diff --git a/Query/sql/v2/runs/v2_cli_20260502_081223_d/c16/artifacts/v2q_c16_024eb99029bb3c59/final_answer.txt b/Query/sql/v2/runs/v2_cli_20260502_081223_d/c16/artifacts/v2q_c16_024eb99029bb3c59/final_answer.txt new file mode 100644 index 0000000000000000000000000000000000000000..63e4eabebcdd14961497d094dc027e6431b07cb4 --- /dev/null +++ b/Query/sql/v2/runs/v2_cli_20260502_081223_d/c16/artifacts/v2q_c16_024eb99029bb3c59/final_answer.txt @@ -0,0 +1,2 @@ +SQL executed successfully for: Use template Grouped Count by Category to probe subgroup_size_stability with semantic role count_distribution. Focus on group_col=ALIVE. +Result preview: [{"ALIVE": "Living Characters", "row_count": 5200}, {"ALIVE": "Deceased Characters", "row_count": 1693}, {"ALIVE": "", "row_count": 3}] \ No newline at end of file diff --git a/Query/sql/v2/runs/v2_cli_20260502_081223_d/c16/artifacts/v2q_c16_024eb99029bb3c59/generated_sql.sql b/Query/sql/v2/runs/v2_cli_20260502_081223_d/c16/artifacts/v2q_c16_024eb99029bb3c59/generated_sql.sql new file mode 100644 index 0000000000000000000000000000000000000000..5b41f7fd09b7a675b6a7d440c5ad72b04130ed40 --- /dev/null +++ b/Query/sql/v2/runs/v2_cli_20260502_081223_d/c16/artifacts/v2q_c16_024eb99029bb3c59/generated_sql.sql @@ -0,0 +1,17 @@ +-- sql_source_version: v2 +-- sql_source_label: v2_current +-- sql_source_run_id: v2_cli_20260502_081223_d +-- sql_source_dataset_id: c16 +-- family_id: subgroup_structure +-- canonical_subitem_id: subgroup_size_stability +-- intended_facet_id: subgroup_distribution_shift +-- variant_semantic_role: count_distribution +-- template_id: tpl_clickbench_group_count +-- query_record_id: v2q_c16_024eb99029bb3c59 +-- problem_id: v2p_c16_b3a47540e6e15f73 +-- realization_mode: agent +-- source_kind: agent +SELECT "ALIVE", COUNT(*) AS row_count +FROM "c16" +GROUP BY "ALIVE" +ORDER BY row_count DESC; diff --git a/Query/sql/v2/runs/v2_cli_20260502_081223_d/c16/artifacts/v2q_c16_024eb99029bb3c59/query_results.jsonl b/Query/sql/v2/runs/v2_cli_20260502_081223_d/c16/artifacts/v2q_c16_024eb99029bb3c59/query_results.jsonl new file mode 100644 index 0000000000000000000000000000000000000000..d867cfeb6c6932d3f82559f0db0dc91a24ce5b1d --- /dev/null +++ b/Query/sql/v2/runs/v2_cli_20260502_081223_d/c16/artifacts/v2q_c16_024eb99029bb3c59/query_results.jsonl @@ -0,0 +1 @@ +{"step_index": 1, "message_index": 0, "node_name": "v2-cli:codex", "tool_name": "sqlite_query", "query": "-- template_id: tpl_clickbench_group_count\nSELECT \"ALIVE\", COUNT(*) AS row_count\nFROM \"c16\"\nGROUP BY \"ALIVE\"\nORDER BY row_count DESC;", "result": "{\"query\": \"-- template_id: tpl_clickbench_group_count\\nSELECT \\\"ALIVE\\\", COUNT(*) AS row_count\\nFROM \\\"c16\\\"\\nGROUP BY \\\"ALIVE\\\"\\nORDER BY row_count DESC;\", \"columns\": [\"ALIVE\", \"row_count\"], \"rows\": [{\"ALIVE\": \"Living Characters\", \"row_count\": 5200}, {\"ALIVE\": \"Deceased Characters\", \"row_count\": 1693}, {\"ALIVE\": \"\", \"row_count\": 3}], \"row_count_returned\": 3, \"row_limit\": 50, \"truncated\": false, \"elapsed_ms\": 5.72}"} diff --git a/Query/sql/v2/runs/v2_cli_20260502_081223_d/c16/artifacts/v2q_c16_024eb99029bb3c59/run_manifest.json b/Query/sql/v2/runs/v2_cli_20260502_081223_d/c16/artifacts/v2q_c16_024eb99029bb3c59/run_manifest.json new file mode 100644 index 0000000000000000000000000000000000000000..78b8559f4e60799cd5c3a4d9daeeaac0f06b8153 --- /dev/null +++ b/Query/sql/v2/runs/v2_cli_20260502_081223_d/c16/artifacts/v2q_c16_024eb99029bb3c59/run_manifest.json @@ -0,0 +1,87 @@ +{ + "run_id": "v2_cli_20260502_081223_d", + "dataset_id": "c16", + "started_at": "2026-05-19T15:33:06.878766+00:00", + "ended_at": "2026-05-19T15:33:25.241720+00:00", + "status": "completed", + "engine": "cli", + "question_record": { + "query_record_id": "v2q_c16_024eb99029bb3c59", + "problem_id": "v2p_c16_b3a47540e6e15f73", + "dataset_id": "c16", + "template_id": "tpl_clickbench_group_count", + "template_name": "Grouped Count by Category", + "family_id": "subgroup_structure", + "canonical_subitem_id": "subgroup_size_stability", + "intended_facet_id": "subgroup_distribution_shift", + "variant_semantic_role": "count_distribution", + "subitem_assignment_source": "planner_selected", + "source_kind": "agent", + "realization_mode": "agent", + "gate_priority": "primary", + "extended_family": false, + "question": "Use template Grouped Count by Category to probe subgroup_size_stability with semantic role count_distribution. Focus on group_col=ALIVE.", + "bindings": { + "group_col": "ALIVE", + "top_k": 14, + "top_n": 6, + "num_tiles": 10, + "percentile_value": 0.9, + "z_threshold": 2.0, + "fraction_threshold": 0.1, + "baseline_multiplier": 1.5, + "baseline_fraction": 0.1, + "min_group_size": 5, + "min_support": 5, + "measure_threshold": 15.0, + "time_grain": "month", + "lookback_rows": 3, + "current_period_start": "'2024-01-01'", + "current_period_end": "'2024-04-01'", + "previous_period_start": "'2023-10-01'", + "previous_period_end": "'2024-01-01'", + "drift_ratio_threshold": 0.8 + }, + "binding_roles": [ + "group_col" + ], + "coverage_target_min": "5", + "runtime_sql_skeleton": "SELECT {group_col}, COUNT(*) AS row_count\nFROM {table}\nGROUP BY {group_col}\nORDER BY row_count DESC;", + "notes": [ + "default_facets=subgroup_distribution_shift", + "template_selection_mode=rule", + "problem_index_within_template=8", + "sql_variant_index=1/1", + "binding_index=19" + ], + "template_selection_mode": "rule", + "selected_template_rank": 2, + "problem_index_within_template": 8, + "sql_variant_index": 1, + "sql_variant_total": 1 + }, + "mode": "subitem_workload_v2", + "sql_source_version": "v2", + "sql_source_label": "v2_current", + "generated_sql_path": "/data/jialinzhang/TabQueryBench/sql_workloads/v2_current/runs_and_launches/runs/v2_cli_20260502_081223_d/c16/sql/v2q_c16_024eb99029bb3c59.sql", + "usage_summary": { + "dataset_id": "c16", + "model": "v2-cli:codex", + "run_id": "v2q_c16_024eb99029bb3c59", + "api_calls": 0, + "input_tokens": 14525, + "cached_input_tokens": 12032, + "output_tokens": 190, + "total_tokens": 14715, + "cost_usd": 0.0, + "ai_cli_calls": 1, + "estimated_input_tokens": 0, + "estimated_output_tokens": 0, + "estimated_total_tokens": 0, + "usage_source": "ai_cli_json_usage", + "cli_elapsed_ms_total": 18349.45, + "sql_execution_elapsed_ms_total": 5.72, + "conversation_log_path": "/data/jialinzhang/TabQueryBench/sql_workloads/v2_current/runs_and_launches/runs/v2_cli_20260502_081223_d/c16/artifacts/v2q_c16_024eb99029bb3c59/cli/conversation.jsonl", + "note": "Executed through a local AI CLI with structured usage metadata." + } +} \ No newline at end of file diff --git a/Query/sql/v2/runs/v2_cli_20260502_081223_d/c16/artifacts/v2q_c16_024eb99029bb3c59/usage_summary.json b/Query/sql/v2/runs/v2_cli_20260502_081223_d/c16/artifacts/v2q_c16_024eb99029bb3c59/usage_summary.json new file mode 100644 index 0000000000000000000000000000000000000000..de4edddb40a364d419e8204b4be3b4bcd17d96de --- /dev/null +++ b/Query/sql/v2/runs/v2_cli_20260502_081223_d/c16/artifacts/v2q_c16_024eb99029bb3c59/usage_summary.json @@ -0,0 +1,20 @@ +{ + "dataset_id": "c16", + "model": "v2-cli:codex", + "run_id": "v2q_c16_024eb99029bb3c59", + "api_calls": 0, + "input_tokens": 14525, + "cached_input_tokens": 12032, + "output_tokens": 190, + "total_tokens": 14715, + "cost_usd": 0.0, + "ai_cli_calls": 1, + "estimated_input_tokens": 0, + "estimated_output_tokens": 0, + "estimated_total_tokens": 0, + "usage_source": "ai_cli_json_usage", + "cli_elapsed_ms_total": 18349.45, + "sql_execution_elapsed_ms_total": 5.72, + "conversation_log_path": "/data/jialinzhang/TabQueryBench/sql_workloads/v2_current/runs_and_launches/runs/v2_cli_20260502_081223_d/c16/artifacts/v2q_c16_024eb99029bb3c59/cli/conversation.jsonl", + "note": "Executed through a local AI CLI with structured usage metadata." +} \ No newline at end of file diff --git a/Query/sql/v2/runs/v2_cli_20260502_081223_d/c16/artifacts/v2q_c16_04b0db29ab4f00b7/final_answer.txt b/Query/sql/v2/runs/v2_cli_20260502_081223_d/c16/artifacts/v2q_c16_04b0db29ab4f00b7/final_answer.txt new file mode 100644 index 0000000000000000000000000000000000000000..ec0c8280fa0965483f958c9098494ae6f247376d --- /dev/null +++ b/Query/sql/v2/runs/v2_cli_20260502_081223_d/c16/artifacts/v2q_c16_04b0db29ab4f00b7/final_answer.txt @@ -0,0 +1,2 @@ +SQL executed successfully for: Use template Grouped Numeric Sum to probe internal_profile_stability with semantic role collapsed_target_view. Focus on group_col=HAIR, measure_col=YEAR. +Result preview: [{"HAIR": "", "total_measure": 4466845}, {"HAIR": "Black Hair", "total_measure": 3095822}, {"HAIR": "Brown Hair", "total_measure": 2273155}, {"HAIR": "Blond Hair", "total_measure": 1463976}, {"HAIR": "Red Hair", "total_measure": 916475}] \ No newline at end of file diff --git a/Query/sql/v2/runs/v2_cli_20260502_081223_d/c16/artifacts/v2q_c16_04b0db29ab4f00b7/generated_sql.sql b/Query/sql/v2/runs/v2_cli_20260502_081223_d/c16/artifacts/v2q_c16_04b0db29ab4f00b7/generated_sql.sql new file mode 100644 index 0000000000000000000000000000000000000000..00cae0ea636f173be4905c15230091a2b3643ef1 --- /dev/null +++ b/Query/sql/v2/runs/v2_cli_20260502_081223_d/c16/artifacts/v2q_c16_04b0db29ab4f00b7/generated_sql.sql @@ -0,0 +1,17 @@ +-- sql_source_version: v2 +-- sql_source_label: v2_current +-- sql_source_run_id: v2_cli_20260502_081223_d +-- sql_source_dataset_id: c16 +-- family_id: subgroup_structure +-- canonical_subitem_id: internal_profile_stability +-- intended_facet_id: subgroup_conditional_contrast +-- variant_semantic_role: collapsed_target_view +-- template_id: tpl_h2o_group_sum +-- query_record_id: v2q_c16_04b0db29ab4f00b7 +-- problem_id: v2p_c16_d3c95af4b3d3dbaa +-- realization_mode: agent +-- source_kind: agent +SELECT "HAIR", SUM(CAST("YEAR" AS INTEGER)) AS total_measure +FROM "c16" +GROUP BY "HAIR" +ORDER BY total_measure DESC; diff --git a/Query/sql/v2/runs/v2_cli_20260502_081223_d/c16/artifacts/v2q_c16_04b0db29ab4f00b7/query_results.jsonl b/Query/sql/v2/runs/v2_cli_20260502_081223_d/c16/artifacts/v2q_c16_04b0db29ab4f00b7/query_results.jsonl new file mode 100644 index 0000000000000000000000000000000000000000..e31971d30c505e7345a7c6d5d782df3ba64e6016 --- /dev/null +++ b/Query/sql/v2/runs/v2_cli_20260502_081223_d/c16/artifacts/v2q_c16_04b0db29ab4f00b7/query_results.jsonl @@ -0,0 +1 @@ +{"step_index": 1, "message_index": 0, "node_name": "v2-cli:codex", "tool_name": "sqlite_query", "query": "-- template_id: tpl_h2o_group_sum\nSELECT \"HAIR\", SUM(CAST(\"YEAR\" AS INTEGER)) AS total_measure\nFROM \"c16\"\nGROUP BY \"HAIR\"\nORDER BY total_measure DESC;", "result": "{\"query\": \"-- template_id: tpl_h2o_group_sum\\nSELECT \\\"HAIR\\\", SUM(CAST(\\\"YEAR\\\" AS INTEGER)) AS total_measure\\nFROM \\\"c16\\\"\\nGROUP BY \\\"HAIR\\\"\\nORDER BY total_measure DESC;\", \"columns\": [\"HAIR\", \"total_measure\"], \"rows\": [{\"HAIR\": \"\", \"total_measure\": 4466845}, {\"HAIR\": \"Black Hair\", \"total_measure\": 3095822}, {\"HAIR\": \"Brown Hair\", \"total_measure\": 2273155}, {\"HAIR\": \"Blond Hair\", \"total_measure\": 1463976}, {\"HAIR\": \"Red Hair\", \"total_measure\": 916475}, {\"HAIR\": \"White Hair\", \"total_measure\": 681373}, {\"HAIR\": \"Grey Hair\", \"total_measure\": 306303}, {\"HAIR\": \"Green Hair\", \"total_measure\": 83510}, {\"HAIR\": \"Blue Hair\", \"total_measure\": 81866}, {\"HAIR\": \"Purple Hair\", \"total_measure\": 63947}, {\"HAIR\": \"Strawberry Blond Hair\", \"total_measure\": 53407}, {\"HAIR\": \"Orange Hair\", \"total_measure\": 41775}, {\"HAIR\": \"Pink Hair\", \"total_measure\": 22005}, {\"HAIR\": \"Gold Hair\", \"total_measure\": 9883}, {\"HAIR\": \"Violet Hair\", \"total_measure\": 7933}, {\"HAIR\": \"Reddish Brown Hair\", \"total_measure\": 5959}, {\"HAIR\": \"Silver Hair\", \"total_measure\": 5945}, {\"HAIR\": \"Platinum Blond Hair\", \"total_measure\": 3958}], \"row_count_returned\": 18, \"row_limit\": 50, \"truncated\": false, \"elapsed_ms\": 8.13}"} diff --git a/Query/sql/v2/runs/v2_cli_20260502_081223_d/c16/artifacts/v2q_c16_04b0db29ab4f00b7/run_manifest.json b/Query/sql/v2/runs/v2_cli_20260502_081223_d/c16/artifacts/v2q_c16_04b0db29ab4f00b7/run_manifest.json new file mode 100644 index 0000000000000000000000000000000000000000..39f6ee85f2c35f179b2d25c7de00845614c06176 --- /dev/null +++ b/Query/sql/v2/runs/v2_cli_20260502_081223_d/c16/artifacts/v2q_c16_04b0db29ab4f00b7/run_manifest.json @@ -0,0 +1,89 @@ +{ + "run_id": "v2_cli_20260502_081223_d", + "dataset_id": "c16", + "started_at": "2026-05-19T15:29:02.117459+00:00", + "ended_at": "2026-05-19T15:29:13.048821+00:00", + "status": "completed", + "engine": "cli", + "question_record": { + "query_record_id": "v2q_c16_04b0db29ab4f00b7", + "problem_id": "v2p_c16_d3c95af4b3d3dbaa", + "dataset_id": "c16", + "template_id": "tpl_h2o_group_sum", + "template_name": "Grouped Numeric Sum", + "family_id": "subgroup_structure", + "canonical_subitem_id": "internal_profile_stability", + "intended_facet_id": "subgroup_conditional_contrast", + "variant_semantic_role": "collapsed_target_view", + "subitem_assignment_source": "planner_selected", + "source_kind": "agent", + "realization_mode": "agent", + "gate_priority": "primary", + "extended_family": false, + "question": "Use template Grouped Numeric Sum to probe internal_profile_stability with semantic role collapsed_target_view. Focus on group_col=HAIR, measure_col=YEAR.", + "bindings": { + "group_col": "HAIR", + "measure_col": "YEAR", + "top_k": 12, + "top_n": 5, + "num_tiles": 10, + "percentile_value": 0.95, + "z_threshold": 2.0, + "fraction_threshold": 0.1, + "baseline_multiplier": 1.5, + "baseline_fraction": 0.1, + "min_group_size": 5, + "min_support": 5, + "measure_threshold": 2003.0, + "time_grain": "month", + "lookback_rows": 3, + "current_period_start": "'2024-01-01'", + "current_period_end": "'2024-04-01'", + "previous_period_start": "'2023-10-01'", + "previous_period_end": "'2024-01-01'", + "drift_ratio_threshold": 0.8 + }, + "binding_roles": [ + "group_col", + "measure_col" + ], + "coverage_target_min": "5", + "runtime_sql_skeleton": "SELECT {group_col}, SUM({measure_col}) AS total_measure\nFROM {table}\nGROUP BY {group_col}\nORDER BY total_measure DESC;", + "notes": [ + "default_facets=subgroup_distribution_shift,subgroup_rank_order,subgroup_conditional_contrast", + "template_selection_mode=rule", + "problem_index_within_template=3", + "sql_variant_index=1/2", + "binding_index=2" + ], + "template_selection_mode": "rule", + "selected_template_rank": 1, + "problem_index_within_template": 3, + "sql_variant_index": 1, + "sql_variant_total": 2 + }, + "mode": "subitem_workload_v2", + "sql_source_version": "v2", + "sql_source_label": "v2_current", + "generated_sql_path": "/data/jialinzhang/TabQueryBench/sql_workloads/v2_current/runs_and_launches/runs/v2_cli_20260502_081223_d/c16/sql/v2q_c16_04b0db29ab4f00b7.sql", + "usage_summary": { + "dataset_id": "c16", + "model": "v2-cli:codex", + "run_id": "v2q_c16_04b0db29ab4f00b7", + "api_calls": 0, + "input_tokens": 14551, + "cached_input_tokens": 0, + "output_tokens": 353, + "total_tokens": 14904, + "cost_usd": 0.0, + "ai_cli_calls": 1, + "estimated_input_tokens": 0, + "estimated_output_tokens": 0, + "estimated_total_tokens": 0, + "usage_source": "ai_cli_json_usage", + "cli_elapsed_ms_total": 10915.87, + "sql_execution_elapsed_ms_total": 8.13, + "conversation_log_path": "/data/jialinzhang/TabQueryBench/sql_workloads/v2_current/runs_and_launches/runs/v2_cli_20260502_081223_d/c16/artifacts/v2q_c16_04b0db29ab4f00b7/cli/conversation.jsonl", + "note": "Executed through a local AI CLI with structured usage metadata." + } +} \ No newline at end of file diff --git a/Query/sql/v2/runs/v2_cli_20260502_081223_d/c16/artifacts/v2q_c16_04b0db29ab4f00b7/trace.jsonl b/Query/sql/v2/runs/v2_cli_20260502_081223_d/c16/artifacts/v2q_c16_04b0db29ab4f00b7/trace.jsonl new file mode 100644 index 0000000000000000000000000000000000000000..14b82815dd596fa8018cdf4846ebc90023fbccc6 --- /dev/null +++ b/Query/sql/v2/runs/v2_cli_20260502_081223_d/c16/artifacts/v2q_c16_04b0db29ab4f00b7/trace.jsonl @@ -0,0 +1 @@ +{"timestamp": "2026-05-19T15:29:13.037637+00:00", "event_type": "ai_cli_sql_generation", "engine": "v2-cli:codex", "attempt": 1, "command": "codex exec --skip-git-repo-check --disable plugins --sandbox read-only --cd \"/data/jialinzhang/SQLagent\" -m gpt-5.4 --json -", "returncode": 0, "elapsed_ms": 10915.87, "started_at": "2026-05-19T15:29:02.120195+00:00", "ended_at": "2026-05-19T15:29:13.036111+00:00", "prompt_metrics": {"chars": 8473, "bytes_utf8": 8473, "lines": 252, "estimated_tokens": null}, "response_metrics": {"chars": 369, "bytes_utf8": 369, "lines": 1, "estimated_tokens": null}, "usage": {"input_tokens": 14551, "cached_input_tokens": 0, "output_tokens": 353, "reasoning_output_tokens": 251}, "stderr_preview": "", "stdout_preview": "{\"sql\":\"-- template_id: tpl_h2o_group_sum\\nSELECT \\\"HAIR\\\", SUM(CAST(\\\"YEAR\\\" AS INTEGER)) AS total_measure\\nFROM \\\"c16\\\"\\nGROUP BY \\\"HAIR\\\"\\nORDER BY total_measure DESC;\",\"notes\":\"Used the planned Grouped Numeric Sum template with group_col=\\\"HAIR\\\" and measure_col=\\\"YEAR\\\". Cast \\\"YEAR\\\" to INTEGER because the schema snapshot shows it is stored as TEXT in SQLite.\"}"} diff --git a/Query/sql/v2/runs/v2_cli_20260502_081223_d/c16/artifacts/v2q_c16_04b0db29ab4f00b7/usage_summary.json b/Query/sql/v2/runs/v2_cli_20260502_081223_d/c16/artifacts/v2q_c16_04b0db29ab4f00b7/usage_summary.json new file mode 100644 index 0000000000000000000000000000000000000000..9d024afee6025b61153054c807969da8d95dcb6a --- /dev/null +++ b/Query/sql/v2/runs/v2_cli_20260502_081223_d/c16/artifacts/v2q_c16_04b0db29ab4f00b7/usage_summary.json @@ -0,0 +1,20 @@ +{ + "dataset_id": "c16", + "model": "v2-cli:codex", + "run_id": "v2q_c16_04b0db29ab4f00b7", + "api_calls": 0, + "input_tokens": 14551, + "cached_input_tokens": 0, + "output_tokens": 353, + "total_tokens": 14904, + "cost_usd": 0.0, + "ai_cli_calls": 1, + "estimated_input_tokens": 0, + "estimated_output_tokens": 0, + "estimated_total_tokens": 0, + "usage_source": "ai_cli_json_usage", + "cli_elapsed_ms_total": 10915.87, + "sql_execution_elapsed_ms_total": 8.13, + "conversation_log_path": "/data/jialinzhang/TabQueryBench/sql_workloads/v2_current/runs_and_launches/runs/v2_cli_20260502_081223_d/c16/artifacts/v2q_c16_04b0db29ab4f00b7/cli/conversation.jsonl", + "note": "Executed through a local AI CLI with structured usage metadata." +} \ No newline at end of file diff --git a/Query/sql/v2/runs/v2_cli_20260502_081223_d/c16/artifacts/v2q_c16_0611709e20fee1c8/final_answer.txt b/Query/sql/v2/runs/v2_cli_20260502_081223_d/c16/artifacts/v2q_c16_0611709e20fee1c8/final_answer.txt new file mode 100644 index 0000000000000000000000000000000000000000..5da4210e8f73b02a91495d67162cb51a9ec502e6 --- /dev/null +++ b/Query/sql/v2/runs/v2_cli_20260502_081223_d/c16/artifacts/v2q_c16_0611709e20fee1c8/final_answer.txt @@ -0,0 +1,2 @@ +SQL executed successfully for: Use template Relative-to-Total Extreme Threshold to probe tail_mass_similarity with semantic role filtered_stable_view. Focus on group_col=YEAR, measure_col=APPEARANCES. +Result preview: [{"YEAR": "1940", "group_value": 9123.0}, {"YEAR": "1987", "group_value": 7777.0}] \ No newline at end of file diff --git a/Query/sql/v2/runs/v2_cli_20260502_081223_d/c16/artifacts/v2q_c16_0611709e20fee1c8/generated_sql.sql b/Query/sql/v2/runs/v2_cli_20260502_081223_d/c16/artifacts/v2q_c16_0611709e20fee1c8/generated_sql.sql new file mode 100644 index 0000000000000000000000000000000000000000..830b1e78e803a4d65e827591c5b4f640b2b18365 --- /dev/null +++ b/Query/sql/v2/runs/v2_cli_20260502_081223_d/c16/artifacts/v2q_c16_0611709e20fee1c8/generated_sql.sql @@ -0,0 +1,30 @@ +-- sql_source_version: v2 +-- sql_source_label: v2_current +-- sql_source_run_id: v2_cli_20260502_081223_d +-- sql_source_dataset_id: c16 +-- family_id: tail_rarity_structure +-- canonical_subitem_id: tail_mass_similarity +-- intended_facet_id: tail_ranked_signal +-- variant_semantic_role: filtered_stable_view +-- template_id: tpl_tpch_relative_total_threshold +-- query_record_id: v2q_c16_0611709e20fee1c8 +-- problem_id: v2p_c16_92055d6cbb88da59 +-- realization_mode: agent +-- source_kind: agent +WITH grouped AS ( + SELECT "YEAR", SUM(CAST("APPEARANCES" AS REAL)) AS group_value + FROM "c16" + WHERE "YEAR" IS NOT NULL + AND "YEAR" <> '' + AND "APPEARANCES" IS NOT NULL + AND "APPEARANCES" <> '' + GROUP BY "YEAR" +), total AS ( + SELECT SUM(group_value) AS total_value + FROM grouped +) +SELECT g."YEAR", g.group_value +FROM grouped AS g +CROSS JOIN total AS t +WHERE g.group_value > t.total_value * 0.05 +ORDER BY g.group_value DESC; diff --git a/Query/sql/v2/runs/v2_cli_20260502_081223_d/c16/artifacts/v2q_c16_0611709e20fee1c8/query_results.jsonl b/Query/sql/v2/runs/v2_cli_20260502_081223_d/c16/artifacts/v2q_c16_0611709e20fee1c8/query_results.jsonl new file mode 100644 index 0000000000000000000000000000000000000000..1602df69be708a024dfc43da88590e8296f46997 --- /dev/null +++ b/Query/sql/v2/runs/v2_cli_20260502_081223_d/c16/artifacts/v2q_c16_0611709e20fee1c8/query_results.jsonl @@ -0,0 +1 @@ +{"step_index": 1, "message_index": 0, "node_name": "v2-cli:codex", "tool_name": "sqlite_query", "query": "-- template_id: tpl_tpch_relative_total_threshold\nWITH grouped AS (\n SELECT \"YEAR\", SUM(CAST(\"APPEARANCES\" AS REAL)) AS group_value\n FROM \"c16\"\n WHERE \"YEAR\" IS NOT NULL\n AND \"YEAR\" <> ''\n AND \"APPEARANCES\" IS NOT NULL\n AND \"APPEARANCES\" <> ''\n GROUP BY \"YEAR\"\n), total AS (\n SELECT SUM(group_value) AS total_value\n FROM grouped\n)\nSELECT g.\"YEAR\", g.group_value\nFROM grouped AS g\nCROSS JOIN total AS t\nWHERE g.group_value > t.total_value * 0.05\nORDER BY g.group_value DESC;", "result": "{\"query\": \"-- template_id: tpl_tpch_relative_total_threshold\\nWITH grouped AS (\\n SELECT \\\"YEAR\\\", SUM(CAST(\\\"APPEARANCES\\\" AS REAL)) AS group_value\\n FROM \\\"c16\\\"\\n WHERE \\\"YEAR\\\" IS NOT NULL\\n AND \\\"YEAR\\\" <> ''\\n AND \\\"APPEARANCES\\\" IS NOT NULL\\n AND \\\"APPEARANCES\\\" <> ''\\n GROUP BY \\\"YEAR\\\"\\n), total AS (\\n SELECT SUM(group_value) AS total_value\\n FROM grouped\\n)\\nSELECT g.\\\"YEAR\\\", g.group_value\\nFROM grouped AS g\\nCROSS JOIN total AS t\\nWHERE g.group_value > t.total_value * 0.05\\nORDER BY g.group_value DESC;\", \"columns\": [\"YEAR\", \"group_value\"], \"rows\": [{\"YEAR\": \"1940\", \"group_value\": 9123.0}, {\"YEAR\": \"1987\", \"group_value\": 7777.0}], \"row_count_returned\": 2, \"row_limit\": 50, \"truncated\": false, \"elapsed_ms\": 3.85}"} diff --git a/Query/sql/v2/runs/v2_cli_20260502_081223_d/c16/artifacts/v2q_c16_0611709e20fee1c8/run_manifest.json b/Query/sql/v2/runs/v2_cli_20260502_081223_d/c16/artifacts/v2q_c16_0611709e20fee1c8/run_manifest.json new file mode 100644 index 0000000000000000000000000000000000000000..0220b4f8d200ac447552899774e19e72b8486cda --- /dev/null +++ b/Query/sql/v2/runs/v2_cli_20260502_081223_d/c16/artifacts/v2q_c16_0611709e20fee1c8/run_manifest.json @@ -0,0 +1,89 @@ +{ + "run_id": "v2_cli_20260502_081223_d", + "dataset_id": "c16", + "started_at": "2026-05-19T15:47:56.294027+00:00", + "ended_at": "2026-05-19T15:48:09.276802+00:00", + "status": "completed", + "engine": "cli", + "question_record": { + "query_record_id": "v2q_c16_0611709e20fee1c8", + "problem_id": "v2p_c16_92055d6cbb88da59", + "dataset_id": "c16", + "template_id": "tpl_tpch_relative_total_threshold", + "template_name": "Relative-to-Total Extreme Threshold", + "family_id": "tail_rarity_structure", + "canonical_subitem_id": "tail_mass_similarity", + "intended_facet_id": "tail_ranked_signal", + "variant_semantic_role": "filtered_stable_view", + "subitem_assignment_source": "planner_selected", + "source_kind": "agent", + "realization_mode": "agent", + "gate_priority": "primary", + "extended_family": false, + "question": "Use template Relative-to-Total Extreme Threshold to probe tail_mass_similarity with semantic role filtered_stable_view. Focus on group_col=YEAR, measure_col=APPEARANCES.", + "bindings": { + "group_col": "YEAR", + "measure_col": "APPEARANCES", + "top_k": 16, + "top_n": 4, + "num_tiles": 10, + "percentile_value": 0.9, + "z_threshold": 2.0, + "fraction_threshold": 0.05, + "baseline_multiplier": 1.75, + "baseline_fraction": 0.1, + "min_group_size": 5, + "min_support": 4, + "measure_threshold": 11.0, + "time_grain": "month", + "lookback_rows": 3, + "current_period_start": "'2024-01-01'", + "current_period_end": "'2024-04-01'", + "previous_period_start": "'2023-10-01'", + "previous_period_end": "'2024-01-01'", + "drift_ratio_threshold": 0.8 + }, + "binding_roles": [ + "group_col", + "measure_col" + ], + "coverage_target_min": "5", + "runtime_sql_skeleton": "WITH grouped AS (\n SELECT {group_col}, SUM({measure_col}) AS group_value\n FROM {table}\n GROUP BY {group_col}\n), total AS (\n SELECT SUM(group_value) AS total_value\n FROM grouped\n)\nSELECT g.{group_col}, g.group_value\nFROM grouped AS g\nCROSS JOIN total AS t\nWHERE g.group_value > t.total_value * {fraction_threshold}\nORDER BY g.group_value DESC;", + "notes": [ + "default_facets=tail_ranked_signal", + "template_selection_mode=rule", + "problem_index_within_template=5", + "sql_variant_index=2/2", + "binding_index=76" + ], + "template_selection_mode": "rule", + "selected_template_rank": 7, + "problem_index_within_template": 5, + "sql_variant_index": 2, + "sql_variant_total": 2 + }, + "mode": "subitem_workload_v2", + "sql_source_version": "v2", + "sql_source_label": "v2_current", + "generated_sql_path": "/data/jialinzhang/TabQueryBench/sql_workloads/v2_current/runs_and_launches/runs/v2_cli_20260502_081223_d/c16/sql/v2q_c16_0611709e20fee1c8.sql", + "usage_summary": { + "dataset_id": "c16", + "model": "v2-cli:codex", + "run_id": "v2q_c16_0611709e20fee1c8", + "api_calls": 0, + "input_tokens": 14692, + "cached_input_tokens": 13696, + "output_tokens": 615, + "total_tokens": 15307, + "cost_usd": 0.0, + "ai_cli_calls": 1, + "estimated_input_tokens": 0, + "estimated_output_tokens": 0, + "estimated_total_tokens": 0, + "usage_source": "ai_cli_json_usage", + "cli_elapsed_ms_total": 12974.15, + "sql_execution_elapsed_ms_total": 3.85, + "conversation_log_path": "/data/jialinzhang/TabQueryBench/sql_workloads/v2_current/runs_and_launches/runs/v2_cli_20260502_081223_d/c16/artifacts/v2q_c16_0611709e20fee1c8/cli/conversation.jsonl", + "note": "Executed through a local AI CLI with structured usage metadata." + } +} \ No newline at end of file diff --git a/Query/sql/v2/runs/v2_cli_20260502_081223_d/c16/artifacts/v2q_c16_0611709e20fee1c8/trace.jsonl b/Query/sql/v2/runs/v2_cli_20260502_081223_d/c16/artifacts/v2q_c16_0611709e20fee1c8/trace.jsonl new file mode 100644 index 0000000000000000000000000000000000000000..dddeccfa9146ddf2c7b38c3105b7b447ac4dba41 --- /dev/null +++ b/Query/sql/v2/runs/v2_cli_20260502_081223_d/c16/artifacts/v2q_c16_0611709e20fee1c8/trace.jsonl @@ -0,0 +1 @@ +{"timestamp": "2026-05-19T15:48:09.271503+00:00", "event_type": "ai_cli_sql_generation", "engine": "v2-cli:codex", "attempt": 1, "command": "codex exec --skip-git-repo-check --disable plugins --sandbox read-only --cd \"/data/jialinzhang/SQLagent\" -m gpt-5.4 --json -", "returncode": 0, "elapsed_ms": 12974.15, "started_at": "2026-05-19T15:47:56.296445+00:00", "ended_at": "2026-05-19T15:48:09.270631+00:00", "prompt_metrics": {"chars": 9042, "bytes_utf8": 9042, "lines": 252, "estimated_tokens": null}, "response_metrics": {"chars": 835, "bytes_utf8": 835, "lines": 1, "estimated_tokens": null}, "usage": {"input_tokens": 14692, "cached_input_tokens": 13696, "output_tokens": 615, "reasoning_output_tokens": 385}, "stderr_preview": "", "stdout_preview": "{\"sql\":\"-- template_id: tpl_tpch_relative_total_threshold\\nWITH grouped AS (\\n SELECT \\\"YEAR\\\", SUM(CAST(\\\"APPEARANCES\\\" AS REAL)) AS group_value\\n FROM \\\"c16\\\"\\n WHERE \\\"YEAR\\\" IS NOT NULL\\n AND \\\"YEAR\\\" <> ''\\n AND \\\"APPEARANCES\\\" IS NOT NULL\\n AND \\\"APPEARANCES\\\" <> ''\\n GROUP BY \\\"YEAR\\\"\\n), total AS (\\n SELECT SUM(group_value) AS total_value\\n FROM grouped\\n)\\nSELECT g.\\\"YEAR\\\", g.group_value\\nFROM grouped AS g\\nCROSS JOIN total AS t\\nWHERE g.group_value > t.total_value * 0.05\\nORDER BY g.group_value DESC;\",\"notes\":\"Uses the planned relative-to-total threshold template with group_col=\\\"YEAR\\\" and measure_col=\\\"APPEARANCES\\\". \\\"APPEARANCES\\\" is cast to REAL because the schema stores it as TEXT, and empty/null YEAR or APPEARANCES values are filtered out for a stable grouped aggregation.\"}"} diff --git a/Query/sql/v2/runs/v2_cli_20260502_081223_d/c16/artifacts/v2q_c16_0611709e20fee1c8/usage_summary.json b/Query/sql/v2/runs/v2_cli_20260502_081223_d/c16/artifacts/v2q_c16_0611709e20fee1c8/usage_summary.json new file mode 100644 index 0000000000000000000000000000000000000000..78306fd9d60196c3ce751b13779c08b0e12511d3 --- /dev/null +++ b/Query/sql/v2/runs/v2_cli_20260502_081223_d/c16/artifacts/v2q_c16_0611709e20fee1c8/usage_summary.json @@ -0,0 +1,20 @@ +{ + "dataset_id": "c16", + "model": "v2-cli:codex", + "run_id": "v2q_c16_0611709e20fee1c8", + "api_calls": 0, + "input_tokens": 14692, + "cached_input_tokens": 13696, + "output_tokens": 615, + "total_tokens": 15307, + "cost_usd": 0.0, + "ai_cli_calls": 1, + "estimated_input_tokens": 0, + "estimated_output_tokens": 0, + "estimated_total_tokens": 0, + "usage_source": "ai_cli_json_usage", + "cli_elapsed_ms_total": 12974.15, + "sql_execution_elapsed_ms_total": 3.85, + "conversation_log_path": "/data/jialinzhang/TabQueryBench/sql_workloads/v2_current/runs_and_launches/runs/v2_cli_20260502_081223_d/c16/artifacts/v2q_c16_0611709e20fee1c8/cli/conversation.jsonl", + "note": "Executed through a local AI CLI with structured usage metadata." +} \ No newline at end of file diff --git a/Query/sql/v2/runs/v2_cli_20260502_081223_d/c16/artifacts/v2q_c16_0b313429a0f6899f/final_answer.txt b/Query/sql/v2/runs/v2_cli_20260502_081223_d/c16/artifacts/v2q_c16_0b313429a0f6899f/final_answer.txt new file mode 100644 index 0000000000000000000000000000000000000000..c38660c3e7179a7f8b6af6f64ebbf6dd663824da --- /dev/null +++ b/Query/sql/v2/runs/v2_cli_20260502_081223_d/c16/artifacts/v2q_c16_0b313429a0f6899f/final_answer.txt @@ -0,0 +1,2 @@ +SQL executed successfully for: Use template Filtered Two-Dimensional Group Count to probe slice_level_consistency with semantic role count_distribution. Focus on group_col=HAIR, group_col_2=SEX. +Result preview: [{"HAIR": "White Hair", "SEX": "Male Characters", "row_count": 273}, {"HAIR": "White Hair", "SEX": "Female Characters", "row_count": 73}] \ No newline at end of file diff --git a/Query/sql/v2/runs/v2_cli_20260502_081223_d/c16/artifacts/v2q_c16_0b313429a0f6899f/generated_sql.sql b/Query/sql/v2/runs/v2_cli_20260502_081223_d/c16/artifacts/v2q_c16_0b313429a0f6899f/generated_sql.sql new file mode 100644 index 0000000000000000000000000000000000000000..f05cf563558c852c135604b6e7e0ca66e4ac1365 --- /dev/null +++ b/Query/sql/v2/runs/v2_cli_20260502_081223_d/c16/artifacts/v2q_c16_0b313429a0f6899f/generated_sql.sql @@ -0,0 +1,18 @@ +-- sql_source_version: v2 +-- sql_source_label: v2_current +-- sql_source_run_id: v2_cli_20260502_081223_d +-- sql_source_dataset_id: c16 +-- family_id: conditional_dependency_structure +-- canonical_subitem_id: slice_level_consistency +-- intended_facet_id: conditional_interaction_hotspots +-- variant_semantic_role: count_distribution +-- template_id: tpl_c2_filtered_group_count_2d +-- query_record_id: v2q_c16_0b313429a0f6899f +-- problem_id: v2p_c16_3f301355a5b8db53 +-- realization_mode: agent +-- source_kind: agent +SELECT "HAIR", "SEX", COUNT(*) AS row_count +FROM "c16" +WHERE "HAIR" = 'White Hair' +GROUP BY "HAIR", "SEX" +ORDER BY row_count DESC; diff --git a/Query/sql/v2/runs/v2_cli_20260502_081223_d/c16/artifacts/v2q_c16_0b313429a0f6899f/query_results.jsonl b/Query/sql/v2/runs/v2_cli_20260502_081223_d/c16/artifacts/v2q_c16_0b313429a0f6899f/query_results.jsonl new file mode 100644 index 0000000000000000000000000000000000000000..95ea1d562ace4215693473e052ce2f97127417d8 --- /dev/null +++ b/Query/sql/v2/runs/v2_cli_20260502_081223_d/c16/artifacts/v2q_c16_0b313429a0f6899f/query_results.jsonl @@ -0,0 +1 @@ +{"step_index": 1, "message_index": 0, "node_name": "v2-cli:codex", "tool_name": "sqlite_query", "query": "-- template_id: tpl_c2_filtered_group_count_2d\nSELECT \"HAIR\", \"SEX\", COUNT(*) AS row_count\nFROM \"c16\"\nWHERE \"HAIR\" = 'White Hair'\nGROUP BY \"HAIR\", \"SEX\"\nORDER BY row_count DESC;", "result": "{\"query\": \"-- template_id: tpl_c2_filtered_group_count_2d\\nSELECT \\\"HAIR\\\", \\\"SEX\\\", COUNT(*) AS row_count\\nFROM \\\"c16\\\"\\nWHERE \\\"HAIR\\\" = 'White Hair'\\nGROUP BY \\\"HAIR\\\", \\\"SEX\\\"\\nORDER BY row_count DESC;\", \"columns\": [\"HAIR\", \"SEX\", \"row_count\"], \"rows\": [{\"HAIR\": \"White Hair\", \"SEX\": \"Male Characters\", \"row_count\": 273}, {\"HAIR\": \"White Hair\", \"SEX\": \"Female Characters\", \"row_count\": 73}], \"row_count_returned\": 2, \"row_limit\": 50, \"truncated\": false, \"elapsed_ms\": 1.63}"} diff --git a/Query/sql/v2/runs/v2_cli_20260502_081223_d/c16/artifacts/v2q_c16_0b313429a0f6899f/run_manifest.json b/Query/sql/v2/runs/v2_cli_20260502_081223_d/c16/artifacts/v2q_c16_0b313429a0f6899f/run_manifest.json new file mode 100644 index 0000000000000000000000000000000000000000..0f68cdb21c57edf7e1c318df71cb3810eb473242 --- /dev/null +++ b/Query/sql/v2/runs/v2_cli_20260502_081223_d/c16/artifacts/v2q_c16_0b313429a0f6899f/run_manifest.json @@ -0,0 +1,93 @@ +{ + "run_id": "v2_cli_20260502_081223_d", + "dataset_id": "c16", + "started_at": "2026-05-19T15:42:36.132522+00:00", + "ended_at": "2026-05-19T15:42:47.727459+00:00", + "status": "completed", + "engine": "cli", + "question_record": { + "query_record_id": "v2q_c16_0b313429a0f6899f", + "problem_id": "v2p_c16_3f301355a5b8db53", + "dataset_id": "c16", + "template_id": "tpl_c2_filtered_group_count_2d", + "template_name": "Filtered Two-Dimensional Group Count", + "family_id": "conditional_dependency_structure", + "canonical_subitem_id": "slice_level_consistency", + "intended_facet_id": "conditional_interaction_hotspots", + "variant_semantic_role": "count_distribution", + "subitem_assignment_source": "planner_selected", + "source_kind": "agent", + "realization_mode": "agent", + "gate_priority": "primary", + "extended_family": false, + "question": "Use template Filtered Two-Dimensional Group Count to probe slice_level_consistency with semantic role count_distribution. Focus on group_col=HAIR, group_col_2=SEX.", + "bindings": { + "group_col": "HAIR", + "group_col_2": "SEX", + "predicate_col": "HAIR", + "predicate_op": "=", + "predicate_value": "White Hair", + "top_k": 13, + "top_n": 4, + "num_tiles": 10, + "percentile_value": 0.9, + "z_threshold": 2.0, + "fraction_threshold": 0.1, + "baseline_multiplier": 1.5, + "baseline_fraction": 0.1, + "min_group_size": 5, + "min_support": 5, + "measure_threshold": 2003.0, + "time_grain": "month", + "lookback_rows": 3, + "current_period_start": "'2024-01-01'", + "current_period_end": "'2024-04-01'", + "previous_period_start": "'2023-10-01'", + "previous_period_end": "'2024-01-01'", + "drift_ratio_threshold": 0.8 + }, + "binding_roles": [ + "group_col", + "group_col_2", + "predicate_col" + ], + "coverage_target_min": "5", + "runtime_sql_skeleton": "SELECT {group_col}, {group_col_2}, COUNT(*) AS row_count\nFROM {table}\nWHERE {predicate_col} {predicate_op} {predicate_value}\nGROUP BY {group_col}, {group_col_2}\nORDER BY row_count DESC;", + "notes": [ + "default_facets=conditional_interaction_hotspots", + "template_selection_mode=rule", + "problem_index_within_template=6", + "sql_variant_index=1/1", + "binding_index=53" + ], + "template_selection_mode": "rule", + "selected_template_rank": 5, + "problem_index_within_template": 6, + "sql_variant_index": 1, + "sql_variant_total": 1 + }, + "mode": "subitem_workload_v2", + "sql_source_version": "v2", + "sql_source_label": "v2_current", + "generated_sql_path": "/data/jialinzhang/TabQueryBench/sql_workloads/v2_current/runs_and_launches/runs/v2_cli_20260502_081223_d/c16/sql/v2q_c16_0b313429a0f6899f.sql", + "usage_summary": { + "dataset_id": "c16", + "model": "v2-cli:codex", + "run_id": "v2q_c16_0b313429a0f6899f", + "api_calls": 0, + "input_tokens": 14644, + "cached_input_tokens": 13696, + "output_tokens": 348, + "total_tokens": 14992, + "cost_usd": 0.0, + "ai_cli_calls": 1, + "estimated_input_tokens": 0, + "estimated_output_tokens": 0, + "estimated_total_tokens": 0, + "usage_source": "ai_cli_json_usage", + "cli_elapsed_ms_total": 11588.39, + "sql_execution_elapsed_ms_total": 1.63, + "conversation_log_path": "/data/jialinzhang/TabQueryBench/sql_workloads/v2_current/runs_and_launches/runs/v2_cli_20260502_081223_d/c16/artifacts/v2q_c16_0b313429a0f6899f/cli/conversation.jsonl", + "note": "Executed through a local AI CLI with structured usage metadata." + } +} \ No newline at end of file diff --git a/Query/sql/v2/runs/v2_cli_20260502_081223_d/c16/artifacts/v2q_c16_0b313429a0f6899f/trace.jsonl b/Query/sql/v2/runs/v2_cli_20260502_081223_d/c16/artifacts/v2q_c16_0b313429a0f6899f/trace.jsonl new file mode 100644 index 0000000000000000000000000000000000000000..03192357108e778d06ca5ba7b469941aa4f4d684 --- /dev/null +++ b/Query/sql/v2/runs/v2_cli_20260502_081223_d/c16/artifacts/v2q_c16_0b313429a0f6899f/trace.jsonl @@ -0,0 +1 @@ +{"timestamp": "2026-05-19T15:42:47.724410+00:00", "event_type": "ai_cli_sql_generation", "engine": "v2-cli:codex", "attempt": 1, "command": "codex exec --skip-git-repo-check --disable plugins --sandbox read-only --cd \"/data/jialinzhang/SQLagent\" -m gpt-5.4 --json -", "returncode": 0, "elapsed_ms": 11588.39, "started_at": "2026-05-19T15:42:36.135174+00:00", "ended_at": "2026-05-19T15:42:47.723598+00:00", "prompt_metrics": {"chars": 8784, "bytes_utf8": 8784, "lines": 256, "estimated_tokens": null}, "response_metrics": {"chars": 438, "bytes_utf8": 438, "lines": 1, "estimated_tokens": null}, "usage": {"input_tokens": 14644, "cached_input_tokens": 13696, "output_tokens": 348, "reasoning_output_tokens": 227}, "stderr_preview": "", "stdout_preview": "{\"sql\":\"-- template_id: tpl_c2_filtered_group_count_2d\\nSELECT \\\"HAIR\\\", \\\"SEX\\\", COUNT(*) AS row_count\\nFROM \\\"c16\\\"\\nWHERE \\\"HAIR\\\" = 'White Hair'\\nGROUP BY \\\"HAIR\\\", \\\"SEX\\\"\\nORDER BY row_count DESC;\",\"notes\":\"Instantiated the provided filtered two-dimensional group count template with group_col=\\\"HAIR\\\", group_col_2=\\\"SEX\\\", and predicate \\\"HAIR\\\" = 'White Hair'. This will return the sex distribution within the White Hair slice.\"}"} diff --git a/Query/sql/v2/runs/v2_cli_20260502_081223_d/c16/artifacts/v2q_c16_0b313429a0f6899f/usage_summary.json b/Query/sql/v2/runs/v2_cli_20260502_081223_d/c16/artifacts/v2q_c16_0b313429a0f6899f/usage_summary.json new file mode 100644 index 0000000000000000000000000000000000000000..e5fe54f7c88c9e53ef6673d9ba82d7fc91be1726 --- /dev/null +++ b/Query/sql/v2/runs/v2_cli_20260502_081223_d/c16/artifacts/v2q_c16_0b313429a0f6899f/usage_summary.json @@ -0,0 +1,20 @@ +{ + "dataset_id": "c16", + "model": "v2-cli:codex", + "run_id": "v2q_c16_0b313429a0f6899f", + "api_calls": 0, + "input_tokens": 14644, + "cached_input_tokens": 13696, + "output_tokens": 348, + "total_tokens": 14992, + "cost_usd": 0.0, + "ai_cli_calls": 1, + "estimated_input_tokens": 0, + "estimated_output_tokens": 0, + "estimated_total_tokens": 0, + "usage_source": "ai_cli_json_usage", + "cli_elapsed_ms_total": 11588.39, + "sql_execution_elapsed_ms_total": 1.63, + "conversation_log_path": "/data/jialinzhang/TabQueryBench/sql_workloads/v2_current/runs_and_launches/runs/v2_cli_20260502_081223_d/c16/artifacts/v2q_c16_0b313429a0f6899f/cli/conversation.jsonl", + "note": "Executed through a local AI CLI with structured usage metadata." +} \ No newline at end of file diff --git a/Query/sql/v2/runs/v2_cli_20260502_081223_d/c16/artifacts/v2q_c16_0f7ac4ff2707e5fd/final_answer.txt b/Query/sql/v2/runs/v2_cli_20260502_081223_d/c16/artifacts/v2q_c16_0f7ac4ff2707e5fd/final_answer.txt new file mode 100644 index 0000000000000000000000000000000000000000..389e9e35830d705986e207285ee8313abecac526 --- /dev/null +++ b/Query/sql/v2/runs/v2_cli_20260502_081223_d/c16/artifacts/v2q_c16_0f7ac4ff2707e5fd/final_answer.txt @@ -0,0 +1,2 @@ +SQL executed successfully for: Use template Grouped Condition Rate to probe dependency_strength_similarity with semantic role within_group_proportion. Focus on group_col=ALIVE, condition_col=EYE. +Result preview: [{"ALIVE": "Deceased Characters", "condition_rate": 0.18015357353809805}, {"ALIVE": "Living Characters", "condition_rate": 0.15326923076923077}, {"ALIVE": "", "condition_rate": 0.0}] \ No newline at end of file diff --git a/Query/sql/v2/runs/v2_cli_20260502_081223_d/c16/artifacts/v2q_c16_0f7ac4ff2707e5fd/generated_sql.sql b/Query/sql/v2/runs/v2_cli_20260502_081223_d/c16/artifacts/v2q_c16_0f7ac4ff2707e5fd/generated_sql.sql new file mode 100644 index 0000000000000000000000000000000000000000..8920057f6f2c6ab4076e4910b2ca769b1678f262 --- /dev/null +++ b/Query/sql/v2/runs/v2_cli_20260502_081223_d/c16/artifacts/v2q_c16_0f7ac4ff2707e5fd/generated_sql.sql @@ -0,0 +1,18 @@ +-- sql_source_version: v2 +-- sql_source_label: v2_current +-- sql_source_run_id: v2_cli_20260502_081223_d +-- sql_source_dataset_id: c16 +-- family_id: conditional_dependency_structure +-- canonical_subitem_id: dependency_strength_similarity +-- intended_facet_id: pairwise_conditional_dependency +-- variant_semantic_role: within_group_proportion +-- template_id: tpl_m4_group_condition_rate +-- query_record_id: v2q_c16_0f7ac4ff2707e5fd +-- problem_id: v2p_c16_750501d83ba4a7cb +-- realization_mode: agent +-- source_kind: agent +SELECT "ALIVE", + AVG(CASE WHEN "EYE" = 'Blue Eyes' THEN 1 ELSE 0 END) AS condition_rate +FROM "c16" +GROUP BY "ALIVE" +ORDER BY condition_rate DESC; diff --git a/Query/sql/v2/runs/v2_cli_20260502_081223_d/c16/artifacts/v2q_c16_0f7ac4ff2707e5fd/query_results.jsonl b/Query/sql/v2/runs/v2_cli_20260502_081223_d/c16/artifacts/v2q_c16_0f7ac4ff2707e5fd/query_results.jsonl new file mode 100644 index 0000000000000000000000000000000000000000..0ea25eb15602df3a92a359feea189620cefee971 --- /dev/null +++ b/Query/sql/v2/runs/v2_cli_20260502_081223_d/c16/artifacts/v2q_c16_0f7ac4ff2707e5fd/query_results.jsonl @@ -0,0 +1 @@ +{"step_index": 1, "message_index": 0, "node_name": "v2-cli:codex", "tool_name": "sqlite_query", "query": "-- template_id: tpl_m4_group_condition_rate\nSELECT \"ALIVE\",\n AVG(CASE WHEN \"EYE\" = 'Blue Eyes' THEN 1 ELSE 0 END) AS condition_rate\nFROM \"c16\"\nGROUP BY \"ALIVE\"\nORDER BY condition_rate DESC;", "result": "{\"query\": \"-- template_id: tpl_m4_group_condition_rate\\nSELECT \\\"ALIVE\\\",\\n AVG(CASE WHEN \\\"EYE\\\" = 'Blue Eyes' THEN 1 ELSE 0 END) AS condition_rate\\nFROM \\\"c16\\\"\\nGROUP BY \\\"ALIVE\\\"\\nORDER BY condition_rate DESC;\", \"columns\": [\"ALIVE\", \"condition_rate\"], \"rows\": [{\"ALIVE\": \"Deceased Characters\", \"condition_rate\": 0.18015357353809805}, {\"ALIVE\": \"Living Characters\", \"condition_rate\": 0.15326923076923077}, {\"ALIVE\": \"\", \"condition_rate\": 0.0}], \"row_count_returned\": 3, \"row_limit\": 50, \"truncated\": false, \"elapsed_ms\": 3.34}"} diff --git a/Query/sql/v2/runs/v2_cli_20260502_081223_d/c16/artifacts/v2q_c16_0f7ac4ff2707e5fd/run_manifest.json b/Query/sql/v2/runs/v2_cli_20260502_081223_d/c16/artifacts/v2q_c16_0f7ac4ff2707e5fd/run_manifest.json new file mode 100644 index 0000000000000000000000000000000000000000..4d5525d83438945d28a9807d2c9bdc2f0129a0d3 --- /dev/null +++ b/Query/sql/v2/runs/v2_cli_20260502_081223_d/c16/artifacts/v2q_c16_0f7ac4ff2707e5fd/run_manifest.json @@ -0,0 +1,92 @@ +{ + "run_id": "v2_cli_20260502_081223_d", + "dataset_id": "c16", + "started_at": "2026-05-19T16:01:15.927832+00:00", + "ended_at": "2026-05-19T16:01:30.371890+00:00", + "status": "completed", + "engine": "cli", + "question_record": { + "query_record_id": "v2q_c16_0f7ac4ff2707e5fd", + "problem_id": "v2p_c16_750501d83ba4a7cb", + "dataset_id": "c16", + "template_id": "tpl_m4_group_condition_rate", + "template_name": "Grouped Condition Rate", + "family_id": "conditional_dependency_structure", + "canonical_subitem_id": "dependency_strength_similarity", + "intended_facet_id": "pairwise_conditional_dependency", + "variant_semantic_role": "within_group_proportion", + "subitem_assignment_source": "planner_selected", + "source_kind": "agent", + "realization_mode": "agent", + "gate_priority": "primary", + "extended_family": false, + "question": "Use template Grouped Condition Rate to probe dependency_strength_similarity with semantic role within_group_proportion. Focus on group_col=ALIVE, condition_col=EYE.", + "bindings": { + "group_col": "ALIVE", + "condition_col": "EYE", + "condition_value": "", + "positive_value": "", + "negative_value": "Blue Eyes", + "top_k": 11, + "top_n": 3, + "num_tiles": 10, + "percentile_value": 0.95, + "z_threshold": 2.0, + "fraction_threshold": 0.1, + "baseline_multiplier": 1.5, + "baseline_fraction": 0.1, + "min_group_size": 5, + "min_support": 5, + "measure_threshold": 213203.0, + "time_grain": "month", + "lookback_rows": 3, + "current_period_start": "'2024-01-01'", + "current_period_end": "'2024-04-01'", + "previous_period_start": "'2023-10-01'", + "previous_period_end": "'2024-01-01'", + "drift_ratio_threshold": 0.8 + }, + "binding_roles": [ + "group_col", + "condition_col" + ], + "coverage_target_min": "5", + "runtime_sql_skeleton": "SELECT {group_col},\n AVG(CASE WHEN {condition_col} = {condition_value} THEN 1 ELSE 0 END) AS condition_rate\nFROM {table}\nGROUP BY {group_col}\nORDER BY condition_rate DESC;", + "notes": [ + "default_facets=pairwise_conditional_dependency", + "template_selection_mode=rule", + "problem_index_within_template=1", + "sql_variant_index=1/2", + "binding_index=96" + ], + "template_selection_mode": "rule", + "selected_template_rank": 9, + "problem_index_within_template": 1, + "sql_variant_index": 1, + "sql_variant_total": 2 + }, + "mode": "subitem_workload_v2", + "sql_source_version": "v2", + "sql_source_label": "v2_current", + "generated_sql_path": "/data/jialinzhang/TabQueryBench/sql_workloads/v2_current/runs_and_launches/runs/v2_cli_20260502_081223_d/c16/sql/v2q_c16_0f7ac4ff2707e5fd.sql", + "usage_summary": { + "dataset_id": "c16", + "model": "v2-cli:codex", + "run_id": "v2q_c16_0f7ac4ff2707e5fd", + "api_calls": 0, + "input_tokens": 14612, + "cached_input_tokens": 12032, + "output_tokens": 647, + "total_tokens": 15259, + "cost_usd": 0.0, + "ai_cli_calls": 1, + "estimated_input_tokens": 0, + "estimated_output_tokens": 0, + "estimated_total_tokens": 0, + "usage_source": "ai_cli_json_usage", + "cli_elapsed_ms_total": 14437.25, + "sql_execution_elapsed_ms_total": 3.34, + "conversation_log_path": "/data/jialinzhang/TabQueryBench/sql_workloads/v2_current/runs_and_launches/runs/v2_cli_20260502_081223_d/c16/artifacts/v2q_c16_0f7ac4ff2707e5fd/cli/conversation.jsonl", + "note": "Executed through a local AI CLI with structured usage metadata." + } +} \ No newline at end of file diff --git a/Query/sql/v2/runs/v2_cli_20260502_081223_d/c16/artifacts/v2q_c16_0f7ac4ff2707e5fd/trace.jsonl b/Query/sql/v2/runs/v2_cli_20260502_081223_d/c16/artifacts/v2q_c16_0f7ac4ff2707e5fd/trace.jsonl new file mode 100644 index 0000000000000000000000000000000000000000..ebd933c9103879b859aeb815b5b5eb91886b6ec2 --- /dev/null +++ b/Query/sql/v2/runs/v2_cli_20260502_081223_d/c16/artifacts/v2q_c16_0f7ac4ff2707e5fd/trace.jsonl @@ -0,0 +1 @@ +{"timestamp": "2026-05-19T16:01:30.367236+00:00", "event_type": "ai_cli_sql_generation", "engine": "v2-cli:codex", "attempt": 1, "command": "codex exec --skip-git-repo-check --disable plugins --sandbox read-only --cd \"/data/jialinzhang/SQLagent\" -m gpt-5.4 --json -", "returncode": 0, "elapsed_ms": 14437.25, "started_at": "2026-05-19T16:01:15.929178+00:00", "ended_at": "2026-05-19T16:01:30.366453+00:00", "prompt_metrics": {"chars": 8731, "bytes_utf8": 8731, "lines": 255, "estimated_tokens": null}, "response_metrics": {"chars": 508, "bytes_utf8": 508, "lines": 1, "estimated_tokens": null}, "usage": {"input_tokens": 14612, "cached_input_tokens": 12032, "output_tokens": 647, "reasoning_output_tokens": 516}, "stderr_preview": "", "stdout_preview": "{\"sql\":\"-- template_id: tpl_m4_group_condition_rate\\nSELECT \\\"ALIVE\\\",\\n AVG(CASE WHEN \\\"EYE\\\" = 'Blue Eyes' THEN 1 ELSE 0 END) AS condition_rate\\nFROM \\\"c16\\\"\\nGROUP BY \\\"ALIVE\\\"\\nORDER BY condition_rate DESC;\",\"notes\":\"Used the provided Grouped Condition Rate template with group_col bound to ALIVE and condition_col bound to EYE. Since condition_value was empty, I used the provided bound eye category 'Blue Eyes' as the condition value to compute the within-group proportion for each ALIVE group.\"}"} diff --git a/Query/sql/v2/runs/v2_cli_20260502_081223_d/c16/artifacts/v2q_c16_0f7ac4ff2707e5fd/usage_summary.json b/Query/sql/v2/runs/v2_cli_20260502_081223_d/c16/artifacts/v2q_c16_0f7ac4ff2707e5fd/usage_summary.json new file mode 100644 index 0000000000000000000000000000000000000000..ce974b16dcf14f387665d8d1848cfac3aa48059b --- /dev/null +++ b/Query/sql/v2/runs/v2_cli_20260502_081223_d/c16/artifacts/v2q_c16_0f7ac4ff2707e5fd/usage_summary.json @@ -0,0 +1,20 @@ +{ + "dataset_id": "c16", + "model": "v2-cli:codex", + "run_id": "v2q_c16_0f7ac4ff2707e5fd", + "api_calls": 0, + "input_tokens": 14612, + "cached_input_tokens": 12032, + "output_tokens": 647, + "total_tokens": 15259, + "cost_usd": 0.0, + "ai_cli_calls": 1, + "estimated_input_tokens": 0, + "estimated_output_tokens": 0, + "estimated_total_tokens": 0, + "usage_source": "ai_cli_json_usage", + "cli_elapsed_ms_total": 14437.25, + "sql_execution_elapsed_ms_total": 3.34, + "conversation_log_path": "/data/jialinzhang/TabQueryBench/sql_workloads/v2_current/runs_and_launches/runs/v2_cli_20260502_081223_d/c16/artifacts/v2q_c16_0f7ac4ff2707e5fd/cli/conversation.jsonl", + "note": "Executed through a local AI CLI with structured usage metadata." +} \ No newline at end of file diff --git a/Query/sql/v2/runs/v2_cli_20260502_081223_d/c16/artifacts/v2q_c16_1727fec74510f83e/final_answer.txt b/Query/sql/v2/runs/v2_cli_20260502_081223_d/c16/artifacts/v2q_c16_1727fec74510f83e/final_answer.txt new file mode 100644 index 0000000000000000000000000000000000000000..5ef3461551d9df0699ff1380cc1b4423fcc099d3 --- /dev/null +++ b/Query/sql/v2/runs/v2_cli_20260502_081223_d/c16/artifacts/v2q_c16_1727fec74510f83e/final_answer.txt @@ -0,0 +1,2 @@ +SQL executed successfully for: Use template Grouped Percentile Point to probe tail_concentration_consistency with semantic role ranked_signal_view. Focus on group_col=ALIVE, measure_col=YEAR. +Result preview: [{"ALIVE": "Living Characters", "percentile_measure": 2009}, {"ALIVE": "Deceased Characters", "percentile_measure": 2007}] \ No newline at end of file diff --git a/Query/sql/v2/runs/v2_cli_20260502_081223_d/c16/artifacts/v2q_c16_1727fec74510f83e/generated_sql.sql b/Query/sql/v2/runs/v2_cli_20260502_081223_d/c16/artifacts/v2q_c16_1727fec74510f83e/generated_sql.sql new file mode 100644 index 0000000000000000000000000000000000000000..2d4ef6423b73256e24e8ee5872263a1710636f7b --- /dev/null +++ b/Query/sql/v2/runs/v2_cli_20260502_081223_d/c16/artifacts/v2q_c16_1727fec74510f83e/generated_sql.sql @@ -0,0 +1,46 @@ +-- sql_source_version: v2 +-- sql_source_label: v2_current +-- sql_source_run_id: v2_cli_20260502_081223_d +-- sql_source_dataset_id: c16 +-- family_id: tail_rarity_structure +-- canonical_subitem_id: tail_concentration_consistency +-- intended_facet_id: rare_target_concentration +-- variant_semantic_role: ranked_signal_view +-- template_id: tpl_grouped_percentile_point +-- query_record_id: v2q_c16_1727fec74510f83e +-- problem_id: v2p_c16_c4cda78613a2c2d5 +-- realization_mode: agent +-- source_kind: agent +WITH "ranked" AS ( + SELECT + "ALIVE", + CAST("YEAR" AS INTEGER) AS "year_value", + ROW_NUMBER() OVER ( + PARTITION BY "ALIVE" + ORDER BY CAST("YEAR" AS INTEGER) + ) AS "rn", + COUNT(*) OVER ( + PARTITION BY "ALIVE" + ) AS "cnt" + FROM "c16" + WHERE "ALIVE" IS NOT NULL + AND "ALIVE" <> '' + AND "YEAR" IS NOT NULL + AND "YEAR" <> '' +), +"percentile_pick" AS ( + SELECT + "ALIVE", + "year_value" AS "percentile_measure", + "rn", + "cnt", + CAST((0.9 * "cnt") + 0.999999999 AS INTEGER) AS "target_rn" + FROM "ranked" +) +SELECT + "ALIVE", + "percentile_measure" +FROM "percentile_pick" +WHERE "cnt" >= 5 + AND "rn" = "target_rn" +ORDER BY "percentile_measure" DESC; diff --git a/Query/sql/v2/runs/v2_cli_20260502_081223_d/c16/artifacts/v2q_c16_1727fec74510f83e/query_results.jsonl b/Query/sql/v2/runs/v2_cli_20260502_081223_d/c16/artifacts/v2q_c16_1727fec74510f83e/query_results.jsonl new file mode 100644 index 0000000000000000000000000000000000000000..4e2f502ede56b6429420b28eb0e6caeda01380aa --- /dev/null +++ b/Query/sql/v2/runs/v2_cli_20260502_081223_d/c16/artifacts/v2q_c16_1727fec74510f83e/query_results.jsonl @@ -0,0 +1 @@ +{"step_index": 1, "message_index": 0, "node_name": "v2-cli:codex", "tool_name": "sqlite_query", "query": "-- template_id: tpl_grouped_percentile_point\nWITH \"ranked\" AS (\n SELECT\n \"ALIVE\",\n CAST(\"YEAR\" AS INTEGER) AS \"year_value\",\n ROW_NUMBER() OVER (\n PARTITION BY \"ALIVE\"\n ORDER BY CAST(\"YEAR\" AS INTEGER)\n ) AS \"rn\",\n COUNT(*) OVER (\n PARTITION BY \"ALIVE\"\n ) AS \"cnt\"\n FROM \"c16\"\n WHERE \"ALIVE\" IS NOT NULL\n AND \"ALIVE\" <> ''\n AND \"YEAR\" IS NOT NULL\n AND \"YEAR\" <> ''\n),\n\"percentile_pick\" AS (\n SELECT\n \"ALIVE\",\n \"year_value\" AS \"percentile_measure\",\n \"rn\",\n \"cnt\",\n CAST((0.9 * \"cnt\") + 0.999999999 AS INTEGER) AS \"target_rn\"\n FROM \"ranked\"\n)\nSELECT\n \"ALIVE\",\n \"percentile_measure\"\nFROM \"percentile_pick\"\nWHERE \"cnt\" >= 5\n AND \"rn\" = \"target_rn\"\nORDER BY \"percentile_measure\" DESC;", "result": "{\"query\": \"-- template_id: tpl_grouped_percentile_point\\nWITH \\\"ranked\\\" AS (\\n SELECT\\n \\\"ALIVE\\\",\\n CAST(\\\"YEAR\\\" AS INTEGER) AS \\\"year_value\\\",\\n ROW_NUMBER() OVER (\\n PARTITION BY \\\"ALIVE\\\"\\n ORDER BY CAST(\\\"YEAR\\\" AS INTEGER)\\n ) AS \\\"rn\\\",\\n COUNT(*) OVER (\\n PARTITION BY \\\"ALIVE\\\"\\n ) AS \\\"cnt\\\"\\n FROM \\\"c16\\\"\\n WHERE \\\"ALIVE\\\" IS NOT NULL\\n AND \\\"ALIVE\\\" <> ''\\n AND \\\"YEAR\\\" IS NOT NULL\\n AND \\\"YEAR\\\" <> ''\\n),\\n\\\"percentile_pick\\\" AS (\\n SELECT\\n \\\"ALIVE\\\",\\n \\\"year_value\\\" AS \\\"percentile_measure\\\",\\n \\\"rn\\\",\\n \\\"cnt\\\",\\n CAST((0.9 * \\\"cnt\\\") + 0.999999999 AS INTEGER) AS \\\"target_rn\\\"\\n FROM \\\"ranked\\\"\\n)\\nSELECT\\n \\\"ALIVE\\\",\\n \\\"percentile_measure\\\"\\nFROM \\\"percentile_pick\\\"\\nWHERE \\\"cnt\\\" >= 5\\n AND \\\"rn\\\" = \\\"target_rn\\\"\\nORDER BY \\\"percentile_measure\\\" DESC;\", \"columns\": [\"ALIVE\", \"percentile_measure\"], \"rows\": [{\"ALIVE\": \"Living Characters\", \"percentile_measure\": 2009}, {\"ALIVE\": \"Deceased Characters\", \"percentile_measure\": 2007}], \"row_count_returned\": 2, \"row_limit\": 50, \"truncated\": false, \"elapsed_ms\": 39.41}"} diff --git a/Query/sql/v2/runs/v2_cli_20260502_081223_d/c16/artifacts/v2q_c16_1727fec74510f83e/run_manifest.json b/Query/sql/v2/runs/v2_cli_20260502_081223_d/c16/artifacts/v2q_c16_1727fec74510f83e/run_manifest.json new file mode 100644 index 0000000000000000000000000000000000000000..9f47bf08ad1219f9f9a965a8a134bf89672da359 --- /dev/null +++ b/Query/sql/v2/runs/v2_cli_20260502_081223_d/c16/artifacts/v2q_c16_1727fec74510f83e/run_manifest.json @@ -0,0 +1,89 @@ +{ + "run_id": "v2_cli_20260502_081223_d", + "dataset_id": "c16", + "started_at": "2026-05-19T15:56:04.345406+00:00", + "ended_at": "2026-05-19T15:56:36.096664+00:00", + "status": "completed", + "engine": "cli", + "question_record": { + "query_record_id": "v2q_c16_1727fec74510f83e", + "problem_id": "v2p_c16_c4cda78613a2c2d5", + "dataset_id": "c16", + "template_id": "tpl_grouped_percentile_point", + "template_name": "Grouped Percentile Point", + "family_id": "tail_rarity_structure", + "canonical_subitem_id": "tail_concentration_consistency", + "intended_facet_id": "rare_target_concentration", + "variant_semantic_role": "ranked_signal_view", + "subitem_assignment_source": "planner_selected", + "source_kind": "agent", + "realization_mode": "agent", + "gate_priority": "primary", + "extended_family": false, + "question": "Use template Grouped Percentile Point to probe tail_concentration_consistency with semantic role ranked_signal_view. Focus on group_col=ALIVE, measure_col=YEAR.", + "bindings": { + "group_col": "ALIVE", + "measure_col": "YEAR", + "top_k": 14, + "top_n": 4, + "num_tiles": 10, + "percentile_value": 0.9, + "z_threshold": 2.0, + "fraction_threshold": 0.1, + "baseline_multiplier": 1.5, + "baseline_fraction": 0.1, + "min_group_size": 5, + "min_support": 5, + "measure_threshold": 2003.0, + "time_grain": "month", + "lookback_rows": 3, + "current_period_start": "'2024-01-01'", + "current_period_end": "'2024-04-01'", + "previous_period_start": "'2023-10-01'", + "previous_period_end": "'2024-01-01'", + "drift_ratio_threshold": 0.8 + }, + "binding_roles": [ + "group_col", + "measure_col" + ], + "coverage_target_min": "5", + "runtime_sql_skeleton": "SELECT {group_col},\n PERCENTILE_CONT({percentile_value}) WITHIN GROUP (ORDER BY {measure_col}) AS percentile_measure\nFROM {table}\nGROUP BY {group_col}\nORDER BY percentile_measure DESC;", + "notes": [ + "default_facets=rare_target_concentration", + "template_selection_mode=rule", + "problem_index_within_template=6", + "sql_variant_index=1/2", + "binding_index=89" + ], + "template_selection_mode": "rule", + "selected_template_rank": 8, + "problem_index_within_template": 6, + "sql_variant_index": 1, + "sql_variant_total": 2 + }, + "mode": "subitem_workload_v2", + "sql_source_version": "v2", + "sql_source_label": "v2_current", + "generated_sql_path": "/data/jialinzhang/TabQueryBench/sql_workloads/v2_current/runs_and_launches/runs/v2_cli_20260502_081223_d/c16/sql/v2q_c16_1727fec74510f83e.sql", + "usage_summary": { + "dataset_id": "c16", + "model": "v2-cli:codex", + "run_id": "v2q_c16_1727fec74510f83e", + "api_calls": 0, + "input_tokens": 14589, + "cached_input_tokens": 13696, + "output_tokens": 1897, + "total_tokens": 16486, + "cost_usd": 0.0, + "ai_cli_calls": 1, + "estimated_input_tokens": 0, + "estimated_output_tokens": 0, + "estimated_total_tokens": 0, + "usage_source": "ai_cli_json_usage", + "cli_elapsed_ms_total": 31705.72, + "sql_execution_elapsed_ms_total": 39.41, + "conversation_log_path": "/data/jialinzhang/TabQueryBench/sql_workloads/v2_current/runs_and_launches/runs/v2_cli_20260502_081223_d/c16/artifacts/v2q_c16_1727fec74510f83e/cli/conversation.jsonl", + "note": "Executed through a local AI CLI with structured usage metadata." + } +} \ No newline at end of file diff --git a/Query/sql/v2/runs/v2_cli_20260502_081223_d/c16/artifacts/v2q_c16_1727fec74510f83e/trace.jsonl b/Query/sql/v2/runs/v2_cli_20260502_081223_d/c16/artifacts/v2q_c16_1727fec74510f83e/trace.jsonl new file mode 100644 index 0000000000000000000000000000000000000000..b11d6d8d4b1b84b5b5e37df8380f9bf36c7fbbbb --- /dev/null +++ b/Query/sql/v2/runs/v2_cli_20260502_081223_d/c16/artifacts/v2q_c16_1727fec74510f83e/trace.jsonl @@ -0,0 +1 @@ +{"timestamp": "2026-05-19T15:56:36.054323+00:00", "event_type": "ai_cli_sql_generation", "engine": "v2-cli:codex", "attempt": 1, "command": "codex exec --skip-git-repo-check --disable plugins --sandbox read-only --cd \"/data/jialinzhang/SQLagent\" -m gpt-5.4 --json -", "returncode": 0, "elapsed_ms": 31705.72, "started_at": "2026-05-19T15:56:04.346973+00:00", "ended_at": "2026-05-19T15:56:36.052721+00:00", "prompt_metrics": {"chars": 8652, "bytes_utf8": 8652, "lines": 252, "estimated_tokens": null}, "response_metrics": {"chars": 1128, "bytes_utf8": 1128, "lines": 1, "estimated_tokens": null}, "usage": {"input_tokens": 14589, "cached_input_tokens": 13696, "output_tokens": 1897, "reasoning_output_tokens": 1552}, "stderr_preview": "", "stdout_preview": "{\"sql\":\"-- template_id: tpl_grouped_percentile_point\\nWITH \\\"ranked\\\" AS (\\n SELECT\\n \\\"ALIVE\\\",\\n CAST(\\\"YEAR\\\" AS INTEGER) AS \\\"year_value\\\",\\n ROW_NUMBER() OVER (\\n PARTITION BY \\\"ALIVE\\\"\\n ORDER BY CAST(\\\"YEAR\\\" AS INTEGER)\\n ) AS \\\"rn\\\",\\n COUNT(*) OVER (\\n PARTITION BY \\\"ALIVE\\\"\\n ) AS \\\"cnt\\\"\\n FROM \\\"c16\\\"\\n WHERE \\\"ALIVE\\\" IS NOT NULL\\n AND \\\"ALIVE\\\" <> ''\\n AND \\\"YEAR\\\" IS NOT NULL\\n AND \\\"YEAR\\\" <> ''\\n),\\n\\\"percentile_pick\\\" AS (\\n SELECT\\n \\\"ALIVE\\\",\\n \\\"year_value\\\" AS \\\"percentile_measure\\\",\\n \\\"rn\\\",\\n \\\"cnt\\\",\\n CAST((0.9 * \\\"cnt\\\") + 0.999999999 AS INTEGER) AS \\\"target_rn\\\"\\n FROM \\\"ranked\\\"\\n)\\nSELECT\\n \\\"ALIVE\\\",\\n \\\"percentile_measure\\\"\\nFROM \\\"percentile_pick\\\"\\nWHERE \\\"cnt\\\" >= 5\\n AND \\\"rn\\\" = \\\"target_rn\\\"\\nORDER BY \\\"percentile_measure\\\" DESC;\",\"notes\":\"SQLite does not support PERCENTILE_CONT, so this uses a nearest-rank discrete 90th percentile per \\\"ALIVE\\\" group via window functions. It casts "} diff --git a/Query/sql/v2/runs/v2_cli_20260502_081223_d/c16/artifacts/v2q_c16_1727fec74510f83e/usage_summary.json b/Query/sql/v2/runs/v2_cli_20260502_081223_d/c16/artifacts/v2q_c16_1727fec74510f83e/usage_summary.json new file mode 100644 index 0000000000000000000000000000000000000000..4b41ed8b3f87e963c855e2fb9603984b0297647d --- /dev/null +++ b/Query/sql/v2/runs/v2_cli_20260502_081223_d/c16/artifacts/v2q_c16_1727fec74510f83e/usage_summary.json @@ -0,0 +1,20 @@ +{ + "dataset_id": "c16", + "model": "v2-cli:codex", + "run_id": "v2q_c16_1727fec74510f83e", + "api_calls": 0, + "input_tokens": 14589, + "cached_input_tokens": 13696, + "output_tokens": 1897, + "total_tokens": 16486, + "cost_usd": 0.0, + "ai_cli_calls": 1, + "estimated_input_tokens": 0, + "estimated_output_tokens": 0, + "estimated_total_tokens": 0, + "usage_source": "ai_cli_json_usage", + "cli_elapsed_ms_total": 31705.72, + "sql_execution_elapsed_ms_total": 39.41, + "conversation_log_path": "/data/jialinzhang/TabQueryBench/sql_workloads/v2_current/runs_and_launches/runs/v2_cli_20260502_081223_d/c16/artifacts/v2q_c16_1727fec74510f83e/cli/conversation.jsonl", + "note": "Executed through a local AI CLI with structured usage metadata." +} \ No newline at end of file diff --git a/Query/sql/v2/runs/v2_cli_20260502_081223_d/c16/artifacts/v2q_c16_1843b940d2e4253c/run_manifest.json b/Query/sql/v2/runs/v2_cli_20260502_081223_d/c16/artifacts/v2q_c16_1843b940d2e4253c/run_manifest.json new file mode 100644 index 0000000000000000000000000000000000000000..5a12aee9adbeac99e358dde7015e14bb8fd7098a --- /dev/null +++ b/Query/sql/v2/runs/v2_cli_20260502_081223_d/c16/artifacts/v2q_c16_1843b940d2e4253c/run_manifest.json @@ -0,0 +1,69 @@ +{ + "run_id": "v2_cli_20260502_081223_d", + "dataset_id": "c16", + "started_at": "2026-05-19T16:09:44.420493+00:00", + "ended_at": "2026-05-19T16:09:51.982059+00:00", + "status": "failed", + "engine": "cli", + "question_record": { + "query_record_id": "v2q_c16_1843b940d2e4253c", + "problem_id": "v2p_c16_b66e7f4030162a67", + "dataset_id": "c16", + "template_id": "tpl_m4_window_partition_avg", + "template_name": "Window Partition Average", + "family_id": "conditional_dependency_structure", + "canonical_subitem_id": "direction_consistency", + "intended_facet_id": "conditional_rate_shift", + "variant_semantic_role": "ranked_signal_view", + "subitem_assignment_source": "planner_selected", + "source_kind": "agent", + "realization_mode": "agent", + "gate_priority": "primary", + "extended_family": false, + "question": "Use template Window Partition Average to probe direction_consistency with semantic role ranked_signal_view. Focus on group_col=GSM, measure_col=YEAR.", + "bindings": { + "group_col": "GSM", + "measure_col": "YEAR", + "top_k": 12, + "top_n": 4, + "num_tiles": 10, + "percentile_value": 0.9, + "z_threshold": 2.0, + "fraction_threshold": 0.1, + "baseline_multiplier": 1.5, + "baseline_fraction": 0.1, + "min_group_size": 5, + "min_support": 5, + "measure_threshold": 2003.0, + "time_grain": "month", + "lookback_rows": 3, + "current_period_start": "'2024-01-01'", + "current_period_end": "'2024-04-01'", + "previous_period_start": "'2023-10-01'", + "previous_period_end": "'2024-01-01'", + "drift_ratio_threshold": 0.8 + }, + "binding_roles": [ + "group_col", + "measure_col" + ], + "coverage_target_min": "5", + "runtime_sql_skeleton": "SELECT DISTINCT {group_col},\n AVG({measure_col}) OVER (PARTITION BY {group_col}) AS avg_measure\nFROM {table}\nORDER BY avg_measure DESC;", + "notes": [ + "default_facets=conditional_rate_shift", + "template_selection_mode=rule", + "problem_index_within_template=6", + "sql_variant_index=1/2", + "binding_index=137" + ], + "template_selection_mode": "rule", + "selected_template_rank": 12, + "problem_index_within_template": 6, + "sql_variant_index": 1, + "sql_variant_total": 2 + }, + "mode": "subitem_workload_v2", + "sql_source_version": "v2", + "sql_source_label": "v2_current", + "error": "AI CLI command failed with exit code 1: " +} \ No newline at end of file diff --git a/Query/sql/v2/runs/v2_cli_20260502_081223_d/c16/artifacts/v2q_c16_1843b940d2e4253c/trace.jsonl b/Query/sql/v2/runs/v2_cli_20260502_081223_d/c16/artifacts/v2q_c16_1843b940d2e4253c/trace.jsonl new file mode 100644 index 0000000000000000000000000000000000000000..6bb484ed2a0979ef2610a12ccf530fbbf210d14f --- /dev/null +++ b/Query/sql/v2/runs/v2_cli_20260502_081223_d/c16/artifacts/v2q_c16_1843b940d2e4253c/trace.jsonl @@ -0,0 +1,2 @@ +{"timestamp": "2026-05-19T16:09:47.494853+00:00", "event_type": "ai_cli_sql_generation_error", "engine": "v2-cli:codex", "attempt": 1, "command": "codex exec --skip-git-repo-check --disable plugins --sandbox read-only --cd \"/data/jialinzhang/SQLagent\" -m gpt-5.4 --json -", "returncode": 1, "elapsed_ms": 3071.99, "started_at": "2026-05-19T16:09:44.422141+00:00", "ended_at": "2026-05-19T16:09:47.494158+00:00", "prompt_metrics": {"chars": 8552, "bytes_utf8": 8552, "lines": 252, "estimated_tokens": null}, "response_metrics": {"chars": 280, "bytes_utf8": 280, "lines": 4, "estimated_tokens": null}, "usage": {}, "stderr_preview": "", "stdout_preview": "{\"type\":\"thread.started\",\"thread_id\":\"019e4100-140a-7c41-871b-3e82662fe1d6\"}\n{\"type\":\"turn.started\"}\n{\"type\":\"error\",\"message\":\"Quota exceeded. Check your plan and billing details.\"}\n{\"type\":\"turn.failed\",\"error\":{\"message\":\"Quota exceeded. Check your plan and billing details.\"}}", "error": "AI CLI command failed with exit code 1: "} +{"timestamp": "2026-05-19T16:09:51.981899+00:00", "event_type": "ai_cli_sql_generation_error", "engine": "v2-cli:codex", "attempt": 2, "command": "codex exec --skip-git-repo-check --disable plugins --sandbox read-only --cd \"/data/jialinzhang/SQLagent\" -m gpt-5.4 --json -", "returncode": 1, "elapsed_ms": 3485.47, "started_at": "2026-05-19T16:09:48.495646+00:00", "ended_at": "2026-05-19T16:09:51.981146+00:00", "prompt_metrics": {"chars": 8552, "bytes_utf8": 8552, "lines": 252, "estimated_tokens": null}, "response_metrics": {"chars": 280, "bytes_utf8": 280, "lines": 4, "estimated_tokens": null}, "usage": {}, "stderr_preview": "", "stdout_preview": "{\"type\":\"thread.started\",\"thread_id\":\"019e4100-23d7-71e0-9c21-eea0cd7144ad\"}\n{\"type\":\"turn.started\"}\n{\"type\":\"error\",\"message\":\"Quota exceeded. Check your plan and billing details.\"}\n{\"type\":\"turn.failed\",\"error\":{\"message\":\"Quota exceeded. Check your plan and billing details.\"}}", "error": "AI CLI command failed with exit code 1: "} diff --git a/Query/sql/v2/runs/v2_cli_20260502_081223_d/c16/artifacts/v2q_c16_332bd5ff14cb9a67/final_answer.txt b/Query/sql/v2/runs/v2_cli_20260502_081223_d/c16/artifacts/v2q_c16_332bd5ff14cb9a67/final_answer.txt new file mode 100644 index 0000000000000000000000000000000000000000..0bd250522a80a637857563e2d8695cd2e4aab84c --- /dev/null +++ b/Query/sql/v2/runs/v2_cli_20260502_081223_d/c16/artifacts/v2q_c16_332bd5ff14cb9a67/final_answer.txt @@ -0,0 +1 @@ +{"row_count": null, "preview_rows": [{"value_label": "2006", "support": 303, "support_share": 0.0439385150812065, "support_rank": 1}, {"value_label": "1988", "support": 286, "support_share": 0.04147331786542923, "support_rank": 2}, {"value_label": "2010", "support": 279, "support_share": 0.040458236658932716, "support_rank": 3}, {"value_label": "1989", "support": 266, "support_share": 0.03857308584686775, "support_rank": 4}, {"value_label": "1987", "support": 254, "support_share": 0.036832946635730855, "support_rank": 5}]} \ No newline at end of file diff --git a/Query/sql/v2/runs/v2_cli_20260502_081223_d/c16/artifacts/v2q_c16_332bd5ff14cb9a67/generated_sql.sql b/Query/sql/v2/runs/v2_cli_20260502_081223_d/c16/artifacts/v2q_c16_332bd5ff14cb9a67/generated_sql.sql new file mode 100644 index 0000000000000000000000000000000000000000..eec8a14b41adc15f9a23272e0b3c4fcd44497476 --- /dev/null +++ b/Query/sql/v2/runs/v2_cli_20260502_081223_d/c16/artifacts/v2q_c16_332bd5ff14cb9a67/generated_sql.sql @@ -0,0 +1,25 @@ +-- sql_source_version: v2 +-- sql_source_label: v2_current +-- sql_source_run_id: v2_cli_20260502_081223_d +-- sql_source_dataset_id: c16 +-- family_id: cardinality_structure +-- canonical_subitem_id: support_rank_profile_consistency +-- intended_facet_id: support_concentration +-- variant_semantic_role: count_distribution +-- template_id: tpl_cardinality_support_rank_profile +-- query_record_id: v2q_c16_332bd5ff14cb9a67 +-- problem_id: v2p_c16_45a54be57ce4c177 +-- realization_mode: deterministic +-- source_kind: deterministic +WITH grouped AS ( + SELECT "YEAR" AS value_label, COUNT(*) AS support + FROM "c16" + GROUP BY "YEAR" +) +SELECT + value_label, + support, + CAST(support AS FLOAT) / NULLIF(SUM(support) OVER (), 0) AS support_share, + ROW_NUMBER() OVER (ORDER BY support DESC, value_label) AS support_rank +FROM grouped +ORDER BY support DESC, value_label; diff --git a/Query/sql/v2/runs/v2_cli_20260502_081223_d/c16/artifacts/v2q_c16_332bd5ff14cb9a67/query_results.jsonl b/Query/sql/v2/runs/v2_cli_20260502_081223_d/c16/artifacts/v2q_c16_332bd5ff14cb9a67/query_results.jsonl new file mode 100644 index 0000000000000000000000000000000000000000..751ecebde67f0fdc76dead4a7231e1aab3eb24fd --- /dev/null +++ b/Query/sql/v2/runs/v2_cli_20260502_081223_d/c16/artifacts/v2q_c16_332bd5ff14cb9a67/query_results.jsonl @@ -0,0 +1 @@ +{"node_name": "v2_template", "tool_name": "sqlite_query", "query": "-- sql_source_version: v2\n-- sql_source_label: v2_current\n-- sql_source_run_id: v2_cli_20260502_081223_d\n-- sql_source_dataset_id: c16\n-- family_id: cardinality_structure\n-- canonical_subitem_id: support_rank_profile_consistency\n-- intended_facet_id: support_concentration\n-- variant_semantic_role: count_distribution\n-- template_id: tpl_cardinality_support_rank_profile\n-- query_record_id: v2q_c16_332bd5ff14cb9a67\n-- problem_id: v2p_c16_45a54be57ce4c177\n-- realization_mode: deterministic\n-- source_kind: deterministic\nWITH grouped AS (\n SELECT \"YEAR\" AS value_label, COUNT(*) AS support\n FROM \"c16\"\n GROUP BY \"YEAR\"\n)\nSELECT\n value_label,\n support,\n CAST(support AS FLOAT) / NULLIF(SUM(support) OVER (), 0) AS support_share,\n ROW_NUMBER() OVER (ORDER BY support DESC, value_label) AS support_rank\nFROM grouped\nORDER BY support DESC, value_label;", "result": "{\"query\": \"-- sql_source_version: v2\\n-- sql_source_label: v2_current\\n-- sql_source_run_id: v2_cli_20260502_081223_d\\n-- sql_source_dataset_id: c16\\n-- family_id: cardinality_structure\\n-- canonical_subitem_id: support_rank_profile_consistency\\n-- intended_facet_id: support_concentration\\n-- variant_semantic_role: count_distribution\\n-- template_id: tpl_cardinality_support_rank_profile\\n-- query_record_id: v2q_c16_332bd5ff14cb9a67\\n-- problem_id: v2p_c16_45a54be57ce4c177\\n-- realization_mode: deterministic\\n-- source_kind: deterministic\\nWITH grouped AS (\\n SELECT \\\"YEAR\\\" AS value_label, COUNT(*) AS support\\n FROM \\\"c16\\\"\\n GROUP BY \\\"YEAR\\\"\\n)\\nSELECT\\n value_label,\\n support,\\n CAST(support AS FLOAT) / NULLIF(SUM(support) OVER (), 0) AS support_share,\\n ROW_NUMBER() OVER (ORDER BY support DESC, value_label) AS support_rank\\nFROM grouped\\nORDER BY support DESC, value_label;\", \"columns\": [\"value_label\", \"support\", \"support_share\", \"support_rank\"], \"rows\": [{\"value_label\": \"2006\", \"support\": 303, \"support_share\": 0.0439385150812065, \"support_rank\": 1}, {\"value_label\": \"1988\", \"support\": 286, \"support_share\": 0.04147331786542923, \"support_rank\": 2}, {\"value_label\": \"2010\", \"support\": 279, \"support_share\": 0.040458236658932716, \"support_rank\": 3}, {\"value_label\": \"1989\", \"support\": 266, \"support_share\": 0.03857308584686775, \"support_rank\": 4}, {\"value_label\": \"1987\", \"support\": 254, \"support_share\": 0.036832946635730855, \"support_rank\": 5}, {\"value_label\": \"1994\", \"support\": 230, \"support_share\": 0.03335266821345707, \"support_rank\": 6}, {\"value_label\": \"2009\", \"support\": 226, \"support_share\": 0.03277262180974478, \"support_rank\": 7}, {\"value_label\": \"2008\", \"support\": 211, \"support_share\": 0.030597447795823667, \"support_rank\": 8}, {\"value_label\": \"1993\", \"support\": 209, \"support_share\": 0.030307424593967517, \"support_rank\": 9}, {\"value_label\": \"1997\", \"support\": 189, \"support_share\": 0.027407192575406032, \"support_rank\": 10}, {\"value_label\": \"1996\", \"support\": 188, \"support_share\": 0.027262180974477957, \"support_rank\": 11}, {\"value_label\": \"2007\", \"support\": 188, \"support_share\": 0.027262180974477957, \"support_rank\": 12}, {\"value_label\": \"1999\", \"support\": 179, \"support_share\": 0.02595707656612529, \"support_rank\": 13}, {\"value_label\": \"1992\", \"support\": 178, \"support_share\": 0.025812064965197216, \"support_rank\": 14}, {\"value_label\": \"1990\", \"support\": 175, \"support_share\": 0.025377030162412995, \"support_rank\": 15}, {\"value_label\": \"1995\", \"support\": 172, \"support_share\": 0.02494199535962877, \"support_rank\": 16}, {\"value_label\": \"1983\", \"support\": 161, \"support_share\": 0.023346867749419953, \"support_rank\": 17}, {\"value_label\": \"2005\", \"support\": 159, \"support_share\": 0.023056844547563807, \"support_rank\": 18}, {\"value_label\": \"2011\", \"support\": 155, \"support_share\": 0.02247679814385151, \"support_rank\": 19}, {\"value_label\": \"2000\", \"support\": 152, \"support_share\": 0.022041763341067284, \"support_rank\": 20}, {\"value_label\": \"1991\", \"support\": 145, \"support_share\": 0.021026682134570766, \"support_rank\": 21}, {\"value_label\": \"1998\", \"support\": 143, \"support_share\": 0.020736658932714615, \"support_rank\": 22}, {\"value_label\": \"1984\", \"support\": 141, \"support_share\": 0.02044663573085847, \"support_rank\": 23}, {\"value_label\": \"1986\", \"support\": 132, \"support_share\": 0.0191415313225058, \"support_rank\": 24}, {\"value_label\": \"1981\", \"support\": 119, \"support_share\": 0.017256380510440834, \"support_rank\": 25}, {\"value_label\": \"1985\", \"support\": 115, \"support_share\": 0.016676334106728537, \"support_rank\": 26}, {\"value_label\": \"2002\", \"support\": 115, \"support_share\": 0.016676334106728537, \"support_rank\": 27}, {\"value_label\": \"1982\", \"support\": 111, \"support_share\": 0.01609628770301624, \"support_rank\": 28}, {\"value_label\": \"2003\", \"support\": 103, \"support_share\": 0.014936194895591648, \"support_rank\": 29}, {\"value_label\": \"2004\", \"support\": 102, \"support_share\": 0.014791183294663572, \"support_rank\": 30}, {\"value_label\": \"2001\", \"support\": 99, \"support_share\": 0.01435614849187935, \"support_rank\": 31}, {\"value_label\": \"\", \"support\": 69, \"support_share\": 0.010005800464037123, \"support_rank\": 32}, {\"value_label\": \"1971\", \"support\": 65, \"support_share\": 0.009425754060324826, \"support_rank\": 33}, {\"value_label\": \"1940\", \"support\": 64, \"support_share\": 0.009280742459396751, \"support_rank\": 34}, {\"value_label\": \"1941\", \"support\": 61, \"support_share\": 0.00884570765661253, \"support_rank\": 35}, {\"value_label\": \"1966\", \"support\": 61, \"support_share\": 0.00884570765661253, \"support_rank\": 36}, {\"value_label\": \"1968\", \"support\": 61, \"support_share\": 0.00884570765661253, \"support_rank\": 37}, {\"value_label\": \"1972\", \"support\": 61, \"support_share\": 0.00884570765661253, \"support_rank\": 38}, {\"value_label\": \"1978\", \"support\": 60, \"support_share\": 0.008700696055684454, \"support_rank\": 39}, {\"value_label\": \"1967\", \"support\": 56, \"support_share\": 0.008120649651972157, \"support_rank\": 40}, {\"value_label\": \"1942\", \"support\": 52, \"support_share\": 0.0075406032482598605, \"support_rank\": 41}, {\"value_label\": \"1977\", \"support\": 52, \"support_share\": 0.0075406032482598605, \"support_rank\": 42}, {\"value_label\": \"1961\", \"support\": 50, \"support_share\": 0.007250580046403712, \"support_rank\": 43}, {\"value_label\": \"1965\", \"support\": 50, \"support_share\": 0.007250580046403712, \"support_rank\": 44}, {\"value_label\": \"1976\", \"support\": 45, \"support_share\": 0.006525522041763341, \"support_rank\": 45}, {\"value_label\": \"1962\", \"support\": 42, \"support_share\": 0.006090487238979118, \"support_rank\": 46}, {\"value_label\": \"1963\", \"support\": 40, \"support_share\": 0.00580046403712297, \"support_rank\": 47}, {\"value_label\": \"1960\", \"support\": 39, \"support_share\": 0.005655452436194895, \"support_rank\": 48}, {\"value_label\": \"1975\", \"support\": 39, \"support_share\": 0.005655452436194895, \"support_rank\": 49}, {\"value_label\": \"1980\", \"support\": 36, \"support_share\": 0.005220417633410673, \"support_rank\": 50}], \"row_count_returned\": 50, \"row_limit\": 50, \"truncated\": true, \"elapsed_ms\": 2.6}"} diff --git a/Query/sql/v2/runs/v2_cli_20260502_081223_d/c16/artifacts/v2q_c16_332bd5ff14cb9a67/run_manifest.json b/Query/sql/v2/runs/v2_cli_20260502_081223_d/c16/artifacts/v2q_c16_332bd5ff14cb9a67/run_manifest.json new file mode 100644 index 0000000000000000000000000000000000000000..b8c3143aa0d213358c87bccfa8385d53e70c551e --- /dev/null +++ b/Query/sql/v2/runs/v2_cli_20260502_081223_d/c16/artifacts/v2q_c16_332bd5ff14cb9a67/run_manifest.json @@ -0,0 +1,57 @@ +{ + "run_id": "v2_cli_20260502_081223_d", + "dataset_id": "c16", + "started_at": "2026-05-19T16:10:30.497372+00:00", + "ended_at": "2026-05-19T16:10:30.500755+00:00", + "status": "completed", + "engine": "cli", + "question_record": { + "query_record_id": "v2q_c16_332bd5ff14cb9a67", + "problem_id": "v2p_c16_45a54be57ce4c177", + "dataset_id": "c16", + "template_id": "tpl_cardinality_support_rank_profile", + "template_name": "Cardinality Support Rank Profile", + "family_id": "cardinality_structure", + "canonical_subitem_id": "support_rank_profile_consistency", + "intended_facet_id": "support_concentration", + "variant_semantic_role": "count_distribution", + "subitem_assignment_source": "template_fixed", + "source_kind": "deterministic", + "realization_mode": "deterministic", + "gate_priority": "deterministic", + "extended_family": true, + "question": "Use template Cardinality Support Rank Profile to probe support_rank_profile_consistency with semantic role count_distribution. Focus on group_col=YEAR.", + "bindings": { + "group_col": "YEAR" + }, + "binding_roles": [ + "group_col" + ], + "coverage_target_min": "enumerate_all_applicable", + "runtime_sql_skeleton": "WITH grouped AS (\n SELECT {group_col} AS value_label, COUNT(*) AS support\n FROM {table}\n GROUP BY {group_col}\n)\nSELECT\n value_label,\n support,\n CAST(support AS FLOAT) / NULLIF(SUM(support) OVER (), 0) AS support_share,\n ROW_NUMBER() OVER (ORDER BY support DESC, value_label) AS support_rank\nFROM grouped\nORDER BY support DESC, value_label;", + "notes": [ + "default_facets=support_concentration,value_imbalance_profile", + "template_selection_mode=deterministic", + "problem_index_within_template=7", + "sql_variant_index=1/1" + ], + "template_selection_mode": "deterministic", + "selected_template_rank": 0, + "problem_index_within_template": 7, + "sql_variant_index": 1, + "sql_variant_total": 1 + }, + "mode": "subitem_workload_v2", + "sql_source_version": "v2", + "sql_source_label": "v2_current", + "generated_sql_path": "/data/jialinzhang/TabQueryBench/sql_workloads/v2_current/runs_and_launches/runs/v2_cli_20260502_081223_d/c16/sql/v2q_c16_332bd5ff14cb9a67.sql", + "usage_summary": { + "engine": "template", + "input_tokens": 0, + "cached_input_tokens": 0, + "output_tokens": 0, + "total_tokens": 0, + "estimated_total_tokens": 0, + "usage_source": "none" + } +} \ No newline at end of file diff --git a/Query/sql/v2/runs/v2_cli_20260502_081223_d/c16/artifacts/v2q_c16_332bd5ff14cb9a67/usage_summary.json b/Query/sql/v2/runs/v2_cli_20260502_081223_d/c16/artifacts/v2q_c16_332bd5ff14cb9a67/usage_summary.json new file mode 100644 index 0000000000000000000000000000000000000000..96c9ff4feec395919fc26411d18d078b8af6e1c7 --- /dev/null +++ b/Query/sql/v2/runs/v2_cli_20260502_081223_d/c16/artifacts/v2q_c16_332bd5ff14cb9a67/usage_summary.json @@ -0,0 +1,9 @@ +{ + "engine": "template", + "input_tokens": 0, + "cached_input_tokens": 0, + "output_tokens": 0, + "total_tokens": 0, + "estimated_total_tokens": 0, + "usage_source": "none" +} \ No newline at end of file diff --git a/Query/sql/v2/runs/v2_cli_20260502_081223_d/c16/artifacts/v2q_c16_389ecc90400bf53c/final_answer.txt b/Query/sql/v2/runs/v2_cli_20260502_081223_d/c16/artifacts/v2q_c16_389ecc90400bf53c/final_answer.txt new file mode 100644 index 0000000000000000000000000000000000000000..f67ea945525c38f524dd7d1407a1dcfac8eff95c --- /dev/null +++ b/Query/sql/v2/runs/v2_cli_20260502_081223_d/c16/artifacts/v2q_c16_389ecc90400bf53c/final_answer.txt @@ -0,0 +1,2 @@ +SQL executed successfully for: Use template Within-Group Share of Total to probe dependency_strength_similarity with semantic role focused_target_view. Focus on group_col=GSM, measure_col=YEAR. +Result preview: [{"GSM": "", "APPEARANCES": "1", "total_measure": 1977551.0, "share_within_group": 14.693566605427096}, {"GSM": "", "APPEARANCES": "2", "total_measure": 1375780.0, "share_within_group": 10.222297712885528}, {"GSM": "Bisexual Characters", "APPEARANCES": "100", "total_measure": 2003.0, "share_within_group": 10.077987421383648}, {"GSM": "Bisexual Characters", "APPEARANCES": "16", "total_measure": 1997.0, "share_within_group": 10.047798742138365}, {"GSM": "Bisexual Characters", "APPEARANCES": "17", "total_measure": 1997.0, "share_within_group": 10.047798742138365}] Results were truncated. \ No newline at end of file diff --git a/Query/sql/v2/runs/v2_cli_20260502_081223_d/c16/artifacts/v2q_c16_389ecc90400bf53c/generated_sql.sql b/Query/sql/v2/runs/v2_cli_20260502_081223_d/c16/artifacts/v2q_c16_389ecc90400bf53c/generated_sql.sql new file mode 100644 index 0000000000000000000000000000000000000000..93b256b6e894c7d97a6abca20dafa26b8a0dc7f2 --- /dev/null +++ b/Query/sql/v2/runs/v2_cli_20260502_081223_d/c16/artifacts/v2q_c16_389ecc90400bf53c/generated_sql.sql @@ -0,0 +1,22 @@ +-- sql_source_version: v2 +-- sql_source_label: v2_current +-- sql_source_run_id: v2_cli_20260502_081223_d +-- sql_source_dataset_id: c16 +-- family_id: conditional_dependency_structure +-- canonical_subitem_id: dependency_strength_similarity +-- intended_facet_id: pairwise_conditional_dependency +-- variant_semantic_role: focused_target_view +-- template_id: tpl_tpcds_within_group_share +-- query_record_id: v2q_c16_389ecc90400bf53c +-- problem_id: v2p_c16_dfb68d6eaf359fd0 +-- realization_mode: agent +-- source_kind: agent +SELECT + "GSM", + "APPEARANCES", + SUM(CAST(NULLIF("YEAR", '') AS REAL)) AS "total_measure", + SUM(CAST(NULLIF("YEAR", '') AS REAL)) * 100.0 / SUM(SUM(CAST(NULLIF("YEAR", '') AS REAL))) OVER (PARTITION BY "GSM") AS "share_within_group" +FROM "c16" +WHERE NULLIF("YEAR", '') IS NOT NULL +GROUP BY "GSM", "APPEARANCES" +ORDER BY "share_within_group" DESC; diff --git a/Query/sql/v2/runs/v2_cli_20260502_081223_d/c16/artifacts/v2q_c16_389ecc90400bf53c/query_results.jsonl b/Query/sql/v2/runs/v2_cli_20260502_081223_d/c16/artifacts/v2q_c16_389ecc90400bf53c/query_results.jsonl new file mode 100644 index 0000000000000000000000000000000000000000..f061e40b76d6e656f57d18f0b962aa651124f3de --- /dev/null +++ b/Query/sql/v2/runs/v2_cli_20260502_081223_d/c16/artifacts/v2q_c16_389ecc90400bf53c/query_results.jsonl @@ -0,0 +1 @@ +{"step_index": 1, "message_index": 0, "node_name": "v2-cli:codex", "tool_name": "sqlite_query", "query": "-- template_id: tpl_tpcds_within_group_share\nSELECT\n \"GSM\",\n \"APPEARANCES\",\n SUM(CAST(NULLIF(\"YEAR\", '') AS REAL)) AS \"total_measure\",\n SUM(CAST(NULLIF(\"YEAR\", '') AS REAL)) * 100.0 / SUM(SUM(CAST(NULLIF(\"YEAR\", '') AS REAL))) OVER (PARTITION BY \"GSM\") AS \"share_within_group\"\nFROM \"c16\"\nWHERE NULLIF(\"YEAR\", '') IS NOT NULL\nGROUP BY \"GSM\", \"APPEARANCES\"\nORDER BY \"share_within_group\" DESC;", "result": "{\"query\": \"-- template_id: tpl_tpcds_within_group_share\\nSELECT\\n \\\"GSM\\\",\\n \\\"APPEARANCES\\\",\\n SUM(CAST(NULLIF(\\\"YEAR\\\", '') AS REAL)) AS \\\"total_measure\\\",\\n SUM(CAST(NULLIF(\\\"YEAR\\\", '') AS REAL)) * 100.0 / SUM(SUM(CAST(NULLIF(\\\"YEAR\\\", '') AS REAL))) OVER (PARTITION BY \\\"GSM\\\") AS \\\"share_within_group\\\"\\nFROM \\\"c16\\\"\\nWHERE NULLIF(\\\"YEAR\\\", '') IS NOT NULL\\nGROUP BY \\\"GSM\\\", \\\"APPEARANCES\\\"\\nORDER BY \\\"share_within_group\\\" DESC;\", \"columns\": [\"GSM\", \"APPEARANCES\", \"total_measure\", \"share_within_group\"], \"rows\": [{\"GSM\": \"\", \"APPEARANCES\": \"1\", \"total_measure\": 1977551.0, \"share_within_group\": 14.693566605427096}, {\"GSM\": \"\", \"APPEARANCES\": \"2\", \"total_measure\": 1375780.0, \"share_within_group\": 10.222297712885528}, {\"GSM\": \"Bisexual Characters\", \"APPEARANCES\": \"100\", \"total_measure\": 2003.0, \"share_within_group\": 10.077987421383648}, {\"GSM\": \"Bisexual Characters\", \"APPEARANCES\": \"16\", \"total_measure\": 1997.0, \"share_within_group\": 10.047798742138365}, {\"GSM\": \"Bisexual Characters\", \"APPEARANCES\": \"17\", \"total_measure\": 1997.0, \"share_within_group\": 10.047798742138365}, {\"GSM\": \"Bisexual Characters\", \"APPEARANCES\": \"53\", \"total_measure\": 1994.0, \"share_within_group\": 10.032704402515723}, {\"GSM\": \"Bisexual Characters\", \"APPEARANCES\": \"34\", \"total_measure\": 1993.0, \"share_within_group\": 10.027672955974843}, {\"GSM\": \"Bisexual Characters\", \"APPEARANCES\": \"20\", \"total_measure\": 1989.0, \"share_within_group\": 10.007547169811321}, {\"GSM\": \"Bisexual Characters\", \"APPEARANCES\": \"97\", \"total_measure\": 1989.0, \"share_within_group\": 10.007547169811321}, {\"GSM\": \"Bisexual Characters\", \"APPEARANCES\": \"38\", \"total_measure\": 1986.0, \"share_within_group\": 9.992452830188679}, {\"GSM\": \"Bisexual Characters\", \"APPEARANCES\": \"371\", \"total_measure\": 1984.0, \"share_within_group\": 9.982389937106918}, {\"GSM\": \"Bisexual Characters\", \"APPEARANCES\": \"32\", \"total_measure\": 1943.0, \"share_within_group\": 9.776100628930818}, {\"GSM\": \"Homosexual Characters\", \"APPEARANCES\": \"2\", \"total_measure\": 9993.0, \"share_within_group\": 9.459126878951952}, {\"GSM\": \"Homosexual Characters\", \"APPEARANCES\": \"10\", \"total_measure\": 7987.0, \"share_within_group\": 7.5602968460111315}, {\"GSM\": \"\", \"APPEARANCES\": \"3\", \"total_measure\": 996920.0, \"share_within_group\": 7.4072984313842625}, {\"GSM\": \"\", \"APPEARANCES\": \"4\", \"total_measure\": 983118.0, \"share_within_group\": 7.3047470401492935}, {\"GSM\": \"\", \"APPEARANCES\": \"5\", \"total_measure\": 763652.0, \"share_within_group\": 5.674074410909054}, {\"GSM\": \"Homosexual Characters\", \"APPEARANCES\": \"8\", \"total_measure\": 5969.0, \"share_within_group\": 5.650107909583128}, {\"GSM\": \"\", \"APPEARANCES\": \"\", \"total_measure\": 688967.0, \"share_within_group\": 5.119151163960519}, {\"GSM\": \"\", \"APPEARANCES\": \"6\", \"total_measure\": 644018.0, \"share_within_group\": 4.785171850482716}, {\"GSM\": \"\", \"APPEARANCES\": \"7\", \"total_measure\": 520196.0, \"share_within_group\": 3.865151681992906}, {\"GSM\": \"Homosexual Characters\", \"APPEARANCES\": \"1\", \"total_measure\": 4003.0, \"share_within_group\": 3.7891408882662527}, {\"GSM\": \"Homosexual Characters\", \"APPEARANCES\": \"6\", \"total_measure\": 4003.0, \"share_within_group\": 3.7891408882662527}, {\"GSM\": \"Homosexual Characters\", \"APPEARANCES\": \"32\", \"total_measure\": 3997.0, \"share_within_group\": 3.783461436522661}, {\"GSM\": \"Homosexual Characters\", \"APPEARANCES\": \"25\", \"total_measure\": 3992.0, \"share_within_group\": 3.778728560069668}, {\"GSM\": \"Homosexual Characters\", \"APPEARANCES\": \"65\", \"total_measure\": 3992.0, \"share_within_group\": 3.778728560069668}, {\"GSM\": \"Homosexual Characters\", \"APPEARANCES\": \"14\", \"total_measure\": 3991.0, \"share_within_group\": 3.7777819847790695}, {\"GSM\": \"Homosexual Characters\", \"APPEARANCES\": \"12\", \"total_measure\": 3985.0, \"share_within_group\": 3.772102533035478}, {\"GSM\": \"Homosexual Characters\", \"APPEARANCES\": \"4\", \"total_measure\": 3983.0, \"share_within_group\": 3.7702093824542806}, {\"GSM\": \"Homosexual Characters\", \"APPEARANCES\": \"3\", \"total_measure\": 3976.0, \"share_within_group\": 3.76358335542009}, {\"GSM\": \"\", \"APPEARANCES\": \"8\", \"total_measure\": 471636.0, \"share_within_group\": 3.5043419762712635}, {\"GSM\": \"\", \"APPEARANCES\": \"9\", \"total_measure\": 370163.0, \"share_within_group\": 2.7503789765041255}, {\"GSM\": \"\", \"APPEARANCES\": \"10\", \"total_measure\": 350341.0, \"share_within_group\": 2.6030978812237633}, {\"GSM\": \"\", \"APPEARANCES\": \"11\", \"total_measure\": 320208.0, \"share_within_group\": 2.3792041649447215}, {\"GSM\": \"Homosexual Characters\", \"APPEARANCES\": \"17\", \"total_measure\": 2009.0, \"share_within_group\": 1.901669758812616}, {\"GSM\": \"Homosexual Characters\", \"APPEARANCES\": \"19\", \"total_measure\": 2006.0, \"share_within_group\": 1.89883003294082}, {\"GSM\": \"Homosexual Characters\", \"APPEARANCES\": \"51\", \"total_measure\": 2006.0, \"share_within_group\": 1.89883003294082}, {\"GSM\": \"Homosexual Characters\", \"APPEARANCES\": \"36\", \"total_measure\": 2004.0, \"share_within_group\": 1.8969368823596229}, {\"GSM\": \"Homosexual Characters\", \"APPEARANCES\": \"15\", \"total_measure\": 2003.0, \"share_within_group\": 1.8959903070690243}, {\"GSM\": \"Homosexual Characters\", \"APPEARANCES\": \"92\", \"total_measure\": 2003.0, \"share_within_group\": 1.8959903070690243}, {\"GSM\": \"Homosexual Characters\", \"APPEARANCES\": \"20\", \"total_measure\": 2002.0, \"share_within_group\": 1.8950437317784257}, {\"GSM\": \"Homosexual Characters\", \"APPEARANCES\": \"31\", \"total_measure\": 2002.0, \"share_within_group\": 1.8950437317784257}, {\"GSM\": \"Homosexual Characters\", \"APPEARANCES\": \"21\", \"total_measure\": 1996.0, \"share_within_group\": 1.889364280034834}, {\"GSM\": \"Homosexual Characters\", \"APPEARANCES\": \"34\", \"total_measure\": 1994.0, \"share_within_group\": 1.8874711294536368}, {\"GSM\": \"Homosexual Characters\", \"APPEARANCES\": \"308\", \"total_measure\": 1992.0, \"share_within_group\": 1.8855779788724396}, {\"GSM\": \"Homosexual Characters\", \"APPEARANCES\": \"24\", \"total_measure\": 1988.0, \"share_within_group\": 1.881791677710045}, {\"GSM\": \"Homosexual Characters\", \"APPEARANCES\": \"29\", \"total_measure\": 1988.0, \"share_within_group\": 1.881791677710045}, {\"GSM\": \"Homosexual Characters\", \"APPEARANCES\": \"11\", \"total_measure\": 1987.0, \"share_within_group\": 1.8808451024194464}, {\"GSM\": \"Homosexual Characters\", \"APPEARANCES\": \"114\", \"total_measure\": 1987.0, \"share_within_group\": 1.8808451024194464}, {\"GSM\": \"Homosexual Characters\", \"APPEARANCES\": \"180\", \"total_measure\": 1987.0, \"share_within_group\": 1.8808451024194464}], \"row_count_returned\": 50, \"row_limit\": 50, \"truncated\": true, \"elapsed_ms\": 9.78}"} diff --git a/Query/sql/v2/runs/v2_cli_20260502_081223_d/c16/artifacts/v2q_c16_389ecc90400bf53c/run_manifest.json b/Query/sql/v2/runs/v2_cli_20260502_081223_d/c16/artifacts/v2q_c16_389ecc90400bf53c/run_manifest.json new file mode 100644 index 0000000000000000000000000000000000000000..9520bf54c8d34e7f7e5470fd395a9bfb38f2c84d --- /dev/null +++ b/Query/sql/v2/runs/v2_cli_20260502_081223_d/c16/artifacts/v2q_c16_389ecc90400bf53c/run_manifest.json @@ -0,0 +1,91 @@ +{ + "run_id": "v2_cli_20260502_081223_d", + "dataset_id": "c16", + "started_at": "2026-05-19T15:38:17.585769+00:00", + "ended_at": "2026-05-19T15:38:32.813796+00:00", + "status": "completed", + "engine": "cli", + "question_record": { + "query_record_id": "v2q_c16_389ecc90400bf53c", + "problem_id": "v2p_c16_dfb68d6eaf359fd0", + "dataset_id": "c16", + "template_id": "tpl_tpcds_within_group_share", + "template_name": "Within-Group Share of Total", + "family_id": "conditional_dependency_structure", + "canonical_subitem_id": "dependency_strength_similarity", + "intended_facet_id": "pairwise_conditional_dependency", + "variant_semantic_role": "focused_target_view", + "subitem_assignment_source": "planner_selected", + "source_kind": "agent", + "realization_mode": "agent", + "gate_priority": "primary", + "extended_family": false, + "question": "Use template Within-Group Share of Total to probe dependency_strength_similarity with semantic role focused_target_view. Focus on group_col=GSM, measure_col=YEAR.", + "bindings": { + "group_col": "GSM", + "measure_col": "YEAR", + "item_col": "APPEARANCES", + "top_k": 17, + "top_n": 4, + "num_tiles": 10, + "percentile_value": 0.9, + "z_threshold": 2.0, + "fraction_threshold": 0.05, + "baseline_multiplier": 1.75, + "baseline_fraction": 0.1, + "min_group_size": 5, + "min_support": 4, + "measure_threshold": 1998.0, + "time_grain": "month", + "lookback_rows": 3, + "current_period_start": "'2024-01-01'", + "current_period_end": "'2024-04-01'", + "previous_period_start": "'2023-10-01'", + "previous_period_end": "'2024-01-01'", + "drift_ratio_threshold": 0.8 + }, + "binding_roles": [ + "group_col", + "item_col", + "measure_col" + ], + "coverage_target_min": "5", + "runtime_sql_skeleton": "SELECT {group_col}, {item_col},\n SUM({measure_col}) AS total_measure,\n SUM({measure_col}) * 100.0 / SUM(SUM({measure_col})) OVER (PARTITION BY {group_col}) AS share_within_group\nFROM {table}\nGROUP BY {group_col}, {item_col}\nORDER BY share_within_group DESC;", + "notes": [ + "default_facets=pairwise_conditional_dependency", + "template_selection_mode=rule", + "problem_index_within_template=9", + "sql_variant_index=2/2", + "binding_index=32" + ], + "template_selection_mode": "rule", + "selected_template_rank": 3, + "problem_index_within_template": 9, + "sql_variant_index": 2, + "sql_variant_total": 2 + }, + "mode": "subitem_workload_v2", + "sql_source_version": "v2", + "sql_source_label": "v2_current", + "generated_sql_path": "/data/jialinzhang/TabQueryBench/sql_workloads/v2_current/runs_and_launches/runs/v2_cli_20260502_081223_d/c16/sql/v2q_c16_389ecc90400bf53c.sql", + "usage_summary": { + "dataset_id": "c16", + "model": "v2-cli:codex", + "run_id": "v2q_c16_389ecc90400bf53c", + "api_calls": 0, + "input_tokens": 14671, + "cached_input_tokens": 13696, + "output_tokens": 723, + "total_tokens": 15394, + "cost_usd": 0.0, + "ai_cli_calls": 1, + "estimated_input_tokens": 0, + "estimated_output_tokens": 0, + "estimated_total_tokens": 0, + "usage_source": "ai_cli_json_usage", + "cli_elapsed_ms_total": 15211.53, + "sql_execution_elapsed_ms_total": 9.78, + "conversation_log_path": "/data/jialinzhang/TabQueryBench/sql_workloads/v2_current/runs_and_launches/runs/v2_cli_20260502_081223_d/c16/artifacts/v2q_c16_389ecc90400bf53c/cli/conversation.jsonl", + "note": "Executed through a local AI CLI with structured usage metadata." + } +} \ No newline at end of file diff --git a/Query/sql/v2/runs/v2_cli_20260502_081223_d/c16/artifacts/v2q_c16_389ecc90400bf53c/trace.jsonl b/Query/sql/v2/runs/v2_cli_20260502_081223_d/c16/artifacts/v2q_c16_389ecc90400bf53c/trace.jsonl new file mode 100644 index 0000000000000000000000000000000000000000..9e0ad7cd3310fc831d301340e407aaa8bd8dc7aa --- /dev/null +++ b/Query/sql/v2/runs/v2_cli_20260502_081223_d/c16/artifacts/v2q_c16_389ecc90400bf53c/trace.jsonl @@ -0,0 +1 @@ +{"timestamp": "2026-05-19T15:38:32.801409+00:00", "event_type": "ai_cli_sql_generation", "engine": "v2-cli:codex", "attempt": 1, "command": "codex exec --skip-git-repo-check --disable plugins --sandbox read-only --cd \"/data/jialinzhang/SQLagent\" -m gpt-5.4 --json -", "returncode": 0, "elapsed_ms": 15211.53, "started_at": "2026-05-19T15:38:17.588514+00:00", "ended_at": "2026-05-19T15:38:32.800079+00:00", "prompt_metrics": {"chars": 8881, "bytes_utf8": 8881, "lines": 254, "estimated_tokens": null}, "response_metrics": {"chars": 698, "bytes_utf8": 698, "lines": 1, "estimated_tokens": null}, "usage": {"input_tokens": 14671, "cached_input_tokens": 13696, "output_tokens": 723, "reasoning_output_tokens": 516}, "stderr_preview": "", "stdout_preview": "{\"sql\":\"-- template_id: tpl_tpcds_within_group_share\\nSELECT\\n \\\"GSM\\\",\\n \\\"APPEARANCES\\\",\\n SUM(CAST(NULLIF(\\\"YEAR\\\", '') AS REAL)) AS \\\"total_measure\\\",\\n SUM(CAST(NULLIF(\\\"YEAR\\\", '') AS REAL)) * 100.0 / SUM(SUM(CAST(NULLIF(\\\"YEAR\\\", '') AS REAL))) OVER (PARTITION BY \\\"GSM\\\") AS \\\"share_within_group\\\"\\nFROM \\\"c16\\\"\\nWHERE NULLIF(\\\"YEAR\\\", '') IS NOT NULL\\nGROUP BY \\\"GSM\\\", \\\"APPEARANCES\\\"\\nORDER BY \\\"share_within_group\\\" DESC;\",\"notes\":\"Used the provided Within-Group Share of Total template with group_col=\\\"GSM\\\", item_col=\\\"APPEARANCES\\\", and measure_col=\\\"YEAR\\\". Since \\\"YEAR\\\" is stored as TEXT in SQLite, the query casts it to REAL and excludes empty strings before aggregation.\"}"} diff --git a/Query/sql/v2/runs/v2_cli_20260502_081223_d/c16/artifacts/v2q_c16_389ecc90400bf53c/usage_summary.json b/Query/sql/v2/runs/v2_cli_20260502_081223_d/c16/artifacts/v2q_c16_389ecc90400bf53c/usage_summary.json new file mode 100644 index 0000000000000000000000000000000000000000..5ba6292a8482cfdc7324298b694759fcf7adfaad --- /dev/null +++ b/Query/sql/v2/runs/v2_cli_20260502_081223_d/c16/artifacts/v2q_c16_389ecc90400bf53c/usage_summary.json @@ -0,0 +1,20 @@ +{ + "dataset_id": "c16", + "model": "v2-cli:codex", + "run_id": "v2q_c16_389ecc90400bf53c", + "api_calls": 0, + "input_tokens": 14671, + "cached_input_tokens": 13696, + "output_tokens": 723, + "total_tokens": 15394, + "cost_usd": 0.0, + "ai_cli_calls": 1, + "estimated_input_tokens": 0, + "estimated_output_tokens": 0, + "estimated_total_tokens": 0, + "usage_source": "ai_cli_json_usage", + "cli_elapsed_ms_total": 15211.53, + "sql_execution_elapsed_ms_total": 9.78, + "conversation_log_path": "/data/jialinzhang/TabQueryBench/sql_workloads/v2_current/runs_and_launches/runs/v2_cli_20260502_081223_d/c16/artifacts/v2q_c16_389ecc90400bf53c/cli/conversation.jsonl", + "note": "Executed through a local AI CLI with structured usage metadata." +} \ No newline at end of file diff --git a/Query/sql/v2/runs/v2_cli_20260502_081223_d/c16/artifacts/v2q_c16_399a160974349c8a/run_manifest.json b/Query/sql/v2/runs/v2_cli_20260502_081223_d/c16/artifacts/v2q_c16_399a160974349c8a/run_manifest.json new file mode 100644 index 0000000000000000000000000000000000000000..bfa986a0a2961ab807f4452d900703fe09e02318 --- /dev/null +++ b/Query/sql/v2/runs/v2_cli_20260502_081223_d/c16/artifacts/v2q_c16_399a160974349c8a/run_manifest.json @@ -0,0 +1,69 @@ +{ + "run_id": "v2_cli_20260502_081223_d", + "dataset_id": "c16", + "started_at": "2026-05-19T16:09:51.983080+00:00", + "ended_at": "2026-05-19T16:09:59.145028+00:00", + "status": "failed", + "engine": "cli", + "question_record": { + "query_record_id": "v2q_c16_399a160974349c8a", + "problem_id": "v2p_c16_89fc86113d106a05", + "dataset_id": "c16", + "template_id": "tpl_m4_window_partition_avg", + "template_name": "Window Partition Average", + "family_id": "conditional_dependency_structure", + "canonical_subitem_id": "direction_consistency", + "intended_facet_id": "conditional_rate_shift", + "variant_semantic_role": "filtered_stable_view", + "subitem_assignment_source": "planner_selected", + "source_kind": "agent", + "realization_mode": "agent", + "gate_priority": "primary", + "extended_family": false, + "question": "Use template Window Partition Average to probe direction_consistency with semantic role filtered_stable_view. Focus on group_col=GSM, measure_col=YEAR.", + "bindings": { + "group_col": "GSM", + "measure_col": "YEAR", + "top_k": 17, + "top_n": 5, + "num_tiles": 10, + "percentile_value": 0.95, + "z_threshold": 2.0, + "fraction_threshold": 0.05, + "baseline_multiplier": 1.75, + "baseline_fraction": 0.1, + "min_group_size": 5, + "min_support": 4, + "measure_threshold": 1998.0, + "time_grain": "month", + "lookback_rows": 3, + "current_period_start": "'2024-01-01'", + "current_period_end": "'2024-04-01'", + "previous_period_start": "'2023-10-01'", + "previous_period_end": "'2024-01-01'", + "drift_ratio_threshold": 0.8 + }, + "binding_roles": [ + "group_col", + "measure_col" + ], + "coverage_target_min": "5", + "runtime_sql_skeleton": "SELECT DISTINCT {group_col},\n AVG({measure_col}) OVER (PARTITION BY {group_col}) AS avg_measure\nFROM {table}\nORDER BY avg_measure DESC;", + "notes": [ + "default_facets=conditional_rate_shift", + "template_selection_mode=rule", + "problem_index_within_template=6", + "sql_variant_index=2/2", + "binding_index=137" + ], + "template_selection_mode": "rule", + "selected_template_rank": 12, + "problem_index_within_template": 6, + "sql_variant_index": 2, + "sql_variant_total": 2 + }, + "mode": "subitem_workload_v2", + "sql_source_version": "v2", + "sql_source_label": "v2_current", + "error": "AI CLI command failed with exit code 1: " +} \ No newline at end of file diff --git a/Query/sql/v2/runs/v2_cli_20260502_081223_d/c16/artifacts/v2q_c16_399a160974349c8a/trace.jsonl b/Query/sql/v2/runs/v2_cli_20260502_081223_d/c16/artifacts/v2q_c16_399a160974349c8a/trace.jsonl new file mode 100644 index 0000000000000000000000000000000000000000..2e8c54b076ad059cd8a9b0f686e835f6be475859 --- /dev/null +++ b/Query/sql/v2/runs/v2_cli_20260502_081223_d/c16/artifacts/v2q_c16_399a160974349c8a/trace.jsonl @@ -0,0 +1,2 @@ +{"timestamp": "2026-05-19T16:09:54.925308+00:00", "event_type": "ai_cli_sql_generation_error", "engine": "v2-cli:codex", "attempt": 1, "command": "codex exec --skip-git-repo-check --disable plugins --sandbox read-only --cd \"/data/jialinzhang/SQLagent\" -m gpt-5.4 --json -", "returncode": 1, "elapsed_ms": 2938.18, "started_at": "2026-05-19T16:09:51.986024+00:00", "ended_at": "2026-05-19T16:09:54.924232+00:00", "prompt_metrics": {"chars": 8557, "bytes_utf8": 8557, "lines": 252, "estimated_tokens": null}, "response_metrics": {"chars": 280, "bytes_utf8": 280, "lines": 4, "estimated_tokens": null}, "usage": {}, "stderr_preview": "", "stdout_preview": "{\"type\":\"thread.started\",\"thread_id\":\"019e4100-3176-7342-a4dc-9620981c1d6b\"}\n{\"type\":\"turn.started\"}\n{\"type\":\"error\",\"message\":\"Quota exceeded. Check your plan and billing details.\"}\n{\"type\":\"turn.failed\",\"error\":{\"message\":\"Quota exceeded. Check your plan and billing details.\"}}", "error": "AI CLI command failed with exit code 1: "} +{"timestamp": "2026-05-19T16:09:59.144901+00:00", "event_type": "ai_cli_sql_generation_error", "engine": "v2-cli:codex", "attempt": 2, "command": "codex exec --skip-git-repo-check --disable plugins --sandbox read-only --cd \"/data/jialinzhang/SQLagent\" -m gpt-5.4 --json -", "returncode": 1, "elapsed_ms": 3217.59, "started_at": "2026-05-19T16:09:55.926347+00:00", "ended_at": "2026-05-19T16:09:59.143968+00:00", "prompt_metrics": {"chars": 8557, "bytes_utf8": 8557, "lines": 252, "estimated_tokens": null}, "response_metrics": {"chars": 280, "bytes_utf8": 280, "lines": 4, "estimated_tokens": null}, "usage": {}, "stderr_preview": "", "stdout_preview": "{\"type\":\"thread.started\",\"thread_id\":\"019e4100-40c2-7c62-ad64-2d9d909786a7\"}\n{\"type\":\"turn.started\"}\n{\"type\":\"error\",\"message\":\"Quota exceeded. Check your plan and billing details.\"}\n{\"type\":\"turn.failed\",\"error\":{\"message\":\"Quota exceeded. Check your plan and billing details.\"}}", "error": "AI CLI command failed with exit code 1: "} diff --git a/Query/sql/v2/runs/v2_cli_20260502_081223_d/c16/artifacts/v2q_c16_3c479363841374cd/final_answer.txt b/Query/sql/v2/runs/v2_cli_20260502_081223_d/c16/artifacts/v2q_c16_3c479363841374cd/final_answer.txt new file mode 100644 index 0000000000000000000000000000000000000000..88d79bfe9caae0999386104795b79beb33935b0c --- /dev/null +++ b/Query/sql/v2/runs/v2_cli_20260502_081223_d/c16/artifacts/v2q_c16_3c479363841374cd/final_answer.txt @@ -0,0 +1 @@ +{"row_count": null, "preview_rows": [{"GSM": "", "total_rows": 6832, "missing_rows": 0, "missing_rate": 0.0}, {"GSM": "Homosexual Characters", "total_rows": 54, "missing_rows": 0, "missing_rate": 0.0}, {"GSM": "Bisexual Characters", "total_rows": 10, "missing_rows": 0, "missing_rate": 0.0}]} \ No newline at end of file diff --git a/Query/sql/v2/runs/v2_cli_20260502_081223_d/c16/artifacts/v2q_c16_3c479363841374cd/generated_sql.sql b/Query/sql/v2/runs/v2_cli_20260502_081223_d/c16/artifacts/v2q_c16_3c479363841374cd/generated_sql.sql new file mode 100644 index 0000000000000000000000000000000000000000..b52bd5a5b1fc4298d1b634df02928af61939d03a --- /dev/null +++ b/Query/sql/v2/runs/v2_cli_20260502_081223_d/c16/artifacts/v2q_c16_3c479363841374cd/generated_sql.sql @@ -0,0 +1,21 @@ +-- sql_source_version: v2 +-- sql_source_label: v2_current +-- sql_source_run_id: v2_cli_20260502_081223_d +-- sql_source_dataset_id: c16 +-- family_id: missingness_structure +-- canonical_subitem_id: co_missingness_pattern_consistency +-- intended_facet_id: missing_target_interaction +-- variant_semantic_role: missing_target_interaction +-- template_id: tpl_missing_target_interaction +-- query_record_id: v2q_c16_3c479363841374cd +-- problem_id: v2p_c16_fa177481fdd5edd9 +-- realization_mode: deterministic +-- source_kind: deterministic +SELECT + "GSM", + COUNT(*) AS total_rows, + SUM(CASE WHEN "APPEARANCES" IS NULL THEN 1 ELSE 0 END) AS missing_rows, + AVG(CASE WHEN "APPEARANCES" IS NULL THEN 1.0 ELSE 0.0 END) AS missing_rate +FROM "c16" +GROUP BY "GSM" +ORDER BY missing_rate DESC, total_rows DESC; diff --git a/Query/sql/v2/runs/v2_cli_20260502_081223_d/c16/artifacts/v2q_c16_3c479363841374cd/query_results.jsonl b/Query/sql/v2/runs/v2_cli_20260502_081223_d/c16/artifacts/v2q_c16_3c479363841374cd/query_results.jsonl new file mode 100644 index 0000000000000000000000000000000000000000..f426ee241058f1820e4f769fa436a0aef501bcc2 --- /dev/null +++ b/Query/sql/v2/runs/v2_cli_20260502_081223_d/c16/artifacts/v2q_c16_3c479363841374cd/query_results.jsonl @@ -0,0 +1 @@ +{"node_name": "v2_template", "tool_name": "sqlite_query", "query": "-- sql_source_version: v2\n-- sql_source_label: v2_current\n-- sql_source_run_id: v2_cli_20260502_081223_d\n-- sql_source_dataset_id: c16\n-- family_id: missingness_structure\n-- canonical_subitem_id: co_missingness_pattern_consistency\n-- intended_facet_id: missing_target_interaction\n-- variant_semantic_role: missing_target_interaction\n-- template_id: tpl_missing_target_interaction\n-- query_record_id: v2q_c16_3c479363841374cd\n-- problem_id: v2p_c16_fa177481fdd5edd9\n-- realization_mode: deterministic\n-- source_kind: deterministic\nSELECT\n \"GSM\",\n COUNT(*) AS total_rows,\n SUM(CASE WHEN \"APPEARANCES\" IS NULL THEN 1 ELSE 0 END) AS missing_rows,\n AVG(CASE WHEN \"APPEARANCES\" IS NULL THEN 1.0 ELSE 0.0 END) AS missing_rate\nFROM \"c16\"\nGROUP BY \"GSM\"\nORDER BY missing_rate DESC, total_rows DESC;", "result": "{\"query\": \"-- sql_source_version: v2\\n-- sql_source_label: v2_current\\n-- sql_source_run_id: v2_cli_20260502_081223_d\\n-- sql_source_dataset_id: c16\\n-- family_id: missingness_structure\\n-- canonical_subitem_id: co_missingness_pattern_consistency\\n-- intended_facet_id: missing_target_interaction\\n-- variant_semantic_role: missing_target_interaction\\n-- template_id: tpl_missing_target_interaction\\n-- query_record_id: v2q_c16_3c479363841374cd\\n-- problem_id: v2p_c16_fa177481fdd5edd9\\n-- realization_mode: deterministic\\n-- source_kind: deterministic\\nSELECT\\n \\\"GSM\\\",\\n COUNT(*) AS total_rows,\\n SUM(CASE WHEN \\\"APPEARANCES\\\" IS NULL THEN 1 ELSE 0 END) AS missing_rows,\\n AVG(CASE WHEN \\\"APPEARANCES\\\" IS NULL THEN 1.0 ELSE 0.0 END) AS missing_rate\\nFROM \\\"c16\\\"\\nGROUP BY \\\"GSM\\\"\\nORDER BY missing_rate DESC, total_rows DESC;\", \"columns\": [\"GSM\", \"total_rows\", \"missing_rows\", \"missing_rate\"], \"rows\": [{\"GSM\": \"\", \"total_rows\": 6832, \"missing_rows\": 0, \"missing_rate\": 0.0}, {\"GSM\": \"Homosexual Characters\", \"total_rows\": 54, \"missing_rows\": 0, \"missing_rate\": 0.0}, {\"GSM\": \"Bisexual Characters\", \"total_rows\": 10, \"missing_rows\": 0, \"missing_rate\": 0.0}], \"row_count_returned\": 3, \"row_limit\": 50, \"truncated\": false, \"elapsed_ms\": 2.66}"} diff --git a/Query/sql/v2/runs/v2_cli_20260502_081223_d/c16/artifacts/v2q_c16_3c479363841374cd/run_manifest.json b/Query/sql/v2/runs/v2_cli_20260502_081223_d/c16/artifacts/v2q_c16_3c479363841374cd/run_manifest.json new file mode 100644 index 0000000000000000000000000000000000000000..90ca5715e0f8fddfd88910dd08cef67065272d61 --- /dev/null +++ b/Query/sql/v2/runs/v2_cli_20260502_081223_d/c16/artifacts/v2q_c16_3c479363841374cd/run_manifest.json @@ -0,0 +1,59 @@ +{ + "run_id": "v2_cli_20260502_081223_d", + "dataset_id": "c16", + "started_at": "2026-05-19T16:10:30.437926+00:00", + "ended_at": "2026-05-19T16:10:30.441217+00:00", + "status": "completed", + "engine": "cli", + "question_record": { + "query_record_id": "v2q_c16_3c479363841374cd", + "problem_id": "v2p_c16_fa177481fdd5edd9", + "dataset_id": "c16", + "template_id": "tpl_missing_target_interaction", + "template_name": "Missingness-Target Interaction", + "family_id": "missingness_structure", + "canonical_subitem_id": "co_missingness_pattern_consistency", + "intended_facet_id": "missing_target_interaction", + "variant_semantic_role": "missing_target_interaction", + "subitem_assignment_source": "template_fixed", + "source_kind": "deterministic", + "realization_mode": "deterministic", + "gate_priority": "deterministic", + "extended_family": false, + "question": "Use template Missingness-Target Interaction to probe co_missingness_pattern_consistency with semantic role missing_target_interaction. Focus on target_col=GSM, missing_col=APPEARANCES.", + "bindings": { + "missing_col": "APPEARANCES", + "target_col": "GSM" + }, + "binding_roles": [ + "missing_col", + "target_col" + ], + "coverage_target_min": "enumerate_all_applicable", + "runtime_sql_skeleton": "SELECT\n {target_col},\n COUNT(*) AS total_rows,\n SUM(CASE WHEN {missing_col} IS NULL THEN 1 ELSE 0 END) AS missing_rows,\n AVG(CASE WHEN {missing_col} IS NULL THEN 1.0 ELSE 0.0 END) AS missing_rate\nFROM {table}\nGROUP BY {target_col}\nORDER BY missing_rate DESC, total_rows DESC;", + "notes": [ + "default_facets=missing_rate_by_subgroup,missing_target_interaction", + "template_selection_mode=deterministic", + "problem_index_within_template=10", + "sql_variant_index=1/1" + ], + "template_selection_mode": "deterministic", + "selected_template_rank": 0, + "problem_index_within_template": 10, + "sql_variant_index": 1, + "sql_variant_total": 1 + }, + "mode": "subitem_workload_v2", + "sql_source_version": "v2", + "sql_source_label": "v2_current", + "generated_sql_path": "/data/jialinzhang/TabQueryBench/sql_workloads/v2_current/runs_and_launches/runs/v2_cli_20260502_081223_d/c16/sql/v2q_c16_3c479363841374cd.sql", + "usage_summary": { + "engine": "template", + "input_tokens": 0, + "cached_input_tokens": 0, + "output_tokens": 0, + "total_tokens": 0, + "estimated_total_tokens": 0, + "usage_source": "none" + } +} \ No newline at end of file diff --git a/Query/sql/v2/runs/v2_cli_20260502_081223_d/c16/artifacts/v2q_c16_3c479363841374cd/usage_summary.json b/Query/sql/v2/runs/v2_cli_20260502_081223_d/c16/artifacts/v2q_c16_3c479363841374cd/usage_summary.json new file mode 100644 index 0000000000000000000000000000000000000000..96c9ff4feec395919fc26411d18d078b8af6e1c7 --- /dev/null +++ b/Query/sql/v2/runs/v2_cli_20260502_081223_d/c16/artifacts/v2q_c16_3c479363841374cd/usage_summary.json @@ -0,0 +1,9 @@ +{ + "engine": "template", + "input_tokens": 0, + "cached_input_tokens": 0, + "output_tokens": 0, + "total_tokens": 0, + "estimated_total_tokens": 0, + "usage_source": "none" +} \ No newline at end of file diff --git a/Query/sql/v2/runs/v2_cli_20260502_081223_d/c16/artifacts/v2q_c16_419c539d87e25abc/final_answer.txt b/Query/sql/v2/runs/v2_cli_20260502_081223_d/c16/artifacts/v2q_c16_419c539d87e25abc/final_answer.txt new file mode 100644 index 0000000000000000000000000000000000000000..1e5bb422f3e67042156c6e1358f1280d58c7071e --- /dev/null +++ b/Query/sql/v2/runs/v2_cli_20260502_081223_d/c16/artifacts/v2q_c16_419c539d87e25abc/final_answer.txt @@ -0,0 +1,2 @@ +SQL executed successfully for: Use template Threshold Rarity CDF to probe tail_set_consistency with semantic role rare_extreme_view. Focus on measure_col=page_id. +Result preview: [{"empirical_cdf_at_threshold": 0.75}] \ No newline at end of file diff --git a/Query/sql/v2/runs/v2_cli_20260502_081223_d/c16/artifacts/v2q_c16_419c539d87e25abc/generated_sql.sql b/Query/sql/v2/runs/v2_cli_20260502_081223_d/c16/artifacts/v2q_c16_419c539d87e25abc/generated_sql.sql new file mode 100644 index 0000000000000000000000000000000000000000..d98a4b50b4eb27253570283d65809200459484bb --- /dev/null +++ b/Query/sql/v2/runs/v2_cli_20260502_081223_d/c16/artifacts/v2q_c16_419c539d87e25abc/generated_sql.sql @@ -0,0 +1,15 @@ +-- sql_source_version: v2 +-- sql_source_label: v2_current +-- sql_source_run_id: v2_cli_20260502_081223_d +-- sql_source_dataset_id: c16 +-- family_id: tail_rarity_structure +-- canonical_subitem_id: tail_set_consistency +-- intended_facet_id: low_support_extremes +-- variant_semantic_role: rare_extreme_view +-- template_id: tpl_threshold_rarity_cdf +-- query_record_id: v2q_c16_419c539d87e25abc +-- problem_id: v2p_c16_bdb8a2a312942158 +-- realization_mode: agent +-- source_kind: agent +SELECT AVG(CASE WHEN CAST("page_id" AS REAL) <= 213203.0 THEN 1 ELSE 0 END) AS "empirical_cdf_at_threshold" +FROM "c16"; diff --git a/Query/sql/v2/runs/v2_cli_20260502_081223_d/c16/artifacts/v2q_c16_419c539d87e25abc/query_results.jsonl b/Query/sql/v2/runs/v2_cli_20260502_081223_d/c16/artifacts/v2q_c16_419c539d87e25abc/query_results.jsonl new file mode 100644 index 0000000000000000000000000000000000000000..71ccebd01d60c1f13c46e266ef25c2553e238b62 --- /dev/null +++ b/Query/sql/v2/runs/v2_cli_20260502_081223_d/c16/artifacts/v2q_c16_419c539d87e25abc/query_results.jsonl @@ -0,0 +1 @@ +{"step_index": 1, "message_index": 0, "node_name": "v2-cli:codex", "tool_name": "sqlite_query", "query": "-- template_id: tpl_threshold_rarity_cdf\nSELECT AVG(CASE WHEN CAST(\"page_id\" AS REAL) <= 213203.0 THEN 1 ELSE 0 END) AS \"empirical_cdf_at_threshold\"\nFROM \"c16\";", "result": "{\"query\": \"-- template_id: tpl_threshold_rarity_cdf\\nSELECT AVG(CASE WHEN CAST(\\\"page_id\\\" AS REAL) <= 213203.0 THEN 1 ELSE 0 END) AS \\\"empirical_cdf_at_threshold\\\"\\nFROM \\\"c16\\\";\", \"columns\": [\"empirical_cdf_at_threshold\"], \"rows\": [{\"empirical_cdf_at_threshold\": 0.75}], \"row_count_returned\": 1, \"row_limit\": 50, \"truncated\": false, \"elapsed_ms\": 1.68}"} diff --git a/Query/sql/v2/runs/v2_cli_20260502_081223_d/c16/artifacts/v2q_c16_419c539d87e25abc/run_manifest.json b/Query/sql/v2/runs/v2_cli_20260502_081223_d/c16/artifacts/v2q_c16_419c539d87e25abc/run_manifest.json new file mode 100644 index 0000000000000000000000000000000000000000..074492b3e0e89f5be9dbf64538371effc7c7d054 --- /dev/null +++ b/Query/sql/v2/runs/v2_cli_20260502_081223_d/c16/artifacts/v2q_c16_419c539d87e25abc/run_manifest.json @@ -0,0 +1,87 @@ +{ + "run_id": "v2_cli_20260502_081223_d", + "dataset_id": "c16", + "started_at": "2026-05-19T16:05:59.364381+00:00", + "ended_at": "2026-05-19T16:06:07.510331+00:00", + "status": "completed", + "engine": "cli", + "question_record": { + "query_record_id": "v2q_c16_419c539d87e25abc", + "problem_id": "v2p_c16_bdb8a2a312942158", + "dataset_id": "c16", + "template_id": "tpl_threshold_rarity_cdf", + "template_name": "Threshold Rarity CDF", + "family_id": "tail_rarity_structure", + "canonical_subitem_id": "tail_set_consistency", + "intended_facet_id": "low_support_extremes", + "variant_semantic_role": "rare_extreme_view", + "subitem_assignment_source": "planner_selected", + "source_kind": "agent", + "realization_mode": "agent", + "gate_priority": "primary", + "extended_family": false, + "question": "Use template Threshold Rarity CDF to probe tail_set_consistency with semantic role rare_extreme_view. Focus on measure_col=page_id.", + "bindings": { + "measure_col": "page_id", + "top_k": 14, + "top_n": 5, + "num_tiles": 10, + "percentile_value": 0.95, + "z_threshold": 2.0, + "fraction_threshold": 0.1, + "baseline_multiplier": 1.5, + "baseline_fraction": 0.1, + "min_group_size": 5, + "min_support": 5, + "measure_threshold": 213203.0, + "time_grain": "month", + "lookback_rows": 3, + "current_period_start": "'2024-01-01'", + "current_period_end": "'2024-04-01'", + "previous_period_start": "'2023-10-01'", + "previous_period_end": "'2024-01-01'", + "drift_ratio_threshold": 0.8 + }, + "binding_roles": [ + "measure_col" + ], + "coverage_target_min": "5", + "runtime_sql_skeleton": "SELECT AVG(CASE WHEN {measure_col} <= {measure_threshold} THEN 1 ELSE 0 END) AS empirical_cdf_at_threshold\nFROM {table};", + "notes": [ + "default_facets=low_support_extremes", + "template_selection_mode=rule", + "problem_index_within_template=7", + "sql_variant_index=1/1", + "binding_index=114" + ], + "template_selection_mode": "rule", + "selected_template_rank": 10, + "problem_index_within_template": 7, + "sql_variant_index": 1, + "sql_variant_total": 1 + }, + "mode": "subitem_workload_v2", + "sql_source_version": "v2", + "sql_source_label": "v2_current", + "generated_sql_path": "/data/jialinzhang/TabQueryBench/sql_workloads/v2_current/runs_and_launches/runs/v2_cli_20260502_081223_d/c16/sql/v2q_c16_419c539d87e25abc.sql", + "usage_summary": { + "dataset_id": "c16", + "model": "v2-cli:codex", + "run_id": "v2q_c16_419c539d87e25abc", + "api_calls": 0, + "input_tokens": 14542, + "cached_input_tokens": 12032, + "output_tokens": 274, + "total_tokens": 14816, + "cost_usd": 0.0, + "ai_cli_calls": 1, + "estimated_input_tokens": 0, + "estimated_output_tokens": 0, + "estimated_total_tokens": 0, + "usage_source": "ai_cli_json_usage", + "cli_elapsed_ms_total": 8139.56, + "sql_execution_elapsed_ms_total": 1.68, + "conversation_log_path": "/data/jialinzhang/TabQueryBench/sql_workloads/v2_current/runs_and_launches/runs/v2_cli_20260502_081223_d/c16/artifacts/v2q_c16_419c539d87e25abc/cli/conversation.jsonl", + "note": "Executed through a local AI CLI with structured usage metadata." + } +} \ No newline at end of file diff --git a/Query/sql/v2/runs/v2_cli_20260502_081223_d/c16/artifacts/v2q_c16_419c539d87e25abc/trace.jsonl b/Query/sql/v2/runs/v2_cli_20260502_081223_d/c16/artifacts/v2q_c16_419c539d87e25abc/trace.jsonl new file mode 100644 index 0000000000000000000000000000000000000000..f4ae7895750065feb17b1c1bfa0896b231c776c1 --- /dev/null +++ b/Query/sql/v2/runs/v2_cli_20260502_081223_d/c16/artifacts/v2q_c16_419c539d87e25abc/trace.jsonl @@ -0,0 +1 @@ +{"timestamp": "2026-05-19T16:06:07.507381+00:00", "event_type": "ai_cli_sql_generation", "engine": "v2-cli:codex", "attempt": 1, "command": "codex exec --skip-git-repo-check --disable plugins --sandbox read-only --cd \"/data/jialinzhang/SQLagent\" -m gpt-5.4 --json -", "returncode": 0, "elapsed_ms": 8139.56, "started_at": "2026-05-19T16:05:59.367037+00:00", "ended_at": "2026-05-19T16:06:07.506619+00:00", "prompt_metrics": {"chars": 8426, "bytes_utf8": 8426, "lines": 250, "estimated_tokens": null}, "response_metrics": {"chars": 326, "bytes_utf8": 326, "lines": 1, "estimated_tokens": null}, "usage": {"input_tokens": 14542, "cached_input_tokens": 12032, "output_tokens": 274, "reasoning_output_tokens": 178}, "stderr_preview": "", "stdout_preview": "{\"sql\":\"-- template_id: tpl_threshold_rarity_cdf\\nSELECT AVG(CASE WHEN CAST(\\\"page_id\\\" AS REAL) <= 213203.0 THEN 1 ELSE 0 END) AS \\\"empirical_cdf_at_threshold\\\"\\nFROM \\\"c16\\\";\",\"notes\":\"Applied the planned Threshold Rarity CDF template on \\\"page_id\\\" and cast it to REAL because the schema snapshot shows it stored as TEXT.\"}"} diff --git a/Query/sql/v2/runs/v2_cli_20260502_081223_d/c16/artifacts/v2q_c16_419c539d87e25abc/usage_summary.json b/Query/sql/v2/runs/v2_cli_20260502_081223_d/c16/artifacts/v2q_c16_419c539d87e25abc/usage_summary.json new file mode 100644 index 0000000000000000000000000000000000000000..27feeb9ecbcc10069192d57aa3027217052b4c30 --- /dev/null +++ b/Query/sql/v2/runs/v2_cli_20260502_081223_d/c16/artifacts/v2q_c16_419c539d87e25abc/usage_summary.json @@ -0,0 +1,20 @@ +{ + "dataset_id": "c16", + "model": "v2-cli:codex", + "run_id": "v2q_c16_419c539d87e25abc", + "api_calls": 0, + "input_tokens": 14542, + "cached_input_tokens": 12032, + "output_tokens": 274, + "total_tokens": 14816, + "cost_usd": 0.0, + "ai_cli_calls": 1, + "estimated_input_tokens": 0, + "estimated_output_tokens": 0, + "estimated_total_tokens": 0, + "usage_source": "ai_cli_json_usage", + "cli_elapsed_ms_total": 8139.56, + "sql_execution_elapsed_ms_total": 1.68, + "conversation_log_path": "/data/jialinzhang/TabQueryBench/sql_workloads/v2_current/runs_and_launches/runs/v2_cli_20260502_081223_d/c16/artifacts/v2q_c16_419c539d87e25abc/cli/conversation.jsonl", + "note": "Executed through a local AI CLI with structured usage metadata." +} \ No newline at end of file diff --git a/Query/sql/v2/runs/v2_cli_20260502_081223_d/c16/artifacts/v2q_c16_48a7217537e0b485/final_answer.txt b/Query/sql/v2/runs/v2_cli_20260502_081223_d/c16/artifacts/v2q_c16_48a7217537e0b485/final_answer.txt new file mode 100644 index 0000000000000000000000000000000000000000..6e6352d3d645a499d2d51a1da64491e8dbb7db08 --- /dev/null +++ b/Query/sql/v2/runs/v2_cli_20260502_081223_d/c16/artifacts/v2q_c16_48a7217537e0b485/final_answer.txt @@ -0,0 +1 @@ +{"row_count": null, "preview_rows": [{"SEX": "Male Characters", "total_rows": 4783, "missing_rows": 0, "missing_rate": 0.0}, {"SEX": "Female Characters", "total_rows": 1967, "missing_rows": 0, "missing_rate": 0.0}, {"SEX": "", "total_rows": 125, "missing_rows": 0, "missing_rate": 0.0}, {"SEX": "Genderless Characters", "total_rows": 20, "missing_rows": 0, "missing_rate": 0.0}, {"SEX": "Transgender Characters", "total_rows": 1, "missing_rows": 0, "missing_rate": 0.0}]} \ No newline at end of file diff --git a/Query/sql/v2/runs/v2_cli_20260502_081223_d/c16/artifacts/v2q_c16_48a7217537e0b485/generated_sql.sql b/Query/sql/v2/runs/v2_cli_20260502_081223_d/c16/artifacts/v2q_c16_48a7217537e0b485/generated_sql.sql new file mode 100644 index 0000000000000000000000000000000000000000..b3898f1ff9a179bd613cbca8e7d19758130e5dcf --- /dev/null +++ b/Query/sql/v2/runs/v2_cli_20260502_081223_d/c16/artifacts/v2q_c16_48a7217537e0b485/generated_sql.sql @@ -0,0 +1,21 @@ +-- sql_source_version: v2 +-- sql_source_label: v2_current +-- sql_source_run_id: v2_cli_20260502_081223_d +-- sql_source_dataset_id: c16 +-- family_id: missingness_structure +-- canonical_subitem_id: co_missingness_pattern_consistency +-- intended_facet_id: missing_target_interaction +-- variant_semantic_role: missing_target_interaction +-- template_id: tpl_missing_target_interaction +-- query_record_id: v2q_c16_48a7217537e0b485 +-- problem_id: v2p_c16_10c707119a758127 +-- realization_mode: deterministic +-- source_kind: deterministic +SELECT + "SEX", + COUNT(*) AS total_rows, + SUM(CASE WHEN "FIRST APPEARANCE" IS NULL THEN 1 ELSE 0 END) AS missing_rows, + AVG(CASE WHEN "FIRST APPEARANCE" IS NULL THEN 1.0 ELSE 0.0 END) AS missing_rate +FROM "c16" +GROUP BY "SEX" +ORDER BY missing_rate DESC, total_rows DESC; diff --git a/Query/sql/v2/runs/v2_cli_20260502_081223_d/c16/artifacts/v2q_c16_48a7217537e0b485/query_results.jsonl b/Query/sql/v2/runs/v2_cli_20260502_081223_d/c16/artifacts/v2q_c16_48a7217537e0b485/query_results.jsonl new file mode 100644 index 0000000000000000000000000000000000000000..e34360978a3efeb86caa6751a7544f9c4079a9dc --- /dev/null +++ b/Query/sql/v2/runs/v2_cli_20260502_081223_d/c16/artifacts/v2q_c16_48a7217537e0b485/query_results.jsonl @@ -0,0 +1 @@ +{"node_name": "v2_template", "tool_name": "sqlite_query", "query": "-- sql_source_version: v2\n-- sql_source_label: v2_current\n-- sql_source_run_id: v2_cli_20260502_081223_d\n-- sql_source_dataset_id: c16\n-- family_id: missingness_structure\n-- canonical_subitem_id: co_missingness_pattern_consistency\n-- intended_facet_id: missing_target_interaction\n-- variant_semantic_role: missing_target_interaction\n-- template_id: tpl_missing_target_interaction\n-- query_record_id: v2q_c16_48a7217537e0b485\n-- problem_id: v2p_c16_10c707119a758127\n-- realization_mode: deterministic\n-- source_kind: deterministic\nSELECT\n \"SEX\",\n COUNT(*) AS total_rows,\n SUM(CASE WHEN \"FIRST APPEARANCE\" IS NULL THEN 1 ELSE 0 END) AS missing_rows,\n AVG(CASE WHEN \"FIRST APPEARANCE\" IS NULL THEN 1.0 ELSE 0.0 END) AS missing_rate\nFROM \"c16\"\nGROUP BY \"SEX\"\nORDER BY missing_rate DESC, total_rows DESC;", "result": "{\"query\": \"-- sql_source_version: v2\\n-- sql_source_label: v2_current\\n-- sql_source_run_id: v2_cli_20260502_081223_d\\n-- sql_source_dataset_id: c16\\n-- family_id: missingness_structure\\n-- canonical_subitem_id: co_missingness_pattern_consistency\\n-- intended_facet_id: missing_target_interaction\\n-- variant_semantic_role: missing_target_interaction\\n-- template_id: tpl_missing_target_interaction\\n-- query_record_id: v2q_c16_48a7217537e0b485\\n-- problem_id: v2p_c16_10c707119a758127\\n-- realization_mode: deterministic\\n-- source_kind: deterministic\\nSELECT\\n \\\"SEX\\\",\\n COUNT(*) AS total_rows,\\n SUM(CASE WHEN \\\"FIRST APPEARANCE\\\" IS NULL THEN 1 ELSE 0 END) AS missing_rows,\\n AVG(CASE WHEN \\\"FIRST APPEARANCE\\\" IS NULL THEN 1.0 ELSE 0.0 END) AS missing_rate\\nFROM \\\"c16\\\"\\nGROUP BY \\\"SEX\\\"\\nORDER BY missing_rate DESC, total_rows DESC;\", \"columns\": [\"SEX\", \"total_rows\", \"missing_rows\", \"missing_rate\"], \"rows\": [{\"SEX\": \"Male Characters\", \"total_rows\": 4783, \"missing_rows\": 0, \"missing_rate\": 0.0}, {\"SEX\": \"Female Characters\", \"total_rows\": 1967, \"missing_rows\": 0, \"missing_rate\": 0.0}, {\"SEX\": \"\", \"total_rows\": 125, \"missing_rows\": 0, \"missing_rate\": 0.0}, {\"SEX\": \"Genderless Characters\", \"total_rows\": 20, \"missing_rows\": 0, \"missing_rate\": 0.0}, {\"SEX\": \"Transgender Characters\", \"total_rows\": 1, \"missing_rows\": 0, \"missing_rate\": 0.0}], \"row_count_returned\": 5, \"row_limit\": 50, \"truncated\": false, \"elapsed_ms\": 3.0}"} diff --git a/Query/sql/v2/runs/v2_cli_20260502_081223_d/c16/artifacts/v2q_c16_48a7217537e0b485/run_manifest.json b/Query/sql/v2/runs/v2_cli_20260502_081223_d/c16/artifacts/v2q_c16_48a7217537e0b485/run_manifest.json new file mode 100644 index 0000000000000000000000000000000000000000..1f2800db10c4515efb9e835e8de939b93efb4b1a --- /dev/null +++ b/Query/sql/v2/runs/v2_cli_20260502_081223_d/c16/artifacts/v2q_c16_48a7217537e0b485/run_manifest.json @@ -0,0 +1,59 @@ +{ + "run_id": "v2_cli_20260502_081223_d", + "dataset_id": "c16", + "started_at": "2026-05-19T16:10:30.441651+00:00", + "ended_at": "2026-05-19T16:10:30.445327+00:00", + "status": "completed", + "engine": "cli", + "question_record": { + "query_record_id": "v2q_c16_48a7217537e0b485", + "problem_id": "v2p_c16_10c707119a758127", + "dataset_id": "c16", + "template_id": "tpl_missing_target_interaction", + "template_name": "Missingness-Target Interaction", + "family_id": "missingness_structure", + "canonical_subitem_id": "co_missingness_pattern_consistency", + "intended_facet_id": "missing_target_interaction", + "variant_semantic_role": "missing_target_interaction", + "subitem_assignment_source": "template_fixed", + "source_kind": "deterministic", + "realization_mode": "deterministic", + "gate_priority": "deterministic", + "extended_family": false, + "question": "Use template Missingness-Target Interaction to probe co_missingness_pattern_consistency with semantic role missing_target_interaction. Focus on target_col=SEX, missing_col=FIRST APPEARANCE.", + "bindings": { + "missing_col": "FIRST APPEARANCE", + "target_col": "SEX" + }, + "binding_roles": [ + "missing_col", + "target_col" + ], + "coverage_target_min": "enumerate_all_applicable", + "runtime_sql_skeleton": "SELECT\n {target_col},\n COUNT(*) AS total_rows,\n SUM(CASE WHEN {missing_col} IS NULL THEN 1 ELSE 0 END) AS missing_rows,\n AVG(CASE WHEN {missing_col} IS NULL THEN 1.0 ELSE 0.0 END) AS missing_rate\nFROM {table}\nGROUP BY {target_col}\nORDER BY missing_rate DESC, total_rows DESC;", + "notes": [ + "default_facets=missing_rate_by_subgroup,missing_target_interaction", + "template_selection_mode=deterministic", + "problem_index_within_template=11", + "sql_variant_index=1/1" + ], + "template_selection_mode": "deterministic", + "selected_template_rank": 0, + "problem_index_within_template": 11, + "sql_variant_index": 1, + "sql_variant_total": 1 + }, + "mode": "subitem_workload_v2", + "sql_source_version": "v2", + "sql_source_label": "v2_current", + "generated_sql_path": "/data/jialinzhang/TabQueryBench/sql_workloads/v2_current/runs_and_launches/runs/v2_cli_20260502_081223_d/c16/sql/v2q_c16_48a7217537e0b485.sql", + "usage_summary": { + "engine": "template", + "input_tokens": 0, + "cached_input_tokens": 0, + "output_tokens": 0, + "total_tokens": 0, + "estimated_total_tokens": 0, + "usage_source": "none" + } +} \ No newline at end of file diff --git a/Query/sql/v2/runs/v2_cli_20260502_081223_d/c16/artifacts/v2q_c16_48a7217537e0b485/usage_summary.json b/Query/sql/v2/runs/v2_cli_20260502_081223_d/c16/artifacts/v2q_c16_48a7217537e0b485/usage_summary.json new file mode 100644 index 0000000000000000000000000000000000000000..96c9ff4feec395919fc26411d18d078b8af6e1c7 --- /dev/null +++ b/Query/sql/v2/runs/v2_cli_20260502_081223_d/c16/artifacts/v2q_c16_48a7217537e0b485/usage_summary.json @@ -0,0 +1,9 @@ +{ + "engine": "template", + "input_tokens": 0, + "cached_input_tokens": 0, + "output_tokens": 0, + "total_tokens": 0, + "estimated_total_tokens": 0, + "usage_source": "none" +} \ No newline at end of file diff --git a/Query/sql/v2/runs/v2_cli_20260502_081223_d/c16/artifacts/v2q_c16_4aae0650ea810862/final_answer.txt b/Query/sql/v2/runs/v2_cli_20260502_081223_d/c16/artifacts/v2q_c16_4aae0650ea810862/final_answer.txt new file mode 100644 index 0000000000000000000000000000000000000000..486c127599b33d1f63ac56a6264dae8fdf11ec0d --- /dev/null +++ b/Query/sql/v2/runs/v2_cli_20260502_081223_d/c16/artifacts/v2q_c16_4aae0650ea810862/final_answer.txt @@ -0,0 +1,2 @@ +SQL executed successfully for: Use template Relative-to-Total Extreme Threshold to probe tail_mass_similarity with semantic role filtered_stable_view. Focus on group_col=EYE, measure_col=page_id. +Result preview: [{"EYE": "Brown Eyes", "group_value": 119726410.0}, {"EYE": "Blue Eyes", "group_value": 108477636.0}, {"EYE": "Black Eyes", "group_value": 51873567.0}, {"EYE": "Green Eyes", "group_value": 34400378.0}, {"EYE": "Red Eyes", "group_value": 25818154.0}] \ No newline at end of file diff --git a/Query/sql/v2/runs/v2_cli_20260502_081223_d/c16/artifacts/v2q_c16_4aae0650ea810862/generated_sql.sql b/Query/sql/v2/runs/v2_cli_20260502_081223_d/c16/artifacts/v2q_c16_4aae0650ea810862/generated_sql.sql new file mode 100644 index 0000000000000000000000000000000000000000..81fdeebcfdb9ef086ca9c41f9055b66fbb52d229 --- /dev/null +++ b/Query/sql/v2/runs/v2_cli_20260502_081223_d/c16/artifacts/v2q_c16_4aae0650ea810862/generated_sql.sql @@ -0,0 +1,30 @@ +-- sql_source_version: v2 +-- sql_source_label: v2_current +-- sql_source_run_id: v2_cli_20260502_081223_d +-- sql_source_dataset_id: c16 +-- family_id: tail_rarity_structure +-- canonical_subitem_id: tail_mass_similarity +-- intended_facet_id: tail_ranked_signal +-- variant_semantic_role: filtered_stable_view +-- template_id: tpl_tpch_relative_total_threshold +-- query_record_id: v2q_c16_4aae0650ea810862 +-- problem_id: v2p_c16_0d08289cb4e63265 +-- realization_mode: agent +-- source_kind: agent +WITH "grouped" AS ( + SELECT "EYE", SUM(CAST("page_id" AS REAL)) AS "group_value" + FROM "c16" + WHERE "EYE" IS NOT NULL + AND "EYE" <> '' + AND "page_id" IS NOT NULL + AND "page_id" <> '' + GROUP BY "EYE" +), "total" AS ( + SELECT SUM("group_value") AS "total_value" + FROM "grouped" +) +SELECT g."EYE", g."group_value" +FROM "grouped" AS g +CROSS JOIN "total" AS t +WHERE g."group_value" > t."total_value" * 0.05 +ORDER BY g."group_value" DESC; diff --git a/Query/sql/v2/runs/v2_cli_20260502_081223_d/c16/artifacts/v2q_c16_4aae0650ea810862/query_results.jsonl b/Query/sql/v2/runs/v2_cli_20260502_081223_d/c16/artifacts/v2q_c16_4aae0650ea810862/query_results.jsonl new file mode 100644 index 0000000000000000000000000000000000000000..1e74f5d99dd761f6108aac711bb5d2a0e9ff4321 --- /dev/null +++ b/Query/sql/v2/runs/v2_cli_20260502_081223_d/c16/artifacts/v2q_c16_4aae0650ea810862/query_results.jsonl @@ -0,0 +1 @@ +{"step_index": 1, "message_index": 0, "node_name": "v2-cli:codex", "tool_name": "sqlite_query", "query": "-- template_id: tpl_tpch_relative_total_threshold\nWITH \"grouped\" AS (\n SELECT \"EYE\", SUM(CAST(\"page_id\" AS REAL)) AS \"group_value\"\n FROM \"c16\"\n WHERE \"EYE\" IS NOT NULL\n AND \"EYE\" <> ''\n AND \"page_id\" IS NOT NULL\n AND \"page_id\" <> ''\n GROUP BY \"EYE\"\n), \"total\" AS (\n SELECT SUM(\"group_value\") AS \"total_value\"\n FROM \"grouped\"\n)\nSELECT g.\"EYE\", g.\"group_value\"\nFROM \"grouped\" AS g\nCROSS JOIN \"total\" AS t\nWHERE g.\"group_value\" > t.\"total_value\" * 0.05\nORDER BY g.\"group_value\" DESC;", "result": "{\"query\": \"-- template_id: tpl_tpch_relative_total_threshold\\nWITH \\\"grouped\\\" AS (\\n SELECT \\\"EYE\\\", SUM(CAST(\\\"page_id\\\" AS REAL)) AS \\\"group_value\\\"\\n FROM \\\"c16\\\"\\n WHERE \\\"EYE\\\" IS NOT NULL\\n AND \\\"EYE\\\" <> ''\\n AND \\\"page_id\\\" IS NOT NULL\\n AND \\\"page_id\\\" <> ''\\n GROUP BY \\\"EYE\\\"\\n), \\\"total\\\" AS (\\n SELECT SUM(\\\"group_value\\\") AS \\\"total_value\\\"\\n FROM \\\"grouped\\\"\\n)\\nSELECT g.\\\"EYE\\\", g.\\\"group_value\\\"\\nFROM \\\"grouped\\\" AS g\\nCROSS JOIN \\\"total\\\" AS t\\nWHERE g.\\\"group_value\\\" > t.\\\"total_value\\\" * 0.05\\nORDER BY g.\\\"group_value\\\" DESC;\", \"columns\": [\"EYE\", \"group_value\"], \"rows\": [{\"EYE\": \"Brown Eyes\", \"group_value\": 119726410.0}, {\"EYE\": \"Blue Eyes\", \"group_value\": 108477636.0}, {\"EYE\": \"Black Eyes\", \"group_value\": 51873567.0}, {\"EYE\": \"Green Eyes\", \"group_value\": 34400378.0}, {\"EYE\": \"Red Eyes\", \"group_value\": 25818154.0}], \"row_count_returned\": 5, \"row_limit\": 50, \"truncated\": false, \"elapsed_ms\": 2.85}"} diff --git a/Query/sql/v2/runs/v2_cli_20260502_081223_d/c16/artifacts/v2q_c16_4aae0650ea810862/run_manifest.json b/Query/sql/v2/runs/v2_cli_20260502_081223_d/c16/artifacts/v2q_c16_4aae0650ea810862/run_manifest.json new file mode 100644 index 0000000000000000000000000000000000000000..ce7986b0421a54316778a6afb9a49e58a06426f2 --- /dev/null +++ b/Query/sql/v2/runs/v2_cli_20260502_081223_d/c16/artifacts/v2q_c16_4aae0650ea810862/run_manifest.json @@ -0,0 +1,89 @@ +{ + "run_id": "v2_cli_20260502_081223_d", + "dataset_id": "c16", + "started_at": "2026-05-19T15:48:58.069314+00:00", + "ended_at": "2026-05-19T15:49:15.712640+00:00", + "status": "completed", + "engine": "cli", + "question_record": { + "query_record_id": "v2q_c16_4aae0650ea810862", + "problem_id": "v2p_c16_0d08289cb4e63265", + "dataset_id": "c16", + "template_id": "tpl_tpch_relative_total_threshold", + "template_name": "Relative-to-Total Extreme Threshold", + "family_id": "tail_rarity_structure", + "canonical_subitem_id": "tail_mass_similarity", + "intended_facet_id": "tail_ranked_signal", + "variant_semantic_role": "filtered_stable_view", + "subitem_assignment_source": "planner_selected", + "source_kind": "agent", + "realization_mode": "agent", + "gate_priority": "primary", + "extended_family": false, + "question": "Use template Relative-to-Total Extreme Threshold to probe tail_mass_similarity with semantic role filtered_stable_view. Focus on group_col=EYE, measure_col=page_id.", + "bindings": { + "group_col": "EYE", + "measure_col": "page_id", + "top_k": 18, + "top_n": 6, + "num_tiles": 10, + "percentile_value": 0.9, + "z_threshold": 2.0, + "fraction_threshold": 0.05, + "baseline_multiplier": 1.75, + "baseline_fraction": 0.1, + "min_group_size": 5, + "min_support": 4, + "measure_threshold": 187526.7, + "time_grain": "month", + "lookback_rows": 3, + "current_period_start": "'2024-01-01'", + "current_period_end": "'2024-04-01'", + "previous_period_start": "'2023-10-01'", + "previous_period_end": "'2024-01-01'", + "drift_ratio_threshold": 0.8 + }, + "binding_roles": [ + "group_col", + "measure_col" + ], + "coverage_target_min": "5", + "runtime_sql_skeleton": "WITH grouped AS (\n SELECT {group_col}, SUM({measure_col}) AS group_value\n FROM {table}\n GROUP BY {group_col}\n), total AS (\n SELECT SUM(group_value) AS total_value\n FROM grouped\n)\nSELECT g.{group_col}, g.group_value\nFROM grouped AS g\nCROSS JOIN total AS t\nWHERE g.group_value > t.total_value * {fraction_threshold}\nORDER BY g.group_value DESC;", + "notes": [ + "default_facets=tail_ranked_signal", + "template_selection_mode=rule", + "problem_index_within_template=7", + "sql_variant_index=2/2", + "binding_index=78" + ], + "template_selection_mode": "rule", + "selected_template_rank": 7, + "problem_index_within_template": 7, + "sql_variant_index": 2, + "sql_variant_total": 2 + }, + "mode": "subitem_workload_v2", + "sql_source_version": "v2", + "sql_source_label": "v2_current", + "generated_sql_path": "/data/jialinzhang/TabQueryBench/sql_workloads/v2_current/runs_and_launches/runs/v2_cli_20260502_081223_d/c16/sql/v2q_c16_4aae0650ea810862.sql", + "usage_summary": { + "dataset_id": "c16", + "model": "v2-cli:codex", + "run_id": "v2q_c16_4aae0650ea810862", + "api_calls": 0, + "input_tokens": 14691, + "cached_input_tokens": 12032, + "output_tokens": 1038, + "total_tokens": 15729, + "cost_usd": 0.0, + "ai_cli_calls": 1, + "estimated_input_tokens": 0, + "estimated_output_tokens": 0, + "estimated_total_tokens": 0, + "usage_source": "ai_cli_json_usage", + "cli_elapsed_ms_total": 17636.8, + "sql_execution_elapsed_ms_total": 2.85, + "conversation_log_path": "/data/jialinzhang/TabQueryBench/sql_workloads/v2_current/runs_and_launches/runs/v2_cli_20260502_081223_d/c16/artifacts/v2q_c16_4aae0650ea810862/cli/conversation.jsonl", + "note": "Executed through a local AI CLI with structured usage metadata." + } +} \ No newline at end of file diff --git a/Query/sql/v2/runs/v2_cli_20260502_081223_d/c16/artifacts/v2q_c16_4aae0650ea810862/trace.jsonl b/Query/sql/v2/runs/v2_cli_20260502_081223_d/c16/artifacts/v2q_c16_4aae0650ea810862/trace.jsonl new file mode 100644 index 0000000000000000000000000000000000000000..efd90a19fc07d44519557e7374e3e52b5fa7a455 --- /dev/null +++ b/Query/sql/v2/runs/v2_cli_20260502_081223_d/c16/artifacts/v2q_c16_4aae0650ea810862/trace.jsonl @@ -0,0 +1 @@ +{"timestamp": "2026-05-19T15:49:15.708475+00:00", "event_type": "ai_cli_sql_generation", "engine": "v2-cli:codex", "attempt": 1, "command": "codex exec --skip-git-repo-check --disable plugins --sandbox read-only --cd \"/data/jialinzhang/SQLagent\" -m gpt-5.4 --json -", "returncode": 0, "elapsed_ms": 17636.8, "started_at": "2026-05-19T15:48:58.070871+00:00", "ended_at": "2026-05-19T15:49:15.707696+00:00", "prompt_metrics": {"chars": 9036, "bytes_utf8": 9036, "lines": 252, "estimated_tokens": null}, "response_metrics": {"chars": 872, "bytes_utf8": 872, "lines": 1, "estimated_tokens": null}, "usage": {"input_tokens": 14691, "cached_input_tokens": 12032, "output_tokens": 1038, "reasoning_output_tokens": 778}, "stderr_preview": "", "stdout_preview": "{\"sql\":\"-- template_id: tpl_tpch_relative_total_threshold\\nWITH \\\"grouped\\\" AS (\\n SELECT \\\"EYE\\\", SUM(CAST(\\\"page_id\\\" AS REAL)) AS \\\"group_value\\\"\\n FROM \\\"c16\\\"\\n WHERE \\\"EYE\\\" IS NOT NULL\\n AND \\\"EYE\\\" <> ''\\n AND \\\"page_id\\\" IS NOT NULL\\n AND \\\"page_id\\\" <> ''\\n GROUP BY \\\"EYE\\\"\\n), \\\"total\\\" AS (\\n SELECT SUM(\\\"group_value\\\") AS \\\"total_value\\\"\\n FROM \\\"grouped\\\"\\n)\\nSELECT g.\\\"EYE\\\", g.\\\"group_value\\\"\\nFROM \\\"grouped\\\" AS g\\nCROSS JOIN \\\"total\\\" AS t\\nWHERE g.\\\"group_value\\\" > t.\\\"total_value\\\" * 0.05\\nORDER BY g.\\\"group_value\\\" DESC;\",\"notes\":\"Applied the requested Relative-to-Total Extreme Threshold template with group_col=\\\"EYE\\\" and measure_col=\\\"page_id\\\". Because \\\"page_id\\\" is stored as TEXT, it is cast to REAL for SUM(), and null/empty \\\"EYE\\\" and \\\"page_id\\\" values are excluded to keep the grouped view stable.\"}"} diff --git a/Query/sql/v2/runs/v2_cli_20260502_081223_d/c16/artifacts/v2q_c16_4aae0650ea810862/usage_summary.json b/Query/sql/v2/runs/v2_cli_20260502_081223_d/c16/artifacts/v2q_c16_4aae0650ea810862/usage_summary.json new file mode 100644 index 0000000000000000000000000000000000000000..41b1d497bc7221e8cf5b1cc530404d166c7a6a36 --- /dev/null +++ b/Query/sql/v2/runs/v2_cli_20260502_081223_d/c16/artifacts/v2q_c16_4aae0650ea810862/usage_summary.json @@ -0,0 +1,20 @@ +{ + "dataset_id": "c16", + "model": "v2-cli:codex", + "run_id": "v2q_c16_4aae0650ea810862", + "api_calls": 0, + "input_tokens": 14691, + "cached_input_tokens": 12032, + "output_tokens": 1038, + "total_tokens": 15729, + "cost_usd": 0.0, + "ai_cli_calls": 1, + "estimated_input_tokens": 0, + "estimated_output_tokens": 0, + "estimated_total_tokens": 0, + "usage_source": "ai_cli_json_usage", + "cli_elapsed_ms_total": 17636.8, + "sql_execution_elapsed_ms_total": 2.85, + "conversation_log_path": "/data/jialinzhang/TabQueryBench/sql_workloads/v2_current/runs_and_launches/runs/v2_cli_20260502_081223_d/c16/artifacts/v2q_c16_4aae0650ea810862/cli/conversation.jsonl", + "note": "Executed through a local AI CLI with structured usage metadata." +} \ No newline at end of file diff --git a/Query/sql/v2/runs/v2_cli_20260502_081223_d/c16/artifacts/v2q_c16_50552f0498f21327/final_answer.txt b/Query/sql/v2/runs/v2_cli_20260502_081223_d/c16/artifacts/v2q_c16_50552f0498f21327/final_answer.txt new file mode 100644 index 0000000000000000000000000000000000000000..1a909eb1a5d5c453162411a35a33836aa171270b --- /dev/null +++ b/Query/sql/v2/runs/v2_cli_20260502_081223_d/c16/artifacts/v2q_c16_50552f0498f21327/final_answer.txt @@ -0,0 +1 @@ +{"row_count": null, "preview_rows": [{"total_rows": 6896, "missing_rows": 0, "missing_rate": 0.0}]} \ No newline at end of file diff --git a/Query/sql/v2/runs/v2_cli_20260502_081223_d/c16/artifacts/v2q_c16_50552f0498f21327/generated_sql.sql b/Query/sql/v2/runs/v2_cli_20260502_081223_d/c16/artifacts/v2q_c16_50552f0498f21327/generated_sql.sql new file mode 100644 index 0000000000000000000000000000000000000000..db2c9fa0815433f8c71cb62c5c2060fa9bba7864 --- /dev/null +++ b/Query/sql/v2/runs/v2_cli_20260502_081223_d/c16/artifacts/v2q_c16_50552f0498f21327/generated_sql.sql @@ -0,0 +1,18 @@ +-- sql_source_version: v2 +-- sql_source_label: v2_current +-- sql_source_run_id: v2_cli_20260502_081223_d +-- sql_source_dataset_id: c16 +-- family_id: missingness_structure +-- canonical_subitem_id: marginal_missing_rate_consistency +-- intended_facet_id: missing_indicator_distribution +-- variant_semantic_role: missing_indicator_view +-- template_id: tpl_missing_marginal_rate_profile +-- query_record_id: v2q_c16_50552f0498f21327 +-- problem_id: v2p_c16_e24ba5753973434c +-- realization_mode: deterministic +-- source_kind: deterministic +SELECT + COUNT(*) AS total_rows, + SUM(CASE WHEN "EYE" IS NULL THEN 1 ELSE 0 END) AS missing_rows, + AVG(CASE WHEN "EYE" IS NULL THEN 1.0 ELSE 0.0 END) AS missing_rate +FROM "c16"; diff --git a/Query/sql/v2/runs/v2_cli_20260502_081223_d/c16/artifacts/v2q_c16_50552f0498f21327/query_results.jsonl b/Query/sql/v2/runs/v2_cli_20260502_081223_d/c16/artifacts/v2q_c16_50552f0498f21327/query_results.jsonl new file mode 100644 index 0000000000000000000000000000000000000000..cd829403b9e236f54b66e66ba083d8478b97d548 --- /dev/null +++ b/Query/sql/v2/runs/v2_cli_20260502_081223_d/c16/artifacts/v2q_c16_50552f0498f21327/query_results.jsonl @@ -0,0 +1 @@ +{"node_name": "v2_template", "tool_name": "sqlite_query", "query": "-- sql_source_version: v2\n-- sql_source_label: v2_current\n-- sql_source_run_id: v2_cli_20260502_081223_d\n-- sql_source_dataset_id: c16\n-- family_id: missingness_structure\n-- canonical_subitem_id: marginal_missing_rate_consistency\n-- intended_facet_id: missing_indicator_distribution\n-- variant_semantic_role: missing_indicator_view\n-- template_id: tpl_missing_marginal_rate_profile\n-- query_record_id: v2q_c16_50552f0498f21327\n-- problem_id: v2p_c16_e24ba5753973434c\n-- realization_mode: deterministic\n-- source_kind: deterministic\nSELECT\n COUNT(*) AS total_rows,\n SUM(CASE WHEN \"EYE\" IS NULL THEN 1 ELSE 0 END) AS missing_rows,\n AVG(CASE WHEN \"EYE\" IS NULL THEN 1.0 ELSE 0.0 END) AS missing_rate\nFROM \"c16\";", "result": "{\"query\": \"-- sql_source_version: v2\\n-- sql_source_label: v2_current\\n-- sql_source_run_id: v2_cli_20260502_081223_d\\n-- sql_source_dataset_id: c16\\n-- family_id: missingness_structure\\n-- canonical_subitem_id: marginal_missing_rate_consistency\\n-- intended_facet_id: missing_indicator_distribution\\n-- variant_semantic_role: missing_indicator_view\\n-- template_id: tpl_missing_marginal_rate_profile\\n-- query_record_id: v2q_c16_50552f0498f21327\\n-- problem_id: v2p_c16_e24ba5753973434c\\n-- realization_mode: deterministic\\n-- source_kind: deterministic\\nSELECT\\n COUNT(*) AS total_rows,\\n SUM(CASE WHEN \\\"EYE\\\" IS NULL THEN 1 ELSE 0 END) AS missing_rows,\\n AVG(CASE WHEN \\\"EYE\\\" IS NULL THEN 1.0 ELSE 0.0 END) AS missing_rate\\nFROM \\\"c16\\\";\", \"columns\": [\"total_rows\", \"missing_rows\", \"missing_rate\"], \"rows\": [{\"total_rows\": 6896, \"missing_rows\": 0, \"missing_rate\": 0.0}], \"row_count_returned\": 1, \"row_limit\": 50, \"truncated\": false, \"elapsed_ms\": 1.33}"} diff --git a/Query/sql/v2/runs/v2_cli_20260502_081223_d/c16/artifacts/v2q_c16_50552f0498f21327/run_manifest.json b/Query/sql/v2/runs/v2_cli_20260502_081223_d/c16/artifacts/v2q_c16_50552f0498f21327/run_manifest.json new file mode 100644 index 0000000000000000000000000000000000000000..2c46f1357fe8fdefcc1b6a79829e60a3141d9bfe --- /dev/null +++ b/Query/sql/v2/runs/v2_cli_20260502_081223_d/c16/artifacts/v2q_c16_50552f0498f21327/run_manifest.json @@ -0,0 +1,57 @@ +{ + "run_id": "v2_cli_20260502_081223_d", + "dataset_id": "c16", + "started_at": "2026-05-19T16:10:30.327307+00:00", + "ended_at": "2026-05-19T16:10:30.329275+00:00", + "status": "completed", + "engine": "cli", + "question_record": { + "query_record_id": "v2q_c16_50552f0498f21327", + "problem_id": "v2p_c16_e24ba5753973434c", + "dataset_id": "c16", + "template_id": "tpl_missing_marginal_rate_profile", + "template_name": "Marginal Missing Rate Profile", + "family_id": "missingness_structure", + "canonical_subitem_id": "marginal_missing_rate_consistency", + "intended_facet_id": "missing_indicator_distribution", + "variant_semantic_role": "missing_indicator_view", + "subitem_assignment_source": "template_fixed", + "source_kind": "deterministic", + "realization_mode": "deterministic", + "gate_priority": "deterministic", + "extended_family": false, + "question": "Use template Marginal Missing Rate Profile to probe marginal_missing_rate_consistency with semantic role missing_indicator_view. Focus on missing_col=EYE.", + "bindings": { + "missing_col": "EYE" + }, + "binding_roles": [ + "missing_col" + ], + "coverage_target_min": "enumerate_all_applicable", + "runtime_sql_skeleton": "SELECT\n COUNT(*) AS total_rows,\n SUM(CASE WHEN {missing_col} IS NULL THEN 1 ELSE 0 END) AS missing_rows,\n AVG(CASE WHEN {missing_col} IS NULL THEN 1.0 ELSE 0.0 END) AS missing_rate\nFROM {table};", + "notes": [ + "default_facets=missing_indicator_distribution", + "template_selection_mode=deterministic", + "problem_index_within_template=3", + "sql_variant_index=1/1" + ], + "template_selection_mode": "deterministic", + "selected_template_rank": 0, + "problem_index_within_template": 3, + "sql_variant_index": 1, + "sql_variant_total": 1 + }, + "mode": "subitem_workload_v2", + "sql_source_version": "v2", + "sql_source_label": "v2_current", + "generated_sql_path": "/data/jialinzhang/TabQueryBench/sql_workloads/v2_current/runs_and_launches/runs/v2_cli_20260502_081223_d/c16/sql/v2q_c16_50552f0498f21327.sql", + "usage_summary": { + "engine": "template", + "input_tokens": 0, + "cached_input_tokens": 0, + "output_tokens": 0, + "total_tokens": 0, + "estimated_total_tokens": 0, + "usage_source": "none" + } +} \ No newline at end of file diff --git a/Query/sql/v2/runs/v2_cli_20260502_081223_d/c16/artifacts/v2q_c16_50552f0498f21327/usage_summary.json b/Query/sql/v2/runs/v2_cli_20260502_081223_d/c16/artifacts/v2q_c16_50552f0498f21327/usage_summary.json new file mode 100644 index 0000000000000000000000000000000000000000..96c9ff4feec395919fc26411d18d078b8af6e1c7 --- /dev/null +++ b/Query/sql/v2/runs/v2_cli_20260502_081223_d/c16/artifacts/v2q_c16_50552f0498f21327/usage_summary.json @@ -0,0 +1,9 @@ +{ + "engine": "template", + "input_tokens": 0, + "cached_input_tokens": 0, + "output_tokens": 0, + "total_tokens": 0, + "estimated_total_tokens": 0, + "usage_source": "none" +} \ No newline at end of file diff --git a/Query/sql/v2/runs/v2_cli_20260502_081223_d/c16/artifacts/v2q_c16_52acc7460fdb25fe/run_manifest.json b/Query/sql/v2/runs/v2_cli_20260502_081223_d/c16/artifacts/v2q_c16_52acc7460fdb25fe/run_manifest.json new file mode 100644 index 0000000000000000000000000000000000000000..0022f66c475695772174388ab8507e0118559cf9 --- /dev/null +++ b/Query/sql/v2/runs/v2_cli_20260502_081223_d/c16/artifacts/v2q_c16_52acc7460fdb25fe/run_manifest.json @@ -0,0 +1,67 @@ +{ + "run_id": "v2_cli_20260502_081223_d", + "dataset_id": "c16", + "started_at": "2026-05-19T16:06:54.410141+00:00", + "ended_at": "2026-05-19T16:07:01.520662+00:00", + "status": "failed", + "engine": "cli", + "question_record": { + "query_record_id": "v2q_c16_52acc7460fdb25fe", + "problem_id": "v2p_c16_1a6a29fa08e4702e", + "dataset_id": "c16", + "template_id": "tpl_tail_low_support_group_count_v2", + "template_name": "Low-Support Group Count", + "family_id": "tail_rarity_structure", + "canonical_subitem_id": "tail_set_consistency", + "intended_facet_id": "low_support_extremes", + "variant_semantic_role": "count_distribution", + "subitem_assignment_source": "planner_selected", + "source_kind": "agent", + "realization_mode": "agent", + "gate_priority": "primary", + "extended_family": false, + "question": "Use template Low-Support Group Count to probe tail_set_consistency with semantic role count_distribution. Focus on group_col=SEX.", + "bindings": { + "group_col": "SEX", + "top_k": 17, + "top_n": 6, + "num_tiles": 10, + "percentile_value": 0.9, + "z_threshold": 2.0, + "fraction_threshold": 0.05, + "baseline_multiplier": 1.75, + "baseline_fraction": 0.1, + "min_group_size": 5, + "min_support": 4, + "measure_threshold": 2003.0, + "time_grain": "month", + "lookback_rows": 3, + "current_period_start": "'2024-01-01'", + "current_period_end": "'2024-04-01'", + "previous_period_start": "'2023-10-01'", + "previous_period_end": "'2024-01-01'", + "drift_ratio_threshold": 0.8 + }, + "binding_roles": [ + "group_col" + ], + "coverage_target_min": "5", + "runtime_sql_skeleton": "SELECT\n {group_col},\n COUNT(*) AS support\nFROM {table}\nGROUP BY {group_col}\nORDER BY support ASC, {group_col}\nLIMIT {top_k};", + "notes": [ + "default_facets=low_support_extremes", + "template_selection_mode=rule", + "problem_index_within_template=3", + "sql_variant_index=2/2", + "binding_index=122" + ], + "template_selection_mode": "rule", + "selected_template_rank": 11, + "problem_index_within_template": 3, + "sql_variant_index": 2, + "sql_variant_total": 2 + }, + "mode": "subitem_workload_v2", + "sql_source_version": "v2", + "sql_source_label": "v2_current", + "error": "AI CLI command failed with exit code 1: " +} \ No newline at end of file diff --git a/Query/sql/v2/runs/v2_cli_20260502_081223_d/c16/artifacts/v2q_c16_52acc7460fdb25fe/trace.jsonl b/Query/sql/v2/runs/v2_cli_20260502_081223_d/c16/artifacts/v2q_c16_52acc7460fdb25fe/trace.jsonl new file mode 100644 index 0000000000000000000000000000000000000000..a48d5697e96a6bd7c091ba7fd73b04cb3e6338e8 --- /dev/null +++ b/Query/sql/v2/runs/v2_cli_20260502_081223_d/c16/artifacts/v2q_c16_52acc7460fdb25fe/trace.jsonl @@ -0,0 +1,2 @@ +{"timestamp": "2026-05-19T16:06:57.577095+00:00", "event_type": "ai_cli_sql_generation_error", "engine": "v2-cli:codex", "attempt": 1, "command": "codex exec --skip-git-repo-check --disable plugins --sandbox read-only --cd \"/data/jialinzhang/SQLagent\" -m gpt-5.4 --json -", "returncode": 1, "elapsed_ms": 3164.52, "started_at": "2026-05-19T16:06:54.411811+00:00", "ended_at": "2026-05-19T16:06:57.576353+00:00", "prompt_metrics": {"chars": 8470, "bytes_utf8": 8470, "lines": 250, "estimated_tokens": null}, "response_metrics": {"chars": 280, "bytes_utf8": 280, "lines": 4, "estimated_tokens": null}, "usage": {}, "stderr_preview": "", "stdout_preview": "{\"type\":\"thread.started\",\"thread_id\":\"019e40fd-7bcd-7e63-8484-21d9fecc9b84\"}\n{\"type\":\"turn.started\"}\n{\"type\":\"error\",\"message\":\"Quota exceeded. Check your plan and billing details.\"}\n{\"type\":\"turn.failed\",\"error\":{\"message\":\"Quota exceeded. Check your plan and billing details.\"}}", "error": "AI CLI command failed with exit code 1: "} +{"timestamp": "2026-05-19T16:07:01.520577+00:00", "event_type": "ai_cli_sql_generation_error", "engine": "v2-cli:codex", "attempt": 2, "command": "codex exec --skip-git-repo-check --disable plugins --sandbox read-only --cd \"/data/jialinzhang/SQLagent\" -m gpt-5.4 --json -", "returncode": 1, "elapsed_ms": 2941.9, "started_at": "2026-05-19T16:06:58.577918+00:00", "ended_at": "2026-05-19T16:07:01.519850+00:00", "prompt_metrics": {"chars": 8470, "bytes_utf8": 8470, "lines": 250, "estimated_tokens": null}, "response_metrics": {"chars": 280, "bytes_utf8": 280, "lines": 4, "estimated_tokens": null}, "usage": {}, "stderr_preview": "", "stdout_preview": "{\"type\":\"thread.started\",\"thread_id\":\"019e40fd-8c0b-7352-9b2a-095a0439290f\"}\n{\"type\":\"turn.started\"}\n{\"type\":\"error\",\"message\":\"Quota exceeded. Check your plan and billing details.\"}\n{\"type\":\"turn.failed\",\"error\":{\"message\":\"Quota exceeded. Check your plan and billing details.\"}}", "error": "AI CLI command failed with exit code 1: "} diff --git a/Query/sql/v2/runs/v2_cli_20260502_081223_d/c16/artifacts/v2q_c16_5963cafd9cc76bc4/final_answer.txt b/Query/sql/v2/runs/v2_cli_20260502_081223_d/c16/artifacts/v2q_c16_5963cafd9cc76bc4/final_answer.txt new file mode 100644 index 0000000000000000000000000000000000000000..35af9aff66bda3c5dccfb525577e0774b7f0d090 --- /dev/null +++ b/Query/sql/v2/runs/v2_cli_20260502_081223_d/c16/artifacts/v2q_c16_5963cafd9cc76bc4/final_answer.txt @@ -0,0 +1,2 @@ +SQL executed successfully for: Use template Grouped Percentile Point to probe tail_concentration_consistency with semantic role focused_target_view. Focus on group_col=ALIGN, measure_col=page_id. +Result preview: [{"ALIGN": "", "percentile_measure": 345517.0}, {"ALIGN": "Good Characters", "percentile_measure": 345130.5}, {"ALIGN": "Neutral Characters", "percentile_measure": 341984.2}, {"ALIGN": "Reformed Criminals", "percentile_measure": 329999.8}, {"ALIGN": "Bad Characters", "percentile_measure": 315979.3}] \ No newline at end of file diff --git a/Query/sql/v2/runs/v2_cli_20260502_081223_d/c16/artifacts/v2q_c16_5963cafd9cc76bc4/generated_sql.sql b/Query/sql/v2/runs/v2_cli_20260502_081223_d/c16/artifacts/v2q_c16_5963cafd9cc76bc4/generated_sql.sql new file mode 100644 index 0000000000000000000000000000000000000000..72e7d9723a48978d7d88cb434ca5ee4656f96d6e --- /dev/null +++ b/Query/sql/v2/runs/v2_cli_20260502_081223_d/c16/artifacts/v2q_c16_5963cafd9cc76bc4/generated_sql.sql @@ -0,0 +1,62 @@ +-- sql_source_version: v2 +-- sql_source_label: v2_current +-- sql_source_run_id: v2_cli_20260502_081223_d +-- sql_source_dataset_id: c16 +-- family_id: tail_rarity_structure +-- canonical_subitem_id: tail_concentration_consistency +-- intended_facet_id: rare_target_concentration +-- variant_semantic_role: focused_target_view +-- template_id: tpl_grouped_percentile_point +-- query_record_id: v2q_c16_5963cafd9cc76bc4 +-- problem_id: v2p_c16_cbcb83759450f843 +-- realization_mode: agent +-- source_kind: agent +WITH "ordered" AS ( + SELECT + "ALIGN" AS "group_col", + CAST("page_id" AS REAL) AS "measure_col", + ROW_NUMBER() OVER ( + PARTITION BY "ALIGN" + ORDER BY CAST("page_id" AS REAL) + ) AS "rn", + COUNT(*) OVER ( + PARTITION BY "ALIGN" + ) AS "cnt" + FROM "c16" + WHERE "ALIGN" IS NOT NULL + AND "page_id" IS NOT NULL + AND TRIM("page_id") <> '' +), +"bounds" AS ( + SELECT DISTINCT + "group_col", + (1.0 + (0.95 * ("cnt" - 1))) AS "target_pos", + CAST(FLOOR(1.0 + (0.95 * ("cnt" - 1))) AS INTEGER) AS "lower_rn", + CAST(CEIL(1.0 + (0.95 * ("cnt" - 1))) AS INTEGER) AS "upper_rn" + FROM "ordered" +), +"picked" AS ( + SELECT + o."group_col", + b."target_pos", + b."lower_rn", + b."upper_rn", + MAX(CASE WHEN o."rn" = b."lower_rn" THEN o."measure_col" END) AS "lower_val", + MAX(CASE WHEN o."rn" = b."upper_rn" THEN o."measure_col" END) AS "upper_val" + FROM "ordered" AS o + JOIN "bounds" AS b + ON o."group_col" = b."group_col" + GROUP BY + o."group_col", + b."target_pos", + b."lower_rn", + b."upper_rn" +) +SELECT + "group_col" AS "ALIGN", + CASE + WHEN "lower_rn" = "upper_rn" THEN "lower_val" + ELSE "lower_val" + (("target_pos" - "lower_rn") * ("upper_val" - "lower_val")) + END AS "percentile_measure" +FROM "picked" +ORDER BY "percentile_measure" DESC; diff --git a/Query/sql/v2/runs/v2_cli_20260502_081223_d/c16/artifacts/v2q_c16_5963cafd9cc76bc4/query_results.jsonl b/Query/sql/v2/runs/v2_cli_20260502_081223_d/c16/artifacts/v2q_c16_5963cafd9cc76bc4/query_results.jsonl new file mode 100644 index 0000000000000000000000000000000000000000..228284e5c23c5dbd0a64ba5978aabf278fbbf99b --- /dev/null +++ b/Query/sql/v2/runs/v2_cli_20260502_081223_d/c16/artifacts/v2q_c16_5963cafd9cc76bc4/query_results.jsonl @@ -0,0 +1 @@ +{"step_index": 1, "message_index": 0, "node_name": "v2-cli:codex", "tool_name": "sqlite_query", "query": "-- template_id: tpl_grouped_percentile_point\nWITH \"ordered\" AS (\n SELECT\n \"ALIGN\" AS \"group_col\",\n CAST(\"page_id\" AS REAL) AS \"measure_col\",\n ROW_NUMBER() OVER (\n PARTITION BY \"ALIGN\"\n ORDER BY CAST(\"page_id\" AS REAL)\n ) AS \"rn\",\n COUNT(*) OVER (\n PARTITION BY \"ALIGN\"\n ) AS \"cnt\"\n FROM \"c16\"\n WHERE \"ALIGN\" IS NOT NULL\n AND \"page_id\" IS NOT NULL\n AND TRIM(\"page_id\") <> ''\n),\n\"bounds\" AS (\n SELECT DISTINCT\n \"group_col\",\n (1.0 + (0.95 * (\"cnt\" - 1))) AS \"target_pos\",\n CAST(FLOOR(1.0 + (0.95 * (\"cnt\" - 1))) AS INTEGER) AS \"lower_rn\",\n CAST(CEIL(1.0 + (0.95 * (\"cnt\" - 1))) AS INTEGER) AS \"upper_rn\"\n FROM \"ordered\"\n),\n\"picked\" AS (\n SELECT\n o.\"group_col\",\n b.\"target_pos\",\n b.\"lower_rn\",\n b.\"upper_rn\",\n MAX(CASE WHEN o.\"rn\" = b.\"lower_rn\" THEN o.\"measure_col\" END) AS \"lower_val\",\n MAX(CASE WHEN o.\"rn\" = b.\"upper_rn\" THEN o.\"measure_col\" END) AS \"upper_val\"\n FROM \"ordered\" AS o\n JOIN \"bounds\" AS b\n ON o.\"group_col\" = b.\"group_col\"\n GROUP BY\n o.\"group_col\",\n b.\"target_pos\",\n b.\"lower_rn\",\n b.\"upper_rn\"\n)\nSELECT\n \"group_col\" AS \"ALIGN\",\n CASE\n WHEN \"lower_rn\" = \"upper_rn\" THEN \"lower_val\"\n ELSE \"lower_val\" + ((\"target_pos\" - \"lower_rn\") * (\"upper_val\" - \"lower_val\"))\n END AS \"percentile_measure\"\nFROM \"picked\"\nORDER BY \"percentile_measure\" DESC;", "result": "{\"query\": \"-- template_id: tpl_grouped_percentile_point\\nWITH \\\"ordered\\\" AS (\\n SELECT\\n \\\"ALIGN\\\" AS \\\"group_col\\\",\\n CAST(\\\"page_id\\\" AS REAL) AS \\\"measure_col\\\",\\n ROW_NUMBER() OVER (\\n PARTITION BY \\\"ALIGN\\\"\\n ORDER BY CAST(\\\"page_id\\\" AS REAL)\\n ) AS \\\"rn\\\",\\n COUNT(*) OVER (\\n PARTITION BY \\\"ALIGN\\\"\\n ) AS \\\"cnt\\\"\\n FROM \\\"c16\\\"\\n WHERE \\\"ALIGN\\\" IS NOT NULL\\n AND \\\"page_id\\\" IS NOT NULL\\n AND TRIM(\\\"page_id\\\") <> ''\\n),\\n\\\"bounds\\\" AS (\\n SELECT DISTINCT\\n \\\"group_col\\\",\\n (1.0 + (0.95 * (\\\"cnt\\\" - 1))) AS \\\"target_pos\\\",\\n CAST(FLOOR(1.0 + (0.95 * (\\\"cnt\\\" - 1))) AS INTEGER) AS \\\"lower_rn\\\",\\n CAST(CEIL(1.0 + (0.95 * (\\\"cnt\\\" - 1))) AS INTEGER) AS \\\"upper_rn\\\"\\n FROM \\\"ordered\\\"\\n),\\n\\\"picked\\\" AS (\\n SELECT\\n o.\\\"group_col\\\",\\n b.\\\"target_pos\\\",\\n b.\\\"lower_rn\\\",\\n b.\\\"upper_rn\\\",\\n MAX(CASE WHEN o.\\\"rn\\\" = b.\\\"lower_rn\\\" THEN o.\\\"measure_col\\\" END) AS \\\"lower_val\\\",\\n MAX(CASE WHEN o.\\\"rn\\\" = b.\\\"upper_rn\\\" THEN o.\\\"measure_col\\\" END) AS \\\"upper_val\\\"\\n FROM \\\"ordered\\\" AS o\\n JOIN \\\"bounds\\\" AS b\\n ON o.\\\"group_col\\\" = b.\\\"group_col\\\"\\n GROUP BY\\n o.\\\"group_col\\\",\\n b.\\\"target_pos\\\",\\n b.\\\"lower_rn\\\",\\n b.\\\"upper_rn\\\"\\n)\\nSELECT\\n \\\"group_col\\\" AS \\\"ALIGN\\\",\\n CASE\\n WHEN \\\"lower_rn\\\" = \\\"upper_rn\\\" THEN \\\"lower_val\\\"\\n ELSE \\\"lower_val\\\" + ((\\\"target_pos\\\" - \\\"lower_rn\\\") * (\\\"upper_val\\\" - \\\"lower_val\\\"))\\n END AS \\\"percentile_measure\\\"\\nFROM \\\"picked\\\"\\nORDER BY \\\"percentile_measure\\\" DESC;\", \"columns\": [\"ALIGN\", \"percentile_measure\"], \"rows\": [{\"ALIGN\": \"\", \"percentile_measure\": 345517.0}, {\"ALIGN\": \"Good Characters\", \"percentile_measure\": 345130.5}, {\"ALIGN\": \"Neutral Characters\", \"percentile_measure\": 341984.2}, {\"ALIGN\": \"Reformed Criminals\", \"percentile_measure\": 329999.8}, {\"ALIGN\": \"Bad Characters\", \"percentile_measure\": 315979.3}], \"row_count_returned\": 5, \"row_limit\": 50, \"truncated\": false, \"elapsed_ms\": 44.89}"} diff --git a/Query/sql/v2/runs/v2_cli_20260502_081223_d/c16/artifacts/v2q_c16_5963cafd9cc76bc4/run_manifest.json b/Query/sql/v2/runs/v2_cli_20260502_081223_d/c16/artifacts/v2q_c16_5963cafd9cc76bc4/run_manifest.json new file mode 100644 index 0000000000000000000000000000000000000000..4362014b5e745deb9bb6785f2a4c53eabf7b312a --- /dev/null +++ b/Query/sql/v2/runs/v2_cli_20260502_081223_d/c16/artifacts/v2q_c16_5963cafd9cc76bc4/run_manifest.json @@ -0,0 +1,89 @@ +{ + "run_id": "v2_cli_20260502_081223_d", + "dataset_id": "c16", + "started_at": "2026-05-19T15:50:18.109211+00:00", + "ended_at": "2026-05-19T15:50:36.544309+00:00", + "status": "completed", + "engine": "cli", + "question_record": { + "query_record_id": "v2q_c16_5963cafd9cc76bc4", + "problem_id": "v2p_c16_cbcb83759450f843", + "dataset_id": "c16", + "template_id": "tpl_grouped_percentile_point", + "template_name": "Grouped Percentile Point", + "family_id": "tail_rarity_structure", + "canonical_subitem_id": "tail_concentration_consistency", + "intended_facet_id": "rare_target_concentration", + "variant_semantic_role": "focused_target_view", + "subitem_assignment_source": "planner_selected", + "source_kind": "agent", + "realization_mode": "agent", + "gate_priority": "primary", + "extended_family": false, + "question": "Use template Grouped Percentile Point to probe tail_concentration_consistency with semantic role focused_target_view. Focus on group_col=ALIGN, measure_col=page_id.", + "bindings": { + "group_col": "ALIGN", + "measure_col": "page_id", + "top_k": 14, + "top_n": 3, + "num_tiles": 10, + "percentile_value": 0.95, + "z_threshold": 2.0, + "fraction_threshold": 0.1, + "baseline_multiplier": 1.5, + "baseline_fraction": 0.1, + "min_group_size": 5, + "min_support": 5, + "measure_threshold": 213203.0, + "time_grain": "month", + "lookback_rows": 3, + "current_period_start": "'2024-01-01'", + "current_period_end": "'2024-04-01'", + "previous_period_start": "'2023-10-01'", + "previous_period_end": "'2024-01-01'", + "drift_ratio_threshold": 0.8 + }, + "binding_roles": [ + "group_col", + "measure_col" + ], + "coverage_target_min": "5", + "runtime_sql_skeleton": "SELECT {group_col},\n PERCENTILE_CONT({percentile_value}) WITHIN GROUP (ORDER BY {measure_col}) AS percentile_measure\nFROM {table}\nGROUP BY {group_col}\nORDER BY percentile_measure DESC;", + "notes": [ + "default_facets=rare_target_concentration", + "template_selection_mode=rule", + "problem_index_within_template=1", + "sql_variant_index=1/2", + "binding_index=84" + ], + "template_selection_mode": "rule", + "selected_template_rank": 8, + "problem_index_within_template": 1, + "sql_variant_index": 1, + "sql_variant_total": 2 + }, + "mode": "subitem_workload_v2", + "sql_source_version": "v2", + "sql_source_label": "v2_current", + "generated_sql_path": "/data/jialinzhang/TabQueryBench/sql_workloads/v2_current/runs_and_launches/runs/v2_cli_20260502_081223_d/c16/sql/v2q_c16_5963cafd9cc76bc4.sql", + "usage_summary": { + "dataset_id": "c16", + "model": "v2-cli:codex", + "run_id": "v2q_c16_5963cafd9cc76bc4", + "api_calls": 0, + "input_tokens": 14589, + "cached_input_tokens": 13696, + "output_tokens": 1081, + "total_tokens": 15670, + "cost_usd": 0.0, + "ai_cli_calls": 1, + "estimated_input_tokens": 0, + "estimated_output_tokens": 0, + "estimated_total_tokens": 0, + "usage_source": "ai_cli_json_usage", + "cli_elapsed_ms_total": 18384.85, + "sql_execution_elapsed_ms_total": 44.89, + "conversation_log_path": "/data/jialinzhang/TabQueryBench/sql_workloads/v2_current/runs_and_launches/runs/v2_cli_20260502_081223_d/c16/artifacts/v2q_c16_5963cafd9cc76bc4/cli/conversation.jsonl", + "note": "Executed through a local AI CLI with structured usage metadata." + } +} \ No newline at end of file diff --git a/Query/sql/v2/runs/v2_cli_20260502_081223_d/c16/artifacts/v2q_c16_5963cafd9cc76bc4/trace.jsonl b/Query/sql/v2/runs/v2_cli_20260502_081223_d/c16/artifacts/v2q_c16_5963cafd9cc76bc4/trace.jsonl new file mode 100644 index 0000000000000000000000000000000000000000..4163615621e5de6f94d4c9957d3f734a562646ae --- /dev/null +++ b/Query/sql/v2/runs/v2_cli_20260502_081223_d/c16/artifacts/v2q_c16_5963cafd9cc76bc4/trace.jsonl @@ -0,0 +1 @@ +{"timestamp": "2026-05-19T15:50:36.496681+00:00", "event_type": "ai_cli_sql_generation", "engine": "v2-cli:codex", "attempt": 1, "command": "codex exec --skip-git-repo-check --disable plugins --sandbox read-only --cd \"/data/jialinzhang/SQLagent\" -m gpt-5.4 --json -", "returncode": 0, "elapsed_ms": 18384.85, "started_at": "2026-05-19T15:50:18.110649+00:00", "ended_at": "2026-05-19T15:50:36.495524+00:00", "prompt_metrics": {"chars": 8662, "bytes_utf8": 8662, "lines": 252, "estimated_tokens": null}, "response_metrics": {"chars": 1733, "bytes_utf8": 1733, "lines": 1, "estimated_tokens": null}, "usage": {"input_tokens": 14589, "cached_input_tokens": 13696, "output_tokens": 1081, "reasoning_output_tokens": 516}, "stderr_preview": "", "stdout_preview": "{\"sql\":\"-- template_id: tpl_grouped_percentile_point\\nWITH \\\"ordered\\\" AS (\\n SELECT\\n \\\"ALIGN\\\" AS \\\"group_col\\\",\\n CAST(\\\"page_id\\\" AS REAL) AS \\\"measure_col\\\",\\n ROW_NUMBER() OVER (\\n PARTITION BY \\\"ALIGN\\\"\\n ORDER BY CAST(\\\"page_id\\\" AS REAL)\\n ) AS \\\"rn\\\",\\n COUNT(*) OVER (\\n PARTITION BY \\\"ALIGN\\\"\\n ) AS \\\"cnt\\\"\\n FROM \\\"c16\\\"\\n WHERE \\\"ALIGN\\\" IS NOT NULL\\n AND \\\"page_id\\\" IS NOT NULL\\n AND TRIM(\\\"page_id\\\") <> ''\\n),\\n\\\"bounds\\\" AS (\\n SELECT DISTINCT\\n \\\"group_col\\\",\\n (1.0 + (0.95 * (\\\"cnt\\\" - 1))) AS \\\"target_pos\\\",\\n CAST(FLOOR(1.0 + (0.95 * (\\\"cnt\\\" - 1))) AS INTEGER) AS \\\"lower_rn\\\",\\n CAST(CEIL(1.0 + (0.95 * (\\\"cnt\\\" - 1))) AS INTEGER) AS \\\"upper_rn\\\"\\n FROM \\\"ordered\\\"\\n),\\n\\\"picked\\\" AS (\\n SELECT\\n o.\\\"group_col\\\",\\n b.\\\"target_pos\\\",\\n b.\\\"lower_rn\\\",\\n b.\\\"upper_rn\\\",\\n MAX(CASE WHEN o.\\\"rn\\\" = b.\\\"lower_rn\\\" THEN o.\\\"measure_col\\\" END) AS \\\"lower_val\\\",\\n MAX(CASE WHEN o.\\\"rn\\\" = b.\\\"upper_r"} diff --git a/Query/sql/v2/runs/v2_cli_20260502_081223_d/c16/artifacts/v2q_c16_5963cafd9cc76bc4/usage_summary.json b/Query/sql/v2/runs/v2_cli_20260502_081223_d/c16/artifacts/v2q_c16_5963cafd9cc76bc4/usage_summary.json new file mode 100644 index 0000000000000000000000000000000000000000..89d54a01a062d4bb63ffd53b93f9af59785e110d --- /dev/null +++ b/Query/sql/v2/runs/v2_cli_20260502_081223_d/c16/artifacts/v2q_c16_5963cafd9cc76bc4/usage_summary.json @@ -0,0 +1,20 @@ +{ + "dataset_id": "c16", + "model": "v2-cli:codex", + "run_id": "v2q_c16_5963cafd9cc76bc4", + "api_calls": 0, + "input_tokens": 14589, + "cached_input_tokens": 13696, + "output_tokens": 1081, + "total_tokens": 15670, + "cost_usd": 0.0, + "ai_cli_calls": 1, + "estimated_input_tokens": 0, + "estimated_output_tokens": 0, + "estimated_total_tokens": 0, + "usage_source": "ai_cli_json_usage", + "cli_elapsed_ms_total": 18384.85, + "sql_execution_elapsed_ms_total": 44.89, + "conversation_log_path": "/data/jialinzhang/TabQueryBench/sql_workloads/v2_current/runs_and_launches/runs/v2_cli_20260502_081223_d/c16/artifacts/v2q_c16_5963cafd9cc76bc4/cli/conversation.jsonl", + "note": "Executed through a local AI CLI with structured usage metadata." +} \ No newline at end of file diff --git a/Query/sql/v2/runs/v2_cli_20260502_081223_d/c16/artifacts/v2q_c16_6370fde98babd4cf/final_answer.txt b/Query/sql/v2/runs/v2_cli_20260502_081223_d/c16/artifacts/v2q_c16_6370fde98babd4cf/final_answer.txt new file mode 100644 index 0000000000000000000000000000000000000000..a3cffbb0942f4b345b9792c158ec924c830c66ec --- /dev/null +++ b/Query/sql/v2/runs/v2_cli_20260502_081223_d/c16/artifacts/v2q_c16_6370fde98babd4cf/final_answer.txt @@ -0,0 +1,2 @@ +SQL executed successfully for: Use template Relative-to-Total Extreme Threshold to probe tail_mass_similarity with semantic role filtered_stable_view. Focus on group_col=SEX, measure_col=APPEARANCES. +Result preview: [{"SEX": "Male Characters", "group_value": 110911.0}, {"SEX": "Female Characters", "group_value": 42271.0}] \ No newline at end of file diff --git a/Query/sql/v2/runs/v2_cli_20260502_081223_d/c16/artifacts/v2q_c16_6370fde98babd4cf/generated_sql.sql b/Query/sql/v2/runs/v2_cli_20260502_081223_d/c16/artifacts/v2q_c16_6370fde98babd4cf/generated_sql.sql new file mode 100644 index 0000000000000000000000000000000000000000..da0e989e4452944340ab9d23b7084fffe686f06d --- /dev/null +++ b/Query/sql/v2/runs/v2_cli_20260502_081223_d/c16/artifacts/v2q_c16_6370fde98babd4cf/generated_sql.sql @@ -0,0 +1,30 @@ +-- sql_source_version: v2 +-- sql_source_label: v2_current +-- sql_source_run_id: v2_cli_20260502_081223_d +-- sql_source_dataset_id: c16 +-- family_id: tail_rarity_structure +-- canonical_subitem_id: tail_mass_similarity +-- intended_facet_id: tail_ranked_signal +-- variant_semantic_role: filtered_stable_view +-- template_id: tpl_tpch_relative_total_threshold +-- query_record_id: v2q_c16_6370fde98babd4cf +-- problem_id: v2p_c16_bda9652f8374c8e3 +-- realization_mode: agent +-- source_kind: agent +WITH grouped AS ( + SELECT "SEX", SUM(CAST("APPEARANCES" AS REAL)) AS group_value + FROM "c16" + WHERE "SEX" IS NOT NULL + AND TRIM("SEX") <> '' + AND "APPEARANCES" IS NOT NULL + AND TRIM("APPEARANCES") <> '' + GROUP BY "SEX" +), total AS ( + SELECT SUM(group_value) AS total_value + FROM grouped +) +SELECT g."SEX", g.group_value +FROM grouped AS g +CROSS JOIN total AS t +WHERE g.group_value > t.total_value * 0.1 +ORDER BY g.group_value DESC; diff --git a/Query/sql/v2/runs/v2_cli_20260502_081223_d/c16/artifacts/v2q_c16_6370fde98babd4cf/query_results.jsonl b/Query/sql/v2/runs/v2_cli_20260502_081223_d/c16/artifacts/v2q_c16_6370fde98babd4cf/query_results.jsonl new file mode 100644 index 0000000000000000000000000000000000000000..f91fe6c97e4c26834ea9d901683774c2732b0e7f --- /dev/null +++ b/Query/sql/v2/runs/v2_cli_20260502_081223_d/c16/artifacts/v2q_c16_6370fde98babd4cf/query_results.jsonl @@ -0,0 +1 @@ +{"step_index": 1, "message_index": 0, "node_name": "v2-cli:codex", "tool_name": "sqlite_query", "query": "-- template_id: tpl_tpch_relative_total_threshold\nWITH grouped AS (\n SELECT \"SEX\", SUM(CAST(\"APPEARANCES\" AS REAL)) AS group_value\n FROM \"c16\"\n WHERE \"SEX\" IS NOT NULL\n AND TRIM(\"SEX\") <> ''\n AND \"APPEARANCES\" IS NOT NULL\n AND TRIM(\"APPEARANCES\") <> ''\n GROUP BY \"SEX\"\n), total AS (\n SELECT SUM(group_value) AS total_value\n FROM grouped\n)\nSELECT g.\"SEX\", g.group_value\nFROM grouped AS g\nCROSS JOIN total AS t\nWHERE g.group_value > t.total_value * 0.1\nORDER BY g.group_value DESC;", "result": "{\"query\": \"-- template_id: tpl_tpch_relative_total_threshold\\nWITH grouped AS (\\n SELECT \\\"SEX\\\", SUM(CAST(\\\"APPEARANCES\\\" AS REAL)) AS group_value\\n FROM \\\"c16\\\"\\n WHERE \\\"SEX\\\" IS NOT NULL\\n AND TRIM(\\\"SEX\\\") <> ''\\n AND \\\"APPEARANCES\\\" IS NOT NULL\\n AND TRIM(\\\"APPEARANCES\\\") <> ''\\n GROUP BY \\\"SEX\\\"\\n), total AS (\\n SELECT SUM(group_value) AS total_value\\n FROM grouped\\n)\\nSELECT g.\\\"SEX\\\", g.group_value\\nFROM grouped AS g\\nCROSS JOIN total AS t\\nWHERE g.group_value > t.total_value * 0.1\\nORDER BY g.group_value DESC;\", \"columns\": [\"SEX\", \"group_value\"], \"rows\": [{\"SEX\": \"Male Characters\", \"group_value\": 110911.0}, {\"SEX\": \"Female Characters\", \"group_value\": 42271.0}], \"row_count_returned\": 2, \"row_limit\": 50, \"truncated\": false, \"elapsed_ms\": 5.06}"} diff --git a/Query/sql/v2/runs/v2_cli_20260502_081223_d/c16/artifacts/v2q_c16_6370fde98babd4cf/run_manifest.json b/Query/sql/v2/runs/v2_cli_20260502_081223_d/c16/artifacts/v2q_c16_6370fde98babd4cf/run_manifest.json new file mode 100644 index 0000000000000000000000000000000000000000..868ae09efe23b89c889dcfbc51a2ad99903c780c --- /dev/null +++ b/Query/sql/v2/runs/v2_cli_20260502_081223_d/c16/artifacts/v2q_c16_6370fde98babd4cf/run_manifest.json @@ -0,0 +1,89 @@ +{ + "run_id": "v2_cli_20260502_081223_d", + "dataset_id": "c16", + "started_at": "2026-05-19T15:46:06.651616+00:00", + "ended_at": "2026-05-19T15:46:22.287041+00:00", + "status": "completed", + "engine": "cli", + "question_record": { + "query_record_id": "v2q_c16_6370fde98babd4cf", + "problem_id": "v2p_c16_bda9652f8374c8e3", + "dataset_id": "c16", + "template_id": "tpl_tpch_relative_total_threshold", + "template_name": "Relative-to-Total Extreme Threshold", + "family_id": "tail_rarity_structure", + "canonical_subitem_id": "tail_mass_similarity", + "intended_facet_id": "tail_ranked_signal", + "variant_semantic_role": "filtered_stable_view", + "subitem_assignment_source": "planner_selected", + "source_kind": "agent", + "realization_mode": "agent", + "gate_priority": "primary", + "extended_family": false, + "question": "Use template Relative-to-Total Extreme Threshold to probe tail_mass_similarity with semantic role filtered_stable_view. Focus on group_col=SEX, measure_col=APPEARANCES.", + "bindings": { + "group_col": "SEX", + "measure_col": "APPEARANCES", + "top_k": 13, + "top_n": 4, + "num_tiles": 10, + "percentile_value": 0.9, + "z_threshold": 2.0, + "fraction_threshold": 0.1, + "baseline_multiplier": 1.5, + "baseline_fraction": 0.1, + "min_group_size": 5, + "min_support": 5, + "measure_threshold": 15.0, + "time_grain": "month", + "lookback_rows": 3, + "current_period_start": "'2024-01-01'", + "current_period_end": "'2024-04-01'", + "previous_period_start": "'2023-10-01'", + "previous_period_end": "'2024-01-01'", + "drift_ratio_threshold": 0.8 + }, + "binding_roles": [ + "group_col", + "measure_col" + ], + "coverage_target_min": "5", + "runtime_sql_skeleton": "WITH grouped AS (\n SELECT {group_col}, SUM({measure_col}) AS group_value\n FROM {table}\n GROUP BY {group_col}\n), total AS (\n SELECT SUM(group_value) AS total_value\n FROM grouped\n)\nSELECT g.{group_col}, g.group_value\nFROM grouped AS g\nCROSS JOIN total AS t\nWHERE g.group_value > t.total_value * {fraction_threshold}\nORDER BY g.group_value DESC;", + "notes": [ + "default_facets=tail_ranked_signal", + "template_selection_mode=rule", + "problem_index_within_template=2", + "sql_variant_index=1/2", + "binding_index=73" + ], + "template_selection_mode": "rule", + "selected_template_rank": 7, + "problem_index_within_template": 2, + "sql_variant_index": 1, + "sql_variant_total": 2 + }, + "mode": "subitem_workload_v2", + "sql_source_version": "v2", + "sql_source_label": "v2_current", + "generated_sql_path": "/data/jialinzhang/TabQueryBench/sql_workloads/v2_current/runs_and_launches/runs/v2_cli_20260502_081223_d/c16/sql/v2q_c16_6370fde98babd4cf.sql", + "usage_summary": { + "dataset_id": "c16", + "model": "v2-cli:codex", + "run_id": "v2q_c16_6370fde98babd4cf", + "api_calls": 0, + "input_tokens": 14692, + "cached_input_tokens": 13696, + "output_tokens": 714, + "total_tokens": 15406, + "cost_usd": 0.0, + "ai_cli_calls": 1, + "estimated_input_tokens": 0, + "estimated_output_tokens": 0, + "estimated_total_tokens": 0, + "usage_source": "ai_cli_json_usage", + "cli_elapsed_ms_total": 15625.31, + "sql_execution_elapsed_ms_total": 5.06, + "conversation_log_path": "/data/jialinzhang/TabQueryBench/sql_workloads/v2_current/runs_and_launches/runs/v2_cli_20260502_081223_d/c16/artifacts/v2q_c16_6370fde98babd4cf/cli/conversation.jsonl", + "note": "Executed through a local AI CLI with structured usage metadata." + } +} \ No newline at end of file diff --git a/Query/sql/v2/runs/v2_cli_20260502_081223_d/c16/artifacts/v2q_c16_6370fde98babd4cf/trace.jsonl b/Query/sql/v2/runs/v2_cli_20260502_081223_d/c16/artifacts/v2q_c16_6370fde98babd4cf/trace.jsonl new file mode 100644 index 0000000000000000000000000000000000000000..420a5bcf8e8ad7837f26041497764cacf7f300df --- /dev/null +++ b/Query/sql/v2/runs/v2_cli_20260502_081223_d/c16/artifacts/v2q_c16_6370fde98babd4cf/trace.jsonl @@ -0,0 +1 @@ +{"timestamp": "2026-05-19T15:46:22.279438+00:00", "event_type": "ai_cli_sql_generation", "engine": "v2-cli:codex", "attempt": 1, "command": "codex exec --skip-git-repo-check --disable plugins --sandbox read-only --cd \"/data/jialinzhang/SQLagent\" -m gpt-5.4 --json -", "returncode": 0, "elapsed_ms": 15625.31, "started_at": "2026-05-19T15:46:06.653009+00:00", "ended_at": "2026-05-19T15:46:22.278354+00:00", "prompt_metrics": {"chars": 9038, "bytes_utf8": 9038, "lines": 252, "estimated_tokens": null}, "response_metrics": {"chars": 850, "bytes_utf8": 850, "lines": 1, "estimated_tokens": null}, "usage": {"input_tokens": 14692, "cached_input_tokens": 13696, "output_tokens": 714, "reasoning_output_tokens": 472}, "stderr_preview": "", "stdout_preview": "{\"sql\":\"-- template_id: tpl_tpch_relative_total_threshold\\nWITH grouped AS (\\n SELECT \\\"SEX\\\", SUM(CAST(\\\"APPEARANCES\\\" AS REAL)) AS group_value\\n FROM \\\"c16\\\"\\n WHERE \\\"SEX\\\" IS NOT NULL\\n AND TRIM(\\\"SEX\\\") <> ''\\n AND \\\"APPEARANCES\\\" IS NOT NULL\\n AND TRIM(\\\"APPEARANCES\\\") <> ''\\n GROUP BY \\\"SEX\\\"\\n), total AS (\\n SELECT SUM(group_value) AS total_value\\n FROM grouped\\n)\\nSELECT g.\\\"SEX\\\", g.group_value\\nFROM grouped AS g\\nCROSS JOIN total AS t\\nWHERE g.group_value > t.total_value * 0.1\\nORDER BY g.group_value DESC;\",\"notes\":\"Uses the planned Relative-to-Total Extreme Threshold template with group_col=\\\"SEX\\\" and measure_col=\\\"APPEARANCES\\\". Since \\\"APPEARANCES\\\" is stored as TEXT in SQLite, it is cast to REAL, and blank/null \\\"SEX\\\" and \\\"APPEARANCES\\\" values are filtered out for a stable grouped view.\"}"} diff --git a/Query/sql/v2/runs/v2_cli_20260502_081223_d/c16/artifacts/v2q_c16_6370fde98babd4cf/usage_summary.json b/Query/sql/v2/runs/v2_cli_20260502_081223_d/c16/artifacts/v2q_c16_6370fde98babd4cf/usage_summary.json new file mode 100644 index 0000000000000000000000000000000000000000..b9e1853baf7768b8a36a2a70c3315c67652d0b98 --- /dev/null +++ b/Query/sql/v2/runs/v2_cli_20260502_081223_d/c16/artifacts/v2q_c16_6370fde98babd4cf/usage_summary.json @@ -0,0 +1,20 @@ +{ + "dataset_id": "c16", + "model": "v2-cli:codex", + "run_id": "v2q_c16_6370fde98babd4cf", + "api_calls": 0, + "input_tokens": 14692, + "cached_input_tokens": 13696, + "output_tokens": 714, + "total_tokens": 15406, + "cost_usd": 0.0, + "ai_cli_calls": 1, + "estimated_input_tokens": 0, + "estimated_output_tokens": 0, + "estimated_total_tokens": 0, + "usage_source": "ai_cli_json_usage", + "cli_elapsed_ms_total": 15625.31, + "sql_execution_elapsed_ms_total": 5.06, + "conversation_log_path": "/data/jialinzhang/TabQueryBench/sql_workloads/v2_current/runs_and_launches/runs/v2_cli_20260502_081223_d/c16/artifacts/v2q_c16_6370fde98babd4cf/cli/conversation.jsonl", + "note": "Executed through a local AI CLI with structured usage metadata." +} \ No newline at end of file diff --git a/Query/sql/v2/runs/v2_cli_20260502_081223_d/c16/artifacts/v2q_c16_63c0915455b6482b/run_manifest.json b/Query/sql/v2/runs/v2_cli_20260502_081223_d/c16/artifacts/v2q_c16_63c0915455b6482b/run_manifest.json new file mode 100644 index 0000000000000000000000000000000000000000..57cd02485e11a618e90deebda6c6247cea728f03 --- /dev/null +++ b/Query/sql/v2/runs/v2_cli_20260502_081223_d/c16/artifacts/v2q_c16_63c0915455b6482b/run_manifest.json @@ -0,0 +1,67 @@ +{ + "run_id": "v2_cli_20260502_081223_d", + "dataset_id": "c16", + "started_at": "2026-05-19T16:07:58.174700+00:00", + "ended_at": "2026-05-19T16:08:04.636479+00:00", + "status": "failed", + "engine": "cli", + "question_record": { + "query_record_id": "v2q_c16_63c0915455b6482b", + "problem_id": "v2p_c16_deda57f374256eea", + "dataset_id": "c16", + "template_id": "tpl_tail_low_support_group_count_v2", + "template_name": "Low-Support Group Count", + "family_id": "tail_rarity_structure", + "canonical_subitem_id": "tail_set_consistency", + "intended_facet_id": "low_support_extremes", + "variant_semantic_role": "count_distribution", + "subitem_assignment_source": "planner_selected", + "source_kind": "agent", + "realization_mode": "agent", + "gate_priority": "primary", + "extended_family": false, + "question": "Use template Low-Support Group Count to probe tail_set_consistency with semantic role count_distribution. Focus on group_col=ALIGN.", + "bindings": { + "group_col": "ALIGN", + "top_k": 16, + "top_n": 6, + "num_tiles": 10, + "percentile_value": 0.9, + "z_threshold": 2.0, + "fraction_threshold": 0.05, + "baseline_multiplier": 1.75, + "baseline_fraction": 0.1, + "min_group_size": 5, + "min_support": 4, + "measure_threshold": 213203.0, + "time_grain": "month", + "lookback_rows": 3, + "current_period_start": "'2024-01-01'", + "current_period_end": "'2024-04-01'", + "previous_period_start": "'2023-10-01'", + "previous_period_end": "'2024-01-01'", + "drift_ratio_threshold": 0.8 + }, + "binding_roles": [ + "group_col" + ], + "coverage_target_min": "5", + "runtime_sql_skeleton": "SELECT\n {group_col},\n COUNT(*) AS support\nFROM {table}\nGROUP BY {group_col}\nORDER BY support ASC, {group_col}\nLIMIT {top_k};", + "notes": [ + "default_facets=low_support_extremes", + "template_selection_mode=rule", + "problem_index_within_template=7", + "sql_variant_index=2/2", + "binding_index=126" + ], + "template_selection_mode": "rule", + "selected_template_rank": 11, + "problem_index_within_template": 7, + "sql_variant_index": 2, + "sql_variant_total": 2 + }, + "mode": "subitem_workload_v2", + "sql_source_version": "v2", + "sql_source_label": "v2_current", + "error": "AI CLI command failed with exit code 1: " +} \ No newline at end of file diff --git a/Query/sql/v2/runs/v2_cli_20260502_081223_d/c16/artifacts/v2q_c16_63c0915455b6482b/trace.jsonl b/Query/sql/v2/runs/v2_cli_20260502_081223_d/c16/artifacts/v2q_c16_63c0915455b6482b/trace.jsonl new file mode 100644 index 0000000000000000000000000000000000000000..1d0176d04ff588dafdb30d9388862cad0da1e72f --- /dev/null +++ b/Query/sql/v2/runs/v2_cli_20260502_081223_d/c16/artifacts/v2q_c16_63c0915455b6482b/trace.jsonl @@ -0,0 +1,2 @@ +{"timestamp": "2026-05-19T16:08:00.732808+00:00", "event_type": "ai_cli_sql_generation_error", "engine": "v2-cli:codex", "attempt": 1, "command": "codex exec --skip-git-repo-check --disable plugins --sandbox read-only --cd \"/data/jialinzhang/SQLagent\" -m gpt-5.4 --json -", "returncode": 1, "elapsed_ms": 2555.3, "started_at": "2026-05-19T16:07:58.176451+00:00", "ended_at": "2026-05-19T16:08:00.731781+00:00", "prompt_metrics": {"chars": 8476, "bytes_utf8": 8476, "lines": 250, "estimated_tokens": null}, "response_metrics": {"chars": 280, "bytes_utf8": 280, "lines": 4, "estimated_tokens": null}, "usage": {}, "stderr_preview": "", "stdout_preview": "{\"type\":\"thread.started\",\"thread_id\":\"019e40fe-74d4-78b2-990b-9c61bc9d3cc4\"}\n{\"type\":\"turn.started\"}\n{\"type\":\"error\",\"message\":\"Quota exceeded. Check your plan and billing details.\"}\n{\"type\":\"turn.failed\",\"error\":{\"message\":\"Quota exceeded. Check your plan and billing details.\"}}", "error": "AI CLI command failed with exit code 1: "} +{"timestamp": "2026-05-19T16:08:04.636393+00:00", "event_type": "ai_cli_sql_generation_error", "engine": "v2-cli:codex", "attempt": 2, "command": "codex exec --skip-git-repo-check --disable plugins --sandbox read-only --cd \"/data/jialinzhang/SQLagent\" -m gpt-5.4 --json -", "returncode": 1, "elapsed_ms": 2901.98, "started_at": "2026-05-19T16:08:01.733664+00:00", "ended_at": "2026-05-19T16:08:04.635680+00:00", "prompt_metrics": {"chars": 8476, "bytes_utf8": 8476, "lines": 250, "estimated_tokens": null}, "response_metrics": {"chars": 280, "bytes_utf8": 280, "lines": 4, "estimated_tokens": null}, "usage": {}, "stderr_preview": "", "stdout_preview": "{\"type\":\"thread.started\",\"thread_id\":\"019e40fe-82dc-7122-b427-2e61742e5720\"}\n{\"type\":\"turn.started\"}\n{\"type\":\"error\",\"message\":\"Quota exceeded. Check your plan and billing details.\"}\n{\"type\":\"turn.failed\",\"error\":{\"message\":\"Quota exceeded. Check your plan and billing details.\"}}", "error": "AI CLI command failed with exit code 1: "} diff --git a/Query/sql/v2/runs/v2_cli_20260502_081223_d/c16/artifacts/v2q_c16_678c130a51808352/final_answer.txt b/Query/sql/v2/runs/v2_cli_20260502_081223_d/c16/artifacts/v2q_c16_678c130a51808352/final_answer.txt new file mode 100644 index 0000000000000000000000000000000000000000..de422a7dfad0e175a48be01691b4e30265e33ff1 --- /dev/null +++ b/Query/sql/v2/runs/v2_cli_20260502_081223_d/c16/artifacts/v2q_c16_678c130a51808352/final_answer.txt @@ -0,0 +1 @@ +{"row_count": null, "preview_rows": [{"FIRST APPEARANCE": "2010, December", "support": 78, "avg_response": 2010.0}, {"FIRST APPEARANCE": "", "support": 69, "avg_response": 0.0}, {"FIRST APPEARANCE": "2006, June", "support": 48, "avg_response": 2006.0}, {"FIRST APPEARANCE": "1989, January", "support": 45, "avg_response": 1989.0}, {"FIRST APPEARANCE": "2009, October", "support": 44, "avg_response": 2009.0}]} \ No newline at end of file diff --git a/Query/sql/v2/runs/v2_cli_20260502_081223_d/c16/artifacts/v2q_c16_678c130a51808352/generated_sql.sql b/Query/sql/v2/runs/v2_cli_20260502_081223_d/c16/artifacts/v2q_c16_678c130a51808352/generated_sql.sql new file mode 100644 index 0000000000000000000000000000000000000000..1af2cd13e0b52d986fb98bf025a5567606fcd3da --- /dev/null +++ b/Query/sql/v2/runs/v2_cli_20260502_081223_d/c16/artifacts/v2q_c16_678c130a51808352/generated_sql.sql @@ -0,0 +1,21 @@ +-- sql_source_version: v2 +-- sql_source_label: v2_current +-- sql_source_run_id: v2_cli_20260502_081223_d +-- sql_source_dataset_id: c16 +-- family_id: cardinality_structure +-- canonical_subitem_id: high_cardinality_response_stability +-- intended_facet_id: target_cardinality_cross_section +-- variant_semantic_role: focused_target_view +-- template_id: tpl_cardinality_high_card_response_stability +-- query_record_id: v2q_c16_678c130a51808352 +-- problem_id: v2p_c16_9b281a7bbb37590a +-- realization_mode: deterministic +-- source_kind: deterministic +SELECT + "FIRST APPEARANCE", + COUNT(*) AS support, + AVG("YEAR") AS avg_response +FROM "c16" +GROUP BY "FIRST APPEARANCE" +HAVING COUNT(*) >= 5.0 +ORDER BY support DESC, avg_response DESC; diff --git a/Query/sql/v2/runs/v2_cli_20260502_081223_d/c16/artifacts/v2q_c16_678c130a51808352/query_results.jsonl b/Query/sql/v2/runs/v2_cli_20260502_081223_d/c16/artifacts/v2q_c16_678c130a51808352/query_results.jsonl new file mode 100644 index 0000000000000000000000000000000000000000..5aa784fc7582f22a458133156af164156227af9a --- /dev/null +++ b/Query/sql/v2/runs/v2_cli_20260502_081223_d/c16/artifacts/v2q_c16_678c130a51808352/query_results.jsonl @@ -0,0 +1 @@ +{"node_name": "v2_template", "tool_name": "sqlite_query", "query": "-- sql_source_version: v2\n-- sql_source_label: v2_current\n-- sql_source_run_id: v2_cli_20260502_081223_d\n-- sql_source_dataset_id: c16\n-- family_id: cardinality_structure\n-- canonical_subitem_id: high_cardinality_response_stability\n-- intended_facet_id: target_cardinality_cross_section\n-- variant_semantic_role: focused_target_view\n-- template_id: tpl_cardinality_high_card_response_stability\n-- query_record_id: v2q_c16_678c130a51808352\n-- problem_id: v2p_c16_9b281a7bbb37590a\n-- realization_mode: deterministic\n-- source_kind: deterministic\nSELECT\n \"FIRST APPEARANCE\",\n COUNT(*) AS support,\n AVG(\"YEAR\") AS avg_response\nFROM \"c16\"\nGROUP BY \"FIRST APPEARANCE\"\nHAVING COUNT(*) >= 5.0\nORDER BY support DESC, avg_response DESC;", "result": "{\"query\": \"-- sql_source_version: v2\\n-- sql_source_label: v2_current\\n-- sql_source_run_id: v2_cli_20260502_081223_d\\n-- sql_source_dataset_id: c16\\n-- family_id: cardinality_structure\\n-- canonical_subitem_id: high_cardinality_response_stability\\n-- intended_facet_id: target_cardinality_cross_section\\n-- variant_semantic_role: focused_target_view\\n-- template_id: tpl_cardinality_high_card_response_stability\\n-- query_record_id: v2q_c16_678c130a51808352\\n-- problem_id: v2p_c16_9b281a7bbb37590a\\n-- realization_mode: deterministic\\n-- source_kind: deterministic\\nSELECT\\n \\\"FIRST APPEARANCE\\\",\\n COUNT(*) AS support,\\n AVG(\\\"YEAR\\\") AS avg_response\\nFROM \\\"c16\\\"\\nGROUP BY \\\"FIRST APPEARANCE\\\"\\nHAVING COUNT(*) >= 5.0\\nORDER BY support DESC, avg_response DESC;\", \"columns\": [\"FIRST APPEARANCE\", \"support\", \"avg_response\"], \"rows\": [{\"FIRST APPEARANCE\": \"2010, December\", \"support\": 78, \"avg_response\": 2010.0}, {\"FIRST APPEARANCE\": \"\", \"support\": 69, \"avg_response\": 0.0}, {\"FIRST APPEARANCE\": \"2006, June\", \"support\": 48, \"avg_response\": 2006.0}, {\"FIRST APPEARANCE\": \"1989, January\", \"support\": 45, \"avg_response\": 1989.0}, {\"FIRST APPEARANCE\": \"2009, October\", \"support\": 44, \"avg_response\": 2009.0}, {\"FIRST APPEARANCE\": \"1988, March\", \"support\": 40, \"avg_response\": 1988.0}, {\"FIRST APPEARANCE\": \"2007, August\", \"support\": 39, \"avg_response\": 2007.0}, {\"FIRST APPEARANCE\": \"2009, August\", \"support\": 37, \"avg_response\": 2009.0}, {\"FIRST APPEARANCE\": \"2006, September\", \"support\": 36, \"avg_response\": 2006.0}, {\"FIRST APPEARANCE\": \"1996, September\", \"support\": 36, \"avg_response\": 1996.0}, {\"FIRST APPEARANCE\": \"2006, October\", \"support\": 34, \"avg_response\": 2006.0}, {\"FIRST APPEARANCE\": \"1983, August\", \"support\": 32, \"avg_response\": 1983.0}, {\"FIRST APPEARANCE\": \"2006, May\", \"support\": 31, \"avg_response\": 2006.0}, {\"FIRST APPEARANCE\": \"1994, March\", \"support\": 31, \"avg_response\": 1994.0}, {\"FIRST APPEARANCE\": \"1993, August\", \"support\": 31, \"avg_response\": 1993.0}, {\"FIRST APPEARANCE\": \"1997, August\", \"support\": 30, \"avg_response\": 1997.0}, {\"FIRST APPEARANCE\": \"2006, November\", \"support\": 29, \"avg_response\": 2006.0}, {\"FIRST APPEARANCE\": \"2005, November\", \"support\": 29, \"avg_response\": 2005.0}, {\"FIRST APPEARANCE\": \"1987, February\", \"support\": 29, \"avg_response\": 1987.0}, {\"FIRST APPEARANCE\": \"2006, January\", \"support\": 28, \"avg_response\": 2006.0}, {\"FIRST APPEARANCE\": \"1993\", \"support\": 28, \"avg_response\": 1993.0}, {\"FIRST APPEARANCE\": \"2006, August\", \"support\": 27, \"avg_response\": 2006.0}, {\"FIRST APPEARANCE\": \"1999, July\", \"support\": 27, \"avg_response\": 1999.0}, {\"FIRST APPEARANCE\": \"1988, April\", \"support\": 27, \"avg_response\": 1988.0}, {\"FIRST APPEARANCE\": \"1987, December\", \"support\": 27, \"avg_response\": 1987.0}, {\"FIRST APPEARANCE\": \"2010, August\", \"support\": 26, \"avg_response\": 2010.0}, {\"FIRST APPEARANCE\": \"2008, January\", \"support\": 26, \"avg_response\": 2008.0}, {\"FIRST APPEARANCE\": \"2008, June\", \"support\": 26, \"avg_response\": 2008.0}, {\"FIRST APPEARANCE\": \"2006, December\", \"support\": 26, \"avg_response\": 2006.0}, {\"FIRST APPEARANCE\": \"2000, March\", \"support\": 26, \"avg_response\": 2000.0}, {\"FIRST APPEARANCE\": \"1993, June\", \"support\": 26, \"avg_response\": 1993.0}, {\"FIRST APPEARANCE\": \"2011, January\", \"support\": 25, \"avg_response\": 2011.0}, {\"FIRST APPEARANCE\": \"2008, February\", \"support\": 25, \"avg_response\": 2008.0}, {\"FIRST APPEARANCE\": \"1999, December\", \"support\": 25, \"avg_response\": 1999.0}, {\"FIRST APPEARANCE\": \"1989, August\", \"support\": 25, \"avg_response\": 1989.0}, {\"FIRST APPEARANCE\": \"1988, December\", \"support\": 25, \"avg_response\": 1988.0}, {\"FIRST APPEARANCE\": \"1988, October\", \"support\": 25, \"avg_response\": 1988.0}, {\"FIRST APPEARANCE\": \"1987, March\", \"support\": 25, \"avg_response\": 1987.0}, {\"FIRST APPEARANCE\": \"1982, June\", \"support\": 25, \"avg_response\": 1982.0}, {\"FIRST APPEARANCE\": \"2011, February\", \"support\": 24, \"avg_response\": 2011.0}, {\"FIRST APPEARANCE\": \"2008, July\", \"support\": 24, \"avg_response\": 2008.0}, {\"FIRST APPEARANCE\": \"1994, November\", \"support\": 24, \"avg_response\": 1994.0}, {\"FIRST APPEARANCE\": \"1989, April\", \"support\": 24, \"avg_response\": 1989.0}, {\"FIRST APPEARANCE\": \"1988, July\", \"support\": 24, \"avg_response\": 1988.0}, {\"FIRST APPEARANCE\": \"1987, June\", \"support\": 24, \"avg_response\": 1987.0}, {\"FIRST APPEARANCE\": \"2010, March\", \"support\": 23, \"avg_response\": 2010.0}, {\"FIRST APPEARANCE\": \"2009, July\", \"support\": 23, \"avg_response\": 2009.0}, {\"FIRST APPEARANCE\": \"1998, December\", \"support\": 23, \"avg_response\": 1998.0}, {\"FIRST APPEARANCE\": \"1996, December\", \"support\": 23, \"avg_response\": 1996.0}, {\"FIRST APPEARANCE\": \"1994, January\", \"support\": 23, \"avg_response\": 1994.0}], \"row_count_returned\": 50, \"row_limit\": 50, \"truncated\": true, \"elapsed_ms\": 3.65}"} diff --git a/Query/sql/v2/runs/v2_cli_20260502_081223_d/c16/artifacts/v2q_c16_678c130a51808352/run_manifest.json b/Query/sql/v2/runs/v2_cli_20260502_081223_d/c16/artifacts/v2q_c16_678c130a51808352/run_manifest.json new file mode 100644 index 0000000000000000000000000000000000000000..3d90465dafc615fd83c491f41a02ede9d8ae61b6 --- /dev/null +++ b/Query/sql/v2/runs/v2_cli_20260502_081223_d/c16/artifacts/v2q_c16_678c130a51808352/run_manifest.json @@ -0,0 +1,60 @@ +{ + "run_id": "v2_cli_20260502_081223_d", + "dataset_id": "c16", + "started_at": "2026-05-19T16:10:30.557426+00:00", + "ended_at": "2026-05-19T16:10:30.561812+00:00", + "status": "completed", + "engine": "cli", + "question_record": { + "query_record_id": "v2q_c16_678c130a51808352", + "problem_id": "v2p_c16_9b281a7bbb37590a", + "dataset_id": "c16", + "template_id": "tpl_cardinality_high_card_response_stability", + "template_name": "High-Cardinality Response Stability", + "family_id": "cardinality_structure", + "canonical_subitem_id": "high_cardinality_response_stability", + "intended_facet_id": "target_cardinality_cross_section", + "variant_semantic_role": "focused_target_view", + "subitem_assignment_source": "template_fixed", + "source_kind": "deterministic", + "realization_mode": "deterministic", + "gate_priority": "deterministic", + "extended_family": true, + "question": "Use template High-Cardinality Response Stability to probe high_cardinality_response_stability with semantic role focused_target_view. Focus on measure_col=YEAR, key_col=FIRST APPEARANCE.", + "bindings": { + "key_col": "FIRST APPEARANCE", + "measure_col": "YEAR", + "min_support": 5 + }, + "binding_roles": [ + "key_col", + "target_col" + ], + "coverage_target_min": "enumerate_all_applicable", + "runtime_sql_skeleton": "SELECT\n {key_col},\n COUNT(*) AS support,\n AVG({measure_col}) AS avg_response\nFROM {table}\nGROUP BY {key_col}\nHAVING COUNT(*) >= {min_support}\nORDER BY support DESC, avg_response DESC;", + "notes": [ + "default_facets=target_cardinality_cross_section", + "template_selection_mode=deterministic", + "problem_index_within_template=11", + "sql_variant_index=1/1" + ], + "template_selection_mode": "deterministic", + "selected_template_rank": 0, + "problem_index_within_template": 11, + "sql_variant_index": 1, + "sql_variant_total": 1 + }, + "mode": "subitem_workload_v2", + "sql_source_version": "v2", + "sql_source_label": "v2_current", + "generated_sql_path": "/data/jialinzhang/TabQueryBench/sql_workloads/v2_current/runs_and_launches/runs/v2_cli_20260502_081223_d/c16/sql/v2q_c16_678c130a51808352.sql", + "usage_summary": { + "engine": "template", + "input_tokens": 0, + "cached_input_tokens": 0, + "output_tokens": 0, + "total_tokens": 0, + "estimated_total_tokens": 0, + "usage_source": "none" + } +} \ No newline at end of file diff --git a/Query/sql/v2/runs/v2_cli_20260502_081223_d/c16/artifacts/v2q_c16_678c130a51808352/usage_summary.json b/Query/sql/v2/runs/v2_cli_20260502_081223_d/c16/artifacts/v2q_c16_678c130a51808352/usage_summary.json new file mode 100644 index 0000000000000000000000000000000000000000..96c9ff4feec395919fc26411d18d078b8af6e1c7 --- /dev/null +++ b/Query/sql/v2/runs/v2_cli_20260502_081223_d/c16/artifacts/v2q_c16_678c130a51808352/usage_summary.json @@ -0,0 +1,9 @@ +{ + "engine": "template", + "input_tokens": 0, + "cached_input_tokens": 0, + "output_tokens": 0, + "total_tokens": 0, + "estimated_total_tokens": 0, + "usage_source": "none" +} \ No newline at end of file diff --git a/Query/sql/v2/runs/v2_cli_20260502_081223_d/c16/artifacts/v2q_c16_6da83c8169121cc6/final_answer.txt b/Query/sql/v2/runs/v2_cli_20260502_081223_d/c16/artifacts/v2q_c16_6da83c8169121cc6/final_answer.txt new file mode 100644 index 0000000000000000000000000000000000000000..ec0c8280fa0965483f958c9098494ae6f247376d --- /dev/null +++ b/Query/sql/v2/runs/v2_cli_20260502_081223_d/c16/artifacts/v2q_c16_6da83c8169121cc6/final_answer.txt @@ -0,0 +1,2 @@ +SQL executed successfully for: Use template Grouped Numeric Sum to probe internal_profile_stability with semantic role collapsed_target_view. Focus on group_col=HAIR, measure_col=YEAR. +Result preview: [{"HAIR": "", "total_measure": 4466845}, {"HAIR": "Black Hair", "total_measure": 3095822}, {"HAIR": "Brown Hair", "total_measure": 2273155}, {"HAIR": "Blond Hair", "total_measure": 1463976}, {"HAIR": "Red Hair", "total_measure": 916475}] \ No newline at end of file diff --git a/Query/sql/v2/runs/v2_cli_20260502_081223_d/c16/artifacts/v2q_c16_6da83c8169121cc6/generated_sql.sql b/Query/sql/v2/runs/v2_cli_20260502_081223_d/c16/artifacts/v2q_c16_6da83c8169121cc6/generated_sql.sql new file mode 100644 index 0000000000000000000000000000000000000000..cf04f2317a48185bed41a45d8e8ff341458cfc6a --- /dev/null +++ b/Query/sql/v2/runs/v2_cli_20260502_081223_d/c16/artifacts/v2q_c16_6da83c8169121cc6/generated_sql.sql @@ -0,0 +1,17 @@ +-- sql_source_version: v2 +-- sql_source_label: v2_current +-- sql_source_run_id: v2_cli_20260502_081223_d +-- sql_source_dataset_id: c16 +-- family_id: subgroup_structure +-- canonical_subitem_id: internal_profile_stability +-- intended_facet_id: subgroup_distribution_shift +-- variant_semantic_role: collapsed_target_view +-- template_id: tpl_h2o_group_sum +-- query_record_id: v2q_c16_6da83c8169121cc6 +-- problem_id: v2p_c16_65a969dc6a31693c +-- realization_mode: agent +-- source_kind: agent +SELECT "HAIR", SUM(CAST("YEAR" AS INTEGER)) AS total_measure +FROM "c16" +GROUP BY "HAIR" +ORDER BY total_measure DESC; diff --git a/Query/sql/v2/runs/v2_cli_20260502_081223_d/c16/artifacts/v2q_c16_6da83c8169121cc6/query_results.jsonl b/Query/sql/v2/runs/v2_cli_20260502_081223_d/c16/artifacts/v2q_c16_6da83c8169121cc6/query_results.jsonl new file mode 100644 index 0000000000000000000000000000000000000000..0af8be596068abd070f8f880432864b0033f3ef8 --- /dev/null +++ b/Query/sql/v2/runs/v2_cli_20260502_081223_d/c16/artifacts/v2q_c16_6da83c8169121cc6/query_results.jsonl @@ -0,0 +1 @@ +{"step_index": 1, "message_index": 0, "node_name": "v2-cli:codex", "tool_name": "sqlite_query", "query": "-- template_id: tpl_h2o_group_sum\nSELECT \"HAIR\", SUM(CAST(\"YEAR\" AS INTEGER)) AS total_measure\nFROM \"c16\"\nGROUP BY \"HAIR\"\nORDER BY total_measure DESC;", "result": "{\"query\": \"-- template_id: tpl_h2o_group_sum\\nSELECT \\\"HAIR\\\", SUM(CAST(\\\"YEAR\\\" AS INTEGER)) AS total_measure\\nFROM \\\"c16\\\"\\nGROUP BY \\\"HAIR\\\"\\nORDER BY total_measure DESC;\", \"columns\": [\"HAIR\", \"total_measure\"], \"rows\": [{\"HAIR\": \"\", \"total_measure\": 4466845}, {\"HAIR\": \"Black Hair\", \"total_measure\": 3095822}, {\"HAIR\": \"Brown Hair\", \"total_measure\": 2273155}, {\"HAIR\": \"Blond Hair\", \"total_measure\": 1463976}, {\"HAIR\": \"Red Hair\", \"total_measure\": 916475}, {\"HAIR\": \"White Hair\", \"total_measure\": 681373}, {\"HAIR\": \"Grey Hair\", \"total_measure\": 306303}, {\"HAIR\": \"Green Hair\", \"total_measure\": 83510}, {\"HAIR\": \"Blue Hair\", \"total_measure\": 81866}, {\"HAIR\": \"Purple Hair\", \"total_measure\": 63947}, {\"HAIR\": \"Strawberry Blond Hair\", \"total_measure\": 53407}, {\"HAIR\": \"Orange Hair\", \"total_measure\": 41775}, {\"HAIR\": \"Pink Hair\", \"total_measure\": 22005}, {\"HAIR\": \"Gold Hair\", \"total_measure\": 9883}, {\"HAIR\": \"Violet Hair\", \"total_measure\": 7933}, {\"HAIR\": \"Reddish Brown Hair\", \"total_measure\": 5959}, {\"HAIR\": \"Silver Hair\", \"total_measure\": 5945}, {\"HAIR\": \"Platinum Blond Hair\", \"total_measure\": 3958}], \"row_count_returned\": 18, \"row_limit\": 50, \"truncated\": false, \"elapsed_ms\": 4.18}"} diff --git a/Query/sql/v2/runs/v2_cli_20260502_081223_d/c16/artifacts/v2q_c16_6da83c8169121cc6/run_manifest.json b/Query/sql/v2/runs/v2_cli_20260502_081223_d/c16/artifacts/v2q_c16_6da83c8169121cc6/run_manifest.json new file mode 100644 index 0000000000000000000000000000000000000000..35d4b6f66bd6b767316fbdf454796fe3dc32a43e --- /dev/null +++ b/Query/sql/v2/runs/v2_cli_20260502_081223_d/c16/artifacts/v2q_c16_6da83c8169121cc6/run_manifest.json @@ -0,0 +1,89 @@ +{ + "run_id": "v2_cli_20260502_081223_d", + "dataset_id": "c16", + "started_at": "2026-05-19T15:29:13.050023+00:00", + "ended_at": "2026-05-19T15:29:23.153285+00:00", + "status": "completed", + "engine": "cli", + "question_record": { + "query_record_id": "v2q_c16_6da83c8169121cc6", + "problem_id": "v2p_c16_65a969dc6a31693c", + "dataset_id": "c16", + "template_id": "tpl_h2o_group_sum", + "template_name": "Grouped Numeric Sum", + "family_id": "subgroup_structure", + "canonical_subitem_id": "internal_profile_stability", + "intended_facet_id": "subgroup_distribution_shift", + "variant_semantic_role": "collapsed_target_view", + "subitem_assignment_source": "planner_selected", + "source_kind": "agent", + "realization_mode": "agent", + "gate_priority": "primary", + "extended_family": false, + "question": "Use template Grouped Numeric Sum to probe internal_profile_stability with semantic role collapsed_target_view. Focus on group_col=HAIR, measure_col=YEAR.", + "bindings": { + "group_col": "HAIR", + "measure_col": "YEAR", + "top_k": 17, + "top_n": 6, + "num_tiles": 10, + "percentile_value": 0.9, + "z_threshold": 2.0, + "fraction_threshold": 0.05, + "baseline_multiplier": 1.75, + "baseline_fraction": 0.1, + "min_group_size": 5, + "min_support": 4, + "measure_threshold": 1998.0, + "time_grain": "month", + "lookback_rows": 3, + "current_period_start": "'2024-01-01'", + "current_period_end": "'2024-04-01'", + "previous_period_start": "'2023-10-01'", + "previous_period_end": "'2024-01-01'", + "drift_ratio_threshold": 0.8 + }, + "binding_roles": [ + "group_col", + "measure_col" + ], + "coverage_target_min": "5", + "runtime_sql_skeleton": "SELECT {group_col}, SUM({measure_col}) AS total_measure\nFROM {table}\nGROUP BY {group_col}\nORDER BY total_measure DESC;", + "notes": [ + "default_facets=subgroup_distribution_shift,subgroup_rank_order,subgroup_conditional_contrast", + "template_selection_mode=rule", + "problem_index_within_template=3", + "sql_variant_index=2/2", + "binding_index=2" + ], + "template_selection_mode": "rule", + "selected_template_rank": 1, + "problem_index_within_template": 3, + "sql_variant_index": 2, + "sql_variant_total": 2 + }, + "mode": "subitem_workload_v2", + "sql_source_version": "v2", + "sql_source_label": "v2_current", + "generated_sql_path": "/data/jialinzhang/TabQueryBench/sql_workloads/v2_current/runs_and_launches/runs/v2_cli_20260502_081223_d/c16/sql/v2q_c16_6da83c8169121cc6.sql", + "usage_summary": { + "dataset_id": "c16", + "model": "v2-cli:codex", + "run_id": "v2q_c16_6da83c8169121cc6", + "api_calls": 0, + "input_tokens": 14551, + "cached_input_tokens": 12032, + "output_tokens": 370, + "total_tokens": 14921, + "cost_usd": 0.0, + "ai_cli_calls": 1, + "estimated_input_tokens": 0, + "estimated_output_tokens": 0, + "estimated_total_tokens": 0, + "usage_source": "ai_cli_json_usage", + "cli_elapsed_ms_total": 10093.12, + "sql_execution_elapsed_ms_total": 4.18, + "conversation_log_path": "/data/jialinzhang/TabQueryBench/sql_workloads/v2_current/runs_and_launches/runs/v2_cli_20260502_081223_d/c16/artifacts/v2q_c16_6da83c8169121cc6/cli/conversation.jsonl", + "note": "Executed through a local AI CLI with structured usage metadata." + } +} \ No newline at end of file diff --git a/Query/sql/v2/runs/v2_cli_20260502_081223_d/c16/artifacts/v2q_c16_6da83c8169121cc6/trace.jsonl b/Query/sql/v2/runs/v2_cli_20260502_081223_d/c16/artifacts/v2q_c16_6da83c8169121cc6/trace.jsonl new file mode 100644 index 0000000000000000000000000000000000000000..7c4ade384ec7abccde20da445fa75378b6a4cea9 --- /dev/null +++ b/Query/sql/v2/runs/v2_cli_20260502_081223_d/c16/artifacts/v2q_c16_6da83c8169121cc6/trace.jsonl @@ -0,0 +1 @@ +{"timestamp": "2026-05-19T15:29:23.146965+00:00", "event_type": "ai_cli_sql_generation", "engine": "v2-cli:codex", "attempt": 1, "command": "codex exec --skip-git-repo-check --disable plugins --sandbox read-only --cd \"/data/jialinzhang/SQLagent\" -m gpt-5.4 --json -", "returncode": 0, "elapsed_ms": 10093.12, "started_at": "2026-05-19T15:29:13.052772+00:00", "ended_at": "2026-05-19T15:29:23.145935+00:00", "prompt_metrics": {"chars": 8474, "bytes_utf8": 8474, "lines": 252, "estimated_tokens": null}, "response_metrics": {"chars": 350, "bytes_utf8": 350, "lines": 1, "estimated_tokens": null}, "usage": {"input_tokens": 14551, "cached_input_tokens": 12032, "output_tokens": 370, "reasoning_output_tokens": 272}, "stderr_preview": "", "stdout_preview": "{\"sql\":\"-- template_id: tpl_h2o_group_sum\\nSELECT \\\"HAIR\\\", SUM(CAST(\\\"YEAR\\\" AS INTEGER)) AS total_measure\\nFROM \\\"c16\\\"\\nGROUP BY \\\"HAIR\\\"\\nORDER BY total_measure DESC;\",\"notes\":\"Used the requested Grouped Numeric Sum template with group_col=\\\"HAIR\\\" and measure_col=\\\"YEAR\\\". Cast \\\"YEAR\\\" to INTEGER because the SQLite schema stores it as TEXT.\"}"} diff --git a/Query/sql/v2/runs/v2_cli_20260502_081223_d/c16/artifacts/v2q_c16_6da83c8169121cc6/usage_summary.json b/Query/sql/v2/runs/v2_cli_20260502_081223_d/c16/artifacts/v2q_c16_6da83c8169121cc6/usage_summary.json new file mode 100644 index 0000000000000000000000000000000000000000..c998e2e7d919c447dbcac4c60dc45d685d34c352 --- /dev/null +++ b/Query/sql/v2/runs/v2_cli_20260502_081223_d/c16/artifacts/v2q_c16_6da83c8169121cc6/usage_summary.json @@ -0,0 +1,20 @@ +{ + "dataset_id": "c16", + "model": "v2-cli:codex", + "run_id": "v2q_c16_6da83c8169121cc6", + "api_calls": 0, + "input_tokens": 14551, + "cached_input_tokens": 12032, + "output_tokens": 370, + "total_tokens": 14921, + "cost_usd": 0.0, + "ai_cli_calls": 1, + "estimated_input_tokens": 0, + "estimated_output_tokens": 0, + "estimated_total_tokens": 0, + "usage_source": "ai_cli_json_usage", + "cli_elapsed_ms_total": 10093.12, + "sql_execution_elapsed_ms_total": 4.18, + "conversation_log_path": "/data/jialinzhang/TabQueryBench/sql_workloads/v2_current/runs_and_launches/runs/v2_cli_20260502_081223_d/c16/artifacts/v2q_c16_6da83c8169121cc6/cli/conversation.jsonl", + "note": "Executed through a local AI CLI with structured usage metadata." +} \ No newline at end of file diff --git a/Query/sql/v2/runs/v2_cli_20260502_081223_d/c16/artifacts/v2q_c16_6df98b7861fe2cef/final_answer.txt b/Query/sql/v2/runs/v2_cli_20260502_081223_d/c16/artifacts/v2q_c16_6df98b7861fe2cef/final_answer.txt new file mode 100644 index 0000000000000000000000000000000000000000..9f9a5d6a96e7b9fc5a1efeabaf3cdbc9f9e06602 --- /dev/null +++ b/Query/sql/v2/runs/v2_cli_20260502_081223_d/c16/artifacts/v2q_c16_6df98b7861fe2cef/final_answer.txt @@ -0,0 +1,2 @@ +SQL executed successfully for: Use template Grouped Ratio of Two Conditions to probe direction_consistency with semantic role contrastive_conditional_view. Focus on group_col=EYE, condition_col=EYE. +Result preview: [{"EYE": "Blue Eyes", "condition_ratio": 0.0}, {"EYE": "", "condition_ratio": null}, {"EYE": "Amber Eyes", "condition_ratio": null}, {"EYE": "Auburn Hair", "condition_ratio": null}, {"EYE": "Black Eyes", "condition_ratio": null}] \ No newline at end of file diff --git a/Query/sql/v2/runs/v2_cli_20260502_081223_d/c16/artifacts/v2q_c16_6df98b7861fe2cef/generated_sql.sql b/Query/sql/v2/runs/v2_cli_20260502_081223_d/c16/artifacts/v2q_c16_6df98b7861fe2cef/generated_sql.sql new file mode 100644 index 0000000000000000000000000000000000000000..f636e61c94799e4fd69e326851103f76d43576c4 --- /dev/null +++ b/Query/sql/v2/runs/v2_cli_20260502_081223_d/c16/artifacts/v2q_c16_6df98b7861fe2cef/generated_sql.sql @@ -0,0 +1,24 @@ +-- sql_source_version: v2 +-- sql_source_label: v2_current +-- sql_source_run_id: v2_cli_20260502_081223_d +-- sql_source_dataset_id: c16 +-- family_id: conditional_dependency_structure +-- canonical_subitem_id: direction_consistency +-- intended_facet_id: conditional_rate_shift +-- variant_semantic_role: contrastive_conditional_view +-- template_id: tpl_m4_group_ratio_two_conditions +-- query_record_id: v2q_c16_6df98b7861fe2cef +-- problem_id: v2p_c16_948e5c1775f15b26 +-- realization_mode: agent +-- source_kind: agent +WITH grouped AS ( + SELECT "EYE", + SUM(CASE WHEN "EYE" = '' THEN 1 ELSE 0 END) AS numerator_count, + SUM(CASE WHEN "EYE" = 'Blue Eyes' THEN 1 ELSE 0 END) AS denominator_count + FROM "c16" + GROUP BY "EYE" +) +SELECT "EYE", + CAST(numerator_count AS FLOAT) / NULLIF(denominator_count, 0) AS condition_ratio +FROM grouped +ORDER BY condition_ratio DESC; diff --git a/Query/sql/v2/runs/v2_cli_20260502_081223_d/c16/artifacts/v2q_c16_6df98b7861fe2cef/query_results.jsonl b/Query/sql/v2/runs/v2_cli_20260502_081223_d/c16/artifacts/v2q_c16_6df98b7861fe2cef/query_results.jsonl new file mode 100644 index 0000000000000000000000000000000000000000..3215065d950af3ebb233f75c27a4a027cbfedc10 --- /dev/null +++ b/Query/sql/v2/runs/v2_cli_20260502_081223_d/c16/artifacts/v2q_c16_6df98b7861fe2cef/query_results.jsonl @@ -0,0 +1 @@ +{"step_index": 1, "message_index": 0, "node_name": "v2-cli:codex", "tool_name": "sqlite_query", "query": "-- template_id: tpl_m4_group_ratio_two_conditions\nWITH grouped AS (\n SELECT \"EYE\",\n SUM(CASE WHEN \"EYE\" = '' THEN 1 ELSE 0 END) AS numerator_count,\n SUM(CASE WHEN \"EYE\" = 'Blue Eyes' THEN 1 ELSE 0 END) AS denominator_count\n FROM \"c16\"\n GROUP BY \"EYE\"\n)\nSELECT \"EYE\",\n CAST(numerator_count AS FLOAT) / NULLIF(denominator_count, 0) AS condition_ratio\nFROM grouped\nORDER BY condition_ratio DESC;", "result": "{\"query\": \"-- template_id: tpl_m4_group_ratio_two_conditions\\nWITH grouped AS (\\n SELECT \\\"EYE\\\",\\n SUM(CASE WHEN \\\"EYE\\\" = '' THEN 1 ELSE 0 END) AS numerator_count,\\n SUM(CASE WHEN \\\"EYE\\\" = 'Blue Eyes' THEN 1 ELSE 0 END) AS denominator_count\\n FROM \\\"c16\\\"\\n GROUP BY \\\"EYE\\\"\\n)\\nSELECT \\\"EYE\\\",\\n CAST(numerator_count AS FLOAT) / NULLIF(denominator_count, 0) AS condition_ratio\\nFROM grouped\\nORDER BY condition_ratio DESC;\", \"columns\": [\"EYE\", \"condition_ratio\"], \"rows\": [{\"EYE\": \"Blue Eyes\", \"condition_ratio\": 0.0}, {\"EYE\": \"\", \"condition_ratio\": null}, {\"EYE\": \"Amber Eyes\", \"condition_ratio\": null}, {\"EYE\": \"Auburn Hair\", \"condition_ratio\": null}, {\"EYE\": \"Black Eyes\", \"condition_ratio\": null}, {\"EYE\": \"Brown Eyes\", \"condition_ratio\": null}, {\"EYE\": \"Gold Eyes\", \"condition_ratio\": null}, {\"EYE\": \"Green Eyes\", \"condition_ratio\": null}, {\"EYE\": \"Grey Eyes\", \"condition_ratio\": null}, {\"EYE\": \"Hazel Eyes\", \"condition_ratio\": null}, {\"EYE\": \"Orange Eyes\", \"condition_ratio\": null}, {\"EYE\": \"Photocellular Eyes\", \"condition_ratio\": null}, {\"EYE\": \"Pink Eyes\", \"condition_ratio\": null}, {\"EYE\": \"Purple Eyes\", \"condition_ratio\": null}, {\"EYE\": \"Red Eyes\", \"condition_ratio\": null}, {\"EYE\": \"Violet Eyes\", \"condition_ratio\": null}, {\"EYE\": \"White Eyes\", \"condition_ratio\": null}, {\"EYE\": \"Yellow Eyes\", \"condition_ratio\": null}], \"row_count_returned\": 18, \"row_limit\": 50, \"truncated\": false, \"elapsed_ms\": 6.67}"} diff --git a/Query/sql/v2/runs/v2_cli_20260502_081223_d/c16/artifacts/v2q_c16_6df98b7861fe2cef/run_manifest.json b/Query/sql/v2/runs/v2_cli_20260502_081223_d/c16/artifacts/v2q_c16_6df98b7861fe2cef/run_manifest.json new file mode 100644 index 0000000000000000000000000000000000000000..a012b07399f4f3cfd6c0d82625f5c9fcbea18feb --- /dev/null +++ b/Query/sql/v2/runs/v2_cli_20260502_081223_d/c16/artifacts/v2q_c16_6df98b7861fe2cef/run_manifest.json @@ -0,0 +1,92 @@ +{ + "run_id": "v2_cli_20260502_081223_d", + "dataset_id": "c16", + "started_at": "2026-05-19T15:39:02.818561+00:00", + "ended_at": "2026-05-19T15:39:17.932411+00:00", + "status": "completed", + "engine": "cli", + "question_record": { + "query_record_id": "v2q_c16_6df98b7861fe2cef", + "problem_id": "v2p_c16_948e5c1775f15b26", + "dataset_id": "c16", + "template_id": "tpl_m4_group_ratio_two_conditions", + "template_name": "Grouped Ratio of Two Conditions", + "family_id": "conditional_dependency_structure", + "canonical_subitem_id": "direction_consistency", + "intended_facet_id": "conditional_rate_shift", + "variant_semantic_role": "contrastive_conditional_view", + "subitem_assignment_source": "planner_selected", + "source_kind": "agent", + "realization_mode": "agent", + "gate_priority": "primary", + "extended_family": false, + "question": "Use template Grouped Ratio of Two Conditions to probe direction_consistency with semantic role contrastive_conditional_view. Focus on group_col=EYE, condition_col=EYE.", + "bindings": { + "group_col": "EYE", + "condition_col": "EYE", + "condition_value": "", + "positive_value": "", + "negative_value": "Blue Eyes", + "top_k": 11, + "top_n": 3, + "num_tiles": 10, + "percentile_value": 0.95, + "z_threshold": 2.0, + "fraction_threshold": 0.1, + "baseline_multiplier": 1.5, + "baseline_fraction": 0.1, + "min_group_size": 5, + "min_support": 5, + "measure_threshold": 213203.0, + "time_grain": "month", + "lookback_rows": 3, + "current_period_start": "'2024-01-01'", + "current_period_end": "'2024-04-01'", + "previous_period_start": "'2023-10-01'", + "previous_period_end": "'2024-01-01'", + "drift_ratio_threshold": 0.8 + }, + "binding_roles": [ + "group_col", + "condition_col" + ], + "coverage_target_min": "5", + "runtime_sql_skeleton": "WITH grouped AS (\n SELECT {group_col},\n SUM(CASE WHEN {condition_col} = {positive_value} THEN 1 ELSE 0 END) AS numerator_count,\n SUM(CASE WHEN {condition_col} = {negative_value} THEN 1 ELSE 0 END) AS denominator_count\n FROM {table}\n GROUP BY {group_col}\n)\nSELECT {group_col},\n CAST(numerator_count AS FLOAT) / NULLIF(denominator_count, 0) AS condition_ratio\nFROM grouped\nORDER BY condition_ratio DESC;", + "notes": [ + "default_facets=conditional_rate_shift", + "template_selection_mode=rule", + "problem_index_within_template=1", + "sql_variant_index=1/1", + "binding_index=36" + ], + "template_selection_mode": "rule", + "selected_template_rank": 4, + "problem_index_within_template": 1, + "sql_variant_index": 1, + "sql_variant_total": 1 + }, + "mode": "subitem_workload_v2", + "sql_source_version": "v2", + "sql_source_label": "v2_current", + "generated_sql_path": "/data/jialinzhang/TabQueryBench/sql_workloads/v2_current/runs_and_launches/runs/v2_cli_20260502_081223_d/c16/sql/v2q_c16_6df98b7861fe2cef.sql", + "usage_summary": { + "dataset_id": "c16", + "model": "v2-cli:codex", + "run_id": "v2q_c16_6df98b7861fe2cef", + "api_calls": 0, + "input_tokens": 14759, + "cached_input_tokens": 12032, + "output_tokens": 685, + "total_tokens": 15444, + "cost_usd": 0.0, + "ai_cli_calls": 1, + "estimated_input_tokens": 0, + "estimated_output_tokens": 0, + "estimated_total_tokens": 0, + "usage_source": "ai_cli_json_usage", + "cli_elapsed_ms_total": 15101.21, + "sql_execution_elapsed_ms_total": 6.67, + "conversation_log_path": "/data/jialinzhang/TabQueryBench/sql_workloads/v2_current/runs_and_launches/runs/v2_cli_20260502_081223_d/c16/artifacts/v2q_c16_6df98b7861fe2cef/cli/conversation.jsonl", + "note": "Executed through a local AI CLI with structured usage metadata." + } +} \ No newline at end of file diff --git a/Query/sql/v2/runs/v2_cli_20260502_081223_d/c16/artifacts/v2q_c16_6df98b7861fe2cef/trace.jsonl b/Query/sql/v2/runs/v2_cli_20260502_081223_d/c16/artifacts/v2q_c16_6df98b7861fe2cef/trace.jsonl new file mode 100644 index 0000000000000000000000000000000000000000..2041a3ac031e4e920f8fc61f4c96506091580ba8 --- /dev/null +++ b/Query/sql/v2/runs/v2_cli_20260502_081223_d/c16/artifacts/v2q_c16_6df98b7861fe2cef/trace.jsonl @@ -0,0 +1 @@ +{"timestamp": "2026-05-19T15:39:17.923436+00:00", "event_type": "ai_cli_sql_generation", "engine": "v2-cli:codex", "attempt": 1, "command": "codex exec --skip-git-repo-check --disable plugins --sandbox read-only --cd \"/data/jialinzhang/SQLagent\" -m gpt-5.4 --json -", "returncode": 0, "elapsed_ms": 15101.21, "started_at": "2026-05-19T15:39:02.820749+00:00", "ended_at": "2026-05-19T15:39:17.922029+00:00", "prompt_metrics": {"chars": 9283, "bytes_utf8": 9283, "lines": 255, "estimated_tokens": null}, "response_metrics": {"chars": 647, "bytes_utf8": 647, "lines": 1, "estimated_tokens": null}, "usage": {"input_tokens": 14759, "cached_input_tokens": 12032, "output_tokens": 685, "reasoning_output_tokens": 504}, "stderr_preview": "", "stdout_preview": "{\"sql\":\"-- template_id: tpl_m4_group_ratio_two_conditions\\nWITH grouped AS (\\n SELECT \\\"EYE\\\",\\n SUM(CASE WHEN \\\"EYE\\\" = '' THEN 1 ELSE 0 END) AS numerator_count,\\n SUM(CASE WHEN \\\"EYE\\\" = 'Blue Eyes' THEN 1 ELSE 0 END) AS denominator_count\\n FROM \\\"c16\\\"\\n GROUP BY \\\"EYE\\\"\\n)\\nSELECT \\\"EYE\\\",\\n CAST(numerator_count AS FLOAT) / NULLIF(denominator_count, 0) AS condition_ratio\\nFROM grouped\\nORDER BY condition_ratio DESC;\",\"notes\":\"Applied the provided grouped-ratio template with group_col=\\\"EYE\\\" and condition_col=\\\"EYE\\\"; numerator counts empty-string \\\"EYE\\\" values and denominator counts \\\"Blue Eyes\\\".\"}"} diff --git a/Query/sql/v2/runs/v2_cli_20260502_081223_d/c16/artifacts/v2q_c16_6df98b7861fe2cef/usage_summary.json b/Query/sql/v2/runs/v2_cli_20260502_081223_d/c16/artifacts/v2q_c16_6df98b7861fe2cef/usage_summary.json new file mode 100644 index 0000000000000000000000000000000000000000..b442e7b6ccaacaa39de15e95a9f7a1e2dbdd2a48 --- /dev/null +++ b/Query/sql/v2/runs/v2_cli_20260502_081223_d/c16/artifacts/v2q_c16_6df98b7861fe2cef/usage_summary.json @@ -0,0 +1,20 @@ +{ + "dataset_id": "c16", + "model": "v2-cli:codex", + "run_id": "v2q_c16_6df98b7861fe2cef", + "api_calls": 0, + "input_tokens": 14759, + "cached_input_tokens": 12032, + "output_tokens": 685, + "total_tokens": 15444, + "cost_usd": 0.0, + "ai_cli_calls": 1, + "estimated_input_tokens": 0, + "estimated_output_tokens": 0, + "estimated_total_tokens": 0, + "usage_source": "ai_cli_json_usage", + "cli_elapsed_ms_total": 15101.21, + "sql_execution_elapsed_ms_total": 6.67, + "conversation_log_path": "/data/jialinzhang/TabQueryBench/sql_workloads/v2_current/runs_and_launches/runs/v2_cli_20260502_081223_d/c16/artifacts/v2q_c16_6df98b7861fe2cef/cli/conversation.jsonl", + "note": "Executed through a local AI CLI with structured usage metadata." +} \ No newline at end of file diff --git a/Query/sql/v2/runs/v2_cli_20260502_081223_d/c16/artifacts/v2q_c16_7ce102b5cd752fd9/final_answer.txt b/Query/sql/v2/runs/v2_cli_20260502_081223_d/c16/artifacts/v2q_c16_7ce102b5cd752fd9/final_answer.txt new file mode 100644 index 0000000000000000000000000000000000000000..1d2d4a31ac363105298e7637200f995e12c9075b --- /dev/null +++ b/Query/sql/v2/runs/v2_cli_20260502_081223_d/c16/artifacts/v2q_c16_7ce102b5cd752fd9/final_answer.txt @@ -0,0 +1 @@ +{"row_count": null, "preview_rows": [{"APPEARANCES": "1", "support": 1002, "avg_response": 206706.81836327346}, {"APPEARANCES": "2", "support": 709, "avg_response": 192542.68406205924}, {"APPEARANCES": "3", "support": 506, "avg_response": 186036.87747035574}, {"APPEARANCES": "4", "support": 499, "avg_response": 177550.85771543087}, {"APPEARANCES": "5", "support": 389, "avg_response": 178925.5732647815}]} \ No newline at end of file diff --git a/Query/sql/v2/runs/v2_cli_20260502_081223_d/c16/artifacts/v2q_c16_7ce102b5cd752fd9/generated_sql.sql b/Query/sql/v2/runs/v2_cli_20260502_081223_d/c16/artifacts/v2q_c16_7ce102b5cd752fd9/generated_sql.sql new file mode 100644 index 0000000000000000000000000000000000000000..d551382f6e25bf828e967277dbb92283d9050dad --- /dev/null +++ b/Query/sql/v2/runs/v2_cli_20260502_081223_d/c16/artifacts/v2q_c16_7ce102b5cd752fd9/generated_sql.sql @@ -0,0 +1,21 @@ +-- sql_source_version: v2 +-- sql_source_label: v2_current +-- sql_source_run_id: v2_cli_20260502_081223_d +-- sql_source_dataset_id: c16 +-- family_id: cardinality_structure +-- canonical_subitem_id: high_cardinality_response_stability +-- intended_facet_id: target_cardinality_cross_section +-- variant_semantic_role: focused_target_view +-- template_id: tpl_cardinality_high_card_response_stability +-- query_record_id: v2q_c16_7ce102b5cd752fd9 +-- problem_id: v2p_c16_9bd4e7a3b853b7e8 +-- realization_mode: deterministic +-- source_kind: deterministic +SELECT + "APPEARANCES", + COUNT(*) AS support, + AVG("page_id") AS avg_response +FROM "c16" +GROUP BY "APPEARANCES" +HAVING COUNT(*) >= 5.0 +ORDER BY support DESC, avg_response DESC; diff --git a/Query/sql/v2/runs/v2_cli_20260502_081223_d/c16/artifacts/v2q_c16_7ce102b5cd752fd9/query_results.jsonl b/Query/sql/v2/runs/v2_cli_20260502_081223_d/c16/artifacts/v2q_c16_7ce102b5cd752fd9/query_results.jsonl new file mode 100644 index 0000000000000000000000000000000000000000..73500fb75dabf2fa671246ffd3532845db0c527e --- /dev/null +++ b/Query/sql/v2/runs/v2_cli_20260502_081223_d/c16/artifacts/v2q_c16_7ce102b5cd752fd9/query_results.jsonl @@ -0,0 +1 @@ +{"node_name": "v2_template", "tool_name": "sqlite_query", "query": "-- sql_source_version: v2\n-- sql_source_label: v2_current\n-- sql_source_run_id: v2_cli_20260502_081223_d\n-- sql_source_dataset_id: c16\n-- family_id: cardinality_structure\n-- canonical_subitem_id: high_cardinality_response_stability\n-- intended_facet_id: target_cardinality_cross_section\n-- variant_semantic_role: focused_target_view\n-- template_id: tpl_cardinality_high_card_response_stability\n-- query_record_id: v2q_c16_7ce102b5cd752fd9\n-- problem_id: v2p_c16_9bd4e7a3b853b7e8\n-- realization_mode: deterministic\n-- source_kind: deterministic\nSELECT\n \"APPEARANCES\",\n COUNT(*) AS support,\n AVG(\"page_id\") AS avg_response\nFROM \"c16\"\nGROUP BY \"APPEARANCES\"\nHAVING COUNT(*) >= 5.0\nORDER BY support DESC, avg_response DESC;", "result": "{\"query\": \"-- sql_source_version: v2\\n-- sql_source_label: v2_current\\n-- sql_source_run_id: v2_cli_20260502_081223_d\\n-- sql_source_dataset_id: c16\\n-- family_id: cardinality_structure\\n-- canonical_subitem_id: high_cardinality_response_stability\\n-- intended_facet_id: target_cardinality_cross_section\\n-- variant_semantic_role: focused_target_view\\n-- template_id: tpl_cardinality_high_card_response_stability\\n-- query_record_id: v2q_c16_7ce102b5cd752fd9\\n-- problem_id: v2p_c16_9bd4e7a3b853b7e8\\n-- realization_mode: deterministic\\n-- source_kind: deterministic\\nSELECT\\n \\\"APPEARANCES\\\",\\n COUNT(*) AS support,\\n AVG(\\\"page_id\\\") AS avg_response\\nFROM \\\"c16\\\"\\nGROUP BY \\\"APPEARANCES\\\"\\nHAVING COUNT(*) >= 5.0\\nORDER BY support DESC, avg_response DESC;\", \"columns\": [\"APPEARANCES\", \"support\", \"avg_response\"], \"rows\": [{\"APPEARANCES\": \"1\", \"support\": 1002, \"avg_response\": 206706.81836327346}, {\"APPEARANCES\": \"2\", \"support\": 709, \"avg_response\": 192542.68406205924}, {\"APPEARANCES\": \"3\", \"support\": 506, \"avg_response\": 186036.87747035574}, {\"APPEARANCES\": \"4\", \"support\": 499, \"avg_response\": 177550.85771543087}, {\"APPEARANCES\": \"5\", \"support\": 389, \"avg_response\": 178925.5732647815}, {\"APPEARANCES\": \"\", \"support\": 355, \"avg_response\": 175364.54929577466}, {\"APPEARANCES\": \"6\", \"support\": 330, \"avg_response\": 171588.83333333334}, {\"APPEARANCES\": \"7\", \"support\": 263, \"avg_response\": 154721.27376425854}, {\"APPEARANCES\": \"8\", \"support\": 242, \"avg_response\": 151982.2561983471}, {\"APPEARANCES\": \"9\", \"support\": 186, \"avg_response\": 149101.79032258064}, {\"APPEARANCES\": \"10\", \"support\": 182, \"avg_response\": 139028.11538461538}, {\"APPEARANCES\": \"11\", \"support\": 165, \"avg_response\": 132542.14545454545}, {\"APPEARANCES\": \"12\", \"support\": 129, \"avg_response\": 122754.55813953489}, {\"APPEARANCES\": \"14\", \"support\": 124, \"avg_response\": 107993.60483870968}, {\"APPEARANCES\": \"13\", \"support\": 106, \"avg_response\": 126072.39622641509}, {\"APPEARANCES\": \"15\", \"support\": 95, \"avg_response\": 113030.56842105264}, {\"APPEARANCES\": \"19\", \"support\": 80, \"avg_response\": 80950.9}, {\"APPEARANCES\": \"16\", \"support\": 77, \"avg_response\": 127008.28571428571}, {\"APPEARANCES\": \"17\", \"support\": 68, \"avg_response\": 110544.35294117648}, {\"APPEARANCES\": \"18\", \"support\": 67, \"avg_response\": 100613.64179104478}, {\"APPEARANCES\": \"20\", \"support\": 59, \"avg_response\": 95265.96610169491}, {\"APPEARANCES\": \"21\", \"support\": 47, \"avg_response\": 109740.63829787234}, {\"APPEARANCES\": \"23\", \"support\": 44, \"avg_response\": 60896.795454545456}, {\"APPEARANCES\": \"25\", \"support\": 40, \"avg_response\": 98577.725}, {\"APPEARANCES\": \"24\", \"support\": 37, \"avg_response\": 82825.56756756757}, {\"APPEARANCES\": \"32\", \"support\": 36, \"avg_response\": 52497.166666666664}, {\"APPEARANCES\": \"27\", \"support\": 32, \"avg_response\": 101432.5}, {\"APPEARANCES\": \"22\", \"support\": 32, \"avg_response\": 100943.96875}, {\"APPEARANCES\": \"26\", \"support\": 32, \"avg_response\": 88335.3125}, {\"APPEARANCES\": \"28\", \"support\": 30, \"avg_response\": 80142.76666666666}, {\"APPEARANCES\": \"30\", \"support\": 29, \"avg_response\": 92839.13793103448}, {\"APPEARANCES\": \"29\", \"support\": 28, \"avg_response\": 92294.60714285714}, {\"APPEARANCES\": \"36\", \"support\": 24, \"avg_response\": 60823.958333333336}, {\"APPEARANCES\": \"31\", \"support\": 22, \"avg_response\": 63838.818181818184}, {\"APPEARANCES\": \"35\", \"support\": 20, \"avg_response\": 73490.2}, {\"APPEARANCES\": \"42\", \"support\": 20, \"avg_response\": 33300.35}, {\"APPEARANCES\": \"40\", \"support\": 18, \"avg_response\": 62177.944444444445}, {\"APPEARANCES\": \"34\", \"support\": 18, \"avg_response\": 60691.88888888889}, {\"APPEARANCES\": \"38\", \"support\": 18, \"avg_response\": 53994.22222222222}, {\"APPEARANCES\": \"39\", \"support\": 18, \"avg_response\": 42442.77777777778}, {\"APPEARANCES\": \"43\", \"support\": 17, \"avg_response\": 46701.05882352941}, {\"APPEARANCES\": \"33\", \"support\": 17, \"avg_response\": 42308.0}, {\"APPEARANCES\": \"41\", \"support\": 15, \"avg_response\": 54222.13333333333}, {\"APPEARANCES\": \"37\", \"support\": 15, \"avg_response\": 32966.933333333334}, {\"APPEARANCES\": \"44\", \"support\": 14, \"avg_response\": 36322.57142857143}, {\"APPEARANCES\": \"55\", \"support\": 13, \"avg_response\": 62600.46153846154}, {\"APPEARANCES\": \"46\", \"support\": 13, \"avg_response\": 30270.23076923077}, {\"APPEARANCES\": \"51\", \"support\": 12, \"avg_response\": 24713.166666666668}, {\"APPEARANCES\": \"45\", \"support\": 12, \"avg_response\": 15927.166666666666}, {\"APPEARANCES\": \"68\", \"support\": 11, \"avg_response\": 64668.90909090909}], \"row_count_returned\": 50, \"row_limit\": 50, \"truncated\": true, \"elapsed_ms\": 2.7}"} diff --git a/Query/sql/v2/runs/v2_cli_20260502_081223_d/c16/artifacts/v2q_c16_7ce102b5cd752fd9/run_manifest.json b/Query/sql/v2/runs/v2_cli_20260502_081223_d/c16/artifacts/v2q_c16_7ce102b5cd752fd9/run_manifest.json new file mode 100644 index 0000000000000000000000000000000000000000..738eeddf2beb207a517cde98878e6dc028b195b0 --- /dev/null +++ b/Query/sql/v2/runs/v2_cli_20260502_081223_d/c16/artifacts/v2q_c16_7ce102b5cd752fd9/run_manifest.json @@ -0,0 +1,60 @@ +{ + "run_id": "v2_cli_20260502_081223_d", + "dataset_id": "c16", + "started_at": "2026-05-19T16:10:30.543573+00:00", + "ended_at": "2026-05-19T16:10:30.547024+00:00", + "status": "completed", + "engine": "cli", + "question_record": { + "query_record_id": "v2q_c16_7ce102b5cd752fd9", + "problem_id": "v2p_c16_9bd4e7a3b853b7e8", + "dataset_id": "c16", + "template_id": "tpl_cardinality_high_card_response_stability", + "template_name": "High-Cardinality Response Stability", + "family_id": "cardinality_structure", + "canonical_subitem_id": "high_cardinality_response_stability", + "intended_facet_id": "target_cardinality_cross_section", + "variant_semantic_role": "focused_target_view", + "subitem_assignment_source": "template_fixed", + "source_kind": "deterministic", + "realization_mode": "deterministic", + "gate_priority": "deterministic", + "extended_family": true, + "question": "Use template High-Cardinality Response Stability to probe high_cardinality_response_stability with semantic role focused_target_view. Focus on measure_col=page_id, key_col=APPEARANCES.", + "bindings": { + "key_col": "APPEARANCES", + "measure_col": "page_id", + "min_support": 5 + }, + "binding_roles": [ + "key_col", + "target_col" + ], + "coverage_target_min": "enumerate_all_applicable", + "runtime_sql_skeleton": "SELECT\n {key_col},\n COUNT(*) AS support,\n AVG({measure_col}) AS avg_response\nFROM {table}\nGROUP BY {key_col}\nHAVING COUNT(*) >= {min_support}\nORDER BY support DESC, avg_response DESC;", + "notes": [ + "default_facets=target_cardinality_cross_section", + "template_selection_mode=deterministic", + "problem_index_within_template=8", + "sql_variant_index=1/1" + ], + "template_selection_mode": "deterministic", + "selected_template_rank": 0, + "problem_index_within_template": 8, + "sql_variant_index": 1, + "sql_variant_total": 1 + }, + "mode": "subitem_workload_v2", + "sql_source_version": "v2", + "sql_source_label": "v2_current", + "generated_sql_path": "/data/jialinzhang/TabQueryBench/sql_workloads/v2_current/runs_and_launches/runs/v2_cli_20260502_081223_d/c16/sql/v2q_c16_7ce102b5cd752fd9.sql", + "usage_summary": { + "engine": "template", + "input_tokens": 0, + "cached_input_tokens": 0, + "output_tokens": 0, + "total_tokens": 0, + "estimated_total_tokens": 0, + "usage_source": "none" + } +} \ No newline at end of file diff --git a/Query/sql/v2/runs/v2_cli_20260502_081223_d/c16/artifacts/v2q_c16_7ce102b5cd752fd9/usage_summary.json b/Query/sql/v2/runs/v2_cli_20260502_081223_d/c16/artifacts/v2q_c16_7ce102b5cd752fd9/usage_summary.json new file mode 100644 index 0000000000000000000000000000000000000000..96c9ff4feec395919fc26411d18d078b8af6e1c7 --- /dev/null +++ b/Query/sql/v2/runs/v2_cli_20260502_081223_d/c16/artifacts/v2q_c16_7ce102b5cd752fd9/usage_summary.json @@ -0,0 +1,9 @@ +{ + "engine": "template", + "input_tokens": 0, + "cached_input_tokens": 0, + "output_tokens": 0, + "total_tokens": 0, + "estimated_total_tokens": 0, + "usage_source": "none" +} \ No newline at end of file diff --git a/Query/sql/v2/runs/v2_cli_20260502_081223_d/c16/artifacts/v2q_c16_7ea8660fba469b20/final_answer.txt b/Query/sql/v2/runs/v2_cli_20260502_081223_d/c16/artifacts/v2q_c16_7ea8660fba469b20/final_answer.txt new file mode 100644 index 0000000000000000000000000000000000000000..0c32e68931ead7fd6c4bb95f211a947f54c7e962 --- /dev/null +++ b/Query/sql/v2/runs/v2_cli_20260502_081223_d/c16/artifacts/v2q_c16_7ea8660fba469b20/final_answer.txt @@ -0,0 +1 @@ +{"row_count": null, "preview_rows": [{"value_label": "", "support": 6832, "support_share": 0.9907192575406032, "cumulative_support": 6832}, {"value_label": "Homosexual Characters", "support": 54, "support_share": 0.007830626450116009, "cumulative_support": 6886}, {"value_label": "Bisexual Characters", "support": 10, "support_share": 0.0014501160092807424, "cumulative_support": 6896}]} \ No newline at end of file diff --git a/Query/sql/v2/runs/v2_cli_20260502_081223_d/c16/artifacts/v2q_c16_7ea8660fba469b20/generated_sql.sql b/Query/sql/v2/runs/v2_cli_20260502_081223_d/c16/artifacts/v2q_c16_7ea8660fba469b20/generated_sql.sql new file mode 100644 index 0000000000000000000000000000000000000000..07c79e8631a05b8b80587fa1dff9818fe6bb8e8b --- /dev/null +++ b/Query/sql/v2/runs/v2_cli_20260502_081223_d/c16/artifacts/v2q_c16_7ea8660fba469b20/generated_sql.sql @@ -0,0 +1,28 @@ +-- sql_source_version: v2 +-- sql_source_label: v2_current +-- sql_source_run_id: v2_cli_20260502_081223_d +-- sql_source_dataset_id: c16 +-- family_id: cardinality_structure +-- canonical_subitem_id: support_rank_profile_consistency +-- intended_facet_id: support_concentration +-- variant_semantic_role: ranked_signal_view +-- template_id: tpl_cardinality_distinct_share_profile +-- query_record_id: v2q_c16_7ea8660fba469b20 +-- problem_id: v2p_c16_30897436b3748ba9 +-- realization_mode: deterministic +-- source_kind: deterministic +WITH grouped AS ( + SELECT "GSM" AS value_label, COUNT(*) AS support + FROM "c16" + GROUP BY "GSM" +), ranked AS ( + SELECT + value_label, + support, + CAST(support AS FLOAT) / NULLIF(SUM(support) OVER (), 0) AS support_share, + SUM(support) OVER (ORDER BY support DESC, value_label ROWS UNBOUNDED PRECEDING) AS cumulative_support + FROM grouped +) +SELECT * +FROM ranked +ORDER BY support DESC, value_label; diff --git a/Query/sql/v2/runs/v2_cli_20260502_081223_d/c16/artifacts/v2q_c16_7ea8660fba469b20/query_results.jsonl b/Query/sql/v2/runs/v2_cli_20260502_081223_d/c16/artifacts/v2q_c16_7ea8660fba469b20/query_results.jsonl new file mode 100644 index 0000000000000000000000000000000000000000..ba42b022553de69db7a319a1e18b37433a22933d --- /dev/null +++ b/Query/sql/v2/runs/v2_cli_20260502_081223_d/c16/artifacts/v2q_c16_7ea8660fba469b20/query_results.jsonl @@ -0,0 +1 @@ +{"node_name": "v2_template", "tool_name": "sqlite_query", "query": "-- sql_source_version: v2\n-- sql_source_label: v2_current\n-- sql_source_run_id: v2_cli_20260502_081223_d\n-- sql_source_dataset_id: c16\n-- family_id: cardinality_structure\n-- canonical_subitem_id: support_rank_profile_consistency\n-- intended_facet_id: support_concentration\n-- variant_semantic_role: ranked_signal_view\n-- template_id: tpl_cardinality_distinct_share_profile\n-- query_record_id: v2q_c16_7ea8660fba469b20\n-- problem_id: v2p_c16_30897436b3748ba9\n-- realization_mode: deterministic\n-- source_kind: deterministic\nWITH grouped AS (\n SELECT \"GSM\" AS value_label, COUNT(*) AS support\n FROM \"c16\"\n GROUP BY \"GSM\"\n), ranked AS (\n SELECT\n value_label,\n support,\n CAST(support AS FLOAT) / NULLIF(SUM(support) OVER (), 0) AS support_share,\n SUM(support) OVER (ORDER BY support DESC, value_label ROWS UNBOUNDED PRECEDING) AS cumulative_support\n FROM grouped\n)\nSELECT *\nFROM ranked\nORDER BY support DESC, value_label;", "result": "{\"query\": \"-- sql_source_version: v2\\n-- sql_source_label: v2_current\\n-- sql_source_run_id: v2_cli_20260502_081223_d\\n-- sql_source_dataset_id: c16\\n-- family_id: cardinality_structure\\n-- canonical_subitem_id: support_rank_profile_consistency\\n-- intended_facet_id: support_concentration\\n-- variant_semantic_role: ranked_signal_view\\n-- template_id: tpl_cardinality_distinct_share_profile\\n-- query_record_id: v2q_c16_7ea8660fba469b20\\n-- problem_id: v2p_c16_30897436b3748ba9\\n-- realization_mode: deterministic\\n-- source_kind: deterministic\\nWITH grouped AS (\\n SELECT \\\"GSM\\\" AS value_label, COUNT(*) AS support\\n FROM \\\"c16\\\"\\n GROUP BY \\\"GSM\\\"\\n), ranked AS (\\n SELECT\\n value_label,\\n support,\\n CAST(support AS FLOAT) / NULLIF(SUM(support) OVER (), 0) AS support_share,\\n SUM(support) OVER (ORDER BY support DESC, value_label ROWS UNBOUNDED PRECEDING) AS cumulative_support\\n FROM grouped\\n)\\nSELECT *\\nFROM ranked\\nORDER BY support DESC, value_label;\", \"columns\": [\"value_label\", \"support\", \"support_share\", \"cumulative_support\"], \"rows\": [{\"value_label\": \"\", \"support\": 6832, \"support_share\": 0.9907192575406032, \"cumulative_support\": 6832}, {\"value_label\": \"Homosexual Characters\", \"support\": 54, \"support_share\": 0.007830626450116009, \"cumulative_support\": 6886}, {\"value_label\": \"Bisexual Characters\", \"support\": 10, \"support_share\": 0.0014501160092807424, \"cumulative_support\": 6896}], \"row_count_returned\": 3, \"row_limit\": 50, \"truncated\": false, \"elapsed_ms\": 2.08}"} diff --git a/Query/sql/v2/runs/v2_cli_20260502_081223_d/c16/artifacts/v2q_c16_7ea8660fba469b20/run_manifest.json b/Query/sql/v2/runs/v2_cli_20260502_081223_d/c16/artifacts/v2q_c16_7ea8660fba469b20/run_manifest.json new file mode 100644 index 0000000000000000000000000000000000000000..01240338ebed2b6f7a60ad055df845e857a446c8 --- /dev/null +++ b/Query/sql/v2/runs/v2_cli_20260502_081223_d/c16/artifacts/v2q_c16_7ea8660fba469b20/run_manifest.json @@ -0,0 +1,57 @@ +{ + "run_id": "v2_cli_20260502_081223_d", + "dataset_id": "c16", + "started_at": "2026-05-19T16:10:30.464971+00:00", + "ended_at": "2026-05-19T16:10:30.467721+00:00", + "status": "completed", + "engine": "cli", + "question_record": { + "query_record_id": "v2q_c16_7ea8660fba469b20", + "problem_id": "v2p_c16_30897436b3748ba9", + "dataset_id": "c16", + "template_id": "tpl_cardinality_distinct_share_profile", + "template_name": "Cardinality Distinct Share Profile", + "family_id": "cardinality_structure", + "canonical_subitem_id": "support_rank_profile_consistency", + "intended_facet_id": "support_concentration", + "variant_semantic_role": "ranked_signal_view", + "subitem_assignment_source": "template_fixed", + "source_kind": "deterministic", + "realization_mode": "deterministic", + "gate_priority": "deterministic", + "extended_family": true, + "question": "Use template Cardinality Distinct Share Profile to probe support_rank_profile_consistency with semantic role ranked_signal_view. Focus on group_col=GSM.", + "bindings": { + "group_col": "GSM" + }, + "binding_roles": [ + "group_col" + ], + "coverage_target_min": "enumerate_all_applicable", + "runtime_sql_skeleton": "WITH grouped AS (\n SELECT {group_col} AS value_label, COUNT(*) AS support\n FROM {table}\n GROUP BY {group_col}\n), ranked AS (\n SELECT\n value_label,\n support,\n CAST(support AS FLOAT) / NULLIF(SUM(support) OVER (), 0) AS support_share,\n SUM(support) OVER (ORDER BY support DESC, value_label ROWS UNBOUNDED PRECEDING) AS cumulative_support\n FROM grouped\n)\nSELECT *\nFROM ranked\nORDER BY support DESC, value_label;", + "notes": [ + "default_facets=support_concentration,value_imbalance_profile", + "template_selection_mode=deterministic", + "problem_index_within_template=5", + "sql_variant_index=1/1" + ], + "template_selection_mode": "deterministic", + "selected_template_rank": 0, + "problem_index_within_template": 5, + "sql_variant_index": 1, + "sql_variant_total": 1 + }, + "mode": "subitem_workload_v2", + "sql_source_version": "v2", + "sql_source_label": "v2_current", + "generated_sql_path": "/data/jialinzhang/TabQueryBench/sql_workloads/v2_current/runs_and_launches/runs/v2_cli_20260502_081223_d/c16/sql/v2q_c16_7ea8660fba469b20.sql", + "usage_summary": { + "engine": "template", + "input_tokens": 0, + "cached_input_tokens": 0, + "output_tokens": 0, + "total_tokens": 0, + "estimated_total_tokens": 0, + "usage_source": "none" + } +} \ No newline at end of file diff --git a/Query/sql/v2/runs/v2_cli_20260502_081223_d/c16/artifacts/v2q_c16_7ea8660fba469b20/usage_summary.json b/Query/sql/v2/runs/v2_cli_20260502_081223_d/c16/artifacts/v2q_c16_7ea8660fba469b20/usage_summary.json new file mode 100644 index 0000000000000000000000000000000000000000..96c9ff4feec395919fc26411d18d078b8af6e1c7 --- /dev/null +++ b/Query/sql/v2/runs/v2_cli_20260502_081223_d/c16/artifacts/v2q_c16_7ea8660fba469b20/usage_summary.json @@ -0,0 +1,9 @@ +{ + "engine": "template", + "input_tokens": 0, + "cached_input_tokens": 0, + "output_tokens": 0, + "total_tokens": 0, + "estimated_total_tokens": 0, + "usage_source": "none" +} \ No newline at end of file diff --git a/Query/sql/v2/runs/v2_cli_20260502_081223_d/c16/artifacts/v2q_c16_845abf0dece8b20a/final_answer.txt b/Query/sql/v2/runs/v2_cli_20260502_081223_d/c16/artifacts/v2q_c16_845abf0dece8b20a/final_answer.txt new file mode 100644 index 0000000000000000000000000000000000000000..7e4b9735eb60728a5ac2bfac8422fbf157117b6f --- /dev/null +++ b/Query/sql/v2/runs/v2_cli_20260502_081223_d/c16/artifacts/v2q_c16_845abf0dece8b20a/final_answer.txt @@ -0,0 +1 @@ +{"row_count": null, "preview_rows": [{"value_label": "", "support": 6832, "support_share": 0.9907192575406032, "support_rank": 1}, {"value_label": "Homosexual Characters", "support": 54, "support_share": 0.007830626450116009, "support_rank": 2}, {"value_label": "Bisexual Characters", "support": 10, "support_share": 0.0014501160092807424, "support_rank": 3}]} \ No newline at end of file diff --git a/Query/sql/v2/runs/v2_cli_20260502_081223_d/c16/artifacts/v2q_c16_845abf0dece8b20a/generated_sql.sql b/Query/sql/v2/runs/v2_cli_20260502_081223_d/c16/artifacts/v2q_c16_845abf0dece8b20a/generated_sql.sql new file mode 100644 index 0000000000000000000000000000000000000000..94c89eefbcef2aba0038e68a760a506869d16f34 --- /dev/null +++ b/Query/sql/v2/runs/v2_cli_20260502_081223_d/c16/artifacts/v2q_c16_845abf0dece8b20a/generated_sql.sql @@ -0,0 +1,25 @@ +-- sql_source_version: v2 +-- sql_source_label: v2_current +-- sql_source_run_id: v2_cli_20260502_081223_d +-- sql_source_dataset_id: c16 +-- family_id: cardinality_structure +-- canonical_subitem_id: support_rank_profile_consistency +-- intended_facet_id: support_concentration +-- variant_semantic_role: count_distribution +-- template_id: tpl_cardinality_support_rank_profile +-- query_record_id: v2q_c16_845abf0dece8b20a +-- problem_id: v2p_c16_ad39351e02f96964 +-- realization_mode: deterministic +-- source_kind: deterministic +WITH grouped AS ( + SELECT "GSM" AS value_label, COUNT(*) AS support + FROM "c16" + GROUP BY "GSM" +) +SELECT + value_label, + support, + CAST(support AS FLOAT) / NULLIF(SUM(support) OVER (), 0) AS support_share, + ROW_NUMBER() OVER (ORDER BY support DESC, value_label) AS support_rank +FROM grouped +ORDER BY support DESC, value_label; diff --git a/Query/sql/v2/runs/v2_cli_20260502_081223_d/c16/artifacts/v2q_c16_845abf0dece8b20a/query_results.jsonl b/Query/sql/v2/runs/v2_cli_20260502_081223_d/c16/artifacts/v2q_c16_845abf0dece8b20a/query_results.jsonl new file mode 100644 index 0000000000000000000000000000000000000000..320efc594083bad35625f67cb452dc68456184c8 --- /dev/null +++ b/Query/sql/v2/runs/v2_cli_20260502_081223_d/c16/artifacts/v2q_c16_845abf0dece8b20a/query_results.jsonl @@ -0,0 +1 @@ +{"node_name": "v2_template", "tool_name": "sqlite_query", "query": "-- sql_source_version: v2\n-- sql_source_label: v2_current\n-- sql_source_run_id: v2_cli_20260502_081223_d\n-- sql_source_dataset_id: c16\n-- family_id: cardinality_structure\n-- canonical_subitem_id: support_rank_profile_consistency\n-- intended_facet_id: support_concentration\n-- variant_semantic_role: count_distribution\n-- template_id: tpl_cardinality_support_rank_profile\n-- query_record_id: v2q_c16_845abf0dece8b20a\n-- problem_id: v2p_c16_ad39351e02f96964\n-- realization_mode: deterministic\n-- source_kind: deterministic\nWITH grouped AS (\n SELECT \"GSM\" AS value_label, COUNT(*) AS support\n FROM \"c16\"\n GROUP BY \"GSM\"\n)\nSELECT\n value_label,\n support,\n CAST(support AS FLOAT) / NULLIF(SUM(support) OVER (), 0) AS support_share,\n ROW_NUMBER() OVER (ORDER BY support DESC, value_label) AS support_rank\nFROM grouped\nORDER BY support DESC, value_label;", "result": "{\"query\": \"-- sql_source_version: v2\\n-- sql_source_label: v2_current\\n-- sql_source_run_id: v2_cli_20260502_081223_d\\n-- sql_source_dataset_id: c16\\n-- family_id: cardinality_structure\\n-- canonical_subitem_id: support_rank_profile_consistency\\n-- intended_facet_id: support_concentration\\n-- variant_semantic_role: count_distribution\\n-- template_id: tpl_cardinality_support_rank_profile\\n-- query_record_id: v2q_c16_845abf0dece8b20a\\n-- problem_id: v2p_c16_ad39351e02f96964\\n-- realization_mode: deterministic\\n-- source_kind: deterministic\\nWITH grouped AS (\\n SELECT \\\"GSM\\\" AS value_label, COUNT(*) AS support\\n FROM \\\"c16\\\"\\n GROUP BY \\\"GSM\\\"\\n)\\nSELECT\\n value_label,\\n support,\\n CAST(support AS FLOAT) / NULLIF(SUM(support) OVER (), 0) AS support_share,\\n ROW_NUMBER() OVER (ORDER BY support DESC, value_label) AS support_rank\\nFROM grouped\\nORDER BY support DESC, value_label;\", \"columns\": [\"value_label\", \"support\", \"support_share\", \"support_rank\"], \"rows\": [{\"value_label\": \"\", \"support\": 6832, \"support_share\": 0.9907192575406032, \"support_rank\": 1}, {\"value_label\": \"Homosexual Characters\", \"support\": 54, \"support_share\": 0.007830626450116009, \"support_rank\": 2}, {\"value_label\": \"Bisexual Characters\", \"support\": 10, \"support_share\": 0.0014501160092807424, \"support_rank\": 3}], \"row_count_returned\": 3, \"row_limit\": 50, \"truncated\": false, \"elapsed_ms\": 2.1}"} diff --git a/Query/sql/v2/runs/v2_cli_20260502_081223_d/c16/artifacts/v2q_c16_845abf0dece8b20a/run_manifest.json b/Query/sql/v2/runs/v2_cli_20260502_081223_d/c16/artifacts/v2q_c16_845abf0dece8b20a/run_manifest.json new file mode 100644 index 0000000000000000000000000000000000000000..605a67768e48c2756f278322579ea12ea64c659b --- /dev/null +++ b/Query/sql/v2/runs/v2_cli_20260502_081223_d/c16/artifacts/v2q_c16_845abf0dece8b20a/run_manifest.json @@ -0,0 +1,57 @@ +{ + "run_id": "v2_cli_20260502_081223_d", + "dataset_id": "c16", + "started_at": "2026-05-19T16:10:30.490600+00:00", + "ended_at": "2026-05-19T16:10:30.493547+00:00", + "status": "completed", + "engine": "cli", + "question_record": { + "query_record_id": "v2q_c16_845abf0dece8b20a", + "problem_id": "v2p_c16_ad39351e02f96964", + "dataset_id": "c16", + "template_id": "tpl_cardinality_support_rank_profile", + "template_name": "Cardinality Support Rank Profile", + "family_id": "cardinality_structure", + "canonical_subitem_id": "support_rank_profile_consistency", + "intended_facet_id": "support_concentration", + "variant_semantic_role": "count_distribution", + "subitem_assignment_source": "template_fixed", + "source_kind": "deterministic", + "realization_mode": "deterministic", + "gate_priority": "deterministic", + "extended_family": true, + "question": "Use template Cardinality Support Rank Profile to probe support_rank_profile_consistency with semantic role count_distribution. Focus on group_col=GSM.", + "bindings": { + "group_col": "GSM" + }, + "binding_roles": [ + "group_col" + ], + "coverage_target_min": "enumerate_all_applicable", + "runtime_sql_skeleton": "WITH grouped AS (\n SELECT {group_col} AS value_label, COUNT(*) AS support\n FROM {table}\n GROUP BY {group_col}\n)\nSELECT\n value_label,\n support,\n CAST(support AS FLOAT) / NULLIF(SUM(support) OVER (), 0) AS support_share,\n ROW_NUMBER() OVER (ORDER BY support DESC, value_label) AS support_rank\nFROM grouped\nORDER BY support DESC, value_label;", + "notes": [ + "default_facets=support_concentration,value_imbalance_profile", + "template_selection_mode=deterministic", + "problem_index_within_template=5", + "sql_variant_index=1/1" + ], + "template_selection_mode": "deterministic", + "selected_template_rank": 0, + "problem_index_within_template": 5, + "sql_variant_index": 1, + "sql_variant_total": 1 + }, + "mode": "subitem_workload_v2", + "sql_source_version": "v2", + "sql_source_label": "v2_current", + "generated_sql_path": "/data/jialinzhang/TabQueryBench/sql_workloads/v2_current/runs_and_launches/runs/v2_cli_20260502_081223_d/c16/sql/v2q_c16_845abf0dece8b20a.sql", + "usage_summary": { + "engine": "template", + "input_tokens": 0, + "cached_input_tokens": 0, + "output_tokens": 0, + "total_tokens": 0, + "estimated_total_tokens": 0, + "usage_source": "none" + } +} \ No newline at end of file diff --git a/Query/sql/v2/runs/v2_cli_20260502_081223_d/c16/artifacts/v2q_c16_845abf0dece8b20a/usage_summary.json b/Query/sql/v2/runs/v2_cli_20260502_081223_d/c16/artifacts/v2q_c16_845abf0dece8b20a/usage_summary.json new file mode 100644 index 0000000000000000000000000000000000000000..96c9ff4feec395919fc26411d18d078b8af6e1c7 --- /dev/null +++ b/Query/sql/v2/runs/v2_cli_20260502_081223_d/c16/artifacts/v2q_c16_845abf0dece8b20a/usage_summary.json @@ -0,0 +1,9 @@ +{ + "engine": "template", + "input_tokens": 0, + "cached_input_tokens": 0, + "output_tokens": 0, + "total_tokens": 0, + "estimated_total_tokens": 0, + "usage_source": "none" +} \ No newline at end of file diff --git a/Query/sql/v2/runs/v2_cli_20260502_081223_d/c16/artifacts/v2q_c16_866a8f1cacbfe02f/final_answer.txt b/Query/sql/v2/runs/v2_cli_20260502_081223_d/c16/artifacts/v2q_c16_866a8f1cacbfe02f/final_answer.txt new file mode 100644 index 0000000000000000000000000000000000000000..402dc602a6ff799214b5ea71fe3c6e151dcc5800 --- /dev/null +++ b/Query/sql/v2/runs/v2_cli_20260502_081223_d/c16/artifacts/v2q_c16_866a8f1cacbfe02f/final_answer.txt @@ -0,0 +1,2 @@ +SQL executed successfully for: Use template Within-Group Share of Total to probe dependency_strength_similarity with semantic role focused_target_view. Focus on group_col=HAIR, measure_col=page_id. +Result preview: [{"HAIR": "Silver Hair", "name": "Pasquale Galante, Jr. (New Earth)", "total_measure": 280536, "share_within_group": 98.8352675079798}, {"HAIR": "Reddish Brown Hair", "name": "Deron Ved (Earth-One)", "total_measure": 127945, "share_within_group": 89.47578220065178}, {"HAIR": "Violet Hair", "name": "Gretti (New Earth)", "total_measure": 132735, "share_within_group": 78.73616400327437}, {"HAIR": "Gold Hair", "name": "Ahura-Mazda (New Earth)", "total_measure": 353950, "share_within_group": 67.15630656441276}, {"HAIR": "Platinum Blond Hair", "name": "Brittany Brandon (New Earth)", "total_measure": 129703, "share_within_group": 51.979128678430165}] Results were truncated. \ No newline at end of file diff --git a/Query/sql/v2/runs/v2_cli_20260502_081223_d/c16/artifacts/v2q_c16_866a8f1cacbfe02f/generated_sql.sql b/Query/sql/v2/runs/v2_cli_20260502_081223_d/c16/artifacts/v2q_c16_866a8f1cacbfe02f/generated_sql.sql new file mode 100644 index 0000000000000000000000000000000000000000..39eba1ba076112c3b810fe198868291fca8c7aa3 --- /dev/null +++ b/Query/sql/v2/runs/v2_cli_20260502_081223_d/c16/artifacts/v2q_c16_866a8f1cacbfe02f/generated_sql.sql @@ -0,0 +1,19 @@ +-- sql_source_version: v2 +-- sql_source_label: v2_current +-- sql_source_run_id: v2_cli_20260502_081223_d +-- sql_source_dataset_id: c16 +-- family_id: conditional_dependency_structure +-- canonical_subitem_id: dependency_strength_similarity +-- intended_facet_id: pairwise_conditional_dependency +-- variant_semantic_role: focused_target_view +-- template_id: tpl_tpcds_within_group_share +-- query_record_id: v2q_c16_866a8f1cacbfe02f +-- problem_id: v2p_c16_c5bac3a67809aa33 +-- realization_mode: agent +-- source_kind: agent +SELECT "HAIR", "name", + SUM("page_id") AS "total_measure", + SUM("page_id") * 100.0 / SUM(SUM("page_id")) OVER (PARTITION BY "HAIR") AS "share_within_group" +FROM "c16" +GROUP BY "HAIR", "name" +ORDER BY "share_within_group" DESC; diff --git a/Query/sql/v2/runs/v2_cli_20260502_081223_d/c16/artifacts/v2q_c16_866a8f1cacbfe02f/query_results.jsonl b/Query/sql/v2/runs/v2_cli_20260502_081223_d/c16/artifacts/v2q_c16_866a8f1cacbfe02f/query_results.jsonl new file mode 100644 index 0000000000000000000000000000000000000000..c0beffc0e0b0d0e058056db6c68fc65327a277e5 --- /dev/null +++ b/Query/sql/v2/runs/v2_cli_20260502_081223_d/c16/artifacts/v2q_c16_866a8f1cacbfe02f/query_results.jsonl @@ -0,0 +1 @@ +{"step_index": 1, "message_index": 0, "node_name": "v2-cli:codex", "tool_name": "sqlite_query", "query": "-- template_id: tpl_tpcds_within_group_share\nSELECT \"HAIR\", \"name\",\n SUM(\"page_id\") AS \"total_measure\",\n SUM(\"page_id\") * 100.0 / SUM(SUM(\"page_id\")) OVER (PARTITION BY \"HAIR\") AS \"share_within_group\"\nFROM \"c16\"\nGROUP BY \"HAIR\", \"name\"\nORDER BY \"share_within_group\" DESC;", "result": "{\"query\": \"-- template_id: tpl_tpcds_within_group_share\\nSELECT \\\"HAIR\\\", \\\"name\\\",\\n SUM(\\\"page_id\\\") AS \\\"total_measure\\\",\\n SUM(\\\"page_id\\\") * 100.0 / SUM(SUM(\\\"page_id\\\")) OVER (PARTITION BY \\\"HAIR\\\") AS \\\"share_within_group\\\"\\nFROM \\\"c16\\\"\\nGROUP BY \\\"HAIR\\\", \\\"name\\\"\\nORDER BY \\\"share_within_group\\\" DESC;\", \"columns\": [\"HAIR\", \"name\", \"total_measure\", \"share_within_group\"], \"rows\": [{\"HAIR\": \"Silver Hair\", \"name\": \"Pasquale Galante, Jr. (New Earth)\", \"total_measure\": 280536, \"share_within_group\": 98.8352675079798}, {\"HAIR\": \"Reddish Brown Hair\", \"name\": \"Deron Ved (Earth-One)\", \"total_measure\": 127945, \"share_within_group\": 89.47578220065178}, {\"HAIR\": \"Violet Hair\", \"name\": \"Gretti (New Earth)\", \"total_measure\": 132735, \"share_within_group\": 78.73616400327437}, {\"HAIR\": \"Gold Hair\", \"name\": \"Ahura-Mazda (New Earth)\", \"total_measure\": 353950, \"share_within_group\": 67.15630656441276}, {\"HAIR\": \"Platinum Blond Hair\", \"name\": \"Brittany Brandon (New Earth)\", \"total_measure\": 129703, \"share_within_group\": 51.979128678430165}, {\"HAIR\": \"Platinum Blond Hair\", \"name\": \"Gilotina (New Earth)\", \"total_measure\": 119826, \"share_within_group\": 48.020871321569835}, {\"HAIR\": \"Pink Hair\", \"name\": \"Silica (New Earth)\", \"total_measure\": 296697, \"share_within_group\": 17.366677066096475}, {\"HAIR\": \"Pink Hair\", \"name\": \"Millie (New Earth)\", \"total_measure\": 273695, \"share_within_group\": 16.02029235079989}, {\"HAIR\": \"Pink Hair\", \"name\": \"Hoppy (New Earth)\", \"total_measure\": 273686, \"share_within_group\": 16.019765550415674}, {\"HAIR\": \"Gold Hair\", \"name\": \"Lambien (New Earth)\", \"total_measure\": 81515, \"share_within_group\": 15.466157167956224}, {\"HAIR\": \"Gold Hair\", \"name\": \"Kal-El (DC One Million)\", \"total_measure\": 69851, \"share_within_group\": 13.25310120025652}, {\"HAIR\": \"Pink Hair\", \"name\": \"B'aad (New Earth)\", \"total_measure\": 206817, \"share_within_group\": 12.105697229088513}, {\"HAIR\": \"Strawberry Blond Hair\", \"name\": \"Diana Lincoln (New Earth)\", \"total_measure\": 375674, \"share_within_group\": 11.80921006099909}, {\"HAIR\": \"Strawberry Blond Hair\", \"name\": \"Marie Saloppe (New Earth)\", \"total_measure\": 353047, \"share_within_group\": 11.097936467270946}, {\"HAIR\": \"Orange Hair\", \"name\": \"Sparta (New Earth)\", \"total_measure\": 365358, \"share_within_group\": 10.891276527435668}, {\"HAIR\": \"Strawberry Blond Hair\", \"name\": \"Alcinoe (New Earth)\", \"total_measure\": 344919, \"share_within_group\": 10.842434996911537}, {\"HAIR\": \"Strawberry Blond Hair\", \"name\": \"Cydippe (New Earth)\", \"total_measure\": 344310, \"share_within_group\": 10.823291247471468}, {\"HAIR\": \"Strawberry Blond Hair\", \"name\": \"Julianna Hut\\\\u00f6ff (New Earth)\", \"total_measure\": 344292, \"share_within_group\": 10.822725422364867}, {\"HAIR\": \"Strawberry Blond Hair\", \"name\": \"Marion Winston (New Earth)\", \"total_measure\": 337012, \"share_within_group\": 10.593880601472089}, {\"HAIR\": \"Orange Hair\", \"name\": \"Kamah (New Earth)\", \"total_measure\": 332809, \"share_within_group\": 9.92099488671204}, {\"HAIR\": \"Orange Hair\", \"name\": \"Kyesha Salton (New Earth)\", \"total_measure\": 332077, \"share_within_group\": 9.899174057776905}, {\"HAIR\": \"Reddish Brown Hair\", \"name\": \"Yolanda Montez (New Earth)\", \"total_measure\": 13337, \"share_within_group\": 9.326964767752493}, {\"HAIR\": \"Orange Hair\", \"name\": \"Joanqin (New Earth)\", \"total_measure\": 304335, \"share_within_group\": 9.07218848903578}, {\"HAIR\": \"Pink Hair\", \"name\": \"Vera Klopis (New Earth)\", \"total_measure\": 154648, \"share_within_group\": 9.05206953530938}, {\"HAIR\": \"Pink Hair\", \"name\": \"Eduardo Flamingo (New Earth)\", \"total_measure\": 152150, \"share_within_group\": 8.90585316200224}, {\"HAIR\": \"Orange Hair\", \"name\": \"Kwintz (New Earth)\", \"total_measure\": 269379, \"share_within_group\": 8.030154477756318}, {\"HAIR\": \"Orange Hair\", \"name\": \"Grand Druid (New Earth)\", \"total_measure\": 262507, \"share_within_group\": 7.825301012671284}, {\"HAIR\": \"Pink Hair\", \"name\": \"Venizz (New Earth)\", \"total_measure\": 132760, \"share_within_group\": 7.77089100090317}, {\"HAIR\": \"Orange Hair\", \"name\": \"Qanda (New Earth)\", \"total_measure\": 253887, \"share_within_group\": 7.568339885047158}, {\"HAIR\": \"Purple Hair\", \"name\": \"Lotta (New Earth)\", \"total_measure\": 284595, \"share_within_group\": 7.5330237310298}, {\"HAIR\": \"Green Hair\", \"name\": \"Clot (New Earth)\", \"total_measure\": 369922, \"share_within_group\": 7.145176365649024}, {\"HAIR\": \"Violet Hair\", \"name\": \"Susan Linden II (New Earth)\", \"total_measure\": 11950, \"share_within_group\": 7.088538515381239}, {\"HAIR\": \"Violet Hair\", \"name\": \"Flora Black (New Earth)\", \"total_measure\": 11949, \"share_within_group\": 7.087945332241876}, {\"HAIR\": \"Violet Hair\", \"name\": \"Susan Linden I (New Earth)\", \"total_measure\": 11948, \"share_within_group\": 7.0873521491025135}, {\"HAIR\": \"Pink Hair\", \"name\": \"Vanessa Kingsbury (New Earth)\", \"total_measure\": 119936, \"share_within_group\": 7.020258986775555}, {\"HAIR\": \"Green Hair\", \"name\": \"Guardian of Hy-Brasil (New Earth)\", \"total_measure\": 332087, \"share_within_group\": 6.414379744214422}, {\"HAIR\": \"Purple Hair\", \"name\": \"Emily Sung (New Earth)\", \"total_measure\": 242097, \"share_within_group\": 6.408132420496219}, {\"HAIR\": \"Purple Hair\", \"name\": \"Qurina Vint (New Earth)\", \"total_measure\": 238455, \"share_within_group\": 6.31173131566862}, {\"HAIR\": \"Strawberry Blond Hair\", \"name\": \"Thunder II (New Earth)\", \"total_measure\": 194096, \"share_within_group\": 6.101354993956673}, {\"HAIR\": \"Purple Hair\", \"name\": \"Tanga (New Earth)\", \"total_measure\": 226037, \"share_within_group\": 5.983035840723776}, {\"HAIR\": \"Green Hair\", \"name\": \"Bizarro Joker (New Earth)\", \"total_measure\": 308374, \"share_within_group\": 5.95635462768003}, {\"HAIR\": \"Green Hair\", \"name\": \"Basqat (New Earth)\", \"total_measure\": 300901, \"share_within_group\": 5.812010947173071}, {\"HAIR\": \"Green Hair\", \"name\": \"Druu (New Earth)\", \"total_measure\": 296542, \"share_within_group\": 5.727815295717186}, {\"HAIR\": \"Purple Hair\", \"name\": \"Witchazel (New Earth)\", \"total_measure\": 213189, \"share_within_group\": 5.642958576905821}, {\"HAIR\": \"Pink Hair\", \"name\": \"Poprocket (New Earth)\", \"total_measure\": 94578, \"share_within_group\": 5.535969637567189}, {\"HAIR\": \"Green Hair\", \"name\": \"Xenofobe (New Earth)\", \"total_measure\": 282833, \"share_within_group\": 5.463021034233191}, {\"HAIR\": \"Strawberry Blond Hair\", \"name\": \"Mary James (New Earth)\", \"total_measure\": 173440, \"share_within_group\": 5.452039249401562}, {\"HAIR\": \"Blue Hair\", \"name\": \"Athyns (New Earth)\", \"total_measure\": 365361, \"share_within_group\": 5.32380231168609}, {\"HAIR\": \"Blue Hair\", \"name\": \"Doranchatok (New Earth)\", \"total_measure\": 363561, \"share_within_group\": 5.297573885113372}, {\"HAIR\": \"Purple Hair\", \"name\": \"Kentor Omoto (New Earth)\", \"total_measure\": 199361, \"share_within_group\": 5.276941422167754}], \"row_count_returned\": 50, \"row_limit\": 50, \"truncated\": true, \"elapsed_ms\": 27.27}"} diff --git a/Query/sql/v2/runs/v2_cli_20260502_081223_d/c16/artifacts/v2q_c16_866a8f1cacbfe02f/run_manifest.json b/Query/sql/v2/runs/v2_cli_20260502_081223_d/c16/artifacts/v2q_c16_866a8f1cacbfe02f/run_manifest.json new file mode 100644 index 0000000000000000000000000000000000000000..91f8c94f3603f861e78bb22d05a4f35a9acc56da --- /dev/null +++ b/Query/sql/v2/runs/v2_cli_20260502_081223_d/c16/artifacts/v2q_c16_866a8f1cacbfe02f/run_manifest.json @@ -0,0 +1,91 @@ +{ + "run_id": "v2_cli_20260502_081223_d", + "dataset_id": "c16", + "started_at": "2026-05-19T15:37:01.889948+00:00", + "ended_at": "2026-05-19T15:37:29.828400+00:00", + "status": "completed", + "engine": "cli", + "question_record": { + "query_record_id": "v2q_c16_866a8f1cacbfe02f", + "problem_id": "v2p_c16_c5bac3a67809aa33", + "dataset_id": "c16", + "template_id": "tpl_tpcds_within_group_share", + "template_name": "Within-Group Share of Total", + "family_id": "conditional_dependency_structure", + "canonical_subitem_id": "dependency_strength_similarity", + "intended_facet_id": "pairwise_conditional_dependency", + "variant_semantic_role": "focused_target_view", + "subitem_assignment_source": "planner_selected", + "source_kind": "agent", + "realization_mode": "agent", + "gate_priority": "primary", + "extended_family": false, + "question": "Use template Within-Group Share of Total to probe dependency_strength_similarity with semantic role focused_target_view. Focus on group_col=HAIR, measure_col=page_id.", + "bindings": { + "group_col": "HAIR", + "measure_col": "page_id", + "item_col": "name", + "top_k": 15, + "top_n": 6, + "num_tiles": 10, + "percentile_value": 0.9, + "z_threshold": 2.0, + "fraction_threshold": 0.05, + "baseline_multiplier": 1.75, + "baseline_fraction": 0.1, + "min_group_size": 5, + "min_support": 4, + "measure_threshold": 187526.7, + "time_grain": "month", + "lookback_rows": 3, + "current_period_start": "'2024-01-01'", + "current_period_end": "'2024-04-01'", + "previous_period_start": "'2023-10-01'", + "previous_period_end": "'2024-01-01'", + "drift_ratio_threshold": 0.8 + }, + "binding_roles": [ + "group_col", + "item_col", + "measure_col" + ], + "coverage_target_min": "5", + "runtime_sql_skeleton": "SELECT {group_col}, {item_col},\n SUM({measure_col}) AS total_measure,\n SUM({measure_col}) * 100.0 / SUM(SUM({measure_col})) OVER (PARTITION BY {group_col}) AS share_within_group\nFROM {table}\nGROUP BY {group_col}, {item_col}\nORDER BY share_within_group DESC;", + "notes": [ + "default_facets=pairwise_conditional_dependency", + "template_selection_mode=rule", + "problem_index_within_template=7", + "sql_variant_index=2/2", + "binding_index=30" + ], + "template_selection_mode": "rule", + "selected_template_rank": 3, + "problem_index_within_template": 7, + "sql_variant_index": 2, + "sql_variant_total": 2 + }, + "mode": "subitem_workload_v2", + "sql_source_version": "v2", + "sql_source_label": "v2_current", + "generated_sql_path": "/data/jialinzhang/TabQueryBench/sql_workloads/v2_current/runs_and_launches/runs/v2_cli_20260502_081223_d/c16/sql/v2q_c16_866a8f1cacbfe02f.sql", + "usage_summary": { + "dataset_id": "c16", + "model": "v2-cli:codex", + "run_id": "v2q_c16_866a8f1cacbfe02f", + "api_calls": 0, + "input_tokens": 14670, + "cached_input_tokens": 13696, + "output_tokens": 1777, + "total_tokens": 16447, + "cost_usd": 0.0, + "ai_cli_calls": 1, + "estimated_input_tokens": 0, + "estimated_output_tokens": 0, + "estimated_total_tokens": 0, + "usage_source": "ai_cli_json_usage", + "cli_elapsed_ms_total": 27906.41, + "sql_execution_elapsed_ms_total": 27.27, + "conversation_log_path": "/data/jialinzhang/TabQueryBench/sql_workloads/v2_current/runs_and_launches/runs/v2_cli_20260502_081223_d/c16/artifacts/v2q_c16_866a8f1cacbfe02f/cli/conversation.jsonl", + "note": "Executed through a local AI CLI with structured usage metadata." + } +} \ No newline at end of file diff --git a/Query/sql/v2/runs/v2_cli_20260502_081223_d/c16/artifacts/v2q_c16_866a8f1cacbfe02f/trace.jsonl b/Query/sql/v2/runs/v2_cli_20260502_081223_d/c16/artifacts/v2q_c16_866a8f1cacbfe02f/trace.jsonl new file mode 100644 index 0000000000000000000000000000000000000000..34752e79877b2ed2ae5fbdbe05f7571da6802f12 --- /dev/null +++ b/Query/sql/v2/runs/v2_cli_20260502_081223_d/c16/artifacts/v2q_c16_866a8f1cacbfe02f/trace.jsonl @@ -0,0 +1 @@ +{"timestamp": "2026-05-19T15:37:29.799225+00:00", "event_type": "ai_cli_sql_generation", "engine": "v2-cli:codex", "attempt": 1, "command": "codex exec --skip-git-repo-check --disable plugins --sandbox read-only --cd \"/data/jialinzhang/SQLagent\" -m gpt-5.4 --json -", "returncode": 0, "elapsed_ms": 27906.41, "started_at": "2026-05-19T15:37:01.891938+00:00", "ended_at": "2026-05-19T15:37:29.798382+00:00", "prompt_metrics": {"chars": 8884, "bytes_utf8": 8884, "lines": 254, "estimated_tokens": null}, "response_metrics": {"chars": 566, "bytes_utf8": 566, "lines": 1, "estimated_tokens": null}, "usage": {"input_tokens": 14670, "cached_input_tokens": 13696, "output_tokens": 1777, "reasoning_output_tokens": 1615}, "stderr_preview": "", "stdout_preview": "{\"sql\":\"-- template_id: tpl_tpcds_within_group_share\\nSELECT \\\"HAIR\\\", \\\"name\\\",\\n SUM(\\\"page_id\\\") AS \\\"total_measure\\\",\\n SUM(\\\"page_id\\\") * 100.0 / SUM(SUM(\\\"page_id\\\")) OVER (PARTITION BY \\\"HAIR\\\") AS \\\"share_within_group\\\"\\nFROM \\\"c16\\\"\\nGROUP BY \\\"HAIR\\\", \\\"name\\\"\\nORDER BY \\\"share_within_group\\\" DESC;\",\"notes\":\"Used the provided template with group_col=\\\"HAIR\\\", item_col=\\\"name\\\", and measure_col=\\\"page_id\\\" exactly as bound. \\\"page_id\\\" is stored as TEXT in the snapshot, so SQLite will numerically coerce numeric-looking values during SUM.\"}"} diff --git a/Query/sql/v2/runs/v2_cli_20260502_081223_d/c16/artifacts/v2q_c16_866a8f1cacbfe02f/usage_summary.json b/Query/sql/v2/runs/v2_cli_20260502_081223_d/c16/artifacts/v2q_c16_866a8f1cacbfe02f/usage_summary.json new file mode 100644 index 0000000000000000000000000000000000000000..b67aa72bcc83d3ab358d73897b972daf0515abc5 --- /dev/null +++ b/Query/sql/v2/runs/v2_cli_20260502_081223_d/c16/artifacts/v2q_c16_866a8f1cacbfe02f/usage_summary.json @@ -0,0 +1,20 @@ +{ + "dataset_id": "c16", + "model": "v2-cli:codex", + "run_id": "v2q_c16_866a8f1cacbfe02f", + "api_calls": 0, + "input_tokens": 14670, + "cached_input_tokens": 13696, + "output_tokens": 1777, + "total_tokens": 16447, + "cost_usd": 0.0, + "ai_cli_calls": 1, + "estimated_input_tokens": 0, + "estimated_output_tokens": 0, + "estimated_total_tokens": 0, + "usage_source": "ai_cli_json_usage", + "cli_elapsed_ms_total": 27906.41, + "sql_execution_elapsed_ms_total": 27.27, + "conversation_log_path": "/data/jialinzhang/TabQueryBench/sql_workloads/v2_current/runs_and_launches/runs/v2_cli_20260502_081223_d/c16/artifacts/v2q_c16_866a8f1cacbfe02f/cli/conversation.jsonl", + "note": "Executed through a local AI CLI with structured usage metadata." +} \ No newline at end of file diff --git a/Query/sql/v2/runs/v2_cli_20260502_081223_d/c16/artifacts/v2q_c16_87733ae369b5bc7c/final_answer.txt b/Query/sql/v2/runs/v2_cli_20260502_081223_d/c16/artifacts/v2q_c16_87733ae369b5bc7c/final_answer.txt new file mode 100644 index 0000000000000000000000000000000000000000..1a909eb1a5d5c453162411a35a33836aa171270b --- /dev/null +++ b/Query/sql/v2/runs/v2_cli_20260502_081223_d/c16/artifacts/v2q_c16_87733ae369b5bc7c/final_answer.txt @@ -0,0 +1 @@ +{"row_count": null, "preview_rows": [{"total_rows": 6896, "missing_rows": 0, "missing_rate": 0.0}]} \ No newline at end of file diff --git a/Query/sql/v2/runs/v2_cli_20260502_081223_d/c16/artifacts/v2q_c16_87733ae369b5bc7c/generated_sql.sql b/Query/sql/v2/runs/v2_cli_20260502_081223_d/c16/artifacts/v2q_c16_87733ae369b5bc7c/generated_sql.sql new file mode 100644 index 0000000000000000000000000000000000000000..132c3dad7a90848f428aca137fe6a5c42246d2d2 --- /dev/null +++ b/Query/sql/v2/runs/v2_cli_20260502_081223_d/c16/artifacts/v2q_c16_87733ae369b5bc7c/generated_sql.sql @@ -0,0 +1,18 @@ +-- sql_source_version: v2 +-- sql_source_label: v2_current +-- sql_source_run_id: v2_cli_20260502_081223_d +-- sql_source_dataset_id: c16 +-- family_id: missingness_structure +-- canonical_subitem_id: marginal_missing_rate_consistency +-- intended_facet_id: missing_indicator_distribution +-- variant_semantic_role: missing_indicator_view +-- template_id: tpl_missing_marginal_rate_profile +-- query_record_id: v2q_c16_87733ae369b5bc7c +-- problem_id: v2p_c16_b95ac7eca8a13add +-- realization_mode: deterministic +-- source_kind: deterministic +SELECT + COUNT(*) AS total_rows, + SUM(CASE WHEN "HAIR" IS NULL THEN 1 ELSE 0 END) AS missing_rows, + AVG(CASE WHEN "HAIR" IS NULL THEN 1.0 ELSE 0.0 END) AS missing_rate +FROM "c16"; diff --git a/Query/sql/v2/runs/v2_cli_20260502_081223_d/c16/artifacts/v2q_c16_87733ae369b5bc7c/query_results.jsonl b/Query/sql/v2/runs/v2_cli_20260502_081223_d/c16/artifacts/v2q_c16_87733ae369b5bc7c/query_results.jsonl new file mode 100644 index 0000000000000000000000000000000000000000..5e5204bcccccab07fbcdc38a9cf689718fc4e8d6 --- /dev/null +++ b/Query/sql/v2/runs/v2_cli_20260502_081223_d/c16/artifacts/v2q_c16_87733ae369b5bc7c/query_results.jsonl @@ -0,0 +1 @@ +{"node_name": "v2_template", "tool_name": "sqlite_query", "query": "-- sql_source_version: v2\n-- sql_source_label: v2_current\n-- sql_source_run_id: v2_cli_20260502_081223_d\n-- sql_source_dataset_id: c16\n-- family_id: missingness_structure\n-- canonical_subitem_id: marginal_missing_rate_consistency\n-- intended_facet_id: missing_indicator_distribution\n-- variant_semantic_role: missing_indicator_view\n-- template_id: tpl_missing_marginal_rate_profile\n-- query_record_id: v2q_c16_87733ae369b5bc7c\n-- problem_id: v2p_c16_b95ac7eca8a13add\n-- realization_mode: deterministic\n-- source_kind: deterministic\nSELECT\n COUNT(*) AS total_rows,\n SUM(CASE WHEN \"HAIR\" IS NULL THEN 1 ELSE 0 END) AS missing_rows,\n AVG(CASE WHEN \"HAIR\" IS NULL THEN 1.0 ELSE 0.0 END) AS missing_rate\nFROM \"c16\";", "result": "{\"query\": \"-- sql_source_version: v2\\n-- sql_source_label: v2_current\\n-- sql_source_run_id: v2_cli_20260502_081223_d\\n-- sql_source_dataset_id: c16\\n-- family_id: missingness_structure\\n-- canonical_subitem_id: marginal_missing_rate_consistency\\n-- intended_facet_id: missing_indicator_distribution\\n-- variant_semantic_role: missing_indicator_view\\n-- template_id: tpl_missing_marginal_rate_profile\\n-- query_record_id: v2q_c16_87733ae369b5bc7c\\n-- problem_id: v2p_c16_b95ac7eca8a13add\\n-- realization_mode: deterministic\\n-- source_kind: deterministic\\nSELECT\\n COUNT(*) AS total_rows,\\n SUM(CASE WHEN \\\"HAIR\\\" IS NULL THEN 1 ELSE 0 END) AS missing_rows,\\n AVG(CASE WHEN \\\"HAIR\\\" IS NULL THEN 1.0 ELSE 0.0 END) AS missing_rate\\nFROM \\\"c16\\\";\", \"columns\": [\"total_rows\", \"missing_rows\", \"missing_rate\"], \"rows\": [{\"total_rows\": 6896, \"missing_rows\": 0, \"missing_rate\": 0.0}], \"row_count_returned\": 1, \"row_limit\": 50, \"truncated\": false, \"elapsed_ms\": 1.67}"} diff --git a/Query/sql/v2/runs/v2_cli_20260502_081223_d/c16/artifacts/v2q_c16_87733ae369b5bc7c/run_manifest.json b/Query/sql/v2/runs/v2_cli_20260502_081223_d/c16/artifacts/v2q_c16_87733ae369b5bc7c/run_manifest.json new file mode 100644 index 0000000000000000000000000000000000000000..6ddfdd18dbf1918017cc3f13e556d589039f8271 --- /dev/null +++ b/Query/sql/v2/runs/v2_cli_20260502_081223_d/c16/artifacts/v2q_c16_87733ae369b5bc7c/run_manifest.json @@ -0,0 +1,57 @@ +{ + "run_id": "v2_cli_20260502_081223_d", + "dataset_id": "c16", + "started_at": "2026-05-19T16:10:30.329804+00:00", + "ended_at": "2026-05-19T16:10:30.332212+00:00", + "status": "completed", + "engine": "cli", + "question_record": { + "query_record_id": "v2q_c16_87733ae369b5bc7c", + "problem_id": "v2p_c16_b95ac7eca8a13add", + "dataset_id": "c16", + "template_id": "tpl_missing_marginal_rate_profile", + "template_name": "Marginal Missing Rate Profile", + "family_id": "missingness_structure", + "canonical_subitem_id": "marginal_missing_rate_consistency", + "intended_facet_id": "missing_indicator_distribution", + "variant_semantic_role": "missing_indicator_view", + "subitem_assignment_source": "template_fixed", + "source_kind": "deterministic", + "realization_mode": "deterministic", + "gate_priority": "deterministic", + "extended_family": false, + "question": "Use template Marginal Missing Rate Profile to probe marginal_missing_rate_consistency with semantic role missing_indicator_view. Focus on missing_col=HAIR.", + "bindings": { + "missing_col": "HAIR" + }, + "binding_roles": [ + "missing_col" + ], + "coverage_target_min": "enumerate_all_applicable", + "runtime_sql_skeleton": "SELECT\n COUNT(*) AS total_rows,\n SUM(CASE WHEN {missing_col} IS NULL THEN 1 ELSE 0 END) AS missing_rows,\n AVG(CASE WHEN {missing_col} IS NULL THEN 1.0 ELSE 0.0 END) AS missing_rate\nFROM {table};", + "notes": [ + "default_facets=missing_indicator_distribution", + "template_selection_mode=deterministic", + "problem_index_within_template=4", + "sql_variant_index=1/1" + ], + "template_selection_mode": "deterministic", + "selected_template_rank": 0, + "problem_index_within_template": 4, + "sql_variant_index": 1, + "sql_variant_total": 1 + }, + "mode": "subitem_workload_v2", + "sql_source_version": "v2", + "sql_source_label": "v2_current", + "generated_sql_path": "/data/jialinzhang/TabQueryBench/sql_workloads/v2_current/runs_and_launches/runs/v2_cli_20260502_081223_d/c16/sql/v2q_c16_87733ae369b5bc7c.sql", + "usage_summary": { + "engine": "template", + "input_tokens": 0, + "cached_input_tokens": 0, + "output_tokens": 0, + "total_tokens": 0, + "estimated_total_tokens": 0, + "usage_source": "none" + } +} \ No newline at end of file diff --git a/Query/sql/v2/runs/v2_cli_20260502_081223_d/c16/artifacts/v2q_c16_87733ae369b5bc7c/usage_summary.json b/Query/sql/v2/runs/v2_cli_20260502_081223_d/c16/artifacts/v2q_c16_87733ae369b5bc7c/usage_summary.json new file mode 100644 index 0000000000000000000000000000000000000000..96c9ff4feec395919fc26411d18d078b8af6e1c7 --- /dev/null +++ b/Query/sql/v2/runs/v2_cli_20260502_081223_d/c16/artifacts/v2q_c16_87733ae369b5bc7c/usage_summary.json @@ -0,0 +1,9 @@ +{ + "engine": "template", + "input_tokens": 0, + "cached_input_tokens": 0, + "output_tokens": 0, + "total_tokens": 0, + "estimated_total_tokens": 0, + "usage_source": "none" +} \ No newline at end of file diff --git a/Query/sql/v2/runs/v2_cli_20260502_081223_d/c16/artifacts/v2q_c16_87ca32966c89fe3c/final_answer.txt b/Query/sql/v2/runs/v2_cli_20260502_081223_d/c16/artifacts/v2q_c16_87ca32966c89fe3c/final_answer.txt new file mode 100644 index 0000000000000000000000000000000000000000..d77ea007a7de7960f9ffc2f7144a23f6295a47c2 --- /dev/null +++ b/Query/sql/v2/runs/v2_cli_20260502_081223_d/c16/artifacts/v2q_c16_87ca32966c89fe3c/final_answer.txt @@ -0,0 +1 @@ +{"row_count": null, "preview_rows": [{"ALIVE": "Living Characters", "total_rows": 5200, "missing_rows": 0, "missing_rate": 0.0}, {"ALIVE": "Deceased Characters", "total_rows": 1693, "missing_rows": 0, "missing_rate": 0.0}, {"ALIVE": "", "total_rows": 3, "missing_rows": 0, "missing_rate": 0.0}]} \ No newline at end of file diff --git a/Query/sql/v2/runs/v2_cli_20260502_081223_d/c16/artifacts/v2q_c16_87ca32966c89fe3c/generated_sql.sql b/Query/sql/v2/runs/v2_cli_20260502_081223_d/c16/artifacts/v2q_c16_87ca32966c89fe3c/generated_sql.sql new file mode 100644 index 0000000000000000000000000000000000000000..3445cd189f9c5190282235c8d03c80b28e9dddd7 --- /dev/null +++ b/Query/sql/v2/runs/v2_cli_20260502_081223_d/c16/artifacts/v2q_c16_87ca32966c89fe3c/generated_sql.sql @@ -0,0 +1,21 @@ +-- sql_source_version: v2 +-- sql_source_label: v2_current +-- sql_source_run_id: v2_cli_20260502_081223_d +-- sql_source_dataset_id: c16 +-- family_id: missingness_structure +-- canonical_subitem_id: co_missingness_pattern_consistency +-- intended_facet_id: missing_target_interaction +-- variant_semantic_role: missing_target_interaction +-- template_id: tpl_missing_target_interaction +-- query_record_id: v2q_c16_87ca32966c89fe3c +-- problem_id: v2p_c16_4f03d164c8870341 +-- realization_mode: deterministic +-- source_kind: deterministic +SELECT + "ALIVE", + COUNT(*) AS total_rows, + SUM(CASE WHEN "ID" IS NULL THEN 1 ELSE 0 END) AS missing_rows, + AVG(CASE WHEN "ID" IS NULL THEN 1.0 ELSE 0.0 END) AS missing_rate +FROM "c16" +GROUP BY "ALIVE" +ORDER BY missing_rate DESC, total_rows DESC; diff --git a/Query/sql/v2/runs/v2_cli_20260502_081223_d/c16/artifacts/v2q_c16_87ca32966c89fe3c/query_results.jsonl b/Query/sql/v2/runs/v2_cli_20260502_081223_d/c16/artifacts/v2q_c16_87ca32966c89fe3c/query_results.jsonl new file mode 100644 index 0000000000000000000000000000000000000000..91e7adaf3b3c832d48424dde451ddd183b996ed9 --- /dev/null +++ b/Query/sql/v2/runs/v2_cli_20260502_081223_d/c16/artifacts/v2q_c16_87ca32966c89fe3c/query_results.jsonl @@ -0,0 +1 @@ +{"node_name": "v2_template", "tool_name": "sqlite_query", "query": "-- sql_source_version: v2\n-- sql_source_label: v2_current\n-- sql_source_run_id: v2_cli_20260502_081223_d\n-- sql_source_dataset_id: c16\n-- family_id: missingness_structure\n-- canonical_subitem_id: co_missingness_pattern_consistency\n-- intended_facet_id: missing_target_interaction\n-- variant_semantic_role: missing_target_interaction\n-- template_id: tpl_missing_target_interaction\n-- query_record_id: v2q_c16_87ca32966c89fe3c\n-- problem_id: v2p_c16_4f03d164c8870341\n-- realization_mode: deterministic\n-- source_kind: deterministic\nSELECT\n \"ALIVE\",\n COUNT(*) AS total_rows,\n SUM(CASE WHEN \"ID\" IS NULL THEN 1 ELSE 0 END) AS missing_rows,\n AVG(CASE WHEN \"ID\" IS NULL THEN 1.0 ELSE 0.0 END) AS missing_rate\nFROM \"c16\"\nGROUP BY \"ALIVE\"\nORDER BY missing_rate DESC, total_rows DESC;", "result": "{\"query\": \"-- sql_source_version: v2\\n-- sql_source_label: v2_current\\n-- sql_source_run_id: v2_cli_20260502_081223_d\\n-- sql_source_dataset_id: c16\\n-- family_id: missingness_structure\\n-- canonical_subitem_id: co_missingness_pattern_consistency\\n-- intended_facet_id: missing_target_interaction\\n-- variant_semantic_role: missing_target_interaction\\n-- template_id: tpl_missing_target_interaction\\n-- query_record_id: v2q_c16_87ca32966c89fe3c\\n-- problem_id: v2p_c16_4f03d164c8870341\\n-- realization_mode: deterministic\\n-- source_kind: deterministic\\nSELECT\\n \\\"ALIVE\\\",\\n COUNT(*) AS total_rows,\\n SUM(CASE WHEN \\\"ID\\\" IS NULL THEN 1 ELSE 0 END) AS missing_rows,\\n AVG(CASE WHEN \\\"ID\\\" IS NULL THEN 1.0 ELSE 0.0 END) AS missing_rate\\nFROM \\\"c16\\\"\\nGROUP BY \\\"ALIVE\\\"\\nORDER BY missing_rate DESC, total_rows DESC;\", \"columns\": [\"ALIVE\", \"total_rows\", \"missing_rows\", \"missing_rate\"], \"rows\": [{\"ALIVE\": \"Living Characters\", \"total_rows\": 5200, \"missing_rows\": 0, \"missing_rate\": 0.0}, {\"ALIVE\": \"Deceased Characters\", \"total_rows\": 1693, \"missing_rows\": 0, \"missing_rate\": 0.0}, {\"ALIVE\": \"\", \"total_rows\": 3, \"missing_rows\": 0, \"missing_rate\": 0.0}], \"row_count_returned\": 3, \"row_limit\": 50, \"truncated\": false, \"elapsed_ms\": 2.8}"} diff --git a/Query/sql/v2/runs/v2_cli_20260502_081223_d/c16/artifacts/v2q_c16_87ca32966c89fe3c/run_manifest.json b/Query/sql/v2/runs/v2_cli_20260502_081223_d/c16/artifacts/v2q_c16_87ca32966c89fe3c/run_manifest.json new file mode 100644 index 0000000000000000000000000000000000000000..7793eabc8454a5842e7cb169efde712d43a82fb2 --- /dev/null +++ b/Query/sql/v2/runs/v2_cli_20260502_081223_d/c16/artifacts/v2q_c16_87ca32966c89fe3c/run_manifest.json @@ -0,0 +1,59 @@ +{ + "run_id": "v2_cli_20260502_081223_d", + "dataset_id": "c16", + "started_at": "2026-05-19T16:10:30.405510+00:00", + "ended_at": "2026-05-19T16:10:30.408925+00:00", + "status": "completed", + "engine": "cli", + "question_record": { + "query_record_id": "v2q_c16_87ca32966c89fe3c", + "problem_id": "v2p_c16_4f03d164c8870341", + "dataset_id": "c16", + "template_id": "tpl_missing_target_interaction", + "template_name": "Missingness-Target Interaction", + "family_id": "missingness_structure", + "canonical_subitem_id": "co_missingness_pattern_consistency", + "intended_facet_id": "missing_target_interaction", + "variant_semantic_role": "missing_target_interaction", + "subitem_assignment_source": "template_fixed", + "source_kind": "deterministic", + "realization_mode": "deterministic", + "gate_priority": "deterministic", + "extended_family": false, + "question": "Use template Missingness-Target Interaction to probe co_missingness_pattern_consistency with semantic role missing_target_interaction. Focus on target_col=ALIVE, missing_col=ID.", + "bindings": { + "missing_col": "ID", + "target_col": "ALIVE" + }, + "binding_roles": [ + "missing_col", + "target_col" + ], + "coverage_target_min": "enumerate_all_applicable", + "runtime_sql_skeleton": "SELECT\n {target_col},\n COUNT(*) AS total_rows,\n SUM(CASE WHEN {missing_col} IS NULL THEN 1 ELSE 0 END) AS missing_rows,\n AVG(CASE WHEN {missing_col} IS NULL THEN 1.0 ELSE 0.0 END) AS missing_rate\nFROM {table}\nGROUP BY {target_col}\nORDER BY missing_rate DESC, total_rows DESC;", + "notes": [ + "default_facets=missing_rate_by_subgroup,missing_target_interaction", + "template_selection_mode=deterministic", + "problem_index_within_template=2", + "sql_variant_index=1/1" + ], + "template_selection_mode": "deterministic", + "selected_template_rank": 0, + "problem_index_within_template": 2, + "sql_variant_index": 1, + "sql_variant_total": 1 + }, + "mode": "subitem_workload_v2", + "sql_source_version": "v2", + "sql_source_label": "v2_current", + "generated_sql_path": "/data/jialinzhang/TabQueryBench/sql_workloads/v2_current/runs_and_launches/runs/v2_cli_20260502_081223_d/c16/sql/v2q_c16_87ca32966c89fe3c.sql", + "usage_summary": { + "engine": "template", + "input_tokens": 0, + "cached_input_tokens": 0, + "output_tokens": 0, + "total_tokens": 0, + "estimated_total_tokens": 0, + "usage_source": "none" + } +} \ No newline at end of file diff --git a/Query/sql/v2/runs/v2_cli_20260502_081223_d/c16/artifacts/v2q_c16_87ca32966c89fe3c/usage_summary.json b/Query/sql/v2/runs/v2_cli_20260502_081223_d/c16/artifacts/v2q_c16_87ca32966c89fe3c/usage_summary.json new file mode 100644 index 0000000000000000000000000000000000000000..96c9ff4feec395919fc26411d18d078b8af6e1c7 --- /dev/null +++ b/Query/sql/v2/runs/v2_cli_20260502_081223_d/c16/artifacts/v2q_c16_87ca32966c89fe3c/usage_summary.json @@ -0,0 +1,9 @@ +{ + "engine": "template", + "input_tokens": 0, + "cached_input_tokens": 0, + "output_tokens": 0, + "total_tokens": 0, + "estimated_total_tokens": 0, + "usage_source": "none" +} \ No newline at end of file diff --git a/Query/sql/v2/runs/v2_cli_20260502_081223_d/c16/artifacts/v2q_c16_885f2d9787f8c80a/final_answer.txt b/Query/sql/v2/runs/v2_cli_20260502_081223_d/c16/artifacts/v2q_c16_885f2d9787f8c80a/final_answer.txt new file mode 100644 index 0000000000000000000000000000000000000000..a0408c9bcc5af67548a5f506ee0d653f615d9eb0 --- /dev/null +++ b/Query/sql/v2/runs/v2_cli_20260502_081223_d/c16/artifacts/v2q_c16_885f2d9787f8c80a/final_answer.txt @@ -0,0 +1,2 @@ +SQL executed successfully for: Use template Relative-to-Total Extreme Threshold to probe tail_mass_similarity with semantic role filtered_stable_view. Focus on group_col=GSM, measure_col=YEAR. +Result preview: [{"GSM": "Homosexual Characters", "group_value": 105644}, {"GSM": "Bisexual Characters", "group_value": 19875}] \ No newline at end of file diff --git a/Query/sql/v2/runs/v2_cli_20260502_081223_d/c16/artifacts/v2q_c16_885f2d9787f8c80a/generated_sql.sql b/Query/sql/v2/runs/v2_cli_20260502_081223_d/c16/artifacts/v2q_c16_885f2d9787f8c80a/generated_sql.sql new file mode 100644 index 0000000000000000000000000000000000000000..5613baa94b9b7541059fc687a98603674029f59d --- /dev/null +++ b/Query/sql/v2/runs/v2_cli_20260502_081223_d/c16/artifacts/v2q_c16_885f2d9787f8c80a/generated_sql.sql @@ -0,0 +1,30 @@ +-- sql_source_version: v2 +-- sql_source_label: v2_current +-- sql_source_run_id: v2_cli_20260502_081223_d +-- sql_source_dataset_id: c16 +-- family_id: tail_rarity_structure +-- canonical_subitem_id: tail_mass_similarity +-- intended_facet_id: tail_ranked_signal +-- variant_semantic_role: filtered_stable_view +-- template_id: tpl_tpch_relative_total_threshold +-- query_record_id: v2q_c16_885f2d9787f8c80a +-- problem_id: v2p_c16_374146d46ba620a3 +-- realization_mode: agent +-- source_kind: agent +WITH grouped AS ( + SELECT "GSM", SUM(CAST("YEAR" AS INTEGER)) AS group_value + FROM "c16" + WHERE "GSM" IS NOT NULL + AND "GSM" <> '' + AND "YEAR" IS NOT NULL + AND "YEAR" <> '' + GROUP BY "GSM" +), total AS ( + SELECT SUM(group_value) AS total_value + FROM grouped +) +SELECT g."GSM", g.group_value +FROM grouped AS g +CROSS JOIN total AS t +WHERE g.group_value > t.total_value * 0.05 +ORDER BY g.group_value DESC; diff --git a/Query/sql/v2/runs/v2_cli_20260502_081223_d/c16/artifacts/v2q_c16_885f2d9787f8c80a/query_results.jsonl b/Query/sql/v2/runs/v2_cli_20260502_081223_d/c16/artifacts/v2q_c16_885f2d9787f8c80a/query_results.jsonl new file mode 100644 index 0000000000000000000000000000000000000000..13312c2947235c83cb9ea81facfb740c81ee2126 --- /dev/null +++ b/Query/sql/v2/runs/v2_cli_20260502_081223_d/c16/artifacts/v2q_c16_885f2d9787f8c80a/query_results.jsonl @@ -0,0 +1 @@ +{"step_index": 1, "message_index": 0, "node_name": "v2-cli:codex", "tool_name": "sqlite_query", "query": "-- template_id: tpl_tpch_relative_total_threshold\nWITH grouped AS (\n SELECT \"GSM\", SUM(CAST(\"YEAR\" AS INTEGER)) AS group_value\n FROM \"c16\"\n WHERE \"GSM\" IS NOT NULL\n AND \"GSM\" <> ''\n AND \"YEAR\" IS NOT NULL\n AND \"YEAR\" <> ''\n GROUP BY \"GSM\"\n), total AS (\n SELECT SUM(group_value) AS total_value\n FROM grouped\n)\nSELECT g.\"GSM\", g.group_value\nFROM grouped AS g\nCROSS JOIN total AS t\nWHERE g.group_value > t.total_value * 0.05\nORDER BY g.group_value DESC;", "result": "{\"query\": \"-- template_id: tpl_tpch_relative_total_threshold\\nWITH grouped AS (\\n SELECT \\\"GSM\\\", SUM(CAST(\\\"YEAR\\\" AS INTEGER)) AS group_value\\n FROM \\\"c16\\\"\\n WHERE \\\"GSM\\\" IS NOT NULL\\n AND \\\"GSM\\\" <> ''\\n AND \\\"YEAR\\\" IS NOT NULL\\n AND \\\"YEAR\\\" <> ''\\n GROUP BY \\\"GSM\\\"\\n), total AS (\\n SELECT SUM(group_value) AS total_value\\n FROM grouped\\n)\\nSELECT g.\\\"GSM\\\", g.group_value\\nFROM grouped AS g\\nCROSS JOIN total AS t\\nWHERE g.group_value > t.total_value * 0.05\\nORDER BY g.group_value DESC;\", \"columns\": [\"GSM\", \"group_value\"], \"rows\": [{\"GSM\": \"Homosexual Characters\", \"group_value\": 105644}, {\"GSM\": \"Bisexual Characters\", \"group_value\": 19875}], \"row_count_returned\": 2, \"row_limit\": 50, \"truncated\": false, \"elapsed_ms\": 2.86}"} diff --git a/Query/sql/v2/runs/v2_cli_20260502_081223_d/c16/artifacts/v2q_c16_885f2d9787f8c80a/run_manifest.json b/Query/sql/v2/runs/v2_cli_20260502_081223_d/c16/artifacts/v2q_c16_885f2d9787f8c80a/run_manifest.json new file mode 100644 index 0000000000000000000000000000000000000000..a7f69ece5f70e2ec401f93017b8f1b41ca184558 --- /dev/null +++ b/Query/sql/v2/runs/v2_cli_20260502_081223_d/c16/artifacts/v2q_c16_885f2d9787f8c80a/run_manifest.json @@ -0,0 +1,89 @@ +{ + "run_id": "v2_cli_20260502_081223_d", + "dataset_id": "c16", + "started_at": "2026-05-19T15:46:48.687689+00:00", + "ended_at": "2026-05-19T15:47:03.137730+00:00", + "status": "completed", + "engine": "cli", + "question_record": { + "query_record_id": "v2q_c16_885f2d9787f8c80a", + "problem_id": "v2p_c16_374146d46ba620a3", + "dataset_id": "c16", + "template_id": "tpl_tpch_relative_total_threshold", + "template_name": "Relative-to-Total Extreme Threshold", + "family_id": "tail_rarity_structure", + "canonical_subitem_id": "tail_mass_similarity", + "intended_facet_id": "tail_ranked_signal", + "variant_semantic_role": "filtered_stable_view", + "subitem_assignment_source": "planner_selected", + "source_kind": "agent", + "realization_mode": "agent", + "gate_priority": "primary", + "extended_family": false, + "question": "Use template Relative-to-Total Extreme Threshold to probe tail_mass_similarity with semantic role filtered_stable_view. Focus on group_col=GSM, measure_col=YEAR.", + "bindings": { + "group_col": "GSM", + "measure_col": "YEAR", + "top_k": 19, + "top_n": 6, + "num_tiles": 10, + "percentile_value": 0.9, + "z_threshold": 2.0, + "fraction_threshold": 0.05, + "baseline_multiplier": 1.75, + "baseline_fraction": 0.1, + "min_group_size": 5, + "min_support": 4, + "measure_threshold": 1998.0, + "time_grain": "month", + "lookback_rows": 3, + "current_period_start": "'2024-01-01'", + "current_period_end": "'2024-04-01'", + "previous_period_start": "'2023-10-01'", + "previous_period_end": "'2024-01-01'", + "drift_ratio_threshold": 0.8 + }, + "binding_roles": [ + "group_col", + "measure_col" + ], + "coverage_target_min": "5", + "runtime_sql_skeleton": "WITH grouped AS (\n SELECT {group_col}, SUM({measure_col}) AS group_value\n FROM {table}\n GROUP BY {group_col}\n), total AS (\n SELECT SUM(group_value) AS total_value\n FROM grouped\n)\nSELECT g.{group_col}, g.group_value\nFROM grouped AS g\nCROSS JOIN total AS t\nWHERE g.group_value > t.total_value * {fraction_threshold}\nORDER BY g.group_value DESC;", + "notes": [ + "default_facets=tail_ranked_signal", + "template_selection_mode=rule", + "problem_index_within_template=3", + "sql_variant_index=2/2", + "binding_index=74" + ], + "template_selection_mode": "rule", + "selected_template_rank": 7, + "problem_index_within_template": 3, + "sql_variant_index": 2, + "sql_variant_total": 2 + }, + "mode": "subitem_workload_v2", + "sql_source_version": "v2", + "sql_source_label": "v2_current", + "generated_sql_path": "/data/jialinzhang/TabQueryBench/sql_workloads/v2_current/runs_and_launches/runs/v2_cli_20260502_081223_d/c16/sql/v2q_c16_885f2d9787f8c80a.sql", + "usage_summary": { + "dataset_id": "c16", + "model": "v2-cli:codex", + "run_id": "v2q_c16_885f2d9787f8c80a", + "api_calls": 0, + "input_tokens": 14689, + "cached_input_tokens": 13696, + "output_tokens": 708, + "total_tokens": 15397, + "cost_usd": 0.0, + "ai_cli_calls": 1, + "estimated_input_tokens": 0, + "estimated_output_tokens": 0, + "estimated_total_tokens": 0, + "usage_source": "ai_cli_json_usage", + "cli_elapsed_ms_total": 14440.45, + "sql_execution_elapsed_ms_total": 2.86, + "conversation_log_path": "/data/jialinzhang/TabQueryBench/sql_workloads/v2_current/runs_and_launches/runs/v2_cli_20260502_081223_d/c16/artifacts/v2q_c16_885f2d9787f8c80a/cli/conversation.jsonl", + "note": "Executed through a local AI CLI with structured usage metadata." + } +} \ No newline at end of file diff --git a/Query/sql/v2/runs/v2_cli_20260502_081223_d/c16/artifacts/v2q_c16_885f2d9787f8c80a/trace.jsonl b/Query/sql/v2/runs/v2_cli_20260502_081223_d/c16/artifacts/v2q_c16_885f2d9787f8c80a/trace.jsonl new file mode 100644 index 0000000000000000000000000000000000000000..4b8afbacfb5d1adf2cad9f8844172a958cac2c61 --- /dev/null +++ b/Query/sql/v2/runs/v2_cli_20260502_081223_d/c16/artifacts/v2q_c16_885f2d9787f8c80a/trace.jsonl @@ -0,0 +1 @@ +{"timestamp": "2026-05-19T15:47:03.132139+00:00", "event_type": "ai_cli_sql_generation", "engine": "v2-cli:codex", "attempt": 1, "command": "codex exec --skip-git-repo-check --disable plugins --sandbox read-only --cd \"/data/jialinzhang/SQLagent\" -m gpt-5.4 --json -", "returncode": 0, "elapsed_ms": 14440.45, "started_at": "2026-05-19T15:46:48.690168+00:00", "ended_at": "2026-05-19T15:47:03.130666+00:00", "prompt_metrics": {"chars": 9028, "bytes_utf8": 9028, "lines": 252, "estimated_tokens": null}, "response_metrics": {"chars": 761, "bytes_utf8": 761, "lines": 1, "estimated_tokens": null}, "usage": {"input_tokens": 14689, "cached_input_tokens": 13696, "output_tokens": 708, "reasoning_output_tokens": 496}, "stderr_preview": "", "stdout_preview": "{\"sql\":\"-- template_id: tpl_tpch_relative_total_threshold\\nWITH grouped AS (\\n SELECT \\\"GSM\\\", SUM(CAST(\\\"YEAR\\\" AS INTEGER)) AS group_value\\n FROM \\\"c16\\\"\\n WHERE \\\"GSM\\\" IS NOT NULL\\n AND \\\"GSM\\\" <> ''\\n AND \\\"YEAR\\\" IS NOT NULL\\n AND \\\"YEAR\\\" <> ''\\n GROUP BY \\\"GSM\\\"\\n), total AS (\\n SELECT SUM(group_value) AS total_value\\n FROM grouped\\n)\\nSELECT g.\\\"GSM\\\", g.group_value\\nFROM grouped AS g\\nCROSS JOIN total AS t\\nWHERE g.group_value > t.total_value * 0.05\\nORDER BY g.group_value DESC;\",\"notes\":\"Uses the planned relative-to-total threshold template with group_col=\\\"GSM\\\" and measure_col=\\\"YEAR\\\". YEAR is cast to INTEGER for aggregation, and null/empty GSM and YEAR values are filtered out for a stable grouped view.\"}"} diff --git a/Query/sql/v2/runs/v2_cli_20260502_081223_d/c16/artifacts/v2q_c16_885f2d9787f8c80a/usage_summary.json b/Query/sql/v2/runs/v2_cli_20260502_081223_d/c16/artifacts/v2q_c16_885f2d9787f8c80a/usage_summary.json new file mode 100644 index 0000000000000000000000000000000000000000..50ea9d03faebb46265a7db8feeb4a957f0d7642d --- /dev/null +++ b/Query/sql/v2/runs/v2_cli_20260502_081223_d/c16/artifacts/v2q_c16_885f2d9787f8c80a/usage_summary.json @@ -0,0 +1,20 @@ +{ + "dataset_id": "c16", + "model": "v2-cli:codex", + "run_id": "v2q_c16_885f2d9787f8c80a", + "api_calls": 0, + "input_tokens": 14689, + "cached_input_tokens": 13696, + "output_tokens": 708, + "total_tokens": 15397, + "cost_usd": 0.0, + "ai_cli_calls": 1, + "estimated_input_tokens": 0, + "estimated_output_tokens": 0, + "estimated_total_tokens": 0, + "usage_source": "ai_cli_json_usage", + "cli_elapsed_ms_total": 14440.45, + "sql_execution_elapsed_ms_total": 2.86, + "conversation_log_path": "/data/jialinzhang/TabQueryBench/sql_workloads/v2_current/runs_and_launches/runs/v2_cli_20260502_081223_d/c16/artifacts/v2q_c16_885f2d9787f8c80a/cli/conversation.jsonl", + "note": "Executed through a local AI CLI with structured usage metadata." +} \ No newline at end of file diff --git a/Query/sql/v2/runs/v2_cli_20260502_081223_d/c16/artifacts/v2q_c16_8877869618b52198/final_answer.txt b/Query/sql/v2/runs/v2_cli_20260502_081223_d/c16/artifacts/v2q_c16_8877869618b52198/final_answer.txt new file mode 100644 index 0000000000000000000000000000000000000000..75efdd2435a3be851318a561192983035b441cf1 --- /dev/null +++ b/Query/sql/v2/runs/v2_cli_20260502_081223_d/c16/artifacts/v2q_c16_8877869618b52198/final_answer.txt @@ -0,0 +1,2 @@ +SQL executed successfully for: Use template Low-Support Group Count to probe tail_set_consistency with semantic role rare_extreme_view. Focus on group_col=ALIGN. +Result preview: [{"ALIGN": "Reformed Criminals", "support": 3}, {"ALIGN": "Neutral Characters", "support": 565}, {"ALIGN": "", "support": 601}, {"ALIGN": "Good Characters", "support": 2832}, {"ALIGN": "Bad Characters", "support": 2895}] \ No newline at end of file diff --git a/Query/sql/v2/runs/v2_cli_20260502_081223_d/c16/artifacts/v2q_c16_8877869618b52198/generated_sql.sql b/Query/sql/v2/runs/v2_cli_20260502_081223_d/c16/artifacts/v2q_c16_8877869618b52198/generated_sql.sql new file mode 100644 index 0000000000000000000000000000000000000000..fb82e282b03a863caa335797b78f9c0ba3c1d343 --- /dev/null +++ b/Query/sql/v2/runs/v2_cli_20260502_081223_d/c16/artifacts/v2q_c16_8877869618b52198/generated_sql.sql @@ -0,0 +1,20 @@ +-- sql_source_version: v2 +-- sql_source_label: v2_current +-- sql_source_run_id: v2_cli_20260502_081223_d +-- sql_source_dataset_id: c16 +-- family_id: tail_rarity_structure +-- canonical_subitem_id: tail_set_consistency +-- intended_facet_id: low_support_extremes +-- variant_semantic_role: rare_extreme_view +-- template_id: tpl_tail_low_support_group_count_v2 +-- query_record_id: v2q_c16_8877869618b52198 +-- problem_id: v2p_c16_bad884dade33e07b +-- realization_mode: agent +-- source_kind: agent +SELECT + "ALIGN", + COUNT(*) AS support +FROM "c16" +GROUP BY "ALIGN" +ORDER BY support ASC, "ALIGN" +LIMIT 11; diff --git a/Query/sql/v2/runs/v2_cli_20260502_081223_d/c16/artifacts/v2q_c16_8877869618b52198/query_results.jsonl b/Query/sql/v2/runs/v2_cli_20260502_081223_d/c16/artifacts/v2q_c16_8877869618b52198/query_results.jsonl new file mode 100644 index 0000000000000000000000000000000000000000..1309792e65d76221c42465066c7a4bf965696a53 --- /dev/null +++ b/Query/sql/v2/runs/v2_cli_20260502_081223_d/c16/artifacts/v2q_c16_8877869618b52198/query_results.jsonl @@ -0,0 +1 @@ +{"step_index": 1, "message_index": 0, "node_name": "v2-cli:codex", "tool_name": "sqlite_query", "query": "-- template_id: tpl_tail_low_support_group_count_v2\nSELECT\n \"ALIGN\",\n COUNT(*) AS support\nFROM \"c16\"\nGROUP BY \"ALIGN\"\nORDER BY support ASC, \"ALIGN\"\nLIMIT 11;", "result": "{\"query\": \"-- template_id: tpl_tail_low_support_group_count_v2\\nSELECT\\n \\\"ALIGN\\\",\\n COUNT(*) AS support\\nFROM \\\"c16\\\"\\nGROUP BY \\\"ALIGN\\\"\\nORDER BY support ASC, \\\"ALIGN\\\"\\nLIMIT 11;\", \"columns\": [\"ALIGN\", \"support\"], \"rows\": [{\"ALIGN\": \"Reformed Criminals\", \"support\": 3}, {\"ALIGN\": \"Neutral Characters\", \"support\": 565}, {\"ALIGN\": \"\", \"support\": 601}, {\"ALIGN\": \"Good Characters\", \"support\": 2832}, {\"ALIGN\": \"Bad Characters\", \"support\": 2895}], \"row_count_returned\": 5, \"row_limit\": 50, \"truncated\": false, \"elapsed_ms\": 2.96}"} diff --git a/Query/sql/v2/runs/v2_cli_20260502_081223_d/c16/artifacts/v2q_c16_8877869618b52198/run_manifest.json b/Query/sql/v2/runs/v2_cli_20260502_081223_d/c16/artifacts/v2q_c16_8877869618b52198/run_manifest.json new file mode 100644 index 0000000000000000000000000000000000000000..df764f1b2e31a744ef955ef0959560a56cd0a05c --- /dev/null +++ b/Query/sql/v2/runs/v2_cli_20260502_081223_d/c16/artifacts/v2q_c16_8877869618b52198/run_manifest.json @@ -0,0 +1,87 @@ +{ + "run_id": "v2_cli_20260502_081223_d", + "dataset_id": "c16", + "started_at": "2026-05-19T16:07:50.477012+00:00", + "ended_at": "2026-05-19T16:07:58.173912+00:00", + "status": "completed", + "engine": "cli", + "question_record": { + "query_record_id": "v2q_c16_8877869618b52198", + "problem_id": "v2p_c16_bad884dade33e07b", + "dataset_id": "c16", + "template_id": "tpl_tail_low_support_group_count_v2", + "template_name": "Low-Support Group Count", + "family_id": "tail_rarity_structure", + "canonical_subitem_id": "tail_set_consistency", + "intended_facet_id": "low_support_extremes", + "variant_semantic_role": "rare_extreme_view", + "subitem_assignment_source": "planner_selected", + "source_kind": "agent", + "realization_mode": "agent", + "gate_priority": "primary", + "extended_family": false, + "question": "Use template Low-Support Group Count to probe tail_set_consistency with semantic role rare_extreme_view. Focus on group_col=ALIGN.", + "bindings": { + "group_col": "ALIGN", + "top_k": 11, + "top_n": 5, + "num_tiles": 10, + "percentile_value": 0.95, + "z_threshold": 2.0, + "fraction_threshold": 0.1, + "baseline_multiplier": 1.5, + "baseline_fraction": 0.1, + "min_group_size": 5, + "min_support": 5, + "measure_threshold": 213203.0, + "time_grain": "month", + "lookback_rows": 3, + "current_period_start": "'2024-01-01'", + "current_period_end": "'2024-04-01'", + "previous_period_start": "'2023-10-01'", + "previous_period_end": "'2024-01-01'", + "drift_ratio_threshold": 0.8 + }, + "binding_roles": [ + "group_col" + ], + "coverage_target_min": "5", + "runtime_sql_skeleton": "SELECT\n {group_col},\n COUNT(*) AS support\nFROM {table}\nGROUP BY {group_col}\nORDER BY support ASC, {group_col}\nLIMIT {top_k};", + "notes": [ + "default_facets=low_support_extremes", + "template_selection_mode=rule", + "problem_index_within_template=7", + "sql_variant_index=1/2", + "binding_index=126" + ], + "template_selection_mode": "rule", + "selected_template_rank": 11, + "problem_index_within_template": 7, + "sql_variant_index": 1, + "sql_variant_total": 2 + }, + "mode": "subitem_workload_v2", + "sql_source_version": "v2", + "sql_source_label": "v2_current", + "generated_sql_path": "/data/jialinzhang/TabQueryBench/sql_workloads/v2_current/runs_and_launches/runs/v2_cli_20260502_081223_d/c16/sql/v2q_c16_8877869618b52198.sql", + "usage_summary": { + "dataset_id": "c16", + "model": "v2-cli:codex", + "run_id": "v2q_c16_8877869618b52198", + "api_calls": 0, + "input_tokens": 14562, + "cached_input_tokens": 12032, + "output_tokens": 229, + "total_tokens": 14791, + "cost_usd": 0.0, + "ai_cli_calls": 1, + "estimated_input_tokens": 0, + "estimated_output_tokens": 0, + "estimated_total_tokens": 0, + "usage_source": "ai_cli_json_usage", + "cli_elapsed_ms_total": 7690.42, + "sql_execution_elapsed_ms_total": 2.96, + "conversation_log_path": "/data/jialinzhang/TabQueryBench/sql_workloads/v2_current/runs_and_launches/runs/v2_cli_20260502_081223_d/c16/artifacts/v2q_c16_8877869618b52198/cli/conversation.jsonl", + "note": "Executed through a local AI CLI with structured usage metadata." + } +} \ No newline at end of file diff --git a/Query/sql/v2/runs/v2_cli_20260502_081223_d/c16/artifacts/v2q_c16_8877869618b52198/trace.jsonl b/Query/sql/v2/runs/v2_cli_20260502_081223_d/c16/artifacts/v2q_c16_8877869618b52198/trace.jsonl new file mode 100644 index 0000000000000000000000000000000000000000..9963ff6c62fbdb9fb7a4553b5080e2699aba4f71 --- /dev/null +++ b/Query/sql/v2/runs/v2_cli_20260502_081223_d/c16/artifacts/v2q_c16_8877869618b52198/trace.jsonl @@ -0,0 +1 @@ +{"timestamp": "2026-05-19T16:07:58.169545+00:00", "event_type": "ai_cli_sql_generation", "engine": "v2-cli:codex", "attempt": 1, "command": "codex exec --skip-git-repo-check --disable plugins --sandbox read-only --cd \"/data/jialinzhang/SQLagent\" -m gpt-5.4 --json -", "returncode": 0, "elapsed_ms": 7690.42, "started_at": "2026-05-19T16:07:50.478322+00:00", "ended_at": "2026-05-19T16:07:58.168764+00:00", "prompt_metrics": {"chars": 8474, "bytes_utf8": 8474, "lines": 250, "estimated_tokens": null}, "response_metrics": {"chars": 288, "bytes_utf8": 288, "lines": 1, "estimated_tokens": null}, "usage": {"input_tokens": 14562, "cached_input_tokens": 12032, "output_tokens": 229, "reasoning_output_tokens": 143}, "stderr_preview": "", "stdout_preview": "{\"sql\":\"-- template_id: tpl_tail_low_support_group_count_v2\\nSELECT\\n \\\"ALIGN\\\",\\n COUNT(*) AS support\\nFROM \\\"c16\\\"\\nGROUP BY \\\"ALIGN\\\"\\nORDER BY support ASC, \\\"ALIGN\\\"\\nLIMIT 11;\",\"notes\":\"Uses the Low-Support Group Count template with group_col bound to \\\"ALIGN\\\" and top_k=11.\"}"} diff --git a/Query/sql/v2/runs/v2_cli_20260502_081223_d/c16/artifacts/v2q_c16_8877869618b52198/usage_summary.json b/Query/sql/v2/runs/v2_cli_20260502_081223_d/c16/artifacts/v2q_c16_8877869618b52198/usage_summary.json new file mode 100644 index 0000000000000000000000000000000000000000..92efc03978b5c4e54d335b1ad041e154f3bbed9b --- /dev/null +++ b/Query/sql/v2/runs/v2_cli_20260502_081223_d/c16/artifacts/v2q_c16_8877869618b52198/usage_summary.json @@ -0,0 +1,20 @@ +{ + "dataset_id": "c16", + "model": "v2-cli:codex", + "run_id": "v2q_c16_8877869618b52198", + "api_calls": 0, + "input_tokens": 14562, + "cached_input_tokens": 12032, + "output_tokens": 229, + "total_tokens": 14791, + "cost_usd": 0.0, + "ai_cli_calls": 1, + "estimated_input_tokens": 0, + "estimated_output_tokens": 0, + "estimated_total_tokens": 0, + "usage_source": "ai_cli_json_usage", + "cli_elapsed_ms_total": 7690.42, + "sql_execution_elapsed_ms_total": 2.96, + "conversation_log_path": "/data/jialinzhang/TabQueryBench/sql_workloads/v2_current/runs_and_launches/runs/v2_cli_20260502_081223_d/c16/artifacts/v2q_c16_8877869618b52198/cli/conversation.jsonl", + "note": "Executed through a local AI CLI with structured usage metadata." +} \ No newline at end of file diff --git a/Query/sql/v2/runs/v2_cli_20260502_081223_d/c16/artifacts/v2q_c16_8b64240a8d9755d1/final_answer.txt b/Query/sql/v2/runs/v2_cli_20260502_081223_d/c16/artifacts/v2q_c16_8b64240a8d9755d1/final_answer.txt new file mode 100644 index 0000000000000000000000000000000000000000..658e7bbad432447f553c3cbad14c920a8af63b32 --- /dev/null +++ b/Query/sql/v2/runs/v2_cli_20260502_081223_d/c16/artifacts/v2q_c16_8b64240a8d9755d1/final_answer.txt @@ -0,0 +1 @@ +{"row_count": null, "preview_rows": [{"EYE": "", "total_rows": 3628, "missing_rows": 0, "missing_rate": 0.0}, {"EYE": "Blue Eyes", "total_rows": 1102, "missing_rows": 0, "missing_rate": 0.0}, {"EYE": "Brown Eyes", "total_rows": 879, "missing_rows": 0, "missing_rate": 0.0}, {"EYE": "Black Eyes", "total_rows": 412, "missing_rows": 0, "missing_rate": 0.0}, {"EYE": "Green Eyes", "total_rows": 291, "missing_rows": 0, "missing_rate": 0.0}]} \ No newline at end of file diff --git a/Query/sql/v2/runs/v2_cli_20260502_081223_d/c16/artifacts/v2q_c16_8b64240a8d9755d1/generated_sql.sql b/Query/sql/v2/runs/v2_cli_20260502_081223_d/c16/artifacts/v2q_c16_8b64240a8d9755d1/generated_sql.sql new file mode 100644 index 0000000000000000000000000000000000000000..064308a49e41026aae15c127b237969088119653 --- /dev/null +++ b/Query/sql/v2/runs/v2_cli_20260502_081223_d/c16/artifacts/v2q_c16_8b64240a8d9755d1/generated_sql.sql @@ -0,0 +1,21 @@ +-- sql_source_version: v2 +-- sql_source_label: v2_current +-- sql_source_run_id: v2_cli_20260502_081223_d +-- sql_source_dataset_id: c16 +-- family_id: missingness_structure +-- canonical_subitem_id: co_missingness_pattern_consistency +-- intended_facet_id: missing_target_interaction +-- variant_semantic_role: missing_target_interaction +-- template_id: tpl_missing_target_interaction +-- query_record_id: v2q_c16_8b64240a8d9755d1 +-- problem_id: v2p_c16_0e8f93410a665d4a +-- realization_mode: deterministic +-- source_kind: deterministic +SELECT + "EYE", + COUNT(*) AS total_rows, + SUM(CASE WHEN "ALIVE" IS NULL THEN 1 ELSE 0 END) AS missing_rows, + AVG(CASE WHEN "ALIVE" IS NULL THEN 1.0 ELSE 0.0 END) AS missing_rate +FROM "c16" +GROUP BY "EYE" +ORDER BY missing_rate DESC, total_rows DESC; diff --git a/Query/sql/v2/runs/v2_cli_20260502_081223_d/c16/artifacts/v2q_c16_8b64240a8d9755d1/query_results.jsonl b/Query/sql/v2/runs/v2_cli_20260502_081223_d/c16/artifacts/v2q_c16_8b64240a8d9755d1/query_results.jsonl new file mode 100644 index 0000000000000000000000000000000000000000..669c4487226115561d8b97e632fee7c7b0047526 --- /dev/null +++ b/Query/sql/v2/runs/v2_cli_20260502_081223_d/c16/artifacts/v2q_c16_8b64240a8d9755d1/query_results.jsonl @@ -0,0 +1 @@ +{"node_name": "v2_template", "tool_name": "sqlite_query", "query": "-- sql_source_version: v2\n-- sql_source_label: v2_current\n-- sql_source_run_id: v2_cli_20260502_081223_d\n-- sql_source_dataset_id: c16\n-- family_id: missingness_structure\n-- canonical_subitem_id: co_missingness_pattern_consistency\n-- intended_facet_id: missing_target_interaction\n-- variant_semantic_role: missing_target_interaction\n-- template_id: tpl_missing_target_interaction\n-- query_record_id: v2q_c16_8b64240a8d9755d1\n-- problem_id: v2p_c16_0e8f93410a665d4a\n-- realization_mode: deterministic\n-- source_kind: deterministic\nSELECT\n \"EYE\",\n COUNT(*) AS total_rows,\n SUM(CASE WHEN \"ALIVE\" IS NULL THEN 1 ELSE 0 END) AS missing_rows,\n AVG(CASE WHEN \"ALIVE\" IS NULL THEN 1.0 ELSE 0.0 END) AS missing_rate\nFROM \"c16\"\nGROUP BY \"EYE\"\nORDER BY missing_rate DESC, total_rows DESC;", "result": "{\"query\": \"-- sql_source_version: v2\\n-- sql_source_label: v2_current\\n-- sql_source_run_id: v2_cli_20260502_081223_d\\n-- sql_source_dataset_id: c16\\n-- family_id: missingness_structure\\n-- canonical_subitem_id: co_missingness_pattern_consistency\\n-- intended_facet_id: missing_target_interaction\\n-- variant_semantic_role: missing_target_interaction\\n-- template_id: tpl_missing_target_interaction\\n-- query_record_id: v2q_c16_8b64240a8d9755d1\\n-- problem_id: v2p_c16_0e8f93410a665d4a\\n-- realization_mode: deterministic\\n-- source_kind: deterministic\\nSELECT\\n \\\"EYE\\\",\\n COUNT(*) AS total_rows,\\n SUM(CASE WHEN \\\"ALIVE\\\" IS NULL THEN 1 ELSE 0 END) AS missing_rows,\\n AVG(CASE WHEN \\\"ALIVE\\\" IS NULL THEN 1.0 ELSE 0.0 END) AS missing_rate\\nFROM \\\"c16\\\"\\nGROUP BY \\\"EYE\\\"\\nORDER BY missing_rate DESC, total_rows DESC;\", \"columns\": [\"EYE\", \"total_rows\", \"missing_rows\", \"missing_rate\"], \"rows\": [{\"EYE\": \"\", \"total_rows\": 3628, \"missing_rows\": 0, \"missing_rate\": 0.0}, {\"EYE\": \"Blue Eyes\", \"total_rows\": 1102, \"missing_rows\": 0, \"missing_rate\": 0.0}, {\"EYE\": \"Brown Eyes\", \"total_rows\": 879, \"missing_rows\": 0, \"missing_rate\": 0.0}, {\"EYE\": \"Black Eyes\", \"total_rows\": 412, \"missing_rows\": 0, \"missing_rate\": 0.0}, {\"EYE\": \"Green Eyes\", \"total_rows\": 291, \"missing_rows\": 0, \"missing_rate\": 0.0}, {\"EYE\": \"Red Eyes\", \"total_rows\": 208, \"missing_rows\": 0, \"missing_rate\": 0.0}, {\"EYE\": \"White Eyes\", \"total_rows\": 116, \"missing_rows\": 0, \"missing_rate\": 0.0}, {\"EYE\": \"Yellow Eyes\", \"total_rows\": 86, \"missing_rows\": 0, \"missing_rate\": 0.0}, {\"EYE\": \"Photocellular Eyes\", \"total_rows\": 48, \"missing_rows\": 0, \"missing_rate\": 0.0}, {\"EYE\": \"Grey Eyes\", \"total_rows\": 40, \"missing_rows\": 0, \"missing_rate\": 0.0}, {\"EYE\": \"Hazel Eyes\", \"total_rows\": 23, \"missing_rows\": 0, \"missing_rate\": 0.0}, {\"EYE\": \"Purple Eyes\", \"total_rows\": 14, \"missing_rows\": 0, \"missing_rate\": 0.0}, {\"EYE\": \"Violet Eyes\", \"total_rows\": 12, \"missing_rows\": 0, \"missing_rate\": 0.0}, {\"EYE\": \"Orange Eyes\", \"total_rows\": 10, \"missing_rows\": 0, \"missing_rate\": 0.0}, {\"EYE\": \"Gold Eyes\", \"total_rows\": 9, \"missing_rows\": 0, \"missing_rate\": 0.0}, {\"EYE\": \"Auburn Hair\", \"total_rows\": 7, \"missing_rows\": 0, \"missing_rate\": 0.0}, {\"EYE\": \"Pink Eyes\", \"total_rows\": 6, \"missing_rows\": 0, \"missing_rate\": 0.0}, {\"EYE\": \"Amber Eyes\", \"total_rows\": 5, \"missing_rows\": 0, \"missing_rate\": 0.0}], \"row_count_returned\": 18, \"row_limit\": 50, \"truncated\": false, \"elapsed_ms\": 3.28}"} diff --git a/Query/sql/v2/runs/v2_cli_20260502_081223_d/c16/artifacts/v2q_c16_8b64240a8d9755d1/run_manifest.json b/Query/sql/v2/runs/v2_cli_20260502_081223_d/c16/artifacts/v2q_c16_8b64240a8d9755d1/run_manifest.json new file mode 100644 index 0000000000000000000000000000000000000000..058ebcc801404c81973978aaf1acd4c6cbcd08b5 --- /dev/null +++ b/Query/sql/v2/runs/v2_cli_20260502_081223_d/c16/artifacts/v2q_c16_8b64240a8d9755d1/run_manifest.json @@ -0,0 +1,59 @@ +{ + "run_id": "v2_cli_20260502_081223_d", + "dataset_id": "c16", + "started_at": "2026-05-19T16:10:30.429193+00:00", + "ended_at": "2026-05-19T16:10:30.433139+00:00", + "status": "completed", + "engine": "cli", + "question_record": { + "query_record_id": "v2q_c16_8b64240a8d9755d1", + "problem_id": "v2p_c16_0e8f93410a665d4a", + "dataset_id": "c16", + "template_id": "tpl_missing_target_interaction", + "template_name": "Missingness-Target Interaction", + "family_id": "missingness_structure", + "canonical_subitem_id": "co_missingness_pattern_consistency", + "intended_facet_id": "missing_target_interaction", + "variant_semantic_role": "missing_target_interaction", + "subitem_assignment_source": "template_fixed", + "source_kind": "deterministic", + "realization_mode": "deterministic", + "gate_priority": "deterministic", + "extended_family": false, + "question": "Use template Missingness-Target Interaction to probe co_missingness_pattern_consistency with semantic role missing_target_interaction. Focus on target_col=EYE, missing_col=ALIVE.", + "bindings": { + "missing_col": "ALIVE", + "target_col": "EYE" + }, + "binding_roles": [ + "missing_col", + "target_col" + ], + "coverage_target_min": "enumerate_all_applicable", + "runtime_sql_skeleton": "SELECT\n {target_col},\n COUNT(*) AS total_rows,\n SUM(CASE WHEN {missing_col} IS NULL THEN 1 ELSE 0 END) AS missing_rows,\n AVG(CASE WHEN {missing_col} IS NULL THEN 1.0 ELSE 0.0 END) AS missing_rate\nFROM {table}\nGROUP BY {target_col}\nORDER BY missing_rate DESC, total_rows DESC;", + "notes": [ + "default_facets=missing_rate_by_subgroup,missing_target_interaction", + "template_selection_mode=deterministic", + "problem_index_within_template=8", + "sql_variant_index=1/1" + ], + "template_selection_mode": "deterministic", + "selected_template_rank": 0, + "problem_index_within_template": 8, + "sql_variant_index": 1, + "sql_variant_total": 1 + }, + "mode": "subitem_workload_v2", + "sql_source_version": "v2", + "sql_source_label": "v2_current", + "generated_sql_path": "/data/jialinzhang/TabQueryBench/sql_workloads/v2_current/runs_and_launches/runs/v2_cli_20260502_081223_d/c16/sql/v2q_c16_8b64240a8d9755d1.sql", + "usage_summary": { + "engine": "template", + "input_tokens": 0, + "cached_input_tokens": 0, + "output_tokens": 0, + "total_tokens": 0, + "estimated_total_tokens": 0, + "usage_source": "none" + } +} \ No newline at end of file diff --git a/Query/sql/v2/runs/v2_cli_20260502_081223_d/c16/artifacts/v2q_c16_8b64240a8d9755d1/usage_summary.json b/Query/sql/v2/runs/v2_cli_20260502_081223_d/c16/artifacts/v2q_c16_8b64240a8d9755d1/usage_summary.json new file mode 100644 index 0000000000000000000000000000000000000000..96c9ff4feec395919fc26411d18d078b8af6e1c7 --- /dev/null +++ b/Query/sql/v2/runs/v2_cli_20260502_081223_d/c16/artifacts/v2q_c16_8b64240a8d9755d1/usage_summary.json @@ -0,0 +1,9 @@ +{ + "engine": "template", + "input_tokens": 0, + "cached_input_tokens": 0, + "output_tokens": 0, + "total_tokens": 0, + "estimated_total_tokens": 0, + "usage_source": "none" +} \ No newline at end of file diff --git a/Query/sql/v2/runs/v2_cli_20260502_081223_d/c16/artifacts/v2q_c16_8b79b00c87ec0d7b/final_answer.txt b/Query/sql/v2/runs/v2_cli_20260502_081223_d/c16/artifacts/v2q_c16_8b79b00c87ec0d7b/final_answer.txt new file mode 100644 index 0000000000000000000000000000000000000000..4161bee944b98adfd63c37a56f0c1985ab67dda7 --- /dev/null +++ b/Query/sql/v2/runs/v2_cli_20260502_081223_d/c16/artifacts/v2q_c16_8b79b00c87ec0d7b/final_answer.txt @@ -0,0 +1 @@ +No rows were returned for: Use template Relative-to-Total Extreme Threshold to probe tail_mass_similarity with semantic role count_distribution. Focus on group_col=YEAR, measure_col=APPEARANCES. \ No newline at end of file diff --git a/Query/sql/v2/runs/v2_cli_20260502_081223_d/c16/artifacts/v2q_c16_8b79b00c87ec0d7b/generated_sql.sql b/Query/sql/v2/runs/v2_cli_20260502_081223_d/c16/artifacts/v2q_c16_8b79b00c87ec0d7b/generated_sql.sql new file mode 100644 index 0000000000000000000000000000000000000000..a7a68737e251c311665cf57f13c12629798d0139 --- /dev/null +++ b/Query/sql/v2/runs/v2_cli_20260502_081223_d/c16/artifacts/v2q_c16_8b79b00c87ec0d7b/generated_sql.sql @@ -0,0 +1,27 @@ +-- sql_source_version: v2 +-- sql_source_label: v2_current +-- sql_source_run_id: v2_cli_20260502_081223_d +-- sql_source_dataset_id: c16 +-- family_id: tail_rarity_structure +-- canonical_subitem_id: tail_mass_similarity +-- intended_facet_id: tail_ranked_signal +-- variant_semantic_role: count_distribution +-- template_id: tpl_tpch_relative_total_threshold +-- query_record_id: v2q_c16_8b79b00c87ec0d7b +-- problem_id: v2p_c16_0fe3f5d31ceff292 +-- realization_mode: agent +-- source_kind: agent +WITH grouped AS ( + SELECT "YEAR", SUM(CAST(NULLIF("APPEARANCES", '') AS REAL)) AS "group_value" + FROM "c16" + WHERE NULLIF("YEAR", '') IS NOT NULL + GROUP BY "YEAR" +), total AS ( + SELECT SUM("group_value") AS "total_value" + FROM grouped +) +SELECT g."YEAR", g."group_value" +FROM grouped AS g +CROSS JOIN total AS t +WHERE g."group_value" > t."total_value" * 0.1 +ORDER BY g."group_value" DESC; diff --git a/Query/sql/v2/runs/v2_cli_20260502_081223_d/c16/artifacts/v2q_c16_8b79b00c87ec0d7b/query_results.jsonl b/Query/sql/v2/runs/v2_cli_20260502_081223_d/c16/artifacts/v2q_c16_8b79b00c87ec0d7b/query_results.jsonl new file mode 100644 index 0000000000000000000000000000000000000000..c33d56b93afbf71d1233d5e1fe62fc0d55d35575 --- /dev/null +++ b/Query/sql/v2/runs/v2_cli_20260502_081223_d/c16/artifacts/v2q_c16_8b79b00c87ec0d7b/query_results.jsonl @@ -0,0 +1 @@ +{"step_index": 1, "message_index": 0, "node_name": "v2-cli:codex", "tool_name": "sqlite_query", "query": "-- template_id: tpl_tpch_relative_total_threshold\nWITH grouped AS (\n SELECT \"YEAR\", SUM(CAST(NULLIF(\"APPEARANCES\", '') AS REAL)) AS \"group_value\"\n FROM \"c16\"\n WHERE NULLIF(\"YEAR\", '') IS NOT NULL\n GROUP BY \"YEAR\"\n), total AS (\n SELECT SUM(\"group_value\") AS \"total_value\"\n FROM grouped\n)\nSELECT g.\"YEAR\", g.\"group_value\"\nFROM grouped AS g\nCROSS JOIN total AS t\nWHERE g.\"group_value\" > t.\"total_value\" * 0.1\nORDER BY g.\"group_value\" DESC;", "result": "{\"query\": \"-- template_id: tpl_tpch_relative_total_threshold\\nWITH grouped AS (\\n SELECT \\\"YEAR\\\", SUM(CAST(NULLIF(\\\"APPEARANCES\\\", '') AS REAL)) AS \\\"group_value\\\"\\n FROM \\\"c16\\\"\\n WHERE NULLIF(\\\"YEAR\\\", '') IS NOT NULL\\n GROUP BY \\\"YEAR\\\"\\n), total AS (\\n SELECT SUM(\\\"group_value\\\") AS \\\"total_value\\\"\\n FROM grouped\\n)\\nSELECT g.\\\"YEAR\\\", g.\\\"group_value\\\"\\nFROM grouped AS g\\nCROSS JOIN total AS t\\nWHERE g.\\\"group_value\\\" > t.\\\"total_value\\\" * 0.1\\nORDER BY g.\\\"group_value\\\" DESC;\", \"columns\": [\"YEAR\", \"group_value\"], \"rows\": [], \"row_count_returned\": 0, \"row_limit\": 50, \"truncated\": false, \"elapsed_ms\": 5.59}"} diff --git a/Query/sql/v2/runs/v2_cli_20260502_081223_d/c16/artifacts/v2q_c16_8b79b00c87ec0d7b/run_manifest.json b/Query/sql/v2/runs/v2_cli_20260502_081223_d/c16/artifacts/v2q_c16_8b79b00c87ec0d7b/run_manifest.json new file mode 100644 index 0000000000000000000000000000000000000000..f6cdfab20e19dc8a227e2e10bbda7cd9abf6e8dd --- /dev/null +++ b/Query/sql/v2/runs/v2_cli_20260502_081223_d/c16/artifacts/v2q_c16_8b79b00c87ec0d7b/run_manifest.json @@ -0,0 +1,89 @@ +{ + "run_id": "v2_cli_20260502_081223_d", + "dataset_id": "c16", + "started_at": "2026-05-19T15:47:42.880673+00:00", + "ended_at": "2026-05-19T15:47:56.293270+00:00", + "status": "completed", + "engine": "cli", + "question_record": { + "query_record_id": "v2q_c16_8b79b00c87ec0d7b", + "problem_id": "v2p_c16_0fe3f5d31ceff292", + "dataset_id": "c16", + "template_id": "tpl_tpch_relative_total_threshold", + "template_name": "Relative-to-Total Extreme Threshold", + "family_id": "tail_rarity_structure", + "canonical_subitem_id": "tail_mass_similarity", + "intended_facet_id": "tail_ranked_signal", + "variant_semantic_role": "count_distribution", + "subitem_assignment_source": "planner_selected", + "source_kind": "agent", + "realization_mode": "agent", + "gate_priority": "primary", + "extended_family": false, + "question": "Use template Relative-to-Total Extreme Threshold to probe tail_mass_similarity with semantic role count_distribution. Focus on group_col=YEAR, measure_col=APPEARANCES.", + "bindings": { + "group_col": "YEAR", + "measure_col": "APPEARANCES", + "top_k": 11, + "top_n": 3, + "num_tiles": 10, + "percentile_value": 0.95, + "z_threshold": 2.0, + "fraction_threshold": 0.1, + "baseline_multiplier": 1.5, + "baseline_fraction": 0.1, + "min_group_size": 5, + "min_support": 5, + "measure_threshold": 15.0, + "time_grain": "month", + "lookback_rows": 3, + "current_period_start": "'2024-01-01'", + "current_period_end": "'2024-04-01'", + "previous_period_start": "'2023-10-01'", + "previous_period_end": "'2024-01-01'", + "drift_ratio_threshold": 0.8 + }, + "binding_roles": [ + "group_col", + "measure_col" + ], + "coverage_target_min": "5", + "runtime_sql_skeleton": "WITH grouped AS (\n SELECT {group_col}, SUM({measure_col}) AS group_value\n FROM {table}\n GROUP BY {group_col}\n), total AS (\n SELECT SUM(group_value) AS total_value\n FROM grouped\n)\nSELECT g.{group_col}, g.group_value\nFROM grouped AS g\nCROSS JOIN total AS t\nWHERE g.group_value > t.total_value * {fraction_threshold}\nORDER BY g.group_value DESC;", + "notes": [ + "default_facets=tail_ranked_signal", + "template_selection_mode=rule", + "problem_index_within_template=5", + "sql_variant_index=1/2", + "binding_index=76" + ], + "template_selection_mode": "rule", + "selected_template_rank": 7, + "problem_index_within_template": 5, + "sql_variant_index": 1, + "sql_variant_total": 2 + }, + "mode": "subitem_workload_v2", + "sql_source_version": "v2", + "sql_source_label": "v2_current", + "generated_sql_path": "/data/jialinzhang/TabQueryBench/sql_workloads/v2_current/runs_and_launches/runs/v2_cli_20260502_081223_d/c16/sql/v2q_c16_8b79b00c87ec0d7b.sql", + "usage_summary": { + "dataset_id": "c16", + "model": "v2-cli:codex", + "run_id": "v2q_c16_8b79b00c87ec0d7b", + "api_calls": 0, + "input_tokens": 14690, + "cached_input_tokens": 13696, + "output_tokens": 722, + "total_tokens": 15412, + "cost_usd": 0.0, + "ai_cli_calls": 1, + "estimated_input_tokens": 0, + "estimated_output_tokens": 0, + "estimated_total_tokens": 0, + "usage_source": "ai_cli_json_usage", + "cli_elapsed_ms_total": 13401.81, + "sql_execution_elapsed_ms_total": 5.59, + "conversation_log_path": "/data/jialinzhang/TabQueryBench/sql_workloads/v2_current/runs_and_launches/runs/v2_cli_20260502_081223_d/c16/artifacts/v2q_c16_8b79b00c87ec0d7b/cli/conversation.jsonl", + "note": "Executed through a local AI CLI with structured usage metadata." + } +} \ No newline at end of file diff --git a/Query/sql/v2/runs/v2_cli_20260502_081223_d/c16/artifacts/v2q_c16_8b79b00c87ec0d7b/trace.jsonl b/Query/sql/v2/runs/v2_cli_20260502_081223_d/c16/artifacts/v2q_c16_8b79b00c87ec0d7b/trace.jsonl new file mode 100644 index 0000000000000000000000000000000000000000..41e3abed9fb0ac318c80de093e9b7b3bb1c14ab4 --- /dev/null +++ b/Query/sql/v2/runs/v2_cli_20260502_081223_d/c16/artifacts/v2q_c16_8b79b00c87ec0d7b/trace.jsonl @@ -0,0 +1 @@ +{"timestamp": "2026-05-19T15:47:56.285615+00:00", "event_type": "ai_cli_sql_generation", "engine": "v2-cli:codex", "attempt": 1, "command": "codex exec --skip-git-repo-check --disable plugins --sandbox read-only --cd \"/data/jialinzhang/SQLagent\" -m gpt-5.4 --json -", "returncode": 0, "elapsed_ms": 13401.81, "started_at": "2026-05-19T15:47:42.882544+00:00", "ended_at": "2026-05-19T15:47:56.284399+00:00", "prompt_metrics": {"chars": 9039, "bytes_utf8": 9039, "lines": 252, "estimated_tokens": null}, "response_metrics": {"chars": 739, "bytes_utf8": 739, "lines": 1, "estimated_tokens": null}, "usage": {"input_tokens": 14690, "cached_input_tokens": 13696, "output_tokens": 722, "reasoning_output_tokens": 516}, "stderr_preview": "", "stdout_preview": "{\"sql\":\"-- template_id: tpl_tpch_relative_total_threshold\\nWITH grouped AS (\\n SELECT \\\"YEAR\\\", SUM(CAST(NULLIF(\\\"APPEARANCES\\\", '') AS REAL)) AS \\\"group_value\\\"\\n FROM \\\"c16\\\"\\n WHERE NULLIF(\\\"YEAR\\\", '') IS NOT NULL\\n GROUP BY \\\"YEAR\\\"\\n), total AS (\\n SELECT SUM(\\\"group_value\\\") AS \\\"total_value\\\"\\n FROM grouped\\n)\\nSELECT g.\\\"YEAR\\\", g.\\\"group_value\\\"\\nFROM grouped AS g\\nCROSS JOIN total AS t\\nWHERE g.\\\"group_value\\\" > t.\\\"total_value\\\" * 0.1\\nORDER BY g.\\\"group_value\\\" DESC;\",\"notes\":\"Applied the planned relative-to-total threshold template with group_col=\\\"YEAR\\\" and measure_col=\\\"APPEARANCES\\\". \\\"APPEARANCES\\\" is stored as TEXT in the snapshot, so the query casts non-empty values to REAL before summing.\"}"} diff --git a/Query/sql/v2/runs/v2_cli_20260502_081223_d/c16/artifacts/v2q_c16_8b79b00c87ec0d7b/usage_summary.json b/Query/sql/v2/runs/v2_cli_20260502_081223_d/c16/artifacts/v2q_c16_8b79b00c87ec0d7b/usage_summary.json new file mode 100644 index 0000000000000000000000000000000000000000..e0567cefee8a8c40d8fe7e50c976080617f199f5 --- /dev/null +++ b/Query/sql/v2/runs/v2_cli_20260502_081223_d/c16/artifacts/v2q_c16_8b79b00c87ec0d7b/usage_summary.json @@ -0,0 +1,20 @@ +{ + "dataset_id": "c16", + "model": "v2-cli:codex", + "run_id": "v2q_c16_8b79b00c87ec0d7b", + "api_calls": 0, + "input_tokens": 14690, + "cached_input_tokens": 13696, + "output_tokens": 722, + "total_tokens": 15412, + "cost_usd": 0.0, + "ai_cli_calls": 1, + "estimated_input_tokens": 0, + "estimated_output_tokens": 0, + "estimated_total_tokens": 0, + "usage_source": "ai_cli_json_usage", + "cli_elapsed_ms_total": 13401.81, + "sql_execution_elapsed_ms_total": 5.59, + "conversation_log_path": "/data/jialinzhang/TabQueryBench/sql_workloads/v2_current/runs_and_launches/runs/v2_cli_20260502_081223_d/c16/artifacts/v2q_c16_8b79b00c87ec0d7b/cli/conversation.jsonl", + "note": "Executed through a local AI CLI with structured usage metadata." +} \ No newline at end of file diff --git a/Query/sql/v2/runs/v2_cli_20260502_081223_d/c16/artifacts/v2q_c16_9eaa6b1188cd150b/final_answer.txt b/Query/sql/v2/runs/v2_cli_20260502_081223_d/c16/artifacts/v2q_c16_9eaa6b1188cd150b/final_answer.txt new file mode 100644 index 0000000000000000000000000000000000000000..63361faf1cea7b81d1a7b44d8178367b4f287c40 --- /dev/null +++ b/Query/sql/v2/runs/v2_cli_20260502_081223_d/c16/artifacts/v2q_c16_9eaa6b1188cd150b/final_answer.txt @@ -0,0 +1,2 @@ +SQL executed successfully for: Use template Grouped Ratio of Two Conditions to probe direction_consistency with semantic role contrastive_conditional_view. Focus on group_col=ALIVE, condition_col=GSM. +Result preview: [{"ALIVE": "Deceased Characters", "condition_ratio": 337.4}, {"ALIVE": "Living Characters", "condition_ratio": 107.14583333333333}, {"ALIVE": "", "condition_ratio": 2.0}] \ No newline at end of file diff --git a/Query/sql/v2/runs/v2_cli_20260502_081223_d/c16/artifacts/v2q_c16_9eaa6b1188cd150b/generated_sql.sql b/Query/sql/v2/runs/v2_cli_20260502_081223_d/c16/artifacts/v2q_c16_9eaa6b1188cd150b/generated_sql.sql new file mode 100644 index 0000000000000000000000000000000000000000..b97211e4bb0588c9054258954ab32d5acaff61dc --- /dev/null +++ b/Query/sql/v2/runs/v2_cli_20260502_081223_d/c16/artifacts/v2q_c16_9eaa6b1188cd150b/generated_sql.sql @@ -0,0 +1,24 @@ +-- sql_source_version: v2 +-- sql_source_label: v2_current +-- sql_source_run_id: v2_cli_20260502_081223_d +-- sql_source_dataset_id: c16 +-- family_id: conditional_dependency_structure +-- canonical_subitem_id: direction_consistency +-- intended_facet_id: conditional_rate_shift +-- variant_semantic_role: contrastive_conditional_view +-- template_id: tpl_m4_group_ratio_two_conditions +-- query_record_id: v2q_c16_9eaa6b1188cd150b +-- problem_id: v2p_c16_fc23db917ca55442 +-- realization_mode: agent +-- source_kind: agent +WITH grouped AS ( + SELECT "ALIVE", + SUM(CASE WHEN "GSM" = '' THEN 1 ELSE 0 END) AS numerator_count, + SUM(CASE WHEN "GSM" = 'Homosexual Characters' THEN 1 ELSE 0 END) AS denominator_count + FROM "c16" + GROUP BY "ALIVE" +) +SELECT "ALIVE", + CAST(numerator_count AS FLOAT) / NULLIF(denominator_count, 0) AS condition_ratio +FROM grouped +ORDER BY condition_ratio DESC; diff --git a/Query/sql/v2/runs/v2_cli_20260502_081223_d/c16/artifacts/v2q_c16_9eaa6b1188cd150b/query_results.jsonl b/Query/sql/v2/runs/v2_cli_20260502_081223_d/c16/artifacts/v2q_c16_9eaa6b1188cd150b/query_results.jsonl new file mode 100644 index 0000000000000000000000000000000000000000..751685b92188b5fd64fd0db223efd2da7514c7b3 --- /dev/null +++ b/Query/sql/v2/runs/v2_cli_20260502_081223_d/c16/artifacts/v2q_c16_9eaa6b1188cd150b/query_results.jsonl @@ -0,0 +1 @@ +{"step_index": 1, "message_index": 0, "node_name": "v2-cli:codex", "tool_name": "sqlite_query", "query": "-- template_id: tpl_m4_group_ratio_two_conditions\nWITH grouped AS (\n SELECT \"ALIVE\",\n SUM(CASE WHEN \"GSM\" = '' THEN 1 ELSE 0 END) AS numerator_count,\n SUM(CASE WHEN \"GSM\" = 'Homosexual Characters' THEN 1 ELSE 0 END) AS denominator_count\n FROM \"c16\"\n GROUP BY \"ALIVE\"\n)\nSELECT \"ALIVE\",\n CAST(numerator_count AS FLOAT) / NULLIF(denominator_count, 0) AS condition_ratio\nFROM grouped\nORDER BY condition_ratio DESC;", "result": "{\"query\": \"-- template_id: tpl_m4_group_ratio_two_conditions\\nWITH grouped AS (\\n SELECT \\\"ALIVE\\\",\\n SUM(CASE WHEN \\\"GSM\\\" = '' THEN 1 ELSE 0 END) AS numerator_count,\\n SUM(CASE WHEN \\\"GSM\\\" = 'Homosexual Characters' THEN 1 ELSE 0 END) AS denominator_count\\n FROM \\\"c16\\\"\\n GROUP BY \\\"ALIVE\\\"\\n)\\nSELECT \\\"ALIVE\\\",\\n CAST(numerator_count AS FLOAT) / NULLIF(denominator_count, 0) AS condition_ratio\\nFROM grouped\\nORDER BY condition_ratio DESC;\", \"columns\": [\"ALIVE\", \"condition_ratio\"], \"rows\": [{\"ALIVE\": \"Deceased Characters\", \"condition_ratio\": 337.4}, {\"ALIVE\": \"Living Characters\", \"condition_ratio\": 107.14583333333333}, {\"ALIVE\": \"\", \"condition_ratio\": 2.0}], \"row_count_returned\": 3, \"row_limit\": 50, \"truncated\": false, \"elapsed_ms\": 4.68}"} diff --git a/Query/sql/v2/runs/v2_cli_20260502_081223_d/c16/artifacts/v2q_c16_9eaa6b1188cd150b/run_manifest.json b/Query/sql/v2/runs/v2_cli_20260502_081223_d/c16/artifacts/v2q_c16_9eaa6b1188cd150b/run_manifest.json new file mode 100644 index 0000000000000000000000000000000000000000..ee9069d7d30e388ed2d148f8c1d20d083ad96db7 --- /dev/null +++ b/Query/sql/v2/runs/v2_cli_20260502_081223_d/c16/artifacts/v2q_c16_9eaa6b1188cd150b/run_manifest.json @@ -0,0 +1,92 @@ +{ + "run_id": "v2_cli_20260502_081223_d", + "dataset_id": "c16", + "started_at": "2026-05-19T15:39:55.177942+00:00", + "ended_at": "2026-05-19T15:40:07.148856+00:00", + "status": "completed", + "engine": "cli", + "question_record": { + "query_record_id": "v2q_c16_9eaa6b1188cd150b", + "problem_id": "v2p_c16_fc23db917ca55442", + "dataset_id": "c16", + "template_id": "tpl_m4_group_ratio_two_conditions", + "template_name": "Grouped Ratio of Two Conditions", + "family_id": "conditional_dependency_structure", + "canonical_subitem_id": "direction_consistency", + "intended_facet_id": "conditional_rate_shift", + "variant_semantic_role": "contrastive_conditional_view", + "subitem_assignment_source": "planner_selected", + "source_kind": "agent", + "realization_mode": "agent", + "gate_priority": "primary", + "extended_family": false, + "question": "Use template Grouped Ratio of Two Conditions to probe direction_consistency with semantic role contrastive_conditional_view. Focus on group_col=ALIVE, condition_col=GSM.", + "bindings": { + "group_col": "ALIVE", + "condition_col": "GSM", + "condition_value": "", + "positive_value": "", + "negative_value": "Homosexual Characters", + "top_k": 10, + "top_n": 3, + "num_tiles": 10, + "percentile_value": 0.95, + "z_threshold": 2.0, + "fraction_threshold": 0.1, + "baseline_multiplier": 1.5, + "baseline_fraction": 0.1, + "min_group_size": 5, + "min_support": 5, + "measure_threshold": 15.0, + "time_grain": "month", + "lookback_rows": 3, + "current_period_start": "'2024-01-01'", + "current_period_end": "'2024-04-01'", + "previous_period_start": "'2023-10-01'", + "previous_period_end": "'2024-01-01'", + "drift_ratio_threshold": 0.8 + }, + "binding_roles": [ + "group_col", + "condition_col" + ], + "coverage_target_min": "5", + "runtime_sql_skeleton": "WITH grouped AS (\n SELECT {group_col},\n SUM(CASE WHEN {condition_col} = {positive_value} THEN 1 ELSE 0 END) AS numerator_count,\n SUM(CASE WHEN {condition_col} = {negative_value} THEN 1 ELSE 0 END) AS denominator_count\n FROM {table}\n GROUP BY {group_col}\n)\nSELECT {group_col},\n CAST(numerator_count AS FLOAT) / NULLIF(denominator_count, 0) AS condition_ratio\nFROM grouped\nORDER BY condition_ratio DESC;", + "notes": [ + "default_facets=conditional_rate_shift", + "template_selection_mode=rule", + "problem_index_within_template=5", + "sql_variant_index=1/1", + "binding_index=40" + ], + "template_selection_mode": "rule", + "selected_template_rank": 4, + "problem_index_within_template": 5, + "sql_variant_index": 1, + "sql_variant_total": 1 + }, + "mode": "subitem_workload_v2", + "sql_source_version": "v2", + "sql_source_label": "v2_current", + "generated_sql_path": "/data/jialinzhang/TabQueryBench/sql_workloads/v2_current/runs_and_launches/runs/v2_cli_20260502_081223_d/c16/sql/v2q_c16_9eaa6b1188cd150b.sql", + "usage_summary": { + "dataset_id": "c16", + "model": "v2-cli:codex", + "run_id": "v2q_c16_9eaa6b1188cd150b", + "api_calls": 0, + "input_tokens": 14759, + "cached_input_tokens": 13696, + "output_tokens": 576, + "total_tokens": 15335, + "cost_usd": 0.0, + "ai_cli_calls": 1, + "estimated_input_tokens": 0, + "estimated_output_tokens": 0, + "estimated_total_tokens": 0, + "usage_source": "ai_cli_json_usage", + "cli_elapsed_ms_total": 11961.96, + "sql_execution_elapsed_ms_total": 4.68, + "conversation_log_path": "/data/jialinzhang/TabQueryBench/sql_workloads/v2_current/runs_and_launches/runs/v2_cli_20260502_081223_d/c16/artifacts/v2q_c16_9eaa6b1188cd150b/cli/conversation.jsonl", + "note": "Executed through a local AI CLI with structured usage metadata." + } +} \ No newline at end of file diff --git a/Query/sql/v2/runs/v2_cli_20260502_081223_d/c16/artifacts/v2q_c16_9eaa6b1188cd150b/trace.jsonl b/Query/sql/v2/runs/v2_cli_20260502_081223_d/c16/artifacts/v2q_c16_9eaa6b1188cd150b/trace.jsonl new file mode 100644 index 0000000000000000000000000000000000000000..df1faa67ce3eea9db43c1b58a6b0ec522f9cb3f0 --- /dev/null +++ b/Query/sql/v2/runs/v2_cli_20260502_081223_d/c16/artifacts/v2q_c16_9eaa6b1188cd150b/trace.jsonl @@ -0,0 +1 @@ +{"timestamp": "2026-05-19T15:40:07.142251+00:00", "event_type": "ai_cli_sql_generation", "engine": "v2-cli:codex", "attempt": 1, "command": "codex exec --skip-git-repo-check --disable plugins --sandbox read-only --cd \"/data/jialinzhang/SQLagent\" -m gpt-5.4 --json -", "returncode": 0, "elapsed_ms": 11961.96, "started_at": "2026-05-19T15:39:55.179265+00:00", "ended_at": "2026-05-19T15:40:07.141254+00:00", "prompt_metrics": {"chars": 9295, "bytes_utf8": 9295, "lines": 255, "estimated_tokens": null}, "response_metrics": {"chars": 698, "bytes_utf8": 698, "lines": 1, "estimated_tokens": null}, "usage": {"input_tokens": 14759, "cached_input_tokens": 13696, "output_tokens": 576, "reasoning_output_tokens": 389}, "stderr_preview": "", "stdout_preview": "{\"sql\":\"-- template_id: tpl_m4_group_ratio_two_conditions\\nWITH grouped AS (\\n SELECT \\\"ALIVE\\\",\\n SUM(CASE WHEN \\\"GSM\\\" = '' THEN 1 ELSE 0 END) AS numerator_count,\\n SUM(CASE WHEN \\\"GSM\\\" = 'Homosexual Characters' THEN 1 ELSE 0 END) AS denominator_count\\n FROM \\\"c16\\\"\\n GROUP BY \\\"ALIVE\\\"\\n)\\nSELECT \\\"ALIVE\\\",\\n CAST(numerator_count AS FLOAT) / NULLIF(denominator_count, 0) AS condition_ratio\\nFROM grouped\\nORDER BY condition_ratio DESC;\",\"notes\":\"Computes the grouped ratio for group_col=\\\"ALIVE\\\" and condition_col=\\\"GSM\\\", using the bound positive_value as the empty string and negative_value as 'Homosexual Characters'. NULLIF prevents division-by-zero.\"}"} diff --git a/Query/sql/v2/runs/v2_cli_20260502_081223_d/c16/artifacts/v2q_c16_9eaa6b1188cd150b/usage_summary.json b/Query/sql/v2/runs/v2_cli_20260502_081223_d/c16/artifacts/v2q_c16_9eaa6b1188cd150b/usage_summary.json new file mode 100644 index 0000000000000000000000000000000000000000..55a1a9b22891d350f6b2f3f8380bb84013cffc2c --- /dev/null +++ b/Query/sql/v2/runs/v2_cli_20260502_081223_d/c16/artifacts/v2q_c16_9eaa6b1188cd150b/usage_summary.json @@ -0,0 +1,20 @@ +{ + "dataset_id": "c16", + "model": "v2-cli:codex", + "run_id": "v2q_c16_9eaa6b1188cd150b", + "api_calls": 0, + "input_tokens": 14759, + "cached_input_tokens": 13696, + "output_tokens": 576, + "total_tokens": 15335, + "cost_usd": 0.0, + "ai_cli_calls": 1, + "estimated_input_tokens": 0, + "estimated_output_tokens": 0, + "estimated_total_tokens": 0, + "usage_source": "ai_cli_json_usage", + "cli_elapsed_ms_total": 11961.96, + "sql_execution_elapsed_ms_total": 4.68, + "conversation_log_path": "/data/jialinzhang/TabQueryBench/sql_workloads/v2_current/runs_and_launches/runs/v2_cli_20260502_081223_d/c16/artifacts/v2q_c16_9eaa6b1188cd150b/cli/conversation.jsonl", + "note": "Executed through a local AI CLI with structured usage metadata." +} \ No newline at end of file diff --git a/Query/sql/v2/runs/v2_cli_20260502_081223_d/c16/artifacts/v2q_c16_a8f824bd83c590bb/final_answer.txt b/Query/sql/v2/runs/v2_cli_20260502_081223_d/c16/artifacts/v2q_c16_a8f824bd83c590bb/final_answer.txt new file mode 100644 index 0000000000000000000000000000000000000000..f2b8c37aed267baaa50ade3e3a8e80618f877fbb --- /dev/null +++ b/Query/sql/v2/runs/v2_cli_20260502_081223_d/c16/artifacts/v2q_c16_a8f824bd83c590bb/final_answer.txt @@ -0,0 +1,2 @@ +SQL executed successfully for: Use template Grouped Condition Rate to probe direction_consistency with semantic role focused_target_view. Focus on group_col=YEAR, condition_col=ID. +Result preview: [{"YEAR": "1950", "condition_rate": 0.6666666666666666}, {"YEAR": "1944", "condition_rate": 0.6666666666666666}, {"YEAR": "1969", "condition_rate": 0.6521739130434783}, {"YEAR": "1959", "condition_rate": 0.6176470588235294}, {"YEAR": "1951", "condition_rate": 0.5833333333333334}] Results were truncated. \ No newline at end of file diff --git a/Query/sql/v2/runs/v2_cli_20260502_081223_d/c16/artifacts/v2q_c16_a8f824bd83c590bb/generated_sql.sql b/Query/sql/v2/runs/v2_cli_20260502_081223_d/c16/artifacts/v2q_c16_a8f824bd83c590bb/generated_sql.sql new file mode 100644 index 0000000000000000000000000000000000000000..bf0020a409a7e0b994f9e97f5b9c8a2289768670 --- /dev/null +++ b/Query/sql/v2/runs/v2_cli_20260502_081223_d/c16/artifacts/v2q_c16_a8f824bd83c590bb/generated_sql.sql @@ -0,0 +1,21 @@ +-- sql_source_version: v2 +-- sql_source_label: v2_current +-- sql_source_run_id: v2_cli_20260502_081223_d +-- sql_source_dataset_id: c16 +-- family_id: conditional_dependency_structure +-- canonical_subitem_id: direction_consistency +-- intended_facet_id: conditional_rate_shift +-- variant_semantic_role: focused_target_view +-- template_id: tpl_m4_group_condition_rate +-- query_record_id: v2q_c16_a8f824bd83c590bb +-- problem_id: v2p_c16_256ac39bb46557a9 +-- realization_mode: agent +-- source_kind: agent +SELECT + "YEAR", + AVG(CASE WHEN "ID" = 'Public Identity' THEN 1.0 ELSE 0.0 END) AS "condition_rate" +FROM "c16" +WHERE "YEAR" IS NOT NULL AND "YEAR" <> '' +GROUP BY "YEAR" +HAVING COUNT(*) >= 5 +ORDER BY "condition_rate" DESC; diff --git a/Query/sql/v2/runs/v2_cli_20260502_081223_d/c16/artifacts/v2q_c16_a8f824bd83c590bb/query_results.jsonl b/Query/sql/v2/runs/v2_cli_20260502_081223_d/c16/artifacts/v2q_c16_a8f824bd83c590bb/query_results.jsonl new file mode 100644 index 0000000000000000000000000000000000000000..342f1a11d9ab086d0113027389668e99ddaf732f --- /dev/null +++ b/Query/sql/v2/runs/v2_cli_20260502_081223_d/c16/artifacts/v2q_c16_a8f824bd83c590bb/query_results.jsonl @@ -0,0 +1 @@ +{"step_index": 1, "message_index": 0, "node_name": "v2-cli:codex", "tool_name": "sqlite_query", "query": "-- template_id: tpl_m4_group_condition_rate\nSELECT\n \"YEAR\",\n AVG(CASE WHEN \"ID\" = 'Public Identity' THEN 1.0 ELSE 0.0 END) AS \"condition_rate\"\nFROM \"c16\"\nWHERE \"YEAR\" IS NOT NULL AND \"YEAR\" <> ''\nGROUP BY \"YEAR\"\nHAVING COUNT(*) >= 5\nORDER BY \"condition_rate\" DESC;", "result": "{\"query\": \"-- template_id: tpl_m4_group_condition_rate\\nSELECT\\n \\\"YEAR\\\",\\n AVG(CASE WHEN \\\"ID\\\" = 'Public Identity' THEN 1.0 ELSE 0.0 END) AS \\\"condition_rate\\\"\\nFROM \\\"c16\\\"\\nWHERE \\\"YEAR\\\" IS NOT NULL AND \\\"YEAR\\\" <> ''\\nGROUP BY \\\"YEAR\\\"\\nHAVING COUNT(*) >= 5\\nORDER BY \\\"condition_rate\\\" DESC;\", \"columns\": [\"YEAR\", \"condition_rate\"], \"rows\": [{\"YEAR\": \"1950\", \"condition_rate\": 0.6666666666666666}, {\"YEAR\": \"1944\", \"condition_rate\": 0.6666666666666666}, {\"YEAR\": \"1969\", \"condition_rate\": 0.6521739130434783}, {\"YEAR\": \"1959\", \"condition_rate\": 0.6176470588235294}, {\"YEAR\": \"1951\", \"condition_rate\": 0.5833333333333334}, {\"YEAR\": \"1945\", \"condition_rate\": 0.5714285714285714}, {\"YEAR\": \"1936\", \"condition_rate\": 0.5555555555555556}, {\"YEAR\": \"1961\", \"condition_rate\": 0.54}, {\"YEAR\": \"1957\", \"condition_rate\": 0.5384615384615384}, {\"YEAR\": \"1972\", \"condition_rate\": 0.5245901639344263}, {\"YEAR\": \"1960\", \"condition_rate\": 0.48717948717948717}, {\"YEAR\": \"1942\", \"condition_rate\": 0.46153846153846156}, {\"YEAR\": \"1985\", \"condition_rate\": 0.4608695652173913}, {\"YEAR\": \"1990\", \"condition_rate\": 0.45714285714285713}, {\"YEAR\": \"2003\", \"condition_rate\": 0.44660194174757284}, {\"YEAR\": \"1946\", \"condition_rate\": 0.4444444444444444}, {\"YEAR\": \"1975\", \"condition_rate\": 0.4358974358974359}, {\"YEAR\": \"1988\", \"condition_rate\": 0.43356643356643354}, {\"YEAR\": \"1989\", \"condition_rate\": 0.4323308270676692}, {\"YEAR\": \"1986\", \"condition_rate\": 0.4318181818181818}, {\"YEAR\": \"2004\", \"condition_rate\": 0.43137254901960786}, {\"YEAR\": \"1970\", \"condition_rate\": 0.42857142857142855}, {\"YEAR\": \"1954\", \"condition_rate\": 0.42857142857142855}, {\"YEAR\": \"1996\", \"condition_rate\": 0.425531914893617}, {\"YEAR\": \"1963\", \"condition_rate\": 0.425}, {\"YEAR\": \"1997\", \"condition_rate\": 0.42328042328042326}, {\"YEAR\": \"1987\", \"condition_rate\": 0.421259842519685}, {\"YEAR\": \"1955\", \"condition_rate\": 0.4166666666666667}, {\"YEAR\": \"1992\", \"condition_rate\": 0.4044943820224719}, {\"YEAR\": \"2012\", \"condition_rate\": 0.4}, {\"YEAR\": \"1973\", \"condition_rate\": 0.4}, {\"YEAR\": \"1952\", \"condition_rate\": 0.4}, {\"YEAR\": \"1938\", \"condition_rate\": 0.4}, {\"YEAR\": \"1995\", \"condition_rate\": 0.3953488372093023}, {\"YEAR\": \"1964\", \"condition_rate\": 0.3870967741935484}, {\"YEAR\": \"1941\", \"condition_rate\": 0.3770491803278688}, {\"YEAR\": \"1971\", \"condition_rate\": 0.36923076923076925}, {\"YEAR\": \"1991\", \"condition_rate\": 0.36551724137931035}, {\"YEAR\": \"1999\", \"condition_rate\": 0.35195530726256985}, {\"YEAR\": \"1979\", \"condition_rate\": 0.3448275862068966}, {\"YEAR\": \"1940\", \"condition_rate\": 0.34375}, {\"YEAR\": \"2001\", \"condition_rate\": 0.3434343434343434}, {\"YEAR\": \"1965\", \"condition_rate\": 0.34}, {\"YEAR\": \"2006\", \"condition_rate\": 0.33993399339933994}, {\"YEAR\": \"1967\", \"condition_rate\": 0.3392857142857143}, {\"YEAR\": \"1978\", \"condition_rate\": 0.3333333333333333}, {\"YEAR\": \"1958\", \"condition_rate\": 0.3333333333333333}, {\"YEAR\": \"1949\", \"condition_rate\": 0.3333333333333333}, {\"YEAR\": \"2002\", \"condition_rate\": 0.33043478260869563}, {\"YEAR\": \"1983\", \"condition_rate\": 0.32919254658385094}], \"row_count_returned\": 50, \"row_limit\": 50, \"truncated\": true, \"elapsed_ms\": 7.04}"} diff --git a/Query/sql/v2/runs/v2_cli_20260502_081223_d/c16/artifacts/v2q_c16_a8f824bd83c590bb/run_manifest.json b/Query/sql/v2/runs/v2_cli_20260502_081223_d/c16/artifacts/v2q_c16_a8f824bd83c590bb/run_manifest.json new file mode 100644 index 0000000000000000000000000000000000000000..ffa9b1a5b0c8bee44b315f3e42fd780264b431bd --- /dev/null +++ b/Query/sql/v2/runs/v2_cli_20260502_081223_d/c16/artifacts/v2q_c16_a8f824bd83c590bb/run_manifest.json @@ -0,0 +1,92 @@ +{ + "run_id": "v2_cli_20260502_081223_d", + "dataset_id": "c16", + "started_at": "2026-05-19T16:01:37.544561+00:00", + "ended_at": "2026-05-19T16:01:52.604684+00:00", + "status": "completed", + "engine": "cli", + "question_record": { + "query_record_id": "v2q_c16_a8f824bd83c590bb", + "problem_id": "v2p_c16_256ac39bb46557a9", + "dataset_id": "c16", + "template_id": "tpl_m4_group_condition_rate", + "template_name": "Grouped Condition Rate", + "family_id": "conditional_dependency_structure", + "canonical_subitem_id": "direction_consistency", + "intended_facet_id": "conditional_rate_shift", + "variant_semantic_role": "focused_target_view", + "subitem_assignment_source": "planner_selected", + "source_kind": "agent", + "realization_mode": "agent", + "gate_priority": "primary", + "extended_family": false, + "question": "Use template Grouped Condition Rate to probe direction_consistency with semantic role focused_target_view. Focus on group_col=YEAR, condition_col=ID.", + "bindings": { + "group_col": "YEAR", + "condition_col": "ID", + "condition_value": "Public Identity", + "positive_value": "Public Identity", + "negative_value": "Secret Identity", + "top_k": 12, + "top_n": 4, + "num_tiles": 10, + "percentile_value": 0.9, + "z_threshold": 2.0, + "fraction_threshold": 0.1, + "baseline_multiplier": 1.5, + "baseline_fraction": 0.1, + "min_group_size": 5, + "min_support": 5, + "measure_threshold": 15.0, + "time_grain": "month", + "lookback_rows": 3, + "current_period_start": "'2024-01-01'", + "current_period_end": "'2024-04-01'", + "previous_period_start": "'2023-10-01'", + "previous_period_end": "'2024-01-01'", + "drift_ratio_threshold": 0.8 + }, + "binding_roles": [ + "group_col", + "condition_col" + ], + "coverage_target_min": "5", + "runtime_sql_skeleton": "SELECT {group_col},\n AVG(CASE WHEN {condition_col} = {condition_value} THEN 1 ELSE 0 END) AS condition_rate\nFROM {table}\nGROUP BY {group_col}\nORDER BY condition_rate DESC;", + "notes": [ + "default_facets=conditional_rate_shift", + "template_selection_mode=rule", + "problem_index_within_template=2", + "sql_variant_index=1/2", + "binding_index=97" + ], + "template_selection_mode": "rule", + "selected_template_rank": 9, + "problem_index_within_template": 2, + "sql_variant_index": 1, + "sql_variant_total": 2 + }, + "mode": "subitem_workload_v2", + "sql_source_version": "v2", + "sql_source_label": "v2_current", + "generated_sql_path": "/data/jialinzhang/TabQueryBench/sql_workloads/v2_current/runs_and_launches/runs/v2_cli_20260502_081223_d/c16/sql/v2q_c16_a8f824bd83c590bb.sql", + "usage_summary": { + "dataset_id": "c16", + "model": "v2-cli:codex", + "run_id": "v2q_c16_a8f824bd83c590bb", + "api_calls": 0, + "input_tokens": 14612, + "cached_input_tokens": 12032, + "output_tokens": 654, + "total_tokens": 15266, + "cost_usd": 0.0, + "ai_cli_calls": 1, + "estimated_input_tokens": 0, + "estimated_output_tokens": 0, + "estimated_total_tokens": 0, + "usage_source": "ai_cli_json_usage", + "cli_elapsed_ms_total": 15047.33, + "sql_execution_elapsed_ms_total": 7.04, + "conversation_log_path": "/data/jialinzhang/TabQueryBench/sql_workloads/v2_current/runs_and_launches/runs/v2_cli_20260502_081223_d/c16/artifacts/v2q_c16_a8f824bd83c590bb/cli/conversation.jsonl", + "note": "Executed through a local AI CLI with structured usage metadata." + } +} \ No newline at end of file diff --git a/Query/sql/v2/runs/v2_cli_20260502_081223_d/c16/artifacts/v2q_c16_a8f824bd83c590bb/trace.jsonl b/Query/sql/v2/runs/v2_cli_20260502_081223_d/c16/artifacts/v2q_c16_a8f824bd83c590bb/trace.jsonl new file mode 100644 index 0000000000000000000000000000000000000000..54f7829e7c485723001a8bb2efc6bed5aa541165 --- /dev/null +++ b/Query/sql/v2/runs/v2_cli_20260502_081223_d/c16/artifacts/v2q_c16_a8f824bd83c590bb/trace.jsonl @@ -0,0 +1 @@ +{"timestamp": "2026-05-19T16:01:52.594652+00:00", "event_type": "ai_cli_sql_generation", "engine": "v2-cli:codex", "attempt": 1, "command": "codex exec --skip-git-repo-check --disable plugins --sandbox read-only --cd \"/data/jialinzhang/SQLagent\" -m gpt-5.4 --json -", "returncode": 0, "elapsed_ms": 15047.33, "started_at": "2026-05-19T16:01:37.546246+00:00", "ended_at": "2026-05-19T16:01:52.593608+00:00", "prompt_metrics": {"chars": 8745, "bytes_utf8": 8745, "lines": 255, "estimated_tokens": null}, "response_metrics": {"chars": 470, "bytes_utf8": 470, "lines": 1, "estimated_tokens": null}, "usage": {"input_tokens": 14612, "cached_input_tokens": 12032, "output_tokens": 654, "reasoning_output_tokens": 516}, "stderr_preview": "", "stdout_preview": "{\"sql\":\"-- template_id: tpl_m4_group_condition_rate\\nSELECT\\n \\\"YEAR\\\",\\n AVG(CASE WHEN \\\"ID\\\" = 'Public Identity' THEN 1.0 ELSE 0.0 END) AS \\\"condition_rate\\\"\\nFROM \\\"c16\\\"\\nWHERE \\\"YEAR\\\" IS NOT NULL AND \\\"YEAR\\\" <> ''\\nGROUP BY \\\"YEAR\\\"\\nHAVING COUNT(*) >= 5\\nORDER BY \\\"condition_rate\\\" DESC;\",\"notes\":\"Groups records by YEAR and computes the rate of rows where ID is 'Public Identity'. Excludes missing YEAR values and applies the bound minimum group size of 5.\"}"} diff --git a/Query/sql/v2/runs/v2_cli_20260502_081223_d/c16/artifacts/v2q_c16_a8f824bd83c590bb/usage_summary.json b/Query/sql/v2/runs/v2_cli_20260502_081223_d/c16/artifacts/v2q_c16_a8f824bd83c590bb/usage_summary.json new file mode 100644 index 0000000000000000000000000000000000000000..c09d7fdfaa721f5deb19c724497d58d7e3651eae --- /dev/null +++ b/Query/sql/v2/runs/v2_cli_20260502_081223_d/c16/artifacts/v2q_c16_a8f824bd83c590bb/usage_summary.json @@ -0,0 +1,20 @@ +{ + "dataset_id": "c16", + "model": "v2-cli:codex", + "run_id": "v2q_c16_a8f824bd83c590bb", + "api_calls": 0, + "input_tokens": 14612, + "cached_input_tokens": 12032, + "output_tokens": 654, + "total_tokens": 15266, + "cost_usd": 0.0, + "ai_cli_calls": 1, + "estimated_input_tokens": 0, + "estimated_output_tokens": 0, + "estimated_total_tokens": 0, + "usage_source": "ai_cli_json_usage", + "cli_elapsed_ms_total": 15047.33, + "sql_execution_elapsed_ms_total": 7.04, + "conversation_log_path": "/data/jialinzhang/TabQueryBench/sql_workloads/v2_current/runs_and_launches/runs/v2_cli_20260502_081223_d/c16/artifacts/v2q_c16_a8f824bd83c590bb/cli/conversation.jsonl", + "note": "Executed through a local AI CLI with structured usage metadata." +} \ No newline at end of file diff --git a/Query/sql/v2/runs/v2_cli_20260502_081223_d/c16/artifacts/v2q_c16_a9679a306c8b7605/final_answer.txt b/Query/sql/v2/runs/v2_cli_20260502_081223_d/c16/artifacts/v2q_c16_a9679a306c8b7605/final_answer.txt new file mode 100644 index 0000000000000000000000000000000000000000..666e5ca763dadbd5974f1bf9cb2c69d97efd2817 --- /dev/null +++ b/Query/sql/v2/runs/v2_cli_20260502_081223_d/c16/artifacts/v2q_c16_a9679a306c8b7605/final_answer.txt @@ -0,0 +1,2 @@ +SQL executed successfully for: Use template Relative-to-Total Extreme Threshold to probe tail_mass_similarity with semantic role count_distribution. Focus on group_col=ALIGN, measure_col=YEAR. +Result preview: [{"ALIGN": "Bad Characters", "group_value": 5734042}, {"ALIGN": "Good Characters", "group_value": 5557336}, {"ALIGN": "", "group_value": 1179700}, {"ALIGN": "Neutral Characters", "group_value": 1107064}] \ No newline at end of file diff --git a/Query/sql/v2/runs/v2_cli_20260502_081223_d/c16/artifacts/v2q_c16_a9679a306c8b7605/generated_sql.sql b/Query/sql/v2/runs/v2_cli_20260502_081223_d/c16/artifacts/v2q_c16_a9679a306c8b7605/generated_sql.sql new file mode 100644 index 0000000000000000000000000000000000000000..bd0464b39333fb320980e35f636d2858960ade4f --- /dev/null +++ b/Query/sql/v2/runs/v2_cli_20260502_081223_d/c16/artifacts/v2q_c16_a9679a306c8b7605/generated_sql.sql @@ -0,0 +1,26 @@ +-- sql_source_version: v2 +-- sql_source_label: v2_current +-- sql_source_run_id: v2_cli_20260502_081223_d +-- sql_source_dataset_id: c16 +-- family_id: tail_rarity_structure +-- canonical_subitem_id: tail_mass_similarity +-- intended_facet_id: tail_ranked_signal +-- variant_semantic_role: count_distribution +-- template_id: tpl_tpch_relative_total_threshold +-- query_record_id: v2q_c16_a9679a306c8b7605 +-- problem_id: v2p_c16_6cb55031d91454f2 +-- realization_mode: agent +-- source_kind: agent +WITH grouped AS ( + SELECT "ALIGN", SUM(CAST(NULLIF("YEAR", '') AS NUMERIC)) AS group_value + FROM "c16" + GROUP BY "ALIGN" +), total AS ( + SELECT SUM(group_value) AS total_value + FROM grouped +) +SELECT g."ALIGN", g.group_value +FROM grouped AS g +CROSS JOIN total AS t +WHERE g.group_value > t.total_value * 0.05 +ORDER BY g.group_value DESC; diff --git a/Query/sql/v2/runs/v2_cli_20260502_081223_d/c16/artifacts/v2q_c16_a9679a306c8b7605/query_results.jsonl b/Query/sql/v2/runs/v2_cli_20260502_081223_d/c16/artifacts/v2q_c16_a9679a306c8b7605/query_results.jsonl new file mode 100644 index 0000000000000000000000000000000000000000..a349dff11f6cdd6bb4c395016430054b901fc56e --- /dev/null +++ b/Query/sql/v2/runs/v2_cli_20260502_081223_d/c16/artifacts/v2q_c16_a9679a306c8b7605/query_results.jsonl @@ -0,0 +1 @@ +{"step_index": 1, "message_index": 0, "node_name": "v2-cli:codex", "tool_name": "sqlite_query", "query": "-- template_id: tpl_tpch_relative_total_threshold\nWITH grouped AS (\n SELECT \"ALIGN\", SUM(CAST(NULLIF(\"YEAR\", '') AS NUMERIC)) AS group_value\n FROM \"c16\"\n GROUP BY \"ALIGN\"\n), total AS (\n SELECT SUM(group_value) AS total_value\n FROM grouped\n)\nSELECT g.\"ALIGN\", g.group_value\nFROM grouped AS g\nCROSS JOIN total AS t\nWHERE g.group_value > t.total_value * 0.05\nORDER BY g.group_value DESC;", "result": "{\"query\": \"-- template_id: tpl_tpch_relative_total_threshold\\nWITH grouped AS (\\n SELECT \\\"ALIGN\\\", SUM(CAST(NULLIF(\\\"YEAR\\\", '') AS NUMERIC)) AS group_value\\n FROM \\\"c16\\\"\\n GROUP BY \\\"ALIGN\\\"\\n), total AS (\\n SELECT SUM(group_value) AS total_value\\n FROM grouped\\n)\\nSELECT g.\\\"ALIGN\\\", g.group_value\\nFROM grouped AS g\\nCROSS JOIN total AS t\\nWHERE g.group_value > t.total_value * 0.05\\nORDER BY g.group_value DESC;\", \"columns\": [\"ALIGN\", \"group_value\"], \"rows\": [{\"ALIGN\": \"Bad Characters\", \"group_value\": 5734042}, {\"ALIGN\": \"Good Characters\", \"group_value\": 5557336}, {\"ALIGN\": \"\", \"group_value\": 1179700}, {\"ALIGN\": \"Neutral Characters\", \"group_value\": 1107064}], \"row_count_returned\": 4, \"row_limit\": 50, \"truncated\": false, \"elapsed_ms\": 3.95}"} diff --git a/Query/sql/v2/runs/v2_cli_20260502_081223_d/c16/artifacts/v2q_c16_a9679a306c8b7605/run_manifest.json b/Query/sql/v2/runs/v2_cli_20260502_081223_d/c16/artifacts/v2q_c16_a9679a306c8b7605/run_manifest.json new file mode 100644 index 0000000000000000000000000000000000000000..f69d72a17ea3bbe7d30f1cd586c334fc3a6dc9ec --- /dev/null +++ b/Query/sql/v2/runs/v2_cli_20260502_081223_d/c16/artifacts/v2q_c16_a9679a306c8b7605/run_manifest.json @@ -0,0 +1,89 @@ +{ + "run_id": "v2_cli_20260502_081223_d", + "dataset_id": "c16", + "started_at": "2026-05-19T15:48:28.380203+00:00", + "ended_at": "2026-05-19T15:48:39.371093+00:00", + "status": "completed", + "engine": "cli", + "question_record": { + "query_record_id": "v2q_c16_a9679a306c8b7605", + "problem_id": "v2p_c16_6cb55031d91454f2", + "dataset_id": "c16", + "template_id": "tpl_tpch_relative_total_threshold", + "template_name": "Relative-to-Total Extreme Threshold", + "family_id": "tail_rarity_structure", + "canonical_subitem_id": "tail_mass_similarity", + "intended_facet_id": "tail_ranked_signal", + "variant_semantic_role": "count_distribution", + "subitem_assignment_source": "planner_selected", + "source_kind": "agent", + "realization_mode": "agent", + "gate_priority": "primary", + "extended_family": false, + "question": "Use template Relative-to-Total Extreme Threshold to probe tail_mass_similarity with semantic role count_distribution. Focus on group_col=ALIGN, measure_col=YEAR.", + "bindings": { + "group_col": "ALIGN", + "measure_col": "YEAR", + "top_k": 17, + "top_n": 5, + "num_tiles": 10, + "percentile_value": 0.95, + "z_threshold": 2.0, + "fraction_threshold": 0.05, + "baseline_multiplier": 1.75, + "baseline_fraction": 0.1, + "min_group_size": 5, + "min_support": 4, + "measure_threshold": 1998.0, + "time_grain": "month", + "lookback_rows": 3, + "current_period_start": "'2024-01-01'", + "current_period_end": "'2024-04-01'", + "previous_period_start": "'2023-10-01'", + "previous_period_end": "'2024-01-01'", + "drift_ratio_threshold": 0.8 + }, + "binding_roles": [ + "group_col", + "measure_col" + ], + "coverage_target_min": "5", + "runtime_sql_skeleton": "WITH grouped AS (\n SELECT {group_col}, SUM({measure_col}) AS group_value\n FROM {table}\n GROUP BY {group_col}\n), total AS (\n SELECT SUM(group_value) AS total_value\n FROM grouped\n)\nSELECT g.{group_col}, g.group_value\nFROM grouped AS g\nCROSS JOIN total AS t\nWHERE g.group_value > t.total_value * {fraction_threshold}\nORDER BY g.group_value DESC;", + "notes": [ + "default_facets=tail_ranked_signal", + "template_selection_mode=rule", + "problem_index_within_template=6", + "sql_variant_index=2/2", + "binding_index=77" + ], + "template_selection_mode": "rule", + "selected_template_rank": 7, + "problem_index_within_template": 6, + "sql_variant_index": 2, + "sql_variant_total": 2 + }, + "mode": "subitem_workload_v2", + "sql_source_version": "v2", + "sql_source_label": "v2_current", + "generated_sql_path": "/data/jialinzhang/TabQueryBench/sql_workloads/v2_current/runs_and_launches/runs/v2_cli_20260502_081223_d/c16/sql/v2q_c16_a9679a306c8b7605.sql", + "usage_summary": { + "dataset_id": "c16", + "model": "v2-cli:codex", + "run_id": "v2q_c16_a9679a306c8b7605", + "api_calls": 0, + "input_tokens": 14685, + "cached_input_tokens": 13696, + "output_tokens": 373, + "total_tokens": 15058, + "cost_usd": 0.0, + "ai_cli_calls": 1, + "estimated_input_tokens": 0, + "estimated_output_tokens": 0, + "estimated_total_tokens": 0, + "usage_source": "ai_cli_json_usage", + "cli_elapsed_ms_total": 10982.56, + "sql_execution_elapsed_ms_total": 3.95, + "conversation_log_path": "/data/jialinzhang/TabQueryBench/sql_workloads/v2_current/runs_and_launches/runs/v2_cli_20260502_081223_d/c16/artifacts/v2q_c16_a9679a306c8b7605/cli/conversation.jsonl", + "note": "Executed through a local AI CLI with structured usage metadata." + } +} \ No newline at end of file diff --git a/Query/sql/v2/runs/v2_cli_20260502_081223_d/c16/artifacts/v2q_c16_a9679a306c8b7605/trace.jsonl b/Query/sql/v2/runs/v2_cli_20260502_081223_d/c16/artifacts/v2q_c16_a9679a306c8b7605/trace.jsonl new file mode 100644 index 0000000000000000000000000000000000000000..07a08cf0342c4c664f03efe12feafcac27b70d1d --- /dev/null +++ b/Query/sql/v2/runs/v2_cli_20260502_081223_d/c16/artifacts/v2q_c16_a9679a306c8b7605/trace.jsonl @@ -0,0 +1 @@ +{"timestamp": "2026-05-19T15:48:39.365393+00:00", "event_type": "ai_cli_sql_generation", "engine": "v2-cli:codex", "attempt": 1, "command": "codex exec --skip-git-repo-check --disable plugins --sandbox read-only --cd \"/data/jialinzhang/SQLagent\" -m gpt-5.4 --json -", "returncode": 0, "elapsed_ms": 10982.56, "started_at": "2026-05-19T15:48:28.381915+00:00", "ended_at": "2026-05-19T15:48:39.364508+00:00", "prompt_metrics": {"chars": 9031, "bytes_utf8": 9031, "lines": 252, "estimated_tokens": null}, "response_metrics": {"chars": 652, "bytes_utf8": 652, "lines": 1, "estimated_tokens": null}, "usage": {"input_tokens": 14685, "cached_input_tokens": 13696, "output_tokens": 373, "reasoning_output_tokens": 198}, "stderr_preview": "", "stdout_preview": "{\"sql\":\"-- template_id: tpl_tpch_relative_total_threshold\\nWITH grouped AS (\\n SELECT \\\"ALIGN\\\", SUM(CAST(NULLIF(\\\"YEAR\\\", '') AS NUMERIC)) AS group_value\\n FROM \\\"c16\\\"\\n GROUP BY \\\"ALIGN\\\"\\n), total AS (\\n SELECT SUM(group_value) AS total_value\\n FROM grouped\\n)\\nSELECT g.\\\"ALIGN\\\", g.group_value\\nFROM grouped AS g\\nCROSS JOIN total AS t\\nWHERE g.group_value > t.total_value * 0.05\\nORDER BY g.group_value DESC;\",\"notes\":\"Applied the required template with group_col=\\\"ALIGN\\\" and measure_col=\\\"YEAR\\\". \\\"YEAR\\\" is stored as TEXT in the snapshot, so the query casts it to NUMERIC and uses NULLIF to ignore empty strings during SUM.\"}"} diff --git a/Query/sql/v2/runs/v2_cli_20260502_081223_d/c16/artifacts/v2q_c16_a9679a306c8b7605/usage_summary.json b/Query/sql/v2/runs/v2_cli_20260502_081223_d/c16/artifacts/v2q_c16_a9679a306c8b7605/usage_summary.json new file mode 100644 index 0000000000000000000000000000000000000000..6d249aa6da2669c57cc0c3ab4627424815541f64 --- /dev/null +++ b/Query/sql/v2/runs/v2_cli_20260502_081223_d/c16/artifacts/v2q_c16_a9679a306c8b7605/usage_summary.json @@ -0,0 +1,20 @@ +{ + "dataset_id": "c16", + "model": "v2-cli:codex", + "run_id": "v2q_c16_a9679a306c8b7605", + "api_calls": 0, + "input_tokens": 14685, + "cached_input_tokens": 13696, + "output_tokens": 373, + "total_tokens": 15058, + "cost_usd": 0.0, + "ai_cli_calls": 1, + "estimated_input_tokens": 0, + "estimated_output_tokens": 0, + "estimated_total_tokens": 0, + "usage_source": "ai_cli_json_usage", + "cli_elapsed_ms_total": 10982.56, + "sql_execution_elapsed_ms_total": 3.95, + "conversation_log_path": "/data/jialinzhang/TabQueryBench/sql_workloads/v2_current/runs_and_launches/runs/v2_cli_20260502_081223_d/c16/artifacts/v2q_c16_a9679a306c8b7605/cli/conversation.jsonl", + "note": "Executed through a local AI CLI with structured usage metadata." +} \ No newline at end of file diff --git a/Query/sql/v2/runs/v2_cli_20260502_081223_d/c16/artifacts/v2q_c16_afdd6155facfb970/final_answer.txt b/Query/sql/v2/runs/v2_cli_20260502_081223_d/c16/artifacts/v2q_c16_afdd6155facfb970/final_answer.txt new file mode 100644 index 0000000000000000000000000000000000000000..a045e8494a34fb43ce20a680c32bbcdf486e53f3 --- /dev/null +++ b/Query/sql/v2/runs/v2_cli_20260502_081223_d/c16/artifacts/v2q_c16_afdd6155facfb970/final_answer.txt @@ -0,0 +1,2 @@ +SQL executed successfully for: Use template Grouped Count by Category to probe subgroup_size_stability with semantic role count_distribution. Focus on group_col=GSM. +Result preview: [{"GSM": "", "row_count": 6832}, {"GSM": "Homosexual Characters", "row_count": 54}, {"GSM": "Bisexual Characters", "row_count": 10}] \ No newline at end of file diff --git a/Query/sql/v2/runs/v2_cli_20260502_081223_d/c16/artifacts/v2q_c16_afdd6155facfb970/generated_sql.sql b/Query/sql/v2/runs/v2_cli_20260502_081223_d/c16/artifacts/v2q_c16_afdd6155facfb970/generated_sql.sql new file mode 100644 index 0000000000000000000000000000000000000000..5361840487365ddde154f5a56a7a51f6d55de063 --- /dev/null +++ b/Query/sql/v2/runs/v2_cli_20260502_081223_d/c16/artifacts/v2q_c16_afdd6155facfb970/generated_sql.sql @@ -0,0 +1,17 @@ +-- sql_source_version: v2 +-- sql_source_label: v2_current +-- sql_source_run_id: v2_cli_20260502_081223_d +-- sql_source_dataset_id: c16 +-- family_id: subgroup_structure +-- canonical_subitem_id: subgroup_size_stability +-- intended_facet_id: subgroup_distribution_shift +-- variant_semantic_role: count_distribution +-- template_id: tpl_clickbench_group_count +-- query_record_id: v2q_c16_afdd6155facfb970 +-- problem_id: v2p_c16_0823595fbb0bc423 +-- realization_mode: agent +-- source_kind: agent +SELECT "GSM", COUNT(*) AS row_count +FROM "c16" +GROUP BY "GSM" +ORDER BY row_count DESC; diff --git a/Query/sql/v2/runs/v2_cli_20260502_081223_d/c16/artifacts/v2q_c16_afdd6155facfb970/query_results.jsonl b/Query/sql/v2/runs/v2_cli_20260502_081223_d/c16/artifacts/v2q_c16_afdd6155facfb970/query_results.jsonl new file mode 100644 index 0000000000000000000000000000000000000000..ea0b8041c816ae05074b28f7736254f0edab6f3d --- /dev/null +++ b/Query/sql/v2/runs/v2_cli_20260502_081223_d/c16/artifacts/v2q_c16_afdd6155facfb970/query_results.jsonl @@ -0,0 +1 @@ +{"step_index": 1, "message_index": 0, "node_name": "v2-cli:codex", "tool_name": "sqlite_query", "query": "-- template_id: tpl_clickbench_group_count\nSELECT \"GSM\", COUNT(*) AS row_count\nFROM \"c16\"\nGROUP BY \"GSM\"\nORDER BY row_count DESC;", "result": "{\"query\": \"-- template_id: tpl_clickbench_group_count\\nSELECT \\\"GSM\\\", COUNT(*) AS row_count\\nFROM \\\"c16\\\"\\nGROUP BY \\\"GSM\\\"\\nORDER BY row_count DESC;\", \"columns\": [\"GSM\", \"row_count\"], \"rows\": [{\"GSM\": \"\", \"row_count\": 6832}, {\"GSM\": \"Homosexual Characters\", \"row_count\": 54}, {\"GSM\": \"Bisexual Characters\", \"row_count\": 10}], \"row_count_returned\": 3, \"row_limit\": 50, \"truncated\": false, \"elapsed_ms\": 4.3}"} diff --git a/Query/sql/v2/runs/v2_cli_20260502_081223_d/c16/artifacts/v2q_c16_afdd6155facfb970/run_manifest.json b/Query/sql/v2/runs/v2_cli_20260502_081223_d/c16/artifacts/v2q_c16_afdd6155facfb970/run_manifest.json new file mode 100644 index 0000000000000000000000000000000000000000..2cd4612047d96964cddd9ed5bf615222090b57e9 --- /dev/null +++ b/Query/sql/v2/runs/v2_cli_20260502_081223_d/c16/artifacts/v2q_c16_afdd6155facfb970/run_manifest.json @@ -0,0 +1,87 @@ +{ + "run_id": "v2_cli_20260502_081223_d", + "dataset_id": "c16", + "started_at": "2026-05-19T15:32:56.783931+00:00", + "ended_at": "2026-05-19T15:33:06.877642+00:00", + "status": "completed", + "engine": "cli", + "question_record": { + "query_record_id": "v2q_c16_afdd6155facfb970", + "problem_id": "v2p_c16_0823595fbb0bc423", + "dataset_id": "c16", + "template_id": "tpl_clickbench_group_count", + "template_name": "Grouped Count by Category", + "family_id": "subgroup_structure", + "canonical_subitem_id": "subgroup_size_stability", + "intended_facet_id": "subgroup_distribution_shift", + "variant_semantic_role": "count_distribution", + "subitem_assignment_source": "planner_selected", + "source_kind": "agent", + "realization_mode": "agent", + "gate_priority": "primary", + "extended_family": false, + "question": "Use template Grouped Count by Category to probe subgroup_size_stability with semantic role count_distribution. Focus on group_col=GSM.", + "bindings": { + "group_col": "GSM", + "top_k": 13, + "top_n": 5, + "num_tiles": 10, + "percentile_value": 0.95, + "z_threshold": 2.0, + "fraction_threshold": 0.1, + "baseline_multiplier": 1.5, + "baseline_fraction": 0.1, + "min_group_size": 5, + "min_support": 5, + "measure_threshold": 213203.0, + "time_grain": "month", + "lookback_rows": 3, + "current_period_start": "'2024-01-01'", + "current_period_end": "'2024-04-01'", + "previous_period_start": "'2023-10-01'", + "previous_period_end": "'2024-01-01'", + "drift_ratio_threshold": 0.8 + }, + "binding_roles": [ + "group_col" + ], + "coverage_target_min": "5", + "runtime_sql_skeleton": "SELECT {group_col}, COUNT(*) AS row_count\nFROM {table}\nGROUP BY {group_col}\nORDER BY row_count DESC;", + "notes": [ + "default_facets=subgroup_distribution_shift", + "template_selection_mode=rule", + "problem_index_within_template=7", + "sql_variant_index=1/1", + "binding_index=18" + ], + "template_selection_mode": "rule", + "selected_template_rank": 2, + "problem_index_within_template": 7, + "sql_variant_index": 1, + "sql_variant_total": 1 + }, + "mode": "subitem_workload_v2", + "sql_source_version": "v2", + "sql_source_label": "v2_current", + "generated_sql_path": "/data/jialinzhang/TabQueryBench/sql_workloads/v2_current/runs_and_launches/runs/v2_cli_20260502_081223_d/c16/sql/v2q_c16_afdd6155facfb970.sql", + "usage_summary": { + "dataset_id": "c16", + "model": "v2-cli:codex", + "run_id": "v2q_c16_afdd6155facfb970", + "api_calls": 0, + "input_tokens": 14526, + "cached_input_tokens": 12032, + "output_tokens": 219, + "total_tokens": 14745, + "cost_usd": 0.0, + "ai_cli_calls": 1, + "estimated_input_tokens": 0, + "estimated_output_tokens": 0, + "estimated_total_tokens": 0, + "usage_source": "ai_cli_json_usage", + "cli_elapsed_ms_total": 10083.15, + "sql_execution_elapsed_ms_total": 4.3, + "conversation_log_path": "/data/jialinzhang/TabQueryBench/sql_workloads/v2_current/runs_and_launches/runs/v2_cli_20260502_081223_d/c16/artifacts/v2q_c16_afdd6155facfb970/cli/conversation.jsonl", + "note": "Executed through a local AI CLI with structured usage metadata." + } +} \ No newline at end of file diff --git a/Query/sql/v2/runs/v2_cli_20260502_081223_d/c16/artifacts/v2q_c16_afdd6155facfb970/trace.jsonl b/Query/sql/v2/runs/v2_cli_20260502_081223_d/c16/artifacts/v2q_c16_afdd6155facfb970/trace.jsonl new file mode 100644 index 0000000000000000000000000000000000000000..d7a2605ff3b24a85bd4c664e69661036912e01c1 --- /dev/null +++ b/Query/sql/v2/runs/v2_cli_20260502_081223_d/c16/artifacts/v2q_c16_afdd6155facfb970/trace.jsonl @@ -0,0 +1 @@ +{"timestamp": "2026-05-19T15:33:06.870820+00:00", "event_type": "ai_cli_sql_generation", "engine": "v2-cli:codex", "attempt": 1, "command": "codex exec --skip-git-repo-check --disable plugins --sandbox read-only --cd \"/data/jialinzhang/SQLagent\" -m gpt-5.4 --json -", "returncode": 0, "elapsed_ms": 10083.15, "started_at": "2026-05-19T15:32:56.785662+00:00", "ended_at": "2026-05-19T15:33:06.868849+00:00", "prompt_metrics": {"chars": 8391, "bytes_utf8": 8391, "lines": 250, "estimated_tokens": null}, "response_metrics": {"chars": 261, "bytes_utf8": 261, "lines": 1, "estimated_tokens": null}, "usage": {"input_tokens": 14526, "cached_input_tokens": 12032, "output_tokens": 219, "reasoning_output_tokens": 142}, "stderr_preview": "", "stdout_preview": "{\"sql\":\"-- template_id: tpl_clickbench_group_count\\nSELECT \\\"GSM\\\", COUNT(*) AS row_count\\nFROM \\\"c16\\\"\\nGROUP BY \\\"GSM\\\"\\nORDER BY row_count DESC;\",\"notes\":\"Uses the provided grouped-count template with group_col bound to \\\"GSM\\\" on the single table \\\"c16\\\".\"}"} diff --git a/Query/sql/v2/runs/v2_cli_20260502_081223_d/c16/artifacts/v2q_c16_afdd6155facfb970/usage_summary.json b/Query/sql/v2/runs/v2_cli_20260502_081223_d/c16/artifacts/v2q_c16_afdd6155facfb970/usage_summary.json new file mode 100644 index 0000000000000000000000000000000000000000..16051e1aa19ad6d417593fff64f233a9044bb10b --- /dev/null +++ b/Query/sql/v2/runs/v2_cli_20260502_081223_d/c16/artifacts/v2q_c16_afdd6155facfb970/usage_summary.json @@ -0,0 +1,20 @@ +{ + "dataset_id": "c16", + "model": "v2-cli:codex", + "run_id": "v2q_c16_afdd6155facfb970", + "api_calls": 0, + "input_tokens": 14526, + "cached_input_tokens": 12032, + "output_tokens": 219, + "total_tokens": 14745, + "cost_usd": 0.0, + "ai_cli_calls": 1, + "estimated_input_tokens": 0, + "estimated_output_tokens": 0, + "estimated_total_tokens": 0, + "usage_source": "ai_cli_json_usage", + "cli_elapsed_ms_total": 10083.15, + "sql_execution_elapsed_ms_total": 4.3, + "conversation_log_path": "/data/jialinzhang/TabQueryBench/sql_workloads/v2_current/runs_and_launches/runs/v2_cli_20260502_081223_d/c16/artifacts/v2q_c16_afdd6155facfb970/cli/conversation.jsonl", + "note": "Executed through a local AI CLI with structured usage metadata." +} \ No newline at end of file diff --git a/Query/sql/v2/runs/v2_cli_20260502_081223_d/c16/artifacts/v2q_c16_b9c15f2f03749a9e/final_answer.txt b/Query/sql/v2/runs/v2_cli_20260502_081223_d/c16/artifacts/v2q_c16_b9c15f2f03749a9e/final_answer.txt new file mode 100644 index 0000000000000000000000000000000000000000..d6d06e7441299f3ba31c6c59baeeb57ac8ac3a5d --- /dev/null +++ b/Query/sql/v2/runs/v2_cli_20260502_081223_d/c16/artifacts/v2q_c16_b9c15f2f03749a9e/final_answer.txt @@ -0,0 +1,2 @@ +SQL executed successfully for: Use template Grouped Condition Rate to probe direction_consistency with semantic role focused_target_view. Focus on group_col=ALIVE, condition_col=ID. +Result preview: [{"ALIVE": "", "condition_rate": 0.6666666666666666}, {"ALIVE": "Deceased Characters", "condition_rate": 0.377436503248671}, {"ALIVE": "Living Characters", "condition_rate": 0.35096153846153844}] \ No newline at end of file diff --git a/Query/sql/v2/runs/v2_cli_20260502_081223_d/c16/artifacts/v2q_c16_b9c15f2f03749a9e/generated_sql.sql b/Query/sql/v2/runs/v2_cli_20260502_081223_d/c16/artifacts/v2q_c16_b9c15f2f03749a9e/generated_sql.sql new file mode 100644 index 0000000000000000000000000000000000000000..697ff8339183e3401fbf0af37964a8cc1e61383a --- /dev/null +++ b/Query/sql/v2/runs/v2_cli_20260502_081223_d/c16/artifacts/v2q_c16_b9c15f2f03749a9e/generated_sql.sql @@ -0,0 +1,18 @@ +-- sql_source_version: v2 +-- sql_source_label: v2_current +-- sql_source_run_id: v2_cli_20260502_081223_d +-- sql_source_dataset_id: c16 +-- family_id: conditional_dependency_structure +-- canonical_subitem_id: direction_consistency +-- intended_facet_id: conditional_rate_shift +-- variant_semantic_role: focused_target_view +-- template_id: tpl_m4_group_condition_rate +-- query_record_id: v2q_c16_b9c15f2f03749a9e +-- problem_id: v2p_c16_37f8053beb7f44d2 +-- realization_mode: agent +-- source_kind: agent +SELECT "ALIVE", + AVG(CASE WHEN "ID" = 'Public Identity' THEN 1 ELSE 0 END) AS condition_rate +FROM "c16" +GROUP BY "ALIVE" +ORDER BY condition_rate DESC; diff --git a/Query/sql/v2/runs/v2_cli_20260502_081223_d/c16/artifacts/v2q_c16_b9c15f2f03749a9e/query_results.jsonl b/Query/sql/v2/runs/v2_cli_20260502_081223_d/c16/artifacts/v2q_c16_b9c15f2f03749a9e/query_results.jsonl new file mode 100644 index 0000000000000000000000000000000000000000..502ba67f8d918a885db8090016026205b972c540 --- /dev/null +++ b/Query/sql/v2/runs/v2_cli_20260502_081223_d/c16/artifacts/v2q_c16_b9c15f2f03749a9e/query_results.jsonl @@ -0,0 +1 @@ +{"step_index": 1, "message_index": 0, "node_name": "v2-cli:codex", "tool_name": "sqlite_query", "query": "-- template_id: tpl_m4_group_condition_rate\nSELECT \"ALIVE\",\n AVG(CASE WHEN \"ID\" = 'Public Identity' THEN 1 ELSE 0 END) AS condition_rate\nFROM \"c16\"\nGROUP BY \"ALIVE\"\nORDER BY condition_rate DESC;", "result": "{\"query\": \"-- template_id: tpl_m4_group_condition_rate\\nSELECT \\\"ALIVE\\\",\\n AVG(CASE WHEN \\\"ID\\\" = 'Public Identity' THEN 1 ELSE 0 END) AS condition_rate\\nFROM \\\"c16\\\"\\nGROUP BY \\\"ALIVE\\\"\\nORDER BY condition_rate DESC;\", \"columns\": [\"ALIVE\", \"condition_rate\"], \"rows\": [{\"ALIVE\": \"\", \"condition_rate\": 0.6666666666666666}, {\"ALIVE\": \"Deceased Characters\", \"condition_rate\": 0.377436503248671}, {\"ALIVE\": \"Living Characters\", \"condition_rate\": 0.35096153846153844}], \"row_count_returned\": 3, \"row_limit\": 50, \"truncated\": false, \"elapsed_ms\": 4.63}"} diff --git a/Query/sql/v2/runs/v2_cli_20260502_081223_d/c16/artifacts/v2q_c16_b9c15f2f03749a9e/run_manifest.json b/Query/sql/v2/runs/v2_cli_20260502_081223_d/c16/artifacts/v2q_c16_b9c15f2f03749a9e/run_manifest.json new file mode 100644 index 0000000000000000000000000000000000000000..eb38f368e25796c4600240b103b232aca576e119 --- /dev/null +++ b/Query/sql/v2/runs/v2_cli_20260502_081223_d/c16/artifacts/v2q_c16_b9c15f2f03749a9e/run_manifest.json @@ -0,0 +1,92 @@ +{ + "run_id": "v2_cli_20260502_081223_d", + "dataset_id": "c16", + "started_at": "2026-05-19T16:03:50.675856+00:00", + "ended_at": "2026-05-19T16:03:58.746244+00:00", + "status": "completed", + "engine": "cli", + "question_record": { + "query_record_id": "v2q_c16_b9c15f2f03749a9e", + "problem_id": "v2p_c16_37f8053beb7f44d2", + "dataset_id": "c16", + "template_id": "tpl_m4_group_condition_rate", + "template_name": "Grouped Condition Rate", + "family_id": "conditional_dependency_structure", + "canonical_subitem_id": "direction_consistency", + "intended_facet_id": "conditional_rate_shift", + "variant_semantic_role": "focused_target_view", + "subitem_assignment_source": "planner_selected", + "source_kind": "agent", + "realization_mode": "agent", + "gate_priority": "primary", + "extended_family": false, + "question": "Use template Grouped Condition Rate to probe direction_consistency with semantic role focused_target_view. Focus on group_col=ALIVE, condition_col=ID.", + "bindings": { + "group_col": "ALIVE", + "condition_col": "ID", + "condition_value": "Public Identity", + "positive_value": "Public Identity", + "negative_value": "Secret Identity", + "top_k": 13, + "top_n": 6, + "num_tiles": 10, + "percentile_value": 0.9, + "z_threshold": 2.0, + "fraction_threshold": 0.1, + "baseline_multiplier": 1.5, + "baseline_fraction": 0.1, + "min_group_size": 5, + "min_support": 5, + "measure_threshold": 15.0, + "time_grain": "month", + "lookback_rows": 3, + "current_period_start": "'2024-01-01'", + "current_period_end": "'2024-04-01'", + "previous_period_start": "'2023-10-01'", + "previous_period_end": "'2024-01-01'", + "drift_ratio_threshold": 0.8 + }, + "binding_roles": [ + "group_col", + "condition_col" + ], + "coverage_target_min": "5", + "runtime_sql_skeleton": "SELECT {group_col},\n AVG(CASE WHEN {condition_col} = {condition_value} THEN 1 ELSE 0 END) AS condition_rate\nFROM {table}\nGROUP BY {group_col}\nORDER BY condition_rate DESC;", + "notes": [ + "default_facets=conditional_rate_shift", + "template_selection_mode=rule", + "problem_index_within_template=8", + "sql_variant_index=1/2", + "binding_index=103" + ], + "template_selection_mode": "rule", + "selected_template_rank": 9, + "problem_index_within_template": 8, + "sql_variant_index": 1, + "sql_variant_total": 2 + }, + "mode": "subitem_workload_v2", + "sql_source_version": "v2", + "sql_source_label": "v2_current", + "generated_sql_path": "/data/jialinzhang/TabQueryBench/sql_workloads/v2_current/runs_and_launches/runs/v2_cli_20260502_081223_d/c16/sql/v2q_c16_b9c15f2f03749a9e.sql", + "usage_summary": { + "dataset_id": "c16", + "model": "v2-cli:codex", + "run_id": "v2q_c16_b9c15f2f03749a9e", + "api_calls": 0, + "input_tokens": 14614, + "cached_input_tokens": 13696, + "output_tokens": 250, + "total_tokens": 14864, + "cost_usd": 0.0, + "ai_cli_calls": 1, + "estimated_input_tokens": 0, + "estimated_output_tokens": 0, + "estimated_total_tokens": 0, + "usage_source": "ai_cli_json_usage", + "cli_elapsed_ms_total": 8059.81, + "sql_execution_elapsed_ms_total": 4.63, + "conversation_log_path": "/data/jialinzhang/TabQueryBench/sql_workloads/v2_current/runs_and_launches/runs/v2_cli_20260502_081223_d/c16/artifacts/v2q_c16_b9c15f2f03749a9e/cli/conversation.jsonl", + "note": "Executed through a local AI CLI with structured usage metadata." + } +} \ No newline at end of file diff --git a/Query/sql/v2/runs/v2_cli_20260502_081223_d/c16/artifacts/v2q_c16_b9c15f2f03749a9e/trace.jsonl b/Query/sql/v2/runs/v2_cli_20260502_081223_d/c16/artifacts/v2q_c16_b9c15f2f03749a9e/trace.jsonl new file mode 100644 index 0000000000000000000000000000000000000000..7d3bb85b5dde8b43de6cf0b3d1a14dd44f029fc8 --- /dev/null +++ b/Query/sql/v2/runs/v2_cli_20260502_081223_d/c16/artifacts/v2q_c16_b9c15f2f03749a9e/trace.jsonl @@ -0,0 +1 @@ +{"timestamp": "2026-05-19T16:03:58.739139+00:00", "event_type": "ai_cli_sql_generation", "engine": "v2-cli:codex", "attempt": 1, "command": "codex exec --skip-git-repo-check --disable plugins --sandbox read-only --cd \"/data/jialinzhang/SQLagent\" -m gpt-5.4 --json -", "returncode": 0, "elapsed_ms": 8059.81, "started_at": "2026-05-19T16:03:50.677686+00:00", "ended_at": "2026-05-19T16:03:58.737530+00:00", "prompt_metrics": {"chars": 8747, "bytes_utf8": 8747, "lines": 255, "estimated_tokens": null}, "response_metrics": {"chars": 416, "bytes_utf8": 416, "lines": 1, "estimated_tokens": null}, "usage": {"input_tokens": 14614, "cached_input_tokens": 13696, "output_tokens": 250, "reasoning_output_tokens": 137}, "stderr_preview": "", "stdout_preview": "{\"sql\":\"-- template_id: tpl_m4_group_condition_rate\\nSELECT \\\"ALIVE\\\",\\n AVG(CASE WHEN \\\"ID\\\" = 'Public Identity' THEN 1 ELSE 0 END) AS condition_rate\\nFROM \\\"c16\\\"\\nGROUP BY \\\"ALIVE\\\"\\nORDER BY condition_rate DESC;\",\"notes\":\"Uses the planned Grouped Condition Rate template with group_col=\\\"ALIVE\\\" and condition_col=\\\"ID\\\", measuring the share of rows where ID is 'Public Identity' within each ALIVE group.\"}"} diff --git a/Query/sql/v2/runs/v2_cli_20260502_081223_d/c16/artifacts/v2q_c16_b9c15f2f03749a9e/usage_summary.json b/Query/sql/v2/runs/v2_cli_20260502_081223_d/c16/artifacts/v2q_c16_b9c15f2f03749a9e/usage_summary.json new file mode 100644 index 0000000000000000000000000000000000000000..dc6dc02713287d3c063916ab8ce00ce14c39f038 --- /dev/null +++ b/Query/sql/v2/runs/v2_cli_20260502_081223_d/c16/artifacts/v2q_c16_b9c15f2f03749a9e/usage_summary.json @@ -0,0 +1,20 @@ +{ + "dataset_id": "c16", + "model": "v2-cli:codex", + "run_id": "v2q_c16_b9c15f2f03749a9e", + "api_calls": 0, + "input_tokens": 14614, + "cached_input_tokens": 13696, + "output_tokens": 250, + "total_tokens": 14864, + "cost_usd": 0.0, + "ai_cli_calls": 1, + "estimated_input_tokens": 0, + "estimated_output_tokens": 0, + "estimated_total_tokens": 0, + "usage_source": "ai_cli_json_usage", + "cli_elapsed_ms_total": 8059.81, + "sql_execution_elapsed_ms_total": 4.63, + "conversation_log_path": "/data/jialinzhang/TabQueryBench/sql_workloads/v2_current/runs_and_launches/runs/v2_cli_20260502_081223_d/c16/artifacts/v2q_c16_b9c15f2f03749a9e/cli/conversation.jsonl", + "note": "Executed through a local AI CLI with structured usage metadata." +} \ No newline at end of file diff --git a/Query/sql/v2/runs/v2_cli_20260502_081223_d/c16/artifacts/v2q_c16_bd3ff6d30fc51928/final_answer.txt b/Query/sql/v2/runs/v2_cli_20260502_081223_d/c16/artifacts/v2q_c16_bd3ff6d30fc51928/final_answer.txt new file mode 100644 index 0000000000000000000000000000000000000000..5d5cece227f45628fc652805757837924f5205a0 --- /dev/null +++ b/Query/sql/v2/runs/v2_cli_20260502_081223_d/c16/artifacts/v2q_c16_bd3ff6d30fc51928/final_answer.txt @@ -0,0 +1 @@ +{"row_count": null, "preview_rows": [{"YEAR": "2006", "support": 303, "avg_response": 109191.54785478547}, {"YEAR": "1988", "support": 286, "avg_response": 151958.9055944056}, {"YEAR": "2010", "support": 279, "avg_response": 215132.02150537635}, {"YEAR": "1989", "support": 266, "avg_response": 174980.55639097744}, {"YEAR": "1987", "support": 254, "avg_response": 119760.92913385827}]} \ No newline at end of file diff --git a/Query/sql/v2/runs/v2_cli_20260502_081223_d/c16/artifacts/v2q_c16_bd3ff6d30fc51928/generated_sql.sql b/Query/sql/v2/runs/v2_cli_20260502_081223_d/c16/artifacts/v2q_c16_bd3ff6d30fc51928/generated_sql.sql new file mode 100644 index 0000000000000000000000000000000000000000..efc239b272005d2fa10fc996246a7defe6da8366 --- /dev/null +++ b/Query/sql/v2/runs/v2_cli_20260502_081223_d/c16/artifacts/v2q_c16_bd3ff6d30fc51928/generated_sql.sql @@ -0,0 +1,21 @@ +-- sql_source_version: v2 +-- sql_source_label: v2_current +-- sql_source_run_id: v2_cli_20260502_081223_d +-- sql_source_dataset_id: c16 +-- family_id: cardinality_structure +-- canonical_subitem_id: high_cardinality_response_stability +-- intended_facet_id: target_cardinality_cross_section +-- variant_semantic_role: focused_target_view +-- template_id: tpl_cardinality_high_card_response_stability +-- query_record_id: v2q_c16_bd3ff6d30fc51928 +-- problem_id: v2p_c16_3b42582ef3ff9a8b +-- realization_mode: deterministic +-- source_kind: deterministic +SELECT + "YEAR", + COUNT(*) AS support, + AVG("page_id") AS avg_response +FROM "c16" +GROUP BY "YEAR" +HAVING COUNT(*) >= 5.0 +ORDER BY support DESC, avg_response DESC; diff --git a/Query/sql/v2/runs/v2_cli_20260502_081223_d/c16/artifacts/v2q_c16_bd3ff6d30fc51928/query_results.jsonl b/Query/sql/v2/runs/v2_cli_20260502_081223_d/c16/artifacts/v2q_c16_bd3ff6d30fc51928/query_results.jsonl new file mode 100644 index 0000000000000000000000000000000000000000..131f6f93b6b7e462953d3cd70be738b80f5c64c6 --- /dev/null +++ b/Query/sql/v2/runs/v2_cli_20260502_081223_d/c16/artifacts/v2q_c16_bd3ff6d30fc51928/query_results.jsonl @@ -0,0 +1 @@ +{"node_name": "v2_template", "tool_name": "sqlite_query", "query": "-- sql_source_version: v2\n-- sql_source_label: v2_current\n-- sql_source_run_id: v2_cli_20260502_081223_d\n-- sql_source_dataset_id: c16\n-- family_id: cardinality_structure\n-- canonical_subitem_id: high_cardinality_response_stability\n-- intended_facet_id: target_cardinality_cross_section\n-- variant_semantic_role: focused_target_view\n-- template_id: tpl_cardinality_high_card_response_stability\n-- query_record_id: v2q_c16_bd3ff6d30fc51928\n-- problem_id: v2p_c16_3b42582ef3ff9a8b\n-- realization_mode: deterministic\n-- source_kind: deterministic\nSELECT\n \"YEAR\",\n COUNT(*) AS support,\n AVG(\"page_id\") AS avg_response\nFROM \"c16\"\nGROUP BY \"YEAR\"\nHAVING COUNT(*) >= 5.0\nORDER BY support DESC, avg_response DESC;", "result": "{\"query\": \"-- sql_source_version: v2\\n-- sql_source_label: v2_current\\n-- sql_source_run_id: v2_cli_20260502_081223_d\\n-- sql_source_dataset_id: c16\\n-- family_id: cardinality_structure\\n-- canonical_subitem_id: high_cardinality_response_stability\\n-- intended_facet_id: target_cardinality_cross_section\\n-- variant_semantic_role: focused_target_view\\n-- template_id: tpl_cardinality_high_card_response_stability\\n-- query_record_id: v2q_c16_bd3ff6d30fc51928\\n-- problem_id: v2p_c16_3b42582ef3ff9a8b\\n-- realization_mode: deterministic\\n-- source_kind: deterministic\\nSELECT\\n \\\"YEAR\\\",\\n COUNT(*) AS support,\\n AVG(\\\"page_id\\\") AS avg_response\\nFROM \\\"c16\\\"\\nGROUP BY \\\"YEAR\\\"\\nHAVING COUNT(*) >= 5.0\\nORDER BY support DESC, avg_response DESC;\", \"columns\": [\"YEAR\", \"support\", \"avg_response\"], \"rows\": [{\"YEAR\": \"2006\", \"support\": 303, \"avg_response\": 109191.54785478547}, {\"YEAR\": \"1988\", \"support\": 286, \"avg_response\": 151958.9055944056}, {\"YEAR\": \"2010\", \"support\": 279, \"avg_response\": 215132.02150537635}, {\"YEAR\": \"1989\", \"support\": 266, \"avg_response\": 174980.55639097744}, {\"YEAR\": \"1987\", \"support\": 254, \"avg_response\": 119760.92913385827}, {\"YEAR\": \"1994\", \"support\": 230, \"avg_response\": 153304.26086956522}, {\"YEAR\": \"2009\", \"support\": 226, \"avg_response\": 192598.30973451328}, {\"YEAR\": \"2008\", \"support\": 211, \"avg_response\": 152708.72511848342}, {\"YEAR\": \"1993\", \"support\": 209, \"avg_response\": 139529.87559808613}, {\"YEAR\": \"1997\", \"support\": 189, \"avg_response\": 164531.20105820106}, {\"YEAR\": \"1996\", \"support\": 188, \"avg_response\": 198550.30319148937}, {\"YEAR\": \"2007\", \"support\": 188, \"avg_response\": 128771.80319148937}, {\"YEAR\": \"1999\", \"support\": 179, \"avg_response\": 158916.5754189944}, {\"YEAR\": \"1992\", \"support\": 178, \"avg_response\": 168643.59550561797}, {\"YEAR\": \"1990\", \"support\": 175, \"avg_response\": 174184.25714285715}, {\"YEAR\": \"1995\", \"support\": 172, \"avg_response\": 172219.66860465117}, {\"YEAR\": \"1983\", \"support\": 161, \"avg_response\": 181315.26708074534}, {\"YEAR\": \"2005\", \"support\": 159, \"avg_response\": 166234.88050314467}, {\"YEAR\": \"2011\", \"support\": 155, \"avg_response\": 243595.4}, {\"YEAR\": \"2000\", \"support\": 152, \"avg_response\": 152276.3552631579}, {\"YEAR\": \"1991\", \"support\": 145, \"avg_response\": 168629.0827586207}, {\"YEAR\": \"1998\", \"support\": 143, \"avg_response\": 172655.37762237762}, {\"YEAR\": \"1984\", \"support\": 141, \"avg_response\": 156050.63829787233}, {\"YEAR\": \"1986\", \"support\": 132, \"avg_response\": 113832.25757575757}, {\"YEAR\": \"1981\", \"support\": 119, \"avg_response\": 129972.74789915966}, {\"YEAR\": \"2002\", \"support\": 115, \"avg_response\": 182801.31304347827}, {\"YEAR\": \"1985\", \"support\": 115, \"avg_response\": 131549.2695652174}, {\"YEAR\": \"1982\", \"support\": 111, \"avg_response\": 115517.36036036036}, {\"YEAR\": \"2003\", \"support\": 103, \"avg_response\": 147679.1359223301}, {\"YEAR\": \"2004\", \"support\": 102, \"avg_response\": 141055.49019607843}, {\"YEAR\": \"2001\", \"support\": 99, \"avg_response\": 154345.12121212122}, {\"YEAR\": \"\", \"support\": 69, \"avg_response\": 181974.73913043478}, {\"YEAR\": \"1971\", \"support\": 65, \"avg_response\": 57491.692307692305}, {\"YEAR\": \"1940\", \"support\": 64, \"avg_response\": 75633.578125}, {\"YEAR\": \"1972\", \"support\": 61, \"avg_response\": 128085.45901639345}, {\"YEAR\": \"1968\", \"support\": 61, \"avg_response\": 124599.67213114754}, {\"YEAR\": \"1966\", \"support\": 61, \"avg_response\": 113199.9344262295}, {\"YEAR\": \"1941\", \"support\": 61, \"avg_response\": 49809.18032786885}, {\"YEAR\": \"1978\", \"support\": 60, \"avg_response\": 138505.55}, {\"YEAR\": \"1967\", \"support\": 56, \"avg_response\": 94999.71428571429}, {\"YEAR\": \"1977\", \"support\": 52, \"avg_response\": 127038.59615384616}, {\"YEAR\": \"1942\", \"support\": 52, \"avg_response\": 78451.26923076923}, {\"YEAR\": \"1965\", \"support\": 50, \"avg_response\": 88905.36}, {\"YEAR\": \"1961\", \"support\": 50, \"avg_response\": 56309.66}, {\"YEAR\": \"1976\", \"support\": 45, \"avg_response\": 98260.02222222222}, {\"YEAR\": \"1962\", \"support\": 42, \"avg_response\": 52060.57142857143}, {\"YEAR\": \"1963\", \"support\": 40, \"avg_response\": 107232.6}, {\"YEAR\": \"1975\", \"support\": 39, \"avg_response\": 133193.41025641025}, {\"YEAR\": \"1960\", \"support\": 39, \"avg_response\": 70384.53846153847}, {\"YEAR\": \"1980\", \"support\": 36, \"avg_response\": 79557.83333333333}], \"row_count_returned\": 50, \"row_limit\": 50, \"truncated\": true, \"elapsed_ms\": 3.0}"} diff --git a/Query/sql/v2/runs/v2_cli_20260502_081223_d/c16/artifacts/v2q_c16_bd3ff6d30fc51928/run_manifest.json b/Query/sql/v2/runs/v2_cli_20260502_081223_d/c16/artifacts/v2q_c16_bd3ff6d30fc51928/run_manifest.json new file mode 100644 index 0000000000000000000000000000000000000000..15d95d2cf4f56f0401e284ff1a25078a29c1f0fb --- /dev/null +++ b/Query/sql/v2/runs/v2_cli_20260502_081223_d/c16/artifacts/v2q_c16_bd3ff6d30fc51928/run_manifest.json @@ -0,0 +1,60 @@ +{ + "run_id": "v2_cli_20260502_081223_d", + "dataset_id": "c16", + "started_at": "2026-05-19T16:10:30.562266+00:00", + "ended_at": "2026-05-19T16:10:30.565980+00:00", + "status": "completed", + "engine": "cli", + "question_record": { + "query_record_id": "v2q_c16_bd3ff6d30fc51928", + "problem_id": "v2p_c16_3b42582ef3ff9a8b", + "dataset_id": "c16", + "template_id": "tpl_cardinality_high_card_response_stability", + "template_name": "High-Cardinality Response Stability", + "family_id": "cardinality_structure", + "canonical_subitem_id": "high_cardinality_response_stability", + "intended_facet_id": "target_cardinality_cross_section", + "variant_semantic_role": "focused_target_view", + "subitem_assignment_source": "template_fixed", + "source_kind": "deterministic", + "realization_mode": "deterministic", + "gate_priority": "deterministic", + "extended_family": true, + "question": "Use template High-Cardinality Response Stability to probe high_cardinality_response_stability with semantic role focused_target_view. Focus on measure_col=page_id, key_col=YEAR.", + "bindings": { + "key_col": "YEAR", + "measure_col": "page_id", + "min_support": 5 + }, + "binding_roles": [ + "key_col", + "target_col" + ], + "coverage_target_min": "enumerate_all_applicable", + "runtime_sql_skeleton": "SELECT\n {key_col},\n COUNT(*) AS support,\n AVG({measure_col}) AS avg_response\nFROM {table}\nGROUP BY {key_col}\nHAVING COUNT(*) >= {min_support}\nORDER BY support DESC, avg_response DESC;", + "notes": [ + "default_facets=target_cardinality_cross_section", + "template_selection_mode=deterministic", + "problem_index_within_template=12", + "sql_variant_index=1/1" + ], + "template_selection_mode": "deterministic", + "selected_template_rank": 0, + "problem_index_within_template": 12, + "sql_variant_index": 1, + "sql_variant_total": 1 + }, + "mode": "subitem_workload_v2", + "sql_source_version": "v2", + "sql_source_label": "v2_current", + "generated_sql_path": "/data/jialinzhang/TabQueryBench/sql_workloads/v2_current/runs_and_launches/runs/v2_cli_20260502_081223_d/c16/sql/v2q_c16_bd3ff6d30fc51928.sql", + "usage_summary": { + "engine": "template", + "input_tokens": 0, + "cached_input_tokens": 0, + "output_tokens": 0, + "total_tokens": 0, + "estimated_total_tokens": 0, + "usage_source": "none" + } +} \ No newline at end of file diff --git a/Query/sql/v2/runs/v2_cli_20260502_081223_d/c16/artifacts/v2q_c16_bd3ff6d30fc51928/usage_summary.json b/Query/sql/v2/runs/v2_cli_20260502_081223_d/c16/artifacts/v2q_c16_bd3ff6d30fc51928/usage_summary.json new file mode 100644 index 0000000000000000000000000000000000000000..96c9ff4feec395919fc26411d18d078b8af6e1c7 --- /dev/null +++ b/Query/sql/v2/runs/v2_cli_20260502_081223_d/c16/artifacts/v2q_c16_bd3ff6d30fc51928/usage_summary.json @@ -0,0 +1,9 @@ +{ + "engine": "template", + "input_tokens": 0, + "cached_input_tokens": 0, + "output_tokens": 0, + "total_tokens": 0, + "estimated_total_tokens": 0, + "usage_source": "none" +} \ No newline at end of file diff --git a/Query/sql/v2/runs/v2_cli_20260502_081223_d/c16/artifacts/v2q_c16_ddf4263136c55fa0/final_answer.txt b/Query/sql/v2/runs/v2_cli_20260502_081223_d/c16/artifacts/v2q_c16_ddf4263136c55fa0/final_answer.txt new file mode 100644 index 0000000000000000000000000000000000000000..6c4212c9bf82944377622ea4e2d7dd9327c5c5b6 --- /dev/null +++ b/Query/sql/v2/runs/v2_cli_20260502_081223_d/c16/artifacts/v2q_c16_ddf4263136c55fa0/final_answer.txt @@ -0,0 +1,2 @@ +SQL executed successfully for: Use template Grouped Ratio of Two Conditions to probe direction_consistency with semantic role contrastive_conditional_view. Focus on group_col=HAIR, condition_col=ID. +Result preview: [{"HAIR": "Orange Hair", "condition_ratio": 16.0}, {"HAIR": "Grey Hair", "condition_ratio": 2.2972972972972974}, {"HAIR": "Purple Hair", "condition_ratio": 2.0}, {"HAIR": "Pink Hair", "condition_ratio": 1.6666666666666667}, {"HAIR": "White Hair", "condition_ratio": 1.5288461538461537}] \ No newline at end of file diff --git a/Query/sql/v2/runs/v2_cli_20260502_081223_d/c16/artifacts/v2q_c16_ddf4263136c55fa0/generated_sql.sql b/Query/sql/v2/runs/v2_cli_20260502_081223_d/c16/artifacts/v2q_c16_ddf4263136c55fa0/generated_sql.sql new file mode 100644 index 0000000000000000000000000000000000000000..bf53983799f367d488ac77f3f8c9e5bcec1fe01c --- /dev/null +++ b/Query/sql/v2/runs/v2_cli_20260502_081223_d/c16/artifacts/v2q_c16_ddf4263136c55fa0/generated_sql.sql @@ -0,0 +1,24 @@ +-- sql_source_version: v2 +-- sql_source_label: v2_current +-- sql_source_run_id: v2_cli_20260502_081223_d +-- sql_source_dataset_id: c16 +-- family_id: conditional_dependency_structure +-- canonical_subitem_id: direction_consistency +-- intended_facet_id: conditional_rate_shift +-- variant_semantic_role: contrastive_conditional_view +-- template_id: tpl_m4_group_ratio_two_conditions +-- query_record_id: v2q_c16_ddf4263136c55fa0 +-- problem_id: v2p_c16_2d4479d360cbd6a5 +-- realization_mode: agent +-- source_kind: agent +WITH grouped AS ( + SELECT "HAIR", + SUM(CASE WHEN "ID" = 'Public Identity' THEN 1 ELSE 0 END) AS numerator_count, + SUM(CASE WHEN "ID" = 'Secret Identity' THEN 1 ELSE 0 END) AS denominator_count + FROM "c16" + GROUP BY "HAIR" +) +SELECT "HAIR", + CAST(numerator_count AS FLOAT) / NULLIF(denominator_count, 0) AS condition_ratio +FROM grouped +ORDER BY condition_ratio DESC; diff --git a/Query/sql/v2/runs/v2_cli_20260502_081223_d/c16/artifacts/v2q_c16_ddf4263136c55fa0/query_results.jsonl b/Query/sql/v2/runs/v2_cli_20260502_081223_d/c16/artifacts/v2q_c16_ddf4263136c55fa0/query_results.jsonl new file mode 100644 index 0000000000000000000000000000000000000000..7c372df0cf3eabcfdb8d56d9c1078ea84da7791d --- /dev/null +++ b/Query/sql/v2/runs/v2_cli_20260502_081223_d/c16/artifacts/v2q_c16_ddf4263136c55fa0/query_results.jsonl @@ -0,0 +1 @@ +{"step_index": 1, "message_index": 0, "node_name": "v2-cli:codex", "tool_name": "sqlite_query", "query": "-- template_id: tpl_m4_group_ratio_two_conditions\nWITH grouped AS (\n SELECT \"HAIR\",\n SUM(CASE WHEN \"ID\" = 'Public Identity' THEN 1 ELSE 0 END) AS numerator_count,\n SUM(CASE WHEN \"ID\" = 'Secret Identity' THEN 1 ELSE 0 END) AS denominator_count\n FROM \"c16\"\n GROUP BY \"HAIR\"\n)\nSELECT \"HAIR\",\n CAST(numerator_count AS FLOAT) / NULLIF(denominator_count, 0) AS condition_ratio\nFROM grouped\nORDER BY condition_ratio DESC;", "result": "{\"query\": \"-- template_id: tpl_m4_group_ratio_two_conditions\\nWITH grouped AS (\\n SELECT \\\"HAIR\\\",\\n SUM(CASE WHEN \\\"ID\\\" = 'Public Identity' THEN 1 ELSE 0 END) AS numerator_count,\\n SUM(CASE WHEN \\\"ID\\\" = 'Secret Identity' THEN 1 ELSE 0 END) AS denominator_count\\n FROM \\\"c16\\\"\\n GROUP BY \\\"HAIR\\\"\\n)\\nSELECT \\\"HAIR\\\",\\n CAST(numerator_count AS FLOAT) / NULLIF(denominator_count, 0) AS condition_ratio\\nFROM grouped\\nORDER BY condition_ratio DESC;\", \"columns\": [\"HAIR\", \"condition_ratio\"], \"rows\": [{\"HAIR\": \"Orange Hair\", \"condition_ratio\": 16.0}, {\"HAIR\": \"Grey Hair\", \"condition_ratio\": 2.2972972972972974}, {\"HAIR\": \"Purple Hair\", \"condition_ratio\": 2.0}, {\"HAIR\": \"Pink Hair\", \"condition_ratio\": 1.6666666666666667}, {\"HAIR\": \"White Hair\", \"condition_ratio\": 1.5288461538461537}, {\"HAIR\": \"Brown Hair\", \"condition_ratio\": 1.4080459770114941}, {\"HAIR\": \"Strawberry Blond Hair\", \"condition_ratio\": 1.4}, {\"HAIR\": \"Blond Hair\", \"condition_ratio\": 1.065217391304348}, {\"HAIR\": \"Blue Hair\", \"condition_ratio\": 1.0625}, {\"HAIR\": \"Black Hair\", \"condition_ratio\": 1.0398671096345515}, {\"HAIR\": \"Red Hair\", \"condition_ratio\": 1.0113636363636365}, {\"HAIR\": \"Gold Hair\", \"condition_ratio\": 1.0}, {\"HAIR\": \"Reddish Brown Hair\", \"condition_ratio\": 1.0}, {\"HAIR\": \"Green Hair\", \"condition_ratio\": 0.7}, {\"HAIR\": \"\", \"condition_ratio\": 0.681704260651629}, {\"HAIR\": \"Violet Hair\", \"condition_ratio\": 0.3333333333333333}, {\"HAIR\": \"Platinum Blond Hair\", \"condition_ratio\": 0.0}, {\"HAIR\": \"Silver Hair\", \"condition_ratio\": null}], \"row_count_returned\": 18, \"row_limit\": 50, \"truncated\": false, \"elapsed_ms\": 4.07}"} diff --git a/Query/sql/v2/runs/v2_cli_20260502_081223_d/c16/artifacts/v2q_c16_ddf4263136c55fa0/run_manifest.json b/Query/sql/v2/runs/v2_cli_20260502_081223_d/c16/artifacts/v2q_c16_ddf4263136c55fa0/run_manifest.json new file mode 100644 index 0000000000000000000000000000000000000000..4d38e59b0f4be730d2e197c3d25fd933210fe0bd --- /dev/null +++ b/Query/sql/v2/runs/v2_cli_20260502_081223_d/c16/artifacts/v2q_c16_ddf4263136c55fa0/run_manifest.json @@ -0,0 +1,92 @@ +{ + "run_id": "v2_cli_20260502_081223_d", + "dataset_id": "c16", + "started_at": "2026-05-19T15:39:17.933129+00:00", + "ended_at": "2026-05-19T15:39:29.570891+00:00", + "status": "completed", + "engine": "cli", + "question_record": { + "query_record_id": "v2q_c16_ddf4263136c55fa0", + "problem_id": "v2p_c16_2d4479d360cbd6a5", + "dataset_id": "c16", + "template_id": "tpl_m4_group_ratio_two_conditions", + "template_name": "Grouped Ratio of Two Conditions", + "family_id": "conditional_dependency_structure", + "canonical_subitem_id": "direction_consistency", + "intended_facet_id": "conditional_rate_shift", + "variant_semantic_role": "contrastive_conditional_view", + "subitem_assignment_source": "planner_selected", + "source_kind": "agent", + "realization_mode": "agent", + "gate_priority": "primary", + "extended_family": false, + "question": "Use template Grouped Ratio of Two Conditions to probe direction_consistency with semantic role contrastive_conditional_view. Focus on group_col=HAIR, condition_col=ID.", + "bindings": { + "group_col": "HAIR", + "condition_col": "ID", + "condition_value": "Public Identity", + "positive_value": "Public Identity", + "negative_value": "Secret Identity", + "top_k": 12, + "top_n": 4, + "num_tiles": 10, + "percentile_value": 0.9, + "z_threshold": 2.0, + "fraction_threshold": 0.1, + "baseline_multiplier": 1.5, + "baseline_fraction": 0.1, + "min_group_size": 5, + "min_support": 5, + "measure_threshold": 15.0, + "time_grain": "month", + "lookback_rows": 3, + "current_period_start": "'2024-01-01'", + "current_period_end": "'2024-04-01'", + "previous_period_start": "'2023-10-01'", + "previous_period_end": "'2024-01-01'", + "drift_ratio_threshold": 0.8 + }, + "binding_roles": [ + "group_col", + "condition_col" + ], + "coverage_target_min": "5", + "runtime_sql_skeleton": "WITH grouped AS (\n SELECT {group_col},\n SUM(CASE WHEN {condition_col} = {positive_value} THEN 1 ELSE 0 END) AS numerator_count,\n SUM(CASE WHEN {condition_col} = {negative_value} THEN 1 ELSE 0 END) AS denominator_count\n FROM {table}\n GROUP BY {group_col}\n)\nSELECT {group_col},\n CAST(numerator_count AS FLOAT) / NULLIF(denominator_count, 0) AS condition_ratio\nFROM grouped\nORDER BY condition_ratio DESC;", + "notes": [ + "default_facets=conditional_rate_shift", + "template_selection_mode=rule", + "problem_index_within_template=2", + "sql_variant_index=1/1", + "binding_index=37" + ], + "template_selection_mode": "rule", + "selected_template_rank": 4, + "problem_index_within_template": 2, + "sql_variant_index": 1, + "sql_variant_total": 1 + }, + "mode": "subitem_workload_v2", + "sql_source_version": "v2", + "sql_source_label": "v2_current", + "generated_sql_path": "/data/jialinzhang/TabQueryBench/sql_workloads/v2_current/runs_and_launches/runs/v2_cli_20260502_081223_d/c16/sql/v2q_c16_ddf4263136c55fa0.sql", + "usage_summary": { + "dataset_id": "c16", + "model": "v2-cli:codex", + "run_id": "v2q_c16_ddf4263136c55fa0", + "api_calls": 0, + "input_tokens": 14762, + "cached_input_tokens": 13696, + "output_tokens": 518, + "total_tokens": 15280, + "cost_usd": 0.0, + "ai_cli_calls": 1, + "estimated_input_tokens": 0, + "estimated_output_tokens": 0, + "estimated_total_tokens": 0, + "usage_source": "ai_cli_json_usage", + "cli_elapsed_ms_total": 11629.28, + "sql_execution_elapsed_ms_total": 4.07, + "conversation_log_path": "/data/jialinzhang/TabQueryBench/sql_workloads/v2_current/runs_and_launches/runs/v2_cli_20260502_081223_d/c16/artifacts/v2q_c16_ddf4263136c55fa0/cli/conversation.jsonl", + "note": "Executed through a local AI CLI with structured usage metadata." + } +} \ No newline at end of file diff --git a/Query/sql/v2/runs/v2_cli_20260502_081223_d/c16/artifacts/v2q_c16_ddf4263136c55fa0/trace.jsonl b/Query/sql/v2/runs/v2_cli_20260502_081223_d/c16/artifacts/v2q_c16_ddf4263136c55fa0/trace.jsonl new file mode 100644 index 0000000000000000000000000000000000000000..bc435746d1a3c0d8fa13ae02c648b9e1898e7085 --- /dev/null +++ b/Query/sql/v2/runs/v2_cli_20260502_081223_d/c16/artifacts/v2q_c16_ddf4263136c55fa0/trace.jsonl @@ -0,0 +1 @@ +{"timestamp": "2026-05-19T15:39:29.564839+00:00", "event_type": "ai_cli_sql_generation", "engine": "v2-cli:codex", "attempt": 1, "command": "codex exec --skip-git-repo-check --disable plugins --sandbox read-only --cd \"/data/jialinzhang/SQLagent\" -m gpt-5.4 --json -", "returncode": 0, "elapsed_ms": 11629.28, "started_at": "2026-05-19T15:39:17.934757+00:00", "ended_at": "2026-05-19T15:39:29.564064+00:00", "prompt_metrics": {"chars": 9314, "bytes_utf8": 9314, "lines": 255, "estimated_tokens": null}, "response_metrics": {"chars": 642, "bytes_utf8": 642, "lines": 1, "estimated_tokens": null}, "usage": {"input_tokens": 14762, "cached_input_tokens": 13696, "output_tokens": 518, "reasoning_output_tokens": 338}, "stderr_preview": "", "stdout_preview": "{\"sql\":\"-- template_id: tpl_m4_group_ratio_two_conditions\\nWITH grouped AS (\\n SELECT \\\"HAIR\\\",\\n SUM(CASE WHEN \\\"ID\\\" = 'Public Identity' THEN 1 ELSE 0 END) AS numerator_count,\\n SUM(CASE WHEN \\\"ID\\\" = 'Secret Identity' THEN 1 ELSE 0 END) AS denominator_count\\n FROM \\\"c16\\\"\\n GROUP BY \\\"HAIR\\\"\\n)\\nSELECT \\\"HAIR\\\",\\n CAST(numerator_count AS FLOAT) / NULLIF(denominator_count, 0) AS condition_ratio\\nFROM grouped\\nORDER BY condition_ratio DESC;\",\"notes\":\"Computes the per-HAIR ratio of rows with \\\"ID\\\" = 'Public Identity' to rows with \\\"ID\\\" = 'Secret Identity' using the planned grouped ratio template.\"}"} diff --git a/Query/sql/v2/runs/v2_cli_20260502_081223_d/c16/artifacts/v2q_c16_ddf4263136c55fa0/usage_summary.json b/Query/sql/v2/runs/v2_cli_20260502_081223_d/c16/artifacts/v2q_c16_ddf4263136c55fa0/usage_summary.json new file mode 100644 index 0000000000000000000000000000000000000000..f98c482bb64c43859110a39777ebe00ac4be9d48 --- /dev/null +++ b/Query/sql/v2/runs/v2_cli_20260502_081223_d/c16/artifacts/v2q_c16_ddf4263136c55fa0/usage_summary.json @@ -0,0 +1,20 @@ +{ + "dataset_id": "c16", + "model": "v2-cli:codex", + "run_id": "v2q_c16_ddf4263136c55fa0", + "api_calls": 0, + "input_tokens": 14762, + "cached_input_tokens": 13696, + "output_tokens": 518, + "total_tokens": 15280, + "cost_usd": 0.0, + "ai_cli_calls": 1, + "estimated_input_tokens": 0, + "estimated_output_tokens": 0, + "estimated_total_tokens": 0, + "usage_source": "ai_cli_json_usage", + "cli_elapsed_ms_total": 11629.28, + "sql_execution_elapsed_ms_total": 4.07, + "conversation_log_path": "/data/jialinzhang/TabQueryBench/sql_workloads/v2_current/runs_and_launches/runs/v2_cli_20260502_081223_d/c16/artifacts/v2q_c16_ddf4263136c55fa0/cli/conversation.jsonl", + "note": "Executed through a local AI CLI with structured usage metadata." +} \ No newline at end of file diff --git a/Query/sql/v2/runs/v2_cli_20260502_081223_d/c16/artifacts/v2q_c16_debbebd8e937751f/final_answer.txt b/Query/sql/v2/runs/v2_cli_20260502_081223_d/c16/artifacts/v2q_c16_debbebd8e937751f/final_answer.txt new file mode 100644 index 0000000000000000000000000000000000000000..02736cc35bdb6e71b65f34c17fe9d5dde39d3b73 --- /dev/null +++ b/Query/sql/v2/runs/v2_cli_20260502_081223_d/c16/artifacts/v2q_c16_debbebd8e937751f/final_answer.txt @@ -0,0 +1,2 @@ +SQL executed successfully for: Use template Grouped Count by Category to probe subgroup_size_stability with semantic role count_distribution. Focus on group_col=EYE. +Result preview: [{"EYE": "", "row_count": 3628}, {"EYE": "Blue Eyes", "row_count": 1102}, {"EYE": "Brown Eyes", "row_count": 879}, {"EYE": "Black Eyes", "row_count": 412}, {"EYE": "Green Eyes", "row_count": 291}] \ No newline at end of file diff --git a/Query/sql/v2/runs/v2_cli_20260502_081223_d/c16/artifacts/v2q_c16_debbebd8e937751f/generated_sql.sql b/Query/sql/v2/runs/v2_cli_20260502_081223_d/c16/artifacts/v2q_c16_debbebd8e937751f/generated_sql.sql new file mode 100644 index 0000000000000000000000000000000000000000..03f87fbcacdd29e18eb3d88bc9492c361fb02b67 --- /dev/null +++ b/Query/sql/v2/runs/v2_cli_20260502_081223_d/c16/artifacts/v2q_c16_debbebd8e937751f/generated_sql.sql @@ -0,0 +1,17 @@ +-- sql_source_version: v2 +-- sql_source_label: v2_current +-- sql_source_run_id: v2_cli_20260502_081223_d +-- sql_source_dataset_id: c16 +-- family_id: subgroup_structure +-- canonical_subitem_id: subgroup_size_stability +-- intended_facet_id: subgroup_distribution_shift +-- variant_semantic_role: count_distribution +-- template_id: tpl_clickbench_group_count +-- query_record_id: v2q_c16_debbebd8e937751f +-- problem_id: v2p_c16_3dc233c26ce4e9a1 +-- realization_mode: agent +-- source_kind: agent +SELECT "EYE", COUNT(*) AS row_count +FROM "c16" +GROUP BY "EYE" +ORDER BY row_count DESC; diff --git a/Query/sql/v2/runs/v2_cli_20260502_081223_d/c16/artifacts/v2q_c16_debbebd8e937751f/query_results.jsonl b/Query/sql/v2/runs/v2_cli_20260502_081223_d/c16/artifacts/v2q_c16_debbebd8e937751f/query_results.jsonl new file mode 100644 index 0000000000000000000000000000000000000000..7a5c5848b0beffa1b08fcba5931f5aa0fc1a123a --- /dev/null +++ b/Query/sql/v2/runs/v2_cli_20260502_081223_d/c16/artifacts/v2q_c16_debbebd8e937751f/query_results.jsonl @@ -0,0 +1 @@ +{"step_index": 1, "message_index": 0, "node_name": "v2-cli:codex", "tool_name": "sqlite_query", "query": "-- template_id: tpl_clickbench_group_count\nSELECT \"EYE\", COUNT(*) AS row_count\nFROM \"c16\"\nGROUP BY \"EYE\"\nORDER BY row_count DESC;", "result": "{\"query\": \"-- template_id: tpl_clickbench_group_count\\nSELECT \\\"EYE\\\", COUNT(*) AS row_count\\nFROM \\\"c16\\\"\\nGROUP BY \\\"EYE\\\"\\nORDER BY row_count DESC;\", \"columns\": [\"EYE\", \"row_count\"], \"rows\": [{\"EYE\": \"\", \"row_count\": 3628}, {\"EYE\": \"Blue Eyes\", \"row_count\": 1102}, {\"EYE\": \"Brown Eyes\", \"row_count\": 879}, {\"EYE\": \"Black Eyes\", \"row_count\": 412}, {\"EYE\": \"Green Eyes\", \"row_count\": 291}, {\"EYE\": \"Red Eyes\", \"row_count\": 208}, {\"EYE\": \"White Eyes\", \"row_count\": 116}, {\"EYE\": \"Yellow Eyes\", \"row_count\": 86}, {\"EYE\": \"Photocellular Eyes\", \"row_count\": 48}, {\"EYE\": \"Grey Eyes\", \"row_count\": 40}, {\"EYE\": \"Hazel Eyes\", \"row_count\": 23}, {\"EYE\": \"Purple Eyes\", \"row_count\": 14}, {\"EYE\": \"Violet Eyes\", \"row_count\": 12}, {\"EYE\": \"Orange Eyes\", \"row_count\": 10}, {\"EYE\": \"Gold Eyes\", \"row_count\": 9}, {\"EYE\": \"Auburn Hair\", \"row_count\": 7}, {\"EYE\": \"Pink Eyes\", \"row_count\": 6}, {\"EYE\": \"Amber Eyes\", \"row_count\": 5}], \"row_count_returned\": 18, \"row_limit\": 50, \"truncated\": false, \"elapsed_ms\": 3.09}"} diff --git a/Query/sql/v2/runs/v2_cli_20260502_081223_d/c16/artifacts/v2q_c16_debbebd8e937751f/run_manifest.json b/Query/sql/v2/runs/v2_cli_20260502_081223_d/c16/artifacts/v2q_c16_debbebd8e937751f/run_manifest.json new file mode 100644 index 0000000000000000000000000000000000000000..d853b1ba0e127758ae262ff7f6dffe8152e5e8ce --- /dev/null +++ b/Query/sql/v2/runs/v2_cli_20260502_081223_d/c16/artifacts/v2q_c16_debbebd8e937751f/run_manifest.json @@ -0,0 +1,87 @@ +{ + "run_id": "v2_cli_20260502_081223_d", + "dataset_id": "c16", + "started_at": "2026-05-19T15:32:15.881282+00:00", + "ended_at": "2026-05-19T15:32:21.990757+00:00", + "status": "completed", + "engine": "cli", + "question_record": { + "query_record_id": "v2q_c16_debbebd8e937751f", + "problem_id": "v2p_c16_3dc233c26ce4e9a1", + "dataset_id": "c16", + "template_id": "tpl_clickbench_group_count", + "template_name": "Grouped Count by Category", + "family_id": "subgroup_structure", + "canonical_subitem_id": "subgroup_size_stability", + "intended_facet_id": "subgroup_distribution_shift", + "variant_semantic_role": "count_distribution", + "subitem_assignment_source": "planner_selected", + "source_kind": "agent", + "realization_mode": "agent", + "gate_priority": "primary", + "extended_family": false, + "question": "Use template Grouped Count by Category to probe subgroup_size_stability with semantic role count_distribution. Focus on group_col=EYE.", + "bindings": { + "group_col": "EYE", + "top_k": 10, + "top_n": 6, + "num_tiles": 10, + "percentile_value": 0.9, + "z_threshold": 2.0, + "fraction_threshold": 0.1, + "baseline_multiplier": 1.5, + "baseline_fraction": 0.1, + "min_group_size": 5, + "min_support": 5, + "measure_threshold": 213203.0, + "time_grain": "month", + "lookback_rows": 3, + "current_period_start": "'2024-01-01'", + "current_period_end": "'2024-04-01'", + "previous_period_start": "'2023-10-01'", + "previous_period_end": "'2024-01-01'", + "drift_ratio_threshold": 0.8 + }, + "binding_roles": [ + "group_col" + ], + "coverage_target_min": "5", + "runtime_sql_skeleton": "SELECT {group_col}, COUNT(*) AS row_count\nFROM {table}\nGROUP BY {group_col}\nORDER BY row_count DESC;", + "notes": [ + "default_facets=subgroup_distribution_shift", + "template_selection_mode=rule", + "problem_index_within_template=4", + "sql_variant_index=1/1", + "binding_index=15" + ], + "template_selection_mode": "rule", + "selected_template_rank": 2, + "problem_index_within_template": 4, + "sql_variant_index": 1, + "sql_variant_total": 1 + }, + "mode": "subitem_workload_v2", + "sql_source_version": "v2", + "sql_source_label": "v2_current", + "generated_sql_path": "/data/jialinzhang/TabQueryBench/sql_workloads/v2_current/runs_and_launches/runs/v2_cli_20260502_081223_d/c16/sql/v2q_c16_debbebd8e937751f.sql", + "usage_summary": { + "dataset_id": "c16", + "model": "v2-cli:codex", + "run_id": "v2q_c16_debbebd8e937751f", + "api_calls": 0, + "input_tokens": 14526, + "cached_input_tokens": 12032, + "output_tokens": 202, + "total_tokens": 14728, + "cost_usd": 0.0, + "ai_cli_calls": 1, + "estimated_input_tokens": 0, + "estimated_output_tokens": 0, + "estimated_total_tokens": 0, + "usage_source": "ai_cli_json_usage", + "cli_elapsed_ms_total": 6102.59, + "sql_execution_elapsed_ms_total": 3.09, + "conversation_log_path": "/data/jialinzhang/TabQueryBench/sql_workloads/v2_current/runs_and_launches/runs/v2_cli_20260502_081223_d/c16/artifacts/v2q_c16_debbebd8e937751f/cli/conversation.jsonl", + "note": "Executed through a local AI CLI with structured usage metadata." + } +} \ No newline at end of file diff --git a/Query/sql/v2/runs/v2_cli_20260502_081223_d/c16/artifacts/v2q_c16_debbebd8e937751f/trace.jsonl b/Query/sql/v2/runs/v2_cli_20260502_081223_d/c16/artifacts/v2q_c16_debbebd8e937751f/trace.jsonl new file mode 100644 index 0000000000000000000000000000000000000000..b5202d287fcef42b89406a4e709357bc37f34fcd --- /dev/null +++ b/Query/sql/v2/runs/v2_cli_20260502_081223_d/c16/artifacts/v2q_c16_debbebd8e937751f/trace.jsonl @@ -0,0 +1 @@ +{"timestamp": "2026-05-19T15:32:21.986099+00:00", "event_type": "ai_cli_sql_generation", "engine": "v2-cli:codex", "attempt": 1, "command": "codex exec --skip-git-repo-check --disable plugins --sandbox read-only --cd \"/data/jialinzhang/SQLagent\" -m gpt-5.4 --json -", "returncode": 0, "elapsed_ms": 6102.59, "started_at": "2026-05-19T15:32:15.882645+00:00", "ended_at": "2026-05-19T15:32:21.985265+00:00", "prompt_metrics": {"chars": 8390, "bytes_utf8": 8390, "lines": 250, "estimated_tokens": null}, "response_metrics": {"chars": 261, "bytes_utf8": 261, "lines": 1, "estimated_tokens": null}, "usage": {"input_tokens": 14526, "cached_input_tokens": 12032, "output_tokens": 202, "reasoning_output_tokens": 125}, "stderr_preview": "", "stdout_preview": "{\"sql\":\"-- template_id: tpl_clickbench_group_count\\nSELECT \\\"EYE\\\", COUNT(*) AS row_count\\nFROM \\\"c16\\\"\\nGROUP BY \\\"EYE\\\"\\nORDER BY row_count DESC;\",\"notes\":\"Uses the required grouped-count template with group_col bound to \\\"EYE\\\" on the single table \\\"c16\\\".\"}"} diff --git a/Query/sql/v2/runs/v2_cli_20260502_081223_d/c16/artifacts/v2q_c16_debbebd8e937751f/usage_summary.json b/Query/sql/v2/runs/v2_cli_20260502_081223_d/c16/artifacts/v2q_c16_debbebd8e937751f/usage_summary.json new file mode 100644 index 0000000000000000000000000000000000000000..c7a3b9c94eba09bddfa0482e651fe10e06bdd42f --- /dev/null +++ b/Query/sql/v2/runs/v2_cli_20260502_081223_d/c16/artifacts/v2q_c16_debbebd8e937751f/usage_summary.json @@ -0,0 +1,20 @@ +{ + "dataset_id": "c16", + "model": "v2-cli:codex", + "run_id": "v2q_c16_debbebd8e937751f", + "api_calls": 0, + "input_tokens": 14526, + "cached_input_tokens": 12032, + "output_tokens": 202, + "total_tokens": 14728, + "cost_usd": 0.0, + "ai_cli_calls": 1, + "estimated_input_tokens": 0, + "estimated_output_tokens": 0, + "estimated_total_tokens": 0, + "usage_source": "ai_cli_json_usage", + "cli_elapsed_ms_total": 6102.59, + "sql_execution_elapsed_ms_total": 3.09, + "conversation_log_path": "/data/jialinzhang/TabQueryBench/sql_workloads/v2_current/runs_and_launches/runs/v2_cli_20260502_081223_d/c16/artifacts/v2q_c16_debbebd8e937751f/cli/conversation.jsonl", + "note": "Executed through a local AI CLI with structured usage metadata." +} \ No newline at end of file diff --git a/Query/sql/v2/runs/v2_cli_20260502_081223_d/c16/artifacts/v2q_c16_e56915a91cf51436/run_manifest.json b/Query/sql/v2/runs/v2_cli_20260502_081223_d/c16/artifacts/v2q_c16_e56915a91cf51436/run_manifest.json new file mode 100644 index 0000000000000000000000000000000000000000..347902a29e66aadd88fb379ac8fed48e4c1fe67e --- /dev/null +++ b/Query/sql/v2/runs/v2_cli_20260502_081223_d/c16/artifacts/v2q_c16_e56915a91cf51436/run_manifest.json @@ -0,0 +1,67 @@ +{ + "run_id": "v2_cli_20260502_081223_d", + "dataset_id": "c16", + "started_at": "2026-05-19T16:08:11.645861+00:00", + "ended_at": "2026-05-19T16:08:19.654432+00:00", + "status": "failed", + "engine": "cli", + "question_record": { + "query_record_id": "v2q_c16_e56915a91cf51436", + "problem_id": "v2p_c16_0fd58070cd87174f", + "dataset_id": "c16", + "template_id": "tpl_tail_low_support_group_count_v2", + "template_name": "Low-Support Group Count", + "family_id": "tail_rarity_structure", + "canonical_subitem_id": "tail_mass_similarity", + "intended_facet_id": "tail_ranked_signal", + "variant_semantic_role": "rare_extreme_view", + "subitem_assignment_source": "planner_selected", + "source_kind": "agent", + "realization_mode": "agent", + "gate_priority": "primary", + "extended_family": false, + "question": "Use template Low-Support Group Count to probe tail_mass_similarity with semantic role rare_extreme_view. Focus on group_col=EYE.", + "bindings": { + "group_col": "EYE", + "top_k": 17, + "top_n": 7, + "num_tiles": 10, + "percentile_value": 0.95, + "z_threshold": 2.0, + "fraction_threshold": 0.05, + "baseline_multiplier": 1.75, + "baseline_fraction": 0.1, + "min_group_size": 5, + "min_support": 4, + "measure_threshold": 15.0, + "time_grain": "month", + "lookback_rows": 3, + "current_period_start": "'2024-01-01'", + "current_period_end": "'2024-04-01'", + "previous_period_start": "'2023-10-01'", + "previous_period_end": "'2024-01-01'", + "drift_ratio_threshold": 0.8 + }, + "binding_roles": [ + "group_col" + ], + "coverage_target_min": "5", + "runtime_sql_skeleton": "SELECT\n {group_col},\n COUNT(*) AS support\nFROM {table}\nGROUP BY {group_col}\nORDER BY support ASC, {group_col}\nLIMIT {top_k};", + "notes": [ + "default_facets=tail_ranked_signal", + "template_selection_mode=rule", + "problem_index_within_template=8", + "sql_variant_index=2/2", + "binding_index=127" + ], + "template_selection_mode": "rule", + "selected_template_rank": 11, + "problem_index_within_template": 8, + "sql_variant_index": 2, + "sql_variant_total": 2 + }, + "mode": "subitem_workload_v2", + "sql_source_version": "v2", + "sql_source_label": "v2_current", + "error": "AI CLI command failed with exit code 1: " +} \ No newline at end of file diff --git a/Query/sql/v2/runs/v2_cli_20260502_081223_d/c16/artifacts/v2q_c16_e56915a91cf51436/trace.jsonl b/Query/sql/v2/runs/v2_cli_20260502_081223_d/c16/artifacts/v2q_c16_e56915a91cf51436/trace.jsonl new file mode 100644 index 0000000000000000000000000000000000000000..9e9bf2f916238ffe42862cd040315e2ec1a33089 --- /dev/null +++ b/Query/sql/v2/runs/v2_cli_20260502_081223_d/c16/artifacts/v2q_c16_e56915a91cf51436/trace.jsonl @@ -0,0 +1,2 @@ +{"timestamp": "2026-05-19T16:08:15.513912+00:00", "event_type": "ai_cli_sql_generation_error", "engine": "v2-cli:codex", "attempt": 1, "command": "codex exec --skip-git-repo-check --disable plugins --sandbox read-only --cd \"/data/jialinzhang/SQLagent\" -m gpt-5.4 --json -", "returncode": 1, "elapsed_ms": 3865.75, "started_at": "2026-05-19T16:08:11.647471+00:00", "ended_at": "2026-05-19T16:08:15.513240+00:00", "prompt_metrics": {"chars": 8468, "bytes_utf8": 8468, "lines": 250, "estimated_tokens": null}, "response_metrics": {"chars": 280, "bytes_utf8": 280, "lines": 4, "estimated_tokens": null}, "usage": {}, "stderr_preview": "", "stdout_preview": "{\"type\":\"thread.started\",\"thread_id\":\"019e40fe-a973-70c3-b1c3-8d0b43866d90\"}\n{\"type\":\"turn.started\"}\n{\"type\":\"error\",\"message\":\"Quota exceeded. Check your plan and billing details.\"}\n{\"type\":\"turn.failed\",\"error\":{\"message\":\"Quota exceeded. Check your plan and billing details.\"}}", "error": "AI CLI command failed with exit code 1: "} +{"timestamp": "2026-05-19T16:08:19.654343+00:00", "event_type": "ai_cli_sql_generation_error", "engine": "v2-cli:codex", "attempt": 2, "command": "codex exec --skip-git-repo-check --disable plugins --sandbox read-only --cd \"/data/jialinzhang/SQLagent\" -m gpt-5.4 --json -", "returncode": 1, "elapsed_ms": 3138.24, "started_at": "2026-05-19T16:08:16.515316+00:00", "ended_at": "2026-05-19T16:08:19.653599+00:00", "prompt_metrics": {"chars": 8468, "bytes_utf8": 8468, "lines": 250, "estimated_tokens": null}, "response_metrics": {"chars": 280, "bytes_utf8": 280, "lines": 4, "estimated_tokens": null}, "usage": {}, "stderr_preview": "", "stdout_preview": "{\"type\":\"thread.started\",\"thread_id\":\"019e40fe-bc82-7d33-90ac-95a6d4d4d0c2\"}\n{\"type\":\"turn.started\"}\n{\"type\":\"error\",\"message\":\"Quota exceeded. Check your plan and billing details.\"}\n{\"type\":\"turn.failed\",\"error\":{\"message\":\"Quota exceeded. Check your plan and billing details.\"}}", "error": "AI CLI command failed with exit code 1: "} diff --git a/Query/sql/v2/runs/v2_cli_20260502_081223_d/c16/artifacts/v2q_c16_e997ffc8c751d2e8/final_answer.txt b/Query/sql/v2/runs/v2_cli_20260502_081223_d/c16/artifacts/v2q_c16_e997ffc8c751d2e8/final_answer.txt new file mode 100644 index 0000000000000000000000000000000000000000..5432660caac5fcd0b4b39a855f1f21a8f928e680 --- /dev/null +++ b/Query/sql/v2/runs/v2_cli_20260502_081223_d/c16/artifacts/v2q_c16_e997ffc8c751d2e8/final_answer.txt @@ -0,0 +1,2 @@ +SQL executed successfully for: Use template Grouped Condition Rate to probe dependency_strength_similarity with semantic role focused_target_view. Focus on group_col=ALIGN, condition_col=ALIGN. +Result preview: [{"ALIGN": "Good Characters", "condition_rate": 1.0}, {"ALIGN": "Reformed Criminals", "condition_rate": 0.0}, {"ALIGN": "Neutral Characters", "condition_rate": 0.0}, {"ALIGN": "Bad Characters", "condition_rate": 0.0}, {"ALIGN": "", "condition_rate": 0.0}] \ No newline at end of file diff --git a/Query/sql/v2/runs/v2_cli_20260502_081223_d/c16/artifacts/v2q_c16_e997ffc8c751d2e8/generated_sql.sql b/Query/sql/v2/runs/v2_cli_20260502_081223_d/c16/artifacts/v2q_c16_e997ffc8c751d2e8/generated_sql.sql new file mode 100644 index 0000000000000000000000000000000000000000..54b0f0f2b61e41acb9f798cde79a6f27bf605fb9 --- /dev/null +++ b/Query/sql/v2/runs/v2_cli_20260502_081223_d/c16/artifacts/v2q_c16_e997ffc8c751d2e8/generated_sql.sql @@ -0,0 +1,18 @@ +-- sql_source_version: v2 +-- sql_source_label: v2_current +-- sql_source_run_id: v2_cli_20260502_081223_d +-- sql_source_dataset_id: c16 +-- family_id: conditional_dependency_structure +-- canonical_subitem_id: dependency_strength_similarity +-- intended_facet_id: pairwise_conditional_dependency +-- variant_semantic_role: focused_target_view +-- template_id: tpl_m4_group_condition_rate +-- query_record_id: v2q_c16_e997ffc8c751d2e8 +-- problem_id: v2p_c16_b98618b993ee313c +-- realization_mode: agent +-- source_kind: agent +SELECT "ALIGN", + AVG(CASE WHEN "ALIGN" = 'Good Characters' THEN 1 ELSE 0 END) AS condition_rate +FROM "c16" +GROUP BY "ALIGN" +ORDER BY condition_rate DESC; diff --git a/Query/sql/v2/runs/v2_cli_20260502_081223_d/c16/artifacts/v2q_c16_e997ffc8c751d2e8/query_results.jsonl b/Query/sql/v2/runs/v2_cli_20260502_081223_d/c16/artifacts/v2q_c16_e997ffc8c751d2e8/query_results.jsonl new file mode 100644 index 0000000000000000000000000000000000000000..a83c6f2c9e3e7684de3059c61ea6c75b564b125a --- /dev/null +++ b/Query/sql/v2/runs/v2_cli_20260502_081223_d/c16/artifacts/v2q_c16_e997ffc8c751d2e8/query_results.jsonl @@ -0,0 +1 @@ +{"step_index": 1, "message_index": 0, "node_name": "v2-cli:codex", "tool_name": "sqlite_query", "query": "-- template_id: tpl_m4_group_condition_rate\nSELECT \"ALIGN\",\n AVG(CASE WHEN \"ALIGN\" = 'Good Characters' THEN 1 ELSE 0 END) AS condition_rate\nFROM \"c16\"\nGROUP BY \"ALIGN\"\nORDER BY condition_rate DESC;", "result": "{\"query\": \"-- template_id: tpl_m4_group_condition_rate\\nSELECT \\\"ALIGN\\\",\\n AVG(CASE WHEN \\\"ALIGN\\\" = 'Good Characters' THEN 1 ELSE 0 END) AS condition_rate\\nFROM \\\"c16\\\"\\nGROUP BY \\\"ALIGN\\\"\\nORDER BY condition_rate DESC;\", \"columns\": [\"ALIGN\", \"condition_rate\"], \"rows\": [{\"ALIGN\": \"Good Characters\", \"condition_rate\": 1.0}, {\"ALIGN\": \"Reformed Criminals\", \"condition_rate\": 0.0}, {\"ALIGN\": \"Neutral Characters\", \"condition_rate\": 0.0}, {\"ALIGN\": \"Bad Characters\", \"condition_rate\": 0.0}, {\"ALIGN\": \"\", \"condition_rate\": 0.0}], \"row_count_returned\": 5, \"row_limit\": 50, \"truncated\": false, \"elapsed_ms\": 3.2}"} diff --git a/Query/sql/v2/runs/v2_cli_20260502_081223_d/c16/artifacts/v2q_c16_e997ffc8c751d2e8/run_manifest.json b/Query/sql/v2/runs/v2_cli_20260502_081223_d/c16/artifacts/v2q_c16_e997ffc8c751d2e8/run_manifest.json new file mode 100644 index 0000000000000000000000000000000000000000..6a4b5b7a27a9510469679c4e3d5222cb34cf3a90 --- /dev/null +++ b/Query/sql/v2/runs/v2_cli_20260502_081223_d/c16/artifacts/v2q_c16_e997ffc8c751d2e8/run_manifest.json @@ -0,0 +1,92 @@ +{ + "run_id": "v2_cli_20260502_081223_d", + "dataset_id": "c16", + "started_at": "2026-05-19T16:02:12.937693+00:00", + "ended_at": "2026-05-19T16:02:22.391263+00:00", + "status": "completed", + "engine": "cli", + "question_record": { + "query_record_id": "v2q_c16_e997ffc8c751d2e8", + "problem_id": "v2p_c16_b98618b993ee313c", + "dataset_id": "c16", + "template_id": "tpl_m4_group_condition_rate", + "template_name": "Grouped Condition Rate", + "family_id": "conditional_dependency_structure", + "canonical_subitem_id": "dependency_strength_similarity", + "intended_facet_id": "pairwise_conditional_dependency", + "variant_semantic_role": "focused_target_view", + "subitem_assignment_source": "planner_selected", + "source_kind": "agent", + "realization_mode": "agent", + "gate_priority": "primary", + "extended_family": false, + "question": "Use template Grouped Condition Rate to probe dependency_strength_similarity with semantic role focused_target_view. Focus on group_col=ALIGN, condition_col=ALIGN.", + "bindings": { + "group_col": "ALIGN", + "condition_col": "ALIGN", + "condition_value": "Good Characters", + "positive_value": "Bad Characters", + "negative_value": "Good Characters", + "top_k": 18, + "top_n": 6, + "num_tiles": 10, + "percentile_value": 0.9, + "z_threshold": 2.0, + "fraction_threshold": 0.05, + "baseline_multiplier": 1.75, + "baseline_fraction": 0.1, + "min_group_size": 5, + "min_support": 4, + "measure_threshold": 2003.0, + "time_grain": "month", + "lookback_rows": 3, + "current_period_start": "'2024-01-01'", + "current_period_end": "'2024-04-01'", + "previous_period_start": "'2023-10-01'", + "previous_period_end": "'2024-01-01'", + "drift_ratio_threshold": 0.8 + }, + "binding_roles": [ + "group_col", + "condition_col" + ], + "coverage_target_min": "5", + "runtime_sql_skeleton": "SELECT {group_col},\n AVG(CASE WHEN {condition_col} = {condition_value} THEN 1 ELSE 0 END) AS condition_rate\nFROM {table}\nGROUP BY {group_col}\nORDER BY condition_rate DESC;", + "notes": [ + "default_facets=pairwise_conditional_dependency", + "template_selection_mode=rule", + "problem_index_within_template=3", + "sql_variant_index=2/2", + "binding_index=98" + ], + "template_selection_mode": "rule", + "selected_template_rank": 9, + "problem_index_within_template": 3, + "sql_variant_index": 2, + "sql_variant_total": 2 + }, + "mode": "subitem_workload_v2", + "sql_source_version": "v2", + "sql_source_label": "v2_current", + "generated_sql_path": "/data/jialinzhang/TabQueryBench/sql_workloads/v2_current/runs_and_launches/runs/v2_cli_20260502_081223_d/c16/sql/v2q_c16_e997ffc8c751d2e8.sql", + "usage_summary": { + "dataset_id": "c16", + "model": "v2-cli:codex", + "run_id": "v2q_c16_e997ffc8c751d2e8", + "api_calls": 0, + "input_tokens": 14613, + "cached_input_tokens": 12032, + "output_tokens": 422, + "total_tokens": 15035, + "cost_usd": 0.0, + "ai_cli_calls": 1, + "estimated_input_tokens": 0, + "estimated_output_tokens": 0, + "estimated_total_tokens": 0, + "usage_source": "ai_cli_json_usage", + "cli_elapsed_ms_total": 9446.95, + "sql_execution_elapsed_ms_total": 3.2, + "conversation_log_path": "/data/jialinzhang/TabQueryBench/sql_workloads/v2_current/runs_and_launches/runs/v2_cli_20260502_081223_d/c16/artifacts/v2q_c16_e997ffc8c751d2e8/cli/conversation.jsonl", + "note": "Executed through a local AI CLI with structured usage metadata." + } +} \ No newline at end of file diff --git a/Query/sql/v2/runs/v2_cli_20260502_081223_d/c16/artifacts/v2q_c16_e997ffc8c751d2e8/trace.jsonl b/Query/sql/v2/runs/v2_cli_20260502_081223_d/c16/artifacts/v2q_c16_e997ffc8c751d2e8/trace.jsonl new file mode 100644 index 0000000000000000000000000000000000000000..e1515f5c4dd712f44e4fc82b56daa403edcbf363 --- /dev/null +++ b/Query/sql/v2/runs/v2_cli_20260502_081223_d/c16/artifacts/v2q_c16_e997ffc8c751d2e8/trace.jsonl @@ -0,0 +1 @@ +{"timestamp": "2026-05-19T16:02:22.386656+00:00", "event_type": "ai_cli_sql_generation", "engine": "v2-cli:codex", "attempt": 1, "command": "codex exec --skip-git-repo-check --disable plugins --sandbox read-only --cd \"/data/jialinzhang/SQLagent\" -m gpt-5.4 --json -", "returncode": 0, "elapsed_ms": 9446.95, "started_at": "2026-05-19T16:02:12.938885+00:00", "ended_at": "2026-05-19T16:02:22.385862+00:00", "prompt_metrics": {"chars": 8765, "bytes_utf8": 8765, "lines": 255, "estimated_tokens": null}, "response_metrics": {"chars": 442, "bytes_utf8": 442, "lines": 1, "estimated_tokens": null}, "usage": {"input_tokens": 14613, "cached_input_tokens": 12032, "output_tokens": 422, "reasoning_output_tokens": 311}, "stderr_preview": "", "stdout_preview": "{\"sql\":\"-- template_id: tpl_m4_group_condition_rate\\nSELECT \\\"ALIGN\\\",\\n AVG(CASE WHEN \\\"ALIGN\\\" = 'Good Characters' THEN 1 ELSE 0 END) AS condition_rate\\nFROM \\\"c16\\\"\\nGROUP BY \\\"ALIGN\\\"\\nORDER BY condition_rate DESC;\",\"notes\":\"Uses the planned Grouped Condition Rate template with both group_col and condition_col bound to \\\"ALIGN\\\", so each ALIGN category gets the rate of rows matching 'Good Characters' within that same category.\"}"} diff --git a/Query/sql/v2/runs/v2_cli_20260502_081223_d/c16/artifacts/v2q_c16_e997ffc8c751d2e8/usage_summary.json b/Query/sql/v2/runs/v2_cli_20260502_081223_d/c16/artifacts/v2q_c16_e997ffc8c751d2e8/usage_summary.json new file mode 100644 index 0000000000000000000000000000000000000000..147347957e589a563803084ddc11ba9e1840cb59 --- /dev/null +++ b/Query/sql/v2/runs/v2_cli_20260502_081223_d/c16/artifacts/v2q_c16_e997ffc8c751d2e8/usage_summary.json @@ -0,0 +1,20 @@ +{ + "dataset_id": "c16", + "model": "v2-cli:codex", + "run_id": "v2q_c16_e997ffc8c751d2e8", + "api_calls": 0, + "input_tokens": 14613, + "cached_input_tokens": 12032, + "output_tokens": 422, + "total_tokens": 15035, + "cost_usd": 0.0, + "ai_cli_calls": 1, + "estimated_input_tokens": 0, + "estimated_output_tokens": 0, + "estimated_total_tokens": 0, + "usage_source": "ai_cli_json_usage", + "cli_elapsed_ms_total": 9446.95, + "sql_execution_elapsed_ms_total": 3.2, + "conversation_log_path": "/data/jialinzhang/TabQueryBench/sql_workloads/v2_current/runs_and_launches/runs/v2_cli_20260502_081223_d/c16/artifacts/v2q_c16_e997ffc8c751d2e8/cli/conversation.jsonl", + "note": "Executed through a local AI CLI with structured usage metadata." +} \ No newline at end of file diff --git a/Query/sql/v2/runs/v2_cli_20260502_081223_d/c16/artifacts/v2q_c16_ee601ee414432d00/final_answer.txt b/Query/sql/v2/runs/v2_cli_20260502_081223_d/c16/artifacts/v2q_c16_ee601ee414432d00/final_answer.txt new file mode 100644 index 0000000000000000000000000000000000000000..cb7962f3824cee8422af5117beff6300e404a6ac --- /dev/null +++ b/Query/sql/v2/runs/v2_cli_20260502_081223_d/c16/artifacts/v2q_c16_ee601ee414432d00/final_answer.txt @@ -0,0 +1,2 @@ +SQL executed successfully for: Use template Within-Group Share of Total to probe dependency_strength_similarity with semantic role within_group_proportion. Focus on group_col=GSM, measure_col=YEAR. +Result preview: [{"GSM": "", "APPEARANCES": "1", "total_measure": 1977551.0, "share_within_group": 14.693566605427096}, {"GSM": "", "APPEARANCES": "2", "total_measure": 1375780.0, "share_within_group": 10.222297712885528}, {"GSM": "Bisexual Characters", "APPEARANCES": "100", "total_measure": 2003.0, "share_within_group": 10.077987421383648}, {"GSM": "Bisexual Characters", "APPEARANCES": "16", "total_measure": 1997.0, "share_within_group": 10.047798742138365}, {"GSM": "Bisexual Characters", "APPEARANCES": "17", "total_measure": 1997.0, "share_within_group": 10.047798742138365}] Results were truncated. \ No newline at end of file diff --git a/Query/sql/v2/runs/v2_cli_20260502_081223_d/c16/artifacts/v2q_c16_ee601ee414432d00/generated_sql.sql b/Query/sql/v2/runs/v2_cli_20260502_081223_d/c16/artifacts/v2q_c16_ee601ee414432d00/generated_sql.sql new file mode 100644 index 0000000000000000000000000000000000000000..fb27f31acac73f3ea7b0e2d785dee612519c7364 --- /dev/null +++ b/Query/sql/v2/runs/v2_cli_20260502_081223_d/c16/artifacts/v2q_c16_ee601ee414432d00/generated_sql.sql @@ -0,0 +1,20 @@ +-- sql_source_version: v2 +-- sql_source_label: v2_current +-- sql_source_run_id: v2_cli_20260502_081223_d +-- sql_source_dataset_id: c16 +-- family_id: conditional_dependency_structure +-- canonical_subitem_id: dependency_strength_similarity +-- intended_facet_id: pairwise_conditional_dependency +-- variant_semantic_role: within_group_proportion +-- template_id: tpl_tpcds_within_group_share +-- query_record_id: v2q_c16_ee601ee414432d00 +-- problem_id: v2p_c16_b26f643d17770f11 +-- realization_mode: agent +-- source_kind: agent +SELECT "GSM", "APPEARANCES", + SUM(CAST(NULLIF("YEAR", '') AS REAL)) AS total_measure, + SUM(CAST(NULLIF("YEAR", '') AS REAL)) * 100.0 + / SUM(SUM(CAST(NULLIF("YEAR", '') AS REAL))) OVER (PARTITION BY "GSM") AS share_within_group +FROM "c16" +GROUP BY "GSM", "APPEARANCES" +ORDER BY share_within_group DESC; diff --git a/Query/sql/v2/runs/v2_cli_20260502_081223_d/c16/artifacts/v2q_c16_ee601ee414432d00/query_results.jsonl b/Query/sql/v2/runs/v2_cli_20260502_081223_d/c16/artifacts/v2q_c16_ee601ee414432d00/query_results.jsonl new file mode 100644 index 0000000000000000000000000000000000000000..e8ada5bd1b69d5165021a9a7135219b2aae59e4a --- /dev/null +++ b/Query/sql/v2/runs/v2_cli_20260502_081223_d/c16/artifacts/v2q_c16_ee601ee414432d00/query_results.jsonl @@ -0,0 +1 @@ +{"step_index": 1, "message_index": 0, "node_name": "v2-cli:codex", "tool_name": "sqlite_query", "query": "-- template_id: tpl_tpcds_within_group_share\nSELECT \"GSM\", \"APPEARANCES\",\n SUM(CAST(NULLIF(\"YEAR\", '') AS REAL)) AS total_measure,\n SUM(CAST(NULLIF(\"YEAR\", '') AS REAL)) * 100.0\n / SUM(SUM(CAST(NULLIF(\"YEAR\", '') AS REAL))) OVER (PARTITION BY \"GSM\") AS share_within_group\nFROM \"c16\"\nGROUP BY \"GSM\", \"APPEARANCES\"\nORDER BY share_within_group DESC;", "result": "{\"query\": \"-- template_id: tpl_tpcds_within_group_share\\nSELECT \\\"GSM\\\", \\\"APPEARANCES\\\",\\n SUM(CAST(NULLIF(\\\"YEAR\\\", '') AS REAL)) AS total_measure,\\n SUM(CAST(NULLIF(\\\"YEAR\\\", '') AS REAL)) * 100.0\\n / SUM(SUM(CAST(NULLIF(\\\"YEAR\\\", '') AS REAL))) OVER (PARTITION BY \\\"GSM\\\") AS share_within_group\\nFROM \\\"c16\\\"\\nGROUP BY \\\"GSM\\\", \\\"APPEARANCES\\\"\\nORDER BY share_within_group DESC;\", \"columns\": [\"GSM\", \"APPEARANCES\", \"total_measure\", \"share_within_group\"], \"rows\": [{\"GSM\": \"\", \"APPEARANCES\": \"1\", \"total_measure\": 1977551.0, \"share_within_group\": 14.693566605427096}, {\"GSM\": \"\", \"APPEARANCES\": \"2\", \"total_measure\": 1375780.0, \"share_within_group\": 10.222297712885528}, {\"GSM\": \"Bisexual Characters\", \"APPEARANCES\": \"100\", \"total_measure\": 2003.0, \"share_within_group\": 10.077987421383648}, {\"GSM\": \"Bisexual Characters\", \"APPEARANCES\": \"16\", \"total_measure\": 1997.0, \"share_within_group\": 10.047798742138365}, {\"GSM\": \"Bisexual Characters\", \"APPEARANCES\": \"17\", \"total_measure\": 1997.0, \"share_within_group\": 10.047798742138365}, {\"GSM\": \"Bisexual Characters\", \"APPEARANCES\": \"53\", \"total_measure\": 1994.0, \"share_within_group\": 10.032704402515723}, {\"GSM\": \"Bisexual Characters\", \"APPEARANCES\": \"34\", \"total_measure\": 1993.0, \"share_within_group\": 10.027672955974843}, {\"GSM\": \"Bisexual Characters\", \"APPEARANCES\": \"20\", \"total_measure\": 1989.0, \"share_within_group\": 10.007547169811321}, {\"GSM\": \"Bisexual Characters\", \"APPEARANCES\": \"97\", \"total_measure\": 1989.0, \"share_within_group\": 10.007547169811321}, {\"GSM\": \"Bisexual Characters\", \"APPEARANCES\": \"38\", \"total_measure\": 1986.0, \"share_within_group\": 9.992452830188679}, {\"GSM\": \"Bisexual Characters\", \"APPEARANCES\": \"371\", \"total_measure\": 1984.0, \"share_within_group\": 9.982389937106918}, {\"GSM\": \"Bisexual Characters\", \"APPEARANCES\": \"32\", \"total_measure\": 1943.0, \"share_within_group\": 9.776100628930818}, {\"GSM\": \"Homosexual Characters\", \"APPEARANCES\": \"2\", \"total_measure\": 9993.0, \"share_within_group\": 9.459126878951952}, {\"GSM\": \"Homosexual Characters\", \"APPEARANCES\": \"10\", \"total_measure\": 7987.0, \"share_within_group\": 7.5602968460111315}, {\"GSM\": \"\", \"APPEARANCES\": \"3\", \"total_measure\": 996920.0, \"share_within_group\": 7.4072984313842625}, {\"GSM\": \"\", \"APPEARANCES\": \"4\", \"total_measure\": 983118.0, \"share_within_group\": 7.3047470401492935}, {\"GSM\": \"\", \"APPEARANCES\": \"5\", \"total_measure\": 763652.0, \"share_within_group\": 5.674074410909054}, {\"GSM\": \"Homosexual Characters\", \"APPEARANCES\": \"8\", \"total_measure\": 5969.0, \"share_within_group\": 5.650107909583128}, {\"GSM\": \"\", \"APPEARANCES\": \"\", \"total_measure\": 688967.0, \"share_within_group\": 5.119151163960519}, {\"GSM\": \"\", \"APPEARANCES\": \"6\", \"total_measure\": 644018.0, \"share_within_group\": 4.785171850482716}, {\"GSM\": \"\", \"APPEARANCES\": \"7\", \"total_measure\": 520196.0, \"share_within_group\": 3.865151681992906}, {\"GSM\": \"Homosexual Characters\", \"APPEARANCES\": \"1\", \"total_measure\": 4003.0, \"share_within_group\": 3.7891408882662527}, {\"GSM\": \"Homosexual Characters\", \"APPEARANCES\": \"6\", \"total_measure\": 4003.0, \"share_within_group\": 3.7891408882662527}, {\"GSM\": \"Homosexual Characters\", \"APPEARANCES\": \"32\", \"total_measure\": 3997.0, \"share_within_group\": 3.783461436522661}, {\"GSM\": \"Homosexual Characters\", \"APPEARANCES\": \"25\", \"total_measure\": 3992.0, \"share_within_group\": 3.778728560069668}, {\"GSM\": \"Homosexual Characters\", \"APPEARANCES\": \"65\", \"total_measure\": 3992.0, \"share_within_group\": 3.778728560069668}, {\"GSM\": \"Homosexual Characters\", \"APPEARANCES\": \"14\", \"total_measure\": 3991.0, \"share_within_group\": 3.7777819847790695}, {\"GSM\": \"Homosexual Characters\", \"APPEARANCES\": \"12\", \"total_measure\": 3985.0, \"share_within_group\": 3.772102533035478}, {\"GSM\": \"Homosexual Characters\", \"APPEARANCES\": \"4\", \"total_measure\": 3983.0, \"share_within_group\": 3.7702093824542806}, {\"GSM\": \"Homosexual Characters\", \"APPEARANCES\": \"3\", \"total_measure\": 3976.0, \"share_within_group\": 3.76358335542009}, {\"GSM\": \"\", \"APPEARANCES\": \"8\", \"total_measure\": 471636.0, \"share_within_group\": 3.5043419762712635}, {\"GSM\": \"\", \"APPEARANCES\": \"9\", \"total_measure\": 370163.0, \"share_within_group\": 2.7503789765041255}, {\"GSM\": \"\", \"APPEARANCES\": \"10\", \"total_measure\": 350341.0, \"share_within_group\": 2.6030978812237633}, {\"GSM\": \"\", \"APPEARANCES\": \"11\", \"total_measure\": 320208.0, \"share_within_group\": 2.3792041649447215}, {\"GSM\": \"Homosexual Characters\", \"APPEARANCES\": \"17\", \"total_measure\": 2009.0, \"share_within_group\": 1.901669758812616}, {\"GSM\": \"Homosexual Characters\", \"APPEARANCES\": \"19\", \"total_measure\": 2006.0, \"share_within_group\": 1.89883003294082}, {\"GSM\": \"Homosexual Characters\", \"APPEARANCES\": \"51\", \"total_measure\": 2006.0, \"share_within_group\": 1.89883003294082}, {\"GSM\": \"Homosexual Characters\", \"APPEARANCES\": \"36\", \"total_measure\": 2004.0, \"share_within_group\": 1.8969368823596229}, {\"GSM\": \"Homosexual Characters\", \"APPEARANCES\": \"15\", \"total_measure\": 2003.0, \"share_within_group\": 1.8959903070690243}, {\"GSM\": \"Homosexual Characters\", \"APPEARANCES\": \"92\", \"total_measure\": 2003.0, \"share_within_group\": 1.8959903070690243}, {\"GSM\": \"Homosexual Characters\", \"APPEARANCES\": \"20\", \"total_measure\": 2002.0, \"share_within_group\": 1.8950437317784257}, {\"GSM\": \"Homosexual Characters\", \"APPEARANCES\": \"31\", \"total_measure\": 2002.0, \"share_within_group\": 1.8950437317784257}, {\"GSM\": \"Homosexual Characters\", \"APPEARANCES\": \"21\", \"total_measure\": 1996.0, \"share_within_group\": 1.889364280034834}, {\"GSM\": \"Homosexual Characters\", \"APPEARANCES\": \"34\", \"total_measure\": 1994.0, \"share_within_group\": 1.8874711294536368}, {\"GSM\": \"Homosexual Characters\", \"APPEARANCES\": \"308\", \"total_measure\": 1992.0, \"share_within_group\": 1.8855779788724396}, {\"GSM\": \"Homosexual Characters\", \"APPEARANCES\": \"24\", \"total_measure\": 1988.0, \"share_within_group\": 1.881791677710045}, {\"GSM\": \"Homosexual Characters\", \"APPEARANCES\": \"29\", \"total_measure\": 1988.0, \"share_within_group\": 1.881791677710045}, {\"GSM\": \"Homosexual Characters\", \"APPEARANCES\": \"11\", \"total_measure\": 1987.0, \"share_within_group\": 1.8808451024194464}, {\"GSM\": \"Homosexual Characters\", \"APPEARANCES\": \"114\", \"total_measure\": 1987.0, \"share_within_group\": 1.8808451024194464}, {\"GSM\": \"Homosexual Characters\", \"APPEARANCES\": \"180\", \"total_measure\": 1987.0, \"share_within_group\": 1.8808451024194464}], \"row_count_returned\": 50, \"row_limit\": 50, \"truncated\": true, \"elapsed_ms\": 10.71}"} diff --git a/Query/sql/v2/runs/v2_cli_20260502_081223_d/c16/artifacts/v2q_c16_ee601ee414432d00/run_manifest.json b/Query/sql/v2/runs/v2_cli_20260502_081223_d/c16/artifacts/v2q_c16_ee601ee414432d00/run_manifest.json new file mode 100644 index 0000000000000000000000000000000000000000..768453dd77de19d8ad1d9e173cf756169af2b7c5 --- /dev/null +++ b/Query/sql/v2/runs/v2_cli_20260502_081223_d/c16/artifacts/v2q_c16_ee601ee414432d00/run_manifest.json @@ -0,0 +1,91 @@ +{ + "run_id": "v2_cli_20260502_081223_d", + "dataset_id": "c16", + "started_at": "2026-05-19T15:38:01.683803+00:00", + "ended_at": "2026-05-19T15:38:17.584807+00:00", + "status": "completed", + "engine": "cli", + "question_record": { + "query_record_id": "v2q_c16_ee601ee414432d00", + "problem_id": "v2p_c16_b26f643d17770f11", + "dataset_id": "c16", + "template_id": "tpl_tpcds_within_group_share", + "template_name": "Within-Group Share of Total", + "family_id": "conditional_dependency_structure", + "canonical_subitem_id": "dependency_strength_similarity", + "intended_facet_id": "pairwise_conditional_dependency", + "variant_semantic_role": "within_group_proportion", + "subitem_assignment_source": "planner_selected", + "source_kind": "agent", + "realization_mode": "agent", + "gate_priority": "primary", + "extended_family": false, + "question": "Use template Within-Group Share of Total to probe dependency_strength_similarity with semantic role within_group_proportion. Focus on group_col=GSM, measure_col=YEAR.", + "bindings": { + "group_col": "GSM", + "measure_col": "YEAR", + "item_col": "APPEARANCES", + "top_k": 12, + "top_n": 3, + "num_tiles": 10, + "percentile_value": 0.95, + "z_threshold": 2.0, + "fraction_threshold": 0.1, + "baseline_multiplier": 1.5, + "baseline_fraction": 0.1, + "min_group_size": 5, + "min_support": 5, + "measure_threshold": 2003.0, + "time_grain": "month", + "lookback_rows": 3, + "current_period_start": "'2024-01-01'", + "current_period_end": "'2024-04-01'", + "previous_period_start": "'2023-10-01'", + "previous_period_end": "'2024-01-01'", + "drift_ratio_threshold": 0.8 + }, + "binding_roles": [ + "group_col", + "item_col", + "measure_col" + ], + "coverage_target_min": "5", + "runtime_sql_skeleton": "SELECT {group_col}, {item_col},\n SUM({measure_col}) AS total_measure,\n SUM({measure_col}) * 100.0 / SUM(SUM({measure_col})) OVER (PARTITION BY {group_col}) AS share_within_group\nFROM {table}\nGROUP BY {group_col}, {item_col}\nORDER BY share_within_group DESC;", + "notes": [ + "default_facets=pairwise_conditional_dependency", + "template_selection_mode=rule", + "problem_index_within_template=9", + "sql_variant_index=1/2", + "binding_index=32" + ], + "template_selection_mode": "rule", + "selected_template_rank": 3, + "problem_index_within_template": 9, + "sql_variant_index": 1, + "sql_variant_total": 2 + }, + "mode": "subitem_workload_v2", + "sql_source_version": "v2", + "sql_source_label": "v2_current", + "generated_sql_path": "/data/jialinzhang/TabQueryBench/sql_workloads/v2_current/runs_and_launches/runs/v2_cli_20260502_081223_d/c16/sql/v2q_c16_ee601ee414432d00.sql", + "usage_summary": { + "dataset_id": "c16", + "model": "v2-cli:codex", + "run_id": "v2q_c16_ee601ee414432d00", + "api_calls": 0, + "input_tokens": 14672, + "cached_input_tokens": 12032, + "output_tokens": 706, + "total_tokens": 15378, + "cost_usd": 0.0, + "ai_cli_calls": 1, + "estimated_input_tokens": 0, + "estimated_output_tokens": 0, + "estimated_total_tokens": 0, + "usage_source": "ai_cli_json_usage", + "cli_elapsed_ms_total": 15883.75, + "sql_execution_elapsed_ms_total": 10.71, + "conversation_log_path": "/data/jialinzhang/TabQueryBench/sql_workloads/v2_current/runs_and_launches/runs/v2_cli_20260502_081223_d/c16/artifacts/v2q_c16_ee601ee414432d00/cli/conversation.jsonl", + "note": "Executed through a local AI CLI with structured usage metadata." + } +} \ No newline at end of file diff --git a/Query/sql/v2/runs/v2_cli_20260502_081223_d/c16/artifacts/v2q_c16_ee601ee414432d00/trace.jsonl b/Query/sql/v2/runs/v2_cli_20260502_081223_d/c16/artifacts/v2q_c16_ee601ee414432d00/trace.jsonl new file mode 100644 index 0000000000000000000000000000000000000000..930c9762ea1ad96e0b5bedb9745113c91756889f --- /dev/null +++ b/Query/sql/v2/runs/v2_cli_20260502_081223_d/c16/artifacts/v2q_c16_ee601ee414432d00/trace.jsonl @@ -0,0 +1 @@ +{"timestamp": "2026-05-19T15:38:17.571195+00:00", "event_type": "ai_cli_sql_generation", "engine": "v2-cli:codex", "attempt": 1, "command": "codex exec --skip-git-repo-check --disable plugins --sandbox read-only --cd \"/data/jialinzhang/SQLagent\" -m gpt-5.4 --json -", "returncode": 0, "elapsed_ms": 15883.75, "started_at": "2026-05-19T15:38:01.685650+00:00", "ended_at": "2026-05-19T15:38:17.569439+00:00", "prompt_metrics": {"chars": 8884, "bytes_utf8": 8884, "lines": 254, "estimated_tokens": null}, "response_metrics": {"chars": 645, "bytes_utf8": 645, "lines": 1, "estimated_tokens": null}, "usage": {"input_tokens": 14672, "cached_input_tokens": 12032, "output_tokens": 706, "reasoning_output_tokens": 516}, "stderr_preview": "", "stdout_preview": "{\"sql\":\"-- template_id: tpl_tpcds_within_group_share\\nSELECT \\\"GSM\\\", \\\"APPEARANCES\\\",\\n SUM(CAST(NULLIF(\\\"YEAR\\\", '') AS REAL)) AS total_measure,\\n SUM(CAST(NULLIF(\\\"YEAR\\\", '') AS REAL)) * 100.0\\n / SUM(SUM(CAST(NULLIF(\\\"YEAR\\\", '') AS REAL))) OVER (PARTITION BY \\\"GSM\\\") AS share_within_group\\nFROM \\\"c16\\\"\\nGROUP BY \\\"GSM\\\", \\\"APPEARANCES\\\"\\nORDER BY share_within_group DESC;\",\"notes\":\"Applied the provided Within-Group Share of Total template with group_col=\\\"GSM\\\", item_col=\\\"APPEARANCES\\\", and measure_col=\\\"YEAR\\\". Since \\\"YEAR\\\" is stored as TEXT in SQLite, NULLIF+CAST is used so blank values are ignored by SUM.\"}"} diff --git a/Query/sql/v2/runs/v2_cli_20260502_081223_d/c16/artifacts/v2q_c16_ee601ee414432d00/usage_summary.json b/Query/sql/v2/runs/v2_cli_20260502_081223_d/c16/artifacts/v2q_c16_ee601ee414432d00/usage_summary.json new file mode 100644 index 0000000000000000000000000000000000000000..3de9d2c057e85ed8fc9c1660eddb18a102a2be4b --- /dev/null +++ b/Query/sql/v2/runs/v2_cli_20260502_081223_d/c16/artifacts/v2q_c16_ee601ee414432d00/usage_summary.json @@ -0,0 +1,20 @@ +{ + "dataset_id": "c16", + "model": "v2-cli:codex", + "run_id": "v2q_c16_ee601ee414432d00", + "api_calls": 0, + "input_tokens": 14672, + "cached_input_tokens": 12032, + "output_tokens": 706, + "total_tokens": 15378, + "cost_usd": 0.0, + "ai_cli_calls": 1, + "estimated_input_tokens": 0, + "estimated_output_tokens": 0, + "estimated_total_tokens": 0, + "usage_source": "ai_cli_json_usage", + "cli_elapsed_ms_total": 15883.75, + "sql_execution_elapsed_ms_total": 10.71, + "conversation_log_path": "/data/jialinzhang/TabQueryBench/sql_workloads/v2_current/runs_and_launches/runs/v2_cli_20260502_081223_d/c16/artifacts/v2q_c16_ee601ee414432d00/cli/conversation.jsonl", + "note": "Executed through a local AI CLI with structured usage metadata." +} \ No newline at end of file diff --git a/Query/sql/v2/runs/v2_cli_20260502_081223_d/c16/artifacts/v2q_c16_efa91f27a0253fd3/final_answer.txt b/Query/sql/v2/runs/v2_cli_20260502_081223_d/c16/artifacts/v2q_c16_efa91f27a0253fd3/final_answer.txt new file mode 100644 index 0000000000000000000000000000000000000000..9049f868c198b25fd1aa057b01f22de46f8ec5d4 --- /dev/null +++ b/Query/sql/v2/runs/v2_cli_20260502_081223_d/c16/artifacts/v2q_c16_efa91f27a0253fd3/final_answer.txt @@ -0,0 +1,2 @@ +SQL executed successfully for: Use template Quantile Tail Slice to probe tail_set_consistency with semantic role rare_extreme_view. Focus on measure_col=APPEARANCES. +Result preview: [{"APPEARANCES": "3093"}, {"APPEARANCES": "2496"}, {"APPEARANCES": "1565"}, {"APPEARANCES": "1316"}, {"APPEARANCES": "1237"}] Results were truncated. \ No newline at end of file diff --git a/Query/sql/v2/runs/v2_cli_20260502_081223_d/c16/artifacts/v2q_c16_efa91f27a0253fd3/generated_sql.sql b/Query/sql/v2/runs/v2_cli_20260502_081223_d/c16/artifacts/v2q_c16_efa91f27a0253fd3/generated_sql.sql new file mode 100644 index 0000000000000000000000000000000000000000..02787b940543c67e55e2a81de28e7c5dbb89e09a --- /dev/null +++ b/Query/sql/v2/runs/v2_cli_20260502_081223_d/c16/artifacts/v2q_c16_efa91f27a0253fd3/generated_sql.sql @@ -0,0 +1,25 @@ +-- sql_source_version: v2 +-- sql_source_label: v2_current +-- sql_source_run_id: v2_cli_20260502_081223_d +-- sql_source_dataset_id: c16 +-- family_id: tail_rarity_structure +-- canonical_subitem_id: tail_set_consistency +-- intended_facet_id: low_support_extremes +-- variant_semantic_role: rare_extreme_view +-- template_id: tpl_m4_quantile_tail_slice +-- query_record_id: v2q_c16_efa91f27a0253fd3 +-- problem_id: v2p_c16_e51fc69c73f6fc5a +-- realization_mode: agent +-- source_kind: agent +WITH "buckets" AS ( + SELECT + "APPEARANCES", + NTILE(10) OVER (ORDER BY CAST("APPEARANCES" AS REAL) DESC) AS "tail_bucket" + FROM "c16" + WHERE "APPEARANCES" IS NOT NULL + AND TRIM("APPEARANCES") <> '' +) +SELECT "APPEARANCES" +FROM "buckets" +WHERE "tail_bucket" = 1 +ORDER BY CAST("APPEARANCES" AS REAL) DESC; diff --git a/Query/sql/v2/runs/v2_cli_20260502_081223_d/c16/artifacts/v2q_c16_efa91f27a0253fd3/query_results.jsonl b/Query/sql/v2/runs/v2_cli_20260502_081223_d/c16/artifacts/v2q_c16_efa91f27a0253fd3/query_results.jsonl new file mode 100644 index 0000000000000000000000000000000000000000..26def36e97c6f9abe53be2dcc9dc09c8fcd3e1e6 --- /dev/null +++ b/Query/sql/v2/runs/v2_cli_20260502_081223_d/c16/artifacts/v2q_c16_efa91f27a0253fd3/query_results.jsonl @@ -0,0 +1 @@ +{"step_index": 1, "message_index": 0, "node_name": "v2-cli:codex", "tool_name": "sqlite_query", "query": "-- template_id: tpl_m4_quantile_tail_slice\nWITH \"buckets\" AS (\n SELECT\n \"APPEARANCES\",\n NTILE(10) OVER (ORDER BY CAST(\"APPEARANCES\" AS REAL) DESC) AS \"tail_bucket\"\n FROM \"c16\"\n WHERE \"APPEARANCES\" IS NOT NULL\n AND TRIM(\"APPEARANCES\") <> ''\n)\nSELECT \"APPEARANCES\"\nFROM \"buckets\"\nWHERE \"tail_bucket\" = 1\nORDER BY CAST(\"APPEARANCES\" AS REAL) DESC;", "result": "{\"query\": \"-- template_id: tpl_m4_quantile_tail_slice\\nWITH \\\"buckets\\\" AS (\\n SELECT\\n \\\"APPEARANCES\\\",\\n NTILE(10) OVER (ORDER BY CAST(\\\"APPEARANCES\\\" AS REAL) DESC) AS \\\"tail_bucket\\\"\\n FROM \\\"c16\\\"\\n WHERE \\\"APPEARANCES\\\" IS NOT NULL\\n AND TRIM(\\\"APPEARANCES\\\") <> ''\\n)\\nSELECT \\\"APPEARANCES\\\"\\nFROM \\\"buckets\\\"\\nWHERE \\\"tail_bucket\\\" = 1\\nORDER BY CAST(\\\"APPEARANCES\\\" AS REAL) DESC;\", \"columns\": [\"APPEARANCES\"], \"rows\": [{\"APPEARANCES\": \"3093\"}, {\"APPEARANCES\": \"2496\"}, {\"APPEARANCES\": \"1565\"}, {\"APPEARANCES\": \"1316\"}, {\"APPEARANCES\": \"1237\"}, {\"APPEARANCES\": \"1231\"}, {\"APPEARANCES\": \"1121\"}, {\"APPEARANCES\": \"1095\"}, {\"APPEARANCES\": \"1075\"}, {\"APPEARANCES\": \"1028\"}, {\"APPEARANCES\": \"1028\"}, {\"APPEARANCES\": \"969\"}, {\"APPEARANCES\": \"951\"}, {\"APPEARANCES\": \"951\"}, {\"APPEARANCES\": \"934\"}, {\"APPEARANCES\": \"930\"}, {\"APPEARANCES\": \"803\"}, {\"APPEARANCES\": \"716\"}, {\"APPEARANCES\": \"706\"}, {\"APPEARANCES\": \"677\"}, {\"APPEARANCES\": \"654\"}, {\"APPEARANCES\": \"635\"}, {\"APPEARANCES\": \"605\"}, {\"APPEARANCES\": \"595\"}, {\"APPEARANCES\": \"593\"}, {\"APPEARANCES\": \"584\"}, {\"APPEARANCES\": \"560\"}, {\"APPEARANCES\": \"558\"}, {\"APPEARANCES\": \"557\"}, {\"APPEARANCES\": \"549\"}, {\"APPEARANCES\": \"517\"}, {\"APPEARANCES\": \"492\"}, {\"APPEARANCES\": \"487\"}, {\"APPEARANCES\": \"470\"}, {\"APPEARANCES\": \"439\"}, {\"APPEARANCES\": \"436\"}, {\"APPEARANCES\": \"429\"}, {\"APPEARANCES\": \"427\"}, {\"APPEARANCES\": \"423\"}, {\"APPEARANCES\": \"422\"}, {\"APPEARANCES\": \"413\"}, {\"APPEARANCES\": \"399\"}, {\"APPEARANCES\": \"393\"}, {\"APPEARANCES\": \"391\"}, {\"APPEARANCES\": \"388\"}, {\"APPEARANCES\": \"386\"}, {\"APPEARANCES\": \"386\"}, {\"APPEARANCES\": \"374\"}, {\"APPEARANCES\": \"371\"}, {\"APPEARANCES\": \"370\"}], \"row_count_returned\": 50, \"row_limit\": 50, \"truncated\": true, \"elapsed_ms\": 20.86}"} diff --git a/Query/sql/v2/runs/v2_cli_20260502_081223_d/c16/artifacts/v2q_c16_efa91f27a0253fd3/run_manifest.json b/Query/sql/v2/runs/v2_cli_20260502_081223_d/c16/artifacts/v2q_c16_efa91f27a0253fd3/run_manifest.json new file mode 100644 index 0000000000000000000000000000000000000000..a44d0931cdde03c9e3ce375ea48ae965c474ec26 --- /dev/null +++ b/Query/sql/v2/runs/v2_cli_20260502_081223_d/c16/artifacts/v2q_c16_efa91f27a0253fd3/run_manifest.json @@ -0,0 +1,87 @@ +{ + "run_id": "v2_cli_20260502_081223_d", + "dataset_id": "c16", + "started_at": "2026-05-19T15:43:43.868295+00:00", + "ended_at": "2026-05-19T15:43:59.640947+00:00", + "status": "completed", + "engine": "cli", + "question_record": { + "query_record_id": "v2q_c16_efa91f27a0253fd3", + "problem_id": "v2p_c16_e51fc69c73f6fc5a", + "dataset_id": "c16", + "template_id": "tpl_m4_quantile_tail_slice", + "template_name": "Quantile Tail Slice", + "family_id": "tail_rarity_structure", + "canonical_subitem_id": "tail_set_consistency", + "intended_facet_id": "low_support_extremes", + "variant_semantic_role": "rare_extreme_view", + "subitem_assignment_source": "planner_selected", + "source_kind": "agent", + "realization_mode": "agent", + "gate_priority": "primary", + "extended_family": false, + "question": "Use template Quantile Tail Slice to probe tail_set_consistency with semantic role rare_extreme_view. Focus on measure_col=APPEARANCES.", + "bindings": { + "measure_col": "APPEARANCES", + "top_k": 11, + "top_n": 4, + "num_tiles": 10, + "percentile_value": 0.9, + "z_threshold": 2.0, + "fraction_threshold": 0.1, + "baseline_multiplier": 1.5, + "baseline_fraction": 0.1, + "min_group_size": 5, + "min_support": 5, + "measure_threshold": 15.0, + "time_grain": "month", + "lookback_rows": 3, + "current_period_start": "'2024-01-01'", + "current_period_end": "'2024-04-01'", + "previous_period_start": "'2023-10-01'", + "previous_period_end": "'2024-01-01'", + "drift_ratio_threshold": 0.8 + }, + "binding_roles": [ + "measure_col" + ], + "coverage_target_min": "5", + "runtime_sql_skeleton": "WITH buckets AS (\n SELECT {measure_col},\n NTILE({num_tiles}) OVER (ORDER BY {measure_col} DESC) AS tail_bucket\n FROM {table}\n)\nSELECT {measure_col}\nFROM buckets\nWHERE tail_bucket = 1\nORDER BY {measure_col} DESC;", + "notes": [ + "default_facets=low_support_extremes", + "template_selection_mode=rule", + "problem_index_within_template=2", + "sql_variant_index=1/1", + "binding_index=61" + ], + "template_selection_mode": "rule", + "selected_template_rank": 6, + "problem_index_within_template": 2, + "sql_variant_index": 1, + "sql_variant_total": 1 + }, + "mode": "subitem_workload_v2", + "sql_source_version": "v2", + "sql_source_label": "v2_current", + "generated_sql_path": "/data/jialinzhang/TabQueryBench/sql_workloads/v2_current/runs_and_launches/runs/v2_cli_20260502_081223_d/c16/sql/v2q_c16_efa91f27a0253fd3.sql", + "usage_summary": { + "dataset_id": "c16", + "model": "v2-cli:codex", + "run_id": "v2q_c16_efa91f27a0253fd3", + "api_calls": 0, + "input_tokens": 14609, + "cached_input_tokens": 12032, + "output_tokens": 654, + "total_tokens": 15263, + "cost_usd": 0.0, + "ai_cli_calls": 1, + "estimated_input_tokens": 0, + "estimated_output_tokens": 0, + "estimated_total_tokens": 0, + "usage_source": "ai_cli_json_usage", + "cli_elapsed_ms_total": 15745.36, + "sql_execution_elapsed_ms_total": 20.86, + "conversation_log_path": "/data/jialinzhang/TabQueryBench/sql_workloads/v2_current/runs_and_launches/runs/v2_cli_20260502_081223_d/c16/artifacts/v2q_c16_efa91f27a0253fd3/cli/conversation.jsonl", + "note": "Executed through a local AI CLI with structured usage metadata." + } +} \ No newline at end of file diff --git a/Query/sql/v2/runs/v2_cli_20260502_081223_d/c16/artifacts/v2q_c16_efa91f27a0253fd3/trace.jsonl b/Query/sql/v2/runs/v2_cli_20260502_081223_d/c16/artifacts/v2q_c16_efa91f27a0253fd3/trace.jsonl new file mode 100644 index 0000000000000000000000000000000000000000..42cecfa767fb6cd3a50ecacf738c048ad72743c4 --- /dev/null +++ b/Query/sql/v2/runs/v2_cli_20260502_081223_d/c16/artifacts/v2q_c16_efa91f27a0253fd3/trace.jsonl @@ -0,0 +1 @@ +{"timestamp": "2026-05-19T15:43:59.616985+00:00", "event_type": "ai_cli_sql_generation", "engine": "v2-cli:codex", "attempt": 1, "command": "codex exec --skip-git-repo-check --disable plugins --sandbox read-only --cd \"/data/jialinzhang/SQLagent\" -m gpt-5.4 --json -", "returncode": 0, "elapsed_ms": 15745.36, "started_at": "2026-05-19T15:43:43.869895+00:00", "ended_at": "2026-05-19T15:43:59.615292+00:00", "prompt_metrics": {"chars": 8663, "bytes_utf8": 8663, "lines": 250, "estimated_tokens": null}, "response_metrics": {"chars": 645, "bytes_utf8": 645, "lines": 1, "estimated_tokens": null}, "usage": {"input_tokens": 14609, "cached_input_tokens": 12032, "output_tokens": 654, "reasoning_output_tokens": 458}, "stderr_preview": "", "stdout_preview": "{\"sql\":\"-- template_id: tpl_m4_quantile_tail_slice\\nWITH \\\"buckets\\\" AS (\\n SELECT\\n \\\"APPEARANCES\\\",\\n NTILE(10) OVER (ORDER BY CAST(\\\"APPEARANCES\\\" AS REAL) DESC) AS \\\"tail_bucket\\\"\\n FROM \\\"c16\\\"\\n WHERE \\\"APPEARANCES\\\" IS NOT NULL\\n AND TRIM(\\\"APPEARANCES\\\") <> ''\\n)\\nSELECT \\\"APPEARANCES\\\"\\nFROM \\\"buckets\\\"\\nWHERE \\\"tail_bucket\\\" = 1\\nORDER BY CAST(\\\"APPEARANCES\\\" AS REAL) DESC;\",\"notes\":\"Used the Quantile Tail Slice template with measure_col bound to \\\"APPEARANCES\\\" and num_tiles=10. Casts \\\"APPEARANCES\\\" from TEXT to REAL for correct numeric tail bucketing and ordering, and excludes null/blank values.\"}"} diff --git a/Query/sql/v2/runs/v2_cli_20260502_081223_d/c16/artifacts/v2q_c16_efa91f27a0253fd3/usage_summary.json b/Query/sql/v2/runs/v2_cli_20260502_081223_d/c16/artifacts/v2q_c16_efa91f27a0253fd3/usage_summary.json new file mode 100644 index 0000000000000000000000000000000000000000..526b8d5924cf905204c234f4e265e72d60b2799d --- /dev/null +++ b/Query/sql/v2/runs/v2_cli_20260502_081223_d/c16/artifacts/v2q_c16_efa91f27a0253fd3/usage_summary.json @@ -0,0 +1,20 @@ +{ + "dataset_id": "c16", + "model": "v2-cli:codex", + "run_id": "v2q_c16_efa91f27a0253fd3", + "api_calls": 0, + "input_tokens": 14609, + "cached_input_tokens": 12032, + "output_tokens": 654, + "total_tokens": 15263, + "cost_usd": 0.0, + "ai_cli_calls": 1, + "estimated_input_tokens": 0, + "estimated_output_tokens": 0, + "estimated_total_tokens": 0, + "usage_source": "ai_cli_json_usage", + "cli_elapsed_ms_total": 15745.36, + "sql_execution_elapsed_ms_total": 20.86, + "conversation_log_path": "/data/jialinzhang/TabQueryBench/sql_workloads/v2_current/runs_and_launches/runs/v2_cli_20260502_081223_d/c16/artifacts/v2q_c16_efa91f27a0253fd3/cli/conversation.jsonl", + "note": "Executed through a local AI CLI with structured usage metadata." +} \ No newline at end of file diff --git a/Query/sql/v2/runs/v2_cli_20260502_081223_d/c16/artifacts/v2q_c16_f0a063225961af10/final_answer.txt b/Query/sql/v2/runs/v2_cli_20260502_081223_d/c16/artifacts/v2q_c16_f0a063225961af10/final_answer.txt new file mode 100644 index 0000000000000000000000000000000000000000..741ca4ecc00d3e17cb5247c9bf3a904b8ec8fa9c --- /dev/null +++ b/Query/sql/v2/runs/v2_cli_20260502_081223_d/c16/artifacts/v2q_c16_f0a063225961af10/final_answer.txt @@ -0,0 +1,2 @@ +SQL executed successfully for: Use template Grouped Ratio of Two Conditions to probe direction_consistency with semantic role contrastive_conditional_view. Focus on group_col=GSM, condition_col=SEX. +Result preview: [{"GSM": "", "condition_ratio": 2.4504643962848296}, {"GSM": "Homosexual Characters", "condition_ratio": 1.25}, {"GSM": "Bisexual Characters", "condition_ratio": 0.8}] \ No newline at end of file diff --git a/Query/sql/v2/runs/v2_cli_20260502_081223_d/c16/artifacts/v2q_c16_f0a063225961af10/generated_sql.sql b/Query/sql/v2/runs/v2_cli_20260502_081223_d/c16/artifacts/v2q_c16_f0a063225961af10/generated_sql.sql new file mode 100644 index 0000000000000000000000000000000000000000..b3b78f8e83c8ca072601429f6bba12dd288f8f87 --- /dev/null +++ b/Query/sql/v2/runs/v2_cli_20260502_081223_d/c16/artifacts/v2q_c16_f0a063225961af10/generated_sql.sql @@ -0,0 +1,24 @@ +-- sql_source_version: v2 +-- sql_source_label: v2_current +-- sql_source_run_id: v2_cli_20260502_081223_d +-- sql_source_dataset_id: c16 +-- family_id: conditional_dependency_structure +-- canonical_subitem_id: direction_consistency +-- intended_facet_id: conditional_rate_shift +-- variant_semantic_role: contrastive_conditional_view +-- template_id: tpl_m4_group_ratio_two_conditions +-- query_record_id: v2q_c16_f0a063225961af10 +-- problem_id: v2p_c16_edb61dab019eb4a8 +-- realization_mode: agent +-- source_kind: agent +WITH grouped AS ( + SELECT "GSM", + SUM(CASE WHEN "SEX" = 'Male Characters' THEN 1 ELSE 0 END) AS numerator_count, + SUM(CASE WHEN "SEX" = 'Female Characters' THEN 1 ELSE 0 END) AS denominator_count + FROM "c16" + GROUP BY "GSM" +) +SELECT "GSM", + CAST(numerator_count AS FLOAT) / NULLIF(denominator_count, 0) AS condition_ratio +FROM grouped +ORDER BY condition_ratio DESC; diff --git a/Query/sql/v2/runs/v2_cli_20260502_081223_d/c16/artifacts/v2q_c16_f0a063225961af10/query_results.jsonl b/Query/sql/v2/runs/v2_cli_20260502_081223_d/c16/artifacts/v2q_c16_f0a063225961af10/query_results.jsonl new file mode 100644 index 0000000000000000000000000000000000000000..46b200595b036db2b43c6230d0569cd37865b394 --- /dev/null +++ b/Query/sql/v2/runs/v2_cli_20260502_081223_d/c16/artifacts/v2q_c16_f0a063225961af10/query_results.jsonl @@ -0,0 +1 @@ +{"step_index": 1, "message_index": 0, "node_name": "v2-cli:codex", "tool_name": "sqlite_query", "query": "-- template_id: tpl_m4_group_ratio_two_conditions\nWITH grouped AS (\n SELECT \"GSM\",\n SUM(CASE WHEN \"SEX\" = 'Male Characters' THEN 1 ELSE 0 END) AS numerator_count,\n SUM(CASE WHEN \"SEX\" = 'Female Characters' THEN 1 ELSE 0 END) AS denominator_count\n FROM \"c16\"\n GROUP BY \"GSM\"\n)\nSELECT \"GSM\",\n CAST(numerator_count AS FLOAT) / NULLIF(denominator_count, 0) AS condition_ratio\nFROM grouped\nORDER BY condition_ratio DESC;", "result": "{\"query\": \"-- template_id: tpl_m4_group_ratio_two_conditions\\nWITH grouped AS (\\n SELECT \\\"GSM\\\",\\n SUM(CASE WHEN \\\"SEX\\\" = 'Male Characters' THEN 1 ELSE 0 END) AS numerator_count,\\n SUM(CASE WHEN \\\"SEX\\\" = 'Female Characters' THEN 1 ELSE 0 END) AS denominator_count\\n FROM \\\"c16\\\"\\n GROUP BY \\\"GSM\\\"\\n)\\nSELECT \\\"GSM\\\",\\n CAST(numerator_count AS FLOAT) / NULLIF(denominator_count, 0) AS condition_ratio\\nFROM grouped\\nORDER BY condition_ratio DESC;\", \"columns\": [\"GSM\", \"condition_ratio\"], \"rows\": [{\"GSM\": \"\", \"condition_ratio\": 2.4504643962848296}, {\"GSM\": \"Homosexual Characters\", \"condition_ratio\": 1.25}, {\"GSM\": \"Bisexual Characters\", \"condition_ratio\": 0.8}], \"row_count_returned\": 3, \"row_limit\": 50, \"truncated\": false, \"elapsed_ms\": 3.33}"} diff --git a/Query/sql/v2/runs/v2_cli_20260502_081223_d/c16/artifacts/v2q_c16_f0a063225961af10/run_manifest.json b/Query/sql/v2/runs/v2_cli_20260502_081223_d/c16/artifacts/v2q_c16_f0a063225961af10/run_manifest.json new file mode 100644 index 0000000000000000000000000000000000000000..0acdf2e89c9ffc52c9c9145e8427fb588359e93e --- /dev/null +++ b/Query/sql/v2/runs/v2_cli_20260502_081223_d/c16/artifacts/v2q_c16_f0a063225961af10/run_manifest.json @@ -0,0 +1,92 @@ +{ + "run_id": "v2_cli_20260502_081223_d", + "dataset_id": "c16", + "started_at": "2026-05-19T15:39:39.606205+00:00", + "ended_at": "2026-05-19T15:39:55.177041+00:00", + "status": "completed", + "engine": "cli", + "question_record": { + "query_record_id": "v2q_c16_f0a063225961af10", + "problem_id": "v2p_c16_edb61dab019eb4a8", + "dataset_id": "c16", + "template_id": "tpl_m4_group_ratio_two_conditions", + "template_name": "Grouped Ratio of Two Conditions", + "family_id": "conditional_dependency_structure", + "canonical_subitem_id": "direction_consistency", + "intended_facet_id": "conditional_rate_shift", + "variant_semantic_role": "contrastive_conditional_view", + "subitem_assignment_source": "planner_selected", + "source_kind": "agent", + "realization_mode": "agent", + "gate_priority": "primary", + "extended_family": false, + "question": "Use template Grouped Ratio of Two Conditions to probe direction_consistency with semantic role contrastive_conditional_view. Focus on group_col=GSM, condition_col=SEX.", + "bindings": { + "group_col": "GSM", + "condition_col": "SEX", + "condition_value": "Male Characters", + "positive_value": "Male Characters", + "negative_value": "Female Characters", + "top_k": 14, + "top_n": 6, + "num_tiles": 10, + "percentile_value": 0.9, + "z_threshold": 2.0, + "fraction_threshold": 0.1, + "baseline_multiplier": 1.5, + "baseline_fraction": 0.1, + "min_group_size": 5, + "min_support": 5, + "measure_threshold": 213203.0, + "time_grain": "month", + "lookback_rows": 3, + "current_period_start": "'2024-01-01'", + "current_period_end": "'2024-04-01'", + "previous_period_start": "'2023-10-01'", + "previous_period_end": "'2024-01-01'", + "drift_ratio_threshold": 0.8 + }, + "binding_roles": [ + "group_col", + "condition_col" + ], + "coverage_target_min": "5", + "runtime_sql_skeleton": "WITH grouped AS (\n SELECT {group_col},\n SUM(CASE WHEN {condition_col} = {positive_value} THEN 1 ELSE 0 END) AS numerator_count,\n SUM(CASE WHEN {condition_col} = {negative_value} THEN 1 ELSE 0 END) AS denominator_count\n FROM {table}\n GROUP BY {group_col}\n)\nSELECT {group_col},\n CAST(numerator_count AS FLOAT) / NULLIF(denominator_count, 0) AS condition_ratio\nFROM grouped\nORDER BY condition_ratio DESC;", + "notes": [ + "default_facets=conditional_rate_shift", + "template_selection_mode=rule", + "problem_index_within_template=4", + "sql_variant_index=1/1", + "binding_index=39" + ], + "template_selection_mode": "rule", + "selected_template_rank": 4, + "problem_index_within_template": 4, + "sql_variant_index": 1, + "sql_variant_total": 1 + }, + "mode": "subitem_workload_v2", + "sql_source_version": "v2", + "sql_source_label": "v2_current", + "generated_sql_path": "/data/jialinzhang/TabQueryBench/sql_workloads/v2_current/runs_and_launches/runs/v2_cli_20260502_081223_d/c16/sql/v2q_c16_f0a063225961af10.sql", + "usage_summary": { + "dataset_id": "c16", + "model": "v2-cli:codex", + "run_id": "v2q_c16_f0a063225961af10", + "api_calls": 0, + "input_tokens": 14763, + "cached_input_tokens": 12032, + "output_tokens": 572, + "total_tokens": 15335, + "cost_usd": 0.0, + "ai_cli_calls": 1, + "estimated_input_tokens": 0, + "estimated_output_tokens": 0, + "estimated_total_tokens": 0, + "usage_source": "ai_cli_json_usage", + "cli_elapsed_ms_total": 15563.82, + "sql_execution_elapsed_ms_total": 3.33, + "conversation_log_path": "/data/jialinzhang/TabQueryBench/sql_workloads/v2_current/runs_and_launches/runs/v2_cli_20260502_081223_d/c16/artifacts/v2q_c16_f0a063225961af10/cli/conversation.jsonl", + "note": "Executed through a local AI CLI with structured usage metadata." + } +} \ No newline at end of file diff --git a/Query/sql/v2/runs/v2_cli_20260502_081223_d/c16/artifacts/v2q_c16_f0a063225961af10/trace.jsonl b/Query/sql/v2/runs/v2_cli_20260502_081223_d/c16/artifacts/v2q_c16_f0a063225961af10/trace.jsonl new file mode 100644 index 0000000000000000000000000000000000000000..793d7ec91428627092c7d4d7eecf7dff6c736217 --- /dev/null +++ b/Query/sql/v2/runs/v2_cli_20260502_081223_d/c16/artifacts/v2q_c16_f0a063225961af10/trace.jsonl @@ -0,0 +1 @@ +{"timestamp": "2026-05-19T15:39:55.172173+00:00", "event_type": "ai_cli_sql_generation", "engine": "v2-cli:codex", "attempt": 1, "command": "codex exec --skip-git-repo-check --disable plugins --sandbox read-only --cd \"/data/jialinzhang/SQLagent\" -m gpt-5.4 --json -", "returncode": 0, "elapsed_ms": 15563.82, "started_at": "2026-05-19T15:39:39.607584+00:00", "ended_at": "2026-05-19T15:39:55.171425+00:00", "prompt_metrics": {"chars": 9320, "bytes_utf8": 9320, "lines": 255, "estimated_tokens": null}, "response_metrics": {"chars": 606, "bytes_utf8": 606, "lines": 1, "estimated_tokens": null}, "usage": {"input_tokens": 14763, "cached_input_tokens": 12032, "output_tokens": 572, "reasoning_output_tokens": 408}, "stderr_preview": "", "stdout_preview": "{\"sql\":\"-- template_id: tpl_m4_group_ratio_two_conditions\\nWITH grouped AS (\\n SELECT \\\"GSM\\\",\\n SUM(CASE WHEN \\\"SEX\\\" = 'Male Characters' THEN 1 ELSE 0 END) AS numerator_count,\\n SUM(CASE WHEN \\\"SEX\\\" = 'Female Characters' THEN 1 ELSE 0 END) AS denominator_count\\n FROM \\\"c16\\\"\\n GROUP BY \\\"GSM\\\"\\n)\\nSELECT \\\"GSM\\\",\\n CAST(numerator_count AS FLOAT) / NULLIF(denominator_count, 0) AS condition_ratio\\nFROM grouped\\nORDER BY condition_ratio DESC;\",\"notes\":\"Computes the ratio of male to female characters within each GSM group using the provided grouped-ratio template.\"}"} diff --git a/Query/sql/v2/runs/v2_cli_20260502_081223_d/c16/artifacts/v2q_c16_f0a063225961af10/usage_summary.json b/Query/sql/v2/runs/v2_cli_20260502_081223_d/c16/artifacts/v2q_c16_f0a063225961af10/usage_summary.json new file mode 100644 index 0000000000000000000000000000000000000000..09214009959c9a4440468bd7dff8015c6e7db747 --- /dev/null +++ b/Query/sql/v2/runs/v2_cli_20260502_081223_d/c16/artifacts/v2q_c16_f0a063225961af10/usage_summary.json @@ -0,0 +1,20 @@ +{ + "dataset_id": "c16", + "model": "v2-cli:codex", + "run_id": "v2q_c16_f0a063225961af10", + "api_calls": 0, + "input_tokens": 14763, + "cached_input_tokens": 12032, + "output_tokens": 572, + "total_tokens": 15335, + "cost_usd": 0.0, + "ai_cli_calls": 1, + "estimated_input_tokens": 0, + "estimated_output_tokens": 0, + "estimated_total_tokens": 0, + "usage_source": "ai_cli_json_usage", + "cli_elapsed_ms_total": 15563.82, + "sql_execution_elapsed_ms_total": 3.33, + "conversation_log_path": "/data/jialinzhang/TabQueryBench/sql_workloads/v2_current/runs_and_launches/runs/v2_cli_20260502_081223_d/c16/artifacts/v2q_c16_f0a063225961af10/cli/conversation.jsonl", + "note": "Executed through a local AI CLI with structured usage metadata." +} \ No newline at end of file diff --git a/Query/sql/v2/runs/v2_cli_20260502_081223_d/c16/artifacts/v2q_c16_f2b81d352e894fce/final_answer.txt b/Query/sql/v2/runs/v2_cli_20260502_081223_d/c16/artifacts/v2q_c16_f2b81d352e894fce/final_answer.txt new file mode 100644 index 0000000000000000000000000000000000000000..6fbc569170cd295a893e9cb4d1798d160bf0cb3a --- /dev/null +++ b/Query/sql/v2/runs/v2_cli_20260502_081223_d/c16/artifacts/v2q_c16_f2b81d352e894fce/final_answer.txt @@ -0,0 +1,2 @@ +SQL executed successfully for: Use template Grouped Condition Rate to probe dependency_strength_similarity with semantic role focused_target_view. Focus on group_col=YEAR, condition_col=ALIGN. +Result preview: [{"YEAR": "1955", "condition_rate": 0.8333333333333334}, {"YEAR": "1951", "condition_rate": 0.8333333333333334}, {"YEAR": "1938", "condition_rate": 0.8}, {"YEAR": "1950", "condition_rate": 0.7777777777777778}, {"YEAR": "1939", "condition_rate": 0.7222222222222222}] Results were truncated. \ No newline at end of file diff --git a/Query/sql/v2/runs/v2_cli_20260502_081223_d/c16/artifacts/v2q_c16_f2b81d352e894fce/generated_sql.sql b/Query/sql/v2/runs/v2_cli_20260502_081223_d/c16/artifacts/v2q_c16_f2b81d352e894fce/generated_sql.sql new file mode 100644 index 0000000000000000000000000000000000000000..a66f78de94938304719d80abbe076e16d5f59c93 --- /dev/null +++ b/Query/sql/v2/runs/v2_cli_20260502_081223_d/c16/artifacts/v2q_c16_f2b81d352e894fce/generated_sql.sql @@ -0,0 +1,22 @@ +-- sql_source_version: v2 +-- sql_source_label: v2_current +-- sql_source_run_id: v2_cli_20260502_081223_d +-- sql_source_dataset_id: c16 +-- family_id: conditional_dependency_structure +-- canonical_subitem_id: dependency_strength_similarity +-- intended_facet_id: pairwise_conditional_dependency +-- variant_semantic_role: focused_target_view +-- template_id: tpl_m4_group_condition_rate +-- query_record_id: v2q_c16_f2b81d352e894fce +-- problem_id: v2p_c16_8f80c73f6c257214 +-- realization_mode: agent +-- source_kind: agent +SELECT + "YEAR", + AVG(CASE WHEN "ALIGN" = 'Good Characters' THEN 1.0 ELSE 0.0 END) AS "condition_rate" +FROM "c16" +WHERE "YEAR" IS NOT NULL + AND "YEAR" <> '' +GROUP BY "YEAR" +HAVING COUNT(*) >= 5 +ORDER BY "condition_rate" DESC; diff --git a/Query/sql/v2/runs/v2_cli_20260502_081223_d/c16/artifacts/v2q_c16_f2b81d352e894fce/query_results.jsonl b/Query/sql/v2/runs/v2_cli_20260502_081223_d/c16/artifacts/v2q_c16_f2b81d352e894fce/query_results.jsonl new file mode 100644 index 0000000000000000000000000000000000000000..fe4763d83d1728478704612ad79ef0a0dc59b721 --- /dev/null +++ b/Query/sql/v2/runs/v2_cli_20260502_081223_d/c16/artifacts/v2q_c16_f2b81d352e894fce/query_results.jsonl @@ -0,0 +1 @@ +{"step_index": 2, "message_index": 0, "node_name": "v2-cli:codex", "tool_name": "sqlite_query", "query": "-- template_id: tpl_m4_group_condition_rate\nSELECT\n \"YEAR\",\n AVG(CASE WHEN \"ALIGN\" = 'Good Characters' THEN 1.0 ELSE 0.0 END) AS \"condition_rate\"\nFROM \"c16\"\nWHERE \"YEAR\" IS NOT NULL\n AND \"YEAR\" <> ''\nGROUP BY \"YEAR\"\nHAVING COUNT(*) >= 5\nORDER BY \"condition_rate\" DESC;", "result": "{\"query\": \"-- template_id: tpl_m4_group_condition_rate\\nSELECT\\n \\\"YEAR\\\",\\n AVG(CASE WHEN \\\"ALIGN\\\" = 'Good Characters' THEN 1.0 ELSE 0.0 END) AS \\\"condition_rate\\\"\\nFROM \\\"c16\\\"\\nWHERE \\\"YEAR\\\" IS NOT NULL\\n AND \\\"YEAR\\\" <> ''\\nGROUP BY \\\"YEAR\\\"\\nHAVING COUNT(*) >= 5\\nORDER BY \\\"condition_rate\\\" DESC;\", \"columns\": [\"YEAR\", \"condition_rate\"], \"rows\": [{\"YEAR\": \"1955\", \"condition_rate\": 0.8333333333333334}, {\"YEAR\": \"1951\", \"condition_rate\": 0.8333333333333334}, {\"YEAR\": \"1938\", \"condition_rate\": 0.8}, {\"YEAR\": \"1950\", \"condition_rate\": 0.7777777777777778}, {\"YEAR\": \"1939\", \"condition_rate\": 0.7222222222222222}, {\"YEAR\": \"1970\", \"condition_rate\": 0.6785714285714286}, {\"YEAR\": \"1975\", \"condition_rate\": 0.6666666666666666}, {\"YEAR\": \"1936\", \"condition_rate\": 0.6666666666666666}, {\"YEAR\": \"1959\", \"condition_rate\": 0.6470588235294118}, {\"YEAR\": \"1942\", \"condition_rate\": 0.6346153846153846}, {\"YEAR\": \"1989\", \"condition_rate\": 0.6203007518796992}, {\"YEAR\": \"1956\", \"condition_rate\": 0.6153846153846154}, {\"YEAR\": \"1940\", \"condition_rate\": 0.609375}, {\"YEAR\": \"1961\", \"condition_rate\": 0.6}, {\"YEAR\": \"1967\", \"condition_rate\": 0.5892857142857143}, {\"YEAR\": \"1980\", \"condition_rate\": 0.5833333333333334}, {\"YEAR\": \"1954\", \"condition_rate\": 0.5714285714285714}, {\"YEAR\": \"1968\", \"condition_rate\": 0.5409836065573771}, {\"YEAR\": \"1941\", \"condition_rate\": 0.5409836065573771}, {\"YEAR\": \"1976\", \"condition_rate\": 0.5333333333333333}, {\"YEAR\": \"1969\", \"condition_rate\": 0.5217391304347826}, {\"YEAR\": \"1949\", \"condition_rate\": 0.5}, {\"YEAR\": \"1993\", \"condition_rate\": 0.49282296650717705}, {\"YEAR\": \"2003\", \"condition_rate\": 0.4854368932038835}, {\"YEAR\": \"1971\", \"condition_rate\": 0.47692307692307695}, {\"YEAR\": \"1944\", \"condition_rate\": 0.4666666666666667}, {\"YEAR\": \"1988\", \"condition_rate\": 0.46153846153846156}, {\"YEAR\": \"1957\", \"condition_rate\": 0.46153846153846156}, {\"YEAR\": \"1972\", \"condition_rate\": 0.45901639344262296}, {\"YEAR\": \"1983\", \"condition_rate\": 0.43478260869565216}, {\"YEAR\": \"1990\", \"condition_rate\": 0.4342857142857143}, {\"YEAR\": \"1986\", \"condition_rate\": 0.4318181818181818}, {\"YEAR\": \"1992\", \"condition_rate\": 0.42134831460674155}, {\"YEAR\": \"1996\", \"condition_rate\": 0.4148936170212766}, {\"YEAR\": \"1960\", \"condition_rate\": 0.41025641025641024}, {\"YEAR\": \"1984\", \"condition_rate\": 0.40425531914893614}, {\"YEAR\": \"1978\", \"condition_rate\": 0.4}, {\"YEAR\": \"1973\", \"condition_rate\": 0.4}, {\"YEAR\": \"1952\", \"condition_rate\": 0.4}, {\"YEAR\": \"1948\", \"condition_rate\": 0.4}, {\"YEAR\": \"1947\", \"condition_rate\": 0.4}, {\"YEAR\": \"2006\", \"condition_rate\": 0.3927392739273927}, {\"YEAR\": \"2005\", \"condition_rate\": 0.389937106918239}, {\"YEAR\": \"1987\", \"condition_rate\": 0.38976377952755903}, {\"YEAR\": \"1982\", \"condition_rate\": 0.38738738738738737}, {\"YEAR\": \"2011\", \"condition_rate\": 0.3870967741935484}, {\"YEAR\": \"1964\", \"condition_rate\": 0.3870967741935484}, {\"YEAR\": \"1998\", \"condition_rate\": 0.38461538461538464}, {\"YEAR\": \"1994\", \"condition_rate\": 0.3826086956521739}, {\"YEAR\": \"2004\", \"condition_rate\": 0.38235294117647056}], \"row_count_returned\": 50, \"row_limit\": 50, \"truncated\": true, \"elapsed_ms\": 4.18}"} diff --git a/Query/sql/v2/runs/v2_cli_20260502_081223_d/c16/artifacts/v2q_c16_f2b81d352e894fce/run_manifest.json b/Query/sql/v2/runs/v2_cli_20260502_081223_d/c16/artifacts/v2q_c16_f2b81d352e894fce/run_manifest.json new file mode 100644 index 0000000000000000000000000000000000000000..1bff1afd0a6e101c07020587b59dbc022856c3ab --- /dev/null +++ b/Query/sql/v2/runs/v2_cli_20260502_081223_d/c16/artifacts/v2q_c16_f2b81d352e894fce/run_manifest.json @@ -0,0 +1,92 @@ +{ + "run_id": "v2_cli_20260502_081223_d", + "dataset_id": "c16", + "started_at": "2026-05-19T16:04:26.819680+00:00", + "ended_at": "2026-05-19T16:04:46.050751+00:00", + "status": "completed", + "engine": "cli", + "question_record": { + "query_record_id": "v2q_c16_f2b81d352e894fce", + "problem_id": "v2p_c16_8f80c73f6c257214", + "dataset_id": "c16", + "template_id": "tpl_m4_group_condition_rate", + "template_name": "Grouped Condition Rate", + "family_id": "conditional_dependency_structure", + "canonical_subitem_id": "dependency_strength_similarity", + "intended_facet_id": "pairwise_conditional_dependency", + "variant_semantic_role": "focused_target_view", + "subitem_assignment_source": "planner_selected", + "source_kind": "agent", + "realization_mode": "agent", + "gate_priority": "primary", + "extended_family": false, + "question": "Use template Grouped Condition Rate to probe dependency_strength_similarity with semantic role focused_target_view. Focus on group_col=YEAR, condition_col=ALIGN.", + "bindings": { + "group_col": "YEAR", + "condition_col": "ALIGN", + "condition_value": "Good Characters", + "positive_value": "Bad Characters", + "negative_value": "Good Characters", + "top_k": 19, + "top_n": 4, + "num_tiles": 10, + "percentile_value": 0.9, + "z_threshold": 2.0, + "fraction_threshold": 0.05, + "baseline_multiplier": 1.75, + "baseline_fraction": 0.1, + "min_group_size": 5, + "min_support": 4, + "measure_threshold": 2003.0, + "time_grain": "month", + "lookback_rows": 3, + "current_period_start": "'2024-01-01'", + "current_period_end": "'2024-04-01'", + "previous_period_start": "'2023-10-01'", + "previous_period_end": "'2024-01-01'", + "drift_ratio_threshold": 0.8 + }, + "binding_roles": [ + "group_col", + "condition_col" + ], + "coverage_target_min": "5", + "runtime_sql_skeleton": "SELECT {group_col},\n AVG(CASE WHEN {condition_col} = {condition_value} THEN 1 ELSE 0 END) AS condition_rate\nFROM {table}\nGROUP BY {group_col}\nORDER BY condition_rate DESC;", + "notes": [ + "default_facets=pairwise_conditional_dependency", + "template_selection_mode=rule", + "problem_index_within_template=9", + "sql_variant_index=2/2", + "binding_index=104" + ], + "template_selection_mode": "rule", + "selected_template_rank": 9, + "problem_index_within_template": 9, + "sql_variant_index": 2, + "sql_variant_total": 2 + }, + "mode": "subitem_workload_v2", + "sql_source_version": "v2", + "sql_source_label": "v2_current", + "generated_sql_path": "/data/jialinzhang/TabQueryBench/sql_workloads/v2_current/runs_and_launches/runs/v2_cli_20260502_081223_d/c16/sql/v2q_c16_f2b81d352e894fce.sql", + "usage_summary": { + "dataset_id": "c16", + "model": "v2-cli:codex", + "run_id": "v2q_c16_f2b81d352e894fce", + "api_calls": 0, + "input_tokens": 14613, + "cached_input_tokens": 12032, + "output_tokens": 666, + "total_tokens": 15279, + "cost_usd": 0.0, + "ai_cli_calls": 2, + "estimated_input_tokens": 0, + "estimated_output_tokens": 0, + "estimated_total_tokens": 0, + "usage_source": "ai_cli_json_usage", + "cli_elapsed_ms_total": 18220.75, + "sql_execution_elapsed_ms_total": 4.18, + "conversation_log_path": "/data/jialinzhang/TabQueryBench/sql_workloads/v2_current/runs_and_launches/runs/v2_cli_20260502_081223_d/c16/artifacts/v2q_c16_f2b81d352e894fce/cli/conversation.jsonl", + "note": "Executed through a local AI CLI with structured usage metadata." + } +} \ No newline at end of file diff --git a/Query/sql/v2/runs/v2_cli_20260502_081223_d/c16/artifacts/v2q_c16_f2b81d352e894fce/trace.jsonl b/Query/sql/v2/runs/v2_cli_20260502_081223_d/c16/artifacts/v2q_c16_f2b81d352e894fce/trace.jsonl new file mode 100644 index 0000000000000000000000000000000000000000..ca3e6085c0af6fc3b002f822db0761fb326cd4b0 --- /dev/null +++ b/Query/sql/v2/runs/v2_cli_20260502_081223_d/c16/artifacts/v2q_c16_f2b81d352e894fce/trace.jsonl @@ -0,0 +1,2 @@ +{"timestamp": "2026-05-19T16:04:30.918242+00:00", "event_type": "ai_cli_sql_generation_error", "engine": "v2-cli:codex", "attempt": 1, "command": "codex exec --skip-git-repo-check --disable plugins --sandbox read-only --cd \"/data/jialinzhang/SQLagent\" -m gpt-5.4 --json -", "returncode": 1, "elapsed_ms": 4096.16, "started_at": "2026-05-19T16:04:26.821345+00:00", "ended_at": "2026-05-19T16:04:30.917528+00:00", "prompt_metrics": {"chars": 8763, "bytes_utf8": 8763, "lines": 255, "estimated_tokens": null}, "response_metrics": {"chars": 280, "bytes_utf8": 280, "lines": 4, "estimated_tokens": null}, "usage": {}, "stderr_preview": "", "stdout_preview": "{\"type\":\"thread.started\",\"thread_id\":\"019e40fb-3b42-72c0-9ede-34f0994d370a\"}\n{\"type\":\"turn.started\"}\n{\"type\":\"error\",\"message\":\"Quota exceeded. Check your plan and billing details.\"}\n{\"type\":\"turn.failed\",\"error\":{\"message\":\"Quota exceeded. Check your plan and billing details.\"}}", "error": "AI CLI command failed with exit code 1: "} +{"timestamp": "2026-05-19T16:04:46.045094+00:00", "event_type": "ai_cli_sql_generation", "engine": "v2-cli:codex", "attempt": 2, "command": "codex exec --skip-git-repo-check --disable plugins --sandbox read-only --cd \"/data/jialinzhang/SQLagent\" -m gpt-5.4 --json -", "returncode": 0, "elapsed_ms": 14124.59, "started_at": "2026-05-19T16:04:31.919661+00:00", "ended_at": "2026-05-19T16:04:46.044295+00:00", "prompt_metrics": {"chars": 8763, "bytes_utf8": 8763, "lines": 255, "estimated_tokens": null}, "response_metrics": {"chars": 536, "bytes_utf8": 536, "lines": 1, "estimated_tokens": null}, "usage": {"input_tokens": 14613, "cached_input_tokens": 12032, "output_tokens": 666, "reasoning_output_tokens": 516}, "stderr_preview": "", "stdout_preview": "{\"sql\":\"-- template_id: tpl_m4_group_condition_rate\\nSELECT\\n \\\"YEAR\\\",\\n AVG(CASE WHEN \\\"ALIGN\\\" = 'Good Characters' THEN 1.0 ELSE 0.0 END) AS \\\"condition_rate\\\"\\nFROM \\\"c16\\\"\\nWHERE \\\"YEAR\\\" IS NOT NULL\\n AND \\\"YEAR\\\" <> ''\\nGROUP BY \\\"YEAR\\\"\\nHAVING COUNT(*) >= 5\\nORDER BY \\\"condition_rate\\\" DESC;\",\"notes\":\"Applies the grouped condition rate template with group_col=\\\"YEAR\\\" and condition_col=\\\"ALIGN\\\", measuring the share of rows labeled 'Good Characters' within each year and enforcing the provided minimum group size of 5.\"}"} diff --git a/Query/sql/v2/runs/v2_cli_20260502_081223_d/c16/artifacts/v2q_c16_f2b81d352e894fce/usage_summary.json b/Query/sql/v2/runs/v2_cli_20260502_081223_d/c16/artifacts/v2q_c16_f2b81d352e894fce/usage_summary.json new file mode 100644 index 0000000000000000000000000000000000000000..7401f42a688592897752c5197cfe62acde52f82c --- /dev/null +++ b/Query/sql/v2/runs/v2_cli_20260502_081223_d/c16/artifacts/v2q_c16_f2b81d352e894fce/usage_summary.json @@ -0,0 +1,20 @@ +{ + "dataset_id": "c16", + "model": "v2-cli:codex", + "run_id": "v2q_c16_f2b81d352e894fce", + "api_calls": 0, + "input_tokens": 14613, + "cached_input_tokens": 12032, + "output_tokens": 666, + "total_tokens": 15279, + "cost_usd": 0.0, + "ai_cli_calls": 2, + "estimated_input_tokens": 0, + "estimated_output_tokens": 0, + "estimated_total_tokens": 0, + "usage_source": "ai_cli_json_usage", + "cli_elapsed_ms_total": 18220.75, + "sql_execution_elapsed_ms_total": 4.18, + "conversation_log_path": "/data/jialinzhang/TabQueryBench/sql_workloads/v2_current/runs_and_launches/runs/v2_cli_20260502_081223_d/c16/artifacts/v2q_c16_f2b81d352e894fce/cli/conversation.jsonl", + "note": "Executed through a local AI CLI with structured usage metadata." +} \ No newline at end of file diff --git a/Query/sql/v2/runs/v2_cli_20260502_081223_d/c16/artifacts/v2q_c16_fa994896bcb619ab/final_answer.txt b/Query/sql/v2/runs/v2_cli_20260502_081223_d/c16/artifacts/v2q_c16_fa994896bcb619ab/final_answer.txt new file mode 100644 index 0000000000000000000000000000000000000000..b4c9681b8049e3e69287cd4fbb8419c57b10e4f6 --- /dev/null +++ b/Query/sql/v2/runs/v2_cli_20260502_081223_d/c16/artifacts/v2q_c16_fa994896bcb619ab/final_answer.txt @@ -0,0 +1 @@ +{"row_count": null, "preview_rows": []} \ No newline at end of file diff --git a/Query/sql/v2/runs/v2_cli_20260502_081223_d/c16/artifacts/v2q_c16_fa994896bcb619ab/generated_sql.sql b/Query/sql/v2/runs/v2_cli_20260502_081223_d/c16/artifacts/v2q_c16_fa994896bcb619ab/generated_sql.sql new file mode 100644 index 0000000000000000000000000000000000000000..4c4e02a3fc3ebb5e25470a2441827c113d15e9f4 --- /dev/null +++ b/Query/sql/v2/runs/v2_cli_20260502_081223_d/c16/artifacts/v2q_c16_fa994896bcb619ab/generated_sql.sql @@ -0,0 +1,21 @@ +-- sql_source_version: v2 +-- sql_source_label: v2_current +-- sql_source_run_id: v2_cli_20260502_081223_d +-- sql_source_dataset_id: c16 +-- family_id: cardinality_structure +-- canonical_subitem_id: high_cardinality_response_stability +-- intended_facet_id: target_cardinality_cross_section +-- variant_semantic_role: focused_target_view +-- template_id: tpl_cardinality_high_card_response_stability +-- query_record_id: v2q_c16_fa994896bcb619ab +-- problem_id: v2p_c16_a57649f7554d7264 +-- realization_mode: deterministic +-- source_kind: deterministic +SELECT + "page_id", + COUNT(*) AS support, + AVG("APPEARANCES") AS avg_response +FROM "c16" +GROUP BY "page_id" +HAVING COUNT(*) >= 5.0 +ORDER BY support DESC, avg_response DESC; diff --git a/Query/sql/v2/runs/v2_cli_20260502_081223_d/c16/artifacts/v2q_c16_fa994896bcb619ab/query_results.jsonl b/Query/sql/v2/runs/v2_cli_20260502_081223_d/c16/artifacts/v2q_c16_fa994896bcb619ab/query_results.jsonl new file mode 100644 index 0000000000000000000000000000000000000000..78446ea0c3dfce355692c221d690446810eda75c --- /dev/null +++ b/Query/sql/v2/runs/v2_cli_20260502_081223_d/c16/artifacts/v2q_c16_fa994896bcb619ab/query_results.jsonl @@ -0,0 +1 @@ +{"node_name": "v2_template", "tool_name": "sqlite_query", "query": "-- sql_source_version: v2\n-- sql_source_label: v2_current\n-- sql_source_run_id: v2_cli_20260502_081223_d\n-- sql_source_dataset_id: c16\n-- family_id: cardinality_structure\n-- canonical_subitem_id: high_cardinality_response_stability\n-- intended_facet_id: target_cardinality_cross_section\n-- variant_semantic_role: focused_target_view\n-- template_id: tpl_cardinality_high_card_response_stability\n-- query_record_id: v2q_c16_fa994896bcb619ab\n-- problem_id: v2p_c16_a57649f7554d7264\n-- realization_mode: deterministic\n-- source_kind: deterministic\nSELECT\n \"page_id\",\n COUNT(*) AS support,\n AVG(\"APPEARANCES\") AS avg_response\nFROM \"c16\"\nGROUP BY \"page_id\"\nHAVING COUNT(*) >= 5.0\nORDER BY support DESC, avg_response DESC;", "result": "{\"query\": \"-- sql_source_version: v2\\n-- sql_source_label: v2_current\\n-- sql_source_run_id: v2_cli_20260502_081223_d\\n-- sql_source_dataset_id: c16\\n-- family_id: cardinality_structure\\n-- canonical_subitem_id: high_cardinality_response_stability\\n-- intended_facet_id: target_cardinality_cross_section\\n-- variant_semantic_role: focused_target_view\\n-- template_id: tpl_cardinality_high_card_response_stability\\n-- query_record_id: v2q_c16_fa994896bcb619ab\\n-- problem_id: v2p_c16_a57649f7554d7264\\n-- realization_mode: deterministic\\n-- source_kind: deterministic\\nSELECT\\n \\\"page_id\\\",\\n COUNT(*) AS support,\\n AVG(\\\"APPEARANCES\\\") AS avg_response\\nFROM \\\"c16\\\"\\nGROUP BY \\\"page_id\\\"\\nHAVING COUNT(*) >= 5.0\\nORDER BY support DESC, avg_response DESC;\", \"columns\": [\"page_id\", \"support\", \"avg_response\"], \"rows\": [], \"row_count_returned\": 0, \"row_limit\": 50, \"truncated\": false, \"elapsed_ms\": 4.71}"} diff --git a/Query/sql/v2/runs/v2_cli_20260502_081223_d/c16/artifacts/v2q_c16_fa994896bcb619ab/run_manifest.json b/Query/sql/v2/runs/v2_cli_20260502_081223_d/c16/artifacts/v2q_c16_fa994896bcb619ab/run_manifest.json new file mode 100644 index 0000000000000000000000000000000000000000..ccc037b863888ec936e169a9bfb2ac417e67ca89 --- /dev/null +++ b/Query/sql/v2/runs/v2_cli_20260502_081223_d/c16/artifacts/v2q_c16_fa994896bcb619ab/run_manifest.json @@ -0,0 +1,60 @@ +{ + "run_id": "v2_cli_20260502_081223_d", + "dataset_id": "c16", + "started_at": "2026-05-19T16:10:30.501240+00:00", + "ended_at": "2026-05-19T16:10:30.506564+00:00", + "status": "completed", + "engine": "cli", + "question_record": { + "query_record_id": "v2q_c16_fa994896bcb619ab", + "problem_id": "v2p_c16_a57649f7554d7264", + "dataset_id": "c16", + "template_id": "tpl_cardinality_high_card_response_stability", + "template_name": "High-Cardinality Response Stability", + "family_id": "cardinality_structure", + "canonical_subitem_id": "high_cardinality_response_stability", + "intended_facet_id": "target_cardinality_cross_section", + "variant_semantic_role": "focused_target_view", + "subitem_assignment_source": "template_fixed", + "source_kind": "deterministic", + "realization_mode": "deterministic", + "gate_priority": "deterministic", + "extended_family": true, + "question": "Use template High-Cardinality Response Stability to probe high_cardinality_response_stability with semantic role focused_target_view. Focus on measure_col=APPEARANCES, key_col=page_id.", + "bindings": { + "key_col": "page_id", + "measure_col": "APPEARANCES", + "min_support": 5 + }, + "binding_roles": [ + "key_col", + "target_col" + ], + "coverage_target_min": "enumerate_all_applicable", + "runtime_sql_skeleton": "SELECT\n {key_col},\n COUNT(*) AS support,\n AVG({measure_col}) AS avg_response\nFROM {table}\nGROUP BY {key_col}\nHAVING COUNT(*) >= {min_support}\nORDER BY support DESC, avg_response DESC;", + "notes": [ + "default_facets=target_cardinality_cross_section", + "template_selection_mode=deterministic", + "problem_index_within_template=1", + "sql_variant_index=1/1" + ], + "template_selection_mode": "deterministic", + "selected_template_rank": 0, + "problem_index_within_template": 1, + "sql_variant_index": 1, + "sql_variant_total": 1 + }, + "mode": "subitem_workload_v2", + "sql_source_version": "v2", + "sql_source_label": "v2_current", + "generated_sql_path": "/data/jialinzhang/TabQueryBench/sql_workloads/v2_current/runs_and_launches/runs/v2_cli_20260502_081223_d/c16/sql/v2q_c16_fa994896bcb619ab.sql", + "usage_summary": { + "engine": "template", + "input_tokens": 0, + "cached_input_tokens": 0, + "output_tokens": 0, + "total_tokens": 0, + "estimated_total_tokens": 0, + "usage_source": "none" + } +} \ No newline at end of file diff --git a/Query/sql/v2/runs/v2_cli_20260502_081223_d/c16/artifacts/v2q_c16_fa994896bcb619ab/usage_summary.json b/Query/sql/v2/runs/v2_cli_20260502_081223_d/c16/artifacts/v2q_c16_fa994896bcb619ab/usage_summary.json new file mode 100644 index 0000000000000000000000000000000000000000..96c9ff4feec395919fc26411d18d078b8af6e1c7 --- /dev/null +++ b/Query/sql/v2/runs/v2_cli_20260502_081223_d/c16/artifacts/v2q_c16_fa994896bcb619ab/usage_summary.json @@ -0,0 +1,9 @@ +{ + "engine": "template", + "input_tokens": 0, + "cached_input_tokens": 0, + "output_tokens": 0, + "total_tokens": 0, + "estimated_total_tokens": 0, + "usage_source": "none" +} \ No newline at end of file diff --git a/Query/sql/v2/runs/v2_cli_20260502_081223_d/c16/artifacts/v2q_c16_fd5cb1ea99f55061/final_answer.txt b/Query/sql/v2/runs/v2_cli_20260502_081223_d/c16/artifacts/v2q_c16_fd5cb1ea99f55061/final_answer.txt new file mode 100644 index 0000000000000000000000000000000000000000..0c3518eabcde870038480f091606afe9b6151592 --- /dev/null +++ b/Query/sql/v2/runs/v2_cli_20260502_081223_d/c16/artifacts/v2q_c16_fd5cb1ea99f55061/final_answer.txt @@ -0,0 +1,2 @@ +SQL executed successfully for: Use template Filtered Two-Dimensional Group Count to probe slice_level_consistency with semantic role count_distribution. Focus on group_col=EYE, group_col_2=GSM. +Result preview: [{"EYE": "", "GSM": "", "row_count": 1075}, {"EYE": "Brown Eyes", "GSM": "", "row_count": 214}, {"EYE": "Blue Eyes", "GSM": "", "row_count": 152}, {"EYE": "Black Eyes", "GSM": "", "row_count": 86}, {"EYE": "Green Eyes", "GSM": "", "row_count": 54}] \ No newline at end of file diff --git a/Query/sql/v2/runs/v2_cli_20260502_081223_d/c16/artifacts/v2q_c16_fd5cb1ea99f55061/generated_sql.sql b/Query/sql/v2/runs/v2_cli_20260502_081223_d/c16/artifacts/v2q_c16_fd5cb1ea99f55061/generated_sql.sql new file mode 100644 index 0000000000000000000000000000000000000000..e3262aa1069b0445abd22666f7c79564e4198d34 --- /dev/null +++ b/Query/sql/v2/runs/v2_cli_20260502_081223_d/c16/artifacts/v2q_c16_fd5cb1ea99f55061/generated_sql.sql @@ -0,0 +1,18 @@ +-- sql_source_version: v2 +-- sql_source_label: v2_current +-- sql_source_run_id: v2_cli_20260502_081223_d +-- sql_source_dataset_id: c16 +-- family_id: conditional_dependency_structure +-- canonical_subitem_id: slice_level_consistency +-- intended_facet_id: conditional_interaction_hotspots +-- variant_semantic_role: count_distribution +-- template_id: tpl_c2_filtered_group_count_2d +-- query_record_id: v2q_c16_fd5cb1ea99f55061 +-- problem_id: v2p_c16_15146fe6e345229b +-- realization_mode: agent +-- source_kind: agent +SELECT "EYE", "GSM", COUNT(*) AS row_count +FROM "c16" +WHERE CAST("page_id" AS REAL) >= 213203.0 +GROUP BY "EYE", "GSM" +ORDER BY row_count DESC; diff --git a/Query/sql/v2/runs/v2_cli_20260502_081223_d/c16/artifacts/v2q_c16_fd5cb1ea99f55061/query_results.jsonl b/Query/sql/v2/runs/v2_cli_20260502_081223_d/c16/artifacts/v2q_c16_fd5cb1ea99f55061/query_results.jsonl new file mode 100644 index 0000000000000000000000000000000000000000..a62b5b00a84195c4a9307fcbcf1a708febb96a8a --- /dev/null +++ b/Query/sql/v2/runs/v2_cli_20260502_081223_d/c16/artifacts/v2q_c16_fd5cb1ea99f55061/query_results.jsonl @@ -0,0 +1 @@ +{"step_index": 1, "message_index": 0, "node_name": "v2-cli:codex", "tool_name": "sqlite_query", "query": "-- template_id: tpl_c2_filtered_group_count_2d\nSELECT \"EYE\", \"GSM\", COUNT(*) AS row_count\nFROM \"c16\"\nWHERE CAST(\"page_id\" AS REAL) >= 213203.0\nGROUP BY \"EYE\", \"GSM\"\nORDER BY row_count DESC;", "result": "{\"query\": \"-- template_id: tpl_c2_filtered_group_count_2d\\nSELECT \\\"EYE\\\", \\\"GSM\\\", COUNT(*) AS row_count\\nFROM \\\"c16\\\"\\nWHERE CAST(\\\"page_id\\\" AS REAL) >= 213203.0\\nGROUP BY \\\"EYE\\\", \\\"GSM\\\"\\nORDER BY row_count DESC;\", \"columns\": [\"EYE\", \"GSM\", \"row_count\"], \"rows\": [{\"EYE\": \"\", \"GSM\": \"\", \"row_count\": 1075}, {\"EYE\": \"Brown Eyes\", \"GSM\": \"\", \"row_count\": 214}, {\"EYE\": \"Blue Eyes\", \"GSM\": \"\", \"row_count\": 152}, {\"EYE\": \"Black Eyes\", \"GSM\": \"\", \"row_count\": 86}, {\"EYE\": \"Green Eyes\", \"GSM\": \"\", \"row_count\": 54}, {\"EYE\": \"Red Eyes\", \"GSM\": \"\", \"row_count\": 52}, {\"EYE\": \"Yellow Eyes\", \"GSM\": \"\", \"row_count\": 25}, {\"EYE\": \"Photocellular Eyes\", \"GSM\": \"\", \"row_count\": 22}, {\"EYE\": \"White Eyes\", \"GSM\": \"\", \"row_count\": 21}, {\"EYE\": \"\", \"GSM\": \"Homosexual Characters\", \"row_count\": 6}, {\"EYE\": \"Grey Eyes\", \"GSM\": \"\", \"row_count\": 6}, {\"EYE\": \"Hazel Eyes\", \"GSM\": \"\", \"row_count\": 3}, {\"EYE\": \"Brown Eyes\", \"GSM\": \"Homosexual Characters\", \"row_count\": 2}, {\"EYE\": \"Gold Eyes\", \"GSM\": \"\", \"row_count\": 2}, {\"EYE\": \"Orange Eyes\", \"GSM\": \"\", \"row_count\": 2}, {\"EYE\": \"Violet Eyes\", \"GSM\": \"\", \"row_count\": 2}], \"row_count_returned\": 16, \"row_limit\": 50, \"truncated\": false, \"elapsed_ms\": 3.26}"} diff --git a/Query/sql/v2/runs/v2_cli_20260502_081223_d/c16/artifacts/v2q_c16_fd5cb1ea99f55061/run_manifest.json b/Query/sql/v2/runs/v2_cli_20260502_081223_d/c16/artifacts/v2q_c16_fd5cb1ea99f55061/run_manifest.json new file mode 100644 index 0000000000000000000000000000000000000000..12098ce4492be17324852f37501fdf59269f810b --- /dev/null +++ b/Query/sql/v2/runs/v2_cli_20260502_081223_d/c16/artifacts/v2q_c16_fd5cb1ea99f55061/run_manifest.json @@ -0,0 +1,93 @@ +{ + "run_id": "v2_cli_20260502_081223_d", + "dataset_id": "c16", + "started_at": "2026-05-19T15:41:41.862214+00:00", + "ended_at": "2026-05-19T15:42:02.029394+00:00", + "status": "completed", + "engine": "cli", + "question_record": { + "query_record_id": "v2q_c16_fd5cb1ea99f55061", + "problem_id": "v2p_c16_15146fe6e345229b", + "dataset_id": "c16", + "template_id": "tpl_c2_filtered_group_count_2d", + "template_name": "Filtered Two-Dimensional Group Count", + "family_id": "conditional_dependency_structure", + "canonical_subitem_id": "slice_level_consistency", + "intended_facet_id": "conditional_interaction_hotspots", + "variant_semantic_role": "count_distribution", + "subitem_assignment_source": "planner_selected", + "source_kind": "agent", + "realization_mode": "agent", + "gate_priority": "primary", + "extended_family": false, + "question": "Use template Filtered Two-Dimensional Group Count to probe slice_level_consistency with semantic role count_distribution. Focus on group_col=EYE, group_col_2=GSM.", + "bindings": { + "group_col": "EYE", + "group_col_2": "GSM", + "predicate_col": "page_id", + "predicate_op": ">=", + "predicate_value": 213203.0, + "top_k": 10, + "top_n": 5, + "num_tiles": 10, + "percentile_value": 0.95, + "z_threshold": 2.0, + "fraction_threshold": 0.1, + "baseline_multiplier": 1.5, + "baseline_fraction": 0.1, + "min_group_size": 5, + "min_support": 5, + "measure_threshold": 2003.0, + "time_grain": "month", + "lookback_rows": 3, + "current_period_start": "'2024-01-01'", + "current_period_end": "'2024-04-01'", + "previous_period_start": "'2023-10-01'", + "previous_period_end": "'2024-01-01'", + "drift_ratio_threshold": 0.8 + }, + "binding_roles": [ + "group_col", + "group_col_2", + "predicate_col" + ], + "coverage_target_min": "5", + "runtime_sql_skeleton": "SELECT {group_col}, {group_col_2}, COUNT(*) AS row_count\nFROM {table}\nWHERE {predicate_col} {predicate_op} {predicate_value}\nGROUP BY {group_col}, {group_col_2}\nORDER BY row_count DESC;", + "notes": [ + "default_facets=conditional_interaction_hotspots", + "template_selection_mode=rule", + "problem_index_within_template=3", + "sql_variant_index=1/1", + "binding_index=50" + ], + "template_selection_mode": "rule", + "selected_template_rank": 5, + "problem_index_within_template": 3, + "sql_variant_index": 1, + "sql_variant_total": 1 + }, + "mode": "subitem_workload_v2", + "sql_source_version": "v2", + "sql_source_label": "v2_current", + "generated_sql_path": "/data/jialinzhang/TabQueryBench/sql_workloads/v2_current/runs_and_launches/runs/v2_cli_20260502_081223_d/c16/sql/v2q_c16_fd5cb1ea99f55061.sql", + "usage_summary": { + "dataset_id": "c16", + "model": "v2-cli:codex", + "run_id": "v2q_c16_fd5cb1ea99f55061", + "api_calls": 0, + "input_tokens": 14649, + "cached_input_tokens": 12032, + "output_tokens": 340, + "total_tokens": 14989, + "cost_usd": 0.0, + "ai_cli_calls": 1, + "estimated_input_tokens": 0, + "estimated_output_tokens": 0, + "estimated_total_tokens": 0, + "usage_source": "ai_cli_json_usage", + "cli_elapsed_ms_total": 20158.84, + "sql_execution_elapsed_ms_total": 3.26, + "conversation_log_path": "/data/jialinzhang/TabQueryBench/sql_workloads/v2_current/runs_and_launches/runs/v2_cli_20260502_081223_d/c16/artifacts/v2q_c16_fd5cb1ea99f55061/cli/conversation.jsonl", + "note": "Executed through a local AI CLI with structured usage metadata." + } +} \ No newline at end of file diff --git a/Query/sql/v2/runs/v2_cli_20260502_081223_d/c16/artifacts/v2q_c16_fd5cb1ea99f55061/trace.jsonl b/Query/sql/v2/runs/v2_cli_20260502_081223_d/c16/artifacts/v2q_c16_fd5cb1ea99f55061/trace.jsonl new file mode 100644 index 0000000000000000000000000000000000000000..ef2610b0745230af8a7800c1d49fca1f1488e3c4 --- /dev/null +++ b/Query/sql/v2/runs/v2_cli_20260502_081223_d/c16/artifacts/v2q_c16_fd5cb1ea99f55061/trace.jsonl @@ -0,0 +1 @@ +{"timestamp": "2026-05-19T15:42:02.023843+00:00", "event_type": "ai_cli_sql_generation", "engine": "v2-cli:codex", "attempt": 1, "command": "codex exec --skip-git-repo-check --disable plugins --sandbox read-only --cd \"/data/jialinzhang/SQLagent\" -m gpt-5.4 --json -", "returncode": 0, "elapsed_ms": 20158.84, "started_at": "2026-05-19T15:41:41.863909+00:00", "ended_at": "2026-05-19T15:42:02.022785+00:00", "prompt_metrics": {"chars": 8783, "bytes_utf8": 8783, "lines": 256, "estimated_tokens": null}, "response_metrics": {"chars": 475, "bytes_utf8": 475, "lines": 1, "estimated_tokens": null}, "usage": {"input_tokens": 14649, "cached_input_tokens": 12032, "output_tokens": 340, "reasoning_output_tokens": 204}, "stderr_preview": "", "stdout_preview": "{\"sql\":\"-- template_id: tpl_c2_filtered_group_count_2d\\nSELECT \\\"EYE\\\", \\\"GSM\\\", COUNT(*) AS row_count\\nFROM \\\"c16\\\"\\nWHERE CAST(\\\"page_id\\\" AS REAL) >= 213203.0\\nGROUP BY \\\"EYE\\\", \\\"GSM\\\"\\nORDER BY row_count DESC;\",\"notes\":\"Used the planned filtered two-dimensional group count template with group_col=\\\"EYE\\\", group_col_2=\\\"GSM\\\", and predicate on \\\"page_id\\\". CAST was added because \\\"page_id\\\" is stored as TEXT in the schema while the bound predicate value is numeric.\"}"} diff --git a/Query/sql/v2/runs/v2_cli_20260502_081223_d/c16/artifacts/v2q_c16_fd5cb1ea99f55061/usage_summary.json b/Query/sql/v2/runs/v2_cli_20260502_081223_d/c16/artifacts/v2q_c16_fd5cb1ea99f55061/usage_summary.json new file mode 100644 index 0000000000000000000000000000000000000000..50e9d635fda30c765997c158507d244b5cd3fb94 --- /dev/null +++ b/Query/sql/v2/runs/v2_cli_20260502_081223_d/c16/artifacts/v2q_c16_fd5cb1ea99f55061/usage_summary.json @@ -0,0 +1,20 @@ +{ + "dataset_id": "c16", + "model": "v2-cli:codex", + "run_id": "v2q_c16_fd5cb1ea99f55061", + "api_calls": 0, + "input_tokens": 14649, + "cached_input_tokens": 12032, + "output_tokens": 340, + "total_tokens": 14989, + "cost_usd": 0.0, + "ai_cli_calls": 1, + "estimated_input_tokens": 0, + "estimated_output_tokens": 0, + "estimated_total_tokens": 0, + "usage_source": "ai_cli_json_usage", + "cli_elapsed_ms_total": 20158.84, + "sql_execution_elapsed_ms_total": 3.26, + "conversation_log_path": "/data/jialinzhang/TabQueryBench/sql_workloads/v2_current/runs_and_launches/runs/v2_cli_20260502_081223_d/c16/artifacts/v2q_c16_fd5cb1ea99f55061/cli/conversation.jsonl", + "note": "Executed through a local AI CLI with structured usage metadata." +} \ No newline at end of file diff --git a/Query/sql/v2/runs/v2_cli_20260502_081223_d/c16/sql/v2q_c16_015d5c3e34621a73.sql b/Query/sql/v2/runs/v2_cli_20260502_081223_d/c16/sql/v2q_c16_015d5c3e34621a73.sql new file mode 100644 index 0000000000000000000000000000000000000000..6cf43f1c2e4d78c12be2313e61d3c66afa63aacd --- /dev/null +++ b/Query/sql/v2/runs/v2_cli_20260502_081223_d/c16/sql/v2q_c16_015d5c3e34621a73.sql @@ -0,0 +1,24 @@ +-- sql_source_version: v2 +-- sql_source_label: v2_current +-- sql_source_run_id: v2_cli_20260502_081223_d +-- sql_source_dataset_id: c16 +-- family_id: conditional_dependency_structure +-- canonical_subitem_id: direction_consistency +-- intended_facet_id: conditional_rate_shift +-- variant_semantic_role: contrastive_conditional_view +-- template_id: tpl_m4_group_ratio_two_conditions +-- query_record_id: v2q_c16_015d5c3e34621a73 +-- problem_id: v2p_c16_bf1559c8457d8261 +-- realization_mode: agent +-- source_kind: agent +WITH grouped AS ( + SELECT "HAIR", + SUM(CASE WHEN "ALIGN" = 'Bad Characters' THEN 1 ELSE 0 END) AS numerator_count, + SUM(CASE WHEN "ALIGN" = 'Good Characters' THEN 1 ELSE 0 END) AS denominator_count + FROM "c16" + GROUP BY "HAIR" +) +SELECT "HAIR", + CAST(numerator_count AS FLOAT) / NULLIF(denominator_count, 0) AS condition_ratio +FROM grouped +ORDER BY condition_ratio DESC; diff --git a/Query/sql/v2/runs/v2_cli_20260502_081223_d/c16/sql/v2q_c16_024eb99029bb3c59.sql b/Query/sql/v2/runs/v2_cli_20260502_081223_d/c16/sql/v2q_c16_024eb99029bb3c59.sql new file mode 100644 index 0000000000000000000000000000000000000000..5b41f7fd09b7a675b6a7d440c5ad72b04130ed40 --- /dev/null +++ b/Query/sql/v2/runs/v2_cli_20260502_081223_d/c16/sql/v2q_c16_024eb99029bb3c59.sql @@ -0,0 +1,17 @@ +-- sql_source_version: v2 +-- sql_source_label: v2_current +-- sql_source_run_id: v2_cli_20260502_081223_d +-- sql_source_dataset_id: c16 +-- family_id: subgroup_structure +-- canonical_subitem_id: subgroup_size_stability +-- intended_facet_id: subgroup_distribution_shift +-- variant_semantic_role: count_distribution +-- template_id: tpl_clickbench_group_count +-- query_record_id: v2q_c16_024eb99029bb3c59 +-- problem_id: v2p_c16_b3a47540e6e15f73 +-- realization_mode: agent +-- source_kind: agent +SELECT "ALIVE", COUNT(*) AS row_count +FROM "c16" +GROUP BY "ALIVE" +ORDER BY row_count DESC; diff --git a/Query/sql/v2/runs/v2_cli_20260502_081223_d/c16/sql/v2q_c16_03681b5c99b9253d.sql b/Query/sql/v2/runs/v2_cli_20260502_081223_d/c16/sql/v2q_c16_03681b5c99b9253d.sql new file mode 100644 index 0000000000000000000000000000000000000000..cc2d0b6f79992154dcdbed2cc4bd7cfd182984cd --- /dev/null +++ b/Query/sql/v2/runs/v2_cli_20260502_081223_d/c16/sql/v2q_c16_03681b5c99b9253d.sql @@ -0,0 +1,18 @@ +-- sql_source_version: v2 +-- sql_source_label: v2_current +-- sql_source_run_id: v2_cli_20260502_081223_d +-- sql_source_dataset_id: c16 +-- family_id: conditional_dependency_structure +-- canonical_subitem_id: slice_level_consistency +-- intended_facet_id: conditional_interaction_hotspots +-- variant_semantic_role: count_distribution +-- template_id: tpl_c2_filtered_group_count_2d +-- query_record_id: v2q_c16_03681b5c99b9253d +-- problem_id: v2p_c16_43dd6016c2c9de35 +-- realization_mode: agent +-- source_kind: agent +SELECT "EYE", "YEAR", COUNT(*) AS row_count +FROM "c16" +WHERE "EYE" = 'Green Eyes' +GROUP BY "EYE", "YEAR" +ORDER BY row_count DESC; diff --git a/Query/sql/v2/runs/v2_cli_20260502_081223_d/c16/sql/v2q_c16_04b0db29ab4f00b7.sql b/Query/sql/v2/runs/v2_cli_20260502_081223_d/c16/sql/v2q_c16_04b0db29ab4f00b7.sql new file mode 100644 index 0000000000000000000000000000000000000000..00cae0ea636f173be4905c15230091a2b3643ef1 --- /dev/null +++ b/Query/sql/v2/runs/v2_cli_20260502_081223_d/c16/sql/v2q_c16_04b0db29ab4f00b7.sql @@ -0,0 +1,17 @@ +-- sql_source_version: v2 +-- sql_source_label: v2_current +-- sql_source_run_id: v2_cli_20260502_081223_d +-- sql_source_dataset_id: c16 +-- family_id: subgroup_structure +-- canonical_subitem_id: internal_profile_stability +-- intended_facet_id: subgroup_conditional_contrast +-- variant_semantic_role: collapsed_target_view +-- template_id: tpl_h2o_group_sum +-- query_record_id: v2q_c16_04b0db29ab4f00b7 +-- problem_id: v2p_c16_d3c95af4b3d3dbaa +-- realization_mode: agent +-- source_kind: agent +SELECT "HAIR", SUM(CAST("YEAR" AS INTEGER)) AS total_measure +FROM "c16" +GROUP BY "HAIR" +ORDER BY total_measure DESC; diff --git a/Query/sql/v2/runs/v2_cli_20260502_081223_d/c16/sql/v2q_c16_0519eb075403405c.sql b/Query/sql/v2/runs/v2_cli_20260502_081223_d/c16/sql/v2q_c16_0519eb075403405c.sql new file mode 100644 index 0000000000000000000000000000000000000000..ee312f01075ebbad393353e009e3e64d50dab5f6 --- /dev/null +++ b/Query/sql/v2/runs/v2_cli_20260502_081223_d/c16/sql/v2q_c16_0519eb075403405c.sql @@ -0,0 +1,31 @@ +-- sql_source_version: v2 +-- sql_source_label: v2_current +-- sql_source_run_id: v2_cli_20260502_081223_d +-- sql_source_dataset_id: c16 +-- family_id: tail_rarity_structure +-- canonical_subitem_id: tail_mass_similarity +-- intended_facet_id: tail_ranked_signal +-- variant_semantic_role: count_distribution +-- template_id: tpl_tpch_relative_total_threshold +-- query_record_id: v2q_c16_0519eb075403405c +-- problem_id: v2p_c16_6c39fa1280510347 +-- realization_mode: agent +-- source_kind: agent +WITH "grouped" AS ( + SELECT + "SEX", + SUM(COALESCE(CAST(NULLIF("APPEARANCES", '') AS REAL), 0)) AS "group_value" + FROM "c16" + GROUP BY "SEX" +), +"total" AS ( + SELECT SUM("group_value") AS "total_value" + FROM "grouped" +) +SELECT + g."SEX", + g."group_value" +FROM "grouped" AS g +CROSS JOIN "total" AS t +WHERE g."group_value" > t."total_value" * 0.05 +ORDER BY g."group_value" DESC; diff --git a/Query/sql/v2/runs/v2_cli_20260502_081223_d/c16/sql/v2q_c16_0611709e20fee1c8.sql b/Query/sql/v2/runs/v2_cli_20260502_081223_d/c16/sql/v2q_c16_0611709e20fee1c8.sql new file mode 100644 index 0000000000000000000000000000000000000000..830b1e78e803a4d65e827591c5b4f640b2b18365 --- /dev/null +++ b/Query/sql/v2/runs/v2_cli_20260502_081223_d/c16/sql/v2q_c16_0611709e20fee1c8.sql @@ -0,0 +1,30 @@ +-- sql_source_version: v2 +-- sql_source_label: v2_current +-- sql_source_run_id: v2_cli_20260502_081223_d +-- sql_source_dataset_id: c16 +-- family_id: tail_rarity_structure +-- canonical_subitem_id: tail_mass_similarity +-- intended_facet_id: tail_ranked_signal +-- variant_semantic_role: filtered_stable_view +-- template_id: tpl_tpch_relative_total_threshold +-- query_record_id: v2q_c16_0611709e20fee1c8 +-- problem_id: v2p_c16_92055d6cbb88da59 +-- realization_mode: agent +-- source_kind: agent +WITH grouped AS ( + SELECT "YEAR", SUM(CAST("APPEARANCES" AS REAL)) AS group_value + FROM "c16" + WHERE "YEAR" IS NOT NULL + AND "YEAR" <> '' + AND "APPEARANCES" IS NOT NULL + AND "APPEARANCES" <> '' + GROUP BY "YEAR" +), total AS ( + SELECT SUM(group_value) AS total_value + FROM grouped +) +SELECT g."YEAR", g.group_value +FROM grouped AS g +CROSS JOIN total AS t +WHERE g.group_value > t.total_value * 0.05 +ORDER BY g.group_value DESC; diff --git a/Query/sql/v2/runs/v2_cli_20260502_081223_d/c16/sql/v2q_c16_08018b6323e9cef0.sql b/Query/sql/v2/runs/v2_cli_20260502_081223_d/c16/sql/v2q_c16_08018b6323e9cef0.sql new file mode 100644 index 0000000000000000000000000000000000000000..8b524197634a6786a3194980132cada7bfaa1cd3 --- /dev/null +++ b/Query/sql/v2/runs/v2_cli_20260502_081223_d/c16/sql/v2q_c16_08018b6323e9cef0.sql @@ -0,0 +1,18 @@ +-- sql_source_version: v2 +-- sql_source_label: v2_current +-- sql_source_run_id: v2_cli_20260502_081223_d +-- sql_source_dataset_id: c16 +-- family_id: conditional_dependency_structure +-- canonical_subitem_id: slice_level_consistency +-- intended_facet_id: conditional_interaction_hotspots +-- variant_semantic_role: count_distribution +-- template_id: tpl_c2_filtered_group_count_2d +-- query_record_id: v2q_c16_08018b6323e9cef0 +-- problem_id: v2p_c16_a3f6b7953de7d714 +-- realization_mode: agent +-- source_kind: agent +SELECT "HAIR", "GSM", COUNT(*) AS "row_count" +FROM "c16" +WHERE "SEX" = 'Transgender Characters' +GROUP BY "HAIR", "GSM" +ORDER BY "row_count" DESC; diff --git a/Query/sql/v2/runs/v2_cli_20260502_081223_d/c16/sql/v2q_c16_0ac334183130637b.sql b/Query/sql/v2/runs/v2_cli_20260502_081223_d/c16/sql/v2q_c16_0ac334183130637b.sql new file mode 100644 index 0000000000000000000000000000000000000000..bd101912af478ac1bc77940beab4619f120ecb91 --- /dev/null +++ b/Query/sql/v2/runs/v2_cli_20260502_081223_d/c16/sql/v2q_c16_0ac334183130637b.sql @@ -0,0 +1,22 @@ +-- sql_source_version: v2 +-- sql_source_label: v2_current +-- sql_source_run_id: v2_cli_20260502_081223_d +-- sql_source_dataset_id: c16 +-- family_id: conditional_dependency_structure +-- canonical_subitem_id: dependency_strength_similarity +-- intended_facet_id: pairwise_conditional_dependency +-- variant_semantic_role: within_group_proportion +-- template_id: tpl_tpcds_within_group_share +-- query_record_id: v2q_c16_0ac334183130637b +-- problem_id: v2p_c16_fe3b9c97b058bae0 +-- realization_mode: agent +-- source_kind: agent +SELECT + "ALIVE", + "FIRST APPEARANCE", + SUM(CAST("page_id" AS REAL)) AS "total_measure", + SUM(CAST("page_id" AS REAL)) * 100.0 / SUM(SUM(CAST("page_id" AS REAL))) OVER (PARTITION BY "ALIVE") AS "share_within_group" +FROM "c16" +GROUP BY "ALIVE", "FIRST APPEARANCE" +ORDER BY "share_within_group" DESC +LIMIT 18; diff --git a/Query/sql/v2/runs/v2_cli_20260502_081223_d/c16/sql/v2q_c16_0b313429a0f6899f.sql b/Query/sql/v2/runs/v2_cli_20260502_081223_d/c16/sql/v2q_c16_0b313429a0f6899f.sql new file mode 100644 index 0000000000000000000000000000000000000000..f05cf563558c852c135604b6e7e0ca66e4ac1365 --- /dev/null +++ b/Query/sql/v2/runs/v2_cli_20260502_081223_d/c16/sql/v2q_c16_0b313429a0f6899f.sql @@ -0,0 +1,18 @@ +-- sql_source_version: v2 +-- sql_source_label: v2_current +-- sql_source_run_id: v2_cli_20260502_081223_d +-- sql_source_dataset_id: c16 +-- family_id: conditional_dependency_structure +-- canonical_subitem_id: slice_level_consistency +-- intended_facet_id: conditional_interaction_hotspots +-- variant_semantic_role: count_distribution +-- template_id: tpl_c2_filtered_group_count_2d +-- query_record_id: v2q_c16_0b313429a0f6899f +-- problem_id: v2p_c16_3f301355a5b8db53 +-- realization_mode: agent +-- source_kind: agent +SELECT "HAIR", "SEX", COUNT(*) AS row_count +FROM "c16" +WHERE "HAIR" = 'White Hair' +GROUP BY "HAIR", "SEX" +ORDER BY row_count DESC; diff --git a/Query/sql/v2/runs/v2_cli_20260502_081223_d/c16/sql/v2q_c16_0c5e13351b1a73a0.sql b/Query/sql/v2/runs/v2_cli_20260502_081223_d/c16/sql/v2q_c16_0c5e13351b1a73a0.sql new file mode 100644 index 0000000000000000000000000000000000000000..626a95d39d8b40cbe731af96752a1e137fbeeeae --- /dev/null +++ b/Query/sql/v2/runs/v2_cli_20260502_081223_d/c16/sql/v2q_c16_0c5e13351b1a73a0.sql @@ -0,0 +1,25 @@ +-- sql_source_version: v2 +-- sql_source_label: v2_current +-- sql_source_run_id: v2_cli_20260502_081223_d +-- sql_source_dataset_id: c16 +-- family_id: cardinality_structure +-- canonical_subitem_id: support_rank_profile_consistency +-- intended_facet_id: support_concentration +-- variant_semantic_role: count_distribution +-- template_id: tpl_cardinality_support_rank_profile +-- query_record_id: v2q_c16_0c5e13351b1a73a0 +-- problem_id: v2p_c16_1aaedbc26241c569 +-- realization_mode: deterministic +-- source_kind: deterministic +WITH grouped AS ( + SELECT "ALIGN" AS value_label, COUNT(*) AS support + FROM "c16" + GROUP BY "ALIGN" +) +SELECT + value_label, + support, + CAST(support AS FLOAT) / NULLIF(SUM(support) OVER (), 0) AS support_share, + ROW_NUMBER() OVER (ORDER BY support DESC, value_label) AS support_rank +FROM grouped +ORDER BY support DESC, value_label; diff --git a/Query/sql/v2/runs/v2_cli_20260502_081223_d/c16/sql/v2q_c16_0f7ac4ff2707e5fd.sql b/Query/sql/v2/runs/v2_cli_20260502_081223_d/c16/sql/v2q_c16_0f7ac4ff2707e5fd.sql new file mode 100644 index 0000000000000000000000000000000000000000..8920057f6f2c6ab4076e4910b2ca769b1678f262 --- /dev/null +++ b/Query/sql/v2/runs/v2_cli_20260502_081223_d/c16/sql/v2q_c16_0f7ac4ff2707e5fd.sql @@ -0,0 +1,18 @@ +-- sql_source_version: v2 +-- sql_source_label: v2_current +-- sql_source_run_id: v2_cli_20260502_081223_d +-- sql_source_dataset_id: c16 +-- family_id: conditional_dependency_structure +-- canonical_subitem_id: dependency_strength_similarity +-- intended_facet_id: pairwise_conditional_dependency +-- variant_semantic_role: within_group_proportion +-- template_id: tpl_m4_group_condition_rate +-- query_record_id: v2q_c16_0f7ac4ff2707e5fd +-- problem_id: v2p_c16_750501d83ba4a7cb +-- realization_mode: agent +-- source_kind: agent +SELECT "ALIVE", + AVG(CASE WHEN "EYE" = 'Blue Eyes' THEN 1 ELSE 0 END) AS condition_rate +FROM "c16" +GROUP BY "ALIVE" +ORDER BY condition_rate DESC; diff --git a/Query/sql/v2/runs/v2_cli_20260502_081223_d/c16/sql/v2q_c16_11202cdf28db1e64.sql b/Query/sql/v2/runs/v2_cli_20260502_081223_d/c16/sql/v2q_c16_11202cdf28db1e64.sql new file mode 100644 index 0000000000000000000000000000000000000000..a0e5e442ded6dec7a2ffed0000eb17ebb5a52744 --- /dev/null +++ b/Query/sql/v2/runs/v2_cli_20260502_081223_d/c16/sql/v2q_c16_11202cdf28db1e64.sql @@ -0,0 +1,17 @@ +-- sql_source_version: v2 +-- sql_source_label: v2_current +-- sql_source_run_id: v2_cli_20260502_081223_d +-- sql_source_dataset_id: c16 +-- family_id: subgroup_structure +-- canonical_subitem_id: subgroup_size_stability +-- intended_facet_id: subgroup_distribution_shift +-- variant_semantic_role: count_distribution +-- template_id: tpl_clickbench_group_count +-- query_record_id: v2q_c16_11202cdf28db1e64 +-- problem_id: v2p_c16_af215dec6f2c4bdc +-- realization_mode: agent +-- source_kind: agent +SELECT "SEX", COUNT(*) AS "row_count" +FROM "c16" +GROUP BY "SEX" +ORDER BY "row_count" DESC; diff --git a/Query/sql/v2/runs/v2_cli_20260502_081223_d/c16/sql/v2q_c16_1359e5914630d39c.sql b/Query/sql/v2/runs/v2_cli_20260502_081223_d/c16/sql/v2q_c16_1359e5914630d39c.sql new file mode 100644 index 0000000000000000000000000000000000000000..0f1f7331edadb5e8d534c48d01bc9544634148bb --- /dev/null +++ b/Query/sql/v2/runs/v2_cli_20260502_081223_d/c16/sql/v2q_c16_1359e5914630d39c.sql @@ -0,0 +1,18 @@ +-- sql_source_version: v2 +-- sql_source_label: v2_current +-- sql_source_run_id: v2_cli_20260502_081223_d +-- sql_source_dataset_id: c16 +-- family_id: missingness_structure +-- canonical_subitem_id: marginal_missing_rate_consistency +-- intended_facet_id: missing_indicator_distribution +-- variant_semantic_role: missing_indicator_view +-- template_id: tpl_missing_marginal_rate_profile +-- query_record_id: v2q_c16_1359e5914630d39c +-- problem_id: v2p_c16_4d9bf67e7f24662f +-- realization_mode: deterministic +-- source_kind: deterministic +SELECT + COUNT(*) AS total_rows, + SUM(CASE WHEN "ID" IS NULL THEN 1 ELSE 0 END) AS missing_rows, + AVG(CASE WHEN "ID" IS NULL THEN 1.0 ELSE 0.0 END) AS missing_rate +FROM "c16"; diff --git a/Query/sql/v2/runs/v2_cli_20260502_081223_d/c16/sql/v2q_c16_1464c4e9967333a1.sql b/Query/sql/v2/runs/v2_cli_20260502_081223_d/c16/sql/v2q_c16_1464c4e9967333a1.sql new file mode 100644 index 0000000000000000000000000000000000000000..ce99acbd551e50b5ec94390e0dd1aab09ccd7282 --- /dev/null +++ b/Query/sql/v2/runs/v2_cli_20260502_081223_d/c16/sql/v2q_c16_1464c4e9967333a1.sql @@ -0,0 +1,28 @@ +-- sql_source_version: v2 +-- sql_source_label: v2_current +-- sql_source_run_id: v2_cli_20260502_081223_d +-- sql_source_dataset_id: c16 +-- family_id: conditional_dependency_structure +-- canonical_subitem_id: dependency_strength_similarity +-- intended_facet_id: pairwise_conditional_dependency +-- variant_semantic_role: focused_target_view +-- template_id: tpl_tpcds_within_group_share +-- query_record_id: v2q_c16_1464c4e9967333a1 +-- problem_id: v2p_c16_e6f06ee4a1f2b869 +-- realization_mode: agent +-- source_kind: agent +SELECT + "ALIVE", + "APPEARANCES", + SUM(CAST("YEAR" AS INTEGER)) AS "total_measure", + SUM(CAST("YEAR" AS INTEGER)) * 100.0 / SUM(SUM(CAST("YEAR" AS INTEGER))) OVER (PARTITION BY "ALIVE") AS "share_within_group" +FROM "c16" +WHERE "ALIVE" IS NOT NULL + AND "ALIVE" <> '' + AND "APPEARANCES" IS NOT NULL + AND "APPEARANCES" <> '' + AND "YEAR" IS NOT NULL + AND "YEAR" <> '' +GROUP BY "ALIVE", "APPEARANCES" +ORDER BY "share_within_group" DESC +LIMIT 16; diff --git a/Query/sql/v2/runs/v2_cli_20260502_081223_d/c16/sql/v2q_c16_14abf488746910db.sql b/Query/sql/v2/runs/v2_cli_20260502_081223_d/c16/sql/v2q_c16_14abf488746910db.sql new file mode 100644 index 0000000000000000000000000000000000000000..4f2191cd7abe18d5e83d54e273b08db16a6df7d0 --- /dev/null +++ b/Query/sql/v2/runs/v2_cli_20260502_081223_d/c16/sql/v2q_c16_14abf488746910db.sql @@ -0,0 +1,19 @@ +-- sql_source_version: v2 +-- sql_source_label: v2_current +-- sql_source_run_id: v2_cli_20260502_081223_d +-- sql_source_dataset_id: c16 +-- family_id: conditional_dependency_structure +-- canonical_subitem_id: dependency_strength_similarity +-- intended_facet_id: pairwise_conditional_dependency +-- variant_semantic_role: within_group_proportion +-- template_id: tpl_tpcds_within_group_share +-- query_record_id: v2q_c16_14abf488746910db +-- problem_id: v2p_c16_d72353d0c23ae1d6 +-- realization_mode: agent +-- source_kind: agent +SELECT "YEAR", "FIRST APPEARANCE", + SUM(CAST("page_id" AS REAL)) AS total_measure, + SUM(CAST("page_id" AS REAL)) * 100.0 / SUM(SUM(CAST("page_id" AS REAL))) OVER (PARTITION BY "YEAR") AS share_within_group +FROM "c16" +GROUP BY "YEAR", "FIRST APPEARANCE" +ORDER BY share_within_group DESC; diff --git a/Query/sql/v2/runs/v2_cli_20260502_081223_d/c16/sql/v2q_c16_15ac29532f63efbe.sql b/Query/sql/v2/runs/v2_cli_20260502_081223_d/c16/sql/v2q_c16_15ac29532f63efbe.sql new file mode 100644 index 0000000000000000000000000000000000000000..5a6043c36cf8c5a31e66410e5b8bc31be81ee7d5 --- /dev/null +++ b/Query/sql/v2/runs/v2_cli_20260502_081223_d/c16/sql/v2q_c16_15ac29532f63efbe.sql @@ -0,0 +1,17 @@ +-- sql_source_version: v2 +-- sql_source_label: v2_current +-- sql_source_run_id: v2_cli_20260502_081223_d +-- sql_source_dataset_id: c16 +-- family_id: subgroup_structure +-- canonical_subitem_id: internal_profile_stability +-- intended_facet_id: subgroup_conditional_contrast +-- variant_semantic_role: collapsed_target_view +-- template_id: tpl_h2o_group_sum +-- query_record_id: v2q_c16_15ac29532f63efbe +-- problem_id: v2p_c16_0fb2eb3b44527a8f +-- realization_mode: agent +-- source_kind: agent +SELECT "ALIGN", SUM(CAST("APPEARANCES" AS REAL)) AS total_measure +FROM "c16" +GROUP BY "ALIGN" +ORDER BY total_measure DESC; diff --git a/Query/sql/v2/runs/v2_cli_20260502_081223_d/c16/sql/v2q_c16_16a04539c6fc1892.sql b/Query/sql/v2/runs/v2_cli_20260502_081223_d/c16/sql/v2q_c16_16a04539c6fc1892.sql new file mode 100644 index 0000000000000000000000000000000000000000..201f01e47337eca022b300303b0291eed294b9c3 --- /dev/null +++ b/Query/sql/v2/runs/v2_cli_20260502_081223_d/c16/sql/v2q_c16_16a04539c6fc1892.sql @@ -0,0 +1,20 @@ +-- sql_source_version: v2 +-- sql_source_label: v2_current +-- sql_source_run_id: v2_cli_20260502_081223_d +-- sql_source_dataset_id: c16 +-- family_id: conditional_dependency_structure +-- canonical_subitem_id: dependency_strength_similarity +-- intended_facet_id: pairwise_conditional_dependency +-- variant_semantic_role: focused_target_view +-- template_id: tpl_tpcds_within_group_share +-- query_record_id: v2q_c16_16a04539c6fc1892 +-- problem_id: v2p_c16_7d92e98250e97d6a +-- realization_mode: agent +-- source_kind: agent +SELECT "SEX", "name", + SUM(CAST("page_id" AS REAL)) AS total_measure, + SUM(CAST("page_id" AS REAL)) * 100.0 / SUM(SUM(CAST("page_id" AS REAL))) OVER (PARTITION BY "SEX") AS share_within_group +FROM "c16" +GROUP BY "SEX", "name" +ORDER BY share_within_group DESC +LIMIT 19; diff --git a/Query/sql/v2/runs/v2_cli_20260502_081223_d/c16/sql/v2q_c16_1727fec74510f83e.sql b/Query/sql/v2/runs/v2_cli_20260502_081223_d/c16/sql/v2q_c16_1727fec74510f83e.sql new file mode 100644 index 0000000000000000000000000000000000000000..2d4ef6423b73256e24e8ee5872263a1710636f7b --- /dev/null +++ b/Query/sql/v2/runs/v2_cli_20260502_081223_d/c16/sql/v2q_c16_1727fec74510f83e.sql @@ -0,0 +1,46 @@ +-- sql_source_version: v2 +-- sql_source_label: v2_current +-- sql_source_run_id: v2_cli_20260502_081223_d +-- sql_source_dataset_id: c16 +-- family_id: tail_rarity_structure +-- canonical_subitem_id: tail_concentration_consistency +-- intended_facet_id: rare_target_concentration +-- variant_semantic_role: ranked_signal_view +-- template_id: tpl_grouped_percentile_point +-- query_record_id: v2q_c16_1727fec74510f83e +-- problem_id: v2p_c16_c4cda78613a2c2d5 +-- realization_mode: agent +-- source_kind: agent +WITH "ranked" AS ( + SELECT + "ALIVE", + CAST("YEAR" AS INTEGER) AS "year_value", + ROW_NUMBER() OVER ( + PARTITION BY "ALIVE" + ORDER BY CAST("YEAR" AS INTEGER) + ) AS "rn", + COUNT(*) OVER ( + PARTITION BY "ALIVE" + ) AS "cnt" + FROM "c16" + WHERE "ALIVE" IS NOT NULL + AND "ALIVE" <> '' + AND "YEAR" IS NOT NULL + AND "YEAR" <> '' +), +"percentile_pick" AS ( + SELECT + "ALIVE", + "year_value" AS "percentile_measure", + "rn", + "cnt", + CAST((0.9 * "cnt") + 0.999999999 AS INTEGER) AS "target_rn" + FROM "ranked" +) +SELECT + "ALIVE", + "percentile_measure" +FROM "percentile_pick" +WHERE "cnt" >= 5 + AND "rn" = "target_rn" +ORDER BY "percentile_measure" DESC; diff --git a/Query/sql/v2/runs/v2_cli_20260502_081223_d/c16/sql/v2q_c16_17a71ce17ae89c11.sql b/Query/sql/v2/runs/v2_cli_20260502_081223_d/c16/sql/v2q_c16_17a71ce17ae89c11.sql new file mode 100644 index 0000000000000000000000000000000000000000..335f960eef55bd225e5d752a669b45650797937f --- /dev/null +++ b/Query/sql/v2/runs/v2_cli_20260502_081223_d/c16/sql/v2q_c16_17a71ce17ae89c11.sql @@ -0,0 +1,21 @@ +-- sql_source_version: v2 +-- sql_source_label: v2_current +-- sql_source_run_id: v2_cli_20260502_081223_d +-- sql_source_dataset_id: c16 +-- family_id: cardinality_structure +-- canonical_subitem_id: high_cardinality_response_stability +-- intended_facet_id: target_cardinality_cross_section +-- variant_semantic_role: focused_target_view +-- template_id: tpl_cardinality_high_card_response_stability +-- query_record_id: v2q_c16_17a71ce17ae89c11 +-- problem_id: v2p_c16_e284081765d15fef +-- realization_mode: deterministic +-- source_kind: deterministic +SELECT + "FIRST APPEARANCE", + COUNT(*) AS support, + AVG("page_id") AS avg_response +FROM "c16" +GROUP BY "FIRST APPEARANCE" +HAVING COUNT(*) >= 5.0 +ORDER BY support DESC, avg_response DESC; diff --git a/Query/sql/v2/runs/v2_cli_20260502_081223_d/c16/sql/v2q_c16_17c17a5dd4825eeb.sql b/Query/sql/v2/runs/v2_cli_20260502_081223_d/c16/sql/v2q_c16_17c17a5dd4825eeb.sql new file mode 100644 index 0000000000000000000000000000000000000000..b14753fc6ca3aedf700f1571df6c195a53d606b9 --- /dev/null +++ b/Query/sql/v2/runs/v2_cli_20260502_081223_d/c16/sql/v2q_c16_17c17a5dd4825eeb.sql @@ -0,0 +1,25 @@ +-- sql_source_version: v2 +-- sql_source_label: v2_current +-- sql_source_run_id: v2_cli_20260502_081223_d +-- sql_source_dataset_id: c16 +-- family_id: conditional_dependency_structure +-- canonical_subitem_id: dependency_strength_similarity +-- intended_facet_id: pairwise_conditional_dependency +-- variant_semantic_role: within_group_proportion +-- template_id: tpl_tpcds_within_group_share +-- query_record_id: v2q_c16_17c17a5dd4825eeb +-- problem_id: v2p_c16_173ccaf09e0de6d7 +-- realization_mode: agent +-- source_kind: agent +SELECT "SEX", "urlslug", + SUM(CAST("APPEARANCES" AS REAL)) AS total_measure, + SUM(CAST("APPEARANCES" AS REAL)) * 100.0 / SUM(SUM(CAST("APPEARANCES" AS REAL))) OVER (PARTITION BY "SEX") AS share_within_group +FROM "c16" +WHERE "APPEARANCES" IS NOT NULL + AND TRIM("APPEARANCES") <> '' + AND "SEX" IS NOT NULL + AND TRIM("SEX") <> '' + AND "urlslug" IS NOT NULL + AND TRIM("urlslug") <> '' +GROUP BY "SEX", "urlslug" +ORDER BY share_within_group DESC; diff --git a/Query/sql/v2/runs/v2_cli_20260502_081223_d/c16/sql/v2q_c16_17f00b4a77414fd2.sql b/Query/sql/v2/runs/v2_cli_20260502_081223_d/c16/sql/v2q_c16_17f00b4a77414fd2.sql new file mode 100644 index 0000000000000000000000000000000000000000..3f556a88208feda117dc4037af1369c317f95142 --- /dev/null +++ b/Query/sql/v2/runs/v2_cli_20260502_081223_d/c16/sql/v2q_c16_17f00b4a77414fd2.sql @@ -0,0 +1,22 @@ +-- sql_source_version: v2 +-- sql_source_label: v2_current +-- sql_source_run_id: v2_cli_20260502_081223_d +-- sql_source_dataset_id: c16 +-- family_id: conditional_dependency_structure +-- canonical_subitem_id: dependency_strength_similarity +-- intended_facet_id: pairwise_conditional_dependency +-- variant_semantic_role: focused_target_view +-- template_id: tpl_tpcds_within_group_share +-- query_record_id: v2q_c16_17f00b4a77414fd2 +-- problem_id: v2p_c16_fcf26f8f020f6ddf +-- realization_mode: agent +-- source_kind: agent +SELECT "ALIGN", "YEAR", + SUM(CAST(NULLIF("APPEARANCES", '') AS REAL)) AS total_measure, + SUM(CAST(NULLIF("APPEARANCES", '') AS REAL)) * 100.0 / SUM(SUM(CAST(NULLIF("APPEARANCES", '') AS REAL))) OVER (PARTITION BY "ALIGN") AS share_within_group +FROM "c16" +WHERE "ALIGN" IS NOT NULL AND TRIM("ALIGN") <> '' + AND "YEAR" IS NOT NULL AND TRIM("YEAR") <> '' + AND NULLIF("APPEARANCES", '') IS NOT NULL +GROUP BY "ALIGN", "YEAR" +ORDER BY share_within_group DESC; diff --git a/Query/sql/v2/runs/v2_cli_20260502_081223_d/c16/sql/v2q_c16_18d6de6c0dc75bd6.sql b/Query/sql/v2/runs/v2_cli_20260502_081223_d/c16/sql/v2q_c16_18d6de6c0dc75bd6.sql new file mode 100644 index 0000000000000000000000000000000000000000..4a843891fc43364f1165c1973a2abc7072d48a52 --- /dev/null +++ b/Query/sql/v2/runs/v2_cli_20260502_081223_d/c16/sql/v2q_c16_18d6de6c0dc75bd6.sql @@ -0,0 +1,18 @@ +-- sql_source_version: v2 +-- sql_source_label: v2_current +-- sql_source_run_id: v2_cli_20260502_081223_d +-- sql_source_dataset_id: c16 +-- family_id: conditional_dependency_structure +-- canonical_subitem_id: direction_consistency +-- intended_facet_id: conditional_rate_shift +-- variant_semantic_role: within_group_proportion +-- template_id: tpl_m4_group_condition_rate +-- query_record_id: v2q_c16_18d6de6c0dc75bd6 +-- problem_id: v2p_c16_0399828de96e221f +-- realization_mode: agent +-- source_kind: agent +SELECT "EYE", + AVG(CASE WHEN "SEX" = 'Female Characters' THEN 1 ELSE 0 END) AS condition_rate +FROM "c16" +GROUP BY "EYE" +ORDER BY condition_rate DESC; diff --git a/Query/sql/v2/runs/v2_cli_20260502_081223_d/c16/sql/v2q_c16_1e417ca72483aff2.sql b/Query/sql/v2/runs/v2_cli_20260502_081223_d/c16/sql/v2q_c16_1e417ca72483aff2.sql new file mode 100644 index 0000000000000000000000000000000000000000..2ca96d7358c35d1ac09202053c9e47bcbbf35740 --- /dev/null +++ b/Query/sql/v2/runs/v2_cli_20260502_081223_d/c16/sql/v2q_c16_1e417ca72483aff2.sql @@ -0,0 +1,18 @@ +-- sql_source_version: v2 +-- sql_source_label: v2_current +-- sql_source_run_id: v2_cli_20260502_081223_d +-- sql_source_dataset_id: c16 +-- family_id: missingness_structure +-- canonical_subitem_id: marginal_missing_rate_consistency +-- intended_facet_id: missing_indicator_distribution +-- variant_semantic_role: missing_indicator_view +-- template_id: tpl_missing_marginal_rate_profile +-- query_record_id: v2q_c16_1e417ca72483aff2 +-- problem_id: v2p_c16_41f769775de964fb +-- realization_mode: deterministic +-- source_kind: deterministic +SELECT + COUNT(*) AS total_rows, + SUM(CASE WHEN "APPEARANCES" IS NULL THEN 1 ELSE 0 END) AS missing_rows, + AVG(CASE WHEN "APPEARANCES" IS NULL THEN 1.0 ELSE 0.0 END) AS missing_rate +FROM "c16"; diff --git a/Query/sql/v2/runs/v2_cli_20260502_081223_d/c16/sql/v2q_c16_219c9329fd32458e.sql b/Query/sql/v2/runs/v2_cli_20260502_081223_d/c16/sql/v2q_c16_219c9329fd32458e.sql new file mode 100644 index 0000000000000000000000000000000000000000..69a0aa2808cd822d82909d2be4c64cd61a3c7e1d --- /dev/null +++ b/Query/sql/v2/runs/v2_cli_20260502_081223_d/c16/sql/v2q_c16_219c9329fd32458e.sql @@ -0,0 +1,25 @@ +-- sql_source_version: v2 +-- sql_source_label: v2_current +-- sql_source_run_id: v2_cli_20260502_081223_d +-- sql_source_dataset_id: c16 +-- family_id: tail_rarity_structure +-- canonical_subitem_id: tail_set_consistency +-- intended_facet_id: low_support_extremes +-- variant_semantic_role: rare_extreme_view +-- template_id: tpl_m4_quantile_tail_slice +-- query_record_id: v2q_c16_219c9329fd32458e +-- problem_id: v2p_c16_27ffed3380aef0d1 +-- realization_mode: agent +-- source_kind: agent +WITH "buckets" AS ( + SELECT + CAST("APPEARANCES" AS INTEGER) AS "APPEARANCES", + NTILE(10) OVER (ORDER BY CAST("APPEARANCES" AS INTEGER) DESC) AS "tail_bucket" + FROM "c16" + WHERE "APPEARANCES" IS NOT NULL + AND TRIM("APPEARANCES") <> '' +) +SELECT "APPEARANCES" +FROM "buckets" +WHERE "tail_bucket" = 1 +ORDER BY "APPEARANCES" DESC; diff --git a/Query/sql/v2/runs/v2_cli_20260502_081223_d/c16/sql/v2q_c16_244d52072d12a43b.sql b/Query/sql/v2/runs/v2_cli_20260502_081223_d/c16/sql/v2q_c16_244d52072d12a43b.sql new file mode 100644 index 0000000000000000000000000000000000000000..baa75a121528dabeac86adfe23ba423cd6d6d680 --- /dev/null +++ b/Query/sql/v2/runs/v2_cli_20260502_081223_d/c16/sql/v2q_c16_244d52072d12a43b.sql @@ -0,0 +1,17 @@ +-- sql_source_version: v2 +-- sql_source_label: v2_current +-- sql_source_run_id: v2_cli_20260502_081223_d +-- sql_source_dataset_id: c16 +-- family_id: subgroup_structure +-- canonical_subitem_id: internal_profile_stability +-- intended_facet_id: subgroup_rank_order +-- variant_semantic_role: collapsed_target_view +-- template_id: tpl_h2o_group_sum +-- query_record_id: v2q_c16_244d52072d12a43b +-- problem_id: v2p_c16_2553365ecacecde0 +-- realization_mode: agent +-- source_kind: agent +SELECT "GSM", SUM(CAST("APPEARANCES" AS REAL)) AS "total_measure" +FROM "c16" +GROUP BY "GSM" +ORDER BY "total_measure" DESC; diff --git a/Query/sql/v2/runs/v2_cli_20260502_081223_d/c16/sql/v2q_c16_255b1207e833ebd1.sql b/Query/sql/v2/runs/v2_cli_20260502_081223_d/c16/sql/v2q_c16_255b1207e833ebd1.sql new file mode 100644 index 0000000000000000000000000000000000000000..a012dd3c0d981842ec7f5e0f0073e72cbe46b714 --- /dev/null +++ b/Query/sql/v2/runs/v2_cli_20260502_081223_d/c16/sql/v2q_c16_255b1207e833ebd1.sql @@ -0,0 +1,17 @@ +-- sql_source_version: v2 +-- sql_source_label: v2_current +-- sql_source_run_id: v2_cli_20260502_081223_d +-- sql_source_dataset_id: c16 +-- family_id: subgroup_structure +-- canonical_subitem_id: internal_profile_stability +-- intended_facet_id: subgroup_rank_order +-- variant_semantic_role: collapsed_target_view +-- template_id: tpl_h2o_group_sum +-- query_record_id: v2q_c16_255b1207e833ebd1 +-- problem_id: v2p_c16_8fdfc8877d61d2e0 +-- realization_mode: agent +-- source_kind: agent +SELECT "ALIGN", SUM(CAST("APPEARANCES" AS REAL)) AS "total_measure" +FROM "c16" +GROUP BY "ALIGN" +ORDER BY "total_measure" DESC; diff --git a/Query/sql/v2/runs/v2_cli_20260502_081223_d/c16/sql/v2q_c16_28170758cce5434f.sql b/Query/sql/v2/runs/v2_cli_20260502_081223_d/c16/sql/v2q_c16_28170758cce5434f.sql new file mode 100644 index 0000000000000000000000000000000000000000..2b94ae0d6b2d65ea62b70d05d8ec898c33de0163 --- /dev/null +++ b/Query/sql/v2/runs/v2_cli_20260502_081223_d/c16/sql/v2q_c16_28170758cce5434f.sql @@ -0,0 +1,17 @@ +-- sql_source_version: v2 +-- sql_source_label: v2_current +-- sql_source_run_id: v2_cli_20260502_081223_d +-- sql_source_dataset_id: c16 +-- family_id: subgroup_structure +-- canonical_subitem_id: subgroup_size_stability +-- intended_facet_id: subgroup_distribution_shift +-- variant_semantic_role: count_distribution +-- template_id: tpl_clickbench_group_count +-- query_record_id: v2q_c16_28170758cce5434f +-- problem_id: v2p_c16_9e5f52ba4afc68bf +-- realization_mode: agent +-- source_kind: agent +SELECT "ALIGN", COUNT(*) AS "row_count" +FROM "c16" +GROUP BY "ALIGN" +ORDER BY "row_count" DESC; diff --git a/Query/sql/v2/runs/v2_cli_20260502_081223_d/c16/sql/v2q_c16_2bcc89a449f4980a.sql b/Query/sql/v2/runs/v2_cli_20260502_081223_d/c16/sql/v2q_c16_2bcc89a449f4980a.sql new file mode 100644 index 0000000000000000000000000000000000000000..59f0c36073c4852097d20497112f88b08197ff9b --- /dev/null +++ b/Query/sql/v2/runs/v2_cli_20260502_081223_d/c16/sql/v2q_c16_2bcc89a449f4980a.sql @@ -0,0 +1,44 @@ +-- sql_source_version: v2 +-- sql_source_label: v2_current +-- sql_source_run_id: v2_cli_20260502_081223_d +-- sql_source_dataset_id: c16 +-- family_id: tail_rarity_structure +-- canonical_subitem_id: tail_concentration_consistency +-- intended_facet_id: rare_target_concentration +-- variant_semantic_role: ranked_signal_view +-- template_id: tpl_grouped_percentile_point +-- query_record_id: v2q_c16_2bcc89a449f4980a +-- problem_id: v2p_c16_e0ef7ca040665773 +-- realization_mode: agent +-- source_kind: agent +WITH "base" AS ( + SELECT + "EYE", + CAST("YEAR" AS INTEGER) AS "YEAR_num" + FROM "c16" + WHERE "EYE" IS NOT NULL + AND TRIM("EYE") <> '' + AND "YEAR" GLOB '[0-9][0-9][0-9][0-9]' +), +"ranked" AS ( + SELECT + "EYE", + "YEAR_num", + CUME_DIST() OVER ( + PARTITION BY "EYE" + ORDER BY "YEAR_num" + ) AS "cume_dist", + COUNT(*) OVER ( + PARTITION BY "EYE" + ) AS "group_size" + FROM "base" +) +SELECT + "EYE", + MIN("YEAR_num") AS "percentile_measure" +FROM "ranked" +WHERE "cume_dist" >= 0.9 +GROUP BY "EYE" +HAVING MAX("group_size") >= 5 +ORDER BY "percentile_measure" DESC, + "EYE" ASC; diff --git a/Query/sql/v2/runs/v2_cli_20260502_081223_d/c16/sql/v2q_c16_2c064699fe9fbcb7.sql b/Query/sql/v2/runs/v2_cli_20260502_081223_d/c16/sql/v2q_c16_2c064699fe9fbcb7.sql new file mode 100644 index 0000000000000000000000000000000000000000..c29774d9d56b1393352f1f9945fa9182c01f9c69 --- /dev/null +++ b/Query/sql/v2/runs/v2_cli_20260502_081223_d/c16/sql/v2q_c16_2c064699fe9fbcb7.sql @@ -0,0 +1,18 @@ +-- sql_source_version: v2 +-- sql_source_label: v2_current +-- sql_source_run_id: v2_cli_20260502_081223_d +-- sql_source_dataset_id: c16 +-- family_id: conditional_dependency_structure +-- canonical_subitem_id: slice_level_consistency +-- intended_facet_id: conditional_interaction_hotspots +-- variant_semantic_role: count_distribution +-- template_id: tpl_c2_filtered_group_count_2d +-- query_record_id: v2q_c16_2c064699fe9fbcb7 +-- problem_id: v2p_c16_dcfec2a546794a3b +-- realization_mode: agent +-- source_kind: agent +SELECT "HAIR", "YEAR", COUNT(*) AS row_count +FROM "c16" +WHERE "ALIVE" = '' +GROUP BY "HAIR", "YEAR" +ORDER BY row_count DESC; diff --git a/Query/sql/v2/runs/v2_cli_20260502_081223_d/c16/sql/v2q_c16_2c1991b104e63bba.sql b/Query/sql/v2/runs/v2_cli_20260502_081223_d/c16/sql/v2q_c16_2c1991b104e63bba.sql new file mode 100644 index 0000000000000000000000000000000000000000..11833d0ccecad5cc79437e4bcda1e79a732cdcab --- /dev/null +++ b/Query/sql/v2/runs/v2_cli_20260502_081223_d/c16/sql/v2q_c16_2c1991b104e63bba.sql @@ -0,0 +1,21 @@ +-- sql_source_version: v2 +-- sql_source_label: v2_current +-- sql_source_run_id: v2_cli_20260502_081223_d +-- sql_source_dataset_id: c16 +-- family_id: missingness_structure +-- canonical_subitem_id: co_missingness_pattern_consistency +-- intended_facet_id: missing_rate_by_subgroup +-- variant_semantic_role: missing_rate_by_subgroup +-- template_id: tpl_missing_rate_by_subgroup +-- query_record_id: v2q_c16_2c1991b104e63bba +-- problem_id: v2p_c16_1463777577d94db2 +-- realization_mode: deterministic +-- source_kind: deterministic +SELECT + "SEX", + COUNT(*) AS total_rows, + SUM(CASE WHEN "HAIR" IS NULL THEN 1 ELSE 0 END) AS missing_rows, + AVG(CASE WHEN "HAIR" IS NULL THEN 1.0 ELSE 0.0 END) AS missing_rate +FROM "c16" +GROUP BY "SEX" +ORDER BY missing_rate DESC, total_rows DESC; diff --git a/Query/sql/v2/runs/v2_cli_20260502_081223_d/c16/sql/v2q_c16_2c5de40636932254.sql b/Query/sql/v2/runs/v2_cli_20260502_081223_d/c16/sql/v2q_c16_2c5de40636932254.sql new file mode 100644 index 0000000000000000000000000000000000000000..0992dc7f7c1e9d694000e69eaf9e322462567137 --- /dev/null +++ b/Query/sql/v2/runs/v2_cli_20260502_081223_d/c16/sql/v2q_c16_2c5de40636932254.sql @@ -0,0 +1,21 @@ +-- sql_source_version: v2 +-- sql_source_label: v2_current +-- sql_source_run_id: v2_cli_20260502_081223_d +-- sql_source_dataset_id: c16 +-- family_id: missingness_structure +-- canonical_subitem_id: co_missingness_pattern_consistency +-- intended_facet_id: missing_target_interaction +-- variant_semantic_role: missing_target_interaction +-- template_id: tpl_missing_target_interaction +-- query_record_id: v2q_c16_2c5de40636932254 +-- problem_id: v2p_c16_d14844d06b8fcc1c +-- realization_mode: deterministic +-- source_kind: deterministic +SELECT + "EYE", + COUNT(*) AS total_rows, + SUM(CASE WHEN "ID" IS NULL THEN 1 ELSE 0 END) AS missing_rows, + AVG(CASE WHEN "ID" IS NULL THEN 1.0 ELSE 0.0 END) AS missing_rate +FROM "c16" +GROUP BY "EYE" +ORDER BY missing_rate DESC, total_rows DESC; diff --git a/Query/sql/v2/runs/v2_cli_20260502_081223_d/c16/sql/v2q_c16_2d2f4089df7eaf33.sql b/Query/sql/v2/runs/v2_cli_20260502_081223_d/c16/sql/v2q_c16_2d2f4089df7eaf33.sql new file mode 100644 index 0000000000000000000000000000000000000000..8eabcf2e6b59b22b49e52be7db1229a1d4d028cb --- /dev/null +++ b/Query/sql/v2/runs/v2_cli_20260502_081223_d/c16/sql/v2q_c16_2d2f4089df7eaf33.sql @@ -0,0 +1,21 @@ +-- sql_source_version: v2 +-- sql_source_label: v2_current +-- sql_source_run_id: v2_cli_20260502_081223_d +-- sql_source_dataset_id: c16 +-- family_id: cardinality_structure +-- canonical_subitem_id: high_cardinality_response_stability +-- intended_facet_id: target_cardinality_cross_section +-- variant_semantic_role: focused_target_view +-- template_id: tpl_cardinality_high_card_response_stability +-- query_record_id: v2q_c16_2d2f4089df7eaf33 +-- problem_id: v2p_c16_8429c7c8ea4a8a06 +-- realization_mode: deterministic +-- source_kind: deterministic +SELECT + "name", + COUNT(*) AS support, + AVG("APPEARANCES") AS avg_response +FROM "c16" +GROUP BY "name" +HAVING COUNT(*) >= 5.0 +ORDER BY support DESC, avg_response DESC; diff --git a/Query/sql/v2/runs/v2_cli_20260502_081223_d/c16/sql/v2q_c16_2e91ac95ba038036.sql b/Query/sql/v2/runs/v2_cli_20260502_081223_d/c16/sql/v2q_c16_2e91ac95ba038036.sql new file mode 100644 index 0000000000000000000000000000000000000000..512e08d87bdfe45735d9e9517e575fc2eda1c0bf --- /dev/null +++ b/Query/sql/v2/runs/v2_cli_20260502_081223_d/c16/sql/v2q_c16_2e91ac95ba038036.sql @@ -0,0 +1,24 @@ +-- sql_source_version: v2 +-- sql_source_label: v2_current +-- sql_source_run_id: v2_cli_20260502_081223_d +-- sql_source_dataset_id: c16 +-- family_id: tail_rarity_structure +-- canonical_subitem_id: tail_set_consistency +-- intended_facet_id: low_support_extremes +-- variant_semantic_role: rare_extreme_view +-- template_id: tpl_m4_quantile_tail_slice +-- query_record_id: v2q_c16_2e91ac95ba038036 +-- problem_id: v2p_c16_0c925ad0109d5317 +-- realization_mode: agent +-- source_kind: agent +WITH buckets AS ( + SELECT + "page_id", + NTILE(10) OVER (ORDER BY CAST("page_id" AS INTEGER) DESC) AS "tail_bucket" + FROM "c16" + WHERE "page_id" IS NOT NULL AND TRIM("page_id") <> '' +) +SELECT "page_id" +FROM buckets +WHERE "tail_bucket" = 1 +ORDER BY CAST("page_id" AS INTEGER) DESC; diff --git a/Query/sql/v2/runs/v2_cli_20260502_081223_d/c16/sql/v2q_c16_332bd5ff14cb9a67.sql b/Query/sql/v2/runs/v2_cli_20260502_081223_d/c16/sql/v2q_c16_332bd5ff14cb9a67.sql new file mode 100644 index 0000000000000000000000000000000000000000..eec8a14b41adc15f9a23272e0b3c4fcd44497476 --- /dev/null +++ b/Query/sql/v2/runs/v2_cli_20260502_081223_d/c16/sql/v2q_c16_332bd5ff14cb9a67.sql @@ -0,0 +1,25 @@ +-- sql_source_version: v2 +-- sql_source_label: v2_current +-- sql_source_run_id: v2_cli_20260502_081223_d +-- sql_source_dataset_id: c16 +-- family_id: cardinality_structure +-- canonical_subitem_id: support_rank_profile_consistency +-- intended_facet_id: support_concentration +-- variant_semantic_role: count_distribution +-- template_id: tpl_cardinality_support_rank_profile +-- query_record_id: v2q_c16_332bd5ff14cb9a67 +-- problem_id: v2p_c16_45a54be57ce4c177 +-- realization_mode: deterministic +-- source_kind: deterministic +WITH grouped AS ( + SELECT "YEAR" AS value_label, COUNT(*) AS support + FROM "c16" + GROUP BY "YEAR" +) +SELECT + value_label, + support, + CAST(support AS FLOAT) / NULLIF(SUM(support) OVER (), 0) AS support_share, + ROW_NUMBER() OVER (ORDER BY support DESC, value_label) AS support_rank +FROM grouped +ORDER BY support DESC, value_label; diff --git a/Query/sql/v2/runs/v2_cli_20260502_081223_d/c16/sql/v2q_c16_33b571b5728f7a1b.sql b/Query/sql/v2/runs/v2_cli_20260502_081223_d/c16/sql/v2q_c16_33b571b5728f7a1b.sql new file mode 100644 index 0000000000000000000000000000000000000000..644b1ab8e472185aa9d9a419beb7e877adb8a080 --- /dev/null +++ b/Query/sql/v2/runs/v2_cli_20260502_081223_d/c16/sql/v2q_c16_33b571b5728f7a1b.sql @@ -0,0 +1,21 @@ +-- sql_source_version: v2 +-- sql_source_label: v2_current +-- sql_source_run_id: v2_cli_20260502_081223_d +-- sql_source_dataset_id: c16 +-- family_id: missingness_structure +-- canonical_subitem_id: co_missingness_pattern_consistency +-- intended_facet_id: missing_target_interaction +-- variant_semantic_role: missing_target_interaction +-- template_id: tpl_missing_target_interaction +-- query_record_id: v2q_c16_33b571b5728f7a1b +-- problem_id: v2p_c16_742e1a482ce30f04 +-- realization_mode: deterministic +-- source_kind: deterministic +SELECT + "ID", + COUNT(*) AS total_rows, + SUM(CASE WHEN "SEX" IS NULL THEN 1 ELSE 0 END) AS missing_rows, + AVG(CASE WHEN "SEX" IS NULL THEN 1.0 ELSE 0.0 END) AS missing_rate +FROM "c16" +GROUP BY "ID" +ORDER BY missing_rate DESC, total_rows DESC; diff --git a/Query/sql/v2/runs/v2_cli_20260502_081223_d/c16/sql/v2q_c16_350e77766ab5c5cd.sql b/Query/sql/v2/runs/v2_cli_20260502_081223_d/c16/sql/v2q_c16_350e77766ab5c5cd.sql new file mode 100644 index 0000000000000000000000000000000000000000..3b5605f70d1c9f6444924f78a0bffa54c569fa97 --- /dev/null +++ b/Query/sql/v2/runs/v2_cli_20260502_081223_d/c16/sql/v2q_c16_350e77766ab5c5cd.sql @@ -0,0 +1,18 @@ +-- sql_source_version: v2 +-- sql_source_label: v2_current +-- sql_source_run_id: v2_cli_20260502_081223_d +-- sql_source_dataset_id: c16 +-- family_id: missingness_structure +-- canonical_subitem_id: marginal_missing_rate_consistency +-- intended_facet_id: missing_indicator_distribution +-- variant_semantic_role: missing_indicator_view +-- template_id: tpl_missing_marginal_rate_profile +-- query_record_id: v2q_c16_350e77766ab5c5cd +-- problem_id: v2p_c16_c70b5b47dd88062b +-- realization_mode: deterministic +-- source_kind: deterministic +SELECT + COUNT(*) AS total_rows, + SUM(CASE WHEN "GSM" IS NULL THEN 1 ELSE 0 END) AS missing_rows, + AVG(CASE WHEN "GSM" IS NULL THEN 1.0 ELSE 0.0 END) AS missing_rate +FROM "c16"; diff --git a/Query/sql/v2/runs/v2_cli_20260502_081223_d/c16/sql/v2q_c16_357156cefb19fef7.sql b/Query/sql/v2/runs/v2_cli_20260502_081223_d/c16/sql/v2q_c16_357156cefb19fef7.sql new file mode 100644 index 0000000000000000000000000000000000000000..0f0c44ff99a68bf30052ce053fea6b38b83bb1f8 --- /dev/null +++ b/Query/sql/v2/runs/v2_cli_20260502_081223_d/c16/sql/v2q_c16_357156cefb19fef7.sql @@ -0,0 +1,22 @@ +-- sql_source_version: v2 +-- sql_source_label: v2_current +-- sql_source_run_id: v2_cli_20260502_081223_d +-- sql_source_dataset_id: c16 +-- family_id: conditional_dependency_structure +-- canonical_subitem_id: dependency_strength_similarity +-- intended_facet_id: pairwise_conditional_dependency +-- variant_semantic_role: within_group_proportion +-- template_id: tpl_tpcds_within_group_share +-- query_record_id: v2q_c16_357156cefb19fef7 +-- problem_id: v2p_c16_f7d751729c22d8a3 +-- realization_mode: agent +-- source_kind: agent +SELECT "EYE", "page_id", + SUM(CAST("YEAR" AS REAL)) AS total_measure, + SUM(CAST("YEAR" AS REAL)) * 100.0 / SUM(SUM(CAST("YEAR" AS REAL))) OVER (PARTITION BY "EYE") AS share_within_group +FROM "c16" +WHERE "EYE" IS NOT NULL AND "EYE" <> '' + AND "page_id" IS NOT NULL AND "page_id" <> '' + AND "YEAR" IS NOT NULL AND "YEAR" <> '' +GROUP BY "EYE", "page_id" +ORDER BY share_within_group DESC; diff --git a/Query/sql/v2/runs/v2_cli_20260502_081223_d/c16/sql/v2q_c16_3588ac07075ecd76.sql b/Query/sql/v2/runs/v2_cli_20260502_081223_d/c16/sql/v2q_c16_3588ac07075ecd76.sql new file mode 100644 index 0000000000000000000000000000000000000000..191ea1c4a3cb93ea1838b5464a888096be23296a --- /dev/null +++ b/Query/sql/v2/runs/v2_cli_20260502_081223_d/c16/sql/v2q_c16_3588ac07075ecd76.sql @@ -0,0 +1,66 @@ +-- sql_source_version: v2 +-- sql_source_label: v2_current +-- sql_source_run_id: v2_cli_20260502_081223_d +-- sql_source_dataset_id: c16 +-- family_id: tail_rarity_structure +-- canonical_subitem_id: tail_concentration_consistency +-- intended_facet_id: rare_target_concentration +-- variant_semantic_role: ranked_signal_view +-- template_id: tpl_grouped_percentile_point +-- query_record_id: v2q_c16_3588ac07075ecd76 +-- problem_id: v2p_c16_3c51e9ccb70dfb52 +-- realization_mode: agent +-- source_kind: agent +WITH "base" AS ( + SELECT + "YEAR" AS "YEAR", + CAST("page_id" AS REAL) AS "measure" + FROM "c16" + WHERE "YEAR" IS NOT NULL + AND TRIM("YEAR") <> '' + AND "page_id" IS NOT NULL + AND TRIM("page_id") <> '' +), +"ordered" AS ( + SELECT + "YEAR", + "measure", + ROW_NUMBER() OVER (PARTITION BY "YEAR" ORDER BY "measure") AS "rn", + COUNT(*) OVER (PARTITION BY "YEAR") AS "cnt" + FROM "base" +), +"params" AS ( + SELECT DISTINCT + "YEAR", + "cnt", + (1.0 + 0.9 * ("cnt" - 1)) AS "pos", + CAST((1.0 + 0.9 * ("cnt" - 1)) AS INTEGER) AS "lo_rn", + CASE + WHEN (1.0 + 0.9 * ("cnt" - 1)) = CAST((1.0 + 0.9 * ("cnt" - 1)) AS INTEGER) + THEN CAST((1.0 + 0.9 * ("cnt" - 1)) AS INTEGER) + ELSE CAST((1.0 + 0.9 * ("cnt" - 1)) AS INTEGER) + 1 + END AS "hi_rn" + FROM "ordered" +) +SELECT + p."YEAR", + CASE + WHEN p."lo_rn" = p."hi_rn" THEN MAX(CASE WHEN o."rn" = p."lo_rn" THEN o."measure" END) + ELSE MAX(CASE WHEN o."rn" = p."lo_rn" THEN o."measure" END) + + (p."pos" - p."lo_rn") + * ( + MAX(CASE WHEN o."rn" = p."hi_rn" THEN o."measure" END) + - MAX(CASE WHEN o."rn" = p."lo_rn" THEN o."measure" END) + ) + END AS "percentile_measure" +FROM "params" AS p +JOIN "ordered" AS o + ON o."YEAR" = p."YEAR" +GROUP BY + p."YEAR", + p."cnt", + p."pos", + p."lo_rn", + p."hi_rn" +HAVING p."cnt" >= 5 +ORDER BY "percentile_measure" DESC; diff --git a/Query/sql/v2/runs/v2_cli_20260502_081223_d/c16/sql/v2q_c16_35eed337063eff10.sql b/Query/sql/v2/runs/v2_cli_20260502_081223_d/c16/sql/v2q_c16_35eed337063eff10.sql new file mode 100644 index 0000000000000000000000000000000000000000..4fda342a404a4a72bb2cec339240f71712ad6ab5 --- /dev/null +++ b/Query/sql/v2/runs/v2_cli_20260502_081223_d/c16/sql/v2q_c16_35eed337063eff10.sql @@ -0,0 +1,15 @@ +-- sql_source_version: v2 +-- sql_source_label: v2_current +-- sql_source_run_id: v2_cli_20260502_081223_d +-- sql_source_dataset_id: c16 +-- family_id: tail_rarity_structure +-- canonical_subitem_id: tail_set_consistency +-- intended_facet_id: low_support_extremes +-- variant_semantic_role: rare_extreme_view +-- template_id: tpl_threshold_rarity_cdf +-- query_record_id: v2q_c16_35eed337063eff10 +-- problem_id: v2p_c16_3ee326270c734658 +-- realization_mode: agent +-- source_kind: agent +SELECT AVG(CASE WHEN CAST("page_id" AS REAL) <= 213203.0 THEN 1 ELSE 0 END) AS "empirical_cdf_at_threshold" +FROM "c16"; diff --git a/Query/sql/v2/runs/v2_cli_20260502_081223_d/c16/sql/v2q_c16_389ecc90400bf53c.sql b/Query/sql/v2/runs/v2_cli_20260502_081223_d/c16/sql/v2q_c16_389ecc90400bf53c.sql new file mode 100644 index 0000000000000000000000000000000000000000..93b256b6e894c7d97a6abca20dafa26b8a0dc7f2 --- /dev/null +++ b/Query/sql/v2/runs/v2_cli_20260502_081223_d/c16/sql/v2q_c16_389ecc90400bf53c.sql @@ -0,0 +1,22 @@ +-- sql_source_version: v2 +-- sql_source_label: v2_current +-- sql_source_run_id: v2_cli_20260502_081223_d +-- sql_source_dataset_id: c16 +-- family_id: conditional_dependency_structure +-- canonical_subitem_id: dependency_strength_similarity +-- intended_facet_id: pairwise_conditional_dependency +-- variant_semantic_role: focused_target_view +-- template_id: tpl_tpcds_within_group_share +-- query_record_id: v2q_c16_389ecc90400bf53c +-- problem_id: v2p_c16_dfb68d6eaf359fd0 +-- realization_mode: agent +-- source_kind: agent +SELECT + "GSM", + "APPEARANCES", + SUM(CAST(NULLIF("YEAR", '') AS REAL)) AS "total_measure", + SUM(CAST(NULLIF("YEAR", '') AS REAL)) * 100.0 / SUM(SUM(CAST(NULLIF("YEAR", '') AS REAL))) OVER (PARTITION BY "GSM") AS "share_within_group" +FROM "c16" +WHERE NULLIF("YEAR", '') IS NOT NULL +GROUP BY "GSM", "APPEARANCES" +ORDER BY "share_within_group" DESC; diff --git a/Query/sql/v2/runs/v2_cli_20260502_081223_d/c16/sql/v2q_c16_3b06263ae10e7d17.sql b/Query/sql/v2/runs/v2_cli_20260502_081223_d/c16/sql/v2q_c16_3b06263ae10e7d17.sql new file mode 100644 index 0000000000000000000000000000000000000000..8e9c13e880c457f0ca65911b68d68c466606d439 --- /dev/null +++ b/Query/sql/v2/runs/v2_cli_20260502_081223_d/c16/sql/v2q_c16_3b06263ae10e7d17.sql @@ -0,0 +1,18 @@ +-- sql_source_version: v2 +-- sql_source_label: v2_current +-- sql_source_run_id: v2_cli_20260502_081223_d +-- sql_source_dataset_id: c16 +-- family_id: conditional_dependency_structure +-- canonical_subitem_id: dependency_strength_similarity +-- intended_facet_id: pairwise_conditional_dependency +-- variant_semantic_role: within_group_proportion +-- template_id: tpl_m4_group_condition_rate +-- query_record_id: v2q_c16_3b06263ae10e7d17 +-- problem_id: v2p_c16_a3e01251b11a82e5 +-- realization_mode: agent +-- source_kind: agent +SELECT "YEAR", + AVG(CASE WHEN "ALIGN" = 'Bad Characters' THEN 1 ELSE 0 END) AS condition_rate +FROM "c16" +GROUP BY "YEAR" +ORDER BY condition_rate DESC; diff --git a/Query/sql/v2/runs/v2_cli_20260502_081223_d/c16/sql/v2q_c16_3c479363841374cd.sql b/Query/sql/v2/runs/v2_cli_20260502_081223_d/c16/sql/v2q_c16_3c479363841374cd.sql new file mode 100644 index 0000000000000000000000000000000000000000..b52bd5a5b1fc4298d1b634df02928af61939d03a --- /dev/null +++ b/Query/sql/v2/runs/v2_cli_20260502_081223_d/c16/sql/v2q_c16_3c479363841374cd.sql @@ -0,0 +1,21 @@ +-- sql_source_version: v2 +-- sql_source_label: v2_current +-- sql_source_run_id: v2_cli_20260502_081223_d +-- sql_source_dataset_id: c16 +-- family_id: missingness_structure +-- canonical_subitem_id: co_missingness_pattern_consistency +-- intended_facet_id: missing_target_interaction +-- variant_semantic_role: missing_target_interaction +-- template_id: tpl_missing_target_interaction +-- query_record_id: v2q_c16_3c479363841374cd +-- problem_id: v2p_c16_fa177481fdd5edd9 +-- realization_mode: deterministic +-- source_kind: deterministic +SELECT + "GSM", + COUNT(*) AS total_rows, + SUM(CASE WHEN "APPEARANCES" IS NULL THEN 1 ELSE 0 END) AS missing_rows, + AVG(CASE WHEN "APPEARANCES" IS NULL THEN 1.0 ELSE 0.0 END) AS missing_rate +FROM "c16" +GROUP BY "GSM" +ORDER BY missing_rate DESC, total_rows DESC; diff --git a/Query/sql/v2/runs/v2_cli_20260502_081223_d/c16/sql/v2q_c16_3cab47d18e542cda.sql b/Query/sql/v2/runs/v2_cli_20260502_081223_d/c16/sql/v2q_c16_3cab47d18e542cda.sql new file mode 100644 index 0000000000000000000000000000000000000000..c4e862ac8f97276e0eaf95e0d34d2a5a952cf718 --- /dev/null +++ b/Query/sql/v2/runs/v2_cli_20260502_081223_d/c16/sql/v2q_c16_3cab47d18e542cda.sql @@ -0,0 +1,19 @@ +-- sql_source_version: v2 +-- sql_source_label: v2_current +-- sql_source_run_id: v2_cli_20260502_081223_d +-- sql_source_dataset_id: c16 +-- family_id: conditional_dependency_structure +-- canonical_subitem_id: dependency_strength_similarity +-- intended_facet_id: pairwise_conditional_dependency +-- variant_semantic_role: focused_target_view +-- template_id: tpl_tpcds_within_group_share +-- query_record_id: v2q_c16_3cab47d18e542cda +-- problem_id: v2p_c16_f1158bf2fdc43157 +-- realization_mode: agent +-- source_kind: agent +SELECT "YEAR", "FIRST APPEARANCE", + SUM(CAST("page_id" AS NUMERIC)) AS total_measure, + SUM(CAST("page_id" AS NUMERIC)) * 100.0 / SUM(SUM(CAST("page_id" AS NUMERIC))) OVER (PARTITION BY "YEAR") AS share_within_group +FROM "c16" +GROUP BY "YEAR", "FIRST APPEARANCE" +ORDER BY share_within_group DESC; diff --git a/Query/sql/v2/runs/v2_cli_20260502_081223_d/c16/sql/v2q_c16_3cc4a3dd9ffa3d8c.sql b/Query/sql/v2/runs/v2_cli_20260502_081223_d/c16/sql/v2q_c16_3cc4a3dd9ffa3d8c.sql new file mode 100644 index 0000000000000000000000000000000000000000..0f32518d5efd0e17ce9fa75e29c76662de5d562f --- /dev/null +++ b/Query/sql/v2/runs/v2_cli_20260502_081223_d/c16/sql/v2q_c16_3cc4a3dd9ffa3d8c.sql @@ -0,0 +1,18 @@ +-- sql_source_version: v2 +-- sql_source_label: v2_current +-- sql_source_run_id: v2_cli_20260502_081223_d +-- sql_source_dataset_id: c16 +-- family_id: conditional_dependency_structure +-- canonical_subitem_id: dependency_strength_similarity +-- intended_facet_id: pairwise_conditional_dependency +-- variant_semantic_role: focused_target_view +-- template_id: tpl_m4_group_condition_rate +-- query_record_id: v2q_c16_3cc4a3dd9ffa3d8c +-- problem_id: v2p_c16_d07412c8c8a5d04a +-- realization_mode: agent +-- source_kind: agent +SELECT "HAIR", + AVG(CASE WHEN "GSM" = 'Homosexual Characters' THEN 1 ELSE 0 END) AS condition_rate +FROM "c16" +GROUP BY "HAIR" +ORDER BY condition_rate DESC; diff --git a/Query/sql/v2/runs/v2_cli_20260502_081223_d/c16/sql/v2q_c16_3df9badec3179a5a.sql b/Query/sql/v2/runs/v2_cli_20260502_081223_d/c16/sql/v2q_c16_3df9badec3179a5a.sql new file mode 100644 index 0000000000000000000000000000000000000000..45347fb761f5931d257d960426abe10c17186fda --- /dev/null +++ b/Query/sql/v2/runs/v2_cli_20260502_081223_d/c16/sql/v2q_c16_3df9badec3179a5a.sql @@ -0,0 +1,23 @@ +-- sql_source_version: v2 +-- sql_source_label: v2_current +-- sql_source_run_id: v2_cli_20260502_081223_d +-- sql_source_dataset_id: c16 +-- family_id: conditional_dependency_structure +-- canonical_subitem_id: dependency_strength_similarity +-- intended_facet_id: pairwise_conditional_dependency +-- variant_semantic_role: focused_target_view +-- template_id: tpl_tpcds_within_group_share +-- query_record_id: v2q_c16_3df9badec3179a5a +-- problem_id: v2p_c16_d26db5c3236d92d4 +-- realization_mode: agent +-- source_kind: agent +SELECT "ALIVE", "FIRST APPEARANCE", + SUM(CAST("page_id" AS NUMERIC)) AS total_measure, + SUM(CAST("page_id" AS NUMERIC)) * 100.0 / SUM(SUM(CAST("page_id" AS NUMERIC))) OVER (PARTITION BY "ALIVE") AS share_within_group +FROM "c16" +WHERE "ALIVE" IS NOT NULL AND "ALIVE" <> '' + AND "FIRST APPEARANCE" IS NOT NULL AND "FIRST APPEARANCE" <> '' + AND "page_id" IS NOT NULL AND "page_id" <> '' +GROUP BY "ALIVE", "FIRST APPEARANCE" +ORDER BY share_within_group DESC +LIMIT 13; diff --git a/Query/sql/v2/runs/v2_cli_20260502_081223_d/c16/sql/v2q_c16_3f03516f7ecbbb04.sql b/Query/sql/v2/runs/v2_cli_20260502_081223_d/c16/sql/v2q_c16_3f03516f7ecbbb04.sql new file mode 100644 index 0000000000000000000000000000000000000000..fa42422528075bf0157fb80c4cd6bf8c29dc1168 --- /dev/null +++ b/Query/sql/v2/runs/v2_cli_20260502_081223_d/c16/sql/v2q_c16_3f03516f7ecbbb04.sql @@ -0,0 +1,17 @@ +-- sql_source_version: v2 +-- sql_source_label: v2_current +-- sql_source_run_id: v2_cli_20260502_081223_d +-- sql_source_dataset_id: c16 +-- family_id: subgroup_structure +-- canonical_subitem_id: internal_profile_stability +-- intended_facet_id: subgroup_conditional_contrast +-- variant_semantic_role: collapsed_target_view +-- template_id: tpl_h2o_group_sum +-- query_record_id: v2q_c16_3f03516f7ecbbb04 +-- problem_id: v2p_c16_c6f7daaea793bf5e +-- realization_mode: agent +-- source_kind: agent +SELECT "ALIVE", SUM(CAST("YEAR" AS INTEGER)) AS total_measure +FROM "c16" +GROUP BY "ALIVE" +ORDER BY total_measure DESC; diff --git a/Query/sql/v2/runs/v2_cli_20260502_081223_d/c16/sql/v2q_c16_419c539d87e25abc.sql b/Query/sql/v2/runs/v2_cli_20260502_081223_d/c16/sql/v2q_c16_419c539d87e25abc.sql new file mode 100644 index 0000000000000000000000000000000000000000..d98a4b50b4eb27253570283d65809200459484bb --- /dev/null +++ b/Query/sql/v2/runs/v2_cli_20260502_081223_d/c16/sql/v2q_c16_419c539d87e25abc.sql @@ -0,0 +1,15 @@ +-- sql_source_version: v2 +-- sql_source_label: v2_current +-- sql_source_run_id: v2_cli_20260502_081223_d +-- sql_source_dataset_id: c16 +-- family_id: tail_rarity_structure +-- canonical_subitem_id: tail_set_consistency +-- intended_facet_id: low_support_extremes +-- variant_semantic_role: rare_extreme_view +-- template_id: tpl_threshold_rarity_cdf +-- query_record_id: v2q_c16_419c539d87e25abc +-- problem_id: v2p_c16_bdb8a2a312942158 +-- realization_mode: agent +-- source_kind: agent +SELECT AVG(CASE WHEN CAST("page_id" AS REAL) <= 213203.0 THEN 1 ELSE 0 END) AS "empirical_cdf_at_threshold" +FROM "c16"; diff --git a/Query/sql/v2/runs/v2_cli_20260502_081223_d/c16/sql/v2q_c16_41ce937f069df2fa.sql b/Query/sql/v2/runs/v2_cli_20260502_081223_d/c16/sql/v2q_c16_41ce937f069df2fa.sql new file mode 100644 index 0000000000000000000000000000000000000000..94201bef0c0a313b85c36a665efc6a1109ac77a6 --- /dev/null +++ b/Query/sql/v2/runs/v2_cli_20260502_081223_d/c16/sql/v2q_c16_41ce937f069df2fa.sql @@ -0,0 +1,17 @@ +-- sql_source_version: v2 +-- sql_source_label: v2_current +-- sql_source_run_id: v2_cli_20260502_081223_d +-- sql_source_dataset_id: c16 +-- family_id: subgroup_structure +-- canonical_subitem_id: internal_profile_stability +-- intended_facet_id: subgroup_distribution_shift +-- variant_semantic_role: collapsed_target_view +-- template_id: tpl_h2o_group_sum +-- query_record_id: v2q_c16_41ce937f069df2fa +-- problem_id: v2p_c16_54fa7b2264b5d4d7 +-- realization_mode: agent +-- source_kind: agent +SELECT "ALIGN", SUM(CAST("page_id" AS NUMERIC)) AS total_measure +FROM "c16" +GROUP BY "ALIGN" +ORDER BY total_measure DESC; diff --git a/Query/sql/v2/runs/v2_cli_20260502_081223_d/c16/sql/v2q_c16_45edb2f404e46613.sql b/Query/sql/v2/runs/v2_cli_20260502_081223_d/c16/sql/v2q_c16_45edb2f404e46613.sql new file mode 100644 index 0000000000000000000000000000000000000000..cb43f0174e9daa83c0595fb52ce837b8f1df2e5e --- /dev/null +++ b/Query/sql/v2/runs/v2_cli_20260502_081223_d/c16/sql/v2q_c16_45edb2f404e46613.sql @@ -0,0 +1,18 @@ +-- sql_source_version: v2 +-- sql_source_label: v2_current +-- sql_source_run_id: v2_cli_20260502_081223_d +-- sql_source_dataset_id: c16 +-- family_id: conditional_dependency_structure +-- canonical_subitem_id: slice_level_consistency +-- intended_facet_id: conditional_interaction_hotspots +-- variant_semantic_role: count_distribution +-- template_id: tpl_c2_filtered_group_count_2d +-- query_record_id: v2q_c16_45edb2f404e46613 +-- problem_id: v2p_c16_1d562af65f72b40e +-- realization_mode: agent +-- source_kind: agent +SELECT "EYE", "SEX", COUNT(*) AS "row_count" +FROM "c16" +WHERE CAST("YEAR" AS REAL) >= 2003.0 +GROUP BY "EYE", "SEX" +ORDER BY "row_count" DESC; diff --git a/Query/sql/v2/runs/v2_cli_20260502_081223_d/c16/sql/v2q_c16_46aaa9bda2f2999b.sql b/Query/sql/v2/runs/v2_cli_20260502_081223_d/c16/sql/v2q_c16_46aaa9bda2f2999b.sql new file mode 100644 index 0000000000000000000000000000000000000000..2a7626857c15af6aa8b136e3505619feb09bb5d1 --- /dev/null +++ b/Query/sql/v2/runs/v2_cli_20260502_081223_d/c16/sql/v2q_c16_46aaa9bda2f2999b.sql @@ -0,0 +1,59 @@ +-- sql_source_version: v2 +-- sql_source_label: v2_current +-- sql_source_run_id: v2_cli_20260502_081223_d +-- sql_source_dataset_id: c16 +-- family_id: tail_rarity_structure +-- canonical_subitem_id: tail_concentration_consistency +-- intended_facet_id: rare_target_concentration +-- variant_semantic_role: focused_target_view +-- template_id: tpl_grouped_percentile_point +-- query_record_id: v2q_c16_46aaa9bda2f2999b +-- problem_id: v2p_c16_bf478017b6015198 +-- realization_mode: agent +-- source_kind: agent +WITH "base" AS ( + SELECT + "EYE", + CAST("APPEARANCES" AS REAL) AS "measure" + FROM "c16" + WHERE "EYE" IS NOT NULL + AND TRIM("EYE") <> '' + AND "APPEARANCES" IS NOT NULL + AND TRIM("APPEARANCES") <> '' + AND TRIM("APPEARANCES") NOT GLOB '*[^0-9]*' +), +"ranked" AS ( + SELECT + "EYE", + "measure", + ROW_NUMBER() OVER (PARTITION BY "EYE" ORDER BY "measure") AS "rn", + COUNT(*) OVER (PARTITION BY "EYE") AS "cnt" + FROM "base" +), +"pos" AS ( + SELECT DISTINCT + "EYE", + "cnt", + 1.0 + (95.0 * ("cnt" - 1)) / 100.0 AS "p", + 1 + CAST((95.0 * ("cnt" - 1)) / 100.0 AS INTEGER) AS "lo", + CASE + WHEN (95 * ("cnt" - 1)) % 100 = 0 THEN 1 + CAST((95.0 * ("cnt" - 1)) / 100.0 AS INTEGER) + ELSE 2 + CAST((95.0 * ("cnt" - 1)) / 100.0 AS INTEGER) + END AS "hi" + FROM "ranked" + WHERE "cnt" >= 5 +) +SELECT + "pos"."EYE", + CASE + WHEN "pos"."lo" = "pos"."hi" THEN "lo_row"."measure" + ELSE "lo_row"."measure" + ("pos"."p" - "pos"."lo") * ("hi_row"."measure" - "lo_row"."measure") + END AS "percentile_measure" +FROM "pos" +JOIN "ranked" AS "lo_row" + ON "lo_row"."EYE" = "pos"."EYE" + AND "lo_row"."rn" = "pos"."lo" +JOIN "ranked" AS "hi_row" + ON "hi_row"."EYE" = "pos"."EYE" + AND "hi_row"."rn" = "pos"."hi" +ORDER BY "percentile_measure" DESC; diff --git a/Query/sql/v2/runs/v2_cli_20260502_081223_d/c16/sql/v2q_c16_47d2b06a073346de.sql b/Query/sql/v2/runs/v2_cli_20260502_081223_d/c16/sql/v2q_c16_47d2b06a073346de.sql new file mode 100644 index 0000000000000000000000000000000000000000..0bbcd2877698042a5b8c1d41d4378d9e7f79eb68 --- /dev/null +++ b/Query/sql/v2/runs/v2_cli_20260502_081223_d/c16/sql/v2q_c16_47d2b06a073346de.sql @@ -0,0 +1,18 @@ +-- sql_source_version: v2 +-- sql_source_label: v2_current +-- sql_source_run_id: v2_cli_20260502_081223_d +-- sql_source_dataset_id: c16 +-- family_id: conditional_dependency_structure +-- canonical_subitem_id: slice_level_consistency +-- intended_facet_id: conditional_interaction_hotspots +-- variant_semantic_role: count_distribution +-- template_id: tpl_c2_filtered_group_count_2d +-- query_record_id: v2q_c16_47d2b06a073346de +-- problem_id: v2p_c16_dc048d14acbf7281 +-- realization_mode: agent +-- source_kind: agent +SELECT "EYE", "ALIVE", COUNT(*) AS row_count +FROM "c16" +WHERE "ALIGN" = 'Good Characters' +GROUP BY "EYE", "ALIVE" +ORDER BY row_count DESC; diff --git a/Query/sql/v2/runs/v2_cli_20260502_081223_d/c16/sql/v2q_c16_48a7217537e0b485.sql b/Query/sql/v2/runs/v2_cli_20260502_081223_d/c16/sql/v2q_c16_48a7217537e0b485.sql new file mode 100644 index 0000000000000000000000000000000000000000..b3898f1ff9a179bd613cbca8e7d19758130e5dcf --- /dev/null +++ b/Query/sql/v2/runs/v2_cli_20260502_081223_d/c16/sql/v2q_c16_48a7217537e0b485.sql @@ -0,0 +1,21 @@ +-- sql_source_version: v2 +-- sql_source_label: v2_current +-- sql_source_run_id: v2_cli_20260502_081223_d +-- sql_source_dataset_id: c16 +-- family_id: missingness_structure +-- canonical_subitem_id: co_missingness_pattern_consistency +-- intended_facet_id: missing_target_interaction +-- variant_semantic_role: missing_target_interaction +-- template_id: tpl_missing_target_interaction +-- query_record_id: v2q_c16_48a7217537e0b485 +-- problem_id: v2p_c16_10c707119a758127 +-- realization_mode: deterministic +-- source_kind: deterministic +SELECT + "SEX", + COUNT(*) AS total_rows, + SUM(CASE WHEN "FIRST APPEARANCE" IS NULL THEN 1 ELSE 0 END) AS missing_rows, + AVG(CASE WHEN "FIRST APPEARANCE" IS NULL THEN 1.0 ELSE 0.0 END) AS missing_rate +FROM "c16" +GROUP BY "SEX" +ORDER BY missing_rate DESC, total_rows DESC; diff --git a/Query/sql/v2/runs/v2_cli_20260502_081223_d/c16/sql/v2q_c16_4aae0650ea810862.sql b/Query/sql/v2/runs/v2_cli_20260502_081223_d/c16/sql/v2q_c16_4aae0650ea810862.sql new file mode 100644 index 0000000000000000000000000000000000000000..81fdeebcfdb9ef086ca9c41f9055b66fbb52d229 --- /dev/null +++ b/Query/sql/v2/runs/v2_cli_20260502_081223_d/c16/sql/v2q_c16_4aae0650ea810862.sql @@ -0,0 +1,30 @@ +-- sql_source_version: v2 +-- sql_source_label: v2_current +-- sql_source_run_id: v2_cli_20260502_081223_d +-- sql_source_dataset_id: c16 +-- family_id: tail_rarity_structure +-- canonical_subitem_id: tail_mass_similarity +-- intended_facet_id: tail_ranked_signal +-- variant_semantic_role: filtered_stable_view +-- template_id: tpl_tpch_relative_total_threshold +-- query_record_id: v2q_c16_4aae0650ea810862 +-- problem_id: v2p_c16_0d08289cb4e63265 +-- realization_mode: agent +-- source_kind: agent +WITH "grouped" AS ( + SELECT "EYE", SUM(CAST("page_id" AS REAL)) AS "group_value" + FROM "c16" + WHERE "EYE" IS NOT NULL + AND "EYE" <> '' + AND "page_id" IS NOT NULL + AND "page_id" <> '' + GROUP BY "EYE" +), "total" AS ( + SELECT SUM("group_value") AS "total_value" + FROM "grouped" +) +SELECT g."EYE", g."group_value" +FROM "grouped" AS g +CROSS JOIN "total" AS t +WHERE g."group_value" > t."total_value" * 0.05 +ORDER BY g."group_value" DESC; diff --git a/Query/sql/v2/runs/v2_cli_20260502_081223_d/c16/sql/v2q_c16_4d071bc74ec8924a.sql b/Query/sql/v2/runs/v2_cli_20260502_081223_d/c16/sql/v2q_c16_4d071bc74ec8924a.sql new file mode 100644 index 0000000000000000000000000000000000000000..0ee759ca7ef5749d97842be707a6de974bfdda7d --- /dev/null +++ b/Query/sql/v2/runs/v2_cli_20260502_081223_d/c16/sql/v2q_c16_4d071bc74ec8924a.sql @@ -0,0 +1,75 @@ +-- sql_source_version: v2 +-- sql_source_label: v2_current +-- sql_source_run_id: v2_cli_20260502_081223_d +-- sql_source_dataset_id: c16 +-- family_id: tail_rarity_structure +-- canonical_subitem_id: tail_concentration_consistency +-- intended_facet_id: rare_target_concentration +-- variant_semantic_role: focused_target_view +-- template_id: tpl_grouped_percentile_point +-- query_record_id: v2q_c16_4d071bc74ec8924a +-- problem_id: v2p_c16_b081f47d9dec4317 +-- realization_mode: agent +-- source_kind: agent +WITH "base" AS ( + SELECT + "ALIGN" AS "group_col", + CAST("APPEARANCES" AS REAL) AS "measure" + FROM "c16" + WHERE "ALIGN" IS NOT NULL + AND TRIM("ALIGN") <> '' + AND "APPEARANCES" IS NOT NULL + AND TRIM("APPEARANCES") <> '' +), +"ranked" AS ( + SELECT + "group_col", + "measure", + ROW_NUMBER() OVER ( + PARTITION BY "group_col" + ORDER BY "measure" + ) AS "rn", + COUNT(*) OVER ( + PARTITION BY "group_col" + ) AS "cnt" + FROM "base" +), +"params" AS ( + SELECT DISTINCT + "group_col", + "cnt", + (95 * "cnt" + 5) AS "pos_num", + CAST((95 * "cnt" + 5) / 100 AS INTEGER) AS "lower_rn", + CASE + WHEN (95 * "cnt" + 5) % 100 = 0 THEN CAST((95 * "cnt" + 5) / 100 AS INTEGER) + ELSE CAST((95 * "cnt" + 5) / 100 AS INTEGER) + 1 + END AS "upper_rn" + FROM "ranked" + WHERE "cnt" >= 5 +), +"picked" AS ( + SELECT + p."group_col", + p."pos_num", + p."lower_rn", + p."upper_rn", + MAX(CASE WHEN r."rn" = p."lower_rn" THEN r."measure" END) AS "lower_val", + MAX(CASE WHEN r."rn" = p."upper_rn" THEN r."measure" END) AS "upper_val" + FROM "params" AS p + JOIN "ranked" AS r + ON r."group_col" = p."group_col" + GROUP BY + p."group_col", + p."pos_num", + p."lower_rn", + p."upper_rn" +) +SELECT + "group_col" AS "ALIGN", + CASE + WHEN "lower_val" IS NULL THEN NULL + WHEN "upper_val" IS NULL OR "lower_rn" = "upper_rn" THEN "lower_val" + ELSE "lower_val" + (("pos_num" % 100) / 100.0) * ("upper_val" - "lower_val") + END AS "percentile_measure" +FROM "picked" +ORDER BY "percentile_measure" DESC, "ALIGN" ASC; diff --git a/Query/sql/v2/runs/v2_cli_20260502_081223_d/c16/sql/v2q_c16_4dfa9069cbfb714c.sql b/Query/sql/v2/runs/v2_cli_20260502_081223_d/c16/sql/v2q_c16_4dfa9069cbfb714c.sql new file mode 100644 index 0000000000000000000000000000000000000000..b52520faae594418701ce0ff032dc0942b40b7f9 --- /dev/null +++ b/Query/sql/v2/runs/v2_cli_20260502_081223_d/c16/sql/v2q_c16_4dfa9069cbfb714c.sql @@ -0,0 +1,34 @@ +-- sql_source_version: v2 +-- sql_source_label: v2_current +-- sql_source_run_id: v2_cli_20260502_081223_d +-- sql_source_dataset_id: c16 +-- family_id: tail_rarity_structure +-- canonical_subitem_id: tail_mass_similarity +-- intended_facet_id: tail_ranked_signal +-- variant_semantic_role: filtered_stable_view +-- template_id: tpl_tpch_relative_total_threshold +-- query_record_id: v2q_c16_4dfa9069cbfb714c +-- problem_id: v2p_c16_265cd5a88d9a2097 +-- realization_mode: agent +-- source_kind: agent +WITH grouped AS ( + SELECT + "HAIR", + SUM(CAST("APPEARANCES" AS REAL)) AS group_value + FROM "c16" + WHERE "HAIR" IS NOT NULL + AND TRIM("HAIR") <> '' + AND "APPEARANCES" IS NOT NULL + AND TRIM("APPEARANCES") <> '' + GROUP BY "HAIR" +), total AS ( + SELECT SUM(group_value) AS total_value + FROM grouped +) +SELECT + g."HAIR", + g.group_value +FROM grouped AS g +CROSS JOIN total AS t +WHERE g.group_value > t.total_value * 0.1 +ORDER BY g.group_value DESC; diff --git a/Query/sql/v2/runs/v2_cli_20260502_081223_d/c16/sql/v2q_c16_4f2787966423a76a.sql b/Query/sql/v2/runs/v2_cli_20260502_081223_d/c16/sql/v2q_c16_4f2787966423a76a.sql new file mode 100644 index 0000000000000000000000000000000000000000..03e8c808d3873484e46413a6826adaac3d76ecb8 --- /dev/null +++ b/Query/sql/v2/runs/v2_cli_20260502_081223_d/c16/sql/v2q_c16_4f2787966423a76a.sql @@ -0,0 +1,21 @@ +-- sql_source_version: v2 +-- sql_source_label: v2_current +-- sql_source_run_id: v2_cli_20260502_081223_d +-- sql_source_dataset_id: c16 +-- family_id: conditional_dependency_structure +-- canonical_subitem_id: dependency_strength_similarity +-- intended_facet_id: pairwise_conditional_dependency +-- variant_semantic_role: within_group_proportion +-- template_id: tpl_tpcds_within_group_share +-- query_record_id: v2q_c16_4f2787966423a76a +-- problem_id: v2p_c16_2affa44e7c4c9ba4 +-- realization_mode: agent +-- source_kind: agent +SELECT + "SEX", + "name", + SUM(CAST("page_id" AS NUMERIC)) AS "total_measure", + SUM(CAST("page_id" AS NUMERIC)) * 100.0 / SUM(SUM(CAST("page_id" AS NUMERIC))) OVER (PARTITION BY "SEX") AS "share_within_group" +FROM "c16" +GROUP BY "SEX", "name" +ORDER BY "share_within_group" DESC; diff --git a/Query/sql/v2/runs/v2_cli_20260502_081223_d/c16/sql/v2q_c16_4feff237305e42a2.sql b/Query/sql/v2/runs/v2_cli_20260502_081223_d/c16/sql/v2q_c16_4feff237305e42a2.sql new file mode 100644 index 0000000000000000000000000000000000000000..0b49af48ffaf1f436a354dae5fb93e4fc107b46d --- /dev/null +++ b/Query/sql/v2/runs/v2_cli_20260502_081223_d/c16/sql/v2q_c16_4feff237305e42a2.sql @@ -0,0 +1,25 @@ +-- sql_source_version: v2 +-- sql_source_label: v2_current +-- sql_source_run_id: v2_cli_20260502_081223_d +-- sql_source_dataset_id: c16 +-- family_id: cardinality_structure +-- canonical_subitem_id: support_rank_profile_consistency +-- intended_facet_id: support_concentration +-- variant_semantic_role: count_distribution +-- template_id: tpl_cardinality_support_rank_profile +-- query_record_id: v2q_c16_4feff237305e42a2 +-- problem_id: v2p_c16_0436539fe3b9bad4 +-- realization_mode: deterministic +-- source_kind: deterministic +WITH grouped AS ( + SELECT "HAIR" AS value_label, COUNT(*) AS support + FROM "c16" + GROUP BY "HAIR" +) +SELECT + value_label, + support, + CAST(support AS FLOAT) / NULLIF(SUM(support) OVER (), 0) AS support_share, + ROW_NUMBER() OVER (ORDER BY support DESC, value_label) AS support_rank +FROM grouped +ORDER BY support DESC, value_label; diff --git a/Query/sql/v2/runs/v2_cli_20260502_081223_d/c16/sql/v2q_c16_50552f0498f21327.sql b/Query/sql/v2/runs/v2_cli_20260502_081223_d/c16/sql/v2q_c16_50552f0498f21327.sql new file mode 100644 index 0000000000000000000000000000000000000000..db2c9fa0815433f8c71cb62c5c2060fa9bba7864 --- /dev/null +++ b/Query/sql/v2/runs/v2_cli_20260502_081223_d/c16/sql/v2q_c16_50552f0498f21327.sql @@ -0,0 +1,18 @@ +-- sql_source_version: v2 +-- sql_source_label: v2_current +-- sql_source_run_id: v2_cli_20260502_081223_d +-- sql_source_dataset_id: c16 +-- family_id: missingness_structure +-- canonical_subitem_id: marginal_missing_rate_consistency +-- intended_facet_id: missing_indicator_distribution +-- variant_semantic_role: missing_indicator_view +-- template_id: tpl_missing_marginal_rate_profile +-- query_record_id: v2q_c16_50552f0498f21327 +-- problem_id: v2p_c16_e24ba5753973434c +-- realization_mode: deterministic +-- source_kind: deterministic +SELECT + COUNT(*) AS total_rows, + SUM(CASE WHEN "EYE" IS NULL THEN 1 ELSE 0 END) AS missing_rows, + AVG(CASE WHEN "EYE" IS NULL THEN 1.0 ELSE 0.0 END) AS missing_rate +FROM "c16"; diff --git a/Query/sql/v2/runs/v2_cli_20260502_081223_d/c16/sql/v2q_c16_51bbe73bebc4673c.sql b/Query/sql/v2/runs/v2_cli_20260502_081223_d/c16/sql/v2q_c16_51bbe73bebc4673c.sql new file mode 100644 index 0000000000000000000000000000000000000000..b74ffcc1f7981bcb0b7296195b8a19c408a67907 --- /dev/null +++ b/Query/sql/v2/runs/v2_cli_20260502_081223_d/c16/sql/v2q_c16_51bbe73bebc4673c.sql @@ -0,0 +1,17 @@ +-- sql_source_version: v2 +-- sql_source_label: v2_current +-- sql_source_run_id: v2_cli_20260502_081223_d +-- sql_source_dataset_id: c16 +-- family_id: subgroup_structure +-- canonical_subitem_id: subgroup_size_stability +-- intended_facet_id: subgroup_distribution_shift +-- variant_semantic_role: count_distribution +-- template_id: tpl_clickbench_group_count +-- query_record_id: v2q_c16_51bbe73bebc4673c +-- problem_id: v2p_c16_a81e675c61097a93 +-- realization_mode: agent +-- source_kind: agent +SELECT "HAIR", COUNT(*) AS row_count +FROM "c16" +GROUP BY "HAIR" +ORDER BY row_count DESC; diff --git a/Query/sql/v2/runs/v2_cli_20260502_081223_d/c16/sql/v2q_c16_51ed98fba7792934.sql b/Query/sql/v2/runs/v2_cli_20260502_081223_d/c16/sql/v2q_c16_51ed98fba7792934.sql new file mode 100644 index 0000000000000000000000000000000000000000..2c3defe63dfc0edf670a1fc892699565cb840058 --- /dev/null +++ b/Query/sql/v2/runs/v2_cli_20260502_081223_d/c16/sql/v2q_c16_51ed98fba7792934.sql @@ -0,0 +1,17 @@ +-- sql_source_version: v2 +-- sql_source_label: v2_current +-- sql_source_run_id: v2_cli_20260502_081223_d +-- sql_source_dataset_id: c16 +-- family_id: subgroup_structure +-- canonical_subitem_id: subgroup_size_stability +-- intended_facet_id: subgroup_distribution_shift +-- variant_semantic_role: count_distribution +-- template_id: tpl_clickbench_group_count +-- query_record_id: v2q_c16_51ed98fba7792934 +-- problem_id: v2p_c16_3d3077403ad22c6c +-- realization_mode: agent +-- source_kind: agent +SELECT "ALIVE", COUNT(*) AS row_count +FROM "c16" +GROUP BY "ALIVE" +ORDER BY row_count DESC; diff --git a/Query/sql/v2/runs/v2_cli_20260502_081223_d/c16/sql/v2q_c16_53c9643f5adb2f85.sql b/Query/sql/v2/runs/v2_cli_20260502_081223_d/c16/sql/v2q_c16_53c9643f5adb2f85.sql new file mode 100644 index 0000000000000000000000000000000000000000..055a05e7813d6e45f94b8a349a1d4ee665096425 --- /dev/null +++ b/Query/sql/v2/runs/v2_cli_20260502_081223_d/c16/sql/v2q_c16_53c9643f5adb2f85.sql @@ -0,0 +1,24 @@ +-- sql_source_version: v2 +-- sql_source_label: v2_current +-- sql_source_run_id: v2_cli_20260502_081223_d +-- sql_source_dataset_id: c16 +-- family_id: conditional_dependency_structure +-- canonical_subitem_id: direction_consistency +-- intended_facet_id: conditional_rate_shift +-- variant_semantic_role: contrastive_conditional_view +-- template_id: tpl_m4_group_ratio_two_conditions +-- query_record_id: v2q_c16_53c9643f5adb2f85 +-- problem_id: v2p_c16_e4eb1c1d37470cce +-- realization_mode: agent +-- source_kind: agent +WITH grouped AS ( + SELECT "SEX", + SUM(CASE WHEN "ALIGN" = 'Bad Characters' THEN 1 ELSE 0 END) AS numerator_count, + SUM(CASE WHEN "ALIGN" = 'Good Characters' THEN 1 ELSE 0 END) AS denominator_count + FROM "c16" + GROUP BY "SEX" +) +SELECT "SEX", + CAST(numerator_count AS FLOAT) / NULLIF(denominator_count, 0) AS condition_ratio +FROM grouped +ORDER BY condition_ratio DESC; diff --git a/Query/sql/v2/runs/v2_cli_20260502_081223_d/c16/sql/v2q_c16_55be888ef89c5c5d.sql b/Query/sql/v2/runs/v2_cli_20260502_081223_d/c16/sql/v2q_c16_55be888ef89c5c5d.sql new file mode 100644 index 0000000000000000000000000000000000000000..129e7725f4f29826445c0d583d389a141a45e36a --- /dev/null +++ b/Query/sql/v2/runs/v2_cli_20260502_081223_d/c16/sql/v2q_c16_55be888ef89c5c5d.sql @@ -0,0 +1,64 @@ +-- sql_source_version: v2 +-- sql_source_label: v2_current +-- sql_source_run_id: v2_cli_20260502_081223_d +-- sql_source_dataset_id: c16 +-- family_id: tail_rarity_structure +-- canonical_subitem_id: tail_concentration_consistency +-- intended_facet_id: rare_target_concentration +-- variant_semantic_role: ranked_signal_view +-- template_id: tpl_grouped_percentile_point +-- query_record_id: v2q_c16_55be888ef89c5c5d +-- problem_id: v2p_c16_7caaf8666785f907 +-- realization_mode: agent +-- source_kind: agent +WITH "base" AS ( + SELECT + "EYE", + CAST(NULLIF(TRIM("APPEARANCES"), '') AS REAL) AS "measure_value" + FROM "c16" + WHERE NULLIF(TRIM("EYE"), '') IS NOT NULL + AND NULLIF(TRIM("APPEARANCES"), '') IS NOT NULL +), +"ordered" AS ( + SELECT + "EYE", + "measure_value", + ROW_NUMBER() OVER (PARTITION BY "EYE" ORDER BY "measure_value") AS "rn", + COUNT(*) OVER (PARTITION BY "EYE") AS "cnt" + FROM "base" +), +"params" AS ( + SELECT DISTINCT + "EYE", + "cnt", + ((9.0 * "cnt") + 1.0) / 10.0 AS "pos", + CAST(((9 * "cnt") + 1) / 10 AS INTEGER) AS "lo_rn", + CASE + WHEN ((9 * "cnt") + 1) % 10 = 0 THEN CAST(((9 * "cnt") + 1) / 10 AS INTEGER) + ELSE CAST(((9 * "cnt") + 1) / 10 AS INTEGER) + 1 + END AS "hi_rn" + FROM "ordered" + WHERE "cnt" >= 5 +) +SELECT + p."EYE", + CASE + WHEN p."lo_rn" = p."hi_rn" THEN MAX(CASE WHEN o."rn" = p."lo_rn" THEN o."measure_value" END) + ELSE + MAX(CASE WHEN o."rn" = p."lo_rn" THEN o."measure_value" END) + + (p."pos" - p."lo_rn") * ( + MAX(CASE WHEN o."rn" = p."hi_rn" THEN o."measure_value" END) - + MAX(CASE WHEN o."rn" = p."lo_rn" THEN o."measure_value" END) + ) + END AS "percentile_measure" +FROM "params" AS p +JOIN "ordered" AS o + ON o."EYE" = p."EYE" +GROUP BY + p."EYE", + p."cnt", + p."pos", + p."lo_rn", + p."hi_rn" +ORDER BY "percentile_measure" DESC +LIMIT 10; diff --git a/Query/sql/v2/runs/v2_cli_20260502_081223_d/c16/sql/v2q_c16_579ab388bd086826.sql b/Query/sql/v2/runs/v2_cli_20260502_081223_d/c16/sql/v2q_c16_579ab388bd086826.sql new file mode 100644 index 0000000000000000000000000000000000000000..48e6c4b97a6ebc2d0c7d2c67ba747cf3fdb74c51 --- /dev/null +++ b/Query/sql/v2/runs/v2_cli_20260502_081223_d/c16/sql/v2q_c16_579ab388bd086826.sql @@ -0,0 +1,18 @@ +-- sql_source_version: v2 +-- sql_source_label: v2_current +-- sql_source_run_id: v2_cli_20260502_081223_d +-- sql_source_dataset_id: c16 +-- family_id: conditional_dependency_structure +-- canonical_subitem_id: slice_level_consistency +-- intended_facet_id: conditional_interaction_hotspots +-- variant_semantic_role: count_distribution +-- template_id: tpl_c2_filtered_group_count_2d +-- query_record_id: v2q_c16_579ab388bd086826 +-- problem_id: v2p_c16_db65c1f5066a9ba2 +-- realization_mode: agent +-- source_kind: agent +SELECT "HAIR", "ALIVE", COUNT(*) AS row_count +FROM "c16" +WHERE "GSM" = 'Homosexual Characters' +GROUP BY "HAIR", "ALIVE" +ORDER BY row_count DESC; diff --git a/Query/sql/v2/runs/v2_cli_20260502_081223_d/c16/sql/v2q_c16_58f898d64876a4ab.sql b/Query/sql/v2/runs/v2_cli_20260502_081223_d/c16/sql/v2q_c16_58f898d64876a4ab.sql new file mode 100644 index 0000000000000000000000000000000000000000..7474932da5aae834c771197a627801b0bb0fad71 --- /dev/null +++ b/Query/sql/v2/runs/v2_cli_20260502_081223_d/c16/sql/v2q_c16_58f898d64876a4ab.sql @@ -0,0 +1,18 @@ +-- sql_source_version: v2 +-- sql_source_label: v2_current +-- sql_source_run_id: v2_cli_20260502_081223_d +-- sql_source_dataset_id: c16 +-- family_id: conditional_dependency_structure +-- canonical_subitem_id: dependency_strength_similarity +-- intended_facet_id: pairwise_conditional_dependency +-- variant_semantic_role: within_group_proportion +-- template_id: tpl_m4_group_condition_rate +-- query_record_id: v2q_c16_58f898d64876a4ab +-- problem_id: v2p_c16_fd2d656aecf0cb5d +-- realization_mode: agent +-- source_kind: agent +SELECT "HAIR", + AVG(CASE WHEN "GSM" = '' THEN 1.0 ELSE 0.0 END) AS "condition_rate" +FROM "c16" +GROUP BY "HAIR" +ORDER BY "condition_rate" DESC; diff --git a/Query/sql/v2/runs/v2_cli_20260502_081223_d/c16/sql/v2q_c16_5963cafd9cc76bc4.sql b/Query/sql/v2/runs/v2_cli_20260502_081223_d/c16/sql/v2q_c16_5963cafd9cc76bc4.sql new file mode 100644 index 0000000000000000000000000000000000000000..72e7d9723a48978d7d88cb434ca5ee4656f96d6e --- /dev/null +++ b/Query/sql/v2/runs/v2_cli_20260502_081223_d/c16/sql/v2q_c16_5963cafd9cc76bc4.sql @@ -0,0 +1,62 @@ +-- sql_source_version: v2 +-- sql_source_label: v2_current +-- sql_source_run_id: v2_cli_20260502_081223_d +-- sql_source_dataset_id: c16 +-- family_id: tail_rarity_structure +-- canonical_subitem_id: tail_concentration_consistency +-- intended_facet_id: rare_target_concentration +-- variant_semantic_role: focused_target_view +-- template_id: tpl_grouped_percentile_point +-- query_record_id: v2q_c16_5963cafd9cc76bc4 +-- problem_id: v2p_c16_cbcb83759450f843 +-- realization_mode: agent +-- source_kind: agent +WITH "ordered" AS ( + SELECT + "ALIGN" AS "group_col", + CAST("page_id" AS REAL) AS "measure_col", + ROW_NUMBER() OVER ( + PARTITION BY "ALIGN" + ORDER BY CAST("page_id" AS REAL) + ) AS "rn", + COUNT(*) OVER ( + PARTITION BY "ALIGN" + ) AS "cnt" + FROM "c16" + WHERE "ALIGN" IS NOT NULL + AND "page_id" IS NOT NULL + AND TRIM("page_id") <> '' +), +"bounds" AS ( + SELECT DISTINCT + "group_col", + (1.0 + (0.95 * ("cnt" - 1))) AS "target_pos", + CAST(FLOOR(1.0 + (0.95 * ("cnt" - 1))) AS INTEGER) AS "lower_rn", + CAST(CEIL(1.0 + (0.95 * ("cnt" - 1))) AS INTEGER) AS "upper_rn" + FROM "ordered" +), +"picked" AS ( + SELECT + o."group_col", + b."target_pos", + b."lower_rn", + b."upper_rn", + MAX(CASE WHEN o."rn" = b."lower_rn" THEN o."measure_col" END) AS "lower_val", + MAX(CASE WHEN o."rn" = b."upper_rn" THEN o."measure_col" END) AS "upper_val" + FROM "ordered" AS o + JOIN "bounds" AS b + ON o."group_col" = b."group_col" + GROUP BY + o."group_col", + b."target_pos", + b."lower_rn", + b."upper_rn" +) +SELECT + "group_col" AS "ALIGN", + CASE + WHEN "lower_rn" = "upper_rn" THEN "lower_val" + ELSE "lower_val" + (("target_pos" - "lower_rn") * ("upper_val" - "lower_val")) + END AS "percentile_measure" +FROM "picked" +ORDER BY "percentile_measure" DESC; diff --git a/Query/sql/v2/runs/v2_cli_20260502_081223_d/c16/sql/v2q_c16_5b52940ee7a88fb3.sql b/Query/sql/v2/runs/v2_cli_20260502_081223_d/c16/sql/v2q_c16_5b52940ee7a88fb3.sql new file mode 100644 index 0000000000000000000000000000000000000000..08cff1cd0de4e33aef85a07a4462f58cfd85f3c7 --- /dev/null +++ b/Query/sql/v2/runs/v2_cli_20260502_081223_d/c16/sql/v2q_c16_5b52940ee7a88fb3.sql @@ -0,0 +1,27 @@ +-- sql_source_version: v2 +-- sql_source_label: v2_current +-- sql_source_run_id: v2_cli_20260502_081223_d +-- sql_source_dataset_id: c16 +-- family_id: tail_rarity_structure +-- canonical_subitem_id: tail_mass_similarity +-- intended_facet_id: tail_ranked_signal +-- variant_semantic_role: count_distribution +-- template_id: tpl_tpch_relative_total_threshold +-- query_record_id: v2q_c16_5b52940ee7a88fb3 +-- problem_id: v2p_c16_343df9152e5c5507 +-- realization_mode: agent +-- source_kind: agent +WITH grouped AS ( + SELECT "SEX", SUM(CAST(NULLIF("YEAR", '') AS INTEGER)) AS group_value + FROM "c16" + WHERE NULLIF("YEAR", '') IS NOT NULL + GROUP BY "SEX" +), total AS ( + SELECT SUM(group_value) AS total_value + FROM grouped +) +SELECT g."SEX", g.group_value +FROM grouped AS g +CROSS JOIN total AS t +WHERE g.group_value > t.total_value * 0.1 +ORDER BY g.group_value DESC; diff --git a/Query/sql/v2/runs/v2_cli_20260502_081223_d/c16/sql/v2q_c16_5d04d48b0d66a8ee.sql b/Query/sql/v2/runs/v2_cli_20260502_081223_d/c16/sql/v2q_c16_5d04d48b0d66a8ee.sql new file mode 100644 index 0000000000000000000000000000000000000000..b4aab00e9c1215cfa157febd9bd2106cdf2b149c --- /dev/null +++ b/Query/sql/v2/runs/v2_cli_20260502_081223_d/c16/sql/v2q_c16_5d04d48b0d66a8ee.sql @@ -0,0 +1,21 @@ +-- sql_source_version: v2 +-- sql_source_label: v2_current +-- sql_source_run_id: v2_cli_20260502_081223_d +-- sql_source_dataset_id: c16 +-- family_id: missingness_structure +-- canonical_subitem_id: co_missingness_pattern_consistency +-- intended_facet_id: missing_rate_by_subgroup +-- variant_semantic_role: missing_rate_by_subgroup +-- template_id: tpl_missing_rate_by_subgroup +-- query_record_id: v2q_c16_5d04d48b0d66a8ee +-- problem_id: v2p_c16_02132d3fe36b26c1 +-- realization_mode: deterministic +-- source_kind: deterministic +SELECT + "EYE", + COUNT(*) AS total_rows, + SUM(CASE WHEN "SEX" IS NULL THEN 1 ELSE 0 END) AS missing_rows, + AVG(CASE WHEN "SEX" IS NULL THEN 1.0 ELSE 0.0 END) AS missing_rate +FROM "c16" +GROUP BY "EYE" +ORDER BY missing_rate DESC, total_rows DESC; diff --git a/Query/sql/v2/runs/v2_cli_20260502_081223_d/c16/sql/v2q_c16_5d08efd2e867983c.sql b/Query/sql/v2/runs/v2_cli_20260502_081223_d/c16/sql/v2q_c16_5d08efd2e867983c.sql new file mode 100644 index 0000000000000000000000000000000000000000..61225697b4a40ba274dbeda4131664a5b85da35c --- /dev/null +++ b/Query/sql/v2/runs/v2_cli_20260502_081223_d/c16/sql/v2q_c16_5d08efd2e867983c.sql @@ -0,0 +1,20 @@ +-- sql_source_version: v2 +-- sql_source_label: v2_current +-- sql_source_run_id: v2_cli_20260502_081223_d +-- sql_source_dataset_id: c16 +-- family_id: tail_rarity_structure +-- canonical_subitem_id: tail_set_consistency +-- intended_facet_id: low_support_extremes +-- variant_semantic_role: rare_extreme_view +-- template_id: tpl_tail_low_support_group_count_v2 +-- query_record_id: v2q_c16_5d08efd2e867983c +-- problem_id: v2p_c16_31887d2f66ea4ffc +-- realization_mode: agent +-- source_kind: agent +SELECT + "ALIVE", + COUNT(*) AS support +FROM "c16" +GROUP BY "ALIVE" +ORDER BY support ASC, "ALIVE" +LIMIT 14; diff --git a/Query/sql/v2/runs/v2_cli_20260502_081223_d/c16/sql/v2q_c16_5e19af4d1c014765.sql b/Query/sql/v2/runs/v2_cli_20260502_081223_d/c16/sql/v2q_c16_5e19af4d1c014765.sql new file mode 100644 index 0000000000000000000000000000000000000000..232df6fd53e266c06bcf84620955c094eab41f42 --- /dev/null +++ b/Query/sql/v2/runs/v2_cli_20260502_081223_d/c16/sql/v2q_c16_5e19af4d1c014765.sql @@ -0,0 +1,18 @@ +-- sql_source_version: v2 +-- sql_source_label: v2_current +-- sql_source_run_id: v2_cli_20260502_081223_d +-- sql_source_dataset_id: c16 +-- family_id: conditional_dependency_structure +-- canonical_subitem_id: dependency_strength_similarity +-- intended_facet_id: pairwise_conditional_dependency +-- variant_semantic_role: within_group_proportion +-- template_id: tpl_m4_group_condition_rate +-- query_record_id: v2q_c16_5e19af4d1c014765 +-- problem_id: v2p_c16_39335378b6d68934 +-- realization_mode: agent +-- source_kind: agent +SELECT "ALIGN", + AVG(CASE WHEN "ALIGN" = 'Bad Characters' THEN 1.0 ELSE 0.0 END) AS "condition_rate" +FROM "c16" +GROUP BY "ALIGN" +ORDER BY "condition_rate" DESC; diff --git a/Query/sql/v2/runs/v2_cli_20260502_081223_d/c16/sql/v2q_c16_5e5d2caa5cfae60b.sql b/Query/sql/v2/runs/v2_cli_20260502_081223_d/c16/sql/v2q_c16_5e5d2caa5cfae60b.sql new file mode 100644 index 0000000000000000000000000000000000000000..ad33b24609d9f802473b861725f8f7358674720d --- /dev/null +++ b/Query/sql/v2/runs/v2_cli_20260502_081223_d/c16/sql/v2q_c16_5e5d2caa5cfae60b.sql @@ -0,0 +1,21 @@ +-- sql_source_version: v2 +-- sql_source_label: v2_current +-- sql_source_run_id: v2_cli_20260502_081223_d +-- sql_source_dataset_id: c16 +-- family_id: missingness_structure +-- canonical_subitem_id: co_missingness_pattern_consistency +-- intended_facet_id: missing_rate_by_subgroup +-- variant_semantic_role: missing_rate_by_subgroup +-- template_id: tpl_missing_rate_by_subgroup +-- query_record_id: v2q_c16_5e5d2caa5cfae60b +-- problem_id: v2p_c16_74ad12885e4c803a +-- realization_mode: deterministic +-- source_kind: deterministic +SELECT + "GSM", + COUNT(*) AS total_rows, + SUM(CASE WHEN "ALIGN" IS NULL THEN 1 ELSE 0 END) AS missing_rows, + AVG(CASE WHEN "ALIGN" IS NULL THEN 1.0 ELSE 0.0 END) AS missing_rate +FROM "c16" +GROUP BY "GSM" +ORDER BY missing_rate DESC, total_rows DESC; diff --git a/Query/sql/v2/runs/v2_cli_20260502_081223_d/c16/sql/v2q_c16_5ebbb82de85a796b.sql b/Query/sql/v2/runs/v2_cli_20260502_081223_d/c16/sql/v2q_c16_5ebbb82de85a796b.sql new file mode 100644 index 0000000000000000000000000000000000000000..59e819a84b8f01a05c22eb8afd8cddb5418827ef --- /dev/null +++ b/Query/sql/v2/runs/v2_cli_20260502_081223_d/c16/sql/v2q_c16_5ebbb82de85a796b.sql @@ -0,0 +1,21 @@ +-- sql_source_version: v2 +-- sql_source_label: v2_current +-- sql_source_run_id: v2_cli_20260502_081223_d +-- sql_source_dataset_id: c16 +-- family_id: cardinality_structure +-- canonical_subitem_id: high_cardinality_response_stability +-- intended_facet_id: target_cardinality_cross_section +-- variant_semantic_role: focused_target_view +-- template_id: tpl_cardinality_high_card_response_stability +-- query_record_id: v2q_c16_5ebbb82de85a796b +-- problem_id: v2p_c16_ed6b7e60ecae1839 +-- realization_mode: deterministic +-- source_kind: deterministic +SELECT + "urlslug", + COUNT(*) AS support, + AVG("APPEARANCES") AS avg_response +FROM "c16" +GROUP BY "urlslug" +HAVING COUNT(*) >= 5.0 +ORDER BY support DESC, avg_response DESC; diff --git a/Query/sql/v2/runs/v2_cli_20260502_081223_d/c16/sql/v2q_c16_5f4bd4620131a777.sql b/Query/sql/v2/runs/v2_cli_20260502_081223_d/c16/sql/v2q_c16_5f4bd4620131a777.sql new file mode 100644 index 0000000000000000000000000000000000000000..01ad2181f1afb949a7f64ab4c7eaeca3444003a4 --- /dev/null +++ b/Query/sql/v2/runs/v2_cli_20260502_081223_d/c16/sql/v2q_c16_5f4bd4620131a777.sql @@ -0,0 +1,26 @@ +-- sql_source_version: v2 +-- sql_source_label: v2_current +-- sql_source_run_id: v2_cli_20260502_081223_d +-- sql_source_dataset_id: c16 +-- family_id: tail_rarity_structure +-- canonical_subitem_id: tail_mass_similarity +-- intended_facet_id: tail_ranked_signal +-- variant_semantic_role: count_distribution +-- template_id: tpl_tpch_relative_total_threshold +-- query_record_id: v2q_c16_5f4bd4620131a777 +-- problem_id: v2p_c16_c62750624a795906 +-- realization_mode: agent +-- source_kind: agent +WITH "grouped" AS ( + SELECT "HAIR", COUNT("page_id") AS "group_value" + FROM "c16" + GROUP BY "HAIR" +), "total" AS ( + SELECT SUM("group_value") AS "total_value" + FROM "grouped" +) +SELECT g."HAIR", g."group_value" +FROM "grouped" AS g +CROSS JOIN "total" AS t +WHERE g."group_value" > t."total_value" * 0.1 +ORDER BY g."group_value" DESC; diff --git a/Query/sql/v2/runs/v2_cli_20260502_081223_d/c16/sql/v2q_c16_6370fde98babd4cf.sql b/Query/sql/v2/runs/v2_cli_20260502_081223_d/c16/sql/v2q_c16_6370fde98babd4cf.sql new file mode 100644 index 0000000000000000000000000000000000000000..da0e989e4452944340ab9d23b7084fffe686f06d --- /dev/null +++ b/Query/sql/v2/runs/v2_cli_20260502_081223_d/c16/sql/v2q_c16_6370fde98babd4cf.sql @@ -0,0 +1,30 @@ +-- sql_source_version: v2 +-- sql_source_label: v2_current +-- sql_source_run_id: v2_cli_20260502_081223_d +-- sql_source_dataset_id: c16 +-- family_id: tail_rarity_structure +-- canonical_subitem_id: tail_mass_similarity +-- intended_facet_id: tail_ranked_signal +-- variant_semantic_role: filtered_stable_view +-- template_id: tpl_tpch_relative_total_threshold +-- query_record_id: v2q_c16_6370fde98babd4cf +-- problem_id: v2p_c16_bda9652f8374c8e3 +-- realization_mode: agent +-- source_kind: agent +WITH grouped AS ( + SELECT "SEX", SUM(CAST("APPEARANCES" AS REAL)) AS group_value + FROM "c16" + WHERE "SEX" IS NOT NULL + AND TRIM("SEX") <> '' + AND "APPEARANCES" IS NOT NULL + AND TRIM("APPEARANCES") <> '' + GROUP BY "SEX" +), total AS ( + SELECT SUM(group_value) AS total_value + FROM grouped +) +SELECT g."SEX", g.group_value +FROM grouped AS g +CROSS JOIN total AS t +WHERE g.group_value > t.total_value * 0.1 +ORDER BY g.group_value DESC; diff --git a/Query/sql/v2/runs/v2_cli_20260502_081223_d/c16/sql/v2q_c16_6410a3fdb9110dd7.sql b/Query/sql/v2/runs/v2_cli_20260502_081223_d/c16/sql/v2q_c16_6410a3fdb9110dd7.sql new file mode 100644 index 0000000000000000000000000000000000000000..183b7be22aa4389f05fedbe407b3dff965ed8cf9 --- /dev/null +++ b/Query/sql/v2/runs/v2_cli_20260502_081223_d/c16/sql/v2q_c16_6410a3fdb9110dd7.sql @@ -0,0 +1,18 @@ +-- sql_source_version: v2 +-- sql_source_label: v2_current +-- sql_source_run_id: v2_cli_20260502_081223_d +-- sql_source_dataset_id: c16 +-- family_id: subgroup_structure +-- canonical_subitem_id: internal_profile_stability +-- intended_facet_id: subgroup_distribution_shift +-- variant_semantic_role: collapsed_target_view +-- template_id: tpl_h2o_group_sum +-- query_record_id: v2q_c16_6410a3fdb9110dd7 +-- problem_id: v2p_c16_0c29258618679c82 +-- realization_mode: agent +-- source_kind: agent +SELECT "ALIVE", SUM(CAST("YEAR" AS INTEGER)) AS total_measure +FROM "c16" +WHERE "ALIVE" IS NOT NULL AND "ALIVE" <> '' AND "YEAR" IS NOT NULL AND "YEAR" <> '' +GROUP BY "ALIVE" +ORDER BY total_measure DESC; diff --git a/Query/sql/v2/runs/v2_cli_20260502_081223_d/c16/sql/v2q_c16_6454d98656d3712e.sql b/Query/sql/v2/runs/v2_cli_20260502_081223_d/c16/sql/v2q_c16_6454d98656d3712e.sql new file mode 100644 index 0000000000000000000000000000000000000000..c62c1be8b83f8005015500241d5e051ffd963823 --- /dev/null +++ b/Query/sql/v2/runs/v2_cli_20260502_081223_d/c16/sql/v2q_c16_6454d98656d3712e.sql @@ -0,0 +1,36 @@ +-- sql_source_version: v2 +-- sql_source_label: v2_current +-- sql_source_run_id: v2_cli_20260502_081223_d +-- sql_source_dataset_id: c16 +-- family_id: tail_rarity_structure +-- canonical_subitem_id: tail_mass_similarity +-- intended_facet_id: tail_ranked_signal +-- variant_semantic_role: filtered_stable_view +-- template_id: tpl_tpch_relative_total_threshold +-- query_record_id: v2q_c16_6454d98656d3712e +-- problem_id: v2p_c16_ca7572c11d2f5ed5 +-- realization_mode: agent +-- source_kind: agent +WITH "grouped" AS ( + SELECT + "ALIGN" AS "ALIGN", + SUM(CAST("YEAR" AS REAL)) AS "group_value" + FROM "c16" + WHERE "ALIGN" IS NOT NULL + AND TRIM("ALIGN") <> '' + AND "YEAR" IS NOT NULL + AND TRIM("YEAR") <> '' + AND TRIM("YEAR") GLOB '[0-9]*' + GROUP BY "ALIGN" +), +"total" AS ( + SELECT SUM("group_value") AS "total_value" + FROM "grouped" +) +SELECT + g."ALIGN", + g."group_value" +FROM "grouped" AS g +CROSS JOIN "total" AS t +WHERE g."group_value" > t."total_value" * 0.1 +ORDER BY g."group_value" DESC; diff --git a/Query/sql/v2/runs/v2_cli_20260502_081223_d/c16/sql/v2q_c16_64c3a9e020e9d350.sql b/Query/sql/v2/runs/v2_cli_20260502_081223_d/c16/sql/v2q_c16_64c3a9e020e9d350.sql new file mode 100644 index 0000000000000000000000000000000000000000..eb855b27348c9e19c2783a7339533fdfc1b4cb37 --- /dev/null +++ b/Query/sql/v2/runs/v2_cli_20260502_081223_d/c16/sql/v2q_c16_64c3a9e020e9d350.sql @@ -0,0 +1,21 @@ +-- sql_source_version: v2 +-- sql_source_label: v2_current +-- sql_source_run_id: v2_cli_20260502_081223_d +-- sql_source_dataset_id: c16 +-- family_id: missingness_structure +-- canonical_subitem_id: co_missingness_pattern_consistency +-- intended_facet_id: missing_rate_by_subgroup +-- variant_semantic_role: missing_rate_by_subgroup +-- template_id: tpl_missing_rate_by_subgroup +-- query_record_id: v2q_c16_64c3a9e020e9d350 +-- problem_id: v2p_c16_942bd0036da75340 +-- realization_mode: deterministic +-- source_kind: deterministic +SELECT + "GSM", + COUNT(*) AS total_rows, + SUM(CASE WHEN "APPEARANCES" IS NULL THEN 1 ELSE 0 END) AS missing_rows, + AVG(CASE WHEN "APPEARANCES" IS NULL THEN 1.0 ELSE 0.0 END) AS missing_rate +FROM "c16" +GROUP BY "GSM" +ORDER BY missing_rate DESC, total_rows DESC; diff --git a/Query/sql/v2/runs/v2_cli_20260502_081223_d/c16/sql/v2q_c16_6688edb8ccaf0030.sql b/Query/sql/v2/runs/v2_cli_20260502_081223_d/c16/sql/v2q_c16_6688edb8ccaf0030.sql new file mode 100644 index 0000000000000000000000000000000000000000..af34464ead1b39dcaf27dc29d77824718a968052 --- /dev/null +++ b/Query/sql/v2/runs/v2_cli_20260502_081223_d/c16/sql/v2q_c16_6688edb8ccaf0030.sql @@ -0,0 +1,33 @@ +-- sql_source_version: v2 +-- sql_source_label: v2_current +-- sql_source_run_id: v2_cli_20260502_081223_d +-- sql_source_dataset_id: c16 +-- family_id: tail_rarity_structure +-- canonical_subitem_id: tail_concentration_consistency +-- intended_facet_id: rare_target_concentration +-- variant_semantic_role: focused_target_view +-- template_id: tpl_grouped_percentile_point +-- query_record_id: v2q_c16_6688edb8ccaf0030 +-- problem_id: v2p_c16_b50ca4b23f9d4b4c +-- realization_mode: agent +-- source_kind: agent +WITH ranked AS ( + SELECT + "ALIVE", + CAST("YEAR" AS INTEGER) AS year_value, + ROW_NUMBER() OVER ( + PARTITION BY "ALIVE" + ORDER BY CAST("YEAR" AS INTEGER) + ) AS rn, + COUNT(*) OVER (PARTITION BY "ALIVE") AS cnt + FROM "c16" + WHERE "YEAR" IS NOT NULL + AND TRIM("YEAR") <> '' +) +SELECT + "ALIVE", + MIN(year_value) AS percentile_measure +FROM ranked +WHERE rn >= ((cnt * 95 + 99) / 100) +GROUP BY "ALIVE" +ORDER BY percentile_measure DESC; diff --git a/Query/sql/v2/runs/v2_cli_20260502_081223_d/c16/sql/v2q_c16_678c130a51808352.sql b/Query/sql/v2/runs/v2_cli_20260502_081223_d/c16/sql/v2q_c16_678c130a51808352.sql new file mode 100644 index 0000000000000000000000000000000000000000..1af2cd13e0b52d986fb98bf025a5567606fcd3da --- /dev/null +++ b/Query/sql/v2/runs/v2_cli_20260502_081223_d/c16/sql/v2q_c16_678c130a51808352.sql @@ -0,0 +1,21 @@ +-- sql_source_version: v2 +-- sql_source_label: v2_current +-- sql_source_run_id: v2_cli_20260502_081223_d +-- sql_source_dataset_id: c16 +-- family_id: cardinality_structure +-- canonical_subitem_id: high_cardinality_response_stability +-- intended_facet_id: target_cardinality_cross_section +-- variant_semantic_role: focused_target_view +-- template_id: tpl_cardinality_high_card_response_stability +-- query_record_id: v2q_c16_678c130a51808352 +-- problem_id: v2p_c16_9b281a7bbb37590a +-- realization_mode: deterministic +-- source_kind: deterministic +SELECT + "FIRST APPEARANCE", + COUNT(*) AS support, + AVG("YEAR") AS avg_response +FROM "c16" +GROUP BY "FIRST APPEARANCE" +HAVING COUNT(*) >= 5.0 +ORDER BY support DESC, avg_response DESC; diff --git a/Query/sql/v2/runs/v2_cli_20260502_081223_d/c16/sql/v2q_c16_69e1e36aea8a384e.sql b/Query/sql/v2/runs/v2_cli_20260502_081223_d/c16/sql/v2q_c16_69e1e36aea8a384e.sql new file mode 100644 index 0000000000000000000000000000000000000000..7734199fd6858377c80b12ad187e2abad120f0fd --- /dev/null +++ b/Query/sql/v2/runs/v2_cli_20260502_081223_d/c16/sql/v2q_c16_69e1e36aea8a384e.sql @@ -0,0 +1,26 @@ +-- sql_source_version: v2 +-- sql_source_label: v2_current +-- sql_source_run_id: v2_cli_20260502_081223_d +-- sql_source_dataset_id: c16 +-- family_id: tail_rarity_structure +-- canonical_subitem_id: tail_mass_similarity +-- intended_facet_id: tail_ranked_signal +-- variant_semantic_role: count_distribution +-- template_id: tpl_tpch_relative_total_threshold +-- query_record_id: v2q_c16_69e1e36aea8a384e +-- problem_id: v2p_c16_3f560b3efd852473 +-- realization_mode: agent +-- source_kind: agent +WITH grouped AS ( + SELECT "ALIVE", COUNT("page_id") AS group_value + FROM "c16" + GROUP BY "ALIVE" +), total AS ( + SELECT SUM(group_value) AS total_value + FROM grouped +) +SELECT g."ALIVE", g.group_value +FROM grouped AS g +CROSS JOIN total AS t +WHERE g.group_value > t.total_value * 0.05 +ORDER BY g.group_value DESC; diff --git a/Query/sql/v2/runs/v2_cli_20260502_081223_d/c16/sql/v2q_c16_6da83c8169121cc6.sql b/Query/sql/v2/runs/v2_cli_20260502_081223_d/c16/sql/v2q_c16_6da83c8169121cc6.sql new file mode 100644 index 0000000000000000000000000000000000000000..cf04f2317a48185bed41a45d8e8ff341458cfc6a --- /dev/null +++ b/Query/sql/v2/runs/v2_cli_20260502_081223_d/c16/sql/v2q_c16_6da83c8169121cc6.sql @@ -0,0 +1,17 @@ +-- sql_source_version: v2 +-- sql_source_label: v2_current +-- sql_source_run_id: v2_cli_20260502_081223_d +-- sql_source_dataset_id: c16 +-- family_id: subgroup_structure +-- canonical_subitem_id: internal_profile_stability +-- intended_facet_id: subgroup_distribution_shift +-- variant_semantic_role: collapsed_target_view +-- template_id: tpl_h2o_group_sum +-- query_record_id: v2q_c16_6da83c8169121cc6 +-- problem_id: v2p_c16_65a969dc6a31693c +-- realization_mode: agent +-- source_kind: agent +SELECT "HAIR", SUM(CAST("YEAR" AS INTEGER)) AS total_measure +FROM "c16" +GROUP BY "HAIR" +ORDER BY total_measure DESC; diff --git a/Query/sql/v2/runs/v2_cli_20260502_081223_d/c16/sql/v2q_c16_6df98b7861fe2cef.sql b/Query/sql/v2/runs/v2_cli_20260502_081223_d/c16/sql/v2q_c16_6df98b7861fe2cef.sql new file mode 100644 index 0000000000000000000000000000000000000000..f636e61c94799e4fd69e326851103f76d43576c4 --- /dev/null +++ b/Query/sql/v2/runs/v2_cli_20260502_081223_d/c16/sql/v2q_c16_6df98b7861fe2cef.sql @@ -0,0 +1,24 @@ +-- sql_source_version: v2 +-- sql_source_label: v2_current +-- sql_source_run_id: v2_cli_20260502_081223_d +-- sql_source_dataset_id: c16 +-- family_id: conditional_dependency_structure +-- canonical_subitem_id: direction_consistency +-- intended_facet_id: conditional_rate_shift +-- variant_semantic_role: contrastive_conditional_view +-- template_id: tpl_m4_group_ratio_two_conditions +-- query_record_id: v2q_c16_6df98b7861fe2cef +-- problem_id: v2p_c16_948e5c1775f15b26 +-- realization_mode: agent +-- source_kind: agent +WITH grouped AS ( + SELECT "EYE", + SUM(CASE WHEN "EYE" = '' THEN 1 ELSE 0 END) AS numerator_count, + SUM(CASE WHEN "EYE" = 'Blue Eyes' THEN 1 ELSE 0 END) AS denominator_count + FROM "c16" + GROUP BY "EYE" +) +SELECT "EYE", + CAST(numerator_count AS FLOAT) / NULLIF(denominator_count, 0) AS condition_ratio +FROM grouped +ORDER BY condition_ratio DESC; diff --git a/Query/sql/v2/runs/v2_cli_20260502_081223_d/c16/sql/v2q_c16_6e29d5ff94910515.sql b/Query/sql/v2/runs/v2_cli_20260502_081223_d/c16/sql/v2q_c16_6e29d5ff94910515.sql new file mode 100644 index 0000000000000000000000000000000000000000..d9c72ceb946dee69120166c3e059bdbd39b9254b --- /dev/null +++ b/Query/sql/v2/runs/v2_cli_20260502_081223_d/c16/sql/v2q_c16_6e29d5ff94910515.sql @@ -0,0 +1,25 @@ +-- sql_source_version: v2 +-- sql_source_label: v2_current +-- sql_source_run_id: v2_cli_20260502_081223_d +-- sql_source_dataset_id: c16 +-- family_id: cardinality_structure +-- canonical_subitem_id: support_rank_profile_consistency +-- intended_facet_id: value_imbalance_profile +-- variant_semantic_role: count_distribution +-- template_id: tpl_cardinality_support_rank_profile +-- query_record_id: v2q_c16_6e29d5ff94910515 +-- problem_id: v2p_c16_99cc87c4faf18982 +-- realization_mode: deterministic +-- source_kind: deterministic +WITH grouped AS ( + SELECT "SEX" AS value_label, COUNT(*) AS support + FROM "c16" + GROUP BY "SEX" +) +SELECT + value_label, + support, + CAST(support AS FLOAT) / NULLIF(SUM(support) OVER (), 0) AS support_share, + ROW_NUMBER() OVER (ORDER BY support DESC, value_label) AS support_rank +FROM grouped +ORDER BY support DESC, value_label; diff --git a/Query/sql/v2/runs/v2_cli_20260502_081223_d/c16/sql/v2q_c16_717f842ae0016879.sql b/Query/sql/v2/runs/v2_cli_20260502_081223_d/c16/sql/v2q_c16_717f842ae0016879.sql new file mode 100644 index 0000000000000000000000000000000000000000..55c266a2d824e2168f04f01c3d0f3c43d073c574 --- /dev/null +++ b/Query/sql/v2/runs/v2_cli_20260502_081223_d/c16/sql/v2q_c16_717f842ae0016879.sql @@ -0,0 +1,21 @@ +-- sql_source_version: v2 +-- sql_source_label: v2_current +-- sql_source_run_id: v2_cli_20260502_081223_d +-- sql_source_dataset_id: c16 +-- family_id: cardinality_structure +-- canonical_subitem_id: high_cardinality_response_stability +-- intended_facet_id: target_cardinality_cross_section +-- variant_semantic_role: focused_target_view +-- template_id: tpl_cardinality_high_card_response_stability +-- query_record_id: v2q_c16_717f842ae0016879 +-- problem_id: v2p_c16_6fb58467fa3dd169 +-- realization_mode: deterministic +-- source_kind: deterministic +SELECT + "FIRST APPEARANCE", + COUNT(*) AS support, + AVG("APPEARANCES") AS avg_response +FROM "c16" +GROUP BY "FIRST APPEARANCE" +HAVING COUNT(*) >= 5.0 +ORDER BY support DESC, avg_response DESC; diff --git a/Query/sql/v2/runs/v2_cli_20260502_081223_d/c16/sql/v2q_c16_730272f8f6fd8175.sql b/Query/sql/v2/runs/v2_cli_20260502_081223_d/c16/sql/v2q_c16_730272f8f6fd8175.sql new file mode 100644 index 0000000000000000000000000000000000000000..8bc0db769018e1cb58f22f2a4b523da9fec8a075 --- /dev/null +++ b/Query/sql/v2/runs/v2_cli_20260502_081223_d/c16/sql/v2q_c16_730272f8f6fd8175.sql @@ -0,0 +1,21 @@ +-- sql_source_version: v2 +-- sql_source_label: v2_current +-- sql_source_run_id: v2_cli_20260502_081223_d +-- sql_source_dataset_id: c16 +-- family_id: missingness_structure +-- canonical_subitem_id: co_missingness_pattern_consistency +-- intended_facet_id: missing_target_interaction +-- variant_semantic_role: missing_target_interaction +-- template_id: tpl_missing_target_interaction +-- query_record_id: v2q_c16_730272f8f6fd8175 +-- problem_id: v2p_c16_d22f0cbab31c557d +-- realization_mode: deterministic +-- source_kind: deterministic +SELECT + "EYE", + COUNT(*) AS total_rows, + SUM(CASE WHEN "APPEARANCES" IS NULL THEN 1 ELSE 0 END) AS missing_rows, + AVG(CASE WHEN "APPEARANCES" IS NULL THEN 1.0 ELSE 0.0 END) AS missing_rate +FROM "c16" +GROUP BY "EYE" +ORDER BY missing_rate DESC, total_rows DESC; diff --git a/Query/sql/v2/runs/v2_cli_20260502_081223_d/c16/sql/v2q_c16_741db70a54ec1aad.sql b/Query/sql/v2/runs/v2_cli_20260502_081223_d/c16/sql/v2q_c16_741db70a54ec1aad.sql new file mode 100644 index 0000000000000000000000000000000000000000..1cc76f3430e9f437290bace9495aead043162c77 --- /dev/null +++ b/Query/sql/v2/runs/v2_cli_20260502_081223_d/c16/sql/v2q_c16_741db70a54ec1aad.sql @@ -0,0 +1,21 @@ +-- sql_source_version: v2 +-- sql_source_label: v2_current +-- sql_source_run_id: v2_cli_20260502_081223_d +-- sql_source_dataset_id: c16 +-- family_id: missingness_structure +-- canonical_subitem_id: co_missingness_pattern_consistency +-- intended_facet_id: missing_rate_by_subgroup +-- variant_semantic_role: missing_rate_by_subgroup +-- template_id: tpl_missing_rate_by_subgroup +-- query_record_id: v2q_c16_741db70a54ec1aad +-- problem_id: v2p_c16_9b5e60c34ce1e6b6 +-- realization_mode: deterministic +-- source_kind: deterministic +SELECT + "YEAR", + COUNT(*) AS total_rows, + SUM(CASE WHEN "GSM" IS NULL THEN 1 ELSE 0 END) AS missing_rows, + AVG(CASE WHEN "GSM" IS NULL THEN 1.0 ELSE 0.0 END) AS missing_rate +FROM "c16" +GROUP BY "YEAR" +ORDER BY missing_rate DESC, total_rows DESC; diff --git a/Query/sql/v2/runs/v2_cli_20260502_081223_d/c16/sql/v2q_c16_75370208d63df2dc.sql b/Query/sql/v2/runs/v2_cli_20260502_081223_d/c16/sql/v2q_c16_75370208d63df2dc.sql new file mode 100644 index 0000000000000000000000000000000000000000..85a83c345851d7ae22c9c228e5eb19b5cbc71483 --- /dev/null +++ b/Query/sql/v2/runs/v2_cli_20260502_081223_d/c16/sql/v2q_c16_75370208d63df2dc.sql @@ -0,0 +1,26 @@ +-- sql_source_version: v2 +-- sql_source_label: v2_current +-- sql_source_run_id: v2_cli_20260502_081223_d +-- sql_source_dataset_id: c16 +-- family_id: conditional_dependency_structure +-- canonical_subitem_id: direction_consistency +-- intended_facet_id: conditional_rate_shift +-- variant_semantic_role: contrastive_conditional_view +-- template_id: tpl_m4_group_ratio_two_conditions +-- query_record_id: v2q_c16_75370208d63df2dc +-- problem_id: v2p_c16_bcb79291c6b6566d +-- realization_mode: agent +-- source_kind: agent +WITH grouped AS ( + SELECT + "ALIGN", + SUM(CASE WHEN COALESCE("EYE", '') = '' THEN 1 ELSE 0 END) AS numerator_count, + SUM(CASE WHEN "EYE" = 'Blue Eyes' THEN 1 ELSE 0 END) AS denominator_count + FROM "c16" + GROUP BY "ALIGN" +) +SELECT + "ALIGN", + CAST(numerator_count AS FLOAT) / NULLIF(denominator_count, 0) AS condition_ratio +FROM grouped +ORDER BY condition_ratio DESC; diff --git a/Query/sql/v2/runs/v2_cli_20260502_081223_d/c16/sql/v2q_c16_76782777597c7556.sql b/Query/sql/v2/runs/v2_cli_20260502_081223_d/c16/sql/v2q_c16_76782777597c7556.sql new file mode 100644 index 0000000000000000000000000000000000000000..054da80b01be8e3340bc1af51d7ffcf2d56c2859 --- /dev/null +++ b/Query/sql/v2/runs/v2_cli_20260502_081223_d/c16/sql/v2q_c16_76782777597c7556.sql @@ -0,0 +1,25 @@ +-- sql_source_version: v2 +-- sql_source_label: v2_current +-- sql_source_run_id: v2_cli_20260502_081223_d +-- sql_source_dataset_id: c16 +-- family_id: cardinality_structure +-- canonical_subitem_id: support_rank_profile_consistency +-- intended_facet_id: value_imbalance_profile +-- variant_semantic_role: count_distribution +-- template_id: tpl_cardinality_support_rank_profile +-- query_record_id: v2q_c16_76782777597c7556 +-- problem_id: v2p_c16_c03dc09a70d40f68 +-- realization_mode: deterministic +-- source_kind: deterministic +WITH grouped AS ( + SELECT "EYE" AS value_label, COUNT(*) AS support + FROM "c16" + GROUP BY "EYE" +) +SELECT + value_label, + support, + CAST(support AS FLOAT) / NULLIF(SUM(support) OVER (), 0) AS support_share, + ROW_NUMBER() OVER (ORDER BY support DESC, value_label) AS support_rank +FROM grouped +ORDER BY support DESC, value_label; diff --git a/Query/sql/v2/runs/v2_cli_20260502_081223_d/c16/sql/v2q_c16_7ce102b5cd752fd9.sql b/Query/sql/v2/runs/v2_cli_20260502_081223_d/c16/sql/v2q_c16_7ce102b5cd752fd9.sql new file mode 100644 index 0000000000000000000000000000000000000000..d551382f6e25bf828e967277dbb92283d9050dad --- /dev/null +++ b/Query/sql/v2/runs/v2_cli_20260502_081223_d/c16/sql/v2q_c16_7ce102b5cd752fd9.sql @@ -0,0 +1,21 @@ +-- sql_source_version: v2 +-- sql_source_label: v2_current +-- sql_source_run_id: v2_cli_20260502_081223_d +-- sql_source_dataset_id: c16 +-- family_id: cardinality_structure +-- canonical_subitem_id: high_cardinality_response_stability +-- intended_facet_id: target_cardinality_cross_section +-- variant_semantic_role: focused_target_view +-- template_id: tpl_cardinality_high_card_response_stability +-- query_record_id: v2q_c16_7ce102b5cd752fd9 +-- problem_id: v2p_c16_9bd4e7a3b853b7e8 +-- realization_mode: deterministic +-- source_kind: deterministic +SELECT + "APPEARANCES", + COUNT(*) AS support, + AVG("page_id") AS avg_response +FROM "c16" +GROUP BY "APPEARANCES" +HAVING COUNT(*) >= 5.0 +ORDER BY support DESC, avg_response DESC; diff --git a/Query/sql/v2/runs/v2_cli_20260502_081223_d/c16/sql/v2q_c16_7d7f7083a6998c73.sql b/Query/sql/v2/runs/v2_cli_20260502_081223_d/c16/sql/v2q_c16_7d7f7083a6998c73.sql new file mode 100644 index 0000000000000000000000000000000000000000..d2120b7a0befe71cfd99de89de4ea740de589f1c --- /dev/null +++ b/Query/sql/v2/runs/v2_cli_20260502_081223_d/c16/sql/v2q_c16_7d7f7083a6998c73.sql @@ -0,0 +1,28 @@ +-- sql_source_version: v2 +-- sql_source_label: v2_current +-- sql_source_run_id: v2_cli_20260502_081223_d +-- sql_source_dataset_id: c16 +-- family_id: cardinality_structure +-- canonical_subitem_id: support_rank_profile_consistency +-- intended_facet_id: value_imbalance_profile +-- variant_semantic_role: ranked_signal_view +-- template_id: tpl_cardinality_distinct_share_profile +-- query_record_id: v2q_c16_7d7f7083a6998c73 +-- problem_id: v2p_c16_488d653b6a4c6555 +-- realization_mode: deterministic +-- source_kind: deterministic +WITH grouped AS ( + SELECT "SEX" AS value_label, COUNT(*) AS support + FROM "c16" + GROUP BY "SEX" +), ranked AS ( + SELECT + value_label, + support, + CAST(support AS FLOAT) / NULLIF(SUM(support) OVER (), 0) AS support_share, + SUM(support) OVER (ORDER BY support DESC, value_label ROWS UNBOUNDED PRECEDING) AS cumulative_support + FROM grouped +) +SELECT * +FROM ranked +ORDER BY support DESC, value_label; diff --git a/Query/sql/v2/runs/v2_cli_20260502_081223_d/c16/sql/v2q_c16_7ea8660fba469b20.sql b/Query/sql/v2/runs/v2_cli_20260502_081223_d/c16/sql/v2q_c16_7ea8660fba469b20.sql new file mode 100644 index 0000000000000000000000000000000000000000..07c79e8631a05b8b80587fa1dff9818fe6bb8e8b --- /dev/null +++ b/Query/sql/v2/runs/v2_cli_20260502_081223_d/c16/sql/v2q_c16_7ea8660fba469b20.sql @@ -0,0 +1,28 @@ +-- sql_source_version: v2 +-- sql_source_label: v2_current +-- sql_source_run_id: v2_cli_20260502_081223_d +-- sql_source_dataset_id: c16 +-- family_id: cardinality_structure +-- canonical_subitem_id: support_rank_profile_consistency +-- intended_facet_id: support_concentration +-- variant_semantic_role: ranked_signal_view +-- template_id: tpl_cardinality_distinct_share_profile +-- query_record_id: v2q_c16_7ea8660fba469b20 +-- problem_id: v2p_c16_30897436b3748ba9 +-- realization_mode: deterministic +-- source_kind: deterministic +WITH grouped AS ( + SELECT "GSM" AS value_label, COUNT(*) AS support + FROM "c16" + GROUP BY "GSM" +), ranked AS ( + SELECT + value_label, + support, + CAST(support AS FLOAT) / NULLIF(SUM(support) OVER (), 0) AS support_share, + SUM(support) OVER (ORDER BY support DESC, value_label ROWS UNBOUNDED PRECEDING) AS cumulative_support + FROM grouped +) +SELECT * +FROM ranked +ORDER BY support DESC, value_label; diff --git a/Query/sql/v2/runs/v2_cli_20260502_081223_d/c16/sql/v2q_c16_800262cac851b2ed.sql b/Query/sql/v2/runs/v2_cli_20260502_081223_d/c16/sql/v2q_c16_800262cac851b2ed.sql new file mode 100644 index 0000000000000000000000000000000000000000..40fec9dcd0adbba2a0a59528232ca4a5e8a8894f --- /dev/null +++ b/Query/sql/v2/runs/v2_cli_20260502_081223_d/c16/sql/v2q_c16_800262cac851b2ed.sql @@ -0,0 +1,22 @@ +-- sql_source_version: v2 +-- sql_source_label: v2_current +-- sql_source_run_id: v2_cli_20260502_081223_d +-- sql_source_dataset_id: c16 +-- family_id: conditional_dependency_structure +-- canonical_subitem_id: dependency_strength_similarity +-- intended_facet_id: pairwise_conditional_dependency +-- variant_semantic_role: within_group_proportion +-- template_id: tpl_tpcds_within_group_share +-- query_record_id: v2q_c16_800262cac851b2ed +-- problem_id: v2p_c16_f1883c03cbd66936 +-- realization_mode: agent +-- source_kind: agent +SELECT + "ALIVE", + "APPEARANCES", + SUM(CAST("YEAR" AS REAL)) AS "total_measure", + SUM(CAST("YEAR" AS REAL)) * 100.0 / SUM(SUM(CAST("YEAR" AS REAL))) OVER (PARTITION BY "ALIVE") AS "share_within_group" +FROM "c16" +WHERE "YEAR" IS NOT NULL AND TRIM("YEAR") <> '' +GROUP BY "ALIVE", "APPEARANCES" +ORDER BY "share_within_group" DESC; diff --git a/Query/sql/v2/runs/v2_cli_20260502_081223_d/c16/sql/v2q_c16_80e8766b25eed988.sql b/Query/sql/v2/runs/v2_cli_20260502_081223_d/c16/sql/v2q_c16_80e8766b25eed988.sql new file mode 100644 index 0000000000000000000000000000000000000000..08f151d7924bd36644897dc0271cb2d079d8af73 --- /dev/null +++ b/Query/sql/v2/runs/v2_cli_20260502_081223_d/c16/sql/v2q_c16_80e8766b25eed988.sql @@ -0,0 +1,21 @@ +-- sql_source_version: v2 +-- sql_source_label: v2_current +-- sql_source_run_id: v2_cli_20260502_081223_d +-- sql_source_dataset_id: c16 +-- family_id: conditional_dependency_structure +-- canonical_subitem_id: dependency_strength_similarity +-- intended_facet_id: pairwise_conditional_dependency +-- variant_semantic_role: within_group_proportion +-- template_id: tpl_tpcds_within_group_share +-- query_record_id: v2q_c16_80e8766b25eed988 +-- problem_id: v2p_c16_9d89b59dd6d2db4a +-- realization_mode: agent +-- source_kind: agent +SELECT + "HAIR", + "name", + SUM(CAST("page_id" AS NUMERIC)) AS total_measure, + SUM(CAST("page_id" AS NUMERIC)) * 100.0 / SUM(SUM(CAST("page_id" AS NUMERIC))) OVER (PARTITION BY "HAIR") AS share_within_group +FROM "c16" +GROUP BY "HAIR", "name" +ORDER BY share_within_group DESC; diff --git a/Query/sql/v2/runs/v2_cli_20260502_081223_d/c16/sql/v2q_c16_845abf0dece8b20a.sql b/Query/sql/v2/runs/v2_cli_20260502_081223_d/c16/sql/v2q_c16_845abf0dece8b20a.sql new file mode 100644 index 0000000000000000000000000000000000000000..94c89eefbcef2aba0038e68a760a506869d16f34 --- /dev/null +++ b/Query/sql/v2/runs/v2_cli_20260502_081223_d/c16/sql/v2q_c16_845abf0dece8b20a.sql @@ -0,0 +1,25 @@ +-- sql_source_version: v2 +-- sql_source_label: v2_current +-- sql_source_run_id: v2_cli_20260502_081223_d +-- sql_source_dataset_id: c16 +-- family_id: cardinality_structure +-- canonical_subitem_id: support_rank_profile_consistency +-- intended_facet_id: support_concentration +-- variant_semantic_role: count_distribution +-- template_id: tpl_cardinality_support_rank_profile +-- query_record_id: v2q_c16_845abf0dece8b20a +-- problem_id: v2p_c16_ad39351e02f96964 +-- realization_mode: deterministic +-- source_kind: deterministic +WITH grouped AS ( + SELECT "GSM" AS value_label, COUNT(*) AS support + FROM "c16" + GROUP BY "GSM" +) +SELECT + value_label, + support, + CAST(support AS FLOAT) / NULLIF(SUM(support) OVER (), 0) AS support_share, + ROW_NUMBER() OVER (ORDER BY support DESC, value_label) AS support_rank +FROM grouped +ORDER BY support DESC, value_label; diff --git a/Query/sql/v2/runs/v2_cli_20260502_081223_d/c16/sql/v2q_c16_855f1db5e6ee4452.sql b/Query/sql/v2/runs/v2_cli_20260502_081223_d/c16/sql/v2q_c16_855f1db5e6ee4452.sql new file mode 100644 index 0000000000000000000000000000000000000000..df1afece764538cb826be519bb73598e0884cacd --- /dev/null +++ b/Query/sql/v2/runs/v2_cli_20260502_081223_d/c16/sql/v2q_c16_855f1db5e6ee4452.sql @@ -0,0 +1,19 @@ +-- sql_source_version: v2 +-- sql_source_label: v2_current +-- sql_source_run_id: v2_cli_20260502_081223_d +-- sql_source_dataset_id: c16 +-- family_id: conditional_dependency_structure +-- canonical_subitem_id: dependency_strength_similarity +-- intended_facet_id: pairwise_conditional_dependency +-- variant_semantic_role: focused_target_view +-- template_id: tpl_tpcds_within_group_share +-- query_record_id: v2q_c16_855f1db5e6ee4452 +-- problem_id: v2p_c16_ed20c793472e99e2 +-- realization_mode: agent +-- source_kind: agent +SELECT "EYE", "page_id", + SUM(CAST("YEAR" AS REAL)) AS total_measure, + SUM(CAST("YEAR" AS REAL)) * 100.0 / SUM(SUM(CAST("YEAR" AS REAL))) OVER (PARTITION BY "EYE") AS share_within_group +FROM "c16" +GROUP BY "EYE", "page_id" +ORDER BY share_within_group DESC; diff --git a/Query/sql/v2/runs/v2_cli_20260502_081223_d/c16/sql/v2q_c16_866a8f1cacbfe02f.sql b/Query/sql/v2/runs/v2_cli_20260502_081223_d/c16/sql/v2q_c16_866a8f1cacbfe02f.sql new file mode 100644 index 0000000000000000000000000000000000000000..39eba1ba076112c3b810fe198868291fca8c7aa3 --- /dev/null +++ b/Query/sql/v2/runs/v2_cli_20260502_081223_d/c16/sql/v2q_c16_866a8f1cacbfe02f.sql @@ -0,0 +1,19 @@ +-- sql_source_version: v2 +-- sql_source_label: v2_current +-- sql_source_run_id: v2_cli_20260502_081223_d +-- sql_source_dataset_id: c16 +-- family_id: conditional_dependency_structure +-- canonical_subitem_id: dependency_strength_similarity +-- intended_facet_id: pairwise_conditional_dependency +-- variant_semantic_role: focused_target_view +-- template_id: tpl_tpcds_within_group_share +-- query_record_id: v2q_c16_866a8f1cacbfe02f +-- problem_id: v2p_c16_c5bac3a67809aa33 +-- realization_mode: agent +-- source_kind: agent +SELECT "HAIR", "name", + SUM("page_id") AS "total_measure", + SUM("page_id") * 100.0 / SUM(SUM("page_id")) OVER (PARTITION BY "HAIR") AS "share_within_group" +FROM "c16" +GROUP BY "HAIR", "name" +ORDER BY "share_within_group" DESC; diff --git a/Query/sql/v2/runs/v2_cli_20260502_081223_d/c16/sql/v2q_c16_87733ae369b5bc7c.sql b/Query/sql/v2/runs/v2_cli_20260502_081223_d/c16/sql/v2q_c16_87733ae369b5bc7c.sql new file mode 100644 index 0000000000000000000000000000000000000000..132c3dad7a90848f428aca137fe6a5c42246d2d2 --- /dev/null +++ b/Query/sql/v2/runs/v2_cli_20260502_081223_d/c16/sql/v2q_c16_87733ae369b5bc7c.sql @@ -0,0 +1,18 @@ +-- sql_source_version: v2 +-- sql_source_label: v2_current +-- sql_source_run_id: v2_cli_20260502_081223_d +-- sql_source_dataset_id: c16 +-- family_id: missingness_structure +-- canonical_subitem_id: marginal_missing_rate_consistency +-- intended_facet_id: missing_indicator_distribution +-- variant_semantic_role: missing_indicator_view +-- template_id: tpl_missing_marginal_rate_profile +-- query_record_id: v2q_c16_87733ae369b5bc7c +-- problem_id: v2p_c16_b95ac7eca8a13add +-- realization_mode: deterministic +-- source_kind: deterministic +SELECT + COUNT(*) AS total_rows, + SUM(CASE WHEN "HAIR" IS NULL THEN 1 ELSE 0 END) AS missing_rows, + AVG(CASE WHEN "HAIR" IS NULL THEN 1.0 ELSE 0.0 END) AS missing_rate +FROM "c16"; diff --git a/Query/sql/v2/runs/v2_cli_20260502_081223_d/c16/sql/v2q_c16_87ca32966c89fe3c.sql b/Query/sql/v2/runs/v2_cli_20260502_081223_d/c16/sql/v2q_c16_87ca32966c89fe3c.sql new file mode 100644 index 0000000000000000000000000000000000000000..3445cd189f9c5190282235c8d03c80b28e9dddd7 --- /dev/null +++ b/Query/sql/v2/runs/v2_cli_20260502_081223_d/c16/sql/v2q_c16_87ca32966c89fe3c.sql @@ -0,0 +1,21 @@ +-- sql_source_version: v2 +-- sql_source_label: v2_current +-- sql_source_run_id: v2_cli_20260502_081223_d +-- sql_source_dataset_id: c16 +-- family_id: missingness_structure +-- canonical_subitem_id: co_missingness_pattern_consistency +-- intended_facet_id: missing_target_interaction +-- variant_semantic_role: missing_target_interaction +-- template_id: tpl_missing_target_interaction +-- query_record_id: v2q_c16_87ca32966c89fe3c +-- problem_id: v2p_c16_4f03d164c8870341 +-- realization_mode: deterministic +-- source_kind: deterministic +SELECT + "ALIVE", + COUNT(*) AS total_rows, + SUM(CASE WHEN "ID" IS NULL THEN 1 ELSE 0 END) AS missing_rows, + AVG(CASE WHEN "ID" IS NULL THEN 1.0 ELSE 0.0 END) AS missing_rate +FROM "c16" +GROUP BY "ALIVE" +ORDER BY missing_rate DESC, total_rows DESC; diff --git a/Query/sql/v2/runs/v2_cli_20260502_081223_d/c16/sql/v2q_c16_885f2d9787f8c80a.sql b/Query/sql/v2/runs/v2_cli_20260502_081223_d/c16/sql/v2q_c16_885f2d9787f8c80a.sql new file mode 100644 index 0000000000000000000000000000000000000000..5613baa94b9b7541059fc687a98603674029f59d --- /dev/null +++ b/Query/sql/v2/runs/v2_cli_20260502_081223_d/c16/sql/v2q_c16_885f2d9787f8c80a.sql @@ -0,0 +1,30 @@ +-- sql_source_version: v2 +-- sql_source_label: v2_current +-- sql_source_run_id: v2_cli_20260502_081223_d +-- sql_source_dataset_id: c16 +-- family_id: tail_rarity_structure +-- canonical_subitem_id: tail_mass_similarity +-- intended_facet_id: tail_ranked_signal +-- variant_semantic_role: filtered_stable_view +-- template_id: tpl_tpch_relative_total_threshold +-- query_record_id: v2q_c16_885f2d9787f8c80a +-- problem_id: v2p_c16_374146d46ba620a3 +-- realization_mode: agent +-- source_kind: agent +WITH grouped AS ( + SELECT "GSM", SUM(CAST("YEAR" AS INTEGER)) AS group_value + FROM "c16" + WHERE "GSM" IS NOT NULL + AND "GSM" <> '' + AND "YEAR" IS NOT NULL + AND "YEAR" <> '' + GROUP BY "GSM" +), total AS ( + SELECT SUM(group_value) AS total_value + FROM grouped +) +SELECT g."GSM", g.group_value +FROM grouped AS g +CROSS JOIN total AS t +WHERE g.group_value > t.total_value * 0.05 +ORDER BY g.group_value DESC; diff --git a/Query/sql/v2/runs/v2_cli_20260502_081223_d/c16/sql/v2q_c16_8877869618b52198.sql b/Query/sql/v2/runs/v2_cli_20260502_081223_d/c16/sql/v2q_c16_8877869618b52198.sql new file mode 100644 index 0000000000000000000000000000000000000000..fb82e282b03a863caa335797b78f9c0ba3c1d343 --- /dev/null +++ b/Query/sql/v2/runs/v2_cli_20260502_081223_d/c16/sql/v2q_c16_8877869618b52198.sql @@ -0,0 +1,20 @@ +-- sql_source_version: v2 +-- sql_source_label: v2_current +-- sql_source_run_id: v2_cli_20260502_081223_d +-- sql_source_dataset_id: c16 +-- family_id: tail_rarity_structure +-- canonical_subitem_id: tail_set_consistency +-- intended_facet_id: low_support_extremes +-- variant_semantic_role: rare_extreme_view +-- template_id: tpl_tail_low_support_group_count_v2 +-- query_record_id: v2q_c16_8877869618b52198 +-- problem_id: v2p_c16_bad884dade33e07b +-- realization_mode: agent +-- source_kind: agent +SELECT + "ALIGN", + COUNT(*) AS support +FROM "c16" +GROUP BY "ALIGN" +ORDER BY support ASC, "ALIGN" +LIMIT 11; diff --git a/Query/sql/v2/runs/v2_cli_20260502_081223_d/c16/sql/v2q_c16_88808374b1af8058.sql b/Query/sql/v2/runs/v2_cli_20260502_081223_d/c16/sql/v2q_c16_88808374b1af8058.sql new file mode 100644 index 0000000000000000000000000000000000000000..2de9b858a34820acfe1a0358dd85e5ab27fd64d3 --- /dev/null +++ b/Query/sql/v2/runs/v2_cli_20260502_081223_d/c16/sql/v2q_c16_88808374b1af8058.sql @@ -0,0 +1,21 @@ +-- sql_source_version: v2 +-- sql_source_label: v2_current +-- sql_source_run_id: v2_cli_20260502_081223_d +-- sql_source_dataset_id: c16 +-- family_id: missingness_structure +-- canonical_subitem_id: co_missingness_pattern_consistency +-- intended_facet_id: missing_rate_by_subgroup +-- variant_semantic_role: missing_rate_by_subgroup +-- template_id: tpl_missing_rate_by_subgroup +-- query_record_id: v2q_c16_88808374b1af8058 +-- problem_id: v2p_c16_fe48d7a085907321 +-- realization_mode: deterministic +-- source_kind: deterministic +SELECT + "ALIVE", + COUNT(*) AS total_rows, + SUM(CASE WHEN "ID" IS NULL THEN 1 ELSE 0 END) AS missing_rows, + AVG(CASE WHEN "ID" IS NULL THEN 1.0 ELSE 0.0 END) AS missing_rate +FROM "c16" +GROUP BY "ALIVE" +ORDER BY missing_rate DESC, total_rows DESC; diff --git a/Query/sql/v2/runs/v2_cli_20260502_081223_d/c16/sql/v2q_c16_89735d1b4b0c2723.sql b/Query/sql/v2/runs/v2_cli_20260502_081223_d/c16/sql/v2q_c16_89735d1b4b0c2723.sql new file mode 100644 index 0000000000000000000000000000000000000000..b41ff1a2437c8d7f0705680bd4d13be4286fcb1c --- /dev/null +++ b/Query/sql/v2/runs/v2_cli_20260502_081223_d/c16/sql/v2q_c16_89735d1b4b0c2723.sql @@ -0,0 +1,21 @@ +-- sql_source_version: v2 +-- sql_source_label: v2_current +-- sql_source_run_id: v2_cli_20260502_081223_d +-- sql_source_dataset_id: c16 +-- family_id: missingness_structure +-- canonical_subitem_id: co_missingness_pattern_consistency +-- intended_facet_id: missing_target_interaction +-- variant_semantic_role: missing_target_interaction +-- template_id: tpl_missing_target_interaction +-- query_record_id: v2q_c16_89735d1b4b0c2723 +-- problem_id: v2p_c16_22497030aee4dd2b +-- realization_mode: deterministic +-- source_kind: deterministic +SELECT + "ALIVE", + COUNT(*) AS total_rows, + SUM(CASE WHEN "ALIGN" IS NULL THEN 1 ELSE 0 END) AS missing_rows, + AVG(CASE WHEN "ALIGN" IS NULL THEN 1.0 ELSE 0.0 END) AS missing_rate +FROM "c16" +GROUP BY "ALIVE" +ORDER BY missing_rate DESC, total_rows DESC; diff --git a/Query/sql/v2/runs/v2_cli_20260502_081223_d/c16/sql/v2q_c16_8ad879c338b67fae.sql b/Query/sql/v2/runs/v2_cli_20260502_081223_d/c16/sql/v2q_c16_8ad879c338b67fae.sql new file mode 100644 index 0000000000000000000000000000000000000000..ea6b815732d21125c0d2b4223b194525ae173db9 --- /dev/null +++ b/Query/sql/v2/runs/v2_cli_20260502_081223_d/c16/sql/v2q_c16_8ad879c338b67fae.sql @@ -0,0 +1,17 @@ +-- sql_source_version: v2 +-- sql_source_label: v2_current +-- sql_source_run_id: v2_cli_20260502_081223_d +-- sql_source_dataset_id: c16 +-- family_id: subgroup_structure +-- canonical_subitem_id: internal_profile_stability +-- intended_facet_id: subgroup_rank_order +-- variant_semantic_role: collapsed_target_view +-- template_id: tpl_h2o_group_sum +-- query_record_id: v2q_c16_8ad879c338b67fae +-- problem_id: v2p_c16_2eae4ea8d94552c2 +-- realization_mode: agent +-- source_kind: agent +SELECT "ALIGN", SUM(CAST("page_id" AS INTEGER)) AS total_measure +FROM "c16" +GROUP BY "ALIGN" +ORDER BY total_measure DESC; diff --git a/Query/sql/v2/runs/v2_cli_20260502_081223_d/c16/sql/v2q_c16_8b64240a8d9755d1.sql b/Query/sql/v2/runs/v2_cli_20260502_081223_d/c16/sql/v2q_c16_8b64240a8d9755d1.sql new file mode 100644 index 0000000000000000000000000000000000000000..064308a49e41026aae15c127b237969088119653 --- /dev/null +++ b/Query/sql/v2/runs/v2_cli_20260502_081223_d/c16/sql/v2q_c16_8b64240a8d9755d1.sql @@ -0,0 +1,21 @@ +-- sql_source_version: v2 +-- sql_source_label: v2_current +-- sql_source_run_id: v2_cli_20260502_081223_d +-- sql_source_dataset_id: c16 +-- family_id: missingness_structure +-- canonical_subitem_id: co_missingness_pattern_consistency +-- intended_facet_id: missing_target_interaction +-- variant_semantic_role: missing_target_interaction +-- template_id: tpl_missing_target_interaction +-- query_record_id: v2q_c16_8b64240a8d9755d1 +-- problem_id: v2p_c16_0e8f93410a665d4a +-- realization_mode: deterministic +-- source_kind: deterministic +SELECT + "EYE", + COUNT(*) AS total_rows, + SUM(CASE WHEN "ALIVE" IS NULL THEN 1 ELSE 0 END) AS missing_rows, + AVG(CASE WHEN "ALIVE" IS NULL THEN 1.0 ELSE 0.0 END) AS missing_rate +FROM "c16" +GROUP BY "EYE" +ORDER BY missing_rate DESC, total_rows DESC; diff --git a/Query/sql/v2/runs/v2_cli_20260502_081223_d/c16/sql/v2q_c16_8b79b00c87ec0d7b.sql b/Query/sql/v2/runs/v2_cli_20260502_081223_d/c16/sql/v2q_c16_8b79b00c87ec0d7b.sql new file mode 100644 index 0000000000000000000000000000000000000000..a7a68737e251c311665cf57f13c12629798d0139 --- /dev/null +++ b/Query/sql/v2/runs/v2_cli_20260502_081223_d/c16/sql/v2q_c16_8b79b00c87ec0d7b.sql @@ -0,0 +1,27 @@ +-- sql_source_version: v2 +-- sql_source_label: v2_current +-- sql_source_run_id: v2_cli_20260502_081223_d +-- sql_source_dataset_id: c16 +-- family_id: tail_rarity_structure +-- canonical_subitem_id: tail_mass_similarity +-- intended_facet_id: tail_ranked_signal +-- variant_semantic_role: count_distribution +-- template_id: tpl_tpch_relative_total_threshold +-- query_record_id: v2q_c16_8b79b00c87ec0d7b +-- problem_id: v2p_c16_0fe3f5d31ceff292 +-- realization_mode: agent +-- source_kind: agent +WITH grouped AS ( + SELECT "YEAR", SUM(CAST(NULLIF("APPEARANCES", '') AS REAL)) AS "group_value" + FROM "c16" + WHERE NULLIF("YEAR", '') IS NOT NULL + GROUP BY "YEAR" +), total AS ( + SELECT SUM("group_value") AS "total_value" + FROM grouped +) +SELECT g."YEAR", g."group_value" +FROM grouped AS g +CROSS JOIN total AS t +WHERE g."group_value" > t."total_value" * 0.1 +ORDER BY g."group_value" DESC; diff --git a/Query/sql/v2/runs/v2_cli_20260502_081223_d/c16/sql/v2q_c16_920cd19b1487e3c8.sql b/Query/sql/v2/runs/v2_cli_20260502_081223_d/c16/sql/v2q_c16_920cd19b1487e3c8.sql new file mode 100644 index 0000000000000000000000000000000000000000..85567c21a02e2b2c6a2ae994321e801b1a764d06 --- /dev/null +++ b/Query/sql/v2/runs/v2_cli_20260502_081223_d/c16/sql/v2q_c16_920cd19b1487e3c8.sql @@ -0,0 +1,28 @@ +-- sql_source_version: v2 +-- sql_source_label: v2_current +-- sql_source_run_id: v2_cli_20260502_081223_d +-- sql_source_dataset_id: c16 +-- family_id: cardinality_structure +-- canonical_subitem_id: support_rank_profile_consistency +-- intended_facet_id: value_imbalance_profile +-- variant_semantic_role: ranked_signal_view +-- template_id: tpl_cardinality_distinct_share_profile +-- query_record_id: v2q_c16_920cd19b1487e3c8 +-- problem_id: v2p_c16_6cea0787ee2fe2e2 +-- realization_mode: deterministic +-- source_kind: deterministic +WITH grouped AS ( + SELECT "ALIVE" AS value_label, COUNT(*) AS support + FROM "c16" + GROUP BY "ALIVE" +), ranked AS ( + SELECT + value_label, + support, + CAST(support AS FLOAT) / NULLIF(SUM(support) OVER (), 0) AS support_share, + SUM(support) OVER (ORDER BY support DESC, value_label ROWS UNBOUNDED PRECEDING) AS cumulative_support + FROM grouped +) +SELECT * +FROM ranked +ORDER BY support DESC, value_label; diff --git a/Query/sql/v2/runs/v2_cli_20260502_081223_d/c16/sql/v2q_c16_93c9b539df822f11.sql b/Query/sql/v2/runs/v2_cli_20260502_081223_d/c16/sql/v2q_c16_93c9b539df822f11.sql new file mode 100644 index 0000000000000000000000000000000000000000..51fd8e61abb600b4aa9991505bce8c0845702391 --- /dev/null +++ b/Query/sql/v2/runs/v2_cli_20260502_081223_d/c16/sql/v2q_c16_93c9b539df822f11.sql @@ -0,0 +1,17 @@ +-- sql_source_version: v2 +-- sql_source_label: v2_current +-- sql_source_run_id: v2_cli_20260502_081223_d +-- sql_source_dataset_id: c16 +-- family_id: subgroup_structure +-- canonical_subitem_id: internal_profile_stability +-- intended_facet_id: subgroup_distribution_shift +-- variant_semantic_role: collapsed_target_view +-- template_id: tpl_h2o_group_sum +-- query_record_id: v2q_c16_93c9b539df822f11 +-- problem_id: v2p_c16_eb9ec507ce2f25de +-- realization_mode: agent +-- source_kind: agent +SELECT "YEAR", SUM(CAST("page_id" AS NUMERIC)) AS total_measure +FROM "c16" +GROUP BY "YEAR" +ORDER BY total_measure DESC; diff --git a/Query/sql/v2/runs/v2_cli_20260502_081223_d/c16/sql/v2q_c16_93ce16df734f5dee.sql b/Query/sql/v2/runs/v2_cli_20260502_081223_d/c16/sql/v2q_c16_93ce16df734f5dee.sql new file mode 100644 index 0000000000000000000000000000000000000000..a4156088804662966fef9d46c47719a9121e532d --- /dev/null +++ b/Query/sql/v2/runs/v2_cli_20260502_081223_d/c16/sql/v2q_c16_93ce16df734f5dee.sql @@ -0,0 +1,21 @@ +-- sql_source_version: v2 +-- sql_source_label: v2_current +-- sql_source_run_id: v2_cli_20260502_081223_d +-- sql_source_dataset_id: c16 +-- family_id: missingness_structure +-- canonical_subitem_id: co_missingness_pattern_consistency +-- intended_facet_id: missing_rate_by_subgroup +-- variant_semantic_role: missing_rate_by_subgroup +-- template_id: tpl_missing_rate_by_subgroup +-- query_record_id: v2q_c16_93ce16df734f5dee +-- problem_id: v2p_c16_a231de0ce5b60604 +-- realization_mode: deterministic +-- source_kind: deterministic +SELECT + "ALIGN", + COUNT(*) AS total_rows, + SUM(CASE WHEN "YEAR" IS NULL THEN 1 ELSE 0 END) AS missing_rows, + AVG(CASE WHEN "YEAR" IS NULL THEN 1.0 ELSE 0.0 END) AS missing_rate +FROM "c16" +GROUP BY "ALIGN" +ORDER BY missing_rate DESC, total_rows DESC; diff --git a/Query/sql/v2/runs/v2_cli_20260502_081223_d/c16/sql/v2q_c16_9be26297701a0308.sql b/Query/sql/v2/runs/v2_cli_20260502_081223_d/c16/sql/v2q_c16_9be26297701a0308.sql new file mode 100644 index 0000000000000000000000000000000000000000..fb1266ce181292acfab38729c933ad0e2d6d46b6 --- /dev/null +++ b/Query/sql/v2/runs/v2_cli_20260502_081223_d/c16/sql/v2q_c16_9be26297701a0308.sql @@ -0,0 +1,65 @@ +-- sql_source_version: v2 +-- sql_source_label: v2_current +-- sql_source_run_id: v2_cli_20260502_081223_d +-- sql_source_dataset_id: c16 +-- family_id: tail_rarity_structure +-- canonical_subitem_id: tail_concentration_consistency +-- intended_facet_id: rare_target_concentration +-- variant_semantic_role: focused_target_view +-- template_id: tpl_grouped_percentile_point +-- query_record_id: v2q_c16_9be26297701a0308 +-- problem_id: v2p_c16_d550ca1611f6f0c0 +-- realization_mode: agent +-- source_kind: agent +WITH "ranked" AS ( + SELECT + "SEX" AS "group_col", + CAST("page_id" AS REAL) AS "measure_value", + ROW_NUMBER() OVER ( + PARTITION BY "SEX" + ORDER BY CAST("page_id" AS REAL) + ) AS "rn", + COUNT(*) OVER ( + PARTITION BY "SEX" + ) AS "cnt" + FROM "c16" + WHERE "page_id" IS NOT NULL + AND TRIM("page_id") <> '' +), +"positions" AS ( + SELECT DISTINCT + "group_col", + (1.0 + 0.95 * ("cnt" - 1)) AS "pos", + CAST((1.0 + 0.95 * ("cnt" - 1)) AS INTEGER) AS "lower_rn", + CASE + WHEN (1.0 + 0.95 * ("cnt" - 1)) = CAST((1.0 + 0.95 * ("cnt" - 1)) AS INTEGER) + THEN CAST((1.0 + 0.95 * ("cnt" - 1)) AS INTEGER) + ELSE CAST((1.0 + 0.95 * ("cnt" - 1)) AS INTEGER) + 1 + END AS "upper_rn" + FROM "ranked" +), +"bounds" AS ( + SELECT + p."group_col", + p."pos", + p."lower_rn", + p."upper_rn", + MAX(CASE WHEN r."rn" = p."lower_rn" THEN r."measure_value" END) AS "lower_val", + MAX(CASE WHEN r."rn" = p."upper_rn" THEN r."measure_value" END) AS "upper_val" + FROM "positions" p + JOIN "ranked" r + ON r."group_col" IS p."group_col" + GROUP BY + p."group_col", + p."pos", + p."lower_rn", + p."upper_rn" +) +SELECT + "group_col" AS "SEX", + CASE + WHEN "lower_rn" = "upper_rn" THEN "lower_val" + ELSE "lower_val" + (("pos" - "lower_rn") * ("upper_val" - "lower_val")) + END AS "percentile_measure" +FROM "bounds" +ORDER BY "percentile_measure" DESC; diff --git a/Query/sql/v2/runs/v2_cli_20260502_081223_d/c16/sql/v2q_c16_9ca76e05b054e7cb.sql b/Query/sql/v2/runs/v2_cli_20260502_081223_d/c16/sql/v2q_c16_9ca76e05b054e7cb.sql new file mode 100644 index 0000000000000000000000000000000000000000..768382d67efdb3dfa715a512bb18a3092a01b7c8 --- /dev/null +++ b/Query/sql/v2/runs/v2_cli_20260502_081223_d/c16/sql/v2q_c16_9ca76e05b054e7cb.sql @@ -0,0 +1,30 @@ +-- sql_source_version: v2 +-- sql_source_label: v2_current +-- sql_source_run_id: v2_cli_20260502_081223_d +-- sql_source_dataset_id: c16 +-- family_id: tail_rarity_structure +-- canonical_subitem_id: tail_mass_similarity +-- intended_facet_id: tail_ranked_signal +-- variant_semantic_role: count_distribution +-- template_id: tpl_tpch_relative_total_threshold +-- query_record_id: v2q_c16_9ca76e05b054e7cb +-- problem_id: v2p_c16_363d28efa2da1690 +-- realization_mode: agent +-- source_kind: agent +WITH "grouped" AS ( + SELECT + "HAIR", + SUM(CAST(NULLIF("APPEARANCES", '') AS REAL)) AS "group_value" + FROM "c16" + GROUP BY "HAIR" +), "total" AS ( + SELECT SUM("group_value") AS "total_value" + FROM "grouped" +) +SELECT + g."HAIR", + g."group_value" +FROM "grouped" AS g +CROSS JOIN "total" AS t +WHERE g."group_value" > t."total_value" * 0.05 +ORDER BY g."group_value" DESC; diff --git a/Query/sql/v2/runs/v2_cli_20260502_081223_d/c16/sql/v2q_c16_9d07eb8cc2bcc3e1.sql b/Query/sql/v2/runs/v2_cli_20260502_081223_d/c16/sql/v2q_c16_9d07eb8cc2bcc3e1.sql new file mode 100644 index 0000000000000000000000000000000000000000..ad8b3077d6ebed7104f963e922996267e1f6a977 --- /dev/null +++ b/Query/sql/v2/runs/v2_cli_20260502_081223_d/c16/sql/v2q_c16_9d07eb8cc2bcc3e1.sql @@ -0,0 +1,76 @@ +-- sql_source_version: v2 +-- sql_source_label: v2_current +-- sql_source_run_id: v2_cli_20260502_081223_d +-- sql_source_dataset_id: c16 +-- family_id: tail_rarity_structure +-- canonical_subitem_id: tail_concentration_consistency +-- intended_facet_id: rare_target_concentration +-- variant_semantic_role: ranked_signal_view +-- template_id: tpl_grouped_percentile_point +-- query_record_id: v2q_c16_9d07eb8cc2bcc3e1 +-- problem_id: v2p_c16_69db1c7ebf252b5b +-- realization_mode: agent +-- source_kind: agent +WITH "base" AS ( + SELECT + "ALIGN" AS "group_col", + CAST("page_id" AS REAL) AS "measure_col" + FROM "c16" + WHERE "ALIGN" IS NOT NULL + AND TRIM("ALIGN") <> '' + AND "page_id" IS NOT NULL + AND TRIM("page_id") <> '' +), +"counts" AS ( + SELECT + "group_col", + COUNT(*) AS "cnt" + FROM "base" + GROUP BY "group_col" +), +"params" AS ( + SELECT + "group_col", + "cnt", + (1.0 + 0.9 * ("cnt" - 1)) AS "pos", + CAST((1.0 + 0.9 * ("cnt" - 1)) AS INTEGER) AS "lower_rn", + CASE + WHEN (1.0 + 0.9 * ("cnt" - 1)) > CAST((1.0 + 0.9 * ("cnt" - 1)) AS INTEGER) + THEN CAST((1.0 + 0.9 * ("cnt" - 1)) AS INTEGER) + 1 + ELSE CAST((1.0 + 0.9 * ("cnt" - 1)) AS INTEGER) + END AS "upper_rn" + FROM "counts" +), +"ranked" AS ( + SELECT + "group_col", + "measure_col", + ROW_NUMBER() OVER ( + PARTITION BY "group_col" + ORDER BY "measure_col" + ) AS "rn" + FROM "base" +) +SELECT + "p"."group_col" AS "ALIGN", + CASE + WHEN "p"."lower_rn" = "p"."upper_rn" THEN + MAX(CASE WHEN "r"."rn" = "p"."lower_rn" THEN "r"."measure_col" END) + ELSE + MAX(CASE WHEN "r"."rn" = "p"."lower_rn" THEN "r"."measure_col" END) + + ("p"."pos" - "p"."lower_rn") * + ( + MAX(CASE WHEN "r"."rn" = "p"."upper_rn" THEN "r"."measure_col" END) - + MAX(CASE WHEN "r"."rn" = "p"."lower_rn" THEN "r"."measure_col" END) + ) + END AS "percentile_measure" +FROM "params" AS "p" +JOIN "ranked" AS "r" + ON "r"."group_col" = "p"."group_col" + AND "r"."rn" IN ("p"."lower_rn", "p"."upper_rn") +GROUP BY + "p"."group_col", + "p"."pos", + "p"."lower_rn", + "p"."upper_rn" +ORDER BY "percentile_measure" DESC; diff --git a/Query/sql/v2/runs/v2_cli_20260502_081223_d/c16/sql/v2q_c16_9d4fae7ea34359cb.sql b/Query/sql/v2/runs/v2_cli_20260502_081223_d/c16/sql/v2q_c16_9d4fae7ea34359cb.sql new file mode 100644 index 0000000000000000000000000000000000000000..6cf98dd06018f0588650b76802b9e5c6e9050f1a --- /dev/null +++ b/Query/sql/v2/runs/v2_cli_20260502_081223_d/c16/sql/v2q_c16_9d4fae7ea34359cb.sql @@ -0,0 +1,18 @@ +-- sql_source_version: v2 +-- sql_source_label: v2_current +-- sql_source_run_id: v2_cli_20260502_081223_d +-- sql_source_dataset_id: c16 +-- family_id: missingness_structure +-- canonical_subitem_id: marginal_missing_rate_consistency +-- intended_facet_id: missing_indicator_distribution +-- variant_semantic_role: missing_indicator_view +-- template_id: tpl_missing_marginal_rate_profile +-- query_record_id: v2q_c16_9d4fae7ea34359cb +-- problem_id: v2p_c16_c0b792ab943bcced +-- realization_mode: deterministic +-- source_kind: deterministic +SELECT + COUNT(*) AS total_rows, + SUM(CASE WHEN "SEX" IS NULL THEN 1 ELSE 0 END) AS missing_rows, + AVG(CASE WHEN "SEX" IS NULL THEN 1.0 ELSE 0.0 END) AS missing_rate +FROM "c16"; diff --git a/Query/sql/v2/runs/v2_cli_20260502_081223_d/c16/sql/v2q_c16_9eaa6b1188cd150b.sql b/Query/sql/v2/runs/v2_cli_20260502_081223_d/c16/sql/v2q_c16_9eaa6b1188cd150b.sql new file mode 100644 index 0000000000000000000000000000000000000000..b97211e4bb0588c9054258954ab32d5acaff61dc --- /dev/null +++ b/Query/sql/v2/runs/v2_cli_20260502_081223_d/c16/sql/v2q_c16_9eaa6b1188cd150b.sql @@ -0,0 +1,24 @@ +-- sql_source_version: v2 +-- sql_source_label: v2_current +-- sql_source_run_id: v2_cli_20260502_081223_d +-- sql_source_dataset_id: c16 +-- family_id: conditional_dependency_structure +-- canonical_subitem_id: direction_consistency +-- intended_facet_id: conditional_rate_shift +-- variant_semantic_role: contrastive_conditional_view +-- template_id: tpl_m4_group_ratio_two_conditions +-- query_record_id: v2q_c16_9eaa6b1188cd150b +-- problem_id: v2p_c16_fc23db917ca55442 +-- realization_mode: agent +-- source_kind: agent +WITH grouped AS ( + SELECT "ALIVE", + SUM(CASE WHEN "GSM" = '' THEN 1 ELSE 0 END) AS numerator_count, + SUM(CASE WHEN "GSM" = 'Homosexual Characters' THEN 1 ELSE 0 END) AS denominator_count + FROM "c16" + GROUP BY "ALIVE" +) +SELECT "ALIVE", + CAST(numerator_count AS FLOAT) / NULLIF(denominator_count, 0) AS condition_ratio +FROM grouped +ORDER BY condition_ratio DESC; diff --git a/Query/sql/v2/runs/v2_cli_20260502_081223_d/c16/sql/v2q_c16_9f4df9e0d5803dc1.sql b/Query/sql/v2/runs/v2_cli_20260502_081223_d/c16/sql/v2q_c16_9f4df9e0d5803dc1.sql new file mode 100644 index 0000000000000000000000000000000000000000..48dc6bb0e3a244a9cc368afa353228f589a2bdc3 --- /dev/null +++ b/Query/sql/v2/runs/v2_cli_20260502_081223_d/c16/sql/v2q_c16_9f4df9e0d5803dc1.sql @@ -0,0 +1,18 @@ +-- sql_source_version: v2 +-- sql_source_label: v2_current +-- sql_source_run_id: v2_cli_20260502_081223_d +-- sql_source_dataset_id: c16 +-- family_id: missingness_structure +-- canonical_subitem_id: marginal_missing_rate_consistency +-- intended_facet_id: missing_indicator_distribution +-- variant_semantic_role: missing_indicator_view +-- template_id: tpl_missing_marginal_rate_profile +-- query_record_id: v2q_c16_9f4df9e0d5803dc1 +-- problem_id: v2p_c16_aa785cdabaf58dd9 +-- realization_mode: deterministic +-- source_kind: deterministic +SELECT + COUNT(*) AS total_rows, + SUM(CASE WHEN "ALIVE" IS NULL THEN 1 ELSE 0 END) AS missing_rows, + AVG(CASE WHEN "ALIVE" IS NULL THEN 1.0 ELSE 0.0 END) AS missing_rate +FROM "c16"; diff --git a/Query/sql/v2/runs/v2_cli_20260502_081223_d/c16/sql/v2q_c16_a004ced42f872c22.sql b/Query/sql/v2/runs/v2_cli_20260502_081223_d/c16/sql/v2q_c16_a004ced42f872c22.sql new file mode 100644 index 0000000000000000000000000000000000000000..78ead8c84d1f437c8419d988f5862fca2c30f53f --- /dev/null +++ b/Query/sql/v2/runs/v2_cli_20260502_081223_d/c16/sql/v2q_c16_a004ced42f872c22.sql @@ -0,0 +1,25 @@ +-- sql_source_version: v2 +-- sql_source_label: v2_current +-- sql_source_run_id: v2_cli_20260502_081223_d +-- sql_source_dataset_id: c16 +-- family_id: conditional_dependency_structure +-- canonical_subitem_id: dependency_strength_similarity +-- intended_facet_id: pairwise_conditional_dependency +-- variant_semantic_role: within_group_proportion +-- template_id: tpl_tpcds_within_group_share +-- query_record_id: v2q_c16_a004ced42f872c22 +-- problem_id: v2p_c16_2e23ccc6b272f7a7 +-- realization_mode: agent +-- source_kind: agent +SELECT + "GSM", + "urlslug", + SUM(CAST("APPEARANCES" AS REAL)) AS total_measure, + SUM(CAST("APPEARANCES" AS REAL)) * 100.0 + / SUM(SUM(CAST("APPEARANCES" AS REAL))) OVER (PARTITION BY "GSM") AS share_within_group +FROM "c16" +WHERE "APPEARANCES" IS NOT NULL + AND TRIM("APPEARANCES") <> '' +GROUP BY "GSM", "urlslug" +ORDER BY share_within_group DESC +LIMIT 15; diff --git a/Query/sql/v2/runs/v2_cli_20260502_081223_d/c16/sql/v2q_c16_a0d4f08848c08b0c.sql b/Query/sql/v2/runs/v2_cli_20260502_081223_d/c16/sql/v2q_c16_a0d4f08848c08b0c.sql new file mode 100644 index 0000000000000000000000000000000000000000..738be659bb256323c84c083545517325eb8e7d00 --- /dev/null +++ b/Query/sql/v2/runs/v2_cli_20260502_081223_d/c16/sql/v2q_c16_a0d4f08848c08b0c.sql @@ -0,0 +1,17 @@ +-- sql_source_version: v2 +-- sql_source_label: v2_current +-- sql_source_run_id: v2_cli_20260502_081223_d +-- sql_source_dataset_id: c16 +-- family_id: subgroup_structure +-- canonical_subitem_id: internal_profile_stability +-- intended_facet_id: subgroup_rank_order +-- variant_semantic_role: collapsed_target_view +-- template_id: tpl_h2o_group_sum +-- query_record_id: v2q_c16_a0d4f08848c08b0c +-- problem_id: v2p_c16_c7dc33fda1b1e4aa +-- realization_mode: agent +-- source_kind: agent +SELECT "YEAR", SUM(CAST("page_id" AS NUMERIC)) AS total_measure +FROM "c16" +GROUP BY "YEAR" +ORDER BY total_measure DESC; diff --git a/Query/sql/v2/runs/v2_cli_20260502_081223_d/c16/sql/v2q_c16_a352472f18a4ebc2.sql b/Query/sql/v2/runs/v2_cli_20260502_081223_d/c16/sql/v2q_c16_a352472f18a4ebc2.sql new file mode 100644 index 0000000000000000000000000000000000000000..aec7c9af74e8808e2c9ad6a47c60197508c031f0 --- /dev/null +++ b/Query/sql/v2/runs/v2_cli_20260502_081223_d/c16/sql/v2q_c16_a352472f18a4ebc2.sql @@ -0,0 +1,17 @@ +-- sql_source_version: v2 +-- sql_source_label: v2_current +-- sql_source_run_id: v2_cli_20260502_081223_d +-- sql_source_dataset_id: c16 +-- family_id: subgroup_structure +-- canonical_subitem_id: subgroup_size_stability +-- intended_facet_id: subgroup_distribution_shift +-- variant_semantic_role: count_distribution +-- template_id: tpl_clickbench_group_count +-- query_record_id: v2q_c16_a352472f18a4ebc2 +-- problem_id: v2p_c16_bcb26334ef646b73 +-- realization_mode: agent +-- source_kind: agent +SELECT "YEAR", COUNT(*) AS row_count +FROM "c16" +GROUP BY "YEAR" +ORDER BY row_count DESC; diff --git a/Query/sql/v2/runs/v2_cli_20260502_081223_d/c16/sql/v2q_c16_a57a99e2cc984fa9.sql b/Query/sql/v2/runs/v2_cli_20260502_081223_d/c16/sql/v2q_c16_a57a99e2cc984fa9.sql new file mode 100644 index 0000000000000000000000000000000000000000..b379575732240c5e685ea3481b882f6b6c0dfc93 --- /dev/null +++ b/Query/sql/v2/runs/v2_cli_20260502_081223_d/c16/sql/v2q_c16_a57a99e2cc984fa9.sql @@ -0,0 +1,24 @@ +-- sql_source_version: v2 +-- sql_source_label: v2_current +-- sql_source_run_id: v2_cli_20260502_081223_d +-- sql_source_dataset_id: c16 +-- family_id: tail_rarity_structure +-- canonical_subitem_id: tail_set_consistency +-- intended_facet_id: low_support_extremes +-- variant_semantic_role: rare_extreme_view +-- template_id: tpl_m4_quantile_tail_slice +-- query_record_id: v2q_c16_a57a99e2cc984fa9 +-- problem_id: v2p_c16_efbf1508a88e2a0e +-- realization_mode: agent +-- source_kind: agent +WITH "buckets" AS ( + SELECT + "page_id", + NTILE(10) OVER (ORDER BY CAST("page_id" AS INTEGER) DESC) AS "tail_bucket" + FROM "c16" + WHERE "page_id" IS NOT NULL AND TRIM("page_id") <> '' +) +SELECT "page_id" +FROM "buckets" +WHERE "tail_bucket" = 1 +ORDER BY CAST("page_id" AS INTEGER) DESC; diff --git a/Query/sql/v2/runs/v2_cli_20260502_081223_d/c16/sql/v2q_c16_a8f824bd83c590bb.sql b/Query/sql/v2/runs/v2_cli_20260502_081223_d/c16/sql/v2q_c16_a8f824bd83c590bb.sql new file mode 100644 index 0000000000000000000000000000000000000000..bf0020a409a7e0b994f9e97f5b9c8a2289768670 --- /dev/null +++ b/Query/sql/v2/runs/v2_cli_20260502_081223_d/c16/sql/v2q_c16_a8f824bd83c590bb.sql @@ -0,0 +1,21 @@ +-- sql_source_version: v2 +-- sql_source_label: v2_current +-- sql_source_run_id: v2_cli_20260502_081223_d +-- sql_source_dataset_id: c16 +-- family_id: conditional_dependency_structure +-- canonical_subitem_id: direction_consistency +-- intended_facet_id: conditional_rate_shift +-- variant_semantic_role: focused_target_view +-- template_id: tpl_m4_group_condition_rate +-- query_record_id: v2q_c16_a8f824bd83c590bb +-- problem_id: v2p_c16_256ac39bb46557a9 +-- realization_mode: agent +-- source_kind: agent +SELECT + "YEAR", + AVG(CASE WHEN "ID" = 'Public Identity' THEN 1.0 ELSE 0.0 END) AS "condition_rate" +FROM "c16" +WHERE "YEAR" IS NOT NULL AND "YEAR" <> '' +GROUP BY "YEAR" +HAVING COUNT(*) >= 5 +ORDER BY "condition_rate" DESC; diff --git a/Query/sql/v2/runs/v2_cli_20260502_081223_d/c16/sql/v2q_c16_a9679a306c8b7605.sql b/Query/sql/v2/runs/v2_cli_20260502_081223_d/c16/sql/v2q_c16_a9679a306c8b7605.sql new file mode 100644 index 0000000000000000000000000000000000000000..bd0464b39333fb320980e35f636d2858960ade4f --- /dev/null +++ b/Query/sql/v2/runs/v2_cli_20260502_081223_d/c16/sql/v2q_c16_a9679a306c8b7605.sql @@ -0,0 +1,26 @@ +-- sql_source_version: v2 +-- sql_source_label: v2_current +-- sql_source_run_id: v2_cli_20260502_081223_d +-- sql_source_dataset_id: c16 +-- family_id: tail_rarity_structure +-- canonical_subitem_id: tail_mass_similarity +-- intended_facet_id: tail_ranked_signal +-- variant_semantic_role: count_distribution +-- template_id: tpl_tpch_relative_total_threshold +-- query_record_id: v2q_c16_a9679a306c8b7605 +-- problem_id: v2p_c16_6cb55031d91454f2 +-- realization_mode: agent +-- source_kind: agent +WITH grouped AS ( + SELECT "ALIGN", SUM(CAST(NULLIF("YEAR", '') AS NUMERIC)) AS group_value + FROM "c16" + GROUP BY "ALIGN" +), total AS ( + SELECT SUM(group_value) AS total_value + FROM grouped +) +SELECT g."ALIGN", g.group_value +FROM grouped AS g +CROSS JOIN total AS t +WHERE g.group_value > t.total_value * 0.05 +ORDER BY g.group_value DESC; diff --git a/Query/sql/v2/runs/v2_cli_20260502_081223_d/c16/sql/v2q_c16_aa3a665613efb87f.sql b/Query/sql/v2/runs/v2_cli_20260502_081223_d/c16/sql/v2q_c16_aa3a665613efb87f.sql new file mode 100644 index 0000000000000000000000000000000000000000..3bb4d22a95f7d4a17ac94be0262b9d6337fe2378 --- /dev/null +++ b/Query/sql/v2/runs/v2_cli_20260502_081223_d/c16/sql/v2q_c16_aa3a665613efb87f.sql @@ -0,0 +1,66 @@ +-- sql_source_version: v2 +-- sql_source_label: v2_current +-- sql_source_run_id: v2_cli_20260502_081223_d +-- sql_source_dataset_id: c16 +-- family_id: tail_rarity_structure +-- canonical_subitem_id: tail_concentration_consistency +-- intended_facet_id: rare_target_concentration +-- variant_semantic_role: ranked_signal_view +-- template_id: tpl_grouped_percentile_point +-- query_record_id: v2q_c16_aa3a665613efb87f +-- problem_id: v2p_c16_2f2f5e31360969e1 +-- realization_mode: agent +-- source_kind: agent +WITH "__base" AS ( + SELECT + "SEX" AS "SEX", + CAST("page_id" AS REAL) AS "measure" + FROM "c16" + WHERE "SEX" IS NOT NULL + AND TRIM("SEX") <> '' + AND "page_id" IS NOT NULL + AND TRIM("page_id") <> '' +), +"__ordered" AS ( + SELECT + "SEX", + "measure", + ROW_NUMBER() OVER (PARTITION BY "SEX" ORDER BY "measure") AS "rn", + COUNT(*) OVER (PARTITION BY "SEX") AS "cnt" + FROM "__base" +), +"__positions" AS ( + SELECT + "SEX", + (0.9 * ("cnt" - 1) + 1.0) AS "pos", + CAST((0.9 * ("cnt" - 1) + 1.0) AS INTEGER) AS "lo_rn", + CASE + WHEN (0.9 * ("cnt" - 1) + 1.0) > CAST((0.9 * ("cnt" - 1) + 1.0) AS INTEGER) + THEN CAST((0.9 * ("cnt" - 1) + 1.0) AS INTEGER) + 1 + ELSE CAST((0.9 * ("cnt" - 1) + 1.0) AS INTEGER) + END AS "hi_rn" + FROM "__ordered" + GROUP BY "SEX", "cnt" +), +"__picked" AS ( + SELECT + p."SEX", + p."pos", + p."lo_rn", + p."hi_rn", + MAX(CASE WHEN o."rn" = p."lo_rn" THEN o."measure" END) AS "lo_val", + MAX(CASE WHEN o."rn" = p."hi_rn" THEN o."measure" END) AS "hi_val" + FROM "__positions" AS p + JOIN "__ordered" AS o + ON o."SEX" = p."SEX" + GROUP BY p."SEX", p."pos", p."lo_rn", p."hi_rn" +) +SELECT + "SEX", + CASE + WHEN "lo_val" IS NULL THEN NULL + WHEN "hi_val" IS NULL OR "hi_rn" = "lo_rn" THEN "lo_val" + ELSE "lo_val" + ("pos" - "lo_rn") * ("hi_val" - "lo_val") + END AS "percentile_measure" +FROM "__picked" +ORDER BY "percentile_measure" DESC, "SEX"; diff --git a/Query/sql/v2/runs/v2_cli_20260502_081223_d/c16/sql/v2q_c16_ad2acb4bcf164394.sql b/Query/sql/v2/runs/v2_cli_20260502_081223_d/c16/sql/v2q_c16_ad2acb4bcf164394.sql new file mode 100644 index 0000000000000000000000000000000000000000..1d1276578dd3fe431758a1c17d94a93b276fc086 --- /dev/null +++ b/Query/sql/v2/runs/v2_cli_20260502_081223_d/c16/sql/v2q_c16_ad2acb4bcf164394.sql @@ -0,0 +1,28 @@ +-- sql_source_version: v2 +-- sql_source_label: v2_current +-- sql_source_run_id: v2_cli_20260502_081223_d +-- sql_source_dataset_id: c16 +-- family_id: cardinality_structure +-- canonical_subitem_id: support_rank_profile_consistency +-- intended_facet_id: support_concentration +-- variant_semantic_role: ranked_signal_view +-- template_id: tpl_cardinality_distinct_share_profile +-- query_record_id: v2q_c16_ad2acb4bcf164394 +-- problem_id: v2p_c16_f8949d2c94e6cfd7 +-- realization_mode: deterministic +-- source_kind: deterministic +WITH grouped AS ( + SELECT "YEAR" AS value_label, COUNT(*) AS support + FROM "c16" + GROUP BY "YEAR" +), ranked AS ( + SELECT + value_label, + support, + CAST(support AS FLOAT) / NULLIF(SUM(support) OVER (), 0) AS support_share, + SUM(support) OVER (ORDER BY support DESC, value_label ROWS UNBOUNDED PRECEDING) AS cumulative_support + FROM grouped +) +SELECT * +FROM ranked +ORDER BY support DESC, value_label; diff --git a/Query/sql/v2/runs/v2_cli_20260502_081223_d/c16/sql/v2q_c16_aebcbf58f3ed0f1d.sql b/Query/sql/v2/runs/v2_cli_20260502_081223_d/c16/sql/v2q_c16_aebcbf58f3ed0f1d.sql new file mode 100644 index 0000000000000000000000000000000000000000..d1634036b7a19860f64b50fd5915ce4d7c840ca3 --- /dev/null +++ b/Query/sql/v2/runs/v2_cli_20260502_081223_d/c16/sql/v2q_c16_aebcbf58f3ed0f1d.sql @@ -0,0 +1,15 @@ +-- sql_source_version: v2 +-- sql_source_label: v2_current +-- sql_source_run_id: v2_cli_20260502_081223_d +-- sql_source_dataset_id: c16 +-- family_id: tail_rarity_structure +-- canonical_subitem_id: tail_set_consistency +-- intended_facet_id: low_support_extremes +-- variant_semantic_role: rare_extreme_view +-- template_id: tpl_threshold_rarity_cdf +-- query_record_id: v2q_c16_aebcbf58f3ed0f1d +-- problem_id: v2p_c16_10c476d845999365 +-- realization_mode: agent +-- source_kind: agent +SELECT AVG(CASE WHEN CAST("YEAR" AS REAL) <= 2003.0 THEN 1 ELSE 0 END) AS "empirical_cdf_at_threshold" +FROM "c16"; diff --git a/Query/sql/v2/runs/v2_cli_20260502_081223_d/c16/sql/v2q_c16_aef8d7d2bdd47583.sql b/Query/sql/v2/runs/v2_cli_20260502_081223_d/c16/sql/v2q_c16_aef8d7d2bdd47583.sql new file mode 100644 index 0000000000000000000000000000000000000000..8eedbc47ca0da5ebb65553e9b49cbd973f2a6023 --- /dev/null +++ b/Query/sql/v2/runs/v2_cli_20260502_081223_d/c16/sql/v2q_c16_aef8d7d2bdd47583.sql @@ -0,0 +1,20 @@ +-- sql_source_version: v2 +-- sql_source_label: v2_current +-- sql_source_run_id: v2_cli_20260502_081223_d +-- sql_source_dataset_id: c16 +-- family_id: tail_rarity_structure +-- canonical_subitem_id: tail_mass_similarity +-- intended_facet_id: tail_ranked_signal +-- variant_semantic_role: rare_extreme_view +-- template_id: tpl_tail_low_support_group_count_v2 +-- query_record_id: v2q_c16_aef8d7d2bdd47583 +-- problem_id: v2p_c16_35505d9acb5c0852 +-- realization_mode: agent +-- source_kind: agent +SELECT + "YEAR", + COUNT(*) AS support +FROM "c16" +GROUP BY "YEAR" +ORDER BY support ASC, "YEAR" +LIMIT 15; diff --git a/Query/sql/v2/runs/v2_cli_20260502_081223_d/c16/sql/v2q_c16_afdd6155facfb970.sql b/Query/sql/v2/runs/v2_cli_20260502_081223_d/c16/sql/v2q_c16_afdd6155facfb970.sql new file mode 100644 index 0000000000000000000000000000000000000000..5361840487365ddde154f5a56a7a51f6d55de063 --- /dev/null +++ b/Query/sql/v2/runs/v2_cli_20260502_081223_d/c16/sql/v2q_c16_afdd6155facfb970.sql @@ -0,0 +1,17 @@ +-- sql_source_version: v2 +-- sql_source_label: v2_current +-- sql_source_run_id: v2_cli_20260502_081223_d +-- sql_source_dataset_id: c16 +-- family_id: subgroup_structure +-- canonical_subitem_id: subgroup_size_stability +-- intended_facet_id: subgroup_distribution_shift +-- variant_semantic_role: count_distribution +-- template_id: tpl_clickbench_group_count +-- query_record_id: v2q_c16_afdd6155facfb970 +-- problem_id: v2p_c16_0823595fbb0bc423 +-- realization_mode: agent +-- source_kind: agent +SELECT "GSM", COUNT(*) AS row_count +FROM "c16" +GROUP BY "GSM" +ORDER BY row_count DESC; diff --git a/Query/sql/v2/runs/v2_cli_20260502_081223_d/c16/sql/v2q_c16_b01f2941089db58d.sql b/Query/sql/v2/runs/v2_cli_20260502_081223_d/c16/sql/v2q_c16_b01f2941089db58d.sql new file mode 100644 index 0000000000000000000000000000000000000000..68456ac6e0c6ec133d300ee433a3de91048e31f9 --- /dev/null +++ b/Query/sql/v2/runs/v2_cli_20260502_081223_d/c16/sql/v2q_c16_b01f2941089db58d.sql @@ -0,0 +1,59 @@ +-- sql_source_version: v2 +-- sql_source_label: v2_current +-- sql_source_run_id: v2_cli_20260502_081223_d +-- sql_source_dataset_id: c16 +-- family_id: tail_rarity_structure +-- canonical_subitem_id: tail_concentration_consistency +-- intended_facet_id: rare_target_concentration +-- variant_semantic_role: focused_target_view +-- template_id: tpl_grouped_percentile_point +-- query_record_id: v2q_c16_b01f2941089db58d +-- problem_id: v2p_c16_a0cf45880f57018c +-- realization_mode: agent +-- source_kind: agent +WITH "base" AS ( + SELECT + "EYE" AS "eye_group", + CAST(TRIM("YEAR") AS REAL) AS "year_value" + FROM "c16" + WHERE "EYE" IS NOT NULL + AND TRIM("EYE") <> '' + AND TRIM("YEAR") GLOB '[0-9][0-9][0-9][0-9]' +), +"ranked" AS ( + SELECT + "eye_group", + "year_value", + ROW_NUMBER() OVER (PARTITION BY "eye_group" ORDER BY "year_value") AS "rn", + COUNT(*) OVER (PARTITION BY "eye_group") AS "n" + FROM "base" +), +"params" AS ( + SELECT + "eye_group", + MIN("n") AS "n", + (95 * MIN("n") + 5) AS "num" + FROM "ranked" + GROUP BY "eye_group" + HAVING MIN("n") >= 5 +), +"picked" AS ( + SELECT + p."eye_group", + MAX(CASE WHEN r."rn" = CAST(p."num" / 100 AS INTEGER) THEN r."year_value" END) AS "lo_val", + MAX(CASE WHEN r."rn" = CAST(p."num" / 100 AS INTEGER) + CASE WHEN (p."num" % 100) = 0 THEN 0 ELSE 1 END THEN r."year_value" END) AS "hi_val", + ((p."num" % 100) / 100.0) AS "frac" + FROM "params" AS p + JOIN "ranked" AS r + ON r."eye_group" = p."eye_group" + GROUP BY p."eye_group", p."num" +) +SELECT + "eye_group" AS "EYE", + CASE + WHEN "lo_val" IS NULL OR "hi_val" IS NULL THEN NULL + WHEN "frac" = 0 THEN "lo_val" + ELSE "lo_val" + "frac" * ("hi_val" - "lo_val") + END AS "percentile_measure" +FROM "picked" +ORDER BY "percentile_measure" DESC, "EYE" ASC; diff --git a/Query/sql/v2/runs/v2_cli_20260502_081223_d/c16/sql/v2q_c16_b14901b0b3adc6c0.sql b/Query/sql/v2/runs/v2_cli_20260502_081223_d/c16/sql/v2q_c16_b14901b0b3adc6c0.sql new file mode 100644 index 0000000000000000000000000000000000000000..f5da6ad4e98d8327b81cb5a4edf39da6b3696163 --- /dev/null +++ b/Query/sql/v2/runs/v2_cli_20260502_081223_d/c16/sql/v2q_c16_b14901b0b3adc6c0.sql @@ -0,0 +1,18 @@ +-- sql_source_version: v2 +-- sql_source_label: v2_current +-- sql_source_run_id: v2_cli_20260502_081223_d +-- sql_source_dataset_id: c16 +-- family_id: conditional_dependency_structure +-- canonical_subitem_id: direction_consistency +-- intended_facet_id: conditional_rate_shift +-- variant_semantic_role: within_group_proportion +-- template_id: tpl_m4_group_condition_rate +-- query_record_id: v2q_c16_b14901b0b3adc6c0 +-- problem_id: v2p_c16_b67868ecc2474114 +-- realization_mode: agent +-- source_kind: agent +SELECT "SEX", + AVG(CASE WHEN "ALIVE" = 'Deceased Characters' THEN 1.0 ELSE 0.0 END) AS "condition_rate" +FROM "c16" +GROUP BY "SEX" +ORDER BY "condition_rate" DESC; diff --git a/Query/sql/v2/runs/v2_cli_20260502_081223_d/c16/sql/v2q_c16_b1fddc07a4b0cbe3.sql b/Query/sql/v2/runs/v2_cli_20260502_081223_d/c16/sql/v2q_c16_b1fddc07a4b0cbe3.sql new file mode 100644 index 0000000000000000000000000000000000000000..fb4934e4b23de87aeb19623ea5fb6af9c6c26ef4 --- /dev/null +++ b/Query/sql/v2/runs/v2_cli_20260502_081223_d/c16/sql/v2q_c16_b1fddc07a4b0cbe3.sql @@ -0,0 +1,35 @@ +-- sql_source_version: v2 +-- sql_source_label: v2_current +-- sql_source_run_id: v2_cli_20260502_081223_d +-- sql_source_dataset_id: c16 +-- family_id: tail_rarity_structure +-- canonical_subitem_id: tail_mass_similarity +-- intended_facet_id: tail_ranked_signal +-- variant_semantic_role: filtered_stable_view +-- template_id: tpl_tpch_relative_total_threshold +-- query_record_id: v2q_c16_b1fddc07a4b0cbe3 +-- problem_id: v2p_c16_5b768ea73247aa10 +-- realization_mode: agent +-- source_kind: agent +WITH "grouped" AS ( + SELECT + "SEX", + SUM(CAST(NULLIF("YEAR", '') AS REAL)) AS "group_value" + FROM "c16" + WHERE "SEX" IS NOT NULL + AND TRIM("SEX") <> '' + AND "YEAR" IS NOT NULL + AND TRIM("YEAR") <> '' + GROUP BY "SEX" +), +"total" AS ( + SELECT SUM("group_value") AS "total_value" + FROM "grouped" +) +SELECT + g."SEX", + g."group_value" +FROM "grouped" AS g +CROSS JOIN "total" AS t +WHERE g."group_value" > t."total_value" * 0.05 +ORDER BY g."group_value" DESC; diff --git a/Query/sql/v2/runs/v2_cli_20260502_081223_d/c16/sql/v2q_c16_b27a6ec3b48a2b57.sql b/Query/sql/v2/runs/v2_cli_20260502_081223_d/c16/sql/v2q_c16_b27a6ec3b48a2b57.sql new file mode 100644 index 0000000000000000000000000000000000000000..3f303833203478ee4669794a5ad07d3a8bc11f5f --- /dev/null +++ b/Query/sql/v2/runs/v2_cli_20260502_081223_d/c16/sql/v2q_c16_b27a6ec3b48a2b57.sql @@ -0,0 +1,21 @@ +-- sql_source_version: v2 +-- sql_source_label: v2_current +-- sql_source_run_id: v2_cli_20260502_081223_d +-- sql_source_dataset_id: c16 +-- family_id: missingness_structure +-- canonical_subitem_id: co_missingness_pattern_consistency +-- intended_facet_id: missing_target_interaction +-- variant_semantic_role: missing_target_interaction +-- template_id: tpl_missing_target_interaction +-- query_record_id: v2q_c16_b27a6ec3b48a2b57 +-- problem_id: v2p_c16_5933fb232a64f637 +-- realization_mode: deterministic +-- source_kind: deterministic +SELECT + "SEX", + COUNT(*) AS total_rows, + SUM(CASE WHEN "HAIR" IS NULL THEN 1 ELSE 0 END) AS missing_rows, + AVG(CASE WHEN "HAIR" IS NULL THEN 1.0 ELSE 0.0 END) AS missing_rate +FROM "c16" +GROUP BY "SEX" +ORDER BY missing_rate DESC, total_rows DESC; diff --git a/Query/sql/v2/runs/v2_cli_20260502_081223_d/c16/sql/v2q_c16_b60330a469f81d81.sql b/Query/sql/v2/runs/v2_cli_20260502_081223_d/c16/sql/v2q_c16_b60330a469f81d81.sql new file mode 100644 index 0000000000000000000000000000000000000000..2c93eb597765caf7a0d9aae54a5ba59447833b4d --- /dev/null +++ b/Query/sql/v2/runs/v2_cli_20260502_081223_d/c16/sql/v2q_c16_b60330a469f81d81.sql @@ -0,0 +1,22 @@ +-- sql_source_version: v2 +-- sql_source_label: v2_current +-- sql_source_run_id: v2_cli_20260502_081223_d +-- sql_source_dataset_id: c16 +-- family_id: conditional_dependency_structure +-- canonical_subitem_id: dependency_strength_similarity +-- intended_facet_id: pairwise_conditional_dependency +-- variant_semantic_role: focused_target_view +-- template_id: tpl_tpcds_within_group_share +-- query_record_id: v2q_c16_b60330a469f81d81 +-- problem_id: v2p_c16_2afd673c6e3b635d +-- realization_mode: agent +-- source_kind: agent +SELECT "SEX", "urlslug", + SUM(CAST("APPEARANCES" AS REAL)) AS total_measure, + SUM(CAST("APPEARANCES" AS REAL)) * 100.0 / SUM(SUM(CAST("APPEARANCES" AS REAL))) OVER (PARTITION BY "SEX") AS share_within_group +FROM "c16" +WHERE "SEX" IS NOT NULL AND "SEX" <> '' + AND "urlslug" IS NOT NULL AND "urlslug" <> '' + AND "APPEARANCES" IS NOT NULL AND "APPEARANCES" <> '' +GROUP BY "SEX", "urlslug" +ORDER BY share_within_group DESC; diff --git a/Query/sql/v2/runs/v2_cli_20260502_081223_d/c16/sql/v2q_c16_b62e6aedd5ff5316.sql b/Query/sql/v2/runs/v2_cli_20260502_081223_d/c16/sql/v2q_c16_b62e6aedd5ff5316.sql new file mode 100644 index 0000000000000000000000000000000000000000..f13adb090d791bba87edaa19bc3796f9c90de0ad --- /dev/null +++ b/Query/sql/v2/runs/v2_cli_20260502_081223_d/c16/sql/v2q_c16_b62e6aedd5ff5316.sql @@ -0,0 +1,21 @@ +-- sql_source_version: v2 +-- sql_source_label: v2_current +-- sql_source_run_id: v2_cli_20260502_081223_d +-- sql_source_dataset_id: c16 +-- family_id: missingness_structure +-- canonical_subitem_id: co_missingness_pattern_consistency +-- intended_facet_id: missing_rate_by_subgroup +-- variant_semantic_role: missing_rate_by_subgroup +-- template_id: tpl_missing_rate_by_subgroup +-- query_record_id: v2q_c16_b62e6aedd5ff5316 +-- problem_id: v2p_c16_eb815b7f945039f2 +-- realization_mode: deterministic +-- source_kind: deterministic +SELECT + "SEX", + COUNT(*) AS total_rows, + SUM(CASE WHEN "EYE" IS NULL THEN 1 ELSE 0 END) AS missing_rows, + AVG(CASE WHEN "EYE" IS NULL THEN 1.0 ELSE 0.0 END) AS missing_rate +FROM "c16" +GROUP BY "SEX" +ORDER BY missing_rate DESC, total_rows DESC; diff --git a/Query/sql/v2/runs/v2_cli_20260502_081223_d/c16/sql/v2q_c16_b71c26457f79e58e.sql b/Query/sql/v2/runs/v2_cli_20260502_081223_d/c16/sql/v2q_c16_b71c26457f79e58e.sql new file mode 100644 index 0000000000000000000000000000000000000000..5445eabb2f614f361994cd0b9f282332715490c1 --- /dev/null +++ b/Query/sql/v2/runs/v2_cli_20260502_081223_d/c16/sql/v2q_c16_b71c26457f79e58e.sql @@ -0,0 +1,23 @@ +-- sql_source_version: v2 +-- sql_source_label: v2_current +-- sql_source_run_id: v2_cli_20260502_081223_d +-- sql_source_dataset_id: c16 +-- family_id: tail_rarity_structure +-- canonical_subitem_id: tail_set_consistency +-- intended_facet_id: low_support_extremes +-- variant_semantic_role: rare_extreme_view +-- template_id: tpl_m4_quantile_tail_slice +-- query_record_id: v2q_c16_b71c26457f79e58e +-- problem_id: v2p_c16_fb83f149c215b953 +-- realization_mode: agent +-- source_kind: agent +WITH buckets AS ( + SELECT CAST("YEAR" AS INTEGER) AS "YEAR", + NTILE(10) OVER (ORDER BY CAST("YEAR" AS INTEGER) DESC) AS tail_bucket + FROM "c16" + WHERE "YEAR" IS NOT NULL AND TRIM("YEAR") <> "" +) +SELECT "YEAR" +FROM buckets +WHERE tail_bucket = 1 +ORDER BY "YEAR" DESC; diff --git a/Query/sql/v2/runs/v2_cli_20260502_081223_d/c16/sql/v2q_c16_b764c6c544831a2f.sql b/Query/sql/v2/runs/v2_cli_20260502_081223_d/c16/sql/v2q_c16_b764c6c544831a2f.sql new file mode 100644 index 0000000000000000000000000000000000000000..6a4a6e363d834a1b15672ee6a8d5e71e5fbb68e1 --- /dev/null +++ b/Query/sql/v2/runs/v2_cli_20260502_081223_d/c16/sql/v2q_c16_b764c6c544831a2f.sql @@ -0,0 +1,21 @@ +-- sql_source_version: v2 +-- sql_source_label: v2_current +-- sql_source_run_id: v2_cli_20260502_081223_d +-- sql_source_dataset_id: c16 +-- family_id: missingness_structure +-- canonical_subitem_id: co_missingness_pattern_consistency +-- intended_facet_id: missing_rate_by_subgroup +-- variant_semantic_role: missing_rate_by_subgroup +-- template_id: tpl_missing_rate_by_subgroup +-- query_record_id: v2q_c16_b764c6c544831a2f +-- problem_id: v2p_c16_0f32eacd4059d8a5 +-- realization_mode: deterministic +-- source_kind: deterministic +SELECT + "ALIGN", + COUNT(*) AS total_rows, + SUM(CASE WHEN "ID" IS NULL THEN 1 ELSE 0 END) AS missing_rows, + AVG(CASE WHEN "ID" IS NULL THEN 1.0 ELSE 0.0 END) AS missing_rate +FROM "c16" +GROUP BY "ALIGN" +ORDER BY missing_rate DESC, total_rows DESC; diff --git a/Query/sql/v2/runs/v2_cli_20260502_081223_d/c16/sql/v2q_c16_b9481ae0760ec4bc.sql b/Query/sql/v2/runs/v2_cli_20260502_081223_d/c16/sql/v2q_c16_b9481ae0760ec4bc.sql new file mode 100644 index 0000000000000000000000000000000000000000..f5f702b3ee0fc9c7c6738dbc0b83a3ecb1a2c14e --- /dev/null +++ b/Query/sql/v2/runs/v2_cli_20260502_081223_d/c16/sql/v2q_c16_b9481ae0760ec4bc.sql @@ -0,0 +1,33 @@ +-- sql_source_version: v2 +-- sql_source_label: v2_current +-- sql_source_run_id: v2_cli_20260502_081223_d +-- sql_source_dataset_id: c16 +-- family_id: conditional_dependency_structure +-- canonical_subitem_id: dependency_strength_similarity +-- intended_facet_id: pairwise_conditional_dependency +-- variant_semantic_role: within_group_proportion +-- template_id: tpl_tpcds_within_group_share +-- query_record_id: v2q_c16_b9481ae0760ec4bc +-- problem_id: v2p_c16_11e056559c13c8ba +-- realization_mode: agent +-- source_kind: agent +WITH "base" AS ( + SELECT + "ALIGN", + "YEAR", + CAST(NULLIF("APPEARANCES", '') AS REAL) AS "appearances_value" + FROM "c16" + WHERE "ALIGN" IS NOT NULL + AND "ALIGN" <> '' + AND "YEAR" IS NOT NULL + AND "YEAR" <> '' + AND NULLIF("APPEARANCES", '') IS NOT NULL +) +SELECT + "ALIGN", + "YEAR", + SUM("appearances_value") AS total_measure, + SUM("appearances_value") * 100.0 / SUM(SUM("appearances_value")) OVER (PARTITION BY "ALIGN") AS share_within_group +FROM "base" +GROUP BY "ALIGN", "YEAR" +ORDER BY share_within_group DESC; diff --git a/Query/sql/v2/runs/v2_cli_20260502_081223_d/c16/sql/v2q_c16_b9c15f2f03749a9e.sql b/Query/sql/v2/runs/v2_cli_20260502_081223_d/c16/sql/v2q_c16_b9c15f2f03749a9e.sql new file mode 100644 index 0000000000000000000000000000000000000000..697ff8339183e3401fbf0af37964a8cc1e61383a --- /dev/null +++ b/Query/sql/v2/runs/v2_cli_20260502_081223_d/c16/sql/v2q_c16_b9c15f2f03749a9e.sql @@ -0,0 +1,18 @@ +-- sql_source_version: v2 +-- sql_source_label: v2_current +-- sql_source_run_id: v2_cli_20260502_081223_d +-- sql_source_dataset_id: c16 +-- family_id: conditional_dependency_structure +-- canonical_subitem_id: direction_consistency +-- intended_facet_id: conditional_rate_shift +-- variant_semantic_role: focused_target_view +-- template_id: tpl_m4_group_condition_rate +-- query_record_id: v2q_c16_b9c15f2f03749a9e +-- problem_id: v2p_c16_37f8053beb7f44d2 +-- realization_mode: agent +-- source_kind: agent +SELECT "ALIVE", + AVG(CASE WHEN "ID" = 'Public Identity' THEN 1 ELSE 0 END) AS condition_rate +FROM "c16" +GROUP BY "ALIVE" +ORDER BY condition_rate DESC; diff --git a/Query/sql/v2/runs/v2_cli_20260502_081223_d/c16/sql/v2q_c16_bb67fb4bfb2f2284.sql b/Query/sql/v2/runs/v2_cli_20260502_081223_d/c16/sql/v2q_c16_bb67fb4bfb2f2284.sql new file mode 100644 index 0000000000000000000000000000000000000000..4fb88916e70e9cea14767f5a50b04d4969221b54 --- /dev/null +++ b/Query/sql/v2/runs/v2_cli_20260502_081223_d/c16/sql/v2q_c16_bb67fb4bfb2f2284.sql @@ -0,0 +1,18 @@ +-- sql_source_version: v2 +-- sql_source_label: v2_current +-- sql_source_run_id: v2_cli_20260502_081223_d +-- sql_source_dataset_id: c16 +-- family_id: conditional_dependency_structure +-- canonical_subitem_id: direction_consistency +-- intended_facet_id: conditional_rate_shift +-- variant_semantic_role: focused_target_view +-- template_id: tpl_m4_group_condition_rate +-- query_record_id: v2q_c16_bb67fb4bfb2f2284 +-- problem_id: v2p_c16_764cec6af45df7a2 +-- realization_mode: agent +-- source_kind: agent +SELECT "SEX", + AVG(CASE WHEN "ALIVE" = 'Living Characters' THEN 1 ELSE 0 END) AS "condition_rate" +FROM "c16" +GROUP BY "SEX" +ORDER BY "condition_rate" DESC; diff --git a/Query/sql/v2/runs/v2_cli_20260502_081223_d/c16/sql/v2q_c16_bce9dcfa01477ceb.sql b/Query/sql/v2/runs/v2_cli_20260502_081223_d/c16/sql/v2q_c16_bce9dcfa01477ceb.sql new file mode 100644 index 0000000000000000000000000000000000000000..7cc4b5a8d657f0ffa56f0e21b57b75a98b915750 --- /dev/null +++ b/Query/sql/v2/runs/v2_cli_20260502_081223_d/c16/sql/v2q_c16_bce9dcfa01477ceb.sql @@ -0,0 +1,25 @@ +-- sql_source_version: v2 +-- sql_source_label: v2_current +-- sql_source_run_id: v2_cli_20260502_081223_d +-- sql_source_dataset_id: c16 +-- family_id: tail_rarity_structure +-- canonical_subitem_id: tail_set_consistency +-- intended_facet_id: low_support_extremes +-- variant_semantic_role: rare_extreme_view +-- template_id: tpl_m4_quantile_tail_slice +-- query_record_id: v2q_c16_bce9dcfa01477ceb +-- problem_id: v2p_c16_97ca98656b42ec48 +-- realization_mode: agent +-- source_kind: agent +WITH buckets AS ( + SELECT + "APPEARANCES", + NTILE(10) OVER (ORDER BY CAST("APPEARANCES" AS INTEGER) DESC) AS tail_bucket + FROM "c16" + WHERE "APPEARANCES" IS NOT NULL + AND TRIM("APPEARANCES") <> '' +) +SELECT "APPEARANCES" +FROM buckets +WHERE tail_bucket = 1 +ORDER BY CAST("APPEARANCES" AS INTEGER) DESC; diff --git a/Query/sql/v2/runs/v2_cli_20260502_081223_d/c16/sql/v2q_c16_bd3ff6d30fc51928.sql b/Query/sql/v2/runs/v2_cli_20260502_081223_d/c16/sql/v2q_c16_bd3ff6d30fc51928.sql new file mode 100644 index 0000000000000000000000000000000000000000..efc239b272005d2fa10fc996246a7defe6da8366 --- /dev/null +++ b/Query/sql/v2/runs/v2_cli_20260502_081223_d/c16/sql/v2q_c16_bd3ff6d30fc51928.sql @@ -0,0 +1,21 @@ +-- sql_source_version: v2 +-- sql_source_label: v2_current +-- sql_source_run_id: v2_cli_20260502_081223_d +-- sql_source_dataset_id: c16 +-- family_id: cardinality_structure +-- canonical_subitem_id: high_cardinality_response_stability +-- intended_facet_id: target_cardinality_cross_section +-- variant_semantic_role: focused_target_view +-- template_id: tpl_cardinality_high_card_response_stability +-- query_record_id: v2q_c16_bd3ff6d30fc51928 +-- problem_id: v2p_c16_3b42582ef3ff9a8b +-- realization_mode: deterministic +-- source_kind: deterministic +SELECT + "YEAR", + COUNT(*) AS support, + AVG("page_id") AS avg_response +FROM "c16" +GROUP BY "YEAR" +HAVING COUNT(*) >= 5.0 +ORDER BY support DESC, avg_response DESC; diff --git a/Query/sql/v2/runs/v2_cli_20260502_081223_d/c16/sql/v2q_c16_bff76b91506b05ff.sql b/Query/sql/v2/runs/v2_cli_20260502_081223_d/c16/sql/v2q_c16_bff76b91506b05ff.sql new file mode 100644 index 0000000000000000000000000000000000000000..d54c60ddbeb5fae3c4245e49f84c18e534536484 --- /dev/null +++ b/Query/sql/v2/runs/v2_cli_20260502_081223_d/c16/sql/v2q_c16_bff76b91506b05ff.sql @@ -0,0 +1,21 @@ +-- sql_source_version: v2 +-- sql_source_label: v2_current +-- sql_source_run_id: v2_cli_20260502_081223_d +-- sql_source_dataset_id: c16 +-- family_id: cardinality_structure +-- canonical_subitem_id: high_cardinality_response_stability +-- intended_facet_id: target_cardinality_cross_section +-- variant_semantic_role: focused_target_view +-- template_id: tpl_cardinality_high_card_response_stability +-- query_record_id: v2q_c16_bff76b91506b05ff +-- problem_id: v2p_c16_7196f8c0a2c46257 +-- realization_mode: deterministic +-- source_kind: deterministic +SELECT + "urlslug", + COUNT(*) AS support, + AVG("page_id") AS avg_response +FROM "c16" +GROUP BY "urlslug" +HAVING COUNT(*) >= 5.0 +ORDER BY support DESC, avg_response DESC; diff --git a/Query/sql/v2/runs/v2_cli_20260502_081223_d/c16/sql/v2q_c16_c303f399c1809ad8.sql b/Query/sql/v2/runs/v2_cli_20260502_081223_d/c16/sql/v2q_c16_c303f399c1809ad8.sql new file mode 100644 index 0000000000000000000000000000000000000000..21d5d1bfb279667f3e6c30aff6962e71ccb057c8 --- /dev/null +++ b/Query/sql/v2/runs/v2_cli_20260502_081223_d/c16/sql/v2q_c16_c303f399c1809ad8.sql @@ -0,0 +1,18 @@ +-- sql_source_version: v2 +-- sql_source_label: v2_current +-- sql_source_run_id: v2_cli_20260502_081223_d +-- sql_source_dataset_id: c16 +-- family_id: missingness_structure +-- canonical_subitem_id: marginal_missing_rate_consistency +-- intended_facet_id: missing_indicator_distribution +-- variant_semantic_role: missing_indicator_view +-- template_id: tpl_missing_marginal_rate_profile +-- query_record_id: v2q_c16_c303f399c1809ad8 +-- problem_id: v2p_c16_24c5a8bb6a126002 +-- realization_mode: deterministic +-- source_kind: deterministic +SELECT + COUNT(*) AS total_rows, + SUM(CASE WHEN "YEAR" IS NULL THEN 1 ELSE 0 END) AS missing_rows, + AVG(CASE WHEN "YEAR" IS NULL THEN 1.0 ELSE 0.0 END) AS missing_rate +FROM "c16"; diff --git a/Query/sql/v2/runs/v2_cli_20260502_081223_d/c16/sql/v2q_c16_c304d2459f68e3ae.sql b/Query/sql/v2/runs/v2_cli_20260502_081223_d/c16/sql/v2q_c16_c304d2459f68e3ae.sql new file mode 100644 index 0000000000000000000000000000000000000000..7536f7ba01d5b8ef525e858f7dbd265a0cc8b30b --- /dev/null +++ b/Query/sql/v2/runs/v2_cli_20260502_081223_d/c16/sql/v2q_c16_c304d2459f68e3ae.sql @@ -0,0 +1,21 @@ +-- sql_source_version: v2 +-- sql_source_label: v2_current +-- sql_source_run_id: v2_cli_20260502_081223_d +-- sql_source_dataset_id: c16 +-- family_id: missingness_structure +-- canonical_subitem_id: co_missingness_pattern_consistency +-- intended_facet_id: missing_target_interaction +-- variant_semantic_role: missing_target_interaction +-- template_id: tpl_missing_target_interaction +-- query_record_id: v2q_c16_c304d2459f68e3ae +-- problem_id: v2p_c16_8774e8082c442878 +-- realization_mode: deterministic +-- source_kind: deterministic +SELECT + "ID", + COUNT(*) AS total_rows, + SUM(CASE WHEN "YEAR" IS NULL THEN 1 ELSE 0 END) AS missing_rows, + AVG(CASE WHEN "YEAR" IS NULL THEN 1.0 ELSE 0.0 END) AS missing_rate +FROM "c16" +GROUP BY "ID" +ORDER BY missing_rate DESC, total_rows DESC; diff --git a/Query/sql/v2/runs/v2_cli_20260502_081223_d/c16/sql/v2q_c16_c314a86a3d76ab54.sql b/Query/sql/v2/runs/v2_cli_20260502_081223_d/c16/sql/v2q_c16_c314a86a3d76ab54.sql new file mode 100644 index 0000000000000000000000000000000000000000..13fc58545c8fe599618a3a696657edde84d049bd --- /dev/null +++ b/Query/sql/v2/runs/v2_cli_20260502_081223_d/c16/sql/v2q_c16_c314a86a3d76ab54.sql @@ -0,0 +1,18 @@ +-- sql_source_version: v2 +-- sql_source_label: v2_current +-- sql_source_run_id: v2_cli_20260502_081223_d +-- sql_source_dataset_id: c16 +-- family_id: conditional_dependency_structure +-- canonical_subitem_id: direction_consistency +-- intended_facet_id: conditional_rate_shift +-- variant_semantic_role: within_group_proportion +-- template_id: tpl_m4_group_condition_rate +-- query_record_id: v2q_c16_c314a86a3d76ab54 +-- problem_id: v2p_c16_a6e66af748d69291 +-- realization_mode: agent +-- source_kind: agent +SELECT "ALIVE", + AVG(CASE WHEN "ID" = 'Secret Identity' THEN 1 ELSE 0 END) AS condition_rate +FROM "c16" +GROUP BY "ALIVE" +ORDER BY condition_rate DESC; diff --git a/Query/sql/v2/runs/v2_cli_20260502_081223_d/c16/sql/v2q_c16_c34c907e9b018734.sql b/Query/sql/v2/runs/v2_cli_20260502_081223_d/c16/sql/v2q_c16_c34c907e9b018734.sql new file mode 100644 index 0000000000000000000000000000000000000000..27a57a92816690d3f608cefc3a6ec0e6c62f2568 --- /dev/null +++ b/Query/sql/v2/runs/v2_cli_20260502_081223_d/c16/sql/v2q_c16_c34c907e9b018734.sql @@ -0,0 +1,21 @@ +-- sql_source_version: v2 +-- sql_source_label: v2_current +-- sql_source_run_id: v2_cli_20260502_081223_d +-- sql_source_dataset_id: c16 +-- family_id: missingness_structure +-- canonical_subitem_id: co_missingness_pattern_consistency +-- intended_facet_id: missing_rate_by_subgroup +-- variant_semantic_role: missing_rate_by_subgroup +-- template_id: tpl_missing_rate_by_subgroup +-- query_record_id: v2q_c16_c34c907e9b018734 +-- problem_id: v2p_c16_6e08ae99cfc69f66 +-- realization_mode: deterministic +-- source_kind: deterministic +SELECT + "HAIR", + COUNT(*) AS total_rows, + SUM(CASE WHEN "FIRST APPEARANCE" IS NULL THEN 1 ELSE 0 END) AS missing_rows, + AVG(CASE WHEN "FIRST APPEARANCE" IS NULL THEN 1.0 ELSE 0.0 END) AS missing_rate +FROM "c16" +GROUP BY "HAIR" +ORDER BY missing_rate DESC, total_rows DESC; diff --git a/Query/sql/v2/runs/v2_cli_20260502_081223_d/c16/sql/v2q_c16_c545758d47ac54b8.sql b/Query/sql/v2/runs/v2_cli_20260502_081223_d/c16/sql/v2q_c16_c545758d47ac54b8.sql new file mode 100644 index 0000000000000000000000000000000000000000..ec82bc8b2f9e350c9a805dd1ab849d46667a5199 --- /dev/null +++ b/Query/sql/v2/runs/v2_cli_20260502_081223_d/c16/sql/v2q_c16_c545758d47ac54b8.sql @@ -0,0 +1,21 @@ +-- sql_source_version: v2 +-- sql_source_label: v2_current +-- sql_source_run_id: v2_cli_20260502_081223_d +-- sql_source_dataset_id: c16 +-- family_id: missingness_structure +-- canonical_subitem_id: co_missingness_pattern_consistency +-- intended_facet_id: missing_rate_by_subgroup +-- variant_semantic_role: missing_rate_by_subgroup +-- template_id: tpl_missing_rate_by_subgroup +-- query_record_id: v2q_c16_c545758d47ac54b8 +-- problem_id: v2p_c16_3d1d0287e5687d64 +-- realization_mode: deterministic +-- source_kind: deterministic +SELECT + "YEAR", + COUNT(*) AS total_rows, + SUM(CASE WHEN "ALIVE" IS NULL THEN 1 ELSE 0 END) AS missing_rows, + AVG(CASE WHEN "ALIVE" IS NULL THEN 1.0 ELSE 0.0 END) AS missing_rate +FROM "c16" +GROUP BY "YEAR" +ORDER BY missing_rate DESC, total_rows DESC; diff --git a/Query/sql/v2/runs/v2_cli_20260502_081223_d/c16/sql/v2q_c16_c64153b758f846f5.sql b/Query/sql/v2/runs/v2_cli_20260502_081223_d/c16/sql/v2q_c16_c64153b758f846f5.sql new file mode 100644 index 0000000000000000000000000000000000000000..467655499981c3a8eb78646775db33909bc71196 --- /dev/null +++ b/Query/sql/v2/runs/v2_cli_20260502_081223_d/c16/sql/v2q_c16_c64153b758f846f5.sql @@ -0,0 +1,23 @@ +-- sql_source_version: v2 +-- sql_source_label: v2_current +-- sql_source_run_id: v2_cli_20260502_081223_d +-- sql_source_dataset_id: c16 +-- family_id: conditional_dependency_structure +-- canonical_subitem_id: dependency_strength_similarity +-- intended_facet_id: pairwise_conditional_dependency +-- variant_semantic_role: focused_target_view +-- template_id: tpl_tpcds_within_group_share +-- query_record_id: v2q_c16_c64153b758f846f5 +-- problem_id: v2p_c16_ee148f0df1a774c5 +-- realization_mode: agent +-- source_kind: agent +SELECT + "GSM", + "urlslug", + SUM(CAST(NULLIF("APPEARANCES", '') AS REAL)) AS total_measure, + SUM(CAST(NULLIF("APPEARANCES", '') AS REAL)) * 100.0 + / SUM(SUM(CAST(NULLIF("APPEARANCES", '') AS REAL))) OVER (PARTITION BY "GSM") AS share_within_group +FROM "c16" +GROUP BY "GSM", "urlslug" +ORDER BY share_within_group DESC +LIMIT 10; diff --git a/Query/sql/v2/runs/v2_cli_20260502_081223_d/c16/sql/v2q_c16_c84be8bcab5ca2bb.sql b/Query/sql/v2/runs/v2_cli_20260502_081223_d/c16/sql/v2q_c16_c84be8bcab5ca2bb.sql new file mode 100644 index 0000000000000000000000000000000000000000..920037143c33be5a2cb30f1686fb6cd7ba740c00 --- /dev/null +++ b/Query/sql/v2/runs/v2_cli_20260502_081223_d/c16/sql/v2q_c16_c84be8bcab5ca2bb.sql @@ -0,0 +1,18 @@ +-- sql_source_version: v2 +-- sql_source_label: v2_current +-- sql_source_run_id: v2_cli_20260502_081223_d +-- sql_source_dataset_id: c16 +-- family_id: conditional_dependency_structure +-- canonical_subitem_id: dependency_strength_similarity +-- intended_facet_id: pairwise_conditional_dependency +-- variant_semantic_role: focused_target_view +-- template_id: tpl_m4_group_condition_rate +-- query_record_id: v2q_c16_c84be8bcab5ca2bb +-- problem_id: v2p_c16_2f61cacd3efe1700 +-- realization_mode: agent +-- source_kind: agent +SELECT "GSM", + AVG(CASE WHEN "EYE" = 'Blue Eyes' THEN 1 ELSE 0 END) AS condition_rate +FROM "c16" +GROUP BY "GSM" +ORDER BY condition_rate DESC; diff --git a/Query/sql/v2/runs/v2_cli_20260502_081223_d/c16/sql/v2q_c16_c90063b15ce3e6c3.sql b/Query/sql/v2/runs/v2_cli_20260502_081223_d/c16/sql/v2q_c16_c90063b15ce3e6c3.sql new file mode 100644 index 0000000000000000000000000000000000000000..9cca6de1431db0c28a887c11a308fcb4306868ce --- /dev/null +++ b/Query/sql/v2/runs/v2_cli_20260502_081223_d/c16/sql/v2q_c16_c90063b15ce3e6c3.sql @@ -0,0 +1,21 @@ +-- sql_source_version: v2 +-- sql_source_label: v2_current +-- sql_source_run_id: v2_cli_20260502_081223_d +-- sql_source_dataset_id: c16 +-- family_id: missingness_structure +-- canonical_subitem_id: co_missingness_pattern_consistency +-- intended_facet_id: missing_rate_by_subgroup +-- variant_semantic_role: missing_rate_by_subgroup +-- template_id: tpl_missing_rate_by_subgroup +-- query_record_id: v2q_c16_c90063b15ce3e6c3 +-- problem_id: v2p_c16_170f30cd25477eb7 +-- realization_mode: deterministic +-- source_kind: deterministic +SELECT + "ALIGN", + COUNT(*) AS total_rows, + SUM(CASE WHEN "GSM" IS NULL THEN 1 ELSE 0 END) AS missing_rows, + AVG(CASE WHEN "GSM" IS NULL THEN 1.0 ELSE 0.0 END) AS missing_rate +FROM "c16" +GROUP BY "ALIGN" +ORDER BY missing_rate DESC, total_rows DESC; diff --git a/Query/sql/v2/runs/v2_cli_20260502_081223_d/c16/sql/v2q_c16_cb4f070c67cf68f9.sql b/Query/sql/v2/runs/v2_cli_20260502_081223_d/c16/sql/v2q_c16_cb4f070c67cf68f9.sql new file mode 100644 index 0000000000000000000000000000000000000000..233cde5c6b430767db86ca5dd22b20624feff299 --- /dev/null +++ b/Query/sql/v2/runs/v2_cli_20260502_081223_d/c16/sql/v2q_c16_cb4f070c67cf68f9.sql @@ -0,0 +1,15 @@ +-- sql_source_version: v2 +-- sql_source_label: v2_current +-- sql_source_run_id: v2_cli_20260502_081223_d +-- sql_source_dataset_id: c16 +-- family_id: tail_rarity_structure +-- canonical_subitem_id: tail_set_consistency +-- intended_facet_id: low_support_extremes +-- variant_semantic_role: rare_extreme_view +-- template_id: tpl_threshold_rarity_cdf +-- query_record_id: v2q_c16_cb4f070c67cf68f9 +-- problem_id: v2p_c16_1b1923619b0f667e +-- realization_mode: agent +-- source_kind: agent +SELECT AVG(CASE WHEN NULLIF(TRIM("YEAR"), '') IS NOT NULL AND CAST("YEAR" AS REAL) <= 2003.0 THEN 1 WHEN NULLIF(TRIM("YEAR"), '') IS NOT NULL THEN 0 END) AS empirical_cdf_at_threshold +FROM "c16"; diff --git a/Query/sql/v2/runs/v2_cli_20260502_081223_d/c16/sql/v2q_c16_cd4adce329a71c06.sql b/Query/sql/v2/runs/v2_cli_20260502_081223_d/c16/sql/v2q_c16_cd4adce329a71c06.sql new file mode 100644 index 0000000000000000000000000000000000000000..e7c77bcf4904a7e19c8faf1edcbe092daa991e55 --- /dev/null +++ b/Query/sql/v2/runs/v2_cli_20260502_081223_d/c16/sql/v2q_c16_cd4adce329a71c06.sql @@ -0,0 +1,18 @@ +-- sql_source_version: v2 +-- sql_source_label: v2_current +-- sql_source_run_id: v2_cli_20260502_081223_d +-- sql_source_dataset_id: c16 +-- family_id: missingness_structure +-- canonical_subitem_id: marginal_missing_rate_consistency +-- intended_facet_id: missing_indicator_distribution +-- variant_semantic_role: missing_indicator_view +-- template_id: tpl_missing_marginal_rate_profile +-- query_record_id: v2q_c16_cd4adce329a71c06 +-- problem_id: v2p_c16_07d64872d305de02 +-- realization_mode: deterministic +-- source_kind: deterministic +SELECT + COUNT(*) AS total_rows, + SUM(CASE WHEN "FIRST APPEARANCE" IS NULL THEN 1 ELSE 0 END) AS missing_rows, + AVG(CASE WHEN "FIRST APPEARANCE" IS NULL THEN 1.0 ELSE 0.0 END) AS missing_rate +FROM "c16"; diff --git a/Query/sql/v2/runs/v2_cli_20260502_081223_d/c16/sql/v2q_c16_cf5661ae5e9f6d77.sql b/Query/sql/v2/runs/v2_cli_20260502_081223_d/c16/sql/v2q_c16_cf5661ae5e9f6d77.sql new file mode 100644 index 0000000000000000000000000000000000000000..2e572234c270cc92280c83205a80de1d2122f503 --- /dev/null +++ b/Query/sql/v2/runs/v2_cli_20260502_081223_d/c16/sql/v2q_c16_cf5661ae5e9f6d77.sql @@ -0,0 +1,24 @@ +-- sql_source_version: v2 +-- sql_source_label: v2_current +-- sql_source_run_id: v2_cli_20260502_081223_d +-- sql_source_dataset_id: c16 +-- family_id: tail_rarity_structure +-- canonical_subitem_id: tail_set_consistency +-- intended_facet_id: low_support_extremes +-- variant_semantic_role: rare_extreme_view +-- template_id: tpl_m4_quantile_tail_slice +-- query_record_id: v2q_c16_cf5661ae5e9f6d77 +-- problem_id: v2p_c16_6a4992dbf9dc0233 +-- realization_mode: agent +-- source_kind: agent +WITH "buckets" AS ( + SELECT + "page_id", + NTILE(10) OVER (ORDER BY CAST("page_id" AS NUMERIC) DESC) AS "tail_bucket" + FROM "c16" + WHERE "page_id" IS NOT NULL AND "page_id" <> '' +) +SELECT "page_id" +FROM "buckets" +WHERE "tail_bucket" = 1 +ORDER BY CAST("page_id" AS NUMERIC) DESC; diff --git a/Query/sql/v2/runs/v2_cli_20260502_081223_d/c16/sql/v2q_c16_d0073fc8693f9e0d.sql b/Query/sql/v2/runs/v2_cli_20260502_081223_d/c16/sql/v2q_c16_d0073fc8693f9e0d.sql new file mode 100644 index 0000000000000000000000000000000000000000..2afc756a9c92333f07b355cb2e5ba8c54836065c --- /dev/null +++ b/Query/sql/v2/runs/v2_cli_20260502_081223_d/c16/sql/v2q_c16_d0073fc8693f9e0d.sql @@ -0,0 +1,30 @@ +-- sql_source_version: v2 +-- sql_source_label: v2_current +-- sql_source_run_id: v2_cli_20260502_081223_d +-- sql_source_dataset_id: c16 +-- family_id: tail_rarity_structure +-- canonical_subitem_id: tail_mass_similarity +-- intended_facet_id: tail_ranked_signal +-- variant_semantic_role: filtered_stable_view +-- template_id: tpl_tpch_relative_total_threshold +-- query_record_id: v2q_c16_d0073fc8693f9e0d +-- problem_id: v2p_c16_2456063c164935da +-- realization_mode: agent +-- source_kind: agent +WITH grouped AS ( + SELECT "HAIR", SUM(CAST("page_id" AS REAL)) AS group_value + FROM "c16" + WHERE "HAIR" IS NOT NULL + AND TRIM("HAIR") <> '' + AND "page_id" IS NOT NULL + AND TRIM("page_id") <> '' + GROUP BY "HAIR" +), total AS ( + SELECT SUM(group_value) AS total_value + FROM grouped +) +SELECT g."HAIR", g.group_value +FROM grouped AS g +CROSS JOIN total AS t +WHERE g.group_value > t.total_value * 0.05 +ORDER BY g.group_value DESC; diff --git a/Query/sql/v2/runs/v2_cli_20260502_081223_d/c16/sql/v2q_c16_d2ade550b5d14658.sql b/Query/sql/v2/runs/v2_cli_20260502_081223_d/c16/sql/v2q_c16_d2ade550b5d14658.sql new file mode 100644 index 0000000000000000000000000000000000000000..1fb453280d2ed4100f8f31ad749b6a7f3093d287 --- /dev/null +++ b/Query/sql/v2/runs/v2_cli_20260502_081223_d/c16/sql/v2q_c16_d2ade550b5d14658.sql @@ -0,0 +1,28 @@ +-- sql_source_version: v2 +-- sql_source_label: v2_current +-- sql_source_run_id: v2_cli_20260502_081223_d +-- sql_source_dataset_id: c16 +-- family_id: cardinality_structure +-- canonical_subitem_id: support_rank_profile_consistency +-- intended_facet_id: support_concentration +-- variant_semantic_role: ranked_signal_view +-- template_id: tpl_cardinality_distinct_share_profile +-- query_record_id: v2q_c16_d2ade550b5d14658 +-- problem_id: v2p_c16_a40b4c94aba90c44 +-- realization_mode: deterministic +-- source_kind: deterministic +WITH grouped AS ( + SELECT "HAIR" AS value_label, COUNT(*) AS support + FROM "c16" + GROUP BY "HAIR" +), ranked AS ( + SELECT + value_label, + support, + CAST(support AS FLOAT) / NULLIF(SUM(support) OVER (), 0) AS support_share, + SUM(support) OVER (ORDER BY support DESC, value_label ROWS UNBOUNDED PRECEDING) AS cumulative_support + FROM grouped +) +SELECT * +FROM ranked +ORDER BY support DESC, value_label; diff --git a/Query/sql/v2/runs/v2_cli_20260502_081223_d/c16/sql/v2q_c16_d57fdd3d9f74bcd7.sql b/Query/sql/v2/runs/v2_cli_20260502_081223_d/c16/sql/v2q_c16_d57fdd3d9f74bcd7.sql new file mode 100644 index 0000000000000000000000000000000000000000..c6adf562ab0cbba7293f8cb0c7e330044b2d0bcb --- /dev/null +++ b/Query/sql/v2/runs/v2_cli_20260502_081223_d/c16/sql/v2q_c16_d57fdd3d9f74bcd7.sql @@ -0,0 +1,30 @@ +-- sql_source_version: v2 +-- sql_source_label: v2_current +-- sql_source_run_id: v2_cli_20260502_081223_d +-- sql_source_dataset_id: c16 +-- family_id: tail_rarity_structure +-- canonical_subitem_id: tail_mass_similarity +-- intended_facet_id: tail_ranked_signal +-- variant_semantic_role: filtered_stable_view +-- template_id: tpl_tpch_relative_total_threshold +-- query_record_id: v2q_c16_d57fdd3d9f74bcd7 +-- problem_id: v2p_c16_b69e223a1c08d49a +-- realization_mode: agent +-- source_kind: agent +WITH "grouped" AS ( + SELECT "ALIVE", SUM(CAST("page_id" AS NUMERIC)) AS "group_value" + FROM "c16" + WHERE "ALIVE" IS NOT NULL + AND TRIM("ALIVE") <> '' + AND "page_id" IS NOT NULL + AND TRIM("page_id") <> '' + GROUP BY "ALIVE" +), "total" AS ( + SELECT SUM("group_value") AS "total_value" + FROM "grouped" +) +SELECT "g"."ALIVE", "g"."group_value" +FROM "grouped" AS "g" +CROSS JOIN "total" AS "t" +WHERE "g"."group_value" > "t"."total_value" * 0.1 +ORDER BY "g"."group_value" DESC; diff --git a/Query/sql/v2/runs/v2_cli_20260502_081223_d/c16/sql/v2q_c16_dac8eb31d2ff5acf.sql b/Query/sql/v2/runs/v2_cli_20260502_081223_d/c16/sql/v2q_c16_dac8eb31d2ff5acf.sql new file mode 100644 index 0000000000000000000000000000000000000000..bbf9ba421bebc0ddd9f0cb12c9de707ba1b89a76 --- /dev/null +++ b/Query/sql/v2/runs/v2_cli_20260502_081223_d/c16/sql/v2q_c16_dac8eb31d2ff5acf.sql @@ -0,0 +1,21 @@ +-- sql_source_version: v2 +-- sql_source_label: v2_current +-- sql_source_run_id: v2_cli_20260502_081223_d +-- sql_source_dataset_id: c16 +-- family_id: missingness_structure +-- canonical_subitem_id: co_missingness_pattern_consistency +-- intended_facet_id: missing_target_interaction +-- variant_semantic_role: missing_target_interaction +-- template_id: tpl_missing_target_interaction +-- query_record_id: v2q_c16_dac8eb31d2ff5acf +-- problem_id: v2p_c16_d990363864664831 +-- realization_mode: deterministic +-- source_kind: deterministic +SELECT + "ID", + COUNT(*) AS total_rows, + SUM(CASE WHEN "GSM" IS NULL THEN 1 ELSE 0 END) AS missing_rows, + AVG(CASE WHEN "GSM" IS NULL THEN 1.0 ELSE 0.0 END) AS missing_rate +FROM "c16" +GROUP BY "ID" +ORDER BY missing_rate DESC, total_rows DESC; diff --git a/Query/sql/v2/runs/v2_cli_20260502_081223_d/c16/sql/v2q_c16_ddf4263136c55fa0.sql b/Query/sql/v2/runs/v2_cli_20260502_081223_d/c16/sql/v2q_c16_ddf4263136c55fa0.sql new file mode 100644 index 0000000000000000000000000000000000000000..bf53983799f367d488ac77f3f8c9e5bcec1fe01c --- /dev/null +++ b/Query/sql/v2/runs/v2_cli_20260502_081223_d/c16/sql/v2q_c16_ddf4263136c55fa0.sql @@ -0,0 +1,24 @@ +-- sql_source_version: v2 +-- sql_source_label: v2_current +-- sql_source_run_id: v2_cli_20260502_081223_d +-- sql_source_dataset_id: c16 +-- family_id: conditional_dependency_structure +-- canonical_subitem_id: direction_consistency +-- intended_facet_id: conditional_rate_shift +-- variant_semantic_role: contrastive_conditional_view +-- template_id: tpl_m4_group_ratio_two_conditions +-- query_record_id: v2q_c16_ddf4263136c55fa0 +-- problem_id: v2p_c16_2d4479d360cbd6a5 +-- realization_mode: agent +-- source_kind: agent +WITH grouped AS ( + SELECT "HAIR", + SUM(CASE WHEN "ID" = 'Public Identity' THEN 1 ELSE 0 END) AS numerator_count, + SUM(CASE WHEN "ID" = 'Secret Identity' THEN 1 ELSE 0 END) AS denominator_count + FROM "c16" + GROUP BY "HAIR" +) +SELECT "HAIR", + CAST(numerator_count AS FLOAT) / NULLIF(denominator_count, 0) AS condition_ratio +FROM grouped +ORDER BY condition_ratio DESC; diff --git a/Query/sql/v2/runs/v2_cli_20260502_081223_d/c16/sql/v2q_c16_debbebd8e937751f.sql b/Query/sql/v2/runs/v2_cli_20260502_081223_d/c16/sql/v2q_c16_debbebd8e937751f.sql new file mode 100644 index 0000000000000000000000000000000000000000..03f87fbcacdd29e18eb3d88bc9492c361fb02b67 --- /dev/null +++ b/Query/sql/v2/runs/v2_cli_20260502_081223_d/c16/sql/v2q_c16_debbebd8e937751f.sql @@ -0,0 +1,17 @@ +-- sql_source_version: v2 +-- sql_source_label: v2_current +-- sql_source_run_id: v2_cli_20260502_081223_d +-- sql_source_dataset_id: c16 +-- family_id: subgroup_structure +-- canonical_subitem_id: subgroup_size_stability +-- intended_facet_id: subgroup_distribution_shift +-- variant_semantic_role: count_distribution +-- template_id: tpl_clickbench_group_count +-- query_record_id: v2q_c16_debbebd8e937751f +-- problem_id: v2p_c16_3dc233c26ce4e9a1 +-- realization_mode: agent +-- source_kind: agent +SELECT "EYE", COUNT(*) AS row_count +FROM "c16" +GROUP BY "EYE" +ORDER BY row_count DESC; diff --git a/Query/sql/v2/runs/v2_cli_20260502_081223_d/c16/sql/v2q_c16_e07a74ec8b7e0172.sql b/Query/sql/v2/runs/v2_cli_20260502_081223_d/c16/sql/v2q_c16_e07a74ec8b7e0172.sql new file mode 100644 index 0000000000000000000000000000000000000000..7cd5b6f00e9b75e09797f8ed279a7b38d200bba4 --- /dev/null +++ b/Query/sql/v2/runs/v2_cli_20260502_081223_d/c16/sql/v2q_c16_e07a74ec8b7e0172.sql @@ -0,0 +1,25 @@ +-- sql_source_version: v2 +-- sql_source_label: v2_current +-- sql_source_run_id: v2_cli_20260502_081223_d +-- sql_source_dataset_id: c16 +-- family_id: cardinality_structure +-- canonical_subitem_id: support_rank_profile_consistency +-- intended_facet_id: value_imbalance_profile +-- variant_semantic_role: count_distribution +-- template_id: tpl_cardinality_support_rank_profile +-- query_record_id: v2q_c16_e07a74ec8b7e0172 +-- problem_id: v2p_c16_1ee2afda51b98ca1 +-- realization_mode: deterministic +-- source_kind: deterministic +WITH grouped AS ( + SELECT "ALIVE" AS value_label, COUNT(*) AS support + FROM "c16" + GROUP BY "ALIVE" +) +SELECT + value_label, + support, + CAST(support AS FLOAT) / NULLIF(SUM(support) OVER (), 0) AS support_share, + ROW_NUMBER() OVER (ORDER BY support DESC, value_label) AS support_rank +FROM grouped +ORDER BY support DESC, value_label; diff --git a/Query/sql/v2/runs/v2_cli_20260502_081223_d/c16/sql/v2q_c16_e0b70f5321149caa.sql b/Query/sql/v2/runs/v2_cli_20260502_081223_d/c16/sql/v2q_c16_e0b70f5321149caa.sql new file mode 100644 index 0000000000000000000000000000000000000000..02e76e9e74f127f9ff6781e38733d036f8aa3bda --- /dev/null +++ b/Query/sql/v2/runs/v2_cli_20260502_081223_d/c16/sql/v2q_c16_e0b70f5321149caa.sql @@ -0,0 +1,76 @@ +-- sql_source_version: v2 +-- sql_source_label: v2_current +-- sql_source_run_id: v2_cli_20260502_081223_d +-- sql_source_dataset_id: c16 +-- family_id: tail_rarity_structure +-- canonical_subitem_id: tail_concentration_consistency +-- intended_facet_id: rare_target_concentration +-- variant_semantic_role: ranked_signal_view +-- template_id: tpl_grouped_percentile_point +-- query_record_id: v2q_c16_e0b70f5321149caa +-- problem_id: v2p_c16_68d49061ec4331dc +-- realization_mode: agent +-- source_kind: agent +WITH "base" AS ( + SELECT + "HAIR" AS "group_col", + CAST("YEAR" AS REAL) AS "measure" + FROM "c16" + WHERE "HAIR" IS NOT NULL + AND "HAIR" <> '' + AND "YEAR" IS NOT NULL + AND "YEAR" <> '' +), +"ranked" AS ( + SELECT + "group_col", + "measure", + ROW_NUMBER() OVER ( + PARTITION BY "group_col" + ORDER BY "measure" + ) AS "rn", + COUNT(*) OVER ( + PARTITION BY "group_col" + ) AS "cnt" + FROM "base" +), +"params" AS ( + SELECT + "group_col", + MAX("cnt") AS "cnt", + (1.0 + 0.9 * (MAX("cnt") - 1)) AS "pos", + CAST((1.0 + 0.9 * (MAX("cnt") - 1)) AS INTEGER) AS "lower_rn", + CASE + WHEN (1.0 + 0.9 * (MAX("cnt") - 1)) = CAST((1.0 + 0.9 * (MAX("cnt") - 1)) AS INTEGER) + THEN CAST((1.0 + 0.9 * (MAX("cnt") - 1)) AS INTEGER) + ELSE CAST((1.0 + 0.9 * (MAX("cnt") - 1)) AS INTEGER) + 1 + END AS "upper_rn" + FROM "ranked" + GROUP BY "group_col" + HAVING MAX("cnt") >= 5 +), +"bounds" AS ( + SELECT + p."group_col", + p."pos", + p."lower_rn", + p."upper_rn", + MAX(CASE WHEN r."rn" = p."lower_rn" THEN r."measure" END) AS "lower_val", + MAX(CASE WHEN r."rn" = p."upper_rn" THEN r."measure" END) AS "upper_val" + FROM "params" AS p + JOIN "ranked" AS r + ON r."group_col" = p."group_col" + GROUP BY + p."group_col", + p."pos", + p."lower_rn", + p."upper_rn" +) +SELECT + "group_col" AS "HAIR", + CASE + WHEN "lower_rn" = "upper_rn" THEN "lower_val" + ELSE "lower_val" + (("pos" - "lower_rn") * ("upper_val" - "lower_val")) + END AS "percentile_measure" +FROM "bounds" +ORDER BY "percentile_measure" DESC; diff --git a/Query/sql/v2/runs/v2_cli_20260502_081223_d/c16/sql/v2q_c16_e1fd4444a2f8c105.sql b/Query/sql/v2/runs/v2_cli_20260502_081223_d/c16/sql/v2q_c16_e1fd4444a2f8c105.sql new file mode 100644 index 0000000000000000000000000000000000000000..9afaaf04b58dc775ff7e1b846a5dc4e27bfe42f6 --- /dev/null +++ b/Query/sql/v2/runs/v2_cli_20260502_081223_d/c16/sql/v2q_c16_e1fd4444a2f8c105.sql @@ -0,0 +1,28 @@ +-- sql_source_version: v2 +-- sql_source_label: v2_current +-- sql_source_run_id: v2_cli_20260502_081223_d +-- sql_source_dataset_id: c16 +-- family_id: cardinality_structure +-- canonical_subitem_id: support_rank_profile_consistency +-- intended_facet_id: support_concentration +-- variant_semantic_role: ranked_signal_view +-- template_id: tpl_cardinality_distinct_share_profile +-- query_record_id: v2q_c16_e1fd4444a2f8c105 +-- problem_id: v2p_c16_acf547281dcfb576 +-- realization_mode: deterministic +-- source_kind: deterministic +WITH grouped AS ( + SELECT "ALIGN" AS value_label, COUNT(*) AS support + FROM "c16" + GROUP BY "ALIGN" +), ranked AS ( + SELECT + value_label, + support, + CAST(support AS FLOAT) / NULLIF(SUM(support) OVER (), 0) AS support_share, + SUM(support) OVER (ORDER BY support DESC, value_label ROWS UNBOUNDED PRECEDING) AS cumulative_support + FROM grouped +) +SELECT * +FROM ranked +ORDER BY support DESC, value_label; diff --git a/Query/sql/v2/runs/v2_cli_20260502_081223_d/c16/sql/v2q_c16_e3f3b4f9794f63db.sql b/Query/sql/v2/runs/v2_cli_20260502_081223_d/c16/sql/v2q_c16_e3f3b4f9794f63db.sql new file mode 100644 index 0000000000000000000000000000000000000000..3994ff2fe3256611afae398be1b1e1a3926223bd --- /dev/null +++ b/Query/sql/v2/runs/v2_cli_20260502_081223_d/c16/sql/v2q_c16_e3f3b4f9794f63db.sql @@ -0,0 +1,26 @@ +-- sql_source_version: v2 +-- sql_source_label: v2_current +-- sql_source_run_id: v2_cli_20260502_081223_d +-- sql_source_dataset_id: c16 +-- family_id: tail_rarity_structure +-- canonical_subitem_id: tail_mass_similarity +-- intended_facet_id: tail_ranked_signal +-- variant_semantic_role: count_distribution +-- template_id: tpl_tpch_relative_total_threshold +-- query_record_id: v2q_c16_e3f3b4f9794f63db +-- problem_id: v2p_c16_a260ec9e67966164 +-- realization_mode: agent +-- source_kind: agent +WITH grouped AS ( + SELECT "GSM", SUM(CAST("YEAR" AS REAL)) AS group_value + FROM "c16" + GROUP BY "GSM" +), total AS ( + SELECT SUM(group_value) AS total_value + FROM grouped +) +SELECT g."GSM", g.group_value +FROM grouped AS g +CROSS JOIN total AS t +WHERE g.group_value > t.total_value * 0.1 +ORDER BY g.group_value DESC; diff --git a/Query/sql/v2/runs/v2_cli_20260502_081223_d/c16/sql/v2q_c16_e592c729e3d55dc4.sql b/Query/sql/v2/runs/v2_cli_20260502_081223_d/c16/sql/v2q_c16_e592c729e3d55dc4.sql new file mode 100644 index 0000000000000000000000000000000000000000..74a5b23d839cba6c006ee8938b5baad5dbc74ec3 --- /dev/null +++ b/Query/sql/v2/runs/v2_cli_20260502_081223_d/c16/sql/v2q_c16_e592c729e3d55dc4.sql @@ -0,0 +1,24 @@ +-- sql_source_version: v2 +-- sql_source_label: v2_current +-- sql_source_run_id: v2_cli_20260502_081223_d +-- sql_source_dataset_id: c16 +-- family_id: tail_rarity_structure +-- canonical_subitem_id: tail_set_consistency +-- intended_facet_id: low_support_extremes +-- variant_semantic_role: rare_extreme_view +-- template_id: tpl_m4_quantile_tail_slice +-- query_record_id: v2q_c16_e592c729e3d55dc4 +-- problem_id: v2p_c16_1fcec09f2f269912 +-- realization_mode: agent +-- source_kind: agent +WITH "buckets" AS ( + SELECT + "YEAR", + NTILE(10) OVER (ORDER BY CAST("YEAR" AS INTEGER) DESC) AS "tail_bucket" + FROM "c16" + WHERE "YEAR" IS NOT NULL AND TRIM("YEAR") <> '' +) +SELECT "YEAR" +FROM "buckets" +WHERE "tail_bucket" = 1 +ORDER BY CAST("YEAR" AS INTEGER) DESC; diff --git a/Query/sql/v2/runs/v2_cli_20260502_081223_d/c16/sql/v2q_c16_e5a4a1c178df4e5c.sql b/Query/sql/v2/runs/v2_cli_20260502_081223_d/c16/sql/v2q_c16_e5a4a1c178df4e5c.sql new file mode 100644 index 0000000000000000000000000000000000000000..1384a39307296f20fa6ecc165c8dbe4fc5d5f783 --- /dev/null +++ b/Query/sql/v2/runs/v2_cli_20260502_081223_d/c16/sql/v2q_c16_e5a4a1c178df4e5c.sql @@ -0,0 +1,18 @@ +-- sql_source_version: v2 +-- sql_source_label: v2_current +-- sql_source_run_id: v2_cli_20260502_081223_d +-- sql_source_dataset_id: c16 +-- family_id: missingness_structure +-- canonical_subitem_id: marginal_missing_rate_consistency +-- intended_facet_id: missing_indicator_distribution +-- variant_semantic_role: missing_indicator_view +-- template_id: tpl_missing_marginal_rate_profile +-- query_record_id: v2q_c16_e5a4a1c178df4e5c +-- problem_id: v2p_c16_fb05f536267f91bd +-- realization_mode: deterministic +-- source_kind: deterministic +SELECT + COUNT(*) AS total_rows, + SUM(CASE WHEN "ALIGN" IS NULL THEN 1 ELSE 0 END) AS missing_rows, + AVG(CASE WHEN "ALIGN" IS NULL THEN 1.0 ELSE 0.0 END) AS missing_rate +FROM "c16"; diff --git a/Query/sql/v2/runs/v2_cli_20260502_081223_d/c16/sql/v2q_c16_e765e195b8f96517.sql b/Query/sql/v2/runs/v2_cli_20260502_081223_d/c16/sql/v2q_c16_e765e195b8f96517.sql new file mode 100644 index 0000000000000000000000000000000000000000..008174e615ae25c073c86f9075750061aa18cdc9 --- /dev/null +++ b/Query/sql/v2/runs/v2_cli_20260502_081223_d/c16/sql/v2q_c16_e765e195b8f96517.sql @@ -0,0 +1,21 @@ +-- sql_source_version: v2 +-- sql_source_label: v2_current +-- sql_source_run_id: v2_cli_20260502_081223_d +-- sql_source_dataset_id: c16 +-- family_id: cardinality_structure +-- canonical_subitem_id: high_cardinality_response_stability +-- intended_facet_id: target_cardinality_cross_section +-- variant_semantic_role: focused_target_view +-- template_id: tpl_cardinality_high_card_response_stability +-- query_record_id: v2q_c16_e765e195b8f96517 +-- problem_id: v2p_c16_01190ce39dfb83d6 +-- realization_mode: deterministic +-- source_kind: deterministic +SELECT + "name", + COUNT(*) AS support, + AVG("page_id") AS avg_response +FROM "c16" +GROUP BY "name" +HAVING COUNT(*) >= 5.0 +ORDER BY support DESC, avg_response DESC; diff --git a/Query/sql/v2/runs/v2_cli_20260502_081223_d/c16/sql/v2q_c16_e7900821675282d2.sql b/Query/sql/v2/runs/v2_cli_20260502_081223_d/c16/sql/v2q_c16_e7900821675282d2.sql new file mode 100644 index 0000000000000000000000000000000000000000..e6af29b22f501dcfc72b6c63d9a9a3f6f71cc12a --- /dev/null +++ b/Query/sql/v2/runs/v2_cli_20260502_081223_d/c16/sql/v2q_c16_e7900821675282d2.sql @@ -0,0 +1,21 @@ +-- sql_source_version: v2 +-- sql_source_label: v2_current +-- sql_source_run_id: v2_cli_20260502_081223_d +-- sql_source_dataset_id: c16 +-- family_id: cardinality_structure +-- canonical_subitem_id: high_cardinality_response_stability +-- intended_facet_id: target_cardinality_cross_section +-- variant_semantic_role: focused_target_view +-- template_id: tpl_cardinality_high_card_response_stability +-- query_record_id: v2q_c16_e7900821675282d2 +-- problem_id: v2p_c16_9fa7d2066bd776e6 +-- realization_mode: deterministic +-- source_kind: deterministic +SELECT + "page_id", + COUNT(*) AS support, + AVG("YEAR") AS avg_response +FROM "c16" +GROUP BY "page_id" +HAVING COUNT(*) >= 5.0 +ORDER BY support DESC, avg_response DESC; diff --git a/Query/sql/v2/runs/v2_cli_20260502_081223_d/c16/sql/v2q_c16_e997ffc8c751d2e8.sql b/Query/sql/v2/runs/v2_cli_20260502_081223_d/c16/sql/v2q_c16_e997ffc8c751d2e8.sql new file mode 100644 index 0000000000000000000000000000000000000000..54b0f0f2b61e41acb9f798cde79a6f27bf605fb9 --- /dev/null +++ b/Query/sql/v2/runs/v2_cli_20260502_081223_d/c16/sql/v2q_c16_e997ffc8c751d2e8.sql @@ -0,0 +1,18 @@ +-- sql_source_version: v2 +-- sql_source_label: v2_current +-- sql_source_run_id: v2_cli_20260502_081223_d +-- sql_source_dataset_id: c16 +-- family_id: conditional_dependency_structure +-- canonical_subitem_id: dependency_strength_similarity +-- intended_facet_id: pairwise_conditional_dependency +-- variant_semantic_role: focused_target_view +-- template_id: tpl_m4_group_condition_rate +-- query_record_id: v2q_c16_e997ffc8c751d2e8 +-- problem_id: v2p_c16_b98618b993ee313c +-- realization_mode: agent +-- source_kind: agent +SELECT "ALIGN", + AVG(CASE WHEN "ALIGN" = 'Good Characters' THEN 1 ELSE 0 END) AS condition_rate +FROM "c16" +GROUP BY "ALIGN" +ORDER BY condition_rate DESC; diff --git a/Query/sql/v2/runs/v2_cli_20260502_081223_d/c16/sql/v2q_c16_eab18e325e8b60f9.sql b/Query/sql/v2/runs/v2_cli_20260502_081223_d/c16/sql/v2q_c16_eab18e325e8b60f9.sql new file mode 100644 index 0000000000000000000000000000000000000000..386e107812d40fe936fcfc4dea8bbba5fd0ae25d --- /dev/null +++ b/Query/sql/v2/runs/v2_cli_20260502_081223_d/c16/sql/v2q_c16_eab18e325e8b60f9.sql @@ -0,0 +1,17 @@ +-- sql_source_version: v2 +-- sql_source_label: v2_current +-- sql_source_run_id: v2_cli_20260502_081223_d +-- sql_source_dataset_id: c16 +-- family_id: subgroup_structure +-- canonical_subitem_id: internal_profile_stability +-- intended_facet_id: subgroup_conditional_contrast +-- variant_semantic_role: collapsed_target_view +-- template_id: tpl_h2o_group_sum +-- query_record_id: v2q_c16_eab18e325e8b60f9 +-- problem_id: v2p_c16_30574a8c4f4868f5 +-- realization_mode: agent +-- source_kind: agent +SELECT "GSM", SUM(CAST("APPEARANCES" AS INTEGER)) AS total_measure +FROM "c16" +GROUP BY "GSM" +ORDER BY total_measure DESC; diff --git a/Query/sql/v2/runs/v2_cli_20260502_081223_d/c16/sql/v2q_c16_edb35045c2be6c90.sql b/Query/sql/v2/runs/v2_cli_20260502_081223_d/c16/sql/v2q_c16_edb35045c2be6c90.sql new file mode 100644 index 0000000000000000000000000000000000000000..5b87ebb13ce242e485b4f8fccea96ea2c2c78a2a --- /dev/null +++ b/Query/sql/v2/runs/v2_cli_20260502_081223_d/c16/sql/v2q_c16_edb35045c2be6c90.sql @@ -0,0 +1,24 @@ +-- sql_source_version: v2 +-- sql_source_label: v2_current +-- sql_source_run_id: v2_cli_20260502_081223_d +-- sql_source_dataset_id: c16 +-- family_id: conditional_dependency_structure +-- canonical_subitem_id: direction_consistency +-- intended_facet_id: conditional_rate_shift +-- variant_semantic_role: contrastive_conditional_view +-- template_id: tpl_m4_group_ratio_two_conditions +-- query_record_id: v2q_c16_edb35045c2be6c90 +-- problem_id: v2p_c16_6d103687e2196fc4 +-- realization_mode: agent +-- source_kind: agent +WITH grouped AS ( + SELECT "EYE", + SUM(CASE WHEN "ID" = 'Public Identity' THEN 1 ELSE 0 END) AS numerator_count, + SUM(CASE WHEN "ID" = 'Secret Identity' THEN 1 ELSE 0 END) AS denominator_count + FROM "c16" + GROUP BY "EYE" +) +SELECT "EYE", + CAST(numerator_count AS FLOAT) / NULLIF(denominator_count, 0) AS condition_ratio +FROM grouped +ORDER BY condition_ratio DESC; diff --git a/Query/sql/v2/runs/v2_cli_20260502_081223_d/c16/sql/v2q_c16_ee601ee414432d00.sql b/Query/sql/v2/runs/v2_cli_20260502_081223_d/c16/sql/v2q_c16_ee601ee414432d00.sql new file mode 100644 index 0000000000000000000000000000000000000000..fb27f31acac73f3ea7b0e2d785dee612519c7364 --- /dev/null +++ b/Query/sql/v2/runs/v2_cli_20260502_081223_d/c16/sql/v2q_c16_ee601ee414432d00.sql @@ -0,0 +1,20 @@ +-- sql_source_version: v2 +-- sql_source_label: v2_current +-- sql_source_run_id: v2_cli_20260502_081223_d +-- sql_source_dataset_id: c16 +-- family_id: conditional_dependency_structure +-- canonical_subitem_id: dependency_strength_similarity +-- intended_facet_id: pairwise_conditional_dependency +-- variant_semantic_role: within_group_proportion +-- template_id: tpl_tpcds_within_group_share +-- query_record_id: v2q_c16_ee601ee414432d00 +-- problem_id: v2p_c16_b26f643d17770f11 +-- realization_mode: agent +-- source_kind: agent +SELECT "GSM", "APPEARANCES", + SUM(CAST(NULLIF("YEAR", '') AS REAL)) AS total_measure, + SUM(CAST(NULLIF("YEAR", '') AS REAL)) * 100.0 + / SUM(SUM(CAST(NULLIF("YEAR", '') AS REAL))) OVER (PARTITION BY "GSM") AS share_within_group +FROM "c16" +GROUP BY "GSM", "APPEARANCES" +ORDER BY share_within_group DESC; diff --git a/Query/sql/v2/runs/v2_cli_20260502_081223_d/c16/sql/v2q_c16_efa91f27a0253fd3.sql b/Query/sql/v2/runs/v2_cli_20260502_081223_d/c16/sql/v2q_c16_efa91f27a0253fd3.sql new file mode 100644 index 0000000000000000000000000000000000000000..02787b940543c67e55e2a81de28e7c5dbb89e09a --- /dev/null +++ b/Query/sql/v2/runs/v2_cli_20260502_081223_d/c16/sql/v2q_c16_efa91f27a0253fd3.sql @@ -0,0 +1,25 @@ +-- sql_source_version: v2 +-- sql_source_label: v2_current +-- sql_source_run_id: v2_cli_20260502_081223_d +-- sql_source_dataset_id: c16 +-- family_id: tail_rarity_structure +-- canonical_subitem_id: tail_set_consistency +-- intended_facet_id: low_support_extremes +-- variant_semantic_role: rare_extreme_view +-- template_id: tpl_m4_quantile_tail_slice +-- query_record_id: v2q_c16_efa91f27a0253fd3 +-- problem_id: v2p_c16_e51fc69c73f6fc5a +-- realization_mode: agent +-- source_kind: agent +WITH "buckets" AS ( + SELECT + "APPEARANCES", + NTILE(10) OVER (ORDER BY CAST("APPEARANCES" AS REAL) DESC) AS "tail_bucket" + FROM "c16" + WHERE "APPEARANCES" IS NOT NULL + AND TRIM("APPEARANCES") <> '' +) +SELECT "APPEARANCES" +FROM "buckets" +WHERE "tail_bucket" = 1 +ORDER BY CAST("APPEARANCES" AS REAL) DESC; diff --git a/Query/sql/v2/runs/v2_cli_20260502_081223_d/c16/sql/v2q_c16_efea7c4546692339.sql b/Query/sql/v2/runs/v2_cli_20260502_081223_d/c16/sql/v2q_c16_efea7c4546692339.sql new file mode 100644 index 0000000000000000000000000000000000000000..d8546f3b37adb19317bb0127150ba2b6cc96a062 --- /dev/null +++ b/Query/sql/v2/runs/v2_cli_20260502_081223_d/c16/sql/v2q_c16_efea7c4546692339.sql @@ -0,0 +1,76 @@ +-- sql_source_version: v2 +-- sql_source_label: v2_current +-- sql_source_run_id: v2_cli_20260502_081223_d +-- sql_source_dataset_id: c16 +-- family_id: tail_rarity_structure +-- canonical_subitem_id: tail_concentration_consistency +-- intended_facet_id: rare_target_concentration +-- variant_semantic_role: focused_target_view +-- template_id: tpl_grouped_percentile_point +-- query_record_id: v2q_c16_efea7c4546692339 +-- problem_id: v2p_c16_ccde493b513c429f +-- realization_mode: agent +-- source_kind: agent +WITH "base" AS ( + SELECT + "HAIR" AS "group_col", + CAST("YEAR" AS REAL) AS "measure_col" + FROM "c16" + WHERE "HAIR" IS NOT NULL + AND TRIM("HAIR") <> '' + AND "YEAR" IS NOT NULL + AND TRIM("YEAR") <> '' + AND "EYE" IS NOT NULL + AND TRIM("EYE") <> '' +), +"eligible_groups" AS ( + SELECT "group_col" + FROM "base" + GROUP BY "group_col" + HAVING COUNT(*) >= 5 +), +"ranked" AS ( + SELECT + b."group_col", + b."measure_col", + ROW_NUMBER() OVER ( + PARTITION BY b."group_col" + ORDER BY b."measure_col" + ) AS "rn", + COUNT(*) OVER (PARTITION BY b."group_col") AS "cnt" + FROM "base" b + JOIN "eligible_groups" eg + ON eg."group_col" = b."group_col" +), +"positions" AS ( + SELECT DISTINCT + "group_col", + "cnt", + (1.0 + (0.95 * ("cnt" - 1))) AS "pos", + CAST(1.0 + (0.95 * ("cnt" - 1)) AS INTEGER) AS "lo_rn", + CAST(1.0 + (0.95 * ("cnt" - 1)) AS INTEGER) + CASE + WHEN (1.0 + (0.95 * ("cnt" - 1))) > CAST(1.0 + (0.95 * ("cnt" - 1)) AS INTEGER) THEN 1 + ELSE 0 + END AS "hi_rn" + FROM "ranked" +), +"picked" AS ( + SELECT + p."group_col", + p."pos", + p."lo_rn", + MAX(CASE WHEN r."rn" = p."lo_rn" THEN r."measure_col" END) AS "lo_val", + MAX(CASE WHEN r."rn" = p."hi_rn" THEN r."measure_col" END) AS "hi_val" + FROM "positions" p + JOIN "ranked" r + ON r."group_col" = p."group_col" + GROUP BY p."group_col", p."pos", p."lo_rn", p."hi_rn" +) +SELECT + "group_col" AS "HAIR", + CASE + WHEN "pos" = "lo_rn" THEN "lo_val" + ELSE "lo_val" + (("pos" - "lo_rn") * ("hi_val" - "lo_val")) + END AS "percentile_measure" +FROM "picked" +ORDER BY "percentile_measure" DESC, "HAIR"; diff --git a/Query/sql/v2/runs/v2_cli_20260502_081223_d/c16/sql/v2q_c16_f0a063225961af10.sql b/Query/sql/v2/runs/v2_cli_20260502_081223_d/c16/sql/v2q_c16_f0a063225961af10.sql new file mode 100644 index 0000000000000000000000000000000000000000..b3b78f8e83c8ca072601429f6bba12dd288f8f87 --- /dev/null +++ b/Query/sql/v2/runs/v2_cli_20260502_081223_d/c16/sql/v2q_c16_f0a063225961af10.sql @@ -0,0 +1,24 @@ +-- sql_source_version: v2 +-- sql_source_label: v2_current +-- sql_source_run_id: v2_cli_20260502_081223_d +-- sql_source_dataset_id: c16 +-- family_id: conditional_dependency_structure +-- canonical_subitem_id: direction_consistency +-- intended_facet_id: conditional_rate_shift +-- variant_semantic_role: contrastive_conditional_view +-- template_id: tpl_m4_group_ratio_two_conditions +-- query_record_id: v2q_c16_f0a063225961af10 +-- problem_id: v2p_c16_edb61dab019eb4a8 +-- realization_mode: agent +-- source_kind: agent +WITH grouped AS ( + SELECT "GSM", + SUM(CASE WHEN "SEX" = 'Male Characters' THEN 1 ELSE 0 END) AS numerator_count, + SUM(CASE WHEN "SEX" = 'Female Characters' THEN 1 ELSE 0 END) AS denominator_count + FROM "c16" + GROUP BY "GSM" +) +SELECT "GSM", + CAST(numerator_count AS FLOAT) / NULLIF(denominator_count, 0) AS condition_ratio +FROM grouped +ORDER BY condition_ratio DESC; diff --git a/Query/sql/v2/runs/v2_cli_20260502_081223_d/c16/sql/v2q_c16_f0d01b0758b6be2f.sql b/Query/sql/v2/runs/v2_cli_20260502_081223_d/c16/sql/v2q_c16_f0d01b0758b6be2f.sql new file mode 100644 index 0000000000000000000000000000000000000000..75686f922725eb6dd3415692d422d0b02eb205c5 --- /dev/null +++ b/Query/sql/v2/runs/v2_cli_20260502_081223_d/c16/sql/v2q_c16_f0d01b0758b6be2f.sql @@ -0,0 +1,24 @@ +-- sql_source_version: v2 +-- sql_source_label: v2_current +-- sql_source_run_id: v2_cli_20260502_081223_d +-- sql_source_dataset_id: c16 +-- family_id: conditional_dependency_structure +-- canonical_subitem_id: direction_consistency +-- intended_facet_id: conditional_rate_shift +-- variant_semantic_role: contrastive_conditional_view +-- template_id: tpl_m4_group_ratio_two_conditions +-- query_record_id: v2q_c16_f0d01b0758b6be2f +-- problem_id: v2p_c16_6e2fceffc5495dda +-- realization_mode: agent +-- source_kind: agent +WITH grouped AS ( + SELECT "YEAR", + SUM(CASE WHEN "ALIVE" = 'Living Characters' THEN 1 ELSE 0 END) AS numerator_count, + SUM(CASE WHEN "ALIVE" = 'Deceased Characters' THEN 1 ELSE 0 END) AS denominator_count + FROM "c16" + GROUP BY "YEAR" +) +SELECT "YEAR", + CAST(numerator_count AS FLOAT) / NULLIF(denominator_count, 0) AS condition_ratio +FROM grouped +ORDER BY condition_ratio DESC; diff --git a/Query/sql/v2/runs/v2_cli_20260502_081223_d/c16/sql/v2q_c16_f2b81d352e894fce.sql b/Query/sql/v2/runs/v2_cli_20260502_081223_d/c16/sql/v2q_c16_f2b81d352e894fce.sql new file mode 100644 index 0000000000000000000000000000000000000000..a66f78de94938304719d80abbe076e16d5f59c93 --- /dev/null +++ b/Query/sql/v2/runs/v2_cli_20260502_081223_d/c16/sql/v2q_c16_f2b81d352e894fce.sql @@ -0,0 +1,22 @@ +-- sql_source_version: v2 +-- sql_source_label: v2_current +-- sql_source_run_id: v2_cli_20260502_081223_d +-- sql_source_dataset_id: c16 +-- family_id: conditional_dependency_structure +-- canonical_subitem_id: dependency_strength_similarity +-- intended_facet_id: pairwise_conditional_dependency +-- variant_semantic_role: focused_target_view +-- template_id: tpl_m4_group_condition_rate +-- query_record_id: v2q_c16_f2b81d352e894fce +-- problem_id: v2p_c16_8f80c73f6c257214 +-- realization_mode: agent +-- source_kind: agent +SELECT + "YEAR", + AVG(CASE WHEN "ALIGN" = 'Good Characters' THEN 1.0 ELSE 0.0 END) AS "condition_rate" +FROM "c16" +WHERE "YEAR" IS NOT NULL + AND "YEAR" <> '' +GROUP BY "YEAR" +HAVING COUNT(*) >= 5 +ORDER BY "condition_rate" DESC; diff --git a/Query/sql/v2/runs/v2_cli_20260502_081223_d/c16/sql/v2q_c16_f482142a43b62d2c.sql b/Query/sql/v2/runs/v2_cli_20260502_081223_d/c16/sql/v2q_c16_f482142a43b62d2c.sql new file mode 100644 index 0000000000000000000000000000000000000000..74dc03bd9e9fc3af01d15f8bfdd0f5d3d8116a75 --- /dev/null +++ b/Query/sql/v2/runs/v2_cli_20260502_081223_d/c16/sql/v2q_c16_f482142a43b62d2c.sql @@ -0,0 +1,26 @@ +-- sql_source_version: v2 +-- sql_source_label: v2_current +-- sql_source_run_id: v2_cli_20260502_081223_d +-- sql_source_dataset_id: c16 +-- family_id: tail_rarity_structure +-- canonical_subitem_id: tail_mass_similarity +-- intended_facet_id: tail_ranked_signal +-- variant_semantic_role: count_distribution +-- template_id: tpl_tpch_relative_total_threshold +-- query_record_id: v2q_c16_f482142a43b62d2c +-- problem_id: v2p_c16_bdd11be1f06d71b0 +-- realization_mode: agent +-- source_kind: agent +WITH grouped AS ( + SELECT "EYE", COUNT("page_id") AS group_value + FROM "c16" + GROUP BY "EYE" +), total AS ( + SELECT SUM(group_value) AS total_value + FROM grouped +) +SELECT g."EYE", g.group_value +FROM grouped AS g +CROSS JOIN total AS t +WHERE g.group_value > t.total_value * 0.1 +ORDER BY g.group_value DESC; diff --git a/Query/sql/v2/runs/v2_cli_20260502_081223_d/c16/sql/v2q_c16_f66a38beeca4d7c7.sql b/Query/sql/v2/runs/v2_cli_20260502_081223_d/c16/sql/v2q_c16_f66a38beeca4d7c7.sql new file mode 100644 index 0000000000000000000000000000000000000000..3a1c219e5d626fdde5981c93c9907408e8431756 --- /dev/null +++ b/Query/sql/v2/runs/v2_cli_20260502_081223_d/c16/sql/v2q_c16_f66a38beeca4d7c7.sql @@ -0,0 +1,21 @@ +-- sql_source_version: v2 +-- sql_source_label: v2_current +-- sql_source_run_id: v2_cli_20260502_081223_d +-- sql_source_dataset_id: c16 +-- family_id: missingness_structure +-- canonical_subitem_id: co_missingness_pattern_consistency +-- intended_facet_id: missing_target_interaction +-- variant_semantic_role: missing_target_interaction +-- template_id: tpl_missing_target_interaction +-- query_record_id: v2q_c16_f66a38beeca4d7c7 +-- problem_id: v2p_c16_76f49de554ee548c +-- realization_mode: deterministic +-- source_kind: deterministic +SELECT + "GSM", + COUNT(*) AS total_rows, + SUM(CASE WHEN "EYE" IS NULL THEN 1 ELSE 0 END) AS missing_rows, + AVG(CASE WHEN "EYE" IS NULL THEN 1.0 ELSE 0.0 END) AS missing_rate +FROM "c16" +GROUP BY "GSM" +ORDER BY missing_rate DESC, total_rows DESC; diff --git a/Query/sql/v2/runs/v2_cli_20260502_081223_d/c16/sql/v2q_c16_f8b249383211d4c5.sql b/Query/sql/v2/runs/v2_cli_20260502_081223_d/c16/sql/v2q_c16_f8b249383211d4c5.sql new file mode 100644 index 0000000000000000000000000000000000000000..5de72a8a2777f69d33cbc1ed3ae00bb35636abfd --- /dev/null +++ b/Query/sql/v2/runs/v2_cli_20260502_081223_d/c16/sql/v2q_c16_f8b249383211d4c5.sql @@ -0,0 +1,73 @@ +-- sql_source_version: v2 +-- sql_source_label: v2_current +-- sql_source_run_id: v2_cli_20260502_081223_d +-- sql_source_dataset_id: c16 +-- family_id: tail_rarity_structure +-- canonical_subitem_id: tail_concentration_consistency +-- intended_facet_id: rare_target_concentration +-- variant_semantic_role: focused_target_view +-- template_id: tpl_grouped_percentile_point +-- query_record_id: v2q_c16_f8b249383211d4c5 +-- problem_id: v2p_c16_dd024efa2aaea2b6 +-- realization_mode: agent +-- source_kind: agent +WITH base AS ( + SELECT + "YEAR", + CAST("page_id" AS REAL) AS "measure_value" + FROM "c16" + WHERE "YEAR" IS NOT NULL + AND TRIM("YEAR") <> '' + AND "page_id" IS NOT NULL + AND TRIM("page_id") <> '' + AND TRIM("page_id") NOT GLOB '*[^0-9]*' +), +bounds AS ( + SELECT + "YEAR", + 1.0 + (COUNT(*) - 1) * 0.95 AS "pos", + CAST(1.0 + (COUNT(*) - 1) * 0.95 AS INTEGER) AS "lo", + CASE + WHEN (1.0 + (COUNT(*) - 1) * 0.95) = CAST(1.0 + (COUNT(*) - 1) * 0.95 AS INTEGER) + THEN CAST(1.0 + (COUNT(*) - 1) * 0.95 AS INTEGER) + ELSE CAST(1.0 + (COUNT(*) - 1) * 0.95 AS INTEGER) + 1 + END AS "hi" + FROM base + GROUP BY "YEAR" + HAVING COUNT(*) >= 5 +), +ordered AS ( + SELECT + "YEAR", + "measure_value", + ROW_NUMBER() OVER ( + PARTITION BY "YEAR" + ORDER BY "measure_value" + ) AS "rn" + FROM base +), +picked AS ( + SELECT + o."YEAR", + b."pos", + b."lo", + b."hi", + MAX(CASE WHEN o."rn" = b."lo" THEN o."measure_value" END) AS "lo_val", + MAX(CASE WHEN o."rn" = b."hi" THEN o."measure_value" END) AS "hi_val" + FROM ordered AS o + JOIN bounds AS b + ON o."YEAR" = b."YEAR" + GROUP BY + o."YEAR", + b."pos", + b."lo", + b."hi" +) +SELECT + "YEAR", + CASE + WHEN "lo" = "hi" THEN "lo_val" + ELSE "lo_val" + ("pos" - "lo") * ("hi_val" - "lo_val") + END AS "percentile_measure" +FROM picked +ORDER BY "percentile_measure" DESC; diff --git a/Query/sql/v2/runs/v2_cli_20260502_081223_d/c16/sql/v2q_c16_fa70f97aef4bac74.sql b/Query/sql/v2/runs/v2_cli_20260502_081223_d/c16/sql/v2q_c16_fa70f97aef4bac74.sql new file mode 100644 index 0000000000000000000000000000000000000000..78f954aeab3a49a912688088b45abfa32b15520b --- /dev/null +++ b/Query/sql/v2/runs/v2_cli_20260502_081223_d/c16/sql/v2q_c16_fa70f97aef4bac74.sql @@ -0,0 +1,17 @@ +-- sql_source_version: v2 +-- sql_source_label: v2_current +-- sql_source_run_id: v2_cli_20260502_081223_d +-- sql_source_dataset_id: c16 +-- family_id: subgroup_structure +-- canonical_subitem_id: internal_profile_stability +-- intended_facet_id: subgroup_rank_order +-- variant_semantic_role: collapsed_target_view +-- template_id: tpl_h2o_group_sum +-- query_record_id: v2q_c16_fa70f97aef4bac74 +-- problem_id: v2p_c16_af37985ddf8ec001 +-- realization_mode: agent +-- source_kind: agent +SELECT "SEX", SUM(CAST("page_id" AS NUMERIC)) AS "total_measure" +FROM "c16" +GROUP BY "SEX" +ORDER BY "total_measure" DESC; diff --git a/Query/sql/v2/runs/v2_cli_20260502_081223_d/c16/sql/v2q_c16_fa994896bcb619ab.sql b/Query/sql/v2/runs/v2_cli_20260502_081223_d/c16/sql/v2q_c16_fa994896bcb619ab.sql new file mode 100644 index 0000000000000000000000000000000000000000..4c4e02a3fc3ebb5e25470a2441827c113d15e9f4 --- /dev/null +++ b/Query/sql/v2/runs/v2_cli_20260502_081223_d/c16/sql/v2q_c16_fa994896bcb619ab.sql @@ -0,0 +1,21 @@ +-- sql_source_version: v2 +-- sql_source_label: v2_current +-- sql_source_run_id: v2_cli_20260502_081223_d +-- sql_source_dataset_id: c16 +-- family_id: cardinality_structure +-- canonical_subitem_id: high_cardinality_response_stability +-- intended_facet_id: target_cardinality_cross_section +-- variant_semantic_role: focused_target_view +-- template_id: tpl_cardinality_high_card_response_stability +-- query_record_id: v2q_c16_fa994896bcb619ab +-- problem_id: v2p_c16_a57649f7554d7264 +-- realization_mode: deterministic +-- source_kind: deterministic +SELECT + "page_id", + COUNT(*) AS support, + AVG("APPEARANCES") AS avg_response +FROM "c16" +GROUP BY "page_id" +HAVING COUNT(*) >= 5.0 +ORDER BY support DESC, avg_response DESC; diff --git a/Query/sql/v2/runs/v2_cli_20260502_081223_d/c16/sql/v2q_c16_fb6723141ceece9d.sql b/Query/sql/v2/runs/v2_cli_20260502_081223_d/c16/sql/v2q_c16_fb6723141ceece9d.sql new file mode 100644 index 0000000000000000000000000000000000000000..b1ab43edeaa1238686afbc63bdce5d5f84e7eb66 --- /dev/null +++ b/Query/sql/v2/runs/v2_cli_20260502_081223_d/c16/sql/v2q_c16_fb6723141ceece9d.sql @@ -0,0 +1,17 @@ +-- sql_source_version: v2 +-- sql_source_label: v2_current +-- sql_source_run_id: v2_cli_20260502_081223_d +-- sql_source_dataset_id: c16 +-- family_id: subgroup_structure +-- canonical_subitem_id: internal_profile_stability +-- intended_facet_id: subgroup_rank_order +-- variant_semantic_role: collapsed_target_view +-- template_id: tpl_h2o_group_sum +-- query_record_id: v2q_c16_fb6723141ceece9d +-- problem_id: v2p_c16_9a3a8c6773a5a614 +-- realization_mode: agent +-- source_kind: agent +SELECT "EYE", SUM(CAST(NULLIF("APPEARANCES", '') AS REAL)) AS "total_measure" +FROM "c16" +GROUP BY "EYE" +ORDER BY "total_measure" DESC; diff --git a/Query/sql/v2/runs/v2_cli_20260502_081223_d/c16/sql/v2q_c16_fb701cc7dca0a971.sql b/Query/sql/v2/runs/v2_cli_20260502_081223_d/c16/sql/v2q_c16_fb701cc7dca0a971.sql new file mode 100644 index 0000000000000000000000000000000000000000..911c16efc572d6904719f24c2e106ec1418798aa --- /dev/null +++ b/Query/sql/v2/runs/v2_cli_20260502_081223_d/c16/sql/v2q_c16_fb701cc7dca0a971.sql @@ -0,0 +1,19 @@ +-- sql_source_version: v2 +-- sql_source_label: v2_current +-- sql_source_run_id: v2_cli_20260502_081223_d +-- sql_source_dataset_id: c16 +-- family_id: subgroup_structure +-- canonical_subitem_id: internal_profile_stability +-- intended_facet_id: subgroup_conditional_contrast +-- variant_semantic_role: collapsed_target_view +-- template_id: tpl_h2o_group_sum +-- query_record_id: v2q_c16_fb701cc7dca0a971 +-- problem_id: v2p_c16_db5912f2ea546ff5 +-- realization_mode: agent +-- source_kind: agent +SELECT "EYE", SUM(CAST("APPEARANCES" AS REAL)) AS "total_measure" +FROM "c16" +WHERE "EYE" IS NOT NULL AND "EYE" <> '' + AND "APPEARANCES" IS NOT NULL AND "APPEARANCES" <> '' +GROUP BY "EYE" +ORDER BY "total_measure" DESC; diff --git a/Query/sql/v2/runs/v2_cli_20260502_081223_d/c16/sql/v2q_c16_fb987a0a97e726f6.sql b/Query/sql/v2/runs/v2_cli_20260502_081223_d/c16/sql/v2q_c16_fb987a0a97e726f6.sql new file mode 100644 index 0000000000000000000000000000000000000000..3254ad59a9db85f4f149b41da5721588c552a78b --- /dev/null +++ b/Query/sql/v2/runs/v2_cli_20260502_081223_d/c16/sql/v2q_c16_fb987a0a97e726f6.sql @@ -0,0 +1,21 @@ +-- sql_source_version: v2 +-- sql_source_label: v2_current +-- sql_source_run_id: v2_cli_20260502_081223_d +-- sql_source_dataset_id: c16 +-- family_id: cardinality_structure +-- canonical_subitem_id: high_cardinality_response_stability +-- intended_facet_id: target_cardinality_cross_section +-- variant_semantic_role: focused_target_view +-- template_id: tpl_cardinality_high_card_response_stability +-- query_record_id: v2q_c16_fb987a0a97e726f6 +-- problem_id: v2p_c16_062ca2d935bc26a6 +-- realization_mode: deterministic +-- source_kind: deterministic +SELECT + "urlslug", + COUNT(*) AS support, + AVG("YEAR") AS avg_response +FROM "c16" +GROUP BY "urlslug" +HAVING COUNT(*) >= 5.0 +ORDER BY support DESC, avg_response DESC; diff --git a/Query/sql/v2/runs/v2_cli_20260502_081223_d/c16/sql/v2q_c16_fc5067d08fed7c7e.sql b/Query/sql/v2/runs/v2_cli_20260502_081223_d/c16/sql/v2q_c16_fc5067d08fed7c7e.sql new file mode 100644 index 0000000000000000000000000000000000000000..c15cf8cc263c5cad2397a8c183cb7861175cf855 --- /dev/null +++ b/Query/sql/v2/runs/v2_cli_20260502_081223_d/c16/sql/v2q_c16_fc5067d08fed7c7e.sql @@ -0,0 +1,22 @@ +-- sql_source_version: v2 +-- sql_source_label: v2_current +-- sql_source_run_id: v2_cli_20260502_081223_d +-- sql_source_dataset_id: c16 +-- family_id: conditional_dependency_structure +-- canonical_subitem_id: direction_consistency +-- intended_facet_id: conditional_rate_shift +-- variant_semantic_role: ranked_signal_view +-- template_id: tpl_m4_window_partition_avg +-- query_record_id: v2q_c16_fc5067d08fed7c7e +-- problem_id: v2p_c16_fed567c63aafe051 +-- realization_mode: agent +-- source_kind: agent +SELECT DISTINCT + "ALIGN", + AVG(CAST("APPEARANCES" AS REAL)) OVER (PARTITION BY "ALIGN") AS "avg_measure" +FROM "c16" +WHERE "ALIGN" IS NOT NULL + AND TRIM("ALIGN") <> '' + AND "APPEARANCES" IS NOT NULL + AND TRIM("APPEARANCES") <> '' +ORDER BY "avg_measure" DESC; diff --git a/Query/sql/v2/runs/v2_cli_20260502_081223_d/c16/sql/v2q_c16_fc9712a446524eb6.sql b/Query/sql/v2/runs/v2_cli_20260502_081223_d/c16/sql/v2q_c16_fc9712a446524eb6.sql new file mode 100644 index 0000000000000000000000000000000000000000..ba2c40ccc3095a55e50fcd0d2c3dd20d2182cd88 --- /dev/null +++ b/Query/sql/v2/runs/v2_cli_20260502_081223_d/c16/sql/v2q_c16_fc9712a446524eb6.sql @@ -0,0 +1,18 @@ +-- sql_source_version: v2 +-- sql_source_label: v2_current +-- sql_source_run_id: v2_cli_20260502_081223_d +-- sql_source_dataset_id: c16 +-- family_id: conditional_dependency_structure +-- canonical_subitem_id: dependency_strength_similarity +-- intended_facet_id: pairwise_conditional_dependency +-- variant_semantic_role: within_group_proportion +-- template_id: tpl_m4_group_condition_rate +-- query_record_id: v2q_c16_fc9712a446524eb6 +-- problem_id: v2p_c16_ff89163b136aa5f4 +-- realization_mode: agent +-- source_kind: agent +SELECT "GSM", + AVG(CASE WHEN "EYE" = '' THEN 1 ELSE 0 END) AS condition_rate +FROM "c16" +GROUP BY "GSM" +ORDER BY condition_rate DESC; diff --git a/Query/sql/v2/runs/v2_cli_20260502_081223_d/c16/sql/v2q_c16_fd5cb1ea99f55061.sql b/Query/sql/v2/runs/v2_cli_20260502_081223_d/c16/sql/v2q_c16_fd5cb1ea99f55061.sql new file mode 100644 index 0000000000000000000000000000000000000000..e3262aa1069b0445abd22666f7c79564e4198d34 --- /dev/null +++ b/Query/sql/v2/runs/v2_cli_20260502_081223_d/c16/sql/v2q_c16_fd5cb1ea99f55061.sql @@ -0,0 +1,18 @@ +-- sql_source_version: v2 +-- sql_source_label: v2_current +-- sql_source_run_id: v2_cli_20260502_081223_d +-- sql_source_dataset_id: c16 +-- family_id: conditional_dependency_structure +-- canonical_subitem_id: slice_level_consistency +-- intended_facet_id: conditional_interaction_hotspots +-- variant_semantic_role: count_distribution +-- template_id: tpl_c2_filtered_group_count_2d +-- query_record_id: v2q_c16_fd5cb1ea99f55061 +-- problem_id: v2p_c16_15146fe6e345229b +-- realization_mode: agent +-- source_kind: agent +SELECT "EYE", "GSM", COUNT(*) AS row_count +FROM "c16" +WHERE CAST("page_id" AS REAL) >= 213203.0 +GROUP BY "EYE", "GSM" +ORDER BY row_count DESC; diff --git a/Query/sql/v2/runs/v2_cli_20260502_081223_d/c16/sql/v2q_c16_fe58744cb65b6f54.sql b/Query/sql/v2/runs/v2_cli_20260502_081223_d/c16/sql/v2q_c16_fe58744cb65b6f54.sql new file mode 100644 index 0000000000000000000000000000000000000000..e3e57534f3fa0b8b8be73fb4bff6e050e6546beb --- /dev/null +++ b/Query/sql/v2/runs/v2_cli_20260502_081223_d/c16/sql/v2q_c16_fe58744cb65b6f54.sql @@ -0,0 +1,28 @@ +-- sql_source_version: v2 +-- sql_source_label: v2_current +-- sql_source_run_id: v2_cli_20260502_081223_d +-- sql_source_dataset_id: c16 +-- family_id: cardinality_structure +-- canonical_subitem_id: support_rank_profile_consistency +-- intended_facet_id: value_imbalance_profile +-- variant_semantic_role: ranked_signal_view +-- template_id: tpl_cardinality_distinct_share_profile +-- query_record_id: v2q_c16_fe58744cb65b6f54 +-- problem_id: v2p_c16_f4b9ff0f9b3a8a74 +-- realization_mode: deterministic +-- source_kind: deterministic +WITH grouped AS ( + SELECT "EYE" AS value_label, COUNT(*) AS support + FROM "c16" + GROUP BY "EYE" +), ranked AS ( + SELECT + value_label, + support, + CAST(support AS FLOAT) / NULLIF(SUM(support) OVER (), 0) AS support_share, + SUM(support) OVER (ORDER BY support DESC, value_label ROWS UNBOUNDED PRECEDING) AS cumulative_support + FROM grouped +) +SELECT * +FROM ranked +ORDER BY support DESC, value_label; diff --git a/Query/sql/v2/runs/v2_cli_20260502_081223_d/c16/sql/v2q_c16_feb83d4a830c5406.sql b/Query/sql/v2/runs/v2_cli_20260502_081223_d/c16/sql/v2q_c16_feb83d4a830c5406.sql new file mode 100644 index 0000000000000000000000000000000000000000..c0e477583c321e802a4cb4f5ae21025fe7a53884 --- /dev/null +++ b/Query/sql/v2/runs/v2_cli_20260502_081223_d/c16/sql/v2q_c16_feb83d4a830c5406.sql @@ -0,0 +1,17 @@ +-- sql_source_version: v2 +-- sql_source_label: v2_current +-- sql_source_run_id: v2_cli_20260502_081223_d +-- sql_source_dataset_id: c16 +-- family_id: subgroup_structure +-- canonical_subitem_id: internal_profile_stability +-- intended_facet_id: subgroup_distribution_shift +-- variant_semantic_role: collapsed_target_view +-- template_id: tpl_h2o_group_sum +-- query_record_id: v2q_c16_feb83d4a830c5406 +-- problem_id: v2p_c16_a6731e0bafe85913 +-- realization_mode: agent +-- source_kind: agent +SELECT "SEX", SUM(CAST("page_id" AS INTEGER)) AS "total_measure" +FROM "c16" +GROUP BY "SEX" +ORDER BY "total_measure" DESC; diff --git a/Query/sql/v2/runs/v2_cli_20260502_081223_d/c16/sql/v2q_c16_ff361e87a46f0b35.sql b/Query/sql/v2/runs/v2_cli_20260502_081223_d/c16/sql/v2q_c16_ff361e87a46f0b35.sql new file mode 100644 index 0000000000000000000000000000000000000000..cc2651e9fb23e994d69ad0572e622232d663e72e --- /dev/null +++ b/Query/sql/v2/runs/v2_cli_20260502_081223_d/c16/sql/v2q_c16_ff361e87a46f0b35.sql @@ -0,0 +1,18 @@ +-- sql_source_version: v2 +-- sql_source_label: v2_current +-- sql_source_run_id: v2_cli_20260502_081223_d +-- sql_source_dataset_id: c16 +-- family_id: conditional_dependency_structure +-- canonical_subitem_id: slice_level_consistency +-- intended_facet_id: conditional_interaction_hotspots +-- variant_semantic_role: count_distribution +-- template_id: tpl_c2_filtered_group_count_2d +-- query_record_id: v2q_c16_ff361e87a46f0b35 +-- problem_id: v2p_c16_eead3dd2e91b22bb +-- realization_mode: agent +-- source_kind: agent +SELECT "EYE", "HAIR", COUNT(*) AS "row_count" +FROM "c16" +WHERE "FIRST APPEARANCE" = '2010, December' +GROUP BY "EYE", "HAIR" +ORDER BY "row_count" DESC; diff --git a/Query/sql/v2/runs/v2_cli_20260502_081223_d/n1/artifacts/v2q_n1_05e1d36cff8070c8/cli/conversation.jsonl b/Query/sql/v2/runs/v2_cli_20260502_081223_d/n1/artifacts/v2q_n1_05e1d36cff8070c8/cli/conversation.jsonl new file mode 100644 index 0000000000000000000000000000000000000000..e08dd3f6cd161c740937a10aabb2f9ba4711213e --- /dev/null +++ b/Query/sql/v2/runs/v2_cli_20260502_081223_d/n1/artifacts/v2q_n1_05e1d36cff8070c8/cli/conversation.jsonl @@ -0,0 +1,2 @@ +{"attempt": 1, "phase": "sql_generation", "role": "user", "content_path": "cli/sql_prompt_attempt_1.txt", "metrics": {"chars": 29910, "bytes_utf8": 29910, "lines": 792, "estimated_tokens": null}} +{"attempt": 1, "phase": "sql_generation", "role": "assistant", "content_path": "cli/sql_response_attempt_1.txt", "raw_content_path": "cli/sql_response_attempt_1.raw.txt", "stderr_path": "cli/sql_stderr_attempt_1.txt", "metrics": {"chars": 653, "bytes_utf8": 653, "lines": 1, "estimated_tokens": null}, "usage": {"input_tokens": 20451, "cached_input_tokens": 19840, "output_tokens": 554, "reasoning_output_tokens": 368}} diff --git a/Query/sql/v2/runs/v2_cli_20260502_081223_d/n1/artifacts/v2q_n1_05e1d36cff8070c8/cli/session_summary.json b/Query/sql/v2/runs/v2_cli_20260502_081223_d/n1/artifacts/v2q_n1_05e1d36cff8070c8/cli/session_summary.json new file mode 100644 index 0000000000000000000000000000000000000000..069a913f7a5440363838f288cd0346f39571ea2e --- /dev/null +++ b/Query/sql/v2/runs/v2_cli_20260502_081223_d/n1/artifacts/v2q_n1_05e1d36cff8070c8/cli/session_summary.json @@ -0,0 +1,25 @@ +{ + "engine": "v2-cli:codex", + "command": "codex exec --skip-git-repo-check --disable plugins --sandbox read-only --cd \"/data/jialinzhang/SQLagent\" -m gpt-5.4 --json -", + "ai_cli_calls": 1, + "usage_summary": { + "dataset_id": "n1", + "model": "v2-cli:codex", + "run_id": "v2q_n1_05e1d36cff8070c8", + "api_calls": 0, + "input_tokens": 20451, + "cached_input_tokens": 19840, + "output_tokens": 554, + "total_tokens": 21005, + "cost_usd": 0.0, + "ai_cli_calls": 1, + "estimated_input_tokens": 0, + "estimated_output_tokens": 0, + "estimated_total_tokens": 0, + "usage_source": "ai_cli_json_usage", + "cli_elapsed_ms_total": 11352.4, + "sql_execution_elapsed_ms_total": 3.95, + "conversation_log_path": "/data/jialinzhang/TabQueryBench/sql_workloads/v2_current/runs_and_launches/runs/v2_cli_20260502_081223_d/n1/artifacts/v2q_n1_05e1d36cff8070c8/cli/conversation.jsonl", + "note": "Executed through a local AI CLI with structured usage metadata." + } +} \ No newline at end of file diff --git a/Query/sql/v2/runs/v2_cli_20260502_081223_d/n1/artifacts/v2q_n1_05e1d36cff8070c8/cli/sql_attempt_1.metadata.json b/Query/sql/v2/runs/v2_cli_20260502_081223_d/n1/artifacts/v2q_n1_05e1d36cff8070c8/cli/sql_attempt_1.metadata.json new file mode 100644 index 0000000000000000000000000000000000000000..576443977b881a7b119a0a08af3204d4a9cde05e --- /dev/null +++ b/Query/sql/v2/runs/v2_cli_20260502_081223_d/n1/artifacts/v2q_n1_05e1d36cff8070c8/cli/sql_attempt_1.metadata.json @@ -0,0 +1,45 @@ +{ + "attempt": 1, + "phase": "sql_generation", + "command": "codex exec --skip-git-repo-check --disable plugins --sandbox read-only --cd \"/data/jialinzhang/SQLagent\" -m gpt-5.4 --json -", + "started_at": "2026-05-19T15:49:10.703862+00:00", + "ended_at": "2026-05-19T15:49:22.056303+00:00", + "elapsed_ms": 11352.4, + "prompt_metrics": { + "chars": 29910, + "bytes_utf8": 29910, + "lines": 792, + "estimated_tokens": null + }, + "stdout_metrics": { + "chars": 1068, + "bytes_utf8": 1068, + "lines": 4, + "estimated_tokens": null + }, + "stderr_metrics": { + "chars": 0, + "bytes_utf8": 0, + "lines": 0, + "estimated_tokens": null + }, + "parsed_output": { + "format": "jsonl_events", + "text_metrics": { + "chars": 653, + "bytes_utf8": 653, + "lines": 1, + "estimated_tokens": null + }, + "usage": { + "input_tokens": 20451, + "cached_input_tokens": 19840, + "output_tokens": 554, + "reasoning_output_tokens": 368 + } + }, + "prompt_path": "cli/sql_prompt_attempt_1.txt", + "response_path": "cli/sql_response_attempt_1.txt", + "raw_response_path": "cli/sql_response_attempt_1.raw.txt", + "stderr_path": "cli/sql_stderr_attempt_1.txt" +} \ No newline at end of file diff --git a/Query/sql/v2/runs/v2_cli_20260502_081223_d/n1/artifacts/v2q_n1_05e1d36cff8070c8/cli/sql_prompt_attempt_1.txt b/Query/sql/v2/runs/v2_cli_20260502_081223_d/n1/artifacts/v2q_n1_05e1d36cff8070c8/cli/sql_prompt_attempt_1.txt new file mode 100644 index 0000000000000000000000000000000000000000..5f14659f5bfe766840ebc02b2beee088d1a02035 --- /dev/null +++ b/Query/sql/v2/runs/v2_cli_20260502_081223_d/n1/artifacts/v2q_n1_05e1d36cff8070c8/cli/sql_prompt_attempt_1.txt @@ -0,0 +1,792 @@ +You are generating one SQLite SELECT query for a single-table SQL QA task. +Return strict JSON only, with this schema: {"sql": "...", "notes": "..."}. +Rules: +- Use only the provided table and columns. +- Do not write INSERT, UPDATE, DELETE, DROP, ALTER, CREATE, PRAGMA, ATTACH, DETACH, or VACUUM. +- Prefer the planned template and bound roles when provided. +- Add a leading SQL comment exactly like: -- template_id: . +- Generate SQLite-compatible SQL. SQLite does not support PERCENTILE_CONT or STDDEV. +- Quote identifiers with double quotes. +- Return no markdown and no extra prose. + +Dataset context: +Dataset context for SQL QA: +- dataset_id: n1 +- dataset_name: Spambase +- table_name: n1 +- table_layout: single-table dataset (do not assume joins). +- row_semantics: One row is one email represented by engineered token/character frequency and capitalization-run features. +- task_type: classification +- target_column: class +- main_row_count: 4601 +- important_fields: +- word_freq_make: role=feature, type=numeric_sparse_frequency. tags=['condition_candidate', 'measure', 'rare_pattern_candidate'] desc=Frequency feature for token 'make' in an email message. +- word_freq_address: role=feature, type=numeric_sparse_frequency. tags=['condition_candidate', 'measure', 'rare_pattern_candidate'] desc=Frequency feature for token 'address' in an email message. +- word_freq_all: role=feature, type=numeric_sparse_frequency. tags=['condition_candidate', 'measure', 'rare_pattern_candidate'] desc=Frequency feature for token 'all' in an email message. +- word_freq_3d: role=feature, type=numeric_sparse_frequency. tags=['condition_candidate', 'measure', 'rare_pattern_candidate'] desc=Frequency feature for token '3d' in an email message. +- word_freq_our: role=feature, type=numeric_sparse_frequency. tags=['condition_candidate', 'measure', 'rare_pattern_candidate'] desc=Frequency feature for token 'our' in an email message. +- word_freq_over: role=feature, type=numeric_sparse_frequency. tags=['condition_candidate', 'measure', 'rare_pattern_candidate'] desc=Frequency feature for token 'over' in an email message. +- word_freq_remove: role=feature, type=numeric_sparse_frequency. tags=['condition_candidate', 'measure', 'rare_pattern_candidate'] desc=Frequency feature for token 'remove' in an email message. +- word_freq_internet: role=feature, type=numeric_sparse_frequency. tags=['condition_candidate', 'measure', 'rare_pattern_candidate'] desc=Frequency feature for token 'internet' in an email message. +- word_freq_order: role=feature, type=numeric_sparse_frequency. tags=['condition_candidate', 'measure', 'rare_pattern_candidate'] desc=Frequency feature for token 'order' in an email message. +- word_freq_mail: role=feature, type=numeric_sparse_frequency. tags=['condition_candidate', 'measure', 'rare_pattern_candidate'] desc=Frequency feature for token 'mail' in an email message. +- word_freq_receive: role=feature, type=numeric_sparse_frequency. tags=['condition_candidate', 'measure', 'rare_pattern_candidate'] desc=Frequency feature for token 'receive' in an email message. +- word_freq_will: role=feature, type=numeric_sparse_frequency. tags=['condition_candidate', 'measure', 'rare_pattern_candidate'] desc=Frequency feature for token 'will' in an email message. +- word_freq_people: role=feature, type=numeric_sparse_frequency. tags=['condition_candidate', 'measure', 'rare_pattern_candidate'] desc=Frequency feature for token 'people' in an email message. +- word_freq_report: role=feature, type=numeric_sparse_frequency. tags=['condition_candidate', 'measure', 'rare_pattern_candidate'] desc=Frequency feature for token 'report' in an email message. +- word_freq_addresses: role=feature, type=numeric_sparse_frequency. tags=['condition_candidate', 'measure', 'rare_pattern_candidate'] desc=Frequency feature for token 'addresses' in an email message. +- word_freq_free: role=feature, type=numeric_sparse_frequency. tags=['condition_candidate', 'measure', 'rare_pattern_candidate'] desc=Frequency feature for token 'free' in an email message. +- word_freq_business: role=feature, type=numeric_sparse_frequency. tags=['condition_candidate', 'measure', 'rare_pattern_candidate'] desc=Frequency feature for token 'business' in an email message. +- word_freq_email: role=feature, type=numeric_sparse_frequency. tags=['condition_candidate', 'measure', 'rare_pattern_candidate'] desc=Frequency feature for token 'email' in an email message. +- word_freq_you: role=feature, type=numeric_sparse_frequency. tags=['condition_candidate', 'measure', 'rare_pattern_candidate'] desc=Frequency feature for token 'you' in an email message. +- word_freq_credit: role=feature, type=numeric_sparse_frequency. tags=['condition_candidate', 'measure', 'rare_pattern_candidate'] desc=Frequency feature for token 'credit' in an email message. +- word_freq_your: role=feature, type=numeric_sparse_frequency. tags=['condition_candidate', 'measure', 'rare_pattern_candidate'] desc=Frequency feature for token 'your' in an email message. +- word_freq_font: role=feature, type=numeric_sparse_frequency. tags=['condition_candidate', 'measure', 'rare_pattern_candidate'] desc=Frequency feature for token 'font' in an email message. +- word_freq_000: role=feature, type=numeric_sparse_frequency. tags=['condition_candidate', 'measure', 'rare_pattern_candidate'] desc=Frequency feature for token '000' in an email message. +- word_freq_money: role=feature, type=numeric_sparse_frequency. tags=['condition_candidate', 'measure', 'rare_pattern_candidate'] desc=Frequency feature for token 'money' in an email message. +- word_freq_hp: role=feature, type=numeric_sparse_frequency. tags=['condition_candidate', 'measure', 'rare_pattern_candidate'] desc=Frequency feature for token 'hp' in an email message. +- word_freq_hpl: role=feature, type=numeric_sparse_frequency. tags=['condition_candidate', 'measure', 'rare_pattern_candidate'] desc=Frequency feature for token 'hpl' in an email message. +- word_freq_george: role=feature, type=numeric_sparse_frequency. tags=['condition_candidate', 'measure', 'rare_pattern_candidate'] desc=Frequency feature for token 'george' in an email message. +- word_freq_650: role=feature, type=numeric_sparse_frequency. tags=['condition_candidate', 'measure', 'rare_pattern_candidate'] desc=Frequency feature for token '650' in an email message. +- word_freq_lab: role=feature, type=numeric_sparse_frequency. tags=['condition_candidate', 'measure', 'rare_pattern_candidate'] desc=Frequency feature for token 'lab' in an email message. +- word_freq_labs: role=feature, type=numeric_sparse_frequency. tags=['condition_candidate', 'measure', 'rare_pattern_candidate'] desc=Frequency feature for token 'labs' in an email message. +- word_freq_telnet: role=feature, type=numeric_sparse_frequency. tags=['condition_candidate', 'measure', 'rare_pattern_candidate'] desc=Frequency feature for token 'telnet' in an email message. +- word_freq_857: role=feature, type=numeric_sparse_frequency. tags=['condition_candidate', 'measure', 'rare_pattern_candidate'] desc=Frequency feature for token '857' in an email message. +- word_freq_data: role=feature, type=numeric_sparse_frequency. tags=['condition_candidate', 'measure', 'rare_pattern_candidate'] desc=Frequency feature for token 'data' in an email message. +- word_freq_415: role=feature, type=numeric_sparse_frequency. tags=['condition_candidate', 'measure', 'rare_pattern_candidate'] desc=Frequency feature for token '415' in an email message. +- word_freq_85: role=feature, type=numeric_sparse_frequency. tags=['condition_candidate', 'measure', 'rare_pattern_candidate'] desc=Frequency feature for token '85' in an email message. +- word_freq_technology: role=feature, type=numeric_sparse_frequency. tags=['condition_candidate', 'measure', 'rare_pattern_candidate'] desc=Frequency feature for token 'technology' in an email message. +- word_freq_1999: role=feature, type=numeric_sparse_frequency. tags=['condition_candidate', 'measure', 'rare_pattern_candidate'] desc=Frequency feature for token '1999' in an email message. +- word_freq_parts: role=feature, type=numeric_sparse_frequency. tags=['condition_candidate', 'measure', 'rare_pattern_candidate'] desc=Frequency feature for token 'parts' in an email message. +- word_freq_pm: role=feature, type=numeric_sparse_frequency. tags=['condition_candidate', 'measure', 'rare_pattern_candidate'] desc=Frequency feature for token 'pm' in an email message. +- word_freq_direct: role=feature, type=numeric_sparse_frequency. tags=['condition_candidate', 'measure', 'rare_pattern_candidate'] desc=Frequency feature for token 'direct' in an email message. +- word_freq_cs: role=feature, type=numeric_sparse_frequency. tags=['condition_candidate', 'measure', 'rare_pattern_candidate'] desc=Frequency feature for token 'cs' in an email message. +- word_freq_meeting: role=feature, type=numeric_sparse_frequency. tags=['condition_candidate', 'measure', 'rare_pattern_candidate'] desc=Frequency feature for token 'meeting' in an email message. +- word_freq_original: role=feature, type=numeric_sparse_frequency. tags=['condition_candidate', 'measure', 'rare_pattern_candidate'] desc=Frequency feature for token 'original' in an email message. +- word_freq_project: role=feature, type=numeric_sparse_frequency. tags=['condition_candidate', 'measure', 'rare_pattern_candidate'] desc=Frequency feature for token 'project' in an email message. +- word_freq_re: role=feature, type=numeric_sparse_frequency. tags=['condition_candidate', 'measure', 'rare_pattern_candidate'] desc=Frequency feature for token 're' in an email message. +- word_freq_edu: role=feature, type=numeric_sparse_frequency. tags=['condition_candidate', 'measure', 'rare_pattern_candidate'] desc=Frequency feature for token 'edu' in an email message. +- word_freq_table: role=feature, type=numeric_sparse_frequency. tags=['condition_candidate', 'measure', 'rare_pattern_candidate'] desc=Frequency feature for token 'table' in an email message. +- word_freq_conference: role=feature, type=numeric_sparse_frequency. tags=['condition_candidate', 'measure', 'rare_pattern_candidate'] desc=Frequency feature for token 'conference' in an email message. +- char_freq_%3B: role=feature, type=numeric_sparse_frequency. tags=['condition_candidate', 'measure', 'rare_pattern_candidate'] desc=Character frequency feature for symbol ';'. +- char_freq_%28: role=feature, type=numeric_sparse_frequency. tags=['condition_candidate', 'measure', 'rare_pattern_candidate'] desc=Character frequency feature for symbol '('. +- char_freq_%5B: role=feature, type=numeric_sparse_frequency. tags=['condition_candidate', 'measure', 'rare_pattern_candidate'] desc=Character frequency feature for symbol '['. +- char_freq_%21: role=feature, type=numeric_sparse_frequency. tags=['condition_candidate', 'measure', 'rare_pattern_candidate'] desc=Character frequency feature for symbol '!'. +- char_freq_%24: role=feature, type=numeric_sparse_frequency. tags=['condition_candidate', 'measure', 'rare_pattern_candidate'] desc=Character frequency feature for symbol '$'. +- char_freq_%23: role=feature, type=numeric_sparse_frequency. tags=['condition_candidate', 'measure', 'rare_pattern_candidate'] desc=Character frequency feature for symbol '#'. +- capital_run_length_average: role=feature, type=numeric_heavy_tail. tags=['condition_candidate', 'measure', 'rare_pattern_candidate'] desc=Average length of uninterrupted capital-letter runs. +- capital_run_length_longest: role=feature, type=numeric_heavy_tail. tags=['condition_candidate', 'measure', 'rare_pattern_candidate'] desc=Longest uninterrupted capital-letter run. +- capital_run_length_total: role=feature, type=numeric_heavy_tail. tags=['condition_candidate', 'measure', 'rare_pattern_candidate'] desc=Total number of capital letters in runs. +- class: role=target, type=binary_target. tags=['target_candidate'] desc=Binary spam label (1=spam, 0=non-spam). +- useful_field_combinations: [['word_freq_free', 'word_freq_you', 'class'], ['char_freq_%21', 'capital_run_length_longest', 'class'], ['word_freq_remove', 'word_freq_money', 'class']] +- fields_requiring_caution: ['capital_run_length_average', 'capital_run_length_longest', 'capital_run_length_total'] +- source_url: https://www.openml.org/d/44 + +SQLite schema snapshot: +{ + "table_name": "n1", + "quoted_table_name": "\"n1\"", + "row_count": 4601, + "columns": [ + { + "name": "word_freq_make", + "type": "TEXT", + "notnull": false, + "pk": false + }, + { + "name": "word_freq_address", + "type": "TEXT", + "notnull": false, + "pk": false + }, + { + "name": "word_freq_all", + "type": "TEXT", + "notnull": false, + "pk": false + }, + { + "name": "word_freq_3d", + "type": "TEXT", + "notnull": false, + "pk": false + }, + { + "name": "word_freq_our", + "type": "TEXT", + "notnull": false, + "pk": false + }, + { + "name": "word_freq_over", + "type": "TEXT", + "notnull": false, + "pk": false + }, + { + "name": "word_freq_remove", + "type": "TEXT", + "notnull": false, + "pk": false + }, + { + "name": "word_freq_internet", + "type": "TEXT", + "notnull": false, + "pk": false + }, + { + "name": "word_freq_order", + "type": "TEXT", + "notnull": false, + "pk": false + }, + { + "name": "word_freq_mail", + "type": "TEXT", + "notnull": false, + "pk": false + }, + { + "name": "word_freq_receive", + "type": "TEXT", + "notnull": false, + "pk": false + }, + { + "name": "word_freq_will", + "type": "TEXT", + "notnull": false, + "pk": false + }, + { + "name": "word_freq_people", + "type": "TEXT", + "notnull": false, + "pk": false + }, + { + "name": "word_freq_report", + "type": "TEXT", + "notnull": false, + "pk": false + }, + { + "name": "word_freq_addresses", + "type": "TEXT", + "notnull": false, + "pk": false + }, + { + "name": "word_freq_free", + "type": "TEXT", + "notnull": false, + "pk": false + }, + { + "name": "word_freq_business", + "type": "TEXT", + "notnull": false, + "pk": false + }, + { + "name": "word_freq_email", + "type": "TEXT", + "notnull": false, + "pk": false + }, + { + "name": "word_freq_you", + "type": "TEXT", + "notnull": false, + "pk": false + }, + { + "name": "word_freq_credit", + "type": "TEXT", + "notnull": false, + "pk": false + }, + { + "name": "word_freq_your", + "type": "TEXT", + "notnull": false, + "pk": false + }, + { + "name": "word_freq_font", + "type": "TEXT", + "notnull": false, + "pk": false + }, + { + "name": "word_freq_000", + "type": "TEXT", + "notnull": false, + "pk": false + }, + { + "name": "word_freq_money", + "type": "TEXT", + "notnull": false, + "pk": false + }, + { + "name": "word_freq_hp", + "type": "TEXT", + "notnull": false, + "pk": false + }, + { + "name": "word_freq_hpl", + "type": "TEXT", + "notnull": false, + "pk": false + }, + { + "name": "word_freq_george", + "type": "TEXT", + "notnull": false, + "pk": false + }, + { + "name": "word_freq_650", + "type": "TEXT", + "notnull": false, + "pk": false + }, + { + "name": "word_freq_lab", + "type": "TEXT", + "notnull": false, + "pk": false + }, + { + "name": "word_freq_labs", + "type": "TEXT", + "notnull": false, + "pk": false + }, + { + "name": "word_freq_telnet", + "type": "TEXT", + "notnull": false, + "pk": false + }, + { + "name": "word_freq_857", + "type": "TEXT", + "notnull": false, + "pk": false + }, + { + "name": "word_freq_data", + "type": "TEXT", + "notnull": false, + "pk": false + }, + { + "name": "word_freq_415", + "type": "TEXT", + "notnull": false, + "pk": false + }, + { + "name": "word_freq_85", + "type": "TEXT", + "notnull": false, + "pk": false + }, + { + "name": "word_freq_technology", + "type": "TEXT", + "notnull": false, + "pk": false + }, + { + "name": "word_freq_1999", + "type": "TEXT", + "notnull": false, + "pk": false + }, + { + "name": "word_freq_parts", + "type": "TEXT", + "notnull": false, + "pk": false + }, + { + "name": "word_freq_pm", + "type": "TEXT", + "notnull": false, + "pk": false + }, + { + "name": "word_freq_direct", + "type": "TEXT", + "notnull": false, + "pk": false + }, + { + "name": "word_freq_cs", + "type": "TEXT", + "notnull": false, + "pk": false + }, + { + "name": "word_freq_meeting", + "type": "TEXT", + "notnull": false, + "pk": false + }, + { + "name": "word_freq_original", + "type": "TEXT", + "notnull": false, + "pk": false + }, + { + "name": "word_freq_project", + "type": "TEXT", + "notnull": false, + "pk": false + }, + { + "name": "word_freq_re", + "type": "TEXT", + "notnull": false, + "pk": false + }, + { + "name": "word_freq_edu", + "type": "TEXT", + "notnull": false, + "pk": false + }, + { + "name": "word_freq_table", + "type": "TEXT", + "notnull": false, + "pk": false + }, + { + "name": "word_freq_conference", + "type": "TEXT", + "notnull": false, + "pk": false + }, + { + "name": "char_freq_%3B", + "type": "TEXT", + "notnull": false, + "pk": false + }, + { + "name": "char_freq_%28", + "type": "TEXT", + "notnull": false, + "pk": false + }, + { + "name": "char_freq_%5B", + "type": "TEXT", + "notnull": false, + "pk": false + }, + { + "name": "char_freq_%21", + "type": "TEXT", + "notnull": false, + "pk": false + }, + { + "name": "char_freq_%24", + "type": "TEXT", + "notnull": false, + "pk": false + }, + { + "name": "char_freq_%23", + "type": "TEXT", + "notnull": false, + "pk": false + }, + { + "name": "capital_run_length_average", + "type": "TEXT", + "notnull": false, + "pk": false + }, + { + "name": "capital_run_length_longest", + "type": "TEXT", + "notnull": false, + "pk": false + }, + { + "name": "capital_run_length_total", + "type": "TEXT", + "notnull": false, + "pk": false + }, + { + "name": "class", + "type": "TEXT", + "notnull": false, + "pk": false + } + ], + "sample_rows": [ + { + "word_freq_make": "0", + "word_freq_address": "0.64", + "word_freq_all": "0.64", + "word_freq_3d": "0", + "word_freq_our": "0.32", + "word_freq_over": "0", + "word_freq_remove": "0", + "word_freq_internet": "0", + "word_freq_order": "0", + "word_freq_mail": "0", + "word_freq_receive": "0", + "word_freq_will": "0.64", + "word_freq_people": "0", + "word_freq_report": "0", + "word_freq_addresses": "0", + "word_freq_free": "0.32", + "word_freq_business": "0", + "word_freq_email": "1.29", + "word_freq_you": "1.93", + "word_freq_credit": "0", + "word_freq_your": "0.96", + "word_freq_font": "0", + "word_freq_000": "0", + "word_freq_money": "0", + "word_freq_hp": "0", + "word_freq_hpl": "0", + "word_freq_george": "0", + "word_freq_650": "0", + "word_freq_lab": "0", + "word_freq_labs": "0", + "word_freq_telnet": "0", + "word_freq_857": "0", + "word_freq_data": "0", + "word_freq_415": "0", + "word_freq_85": "0", + "word_freq_technology": "0", + "word_freq_1999": "0", + "word_freq_parts": "0", + "word_freq_pm": "0", + "word_freq_direct": "0", + "word_freq_cs": "0", + "word_freq_meeting": "0", + "word_freq_original": "0", + "word_freq_project": "0", + "word_freq_re": "0", + "word_freq_edu": "0", + "word_freq_table": "0", + "word_freq_conference": "0", + "char_freq_%3B": "0", + "char_freq_%28": "0", + "char_freq_%5B": "0", + "char_freq_%21": "0.778", + "char_freq_%24": "0", + "char_freq_%23": "0", + "capital_run_length_average": "3.756", + "capital_run_length_longest": "61", + "capital_run_length_total": "278", + "class": "1" + }, + { + "word_freq_make": "0.21", + "word_freq_address": "0.28", + "word_freq_all": "0.5", + "word_freq_3d": "0", + "word_freq_our": "0.14", + "word_freq_over": "0.28", + "word_freq_remove": "0.21", + "word_freq_internet": "0.07", + "word_freq_order": "0", + "word_freq_mail": "0.94", + "word_freq_receive": "0.21", + "word_freq_will": "0.79", + "word_freq_people": "0.65", + "word_freq_report": "0.21", + "word_freq_addresses": "0.14", + "word_freq_free": "0.14", + "word_freq_business": "0.07", + "word_freq_email": "0.28", + "word_freq_you": "3.47", + "word_freq_credit": "0", + "word_freq_your": "1.59", + "word_freq_font": "0", + "word_freq_000": "0.43", + "word_freq_money": "0.43", + "word_freq_hp": "0", + "word_freq_hpl": "0", + "word_freq_george": "0", + "word_freq_650": "0", + "word_freq_lab": "0", + "word_freq_labs": "0", + "word_freq_telnet": "0", + "word_freq_857": "0", + "word_freq_data": "0", + "word_freq_415": "0", + "word_freq_85": "0", + "word_freq_technology": "0", + "word_freq_1999": "0.07", + "word_freq_parts": "0", + "word_freq_pm": "0", + "word_freq_direct": "0", + "word_freq_cs": "0", + "word_freq_meeting": "0", + "word_freq_original": "0", + "word_freq_project": "0", + "word_freq_re": "0", + "word_freq_edu": "0", + "word_freq_table": "0", + "word_freq_conference": "0", + "char_freq_%3B": "0", + "char_freq_%28": "0.132", + "char_freq_%5B": "0", + "char_freq_%21": "0.372", + "char_freq_%24": "0.18", + "char_freq_%23": "0.048", + "capital_run_length_average": "5.114", + "capital_run_length_longest": "101", + "capital_run_length_total": "1028", + "class": "1" + }, + { + "word_freq_make": "0.06", + "word_freq_address": "0", + "word_freq_all": "0.71", + "word_freq_3d": "0", + "word_freq_our": "1.23", + "word_freq_over": "0.19", + "word_freq_remove": "0.19", + "word_freq_internet": "0.12", + "word_freq_order": "0.64", + "word_freq_mail": "0.25", + "word_freq_receive": "0.38", + "word_freq_will": "0.45", + "word_freq_people": "0.12", + "word_freq_report": "0", + "word_freq_addresses": "1.75", + "word_freq_free": "0.06", + "word_freq_business": "0.06", + "word_freq_email": "1.03", + "word_freq_you": "1.36", + "word_freq_credit": "0.32", + "word_freq_your": "0.51", + "word_freq_font": "0", + "word_freq_000": "1.16", + "word_freq_money": "0.06", + "word_freq_hp": "0", + "word_freq_hpl": "0", + "word_freq_george": "0", + "word_freq_650": "0", + "word_freq_lab": "0", + "word_freq_labs": "0", + "word_freq_telnet": "0", + "word_freq_857": "0", + "word_freq_data": "0", + "word_freq_415": "0", + "word_freq_85": "0", + "word_freq_technology": "0", + "word_freq_1999": "0", + "word_freq_parts": "0", + "word_freq_pm": "0", + "word_freq_direct": "0.06", + "word_freq_cs": "0", + "word_freq_meeting": "0", + "word_freq_original": "0.12", + "word_freq_project": "0", + "word_freq_re": "0.06", + "word_freq_edu": "0.06", + "word_freq_table": "0", + "word_freq_conference": "0", + "char_freq_%3B": "0.01", + "char_freq_%28": "0.143", + "char_freq_%5B": "0", + "char_freq_%21": "0.276", + "char_freq_%24": "0.184", + "char_freq_%23": "0.01", + "capital_run_length_average": "9.821", + "capital_run_length_longest": "485", + "capital_run_length_total": "2259", + "class": "1" + }, + { + "word_freq_make": "0", + "word_freq_address": "0", + "word_freq_all": "0", + "word_freq_3d": "0", + "word_freq_our": "0.63", + "word_freq_over": "0", + "word_freq_remove": "0.31", + "word_freq_internet": "0.63", + "word_freq_order": "0.31", + "word_freq_mail": "0.63", + "word_freq_receive": "0.31", + "word_freq_will": "0.31", + "word_freq_people": "0.31", + "word_freq_report": "0", + "word_freq_addresses": "0", + "word_freq_free": "0.31", + "word_freq_business": "0", + "word_freq_email": "0", + "word_freq_you": "3.18", + "word_freq_credit": "0", + "word_freq_your": "0.31", + "word_freq_font": "0", + "word_freq_000": "0", + "word_freq_money": "0", + "word_freq_hp": "0", + "word_freq_hpl": "0", + "word_freq_george": "0", + "word_freq_650": "0", + "word_freq_lab": "0", + "word_freq_labs": "0", + "word_freq_telnet": "0", + "word_freq_857": "0", + "word_freq_data": "0", + "word_freq_415": "0", + "word_freq_85": "0", + "word_freq_technology": "0", + "word_freq_1999": "0", + "word_freq_parts": "0", + "word_freq_pm": "0", + "word_freq_direct": "0", + "word_freq_cs": "0", + "word_freq_meeting": "0", + "word_freq_original": "0", + "word_freq_project": "0", + "word_freq_re": "0", + "word_freq_edu": "0", + "word_freq_table": "0", + "word_freq_conference": "0", + "char_freq_%3B": "0", + "char_freq_%28": "0.137", + "char_freq_%5B": "0", + "char_freq_%21": "0.137", + "char_freq_%24": "0", + "char_freq_%23": "0", + "capital_run_length_average": "3.537", + "capital_run_length_longest": "40", + "capital_run_length_total": "191", + "class": "1" + }, + { + "word_freq_make": "0", + "word_freq_address": "0", + "word_freq_all": "0", + "word_freq_3d": "0", + "word_freq_our": "0.63", + "word_freq_over": "0", + "word_freq_remove": "0.31", + "word_freq_internet": "0.63", + "word_freq_order": "0.31", + "word_freq_mail": "0.63", + "word_freq_receive": "0.31", + "word_freq_will": "0.31", + "word_freq_people": "0.31", + "word_freq_report": "0", + "word_freq_addresses": "0", + "word_freq_free": "0.31", + "word_freq_business": "0", + "word_freq_email": "0", + "word_freq_you": "3.18", + "word_freq_credit": "0", + "word_freq_your": "0.31", + "word_freq_font": "0", + "word_freq_000": "0", + "word_freq_money": "0", + "word_freq_hp": "0", + "word_freq_hpl": "0", + "word_freq_george": "0", + "word_freq_650": "0", + "word_freq_lab": "0", + "word_freq_labs": "0", + "word_freq_telnet": "0", + "word_freq_857": "0", + "word_freq_data": "0", + "word_freq_415": "0", + "word_freq_85": "0", + "word_freq_technology": "0", + "word_freq_1999": "0", + "word_freq_parts": "0", + "word_freq_pm": "0", + "word_freq_direct": "0", + "word_freq_cs": "0", + "word_freq_meeting": "0", + "word_freq_original": "0", + "word_freq_project": "0", + "word_freq_re": "0", + "word_freq_edu": "0", + "word_freq_table": "0", + "word_freq_conference": "0", + "char_freq_%3B": "0", + "char_freq_%28": "0.135", + "char_freq_%5B": "0", + "char_freq_%21": "0.135", + "char_freq_%24": "0", + "char_freq_%23": "0", + "capital_run_length_average": "3.537", + "capital_run_length_longest": "40", + "capital_run_length_total": "191", + "class": "1" + } + ] +} + +Shortlisted templates: +[ + { + "template_id": "tpl_tpch_relative_total_threshold", + "template_name": "Relative-to-Total Extreme Threshold", + "primary_family": "tail_rarity_structure", + "portability": "partial", + "sql_skeleton": "WITH grouped AS (\n SELECT {group_col}, SUM({measure_col}) AS group_value\n FROM {table}\n GROUP BY {group_col}\n), total AS (\n SELECT SUM(group_value) AS total_value\n FROM grouped\n)\nSELECT g.{group_col}, g.group_value\nFROM grouped AS g\nCROSS JOIN total AS t\nWHERE g.group_value > t.total_value * {fraction_threshold}\nORDER BY g.group_value DESC;", + "required_roles": [ + "group_col", + "measure_col" + ] + } +] + +Problem instance: +{ + "dataset_id": "n1", + "question": "Use template Relative-to-Total Extreme Threshold to probe tail_mass_similarity with semantic role count_distribution. Focus on group_col=class, measure_col=word_freq_font.", + "planned_template_id": "tpl_tpch_relative_total_threshold", + "bindings": { + "group_col": "class", + "measure_col": "word_freq_font", + "top_k": 13, + "top_n": 5, + "num_tiles": 10, + "percentile_value": 0.95, + "z_threshold": 2.0, + "fraction_threshold": 0.1, + "baseline_multiplier": 1.5, + "baseline_fraction": 0.1, + "min_group_size": 5, + "min_support": 5, + "measure_threshold": 0.0, + "time_grain": "month", + "lookback_rows": 3, + "current_period_start": "'2024-01-01'", + "current_period_end": "'2024-04-01'", + "previous_period_start": "'2023-10-01'", + "previous_period_end": "'2024-01-01'", + "drift_ratio_threshold": 0.8 + }, + "can_vary": [], + "must_fix": [], + "runtime_sql_skeleton": "WITH grouped AS (\n SELECT {group_col}, SUM({measure_col}) AS group_value\n FROM {table}\n GROUP BY {group_col}\n), total AS (\n SELECT SUM(group_value) AS total_value\n FROM grouped\n)\nSELECT g.{group_col}, g.group_value\nFROM grouped AS g\nCROSS JOIN total AS t\nWHERE g.group_value > t.total_value * {fraction_threshold}\nORDER BY g.group_value DESC;" +} + +Repair context: +{} diff --git a/Query/sql/v2/runs/v2_cli_20260502_081223_d/n1/artifacts/v2q_n1_05e1d36cff8070c8/cli/sql_response_attempt_1.raw.txt b/Query/sql/v2/runs/v2_cli_20260502_081223_d/n1/artifacts/v2q_n1_05e1d36cff8070c8/cli/sql_response_attempt_1.raw.txt new file mode 100644 index 0000000000000000000000000000000000000000..a2ff04708a2eb3c054642c9111215c550148f878 --- /dev/null +++ b/Query/sql/v2/runs/v2_cli_20260502_081223_d/n1/artifacts/v2q_n1_05e1d36cff8070c8/cli/sql_response_attempt_1.raw.txt @@ -0,0 +1,4 @@ +{"type":"thread.started","thread_id":"019e40ed-40b6-71a0-af18-d994744c0ab9"} +{"type":"turn.started"} +{"type":"item.completed","item":{"id":"item_0","type":"agent_message","text":"{\"sql\":\"-- template_id: tpl_tpch_relative_total_threshold\\nWITH \\\"grouped\\\" AS (\\n SELECT \\\"class\\\", SUM(CAST(\\\"word_freq_font\\\" AS REAL)) AS \\\"group_value\\\"\\n FROM \\\"n1\\\"\\n GROUP BY \\\"class\\\"\\n), \\\"total\\\" AS (\\n SELECT SUM(\\\"group_value\\\") AS \\\"total_value\\\"\\n FROM \\\"grouped\\\"\\n)\\nSELECT g.\\\"class\\\", g.\\\"group_value\\\"\\nFROM \\\"grouped\\\" AS g\\nCROSS JOIN \\\"total\\\" AS t\\nWHERE g.\\\"group_value\\\" > t.\\\"total_value\\\" * 0.1\\nORDER BY g.\\\"group_value\\\" DESC;\",\"notes\":\"Applied the required template with group_col=\\\"class\\\" and measure_col=\\\"word_freq_font\\\". CAST to REAL is used because the schema stores numeric-looking fields as TEXT.\"}"}} +{"type":"turn.completed","usage":{"input_tokens":20451,"cached_input_tokens":19840,"output_tokens":554,"reasoning_output_tokens":368}} diff --git a/Query/sql/v2/runs/v2_cli_20260502_081223_d/n1/artifacts/v2q_n1_05e1d36cff8070c8/cli/sql_response_attempt_1.txt b/Query/sql/v2/runs/v2_cli_20260502_081223_d/n1/artifacts/v2q_n1_05e1d36cff8070c8/cli/sql_response_attempt_1.txt new file mode 100644 index 0000000000000000000000000000000000000000..1fa84f9ed4dae75d09eb4a9b0c4f15e3e9d6d510 --- /dev/null +++ b/Query/sql/v2/runs/v2_cli_20260502_081223_d/n1/artifacts/v2q_n1_05e1d36cff8070c8/cli/sql_response_attempt_1.txt @@ -0,0 +1 @@ +{"sql":"-- template_id: tpl_tpch_relative_total_threshold\nWITH \"grouped\" AS (\n SELECT \"class\", SUM(CAST(\"word_freq_font\" AS REAL)) AS \"group_value\"\n FROM \"n1\"\n GROUP BY \"class\"\n), \"total\" AS (\n SELECT SUM(\"group_value\") AS \"total_value\"\n FROM \"grouped\"\n)\nSELECT g.\"class\", g.\"group_value\"\nFROM \"grouped\" AS g\nCROSS JOIN \"total\" AS t\nWHERE g.\"group_value\" > t.\"total_value\" * 0.1\nORDER BY g.\"group_value\" DESC;","notes":"Applied the required template with group_col=\"class\" and measure_col=\"word_freq_font\". CAST to REAL is used because the schema stores numeric-looking fields as TEXT."} \ No newline at end of file diff --git a/Query/sql/v2/runs/v2_cli_20260502_081223_d/n1/artifacts/v2q_n1_05e1d36cff8070c8/cli/sql_stderr_attempt_1.txt b/Query/sql/v2/runs/v2_cli_20260502_081223_d/n1/artifacts/v2q_n1_05e1d36cff8070c8/cli/sql_stderr_attempt_1.txt new file mode 100644 index 0000000000000000000000000000000000000000..e69de29bb2d1d6434b8b29ae775ad8c2e48c5391 diff --git a/Query/sql/v2/runs/v2_cli_20260502_081223_d/n1/artifacts/v2q_n1_06a5b861b6b26a1c/cli/conversation.jsonl b/Query/sql/v2/runs/v2_cli_20260502_081223_d/n1/artifacts/v2q_n1_06a5b861b6b26a1c/cli/conversation.jsonl new file mode 100644 index 0000000000000000000000000000000000000000..fd3b3487cd8de3580f9688b6e948bf9213031c30 --- /dev/null +++ b/Query/sql/v2/runs/v2_cli_20260502_081223_d/n1/artifacts/v2q_n1_06a5b861b6b26a1c/cli/conversation.jsonl @@ -0,0 +1,2 @@ +{"attempt": 1, "phase": "sql_generation", "role": "user", "content_path": "cli/sql_prompt_attempt_1.txt", "metrics": {"chars": 29440, "bytes_utf8": 29440, "lines": 792, "estimated_tokens": null}} +{"attempt": 1, "phase": "sql_generation", "role": "assistant", "content_path": "cli/sql_response_attempt_1.txt", "raw_content_path": "cli/sql_response_attempt_1.raw.txt", "stderr_path": "cli/sql_stderr_attempt_1.txt", "metrics": {"chars": 430, "bytes_utf8": 430, "lines": 1, "estimated_tokens": null}, "usage": {"input_tokens": 20331, "cached_input_tokens": 19840, "output_tokens": 505, "reasoning_output_tokens": 390}} diff --git a/Query/sql/v2/runs/v2_cli_20260502_081223_d/n1/artifacts/v2q_n1_06a5b861b6b26a1c/cli/session_summary.json b/Query/sql/v2/runs/v2_cli_20260502_081223_d/n1/artifacts/v2q_n1_06a5b861b6b26a1c/cli/session_summary.json new file mode 100644 index 0000000000000000000000000000000000000000..6f3b9f9e0c7f14b9d7440b632f4a9f3851bb7bb5 --- /dev/null +++ b/Query/sql/v2/runs/v2_cli_20260502_081223_d/n1/artifacts/v2q_n1_06a5b861b6b26a1c/cli/session_summary.json @@ -0,0 +1,25 @@ +{ + "engine": "v2-cli:codex", + "command": "codex exec --skip-git-repo-check --disable plugins --sandbox read-only --cd \"/data/jialinzhang/SQLagent\" -m gpt-5.4 --json -", + "ai_cli_calls": 1, + "usage_summary": { + "dataset_id": "n1", + "model": "v2-cli:codex", + "run_id": "v2q_n1_06a5b861b6b26a1c", + "api_calls": 0, + "input_tokens": 20331, + "cached_input_tokens": 19840, + "output_tokens": 505, + "total_tokens": 20836, + "cost_usd": 0.0, + "ai_cli_calls": 1, + "estimated_input_tokens": 0, + "estimated_output_tokens": 0, + "estimated_total_tokens": 0, + "usage_source": "ai_cli_json_usage", + "cli_elapsed_ms_total": 11207.34, + "sql_execution_elapsed_ms_total": 8.27, + "conversation_log_path": "/data/jialinzhang/TabQueryBench/sql_workloads/v2_current/runs_and_launches/runs/v2_cli_20260502_081223_d/n1/artifacts/v2q_n1_06a5b861b6b26a1c/cli/conversation.jsonl", + "note": "Executed through a local AI CLI with structured usage metadata." + } +} \ No newline at end of file diff --git a/Query/sql/v2/runs/v2_cli_20260502_081223_d/n1/artifacts/v2q_n1_06a5b861b6b26a1c/cli/sql_attempt_1.metadata.json b/Query/sql/v2/runs/v2_cli_20260502_081223_d/n1/artifacts/v2q_n1_06a5b861b6b26a1c/cli/sql_attempt_1.metadata.json new file mode 100644 index 0000000000000000000000000000000000000000..5c469098e2be63b9d8335ab6428ca3f85bce5db6 --- /dev/null +++ b/Query/sql/v2/runs/v2_cli_20260502_081223_d/n1/artifacts/v2q_n1_06a5b861b6b26a1c/cli/sql_attempt_1.metadata.json @@ -0,0 +1,45 @@ +{ + "attempt": 1, + "phase": "sql_generation", + "command": "codex exec --skip-git-repo-check --disable plugins --sandbox read-only --cd \"/data/jialinzhang/SQLagent\" -m gpt-5.4 --json -", + "started_at": "2026-05-19T15:41:15.532632+00:00", + "ended_at": "2026-05-19T15:41:26.740024+00:00", + "elapsed_ms": 11207.34, + "prompt_metrics": { + "chars": 29440, + "bytes_utf8": 29440, + "lines": 792, + "estimated_tokens": null + }, + "stdout_metrics": { + "chars": 793, + "bytes_utf8": 793, + "lines": 4, + "estimated_tokens": null + }, + "stderr_metrics": { + "chars": 0, + "bytes_utf8": 0, + "lines": 0, + "estimated_tokens": null + }, + "parsed_output": { + "format": "jsonl_events", + "text_metrics": { + "chars": 430, + "bytes_utf8": 430, + "lines": 1, + "estimated_tokens": null + }, + "usage": { + "input_tokens": 20331, + "cached_input_tokens": 19840, + "output_tokens": 505, + "reasoning_output_tokens": 390 + } + }, + "prompt_path": "cli/sql_prompt_attempt_1.txt", + "response_path": "cli/sql_response_attempt_1.txt", + "raw_response_path": "cli/sql_response_attempt_1.raw.txt", + "stderr_path": "cli/sql_stderr_attempt_1.txt" +} \ No newline at end of file diff --git a/Query/sql/v2/runs/v2_cli_20260502_081223_d/n1/artifacts/v2q_n1_06a5b861b6b26a1c/cli/sql_prompt_attempt_1.txt b/Query/sql/v2/runs/v2_cli_20260502_081223_d/n1/artifacts/v2q_n1_06a5b861b6b26a1c/cli/sql_prompt_attempt_1.txt new file mode 100644 index 0000000000000000000000000000000000000000..8ce56d307baaf6bbb2e01be062dfee1f2f492275 --- /dev/null +++ b/Query/sql/v2/runs/v2_cli_20260502_081223_d/n1/artifacts/v2q_n1_06a5b861b6b26a1c/cli/sql_prompt_attempt_1.txt @@ -0,0 +1,792 @@ +You are generating one SQLite SELECT query for a single-table SQL QA task. +Return strict JSON only, with this schema: {"sql": "...", "notes": "..."}. +Rules: +- Use only the provided table and columns. +- Do not write INSERT, UPDATE, DELETE, DROP, ALTER, CREATE, PRAGMA, ATTACH, DETACH, or VACUUM. +- Prefer the planned template and bound roles when provided. +- Add a leading SQL comment exactly like: -- template_id: . +- Generate SQLite-compatible SQL. SQLite does not support PERCENTILE_CONT or STDDEV. +- Quote identifiers with double quotes. +- Return no markdown and no extra prose. + +Dataset context: +Dataset context for SQL QA: +- dataset_id: n1 +- dataset_name: Spambase +- table_name: n1 +- table_layout: single-table dataset (do not assume joins). +- row_semantics: One row is one email represented by engineered token/character frequency and capitalization-run features. +- task_type: classification +- target_column: class +- main_row_count: 4601 +- important_fields: +- word_freq_make: role=feature, type=numeric_sparse_frequency. tags=['condition_candidate', 'measure', 'rare_pattern_candidate'] desc=Frequency feature for token 'make' in an email message. +- word_freq_address: role=feature, type=numeric_sparse_frequency. tags=['condition_candidate', 'measure', 'rare_pattern_candidate'] desc=Frequency feature for token 'address' in an email message. +- word_freq_all: role=feature, type=numeric_sparse_frequency. tags=['condition_candidate', 'measure', 'rare_pattern_candidate'] desc=Frequency feature for token 'all' in an email message. +- word_freq_3d: role=feature, type=numeric_sparse_frequency. tags=['condition_candidate', 'measure', 'rare_pattern_candidate'] desc=Frequency feature for token '3d' in an email message. +- word_freq_our: role=feature, type=numeric_sparse_frequency. tags=['condition_candidate', 'measure', 'rare_pattern_candidate'] desc=Frequency feature for token 'our' in an email message. +- word_freq_over: role=feature, type=numeric_sparse_frequency. tags=['condition_candidate', 'measure', 'rare_pattern_candidate'] desc=Frequency feature for token 'over' in an email message. +- word_freq_remove: role=feature, type=numeric_sparse_frequency. tags=['condition_candidate', 'measure', 'rare_pattern_candidate'] desc=Frequency feature for token 'remove' in an email message. +- word_freq_internet: role=feature, type=numeric_sparse_frequency. tags=['condition_candidate', 'measure', 'rare_pattern_candidate'] desc=Frequency feature for token 'internet' in an email message. +- word_freq_order: role=feature, type=numeric_sparse_frequency. tags=['condition_candidate', 'measure', 'rare_pattern_candidate'] desc=Frequency feature for token 'order' in an email message. +- word_freq_mail: role=feature, type=numeric_sparse_frequency. tags=['condition_candidate', 'measure', 'rare_pattern_candidate'] desc=Frequency feature for token 'mail' in an email message. +- word_freq_receive: role=feature, type=numeric_sparse_frequency. tags=['condition_candidate', 'measure', 'rare_pattern_candidate'] desc=Frequency feature for token 'receive' in an email message. +- word_freq_will: role=feature, type=numeric_sparse_frequency. tags=['condition_candidate', 'measure', 'rare_pattern_candidate'] desc=Frequency feature for token 'will' in an email message. +- word_freq_people: role=feature, type=numeric_sparse_frequency. tags=['condition_candidate', 'measure', 'rare_pattern_candidate'] desc=Frequency feature for token 'people' in an email message. +- word_freq_report: role=feature, type=numeric_sparse_frequency. tags=['condition_candidate', 'measure', 'rare_pattern_candidate'] desc=Frequency feature for token 'report' in an email message. +- word_freq_addresses: role=feature, type=numeric_sparse_frequency. tags=['condition_candidate', 'measure', 'rare_pattern_candidate'] desc=Frequency feature for token 'addresses' in an email message. +- word_freq_free: role=feature, type=numeric_sparse_frequency. tags=['condition_candidate', 'measure', 'rare_pattern_candidate'] desc=Frequency feature for token 'free' in an email message. +- word_freq_business: role=feature, type=numeric_sparse_frequency. tags=['condition_candidate', 'measure', 'rare_pattern_candidate'] desc=Frequency feature for token 'business' in an email message. +- word_freq_email: role=feature, type=numeric_sparse_frequency. tags=['condition_candidate', 'measure', 'rare_pattern_candidate'] desc=Frequency feature for token 'email' in an email message. +- word_freq_you: role=feature, type=numeric_sparse_frequency. tags=['condition_candidate', 'measure', 'rare_pattern_candidate'] desc=Frequency feature for token 'you' in an email message. +- word_freq_credit: role=feature, type=numeric_sparse_frequency. tags=['condition_candidate', 'measure', 'rare_pattern_candidate'] desc=Frequency feature for token 'credit' in an email message. +- word_freq_your: role=feature, type=numeric_sparse_frequency. tags=['condition_candidate', 'measure', 'rare_pattern_candidate'] desc=Frequency feature for token 'your' in an email message. +- word_freq_font: role=feature, type=numeric_sparse_frequency. tags=['condition_candidate', 'measure', 'rare_pattern_candidate'] desc=Frequency feature for token 'font' in an email message. +- word_freq_000: role=feature, type=numeric_sparse_frequency. tags=['condition_candidate', 'measure', 'rare_pattern_candidate'] desc=Frequency feature for token '000' in an email message. +- word_freq_money: role=feature, type=numeric_sparse_frequency. tags=['condition_candidate', 'measure', 'rare_pattern_candidate'] desc=Frequency feature for token 'money' in an email message. +- word_freq_hp: role=feature, type=numeric_sparse_frequency. tags=['condition_candidate', 'measure', 'rare_pattern_candidate'] desc=Frequency feature for token 'hp' in an email message. +- word_freq_hpl: role=feature, type=numeric_sparse_frequency. tags=['condition_candidate', 'measure', 'rare_pattern_candidate'] desc=Frequency feature for token 'hpl' in an email message. +- word_freq_george: role=feature, type=numeric_sparse_frequency. tags=['condition_candidate', 'measure', 'rare_pattern_candidate'] desc=Frequency feature for token 'george' in an email message. +- word_freq_650: role=feature, type=numeric_sparse_frequency. tags=['condition_candidate', 'measure', 'rare_pattern_candidate'] desc=Frequency feature for token '650' in an email message. +- word_freq_lab: role=feature, type=numeric_sparse_frequency. tags=['condition_candidate', 'measure', 'rare_pattern_candidate'] desc=Frequency feature for token 'lab' in an email message. +- word_freq_labs: role=feature, type=numeric_sparse_frequency. tags=['condition_candidate', 'measure', 'rare_pattern_candidate'] desc=Frequency feature for token 'labs' in an email message. +- word_freq_telnet: role=feature, type=numeric_sparse_frequency. tags=['condition_candidate', 'measure', 'rare_pattern_candidate'] desc=Frequency feature for token 'telnet' in an email message. +- word_freq_857: role=feature, type=numeric_sparse_frequency. tags=['condition_candidate', 'measure', 'rare_pattern_candidate'] desc=Frequency feature for token '857' in an email message. +- word_freq_data: role=feature, type=numeric_sparse_frequency. tags=['condition_candidate', 'measure', 'rare_pattern_candidate'] desc=Frequency feature for token 'data' in an email message. +- word_freq_415: role=feature, type=numeric_sparse_frequency. tags=['condition_candidate', 'measure', 'rare_pattern_candidate'] desc=Frequency feature for token '415' in an email message. +- word_freq_85: role=feature, type=numeric_sparse_frequency. tags=['condition_candidate', 'measure', 'rare_pattern_candidate'] desc=Frequency feature for token '85' in an email message. +- word_freq_technology: role=feature, type=numeric_sparse_frequency. tags=['condition_candidate', 'measure', 'rare_pattern_candidate'] desc=Frequency feature for token 'technology' in an email message. +- word_freq_1999: role=feature, type=numeric_sparse_frequency. tags=['condition_candidate', 'measure', 'rare_pattern_candidate'] desc=Frequency feature for token '1999' in an email message. +- word_freq_parts: role=feature, type=numeric_sparse_frequency. tags=['condition_candidate', 'measure', 'rare_pattern_candidate'] desc=Frequency feature for token 'parts' in an email message. +- word_freq_pm: role=feature, type=numeric_sparse_frequency. tags=['condition_candidate', 'measure', 'rare_pattern_candidate'] desc=Frequency feature for token 'pm' in an email message. +- word_freq_direct: role=feature, type=numeric_sparse_frequency. tags=['condition_candidate', 'measure', 'rare_pattern_candidate'] desc=Frequency feature for token 'direct' in an email message. +- word_freq_cs: role=feature, type=numeric_sparse_frequency. tags=['condition_candidate', 'measure', 'rare_pattern_candidate'] desc=Frequency feature for token 'cs' in an email message. +- word_freq_meeting: role=feature, type=numeric_sparse_frequency. tags=['condition_candidate', 'measure', 'rare_pattern_candidate'] desc=Frequency feature for token 'meeting' in an email message. +- word_freq_original: role=feature, type=numeric_sparse_frequency. tags=['condition_candidate', 'measure', 'rare_pattern_candidate'] desc=Frequency feature for token 'original' in an email message. +- word_freq_project: role=feature, type=numeric_sparse_frequency. tags=['condition_candidate', 'measure', 'rare_pattern_candidate'] desc=Frequency feature for token 'project' in an email message. +- word_freq_re: role=feature, type=numeric_sparse_frequency. tags=['condition_candidate', 'measure', 'rare_pattern_candidate'] desc=Frequency feature for token 're' in an email message. +- word_freq_edu: role=feature, type=numeric_sparse_frequency. tags=['condition_candidate', 'measure', 'rare_pattern_candidate'] desc=Frequency feature for token 'edu' in an email message. +- word_freq_table: role=feature, type=numeric_sparse_frequency. tags=['condition_candidate', 'measure', 'rare_pattern_candidate'] desc=Frequency feature for token 'table' in an email message. +- word_freq_conference: role=feature, type=numeric_sparse_frequency. tags=['condition_candidate', 'measure', 'rare_pattern_candidate'] desc=Frequency feature for token 'conference' in an email message. +- char_freq_%3B: role=feature, type=numeric_sparse_frequency. tags=['condition_candidate', 'measure', 'rare_pattern_candidate'] desc=Character frequency feature for symbol ';'. +- char_freq_%28: role=feature, type=numeric_sparse_frequency. tags=['condition_candidate', 'measure', 'rare_pattern_candidate'] desc=Character frequency feature for symbol '('. +- char_freq_%5B: role=feature, type=numeric_sparse_frequency. tags=['condition_candidate', 'measure', 'rare_pattern_candidate'] desc=Character frequency feature for symbol '['. +- char_freq_%21: role=feature, type=numeric_sparse_frequency. tags=['condition_candidate', 'measure', 'rare_pattern_candidate'] desc=Character frequency feature for symbol '!'. +- char_freq_%24: role=feature, type=numeric_sparse_frequency. tags=['condition_candidate', 'measure', 'rare_pattern_candidate'] desc=Character frequency feature for symbol '$'. +- char_freq_%23: role=feature, type=numeric_sparse_frequency. tags=['condition_candidate', 'measure', 'rare_pattern_candidate'] desc=Character frequency feature for symbol '#'. +- capital_run_length_average: role=feature, type=numeric_heavy_tail. tags=['condition_candidate', 'measure', 'rare_pattern_candidate'] desc=Average length of uninterrupted capital-letter runs. +- capital_run_length_longest: role=feature, type=numeric_heavy_tail. tags=['condition_candidate', 'measure', 'rare_pattern_candidate'] desc=Longest uninterrupted capital-letter run. +- capital_run_length_total: role=feature, type=numeric_heavy_tail. tags=['condition_candidate', 'measure', 'rare_pattern_candidate'] desc=Total number of capital letters in runs. +- class: role=target, type=binary_target. tags=['target_candidate'] desc=Binary spam label (1=spam, 0=non-spam). +- useful_field_combinations: [['word_freq_free', 'word_freq_you', 'class'], ['char_freq_%21', 'capital_run_length_longest', 'class'], ['word_freq_remove', 'word_freq_money', 'class']] +- fields_requiring_caution: ['capital_run_length_average', 'capital_run_length_longest', 'capital_run_length_total'] +- source_url: https://www.openml.org/d/44 + +SQLite schema snapshot: +{ + "table_name": "n1", + "quoted_table_name": "\"n1\"", + "row_count": 4601, + "columns": [ + { + "name": "word_freq_make", + "type": "TEXT", + "notnull": false, + "pk": false + }, + { + "name": "word_freq_address", + "type": "TEXT", + "notnull": false, + "pk": false + }, + { + "name": "word_freq_all", + "type": "TEXT", + "notnull": false, + "pk": false + }, + { + "name": "word_freq_3d", + "type": "TEXT", + "notnull": false, + "pk": false + }, + { + "name": "word_freq_our", + "type": "TEXT", + "notnull": false, + "pk": false + }, + { + "name": "word_freq_over", + "type": "TEXT", + "notnull": false, + "pk": false + }, + { + "name": "word_freq_remove", + "type": "TEXT", + "notnull": false, + "pk": false + }, + { + "name": "word_freq_internet", + "type": "TEXT", + "notnull": false, + "pk": false + }, + { + "name": "word_freq_order", + "type": "TEXT", + "notnull": false, + "pk": false + }, + { + "name": "word_freq_mail", + "type": "TEXT", + "notnull": false, + "pk": false + }, + { + "name": "word_freq_receive", + "type": "TEXT", + "notnull": false, + "pk": false + }, + { + "name": "word_freq_will", + "type": "TEXT", + "notnull": false, + "pk": false + }, + { + "name": "word_freq_people", + "type": "TEXT", + "notnull": false, + "pk": false + }, + { + "name": "word_freq_report", + "type": "TEXT", + "notnull": false, + "pk": false + }, + { + "name": "word_freq_addresses", + "type": "TEXT", + "notnull": false, + "pk": false + }, + { + "name": "word_freq_free", + "type": "TEXT", + "notnull": false, + "pk": false + }, + { + "name": "word_freq_business", + "type": "TEXT", + "notnull": false, + "pk": false + }, + { + "name": "word_freq_email", + "type": "TEXT", + "notnull": false, + "pk": false + }, + { + "name": "word_freq_you", + "type": "TEXT", + "notnull": false, + "pk": false + }, + { + "name": "word_freq_credit", + "type": "TEXT", + "notnull": false, + "pk": false + }, + { + "name": "word_freq_your", + "type": "TEXT", + "notnull": false, + "pk": false + }, + { + "name": "word_freq_font", + "type": "TEXT", + "notnull": false, + "pk": false + }, + { + "name": "word_freq_000", + "type": "TEXT", + "notnull": false, + "pk": false + }, + { + "name": "word_freq_money", + "type": "TEXT", + "notnull": false, + "pk": false + }, + { + "name": "word_freq_hp", + "type": "TEXT", + "notnull": false, + "pk": false + }, + { + "name": "word_freq_hpl", + "type": "TEXT", + "notnull": false, + "pk": false + }, + { + "name": "word_freq_george", + "type": "TEXT", + "notnull": false, + "pk": false + }, + { + "name": "word_freq_650", + "type": "TEXT", + "notnull": false, + "pk": false + }, + { + "name": "word_freq_lab", + "type": "TEXT", + "notnull": false, + "pk": false + }, + { + "name": "word_freq_labs", + "type": "TEXT", + "notnull": false, + "pk": false + }, + { + "name": "word_freq_telnet", + "type": "TEXT", + "notnull": false, + "pk": false + }, + { + "name": "word_freq_857", + "type": "TEXT", + "notnull": false, + "pk": false + }, + { + "name": "word_freq_data", + "type": "TEXT", + "notnull": false, + "pk": false + }, + { + "name": "word_freq_415", + "type": "TEXT", + "notnull": false, + "pk": false + }, + { + "name": "word_freq_85", + "type": "TEXT", + "notnull": false, + "pk": false + }, + { + "name": "word_freq_technology", + "type": "TEXT", + "notnull": false, + "pk": false + }, + { + "name": "word_freq_1999", + "type": "TEXT", + "notnull": false, + "pk": false + }, + { + "name": "word_freq_parts", + "type": "TEXT", + "notnull": false, + "pk": false + }, + { + "name": "word_freq_pm", + "type": "TEXT", + "notnull": false, + "pk": false + }, + { + "name": "word_freq_direct", + "type": "TEXT", + "notnull": false, + "pk": false + }, + { + "name": "word_freq_cs", + "type": "TEXT", + "notnull": false, + "pk": false + }, + { + "name": "word_freq_meeting", + "type": "TEXT", + "notnull": false, + "pk": false + }, + { + "name": "word_freq_original", + "type": "TEXT", + "notnull": false, + "pk": false + }, + { + "name": "word_freq_project", + "type": "TEXT", + "notnull": false, + "pk": false + }, + { + "name": "word_freq_re", + "type": "TEXT", + "notnull": false, + "pk": false + }, + { + "name": "word_freq_edu", + "type": "TEXT", + "notnull": false, + "pk": false + }, + { + "name": "word_freq_table", + "type": "TEXT", + "notnull": false, + "pk": false + }, + { + "name": "word_freq_conference", + "type": "TEXT", + "notnull": false, + "pk": false + }, + { + "name": "char_freq_%3B", + "type": "TEXT", + "notnull": false, + "pk": false + }, + { + "name": "char_freq_%28", + "type": "TEXT", + "notnull": false, + "pk": false + }, + { + "name": "char_freq_%5B", + "type": "TEXT", + "notnull": false, + "pk": false + }, + { + "name": "char_freq_%21", + "type": "TEXT", + "notnull": false, + "pk": false + }, + { + "name": "char_freq_%24", + "type": "TEXT", + "notnull": false, + "pk": false + }, + { + "name": "char_freq_%23", + "type": "TEXT", + "notnull": false, + "pk": false + }, + { + "name": "capital_run_length_average", + "type": "TEXT", + "notnull": false, + "pk": false + }, + { + "name": "capital_run_length_longest", + "type": "TEXT", + "notnull": false, + "pk": false + }, + { + "name": "capital_run_length_total", + "type": "TEXT", + "notnull": false, + "pk": false + }, + { + "name": "class", + "type": "TEXT", + "notnull": false, + "pk": false + } + ], + "sample_rows": [ + { + "word_freq_make": "0", + "word_freq_address": "0.64", + "word_freq_all": "0.64", + "word_freq_3d": "0", + "word_freq_our": "0.32", + "word_freq_over": "0", + "word_freq_remove": "0", + "word_freq_internet": "0", + "word_freq_order": "0", + "word_freq_mail": "0", + "word_freq_receive": "0", + "word_freq_will": "0.64", + "word_freq_people": "0", + "word_freq_report": "0", + "word_freq_addresses": "0", + "word_freq_free": "0.32", + "word_freq_business": "0", + "word_freq_email": "1.29", + "word_freq_you": "1.93", + "word_freq_credit": "0", + "word_freq_your": "0.96", + "word_freq_font": "0", + "word_freq_000": "0", + "word_freq_money": "0", + "word_freq_hp": "0", + "word_freq_hpl": "0", + "word_freq_george": "0", + "word_freq_650": "0", + "word_freq_lab": "0", + "word_freq_labs": "0", + "word_freq_telnet": "0", + "word_freq_857": "0", + "word_freq_data": "0", + "word_freq_415": "0", + "word_freq_85": "0", + "word_freq_technology": "0", + "word_freq_1999": "0", + "word_freq_parts": "0", + "word_freq_pm": "0", + "word_freq_direct": "0", + "word_freq_cs": "0", + "word_freq_meeting": "0", + "word_freq_original": "0", + "word_freq_project": "0", + "word_freq_re": "0", + "word_freq_edu": "0", + "word_freq_table": "0", + "word_freq_conference": "0", + "char_freq_%3B": "0", + "char_freq_%28": "0", + "char_freq_%5B": "0", + "char_freq_%21": "0.778", + "char_freq_%24": "0", + "char_freq_%23": "0", + "capital_run_length_average": "3.756", + "capital_run_length_longest": "61", + "capital_run_length_total": "278", + "class": "1" + }, + { + "word_freq_make": "0.21", + "word_freq_address": "0.28", + "word_freq_all": "0.5", + "word_freq_3d": "0", + "word_freq_our": "0.14", + "word_freq_over": "0.28", + "word_freq_remove": "0.21", + "word_freq_internet": "0.07", + "word_freq_order": "0", + "word_freq_mail": "0.94", + "word_freq_receive": "0.21", + "word_freq_will": "0.79", + "word_freq_people": "0.65", + "word_freq_report": "0.21", + "word_freq_addresses": "0.14", + "word_freq_free": "0.14", + "word_freq_business": "0.07", + "word_freq_email": "0.28", + "word_freq_you": "3.47", + "word_freq_credit": "0", + "word_freq_your": "1.59", + "word_freq_font": "0", + "word_freq_000": "0.43", + "word_freq_money": "0.43", + "word_freq_hp": "0", + "word_freq_hpl": "0", + "word_freq_george": "0", + "word_freq_650": "0", + "word_freq_lab": "0", + "word_freq_labs": "0", + "word_freq_telnet": "0", + "word_freq_857": "0", + "word_freq_data": "0", + "word_freq_415": "0", + "word_freq_85": "0", + "word_freq_technology": "0", + "word_freq_1999": "0.07", + "word_freq_parts": "0", + "word_freq_pm": "0", + "word_freq_direct": "0", + "word_freq_cs": "0", + "word_freq_meeting": "0", + "word_freq_original": "0", + "word_freq_project": "0", + "word_freq_re": "0", + "word_freq_edu": "0", + "word_freq_table": "0", + "word_freq_conference": "0", + "char_freq_%3B": "0", + "char_freq_%28": "0.132", + "char_freq_%5B": "0", + "char_freq_%21": "0.372", + "char_freq_%24": "0.18", + "char_freq_%23": "0.048", + "capital_run_length_average": "5.114", + "capital_run_length_longest": "101", + "capital_run_length_total": "1028", + "class": "1" + }, + { + "word_freq_make": "0.06", + "word_freq_address": "0", + "word_freq_all": "0.71", + "word_freq_3d": "0", + "word_freq_our": "1.23", + "word_freq_over": "0.19", + "word_freq_remove": "0.19", + "word_freq_internet": "0.12", + "word_freq_order": "0.64", + "word_freq_mail": "0.25", + "word_freq_receive": "0.38", + "word_freq_will": "0.45", + "word_freq_people": "0.12", + "word_freq_report": "0", + "word_freq_addresses": "1.75", + "word_freq_free": "0.06", + "word_freq_business": "0.06", + "word_freq_email": "1.03", + "word_freq_you": "1.36", + "word_freq_credit": "0.32", + "word_freq_your": "0.51", + "word_freq_font": "0", + "word_freq_000": "1.16", + "word_freq_money": "0.06", + "word_freq_hp": "0", + "word_freq_hpl": "0", + "word_freq_george": "0", + "word_freq_650": "0", + "word_freq_lab": "0", + "word_freq_labs": "0", + "word_freq_telnet": "0", + "word_freq_857": "0", + "word_freq_data": "0", + "word_freq_415": "0", + "word_freq_85": "0", + "word_freq_technology": "0", + "word_freq_1999": "0", + "word_freq_parts": "0", + "word_freq_pm": "0", + "word_freq_direct": "0.06", + "word_freq_cs": "0", + "word_freq_meeting": "0", + "word_freq_original": "0.12", + "word_freq_project": "0", + "word_freq_re": "0.06", + "word_freq_edu": "0.06", + "word_freq_table": "0", + "word_freq_conference": "0", + "char_freq_%3B": "0.01", + "char_freq_%28": "0.143", + "char_freq_%5B": "0", + "char_freq_%21": "0.276", + "char_freq_%24": "0.184", + "char_freq_%23": "0.01", + "capital_run_length_average": "9.821", + "capital_run_length_longest": "485", + "capital_run_length_total": "2259", + "class": "1" + }, + { + "word_freq_make": "0", + "word_freq_address": "0", + "word_freq_all": "0", + "word_freq_3d": "0", + "word_freq_our": "0.63", + "word_freq_over": "0", + "word_freq_remove": "0.31", + "word_freq_internet": "0.63", + "word_freq_order": "0.31", + "word_freq_mail": "0.63", + "word_freq_receive": "0.31", + "word_freq_will": "0.31", + "word_freq_people": "0.31", + "word_freq_report": "0", + "word_freq_addresses": "0", + "word_freq_free": "0.31", + "word_freq_business": "0", + "word_freq_email": "0", + "word_freq_you": "3.18", + "word_freq_credit": "0", + "word_freq_your": "0.31", + "word_freq_font": "0", + "word_freq_000": "0", + "word_freq_money": "0", + "word_freq_hp": "0", + "word_freq_hpl": "0", + "word_freq_george": "0", + "word_freq_650": "0", + "word_freq_lab": "0", + "word_freq_labs": "0", + "word_freq_telnet": "0", + "word_freq_857": "0", + "word_freq_data": "0", + "word_freq_415": "0", + "word_freq_85": "0", + "word_freq_technology": "0", + "word_freq_1999": "0", + "word_freq_parts": "0", + "word_freq_pm": "0", + "word_freq_direct": "0", + "word_freq_cs": "0", + "word_freq_meeting": "0", + "word_freq_original": "0", + "word_freq_project": "0", + "word_freq_re": "0", + "word_freq_edu": "0", + "word_freq_table": "0", + "word_freq_conference": "0", + "char_freq_%3B": "0", + "char_freq_%28": "0.137", + "char_freq_%5B": "0", + "char_freq_%21": "0.137", + "char_freq_%24": "0", + "char_freq_%23": "0", + "capital_run_length_average": "3.537", + "capital_run_length_longest": "40", + "capital_run_length_total": "191", + "class": "1" + }, + { + "word_freq_make": "0", + "word_freq_address": "0", + "word_freq_all": "0", + "word_freq_3d": "0", + "word_freq_our": "0.63", + "word_freq_over": "0", + "word_freq_remove": "0.31", + "word_freq_internet": "0.63", + "word_freq_order": "0.31", + "word_freq_mail": "0.63", + "word_freq_receive": "0.31", + "word_freq_will": "0.31", + "word_freq_people": "0.31", + "word_freq_report": "0", + "word_freq_addresses": "0", + "word_freq_free": "0.31", + "word_freq_business": "0", + "word_freq_email": "0", + "word_freq_you": "3.18", + "word_freq_credit": "0", + "word_freq_your": "0.31", + "word_freq_font": "0", + "word_freq_000": "0", + "word_freq_money": "0", + "word_freq_hp": "0", + "word_freq_hpl": "0", + "word_freq_george": "0", + "word_freq_650": "0", + "word_freq_lab": "0", + "word_freq_labs": "0", + "word_freq_telnet": "0", + "word_freq_857": "0", + "word_freq_data": "0", + "word_freq_415": "0", + "word_freq_85": "0", + "word_freq_technology": "0", + "word_freq_1999": "0", + "word_freq_parts": "0", + "word_freq_pm": "0", + "word_freq_direct": "0", + "word_freq_cs": "0", + "word_freq_meeting": "0", + "word_freq_original": "0", + "word_freq_project": "0", + "word_freq_re": "0", + "word_freq_edu": "0", + "word_freq_table": "0", + "word_freq_conference": "0", + "char_freq_%3B": "0", + "char_freq_%28": "0.135", + "char_freq_%5B": "0", + "char_freq_%21": "0.135", + "char_freq_%24": "0", + "char_freq_%23": "0", + "capital_run_length_average": "3.537", + "capital_run_length_longest": "40", + "capital_run_length_total": "191", + "class": "1" + } + ] +} + +Shortlisted templates: +[ + { + "template_id": "tpl_m4_window_partition_avg", + "template_name": "Window Partition Average", + "primary_family": "conditional_dependency_structure", + "portability": "partial", + "sql_skeleton": "SELECT DISTINCT {group_col},\n AVG({measure_col}) OVER (PARTITION BY {group_col}) AS avg_measure\nFROM {table}\nORDER BY avg_measure DESC;", + "required_roles": [ + "group_col", + "measure_col" + ] + } +] + +Problem instance: +{ + "dataset_id": "n1", + "question": "Use template Window Partition Average to probe slice_level_consistency with semantic role filtered_stable_view. Focus on group_col=class, measure_col=char_freq_%3B.", + "planned_template_id": "tpl_m4_window_partition_avg", + "bindings": { + "group_col": "class", + "measure_col": "char_freq_%3B", + "top_k": 13, + "top_n": 3, + "num_tiles": 10, + "percentile_value": 0.95, + "z_threshold": 2.0, + "fraction_threshold": 0.1, + "baseline_multiplier": 1.5, + "baseline_fraction": 0.1, + "min_group_size": 5, + "min_support": 5, + "measure_threshold": 0.0, + "time_grain": "month", + "lookback_rows": 3, + "current_period_start": "'2024-01-01'", + "current_period_end": "'2024-04-01'", + "previous_period_start": "'2023-10-01'", + "previous_period_end": "'2024-01-01'", + "drift_ratio_threshold": 0.8 + }, + "can_vary": [], + "must_fix": [], + "runtime_sql_skeleton": "SELECT DISTINCT {group_col},\n AVG({measure_col}) OVER (PARTITION BY {group_col}) AS avg_measure\nFROM {table}\nORDER BY avg_measure DESC;" +} + +Repair context: +{} diff --git a/Query/sql/v2/runs/v2_cli_20260502_081223_d/n1/artifacts/v2q_n1_06a5b861b6b26a1c/cli/sql_response_attempt_1.raw.txt b/Query/sql/v2/runs/v2_cli_20260502_081223_d/n1/artifacts/v2q_n1_06a5b861b6b26a1c/cli/sql_response_attempt_1.raw.txt new file mode 100644 index 0000000000000000000000000000000000000000..0a4f0e3698853db23a565937017067d03fb02657 --- /dev/null +++ b/Query/sql/v2/runs/v2_cli_20260502_081223_d/n1/artifacts/v2q_n1_06a5b861b6b26a1c/cli/sql_response_attempt_1.raw.txt @@ -0,0 +1,4 @@ +{"type":"thread.started","thread_id":"019e40e6-007b-7280-90a1-668df22a5ef8"} +{"type":"turn.started"} +{"type":"item.completed","item":{"id":"item_0","type":"agent_message","text":"{\"sql\":\"-- template_id: tpl_m4_window_partition_avg\\nSELECT DISTINCT\\n \\\"class\\\",\\n AVG(CAST(\\\"char_freq_%3B\\\" AS REAL)) OVER (PARTITION BY \\\"class\\\") AS \\\"avg_measure\\\"\\nFROM \\\"n1\\\"\\nORDER BY \\\"avg_measure\\\" DESC;\",\"notes\":\"Uses the planned Window Partition Average template with group_col=\\\"class\\\" and measure_col=\\\"char_freq_%3B\\\". CAST to REAL is added because the schema snapshot shows the measure column stored as TEXT.\"}"}} +{"type":"turn.completed","usage":{"input_tokens":20331,"cached_input_tokens":19840,"output_tokens":505,"reasoning_output_tokens":390}} diff --git a/Query/sql/v2/runs/v2_cli_20260502_081223_d/n1/artifacts/v2q_n1_06a5b861b6b26a1c/cli/sql_response_attempt_1.txt b/Query/sql/v2/runs/v2_cli_20260502_081223_d/n1/artifacts/v2q_n1_06a5b861b6b26a1c/cli/sql_response_attempt_1.txt new file mode 100644 index 0000000000000000000000000000000000000000..97b755a58ab930c01813711871a2008f11f7dc92 --- /dev/null +++ b/Query/sql/v2/runs/v2_cli_20260502_081223_d/n1/artifacts/v2q_n1_06a5b861b6b26a1c/cli/sql_response_attempt_1.txt @@ -0,0 +1 @@ +{"sql":"-- template_id: tpl_m4_window_partition_avg\nSELECT DISTINCT\n \"class\",\n AVG(CAST(\"char_freq_%3B\" AS REAL)) OVER (PARTITION BY \"class\") AS \"avg_measure\"\nFROM \"n1\"\nORDER BY \"avg_measure\" DESC;","notes":"Uses the planned Window Partition Average template with group_col=\"class\" and measure_col=\"char_freq_%3B\". CAST to REAL is added because the schema snapshot shows the measure column stored as TEXT."} \ No newline at end of file diff --git a/Query/sql/v2/runs/v2_cli_20260502_081223_d/n1/artifacts/v2q_n1_06a5b861b6b26a1c/cli/sql_stderr_attempt_1.txt b/Query/sql/v2/runs/v2_cli_20260502_081223_d/n1/artifacts/v2q_n1_06a5b861b6b26a1c/cli/sql_stderr_attempt_1.txt new file mode 100644 index 0000000000000000000000000000000000000000..e69de29bb2d1d6434b8b29ae775ad8c2e48c5391 diff --git a/Query/sql/v2/runs/v2_cli_20260502_081223_d/n1/artifacts/v2q_n1_06f410de776111c5/cli/conversation.jsonl b/Query/sql/v2/runs/v2_cli_20260502_081223_d/n1/artifacts/v2q_n1_06f410de776111c5/cli/conversation.jsonl new file mode 100644 index 0000000000000000000000000000000000000000..a07fb9a2f44c965909effe2332937c3bf7d020c6 --- /dev/null +++ b/Query/sql/v2/runs/v2_cli_20260502_081223_d/n1/artifacts/v2q_n1_06f410de776111c5/cli/conversation.jsonl @@ -0,0 +1,2 @@ +{"attempt": 1, "phase": "sql_generation", "role": "user", "content_path": "cli/sql_prompt_attempt_1.txt", "metrics": {"chars": 29522, "bytes_utf8": 29522, "lines": 792, "estimated_tokens": null}} +{"attempt": 1, "phase": "sql_generation", "role": "assistant", "content_path": "cli/sql_response_attempt_1.txt", "raw_content_path": "cli/sql_response_attempt_1.raw.txt", "stderr_path": "cli/sql_stderr_attempt_1.txt", "metrics": {"chars": 470, "bytes_utf8": 470, "lines": 1, "estimated_tokens": null}, "usage": {"input_tokens": 20360, "cached_input_tokens": 12032, "output_tokens": 544, "reasoning_output_tokens": 419}} diff --git a/Query/sql/v2/runs/v2_cli_20260502_081223_d/n1/artifacts/v2q_n1_06f410de776111c5/cli/session_summary.json b/Query/sql/v2/runs/v2_cli_20260502_081223_d/n1/artifacts/v2q_n1_06f410de776111c5/cli/session_summary.json new file mode 100644 index 0000000000000000000000000000000000000000..84fc718aee5207c7f8afe23b7596f71ccfea16a1 --- /dev/null +++ b/Query/sql/v2/runs/v2_cli_20260502_081223_d/n1/artifacts/v2q_n1_06f410de776111c5/cli/session_summary.json @@ -0,0 +1,25 @@ +{ + "engine": "v2-cli:codex", + "command": "codex exec --skip-git-repo-check --disable plugins --sandbox read-only --cd \"/data/jialinzhang/SQLagent\" -m gpt-5.4 --json -", + "ai_cli_calls": 1, + "usage_summary": { + "dataset_id": "n1", + "model": "v2-cli:codex", + "run_id": "v2q_n1_06f410de776111c5", + "api_calls": 0, + "input_tokens": 20360, + "cached_input_tokens": 12032, + "output_tokens": 544, + "total_tokens": 20904, + "cost_usd": 0.0, + "ai_cli_calls": 1, + "estimated_input_tokens": 0, + "estimated_output_tokens": 0, + "estimated_total_tokens": 0, + "usage_source": "ai_cli_json_usage", + "cli_elapsed_ms_total": 11979.35, + "sql_execution_elapsed_ms_total": 3.25, + "conversation_log_path": "/data/jialinzhang/TabQueryBench/sql_workloads/v2_current/runs_and_launches/runs/v2_cli_20260502_081223_d/n1/artifacts/v2q_n1_06f410de776111c5/cli/conversation.jsonl", + "note": "Executed through a local AI CLI with structured usage metadata." + } +} \ No newline at end of file diff --git a/Query/sql/v2/runs/v2_cli_20260502_081223_d/n1/artifacts/v2q_n1_06f410de776111c5/cli/sql_attempt_1.metadata.json b/Query/sql/v2/runs/v2_cli_20260502_081223_d/n1/artifacts/v2q_n1_06f410de776111c5/cli/sql_attempt_1.metadata.json new file mode 100644 index 0000000000000000000000000000000000000000..b5b8a445a88c60f4593d0b76b52a8b59bed56aac --- /dev/null +++ b/Query/sql/v2/runs/v2_cli_20260502_081223_d/n1/artifacts/v2q_n1_06f410de776111c5/cli/sql_attempt_1.metadata.json @@ -0,0 +1,45 @@ +{ + "attempt": 1, + "phase": "sql_generation", + "command": "codex exec --skip-git-repo-check --disable plugins --sandbox read-only --cd \"/data/jialinzhang/SQLagent\" -m gpt-5.4 --json -", + "started_at": "2026-05-19T16:03:47.790570+00:00", + "ended_at": "2026-05-19T16:03:59.769951+00:00", + "elapsed_ms": 11979.35, + "prompt_metrics": { + "chars": 29522, + "bytes_utf8": 29522, + "lines": 792, + "estimated_tokens": null + }, + "stdout_metrics": { + "chars": 830, + "bytes_utf8": 830, + "lines": 4, + "estimated_tokens": null + }, + "stderr_metrics": { + "chars": 0, + "bytes_utf8": 0, + "lines": 0, + "estimated_tokens": null + }, + "parsed_output": { + "format": "jsonl_events", + "text_metrics": { + "chars": 470, + "bytes_utf8": 470, + "lines": 1, + "estimated_tokens": null + }, + "usage": { + "input_tokens": 20360, + "cached_input_tokens": 12032, + "output_tokens": 544, + "reasoning_output_tokens": 419 + } + }, + "prompt_path": "cli/sql_prompt_attempt_1.txt", + "response_path": "cli/sql_response_attempt_1.txt", + "raw_response_path": "cli/sql_response_attempt_1.raw.txt", + "stderr_path": "cli/sql_stderr_attempt_1.txt" +} \ No newline at end of file diff --git a/Query/sql/v2/runs/v2_cli_20260502_081223_d/n1/artifacts/v2q_n1_06f410de776111c5/cli/sql_prompt_attempt_1.txt b/Query/sql/v2/runs/v2_cli_20260502_081223_d/n1/artifacts/v2q_n1_06f410de776111c5/cli/sql_prompt_attempt_1.txt new file mode 100644 index 0000000000000000000000000000000000000000..f805fffb747575ab4255926686fbb8fe4d840a39 --- /dev/null +++ b/Query/sql/v2/runs/v2_cli_20260502_081223_d/n1/artifacts/v2q_n1_06f410de776111c5/cli/sql_prompt_attempt_1.txt @@ -0,0 +1,792 @@ +You are generating one SQLite SELECT query for a single-table SQL QA task. +Return strict JSON only, with this schema: {"sql": "...", "notes": "..."}. +Rules: +- Use only the provided table and columns. +- Do not write INSERT, UPDATE, DELETE, DROP, ALTER, CREATE, PRAGMA, ATTACH, DETACH, or VACUUM. +- Prefer the planned template and bound roles when provided. +- Add a leading SQL comment exactly like: -- template_id: . +- Generate SQLite-compatible SQL. SQLite does not support PERCENTILE_CONT or STDDEV. +- Quote identifiers with double quotes. +- Return no markdown and no extra prose. + +Dataset context: +Dataset context for SQL QA: +- dataset_id: n1 +- dataset_name: Spambase +- table_name: n1 +- table_layout: single-table dataset (do not assume joins). +- row_semantics: One row is one email represented by engineered token/character frequency and capitalization-run features. +- task_type: classification +- target_column: class +- main_row_count: 4601 +- important_fields: +- word_freq_make: role=feature, type=numeric_sparse_frequency. tags=['condition_candidate', 'measure', 'rare_pattern_candidate'] desc=Frequency feature for token 'make' in an email message. +- word_freq_address: role=feature, type=numeric_sparse_frequency. tags=['condition_candidate', 'measure', 'rare_pattern_candidate'] desc=Frequency feature for token 'address' in an email message. +- word_freq_all: role=feature, type=numeric_sparse_frequency. tags=['condition_candidate', 'measure', 'rare_pattern_candidate'] desc=Frequency feature for token 'all' in an email message. +- word_freq_3d: role=feature, type=numeric_sparse_frequency. tags=['condition_candidate', 'measure', 'rare_pattern_candidate'] desc=Frequency feature for token '3d' in an email message. +- word_freq_our: role=feature, type=numeric_sparse_frequency. tags=['condition_candidate', 'measure', 'rare_pattern_candidate'] desc=Frequency feature for token 'our' in an email message. +- word_freq_over: role=feature, type=numeric_sparse_frequency. tags=['condition_candidate', 'measure', 'rare_pattern_candidate'] desc=Frequency feature for token 'over' in an email message. +- word_freq_remove: role=feature, type=numeric_sparse_frequency. tags=['condition_candidate', 'measure', 'rare_pattern_candidate'] desc=Frequency feature for token 'remove' in an email message. +- word_freq_internet: role=feature, type=numeric_sparse_frequency. tags=['condition_candidate', 'measure', 'rare_pattern_candidate'] desc=Frequency feature for token 'internet' in an email message. +- word_freq_order: role=feature, type=numeric_sparse_frequency. tags=['condition_candidate', 'measure', 'rare_pattern_candidate'] desc=Frequency feature for token 'order' in an email message. +- word_freq_mail: role=feature, type=numeric_sparse_frequency. tags=['condition_candidate', 'measure', 'rare_pattern_candidate'] desc=Frequency feature for token 'mail' in an email message. +- word_freq_receive: role=feature, type=numeric_sparse_frequency. tags=['condition_candidate', 'measure', 'rare_pattern_candidate'] desc=Frequency feature for token 'receive' in an email message. +- word_freq_will: role=feature, type=numeric_sparse_frequency. tags=['condition_candidate', 'measure', 'rare_pattern_candidate'] desc=Frequency feature for token 'will' in an email message. +- word_freq_people: role=feature, type=numeric_sparse_frequency. tags=['condition_candidate', 'measure', 'rare_pattern_candidate'] desc=Frequency feature for token 'people' in an email message. +- word_freq_report: role=feature, type=numeric_sparse_frequency. tags=['condition_candidate', 'measure', 'rare_pattern_candidate'] desc=Frequency feature for token 'report' in an email message. +- word_freq_addresses: role=feature, type=numeric_sparse_frequency. tags=['condition_candidate', 'measure', 'rare_pattern_candidate'] desc=Frequency feature for token 'addresses' in an email message. +- word_freq_free: role=feature, type=numeric_sparse_frequency. tags=['condition_candidate', 'measure', 'rare_pattern_candidate'] desc=Frequency feature for token 'free' in an email message. +- word_freq_business: role=feature, type=numeric_sparse_frequency. tags=['condition_candidate', 'measure', 'rare_pattern_candidate'] desc=Frequency feature for token 'business' in an email message. +- word_freq_email: role=feature, type=numeric_sparse_frequency. tags=['condition_candidate', 'measure', 'rare_pattern_candidate'] desc=Frequency feature for token 'email' in an email message. +- word_freq_you: role=feature, type=numeric_sparse_frequency. tags=['condition_candidate', 'measure', 'rare_pattern_candidate'] desc=Frequency feature for token 'you' in an email message. +- word_freq_credit: role=feature, type=numeric_sparse_frequency. tags=['condition_candidate', 'measure', 'rare_pattern_candidate'] desc=Frequency feature for token 'credit' in an email message. +- word_freq_your: role=feature, type=numeric_sparse_frequency. tags=['condition_candidate', 'measure', 'rare_pattern_candidate'] desc=Frequency feature for token 'your' in an email message. +- word_freq_font: role=feature, type=numeric_sparse_frequency. tags=['condition_candidate', 'measure', 'rare_pattern_candidate'] desc=Frequency feature for token 'font' in an email message. +- word_freq_000: role=feature, type=numeric_sparse_frequency. tags=['condition_candidate', 'measure', 'rare_pattern_candidate'] desc=Frequency feature for token '000' in an email message. +- word_freq_money: role=feature, type=numeric_sparse_frequency. tags=['condition_candidate', 'measure', 'rare_pattern_candidate'] desc=Frequency feature for token 'money' in an email message. +- word_freq_hp: role=feature, type=numeric_sparse_frequency. tags=['condition_candidate', 'measure', 'rare_pattern_candidate'] desc=Frequency feature for token 'hp' in an email message. +- word_freq_hpl: role=feature, type=numeric_sparse_frequency. tags=['condition_candidate', 'measure', 'rare_pattern_candidate'] desc=Frequency feature for token 'hpl' in an email message. +- word_freq_george: role=feature, type=numeric_sparse_frequency. tags=['condition_candidate', 'measure', 'rare_pattern_candidate'] desc=Frequency feature for token 'george' in an email message. +- word_freq_650: role=feature, type=numeric_sparse_frequency. tags=['condition_candidate', 'measure', 'rare_pattern_candidate'] desc=Frequency feature for token '650' in an email message. +- word_freq_lab: role=feature, type=numeric_sparse_frequency. tags=['condition_candidate', 'measure', 'rare_pattern_candidate'] desc=Frequency feature for token 'lab' in an email message. +- word_freq_labs: role=feature, type=numeric_sparse_frequency. tags=['condition_candidate', 'measure', 'rare_pattern_candidate'] desc=Frequency feature for token 'labs' in an email message. +- word_freq_telnet: role=feature, type=numeric_sparse_frequency. tags=['condition_candidate', 'measure', 'rare_pattern_candidate'] desc=Frequency feature for token 'telnet' in an email message. +- word_freq_857: role=feature, type=numeric_sparse_frequency. tags=['condition_candidate', 'measure', 'rare_pattern_candidate'] desc=Frequency feature for token '857' in an email message. +- word_freq_data: role=feature, type=numeric_sparse_frequency. tags=['condition_candidate', 'measure', 'rare_pattern_candidate'] desc=Frequency feature for token 'data' in an email message. +- word_freq_415: role=feature, type=numeric_sparse_frequency. tags=['condition_candidate', 'measure', 'rare_pattern_candidate'] desc=Frequency feature for token '415' in an email message. +- word_freq_85: role=feature, type=numeric_sparse_frequency. tags=['condition_candidate', 'measure', 'rare_pattern_candidate'] desc=Frequency feature for token '85' in an email message. +- word_freq_technology: role=feature, type=numeric_sparse_frequency. tags=['condition_candidate', 'measure', 'rare_pattern_candidate'] desc=Frequency feature for token 'technology' in an email message. +- word_freq_1999: role=feature, type=numeric_sparse_frequency. tags=['condition_candidate', 'measure', 'rare_pattern_candidate'] desc=Frequency feature for token '1999' in an email message. +- word_freq_parts: role=feature, type=numeric_sparse_frequency. tags=['condition_candidate', 'measure', 'rare_pattern_candidate'] desc=Frequency feature for token 'parts' in an email message. +- word_freq_pm: role=feature, type=numeric_sparse_frequency. tags=['condition_candidate', 'measure', 'rare_pattern_candidate'] desc=Frequency feature for token 'pm' in an email message. +- word_freq_direct: role=feature, type=numeric_sparse_frequency. tags=['condition_candidate', 'measure', 'rare_pattern_candidate'] desc=Frequency feature for token 'direct' in an email message. +- word_freq_cs: role=feature, type=numeric_sparse_frequency. tags=['condition_candidate', 'measure', 'rare_pattern_candidate'] desc=Frequency feature for token 'cs' in an email message. +- word_freq_meeting: role=feature, type=numeric_sparse_frequency. tags=['condition_candidate', 'measure', 'rare_pattern_candidate'] desc=Frequency feature for token 'meeting' in an email message. +- word_freq_original: role=feature, type=numeric_sparse_frequency. tags=['condition_candidate', 'measure', 'rare_pattern_candidate'] desc=Frequency feature for token 'original' in an email message. +- word_freq_project: role=feature, type=numeric_sparse_frequency. tags=['condition_candidate', 'measure', 'rare_pattern_candidate'] desc=Frequency feature for token 'project' in an email message. +- word_freq_re: role=feature, type=numeric_sparse_frequency. tags=['condition_candidate', 'measure', 'rare_pattern_candidate'] desc=Frequency feature for token 're' in an email message. +- word_freq_edu: role=feature, type=numeric_sparse_frequency. tags=['condition_candidate', 'measure', 'rare_pattern_candidate'] desc=Frequency feature for token 'edu' in an email message. +- word_freq_table: role=feature, type=numeric_sparse_frequency. tags=['condition_candidate', 'measure', 'rare_pattern_candidate'] desc=Frequency feature for token 'table' in an email message. +- word_freq_conference: role=feature, type=numeric_sparse_frequency. tags=['condition_candidate', 'measure', 'rare_pattern_candidate'] desc=Frequency feature for token 'conference' in an email message. +- char_freq_%3B: role=feature, type=numeric_sparse_frequency. tags=['condition_candidate', 'measure', 'rare_pattern_candidate'] desc=Character frequency feature for symbol ';'. +- char_freq_%28: role=feature, type=numeric_sparse_frequency. tags=['condition_candidate', 'measure', 'rare_pattern_candidate'] desc=Character frequency feature for symbol '('. +- char_freq_%5B: role=feature, type=numeric_sparse_frequency. tags=['condition_candidate', 'measure', 'rare_pattern_candidate'] desc=Character frequency feature for symbol '['. +- char_freq_%21: role=feature, type=numeric_sparse_frequency. tags=['condition_candidate', 'measure', 'rare_pattern_candidate'] desc=Character frequency feature for symbol '!'. +- char_freq_%24: role=feature, type=numeric_sparse_frequency. tags=['condition_candidate', 'measure', 'rare_pattern_candidate'] desc=Character frequency feature for symbol '$'. +- char_freq_%23: role=feature, type=numeric_sparse_frequency. tags=['condition_candidate', 'measure', 'rare_pattern_candidate'] desc=Character frequency feature for symbol '#'. +- capital_run_length_average: role=feature, type=numeric_heavy_tail. tags=['condition_candidate', 'measure', 'rare_pattern_candidate'] desc=Average length of uninterrupted capital-letter runs. +- capital_run_length_longest: role=feature, type=numeric_heavy_tail. tags=['condition_candidate', 'measure', 'rare_pattern_candidate'] desc=Longest uninterrupted capital-letter run. +- capital_run_length_total: role=feature, type=numeric_heavy_tail. tags=['condition_candidate', 'measure', 'rare_pattern_candidate'] desc=Total number of capital letters in runs. +- class: role=target, type=binary_target. tags=['target_candidate'] desc=Binary spam label (1=spam, 0=non-spam). +- useful_field_combinations: [['word_freq_free', 'word_freq_you', 'class'], ['char_freq_%21', 'capital_run_length_longest', 'class'], ['word_freq_remove', 'word_freq_money', 'class']] +- fields_requiring_caution: ['capital_run_length_average', 'capital_run_length_longest', 'capital_run_length_total'] +- source_url: https://www.openml.org/d/44 + +SQLite schema snapshot: +{ + "table_name": "n1", + "quoted_table_name": "\"n1\"", + "row_count": 4601, + "columns": [ + { + "name": "word_freq_make", + "type": "TEXT", + "notnull": false, + "pk": false + }, + { + "name": "word_freq_address", + "type": "TEXT", + "notnull": false, + "pk": false + }, + { + "name": "word_freq_all", + "type": "TEXT", + "notnull": false, + "pk": false + }, + { + "name": "word_freq_3d", + "type": "TEXT", + "notnull": false, + "pk": false + }, + { + "name": "word_freq_our", + "type": "TEXT", + "notnull": false, + "pk": false + }, + { + "name": "word_freq_over", + "type": "TEXT", + "notnull": false, + "pk": false + }, + { + "name": "word_freq_remove", + "type": "TEXT", + "notnull": false, + "pk": false + }, + { + "name": "word_freq_internet", + "type": "TEXT", + "notnull": false, + "pk": false + }, + { + "name": "word_freq_order", + "type": "TEXT", + "notnull": false, + "pk": false + }, + { + "name": "word_freq_mail", + "type": "TEXT", + "notnull": false, + "pk": false + }, + { + "name": "word_freq_receive", + "type": "TEXT", + "notnull": false, + "pk": false + }, + { + "name": "word_freq_will", + "type": "TEXT", + "notnull": false, + "pk": false + }, + { + "name": "word_freq_people", + "type": "TEXT", + "notnull": false, + "pk": false + }, + { + "name": "word_freq_report", + "type": "TEXT", + "notnull": false, + "pk": false + }, + { + "name": "word_freq_addresses", + "type": "TEXT", + "notnull": false, + "pk": false + }, + { + "name": "word_freq_free", + "type": "TEXT", + "notnull": false, + "pk": false + }, + { + "name": "word_freq_business", + "type": "TEXT", + "notnull": false, + "pk": false + }, + { + "name": "word_freq_email", + "type": "TEXT", + "notnull": false, + "pk": false + }, + { + "name": "word_freq_you", + "type": "TEXT", + "notnull": false, + "pk": false + }, + { + "name": "word_freq_credit", + "type": "TEXT", + "notnull": false, + "pk": false + }, + { + "name": "word_freq_your", + "type": "TEXT", + "notnull": false, + "pk": false + }, + { + "name": "word_freq_font", + "type": "TEXT", + "notnull": false, + "pk": false + }, + { + "name": "word_freq_000", + "type": "TEXT", + "notnull": false, + "pk": false + }, + { + "name": "word_freq_money", + "type": "TEXT", + "notnull": false, + "pk": false + }, + { + "name": "word_freq_hp", + "type": "TEXT", + "notnull": false, + "pk": false + }, + { + "name": "word_freq_hpl", + "type": "TEXT", + "notnull": false, + "pk": false + }, + { + "name": "word_freq_george", + "type": "TEXT", + "notnull": false, + "pk": false + }, + { + "name": "word_freq_650", + "type": "TEXT", + "notnull": false, + "pk": false + }, + { + "name": "word_freq_lab", + "type": "TEXT", + "notnull": false, + "pk": false + }, + { + "name": "word_freq_labs", + "type": "TEXT", + "notnull": false, + "pk": false + }, + { + "name": "word_freq_telnet", + "type": "TEXT", + "notnull": false, + "pk": false + }, + { + "name": "word_freq_857", + "type": "TEXT", + "notnull": false, + "pk": false + }, + { + "name": "word_freq_data", + "type": "TEXT", + "notnull": false, + "pk": false + }, + { + "name": "word_freq_415", + "type": "TEXT", + "notnull": false, + "pk": false + }, + { + "name": "word_freq_85", + "type": "TEXT", + "notnull": false, + "pk": false + }, + { + "name": "word_freq_technology", + "type": "TEXT", + "notnull": false, + "pk": false + }, + { + "name": "word_freq_1999", + "type": "TEXT", + "notnull": false, + "pk": false + }, + { + "name": "word_freq_parts", + "type": "TEXT", + "notnull": false, + "pk": false + }, + { + "name": "word_freq_pm", + "type": "TEXT", + "notnull": false, + "pk": false + }, + { + "name": "word_freq_direct", + "type": "TEXT", + "notnull": false, + "pk": false + }, + { + "name": "word_freq_cs", + "type": "TEXT", + "notnull": false, + "pk": false + }, + { + "name": "word_freq_meeting", + "type": "TEXT", + "notnull": false, + "pk": false + }, + { + "name": "word_freq_original", + "type": "TEXT", + "notnull": false, + "pk": false + }, + { + "name": "word_freq_project", + "type": "TEXT", + "notnull": false, + "pk": false + }, + { + "name": "word_freq_re", + "type": "TEXT", + "notnull": false, + "pk": false + }, + { + "name": "word_freq_edu", + "type": "TEXT", + "notnull": false, + "pk": false + }, + { + "name": "word_freq_table", + "type": "TEXT", + "notnull": false, + "pk": false + }, + { + "name": "word_freq_conference", + "type": "TEXT", + "notnull": false, + "pk": false + }, + { + "name": "char_freq_%3B", + "type": "TEXT", + "notnull": false, + "pk": false + }, + { + "name": "char_freq_%28", + "type": "TEXT", + "notnull": false, + "pk": false + }, + { + "name": "char_freq_%5B", + "type": "TEXT", + "notnull": false, + "pk": false + }, + { + "name": "char_freq_%21", + "type": "TEXT", + "notnull": false, + "pk": false + }, + { + "name": "char_freq_%24", + "type": "TEXT", + "notnull": false, + "pk": false + }, + { + "name": "char_freq_%23", + "type": "TEXT", + "notnull": false, + "pk": false + }, + { + "name": "capital_run_length_average", + "type": "TEXT", + "notnull": false, + "pk": false + }, + { + "name": "capital_run_length_longest", + "type": "TEXT", + "notnull": false, + "pk": false + }, + { + "name": "capital_run_length_total", + "type": "TEXT", + "notnull": false, + "pk": false + }, + { + "name": "class", + "type": "TEXT", + "notnull": false, + "pk": false + } + ], + "sample_rows": [ + { + "word_freq_make": "0", + "word_freq_address": "0.64", + "word_freq_all": "0.64", + "word_freq_3d": "0", + "word_freq_our": "0.32", + "word_freq_over": "0", + "word_freq_remove": "0", + "word_freq_internet": "0", + "word_freq_order": "0", + "word_freq_mail": "0", + "word_freq_receive": "0", + "word_freq_will": "0.64", + "word_freq_people": "0", + "word_freq_report": "0", + "word_freq_addresses": "0", + "word_freq_free": "0.32", + "word_freq_business": "0", + "word_freq_email": "1.29", + "word_freq_you": "1.93", + "word_freq_credit": "0", + "word_freq_your": "0.96", + "word_freq_font": "0", + "word_freq_000": "0", + "word_freq_money": "0", + "word_freq_hp": "0", + "word_freq_hpl": "0", + "word_freq_george": "0", + "word_freq_650": "0", + "word_freq_lab": "0", + "word_freq_labs": "0", + "word_freq_telnet": "0", + "word_freq_857": "0", + "word_freq_data": "0", + "word_freq_415": "0", + "word_freq_85": "0", + "word_freq_technology": "0", + "word_freq_1999": "0", + "word_freq_parts": "0", + "word_freq_pm": "0", + "word_freq_direct": "0", + "word_freq_cs": "0", + "word_freq_meeting": "0", + "word_freq_original": "0", + "word_freq_project": "0", + "word_freq_re": "0", + "word_freq_edu": "0", + "word_freq_table": "0", + "word_freq_conference": "0", + "char_freq_%3B": "0", + "char_freq_%28": "0", + "char_freq_%5B": "0", + "char_freq_%21": "0.778", + "char_freq_%24": "0", + "char_freq_%23": "0", + "capital_run_length_average": "3.756", + "capital_run_length_longest": "61", + "capital_run_length_total": "278", + "class": "1" + }, + { + "word_freq_make": "0.21", + "word_freq_address": "0.28", + "word_freq_all": "0.5", + "word_freq_3d": "0", + "word_freq_our": "0.14", + "word_freq_over": "0.28", + "word_freq_remove": "0.21", + "word_freq_internet": "0.07", + "word_freq_order": "0", + "word_freq_mail": "0.94", + "word_freq_receive": "0.21", + "word_freq_will": "0.79", + "word_freq_people": "0.65", + "word_freq_report": "0.21", + "word_freq_addresses": "0.14", + "word_freq_free": "0.14", + "word_freq_business": "0.07", + "word_freq_email": "0.28", + "word_freq_you": "3.47", + "word_freq_credit": "0", + "word_freq_your": "1.59", + "word_freq_font": "0", + "word_freq_000": "0.43", + "word_freq_money": "0.43", + "word_freq_hp": "0", + "word_freq_hpl": "0", + "word_freq_george": "0", + "word_freq_650": "0", + "word_freq_lab": "0", + "word_freq_labs": "0", + "word_freq_telnet": "0", + "word_freq_857": "0", + "word_freq_data": "0", + "word_freq_415": "0", + "word_freq_85": "0", + "word_freq_technology": "0", + "word_freq_1999": "0.07", + "word_freq_parts": "0", + "word_freq_pm": "0", + "word_freq_direct": "0", + "word_freq_cs": "0", + "word_freq_meeting": "0", + "word_freq_original": "0", + "word_freq_project": "0", + "word_freq_re": "0", + "word_freq_edu": "0", + "word_freq_table": "0", + "word_freq_conference": "0", + "char_freq_%3B": "0", + "char_freq_%28": "0.132", + "char_freq_%5B": "0", + "char_freq_%21": "0.372", + "char_freq_%24": "0.18", + "char_freq_%23": "0.048", + "capital_run_length_average": "5.114", + "capital_run_length_longest": "101", + "capital_run_length_total": "1028", + "class": "1" + }, + { + "word_freq_make": "0.06", + "word_freq_address": "0", + "word_freq_all": "0.71", + "word_freq_3d": "0", + "word_freq_our": "1.23", + "word_freq_over": "0.19", + "word_freq_remove": "0.19", + "word_freq_internet": "0.12", + "word_freq_order": "0.64", + "word_freq_mail": "0.25", + "word_freq_receive": "0.38", + "word_freq_will": "0.45", + "word_freq_people": "0.12", + "word_freq_report": "0", + "word_freq_addresses": "1.75", + "word_freq_free": "0.06", + "word_freq_business": "0.06", + "word_freq_email": "1.03", + "word_freq_you": "1.36", + "word_freq_credit": "0.32", + "word_freq_your": "0.51", + "word_freq_font": "0", + "word_freq_000": "1.16", + "word_freq_money": "0.06", + "word_freq_hp": "0", + "word_freq_hpl": "0", + "word_freq_george": "0", + "word_freq_650": "0", + "word_freq_lab": "0", + "word_freq_labs": "0", + "word_freq_telnet": "0", + "word_freq_857": "0", + "word_freq_data": "0", + "word_freq_415": "0", + "word_freq_85": "0", + "word_freq_technology": "0", + "word_freq_1999": "0", + "word_freq_parts": "0", + "word_freq_pm": "0", + "word_freq_direct": "0.06", + "word_freq_cs": "0", + "word_freq_meeting": "0", + "word_freq_original": "0.12", + "word_freq_project": "0", + "word_freq_re": "0.06", + "word_freq_edu": "0.06", + "word_freq_table": "0", + "word_freq_conference": "0", + "char_freq_%3B": "0.01", + "char_freq_%28": "0.143", + "char_freq_%5B": "0", + "char_freq_%21": "0.276", + "char_freq_%24": "0.184", + "char_freq_%23": "0.01", + "capital_run_length_average": "9.821", + "capital_run_length_longest": "485", + "capital_run_length_total": "2259", + "class": "1" + }, + { + "word_freq_make": "0", + "word_freq_address": "0", + "word_freq_all": "0", + "word_freq_3d": "0", + "word_freq_our": "0.63", + "word_freq_over": "0", + "word_freq_remove": "0.31", + "word_freq_internet": "0.63", + "word_freq_order": "0.31", + "word_freq_mail": "0.63", + "word_freq_receive": "0.31", + "word_freq_will": "0.31", + "word_freq_people": "0.31", + "word_freq_report": "0", + "word_freq_addresses": "0", + "word_freq_free": "0.31", + "word_freq_business": "0", + "word_freq_email": "0", + "word_freq_you": "3.18", + "word_freq_credit": "0", + "word_freq_your": "0.31", + "word_freq_font": "0", + "word_freq_000": "0", + "word_freq_money": "0", + "word_freq_hp": "0", + "word_freq_hpl": "0", + "word_freq_george": "0", + "word_freq_650": "0", + "word_freq_lab": "0", + "word_freq_labs": "0", + "word_freq_telnet": "0", + "word_freq_857": "0", + "word_freq_data": "0", + "word_freq_415": "0", + "word_freq_85": "0", + "word_freq_technology": "0", + "word_freq_1999": "0", + "word_freq_parts": "0", + "word_freq_pm": "0", + "word_freq_direct": "0", + "word_freq_cs": "0", + "word_freq_meeting": "0", + "word_freq_original": "0", + "word_freq_project": "0", + "word_freq_re": "0", + "word_freq_edu": "0", + "word_freq_table": "0", + "word_freq_conference": "0", + "char_freq_%3B": "0", + "char_freq_%28": "0.137", + "char_freq_%5B": "0", + "char_freq_%21": "0.137", + "char_freq_%24": "0", + "char_freq_%23": "0", + "capital_run_length_average": "3.537", + "capital_run_length_longest": "40", + "capital_run_length_total": "191", + "class": "1" + }, + { + "word_freq_make": "0", + "word_freq_address": "0", + "word_freq_all": "0", + "word_freq_3d": "0", + "word_freq_our": "0.63", + "word_freq_over": "0", + "word_freq_remove": "0.31", + "word_freq_internet": "0.63", + "word_freq_order": "0.31", + "word_freq_mail": "0.63", + "word_freq_receive": "0.31", + "word_freq_will": "0.31", + "word_freq_people": "0.31", + "word_freq_report": "0", + "word_freq_addresses": "0", + "word_freq_free": "0.31", + "word_freq_business": "0", + "word_freq_email": "0", + "word_freq_you": "3.18", + "word_freq_credit": "0", + "word_freq_your": "0.31", + "word_freq_font": "0", + "word_freq_000": "0", + "word_freq_money": "0", + "word_freq_hp": "0", + "word_freq_hpl": "0", + "word_freq_george": "0", + "word_freq_650": "0", + "word_freq_lab": "0", + "word_freq_labs": "0", + "word_freq_telnet": "0", + "word_freq_857": "0", + "word_freq_data": "0", + "word_freq_415": "0", + "word_freq_85": "0", + "word_freq_technology": "0", + "word_freq_1999": "0", + "word_freq_parts": "0", + "word_freq_pm": "0", + "word_freq_direct": "0", + "word_freq_cs": "0", + "word_freq_meeting": "0", + "word_freq_original": "0", + "word_freq_project": "0", + "word_freq_re": "0", + "word_freq_edu": "0", + "word_freq_table": "0", + "word_freq_conference": "0", + "char_freq_%3B": "0", + "char_freq_%28": "0.135", + "char_freq_%5B": "0", + "char_freq_%21": "0.135", + "char_freq_%24": "0", + "char_freq_%23": "0", + "capital_run_length_average": "3.537", + "capital_run_length_longest": "40", + "capital_run_length_total": "191", + "class": "1" + } + ] +} + +Shortlisted templates: +[ + { + "template_id": "tpl_tpch_thresholded_group_ranking", + "template_name": "Thresholded Group Ranking", + "primary_family": "tail_rarity_structure", + "portability": "partial", + "sql_skeleton": "SELECT {group_col}, SUM({measure_col}) AS total_measure\nFROM {table}\nGROUP BY {group_col}\nHAVING SUM({measure_col}) > {measure_threshold}\nORDER BY total_measure DESC\nLIMIT {top_k};", + "required_roles": [ + "group_col", + "measure_col" + ] + } +] + +Problem instance: +{ + "dataset_id": "n1", + "question": "Use template Thresholded Group Ranking to probe tail_set_consistency with semantic role rare_extreme_view. Focus on group_col=class, measure_col=word_freq_you.", + "planned_template_id": "tpl_tpch_thresholded_group_ranking", + "bindings": { + "group_col": "class", + "measure_col": "word_freq_you", + "top_k": 12, + "top_n": 3, + "num_tiles": 10, + "percentile_value": 0.95, + "z_threshold": 2.0, + "fraction_threshold": 0.1, + "baseline_multiplier": 1.5, + "baseline_fraction": 0.1, + "min_group_size": 5, + "min_support": 5, + "measure_threshold": 2.64, + "time_grain": "month", + "lookback_rows": 3, + "current_period_start": "'2024-01-01'", + "current_period_end": "'2024-04-01'", + "previous_period_start": "'2023-10-01'", + "previous_period_end": "'2024-01-01'", + "drift_ratio_threshold": 0.8 + }, + "can_vary": [], + "must_fix": [], + "runtime_sql_skeleton": "SELECT {group_col}, SUM({measure_col}) AS total_measure\nFROM {table}\nGROUP BY {group_col}\nHAVING SUM({measure_col}) > {measure_threshold}\nORDER BY total_measure DESC\nLIMIT {top_k};" +} + +Repair context: +{} diff --git a/Query/sql/v2/runs/v2_cli_20260502_081223_d/n1/artifacts/v2q_n1_06f410de776111c5/cli/sql_response_attempt_1.raw.txt b/Query/sql/v2/runs/v2_cli_20260502_081223_d/n1/artifacts/v2q_n1_06f410de776111c5/cli/sql_response_attempt_1.raw.txt new file mode 100644 index 0000000000000000000000000000000000000000..47eb6acb879586cc578ac06b50270520a591a43f --- /dev/null +++ b/Query/sql/v2/runs/v2_cli_20260502_081223_d/n1/artifacts/v2q_n1_06f410de776111c5/cli/sql_response_attempt_1.raw.txt @@ -0,0 +1,4 @@ +{"type":"thread.started","thread_id":"019e40fa-a2d9-71d2-88e1-aff1c376d3b4"} +{"type":"turn.started"} +{"type":"item.completed","item":{"id":"item_0","type":"agent_message","text":"{\"sql\":\"-- template_id: tpl_tpch_thresholded_group_ranking\\nSELECT \\\"class\\\", SUM(CAST(\\\"word_freq_you\\\" AS REAL)) AS total_measure\\nFROM \\\"n1\\\"\\nGROUP BY \\\"class\\\"\\nHAVING SUM(CAST(\\\"word_freq_you\\\" AS REAL)) > 2.64\\nORDER BY total_measure DESC\\nLIMIT 12;\",\"notes\":\"Used the required Thresholded Group Ranking template with group_col=\\\"class\\\" and measure_col=\\\"word_freq_you\\\". CAST to REAL is included because the schema snapshot shows these columns stored as TEXT.\"}"}} +{"type":"turn.completed","usage":{"input_tokens":20360,"cached_input_tokens":12032,"output_tokens":544,"reasoning_output_tokens":419}} diff --git a/Query/sql/v2/runs/v2_cli_20260502_081223_d/n1/artifacts/v2q_n1_06f410de776111c5/cli/sql_response_attempt_1.txt b/Query/sql/v2/runs/v2_cli_20260502_081223_d/n1/artifacts/v2q_n1_06f410de776111c5/cli/sql_response_attempt_1.txt new file mode 100644 index 0000000000000000000000000000000000000000..d36185b97b81eecb3c863ee59a41b5b42ca5caeb --- /dev/null +++ b/Query/sql/v2/runs/v2_cli_20260502_081223_d/n1/artifacts/v2q_n1_06f410de776111c5/cli/sql_response_attempt_1.txt @@ -0,0 +1 @@ +{"sql":"-- template_id: tpl_tpch_thresholded_group_ranking\nSELECT \"class\", SUM(CAST(\"word_freq_you\" AS REAL)) AS total_measure\nFROM \"n1\"\nGROUP BY \"class\"\nHAVING SUM(CAST(\"word_freq_you\" AS REAL)) > 2.64\nORDER BY total_measure DESC\nLIMIT 12;","notes":"Used the required Thresholded Group Ranking template with group_col=\"class\" and measure_col=\"word_freq_you\". CAST to REAL is included because the schema snapshot shows these columns stored as TEXT."} \ No newline at end of file diff --git a/Query/sql/v2/runs/v2_cli_20260502_081223_d/n1/artifacts/v2q_n1_06f410de776111c5/cli/sql_stderr_attempt_1.txt b/Query/sql/v2/runs/v2_cli_20260502_081223_d/n1/artifacts/v2q_n1_06f410de776111c5/cli/sql_stderr_attempt_1.txt new file mode 100644 index 0000000000000000000000000000000000000000..e69de29bb2d1d6434b8b29ae775ad8c2e48c5391 diff --git a/Query/sql/v2/runs/v2_cli_20260502_081223_d/n1/artifacts/v2q_n1_088dd75b53afc004/cli/conversation.jsonl b/Query/sql/v2/runs/v2_cli_20260502_081223_d/n1/artifacts/v2q_n1_088dd75b53afc004/cli/conversation.jsonl new file mode 100644 index 0000000000000000000000000000000000000000..84ac3c2357e58fb4503892f954341cb926de7216 --- /dev/null +++ b/Query/sql/v2/runs/v2_cli_20260502_081223_d/n1/artifacts/v2q_n1_088dd75b53afc004/cli/conversation.jsonl @@ -0,0 +1,4 @@ +{"attempt": 1, "phase": "sql_generation", "role": "user", "content_path": "cli/sql_prompt_attempt_1.txt", "metrics": {"chars": 29335, "bytes_utf8": 29335, "lines": 790, "estimated_tokens": null}} +{"attempt": 1, "phase": "sql_generation", "role": "assistant", "content_path": "cli/sql_response_attempt_1.txt", "raw_content_path": "cli/sql_response_attempt_1.raw.txt", "stderr_path": "cli/sql_stderr_attempt_1.txt", "metrics": {"chars": 280, "bytes_utf8": 280, "lines": 4, "estimated_tokens": null}, "usage": {}, "status": "failed", "error": "AI CLI command failed with exit code 1: "} +{"attempt": 2, "phase": "sql_generation", "role": "user", "content_path": "cli/sql_prompt_attempt_2.txt", "metrics": {"chars": 29335, "bytes_utf8": 29335, "lines": 790, "estimated_tokens": null}} +{"attempt": 2, "phase": "sql_generation", "role": "assistant", "content_path": "cli/sql_response_attempt_2.txt", "raw_content_path": "cli/sql_response_attempt_2.raw.txt", "stderr_path": "cli/sql_stderr_attempt_2.txt", "metrics": {"chars": 310, "bytes_utf8": 310, "lines": 1, "estimated_tokens": null}, "usage": {"input_tokens": 20322, "cached_input_tokens": 19840, "output_tokens": 298, "reasoning_output_tokens": 208}} diff --git a/Query/sql/v2/runs/v2_cli_20260502_081223_d/n1/artifacts/v2q_n1_088dd75b53afc004/cli/session_summary.json b/Query/sql/v2/runs/v2_cli_20260502_081223_d/n1/artifacts/v2q_n1_088dd75b53afc004/cli/session_summary.json new file mode 100644 index 0000000000000000000000000000000000000000..66a9d03f4754dc1f2df44877f711fc8396064e52 --- /dev/null +++ b/Query/sql/v2/runs/v2_cli_20260502_081223_d/n1/artifacts/v2q_n1_088dd75b53afc004/cli/session_summary.json @@ -0,0 +1,25 @@ +{ + "engine": "v2-cli:codex", + "command": "codex exec --skip-git-repo-check --disable plugins --sandbox read-only --cd \"/data/jialinzhang/SQLagent\" -m gpt-5.4 --json -", + "ai_cli_calls": 2, + "usage_summary": { + "dataset_id": "n1", + "model": "v2-cli:codex", + "run_id": "v2q_n1_088dd75b53afc004", + "api_calls": 0, + "input_tokens": 20322, + "cached_input_tokens": 19840, + "output_tokens": 298, + "total_tokens": 20620, + "cost_usd": 0.0, + "ai_cli_calls": 2, + "estimated_input_tokens": 0, + "estimated_output_tokens": 0, + "estimated_total_tokens": 0, + "usage_source": "ai_cli_json_usage", + "cli_elapsed_ms_total": 12951.89, + "sql_execution_elapsed_ms_total": 2.94, + "conversation_log_path": "/data/jialinzhang/TabQueryBench/sql_workloads/v2_current/runs_and_launches/runs/v2_cli_20260502_081223_d/n1/artifacts/v2q_n1_088dd75b53afc004/cli/conversation.jsonl", + "note": "Executed through a local AI CLI with structured usage metadata." + } +} \ No newline at end of file diff --git a/Query/sql/v2/runs/v2_cli_20260502_081223_d/n1/artifacts/v2q_n1_088dd75b53afc004/cli/sql_attempt_1.metadata.json b/Query/sql/v2/runs/v2_cli_20260502_081223_d/n1/artifacts/v2q_n1_088dd75b53afc004/cli/sql_attempt_1.metadata.json new file mode 100644 index 0000000000000000000000000000000000000000..8760ca63204e7c52e769568512bbe9b5d62094d8 --- /dev/null +++ b/Query/sql/v2/runs/v2_cli_20260502_081223_d/n1/artifacts/v2q_n1_088dd75b53afc004/cli/sql_attempt_1.metadata.json @@ -0,0 +1,43 @@ +{ + "attempt": 1, + "phase": "sql_generation", + "command": "codex exec --skip-git-repo-check --disable plugins --sandbox read-only --cd \"/data/jialinzhang/SQLagent\" -m gpt-5.4 --json -", + "started_at": "2026-05-19T16:03:01.172817+00:00", + "ended_at": "2026-05-19T16:03:05.504976+00:00", + "elapsed_ms": 4332.13, + "returncode": 1, + "prompt_metrics": { + "chars": 29335, + "bytes_utf8": 29335, + "lines": 790, + "estimated_tokens": null + }, + "stdout_metrics": { + "chars": 281, + "bytes_utf8": 281, + "lines": 4, + "estimated_tokens": null + }, + "stderr_metrics": { + "chars": 0, + "bytes_utf8": 0, + "lines": 0, + "estimated_tokens": null + }, + "parsed_output": { + "format": "jsonl_events", + "text_metrics": { + "chars": 280, + "bytes_utf8": 280, + "lines": 4, + "estimated_tokens": null + }, + "usage": {} + }, + "status": "failed", + "error": "AI CLI command failed with exit code 1: ", + "prompt_path": "cli/sql_prompt_attempt_1.txt", + "response_path": "cli/sql_response_attempt_1.txt", + "raw_response_path": "cli/sql_response_attempt_1.raw.txt", + "stderr_path": "cli/sql_stderr_attempt_1.txt" +} \ No newline at end of file diff --git a/Query/sql/v2/runs/v2_cli_20260502_081223_d/n1/artifacts/v2q_n1_088dd75b53afc004/cli/sql_attempt_2.metadata.json b/Query/sql/v2/runs/v2_cli_20260502_081223_d/n1/artifacts/v2q_n1_088dd75b53afc004/cli/sql_attempt_2.metadata.json new file mode 100644 index 0000000000000000000000000000000000000000..f55ea3fb6ffe2d2c84aaf8ed476fc60d2881eb73 --- /dev/null +++ b/Query/sql/v2/runs/v2_cli_20260502_081223_d/n1/artifacts/v2q_n1_088dd75b53afc004/cli/sql_attempt_2.metadata.json @@ -0,0 +1,45 @@ +{ + "attempt": 2, + "phase": "sql_generation", + "command": "codex exec --skip-git-repo-check --disable plugins --sandbox read-only --cd \"/data/jialinzhang/SQLagent\" -m gpt-5.4 --json -", + "started_at": "2026-05-19T16:03:06.506945+00:00", + "ended_at": "2026-05-19T16:03:15.126737+00:00", + "elapsed_ms": 8619.76, + "prompt_metrics": { + "chars": 29335, + "bytes_utf8": 29335, + "lines": 790, + "estimated_tokens": null + }, + "stdout_metrics": { + "chars": 667, + "bytes_utf8": 667, + "lines": 4, + "estimated_tokens": null + }, + "stderr_metrics": { + "chars": 0, + "bytes_utf8": 0, + "lines": 0, + "estimated_tokens": null + }, + "parsed_output": { + "format": "jsonl_events", + "text_metrics": { + "chars": 310, + "bytes_utf8": 310, + "lines": 1, + "estimated_tokens": null + }, + "usage": { + "input_tokens": 20322, + "cached_input_tokens": 19840, + "output_tokens": 298, + "reasoning_output_tokens": 208 + } + }, + "prompt_path": "cli/sql_prompt_attempt_2.txt", + "response_path": "cli/sql_response_attempt_2.txt", + "raw_response_path": "cli/sql_response_attempt_2.raw.txt", + "stderr_path": "cli/sql_stderr_attempt_2.txt" +} \ No newline at end of file diff --git a/Query/sql/v2/runs/v2_cli_20260502_081223_d/n1/artifacts/v2q_n1_088dd75b53afc004/cli/sql_prompt_attempt_1.txt b/Query/sql/v2/runs/v2_cli_20260502_081223_d/n1/artifacts/v2q_n1_088dd75b53afc004/cli/sql_prompt_attempt_1.txt new file mode 100644 index 0000000000000000000000000000000000000000..ca35bfc5e3e4e856b4045251c3b6dd3964b94563 --- /dev/null +++ b/Query/sql/v2/runs/v2_cli_20260502_081223_d/n1/artifacts/v2q_n1_088dd75b53afc004/cli/sql_prompt_attempt_1.txt @@ -0,0 +1,790 @@ +You are generating one SQLite SELECT query for a single-table SQL QA task. +Return strict JSON only, with this schema: {"sql": "...", "notes": "..."}. +Rules: +- Use only the provided table and columns. +- Do not write INSERT, UPDATE, DELETE, DROP, ALTER, CREATE, PRAGMA, ATTACH, DETACH, or VACUUM. +- Prefer the planned template and bound roles when provided. +- Add a leading SQL comment exactly like: -- template_id: . +- Generate SQLite-compatible SQL. SQLite does not support PERCENTILE_CONT or STDDEV. +- Quote identifiers with double quotes. +- Return no markdown and no extra prose. + +Dataset context: +Dataset context for SQL QA: +- dataset_id: n1 +- dataset_name: Spambase +- table_name: n1 +- table_layout: single-table dataset (do not assume joins). +- row_semantics: One row is one email represented by engineered token/character frequency and capitalization-run features. +- task_type: classification +- target_column: class +- main_row_count: 4601 +- important_fields: +- word_freq_make: role=feature, type=numeric_sparse_frequency. tags=['condition_candidate', 'measure', 'rare_pattern_candidate'] desc=Frequency feature for token 'make' in an email message. +- word_freq_address: role=feature, type=numeric_sparse_frequency. tags=['condition_candidate', 'measure', 'rare_pattern_candidate'] desc=Frequency feature for token 'address' in an email message. +- word_freq_all: role=feature, type=numeric_sparse_frequency. tags=['condition_candidate', 'measure', 'rare_pattern_candidate'] desc=Frequency feature for token 'all' in an email message. +- word_freq_3d: role=feature, type=numeric_sparse_frequency. tags=['condition_candidate', 'measure', 'rare_pattern_candidate'] desc=Frequency feature for token '3d' in an email message. +- word_freq_our: role=feature, type=numeric_sparse_frequency. tags=['condition_candidate', 'measure', 'rare_pattern_candidate'] desc=Frequency feature for token 'our' in an email message. +- word_freq_over: role=feature, type=numeric_sparse_frequency. tags=['condition_candidate', 'measure', 'rare_pattern_candidate'] desc=Frequency feature for token 'over' in an email message. +- word_freq_remove: role=feature, type=numeric_sparse_frequency. tags=['condition_candidate', 'measure', 'rare_pattern_candidate'] desc=Frequency feature for token 'remove' in an email message. +- word_freq_internet: role=feature, type=numeric_sparse_frequency. tags=['condition_candidate', 'measure', 'rare_pattern_candidate'] desc=Frequency feature for token 'internet' in an email message. +- word_freq_order: role=feature, type=numeric_sparse_frequency. tags=['condition_candidate', 'measure', 'rare_pattern_candidate'] desc=Frequency feature for token 'order' in an email message. +- word_freq_mail: role=feature, type=numeric_sparse_frequency. tags=['condition_candidate', 'measure', 'rare_pattern_candidate'] desc=Frequency feature for token 'mail' in an email message. +- word_freq_receive: role=feature, type=numeric_sparse_frequency. tags=['condition_candidate', 'measure', 'rare_pattern_candidate'] desc=Frequency feature for token 'receive' in an email message. +- word_freq_will: role=feature, type=numeric_sparse_frequency. tags=['condition_candidate', 'measure', 'rare_pattern_candidate'] desc=Frequency feature for token 'will' in an email message. +- word_freq_people: role=feature, type=numeric_sparse_frequency. tags=['condition_candidate', 'measure', 'rare_pattern_candidate'] desc=Frequency feature for token 'people' in an email message. +- word_freq_report: role=feature, type=numeric_sparse_frequency. tags=['condition_candidate', 'measure', 'rare_pattern_candidate'] desc=Frequency feature for token 'report' in an email message. +- word_freq_addresses: role=feature, type=numeric_sparse_frequency. tags=['condition_candidate', 'measure', 'rare_pattern_candidate'] desc=Frequency feature for token 'addresses' in an email message. +- word_freq_free: role=feature, type=numeric_sparse_frequency. tags=['condition_candidate', 'measure', 'rare_pattern_candidate'] desc=Frequency feature for token 'free' in an email message. +- word_freq_business: role=feature, type=numeric_sparse_frequency. tags=['condition_candidate', 'measure', 'rare_pattern_candidate'] desc=Frequency feature for token 'business' in an email message. +- word_freq_email: role=feature, type=numeric_sparse_frequency. tags=['condition_candidate', 'measure', 'rare_pattern_candidate'] desc=Frequency feature for token 'email' in an email message. +- word_freq_you: role=feature, type=numeric_sparse_frequency. tags=['condition_candidate', 'measure', 'rare_pattern_candidate'] desc=Frequency feature for token 'you' in an email message. +- word_freq_credit: role=feature, type=numeric_sparse_frequency. tags=['condition_candidate', 'measure', 'rare_pattern_candidate'] desc=Frequency feature for token 'credit' in an email message. +- word_freq_your: role=feature, type=numeric_sparse_frequency. tags=['condition_candidate', 'measure', 'rare_pattern_candidate'] desc=Frequency feature for token 'your' in an email message. +- word_freq_font: role=feature, type=numeric_sparse_frequency. tags=['condition_candidate', 'measure', 'rare_pattern_candidate'] desc=Frequency feature for token 'font' in an email message. +- word_freq_000: role=feature, type=numeric_sparse_frequency. tags=['condition_candidate', 'measure', 'rare_pattern_candidate'] desc=Frequency feature for token '000' in an email message. +- word_freq_money: role=feature, type=numeric_sparse_frequency. tags=['condition_candidate', 'measure', 'rare_pattern_candidate'] desc=Frequency feature for token 'money' in an email message. +- word_freq_hp: role=feature, type=numeric_sparse_frequency. tags=['condition_candidate', 'measure', 'rare_pattern_candidate'] desc=Frequency feature for token 'hp' in an email message. +- word_freq_hpl: role=feature, type=numeric_sparse_frequency. tags=['condition_candidate', 'measure', 'rare_pattern_candidate'] desc=Frequency feature for token 'hpl' in an email message. +- word_freq_george: role=feature, type=numeric_sparse_frequency. tags=['condition_candidate', 'measure', 'rare_pattern_candidate'] desc=Frequency feature for token 'george' in an email message. +- word_freq_650: role=feature, type=numeric_sparse_frequency. tags=['condition_candidate', 'measure', 'rare_pattern_candidate'] desc=Frequency feature for token '650' in an email message. +- word_freq_lab: role=feature, type=numeric_sparse_frequency. tags=['condition_candidate', 'measure', 'rare_pattern_candidate'] desc=Frequency feature for token 'lab' in an email message. +- word_freq_labs: role=feature, type=numeric_sparse_frequency. tags=['condition_candidate', 'measure', 'rare_pattern_candidate'] desc=Frequency feature for token 'labs' in an email message. +- word_freq_telnet: role=feature, type=numeric_sparse_frequency. tags=['condition_candidate', 'measure', 'rare_pattern_candidate'] desc=Frequency feature for token 'telnet' in an email message. +- word_freq_857: role=feature, type=numeric_sparse_frequency. tags=['condition_candidate', 'measure', 'rare_pattern_candidate'] desc=Frequency feature for token '857' in an email message. +- word_freq_data: role=feature, type=numeric_sparse_frequency. tags=['condition_candidate', 'measure', 'rare_pattern_candidate'] desc=Frequency feature for token 'data' in an email message. +- word_freq_415: role=feature, type=numeric_sparse_frequency. tags=['condition_candidate', 'measure', 'rare_pattern_candidate'] desc=Frequency feature for token '415' in an email message. +- word_freq_85: role=feature, type=numeric_sparse_frequency. tags=['condition_candidate', 'measure', 'rare_pattern_candidate'] desc=Frequency feature for token '85' in an email message. +- word_freq_technology: role=feature, type=numeric_sparse_frequency. tags=['condition_candidate', 'measure', 'rare_pattern_candidate'] desc=Frequency feature for token 'technology' in an email message. +- word_freq_1999: role=feature, type=numeric_sparse_frequency. tags=['condition_candidate', 'measure', 'rare_pattern_candidate'] desc=Frequency feature for token '1999' in an email message. +- word_freq_parts: role=feature, type=numeric_sparse_frequency. tags=['condition_candidate', 'measure', 'rare_pattern_candidate'] desc=Frequency feature for token 'parts' in an email message. +- word_freq_pm: role=feature, type=numeric_sparse_frequency. tags=['condition_candidate', 'measure', 'rare_pattern_candidate'] desc=Frequency feature for token 'pm' in an email message. +- word_freq_direct: role=feature, type=numeric_sparse_frequency. tags=['condition_candidate', 'measure', 'rare_pattern_candidate'] desc=Frequency feature for token 'direct' in an email message. +- word_freq_cs: role=feature, type=numeric_sparse_frequency. tags=['condition_candidate', 'measure', 'rare_pattern_candidate'] desc=Frequency feature for token 'cs' in an email message. +- word_freq_meeting: role=feature, type=numeric_sparse_frequency. tags=['condition_candidate', 'measure', 'rare_pattern_candidate'] desc=Frequency feature for token 'meeting' in an email message. +- word_freq_original: role=feature, type=numeric_sparse_frequency. tags=['condition_candidate', 'measure', 'rare_pattern_candidate'] desc=Frequency feature for token 'original' in an email message. +- word_freq_project: role=feature, type=numeric_sparse_frequency. tags=['condition_candidate', 'measure', 'rare_pattern_candidate'] desc=Frequency feature for token 'project' in an email message. +- word_freq_re: role=feature, type=numeric_sparse_frequency. tags=['condition_candidate', 'measure', 'rare_pattern_candidate'] desc=Frequency feature for token 're' in an email message. +- word_freq_edu: role=feature, type=numeric_sparse_frequency. tags=['condition_candidate', 'measure', 'rare_pattern_candidate'] desc=Frequency feature for token 'edu' in an email message. +- word_freq_table: role=feature, type=numeric_sparse_frequency. tags=['condition_candidate', 'measure', 'rare_pattern_candidate'] desc=Frequency feature for token 'table' in an email message. +- word_freq_conference: role=feature, type=numeric_sparse_frequency. tags=['condition_candidate', 'measure', 'rare_pattern_candidate'] desc=Frequency feature for token 'conference' in an email message. +- char_freq_%3B: role=feature, type=numeric_sparse_frequency. tags=['condition_candidate', 'measure', 'rare_pattern_candidate'] desc=Character frequency feature for symbol ';'. +- char_freq_%28: role=feature, type=numeric_sparse_frequency. tags=['condition_candidate', 'measure', 'rare_pattern_candidate'] desc=Character frequency feature for symbol '('. +- char_freq_%5B: role=feature, type=numeric_sparse_frequency. tags=['condition_candidate', 'measure', 'rare_pattern_candidate'] desc=Character frequency feature for symbol '['. +- char_freq_%21: role=feature, type=numeric_sparse_frequency. tags=['condition_candidate', 'measure', 'rare_pattern_candidate'] desc=Character frequency feature for symbol '!'. +- char_freq_%24: role=feature, type=numeric_sparse_frequency. tags=['condition_candidate', 'measure', 'rare_pattern_candidate'] desc=Character frequency feature for symbol '$'. +- char_freq_%23: role=feature, type=numeric_sparse_frequency. tags=['condition_candidate', 'measure', 'rare_pattern_candidate'] desc=Character frequency feature for symbol '#'. +- capital_run_length_average: role=feature, type=numeric_heavy_tail. tags=['condition_candidate', 'measure', 'rare_pattern_candidate'] desc=Average length of uninterrupted capital-letter runs. +- capital_run_length_longest: role=feature, type=numeric_heavy_tail. tags=['condition_candidate', 'measure', 'rare_pattern_candidate'] desc=Longest uninterrupted capital-letter run. +- capital_run_length_total: role=feature, type=numeric_heavy_tail. tags=['condition_candidate', 'measure', 'rare_pattern_candidate'] desc=Total number of capital letters in runs. +- class: role=target, type=binary_target. tags=['target_candidate'] desc=Binary spam label (1=spam, 0=non-spam). +- useful_field_combinations: [['word_freq_free', 'word_freq_you', 'class'], ['char_freq_%21', 'capital_run_length_longest', 'class'], ['word_freq_remove', 'word_freq_money', 'class']] +- fields_requiring_caution: ['capital_run_length_average', 'capital_run_length_longest', 'capital_run_length_total'] +- source_url: https://www.openml.org/d/44 + +SQLite schema snapshot: +{ + "table_name": "n1", + "quoted_table_name": "\"n1\"", + "row_count": 4601, + "columns": [ + { + "name": "word_freq_make", + "type": "TEXT", + "notnull": false, + "pk": false + }, + { + "name": "word_freq_address", + "type": "TEXT", + "notnull": false, + "pk": false + }, + { + "name": "word_freq_all", + "type": "TEXT", + "notnull": false, + "pk": false + }, + { + "name": "word_freq_3d", + "type": "TEXT", + "notnull": false, + "pk": false + }, + { + "name": "word_freq_our", + "type": "TEXT", + "notnull": false, + "pk": false + }, + { + "name": "word_freq_over", + "type": "TEXT", + "notnull": false, + "pk": false + }, + { + "name": "word_freq_remove", + "type": "TEXT", + "notnull": false, + "pk": false + }, + { + "name": "word_freq_internet", + "type": "TEXT", + "notnull": false, + "pk": false + }, + { + "name": "word_freq_order", + "type": "TEXT", + "notnull": false, + "pk": false + }, + { + "name": "word_freq_mail", + "type": "TEXT", + "notnull": false, + "pk": false + }, + { + "name": "word_freq_receive", + "type": "TEXT", + "notnull": false, + "pk": false + }, + { + "name": "word_freq_will", + "type": "TEXT", + "notnull": false, + "pk": false + }, + { + "name": "word_freq_people", + "type": "TEXT", + "notnull": false, + "pk": false + }, + { + "name": "word_freq_report", + "type": "TEXT", + "notnull": false, + "pk": false + }, + { + "name": "word_freq_addresses", + "type": "TEXT", + "notnull": false, + "pk": false + }, + { + "name": "word_freq_free", + "type": "TEXT", + "notnull": false, + "pk": false + }, + { + "name": "word_freq_business", + "type": "TEXT", + "notnull": false, + "pk": false + }, + { + "name": "word_freq_email", + "type": "TEXT", + "notnull": false, + "pk": false + }, + { + "name": "word_freq_you", + "type": "TEXT", + "notnull": false, + "pk": false + }, + { + "name": "word_freq_credit", + "type": "TEXT", + "notnull": false, + "pk": false + }, + { + "name": "word_freq_your", + "type": "TEXT", + "notnull": false, + "pk": false + }, + { + "name": "word_freq_font", + "type": "TEXT", + "notnull": false, + "pk": false + }, + { + "name": "word_freq_000", + "type": "TEXT", + "notnull": false, + "pk": false + }, + { + "name": "word_freq_money", + "type": "TEXT", + "notnull": false, + "pk": false + }, + { + "name": "word_freq_hp", + "type": "TEXT", + "notnull": false, + "pk": false + }, + { + "name": "word_freq_hpl", + "type": "TEXT", + "notnull": false, + "pk": false + }, + { + "name": "word_freq_george", + "type": "TEXT", + "notnull": false, + "pk": false + }, + { + "name": "word_freq_650", + "type": "TEXT", + "notnull": false, + "pk": false + }, + { + "name": "word_freq_lab", + "type": "TEXT", + "notnull": false, + "pk": false + }, + { + "name": "word_freq_labs", + "type": "TEXT", + "notnull": false, + "pk": false + }, + { + "name": "word_freq_telnet", + "type": "TEXT", + "notnull": false, + "pk": false + }, + { + "name": "word_freq_857", + "type": "TEXT", + "notnull": false, + "pk": false + }, + { + "name": "word_freq_data", + "type": "TEXT", + "notnull": false, + "pk": false + }, + { + "name": "word_freq_415", + "type": "TEXT", + "notnull": false, + "pk": false + }, + { + "name": "word_freq_85", + "type": "TEXT", + "notnull": false, + "pk": false + }, + { + "name": "word_freq_technology", + "type": "TEXT", + "notnull": false, + "pk": false + }, + { + "name": "word_freq_1999", + "type": "TEXT", + "notnull": false, + "pk": false + }, + { + "name": "word_freq_parts", + "type": "TEXT", + "notnull": false, + "pk": false + }, + { + "name": "word_freq_pm", + "type": "TEXT", + "notnull": false, + "pk": false + }, + { + "name": "word_freq_direct", + "type": "TEXT", + "notnull": false, + "pk": false + }, + { + "name": "word_freq_cs", + "type": "TEXT", + "notnull": false, + "pk": false + }, + { + "name": "word_freq_meeting", + "type": "TEXT", + "notnull": false, + "pk": false + }, + { + "name": "word_freq_original", + "type": "TEXT", + "notnull": false, + "pk": false + }, + { + "name": "word_freq_project", + "type": "TEXT", + "notnull": false, + "pk": false + }, + { + "name": "word_freq_re", + "type": "TEXT", + "notnull": false, + "pk": false + }, + { + "name": "word_freq_edu", + "type": "TEXT", + "notnull": false, + "pk": false + }, + { + "name": "word_freq_table", + "type": "TEXT", + "notnull": false, + "pk": false + }, + { + "name": "word_freq_conference", + "type": "TEXT", + "notnull": false, + "pk": false + }, + { + "name": "char_freq_%3B", + "type": "TEXT", + "notnull": false, + "pk": false + }, + { + "name": "char_freq_%28", + "type": "TEXT", + "notnull": false, + "pk": false + }, + { + "name": "char_freq_%5B", + "type": "TEXT", + "notnull": false, + "pk": false + }, + { + "name": "char_freq_%21", + "type": "TEXT", + "notnull": false, + "pk": false + }, + { + "name": "char_freq_%24", + "type": "TEXT", + "notnull": false, + "pk": false + }, + { + "name": "char_freq_%23", + "type": "TEXT", + "notnull": false, + "pk": false + }, + { + "name": "capital_run_length_average", + "type": "TEXT", + "notnull": false, + "pk": false + }, + { + "name": "capital_run_length_longest", + "type": "TEXT", + "notnull": false, + "pk": false + }, + { + "name": "capital_run_length_total", + "type": "TEXT", + "notnull": false, + "pk": false + }, + { + "name": "class", + "type": "TEXT", + "notnull": false, + "pk": false + } + ], + "sample_rows": [ + { + "word_freq_make": "0", + "word_freq_address": "0.64", + "word_freq_all": "0.64", + "word_freq_3d": "0", + "word_freq_our": "0.32", + "word_freq_over": "0", + "word_freq_remove": "0", + "word_freq_internet": "0", + "word_freq_order": "0", + "word_freq_mail": "0", + "word_freq_receive": "0", + "word_freq_will": "0.64", + "word_freq_people": "0", + "word_freq_report": "0", + "word_freq_addresses": "0", + "word_freq_free": "0.32", + "word_freq_business": "0", + "word_freq_email": "1.29", + "word_freq_you": "1.93", + "word_freq_credit": "0", + "word_freq_your": "0.96", + "word_freq_font": "0", + "word_freq_000": "0", + "word_freq_money": "0", + "word_freq_hp": "0", + "word_freq_hpl": "0", + "word_freq_george": "0", + "word_freq_650": "0", + "word_freq_lab": "0", + "word_freq_labs": "0", + "word_freq_telnet": "0", + "word_freq_857": "0", + "word_freq_data": "0", + "word_freq_415": "0", + "word_freq_85": "0", + "word_freq_technology": "0", + "word_freq_1999": "0", + "word_freq_parts": "0", + "word_freq_pm": "0", + "word_freq_direct": "0", + "word_freq_cs": "0", + "word_freq_meeting": "0", + "word_freq_original": "0", + "word_freq_project": "0", + "word_freq_re": "0", + "word_freq_edu": "0", + "word_freq_table": "0", + "word_freq_conference": "0", + "char_freq_%3B": "0", + "char_freq_%28": "0", + "char_freq_%5B": "0", + "char_freq_%21": "0.778", + "char_freq_%24": "0", + "char_freq_%23": "0", + "capital_run_length_average": "3.756", + "capital_run_length_longest": "61", + "capital_run_length_total": "278", + "class": "1" + }, + { + "word_freq_make": "0.21", + "word_freq_address": "0.28", + "word_freq_all": "0.5", + "word_freq_3d": "0", + "word_freq_our": "0.14", + "word_freq_over": "0.28", + "word_freq_remove": "0.21", + "word_freq_internet": "0.07", + "word_freq_order": "0", + "word_freq_mail": "0.94", + "word_freq_receive": "0.21", + "word_freq_will": "0.79", + "word_freq_people": "0.65", + "word_freq_report": "0.21", + "word_freq_addresses": "0.14", + "word_freq_free": "0.14", + "word_freq_business": "0.07", + "word_freq_email": "0.28", + "word_freq_you": "3.47", + "word_freq_credit": "0", + "word_freq_your": "1.59", + "word_freq_font": "0", + "word_freq_000": "0.43", + "word_freq_money": "0.43", + "word_freq_hp": "0", + "word_freq_hpl": "0", + "word_freq_george": "0", + "word_freq_650": "0", + "word_freq_lab": "0", + "word_freq_labs": "0", + "word_freq_telnet": "0", + "word_freq_857": "0", + "word_freq_data": "0", + "word_freq_415": "0", + "word_freq_85": "0", + "word_freq_technology": "0", + "word_freq_1999": "0.07", + "word_freq_parts": "0", + "word_freq_pm": "0", + "word_freq_direct": "0", + "word_freq_cs": "0", + "word_freq_meeting": "0", + "word_freq_original": "0", + "word_freq_project": "0", + "word_freq_re": "0", + "word_freq_edu": "0", + "word_freq_table": "0", + "word_freq_conference": "0", + "char_freq_%3B": "0", + "char_freq_%28": "0.132", + "char_freq_%5B": "0", + "char_freq_%21": "0.372", + "char_freq_%24": "0.18", + "char_freq_%23": "0.048", + "capital_run_length_average": "5.114", + "capital_run_length_longest": "101", + "capital_run_length_total": "1028", + "class": "1" + }, + { + "word_freq_make": "0.06", + "word_freq_address": "0", + "word_freq_all": "0.71", + "word_freq_3d": "0", + "word_freq_our": "1.23", + "word_freq_over": "0.19", + "word_freq_remove": "0.19", + "word_freq_internet": "0.12", + "word_freq_order": "0.64", + "word_freq_mail": "0.25", + "word_freq_receive": "0.38", + "word_freq_will": "0.45", + "word_freq_people": "0.12", + "word_freq_report": "0", + "word_freq_addresses": "1.75", + "word_freq_free": "0.06", + "word_freq_business": "0.06", + "word_freq_email": "1.03", + "word_freq_you": "1.36", + "word_freq_credit": "0.32", + "word_freq_your": "0.51", + "word_freq_font": "0", + "word_freq_000": "1.16", + "word_freq_money": "0.06", + "word_freq_hp": "0", + "word_freq_hpl": "0", + "word_freq_george": "0", + "word_freq_650": "0", + "word_freq_lab": "0", + "word_freq_labs": "0", + "word_freq_telnet": "0", + "word_freq_857": "0", + "word_freq_data": "0", + "word_freq_415": "0", + "word_freq_85": "0", + "word_freq_technology": "0", + "word_freq_1999": "0", + "word_freq_parts": "0", + "word_freq_pm": "0", + "word_freq_direct": "0.06", + "word_freq_cs": "0", + "word_freq_meeting": "0", + "word_freq_original": "0.12", + "word_freq_project": "0", + "word_freq_re": "0.06", + "word_freq_edu": "0.06", + "word_freq_table": "0", + "word_freq_conference": "0", + "char_freq_%3B": "0.01", + "char_freq_%28": "0.143", + "char_freq_%5B": "0", + "char_freq_%21": "0.276", + "char_freq_%24": "0.184", + "char_freq_%23": "0.01", + "capital_run_length_average": "9.821", + "capital_run_length_longest": "485", + "capital_run_length_total": "2259", + "class": "1" + }, + { + "word_freq_make": "0", + "word_freq_address": "0", + "word_freq_all": "0", + "word_freq_3d": "0", + "word_freq_our": "0.63", + "word_freq_over": "0", + "word_freq_remove": "0.31", + "word_freq_internet": "0.63", + "word_freq_order": "0.31", + "word_freq_mail": "0.63", + "word_freq_receive": "0.31", + "word_freq_will": "0.31", + "word_freq_people": "0.31", + "word_freq_report": "0", + "word_freq_addresses": "0", + "word_freq_free": "0.31", + "word_freq_business": "0", + "word_freq_email": "0", + "word_freq_you": "3.18", + "word_freq_credit": "0", + "word_freq_your": "0.31", + "word_freq_font": "0", + "word_freq_000": "0", + "word_freq_money": "0", + "word_freq_hp": "0", + "word_freq_hpl": "0", + "word_freq_george": "0", + "word_freq_650": "0", + "word_freq_lab": "0", + "word_freq_labs": "0", + "word_freq_telnet": "0", + "word_freq_857": "0", + "word_freq_data": "0", + "word_freq_415": "0", + "word_freq_85": "0", + "word_freq_technology": "0", + "word_freq_1999": "0", + "word_freq_parts": "0", + "word_freq_pm": "0", + "word_freq_direct": "0", + "word_freq_cs": "0", + "word_freq_meeting": "0", + "word_freq_original": "0", + "word_freq_project": "0", + "word_freq_re": "0", + "word_freq_edu": "0", + "word_freq_table": "0", + "word_freq_conference": "0", + "char_freq_%3B": "0", + "char_freq_%28": "0.137", + "char_freq_%5B": "0", + "char_freq_%21": "0.137", + "char_freq_%24": "0", + "char_freq_%23": "0", + "capital_run_length_average": "3.537", + "capital_run_length_longest": "40", + "capital_run_length_total": "191", + "class": "1" + }, + { + "word_freq_make": "0", + "word_freq_address": "0", + "word_freq_all": "0", + "word_freq_3d": "0", + "word_freq_our": "0.63", + "word_freq_over": "0", + "word_freq_remove": "0.31", + "word_freq_internet": "0.63", + "word_freq_order": "0.31", + "word_freq_mail": "0.63", + "word_freq_receive": "0.31", + "word_freq_will": "0.31", + "word_freq_people": "0.31", + "word_freq_report": "0", + "word_freq_addresses": "0", + "word_freq_free": "0.31", + "word_freq_business": "0", + "word_freq_email": "0", + "word_freq_you": "3.18", + "word_freq_credit": "0", + "word_freq_your": "0.31", + "word_freq_font": "0", + "word_freq_000": "0", + "word_freq_money": "0", + "word_freq_hp": "0", + "word_freq_hpl": "0", + "word_freq_george": "0", + "word_freq_650": "0", + "word_freq_lab": "0", + "word_freq_labs": "0", + "word_freq_telnet": "0", + "word_freq_857": "0", + "word_freq_data": "0", + "word_freq_415": "0", + "word_freq_85": "0", + "word_freq_technology": "0", + "word_freq_1999": "0", + "word_freq_parts": "0", + "word_freq_pm": "0", + "word_freq_direct": "0", + "word_freq_cs": "0", + "word_freq_meeting": "0", + "word_freq_original": "0", + "word_freq_project": "0", + "word_freq_re": "0", + "word_freq_edu": "0", + "word_freq_table": "0", + "word_freq_conference": "0", + "char_freq_%3B": "0", + "char_freq_%28": "0.135", + "char_freq_%5B": "0", + "char_freq_%21": "0.135", + "char_freq_%24": "0", + "char_freq_%23": "0", + "capital_run_length_average": "3.537", + "capital_run_length_longest": "40", + "capital_run_length_total": "191", + "class": "1" + } + ] +} + +Shortlisted templates: +[ + { + "template_id": "tpl_tail_low_support_group_count_v2", + "template_name": "Low-Support Group Count", + "primary_family": "tail_rarity_structure", + "portability": "yes", + "sql_skeleton": "SELECT\n {group_col},\n COUNT(*) AS support\nFROM {table}\nGROUP BY {group_col}\nORDER BY support ASC, {group_col}\nLIMIT {top_k};", + "required_roles": [ + "group_col" + ] + } +] + +Problem instance: +{ + "dataset_id": "n1", + "question": "Use template Low-Support Group Count to probe tail_set_consistency with semantic role count_distribution. Focus on group_col=class.", + "planned_template_id": "tpl_tail_low_support_group_count_v2", + "bindings": { + "group_col": "class", + "top_k": 19, + "top_n": 4, + "num_tiles": 10, + "percentile_value": 0.9, + "z_threshold": 2.0, + "fraction_threshold": 0.05, + "baseline_multiplier": 1.75, + "baseline_fraction": 0.1, + "min_group_size": 5, + "min_support": 4, + "measure_threshold": 0.0, + "time_grain": "month", + "lookback_rows": 3, + "current_period_start": "'2024-01-01'", + "current_period_end": "'2024-04-01'", + "previous_period_start": "'2023-10-01'", + "previous_period_end": "'2024-01-01'", + "drift_ratio_threshold": 0.8 + }, + "can_vary": [], + "must_fix": [], + "runtime_sql_skeleton": "SELECT\n {group_col},\n COUNT(*) AS support\nFROM {table}\nGROUP BY {group_col}\nORDER BY support ASC, {group_col}\nLIMIT {top_k};" +} + +Repair context: +{} diff --git a/Query/sql/v2/runs/v2_cli_20260502_081223_d/n1/artifacts/v2q_n1_088dd75b53afc004/cli/sql_prompt_attempt_2.txt b/Query/sql/v2/runs/v2_cli_20260502_081223_d/n1/artifacts/v2q_n1_088dd75b53afc004/cli/sql_prompt_attempt_2.txt new file mode 100644 index 0000000000000000000000000000000000000000..ca35bfc5e3e4e856b4045251c3b6dd3964b94563 --- /dev/null +++ b/Query/sql/v2/runs/v2_cli_20260502_081223_d/n1/artifacts/v2q_n1_088dd75b53afc004/cli/sql_prompt_attempt_2.txt @@ -0,0 +1,790 @@ +You are generating one SQLite SELECT query for a single-table SQL QA task. +Return strict JSON only, with this schema: {"sql": "...", "notes": "..."}. +Rules: +- Use only the provided table and columns. +- Do not write INSERT, UPDATE, DELETE, DROP, ALTER, CREATE, PRAGMA, ATTACH, DETACH, or VACUUM. +- Prefer the planned template and bound roles when provided. +- Add a leading SQL comment exactly like: -- template_id: . +- Generate SQLite-compatible SQL. SQLite does not support PERCENTILE_CONT or STDDEV. +- Quote identifiers with double quotes. +- Return no markdown and no extra prose. + +Dataset context: +Dataset context for SQL QA: +- dataset_id: n1 +- dataset_name: Spambase +- table_name: n1 +- table_layout: single-table dataset (do not assume joins). +- row_semantics: One row is one email represented by engineered token/character frequency and capitalization-run features. +- task_type: classification +- target_column: class +- main_row_count: 4601 +- important_fields: +- word_freq_make: role=feature, type=numeric_sparse_frequency. tags=['condition_candidate', 'measure', 'rare_pattern_candidate'] desc=Frequency feature for token 'make' in an email message. +- word_freq_address: role=feature, type=numeric_sparse_frequency. tags=['condition_candidate', 'measure', 'rare_pattern_candidate'] desc=Frequency feature for token 'address' in an email message. +- word_freq_all: role=feature, type=numeric_sparse_frequency. tags=['condition_candidate', 'measure', 'rare_pattern_candidate'] desc=Frequency feature for token 'all' in an email message. +- word_freq_3d: role=feature, type=numeric_sparse_frequency. tags=['condition_candidate', 'measure', 'rare_pattern_candidate'] desc=Frequency feature for token '3d' in an email message. +- word_freq_our: role=feature, type=numeric_sparse_frequency. tags=['condition_candidate', 'measure', 'rare_pattern_candidate'] desc=Frequency feature for token 'our' in an email message. +- word_freq_over: role=feature, type=numeric_sparse_frequency. tags=['condition_candidate', 'measure', 'rare_pattern_candidate'] desc=Frequency feature for token 'over' in an email message. +- word_freq_remove: role=feature, type=numeric_sparse_frequency. tags=['condition_candidate', 'measure', 'rare_pattern_candidate'] desc=Frequency feature for token 'remove' in an email message. +- word_freq_internet: role=feature, type=numeric_sparse_frequency. tags=['condition_candidate', 'measure', 'rare_pattern_candidate'] desc=Frequency feature for token 'internet' in an email message. +- word_freq_order: role=feature, type=numeric_sparse_frequency. tags=['condition_candidate', 'measure', 'rare_pattern_candidate'] desc=Frequency feature for token 'order' in an email message. +- word_freq_mail: role=feature, type=numeric_sparse_frequency. tags=['condition_candidate', 'measure', 'rare_pattern_candidate'] desc=Frequency feature for token 'mail' in an email message. +- word_freq_receive: role=feature, type=numeric_sparse_frequency. tags=['condition_candidate', 'measure', 'rare_pattern_candidate'] desc=Frequency feature for token 'receive' in an email message. +- word_freq_will: role=feature, type=numeric_sparse_frequency. tags=['condition_candidate', 'measure', 'rare_pattern_candidate'] desc=Frequency feature for token 'will' in an email message. +- word_freq_people: role=feature, type=numeric_sparse_frequency. tags=['condition_candidate', 'measure', 'rare_pattern_candidate'] desc=Frequency feature for token 'people' in an email message. +- word_freq_report: role=feature, type=numeric_sparse_frequency. tags=['condition_candidate', 'measure', 'rare_pattern_candidate'] desc=Frequency feature for token 'report' in an email message. +- word_freq_addresses: role=feature, type=numeric_sparse_frequency. tags=['condition_candidate', 'measure', 'rare_pattern_candidate'] desc=Frequency feature for token 'addresses' in an email message. +- word_freq_free: role=feature, type=numeric_sparse_frequency. tags=['condition_candidate', 'measure', 'rare_pattern_candidate'] desc=Frequency feature for token 'free' in an email message. +- word_freq_business: role=feature, type=numeric_sparse_frequency. tags=['condition_candidate', 'measure', 'rare_pattern_candidate'] desc=Frequency feature for token 'business' in an email message. +- word_freq_email: role=feature, type=numeric_sparse_frequency. tags=['condition_candidate', 'measure', 'rare_pattern_candidate'] desc=Frequency feature for token 'email' in an email message. +- word_freq_you: role=feature, type=numeric_sparse_frequency. tags=['condition_candidate', 'measure', 'rare_pattern_candidate'] desc=Frequency feature for token 'you' in an email message. +- word_freq_credit: role=feature, type=numeric_sparse_frequency. tags=['condition_candidate', 'measure', 'rare_pattern_candidate'] desc=Frequency feature for token 'credit' in an email message. +- word_freq_your: role=feature, type=numeric_sparse_frequency. tags=['condition_candidate', 'measure', 'rare_pattern_candidate'] desc=Frequency feature for token 'your' in an email message. +- word_freq_font: role=feature, type=numeric_sparse_frequency. tags=['condition_candidate', 'measure', 'rare_pattern_candidate'] desc=Frequency feature for token 'font' in an email message. +- word_freq_000: role=feature, type=numeric_sparse_frequency. tags=['condition_candidate', 'measure', 'rare_pattern_candidate'] desc=Frequency feature for token '000' in an email message. +- word_freq_money: role=feature, type=numeric_sparse_frequency. tags=['condition_candidate', 'measure', 'rare_pattern_candidate'] desc=Frequency feature for token 'money' in an email message. +- word_freq_hp: role=feature, type=numeric_sparse_frequency. tags=['condition_candidate', 'measure', 'rare_pattern_candidate'] desc=Frequency feature for token 'hp' in an email message. +- word_freq_hpl: role=feature, type=numeric_sparse_frequency. tags=['condition_candidate', 'measure', 'rare_pattern_candidate'] desc=Frequency feature for token 'hpl' in an email message. +- word_freq_george: role=feature, type=numeric_sparse_frequency. tags=['condition_candidate', 'measure', 'rare_pattern_candidate'] desc=Frequency feature for token 'george' in an email message. +- word_freq_650: role=feature, type=numeric_sparse_frequency. tags=['condition_candidate', 'measure', 'rare_pattern_candidate'] desc=Frequency feature for token '650' in an email message. +- word_freq_lab: role=feature, type=numeric_sparse_frequency. tags=['condition_candidate', 'measure', 'rare_pattern_candidate'] desc=Frequency feature for token 'lab' in an email message. +- word_freq_labs: role=feature, type=numeric_sparse_frequency. tags=['condition_candidate', 'measure', 'rare_pattern_candidate'] desc=Frequency feature for token 'labs' in an email message. +- word_freq_telnet: role=feature, type=numeric_sparse_frequency. tags=['condition_candidate', 'measure', 'rare_pattern_candidate'] desc=Frequency feature for token 'telnet' in an email message. +- word_freq_857: role=feature, type=numeric_sparse_frequency. tags=['condition_candidate', 'measure', 'rare_pattern_candidate'] desc=Frequency feature for token '857' in an email message. +- word_freq_data: role=feature, type=numeric_sparse_frequency. tags=['condition_candidate', 'measure', 'rare_pattern_candidate'] desc=Frequency feature for token 'data' in an email message. +- word_freq_415: role=feature, type=numeric_sparse_frequency. tags=['condition_candidate', 'measure', 'rare_pattern_candidate'] desc=Frequency feature for token '415' in an email message. +- word_freq_85: role=feature, type=numeric_sparse_frequency. tags=['condition_candidate', 'measure', 'rare_pattern_candidate'] desc=Frequency feature for token '85' in an email message. +- word_freq_technology: role=feature, type=numeric_sparse_frequency. tags=['condition_candidate', 'measure', 'rare_pattern_candidate'] desc=Frequency feature for token 'technology' in an email message. +- word_freq_1999: role=feature, type=numeric_sparse_frequency. tags=['condition_candidate', 'measure', 'rare_pattern_candidate'] desc=Frequency feature for token '1999' in an email message. +- word_freq_parts: role=feature, type=numeric_sparse_frequency. tags=['condition_candidate', 'measure', 'rare_pattern_candidate'] desc=Frequency feature for token 'parts' in an email message. +- word_freq_pm: role=feature, type=numeric_sparse_frequency. tags=['condition_candidate', 'measure', 'rare_pattern_candidate'] desc=Frequency feature for token 'pm' in an email message. +- word_freq_direct: role=feature, type=numeric_sparse_frequency. tags=['condition_candidate', 'measure', 'rare_pattern_candidate'] desc=Frequency feature for token 'direct' in an email message. +- word_freq_cs: role=feature, type=numeric_sparse_frequency. tags=['condition_candidate', 'measure', 'rare_pattern_candidate'] desc=Frequency feature for token 'cs' in an email message. +- word_freq_meeting: role=feature, type=numeric_sparse_frequency. tags=['condition_candidate', 'measure', 'rare_pattern_candidate'] desc=Frequency feature for token 'meeting' in an email message. +- word_freq_original: role=feature, type=numeric_sparse_frequency. tags=['condition_candidate', 'measure', 'rare_pattern_candidate'] desc=Frequency feature for token 'original' in an email message. +- word_freq_project: role=feature, type=numeric_sparse_frequency. tags=['condition_candidate', 'measure', 'rare_pattern_candidate'] desc=Frequency feature for token 'project' in an email message. +- word_freq_re: role=feature, type=numeric_sparse_frequency. tags=['condition_candidate', 'measure', 'rare_pattern_candidate'] desc=Frequency feature for token 're' in an email message. +- word_freq_edu: role=feature, type=numeric_sparse_frequency. tags=['condition_candidate', 'measure', 'rare_pattern_candidate'] desc=Frequency feature for token 'edu' in an email message. +- word_freq_table: role=feature, type=numeric_sparse_frequency. tags=['condition_candidate', 'measure', 'rare_pattern_candidate'] desc=Frequency feature for token 'table' in an email message. +- word_freq_conference: role=feature, type=numeric_sparse_frequency. tags=['condition_candidate', 'measure', 'rare_pattern_candidate'] desc=Frequency feature for token 'conference' in an email message. +- char_freq_%3B: role=feature, type=numeric_sparse_frequency. tags=['condition_candidate', 'measure', 'rare_pattern_candidate'] desc=Character frequency feature for symbol ';'. +- char_freq_%28: role=feature, type=numeric_sparse_frequency. tags=['condition_candidate', 'measure', 'rare_pattern_candidate'] desc=Character frequency feature for symbol '('. +- char_freq_%5B: role=feature, type=numeric_sparse_frequency. tags=['condition_candidate', 'measure', 'rare_pattern_candidate'] desc=Character frequency feature for symbol '['. +- char_freq_%21: role=feature, type=numeric_sparse_frequency. tags=['condition_candidate', 'measure', 'rare_pattern_candidate'] desc=Character frequency feature for symbol '!'. +- char_freq_%24: role=feature, type=numeric_sparse_frequency. tags=['condition_candidate', 'measure', 'rare_pattern_candidate'] desc=Character frequency feature for symbol '$'. +- char_freq_%23: role=feature, type=numeric_sparse_frequency. tags=['condition_candidate', 'measure', 'rare_pattern_candidate'] desc=Character frequency feature for symbol '#'. +- capital_run_length_average: role=feature, type=numeric_heavy_tail. tags=['condition_candidate', 'measure', 'rare_pattern_candidate'] desc=Average length of uninterrupted capital-letter runs. +- capital_run_length_longest: role=feature, type=numeric_heavy_tail. tags=['condition_candidate', 'measure', 'rare_pattern_candidate'] desc=Longest uninterrupted capital-letter run. +- capital_run_length_total: role=feature, type=numeric_heavy_tail. tags=['condition_candidate', 'measure', 'rare_pattern_candidate'] desc=Total number of capital letters in runs. +- class: role=target, type=binary_target. tags=['target_candidate'] desc=Binary spam label (1=spam, 0=non-spam). +- useful_field_combinations: [['word_freq_free', 'word_freq_you', 'class'], ['char_freq_%21', 'capital_run_length_longest', 'class'], ['word_freq_remove', 'word_freq_money', 'class']] +- fields_requiring_caution: ['capital_run_length_average', 'capital_run_length_longest', 'capital_run_length_total'] +- source_url: https://www.openml.org/d/44 + +SQLite schema snapshot: +{ + "table_name": "n1", + "quoted_table_name": "\"n1\"", + "row_count": 4601, + "columns": [ + { + "name": "word_freq_make", + "type": "TEXT", + "notnull": false, + "pk": false + }, + { + "name": "word_freq_address", + "type": "TEXT", + "notnull": false, + "pk": false + }, + { + "name": "word_freq_all", + "type": "TEXT", + "notnull": false, + "pk": false + }, + { + "name": "word_freq_3d", + "type": "TEXT", + "notnull": false, + "pk": false + }, + { + "name": "word_freq_our", + "type": "TEXT", + "notnull": false, + "pk": false + }, + { + "name": "word_freq_over", + "type": "TEXT", + "notnull": false, + "pk": false + }, + { + "name": "word_freq_remove", + "type": "TEXT", + "notnull": false, + "pk": false + }, + { + "name": "word_freq_internet", + "type": "TEXT", + "notnull": false, + "pk": false + }, + { + "name": "word_freq_order", + "type": "TEXT", + "notnull": false, + "pk": false + }, + { + "name": "word_freq_mail", + "type": "TEXT", + "notnull": false, + "pk": false + }, + { + "name": "word_freq_receive", + "type": "TEXT", + "notnull": false, + "pk": false + }, + { + "name": "word_freq_will", + "type": "TEXT", + "notnull": false, + "pk": false + }, + { + "name": "word_freq_people", + "type": "TEXT", + "notnull": false, + "pk": false + }, + { + "name": "word_freq_report", + "type": "TEXT", + "notnull": false, + "pk": false + }, + { + "name": "word_freq_addresses", + "type": "TEXT", + "notnull": false, + "pk": false + }, + { + "name": "word_freq_free", + "type": "TEXT", + "notnull": false, + "pk": false + }, + { + "name": "word_freq_business", + "type": "TEXT", + "notnull": false, + "pk": false + }, + { + "name": "word_freq_email", + "type": "TEXT", + "notnull": false, + "pk": false + }, + { + "name": "word_freq_you", + "type": "TEXT", + "notnull": false, + "pk": false + }, + { + "name": "word_freq_credit", + "type": "TEXT", + "notnull": false, + "pk": false + }, + { + "name": "word_freq_your", + "type": "TEXT", + "notnull": false, + "pk": false + }, + { + "name": "word_freq_font", + "type": "TEXT", + "notnull": false, + "pk": false + }, + { + "name": "word_freq_000", + "type": "TEXT", + "notnull": false, + "pk": false + }, + { + "name": "word_freq_money", + "type": "TEXT", + "notnull": false, + "pk": false + }, + { + "name": "word_freq_hp", + "type": "TEXT", + "notnull": false, + "pk": false + }, + { + "name": "word_freq_hpl", + "type": "TEXT", + "notnull": false, + "pk": false + }, + { + "name": "word_freq_george", + "type": "TEXT", + "notnull": false, + "pk": false + }, + { + "name": "word_freq_650", + "type": "TEXT", + "notnull": false, + "pk": false + }, + { + "name": "word_freq_lab", + "type": "TEXT", + "notnull": false, + "pk": false + }, + { + "name": "word_freq_labs", + "type": "TEXT", + "notnull": false, + "pk": false + }, + { + "name": "word_freq_telnet", + "type": "TEXT", + "notnull": false, + "pk": false + }, + { + "name": "word_freq_857", + "type": "TEXT", + "notnull": false, + "pk": false + }, + { + "name": "word_freq_data", + "type": "TEXT", + "notnull": false, + "pk": false + }, + { + "name": "word_freq_415", + "type": "TEXT", + "notnull": false, + "pk": false + }, + { + "name": "word_freq_85", + "type": "TEXT", + "notnull": false, + "pk": false + }, + { + "name": "word_freq_technology", + "type": "TEXT", + "notnull": false, + "pk": false + }, + { + "name": "word_freq_1999", + "type": "TEXT", + "notnull": false, + "pk": false + }, + { + "name": "word_freq_parts", + "type": "TEXT", + "notnull": false, + "pk": false + }, + { + "name": "word_freq_pm", + "type": "TEXT", + "notnull": false, + "pk": false + }, + { + "name": "word_freq_direct", + "type": "TEXT", + "notnull": false, + "pk": false + }, + { + "name": "word_freq_cs", + "type": "TEXT", + "notnull": false, + "pk": false + }, + { + "name": "word_freq_meeting", + "type": "TEXT", + "notnull": false, + "pk": false + }, + { + "name": "word_freq_original", + "type": "TEXT", + "notnull": false, + "pk": false + }, + { + "name": "word_freq_project", + "type": "TEXT", + "notnull": false, + "pk": false + }, + { + "name": "word_freq_re", + "type": "TEXT", + "notnull": false, + "pk": false + }, + { + "name": "word_freq_edu", + "type": "TEXT", + "notnull": false, + "pk": false + }, + { + "name": "word_freq_table", + "type": "TEXT", + "notnull": false, + "pk": false + }, + { + "name": "word_freq_conference", + "type": "TEXT", + "notnull": false, + "pk": false + }, + { + "name": "char_freq_%3B", + "type": "TEXT", + "notnull": false, + "pk": false + }, + { + "name": "char_freq_%28", + "type": "TEXT", + "notnull": false, + "pk": false + }, + { + "name": "char_freq_%5B", + "type": "TEXT", + "notnull": false, + "pk": false + }, + { + "name": "char_freq_%21", + "type": "TEXT", + "notnull": false, + "pk": false + }, + { + "name": "char_freq_%24", + "type": "TEXT", + "notnull": false, + "pk": false + }, + { + "name": "char_freq_%23", + "type": "TEXT", + "notnull": false, + "pk": false + }, + { + "name": "capital_run_length_average", + "type": "TEXT", + "notnull": false, + "pk": false + }, + { + "name": "capital_run_length_longest", + "type": "TEXT", + "notnull": false, + "pk": false + }, + { + "name": "capital_run_length_total", + "type": "TEXT", + "notnull": false, + "pk": false + }, + { + "name": "class", + "type": "TEXT", + "notnull": false, + "pk": false + } + ], + "sample_rows": [ + { + "word_freq_make": "0", + "word_freq_address": "0.64", + "word_freq_all": "0.64", + "word_freq_3d": "0", + "word_freq_our": "0.32", + "word_freq_over": "0", + "word_freq_remove": "0", + "word_freq_internet": "0", + "word_freq_order": "0", + "word_freq_mail": "0", + "word_freq_receive": "0", + "word_freq_will": "0.64", + "word_freq_people": "0", + "word_freq_report": "0", + "word_freq_addresses": "0", + "word_freq_free": "0.32", + "word_freq_business": "0", + "word_freq_email": "1.29", + "word_freq_you": "1.93", + "word_freq_credit": "0", + "word_freq_your": "0.96", + "word_freq_font": "0", + "word_freq_000": "0", + "word_freq_money": "0", + "word_freq_hp": "0", + "word_freq_hpl": "0", + "word_freq_george": "0", + "word_freq_650": "0", + "word_freq_lab": "0", + "word_freq_labs": "0", + "word_freq_telnet": "0", + "word_freq_857": "0", + "word_freq_data": "0", + "word_freq_415": "0", + "word_freq_85": "0", + "word_freq_technology": "0", + "word_freq_1999": "0", + "word_freq_parts": "0", + "word_freq_pm": "0", + "word_freq_direct": "0", + "word_freq_cs": "0", + "word_freq_meeting": "0", + "word_freq_original": "0", + "word_freq_project": "0", + "word_freq_re": "0", + "word_freq_edu": "0", + "word_freq_table": "0", + "word_freq_conference": "0", + "char_freq_%3B": "0", + "char_freq_%28": "0", + "char_freq_%5B": "0", + "char_freq_%21": "0.778", + "char_freq_%24": "0", + "char_freq_%23": "0", + "capital_run_length_average": "3.756", + "capital_run_length_longest": "61", + "capital_run_length_total": "278", + "class": "1" + }, + { + "word_freq_make": "0.21", + "word_freq_address": "0.28", + "word_freq_all": "0.5", + "word_freq_3d": "0", + "word_freq_our": "0.14", + "word_freq_over": "0.28", + "word_freq_remove": "0.21", + "word_freq_internet": "0.07", + "word_freq_order": "0", + "word_freq_mail": "0.94", + "word_freq_receive": "0.21", + "word_freq_will": "0.79", + "word_freq_people": "0.65", + "word_freq_report": "0.21", + "word_freq_addresses": "0.14", + "word_freq_free": "0.14", + "word_freq_business": "0.07", + "word_freq_email": "0.28", + "word_freq_you": "3.47", + "word_freq_credit": "0", + "word_freq_your": "1.59", + "word_freq_font": "0", + "word_freq_000": "0.43", + "word_freq_money": "0.43", + "word_freq_hp": "0", + "word_freq_hpl": "0", + "word_freq_george": "0", + "word_freq_650": "0", + "word_freq_lab": "0", + "word_freq_labs": "0", + "word_freq_telnet": "0", + "word_freq_857": "0", + "word_freq_data": "0", + "word_freq_415": "0", + "word_freq_85": "0", + "word_freq_technology": "0", + "word_freq_1999": "0.07", + "word_freq_parts": "0", + "word_freq_pm": "0", + "word_freq_direct": "0", + "word_freq_cs": "0", + "word_freq_meeting": "0", + "word_freq_original": "0", + "word_freq_project": "0", + "word_freq_re": "0", + "word_freq_edu": "0", + "word_freq_table": "0", + "word_freq_conference": "0", + "char_freq_%3B": "0", + "char_freq_%28": "0.132", + "char_freq_%5B": "0", + "char_freq_%21": "0.372", + "char_freq_%24": "0.18", + "char_freq_%23": "0.048", + "capital_run_length_average": "5.114", + "capital_run_length_longest": "101", + "capital_run_length_total": "1028", + "class": "1" + }, + { + "word_freq_make": "0.06", + "word_freq_address": "0", + "word_freq_all": "0.71", + "word_freq_3d": "0", + "word_freq_our": "1.23", + "word_freq_over": "0.19", + "word_freq_remove": "0.19", + "word_freq_internet": "0.12", + "word_freq_order": "0.64", + "word_freq_mail": "0.25", + "word_freq_receive": "0.38", + "word_freq_will": "0.45", + "word_freq_people": "0.12", + "word_freq_report": "0", + "word_freq_addresses": "1.75", + "word_freq_free": "0.06", + "word_freq_business": "0.06", + "word_freq_email": "1.03", + "word_freq_you": "1.36", + "word_freq_credit": "0.32", + "word_freq_your": "0.51", + "word_freq_font": "0", + "word_freq_000": "1.16", + "word_freq_money": "0.06", + "word_freq_hp": "0", + "word_freq_hpl": "0", + "word_freq_george": "0", + "word_freq_650": "0", + "word_freq_lab": "0", + "word_freq_labs": "0", + "word_freq_telnet": "0", + "word_freq_857": "0", + "word_freq_data": "0", + "word_freq_415": "0", + "word_freq_85": "0", + "word_freq_technology": "0", + "word_freq_1999": "0", + "word_freq_parts": "0", + "word_freq_pm": "0", + "word_freq_direct": "0.06", + "word_freq_cs": "0", + "word_freq_meeting": "0", + "word_freq_original": "0.12", + "word_freq_project": "0", + "word_freq_re": "0.06", + "word_freq_edu": "0.06", + "word_freq_table": "0", + "word_freq_conference": "0", + "char_freq_%3B": "0.01", + "char_freq_%28": "0.143", + "char_freq_%5B": "0", + "char_freq_%21": "0.276", + "char_freq_%24": "0.184", + "char_freq_%23": "0.01", + "capital_run_length_average": "9.821", + "capital_run_length_longest": "485", + "capital_run_length_total": "2259", + "class": "1" + }, + { + "word_freq_make": "0", + "word_freq_address": "0", + "word_freq_all": "0", + "word_freq_3d": "0", + "word_freq_our": "0.63", + "word_freq_over": "0", + "word_freq_remove": "0.31", + "word_freq_internet": "0.63", + "word_freq_order": "0.31", + "word_freq_mail": "0.63", + "word_freq_receive": "0.31", + "word_freq_will": "0.31", + "word_freq_people": "0.31", + "word_freq_report": "0", + "word_freq_addresses": "0", + "word_freq_free": "0.31", + "word_freq_business": "0", + "word_freq_email": "0", + "word_freq_you": "3.18", + "word_freq_credit": "0", + "word_freq_your": "0.31", + "word_freq_font": "0", + "word_freq_000": "0", + "word_freq_money": "0", + "word_freq_hp": "0", + "word_freq_hpl": "0", + "word_freq_george": "0", + "word_freq_650": "0", + "word_freq_lab": "0", + "word_freq_labs": "0", + "word_freq_telnet": "0", + "word_freq_857": "0", + "word_freq_data": "0", + "word_freq_415": "0", + "word_freq_85": "0", + "word_freq_technology": "0", + "word_freq_1999": "0", + "word_freq_parts": "0", + "word_freq_pm": "0", + "word_freq_direct": "0", + "word_freq_cs": "0", + "word_freq_meeting": "0", + "word_freq_original": "0", + "word_freq_project": "0", + "word_freq_re": "0", + "word_freq_edu": "0", + "word_freq_table": "0", + "word_freq_conference": "0", + "char_freq_%3B": "0", + "char_freq_%28": "0.137", + "char_freq_%5B": "0", + "char_freq_%21": "0.137", + "char_freq_%24": "0", + "char_freq_%23": "0", + "capital_run_length_average": "3.537", + "capital_run_length_longest": "40", + "capital_run_length_total": "191", + "class": "1" + }, + { + "word_freq_make": "0", + "word_freq_address": "0", + "word_freq_all": "0", + "word_freq_3d": "0", + "word_freq_our": "0.63", + "word_freq_over": "0", + "word_freq_remove": "0.31", + "word_freq_internet": "0.63", + "word_freq_order": "0.31", + "word_freq_mail": "0.63", + "word_freq_receive": "0.31", + "word_freq_will": "0.31", + "word_freq_people": "0.31", + "word_freq_report": "0", + "word_freq_addresses": "0", + "word_freq_free": "0.31", + "word_freq_business": "0", + "word_freq_email": "0", + "word_freq_you": "3.18", + "word_freq_credit": "0", + "word_freq_your": "0.31", + "word_freq_font": "0", + "word_freq_000": "0", + "word_freq_money": "0", + "word_freq_hp": "0", + "word_freq_hpl": "0", + "word_freq_george": "0", + "word_freq_650": "0", + "word_freq_lab": "0", + "word_freq_labs": "0", + "word_freq_telnet": "0", + "word_freq_857": "0", + "word_freq_data": "0", + "word_freq_415": "0", + "word_freq_85": "0", + "word_freq_technology": "0", + "word_freq_1999": "0", + "word_freq_parts": "0", + "word_freq_pm": "0", + "word_freq_direct": "0", + "word_freq_cs": "0", + "word_freq_meeting": "0", + "word_freq_original": "0", + "word_freq_project": "0", + "word_freq_re": "0", + "word_freq_edu": "0", + "word_freq_table": "0", + "word_freq_conference": "0", + "char_freq_%3B": "0", + "char_freq_%28": "0.135", + "char_freq_%5B": "0", + "char_freq_%21": "0.135", + "char_freq_%24": "0", + "char_freq_%23": "0", + "capital_run_length_average": "3.537", + "capital_run_length_longest": "40", + "capital_run_length_total": "191", + "class": "1" + } + ] +} + +Shortlisted templates: +[ + { + "template_id": "tpl_tail_low_support_group_count_v2", + "template_name": "Low-Support Group Count", + "primary_family": "tail_rarity_structure", + "portability": "yes", + "sql_skeleton": "SELECT\n {group_col},\n COUNT(*) AS support\nFROM {table}\nGROUP BY {group_col}\nORDER BY support ASC, {group_col}\nLIMIT {top_k};", + "required_roles": [ + "group_col" + ] + } +] + +Problem instance: +{ + "dataset_id": "n1", + "question": "Use template Low-Support Group Count to probe tail_set_consistency with semantic role count_distribution. Focus on group_col=class.", + "planned_template_id": "tpl_tail_low_support_group_count_v2", + "bindings": { + "group_col": "class", + "top_k": 19, + "top_n": 4, + "num_tiles": 10, + "percentile_value": 0.9, + "z_threshold": 2.0, + "fraction_threshold": 0.05, + "baseline_multiplier": 1.75, + "baseline_fraction": 0.1, + "min_group_size": 5, + "min_support": 4, + "measure_threshold": 0.0, + "time_grain": "month", + "lookback_rows": 3, + "current_period_start": "'2024-01-01'", + "current_period_end": "'2024-04-01'", + "previous_period_start": "'2023-10-01'", + "previous_period_end": "'2024-01-01'", + "drift_ratio_threshold": 0.8 + }, + "can_vary": [], + "must_fix": [], + "runtime_sql_skeleton": "SELECT\n {group_col},\n COUNT(*) AS support\nFROM {table}\nGROUP BY {group_col}\nORDER BY support ASC, {group_col}\nLIMIT {top_k};" +} + +Repair context: +{} diff --git a/Query/sql/v2/runs/v2_cli_20260502_081223_d/n1/artifacts/v2q_n1_088dd75b53afc004/cli/sql_response_attempt_1.raw.txt b/Query/sql/v2/runs/v2_cli_20260502_081223_d/n1/artifacts/v2q_n1_088dd75b53afc004/cli/sql_response_attempt_1.raw.txt new file mode 100644 index 0000000000000000000000000000000000000000..7f450f23e38b9ac9024f755a41851d48da7ba92e --- /dev/null +++ b/Query/sql/v2/runs/v2_cli_20260502_081223_d/n1/artifacts/v2q_n1_088dd75b53afc004/cli/sql_response_attempt_1.raw.txt @@ -0,0 +1,4 @@ +{"type":"thread.started","thread_id":"019e40f9-ecc9-7e33-bace-1c6d35859576"} +{"type":"turn.started"} +{"type":"error","message":"Quota exceeded. Check your plan and billing details."} +{"type":"turn.failed","error":{"message":"Quota exceeded. Check your plan and billing details."}} diff --git a/Query/sql/v2/runs/v2_cli_20260502_081223_d/n1/artifacts/v2q_n1_088dd75b53afc004/cli/sql_response_attempt_1.txt b/Query/sql/v2/runs/v2_cli_20260502_081223_d/n1/artifacts/v2q_n1_088dd75b53afc004/cli/sql_response_attempt_1.txt new file mode 100644 index 0000000000000000000000000000000000000000..31cb86a9e8bddb4470627afbc0024192179e251a --- /dev/null +++ b/Query/sql/v2/runs/v2_cli_20260502_081223_d/n1/artifacts/v2q_n1_088dd75b53afc004/cli/sql_response_attempt_1.txt @@ -0,0 +1,4 @@ +{"type":"thread.started","thread_id":"019e40f9-ecc9-7e33-bace-1c6d35859576"} +{"type":"turn.started"} +{"type":"error","message":"Quota exceeded. Check your plan and billing details."} +{"type":"turn.failed","error":{"message":"Quota exceeded. Check your plan and billing details."}} \ No newline at end of file diff --git a/Query/sql/v2/runs/v2_cli_20260502_081223_d/n1/artifacts/v2q_n1_088dd75b53afc004/cli/sql_response_attempt_2.raw.txt b/Query/sql/v2/runs/v2_cli_20260502_081223_d/n1/artifacts/v2q_n1_088dd75b53afc004/cli/sql_response_attempt_2.raw.txt new file mode 100644 index 0000000000000000000000000000000000000000..e5d91c7f02301fb5b62d10abb3c5b4c29638cd33 --- /dev/null +++ b/Query/sql/v2/runs/v2_cli_20260502_081223_d/n1/artifacts/v2q_n1_088dd75b53afc004/cli/sql_response_attempt_2.raw.txt @@ -0,0 +1,4 @@ +{"type":"thread.started","thread_id":"019e40fa-019c-7b42-b987-301963ae2495"} +{"type":"turn.started"} +{"type":"item.completed","item":{"id":"item_0","type":"agent_message","text":"{\"sql\":\"-- template_id: tpl_tail_low_support_group_count_v2\\nSELECT\\n \\\"class\\\",\\n COUNT(*) AS support\\nFROM \\\"n1\\\"\\nGROUP BY \\\"class\\\"\\nORDER BY support ASC, \\\"class\\\"\\nLIMIT 19;\",\"notes\":\"Used the provided Low-Support Group Count template with group_col bound to \\\"class\\\" on the single table \\\"n1\\\".\"}"}} +{"type":"turn.completed","usage":{"input_tokens":20322,"cached_input_tokens":19840,"output_tokens":298,"reasoning_output_tokens":208}} diff --git a/Query/sql/v2/runs/v2_cli_20260502_081223_d/n1/artifacts/v2q_n1_088dd75b53afc004/cli/sql_response_attempt_2.txt b/Query/sql/v2/runs/v2_cli_20260502_081223_d/n1/artifacts/v2q_n1_088dd75b53afc004/cli/sql_response_attempt_2.txt new file mode 100644 index 0000000000000000000000000000000000000000..d1c3d89568163c7fd12f318eb2cac5b18a32adb5 --- /dev/null +++ b/Query/sql/v2/runs/v2_cli_20260502_081223_d/n1/artifacts/v2q_n1_088dd75b53afc004/cli/sql_response_attempt_2.txt @@ -0,0 +1 @@ +{"sql":"-- template_id: tpl_tail_low_support_group_count_v2\nSELECT\n \"class\",\n COUNT(*) AS support\nFROM \"n1\"\nGROUP BY \"class\"\nORDER BY support ASC, \"class\"\nLIMIT 19;","notes":"Used the provided Low-Support Group Count template with group_col bound to \"class\" on the single table \"n1\"."} \ No newline at end of file diff --git a/Query/sql/v2/runs/v2_cli_20260502_081223_d/n1/artifacts/v2q_n1_088dd75b53afc004/cli/sql_stderr_attempt_1.txt b/Query/sql/v2/runs/v2_cli_20260502_081223_d/n1/artifacts/v2q_n1_088dd75b53afc004/cli/sql_stderr_attempt_1.txt new file mode 100644 index 0000000000000000000000000000000000000000..e69de29bb2d1d6434b8b29ae775ad8c2e48c5391 diff --git a/Query/sql/v2/runs/v2_cli_20260502_081223_d/n1/artifacts/v2q_n1_088dd75b53afc004/cli/sql_stderr_attempt_2.txt b/Query/sql/v2/runs/v2_cli_20260502_081223_d/n1/artifacts/v2q_n1_088dd75b53afc004/cli/sql_stderr_attempt_2.txt new file mode 100644 index 0000000000000000000000000000000000000000..e69de29bb2d1d6434b8b29ae775ad8c2e48c5391 diff --git a/Query/sql/v2/runs/v2_cli_20260502_081223_d/n1/artifacts/v2q_n1_0c626140449240f5/cli/conversation.jsonl b/Query/sql/v2/runs/v2_cli_20260502_081223_d/n1/artifacts/v2q_n1_0c626140449240f5/cli/conversation.jsonl new file mode 100644 index 0000000000000000000000000000000000000000..ce7c4e0f07ce93a55f8425545fbe377847bb952d --- /dev/null +++ b/Query/sql/v2/runs/v2_cli_20260502_081223_d/n1/artifacts/v2q_n1_0c626140449240f5/cli/conversation.jsonl @@ -0,0 +1,2 @@ +{"attempt": 1, "phase": "sql_generation", "role": "user", "content_path": "cli/sql_prompt_attempt_1.txt", "metrics": {"chars": 29442, "bytes_utf8": 29442, "lines": 792, "estimated_tokens": null}} +{"attempt": 1, "phase": "sql_generation", "role": "assistant", "content_path": "cli/sql_response_attempt_1.txt", "raw_content_path": "cli/sql_response_attempt_1.raw.txt", "stderr_path": "cli/sql_stderr_attempt_1.txt", "metrics": {"chars": 420, "bytes_utf8": 420, "lines": 1, "estimated_tokens": null}, "usage": {"input_tokens": 20328, "cached_input_tokens": 19840, "output_tokens": 279, "reasoning_output_tokens": 171}} diff --git a/Query/sql/v2/runs/v2_cli_20260502_081223_d/n1/artifacts/v2q_n1_0c626140449240f5/cli/session_summary.json b/Query/sql/v2/runs/v2_cli_20260502_081223_d/n1/artifacts/v2q_n1_0c626140449240f5/cli/session_summary.json new file mode 100644 index 0000000000000000000000000000000000000000..91bbf3db8c315b1233bace1eb44540dd4079c565 --- /dev/null +++ b/Query/sql/v2/runs/v2_cli_20260502_081223_d/n1/artifacts/v2q_n1_0c626140449240f5/cli/session_summary.json @@ -0,0 +1,25 @@ +{ + "engine": "v2-cli:codex", + "command": "codex exec --skip-git-repo-check --disable plugins --sandbox read-only --cd \"/data/jialinzhang/SQLagent\" -m gpt-5.4 --json -", + "ai_cli_calls": 1, + "usage_summary": { + "dataset_id": "n1", + "model": "v2-cli:codex", + "run_id": "v2q_n1_0c626140449240f5", + "api_calls": 0, + "input_tokens": 20328, + "cached_input_tokens": 19840, + "output_tokens": 279, + "total_tokens": 20607, + "cost_usd": 0.0, + "ai_cli_calls": 1, + "estimated_input_tokens": 0, + "estimated_output_tokens": 0, + "estimated_total_tokens": 0, + "usage_source": "ai_cli_json_usage", + "cli_elapsed_ms_total": 11940.84, + "sql_execution_elapsed_ms_total": 17.71, + "conversation_log_path": "/data/jialinzhang/TabQueryBench/sql_workloads/v2_current/runs_and_launches/runs/v2_cli_20260502_081223_d/n1/artifacts/v2q_n1_0c626140449240f5/cli/conversation.jsonl", + "note": "Executed through a local AI CLI with structured usage metadata." + } +} \ No newline at end of file diff --git a/Query/sql/v2/runs/v2_cli_20260502_081223_d/n1/artifacts/v2q_n1_0c626140449240f5/cli/sql_attempt_1.metadata.json b/Query/sql/v2/runs/v2_cli_20260502_081223_d/n1/artifacts/v2q_n1_0c626140449240f5/cli/sql_attempt_1.metadata.json new file mode 100644 index 0000000000000000000000000000000000000000..14ba6767ea98efef5458f5c81972cb2ee301790e --- /dev/null +++ b/Query/sql/v2/runs/v2_cli_20260502_081223_d/n1/artifacts/v2q_n1_0c626140449240f5/cli/sql_attempt_1.metadata.json @@ -0,0 +1,45 @@ +{ + "attempt": 1, + "phase": "sql_generation", + "command": "codex exec --skip-git-repo-check --disable plugins --sandbox read-only --cd \"/data/jialinzhang/SQLagent\" -m gpt-5.4 --json -", + "started_at": "2026-05-19T15:42:51.826972+00:00", + "ended_at": "2026-05-19T15:43:03.767841+00:00", + "elapsed_ms": 11940.84, + "prompt_metrics": { + "chars": 29442, + "bytes_utf8": 29442, + "lines": 792, + "estimated_tokens": null + }, + "stdout_metrics": { + "chars": 782, + "bytes_utf8": 782, + "lines": 4, + "estimated_tokens": null + }, + "stderr_metrics": { + "chars": 0, + "bytes_utf8": 0, + "lines": 0, + "estimated_tokens": null + }, + "parsed_output": { + "format": "jsonl_events", + "text_metrics": { + "chars": 420, + "bytes_utf8": 420, + "lines": 1, + "estimated_tokens": null + }, + "usage": { + "input_tokens": 20328, + "cached_input_tokens": 19840, + "output_tokens": 279, + "reasoning_output_tokens": 171 + } + }, + "prompt_path": "cli/sql_prompt_attempt_1.txt", + "response_path": "cli/sql_response_attempt_1.txt", + "raw_response_path": "cli/sql_response_attempt_1.raw.txt", + "stderr_path": "cli/sql_stderr_attempt_1.txt" +} \ No newline at end of file diff --git a/Query/sql/v2/runs/v2_cli_20260502_081223_d/n1/artifacts/v2q_n1_0c626140449240f5/cli/sql_prompt_attempt_1.txt b/Query/sql/v2/runs/v2_cli_20260502_081223_d/n1/artifacts/v2q_n1_0c626140449240f5/cli/sql_prompt_attempt_1.txt new file mode 100644 index 0000000000000000000000000000000000000000..655c986ef92cd333ab56a9dce2589bb550af02c2 --- /dev/null +++ b/Query/sql/v2/runs/v2_cli_20260502_081223_d/n1/artifacts/v2q_n1_0c626140449240f5/cli/sql_prompt_attempt_1.txt @@ -0,0 +1,792 @@ +You are generating one SQLite SELECT query for a single-table SQL QA task. +Return strict JSON only, with this schema: {"sql": "...", "notes": "..."}. +Rules: +- Use only the provided table and columns. +- Do not write INSERT, UPDATE, DELETE, DROP, ALTER, CREATE, PRAGMA, ATTACH, DETACH, or VACUUM. +- Prefer the planned template and bound roles when provided. +- Add a leading SQL comment exactly like: -- template_id: . +- Generate SQLite-compatible SQL. SQLite does not support PERCENTILE_CONT or STDDEV. +- Quote identifiers with double quotes. +- Return no markdown and no extra prose. + +Dataset context: +Dataset context for SQL QA: +- dataset_id: n1 +- dataset_name: Spambase +- table_name: n1 +- table_layout: single-table dataset (do not assume joins). +- row_semantics: One row is one email represented by engineered token/character frequency and capitalization-run features. +- task_type: classification +- target_column: class +- main_row_count: 4601 +- important_fields: +- word_freq_make: role=feature, type=numeric_sparse_frequency. tags=['condition_candidate', 'measure', 'rare_pattern_candidate'] desc=Frequency feature for token 'make' in an email message. +- word_freq_address: role=feature, type=numeric_sparse_frequency. tags=['condition_candidate', 'measure', 'rare_pattern_candidate'] desc=Frequency feature for token 'address' in an email message. +- word_freq_all: role=feature, type=numeric_sparse_frequency. tags=['condition_candidate', 'measure', 'rare_pattern_candidate'] desc=Frequency feature for token 'all' in an email message. +- word_freq_3d: role=feature, type=numeric_sparse_frequency. tags=['condition_candidate', 'measure', 'rare_pattern_candidate'] desc=Frequency feature for token '3d' in an email message. +- word_freq_our: role=feature, type=numeric_sparse_frequency. tags=['condition_candidate', 'measure', 'rare_pattern_candidate'] desc=Frequency feature for token 'our' in an email message. +- word_freq_over: role=feature, type=numeric_sparse_frequency. tags=['condition_candidate', 'measure', 'rare_pattern_candidate'] desc=Frequency feature for token 'over' in an email message. +- word_freq_remove: role=feature, type=numeric_sparse_frequency. tags=['condition_candidate', 'measure', 'rare_pattern_candidate'] desc=Frequency feature for token 'remove' in an email message. +- word_freq_internet: role=feature, type=numeric_sparse_frequency. tags=['condition_candidate', 'measure', 'rare_pattern_candidate'] desc=Frequency feature for token 'internet' in an email message. +- word_freq_order: role=feature, type=numeric_sparse_frequency. tags=['condition_candidate', 'measure', 'rare_pattern_candidate'] desc=Frequency feature for token 'order' in an email message. +- word_freq_mail: role=feature, type=numeric_sparse_frequency. tags=['condition_candidate', 'measure', 'rare_pattern_candidate'] desc=Frequency feature for token 'mail' in an email message. +- word_freq_receive: role=feature, type=numeric_sparse_frequency. tags=['condition_candidate', 'measure', 'rare_pattern_candidate'] desc=Frequency feature for token 'receive' in an email message. +- word_freq_will: role=feature, type=numeric_sparse_frequency. tags=['condition_candidate', 'measure', 'rare_pattern_candidate'] desc=Frequency feature for token 'will' in an email message. +- word_freq_people: role=feature, type=numeric_sparse_frequency. tags=['condition_candidate', 'measure', 'rare_pattern_candidate'] desc=Frequency feature for token 'people' in an email message. +- word_freq_report: role=feature, type=numeric_sparse_frequency. tags=['condition_candidate', 'measure', 'rare_pattern_candidate'] desc=Frequency feature for token 'report' in an email message. +- word_freq_addresses: role=feature, type=numeric_sparse_frequency. tags=['condition_candidate', 'measure', 'rare_pattern_candidate'] desc=Frequency feature for token 'addresses' in an email message. +- word_freq_free: role=feature, type=numeric_sparse_frequency. tags=['condition_candidate', 'measure', 'rare_pattern_candidate'] desc=Frequency feature for token 'free' in an email message. +- word_freq_business: role=feature, type=numeric_sparse_frequency. tags=['condition_candidate', 'measure', 'rare_pattern_candidate'] desc=Frequency feature for token 'business' in an email message. +- word_freq_email: role=feature, type=numeric_sparse_frequency. tags=['condition_candidate', 'measure', 'rare_pattern_candidate'] desc=Frequency feature for token 'email' in an email message. +- word_freq_you: role=feature, type=numeric_sparse_frequency. tags=['condition_candidate', 'measure', 'rare_pattern_candidate'] desc=Frequency feature for token 'you' in an email message. +- word_freq_credit: role=feature, type=numeric_sparse_frequency. tags=['condition_candidate', 'measure', 'rare_pattern_candidate'] desc=Frequency feature for token 'credit' in an email message. +- word_freq_your: role=feature, type=numeric_sparse_frequency. tags=['condition_candidate', 'measure', 'rare_pattern_candidate'] desc=Frequency feature for token 'your' in an email message. +- word_freq_font: role=feature, type=numeric_sparse_frequency. tags=['condition_candidate', 'measure', 'rare_pattern_candidate'] desc=Frequency feature for token 'font' in an email message. +- word_freq_000: role=feature, type=numeric_sparse_frequency. tags=['condition_candidate', 'measure', 'rare_pattern_candidate'] desc=Frequency feature for token '000' in an email message. +- word_freq_money: role=feature, type=numeric_sparse_frequency. tags=['condition_candidate', 'measure', 'rare_pattern_candidate'] desc=Frequency feature for token 'money' in an email message. +- word_freq_hp: role=feature, type=numeric_sparse_frequency. tags=['condition_candidate', 'measure', 'rare_pattern_candidate'] desc=Frequency feature for token 'hp' in an email message. +- word_freq_hpl: role=feature, type=numeric_sparse_frequency. tags=['condition_candidate', 'measure', 'rare_pattern_candidate'] desc=Frequency feature for token 'hpl' in an email message. +- word_freq_george: role=feature, type=numeric_sparse_frequency. tags=['condition_candidate', 'measure', 'rare_pattern_candidate'] desc=Frequency feature for token 'george' in an email message. +- word_freq_650: role=feature, type=numeric_sparse_frequency. tags=['condition_candidate', 'measure', 'rare_pattern_candidate'] desc=Frequency feature for token '650' in an email message. +- word_freq_lab: role=feature, type=numeric_sparse_frequency. tags=['condition_candidate', 'measure', 'rare_pattern_candidate'] desc=Frequency feature for token 'lab' in an email message. +- word_freq_labs: role=feature, type=numeric_sparse_frequency. tags=['condition_candidate', 'measure', 'rare_pattern_candidate'] desc=Frequency feature for token 'labs' in an email message. +- word_freq_telnet: role=feature, type=numeric_sparse_frequency. tags=['condition_candidate', 'measure', 'rare_pattern_candidate'] desc=Frequency feature for token 'telnet' in an email message. +- word_freq_857: role=feature, type=numeric_sparse_frequency. tags=['condition_candidate', 'measure', 'rare_pattern_candidate'] desc=Frequency feature for token '857' in an email message. +- word_freq_data: role=feature, type=numeric_sparse_frequency. tags=['condition_candidate', 'measure', 'rare_pattern_candidate'] desc=Frequency feature for token 'data' in an email message. +- word_freq_415: role=feature, type=numeric_sparse_frequency. tags=['condition_candidate', 'measure', 'rare_pattern_candidate'] desc=Frequency feature for token '415' in an email message. +- word_freq_85: role=feature, type=numeric_sparse_frequency. tags=['condition_candidate', 'measure', 'rare_pattern_candidate'] desc=Frequency feature for token '85' in an email message. +- word_freq_technology: role=feature, type=numeric_sparse_frequency. tags=['condition_candidate', 'measure', 'rare_pattern_candidate'] desc=Frequency feature for token 'technology' in an email message. +- word_freq_1999: role=feature, type=numeric_sparse_frequency. tags=['condition_candidate', 'measure', 'rare_pattern_candidate'] desc=Frequency feature for token '1999' in an email message. +- word_freq_parts: role=feature, type=numeric_sparse_frequency. tags=['condition_candidate', 'measure', 'rare_pattern_candidate'] desc=Frequency feature for token 'parts' in an email message. +- word_freq_pm: role=feature, type=numeric_sparse_frequency. tags=['condition_candidate', 'measure', 'rare_pattern_candidate'] desc=Frequency feature for token 'pm' in an email message. +- word_freq_direct: role=feature, type=numeric_sparse_frequency. tags=['condition_candidate', 'measure', 'rare_pattern_candidate'] desc=Frequency feature for token 'direct' in an email message. +- word_freq_cs: role=feature, type=numeric_sparse_frequency. tags=['condition_candidate', 'measure', 'rare_pattern_candidate'] desc=Frequency feature for token 'cs' in an email message. +- word_freq_meeting: role=feature, type=numeric_sparse_frequency. tags=['condition_candidate', 'measure', 'rare_pattern_candidate'] desc=Frequency feature for token 'meeting' in an email message. +- word_freq_original: role=feature, type=numeric_sparse_frequency. tags=['condition_candidate', 'measure', 'rare_pattern_candidate'] desc=Frequency feature for token 'original' in an email message. +- word_freq_project: role=feature, type=numeric_sparse_frequency. tags=['condition_candidate', 'measure', 'rare_pattern_candidate'] desc=Frequency feature for token 'project' in an email message. +- word_freq_re: role=feature, type=numeric_sparse_frequency. tags=['condition_candidate', 'measure', 'rare_pattern_candidate'] desc=Frequency feature for token 're' in an email message. +- word_freq_edu: role=feature, type=numeric_sparse_frequency. tags=['condition_candidate', 'measure', 'rare_pattern_candidate'] desc=Frequency feature for token 'edu' in an email message. +- word_freq_table: role=feature, type=numeric_sparse_frequency. tags=['condition_candidate', 'measure', 'rare_pattern_candidate'] desc=Frequency feature for token 'table' in an email message. +- word_freq_conference: role=feature, type=numeric_sparse_frequency. tags=['condition_candidate', 'measure', 'rare_pattern_candidate'] desc=Frequency feature for token 'conference' in an email message. +- char_freq_%3B: role=feature, type=numeric_sparse_frequency. tags=['condition_candidate', 'measure', 'rare_pattern_candidate'] desc=Character frequency feature for symbol ';'. +- char_freq_%28: role=feature, type=numeric_sparse_frequency. tags=['condition_candidate', 'measure', 'rare_pattern_candidate'] desc=Character frequency feature for symbol '('. +- char_freq_%5B: role=feature, type=numeric_sparse_frequency. tags=['condition_candidate', 'measure', 'rare_pattern_candidate'] desc=Character frequency feature for symbol '['. +- char_freq_%21: role=feature, type=numeric_sparse_frequency. tags=['condition_candidate', 'measure', 'rare_pattern_candidate'] desc=Character frequency feature for symbol '!'. +- char_freq_%24: role=feature, type=numeric_sparse_frequency. tags=['condition_candidate', 'measure', 'rare_pattern_candidate'] desc=Character frequency feature for symbol '$'. +- char_freq_%23: role=feature, type=numeric_sparse_frequency. tags=['condition_candidate', 'measure', 'rare_pattern_candidate'] desc=Character frequency feature for symbol '#'. +- capital_run_length_average: role=feature, type=numeric_heavy_tail. tags=['condition_candidate', 'measure', 'rare_pattern_candidate'] desc=Average length of uninterrupted capital-letter runs. +- capital_run_length_longest: role=feature, type=numeric_heavy_tail. tags=['condition_candidate', 'measure', 'rare_pattern_candidate'] desc=Longest uninterrupted capital-letter run. +- capital_run_length_total: role=feature, type=numeric_heavy_tail. tags=['condition_candidate', 'measure', 'rare_pattern_candidate'] desc=Total number of capital letters in runs. +- class: role=target, type=binary_target. tags=['target_candidate'] desc=Binary spam label (1=spam, 0=non-spam). +- useful_field_combinations: [['word_freq_free', 'word_freq_you', 'class'], ['char_freq_%21', 'capital_run_length_longest', 'class'], ['word_freq_remove', 'word_freq_money', 'class']] +- fields_requiring_caution: ['capital_run_length_average', 'capital_run_length_longest', 'capital_run_length_total'] +- source_url: https://www.openml.org/d/44 + +SQLite schema snapshot: +{ + "table_name": "n1", + "quoted_table_name": "\"n1\"", + "row_count": 4601, + "columns": [ + { + "name": "word_freq_make", + "type": "TEXT", + "notnull": false, + "pk": false + }, + { + "name": "word_freq_address", + "type": "TEXT", + "notnull": false, + "pk": false + }, + { + "name": "word_freq_all", + "type": "TEXT", + "notnull": false, + "pk": false + }, + { + "name": "word_freq_3d", + "type": "TEXT", + "notnull": false, + "pk": false + }, + { + "name": "word_freq_our", + "type": "TEXT", + "notnull": false, + "pk": false + }, + { + "name": "word_freq_over", + "type": "TEXT", + "notnull": false, + "pk": false + }, + { + "name": "word_freq_remove", + "type": "TEXT", + "notnull": false, + "pk": false + }, + { + "name": "word_freq_internet", + "type": "TEXT", + "notnull": false, + "pk": false + }, + { + "name": "word_freq_order", + "type": "TEXT", + "notnull": false, + "pk": false + }, + { + "name": "word_freq_mail", + "type": "TEXT", + "notnull": false, + "pk": false + }, + { + "name": "word_freq_receive", + "type": "TEXT", + "notnull": false, + "pk": false + }, + { + "name": "word_freq_will", + "type": "TEXT", + "notnull": false, + "pk": false + }, + { + "name": "word_freq_people", + "type": "TEXT", + "notnull": false, + "pk": false + }, + { + "name": "word_freq_report", + "type": "TEXT", + "notnull": false, + "pk": false + }, + { + "name": "word_freq_addresses", + "type": "TEXT", + "notnull": false, + "pk": false + }, + { + "name": "word_freq_free", + "type": "TEXT", + "notnull": false, + "pk": false + }, + { + "name": "word_freq_business", + "type": "TEXT", + "notnull": false, + "pk": false + }, + { + "name": "word_freq_email", + "type": "TEXT", + "notnull": false, + "pk": false + }, + { + "name": "word_freq_you", + "type": "TEXT", + "notnull": false, + "pk": false + }, + { + "name": "word_freq_credit", + "type": "TEXT", + "notnull": false, + "pk": false + }, + { + "name": "word_freq_your", + "type": "TEXT", + "notnull": false, + "pk": false + }, + { + "name": "word_freq_font", + "type": "TEXT", + "notnull": false, + "pk": false + }, + { + "name": "word_freq_000", + "type": "TEXT", + "notnull": false, + "pk": false + }, + { + "name": "word_freq_money", + "type": "TEXT", + "notnull": false, + "pk": false + }, + { + "name": "word_freq_hp", + "type": "TEXT", + "notnull": false, + "pk": false + }, + { + "name": "word_freq_hpl", + "type": "TEXT", + "notnull": false, + "pk": false + }, + { + "name": "word_freq_george", + "type": "TEXT", + "notnull": false, + "pk": false + }, + { + "name": "word_freq_650", + "type": "TEXT", + "notnull": false, + "pk": false + }, + { + "name": "word_freq_lab", + "type": "TEXT", + "notnull": false, + "pk": false + }, + { + "name": "word_freq_labs", + "type": "TEXT", + "notnull": false, + "pk": false + }, + { + "name": "word_freq_telnet", + "type": "TEXT", + "notnull": false, + "pk": false + }, + { + "name": "word_freq_857", + "type": "TEXT", + "notnull": false, + "pk": false + }, + { + "name": "word_freq_data", + "type": "TEXT", + "notnull": false, + "pk": false + }, + { + "name": "word_freq_415", + "type": "TEXT", + "notnull": false, + "pk": false + }, + { + "name": "word_freq_85", + "type": "TEXT", + "notnull": false, + "pk": false + }, + { + "name": "word_freq_technology", + "type": "TEXT", + "notnull": false, + "pk": false + }, + { + "name": "word_freq_1999", + "type": "TEXT", + "notnull": false, + "pk": false + }, + { + "name": "word_freq_parts", + "type": "TEXT", + "notnull": false, + "pk": false + }, + { + "name": "word_freq_pm", + "type": "TEXT", + "notnull": false, + "pk": false + }, + { + "name": "word_freq_direct", + "type": "TEXT", + "notnull": false, + "pk": false + }, + { + "name": "word_freq_cs", + "type": "TEXT", + "notnull": false, + "pk": false + }, + { + "name": "word_freq_meeting", + "type": "TEXT", + "notnull": false, + "pk": false + }, + { + "name": "word_freq_original", + "type": "TEXT", + "notnull": false, + "pk": false + }, + { + "name": "word_freq_project", + "type": "TEXT", + "notnull": false, + "pk": false + }, + { + "name": "word_freq_re", + "type": "TEXT", + "notnull": false, + "pk": false + }, + { + "name": "word_freq_edu", + "type": "TEXT", + "notnull": false, + "pk": false + }, + { + "name": "word_freq_table", + "type": "TEXT", + "notnull": false, + "pk": false + }, + { + "name": "word_freq_conference", + "type": "TEXT", + "notnull": false, + "pk": false + }, + { + "name": "char_freq_%3B", + "type": "TEXT", + "notnull": false, + "pk": false + }, + { + "name": "char_freq_%28", + "type": "TEXT", + "notnull": false, + "pk": false + }, + { + "name": "char_freq_%5B", + "type": "TEXT", + "notnull": false, + "pk": false + }, + { + "name": "char_freq_%21", + "type": "TEXT", + "notnull": false, + "pk": false + }, + { + "name": "char_freq_%24", + "type": "TEXT", + "notnull": false, + "pk": false + }, + { + "name": "char_freq_%23", + "type": "TEXT", + "notnull": false, + "pk": false + }, + { + "name": "capital_run_length_average", + "type": "TEXT", + "notnull": false, + "pk": false + }, + { + "name": "capital_run_length_longest", + "type": "TEXT", + "notnull": false, + "pk": false + }, + { + "name": "capital_run_length_total", + "type": "TEXT", + "notnull": false, + "pk": false + }, + { + "name": "class", + "type": "TEXT", + "notnull": false, + "pk": false + } + ], + "sample_rows": [ + { + "word_freq_make": "0", + "word_freq_address": "0.64", + "word_freq_all": "0.64", + "word_freq_3d": "0", + "word_freq_our": "0.32", + "word_freq_over": "0", + "word_freq_remove": "0", + "word_freq_internet": "0", + "word_freq_order": "0", + "word_freq_mail": "0", + "word_freq_receive": "0", + "word_freq_will": "0.64", + "word_freq_people": "0", + "word_freq_report": "0", + "word_freq_addresses": "0", + "word_freq_free": "0.32", + "word_freq_business": "0", + "word_freq_email": "1.29", + "word_freq_you": "1.93", + "word_freq_credit": "0", + "word_freq_your": "0.96", + "word_freq_font": "0", + "word_freq_000": "0", + "word_freq_money": "0", + "word_freq_hp": "0", + "word_freq_hpl": "0", + "word_freq_george": "0", + "word_freq_650": "0", + "word_freq_lab": "0", + "word_freq_labs": "0", + "word_freq_telnet": "0", + "word_freq_857": "0", + "word_freq_data": "0", + "word_freq_415": "0", + "word_freq_85": "0", + "word_freq_technology": "0", + "word_freq_1999": "0", + "word_freq_parts": "0", + "word_freq_pm": "0", + "word_freq_direct": "0", + "word_freq_cs": "0", + "word_freq_meeting": "0", + "word_freq_original": "0", + "word_freq_project": "0", + "word_freq_re": "0", + "word_freq_edu": "0", + "word_freq_table": "0", + "word_freq_conference": "0", + "char_freq_%3B": "0", + "char_freq_%28": "0", + "char_freq_%5B": "0", + "char_freq_%21": "0.778", + "char_freq_%24": "0", + "char_freq_%23": "0", + "capital_run_length_average": "3.756", + "capital_run_length_longest": "61", + "capital_run_length_total": "278", + "class": "1" + }, + { + "word_freq_make": "0.21", + "word_freq_address": "0.28", + "word_freq_all": "0.5", + "word_freq_3d": "0", + "word_freq_our": "0.14", + "word_freq_over": "0.28", + "word_freq_remove": "0.21", + "word_freq_internet": "0.07", + "word_freq_order": "0", + "word_freq_mail": "0.94", + "word_freq_receive": "0.21", + "word_freq_will": "0.79", + "word_freq_people": "0.65", + "word_freq_report": "0.21", + "word_freq_addresses": "0.14", + "word_freq_free": "0.14", + "word_freq_business": "0.07", + "word_freq_email": "0.28", + "word_freq_you": "3.47", + "word_freq_credit": "0", + "word_freq_your": "1.59", + "word_freq_font": "0", + "word_freq_000": "0.43", + "word_freq_money": "0.43", + "word_freq_hp": "0", + "word_freq_hpl": "0", + "word_freq_george": "0", + "word_freq_650": "0", + "word_freq_lab": "0", + "word_freq_labs": "0", + "word_freq_telnet": "0", + "word_freq_857": "0", + "word_freq_data": "0", + "word_freq_415": "0", + "word_freq_85": "0", + "word_freq_technology": "0", + "word_freq_1999": "0.07", + "word_freq_parts": "0", + "word_freq_pm": "0", + "word_freq_direct": "0", + "word_freq_cs": "0", + "word_freq_meeting": "0", + "word_freq_original": "0", + "word_freq_project": "0", + "word_freq_re": "0", + "word_freq_edu": "0", + "word_freq_table": "0", + "word_freq_conference": "0", + "char_freq_%3B": "0", + "char_freq_%28": "0.132", + "char_freq_%5B": "0", + "char_freq_%21": "0.372", + "char_freq_%24": "0.18", + "char_freq_%23": "0.048", + "capital_run_length_average": "5.114", + "capital_run_length_longest": "101", + "capital_run_length_total": "1028", + "class": "1" + }, + { + "word_freq_make": "0.06", + "word_freq_address": "0", + "word_freq_all": "0.71", + "word_freq_3d": "0", + "word_freq_our": "1.23", + "word_freq_over": "0.19", + "word_freq_remove": "0.19", + "word_freq_internet": "0.12", + "word_freq_order": "0.64", + "word_freq_mail": "0.25", + "word_freq_receive": "0.38", + "word_freq_will": "0.45", + "word_freq_people": "0.12", + "word_freq_report": "0", + "word_freq_addresses": "1.75", + "word_freq_free": "0.06", + "word_freq_business": "0.06", + "word_freq_email": "1.03", + "word_freq_you": "1.36", + "word_freq_credit": "0.32", + "word_freq_your": "0.51", + "word_freq_font": "0", + "word_freq_000": "1.16", + "word_freq_money": "0.06", + "word_freq_hp": "0", + "word_freq_hpl": "0", + "word_freq_george": "0", + "word_freq_650": "0", + "word_freq_lab": "0", + "word_freq_labs": "0", + "word_freq_telnet": "0", + "word_freq_857": "0", + "word_freq_data": "0", + "word_freq_415": "0", + "word_freq_85": "0", + "word_freq_technology": "0", + "word_freq_1999": "0", + "word_freq_parts": "0", + "word_freq_pm": "0", + "word_freq_direct": "0.06", + "word_freq_cs": "0", + "word_freq_meeting": "0", + "word_freq_original": "0.12", + "word_freq_project": "0", + "word_freq_re": "0.06", + "word_freq_edu": "0.06", + "word_freq_table": "0", + "word_freq_conference": "0", + "char_freq_%3B": "0.01", + "char_freq_%28": "0.143", + "char_freq_%5B": "0", + "char_freq_%21": "0.276", + "char_freq_%24": "0.184", + "char_freq_%23": "0.01", + "capital_run_length_average": "9.821", + "capital_run_length_longest": "485", + "capital_run_length_total": "2259", + "class": "1" + }, + { + "word_freq_make": "0", + "word_freq_address": "0", + "word_freq_all": "0", + "word_freq_3d": "0", + "word_freq_our": "0.63", + "word_freq_over": "0", + "word_freq_remove": "0.31", + "word_freq_internet": "0.63", + "word_freq_order": "0.31", + "word_freq_mail": "0.63", + "word_freq_receive": "0.31", + "word_freq_will": "0.31", + "word_freq_people": "0.31", + "word_freq_report": "0", + "word_freq_addresses": "0", + "word_freq_free": "0.31", + "word_freq_business": "0", + "word_freq_email": "0", + "word_freq_you": "3.18", + "word_freq_credit": "0", + "word_freq_your": "0.31", + "word_freq_font": "0", + "word_freq_000": "0", + "word_freq_money": "0", + "word_freq_hp": "0", + "word_freq_hpl": "0", + "word_freq_george": "0", + "word_freq_650": "0", + "word_freq_lab": "0", + "word_freq_labs": "0", + "word_freq_telnet": "0", + "word_freq_857": "0", + "word_freq_data": "0", + "word_freq_415": "0", + "word_freq_85": "0", + "word_freq_technology": "0", + "word_freq_1999": "0", + "word_freq_parts": "0", + "word_freq_pm": "0", + "word_freq_direct": "0", + "word_freq_cs": "0", + "word_freq_meeting": "0", + "word_freq_original": "0", + "word_freq_project": "0", + "word_freq_re": "0", + "word_freq_edu": "0", + "word_freq_table": "0", + "word_freq_conference": "0", + "char_freq_%3B": "0", + "char_freq_%28": "0.137", + "char_freq_%5B": "0", + "char_freq_%21": "0.137", + "char_freq_%24": "0", + "char_freq_%23": "0", + "capital_run_length_average": "3.537", + "capital_run_length_longest": "40", + "capital_run_length_total": "191", + "class": "1" + }, + { + "word_freq_make": "0", + "word_freq_address": "0", + "word_freq_all": "0", + "word_freq_3d": "0", + "word_freq_our": "0.63", + "word_freq_over": "0", + "word_freq_remove": "0.31", + "word_freq_internet": "0.63", + "word_freq_order": "0.31", + "word_freq_mail": "0.63", + "word_freq_receive": "0.31", + "word_freq_will": "0.31", + "word_freq_people": "0.31", + "word_freq_report": "0", + "word_freq_addresses": "0", + "word_freq_free": "0.31", + "word_freq_business": "0", + "word_freq_email": "0", + "word_freq_you": "3.18", + "word_freq_credit": "0", + "word_freq_your": "0.31", + "word_freq_font": "0", + "word_freq_000": "0", + "word_freq_money": "0", + "word_freq_hp": "0", + "word_freq_hpl": "0", + "word_freq_george": "0", + "word_freq_650": "0", + "word_freq_lab": "0", + "word_freq_labs": "0", + "word_freq_telnet": "0", + "word_freq_857": "0", + "word_freq_data": "0", + "word_freq_415": "0", + "word_freq_85": "0", + "word_freq_technology": "0", + "word_freq_1999": "0", + "word_freq_parts": "0", + "word_freq_pm": "0", + "word_freq_direct": "0", + "word_freq_cs": "0", + "word_freq_meeting": "0", + "word_freq_original": "0", + "word_freq_project": "0", + "word_freq_re": "0", + "word_freq_edu": "0", + "word_freq_table": "0", + "word_freq_conference": "0", + "char_freq_%3B": "0", + "char_freq_%28": "0.135", + "char_freq_%5B": "0", + "char_freq_%21": "0.135", + "char_freq_%24": "0", + "char_freq_%23": "0", + "capital_run_length_average": "3.537", + "capital_run_length_longest": "40", + "capital_run_length_total": "191", + "class": "1" + } + ] +} + +Shortlisted templates: +[ + { + "template_id": "tpl_m4_window_partition_avg", + "template_name": "Window Partition Average", + "primary_family": "conditional_dependency_structure", + "portability": "partial", + "sql_skeleton": "SELECT DISTINCT {group_col},\n AVG({measure_col}) OVER (PARTITION BY {group_col}) AS avg_measure\nFROM {table}\nORDER BY avg_measure DESC;", + "required_roles": [ + "group_col", + "measure_col" + ] + } +] + +Problem instance: +{ + "dataset_id": "n1", + "question": "Use template Window Partition Average to probe direction_consistency with semantic role filtered_stable_view. Focus on group_col=class, measure_col=char_freq_%21.", + "planned_template_id": "tpl_m4_window_partition_avg", + "bindings": { + "group_col": "class", + "measure_col": "char_freq_%21", + "top_k": 16, + "top_n": 7, + "num_tiles": 10, + "percentile_value": 0.95, + "z_threshold": 2.0, + "fraction_threshold": 0.05, + "baseline_multiplier": 1.75, + "baseline_fraction": 0.1, + "min_group_size": 5, + "min_support": 4, + "measure_threshold": 0.171, + "time_grain": "month", + "lookback_rows": 3, + "current_period_start": "'2024-01-01'", + "current_period_end": "'2024-04-01'", + "previous_period_start": "'2023-10-01'", + "previous_period_end": "'2024-01-01'", + "drift_ratio_threshold": 0.8 + }, + "can_vary": [], + "must_fix": [], + "runtime_sql_skeleton": "SELECT DISTINCT {group_col},\n AVG({measure_col}) OVER (PARTITION BY {group_col}) AS avg_measure\nFROM {table}\nORDER BY avg_measure DESC;" +} + +Repair context: +{} diff --git a/Query/sql/v2/runs/v2_cli_20260502_081223_d/n1/artifacts/v2q_n1_0c626140449240f5/cli/sql_response_attempt_1.raw.txt b/Query/sql/v2/runs/v2_cli_20260502_081223_d/n1/artifacts/v2q_n1_0c626140449240f5/cli/sql_response_attempt_1.raw.txt new file mode 100644 index 0000000000000000000000000000000000000000..fe973b69934e5220edc55f7c7d22b8c05b4d9bf2 --- /dev/null +++ b/Query/sql/v2/runs/v2_cli_20260502_081223_d/n1/artifacts/v2q_n1_0c626140449240f5/cli/sql_response_attempt_1.raw.txt @@ -0,0 +1,4 @@ +{"type":"thread.started","thread_id":"019e40e7-78a4-72e1-923b-49db452276d3"} +{"type":"turn.started"} +{"type":"item.completed","item":{"id":"item_0","type":"agent_message","text":"{\"sql\":\"-- template_id: tpl_m4_window_partition_avg\\nSELECT DISTINCT \\\"class\\\",\\n AVG(CAST(\\\"char_freq_%21\\\" AS REAL)) OVER (PARTITION BY \\\"class\\\") AS \\\"avg_measure\\\"\\nFROM \\\"n1\\\"\\nORDER BY \\\"avg_measure\\\" DESC;\",\"notes\":\"Applied the provided Window Partition Average template with group_col=\\\"class\\\" and measure_col=\\\"char_freq_%21\\\". Cast the measure to REAL because the schema stores feature values as TEXT.\"}"}} +{"type":"turn.completed","usage":{"input_tokens":20328,"cached_input_tokens":19840,"output_tokens":279,"reasoning_output_tokens":171}} diff --git a/Query/sql/v2/runs/v2_cli_20260502_081223_d/n1/artifacts/v2q_n1_0c626140449240f5/cli/sql_response_attempt_1.txt b/Query/sql/v2/runs/v2_cli_20260502_081223_d/n1/artifacts/v2q_n1_0c626140449240f5/cli/sql_response_attempt_1.txt new file mode 100644 index 0000000000000000000000000000000000000000..a4fc2b38e2e158d8a0fb290c9f8867aed2022173 --- /dev/null +++ b/Query/sql/v2/runs/v2_cli_20260502_081223_d/n1/artifacts/v2q_n1_0c626140449240f5/cli/sql_response_attempt_1.txt @@ -0,0 +1 @@ +{"sql":"-- template_id: tpl_m4_window_partition_avg\nSELECT DISTINCT \"class\",\n AVG(CAST(\"char_freq_%21\" AS REAL)) OVER (PARTITION BY \"class\") AS \"avg_measure\"\nFROM \"n1\"\nORDER BY \"avg_measure\" DESC;","notes":"Applied the provided Window Partition Average template with group_col=\"class\" and measure_col=\"char_freq_%21\". Cast the measure to REAL because the schema stores feature values as TEXT."} \ No newline at end of file diff --git a/Query/sql/v2/runs/v2_cli_20260502_081223_d/n1/artifacts/v2q_n1_0c626140449240f5/cli/sql_stderr_attempt_1.txt b/Query/sql/v2/runs/v2_cli_20260502_081223_d/n1/artifacts/v2q_n1_0c626140449240f5/cli/sql_stderr_attempt_1.txt new file mode 100644 index 0000000000000000000000000000000000000000..e69de29bb2d1d6434b8b29ae775ad8c2e48c5391 diff --git a/Query/sql/v2/runs/v2_cli_20260502_081223_d/n1/artifacts/v2q_n1_12e9d7ee8ce2ca10/cli/conversation.jsonl b/Query/sql/v2/runs/v2_cli_20260502_081223_d/n1/artifacts/v2q_n1_12e9d7ee8ce2ca10/cli/conversation.jsonl new file mode 100644 index 0000000000000000000000000000000000000000..632938f1fafbb2bea340e2a6b4e6a87661fcc79e --- /dev/null +++ b/Query/sql/v2/runs/v2_cli_20260502_081223_d/n1/artifacts/v2q_n1_12e9d7ee8ce2ca10/cli/conversation.jsonl @@ -0,0 +1,2 @@ +{"attempt": 1, "phase": "sql_generation", "role": "user", "content_path": "cli/sql_prompt_attempt_1.txt", "metrics": {"chars": 29354, "bytes_utf8": 29354, "lines": 792, "estimated_tokens": null}} +{"attempt": 1, "phase": "sql_generation", "role": "assistant", "content_path": "cli/sql_response_attempt_1.txt", "raw_content_path": "cli/sql_response_attempt_1.raw.txt", "stderr_path": "cli/sql_stderr_attempt_1.txt", "metrics": {"chars": 360, "bytes_utf8": 360, "lines": 1, "estimated_tokens": null}, "usage": {"input_tokens": 20319, "cached_input_tokens": 12032, "output_tokens": 303, "reasoning_output_tokens": 203}} diff --git a/Query/sql/v2/runs/v2_cli_20260502_081223_d/n1/artifacts/v2q_n1_12e9d7ee8ce2ca10/cli/session_summary.json b/Query/sql/v2/runs/v2_cli_20260502_081223_d/n1/artifacts/v2q_n1_12e9d7ee8ce2ca10/cli/session_summary.json new file mode 100644 index 0000000000000000000000000000000000000000..03c88f6bc6bd700c317b010aee32eaf448897c9a --- /dev/null +++ b/Query/sql/v2/runs/v2_cli_20260502_081223_d/n1/artifacts/v2q_n1_12e9d7ee8ce2ca10/cli/session_summary.json @@ -0,0 +1,25 @@ +{ + "engine": "v2-cli:codex", + "command": "codex exec --skip-git-repo-check --disable plugins --sandbox read-only --cd \"/data/jialinzhang/SQLagent\" -m gpt-5.4 --json -", + "ai_cli_calls": 1, + "usage_summary": { + "dataset_id": "n1", + "model": "v2-cli:codex", + "run_id": "v2q_n1_12e9d7ee8ce2ca10", + "api_calls": 0, + "input_tokens": 20319, + "cached_input_tokens": 12032, + "output_tokens": 303, + "total_tokens": 20622, + "cost_usd": 0.0, + "ai_cli_calls": 1, + "estimated_input_tokens": 0, + "estimated_output_tokens": 0, + "estimated_total_tokens": 0, + "usage_source": "ai_cli_json_usage", + "cli_elapsed_ms_total": 11245.74, + "sql_execution_elapsed_ms_total": 2.22, + "conversation_log_path": "/data/jialinzhang/TabQueryBench/sql_workloads/v2_current/runs_and_launches/runs/v2_cli_20260502_081223_d/n1/artifacts/v2q_n1_12e9d7ee8ce2ca10/cli/conversation.jsonl", + "note": "Executed through a local AI CLI with structured usage metadata." + } +} \ No newline at end of file diff --git a/Query/sql/v2/runs/v2_cli_20260502_081223_d/n1/artifacts/v2q_n1_12e9d7ee8ce2ca10/cli/sql_attempt_1.metadata.json b/Query/sql/v2/runs/v2_cli_20260502_081223_d/n1/artifacts/v2q_n1_12e9d7ee8ce2ca10/cli/sql_attempt_1.metadata.json new file mode 100644 index 0000000000000000000000000000000000000000..b1f23205a5058c2703c0260b34dce69cb41574ac --- /dev/null +++ b/Query/sql/v2/runs/v2_cli_20260502_081223_d/n1/artifacts/v2q_n1_12e9d7ee8ce2ca10/cli/sql_attempt_1.metadata.json @@ -0,0 +1,45 @@ +{ + "attempt": 1, + "phase": "sql_generation", + "command": "codex exec --skip-git-repo-check --disable plugins --sandbox read-only --cd \"/data/jialinzhang/SQLagent\" -m gpt-5.4 --json -", + "started_at": "2026-05-19T15:29:23.660355+00:00", + "ended_at": "2026-05-19T15:29:34.906127+00:00", + "elapsed_ms": 11245.74, + "prompt_metrics": { + "chars": 29354, + "bytes_utf8": 29354, + "lines": 792, + "estimated_tokens": null + }, + "stdout_metrics": { + "chars": 714, + "bytes_utf8": 714, + "lines": 4, + "estimated_tokens": null + }, + "stderr_metrics": { + "chars": 0, + "bytes_utf8": 0, + "lines": 0, + "estimated_tokens": null + }, + "parsed_output": { + "format": "jsonl_events", + "text_metrics": { + "chars": 360, + "bytes_utf8": 360, + "lines": 1, + "estimated_tokens": null + }, + "usage": { + "input_tokens": 20319, + "cached_input_tokens": 12032, + "output_tokens": 303, + "reasoning_output_tokens": 203 + } + }, + "prompt_path": "cli/sql_prompt_attempt_1.txt", + "response_path": "cli/sql_response_attempt_1.txt", + "raw_response_path": "cli/sql_response_attempt_1.raw.txt", + "stderr_path": "cli/sql_stderr_attempt_1.txt" +} \ No newline at end of file diff --git a/Query/sql/v2/runs/v2_cli_20260502_081223_d/n1/artifacts/v2q_n1_12e9d7ee8ce2ca10/cli/sql_prompt_attempt_1.txt b/Query/sql/v2/runs/v2_cli_20260502_081223_d/n1/artifacts/v2q_n1_12e9d7ee8ce2ca10/cli/sql_prompt_attempt_1.txt new file mode 100644 index 0000000000000000000000000000000000000000..64ea84e3f34556d875ab9b89847429eb6bb69c69 --- /dev/null +++ b/Query/sql/v2/runs/v2_cli_20260502_081223_d/n1/artifacts/v2q_n1_12e9d7ee8ce2ca10/cli/sql_prompt_attempt_1.txt @@ -0,0 +1,792 @@ +You are generating one SQLite SELECT query for a single-table SQL QA task. +Return strict JSON only, with this schema: {"sql": "...", "notes": "..."}. +Rules: +- Use only the provided table and columns. +- Do not write INSERT, UPDATE, DELETE, DROP, ALTER, CREATE, PRAGMA, ATTACH, DETACH, or VACUUM. +- Prefer the planned template and bound roles when provided. +- Add a leading SQL comment exactly like: -- template_id: . +- Generate SQLite-compatible SQL. SQLite does not support PERCENTILE_CONT or STDDEV. +- Quote identifiers with double quotes. +- Return no markdown and no extra prose. + +Dataset context: +Dataset context for SQL QA: +- dataset_id: n1 +- dataset_name: Spambase +- table_name: n1 +- table_layout: single-table dataset (do not assume joins). +- row_semantics: One row is one email represented by engineered token/character frequency and capitalization-run features. +- task_type: classification +- target_column: class +- main_row_count: 4601 +- important_fields: +- word_freq_make: role=feature, type=numeric_sparse_frequency. tags=['condition_candidate', 'measure', 'rare_pattern_candidate'] desc=Frequency feature for token 'make' in an email message. +- word_freq_address: role=feature, type=numeric_sparse_frequency. tags=['condition_candidate', 'measure', 'rare_pattern_candidate'] desc=Frequency feature for token 'address' in an email message. +- word_freq_all: role=feature, type=numeric_sparse_frequency. tags=['condition_candidate', 'measure', 'rare_pattern_candidate'] desc=Frequency feature for token 'all' in an email message. +- word_freq_3d: role=feature, type=numeric_sparse_frequency. tags=['condition_candidate', 'measure', 'rare_pattern_candidate'] desc=Frequency feature for token '3d' in an email message. +- word_freq_our: role=feature, type=numeric_sparse_frequency. tags=['condition_candidate', 'measure', 'rare_pattern_candidate'] desc=Frequency feature for token 'our' in an email message. +- word_freq_over: role=feature, type=numeric_sparse_frequency. tags=['condition_candidate', 'measure', 'rare_pattern_candidate'] desc=Frequency feature for token 'over' in an email message. +- word_freq_remove: role=feature, type=numeric_sparse_frequency. tags=['condition_candidate', 'measure', 'rare_pattern_candidate'] desc=Frequency feature for token 'remove' in an email message. +- word_freq_internet: role=feature, type=numeric_sparse_frequency. tags=['condition_candidate', 'measure', 'rare_pattern_candidate'] desc=Frequency feature for token 'internet' in an email message. +- word_freq_order: role=feature, type=numeric_sparse_frequency. tags=['condition_candidate', 'measure', 'rare_pattern_candidate'] desc=Frequency feature for token 'order' in an email message. +- word_freq_mail: role=feature, type=numeric_sparse_frequency. tags=['condition_candidate', 'measure', 'rare_pattern_candidate'] desc=Frequency feature for token 'mail' in an email message. +- word_freq_receive: role=feature, type=numeric_sparse_frequency. tags=['condition_candidate', 'measure', 'rare_pattern_candidate'] desc=Frequency feature for token 'receive' in an email message. +- word_freq_will: role=feature, type=numeric_sparse_frequency. tags=['condition_candidate', 'measure', 'rare_pattern_candidate'] desc=Frequency feature for token 'will' in an email message. +- word_freq_people: role=feature, type=numeric_sparse_frequency. tags=['condition_candidate', 'measure', 'rare_pattern_candidate'] desc=Frequency feature for token 'people' in an email message. +- word_freq_report: role=feature, type=numeric_sparse_frequency. tags=['condition_candidate', 'measure', 'rare_pattern_candidate'] desc=Frequency feature for token 'report' in an email message. +- word_freq_addresses: role=feature, type=numeric_sparse_frequency. tags=['condition_candidate', 'measure', 'rare_pattern_candidate'] desc=Frequency feature for token 'addresses' in an email message. +- word_freq_free: role=feature, type=numeric_sparse_frequency. tags=['condition_candidate', 'measure', 'rare_pattern_candidate'] desc=Frequency feature for token 'free' in an email message. +- word_freq_business: role=feature, type=numeric_sparse_frequency. tags=['condition_candidate', 'measure', 'rare_pattern_candidate'] desc=Frequency feature for token 'business' in an email message. +- word_freq_email: role=feature, type=numeric_sparse_frequency. tags=['condition_candidate', 'measure', 'rare_pattern_candidate'] desc=Frequency feature for token 'email' in an email message. +- word_freq_you: role=feature, type=numeric_sparse_frequency. tags=['condition_candidate', 'measure', 'rare_pattern_candidate'] desc=Frequency feature for token 'you' in an email message. +- word_freq_credit: role=feature, type=numeric_sparse_frequency. tags=['condition_candidate', 'measure', 'rare_pattern_candidate'] desc=Frequency feature for token 'credit' in an email message. +- word_freq_your: role=feature, type=numeric_sparse_frequency. tags=['condition_candidate', 'measure', 'rare_pattern_candidate'] desc=Frequency feature for token 'your' in an email message. +- word_freq_font: role=feature, type=numeric_sparse_frequency. tags=['condition_candidate', 'measure', 'rare_pattern_candidate'] desc=Frequency feature for token 'font' in an email message. +- word_freq_000: role=feature, type=numeric_sparse_frequency. tags=['condition_candidate', 'measure', 'rare_pattern_candidate'] desc=Frequency feature for token '000' in an email message. +- word_freq_money: role=feature, type=numeric_sparse_frequency. tags=['condition_candidate', 'measure', 'rare_pattern_candidate'] desc=Frequency feature for token 'money' in an email message. +- word_freq_hp: role=feature, type=numeric_sparse_frequency. tags=['condition_candidate', 'measure', 'rare_pattern_candidate'] desc=Frequency feature for token 'hp' in an email message. +- word_freq_hpl: role=feature, type=numeric_sparse_frequency. tags=['condition_candidate', 'measure', 'rare_pattern_candidate'] desc=Frequency feature for token 'hpl' in an email message. +- word_freq_george: role=feature, type=numeric_sparse_frequency. tags=['condition_candidate', 'measure', 'rare_pattern_candidate'] desc=Frequency feature for token 'george' in an email message. +- word_freq_650: role=feature, type=numeric_sparse_frequency. tags=['condition_candidate', 'measure', 'rare_pattern_candidate'] desc=Frequency feature for token '650' in an email message. +- word_freq_lab: role=feature, type=numeric_sparse_frequency. tags=['condition_candidate', 'measure', 'rare_pattern_candidate'] desc=Frequency feature for token 'lab' in an email message. +- word_freq_labs: role=feature, type=numeric_sparse_frequency. tags=['condition_candidate', 'measure', 'rare_pattern_candidate'] desc=Frequency feature for token 'labs' in an email message. +- word_freq_telnet: role=feature, type=numeric_sparse_frequency. tags=['condition_candidate', 'measure', 'rare_pattern_candidate'] desc=Frequency feature for token 'telnet' in an email message. +- word_freq_857: role=feature, type=numeric_sparse_frequency. tags=['condition_candidate', 'measure', 'rare_pattern_candidate'] desc=Frequency feature for token '857' in an email message. +- word_freq_data: role=feature, type=numeric_sparse_frequency. tags=['condition_candidate', 'measure', 'rare_pattern_candidate'] desc=Frequency feature for token 'data' in an email message. +- word_freq_415: role=feature, type=numeric_sparse_frequency. tags=['condition_candidate', 'measure', 'rare_pattern_candidate'] desc=Frequency feature for token '415' in an email message. +- word_freq_85: role=feature, type=numeric_sparse_frequency. tags=['condition_candidate', 'measure', 'rare_pattern_candidate'] desc=Frequency feature for token '85' in an email message. +- word_freq_technology: role=feature, type=numeric_sparse_frequency. tags=['condition_candidate', 'measure', 'rare_pattern_candidate'] desc=Frequency feature for token 'technology' in an email message. +- word_freq_1999: role=feature, type=numeric_sparse_frequency. tags=['condition_candidate', 'measure', 'rare_pattern_candidate'] desc=Frequency feature for token '1999' in an email message. +- word_freq_parts: role=feature, type=numeric_sparse_frequency. tags=['condition_candidate', 'measure', 'rare_pattern_candidate'] desc=Frequency feature for token 'parts' in an email message. +- word_freq_pm: role=feature, type=numeric_sparse_frequency. tags=['condition_candidate', 'measure', 'rare_pattern_candidate'] desc=Frequency feature for token 'pm' in an email message. +- word_freq_direct: role=feature, type=numeric_sparse_frequency. tags=['condition_candidate', 'measure', 'rare_pattern_candidate'] desc=Frequency feature for token 'direct' in an email message. +- word_freq_cs: role=feature, type=numeric_sparse_frequency. tags=['condition_candidate', 'measure', 'rare_pattern_candidate'] desc=Frequency feature for token 'cs' in an email message. +- word_freq_meeting: role=feature, type=numeric_sparse_frequency. tags=['condition_candidate', 'measure', 'rare_pattern_candidate'] desc=Frequency feature for token 'meeting' in an email message. +- word_freq_original: role=feature, type=numeric_sparse_frequency. tags=['condition_candidate', 'measure', 'rare_pattern_candidate'] desc=Frequency feature for token 'original' in an email message. +- word_freq_project: role=feature, type=numeric_sparse_frequency. tags=['condition_candidate', 'measure', 'rare_pattern_candidate'] desc=Frequency feature for token 'project' in an email message. +- word_freq_re: role=feature, type=numeric_sparse_frequency. tags=['condition_candidate', 'measure', 'rare_pattern_candidate'] desc=Frequency feature for token 're' in an email message. +- word_freq_edu: role=feature, type=numeric_sparse_frequency. tags=['condition_candidate', 'measure', 'rare_pattern_candidate'] desc=Frequency feature for token 'edu' in an email message. +- word_freq_table: role=feature, type=numeric_sparse_frequency. tags=['condition_candidate', 'measure', 'rare_pattern_candidate'] desc=Frequency feature for token 'table' in an email message. +- word_freq_conference: role=feature, type=numeric_sparse_frequency. tags=['condition_candidate', 'measure', 'rare_pattern_candidate'] desc=Frequency feature for token 'conference' in an email message. +- char_freq_%3B: role=feature, type=numeric_sparse_frequency. tags=['condition_candidate', 'measure', 'rare_pattern_candidate'] desc=Character frequency feature for symbol ';'. +- char_freq_%28: role=feature, type=numeric_sparse_frequency. tags=['condition_candidate', 'measure', 'rare_pattern_candidate'] desc=Character frequency feature for symbol '('. +- char_freq_%5B: role=feature, type=numeric_sparse_frequency. tags=['condition_candidate', 'measure', 'rare_pattern_candidate'] desc=Character frequency feature for symbol '['. +- char_freq_%21: role=feature, type=numeric_sparse_frequency. tags=['condition_candidate', 'measure', 'rare_pattern_candidate'] desc=Character frequency feature for symbol '!'. +- char_freq_%24: role=feature, type=numeric_sparse_frequency. tags=['condition_candidate', 'measure', 'rare_pattern_candidate'] desc=Character frequency feature for symbol '$'. +- char_freq_%23: role=feature, type=numeric_sparse_frequency. tags=['condition_candidate', 'measure', 'rare_pattern_candidate'] desc=Character frequency feature for symbol '#'. +- capital_run_length_average: role=feature, type=numeric_heavy_tail. tags=['condition_candidate', 'measure', 'rare_pattern_candidate'] desc=Average length of uninterrupted capital-letter runs. +- capital_run_length_longest: role=feature, type=numeric_heavy_tail. tags=['condition_candidate', 'measure', 'rare_pattern_candidate'] desc=Longest uninterrupted capital-letter run. +- capital_run_length_total: role=feature, type=numeric_heavy_tail. tags=['condition_candidate', 'measure', 'rare_pattern_candidate'] desc=Total number of capital letters in runs. +- class: role=target, type=binary_target. tags=['target_candidate'] desc=Binary spam label (1=spam, 0=non-spam). +- useful_field_combinations: [['word_freq_free', 'word_freq_you', 'class'], ['char_freq_%21', 'capital_run_length_longest', 'class'], ['word_freq_remove', 'word_freq_money', 'class']] +- fields_requiring_caution: ['capital_run_length_average', 'capital_run_length_longest', 'capital_run_length_total'] +- source_url: https://www.openml.org/d/44 + +SQLite schema snapshot: +{ + "table_name": "n1", + "quoted_table_name": "\"n1\"", + "row_count": 4601, + "columns": [ + { + "name": "word_freq_make", + "type": "TEXT", + "notnull": false, + "pk": false + }, + { + "name": "word_freq_address", + "type": "TEXT", + "notnull": false, + "pk": false + }, + { + "name": "word_freq_all", + "type": "TEXT", + "notnull": false, + "pk": false + }, + { + "name": "word_freq_3d", + "type": "TEXT", + "notnull": false, + "pk": false + }, + { + "name": "word_freq_our", + "type": "TEXT", + "notnull": false, + "pk": false + }, + { + "name": "word_freq_over", + "type": "TEXT", + "notnull": false, + "pk": false + }, + { + "name": "word_freq_remove", + "type": "TEXT", + "notnull": false, + "pk": false + }, + { + "name": "word_freq_internet", + "type": "TEXT", + "notnull": false, + "pk": false + }, + { + "name": "word_freq_order", + "type": "TEXT", + "notnull": false, + "pk": false + }, + { + "name": "word_freq_mail", + "type": "TEXT", + "notnull": false, + "pk": false + }, + { + "name": "word_freq_receive", + "type": "TEXT", + "notnull": false, + "pk": false + }, + { + "name": "word_freq_will", + "type": "TEXT", + "notnull": false, + "pk": false + }, + { + "name": "word_freq_people", + "type": "TEXT", + "notnull": false, + "pk": false + }, + { + "name": "word_freq_report", + "type": "TEXT", + "notnull": false, + "pk": false + }, + { + "name": "word_freq_addresses", + "type": "TEXT", + "notnull": false, + "pk": false + }, + { + "name": "word_freq_free", + "type": "TEXT", + "notnull": false, + "pk": false + }, + { + "name": "word_freq_business", + "type": "TEXT", + "notnull": false, + "pk": false + }, + { + "name": "word_freq_email", + "type": "TEXT", + "notnull": false, + "pk": false + }, + { + "name": "word_freq_you", + "type": "TEXT", + "notnull": false, + "pk": false + }, + { + "name": "word_freq_credit", + "type": "TEXT", + "notnull": false, + "pk": false + }, + { + "name": "word_freq_your", + "type": "TEXT", + "notnull": false, + "pk": false + }, + { + "name": "word_freq_font", + "type": "TEXT", + "notnull": false, + "pk": false + }, + { + "name": "word_freq_000", + "type": "TEXT", + "notnull": false, + "pk": false + }, + { + "name": "word_freq_money", + "type": "TEXT", + "notnull": false, + "pk": false + }, + { + "name": "word_freq_hp", + "type": "TEXT", + "notnull": false, + "pk": false + }, + { + "name": "word_freq_hpl", + "type": "TEXT", + "notnull": false, + "pk": false + }, + { + "name": "word_freq_george", + "type": "TEXT", + "notnull": false, + "pk": false + }, + { + "name": "word_freq_650", + "type": "TEXT", + "notnull": false, + "pk": false + }, + { + "name": "word_freq_lab", + "type": "TEXT", + "notnull": false, + "pk": false + }, + { + "name": "word_freq_labs", + "type": "TEXT", + "notnull": false, + "pk": false + }, + { + "name": "word_freq_telnet", + "type": "TEXT", + "notnull": false, + "pk": false + }, + { + "name": "word_freq_857", + "type": "TEXT", + "notnull": false, + "pk": false + }, + { + "name": "word_freq_data", + "type": "TEXT", + "notnull": false, + "pk": false + }, + { + "name": "word_freq_415", + "type": "TEXT", + "notnull": false, + "pk": false + }, + { + "name": "word_freq_85", + "type": "TEXT", + "notnull": false, + "pk": false + }, + { + "name": "word_freq_technology", + "type": "TEXT", + "notnull": false, + "pk": false + }, + { + "name": "word_freq_1999", + "type": "TEXT", + "notnull": false, + "pk": false + }, + { + "name": "word_freq_parts", + "type": "TEXT", + "notnull": false, + "pk": false + }, + { + "name": "word_freq_pm", + "type": "TEXT", + "notnull": false, + "pk": false + }, + { + "name": "word_freq_direct", + "type": "TEXT", + "notnull": false, + "pk": false + }, + { + "name": "word_freq_cs", + "type": "TEXT", + "notnull": false, + "pk": false + }, + { + "name": "word_freq_meeting", + "type": "TEXT", + "notnull": false, + "pk": false + }, + { + "name": "word_freq_original", + "type": "TEXT", + "notnull": false, + "pk": false + }, + { + "name": "word_freq_project", + "type": "TEXT", + "notnull": false, + "pk": false + }, + { + "name": "word_freq_re", + "type": "TEXT", + "notnull": false, + "pk": false + }, + { + "name": "word_freq_edu", + "type": "TEXT", + "notnull": false, + "pk": false + }, + { + "name": "word_freq_table", + "type": "TEXT", + "notnull": false, + "pk": false + }, + { + "name": "word_freq_conference", + "type": "TEXT", + "notnull": false, + "pk": false + }, + { + "name": "char_freq_%3B", + "type": "TEXT", + "notnull": false, + "pk": false + }, + { + "name": "char_freq_%28", + "type": "TEXT", + "notnull": false, + "pk": false + }, + { + "name": "char_freq_%5B", + "type": "TEXT", + "notnull": false, + "pk": false + }, + { + "name": "char_freq_%21", + "type": "TEXT", + "notnull": false, + "pk": false + }, + { + "name": "char_freq_%24", + "type": "TEXT", + "notnull": false, + "pk": false + }, + { + "name": "char_freq_%23", + "type": "TEXT", + "notnull": false, + "pk": false + }, + { + "name": "capital_run_length_average", + "type": "TEXT", + "notnull": false, + "pk": false + }, + { + "name": "capital_run_length_longest", + "type": "TEXT", + "notnull": false, + "pk": false + }, + { + "name": "capital_run_length_total", + "type": "TEXT", + "notnull": false, + "pk": false + }, + { + "name": "class", + "type": "TEXT", + "notnull": false, + "pk": false + } + ], + "sample_rows": [ + { + "word_freq_make": "0", + "word_freq_address": "0.64", + "word_freq_all": "0.64", + "word_freq_3d": "0", + "word_freq_our": "0.32", + "word_freq_over": "0", + "word_freq_remove": "0", + "word_freq_internet": "0", + "word_freq_order": "0", + "word_freq_mail": "0", + "word_freq_receive": "0", + "word_freq_will": "0.64", + "word_freq_people": "0", + "word_freq_report": "0", + "word_freq_addresses": "0", + "word_freq_free": "0.32", + "word_freq_business": "0", + "word_freq_email": "1.29", + "word_freq_you": "1.93", + "word_freq_credit": "0", + "word_freq_your": "0.96", + "word_freq_font": "0", + "word_freq_000": "0", + "word_freq_money": "0", + "word_freq_hp": "0", + "word_freq_hpl": "0", + "word_freq_george": "0", + "word_freq_650": "0", + "word_freq_lab": "0", + "word_freq_labs": "0", + "word_freq_telnet": "0", + "word_freq_857": "0", + "word_freq_data": "0", + "word_freq_415": "0", + "word_freq_85": "0", + "word_freq_technology": "0", + "word_freq_1999": "0", + "word_freq_parts": "0", + "word_freq_pm": "0", + "word_freq_direct": "0", + "word_freq_cs": "0", + "word_freq_meeting": "0", + "word_freq_original": "0", + "word_freq_project": "0", + "word_freq_re": "0", + "word_freq_edu": "0", + "word_freq_table": "0", + "word_freq_conference": "0", + "char_freq_%3B": "0", + "char_freq_%28": "0", + "char_freq_%5B": "0", + "char_freq_%21": "0.778", + "char_freq_%24": "0", + "char_freq_%23": "0", + "capital_run_length_average": "3.756", + "capital_run_length_longest": "61", + "capital_run_length_total": "278", + "class": "1" + }, + { + "word_freq_make": "0.21", + "word_freq_address": "0.28", + "word_freq_all": "0.5", + "word_freq_3d": "0", + "word_freq_our": "0.14", + "word_freq_over": "0.28", + "word_freq_remove": "0.21", + "word_freq_internet": "0.07", + "word_freq_order": "0", + "word_freq_mail": "0.94", + "word_freq_receive": "0.21", + "word_freq_will": "0.79", + "word_freq_people": "0.65", + "word_freq_report": "0.21", + "word_freq_addresses": "0.14", + "word_freq_free": "0.14", + "word_freq_business": "0.07", + "word_freq_email": "0.28", + "word_freq_you": "3.47", + "word_freq_credit": "0", + "word_freq_your": "1.59", + "word_freq_font": "0", + "word_freq_000": "0.43", + "word_freq_money": "0.43", + "word_freq_hp": "0", + "word_freq_hpl": "0", + "word_freq_george": "0", + "word_freq_650": "0", + "word_freq_lab": "0", + "word_freq_labs": "0", + "word_freq_telnet": "0", + "word_freq_857": "0", + "word_freq_data": "0", + "word_freq_415": "0", + "word_freq_85": "0", + "word_freq_technology": "0", + "word_freq_1999": "0.07", + "word_freq_parts": "0", + "word_freq_pm": "0", + "word_freq_direct": "0", + "word_freq_cs": "0", + "word_freq_meeting": "0", + "word_freq_original": "0", + "word_freq_project": "0", + "word_freq_re": "0", + "word_freq_edu": "0", + "word_freq_table": "0", + "word_freq_conference": "0", + "char_freq_%3B": "0", + "char_freq_%28": "0.132", + "char_freq_%5B": "0", + "char_freq_%21": "0.372", + "char_freq_%24": "0.18", + "char_freq_%23": "0.048", + "capital_run_length_average": "5.114", + "capital_run_length_longest": "101", + "capital_run_length_total": "1028", + "class": "1" + }, + { + "word_freq_make": "0.06", + "word_freq_address": "0", + "word_freq_all": "0.71", + "word_freq_3d": "0", + "word_freq_our": "1.23", + "word_freq_over": "0.19", + "word_freq_remove": "0.19", + "word_freq_internet": "0.12", + "word_freq_order": "0.64", + "word_freq_mail": "0.25", + "word_freq_receive": "0.38", + "word_freq_will": "0.45", + "word_freq_people": "0.12", + "word_freq_report": "0", + "word_freq_addresses": "1.75", + "word_freq_free": "0.06", + "word_freq_business": "0.06", + "word_freq_email": "1.03", + "word_freq_you": "1.36", + "word_freq_credit": "0.32", + "word_freq_your": "0.51", + "word_freq_font": "0", + "word_freq_000": "1.16", + "word_freq_money": "0.06", + "word_freq_hp": "0", + "word_freq_hpl": "0", + "word_freq_george": "0", + "word_freq_650": "0", + "word_freq_lab": "0", + "word_freq_labs": "0", + "word_freq_telnet": "0", + "word_freq_857": "0", + "word_freq_data": "0", + "word_freq_415": "0", + "word_freq_85": "0", + "word_freq_technology": "0", + "word_freq_1999": "0", + "word_freq_parts": "0", + "word_freq_pm": "0", + "word_freq_direct": "0.06", + "word_freq_cs": "0", + "word_freq_meeting": "0", + "word_freq_original": "0.12", + "word_freq_project": "0", + "word_freq_re": "0.06", + "word_freq_edu": "0.06", + "word_freq_table": "0", + "word_freq_conference": "0", + "char_freq_%3B": "0.01", + "char_freq_%28": "0.143", + "char_freq_%5B": "0", + "char_freq_%21": "0.276", + "char_freq_%24": "0.184", + "char_freq_%23": "0.01", + "capital_run_length_average": "9.821", + "capital_run_length_longest": "485", + "capital_run_length_total": "2259", + "class": "1" + }, + { + "word_freq_make": "0", + "word_freq_address": "0", + "word_freq_all": "0", + "word_freq_3d": "0", + "word_freq_our": "0.63", + "word_freq_over": "0", + "word_freq_remove": "0.31", + "word_freq_internet": "0.63", + "word_freq_order": "0.31", + "word_freq_mail": "0.63", + "word_freq_receive": "0.31", + "word_freq_will": "0.31", + "word_freq_people": "0.31", + "word_freq_report": "0", + "word_freq_addresses": "0", + "word_freq_free": "0.31", + "word_freq_business": "0", + "word_freq_email": "0", + "word_freq_you": "3.18", + "word_freq_credit": "0", + "word_freq_your": "0.31", + "word_freq_font": "0", + "word_freq_000": "0", + "word_freq_money": "0", + "word_freq_hp": "0", + "word_freq_hpl": "0", + "word_freq_george": "0", + "word_freq_650": "0", + "word_freq_lab": "0", + "word_freq_labs": "0", + "word_freq_telnet": "0", + "word_freq_857": "0", + "word_freq_data": "0", + "word_freq_415": "0", + "word_freq_85": "0", + "word_freq_technology": "0", + "word_freq_1999": "0", + "word_freq_parts": "0", + "word_freq_pm": "0", + "word_freq_direct": "0", + "word_freq_cs": "0", + "word_freq_meeting": "0", + "word_freq_original": "0", + "word_freq_project": "0", + "word_freq_re": "0", + "word_freq_edu": "0", + "word_freq_table": "0", + "word_freq_conference": "0", + "char_freq_%3B": "0", + "char_freq_%28": "0.137", + "char_freq_%5B": "0", + "char_freq_%21": "0.137", + "char_freq_%24": "0", + "char_freq_%23": "0", + "capital_run_length_average": "3.537", + "capital_run_length_longest": "40", + "capital_run_length_total": "191", + "class": "1" + }, + { + "word_freq_make": "0", + "word_freq_address": "0", + "word_freq_all": "0", + "word_freq_3d": "0", + "word_freq_our": "0.63", + "word_freq_over": "0", + "word_freq_remove": "0.31", + "word_freq_internet": "0.63", + "word_freq_order": "0.31", + "word_freq_mail": "0.63", + "word_freq_receive": "0.31", + "word_freq_will": "0.31", + "word_freq_people": "0.31", + "word_freq_report": "0", + "word_freq_addresses": "0", + "word_freq_free": "0.31", + "word_freq_business": "0", + "word_freq_email": "0", + "word_freq_you": "3.18", + "word_freq_credit": "0", + "word_freq_your": "0.31", + "word_freq_font": "0", + "word_freq_000": "0", + "word_freq_money": "0", + "word_freq_hp": "0", + "word_freq_hpl": "0", + "word_freq_george": "0", + "word_freq_650": "0", + "word_freq_lab": "0", + "word_freq_labs": "0", + "word_freq_telnet": "0", + "word_freq_857": "0", + "word_freq_data": "0", + "word_freq_415": "0", + "word_freq_85": "0", + "word_freq_technology": "0", + "word_freq_1999": "0", + "word_freq_parts": "0", + "word_freq_pm": "0", + "word_freq_direct": "0", + "word_freq_cs": "0", + "word_freq_meeting": "0", + "word_freq_original": "0", + "word_freq_project": "0", + "word_freq_re": "0", + "word_freq_edu": "0", + "word_freq_table": "0", + "word_freq_conference": "0", + "char_freq_%3B": "0", + "char_freq_%28": "0.135", + "char_freq_%5B": "0", + "char_freq_%21": "0.135", + "char_freq_%24": "0", + "char_freq_%23": "0", + "capital_run_length_average": "3.537", + "capital_run_length_longest": "40", + "capital_run_length_total": "191", + "class": "1" + } + ] +} + +Shortlisted templates: +[ + { + "template_id": "tpl_h2o_group_sum", + "template_name": "Grouped Numeric Sum", + "primary_family": "subgroup_structure", + "portability": "partial", + "sql_skeleton": "SELECT {group_col}, SUM({measure_col}) AS total_measure\nFROM {table}\nGROUP BY {group_col}\nORDER BY total_measure DESC;", + "required_roles": [ + "group_col", + "measure_col" + ] + } +] + +Problem instance: +{ + "dataset_id": "n1", + "question": "Use template Grouped Numeric Sum to probe internal_profile_stability with semantic role collapsed_target_view. Focus on group_col=class, measure_col=word_freq_3d.", + "planned_template_id": "tpl_h2o_group_sum", + "bindings": { + "group_col": "class", + "measure_col": "word_freq_3d", + "top_k": 18, + "top_n": 7, + "num_tiles": 10, + "percentile_value": 0.95, + "z_threshold": 2.0, + "fraction_threshold": 0.05, + "baseline_multiplier": 1.75, + "baseline_fraction": 0.1, + "min_group_size": 5, + "min_support": 4, + "measure_threshold": 0.0, + "time_grain": "month", + "lookback_rows": 3, + "current_period_start": "'2024-01-01'", + "current_period_end": "'2024-04-01'", + "previous_period_start": "'2023-10-01'", + "previous_period_end": "'2024-01-01'", + "drift_ratio_threshold": 0.8 + }, + "can_vary": [], + "must_fix": [], + "runtime_sql_skeleton": "SELECT {group_col}, SUM({measure_col}) AS total_measure\nFROM {table}\nGROUP BY {group_col}\nORDER BY total_measure DESC;" +} + +Repair context: +{} diff --git a/Query/sql/v2/runs/v2_cli_20260502_081223_d/n1/artifacts/v2q_n1_12e9d7ee8ce2ca10/cli/sql_response_attempt_1.raw.txt b/Query/sql/v2/runs/v2_cli_20260502_081223_d/n1/artifacts/v2q_n1_12e9d7ee8ce2ca10/cli/sql_response_attempt_1.raw.txt new file mode 100644 index 0000000000000000000000000000000000000000..8f0b137b23a5659aa0aef0a06d4fa9c3d2470e90 --- /dev/null +++ b/Query/sql/v2/runs/v2_cli_20260502_081223_d/n1/artifacts/v2q_n1_12e9d7ee8ce2ca10/cli/sql_response_attempt_1.raw.txt @@ -0,0 +1,4 @@ +{"type":"thread.started","thread_id":"019e40db-23ee-7730-a2fc-5dd3add4446c"} +{"type":"turn.started"} +{"type":"item.completed","item":{"id":"item_0","type":"agent_message","text":"{\"sql\":\"-- template_id: tpl_h2o_group_sum\\nSELECT \\\"class\\\", SUM(CAST(\\\"word_freq_3d\\\" AS REAL)) AS total_measure\\nFROM \\\"n1\\\"\\nGROUP BY \\\"class\\\"\\nORDER BY total_measure DESC;\",\"notes\":\"Applied the grouped numeric sum template with group_col=\\\"class\\\" and measure_col=\\\"word_freq_3d\\\". CAST to REAL is used because the schema stores feature columns as TEXT.\"}"}} +{"type":"turn.completed","usage":{"input_tokens":20319,"cached_input_tokens":12032,"output_tokens":303,"reasoning_output_tokens":203}} diff --git a/Query/sql/v2/runs/v2_cli_20260502_081223_d/n1/artifacts/v2q_n1_12e9d7ee8ce2ca10/cli/sql_response_attempt_1.txt b/Query/sql/v2/runs/v2_cli_20260502_081223_d/n1/artifacts/v2q_n1_12e9d7ee8ce2ca10/cli/sql_response_attempt_1.txt new file mode 100644 index 0000000000000000000000000000000000000000..15a41e5830087e158f2a03b6472d0642fc5bc177 --- /dev/null +++ b/Query/sql/v2/runs/v2_cli_20260502_081223_d/n1/artifacts/v2q_n1_12e9d7ee8ce2ca10/cli/sql_response_attempt_1.txt @@ -0,0 +1 @@ +{"sql":"-- template_id: tpl_h2o_group_sum\nSELECT \"class\", SUM(CAST(\"word_freq_3d\" AS REAL)) AS total_measure\nFROM \"n1\"\nGROUP BY \"class\"\nORDER BY total_measure DESC;","notes":"Applied the grouped numeric sum template with group_col=\"class\" and measure_col=\"word_freq_3d\". CAST to REAL is used because the schema stores feature columns as TEXT."} \ No newline at end of file diff --git a/Query/sql/v2/runs/v2_cli_20260502_081223_d/n1/artifacts/v2q_n1_12e9d7ee8ce2ca10/cli/sql_stderr_attempt_1.txt b/Query/sql/v2/runs/v2_cli_20260502_081223_d/n1/artifacts/v2q_n1_12e9d7ee8ce2ca10/cli/sql_stderr_attempt_1.txt new file mode 100644 index 0000000000000000000000000000000000000000..e69de29bb2d1d6434b8b29ae775ad8c2e48c5391 diff --git a/Query/sql/v2/runs/v2_cli_20260502_081223_d/n1/artifacts/v2q_n1_159c7dd81ca8414b/cli/conversation.jsonl b/Query/sql/v2/runs/v2_cli_20260502_081223_d/n1/artifacts/v2q_n1_159c7dd81ca8414b/cli/conversation.jsonl new file mode 100644 index 0000000000000000000000000000000000000000..61d983174cf950499c838bc6be087cfce8e37782 --- /dev/null +++ b/Query/sql/v2/runs/v2_cli_20260502_081223_d/n1/artifacts/v2q_n1_159c7dd81ca8414b/cli/conversation.jsonl @@ -0,0 +1,2 @@ +{"attempt": 1, "phase": "sql_generation", "role": "user", "content_path": "cli/sql_prompt_attempt_1.txt", "metrics": {"chars": 29771, "bytes_utf8": 29771, "lines": 794, "estimated_tokens": null}} +{"attempt": 1, "phase": "sql_generation", "role": "assistant", "content_path": "cli/sql_response_attempt_1.txt", "raw_content_path": "cli/sql_response_attempt_1.raw.txt", "stderr_path": "cli/sql_stderr_attempt_1.txt", "metrics": {"chars": 627, "bytes_utf8": 627, "lines": 1, "estimated_tokens": null}, "usage": {"input_tokens": 20437, "cached_input_tokens": 19840, "output_tokens": 1096, "reasoning_output_tokens": 913}} diff --git a/Query/sql/v2/runs/v2_cli_20260502_081223_d/n1/artifacts/v2q_n1_159c7dd81ca8414b/cli/session_summary.json b/Query/sql/v2/runs/v2_cli_20260502_081223_d/n1/artifacts/v2q_n1_159c7dd81ca8414b/cli/session_summary.json new file mode 100644 index 0000000000000000000000000000000000000000..af19043c192901a80c4e23e427dc9ed70671d1f7 --- /dev/null +++ b/Query/sql/v2/runs/v2_cli_20260502_081223_d/n1/artifacts/v2q_n1_159c7dd81ca8414b/cli/session_summary.json @@ -0,0 +1,25 @@ +{ + "engine": "v2-cli:codex", + "command": "codex exec --skip-git-repo-check --disable plugins --sandbox read-only --cd \"/data/jialinzhang/SQLagent\" -m gpt-5.4 --json -", + "ai_cli_calls": 1, + "usage_summary": { + "dataset_id": "n1", + "model": "v2-cli:codex", + "run_id": "v2q_n1_159c7dd81ca8414b", + "api_calls": 0, + "input_tokens": 20437, + "cached_input_tokens": 19840, + "output_tokens": 1096, + "total_tokens": 21533, + "cost_usd": 0.0, + "ai_cli_calls": 1, + "estimated_input_tokens": 0, + "estimated_output_tokens": 0, + "estimated_total_tokens": 0, + "usage_source": "ai_cli_json_usage", + "cli_elapsed_ms_total": 22282.33, + "sql_execution_elapsed_ms_total": 9.11, + "conversation_log_path": "/data/jialinzhang/TabQueryBench/sql_workloads/v2_current/runs_and_launches/runs/v2_cli_20260502_081223_d/n1/artifacts/v2q_n1_159c7dd81ca8414b/cli/conversation.jsonl", + "note": "Executed through a local AI CLI with structured usage metadata." + } +} \ No newline at end of file diff --git a/Query/sql/v2/runs/v2_cli_20260502_081223_d/n1/artifacts/v2q_n1_159c7dd81ca8414b/cli/sql_attempt_1.metadata.json b/Query/sql/v2/runs/v2_cli_20260502_081223_d/n1/artifacts/v2q_n1_159c7dd81ca8414b/cli/sql_attempt_1.metadata.json new file mode 100644 index 0000000000000000000000000000000000000000..7c94105203b3252b12e6f61e88768344d1bec013 --- /dev/null +++ b/Query/sql/v2/runs/v2_cli_20260502_081223_d/n1/artifacts/v2q_n1_159c7dd81ca8414b/cli/sql_attempt_1.metadata.json @@ -0,0 +1,45 @@ +{ + "attempt": 1, + "phase": "sql_generation", + "command": "codex exec --skip-git-repo-check --disable plugins --sandbox read-only --cd \"/data/jialinzhang/SQLagent\" -m gpt-5.4 --json -", + "started_at": "2026-05-19T15:35:35.031830+00:00", + "ended_at": "2026-05-19T15:35:57.314207+00:00", + "elapsed_ms": 22282.33, + "prompt_metrics": { + "chars": 29771, + "bytes_utf8": 29771, + "lines": 794, + "estimated_tokens": null + }, + "stdout_metrics": { + "chars": 1022, + "bytes_utf8": 1022, + "lines": 4, + "estimated_tokens": null + }, + "stderr_metrics": { + "chars": 0, + "bytes_utf8": 0, + "lines": 0, + "estimated_tokens": null + }, + "parsed_output": { + "format": "jsonl_events", + "text_metrics": { + "chars": 627, + "bytes_utf8": 627, + "lines": 1, + "estimated_tokens": null + }, + "usage": { + "input_tokens": 20437, + "cached_input_tokens": 19840, + "output_tokens": 1096, + "reasoning_output_tokens": 913 + } + }, + "prompt_path": "cli/sql_prompt_attempt_1.txt", + "response_path": "cli/sql_response_attempt_1.txt", + "raw_response_path": "cli/sql_response_attempt_1.raw.txt", + "stderr_path": "cli/sql_stderr_attempt_1.txt" +} \ No newline at end of file diff --git a/Query/sql/v2/runs/v2_cli_20260502_081223_d/n1/artifacts/v2q_n1_159c7dd81ca8414b/cli/sql_prompt_attempt_1.txt b/Query/sql/v2/runs/v2_cli_20260502_081223_d/n1/artifacts/v2q_n1_159c7dd81ca8414b/cli/sql_prompt_attempt_1.txt new file mode 100644 index 0000000000000000000000000000000000000000..1616cd9d8ccf87c793d3b856eac3355356b6ea2e --- /dev/null +++ b/Query/sql/v2/runs/v2_cli_20260502_081223_d/n1/artifacts/v2q_n1_159c7dd81ca8414b/cli/sql_prompt_attempt_1.txt @@ -0,0 +1,794 @@ +You are generating one SQLite SELECT query for a single-table SQL QA task. +Return strict JSON only, with this schema: {"sql": "...", "notes": "..."}. +Rules: +- Use only the provided table and columns. +- Do not write INSERT, UPDATE, DELETE, DROP, ALTER, CREATE, PRAGMA, ATTACH, DETACH, or VACUUM. +- Prefer the planned template and bound roles when provided. +- Add a leading SQL comment exactly like: -- template_id: . +- Generate SQLite-compatible SQL. SQLite does not support PERCENTILE_CONT or STDDEV. +- Quote identifiers with double quotes. +- Return no markdown and no extra prose. + +Dataset context: +Dataset context for SQL QA: +- dataset_id: n1 +- dataset_name: Spambase +- table_name: n1 +- table_layout: single-table dataset (do not assume joins). +- row_semantics: One row is one email represented by engineered token/character frequency and capitalization-run features. +- task_type: classification +- target_column: class +- main_row_count: 4601 +- important_fields: +- word_freq_make: role=feature, type=numeric_sparse_frequency. tags=['condition_candidate', 'measure', 'rare_pattern_candidate'] desc=Frequency feature for token 'make' in an email message. +- word_freq_address: role=feature, type=numeric_sparse_frequency. tags=['condition_candidate', 'measure', 'rare_pattern_candidate'] desc=Frequency feature for token 'address' in an email message. +- word_freq_all: role=feature, type=numeric_sparse_frequency. tags=['condition_candidate', 'measure', 'rare_pattern_candidate'] desc=Frequency feature for token 'all' in an email message. +- word_freq_3d: role=feature, type=numeric_sparse_frequency. tags=['condition_candidate', 'measure', 'rare_pattern_candidate'] desc=Frequency feature for token '3d' in an email message. +- word_freq_our: role=feature, type=numeric_sparse_frequency. tags=['condition_candidate', 'measure', 'rare_pattern_candidate'] desc=Frequency feature for token 'our' in an email message. +- word_freq_over: role=feature, type=numeric_sparse_frequency. tags=['condition_candidate', 'measure', 'rare_pattern_candidate'] desc=Frequency feature for token 'over' in an email message. +- word_freq_remove: role=feature, type=numeric_sparse_frequency. tags=['condition_candidate', 'measure', 'rare_pattern_candidate'] desc=Frequency feature for token 'remove' in an email message. +- word_freq_internet: role=feature, type=numeric_sparse_frequency. tags=['condition_candidate', 'measure', 'rare_pattern_candidate'] desc=Frequency feature for token 'internet' in an email message. +- word_freq_order: role=feature, type=numeric_sparse_frequency. tags=['condition_candidate', 'measure', 'rare_pattern_candidate'] desc=Frequency feature for token 'order' in an email message. +- word_freq_mail: role=feature, type=numeric_sparse_frequency. tags=['condition_candidate', 'measure', 'rare_pattern_candidate'] desc=Frequency feature for token 'mail' in an email message. +- word_freq_receive: role=feature, type=numeric_sparse_frequency. tags=['condition_candidate', 'measure', 'rare_pattern_candidate'] desc=Frequency feature for token 'receive' in an email message. +- word_freq_will: role=feature, type=numeric_sparse_frequency. tags=['condition_candidate', 'measure', 'rare_pattern_candidate'] desc=Frequency feature for token 'will' in an email message. +- word_freq_people: role=feature, type=numeric_sparse_frequency. tags=['condition_candidate', 'measure', 'rare_pattern_candidate'] desc=Frequency feature for token 'people' in an email message. +- word_freq_report: role=feature, type=numeric_sparse_frequency. tags=['condition_candidate', 'measure', 'rare_pattern_candidate'] desc=Frequency feature for token 'report' in an email message. +- word_freq_addresses: role=feature, type=numeric_sparse_frequency. tags=['condition_candidate', 'measure', 'rare_pattern_candidate'] desc=Frequency feature for token 'addresses' in an email message. +- word_freq_free: role=feature, type=numeric_sparse_frequency. tags=['condition_candidate', 'measure', 'rare_pattern_candidate'] desc=Frequency feature for token 'free' in an email message. +- word_freq_business: role=feature, type=numeric_sparse_frequency. tags=['condition_candidate', 'measure', 'rare_pattern_candidate'] desc=Frequency feature for token 'business' in an email message. +- word_freq_email: role=feature, type=numeric_sparse_frequency. tags=['condition_candidate', 'measure', 'rare_pattern_candidate'] desc=Frequency feature for token 'email' in an email message. +- word_freq_you: role=feature, type=numeric_sparse_frequency. tags=['condition_candidate', 'measure', 'rare_pattern_candidate'] desc=Frequency feature for token 'you' in an email message. +- word_freq_credit: role=feature, type=numeric_sparse_frequency. tags=['condition_candidate', 'measure', 'rare_pattern_candidate'] desc=Frequency feature for token 'credit' in an email message. +- word_freq_your: role=feature, type=numeric_sparse_frequency. tags=['condition_candidate', 'measure', 'rare_pattern_candidate'] desc=Frequency feature for token 'your' in an email message. +- word_freq_font: role=feature, type=numeric_sparse_frequency. tags=['condition_candidate', 'measure', 'rare_pattern_candidate'] desc=Frequency feature for token 'font' in an email message. +- word_freq_000: role=feature, type=numeric_sparse_frequency. tags=['condition_candidate', 'measure', 'rare_pattern_candidate'] desc=Frequency feature for token '000' in an email message. +- word_freq_money: role=feature, type=numeric_sparse_frequency. tags=['condition_candidate', 'measure', 'rare_pattern_candidate'] desc=Frequency feature for token 'money' in an email message. +- word_freq_hp: role=feature, type=numeric_sparse_frequency. tags=['condition_candidate', 'measure', 'rare_pattern_candidate'] desc=Frequency feature for token 'hp' in an email message. +- word_freq_hpl: role=feature, type=numeric_sparse_frequency. tags=['condition_candidate', 'measure', 'rare_pattern_candidate'] desc=Frequency feature for token 'hpl' in an email message. +- word_freq_george: role=feature, type=numeric_sparse_frequency. tags=['condition_candidate', 'measure', 'rare_pattern_candidate'] desc=Frequency feature for token 'george' in an email message. +- word_freq_650: role=feature, type=numeric_sparse_frequency. tags=['condition_candidate', 'measure', 'rare_pattern_candidate'] desc=Frequency feature for token '650' in an email message. +- word_freq_lab: role=feature, type=numeric_sparse_frequency. tags=['condition_candidate', 'measure', 'rare_pattern_candidate'] desc=Frequency feature for token 'lab' in an email message. +- word_freq_labs: role=feature, type=numeric_sparse_frequency. tags=['condition_candidate', 'measure', 'rare_pattern_candidate'] desc=Frequency feature for token 'labs' in an email message. +- word_freq_telnet: role=feature, type=numeric_sparse_frequency. tags=['condition_candidate', 'measure', 'rare_pattern_candidate'] desc=Frequency feature for token 'telnet' in an email message. +- word_freq_857: role=feature, type=numeric_sparse_frequency. tags=['condition_candidate', 'measure', 'rare_pattern_candidate'] desc=Frequency feature for token '857' in an email message. +- word_freq_data: role=feature, type=numeric_sparse_frequency. tags=['condition_candidate', 'measure', 'rare_pattern_candidate'] desc=Frequency feature for token 'data' in an email message. +- word_freq_415: role=feature, type=numeric_sparse_frequency. tags=['condition_candidate', 'measure', 'rare_pattern_candidate'] desc=Frequency feature for token '415' in an email message. +- word_freq_85: role=feature, type=numeric_sparse_frequency. tags=['condition_candidate', 'measure', 'rare_pattern_candidate'] desc=Frequency feature for token '85' in an email message. +- word_freq_technology: role=feature, type=numeric_sparse_frequency. tags=['condition_candidate', 'measure', 'rare_pattern_candidate'] desc=Frequency feature for token 'technology' in an email message. +- word_freq_1999: role=feature, type=numeric_sparse_frequency. tags=['condition_candidate', 'measure', 'rare_pattern_candidate'] desc=Frequency feature for token '1999' in an email message. +- word_freq_parts: role=feature, type=numeric_sparse_frequency. tags=['condition_candidate', 'measure', 'rare_pattern_candidate'] desc=Frequency feature for token 'parts' in an email message. +- word_freq_pm: role=feature, type=numeric_sparse_frequency. tags=['condition_candidate', 'measure', 'rare_pattern_candidate'] desc=Frequency feature for token 'pm' in an email message. +- word_freq_direct: role=feature, type=numeric_sparse_frequency. tags=['condition_candidate', 'measure', 'rare_pattern_candidate'] desc=Frequency feature for token 'direct' in an email message. +- word_freq_cs: role=feature, type=numeric_sparse_frequency. tags=['condition_candidate', 'measure', 'rare_pattern_candidate'] desc=Frequency feature for token 'cs' in an email message. +- word_freq_meeting: role=feature, type=numeric_sparse_frequency. tags=['condition_candidate', 'measure', 'rare_pattern_candidate'] desc=Frequency feature for token 'meeting' in an email message. +- word_freq_original: role=feature, type=numeric_sparse_frequency. tags=['condition_candidate', 'measure', 'rare_pattern_candidate'] desc=Frequency feature for token 'original' in an email message. +- word_freq_project: role=feature, type=numeric_sparse_frequency. tags=['condition_candidate', 'measure', 'rare_pattern_candidate'] desc=Frequency feature for token 'project' in an email message. +- word_freq_re: role=feature, type=numeric_sparse_frequency. tags=['condition_candidate', 'measure', 'rare_pattern_candidate'] desc=Frequency feature for token 're' in an email message. +- word_freq_edu: role=feature, type=numeric_sparse_frequency. tags=['condition_candidate', 'measure', 'rare_pattern_candidate'] desc=Frequency feature for token 'edu' in an email message. +- word_freq_table: role=feature, type=numeric_sparse_frequency. tags=['condition_candidate', 'measure', 'rare_pattern_candidate'] desc=Frequency feature for token 'table' in an email message. +- word_freq_conference: role=feature, type=numeric_sparse_frequency. tags=['condition_candidate', 'measure', 'rare_pattern_candidate'] desc=Frequency feature for token 'conference' in an email message. +- char_freq_%3B: role=feature, type=numeric_sparse_frequency. tags=['condition_candidate', 'measure', 'rare_pattern_candidate'] desc=Character frequency feature for symbol ';'. +- char_freq_%28: role=feature, type=numeric_sparse_frequency. tags=['condition_candidate', 'measure', 'rare_pattern_candidate'] desc=Character frequency feature for symbol '('. +- char_freq_%5B: role=feature, type=numeric_sparse_frequency. tags=['condition_candidate', 'measure', 'rare_pattern_candidate'] desc=Character frequency feature for symbol '['. +- char_freq_%21: role=feature, type=numeric_sparse_frequency. tags=['condition_candidate', 'measure', 'rare_pattern_candidate'] desc=Character frequency feature for symbol '!'. +- char_freq_%24: role=feature, type=numeric_sparse_frequency. tags=['condition_candidate', 'measure', 'rare_pattern_candidate'] desc=Character frequency feature for symbol '$'. +- char_freq_%23: role=feature, type=numeric_sparse_frequency. tags=['condition_candidate', 'measure', 'rare_pattern_candidate'] desc=Character frequency feature for symbol '#'. +- capital_run_length_average: role=feature, type=numeric_heavy_tail. tags=['condition_candidate', 'measure', 'rare_pattern_candidate'] desc=Average length of uninterrupted capital-letter runs. +- capital_run_length_longest: role=feature, type=numeric_heavy_tail. tags=['condition_candidate', 'measure', 'rare_pattern_candidate'] desc=Longest uninterrupted capital-letter run. +- capital_run_length_total: role=feature, type=numeric_heavy_tail. tags=['condition_candidate', 'measure', 'rare_pattern_candidate'] desc=Total number of capital letters in runs. +- class: role=target, type=binary_target. tags=['target_candidate'] desc=Binary spam label (1=spam, 0=non-spam). +- useful_field_combinations: [['word_freq_free', 'word_freq_you', 'class'], ['char_freq_%21', 'capital_run_length_longest', 'class'], ['word_freq_remove', 'word_freq_money', 'class']] +- fields_requiring_caution: ['capital_run_length_average', 'capital_run_length_longest', 'capital_run_length_total'] +- source_url: https://www.openml.org/d/44 + +SQLite schema snapshot: +{ + "table_name": "n1", + "quoted_table_name": "\"n1\"", + "row_count": 4601, + "columns": [ + { + "name": "word_freq_make", + "type": "TEXT", + "notnull": false, + "pk": false + }, + { + "name": "word_freq_address", + "type": "TEXT", + "notnull": false, + "pk": false + }, + { + "name": "word_freq_all", + "type": "TEXT", + "notnull": false, + "pk": false + }, + { + "name": "word_freq_3d", + "type": "TEXT", + "notnull": false, + "pk": false + }, + { + "name": "word_freq_our", + "type": "TEXT", + "notnull": false, + "pk": false + }, + { + "name": "word_freq_over", + "type": "TEXT", + "notnull": false, + "pk": false + }, + { + "name": "word_freq_remove", + "type": "TEXT", + "notnull": false, + "pk": false + }, + { + "name": "word_freq_internet", + "type": "TEXT", + "notnull": false, + "pk": false + }, + { + "name": "word_freq_order", + "type": "TEXT", + "notnull": false, + "pk": false + }, + { + "name": "word_freq_mail", + "type": "TEXT", + "notnull": false, + "pk": false + }, + { + "name": "word_freq_receive", + "type": "TEXT", + "notnull": false, + "pk": false + }, + { + "name": "word_freq_will", + "type": "TEXT", + "notnull": false, + "pk": false + }, + { + "name": "word_freq_people", + "type": "TEXT", + "notnull": false, + "pk": false + }, + { + "name": "word_freq_report", + "type": "TEXT", + "notnull": false, + "pk": false + }, + { + "name": "word_freq_addresses", + "type": "TEXT", + "notnull": false, + "pk": false + }, + { + "name": "word_freq_free", + "type": "TEXT", + "notnull": false, + "pk": false + }, + { + "name": "word_freq_business", + "type": "TEXT", + "notnull": false, + "pk": false + }, + { + "name": "word_freq_email", + "type": "TEXT", + "notnull": false, + "pk": false + }, + { + "name": "word_freq_you", + "type": "TEXT", + "notnull": false, + "pk": false + }, + { + "name": "word_freq_credit", + "type": "TEXT", + "notnull": false, + "pk": false + }, + { + "name": "word_freq_your", + "type": "TEXT", + "notnull": false, + "pk": false + }, + { + "name": "word_freq_font", + "type": "TEXT", + "notnull": false, + "pk": false + }, + { + "name": "word_freq_000", + "type": "TEXT", + "notnull": false, + "pk": false + }, + { + "name": "word_freq_money", + "type": "TEXT", + "notnull": false, + "pk": false + }, + { + "name": "word_freq_hp", + "type": "TEXT", + "notnull": false, + "pk": false + }, + { + "name": "word_freq_hpl", + "type": "TEXT", + "notnull": false, + "pk": false + }, + { + "name": "word_freq_george", + "type": "TEXT", + "notnull": false, + "pk": false + }, + { + "name": "word_freq_650", + "type": "TEXT", + "notnull": false, + "pk": false + }, + { + "name": "word_freq_lab", + "type": "TEXT", + "notnull": false, + "pk": false + }, + { + "name": "word_freq_labs", + "type": "TEXT", + "notnull": false, + "pk": false + }, + { + "name": "word_freq_telnet", + "type": "TEXT", + "notnull": false, + "pk": false + }, + { + "name": "word_freq_857", + "type": "TEXT", + "notnull": false, + "pk": false + }, + { + "name": "word_freq_data", + "type": "TEXT", + "notnull": false, + "pk": false + }, + { + "name": "word_freq_415", + "type": "TEXT", + "notnull": false, + "pk": false + }, + { + "name": "word_freq_85", + "type": "TEXT", + "notnull": false, + "pk": false + }, + { + "name": "word_freq_technology", + "type": "TEXT", + "notnull": false, + "pk": false + }, + { + "name": "word_freq_1999", + "type": "TEXT", + "notnull": false, + "pk": false + }, + { + "name": "word_freq_parts", + "type": "TEXT", + "notnull": false, + "pk": false + }, + { + "name": "word_freq_pm", + "type": "TEXT", + "notnull": false, + "pk": false + }, + { + "name": "word_freq_direct", + "type": "TEXT", + "notnull": false, + "pk": false + }, + { + "name": "word_freq_cs", + "type": "TEXT", + "notnull": false, + "pk": false + }, + { + "name": "word_freq_meeting", + "type": "TEXT", + "notnull": false, + "pk": false + }, + { + "name": "word_freq_original", + "type": "TEXT", + "notnull": false, + "pk": false + }, + { + "name": "word_freq_project", + "type": "TEXT", + "notnull": false, + "pk": false + }, + { + "name": "word_freq_re", + "type": "TEXT", + "notnull": false, + "pk": false + }, + { + "name": "word_freq_edu", + "type": "TEXT", + "notnull": false, + "pk": false + }, + { + "name": "word_freq_table", + "type": "TEXT", + "notnull": false, + "pk": false + }, + { + "name": "word_freq_conference", + "type": "TEXT", + "notnull": false, + "pk": false + }, + { + "name": "char_freq_%3B", + "type": "TEXT", + "notnull": false, + "pk": false + }, + { + "name": "char_freq_%28", + "type": "TEXT", + "notnull": false, + "pk": false + }, + { + "name": "char_freq_%5B", + "type": "TEXT", + "notnull": false, + "pk": false + }, + { + "name": "char_freq_%21", + "type": "TEXT", + "notnull": false, + "pk": false + }, + { + "name": "char_freq_%24", + "type": "TEXT", + "notnull": false, + "pk": false + }, + { + "name": "char_freq_%23", + "type": "TEXT", + "notnull": false, + "pk": false + }, + { + "name": "capital_run_length_average", + "type": "TEXT", + "notnull": false, + "pk": false + }, + { + "name": "capital_run_length_longest", + "type": "TEXT", + "notnull": false, + "pk": false + }, + { + "name": "capital_run_length_total", + "type": "TEXT", + "notnull": false, + "pk": false + }, + { + "name": "class", + "type": "TEXT", + "notnull": false, + "pk": false + } + ], + "sample_rows": [ + { + "word_freq_make": "0", + "word_freq_address": "0.64", + "word_freq_all": "0.64", + "word_freq_3d": "0", + "word_freq_our": "0.32", + "word_freq_over": "0", + "word_freq_remove": "0", + "word_freq_internet": "0", + "word_freq_order": "0", + "word_freq_mail": "0", + "word_freq_receive": "0", + "word_freq_will": "0.64", + "word_freq_people": "0", + "word_freq_report": "0", + "word_freq_addresses": "0", + "word_freq_free": "0.32", + "word_freq_business": "0", + "word_freq_email": "1.29", + "word_freq_you": "1.93", + "word_freq_credit": "0", + "word_freq_your": "0.96", + "word_freq_font": "0", + "word_freq_000": "0", + "word_freq_money": "0", + "word_freq_hp": "0", + "word_freq_hpl": "0", + "word_freq_george": "0", + "word_freq_650": "0", + "word_freq_lab": "0", + "word_freq_labs": "0", + "word_freq_telnet": "0", + "word_freq_857": "0", + "word_freq_data": "0", + "word_freq_415": "0", + "word_freq_85": "0", + "word_freq_technology": "0", + "word_freq_1999": "0", + "word_freq_parts": "0", + "word_freq_pm": "0", + "word_freq_direct": "0", + "word_freq_cs": "0", + "word_freq_meeting": "0", + "word_freq_original": "0", + "word_freq_project": "0", + "word_freq_re": "0", + "word_freq_edu": "0", + "word_freq_table": "0", + "word_freq_conference": "0", + "char_freq_%3B": "0", + "char_freq_%28": "0", + "char_freq_%5B": "0", + "char_freq_%21": "0.778", + "char_freq_%24": "0", + "char_freq_%23": "0", + "capital_run_length_average": "3.756", + "capital_run_length_longest": "61", + "capital_run_length_total": "278", + "class": "1" + }, + { + "word_freq_make": "0.21", + "word_freq_address": "0.28", + "word_freq_all": "0.5", + "word_freq_3d": "0", + "word_freq_our": "0.14", + "word_freq_over": "0.28", + "word_freq_remove": "0.21", + "word_freq_internet": "0.07", + "word_freq_order": "0", + "word_freq_mail": "0.94", + "word_freq_receive": "0.21", + "word_freq_will": "0.79", + "word_freq_people": "0.65", + "word_freq_report": "0.21", + "word_freq_addresses": "0.14", + "word_freq_free": "0.14", + "word_freq_business": "0.07", + "word_freq_email": "0.28", + "word_freq_you": "3.47", + "word_freq_credit": "0", + "word_freq_your": "1.59", + "word_freq_font": "0", + "word_freq_000": "0.43", + "word_freq_money": "0.43", + "word_freq_hp": "0", + "word_freq_hpl": "0", + "word_freq_george": "0", + "word_freq_650": "0", + "word_freq_lab": "0", + "word_freq_labs": "0", + "word_freq_telnet": "0", + "word_freq_857": "0", + "word_freq_data": "0", + "word_freq_415": "0", + "word_freq_85": "0", + "word_freq_technology": "0", + "word_freq_1999": "0.07", + "word_freq_parts": "0", + "word_freq_pm": "0", + "word_freq_direct": "0", + "word_freq_cs": "0", + "word_freq_meeting": "0", + "word_freq_original": "0", + "word_freq_project": "0", + "word_freq_re": "0", + "word_freq_edu": "0", + "word_freq_table": "0", + "word_freq_conference": "0", + "char_freq_%3B": "0", + "char_freq_%28": "0.132", + "char_freq_%5B": "0", + "char_freq_%21": "0.372", + "char_freq_%24": "0.18", + "char_freq_%23": "0.048", + "capital_run_length_average": "5.114", + "capital_run_length_longest": "101", + "capital_run_length_total": "1028", + "class": "1" + }, + { + "word_freq_make": "0.06", + "word_freq_address": "0", + "word_freq_all": "0.71", + "word_freq_3d": "0", + "word_freq_our": "1.23", + "word_freq_over": "0.19", + "word_freq_remove": "0.19", + "word_freq_internet": "0.12", + "word_freq_order": "0.64", + "word_freq_mail": "0.25", + "word_freq_receive": "0.38", + "word_freq_will": "0.45", + "word_freq_people": "0.12", + "word_freq_report": "0", + "word_freq_addresses": "1.75", + "word_freq_free": "0.06", + "word_freq_business": "0.06", + "word_freq_email": "1.03", + "word_freq_you": "1.36", + "word_freq_credit": "0.32", + "word_freq_your": "0.51", + "word_freq_font": "0", + "word_freq_000": "1.16", + "word_freq_money": "0.06", + "word_freq_hp": "0", + "word_freq_hpl": "0", + "word_freq_george": "0", + "word_freq_650": "0", + "word_freq_lab": "0", + "word_freq_labs": "0", + "word_freq_telnet": "0", + "word_freq_857": "0", + "word_freq_data": "0", + "word_freq_415": "0", + "word_freq_85": "0", + "word_freq_technology": "0", + "word_freq_1999": "0", + "word_freq_parts": "0", + "word_freq_pm": "0", + "word_freq_direct": "0.06", + "word_freq_cs": "0", + "word_freq_meeting": "0", + "word_freq_original": "0.12", + "word_freq_project": "0", + "word_freq_re": "0.06", + "word_freq_edu": "0.06", + "word_freq_table": "0", + "word_freq_conference": "0", + "char_freq_%3B": "0.01", + "char_freq_%28": "0.143", + "char_freq_%5B": "0", + "char_freq_%21": "0.276", + "char_freq_%24": "0.184", + "char_freq_%23": "0.01", + "capital_run_length_average": "9.821", + "capital_run_length_longest": "485", + "capital_run_length_total": "2259", + "class": "1" + }, + { + "word_freq_make": "0", + "word_freq_address": "0", + "word_freq_all": "0", + "word_freq_3d": "0", + "word_freq_our": "0.63", + "word_freq_over": "0", + "word_freq_remove": "0.31", + "word_freq_internet": "0.63", + "word_freq_order": "0.31", + "word_freq_mail": "0.63", + "word_freq_receive": "0.31", + "word_freq_will": "0.31", + "word_freq_people": "0.31", + "word_freq_report": "0", + "word_freq_addresses": "0", + "word_freq_free": "0.31", + "word_freq_business": "0", + "word_freq_email": "0", + "word_freq_you": "3.18", + "word_freq_credit": "0", + "word_freq_your": "0.31", + "word_freq_font": "0", + "word_freq_000": "0", + "word_freq_money": "0", + "word_freq_hp": "0", + "word_freq_hpl": "0", + "word_freq_george": "0", + "word_freq_650": "0", + "word_freq_lab": "0", + "word_freq_labs": "0", + "word_freq_telnet": "0", + "word_freq_857": "0", + "word_freq_data": "0", + "word_freq_415": "0", + "word_freq_85": "0", + "word_freq_technology": "0", + "word_freq_1999": "0", + "word_freq_parts": "0", + "word_freq_pm": "0", + "word_freq_direct": "0", + "word_freq_cs": "0", + "word_freq_meeting": "0", + "word_freq_original": "0", + "word_freq_project": "0", + "word_freq_re": "0", + "word_freq_edu": "0", + "word_freq_table": "0", + "word_freq_conference": "0", + "char_freq_%3B": "0", + "char_freq_%28": "0.137", + "char_freq_%5B": "0", + "char_freq_%21": "0.137", + "char_freq_%24": "0", + "char_freq_%23": "0", + "capital_run_length_average": "3.537", + "capital_run_length_longest": "40", + "capital_run_length_total": "191", + "class": "1" + }, + { + "word_freq_make": "0", + "word_freq_address": "0", + "word_freq_all": "0", + "word_freq_3d": "0", + "word_freq_our": "0.63", + "word_freq_over": "0", + "word_freq_remove": "0.31", + "word_freq_internet": "0.63", + "word_freq_order": "0.31", + "word_freq_mail": "0.63", + "word_freq_receive": "0.31", + "word_freq_will": "0.31", + "word_freq_people": "0.31", + "word_freq_report": "0", + "word_freq_addresses": "0", + "word_freq_free": "0.31", + "word_freq_business": "0", + "word_freq_email": "0", + "word_freq_you": "3.18", + "word_freq_credit": "0", + "word_freq_your": "0.31", + "word_freq_font": "0", + "word_freq_000": "0", + "word_freq_money": "0", + "word_freq_hp": "0", + "word_freq_hpl": "0", + "word_freq_george": "0", + "word_freq_650": "0", + "word_freq_lab": "0", + "word_freq_labs": "0", + "word_freq_telnet": "0", + "word_freq_857": "0", + "word_freq_data": "0", + "word_freq_415": "0", + "word_freq_85": "0", + "word_freq_technology": "0", + "word_freq_1999": "0", + "word_freq_parts": "0", + "word_freq_pm": "0", + "word_freq_direct": "0", + "word_freq_cs": "0", + "word_freq_meeting": "0", + "word_freq_original": "0", + "word_freq_project": "0", + "word_freq_re": "0", + "word_freq_edu": "0", + "word_freq_table": "0", + "word_freq_conference": "0", + "char_freq_%3B": "0", + "char_freq_%28": "0.135", + "char_freq_%5B": "0", + "char_freq_%21": "0.135", + "char_freq_%24": "0", + "char_freq_%23": "0", + "capital_run_length_average": "3.537", + "capital_run_length_longest": "40", + "capital_run_length_total": "191", + "class": "1" + } + ] +} + +Shortlisted templates: +[ + { + "template_id": "tpl_tpcds_within_group_share", + "template_name": "Within-Group Share of Total", + "primary_family": "conditional_dependency_structure", + "portability": "partial", + "sql_skeleton": "SELECT {group_col}, {item_col},\n SUM({measure_col}) AS total_measure,\n SUM({measure_col}) * 100.0 / SUM(SUM({measure_col})) OVER (PARTITION BY {group_col}) AS share_within_group\nFROM {table}\nGROUP BY {group_col}, {item_col}\nORDER BY share_within_group DESC;", + "required_roles": [ + "group_col", + "item_col", + "measure_col" + ] + } +] + +Problem instance: +{ + "dataset_id": "n1", + "question": "Use template Within-Group Share of Total to probe dependency_strength_similarity with semantic role within_group_proportion. Focus on group_col=class, measure_col=word_freq_650.", + "planned_template_id": "tpl_tpcds_within_group_share", + "bindings": { + "group_col": "class", + "measure_col": "word_freq_650", + "item_col": "word_freq_lab", + "top_k": 17, + "top_n": 7, + "num_tiles": 10, + "percentile_value": 0.95, + "z_threshold": 2.0, + "fraction_threshold": 0.05, + "baseline_multiplier": 1.75, + "baseline_fraction": 0.1, + "min_group_size": 5, + "min_support": 4, + "measure_threshold": 0.0, + "time_grain": "month", + "lookback_rows": 3, + "current_period_start": "'2024-01-01'", + "current_period_end": "'2024-04-01'", + "previous_period_start": "'2023-10-01'", + "previous_period_end": "'2024-01-01'", + "drift_ratio_threshold": 0.8 + }, + "can_vary": [], + "must_fix": [], + "runtime_sql_skeleton": "SELECT {group_col}, {item_col},\n SUM({measure_col}) AS total_measure,\n SUM({measure_col}) * 100.0 / SUM(SUM({measure_col})) OVER (PARTITION BY {group_col}) AS share_within_group\nFROM {table}\nGROUP BY {group_col}, {item_col}\nORDER BY share_within_group DESC;" +} + +Repair context: +{} diff --git a/Query/sql/v2/runs/v2_cli_20260502_081223_d/n1/artifacts/v2q_n1_159c7dd81ca8414b/cli/sql_response_attempt_1.raw.txt b/Query/sql/v2/runs/v2_cli_20260502_081223_d/n1/artifacts/v2q_n1_159c7dd81ca8414b/cli/sql_response_attempt_1.raw.txt new file mode 100644 index 0000000000000000000000000000000000000000..c165adbe3b172f4ce3609c2813ea133b436c44df --- /dev/null +++ b/Query/sql/v2/runs/v2_cli_20260502_081223_d/n1/artifacts/v2q_n1_159c7dd81ca8414b/cli/sql_response_attempt_1.raw.txt @@ -0,0 +1,4 @@ +{"type":"thread.started","thread_id":"019e40e0-ce82-7153-91a0-b9561dedd192"} +{"type":"turn.started"} +{"type":"item.completed","item":{"id":"item_0","type":"agent_message","text":"{\"sql\":\"-- template_id: tpl_tpcds_within_group_share.\\nSELECT\\n \\\"class\\\",\\n \\\"word_freq_lab\\\",\\n SUM(CAST(\\\"word_freq_650\\\" AS REAL)) AS \\\"total_measure\\\",\\n SUM(CAST(\\\"word_freq_650\\\" AS REAL)) * 100.0 / SUM(SUM(CAST(\\\"word_freq_650\\\" AS REAL))) OVER (PARTITION BY \\\"class\\\") AS \\\"share_within_group\\\"\\nFROM \\\"n1\\\"\\nGROUP BY \\\"class\\\", \\\"word_freq_lab\\\"\\nORDER BY \\\"share_within_group\\\" DESC;\",\"notes\":\"Applied the planned Within-Group Share of Total template with group_col=\\\"class\\\", item_col=\\\"word_freq_lab\\\", and measure_col=\\\"word_freq_650\\\". CAST to REAL is used because the schema stores feature values as TEXT.\"}"}} +{"type":"turn.completed","usage":{"input_tokens":20437,"cached_input_tokens":19840,"output_tokens":1096,"reasoning_output_tokens":913}} diff --git a/Query/sql/v2/runs/v2_cli_20260502_081223_d/n1/artifacts/v2q_n1_159c7dd81ca8414b/cli/sql_response_attempt_1.txt b/Query/sql/v2/runs/v2_cli_20260502_081223_d/n1/artifacts/v2q_n1_159c7dd81ca8414b/cli/sql_response_attempt_1.txt new file mode 100644 index 0000000000000000000000000000000000000000..fb95709365a0dcae31111a7c3534b41e1fd5e221 --- /dev/null +++ b/Query/sql/v2/runs/v2_cli_20260502_081223_d/n1/artifacts/v2q_n1_159c7dd81ca8414b/cli/sql_response_attempt_1.txt @@ -0,0 +1 @@ +{"sql":"-- template_id: tpl_tpcds_within_group_share.\nSELECT\n \"class\",\n \"word_freq_lab\",\n SUM(CAST(\"word_freq_650\" AS REAL)) AS \"total_measure\",\n SUM(CAST(\"word_freq_650\" AS REAL)) * 100.0 / SUM(SUM(CAST(\"word_freq_650\" AS REAL))) OVER (PARTITION BY \"class\") AS \"share_within_group\"\nFROM \"n1\"\nGROUP BY \"class\", \"word_freq_lab\"\nORDER BY \"share_within_group\" DESC;","notes":"Applied the planned Within-Group Share of Total template with group_col=\"class\", item_col=\"word_freq_lab\", and measure_col=\"word_freq_650\". CAST to REAL is used because the schema stores feature values as TEXT."} \ No newline at end of file diff --git a/Query/sql/v2/runs/v2_cli_20260502_081223_d/n1/artifacts/v2q_n1_159c7dd81ca8414b/cli/sql_stderr_attempt_1.txt b/Query/sql/v2/runs/v2_cli_20260502_081223_d/n1/artifacts/v2q_n1_159c7dd81ca8414b/cli/sql_stderr_attempt_1.txt new file mode 100644 index 0000000000000000000000000000000000000000..e69de29bb2d1d6434b8b29ae775ad8c2e48c5391 diff --git a/Query/sql/v2/runs/v2_cli_20260502_081223_d/n1/artifacts/v2q_n1_15f59192bc30161b/cli/conversation.jsonl b/Query/sql/v2/runs/v2_cli_20260502_081223_d/n1/artifacts/v2q_n1_15f59192bc30161b/cli/conversation.jsonl new file mode 100644 index 0000000000000000000000000000000000000000..d6bb2b5f7745151e3b1c34e1956de3fb7e95bf95 --- /dev/null +++ b/Query/sql/v2/runs/v2_cli_20260502_081223_d/n1/artifacts/v2q_n1_15f59192bc30161b/cli/conversation.jsonl @@ -0,0 +1,2 @@ +{"attempt": 1, "phase": "sql_generation", "role": "user", "content_path": "cli/sql_prompt_attempt_1.txt", "metrics": {"chars": 29775, "bytes_utf8": 29775, "lines": 794, "estimated_tokens": null}} +{"attempt": 1, "phase": "sql_generation", "role": "assistant", "content_path": "cli/sql_response_attempt_1.txt", "raw_content_path": "cli/sql_response_attempt_1.raw.txt", "stderr_path": "cli/sql_stderr_attempt_1.txt", "metrics": {"chars": 667, "bytes_utf8": 667, "lines": 1, "estimated_tokens": null}, "usage": {"input_tokens": 20438, "cached_input_tokens": 19840, "output_tokens": 711, "reasoning_output_tokens": 516}} diff --git a/Query/sql/v2/runs/v2_cli_20260502_081223_d/n1/artifacts/v2q_n1_15f59192bc30161b/cli/session_summary.json b/Query/sql/v2/runs/v2_cli_20260502_081223_d/n1/artifacts/v2q_n1_15f59192bc30161b/cli/session_summary.json new file mode 100644 index 0000000000000000000000000000000000000000..e0f5eddada613d107b227ab7f2e2846d9daa12b3 --- /dev/null +++ b/Query/sql/v2/runs/v2_cli_20260502_081223_d/n1/artifacts/v2q_n1_15f59192bc30161b/cli/session_summary.json @@ -0,0 +1,25 @@ +{ + "engine": "v2-cli:codex", + "command": "codex exec --skip-git-repo-check --disable plugins --sandbox read-only --cd \"/data/jialinzhang/SQLagent\" -m gpt-5.4 --json -", + "ai_cli_calls": 1, + "usage_summary": { + "dataset_id": "n1", + "model": "v2-cli:codex", + "run_id": "v2q_n1_15f59192bc30161b", + "api_calls": 0, + "input_tokens": 20438, + "cached_input_tokens": 19840, + "output_tokens": 711, + "total_tokens": 21149, + "cost_usd": 0.0, + "ai_cli_calls": 1, + "estimated_input_tokens": 0, + "estimated_output_tokens": 0, + "estimated_total_tokens": 0, + "usage_source": "ai_cli_json_usage", + "cli_elapsed_ms_total": 15617.43, + "sql_execution_elapsed_ms_total": 5.93, + "conversation_log_path": "/data/jialinzhang/TabQueryBench/sql_workloads/v2_current/runs_and_launches/runs/v2_cli_20260502_081223_d/n1/artifacts/v2q_n1_15f59192bc30161b/cli/conversation.jsonl", + "note": "Executed through a local AI CLI with structured usage metadata." + } +} \ No newline at end of file diff --git a/Query/sql/v2/runs/v2_cli_20260502_081223_d/n1/artifacts/v2q_n1_15f59192bc30161b/cli/sql_attempt_1.metadata.json b/Query/sql/v2/runs/v2_cli_20260502_081223_d/n1/artifacts/v2q_n1_15f59192bc30161b/cli/sql_attempt_1.metadata.json new file mode 100644 index 0000000000000000000000000000000000000000..ebb0eb93a26556907b5fa1ef2637feff3b06301a --- /dev/null +++ b/Query/sql/v2/runs/v2_cli_20260502_081223_d/n1/artifacts/v2q_n1_15f59192bc30161b/cli/sql_attempt_1.metadata.json @@ -0,0 +1,45 @@ +{ + "attempt": 1, + "phase": "sql_generation", + "command": "codex exec --skip-git-repo-check --disable plugins --sandbox read-only --cd \"/data/jialinzhang/SQLagent\" -m gpt-5.4 --json -", + "started_at": "2026-05-19T15:34:55.648488+00:00", + "ended_at": "2026-05-19T15:35:11.265953+00:00", + "elapsed_ms": 15617.43, + "prompt_metrics": { + "chars": 29775, + "bytes_utf8": 29775, + "lines": 794, + "estimated_tokens": null + }, + "stdout_metrics": { + "chars": 1049, + "bytes_utf8": 1049, + "lines": 4, + "estimated_tokens": null + }, + "stderr_metrics": { + "chars": 0, + "bytes_utf8": 0, + "lines": 0, + "estimated_tokens": null + }, + "parsed_output": { + "format": "jsonl_events", + "text_metrics": { + "chars": 667, + "bytes_utf8": 667, + "lines": 1, + "estimated_tokens": null + }, + "usage": { + "input_tokens": 20438, + "cached_input_tokens": 19840, + "output_tokens": 711, + "reasoning_output_tokens": 516 + } + }, + "prompt_path": "cli/sql_prompt_attempt_1.txt", + "response_path": "cli/sql_response_attempt_1.txt", + "raw_response_path": "cli/sql_response_attempt_1.raw.txt", + "stderr_path": "cli/sql_stderr_attempt_1.txt" +} \ No newline at end of file diff --git a/Query/sql/v2/runs/v2_cli_20260502_081223_d/n1/artifacts/v2q_n1_15f59192bc30161b/cli/sql_prompt_attempt_1.txt b/Query/sql/v2/runs/v2_cli_20260502_081223_d/n1/artifacts/v2q_n1_15f59192bc30161b/cli/sql_prompt_attempt_1.txt new file mode 100644 index 0000000000000000000000000000000000000000..d6f6e3244a37f55852f1532ea0e3cdddefe9649f --- /dev/null +++ b/Query/sql/v2/runs/v2_cli_20260502_081223_d/n1/artifacts/v2q_n1_15f59192bc30161b/cli/sql_prompt_attempt_1.txt @@ -0,0 +1,794 @@ +You are generating one SQLite SELECT query for a single-table SQL QA task. +Return strict JSON only, with this schema: {"sql": "...", "notes": "..."}. +Rules: +- Use only the provided table and columns. +- Do not write INSERT, UPDATE, DELETE, DROP, ALTER, CREATE, PRAGMA, ATTACH, DETACH, or VACUUM. +- Prefer the planned template and bound roles when provided. +- Add a leading SQL comment exactly like: -- template_id: . +- Generate SQLite-compatible SQL. SQLite does not support PERCENTILE_CONT or STDDEV. +- Quote identifiers with double quotes. +- Return no markdown and no extra prose. + +Dataset context: +Dataset context for SQL QA: +- dataset_id: n1 +- dataset_name: Spambase +- table_name: n1 +- table_layout: single-table dataset (do not assume joins). +- row_semantics: One row is one email represented by engineered token/character frequency and capitalization-run features. +- task_type: classification +- target_column: class +- main_row_count: 4601 +- important_fields: +- word_freq_make: role=feature, type=numeric_sparse_frequency. tags=['condition_candidate', 'measure', 'rare_pattern_candidate'] desc=Frequency feature for token 'make' in an email message. +- word_freq_address: role=feature, type=numeric_sparse_frequency. tags=['condition_candidate', 'measure', 'rare_pattern_candidate'] desc=Frequency feature for token 'address' in an email message. +- word_freq_all: role=feature, type=numeric_sparse_frequency. tags=['condition_candidate', 'measure', 'rare_pattern_candidate'] desc=Frequency feature for token 'all' in an email message. +- word_freq_3d: role=feature, type=numeric_sparse_frequency. tags=['condition_candidate', 'measure', 'rare_pattern_candidate'] desc=Frequency feature for token '3d' in an email message. +- word_freq_our: role=feature, type=numeric_sparse_frequency. tags=['condition_candidate', 'measure', 'rare_pattern_candidate'] desc=Frequency feature for token 'our' in an email message. +- word_freq_over: role=feature, type=numeric_sparse_frequency. tags=['condition_candidate', 'measure', 'rare_pattern_candidate'] desc=Frequency feature for token 'over' in an email message. +- word_freq_remove: role=feature, type=numeric_sparse_frequency. tags=['condition_candidate', 'measure', 'rare_pattern_candidate'] desc=Frequency feature for token 'remove' in an email message. +- word_freq_internet: role=feature, type=numeric_sparse_frequency. tags=['condition_candidate', 'measure', 'rare_pattern_candidate'] desc=Frequency feature for token 'internet' in an email message. +- word_freq_order: role=feature, type=numeric_sparse_frequency. tags=['condition_candidate', 'measure', 'rare_pattern_candidate'] desc=Frequency feature for token 'order' in an email message. +- word_freq_mail: role=feature, type=numeric_sparse_frequency. tags=['condition_candidate', 'measure', 'rare_pattern_candidate'] desc=Frequency feature for token 'mail' in an email message. +- word_freq_receive: role=feature, type=numeric_sparse_frequency. tags=['condition_candidate', 'measure', 'rare_pattern_candidate'] desc=Frequency feature for token 'receive' in an email message. +- word_freq_will: role=feature, type=numeric_sparse_frequency. tags=['condition_candidate', 'measure', 'rare_pattern_candidate'] desc=Frequency feature for token 'will' in an email message. +- word_freq_people: role=feature, type=numeric_sparse_frequency. tags=['condition_candidate', 'measure', 'rare_pattern_candidate'] desc=Frequency feature for token 'people' in an email message. +- word_freq_report: role=feature, type=numeric_sparse_frequency. tags=['condition_candidate', 'measure', 'rare_pattern_candidate'] desc=Frequency feature for token 'report' in an email message. +- word_freq_addresses: role=feature, type=numeric_sparse_frequency. tags=['condition_candidate', 'measure', 'rare_pattern_candidate'] desc=Frequency feature for token 'addresses' in an email message. +- word_freq_free: role=feature, type=numeric_sparse_frequency. tags=['condition_candidate', 'measure', 'rare_pattern_candidate'] desc=Frequency feature for token 'free' in an email message. +- word_freq_business: role=feature, type=numeric_sparse_frequency. tags=['condition_candidate', 'measure', 'rare_pattern_candidate'] desc=Frequency feature for token 'business' in an email message. +- word_freq_email: role=feature, type=numeric_sparse_frequency. tags=['condition_candidate', 'measure', 'rare_pattern_candidate'] desc=Frequency feature for token 'email' in an email message. +- word_freq_you: role=feature, type=numeric_sparse_frequency. tags=['condition_candidate', 'measure', 'rare_pattern_candidate'] desc=Frequency feature for token 'you' in an email message. +- word_freq_credit: role=feature, type=numeric_sparse_frequency. tags=['condition_candidate', 'measure', 'rare_pattern_candidate'] desc=Frequency feature for token 'credit' in an email message. +- word_freq_your: role=feature, type=numeric_sparse_frequency. tags=['condition_candidate', 'measure', 'rare_pattern_candidate'] desc=Frequency feature for token 'your' in an email message. +- word_freq_font: role=feature, type=numeric_sparse_frequency. tags=['condition_candidate', 'measure', 'rare_pattern_candidate'] desc=Frequency feature for token 'font' in an email message. +- word_freq_000: role=feature, type=numeric_sparse_frequency. tags=['condition_candidate', 'measure', 'rare_pattern_candidate'] desc=Frequency feature for token '000' in an email message. +- word_freq_money: role=feature, type=numeric_sparse_frequency. tags=['condition_candidate', 'measure', 'rare_pattern_candidate'] desc=Frequency feature for token 'money' in an email message. +- word_freq_hp: role=feature, type=numeric_sparse_frequency. tags=['condition_candidate', 'measure', 'rare_pattern_candidate'] desc=Frequency feature for token 'hp' in an email message. +- word_freq_hpl: role=feature, type=numeric_sparse_frequency. tags=['condition_candidate', 'measure', 'rare_pattern_candidate'] desc=Frequency feature for token 'hpl' in an email message. +- word_freq_george: role=feature, type=numeric_sparse_frequency. tags=['condition_candidate', 'measure', 'rare_pattern_candidate'] desc=Frequency feature for token 'george' in an email message. +- word_freq_650: role=feature, type=numeric_sparse_frequency. tags=['condition_candidate', 'measure', 'rare_pattern_candidate'] desc=Frequency feature for token '650' in an email message. +- word_freq_lab: role=feature, type=numeric_sparse_frequency. tags=['condition_candidate', 'measure', 'rare_pattern_candidate'] desc=Frequency feature for token 'lab' in an email message. +- word_freq_labs: role=feature, type=numeric_sparse_frequency. tags=['condition_candidate', 'measure', 'rare_pattern_candidate'] desc=Frequency feature for token 'labs' in an email message. +- word_freq_telnet: role=feature, type=numeric_sparse_frequency. tags=['condition_candidate', 'measure', 'rare_pattern_candidate'] desc=Frequency feature for token 'telnet' in an email message. +- word_freq_857: role=feature, type=numeric_sparse_frequency. tags=['condition_candidate', 'measure', 'rare_pattern_candidate'] desc=Frequency feature for token '857' in an email message. +- word_freq_data: role=feature, type=numeric_sparse_frequency. tags=['condition_candidate', 'measure', 'rare_pattern_candidate'] desc=Frequency feature for token 'data' in an email message. +- word_freq_415: role=feature, type=numeric_sparse_frequency. tags=['condition_candidate', 'measure', 'rare_pattern_candidate'] desc=Frequency feature for token '415' in an email message. +- word_freq_85: role=feature, type=numeric_sparse_frequency. tags=['condition_candidate', 'measure', 'rare_pattern_candidate'] desc=Frequency feature for token '85' in an email message. +- word_freq_technology: role=feature, type=numeric_sparse_frequency. tags=['condition_candidate', 'measure', 'rare_pattern_candidate'] desc=Frequency feature for token 'technology' in an email message. +- word_freq_1999: role=feature, type=numeric_sparse_frequency. tags=['condition_candidate', 'measure', 'rare_pattern_candidate'] desc=Frequency feature for token '1999' in an email message. +- word_freq_parts: role=feature, type=numeric_sparse_frequency. tags=['condition_candidate', 'measure', 'rare_pattern_candidate'] desc=Frequency feature for token 'parts' in an email message. +- word_freq_pm: role=feature, type=numeric_sparse_frequency. tags=['condition_candidate', 'measure', 'rare_pattern_candidate'] desc=Frequency feature for token 'pm' in an email message. +- word_freq_direct: role=feature, type=numeric_sparse_frequency. tags=['condition_candidate', 'measure', 'rare_pattern_candidate'] desc=Frequency feature for token 'direct' in an email message. +- word_freq_cs: role=feature, type=numeric_sparse_frequency. tags=['condition_candidate', 'measure', 'rare_pattern_candidate'] desc=Frequency feature for token 'cs' in an email message. +- word_freq_meeting: role=feature, type=numeric_sparse_frequency. tags=['condition_candidate', 'measure', 'rare_pattern_candidate'] desc=Frequency feature for token 'meeting' in an email message. +- word_freq_original: role=feature, type=numeric_sparse_frequency. tags=['condition_candidate', 'measure', 'rare_pattern_candidate'] desc=Frequency feature for token 'original' in an email message. +- word_freq_project: role=feature, type=numeric_sparse_frequency. tags=['condition_candidate', 'measure', 'rare_pattern_candidate'] desc=Frequency feature for token 'project' in an email message. +- word_freq_re: role=feature, type=numeric_sparse_frequency. tags=['condition_candidate', 'measure', 'rare_pattern_candidate'] desc=Frequency feature for token 're' in an email message. +- word_freq_edu: role=feature, type=numeric_sparse_frequency. tags=['condition_candidate', 'measure', 'rare_pattern_candidate'] desc=Frequency feature for token 'edu' in an email message. +- word_freq_table: role=feature, type=numeric_sparse_frequency. tags=['condition_candidate', 'measure', 'rare_pattern_candidate'] desc=Frequency feature for token 'table' in an email message. +- word_freq_conference: role=feature, type=numeric_sparse_frequency. tags=['condition_candidate', 'measure', 'rare_pattern_candidate'] desc=Frequency feature for token 'conference' in an email message. +- char_freq_%3B: role=feature, type=numeric_sparse_frequency. tags=['condition_candidate', 'measure', 'rare_pattern_candidate'] desc=Character frequency feature for symbol ';'. +- char_freq_%28: role=feature, type=numeric_sparse_frequency. tags=['condition_candidate', 'measure', 'rare_pattern_candidate'] desc=Character frequency feature for symbol '('. +- char_freq_%5B: role=feature, type=numeric_sparse_frequency. tags=['condition_candidate', 'measure', 'rare_pattern_candidate'] desc=Character frequency feature for symbol '['. +- char_freq_%21: role=feature, type=numeric_sparse_frequency. tags=['condition_candidate', 'measure', 'rare_pattern_candidate'] desc=Character frequency feature for symbol '!'. +- char_freq_%24: role=feature, type=numeric_sparse_frequency. tags=['condition_candidate', 'measure', 'rare_pattern_candidate'] desc=Character frequency feature for symbol '$'. +- char_freq_%23: role=feature, type=numeric_sparse_frequency. tags=['condition_candidate', 'measure', 'rare_pattern_candidate'] desc=Character frequency feature for symbol '#'. +- capital_run_length_average: role=feature, type=numeric_heavy_tail. tags=['condition_candidate', 'measure', 'rare_pattern_candidate'] desc=Average length of uninterrupted capital-letter runs. +- capital_run_length_longest: role=feature, type=numeric_heavy_tail. tags=['condition_candidate', 'measure', 'rare_pattern_candidate'] desc=Longest uninterrupted capital-letter run. +- capital_run_length_total: role=feature, type=numeric_heavy_tail. tags=['condition_candidate', 'measure', 'rare_pattern_candidate'] desc=Total number of capital letters in runs. +- class: role=target, type=binary_target. tags=['target_candidate'] desc=Binary spam label (1=spam, 0=non-spam). +- useful_field_combinations: [['word_freq_free', 'word_freq_you', 'class'], ['char_freq_%21', 'capital_run_length_longest', 'class'], ['word_freq_remove', 'word_freq_money', 'class']] +- fields_requiring_caution: ['capital_run_length_average', 'capital_run_length_longest', 'capital_run_length_total'] +- source_url: https://www.openml.org/d/44 + +SQLite schema snapshot: +{ + "table_name": "n1", + "quoted_table_name": "\"n1\"", + "row_count": 4601, + "columns": [ + { + "name": "word_freq_make", + "type": "TEXT", + "notnull": false, + "pk": false + }, + { + "name": "word_freq_address", + "type": "TEXT", + "notnull": false, + "pk": false + }, + { + "name": "word_freq_all", + "type": "TEXT", + "notnull": false, + "pk": false + }, + { + "name": "word_freq_3d", + "type": "TEXT", + "notnull": false, + "pk": false + }, + { + "name": "word_freq_our", + "type": "TEXT", + "notnull": false, + "pk": false + }, + { + "name": "word_freq_over", + "type": "TEXT", + "notnull": false, + "pk": false + }, + { + "name": "word_freq_remove", + "type": "TEXT", + "notnull": false, + "pk": false + }, + { + "name": "word_freq_internet", + "type": "TEXT", + "notnull": false, + "pk": false + }, + { + "name": "word_freq_order", + "type": "TEXT", + "notnull": false, + "pk": false + }, + { + "name": "word_freq_mail", + "type": "TEXT", + "notnull": false, + "pk": false + }, + { + "name": "word_freq_receive", + "type": "TEXT", + "notnull": false, + "pk": false + }, + { + "name": "word_freq_will", + "type": "TEXT", + "notnull": false, + "pk": false + }, + { + "name": "word_freq_people", + "type": "TEXT", + "notnull": false, + "pk": false + }, + { + "name": "word_freq_report", + "type": "TEXT", + "notnull": false, + "pk": false + }, + { + "name": "word_freq_addresses", + "type": "TEXT", + "notnull": false, + "pk": false + }, + { + "name": "word_freq_free", + "type": "TEXT", + "notnull": false, + "pk": false + }, + { + "name": "word_freq_business", + "type": "TEXT", + "notnull": false, + "pk": false + }, + { + "name": "word_freq_email", + "type": "TEXT", + "notnull": false, + "pk": false + }, + { + "name": "word_freq_you", + "type": "TEXT", + "notnull": false, + "pk": false + }, + { + "name": "word_freq_credit", + "type": "TEXT", + "notnull": false, + "pk": false + }, + { + "name": "word_freq_your", + "type": "TEXT", + "notnull": false, + "pk": false + }, + { + "name": "word_freq_font", + "type": "TEXT", + "notnull": false, + "pk": false + }, + { + "name": "word_freq_000", + "type": "TEXT", + "notnull": false, + "pk": false + }, + { + "name": "word_freq_money", + "type": "TEXT", + "notnull": false, + "pk": false + }, + { + "name": "word_freq_hp", + "type": "TEXT", + "notnull": false, + "pk": false + }, + { + "name": "word_freq_hpl", + "type": "TEXT", + "notnull": false, + "pk": false + }, + { + "name": "word_freq_george", + "type": "TEXT", + "notnull": false, + "pk": false + }, + { + "name": "word_freq_650", + "type": "TEXT", + "notnull": false, + "pk": false + }, + { + "name": "word_freq_lab", + "type": "TEXT", + "notnull": false, + "pk": false + }, + { + "name": "word_freq_labs", + "type": "TEXT", + "notnull": false, + "pk": false + }, + { + "name": "word_freq_telnet", + "type": "TEXT", + "notnull": false, + "pk": false + }, + { + "name": "word_freq_857", + "type": "TEXT", + "notnull": false, + "pk": false + }, + { + "name": "word_freq_data", + "type": "TEXT", + "notnull": false, + "pk": false + }, + { + "name": "word_freq_415", + "type": "TEXT", + "notnull": false, + "pk": false + }, + { + "name": "word_freq_85", + "type": "TEXT", + "notnull": false, + "pk": false + }, + { + "name": "word_freq_technology", + "type": "TEXT", + "notnull": false, + "pk": false + }, + { + "name": "word_freq_1999", + "type": "TEXT", + "notnull": false, + "pk": false + }, + { + "name": "word_freq_parts", + "type": "TEXT", + "notnull": false, + "pk": false + }, + { + "name": "word_freq_pm", + "type": "TEXT", + "notnull": false, + "pk": false + }, + { + "name": "word_freq_direct", + "type": "TEXT", + "notnull": false, + "pk": false + }, + { + "name": "word_freq_cs", + "type": "TEXT", + "notnull": false, + "pk": false + }, + { + "name": "word_freq_meeting", + "type": "TEXT", + "notnull": false, + "pk": false + }, + { + "name": "word_freq_original", + "type": "TEXT", + "notnull": false, + "pk": false + }, + { + "name": "word_freq_project", + "type": "TEXT", + "notnull": false, + "pk": false + }, + { + "name": "word_freq_re", + "type": "TEXT", + "notnull": false, + "pk": false + }, + { + "name": "word_freq_edu", + "type": "TEXT", + "notnull": false, + "pk": false + }, + { + "name": "word_freq_table", + "type": "TEXT", + "notnull": false, + "pk": false + }, + { + "name": "word_freq_conference", + "type": "TEXT", + "notnull": false, + "pk": false + }, + { + "name": "char_freq_%3B", + "type": "TEXT", + "notnull": false, + "pk": false + }, + { + "name": "char_freq_%28", + "type": "TEXT", + "notnull": false, + "pk": false + }, + { + "name": "char_freq_%5B", + "type": "TEXT", + "notnull": false, + "pk": false + }, + { + "name": "char_freq_%21", + "type": "TEXT", + "notnull": false, + "pk": false + }, + { + "name": "char_freq_%24", + "type": "TEXT", + "notnull": false, + "pk": false + }, + { + "name": "char_freq_%23", + "type": "TEXT", + "notnull": false, + "pk": false + }, + { + "name": "capital_run_length_average", + "type": "TEXT", + "notnull": false, + "pk": false + }, + { + "name": "capital_run_length_longest", + "type": "TEXT", + "notnull": false, + "pk": false + }, + { + "name": "capital_run_length_total", + "type": "TEXT", + "notnull": false, + "pk": false + }, + { + "name": "class", + "type": "TEXT", + "notnull": false, + "pk": false + } + ], + "sample_rows": [ + { + "word_freq_make": "0", + "word_freq_address": "0.64", + "word_freq_all": "0.64", + "word_freq_3d": "0", + "word_freq_our": "0.32", + "word_freq_over": "0", + "word_freq_remove": "0", + "word_freq_internet": "0", + "word_freq_order": "0", + "word_freq_mail": "0", + "word_freq_receive": "0", + "word_freq_will": "0.64", + "word_freq_people": "0", + "word_freq_report": "0", + "word_freq_addresses": "0", + "word_freq_free": "0.32", + "word_freq_business": "0", + "word_freq_email": "1.29", + "word_freq_you": "1.93", + "word_freq_credit": "0", + "word_freq_your": "0.96", + "word_freq_font": "0", + "word_freq_000": "0", + "word_freq_money": "0", + "word_freq_hp": "0", + "word_freq_hpl": "0", + "word_freq_george": "0", + "word_freq_650": "0", + "word_freq_lab": "0", + "word_freq_labs": "0", + "word_freq_telnet": "0", + "word_freq_857": "0", + "word_freq_data": "0", + "word_freq_415": "0", + "word_freq_85": "0", + "word_freq_technology": "0", + "word_freq_1999": "0", + "word_freq_parts": "0", + "word_freq_pm": "0", + "word_freq_direct": "0", + "word_freq_cs": "0", + "word_freq_meeting": "0", + "word_freq_original": "0", + "word_freq_project": "0", + "word_freq_re": "0", + "word_freq_edu": "0", + "word_freq_table": "0", + "word_freq_conference": "0", + "char_freq_%3B": "0", + "char_freq_%28": "0", + "char_freq_%5B": "0", + "char_freq_%21": "0.778", + "char_freq_%24": "0", + "char_freq_%23": "0", + "capital_run_length_average": "3.756", + "capital_run_length_longest": "61", + "capital_run_length_total": "278", + "class": "1" + }, + { + "word_freq_make": "0.21", + "word_freq_address": "0.28", + "word_freq_all": "0.5", + "word_freq_3d": "0", + "word_freq_our": "0.14", + "word_freq_over": "0.28", + "word_freq_remove": "0.21", + "word_freq_internet": "0.07", + "word_freq_order": "0", + "word_freq_mail": "0.94", + "word_freq_receive": "0.21", + "word_freq_will": "0.79", + "word_freq_people": "0.65", + "word_freq_report": "0.21", + "word_freq_addresses": "0.14", + "word_freq_free": "0.14", + "word_freq_business": "0.07", + "word_freq_email": "0.28", + "word_freq_you": "3.47", + "word_freq_credit": "0", + "word_freq_your": "1.59", + "word_freq_font": "0", + "word_freq_000": "0.43", + "word_freq_money": "0.43", + "word_freq_hp": "0", + "word_freq_hpl": "0", + "word_freq_george": "0", + "word_freq_650": "0", + "word_freq_lab": "0", + "word_freq_labs": "0", + "word_freq_telnet": "0", + "word_freq_857": "0", + "word_freq_data": "0", + "word_freq_415": "0", + "word_freq_85": "0", + "word_freq_technology": "0", + "word_freq_1999": "0.07", + "word_freq_parts": "0", + "word_freq_pm": "0", + "word_freq_direct": "0", + "word_freq_cs": "0", + "word_freq_meeting": "0", + "word_freq_original": "0", + "word_freq_project": "0", + "word_freq_re": "0", + "word_freq_edu": "0", + "word_freq_table": "0", + "word_freq_conference": "0", + "char_freq_%3B": "0", + "char_freq_%28": "0.132", + "char_freq_%5B": "0", + "char_freq_%21": "0.372", + "char_freq_%24": "0.18", + "char_freq_%23": "0.048", + "capital_run_length_average": "5.114", + "capital_run_length_longest": "101", + "capital_run_length_total": "1028", + "class": "1" + }, + { + "word_freq_make": "0.06", + "word_freq_address": "0", + "word_freq_all": "0.71", + "word_freq_3d": "0", + "word_freq_our": "1.23", + "word_freq_over": "0.19", + "word_freq_remove": "0.19", + "word_freq_internet": "0.12", + "word_freq_order": "0.64", + "word_freq_mail": "0.25", + "word_freq_receive": "0.38", + "word_freq_will": "0.45", + "word_freq_people": "0.12", + "word_freq_report": "0", + "word_freq_addresses": "1.75", + "word_freq_free": "0.06", + "word_freq_business": "0.06", + "word_freq_email": "1.03", + "word_freq_you": "1.36", + "word_freq_credit": "0.32", + "word_freq_your": "0.51", + "word_freq_font": "0", + "word_freq_000": "1.16", + "word_freq_money": "0.06", + "word_freq_hp": "0", + "word_freq_hpl": "0", + "word_freq_george": "0", + "word_freq_650": "0", + "word_freq_lab": "0", + "word_freq_labs": "0", + "word_freq_telnet": "0", + "word_freq_857": "0", + "word_freq_data": "0", + "word_freq_415": "0", + "word_freq_85": "0", + "word_freq_technology": "0", + "word_freq_1999": "0", + "word_freq_parts": "0", + "word_freq_pm": "0", + "word_freq_direct": "0.06", + "word_freq_cs": "0", + "word_freq_meeting": "0", + "word_freq_original": "0.12", + "word_freq_project": "0", + "word_freq_re": "0.06", + "word_freq_edu": "0.06", + "word_freq_table": "0", + "word_freq_conference": "0", + "char_freq_%3B": "0.01", + "char_freq_%28": "0.143", + "char_freq_%5B": "0", + "char_freq_%21": "0.276", + "char_freq_%24": "0.184", + "char_freq_%23": "0.01", + "capital_run_length_average": "9.821", + "capital_run_length_longest": "485", + "capital_run_length_total": "2259", + "class": "1" + }, + { + "word_freq_make": "0", + "word_freq_address": "0", + "word_freq_all": "0", + "word_freq_3d": "0", + "word_freq_our": "0.63", + "word_freq_over": "0", + "word_freq_remove": "0.31", + "word_freq_internet": "0.63", + "word_freq_order": "0.31", + "word_freq_mail": "0.63", + "word_freq_receive": "0.31", + "word_freq_will": "0.31", + "word_freq_people": "0.31", + "word_freq_report": "0", + "word_freq_addresses": "0", + "word_freq_free": "0.31", + "word_freq_business": "0", + "word_freq_email": "0", + "word_freq_you": "3.18", + "word_freq_credit": "0", + "word_freq_your": "0.31", + "word_freq_font": "0", + "word_freq_000": "0", + "word_freq_money": "0", + "word_freq_hp": "0", + "word_freq_hpl": "0", + "word_freq_george": "0", + "word_freq_650": "0", + "word_freq_lab": "0", + "word_freq_labs": "0", + "word_freq_telnet": "0", + "word_freq_857": "0", + "word_freq_data": "0", + "word_freq_415": "0", + "word_freq_85": "0", + "word_freq_technology": "0", + "word_freq_1999": "0", + "word_freq_parts": "0", + "word_freq_pm": "0", + "word_freq_direct": "0", + "word_freq_cs": "0", + "word_freq_meeting": "0", + "word_freq_original": "0", + "word_freq_project": "0", + "word_freq_re": "0", + "word_freq_edu": "0", + "word_freq_table": "0", + "word_freq_conference": "0", + "char_freq_%3B": "0", + "char_freq_%28": "0.137", + "char_freq_%5B": "0", + "char_freq_%21": "0.137", + "char_freq_%24": "0", + "char_freq_%23": "0", + "capital_run_length_average": "3.537", + "capital_run_length_longest": "40", + "capital_run_length_total": "191", + "class": "1" + }, + { + "word_freq_make": "0", + "word_freq_address": "0", + "word_freq_all": "0", + "word_freq_3d": "0", + "word_freq_our": "0.63", + "word_freq_over": "0", + "word_freq_remove": "0.31", + "word_freq_internet": "0.63", + "word_freq_order": "0.31", + "word_freq_mail": "0.63", + "word_freq_receive": "0.31", + "word_freq_will": "0.31", + "word_freq_people": "0.31", + "word_freq_report": "0", + "word_freq_addresses": "0", + "word_freq_free": "0.31", + "word_freq_business": "0", + "word_freq_email": "0", + "word_freq_you": "3.18", + "word_freq_credit": "0", + "word_freq_your": "0.31", + "word_freq_font": "0", + "word_freq_000": "0", + "word_freq_money": "0", + "word_freq_hp": "0", + "word_freq_hpl": "0", + "word_freq_george": "0", + "word_freq_650": "0", + "word_freq_lab": "0", + "word_freq_labs": "0", + "word_freq_telnet": "0", + "word_freq_857": "0", + "word_freq_data": "0", + "word_freq_415": "0", + "word_freq_85": "0", + "word_freq_technology": "0", + "word_freq_1999": "0", + "word_freq_parts": "0", + "word_freq_pm": "0", + "word_freq_direct": "0", + "word_freq_cs": "0", + "word_freq_meeting": "0", + "word_freq_original": "0", + "word_freq_project": "0", + "word_freq_re": "0", + "word_freq_edu": "0", + "word_freq_table": "0", + "word_freq_conference": "0", + "char_freq_%3B": "0", + "char_freq_%28": "0.135", + "char_freq_%5B": "0", + "char_freq_%21": "0.135", + "char_freq_%24": "0", + "char_freq_%23": "0", + "capital_run_length_average": "3.537", + "capital_run_length_longest": "40", + "capital_run_length_total": "191", + "class": "1" + } + ] +} + +Shortlisted templates: +[ + { + "template_id": "tpl_tpcds_within_group_share", + "template_name": "Within-Group Share of Total", + "primary_family": "conditional_dependency_structure", + "portability": "partial", + "sql_skeleton": "SELECT {group_col}, {item_col},\n SUM({measure_col}) AS total_measure,\n SUM({measure_col}) * 100.0 / SUM(SUM({measure_col})) OVER (PARTITION BY {group_col}) AS share_within_group\nFROM {table}\nGROUP BY {group_col}, {item_col}\nORDER BY share_within_group DESC;", + "required_roles": [ + "group_col", + "item_col", + "measure_col" + ] + } +] + +Problem instance: +{ + "dataset_id": "n1", + "question": "Use template Within-Group Share of Total to probe dependency_strength_similarity with semantic role within_group_proportion. Focus on group_col=class, measure_col=word_freq_george.", + "planned_template_id": "tpl_tpcds_within_group_share", + "bindings": { + "group_col": "class", + "measure_col": "word_freq_george", + "item_col": "word_freq_650", + "top_k": 11, + "top_n": 5, + "num_tiles": 10, + "percentile_value": 0.95, + "z_threshold": 2.0, + "fraction_threshold": 0.1, + "baseline_multiplier": 1.5, + "baseline_fraction": 0.1, + "min_group_size": 5, + "min_support": 5, + "measure_threshold": 0.0, + "time_grain": "month", + "lookback_rows": 3, + "current_period_start": "'2024-01-01'", + "current_period_end": "'2024-04-01'", + "previous_period_start": "'2023-10-01'", + "previous_period_end": "'2024-01-01'", + "drift_ratio_threshold": 0.8 + }, + "can_vary": [], + "must_fix": [], + "runtime_sql_skeleton": "SELECT {group_col}, {item_col},\n SUM({measure_col}) AS total_measure,\n SUM({measure_col}) * 100.0 / SUM(SUM({measure_col})) OVER (PARTITION BY {group_col}) AS share_within_group\nFROM {table}\nGROUP BY {group_col}, {item_col}\nORDER BY share_within_group DESC;" +} + +Repair context: +{} diff --git a/Query/sql/v2/runs/v2_cli_20260502_081223_d/n1/artifacts/v2q_n1_15f59192bc30161b/cli/sql_response_attempt_1.raw.txt b/Query/sql/v2/runs/v2_cli_20260502_081223_d/n1/artifacts/v2q_n1_15f59192bc30161b/cli/sql_response_attempt_1.raw.txt new file mode 100644 index 0000000000000000000000000000000000000000..6a426c9b6a4148f7b5c6e37f706cafd979e22162 --- /dev/null +++ b/Query/sql/v2/runs/v2_cli_20260502_081223_d/n1/artifacts/v2q_n1_15f59192bc30161b/cli/sql_response_attempt_1.raw.txt @@ -0,0 +1,4 @@ +{"type":"thread.started","thread_id":"019e40e0-34ab-7d90-812b-5f0510a6d955"} +{"type":"turn.started"} +{"type":"item.completed","item":{"id":"item_0","type":"agent_message","text":"{\"sql\":\"-- template_id: tpl_tpcds_within_group_share\\nSELECT\\n \\\"class\\\",\\n \\\"word_freq_650\\\",\\n SUM(CAST(\\\"word_freq_george\\\" AS REAL)) AS total_measure,\\n SUM(CAST(\\\"word_freq_george\\\" AS REAL)) * 100.0 / NULLIF(SUM(SUM(CAST(\\\"word_freq_george\\\" AS REAL))) OVER (PARTITION BY \\\"class\\\"), 0.0) AS share_within_group\\nFROM \\\"n1\\\"\\nGROUP BY \\\"class\\\", \\\"word_freq_650\\\"\\nORDER BY share_within_group DESC;\",\"notes\":\"Applied the Within-Group Share of Total template with group_col=\\\"class\\\", item_col=\\\"word_freq_650\\\", and measure_col=\\\"word_freq_george\\\". Cast the measure from TEXT to REAL for SQLite numeric aggregation and used NULLIF to avoid divide-by-zero.\"}"}} +{"type":"turn.completed","usage":{"input_tokens":20438,"cached_input_tokens":19840,"output_tokens":711,"reasoning_output_tokens":516}} diff --git a/Query/sql/v2/runs/v2_cli_20260502_081223_d/n1/artifacts/v2q_n1_15f59192bc30161b/cli/sql_response_attempt_1.txt b/Query/sql/v2/runs/v2_cli_20260502_081223_d/n1/artifacts/v2q_n1_15f59192bc30161b/cli/sql_response_attempt_1.txt new file mode 100644 index 0000000000000000000000000000000000000000..de1e37def5ff9faef7163ad39d231e875061c058 --- /dev/null +++ b/Query/sql/v2/runs/v2_cli_20260502_081223_d/n1/artifacts/v2q_n1_15f59192bc30161b/cli/sql_response_attempt_1.txt @@ -0,0 +1 @@ +{"sql":"-- template_id: tpl_tpcds_within_group_share\nSELECT\n \"class\",\n \"word_freq_650\",\n SUM(CAST(\"word_freq_george\" AS REAL)) AS total_measure,\n SUM(CAST(\"word_freq_george\" AS REAL)) * 100.0 / NULLIF(SUM(SUM(CAST(\"word_freq_george\" AS REAL))) OVER (PARTITION BY \"class\"), 0.0) AS share_within_group\nFROM \"n1\"\nGROUP BY \"class\", \"word_freq_650\"\nORDER BY share_within_group DESC;","notes":"Applied the Within-Group Share of Total template with group_col=\"class\", item_col=\"word_freq_650\", and measure_col=\"word_freq_george\". Cast the measure from TEXT to REAL for SQLite numeric aggregation and used NULLIF to avoid divide-by-zero."} \ No newline at end of file diff --git a/Query/sql/v2/runs/v2_cli_20260502_081223_d/n1/artifacts/v2q_n1_15f59192bc30161b/cli/sql_stderr_attempt_1.txt b/Query/sql/v2/runs/v2_cli_20260502_081223_d/n1/artifacts/v2q_n1_15f59192bc30161b/cli/sql_stderr_attempt_1.txt new file mode 100644 index 0000000000000000000000000000000000000000..e69de29bb2d1d6434b8b29ae775ad8c2e48c5391 diff --git a/Query/sql/v2/runs/v2_cli_20260502_081223_d/n1/artifacts/v2q_n1_1b29859e41bcb9ed/cli/sql_attempt_1.metadata.json b/Query/sql/v2/runs/v2_cli_20260502_081223_d/n1/artifacts/v2q_n1_1b29859e41bcb9ed/cli/sql_attempt_1.metadata.json new file mode 100644 index 0000000000000000000000000000000000000000..03a37573204bb109fee1e2fc0ab8f5ce6a64676b --- /dev/null +++ b/Query/sql/v2/runs/v2_cli_20260502_081223_d/n1/artifacts/v2q_n1_1b29859e41bcb9ed/cli/sql_attempt_1.metadata.json @@ -0,0 +1,43 @@ +{ + "attempt": 1, + "phase": "sql_generation", + "command": "codex exec --skip-git-repo-check --disable plugins --sandbox read-only --cd \"/data/jialinzhang/SQLagent\" -m gpt-5.4 --json -", + "started_at": "2026-05-19T15:56:48.321943+00:00", + "ended_at": "2026-05-19T15:56:51.602620+00:00", + "elapsed_ms": 3280.64, + "returncode": 1, + "prompt_metrics": { + "chars": 29533, + "bytes_utf8": 29533, + "lines": 792, + "estimated_tokens": null + }, + "stdout_metrics": { + "chars": 281, + "bytes_utf8": 281, + "lines": 4, + "estimated_tokens": null + }, + "stderr_metrics": { + "chars": 0, + "bytes_utf8": 0, + "lines": 0, + "estimated_tokens": null + }, + "parsed_output": { + "format": "jsonl_events", + "text_metrics": { + "chars": 280, + "bytes_utf8": 280, + "lines": 4, + "estimated_tokens": null + }, + "usage": {} + }, + "status": "failed", + "error": "AI CLI command failed with exit code 1: ", + "prompt_path": "cli/sql_prompt_attempt_1.txt", + "response_path": "cli/sql_response_attempt_1.txt", + "raw_response_path": "cli/sql_response_attempt_1.raw.txt", + "stderr_path": "cli/sql_stderr_attempt_1.txt" +} \ No newline at end of file diff --git a/Query/sql/v2/runs/v2_cli_20260502_081223_d/n1/artifacts/v2q_n1_1b29859e41bcb9ed/cli/sql_attempt_2.metadata.json b/Query/sql/v2/runs/v2_cli_20260502_081223_d/n1/artifacts/v2q_n1_1b29859e41bcb9ed/cli/sql_attempt_2.metadata.json new file mode 100644 index 0000000000000000000000000000000000000000..6cf794155dbbd25bbb200af7f93856ca2f705faf --- /dev/null +++ b/Query/sql/v2/runs/v2_cli_20260502_081223_d/n1/artifacts/v2q_n1_1b29859e41bcb9ed/cli/sql_attempt_2.metadata.json @@ -0,0 +1,43 @@ +{ + "attempt": 2, + "phase": "sql_generation", + "command": "codex exec --skip-git-repo-check --disable plugins --sandbox read-only --cd \"/data/jialinzhang/SQLagent\" -m gpt-5.4 --json -", + "started_at": "2026-05-19T15:56:52.607576+00:00", + "ended_at": "2026-05-19T15:56:57.080203+00:00", + "elapsed_ms": 4472.58, + "returncode": 1, + "prompt_metrics": { + "chars": 29533, + "bytes_utf8": 29533, + "lines": 792, + "estimated_tokens": null + }, + "stdout_metrics": { + "chars": 281, + "bytes_utf8": 281, + "lines": 4, + "estimated_tokens": null + }, + "stderr_metrics": { + "chars": 0, + "bytes_utf8": 0, + "lines": 0, + "estimated_tokens": null + }, + "parsed_output": { + "format": "jsonl_events", + "text_metrics": { + "chars": 280, + "bytes_utf8": 280, + "lines": 4, + "estimated_tokens": null + }, + "usage": {} + }, + "status": "failed", + "error": "AI CLI command failed with exit code 1: ", + "prompt_path": "cli/sql_prompt_attempt_2.txt", + "response_path": "cli/sql_response_attempt_2.txt", + "raw_response_path": "cli/sql_response_attempt_2.raw.txt", + "stderr_path": "cli/sql_stderr_attempt_2.txt" +} \ No newline at end of file diff --git a/Query/sql/v2/runs/v2_cli_20260502_081223_d/n1/artifacts/v2q_n1_1b29859e41bcb9ed/cli/sql_prompt_attempt_1.txt b/Query/sql/v2/runs/v2_cli_20260502_081223_d/n1/artifacts/v2q_n1_1b29859e41bcb9ed/cli/sql_prompt_attempt_1.txt new file mode 100644 index 0000000000000000000000000000000000000000..34be9ccb07be5736dd891608580cf6e22198561d --- /dev/null +++ b/Query/sql/v2/runs/v2_cli_20260502_081223_d/n1/artifacts/v2q_n1_1b29859e41bcb9ed/cli/sql_prompt_attempt_1.txt @@ -0,0 +1,792 @@ +You are generating one SQLite SELECT query for a single-table SQL QA task. +Return strict JSON only, with this schema: {"sql": "...", "notes": "..."}. +Rules: +- Use only the provided table and columns. +- Do not write INSERT, UPDATE, DELETE, DROP, ALTER, CREATE, PRAGMA, ATTACH, DETACH, or VACUUM. +- Prefer the planned template and bound roles when provided. +- Add a leading SQL comment exactly like: -- template_id: . +- Generate SQLite-compatible SQL. SQLite does not support PERCENTILE_CONT or STDDEV. +- Quote identifiers with double quotes. +- Return no markdown and no extra prose. + +Dataset context: +Dataset context for SQL QA: +- dataset_id: n1 +- dataset_name: Spambase +- table_name: n1 +- table_layout: single-table dataset (do not assume joins). +- row_semantics: One row is one email represented by engineered token/character frequency and capitalization-run features. +- task_type: classification +- target_column: class +- main_row_count: 4601 +- important_fields: +- word_freq_make: role=feature, type=numeric_sparse_frequency. tags=['condition_candidate', 'measure', 'rare_pattern_candidate'] desc=Frequency feature for token 'make' in an email message. +- word_freq_address: role=feature, type=numeric_sparse_frequency. tags=['condition_candidate', 'measure', 'rare_pattern_candidate'] desc=Frequency feature for token 'address' in an email message. +- word_freq_all: role=feature, type=numeric_sparse_frequency. tags=['condition_candidate', 'measure', 'rare_pattern_candidate'] desc=Frequency feature for token 'all' in an email message. +- word_freq_3d: role=feature, type=numeric_sparse_frequency. tags=['condition_candidate', 'measure', 'rare_pattern_candidate'] desc=Frequency feature for token '3d' in an email message. +- word_freq_our: role=feature, type=numeric_sparse_frequency. tags=['condition_candidate', 'measure', 'rare_pattern_candidate'] desc=Frequency feature for token 'our' in an email message. +- word_freq_over: role=feature, type=numeric_sparse_frequency. tags=['condition_candidate', 'measure', 'rare_pattern_candidate'] desc=Frequency feature for token 'over' in an email message. +- word_freq_remove: role=feature, type=numeric_sparse_frequency. tags=['condition_candidate', 'measure', 'rare_pattern_candidate'] desc=Frequency feature for token 'remove' in an email message. +- word_freq_internet: role=feature, type=numeric_sparse_frequency. tags=['condition_candidate', 'measure', 'rare_pattern_candidate'] desc=Frequency feature for token 'internet' in an email message. +- word_freq_order: role=feature, type=numeric_sparse_frequency. tags=['condition_candidate', 'measure', 'rare_pattern_candidate'] desc=Frequency feature for token 'order' in an email message. +- word_freq_mail: role=feature, type=numeric_sparse_frequency. tags=['condition_candidate', 'measure', 'rare_pattern_candidate'] desc=Frequency feature for token 'mail' in an email message. +- word_freq_receive: role=feature, type=numeric_sparse_frequency. tags=['condition_candidate', 'measure', 'rare_pattern_candidate'] desc=Frequency feature for token 'receive' in an email message. +- word_freq_will: role=feature, type=numeric_sparse_frequency. tags=['condition_candidate', 'measure', 'rare_pattern_candidate'] desc=Frequency feature for token 'will' in an email message. +- word_freq_people: role=feature, type=numeric_sparse_frequency. tags=['condition_candidate', 'measure', 'rare_pattern_candidate'] desc=Frequency feature for token 'people' in an email message. +- word_freq_report: role=feature, type=numeric_sparse_frequency. tags=['condition_candidate', 'measure', 'rare_pattern_candidate'] desc=Frequency feature for token 'report' in an email message. +- word_freq_addresses: role=feature, type=numeric_sparse_frequency. tags=['condition_candidate', 'measure', 'rare_pattern_candidate'] desc=Frequency feature for token 'addresses' in an email message. +- word_freq_free: role=feature, type=numeric_sparse_frequency. tags=['condition_candidate', 'measure', 'rare_pattern_candidate'] desc=Frequency feature for token 'free' in an email message. +- word_freq_business: role=feature, type=numeric_sparse_frequency. tags=['condition_candidate', 'measure', 'rare_pattern_candidate'] desc=Frequency feature for token 'business' in an email message. +- word_freq_email: role=feature, type=numeric_sparse_frequency. tags=['condition_candidate', 'measure', 'rare_pattern_candidate'] desc=Frequency feature for token 'email' in an email message. +- word_freq_you: role=feature, type=numeric_sparse_frequency. tags=['condition_candidate', 'measure', 'rare_pattern_candidate'] desc=Frequency feature for token 'you' in an email message. +- word_freq_credit: role=feature, type=numeric_sparse_frequency. tags=['condition_candidate', 'measure', 'rare_pattern_candidate'] desc=Frequency feature for token 'credit' in an email message. +- word_freq_your: role=feature, type=numeric_sparse_frequency. tags=['condition_candidate', 'measure', 'rare_pattern_candidate'] desc=Frequency feature for token 'your' in an email message. +- word_freq_font: role=feature, type=numeric_sparse_frequency. tags=['condition_candidate', 'measure', 'rare_pattern_candidate'] desc=Frequency feature for token 'font' in an email message. +- word_freq_000: role=feature, type=numeric_sparse_frequency. tags=['condition_candidate', 'measure', 'rare_pattern_candidate'] desc=Frequency feature for token '000' in an email message. +- word_freq_money: role=feature, type=numeric_sparse_frequency. tags=['condition_candidate', 'measure', 'rare_pattern_candidate'] desc=Frequency feature for token 'money' in an email message. +- word_freq_hp: role=feature, type=numeric_sparse_frequency. tags=['condition_candidate', 'measure', 'rare_pattern_candidate'] desc=Frequency feature for token 'hp' in an email message. +- word_freq_hpl: role=feature, type=numeric_sparse_frequency. tags=['condition_candidate', 'measure', 'rare_pattern_candidate'] desc=Frequency feature for token 'hpl' in an email message. +- word_freq_george: role=feature, type=numeric_sparse_frequency. tags=['condition_candidate', 'measure', 'rare_pattern_candidate'] desc=Frequency feature for token 'george' in an email message. +- word_freq_650: role=feature, type=numeric_sparse_frequency. tags=['condition_candidate', 'measure', 'rare_pattern_candidate'] desc=Frequency feature for token '650' in an email message. +- word_freq_lab: role=feature, type=numeric_sparse_frequency. tags=['condition_candidate', 'measure', 'rare_pattern_candidate'] desc=Frequency feature for token 'lab' in an email message. +- word_freq_labs: role=feature, type=numeric_sparse_frequency. tags=['condition_candidate', 'measure', 'rare_pattern_candidate'] desc=Frequency feature for token 'labs' in an email message. +- word_freq_telnet: role=feature, type=numeric_sparse_frequency. tags=['condition_candidate', 'measure', 'rare_pattern_candidate'] desc=Frequency feature for token 'telnet' in an email message. +- word_freq_857: role=feature, type=numeric_sparse_frequency. tags=['condition_candidate', 'measure', 'rare_pattern_candidate'] desc=Frequency feature for token '857' in an email message. +- word_freq_data: role=feature, type=numeric_sparse_frequency. tags=['condition_candidate', 'measure', 'rare_pattern_candidate'] desc=Frequency feature for token 'data' in an email message. +- word_freq_415: role=feature, type=numeric_sparse_frequency. tags=['condition_candidate', 'measure', 'rare_pattern_candidate'] desc=Frequency feature for token '415' in an email message. +- word_freq_85: role=feature, type=numeric_sparse_frequency. tags=['condition_candidate', 'measure', 'rare_pattern_candidate'] desc=Frequency feature for token '85' in an email message. +- word_freq_technology: role=feature, type=numeric_sparse_frequency. tags=['condition_candidate', 'measure', 'rare_pattern_candidate'] desc=Frequency feature for token 'technology' in an email message. +- word_freq_1999: role=feature, type=numeric_sparse_frequency. tags=['condition_candidate', 'measure', 'rare_pattern_candidate'] desc=Frequency feature for token '1999' in an email message. +- word_freq_parts: role=feature, type=numeric_sparse_frequency. tags=['condition_candidate', 'measure', 'rare_pattern_candidate'] desc=Frequency feature for token 'parts' in an email message. +- word_freq_pm: role=feature, type=numeric_sparse_frequency. tags=['condition_candidate', 'measure', 'rare_pattern_candidate'] desc=Frequency feature for token 'pm' in an email message. +- word_freq_direct: role=feature, type=numeric_sparse_frequency. tags=['condition_candidate', 'measure', 'rare_pattern_candidate'] desc=Frequency feature for token 'direct' in an email message. +- word_freq_cs: role=feature, type=numeric_sparse_frequency. tags=['condition_candidate', 'measure', 'rare_pattern_candidate'] desc=Frequency feature for token 'cs' in an email message. +- word_freq_meeting: role=feature, type=numeric_sparse_frequency. tags=['condition_candidate', 'measure', 'rare_pattern_candidate'] desc=Frequency feature for token 'meeting' in an email message. +- word_freq_original: role=feature, type=numeric_sparse_frequency. tags=['condition_candidate', 'measure', 'rare_pattern_candidate'] desc=Frequency feature for token 'original' in an email message. +- word_freq_project: role=feature, type=numeric_sparse_frequency. tags=['condition_candidate', 'measure', 'rare_pattern_candidate'] desc=Frequency feature for token 'project' in an email message. +- word_freq_re: role=feature, type=numeric_sparse_frequency. tags=['condition_candidate', 'measure', 'rare_pattern_candidate'] desc=Frequency feature for token 're' in an email message. +- word_freq_edu: role=feature, type=numeric_sparse_frequency. tags=['condition_candidate', 'measure', 'rare_pattern_candidate'] desc=Frequency feature for token 'edu' in an email message. +- word_freq_table: role=feature, type=numeric_sparse_frequency. tags=['condition_candidate', 'measure', 'rare_pattern_candidate'] desc=Frequency feature for token 'table' in an email message. +- word_freq_conference: role=feature, type=numeric_sparse_frequency. tags=['condition_candidate', 'measure', 'rare_pattern_candidate'] desc=Frequency feature for token 'conference' in an email message. +- char_freq_%3B: role=feature, type=numeric_sparse_frequency. tags=['condition_candidate', 'measure', 'rare_pattern_candidate'] desc=Character frequency feature for symbol ';'. +- char_freq_%28: role=feature, type=numeric_sparse_frequency. tags=['condition_candidate', 'measure', 'rare_pattern_candidate'] desc=Character frequency feature for symbol '('. +- char_freq_%5B: role=feature, type=numeric_sparse_frequency. tags=['condition_candidate', 'measure', 'rare_pattern_candidate'] desc=Character frequency feature for symbol '['. +- char_freq_%21: role=feature, type=numeric_sparse_frequency. tags=['condition_candidate', 'measure', 'rare_pattern_candidate'] desc=Character frequency feature for symbol '!'. +- char_freq_%24: role=feature, type=numeric_sparse_frequency. tags=['condition_candidate', 'measure', 'rare_pattern_candidate'] desc=Character frequency feature for symbol '$'. +- char_freq_%23: role=feature, type=numeric_sparse_frequency. tags=['condition_candidate', 'measure', 'rare_pattern_candidate'] desc=Character frequency feature for symbol '#'. +- capital_run_length_average: role=feature, type=numeric_heavy_tail. tags=['condition_candidate', 'measure', 'rare_pattern_candidate'] desc=Average length of uninterrupted capital-letter runs. +- capital_run_length_longest: role=feature, type=numeric_heavy_tail. tags=['condition_candidate', 'measure', 'rare_pattern_candidate'] desc=Longest uninterrupted capital-letter run. +- capital_run_length_total: role=feature, type=numeric_heavy_tail. tags=['condition_candidate', 'measure', 'rare_pattern_candidate'] desc=Total number of capital letters in runs. +- class: role=target, type=binary_target. tags=['target_candidate'] desc=Binary spam label (1=spam, 0=non-spam). +- useful_field_combinations: [['word_freq_free', 'word_freq_you', 'class'], ['char_freq_%21', 'capital_run_length_longest', 'class'], ['word_freq_remove', 'word_freq_money', 'class']] +- fields_requiring_caution: ['capital_run_length_average', 'capital_run_length_longest', 'capital_run_length_total'] +- source_url: https://www.openml.org/d/44 + +SQLite schema snapshot: +{ + "table_name": "n1", + "quoted_table_name": "\"n1\"", + "row_count": 4601, + "columns": [ + { + "name": "word_freq_make", + "type": "TEXT", + "notnull": false, + "pk": false + }, + { + "name": "word_freq_address", + "type": "TEXT", + "notnull": false, + "pk": false + }, + { + "name": "word_freq_all", + "type": "TEXT", + "notnull": false, + "pk": false + }, + { + "name": "word_freq_3d", + "type": "TEXT", + "notnull": false, + "pk": false + }, + { + "name": "word_freq_our", + "type": "TEXT", + "notnull": false, + "pk": false + }, + { + "name": "word_freq_over", + "type": "TEXT", + "notnull": false, + "pk": false + }, + { + "name": "word_freq_remove", + "type": "TEXT", + "notnull": false, + "pk": false + }, + { + "name": "word_freq_internet", + "type": "TEXT", + "notnull": false, + "pk": false + }, + { + "name": "word_freq_order", + "type": "TEXT", + "notnull": false, + "pk": false + }, + { + "name": "word_freq_mail", + "type": "TEXT", + "notnull": false, + "pk": false + }, + { + "name": "word_freq_receive", + "type": "TEXT", + "notnull": false, + "pk": false + }, + { + "name": "word_freq_will", + "type": "TEXT", + "notnull": false, + "pk": false + }, + { + "name": "word_freq_people", + "type": "TEXT", + "notnull": false, + "pk": false + }, + { + "name": "word_freq_report", + "type": "TEXT", + "notnull": false, + "pk": false + }, + { + "name": "word_freq_addresses", + "type": "TEXT", + "notnull": false, + "pk": false + }, + { + "name": "word_freq_free", + "type": "TEXT", + "notnull": false, + "pk": false + }, + { + "name": "word_freq_business", + "type": "TEXT", + "notnull": false, + "pk": false + }, + { + "name": "word_freq_email", + "type": "TEXT", + "notnull": false, + "pk": false + }, + { + "name": "word_freq_you", + "type": "TEXT", + "notnull": false, + "pk": false + }, + { + "name": "word_freq_credit", + "type": "TEXT", + "notnull": false, + "pk": false + }, + { + "name": "word_freq_your", + "type": "TEXT", + "notnull": false, + "pk": false + }, + { + "name": "word_freq_font", + "type": "TEXT", + "notnull": false, + "pk": false + }, + { + "name": "word_freq_000", + "type": "TEXT", + "notnull": false, + "pk": false + }, + { + "name": "word_freq_money", + "type": "TEXT", + "notnull": false, + "pk": false + }, + { + "name": "word_freq_hp", + "type": "TEXT", + "notnull": false, + "pk": false + }, + { + "name": "word_freq_hpl", + "type": "TEXT", + "notnull": false, + "pk": false + }, + { + "name": "word_freq_george", + "type": "TEXT", + "notnull": false, + "pk": false + }, + { + "name": "word_freq_650", + "type": "TEXT", + "notnull": false, + "pk": false + }, + { + "name": "word_freq_lab", + "type": "TEXT", + "notnull": false, + "pk": false + }, + { + "name": "word_freq_labs", + "type": "TEXT", + "notnull": false, + "pk": false + }, + { + "name": "word_freq_telnet", + "type": "TEXT", + "notnull": false, + "pk": false + }, + { + "name": "word_freq_857", + "type": "TEXT", + "notnull": false, + "pk": false + }, + { + "name": "word_freq_data", + "type": "TEXT", + "notnull": false, + "pk": false + }, + { + "name": "word_freq_415", + "type": "TEXT", + "notnull": false, + "pk": false + }, + { + "name": "word_freq_85", + "type": "TEXT", + "notnull": false, + "pk": false + }, + { + "name": "word_freq_technology", + "type": "TEXT", + "notnull": false, + "pk": false + }, + { + "name": "word_freq_1999", + "type": "TEXT", + "notnull": false, + "pk": false + }, + { + "name": "word_freq_parts", + "type": "TEXT", + "notnull": false, + "pk": false + }, + { + "name": "word_freq_pm", + "type": "TEXT", + "notnull": false, + "pk": false + }, + { + "name": "word_freq_direct", + "type": "TEXT", + "notnull": false, + "pk": false + }, + { + "name": "word_freq_cs", + "type": "TEXT", + "notnull": false, + "pk": false + }, + { + "name": "word_freq_meeting", + "type": "TEXT", + "notnull": false, + "pk": false + }, + { + "name": "word_freq_original", + "type": "TEXT", + "notnull": false, + "pk": false + }, + { + "name": "word_freq_project", + "type": "TEXT", + "notnull": false, + "pk": false + }, + { + "name": "word_freq_re", + "type": "TEXT", + "notnull": false, + "pk": false + }, + { + "name": "word_freq_edu", + "type": "TEXT", + "notnull": false, + "pk": false + }, + { + "name": "word_freq_table", + "type": "TEXT", + "notnull": false, + "pk": false + }, + { + "name": "word_freq_conference", + "type": "TEXT", + "notnull": false, + "pk": false + }, + { + "name": "char_freq_%3B", + "type": "TEXT", + "notnull": false, + "pk": false + }, + { + "name": "char_freq_%28", + "type": "TEXT", + "notnull": false, + "pk": false + }, + { + "name": "char_freq_%5B", + "type": "TEXT", + "notnull": false, + "pk": false + }, + { + "name": "char_freq_%21", + "type": "TEXT", + "notnull": false, + "pk": false + }, + { + "name": "char_freq_%24", + "type": "TEXT", + "notnull": false, + "pk": false + }, + { + "name": "char_freq_%23", + "type": "TEXT", + "notnull": false, + "pk": false + }, + { + "name": "capital_run_length_average", + "type": "TEXT", + "notnull": false, + "pk": false + }, + { + "name": "capital_run_length_longest", + "type": "TEXT", + "notnull": false, + "pk": false + }, + { + "name": "capital_run_length_total", + "type": "TEXT", + "notnull": false, + "pk": false + }, + { + "name": "class", + "type": "TEXT", + "notnull": false, + "pk": false + } + ], + "sample_rows": [ + { + "word_freq_make": "0", + "word_freq_address": "0.64", + "word_freq_all": "0.64", + "word_freq_3d": "0", + "word_freq_our": "0.32", + "word_freq_over": "0", + "word_freq_remove": "0", + "word_freq_internet": "0", + "word_freq_order": "0", + "word_freq_mail": "0", + "word_freq_receive": "0", + "word_freq_will": "0.64", + "word_freq_people": "0", + "word_freq_report": "0", + "word_freq_addresses": "0", + "word_freq_free": "0.32", + "word_freq_business": "0", + "word_freq_email": "1.29", + "word_freq_you": "1.93", + "word_freq_credit": "0", + "word_freq_your": "0.96", + "word_freq_font": "0", + "word_freq_000": "0", + "word_freq_money": "0", + "word_freq_hp": "0", + "word_freq_hpl": "0", + "word_freq_george": "0", + "word_freq_650": "0", + "word_freq_lab": "0", + "word_freq_labs": "0", + "word_freq_telnet": "0", + "word_freq_857": "0", + "word_freq_data": "0", + "word_freq_415": "0", + "word_freq_85": "0", + "word_freq_technology": "0", + "word_freq_1999": "0", + "word_freq_parts": "0", + "word_freq_pm": "0", + "word_freq_direct": "0", + "word_freq_cs": "0", + "word_freq_meeting": "0", + "word_freq_original": "0", + "word_freq_project": "0", + "word_freq_re": "0", + "word_freq_edu": "0", + "word_freq_table": "0", + "word_freq_conference": "0", + "char_freq_%3B": "0", + "char_freq_%28": "0", + "char_freq_%5B": "0", + "char_freq_%21": "0.778", + "char_freq_%24": "0", + "char_freq_%23": "0", + "capital_run_length_average": "3.756", + "capital_run_length_longest": "61", + "capital_run_length_total": "278", + "class": "1" + }, + { + "word_freq_make": "0.21", + "word_freq_address": "0.28", + "word_freq_all": "0.5", + "word_freq_3d": "0", + "word_freq_our": "0.14", + "word_freq_over": "0.28", + "word_freq_remove": "0.21", + "word_freq_internet": "0.07", + "word_freq_order": "0", + "word_freq_mail": "0.94", + "word_freq_receive": "0.21", + "word_freq_will": "0.79", + "word_freq_people": "0.65", + "word_freq_report": "0.21", + "word_freq_addresses": "0.14", + "word_freq_free": "0.14", + "word_freq_business": "0.07", + "word_freq_email": "0.28", + "word_freq_you": "3.47", + "word_freq_credit": "0", + "word_freq_your": "1.59", + "word_freq_font": "0", + "word_freq_000": "0.43", + "word_freq_money": "0.43", + "word_freq_hp": "0", + "word_freq_hpl": "0", + "word_freq_george": "0", + "word_freq_650": "0", + "word_freq_lab": "0", + "word_freq_labs": "0", + "word_freq_telnet": "0", + "word_freq_857": "0", + "word_freq_data": "0", + "word_freq_415": "0", + "word_freq_85": "0", + "word_freq_technology": "0", + "word_freq_1999": "0.07", + "word_freq_parts": "0", + "word_freq_pm": "0", + "word_freq_direct": "0", + "word_freq_cs": "0", + "word_freq_meeting": "0", + "word_freq_original": "0", + "word_freq_project": "0", + "word_freq_re": "0", + "word_freq_edu": "0", + "word_freq_table": "0", + "word_freq_conference": "0", + "char_freq_%3B": "0", + "char_freq_%28": "0.132", + "char_freq_%5B": "0", + "char_freq_%21": "0.372", + "char_freq_%24": "0.18", + "char_freq_%23": "0.048", + "capital_run_length_average": "5.114", + "capital_run_length_longest": "101", + "capital_run_length_total": "1028", + "class": "1" + }, + { + "word_freq_make": "0.06", + "word_freq_address": "0", + "word_freq_all": "0.71", + "word_freq_3d": "0", + "word_freq_our": "1.23", + "word_freq_over": "0.19", + "word_freq_remove": "0.19", + "word_freq_internet": "0.12", + "word_freq_order": "0.64", + "word_freq_mail": "0.25", + "word_freq_receive": "0.38", + "word_freq_will": "0.45", + "word_freq_people": "0.12", + "word_freq_report": "0", + "word_freq_addresses": "1.75", + "word_freq_free": "0.06", + "word_freq_business": "0.06", + "word_freq_email": "1.03", + "word_freq_you": "1.36", + "word_freq_credit": "0.32", + "word_freq_your": "0.51", + "word_freq_font": "0", + "word_freq_000": "1.16", + "word_freq_money": "0.06", + "word_freq_hp": "0", + "word_freq_hpl": "0", + "word_freq_george": "0", + "word_freq_650": "0", + "word_freq_lab": "0", + "word_freq_labs": "0", + "word_freq_telnet": "0", + "word_freq_857": "0", + "word_freq_data": "0", + "word_freq_415": "0", + "word_freq_85": "0", + "word_freq_technology": "0", + "word_freq_1999": "0", + "word_freq_parts": "0", + "word_freq_pm": "0", + "word_freq_direct": "0.06", + "word_freq_cs": "0", + "word_freq_meeting": "0", + "word_freq_original": "0.12", + "word_freq_project": "0", + "word_freq_re": "0.06", + "word_freq_edu": "0.06", + "word_freq_table": "0", + "word_freq_conference": "0", + "char_freq_%3B": "0.01", + "char_freq_%28": "0.143", + "char_freq_%5B": "0", + "char_freq_%21": "0.276", + "char_freq_%24": "0.184", + "char_freq_%23": "0.01", + "capital_run_length_average": "9.821", + "capital_run_length_longest": "485", + "capital_run_length_total": "2259", + "class": "1" + }, + { + "word_freq_make": "0", + "word_freq_address": "0", + "word_freq_all": "0", + "word_freq_3d": "0", + "word_freq_our": "0.63", + "word_freq_over": "0", + "word_freq_remove": "0.31", + "word_freq_internet": "0.63", + "word_freq_order": "0.31", + "word_freq_mail": "0.63", + "word_freq_receive": "0.31", + "word_freq_will": "0.31", + "word_freq_people": "0.31", + "word_freq_report": "0", + "word_freq_addresses": "0", + "word_freq_free": "0.31", + "word_freq_business": "0", + "word_freq_email": "0", + "word_freq_you": "3.18", + "word_freq_credit": "0", + "word_freq_your": "0.31", + "word_freq_font": "0", + "word_freq_000": "0", + "word_freq_money": "0", + "word_freq_hp": "0", + "word_freq_hpl": "0", + "word_freq_george": "0", + "word_freq_650": "0", + "word_freq_lab": "0", + "word_freq_labs": "0", + "word_freq_telnet": "0", + "word_freq_857": "0", + "word_freq_data": "0", + "word_freq_415": "0", + "word_freq_85": "0", + "word_freq_technology": "0", + "word_freq_1999": "0", + "word_freq_parts": "0", + "word_freq_pm": "0", + "word_freq_direct": "0", + "word_freq_cs": "0", + "word_freq_meeting": "0", + "word_freq_original": "0", + "word_freq_project": "0", + "word_freq_re": "0", + "word_freq_edu": "0", + "word_freq_table": "0", + "word_freq_conference": "0", + "char_freq_%3B": "0", + "char_freq_%28": "0.137", + "char_freq_%5B": "0", + "char_freq_%21": "0.137", + "char_freq_%24": "0", + "char_freq_%23": "0", + "capital_run_length_average": "3.537", + "capital_run_length_longest": "40", + "capital_run_length_total": "191", + "class": "1" + }, + { + "word_freq_make": "0", + "word_freq_address": "0", + "word_freq_all": "0", + "word_freq_3d": "0", + "word_freq_our": "0.63", + "word_freq_over": "0", + "word_freq_remove": "0.31", + "word_freq_internet": "0.63", + "word_freq_order": "0.31", + "word_freq_mail": "0.63", + "word_freq_receive": "0.31", + "word_freq_will": "0.31", + "word_freq_people": "0.31", + "word_freq_report": "0", + "word_freq_addresses": "0", + "word_freq_free": "0.31", + "word_freq_business": "0", + "word_freq_email": "0", + "word_freq_you": "3.18", + "word_freq_credit": "0", + "word_freq_your": "0.31", + "word_freq_font": "0", + "word_freq_000": "0", + "word_freq_money": "0", + "word_freq_hp": "0", + "word_freq_hpl": "0", + "word_freq_george": "0", + "word_freq_650": "0", + "word_freq_lab": "0", + "word_freq_labs": "0", + "word_freq_telnet": "0", + "word_freq_857": "0", + "word_freq_data": "0", + "word_freq_415": "0", + "word_freq_85": "0", + "word_freq_technology": "0", + "word_freq_1999": "0", + "word_freq_parts": "0", + "word_freq_pm": "0", + "word_freq_direct": "0", + "word_freq_cs": "0", + "word_freq_meeting": "0", + "word_freq_original": "0", + "word_freq_project": "0", + "word_freq_re": "0", + "word_freq_edu": "0", + "word_freq_table": "0", + "word_freq_conference": "0", + "char_freq_%3B": "0", + "char_freq_%28": "0.135", + "char_freq_%5B": "0", + "char_freq_%21": "0.135", + "char_freq_%24": "0", + "char_freq_%23": "0", + "capital_run_length_average": "3.537", + "capital_run_length_longest": "40", + "capital_run_length_total": "191", + "class": "1" + } + ] +} + +Shortlisted templates: +[ + { + "template_id": "tpl_grouped_percentile_point", + "template_name": "Grouped Percentile Point", + "primary_family": "tail_rarity_structure", + "portability": "yes", + "sql_skeleton": "SELECT {group_col},\n PERCENTILE_CONT({percentile_value}) WITHIN GROUP (ORDER BY {measure_col}) AS percentile_measure\nFROM {table}\nGROUP BY {group_col}\nORDER BY percentile_measure DESC;", + "required_roles": [ + "group_col", + "measure_col" + ] + } +] + +Problem instance: +{ + "dataset_id": "n1", + "question": "Use template Grouped Percentile Point to probe tail_concentration_consistency with semantic role focused_target_view. Focus on group_col=class, measure_col=word_freq_85.", + "planned_template_id": "tpl_grouped_percentile_point", + "bindings": { + "group_col": "class", + "measure_col": "word_freq_85", + "top_k": 16, + "top_n": 7, + "num_tiles": 10, + "percentile_value": 0.95, + "z_threshold": 2.0, + "fraction_threshold": 0.05, + "baseline_multiplier": 1.75, + "baseline_fraction": 0.1, + "min_group_size": 5, + "min_support": 4, + "measure_threshold": 0.0, + "time_grain": "month", + "lookback_rows": 3, + "current_period_start": "'2024-01-01'", + "current_period_end": "'2024-04-01'", + "previous_period_start": "'2023-10-01'", + "previous_period_end": "'2024-01-01'", + "drift_ratio_threshold": 0.8 + }, + "can_vary": [], + "must_fix": [], + "runtime_sql_skeleton": "SELECT {group_col},\n PERCENTILE_CONT({percentile_value}) WITHIN GROUP (ORDER BY {measure_col}) AS percentile_measure\nFROM {table}\nGROUP BY {group_col}\nORDER BY percentile_measure DESC;" +} + +Repair context: +{} diff --git a/Query/sql/v2/runs/v2_cli_20260502_081223_d/n1/artifacts/v2q_n1_1b29859e41bcb9ed/cli/sql_prompt_attempt_2.txt b/Query/sql/v2/runs/v2_cli_20260502_081223_d/n1/artifacts/v2q_n1_1b29859e41bcb9ed/cli/sql_prompt_attempt_2.txt new file mode 100644 index 0000000000000000000000000000000000000000..34be9ccb07be5736dd891608580cf6e22198561d --- /dev/null +++ b/Query/sql/v2/runs/v2_cli_20260502_081223_d/n1/artifacts/v2q_n1_1b29859e41bcb9ed/cli/sql_prompt_attempt_2.txt @@ -0,0 +1,792 @@ +You are generating one SQLite SELECT query for a single-table SQL QA task. +Return strict JSON only, with this schema: {"sql": "...", "notes": "..."}. +Rules: +- Use only the provided table and columns. +- Do not write INSERT, UPDATE, DELETE, DROP, ALTER, CREATE, PRAGMA, ATTACH, DETACH, or VACUUM. +- Prefer the planned template and bound roles when provided. +- Add a leading SQL comment exactly like: -- template_id: . +- Generate SQLite-compatible SQL. SQLite does not support PERCENTILE_CONT or STDDEV. +- Quote identifiers with double quotes. +- Return no markdown and no extra prose. + +Dataset context: +Dataset context for SQL QA: +- dataset_id: n1 +- dataset_name: Spambase +- table_name: n1 +- table_layout: single-table dataset (do not assume joins). +- row_semantics: One row is one email represented by engineered token/character frequency and capitalization-run features. +- task_type: classification +- target_column: class +- main_row_count: 4601 +- important_fields: +- word_freq_make: role=feature, type=numeric_sparse_frequency. tags=['condition_candidate', 'measure', 'rare_pattern_candidate'] desc=Frequency feature for token 'make' in an email message. +- word_freq_address: role=feature, type=numeric_sparse_frequency. tags=['condition_candidate', 'measure', 'rare_pattern_candidate'] desc=Frequency feature for token 'address' in an email message. +- word_freq_all: role=feature, type=numeric_sparse_frequency. tags=['condition_candidate', 'measure', 'rare_pattern_candidate'] desc=Frequency feature for token 'all' in an email message. +- word_freq_3d: role=feature, type=numeric_sparse_frequency. tags=['condition_candidate', 'measure', 'rare_pattern_candidate'] desc=Frequency feature for token '3d' in an email message. +- word_freq_our: role=feature, type=numeric_sparse_frequency. tags=['condition_candidate', 'measure', 'rare_pattern_candidate'] desc=Frequency feature for token 'our' in an email message. +- word_freq_over: role=feature, type=numeric_sparse_frequency. tags=['condition_candidate', 'measure', 'rare_pattern_candidate'] desc=Frequency feature for token 'over' in an email message. +- word_freq_remove: role=feature, type=numeric_sparse_frequency. tags=['condition_candidate', 'measure', 'rare_pattern_candidate'] desc=Frequency feature for token 'remove' in an email message. +- word_freq_internet: role=feature, type=numeric_sparse_frequency. tags=['condition_candidate', 'measure', 'rare_pattern_candidate'] desc=Frequency feature for token 'internet' in an email message. +- word_freq_order: role=feature, type=numeric_sparse_frequency. tags=['condition_candidate', 'measure', 'rare_pattern_candidate'] desc=Frequency feature for token 'order' in an email message. +- word_freq_mail: role=feature, type=numeric_sparse_frequency. tags=['condition_candidate', 'measure', 'rare_pattern_candidate'] desc=Frequency feature for token 'mail' in an email message. +- word_freq_receive: role=feature, type=numeric_sparse_frequency. tags=['condition_candidate', 'measure', 'rare_pattern_candidate'] desc=Frequency feature for token 'receive' in an email message. +- word_freq_will: role=feature, type=numeric_sparse_frequency. tags=['condition_candidate', 'measure', 'rare_pattern_candidate'] desc=Frequency feature for token 'will' in an email message. +- word_freq_people: role=feature, type=numeric_sparse_frequency. tags=['condition_candidate', 'measure', 'rare_pattern_candidate'] desc=Frequency feature for token 'people' in an email message. +- word_freq_report: role=feature, type=numeric_sparse_frequency. tags=['condition_candidate', 'measure', 'rare_pattern_candidate'] desc=Frequency feature for token 'report' in an email message. +- word_freq_addresses: role=feature, type=numeric_sparse_frequency. tags=['condition_candidate', 'measure', 'rare_pattern_candidate'] desc=Frequency feature for token 'addresses' in an email message. +- word_freq_free: role=feature, type=numeric_sparse_frequency. tags=['condition_candidate', 'measure', 'rare_pattern_candidate'] desc=Frequency feature for token 'free' in an email message. +- word_freq_business: role=feature, type=numeric_sparse_frequency. tags=['condition_candidate', 'measure', 'rare_pattern_candidate'] desc=Frequency feature for token 'business' in an email message. +- word_freq_email: role=feature, type=numeric_sparse_frequency. tags=['condition_candidate', 'measure', 'rare_pattern_candidate'] desc=Frequency feature for token 'email' in an email message. +- word_freq_you: role=feature, type=numeric_sparse_frequency. tags=['condition_candidate', 'measure', 'rare_pattern_candidate'] desc=Frequency feature for token 'you' in an email message. +- word_freq_credit: role=feature, type=numeric_sparse_frequency. tags=['condition_candidate', 'measure', 'rare_pattern_candidate'] desc=Frequency feature for token 'credit' in an email message. +- word_freq_your: role=feature, type=numeric_sparse_frequency. tags=['condition_candidate', 'measure', 'rare_pattern_candidate'] desc=Frequency feature for token 'your' in an email message. +- word_freq_font: role=feature, type=numeric_sparse_frequency. tags=['condition_candidate', 'measure', 'rare_pattern_candidate'] desc=Frequency feature for token 'font' in an email message. +- word_freq_000: role=feature, type=numeric_sparse_frequency. tags=['condition_candidate', 'measure', 'rare_pattern_candidate'] desc=Frequency feature for token '000' in an email message. +- word_freq_money: role=feature, type=numeric_sparse_frequency. tags=['condition_candidate', 'measure', 'rare_pattern_candidate'] desc=Frequency feature for token 'money' in an email message. +- word_freq_hp: role=feature, type=numeric_sparse_frequency. tags=['condition_candidate', 'measure', 'rare_pattern_candidate'] desc=Frequency feature for token 'hp' in an email message. +- word_freq_hpl: role=feature, type=numeric_sparse_frequency. tags=['condition_candidate', 'measure', 'rare_pattern_candidate'] desc=Frequency feature for token 'hpl' in an email message. +- word_freq_george: role=feature, type=numeric_sparse_frequency. tags=['condition_candidate', 'measure', 'rare_pattern_candidate'] desc=Frequency feature for token 'george' in an email message. +- word_freq_650: role=feature, type=numeric_sparse_frequency. tags=['condition_candidate', 'measure', 'rare_pattern_candidate'] desc=Frequency feature for token '650' in an email message. +- word_freq_lab: role=feature, type=numeric_sparse_frequency. tags=['condition_candidate', 'measure', 'rare_pattern_candidate'] desc=Frequency feature for token 'lab' in an email message. +- word_freq_labs: role=feature, type=numeric_sparse_frequency. tags=['condition_candidate', 'measure', 'rare_pattern_candidate'] desc=Frequency feature for token 'labs' in an email message. +- word_freq_telnet: role=feature, type=numeric_sparse_frequency. tags=['condition_candidate', 'measure', 'rare_pattern_candidate'] desc=Frequency feature for token 'telnet' in an email message. +- word_freq_857: role=feature, type=numeric_sparse_frequency. tags=['condition_candidate', 'measure', 'rare_pattern_candidate'] desc=Frequency feature for token '857' in an email message. +- word_freq_data: role=feature, type=numeric_sparse_frequency. tags=['condition_candidate', 'measure', 'rare_pattern_candidate'] desc=Frequency feature for token 'data' in an email message. +- word_freq_415: role=feature, type=numeric_sparse_frequency. tags=['condition_candidate', 'measure', 'rare_pattern_candidate'] desc=Frequency feature for token '415' in an email message. +- word_freq_85: role=feature, type=numeric_sparse_frequency. tags=['condition_candidate', 'measure', 'rare_pattern_candidate'] desc=Frequency feature for token '85' in an email message. +- word_freq_technology: role=feature, type=numeric_sparse_frequency. tags=['condition_candidate', 'measure', 'rare_pattern_candidate'] desc=Frequency feature for token 'technology' in an email message. +- word_freq_1999: role=feature, type=numeric_sparse_frequency. tags=['condition_candidate', 'measure', 'rare_pattern_candidate'] desc=Frequency feature for token '1999' in an email message. +- word_freq_parts: role=feature, type=numeric_sparse_frequency. tags=['condition_candidate', 'measure', 'rare_pattern_candidate'] desc=Frequency feature for token 'parts' in an email message. +- word_freq_pm: role=feature, type=numeric_sparse_frequency. tags=['condition_candidate', 'measure', 'rare_pattern_candidate'] desc=Frequency feature for token 'pm' in an email message. +- word_freq_direct: role=feature, type=numeric_sparse_frequency. tags=['condition_candidate', 'measure', 'rare_pattern_candidate'] desc=Frequency feature for token 'direct' in an email message. +- word_freq_cs: role=feature, type=numeric_sparse_frequency. tags=['condition_candidate', 'measure', 'rare_pattern_candidate'] desc=Frequency feature for token 'cs' in an email message. +- word_freq_meeting: role=feature, type=numeric_sparse_frequency. tags=['condition_candidate', 'measure', 'rare_pattern_candidate'] desc=Frequency feature for token 'meeting' in an email message. +- word_freq_original: role=feature, type=numeric_sparse_frequency. tags=['condition_candidate', 'measure', 'rare_pattern_candidate'] desc=Frequency feature for token 'original' in an email message. +- word_freq_project: role=feature, type=numeric_sparse_frequency. tags=['condition_candidate', 'measure', 'rare_pattern_candidate'] desc=Frequency feature for token 'project' in an email message. +- word_freq_re: role=feature, type=numeric_sparse_frequency. tags=['condition_candidate', 'measure', 'rare_pattern_candidate'] desc=Frequency feature for token 're' in an email message. +- word_freq_edu: role=feature, type=numeric_sparse_frequency. tags=['condition_candidate', 'measure', 'rare_pattern_candidate'] desc=Frequency feature for token 'edu' in an email message. +- word_freq_table: role=feature, type=numeric_sparse_frequency. tags=['condition_candidate', 'measure', 'rare_pattern_candidate'] desc=Frequency feature for token 'table' in an email message. +- word_freq_conference: role=feature, type=numeric_sparse_frequency. tags=['condition_candidate', 'measure', 'rare_pattern_candidate'] desc=Frequency feature for token 'conference' in an email message. +- char_freq_%3B: role=feature, type=numeric_sparse_frequency. tags=['condition_candidate', 'measure', 'rare_pattern_candidate'] desc=Character frequency feature for symbol ';'. +- char_freq_%28: role=feature, type=numeric_sparse_frequency. tags=['condition_candidate', 'measure', 'rare_pattern_candidate'] desc=Character frequency feature for symbol '('. +- char_freq_%5B: role=feature, type=numeric_sparse_frequency. tags=['condition_candidate', 'measure', 'rare_pattern_candidate'] desc=Character frequency feature for symbol '['. +- char_freq_%21: role=feature, type=numeric_sparse_frequency. tags=['condition_candidate', 'measure', 'rare_pattern_candidate'] desc=Character frequency feature for symbol '!'. +- char_freq_%24: role=feature, type=numeric_sparse_frequency. tags=['condition_candidate', 'measure', 'rare_pattern_candidate'] desc=Character frequency feature for symbol '$'. +- char_freq_%23: role=feature, type=numeric_sparse_frequency. tags=['condition_candidate', 'measure', 'rare_pattern_candidate'] desc=Character frequency feature for symbol '#'. +- capital_run_length_average: role=feature, type=numeric_heavy_tail. tags=['condition_candidate', 'measure', 'rare_pattern_candidate'] desc=Average length of uninterrupted capital-letter runs. +- capital_run_length_longest: role=feature, type=numeric_heavy_tail. tags=['condition_candidate', 'measure', 'rare_pattern_candidate'] desc=Longest uninterrupted capital-letter run. +- capital_run_length_total: role=feature, type=numeric_heavy_tail. tags=['condition_candidate', 'measure', 'rare_pattern_candidate'] desc=Total number of capital letters in runs. +- class: role=target, type=binary_target. tags=['target_candidate'] desc=Binary spam label (1=spam, 0=non-spam). +- useful_field_combinations: [['word_freq_free', 'word_freq_you', 'class'], ['char_freq_%21', 'capital_run_length_longest', 'class'], ['word_freq_remove', 'word_freq_money', 'class']] +- fields_requiring_caution: ['capital_run_length_average', 'capital_run_length_longest', 'capital_run_length_total'] +- source_url: https://www.openml.org/d/44 + +SQLite schema snapshot: +{ + "table_name": "n1", + "quoted_table_name": "\"n1\"", + "row_count": 4601, + "columns": [ + { + "name": "word_freq_make", + "type": "TEXT", + "notnull": false, + "pk": false + }, + { + "name": "word_freq_address", + "type": "TEXT", + "notnull": false, + "pk": false + }, + { + "name": "word_freq_all", + "type": "TEXT", + "notnull": false, + "pk": false + }, + { + "name": "word_freq_3d", + "type": "TEXT", + "notnull": false, + "pk": false + }, + { + "name": "word_freq_our", + "type": "TEXT", + "notnull": false, + "pk": false + }, + { + "name": "word_freq_over", + "type": "TEXT", + "notnull": false, + "pk": false + }, + { + "name": "word_freq_remove", + "type": "TEXT", + "notnull": false, + "pk": false + }, + { + "name": "word_freq_internet", + "type": "TEXT", + "notnull": false, + "pk": false + }, + { + "name": "word_freq_order", + "type": "TEXT", + "notnull": false, + "pk": false + }, + { + "name": "word_freq_mail", + "type": "TEXT", + "notnull": false, + "pk": false + }, + { + "name": "word_freq_receive", + "type": "TEXT", + "notnull": false, + "pk": false + }, + { + "name": "word_freq_will", + "type": "TEXT", + "notnull": false, + "pk": false + }, + { + "name": "word_freq_people", + "type": "TEXT", + "notnull": false, + "pk": false + }, + { + "name": "word_freq_report", + "type": "TEXT", + "notnull": false, + "pk": false + }, + { + "name": "word_freq_addresses", + "type": "TEXT", + "notnull": false, + "pk": false + }, + { + "name": "word_freq_free", + "type": "TEXT", + "notnull": false, + "pk": false + }, + { + "name": "word_freq_business", + "type": "TEXT", + "notnull": false, + "pk": false + }, + { + "name": "word_freq_email", + "type": "TEXT", + "notnull": false, + "pk": false + }, + { + "name": "word_freq_you", + "type": "TEXT", + "notnull": false, + "pk": false + }, + { + "name": "word_freq_credit", + "type": "TEXT", + "notnull": false, + "pk": false + }, + { + "name": "word_freq_your", + "type": "TEXT", + "notnull": false, + "pk": false + }, + { + "name": "word_freq_font", + "type": "TEXT", + "notnull": false, + "pk": false + }, + { + "name": "word_freq_000", + "type": "TEXT", + "notnull": false, + "pk": false + }, + { + "name": "word_freq_money", + "type": "TEXT", + "notnull": false, + "pk": false + }, + { + "name": "word_freq_hp", + "type": "TEXT", + "notnull": false, + "pk": false + }, + { + "name": "word_freq_hpl", + "type": "TEXT", + "notnull": false, + "pk": false + }, + { + "name": "word_freq_george", + "type": "TEXT", + "notnull": false, + "pk": false + }, + { + "name": "word_freq_650", + "type": "TEXT", + "notnull": false, + "pk": false + }, + { + "name": "word_freq_lab", + "type": "TEXT", + "notnull": false, + "pk": false + }, + { + "name": "word_freq_labs", + "type": "TEXT", + "notnull": false, + "pk": false + }, + { + "name": "word_freq_telnet", + "type": "TEXT", + "notnull": false, + "pk": false + }, + { + "name": "word_freq_857", + "type": "TEXT", + "notnull": false, + "pk": false + }, + { + "name": "word_freq_data", + "type": "TEXT", + "notnull": false, + "pk": false + }, + { + "name": "word_freq_415", + "type": "TEXT", + "notnull": false, + "pk": false + }, + { + "name": "word_freq_85", + "type": "TEXT", + "notnull": false, + "pk": false + }, + { + "name": "word_freq_technology", + "type": "TEXT", + "notnull": false, + "pk": false + }, + { + "name": "word_freq_1999", + "type": "TEXT", + "notnull": false, + "pk": false + }, + { + "name": "word_freq_parts", + "type": "TEXT", + "notnull": false, + "pk": false + }, + { + "name": "word_freq_pm", + "type": "TEXT", + "notnull": false, + "pk": false + }, + { + "name": "word_freq_direct", + "type": "TEXT", + "notnull": false, + "pk": false + }, + { + "name": "word_freq_cs", + "type": "TEXT", + "notnull": false, + "pk": false + }, + { + "name": "word_freq_meeting", + "type": "TEXT", + "notnull": false, + "pk": false + }, + { + "name": "word_freq_original", + "type": "TEXT", + "notnull": false, + "pk": false + }, + { + "name": "word_freq_project", + "type": "TEXT", + "notnull": false, + "pk": false + }, + { + "name": "word_freq_re", + "type": "TEXT", + "notnull": false, + "pk": false + }, + { + "name": "word_freq_edu", + "type": "TEXT", + "notnull": false, + "pk": false + }, + { + "name": "word_freq_table", + "type": "TEXT", + "notnull": false, + "pk": false + }, + { + "name": "word_freq_conference", + "type": "TEXT", + "notnull": false, + "pk": false + }, + { + "name": "char_freq_%3B", + "type": "TEXT", + "notnull": false, + "pk": false + }, + { + "name": "char_freq_%28", + "type": "TEXT", + "notnull": false, + "pk": false + }, + { + "name": "char_freq_%5B", + "type": "TEXT", + "notnull": false, + "pk": false + }, + { + "name": "char_freq_%21", + "type": "TEXT", + "notnull": false, + "pk": false + }, + { + "name": "char_freq_%24", + "type": "TEXT", + "notnull": false, + "pk": false + }, + { + "name": "char_freq_%23", + "type": "TEXT", + "notnull": false, + "pk": false + }, + { + "name": "capital_run_length_average", + "type": "TEXT", + "notnull": false, + "pk": false + }, + { + "name": "capital_run_length_longest", + "type": "TEXT", + "notnull": false, + "pk": false + }, + { + "name": "capital_run_length_total", + "type": "TEXT", + "notnull": false, + "pk": false + }, + { + "name": "class", + "type": "TEXT", + "notnull": false, + "pk": false + } + ], + "sample_rows": [ + { + "word_freq_make": "0", + "word_freq_address": "0.64", + "word_freq_all": "0.64", + "word_freq_3d": "0", + "word_freq_our": "0.32", + "word_freq_over": "0", + "word_freq_remove": "0", + "word_freq_internet": "0", + "word_freq_order": "0", + "word_freq_mail": "0", + "word_freq_receive": "0", + "word_freq_will": "0.64", + "word_freq_people": "0", + "word_freq_report": "0", + "word_freq_addresses": "0", + "word_freq_free": "0.32", + "word_freq_business": "0", + "word_freq_email": "1.29", + "word_freq_you": "1.93", + "word_freq_credit": "0", + "word_freq_your": "0.96", + "word_freq_font": "0", + "word_freq_000": "0", + "word_freq_money": "0", + "word_freq_hp": "0", + "word_freq_hpl": "0", + "word_freq_george": "0", + "word_freq_650": "0", + "word_freq_lab": "0", + "word_freq_labs": "0", + "word_freq_telnet": "0", + "word_freq_857": "0", + "word_freq_data": "0", + "word_freq_415": "0", + "word_freq_85": "0", + "word_freq_technology": "0", + "word_freq_1999": "0", + "word_freq_parts": "0", + "word_freq_pm": "0", + "word_freq_direct": "0", + "word_freq_cs": "0", + "word_freq_meeting": "0", + "word_freq_original": "0", + "word_freq_project": "0", + "word_freq_re": "0", + "word_freq_edu": "0", + "word_freq_table": "0", + "word_freq_conference": "0", + "char_freq_%3B": "0", + "char_freq_%28": "0", + "char_freq_%5B": "0", + "char_freq_%21": "0.778", + "char_freq_%24": "0", + "char_freq_%23": "0", + "capital_run_length_average": "3.756", + "capital_run_length_longest": "61", + "capital_run_length_total": "278", + "class": "1" + }, + { + "word_freq_make": "0.21", + "word_freq_address": "0.28", + "word_freq_all": "0.5", + "word_freq_3d": "0", + "word_freq_our": "0.14", + "word_freq_over": "0.28", + "word_freq_remove": "0.21", + "word_freq_internet": "0.07", + "word_freq_order": "0", + "word_freq_mail": "0.94", + "word_freq_receive": "0.21", + "word_freq_will": "0.79", + "word_freq_people": "0.65", + "word_freq_report": "0.21", + "word_freq_addresses": "0.14", + "word_freq_free": "0.14", + "word_freq_business": "0.07", + "word_freq_email": "0.28", + "word_freq_you": "3.47", + "word_freq_credit": "0", + "word_freq_your": "1.59", + "word_freq_font": "0", + "word_freq_000": "0.43", + "word_freq_money": "0.43", + "word_freq_hp": "0", + "word_freq_hpl": "0", + "word_freq_george": "0", + "word_freq_650": "0", + "word_freq_lab": "0", + "word_freq_labs": "0", + "word_freq_telnet": "0", + "word_freq_857": "0", + "word_freq_data": "0", + "word_freq_415": "0", + "word_freq_85": "0", + "word_freq_technology": "0", + "word_freq_1999": "0.07", + "word_freq_parts": "0", + "word_freq_pm": "0", + "word_freq_direct": "0", + "word_freq_cs": "0", + "word_freq_meeting": "0", + "word_freq_original": "0", + "word_freq_project": "0", + "word_freq_re": "0", + "word_freq_edu": "0", + "word_freq_table": "0", + "word_freq_conference": "0", + "char_freq_%3B": "0", + "char_freq_%28": "0.132", + "char_freq_%5B": "0", + "char_freq_%21": "0.372", + "char_freq_%24": "0.18", + "char_freq_%23": "0.048", + "capital_run_length_average": "5.114", + "capital_run_length_longest": "101", + "capital_run_length_total": "1028", + "class": "1" + }, + { + "word_freq_make": "0.06", + "word_freq_address": "0", + "word_freq_all": "0.71", + "word_freq_3d": "0", + "word_freq_our": "1.23", + "word_freq_over": "0.19", + "word_freq_remove": "0.19", + "word_freq_internet": "0.12", + "word_freq_order": "0.64", + "word_freq_mail": "0.25", + "word_freq_receive": "0.38", + "word_freq_will": "0.45", + "word_freq_people": "0.12", + "word_freq_report": "0", + "word_freq_addresses": "1.75", + "word_freq_free": "0.06", + "word_freq_business": "0.06", + "word_freq_email": "1.03", + "word_freq_you": "1.36", + "word_freq_credit": "0.32", + "word_freq_your": "0.51", + "word_freq_font": "0", + "word_freq_000": "1.16", + "word_freq_money": "0.06", + "word_freq_hp": "0", + "word_freq_hpl": "0", + "word_freq_george": "0", + "word_freq_650": "0", + "word_freq_lab": "0", + "word_freq_labs": "0", + "word_freq_telnet": "0", + "word_freq_857": "0", + "word_freq_data": "0", + "word_freq_415": "0", + "word_freq_85": "0", + "word_freq_technology": "0", + "word_freq_1999": "0", + "word_freq_parts": "0", + "word_freq_pm": "0", + "word_freq_direct": "0.06", + "word_freq_cs": "0", + "word_freq_meeting": "0", + "word_freq_original": "0.12", + "word_freq_project": "0", + "word_freq_re": "0.06", + "word_freq_edu": "0.06", + "word_freq_table": "0", + "word_freq_conference": "0", + "char_freq_%3B": "0.01", + "char_freq_%28": "0.143", + "char_freq_%5B": "0", + "char_freq_%21": "0.276", + "char_freq_%24": "0.184", + "char_freq_%23": "0.01", + "capital_run_length_average": "9.821", + "capital_run_length_longest": "485", + "capital_run_length_total": "2259", + "class": "1" + }, + { + "word_freq_make": "0", + "word_freq_address": "0", + "word_freq_all": "0", + "word_freq_3d": "0", + "word_freq_our": "0.63", + "word_freq_over": "0", + "word_freq_remove": "0.31", + "word_freq_internet": "0.63", + "word_freq_order": "0.31", + "word_freq_mail": "0.63", + "word_freq_receive": "0.31", + "word_freq_will": "0.31", + "word_freq_people": "0.31", + "word_freq_report": "0", + "word_freq_addresses": "0", + "word_freq_free": "0.31", + "word_freq_business": "0", + "word_freq_email": "0", + "word_freq_you": "3.18", + "word_freq_credit": "0", + "word_freq_your": "0.31", + "word_freq_font": "0", + "word_freq_000": "0", + "word_freq_money": "0", + "word_freq_hp": "0", + "word_freq_hpl": "0", + "word_freq_george": "0", + "word_freq_650": "0", + "word_freq_lab": "0", + "word_freq_labs": "0", + "word_freq_telnet": "0", + "word_freq_857": "0", + "word_freq_data": "0", + "word_freq_415": "0", + "word_freq_85": "0", + "word_freq_technology": "0", + "word_freq_1999": "0", + "word_freq_parts": "0", + "word_freq_pm": "0", + "word_freq_direct": "0", + "word_freq_cs": "0", + "word_freq_meeting": "0", + "word_freq_original": "0", + "word_freq_project": "0", + "word_freq_re": "0", + "word_freq_edu": "0", + "word_freq_table": "0", + "word_freq_conference": "0", + "char_freq_%3B": "0", + "char_freq_%28": "0.137", + "char_freq_%5B": "0", + "char_freq_%21": "0.137", + "char_freq_%24": "0", + "char_freq_%23": "0", + "capital_run_length_average": "3.537", + "capital_run_length_longest": "40", + "capital_run_length_total": "191", + "class": "1" + }, + { + "word_freq_make": "0", + "word_freq_address": "0", + "word_freq_all": "0", + "word_freq_3d": "0", + "word_freq_our": "0.63", + "word_freq_over": "0", + "word_freq_remove": "0.31", + "word_freq_internet": "0.63", + "word_freq_order": "0.31", + "word_freq_mail": "0.63", + "word_freq_receive": "0.31", + "word_freq_will": "0.31", + "word_freq_people": "0.31", + "word_freq_report": "0", + "word_freq_addresses": "0", + "word_freq_free": "0.31", + "word_freq_business": "0", + "word_freq_email": "0", + "word_freq_you": "3.18", + "word_freq_credit": "0", + "word_freq_your": "0.31", + "word_freq_font": "0", + "word_freq_000": "0", + "word_freq_money": "0", + "word_freq_hp": "0", + "word_freq_hpl": "0", + "word_freq_george": "0", + "word_freq_650": "0", + "word_freq_lab": "0", + "word_freq_labs": "0", + "word_freq_telnet": "0", + "word_freq_857": "0", + "word_freq_data": "0", + "word_freq_415": "0", + "word_freq_85": "0", + "word_freq_technology": "0", + "word_freq_1999": "0", + "word_freq_parts": "0", + "word_freq_pm": "0", + "word_freq_direct": "0", + "word_freq_cs": "0", + "word_freq_meeting": "0", + "word_freq_original": "0", + "word_freq_project": "0", + "word_freq_re": "0", + "word_freq_edu": "0", + "word_freq_table": "0", + "word_freq_conference": "0", + "char_freq_%3B": "0", + "char_freq_%28": "0.135", + "char_freq_%5B": "0", + "char_freq_%21": "0.135", + "char_freq_%24": "0", + "char_freq_%23": "0", + "capital_run_length_average": "3.537", + "capital_run_length_longest": "40", + "capital_run_length_total": "191", + "class": "1" + } + ] +} + +Shortlisted templates: +[ + { + "template_id": "tpl_grouped_percentile_point", + "template_name": "Grouped Percentile Point", + "primary_family": "tail_rarity_structure", + "portability": "yes", + "sql_skeleton": "SELECT {group_col},\n PERCENTILE_CONT({percentile_value}) WITHIN GROUP (ORDER BY {measure_col}) AS percentile_measure\nFROM {table}\nGROUP BY {group_col}\nORDER BY percentile_measure DESC;", + "required_roles": [ + "group_col", + "measure_col" + ] + } +] + +Problem instance: +{ + "dataset_id": "n1", + "question": "Use template Grouped Percentile Point to probe tail_concentration_consistency with semantic role focused_target_view. Focus on group_col=class, measure_col=word_freq_85.", + "planned_template_id": "tpl_grouped_percentile_point", + "bindings": { + "group_col": "class", + "measure_col": "word_freq_85", + "top_k": 16, + "top_n": 7, + "num_tiles": 10, + "percentile_value": 0.95, + "z_threshold": 2.0, + "fraction_threshold": 0.05, + "baseline_multiplier": 1.75, + "baseline_fraction": 0.1, + "min_group_size": 5, + "min_support": 4, + "measure_threshold": 0.0, + "time_grain": "month", + "lookback_rows": 3, + "current_period_start": "'2024-01-01'", + "current_period_end": "'2024-04-01'", + "previous_period_start": "'2023-10-01'", + "previous_period_end": "'2024-01-01'", + "drift_ratio_threshold": 0.8 + }, + "can_vary": [], + "must_fix": [], + "runtime_sql_skeleton": "SELECT {group_col},\n PERCENTILE_CONT({percentile_value}) WITHIN GROUP (ORDER BY {measure_col}) AS percentile_measure\nFROM {table}\nGROUP BY {group_col}\nORDER BY percentile_measure DESC;" +} + +Repair context: +{} diff --git a/Query/sql/v2/runs/v2_cli_20260502_081223_d/n1/artifacts/v2q_n1_1b29859e41bcb9ed/cli/sql_response_attempt_1.raw.txt b/Query/sql/v2/runs/v2_cli_20260502_081223_d/n1/artifacts/v2q_n1_1b29859e41bcb9ed/cli/sql_response_attempt_1.raw.txt new file mode 100644 index 0000000000000000000000000000000000000000..1f5f6849cf04b419b2c54e7db8891389156a2ffe --- /dev/null +++ b/Query/sql/v2/runs/v2_cli_20260502_081223_d/n1/artifacts/v2q_n1_1b29859e41bcb9ed/cli/sql_response_attempt_1.raw.txt @@ -0,0 +1,4 @@ +{"type":"thread.started","thread_id":"019e40f4-3c5a-7ee3-9751-67f28d8b9574"} +{"type":"turn.started"} +{"type":"error","message":"Quota exceeded. Check your plan and billing details."} +{"type":"turn.failed","error":{"message":"Quota exceeded. Check your plan and billing details."}} diff --git a/Query/sql/v2/runs/v2_cli_20260502_081223_d/n1/artifacts/v2q_n1_1b29859e41bcb9ed/cli/sql_response_attempt_1.txt b/Query/sql/v2/runs/v2_cli_20260502_081223_d/n1/artifacts/v2q_n1_1b29859e41bcb9ed/cli/sql_response_attempt_1.txt new file mode 100644 index 0000000000000000000000000000000000000000..8c5a5ba621cc15cdb07f0a6fa0f594541671fb2e --- /dev/null +++ b/Query/sql/v2/runs/v2_cli_20260502_081223_d/n1/artifacts/v2q_n1_1b29859e41bcb9ed/cli/sql_response_attempt_1.txt @@ -0,0 +1,4 @@ +{"type":"thread.started","thread_id":"019e40f4-3c5a-7ee3-9751-67f28d8b9574"} +{"type":"turn.started"} +{"type":"error","message":"Quota exceeded. Check your plan and billing details."} +{"type":"turn.failed","error":{"message":"Quota exceeded. Check your plan and billing details."}} \ No newline at end of file diff --git a/Query/sql/v2/runs/v2_cli_20260502_081223_d/n1/artifacts/v2q_n1_1b29859e41bcb9ed/cli/sql_response_attempt_2.raw.txt b/Query/sql/v2/runs/v2_cli_20260502_081223_d/n1/artifacts/v2q_n1_1b29859e41bcb9ed/cli/sql_response_attempt_2.raw.txt new file mode 100644 index 0000000000000000000000000000000000000000..15dff8f148bede876f8ac65029cfc1aff36db3c8 --- /dev/null +++ b/Query/sql/v2/runs/v2_cli_20260502_081223_d/n1/artifacts/v2q_n1_1b29859e41bcb9ed/cli/sql_response_attempt_2.raw.txt @@ -0,0 +1,4 @@ +{"type":"thread.started","thread_id":"019e40f4-4d19-7462-a28d-23ff076da967"} +{"type":"turn.started"} +{"type":"error","message":"Quota exceeded. Check your plan and billing details."} +{"type":"turn.failed","error":{"message":"Quota exceeded. Check your plan and billing details."}} diff --git a/Query/sql/v2/runs/v2_cli_20260502_081223_d/n1/artifacts/v2q_n1_1b29859e41bcb9ed/cli/sql_response_attempt_2.txt b/Query/sql/v2/runs/v2_cli_20260502_081223_d/n1/artifacts/v2q_n1_1b29859e41bcb9ed/cli/sql_response_attempt_2.txt new file mode 100644 index 0000000000000000000000000000000000000000..a5fd96f67af68b7075e3c8cdc6d428ec9c21c5b3 --- /dev/null +++ b/Query/sql/v2/runs/v2_cli_20260502_081223_d/n1/artifacts/v2q_n1_1b29859e41bcb9ed/cli/sql_response_attempt_2.txt @@ -0,0 +1,4 @@ +{"type":"thread.started","thread_id":"019e40f4-4d19-7462-a28d-23ff076da967"} +{"type":"turn.started"} +{"type":"error","message":"Quota exceeded. Check your plan and billing details."} +{"type":"turn.failed","error":{"message":"Quota exceeded. Check your plan and billing details."}} \ No newline at end of file diff --git a/Query/sql/v2/runs/v2_cli_20260502_081223_d/n1/artifacts/v2q_n1_1b29859e41bcb9ed/cli/sql_stderr_attempt_1.txt b/Query/sql/v2/runs/v2_cli_20260502_081223_d/n1/artifacts/v2q_n1_1b29859e41bcb9ed/cli/sql_stderr_attempt_1.txt new file mode 100644 index 0000000000000000000000000000000000000000..e69de29bb2d1d6434b8b29ae775ad8c2e48c5391 diff --git a/Query/sql/v2/runs/v2_cli_20260502_081223_d/n1/artifacts/v2q_n1_1b29859e41bcb9ed/cli/sql_stderr_attempt_2.txt b/Query/sql/v2/runs/v2_cli_20260502_081223_d/n1/artifacts/v2q_n1_1b29859e41bcb9ed/cli/sql_stderr_attempt_2.txt new file mode 100644 index 0000000000000000000000000000000000000000..e69de29bb2d1d6434b8b29ae775ad8c2e48c5391 diff --git a/Query/sql/v2/runs/v2_cli_20260502_081223_d/n1/artifacts/v2q_n1_22c2bb93d6588817/cli/conversation.jsonl b/Query/sql/v2/runs/v2_cli_20260502_081223_d/n1/artifacts/v2q_n1_22c2bb93d6588817/cli/conversation.jsonl new file mode 100644 index 0000000000000000000000000000000000000000..e97967ee87d2e6a5e224fcd7ba70d4057a3cd4c6 --- /dev/null +++ b/Query/sql/v2/runs/v2_cli_20260502_081223_d/n1/artifacts/v2q_n1_22c2bb93d6588817/cli/conversation.jsonl @@ -0,0 +1,2 @@ +{"attempt": 1, "phase": "sql_generation", "role": "user", "content_path": "cli/sql_prompt_attempt_1.txt", "metrics": {"chars": 29361, "bytes_utf8": 29361, "lines": 792, "estimated_tokens": null}} +{"attempt": 1, "phase": "sql_generation", "role": "assistant", "content_path": "cli/sql_response_attempt_1.txt", "raw_content_path": "cli/sql_response_attempt_1.raw.txt", "stderr_path": "cli/sql_stderr_attempt_1.txt", "metrics": {"chars": 393, "bytes_utf8": 393, "lines": 1, "estimated_tokens": null}, "usage": {"input_tokens": 20315, "cached_input_tokens": 12032, "output_tokens": 349, "reasoning_output_tokens": 244}} diff --git a/Query/sql/v2/runs/v2_cli_20260502_081223_d/n1/artifacts/v2q_n1_22c2bb93d6588817/cli/session_summary.json b/Query/sql/v2/runs/v2_cli_20260502_081223_d/n1/artifacts/v2q_n1_22c2bb93d6588817/cli/session_summary.json new file mode 100644 index 0000000000000000000000000000000000000000..6fa768bfe827fa715f9c31635a9a44f0710cf473 --- /dev/null +++ b/Query/sql/v2/runs/v2_cli_20260502_081223_d/n1/artifacts/v2q_n1_22c2bb93d6588817/cli/session_summary.json @@ -0,0 +1,25 @@ +{ + "engine": "v2-cli:codex", + "command": "codex exec --skip-git-repo-check --disable plugins --sandbox read-only --cd \"/data/jialinzhang/SQLagent\" -m gpt-5.4 --json -", + "ai_cli_calls": 1, + "usage_summary": { + "dataset_id": "n1", + "model": "v2-cli:codex", + "run_id": "v2q_n1_22c2bb93d6588817", + "api_calls": 0, + "input_tokens": 20315, + "cached_input_tokens": 12032, + "output_tokens": 349, + "total_tokens": 20664, + "cost_usd": 0.0, + "ai_cli_calls": 1, + "estimated_input_tokens": 0, + "estimated_output_tokens": 0, + "estimated_total_tokens": 0, + "usage_source": "ai_cli_json_usage", + "cli_elapsed_ms_total": 10152.84, + "sql_execution_elapsed_ms_total": 2.77, + "conversation_log_path": "/data/jialinzhang/TabQueryBench/sql_workloads/v2_current/runs_and_launches/runs/v2_cli_20260502_081223_d/n1/artifacts/v2q_n1_22c2bb93d6588817/cli/conversation.jsonl", + "note": "Executed through a local AI CLI with structured usage metadata." + } +} \ No newline at end of file diff --git a/Query/sql/v2/runs/v2_cli_20260502_081223_d/n1/artifacts/v2q_n1_22c2bb93d6588817/cli/sql_attempt_1.metadata.json b/Query/sql/v2/runs/v2_cli_20260502_081223_d/n1/artifacts/v2q_n1_22c2bb93d6588817/cli/sql_attempt_1.metadata.json new file mode 100644 index 0000000000000000000000000000000000000000..fdc74777d2eef7ce29c825924e6487bc80692d39 --- /dev/null +++ b/Query/sql/v2/runs/v2_cli_20260502_081223_d/n1/artifacts/v2q_n1_22c2bb93d6588817/cli/sql_attempt_1.metadata.json @@ -0,0 +1,45 @@ +{ + "attempt": 1, + "phase": "sql_generation", + "command": "codex exec --skip-git-repo-check --disable plugins --sandbox read-only --cd \"/data/jialinzhang/SQLagent\" -m gpt-5.4 --json -", + "started_at": "2026-05-19T15:28:37.352498+00:00", + "ended_at": "2026-05-19T15:28:47.505379+00:00", + "elapsed_ms": 10152.84, + "prompt_metrics": { + "chars": 29361, + "bytes_utf8": 29361, + "lines": 792, + "estimated_tokens": null + }, + "stdout_metrics": { + "chars": 757, + "bytes_utf8": 757, + "lines": 4, + "estimated_tokens": null + }, + "stderr_metrics": { + "chars": 0, + "bytes_utf8": 0, + "lines": 0, + "estimated_tokens": null + }, + "parsed_output": { + "format": "jsonl_events", + "text_metrics": { + "chars": 393, + "bytes_utf8": 393, + "lines": 1, + "estimated_tokens": null + }, + "usage": { + "input_tokens": 20315, + "cached_input_tokens": 12032, + "output_tokens": 349, + "reasoning_output_tokens": 244 + } + }, + "prompt_path": "cli/sql_prompt_attempt_1.txt", + "response_path": "cli/sql_response_attempt_1.txt", + "raw_response_path": "cli/sql_response_attempt_1.raw.txt", + "stderr_path": "cli/sql_stderr_attempt_1.txt" +} \ No newline at end of file diff --git a/Query/sql/v2/runs/v2_cli_20260502_081223_d/n1/artifacts/v2q_n1_22c2bb93d6588817/cli/sql_prompt_attempt_1.txt b/Query/sql/v2/runs/v2_cli_20260502_081223_d/n1/artifacts/v2q_n1_22c2bb93d6588817/cli/sql_prompt_attempt_1.txt new file mode 100644 index 0000000000000000000000000000000000000000..1308c1a14ff53ebaefb789e04dda2b72168f7d91 --- /dev/null +++ b/Query/sql/v2/runs/v2_cli_20260502_081223_d/n1/artifacts/v2q_n1_22c2bb93d6588817/cli/sql_prompt_attempt_1.txt @@ -0,0 +1,792 @@ +You are generating one SQLite SELECT query for a single-table SQL QA task. +Return strict JSON only, with this schema: {"sql": "...", "notes": "..."}. +Rules: +- Use only the provided table and columns. +- Do not write INSERT, UPDATE, DELETE, DROP, ALTER, CREATE, PRAGMA, ATTACH, DETACH, or VACUUM. +- Prefer the planned template and bound roles when provided. +- Add a leading SQL comment exactly like: -- template_id: . +- Generate SQLite-compatible SQL. SQLite does not support PERCENTILE_CONT or STDDEV. +- Quote identifiers with double quotes. +- Return no markdown and no extra prose. + +Dataset context: +Dataset context for SQL QA: +- dataset_id: n1 +- dataset_name: Spambase +- table_name: n1 +- table_layout: single-table dataset (do not assume joins). +- row_semantics: One row is one email represented by engineered token/character frequency and capitalization-run features. +- task_type: classification +- target_column: class +- main_row_count: 4601 +- important_fields: +- word_freq_make: role=feature, type=numeric_sparse_frequency. tags=['condition_candidate', 'measure', 'rare_pattern_candidate'] desc=Frequency feature for token 'make' in an email message. +- word_freq_address: role=feature, type=numeric_sparse_frequency. tags=['condition_candidate', 'measure', 'rare_pattern_candidate'] desc=Frequency feature for token 'address' in an email message. +- word_freq_all: role=feature, type=numeric_sparse_frequency. tags=['condition_candidate', 'measure', 'rare_pattern_candidate'] desc=Frequency feature for token 'all' in an email message. +- word_freq_3d: role=feature, type=numeric_sparse_frequency. tags=['condition_candidate', 'measure', 'rare_pattern_candidate'] desc=Frequency feature for token '3d' in an email message. +- word_freq_our: role=feature, type=numeric_sparse_frequency. tags=['condition_candidate', 'measure', 'rare_pattern_candidate'] desc=Frequency feature for token 'our' in an email message. +- word_freq_over: role=feature, type=numeric_sparse_frequency. tags=['condition_candidate', 'measure', 'rare_pattern_candidate'] desc=Frequency feature for token 'over' in an email message. +- word_freq_remove: role=feature, type=numeric_sparse_frequency. tags=['condition_candidate', 'measure', 'rare_pattern_candidate'] desc=Frequency feature for token 'remove' in an email message. +- word_freq_internet: role=feature, type=numeric_sparse_frequency. tags=['condition_candidate', 'measure', 'rare_pattern_candidate'] desc=Frequency feature for token 'internet' in an email message. +- word_freq_order: role=feature, type=numeric_sparse_frequency. tags=['condition_candidate', 'measure', 'rare_pattern_candidate'] desc=Frequency feature for token 'order' in an email message. +- word_freq_mail: role=feature, type=numeric_sparse_frequency. tags=['condition_candidate', 'measure', 'rare_pattern_candidate'] desc=Frequency feature for token 'mail' in an email message. +- word_freq_receive: role=feature, type=numeric_sparse_frequency. tags=['condition_candidate', 'measure', 'rare_pattern_candidate'] desc=Frequency feature for token 'receive' in an email message. +- word_freq_will: role=feature, type=numeric_sparse_frequency. tags=['condition_candidate', 'measure', 'rare_pattern_candidate'] desc=Frequency feature for token 'will' in an email message. +- word_freq_people: role=feature, type=numeric_sparse_frequency. tags=['condition_candidate', 'measure', 'rare_pattern_candidate'] desc=Frequency feature for token 'people' in an email message. +- word_freq_report: role=feature, type=numeric_sparse_frequency. tags=['condition_candidate', 'measure', 'rare_pattern_candidate'] desc=Frequency feature for token 'report' in an email message. +- word_freq_addresses: role=feature, type=numeric_sparse_frequency. tags=['condition_candidate', 'measure', 'rare_pattern_candidate'] desc=Frequency feature for token 'addresses' in an email message. +- word_freq_free: role=feature, type=numeric_sparse_frequency. tags=['condition_candidate', 'measure', 'rare_pattern_candidate'] desc=Frequency feature for token 'free' in an email message. +- word_freq_business: role=feature, type=numeric_sparse_frequency. tags=['condition_candidate', 'measure', 'rare_pattern_candidate'] desc=Frequency feature for token 'business' in an email message. +- word_freq_email: role=feature, type=numeric_sparse_frequency. tags=['condition_candidate', 'measure', 'rare_pattern_candidate'] desc=Frequency feature for token 'email' in an email message. +- word_freq_you: role=feature, type=numeric_sparse_frequency. tags=['condition_candidate', 'measure', 'rare_pattern_candidate'] desc=Frequency feature for token 'you' in an email message. +- word_freq_credit: role=feature, type=numeric_sparse_frequency. tags=['condition_candidate', 'measure', 'rare_pattern_candidate'] desc=Frequency feature for token 'credit' in an email message. +- word_freq_your: role=feature, type=numeric_sparse_frequency. tags=['condition_candidate', 'measure', 'rare_pattern_candidate'] desc=Frequency feature for token 'your' in an email message. +- word_freq_font: role=feature, type=numeric_sparse_frequency. tags=['condition_candidate', 'measure', 'rare_pattern_candidate'] desc=Frequency feature for token 'font' in an email message. +- word_freq_000: role=feature, type=numeric_sparse_frequency. tags=['condition_candidate', 'measure', 'rare_pattern_candidate'] desc=Frequency feature for token '000' in an email message. +- word_freq_money: role=feature, type=numeric_sparse_frequency. tags=['condition_candidate', 'measure', 'rare_pattern_candidate'] desc=Frequency feature for token 'money' in an email message. +- word_freq_hp: role=feature, type=numeric_sparse_frequency. tags=['condition_candidate', 'measure', 'rare_pattern_candidate'] desc=Frequency feature for token 'hp' in an email message. +- word_freq_hpl: role=feature, type=numeric_sparse_frequency. tags=['condition_candidate', 'measure', 'rare_pattern_candidate'] desc=Frequency feature for token 'hpl' in an email message. +- word_freq_george: role=feature, type=numeric_sparse_frequency. tags=['condition_candidate', 'measure', 'rare_pattern_candidate'] desc=Frequency feature for token 'george' in an email message. +- word_freq_650: role=feature, type=numeric_sparse_frequency. tags=['condition_candidate', 'measure', 'rare_pattern_candidate'] desc=Frequency feature for token '650' in an email message. +- word_freq_lab: role=feature, type=numeric_sparse_frequency. tags=['condition_candidate', 'measure', 'rare_pattern_candidate'] desc=Frequency feature for token 'lab' in an email message. +- word_freq_labs: role=feature, type=numeric_sparse_frequency. tags=['condition_candidate', 'measure', 'rare_pattern_candidate'] desc=Frequency feature for token 'labs' in an email message. +- word_freq_telnet: role=feature, type=numeric_sparse_frequency. tags=['condition_candidate', 'measure', 'rare_pattern_candidate'] desc=Frequency feature for token 'telnet' in an email message. +- word_freq_857: role=feature, type=numeric_sparse_frequency. tags=['condition_candidate', 'measure', 'rare_pattern_candidate'] desc=Frequency feature for token '857' in an email message. +- word_freq_data: role=feature, type=numeric_sparse_frequency. tags=['condition_candidate', 'measure', 'rare_pattern_candidate'] desc=Frequency feature for token 'data' in an email message. +- word_freq_415: role=feature, type=numeric_sparse_frequency. tags=['condition_candidate', 'measure', 'rare_pattern_candidate'] desc=Frequency feature for token '415' in an email message. +- word_freq_85: role=feature, type=numeric_sparse_frequency. tags=['condition_candidate', 'measure', 'rare_pattern_candidate'] desc=Frequency feature for token '85' in an email message. +- word_freq_technology: role=feature, type=numeric_sparse_frequency. tags=['condition_candidate', 'measure', 'rare_pattern_candidate'] desc=Frequency feature for token 'technology' in an email message. +- word_freq_1999: role=feature, type=numeric_sparse_frequency. tags=['condition_candidate', 'measure', 'rare_pattern_candidate'] desc=Frequency feature for token '1999' in an email message. +- word_freq_parts: role=feature, type=numeric_sparse_frequency. tags=['condition_candidate', 'measure', 'rare_pattern_candidate'] desc=Frequency feature for token 'parts' in an email message. +- word_freq_pm: role=feature, type=numeric_sparse_frequency. tags=['condition_candidate', 'measure', 'rare_pattern_candidate'] desc=Frequency feature for token 'pm' in an email message. +- word_freq_direct: role=feature, type=numeric_sparse_frequency. tags=['condition_candidate', 'measure', 'rare_pattern_candidate'] desc=Frequency feature for token 'direct' in an email message. +- word_freq_cs: role=feature, type=numeric_sparse_frequency. tags=['condition_candidate', 'measure', 'rare_pattern_candidate'] desc=Frequency feature for token 'cs' in an email message. +- word_freq_meeting: role=feature, type=numeric_sparse_frequency. tags=['condition_candidate', 'measure', 'rare_pattern_candidate'] desc=Frequency feature for token 'meeting' in an email message. +- word_freq_original: role=feature, type=numeric_sparse_frequency. tags=['condition_candidate', 'measure', 'rare_pattern_candidate'] desc=Frequency feature for token 'original' in an email message. +- word_freq_project: role=feature, type=numeric_sparse_frequency. tags=['condition_candidate', 'measure', 'rare_pattern_candidate'] desc=Frequency feature for token 'project' in an email message. +- word_freq_re: role=feature, type=numeric_sparse_frequency. tags=['condition_candidate', 'measure', 'rare_pattern_candidate'] desc=Frequency feature for token 're' in an email message. +- word_freq_edu: role=feature, type=numeric_sparse_frequency. tags=['condition_candidate', 'measure', 'rare_pattern_candidate'] desc=Frequency feature for token 'edu' in an email message. +- word_freq_table: role=feature, type=numeric_sparse_frequency. tags=['condition_candidate', 'measure', 'rare_pattern_candidate'] desc=Frequency feature for token 'table' in an email message. +- word_freq_conference: role=feature, type=numeric_sparse_frequency. tags=['condition_candidate', 'measure', 'rare_pattern_candidate'] desc=Frequency feature for token 'conference' in an email message. +- char_freq_%3B: role=feature, type=numeric_sparse_frequency. tags=['condition_candidate', 'measure', 'rare_pattern_candidate'] desc=Character frequency feature for symbol ';'. +- char_freq_%28: role=feature, type=numeric_sparse_frequency. tags=['condition_candidate', 'measure', 'rare_pattern_candidate'] desc=Character frequency feature for symbol '('. +- char_freq_%5B: role=feature, type=numeric_sparse_frequency. tags=['condition_candidate', 'measure', 'rare_pattern_candidate'] desc=Character frequency feature for symbol '['. +- char_freq_%21: role=feature, type=numeric_sparse_frequency. tags=['condition_candidate', 'measure', 'rare_pattern_candidate'] desc=Character frequency feature for symbol '!'. +- char_freq_%24: role=feature, type=numeric_sparse_frequency. tags=['condition_candidate', 'measure', 'rare_pattern_candidate'] desc=Character frequency feature for symbol '$'. +- char_freq_%23: role=feature, type=numeric_sparse_frequency. tags=['condition_candidate', 'measure', 'rare_pattern_candidate'] desc=Character frequency feature for symbol '#'. +- capital_run_length_average: role=feature, type=numeric_heavy_tail. tags=['condition_candidate', 'measure', 'rare_pattern_candidate'] desc=Average length of uninterrupted capital-letter runs. +- capital_run_length_longest: role=feature, type=numeric_heavy_tail. tags=['condition_candidate', 'measure', 'rare_pattern_candidate'] desc=Longest uninterrupted capital-letter run. +- capital_run_length_total: role=feature, type=numeric_heavy_tail. tags=['condition_candidate', 'measure', 'rare_pattern_candidate'] desc=Total number of capital letters in runs. +- class: role=target, type=binary_target. tags=['target_candidate'] desc=Binary spam label (1=spam, 0=non-spam). +- useful_field_combinations: [['word_freq_free', 'word_freq_you', 'class'], ['char_freq_%21', 'capital_run_length_longest', 'class'], ['word_freq_remove', 'word_freq_money', 'class']] +- fields_requiring_caution: ['capital_run_length_average', 'capital_run_length_longest', 'capital_run_length_total'] +- source_url: https://www.openml.org/d/44 + +SQLite schema snapshot: +{ + "table_name": "n1", + "quoted_table_name": "\"n1\"", + "row_count": 4601, + "columns": [ + { + "name": "word_freq_make", + "type": "TEXT", + "notnull": false, + "pk": false + }, + { + "name": "word_freq_address", + "type": "TEXT", + "notnull": false, + "pk": false + }, + { + "name": "word_freq_all", + "type": "TEXT", + "notnull": false, + "pk": false + }, + { + "name": "word_freq_3d", + "type": "TEXT", + "notnull": false, + "pk": false + }, + { + "name": "word_freq_our", + "type": "TEXT", + "notnull": false, + "pk": false + }, + { + "name": "word_freq_over", + "type": "TEXT", + "notnull": false, + "pk": false + }, + { + "name": "word_freq_remove", + "type": "TEXT", + "notnull": false, + "pk": false + }, + { + "name": "word_freq_internet", + "type": "TEXT", + "notnull": false, + "pk": false + }, + { + "name": "word_freq_order", + "type": "TEXT", + "notnull": false, + "pk": false + }, + { + "name": "word_freq_mail", + "type": "TEXT", + "notnull": false, + "pk": false + }, + { + "name": "word_freq_receive", + "type": "TEXT", + "notnull": false, + "pk": false + }, + { + "name": "word_freq_will", + "type": "TEXT", + "notnull": false, + "pk": false + }, + { + "name": "word_freq_people", + "type": "TEXT", + "notnull": false, + "pk": false + }, + { + "name": "word_freq_report", + "type": "TEXT", + "notnull": false, + "pk": false + }, + { + "name": "word_freq_addresses", + "type": "TEXT", + "notnull": false, + "pk": false + }, + { + "name": "word_freq_free", + "type": "TEXT", + "notnull": false, + "pk": false + }, + { + "name": "word_freq_business", + "type": "TEXT", + "notnull": false, + "pk": false + }, + { + "name": "word_freq_email", + "type": "TEXT", + "notnull": false, + "pk": false + }, + { + "name": "word_freq_you", + "type": "TEXT", + "notnull": false, + "pk": false + }, + { + "name": "word_freq_credit", + "type": "TEXT", + "notnull": false, + "pk": false + }, + { + "name": "word_freq_your", + "type": "TEXT", + "notnull": false, + "pk": false + }, + { + "name": "word_freq_font", + "type": "TEXT", + "notnull": false, + "pk": false + }, + { + "name": "word_freq_000", + "type": "TEXT", + "notnull": false, + "pk": false + }, + { + "name": "word_freq_money", + "type": "TEXT", + "notnull": false, + "pk": false + }, + { + "name": "word_freq_hp", + "type": "TEXT", + "notnull": false, + "pk": false + }, + { + "name": "word_freq_hpl", + "type": "TEXT", + "notnull": false, + "pk": false + }, + { + "name": "word_freq_george", + "type": "TEXT", + "notnull": false, + "pk": false + }, + { + "name": "word_freq_650", + "type": "TEXT", + "notnull": false, + "pk": false + }, + { + "name": "word_freq_lab", + "type": "TEXT", + "notnull": false, + "pk": false + }, + { + "name": "word_freq_labs", + "type": "TEXT", + "notnull": false, + "pk": false + }, + { + "name": "word_freq_telnet", + "type": "TEXT", + "notnull": false, + "pk": false + }, + { + "name": "word_freq_857", + "type": "TEXT", + "notnull": false, + "pk": false + }, + { + "name": "word_freq_data", + "type": "TEXT", + "notnull": false, + "pk": false + }, + { + "name": "word_freq_415", + "type": "TEXT", + "notnull": false, + "pk": false + }, + { + "name": "word_freq_85", + "type": "TEXT", + "notnull": false, + "pk": false + }, + { + "name": "word_freq_technology", + "type": "TEXT", + "notnull": false, + "pk": false + }, + { + "name": "word_freq_1999", + "type": "TEXT", + "notnull": false, + "pk": false + }, + { + "name": "word_freq_parts", + "type": "TEXT", + "notnull": false, + "pk": false + }, + { + "name": "word_freq_pm", + "type": "TEXT", + "notnull": false, + "pk": false + }, + { + "name": "word_freq_direct", + "type": "TEXT", + "notnull": false, + "pk": false + }, + { + "name": "word_freq_cs", + "type": "TEXT", + "notnull": false, + "pk": false + }, + { + "name": "word_freq_meeting", + "type": "TEXT", + "notnull": false, + "pk": false + }, + { + "name": "word_freq_original", + "type": "TEXT", + "notnull": false, + "pk": false + }, + { + "name": "word_freq_project", + "type": "TEXT", + "notnull": false, + "pk": false + }, + { + "name": "word_freq_re", + "type": "TEXT", + "notnull": false, + "pk": false + }, + { + "name": "word_freq_edu", + "type": "TEXT", + "notnull": false, + "pk": false + }, + { + "name": "word_freq_table", + "type": "TEXT", + "notnull": false, + "pk": false + }, + { + "name": "word_freq_conference", + "type": "TEXT", + "notnull": false, + "pk": false + }, + { + "name": "char_freq_%3B", + "type": "TEXT", + "notnull": false, + "pk": false + }, + { + "name": "char_freq_%28", + "type": "TEXT", + "notnull": false, + "pk": false + }, + { + "name": "char_freq_%5B", + "type": "TEXT", + "notnull": false, + "pk": false + }, + { + "name": "char_freq_%21", + "type": "TEXT", + "notnull": false, + "pk": false + }, + { + "name": "char_freq_%24", + "type": "TEXT", + "notnull": false, + "pk": false + }, + { + "name": "char_freq_%23", + "type": "TEXT", + "notnull": false, + "pk": false + }, + { + "name": "capital_run_length_average", + "type": "TEXT", + "notnull": false, + "pk": false + }, + { + "name": "capital_run_length_longest", + "type": "TEXT", + "notnull": false, + "pk": false + }, + { + "name": "capital_run_length_total", + "type": "TEXT", + "notnull": false, + "pk": false + }, + { + "name": "class", + "type": "TEXT", + "notnull": false, + "pk": false + } + ], + "sample_rows": [ + { + "word_freq_make": "0", + "word_freq_address": "0.64", + "word_freq_all": "0.64", + "word_freq_3d": "0", + "word_freq_our": "0.32", + "word_freq_over": "0", + "word_freq_remove": "0", + "word_freq_internet": "0", + "word_freq_order": "0", + "word_freq_mail": "0", + "word_freq_receive": "0", + "word_freq_will": "0.64", + "word_freq_people": "0", + "word_freq_report": "0", + "word_freq_addresses": "0", + "word_freq_free": "0.32", + "word_freq_business": "0", + "word_freq_email": "1.29", + "word_freq_you": "1.93", + "word_freq_credit": "0", + "word_freq_your": "0.96", + "word_freq_font": "0", + "word_freq_000": "0", + "word_freq_money": "0", + "word_freq_hp": "0", + "word_freq_hpl": "0", + "word_freq_george": "0", + "word_freq_650": "0", + "word_freq_lab": "0", + "word_freq_labs": "0", + "word_freq_telnet": "0", + "word_freq_857": "0", + "word_freq_data": "0", + "word_freq_415": "0", + "word_freq_85": "0", + "word_freq_technology": "0", + "word_freq_1999": "0", + "word_freq_parts": "0", + "word_freq_pm": "0", + "word_freq_direct": "0", + "word_freq_cs": "0", + "word_freq_meeting": "0", + "word_freq_original": "0", + "word_freq_project": "0", + "word_freq_re": "0", + "word_freq_edu": "0", + "word_freq_table": "0", + "word_freq_conference": "0", + "char_freq_%3B": "0", + "char_freq_%28": "0", + "char_freq_%5B": "0", + "char_freq_%21": "0.778", + "char_freq_%24": "0", + "char_freq_%23": "0", + "capital_run_length_average": "3.756", + "capital_run_length_longest": "61", + "capital_run_length_total": "278", + "class": "1" + }, + { + "word_freq_make": "0.21", + "word_freq_address": "0.28", + "word_freq_all": "0.5", + "word_freq_3d": "0", + "word_freq_our": "0.14", + "word_freq_over": "0.28", + "word_freq_remove": "0.21", + "word_freq_internet": "0.07", + "word_freq_order": "0", + "word_freq_mail": "0.94", + "word_freq_receive": "0.21", + "word_freq_will": "0.79", + "word_freq_people": "0.65", + "word_freq_report": "0.21", + "word_freq_addresses": "0.14", + "word_freq_free": "0.14", + "word_freq_business": "0.07", + "word_freq_email": "0.28", + "word_freq_you": "3.47", + "word_freq_credit": "0", + "word_freq_your": "1.59", + "word_freq_font": "0", + "word_freq_000": "0.43", + "word_freq_money": "0.43", + "word_freq_hp": "0", + "word_freq_hpl": "0", + "word_freq_george": "0", + "word_freq_650": "0", + "word_freq_lab": "0", + "word_freq_labs": "0", + "word_freq_telnet": "0", + "word_freq_857": "0", + "word_freq_data": "0", + "word_freq_415": "0", + "word_freq_85": "0", + "word_freq_technology": "0", + "word_freq_1999": "0.07", + "word_freq_parts": "0", + "word_freq_pm": "0", + "word_freq_direct": "0", + "word_freq_cs": "0", + "word_freq_meeting": "0", + "word_freq_original": "0", + "word_freq_project": "0", + "word_freq_re": "0", + "word_freq_edu": "0", + "word_freq_table": "0", + "word_freq_conference": "0", + "char_freq_%3B": "0", + "char_freq_%28": "0.132", + "char_freq_%5B": "0", + "char_freq_%21": "0.372", + "char_freq_%24": "0.18", + "char_freq_%23": "0.048", + "capital_run_length_average": "5.114", + "capital_run_length_longest": "101", + "capital_run_length_total": "1028", + "class": "1" + }, + { + "word_freq_make": "0.06", + "word_freq_address": "0", + "word_freq_all": "0.71", + "word_freq_3d": "0", + "word_freq_our": "1.23", + "word_freq_over": "0.19", + "word_freq_remove": "0.19", + "word_freq_internet": "0.12", + "word_freq_order": "0.64", + "word_freq_mail": "0.25", + "word_freq_receive": "0.38", + "word_freq_will": "0.45", + "word_freq_people": "0.12", + "word_freq_report": "0", + "word_freq_addresses": "1.75", + "word_freq_free": "0.06", + "word_freq_business": "0.06", + "word_freq_email": "1.03", + "word_freq_you": "1.36", + "word_freq_credit": "0.32", + "word_freq_your": "0.51", + "word_freq_font": "0", + "word_freq_000": "1.16", + "word_freq_money": "0.06", + "word_freq_hp": "0", + "word_freq_hpl": "0", + "word_freq_george": "0", + "word_freq_650": "0", + "word_freq_lab": "0", + "word_freq_labs": "0", + "word_freq_telnet": "0", + "word_freq_857": "0", + "word_freq_data": "0", + "word_freq_415": "0", + "word_freq_85": "0", + "word_freq_technology": "0", + "word_freq_1999": "0", + "word_freq_parts": "0", + "word_freq_pm": "0", + "word_freq_direct": "0.06", + "word_freq_cs": "0", + "word_freq_meeting": "0", + "word_freq_original": "0.12", + "word_freq_project": "0", + "word_freq_re": "0.06", + "word_freq_edu": "0.06", + "word_freq_table": "0", + "word_freq_conference": "0", + "char_freq_%3B": "0.01", + "char_freq_%28": "0.143", + "char_freq_%5B": "0", + "char_freq_%21": "0.276", + "char_freq_%24": "0.184", + "char_freq_%23": "0.01", + "capital_run_length_average": "9.821", + "capital_run_length_longest": "485", + "capital_run_length_total": "2259", + "class": "1" + }, + { + "word_freq_make": "0", + "word_freq_address": "0", + "word_freq_all": "0", + "word_freq_3d": "0", + "word_freq_our": "0.63", + "word_freq_over": "0", + "word_freq_remove": "0.31", + "word_freq_internet": "0.63", + "word_freq_order": "0.31", + "word_freq_mail": "0.63", + "word_freq_receive": "0.31", + "word_freq_will": "0.31", + "word_freq_people": "0.31", + "word_freq_report": "0", + "word_freq_addresses": "0", + "word_freq_free": "0.31", + "word_freq_business": "0", + "word_freq_email": "0", + "word_freq_you": "3.18", + "word_freq_credit": "0", + "word_freq_your": "0.31", + "word_freq_font": "0", + "word_freq_000": "0", + "word_freq_money": "0", + "word_freq_hp": "0", + "word_freq_hpl": "0", + "word_freq_george": "0", + "word_freq_650": "0", + "word_freq_lab": "0", + "word_freq_labs": "0", + "word_freq_telnet": "0", + "word_freq_857": "0", + "word_freq_data": "0", + "word_freq_415": "0", + "word_freq_85": "0", + "word_freq_technology": "0", + "word_freq_1999": "0", + "word_freq_parts": "0", + "word_freq_pm": "0", + "word_freq_direct": "0", + "word_freq_cs": "0", + "word_freq_meeting": "0", + "word_freq_original": "0", + "word_freq_project": "0", + "word_freq_re": "0", + "word_freq_edu": "0", + "word_freq_table": "0", + "word_freq_conference": "0", + "char_freq_%3B": "0", + "char_freq_%28": "0.137", + "char_freq_%5B": "0", + "char_freq_%21": "0.137", + "char_freq_%24": "0", + "char_freq_%23": "0", + "capital_run_length_average": "3.537", + "capital_run_length_longest": "40", + "capital_run_length_total": "191", + "class": "1" + }, + { + "word_freq_make": "0", + "word_freq_address": "0", + "word_freq_all": "0", + "word_freq_3d": "0", + "word_freq_our": "0.63", + "word_freq_over": "0", + "word_freq_remove": "0.31", + "word_freq_internet": "0.63", + "word_freq_order": "0.31", + "word_freq_mail": "0.63", + "word_freq_receive": "0.31", + "word_freq_will": "0.31", + "word_freq_people": "0.31", + "word_freq_report": "0", + "word_freq_addresses": "0", + "word_freq_free": "0.31", + "word_freq_business": "0", + "word_freq_email": "0", + "word_freq_you": "3.18", + "word_freq_credit": "0", + "word_freq_your": "0.31", + "word_freq_font": "0", + "word_freq_000": "0", + "word_freq_money": "0", + "word_freq_hp": "0", + "word_freq_hpl": "0", + "word_freq_george": "0", + "word_freq_650": "0", + "word_freq_lab": "0", + "word_freq_labs": "0", + "word_freq_telnet": "0", + "word_freq_857": "0", + "word_freq_data": "0", + "word_freq_415": "0", + "word_freq_85": "0", + "word_freq_technology": "0", + "word_freq_1999": "0", + "word_freq_parts": "0", + "word_freq_pm": "0", + "word_freq_direct": "0", + "word_freq_cs": "0", + "word_freq_meeting": "0", + "word_freq_original": "0", + "word_freq_project": "0", + "word_freq_re": "0", + "word_freq_edu": "0", + "word_freq_table": "0", + "word_freq_conference": "0", + "char_freq_%3B": "0", + "char_freq_%28": "0.135", + "char_freq_%5B": "0", + "char_freq_%21": "0.135", + "char_freq_%24": "0", + "char_freq_%23": "0", + "capital_run_length_average": "3.537", + "capital_run_length_longest": "40", + "capital_run_length_total": "191", + "class": "1" + } + ] +} + +Shortlisted templates: +[ + { + "template_id": "tpl_h2o_group_sum", + "template_name": "Grouped Numeric Sum", + "primary_family": "subgroup_structure", + "portability": "partial", + "sql_skeleton": "SELECT {group_col}, SUM({measure_col}) AS total_measure\nFROM {table}\nGROUP BY {group_col}\nORDER BY total_measure DESC;", + "required_roles": [ + "group_col", + "measure_col" + ] + } +] + +Problem instance: +{ + "dataset_id": "n1", + "question": "Use template Grouped Numeric Sum to probe internal_profile_stability with semantic role collapsed_target_view. Focus on group_col=class, measure_col=word_freq_address.", + "planned_template_id": "tpl_h2o_group_sum", + "bindings": { + "group_col": "class", + "measure_col": "word_freq_address", + "top_k": 11, + "top_n": 4, + "num_tiles": 10, + "percentile_value": 0.9, + "z_threshold": 2.0, + "fraction_threshold": 0.1, + "baseline_multiplier": 1.5, + "baseline_fraction": 0.1, + "min_group_size": 5, + "min_support": 5, + "measure_threshold": 0.0, + "time_grain": "month", + "lookback_rows": 3, + "current_period_start": "'2024-01-01'", + "current_period_end": "'2024-04-01'", + "previous_period_start": "'2023-10-01'", + "previous_period_end": "'2024-01-01'", + "drift_ratio_threshold": 0.8 + }, + "can_vary": [], + "must_fix": [], + "runtime_sql_skeleton": "SELECT {group_col}, SUM({measure_col}) AS total_measure\nFROM {table}\nGROUP BY {group_col}\nORDER BY total_measure DESC;" +} + +Repair context: +{} diff --git a/Query/sql/v2/runs/v2_cli_20260502_081223_d/n1/artifacts/v2q_n1_22c2bb93d6588817/cli/sql_response_attempt_1.raw.txt b/Query/sql/v2/runs/v2_cli_20260502_081223_d/n1/artifacts/v2q_n1_22c2bb93d6588817/cli/sql_response_attempt_1.raw.txt new file mode 100644 index 0000000000000000000000000000000000000000..ef81ea77729228973313634a97cc59b39f69b84a --- /dev/null +++ b/Query/sql/v2/runs/v2_cli_20260502_081223_d/n1/artifacts/v2q_n1_22c2bb93d6588817/cli/sql_response_attempt_1.raw.txt @@ -0,0 +1,4 @@ +{"type":"thread.started","thread_id":"019e40da-6eed-71f3-9ea2-a6d36cf38051"} +{"type":"turn.started"} +{"type":"item.completed","item":{"id":"item_0","type":"agent_message","text":"{\"sql\":\"-- template_id: tpl_h2o_group_sum\\nSELECT\\n \\\"class\\\",\\n SUM(CAST(\\\"word_freq_address\\\" AS REAL)) AS \\\"total_measure\\\"\\nFROM \\\"n1\\\"\\nGROUP BY \\\"class\\\"\\nORDER BY \\\"total_measure\\\" DESC;\",\"notes\":\"Uses the required grouped numeric sum template with group_col=\\\"class\\\" and measure_col=\\\"word_freq_address\\\". CAST to REAL is applied because the schema stores feature columns as TEXT.\"}"}} +{"type":"turn.completed","usage":{"input_tokens":20315,"cached_input_tokens":12032,"output_tokens":349,"reasoning_output_tokens":244}} diff --git a/Query/sql/v2/runs/v2_cli_20260502_081223_d/n1/artifacts/v2q_n1_22c2bb93d6588817/cli/sql_response_attempt_1.txt b/Query/sql/v2/runs/v2_cli_20260502_081223_d/n1/artifacts/v2q_n1_22c2bb93d6588817/cli/sql_response_attempt_1.txt new file mode 100644 index 0000000000000000000000000000000000000000..6f96797b22a39e1c48b5843da9c8c2f11321b802 --- /dev/null +++ b/Query/sql/v2/runs/v2_cli_20260502_081223_d/n1/artifacts/v2q_n1_22c2bb93d6588817/cli/sql_response_attempt_1.txt @@ -0,0 +1 @@ +{"sql":"-- template_id: tpl_h2o_group_sum\nSELECT\n \"class\",\n SUM(CAST(\"word_freq_address\" AS REAL)) AS \"total_measure\"\nFROM \"n1\"\nGROUP BY \"class\"\nORDER BY \"total_measure\" DESC;","notes":"Uses the required grouped numeric sum template with group_col=\"class\" and measure_col=\"word_freq_address\". CAST to REAL is applied because the schema stores feature columns as TEXT."} \ No newline at end of file diff --git a/Query/sql/v2/runs/v2_cli_20260502_081223_d/n1/artifacts/v2q_n1_22c2bb93d6588817/cli/sql_stderr_attempt_1.txt b/Query/sql/v2/runs/v2_cli_20260502_081223_d/n1/artifacts/v2q_n1_22c2bb93d6588817/cli/sql_stderr_attempt_1.txt new file mode 100644 index 0000000000000000000000000000000000000000..e69de29bb2d1d6434b8b29ae775ad8c2e48c5391 diff --git a/Query/sql/v2/runs/v2_cli_20260502_081223_d/n1/artifacts/v2q_n1_23e99d817f436540/cli/conversation.jsonl b/Query/sql/v2/runs/v2_cli_20260502_081223_d/n1/artifacts/v2q_n1_23e99d817f436540/cli/conversation.jsonl new file mode 100644 index 0000000000000000000000000000000000000000..cf80432ea8e4f81da8e28442453b8fcfd3326bea --- /dev/null +++ b/Query/sql/v2/runs/v2_cli_20260502_081223_d/n1/artifacts/v2q_n1_23e99d817f436540/cli/conversation.jsonl @@ -0,0 +1,2 @@ +{"attempt": 1, "phase": "sql_generation", "role": "user", "content_path": "cli/sql_prompt_attempt_1.txt", "metrics": {"chars": 29531, "bytes_utf8": 29531, "lines": 792, "estimated_tokens": null}} +{"attempt": 1, "phase": "sql_generation", "role": "assistant", "content_path": "cli/sql_response_attempt_1.txt", "raw_content_path": "cli/sql_response_attempt_1.raw.txt", "stderr_path": "cli/sql_stderr_attempt_1.txt", "metrics": {"chars": 800, "bytes_utf8": 800, "lines": 1, "estimated_tokens": null}, "usage": {"input_tokens": 20353, "cached_input_tokens": 19840, "output_tokens": 744, "reasoning_output_tokens": 516}} diff --git a/Query/sql/v2/runs/v2_cli_20260502_081223_d/n1/artifacts/v2q_n1_23e99d817f436540/cli/session_summary.json b/Query/sql/v2/runs/v2_cli_20260502_081223_d/n1/artifacts/v2q_n1_23e99d817f436540/cli/session_summary.json new file mode 100644 index 0000000000000000000000000000000000000000..afdd3447e66e66086aa0b1c21c45cd1524c78484 --- /dev/null +++ b/Query/sql/v2/runs/v2_cli_20260502_081223_d/n1/artifacts/v2q_n1_23e99d817f436540/cli/session_summary.json @@ -0,0 +1,25 @@ +{ + "engine": "v2-cli:codex", + "command": "codex exec --skip-git-repo-check --disable plugins --sandbox read-only --cd \"/data/jialinzhang/SQLagent\" -m gpt-5.4 --json -", + "ai_cli_calls": 1, + "usage_summary": { + "dataset_id": "n1", + "model": "v2-cli:codex", + "run_id": "v2q_n1_23e99d817f436540", + "api_calls": 0, + "input_tokens": 20353, + "cached_input_tokens": 19840, + "output_tokens": 744, + "total_tokens": 21097, + "cost_usd": 0.0, + "ai_cli_calls": 1, + "estimated_input_tokens": 0, + "estimated_output_tokens": 0, + "estimated_total_tokens": 0, + "usage_source": "ai_cli_json_usage", + "cli_elapsed_ms_total": 16774.26, + "sql_execution_elapsed_ms_total": 8.33, + "conversation_log_path": "/data/jialinzhang/TabQueryBench/sql_workloads/v2_current/runs_and_launches/runs/v2_cli_20260502_081223_d/n1/artifacts/v2q_n1_23e99d817f436540/cli/conversation.jsonl", + "note": "Executed through a local AI CLI with structured usage metadata." + } +} \ No newline at end of file diff --git a/Query/sql/v2/runs/v2_cli_20260502_081223_d/n1/artifacts/v2q_n1_23e99d817f436540/cli/sql_attempt_1.metadata.json b/Query/sql/v2/runs/v2_cli_20260502_081223_d/n1/artifacts/v2q_n1_23e99d817f436540/cli/sql_attempt_1.metadata.json new file mode 100644 index 0000000000000000000000000000000000000000..22b942fc4cd4fae000cb96575647f2473486e65f --- /dev/null +++ b/Query/sql/v2/runs/v2_cli_20260502_081223_d/n1/artifacts/v2q_n1_23e99d817f436540/cli/sql_attempt_1.metadata.json @@ -0,0 +1,45 @@ +{ + "attempt": 1, + "phase": "sql_generation", + "command": "codex exec --skip-git-repo-check --disable plugins --sandbox read-only --cd \"/data/jialinzhang/SQLagent\" -m gpt-5.4 --json -", + "started_at": "2026-05-19T15:51:46.456984+00:00", + "ended_at": "2026-05-19T15:52:03.231285+00:00", + "elapsed_ms": 16774.26, + "prompt_metrics": { + "chars": 29531, + "bytes_utf8": 29531, + "lines": 792, + "estimated_tokens": null + }, + "stdout_metrics": { + "chars": 1217, + "bytes_utf8": 1217, + "lines": 4, + "estimated_tokens": null + }, + "stderr_metrics": { + "chars": 0, + "bytes_utf8": 0, + "lines": 0, + "estimated_tokens": null + }, + "parsed_output": { + "format": "jsonl_events", + "text_metrics": { + "chars": 800, + "bytes_utf8": 800, + "lines": 1, + "estimated_tokens": null + }, + "usage": { + "input_tokens": 20353, + "cached_input_tokens": 19840, + "output_tokens": 744, + "reasoning_output_tokens": 516 + } + }, + "prompt_path": "cli/sql_prompt_attempt_1.txt", + "response_path": "cli/sql_response_attempt_1.txt", + "raw_response_path": "cli/sql_response_attempt_1.raw.txt", + "stderr_path": "cli/sql_stderr_attempt_1.txt" +} \ No newline at end of file diff --git a/Query/sql/v2/runs/v2_cli_20260502_081223_d/n1/artifacts/v2q_n1_23e99d817f436540/cli/sql_prompt_attempt_1.txt b/Query/sql/v2/runs/v2_cli_20260502_081223_d/n1/artifacts/v2q_n1_23e99d817f436540/cli/sql_prompt_attempt_1.txt new file mode 100644 index 0000000000000000000000000000000000000000..8f388678f8b34c7512eedb2670ce417374dcddbe --- /dev/null +++ b/Query/sql/v2/runs/v2_cli_20260502_081223_d/n1/artifacts/v2q_n1_23e99d817f436540/cli/sql_prompt_attempt_1.txt @@ -0,0 +1,792 @@ +You are generating one SQLite SELECT query for a single-table SQL QA task. +Return strict JSON only, with this schema: {"sql": "...", "notes": "..."}. +Rules: +- Use only the provided table and columns. +- Do not write INSERT, UPDATE, DELETE, DROP, ALTER, CREATE, PRAGMA, ATTACH, DETACH, or VACUUM. +- Prefer the planned template and bound roles when provided. +- Add a leading SQL comment exactly like: -- template_id: . +- Generate SQLite-compatible SQL. SQLite does not support PERCENTILE_CONT or STDDEV. +- Quote identifiers with double quotes. +- Return no markdown and no extra prose. + +Dataset context: +Dataset context for SQL QA: +- dataset_id: n1 +- dataset_name: Spambase +- table_name: n1 +- table_layout: single-table dataset (do not assume joins). +- row_semantics: One row is one email represented by engineered token/character frequency and capitalization-run features. +- task_type: classification +- target_column: class +- main_row_count: 4601 +- important_fields: +- word_freq_make: role=feature, type=numeric_sparse_frequency. tags=['condition_candidate', 'measure', 'rare_pattern_candidate'] desc=Frequency feature for token 'make' in an email message. +- word_freq_address: role=feature, type=numeric_sparse_frequency. tags=['condition_candidate', 'measure', 'rare_pattern_candidate'] desc=Frequency feature for token 'address' in an email message. +- word_freq_all: role=feature, type=numeric_sparse_frequency. tags=['condition_candidate', 'measure', 'rare_pattern_candidate'] desc=Frequency feature for token 'all' in an email message. +- word_freq_3d: role=feature, type=numeric_sparse_frequency. tags=['condition_candidate', 'measure', 'rare_pattern_candidate'] desc=Frequency feature for token '3d' in an email message. +- word_freq_our: role=feature, type=numeric_sparse_frequency. tags=['condition_candidate', 'measure', 'rare_pattern_candidate'] desc=Frequency feature for token 'our' in an email message. +- word_freq_over: role=feature, type=numeric_sparse_frequency. tags=['condition_candidate', 'measure', 'rare_pattern_candidate'] desc=Frequency feature for token 'over' in an email message. +- word_freq_remove: role=feature, type=numeric_sparse_frequency. tags=['condition_candidate', 'measure', 'rare_pattern_candidate'] desc=Frequency feature for token 'remove' in an email message. +- word_freq_internet: role=feature, type=numeric_sparse_frequency. tags=['condition_candidate', 'measure', 'rare_pattern_candidate'] desc=Frequency feature for token 'internet' in an email message. +- word_freq_order: role=feature, type=numeric_sparse_frequency. tags=['condition_candidate', 'measure', 'rare_pattern_candidate'] desc=Frequency feature for token 'order' in an email message. +- word_freq_mail: role=feature, type=numeric_sparse_frequency. tags=['condition_candidate', 'measure', 'rare_pattern_candidate'] desc=Frequency feature for token 'mail' in an email message. +- word_freq_receive: role=feature, type=numeric_sparse_frequency. tags=['condition_candidate', 'measure', 'rare_pattern_candidate'] desc=Frequency feature for token 'receive' in an email message. +- word_freq_will: role=feature, type=numeric_sparse_frequency. tags=['condition_candidate', 'measure', 'rare_pattern_candidate'] desc=Frequency feature for token 'will' in an email message. +- word_freq_people: role=feature, type=numeric_sparse_frequency. tags=['condition_candidate', 'measure', 'rare_pattern_candidate'] desc=Frequency feature for token 'people' in an email message. +- word_freq_report: role=feature, type=numeric_sparse_frequency. tags=['condition_candidate', 'measure', 'rare_pattern_candidate'] desc=Frequency feature for token 'report' in an email message. +- word_freq_addresses: role=feature, type=numeric_sparse_frequency. tags=['condition_candidate', 'measure', 'rare_pattern_candidate'] desc=Frequency feature for token 'addresses' in an email message. +- word_freq_free: role=feature, type=numeric_sparse_frequency. tags=['condition_candidate', 'measure', 'rare_pattern_candidate'] desc=Frequency feature for token 'free' in an email message. +- word_freq_business: role=feature, type=numeric_sparse_frequency. tags=['condition_candidate', 'measure', 'rare_pattern_candidate'] desc=Frequency feature for token 'business' in an email message. +- word_freq_email: role=feature, type=numeric_sparse_frequency. tags=['condition_candidate', 'measure', 'rare_pattern_candidate'] desc=Frequency feature for token 'email' in an email message. +- word_freq_you: role=feature, type=numeric_sparse_frequency. tags=['condition_candidate', 'measure', 'rare_pattern_candidate'] desc=Frequency feature for token 'you' in an email message. +- word_freq_credit: role=feature, type=numeric_sparse_frequency. tags=['condition_candidate', 'measure', 'rare_pattern_candidate'] desc=Frequency feature for token 'credit' in an email message. +- word_freq_your: role=feature, type=numeric_sparse_frequency. tags=['condition_candidate', 'measure', 'rare_pattern_candidate'] desc=Frequency feature for token 'your' in an email message. +- word_freq_font: role=feature, type=numeric_sparse_frequency. tags=['condition_candidate', 'measure', 'rare_pattern_candidate'] desc=Frequency feature for token 'font' in an email message. +- word_freq_000: role=feature, type=numeric_sparse_frequency. tags=['condition_candidate', 'measure', 'rare_pattern_candidate'] desc=Frequency feature for token '000' in an email message. +- word_freq_money: role=feature, type=numeric_sparse_frequency. tags=['condition_candidate', 'measure', 'rare_pattern_candidate'] desc=Frequency feature for token 'money' in an email message. +- word_freq_hp: role=feature, type=numeric_sparse_frequency. tags=['condition_candidate', 'measure', 'rare_pattern_candidate'] desc=Frequency feature for token 'hp' in an email message. +- word_freq_hpl: role=feature, type=numeric_sparse_frequency. tags=['condition_candidate', 'measure', 'rare_pattern_candidate'] desc=Frequency feature for token 'hpl' in an email message. +- word_freq_george: role=feature, type=numeric_sparse_frequency. tags=['condition_candidate', 'measure', 'rare_pattern_candidate'] desc=Frequency feature for token 'george' in an email message. +- word_freq_650: role=feature, type=numeric_sparse_frequency. tags=['condition_candidate', 'measure', 'rare_pattern_candidate'] desc=Frequency feature for token '650' in an email message. +- word_freq_lab: role=feature, type=numeric_sparse_frequency. tags=['condition_candidate', 'measure', 'rare_pattern_candidate'] desc=Frequency feature for token 'lab' in an email message. +- word_freq_labs: role=feature, type=numeric_sparse_frequency. tags=['condition_candidate', 'measure', 'rare_pattern_candidate'] desc=Frequency feature for token 'labs' in an email message. +- word_freq_telnet: role=feature, type=numeric_sparse_frequency. tags=['condition_candidate', 'measure', 'rare_pattern_candidate'] desc=Frequency feature for token 'telnet' in an email message. +- word_freq_857: role=feature, type=numeric_sparse_frequency. tags=['condition_candidate', 'measure', 'rare_pattern_candidate'] desc=Frequency feature for token '857' in an email message. +- word_freq_data: role=feature, type=numeric_sparse_frequency. tags=['condition_candidate', 'measure', 'rare_pattern_candidate'] desc=Frequency feature for token 'data' in an email message. +- word_freq_415: role=feature, type=numeric_sparse_frequency. tags=['condition_candidate', 'measure', 'rare_pattern_candidate'] desc=Frequency feature for token '415' in an email message. +- word_freq_85: role=feature, type=numeric_sparse_frequency. tags=['condition_candidate', 'measure', 'rare_pattern_candidate'] desc=Frequency feature for token '85' in an email message. +- word_freq_technology: role=feature, type=numeric_sparse_frequency. tags=['condition_candidate', 'measure', 'rare_pattern_candidate'] desc=Frequency feature for token 'technology' in an email message. +- word_freq_1999: role=feature, type=numeric_sparse_frequency. tags=['condition_candidate', 'measure', 'rare_pattern_candidate'] desc=Frequency feature for token '1999' in an email message. +- word_freq_parts: role=feature, type=numeric_sparse_frequency. tags=['condition_candidate', 'measure', 'rare_pattern_candidate'] desc=Frequency feature for token 'parts' in an email message. +- word_freq_pm: role=feature, type=numeric_sparse_frequency. tags=['condition_candidate', 'measure', 'rare_pattern_candidate'] desc=Frequency feature for token 'pm' in an email message. +- word_freq_direct: role=feature, type=numeric_sparse_frequency. tags=['condition_candidate', 'measure', 'rare_pattern_candidate'] desc=Frequency feature for token 'direct' in an email message. +- word_freq_cs: role=feature, type=numeric_sparse_frequency. tags=['condition_candidate', 'measure', 'rare_pattern_candidate'] desc=Frequency feature for token 'cs' in an email message. +- word_freq_meeting: role=feature, type=numeric_sparse_frequency. tags=['condition_candidate', 'measure', 'rare_pattern_candidate'] desc=Frequency feature for token 'meeting' in an email message. +- word_freq_original: role=feature, type=numeric_sparse_frequency. tags=['condition_candidate', 'measure', 'rare_pattern_candidate'] desc=Frequency feature for token 'original' in an email message. +- word_freq_project: role=feature, type=numeric_sparse_frequency. tags=['condition_candidate', 'measure', 'rare_pattern_candidate'] desc=Frequency feature for token 'project' in an email message. +- word_freq_re: role=feature, type=numeric_sparse_frequency. tags=['condition_candidate', 'measure', 'rare_pattern_candidate'] desc=Frequency feature for token 're' in an email message. +- word_freq_edu: role=feature, type=numeric_sparse_frequency. tags=['condition_candidate', 'measure', 'rare_pattern_candidate'] desc=Frequency feature for token 'edu' in an email message. +- word_freq_table: role=feature, type=numeric_sparse_frequency. tags=['condition_candidate', 'measure', 'rare_pattern_candidate'] desc=Frequency feature for token 'table' in an email message. +- word_freq_conference: role=feature, type=numeric_sparse_frequency. tags=['condition_candidate', 'measure', 'rare_pattern_candidate'] desc=Frequency feature for token 'conference' in an email message. +- char_freq_%3B: role=feature, type=numeric_sparse_frequency. tags=['condition_candidate', 'measure', 'rare_pattern_candidate'] desc=Character frequency feature for symbol ';'. +- char_freq_%28: role=feature, type=numeric_sparse_frequency. tags=['condition_candidate', 'measure', 'rare_pattern_candidate'] desc=Character frequency feature for symbol '('. +- char_freq_%5B: role=feature, type=numeric_sparse_frequency. tags=['condition_candidate', 'measure', 'rare_pattern_candidate'] desc=Character frequency feature for symbol '['. +- char_freq_%21: role=feature, type=numeric_sparse_frequency. tags=['condition_candidate', 'measure', 'rare_pattern_candidate'] desc=Character frequency feature for symbol '!'. +- char_freq_%24: role=feature, type=numeric_sparse_frequency. tags=['condition_candidate', 'measure', 'rare_pattern_candidate'] desc=Character frequency feature for symbol '$'. +- char_freq_%23: role=feature, type=numeric_sparse_frequency. tags=['condition_candidate', 'measure', 'rare_pattern_candidate'] desc=Character frequency feature for symbol '#'. +- capital_run_length_average: role=feature, type=numeric_heavy_tail. tags=['condition_candidate', 'measure', 'rare_pattern_candidate'] desc=Average length of uninterrupted capital-letter runs. +- capital_run_length_longest: role=feature, type=numeric_heavy_tail. tags=['condition_candidate', 'measure', 'rare_pattern_candidate'] desc=Longest uninterrupted capital-letter run. +- capital_run_length_total: role=feature, type=numeric_heavy_tail. tags=['condition_candidate', 'measure', 'rare_pattern_candidate'] desc=Total number of capital letters in runs. +- class: role=target, type=binary_target. tags=['target_candidate'] desc=Binary spam label (1=spam, 0=non-spam). +- useful_field_combinations: [['word_freq_free', 'word_freq_you', 'class'], ['char_freq_%21', 'capital_run_length_longest', 'class'], ['word_freq_remove', 'word_freq_money', 'class']] +- fields_requiring_caution: ['capital_run_length_average', 'capital_run_length_longest', 'capital_run_length_total'] +- source_url: https://www.openml.org/d/44 + +SQLite schema snapshot: +{ + "table_name": "n1", + "quoted_table_name": "\"n1\"", + "row_count": 4601, + "columns": [ + { + "name": "word_freq_make", + "type": "TEXT", + "notnull": false, + "pk": false + }, + { + "name": "word_freq_address", + "type": "TEXT", + "notnull": false, + "pk": false + }, + { + "name": "word_freq_all", + "type": "TEXT", + "notnull": false, + "pk": false + }, + { + "name": "word_freq_3d", + "type": "TEXT", + "notnull": false, + "pk": false + }, + { + "name": "word_freq_our", + "type": "TEXT", + "notnull": false, + "pk": false + }, + { + "name": "word_freq_over", + "type": "TEXT", + "notnull": false, + "pk": false + }, + { + "name": "word_freq_remove", + "type": "TEXT", + "notnull": false, + "pk": false + }, + { + "name": "word_freq_internet", + "type": "TEXT", + "notnull": false, + "pk": false + }, + { + "name": "word_freq_order", + "type": "TEXT", + "notnull": false, + "pk": false + }, + { + "name": "word_freq_mail", + "type": "TEXT", + "notnull": false, + "pk": false + }, + { + "name": "word_freq_receive", + "type": "TEXT", + "notnull": false, + "pk": false + }, + { + "name": "word_freq_will", + "type": "TEXT", + "notnull": false, + "pk": false + }, + { + "name": "word_freq_people", + "type": "TEXT", + "notnull": false, + "pk": false + }, + { + "name": "word_freq_report", + "type": "TEXT", + "notnull": false, + "pk": false + }, + { + "name": "word_freq_addresses", + "type": "TEXT", + "notnull": false, + "pk": false + }, + { + "name": "word_freq_free", + "type": "TEXT", + "notnull": false, + "pk": false + }, + { + "name": "word_freq_business", + "type": "TEXT", + "notnull": false, + "pk": false + }, + { + "name": "word_freq_email", + "type": "TEXT", + "notnull": false, + "pk": false + }, + { + "name": "word_freq_you", + "type": "TEXT", + "notnull": false, + "pk": false + }, + { + "name": "word_freq_credit", + "type": "TEXT", + "notnull": false, + "pk": false + }, + { + "name": "word_freq_your", + "type": "TEXT", + "notnull": false, + "pk": false + }, + { + "name": "word_freq_font", + "type": "TEXT", + "notnull": false, + "pk": false + }, + { + "name": "word_freq_000", + "type": "TEXT", + "notnull": false, + "pk": false + }, + { + "name": "word_freq_money", + "type": "TEXT", + "notnull": false, + "pk": false + }, + { + "name": "word_freq_hp", + "type": "TEXT", + "notnull": false, + "pk": false + }, + { + "name": "word_freq_hpl", + "type": "TEXT", + "notnull": false, + "pk": false + }, + { + "name": "word_freq_george", + "type": "TEXT", + "notnull": false, + "pk": false + }, + { + "name": "word_freq_650", + "type": "TEXT", + "notnull": false, + "pk": false + }, + { + "name": "word_freq_lab", + "type": "TEXT", + "notnull": false, + "pk": false + }, + { + "name": "word_freq_labs", + "type": "TEXT", + "notnull": false, + "pk": false + }, + { + "name": "word_freq_telnet", + "type": "TEXT", + "notnull": false, + "pk": false + }, + { + "name": "word_freq_857", + "type": "TEXT", + "notnull": false, + "pk": false + }, + { + "name": "word_freq_data", + "type": "TEXT", + "notnull": false, + "pk": false + }, + { + "name": "word_freq_415", + "type": "TEXT", + "notnull": false, + "pk": false + }, + { + "name": "word_freq_85", + "type": "TEXT", + "notnull": false, + "pk": false + }, + { + "name": "word_freq_technology", + "type": "TEXT", + "notnull": false, + "pk": false + }, + { + "name": "word_freq_1999", + "type": "TEXT", + "notnull": false, + "pk": false + }, + { + "name": "word_freq_parts", + "type": "TEXT", + "notnull": false, + "pk": false + }, + { + "name": "word_freq_pm", + "type": "TEXT", + "notnull": false, + "pk": false + }, + { + "name": "word_freq_direct", + "type": "TEXT", + "notnull": false, + "pk": false + }, + { + "name": "word_freq_cs", + "type": "TEXT", + "notnull": false, + "pk": false + }, + { + "name": "word_freq_meeting", + "type": "TEXT", + "notnull": false, + "pk": false + }, + { + "name": "word_freq_original", + "type": "TEXT", + "notnull": false, + "pk": false + }, + { + "name": "word_freq_project", + "type": "TEXT", + "notnull": false, + "pk": false + }, + { + "name": "word_freq_re", + "type": "TEXT", + "notnull": false, + "pk": false + }, + { + "name": "word_freq_edu", + "type": "TEXT", + "notnull": false, + "pk": false + }, + { + "name": "word_freq_table", + "type": "TEXT", + "notnull": false, + "pk": false + }, + { + "name": "word_freq_conference", + "type": "TEXT", + "notnull": false, + "pk": false + }, + { + "name": "char_freq_%3B", + "type": "TEXT", + "notnull": false, + "pk": false + }, + { + "name": "char_freq_%28", + "type": "TEXT", + "notnull": false, + "pk": false + }, + { + "name": "char_freq_%5B", + "type": "TEXT", + "notnull": false, + "pk": false + }, + { + "name": "char_freq_%21", + "type": "TEXT", + "notnull": false, + "pk": false + }, + { + "name": "char_freq_%24", + "type": "TEXT", + "notnull": false, + "pk": false + }, + { + "name": "char_freq_%23", + "type": "TEXT", + "notnull": false, + "pk": false + }, + { + "name": "capital_run_length_average", + "type": "TEXT", + "notnull": false, + "pk": false + }, + { + "name": "capital_run_length_longest", + "type": "TEXT", + "notnull": false, + "pk": false + }, + { + "name": "capital_run_length_total", + "type": "TEXT", + "notnull": false, + "pk": false + }, + { + "name": "class", + "type": "TEXT", + "notnull": false, + "pk": false + } + ], + "sample_rows": [ + { + "word_freq_make": "0", + "word_freq_address": "0.64", + "word_freq_all": "0.64", + "word_freq_3d": "0", + "word_freq_our": "0.32", + "word_freq_over": "0", + "word_freq_remove": "0", + "word_freq_internet": "0", + "word_freq_order": "0", + "word_freq_mail": "0", + "word_freq_receive": "0", + "word_freq_will": "0.64", + "word_freq_people": "0", + "word_freq_report": "0", + "word_freq_addresses": "0", + "word_freq_free": "0.32", + "word_freq_business": "0", + "word_freq_email": "1.29", + "word_freq_you": "1.93", + "word_freq_credit": "0", + "word_freq_your": "0.96", + "word_freq_font": "0", + "word_freq_000": "0", + "word_freq_money": "0", + "word_freq_hp": "0", + "word_freq_hpl": "0", + "word_freq_george": "0", + "word_freq_650": "0", + "word_freq_lab": "0", + "word_freq_labs": "0", + "word_freq_telnet": "0", + "word_freq_857": "0", + "word_freq_data": "0", + "word_freq_415": "0", + "word_freq_85": "0", + "word_freq_technology": "0", + "word_freq_1999": "0", + "word_freq_parts": "0", + "word_freq_pm": "0", + "word_freq_direct": "0", + "word_freq_cs": "0", + "word_freq_meeting": "0", + "word_freq_original": "0", + "word_freq_project": "0", + "word_freq_re": "0", + "word_freq_edu": "0", + "word_freq_table": "0", + "word_freq_conference": "0", + "char_freq_%3B": "0", + "char_freq_%28": "0", + "char_freq_%5B": "0", + "char_freq_%21": "0.778", + "char_freq_%24": "0", + "char_freq_%23": "0", + "capital_run_length_average": "3.756", + "capital_run_length_longest": "61", + "capital_run_length_total": "278", + "class": "1" + }, + { + "word_freq_make": "0.21", + "word_freq_address": "0.28", + "word_freq_all": "0.5", + "word_freq_3d": "0", + "word_freq_our": "0.14", + "word_freq_over": "0.28", + "word_freq_remove": "0.21", + "word_freq_internet": "0.07", + "word_freq_order": "0", + "word_freq_mail": "0.94", + "word_freq_receive": "0.21", + "word_freq_will": "0.79", + "word_freq_people": "0.65", + "word_freq_report": "0.21", + "word_freq_addresses": "0.14", + "word_freq_free": "0.14", + "word_freq_business": "0.07", + "word_freq_email": "0.28", + "word_freq_you": "3.47", + "word_freq_credit": "0", + "word_freq_your": "1.59", + "word_freq_font": "0", + "word_freq_000": "0.43", + "word_freq_money": "0.43", + "word_freq_hp": "0", + "word_freq_hpl": "0", + "word_freq_george": "0", + "word_freq_650": "0", + "word_freq_lab": "0", + "word_freq_labs": "0", + "word_freq_telnet": "0", + "word_freq_857": "0", + "word_freq_data": "0", + "word_freq_415": "0", + "word_freq_85": "0", + "word_freq_technology": "0", + "word_freq_1999": "0.07", + "word_freq_parts": "0", + "word_freq_pm": "0", + "word_freq_direct": "0", + "word_freq_cs": "0", + "word_freq_meeting": "0", + "word_freq_original": "0", + "word_freq_project": "0", + "word_freq_re": "0", + "word_freq_edu": "0", + "word_freq_table": "0", + "word_freq_conference": "0", + "char_freq_%3B": "0", + "char_freq_%28": "0.132", + "char_freq_%5B": "0", + "char_freq_%21": "0.372", + "char_freq_%24": "0.18", + "char_freq_%23": "0.048", + "capital_run_length_average": "5.114", + "capital_run_length_longest": "101", + "capital_run_length_total": "1028", + "class": "1" + }, + { + "word_freq_make": "0.06", + "word_freq_address": "0", + "word_freq_all": "0.71", + "word_freq_3d": "0", + "word_freq_our": "1.23", + "word_freq_over": "0.19", + "word_freq_remove": "0.19", + "word_freq_internet": "0.12", + "word_freq_order": "0.64", + "word_freq_mail": "0.25", + "word_freq_receive": "0.38", + "word_freq_will": "0.45", + "word_freq_people": "0.12", + "word_freq_report": "0", + "word_freq_addresses": "1.75", + "word_freq_free": "0.06", + "word_freq_business": "0.06", + "word_freq_email": "1.03", + "word_freq_you": "1.36", + "word_freq_credit": "0.32", + "word_freq_your": "0.51", + "word_freq_font": "0", + "word_freq_000": "1.16", + "word_freq_money": "0.06", + "word_freq_hp": "0", + "word_freq_hpl": "0", + "word_freq_george": "0", + "word_freq_650": "0", + "word_freq_lab": "0", + "word_freq_labs": "0", + "word_freq_telnet": "0", + "word_freq_857": "0", + "word_freq_data": "0", + "word_freq_415": "0", + "word_freq_85": "0", + "word_freq_technology": "0", + "word_freq_1999": "0", + "word_freq_parts": "0", + "word_freq_pm": "0", + "word_freq_direct": "0.06", + "word_freq_cs": "0", + "word_freq_meeting": "0", + "word_freq_original": "0.12", + "word_freq_project": "0", + "word_freq_re": "0.06", + "word_freq_edu": "0.06", + "word_freq_table": "0", + "word_freq_conference": "0", + "char_freq_%3B": "0.01", + "char_freq_%28": "0.143", + "char_freq_%5B": "0", + "char_freq_%21": "0.276", + "char_freq_%24": "0.184", + "char_freq_%23": "0.01", + "capital_run_length_average": "9.821", + "capital_run_length_longest": "485", + "capital_run_length_total": "2259", + "class": "1" + }, + { + "word_freq_make": "0", + "word_freq_address": "0", + "word_freq_all": "0", + "word_freq_3d": "0", + "word_freq_our": "0.63", + "word_freq_over": "0", + "word_freq_remove": "0.31", + "word_freq_internet": "0.63", + "word_freq_order": "0.31", + "word_freq_mail": "0.63", + "word_freq_receive": "0.31", + "word_freq_will": "0.31", + "word_freq_people": "0.31", + "word_freq_report": "0", + "word_freq_addresses": "0", + "word_freq_free": "0.31", + "word_freq_business": "0", + "word_freq_email": "0", + "word_freq_you": "3.18", + "word_freq_credit": "0", + "word_freq_your": "0.31", + "word_freq_font": "0", + "word_freq_000": "0", + "word_freq_money": "0", + "word_freq_hp": "0", + "word_freq_hpl": "0", + "word_freq_george": "0", + "word_freq_650": "0", + "word_freq_lab": "0", + "word_freq_labs": "0", + "word_freq_telnet": "0", + "word_freq_857": "0", + "word_freq_data": "0", + "word_freq_415": "0", + "word_freq_85": "0", + "word_freq_technology": "0", + "word_freq_1999": "0", + "word_freq_parts": "0", + "word_freq_pm": "0", + "word_freq_direct": "0", + "word_freq_cs": "0", + "word_freq_meeting": "0", + "word_freq_original": "0", + "word_freq_project": "0", + "word_freq_re": "0", + "word_freq_edu": "0", + "word_freq_table": "0", + "word_freq_conference": "0", + "char_freq_%3B": "0", + "char_freq_%28": "0.137", + "char_freq_%5B": "0", + "char_freq_%21": "0.137", + "char_freq_%24": "0", + "char_freq_%23": "0", + "capital_run_length_average": "3.537", + "capital_run_length_longest": "40", + "capital_run_length_total": "191", + "class": "1" + }, + { + "word_freq_make": "0", + "word_freq_address": "0", + "word_freq_all": "0", + "word_freq_3d": "0", + "word_freq_our": "0.63", + "word_freq_over": "0", + "word_freq_remove": "0.31", + "word_freq_internet": "0.63", + "word_freq_order": "0.31", + "word_freq_mail": "0.63", + "word_freq_receive": "0.31", + "word_freq_will": "0.31", + "word_freq_people": "0.31", + "word_freq_report": "0", + "word_freq_addresses": "0", + "word_freq_free": "0.31", + "word_freq_business": "0", + "word_freq_email": "0", + "word_freq_you": "3.18", + "word_freq_credit": "0", + "word_freq_your": "0.31", + "word_freq_font": "0", + "word_freq_000": "0", + "word_freq_money": "0", + "word_freq_hp": "0", + "word_freq_hpl": "0", + "word_freq_george": "0", + "word_freq_650": "0", + "word_freq_lab": "0", + "word_freq_labs": "0", + "word_freq_telnet": "0", + "word_freq_857": "0", + "word_freq_data": "0", + "word_freq_415": "0", + "word_freq_85": "0", + "word_freq_technology": "0", + "word_freq_1999": "0", + "word_freq_parts": "0", + "word_freq_pm": "0", + "word_freq_direct": "0", + "word_freq_cs": "0", + "word_freq_meeting": "0", + "word_freq_original": "0", + "word_freq_project": "0", + "word_freq_re": "0", + "word_freq_edu": "0", + "word_freq_table": "0", + "word_freq_conference": "0", + "char_freq_%3B": "0", + "char_freq_%28": "0.135", + "char_freq_%5B": "0", + "char_freq_%21": "0.135", + "char_freq_%24": "0", + "char_freq_%23": "0", + "capital_run_length_average": "3.537", + "capital_run_length_longest": "40", + "capital_run_length_total": "191", + "class": "1" + } + ] +} + +Shortlisted templates: +[ + { + "template_id": "tpl_grouped_percentile_point", + "template_name": "Grouped Percentile Point", + "primary_family": "tail_rarity_structure", + "portability": "yes", + "sql_skeleton": "SELECT {group_col},\n PERCENTILE_CONT({percentile_value}) WITHIN GROUP (ORDER BY {measure_col}) AS percentile_measure\nFROM {table}\nGROUP BY {group_col}\nORDER BY percentile_measure DESC;", + "required_roles": [ + "group_col", + "measure_col" + ] + } +] + +Problem instance: +{ + "dataset_id": "n1", + "question": "Use template Grouped Percentile Point to probe tail_concentration_consistency with semantic role ranked_signal_view. Focus on group_col=class, measure_col=word_freq_lab.", + "planned_template_id": "tpl_grouped_percentile_point", + "bindings": { + "group_col": "class", + "measure_col": "word_freq_lab", + "top_k": 10, + "top_n": 4, + "num_tiles": 10, + "percentile_value": 0.9, + "z_threshold": 2.0, + "fraction_threshold": 0.1, + "baseline_multiplier": 1.5, + "baseline_fraction": 0.1, + "min_group_size": 5, + "min_support": 5, + "measure_threshold": 0.0, + "time_grain": "month", + "lookback_rows": 3, + "current_period_start": "'2024-01-01'", + "current_period_end": "'2024-04-01'", + "previous_period_start": "'2023-10-01'", + "previous_period_end": "'2024-01-01'", + "drift_ratio_threshold": 0.8 + }, + "can_vary": [], + "must_fix": [], + "runtime_sql_skeleton": "SELECT {group_col},\n PERCENTILE_CONT({percentile_value}) WITHIN GROUP (ORDER BY {measure_col}) AS percentile_measure\nFROM {table}\nGROUP BY {group_col}\nORDER BY percentile_measure DESC;" +} + +Repair context: +{} diff --git a/Query/sql/v2/runs/v2_cli_20260502_081223_d/n1/artifacts/v2q_n1_23e99d817f436540/cli/sql_response_attempt_1.raw.txt b/Query/sql/v2/runs/v2_cli_20260502_081223_d/n1/artifacts/v2q_n1_23e99d817f436540/cli/sql_response_attempt_1.raw.txt new file mode 100644 index 0000000000000000000000000000000000000000..548cd1361688ff13d51c69bb6a7ab1550989efac --- /dev/null +++ b/Query/sql/v2/runs/v2_cli_20260502_081223_d/n1/artifacts/v2q_n1_23e99d817f436540/cli/sql_response_attempt_1.raw.txt @@ -0,0 +1,4 @@ +{"type":"thread.started","thread_id":"019e40ef-a12c-7e91-a830-64e32fe582d7"} +{"type":"turn.started"} +{"type":"item.completed","item":{"id":"item_0","type":"agent_message","text":"{\"sql\":\"-- template_id: tpl_grouped_percentile_point\\nWITH \\\"ranked\\\" AS (\\n SELECT\\n \\\"class\\\",\\n CAST(\\\"word_freq_lab\\\" AS REAL) AS \\\"measure_value\\\",\\n CUME_DIST() OVER (\\n PARTITION BY \\\"class\\\"\\n ORDER BY CAST(\\\"word_freq_lab\\\" AS REAL)\\n ) AS \\\"cum_dist\\\"\\n FROM \\\"n1\\\"\\n WHERE \\\"class\\\" IS NOT NULL\\n AND \\\"word_freq_lab\\\" IS NOT NULL\\n)\\nSELECT\\n \\\"class\\\",\\n MIN(\\\"measure_value\\\") AS \\\"percentile_measure\\\"\\nFROM \\\"ranked\\\"\\nWHERE \\\"cum_dist\\\" >= 0.9\\nGROUP BY \\\"class\\\"\\nORDER BY \\\"percentile_measure\\\" DESC;\",\"notes\":\"SQLite does not support PERCENTILE_CONT, so this uses CUME_DIST to return the nearest-rank 90th percentile point per class. \\\"word_freq_lab\\\" is stored as TEXT in the schema snapshot, so it is cast to REAL before ordering and aggregation.\"}"}} +{"type":"turn.completed","usage":{"input_tokens":20353,"cached_input_tokens":19840,"output_tokens":744,"reasoning_output_tokens":516}} diff --git a/Query/sql/v2/runs/v2_cli_20260502_081223_d/n1/artifacts/v2q_n1_23e99d817f436540/cli/sql_response_attempt_1.txt b/Query/sql/v2/runs/v2_cli_20260502_081223_d/n1/artifacts/v2q_n1_23e99d817f436540/cli/sql_response_attempt_1.txt new file mode 100644 index 0000000000000000000000000000000000000000..1d8043f3fca5cd1b1fb847861d8379e502e48c2a --- /dev/null +++ b/Query/sql/v2/runs/v2_cli_20260502_081223_d/n1/artifacts/v2q_n1_23e99d817f436540/cli/sql_response_attempt_1.txt @@ -0,0 +1 @@ +{"sql":"-- template_id: tpl_grouped_percentile_point\nWITH \"ranked\" AS (\n SELECT\n \"class\",\n CAST(\"word_freq_lab\" AS REAL) AS \"measure_value\",\n CUME_DIST() OVER (\n PARTITION BY \"class\"\n ORDER BY CAST(\"word_freq_lab\" AS REAL)\n ) AS \"cum_dist\"\n FROM \"n1\"\n WHERE \"class\" IS NOT NULL\n AND \"word_freq_lab\" IS NOT NULL\n)\nSELECT\n \"class\",\n MIN(\"measure_value\") AS \"percentile_measure\"\nFROM \"ranked\"\nWHERE \"cum_dist\" >= 0.9\nGROUP BY \"class\"\nORDER BY \"percentile_measure\" DESC;","notes":"SQLite does not support PERCENTILE_CONT, so this uses CUME_DIST to return the nearest-rank 90th percentile point per class. \"word_freq_lab\" is stored as TEXT in the schema snapshot, so it is cast to REAL before ordering and aggregation."} \ No newline at end of file diff --git a/Query/sql/v2/runs/v2_cli_20260502_081223_d/n1/artifacts/v2q_n1_23e99d817f436540/cli/sql_stderr_attempt_1.txt b/Query/sql/v2/runs/v2_cli_20260502_081223_d/n1/artifacts/v2q_n1_23e99d817f436540/cli/sql_stderr_attempt_1.txt new file mode 100644 index 0000000000000000000000000000000000000000..e69de29bb2d1d6434b8b29ae775ad8c2e48c5391 diff --git a/Query/sql/v2/runs/v2_cli_20260502_081223_d/n1/artifacts/v2q_n1_2c2876d22a53423d/cli/conversation.jsonl b/Query/sql/v2/runs/v2_cli_20260502_081223_d/n1/artifacts/v2q_n1_2c2876d22a53423d/cli/conversation.jsonl new file mode 100644 index 0000000000000000000000000000000000000000..dc732c9f12c7228f648b7bda04e34807842dbdb2 --- /dev/null +++ b/Query/sql/v2/runs/v2_cli_20260502_081223_d/n1/artifacts/v2q_n1_2c2876d22a53423d/cli/conversation.jsonl @@ -0,0 +1,2 @@ +{"attempt": 1, "phase": "sql_generation", "role": "user", "content_path": "cli/sql_prompt_attempt_1.txt", "metrics": {"chars": 29911, "bytes_utf8": 29911, "lines": 792, "estimated_tokens": null}} +{"attempt": 1, "phase": "sql_generation", "role": "assistant", "content_path": "cli/sql_response_attempt_1.txt", "raw_content_path": "cli/sql_response_attempt_1.raw.txt", "stderr_path": "cli/sql_stderr_attempt_1.txt", "metrics": {"chars": 604, "bytes_utf8": 604, "lines": 1, "estimated_tokens": null}, "usage": {"input_tokens": 20451, "cached_input_tokens": 19840, "output_tokens": 504, "reasoning_output_tokens": 344}} diff --git a/Query/sql/v2/runs/v2_cli_20260502_081223_d/n1/artifacts/v2q_n1_2c2876d22a53423d/cli/session_summary.json b/Query/sql/v2/runs/v2_cli_20260502_081223_d/n1/artifacts/v2q_n1_2c2876d22a53423d/cli/session_summary.json new file mode 100644 index 0000000000000000000000000000000000000000..bbedab85bcac55e3e489e74a60244d54b4aaffa7 --- /dev/null +++ b/Query/sql/v2/runs/v2_cli_20260502_081223_d/n1/artifacts/v2q_n1_2c2876d22a53423d/cli/session_summary.json @@ -0,0 +1,25 @@ +{ + "engine": "v2-cli:codex", + "command": "codex exec --skip-git-repo-check --disable plugins --sandbox read-only --cd \"/data/jialinzhang/SQLagent\" -m gpt-5.4 --json -", + "ai_cli_calls": 1, + "usage_summary": { + "dataset_id": "n1", + "model": "v2-cli:codex", + "run_id": "v2q_n1_2c2876d22a53423d", + "api_calls": 0, + "input_tokens": 20451, + "cached_input_tokens": 19840, + "output_tokens": 504, + "total_tokens": 20955, + "cost_usd": 0.0, + "ai_cli_calls": 1, + "estimated_input_tokens": 0, + "estimated_output_tokens": 0, + "estimated_total_tokens": 0, + "usage_source": "ai_cli_json_usage", + "cli_elapsed_ms_total": 16107.47, + "sql_execution_elapsed_ms_total": 2.45, + "conversation_log_path": "/data/jialinzhang/TabQueryBench/sql_workloads/v2_current/runs_and_launches/runs/v2_cli_20260502_081223_d/n1/artifacts/v2q_n1_2c2876d22a53423d/cli/conversation.jsonl", + "note": "Executed through a local AI CLI with structured usage metadata." + } +} \ No newline at end of file diff --git a/Query/sql/v2/runs/v2_cli_20260502_081223_d/n1/artifacts/v2q_n1_2c2876d22a53423d/cli/sql_attempt_1.metadata.json b/Query/sql/v2/runs/v2_cli_20260502_081223_d/n1/artifacts/v2q_n1_2c2876d22a53423d/cli/sql_attempt_1.metadata.json new file mode 100644 index 0000000000000000000000000000000000000000..10fbb8b4059187e61078c6a35e51d1ecd547f1b0 --- /dev/null +++ b/Query/sql/v2/runs/v2_cli_20260502_081223_d/n1/artifacts/v2q_n1_2c2876d22a53423d/cli/sql_attempt_1.metadata.json @@ -0,0 +1,45 @@ +{ + "attempt": 1, + "phase": "sql_generation", + "command": "codex exec --skip-git-repo-check --disable plugins --sandbox read-only --cd \"/data/jialinzhang/SQLagent\" -m gpt-5.4 --json -", + "started_at": "2026-05-19T15:47:58.492433+00:00", + "ended_at": "2026-05-19T15:48:14.599956+00:00", + "elapsed_ms": 16107.47, + "prompt_metrics": { + "chars": 29911, + "bytes_utf8": 29911, + "lines": 792, + "estimated_tokens": null + }, + "stdout_metrics": { + "chars": 1304, + "bytes_utf8": 1304, + "lines": 5, + "estimated_tokens": null + }, + "stderr_metrics": { + "chars": 0, + "bytes_utf8": 0, + "lines": 0, + "estimated_tokens": null + }, + "parsed_output": { + "format": "jsonl_events", + "text_metrics": { + "chars": 604, + "bytes_utf8": 604, + "lines": 1, + "estimated_tokens": null + }, + "usage": { + "input_tokens": 20451, + "cached_input_tokens": 19840, + "output_tokens": 504, + "reasoning_output_tokens": 344 + } + }, + "prompt_path": "cli/sql_prompt_attempt_1.txt", + "response_path": "cli/sql_response_attempt_1.txt", + "raw_response_path": "cli/sql_response_attempt_1.raw.txt", + "stderr_path": "cli/sql_stderr_attempt_1.txt" +} \ No newline at end of file diff --git a/Query/sql/v2/runs/v2_cli_20260502_081223_d/n1/artifacts/v2q_n1_2c2876d22a53423d/cli/sql_prompt_attempt_1.txt b/Query/sql/v2/runs/v2_cli_20260502_081223_d/n1/artifacts/v2q_n1_2c2876d22a53423d/cli/sql_prompt_attempt_1.txt new file mode 100644 index 0000000000000000000000000000000000000000..0ac21d660e878430f3acf072f331f34a968861b3 --- /dev/null +++ b/Query/sql/v2/runs/v2_cli_20260502_081223_d/n1/artifacts/v2q_n1_2c2876d22a53423d/cli/sql_prompt_attempt_1.txt @@ -0,0 +1,792 @@ +You are generating one SQLite SELECT query for a single-table SQL QA task. +Return strict JSON only, with this schema: {"sql": "...", "notes": "..."}. +Rules: +- Use only the provided table and columns. +- Do not write INSERT, UPDATE, DELETE, DROP, ALTER, CREATE, PRAGMA, ATTACH, DETACH, or VACUUM. +- Prefer the planned template and bound roles when provided. +- Add a leading SQL comment exactly like: -- template_id: . +- Generate SQLite-compatible SQL. SQLite does not support PERCENTILE_CONT or STDDEV. +- Quote identifiers with double quotes. +- Return no markdown and no extra prose. + +Dataset context: +Dataset context for SQL QA: +- dataset_id: n1 +- dataset_name: Spambase +- table_name: n1 +- table_layout: single-table dataset (do not assume joins). +- row_semantics: One row is one email represented by engineered token/character frequency and capitalization-run features. +- task_type: classification +- target_column: class +- main_row_count: 4601 +- important_fields: +- word_freq_make: role=feature, type=numeric_sparse_frequency. tags=['condition_candidate', 'measure', 'rare_pattern_candidate'] desc=Frequency feature for token 'make' in an email message. +- word_freq_address: role=feature, type=numeric_sparse_frequency. tags=['condition_candidate', 'measure', 'rare_pattern_candidate'] desc=Frequency feature for token 'address' in an email message. +- word_freq_all: role=feature, type=numeric_sparse_frequency. tags=['condition_candidate', 'measure', 'rare_pattern_candidate'] desc=Frequency feature for token 'all' in an email message. +- word_freq_3d: role=feature, type=numeric_sparse_frequency. tags=['condition_candidate', 'measure', 'rare_pattern_candidate'] desc=Frequency feature for token '3d' in an email message. +- word_freq_our: role=feature, type=numeric_sparse_frequency. tags=['condition_candidate', 'measure', 'rare_pattern_candidate'] desc=Frequency feature for token 'our' in an email message. +- word_freq_over: role=feature, type=numeric_sparse_frequency. tags=['condition_candidate', 'measure', 'rare_pattern_candidate'] desc=Frequency feature for token 'over' in an email message. +- word_freq_remove: role=feature, type=numeric_sparse_frequency. tags=['condition_candidate', 'measure', 'rare_pattern_candidate'] desc=Frequency feature for token 'remove' in an email message. +- word_freq_internet: role=feature, type=numeric_sparse_frequency. tags=['condition_candidate', 'measure', 'rare_pattern_candidate'] desc=Frequency feature for token 'internet' in an email message. +- word_freq_order: role=feature, type=numeric_sparse_frequency. tags=['condition_candidate', 'measure', 'rare_pattern_candidate'] desc=Frequency feature for token 'order' in an email message. +- word_freq_mail: role=feature, type=numeric_sparse_frequency. tags=['condition_candidate', 'measure', 'rare_pattern_candidate'] desc=Frequency feature for token 'mail' in an email message. +- word_freq_receive: role=feature, type=numeric_sparse_frequency. tags=['condition_candidate', 'measure', 'rare_pattern_candidate'] desc=Frequency feature for token 'receive' in an email message. +- word_freq_will: role=feature, type=numeric_sparse_frequency. tags=['condition_candidate', 'measure', 'rare_pattern_candidate'] desc=Frequency feature for token 'will' in an email message. +- word_freq_people: role=feature, type=numeric_sparse_frequency. tags=['condition_candidate', 'measure', 'rare_pattern_candidate'] desc=Frequency feature for token 'people' in an email message. +- word_freq_report: role=feature, type=numeric_sparse_frequency. tags=['condition_candidate', 'measure', 'rare_pattern_candidate'] desc=Frequency feature for token 'report' in an email message. +- word_freq_addresses: role=feature, type=numeric_sparse_frequency. tags=['condition_candidate', 'measure', 'rare_pattern_candidate'] desc=Frequency feature for token 'addresses' in an email message. +- word_freq_free: role=feature, type=numeric_sparse_frequency. tags=['condition_candidate', 'measure', 'rare_pattern_candidate'] desc=Frequency feature for token 'free' in an email message. +- word_freq_business: role=feature, type=numeric_sparse_frequency. tags=['condition_candidate', 'measure', 'rare_pattern_candidate'] desc=Frequency feature for token 'business' in an email message. +- word_freq_email: role=feature, type=numeric_sparse_frequency. tags=['condition_candidate', 'measure', 'rare_pattern_candidate'] desc=Frequency feature for token 'email' in an email message. +- word_freq_you: role=feature, type=numeric_sparse_frequency. tags=['condition_candidate', 'measure', 'rare_pattern_candidate'] desc=Frequency feature for token 'you' in an email message. +- word_freq_credit: role=feature, type=numeric_sparse_frequency. tags=['condition_candidate', 'measure', 'rare_pattern_candidate'] desc=Frequency feature for token 'credit' in an email message. +- word_freq_your: role=feature, type=numeric_sparse_frequency. tags=['condition_candidate', 'measure', 'rare_pattern_candidate'] desc=Frequency feature for token 'your' in an email message. +- word_freq_font: role=feature, type=numeric_sparse_frequency. tags=['condition_candidate', 'measure', 'rare_pattern_candidate'] desc=Frequency feature for token 'font' in an email message. +- word_freq_000: role=feature, type=numeric_sparse_frequency. tags=['condition_candidate', 'measure', 'rare_pattern_candidate'] desc=Frequency feature for token '000' in an email message. +- word_freq_money: role=feature, type=numeric_sparse_frequency. tags=['condition_candidate', 'measure', 'rare_pattern_candidate'] desc=Frequency feature for token 'money' in an email message. +- word_freq_hp: role=feature, type=numeric_sparse_frequency. tags=['condition_candidate', 'measure', 'rare_pattern_candidate'] desc=Frequency feature for token 'hp' in an email message. +- word_freq_hpl: role=feature, type=numeric_sparse_frequency. tags=['condition_candidate', 'measure', 'rare_pattern_candidate'] desc=Frequency feature for token 'hpl' in an email message. +- word_freq_george: role=feature, type=numeric_sparse_frequency. tags=['condition_candidate', 'measure', 'rare_pattern_candidate'] desc=Frequency feature for token 'george' in an email message. +- word_freq_650: role=feature, type=numeric_sparse_frequency. tags=['condition_candidate', 'measure', 'rare_pattern_candidate'] desc=Frequency feature for token '650' in an email message. +- word_freq_lab: role=feature, type=numeric_sparse_frequency. tags=['condition_candidate', 'measure', 'rare_pattern_candidate'] desc=Frequency feature for token 'lab' in an email message. +- word_freq_labs: role=feature, type=numeric_sparse_frequency. tags=['condition_candidate', 'measure', 'rare_pattern_candidate'] desc=Frequency feature for token 'labs' in an email message. +- word_freq_telnet: role=feature, type=numeric_sparse_frequency. tags=['condition_candidate', 'measure', 'rare_pattern_candidate'] desc=Frequency feature for token 'telnet' in an email message. +- word_freq_857: role=feature, type=numeric_sparse_frequency. tags=['condition_candidate', 'measure', 'rare_pattern_candidate'] desc=Frequency feature for token '857' in an email message. +- word_freq_data: role=feature, type=numeric_sparse_frequency. tags=['condition_candidate', 'measure', 'rare_pattern_candidate'] desc=Frequency feature for token 'data' in an email message. +- word_freq_415: role=feature, type=numeric_sparse_frequency. tags=['condition_candidate', 'measure', 'rare_pattern_candidate'] desc=Frequency feature for token '415' in an email message. +- word_freq_85: role=feature, type=numeric_sparse_frequency. tags=['condition_candidate', 'measure', 'rare_pattern_candidate'] desc=Frequency feature for token '85' in an email message. +- word_freq_technology: role=feature, type=numeric_sparse_frequency. tags=['condition_candidate', 'measure', 'rare_pattern_candidate'] desc=Frequency feature for token 'technology' in an email message. +- word_freq_1999: role=feature, type=numeric_sparse_frequency. tags=['condition_candidate', 'measure', 'rare_pattern_candidate'] desc=Frequency feature for token '1999' in an email message. +- word_freq_parts: role=feature, type=numeric_sparse_frequency. tags=['condition_candidate', 'measure', 'rare_pattern_candidate'] desc=Frequency feature for token 'parts' in an email message. +- word_freq_pm: role=feature, type=numeric_sparse_frequency. tags=['condition_candidate', 'measure', 'rare_pattern_candidate'] desc=Frequency feature for token 'pm' in an email message. +- word_freq_direct: role=feature, type=numeric_sparse_frequency. tags=['condition_candidate', 'measure', 'rare_pattern_candidate'] desc=Frequency feature for token 'direct' in an email message. +- word_freq_cs: role=feature, type=numeric_sparse_frequency. tags=['condition_candidate', 'measure', 'rare_pattern_candidate'] desc=Frequency feature for token 'cs' in an email message. +- word_freq_meeting: role=feature, type=numeric_sparse_frequency. tags=['condition_candidate', 'measure', 'rare_pattern_candidate'] desc=Frequency feature for token 'meeting' in an email message. +- word_freq_original: role=feature, type=numeric_sparse_frequency. tags=['condition_candidate', 'measure', 'rare_pattern_candidate'] desc=Frequency feature for token 'original' in an email message. +- word_freq_project: role=feature, type=numeric_sparse_frequency. tags=['condition_candidate', 'measure', 'rare_pattern_candidate'] desc=Frequency feature for token 'project' in an email message. +- word_freq_re: role=feature, type=numeric_sparse_frequency. tags=['condition_candidate', 'measure', 'rare_pattern_candidate'] desc=Frequency feature for token 're' in an email message. +- word_freq_edu: role=feature, type=numeric_sparse_frequency. tags=['condition_candidate', 'measure', 'rare_pattern_candidate'] desc=Frequency feature for token 'edu' in an email message. +- word_freq_table: role=feature, type=numeric_sparse_frequency. tags=['condition_candidate', 'measure', 'rare_pattern_candidate'] desc=Frequency feature for token 'table' in an email message. +- word_freq_conference: role=feature, type=numeric_sparse_frequency. tags=['condition_candidate', 'measure', 'rare_pattern_candidate'] desc=Frequency feature for token 'conference' in an email message. +- char_freq_%3B: role=feature, type=numeric_sparse_frequency. tags=['condition_candidate', 'measure', 'rare_pattern_candidate'] desc=Character frequency feature for symbol ';'. +- char_freq_%28: role=feature, type=numeric_sparse_frequency. tags=['condition_candidate', 'measure', 'rare_pattern_candidate'] desc=Character frequency feature for symbol '('. +- char_freq_%5B: role=feature, type=numeric_sparse_frequency. tags=['condition_candidate', 'measure', 'rare_pattern_candidate'] desc=Character frequency feature for symbol '['. +- char_freq_%21: role=feature, type=numeric_sparse_frequency. tags=['condition_candidate', 'measure', 'rare_pattern_candidate'] desc=Character frequency feature for symbol '!'. +- char_freq_%24: role=feature, type=numeric_sparse_frequency. tags=['condition_candidate', 'measure', 'rare_pattern_candidate'] desc=Character frequency feature for symbol '$'. +- char_freq_%23: role=feature, type=numeric_sparse_frequency. tags=['condition_candidate', 'measure', 'rare_pattern_candidate'] desc=Character frequency feature for symbol '#'. +- capital_run_length_average: role=feature, type=numeric_heavy_tail. tags=['condition_candidate', 'measure', 'rare_pattern_candidate'] desc=Average length of uninterrupted capital-letter runs. +- capital_run_length_longest: role=feature, type=numeric_heavy_tail. tags=['condition_candidate', 'measure', 'rare_pattern_candidate'] desc=Longest uninterrupted capital-letter run. +- capital_run_length_total: role=feature, type=numeric_heavy_tail. tags=['condition_candidate', 'measure', 'rare_pattern_candidate'] desc=Total number of capital letters in runs. +- class: role=target, type=binary_target. tags=['target_candidate'] desc=Binary spam label (1=spam, 0=non-spam). +- useful_field_combinations: [['word_freq_free', 'word_freq_you', 'class'], ['char_freq_%21', 'capital_run_length_longest', 'class'], ['word_freq_remove', 'word_freq_money', 'class']] +- fields_requiring_caution: ['capital_run_length_average', 'capital_run_length_longest', 'capital_run_length_total'] +- source_url: https://www.openml.org/d/44 + +SQLite schema snapshot: +{ + "table_name": "n1", + "quoted_table_name": "\"n1\"", + "row_count": 4601, + "columns": [ + { + "name": "word_freq_make", + "type": "TEXT", + "notnull": false, + "pk": false + }, + { + "name": "word_freq_address", + "type": "TEXT", + "notnull": false, + "pk": false + }, + { + "name": "word_freq_all", + "type": "TEXT", + "notnull": false, + "pk": false + }, + { + "name": "word_freq_3d", + "type": "TEXT", + "notnull": false, + "pk": false + }, + { + "name": "word_freq_our", + "type": "TEXT", + "notnull": false, + "pk": false + }, + { + "name": "word_freq_over", + "type": "TEXT", + "notnull": false, + "pk": false + }, + { + "name": "word_freq_remove", + "type": "TEXT", + "notnull": false, + "pk": false + }, + { + "name": "word_freq_internet", + "type": "TEXT", + "notnull": false, + "pk": false + }, + { + "name": "word_freq_order", + "type": "TEXT", + "notnull": false, + "pk": false + }, + { + "name": "word_freq_mail", + "type": "TEXT", + "notnull": false, + "pk": false + }, + { + "name": "word_freq_receive", + "type": "TEXT", + "notnull": false, + "pk": false + }, + { + "name": "word_freq_will", + "type": "TEXT", + "notnull": false, + "pk": false + }, + { + "name": "word_freq_people", + "type": "TEXT", + "notnull": false, + "pk": false + }, + { + "name": "word_freq_report", + "type": "TEXT", + "notnull": false, + "pk": false + }, + { + "name": "word_freq_addresses", + "type": "TEXT", + "notnull": false, + "pk": false + }, + { + "name": "word_freq_free", + "type": "TEXT", + "notnull": false, + "pk": false + }, + { + "name": "word_freq_business", + "type": "TEXT", + "notnull": false, + "pk": false + }, + { + "name": "word_freq_email", + "type": "TEXT", + "notnull": false, + "pk": false + }, + { + "name": "word_freq_you", + "type": "TEXT", + "notnull": false, + "pk": false + }, + { + "name": "word_freq_credit", + "type": "TEXT", + "notnull": false, + "pk": false + }, + { + "name": "word_freq_your", + "type": "TEXT", + "notnull": false, + "pk": false + }, + { + "name": "word_freq_font", + "type": "TEXT", + "notnull": false, + "pk": false + }, + { + "name": "word_freq_000", + "type": "TEXT", + "notnull": false, + "pk": false + }, + { + "name": "word_freq_money", + "type": "TEXT", + "notnull": false, + "pk": false + }, + { + "name": "word_freq_hp", + "type": "TEXT", + "notnull": false, + "pk": false + }, + { + "name": "word_freq_hpl", + "type": "TEXT", + "notnull": false, + "pk": false + }, + { + "name": "word_freq_george", + "type": "TEXT", + "notnull": false, + "pk": false + }, + { + "name": "word_freq_650", + "type": "TEXT", + "notnull": false, + "pk": false + }, + { + "name": "word_freq_lab", + "type": "TEXT", + "notnull": false, + "pk": false + }, + { + "name": "word_freq_labs", + "type": "TEXT", + "notnull": false, + "pk": false + }, + { + "name": "word_freq_telnet", + "type": "TEXT", + "notnull": false, + "pk": false + }, + { + "name": "word_freq_857", + "type": "TEXT", + "notnull": false, + "pk": false + }, + { + "name": "word_freq_data", + "type": "TEXT", + "notnull": false, + "pk": false + }, + { + "name": "word_freq_415", + "type": "TEXT", + "notnull": false, + "pk": false + }, + { + "name": "word_freq_85", + "type": "TEXT", + "notnull": false, + "pk": false + }, + { + "name": "word_freq_technology", + "type": "TEXT", + "notnull": false, + "pk": false + }, + { + "name": "word_freq_1999", + "type": "TEXT", + "notnull": false, + "pk": false + }, + { + "name": "word_freq_parts", + "type": "TEXT", + "notnull": false, + "pk": false + }, + { + "name": "word_freq_pm", + "type": "TEXT", + "notnull": false, + "pk": false + }, + { + "name": "word_freq_direct", + "type": "TEXT", + "notnull": false, + "pk": false + }, + { + "name": "word_freq_cs", + "type": "TEXT", + "notnull": false, + "pk": false + }, + { + "name": "word_freq_meeting", + "type": "TEXT", + "notnull": false, + "pk": false + }, + { + "name": "word_freq_original", + "type": "TEXT", + "notnull": false, + "pk": false + }, + { + "name": "word_freq_project", + "type": "TEXT", + "notnull": false, + "pk": false + }, + { + "name": "word_freq_re", + "type": "TEXT", + "notnull": false, + "pk": false + }, + { + "name": "word_freq_edu", + "type": "TEXT", + "notnull": false, + "pk": false + }, + { + "name": "word_freq_table", + "type": "TEXT", + "notnull": false, + "pk": false + }, + { + "name": "word_freq_conference", + "type": "TEXT", + "notnull": false, + "pk": false + }, + { + "name": "char_freq_%3B", + "type": "TEXT", + "notnull": false, + "pk": false + }, + { + "name": "char_freq_%28", + "type": "TEXT", + "notnull": false, + "pk": false + }, + { + "name": "char_freq_%5B", + "type": "TEXT", + "notnull": false, + "pk": false + }, + { + "name": "char_freq_%21", + "type": "TEXT", + "notnull": false, + "pk": false + }, + { + "name": "char_freq_%24", + "type": "TEXT", + "notnull": false, + "pk": false + }, + { + "name": "char_freq_%23", + "type": "TEXT", + "notnull": false, + "pk": false + }, + { + "name": "capital_run_length_average", + "type": "TEXT", + "notnull": false, + "pk": false + }, + { + "name": "capital_run_length_longest", + "type": "TEXT", + "notnull": false, + "pk": false + }, + { + "name": "capital_run_length_total", + "type": "TEXT", + "notnull": false, + "pk": false + }, + { + "name": "class", + "type": "TEXT", + "notnull": false, + "pk": false + } + ], + "sample_rows": [ + { + "word_freq_make": "0", + "word_freq_address": "0.64", + "word_freq_all": "0.64", + "word_freq_3d": "0", + "word_freq_our": "0.32", + "word_freq_over": "0", + "word_freq_remove": "0", + "word_freq_internet": "0", + "word_freq_order": "0", + "word_freq_mail": "0", + "word_freq_receive": "0", + "word_freq_will": "0.64", + "word_freq_people": "0", + "word_freq_report": "0", + "word_freq_addresses": "0", + "word_freq_free": "0.32", + "word_freq_business": "0", + "word_freq_email": "1.29", + "word_freq_you": "1.93", + "word_freq_credit": "0", + "word_freq_your": "0.96", + "word_freq_font": "0", + "word_freq_000": "0", + "word_freq_money": "0", + "word_freq_hp": "0", + "word_freq_hpl": "0", + "word_freq_george": "0", + "word_freq_650": "0", + "word_freq_lab": "0", + "word_freq_labs": "0", + "word_freq_telnet": "0", + "word_freq_857": "0", + "word_freq_data": "0", + "word_freq_415": "0", + "word_freq_85": "0", + "word_freq_technology": "0", + "word_freq_1999": "0", + "word_freq_parts": "0", + "word_freq_pm": "0", + "word_freq_direct": "0", + "word_freq_cs": "0", + "word_freq_meeting": "0", + "word_freq_original": "0", + "word_freq_project": "0", + "word_freq_re": "0", + "word_freq_edu": "0", + "word_freq_table": "0", + "word_freq_conference": "0", + "char_freq_%3B": "0", + "char_freq_%28": "0", + "char_freq_%5B": "0", + "char_freq_%21": "0.778", + "char_freq_%24": "0", + "char_freq_%23": "0", + "capital_run_length_average": "3.756", + "capital_run_length_longest": "61", + "capital_run_length_total": "278", + "class": "1" + }, + { + "word_freq_make": "0.21", + "word_freq_address": "0.28", + "word_freq_all": "0.5", + "word_freq_3d": "0", + "word_freq_our": "0.14", + "word_freq_over": "0.28", + "word_freq_remove": "0.21", + "word_freq_internet": "0.07", + "word_freq_order": "0", + "word_freq_mail": "0.94", + "word_freq_receive": "0.21", + "word_freq_will": "0.79", + "word_freq_people": "0.65", + "word_freq_report": "0.21", + "word_freq_addresses": "0.14", + "word_freq_free": "0.14", + "word_freq_business": "0.07", + "word_freq_email": "0.28", + "word_freq_you": "3.47", + "word_freq_credit": "0", + "word_freq_your": "1.59", + "word_freq_font": "0", + "word_freq_000": "0.43", + "word_freq_money": "0.43", + "word_freq_hp": "0", + "word_freq_hpl": "0", + "word_freq_george": "0", + "word_freq_650": "0", + "word_freq_lab": "0", + "word_freq_labs": "0", + "word_freq_telnet": "0", + "word_freq_857": "0", + "word_freq_data": "0", + "word_freq_415": "0", + "word_freq_85": "0", + "word_freq_technology": "0", + "word_freq_1999": "0.07", + "word_freq_parts": "0", + "word_freq_pm": "0", + "word_freq_direct": "0", + "word_freq_cs": "0", + "word_freq_meeting": "0", + "word_freq_original": "0", + "word_freq_project": "0", + "word_freq_re": "0", + "word_freq_edu": "0", + "word_freq_table": "0", + "word_freq_conference": "0", + "char_freq_%3B": "0", + "char_freq_%28": "0.132", + "char_freq_%5B": "0", + "char_freq_%21": "0.372", + "char_freq_%24": "0.18", + "char_freq_%23": "0.048", + "capital_run_length_average": "5.114", + "capital_run_length_longest": "101", + "capital_run_length_total": "1028", + "class": "1" + }, + { + "word_freq_make": "0.06", + "word_freq_address": "0", + "word_freq_all": "0.71", + "word_freq_3d": "0", + "word_freq_our": "1.23", + "word_freq_over": "0.19", + "word_freq_remove": "0.19", + "word_freq_internet": "0.12", + "word_freq_order": "0.64", + "word_freq_mail": "0.25", + "word_freq_receive": "0.38", + "word_freq_will": "0.45", + "word_freq_people": "0.12", + "word_freq_report": "0", + "word_freq_addresses": "1.75", + "word_freq_free": "0.06", + "word_freq_business": "0.06", + "word_freq_email": "1.03", + "word_freq_you": "1.36", + "word_freq_credit": "0.32", + "word_freq_your": "0.51", + "word_freq_font": "0", + "word_freq_000": "1.16", + "word_freq_money": "0.06", + "word_freq_hp": "0", + "word_freq_hpl": "0", + "word_freq_george": "0", + "word_freq_650": "0", + "word_freq_lab": "0", + "word_freq_labs": "0", + "word_freq_telnet": "0", + "word_freq_857": "0", + "word_freq_data": "0", + "word_freq_415": "0", + "word_freq_85": "0", + "word_freq_technology": "0", + "word_freq_1999": "0", + "word_freq_parts": "0", + "word_freq_pm": "0", + "word_freq_direct": "0.06", + "word_freq_cs": "0", + "word_freq_meeting": "0", + "word_freq_original": "0.12", + "word_freq_project": "0", + "word_freq_re": "0.06", + "word_freq_edu": "0.06", + "word_freq_table": "0", + "word_freq_conference": "0", + "char_freq_%3B": "0.01", + "char_freq_%28": "0.143", + "char_freq_%5B": "0", + "char_freq_%21": "0.276", + "char_freq_%24": "0.184", + "char_freq_%23": "0.01", + "capital_run_length_average": "9.821", + "capital_run_length_longest": "485", + "capital_run_length_total": "2259", + "class": "1" + }, + { + "word_freq_make": "0", + "word_freq_address": "0", + "word_freq_all": "0", + "word_freq_3d": "0", + "word_freq_our": "0.63", + "word_freq_over": "0", + "word_freq_remove": "0.31", + "word_freq_internet": "0.63", + "word_freq_order": "0.31", + "word_freq_mail": "0.63", + "word_freq_receive": "0.31", + "word_freq_will": "0.31", + "word_freq_people": "0.31", + "word_freq_report": "0", + "word_freq_addresses": "0", + "word_freq_free": "0.31", + "word_freq_business": "0", + "word_freq_email": "0", + "word_freq_you": "3.18", + "word_freq_credit": "0", + "word_freq_your": "0.31", + "word_freq_font": "0", + "word_freq_000": "0", + "word_freq_money": "0", + "word_freq_hp": "0", + "word_freq_hpl": "0", + "word_freq_george": "0", + "word_freq_650": "0", + "word_freq_lab": "0", + "word_freq_labs": "0", + "word_freq_telnet": "0", + "word_freq_857": "0", + "word_freq_data": "0", + "word_freq_415": "0", + "word_freq_85": "0", + "word_freq_technology": "0", + "word_freq_1999": "0", + "word_freq_parts": "0", + "word_freq_pm": "0", + "word_freq_direct": "0", + "word_freq_cs": "0", + "word_freq_meeting": "0", + "word_freq_original": "0", + "word_freq_project": "0", + "word_freq_re": "0", + "word_freq_edu": "0", + "word_freq_table": "0", + "word_freq_conference": "0", + "char_freq_%3B": "0", + "char_freq_%28": "0.137", + "char_freq_%5B": "0", + "char_freq_%21": "0.137", + "char_freq_%24": "0", + "char_freq_%23": "0", + "capital_run_length_average": "3.537", + "capital_run_length_longest": "40", + "capital_run_length_total": "191", + "class": "1" + }, + { + "word_freq_make": "0", + "word_freq_address": "0", + "word_freq_all": "0", + "word_freq_3d": "0", + "word_freq_our": "0.63", + "word_freq_over": "0", + "word_freq_remove": "0.31", + "word_freq_internet": "0.63", + "word_freq_order": "0.31", + "word_freq_mail": "0.63", + "word_freq_receive": "0.31", + "word_freq_will": "0.31", + "word_freq_people": "0.31", + "word_freq_report": "0", + "word_freq_addresses": "0", + "word_freq_free": "0.31", + "word_freq_business": "0", + "word_freq_email": "0", + "word_freq_you": "3.18", + "word_freq_credit": "0", + "word_freq_your": "0.31", + "word_freq_font": "0", + "word_freq_000": "0", + "word_freq_money": "0", + "word_freq_hp": "0", + "word_freq_hpl": "0", + "word_freq_george": "0", + "word_freq_650": "0", + "word_freq_lab": "0", + "word_freq_labs": "0", + "word_freq_telnet": "0", + "word_freq_857": "0", + "word_freq_data": "0", + "word_freq_415": "0", + "word_freq_85": "0", + "word_freq_technology": "0", + "word_freq_1999": "0", + "word_freq_parts": "0", + "word_freq_pm": "0", + "word_freq_direct": "0", + "word_freq_cs": "0", + "word_freq_meeting": "0", + "word_freq_original": "0", + "word_freq_project": "0", + "word_freq_re": "0", + "word_freq_edu": "0", + "word_freq_table": "0", + "word_freq_conference": "0", + "char_freq_%3B": "0", + "char_freq_%28": "0.135", + "char_freq_%5B": "0", + "char_freq_%21": "0.135", + "char_freq_%24": "0", + "char_freq_%23": "0", + "capital_run_length_average": "3.537", + "capital_run_length_longest": "40", + "capital_run_length_total": "191", + "class": "1" + } + ] +} + +Shortlisted templates: +[ + { + "template_id": "tpl_tpch_relative_total_threshold", + "template_name": "Relative-to-Total Extreme Threshold", + "primary_family": "tail_rarity_structure", + "portability": "partial", + "sql_skeleton": "WITH grouped AS (\n SELECT {group_col}, SUM({measure_col}) AS group_value\n FROM {table}\n GROUP BY {group_col}\n), total AS (\n SELECT SUM(group_value) AS total_value\n FROM grouped\n)\nSELECT g.{group_col}, g.group_value\nFROM grouped AS g\nCROSS JOIN total AS t\nWHERE g.group_value > t.total_value * {fraction_threshold}\nORDER BY g.group_value DESC;", + "required_roles": [ + "group_col", + "measure_col" + ] + } +] + +Problem instance: +{ + "dataset_id": "n1", + "question": "Use template Relative-to-Total Extreme Threshold to probe tail_mass_similarity with semantic role count_distribution. Focus on group_col=class, measure_col=word_freq_you.", + "planned_template_id": "tpl_tpch_relative_total_threshold", + "bindings": { + "group_col": "class", + "measure_col": "word_freq_you", + "top_k": 15, + "top_n": 7, + "num_tiles": 10, + "percentile_value": 0.95, + "z_threshold": 2.0, + "fraction_threshold": 0.05, + "baseline_multiplier": 1.75, + "baseline_fraction": 0.1, + "min_group_size": 5, + "min_support": 4, + "measure_threshold": 2.14, + "time_grain": "month", + "lookback_rows": 3, + "current_period_start": "'2024-01-01'", + "current_period_end": "'2024-04-01'", + "previous_period_start": "'2023-10-01'", + "previous_period_end": "'2024-01-01'", + "drift_ratio_threshold": 0.8 + }, + "can_vary": [], + "must_fix": [], + "runtime_sql_skeleton": "WITH grouped AS (\n SELECT {group_col}, SUM({measure_col}) AS group_value\n FROM {table}\n GROUP BY {group_col}\n), total AS (\n SELECT SUM(group_value) AS total_value\n FROM grouped\n)\nSELECT g.{group_col}, g.group_value\nFROM grouped AS g\nCROSS JOIN total AS t\nWHERE g.group_value > t.total_value * {fraction_threshold}\nORDER BY g.group_value DESC;" +} + +Repair context: +{} diff --git a/Query/sql/v2/runs/v2_cli_20260502_081223_d/n1/artifacts/v2q_n1_2c2876d22a53423d/cli/sql_response_attempt_1.raw.txt b/Query/sql/v2/runs/v2_cli_20260502_081223_d/n1/artifacts/v2q_n1_2c2876d22a53423d/cli/sql_response_attempt_1.raw.txt new file mode 100644 index 0000000000000000000000000000000000000000..329fe8cead2626c961aa6aa120c3ab5a05ace56a --- /dev/null +++ b/Query/sql/v2/runs/v2_cli_20260502_081223_d/n1/artifacts/v2q_n1_2c2876d22a53423d/cli/sql_response_attempt_1.raw.txt @@ -0,0 +1,5 @@ +{"type":"thread.started","thread_id":"019e40ec-2707-7280-9d1b-4e280eb401d8"} +{"type":"turn.started"} +{"type":"error","message":"Reconnecting... 2/5 (stream disconnected before completion: Rate limit reached for gpt-5.4 in organization org-yXK0mBGOR7eXhtGEx8v7e3bf on tokens per min (TPM): Limit 2000000, Used 1993747, Requested 21086. Please try again in 444ms. Visit https://platform.openai.com/account/rate-limits to learn more.)"} +{"type":"item.completed","item":{"id":"item_0","type":"agent_message","text":"{\"sql\":\"-- template_id: tpl_tpch_relative_total_threshold\\nWITH grouped AS (\\n SELECT \\\"class\\\", SUM(CAST(\\\"word_freq_you\\\" AS REAL)) AS group_value\\n FROM \\\"n1\\\"\\n GROUP BY \\\"class\\\"\\n), total AS (\\n SELECT SUM(group_value) AS total_value\\n FROM grouped\\n)\\nSELECT g.\\\"class\\\", g.group_value\\nFROM grouped AS g\\nCROSS JOIN total AS t\\nWHERE g.group_value > t.total_value * 0.05\\nORDER BY g.group_value DESC;\",\"notes\":\"Applied the provided template with group_col=\\\"class\\\" and measure_col=\\\"word_freq_you\\\". CAST to REAL is used because the schema stores numeric-looking values as TEXT.\"}"}} +{"type":"turn.completed","usage":{"input_tokens":20451,"cached_input_tokens":19840,"output_tokens":504,"reasoning_output_tokens":344}} diff --git a/Query/sql/v2/runs/v2_cli_20260502_081223_d/n1/artifacts/v2q_n1_2c2876d22a53423d/cli/sql_response_attempt_1.txt b/Query/sql/v2/runs/v2_cli_20260502_081223_d/n1/artifacts/v2q_n1_2c2876d22a53423d/cli/sql_response_attempt_1.txt new file mode 100644 index 0000000000000000000000000000000000000000..ff84053b83aad15ced2de0b3af99abd63f7b1254 --- /dev/null +++ b/Query/sql/v2/runs/v2_cli_20260502_081223_d/n1/artifacts/v2q_n1_2c2876d22a53423d/cli/sql_response_attempt_1.txt @@ -0,0 +1 @@ +{"sql":"-- template_id: tpl_tpch_relative_total_threshold\nWITH grouped AS (\n SELECT \"class\", SUM(CAST(\"word_freq_you\" AS REAL)) AS group_value\n FROM \"n1\"\n GROUP BY \"class\"\n), total AS (\n SELECT SUM(group_value) AS total_value\n FROM grouped\n)\nSELECT g.\"class\", g.group_value\nFROM grouped AS g\nCROSS JOIN total AS t\nWHERE g.group_value > t.total_value * 0.05\nORDER BY g.group_value DESC;","notes":"Applied the provided template with group_col=\"class\" and measure_col=\"word_freq_you\". CAST to REAL is used because the schema stores numeric-looking values as TEXT."} \ No newline at end of file diff --git a/Query/sql/v2/runs/v2_cli_20260502_081223_d/n1/artifacts/v2q_n1_2c2876d22a53423d/cli/sql_stderr_attempt_1.txt b/Query/sql/v2/runs/v2_cli_20260502_081223_d/n1/artifacts/v2q_n1_2c2876d22a53423d/cli/sql_stderr_attempt_1.txt new file mode 100644 index 0000000000000000000000000000000000000000..e69de29bb2d1d6434b8b29ae775ad8c2e48c5391 diff --git a/Query/sql/v2/runs/v2_cli_20260502_081223_d/n1/artifacts/v2q_n1_2d71c7b6d450c813/cli/conversation.jsonl b/Query/sql/v2/runs/v2_cli_20260502_081223_d/n1/artifacts/v2q_n1_2d71c7b6d450c813/cli/conversation.jsonl new file mode 100644 index 0000000000000000000000000000000000000000..8321372cf08fa4e7c890787f673df73c4c12133c --- /dev/null +++ b/Query/sql/v2/runs/v2_cli_20260502_081223_d/n1/artifacts/v2q_n1_2d71c7b6d450c813/cli/conversation.jsonl @@ -0,0 +1,2 @@ +{"attempt": 1, "phase": "sql_generation", "role": "user", "content_path": "cli/sql_prompt_attempt_1.txt", "metrics": {"chars": 29541, "bytes_utf8": 29541, "lines": 790, "estimated_tokens": null}} +{"attempt": 1, "phase": "sql_generation", "role": "assistant", "content_path": "cli/sql_response_attempt_1.txt", "raw_content_path": "cli/sql_response_attempt_1.raw.txt", "stderr_path": "cli/sql_stderr_attempt_1.txt", "metrics": {"chars": 636, "bytes_utf8": 636, "lines": 1, "estimated_tokens": null}, "usage": {"input_tokens": 20372, "cached_input_tokens": 19840, "output_tokens": 445, "reasoning_output_tokens": 270}} diff --git a/Query/sql/v2/runs/v2_cli_20260502_081223_d/n1/artifacts/v2q_n1_2d71c7b6d450c813/cli/session_summary.json b/Query/sql/v2/runs/v2_cli_20260502_081223_d/n1/artifacts/v2q_n1_2d71c7b6d450c813/cli/session_summary.json new file mode 100644 index 0000000000000000000000000000000000000000..154edf3e41093e9780be77c1536c286adc858523 --- /dev/null +++ b/Query/sql/v2/runs/v2_cli_20260502_081223_d/n1/artifacts/v2q_n1_2d71c7b6d450c813/cli/session_summary.json @@ -0,0 +1,25 @@ +{ + "engine": "v2-cli:codex", + "command": "codex exec --skip-git-repo-check --disable plugins --sandbox read-only --cd \"/data/jialinzhang/SQLagent\" -m gpt-5.4 --json -", + "ai_cli_calls": 1, + "usage_summary": { + "dataset_id": "n1", + "model": "v2-cli:codex", + "run_id": "v2q_n1_2d71c7b6d450c813", + "api_calls": 0, + "input_tokens": 20372, + "cached_input_tokens": 19840, + "output_tokens": 445, + "total_tokens": 20817, + "cost_usd": 0.0, + "ai_cli_calls": 1, + "estimated_input_tokens": 0, + "estimated_output_tokens": 0, + "estimated_total_tokens": 0, + "usage_source": "ai_cli_json_usage", + "cli_elapsed_ms_total": 10336.56, + "sql_execution_elapsed_ms_total": 11.1, + "conversation_log_path": "/data/jialinzhang/TabQueryBench/sql_workloads/v2_current/runs_and_launches/runs/v2_cli_20260502_081223_d/n1/artifacts/v2q_n1_2d71c7b6d450c813/cli/conversation.jsonl", + "note": "Executed through a local AI CLI with structured usage metadata." + } +} \ No newline at end of file diff --git a/Query/sql/v2/runs/v2_cli_20260502_081223_d/n1/artifacts/v2q_n1_2d71c7b6d450c813/cli/sql_attempt_1.metadata.json b/Query/sql/v2/runs/v2_cli_20260502_081223_d/n1/artifacts/v2q_n1_2d71c7b6d450c813/cli/sql_attempt_1.metadata.json new file mode 100644 index 0000000000000000000000000000000000000000..332917b56b59fc0de35824ff6d9af648366cbdad --- /dev/null +++ b/Query/sql/v2/runs/v2_cli_20260502_081223_d/n1/artifacts/v2q_n1_2d71c7b6d450c813/cli/sql_attempt_1.metadata.json @@ -0,0 +1,45 @@ +{ + "attempt": 1, + "phase": "sql_generation", + "command": "codex exec --skip-git-repo-check --disable plugins --sandbox read-only --cd \"/data/jialinzhang/SQLagent\" -m gpt-5.4 --json -", + "started_at": "2026-05-19T15:45:35.637160+00:00", + "ended_at": "2026-05-19T15:45:45.973749+00:00", + "elapsed_ms": 10336.56, + "prompt_metrics": { + "chars": 29541, + "bytes_utf8": 29541, + "lines": 790, + "estimated_tokens": null + }, + "stdout_metrics": { + "chars": 1015, + "bytes_utf8": 1015, + "lines": 4, + "estimated_tokens": null + }, + "stderr_metrics": { + "chars": 0, + "bytes_utf8": 0, + "lines": 0, + "estimated_tokens": null + }, + "parsed_output": { + "format": "jsonl_events", + "text_metrics": { + "chars": 636, + "bytes_utf8": 636, + "lines": 1, + "estimated_tokens": null + }, + "usage": { + "input_tokens": 20372, + "cached_input_tokens": 19840, + "output_tokens": 445, + "reasoning_output_tokens": 270 + } + }, + "prompt_path": "cli/sql_prompt_attempt_1.txt", + "response_path": "cli/sql_response_attempt_1.txt", + "raw_response_path": "cli/sql_response_attempt_1.raw.txt", + "stderr_path": "cli/sql_stderr_attempt_1.txt" +} \ No newline at end of file diff --git a/Query/sql/v2/runs/v2_cli_20260502_081223_d/n1/artifacts/v2q_n1_2d71c7b6d450c813/cli/sql_prompt_attempt_1.txt b/Query/sql/v2/runs/v2_cli_20260502_081223_d/n1/artifacts/v2q_n1_2d71c7b6d450c813/cli/sql_prompt_attempt_1.txt new file mode 100644 index 0000000000000000000000000000000000000000..594af669abb735490ddf143b35e57c3a52b483f8 --- /dev/null +++ b/Query/sql/v2/runs/v2_cli_20260502_081223_d/n1/artifacts/v2q_n1_2d71c7b6d450c813/cli/sql_prompt_attempt_1.txt @@ -0,0 +1,790 @@ +You are generating one SQLite SELECT query for a single-table SQL QA task. +Return strict JSON only, with this schema: {"sql": "...", "notes": "..."}. +Rules: +- Use only the provided table and columns. +- Do not write INSERT, UPDATE, DELETE, DROP, ALTER, CREATE, PRAGMA, ATTACH, DETACH, or VACUUM. +- Prefer the planned template and bound roles when provided. +- Add a leading SQL comment exactly like: -- template_id: . +- Generate SQLite-compatible SQL. SQLite does not support PERCENTILE_CONT or STDDEV. +- Quote identifiers with double quotes. +- Return no markdown and no extra prose. + +Dataset context: +Dataset context for SQL QA: +- dataset_id: n1 +- dataset_name: Spambase +- table_name: n1 +- table_layout: single-table dataset (do not assume joins). +- row_semantics: One row is one email represented by engineered token/character frequency and capitalization-run features. +- task_type: classification +- target_column: class +- main_row_count: 4601 +- important_fields: +- word_freq_make: role=feature, type=numeric_sparse_frequency. tags=['condition_candidate', 'measure', 'rare_pattern_candidate'] desc=Frequency feature for token 'make' in an email message. +- word_freq_address: role=feature, type=numeric_sparse_frequency. tags=['condition_candidate', 'measure', 'rare_pattern_candidate'] desc=Frequency feature for token 'address' in an email message. +- word_freq_all: role=feature, type=numeric_sparse_frequency. tags=['condition_candidate', 'measure', 'rare_pattern_candidate'] desc=Frequency feature for token 'all' in an email message. +- word_freq_3d: role=feature, type=numeric_sparse_frequency. tags=['condition_candidate', 'measure', 'rare_pattern_candidate'] desc=Frequency feature for token '3d' in an email message. +- word_freq_our: role=feature, type=numeric_sparse_frequency. tags=['condition_candidate', 'measure', 'rare_pattern_candidate'] desc=Frequency feature for token 'our' in an email message. +- word_freq_over: role=feature, type=numeric_sparse_frequency. tags=['condition_candidate', 'measure', 'rare_pattern_candidate'] desc=Frequency feature for token 'over' in an email message. +- word_freq_remove: role=feature, type=numeric_sparse_frequency. tags=['condition_candidate', 'measure', 'rare_pattern_candidate'] desc=Frequency feature for token 'remove' in an email message. +- word_freq_internet: role=feature, type=numeric_sparse_frequency. tags=['condition_candidate', 'measure', 'rare_pattern_candidate'] desc=Frequency feature for token 'internet' in an email message. +- word_freq_order: role=feature, type=numeric_sparse_frequency. tags=['condition_candidate', 'measure', 'rare_pattern_candidate'] desc=Frequency feature for token 'order' in an email message. +- word_freq_mail: role=feature, type=numeric_sparse_frequency. tags=['condition_candidate', 'measure', 'rare_pattern_candidate'] desc=Frequency feature for token 'mail' in an email message. +- word_freq_receive: role=feature, type=numeric_sparse_frequency. tags=['condition_candidate', 'measure', 'rare_pattern_candidate'] desc=Frequency feature for token 'receive' in an email message. +- word_freq_will: role=feature, type=numeric_sparse_frequency. tags=['condition_candidate', 'measure', 'rare_pattern_candidate'] desc=Frequency feature for token 'will' in an email message. +- word_freq_people: role=feature, type=numeric_sparse_frequency. tags=['condition_candidate', 'measure', 'rare_pattern_candidate'] desc=Frequency feature for token 'people' in an email message. +- word_freq_report: role=feature, type=numeric_sparse_frequency. tags=['condition_candidate', 'measure', 'rare_pattern_candidate'] desc=Frequency feature for token 'report' in an email message. +- word_freq_addresses: role=feature, type=numeric_sparse_frequency. tags=['condition_candidate', 'measure', 'rare_pattern_candidate'] desc=Frequency feature for token 'addresses' in an email message. +- word_freq_free: role=feature, type=numeric_sparse_frequency. tags=['condition_candidate', 'measure', 'rare_pattern_candidate'] desc=Frequency feature for token 'free' in an email message. +- word_freq_business: role=feature, type=numeric_sparse_frequency. tags=['condition_candidate', 'measure', 'rare_pattern_candidate'] desc=Frequency feature for token 'business' in an email message. +- word_freq_email: role=feature, type=numeric_sparse_frequency. tags=['condition_candidate', 'measure', 'rare_pattern_candidate'] desc=Frequency feature for token 'email' in an email message. +- word_freq_you: role=feature, type=numeric_sparse_frequency. tags=['condition_candidate', 'measure', 'rare_pattern_candidate'] desc=Frequency feature for token 'you' in an email message. +- word_freq_credit: role=feature, type=numeric_sparse_frequency. tags=['condition_candidate', 'measure', 'rare_pattern_candidate'] desc=Frequency feature for token 'credit' in an email message. +- word_freq_your: role=feature, type=numeric_sparse_frequency. tags=['condition_candidate', 'measure', 'rare_pattern_candidate'] desc=Frequency feature for token 'your' in an email message. +- word_freq_font: role=feature, type=numeric_sparse_frequency. tags=['condition_candidate', 'measure', 'rare_pattern_candidate'] desc=Frequency feature for token 'font' in an email message. +- word_freq_000: role=feature, type=numeric_sparse_frequency. tags=['condition_candidate', 'measure', 'rare_pattern_candidate'] desc=Frequency feature for token '000' in an email message. +- word_freq_money: role=feature, type=numeric_sparse_frequency. tags=['condition_candidate', 'measure', 'rare_pattern_candidate'] desc=Frequency feature for token 'money' in an email message. +- word_freq_hp: role=feature, type=numeric_sparse_frequency. tags=['condition_candidate', 'measure', 'rare_pattern_candidate'] desc=Frequency feature for token 'hp' in an email message. +- word_freq_hpl: role=feature, type=numeric_sparse_frequency. tags=['condition_candidate', 'measure', 'rare_pattern_candidate'] desc=Frequency feature for token 'hpl' in an email message. +- word_freq_george: role=feature, type=numeric_sparse_frequency. tags=['condition_candidate', 'measure', 'rare_pattern_candidate'] desc=Frequency feature for token 'george' in an email message. +- word_freq_650: role=feature, type=numeric_sparse_frequency. tags=['condition_candidate', 'measure', 'rare_pattern_candidate'] desc=Frequency feature for token '650' in an email message. +- word_freq_lab: role=feature, type=numeric_sparse_frequency. tags=['condition_candidate', 'measure', 'rare_pattern_candidate'] desc=Frequency feature for token 'lab' in an email message. +- word_freq_labs: role=feature, type=numeric_sparse_frequency. tags=['condition_candidate', 'measure', 'rare_pattern_candidate'] desc=Frequency feature for token 'labs' in an email message. +- word_freq_telnet: role=feature, type=numeric_sparse_frequency. tags=['condition_candidate', 'measure', 'rare_pattern_candidate'] desc=Frequency feature for token 'telnet' in an email message. +- word_freq_857: role=feature, type=numeric_sparse_frequency. tags=['condition_candidate', 'measure', 'rare_pattern_candidate'] desc=Frequency feature for token '857' in an email message. +- word_freq_data: role=feature, type=numeric_sparse_frequency. tags=['condition_candidate', 'measure', 'rare_pattern_candidate'] desc=Frequency feature for token 'data' in an email message. +- word_freq_415: role=feature, type=numeric_sparse_frequency. tags=['condition_candidate', 'measure', 'rare_pattern_candidate'] desc=Frequency feature for token '415' in an email message. +- word_freq_85: role=feature, type=numeric_sparse_frequency. tags=['condition_candidate', 'measure', 'rare_pattern_candidate'] desc=Frequency feature for token '85' in an email message. +- word_freq_technology: role=feature, type=numeric_sparse_frequency. tags=['condition_candidate', 'measure', 'rare_pattern_candidate'] desc=Frequency feature for token 'technology' in an email message. +- word_freq_1999: role=feature, type=numeric_sparse_frequency. tags=['condition_candidate', 'measure', 'rare_pattern_candidate'] desc=Frequency feature for token '1999' in an email message. +- word_freq_parts: role=feature, type=numeric_sparse_frequency. tags=['condition_candidate', 'measure', 'rare_pattern_candidate'] desc=Frequency feature for token 'parts' in an email message. +- word_freq_pm: role=feature, type=numeric_sparse_frequency. tags=['condition_candidate', 'measure', 'rare_pattern_candidate'] desc=Frequency feature for token 'pm' in an email message. +- word_freq_direct: role=feature, type=numeric_sparse_frequency. tags=['condition_candidate', 'measure', 'rare_pattern_candidate'] desc=Frequency feature for token 'direct' in an email message. +- word_freq_cs: role=feature, type=numeric_sparse_frequency. tags=['condition_candidate', 'measure', 'rare_pattern_candidate'] desc=Frequency feature for token 'cs' in an email message. +- word_freq_meeting: role=feature, type=numeric_sparse_frequency. tags=['condition_candidate', 'measure', 'rare_pattern_candidate'] desc=Frequency feature for token 'meeting' in an email message. +- word_freq_original: role=feature, type=numeric_sparse_frequency. tags=['condition_candidate', 'measure', 'rare_pattern_candidate'] desc=Frequency feature for token 'original' in an email message. +- word_freq_project: role=feature, type=numeric_sparse_frequency. tags=['condition_candidate', 'measure', 'rare_pattern_candidate'] desc=Frequency feature for token 'project' in an email message. +- word_freq_re: role=feature, type=numeric_sparse_frequency. tags=['condition_candidate', 'measure', 'rare_pattern_candidate'] desc=Frequency feature for token 're' in an email message. +- word_freq_edu: role=feature, type=numeric_sparse_frequency. tags=['condition_candidate', 'measure', 'rare_pattern_candidate'] desc=Frequency feature for token 'edu' in an email message. +- word_freq_table: role=feature, type=numeric_sparse_frequency. tags=['condition_candidate', 'measure', 'rare_pattern_candidate'] desc=Frequency feature for token 'table' in an email message. +- word_freq_conference: role=feature, type=numeric_sparse_frequency. tags=['condition_candidate', 'measure', 'rare_pattern_candidate'] desc=Frequency feature for token 'conference' in an email message. +- char_freq_%3B: role=feature, type=numeric_sparse_frequency. tags=['condition_candidate', 'measure', 'rare_pattern_candidate'] desc=Character frequency feature for symbol ';'. +- char_freq_%28: role=feature, type=numeric_sparse_frequency. tags=['condition_candidate', 'measure', 'rare_pattern_candidate'] desc=Character frequency feature for symbol '('. +- char_freq_%5B: role=feature, type=numeric_sparse_frequency. tags=['condition_candidate', 'measure', 'rare_pattern_candidate'] desc=Character frequency feature for symbol '['. +- char_freq_%21: role=feature, type=numeric_sparse_frequency. tags=['condition_candidate', 'measure', 'rare_pattern_candidate'] desc=Character frequency feature for symbol '!'. +- char_freq_%24: role=feature, type=numeric_sparse_frequency. tags=['condition_candidate', 'measure', 'rare_pattern_candidate'] desc=Character frequency feature for symbol '$'. +- char_freq_%23: role=feature, type=numeric_sparse_frequency. tags=['condition_candidate', 'measure', 'rare_pattern_candidate'] desc=Character frequency feature for symbol '#'. +- capital_run_length_average: role=feature, type=numeric_heavy_tail. tags=['condition_candidate', 'measure', 'rare_pattern_candidate'] desc=Average length of uninterrupted capital-letter runs. +- capital_run_length_longest: role=feature, type=numeric_heavy_tail. tags=['condition_candidate', 'measure', 'rare_pattern_candidate'] desc=Longest uninterrupted capital-letter run. +- capital_run_length_total: role=feature, type=numeric_heavy_tail. tags=['condition_candidate', 'measure', 'rare_pattern_candidate'] desc=Total number of capital letters in runs. +- class: role=target, type=binary_target. tags=['target_candidate'] desc=Binary spam label (1=spam, 0=non-spam). +- useful_field_combinations: [['word_freq_free', 'word_freq_you', 'class'], ['char_freq_%21', 'capital_run_length_longest', 'class'], ['word_freq_remove', 'word_freq_money', 'class']] +- fields_requiring_caution: ['capital_run_length_average', 'capital_run_length_longest', 'capital_run_length_total'] +- source_url: https://www.openml.org/d/44 + +SQLite schema snapshot: +{ + "table_name": "n1", + "quoted_table_name": "\"n1\"", + "row_count": 4601, + "columns": [ + { + "name": "word_freq_make", + "type": "TEXT", + "notnull": false, + "pk": false + }, + { + "name": "word_freq_address", + "type": "TEXT", + "notnull": false, + "pk": false + }, + { + "name": "word_freq_all", + "type": "TEXT", + "notnull": false, + "pk": false + }, + { + "name": "word_freq_3d", + "type": "TEXT", + "notnull": false, + "pk": false + }, + { + "name": "word_freq_our", + "type": "TEXT", + "notnull": false, + "pk": false + }, + { + "name": "word_freq_over", + "type": "TEXT", + "notnull": false, + "pk": false + }, + { + "name": "word_freq_remove", + "type": "TEXT", + "notnull": false, + "pk": false + }, + { + "name": "word_freq_internet", + "type": "TEXT", + "notnull": false, + "pk": false + }, + { + "name": "word_freq_order", + "type": "TEXT", + "notnull": false, + "pk": false + }, + { + "name": "word_freq_mail", + "type": "TEXT", + "notnull": false, + "pk": false + }, + { + "name": "word_freq_receive", + "type": "TEXT", + "notnull": false, + "pk": false + }, + { + "name": "word_freq_will", + "type": "TEXT", + "notnull": false, + "pk": false + }, + { + "name": "word_freq_people", + "type": "TEXT", + "notnull": false, + "pk": false + }, + { + "name": "word_freq_report", + "type": "TEXT", + "notnull": false, + "pk": false + }, + { + "name": "word_freq_addresses", + "type": "TEXT", + "notnull": false, + "pk": false + }, + { + "name": "word_freq_free", + "type": "TEXT", + "notnull": false, + "pk": false + }, + { + "name": "word_freq_business", + "type": "TEXT", + "notnull": false, + "pk": false + }, + { + "name": "word_freq_email", + "type": "TEXT", + "notnull": false, + "pk": false + }, + { + "name": "word_freq_you", + "type": "TEXT", + "notnull": false, + "pk": false + }, + { + "name": "word_freq_credit", + "type": "TEXT", + "notnull": false, + "pk": false + }, + { + "name": "word_freq_your", + "type": "TEXT", + "notnull": false, + "pk": false + }, + { + "name": "word_freq_font", + "type": "TEXT", + "notnull": false, + "pk": false + }, + { + "name": "word_freq_000", + "type": "TEXT", + "notnull": false, + "pk": false + }, + { + "name": "word_freq_money", + "type": "TEXT", + "notnull": false, + "pk": false + }, + { + "name": "word_freq_hp", + "type": "TEXT", + "notnull": false, + "pk": false + }, + { + "name": "word_freq_hpl", + "type": "TEXT", + "notnull": false, + "pk": false + }, + { + "name": "word_freq_george", + "type": "TEXT", + "notnull": false, + "pk": false + }, + { + "name": "word_freq_650", + "type": "TEXT", + "notnull": false, + "pk": false + }, + { + "name": "word_freq_lab", + "type": "TEXT", + "notnull": false, + "pk": false + }, + { + "name": "word_freq_labs", + "type": "TEXT", + "notnull": false, + "pk": false + }, + { + "name": "word_freq_telnet", + "type": "TEXT", + "notnull": false, + "pk": false + }, + { + "name": "word_freq_857", + "type": "TEXT", + "notnull": false, + "pk": false + }, + { + "name": "word_freq_data", + "type": "TEXT", + "notnull": false, + "pk": false + }, + { + "name": "word_freq_415", + "type": "TEXT", + "notnull": false, + "pk": false + }, + { + "name": "word_freq_85", + "type": "TEXT", + "notnull": false, + "pk": false + }, + { + "name": "word_freq_technology", + "type": "TEXT", + "notnull": false, + "pk": false + }, + { + "name": "word_freq_1999", + "type": "TEXT", + "notnull": false, + "pk": false + }, + { + "name": "word_freq_parts", + "type": "TEXT", + "notnull": false, + "pk": false + }, + { + "name": "word_freq_pm", + "type": "TEXT", + "notnull": false, + "pk": false + }, + { + "name": "word_freq_direct", + "type": "TEXT", + "notnull": false, + "pk": false + }, + { + "name": "word_freq_cs", + "type": "TEXT", + "notnull": false, + "pk": false + }, + { + "name": "word_freq_meeting", + "type": "TEXT", + "notnull": false, + "pk": false + }, + { + "name": "word_freq_original", + "type": "TEXT", + "notnull": false, + "pk": false + }, + { + "name": "word_freq_project", + "type": "TEXT", + "notnull": false, + "pk": false + }, + { + "name": "word_freq_re", + "type": "TEXT", + "notnull": false, + "pk": false + }, + { + "name": "word_freq_edu", + "type": "TEXT", + "notnull": false, + "pk": false + }, + { + "name": "word_freq_table", + "type": "TEXT", + "notnull": false, + "pk": false + }, + { + "name": "word_freq_conference", + "type": "TEXT", + "notnull": false, + "pk": false + }, + { + "name": "char_freq_%3B", + "type": "TEXT", + "notnull": false, + "pk": false + }, + { + "name": "char_freq_%28", + "type": "TEXT", + "notnull": false, + "pk": false + }, + { + "name": "char_freq_%5B", + "type": "TEXT", + "notnull": false, + "pk": false + }, + { + "name": "char_freq_%21", + "type": "TEXT", + "notnull": false, + "pk": false + }, + { + "name": "char_freq_%24", + "type": "TEXT", + "notnull": false, + "pk": false + }, + { + "name": "char_freq_%23", + "type": "TEXT", + "notnull": false, + "pk": false + }, + { + "name": "capital_run_length_average", + "type": "TEXT", + "notnull": false, + "pk": false + }, + { + "name": "capital_run_length_longest", + "type": "TEXT", + "notnull": false, + "pk": false + }, + { + "name": "capital_run_length_total", + "type": "TEXT", + "notnull": false, + "pk": false + }, + { + "name": "class", + "type": "TEXT", + "notnull": false, + "pk": false + } + ], + "sample_rows": [ + { + "word_freq_make": "0", + "word_freq_address": "0.64", + "word_freq_all": "0.64", + "word_freq_3d": "0", + "word_freq_our": "0.32", + "word_freq_over": "0", + "word_freq_remove": "0", + "word_freq_internet": "0", + "word_freq_order": "0", + "word_freq_mail": "0", + "word_freq_receive": "0", + "word_freq_will": "0.64", + "word_freq_people": "0", + "word_freq_report": "0", + "word_freq_addresses": "0", + "word_freq_free": "0.32", + "word_freq_business": "0", + "word_freq_email": "1.29", + "word_freq_you": "1.93", + "word_freq_credit": "0", + "word_freq_your": "0.96", + "word_freq_font": "0", + "word_freq_000": "0", + "word_freq_money": "0", + "word_freq_hp": "0", + "word_freq_hpl": "0", + "word_freq_george": "0", + "word_freq_650": "0", + "word_freq_lab": "0", + "word_freq_labs": "0", + "word_freq_telnet": "0", + "word_freq_857": "0", + "word_freq_data": "0", + "word_freq_415": "0", + "word_freq_85": "0", + "word_freq_technology": "0", + "word_freq_1999": "0", + "word_freq_parts": "0", + "word_freq_pm": "0", + "word_freq_direct": "0", + "word_freq_cs": "0", + "word_freq_meeting": "0", + "word_freq_original": "0", + "word_freq_project": "0", + "word_freq_re": "0", + "word_freq_edu": "0", + "word_freq_table": "0", + "word_freq_conference": "0", + "char_freq_%3B": "0", + "char_freq_%28": "0", + "char_freq_%5B": "0", + "char_freq_%21": "0.778", + "char_freq_%24": "0", + "char_freq_%23": "0", + "capital_run_length_average": "3.756", + "capital_run_length_longest": "61", + "capital_run_length_total": "278", + "class": "1" + }, + { + "word_freq_make": "0.21", + "word_freq_address": "0.28", + "word_freq_all": "0.5", + "word_freq_3d": "0", + "word_freq_our": "0.14", + "word_freq_over": "0.28", + "word_freq_remove": "0.21", + "word_freq_internet": "0.07", + "word_freq_order": "0", + "word_freq_mail": "0.94", + "word_freq_receive": "0.21", + "word_freq_will": "0.79", + "word_freq_people": "0.65", + "word_freq_report": "0.21", + "word_freq_addresses": "0.14", + "word_freq_free": "0.14", + "word_freq_business": "0.07", + "word_freq_email": "0.28", + "word_freq_you": "3.47", + "word_freq_credit": "0", + "word_freq_your": "1.59", + "word_freq_font": "0", + "word_freq_000": "0.43", + "word_freq_money": "0.43", + "word_freq_hp": "0", + "word_freq_hpl": "0", + "word_freq_george": "0", + "word_freq_650": "0", + "word_freq_lab": "0", + "word_freq_labs": "0", + "word_freq_telnet": "0", + "word_freq_857": "0", + "word_freq_data": "0", + "word_freq_415": "0", + "word_freq_85": "0", + "word_freq_technology": "0", + "word_freq_1999": "0.07", + "word_freq_parts": "0", + "word_freq_pm": "0", + "word_freq_direct": "0", + "word_freq_cs": "0", + "word_freq_meeting": "0", + "word_freq_original": "0", + "word_freq_project": "0", + "word_freq_re": "0", + "word_freq_edu": "0", + "word_freq_table": "0", + "word_freq_conference": "0", + "char_freq_%3B": "0", + "char_freq_%28": "0.132", + "char_freq_%5B": "0", + "char_freq_%21": "0.372", + "char_freq_%24": "0.18", + "char_freq_%23": "0.048", + "capital_run_length_average": "5.114", + "capital_run_length_longest": "101", + "capital_run_length_total": "1028", + "class": "1" + }, + { + "word_freq_make": "0.06", + "word_freq_address": "0", + "word_freq_all": "0.71", + "word_freq_3d": "0", + "word_freq_our": "1.23", + "word_freq_over": "0.19", + "word_freq_remove": "0.19", + "word_freq_internet": "0.12", + "word_freq_order": "0.64", + "word_freq_mail": "0.25", + "word_freq_receive": "0.38", + "word_freq_will": "0.45", + "word_freq_people": "0.12", + "word_freq_report": "0", + "word_freq_addresses": "1.75", + "word_freq_free": "0.06", + "word_freq_business": "0.06", + "word_freq_email": "1.03", + "word_freq_you": "1.36", + "word_freq_credit": "0.32", + "word_freq_your": "0.51", + "word_freq_font": "0", + "word_freq_000": "1.16", + "word_freq_money": "0.06", + "word_freq_hp": "0", + "word_freq_hpl": "0", + "word_freq_george": "0", + "word_freq_650": "0", + "word_freq_lab": "0", + "word_freq_labs": "0", + "word_freq_telnet": "0", + "word_freq_857": "0", + "word_freq_data": "0", + "word_freq_415": "0", + "word_freq_85": "0", + "word_freq_technology": "0", + "word_freq_1999": "0", + "word_freq_parts": "0", + "word_freq_pm": "0", + "word_freq_direct": "0.06", + "word_freq_cs": "0", + "word_freq_meeting": "0", + "word_freq_original": "0.12", + "word_freq_project": "0", + "word_freq_re": "0.06", + "word_freq_edu": "0.06", + "word_freq_table": "0", + "word_freq_conference": "0", + "char_freq_%3B": "0.01", + "char_freq_%28": "0.143", + "char_freq_%5B": "0", + "char_freq_%21": "0.276", + "char_freq_%24": "0.184", + "char_freq_%23": "0.01", + "capital_run_length_average": "9.821", + "capital_run_length_longest": "485", + "capital_run_length_total": "2259", + "class": "1" + }, + { + "word_freq_make": "0", + "word_freq_address": "0", + "word_freq_all": "0", + "word_freq_3d": "0", + "word_freq_our": "0.63", + "word_freq_over": "0", + "word_freq_remove": "0.31", + "word_freq_internet": "0.63", + "word_freq_order": "0.31", + "word_freq_mail": "0.63", + "word_freq_receive": "0.31", + "word_freq_will": "0.31", + "word_freq_people": "0.31", + "word_freq_report": "0", + "word_freq_addresses": "0", + "word_freq_free": "0.31", + "word_freq_business": "0", + "word_freq_email": "0", + "word_freq_you": "3.18", + "word_freq_credit": "0", + "word_freq_your": "0.31", + "word_freq_font": "0", + "word_freq_000": "0", + "word_freq_money": "0", + "word_freq_hp": "0", + "word_freq_hpl": "0", + "word_freq_george": "0", + "word_freq_650": "0", + "word_freq_lab": "0", + "word_freq_labs": "0", + "word_freq_telnet": "0", + "word_freq_857": "0", + "word_freq_data": "0", + "word_freq_415": "0", + "word_freq_85": "0", + "word_freq_technology": "0", + "word_freq_1999": "0", + "word_freq_parts": "0", + "word_freq_pm": "0", + "word_freq_direct": "0", + "word_freq_cs": "0", + "word_freq_meeting": "0", + "word_freq_original": "0", + "word_freq_project": "0", + "word_freq_re": "0", + "word_freq_edu": "0", + "word_freq_table": "0", + "word_freq_conference": "0", + "char_freq_%3B": "0", + "char_freq_%28": "0.137", + "char_freq_%5B": "0", + "char_freq_%21": "0.137", + "char_freq_%24": "0", + "char_freq_%23": "0", + "capital_run_length_average": "3.537", + "capital_run_length_longest": "40", + "capital_run_length_total": "191", + "class": "1" + }, + { + "word_freq_make": "0", + "word_freq_address": "0", + "word_freq_all": "0", + "word_freq_3d": "0", + "word_freq_our": "0.63", + "word_freq_over": "0", + "word_freq_remove": "0.31", + "word_freq_internet": "0.63", + "word_freq_order": "0.31", + "word_freq_mail": "0.63", + "word_freq_receive": "0.31", + "word_freq_will": "0.31", + "word_freq_people": "0.31", + "word_freq_report": "0", + "word_freq_addresses": "0", + "word_freq_free": "0.31", + "word_freq_business": "0", + "word_freq_email": "0", + "word_freq_you": "3.18", + "word_freq_credit": "0", + "word_freq_your": "0.31", + "word_freq_font": "0", + "word_freq_000": "0", + "word_freq_money": "0", + "word_freq_hp": "0", + "word_freq_hpl": "0", + "word_freq_george": "0", + "word_freq_650": "0", + "word_freq_lab": "0", + "word_freq_labs": "0", + "word_freq_telnet": "0", + "word_freq_857": "0", + "word_freq_data": "0", + "word_freq_415": "0", + "word_freq_85": "0", + "word_freq_technology": "0", + "word_freq_1999": "0", + "word_freq_parts": "0", + "word_freq_pm": "0", + "word_freq_direct": "0", + "word_freq_cs": "0", + "word_freq_meeting": "0", + "word_freq_original": "0", + "word_freq_project": "0", + "word_freq_re": "0", + "word_freq_edu": "0", + "word_freq_table": "0", + "word_freq_conference": "0", + "char_freq_%3B": "0", + "char_freq_%28": "0.135", + "char_freq_%5B": "0", + "char_freq_%21": "0.135", + "char_freq_%24": "0", + "char_freq_%23": "0", + "capital_run_length_average": "3.537", + "capital_run_length_longest": "40", + "capital_run_length_total": "191", + "class": "1" + } + ] +} + +Shortlisted templates: +[ + { + "template_id": "tpl_m4_quantile_tail_slice", + "template_name": "Quantile Tail Slice", + "primary_family": "tail_rarity_structure", + "portability": "partial", + "sql_skeleton": "WITH buckets AS (\n SELECT {measure_col},\n NTILE({num_tiles}) OVER (ORDER BY {measure_col} DESC) AS tail_bucket\n FROM {table}\n)\nSELECT {measure_col}\nFROM buckets\nWHERE tail_bucket = 1\nORDER BY {measure_col} DESC;", + "required_roles": [ + "measure_col" + ] + } +] + +Problem instance: +{ + "dataset_id": "n1", + "question": "Use template Quantile Tail Slice to probe tail_set_consistency with semantic role rare_extreme_view. Focus on measure_col=word_freq_internet.", + "planned_template_id": "tpl_m4_quantile_tail_slice", + "bindings": { + "measure_col": "word_freq_internet", + "top_k": 14, + "top_n": 3, + "num_tiles": 10, + "percentile_value": 0.95, + "z_threshold": 2.0, + "fraction_threshold": 0.1, + "baseline_multiplier": 1.5, + "baseline_fraction": 0.1, + "min_group_size": 5, + "min_support": 5, + "measure_threshold": 0.0, + "time_grain": "month", + "lookback_rows": 3, + "current_period_start": "'2024-01-01'", + "current_period_end": "'2024-04-01'", + "previous_period_start": "'2023-10-01'", + "previous_period_end": "'2024-01-01'", + "drift_ratio_threshold": 0.8 + }, + "can_vary": [], + "must_fix": [], + "runtime_sql_skeleton": "WITH buckets AS (\n SELECT {measure_col},\n NTILE({num_tiles}) OVER (ORDER BY {measure_col} DESC) AS tail_bucket\n FROM {table}\n)\nSELECT {measure_col}\nFROM buckets\nWHERE tail_bucket = 1\nORDER BY {measure_col} DESC;" +} + +Repair context: +{} diff --git a/Query/sql/v2/runs/v2_cli_20260502_081223_d/n1/artifacts/v2q_n1_2d71c7b6d450c813/cli/sql_response_attempt_1.raw.txt b/Query/sql/v2/runs/v2_cli_20260502_081223_d/n1/artifacts/v2q_n1_2d71c7b6d450c813/cli/sql_response_attempt_1.raw.txt new file mode 100644 index 0000000000000000000000000000000000000000..bfacdbbe267a08008eaf82d3d933eccfe11ae1ea --- /dev/null +++ b/Query/sql/v2/runs/v2_cli_20260502_081223_d/n1/artifacts/v2q_n1_2d71c7b6d450c813/cli/sql_response_attempt_1.raw.txt @@ -0,0 +1,4 @@ +{"type":"thread.started","thread_id":"019e40e9-f8b8-7331-8105-b5e0193ab7fe"} +{"type":"turn.started"} +{"type":"item.completed","item":{"id":"item_0","type":"agent_message","text":"{\"sql\":\"-- template_id: tpl_m4_quantile_tail_slice\\nWITH \\\"buckets\\\" AS (\\n SELECT CAST(\\\"word_freq_internet\\\" AS REAL) AS \\\"word_freq_internet\\\",\\n NTILE(10) OVER (ORDER BY CAST(\\\"word_freq_internet\\\" AS REAL) DESC) AS \\\"tail_bucket\\\"\\n FROM \\\"n1\\\"\\n)\\nSELECT \\\"word_freq_internet\\\"\\nFROM \\\"buckets\\\"\\nWHERE \\\"tail_bucket\\\" = 1\\nORDER BY \\\"word_freq_internet\\\" DESC;\",\"notes\":\"Applies the Quantile Tail Slice template with measure_col bound to \\\"word_freq_internet\\\" and num_tiles fixed at 10. CAST to REAL is used because the schema stores feature values as TEXT, and numeric ordering is required for a correct tail slice.\"}"}} +{"type":"turn.completed","usage":{"input_tokens":20372,"cached_input_tokens":19840,"output_tokens":445,"reasoning_output_tokens":270}} diff --git a/Query/sql/v2/runs/v2_cli_20260502_081223_d/n1/artifacts/v2q_n1_2d71c7b6d450c813/cli/sql_response_attempt_1.txt b/Query/sql/v2/runs/v2_cli_20260502_081223_d/n1/artifacts/v2q_n1_2d71c7b6d450c813/cli/sql_response_attempt_1.txt new file mode 100644 index 0000000000000000000000000000000000000000..e057de81e36fe054a649bcfe49dfda64e0712bc2 --- /dev/null +++ b/Query/sql/v2/runs/v2_cli_20260502_081223_d/n1/artifacts/v2q_n1_2d71c7b6d450c813/cli/sql_response_attempt_1.txt @@ -0,0 +1 @@ +{"sql":"-- template_id: tpl_m4_quantile_tail_slice\nWITH \"buckets\" AS (\n SELECT CAST(\"word_freq_internet\" AS REAL) AS \"word_freq_internet\",\n NTILE(10) OVER (ORDER BY CAST(\"word_freq_internet\" AS REAL) DESC) AS \"tail_bucket\"\n FROM \"n1\"\n)\nSELECT \"word_freq_internet\"\nFROM \"buckets\"\nWHERE \"tail_bucket\" = 1\nORDER BY \"word_freq_internet\" DESC;","notes":"Applies the Quantile Tail Slice template with measure_col bound to \"word_freq_internet\" and num_tiles fixed at 10. CAST to REAL is used because the schema stores feature values as TEXT, and numeric ordering is required for a correct tail slice."} \ No newline at end of file diff --git a/Query/sql/v2/runs/v2_cli_20260502_081223_d/n1/artifacts/v2q_n1_2d71c7b6d450c813/cli/sql_stderr_attempt_1.txt b/Query/sql/v2/runs/v2_cli_20260502_081223_d/n1/artifacts/v2q_n1_2d71c7b6d450c813/cli/sql_stderr_attempt_1.txt new file mode 100644 index 0000000000000000000000000000000000000000..e69de29bb2d1d6434b8b29ae775ad8c2e48c5391 diff --git a/Query/sql/v2/runs/v2_cli_20260502_081223_d/n1/artifacts/v2q_n1_33edde959f0b8f98/cli/conversation.jsonl b/Query/sql/v2/runs/v2_cli_20260502_081223_d/n1/artifacts/v2q_n1_33edde959f0b8f98/cli/conversation.jsonl new file mode 100644 index 0000000000000000000000000000000000000000..513ca456d869a9eabc1f137753b15eb2d34f9ee9 --- /dev/null +++ b/Query/sql/v2/runs/v2_cli_20260502_081223_d/n1/artifacts/v2q_n1_33edde959f0b8f98/cli/conversation.jsonl @@ -0,0 +1,2 @@ +{"attempt": 1, "phase": "sql_generation", "role": "user", "content_path": "cli/sql_prompt_attempt_1.txt", "metrics": {"chars": 29534, "bytes_utf8": 29534, "lines": 790, "estimated_tokens": null}} +{"attempt": 1, "phase": "sql_generation", "role": "assistant", "content_path": "cli/sql_response_attempt_1.txt", "raw_content_path": "cli/sql_response_attempt_1.raw.txt", "stderr_path": "cli/sql_stderr_attempt_1.txt", "metrics": {"chars": 542, "bytes_utf8": 542, "lines": 1, "estimated_tokens": null}, "usage": {"input_tokens": 20370, "cached_input_tokens": 19840, "output_tokens": 420, "reasoning_output_tokens": 272}} diff --git a/Query/sql/v2/runs/v2_cli_20260502_081223_d/n1/artifacts/v2q_n1_33edde959f0b8f98/cli/session_summary.json b/Query/sql/v2/runs/v2_cli_20260502_081223_d/n1/artifacts/v2q_n1_33edde959f0b8f98/cli/session_summary.json new file mode 100644 index 0000000000000000000000000000000000000000..167a685e096aa653619bb454b55d0dc61b490a9c --- /dev/null +++ b/Query/sql/v2/runs/v2_cli_20260502_081223_d/n1/artifacts/v2q_n1_33edde959f0b8f98/cli/session_summary.json @@ -0,0 +1,25 @@ +{ + "engine": "v2-cli:codex", + "command": "codex exec --skip-git-repo-check --disable plugins --sandbox read-only --cd \"/data/jialinzhang/SQLagent\" -m gpt-5.4 --json -", + "ai_cli_calls": 1, + "usage_summary": { + "dataset_id": "n1", + "model": "v2-cli:codex", + "run_id": "v2q_n1_33edde959f0b8f98", + "api_calls": 0, + "input_tokens": 20370, + "cached_input_tokens": 19840, + "output_tokens": 420, + "total_tokens": 20790, + "cost_usd": 0.0, + "ai_cli_calls": 1, + "estimated_input_tokens": 0, + "estimated_output_tokens": 0, + "estimated_total_tokens": 0, + "usage_source": "ai_cli_json_usage", + "cli_elapsed_ms_total": 10410.88, + "sql_execution_elapsed_ms_total": 8.29, + "conversation_log_path": "/data/jialinzhang/TabQueryBench/sql_workloads/v2_current/runs_and_launches/runs/v2_cli_20260502_081223_d/n1/artifacts/v2q_n1_33edde959f0b8f98/cli/conversation.jsonl", + "note": "Executed through a local AI CLI with structured usage metadata." + } +} \ No newline at end of file diff --git a/Query/sql/v2/runs/v2_cli_20260502_081223_d/n1/artifacts/v2q_n1_33edde959f0b8f98/cli/sql_attempt_1.metadata.json b/Query/sql/v2/runs/v2_cli_20260502_081223_d/n1/artifacts/v2q_n1_33edde959f0b8f98/cli/sql_attempt_1.metadata.json new file mode 100644 index 0000000000000000000000000000000000000000..bf8e3e567b77d3c0924ad20b5b68d6a04a7270ae --- /dev/null +++ b/Query/sql/v2/runs/v2_cli_20260502_081223_d/n1/artifacts/v2q_n1_33edde959f0b8f98/cli/sql_attempt_1.metadata.json @@ -0,0 +1,45 @@ +{ + "attempt": 1, + "phase": "sql_generation", + "command": "codex exec --skip-git-repo-check --disable plugins --sandbox read-only --cd \"/data/jialinzhang/SQLagent\" -m gpt-5.4 --json -", + "started_at": "2026-05-19T15:45:58.348891+00:00", + "ended_at": "2026-05-19T15:46:08.759795+00:00", + "elapsed_ms": 10410.88, + "prompt_metrics": { + "chars": 29534, + "bytes_utf8": 29534, + "lines": 790, + "estimated_tokens": null + }, + "stdout_metrics": { + "chars": 913, + "bytes_utf8": 913, + "lines": 4, + "estimated_tokens": null + }, + "stderr_metrics": { + "chars": 0, + "bytes_utf8": 0, + "lines": 0, + "estimated_tokens": null + }, + "parsed_output": { + "format": "jsonl_events", + "text_metrics": { + "chars": 542, + "bytes_utf8": 542, + "lines": 1, + "estimated_tokens": null + }, + "usage": { + "input_tokens": 20370, + "cached_input_tokens": 19840, + "output_tokens": 420, + "reasoning_output_tokens": 272 + } + }, + "prompt_path": "cli/sql_prompt_attempt_1.txt", + "response_path": "cli/sql_response_attempt_1.txt", + "raw_response_path": "cli/sql_response_attempt_1.raw.txt", + "stderr_path": "cli/sql_stderr_attempt_1.txt" +} \ No newline at end of file diff --git a/Query/sql/v2/runs/v2_cli_20260502_081223_d/n1/artifacts/v2q_n1_33edde959f0b8f98/cli/sql_prompt_attempt_1.txt b/Query/sql/v2/runs/v2_cli_20260502_081223_d/n1/artifacts/v2q_n1_33edde959f0b8f98/cli/sql_prompt_attempt_1.txt new file mode 100644 index 0000000000000000000000000000000000000000..73886b5d3e87a1e4b9718e3061b54e9b15f86431 --- /dev/null +++ b/Query/sql/v2/runs/v2_cli_20260502_081223_d/n1/artifacts/v2q_n1_33edde959f0b8f98/cli/sql_prompt_attempt_1.txt @@ -0,0 +1,790 @@ +You are generating one SQLite SELECT query for a single-table SQL QA task. +Return strict JSON only, with this schema: {"sql": "...", "notes": "..."}. +Rules: +- Use only the provided table and columns. +- Do not write INSERT, UPDATE, DELETE, DROP, ALTER, CREATE, PRAGMA, ATTACH, DETACH, or VACUUM. +- Prefer the planned template and bound roles when provided. +- Add a leading SQL comment exactly like: -- template_id: . +- Generate SQLite-compatible SQL. SQLite does not support PERCENTILE_CONT or STDDEV. +- Quote identifiers with double quotes. +- Return no markdown and no extra prose. + +Dataset context: +Dataset context for SQL QA: +- dataset_id: n1 +- dataset_name: Spambase +- table_name: n1 +- table_layout: single-table dataset (do not assume joins). +- row_semantics: One row is one email represented by engineered token/character frequency and capitalization-run features. +- task_type: classification +- target_column: class +- main_row_count: 4601 +- important_fields: +- word_freq_make: role=feature, type=numeric_sparse_frequency. tags=['condition_candidate', 'measure', 'rare_pattern_candidate'] desc=Frequency feature for token 'make' in an email message. +- word_freq_address: role=feature, type=numeric_sparse_frequency. tags=['condition_candidate', 'measure', 'rare_pattern_candidate'] desc=Frequency feature for token 'address' in an email message. +- word_freq_all: role=feature, type=numeric_sparse_frequency. tags=['condition_candidate', 'measure', 'rare_pattern_candidate'] desc=Frequency feature for token 'all' in an email message. +- word_freq_3d: role=feature, type=numeric_sparse_frequency. tags=['condition_candidate', 'measure', 'rare_pattern_candidate'] desc=Frequency feature for token '3d' in an email message. +- word_freq_our: role=feature, type=numeric_sparse_frequency. tags=['condition_candidate', 'measure', 'rare_pattern_candidate'] desc=Frequency feature for token 'our' in an email message. +- word_freq_over: role=feature, type=numeric_sparse_frequency. tags=['condition_candidate', 'measure', 'rare_pattern_candidate'] desc=Frequency feature for token 'over' in an email message. +- word_freq_remove: role=feature, type=numeric_sparse_frequency. tags=['condition_candidate', 'measure', 'rare_pattern_candidate'] desc=Frequency feature for token 'remove' in an email message. +- word_freq_internet: role=feature, type=numeric_sparse_frequency. tags=['condition_candidate', 'measure', 'rare_pattern_candidate'] desc=Frequency feature for token 'internet' in an email message. +- word_freq_order: role=feature, type=numeric_sparse_frequency. tags=['condition_candidate', 'measure', 'rare_pattern_candidate'] desc=Frequency feature for token 'order' in an email message. +- word_freq_mail: role=feature, type=numeric_sparse_frequency. tags=['condition_candidate', 'measure', 'rare_pattern_candidate'] desc=Frequency feature for token 'mail' in an email message. +- word_freq_receive: role=feature, type=numeric_sparse_frequency. tags=['condition_candidate', 'measure', 'rare_pattern_candidate'] desc=Frequency feature for token 'receive' in an email message. +- word_freq_will: role=feature, type=numeric_sparse_frequency. tags=['condition_candidate', 'measure', 'rare_pattern_candidate'] desc=Frequency feature for token 'will' in an email message. +- word_freq_people: role=feature, type=numeric_sparse_frequency. tags=['condition_candidate', 'measure', 'rare_pattern_candidate'] desc=Frequency feature for token 'people' in an email message. +- word_freq_report: role=feature, type=numeric_sparse_frequency. tags=['condition_candidate', 'measure', 'rare_pattern_candidate'] desc=Frequency feature for token 'report' in an email message. +- word_freq_addresses: role=feature, type=numeric_sparse_frequency. tags=['condition_candidate', 'measure', 'rare_pattern_candidate'] desc=Frequency feature for token 'addresses' in an email message. +- word_freq_free: role=feature, type=numeric_sparse_frequency. tags=['condition_candidate', 'measure', 'rare_pattern_candidate'] desc=Frequency feature for token 'free' in an email message. +- word_freq_business: role=feature, type=numeric_sparse_frequency. tags=['condition_candidate', 'measure', 'rare_pattern_candidate'] desc=Frequency feature for token 'business' in an email message. +- word_freq_email: role=feature, type=numeric_sparse_frequency. tags=['condition_candidate', 'measure', 'rare_pattern_candidate'] desc=Frequency feature for token 'email' in an email message. +- word_freq_you: role=feature, type=numeric_sparse_frequency. tags=['condition_candidate', 'measure', 'rare_pattern_candidate'] desc=Frequency feature for token 'you' in an email message. +- word_freq_credit: role=feature, type=numeric_sparse_frequency. tags=['condition_candidate', 'measure', 'rare_pattern_candidate'] desc=Frequency feature for token 'credit' in an email message. +- word_freq_your: role=feature, type=numeric_sparse_frequency. tags=['condition_candidate', 'measure', 'rare_pattern_candidate'] desc=Frequency feature for token 'your' in an email message. +- word_freq_font: role=feature, type=numeric_sparse_frequency. tags=['condition_candidate', 'measure', 'rare_pattern_candidate'] desc=Frequency feature for token 'font' in an email message. +- word_freq_000: role=feature, type=numeric_sparse_frequency. tags=['condition_candidate', 'measure', 'rare_pattern_candidate'] desc=Frequency feature for token '000' in an email message. +- word_freq_money: role=feature, type=numeric_sparse_frequency. tags=['condition_candidate', 'measure', 'rare_pattern_candidate'] desc=Frequency feature for token 'money' in an email message. +- word_freq_hp: role=feature, type=numeric_sparse_frequency. tags=['condition_candidate', 'measure', 'rare_pattern_candidate'] desc=Frequency feature for token 'hp' in an email message. +- word_freq_hpl: role=feature, type=numeric_sparse_frequency. tags=['condition_candidate', 'measure', 'rare_pattern_candidate'] desc=Frequency feature for token 'hpl' in an email message. +- word_freq_george: role=feature, type=numeric_sparse_frequency. tags=['condition_candidate', 'measure', 'rare_pattern_candidate'] desc=Frequency feature for token 'george' in an email message. +- word_freq_650: role=feature, type=numeric_sparse_frequency. tags=['condition_candidate', 'measure', 'rare_pattern_candidate'] desc=Frequency feature for token '650' in an email message. +- word_freq_lab: role=feature, type=numeric_sparse_frequency. tags=['condition_candidate', 'measure', 'rare_pattern_candidate'] desc=Frequency feature for token 'lab' in an email message. +- word_freq_labs: role=feature, type=numeric_sparse_frequency. tags=['condition_candidate', 'measure', 'rare_pattern_candidate'] desc=Frequency feature for token 'labs' in an email message. +- word_freq_telnet: role=feature, type=numeric_sparse_frequency. tags=['condition_candidate', 'measure', 'rare_pattern_candidate'] desc=Frequency feature for token 'telnet' in an email message. +- word_freq_857: role=feature, type=numeric_sparse_frequency. tags=['condition_candidate', 'measure', 'rare_pattern_candidate'] desc=Frequency feature for token '857' in an email message. +- word_freq_data: role=feature, type=numeric_sparse_frequency. tags=['condition_candidate', 'measure', 'rare_pattern_candidate'] desc=Frequency feature for token 'data' in an email message. +- word_freq_415: role=feature, type=numeric_sparse_frequency. tags=['condition_candidate', 'measure', 'rare_pattern_candidate'] desc=Frequency feature for token '415' in an email message. +- word_freq_85: role=feature, type=numeric_sparse_frequency. tags=['condition_candidate', 'measure', 'rare_pattern_candidate'] desc=Frequency feature for token '85' in an email message. +- word_freq_technology: role=feature, type=numeric_sparse_frequency. tags=['condition_candidate', 'measure', 'rare_pattern_candidate'] desc=Frequency feature for token 'technology' in an email message. +- word_freq_1999: role=feature, type=numeric_sparse_frequency. tags=['condition_candidate', 'measure', 'rare_pattern_candidate'] desc=Frequency feature for token '1999' in an email message. +- word_freq_parts: role=feature, type=numeric_sparse_frequency. tags=['condition_candidate', 'measure', 'rare_pattern_candidate'] desc=Frequency feature for token 'parts' in an email message. +- word_freq_pm: role=feature, type=numeric_sparse_frequency. tags=['condition_candidate', 'measure', 'rare_pattern_candidate'] desc=Frequency feature for token 'pm' in an email message. +- word_freq_direct: role=feature, type=numeric_sparse_frequency. tags=['condition_candidate', 'measure', 'rare_pattern_candidate'] desc=Frequency feature for token 'direct' in an email message. +- word_freq_cs: role=feature, type=numeric_sparse_frequency. tags=['condition_candidate', 'measure', 'rare_pattern_candidate'] desc=Frequency feature for token 'cs' in an email message. +- word_freq_meeting: role=feature, type=numeric_sparse_frequency. tags=['condition_candidate', 'measure', 'rare_pattern_candidate'] desc=Frequency feature for token 'meeting' in an email message. +- word_freq_original: role=feature, type=numeric_sparse_frequency. tags=['condition_candidate', 'measure', 'rare_pattern_candidate'] desc=Frequency feature for token 'original' in an email message. +- word_freq_project: role=feature, type=numeric_sparse_frequency. tags=['condition_candidate', 'measure', 'rare_pattern_candidate'] desc=Frequency feature for token 'project' in an email message. +- word_freq_re: role=feature, type=numeric_sparse_frequency. tags=['condition_candidate', 'measure', 'rare_pattern_candidate'] desc=Frequency feature for token 're' in an email message. +- word_freq_edu: role=feature, type=numeric_sparse_frequency. tags=['condition_candidate', 'measure', 'rare_pattern_candidate'] desc=Frequency feature for token 'edu' in an email message. +- word_freq_table: role=feature, type=numeric_sparse_frequency. tags=['condition_candidate', 'measure', 'rare_pattern_candidate'] desc=Frequency feature for token 'table' in an email message. +- word_freq_conference: role=feature, type=numeric_sparse_frequency. tags=['condition_candidate', 'measure', 'rare_pattern_candidate'] desc=Frequency feature for token 'conference' in an email message. +- char_freq_%3B: role=feature, type=numeric_sparse_frequency. tags=['condition_candidate', 'measure', 'rare_pattern_candidate'] desc=Character frequency feature for symbol ';'. +- char_freq_%28: role=feature, type=numeric_sparse_frequency. tags=['condition_candidate', 'measure', 'rare_pattern_candidate'] desc=Character frequency feature for symbol '('. +- char_freq_%5B: role=feature, type=numeric_sparse_frequency. tags=['condition_candidate', 'measure', 'rare_pattern_candidate'] desc=Character frequency feature for symbol '['. +- char_freq_%21: role=feature, type=numeric_sparse_frequency. tags=['condition_candidate', 'measure', 'rare_pattern_candidate'] desc=Character frequency feature for symbol '!'. +- char_freq_%24: role=feature, type=numeric_sparse_frequency. tags=['condition_candidate', 'measure', 'rare_pattern_candidate'] desc=Character frequency feature for symbol '$'. +- char_freq_%23: role=feature, type=numeric_sparse_frequency. tags=['condition_candidate', 'measure', 'rare_pattern_candidate'] desc=Character frequency feature for symbol '#'. +- capital_run_length_average: role=feature, type=numeric_heavy_tail. tags=['condition_candidate', 'measure', 'rare_pattern_candidate'] desc=Average length of uninterrupted capital-letter runs. +- capital_run_length_longest: role=feature, type=numeric_heavy_tail. tags=['condition_candidate', 'measure', 'rare_pattern_candidate'] desc=Longest uninterrupted capital-letter run. +- capital_run_length_total: role=feature, type=numeric_heavy_tail. tags=['condition_candidate', 'measure', 'rare_pattern_candidate'] desc=Total number of capital letters in runs. +- class: role=target, type=binary_target. tags=['target_candidate'] desc=Binary spam label (1=spam, 0=non-spam). +- useful_field_combinations: [['word_freq_free', 'word_freq_you', 'class'], ['char_freq_%21', 'capital_run_length_longest', 'class'], ['word_freq_remove', 'word_freq_money', 'class']] +- fields_requiring_caution: ['capital_run_length_average', 'capital_run_length_longest', 'capital_run_length_total'] +- source_url: https://www.openml.org/d/44 + +SQLite schema snapshot: +{ + "table_name": "n1", + "quoted_table_name": "\"n1\"", + "row_count": 4601, + "columns": [ + { + "name": "word_freq_make", + "type": "TEXT", + "notnull": false, + "pk": false + }, + { + "name": "word_freq_address", + "type": "TEXT", + "notnull": false, + "pk": false + }, + { + "name": "word_freq_all", + "type": "TEXT", + "notnull": false, + "pk": false + }, + { + "name": "word_freq_3d", + "type": "TEXT", + "notnull": false, + "pk": false + }, + { + "name": "word_freq_our", + "type": "TEXT", + "notnull": false, + "pk": false + }, + { + "name": "word_freq_over", + "type": "TEXT", + "notnull": false, + "pk": false + }, + { + "name": "word_freq_remove", + "type": "TEXT", + "notnull": false, + "pk": false + }, + { + "name": "word_freq_internet", + "type": "TEXT", + "notnull": false, + "pk": false + }, + { + "name": "word_freq_order", + "type": "TEXT", + "notnull": false, + "pk": false + }, + { + "name": "word_freq_mail", + "type": "TEXT", + "notnull": false, + "pk": false + }, + { + "name": "word_freq_receive", + "type": "TEXT", + "notnull": false, + "pk": false + }, + { + "name": "word_freq_will", + "type": "TEXT", + "notnull": false, + "pk": false + }, + { + "name": "word_freq_people", + "type": "TEXT", + "notnull": false, + "pk": false + }, + { + "name": "word_freq_report", + "type": "TEXT", + "notnull": false, + "pk": false + }, + { + "name": "word_freq_addresses", + "type": "TEXT", + "notnull": false, + "pk": false + }, + { + "name": "word_freq_free", + "type": "TEXT", + "notnull": false, + "pk": false + }, + { + "name": "word_freq_business", + "type": "TEXT", + "notnull": false, + "pk": false + }, + { + "name": "word_freq_email", + "type": "TEXT", + "notnull": false, + "pk": false + }, + { + "name": "word_freq_you", + "type": "TEXT", + "notnull": false, + "pk": false + }, + { + "name": "word_freq_credit", + "type": "TEXT", + "notnull": false, + "pk": false + }, + { + "name": "word_freq_your", + "type": "TEXT", + "notnull": false, + "pk": false + }, + { + "name": "word_freq_font", + "type": "TEXT", + "notnull": false, + "pk": false + }, + { + "name": "word_freq_000", + "type": "TEXT", + "notnull": false, + "pk": false + }, + { + "name": "word_freq_money", + "type": "TEXT", + "notnull": false, + "pk": false + }, + { + "name": "word_freq_hp", + "type": "TEXT", + "notnull": false, + "pk": false + }, + { + "name": "word_freq_hpl", + "type": "TEXT", + "notnull": false, + "pk": false + }, + { + "name": "word_freq_george", + "type": "TEXT", + "notnull": false, + "pk": false + }, + { + "name": "word_freq_650", + "type": "TEXT", + "notnull": false, + "pk": false + }, + { + "name": "word_freq_lab", + "type": "TEXT", + "notnull": false, + "pk": false + }, + { + "name": "word_freq_labs", + "type": "TEXT", + "notnull": false, + "pk": false + }, + { + "name": "word_freq_telnet", + "type": "TEXT", + "notnull": false, + "pk": false + }, + { + "name": "word_freq_857", + "type": "TEXT", + "notnull": false, + "pk": false + }, + { + "name": "word_freq_data", + "type": "TEXT", + "notnull": false, + "pk": false + }, + { + "name": "word_freq_415", + "type": "TEXT", + "notnull": false, + "pk": false + }, + { + "name": "word_freq_85", + "type": "TEXT", + "notnull": false, + "pk": false + }, + { + "name": "word_freq_technology", + "type": "TEXT", + "notnull": false, + "pk": false + }, + { + "name": "word_freq_1999", + "type": "TEXT", + "notnull": false, + "pk": false + }, + { + "name": "word_freq_parts", + "type": "TEXT", + "notnull": false, + "pk": false + }, + { + "name": "word_freq_pm", + "type": "TEXT", + "notnull": false, + "pk": false + }, + { + "name": "word_freq_direct", + "type": "TEXT", + "notnull": false, + "pk": false + }, + { + "name": "word_freq_cs", + "type": "TEXT", + "notnull": false, + "pk": false + }, + { + "name": "word_freq_meeting", + "type": "TEXT", + "notnull": false, + "pk": false + }, + { + "name": "word_freq_original", + "type": "TEXT", + "notnull": false, + "pk": false + }, + { + "name": "word_freq_project", + "type": "TEXT", + "notnull": false, + "pk": false + }, + { + "name": "word_freq_re", + "type": "TEXT", + "notnull": false, + "pk": false + }, + { + "name": "word_freq_edu", + "type": "TEXT", + "notnull": false, + "pk": false + }, + { + "name": "word_freq_table", + "type": "TEXT", + "notnull": false, + "pk": false + }, + { + "name": "word_freq_conference", + "type": "TEXT", + "notnull": false, + "pk": false + }, + { + "name": "char_freq_%3B", + "type": "TEXT", + "notnull": false, + "pk": false + }, + { + "name": "char_freq_%28", + "type": "TEXT", + "notnull": false, + "pk": false + }, + { + "name": "char_freq_%5B", + "type": "TEXT", + "notnull": false, + "pk": false + }, + { + "name": "char_freq_%21", + "type": "TEXT", + "notnull": false, + "pk": false + }, + { + "name": "char_freq_%24", + "type": "TEXT", + "notnull": false, + "pk": false + }, + { + "name": "char_freq_%23", + "type": "TEXT", + "notnull": false, + "pk": false + }, + { + "name": "capital_run_length_average", + "type": "TEXT", + "notnull": false, + "pk": false + }, + { + "name": "capital_run_length_longest", + "type": "TEXT", + "notnull": false, + "pk": false + }, + { + "name": "capital_run_length_total", + "type": "TEXT", + "notnull": false, + "pk": false + }, + { + "name": "class", + "type": "TEXT", + "notnull": false, + "pk": false + } + ], + "sample_rows": [ + { + "word_freq_make": "0", + "word_freq_address": "0.64", + "word_freq_all": "0.64", + "word_freq_3d": "0", + "word_freq_our": "0.32", + "word_freq_over": "0", + "word_freq_remove": "0", + "word_freq_internet": "0", + "word_freq_order": "0", + "word_freq_mail": "0", + "word_freq_receive": "0", + "word_freq_will": "0.64", + "word_freq_people": "0", + "word_freq_report": "0", + "word_freq_addresses": "0", + "word_freq_free": "0.32", + "word_freq_business": "0", + "word_freq_email": "1.29", + "word_freq_you": "1.93", + "word_freq_credit": "0", + "word_freq_your": "0.96", + "word_freq_font": "0", + "word_freq_000": "0", + "word_freq_money": "0", + "word_freq_hp": "0", + "word_freq_hpl": "0", + "word_freq_george": "0", + "word_freq_650": "0", + "word_freq_lab": "0", + "word_freq_labs": "0", + "word_freq_telnet": "0", + "word_freq_857": "0", + "word_freq_data": "0", + "word_freq_415": "0", + "word_freq_85": "0", + "word_freq_technology": "0", + "word_freq_1999": "0", + "word_freq_parts": "0", + "word_freq_pm": "0", + "word_freq_direct": "0", + "word_freq_cs": "0", + "word_freq_meeting": "0", + "word_freq_original": "0", + "word_freq_project": "0", + "word_freq_re": "0", + "word_freq_edu": "0", + "word_freq_table": "0", + "word_freq_conference": "0", + "char_freq_%3B": "0", + "char_freq_%28": "0", + "char_freq_%5B": "0", + "char_freq_%21": "0.778", + "char_freq_%24": "0", + "char_freq_%23": "0", + "capital_run_length_average": "3.756", + "capital_run_length_longest": "61", + "capital_run_length_total": "278", + "class": "1" + }, + { + "word_freq_make": "0.21", + "word_freq_address": "0.28", + "word_freq_all": "0.5", + "word_freq_3d": "0", + "word_freq_our": "0.14", + "word_freq_over": "0.28", + "word_freq_remove": "0.21", + "word_freq_internet": "0.07", + "word_freq_order": "0", + "word_freq_mail": "0.94", + "word_freq_receive": "0.21", + "word_freq_will": "0.79", + "word_freq_people": "0.65", + "word_freq_report": "0.21", + "word_freq_addresses": "0.14", + "word_freq_free": "0.14", + "word_freq_business": "0.07", + "word_freq_email": "0.28", + "word_freq_you": "3.47", + "word_freq_credit": "0", + "word_freq_your": "1.59", + "word_freq_font": "0", + "word_freq_000": "0.43", + "word_freq_money": "0.43", + "word_freq_hp": "0", + "word_freq_hpl": "0", + "word_freq_george": "0", + "word_freq_650": "0", + "word_freq_lab": "0", + "word_freq_labs": "0", + "word_freq_telnet": "0", + "word_freq_857": "0", + "word_freq_data": "0", + "word_freq_415": "0", + "word_freq_85": "0", + "word_freq_technology": "0", + "word_freq_1999": "0.07", + "word_freq_parts": "0", + "word_freq_pm": "0", + "word_freq_direct": "0", + "word_freq_cs": "0", + "word_freq_meeting": "0", + "word_freq_original": "0", + "word_freq_project": "0", + "word_freq_re": "0", + "word_freq_edu": "0", + "word_freq_table": "0", + "word_freq_conference": "0", + "char_freq_%3B": "0", + "char_freq_%28": "0.132", + "char_freq_%5B": "0", + "char_freq_%21": "0.372", + "char_freq_%24": "0.18", + "char_freq_%23": "0.048", + "capital_run_length_average": "5.114", + "capital_run_length_longest": "101", + "capital_run_length_total": "1028", + "class": "1" + }, + { + "word_freq_make": "0.06", + "word_freq_address": "0", + "word_freq_all": "0.71", + "word_freq_3d": "0", + "word_freq_our": "1.23", + "word_freq_over": "0.19", + "word_freq_remove": "0.19", + "word_freq_internet": "0.12", + "word_freq_order": "0.64", + "word_freq_mail": "0.25", + "word_freq_receive": "0.38", + "word_freq_will": "0.45", + "word_freq_people": "0.12", + "word_freq_report": "0", + "word_freq_addresses": "1.75", + "word_freq_free": "0.06", + "word_freq_business": "0.06", + "word_freq_email": "1.03", + "word_freq_you": "1.36", + "word_freq_credit": "0.32", + "word_freq_your": "0.51", + "word_freq_font": "0", + "word_freq_000": "1.16", + "word_freq_money": "0.06", + "word_freq_hp": "0", + "word_freq_hpl": "0", + "word_freq_george": "0", + "word_freq_650": "0", + "word_freq_lab": "0", + "word_freq_labs": "0", + "word_freq_telnet": "0", + "word_freq_857": "0", + "word_freq_data": "0", + "word_freq_415": "0", + "word_freq_85": "0", + "word_freq_technology": "0", + "word_freq_1999": "0", + "word_freq_parts": "0", + "word_freq_pm": "0", + "word_freq_direct": "0.06", + "word_freq_cs": "0", + "word_freq_meeting": "0", + "word_freq_original": "0.12", + "word_freq_project": "0", + "word_freq_re": "0.06", + "word_freq_edu": "0.06", + "word_freq_table": "0", + "word_freq_conference": "0", + "char_freq_%3B": "0.01", + "char_freq_%28": "0.143", + "char_freq_%5B": "0", + "char_freq_%21": "0.276", + "char_freq_%24": "0.184", + "char_freq_%23": "0.01", + "capital_run_length_average": "9.821", + "capital_run_length_longest": "485", + "capital_run_length_total": "2259", + "class": "1" + }, + { + "word_freq_make": "0", + "word_freq_address": "0", + "word_freq_all": "0", + "word_freq_3d": "0", + "word_freq_our": "0.63", + "word_freq_over": "0", + "word_freq_remove": "0.31", + "word_freq_internet": "0.63", + "word_freq_order": "0.31", + "word_freq_mail": "0.63", + "word_freq_receive": "0.31", + "word_freq_will": "0.31", + "word_freq_people": "0.31", + "word_freq_report": "0", + "word_freq_addresses": "0", + "word_freq_free": "0.31", + "word_freq_business": "0", + "word_freq_email": "0", + "word_freq_you": "3.18", + "word_freq_credit": "0", + "word_freq_your": "0.31", + "word_freq_font": "0", + "word_freq_000": "0", + "word_freq_money": "0", + "word_freq_hp": "0", + "word_freq_hpl": "0", + "word_freq_george": "0", + "word_freq_650": "0", + "word_freq_lab": "0", + "word_freq_labs": "0", + "word_freq_telnet": "0", + "word_freq_857": "0", + "word_freq_data": "0", + "word_freq_415": "0", + "word_freq_85": "0", + "word_freq_technology": "0", + "word_freq_1999": "0", + "word_freq_parts": "0", + "word_freq_pm": "0", + "word_freq_direct": "0", + "word_freq_cs": "0", + "word_freq_meeting": "0", + "word_freq_original": "0", + "word_freq_project": "0", + "word_freq_re": "0", + "word_freq_edu": "0", + "word_freq_table": "0", + "word_freq_conference": "0", + "char_freq_%3B": "0", + "char_freq_%28": "0.137", + "char_freq_%5B": "0", + "char_freq_%21": "0.137", + "char_freq_%24": "0", + "char_freq_%23": "0", + "capital_run_length_average": "3.537", + "capital_run_length_longest": "40", + "capital_run_length_total": "191", + "class": "1" + }, + { + "word_freq_make": "0", + "word_freq_address": "0", + "word_freq_all": "0", + "word_freq_3d": "0", + "word_freq_our": "0.63", + "word_freq_over": "0", + "word_freq_remove": "0.31", + "word_freq_internet": "0.63", + "word_freq_order": "0.31", + "word_freq_mail": "0.63", + "word_freq_receive": "0.31", + "word_freq_will": "0.31", + "word_freq_people": "0.31", + "word_freq_report": "0", + "word_freq_addresses": "0", + "word_freq_free": "0.31", + "word_freq_business": "0", + "word_freq_email": "0", + "word_freq_you": "3.18", + "word_freq_credit": "0", + "word_freq_your": "0.31", + "word_freq_font": "0", + "word_freq_000": "0", + "word_freq_money": "0", + "word_freq_hp": "0", + "word_freq_hpl": "0", + "word_freq_george": "0", + "word_freq_650": "0", + "word_freq_lab": "0", + "word_freq_labs": "0", + "word_freq_telnet": "0", + "word_freq_857": "0", + "word_freq_data": "0", + "word_freq_415": "0", + "word_freq_85": "0", + "word_freq_technology": "0", + "word_freq_1999": "0", + "word_freq_parts": "0", + "word_freq_pm": "0", + "word_freq_direct": "0", + "word_freq_cs": "0", + "word_freq_meeting": "0", + "word_freq_original": "0", + "word_freq_project": "0", + "word_freq_re": "0", + "word_freq_edu": "0", + "word_freq_table": "0", + "word_freq_conference": "0", + "char_freq_%3B": "0", + "char_freq_%28": "0.135", + "char_freq_%5B": "0", + "char_freq_%21": "0.135", + "char_freq_%24": "0", + "char_freq_%23": "0", + "capital_run_length_average": "3.537", + "capital_run_length_longest": "40", + "capital_run_length_total": "191", + "class": "1" + } + ] +} + +Shortlisted templates: +[ + { + "template_id": "tpl_m4_quantile_tail_slice", + "template_name": "Quantile Tail Slice", + "primary_family": "tail_rarity_structure", + "portability": "partial", + "sql_skeleton": "WITH buckets AS (\n SELECT {measure_col},\n NTILE({num_tiles}) OVER (ORDER BY {measure_col} DESC) AS tail_bucket\n FROM {table}\n)\nSELECT {measure_col}\nFROM buckets\nWHERE tail_bucket = 1\nORDER BY {measure_col} DESC;", + "required_roles": [ + "measure_col" + ] + } +] + +Problem instance: +{ + "dataset_id": "n1", + "question": "Use template Quantile Tail Slice to probe tail_set_consistency with semantic role rare_extreme_view. Focus on measure_col=word_freq_mail.", + "planned_template_id": "tpl_m4_quantile_tail_slice", + "bindings": { + "measure_col": "word_freq_mail", + "top_k": 11, + "top_n": 5, + "num_tiles": 10, + "percentile_value": 0.95, + "z_threshold": 2.0, + "fraction_threshold": 0.1, + "baseline_multiplier": 1.5, + "baseline_fraction": 0.1, + "min_group_size": 5, + "min_support": 5, + "measure_threshold": 0.16, + "time_grain": "month", + "lookback_rows": 3, + "current_period_start": "'2024-01-01'", + "current_period_end": "'2024-04-01'", + "previous_period_start": "'2023-10-01'", + "previous_period_end": "'2024-01-01'", + "drift_ratio_threshold": 0.8 + }, + "can_vary": [], + "must_fix": [], + "runtime_sql_skeleton": "WITH buckets AS (\n SELECT {measure_col},\n NTILE({num_tiles}) OVER (ORDER BY {measure_col} DESC) AS tail_bucket\n FROM {table}\n)\nSELECT {measure_col}\nFROM buckets\nWHERE tail_bucket = 1\nORDER BY {measure_col} DESC;" +} + +Repair context: +{} diff --git a/Query/sql/v2/runs/v2_cli_20260502_081223_d/n1/artifacts/v2q_n1_33edde959f0b8f98/cli/sql_response_attempt_1.raw.txt b/Query/sql/v2/runs/v2_cli_20260502_081223_d/n1/artifacts/v2q_n1_33edde959f0b8f98/cli/sql_response_attempt_1.raw.txt new file mode 100644 index 0000000000000000000000000000000000000000..ae45385c0bfa526e1d4e3ef6c8532b45580893f0 --- /dev/null +++ b/Query/sql/v2/runs/v2_cli_20260502_081223_d/n1/artifacts/v2q_n1_33edde959f0b8f98/cli/sql_response_attempt_1.raw.txt @@ -0,0 +1,4 @@ +{"type":"thread.started","thread_id":"019e40ea-5143-7db1-be24-e77efdf99d00"} +{"type":"turn.started"} +{"type":"item.completed","item":{"id":"item_0","type":"agent_message","text":"{\"sql\":\"-- template_id: tpl_m4_quantile_tail_slice\\nWITH buckets AS (\\n SELECT CAST(\\\"word_freq_mail\\\" AS REAL) AS \\\"word_freq_mail\\\",\\n NTILE(10) OVER (ORDER BY CAST(\\\"word_freq_mail\\\" AS REAL) DESC) AS \\\"tail_bucket\\\"\\n FROM \\\"n1\\\"\\n)\\nSELECT \\\"word_freq_mail\\\"\\nFROM buckets\\nWHERE \\\"tail_bucket\\\" = 1\\nORDER BY \\\"word_freq_mail\\\" DESC;\",\"notes\":\"Used the provided Quantile Tail Slice template with measure_col bound to \\\"word_freq_mail\\\" and num_tiles=10. Cast to REAL because the schema stores numeric features as TEXT.\"}"}} +{"type":"turn.completed","usage":{"input_tokens":20370,"cached_input_tokens":19840,"output_tokens":420,"reasoning_output_tokens":272}} diff --git a/Query/sql/v2/runs/v2_cli_20260502_081223_d/n1/artifacts/v2q_n1_33edde959f0b8f98/cli/sql_response_attempt_1.txt b/Query/sql/v2/runs/v2_cli_20260502_081223_d/n1/artifacts/v2q_n1_33edde959f0b8f98/cli/sql_response_attempt_1.txt new file mode 100644 index 0000000000000000000000000000000000000000..be0882060f37174381c1d5bde1460edf4ad62241 --- /dev/null +++ b/Query/sql/v2/runs/v2_cli_20260502_081223_d/n1/artifacts/v2q_n1_33edde959f0b8f98/cli/sql_response_attempt_1.txt @@ -0,0 +1 @@ +{"sql":"-- template_id: tpl_m4_quantile_tail_slice\nWITH buckets AS (\n SELECT CAST(\"word_freq_mail\" AS REAL) AS \"word_freq_mail\",\n NTILE(10) OVER (ORDER BY CAST(\"word_freq_mail\" AS REAL) DESC) AS \"tail_bucket\"\n FROM \"n1\"\n)\nSELECT \"word_freq_mail\"\nFROM buckets\nWHERE \"tail_bucket\" = 1\nORDER BY \"word_freq_mail\" DESC;","notes":"Used the provided Quantile Tail Slice template with measure_col bound to \"word_freq_mail\" and num_tiles=10. Cast to REAL because the schema stores numeric features as TEXT."} \ No newline at end of file diff --git a/Query/sql/v2/runs/v2_cli_20260502_081223_d/n1/artifacts/v2q_n1_33edde959f0b8f98/cli/sql_stderr_attempt_1.txt b/Query/sql/v2/runs/v2_cli_20260502_081223_d/n1/artifacts/v2q_n1_33edde959f0b8f98/cli/sql_stderr_attempt_1.txt new file mode 100644 index 0000000000000000000000000000000000000000..e69de29bb2d1d6434b8b29ae775ad8c2e48c5391 diff --git a/Query/sql/v2/runs/v2_cli_20260502_081223_d/n1/artifacts/v2q_n1_36c4dfa9539ee7b0/cli/conversation.jsonl b/Query/sql/v2/runs/v2_cli_20260502_081223_d/n1/artifacts/v2q_n1_36c4dfa9539ee7b0/cli/conversation.jsonl new file mode 100644 index 0000000000000000000000000000000000000000..5d681fcb6c417996251f7e8db61c4f17a428179d --- /dev/null +++ b/Query/sql/v2/runs/v2_cli_20260502_081223_d/n1/artifacts/v2q_n1_36c4dfa9539ee7b0/cli/conversation.jsonl @@ -0,0 +1,2 @@ +{"attempt": 1, "phase": "sql_generation", "role": "user", "content_path": "cli/sql_prompt_attempt_1.txt", "metrics": {"chars": 29361, "bytes_utf8": 29361, "lines": 792, "estimated_tokens": null}} +{"attempt": 1, "phase": "sql_generation", "role": "assistant", "content_path": "cli/sql_response_attempt_1.txt", "raw_content_path": "cli/sql_response_attempt_1.raw.txt", "stderr_path": "cli/sql_stderr_attempt_1.txt", "metrics": {"chars": 378, "bytes_utf8": 378, "lines": 1, "estimated_tokens": null}, "usage": {"input_tokens": 20315, "cached_input_tokens": 19840, "output_tokens": 337, "reasoning_output_tokens": 239}} diff --git a/Query/sql/v2/runs/v2_cli_20260502_081223_d/n1/artifacts/v2q_n1_36c4dfa9539ee7b0/cli/session_summary.json b/Query/sql/v2/runs/v2_cli_20260502_081223_d/n1/artifacts/v2q_n1_36c4dfa9539ee7b0/cli/session_summary.json new file mode 100644 index 0000000000000000000000000000000000000000..603f3d9d97a4bd3db7950f73b0faf46d280f23bd --- /dev/null +++ b/Query/sql/v2/runs/v2_cli_20260502_081223_d/n1/artifacts/v2q_n1_36c4dfa9539ee7b0/cli/session_summary.json @@ -0,0 +1,25 @@ +{ + "engine": "v2-cli:codex", + "command": "codex exec --skip-git-repo-check --disable plugins --sandbox read-only --cd \"/data/jialinzhang/SQLagent\" -m gpt-5.4 --json -", + "ai_cli_calls": 1, + "usage_summary": { + "dataset_id": "n1", + "model": "v2-cli:codex", + "run_id": "v2q_n1_36c4dfa9539ee7b0", + "api_calls": 0, + "input_tokens": 20315, + "cached_input_tokens": 19840, + "output_tokens": 337, + "total_tokens": 20652, + "cost_usd": 0.0, + "ai_cli_calls": 1, + "estimated_input_tokens": 0, + "estimated_output_tokens": 0, + "estimated_total_tokens": 0, + "usage_source": "ai_cli_json_usage", + "cli_elapsed_ms_total": 42566.67, + "sql_execution_elapsed_ms_total": 4.92, + "conversation_log_path": "/data/jialinzhang/TabQueryBench/sql_workloads/v2_current/runs_and_launches/runs/v2_cli_20260502_081223_d/n1/artifacts/v2q_n1_36c4dfa9539ee7b0/cli/conversation.jsonl", + "note": "Executed through a local AI CLI with structured usage metadata." + } +} \ No newline at end of file diff --git a/Query/sql/v2/runs/v2_cli_20260502_081223_d/n1/artifacts/v2q_n1_36c4dfa9539ee7b0/cli/sql_attempt_1.metadata.json b/Query/sql/v2/runs/v2_cli_20260502_081223_d/n1/artifacts/v2q_n1_36c4dfa9539ee7b0/cli/sql_attempt_1.metadata.json new file mode 100644 index 0000000000000000000000000000000000000000..fbff42b8b5865e673727e27f4e67e95afe4d124b --- /dev/null +++ b/Query/sql/v2/runs/v2_cli_20260502_081223_d/n1/artifacts/v2q_n1_36c4dfa9539ee7b0/cli/sql_attempt_1.metadata.json @@ -0,0 +1,45 @@ +{ + "attempt": 1, + "phase": "sql_generation", + "command": "codex exec --skip-git-repo-check --disable plugins --sandbox read-only --cd \"/data/jialinzhang/SQLagent\" -m gpt-5.4 --json -", + "started_at": "2026-05-19T15:30:58.416001+00:00", + "ended_at": "2026-05-19T15:31:40.982707+00:00", + "elapsed_ms": 42566.67, + "prompt_metrics": { + "chars": 29361, + "bytes_utf8": 29361, + "lines": 792, + "estimated_tokens": null + }, + "stdout_metrics": { + "chars": 3729, + "bytes_utf8": 3729, + "lines": 13, + "estimated_tokens": null + }, + "stderr_metrics": { + "chars": 0, + "bytes_utf8": 0, + "lines": 0, + "estimated_tokens": null + }, + "parsed_output": { + "format": "jsonl_events", + "text_metrics": { + "chars": 378, + "bytes_utf8": 378, + "lines": 1, + "estimated_tokens": null + }, + "usage": { + "input_tokens": 20315, + "cached_input_tokens": 19840, + "output_tokens": 337, + "reasoning_output_tokens": 239 + } + }, + "prompt_path": "cli/sql_prompt_attempt_1.txt", + "response_path": "cli/sql_response_attempt_1.txt", + "raw_response_path": "cli/sql_response_attempt_1.raw.txt", + "stderr_path": "cli/sql_stderr_attempt_1.txt" +} \ No newline at end of file diff --git a/Query/sql/v2/runs/v2_cli_20260502_081223_d/n1/artifacts/v2q_n1_36c4dfa9539ee7b0/cli/sql_prompt_attempt_1.txt b/Query/sql/v2/runs/v2_cli_20260502_081223_d/n1/artifacts/v2q_n1_36c4dfa9539ee7b0/cli/sql_prompt_attempt_1.txt new file mode 100644 index 0000000000000000000000000000000000000000..50b4e49ef56afd8281d9c19e61c70cb973064ede --- /dev/null +++ b/Query/sql/v2/runs/v2_cli_20260502_081223_d/n1/artifacts/v2q_n1_36c4dfa9539ee7b0/cli/sql_prompt_attempt_1.txt @@ -0,0 +1,792 @@ +You are generating one SQLite SELECT query for a single-table SQL QA task. +Return strict JSON only, with this schema: {"sql": "...", "notes": "..."}. +Rules: +- Use only the provided table and columns. +- Do not write INSERT, UPDATE, DELETE, DROP, ALTER, CREATE, PRAGMA, ATTACH, DETACH, or VACUUM. +- Prefer the planned template and bound roles when provided. +- Add a leading SQL comment exactly like: -- template_id: . +- Generate SQLite-compatible SQL. SQLite does not support PERCENTILE_CONT or STDDEV. +- Quote identifiers with double quotes. +- Return no markdown and no extra prose. + +Dataset context: +Dataset context for SQL QA: +- dataset_id: n1 +- dataset_name: Spambase +- table_name: n1 +- table_layout: single-table dataset (do not assume joins). +- row_semantics: One row is one email represented by engineered token/character frequency and capitalization-run features. +- task_type: classification +- target_column: class +- main_row_count: 4601 +- important_fields: +- word_freq_make: role=feature, type=numeric_sparse_frequency. tags=['condition_candidate', 'measure', 'rare_pattern_candidate'] desc=Frequency feature for token 'make' in an email message. +- word_freq_address: role=feature, type=numeric_sparse_frequency. tags=['condition_candidate', 'measure', 'rare_pattern_candidate'] desc=Frequency feature for token 'address' in an email message. +- word_freq_all: role=feature, type=numeric_sparse_frequency. tags=['condition_candidate', 'measure', 'rare_pattern_candidate'] desc=Frequency feature for token 'all' in an email message. +- word_freq_3d: role=feature, type=numeric_sparse_frequency. tags=['condition_candidate', 'measure', 'rare_pattern_candidate'] desc=Frequency feature for token '3d' in an email message. +- word_freq_our: role=feature, type=numeric_sparse_frequency. tags=['condition_candidate', 'measure', 'rare_pattern_candidate'] desc=Frequency feature for token 'our' in an email message. +- word_freq_over: role=feature, type=numeric_sparse_frequency. tags=['condition_candidate', 'measure', 'rare_pattern_candidate'] desc=Frequency feature for token 'over' in an email message. +- word_freq_remove: role=feature, type=numeric_sparse_frequency. tags=['condition_candidate', 'measure', 'rare_pattern_candidate'] desc=Frequency feature for token 'remove' in an email message. +- word_freq_internet: role=feature, type=numeric_sparse_frequency. tags=['condition_candidate', 'measure', 'rare_pattern_candidate'] desc=Frequency feature for token 'internet' in an email message. +- word_freq_order: role=feature, type=numeric_sparse_frequency. tags=['condition_candidate', 'measure', 'rare_pattern_candidate'] desc=Frequency feature for token 'order' in an email message. +- word_freq_mail: role=feature, type=numeric_sparse_frequency. tags=['condition_candidate', 'measure', 'rare_pattern_candidate'] desc=Frequency feature for token 'mail' in an email message. +- word_freq_receive: role=feature, type=numeric_sparse_frequency. tags=['condition_candidate', 'measure', 'rare_pattern_candidate'] desc=Frequency feature for token 'receive' in an email message. +- word_freq_will: role=feature, type=numeric_sparse_frequency. tags=['condition_candidate', 'measure', 'rare_pattern_candidate'] desc=Frequency feature for token 'will' in an email message. +- word_freq_people: role=feature, type=numeric_sparse_frequency. tags=['condition_candidate', 'measure', 'rare_pattern_candidate'] desc=Frequency feature for token 'people' in an email message. +- word_freq_report: role=feature, type=numeric_sparse_frequency. tags=['condition_candidate', 'measure', 'rare_pattern_candidate'] desc=Frequency feature for token 'report' in an email message. +- word_freq_addresses: role=feature, type=numeric_sparse_frequency. tags=['condition_candidate', 'measure', 'rare_pattern_candidate'] desc=Frequency feature for token 'addresses' in an email message. +- word_freq_free: role=feature, type=numeric_sparse_frequency. tags=['condition_candidate', 'measure', 'rare_pattern_candidate'] desc=Frequency feature for token 'free' in an email message. +- word_freq_business: role=feature, type=numeric_sparse_frequency. tags=['condition_candidate', 'measure', 'rare_pattern_candidate'] desc=Frequency feature for token 'business' in an email message. +- word_freq_email: role=feature, type=numeric_sparse_frequency. tags=['condition_candidate', 'measure', 'rare_pattern_candidate'] desc=Frequency feature for token 'email' in an email message. +- word_freq_you: role=feature, type=numeric_sparse_frequency. tags=['condition_candidate', 'measure', 'rare_pattern_candidate'] desc=Frequency feature for token 'you' in an email message. +- word_freq_credit: role=feature, type=numeric_sparse_frequency. tags=['condition_candidate', 'measure', 'rare_pattern_candidate'] desc=Frequency feature for token 'credit' in an email message. +- word_freq_your: role=feature, type=numeric_sparse_frequency. tags=['condition_candidate', 'measure', 'rare_pattern_candidate'] desc=Frequency feature for token 'your' in an email message. +- word_freq_font: role=feature, type=numeric_sparse_frequency. tags=['condition_candidate', 'measure', 'rare_pattern_candidate'] desc=Frequency feature for token 'font' in an email message. +- word_freq_000: role=feature, type=numeric_sparse_frequency. tags=['condition_candidate', 'measure', 'rare_pattern_candidate'] desc=Frequency feature for token '000' in an email message. +- word_freq_money: role=feature, type=numeric_sparse_frequency. tags=['condition_candidate', 'measure', 'rare_pattern_candidate'] desc=Frequency feature for token 'money' in an email message. +- word_freq_hp: role=feature, type=numeric_sparse_frequency. tags=['condition_candidate', 'measure', 'rare_pattern_candidate'] desc=Frequency feature for token 'hp' in an email message. +- word_freq_hpl: role=feature, type=numeric_sparse_frequency. tags=['condition_candidate', 'measure', 'rare_pattern_candidate'] desc=Frequency feature for token 'hpl' in an email message. +- word_freq_george: role=feature, type=numeric_sparse_frequency. tags=['condition_candidate', 'measure', 'rare_pattern_candidate'] desc=Frequency feature for token 'george' in an email message. +- word_freq_650: role=feature, type=numeric_sparse_frequency. tags=['condition_candidate', 'measure', 'rare_pattern_candidate'] desc=Frequency feature for token '650' in an email message. +- word_freq_lab: role=feature, type=numeric_sparse_frequency. tags=['condition_candidate', 'measure', 'rare_pattern_candidate'] desc=Frequency feature for token 'lab' in an email message. +- word_freq_labs: role=feature, type=numeric_sparse_frequency. tags=['condition_candidate', 'measure', 'rare_pattern_candidate'] desc=Frequency feature for token 'labs' in an email message. +- word_freq_telnet: role=feature, type=numeric_sparse_frequency. tags=['condition_candidate', 'measure', 'rare_pattern_candidate'] desc=Frequency feature for token 'telnet' in an email message. +- word_freq_857: role=feature, type=numeric_sparse_frequency. tags=['condition_candidate', 'measure', 'rare_pattern_candidate'] desc=Frequency feature for token '857' in an email message. +- word_freq_data: role=feature, type=numeric_sparse_frequency. tags=['condition_candidate', 'measure', 'rare_pattern_candidate'] desc=Frequency feature for token 'data' in an email message. +- word_freq_415: role=feature, type=numeric_sparse_frequency. tags=['condition_candidate', 'measure', 'rare_pattern_candidate'] desc=Frequency feature for token '415' in an email message. +- word_freq_85: role=feature, type=numeric_sparse_frequency. tags=['condition_candidate', 'measure', 'rare_pattern_candidate'] desc=Frequency feature for token '85' in an email message. +- word_freq_technology: role=feature, type=numeric_sparse_frequency. tags=['condition_candidate', 'measure', 'rare_pattern_candidate'] desc=Frequency feature for token 'technology' in an email message. +- word_freq_1999: role=feature, type=numeric_sparse_frequency. tags=['condition_candidate', 'measure', 'rare_pattern_candidate'] desc=Frequency feature for token '1999' in an email message. +- word_freq_parts: role=feature, type=numeric_sparse_frequency. tags=['condition_candidate', 'measure', 'rare_pattern_candidate'] desc=Frequency feature for token 'parts' in an email message. +- word_freq_pm: role=feature, type=numeric_sparse_frequency. tags=['condition_candidate', 'measure', 'rare_pattern_candidate'] desc=Frequency feature for token 'pm' in an email message. +- word_freq_direct: role=feature, type=numeric_sparse_frequency. tags=['condition_candidate', 'measure', 'rare_pattern_candidate'] desc=Frequency feature for token 'direct' in an email message. +- word_freq_cs: role=feature, type=numeric_sparse_frequency. tags=['condition_candidate', 'measure', 'rare_pattern_candidate'] desc=Frequency feature for token 'cs' in an email message. +- word_freq_meeting: role=feature, type=numeric_sparse_frequency. tags=['condition_candidate', 'measure', 'rare_pattern_candidate'] desc=Frequency feature for token 'meeting' in an email message. +- word_freq_original: role=feature, type=numeric_sparse_frequency. tags=['condition_candidate', 'measure', 'rare_pattern_candidate'] desc=Frequency feature for token 'original' in an email message. +- word_freq_project: role=feature, type=numeric_sparse_frequency. tags=['condition_candidate', 'measure', 'rare_pattern_candidate'] desc=Frequency feature for token 'project' in an email message. +- word_freq_re: role=feature, type=numeric_sparse_frequency. tags=['condition_candidate', 'measure', 'rare_pattern_candidate'] desc=Frequency feature for token 're' in an email message. +- word_freq_edu: role=feature, type=numeric_sparse_frequency. tags=['condition_candidate', 'measure', 'rare_pattern_candidate'] desc=Frequency feature for token 'edu' in an email message. +- word_freq_table: role=feature, type=numeric_sparse_frequency. tags=['condition_candidate', 'measure', 'rare_pattern_candidate'] desc=Frequency feature for token 'table' in an email message. +- word_freq_conference: role=feature, type=numeric_sparse_frequency. tags=['condition_candidate', 'measure', 'rare_pattern_candidate'] desc=Frequency feature for token 'conference' in an email message. +- char_freq_%3B: role=feature, type=numeric_sparse_frequency. tags=['condition_candidate', 'measure', 'rare_pattern_candidate'] desc=Character frequency feature for symbol ';'. +- char_freq_%28: role=feature, type=numeric_sparse_frequency. tags=['condition_candidate', 'measure', 'rare_pattern_candidate'] desc=Character frequency feature for symbol '('. +- char_freq_%5B: role=feature, type=numeric_sparse_frequency. tags=['condition_candidate', 'measure', 'rare_pattern_candidate'] desc=Character frequency feature for symbol '['. +- char_freq_%21: role=feature, type=numeric_sparse_frequency. tags=['condition_candidate', 'measure', 'rare_pattern_candidate'] desc=Character frequency feature for symbol '!'. +- char_freq_%24: role=feature, type=numeric_sparse_frequency. tags=['condition_candidate', 'measure', 'rare_pattern_candidate'] desc=Character frequency feature for symbol '$'. +- char_freq_%23: role=feature, type=numeric_sparse_frequency. tags=['condition_candidate', 'measure', 'rare_pattern_candidate'] desc=Character frequency feature for symbol '#'. +- capital_run_length_average: role=feature, type=numeric_heavy_tail. tags=['condition_candidate', 'measure', 'rare_pattern_candidate'] desc=Average length of uninterrupted capital-letter runs. +- capital_run_length_longest: role=feature, type=numeric_heavy_tail. tags=['condition_candidate', 'measure', 'rare_pattern_candidate'] desc=Longest uninterrupted capital-letter run. +- capital_run_length_total: role=feature, type=numeric_heavy_tail. tags=['condition_candidate', 'measure', 'rare_pattern_candidate'] desc=Total number of capital letters in runs. +- class: role=target, type=binary_target. tags=['target_candidate'] desc=Binary spam label (1=spam, 0=non-spam). +- useful_field_combinations: [['word_freq_free', 'word_freq_you', 'class'], ['char_freq_%21', 'capital_run_length_longest', 'class'], ['word_freq_remove', 'word_freq_money', 'class']] +- fields_requiring_caution: ['capital_run_length_average', 'capital_run_length_longest', 'capital_run_length_total'] +- source_url: https://www.openml.org/d/44 + +SQLite schema snapshot: +{ + "table_name": "n1", + "quoted_table_name": "\"n1\"", + "row_count": 4601, + "columns": [ + { + "name": "word_freq_make", + "type": "TEXT", + "notnull": false, + "pk": false + }, + { + "name": "word_freq_address", + "type": "TEXT", + "notnull": false, + "pk": false + }, + { + "name": "word_freq_all", + "type": "TEXT", + "notnull": false, + "pk": false + }, + { + "name": "word_freq_3d", + "type": "TEXT", + "notnull": false, + "pk": false + }, + { + "name": "word_freq_our", + "type": "TEXT", + "notnull": false, + "pk": false + }, + { + "name": "word_freq_over", + "type": "TEXT", + "notnull": false, + "pk": false + }, + { + "name": "word_freq_remove", + "type": "TEXT", + "notnull": false, + "pk": false + }, + { + "name": "word_freq_internet", + "type": "TEXT", + "notnull": false, + "pk": false + }, + { + "name": "word_freq_order", + "type": "TEXT", + "notnull": false, + "pk": false + }, + { + "name": "word_freq_mail", + "type": "TEXT", + "notnull": false, + "pk": false + }, + { + "name": "word_freq_receive", + "type": "TEXT", + "notnull": false, + "pk": false + }, + { + "name": "word_freq_will", + "type": "TEXT", + "notnull": false, + "pk": false + }, + { + "name": "word_freq_people", + "type": "TEXT", + "notnull": false, + "pk": false + }, + { + "name": "word_freq_report", + "type": "TEXT", + "notnull": false, + "pk": false + }, + { + "name": "word_freq_addresses", + "type": "TEXT", + "notnull": false, + "pk": false + }, + { + "name": "word_freq_free", + "type": "TEXT", + "notnull": false, + "pk": false + }, + { + "name": "word_freq_business", + "type": "TEXT", + "notnull": false, + "pk": false + }, + { + "name": "word_freq_email", + "type": "TEXT", + "notnull": false, + "pk": false + }, + { + "name": "word_freq_you", + "type": "TEXT", + "notnull": false, + "pk": false + }, + { + "name": "word_freq_credit", + "type": "TEXT", + "notnull": false, + "pk": false + }, + { + "name": "word_freq_your", + "type": "TEXT", + "notnull": false, + "pk": false + }, + { + "name": "word_freq_font", + "type": "TEXT", + "notnull": false, + "pk": false + }, + { + "name": "word_freq_000", + "type": "TEXT", + "notnull": false, + "pk": false + }, + { + "name": "word_freq_money", + "type": "TEXT", + "notnull": false, + "pk": false + }, + { + "name": "word_freq_hp", + "type": "TEXT", + "notnull": false, + "pk": false + }, + { + "name": "word_freq_hpl", + "type": "TEXT", + "notnull": false, + "pk": false + }, + { + "name": "word_freq_george", + "type": "TEXT", + "notnull": false, + "pk": false + }, + { + "name": "word_freq_650", + "type": "TEXT", + "notnull": false, + "pk": false + }, + { + "name": "word_freq_lab", + "type": "TEXT", + "notnull": false, + "pk": false + }, + { + "name": "word_freq_labs", + "type": "TEXT", + "notnull": false, + "pk": false + }, + { + "name": "word_freq_telnet", + "type": "TEXT", + "notnull": false, + "pk": false + }, + { + "name": "word_freq_857", + "type": "TEXT", + "notnull": false, + "pk": false + }, + { + "name": "word_freq_data", + "type": "TEXT", + "notnull": false, + "pk": false + }, + { + "name": "word_freq_415", + "type": "TEXT", + "notnull": false, + "pk": false + }, + { + "name": "word_freq_85", + "type": "TEXT", + "notnull": false, + "pk": false + }, + { + "name": "word_freq_technology", + "type": "TEXT", + "notnull": false, + "pk": false + }, + { + "name": "word_freq_1999", + "type": "TEXT", + "notnull": false, + "pk": false + }, + { + "name": "word_freq_parts", + "type": "TEXT", + "notnull": false, + "pk": false + }, + { + "name": "word_freq_pm", + "type": "TEXT", + "notnull": false, + "pk": false + }, + { + "name": "word_freq_direct", + "type": "TEXT", + "notnull": false, + "pk": false + }, + { + "name": "word_freq_cs", + "type": "TEXT", + "notnull": false, + "pk": false + }, + { + "name": "word_freq_meeting", + "type": "TEXT", + "notnull": false, + "pk": false + }, + { + "name": "word_freq_original", + "type": "TEXT", + "notnull": false, + "pk": false + }, + { + "name": "word_freq_project", + "type": "TEXT", + "notnull": false, + "pk": false + }, + { + "name": "word_freq_re", + "type": "TEXT", + "notnull": false, + "pk": false + }, + { + "name": "word_freq_edu", + "type": "TEXT", + "notnull": false, + "pk": false + }, + { + "name": "word_freq_table", + "type": "TEXT", + "notnull": false, + "pk": false + }, + { + "name": "word_freq_conference", + "type": "TEXT", + "notnull": false, + "pk": false + }, + { + "name": "char_freq_%3B", + "type": "TEXT", + "notnull": false, + "pk": false + }, + { + "name": "char_freq_%28", + "type": "TEXT", + "notnull": false, + "pk": false + }, + { + "name": "char_freq_%5B", + "type": "TEXT", + "notnull": false, + "pk": false + }, + { + "name": "char_freq_%21", + "type": "TEXT", + "notnull": false, + "pk": false + }, + { + "name": "char_freq_%24", + "type": "TEXT", + "notnull": false, + "pk": false + }, + { + "name": "char_freq_%23", + "type": "TEXT", + "notnull": false, + "pk": false + }, + { + "name": "capital_run_length_average", + "type": "TEXT", + "notnull": false, + "pk": false + }, + { + "name": "capital_run_length_longest", + "type": "TEXT", + "notnull": false, + "pk": false + }, + { + "name": "capital_run_length_total", + "type": "TEXT", + "notnull": false, + "pk": false + }, + { + "name": "class", + "type": "TEXT", + "notnull": false, + "pk": false + } + ], + "sample_rows": [ + { + "word_freq_make": "0", + "word_freq_address": "0.64", + "word_freq_all": "0.64", + "word_freq_3d": "0", + "word_freq_our": "0.32", + "word_freq_over": "0", + "word_freq_remove": "0", + "word_freq_internet": "0", + "word_freq_order": "0", + "word_freq_mail": "0", + "word_freq_receive": "0", + "word_freq_will": "0.64", + "word_freq_people": "0", + "word_freq_report": "0", + "word_freq_addresses": "0", + "word_freq_free": "0.32", + "word_freq_business": "0", + "word_freq_email": "1.29", + "word_freq_you": "1.93", + "word_freq_credit": "0", + "word_freq_your": "0.96", + "word_freq_font": "0", + "word_freq_000": "0", + "word_freq_money": "0", + "word_freq_hp": "0", + "word_freq_hpl": "0", + "word_freq_george": "0", + "word_freq_650": "0", + "word_freq_lab": "0", + "word_freq_labs": "0", + "word_freq_telnet": "0", + "word_freq_857": "0", + "word_freq_data": "0", + "word_freq_415": "0", + "word_freq_85": "0", + "word_freq_technology": "0", + "word_freq_1999": "0", + "word_freq_parts": "0", + "word_freq_pm": "0", + "word_freq_direct": "0", + "word_freq_cs": "0", + "word_freq_meeting": "0", + "word_freq_original": "0", + "word_freq_project": "0", + "word_freq_re": "0", + "word_freq_edu": "0", + "word_freq_table": "0", + "word_freq_conference": "0", + "char_freq_%3B": "0", + "char_freq_%28": "0", + "char_freq_%5B": "0", + "char_freq_%21": "0.778", + "char_freq_%24": "0", + "char_freq_%23": "0", + "capital_run_length_average": "3.756", + "capital_run_length_longest": "61", + "capital_run_length_total": "278", + "class": "1" + }, + { + "word_freq_make": "0.21", + "word_freq_address": "0.28", + "word_freq_all": "0.5", + "word_freq_3d": "0", + "word_freq_our": "0.14", + "word_freq_over": "0.28", + "word_freq_remove": "0.21", + "word_freq_internet": "0.07", + "word_freq_order": "0", + "word_freq_mail": "0.94", + "word_freq_receive": "0.21", + "word_freq_will": "0.79", + "word_freq_people": "0.65", + "word_freq_report": "0.21", + "word_freq_addresses": "0.14", + "word_freq_free": "0.14", + "word_freq_business": "0.07", + "word_freq_email": "0.28", + "word_freq_you": "3.47", + "word_freq_credit": "0", + "word_freq_your": "1.59", + "word_freq_font": "0", + "word_freq_000": "0.43", + "word_freq_money": "0.43", + "word_freq_hp": "0", + "word_freq_hpl": "0", + "word_freq_george": "0", + "word_freq_650": "0", + "word_freq_lab": "0", + "word_freq_labs": "0", + "word_freq_telnet": "0", + "word_freq_857": "0", + "word_freq_data": "0", + "word_freq_415": "0", + "word_freq_85": "0", + "word_freq_technology": "0", + "word_freq_1999": "0.07", + "word_freq_parts": "0", + "word_freq_pm": "0", + "word_freq_direct": "0", + "word_freq_cs": "0", + "word_freq_meeting": "0", + "word_freq_original": "0", + "word_freq_project": "0", + "word_freq_re": "0", + "word_freq_edu": "0", + "word_freq_table": "0", + "word_freq_conference": "0", + "char_freq_%3B": "0", + "char_freq_%28": "0.132", + "char_freq_%5B": "0", + "char_freq_%21": "0.372", + "char_freq_%24": "0.18", + "char_freq_%23": "0.048", + "capital_run_length_average": "5.114", + "capital_run_length_longest": "101", + "capital_run_length_total": "1028", + "class": "1" + }, + { + "word_freq_make": "0.06", + "word_freq_address": "0", + "word_freq_all": "0.71", + "word_freq_3d": "0", + "word_freq_our": "1.23", + "word_freq_over": "0.19", + "word_freq_remove": "0.19", + "word_freq_internet": "0.12", + "word_freq_order": "0.64", + "word_freq_mail": "0.25", + "word_freq_receive": "0.38", + "word_freq_will": "0.45", + "word_freq_people": "0.12", + "word_freq_report": "0", + "word_freq_addresses": "1.75", + "word_freq_free": "0.06", + "word_freq_business": "0.06", + "word_freq_email": "1.03", + "word_freq_you": "1.36", + "word_freq_credit": "0.32", + "word_freq_your": "0.51", + "word_freq_font": "0", + "word_freq_000": "1.16", + "word_freq_money": "0.06", + "word_freq_hp": "0", + "word_freq_hpl": "0", + "word_freq_george": "0", + "word_freq_650": "0", + "word_freq_lab": "0", + "word_freq_labs": "0", + "word_freq_telnet": "0", + "word_freq_857": "0", + "word_freq_data": "0", + "word_freq_415": "0", + "word_freq_85": "0", + "word_freq_technology": "0", + "word_freq_1999": "0", + "word_freq_parts": "0", + "word_freq_pm": "0", + "word_freq_direct": "0.06", + "word_freq_cs": "0", + "word_freq_meeting": "0", + "word_freq_original": "0.12", + "word_freq_project": "0", + "word_freq_re": "0.06", + "word_freq_edu": "0.06", + "word_freq_table": "0", + "word_freq_conference": "0", + "char_freq_%3B": "0.01", + "char_freq_%28": "0.143", + "char_freq_%5B": "0", + "char_freq_%21": "0.276", + "char_freq_%24": "0.184", + "char_freq_%23": "0.01", + "capital_run_length_average": "9.821", + "capital_run_length_longest": "485", + "capital_run_length_total": "2259", + "class": "1" + }, + { + "word_freq_make": "0", + "word_freq_address": "0", + "word_freq_all": "0", + "word_freq_3d": "0", + "word_freq_our": "0.63", + "word_freq_over": "0", + "word_freq_remove": "0.31", + "word_freq_internet": "0.63", + "word_freq_order": "0.31", + "word_freq_mail": "0.63", + "word_freq_receive": "0.31", + "word_freq_will": "0.31", + "word_freq_people": "0.31", + "word_freq_report": "0", + "word_freq_addresses": "0", + "word_freq_free": "0.31", + "word_freq_business": "0", + "word_freq_email": "0", + "word_freq_you": "3.18", + "word_freq_credit": "0", + "word_freq_your": "0.31", + "word_freq_font": "0", + "word_freq_000": "0", + "word_freq_money": "0", + "word_freq_hp": "0", + "word_freq_hpl": "0", + "word_freq_george": "0", + "word_freq_650": "0", + "word_freq_lab": "0", + "word_freq_labs": "0", + "word_freq_telnet": "0", + "word_freq_857": "0", + "word_freq_data": "0", + "word_freq_415": "0", + "word_freq_85": "0", + "word_freq_technology": "0", + "word_freq_1999": "0", + "word_freq_parts": "0", + "word_freq_pm": "0", + "word_freq_direct": "0", + "word_freq_cs": "0", + "word_freq_meeting": "0", + "word_freq_original": "0", + "word_freq_project": "0", + "word_freq_re": "0", + "word_freq_edu": "0", + "word_freq_table": "0", + "word_freq_conference": "0", + "char_freq_%3B": "0", + "char_freq_%28": "0.137", + "char_freq_%5B": "0", + "char_freq_%21": "0.137", + "char_freq_%24": "0", + "char_freq_%23": "0", + "capital_run_length_average": "3.537", + "capital_run_length_longest": "40", + "capital_run_length_total": "191", + "class": "1" + }, + { + "word_freq_make": "0", + "word_freq_address": "0", + "word_freq_all": "0", + "word_freq_3d": "0", + "word_freq_our": "0.63", + "word_freq_over": "0", + "word_freq_remove": "0.31", + "word_freq_internet": "0.63", + "word_freq_order": "0.31", + "word_freq_mail": "0.63", + "word_freq_receive": "0.31", + "word_freq_will": "0.31", + "word_freq_people": "0.31", + "word_freq_report": "0", + "word_freq_addresses": "0", + "word_freq_free": "0.31", + "word_freq_business": "0", + "word_freq_email": "0", + "word_freq_you": "3.18", + "word_freq_credit": "0", + "word_freq_your": "0.31", + "word_freq_font": "0", + "word_freq_000": "0", + "word_freq_money": "0", + "word_freq_hp": "0", + "word_freq_hpl": "0", + "word_freq_george": "0", + "word_freq_650": "0", + "word_freq_lab": "0", + "word_freq_labs": "0", + "word_freq_telnet": "0", + "word_freq_857": "0", + "word_freq_data": "0", + "word_freq_415": "0", + "word_freq_85": "0", + "word_freq_technology": "0", + "word_freq_1999": "0", + "word_freq_parts": "0", + "word_freq_pm": "0", + "word_freq_direct": "0", + "word_freq_cs": "0", + "word_freq_meeting": "0", + "word_freq_original": "0", + "word_freq_project": "0", + "word_freq_re": "0", + "word_freq_edu": "0", + "word_freq_table": "0", + "word_freq_conference": "0", + "char_freq_%3B": "0", + "char_freq_%28": "0.135", + "char_freq_%5B": "0", + "char_freq_%21": "0.135", + "char_freq_%24": "0", + "char_freq_%23": "0", + "capital_run_length_average": "3.537", + "capital_run_length_longest": "40", + "capital_run_length_total": "191", + "class": "1" + } + ] +} + +Shortlisted templates: +[ + { + "template_id": "tpl_h2o_group_sum", + "template_name": "Grouped Numeric Sum", + "primary_family": "subgroup_structure", + "portability": "partial", + "sql_skeleton": "SELECT {group_col}, SUM({measure_col}) AS total_measure\nFROM {table}\nGROUP BY {group_col}\nORDER BY total_measure DESC;", + "required_roles": [ + "group_col", + "measure_col" + ] + } +] + +Problem instance: +{ + "dataset_id": "n1", + "question": "Use template Grouped Numeric Sum to probe internal_profile_stability with semantic role collapsed_target_view. Focus on group_col=class, measure_col=word_freq_remove.", + "planned_template_id": "tpl_h2o_group_sum", + "bindings": { + "group_col": "class", + "measure_col": "word_freq_remove", + "top_k": 16, + "top_n": 6, + "num_tiles": 10, + "percentile_value": 0.9, + "z_threshold": 2.0, + "fraction_threshold": 0.05, + "baseline_multiplier": 1.75, + "baseline_fraction": 0.1, + "min_group_size": 5, + "min_support": 4, + "measure_threshold": 0.0, + "time_grain": "month", + "lookback_rows": 3, + "current_period_start": "'2024-01-01'", + "current_period_end": "'2024-04-01'", + "previous_period_start": "'2023-10-01'", + "previous_period_end": "'2024-01-01'", + "drift_ratio_threshold": 0.8 + }, + "can_vary": [], + "must_fix": [], + "runtime_sql_skeleton": "SELECT {group_col}, SUM({measure_col}) AS total_measure\nFROM {table}\nGROUP BY {group_col}\nORDER BY total_measure DESC;" +} + +Repair context: +{} diff --git a/Query/sql/v2/runs/v2_cli_20260502_081223_d/n1/artifacts/v2q_n1_36c4dfa9539ee7b0/cli/sql_response_attempt_1.raw.txt b/Query/sql/v2/runs/v2_cli_20260502_081223_d/n1/artifacts/v2q_n1_36c4dfa9539ee7b0/cli/sql_response_attempt_1.raw.txt new file mode 100644 index 0000000000000000000000000000000000000000..6f1fef9f3145a1d3323158b429ae43c6566e8c43 --- /dev/null +++ b/Query/sql/v2/runs/v2_cli_20260502_081223_d/n1/artifacts/v2q_n1_36c4dfa9539ee7b0/cli/sql_response_attempt_1.raw.txt @@ -0,0 +1,13 @@ +{"type":"thread.started","thread_id":"019e40dc-9610-7251-8c0f-0218da135f08"} +{"type":"turn.started"} +{"type":"error","message":"Reconnecting... 2/5 (stream disconnected before completion: Rate limit reached for gpt-5.4 in organization org-yXK0mBGOR7eXhtGEx8v7e3bf on tokens per min (TPM): Limit 2000000, Used 2000000, Requested 20950. Please try again in 628ms. Visit https://platform.openai.com/account/rate-limits to learn more.)"} +{"type":"error","message":"Reconnecting... 3/5 (stream disconnected before completion: Rate limit reached for gpt-5.4 in organization org-yXK0mBGOR7eXhtGEx8v7e3bf on tokens per min (TPM): Limit 2000000, Used 1994853, Requested 20950. Please try again in 474ms. Visit https://platform.openai.com/account/rate-limits to learn more.)"} +{"type":"error","message":"Reconnecting... 4/5 (stream disconnected before completion: Rate limit reached for gpt-5.4 in organization org-yXK0mBGOR7eXhtGEx8v7e3bf on tokens per min (TPM): Limit 2000000, Used 2000000, Requested 20950. Please try again in 628ms. Visit https://platform.openai.com/account/rate-limits to learn more.)"} +{"type":"error","message":"Reconnecting... 5/5 (stream disconnected before completion: Rate limit reached for gpt-5.4 in organization org-yXK0mBGOR7eXhtGEx8v7e3bf on tokens per min (TPM): Limit 2000000, Used 1983136, Requested 20950. Please try again in 122ms. Visit https://platform.openai.com/account/rate-limits to learn more.)"} +{"type":"error","message":"Reconnecting... 1/5 (stream disconnected before completion: Rate limit reached for gpt-5.4 in organization org-yXK0mBGOR7eXhtGEx8v7e3bf on tokens per min (TPM): Limit 2000000, Used 1991664, Requested 20950. Please try again in 378ms. Visit https://platform.openai.com/account/rate-limits to learn more.)"} +{"type":"error","message":"Reconnecting... 2/5 (stream disconnected before completion: Rate limit reached for gpt-5.4 in organization org-yXK0mBGOR7eXhtGEx8v7e3bf on tokens per min (TPM): Limit 2000000, Used 1993759, Requested 20950. Please try again in 441ms. Visit https://platform.openai.com/account/rate-limits to learn more.)"} +{"type":"error","message":"Reconnecting... 3/5 (stream disconnected before completion: Rate limit reached for gpt-5.4 in organization org-yXK0mBGOR7eXhtGEx8v7e3bf on tokens per min (TPM): Limit 2000000, Used 1996174, Requested 20950. Please try again in 513ms. Visit https://platform.openai.com/account/rate-limits to learn more.)"} +{"type":"error","message":"Reconnecting... 4/5 (stream disconnected before completion: Rate limit reached for gpt-5.4 in organization org-yXK0mBGOR7eXhtGEx8v7e3bf on tokens per min (TPM): Limit 2000000, Used 1993395, Requested 20950. Please try again in 430ms. Visit https://platform.openai.com/account/rate-limits to learn more.)"} +{"type":"error","message":"Reconnecting... 5/5 (stream disconnected before completion: Rate limit reached for gpt-5.4 in organization org-yXK0mBGOR7eXhtGEx8v7e3bf on tokens per min (TPM): Limit 2000000, Used 1988402, Requested 20950. Please try again in 280ms. Visit https://platform.openai.com/account/rate-limits to learn more.)"} +{"type":"item.completed","item":{"id":"item_0","type":"agent_message","text":"{\"sql\":\"-- template_id: tpl_h2o_group_sum\\nSELECT \\\"class\\\", SUM(CAST(\\\"word_freq_remove\\\" AS REAL)) AS total_measure\\nFROM \\\"n1\\\"\\nGROUP BY \\\"class\\\"\\nORDER BY total_measure DESC;\",\"notes\":\"Uses the requested Grouped Numeric Sum template with group_col=\\\"class\\\" and measure_col=\\\"word_freq_remove\\\". CAST to REAL is included because the schema stores feature values as TEXT.\"}"}} +{"type":"turn.completed","usage":{"input_tokens":20315,"cached_input_tokens":19840,"output_tokens":337,"reasoning_output_tokens":239}} diff --git a/Query/sql/v2/runs/v2_cli_20260502_081223_d/n1/artifacts/v2q_n1_36c4dfa9539ee7b0/cli/sql_response_attempt_1.txt b/Query/sql/v2/runs/v2_cli_20260502_081223_d/n1/artifacts/v2q_n1_36c4dfa9539ee7b0/cli/sql_response_attempt_1.txt new file mode 100644 index 0000000000000000000000000000000000000000..dffc9df76bb1ec1e7d398839c580e0b7881d9bfc --- /dev/null +++ b/Query/sql/v2/runs/v2_cli_20260502_081223_d/n1/artifacts/v2q_n1_36c4dfa9539ee7b0/cli/sql_response_attempt_1.txt @@ -0,0 +1 @@ +{"sql":"-- template_id: tpl_h2o_group_sum\nSELECT \"class\", SUM(CAST(\"word_freq_remove\" AS REAL)) AS total_measure\nFROM \"n1\"\nGROUP BY \"class\"\nORDER BY total_measure DESC;","notes":"Uses the requested Grouped Numeric Sum template with group_col=\"class\" and measure_col=\"word_freq_remove\". CAST to REAL is included because the schema stores feature values as TEXT."} \ No newline at end of file diff --git a/Query/sql/v2/runs/v2_cli_20260502_081223_d/n1/artifacts/v2q_n1_36c4dfa9539ee7b0/cli/sql_stderr_attempt_1.txt b/Query/sql/v2/runs/v2_cli_20260502_081223_d/n1/artifacts/v2q_n1_36c4dfa9539ee7b0/cli/sql_stderr_attempt_1.txt new file mode 100644 index 0000000000000000000000000000000000000000..e69de29bb2d1d6434b8b29ae775ad8c2e48c5391 diff --git a/Query/sql/v2/runs/v2_cli_20260502_081223_d/n1/artifacts/v2q_n1_3be9acbd219c318a/cli/conversation.jsonl b/Query/sql/v2/runs/v2_cli_20260502_081223_d/n1/artifacts/v2q_n1_3be9acbd219c318a/cli/conversation.jsonl new file mode 100644 index 0000000000000000000000000000000000000000..9cf49eacb47309a375379fa5f123e080cbe14e3e --- /dev/null +++ b/Query/sql/v2/runs/v2_cli_20260502_081223_d/n1/artifacts/v2q_n1_3be9acbd219c318a/cli/conversation.jsonl @@ -0,0 +1,2 @@ +{"attempt": 1, "phase": "sql_generation", "role": "user", "content_path": "cli/sql_prompt_attempt_1.txt", "metrics": {"chars": 29255, "bytes_utf8": 29255, "lines": 790, "estimated_tokens": null}} +{"attempt": 1, "phase": "sql_generation", "role": "assistant", "content_path": "cli/sql_response_attempt_1.txt", "raw_content_path": "cli/sql_response_attempt_1.raw.txt", "stderr_path": "cli/sql_stderr_attempt_1.txt", "metrics": {"chars": 268, "bytes_utf8": 268, "lines": 1, "estimated_tokens": null}, "usage": {"input_tokens": 20286, "cached_input_tokens": 19840, "output_tokens": 255, "reasoning_output_tokens": 181}} diff --git a/Query/sql/v2/runs/v2_cli_20260502_081223_d/n1/artifacts/v2q_n1_3be9acbd219c318a/cli/session_summary.json b/Query/sql/v2/runs/v2_cli_20260502_081223_d/n1/artifacts/v2q_n1_3be9acbd219c318a/cli/session_summary.json new file mode 100644 index 0000000000000000000000000000000000000000..b3b474c06535a87867176023872246302c56f6cb --- /dev/null +++ b/Query/sql/v2/runs/v2_cli_20260502_081223_d/n1/artifacts/v2q_n1_3be9acbd219c318a/cli/session_summary.json @@ -0,0 +1,25 @@ +{ + "engine": "v2-cli:codex", + "command": "codex exec --skip-git-repo-check --disable plugins --sandbox read-only --cd \"/data/jialinzhang/SQLagent\" -m gpt-5.4 --json -", + "ai_cli_calls": 1, + "usage_summary": { + "dataset_id": "n1", + "model": "v2-cli:codex", + "run_id": "v2q_n1_3be9acbd219c318a", + "api_calls": 0, + "input_tokens": 20286, + "cached_input_tokens": 19840, + "output_tokens": 255, + "total_tokens": 20541, + "cost_usd": 0.0, + "ai_cli_calls": 1, + "estimated_input_tokens": 0, + "estimated_output_tokens": 0, + "estimated_total_tokens": 0, + "usage_source": "ai_cli_json_usage", + "cli_elapsed_ms_total": 19776.31, + "sql_execution_elapsed_ms_total": 3.89, + "conversation_log_path": "/data/jialinzhang/TabQueryBench/sql_workloads/v2_current/runs_and_launches/runs/v2_cli_20260502_081223_d/n1/artifacts/v2q_n1_3be9acbd219c318a/cli/conversation.jsonl", + "note": "Executed through a local AI CLI with structured usage metadata." + } +} \ No newline at end of file diff --git a/Query/sql/v2/runs/v2_cli_20260502_081223_d/n1/artifacts/v2q_n1_3be9acbd219c318a/cli/sql_attempt_1.metadata.json b/Query/sql/v2/runs/v2_cli_20260502_081223_d/n1/artifacts/v2q_n1_3be9acbd219c318a/cli/sql_attempt_1.metadata.json new file mode 100644 index 0000000000000000000000000000000000000000..fa943e7d762bad7e95d23e6197a1bd5634bc447f --- /dev/null +++ b/Query/sql/v2/runs/v2_cli_20260502_081223_d/n1/artifacts/v2q_n1_3be9acbd219c318a/cli/sql_attempt_1.metadata.json @@ -0,0 +1,45 @@ +{ + "attempt": 1, + "phase": "sql_generation", + "command": "codex exec --skip-git-repo-check --disable plugins --sandbox read-only --cd \"/data/jialinzhang/SQLagent\" -m gpt-5.4 --json -", + "started_at": "2026-05-19T15:33:37.717087+00:00", + "ended_at": "2026-05-19T15:33:57.493436+00:00", + "elapsed_ms": 19776.31, + "prompt_metrics": { + "chars": 29255, + "bytes_utf8": 29255, + "lines": 790, + "estimated_tokens": null + }, + "stdout_metrics": { + "chars": 1621, + "bytes_utf8": 1621, + "lines": 7, + "estimated_tokens": null + }, + "stderr_metrics": { + "chars": 0, + "bytes_utf8": 0, + "lines": 0, + "estimated_tokens": null + }, + "parsed_output": { + "format": "jsonl_events", + "text_metrics": { + "chars": 268, + "bytes_utf8": 268, + "lines": 1, + "estimated_tokens": null + }, + "usage": { + "input_tokens": 20286, + "cached_input_tokens": 19840, + "output_tokens": 255, + "reasoning_output_tokens": 181 + } + }, + "prompt_path": "cli/sql_prompt_attempt_1.txt", + "response_path": "cli/sql_response_attempt_1.txt", + "raw_response_path": "cli/sql_response_attempt_1.raw.txt", + "stderr_path": "cli/sql_stderr_attempt_1.txt" +} \ No newline at end of file diff --git a/Query/sql/v2/runs/v2_cli_20260502_081223_d/n1/artifacts/v2q_n1_3be9acbd219c318a/cli/sql_prompt_attempt_1.txt b/Query/sql/v2/runs/v2_cli_20260502_081223_d/n1/artifacts/v2q_n1_3be9acbd219c318a/cli/sql_prompt_attempt_1.txt new file mode 100644 index 0000000000000000000000000000000000000000..8e49528b23606462c1dbba29e00e3d246319bbc6 --- /dev/null +++ b/Query/sql/v2/runs/v2_cli_20260502_081223_d/n1/artifacts/v2q_n1_3be9acbd219c318a/cli/sql_prompt_attempt_1.txt @@ -0,0 +1,790 @@ +You are generating one SQLite SELECT query for a single-table SQL QA task. +Return strict JSON only, with this schema: {"sql": "...", "notes": "..."}. +Rules: +- Use only the provided table and columns. +- Do not write INSERT, UPDATE, DELETE, DROP, ALTER, CREATE, PRAGMA, ATTACH, DETACH, or VACUUM. +- Prefer the planned template and bound roles when provided. +- Add a leading SQL comment exactly like: -- template_id: . +- Generate SQLite-compatible SQL. SQLite does not support PERCENTILE_CONT or STDDEV. +- Quote identifiers with double quotes. +- Return no markdown and no extra prose. + +Dataset context: +Dataset context for SQL QA: +- dataset_id: n1 +- dataset_name: Spambase +- table_name: n1 +- table_layout: single-table dataset (do not assume joins). +- row_semantics: One row is one email represented by engineered token/character frequency and capitalization-run features. +- task_type: classification +- target_column: class +- main_row_count: 4601 +- important_fields: +- word_freq_make: role=feature, type=numeric_sparse_frequency. tags=['condition_candidate', 'measure', 'rare_pattern_candidate'] desc=Frequency feature for token 'make' in an email message. +- word_freq_address: role=feature, type=numeric_sparse_frequency. tags=['condition_candidate', 'measure', 'rare_pattern_candidate'] desc=Frequency feature for token 'address' in an email message. +- word_freq_all: role=feature, type=numeric_sparse_frequency. tags=['condition_candidate', 'measure', 'rare_pattern_candidate'] desc=Frequency feature for token 'all' in an email message. +- word_freq_3d: role=feature, type=numeric_sparse_frequency. tags=['condition_candidate', 'measure', 'rare_pattern_candidate'] desc=Frequency feature for token '3d' in an email message. +- word_freq_our: role=feature, type=numeric_sparse_frequency. tags=['condition_candidate', 'measure', 'rare_pattern_candidate'] desc=Frequency feature for token 'our' in an email message. +- word_freq_over: role=feature, type=numeric_sparse_frequency. tags=['condition_candidate', 'measure', 'rare_pattern_candidate'] desc=Frequency feature for token 'over' in an email message. +- word_freq_remove: role=feature, type=numeric_sparse_frequency. tags=['condition_candidate', 'measure', 'rare_pattern_candidate'] desc=Frequency feature for token 'remove' in an email message. +- word_freq_internet: role=feature, type=numeric_sparse_frequency. tags=['condition_candidate', 'measure', 'rare_pattern_candidate'] desc=Frequency feature for token 'internet' in an email message. +- word_freq_order: role=feature, type=numeric_sparse_frequency. tags=['condition_candidate', 'measure', 'rare_pattern_candidate'] desc=Frequency feature for token 'order' in an email message. +- word_freq_mail: role=feature, type=numeric_sparse_frequency. tags=['condition_candidate', 'measure', 'rare_pattern_candidate'] desc=Frequency feature for token 'mail' in an email message. +- word_freq_receive: role=feature, type=numeric_sparse_frequency. tags=['condition_candidate', 'measure', 'rare_pattern_candidate'] desc=Frequency feature for token 'receive' in an email message. +- word_freq_will: role=feature, type=numeric_sparse_frequency. tags=['condition_candidate', 'measure', 'rare_pattern_candidate'] desc=Frequency feature for token 'will' in an email message. +- word_freq_people: role=feature, type=numeric_sparse_frequency. tags=['condition_candidate', 'measure', 'rare_pattern_candidate'] desc=Frequency feature for token 'people' in an email message. +- word_freq_report: role=feature, type=numeric_sparse_frequency. tags=['condition_candidate', 'measure', 'rare_pattern_candidate'] desc=Frequency feature for token 'report' in an email message. +- word_freq_addresses: role=feature, type=numeric_sparse_frequency. tags=['condition_candidate', 'measure', 'rare_pattern_candidate'] desc=Frequency feature for token 'addresses' in an email message. +- word_freq_free: role=feature, type=numeric_sparse_frequency. tags=['condition_candidate', 'measure', 'rare_pattern_candidate'] desc=Frequency feature for token 'free' in an email message. +- word_freq_business: role=feature, type=numeric_sparse_frequency. tags=['condition_candidate', 'measure', 'rare_pattern_candidate'] desc=Frequency feature for token 'business' in an email message. +- word_freq_email: role=feature, type=numeric_sparse_frequency. tags=['condition_candidate', 'measure', 'rare_pattern_candidate'] desc=Frequency feature for token 'email' in an email message. +- word_freq_you: role=feature, type=numeric_sparse_frequency. tags=['condition_candidate', 'measure', 'rare_pattern_candidate'] desc=Frequency feature for token 'you' in an email message. +- word_freq_credit: role=feature, type=numeric_sparse_frequency. tags=['condition_candidate', 'measure', 'rare_pattern_candidate'] desc=Frequency feature for token 'credit' in an email message. +- word_freq_your: role=feature, type=numeric_sparse_frequency. tags=['condition_candidate', 'measure', 'rare_pattern_candidate'] desc=Frequency feature for token 'your' in an email message. +- word_freq_font: role=feature, type=numeric_sparse_frequency. tags=['condition_candidate', 'measure', 'rare_pattern_candidate'] desc=Frequency feature for token 'font' in an email message. +- word_freq_000: role=feature, type=numeric_sparse_frequency. tags=['condition_candidate', 'measure', 'rare_pattern_candidate'] desc=Frequency feature for token '000' in an email message. +- word_freq_money: role=feature, type=numeric_sparse_frequency. tags=['condition_candidate', 'measure', 'rare_pattern_candidate'] desc=Frequency feature for token 'money' in an email message. +- word_freq_hp: role=feature, type=numeric_sparse_frequency. tags=['condition_candidate', 'measure', 'rare_pattern_candidate'] desc=Frequency feature for token 'hp' in an email message. +- word_freq_hpl: role=feature, type=numeric_sparse_frequency. tags=['condition_candidate', 'measure', 'rare_pattern_candidate'] desc=Frequency feature for token 'hpl' in an email message. +- word_freq_george: role=feature, type=numeric_sparse_frequency. tags=['condition_candidate', 'measure', 'rare_pattern_candidate'] desc=Frequency feature for token 'george' in an email message. +- word_freq_650: role=feature, type=numeric_sparse_frequency. tags=['condition_candidate', 'measure', 'rare_pattern_candidate'] desc=Frequency feature for token '650' in an email message. +- word_freq_lab: role=feature, type=numeric_sparse_frequency. tags=['condition_candidate', 'measure', 'rare_pattern_candidate'] desc=Frequency feature for token 'lab' in an email message. +- word_freq_labs: role=feature, type=numeric_sparse_frequency. tags=['condition_candidate', 'measure', 'rare_pattern_candidate'] desc=Frequency feature for token 'labs' in an email message. +- word_freq_telnet: role=feature, type=numeric_sparse_frequency. tags=['condition_candidate', 'measure', 'rare_pattern_candidate'] desc=Frequency feature for token 'telnet' in an email message. +- word_freq_857: role=feature, type=numeric_sparse_frequency. tags=['condition_candidate', 'measure', 'rare_pattern_candidate'] desc=Frequency feature for token '857' in an email message. +- word_freq_data: role=feature, type=numeric_sparse_frequency. tags=['condition_candidate', 'measure', 'rare_pattern_candidate'] desc=Frequency feature for token 'data' in an email message. +- word_freq_415: role=feature, type=numeric_sparse_frequency. tags=['condition_candidate', 'measure', 'rare_pattern_candidate'] desc=Frequency feature for token '415' in an email message. +- word_freq_85: role=feature, type=numeric_sparse_frequency. tags=['condition_candidate', 'measure', 'rare_pattern_candidate'] desc=Frequency feature for token '85' in an email message. +- word_freq_technology: role=feature, type=numeric_sparse_frequency. tags=['condition_candidate', 'measure', 'rare_pattern_candidate'] desc=Frequency feature for token 'technology' in an email message. +- word_freq_1999: role=feature, type=numeric_sparse_frequency. tags=['condition_candidate', 'measure', 'rare_pattern_candidate'] desc=Frequency feature for token '1999' in an email message. +- word_freq_parts: role=feature, type=numeric_sparse_frequency. tags=['condition_candidate', 'measure', 'rare_pattern_candidate'] desc=Frequency feature for token 'parts' in an email message. +- word_freq_pm: role=feature, type=numeric_sparse_frequency. tags=['condition_candidate', 'measure', 'rare_pattern_candidate'] desc=Frequency feature for token 'pm' in an email message. +- word_freq_direct: role=feature, type=numeric_sparse_frequency. tags=['condition_candidate', 'measure', 'rare_pattern_candidate'] desc=Frequency feature for token 'direct' in an email message. +- word_freq_cs: role=feature, type=numeric_sparse_frequency. tags=['condition_candidate', 'measure', 'rare_pattern_candidate'] desc=Frequency feature for token 'cs' in an email message. +- word_freq_meeting: role=feature, type=numeric_sparse_frequency. tags=['condition_candidate', 'measure', 'rare_pattern_candidate'] desc=Frequency feature for token 'meeting' in an email message. +- word_freq_original: role=feature, type=numeric_sparse_frequency. tags=['condition_candidate', 'measure', 'rare_pattern_candidate'] desc=Frequency feature for token 'original' in an email message. +- word_freq_project: role=feature, type=numeric_sparse_frequency. tags=['condition_candidate', 'measure', 'rare_pattern_candidate'] desc=Frequency feature for token 'project' in an email message. +- word_freq_re: role=feature, type=numeric_sparse_frequency. tags=['condition_candidate', 'measure', 'rare_pattern_candidate'] desc=Frequency feature for token 're' in an email message. +- word_freq_edu: role=feature, type=numeric_sparse_frequency. tags=['condition_candidate', 'measure', 'rare_pattern_candidate'] desc=Frequency feature for token 'edu' in an email message. +- word_freq_table: role=feature, type=numeric_sparse_frequency. tags=['condition_candidate', 'measure', 'rare_pattern_candidate'] desc=Frequency feature for token 'table' in an email message. +- word_freq_conference: role=feature, type=numeric_sparse_frequency. tags=['condition_candidate', 'measure', 'rare_pattern_candidate'] desc=Frequency feature for token 'conference' in an email message. +- char_freq_%3B: role=feature, type=numeric_sparse_frequency. tags=['condition_candidate', 'measure', 'rare_pattern_candidate'] desc=Character frequency feature for symbol ';'. +- char_freq_%28: role=feature, type=numeric_sparse_frequency. tags=['condition_candidate', 'measure', 'rare_pattern_candidate'] desc=Character frequency feature for symbol '('. +- char_freq_%5B: role=feature, type=numeric_sparse_frequency. tags=['condition_candidate', 'measure', 'rare_pattern_candidate'] desc=Character frequency feature for symbol '['. +- char_freq_%21: role=feature, type=numeric_sparse_frequency. tags=['condition_candidate', 'measure', 'rare_pattern_candidate'] desc=Character frequency feature for symbol '!'. +- char_freq_%24: role=feature, type=numeric_sparse_frequency. tags=['condition_candidate', 'measure', 'rare_pattern_candidate'] desc=Character frequency feature for symbol '$'. +- char_freq_%23: role=feature, type=numeric_sparse_frequency. tags=['condition_candidate', 'measure', 'rare_pattern_candidate'] desc=Character frequency feature for symbol '#'. +- capital_run_length_average: role=feature, type=numeric_heavy_tail. tags=['condition_candidate', 'measure', 'rare_pattern_candidate'] desc=Average length of uninterrupted capital-letter runs. +- capital_run_length_longest: role=feature, type=numeric_heavy_tail. tags=['condition_candidate', 'measure', 'rare_pattern_candidate'] desc=Longest uninterrupted capital-letter run. +- capital_run_length_total: role=feature, type=numeric_heavy_tail. tags=['condition_candidate', 'measure', 'rare_pattern_candidate'] desc=Total number of capital letters in runs. +- class: role=target, type=binary_target. tags=['target_candidate'] desc=Binary spam label (1=spam, 0=non-spam). +- useful_field_combinations: [['word_freq_free', 'word_freq_you', 'class'], ['char_freq_%21', 'capital_run_length_longest', 'class'], ['word_freq_remove', 'word_freq_money', 'class']] +- fields_requiring_caution: ['capital_run_length_average', 'capital_run_length_longest', 'capital_run_length_total'] +- source_url: https://www.openml.org/d/44 + +SQLite schema snapshot: +{ + "table_name": "n1", + "quoted_table_name": "\"n1\"", + "row_count": 4601, + "columns": [ + { + "name": "word_freq_make", + "type": "TEXT", + "notnull": false, + "pk": false + }, + { + "name": "word_freq_address", + "type": "TEXT", + "notnull": false, + "pk": false + }, + { + "name": "word_freq_all", + "type": "TEXT", + "notnull": false, + "pk": false + }, + { + "name": "word_freq_3d", + "type": "TEXT", + "notnull": false, + "pk": false + }, + { + "name": "word_freq_our", + "type": "TEXT", + "notnull": false, + "pk": false + }, + { + "name": "word_freq_over", + "type": "TEXT", + "notnull": false, + "pk": false + }, + { + "name": "word_freq_remove", + "type": "TEXT", + "notnull": false, + "pk": false + }, + { + "name": "word_freq_internet", + "type": "TEXT", + "notnull": false, + "pk": false + }, + { + "name": "word_freq_order", + "type": "TEXT", + "notnull": false, + "pk": false + }, + { + "name": "word_freq_mail", + "type": "TEXT", + "notnull": false, + "pk": false + }, + { + "name": "word_freq_receive", + "type": "TEXT", + "notnull": false, + "pk": false + }, + { + "name": "word_freq_will", + "type": "TEXT", + "notnull": false, + "pk": false + }, + { + "name": "word_freq_people", + "type": "TEXT", + "notnull": false, + "pk": false + }, + { + "name": "word_freq_report", + "type": "TEXT", + "notnull": false, + "pk": false + }, + { + "name": "word_freq_addresses", + "type": "TEXT", + "notnull": false, + "pk": false + }, + { + "name": "word_freq_free", + "type": "TEXT", + "notnull": false, + "pk": false + }, + { + "name": "word_freq_business", + "type": "TEXT", + "notnull": false, + "pk": false + }, + { + "name": "word_freq_email", + "type": "TEXT", + "notnull": false, + "pk": false + }, + { + "name": "word_freq_you", + "type": "TEXT", + "notnull": false, + "pk": false + }, + { + "name": "word_freq_credit", + "type": "TEXT", + "notnull": false, + "pk": false + }, + { + "name": "word_freq_your", + "type": "TEXT", + "notnull": false, + "pk": false + }, + { + "name": "word_freq_font", + "type": "TEXT", + "notnull": false, + "pk": false + }, + { + "name": "word_freq_000", + "type": "TEXT", + "notnull": false, + "pk": false + }, + { + "name": "word_freq_money", + "type": "TEXT", + "notnull": false, + "pk": false + }, + { + "name": "word_freq_hp", + "type": "TEXT", + "notnull": false, + "pk": false + }, + { + "name": "word_freq_hpl", + "type": "TEXT", + "notnull": false, + "pk": false + }, + { + "name": "word_freq_george", + "type": "TEXT", + "notnull": false, + "pk": false + }, + { + "name": "word_freq_650", + "type": "TEXT", + "notnull": false, + "pk": false + }, + { + "name": "word_freq_lab", + "type": "TEXT", + "notnull": false, + "pk": false + }, + { + "name": "word_freq_labs", + "type": "TEXT", + "notnull": false, + "pk": false + }, + { + "name": "word_freq_telnet", + "type": "TEXT", + "notnull": false, + "pk": false + }, + { + "name": "word_freq_857", + "type": "TEXT", + "notnull": false, + "pk": false + }, + { + "name": "word_freq_data", + "type": "TEXT", + "notnull": false, + "pk": false + }, + { + "name": "word_freq_415", + "type": "TEXT", + "notnull": false, + "pk": false + }, + { + "name": "word_freq_85", + "type": "TEXT", + "notnull": false, + "pk": false + }, + { + "name": "word_freq_technology", + "type": "TEXT", + "notnull": false, + "pk": false + }, + { + "name": "word_freq_1999", + "type": "TEXT", + "notnull": false, + "pk": false + }, + { + "name": "word_freq_parts", + "type": "TEXT", + "notnull": false, + "pk": false + }, + { + "name": "word_freq_pm", + "type": "TEXT", + "notnull": false, + "pk": false + }, + { + "name": "word_freq_direct", + "type": "TEXT", + "notnull": false, + "pk": false + }, + { + "name": "word_freq_cs", + "type": "TEXT", + "notnull": false, + "pk": false + }, + { + "name": "word_freq_meeting", + "type": "TEXT", + "notnull": false, + "pk": false + }, + { + "name": "word_freq_original", + "type": "TEXT", + "notnull": false, + "pk": false + }, + { + "name": "word_freq_project", + "type": "TEXT", + "notnull": false, + "pk": false + }, + { + "name": "word_freq_re", + "type": "TEXT", + "notnull": false, + "pk": false + }, + { + "name": "word_freq_edu", + "type": "TEXT", + "notnull": false, + "pk": false + }, + { + "name": "word_freq_table", + "type": "TEXT", + "notnull": false, + "pk": false + }, + { + "name": "word_freq_conference", + "type": "TEXT", + "notnull": false, + "pk": false + }, + { + "name": "char_freq_%3B", + "type": "TEXT", + "notnull": false, + "pk": false + }, + { + "name": "char_freq_%28", + "type": "TEXT", + "notnull": false, + "pk": false + }, + { + "name": "char_freq_%5B", + "type": "TEXT", + "notnull": false, + "pk": false + }, + { + "name": "char_freq_%21", + "type": "TEXT", + "notnull": false, + "pk": false + }, + { + "name": "char_freq_%24", + "type": "TEXT", + "notnull": false, + "pk": false + }, + { + "name": "char_freq_%23", + "type": "TEXT", + "notnull": false, + "pk": false + }, + { + "name": "capital_run_length_average", + "type": "TEXT", + "notnull": false, + "pk": false + }, + { + "name": "capital_run_length_longest", + "type": "TEXT", + "notnull": false, + "pk": false + }, + { + "name": "capital_run_length_total", + "type": "TEXT", + "notnull": false, + "pk": false + }, + { + "name": "class", + "type": "TEXT", + "notnull": false, + "pk": false + } + ], + "sample_rows": [ + { + "word_freq_make": "0", + "word_freq_address": "0.64", + "word_freq_all": "0.64", + "word_freq_3d": "0", + "word_freq_our": "0.32", + "word_freq_over": "0", + "word_freq_remove": "0", + "word_freq_internet": "0", + "word_freq_order": "0", + "word_freq_mail": "0", + "word_freq_receive": "0", + "word_freq_will": "0.64", + "word_freq_people": "0", + "word_freq_report": "0", + "word_freq_addresses": "0", + "word_freq_free": "0.32", + "word_freq_business": "0", + "word_freq_email": "1.29", + "word_freq_you": "1.93", + "word_freq_credit": "0", + "word_freq_your": "0.96", + "word_freq_font": "0", + "word_freq_000": "0", + "word_freq_money": "0", + "word_freq_hp": "0", + "word_freq_hpl": "0", + "word_freq_george": "0", + "word_freq_650": "0", + "word_freq_lab": "0", + "word_freq_labs": "0", + "word_freq_telnet": "0", + "word_freq_857": "0", + "word_freq_data": "0", + "word_freq_415": "0", + "word_freq_85": "0", + "word_freq_technology": "0", + "word_freq_1999": "0", + "word_freq_parts": "0", + "word_freq_pm": "0", + "word_freq_direct": "0", + "word_freq_cs": "0", + "word_freq_meeting": "0", + "word_freq_original": "0", + "word_freq_project": "0", + "word_freq_re": "0", + "word_freq_edu": "0", + "word_freq_table": "0", + "word_freq_conference": "0", + "char_freq_%3B": "0", + "char_freq_%28": "0", + "char_freq_%5B": "0", + "char_freq_%21": "0.778", + "char_freq_%24": "0", + "char_freq_%23": "0", + "capital_run_length_average": "3.756", + "capital_run_length_longest": "61", + "capital_run_length_total": "278", + "class": "1" + }, + { + "word_freq_make": "0.21", + "word_freq_address": "0.28", + "word_freq_all": "0.5", + "word_freq_3d": "0", + "word_freq_our": "0.14", + "word_freq_over": "0.28", + "word_freq_remove": "0.21", + "word_freq_internet": "0.07", + "word_freq_order": "0", + "word_freq_mail": "0.94", + "word_freq_receive": "0.21", + "word_freq_will": "0.79", + "word_freq_people": "0.65", + "word_freq_report": "0.21", + "word_freq_addresses": "0.14", + "word_freq_free": "0.14", + "word_freq_business": "0.07", + "word_freq_email": "0.28", + "word_freq_you": "3.47", + "word_freq_credit": "0", + "word_freq_your": "1.59", + "word_freq_font": "0", + "word_freq_000": "0.43", + "word_freq_money": "0.43", + "word_freq_hp": "0", + "word_freq_hpl": "0", + "word_freq_george": "0", + "word_freq_650": "0", + "word_freq_lab": "0", + "word_freq_labs": "0", + "word_freq_telnet": "0", + "word_freq_857": "0", + "word_freq_data": "0", + "word_freq_415": "0", + "word_freq_85": "0", + "word_freq_technology": "0", + "word_freq_1999": "0.07", + "word_freq_parts": "0", + "word_freq_pm": "0", + "word_freq_direct": "0", + "word_freq_cs": "0", + "word_freq_meeting": "0", + "word_freq_original": "0", + "word_freq_project": "0", + "word_freq_re": "0", + "word_freq_edu": "0", + "word_freq_table": "0", + "word_freq_conference": "0", + "char_freq_%3B": "0", + "char_freq_%28": "0.132", + "char_freq_%5B": "0", + "char_freq_%21": "0.372", + "char_freq_%24": "0.18", + "char_freq_%23": "0.048", + "capital_run_length_average": "5.114", + "capital_run_length_longest": "101", + "capital_run_length_total": "1028", + "class": "1" + }, + { + "word_freq_make": "0.06", + "word_freq_address": "0", + "word_freq_all": "0.71", + "word_freq_3d": "0", + "word_freq_our": "1.23", + "word_freq_over": "0.19", + "word_freq_remove": "0.19", + "word_freq_internet": "0.12", + "word_freq_order": "0.64", + "word_freq_mail": "0.25", + "word_freq_receive": "0.38", + "word_freq_will": "0.45", + "word_freq_people": "0.12", + "word_freq_report": "0", + "word_freq_addresses": "1.75", + "word_freq_free": "0.06", + "word_freq_business": "0.06", + "word_freq_email": "1.03", + "word_freq_you": "1.36", + "word_freq_credit": "0.32", + "word_freq_your": "0.51", + "word_freq_font": "0", + "word_freq_000": "1.16", + "word_freq_money": "0.06", + "word_freq_hp": "0", + "word_freq_hpl": "0", + "word_freq_george": "0", + "word_freq_650": "0", + "word_freq_lab": "0", + "word_freq_labs": "0", + "word_freq_telnet": "0", + "word_freq_857": "0", + "word_freq_data": "0", + "word_freq_415": "0", + "word_freq_85": "0", + "word_freq_technology": "0", + "word_freq_1999": "0", + "word_freq_parts": "0", + "word_freq_pm": "0", + "word_freq_direct": "0.06", + "word_freq_cs": "0", + "word_freq_meeting": "0", + "word_freq_original": "0.12", + "word_freq_project": "0", + "word_freq_re": "0.06", + "word_freq_edu": "0.06", + "word_freq_table": "0", + "word_freq_conference": "0", + "char_freq_%3B": "0.01", + "char_freq_%28": "0.143", + "char_freq_%5B": "0", + "char_freq_%21": "0.276", + "char_freq_%24": "0.184", + "char_freq_%23": "0.01", + "capital_run_length_average": "9.821", + "capital_run_length_longest": "485", + "capital_run_length_total": "2259", + "class": "1" + }, + { + "word_freq_make": "0", + "word_freq_address": "0", + "word_freq_all": "0", + "word_freq_3d": "0", + "word_freq_our": "0.63", + "word_freq_over": "0", + "word_freq_remove": "0.31", + "word_freq_internet": "0.63", + "word_freq_order": "0.31", + "word_freq_mail": "0.63", + "word_freq_receive": "0.31", + "word_freq_will": "0.31", + "word_freq_people": "0.31", + "word_freq_report": "0", + "word_freq_addresses": "0", + "word_freq_free": "0.31", + "word_freq_business": "0", + "word_freq_email": "0", + "word_freq_you": "3.18", + "word_freq_credit": "0", + "word_freq_your": "0.31", + "word_freq_font": "0", + "word_freq_000": "0", + "word_freq_money": "0", + "word_freq_hp": "0", + "word_freq_hpl": "0", + "word_freq_george": "0", + "word_freq_650": "0", + "word_freq_lab": "0", + "word_freq_labs": "0", + "word_freq_telnet": "0", + "word_freq_857": "0", + "word_freq_data": "0", + "word_freq_415": "0", + "word_freq_85": "0", + "word_freq_technology": "0", + "word_freq_1999": "0", + "word_freq_parts": "0", + "word_freq_pm": "0", + "word_freq_direct": "0", + "word_freq_cs": "0", + "word_freq_meeting": "0", + "word_freq_original": "0", + "word_freq_project": "0", + "word_freq_re": "0", + "word_freq_edu": "0", + "word_freq_table": "0", + "word_freq_conference": "0", + "char_freq_%3B": "0", + "char_freq_%28": "0.137", + "char_freq_%5B": "0", + "char_freq_%21": "0.137", + "char_freq_%24": "0", + "char_freq_%23": "0", + "capital_run_length_average": "3.537", + "capital_run_length_longest": "40", + "capital_run_length_total": "191", + "class": "1" + }, + { + "word_freq_make": "0", + "word_freq_address": "0", + "word_freq_all": "0", + "word_freq_3d": "0", + "word_freq_our": "0.63", + "word_freq_over": "0", + "word_freq_remove": "0.31", + "word_freq_internet": "0.63", + "word_freq_order": "0.31", + "word_freq_mail": "0.63", + "word_freq_receive": "0.31", + "word_freq_will": "0.31", + "word_freq_people": "0.31", + "word_freq_report": "0", + "word_freq_addresses": "0", + "word_freq_free": "0.31", + "word_freq_business": "0", + "word_freq_email": "0", + "word_freq_you": "3.18", + "word_freq_credit": "0", + "word_freq_your": "0.31", + "word_freq_font": "0", + "word_freq_000": "0", + "word_freq_money": "0", + "word_freq_hp": "0", + "word_freq_hpl": "0", + "word_freq_george": "0", + "word_freq_650": "0", + "word_freq_lab": "0", + "word_freq_labs": "0", + "word_freq_telnet": "0", + "word_freq_857": "0", + "word_freq_data": "0", + "word_freq_415": "0", + "word_freq_85": "0", + "word_freq_technology": "0", + "word_freq_1999": "0", + "word_freq_parts": "0", + "word_freq_pm": "0", + "word_freq_direct": "0", + "word_freq_cs": "0", + "word_freq_meeting": "0", + "word_freq_original": "0", + "word_freq_project": "0", + "word_freq_re": "0", + "word_freq_edu": "0", + "word_freq_table": "0", + "word_freq_conference": "0", + "char_freq_%3B": "0", + "char_freq_%28": "0.135", + "char_freq_%5B": "0", + "char_freq_%21": "0.135", + "char_freq_%24": "0", + "char_freq_%23": "0", + "capital_run_length_average": "3.537", + "capital_run_length_longest": "40", + "capital_run_length_total": "191", + "class": "1" + } + ] +} + +Shortlisted templates: +[ + { + "template_id": "tpl_clickbench_group_count", + "template_name": "Grouped Count by Category", + "primary_family": "subgroup_structure", + "portability": "yes", + "sql_skeleton": "SELECT {group_col}, COUNT(*) AS row_count\nFROM {table}\nGROUP BY {group_col}\nORDER BY row_count DESC;", + "required_roles": [ + "group_col" + ] + } +] + +Problem instance: +{ + "dataset_id": "n1", + "question": "Use template Grouped Count by Category to probe subgroup_size_stability with semantic role count_distribution. Focus on group_col=class.", + "planned_template_id": "tpl_clickbench_group_count", + "bindings": { + "group_col": "class", + "top_k": 13, + "top_n": 5, + "num_tiles": 10, + "percentile_value": 0.95, + "z_threshold": 2.0, + "fraction_threshold": 0.1, + "baseline_multiplier": 1.5, + "baseline_fraction": 0.1, + "min_group_size": 5, + "min_support": 5, + "measure_threshold": 2.64, + "time_grain": "month", + "lookback_rows": 3, + "current_period_start": "'2024-01-01'", + "current_period_end": "'2024-04-01'", + "previous_period_start": "'2023-10-01'", + "previous_period_end": "'2024-01-01'", + "drift_ratio_threshold": 0.8 + }, + "can_vary": [], + "must_fix": [], + "runtime_sql_skeleton": "SELECT {group_col}, COUNT(*) AS row_count\nFROM {table}\nGROUP BY {group_col}\nORDER BY row_count DESC;" +} + +Repair context: +{} diff --git a/Query/sql/v2/runs/v2_cli_20260502_081223_d/n1/artifacts/v2q_n1_3be9acbd219c318a/cli/sql_response_attempt_1.raw.txt b/Query/sql/v2/runs/v2_cli_20260502_081223_d/n1/artifacts/v2q_n1_3be9acbd219c318a/cli/sql_response_attempt_1.raw.txt new file mode 100644 index 0000000000000000000000000000000000000000..42f7388f4b53d515016636a8027b956afd9debb4 --- /dev/null +++ b/Query/sql/v2/runs/v2_cli_20260502_081223_d/n1/artifacts/v2q_n1_3be9acbd219c318a/cli/sql_response_attempt_1.raw.txt @@ -0,0 +1,7 @@ +{"type":"thread.started","thread_id":"019e40df-0453-7941-a3a5-30e9791725b3"} +{"type":"turn.started"} +{"type":"error","message":"Reconnecting... 2/5 (stream disconnected before completion: Rate limit reached for gpt-5.4 in organization org-yXK0mBGOR7eXhtGEx8v7e3bf on tokens per min (TPM): Limit 2000000, Used 2000000, Requested 20921. Please try again in 627ms. Visit https://platform.openai.com/account/rate-limits to learn more.)"} +{"type":"error","message":"Reconnecting... 3/5 (stream disconnected before completion: Rate limit reached for gpt-5.4 in organization org-yXK0mBGOR7eXhtGEx8v7e3bf on tokens per min (TPM): Limit 2000000, Used 1999576, Requested 20921. Please try again in 614ms. Visit https://platform.openai.com/account/rate-limits to learn more.)"} +{"type":"error","message":"Reconnecting... 4/5 (stream disconnected before completion: Rate limit reached for gpt-5.4 in organization org-yXK0mBGOR7eXhtGEx8v7e3bf on tokens per min (TPM): Limit 2000000, Used 1988708, Requested 20921. Please try again in 288ms. Visit https://platform.openai.com/account/rate-limits to learn more.)"} +{"type":"item.completed","item":{"id":"item_0","type":"agent_message","text":"{\"sql\":\"-- template_id: tpl_clickbench_group_count\\nSELECT \\\"class\\\", COUNT(*) AS \\\"row_count\\\"\\nFROM \\\"n1\\\"\\nGROUP BY \\\"class\\\"\\nORDER BY \\\"row_count\\\" DESC;\",\"notes\":\"Uses the grouped-count template with group_col fixed to \\\"class\\\" to show the class distribution.\"}"}} +{"type":"turn.completed","usage":{"input_tokens":20286,"cached_input_tokens":19840,"output_tokens":255,"reasoning_output_tokens":181}} diff --git a/Query/sql/v2/runs/v2_cli_20260502_081223_d/n1/artifacts/v2q_n1_3be9acbd219c318a/cli/sql_response_attempt_1.txt b/Query/sql/v2/runs/v2_cli_20260502_081223_d/n1/artifacts/v2q_n1_3be9acbd219c318a/cli/sql_response_attempt_1.txt new file mode 100644 index 0000000000000000000000000000000000000000..6a99fc5fddc482fd900ab8dd95768db40e0c9def --- /dev/null +++ b/Query/sql/v2/runs/v2_cli_20260502_081223_d/n1/artifacts/v2q_n1_3be9acbd219c318a/cli/sql_response_attempt_1.txt @@ -0,0 +1 @@ +{"sql":"-- template_id: tpl_clickbench_group_count\nSELECT \"class\", COUNT(*) AS \"row_count\"\nFROM \"n1\"\nGROUP BY \"class\"\nORDER BY \"row_count\" DESC;","notes":"Uses the grouped-count template with group_col fixed to \"class\" to show the class distribution."} \ No newline at end of file diff --git a/Query/sql/v2/runs/v2_cli_20260502_081223_d/n1/artifacts/v2q_n1_3be9acbd219c318a/cli/sql_stderr_attempt_1.txt b/Query/sql/v2/runs/v2_cli_20260502_081223_d/n1/artifacts/v2q_n1_3be9acbd219c318a/cli/sql_stderr_attempt_1.txt new file mode 100644 index 0000000000000000000000000000000000000000..e69de29bb2d1d6434b8b29ae775ad8c2e48c5391 diff --git a/Query/sql/v2/runs/v2_cli_20260502_081223_d/n1/artifacts/v2q_n1_3fbf68ef15f8fe9d/cli/conversation.jsonl b/Query/sql/v2/runs/v2_cli_20260502_081223_d/n1/artifacts/v2q_n1_3fbf68ef15f8fe9d/cli/conversation.jsonl new file mode 100644 index 0000000000000000000000000000000000000000..3388636e703d36dea56d8a30ebd6feaf97f10130 --- /dev/null +++ b/Query/sql/v2/runs/v2_cli_20260502_081223_d/n1/artifacts/v2q_n1_3fbf68ef15f8fe9d/cli/conversation.jsonl @@ -0,0 +1,2 @@ +{"attempt": 1, "phase": "sql_generation", "role": "user", "content_path": "cli/sql_prompt_attempt_1.txt", "metrics": {"chars": 29320, "bytes_utf8": 29320, "lines": 790, "estimated_tokens": null}} +{"attempt": 1, "phase": "sql_generation", "role": "assistant", "content_path": "cli/sql_response_attempt_1.txt", "raw_content_path": "cli/sql_response_attempt_1.raw.txt", "stderr_path": "cli/sql_stderr_attempt_1.txt", "metrics": {"chars": 401, "bytes_utf8": 401, "lines": 1, "estimated_tokens": null}, "usage": {"input_tokens": 20308, "cached_input_tokens": 19840, "output_tokens": 287, "reasoning_output_tokens": 178}} diff --git a/Query/sql/v2/runs/v2_cli_20260502_081223_d/n1/artifacts/v2q_n1_3fbf68ef15f8fe9d/cli/session_summary.json b/Query/sql/v2/runs/v2_cli_20260502_081223_d/n1/artifacts/v2q_n1_3fbf68ef15f8fe9d/cli/session_summary.json new file mode 100644 index 0000000000000000000000000000000000000000..c8c60adc8cf826c283577ef02b594642d1890640 --- /dev/null +++ b/Query/sql/v2/runs/v2_cli_20260502_081223_d/n1/artifacts/v2q_n1_3fbf68ef15f8fe9d/cli/session_summary.json @@ -0,0 +1,25 @@ +{ + "engine": "v2-cli:codex", + "command": "codex exec --skip-git-repo-check --disable plugins --sandbox read-only --cd \"/data/jialinzhang/SQLagent\" -m gpt-5.4 --json -", + "ai_cli_calls": 1, + "usage_summary": { + "dataset_id": "n1", + "model": "v2-cli:codex", + "run_id": "v2q_n1_3fbf68ef15f8fe9d", + "api_calls": 0, + "input_tokens": 20308, + "cached_input_tokens": 19840, + "output_tokens": 287, + "total_tokens": 20595, + "cost_usd": 0.0, + "ai_cli_calls": 1, + "estimated_input_tokens": 0, + "estimated_output_tokens": 0, + "estimated_total_tokens": 0, + "usage_source": "ai_cli_json_usage", + "cli_elapsed_ms_total": 8561.23, + "sql_execution_elapsed_ms_total": 1.66, + "conversation_log_path": "/data/jialinzhang/TabQueryBench/sql_workloads/v2_current/runs_and_launches/runs/v2_cli_20260502_081223_d/n1/artifacts/v2q_n1_3fbf68ef15f8fe9d/cli/conversation.jsonl", + "note": "Executed through a local AI CLI with structured usage metadata." + } +} \ No newline at end of file diff --git a/Query/sql/v2/runs/v2_cli_20260502_081223_d/n1/artifacts/v2q_n1_3fbf68ef15f8fe9d/cli/sql_attempt_1.metadata.json b/Query/sql/v2/runs/v2_cli_20260502_081223_d/n1/artifacts/v2q_n1_3fbf68ef15f8fe9d/cli/sql_attempt_1.metadata.json new file mode 100644 index 0000000000000000000000000000000000000000..683e8b141e486cc67a8f43cf32d742d47f8d73de --- /dev/null +++ b/Query/sql/v2/runs/v2_cli_20260502_081223_d/n1/artifacts/v2q_n1_3fbf68ef15f8fe9d/cli/sql_attempt_1.metadata.json @@ -0,0 +1,45 @@ +{ + "attempt": 1, + "phase": "sql_generation", + "command": "codex exec --skip-git-repo-check --disable plugins --sandbox read-only --cd \"/data/jialinzhang/SQLagent\" -m gpt-5.4 --json -", + "started_at": "2026-05-19T16:01:08.880371+00:00", + "ended_at": "2026-05-19T16:01:17.441623+00:00", + "elapsed_ms": 8561.23, + "prompt_metrics": { + "chars": 29320, + "bytes_utf8": 29320, + "lines": 790, + "estimated_tokens": null + }, + "stdout_metrics": { + "chars": 745, + "bytes_utf8": 745, + "lines": 4, + "estimated_tokens": null + }, + "stderr_metrics": { + "chars": 0, + "bytes_utf8": 0, + "lines": 0, + "estimated_tokens": null + }, + "parsed_output": { + "format": "jsonl_events", + "text_metrics": { + "chars": 401, + "bytes_utf8": 401, + "lines": 1, + "estimated_tokens": null + }, + "usage": { + "input_tokens": 20308, + "cached_input_tokens": 19840, + "output_tokens": 287, + "reasoning_output_tokens": 178 + } + }, + "prompt_path": "cli/sql_prompt_attempt_1.txt", + "response_path": "cli/sql_response_attempt_1.txt", + "raw_response_path": "cli/sql_response_attempt_1.raw.txt", + "stderr_path": "cli/sql_stderr_attempt_1.txt" +} \ No newline at end of file diff --git a/Query/sql/v2/runs/v2_cli_20260502_081223_d/n1/artifacts/v2q_n1_3fbf68ef15f8fe9d/cli/sql_prompt_attempt_1.txt b/Query/sql/v2/runs/v2_cli_20260502_081223_d/n1/artifacts/v2q_n1_3fbf68ef15f8fe9d/cli/sql_prompt_attempt_1.txt new file mode 100644 index 0000000000000000000000000000000000000000..1934d0885a7736611e825a51023411c9139c27fe --- /dev/null +++ b/Query/sql/v2/runs/v2_cli_20260502_081223_d/n1/artifacts/v2q_n1_3fbf68ef15f8fe9d/cli/sql_prompt_attempt_1.txt @@ -0,0 +1,790 @@ +You are generating one SQLite SELECT query for a single-table SQL QA task. +Return strict JSON only, with this schema: {"sql": "...", "notes": "..."}. +Rules: +- Use only the provided table and columns. +- Do not write INSERT, UPDATE, DELETE, DROP, ALTER, CREATE, PRAGMA, ATTACH, DETACH, or VACUUM. +- Prefer the planned template and bound roles when provided. +- Add a leading SQL comment exactly like: -- template_id: . +- Generate SQLite-compatible SQL. SQLite does not support PERCENTILE_CONT or STDDEV. +- Quote identifiers with double quotes. +- Return no markdown and no extra prose. + +Dataset context: +Dataset context for SQL QA: +- dataset_id: n1 +- dataset_name: Spambase +- table_name: n1 +- table_layout: single-table dataset (do not assume joins). +- row_semantics: One row is one email represented by engineered token/character frequency and capitalization-run features. +- task_type: classification +- target_column: class +- main_row_count: 4601 +- important_fields: +- word_freq_make: role=feature, type=numeric_sparse_frequency. tags=['condition_candidate', 'measure', 'rare_pattern_candidate'] desc=Frequency feature for token 'make' in an email message. +- word_freq_address: role=feature, type=numeric_sparse_frequency. tags=['condition_candidate', 'measure', 'rare_pattern_candidate'] desc=Frequency feature for token 'address' in an email message. +- word_freq_all: role=feature, type=numeric_sparse_frequency. tags=['condition_candidate', 'measure', 'rare_pattern_candidate'] desc=Frequency feature for token 'all' in an email message. +- word_freq_3d: role=feature, type=numeric_sparse_frequency. tags=['condition_candidate', 'measure', 'rare_pattern_candidate'] desc=Frequency feature for token '3d' in an email message. +- word_freq_our: role=feature, type=numeric_sparse_frequency. tags=['condition_candidate', 'measure', 'rare_pattern_candidate'] desc=Frequency feature for token 'our' in an email message. +- word_freq_over: role=feature, type=numeric_sparse_frequency. tags=['condition_candidate', 'measure', 'rare_pattern_candidate'] desc=Frequency feature for token 'over' in an email message. +- word_freq_remove: role=feature, type=numeric_sparse_frequency. tags=['condition_candidate', 'measure', 'rare_pattern_candidate'] desc=Frequency feature for token 'remove' in an email message. +- word_freq_internet: role=feature, type=numeric_sparse_frequency. tags=['condition_candidate', 'measure', 'rare_pattern_candidate'] desc=Frequency feature for token 'internet' in an email message. +- word_freq_order: role=feature, type=numeric_sparse_frequency. tags=['condition_candidate', 'measure', 'rare_pattern_candidate'] desc=Frequency feature for token 'order' in an email message. +- word_freq_mail: role=feature, type=numeric_sparse_frequency. tags=['condition_candidate', 'measure', 'rare_pattern_candidate'] desc=Frequency feature for token 'mail' in an email message. +- word_freq_receive: role=feature, type=numeric_sparse_frequency. tags=['condition_candidate', 'measure', 'rare_pattern_candidate'] desc=Frequency feature for token 'receive' in an email message. +- word_freq_will: role=feature, type=numeric_sparse_frequency. tags=['condition_candidate', 'measure', 'rare_pattern_candidate'] desc=Frequency feature for token 'will' in an email message. +- word_freq_people: role=feature, type=numeric_sparse_frequency. tags=['condition_candidate', 'measure', 'rare_pattern_candidate'] desc=Frequency feature for token 'people' in an email message. +- word_freq_report: role=feature, type=numeric_sparse_frequency. tags=['condition_candidate', 'measure', 'rare_pattern_candidate'] desc=Frequency feature for token 'report' in an email message. +- word_freq_addresses: role=feature, type=numeric_sparse_frequency. tags=['condition_candidate', 'measure', 'rare_pattern_candidate'] desc=Frequency feature for token 'addresses' in an email message. +- word_freq_free: role=feature, type=numeric_sparse_frequency. tags=['condition_candidate', 'measure', 'rare_pattern_candidate'] desc=Frequency feature for token 'free' in an email message. +- word_freq_business: role=feature, type=numeric_sparse_frequency. tags=['condition_candidate', 'measure', 'rare_pattern_candidate'] desc=Frequency feature for token 'business' in an email message. +- word_freq_email: role=feature, type=numeric_sparse_frequency. tags=['condition_candidate', 'measure', 'rare_pattern_candidate'] desc=Frequency feature for token 'email' in an email message. +- word_freq_you: role=feature, type=numeric_sparse_frequency. tags=['condition_candidate', 'measure', 'rare_pattern_candidate'] desc=Frequency feature for token 'you' in an email message. +- word_freq_credit: role=feature, type=numeric_sparse_frequency. tags=['condition_candidate', 'measure', 'rare_pattern_candidate'] desc=Frequency feature for token 'credit' in an email message. +- word_freq_your: role=feature, type=numeric_sparse_frequency. tags=['condition_candidate', 'measure', 'rare_pattern_candidate'] desc=Frequency feature for token 'your' in an email message. +- word_freq_font: role=feature, type=numeric_sparse_frequency. tags=['condition_candidate', 'measure', 'rare_pattern_candidate'] desc=Frequency feature for token 'font' in an email message. +- word_freq_000: role=feature, type=numeric_sparse_frequency. tags=['condition_candidate', 'measure', 'rare_pattern_candidate'] desc=Frequency feature for token '000' in an email message. +- word_freq_money: role=feature, type=numeric_sparse_frequency. tags=['condition_candidate', 'measure', 'rare_pattern_candidate'] desc=Frequency feature for token 'money' in an email message. +- word_freq_hp: role=feature, type=numeric_sparse_frequency. tags=['condition_candidate', 'measure', 'rare_pattern_candidate'] desc=Frequency feature for token 'hp' in an email message. +- word_freq_hpl: role=feature, type=numeric_sparse_frequency. tags=['condition_candidate', 'measure', 'rare_pattern_candidate'] desc=Frequency feature for token 'hpl' in an email message. +- word_freq_george: role=feature, type=numeric_sparse_frequency. tags=['condition_candidate', 'measure', 'rare_pattern_candidate'] desc=Frequency feature for token 'george' in an email message. +- word_freq_650: role=feature, type=numeric_sparse_frequency. tags=['condition_candidate', 'measure', 'rare_pattern_candidate'] desc=Frequency feature for token '650' in an email message. +- word_freq_lab: role=feature, type=numeric_sparse_frequency. tags=['condition_candidate', 'measure', 'rare_pattern_candidate'] desc=Frequency feature for token 'lab' in an email message. +- word_freq_labs: role=feature, type=numeric_sparse_frequency. tags=['condition_candidate', 'measure', 'rare_pattern_candidate'] desc=Frequency feature for token 'labs' in an email message. +- word_freq_telnet: role=feature, type=numeric_sparse_frequency. tags=['condition_candidate', 'measure', 'rare_pattern_candidate'] desc=Frequency feature for token 'telnet' in an email message. +- word_freq_857: role=feature, type=numeric_sparse_frequency. tags=['condition_candidate', 'measure', 'rare_pattern_candidate'] desc=Frequency feature for token '857' in an email message. +- word_freq_data: role=feature, type=numeric_sparse_frequency. tags=['condition_candidate', 'measure', 'rare_pattern_candidate'] desc=Frequency feature for token 'data' in an email message. +- word_freq_415: role=feature, type=numeric_sparse_frequency. tags=['condition_candidate', 'measure', 'rare_pattern_candidate'] desc=Frequency feature for token '415' in an email message. +- word_freq_85: role=feature, type=numeric_sparse_frequency. tags=['condition_candidate', 'measure', 'rare_pattern_candidate'] desc=Frequency feature for token '85' in an email message. +- word_freq_technology: role=feature, type=numeric_sparse_frequency. tags=['condition_candidate', 'measure', 'rare_pattern_candidate'] desc=Frequency feature for token 'technology' in an email message. +- word_freq_1999: role=feature, type=numeric_sparse_frequency. tags=['condition_candidate', 'measure', 'rare_pattern_candidate'] desc=Frequency feature for token '1999' in an email message. +- word_freq_parts: role=feature, type=numeric_sparse_frequency. tags=['condition_candidate', 'measure', 'rare_pattern_candidate'] desc=Frequency feature for token 'parts' in an email message. +- word_freq_pm: role=feature, type=numeric_sparse_frequency. tags=['condition_candidate', 'measure', 'rare_pattern_candidate'] desc=Frequency feature for token 'pm' in an email message. +- word_freq_direct: role=feature, type=numeric_sparse_frequency. tags=['condition_candidate', 'measure', 'rare_pattern_candidate'] desc=Frequency feature for token 'direct' in an email message. +- word_freq_cs: role=feature, type=numeric_sparse_frequency. tags=['condition_candidate', 'measure', 'rare_pattern_candidate'] desc=Frequency feature for token 'cs' in an email message. +- word_freq_meeting: role=feature, type=numeric_sparse_frequency. tags=['condition_candidate', 'measure', 'rare_pattern_candidate'] desc=Frequency feature for token 'meeting' in an email message. +- word_freq_original: role=feature, type=numeric_sparse_frequency. tags=['condition_candidate', 'measure', 'rare_pattern_candidate'] desc=Frequency feature for token 'original' in an email message. +- word_freq_project: role=feature, type=numeric_sparse_frequency. tags=['condition_candidate', 'measure', 'rare_pattern_candidate'] desc=Frequency feature for token 'project' in an email message. +- word_freq_re: role=feature, type=numeric_sparse_frequency. tags=['condition_candidate', 'measure', 'rare_pattern_candidate'] desc=Frequency feature for token 're' in an email message. +- word_freq_edu: role=feature, type=numeric_sparse_frequency. tags=['condition_candidate', 'measure', 'rare_pattern_candidate'] desc=Frequency feature for token 'edu' in an email message. +- word_freq_table: role=feature, type=numeric_sparse_frequency. tags=['condition_candidate', 'measure', 'rare_pattern_candidate'] desc=Frequency feature for token 'table' in an email message. +- word_freq_conference: role=feature, type=numeric_sparse_frequency. tags=['condition_candidate', 'measure', 'rare_pattern_candidate'] desc=Frequency feature for token 'conference' in an email message. +- char_freq_%3B: role=feature, type=numeric_sparse_frequency. tags=['condition_candidate', 'measure', 'rare_pattern_candidate'] desc=Character frequency feature for symbol ';'. +- char_freq_%28: role=feature, type=numeric_sparse_frequency. tags=['condition_candidate', 'measure', 'rare_pattern_candidate'] desc=Character frequency feature for symbol '('. +- char_freq_%5B: role=feature, type=numeric_sparse_frequency. tags=['condition_candidate', 'measure', 'rare_pattern_candidate'] desc=Character frequency feature for symbol '['. +- char_freq_%21: role=feature, type=numeric_sparse_frequency. tags=['condition_candidate', 'measure', 'rare_pattern_candidate'] desc=Character frequency feature for symbol '!'. +- char_freq_%24: role=feature, type=numeric_sparse_frequency. tags=['condition_candidate', 'measure', 'rare_pattern_candidate'] desc=Character frequency feature for symbol '$'. +- char_freq_%23: role=feature, type=numeric_sparse_frequency. tags=['condition_candidate', 'measure', 'rare_pattern_candidate'] desc=Character frequency feature for symbol '#'. +- capital_run_length_average: role=feature, type=numeric_heavy_tail. tags=['condition_candidate', 'measure', 'rare_pattern_candidate'] desc=Average length of uninterrupted capital-letter runs. +- capital_run_length_longest: role=feature, type=numeric_heavy_tail. tags=['condition_candidate', 'measure', 'rare_pattern_candidate'] desc=Longest uninterrupted capital-letter run. +- capital_run_length_total: role=feature, type=numeric_heavy_tail. tags=['condition_candidate', 'measure', 'rare_pattern_candidate'] desc=Total number of capital letters in runs. +- class: role=target, type=binary_target. tags=['target_candidate'] desc=Binary spam label (1=spam, 0=non-spam). +- useful_field_combinations: [['word_freq_free', 'word_freq_you', 'class'], ['char_freq_%21', 'capital_run_length_longest', 'class'], ['word_freq_remove', 'word_freq_money', 'class']] +- fields_requiring_caution: ['capital_run_length_average', 'capital_run_length_longest', 'capital_run_length_total'] +- source_url: https://www.openml.org/d/44 + +SQLite schema snapshot: +{ + "table_name": "n1", + "quoted_table_name": "\"n1\"", + "row_count": 4601, + "columns": [ + { + "name": "word_freq_make", + "type": "TEXT", + "notnull": false, + "pk": false + }, + { + "name": "word_freq_address", + "type": "TEXT", + "notnull": false, + "pk": false + }, + { + "name": "word_freq_all", + "type": "TEXT", + "notnull": false, + "pk": false + }, + { + "name": "word_freq_3d", + "type": "TEXT", + "notnull": false, + "pk": false + }, + { + "name": "word_freq_our", + "type": "TEXT", + "notnull": false, + "pk": false + }, + { + "name": "word_freq_over", + "type": "TEXT", + "notnull": false, + "pk": false + }, + { + "name": "word_freq_remove", + "type": "TEXT", + "notnull": false, + "pk": false + }, + { + "name": "word_freq_internet", + "type": "TEXT", + "notnull": false, + "pk": false + }, + { + "name": "word_freq_order", + "type": "TEXT", + "notnull": false, + "pk": false + }, + { + "name": "word_freq_mail", + "type": "TEXT", + "notnull": false, + "pk": false + }, + { + "name": "word_freq_receive", + "type": "TEXT", + "notnull": false, + "pk": false + }, + { + "name": "word_freq_will", + "type": "TEXT", + "notnull": false, + "pk": false + }, + { + "name": "word_freq_people", + "type": "TEXT", + "notnull": false, + "pk": false + }, + { + "name": "word_freq_report", + "type": "TEXT", + "notnull": false, + "pk": false + }, + { + "name": "word_freq_addresses", + "type": "TEXT", + "notnull": false, + "pk": false + }, + { + "name": "word_freq_free", + "type": "TEXT", + "notnull": false, + "pk": false + }, + { + "name": "word_freq_business", + "type": "TEXT", + "notnull": false, + "pk": false + }, + { + "name": "word_freq_email", + "type": "TEXT", + "notnull": false, + "pk": false + }, + { + "name": "word_freq_you", + "type": "TEXT", + "notnull": false, + "pk": false + }, + { + "name": "word_freq_credit", + "type": "TEXT", + "notnull": false, + "pk": false + }, + { + "name": "word_freq_your", + "type": "TEXT", + "notnull": false, + "pk": false + }, + { + "name": "word_freq_font", + "type": "TEXT", + "notnull": false, + "pk": false + }, + { + "name": "word_freq_000", + "type": "TEXT", + "notnull": false, + "pk": false + }, + { + "name": "word_freq_money", + "type": "TEXT", + "notnull": false, + "pk": false + }, + { + "name": "word_freq_hp", + "type": "TEXT", + "notnull": false, + "pk": false + }, + { + "name": "word_freq_hpl", + "type": "TEXT", + "notnull": false, + "pk": false + }, + { + "name": "word_freq_george", + "type": "TEXT", + "notnull": false, + "pk": false + }, + { + "name": "word_freq_650", + "type": "TEXT", + "notnull": false, + "pk": false + }, + { + "name": "word_freq_lab", + "type": "TEXT", + "notnull": false, + "pk": false + }, + { + "name": "word_freq_labs", + "type": "TEXT", + "notnull": false, + "pk": false + }, + { + "name": "word_freq_telnet", + "type": "TEXT", + "notnull": false, + "pk": false + }, + { + "name": "word_freq_857", + "type": "TEXT", + "notnull": false, + "pk": false + }, + { + "name": "word_freq_data", + "type": "TEXT", + "notnull": false, + "pk": false + }, + { + "name": "word_freq_415", + "type": "TEXT", + "notnull": false, + "pk": false + }, + { + "name": "word_freq_85", + "type": "TEXT", + "notnull": false, + "pk": false + }, + { + "name": "word_freq_technology", + "type": "TEXT", + "notnull": false, + "pk": false + }, + { + "name": "word_freq_1999", + "type": "TEXT", + "notnull": false, + "pk": false + }, + { + "name": "word_freq_parts", + "type": "TEXT", + "notnull": false, + "pk": false + }, + { + "name": "word_freq_pm", + "type": "TEXT", + "notnull": false, + "pk": false + }, + { + "name": "word_freq_direct", + "type": "TEXT", + "notnull": false, + "pk": false + }, + { + "name": "word_freq_cs", + "type": "TEXT", + "notnull": false, + "pk": false + }, + { + "name": "word_freq_meeting", + "type": "TEXT", + "notnull": false, + "pk": false + }, + { + "name": "word_freq_original", + "type": "TEXT", + "notnull": false, + "pk": false + }, + { + "name": "word_freq_project", + "type": "TEXT", + "notnull": false, + "pk": false + }, + { + "name": "word_freq_re", + "type": "TEXT", + "notnull": false, + "pk": false + }, + { + "name": "word_freq_edu", + "type": "TEXT", + "notnull": false, + "pk": false + }, + { + "name": "word_freq_table", + "type": "TEXT", + "notnull": false, + "pk": false + }, + { + "name": "word_freq_conference", + "type": "TEXT", + "notnull": false, + "pk": false + }, + { + "name": "char_freq_%3B", + "type": "TEXT", + "notnull": false, + "pk": false + }, + { + "name": "char_freq_%28", + "type": "TEXT", + "notnull": false, + "pk": false + }, + { + "name": "char_freq_%5B", + "type": "TEXT", + "notnull": false, + "pk": false + }, + { + "name": "char_freq_%21", + "type": "TEXT", + "notnull": false, + "pk": false + }, + { + "name": "char_freq_%24", + "type": "TEXT", + "notnull": false, + "pk": false + }, + { + "name": "char_freq_%23", + "type": "TEXT", + "notnull": false, + "pk": false + }, + { + "name": "capital_run_length_average", + "type": "TEXT", + "notnull": false, + "pk": false + }, + { + "name": "capital_run_length_longest", + "type": "TEXT", + "notnull": false, + "pk": false + }, + { + "name": "capital_run_length_total", + "type": "TEXT", + "notnull": false, + "pk": false + }, + { + "name": "class", + "type": "TEXT", + "notnull": false, + "pk": false + } + ], + "sample_rows": [ + { + "word_freq_make": "0", + "word_freq_address": "0.64", + "word_freq_all": "0.64", + "word_freq_3d": "0", + "word_freq_our": "0.32", + "word_freq_over": "0", + "word_freq_remove": "0", + "word_freq_internet": "0", + "word_freq_order": "0", + "word_freq_mail": "0", + "word_freq_receive": "0", + "word_freq_will": "0.64", + "word_freq_people": "0", + "word_freq_report": "0", + "word_freq_addresses": "0", + "word_freq_free": "0.32", + "word_freq_business": "0", + "word_freq_email": "1.29", + "word_freq_you": "1.93", + "word_freq_credit": "0", + "word_freq_your": "0.96", + "word_freq_font": "0", + "word_freq_000": "0", + "word_freq_money": "0", + "word_freq_hp": "0", + "word_freq_hpl": "0", + "word_freq_george": "0", + "word_freq_650": "0", + "word_freq_lab": "0", + "word_freq_labs": "0", + "word_freq_telnet": "0", + "word_freq_857": "0", + "word_freq_data": "0", + "word_freq_415": "0", + "word_freq_85": "0", + "word_freq_technology": "0", + "word_freq_1999": "0", + "word_freq_parts": "0", + "word_freq_pm": "0", + "word_freq_direct": "0", + "word_freq_cs": "0", + "word_freq_meeting": "0", + "word_freq_original": "0", + "word_freq_project": "0", + "word_freq_re": "0", + "word_freq_edu": "0", + "word_freq_table": "0", + "word_freq_conference": "0", + "char_freq_%3B": "0", + "char_freq_%28": "0", + "char_freq_%5B": "0", + "char_freq_%21": "0.778", + "char_freq_%24": "0", + "char_freq_%23": "0", + "capital_run_length_average": "3.756", + "capital_run_length_longest": "61", + "capital_run_length_total": "278", + "class": "1" + }, + { + "word_freq_make": "0.21", + "word_freq_address": "0.28", + "word_freq_all": "0.5", + "word_freq_3d": "0", + "word_freq_our": "0.14", + "word_freq_over": "0.28", + "word_freq_remove": "0.21", + "word_freq_internet": "0.07", + "word_freq_order": "0", + "word_freq_mail": "0.94", + "word_freq_receive": "0.21", + "word_freq_will": "0.79", + "word_freq_people": "0.65", + "word_freq_report": "0.21", + "word_freq_addresses": "0.14", + "word_freq_free": "0.14", + "word_freq_business": "0.07", + "word_freq_email": "0.28", + "word_freq_you": "3.47", + "word_freq_credit": "0", + "word_freq_your": "1.59", + "word_freq_font": "0", + "word_freq_000": "0.43", + "word_freq_money": "0.43", + "word_freq_hp": "0", + "word_freq_hpl": "0", + "word_freq_george": "0", + "word_freq_650": "0", + "word_freq_lab": "0", + "word_freq_labs": "0", + "word_freq_telnet": "0", + "word_freq_857": "0", + "word_freq_data": "0", + "word_freq_415": "0", + "word_freq_85": "0", + "word_freq_technology": "0", + "word_freq_1999": "0.07", + "word_freq_parts": "0", + "word_freq_pm": "0", + "word_freq_direct": "0", + "word_freq_cs": "0", + "word_freq_meeting": "0", + "word_freq_original": "0", + "word_freq_project": "0", + "word_freq_re": "0", + "word_freq_edu": "0", + "word_freq_table": "0", + "word_freq_conference": "0", + "char_freq_%3B": "0", + "char_freq_%28": "0.132", + "char_freq_%5B": "0", + "char_freq_%21": "0.372", + "char_freq_%24": "0.18", + "char_freq_%23": "0.048", + "capital_run_length_average": "5.114", + "capital_run_length_longest": "101", + "capital_run_length_total": "1028", + "class": "1" + }, + { + "word_freq_make": "0.06", + "word_freq_address": "0", + "word_freq_all": "0.71", + "word_freq_3d": "0", + "word_freq_our": "1.23", + "word_freq_over": "0.19", + "word_freq_remove": "0.19", + "word_freq_internet": "0.12", + "word_freq_order": "0.64", + "word_freq_mail": "0.25", + "word_freq_receive": "0.38", + "word_freq_will": "0.45", + "word_freq_people": "0.12", + "word_freq_report": "0", + "word_freq_addresses": "1.75", + "word_freq_free": "0.06", + "word_freq_business": "0.06", + "word_freq_email": "1.03", + "word_freq_you": "1.36", + "word_freq_credit": "0.32", + "word_freq_your": "0.51", + "word_freq_font": "0", + "word_freq_000": "1.16", + "word_freq_money": "0.06", + "word_freq_hp": "0", + "word_freq_hpl": "0", + "word_freq_george": "0", + "word_freq_650": "0", + "word_freq_lab": "0", + "word_freq_labs": "0", + "word_freq_telnet": "0", + "word_freq_857": "0", + "word_freq_data": "0", + "word_freq_415": "0", + "word_freq_85": "0", + "word_freq_technology": "0", + "word_freq_1999": "0", + "word_freq_parts": "0", + "word_freq_pm": "0", + "word_freq_direct": "0.06", + "word_freq_cs": "0", + "word_freq_meeting": "0", + "word_freq_original": "0.12", + "word_freq_project": "0", + "word_freq_re": "0.06", + "word_freq_edu": "0.06", + "word_freq_table": "0", + "word_freq_conference": "0", + "char_freq_%3B": "0.01", + "char_freq_%28": "0.143", + "char_freq_%5B": "0", + "char_freq_%21": "0.276", + "char_freq_%24": "0.184", + "char_freq_%23": "0.01", + "capital_run_length_average": "9.821", + "capital_run_length_longest": "485", + "capital_run_length_total": "2259", + "class": "1" + }, + { + "word_freq_make": "0", + "word_freq_address": "0", + "word_freq_all": "0", + "word_freq_3d": "0", + "word_freq_our": "0.63", + "word_freq_over": "0", + "word_freq_remove": "0.31", + "word_freq_internet": "0.63", + "word_freq_order": "0.31", + "word_freq_mail": "0.63", + "word_freq_receive": "0.31", + "word_freq_will": "0.31", + "word_freq_people": "0.31", + "word_freq_report": "0", + "word_freq_addresses": "0", + "word_freq_free": "0.31", + "word_freq_business": "0", + "word_freq_email": "0", + "word_freq_you": "3.18", + "word_freq_credit": "0", + "word_freq_your": "0.31", + "word_freq_font": "0", + "word_freq_000": "0", + "word_freq_money": "0", + "word_freq_hp": "0", + "word_freq_hpl": "0", + "word_freq_george": "0", + "word_freq_650": "0", + "word_freq_lab": "0", + "word_freq_labs": "0", + "word_freq_telnet": "0", + "word_freq_857": "0", + "word_freq_data": "0", + "word_freq_415": "0", + "word_freq_85": "0", + "word_freq_technology": "0", + "word_freq_1999": "0", + "word_freq_parts": "0", + "word_freq_pm": "0", + "word_freq_direct": "0", + "word_freq_cs": "0", + "word_freq_meeting": "0", + "word_freq_original": "0", + "word_freq_project": "0", + "word_freq_re": "0", + "word_freq_edu": "0", + "word_freq_table": "0", + "word_freq_conference": "0", + "char_freq_%3B": "0", + "char_freq_%28": "0.137", + "char_freq_%5B": "0", + "char_freq_%21": "0.137", + "char_freq_%24": "0", + "char_freq_%23": "0", + "capital_run_length_average": "3.537", + "capital_run_length_longest": "40", + "capital_run_length_total": "191", + "class": "1" + }, + { + "word_freq_make": "0", + "word_freq_address": "0", + "word_freq_all": "0", + "word_freq_3d": "0", + "word_freq_our": "0.63", + "word_freq_over": "0", + "word_freq_remove": "0.31", + "word_freq_internet": "0.63", + "word_freq_order": "0.31", + "word_freq_mail": "0.63", + "word_freq_receive": "0.31", + "word_freq_will": "0.31", + "word_freq_people": "0.31", + "word_freq_report": "0", + "word_freq_addresses": "0", + "word_freq_free": "0.31", + "word_freq_business": "0", + "word_freq_email": "0", + "word_freq_you": "3.18", + "word_freq_credit": "0", + "word_freq_your": "0.31", + "word_freq_font": "0", + "word_freq_000": "0", + "word_freq_money": "0", + "word_freq_hp": "0", + "word_freq_hpl": "0", + "word_freq_george": "0", + "word_freq_650": "0", + "word_freq_lab": "0", + "word_freq_labs": "0", + "word_freq_telnet": "0", + "word_freq_857": "0", + "word_freq_data": "0", + "word_freq_415": "0", + "word_freq_85": "0", + "word_freq_technology": "0", + "word_freq_1999": "0", + "word_freq_parts": "0", + "word_freq_pm": "0", + "word_freq_direct": "0", + "word_freq_cs": "0", + "word_freq_meeting": "0", + "word_freq_original": "0", + "word_freq_project": "0", + "word_freq_re": "0", + "word_freq_edu": "0", + "word_freq_table": "0", + "word_freq_conference": "0", + "char_freq_%3B": "0", + "char_freq_%28": "0.135", + "char_freq_%5B": "0", + "char_freq_%21": "0.135", + "char_freq_%24": "0", + "char_freq_%23": "0", + "capital_run_length_average": "3.537", + "capital_run_length_longest": "40", + "capital_run_length_total": "191", + "class": "1" + } + ] +} + +Shortlisted templates: +[ + { + "template_id": "tpl_threshold_rarity_cdf", + "template_name": "Threshold Rarity CDF", + "primary_family": "tail_rarity_structure", + "portability": "yes", + "sql_skeleton": "SELECT AVG(CASE WHEN {measure_col} <= {measure_threshold} THEN 1 ELSE 0 END) AS empirical_cdf_at_threshold\nFROM {table};", + "required_roles": [ + "measure_col" + ] + } +] + +Problem instance: +{ + "dataset_id": "n1", + "question": "Use template Threshold Rarity CDF to probe tail_set_consistency with semantic role rare_extreme_view. Focus on measure_col=capital_run_length_total.", + "planned_template_id": "tpl_threshold_rarity_cdf", + "bindings": { + "measure_col": "capital_run_length_total", + "top_k": 13, + "top_n": 4, + "num_tiles": 10, + "percentile_value": 0.9, + "z_threshold": 2.0, + "fraction_threshold": 0.1, + "baseline_multiplier": 1.5, + "baseline_fraction": 0.1, + "min_group_size": 5, + "min_support": 5, + "measure_threshold": 266.0, + "time_grain": "month", + "lookback_rows": 3, + "current_period_start": "'2024-01-01'", + "current_period_end": "'2024-04-01'", + "previous_period_start": "'2023-10-01'", + "previous_period_end": "'2024-01-01'", + "drift_ratio_threshold": 0.8 + }, + "can_vary": [], + "must_fix": [], + "runtime_sql_skeleton": "SELECT AVG(CASE WHEN {measure_col} <= {measure_threshold} THEN 1 ELSE 0 END) AS empirical_cdf_at_threshold\nFROM {table};" +} + +Repair context: +{} diff --git a/Query/sql/v2/runs/v2_cli_20260502_081223_d/n1/artifacts/v2q_n1_3fbf68ef15f8fe9d/cli/sql_response_attempt_1.raw.txt b/Query/sql/v2/runs/v2_cli_20260502_081223_d/n1/artifacts/v2q_n1_3fbf68ef15f8fe9d/cli/sql_response_attempt_1.raw.txt new file mode 100644 index 0000000000000000000000000000000000000000..eb8df09d8e8859250bac41575aa6eddc3c776d28 --- /dev/null +++ b/Query/sql/v2/runs/v2_cli_20260502_081223_d/n1/artifacts/v2q_n1_3fbf68ef15f8fe9d/cli/sql_response_attempt_1.raw.txt @@ -0,0 +1,4 @@ +{"type":"thread.started","thread_id":"019e40f8-3635-7e10-b9a5-66c31b4b7552"} +{"type":"turn.started"} +{"type":"item.completed","item":{"id":"item_0","type":"agent_message","text":"{\"sql\":\"-- template_id: tpl_threshold_rarity_cdf\\nSELECT AVG(CASE WHEN CAST(\\\"capital_run_length_total\\\" AS REAL) <= 266.0 THEN 1 ELSE 0 END) AS \\\"empirical_cdf_at_threshold\\\"\\nFROM \\\"n1\\\";\",\"notes\":\"Used the requested Threshold Rarity CDF template with measure_col bound to \\\"capital_run_length_total\\\" and threshold 266.0; CAST to REAL is included because the schema stores feature values as TEXT.\"}"}} +{"type":"turn.completed","usage":{"input_tokens":20308,"cached_input_tokens":19840,"output_tokens":287,"reasoning_output_tokens":178}} diff --git a/Query/sql/v2/runs/v2_cli_20260502_081223_d/n1/artifacts/v2q_n1_3fbf68ef15f8fe9d/cli/sql_response_attempt_1.txt b/Query/sql/v2/runs/v2_cli_20260502_081223_d/n1/artifacts/v2q_n1_3fbf68ef15f8fe9d/cli/sql_response_attempt_1.txt new file mode 100644 index 0000000000000000000000000000000000000000..f0e6d5bbf3ba4fb2ca4f93280256053b3e72fa2f --- /dev/null +++ b/Query/sql/v2/runs/v2_cli_20260502_081223_d/n1/artifacts/v2q_n1_3fbf68ef15f8fe9d/cli/sql_response_attempt_1.txt @@ -0,0 +1 @@ +{"sql":"-- template_id: tpl_threshold_rarity_cdf\nSELECT AVG(CASE WHEN CAST(\"capital_run_length_total\" AS REAL) <= 266.0 THEN 1 ELSE 0 END) AS \"empirical_cdf_at_threshold\"\nFROM \"n1\";","notes":"Used the requested Threshold Rarity CDF template with measure_col bound to \"capital_run_length_total\" and threshold 266.0; CAST to REAL is included because the schema stores feature values as TEXT."} \ No newline at end of file diff --git a/Query/sql/v2/runs/v2_cli_20260502_081223_d/n1/artifacts/v2q_n1_3fbf68ef15f8fe9d/cli/sql_stderr_attempt_1.txt b/Query/sql/v2/runs/v2_cli_20260502_081223_d/n1/artifacts/v2q_n1_3fbf68ef15f8fe9d/cli/sql_stderr_attempt_1.txt new file mode 100644 index 0000000000000000000000000000000000000000..e69de29bb2d1d6434b8b29ae775ad8c2e48c5391 diff --git a/Query/sql/v2/runs/v2_cli_20260502_081223_d/n1/artifacts/v2q_n1_4069c2f7e4b1c80d/cli/conversation.jsonl b/Query/sql/v2/runs/v2_cli_20260502_081223_d/n1/artifacts/v2q_n1_4069c2f7e4b1c80d/cli/conversation.jsonl new file mode 100644 index 0000000000000000000000000000000000000000..934a85ec0cf180a58a0b9e7ffc9710644d2c2bd1 --- /dev/null +++ b/Query/sql/v2/runs/v2_cli_20260502_081223_d/n1/artifacts/v2q_n1_4069c2f7e4b1c80d/cli/conversation.jsonl @@ -0,0 +1,2 @@ +{"attempt": 1, "phase": "sql_generation", "role": "user", "content_path": "cli/sql_prompt_attempt_1.txt", "metrics": {"chars": 30143, "bytes_utf8": 30143, "lines": 795, "estimated_tokens": null}} +{"attempt": 1, "phase": "sql_generation", "role": "assistant", "content_path": "cli/sql_response_attempt_1.txt", "raw_content_path": "cli/sql_response_attempt_1.raw.txt", "stderr_path": "cli/sql_stderr_attempt_1.txt", "metrics": {"chars": 747, "bytes_utf8": 747, "lines": 1, "estimated_tokens": null}, "usage": {"input_tokens": 20520, "cached_input_tokens": 19840, "output_tokens": 473, "reasoning_output_tokens": 257}} diff --git a/Query/sql/v2/runs/v2_cli_20260502_081223_d/n1/artifacts/v2q_n1_4069c2f7e4b1c80d/cli/session_summary.json b/Query/sql/v2/runs/v2_cli_20260502_081223_d/n1/artifacts/v2q_n1_4069c2f7e4b1c80d/cli/session_summary.json new file mode 100644 index 0000000000000000000000000000000000000000..0f67bde255e14c59e71b96eb0dfd600b6de9a558 --- /dev/null +++ b/Query/sql/v2/runs/v2_cli_20260502_081223_d/n1/artifacts/v2q_n1_4069c2f7e4b1c80d/cli/session_summary.json @@ -0,0 +1,25 @@ +{ + "engine": "v2-cli:codex", + "command": "codex exec --skip-git-repo-check --disable plugins --sandbox read-only --cd \"/data/jialinzhang/SQLagent\" -m gpt-5.4 --json -", + "ai_cli_calls": 1, + "usage_summary": { + "dataset_id": "n1", + "model": "v2-cli:codex", + "run_id": "v2q_n1_4069c2f7e4b1c80d", + "api_calls": 0, + "input_tokens": 20520, + "cached_input_tokens": 19840, + "output_tokens": 473, + "total_tokens": 20993, + "cost_usd": 0.0, + "ai_cli_calls": 1, + "estimated_input_tokens": 0, + "estimated_output_tokens": 0, + "estimated_total_tokens": 0, + "usage_source": "ai_cli_json_usage", + "cli_elapsed_ms_total": 20085.87, + "sql_execution_elapsed_ms_total": 5.18, + "conversation_log_path": "/data/jialinzhang/TabQueryBench/sql_workloads/v2_current/runs_and_launches/runs/v2_cli_20260502_081223_d/n1/artifacts/v2q_n1_4069c2f7e4b1c80d/cli/conversation.jsonl", + "note": "Executed through a local AI CLI with structured usage metadata." + } +} \ No newline at end of file diff --git a/Query/sql/v2/runs/v2_cli_20260502_081223_d/n1/artifacts/v2q_n1_4069c2f7e4b1c80d/cli/sql_attempt_1.metadata.json b/Query/sql/v2/runs/v2_cli_20260502_081223_d/n1/artifacts/v2q_n1_4069c2f7e4b1c80d/cli/sql_attempt_1.metadata.json new file mode 100644 index 0000000000000000000000000000000000000000..5389a214ff1533f3f0c33fe29a47a49671acfc4d --- /dev/null +++ b/Query/sql/v2/runs/v2_cli_20260502_081223_d/n1/artifacts/v2q_n1_4069c2f7e4b1c80d/cli/sql_attempt_1.metadata.json @@ -0,0 +1,45 @@ +{ + "attempt": 1, + "phase": "sql_generation", + "command": "codex exec --skip-git-repo-check --disable plugins --sandbox read-only --cd \"/data/jialinzhang/SQLagent\" -m gpt-5.4 --json -", + "started_at": "2026-05-19T15:40:29.457232+00:00", + "ended_at": "2026-05-19T15:40:49.543143+00:00", + "elapsed_ms": 20085.87, + "prompt_metrics": { + "chars": 30143, + "bytes_utf8": 30143, + "lines": 795, + "estimated_tokens": null + }, + "stdout_metrics": { + "chars": 2148, + "bytes_utf8": 2148, + "lines": 7, + "estimated_tokens": null + }, + "stderr_metrics": { + "chars": 0, + "bytes_utf8": 0, + "lines": 0, + "estimated_tokens": null + }, + "parsed_output": { + "format": "jsonl_events", + "text_metrics": { + "chars": 747, + "bytes_utf8": 747, + "lines": 1, + "estimated_tokens": null + }, + "usage": { + "input_tokens": 20520, + "cached_input_tokens": 19840, + "output_tokens": 473, + "reasoning_output_tokens": 257 + } + }, + "prompt_path": "cli/sql_prompt_attempt_1.txt", + "response_path": "cli/sql_response_attempt_1.txt", + "raw_response_path": "cli/sql_response_attempt_1.raw.txt", + "stderr_path": "cli/sql_stderr_attempt_1.txt" +} \ No newline at end of file diff --git a/Query/sql/v2/runs/v2_cli_20260502_081223_d/n1/artifacts/v2q_n1_4069c2f7e4b1c80d/cli/sql_prompt_attempt_1.txt b/Query/sql/v2/runs/v2_cli_20260502_081223_d/n1/artifacts/v2q_n1_4069c2f7e4b1c80d/cli/sql_prompt_attempt_1.txt new file mode 100644 index 0000000000000000000000000000000000000000..7b3e357ce2d0518686660f7afc53f586b686f6c3 --- /dev/null +++ b/Query/sql/v2/runs/v2_cli_20260502_081223_d/n1/artifacts/v2q_n1_4069c2f7e4b1c80d/cli/sql_prompt_attempt_1.txt @@ -0,0 +1,795 @@ +You are generating one SQLite SELECT query for a single-table SQL QA task. +Return strict JSON only, with this schema: {"sql": "...", "notes": "..."}. +Rules: +- Use only the provided table and columns. +- Do not write INSERT, UPDATE, DELETE, DROP, ALTER, CREATE, PRAGMA, ATTACH, DETACH, or VACUUM. +- Prefer the planned template and bound roles when provided. +- Add a leading SQL comment exactly like: -- template_id: . +- Generate SQLite-compatible SQL. SQLite does not support PERCENTILE_CONT or STDDEV. +- Quote identifiers with double quotes. +- Return no markdown and no extra prose. + +Dataset context: +Dataset context for SQL QA: +- dataset_id: n1 +- dataset_name: Spambase +- table_name: n1 +- table_layout: single-table dataset (do not assume joins). +- row_semantics: One row is one email represented by engineered token/character frequency and capitalization-run features. +- task_type: classification +- target_column: class +- main_row_count: 4601 +- important_fields: +- word_freq_make: role=feature, type=numeric_sparse_frequency. tags=['condition_candidate', 'measure', 'rare_pattern_candidate'] desc=Frequency feature for token 'make' in an email message. +- word_freq_address: role=feature, type=numeric_sparse_frequency. tags=['condition_candidate', 'measure', 'rare_pattern_candidate'] desc=Frequency feature for token 'address' in an email message. +- word_freq_all: role=feature, type=numeric_sparse_frequency. tags=['condition_candidate', 'measure', 'rare_pattern_candidate'] desc=Frequency feature for token 'all' in an email message. +- word_freq_3d: role=feature, type=numeric_sparse_frequency. tags=['condition_candidate', 'measure', 'rare_pattern_candidate'] desc=Frequency feature for token '3d' in an email message. +- word_freq_our: role=feature, type=numeric_sparse_frequency. tags=['condition_candidate', 'measure', 'rare_pattern_candidate'] desc=Frequency feature for token 'our' in an email message. +- word_freq_over: role=feature, type=numeric_sparse_frequency. tags=['condition_candidate', 'measure', 'rare_pattern_candidate'] desc=Frequency feature for token 'over' in an email message. +- word_freq_remove: role=feature, type=numeric_sparse_frequency. tags=['condition_candidate', 'measure', 'rare_pattern_candidate'] desc=Frequency feature for token 'remove' in an email message. +- word_freq_internet: role=feature, type=numeric_sparse_frequency. tags=['condition_candidate', 'measure', 'rare_pattern_candidate'] desc=Frequency feature for token 'internet' in an email message. +- word_freq_order: role=feature, type=numeric_sparse_frequency. tags=['condition_candidate', 'measure', 'rare_pattern_candidate'] desc=Frequency feature for token 'order' in an email message. +- word_freq_mail: role=feature, type=numeric_sparse_frequency. tags=['condition_candidate', 'measure', 'rare_pattern_candidate'] desc=Frequency feature for token 'mail' in an email message. +- word_freq_receive: role=feature, type=numeric_sparse_frequency. tags=['condition_candidate', 'measure', 'rare_pattern_candidate'] desc=Frequency feature for token 'receive' in an email message. +- word_freq_will: role=feature, type=numeric_sparse_frequency. tags=['condition_candidate', 'measure', 'rare_pattern_candidate'] desc=Frequency feature for token 'will' in an email message. +- word_freq_people: role=feature, type=numeric_sparse_frequency. tags=['condition_candidate', 'measure', 'rare_pattern_candidate'] desc=Frequency feature for token 'people' in an email message. +- word_freq_report: role=feature, type=numeric_sparse_frequency. tags=['condition_candidate', 'measure', 'rare_pattern_candidate'] desc=Frequency feature for token 'report' in an email message. +- word_freq_addresses: role=feature, type=numeric_sparse_frequency. tags=['condition_candidate', 'measure', 'rare_pattern_candidate'] desc=Frequency feature for token 'addresses' in an email message. +- word_freq_free: role=feature, type=numeric_sparse_frequency. tags=['condition_candidate', 'measure', 'rare_pattern_candidate'] desc=Frequency feature for token 'free' in an email message. +- word_freq_business: role=feature, type=numeric_sparse_frequency. tags=['condition_candidate', 'measure', 'rare_pattern_candidate'] desc=Frequency feature for token 'business' in an email message. +- word_freq_email: role=feature, type=numeric_sparse_frequency. tags=['condition_candidate', 'measure', 'rare_pattern_candidate'] desc=Frequency feature for token 'email' in an email message. +- word_freq_you: role=feature, type=numeric_sparse_frequency. tags=['condition_candidate', 'measure', 'rare_pattern_candidate'] desc=Frequency feature for token 'you' in an email message. +- word_freq_credit: role=feature, type=numeric_sparse_frequency. tags=['condition_candidate', 'measure', 'rare_pattern_candidate'] desc=Frequency feature for token 'credit' in an email message. +- word_freq_your: role=feature, type=numeric_sparse_frequency. tags=['condition_candidate', 'measure', 'rare_pattern_candidate'] desc=Frequency feature for token 'your' in an email message. +- word_freq_font: role=feature, type=numeric_sparse_frequency. tags=['condition_candidate', 'measure', 'rare_pattern_candidate'] desc=Frequency feature for token 'font' in an email message. +- word_freq_000: role=feature, type=numeric_sparse_frequency. tags=['condition_candidate', 'measure', 'rare_pattern_candidate'] desc=Frequency feature for token '000' in an email message. +- word_freq_money: role=feature, type=numeric_sparse_frequency. tags=['condition_candidate', 'measure', 'rare_pattern_candidate'] desc=Frequency feature for token 'money' in an email message. +- word_freq_hp: role=feature, type=numeric_sparse_frequency. tags=['condition_candidate', 'measure', 'rare_pattern_candidate'] desc=Frequency feature for token 'hp' in an email message. +- word_freq_hpl: role=feature, type=numeric_sparse_frequency. tags=['condition_candidate', 'measure', 'rare_pattern_candidate'] desc=Frequency feature for token 'hpl' in an email message. +- word_freq_george: role=feature, type=numeric_sparse_frequency. tags=['condition_candidate', 'measure', 'rare_pattern_candidate'] desc=Frequency feature for token 'george' in an email message. +- word_freq_650: role=feature, type=numeric_sparse_frequency. tags=['condition_candidate', 'measure', 'rare_pattern_candidate'] desc=Frequency feature for token '650' in an email message. +- word_freq_lab: role=feature, type=numeric_sparse_frequency. tags=['condition_candidate', 'measure', 'rare_pattern_candidate'] desc=Frequency feature for token 'lab' in an email message. +- word_freq_labs: role=feature, type=numeric_sparse_frequency. tags=['condition_candidate', 'measure', 'rare_pattern_candidate'] desc=Frequency feature for token 'labs' in an email message. +- word_freq_telnet: role=feature, type=numeric_sparse_frequency. tags=['condition_candidate', 'measure', 'rare_pattern_candidate'] desc=Frequency feature for token 'telnet' in an email message. +- word_freq_857: role=feature, type=numeric_sparse_frequency. tags=['condition_candidate', 'measure', 'rare_pattern_candidate'] desc=Frequency feature for token '857' in an email message. +- word_freq_data: role=feature, type=numeric_sparse_frequency. tags=['condition_candidate', 'measure', 'rare_pattern_candidate'] desc=Frequency feature for token 'data' in an email message. +- word_freq_415: role=feature, type=numeric_sparse_frequency. tags=['condition_candidate', 'measure', 'rare_pattern_candidate'] desc=Frequency feature for token '415' in an email message. +- word_freq_85: role=feature, type=numeric_sparse_frequency. tags=['condition_candidate', 'measure', 'rare_pattern_candidate'] desc=Frequency feature for token '85' in an email message. +- word_freq_technology: role=feature, type=numeric_sparse_frequency. tags=['condition_candidate', 'measure', 'rare_pattern_candidate'] desc=Frequency feature for token 'technology' in an email message. +- word_freq_1999: role=feature, type=numeric_sparse_frequency. tags=['condition_candidate', 'measure', 'rare_pattern_candidate'] desc=Frequency feature for token '1999' in an email message. +- word_freq_parts: role=feature, type=numeric_sparse_frequency. tags=['condition_candidate', 'measure', 'rare_pattern_candidate'] desc=Frequency feature for token 'parts' in an email message. +- word_freq_pm: role=feature, type=numeric_sparse_frequency. tags=['condition_candidate', 'measure', 'rare_pattern_candidate'] desc=Frequency feature for token 'pm' in an email message. +- word_freq_direct: role=feature, type=numeric_sparse_frequency. tags=['condition_candidate', 'measure', 'rare_pattern_candidate'] desc=Frequency feature for token 'direct' in an email message. +- word_freq_cs: role=feature, type=numeric_sparse_frequency. tags=['condition_candidate', 'measure', 'rare_pattern_candidate'] desc=Frequency feature for token 'cs' in an email message. +- word_freq_meeting: role=feature, type=numeric_sparse_frequency. tags=['condition_candidate', 'measure', 'rare_pattern_candidate'] desc=Frequency feature for token 'meeting' in an email message. +- word_freq_original: role=feature, type=numeric_sparse_frequency. tags=['condition_candidate', 'measure', 'rare_pattern_candidate'] desc=Frequency feature for token 'original' in an email message. +- word_freq_project: role=feature, type=numeric_sparse_frequency. tags=['condition_candidate', 'measure', 'rare_pattern_candidate'] desc=Frequency feature for token 'project' in an email message. +- word_freq_re: role=feature, type=numeric_sparse_frequency. tags=['condition_candidate', 'measure', 'rare_pattern_candidate'] desc=Frequency feature for token 're' in an email message. +- word_freq_edu: role=feature, type=numeric_sparse_frequency. tags=['condition_candidate', 'measure', 'rare_pattern_candidate'] desc=Frequency feature for token 'edu' in an email message. +- word_freq_table: role=feature, type=numeric_sparse_frequency. tags=['condition_candidate', 'measure', 'rare_pattern_candidate'] desc=Frequency feature for token 'table' in an email message. +- word_freq_conference: role=feature, type=numeric_sparse_frequency. tags=['condition_candidate', 'measure', 'rare_pattern_candidate'] desc=Frequency feature for token 'conference' in an email message. +- char_freq_%3B: role=feature, type=numeric_sparse_frequency. tags=['condition_candidate', 'measure', 'rare_pattern_candidate'] desc=Character frequency feature for symbol ';'. +- char_freq_%28: role=feature, type=numeric_sparse_frequency. tags=['condition_candidate', 'measure', 'rare_pattern_candidate'] desc=Character frequency feature for symbol '('. +- char_freq_%5B: role=feature, type=numeric_sparse_frequency. tags=['condition_candidate', 'measure', 'rare_pattern_candidate'] desc=Character frequency feature for symbol '['. +- char_freq_%21: role=feature, type=numeric_sparse_frequency. tags=['condition_candidate', 'measure', 'rare_pattern_candidate'] desc=Character frequency feature for symbol '!'. +- char_freq_%24: role=feature, type=numeric_sparse_frequency. tags=['condition_candidate', 'measure', 'rare_pattern_candidate'] desc=Character frequency feature for symbol '$'. +- char_freq_%23: role=feature, type=numeric_sparse_frequency. tags=['condition_candidate', 'measure', 'rare_pattern_candidate'] desc=Character frequency feature for symbol '#'. +- capital_run_length_average: role=feature, type=numeric_heavy_tail. tags=['condition_candidate', 'measure', 'rare_pattern_candidate'] desc=Average length of uninterrupted capital-letter runs. +- capital_run_length_longest: role=feature, type=numeric_heavy_tail. tags=['condition_candidate', 'measure', 'rare_pattern_candidate'] desc=Longest uninterrupted capital-letter run. +- capital_run_length_total: role=feature, type=numeric_heavy_tail. tags=['condition_candidate', 'measure', 'rare_pattern_candidate'] desc=Total number of capital letters in runs. +- class: role=target, type=binary_target. tags=['target_candidate'] desc=Binary spam label (1=spam, 0=non-spam). +- useful_field_combinations: [['word_freq_free', 'word_freq_you', 'class'], ['char_freq_%21', 'capital_run_length_longest', 'class'], ['word_freq_remove', 'word_freq_money', 'class']] +- fields_requiring_caution: ['capital_run_length_average', 'capital_run_length_longest', 'capital_run_length_total'] +- source_url: https://www.openml.org/d/44 + +SQLite schema snapshot: +{ + "table_name": "n1", + "quoted_table_name": "\"n1\"", + "row_count": 4601, + "columns": [ + { + "name": "word_freq_make", + "type": "TEXT", + "notnull": false, + "pk": false + }, + { + "name": "word_freq_address", + "type": "TEXT", + "notnull": false, + "pk": false + }, + { + "name": "word_freq_all", + "type": "TEXT", + "notnull": false, + "pk": false + }, + { + "name": "word_freq_3d", + "type": "TEXT", + "notnull": false, + "pk": false + }, + { + "name": "word_freq_our", + "type": "TEXT", + "notnull": false, + "pk": false + }, + { + "name": "word_freq_over", + "type": "TEXT", + "notnull": false, + "pk": false + }, + { + "name": "word_freq_remove", + "type": "TEXT", + "notnull": false, + "pk": false + }, + { + "name": "word_freq_internet", + "type": "TEXT", + "notnull": false, + "pk": false + }, + { + "name": "word_freq_order", + "type": "TEXT", + "notnull": false, + "pk": false + }, + { + "name": "word_freq_mail", + "type": "TEXT", + "notnull": false, + "pk": false + }, + { + "name": "word_freq_receive", + "type": "TEXT", + "notnull": false, + "pk": false + }, + { + "name": "word_freq_will", + "type": "TEXT", + "notnull": false, + "pk": false + }, + { + "name": "word_freq_people", + "type": "TEXT", + "notnull": false, + "pk": false + }, + { + "name": "word_freq_report", + "type": "TEXT", + "notnull": false, + "pk": false + }, + { + "name": "word_freq_addresses", + "type": "TEXT", + "notnull": false, + "pk": false + }, + { + "name": "word_freq_free", + "type": "TEXT", + "notnull": false, + "pk": false + }, + { + "name": "word_freq_business", + "type": "TEXT", + "notnull": false, + "pk": false + }, + { + "name": "word_freq_email", + "type": "TEXT", + "notnull": false, + "pk": false + }, + { + "name": "word_freq_you", + "type": "TEXT", + "notnull": false, + "pk": false + }, + { + "name": "word_freq_credit", + "type": "TEXT", + "notnull": false, + "pk": false + }, + { + "name": "word_freq_your", + "type": "TEXT", + "notnull": false, + "pk": false + }, + { + "name": "word_freq_font", + "type": "TEXT", + "notnull": false, + "pk": false + }, + { + "name": "word_freq_000", + "type": "TEXT", + "notnull": false, + "pk": false + }, + { + "name": "word_freq_money", + "type": "TEXT", + "notnull": false, + "pk": false + }, + { + "name": "word_freq_hp", + "type": "TEXT", + "notnull": false, + "pk": false + }, + { + "name": "word_freq_hpl", + "type": "TEXT", + "notnull": false, + "pk": false + }, + { + "name": "word_freq_george", + "type": "TEXT", + "notnull": false, + "pk": false + }, + { + "name": "word_freq_650", + "type": "TEXT", + "notnull": false, + "pk": false + }, + { + "name": "word_freq_lab", + "type": "TEXT", + "notnull": false, + "pk": false + }, + { + "name": "word_freq_labs", + "type": "TEXT", + "notnull": false, + "pk": false + }, + { + "name": "word_freq_telnet", + "type": "TEXT", + "notnull": false, + "pk": false + }, + { + "name": "word_freq_857", + "type": "TEXT", + "notnull": false, + "pk": false + }, + { + "name": "word_freq_data", + "type": "TEXT", + "notnull": false, + "pk": false + }, + { + "name": "word_freq_415", + "type": "TEXT", + "notnull": false, + "pk": false + }, + { + "name": "word_freq_85", + "type": "TEXT", + "notnull": false, + "pk": false + }, + { + "name": "word_freq_technology", + "type": "TEXT", + "notnull": false, + "pk": false + }, + { + "name": "word_freq_1999", + "type": "TEXT", + "notnull": false, + "pk": false + }, + { + "name": "word_freq_parts", + "type": "TEXT", + "notnull": false, + "pk": false + }, + { + "name": "word_freq_pm", + "type": "TEXT", + "notnull": false, + "pk": false + }, + { + "name": "word_freq_direct", + "type": "TEXT", + "notnull": false, + "pk": false + }, + { + "name": "word_freq_cs", + "type": "TEXT", + "notnull": false, + "pk": false + }, + { + "name": "word_freq_meeting", + "type": "TEXT", + "notnull": false, + "pk": false + }, + { + "name": "word_freq_original", + "type": "TEXT", + "notnull": false, + "pk": false + }, + { + "name": "word_freq_project", + "type": "TEXT", + "notnull": false, + "pk": false + }, + { + "name": "word_freq_re", + "type": "TEXT", + "notnull": false, + "pk": false + }, + { + "name": "word_freq_edu", + "type": "TEXT", + "notnull": false, + "pk": false + }, + { + "name": "word_freq_table", + "type": "TEXT", + "notnull": false, + "pk": false + }, + { + "name": "word_freq_conference", + "type": "TEXT", + "notnull": false, + "pk": false + }, + { + "name": "char_freq_%3B", + "type": "TEXT", + "notnull": false, + "pk": false + }, + { + "name": "char_freq_%28", + "type": "TEXT", + "notnull": false, + "pk": false + }, + { + "name": "char_freq_%5B", + "type": "TEXT", + "notnull": false, + "pk": false + }, + { + "name": "char_freq_%21", + "type": "TEXT", + "notnull": false, + "pk": false + }, + { + "name": "char_freq_%24", + "type": "TEXT", + "notnull": false, + "pk": false + }, + { + "name": "char_freq_%23", + "type": "TEXT", + "notnull": false, + "pk": false + }, + { + "name": "capital_run_length_average", + "type": "TEXT", + "notnull": false, + "pk": false + }, + { + "name": "capital_run_length_longest", + "type": "TEXT", + "notnull": false, + "pk": false + }, + { + "name": "capital_run_length_total", + "type": "TEXT", + "notnull": false, + "pk": false + }, + { + "name": "class", + "type": "TEXT", + "notnull": false, + "pk": false + } + ], + "sample_rows": [ + { + "word_freq_make": "0", + "word_freq_address": "0.64", + "word_freq_all": "0.64", + "word_freq_3d": "0", + "word_freq_our": "0.32", + "word_freq_over": "0", + "word_freq_remove": "0", + "word_freq_internet": "0", + "word_freq_order": "0", + "word_freq_mail": "0", + "word_freq_receive": "0", + "word_freq_will": "0.64", + "word_freq_people": "0", + "word_freq_report": "0", + "word_freq_addresses": "0", + "word_freq_free": "0.32", + "word_freq_business": "0", + "word_freq_email": "1.29", + "word_freq_you": "1.93", + "word_freq_credit": "0", + "word_freq_your": "0.96", + "word_freq_font": "0", + "word_freq_000": "0", + "word_freq_money": "0", + "word_freq_hp": "0", + "word_freq_hpl": "0", + "word_freq_george": "0", + "word_freq_650": "0", + "word_freq_lab": "0", + "word_freq_labs": "0", + "word_freq_telnet": "0", + "word_freq_857": "0", + "word_freq_data": "0", + "word_freq_415": "0", + "word_freq_85": "0", + "word_freq_technology": "0", + "word_freq_1999": "0", + "word_freq_parts": "0", + "word_freq_pm": "0", + "word_freq_direct": "0", + "word_freq_cs": "0", + "word_freq_meeting": "0", + "word_freq_original": "0", + "word_freq_project": "0", + "word_freq_re": "0", + "word_freq_edu": "0", + "word_freq_table": "0", + "word_freq_conference": "0", + "char_freq_%3B": "0", + "char_freq_%28": "0", + "char_freq_%5B": "0", + "char_freq_%21": "0.778", + "char_freq_%24": "0", + "char_freq_%23": "0", + "capital_run_length_average": "3.756", + "capital_run_length_longest": "61", + "capital_run_length_total": "278", + "class": "1" + }, + { + "word_freq_make": "0.21", + "word_freq_address": "0.28", + "word_freq_all": "0.5", + "word_freq_3d": "0", + "word_freq_our": "0.14", + "word_freq_over": "0.28", + "word_freq_remove": "0.21", + "word_freq_internet": "0.07", + "word_freq_order": "0", + "word_freq_mail": "0.94", + "word_freq_receive": "0.21", + "word_freq_will": "0.79", + "word_freq_people": "0.65", + "word_freq_report": "0.21", + "word_freq_addresses": "0.14", + "word_freq_free": "0.14", + "word_freq_business": "0.07", + "word_freq_email": "0.28", + "word_freq_you": "3.47", + "word_freq_credit": "0", + "word_freq_your": "1.59", + "word_freq_font": "0", + "word_freq_000": "0.43", + "word_freq_money": "0.43", + "word_freq_hp": "0", + "word_freq_hpl": "0", + "word_freq_george": "0", + "word_freq_650": "0", + "word_freq_lab": "0", + "word_freq_labs": "0", + "word_freq_telnet": "0", + "word_freq_857": "0", + "word_freq_data": "0", + "word_freq_415": "0", + "word_freq_85": "0", + "word_freq_technology": "0", + "word_freq_1999": "0.07", + "word_freq_parts": "0", + "word_freq_pm": "0", + "word_freq_direct": "0", + "word_freq_cs": "0", + "word_freq_meeting": "0", + "word_freq_original": "0", + "word_freq_project": "0", + "word_freq_re": "0", + "word_freq_edu": "0", + "word_freq_table": "0", + "word_freq_conference": "0", + "char_freq_%3B": "0", + "char_freq_%28": "0.132", + "char_freq_%5B": "0", + "char_freq_%21": "0.372", + "char_freq_%24": "0.18", + "char_freq_%23": "0.048", + "capital_run_length_average": "5.114", + "capital_run_length_longest": "101", + "capital_run_length_total": "1028", + "class": "1" + }, + { + "word_freq_make": "0.06", + "word_freq_address": "0", + "word_freq_all": "0.71", + "word_freq_3d": "0", + "word_freq_our": "1.23", + "word_freq_over": "0.19", + "word_freq_remove": "0.19", + "word_freq_internet": "0.12", + "word_freq_order": "0.64", + "word_freq_mail": "0.25", + "word_freq_receive": "0.38", + "word_freq_will": "0.45", + "word_freq_people": "0.12", + "word_freq_report": "0", + "word_freq_addresses": "1.75", + "word_freq_free": "0.06", + "word_freq_business": "0.06", + "word_freq_email": "1.03", + "word_freq_you": "1.36", + "word_freq_credit": "0.32", + "word_freq_your": "0.51", + "word_freq_font": "0", + "word_freq_000": "1.16", + "word_freq_money": "0.06", + "word_freq_hp": "0", + "word_freq_hpl": "0", + "word_freq_george": "0", + "word_freq_650": "0", + "word_freq_lab": "0", + "word_freq_labs": "0", + "word_freq_telnet": "0", + "word_freq_857": "0", + "word_freq_data": "0", + "word_freq_415": "0", + "word_freq_85": "0", + "word_freq_technology": "0", + "word_freq_1999": "0", + "word_freq_parts": "0", + "word_freq_pm": "0", + "word_freq_direct": "0.06", + "word_freq_cs": "0", + "word_freq_meeting": "0", + "word_freq_original": "0.12", + "word_freq_project": "0", + "word_freq_re": "0.06", + "word_freq_edu": "0.06", + "word_freq_table": "0", + "word_freq_conference": "0", + "char_freq_%3B": "0.01", + "char_freq_%28": "0.143", + "char_freq_%5B": "0", + "char_freq_%21": "0.276", + "char_freq_%24": "0.184", + "char_freq_%23": "0.01", + "capital_run_length_average": "9.821", + "capital_run_length_longest": "485", + "capital_run_length_total": "2259", + "class": "1" + }, + { + "word_freq_make": "0", + "word_freq_address": "0", + "word_freq_all": "0", + "word_freq_3d": "0", + "word_freq_our": "0.63", + "word_freq_over": "0", + "word_freq_remove": "0.31", + "word_freq_internet": "0.63", + "word_freq_order": "0.31", + "word_freq_mail": "0.63", + "word_freq_receive": "0.31", + "word_freq_will": "0.31", + "word_freq_people": "0.31", + "word_freq_report": "0", + "word_freq_addresses": "0", + "word_freq_free": "0.31", + "word_freq_business": "0", + "word_freq_email": "0", + "word_freq_you": "3.18", + "word_freq_credit": "0", + "word_freq_your": "0.31", + "word_freq_font": "0", + "word_freq_000": "0", + "word_freq_money": "0", + "word_freq_hp": "0", + "word_freq_hpl": "0", + "word_freq_george": "0", + "word_freq_650": "0", + "word_freq_lab": "0", + "word_freq_labs": "0", + "word_freq_telnet": "0", + "word_freq_857": "0", + "word_freq_data": "0", + "word_freq_415": "0", + "word_freq_85": "0", + "word_freq_technology": "0", + "word_freq_1999": "0", + "word_freq_parts": "0", + "word_freq_pm": "0", + "word_freq_direct": "0", + "word_freq_cs": "0", + "word_freq_meeting": "0", + "word_freq_original": "0", + "word_freq_project": "0", + "word_freq_re": "0", + "word_freq_edu": "0", + "word_freq_table": "0", + "word_freq_conference": "0", + "char_freq_%3B": "0", + "char_freq_%28": "0.137", + "char_freq_%5B": "0", + "char_freq_%21": "0.137", + "char_freq_%24": "0", + "char_freq_%23": "0", + "capital_run_length_average": "3.537", + "capital_run_length_longest": "40", + "capital_run_length_total": "191", + "class": "1" + }, + { + "word_freq_make": "0", + "word_freq_address": "0", + "word_freq_all": "0", + "word_freq_3d": "0", + "word_freq_our": "0.63", + "word_freq_over": "0", + "word_freq_remove": "0.31", + "word_freq_internet": "0.63", + "word_freq_order": "0.31", + "word_freq_mail": "0.63", + "word_freq_receive": "0.31", + "word_freq_will": "0.31", + "word_freq_people": "0.31", + "word_freq_report": "0", + "word_freq_addresses": "0", + "word_freq_free": "0.31", + "word_freq_business": "0", + "word_freq_email": "0", + "word_freq_you": "3.18", + "word_freq_credit": "0", + "word_freq_your": "0.31", + "word_freq_font": "0", + "word_freq_000": "0", + "word_freq_money": "0", + "word_freq_hp": "0", + "word_freq_hpl": "0", + "word_freq_george": "0", + "word_freq_650": "0", + "word_freq_lab": "0", + "word_freq_labs": "0", + "word_freq_telnet": "0", + "word_freq_857": "0", + "word_freq_data": "0", + "word_freq_415": "0", + "word_freq_85": "0", + "word_freq_technology": "0", + "word_freq_1999": "0", + "word_freq_parts": "0", + "word_freq_pm": "0", + "word_freq_direct": "0", + "word_freq_cs": "0", + "word_freq_meeting": "0", + "word_freq_original": "0", + "word_freq_project": "0", + "word_freq_re": "0", + "word_freq_edu": "0", + "word_freq_table": "0", + "word_freq_conference": "0", + "char_freq_%3B": "0", + "char_freq_%28": "0.135", + "char_freq_%5B": "0", + "char_freq_%21": "0.135", + "char_freq_%24": "0", + "char_freq_%23": "0", + "capital_run_length_average": "3.537", + "capital_run_length_longest": "40", + "capital_run_length_total": "191", + "class": "1" + } + ] +} + +Shortlisted templates: +[ + { + "template_id": "tpl_m4_group_ratio_two_conditions", + "template_name": "Grouped Ratio of Two Conditions", + "primary_family": "conditional_dependency_structure", + "portability": "yes", + "sql_skeleton": "WITH grouped AS (\n SELECT {group_col},\n SUM(CASE WHEN {condition_col} = {positive_value} THEN 1 ELSE 0 END) AS numerator_count,\n SUM(CASE WHEN {condition_col} = {negative_value} THEN 1 ELSE 0 END) AS denominator_count\n FROM {table}\n GROUP BY {group_col}\n)\nSELECT {group_col},\n CAST(numerator_count AS FLOAT) / NULLIF(denominator_count, 0) AS condition_ratio\nFROM grouped\nORDER BY condition_ratio DESC;", + "required_roles": [ + "group_col", + "condition_col" + ] + } +] + +Problem instance: +{ + "dataset_id": "n1", + "question": "Use template Grouped Ratio of Two Conditions to probe direction_consistency with semantic role contrastive_conditional_view. Focus on group_col=class, condition_col=class.", + "planned_template_id": "tpl_m4_group_ratio_two_conditions", + "bindings": { + "group_col": "class", + "condition_col": "class", + "condition_value": "0", + "positive_value": "0", + "negative_value": "1", + "top_k": 11, + "top_n": 4, + "num_tiles": 10, + "percentile_value": 0.9, + "z_threshold": 2.0, + "fraction_threshold": 0.1, + "baseline_multiplier": 1.5, + "baseline_fraction": 0.1, + "min_group_size": 5, + "min_support": 5, + "measure_threshold": 0.0, + "time_grain": "month", + "lookback_rows": 3, + "current_period_start": "'2024-01-01'", + "current_period_end": "'2024-04-01'", + "previous_period_start": "'2023-10-01'", + "previous_period_end": "'2024-01-01'", + "drift_ratio_threshold": 0.8 + }, + "can_vary": [], + "must_fix": [], + "runtime_sql_skeleton": "WITH grouped AS (\n SELECT {group_col},\n SUM(CASE WHEN {condition_col} = {positive_value} THEN 1 ELSE 0 END) AS numerator_count,\n SUM(CASE WHEN {condition_col} = {negative_value} THEN 1 ELSE 0 END) AS denominator_count\n FROM {table}\n GROUP BY {group_col}\n)\nSELECT {group_col},\n CAST(numerator_count AS FLOAT) / NULLIF(denominator_count, 0) AS condition_ratio\nFROM grouped\nORDER BY condition_ratio DESC;" +} + +Repair context: +{} diff --git a/Query/sql/v2/runs/v2_cli_20260502_081223_d/n1/artifacts/v2q_n1_4069c2f7e4b1c80d/cli/sql_response_attempt_1.raw.txt b/Query/sql/v2/runs/v2_cli_20260502_081223_d/n1/artifacts/v2q_n1_4069c2f7e4b1c80d/cli/sql_response_attempt_1.raw.txt new file mode 100644 index 0000000000000000000000000000000000000000..8e17e97693907204e9a984de5bfba79ded569e65 --- /dev/null +++ b/Query/sql/v2/runs/v2_cli_20260502_081223_d/n1/artifacts/v2q_n1_4069c2f7e4b1c80d/cli/sql_response_attempt_1.raw.txt @@ -0,0 +1,7 @@ +{"type":"thread.started","thread_id":"019e40e5-4c8a-7970-ac47-d493c245a51a"} +{"type":"turn.started"} +{"type":"error","message":"Reconnecting... 2/5 (stream disconnected before completion: Rate limit reached for gpt-5.4 in organization org-yXK0mBGOR7eXhtGEx8v7e3bf on tokens per min (TPM): Limit 2000000, Used 1982774, Requested 21155. Please try again in 117ms. Visit https://platform.openai.com/account/rate-limits to learn more.)"} +{"type":"error","message":"Reconnecting... 3/5 (stream disconnected before completion: Rate limit reached for gpt-5.4 in organization org-yXK0mBGOR7eXhtGEx8v7e3bf on tokens per min (TPM): Limit 2000000, Used 1987510, Requested 21155. Please try again in 259ms. Visit https://platform.openai.com/account/rate-limits to learn more.)"} +{"type":"error","message":"Reconnecting... 4/5 (stream disconnected before completion: Rate limit reached for gpt-5.4 in organization org-yXK0mBGOR7eXhtGEx8v7e3bf on tokens per min (TPM): Limit 2000000, Used 1980203, Requested 21155. Please try again in 40ms. Visit https://platform.openai.com/account/rate-limits to learn more.)"} +{"type":"item.completed","item":{"id":"item_0","type":"agent_message","text":"{\"sql\":\"-- template_id: tpl_m4_group_ratio_two_conditions\\nWITH \\\"grouped\\\" AS (\\n SELECT\\n \\\"class\\\",\\n SUM(CASE WHEN \\\"class\\\" = '0' THEN 1 ELSE 0 END) AS \\\"numerator_count\\\",\\n SUM(CASE WHEN \\\"class\\\" = '1' THEN 1 ELSE 0 END) AS \\\"denominator_count\\\"\\n FROM \\\"n1\\\"\\n GROUP BY \\\"class\\\"\\n)\\nSELECT\\n \\\"class\\\",\\n CAST(\\\"numerator_count\\\" AS FLOAT) / NULLIF(\\\"denominator_count\\\", 0) AS \\\"condition_ratio\\\"\\nFROM \\\"grouped\\\"\\nORDER BY \\\"condition_ratio\\\" DESC;\",\"notes\":\"Applied the requested template with group_col=\\\"class\\\" and condition_col=\\\"class\\\". Because the grouping column and condition column are the same, the '0' group yields a NULL ratio (no '1' rows in that group) and the '1' group yields 0.\"}"}} +{"type":"turn.completed","usage":{"input_tokens":20520,"cached_input_tokens":19840,"output_tokens":473,"reasoning_output_tokens":257}} diff --git a/Query/sql/v2/runs/v2_cli_20260502_081223_d/n1/artifacts/v2q_n1_4069c2f7e4b1c80d/cli/sql_response_attempt_1.txt b/Query/sql/v2/runs/v2_cli_20260502_081223_d/n1/artifacts/v2q_n1_4069c2f7e4b1c80d/cli/sql_response_attempt_1.txt new file mode 100644 index 0000000000000000000000000000000000000000..b1ef0553dc9be95ddb72926d52f1ea9d4c95e893 --- /dev/null +++ b/Query/sql/v2/runs/v2_cli_20260502_081223_d/n1/artifacts/v2q_n1_4069c2f7e4b1c80d/cli/sql_response_attempt_1.txt @@ -0,0 +1 @@ +{"sql":"-- template_id: tpl_m4_group_ratio_two_conditions\nWITH \"grouped\" AS (\n SELECT\n \"class\",\n SUM(CASE WHEN \"class\" = '0' THEN 1 ELSE 0 END) AS \"numerator_count\",\n SUM(CASE WHEN \"class\" = '1' THEN 1 ELSE 0 END) AS \"denominator_count\"\n FROM \"n1\"\n GROUP BY \"class\"\n)\nSELECT\n \"class\",\n CAST(\"numerator_count\" AS FLOAT) / NULLIF(\"denominator_count\", 0) AS \"condition_ratio\"\nFROM \"grouped\"\nORDER BY \"condition_ratio\" DESC;","notes":"Applied the requested template with group_col=\"class\" and condition_col=\"class\". Because the grouping column and condition column are the same, the '0' group yields a NULL ratio (no '1' rows in that group) and the '1' group yields 0."} \ No newline at end of file diff --git a/Query/sql/v2/runs/v2_cli_20260502_081223_d/n1/artifacts/v2q_n1_4069c2f7e4b1c80d/cli/sql_stderr_attempt_1.txt b/Query/sql/v2/runs/v2_cli_20260502_081223_d/n1/artifacts/v2q_n1_4069c2f7e4b1c80d/cli/sql_stderr_attempt_1.txt new file mode 100644 index 0000000000000000000000000000000000000000..e69de29bb2d1d6434b8b29ae775ad8c2e48c5391 diff --git a/Query/sql/v2/runs/v2_cli_20260502_081223_d/n1/artifacts/v2q_n1_4076fdb3e0b8bbdd/cli/sql_attempt_1.metadata.json b/Query/sql/v2/runs/v2_cli_20260502_081223_d/n1/artifacts/v2q_n1_4076fdb3e0b8bbdd/cli/sql_attempt_1.metadata.json new file mode 100644 index 0000000000000000000000000000000000000000..a82c2af3eef503bc378d1bef8b4ca2377a4df62c --- /dev/null +++ b/Query/sql/v2/runs/v2_cli_20260502_081223_d/n1/artifacts/v2q_n1_4076fdb3e0b8bbdd/cli/sql_attempt_1.metadata.json @@ -0,0 +1,43 @@ +{ + "attempt": 1, + "phase": "sql_generation", + "command": "codex exec --skip-git-repo-check --disable plugins --sandbox read-only --cd \"/data/jialinzhang/SQLagent\" -m gpt-5.4 --json -", + "started_at": "2026-05-19T16:03:40.566166+00:00", + "ended_at": "2026-05-19T16:03:43.898790+00:00", + "elapsed_ms": 3332.6, + "returncode": 1, + "prompt_metrics": { + "chars": 29335, + "bytes_utf8": 29335, + "lines": 790, + "estimated_tokens": null + }, + "stdout_metrics": { + "chars": 281, + "bytes_utf8": 281, + "lines": 4, + "estimated_tokens": null + }, + "stderr_metrics": { + "chars": 0, + "bytes_utf8": 0, + "lines": 0, + "estimated_tokens": null + }, + "parsed_output": { + "format": "jsonl_events", + "text_metrics": { + "chars": 280, + "bytes_utf8": 280, + "lines": 4, + "estimated_tokens": null + }, + "usage": {} + }, + "status": "failed", + "error": "AI CLI command failed with exit code 1: ", + "prompt_path": "cli/sql_prompt_attempt_1.txt", + "response_path": "cli/sql_response_attempt_1.txt", + "raw_response_path": "cli/sql_response_attempt_1.raw.txt", + "stderr_path": "cli/sql_stderr_attempt_1.txt" +} \ No newline at end of file diff --git a/Query/sql/v2/runs/v2_cli_20260502_081223_d/n1/artifacts/v2q_n1_4076fdb3e0b8bbdd/cli/sql_attempt_2.metadata.json b/Query/sql/v2/runs/v2_cli_20260502_081223_d/n1/artifacts/v2q_n1_4076fdb3e0b8bbdd/cli/sql_attempt_2.metadata.json new file mode 100644 index 0000000000000000000000000000000000000000..bfc96c89559a25899054f0804c4d4026a74db3d3 --- /dev/null +++ b/Query/sql/v2/runs/v2_cli_20260502_081223_d/n1/artifacts/v2q_n1_4076fdb3e0b8bbdd/cli/sql_attempt_2.metadata.json @@ -0,0 +1,43 @@ +{ + "attempt": 2, + "phase": "sql_generation", + "command": "codex exec --skip-git-repo-check --disable plugins --sandbox read-only --cd \"/data/jialinzhang/SQLagent\" -m gpt-5.4 --json -", + "started_at": "2026-05-19T16:03:44.901062+00:00", + "ended_at": "2026-05-19T16:03:47.787318+00:00", + "elapsed_ms": 2886.22, + "returncode": 1, + "prompt_metrics": { + "chars": 29335, + "bytes_utf8": 29335, + "lines": 790, + "estimated_tokens": null + }, + "stdout_metrics": { + "chars": 281, + "bytes_utf8": 281, + "lines": 4, + "estimated_tokens": null + }, + "stderr_metrics": { + "chars": 0, + "bytes_utf8": 0, + "lines": 0, + "estimated_tokens": null + }, + "parsed_output": { + "format": "jsonl_events", + "text_metrics": { + "chars": 280, + "bytes_utf8": 280, + "lines": 4, + "estimated_tokens": null + }, + "usage": {} + }, + "status": "failed", + "error": "AI CLI command failed with exit code 1: ", + "prompt_path": "cli/sql_prompt_attempt_2.txt", + "response_path": "cli/sql_response_attempt_2.txt", + "raw_response_path": "cli/sql_response_attempt_2.raw.txt", + "stderr_path": "cli/sql_stderr_attempt_2.txt" +} \ No newline at end of file diff --git a/Query/sql/v2/runs/v2_cli_20260502_081223_d/n1/artifacts/v2q_n1_4076fdb3e0b8bbdd/cli/sql_prompt_attempt_1.txt b/Query/sql/v2/runs/v2_cli_20260502_081223_d/n1/artifacts/v2q_n1_4076fdb3e0b8bbdd/cli/sql_prompt_attempt_1.txt new file mode 100644 index 0000000000000000000000000000000000000000..c85f1be6d96dbe7af16358363386c60d75847081 --- /dev/null +++ b/Query/sql/v2/runs/v2_cli_20260502_081223_d/n1/artifacts/v2q_n1_4076fdb3e0b8bbdd/cli/sql_prompt_attempt_1.txt @@ -0,0 +1,790 @@ +You are generating one SQLite SELECT query for a single-table SQL QA task. +Return strict JSON only, with this schema: {"sql": "...", "notes": "..."}. +Rules: +- Use only the provided table and columns. +- Do not write INSERT, UPDATE, DELETE, DROP, ALTER, CREATE, PRAGMA, ATTACH, DETACH, or VACUUM. +- Prefer the planned template and bound roles when provided. +- Add a leading SQL comment exactly like: -- template_id: . +- Generate SQLite-compatible SQL. SQLite does not support PERCENTILE_CONT or STDDEV. +- Quote identifiers with double quotes. +- Return no markdown and no extra prose. + +Dataset context: +Dataset context for SQL QA: +- dataset_id: n1 +- dataset_name: Spambase +- table_name: n1 +- table_layout: single-table dataset (do not assume joins). +- row_semantics: One row is one email represented by engineered token/character frequency and capitalization-run features. +- task_type: classification +- target_column: class +- main_row_count: 4601 +- important_fields: +- word_freq_make: role=feature, type=numeric_sparse_frequency. tags=['condition_candidate', 'measure', 'rare_pattern_candidate'] desc=Frequency feature for token 'make' in an email message. +- word_freq_address: role=feature, type=numeric_sparse_frequency. tags=['condition_candidate', 'measure', 'rare_pattern_candidate'] desc=Frequency feature for token 'address' in an email message. +- word_freq_all: role=feature, type=numeric_sparse_frequency. tags=['condition_candidate', 'measure', 'rare_pattern_candidate'] desc=Frequency feature for token 'all' in an email message. +- word_freq_3d: role=feature, type=numeric_sparse_frequency. tags=['condition_candidate', 'measure', 'rare_pattern_candidate'] desc=Frequency feature for token '3d' in an email message. +- word_freq_our: role=feature, type=numeric_sparse_frequency. tags=['condition_candidate', 'measure', 'rare_pattern_candidate'] desc=Frequency feature for token 'our' in an email message. +- word_freq_over: role=feature, type=numeric_sparse_frequency. tags=['condition_candidate', 'measure', 'rare_pattern_candidate'] desc=Frequency feature for token 'over' in an email message. +- word_freq_remove: role=feature, type=numeric_sparse_frequency. tags=['condition_candidate', 'measure', 'rare_pattern_candidate'] desc=Frequency feature for token 'remove' in an email message. +- word_freq_internet: role=feature, type=numeric_sparse_frequency. tags=['condition_candidate', 'measure', 'rare_pattern_candidate'] desc=Frequency feature for token 'internet' in an email message. +- word_freq_order: role=feature, type=numeric_sparse_frequency. tags=['condition_candidate', 'measure', 'rare_pattern_candidate'] desc=Frequency feature for token 'order' in an email message. +- word_freq_mail: role=feature, type=numeric_sparse_frequency. tags=['condition_candidate', 'measure', 'rare_pattern_candidate'] desc=Frequency feature for token 'mail' in an email message. +- word_freq_receive: role=feature, type=numeric_sparse_frequency. tags=['condition_candidate', 'measure', 'rare_pattern_candidate'] desc=Frequency feature for token 'receive' in an email message. +- word_freq_will: role=feature, type=numeric_sparse_frequency. tags=['condition_candidate', 'measure', 'rare_pattern_candidate'] desc=Frequency feature for token 'will' in an email message. +- word_freq_people: role=feature, type=numeric_sparse_frequency. tags=['condition_candidate', 'measure', 'rare_pattern_candidate'] desc=Frequency feature for token 'people' in an email message. +- word_freq_report: role=feature, type=numeric_sparse_frequency. tags=['condition_candidate', 'measure', 'rare_pattern_candidate'] desc=Frequency feature for token 'report' in an email message. +- word_freq_addresses: role=feature, type=numeric_sparse_frequency. tags=['condition_candidate', 'measure', 'rare_pattern_candidate'] desc=Frequency feature for token 'addresses' in an email message. +- word_freq_free: role=feature, type=numeric_sparse_frequency. tags=['condition_candidate', 'measure', 'rare_pattern_candidate'] desc=Frequency feature for token 'free' in an email message. +- word_freq_business: role=feature, type=numeric_sparse_frequency. tags=['condition_candidate', 'measure', 'rare_pattern_candidate'] desc=Frequency feature for token 'business' in an email message. +- word_freq_email: role=feature, type=numeric_sparse_frequency. tags=['condition_candidate', 'measure', 'rare_pattern_candidate'] desc=Frequency feature for token 'email' in an email message. +- word_freq_you: role=feature, type=numeric_sparse_frequency. tags=['condition_candidate', 'measure', 'rare_pattern_candidate'] desc=Frequency feature for token 'you' in an email message. +- word_freq_credit: role=feature, type=numeric_sparse_frequency. tags=['condition_candidate', 'measure', 'rare_pattern_candidate'] desc=Frequency feature for token 'credit' in an email message. +- word_freq_your: role=feature, type=numeric_sparse_frequency. tags=['condition_candidate', 'measure', 'rare_pattern_candidate'] desc=Frequency feature for token 'your' in an email message. +- word_freq_font: role=feature, type=numeric_sparse_frequency. tags=['condition_candidate', 'measure', 'rare_pattern_candidate'] desc=Frequency feature for token 'font' in an email message. +- word_freq_000: role=feature, type=numeric_sparse_frequency. tags=['condition_candidate', 'measure', 'rare_pattern_candidate'] desc=Frequency feature for token '000' in an email message. +- word_freq_money: role=feature, type=numeric_sparse_frequency. tags=['condition_candidate', 'measure', 'rare_pattern_candidate'] desc=Frequency feature for token 'money' in an email message. +- word_freq_hp: role=feature, type=numeric_sparse_frequency. tags=['condition_candidate', 'measure', 'rare_pattern_candidate'] desc=Frequency feature for token 'hp' in an email message. +- word_freq_hpl: role=feature, type=numeric_sparse_frequency. tags=['condition_candidate', 'measure', 'rare_pattern_candidate'] desc=Frequency feature for token 'hpl' in an email message. +- word_freq_george: role=feature, type=numeric_sparse_frequency. tags=['condition_candidate', 'measure', 'rare_pattern_candidate'] desc=Frequency feature for token 'george' in an email message. +- word_freq_650: role=feature, type=numeric_sparse_frequency. tags=['condition_candidate', 'measure', 'rare_pattern_candidate'] desc=Frequency feature for token '650' in an email message. +- word_freq_lab: role=feature, type=numeric_sparse_frequency. tags=['condition_candidate', 'measure', 'rare_pattern_candidate'] desc=Frequency feature for token 'lab' in an email message. +- word_freq_labs: role=feature, type=numeric_sparse_frequency. tags=['condition_candidate', 'measure', 'rare_pattern_candidate'] desc=Frequency feature for token 'labs' in an email message. +- word_freq_telnet: role=feature, type=numeric_sparse_frequency. tags=['condition_candidate', 'measure', 'rare_pattern_candidate'] desc=Frequency feature for token 'telnet' in an email message. +- word_freq_857: role=feature, type=numeric_sparse_frequency. tags=['condition_candidate', 'measure', 'rare_pattern_candidate'] desc=Frequency feature for token '857' in an email message. +- word_freq_data: role=feature, type=numeric_sparse_frequency. tags=['condition_candidate', 'measure', 'rare_pattern_candidate'] desc=Frequency feature for token 'data' in an email message. +- word_freq_415: role=feature, type=numeric_sparse_frequency. tags=['condition_candidate', 'measure', 'rare_pattern_candidate'] desc=Frequency feature for token '415' in an email message. +- word_freq_85: role=feature, type=numeric_sparse_frequency. tags=['condition_candidate', 'measure', 'rare_pattern_candidate'] desc=Frequency feature for token '85' in an email message. +- word_freq_technology: role=feature, type=numeric_sparse_frequency. tags=['condition_candidate', 'measure', 'rare_pattern_candidate'] desc=Frequency feature for token 'technology' in an email message. +- word_freq_1999: role=feature, type=numeric_sparse_frequency. tags=['condition_candidate', 'measure', 'rare_pattern_candidate'] desc=Frequency feature for token '1999' in an email message. +- word_freq_parts: role=feature, type=numeric_sparse_frequency. tags=['condition_candidate', 'measure', 'rare_pattern_candidate'] desc=Frequency feature for token 'parts' in an email message. +- word_freq_pm: role=feature, type=numeric_sparse_frequency. tags=['condition_candidate', 'measure', 'rare_pattern_candidate'] desc=Frequency feature for token 'pm' in an email message. +- word_freq_direct: role=feature, type=numeric_sparse_frequency. tags=['condition_candidate', 'measure', 'rare_pattern_candidate'] desc=Frequency feature for token 'direct' in an email message. +- word_freq_cs: role=feature, type=numeric_sparse_frequency. tags=['condition_candidate', 'measure', 'rare_pattern_candidate'] desc=Frequency feature for token 'cs' in an email message. +- word_freq_meeting: role=feature, type=numeric_sparse_frequency. tags=['condition_candidate', 'measure', 'rare_pattern_candidate'] desc=Frequency feature for token 'meeting' in an email message. +- word_freq_original: role=feature, type=numeric_sparse_frequency. tags=['condition_candidate', 'measure', 'rare_pattern_candidate'] desc=Frequency feature for token 'original' in an email message. +- word_freq_project: role=feature, type=numeric_sparse_frequency. tags=['condition_candidate', 'measure', 'rare_pattern_candidate'] desc=Frequency feature for token 'project' in an email message. +- word_freq_re: role=feature, type=numeric_sparse_frequency. tags=['condition_candidate', 'measure', 'rare_pattern_candidate'] desc=Frequency feature for token 're' in an email message. +- word_freq_edu: role=feature, type=numeric_sparse_frequency. tags=['condition_candidate', 'measure', 'rare_pattern_candidate'] desc=Frequency feature for token 'edu' in an email message. +- word_freq_table: role=feature, type=numeric_sparse_frequency. tags=['condition_candidate', 'measure', 'rare_pattern_candidate'] desc=Frequency feature for token 'table' in an email message. +- word_freq_conference: role=feature, type=numeric_sparse_frequency. tags=['condition_candidate', 'measure', 'rare_pattern_candidate'] desc=Frequency feature for token 'conference' in an email message. +- char_freq_%3B: role=feature, type=numeric_sparse_frequency. tags=['condition_candidate', 'measure', 'rare_pattern_candidate'] desc=Character frequency feature for symbol ';'. +- char_freq_%28: role=feature, type=numeric_sparse_frequency. tags=['condition_candidate', 'measure', 'rare_pattern_candidate'] desc=Character frequency feature for symbol '('. +- char_freq_%5B: role=feature, type=numeric_sparse_frequency. tags=['condition_candidate', 'measure', 'rare_pattern_candidate'] desc=Character frequency feature for symbol '['. +- char_freq_%21: role=feature, type=numeric_sparse_frequency. tags=['condition_candidate', 'measure', 'rare_pattern_candidate'] desc=Character frequency feature for symbol '!'. +- char_freq_%24: role=feature, type=numeric_sparse_frequency. tags=['condition_candidate', 'measure', 'rare_pattern_candidate'] desc=Character frequency feature for symbol '$'. +- char_freq_%23: role=feature, type=numeric_sparse_frequency. tags=['condition_candidate', 'measure', 'rare_pattern_candidate'] desc=Character frequency feature for symbol '#'. +- capital_run_length_average: role=feature, type=numeric_heavy_tail. tags=['condition_candidate', 'measure', 'rare_pattern_candidate'] desc=Average length of uninterrupted capital-letter runs. +- capital_run_length_longest: role=feature, type=numeric_heavy_tail. tags=['condition_candidate', 'measure', 'rare_pattern_candidate'] desc=Longest uninterrupted capital-letter run. +- capital_run_length_total: role=feature, type=numeric_heavy_tail. tags=['condition_candidate', 'measure', 'rare_pattern_candidate'] desc=Total number of capital letters in runs. +- class: role=target, type=binary_target. tags=['target_candidate'] desc=Binary spam label (1=spam, 0=non-spam). +- useful_field_combinations: [['word_freq_free', 'word_freq_you', 'class'], ['char_freq_%21', 'capital_run_length_longest', 'class'], ['word_freq_remove', 'word_freq_money', 'class']] +- fields_requiring_caution: ['capital_run_length_average', 'capital_run_length_longest', 'capital_run_length_total'] +- source_url: https://www.openml.org/d/44 + +SQLite schema snapshot: +{ + "table_name": "n1", + "quoted_table_name": "\"n1\"", + "row_count": 4601, + "columns": [ + { + "name": "word_freq_make", + "type": "TEXT", + "notnull": false, + "pk": false + }, + { + "name": "word_freq_address", + "type": "TEXT", + "notnull": false, + "pk": false + }, + { + "name": "word_freq_all", + "type": "TEXT", + "notnull": false, + "pk": false + }, + { + "name": "word_freq_3d", + "type": "TEXT", + "notnull": false, + "pk": false + }, + { + "name": "word_freq_our", + "type": "TEXT", + "notnull": false, + "pk": false + }, + { + "name": "word_freq_over", + "type": "TEXT", + "notnull": false, + "pk": false + }, + { + "name": "word_freq_remove", + "type": "TEXT", + "notnull": false, + "pk": false + }, + { + "name": "word_freq_internet", + "type": "TEXT", + "notnull": false, + "pk": false + }, + { + "name": "word_freq_order", + "type": "TEXT", + "notnull": false, + "pk": false + }, + { + "name": "word_freq_mail", + "type": "TEXT", + "notnull": false, + "pk": false + }, + { + "name": "word_freq_receive", + "type": "TEXT", + "notnull": false, + "pk": false + }, + { + "name": "word_freq_will", + "type": "TEXT", + "notnull": false, + "pk": false + }, + { + "name": "word_freq_people", + "type": "TEXT", + "notnull": false, + "pk": false + }, + { + "name": "word_freq_report", + "type": "TEXT", + "notnull": false, + "pk": false + }, + { + "name": "word_freq_addresses", + "type": "TEXT", + "notnull": false, + "pk": false + }, + { + "name": "word_freq_free", + "type": "TEXT", + "notnull": false, + "pk": false + }, + { + "name": "word_freq_business", + "type": "TEXT", + "notnull": false, + "pk": false + }, + { + "name": "word_freq_email", + "type": "TEXT", + "notnull": false, + "pk": false + }, + { + "name": "word_freq_you", + "type": "TEXT", + "notnull": false, + "pk": false + }, + { + "name": "word_freq_credit", + "type": "TEXT", + "notnull": false, + "pk": false + }, + { + "name": "word_freq_your", + "type": "TEXT", + "notnull": false, + "pk": false + }, + { + "name": "word_freq_font", + "type": "TEXT", + "notnull": false, + "pk": false + }, + { + "name": "word_freq_000", + "type": "TEXT", + "notnull": false, + "pk": false + }, + { + "name": "word_freq_money", + "type": "TEXT", + "notnull": false, + "pk": false + }, + { + "name": "word_freq_hp", + "type": "TEXT", + "notnull": false, + "pk": false + }, + { + "name": "word_freq_hpl", + "type": "TEXT", + "notnull": false, + "pk": false + }, + { + "name": "word_freq_george", + "type": "TEXT", + "notnull": false, + "pk": false + }, + { + "name": "word_freq_650", + "type": "TEXT", + "notnull": false, + "pk": false + }, + { + "name": "word_freq_lab", + "type": "TEXT", + "notnull": false, + "pk": false + }, + { + "name": "word_freq_labs", + "type": "TEXT", + "notnull": false, + "pk": false + }, + { + "name": "word_freq_telnet", + "type": "TEXT", + "notnull": false, + "pk": false + }, + { + "name": "word_freq_857", + "type": "TEXT", + "notnull": false, + "pk": false + }, + { + "name": "word_freq_data", + "type": "TEXT", + "notnull": false, + "pk": false + }, + { + "name": "word_freq_415", + "type": "TEXT", + "notnull": false, + "pk": false + }, + { + "name": "word_freq_85", + "type": "TEXT", + "notnull": false, + "pk": false + }, + { + "name": "word_freq_technology", + "type": "TEXT", + "notnull": false, + "pk": false + }, + { + "name": "word_freq_1999", + "type": "TEXT", + "notnull": false, + "pk": false + }, + { + "name": "word_freq_parts", + "type": "TEXT", + "notnull": false, + "pk": false + }, + { + "name": "word_freq_pm", + "type": "TEXT", + "notnull": false, + "pk": false + }, + { + "name": "word_freq_direct", + "type": "TEXT", + "notnull": false, + "pk": false + }, + { + "name": "word_freq_cs", + "type": "TEXT", + "notnull": false, + "pk": false + }, + { + "name": "word_freq_meeting", + "type": "TEXT", + "notnull": false, + "pk": false + }, + { + "name": "word_freq_original", + "type": "TEXT", + "notnull": false, + "pk": false + }, + { + "name": "word_freq_project", + "type": "TEXT", + "notnull": false, + "pk": false + }, + { + "name": "word_freq_re", + "type": "TEXT", + "notnull": false, + "pk": false + }, + { + "name": "word_freq_edu", + "type": "TEXT", + "notnull": false, + "pk": false + }, + { + "name": "word_freq_table", + "type": "TEXT", + "notnull": false, + "pk": false + }, + { + "name": "word_freq_conference", + "type": "TEXT", + "notnull": false, + "pk": false + }, + { + "name": "char_freq_%3B", + "type": "TEXT", + "notnull": false, + "pk": false + }, + { + "name": "char_freq_%28", + "type": "TEXT", + "notnull": false, + "pk": false + }, + { + "name": "char_freq_%5B", + "type": "TEXT", + "notnull": false, + "pk": false + }, + { + "name": "char_freq_%21", + "type": "TEXT", + "notnull": false, + "pk": false + }, + { + "name": "char_freq_%24", + "type": "TEXT", + "notnull": false, + "pk": false + }, + { + "name": "char_freq_%23", + "type": "TEXT", + "notnull": false, + "pk": false + }, + { + "name": "capital_run_length_average", + "type": "TEXT", + "notnull": false, + "pk": false + }, + { + "name": "capital_run_length_longest", + "type": "TEXT", + "notnull": false, + "pk": false + }, + { + "name": "capital_run_length_total", + "type": "TEXT", + "notnull": false, + "pk": false + }, + { + "name": "class", + "type": "TEXT", + "notnull": false, + "pk": false + } + ], + "sample_rows": [ + { + "word_freq_make": "0", + "word_freq_address": "0.64", + "word_freq_all": "0.64", + "word_freq_3d": "0", + "word_freq_our": "0.32", + "word_freq_over": "0", + "word_freq_remove": "0", + "word_freq_internet": "0", + "word_freq_order": "0", + "word_freq_mail": "0", + "word_freq_receive": "0", + "word_freq_will": "0.64", + "word_freq_people": "0", + "word_freq_report": "0", + "word_freq_addresses": "0", + "word_freq_free": "0.32", + "word_freq_business": "0", + "word_freq_email": "1.29", + "word_freq_you": "1.93", + "word_freq_credit": "0", + "word_freq_your": "0.96", + "word_freq_font": "0", + "word_freq_000": "0", + "word_freq_money": "0", + "word_freq_hp": "0", + "word_freq_hpl": "0", + "word_freq_george": "0", + "word_freq_650": "0", + "word_freq_lab": "0", + "word_freq_labs": "0", + "word_freq_telnet": "0", + "word_freq_857": "0", + "word_freq_data": "0", + "word_freq_415": "0", + "word_freq_85": "0", + "word_freq_technology": "0", + "word_freq_1999": "0", + "word_freq_parts": "0", + "word_freq_pm": "0", + "word_freq_direct": "0", + "word_freq_cs": "0", + "word_freq_meeting": "0", + "word_freq_original": "0", + "word_freq_project": "0", + "word_freq_re": "0", + "word_freq_edu": "0", + "word_freq_table": "0", + "word_freq_conference": "0", + "char_freq_%3B": "0", + "char_freq_%28": "0", + "char_freq_%5B": "0", + "char_freq_%21": "0.778", + "char_freq_%24": "0", + "char_freq_%23": "0", + "capital_run_length_average": "3.756", + "capital_run_length_longest": "61", + "capital_run_length_total": "278", + "class": "1" + }, + { + "word_freq_make": "0.21", + "word_freq_address": "0.28", + "word_freq_all": "0.5", + "word_freq_3d": "0", + "word_freq_our": "0.14", + "word_freq_over": "0.28", + "word_freq_remove": "0.21", + "word_freq_internet": "0.07", + "word_freq_order": "0", + "word_freq_mail": "0.94", + "word_freq_receive": "0.21", + "word_freq_will": "0.79", + "word_freq_people": "0.65", + "word_freq_report": "0.21", + "word_freq_addresses": "0.14", + "word_freq_free": "0.14", + "word_freq_business": "0.07", + "word_freq_email": "0.28", + "word_freq_you": "3.47", + "word_freq_credit": "0", + "word_freq_your": "1.59", + "word_freq_font": "0", + "word_freq_000": "0.43", + "word_freq_money": "0.43", + "word_freq_hp": "0", + "word_freq_hpl": "0", + "word_freq_george": "0", + "word_freq_650": "0", + "word_freq_lab": "0", + "word_freq_labs": "0", + "word_freq_telnet": "0", + "word_freq_857": "0", + "word_freq_data": "0", + "word_freq_415": "0", + "word_freq_85": "0", + "word_freq_technology": "0", + "word_freq_1999": "0.07", + "word_freq_parts": "0", + "word_freq_pm": "0", + "word_freq_direct": "0", + "word_freq_cs": "0", + "word_freq_meeting": "0", + "word_freq_original": "0", + "word_freq_project": "0", + "word_freq_re": "0", + "word_freq_edu": "0", + "word_freq_table": "0", + "word_freq_conference": "0", + "char_freq_%3B": "0", + "char_freq_%28": "0.132", + "char_freq_%5B": "0", + "char_freq_%21": "0.372", + "char_freq_%24": "0.18", + "char_freq_%23": "0.048", + "capital_run_length_average": "5.114", + "capital_run_length_longest": "101", + "capital_run_length_total": "1028", + "class": "1" + }, + { + "word_freq_make": "0.06", + "word_freq_address": "0", + "word_freq_all": "0.71", + "word_freq_3d": "0", + "word_freq_our": "1.23", + "word_freq_over": "0.19", + "word_freq_remove": "0.19", + "word_freq_internet": "0.12", + "word_freq_order": "0.64", + "word_freq_mail": "0.25", + "word_freq_receive": "0.38", + "word_freq_will": "0.45", + "word_freq_people": "0.12", + "word_freq_report": "0", + "word_freq_addresses": "1.75", + "word_freq_free": "0.06", + "word_freq_business": "0.06", + "word_freq_email": "1.03", + "word_freq_you": "1.36", + "word_freq_credit": "0.32", + "word_freq_your": "0.51", + "word_freq_font": "0", + "word_freq_000": "1.16", + "word_freq_money": "0.06", + "word_freq_hp": "0", + "word_freq_hpl": "0", + "word_freq_george": "0", + "word_freq_650": "0", + "word_freq_lab": "0", + "word_freq_labs": "0", + "word_freq_telnet": "0", + "word_freq_857": "0", + "word_freq_data": "0", + "word_freq_415": "0", + "word_freq_85": "0", + "word_freq_technology": "0", + "word_freq_1999": "0", + "word_freq_parts": "0", + "word_freq_pm": "0", + "word_freq_direct": "0.06", + "word_freq_cs": "0", + "word_freq_meeting": "0", + "word_freq_original": "0.12", + "word_freq_project": "0", + "word_freq_re": "0.06", + "word_freq_edu": "0.06", + "word_freq_table": "0", + "word_freq_conference": "0", + "char_freq_%3B": "0.01", + "char_freq_%28": "0.143", + "char_freq_%5B": "0", + "char_freq_%21": "0.276", + "char_freq_%24": "0.184", + "char_freq_%23": "0.01", + "capital_run_length_average": "9.821", + "capital_run_length_longest": "485", + "capital_run_length_total": "2259", + "class": "1" + }, + { + "word_freq_make": "0", + "word_freq_address": "0", + "word_freq_all": "0", + "word_freq_3d": "0", + "word_freq_our": "0.63", + "word_freq_over": "0", + "word_freq_remove": "0.31", + "word_freq_internet": "0.63", + "word_freq_order": "0.31", + "word_freq_mail": "0.63", + "word_freq_receive": "0.31", + "word_freq_will": "0.31", + "word_freq_people": "0.31", + "word_freq_report": "0", + "word_freq_addresses": "0", + "word_freq_free": "0.31", + "word_freq_business": "0", + "word_freq_email": "0", + "word_freq_you": "3.18", + "word_freq_credit": "0", + "word_freq_your": "0.31", + "word_freq_font": "0", + "word_freq_000": "0", + "word_freq_money": "0", + "word_freq_hp": "0", + "word_freq_hpl": "0", + "word_freq_george": "0", + "word_freq_650": "0", + "word_freq_lab": "0", + "word_freq_labs": "0", + "word_freq_telnet": "0", + "word_freq_857": "0", + "word_freq_data": "0", + "word_freq_415": "0", + "word_freq_85": "0", + "word_freq_technology": "0", + "word_freq_1999": "0", + "word_freq_parts": "0", + "word_freq_pm": "0", + "word_freq_direct": "0", + "word_freq_cs": "0", + "word_freq_meeting": "0", + "word_freq_original": "0", + "word_freq_project": "0", + "word_freq_re": "0", + "word_freq_edu": "0", + "word_freq_table": "0", + "word_freq_conference": "0", + "char_freq_%3B": "0", + "char_freq_%28": "0.137", + "char_freq_%5B": "0", + "char_freq_%21": "0.137", + "char_freq_%24": "0", + "char_freq_%23": "0", + "capital_run_length_average": "3.537", + "capital_run_length_longest": "40", + "capital_run_length_total": "191", + "class": "1" + }, + { + "word_freq_make": "0", + "word_freq_address": "0", + "word_freq_all": "0", + "word_freq_3d": "0", + "word_freq_our": "0.63", + "word_freq_over": "0", + "word_freq_remove": "0.31", + "word_freq_internet": "0.63", + "word_freq_order": "0.31", + "word_freq_mail": "0.63", + "word_freq_receive": "0.31", + "word_freq_will": "0.31", + "word_freq_people": "0.31", + "word_freq_report": "0", + "word_freq_addresses": "0", + "word_freq_free": "0.31", + "word_freq_business": "0", + "word_freq_email": "0", + "word_freq_you": "3.18", + "word_freq_credit": "0", + "word_freq_your": "0.31", + "word_freq_font": "0", + "word_freq_000": "0", + "word_freq_money": "0", + "word_freq_hp": "0", + "word_freq_hpl": "0", + "word_freq_george": "0", + "word_freq_650": "0", + "word_freq_lab": "0", + "word_freq_labs": "0", + "word_freq_telnet": "0", + "word_freq_857": "0", + "word_freq_data": "0", + "word_freq_415": "0", + "word_freq_85": "0", + "word_freq_technology": "0", + "word_freq_1999": "0", + "word_freq_parts": "0", + "word_freq_pm": "0", + "word_freq_direct": "0", + "word_freq_cs": "0", + "word_freq_meeting": "0", + "word_freq_original": "0", + "word_freq_project": "0", + "word_freq_re": "0", + "word_freq_edu": "0", + "word_freq_table": "0", + "word_freq_conference": "0", + "char_freq_%3B": "0", + "char_freq_%28": "0.135", + "char_freq_%5B": "0", + "char_freq_%21": "0.135", + "char_freq_%24": "0", + "char_freq_%23": "0", + "capital_run_length_average": "3.537", + "capital_run_length_longest": "40", + "capital_run_length_total": "191", + "class": "1" + } + ] +} + +Shortlisted templates: +[ + { + "template_id": "tpl_tail_low_support_group_count_v2", + "template_name": "Low-Support Group Count", + "primary_family": "tail_rarity_structure", + "portability": "yes", + "sql_skeleton": "SELECT\n {group_col},\n COUNT(*) AS support\nFROM {table}\nGROUP BY {group_col}\nORDER BY support ASC, {group_col}\nLIMIT {top_k};", + "required_roles": [ + "group_col" + ] + } +] + +Problem instance: +{ + "dataset_id": "n1", + "question": "Use template Low-Support Group Count to probe tail_set_consistency with semantic role count_distribution. Focus on group_col=class.", + "planned_template_id": "tpl_tail_low_support_group_count_v2", + "bindings": { + "group_col": "class", + "top_k": 16, + "top_n": 6, + "num_tiles": 10, + "percentile_value": 0.9, + "z_threshold": 2.0, + "fraction_threshold": 0.05, + "baseline_multiplier": 1.75, + "baseline_fraction": 0.1, + "min_group_size": 5, + "min_support": 4, + "measure_threshold": 0.0, + "time_grain": "month", + "lookback_rows": 3, + "current_period_start": "'2024-01-01'", + "current_period_end": "'2024-04-01'", + "previous_period_start": "'2023-10-01'", + "previous_period_end": "'2024-01-01'", + "drift_ratio_threshold": 0.8 + }, + "can_vary": [], + "must_fix": [], + "runtime_sql_skeleton": "SELECT\n {group_col},\n COUNT(*) AS support\nFROM {table}\nGROUP BY {group_col}\nORDER BY support ASC, {group_col}\nLIMIT {top_k};" +} + +Repair context: +{} diff --git a/Query/sql/v2/runs/v2_cli_20260502_081223_d/n1/artifacts/v2q_n1_4076fdb3e0b8bbdd/cli/sql_prompt_attempt_2.txt b/Query/sql/v2/runs/v2_cli_20260502_081223_d/n1/artifacts/v2q_n1_4076fdb3e0b8bbdd/cli/sql_prompt_attempt_2.txt new file mode 100644 index 0000000000000000000000000000000000000000..c85f1be6d96dbe7af16358363386c60d75847081 --- /dev/null +++ b/Query/sql/v2/runs/v2_cli_20260502_081223_d/n1/artifacts/v2q_n1_4076fdb3e0b8bbdd/cli/sql_prompt_attempt_2.txt @@ -0,0 +1,790 @@ +You are generating one SQLite SELECT query for a single-table SQL QA task. +Return strict JSON only, with this schema: {"sql": "...", "notes": "..."}. +Rules: +- Use only the provided table and columns. +- Do not write INSERT, UPDATE, DELETE, DROP, ALTER, CREATE, PRAGMA, ATTACH, DETACH, or VACUUM. +- Prefer the planned template and bound roles when provided. +- Add a leading SQL comment exactly like: -- template_id: . +- Generate SQLite-compatible SQL. SQLite does not support PERCENTILE_CONT or STDDEV. +- Quote identifiers with double quotes. +- Return no markdown and no extra prose. + +Dataset context: +Dataset context for SQL QA: +- dataset_id: n1 +- dataset_name: Spambase +- table_name: n1 +- table_layout: single-table dataset (do not assume joins). +- row_semantics: One row is one email represented by engineered token/character frequency and capitalization-run features. +- task_type: classification +- target_column: class +- main_row_count: 4601 +- important_fields: +- word_freq_make: role=feature, type=numeric_sparse_frequency. tags=['condition_candidate', 'measure', 'rare_pattern_candidate'] desc=Frequency feature for token 'make' in an email message. +- word_freq_address: role=feature, type=numeric_sparse_frequency. tags=['condition_candidate', 'measure', 'rare_pattern_candidate'] desc=Frequency feature for token 'address' in an email message. +- word_freq_all: role=feature, type=numeric_sparse_frequency. tags=['condition_candidate', 'measure', 'rare_pattern_candidate'] desc=Frequency feature for token 'all' in an email message. +- word_freq_3d: role=feature, type=numeric_sparse_frequency. tags=['condition_candidate', 'measure', 'rare_pattern_candidate'] desc=Frequency feature for token '3d' in an email message. +- word_freq_our: role=feature, type=numeric_sparse_frequency. tags=['condition_candidate', 'measure', 'rare_pattern_candidate'] desc=Frequency feature for token 'our' in an email message. +- word_freq_over: role=feature, type=numeric_sparse_frequency. tags=['condition_candidate', 'measure', 'rare_pattern_candidate'] desc=Frequency feature for token 'over' in an email message. +- word_freq_remove: role=feature, type=numeric_sparse_frequency. tags=['condition_candidate', 'measure', 'rare_pattern_candidate'] desc=Frequency feature for token 'remove' in an email message. +- word_freq_internet: role=feature, type=numeric_sparse_frequency. tags=['condition_candidate', 'measure', 'rare_pattern_candidate'] desc=Frequency feature for token 'internet' in an email message. +- word_freq_order: role=feature, type=numeric_sparse_frequency. tags=['condition_candidate', 'measure', 'rare_pattern_candidate'] desc=Frequency feature for token 'order' in an email message. +- word_freq_mail: role=feature, type=numeric_sparse_frequency. tags=['condition_candidate', 'measure', 'rare_pattern_candidate'] desc=Frequency feature for token 'mail' in an email message. +- word_freq_receive: role=feature, type=numeric_sparse_frequency. tags=['condition_candidate', 'measure', 'rare_pattern_candidate'] desc=Frequency feature for token 'receive' in an email message. +- word_freq_will: role=feature, type=numeric_sparse_frequency. tags=['condition_candidate', 'measure', 'rare_pattern_candidate'] desc=Frequency feature for token 'will' in an email message. +- word_freq_people: role=feature, type=numeric_sparse_frequency. tags=['condition_candidate', 'measure', 'rare_pattern_candidate'] desc=Frequency feature for token 'people' in an email message. +- word_freq_report: role=feature, type=numeric_sparse_frequency. tags=['condition_candidate', 'measure', 'rare_pattern_candidate'] desc=Frequency feature for token 'report' in an email message. +- word_freq_addresses: role=feature, type=numeric_sparse_frequency. tags=['condition_candidate', 'measure', 'rare_pattern_candidate'] desc=Frequency feature for token 'addresses' in an email message. +- word_freq_free: role=feature, type=numeric_sparse_frequency. tags=['condition_candidate', 'measure', 'rare_pattern_candidate'] desc=Frequency feature for token 'free' in an email message. +- word_freq_business: role=feature, type=numeric_sparse_frequency. tags=['condition_candidate', 'measure', 'rare_pattern_candidate'] desc=Frequency feature for token 'business' in an email message. +- word_freq_email: role=feature, type=numeric_sparse_frequency. tags=['condition_candidate', 'measure', 'rare_pattern_candidate'] desc=Frequency feature for token 'email' in an email message. +- word_freq_you: role=feature, type=numeric_sparse_frequency. tags=['condition_candidate', 'measure', 'rare_pattern_candidate'] desc=Frequency feature for token 'you' in an email message. +- word_freq_credit: role=feature, type=numeric_sparse_frequency. tags=['condition_candidate', 'measure', 'rare_pattern_candidate'] desc=Frequency feature for token 'credit' in an email message. +- word_freq_your: role=feature, type=numeric_sparse_frequency. tags=['condition_candidate', 'measure', 'rare_pattern_candidate'] desc=Frequency feature for token 'your' in an email message. +- word_freq_font: role=feature, type=numeric_sparse_frequency. tags=['condition_candidate', 'measure', 'rare_pattern_candidate'] desc=Frequency feature for token 'font' in an email message. +- word_freq_000: role=feature, type=numeric_sparse_frequency. tags=['condition_candidate', 'measure', 'rare_pattern_candidate'] desc=Frequency feature for token '000' in an email message. +- word_freq_money: role=feature, type=numeric_sparse_frequency. tags=['condition_candidate', 'measure', 'rare_pattern_candidate'] desc=Frequency feature for token 'money' in an email message. +- word_freq_hp: role=feature, type=numeric_sparse_frequency. tags=['condition_candidate', 'measure', 'rare_pattern_candidate'] desc=Frequency feature for token 'hp' in an email message. +- word_freq_hpl: role=feature, type=numeric_sparse_frequency. tags=['condition_candidate', 'measure', 'rare_pattern_candidate'] desc=Frequency feature for token 'hpl' in an email message. +- word_freq_george: role=feature, type=numeric_sparse_frequency. tags=['condition_candidate', 'measure', 'rare_pattern_candidate'] desc=Frequency feature for token 'george' in an email message. +- word_freq_650: role=feature, type=numeric_sparse_frequency. tags=['condition_candidate', 'measure', 'rare_pattern_candidate'] desc=Frequency feature for token '650' in an email message. +- word_freq_lab: role=feature, type=numeric_sparse_frequency. tags=['condition_candidate', 'measure', 'rare_pattern_candidate'] desc=Frequency feature for token 'lab' in an email message. +- word_freq_labs: role=feature, type=numeric_sparse_frequency. tags=['condition_candidate', 'measure', 'rare_pattern_candidate'] desc=Frequency feature for token 'labs' in an email message. +- word_freq_telnet: role=feature, type=numeric_sparse_frequency. tags=['condition_candidate', 'measure', 'rare_pattern_candidate'] desc=Frequency feature for token 'telnet' in an email message. +- word_freq_857: role=feature, type=numeric_sparse_frequency. tags=['condition_candidate', 'measure', 'rare_pattern_candidate'] desc=Frequency feature for token '857' in an email message. +- word_freq_data: role=feature, type=numeric_sparse_frequency. tags=['condition_candidate', 'measure', 'rare_pattern_candidate'] desc=Frequency feature for token 'data' in an email message. +- word_freq_415: role=feature, type=numeric_sparse_frequency. tags=['condition_candidate', 'measure', 'rare_pattern_candidate'] desc=Frequency feature for token '415' in an email message. +- word_freq_85: role=feature, type=numeric_sparse_frequency. tags=['condition_candidate', 'measure', 'rare_pattern_candidate'] desc=Frequency feature for token '85' in an email message. +- word_freq_technology: role=feature, type=numeric_sparse_frequency. tags=['condition_candidate', 'measure', 'rare_pattern_candidate'] desc=Frequency feature for token 'technology' in an email message. +- word_freq_1999: role=feature, type=numeric_sparse_frequency. tags=['condition_candidate', 'measure', 'rare_pattern_candidate'] desc=Frequency feature for token '1999' in an email message. +- word_freq_parts: role=feature, type=numeric_sparse_frequency. tags=['condition_candidate', 'measure', 'rare_pattern_candidate'] desc=Frequency feature for token 'parts' in an email message. +- word_freq_pm: role=feature, type=numeric_sparse_frequency. tags=['condition_candidate', 'measure', 'rare_pattern_candidate'] desc=Frequency feature for token 'pm' in an email message. +- word_freq_direct: role=feature, type=numeric_sparse_frequency. tags=['condition_candidate', 'measure', 'rare_pattern_candidate'] desc=Frequency feature for token 'direct' in an email message. +- word_freq_cs: role=feature, type=numeric_sparse_frequency. tags=['condition_candidate', 'measure', 'rare_pattern_candidate'] desc=Frequency feature for token 'cs' in an email message. +- word_freq_meeting: role=feature, type=numeric_sparse_frequency. tags=['condition_candidate', 'measure', 'rare_pattern_candidate'] desc=Frequency feature for token 'meeting' in an email message. +- word_freq_original: role=feature, type=numeric_sparse_frequency. tags=['condition_candidate', 'measure', 'rare_pattern_candidate'] desc=Frequency feature for token 'original' in an email message. +- word_freq_project: role=feature, type=numeric_sparse_frequency. tags=['condition_candidate', 'measure', 'rare_pattern_candidate'] desc=Frequency feature for token 'project' in an email message. +- word_freq_re: role=feature, type=numeric_sparse_frequency. tags=['condition_candidate', 'measure', 'rare_pattern_candidate'] desc=Frequency feature for token 're' in an email message. +- word_freq_edu: role=feature, type=numeric_sparse_frequency. tags=['condition_candidate', 'measure', 'rare_pattern_candidate'] desc=Frequency feature for token 'edu' in an email message. +- word_freq_table: role=feature, type=numeric_sparse_frequency. tags=['condition_candidate', 'measure', 'rare_pattern_candidate'] desc=Frequency feature for token 'table' in an email message. +- word_freq_conference: role=feature, type=numeric_sparse_frequency. tags=['condition_candidate', 'measure', 'rare_pattern_candidate'] desc=Frequency feature for token 'conference' in an email message. +- char_freq_%3B: role=feature, type=numeric_sparse_frequency. tags=['condition_candidate', 'measure', 'rare_pattern_candidate'] desc=Character frequency feature for symbol ';'. +- char_freq_%28: role=feature, type=numeric_sparse_frequency. tags=['condition_candidate', 'measure', 'rare_pattern_candidate'] desc=Character frequency feature for symbol '('. +- char_freq_%5B: role=feature, type=numeric_sparse_frequency. tags=['condition_candidate', 'measure', 'rare_pattern_candidate'] desc=Character frequency feature for symbol '['. +- char_freq_%21: role=feature, type=numeric_sparse_frequency. tags=['condition_candidate', 'measure', 'rare_pattern_candidate'] desc=Character frequency feature for symbol '!'. +- char_freq_%24: role=feature, type=numeric_sparse_frequency. tags=['condition_candidate', 'measure', 'rare_pattern_candidate'] desc=Character frequency feature for symbol '$'. +- char_freq_%23: role=feature, type=numeric_sparse_frequency. tags=['condition_candidate', 'measure', 'rare_pattern_candidate'] desc=Character frequency feature for symbol '#'. +- capital_run_length_average: role=feature, type=numeric_heavy_tail. tags=['condition_candidate', 'measure', 'rare_pattern_candidate'] desc=Average length of uninterrupted capital-letter runs. +- capital_run_length_longest: role=feature, type=numeric_heavy_tail. tags=['condition_candidate', 'measure', 'rare_pattern_candidate'] desc=Longest uninterrupted capital-letter run. +- capital_run_length_total: role=feature, type=numeric_heavy_tail. tags=['condition_candidate', 'measure', 'rare_pattern_candidate'] desc=Total number of capital letters in runs. +- class: role=target, type=binary_target. tags=['target_candidate'] desc=Binary spam label (1=spam, 0=non-spam). +- useful_field_combinations: [['word_freq_free', 'word_freq_you', 'class'], ['char_freq_%21', 'capital_run_length_longest', 'class'], ['word_freq_remove', 'word_freq_money', 'class']] +- fields_requiring_caution: ['capital_run_length_average', 'capital_run_length_longest', 'capital_run_length_total'] +- source_url: https://www.openml.org/d/44 + +SQLite schema snapshot: +{ + "table_name": "n1", + "quoted_table_name": "\"n1\"", + "row_count": 4601, + "columns": [ + { + "name": "word_freq_make", + "type": "TEXT", + "notnull": false, + "pk": false + }, + { + "name": "word_freq_address", + "type": "TEXT", + "notnull": false, + "pk": false + }, + { + "name": "word_freq_all", + "type": "TEXT", + "notnull": false, + "pk": false + }, + { + "name": "word_freq_3d", + "type": "TEXT", + "notnull": false, + "pk": false + }, + { + "name": "word_freq_our", + "type": "TEXT", + "notnull": false, + "pk": false + }, + { + "name": "word_freq_over", + "type": "TEXT", + "notnull": false, + "pk": false + }, + { + "name": "word_freq_remove", + "type": "TEXT", + "notnull": false, + "pk": false + }, + { + "name": "word_freq_internet", + "type": "TEXT", + "notnull": false, + "pk": false + }, + { + "name": "word_freq_order", + "type": "TEXT", + "notnull": false, + "pk": false + }, + { + "name": "word_freq_mail", + "type": "TEXT", + "notnull": false, + "pk": false + }, + { + "name": "word_freq_receive", + "type": "TEXT", + "notnull": false, + "pk": false + }, + { + "name": "word_freq_will", + "type": "TEXT", + "notnull": false, + "pk": false + }, + { + "name": "word_freq_people", + "type": "TEXT", + "notnull": false, + "pk": false + }, + { + "name": "word_freq_report", + "type": "TEXT", + "notnull": false, + "pk": false + }, + { + "name": "word_freq_addresses", + "type": "TEXT", + "notnull": false, + "pk": false + }, + { + "name": "word_freq_free", + "type": "TEXT", + "notnull": false, + "pk": false + }, + { + "name": "word_freq_business", + "type": "TEXT", + "notnull": false, + "pk": false + }, + { + "name": "word_freq_email", + "type": "TEXT", + "notnull": false, + "pk": false + }, + { + "name": "word_freq_you", + "type": "TEXT", + "notnull": false, + "pk": false + }, + { + "name": "word_freq_credit", + "type": "TEXT", + "notnull": false, + "pk": false + }, + { + "name": "word_freq_your", + "type": "TEXT", + "notnull": false, + "pk": false + }, + { + "name": "word_freq_font", + "type": "TEXT", + "notnull": false, + "pk": false + }, + { + "name": "word_freq_000", + "type": "TEXT", + "notnull": false, + "pk": false + }, + { + "name": "word_freq_money", + "type": "TEXT", + "notnull": false, + "pk": false + }, + { + "name": "word_freq_hp", + "type": "TEXT", + "notnull": false, + "pk": false + }, + { + "name": "word_freq_hpl", + "type": "TEXT", + "notnull": false, + "pk": false + }, + { + "name": "word_freq_george", + "type": "TEXT", + "notnull": false, + "pk": false + }, + { + "name": "word_freq_650", + "type": "TEXT", + "notnull": false, + "pk": false + }, + { + "name": "word_freq_lab", + "type": "TEXT", + "notnull": false, + "pk": false + }, + { + "name": "word_freq_labs", + "type": "TEXT", + "notnull": false, + "pk": false + }, + { + "name": "word_freq_telnet", + "type": "TEXT", + "notnull": false, + "pk": false + }, + { + "name": "word_freq_857", + "type": "TEXT", + "notnull": false, + "pk": false + }, + { + "name": "word_freq_data", + "type": "TEXT", + "notnull": false, + "pk": false + }, + { + "name": "word_freq_415", + "type": "TEXT", + "notnull": false, + "pk": false + }, + { + "name": "word_freq_85", + "type": "TEXT", + "notnull": false, + "pk": false + }, + { + "name": "word_freq_technology", + "type": "TEXT", + "notnull": false, + "pk": false + }, + { + "name": "word_freq_1999", + "type": "TEXT", + "notnull": false, + "pk": false + }, + { + "name": "word_freq_parts", + "type": "TEXT", + "notnull": false, + "pk": false + }, + { + "name": "word_freq_pm", + "type": "TEXT", + "notnull": false, + "pk": false + }, + { + "name": "word_freq_direct", + "type": "TEXT", + "notnull": false, + "pk": false + }, + { + "name": "word_freq_cs", + "type": "TEXT", + "notnull": false, + "pk": false + }, + { + "name": "word_freq_meeting", + "type": "TEXT", + "notnull": false, + "pk": false + }, + { + "name": "word_freq_original", + "type": "TEXT", + "notnull": false, + "pk": false + }, + { + "name": "word_freq_project", + "type": "TEXT", + "notnull": false, + "pk": false + }, + { + "name": "word_freq_re", + "type": "TEXT", + "notnull": false, + "pk": false + }, + { + "name": "word_freq_edu", + "type": "TEXT", + "notnull": false, + "pk": false + }, + { + "name": "word_freq_table", + "type": "TEXT", + "notnull": false, + "pk": false + }, + { + "name": "word_freq_conference", + "type": "TEXT", + "notnull": false, + "pk": false + }, + { + "name": "char_freq_%3B", + "type": "TEXT", + "notnull": false, + "pk": false + }, + { + "name": "char_freq_%28", + "type": "TEXT", + "notnull": false, + "pk": false + }, + { + "name": "char_freq_%5B", + "type": "TEXT", + "notnull": false, + "pk": false + }, + { + "name": "char_freq_%21", + "type": "TEXT", + "notnull": false, + "pk": false + }, + { + "name": "char_freq_%24", + "type": "TEXT", + "notnull": false, + "pk": false + }, + { + "name": "char_freq_%23", + "type": "TEXT", + "notnull": false, + "pk": false + }, + { + "name": "capital_run_length_average", + "type": "TEXT", + "notnull": false, + "pk": false + }, + { + "name": "capital_run_length_longest", + "type": "TEXT", + "notnull": false, + "pk": false + }, + { + "name": "capital_run_length_total", + "type": "TEXT", + "notnull": false, + "pk": false + }, + { + "name": "class", + "type": "TEXT", + "notnull": false, + "pk": false + } + ], + "sample_rows": [ + { + "word_freq_make": "0", + "word_freq_address": "0.64", + "word_freq_all": "0.64", + "word_freq_3d": "0", + "word_freq_our": "0.32", + "word_freq_over": "0", + "word_freq_remove": "0", + "word_freq_internet": "0", + "word_freq_order": "0", + "word_freq_mail": "0", + "word_freq_receive": "0", + "word_freq_will": "0.64", + "word_freq_people": "0", + "word_freq_report": "0", + "word_freq_addresses": "0", + "word_freq_free": "0.32", + "word_freq_business": "0", + "word_freq_email": "1.29", + "word_freq_you": "1.93", + "word_freq_credit": "0", + "word_freq_your": "0.96", + "word_freq_font": "0", + "word_freq_000": "0", + "word_freq_money": "0", + "word_freq_hp": "0", + "word_freq_hpl": "0", + "word_freq_george": "0", + "word_freq_650": "0", + "word_freq_lab": "0", + "word_freq_labs": "0", + "word_freq_telnet": "0", + "word_freq_857": "0", + "word_freq_data": "0", + "word_freq_415": "0", + "word_freq_85": "0", + "word_freq_technology": "0", + "word_freq_1999": "0", + "word_freq_parts": "0", + "word_freq_pm": "0", + "word_freq_direct": "0", + "word_freq_cs": "0", + "word_freq_meeting": "0", + "word_freq_original": "0", + "word_freq_project": "0", + "word_freq_re": "0", + "word_freq_edu": "0", + "word_freq_table": "0", + "word_freq_conference": "0", + "char_freq_%3B": "0", + "char_freq_%28": "0", + "char_freq_%5B": "0", + "char_freq_%21": "0.778", + "char_freq_%24": "0", + "char_freq_%23": "0", + "capital_run_length_average": "3.756", + "capital_run_length_longest": "61", + "capital_run_length_total": "278", + "class": "1" + }, + { + "word_freq_make": "0.21", + "word_freq_address": "0.28", + "word_freq_all": "0.5", + "word_freq_3d": "0", + "word_freq_our": "0.14", + "word_freq_over": "0.28", + "word_freq_remove": "0.21", + "word_freq_internet": "0.07", + "word_freq_order": "0", + "word_freq_mail": "0.94", + "word_freq_receive": "0.21", + "word_freq_will": "0.79", + "word_freq_people": "0.65", + "word_freq_report": "0.21", + "word_freq_addresses": "0.14", + "word_freq_free": "0.14", + "word_freq_business": "0.07", + "word_freq_email": "0.28", + "word_freq_you": "3.47", + "word_freq_credit": "0", + "word_freq_your": "1.59", + "word_freq_font": "0", + "word_freq_000": "0.43", + "word_freq_money": "0.43", + "word_freq_hp": "0", + "word_freq_hpl": "0", + "word_freq_george": "0", + "word_freq_650": "0", + "word_freq_lab": "0", + "word_freq_labs": "0", + "word_freq_telnet": "0", + "word_freq_857": "0", + "word_freq_data": "0", + "word_freq_415": "0", + "word_freq_85": "0", + "word_freq_technology": "0", + "word_freq_1999": "0.07", + "word_freq_parts": "0", + "word_freq_pm": "0", + "word_freq_direct": "0", + "word_freq_cs": "0", + "word_freq_meeting": "0", + "word_freq_original": "0", + "word_freq_project": "0", + "word_freq_re": "0", + "word_freq_edu": "0", + "word_freq_table": "0", + "word_freq_conference": "0", + "char_freq_%3B": "0", + "char_freq_%28": "0.132", + "char_freq_%5B": "0", + "char_freq_%21": "0.372", + "char_freq_%24": "0.18", + "char_freq_%23": "0.048", + "capital_run_length_average": "5.114", + "capital_run_length_longest": "101", + "capital_run_length_total": "1028", + "class": "1" + }, + { + "word_freq_make": "0.06", + "word_freq_address": "0", + "word_freq_all": "0.71", + "word_freq_3d": "0", + "word_freq_our": "1.23", + "word_freq_over": "0.19", + "word_freq_remove": "0.19", + "word_freq_internet": "0.12", + "word_freq_order": "0.64", + "word_freq_mail": "0.25", + "word_freq_receive": "0.38", + "word_freq_will": "0.45", + "word_freq_people": "0.12", + "word_freq_report": "0", + "word_freq_addresses": "1.75", + "word_freq_free": "0.06", + "word_freq_business": "0.06", + "word_freq_email": "1.03", + "word_freq_you": "1.36", + "word_freq_credit": "0.32", + "word_freq_your": "0.51", + "word_freq_font": "0", + "word_freq_000": "1.16", + "word_freq_money": "0.06", + "word_freq_hp": "0", + "word_freq_hpl": "0", + "word_freq_george": "0", + "word_freq_650": "0", + "word_freq_lab": "0", + "word_freq_labs": "0", + "word_freq_telnet": "0", + "word_freq_857": "0", + "word_freq_data": "0", + "word_freq_415": "0", + "word_freq_85": "0", + "word_freq_technology": "0", + "word_freq_1999": "0", + "word_freq_parts": "0", + "word_freq_pm": "0", + "word_freq_direct": "0.06", + "word_freq_cs": "0", + "word_freq_meeting": "0", + "word_freq_original": "0.12", + "word_freq_project": "0", + "word_freq_re": "0.06", + "word_freq_edu": "0.06", + "word_freq_table": "0", + "word_freq_conference": "0", + "char_freq_%3B": "0.01", + "char_freq_%28": "0.143", + "char_freq_%5B": "0", + "char_freq_%21": "0.276", + "char_freq_%24": "0.184", + "char_freq_%23": "0.01", + "capital_run_length_average": "9.821", + "capital_run_length_longest": "485", + "capital_run_length_total": "2259", + "class": "1" + }, + { + "word_freq_make": "0", + "word_freq_address": "0", + "word_freq_all": "0", + "word_freq_3d": "0", + "word_freq_our": "0.63", + "word_freq_over": "0", + "word_freq_remove": "0.31", + "word_freq_internet": "0.63", + "word_freq_order": "0.31", + "word_freq_mail": "0.63", + "word_freq_receive": "0.31", + "word_freq_will": "0.31", + "word_freq_people": "0.31", + "word_freq_report": "0", + "word_freq_addresses": "0", + "word_freq_free": "0.31", + "word_freq_business": "0", + "word_freq_email": "0", + "word_freq_you": "3.18", + "word_freq_credit": "0", + "word_freq_your": "0.31", + "word_freq_font": "0", + "word_freq_000": "0", + "word_freq_money": "0", + "word_freq_hp": "0", + "word_freq_hpl": "0", + "word_freq_george": "0", + "word_freq_650": "0", + "word_freq_lab": "0", + "word_freq_labs": "0", + "word_freq_telnet": "0", + "word_freq_857": "0", + "word_freq_data": "0", + "word_freq_415": "0", + "word_freq_85": "0", + "word_freq_technology": "0", + "word_freq_1999": "0", + "word_freq_parts": "0", + "word_freq_pm": "0", + "word_freq_direct": "0", + "word_freq_cs": "0", + "word_freq_meeting": "0", + "word_freq_original": "0", + "word_freq_project": "0", + "word_freq_re": "0", + "word_freq_edu": "0", + "word_freq_table": "0", + "word_freq_conference": "0", + "char_freq_%3B": "0", + "char_freq_%28": "0.137", + "char_freq_%5B": "0", + "char_freq_%21": "0.137", + "char_freq_%24": "0", + "char_freq_%23": "0", + "capital_run_length_average": "3.537", + "capital_run_length_longest": "40", + "capital_run_length_total": "191", + "class": "1" + }, + { + "word_freq_make": "0", + "word_freq_address": "0", + "word_freq_all": "0", + "word_freq_3d": "0", + "word_freq_our": "0.63", + "word_freq_over": "0", + "word_freq_remove": "0.31", + "word_freq_internet": "0.63", + "word_freq_order": "0.31", + "word_freq_mail": "0.63", + "word_freq_receive": "0.31", + "word_freq_will": "0.31", + "word_freq_people": "0.31", + "word_freq_report": "0", + "word_freq_addresses": "0", + "word_freq_free": "0.31", + "word_freq_business": "0", + "word_freq_email": "0", + "word_freq_you": "3.18", + "word_freq_credit": "0", + "word_freq_your": "0.31", + "word_freq_font": "0", + "word_freq_000": "0", + "word_freq_money": "0", + "word_freq_hp": "0", + "word_freq_hpl": "0", + "word_freq_george": "0", + "word_freq_650": "0", + "word_freq_lab": "0", + "word_freq_labs": "0", + "word_freq_telnet": "0", + "word_freq_857": "0", + "word_freq_data": "0", + "word_freq_415": "0", + "word_freq_85": "0", + "word_freq_technology": "0", + "word_freq_1999": "0", + "word_freq_parts": "0", + "word_freq_pm": "0", + "word_freq_direct": "0", + "word_freq_cs": "0", + "word_freq_meeting": "0", + "word_freq_original": "0", + "word_freq_project": "0", + "word_freq_re": "0", + "word_freq_edu": "0", + "word_freq_table": "0", + "word_freq_conference": "0", + "char_freq_%3B": "0", + "char_freq_%28": "0.135", + "char_freq_%5B": "0", + "char_freq_%21": "0.135", + "char_freq_%24": "0", + "char_freq_%23": "0", + "capital_run_length_average": "3.537", + "capital_run_length_longest": "40", + "capital_run_length_total": "191", + "class": "1" + } + ] +} + +Shortlisted templates: +[ + { + "template_id": "tpl_tail_low_support_group_count_v2", + "template_name": "Low-Support Group Count", + "primary_family": "tail_rarity_structure", + "portability": "yes", + "sql_skeleton": "SELECT\n {group_col},\n COUNT(*) AS support\nFROM {table}\nGROUP BY {group_col}\nORDER BY support ASC, {group_col}\nLIMIT {top_k};", + "required_roles": [ + "group_col" + ] + } +] + +Problem instance: +{ + "dataset_id": "n1", + "question": "Use template Low-Support Group Count to probe tail_set_consistency with semantic role count_distribution. Focus on group_col=class.", + "planned_template_id": "tpl_tail_low_support_group_count_v2", + "bindings": { + "group_col": "class", + "top_k": 16, + "top_n": 6, + "num_tiles": 10, + "percentile_value": 0.9, + "z_threshold": 2.0, + "fraction_threshold": 0.05, + "baseline_multiplier": 1.75, + "baseline_fraction": 0.1, + "min_group_size": 5, + "min_support": 4, + "measure_threshold": 0.0, + "time_grain": "month", + "lookback_rows": 3, + "current_period_start": "'2024-01-01'", + "current_period_end": "'2024-04-01'", + "previous_period_start": "'2023-10-01'", + "previous_period_end": "'2024-01-01'", + "drift_ratio_threshold": 0.8 + }, + "can_vary": [], + "must_fix": [], + "runtime_sql_skeleton": "SELECT\n {group_col},\n COUNT(*) AS support\nFROM {table}\nGROUP BY {group_col}\nORDER BY support ASC, {group_col}\nLIMIT {top_k};" +} + +Repair context: +{} diff --git a/Query/sql/v2/runs/v2_cli_20260502_081223_d/n1/artifacts/v2q_n1_4076fdb3e0b8bbdd/cli/sql_response_attempt_1.raw.txt b/Query/sql/v2/runs/v2_cli_20260502_081223_d/n1/artifacts/v2q_n1_4076fdb3e0b8bbdd/cli/sql_response_attempt_1.raw.txt new file mode 100644 index 0000000000000000000000000000000000000000..bff7a87ee6f44a0946302663627fc1726bc82bc5 --- /dev/null +++ b/Query/sql/v2/runs/v2_cli_20260502_081223_d/n1/artifacts/v2q_n1_4076fdb3e0b8bbdd/cli/sql_response_attempt_1.raw.txt @@ -0,0 +1,4 @@ +{"type":"thread.started","thread_id":"019e40fa-86c6-7682-acf7-1747d85bfdf0"} +{"type":"turn.started"} +{"type":"error","message":"Quota exceeded. Check your plan and billing details."} +{"type":"turn.failed","error":{"message":"Quota exceeded. Check your plan and billing details."}} diff --git a/Query/sql/v2/runs/v2_cli_20260502_081223_d/n1/artifacts/v2q_n1_4076fdb3e0b8bbdd/cli/sql_response_attempt_1.txt b/Query/sql/v2/runs/v2_cli_20260502_081223_d/n1/artifacts/v2q_n1_4076fdb3e0b8bbdd/cli/sql_response_attempt_1.txt new file mode 100644 index 0000000000000000000000000000000000000000..1914d5a649fa8ec4e018ef95799a94ce7d3562f5 --- /dev/null +++ b/Query/sql/v2/runs/v2_cli_20260502_081223_d/n1/artifacts/v2q_n1_4076fdb3e0b8bbdd/cli/sql_response_attempt_1.txt @@ -0,0 +1,4 @@ +{"type":"thread.started","thread_id":"019e40fa-86c6-7682-acf7-1747d85bfdf0"} +{"type":"turn.started"} +{"type":"error","message":"Quota exceeded. Check your plan and billing details."} +{"type":"turn.failed","error":{"message":"Quota exceeded. Check your plan and billing details."}} \ No newline at end of file diff --git a/Query/sql/v2/runs/v2_cli_20260502_081223_d/n1/artifacts/v2q_n1_4076fdb3e0b8bbdd/cli/sql_response_attempt_2.raw.txt b/Query/sql/v2/runs/v2_cli_20260502_081223_d/n1/artifacts/v2q_n1_4076fdb3e0b8bbdd/cli/sql_response_attempt_2.raw.txt new file mode 100644 index 0000000000000000000000000000000000000000..21ad15c66c96f05be37cc633d222c28d348e1799 --- /dev/null +++ b/Query/sql/v2/runs/v2_cli_20260502_081223_d/n1/artifacts/v2q_n1_4076fdb3e0b8bbdd/cli/sql_response_attempt_2.raw.txt @@ -0,0 +1,4 @@ +{"type":"thread.started","thread_id":"019e40fa-9792-7ed2-9d08-2e8a9669b6de"} +{"type":"turn.started"} +{"type":"error","message":"Quota exceeded. Check your plan and billing details."} +{"type":"turn.failed","error":{"message":"Quota exceeded. Check your plan and billing details."}} diff --git a/Query/sql/v2/runs/v2_cli_20260502_081223_d/n1/artifacts/v2q_n1_4076fdb3e0b8bbdd/cli/sql_response_attempt_2.txt b/Query/sql/v2/runs/v2_cli_20260502_081223_d/n1/artifacts/v2q_n1_4076fdb3e0b8bbdd/cli/sql_response_attempt_2.txt new file mode 100644 index 0000000000000000000000000000000000000000..0d27dc839e83bf6317d59f5d3fd48ddd72dc4b0b --- /dev/null +++ b/Query/sql/v2/runs/v2_cli_20260502_081223_d/n1/artifacts/v2q_n1_4076fdb3e0b8bbdd/cli/sql_response_attempt_2.txt @@ -0,0 +1,4 @@ +{"type":"thread.started","thread_id":"019e40fa-9792-7ed2-9d08-2e8a9669b6de"} +{"type":"turn.started"} +{"type":"error","message":"Quota exceeded. Check your plan and billing details."} +{"type":"turn.failed","error":{"message":"Quota exceeded. Check your plan and billing details."}} \ No newline at end of file diff --git a/Query/sql/v2/runs/v2_cli_20260502_081223_d/n1/artifacts/v2q_n1_4076fdb3e0b8bbdd/cli/sql_stderr_attempt_1.txt b/Query/sql/v2/runs/v2_cli_20260502_081223_d/n1/artifacts/v2q_n1_4076fdb3e0b8bbdd/cli/sql_stderr_attempt_1.txt new file mode 100644 index 0000000000000000000000000000000000000000..e69de29bb2d1d6434b8b29ae775ad8c2e48c5391 diff --git a/Query/sql/v2/runs/v2_cli_20260502_081223_d/n1/artifacts/v2q_n1_4076fdb3e0b8bbdd/cli/sql_stderr_attempt_2.txt b/Query/sql/v2/runs/v2_cli_20260502_081223_d/n1/artifacts/v2q_n1_4076fdb3e0b8bbdd/cli/sql_stderr_attempt_2.txt new file mode 100644 index 0000000000000000000000000000000000000000..e69de29bb2d1d6434b8b29ae775ad8c2e48c5391 diff --git a/Query/sql/v2/runs/v2_cli_20260502_081223_d/n1/artifacts/v2q_n1_45cf6899c1a896b8/cli/sql_attempt_1.metadata.json b/Query/sql/v2/runs/v2_cli_20260502_081223_d/n1/artifacts/v2q_n1_45cf6899c1a896b8/cli/sql_attempt_1.metadata.json new file mode 100644 index 0000000000000000000000000000000000000000..69034b5545c4cd41d478a643fd8b52d9bc1d6f53 --- /dev/null +++ b/Query/sql/v2/runs/v2_cli_20260502_081223_d/n1/artifacts/v2q_n1_45cf6899c1a896b8/cli/sql_attempt_1.metadata.json @@ -0,0 +1,43 @@ +{ + "attempt": 1, + "phase": "sql_generation", + "command": "codex exec --skip-git-repo-check --disable plugins --sandbox read-only --cd \"/data/jialinzhang/SQLagent\" -m gpt-5.4 --json -", + "started_at": "2026-05-19T15:58:55.770044+00:00", + "ended_at": "2026-05-19T15:58:58.839162+00:00", + "elapsed_ms": 3069.09, + "returncode": 1, + "prompt_metrics": { + "chars": 29574, + "bytes_utf8": 29574, + "lines": 795, + "estimated_tokens": null + }, + "stdout_metrics": { + "chars": 281, + "bytes_utf8": 281, + "lines": 4, + "estimated_tokens": null + }, + "stderr_metrics": { + "chars": 0, + "bytes_utf8": 0, + "lines": 0, + "estimated_tokens": null + }, + "parsed_output": { + "format": "jsonl_events", + "text_metrics": { + "chars": 280, + "bytes_utf8": 280, + "lines": 4, + "estimated_tokens": null + }, + "usage": {} + }, + "status": "failed", + "error": "AI CLI command failed with exit code 1: ", + "prompt_path": "cli/sql_prompt_attempt_1.txt", + "response_path": "cli/sql_response_attempt_1.txt", + "raw_response_path": "cli/sql_response_attempt_1.raw.txt", + "stderr_path": "cli/sql_stderr_attempt_1.txt" +} \ No newline at end of file diff --git a/Query/sql/v2/runs/v2_cli_20260502_081223_d/n1/artifacts/v2q_n1_45cf6899c1a896b8/cli/sql_attempt_2.metadata.json b/Query/sql/v2/runs/v2_cli_20260502_081223_d/n1/artifacts/v2q_n1_45cf6899c1a896b8/cli/sql_attempt_2.metadata.json new file mode 100644 index 0000000000000000000000000000000000000000..a9031a719b63c1a1c5bc51a1a202d0ced4f07fc3 --- /dev/null +++ b/Query/sql/v2/runs/v2_cli_20260502_081223_d/n1/artifacts/v2q_n1_45cf6899c1a896b8/cli/sql_attempt_2.metadata.json @@ -0,0 +1,43 @@ +{ + "attempt": 2, + "phase": "sql_generation", + "command": "codex exec --skip-git-repo-check --disable plugins --sandbox read-only --cd \"/data/jialinzhang/SQLagent\" -m gpt-5.4 --json -", + "started_at": "2026-05-19T15:58:59.841795+00:00", + "ended_at": "2026-05-19T15:59:02.931076+00:00", + "elapsed_ms": 3089.24, + "returncode": 1, + "prompt_metrics": { + "chars": 29574, + "bytes_utf8": 29574, + "lines": 795, + "estimated_tokens": null + }, + "stdout_metrics": { + "chars": 281, + "bytes_utf8": 281, + "lines": 4, + "estimated_tokens": null + }, + "stderr_metrics": { + "chars": 0, + "bytes_utf8": 0, + "lines": 0, + "estimated_tokens": null + }, + "parsed_output": { + "format": "jsonl_events", + "text_metrics": { + "chars": 280, + "bytes_utf8": 280, + "lines": 4, + "estimated_tokens": null + }, + "usage": {} + }, + "status": "failed", + "error": "AI CLI command failed with exit code 1: ", + "prompt_path": "cli/sql_prompt_attempt_2.txt", + "response_path": "cli/sql_response_attempt_2.txt", + "raw_response_path": "cli/sql_response_attempt_2.raw.txt", + "stderr_path": "cli/sql_stderr_attempt_2.txt" +} \ No newline at end of file diff --git a/Query/sql/v2/runs/v2_cli_20260502_081223_d/n1/artifacts/v2q_n1_45cf6899c1a896b8/cli/sql_prompt_attempt_1.txt b/Query/sql/v2/runs/v2_cli_20260502_081223_d/n1/artifacts/v2q_n1_45cf6899c1a896b8/cli/sql_prompt_attempt_1.txt new file mode 100644 index 0000000000000000000000000000000000000000..7507f77046d1a99048d7f7b2ecb6277faae2dde8 --- /dev/null +++ b/Query/sql/v2/runs/v2_cli_20260502_081223_d/n1/artifacts/v2q_n1_45cf6899c1a896b8/cli/sql_prompt_attempt_1.txt @@ -0,0 +1,795 @@ +You are generating one SQLite SELECT query for a single-table SQL QA task. +Return strict JSON only, with this schema: {"sql": "...", "notes": "..."}. +Rules: +- Use only the provided table and columns. +- Do not write INSERT, UPDATE, DELETE, DROP, ALTER, CREATE, PRAGMA, ATTACH, DETACH, or VACUUM. +- Prefer the planned template and bound roles when provided. +- Add a leading SQL comment exactly like: -- template_id: . +- Generate SQLite-compatible SQL. SQLite does not support PERCENTILE_CONT or STDDEV. +- Quote identifiers with double quotes. +- Return no markdown and no extra prose. + +Dataset context: +Dataset context for SQL QA: +- dataset_id: n1 +- dataset_name: Spambase +- table_name: n1 +- table_layout: single-table dataset (do not assume joins). +- row_semantics: One row is one email represented by engineered token/character frequency and capitalization-run features. +- task_type: classification +- target_column: class +- main_row_count: 4601 +- important_fields: +- word_freq_make: role=feature, type=numeric_sparse_frequency. tags=['condition_candidate', 'measure', 'rare_pattern_candidate'] desc=Frequency feature for token 'make' in an email message. +- word_freq_address: role=feature, type=numeric_sparse_frequency. tags=['condition_candidate', 'measure', 'rare_pattern_candidate'] desc=Frequency feature for token 'address' in an email message. +- word_freq_all: role=feature, type=numeric_sparse_frequency. tags=['condition_candidate', 'measure', 'rare_pattern_candidate'] desc=Frequency feature for token 'all' in an email message. +- word_freq_3d: role=feature, type=numeric_sparse_frequency. tags=['condition_candidate', 'measure', 'rare_pattern_candidate'] desc=Frequency feature for token '3d' in an email message. +- word_freq_our: role=feature, type=numeric_sparse_frequency. tags=['condition_candidate', 'measure', 'rare_pattern_candidate'] desc=Frequency feature for token 'our' in an email message. +- word_freq_over: role=feature, type=numeric_sparse_frequency. tags=['condition_candidate', 'measure', 'rare_pattern_candidate'] desc=Frequency feature for token 'over' in an email message. +- word_freq_remove: role=feature, type=numeric_sparse_frequency. tags=['condition_candidate', 'measure', 'rare_pattern_candidate'] desc=Frequency feature for token 'remove' in an email message. +- word_freq_internet: role=feature, type=numeric_sparse_frequency. tags=['condition_candidate', 'measure', 'rare_pattern_candidate'] desc=Frequency feature for token 'internet' in an email message. +- word_freq_order: role=feature, type=numeric_sparse_frequency. tags=['condition_candidate', 'measure', 'rare_pattern_candidate'] desc=Frequency feature for token 'order' in an email message. +- word_freq_mail: role=feature, type=numeric_sparse_frequency. tags=['condition_candidate', 'measure', 'rare_pattern_candidate'] desc=Frequency feature for token 'mail' in an email message. +- word_freq_receive: role=feature, type=numeric_sparse_frequency. tags=['condition_candidate', 'measure', 'rare_pattern_candidate'] desc=Frequency feature for token 'receive' in an email message. +- word_freq_will: role=feature, type=numeric_sparse_frequency. tags=['condition_candidate', 'measure', 'rare_pattern_candidate'] desc=Frequency feature for token 'will' in an email message. +- word_freq_people: role=feature, type=numeric_sparse_frequency. tags=['condition_candidate', 'measure', 'rare_pattern_candidate'] desc=Frequency feature for token 'people' in an email message. +- word_freq_report: role=feature, type=numeric_sparse_frequency. tags=['condition_candidate', 'measure', 'rare_pattern_candidate'] desc=Frequency feature for token 'report' in an email message. +- word_freq_addresses: role=feature, type=numeric_sparse_frequency. tags=['condition_candidate', 'measure', 'rare_pattern_candidate'] desc=Frequency feature for token 'addresses' in an email message. +- word_freq_free: role=feature, type=numeric_sparse_frequency. tags=['condition_candidate', 'measure', 'rare_pattern_candidate'] desc=Frequency feature for token 'free' in an email message. +- word_freq_business: role=feature, type=numeric_sparse_frequency. tags=['condition_candidate', 'measure', 'rare_pattern_candidate'] desc=Frequency feature for token 'business' in an email message. +- word_freq_email: role=feature, type=numeric_sparse_frequency. tags=['condition_candidate', 'measure', 'rare_pattern_candidate'] desc=Frequency feature for token 'email' in an email message. +- word_freq_you: role=feature, type=numeric_sparse_frequency. tags=['condition_candidate', 'measure', 'rare_pattern_candidate'] desc=Frequency feature for token 'you' in an email message. +- word_freq_credit: role=feature, type=numeric_sparse_frequency. tags=['condition_candidate', 'measure', 'rare_pattern_candidate'] desc=Frequency feature for token 'credit' in an email message. +- word_freq_your: role=feature, type=numeric_sparse_frequency. tags=['condition_candidate', 'measure', 'rare_pattern_candidate'] desc=Frequency feature for token 'your' in an email message. +- word_freq_font: role=feature, type=numeric_sparse_frequency. tags=['condition_candidate', 'measure', 'rare_pattern_candidate'] desc=Frequency feature for token 'font' in an email message. +- word_freq_000: role=feature, type=numeric_sparse_frequency. tags=['condition_candidate', 'measure', 'rare_pattern_candidate'] desc=Frequency feature for token '000' in an email message. +- word_freq_money: role=feature, type=numeric_sparse_frequency. tags=['condition_candidate', 'measure', 'rare_pattern_candidate'] desc=Frequency feature for token 'money' in an email message. +- word_freq_hp: role=feature, type=numeric_sparse_frequency. tags=['condition_candidate', 'measure', 'rare_pattern_candidate'] desc=Frequency feature for token 'hp' in an email message. +- word_freq_hpl: role=feature, type=numeric_sparse_frequency. tags=['condition_candidate', 'measure', 'rare_pattern_candidate'] desc=Frequency feature for token 'hpl' in an email message. +- word_freq_george: role=feature, type=numeric_sparse_frequency. tags=['condition_candidate', 'measure', 'rare_pattern_candidate'] desc=Frequency feature for token 'george' in an email message. +- word_freq_650: role=feature, type=numeric_sparse_frequency. tags=['condition_candidate', 'measure', 'rare_pattern_candidate'] desc=Frequency feature for token '650' in an email message. +- word_freq_lab: role=feature, type=numeric_sparse_frequency. tags=['condition_candidate', 'measure', 'rare_pattern_candidate'] desc=Frequency feature for token 'lab' in an email message. +- word_freq_labs: role=feature, type=numeric_sparse_frequency. tags=['condition_candidate', 'measure', 'rare_pattern_candidate'] desc=Frequency feature for token 'labs' in an email message. +- word_freq_telnet: role=feature, type=numeric_sparse_frequency. tags=['condition_candidate', 'measure', 'rare_pattern_candidate'] desc=Frequency feature for token 'telnet' in an email message. +- word_freq_857: role=feature, type=numeric_sparse_frequency. tags=['condition_candidate', 'measure', 'rare_pattern_candidate'] desc=Frequency feature for token '857' in an email message. +- word_freq_data: role=feature, type=numeric_sparse_frequency. tags=['condition_candidate', 'measure', 'rare_pattern_candidate'] desc=Frequency feature for token 'data' in an email message. +- word_freq_415: role=feature, type=numeric_sparse_frequency. tags=['condition_candidate', 'measure', 'rare_pattern_candidate'] desc=Frequency feature for token '415' in an email message. +- word_freq_85: role=feature, type=numeric_sparse_frequency. tags=['condition_candidate', 'measure', 'rare_pattern_candidate'] desc=Frequency feature for token '85' in an email message. +- word_freq_technology: role=feature, type=numeric_sparse_frequency. tags=['condition_candidate', 'measure', 'rare_pattern_candidate'] desc=Frequency feature for token 'technology' in an email message. +- word_freq_1999: role=feature, type=numeric_sparse_frequency. tags=['condition_candidate', 'measure', 'rare_pattern_candidate'] desc=Frequency feature for token '1999' in an email message. +- word_freq_parts: role=feature, type=numeric_sparse_frequency. tags=['condition_candidate', 'measure', 'rare_pattern_candidate'] desc=Frequency feature for token 'parts' in an email message. +- word_freq_pm: role=feature, type=numeric_sparse_frequency. tags=['condition_candidate', 'measure', 'rare_pattern_candidate'] desc=Frequency feature for token 'pm' in an email message. +- word_freq_direct: role=feature, type=numeric_sparse_frequency. tags=['condition_candidate', 'measure', 'rare_pattern_candidate'] desc=Frequency feature for token 'direct' in an email message. +- word_freq_cs: role=feature, type=numeric_sparse_frequency. tags=['condition_candidate', 'measure', 'rare_pattern_candidate'] desc=Frequency feature for token 'cs' in an email message. +- word_freq_meeting: role=feature, type=numeric_sparse_frequency. tags=['condition_candidate', 'measure', 'rare_pattern_candidate'] desc=Frequency feature for token 'meeting' in an email message. +- word_freq_original: role=feature, type=numeric_sparse_frequency. tags=['condition_candidate', 'measure', 'rare_pattern_candidate'] desc=Frequency feature for token 'original' in an email message. +- word_freq_project: role=feature, type=numeric_sparse_frequency. tags=['condition_candidate', 'measure', 'rare_pattern_candidate'] desc=Frequency feature for token 'project' in an email message. +- word_freq_re: role=feature, type=numeric_sparse_frequency. tags=['condition_candidate', 'measure', 'rare_pattern_candidate'] desc=Frequency feature for token 're' in an email message. +- word_freq_edu: role=feature, type=numeric_sparse_frequency. tags=['condition_candidate', 'measure', 'rare_pattern_candidate'] desc=Frequency feature for token 'edu' in an email message. +- word_freq_table: role=feature, type=numeric_sparse_frequency. tags=['condition_candidate', 'measure', 'rare_pattern_candidate'] desc=Frequency feature for token 'table' in an email message. +- word_freq_conference: role=feature, type=numeric_sparse_frequency. tags=['condition_candidate', 'measure', 'rare_pattern_candidate'] desc=Frequency feature for token 'conference' in an email message. +- char_freq_%3B: role=feature, type=numeric_sparse_frequency. tags=['condition_candidate', 'measure', 'rare_pattern_candidate'] desc=Character frequency feature for symbol ';'. +- char_freq_%28: role=feature, type=numeric_sparse_frequency. tags=['condition_candidate', 'measure', 'rare_pattern_candidate'] desc=Character frequency feature for symbol '('. +- char_freq_%5B: role=feature, type=numeric_sparse_frequency. tags=['condition_candidate', 'measure', 'rare_pattern_candidate'] desc=Character frequency feature for symbol '['. +- char_freq_%21: role=feature, type=numeric_sparse_frequency. tags=['condition_candidate', 'measure', 'rare_pattern_candidate'] desc=Character frequency feature for symbol '!'. +- char_freq_%24: role=feature, type=numeric_sparse_frequency. tags=['condition_candidate', 'measure', 'rare_pattern_candidate'] desc=Character frequency feature for symbol '$'. +- char_freq_%23: role=feature, type=numeric_sparse_frequency. tags=['condition_candidate', 'measure', 'rare_pattern_candidate'] desc=Character frequency feature for symbol '#'. +- capital_run_length_average: role=feature, type=numeric_heavy_tail. tags=['condition_candidate', 'measure', 'rare_pattern_candidate'] desc=Average length of uninterrupted capital-letter runs. +- capital_run_length_longest: role=feature, type=numeric_heavy_tail. tags=['condition_candidate', 'measure', 'rare_pattern_candidate'] desc=Longest uninterrupted capital-letter run. +- capital_run_length_total: role=feature, type=numeric_heavy_tail. tags=['condition_candidate', 'measure', 'rare_pattern_candidate'] desc=Total number of capital letters in runs. +- class: role=target, type=binary_target. tags=['target_candidate'] desc=Binary spam label (1=spam, 0=non-spam). +- useful_field_combinations: [['word_freq_free', 'word_freq_you', 'class'], ['char_freq_%21', 'capital_run_length_longest', 'class'], ['word_freq_remove', 'word_freq_money', 'class']] +- fields_requiring_caution: ['capital_run_length_average', 'capital_run_length_longest', 'capital_run_length_total'] +- source_url: https://www.openml.org/d/44 + +SQLite schema snapshot: +{ + "table_name": "n1", + "quoted_table_name": "\"n1\"", + "row_count": 4601, + "columns": [ + { + "name": "word_freq_make", + "type": "TEXT", + "notnull": false, + "pk": false + }, + { + "name": "word_freq_address", + "type": "TEXT", + "notnull": false, + "pk": false + }, + { + "name": "word_freq_all", + "type": "TEXT", + "notnull": false, + "pk": false + }, + { + "name": "word_freq_3d", + "type": "TEXT", + "notnull": false, + "pk": false + }, + { + "name": "word_freq_our", + "type": "TEXT", + "notnull": false, + "pk": false + }, + { + "name": "word_freq_over", + "type": "TEXT", + "notnull": false, + "pk": false + }, + { + "name": "word_freq_remove", + "type": "TEXT", + "notnull": false, + "pk": false + }, + { + "name": "word_freq_internet", + "type": "TEXT", + "notnull": false, + "pk": false + }, + { + "name": "word_freq_order", + "type": "TEXT", + "notnull": false, + "pk": false + }, + { + "name": "word_freq_mail", + "type": "TEXT", + "notnull": false, + "pk": false + }, + { + "name": "word_freq_receive", + "type": "TEXT", + "notnull": false, + "pk": false + }, + { + "name": "word_freq_will", + "type": "TEXT", + "notnull": false, + "pk": false + }, + { + "name": "word_freq_people", + "type": "TEXT", + "notnull": false, + "pk": false + }, + { + "name": "word_freq_report", + "type": "TEXT", + "notnull": false, + "pk": false + }, + { + "name": "word_freq_addresses", + "type": "TEXT", + "notnull": false, + "pk": false + }, + { + "name": "word_freq_free", + "type": "TEXT", + "notnull": false, + "pk": false + }, + { + "name": "word_freq_business", + "type": "TEXT", + "notnull": false, + "pk": false + }, + { + "name": "word_freq_email", + "type": "TEXT", + "notnull": false, + "pk": false + }, + { + "name": "word_freq_you", + "type": "TEXT", + "notnull": false, + "pk": false + }, + { + "name": "word_freq_credit", + "type": "TEXT", + "notnull": false, + "pk": false + }, + { + "name": "word_freq_your", + "type": "TEXT", + "notnull": false, + "pk": false + }, + { + "name": "word_freq_font", + "type": "TEXT", + "notnull": false, + "pk": false + }, + { + "name": "word_freq_000", + "type": "TEXT", + "notnull": false, + "pk": false + }, + { + "name": "word_freq_money", + "type": "TEXT", + "notnull": false, + "pk": false + }, + { + "name": "word_freq_hp", + "type": "TEXT", + "notnull": false, + "pk": false + }, + { + "name": "word_freq_hpl", + "type": "TEXT", + "notnull": false, + "pk": false + }, + { + "name": "word_freq_george", + "type": "TEXT", + "notnull": false, + "pk": false + }, + { + "name": "word_freq_650", + "type": "TEXT", + "notnull": false, + "pk": false + }, + { + "name": "word_freq_lab", + "type": "TEXT", + "notnull": false, + "pk": false + }, + { + "name": "word_freq_labs", + "type": "TEXT", + "notnull": false, + "pk": false + }, + { + "name": "word_freq_telnet", + "type": "TEXT", + "notnull": false, + "pk": false + }, + { + "name": "word_freq_857", + "type": "TEXT", + "notnull": false, + "pk": false + }, + { + "name": "word_freq_data", + "type": "TEXT", + "notnull": false, + "pk": false + }, + { + "name": "word_freq_415", + "type": "TEXT", + "notnull": false, + "pk": false + }, + { + "name": "word_freq_85", + "type": "TEXT", + "notnull": false, + "pk": false + }, + { + "name": "word_freq_technology", + "type": "TEXT", + "notnull": false, + "pk": false + }, + { + "name": "word_freq_1999", + "type": "TEXT", + "notnull": false, + "pk": false + }, + { + "name": "word_freq_parts", + "type": "TEXT", + "notnull": false, + "pk": false + }, + { + "name": "word_freq_pm", + "type": "TEXT", + "notnull": false, + "pk": false + }, + { + "name": "word_freq_direct", + "type": "TEXT", + "notnull": false, + "pk": false + }, + { + "name": "word_freq_cs", + "type": "TEXT", + "notnull": false, + "pk": false + }, + { + "name": "word_freq_meeting", + "type": "TEXT", + "notnull": false, + "pk": false + }, + { + "name": "word_freq_original", + "type": "TEXT", + "notnull": false, + "pk": false + }, + { + "name": "word_freq_project", + "type": "TEXT", + "notnull": false, + "pk": false + }, + { + "name": "word_freq_re", + "type": "TEXT", + "notnull": false, + "pk": false + }, + { + "name": "word_freq_edu", + "type": "TEXT", + "notnull": false, + "pk": false + }, + { + "name": "word_freq_table", + "type": "TEXT", + "notnull": false, + "pk": false + }, + { + "name": "word_freq_conference", + "type": "TEXT", + "notnull": false, + "pk": false + }, + { + "name": "char_freq_%3B", + "type": "TEXT", + "notnull": false, + "pk": false + }, + { + "name": "char_freq_%28", + "type": "TEXT", + "notnull": false, + "pk": false + }, + { + "name": "char_freq_%5B", + "type": "TEXT", + "notnull": false, + "pk": false + }, + { + "name": "char_freq_%21", + "type": "TEXT", + "notnull": false, + "pk": false + }, + { + "name": "char_freq_%24", + "type": "TEXT", + "notnull": false, + "pk": false + }, + { + "name": "char_freq_%23", + "type": "TEXT", + "notnull": false, + "pk": false + }, + { + "name": "capital_run_length_average", + "type": "TEXT", + "notnull": false, + "pk": false + }, + { + "name": "capital_run_length_longest", + "type": "TEXT", + "notnull": false, + "pk": false + }, + { + "name": "capital_run_length_total", + "type": "TEXT", + "notnull": false, + "pk": false + }, + { + "name": "class", + "type": "TEXT", + "notnull": false, + "pk": false + } + ], + "sample_rows": [ + { + "word_freq_make": "0", + "word_freq_address": "0.64", + "word_freq_all": "0.64", + "word_freq_3d": "0", + "word_freq_our": "0.32", + "word_freq_over": "0", + "word_freq_remove": "0", + "word_freq_internet": "0", + "word_freq_order": "0", + "word_freq_mail": "0", + "word_freq_receive": "0", + "word_freq_will": "0.64", + "word_freq_people": "0", + "word_freq_report": "0", + "word_freq_addresses": "0", + "word_freq_free": "0.32", + "word_freq_business": "0", + "word_freq_email": "1.29", + "word_freq_you": "1.93", + "word_freq_credit": "0", + "word_freq_your": "0.96", + "word_freq_font": "0", + "word_freq_000": "0", + "word_freq_money": "0", + "word_freq_hp": "0", + "word_freq_hpl": "0", + "word_freq_george": "0", + "word_freq_650": "0", + "word_freq_lab": "0", + "word_freq_labs": "0", + "word_freq_telnet": "0", + "word_freq_857": "0", + "word_freq_data": "0", + "word_freq_415": "0", + "word_freq_85": "0", + "word_freq_technology": "0", + "word_freq_1999": "0", + "word_freq_parts": "0", + "word_freq_pm": "0", + "word_freq_direct": "0", + "word_freq_cs": "0", + "word_freq_meeting": "0", + "word_freq_original": "0", + "word_freq_project": "0", + "word_freq_re": "0", + "word_freq_edu": "0", + "word_freq_table": "0", + "word_freq_conference": "0", + "char_freq_%3B": "0", + "char_freq_%28": "0", + "char_freq_%5B": "0", + "char_freq_%21": "0.778", + "char_freq_%24": "0", + "char_freq_%23": "0", + "capital_run_length_average": "3.756", + "capital_run_length_longest": "61", + "capital_run_length_total": "278", + "class": "1" + }, + { + "word_freq_make": "0.21", + "word_freq_address": "0.28", + "word_freq_all": "0.5", + "word_freq_3d": "0", + "word_freq_our": "0.14", + "word_freq_over": "0.28", + "word_freq_remove": "0.21", + "word_freq_internet": "0.07", + "word_freq_order": "0", + "word_freq_mail": "0.94", + "word_freq_receive": "0.21", + "word_freq_will": "0.79", + "word_freq_people": "0.65", + "word_freq_report": "0.21", + "word_freq_addresses": "0.14", + "word_freq_free": "0.14", + "word_freq_business": "0.07", + "word_freq_email": "0.28", + "word_freq_you": "3.47", + "word_freq_credit": "0", + "word_freq_your": "1.59", + "word_freq_font": "0", + "word_freq_000": "0.43", + "word_freq_money": "0.43", + "word_freq_hp": "0", + "word_freq_hpl": "0", + "word_freq_george": "0", + "word_freq_650": "0", + "word_freq_lab": "0", + "word_freq_labs": "0", + "word_freq_telnet": "0", + "word_freq_857": "0", + "word_freq_data": "0", + "word_freq_415": "0", + "word_freq_85": "0", + "word_freq_technology": "0", + "word_freq_1999": "0.07", + "word_freq_parts": "0", + "word_freq_pm": "0", + "word_freq_direct": "0", + "word_freq_cs": "0", + "word_freq_meeting": "0", + "word_freq_original": "0", + "word_freq_project": "0", + "word_freq_re": "0", + "word_freq_edu": "0", + "word_freq_table": "0", + "word_freq_conference": "0", + "char_freq_%3B": "0", + "char_freq_%28": "0.132", + "char_freq_%5B": "0", + "char_freq_%21": "0.372", + "char_freq_%24": "0.18", + "char_freq_%23": "0.048", + "capital_run_length_average": "5.114", + "capital_run_length_longest": "101", + "capital_run_length_total": "1028", + "class": "1" + }, + { + "word_freq_make": "0.06", + "word_freq_address": "0", + "word_freq_all": "0.71", + "word_freq_3d": "0", + "word_freq_our": "1.23", + "word_freq_over": "0.19", + "word_freq_remove": "0.19", + "word_freq_internet": "0.12", + "word_freq_order": "0.64", + "word_freq_mail": "0.25", + "word_freq_receive": "0.38", + "word_freq_will": "0.45", + "word_freq_people": "0.12", + "word_freq_report": "0", + "word_freq_addresses": "1.75", + "word_freq_free": "0.06", + "word_freq_business": "0.06", + "word_freq_email": "1.03", + "word_freq_you": "1.36", + "word_freq_credit": "0.32", + "word_freq_your": "0.51", + "word_freq_font": "0", + "word_freq_000": "1.16", + "word_freq_money": "0.06", + "word_freq_hp": "0", + "word_freq_hpl": "0", + "word_freq_george": "0", + "word_freq_650": "0", + "word_freq_lab": "0", + "word_freq_labs": "0", + "word_freq_telnet": "0", + "word_freq_857": "0", + "word_freq_data": "0", + "word_freq_415": "0", + "word_freq_85": "0", + "word_freq_technology": "0", + "word_freq_1999": "0", + "word_freq_parts": "0", + "word_freq_pm": "0", + "word_freq_direct": "0.06", + "word_freq_cs": "0", + "word_freq_meeting": "0", + "word_freq_original": "0.12", + "word_freq_project": "0", + "word_freq_re": "0.06", + "word_freq_edu": "0.06", + "word_freq_table": "0", + "word_freq_conference": "0", + "char_freq_%3B": "0.01", + "char_freq_%28": "0.143", + "char_freq_%5B": "0", + "char_freq_%21": "0.276", + "char_freq_%24": "0.184", + "char_freq_%23": "0.01", + "capital_run_length_average": "9.821", + "capital_run_length_longest": "485", + "capital_run_length_total": "2259", + "class": "1" + }, + { + "word_freq_make": "0", + "word_freq_address": "0", + "word_freq_all": "0", + "word_freq_3d": "0", + "word_freq_our": "0.63", + "word_freq_over": "0", + "word_freq_remove": "0.31", + "word_freq_internet": "0.63", + "word_freq_order": "0.31", + "word_freq_mail": "0.63", + "word_freq_receive": "0.31", + "word_freq_will": "0.31", + "word_freq_people": "0.31", + "word_freq_report": "0", + "word_freq_addresses": "0", + "word_freq_free": "0.31", + "word_freq_business": "0", + "word_freq_email": "0", + "word_freq_you": "3.18", + "word_freq_credit": "0", + "word_freq_your": "0.31", + "word_freq_font": "0", + "word_freq_000": "0", + "word_freq_money": "0", + "word_freq_hp": "0", + "word_freq_hpl": "0", + "word_freq_george": "0", + "word_freq_650": "0", + "word_freq_lab": "0", + "word_freq_labs": "0", + "word_freq_telnet": "0", + "word_freq_857": "0", + "word_freq_data": "0", + "word_freq_415": "0", + "word_freq_85": "0", + "word_freq_technology": "0", + "word_freq_1999": "0", + "word_freq_parts": "0", + "word_freq_pm": "0", + "word_freq_direct": "0", + "word_freq_cs": "0", + "word_freq_meeting": "0", + "word_freq_original": "0", + "word_freq_project": "0", + "word_freq_re": "0", + "word_freq_edu": "0", + "word_freq_table": "0", + "word_freq_conference": "0", + "char_freq_%3B": "0", + "char_freq_%28": "0.137", + "char_freq_%5B": "0", + "char_freq_%21": "0.137", + "char_freq_%24": "0", + "char_freq_%23": "0", + "capital_run_length_average": "3.537", + "capital_run_length_longest": "40", + "capital_run_length_total": "191", + "class": "1" + }, + { + "word_freq_make": "0", + "word_freq_address": "0", + "word_freq_all": "0", + "word_freq_3d": "0", + "word_freq_our": "0.63", + "word_freq_over": "0", + "word_freq_remove": "0.31", + "word_freq_internet": "0.63", + "word_freq_order": "0.31", + "word_freq_mail": "0.63", + "word_freq_receive": "0.31", + "word_freq_will": "0.31", + "word_freq_people": "0.31", + "word_freq_report": "0", + "word_freq_addresses": "0", + "word_freq_free": "0.31", + "word_freq_business": "0", + "word_freq_email": "0", + "word_freq_you": "3.18", + "word_freq_credit": "0", + "word_freq_your": "0.31", + "word_freq_font": "0", + "word_freq_000": "0", + "word_freq_money": "0", + "word_freq_hp": "0", + "word_freq_hpl": "0", + "word_freq_george": "0", + "word_freq_650": "0", + "word_freq_lab": "0", + "word_freq_labs": "0", + "word_freq_telnet": "0", + "word_freq_857": "0", + "word_freq_data": "0", + "word_freq_415": "0", + "word_freq_85": "0", + "word_freq_technology": "0", + "word_freq_1999": "0", + "word_freq_parts": "0", + "word_freq_pm": "0", + "word_freq_direct": "0", + "word_freq_cs": "0", + "word_freq_meeting": "0", + "word_freq_original": "0", + "word_freq_project": "0", + "word_freq_re": "0", + "word_freq_edu": "0", + "word_freq_table": "0", + "word_freq_conference": "0", + "char_freq_%3B": "0", + "char_freq_%28": "0.135", + "char_freq_%5B": "0", + "char_freq_%21": "0.135", + "char_freq_%24": "0", + "char_freq_%23": "0", + "capital_run_length_average": "3.537", + "capital_run_length_longest": "40", + "capital_run_length_total": "191", + "class": "1" + } + ] +} + +Shortlisted templates: +[ + { + "template_id": "tpl_m4_group_condition_rate", + "template_name": "Grouped Condition Rate", + "primary_family": "conditional_dependency_structure", + "portability": "yes", + "sql_skeleton": "SELECT {group_col},\n AVG(CASE WHEN {condition_col} = {condition_value} THEN 1 ELSE 0 END) AS condition_rate\nFROM {table}\nGROUP BY {group_col}\nORDER BY condition_rate DESC;", + "required_roles": [ + "group_col", + "condition_col" + ] + } +] + +Problem instance: +{ + "dataset_id": "n1", + "question": "Use template Grouped Condition Rate to probe direction_consistency with semantic role focused_target_view. Focus on group_col=class, condition_col=class.", + "planned_template_id": "tpl_m4_group_condition_rate", + "bindings": { + "group_col": "class", + "condition_col": "class", + "condition_value": "0", + "positive_value": "0", + "negative_value": "1", + "top_k": 14, + "top_n": 6, + "num_tiles": 10, + "percentile_value": 0.9, + "z_threshold": 2.0, + "fraction_threshold": 0.1, + "baseline_multiplier": 1.5, + "baseline_fraction": 0.1, + "min_group_size": 5, + "min_support": 5, + "measure_threshold": 0.0, + "time_grain": "month", + "lookback_rows": 3, + "current_period_start": "'2024-01-01'", + "current_period_end": "'2024-04-01'", + "previous_period_start": "'2023-10-01'", + "previous_period_end": "'2024-01-01'", + "drift_ratio_threshold": 0.8 + }, + "can_vary": [], + "must_fix": [], + "runtime_sql_skeleton": "SELECT {group_col},\n AVG(CASE WHEN {condition_col} = {condition_value} THEN 1 ELSE 0 END) AS condition_rate\nFROM {table}\nGROUP BY {group_col}\nORDER BY condition_rate DESC;" +} + +Repair context: +{} diff --git a/Query/sql/v2/runs/v2_cli_20260502_081223_d/n1/artifacts/v2q_n1_45cf6899c1a896b8/cli/sql_prompt_attempt_2.txt b/Query/sql/v2/runs/v2_cli_20260502_081223_d/n1/artifacts/v2q_n1_45cf6899c1a896b8/cli/sql_prompt_attempt_2.txt new file mode 100644 index 0000000000000000000000000000000000000000..7507f77046d1a99048d7f7b2ecb6277faae2dde8 --- /dev/null +++ b/Query/sql/v2/runs/v2_cli_20260502_081223_d/n1/artifacts/v2q_n1_45cf6899c1a896b8/cli/sql_prompt_attempt_2.txt @@ -0,0 +1,795 @@ +You are generating one SQLite SELECT query for a single-table SQL QA task. +Return strict JSON only, with this schema: {"sql": "...", "notes": "..."}. +Rules: +- Use only the provided table and columns. +- Do not write INSERT, UPDATE, DELETE, DROP, ALTER, CREATE, PRAGMA, ATTACH, DETACH, or VACUUM. +- Prefer the planned template and bound roles when provided. +- Add a leading SQL comment exactly like: -- template_id: . +- Generate SQLite-compatible SQL. SQLite does not support PERCENTILE_CONT or STDDEV. +- Quote identifiers with double quotes. +- Return no markdown and no extra prose. + +Dataset context: +Dataset context for SQL QA: +- dataset_id: n1 +- dataset_name: Spambase +- table_name: n1 +- table_layout: single-table dataset (do not assume joins). +- row_semantics: One row is one email represented by engineered token/character frequency and capitalization-run features. +- task_type: classification +- target_column: class +- main_row_count: 4601 +- important_fields: +- word_freq_make: role=feature, type=numeric_sparse_frequency. tags=['condition_candidate', 'measure', 'rare_pattern_candidate'] desc=Frequency feature for token 'make' in an email message. +- word_freq_address: role=feature, type=numeric_sparse_frequency. tags=['condition_candidate', 'measure', 'rare_pattern_candidate'] desc=Frequency feature for token 'address' in an email message. +- word_freq_all: role=feature, type=numeric_sparse_frequency. tags=['condition_candidate', 'measure', 'rare_pattern_candidate'] desc=Frequency feature for token 'all' in an email message. +- word_freq_3d: role=feature, type=numeric_sparse_frequency. tags=['condition_candidate', 'measure', 'rare_pattern_candidate'] desc=Frequency feature for token '3d' in an email message. +- word_freq_our: role=feature, type=numeric_sparse_frequency. tags=['condition_candidate', 'measure', 'rare_pattern_candidate'] desc=Frequency feature for token 'our' in an email message. +- word_freq_over: role=feature, type=numeric_sparse_frequency. tags=['condition_candidate', 'measure', 'rare_pattern_candidate'] desc=Frequency feature for token 'over' in an email message. +- word_freq_remove: role=feature, type=numeric_sparse_frequency. tags=['condition_candidate', 'measure', 'rare_pattern_candidate'] desc=Frequency feature for token 'remove' in an email message. +- word_freq_internet: role=feature, type=numeric_sparse_frequency. tags=['condition_candidate', 'measure', 'rare_pattern_candidate'] desc=Frequency feature for token 'internet' in an email message. +- word_freq_order: role=feature, type=numeric_sparse_frequency. tags=['condition_candidate', 'measure', 'rare_pattern_candidate'] desc=Frequency feature for token 'order' in an email message. +- word_freq_mail: role=feature, type=numeric_sparse_frequency. tags=['condition_candidate', 'measure', 'rare_pattern_candidate'] desc=Frequency feature for token 'mail' in an email message. +- word_freq_receive: role=feature, type=numeric_sparse_frequency. tags=['condition_candidate', 'measure', 'rare_pattern_candidate'] desc=Frequency feature for token 'receive' in an email message. +- word_freq_will: role=feature, type=numeric_sparse_frequency. tags=['condition_candidate', 'measure', 'rare_pattern_candidate'] desc=Frequency feature for token 'will' in an email message. +- word_freq_people: role=feature, type=numeric_sparse_frequency. tags=['condition_candidate', 'measure', 'rare_pattern_candidate'] desc=Frequency feature for token 'people' in an email message. +- word_freq_report: role=feature, type=numeric_sparse_frequency. tags=['condition_candidate', 'measure', 'rare_pattern_candidate'] desc=Frequency feature for token 'report' in an email message. +- word_freq_addresses: role=feature, type=numeric_sparse_frequency. tags=['condition_candidate', 'measure', 'rare_pattern_candidate'] desc=Frequency feature for token 'addresses' in an email message. +- word_freq_free: role=feature, type=numeric_sparse_frequency. tags=['condition_candidate', 'measure', 'rare_pattern_candidate'] desc=Frequency feature for token 'free' in an email message. +- word_freq_business: role=feature, type=numeric_sparse_frequency. tags=['condition_candidate', 'measure', 'rare_pattern_candidate'] desc=Frequency feature for token 'business' in an email message. +- word_freq_email: role=feature, type=numeric_sparse_frequency. tags=['condition_candidate', 'measure', 'rare_pattern_candidate'] desc=Frequency feature for token 'email' in an email message. +- word_freq_you: role=feature, type=numeric_sparse_frequency. tags=['condition_candidate', 'measure', 'rare_pattern_candidate'] desc=Frequency feature for token 'you' in an email message. +- word_freq_credit: role=feature, type=numeric_sparse_frequency. tags=['condition_candidate', 'measure', 'rare_pattern_candidate'] desc=Frequency feature for token 'credit' in an email message. +- word_freq_your: role=feature, type=numeric_sparse_frequency. tags=['condition_candidate', 'measure', 'rare_pattern_candidate'] desc=Frequency feature for token 'your' in an email message. +- word_freq_font: role=feature, type=numeric_sparse_frequency. tags=['condition_candidate', 'measure', 'rare_pattern_candidate'] desc=Frequency feature for token 'font' in an email message. +- word_freq_000: role=feature, type=numeric_sparse_frequency. tags=['condition_candidate', 'measure', 'rare_pattern_candidate'] desc=Frequency feature for token '000' in an email message. +- word_freq_money: role=feature, type=numeric_sparse_frequency. tags=['condition_candidate', 'measure', 'rare_pattern_candidate'] desc=Frequency feature for token 'money' in an email message. +- word_freq_hp: role=feature, type=numeric_sparse_frequency. tags=['condition_candidate', 'measure', 'rare_pattern_candidate'] desc=Frequency feature for token 'hp' in an email message. +- word_freq_hpl: role=feature, type=numeric_sparse_frequency. tags=['condition_candidate', 'measure', 'rare_pattern_candidate'] desc=Frequency feature for token 'hpl' in an email message. +- word_freq_george: role=feature, type=numeric_sparse_frequency. tags=['condition_candidate', 'measure', 'rare_pattern_candidate'] desc=Frequency feature for token 'george' in an email message. +- word_freq_650: role=feature, type=numeric_sparse_frequency. tags=['condition_candidate', 'measure', 'rare_pattern_candidate'] desc=Frequency feature for token '650' in an email message. +- word_freq_lab: role=feature, type=numeric_sparse_frequency. tags=['condition_candidate', 'measure', 'rare_pattern_candidate'] desc=Frequency feature for token 'lab' in an email message. +- word_freq_labs: role=feature, type=numeric_sparse_frequency. tags=['condition_candidate', 'measure', 'rare_pattern_candidate'] desc=Frequency feature for token 'labs' in an email message. +- word_freq_telnet: role=feature, type=numeric_sparse_frequency. tags=['condition_candidate', 'measure', 'rare_pattern_candidate'] desc=Frequency feature for token 'telnet' in an email message. +- word_freq_857: role=feature, type=numeric_sparse_frequency. tags=['condition_candidate', 'measure', 'rare_pattern_candidate'] desc=Frequency feature for token '857' in an email message. +- word_freq_data: role=feature, type=numeric_sparse_frequency. tags=['condition_candidate', 'measure', 'rare_pattern_candidate'] desc=Frequency feature for token 'data' in an email message. +- word_freq_415: role=feature, type=numeric_sparse_frequency. tags=['condition_candidate', 'measure', 'rare_pattern_candidate'] desc=Frequency feature for token '415' in an email message. +- word_freq_85: role=feature, type=numeric_sparse_frequency. tags=['condition_candidate', 'measure', 'rare_pattern_candidate'] desc=Frequency feature for token '85' in an email message. +- word_freq_technology: role=feature, type=numeric_sparse_frequency. tags=['condition_candidate', 'measure', 'rare_pattern_candidate'] desc=Frequency feature for token 'technology' in an email message. +- word_freq_1999: role=feature, type=numeric_sparse_frequency. tags=['condition_candidate', 'measure', 'rare_pattern_candidate'] desc=Frequency feature for token '1999' in an email message. +- word_freq_parts: role=feature, type=numeric_sparse_frequency. tags=['condition_candidate', 'measure', 'rare_pattern_candidate'] desc=Frequency feature for token 'parts' in an email message. +- word_freq_pm: role=feature, type=numeric_sparse_frequency. tags=['condition_candidate', 'measure', 'rare_pattern_candidate'] desc=Frequency feature for token 'pm' in an email message. +- word_freq_direct: role=feature, type=numeric_sparse_frequency. tags=['condition_candidate', 'measure', 'rare_pattern_candidate'] desc=Frequency feature for token 'direct' in an email message. +- word_freq_cs: role=feature, type=numeric_sparse_frequency. tags=['condition_candidate', 'measure', 'rare_pattern_candidate'] desc=Frequency feature for token 'cs' in an email message. +- word_freq_meeting: role=feature, type=numeric_sparse_frequency. tags=['condition_candidate', 'measure', 'rare_pattern_candidate'] desc=Frequency feature for token 'meeting' in an email message. +- word_freq_original: role=feature, type=numeric_sparse_frequency. tags=['condition_candidate', 'measure', 'rare_pattern_candidate'] desc=Frequency feature for token 'original' in an email message. +- word_freq_project: role=feature, type=numeric_sparse_frequency. tags=['condition_candidate', 'measure', 'rare_pattern_candidate'] desc=Frequency feature for token 'project' in an email message. +- word_freq_re: role=feature, type=numeric_sparse_frequency. tags=['condition_candidate', 'measure', 'rare_pattern_candidate'] desc=Frequency feature for token 're' in an email message. +- word_freq_edu: role=feature, type=numeric_sparse_frequency. tags=['condition_candidate', 'measure', 'rare_pattern_candidate'] desc=Frequency feature for token 'edu' in an email message. +- word_freq_table: role=feature, type=numeric_sparse_frequency. tags=['condition_candidate', 'measure', 'rare_pattern_candidate'] desc=Frequency feature for token 'table' in an email message. +- word_freq_conference: role=feature, type=numeric_sparse_frequency. tags=['condition_candidate', 'measure', 'rare_pattern_candidate'] desc=Frequency feature for token 'conference' in an email message. +- char_freq_%3B: role=feature, type=numeric_sparse_frequency. tags=['condition_candidate', 'measure', 'rare_pattern_candidate'] desc=Character frequency feature for symbol ';'. +- char_freq_%28: role=feature, type=numeric_sparse_frequency. tags=['condition_candidate', 'measure', 'rare_pattern_candidate'] desc=Character frequency feature for symbol '('. +- char_freq_%5B: role=feature, type=numeric_sparse_frequency. tags=['condition_candidate', 'measure', 'rare_pattern_candidate'] desc=Character frequency feature for symbol '['. +- char_freq_%21: role=feature, type=numeric_sparse_frequency. tags=['condition_candidate', 'measure', 'rare_pattern_candidate'] desc=Character frequency feature for symbol '!'. +- char_freq_%24: role=feature, type=numeric_sparse_frequency. tags=['condition_candidate', 'measure', 'rare_pattern_candidate'] desc=Character frequency feature for symbol '$'. +- char_freq_%23: role=feature, type=numeric_sparse_frequency. tags=['condition_candidate', 'measure', 'rare_pattern_candidate'] desc=Character frequency feature for symbol '#'. +- capital_run_length_average: role=feature, type=numeric_heavy_tail. tags=['condition_candidate', 'measure', 'rare_pattern_candidate'] desc=Average length of uninterrupted capital-letter runs. +- capital_run_length_longest: role=feature, type=numeric_heavy_tail. tags=['condition_candidate', 'measure', 'rare_pattern_candidate'] desc=Longest uninterrupted capital-letter run. +- capital_run_length_total: role=feature, type=numeric_heavy_tail. tags=['condition_candidate', 'measure', 'rare_pattern_candidate'] desc=Total number of capital letters in runs. +- class: role=target, type=binary_target. tags=['target_candidate'] desc=Binary spam label (1=spam, 0=non-spam). +- useful_field_combinations: [['word_freq_free', 'word_freq_you', 'class'], ['char_freq_%21', 'capital_run_length_longest', 'class'], ['word_freq_remove', 'word_freq_money', 'class']] +- fields_requiring_caution: ['capital_run_length_average', 'capital_run_length_longest', 'capital_run_length_total'] +- source_url: https://www.openml.org/d/44 + +SQLite schema snapshot: +{ + "table_name": "n1", + "quoted_table_name": "\"n1\"", + "row_count": 4601, + "columns": [ + { + "name": "word_freq_make", + "type": "TEXT", + "notnull": false, + "pk": false + }, + { + "name": "word_freq_address", + "type": "TEXT", + "notnull": false, + "pk": false + }, + { + "name": "word_freq_all", + "type": "TEXT", + "notnull": false, + "pk": false + }, + { + "name": "word_freq_3d", + "type": "TEXT", + "notnull": false, + "pk": false + }, + { + "name": "word_freq_our", + "type": "TEXT", + "notnull": false, + "pk": false + }, + { + "name": "word_freq_over", + "type": "TEXT", + "notnull": false, + "pk": false + }, + { + "name": "word_freq_remove", + "type": "TEXT", + "notnull": false, + "pk": false + }, + { + "name": "word_freq_internet", + "type": "TEXT", + "notnull": false, + "pk": false + }, + { + "name": "word_freq_order", + "type": "TEXT", + "notnull": false, + "pk": false + }, + { + "name": "word_freq_mail", + "type": "TEXT", + "notnull": false, + "pk": false + }, + { + "name": "word_freq_receive", + "type": "TEXT", + "notnull": false, + "pk": false + }, + { + "name": "word_freq_will", + "type": "TEXT", + "notnull": false, + "pk": false + }, + { + "name": "word_freq_people", + "type": "TEXT", + "notnull": false, + "pk": false + }, + { + "name": "word_freq_report", + "type": "TEXT", + "notnull": false, + "pk": false + }, + { + "name": "word_freq_addresses", + "type": "TEXT", + "notnull": false, + "pk": false + }, + { + "name": "word_freq_free", + "type": "TEXT", + "notnull": false, + "pk": false + }, + { + "name": "word_freq_business", + "type": "TEXT", + "notnull": false, + "pk": false + }, + { + "name": "word_freq_email", + "type": "TEXT", + "notnull": false, + "pk": false + }, + { + "name": "word_freq_you", + "type": "TEXT", + "notnull": false, + "pk": false + }, + { + "name": "word_freq_credit", + "type": "TEXT", + "notnull": false, + "pk": false + }, + { + "name": "word_freq_your", + "type": "TEXT", + "notnull": false, + "pk": false + }, + { + "name": "word_freq_font", + "type": "TEXT", + "notnull": false, + "pk": false + }, + { + "name": "word_freq_000", + "type": "TEXT", + "notnull": false, + "pk": false + }, + { + "name": "word_freq_money", + "type": "TEXT", + "notnull": false, + "pk": false + }, + { + "name": "word_freq_hp", + "type": "TEXT", + "notnull": false, + "pk": false + }, + { + "name": "word_freq_hpl", + "type": "TEXT", + "notnull": false, + "pk": false + }, + { + "name": "word_freq_george", + "type": "TEXT", + "notnull": false, + "pk": false + }, + { + "name": "word_freq_650", + "type": "TEXT", + "notnull": false, + "pk": false + }, + { + "name": "word_freq_lab", + "type": "TEXT", + "notnull": false, + "pk": false + }, + { + "name": "word_freq_labs", + "type": "TEXT", + "notnull": false, + "pk": false + }, + { + "name": "word_freq_telnet", + "type": "TEXT", + "notnull": false, + "pk": false + }, + { + "name": "word_freq_857", + "type": "TEXT", + "notnull": false, + "pk": false + }, + { + "name": "word_freq_data", + "type": "TEXT", + "notnull": false, + "pk": false + }, + { + "name": "word_freq_415", + "type": "TEXT", + "notnull": false, + "pk": false + }, + { + "name": "word_freq_85", + "type": "TEXT", + "notnull": false, + "pk": false + }, + { + "name": "word_freq_technology", + "type": "TEXT", + "notnull": false, + "pk": false + }, + { + "name": "word_freq_1999", + "type": "TEXT", + "notnull": false, + "pk": false + }, + { + "name": "word_freq_parts", + "type": "TEXT", + "notnull": false, + "pk": false + }, + { + "name": "word_freq_pm", + "type": "TEXT", + "notnull": false, + "pk": false + }, + { + "name": "word_freq_direct", + "type": "TEXT", + "notnull": false, + "pk": false + }, + { + "name": "word_freq_cs", + "type": "TEXT", + "notnull": false, + "pk": false + }, + { + "name": "word_freq_meeting", + "type": "TEXT", + "notnull": false, + "pk": false + }, + { + "name": "word_freq_original", + "type": "TEXT", + "notnull": false, + "pk": false + }, + { + "name": "word_freq_project", + "type": "TEXT", + "notnull": false, + "pk": false + }, + { + "name": "word_freq_re", + "type": "TEXT", + "notnull": false, + "pk": false + }, + { + "name": "word_freq_edu", + "type": "TEXT", + "notnull": false, + "pk": false + }, + { + "name": "word_freq_table", + "type": "TEXT", + "notnull": false, + "pk": false + }, + { + "name": "word_freq_conference", + "type": "TEXT", + "notnull": false, + "pk": false + }, + { + "name": "char_freq_%3B", + "type": "TEXT", + "notnull": false, + "pk": false + }, + { + "name": "char_freq_%28", + "type": "TEXT", + "notnull": false, + "pk": false + }, + { + "name": "char_freq_%5B", + "type": "TEXT", + "notnull": false, + "pk": false + }, + { + "name": "char_freq_%21", + "type": "TEXT", + "notnull": false, + "pk": false + }, + { + "name": "char_freq_%24", + "type": "TEXT", + "notnull": false, + "pk": false + }, + { + "name": "char_freq_%23", + "type": "TEXT", + "notnull": false, + "pk": false + }, + { + "name": "capital_run_length_average", + "type": "TEXT", + "notnull": false, + "pk": false + }, + { + "name": "capital_run_length_longest", + "type": "TEXT", + "notnull": false, + "pk": false + }, + { + "name": "capital_run_length_total", + "type": "TEXT", + "notnull": false, + "pk": false + }, + { + "name": "class", + "type": "TEXT", + "notnull": false, + "pk": false + } + ], + "sample_rows": [ + { + "word_freq_make": "0", + "word_freq_address": "0.64", + "word_freq_all": "0.64", + "word_freq_3d": "0", + "word_freq_our": "0.32", + "word_freq_over": "0", + "word_freq_remove": "0", + "word_freq_internet": "0", + "word_freq_order": "0", + "word_freq_mail": "0", + "word_freq_receive": "0", + "word_freq_will": "0.64", + "word_freq_people": "0", + "word_freq_report": "0", + "word_freq_addresses": "0", + "word_freq_free": "0.32", + "word_freq_business": "0", + "word_freq_email": "1.29", + "word_freq_you": "1.93", + "word_freq_credit": "0", + "word_freq_your": "0.96", + "word_freq_font": "0", + "word_freq_000": "0", + "word_freq_money": "0", + "word_freq_hp": "0", + "word_freq_hpl": "0", + "word_freq_george": "0", + "word_freq_650": "0", + "word_freq_lab": "0", + "word_freq_labs": "0", + "word_freq_telnet": "0", + "word_freq_857": "0", + "word_freq_data": "0", + "word_freq_415": "0", + "word_freq_85": "0", + "word_freq_technology": "0", + "word_freq_1999": "0", + "word_freq_parts": "0", + "word_freq_pm": "0", + "word_freq_direct": "0", + "word_freq_cs": "0", + "word_freq_meeting": "0", + "word_freq_original": "0", + "word_freq_project": "0", + "word_freq_re": "0", + "word_freq_edu": "0", + "word_freq_table": "0", + "word_freq_conference": "0", + "char_freq_%3B": "0", + "char_freq_%28": "0", + "char_freq_%5B": "0", + "char_freq_%21": "0.778", + "char_freq_%24": "0", + "char_freq_%23": "0", + "capital_run_length_average": "3.756", + "capital_run_length_longest": "61", + "capital_run_length_total": "278", + "class": "1" + }, + { + "word_freq_make": "0.21", + "word_freq_address": "0.28", + "word_freq_all": "0.5", + "word_freq_3d": "0", + "word_freq_our": "0.14", + "word_freq_over": "0.28", + "word_freq_remove": "0.21", + "word_freq_internet": "0.07", + "word_freq_order": "0", + "word_freq_mail": "0.94", + "word_freq_receive": "0.21", + "word_freq_will": "0.79", + "word_freq_people": "0.65", + "word_freq_report": "0.21", + "word_freq_addresses": "0.14", + "word_freq_free": "0.14", + "word_freq_business": "0.07", + "word_freq_email": "0.28", + "word_freq_you": "3.47", + "word_freq_credit": "0", + "word_freq_your": "1.59", + "word_freq_font": "0", + "word_freq_000": "0.43", + "word_freq_money": "0.43", + "word_freq_hp": "0", + "word_freq_hpl": "0", + "word_freq_george": "0", + "word_freq_650": "0", + "word_freq_lab": "0", + "word_freq_labs": "0", + "word_freq_telnet": "0", + "word_freq_857": "0", + "word_freq_data": "0", + "word_freq_415": "0", + "word_freq_85": "0", + "word_freq_technology": "0", + "word_freq_1999": "0.07", + "word_freq_parts": "0", + "word_freq_pm": "0", + "word_freq_direct": "0", + "word_freq_cs": "0", + "word_freq_meeting": "0", + "word_freq_original": "0", + "word_freq_project": "0", + "word_freq_re": "0", + "word_freq_edu": "0", + "word_freq_table": "0", + "word_freq_conference": "0", + "char_freq_%3B": "0", + "char_freq_%28": "0.132", + "char_freq_%5B": "0", + "char_freq_%21": "0.372", + "char_freq_%24": "0.18", + "char_freq_%23": "0.048", + "capital_run_length_average": "5.114", + "capital_run_length_longest": "101", + "capital_run_length_total": "1028", + "class": "1" + }, + { + "word_freq_make": "0.06", + "word_freq_address": "0", + "word_freq_all": "0.71", + "word_freq_3d": "0", + "word_freq_our": "1.23", + "word_freq_over": "0.19", + "word_freq_remove": "0.19", + "word_freq_internet": "0.12", + "word_freq_order": "0.64", + "word_freq_mail": "0.25", + "word_freq_receive": "0.38", + "word_freq_will": "0.45", + "word_freq_people": "0.12", + "word_freq_report": "0", + "word_freq_addresses": "1.75", + "word_freq_free": "0.06", + "word_freq_business": "0.06", + "word_freq_email": "1.03", + "word_freq_you": "1.36", + "word_freq_credit": "0.32", + "word_freq_your": "0.51", + "word_freq_font": "0", + "word_freq_000": "1.16", + "word_freq_money": "0.06", + "word_freq_hp": "0", + "word_freq_hpl": "0", + "word_freq_george": "0", + "word_freq_650": "0", + "word_freq_lab": "0", + "word_freq_labs": "0", + "word_freq_telnet": "0", + "word_freq_857": "0", + "word_freq_data": "0", + "word_freq_415": "0", + "word_freq_85": "0", + "word_freq_technology": "0", + "word_freq_1999": "0", + "word_freq_parts": "0", + "word_freq_pm": "0", + "word_freq_direct": "0.06", + "word_freq_cs": "0", + "word_freq_meeting": "0", + "word_freq_original": "0.12", + "word_freq_project": "0", + "word_freq_re": "0.06", + "word_freq_edu": "0.06", + "word_freq_table": "0", + "word_freq_conference": "0", + "char_freq_%3B": "0.01", + "char_freq_%28": "0.143", + "char_freq_%5B": "0", + "char_freq_%21": "0.276", + "char_freq_%24": "0.184", + "char_freq_%23": "0.01", + "capital_run_length_average": "9.821", + "capital_run_length_longest": "485", + "capital_run_length_total": "2259", + "class": "1" + }, + { + "word_freq_make": "0", + "word_freq_address": "0", + "word_freq_all": "0", + "word_freq_3d": "0", + "word_freq_our": "0.63", + "word_freq_over": "0", + "word_freq_remove": "0.31", + "word_freq_internet": "0.63", + "word_freq_order": "0.31", + "word_freq_mail": "0.63", + "word_freq_receive": "0.31", + "word_freq_will": "0.31", + "word_freq_people": "0.31", + "word_freq_report": "0", + "word_freq_addresses": "0", + "word_freq_free": "0.31", + "word_freq_business": "0", + "word_freq_email": "0", + "word_freq_you": "3.18", + "word_freq_credit": "0", + "word_freq_your": "0.31", + "word_freq_font": "0", + "word_freq_000": "0", + "word_freq_money": "0", + "word_freq_hp": "0", + "word_freq_hpl": "0", + "word_freq_george": "0", + "word_freq_650": "0", + "word_freq_lab": "0", + "word_freq_labs": "0", + "word_freq_telnet": "0", + "word_freq_857": "0", + "word_freq_data": "0", + "word_freq_415": "0", + "word_freq_85": "0", + "word_freq_technology": "0", + "word_freq_1999": "0", + "word_freq_parts": "0", + "word_freq_pm": "0", + "word_freq_direct": "0", + "word_freq_cs": "0", + "word_freq_meeting": "0", + "word_freq_original": "0", + "word_freq_project": "0", + "word_freq_re": "0", + "word_freq_edu": "0", + "word_freq_table": "0", + "word_freq_conference": "0", + "char_freq_%3B": "0", + "char_freq_%28": "0.137", + "char_freq_%5B": "0", + "char_freq_%21": "0.137", + "char_freq_%24": "0", + "char_freq_%23": "0", + "capital_run_length_average": "3.537", + "capital_run_length_longest": "40", + "capital_run_length_total": "191", + "class": "1" + }, + { + "word_freq_make": "0", + "word_freq_address": "0", + "word_freq_all": "0", + "word_freq_3d": "0", + "word_freq_our": "0.63", + "word_freq_over": "0", + "word_freq_remove": "0.31", + "word_freq_internet": "0.63", + "word_freq_order": "0.31", + "word_freq_mail": "0.63", + "word_freq_receive": "0.31", + "word_freq_will": "0.31", + "word_freq_people": "0.31", + "word_freq_report": "0", + "word_freq_addresses": "0", + "word_freq_free": "0.31", + "word_freq_business": "0", + "word_freq_email": "0", + "word_freq_you": "3.18", + "word_freq_credit": "0", + "word_freq_your": "0.31", + "word_freq_font": "0", + "word_freq_000": "0", + "word_freq_money": "0", + "word_freq_hp": "0", + "word_freq_hpl": "0", + "word_freq_george": "0", + "word_freq_650": "0", + "word_freq_lab": "0", + "word_freq_labs": "0", + "word_freq_telnet": "0", + "word_freq_857": "0", + "word_freq_data": "0", + "word_freq_415": "0", + "word_freq_85": "0", + "word_freq_technology": "0", + "word_freq_1999": "0", + "word_freq_parts": "0", + "word_freq_pm": "0", + "word_freq_direct": "0", + "word_freq_cs": "0", + "word_freq_meeting": "0", + "word_freq_original": "0", + "word_freq_project": "0", + "word_freq_re": "0", + "word_freq_edu": "0", + "word_freq_table": "0", + "word_freq_conference": "0", + "char_freq_%3B": "0", + "char_freq_%28": "0.135", + "char_freq_%5B": "0", + "char_freq_%21": "0.135", + "char_freq_%24": "0", + "char_freq_%23": "0", + "capital_run_length_average": "3.537", + "capital_run_length_longest": "40", + "capital_run_length_total": "191", + "class": "1" + } + ] +} + +Shortlisted templates: +[ + { + "template_id": "tpl_m4_group_condition_rate", + "template_name": "Grouped Condition Rate", + "primary_family": "conditional_dependency_structure", + "portability": "yes", + "sql_skeleton": "SELECT {group_col},\n AVG(CASE WHEN {condition_col} = {condition_value} THEN 1 ELSE 0 END) AS condition_rate\nFROM {table}\nGROUP BY {group_col}\nORDER BY condition_rate DESC;", + "required_roles": [ + "group_col", + "condition_col" + ] + } +] + +Problem instance: +{ + "dataset_id": "n1", + "question": "Use template Grouped Condition Rate to probe direction_consistency with semantic role focused_target_view. Focus on group_col=class, condition_col=class.", + "planned_template_id": "tpl_m4_group_condition_rate", + "bindings": { + "group_col": "class", + "condition_col": "class", + "condition_value": "0", + "positive_value": "0", + "negative_value": "1", + "top_k": 14, + "top_n": 6, + "num_tiles": 10, + "percentile_value": 0.9, + "z_threshold": 2.0, + "fraction_threshold": 0.1, + "baseline_multiplier": 1.5, + "baseline_fraction": 0.1, + "min_group_size": 5, + "min_support": 5, + "measure_threshold": 0.0, + "time_grain": "month", + "lookback_rows": 3, + "current_period_start": "'2024-01-01'", + "current_period_end": "'2024-04-01'", + "previous_period_start": "'2023-10-01'", + "previous_period_end": "'2024-01-01'", + "drift_ratio_threshold": 0.8 + }, + "can_vary": [], + "must_fix": [], + "runtime_sql_skeleton": "SELECT {group_col},\n AVG(CASE WHEN {condition_col} = {condition_value} THEN 1 ELSE 0 END) AS condition_rate\nFROM {table}\nGROUP BY {group_col}\nORDER BY condition_rate DESC;" +} + +Repair context: +{} diff --git a/Query/sql/v2/runs/v2_cli_20260502_081223_d/n1/artifacts/v2q_n1_45cf6899c1a896b8/cli/sql_response_attempt_1.raw.txt b/Query/sql/v2/runs/v2_cli_20260502_081223_d/n1/artifacts/v2q_n1_45cf6899c1a896b8/cli/sql_response_attempt_1.raw.txt new file mode 100644 index 0000000000000000000000000000000000000000..acce32b7df006621a781f2e8040a682d58ce3ce3 --- /dev/null +++ b/Query/sql/v2/runs/v2_cli_20260502_081223_d/n1/artifacts/v2q_n1_45cf6899c1a896b8/cli/sql_response_attempt_1.raw.txt @@ -0,0 +1,4 @@ +{"type":"thread.started","thread_id":"019e40f6-2e2f-7bc1-a7b1-e2025cea0102"} +{"type":"turn.started"} +{"type":"error","message":"Quota exceeded. Check your plan and billing details."} +{"type":"turn.failed","error":{"message":"Quota exceeded. Check your plan and billing details."}} diff --git a/Query/sql/v2/runs/v2_cli_20260502_081223_d/n1/artifacts/v2q_n1_45cf6899c1a896b8/cli/sql_response_attempt_1.txt b/Query/sql/v2/runs/v2_cli_20260502_081223_d/n1/artifacts/v2q_n1_45cf6899c1a896b8/cli/sql_response_attempt_1.txt new file mode 100644 index 0000000000000000000000000000000000000000..36e3774acd1fe318708b7ac417fbf4b868e87d9c --- /dev/null +++ b/Query/sql/v2/runs/v2_cli_20260502_081223_d/n1/artifacts/v2q_n1_45cf6899c1a896b8/cli/sql_response_attempt_1.txt @@ -0,0 +1,4 @@ +{"type":"thread.started","thread_id":"019e40f6-2e2f-7bc1-a7b1-e2025cea0102"} +{"type":"turn.started"} +{"type":"error","message":"Quota exceeded. Check your plan and billing details."} +{"type":"turn.failed","error":{"message":"Quota exceeded. Check your plan and billing details."}} \ No newline at end of file diff --git a/Query/sql/v2/runs/v2_cli_20260502_081223_d/n1/artifacts/v2q_n1_45cf6899c1a896b8/cli/sql_response_attempt_2.raw.txt b/Query/sql/v2/runs/v2_cli_20260502_081223_d/n1/artifacts/v2q_n1_45cf6899c1a896b8/cli/sql_response_attempt_2.raw.txt new file mode 100644 index 0000000000000000000000000000000000000000..79c8a2ce36521a342fd32086a35e1eabf2dec639 --- /dev/null +++ b/Query/sql/v2/runs/v2_cli_20260502_081223_d/n1/artifacts/v2q_n1_45cf6899c1a896b8/cli/sql_response_attempt_2.raw.txt @@ -0,0 +1,4 @@ +{"type":"thread.started","thread_id":"019e40f6-3e00-7ec2-9c77-3ef7db2a262c"} +{"type":"turn.started"} +{"type":"error","message":"Quota exceeded. Check your plan and billing details."} +{"type":"turn.failed","error":{"message":"Quota exceeded. Check your plan and billing details."}} diff --git a/Query/sql/v2/runs/v2_cli_20260502_081223_d/n1/artifacts/v2q_n1_45cf6899c1a896b8/cli/sql_response_attempt_2.txt b/Query/sql/v2/runs/v2_cli_20260502_081223_d/n1/artifacts/v2q_n1_45cf6899c1a896b8/cli/sql_response_attempt_2.txt new file mode 100644 index 0000000000000000000000000000000000000000..c2c2648f872f0bec26802791d7b468c49abc2eda --- /dev/null +++ b/Query/sql/v2/runs/v2_cli_20260502_081223_d/n1/artifacts/v2q_n1_45cf6899c1a896b8/cli/sql_response_attempt_2.txt @@ -0,0 +1,4 @@ +{"type":"thread.started","thread_id":"019e40f6-3e00-7ec2-9c77-3ef7db2a262c"} +{"type":"turn.started"} +{"type":"error","message":"Quota exceeded. Check your plan and billing details."} +{"type":"turn.failed","error":{"message":"Quota exceeded. Check your plan and billing details."}} \ No newline at end of file diff --git a/Query/sql/v2/runs/v2_cli_20260502_081223_d/n1/artifacts/v2q_n1_45cf6899c1a896b8/cli/sql_stderr_attempt_1.txt b/Query/sql/v2/runs/v2_cli_20260502_081223_d/n1/artifacts/v2q_n1_45cf6899c1a896b8/cli/sql_stderr_attempt_1.txt new file mode 100644 index 0000000000000000000000000000000000000000..e69de29bb2d1d6434b8b29ae775ad8c2e48c5391 diff --git a/Query/sql/v2/runs/v2_cli_20260502_081223_d/n1/artifacts/v2q_n1_45cf6899c1a896b8/cli/sql_stderr_attempt_2.txt b/Query/sql/v2/runs/v2_cli_20260502_081223_d/n1/artifacts/v2q_n1_45cf6899c1a896b8/cli/sql_stderr_attempt_2.txt new file mode 100644 index 0000000000000000000000000000000000000000..e69de29bb2d1d6434b8b29ae775ad8c2e48c5391 diff --git a/Query/sql/v2/runs/v2_cli_20260502_081223_d/n1/artifacts/v2q_n1_4862ee97bb2fdeda/cli/conversation.jsonl b/Query/sql/v2/runs/v2_cli_20260502_081223_d/n1/artifacts/v2q_n1_4862ee97bb2fdeda/cli/conversation.jsonl new file mode 100644 index 0000000000000000000000000000000000000000..d2c91ab01596fb98d66113d00fbaca62b3a5a0e7 --- /dev/null +++ b/Query/sql/v2/runs/v2_cli_20260502_081223_d/n1/artifacts/v2q_n1_4862ee97bb2fdeda/cli/conversation.jsonl @@ -0,0 +1,2 @@ +{"attempt": 1, "phase": "sql_generation", "role": "user", "content_path": "cli/sql_prompt_attempt_1.txt", "metrics": {"chars": 29533, "bytes_utf8": 29533, "lines": 790, "estimated_tokens": null}} +{"attempt": 1, "phase": "sql_generation", "role": "assistant", "content_path": "cli/sql_response_attempt_1.txt", "raw_content_path": "cli/sql_response_attempt_1.raw.txt", "stderr_path": "cli/sql_stderr_attempt_1.txt", "metrics": {"chars": 541, "bytes_utf8": 541, "lines": 1, "estimated_tokens": null}, "usage": {"input_tokens": 20370, "cached_input_tokens": 19840, "output_tokens": 450, "reasoning_output_tokens": 296}} diff --git a/Query/sql/v2/runs/v2_cli_20260502_081223_d/n1/artifacts/v2q_n1_4862ee97bb2fdeda/cli/session_summary.json b/Query/sql/v2/runs/v2_cli_20260502_081223_d/n1/artifacts/v2q_n1_4862ee97bb2fdeda/cli/session_summary.json new file mode 100644 index 0000000000000000000000000000000000000000..8a9fbc69e26b61601328892f97daca50d410f1a6 --- /dev/null +++ b/Query/sql/v2/runs/v2_cli_20260502_081223_d/n1/artifacts/v2q_n1_4862ee97bb2fdeda/cli/session_summary.json @@ -0,0 +1,25 @@ +{ + "engine": "v2-cli:codex", + "command": "codex exec --skip-git-repo-check --disable plugins --sandbox read-only --cd \"/data/jialinzhang/SQLagent\" -m gpt-5.4 --json -", + "ai_cli_calls": 1, + "usage_summary": { + "dataset_id": "n1", + "model": "v2-cli:codex", + "run_id": "v2q_n1_4862ee97bb2fdeda", + "api_calls": 0, + "input_tokens": 20370, + "cached_input_tokens": 19840, + "output_tokens": 450, + "total_tokens": 20820, + "cost_usd": 0.0, + "ai_cli_calls": 1, + "estimated_input_tokens": 0, + "estimated_output_tokens": 0, + "estimated_total_tokens": 0, + "usage_source": "ai_cli_json_usage", + "cli_elapsed_ms_total": 18866.83, + "sql_execution_elapsed_ms_total": 15.61, + "conversation_log_path": "/data/jialinzhang/TabQueryBench/sql_workloads/v2_current/runs_and_launches/runs/v2_cli_20260502_081223_d/n1/artifacts/v2q_n1_4862ee97bb2fdeda/cli/conversation.jsonl", + "note": "Executed through a local AI CLI with structured usage metadata." + } +} \ No newline at end of file diff --git a/Query/sql/v2/runs/v2_cli_20260502_081223_d/n1/artifacts/v2q_n1_4862ee97bb2fdeda/cli/sql_attempt_1.metadata.json b/Query/sql/v2/runs/v2_cli_20260502_081223_d/n1/artifacts/v2q_n1_4862ee97bb2fdeda/cli/sql_attempt_1.metadata.json new file mode 100644 index 0000000000000000000000000000000000000000..2d81cbd5f32b1c8aee01b0106d4d81288542fe5e --- /dev/null +++ b/Query/sql/v2/runs/v2_cli_20260502_081223_d/n1/artifacts/v2q_n1_4862ee97bb2fdeda/cli/sql_attempt_1.metadata.json @@ -0,0 +1,45 @@ +{ + "attempt": 1, + "phase": "sql_generation", + "command": "codex exec --skip-git-repo-check --disable plugins --sandbox read-only --cd \"/data/jialinzhang/SQLagent\" -m gpt-5.4 --json -", + "started_at": "2026-05-19T15:44:57.915850+00:00", + "ended_at": "2026-05-19T15:45:16.782710+00:00", + "elapsed_ms": 18866.83, + "prompt_metrics": { + "chars": 29533, + "bytes_utf8": 29533, + "lines": 790, + "estimated_tokens": null + }, + "stdout_metrics": { + "chars": 921, + "bytes_utf8": 921, + "lines": 4, + "estimated_tokens": null + }, + "stderr_metrics": { + "chars": 0, + "bytes_utf8": 0, + "lines": 0, + "estimated_tokens": null + }, + "parsed_output": { + "format": "jsonl_events", + "text_metrics": { + "chars": 541, + "bytes_utf8": 541, + "lines": 1, + "estimated_tokens": null + }, + "usage": { + "input_tokens": 20370, + "cached_input_tokens": 19840, + "output_tokens": 450, + "reasoning_output_tokens": 296 + } + }, + "prompt_path": "cli/sql_prompt_attempt_1.txt", + "response_path": "cli/sql_response_attempt_1.txt", + "raw_response_path": "cli/sql_response_attempt_1.raw.txt", + "stderr_path": "cli/sql_stderr_attempt_1.txt" +} \ No newline at end of file diff --git a/Query/sql/v2/runs/v2_cli_20260502_081223_d/n1/artifacts/v2q_n1_4862ee97bb2fdeda/cli/sql_prompt_attempt_1.txt b/Query/sql/v2/runs/v2_cli_20260502_081223_d/n1/artifacts/v2q_n1_4862ee97bb2fdeda/cli/sql_prompt_attempt_1.txt new file mode 100644 index 0000000000000000000000000000000000000000..dde9f6653845216d011ec1e0443e25ce13950315 --- /dev/null +++ b/Query/sql/v2/runs/v2_cli_20260502_081223_d/n1/artifacts/v2q_n1_4862ee97bb2fdeda/cli/sql_prompt_attempt_1.txt @@ -0,0 +1,790 @@ +You are generating one SQLite SELECT query for a single-table SQL QA task. +Return strict JSON only, with this schema: {"sql": "...", "notes": "..."}. +Rules: +- Use only the provided table and columns. +- Do not write INSERT, UPDATE, DELETE, DROP, ALTER, CREATE, PRAGMA, ATTACH, DETACH, or VACUUM. +- Prefer the planned template and bound roles when provided. +- Add a leading SQL comment exactly like: -- template_id: . +- Generate SQLite-compatible SQL. SQLite does not support PERCENTILE_CONT or STDDEV. +- Quote identifiers with double quotes. +- Return no markdown and no extra prose. + +Dataset context: +Dataset context for SQL QA: +- dataset_id: n1 +- dataset_name: Spambase +- table_name: n1 +- table_layout: single-table dataset (do not assume joins). +- row_semantics: One row is one email represented by engineered token/character frequency and capitalization-run features. +- task_type: classification +- target_column: class +- main_row_count: 4601 +- important_fields: +- word_freq_make: role=feature, type=numeric_sparse_frequency. tags=['condition_candidate', 'measure', 'rare_pattern_candidate'] desc=Frequency feature for token 'make' in an email message. +- word_freq_address: role=feature, type=numeric_sparse_frequency. tags=['condition_candidate', 'measure', 'rare_pattern_candidate'] desc=Frequency feature for token 'address' in an email message. +- word_freq_all: role=feature, type=numeric_sparse_frequency. tags=['condition_candidate', 'measure', 'rare_pattern_candidate'] desc=Frequency feature for token 'all' in an email message. +- word_freq_3d: role=feature, type=numeric_sparse_frequency. tags=['condition_candidate', 'measure', 'rare_pattern_candidate'] desc=Frequency feature for token '3d' in an email message. +- word_freq_our: role=feature, type=numeric_sparse_frequency. tags=['condition_candidate', 'measure', 'rare_pattern_candidate'] desc=Frequency feature for token 'our' in an email message. +- word_freq_over: role=feature, type=numeric_sparse_frequency. tags=['condition_candidate', 'measure', 'rare_pattern_candidate'] desc=Frequency feature for token 'over' in an email message. +- word_freq_remove: role=feature, type=numeric_sparse_frequency. tags=['condition_candidate', 'measure', 'rare_pattern_candidate'] desc=Frequency feature for token 'remove' in an email message. +- word_freq_internet: role=feature, type=numeric_sparse_frequency. tags=['condition_candidate', 'measure', 'rare_pattern_candidate'] desc=Frequency feature for token 'internet' in an email message. +- word_freq_order: role=feature, type=numeric_sparse_frequency. tags=['condition_candidate', 'measure', 'rare_pattern_candidate'] desc=Frequency feature for token 'order' in an email message. +- word_freq_mail: role=feature, type=numeric_sparse_frequency. tags=['condition_candidate', 'measure', 'rare_pattern_candidate'] desc=Frequency feature for token 'mail' in an email message. +- word_freq_receive: role=feature, type=numeric_sparse_frequency. tags=['condition_candidate', 'measure', 'rare_pattern_candidate'] desc=Frequency feature for token 'receive' in an email message. +- word_freq_will: role=feature, type=numeric_sparse_frequency. tags=['condition_candidate', 'measure', 'rare_pattern_candidate'] desc=Frequency feature for token 'will' in an email message. +- word_freq_people: role=feature, type=numeric_sparse_frequency. tags=['condition_candidate', 'measure', 'rare_pattern_candidate'] desc=Frequency feature for token 'people' in an email message. +- word_freq_report: role=feature, type=numeric_sparse_frequency. tags=['condition_candidate', 'measure', 'rare_pattern_candidate'] desc=Frequency feature for token 'report' in an email message. +- word_freq_addresses: role=feature, type=numeric_sparse_frequency. tags=['condition_candidate', 'measure', 'rare_pattern_candidate'] desc=Frequency feature for token 'addresses' in an email message. +- word_freq_free: role=feature, type=numeric_sparse_frequency. tags=['condition_candidate', 'measure', 'rare_pattern_candidate'] desc=Frequency feature for token 'free' in an email message. +- word_freq_business: role=feature, type=numeric_sparse_frequency. tags=['condition_candidate', 'measure', 'rare_pattern_candidate'] desc=Frequency feature for token 'business' in an email message. +- word_freq_email: role=feature, type=numeric_sparse_frequency. tags=['condition_candidate', 'measure', 'rare_pattern_candidate'] desc=Frequency feature for token 'email' in an email message. +- word_freq_you: role=feature, type=numeric_sparse_frequency. tags=['condition_candidate', 'measure', 'rare_pattern_candidate'] desc=Frequency feature for token 'you' in an email message. +- word_freq_credit: role=feature, type=numeric_sparse_frequency. tags=['condition_candidate', 'measure', 'rare_pattern_candidate'] desc=Frequency feature for token 'credit' in an email message. +- word_freq_your: role=feature, type=numeric_sparse_frequency. tags=['condition_candidate', 'measure', 'rare_pattern_candidate'] desc=Frequency feature for token 'your' in an email message. +- word_freq_font: role=feature, type=numeric_sparse_frequency. tags=['condition_candidate', 'measure', 'rare_pattern_candidate'] desc=Frequency feature for token 'font' in an email message. +- word_freq_000: role=feature, type=numeric_sparse_frequency. tags=['condition_candidate', 'measure', 'rare_pattern_candidate'] desc=Frequency feature for token '000' in an email message. +- word_freq_money: role=feature, type=numeric_sparse_frequency. tags=['condition_candidate', 'measure', 'rare_pattern_candidate'] desc=Frequency feature for token 'money' in an email message. +- word_freq_hp: role=feature, type=numeric_sparse_frequency. tags=['condition_candidate', 'measure', 'rare_pattern_candidate'] desc=Frequency feature for token 'hp' in an email message. +- word_freq_hpl: role=feature, type=numeric_sparse_frequency. tags=['condition_candidate', 'measure', 'rare_pattern_candidate'] desc=Frequency feature for token 'hpl' in an email message. +- word_freq_george: role=feature, type=numeric_sparse_frequency. tags=['condition_candidate', 'measure', 'rare_pattern_candidate'] desc=Frequency feature for token 'george' in an email message. +- word_freq_650: role=feature, type=numeric_sparse_frequency. tags=['condition_candidate', 'measure', 'rare_pattern_candidate'] desc=Frequency feature for token '650' in an email message. +- word_freq_lab: role=feature, type=numeric_sparse_frequency. tags=['condition_candidate', 'measure', 'rare_pattern_candidate'] desc=Frequency feature for token 'lab' in an email message. +- word_freq_labs: role=feature, type=numeric_sparse_frequency. tags=['condition_candidate', 'measure', 'rare_pattern_candidate'] desc=Frequency feature for token 'labs' in an email message. +- word_freq_telnet: role=feature, type=numeric_sparse_frequency. tags=['condition_candidate', 'measure', 'rare_pattern_candidate'] desc=Frequency feature for token 'telnet' in an email message. +- word_freq_857: role=feature, type=numeric_sparse_frequency. tags=['condition_candidate', 'measure', 'rare_pattern_candidate'] desc=Frequency feature for token '857' in an email message. +- word_freq_data: role=feature, type=numeric_sparse_frequency. tags=['condition_candidate', 'measure', 'rare_pattern_candidate'] desc=Frequency feature for token 'data' in an email message. +- word_freq_415: role=feature, type=numeric_sparse_frequency. tags=['condition_candidate', 'measure', 'rare_pattern_candidate'] desc=Frequency feature for token '415' in an email message. +- word_freq_85: role=feature, type=numeric_sparse_frequency. tags=['condition_candidate', 'measure', 'rare_pattern_candidate'] desc=Frequency feature for token '85' in an email message. +- word_freq_technology: role=feature, type=numeric_sparse_frequency. tags=['condition_candidate', 'measure', 'rare_pattern_candidate'] desc=Frequency feature for token 'technology' in an email message. +- word_freq_1999: role=feature, type=numeric_sparse_frequency. tags=['condition_candidate', 'measure', 'rare_pattern_candidate'] desc=Frequency feature for token '1999' in an email message. +- word_freq_parts: role=feature, type=numeric_sparse_frequency. tags=['condition_candidate', 'measure', 'rare_pattern_candidate'] desc=Frequency feature for token 'parts' in an email message. +- word_freq_pm: role=feature, type=numeric_sparse_frequency. tags=['condition_candidate', 'measure', 'rare_pattern_candidate'] desc=Frequency feature for token 'pm' in an email message. +- word_freq_direct: role=feature, type=numeric_sparse_frequency. tags=['condition_candidate', 'measure', 'rare_pattern_candidate'] desc=Frequency feature for token 'direct' in an email message. +- word_freq_cs: role=feature, type=numeric_sparse_frequency. tags=['condition_candidate', 'measure', 'rare_pattern_candidate'] desc=Frequency feature for token 'cs' in an email message. +- word_freq_meeting: role=feature, type=numeric_sparse_frequency. tags=['condition_candidate', 'measure', 'rare_pattern_candidate'] desc=Frequency feature for token 'meeting' in an email message. +- word_freq_original: role=feature, type=numeric_sparse_frequency. tags=['condition_candidate', 'measure', 'rare_pattern_candidate'] desc=Frequency feature for token 'original' in an email message. +- word_freq_project: role=feature, type=numeric_sparse_frequency. tags=['condition_candidate', 'measure', 'rare_pattern_candidate'] desc=Frequency feature for token 'project' in an email message. +- word_freq_re: role=feature, type=numeric_sparse_frequency. tags=['condition_candidate', 'measure', 'rare_pattern_candidate'] desc=Frequency feature for token 're' in an email message. +- word_freq_edu: role=feature, type=numeric_sparse_frequency. tags=['condition_candidate', 'measure', 'rare_pattern_candidate'] desc=Frequency feature for token 'edu' in an email message. +- word_freq_table: role=feature, type=numeric_sparse_frequency. tags=['condition_candidate', 'measure', 'rare_pattern_candidate'] desc=Frequency feature for token 'table' in an email message. +- word_freq_conference: role=feature, type=numeric_sparse_frequency. tags=['condition_candidate', 'measure', 'rare_pattern_candidate'] desc=Frequency feature for token 'conference' in an email message. +- char_freq_%3B: role=feature, type=numeric_sparse_frequency. tags=['condition_candidate', 'measure', 'rare_pattern_candidate'] desc=Character frequency feature for symbol ';'. +- char_freq_%28: role=feature, type=numeric_sparse_frequency. tags=['condition_candidate', 'measure', 'rare_pattern_candidate'] desc=Character frequency feature for symbol '('. +- char_freq_%5B: role=feature, type=numeric_sparse_frequency. tags=['condition_candidate', 'measure', 'rare_pattern_candidate'] desc=Character frequency feature for symbol '['. +- char_freq_%21: role=feature, type=numeric_sparse_frequency. tags=['condition_candidate', 'measure', 'rare_pattern_candidate'] desc=Character frequency feature for symbol '!'. +- char_freq_%24: role=feature, type=numeric_sparse_frequency. tags=['condition_candidate', 'measure', 'rare_pattern_candidate'] desc=Character frequency feature for symbol '$'. +- char_freq_%23: role=feature, type=numeric_sparse_frequency. tags=['condition_candidate', 'measure', 'rare_pattern_candidate'] desc=Character frequency feature for symbol '#'. +- capital_run_length_average: role=feature, type=numeric_heavy_tail. tags=['condition_candidate', 'measure', 'rare_pattern_candidate'] desc=Average length of uninterrupted capital-letter runs. +- capital_run_length_longest: role=feature, type=numeric_heavy_tail. tags=['condition_candidate', 'measure', 'rare_pattern_candidate'] desc=Longest uninterrupted capital-letter run. +- capital_run_length_total: role=feature, type=numeric_heavy_tail. tags=['condition_candidate', 'measure', 'rare_pattern_candidate'] desc=Total number of capital letters in runs. +- class: role=target, type=binary_target. tags=['target_candidate'] desc=Binary spam label (1=spam, 0=non-spam). +- useful_field_combinations: [['word_freq_free', 'word_freq_you', 'class'], ['char_freq_%21', 'capital_run_length_longest', 'class'], ['word_freq_remove', 'word_freq_money', 'class']] +- fields_requiring_caution: ['capital_run_length_average', 'capital_run_length_longest', 'capital_run_length_total'] +- source_url: https://www.openml.org/d/44 + +SQLite schema snapshot: +{ + "table_name": "n1", + "quoted_table_name": "\"n1\"", + "row_count": 4601, + "columns": [ + { + "name": "word_freq_make", + "type": "TEXT", + "notnull": false, + "pk": false + }, + { + "name": "word_freq_address", + "type": "TEXT", + "notnull": false, + "pk": false + }, + { + "name": "word_freq_all", + "type": "TEXT", + "notnull": false, + "pk": false + }, + { + "name": "word_freq_3d", + "type": "TEXT", + "notnull": false, + "pk": false + }, + { + "name": "word_freq_our", + "type": "TEXT", + "notnull": false, + "pk": false + }, + { + "name": "word_freq_over", + "type": "TEXT", + "notnull": false, + "pk": false + }, + { + "name": "word_freq_remove", + "type": "TEXT", + "notnull": false, + "pk": false + }, + { + "name": "word_freq_internet", + "type": "TEXT", + "notnull": false, + "pk": false + }, + { + "name": "word_freq_order", + "type": "TEXT", + "notnull": false, + "pk": false + }, + { + "name": "word_freq_mail", + "type": "TEXT", + "notnull": false, + "pk": false + }, + { + "name": "word_freq_receive", + "type": "TEXT", + "notnull": false, + "pk": false + }, + { + "name": "word_freq_will", + "type": "TEXT", + "notnull": false, + "pk": false + }, + { + "name": "word_freq_people", + "type": "TEXT", + "notnull": false, + "pk": false + }, + { + "name": "word_freq_report", + "type": "TEXT", + "notnull": false, + "pk": false + }, + { + "name": "word_freq_addresses", + "type": "TEXT", + "notnull": false, + "pk": false + }, + { + "name": "word_freq_free", + "type": "TEXT", + "notnull": false, + "pk": false + }, + { + "name": "word_freq_business", + "type": "TEXT", + "notnull": false, + "pk": false + }, + { + "name": "word_freq_email", + "type": "TEXT", + "notnull": false, + "pk": false + }, + { + "name": "word_freq_you", + "type": "TEXT", + "notnull": false, + "pk": false + }, + { + "name": "word_freq_credit", + "type": "TEXT", + "notnull": false, + "pk": false + }, + { + "name": "word_freq_your", + "type": "TEXT", + "notnull": false, + "pk": false + }, + { + "name": "word_freq_font", + "type": "TEXT", + "notnull": false, + "pk": false + }, + { + "name": "word_freq_000", + "type": "TEXT", + "notnull": false, + "pk": false + }, + { + "name": "word_freq_money", + "type": "TEXT", + "notnull": false, + "pk": false + }, + { + "name": "word_freq_hp", + "type": "TEXT", + "notnull": false, + "pk": false + }, + { + "name": "word_freq_hpl", + "type": "TEXT", + "notnull": false, + "pk": false + }, + { + "name": "word_freq_george", + "type": "TEXT", + "notnull": false, + "pk": false + }, + { + "name": "word_freq_650", + "type": "TEXT", + "notnull": false, + "pk": false + }, + { + "name": "word_freq_lab", + "type": "TEXT", + "notnull": false, + "pk": false + }, + { + "name": "word_freq_labs", + "type": "TEXT", + "notnull": false, + "pk": false + }, + { + "name": "word_freq_telnet", + "type": "TEXT", + "notnull": false, + "pk": false + }, + { + "name": "word_freq_857", + "type": "TEXT", + "notnull": false, + "pk": false + }, + { + "name": "word_freq_data", + "type": "TEXT", + "notnull": false, + "pk": false + }, + { + "name": "word_freq_415", + "type": "TEXT", + "notnull": false, + "pk": false + }, + { + "name": "word_freq_85", + "type": "TEXT", + "notnull": false, + "pk": false + }, + { + "name": "word_freq_technology", + "type": "TEXT", + "notnull": false, + "pk": false + }, + { + "name": "word_freq_1999", + "type": "TEXT", + "notnull": false, + "pk": false + }, + { + "name": "word_freq_parts", + "type": "TEXT", + "notnull": false, + "pk": false + }, + { + "name": "word_freq_pm", + "type": "TEXT", + "notnull": false, + "pk": false + }, + { + "name": "word_freq_direct", + "type": "TEXT", + "notnull": false, + "pk": false + }, + { + "name": "word_freq_cs", + "type": "TEXT", + "notnull": false, + "pk": false + }, + { + "name": "word_freq_meeting", + "type": "TEXT", + "notnull": false, + "pk": false + }, + { + "name": "word_freq_original", + "type": "TEXT", + "notnull": false, + "pk": false + }, + { + "name": "word_freq_project", + "type": "TEXT", + "notnull": false, + "pk": false + }, + { + "name": "word_freq_re", + "type": "TEXT", + "notnull": false, + "pk": false + }, + { + "name": "word_freq_edu", + "type": "TEXT", + "notnull": false, + "pk": false + }, + { + "name": "word_freq_table", + "type": "TEXT", + "notnull": false, + "pk": false + }, + { + "name": "word_freq_conference", + "type": "TEXT", + "notnull": false, + "pk": false + }, + { + "name": "char_freq_%3B", + "type": "TEXT", + "notnull": false, + "pk": false + }, + { + "name": "char_freq_%28", + "type": "TEXT", + "notnull": false, + "pk": false + }, + { + "name": "char_freq_%5B", + "type": "TEXT", + "notnull": false, + "pk": false + }, + { + "name": "char_freq_%21", + "type": "TEXT", + "notnull": false, + "pk": false + }, + { + "name": "char_freq_%24", + "type": "TEXT", + "notnull": false, + "pk": false + }, + { + "name": "char_freq_%23", + "type": "TEXT", + "notnull": false, + "pk": false + }, + { + "name": "capital_run_length_average", + "type": "TEXT", + "notnull": false, + "pk": false + }, + { + "name": "capital_run_length_longest", + "type": "TEXT", + "notnull": false, + "pk": false + }, + { + "name": "capital_run_length_total", + "type": "TEXT", + "notnull": false, + "pk": false + }, + { + "name": "class", + "type": "TEXT", + "notnull": false, + "pk": false + } + ], + "sample_rows": [ + { + "word_freq_make": "0", + "word_freq_address": "0.64", + "word_freq_all": "0.64", + "word_freq_3d": "0", + "word_freq_our": "0.32", + "word_freq_over": "0", + "word_freq_remove": "0", + "word_freq_internet": "0", + "word_freq_order": "0", + "word_freq_mail": "0", + "word_freq_receive": "0", + "word_freq_will": "0.64", + "word_freq_people": "0", + "word_freq_report": "0", + "word_freq_addresses": "0", + "word_freq_free": "0.32", + "word_freq_business": "0", + "word_freq_email": "1.29", + "word_freq_you": "1.93", + "word_freq_credit": "0", + "word_freq_your": "0.96", + "word_freq_font": "0", + "word_freq_000": "0", + "word_freq_money": "0", + "word_freq_hp": "0", + "word_freq_hpl": "0", + "word_freq_george": "0", + "word_freq_650": "0", + "word_freq_lab": "0", + "word_freq_labs": "0", + "word_freq_telnet": "0", + "word_freq_857": "0", + "word_freq_data": "0", + "word_freq_415": "0", + "word_freq_85": "0", + "word_freq_technology": "0", + "word_freq_1999": "0", + "word_freq_parts": "0", + "word_freq_pm": "0", + "word_freq_direct": "0", + "word_freq_cs": "0", + "word_freq_meeting": "0", + "word_freq_original": "0", + "word_freq_project": "0", + "word_freq_re": "0", + "word_freq_edu": "0", + "word_freq_table": "0", + "word_freq_conference": "0", + "char_freq_%3B": "0", + "char_freq_%28": "0", + "char_freq_%5B": "0", + "char_freq_%21": "0.778", + "char_freq_%24": "0", + "char_freq_%23": "0", + "capital_run_length_average": "3.756", + "capital_run_length_longest": "61", + "capital_run_length_total": "278", + "class": "1" + }, + { + "word_freq_make": "0.21", + "word_freq_address": "0.28", + "word_freq_all": "0.5", + "word_freq_3d": "0", + "word_freq_our": "0.14", + "word_freq_over": "0.28", + "word_freq_remove": "0.21", + "word_freq_internet": "0.07", + "word_freq_order": "0", + "word_freq_mail": "0.94", + "word_freq_receive": "0.21", + "word_freq_will": "0.79", + "word_freq_people": "0.65", + "word_freq_report": "0.21", + "word_freq_addresses": "0.14", + "word_freq_free": "0.14", + "word_freq_business": "0.07", + "word_freq_email": "0.28", + "word_freq_you": "3.47", + "word_freq_credit": "0", + "word_freq_your": "1.59", + "word_freq_font": "0", + "word_freq_000": "0.43", + "word_freq_money": "0.43", + "word_freq_hp": "0", + "word_freq_hpl": "0", + "word_freq_george": "0", + "word_freq_650": "0", + "word_freq_lab": "0", + "word_freq_labs": "0", + "word_freq_telnet": "0", + "word_freq_857": "0", + "word_freq_data": "0", + "word_freq_415": "0", + "word_freq_85": "0", + "word_freq_technology": "0", + "word_freq_1999": "0.07", + "word_freq_parts": "0", + "word_freq_pm": "0", + "word_freq_direct": "0", + "word_freq_cs": "0", + "word_freq_meeting": "0", + "word_freq_original": "0", + "word_freq_project": "0", + "word_freq_re": "0", + "word_freq_edu": "0", + "word_freq_table": "0", + "word_freq_conference": "0", + "char_freq_%3B": "0", + "char_freq_%28": "0.132", + "char_freq_%5B": "0", + "char_freq_%21": "0.372", + "char_freq_%24": "0.18", + "char_freq_%23": "0.048", + "capital_run_length_average": "5.114", + "capital_run_length_longest": "101", + "capital_run_length_total": "1028", + "class": "1" + }, + { + "word_freq_make": "0.06", + "word_freq_address": "0", + "word_freq_all": "0.71", + "word_freq_3d": "0", + "word_freq_our": "1.23", + "word_freq_over": "0.19", + "word_freq_remove": "0.19", + "word_freq_internet": "0.12", + "word_freq_order": "0.64", + "word_freq_mail": "0.25", + "word_freq_receive": "0.38", + "word_freq_will": "0.45", + "word_freq_people": "0.12", + "word_freq_report": "0", + "word_freq_addresses": "1.75", + "word_freq_free": "0.06", + "word_freq_business": "0.06", + "word_freq_email": "1.03", + "word_freq_you": "1.36", + "word_freq_credit": "0.32", + "word_freq_your": "0.51", + "word_freq_font": "0", + "word_freq_000": "1.16", + "word_freq_money": "0.06", + "word_freq_hp": "0", + "word_freq_hpl": "0", + "word_freq_george": "0", + "word_freq_650": "0", + "word_freq_lab": "0", + "word_freq_labs": "0", + "word_freq_telnet": "0", + "word_freq_857": "0", + "word_freq_data": "0", + "word_freq_415": "0", + "word_freq_85": "0", + "word_freq_technology": "0", + "word_freq_1999": "0", + "word_freq_parts": "0", + "word_freq_pm": "0", + "word_freq_direct": "0.06", + "word_freq_cs": "0", + "word_freq_meeting": "0", + "word_freq_original": "0.12", + "word_freq_project": "0", + "word_freq_re": "0.06", + "word_freq_edu": "0.06", + "word_freq_table": "0", + "word_freq_conference": "0", + "char_freq_%3B": "0.01", + "char_freq_%28": "0.143", + "char_freq_%5B": "0", + "char_freq_%21": "0.276", + "char_freq_%24": "0.184", + "char_freq_%23": "0.01", + "capital_run_length_average": "9.821", + "capital_run_length_longest": "485", + "capital_run_length_total": "2259", + "class": "1" + }, + { + "word_freq_make": "0", + "word_freq_address": "0", + "word_freq_all": "0", + "word_freq_3d": "0", + "word_freq_our": "0.63", + "word_freq_over": "0", + "word_freq_remove": "0.31", + "word_freq_internet": "0.63", + "word_freq_order": "0.31", + "word_freq_mail": "0.63", + "word_freq_receive": "0.31", + "word_freq_will": "0.31", + "word_freq_people": "0.31", + "word_freq_report": "0", + "word_freq_addresses": "0", + "word_freq_free": "0.31", + "word_freq_business": "0", + "word_freq_email": "0", + "word_freq_you": "3.18", + "word_freq_credit": "0", + "word_freq_your": "0.31", + "word_freq_font": "0", + "word_freq_000": "0", + "word_freq_money": "0", + "word_freq_hp": "0", + "word_freq_hpl": "0", + "word_freq_george": "0", + "word_freq_650": "0", + "word_freq_lab": "0", + "word_freq_labs": "0", + "word_freq_telnet": "0", + "word_freq_857": "0", + "word_freq_data": "0", + "word_freq_415": "0", + "word_freq_85": "0", + "word_freq_technology": "0", + "word_freq_1999": "0", + "word_freq_parts": "0", + "word_freq_pm": "0", + "word_freq_direct": "0", + "word_freq_cs": "0", + "word_freq_meeting": "0", + "word_freq_original": "0", + "word_freq_project": "0", + "word_freq_re": "0", + "word_freq_edu": "0", + "word_freq_table": "0", + "word_freq_conference": "0", + "char_freq_%3B": "0", + "char_freq_%28": "0.137", + "char_freq_%5B": "0", + "char_freq_%21": "0.137", + "char_freq_%24": "0", + "char_freq_%23": "0", + "capital_run_length_average": "3.537", + "capital_run_length_longest": "40", + "capital_run_length_total": "191", + "class": "1" + }, + { + "word_freq_make": "0", + "word_freq_address": "0", + "word_freq_all": "0", + "word_freq_3d": "0", + "word_freq_our": "0.63", + "word_freq_over": "0", + "word_freq_remove": "0.31", + "word_freq_internet": "0.63", + "word_freq_order": "0.31", + "word_freq_mail": "0.63", + "word_freq_receive": "0.31", + "word_freq_will": "0.31", + "word_freq_people": "0.31", + "word_freq_report": "0", + "word_freq_addresses": "0", + "word_freq_free": "0.31", + "word_freq_business": "0", + "word_freq_email": "0", + "word_freq_you": "3.18", + "word_freq_credit": "0", + "word_freq_your": "0.31", + "word_freq_font": "0", + "word_freq_000": "0", + "word_freq_money": "0", + "word_freq_hp": "0", + "word_freq_hpl": "0", + "word_freq_george": "0", + "word_freq_650": "0", + "word_freq_lab": "0", + "word_freq_labs": "0", + "word_freq_telnet": "0", + "word_freq_857": "0", + "word_freq_data": "0", + "word_freq_415": "0", + "word_freq_85": "0", + "word_freq_technology": "0", + "word_freq_1999": "0", + "word_freq_parts": "0", + "word_freq_pm": "0", + "word_freq_direct": "0", + "word_freq_cs": "0", + "word_freq_meeting": "0", + "word_freq_original": "0", + "word_freq_project": "0", + "word_freq_re": "0", + "word_freq_edu": "0", + "word_freq_table": "0", + "word_freq_conference": "0", + "char_freq_%3B": "0", + "char_freq_%28": "0.135", + "char_freq_%5B": "0", + "char_freq_%21": "0.135", + "char_freq_%24": "0", + "char_freq_%23": "0", + "capital_run_length_average": "3.537", + "capital_run_length_longest": "40", + "capital_run_length_total": "191", + "class": "1" + } + ] +} + +Shortlisted templates: +[ + { + "template_id": "tpl_m4_quantile_tail_slice", + "template_name": "Quantile Tail Slice", + "primary_family": "tail_rarity_structure", + "portability": "partial", + "sql_skeleton": "WITH buckets AS (\n SELECT {measure_col},\n NTILE({num_tiles}) OVER (ORDER BY {measure_col} DESC) AS tail_bucket\n FROM {table}\n)\nSELECT {measure_col}\nFROM buckets\nWHERE tail_bucket = 1\nORDER BY {measure_col} DESC;", + "required_roles": [ + "measure_col" + ] + } +] + +Problem instance: +{ + "dataset_id": "n1", + "question": "Use template Quantile Tail Slice to probe tail_set_consistency with semantic role rare_extreme_view. Focus on measure_col=word_freq_over.", + "planned_template_id": "tpl_m4_quantile_tail_slice", + "bindings": { + "measure_col": "word_freq_over", + "top_k": 12, + "top_n": 5, + "num_tiles": 10, + "percentile_value": 0.95, + "z_threshold": 2.0, + "fraction_threshold": 0.1, + "baseline_multiplier": 1.5, + "baseline_fraction": 0.1, + "min_group_size": 5, + "min_support": 5, + "measure_threshold": 0.0, + "time_grain": "month", + "lookback_rows": 3, + "current_period_start": "'2024-01-01'", + "current_period_end": "'2024-04-01'", + "previous_period_start": "'2023-10-01'", + "previous_period_end": "'2024-01-01'", + "drift_ratio_threshold": 0.8 + }, + "can_vary": [], + "must_fix": [], + "runtime_sql_skeleton": "WITH buckets AS (\n SELECT {measure_col},\n NTILE({num_tiles}) OVER (ORDER BY {measure_col} DESC) AS tail_bucket\n FROM {table}\n)\nSELECT {measure_col}\nFROM buckets\nWHERE tail_bucket = 1\nORDER BY {measure_col} DESC;" +} + +Repair context: +{} diff --git a/Query/sql/v2/runs/v2_cli_20260502_081223_d/n1/artifacts/v2q_n1_4862ee97bb2fdeda/cli/sql_response_attempt_1.raw.txt b/Query/sql/v2/runs/v2_cli_20260502_081223_d/n1/artifacts/v2q_n1_4862ee97bb2fdeda/cli/sql_response_attempt_1.raw.txt new file mode 100644 index 0000000000000000000000000000000000000000..c8779c68a4a1b87fefe2470c7b7bcc92a3c9ea34 --- /dev/null +++ b/Query/sql/v2/runs/v2_cli_20260502_081223_d/n1/artifacts/v2q_n1_4862ee97bb2fdeda/cli/sql_response_attempt_1.raw.txt @@ -0,0 +1,4 @@ +{"type":"thread.started","thread_id":"019e40e9-6537-7db2-add4-4faa440058de"} +{"type":"turn.started"} +{"type":"item.completed","item":{"id":"item_0","type":"agent_message","text":"{\"sql\":\"-- template_id: tpl_m4_quantile_tail_slice\\nWITH \\\"buckets\\\" AS (\\n SELECT\\n CAST(\\\"word_freq_over\\\" AS REAL) AS \\\"word_freq_over\\\",\\n NTILE(10) OVER (ORDER BY CAST(\\\"word_freq_over\\\" AS REAL) DESC) AS \\\"tail_bucket\\\"\\n FROM \\\"n1\\\"\\n)\\nSELECT \\\"word_freq_over\\\"\\nFROM \\\"buckets\\\"\\nWHERE \\\"tail_bucket\\\" = 1\\nORDER BY \\\"word_freq_over\\\" DESC;\",\"notes\":\"Uses the planned Quantile Tail Slice template with measure_col bound to \\\"word_freq_over\\\" and casts the TEXT-typed feature to REAL for correct numeric ordering in SQLite.\"}"}} +{"type":"turn.completed","usage":{"input_tokens":20370,"cached_input_tokens":19840,"output_tokens":450,"reasoning_output_tokens":296}} diff --git a/Query/sql/v2/runs/v2_cli_20260502_081223_d/n1/artifacts/v2q_n1_4862ee97bb2fdeda/cli/sql_response_attempt_1.txt b/Query/sql/v2/runs/v2_cli_20260502_081223_d/n1/artifacts/v2q_n1_4862ee97bb2fdeda/cli/sql_response_attempt_1.txt new file mode 100644 index 0000000000000000000000000000000000000000..b28ac8b21b71ed88f8c1ffcdc614c2e2fe6c7266 --- /dev/null +++ b/Query/sql/v2/runs/v2_cli_20260502_081223_d/n1/artifacts/v2q_n1_4862ee97bb2fdeda/cli/sql_response_attempt_1.txt @@ -0,0 +1 @@ +{"sql":"-- template_id: tpl_m4_quantile_tail_slice\nWITH \"buckets\" AS (\n SELECT\n CAST(\"word_freq_over\" AS REAL) AS \"word_freq_over\",\n NTILE(10) OVER (ORDER BY CAST(\"word_freq_over\" AS REAL) DESC) AS \"tail_bucket\"\n FROM \"n1\"\n)\nSELECT \"word_freq_over\"\nFROM \"buckets\"\nWHERE \"tail_bucket\" = 1\nORDER BY \"word_freq_over\" DESC;","notes":"Uses the planned Quantile Tail Slice template with measure_col bound to \"word_freq_over\" and casts the TEXT-typed feature to REAL for correct numeric ordering in SQLite."} \ No newline at end of file diff --git a/Query/sql/v2/runs/v2_cli_20260502_081223_d/n1/artifacts/v2q_n1_4862ee97bb2fdeda/cli/sql_stderr_attempt_1.txt b/Query/sql/v2/runs/v2_cli_20260502_081223_d/n1/artifacts/v2q_n1_4862ee97bb2fdeda/cli/sql_stderr_attempt_1.txt new file mode 100644 index 0000000000000000000000000000000000000000..e69de29bb2d1d6434b8b29ae775ad8c2e48c5391 diff --git a/Query/sql/v2/runs/v2_cli_20260502_081223_d/n1/artifacts/v2q_n1_4d17d0fd5ebc19a7/cli/conversation.jsonl b/Query/sql/v2/runs/v2_cli_20260502_081223_d/n1/artifacts/v2q_n1_4d17d0fd5ebc19a7/cli/conversation.jsonl new file mode 100644 index 0000000000000000000000000000000000000000..8b3e62b4a933b039fdb7d15508bf81bb82ea3ff9 --- /dev/null +++ b/Query/sql/v2/runs/v2_cli_20260502_081223_d/n1/artifacts/v2q_n1_4d17d0fd5ebc19a7/cli/conversation.jsonl @@ -0,0 +1,4 @@ +{"attempt": 1, "phase": "sql_generation", "role": "user", "content_path": "cli/sql_prompt_attempt_1.txt", "metrics": {"chars": 29527, "bytes_utf8": 29527, "lines": 792, "estimated_tokens": null}} +{"attempt": 1, "phase": "sql_generation", "role": "assistant", "content_path": "cli/sql_response_attempt_1.txt", "raw_content_path": "cli/sql_response_attempt_1.raw.txt", "stderr_path": "cli/sql_stderr_attempt_1.txt", "metrics": {"chars": 280, "bytes_utf8": 280, "lines": 4, "estimated_tokens": null}, "usage": {}, "status": "failed", "error": "AI CLI command failed with exit code 1: "} +{"attempt": 2, "phase": "sql_generation", "role": "user", "content_path": "cli/sql_prompt_attempt_2.txt", "metrics": {"chars": 29527, "bytes_utf8": 29527, "lines": 792, "estimated_tokens": null}} +{"attempt": 2, "phase": "sql_generation", "role": "assistant", "content_path": "cli/sql_response_attempt_2.txt", "raw_content_path": "cli/sql_response_attempt_2.raw.txt", "stderr_path": "cli/sql_stderr_attempt_2.txt", "metrics": {"chars": 489, "bytes_utf8": 489, "lines": 1, "estimated_tokens": null}, "usage": {"input_tokens": 20359, "cached_input_tokens": 12032, "output_tokens": 391, "reasoning_output_tokens": 260}} diff --git a/Query/sql/v2/runs/v2_cli_20260502_081223_d/n1/artifacts/v2q_n1_4d17d0fd5ebc19a7/cli/session_summary.json b/Query/sql/v2/runs/v2_cli_20260502_081223_d/n1/artifacts/v2q_n1_4d17d0fd5ebc19a7/cli/session_summary.json new file mode 100644 index 0000000000000000000000000000000000000000..1ff07cebca7c35e05d2365b70706ae92daeaccf2 --- /dev/null +++ b/Query/sql/v2/runs/v2_cli_20260502_081223_d/n1/artifacts/v2q_n1_4d17d0fd5ebc19a7/cli/session_summary.json @@ -0,0 +1,25 @@ +{ + "engine": "v2-cli:codex", + "command": "codex exec --skip-git-repo-check --disable plugins --sandbox read-only --cd \"/data/jialinzhang/SQLagent\" -m gpt-5.4 --json -", + "ai_cli_calls": 2, + "usage_summary": { + "dataset_id": "n1", + "model": "v2-cli:codex", + "run_id": "v2q_n1_4d17d0fd5ebc19a7", + "api_calls": 0, + "input_tokens": 20359, + "cached_input_tokens": 12032, + "output_tokens": 391, + "total_tokens": 20750, + "cost_usd": 0.0, + "ai_cli_calls": 2, + "estimated_input_tokens": 0, + "estimated_output_tokens": 0, + "estimated_total_tokens": 0, + "usage_source": "ai_cli_json_usage", + "cli_elapsed_ms_total": 12289.42, + "sql_execution_elapsed_ms_total": 2.37, + "conversation_log_path": "/data/jialinzhang/TabQueryBench/sql_workloads/v2_current/runs_and_launches/runs/v2_cli_20260502_081223_d/n1/artifacts/v2q_n1_4d17d0fd5ebc19a7/cli/conversation.jsonl", + "note": "Executed through a local AI CLI with structured usage metadata." + } +} \ No newline at end of file diff --git a/Query/sql/v2/runs/v2_cli_20260502_081223_d/n1/artifacts/v2q_n1_4d17d0fd5ebc19a7/cli/sql_attempt_1.metadata.json b/Query/sql/v2/runs/v2_cli_20260502_081223_d/n1/artifacts/v2q_n1_4d17d0fd5ebc19a7/cli/sql_attempt_1.metadata.json new file mode 100644 index 0000000000000000000000000000000000000000..d10c91aab3c82b06131921d3ac787f6c00f95a5e --- /dev/null +++ b/Query/sql/v2/runs/v2_cli_20260502_081223_d/n1/artifacts/v2q_n1_4d17d0fd5ebc19a7/cli/sql_attempt_1.metadata.json @@ -0,0 +1,43 @@ +{ + "attempt": 1, + "phase": "sql_generation", + "command": "codex exec --skip-git-repo-check --disable plugins --sandbox read-only --cd \"/data/jialinzhang/SQLagent\" -m gpt-5.4 --json -", + "started_at": "2026-05-19T16:05:45.459561+00:00", + "ended_at": "2026-05-19T16:05:49.045741+00:00", + "elapsed_ms": 3586.15, + "returncode": 1, + "prompt_metrics": { + "chars": 29527, + "bytes_utf8": 29527, + "lines": 792, + "estimated_tokens": null + }, + "stdout_metrics": { + "chars": 281, + "bytes_utf8": 281, + "lines": 4, + "estimated_tokens": null + }, + "stderr_metrics": { + "chars": 0, + "bytes_utf8": 0, + "lines": 0, + "estimated_tokens": null + }, + "parsed_output": { + "format": "jsonl_events", + "text_metrics": { + "chars": 280, + "bytes_utf8": 280, + "lines": 4, + "estimated_tokens": null + }, + "usage": {} + }, + "status": "failed", + "error": "AI CLI command failed with exit code 1: ", + "prompt_path": "cli/sql_prompt_attempt_1.txt", + "response_path": "cli/sql_response_attempt_1.txt", + "raw_response_path": "cli/sql_response_attempt_1.raw.txt", + "stderr_path": "cli/sql_stderr_attempt_1.txt" +} \ No newline at end of file diff --git a/Query/sql/v2/runs/v2_cli_20260502_081223_d/n1/artifacts/v2q_n1_4d17d0fd5ebc19a7/cli/sql_attempt_2.metadata.json b/Query/sql/v2/runs/v2_cli_20260502_081223_d/n1/artifacts/v2q_n1_4d17d0fd5ebc19a7/cli/sql_attempt_2.metadata.json new file mode 100644 index 0000000000000000000000000000000000000000..9a00962bd424dbef9499da115c3e88531c7341df --- /dev/null +++ b/Query/sql/v2/runs/v2_cli_20260502_081223_d/n1/artifacts/v2q_n1_4d17d0fd5ebc19a7/cli/sql_attempt_2.metadata.json @@ -0,0 +1,45 @@ +{ + "attempt": 2, + "phase": "sql_generation", + "command": "codex exec --skip-git-repo-check --disable plugins --sandbox read-only --cd \"/data/jialinzhang/SQLagent\" -m gpt-5.4 --json -", + "started_at": "2026-05-19T16:05:50.047903+00:00", + "ended_at": "2026-05-19T16:05:58.751209+00:00", + "elapsed_ms": 8703.27, + "prompt_metrics": { + "chars": 29527, + "bytes_utf8": 29527, + "lines": 792, + "estimated_tokens": null + }, + "stdout_metrics": { + "chars": 857, + "bytes_utf8": 857, + "lines": 4, + "estimated_tokens": null + }, + "stderr_metrics": { + "chars": 0, + "bytes_utf8": 0, + "lines": 0, + "estimated_tokens": null + }, + "parsed_output": { + "format": "jsonl_events", + "text_metrics": { + "chars": 489, + "bytes_utf8": 489, + "lines": 1, + "estimated_tokens": null + }, + "usage": { + "input_tokens": 20359, + "cached_input_tokens": 12032, + "output_tokens": 391, + "reasoning_output_tokens": 260 + } + }, + "prompt_path": "cli/sql_prompt_attempt_2.txt", + "response_path": "cli/sql_response_attempt_2.txt", + "raw_response_path": "cli/sql_response_attempt_2.raw.txt", + "stderr_path": "cli/sql_stderr_attempt_2.txt" +} \ No newline at end of file diff --git a/Query/sql/v2/runs/v2_cli_20260502_081223_d/n1/artifacts/v2q_n1_4d17d0fd5ebc19a7/cli/sql_prompt_attempt_1.txt b/Query/sql/v2/runs/v2_cli_20260502_081223_d/n1/artifacts/v2q_n1_4d17d0fd5ebc19a7/cli/sql_prompt_attempt_1.txt new file mode 100644 index 0000000000000000000000000000000000000000..11ad6e7642f31f0a25b21b1a5ffa9e6dca162ac6 --- /dev/null +++ b/Query/sql/v2/runs/v2_cli_20260502_081223_d/n1/artifacts/v2q_n1_4d17d0fd5ebc19a7/cli/sql_prompt_attempt_1.txt @@ -0,0 +1,792 @@ +You are generating one SQLite SELECT query for a single-table SQL QA task. +Return strict JSON only, with this schema: {"sql": "...", "notes": "..."}. +Rules: +- Use only the provided table and columns. +- Do not write INSERT, UPDATE, DELETE, DROP, ALTER, CREATE, PRAGMA, ATTACH, DETACH, or VACUUM. +- Prefer the planned template and bound roles when provided. +- Add a leading SQL comment exactly like: -- template_id: . +- Generate SQLite-compatible SQL. SQLite does not support PERCENTILE_CONT or STDDEV. +- Quote identifiers with double quotes. +- Return no markdown and no extra prose. + +Dataset context: +Dataset context for SQL QA: +- dataset_id: n1 +- dataset_name: Spambase +- table_name: n1 +- table_layout: single-table dataset (do not assume joins). +- row_semantics: One row is one email represented by engineered token/character frequency and capitalization-run features. +- task_type: classification +- target_column: class +- main_row_count: 4601 +- important_fields: +- word_freq_make: role=feature, type=numeric_sparse_frequency. tags=['condition_candidate', 'measure', 'rare_pattern_candidate'] desc=Frequency feature for token 'make' in an email message. +- word_freq_address: role=feature, type=numeric_sparse_frequency. tags=['condition_candidate', 'measure', 'rare_pattern_candidate'] desc=Frequency feature for token 'address' in an email message. +- word_freq_all: role=feature, type=numeric_sparse_frequency. tags=['condition_candidate', 'measure', 'rare_pattern_candidate'] desc=Frequency feature for token 'all' in an email message. +- word_freq_3d: role=feature, type=numeric_sparse_frequency. tags=['condition_candidate', 'measure', 'rare_pattern_candidate'] desc=Frequency feature for token '3d' in an email message. +- word_freq_our: role=feature, type=numeric_sparse_frequency. tags=['condition_candidate', 'measure', 'rare_pattern_candidate'] desc=Frequency feature for token 'our' in an email message. +- word_freq_over: role=feature, type=numeric_sparse_frequency. tags=['condition_candidate', 'measure', 'rare_pattern_candidate'] desc=Frequency feature for token 'over' in an email message. +- word_freq_remove: role=feature, type=numeric_sparse_frequency. tags=['condition_candidate', 'measure', 'rare_pattern_candidate'] desc=Frequency feature for token 'remove' in an email message. +- word_freq_internet: role=feature, type=numeric_sparse_frequency. tags=['condition_candidate', 'measure', 'rare_pattern_candidate'] desc=Frequency feature for token 'internet' in an email message. +- word_freq_order: role=feature, type=numeric_sparse_frequency. tags=['condition_candidate', 'measure', 'rare_pattern_candidate'] desc=Frequency feature for token 'order' in an email message. +- word_freq_mail: role=feature, type=numeric_sparse_frequency. tags=['condition_candidate', 'measure', 'rare_pattern_candidate'] desc=Frequency feature for token 'mail' in an email message. +- word_freq_receive: role=feature, type=numeric_sparse_frequency. tags=['condition_candidate', 'measure', 'rare_pattern_candidate'] desc=Frequency feature for token 'receive' in an email message. +- word_freq_will: role=feature, type=numeric_sparse_frequency. tags=['condition_candidate', 'measure', 'rare_pattern_candidate'] desc=Frequency feature for token 'will' in an email message. +- word_freq_people: role=feature, type=numeric_sparse_frequency. tags=['condition_candidate', 'measure', 'rare_pattern_candidate'] desc=Frequency feature for token 'people' in an email message. +- word_freq_report: role=feature, type=numeric_sparse_frequency. tags=['condition_candidate', 'measure', 'rare_pattern_candidate'] desc=Frequency feature for token 'report' in an email message. +- word_freq_addresses: role=feature, type=numeric_sparse_frequency. tags=['condition_candidate', 'measure', 'rare_pattern_candidate'] desc=Frequency feature for token 'addresses' in an email message. +- word_freq_free: role=feature, type=numeric_sparse_frequency. tags=['condition_candidate', 'measure', 'rare_pattern_candidate'] desc=Frequency feature for token 'free' in an email message. +- word_freq_business: role=feature, type=numeric_sparse_frequency. tags=['condition_candidate', 'measure', 'rare_pattern_candidate'] desc=Frequency feature for token 'business' in an email message. +- word_freq_email: role=feature, type=numeric_sparse_frequency. tags=['condition_candidate', 'measure', 'rare_pattern_candidate'] desc=Frequency feature for token 'email' in an email message. +- word_freq_you: role=feature, type=numeric_sparse_frequency. tags=['condition_candidate', 'measure', 'rare_pattern_candidate'] desc=Frequency feature for token 'you' in an email message. +- word_freq_credit: role=feature, type=numeric_sparse_frequency. tags=['condition_candidate', 'measure', 'rare_pattern_candidate'] desc=Frequency feature for token 'credit' in an email message. +- word_freq_your: role=feature, type=numeric_sparse_frequency. tags=['condition_candidate', 'measure', 'rare_pattern_candidate'] desc=Frequency feature for token 'your' in an email message. +- word_freq_font: role=feature, type=numeric_sparse_frequency. tags=['condition_candidate', 'measure', 'rare_pattern_candidate'] desc=Frequency feature for token 'font' in an email message. +- word_freq_000: role=feature, type=numeric_sparse_frequency. tags=['condition_candidate', 'measure', 'rare_pattern_candidate'] desc=Frequency feature for token '000' in an email message. +- word_freq_money: role=feature, type=numeric_sparse_frequency. tags=['condition_candidate', 'measure', 'rare_pattern_candidate'] desc=Frequency feature for token 'money' in an email message. +- word_freq_hp: role=feature, type=numeric_sparse_frequency. tags=['condition_candidate', 'measure', 'rare_pattern_candidate'] desc=Frequency feature for token 'hp' in an email message. +- word_freq_hpl: role=feature, type=numeric_sparse_frequency. tags=['condition_candidate', 'measure', 'rare_pattern_candidate'] desc=Frequency feature for token 'hpl' in an email message. +- word_freq_george: role=feature, type=numeric_sparse_frequency. tags=['condition_candidate', 'measure', 'rare_pattern_candidate'] desc=Frequency feature for token 'george' in an email message. +- word_freq_650: role=feature, type=numeric_sparse_frequency. tags=['condition_candidate', 'measure', 'rare_pattern_candidate'] desc=Frequency feature for token '650' in an email message. +- word_freq_lab: role=feature, type=numeric_sparse_frequency. tags=['condition_candidate', 'measure', 'rare_pattern_candidate'] desc=Frequency feature for token 'lab' in an email message. +- word_freq_labs: role=feature, type=numeric_sparse_frequency. tags=['condition_candidate', 'measure', 'rare_pattern_candidate'] desc=Frequency feature for token 'labs' in an email message. +- word_freq_telnet: role=feature, type=numeric_sparse_frequency. tags=['condition_candidate', 'measure', 'rare_pattern_candidate'] desc=Frequency feature for token 'telnet' in an email message. +- word_freq_857: role=feature, type=numeric_sparse_frequency. tags=['condition_candidate', 'measure', 'rare_pattern_candidate'] desc=Frequency feature for token '857' in an email message. +- word_freq_data: role=feature, type=numeric_sparse_frequency. tags=['condition_candidate', 'measure', 'rare_pattern_candidate'] desc=Frequency feature for token 'data' in an email message. +- word_freq_415: role=feature, type=numeric_sparse_frequency. tags=['condition_candidate', 'measure', 'rare_pattern_candidate'] desc=Frequency feature for token '415' in an email message. +- word_freq_85: role=feature, type=numeric_sparse_frequency. tags=['condition_candidate', 'measure', 'rare_pattern_candidate'] desc=Frequency feature for token '85' in an email message. +- word_freq_technology: role=feature, type=numeric_sparse_frequency. tags=['condition_candidate', 'measure', 'rare_pattern_candidate'] desc=Frequency feature for token 'technology' in an email message. +- word_freq_1999: role=feature, type=numeric_sparse_frequency. tags=['condition_candidate', 'measure', 'rare_pattern_candidate'] desc=Frequency feature for token '1999' in an email message. +- word_freq_parts: role=feature, type=numeric_sparse_frequency. tags=['condition_candidate', 'measure', 'rare_pattern_candidate'] desc=Frequency feature for token 'parts' in an email message. +- word_freq_pm: role=feature, type=numeric_sparse_frequency. tags=['condition_candidate', 'measure', 'rare_pattern_candidate'] desc=Frequency feature for token 'pm' in an email message. +- word_freq_direct: role=feature, type=numeric_sparse_frequency. tags=['condition_candidate', 'measure', 'rare_pattern_candidate'] desc=Frequency feature for token 'direct' in an email message. +- word_freq_cs: role=feature, type=numeric_sparse_frequency. tags=['condition_candidate', 'measure', 'rare_pattern_candidate'] desc=Frequency feature for token 'cs' in an email message. +- word_freq_meeting: role=feature, type=numeric_sparse_frequency. tags=['condition_candidate', 'measure', 'rare_pattern_candidate'] desc=Frequency feature for token 'meeting' in an email message. +- word_freq_original: role=feature, type=numeric_sparse_frequency. tags=['condition_candidate', 'measure', 'rare_pattern_candidate'] desc=Frequency feature for token 'original' in an email message. +- word_freq_project: role=feature, type=numeric_sparse_frequency. tags=['condition_candidate', 'measure', 'rare_pattern_candidate'] desc=Frequency feature for token 'project' in an email message. +- word_freq_re: role=feature, type=numeric_sparse_frequency. tags=['condition_candidate', 'measure', 'rare_pattern_candidate'] desc=Frequency feature for token 're' in an email message. +- word_freq_edu: role=feature, type=numeric_sparse_frequency. tags=['condition_candidate', 'measure', 'rare_pattern_candidate'] desc=Frequency feature for token 'edu' in an email message. +- word_freq_table: role=feature, type=numeric_sparse_frequency. tags=['condition_candidate', 'measure', 'rare_pattern_candidate'] desc=Frequency feature for token 'table' in an email message. +- word_freq_conference: role=feature, type=numeric_sparse_frequency. tags=['condition_candidate', 'measure', 'rare_pattern_candidate'] desc=Frequency feature for token 'conference' in an email message. +- char_freq_%3B: role=feature, type=numeric_sparse_frequency. tags=['condition_candidate', 'measure', 'rare_pattern_candidate'] desc=Character frequency feature for symbol ';'. +- char_freq_%28: role=feature, type=numeric_sparse_frequency. tags=['condition_candidate', 'measure', 'rare_pattern_candidate'] desc=Character frequency feature for symbol '('. +- char_freq_%5B: role=feature, type=numeric_sparse_frequency. tags=['condition_candidate', 'measure', 'rare_pattern_candidate'] desc=Character frequency feature for symbol '['. +- char_freq_%21: role=feature, type=numeric_sparse_frequency. tags=['condition_candidate', 'measure', 'rare_pattern_candidate'] desc=Character frequency feature for symbol '!'. +- char_freq_%24: role=feature, type=numeric_sparse_frequency. tags=['condition_candidate', 'measure', 'rare_pattern_candidate'] desc=Character frequency feature for symbol '$'. +- char_freq_%23: role=feature, type=numeric_sparse_frequency. tags=['condition_candidate', 'measure', 'rare_pattern_candidate'] desc=Character frequency feature for symbol '#'. +- capital_run_length_average: role=feature, type=numeric_heavy_tail. tags=['condition_candidate', 'measure', 'rare_pattern_candidate'] desc=Average length of uninterrupted capital-letter runs. +- capital_run_length_longest: role=feature, type=numeric_heavy_tail. tags=['condition_candidate', 'measure', 'rare_pattern_candidate'] desc=Longest uninterrupted capital-letter run. +- capital_run_length_total: role=feature, type=numeric_heavy_tail. tags=['condition_candidate', 'measure', 'rare_pattern_candidate'] desc=Total number of capital letters in runs. +- class: role=target, type=binary_target. tags=['target_candidate'] desc=Binary spam label (1=spam, 0=non-spam). +- useful_field_combinations: [['word_freq_free', 'word_freq_you', 'class'], ['char_freq_%21', 'capital_run_length_longest', 'class'], ['word_freq_remove', 'word_freq_money', 'class']] +- fields_requiring_caution: ['capital_run_length_average', 'capital_run_length_longest', 'capital_run_length_total'] +- source_url: https://www.openml.org/d/44 + +SQLite schema snapshot: +{ + "table_name": "n1", + "quoted_table_name": "\"n1\"", + "row_count": 4601, + "columns": [ + { + "name": "word_freq_make", + "type": "TEXT", + "notnull": false, + "pk": false + }, + { + "name": "word_freq_address", + "type": "TEXT", + "notnull": false, + "pk": false + }, + { + "name": "word_freq_all", + "type": "TEXT", + "notnull": false, + "pk": false + }, + { + "name": "word_freq_3d", + "type": "TEXT", + "notnull": false, + "pk": false + }, + { + "name": "word_freq_our", + "type": "TEXT", + "notnull": false, + "pk": false + }, + { + "name": "word_freq_over", + "type": "TEXT", + "notnull": false, + "pk": false + }, + { + "name": "word_freq_remove", + "type": "TEXT", + "notnull": false, + "pk": false + }, + { + "name": "word_freq_internet", + "type": "TEXT", + "notnull": false, + "pk": false + }, + { + "name": "word_freq_order", + "type": "TEXT", + "notnull": false, + "pk": false + }, + { + "name": "word_freq_mail", + "type": "TEXT", + "notnull": false, + "pk": false + }, + { + "name": "word_freq_receive", + "type": "TEXT", + "notnull": false, + "pk": false + }, + { + "name": "word_freq_will", + "type": "TEXT", + "notnull": false, + "pk": false + }, + { + "name": "word_freq_people", + "type": "TEXT", + "notnull": false, + "pk": false + }, + { + "name": "word_freq_report", + "type": "TEXT", + "notnull": false, + "pk": false + }, + { + "name": "word_freq_addresses", + "type": "TEXT", + "notnull": false, + "pk": false + }, + { + "name": "word_freq_free", + "type": "TEXT", + "notnull": false, + "pk": false + }, + { + "name": "word_freq_business", + "type": "TEXT", + "notnull": false, + "pk": false + }, + { + "name": "word_freq_email", + "type": "TEXT", + "notnull": false, + "pk": false + }, + { + "name": "word_freq_you", + "type": "TEXT", + "notnull": false, + "pk": false + }, + { + "name": "word_freq_credit", + "type": "TEXT", + "notnull": false, + "pk": false + }, + { + "name": "word_freq_your", + "type": "TEXT", + "notnull": false, + "pk": false + }, + { + "name": "word_freq_font", + "type": "TEXT", + "notnull": false, + "pk": false + }, + { + "name": "word_freq_000", + "type": "TEXT", + "notnull": false, + "pk": false + }, + { + "name": "word_freq_money", + "type": "TEXT", + "notnull": false, + "pk": false + }, + { + "name": "word_freq_hp", + "type": "TEXT", + "notnull": false, + "pk": false + }, + { + "name": "word_freq_hpl", + "type": "TEXT", + "notnull": false, + "pk": false + }, + { + "name": "word_freq_george", + "type": "TEXT", + "notnull": false, + "pk": false + }, + { + "name": "word_freq_650", + "type": "TEXT", + "notnull": false, + "pk": false + }, + { + "name": "word_freq_lab", + "type": "TEXT", + "notnull": false, + "pk": false + }, + { + "name": "word_freq_labs", + "type": "TEXT", + "notnull": false, + "pk": false + }, + { + "name": "word_freq_telnet", + "type": "TEXT", + "notnull": false, + "pk": false + }, + { + "name": "word_freq_857", + "type": "TEXT", + "notnull": false, + "pk": false + }, + { + "name": "word_freq_data", + "type": "TEXT", + "notnull": false, + "pk": false + }, + { + "name": "word_freq_415", + "type": "TEXT", + "notnull": false, + "pk": false + }, + { + "name": "word_freq_85", + "type": "TEXT", + "notnull": false, + "pk": false + }, + { + "name": "word_freq_technology", + "type": "TEXT", + "notnull": false, + "pk": false + }, + { + "name": "word_freq_1999", + "type": "TEXT", + "notnull": false, + "pk": false + }, + { + "name": "word_freq_parts", + "type": "TEXT", + "notnull": false, + "pk": false + }, + { + "name": "word_freq_pm", + "type": "TEXT", + "notnull": false, + "pk": false + }, + { + "name": "word_freq_direct", + "type": "TEXT", + "notnull": false, + "pk": false + }, + { + "name": "word_freq_cs", + "type": "TEXT", + "notnull": false, + "pk": false + }, + { + "name": "word_freq_meeting", + "type": "TEXT", + "notnull": false, + "pk": false + }, + { + "name": "word_freq_original", + "type": "TEXT", + "notnull": false, + "pk": false + }, + { + "name": "word_freq_project", + "type": "TEXT", + "notnull": false, + "pk": false + }, + { + "name": "word_freq_re", + "type": "TEXT", + "notnull": false, + "pk": false + }, + { + "name": "word_freq_edu", + "type": "TEXT", + "notnull": false, + "pk": false + }, + { + "name": "word_freq_table", + "type": "TEXT", + "notnull": false, + "pk": false + }, + { + "name": "word_freq_conference", + "type": "TEXT", + "notnull": false, + "pk": false + }, + { + "name": "char_freq_%3B", + "type": "TEXT", + "notnull": false, + "pk": false + }, + { + "name": "char_freq_%28", + "type": "TEXT", + "notnull": false, + "pk": false + }, + { + "name": "char_freq_%5B", + "type": "TEXT", + "notnull": false, + "pk": false + }, + { + "name": "char_freq_%21", + "type": "TEXT", + "notnull": false, + "pk": false + }, + { + "name": "char_freq_%24", + "type": "TEXT", + "notnull": false, + "pk": false + }, + { + "name": "char_freq_%23", + "type": "TEXT", + "notnull": false, + "pk": false + }, + { + "name": "capital_run_length_average", + "type": "TEXT", + "notnull": false, + "pk": false + }, + { + "name": "capital_run_length_longest", + "type": "TEXT", + "notnull": false, + "pk": false + }, + { + "name": "capital_run_length_total", + "type": "TEXT", + "notnull": false, + "pk": false + }, + { + "name": "class", + "type": "TEXT", + "notnull": false, + "pk": false + } + ], + "sample_rows": [ + { + "word_freq_make": "0", + "word_freq_address": "0.64", + "word_freq_all": "0.64", + "word_freq_3d": "0", + "word_freq_our": "0.32", + "word_freq_over": "0", + "word_freq_remove": "0", + "word_freq_internet": "0", + "word_freq_order": "0", + "word_freq_mail": "0", + "word_freq_receive": "0", + "word_freq_will": "0.64", + "word_freq_people": "0", + "word_freq_report": "0", + "word_freq_addresses": "0", + "word_freq_free": "0.32", + "word_freq_business": "0", + "word_freq_email": "1.29", + "word_freq_you": "1.93", + "word_freq_credit": "0", + "word_freq_your": "0.96", + "word_freq_font": "0", + "word_freq_000": "0", + "word_freq_money": "0", + "word_freq_hp": "0", + "word_freq_hpl": "0", + "word_freq_george": "0", + "word_freq_650": "0", + "word_freq_lab": "0", + "word_freq_labs": "0", + "word_freq_telnet": "0", + "word_freq_857": "0", + "word_freq_data": "0", + "word_freq_415": "0", + "word_freq_85": "0", + "word_freq_technology": "0", + "word_freq_1999": "0", + "word_freq_parts": "0", + "word_freq_pm": "0", + "word_freq_direct": "0", + "word_freq_cs": "0", + "word_freq_meeting": "0", + "word_freq_original": "0", + "word_freq_project": "0", + "word_freq_re": "0", + "word_freq_edu": "0", + "word_freq_table": "0", + "word_freq_conference": "0", + "char_freq_%3B": "0", + "char_freq_%28": "0", + "char_freq_%5B": "0", + "char_freq_%21": "0.778", + "char_freq_%24": "0", + "char_freq_%23": "0", + "capital_run_length_average": "3.756", + "capital_run_length_longest": "61", + "capital_run_length_total": "278", + "class": "1" + }, + { + "word_freq_make": "0.21", + "word_freq_address": "0.28", + "word_freq_all": "0.5", + "word_freq_3d": "0", + "word_freq_our": "0.14", + "word_freq_over": "0.28", + "word_freq_remove": "0.21", + "word_freq_internet": "0.07", + "word_freq_order": "0", + "word_freq_mail": "0.94", + "word_freq_receive": "0.21", + "word_freq_will": "0.79", + "word_freq_people": "0.65", + "word_freq_report": "0.21", + "word_freq_addresses": "0.14", + "word_freq_free": "0.14", + "word_freq_business": "0.07", + "word_freq_email": "0.28", + "word_freq_you": "3.47", + "word_freq_credit": "0", + "word_freq_your": "1.59", + "word_freq_font": "0", + "word_freq_000": "0.43", + "word_freq_money": "0.43", + "word_freq_hp": "0", + "word_freq_hpl": "0", + "word_freq_george": "0", + "word_freq_650": "0", + "word_freq_lab": "0", + "word_freq_labs": "0", + "word_freq_telnet": "0", + "word_freq_857": "0", + "word_freq_data": "0", + "word_freq_415": "0", + "word_freq_85": "0", + "word_freq_technology": "0", + "word_freq_1999": "0.07", + "word_freq_parts": "0", + "word_freq_pm": "0", + "word_freq_direct": "0", + "word_freq_cs": "0", + "word_freq_meeting": "0", + "word_freq_original": "0", + "word_freq_project": "0", + "word_freq_re": "0", + "word_freq_edu": "0", + "word_freq_table": "0", + "word_freq_conference": "0", + "char_freq_%3B": "0", + "char_freq_%28": "0.132", + "char_freq_%5B": "0", + "char_freq_%21": "0.372", + "char_freq_%24": "0.18", + "char_freq_%23": "0.048", + "capital_run_length_average": "5.114", + "capital_run_length_longest": "101", + "capital_run_length_total": "1028", + "class": "1" + }, + { + "word_freq_make": "0.06", + "word_freq_address": "0", + "word_freq_all": "0.71", + "word_freq_3d": "0", + "word_freq_our": "1.23", + "word_freq_over": "0.19", + "word_freq_remove": "0.19", + "word_freq_internet": "0.12", + "word_freq_order": "0.64", + "word_freq_mail": "0.25", + "word_freq_receive": "0.38", + "word_freq_will": "0.45", + "word_freq_people": "0.12", + "word_freq_report": "0", + "word_freq_addresses": "1.75", + "word_freq_free": "0.06", + "word_freq_business": "0.06", + "word_freq_email": "1.03", + "word_freq_you": "1.36", + "word_freq_credit": "0.32", + "word_freq_your": "0.51", + "word_freq_font": "0", + "word_freq_000": "1.16", + "word_freq_money": "0.06", + "word_freq_hp": "0", + "word_freq_hpl": "0", + "word_freq_george": "0", + "word_freq_650": "0", + "word_freq_lab": "0", + "word_freq_labs": "0", + "word_freq_telnet": "0", + "word_freq_857": "0", + "word_freq_data": "0", + "word_freq_415": "0", + "word_freq_85": "0", + "word_freq_technology": "0", + "word_freq_1999": "0", + "word_freq_parts": "0", + "word_freq_pm": "0", + "word_freq_direct": "0.06", + "word_freq_cs": "0", + "word_freq_meeting": "0", + "word_freq_original": "0.12", + "word_freq_project": "0", + "word_freq_re": "0.06", + "word_freq_edu": "0.06", + "word_freq_table": "0", + "word_freq_conference": "0", + "char_freq_%3B": "0.01", + "char_freq_%28": "0.143", + "char_freq_%5B": "0", + "char_freq_%21": "0.276", + "char_freq_%24": "0.184", + "char_freq_%23": "0.01", + "capital_run_length_average": "9.821", + "capital_run_length_longest": "485", + "capital_run_length_total": "2259", + "class": "1" + }, + { + "word_freq_make": "0", + "word_freq_address": "0", + "word_freq_all": "0", + "word_freq_3d": "0", + "word_freq_our": "0.63", + "word_freq_over": "0", + "word_freq_remove": "0.31", + "word_freq_internet": "0.63", + "word_freq_order": "0.31", + "word_freq_mail": "0.63", + "word_freq_receive": "0.31", + "word_freq_will": "0.31", + "word_freq_people": "0.31", + "word_freq_report": "0", + "word_freq_addresses": "0", + "word_freq_free": "0.31", + "word_freq_business": "0", + "word_freq_email": "0", + "word_freq_you": "3.18", + "word_freq_credit": "0", + "word_freq_your": "0.31", + "word_freq_font": "0", + "word_freq_000": "0", + "word_freq_money": "0", + "word_freq_hp": "0", + "word_freq_hpl": "0", + "word_freq_george": "0", + "word_freq_650": "0", + "word_freq_lab": "0", + "word_freq_labs": "0", + "word_freq_telnet": "0", + "word_freq_857": "0", + "word_freq_data": "0", + "word_freq_415": "0", + "word_freq_85": "0", + "word_freq_technology": "0", + "word_freq_1999": "0", + "word_freq_parts": "0", + "word_freq_pm": "0", + "word_freq_direct": "0", + "word_freq_cs": "0", + "word_freq_meeting": "0", + "word_freq_original": "0", + "word_freq_project": "0", + "word_freq_re": "0", + "word_freq_edu": "0", + "word_freq_table": "0", + "word_freq_conference": "0", + "char_freq_%3B": "0", + "char_freq_%28": "0.137", + "char_freq_%5B": "0", + "char_freq_%21": "0.137", + "char_freq_%24": "0", + "char_freq_%23": "0", + "capital_run_length_average": "3.537", + "capital_run_length_longest": "40", + "capital_run_length_total": "191", + "class": "1" + }, + { + "word_freq_make": "0", + "word_freq_address": "0", + "word_freq_all": "0", + "word_freq_3d": "0", + "word_freq_our": "0.63", + "word_freq_over": "0", + "word_freq_remove": "0.31", + "word_freq_internet": "0.63", + "word_freq_order": "0.31", + "word_freq_mail": "0.63", + "word_freq_receive": "0.31", + "word_freq_will": "0.31", + "word_freq_people": "0.31", + "word_freq_report": "0", + "word_freq_addresses": "0", + "word_freq_free": "0.31", + "word_freq_business": "0", + "word_freq_email": "0", + "word_freq_you": "3.18", + "word_freq_credit": "0", + "word_freq_your": "0.31", + "word_freq_font": "0", + "word_freq_000": "0", + "word_freq_money": "0", + "word_freq_hp": "0", + "word_freq_hpl": "0", + "word_freq_george": "0", + "word_freq_650": "0", + "word_freq_lab": "0", + "word_freq_labs": "0", + "word_freq_telnet": "0", + "word_freq_857": "0", + "word_freq_data": "0", + "word_freq_415": "0", + "word_freq_85": "0", + "word_freq_technology": "0", + "word_freq_1999": "0", + "word_freq_parts": "0", + "word_freq_pm": "0", + "word_freq_direct": "0", + "word_freq_cs": "0", + "word_freq_meeting": "0", + "word_freq_original": "0", + "word_freq_project": "0", + "word_freq_re": "0", + "word_freq_edu": "0", + "word_freq_table": "0", + "word_freq_conference": "0", + "char_freq_%3B": "0", + "char_freq_%28": "0.135", + "char_freq_%5B": "0", + "char_freq_%21": "0.135", + "char_freq_%24": "0", + "char_freq_%23": "0", + "capital_run_length_average": "3.537", + "capital_run_length_longest": "40", + "capital_run_length_total": "191", + "class": "1" + } + ] +} + +Shortlisted templates: +[ + { + "template_id": "tpl_tpch_thresholded_group_ranking", + "template_name": "Thresholded Group Ranking", + "primary_family": "tail_rarity_structure", + "portability": "partial", + "sql_skeleton": "SELECT {group_col}, SUM({measure_col}) AS total_measure\nFROM {table}\nGROUP BY {group_col}\nHAVING SUM({measure_col}) > {measure_threshold}\nORDER BY total_measure DESC\nLIMIT {top_k};", + "required_roles": [ + "group_col", + "measure_col" + ] + } +] + +Problem instance: +{ + "dataset_id": "n1", + "question": "Use template Thresholded Group Ranking to probe tail_mass_similarity with semantic role rare_extreme_view. Focus on group_col=class, measure_col=word_freq_money.", + "planned_template_id": "tpl_tpch_thresholded_group_ranking", + "bindings": { + "group_col": "class", + "measure_col": "word_freq_money", + "top_k": 17, + "top_n": 5, + "num_tiles": 10, + "percentile_value": 0.95, + "z_threshold": 2.0, + "fraction_threshold": 0.05, + "baseline_multiplier": 1.75, + "baseline_fraction": 0.1, + "min_group_size": 5, + "min_support": 4, + "measure_threshold": 0.0, + "time_grain": "month", + "lookback_rows": 3, + "current_period_start": "'2024-01-01'", + "current_period_end": "'2024-04-01'", + "previous_period_start": "'2023-10-01'", + "previous_period_end": "'2024-01-01'", + "drift_ratio_threshold": 0.8 + }, + "can_vary": [], + "must_fix": [], + "runtime_sql_skeleton": "SELECT {group_col}, SUM({measure_col}) AS total_measure\nFROM {table}\nGROUP BY {group_col}\nHAVING SUM({measure_col}) > {measure_threshold}\nORDER BY total_measure DESC\nLIMIT {top_k};" +} + +Repair context: +{} diff --git a/Query/sql/v2/runs/v2_cli_20260502_081223_d/n1/artifacts/v2q_n1_4d17d0fd5ebc19a7/cli/sql_prompt_attempt_2.txt b/Query/sql/v2/runs/v2_cli_20260502_081223_d/n1/artifacts/v2q_n1_4d17d0fd5ebc19a7/cli/sql_prompt_attempt_2.txt new file mode 100644 index 0000000000000000000000000000000000000000..11ad6e7642f31f0a25b21b1a5ffa9e6dca162ac6 --- /dev/null +++ b/Query/sql/v2/runs/v2_cli_20260502_081223_d/n1/artifacts/v2q_n1_4d17d0fd5ebc19a7/cli/sql_prompt_attempt_2.txt @@ -0,0 +1,792 @@ +You are generating one SQLite SELECT query for a single-table SQL QA task. +Return strict JSON only, with this schema: {"sql": "...", "notes": "..."}. +Rules: +- Use only the provided table and columns. +- Do not write INSERT, UPDATE, DELETE, DROP, ALTER, CREATE, PRAGMA, ATTACH, DETACH, or VACUUM. +- Prefer the planned template and bound roles when provided. +- Add a leading SQL comment exactly like: -- template_id: . +- Generate SQLite-compatible SQL. SQLite does not support PERCENTILE_CONT or STDDEV. +- Quote identifiers with double quotes. +- Return no markdown and no extra prose. + +Dataset context: +Dataset context for SQL QA: +- dataset_id: n1 +- dataset_name: Spambase +- table_name: n1 +- table_layout: single-table dataset (do not assume joins). +- row_semantics: One row is one email represented by engineered token/character frequency and capitalization-run features. +- task_type: classification +- target_column: class +- main_row_count: 4601 +- important_fields: +- word_freq_make: role=feature, type=numeric_sparse_frequency. tags=['condition_candidate', 'measure', 'rare_pattern_candidate'] desc=Frequency feature for token 'make' in an email message. +- word_freq_address: role=feature, type=numeric_sparse_frequency. tags=['condition_candidate', 'measure', 'rare_pattern_candidate'] desc=Frequency feature for token 'address' in an email message. +- word_freq_all: role=feature, type=numeric_sparse_frequency. tags=['condition_candidate', 'measure', 'rare_pattern_candidate'] desc=Frequency feature for token 'all' in an email message. +- word_freq_3d: role=feature, type=numeric_sparse_frequency. tags=['condition_candidate', 'measure', 'rare_pattern_candidate'] desc=Frequency feature for token '3d' in an email message. +- word_freq_our: role=feature, type=numeric_sparse_frequency. tags=['condition_candidate', 'measure', 'rare_pattern_candidate'] desc=Frequency feature for token 'our' in an email message. +- word_freq_over: role=feature, type=numeric_sparse_frequency. tags=['condition_candidate', 'measure', 'rare_pattern_candidate'] desc=Frequency feature for token 'over' in an email message. +- word_freq_remove: role=feature, type=numeric_sparse_frequency. tags=['condition_candidate', 'measure', 'rare_pattern_candidate'] desc=Frequency feature for token 'remove' in an email message. +- word_freq_internet: role=feature, type=numeric_sparse_frequency. tags=['condition_candidate', 'measure', 'rare_pattern_candidate'] desc=Frequency feature for token 'internet' in an email message. +- word_freq_order: role=feature, type=numeric_sparse_frequency. tags=['condition_candidate', 'measure', 'rare_pattern_candidate'] desc=Frequency feature for token 'order' in an email message. +- word_freq_mail: role=feature, type=numeric_sparse_frequency. tags=['condition_candidate', 'measure', 'rare_pattern_candidate'] desc=Frequency feature for token 'mail' in an email message. +- word_freq_receive: role=feature, type=numeric_sparse_frequency. tags=['condition_candidate', 'measure', 'rare_pattern_candidate'] desc=Frequency feature for token 'receive' in an email message. +- word_freq_will: role=feature, type=numeric_sparse_frequency. tags=['condition_candidate', 'measure', 'rare_pattern_candidate'] desc=Frequency feature for token 'will' in an email message. +- word_freq_people: role=feature, type=numeric_sparse_frequency. tags=['condition_candidate', 'measure', 'rare_pattern_candidate'] desc=Frequency feature for token 'people' in an email message. +- word_freq_report: role=feature, type=numeric_sparse_frequency. tags=['condition_candidate', 'measure', 'rare_pattern_candidate'] desc=Frequency feature for token 'report' in an email message. +- word_freq_addresses: role=feature, type=numeric_sparse_frequency. tags=['condition_candidate', 'measure', 'rare_pattern_candidate'] desc=Frequency feature for token 'addresses' in an email message. +- word_freq_free: role=feature, type=numeric_sparse_frequency. tags=['condition_candidate', 'measure', 'rare_pattern_candidate'] desc=Frequency feature for token 'free' in an email message. +- word_freq_business: role=feature, type=numeric_sparse_frequency. tags=['condition_candidate', 'measure', 'rare_pattern_candidate'] desc=Frequency feature for token 'business' in an email message. +- word_freq_email: role=feature, type=numeric_sparse_frequency. tags=['condition_candidate', 'measure', 'rare_pattern_candidate'] desc=Frequency feature for token 'email' in an email message. +- word_freq_you: role=feature, type=numeric_sparse_frequency. tags=['condition_candidate', 'measure', 'rare_pattern_candidate'] desc=Frequency feature for token 'you' in an email message. +- word_freq_credit: role=feature, type=numeric_sparse_frequency. tags=['condition_candidate', 'measure', 'rare_pattern_candidate'] desc=Frequency feature for token 'credit' in an email message. +- word_freq_your: role=feature, type=numeric_sparse_frequency. tags=['condition_candidate', 'measure', 'rare_pattern_candidate'] desc=Frequency feature for token 'your' in an email message. +- word_freq_font: role=feature, type=numeric_sparse_frequency. tags=['condition_candidate', 'measure', 'rare_pattern_candidate'] desc=Frequency feature for token 'font' in an email message. +- word_freq_000: role=feature, type=numeric_sparse_frequency. tags=['condition_candidate', 'measure', 'rare_pattern_candidate'] desc=Frequency feature for token '000' in an email message. +- word_freq_money: role=feature, type=numeric_sparse_frequency. tags=['condition_candidate', 'measure', 'rare_pattern_candidate'] desc=Frequency feature for token 'money' in an email message. +- word_freq_hp: role=feature, type=numeric_sparse_frequency. tags=['condition_candidate', 'measure', 'rare_pattern_candidate'] desc=Frequency feature for token 'hp' in an email message. +- word_freq_hpl: role=feature, type=numeric_sparse_frequency. tags=['condition_candidate', 'measure', 'rare_pattern_candidate'] desc=Frequency feature for token 'hpl' in an email message. +- word_freq_george: role=feature, type=numeric_sparse_frequency. tags=['condition_candidate', 'measure', 'rare_pattern_candidate'] desc=Frequency feature for token 'george' in an email message. +- word_freq_650: role=feature, type=numeric_sparse_frequency. tags=['condition_candidate', 'measure', 'rare_pattern_candidate'] desc=Frequency feature for token '650' in an email message. +- word_freq_lab: role=feature, type=numeric_sparse_frequency. tags=['condition_candidate', 'measure', 'rare_pattern_candidate'] desc=Frequency feature for token 'lab' in an email message. +- word_freq_labs: role=feature, type=numeric_sparse_frequency. tags=['condition_candidate', 'measure', 'rare_pattern_candidate'] desc=Frequency feature for token 'labs' in an email message. +- word_freq_telnet: role=feature, type=numeric_sparse_frequency. tags=['condition_candidate', 'measure', 'rare_pattern_candidate'] desc=Frequency feature for token 'telnet' in an email message. +- word_freq_857: role=feature, type=numeric_sparse_frequency. tags=['condition_candidate', 'measure', 'rare_pattern_candidate'] desc=Frequency feature for token '857' in an email message. +- word_freq_data: role=feature, type=numeric_sparse_frequency. tags=['condition_candidate', 'measure', 'rare_pattern_candidate'] desc=Frequency feature for token 'data' in an email message. +- word_freq_415: role=feature, type=numeric_sparse_frequency. tags=['condition_candidate', 'measure', 'rare_pattern_candidate'] desc=Frequency feature for token '415' in an email message. +- word_freq_85: role=feature, type=numeric_sparse_frequency. tags=['condition_candidate', 'measure', 'rare_pattern_candidate'] desc=Frequency feature for token '85' in an email message. +- word_freq_technology: role=feature, type=numeric_sparse_frequency. tags=['condition_candidate', 'measure', 'rare_pattern_candidate'] desc=Frequency feature for token 'technology' in an email message. +- word_freq_1999: role=feature, type=numeric_sparse_frequency. tags=['condition_candidate', 'measure', 'rare_pattern_candidate'] desc=Frequency feature for token '1999' in an email message. +- word_freq_parts: role=feature, type=numeric_sparse_frequency. tags=['condition_candidate', 'measure', 'rare_pattern_candidate'] desc=Frequency feature for token 'parts' in an email message. +- word_freq_pm: role=feature, type=numeric_sparse_frequency. tags=['condition_candidate', 'measure', 'rare_pattern_candidate'] desc=Frequency feature for token 'pm' in an email message. +- word_freq_direct: role=feature, type=numeric_sparse_frequency. tags=['condition_candidate', 'measure', 'rare_pattern_candidate'] desc=Frequency feature for token 'direct' in an email message. +- word_freq_cs: role=feature, type=numeric_sparse_frequency. tags=['condition_candidate', 'measure', 'rare_pattern_candidate'] desc=Frequency feature for token 'cs' in an email message. +- word_freq_meeting: role=feature, type=numeric_sparse_frequency. tags=['condition_candidate', 'measure', 'rare_pattern_candidate'] desc=Frequency feature for token 'meeting' in an email message. +- word_freq_original: role=feature, type=numeric_sparse_frequency. tags=['condition_candidate', 'measure', 'rare_pattern_candidate'] desc=Frequency feature for token 'original' in an email message. +- word_freq_project: role=feature, type=numeric_sparse_frequency. tags=['condition_candidate', 'measure', 'rare_pattern_candidate'] desc=Frequency feature for token 'project' in an email message. +- word_freq_re: role=feature, type=numeric_sparse_frequency. tags=['condition_candidate', 'measure', 'rare_pattern_candidate'] desc=Frequency feature for token 're' in an email message. +- word_freq_edu: role=feature, type=numeric_sparse_frequency. tags=['condition_candidate', 'measure', 'rare_pattern_candidate'] desc=Frequency feature for token 'edu' in an email message. +- word_freq_table: role=feature, type=numeric_sparse_frequency. tags=['condition_candidate', 'measure', 'rare_pattern_candidate'] desc=Frequency feature for token 'table' in an email message. +- word_freq_conference: role=feature, type=numeric_sparse_frequency. tags=['condition_candidate', 'measure', 'rare_pattern_candidate'] desc=Frequency feature for token 'conference' in an email message. +- char_freq_%3B: role=feature, type=numeric_sparse_frequency. tags=['condition_candidate', 'measure', 'rare_pattern_candidate'] desc=Character frequency feature for symbol ';'. +- char_freq_%28: role=feature, type=numeric_sparse_frequency. tags=['condition_candidate', 'measure', 'rare_pattern_candidate'] desc=Character frequency feature for symbol '('. +- char_freq_%5B: role=feature, type=numeric_sparse_frequency. tags=['condition_candidate', 'measure', 'rare_pattern_candidate'] desc=Character frequency feature for symbol '['. +- char_freq_%21: role=feature, type=numeric_sparse_frequency. tags=['condition_candidate', 'measure', 'rare_pattern_candidate'] desc=Character frequency feature for symbol '!'. +- char_freq_%24: role=feature, type=numeric_sparse_frequency. tags=['condition_candidate', 'measure', 'rare_pattern_candidate'] desc=Character frequency feature for symbol '$'. +- char_freq_%23: role=feature, type=numeric_sparse_frequency. tags=['condition_candidate', 'measure', 'rare_pattern_candidate'] desc=Character frequency feature for symbol '#'. +- capital_run_length_average: role=feature, type=numeric_heavy_tail. tags=['condition_candidate', 'measure', 'rare_pattern_candidate'] desc=Average length of uninterrupted capital-letter runs. +- capital_run_length_longest: role=feature, type=numeric_heavy_tail. tags=['condition_candidate', 'measure', 'rare_pattern_candidate'] desc=Longest uninterrupted capital-letter run. +- capital_run_length_total: role=feature, type=numeric_heavy_tail. tags=['condition_candidate', 'measure', 'rare_pattern_candidate'] desc=Total number of capital letters in runs. +- class: role=target, type=binary_target. tags=['target_candidate'] desc=Binary spam label (1=spam, 0=non-spam). +- useful_field_combinations: [['word_freq_free', 'word_freq_you', 'class'], ['char_freq_%21', 'capital_run_length_longest', 'class'], ['word_freq_remove', 'word_freq_money', 'class']] +- fields_requiring_caution: ['capital_run_length_average', 'capital_run_length_longest', 'capital_run_length_total'] +- source_url: https://www.openml.org/d/44 + +SQLite schema snapshot: +{ + "table_name": "n1", + "quoted_table_name": "\"n1\"", + "row_count": 4601, + "columns": [ + { + "name": "word_freq_make", + "type": "TEXT", + "notnull": false, + "pk": false + }, + { + "name": "word_freq_address", + "type": "TEXT", + "notnull": false, + "pk": false + }, + { + "name": "word_freq_all", + "type": "TEXT", + "notnull": false, + "pk": false + }, + { + "name": "word_freq_3d", + "type": "TEXT", + "notnull": false, + "pk": false + }, + { + "name": "word_freq_our", + "type": "TEXT", + "notnull": false, + "pk": false + }, + { + "name": "word_freq_over", + "type": "TEXT", + "notnull": false, + "pk": false + }, + { + "name": "word_freq_remove", + "type": "TEXT", + "notnull": false, + "pk": false + }, + { + "name": "word_freq_internet", + "type": "TEXT", + "notnull": false, + "pk": false + }, + { + "name": "word_freq_order", + "type": "TEXT", + "notnull": false, + "pk": false + }, + { + "name": "word_freq_mail", + "type": "TEXT", + "notnull": false, + "pk": false + }, + { + "name": "word_freq_receive", + "type": "TEXT", + "notnull": false, + "pk": false + }, + { + "name": "word_freq_will", + "type": "TEXT", + "notnull": false, + "pk": false + }, + { + "name": "word_freq_people", + "type": "TEXT", + "notnull": false, + "pk": false + }, + { + "name": "word_freq_report", + "type": "TEXT", + "notnull": false, + "pk": false + }, + { + "name": "word_freq_addresses", + "type": "TEXT", + "notnull": false, + "pk": false + }, + { + "name": "word_freq_free", + "type": "TEXT", + "notnull": false, + "pk": false + }, + { + "name": "word_freq_business", + "type": "TEXT", + "notnull": false, + "pk": false + }, + { + "name": "word_freq_email", + "type": "TEXT", + "notnull": false, + "pk": false + }, + { + "name": "word_freq_you", + "type": "TEXT", + "notnull": false, + "pk": false + }, + { + "name": "word_freq_credit", + "type": "TEXT", + "notnull": false, + "pk": false + }, + { + "name": "word_freq_your", + "type": "TEXT", + "notnull": false, + "pk": false + }, + { + "name": "word_freq_font", + "type": "TEXT", + "notnull": false, + "pk": false + }, + { + "name": "word_freq_000", + "type": "TEXT", + "notnull": false, + "pk": false + }, + { + "name": "word_freq_money", + "type": "TEXT", + "notnull": false, + "pk": false + }, + { + "name": "word_freq_hp", + "type": "TEXT", + "notnull": false, + "pk": false + }, + { + "name": "word_freq_hpl", + "type": "TEXT", + "notnull": false, + "pk": false + }, + { + "name": "word_freq_george", + "type": "TEXT", + "notnull": false, + "pk": false + }, + { + "name": "word_freq_650", + "type": "TEXT", + "notnull": false, + "pk": false + }, + { + "name": "word_freq_lab", + "type": "TEXT", + "notnull": false, + "pk": false + }, + { + "name": "word_freq_labs", + "type": "TEXT", + "notnull": false, + "pk": false + }, + { + "name": "word_freq_telnet", + "type": "TEXT", + "notnull": false, + "pk": false + }, + { + "name": "word_freq_857", + "type": "TEXT", + "notnull": false, + "pk": false + }, + { + "name": "word_freq_data", + "type": "TEXT", + "notnull": false, + "pk": false + }, + { + "name": "word_freq_415", + "type": "TEXT", + "notnull": false, + "pk": false + }, + { + "name": "word_freq_85", + "type": "TEXT", + "notnull": false, + "pk": false + }, + { + "name": "word_freq_technology", + "type": "TEXT", + "notnull": false, + "pk": false + }, + { + "name": "word_freq_1999", + "type": "TEXT", + "notnull": false, + "pk": false + }, + { + "name": "word_freq_parts", + "type": "TEXT", + "notnull": false, + "pk": false + }, + { + "name": "word_freq_pm", + "type": "TEXT", + "notnull": false, + "pk": false + }, + { + "name": "word_freq_direct", + "type": "TEXT", + "notnull": false, + "pk": false + }, + { + "name": "word_freq_cs", + "type": "TEXT", + "notnull": false, + "pk": false + }, + { + "name": "word_freq_meeting", + "type": "TEXT", + "notnull": false, + "pk": false + }, + { + "name": "word_freq_original", + "type": "TEXT", + "notnull": false, + "pk": false + }, + { + "name": "word_freq_project", + "type": "TEXT", + "notnull": false, + "pk": false + }, + { + "name": "word_freq_re", + "type": "TEXT", + "notnull": false, + "pk": false + }, + { + "name": "word_freq_edu", + "type": "TEXT", + "notnull": false, + "pk": false + }, + { + "name": "word_freq_table", + "type": "TEXT", + "notnull": false, + "pk": false + }, + { + "name": "word_freq_conference", + "type": "TEXT", + "notnull": false, + "pk": false + }, + { + "name": "char_freq_%3B", + "type": "TEXT", + "notnull": false, + "pk": false + }, + { + "name": "char_freq_%28", + "type": "TEXT", + "notnull": false, + "pk": false + }, + { + "name": "char_freq_%5B", + "type": "TEXT", + "notnull": false, + "pk": false + }, + { + "name": "char_freq_%21", + "type": "TEXT", + "notnull": false, + "pk": false + }, + { + "name": "char_freq_%24", + "type": "TEXT", + "notnull": false, + "pk": false + }, + { + "name": "char_freq_%23", + "type": "TEXT", + "notnull": false, + "pk": false + }, + { + "name": "capital_run_length_average", + "type": "TEXT", + "notnull": false, + "pk": false + }, + { + "name": "capital_run_length_longest", + "type": "TEXT", + "notnull": false, + "pk": false + }, + { + "name": "capital_run_length_total", + "type": "TEXT", + "notnull": false, + "pk": false + }, + { + "name": "class", + "type": "TEXT", + "notnull": false, + "pk": false + } + ], + "sample_rows": [ + { + "word_freq_make": "0", + "word_freq_address": "0.64", + "word_freq_all": "0.64", + "word_freq_3d": "0", + "word_freq_our": "0.32", + "word_freq_over": "0", + "word_freq_remove": "0", + "word_freq_internet": "0", + "word_freq_order": "0", + "word_freq_mail": "0", + "word_freq_receive": "0", + "word_freq_will": "0.64", + "word_freq_people": "0", + "word_freq_report": "0", + "word_freq_addresses": "0", + "word_freq_free": "0.32", + "word_freq_business": "0", + "word_freq_email": "1.29", + "word_freq_you": "1.93", + "word_freq_credit": "0", + "word_freq_your": "0.96", + "word_freq_font": "0", + "word_freq_000": "0", + "word_freq_money": "0", + "word_freq_hp": "0", + "word_freq_hpl": "0", + "word_freq_george": "0", + "word_freq_650": "0", + "word_freq_lab": "0", + "word_freq_labs": "0", + "word_freq_telnet": "0", + "word_freq_857": "0", + "word_freq_data": "0", + "word_freq_415": "0", + "word_freq_85": "0", + "word_freq_technology": "0", + "word_freq_1999": "0", + "word_freq_parts": "0", + "word_freq_pm": "0", + "word_freq_direct": "0", + "word_freq_cs": "0", + "word_freq_meeting": "0", + "word_freq_original": "0", + "word_freq_project": "0", + "word_freq_re": "0", + "word_freq_edu": "0", + "word_freq_table": "0", + "word_freq_conference": "0", + "char_freq_%3B": "0", + "char_freq_%28": "0", + "char_freq_%5B": "0", + "char_freq_%21": "0.778", + "char_freq_%24": "0", + "char_freq_%23": "0", + "capital_run_length_average": "3.756", + "capital_run_length_longest": "61", + "capital_run_length_total": "278", + "class": "1" + }, + { + "word_freq_make": "0.21", + "word_freq_address": "0.28", + "word_freq_all": "0.5", + "word_freq_3d": "0", + "word_freq_our": "0.14", + "word_freq_over": "0.28", + "word_freq_remove": "0.21", + "word_freq_internet": "0.07", + "word_freq_order": "0", + "word_freq_mail": "0.94", + "word_freq_receive": "0.21", + "word_freq_will": "0.79", + "word_freq_people": "0.65", + "word_freq_report": "0.21", + "word_freq_addresses": "0.14", + "word_freq_free": "0.14", + "word_freq_business": "0.07", + "word_freq_email": "0.28", + "word_freq_you": "3.47", + "word_freq_credit": "0", + "word_freq_your": "1.59", + "word_freq_font": "0", + "word_freq_000": "0.43", + "word_freq_money": "0.43", + "word_freq_hp": "0", + "word_freq_hpl": "0", + "word_freq_george": "0", + "word_freq_650": "0", + "word_freq_lab": "0", + "word_freq_labs": "0", + "word_freq_telnet": "0", + "word_freq_857": "0", + "word_freq_data": "0", + "word_freq_415": "0", + "word_freq_85": "0", + "word_freq_technology": "0", + "word_freq_1999": "0.07", + "word_freq_parts": "0", + "word_freq_pm": "0", + "word_freq_direct": "0", + "word_freq_cs": "0", + "word_freq_meeting": "0", + "word_freq_original": "0", + "word_freq_project": "0", + "word_freq_re": "0", + "word_freq_edu": "0", + "word_freq_table": "0", + "word_freq_conference": "0", + "char_freq_%3B": "0", + "char_freq_%28": "0.132", + "char_freq_%5B": "0", + "char_freq_%21": "0.372", + "char_freq_%24": "0.18", + "char_freq_%23": "0.048", + "capital_run_length_average": "5.114", + "capital_run_length_longest": "101", + "capital_run_length_total": "1028", + "class": "1" + }, + { + "word_freq_make": "0.06", + "word_freq_address": "0", + "word_freq_all": "0.71", + "word_freq_3d": "0", + "word_freq_our": "1.23", + "word_freq_over": "0.19", + "word_freq_remove": "0.19", + "word_freq_internet": "0.12", + "word_freq_order": "0.64", + "word_freq_mail": "0.25", + "word_freq_receive": "0.38", + "word_freq_will": "0.45", + "word_freq_people": "0.12", + "word_freq_report": "0", + "word_freq_addresses": "1.75", + "word_freq_free": "0.06", + "word_freq_business": "0.06", + "word_freq_email": "1.03", + "word_freq_you": "1.36", + "word_freq_credit": "0.32", + "word_freq_your": "0.51", + "word_freq_font": "0", + "word_freq_000": "1.16", + "word_freq_money": "0.06", + "word_freq_hp": "0", + "word_freq_hpl": "0", + "word_freq_george": "0", + "word_freq_650": "0", + "word_freq_lab": "0", + "word_freq_labs": "0", + "word_freq_telnet": "0", + "word_freq_857": "0", + "word_freq_data": "0", + "word_freq_415": "0", + "word_freq_85": "0", + "word_freq_technology": "0", + "word_freq_1999": "0", + "word_freq_parts": "0", + "word_freq_pm": "0", + "word_freq_direct": "0.06", + "word_freq_cs": "0", + "word_freq_meeting": "0", + "word_freq_original": "0.12", + "word_freq_project": "0", + "word_freq_re": "0.06", + "word_freq_edu": "0.06", + "word_freq_table": "0", + "word_freq_conference": "0", + "char_freq_%3B": "0.01", + "char_freq_%28": "0.143", + "char_freq_%5B": "0", + "char_freq_%21": "0.276", + "char_freq_%24": "0.184", + "char_freq_%23": "0.01", + "capital_run_length_average": "9.821", + "capital_run_length_longest": "485", + "capital_run_length_total": "2259", + "class": "1" + }, + { + "word_freq_make": "0", + "word_freq_address": "0", + "word_freq_all": "0", + "word_freq_3d": "0", + "word_freq_our": "0.63", + "word_freq_over": "0", + "word_freq_remove": "0.31", + "word_freq_internet": "0.63", + "word_freq_order": "0.31", + "word_freq_mail": "0.63", + "word_freq_receive": "0.31", + "word_freq_will": "0.31", + "word_freq_people": "0.31", + "word_freq_report": "0", + "word_freq_addresses": "0", + "word_freq_free": "0.31", + "word_freq_business": "0", + "word_freq_email": "0", + "word_freq_you": "3.18", + "word_freq_credit": "0", + "word_freq_your": "0.31", + "word_freq_font": "0", + "word_freq_000": "0", + "word_freq_money": "0", + "word_freq_hp": "0", + "word_freq_hpl": "0", + "word_freq_george": "0", + "word_freq_650": "0", + "word_freq_lab": "0", + "word_freq_labs": "0", + "word_freq_telnet": "0", + "word_freq_857": "0", + "word_freq_data": "0", + "word_freq_415": "0", + "word_freq_85": "0", + "word_freq_technology": "0", + "word_freq_1999": "0", + "word_freq_parts": "0", + "word_freq_pm": "0", + "word_freq_direct": "0", + "word_freq_cs": "0", + "word_freq_meeting": "0", + "word_freq_original": "0", + "word_freq_project": "0", + "word_freq_re": "0", + "word_freq_edu": "0", + "word_freq_table": "0", + "word_freq_conference": "0", + "char_freq_%3B": "0", + "char_freq_%28": "0.137", + "char_freq_%5B": "0", + "char_freq_%21": "0.137", + "char_freq_%24": "0", + "char_freq_%23": "0", + "capital_run_length_average": "3.537", + "capital_run_length_longest": "40", + "capital_run_length_total": "191", + "class": "1" + }, + { + "word_freq_make": "0", + "word_freq_address": "0", + "word_freq_all": "0", + "word_freq_3d": "0", + "word_freq_our": "0.63", + "word_freq_over": "0", + "word_freq_remove": "0.31", + "word_freq_internet": "0.63", + "word_freq_order": "0.31", + "word_freq_mail": "0.63", + "word_freq_receive": "0.31", + "word_freq_will": "0.31", + "word_freq_people": "0.31", + "word_freq_report": "0", + "word_freq_addresses": "0", + "word_freq_free": "0.31", + "word_freq_business": "0", + "word_freq_email": "0", + "word_freq_you": "3.18", + "word_freq_credit": "0", + "word_freq_your": "0.31", + "word_freq_font": "0", + "word_freq_000": "0", + "word_freq_money": "0", + "word_freq_hp": "0", + "word_freq_hpl": "0", + "word_freq_george": "0", + "word_freq_650": "0", + "word_freq_lab": "0", + "word_freq_labs": "0", + "word_freq_telnet": "0", + "word_freq_857": "0", + "word_freq_data": "0", + "word_freq_415": "0", + "word_freq_85": "0", + "word_freq_technology": "0", + "word_freq_1999": "0", + "word_freq_parts": "0", + "word_freq_pm": "0", + "word_freq_direct": "0", + "word_freq_cs": "0", + "word_freq_meeting": "0", + "word_freq_original": "0", + "word_freq_project": "0", + "word_freq_re": "0", + "word_freq_edu": "0", + "word_freq_table": "0", + "word_freq_conference": "0", + "char_freq_%3B": "0", + "char_freq_%28": "0.135", + "char_freq_%5B": "0", + "char_freq_%21": "0.135", + "char_freq_%24": "0", + "char_freq_%23": "0", + "capital_run_length_average": "3.537", + "capital_run_length_longest": "40", + "capital_run_length_total": "191", + "class": "1" + } + ] +} + +Shortlisted templates: +[ + { + "template_id": "tpl_tpch_thresholded_group_ranking", + "template_name": "Thresholded Group Ranking", + "primary_family": "tail_rarity_structure", + "portability": "partial", + "sql_skeleton": "SELECT {group_col}, SUM({measure_col}) AS total_measure\nFROM {table}\nGROUP BY {group_col}\nHAVING SUM({measure_col}) > {measure_threshold}\nORDER BY total_measure DESC\nLIMIT {top_k};", + "required_roles": [ + "group_col", + "measure_col" + ] + } +] + +Problem instance: +{ + "dataset_id": "n1", + "question": "Use template Thresholded Group Ranking to probe tail_mass_similarity with semantic role rare_extreme_view. Focus on group_col=class, measure_col=word_freq_money.", + "planned_template_id": "tpl_tpch_thresholded_group_ranking", + "bindings": { + "group_col": "class", + "measure_col": "word_freq_money", + "top_k": 17, + "top_n": 5, + "num_tiles": 10, + "percentile_value": 0.95, + "z_threshold": 2.0, + "fraction_threshold": 0.05, + "baseline_multiplier": 1.75, + "baseline_fraction": 0.1, + "min_group_size": 5, + "min_support": 4, + "measure_threshold": 0.0, + "time_grain": "month", + "lookback_rows": 3, + "current_period_start": "'2024-01-01'", + "current_period_end": "'2024-04-01'", + "previous_period_start": "'2023-10-01'", + "previous_period_end": "'2024-01-01'", + "drift_ratio_threshold": 0.8 + }, + "can_vary": [], + "must_fix": [], + "runtime_sql_skeleton": "SELECT {group_col}, SUM({measure_col}) AS total_measure\nFROM {table}\nGROUP BY {group_col}\nHAVING SUM({measure_col}) > {measure_threshold}\nORDER BY total_measure DESC\nLIMIT {top_k};" +} + +Repair context: +{} diff --git a/Query/sql/v2/runs/v2_cli_20260502_081223_d/n1/artifacts/v2q_n1_4d17d0fd5ebc19a7/cli/sql_response_attempt_1.raw.txt b/Query/sql/v2/runs/v2_cli_20260502_081223_d/n1/artifacts/v2q_n1_4d17d0fd5ebc19a7/cli/sql_response_attempt_1.raw.txt new file mode 100644 index 0000000000000000000000000000000000000000..f0c79239abd1d0edcab55e2afdf336aeedc29c79 --- /dev/null +++ b/Query/sql/v2/runs/v2_cli_20260502_081223_d/n1/artifacts/v2q_n1_4d17d0fd5ebc19a7/cli/sql_response_attempt_1.raw.txt @@ -0,0 +1,4 @@ +{"type":"thread.started","thread_id":"019e40fc-6e88-7b52-897a-a349c1ea8e8d"} +{"type":"turn.started"} +{"type":"error","message":"Quota exceeded. Check your plan and billing details."} +{"type":"turn.failed","error":{"message":"Quota exceeded. Check your plan and billing details."}} diff --git a/Query/sql/v2/runs/v2_cli_20260502_081223_d/n1/artifacts/v2q_n1_4d17d0fd5ebc19a7/cli/sql_response_attempt_1.txt b/Query/sql/v2/runs/v2_cli_20260502_081223_d/n1/artifacts/v2q_n1_4d17d0fd5ebc19a7/cli/sql_response_attempt_1.txt new file mode 100644 index 0000000000000000000000000000000000000000..54fd1ecdc3daf4bd352e9b03eef30d132c9e65aa --- /dev/null +++ b/Query/sql/v2/runs/v2_cli_20260502_081223_d/n1/artifacts/v2q_n1_4d17d0fd5ebc19a7/cli/sql_response_attempt_1.txt @@ -0,0 +1,4 @@ +{"type":"thread.started","thread_id":"019e40fc-6e88-7b52-897a-a349c1ea8e8d"} +{"type":"turn.started"} +{"type":"error","message":"Quota exceeded. Check your plan and billing details."} +{"type":"turn.failed","error":{"message":"Quota exceeded. Check your plan and billing details."}} \ No newline at end of file diff --git a/Query/sql/v2/runs/v2_cli_20260502_081223_d/n1/artifacts/v2q_n1_4d17d0fd5ebc19a7/cli/sql_response_attempt_2.raw.txt b/Query/sql/v2/runs/v2_cli_20260502_081223_d/n1/artifacts/v2q_n1_4d17d0fd5ebc19a7/cli/sql_response_attempt_2.raw.txt new file mode 100644 index 0000000000000000000000000000000000000000..1e350fba287f78920088a2a9acc814c348ab448f --- /dev/null +++ b/Query/sql/v2/runs/v2_cli_20260502_081223_d/n1/artifacts/v2q_n1_4d17d0fd5ebc19a7/cli/sql_response_attempt_2.raw.txt @@ -0,0 +1,4 @@ +{"type":"thread.started","thread_id":"019e40fc-805e-7650-a8d3-b92cfa477f6f"} +{"type":"turn.started"} +{"type":"item.completed","item":{"id":"item_0","type":"agent_message","text":"{\"sql\":\"-- template_id: tpl_tpch_thresholded_group_ranking\\nSELECT \\\"class\\\", SUM(CAST(\\\"word_freq_money\\\" AS REAL)) AS \\\"total_measure\\\"\\nFROM \\\"n1\\\"\\nGROUP BY \\\"class\\\"\\nHAVING SUM(CAST(\\\"word_freq_money\\\" AS REAL)) > 0.0\\nORDER BY \\\"total_measure\\\" DESC\\nLIMIT 17;\",\"notes\":\"Used the required Thresholded Group Ranking template with \\\"class\\\" as the group column and \\\"word_freq_money\\\" as the measure column. CAST to REAL is included because the schema stores feature values as TEXT.\"}"}} +{"type":"turn.completed","usage":{"input_tokens":20359,"cached_input_tokens":12032,"output_tokens":391,"reasoning_output_tokens":260}} diff --git a/Query/sql/v2/runs/v2_cli_20260502_081223_d/n1/artifacts/v2q_n1_4d17d0fd5ebc19a7/cli/sql_response_attempt_2.txt b/Query/sql/v2/runs/v2_cli_20260502_081223_d/n1/artifacts/v2q_n1_4d17d0fd5ebc19a7/cli/sql_response_attempt_2.txt new file mode 100644 index 0000000000000000000000000000000000000000..e34fdf721f3a2691adde70545a940056b0da8513 --- /dev/null +++ b/Query/sql/v2/runs/v2_cli_20260502_081223_d/n1/artifacts/v2q_n1_4d17d0fd5ebc19a7/cli/sql_response_attempt_2.txt @@ -0,0 +1 @@ +{"sql":"-- template_id: tpl_tpch_thresholded_group_ranking\nSELECT \"class\", SUM(CAST(\"word_freq_money\" AS REAL)) AS \"total_measure\"\nFROM \"n1\"\nGROUP BY \"class\"\nHAVING SUM(CAST(\"word_freq_money\" AS REAL)) > 0.0\nORDER BY \"total_measure\" DESC\nLIMIT 17;","notes":"Used the required Thresholded Group Ranking template with \"class\" as the group column and \"word_freq_money\" as the measure column. CAST to REAL is included because the schema stores feature values as TEXT."} \ No newline at end of file diff --git a/Query/sql/v2/runs/v2_cli_20260502_081223_d/n1/artifacts/v2q_n1_4d17d0fd5ebc19a7/cli/sql_stderr_attempt_1.txt b/Query/sql/v2/runs/v2_cli_20260502_081223_d/n1/artifacts/v2q_n1_4d17d0fd5ebc19a7/cli/sql_stderr_attempt_1.txt new file mode 100644 index 0000000000000000000000000000000000000000..e69de29bb2d1d6434b8b29ae775ad8c2e48c5391 diff --git a/Query/sql/v2/runs/v2_cli_20260502_081223_d/n1/artifacts/v2q_n1_4d17d0fd5ebc19a7/cli/sql_stderr_attempt_2.txt b/Query/sql/v2/runs/v2_cli_20260502_081223_d/n1/artifacts/v2q_n1_4d17d0fd5ebc19a7/cli/sql_stderr_attempt_2.txt new file mode 100644 index 0000000000000000000000000000000000000000..e69de29bb2d1d6434b8b29ae775ad8c2e48c5391 diff --git a/Query/sql/v2/runs/v2_cli_20260502_081223_d/n1/artifacts/v2q_n1_4f48bf7d451fc90d/cli/conversation.jsonl b/Query/sql/v2/runs/v2_cli_20260502_081223_d/n1/artifacts/v2q_n1_4f48bf7d451fc90d/cli/conversation.jsonl new file mode 100644 index 0000000000000000000000000000000000000000..e11659cdcb9fa8ddbbf166cb386eb9b100f0a653 --- /dev/null +++ b/Query/sql/v2/runs/v2_cli_20260502_081223_d/n1/artifacts/v2q_n1_4f48bf7d451fc90d/cli/conversation.jsonl @@ -0,0 +1,2 @@ +{"attempt": 1, "phase": "sql_generation", "role": "user", "content_path": "cli/sql_prompt_attempt_1.txt", "metrics": {"chars": 29585, "bytes_utf8": 29585, "lines": 795, "estimated_tokens": null}} +{"attempt": 1, "phase": "sql_generation", "role": "assistant", "content_path": "cli/sql_response_attempt_1.txt", "raw_content_path": "cli/sql_response_attempt_1.raw.txt", "stderr_path": "cli/sql_stderr_attempt_1.txt", "metrics": {"chars": 374, "bytes_utf8": 374, "lines": 1, "estimated_tokens": null}, "usage": {"input_tokens": 20372, "cached_input_tokens": 19840, "output_tokens": 285, "reasoning_output_tokens": 185}} diff --git a/Query/sql/v2/runs/v2_cli_20260502_081223_d/n1/artifacts/v2q_n1_4f48bf7d451fc90d/cli/session_summary.json b/Query/sql/v2/runs/v2_cli_20260502_081223_d/n1/artifacts/v2q_n1_4f48bf7d451fc90d/cli/session_summary.json new file mode 100644 index 0000000000000000000000000000000000000000..20c9fb3ad8f82ffa521acd2eb6146a8b4a9ee63e --- /dev/null +++ b/Query/sql/v2/runs/v2_cli_20260502_081223_d/n1/artifacts/v2q_n1_4f48bf7d451fc90d/cli/session_summary.json @@ -0,0 +1,25 @@ +{ + "engine": "v2-cli:codex", + "command": "codex exec --skip-git-repo-check --disable plugins --sandbox read-only --cd \"/data/jialinzhang/SQLagent\" -m gpt-5.4 --json -", + "ai_cli_calls": 1, + "usage_summary": { + "dataset_id": "n1", + "model": "v2-cli:codex", + "run_id": "v2q_n1_4f48bf7d451fc90d", + "api_calls": 0, + "input_tokens": 20372, + "cached_input_tokens": 19840, + "output_tokens": 285, + "total_tokens": 20657, + "cost_usd": 0.0, + "ai_cli_calls": 1, + "estimated_input_tokens": 0, + "estimated_output_tokens": 0, + "estimated_total_tokens": 0, + "usage_source": "ai_cli_json_usage", + "cli_elapsed_ms_total": 8383.45, + "sql_execution_elapsed_ms_total": 2.2, + "conversation_log_path": "/data/jialinzhang/TabQueryBench/sql_workloads/v2_current/runs_and_launches/runs/v2_cli_20260502_081223_d/n1/artifacts/v2q_n1_4f48bf7d451fc90d/cli/conversation.jsonl", + "note": "Executed through a local AI CLI with structured usage metadata." + } +} \ No newline at end of file diff --git a/Query/sql/v2/runs/v2_cli_20260502_081223_d/n1/artifacts/v2q_n1_4f48bf7d451fc90d/cli/sql_attempt_1.metadata.json b/Query/sql/v2/runs/v2_cli_20260502_081223_d/n1/artifacts/v2q_n1_4f48bf7d451fc90d/cli/sql_attempt_1.metadata.json new file mode 100644 index 0000000000000000000000000000000000000000..db07594c648726e769ab33cfca29e0e449983c40 --- /dev/null +++ b/Query/sql/v2/runs/v2_cli_20260502_081223_d/n1/artifacts/v2q_n1_4f48bf7d451fc90d/cli/sql_attempt_1.metadata.json @@ -0,0 +1,45 @@ +{ + "attempt": 1, + "phase": "sql_generation", + "command": "codex exec --skip-git-repo-check --disable plugins --sandbox read-only --cd \"/data/jialinzhang/SQLagent\" -m gpt-5.4 --json -", + "started_at": "2026-05-19T15:58:03.115427+00:00", + "ended_at": "2026-05-19T15:58:11.498898+00:00", + "elapsed_ms": 8383.45, + "prompt_metrics": { + "chars": 29585, + "bytes_utf8": 29585, + "lines": 795, + "estimated_tokens": null + }, + "stdout_metrics": { + "chars": 725, + "bytes_utf8": 725, + "lines": 4, + "estimated_tokens": null + }, + "stderr_metrics": { + "chars": 0, + "bytes_utf8": 0, + "lines": 0, + "estimated_tokens": null + }, + "parsed_output": { + "format": "jsonl_events", + "text_metrics": { + "chars": 374, + "bytes_utf8": 374, + "lines": 1, + "estimated_tokens": null + }, + "usage": { + "input_tokens": 20372, + "cached_input_tokens": 19840, + "output_tokens": 285, + "reasoning_output_tokens": 185 + } + }, + "prompt_path": "cli/sql_prompt_attempt_1.txt", + "response_path": "cli/sql_response_attempt_1.txt", + "raw_response_path": "cli/sql_response_attempt_1.raw.txt", + "stderr_path": "cli/sql_stderr_attempt_1.txt" +} \ No newline at end of file diff --git a/Query/sql/v2/runs/v2_cli_20260502_081223_d/n1/artifacts/v2q_n1_4f48bf7d451fc90d/cli/sql_prompt_attempt_1.txt b/Query/sql/v2/runs/v2_cli_20260502_081223_d/n1/artifacts/v2q_n1_4f48bf7d451fc90d/cli/sql_prompt_attempt_1.txt new file mode 100644 index 0000000000000000000000000000000000000000..db6829959a444736dd2178d2055148867943b6a6 --- /dev/null +++ b/Query/sql/v2/runs/v2_cli_20260502_081223_d/n1/artifacts/v2q_n1_4f48bf7d451fc90d/cli/sql_prompt_attempt_1.txt @@ -0,0 +1,795 @@ +You are generating one SQLite SELECT query for a single-table SQL QA task. +Return strict JSON only, with this schema: {"sql": "...", "notes": "..."}. +Rules: +- Use only the provided table and columns. +- Do not write INSERT, UPDATE, DELETE, DROP, ALTER, CREATE, PRAGMA, ATTACH, DETACH, or VACUUM. +- Prefer the planned template and bound roles when provided. +- Add a leading SQL comment exactly like: -- template_id: . +- Generate SQLite-compatible SQL. SQLite does not support PERCENTILE_CONT or STDDEV. +- Quote identifiers with double quotes. +- Return no markdown and no extra prose. + +Dataset context: +Dataset context for SQL QA: +- dataset_id: n1 +- dataset_name: Spambase +- table_name: n1 +- table_layout: single-table dataset (do not assume joins). +- row_semantics: One row is one email represented by engineered token/character frequency and capitalization-run features. +- task_type: classification +- target_column: class +- main_row_count: 4601 +- important_fields: +- word_freq_make: role=feature, type=numeric_sparse_frequency. tags=['condition_candidate', 'measure', 'rare_pattern_candidate'] desc=Frequency feature for token 'make' in an email message. +- word_freq_address: role=feature, type=numeric_sparse_frequency. tags=['condition_candidate', 'measure', 'rare_pattern_candidate'] desc=Frequency feature for token 'address' in an email message. +- word_freq_all: role=feature, type=numeric_sparse_frequency. tags=['condition_candidate', 'measure', 'rare_pattern_candidate'] desc=Frequency feature for token 'all' in an email message. +- word_freq_3d: role=feature, type=numeric_sparse_frequency. tags=['condition_candidate', 'measure', 'rare_pattern_candidate'] desc=Frequency feature for token '3d' in an email message. +- word_freq_our: role=feature, type=numeric_sparse_frequency. tags=['condition_candidate', 'measure', 'rare_pattern_candidate'] desc=Frequency feature for token 'our' in an email message. +- word_freq_over: role=feature, type=numeric_sparse_frequency. tags=['condition_candidate', 'measure', 'rare_pattern_candidate'] desc=Frequency feature for token 'over' in an email message. +- word_freq_remove: role=feature, type=numeric_sparse_frequency. tags=['condition_candidate', 'measure', 'rare_pattern_candidate'] desc=Frequency feature for token 'remove' in an email message. +- word_freq_internet: role=feature, type=numeric_sparse_frequency. tags=['condition_candidate', 'measure', 'rare_pattern_candidate'] desc=Frequency feature for token 'internet' in an email message. +- word_freq_order: role=feature, type=numeric_sparse_frequency. tags=['condition_candidate', 'measure', 'rare_pattern_candidate'] desc=Frequency feature for token 'order' in an email message. +- word_freq_mail: role=feature, type=numeric_sparse_frequency. tags=['condition_candidate', 'measure', 'rare_pattern_candidate'] desc=Frequency feature for token 'mail' in an email message. +- word_freq_receive: role=feature, type=numeric_sparse_frequency. tags=['condition_candidate', 'measure', 'rare_pattern_candidate'] desc=Frequency feature for token 'receive' in an email message. +- word_freq_will: role=feature, type=numeric_sparse_frequency. tags=['condition_candidate', 'measure', 'rare_pattern_candidate'] desc=Frequency feature for token 'will' in an email message. +- word_freq_people: role=feature, type=numeric_sparse_frequency. tags=['condition_candidate', 'measure', 'rare_pattern_candidate'] desc=Frequency feature for token 'people' in an email message. +- word_freq_report: role=feature, type=numeric_sparse_frequency. tags=['condition_candidate', 'measure', 'rare_pattern_candidate'] desc=Frequency feature for token 'report' in an email message. +- word_freq_addresses: role=feature, type=numeric_sparse_frequency. tags=['condition_candidate', 'measure', 'rare_pattern_candidate'] desc=Frequency feature for token 'addresses' in an email message. +- word_freq_free: role=feature, type=numeric_sparse_frequency. tags=['condition_candidate', 'measure', 'rare_pattern_candidate'] desc=Frequency feature for token 'free' in an email message. +- word_freq_business: role=feature, type=numeric_sparse_frequency. tags=['condition_candidate', 'measure', 'rare_pattern_candidate'] desc=Frequency feature for token 'business' in an email message. +- word_freq_email: role=feature, type=numeric_sparse_frequency. tags=['condition_candidate', 'measure', 'rare_pattern_candidate'] desc=Frequency feature for token 'email' in an email message. +- word_freq_you: role=feature, type=numeric_sparse_frequency. tags=['condition_candidate', 'measure', 'rare_pattern_candidate'] desc=Frequency feature for token 'you' in an email message. +- word_freq_credit: role=feature, type=numeric_sparse_frequency. tags=['condition_candidate', 'measure', 'rare_pattern_candidate'] desc=Frequency feature for token 'credit' in an email message. +- word_freq_your: role=feature, type=numeric_sparse_frequency. tags=['condition_candidate', 'measure', 'rare_pattern_candidate'] desc=Frequency feature for token 'your' in an email message. +- word_freq_font: role=feature, type=numeric_sparse_frequency. tags=['condition_candidate', 'measure', 'rare_pattern_candidate'] desc=Frequency feature for token 'font' in an email message. +- word_freq_000: role=feature, type=numeric_sparse_frequency. tags=['condition_candidate', 'measure', 'rare_pattern_candidate'] desc=Frequency feature for token '000' in an email message. +- word_freq_money: role=feature, type=numeric_sparse_frequency. tags=['condition_candidate', 'measure', 'rare_pattern_candidate'] desc=Frequency feature for token 'money' in an email message. +- word_freq_hp: role=feature, type=numeric_sparse_frequency. tags=['condition_candidate', 'measure', 'rare_pattern_candidate'] desc=Frequency feature for token 'hp' in an email message. +- word_freq_hpl: role=feature, type=numeric_sparse_frequency. tags=['condition_candidate', 'measure', 'rare_pattern_candidate'] desc=Frequency feature for token 'hpl' in an email message. +- word_freq_george: role=feature, type=numeric_sparse_frequency. tags=['condition_candidate', 'measure', 'rare_pattern_candidate'] desc=Frequency feature for token 'george' in an email message. +- word_freq_650: role=feature, type=numeric_sparse_frequency. tags=['condition_candidate', 'measure', 'rare_pattern_candidate'] desc=Frequency feature for token '650' in an email message. +- word_freq_lab: role=feature, type=numeric_sparse_frequency. tags=['condition_candidate', 'measure', 'rare_pattern_candidate'] desc=Frequency feature for token 'lab' in an email message. +- word_freq_labs: role=feature, type=numeric_sparse_frequency. tags=['condition_candidate', 'measure', 'rare_pattern_candidate'] desc=Frequency feature for token 'labs' in an email message. +- word_freq_telnet: role=feature, type=numeric_sparse_frequency. tags=['condition_candidate', 'measure', 'rare_pattern_candidate'] desc=Frequency feature for token 'telnet' in an email message. +- word_freq_857: role=feature, type=numeric_sparse_frequency. tags=['condition_candidate', 'measure', 'rare_pattern_candidate'] desc=Frequency feature for token '857' in an email message. +- word_freq_data: role=feature, type=numeric_sparse_frequency. tags=['condition_candidate', 'measure', 'rare_pattern_candidate'] desc=Frequency feature for token 'data' in an email message. +- word_freq_415: role=feature, type=numeric_sparse_frequency. tags=['condition_candidate', 'measure', 'rare_pattern_candidate'] desc=Frequency feature for token '415' in an email message. +- word_freq_85: role=feature, type=numeric_sparse_frequency. tags=['condition_candidate', 'measure', 'rare_pattern_candidate'] desc=Frequency feature for token '85' in an email message. +- word_freq_technology: role=feature, type=numeric_sparse_frequency. tags=['condition_candidate', 'measure', 'rare_pattern_candidate'] desc=Frequency feature for token 'technology' in an email message. +- word_freq_1999: role=feature, type=numeric_sparse_frequency. tags=['condition_candidate', 'measure', 'rare_pattern_candidate'] desc=Frequency feature for token '1999' in an email message. +- word_freq_parts: role=feature, type=numeric_sparse_frequency. tags=['condition_candidate', 'measure', 'rare_pattern_candidate'] desc=Frequency feature for token 'parts' in an email message. +- word_freq_pm: role=feature, type=numeric_sparse_frequency. tags=['condition_candidate', 'measure', 'rare_pattern_candidate'] desc=Frequency feature for token 'pm' in an email message. +- word_freq_direct: role=feature, type=numeric_sparse_frequency. tags=['condition_candidate', 'measure', 'rare_pattern_candidate'] desc=Frequency feature for token 'direct' in an email message. +- word_freq_cs: role=feature, type=numeric_sparse_frequency. tags=['condition_candidate', 'measure', 'rare_pattern_candidate'] desc=Frequency feature for token 'cs' in an email message. +- word_freq_meeting: role=feature, type=numeric_sparse_frequency. tags=['condition_candidate', 'measure', 'rare_pattern_candidate'] desc=Frequency feature for token 'meeting' in an email message. +- word_freq_original: role=feature, type=numeric_sparse_frequency. tags=['condition_candidate', 'measure', 'rare_pattern_candidate'] desc=Frequency feature for token 'original' in an email message. +- word_freq_project: role=feature, type=numeric_sparse_frequency. tags=['condition_candidate', 'measure', 'rare_pattern_candidate'] desc=Frequency feature for token 'project' in an email message. +- word_freq_re: role=feature, type=numeric_sparse_frequency. tags=['condition_candidate', 'measure', 'rare_pattern_candidate'] desc=Frequency feature for token 're' in an email message. +- word_freq_edu: role=feature, type=numeric_sparse_frequency. tags=['condition_candidate', 'measure', 'rare_pattern_candidate'] desc=Frequency feature for token 'edu' in an email message. +- word_freq_table: role=feature, type=numeric_sparse_frequency. tags=['condition_candidate', 'measure', 'rare_pattern_candidate'] desc=Frequency feature for token 'table' in an email message. +- word_freq_conference: role=feature, type=numeric_sparse_frequency. tags=['condition_candidate', 'measure', 'rare_pattern_candidate'] desc=Frequency feature for token 'conference' in an email message. +- char_freq_%3B: role=feature, type=numeric_sparse_frequency. tags=['condition_candidate', 'measure', 'rare_pattern_candidate'] desc=Character frequency feature for symbol ';'. +- char_freq_%28: role=feature, type=numeric_sparse_frequency. tags=['condition_candidate', 'measure', 'rare_pattern_candidate'] desc=Character frequency feature for symbol '('. +- char_freq_%5B: role=feature, type=numeric_sparse_frequency. tags=['condition_candidate', 'measure', 'rare_pattern_candidate'] desc=Character frequency feature for symbol '['. +- char_freq_%21: role=feature, type=numeric_sparse_frequency. tags=['condition_candidate', 'measure', 'rare_pattern_candidate'] desc=Character frequency feature for symbol '!'. +- char_freq_%24: role=feature, type=numeric_sparse_frequency. tags=['condition_candidate', 'measure', 'rare_pattern_candidate'] desc=Character frequency feature for symbol '$'. +- char_freq_%23: role=feature, type=numeric_sparse_frequency. tags=['condition_candidate', 'measure', 'rare_pattern_candidate'] desc=Character frequency feature for symbol '#'. +- capital_run_length_average: role=feature, type=numeric_heavy_tail. tags=['condition_candidate', 'measure', 'rare_pattern_candidate'] desc=Average length of uninterrupted capital-letter runs. +- capital_run_length_longest: role=feature, type=numeric_heavy_tail. tags=['condition_candidate', 'measure', 'rare_pattern_candidate'] desc=Longest uninterrupted capital-letter run. +- capital_run_length_total: role=feature, type=numeric_heavy_tail. tags=['condition_candidate', 'measure', 'rare_pattern_candidate'] desc=Total number of capital letters in runs. +- class: role=target, type=binary_target. tags=['target_candidate'] desc=Binary spam label (1=spam, 0=non-spam). +- useful_field_combinations: [['word_freq_free', 'word_freq_you', 'class'], ['char_freq_%21', 'capital_run_length_longest', 'class'], ['word_freq_remove', 'word_freq_money', 'class']] +- fields_requiring_caution: ['capital_run_length_average', 'capital_run_length_longest', 'capital_run_length_total'] +- source_url: https://www.openml.org/d/44 + +SQLite schema snapshot: +{ + "table_name": "n1", + "quoted_table_name": "\"n1\"", + "row_count": 4601, + "columns": [ + { + "name": "word_freq_make", + "type": "TEXT", + "notnull": false, + "pk": false + }, + { + "name": "word_freq_address", + "type": "TEXT", + "notnull": false, + "pk": false + }, + { + "name": "word_freq_all", + "type": "TEXT", + "notnull": false, + "pk": false + }, + { + "name": "word_freq_3d", + "type": "TEXT", + "notnull": false, + "pk": false + }, + { + "name": "word_freq_our", + "type": "TEXT", + "notnull": false, + "pk": false + }, + { + "name": "word_freq_over", + "type": "TEXT", + "notnull": false, + "pk": false + }, + { + "name": "word_freq_remove", + "type": "TEXT", + "notnull": false, + "pk": false + }, + { + "name": "word_freq_internet", + "type": "TEXT", + "notnull": false, + "pk": false + }, + { + "name": "word_freq_order", + "type": "TEXT", + "notnull": false, + "pk": false + }, + { + "name": "word_freq_mail", + "type": "TEXT", + "notnull": false, + "pk": false + }, + { + "name": "word_freq_receive", + "type": "TEXT", + "notnull": false, + "pk": false + }, + { + "name": "word_freq_will", + "type": "TEXT", + "notnull": false, + "pk": false + }, + { + "name": "word_freq_people", + "type": "TEXT", + "notnull": false, + "pk": false + }, + { + "name": "word_freq_report", + "type": "TEXT", + "notnull": false, + "pk": false + }, + { + "name": "word_freq_addresses", + "type": "TEXT", + "notnull": false, + "pk": false + }, + { + "name": "word_freq_free", + "type": "TEXT", + "notnull": false, + "pk": false + }, + { + "name": "word_freq_business", + "type": "TEXT", + "notnull": false, + "pk": false + }, + { + "name": "word_freq_email", + "type": "TEXT", + "notnull": false, + "pk": false + }, + { + "name": "word_freq_you", + "type": "TEXT", + "notnull": false, + "pk": false + }, + { + "name": "word_freq_credit", + "type": "TEXT", + "notnull": false, + "pk": false + }, + { + "name": "word_freq_your", + "type": "TEXT", + "notnull": false, + "pk": false + }, + { + "name": "word_freq_font", + "type": "TEXT", + "notnull": false, + "pk": false + }, + { + "name": "word_freq_000", + "type": "TEXT", + "notnull": false, + "pk": false + }, + { + "name": "word_freq_money", + "type": "TEXT", + "notnull": false, + "pk": false + }, + { + "name": "word_freq_hp", + "type": "TEXT", + "notnull": false, + "pk": false + }, + { + "name": "word_freq_hpl", + "type": "TEXT", + "notnull": false, + "pk": false + }, + { + "name": "word_freq_george", + "type": "TEXT", + "notnull": false, + "pk": false + }, + { + "name": "word_freq_650", + "type": "TEXT", + "notnull": false, + "pk": false + }, + { + "name": "word_freq_lab", + "type": "TEXT", + "notnull": false, + "pk": false + }, + { + "name": "word_freq_labs", + "type": "TEXT", + "notnull": false, + "pk": false + }, + { + "name": "word_freq_telnet", + "type": "TEXT", + "notnull": false, + "pk": false + }, + { + "name": "word_freq_857", + "type": "TEXT", + "notnull": false, + "pk": false + }, + { + "name": "word_freq_data", + "type": "TEXT", + "notnull": false, + "pk": false + }, + { + "name": "word_freq_415", + "type": "TEXT", + "notnull": false, + "pk": false + }, + { + "name": "word_freq_85", + "type": "TEXT", + "notnull": false, + "pk": false + }, + { + "name": "word_freq_technology", + "type": "TEXT", + "notnull": false, + "pk": false + }, + { + "name": "word_freq_1999", + "type": "TEXT", + "notnull": false, + "pk": false + }, + { + "name": "word_freq_parts", + "type": "TEXT", + "notnull": false, + "pk": false + }, + { + "name": "word_freq_pm", + "type": "TEXT", + "notnull": false, + "pk": false + }, + { + "name": "word_freq_direct", + "type": "TEXT", + "notnull": false, + "pk": false + }, + { + "name": "word_freq_cs", + "type": "TEXT", + "notnull": false, + "pk": false + }, + { + "name": "word_freq_meeting", + "type": "TEXT", + "notnull": false, + "pk": false + }, + { + "name": "word_freq_original", + "type": "TEXT", + "notnull": false, + "pk": false + }, + { + "name": "word_freq_project", + "type": "TEXT", + "notnull": false, + "pk": false + }, + { + "name": "word_freq_re", + "type": "TEXT", + "notnull": false, + "pk": false + }, + { + "name": "word_freq_edu", + "type": "TEXT", + "notnull": false, + "pk": false + }, + { + "name": "word_freq_table", + "type": "TEXT", + "notnull": false, + "pk": false + }, + { + "name": "word_freq_conference", + "type": "TEXT", + "notnull": false, + "pk": false + }, + { + "name": "char_freq_%3B", + "type": "TEXT", + "notnull": false, + "pk": false + }, + { + "name": "char_freq_%28", + "type": "TEXT", + "notnull": false, + "pk": false + }, + { + "name": "char_freq_%5B", + "type": "TEXT", + "notnull": false, + "pk": false + }, + { + "name": "char_freq_%21", + "type": "TEXT", + "notnull": false, + "pk": false + }, + { + "name": "char_freq_%24", + "type": "TEXT", + "notnull": false, + "pk": false + }, + { + "name": "char_freq_%23", + "type": "TEXT", + "notnull": false, + "pk": false + }, + { + "name": "capital_run_length_average", + "type": "TEXT", + "notnull": false, + "pk": false + }, + { + "name": "capital_run_length_longest", + "type": "TEXT", + "notnull": false, + "pk": false + }, + { + "name": "capital_run_length_total", + "type": "TEXT", + "notnull": false, + "pk": false + }, + { + "name": "class", + "type": "TEXT", + "notnull": false, + "pk": false + } + ], + "sample_rows": [ + { + "word_freq_make": "0", + "word_freq_address": "0.64", + "word_freq_all": "0.64", + "word_freq_3d": "0", + "word_freq_our": "0.32", + "word_freq_over": "0", + "word_freq_remove": "0", + "word_freq_internet": "0", + "word_freq_order": "0", + "word_freq_mail": "0", + "word_freq_receive": "0", + "word_freq_will": "0.64", + "word_freq_people": "0", + "word_freq_report": "0", + "word_freq_addresses": "0", + "word_freq_free": "0.32", + "word_freq_business": "0", + "word_freq_email": "1.29", + "word_freq_you": "1.93", + "word_freq_credit": "0", + "word_freq_your": "0.96", + "word_freq_font": "0", + "word_freq_000": "0", + "word_freq_money": "0", + "word_freq_hp": "0", + "word_freq_hpl": "0", + "word_freq_george": "0", + "word_freq_650": "0", + "word_freq_lab": "0", + "word_freq_labs": "0", + "word_freq_telnet": "0", + "word_freq_857": "0", + "word_freq_data": "0", + "word_freq_415": "0", + "word_freq_85": "0", + "word_freq_technology": "0", + "word_freq_1999": "0", + "word_freq_parts": "0", + "word_freq_pm": "0", + "word_freq_direct": "0", + "word_freq_cs": "0", + "word_freq_meeting": "0", + "word_freq_original": "0", + "word_freq_project": "0", + "word_freq_re": "0", + "word_freq_edu": "0", + "word_freq_table": "0", + "word_freq_conference": "0", + "char_freq_%3B": "0", + "char_freq_%28": "0", + "char_freq_%5B": "0", + "char_freq_%21": "0.778", + "char_freq_%24": "0", + "char_freq_%23": "0", + "capital_run_length_average": "3.756", + "capital_run_length_longest": "61", + "capital_run_length_total": "278", + "class": "1" + }, + { + "word_freq_make": "0.21", + "word_freq_address": "0.28", + "word_freq_all": "0.5", + "word_freq_3d": "0", + "word_freq_our": "0.14", + "word_freq_over": "0.28", + "word_freq_remove": "0.21", + "word_freq_internet": "0.07", + "word_freq_order": "0", + "word_freq_mail": "0.94", + "word_freq_receive": "0.21", + "word_freq_will": "0.79", + "word_freq_people": "0.65", + "word_freq_report": "0.21", + "word_freq_addresses": "0.14", + "word_freq_free": "0.14", + "word_freq_business": "0.07", + "word_freq_email": "0.28", + "word_freq_you": "3.47", + "word_freq_credit": "0", + "word_freq_your": "1.59", + "word_freq_font": "0", + "word_freq_000": "0.43", + "word_freq_money": "0.43", + "word_freq_hp": "0", + "word_freq_hpl": "0", + "word_freq_george": "0", + "word_freq_650": "0", + "word_freq_lab": "0", + "word_freq_labs": "0", + "word_freq_telnet": "0", + "word_freq_857": "0", + "word_freq_data": "0", + "word_freq_415": "0", + "word_freq_85": "0", + "word_freq_technology": "0", + "word_freq_1999": "0.07", + "word_freq_parts": "0", + "word_freq_pm": "0", + "word_freq_direct": "0", + "word_freq_cs": "0", + "word_freq_meeting": "0", + "word_freq_original": "0", + "word_freq_project": "0", + "word_freq_re": "0", + "word_freq_edu": "0", + "word_freq_table": "0", + "word_freq_conference": "0", + "char_freq_%3B": "0", + "char_freq_%28": "0.132", + "char_freq_%5B": "0", + "char_freq_%21": "0.372", + "char_freq_%24": "0.18", + "char_freq_%23": "0.048", + "capital_run_length_average": "5.114", + "capital_run_length_longest": "101", + "capital_run_length_total": "1028", + "class": "1" + }, + { + "word_freq_make": "0.06", + "word_freq_address": "0", + "word_freq_all": "0.71", + "word_freq_3d": "0", + "word_freq_our": "1.23", + "word_freq_over": "0.19", + "word_freq_remove": "0.19", + "word_freq_internet": "0.12", + "word_freq_order": "0.64", + "word_freq_mail": "0.25", + "word_freq_receive": "0.38", + "word_freq_will": "0.45", + "word_freq_people": "0.12", + "word_freq_report": "0", + "word_freq_addresses": "1.75", + "word_freq_free": "0.06", + "word_freq_business": "0.06", + "word_freq_email": "1.03", + "word_freq_you": "1.36", + "word_freq_credit": "0.32", + "word_freq_your": "0.51", + "word_freq_font": "0", + "word_freq_000": "1.16", + "word_freq_money": "0.06", + "word_freq_hp": "0", + "word_freq_hpl": "0", + "word_freq_george": "0", + "word_freq_650": "0", + "word_freq_lab": "0", + "word_freq_labs": "0", + "word_freq_telnet": "0", + "word_freq_857": "0", + "word_freq_data": "0", + "word_freq_415": "0", + "word_freq_85": "0", + "word_freq_technology": "0", + "word_freq_1999": "0", + "word_freq_parts": "0", + "word_freq_pm": "0", + "word_freq_direct": "0.06", + "word_freq_cs": "0", + "word_freq_meeting": "0", + "word_freq_original": "0.12", + "word_freq_project": "0", + "word_freq_re": "0.06", + "word_freq_edu": "0.06", + "word_freq_table": "0", + "word_freq_conference": "0", + "char_freq_%3B": "0.01", + "char_freq_%28": "0.143", + "char_freq_%5B": "0", + "char_freq_%21": "0.276", + "char_freq_%24": "0.184", + "char_freq_%23": "0.01", + "capital_run_length_average": "9.821", + "capital_run_length_longest": "485", + "capital_run_length_total": "2259", + "class": "1" + }, + { + "word_freq_make": "0", + "word_freq_address": "0", + "word_freq_all": "0", + "word_freq_3d": "0", + "word_freq_our": "0.63", + "word_freq_over": "0", + "word_freq_remove": "0.31", + "word_freq_internet": "0.63", + "word_freq_order": "0.31", + "word_freq_mail": "0.63", + "word_freq_receive": "0.31", + "word_freq_will": "0.31", + "word_freq_people": "0.31", + "word_freq_report": "0", + "word_freq_addresses": "0", + "word_freq_free": "0.31", + "word_freq_business": "0", + "word_freq_email": "0", + "word_freq_you": "3.18", + "word_freq_credit": "0", + "word_freq_your": "0.31", + "word_freq_font": "0", + "word_freq_000": "0", + "word_freq_money": "0", + "word_freq_hp": "0", + "word_freq_hpl": "0", + "word_freq_george": "0", + "word_freq_650": "0", + "word_freq_lab": "0", + "word_freq_labs": "0", + "word_freq_telnet": "0", + "word_freq_857": "0", + "word_freq_data": "0", + "word_freq_415": "0", + "word_freq_85": "0", + "word_freq_technology": "0", + "word_freq_1999": "0", + "word_freq_parts": "0", + "word_freq_pm": "0", + "word_freq_direct": "0", + "word_freq_cs": "0", + "word_freq_meeting": "0", + "word_freq_original": "0", + "word_freq_project": "0", + "word_freq_re": "0", + "word_freq_edu": "0", + "word_freq_table": "0", + "word_freq_conference": "0", + "char_freq_%3B": "0", + "char_freq_%28": "0.137", + "char_freq_%5B": "0", + "char_freq_%21": "0.137", + "char_freq_%24": "0", + "char_freq_%23": "0", + "capital_run_length_average": "3.537", + "capital_run_length_longest": "40", + "capital_run_length_total": "191", + "class": "1" + }, + { + "word_freq_make": "0", + "word_freq_address": "0", + "word_freq_all": "0", + "word_freq_3d": "0", + "word_freq_our": "0.63", + "word_freq_over": "0", + "word_freq_remove": "0.31", + "word_freq_internet": "0.63", + "word_freq_order": "0.31", + "word_freq_mail": "0.63", + "word_freq_receive": "0.31", + "word_freq_will": "0.31", + "word_freq_people": "0.31", + "word_freq_report": "0", + "word_freq_addresses": "0", + "word_freq_free": "0.31", + "word_freq_business": "0", + "word_freq_email": "0", + "word_freq_you": "3.18", + "word_freq_credit": "0", + "word_freq_your": "0.31", + "word_freq_font": "0", + "word_freq_000": "0", + "word_freq_money": "0", + "word_freq_hp": "0", + "word_freq_hpl": "0", + "word_freq_george": "0", + "word_freq_650": "0", + "word_freq_lab": "0", + "word_freq_labs": "0", + "word_freq_telnet": "0", + "word_freq_857": "0", + "word_freq_data": "0", + "word_freq_415": "0", + "word_freq_85": "0", + "word_freq_technology": "0", + "word_freq_1999": "0", + "word_freq_parts": "0", + "word_freq_pm": "0", + "word_freq_direct": "0", + "word_freq_cs": "0", + "word_freq_meeting": "0", + "word_freq_original": "0", + "word_freq_project": "0", + "word_freq_re": "0", + "word_freq_edu": "0", + "word_freq_table": "0", + "word_freq_conference": "0", + "char_freq_%3B": "0", + "char_freq_%28": "0.135", + "char_freq_%5B": "0", + "char_freq_%21": "0.135", + "char_freq_%24": "0", + "char_freq_%23": "0", + "capital_run_length_average": "3.537", + "capital_run_length_longest": "40", + "capital_run_length_total": "191", + "class": "1" + } + ] +} + +Shortlisted templates: +[ + { + "template_id": "tpl_m4_group_condition_rate", + "template_name": "Grouped Condition Rate", + "primary_family": "conditional_dependency_structure", + "portability": "yes", + "sql_skeleton": "SELECT {group_col},\n AVG(CASE WHEN {condition_col} = {condition_value} THEN 1 ELSE 0 END) AS condition_rate\nFROM {table}\nGROUP BY {group_col}\nORDER BY condition_rate DESC;", + "required_roles": [ + "group_col", + "condition_col" + ] + } +] + +Problem instance: +{ + "dataset_id": "n1", + "question": "Use template Grouped Condition Rate to probe dependency_strength_similarity with semantic role focused_target_view. Focus on group_col=class, condition_col=class.", + "planned_template_id": "tpl_m4_group_condition_rate", + "bindings": { + "group_col": "class", + "condition_col": "class", + "condition_value": "1", + "positive_value": "0", + "negative_value": "1", + "top_k": 16, + "top_n": 4, + "num_tiles": 10, + "percentile_value": 0.9, + "z_threshold": 2.0, + "fraction_threshold": 0.05, + "baseline_multiplier": 1.75, + "baseline_fraction": 0.1, + "min_group_size": 5, + "min_support": 4, + "measure_threshold": 0.0, + "time_grain": "month", + "lookback_rows": 3, + "current_period_start": "'2024-01-01'", + "current_period_end": "'2024-04-01'", + "previous_period_start": "'2023-10-01'", + "previous_period_end": "'2024-01-01'", + "drift_ratio_threshold": 0.8 + }, + "can_vary": [], + "must_fix": [], + "runtime_sql_skeleton": "SELECT {group_col},\n AVG(CASE WHEN {condition_col} = {condition_value} THEN 1 ELSE 0 END) AS condition_rate\nFROM {table}\nGROUP BY {group_col}\nORDER BY condition_rate DESC;" +} + +Repair context: +{} diff --git a/Query/sql/v2/runs/v2_cli_20260502_081223_d/n1/artifacts/v2q_n1_4f48bf7d451fc90d/cli/sql_response_attempt_1.raw.txt b/Query/sql/v2/runs/v2_cli_20260502_081223_d/n1/artifacts/v2q_n1_4f48bf7d451fc90d/cli/sql_response_attempt_1.raw.txt new file mode 100644 index 0000000000000000000000000000000000000000..21c59ef6085749aef2557c089257d9fd81ebd3af --- /dev/null +++ b/Query/sql/v2/runs/v2_cli_20260502_081223_d/n1/artifacts/v2q_n1_4f48bf7d451fc90d/cli/sql_response_attempt_1.raw.txt @@ -0,0 +1,4 @@ +{"type":"thread.started","thread_id":"019e40f5-6079-7c50-af07-90bf6669124e"} +{"type":"turn.started"} +{"type":"item.completed","item":{"id":"item_0","type":"agent_message","text":"{\"sql\":\"-- template_id: tpl_m4_group_condition_rate\\nSELECT \\\"class\\\",\\n AVG(CASE WHEN \\\"class\\\" = '1' THEN 1 ELSE 0 END) AS condition_rate\\nFROM \\\"n1\\\"\\nGROUP BY \\\"class\\\"\\nORDER BY condition_rate DESC;\",\"notes\":\"Uses the planned grouped condition rate template with both group_col and condition_col bound to \\\"class\\\", treating the positive condition value as '1'.\"}"}} +{"type":"turn.completed","usage":{"input_tokens":20372,"cached_input_tokens":19840,"output_tokens":285,"reasoning_output_tokens":185}} diff --git a/Query/sql/v2/runs/v2_cli_20260502_081223_d/n1/artifacts/v2q_n1_4f48bf7d451fc90d/cli/sql_response_attempt_1.txt b/Query/sql/v2/runs/v2_cli_20260502_081223_d/n1/artifacts/v2q_n1_4f48bf7d451fc90d/cli/sql_response_attempt_1.txt new file mode 100644 index 0000000000000000000000000000000000000000..a17b440115dfac991d0b4cdda9a8dfda2ea747a9 --- /dev/null +++ b/Query/sql/v2/runs/v2_cli_20260502_081223_d/n1/artifacts/v2q_n1_4f48bf7d451fc90d/cli/sql_response_attempt_1.txt @@ -0,0 +1 @@ +{"sql":"-- template_id: tpl_m4_group_condition_rate\nSELECT \"class\",\n AVG(CASE WHEN \"class\" = '1' THEN 1 ELSE 0 END) AS condition_rate\nFROM \"n1\"\nGROUP BY \"class\"\nORDER BY condition_rate DESC;","notes":"Uses the planned grouped condition rate template with both group_col and condition_col bound to \"class\", treating the positive condition value as '1'."} \ No newline at end of file diff --git a/Query/sql/v2/runs/v2_cli_20260502_081223_d/n1/artifacts/v2q_n1_4f48bf7d451fc90d/cli/sql_stderr_attempt_1.txt b/Query/sql/v2/runs/v2_cli_20260502_081223_d/n1/artifacts/v2q_n1_4f48bf7d451fc90d/cli/sql_stderr_attempt_1.txt new file mode 100644 index 0000000000000000000000000000000000000000..e69de29bb2d1d6434b8b29ae775ad8c2e48c5391 diff --git a/Query/sql/v2/runs/v2_cli_20260502_081223_d/n1/artifacts/v2q_n1_4fe6330800dd43f4/cli/conversation.jsonl b/Query/sql/v2/runs/v2_cli_20260502_081223_d/n1/artifacts/v2q_n1_4fe6330800dd43f4/cli/conversation.jsonl new file mode 100644 index 0000000000000000000000000000000000000000..d6d3c1a7b432992e5930af428fdf8b6f61d6e8c0 --- /dev/null +++ b/Query/sql/v2/runs/v2_cli_20260502_081223_d/n1/artifacts/v2q_n1_4fe6330800dd43f4/cli/conversation.jsonl @@ -0,0 +1,2 @@ +{"attempt": 1, "phase": "sql_generation", "role": "user", "content_path": "cli/sql_prompt_attempt_1.txt", "metrics": {"chars": 29360, "bytes_utf8": 29360, "lines": 792, "estimated_tokens": null}} +{"attempt": 1, "phase": "sql_generation", "role": "assistant", "content_path": "cli/sql_response_attempt_1.txt", "raw_content_path": "cli/sql_response_attempt_1.raw.txt", "stderr_path": "cli/sql_stderr_attempt_1.txt", "metrics": {"chars": 375, "bytes_utf8": 375, "lines": 1, "estimated_tokens": null}, "usage": {"input_tokens": 20315, "cached_input_tokens": 12032, "output_tokens": 349, "reasoning_output_tokens": 248}} diff --git a/Query/sql/v2/runs/v2_cli_20260502_081223_d/n1/artifacts/v2q_n1_4fe6330800dd43f4/cli/session_summary.json b/Query/sql/v2/runs/v2_cli_20260502_081223_d/n1/artifacts/v2q_n1_4fe6330800dd43f4/cli/session_summary.json new file mode 100644 index 0000000000000000000000000000000000000000..003871ec166a96b0d9407d34c56bc1190d417882 --- /dev/null +++ b/Query/sql/v2/runs/v2_cli_20260502_081223_d/n1/artifacts/v2q_n1_4fe6330800dd43f4/cli/session_summary.json @@ -0,0 +1,25 @@ +{ + "engine": "v2-cli:codex", + "command": "codex exec --skip-git-repo-check --disable plugins --sandbox read-only --cd \"/data/jialinzhang/SQLagent\" -m gpt-5.4 --json -", + "ai_cli_calls": 1, + "usage_summary": { + "dataset_id": "n1", + "model": "v2-cli:codex", + "run_id": "v2q_n1_4fe6330800dd43f4", + "api_calls": 0, + "input_tokens": 20315, + "cached_input_tokens": 12032, + "output_tokens": 349, + "total_tokens": 20664, + "cost_usd": 0.0, + "ai_cli_calls": 1, + "estimated_input_tokens": 0, + "estimated_output_tokens": 0, + "estimated_total_tokens": 0, + "usage_source": "ai_cli_json_usage", + "cli_elapsed_ms_total": 12083.22, + "sql_execution_elapsed_ms_total": 4.45, + "conversation_log_path": "/data/jialinzhang/TabQueryBench/sql_workloads/v2_current/runs_and_launches/runs/v2_cli_20260502_081223_d/n1/artifacts/v2q_n1_4fe6330800dd43f4/cli/conversation.jsonl", + "note": "Executed through a local AI CLI with structured usage metadata." + } +} \ No newline at end of file diff --git a/Query/sql/v2/runs/v2_cli_20260502_081223_d/n1/artifacts/v2q_n1_4fe6330800dd43f4/cli/sql_attempt_1.metadata.json b/Query/sql/v2/runs/v2_cli_20260502_081223_d/n1/artifacts/v2q_n1_4fe6330800dd43f4/cli/sql_attempt_1.metadata.json new file mode 100644 index 0000000000000000000000000000000000000000..7592510d102271b392097ac634d4922c1c728fe7 --- /dev/null +++ b/Query/sql/v2/runs/v2_cli_20260502_081223_d/n1/artifacts/v2q_n1_4fe6330800dd43f4/cli/sql_attempt_1.metadata.json @@ -0,0 +1,45 @@ +{ + "attempt": 1, + "phase": "sql_generation", + "command": "codex exec --skip-git-repo-check --disable plugins --sandbox read-only --cd \"/data/jialinzhang/SQLagent\" -m gpt-5.4 --json -", + "started_at": "2026-05-19T15:30:46.323427+00:00", + "ended_at": "2026-05-19T15:30:58.406675+00:00", + "elapsed_ms": 12083.22, + "prompt_metrics": { + "chars": 29360, + "bytes_utf8": 29360, + "lines": 792, + "estimated_tokens": null + }, + "stdout_metrics": { + "chars": 737, + "bytes_utf8": 737, + "lines": 4, + "estimated_tokens": null + }, + "stderr_metrics": { + "chars": 0, + "bytes_utf8": 0, + "lines": 0, + "estimated_tokens": null + }, + "parsed_output": { + "format": "jsonl_events", + "text_metrics": { + "chars": 375, + "bytes_utf8": 375, + "lines": 1, + "estimated_tokens": null + }, + "usage": { + "input_tokens": 20315, + "cached_input_tokens": 12032, + "output_tokens": 349, + "reasoning_output_tokens": 248 + } + }, + "prompt_path": "cli/sql_prompt_attempt_1.txt", + "response_path": "cli/sql_response_attempt_1.txt", + "raw_response_path": "cli/sql_response_attempt_1.raw.txt", + "stderr_path": "cli/sql_stderr_attempt_1.txt" +} \ No newline at end of file diff --git a/Query/sql/v2/runs/v2_cli_20260502_081223_d/n1/artifacts/v2q_n1_4fe6330800dd43f4/cli/sql_prompt_attempt_1.txt b/Query/sql/v2/runs/v2_cli_20260502_081223_d/n1/artifacts/v2q_n1_4fe6330800dd43f4/cli/sql_prompt_attempt_1.txt new file mode 100644 index 0000000000000000000000000000000000000000..8b909e492871186a14c920b6459a13460b456440 --- /dev/null +++ b/Query/sql/v2/runs/v2_cli_20260502_081223_d/n1/artifacts/v2q_n1_4fe6330800dd43f4/cli/sql_prompt_attempt_1.txt @@ -0,0 +1,792 @@ +You are generating one SQLite SELECT query for a single-table SQL QA task. +Return strict JSON only, with this schema: {"sql": "...", "notes": "..."}. +Rules: +- Use only the provided table and columns. +- Do not write INSERT, UPDATE, DELETE, DROP, ALTER, CREATE, PRAGMA, ATTACH, DETACH, or VACUUM. +- Prefer the planned template and bound roles when provided. +- Add a leading SQL comment exactly like: -- template_id: . +- Generate SQLite-compatible SQL. SQLite does not support PERCENTILE_CONT or STDDEV. +- Quote identifiers with double quotes. +- Return no markdown and no extra prose. + +Dataset context: +Dataset context for SQL QA: +- dataset_id: n1 +- dataset_name: Spambase +- table_name: n1 +- table_layout: single-table dataset (do not assume joins). +- row_semantics: One row is one email represented by engineered token/character frequency and capitalization-run features. +- task_type: classification +- target_column: class +- main_row_count: 4601 +- important_fields: +- word_freq_make: role=feature, type=numeric_sparse_frequency. tags=['condition_candidate', 'measure', 'rare_pattern_candidate'] desc=Frequency feature for token 'make' in an email message. +- word_freq_address: role=feature, type=numeric_sparse_frequency. tags=['condition_candidate', 'measure', 'rare_pattern_candidate'] desc=Frequency feature for token 'address' in an email message. +- word_freq_all: role=feature, type=numeric_sparse_frequency. tags=['condition_candidate', 'measure', 'rare_pattern_candidate'] desc=Frequency feature for token 'all' in an email message. +- word_freq_3d: role=feature, type=numeric_sparse_frequency. tags=['condition_candidate', 'measure', 'rare_pattern_candidate'] desc=Frequency feature for token '3d' in an email message. +- word_freq_our: role=feature, type=numeric_sparse_frequency. tags=['condition_candidate', 'measure', 'rare_pattern_candidate'] desc=Frequency feature for token 'our' in an email message. +- word_freq_over: role=feature, type=numeric_sparse_frequency. tags=['condition_candidate', 'measure', 'rare_pattern_candidate'] desc=Frequency feature for token 'over' in an email message. +- word_freq_remove: role=feature, type=numeric_sparse_frequency. tags=['condition_candidate', 'measure', 'rare_pattern_candidate'] desc=Frequency feature for token 'remove' in an email message. +- word_freq_internet: role=feature, type=numeric_sparse_frequency. tags=['condition_candidate', 'measure', 'rare_pattern_candidate'] desc=Frequency feature for token 'internet' in an email message. +- word_freq_order: role=feature, type=numeric_sparse_frequency. tags=['condition_candidate', 'measure', 'rare_pattern_candidate'] desc=Frequency feature for token 'order' in an email message. +- word_freq_mail: role=feature, type=numeric_sparse_frequency. tags=['condition_candidate', 'measure', 'rare_pattern_candidate'] desc=Frequency feature for token 'mail' in an email message. +- word_freq_receive: role=feature, type=numeric_sparse_frequency. tags=['condition_candidate', 'measure', 'rare_pattern_candidate'] desc=Frequency feature for token 'receive' in an email message. +- word_freq_will: role=feature, type=numeric_sparse_frequency. tags=['condition_candidate', 'measure', 'rare_pattern_candidate'] desc=Frequency feature for token 'will' in an email message. +- word_freq_people: role=feature, type=numeric_sparse_frequency. tags=['condition_candidate', 'measure', 'rare_pattern_candidate'] desc=Frequency feature for token 'people' in an email message. +- word_freq_report: role=feature, type=numeric_sparse_frequency. tags=['condition_candidate', 'measure', 'rare_pattern_candidate'] desc=Frequency feature for token 'report' in an email message. +- word_freq_addresses: role=feature, type=numeric_sparse_frequency. tags=['condition_candidate', 'measure', 'rare_pattern_candidate'] desc=Frequency feature for token 'addresses' in an email message. +- word_freq_free: role=feature, type=numeric_sparse_frequency. tags=['condition_candidate', 'measure', 'rare_pattern_candidate'] desc=Frequency feature for token 'free' in an email message. +- word_freq_business: role=feature, type=numeric_sparse_frequency. tags=['condition_candidate', 'measure', 'rare_pattern_candidate'] desc=Frequency feature for token 'business' in an email message. +- word_freq_email: role=feature, type=numeric_sparse_frequency. tags=['condition_candidate', 'measure', 'rare_pattern_candidate'] desc=Frequency feature for token 'email' in an email message. +- word_freq_you: role=feature, type=numeric_sparse_frequency. tags=['condition_candidate', 'measure', 'rare_pattern_candidate'] desc=Frequency feature for token 'you' in an email message. +- word_freq_credit: role=feature, type=numeric_sparse_frequency. tags=['condition_candidate', 'measure', 'rare_pattern_candidate'] desc=Frequency feature for token 'credit' in an email message. +- word_freq_your: role=feature, type=numeric_sparse_frequency. tags=['condition_candidate', 'measure', 'rare_pattern_candidate'] desc=Frequency feature for token 'your' in an email message. +- word_freq_font: role=feature, type=numeric_sparse_frequency. tags=['condition_candidate', 'measure', 'rare_pattern_candidate'] desc=Frequency feature for token 'font' in an email message. +- word_freq_000: role=feature, type=numeric_sparse_frequency. tags=['condition_candidate', 'measure', 'rare_pattern_candidate'] desc=Frequency feature for token '000' in an email message. +- word_freq_money: role=feature, type=numeric_sparse_frequency. tags=['condition_candidate', 'measure', 'rare_pattern_candidate'] desc=Frequency feature for token 'money' in an email message. +- word_freq_hp: role=feature, type=numeric_sparse_frequency. tags=['condition_candidate', 'measure', 'rare_pattern_candidate'] desc=Frequency feature for token 'hp' in an email message. +- word_freq_hpl: role=feature, type=numeric_sparse_frequency. tags=['condition_candidate', 'measure', 'rare_pattern_candidate'] desc=Frequency feature for token 'hpl' in an email message. +- word_freq_george: role=feature, type=numeric_sparse_frequency. tags=['condition_candidate', 'measure', 'rare_pattern_candidate'] desc=Frequency feature for token 'george' in an email message. +- word_freq_650: role=feature, type=numeric_sparse_frequency. tags=['condition_candidate', 'measure', 'rare_pattern_candidate'] desc=Frequency feature for token '650' in an email message. +- word_freq_lab: role=feature, type=numeric_sparse_frequency. tags=['condition_candidate', 'measure', 'rare_pattern_candidate'] desc=Frequency feature for token 'lab' in an email message. +- word_freq_labs: role=feature, type=numeric_sparse_frequency. tags=['condition_candidate', 'measure', 'rare_pattern_candidate'] desc=Frequency feature for token 'labs' in an email message. +- word_freq_telnet: role=feature, type=numeric_sparse_frequency. tags=['condition_candidate', 'measure', 'rare_pattern_candidate'] desc=Frequency feature for token 'telnet' in an email message. +- word_freq_857: role=feature, type=numeric_sparse_frequency. tags=['condition_candidate', 'measure', 'rare_pattern_candidate'] desc=Frequency feature for token '857' in an email message. +- word_freq_data: role=feature, type=numeric_sparse_frequency. tags=['condition_candidate', 'measure', 'rare_pattern_candidate'] desc=Frequency feature for token 'data' in an email message. +- word_freq_415: role=feature, type=numeric_sparse_frequency. tags=['condition_candidate', 'measure', 'rare_pattern_candidate'] desc=Frequency feature for token '415' in an email message. +- word_freq_85: role=feature, type=numeric_sparse_frequency. tags=['condition_candidate', 'measure', 'rare_pattern_candidate'] desc=Frequency feature for token '85' in an email message. +- word_freq_technology: role=feature, type=numeric_sparse_frequency. tags=['condition_candidate', 'measure', 'rare_pattern_candidate'] desc=Frequency feature for token 'technology' in an email message. +- word_freq_1999: role=feature, type=numeric_sparse_frequency. tags=['condition_candidate', 'measure', 'rare_pattern_candidate'] desc=Frequency feature for token '1999' in an email message. +- word_freq_parts: role=feature, type=numeric_sparse_frequency. tags=['condition_candidate', 'measure', 'rare_pattern_candidate'] desc=Frequency feature for token 'parts' in an email message. +- word_freq_pm: role=feature, type=numeric_sparse_frequency. tags=['condition_candidate', 'measure', 'rare_pattern_candidate'] desc=Frequency feature for token 'pm' in an email message. +- word_freq_direct: role=feature, type=numeric_sparse_frequency. tags=['condition_candidate', 'measure', 'rare_pattern_candidate'] desc=Frequency feature for token 'direct' in an email message. +- word_freq_cs: role=feature, type=numeric_sparse_frequency. tags=['condition_candidate', 'measure', 'rare_pattern_candidate'] desc=Frequency feature for token 'cs' in an email message. +- word_freq_meeting: role=feature, type=numeric_sparse_frequency. tags=['condition_candidate', 'measure', 'rare_pattern_candidate'] desc=Frequency feature for token 'meeting' in an email message. +- word_freq_original: role=feature, type=numeric_sparse_frequency. tags=['condition_candidate', 'measure', 'rare_pattern_candidate'] desc=Frequency feature for token 'original' in an email message. +- word_freq_project: role=feature, type=numeric_sparse_frequency. tags=['condition_candidate', 'measure', 'rare_pattern_candidate'] desc=Frequency feature for token 'project' in an email message. +- word_freq_re: role=feature, type=numeric_sparse_frequency. tags=['condition_candidate', 'measure', 'rare_pattern_candidate'] desc=Frequency feature for token 're' in an email message. +- word_freq_edu: role=feature, type=numeric_sparse_frequency. tags=['condition_candidate', 'measure', 'rare_pattern_candidate'] desc=Frequency feature for token 'edu' in an email message. +- word_freq_table: role=feature, type=numeric_sparse_frequency. tags=['condition_candidate', 'measure', 'rare_pattern_candidate'] desc=Frequency feature for token 'table' in an email message. +- word_freq_conference: role=feature, type=numeric_sparse_frequency. tags=['condition_candidate', 'measure', 'rare_pattern_candidate'] desc=Frequency feature for token 'conference' in an email message. +- char_freq_%3B: role=feature, type=numeric_sparse_frequency. tags=['condition_candidate', 'measure', 'rare_pattern_candidate'] desc=Character frequency feature for symbol ';'. +- char_freq_%28: role=feature, type=numeric_sparse_frequency. tags=['condition_candidate', 'measure', 'rare_pattern_candidate'] desc=Character frequency feature for symbol '('. +- char_freq_%5B: role=feature, type=numeric_sparse_frequency. tags=['condition_candidate', 'measure', 'rare_pattern_candidate'] desc=Character frequency feature for symbol '['. +- char_freq_%21: role=feature, type=numeric_sparse_frequency. tags=['condition_candidate', 'measure', 'rare_pattern_candidate'] desc=Character frequency feature for symbol '!'. +- char_freq_%24: role=feature, type=numeric_sparse_frequency. tags=['condition_candidate', 'measure', 'rare_pattern_candidate'] desc=Character frequency feature for symbol '$'. +- char_freq_%23: role=feature, type=numeric_sparse_frequency. tags=['condition_candidate', 'measure', 'rare_pattern_candidate'] desc=Character frequency feature for symbol '#'. +- capital_run_length_average: role=feature, type=numeric_heavy_tail. tags=['condition_candidate', 'measure', 'rare_pattern_candidate'] desc=Average length of uninterrupted capital-letter runs. +- capital_run_length_longest: role=feature, type=numeric_heavy_tail. tags=['condition_candidate', 'measure', 'rare_pattern_candidate'] desc=Longest uninterrupted capital-letter run. +- capital_run_length_total: role=feature, type=numeric_heavy_tail. tags=['condition_candidate', 'measure', 'rare_pattern_candidate'] desc=Total number of capital letters in runs. +- class: role=target, type=binary_target. tags=['target_candidate'] desc=Binary spam label (1=spam, 0=non-spam). +- useful_field_combinations: [['word_freq_free', 'word_freq_you', 'class'], ['char_freq_%21', 'capital_run_length_longest', 'class'], ['word_freq_remove', 'word_freq_money', 'class']] +- fields_requiring_caution: ['capital_run_length_average', 'capital_run_length_longest', 'capital_run_length_total'] +- source_url: https://www.openml.org/d/44 + +SQLite schema snapshot: +{ + "table_name": "n1", + "quoted_table_name": "\"n1\"", + "row_count": 4601, + "columns": [ + { + "name": "word_freq_make", + "type": "TEXT", + "notnull": false, + "pk": false + }, + { + "name": "word_freq_address", + "type": "TEXT", + "notnull": false, + "pk": false + }, + { + "name": "word_freq_all", + "type": "TEXT", + "notnull": false, + "pk": false + }, + { + "name": "word_freq_3d", + "type": "TEXT", + "notnull": false, + "pk": false + }, + { + "name": "word_freq_our", + "type": "TEXT", + "notnull": false, + "pk": false + }, + { + "name": "word_freq_over", + "type": "TEXT", + "notnull": false, + "pk": false + }, + { + "name": "word_freq_remove", + "type": "TEXT", + "notnull": false, + "pk": false + }, + { + "name": "word_freq_internet", + "type": "TEXT", + "notnull": false, + "pk": false + }, + { + "name": "word_freq_order", + "type": "TEXT", + "notnull": false, + "pk": false + }, + { + "name": "word_freq_mail", + "type": "TEXT", + "notnull": false, + "pk": false + }, + { + "name": "word_freq_receive", + "type": "TEXT", + "notnull": false, + "pk": false + }, + { + "name": "word_freq_will", + "type": "TEXT", + "notnull": false, + "pk": false + }, + { + "name": "word_freq_people", + "type": "TEXT", + "notnull": false, + "pk": false + }, + { + "name": "word_freq_report", + "type": "TEXT", + "notnull": false, + "pk": false + }, + { + "name": "word_freq_addresses", + "type": "TEXT", + "notnull": false, + "pk": false + }, + { + "name": "word_freq_free", + "type": "TEXT", + "notnull": false, + "pk": false + }, + { + "name": "word_freq_business", + "type": "TEXT", + "notnull": false, + "pk": false + }, + { + "name": "word_freq_email", + "type": "TEXT", + "notnull": false, + "pk": false + }, + { + "name": "word_freq_you", + "type": "TEXT", + "notnull": false, + "pk": false + }, + { + "name": "word_freq_credit", + "type": "TEXT", + "notnull": false, + "pk": false + }, + { + "name": "word_freq_your", + "type": "TEXT", + "notnull": false, + "pk": false + }, + { + "name": "word_freq_font", + "type": "TEXT", + "notnull": false, + "pk": false + }, + { + "name": "word_freq_000", + "type": "TEXT", + "notnull": false, + "pk": false + }, + { + "name": "word_freq_money", + "type": "TEXT", + "notnull": false, + "pk": false + }, + { + "name": "word_freq_hp", + "type": "TEXT", + "notnull": false, + "pk": false + }, + { + "name": "word_freq_hpl", + "type": "TEXT", + "notnull": false, + "pk": false + }, + { + "name": "word_freq_george", + "type": "TEXT", + "notnull": false, + "pk": false + }, + { + "name": "word_freq_650", + "type": "TEXT", + "notnull": false, + "pk": false + }, + { + "name": "word_freq_lab", + "type": "TEXT", + "notnull": false, + "pk": false + }, + { + "name": "word_freq_labs", + "type": "TEXT", + "notnull": false, + "pk": false + }, + { + "name": "word_freq_telnet", + "type": "TEXT", + "notnull": false, + "pk": false + }, + { + "name": "word_freq_857", + "type": "TEXT", + "notnull": false, + "pk": false + }, + { + "name": "word_freq_data", + "type": "TEXT", + "notnull": false, + "pk": false + }, + { + "name": "word_freq_415", + "type": "TEXT", + "notnull": false, + "pk": false + }, + { + "name": "word_freq_85", + "type": "TEXT", + "notnull": false, + "pk": false + }, + { + "name": "word_freq_technology", + "type": "TEXT", + "notnull": false, + "pk": false + }, + { + "name": "word_freq_1999", + "type": "TEXT", + "notnull": false, + "pk": false + }, + { + "name": "word_freq_parts", + "type": "TEXT", + "notnull": false, + "pk": false + }, + { + "name": "word_freq_pm", + "type": "TEXT", + "notnull": false, + "pk": false + }, + { + "name": "word_freq_direct", + "type": "TEXT", + "notnull": false, + "pk": false + }, + { + "name": "word_freq_cs", + "type": "TEXT", + "notnull": false, + "pk": false + }, + { + "name": "word_freq_meeting", + "type": "TEXT", + "notnull": false, + "pk": false + }, + { + "name": "word_freq_original", + "type": "TEXT", + "notnull": false, + "pk": false + }, + { + "name": "word_freq_project", + "type": "TEXT", + "notnull": false, + "pk": false + }, + { + "name": "word_freq_re", + "type": "TEXT", + "notnull": false, + "pk": false + }, + { + "name": "word_freq_edu", + "type": "TEXT", + "notnull": false, + "pk": false + }, + { + "name": "word_freq_table", + "type": "TEXT", + "notnull": false, + "pk": false + }, + { + "name": "word_freq_conference", + "type": "TEXT", + "notnull": false, + "pk": false + }, + { + "name": "char_freq_%3B", + "type": "TEXT", + "notnull": false, + "pk": false + }, + { + "name": "char_freq_%28", + "type": "TEXT", + "notnull": false, + "pk": false + }, + { + "name": "char_freq_%5B", + "type": "TEXT", + "notnull": false, + "pk": false + }, + { + "name": "char_freq_%21", + "type": "TEXT", + "notnull": false, + "pk": false + }, + { + "name": "char_freq_%24", + "type": "TEXT", + "notnull": false, + "pk": false + }, + { + "name": "char_freq_%23", + "type": "TEXT", + "notnull": false, + "pk": false + }, + { + "name": "capital_run_length_average", + "type": "TEXT", + "notnull": false, + "pk": false + }, + { + "name": "capital_run_length_longest", + "type": "TEXT", + "notnull": false, + "pk": false + }, + { + "name": "capital_run_length_total", + "type": "TEXT", + "notnull": false, + "pk": false + }, + { + "name": "class", + "type": "TEXT", + "notnull": false, + "pk": false + } + ], + "sample_rows": [ + { + "word_freq_make": "0", + "word_freq_address": "0.64", + "word_freq_all": "0.64", + "word_freq_3d": "0", + "word_freq_our": "0.32", + "word_freq_over": "0", + "word_freq_remove": "0", + "word_freq_internet": "0", + "word_freq_order": "0", + "word_freq_mail": "0", + "word_freq_receive": "0", + "word_freq_will": "0.64", + "word_freq_people": "0", + "word_freq_report": "0", + "word_freq_addresses": "0", + "word_freq_free": "0.32", + "word_freq_business": "0", + "word_freq_email": "1.29", + "word_freq_you": "1.93", + "word_freq_credit": "0", + "word_freq_your": "0.96", + "word_freq_font": "0", + "word_freq_000": "0", + "word_freq_money": "0", + "word_freq_hp": "0", + "word_freq_hpl": "0", + "word_freq_george": "0", + "word_freq_650": "0", + "word_freq_lab": "0", + "word_freq_labs": "0", + "word_freq_telnet": "0", + "word_freq_857": "0", + "word_freq_data": "0", + "word_freq_415": "0", + "word_freq_85": "0", + "word_freq_technology": "0", + "word_freq_1999": "0", + "word_freq_parts": "0", + "word_freq_pm": "0", + "word_freq_direct": "0", + "word_freq_cs": "0", + "word_freq_meeting": "0", + "word_freq_original": "0", + "word_freq_project": "0", + "word_freq_re": "0", + "word_freq_edu": "0", + "word_freq_table": "0", + "word_freq_conference": "0", + "char_freq_%3B": "0", + "char_freq_%28": "0", + "char_freq_%5B": "0", + "char_freq_%21": "0.778", + "char_freq_%24": "0", + "char_freq_%23": "0", + "capital_run_length_average": "3.756", + "capital_run_length_longest": "61", + "capital_run_length_total": "278", + "class": "1" + }, + { + "word_freq_make": "0.21", + "word_freq_address": "0.28", + "word_freq_all": "0.5", + "word_freq_3d": "0", + "word_freq_our": "0.14", + "word_freq_over": "0.28", + "word_freq_remove": "0.21", + "word_freq_internet": "0.07", + "word_freq_order": "0", + "word_freq_mail": "0.94", + "word_freq_receive": "0.21", + "word_freq_will": "0.79", + "word_freq_people": "0.65", + "word_freq_report": "0.21", + "word_freq_addresses": "0.14", + "word_freq_free": "0.14", + "word_freq_business": "0.07", + "word_freq_email": "0.28", + "word_freq_you": "3.47", + "word_freq_credit": "0", + "word_freq_your": "1.59", + "word_freq_font": "0", + "word_freq_000": "0.43", + "word_freq_money": "0.43", + "word_freq_hp": "0", + "word_freq_hpl": "0", + "word_freq_george": "0", + "word_freq_650": "0", + "word_freq_lab": "0", + "word_freq_labs": "0", + "word_freq_telnet": "0", + "word_freq_857": "0", + "word_freq_data": "0", + "word_freq_415": "0", + "word_freq_85": "0", + "word_freq_technology": "0", + "word_freq_1999": "0.07", + "word_freq_parts": "0", + "word_freq_pm": "0", + "word_freq_direct": "0", + "word_freq_cs": "0", + "word_freq_meeting": "0", + "word_freq_original": "0", + "word_freq_project": "0", + "word_freq_re": "0", + "word_freq_edu": "0", + "word_freq_table": "0", + "word_freq_conference": "0", + "char_freq_%3B": "0", + "char_freq_%28": "0.132", + "char_freq_%5B": "0", + "char_freq_%21": "0.372", + "char_freq_%24": "0.18", + "char_freq_%23": "0.048", + "capital_run_length_average": "5.114", + "capital_run_length_longest": "101", + "capital_run_length_total": "1028", + "class": "1" + }, + { + "word_freq_make": "0.06", + "word_freq_address": "0", + "word_freq_all": "0.71", + "word_freq_3d": "0", + "word_freq_our": "1.23", + "word_freq_over": "0.19", + "word_freq_remove": "0.19", + "word_freq_internet": "0.12", + "word_freq_order": "0.64", + "word_freq_mail": "0.25", + "word_freq_receive": "0.38", + "word_freq_will": "0.45", + "word_freq_people": "0.12", + "word_freq_report": "0", + "word_freq_addresses": "1.75", + "word_freq_free": "0.06", + "word_freq_business": "0.06", + "word_freq_email": "1.03", + "word_freq_you": "1.36", + "word_freq_credit": "0.32", + "word_freq_your": "0.51", + "word_freq_font": "0", + "word_freq_000": "1.16", + "word_freq_money": "0.06", + "word_freq_hp": "0", + "word_freq_hpl": "0", + "word_freq_george": "0", + "word_freq_650": "0", + "word_freq_lab": "0", + "word_freq_labs": "0", + "word_freq_telnet": "0", + "word_freq_857": "0", + "word_freq_data": "0", + "word_freq_415": "0", + "word_freq_85": "0", + "word_freq_technology": "0", + "word_freq_1999": "0", + "word_freq_parts": "0", + "word_freq_pm": "0", + "word_freq_direct": "0.06", + "word_freq_cs": "0", + "word_freq_meeting": "0", + "word_freq_original": "0.12", + "word_freq_project": "0", + "word_freq_re": "0.06", + "word_freq_edu": "0.06", + "word_freq_table": "0", + "word_freq_conference": "0", + "char_freq_%3B": "0.01", + "char_freq_%28": "0.143", + "char_freq_%5B": "0", + "char_freq_%21": "0.276", + "char_freq_%24": "0.184", + "char_freq_%23": "0.01", + "capital_run_length_average": "9.821", + "capital_run_length_longest": "485", + "capital_run_length_total": "2259", + "class": "1" + }, + { + "word_freq_make": "0", + "word_freq_address": "0", + "word_freq_all": "0", + "word_freq_3d": "0", + "word_freq_our": "0.63", + "word_freq_over": "0", + "word_freq_remove": "0.31", + "word_freq_internet": "0.63", + "word_freq_order": "0.31", + "word_freq_mail": "0.63", + "word_freq_receive": "0.31", + "word_freq_will": "0.31", + "word_freq_people": "0.31", + "word_freq_report": "0", + "word_freq_addresses": "0", + "word_freq_free": "0.31", + "word_freq_business": "0", + "word_freq_email": "0", + "word_freq_you": "3.18", + "word_freq_credit": "0", + "word_freq_your": "0.31", + "word_freq_font": "0", + "word_freq_000": "0", + "word_freq_money": "0", + "word_freq_hp": "0", + "word_freq_hpl": "0", + "word_freq_george": "0", + "word_freq_650": "0", + "word_freq_lab": "0", + "word_freq_labs": "0", + "word_freq_telnet": "0", + "word_freq_857": "0", + "word_freq_data": "0", + "word_freq_415": "0", + "word_freq_85": "0", + "word_freq_technology": "0", + "word_freq_1999": "0", + "word_freq_parts": "0", + "word_freq_pm": "0", + "word_freq_direct": "0", + "word_freq_cs": "0", + "word_freq_meeting": "0", + "word_freq_original": "0", + "word_freq_project": "0", + "word_freq_re": "0", + "word_freq_edu": "0", + "word_freq_table": "0", + "word_freq_conference": "0", + "char_freq_%3B": "0", + "char_freq_%28": "0.137", + "char_freq_%5B": "0", + "char_freq_%21": "0.137", + "char_freq_%24": "0", + "char_freq_%23": "0", + "capital_run_length_average": "3.537", + "capital_run_length_longest": "40", + "capital_run_length_total": "191", + "class": "1" + }, + { + "word_freq_make": "0", + "word_freq_address": "0", + "word_freq_all": "0", + "word_freq_3d": "0", + "word_freq_our": "0.63", + "word_freq_over": "0", + "word_freq_remove": "0.31", + "word_freq_internet": "0.63", + "word_freq_order": "0.31", + "word_freq_mail": "0.63", + "word_freq_receive": "0.31", + "word_freq_will": "0.31", + "word_freq_people": "0.31", + "word_freq_report": "0", + "word_freq_addresses": "0", + "word_freq_free": "0.31", + "word_freq_business": "0", + "word_freq_email": "0", + "word_freq_you": "3.18", + "word_freq_credit": "0", + "word_freq_your": "0.31", + "word_freq_font": "0", + "word_freq_000": "0", + "word_freq_money": "0", + "word_freq_hp": "0", + "word_freq_hpl": "0", + "word_freq_george": "0", + "word_freq_650": "0", + "word_freq_lab": "0", + "word_freq_labs": "0", + "word_freq_telnet": "0", + "word_freq_857": "0", + "word_freq_data": "0", + "word_freq_415": "0", + "word_freq_85": "0", + "word_freq_technology": "0", + "word_freq_1999": "0", + "word_freq_parts": "0", + "word_freq_pm": "0", + "word_freq_direct": "0", + "word_freq_cs": "0", + "word_freq_meeting": "0", + "word_freq_original": "0", + "word_freq_project": "0", + "word_freq_re": "0", + "word_freq_edu": "0", + "word_freq_table": "0", + "word_freq_conference": "0", + "char_freq_%3B": "0", + "char_freq_%28": "0.135", + "char_freq_%5B": "0", + "char_freq_%21": "0.135", + "char_freq_%24": "0", + "char_freq_%23": "0", + "capital_run_length_average": "3.537", + "capital_run_length_longest": "40", + "capital_run_length_total": "191", + "class": "1" + } + ] +} + +Shortlisted templates: +[ + { + "template_id": "tpl_h2o_group_sum", + "template_name": "Grouped Numeric Sum", + "primary_family": "subgroup_structure", + "portability": "partial", + "sql_skeleton": "SELECT {group_col}, SUM({measure_col}) AS total_measure\nFROM {table}\nGROUP BY {group_col}\nORDER BY total_measure DESC;", + "required_roles": [ + "group_col", + "measure_col" + ] + } +] + +Problem instance: +{ + "dataset_id": "n1", + "question": "Use template Grouped Numeric Sum to probe internal_profile_stability with semantic role collapsed_target_view. Focus on group_col=class, measure_col=word_freq_remove.", + "planned_template_id": "tpl_h2o_group_sum", + "bindings": { + "group_col": "class", + "measure_col": "word_freq_remove", + "top_k": 11, + "top_n": 5, + "num_tiles": 10, + "percentile_value": 0.95, + "z_threshold": 2.0, + "fraction_threshold": 0.1, + "baseline_multiplier": 1.5, + "baseline_fraction": 0.1, + "min_group_size": 5, + "min_support": 5, + "measure_threshold": 0.0, + "time_grain": "month", + "lookback_rows": 3, + "current_period_start": "'2024-01-01'", + "current_period_end": "'2024-04-01'", + "previous_period_start": "'2023-10-01'", + "previous_period_end": "'2024-01-01'", + "drift_ratio_threshold": 0.8 + }, + "can_vary": [], + "must_fix": [], + "runtime_sql_skeleton": "SELECT {group_col}, SUM({measure_col}) AS total_measure\nFROM {table}\nGROUP BY {group_col}\nORDER BY total_measure DESC;" +} + +Repair context: +{} diff --git a/Query/sql/v2/runs/v2_cli_20260502_081223_d/n1/artifacts/v2q_n1_4fe6330800dd43f4/cli/sql_response_attempt_1.raw.txt b/Query/sql/v2/runs/v2_cli_20260502_081223_d/n1/artifacts/v2q_n1_4fe6330800dd43f4/cli/sql_response_attempt_1.raw.txt new file mode 100644 index 0000000000000000000000000000000000000000..d70d4af5dbe723a48aa3e337d9192a3956aecfd5 --- /dev/null +++ b/Query/sql/v2/runs/v2_cli_20260502_081223_d/n1/artifacts/v2q_n1_4fe6330800dd43f4/cli/sql_response_attempt_1.raw.txt @@ -0,0 +1,4 @@ +{"type":"thread.started","thread_id":"019e40dc-66ab-75c1-a475-c6e6addc4e07"} +{"type":"turn.started"} +{"type":"item.completed","item":{"id":"item_0","type":"agent_message","text":"{\"sql\":\"-- template_id: tpl_h2o_group_sum\\nSELECT \\\"class\\\", SUM(CAST(\\\"word_freq_remove\\\" AS REAL)) AS \\\"total_measure\\\"\\nFROM \\\"n1\\\"\\nGROUP BY \\\"class\\\"\\nORDER BY \\\"total_measure\\\" DESC;\",\"notes\":\"Uses the required grouped-sum template with group_col=\\\"class\\\" and measure_col=\\\"word_freq_remove\\\". CAST to REAL is added because the schema stores feature columns as TEXT.\"}"}} +{"type":"turn.completed","usage":{"input_tokens":20315,"cached_input_tokens":12032,"output_tokens":349,"reasoning_output_tokens":248}} diff --git a/Query/sql/v2/runs/v2_cli_20260502_081223_d/n1/artifacts/v2q_n1_4fe6330800dd43f4/cli/sql_response_attempt_1.txt b/Query/sql/v2/runs/v2_cli_20260502_081223_d/n1/artifacts/v2q_n1_4fe6330800dd43f4/cli/sql_response_attempt_1.txt new file mode 100644 index 0000000000000000000000000000000000000000..d818eb2fb94f2c2b196d8794a586495887a48667 --- /dev/null +++ b/Query/sql/v2/runs/v2_cli_20260502_081223_d/n1/artifacts/v2q_n1_4fe6330800dd43f4/cli/sql_response_attempt_1.txt @@ -0,0 +1 @@ +{"sql":"-- template_id: tpl_h2o_group_sum\nSELECT \"class\", SUM(CAST(\"word_freq_remove\" AS REAL)) AS \"total_measure\"\nFROM \"n1\"\nGROUP BY \"class\"\nORDER BY \"total_measure\" DESC;","notes":"Uses the required grouped-sum template with group_col=\"class\" and measure_col=\"word_freq_remove\". CAST to REAL is added because the schema stores feature columns as TEXT."} \ No newline at end of file diff --git a/Query/sql/v2/runs/v2_cli_20260502_081223_d/n1/artifacts/v2q_n1_4fe6330800dd43f4/cli/sql_stderr_attempt_1.txt b/Query/sql/v2/runs/v2_cli_20260502_081223_d/n1/artifacts/v2q_n1_4fe6330800dd43f4/cli/sql_stderr_attempt_1.txt new file mode 100644 index 0000000000000000000000000000000000000000..e69de29bb2d1d6434b8b29ae775ad8c2e48c5391 diff --git a/Query/sql/v2/runs/v2_cli_20260502_081223_d/n1/artifacts/v2q_n1_50bcf86bb04ee1de/cli/conversation.jsonl b/Query/sql/v2/runs/v2_cli_20260502_081223_d/n1/artifacts/v2q_n1_50bcf86bb04ee1de/cli/conversation.jsonl new file mode 100644 index 0000000000000000000000000000000000000000..8a16e7e14d188d1630cb1aadc2e0abf9548d9d74 --- /dev/null +++ b/Query/sql/v2/runs/v2_cli_20260502_081223_d/n1/artifacts/v2q_n1_50bcf86bb04ee1de/cli/conversation.jsonl @@ -0,0 +1,2 @@ +{"attempt": 1, "phase": "sql_generation", "role": "user", "content_path": "cli/sql_prompt_attempt_1.txt", "metrics": {"chars": 29335, "bytes_utf8": 29335, "lines": 790, "estimated_tokens": null}} +{"attempt": 1, "phase": "sql_generation", "role": "assistant", "content_path": "cli/sql_response_attempt_1.txt", "raw_content_path": "cli/sql_response_attempt_1.raw.txt", "stderr_path": "cli/sql_stderr_attempt_1.txt", "metrics": {"chars": 307, "bytes_utf8": 307, "lines": 1, "estimated_tokens": null}, "usage": {"input_tokens": 20323, "cached_input_tokens": 12032, "output_tokens": 307, "reasoning_output_tokens": 214}} diff --git a/Query/sql/v2/runs/v2_cli_20260502_081223_d/n1/artifacts/v2q_n1_50bcf86bb04ee1de/cli/session_summary.json b/Query/sql/v2/runs/v2_cli_20260502_081223_d/n1/artifacts/v2q_n1_50bcf86bb04ee1de/cli/session_summary.json new file mode 100644 index 0000000000000000000000000000000000000000..555e304de6f990228bebc982012e028b721f420e --- /dev/null +++ b/Query/sql/v2/runs/v2_cli_20260502_081223_d/n1/artifacts/v2q_n1_50bcf86bb04ee1de/cli/session_summary.json @@ -0,0 +1,25 @@ +{ + "engine": "v2-cli:codex", + "command": "codex exec --skip-git-repo-check --disable plugins --sandbox read-only --cd \"/data/jialinzhang/SQLagent\" -m gpt-5.4 --json -", + "ai_cli_calls": 1, + "usage_summary": { + "dataset_id": "n1", + "model": "v2-cli:codex", + "run_id": "v2q_n1_50bcf86bb04ee1de", + "api_calls": 0, + "input_tokens": 20323, + "cached_input_tokens": 12032, + "output_tokens": 307, + "total_tokens": 20630, + "cost_usd": 0.0, + "ai_cli_calls": 1, + "estimated_input_tokens": 0, + "estimated_output_tokens": 0, + "estimated_total_tokens": 0, + "usage_source": "ai_cli_json_usage", + "cli_elapsed_ms_total": 8664.06, + "sql_execution_elapsed_ms_total": 1.94, + "conversation_log_path": "/data/jialinzhang/TabQueryBench/sql_workloads/v2_current/runs_and_launches/runs/v2_cli_20260502_081223_d/n1/artifacts/v2q_n1_50bcf86bb04ee1de/cli/conversation.jsonl", + "note": "Executed through a local AI CLI with structured usage metadata." + } +} \ No newline at end of file diff --git a/Query/sql/v2/runs/v2_cli_20260502_081223_d/n1/artifacts/v2q_n1_50bcf86bb04ee1de/cli/sql_attempt_1.metadata.json b/Query/sql/v2/runs/v2_cli_20260502_081223_d/n1/artifacts/v2q_n1_50bcf86bb04ee1de/cli/sql_attempt_1.metadata.json new file mode 100644 index 0000000000000000000000000000000000000000..7b8b8fe2fa7d453d8265df0faf52f11a9fb67890 --- /dev/null +++ b/Query/sql/v2/runs/v2_cli_20260502_081223_d/n1/artifacts/v2q_n1_50bcf86bb04ee1de/cli/sql_attempt_1.metadata.json @@ -0,0 +1,45 @@ +{ + "attempt": 1, + "phase": "sql_generation", + "command": "codex exec --skip-git-repo-check --disable plugins --sandbox read-only --cd \"/data/jialinzhang/SQLagent\" -m gpt-5.4 --json -", + "started_at": "2026-05-19T16:03:25.083627+00:00", + "ended_at": "2026-05-19T16:03:33.747710+00:00", + "elapsed_ms": 8664.06, + "prompt_metrics": { + "chars": 29335, + "bytes_utf8": 29335, + "lines": 790, + "estimated_tokens": null + }, + "stdout_metrics": { + "chars": 668, + "bytes_utf8": 668, + "lines": 4, + "estimated_tokens": null + }, + "stderr_metrics": { + "chars": 0, + "bytes_utf8": 0, + "lines": 0, + "estimated_tokens": null + }, + "parsed_output": { + "format": "jsonl_events", + "text_metrics": { + "chars": 307, + "bytes_utf8": 307, + "lines": 1, + "estimated_tokens": null + }, + "usage": { + "input_tokens": 20323, + "cached_input_tokens": 12032, + "output_tokens": 307, + "reasoning_output_tokens": 214 + } + }, + "prompt_path": "cli/sql_prompt_attempt_1.txt", + "response_path": "cli/sql_response_attempt_1.txt", + "raw_response_path": "cli/sql_response_attempt_1.raw.txt", + "stderr_path": "cli/sql_stderr_attempt_1.txt" +} \ No newline at end of file diff --git a/Query/sql/v2/runs/v2_cli_20260502_081223_d/n1/artifacts/v2q_n1_50bcf86bb04ee1de/cli/sql_prompt_attempt_1.txt b/Query/sql/v2/runs/v2_cli_20260502_081223_d/n1/artifacts/v2q_n1_50bcf86bb04ee1de/cli/sql_prompt_attempt_1.txt new file mode 100644 index 0000000000000000000000000000000000000000..53f7a060422dae6cbd1e7aa3f64347db86d10eac --- /dev/null +++ b/Query/sql/v2/runs/v2_cli_20260502_081223_d/n1/artifacts/v2q_n1_50bcf86bb04ee1de/cli/sql_prompt_attempt_1.txt @@ -0,0 +1,790 @@ +You are generating one SQLite SELECT query for a single-table SQL QA task. +Return strict JSON only, with this schema: {"sql": "...", "notes": "..."}. +Rules: +- Use only the provided table and columns. +- Do not write INSERT, UPDATE, DELETE, DROP, ALTER, CREATE, PRAGMA, ATTACH, DETACH, or VACUUM. +- Prefer the planned template and bound roles when provided. +- Add a leading SQL comment exactly like: -- template_id: . +- Generate SQLite-compatible SQL. SQLite does not support PERCENTILE_CONT or STDDEV. +- Quote identifiers with double quotes. +- Return no markdown and no extra prose. + +Dataset context: +Dataset context for SQL QA: +- dataset_id: n1 +- dataset_name: Spambase +- table_name: n1 +- table_layout: single-table dataset (do not assume joins). +- row_semantics: One row is one email represented by engineered token/character frequency and capitalization-run features. +- task_type: classification +- target_column: class +- main_row_count: 4601 +- important_fields: +- word_freq_make: role=feature, type=numeric_sparse_frequency. tags=['condition_candidate', 'measure', 'rare_pattern_candidate'] desc=Frequency feature for token 'make' in an email message. +- word_freq_address: role=feature, type=numeric_sparse_frequency. tags=['condition_candidate', 'measure', 'rare_pattern_candidate'] desc=Frequency feature for token 'address' in an email message. +- word_freq_all: role=feature, type=numeric_sparse_frequency. tags=['condition_candidate', 'measure', 'rare_pattern_candidate'] desc=Frequency feature for token 'all' in an email message. +- word_freq_3d: role=feature, type=numeric_sparse_frequency. tags=['condition_candidate', 'measure', 'rare_pattern_candidate'] desc=Frequency feature for token '3d' in an email message. +- word_freq_our: role=feature, type=numeric_sparse_frequency. tags=['condition_candidate', 'measure', 'rare_pattern_candidate'] desc=Frequency feature for token 'our' in an email message. +- word_freq_over: role=feature, type=numeric_sparse_frequency. tags=['condition_candidate', 'measure', 'rare_pattern_candidate'] desc=Frequency feature for token 'over' in an email message. +- word_freq_remove: role=feature, type=numeric_sparse_frequency. tags=['condition_candidate', 'measure', 'rare_pattern_candidate'] desc=Frequency feature for token 'remove' in an email message. +- word_freq_internet: role=feature, type=numeric_sparse_frequency. tags=['condition_candidate', 'measure', 'rare_pattern_candidate'] desc=Frequency feature for token 'internet' in an email message. +- word_freq_order: role=feature, type=numeric_sparse_frequency. tags=['condition_candidate', 'measure', 'rare_pattern_candidate'] desc=Frequency feature for token 'order' in an email message. +- word_freq_mail: role=feature, type=numeric_sparse_frequency. tags=['condition_candidate', 'measure', 'rare_pattern_candidate'] desc=Frequency feature for token 'mail' in an email message. +- word_freq_receive: role=feature, type=numeric_sparse_frequency. tags=['condition_candidate', 'measure', 'rare_pattern_candidate'] desc=Frequency feature for token 'receive' in an email message. +- word_freq_will: role=feature, type=numeric_sparse_frequency. tags=['condition_candidate', 'measure', 'rare_pattern_candidate'] desc=Frequency feature for token 'will' in an email message. +- word_freq_people: role=feature, type=numeric_sparse_frequency. tags=['condition_candidate', 'measure', 'rare_pattern_candidate'] desc=Frequency feature for token 'people' in an email message. +- word_freq_report: role=feature, type=numeric_sparse_frequency. tags=['condition_candidate', 'measure', 'rare_pattern_candidate'] desc=Frequency feature for token 'report' in an email message. +- word_freq_addresses: role=feature, type=numeric_sparse_frequency. tags=['condition_candidate', 'measure', 'rare_pattern_candidate'] desc=Frequency feature for token 'addresses' in an email message. +- word_freq_free: role=feature, type=numeric_sparse_frequency. tags=['condition_candidate', 'measure', 'rare_pattern_candidate'] desc=Frequency feature for token 'free' in an email message. +- word_freq_business: role=feature, type=numeric_sparse_frequency. tags=['condition_candidate', 'measure', 'rare_pattern_candidate'] desc=Frequency feature for token 'business' in an email message. +- word_freq_email: role=feature, type=numeric_sparse_frequency. tags=['condition_candidate', 'measure', 'rare_pattern_candidate'] desc=Frequency feature for token 'email' in an email message. +- word_freq_you: role=feature, type=numeric_sparse_frequency. tags=['condition_candidate', 'measure', 'rare_pattern_candidate'] desc=Frequency feature for token 'you' in an email message. +- word_freq_credit: role=feature, type=numeric_sparse_frequency. tags=['condition_candidate', 'measure', 'rare_pattern_candidate'] desc=Frequency feature for token 'credit' in an email message. +- word_freq_your: role=feature, type=numeric_sparse_frequency. tags=['condition_candidate', 'measure', 'rare_pattern_candidate'] desc=Frequency feature for token 'your' in an email message. +- word_freq_font: role=feature, type=numeric_sparse_frequency. tags=['condition_candidate', 'measure', 'rare_pattern_candidate'] desc=Frequency feature for token 'font' in an email message. +- word_freq_000: role=feature, type=numeric_sparse_frequency. tags=['condition_candidate', 'measure', 'rare_pattern_candidate'] desc=Frequency feature for token '000' in an email message. +- word_freq_money: role=feature, type=numeric_sparse_frequency. tags=['condition_candidate', 'measure', 'rare_pattern_candidate'] desc=Frequency feature for token 'money' in an email message. +- word_freq_hp: role=feature, type=numeric_sparse_frequency. tags=['condition_candidate', 'measure', 'rare_pattern_candidate'] desc=Frequency feature for token 'hp' in an email message. +- word_freq_hpl: role=feature, type=numeric_sparse_frequency. tags=['condition_candidate', 'measure', 'rare_pattern_candidate'] desc=Frequency feature for token 'hpl' in an email message. +- word_freq_george: role=feature, type=numeric_sparse_frequency. tags=['condition_candidate', 'measure', 'rare_pattern_candidate'] desc=Frequency feature for token 'george' in an email message. +- word_freq_650: role=feature, type=numeric_sparse_frequency. tags=['condition_candidate', 'measure', 'rare_pattern_candidate'] desc=Frequency feature for token '650' in an email message. +- word_freq_lab: role=feature, type=numeric_sparse_frequency. tags=['condition_candidate', 'measure', 'rare_pattern_candidate'] desc=Frequency feature for token 'lab' in an email message. +- word_freq_labs: role=feature, type=numeric_sparse_frequency. tags=['condition_candidate', 'measure', 'rare_pattern_candidate'] desc=Frequency feature for token 'labs' in an email message. +- word_freq_telnet: role=feature, type=numeric_sparse_frequency. tags=['condition_candidate', 'measure', 'rare_pattern_candidate'] desc=Frequency feature for token 'telnet' in an email message. +- word_freq_857: role=feature, type=numeric_sparse_frequency. tags=['condition_candidate', 'measure', 'rare_pattern_candidate'] desc=Frequency feature for token '857' in an email message. +- word_freq_data: role=feature, type=numeric_sparse_frequency. tags=['condition_candidate', 'measure', 'rare_pattern_candidate'] desc=Frequency feature for token 'data' in an email message. +- word_freq_415: role=feature, type=numeric_sparse_frequency. tags=['condition_candidate', 'measure', 'rare_pattern_candidate'] desc=Frequency feature for token '415' in an email message. +- word_freq_85: role=feature, type=numeric_sparse_frequency. tags=['condition_candidate', 'measure', 'rare_pattern_candidate'] desc=Frequency feature for token '85' in an email message. +- word_freq_technology: role=feature, type=numeric_sparse_frequency. tags=['condition_candidate', 'measure', 'rare_pattern_candidate'] desc=Frequency feature for token 'technology' in an email message. +- word_freq_1999: role=feature, type=numeric_sparse_frequency. tags=['condition_candidate', 'measure', 'rare_pattern_candidate'] desc=Frequency feature for token '1999' in an email message. +- word_freq_parts: role=feature, type=numeric_sparse_frequency. tags=['condition_candidate', 'measure', 'rare_pattern_candidate'] desc=Frequency feature for token 'parts' in an email message. +- word_freq_pm: role=feature, type=numeric_sparse_frequency. tags=['condition_candidate', 'measure', 'rare_pattern_candidate'] desc=Frequency feature for token 'pm' in an email message. +- word_freq_direct: role=feature, type=numeric_sparse_frequency. tags=['condition_candidate', 'measure', 'rare_pattern_candidate'] desc=Frequency feature for token 'direct' in an email message. +- word_freq_cs: role=feature, type=numeric_sparse_frequency. tags=['condition_candidate', 'measure', 'rare_pattern_candidate'] desc=Frequency feature for token 'cs' in an email message. +- word_freq_meeting: role=feature, type=numeric_sparse_frequency. tags=['condition_candidate', 'measure', 'rare_pattern_candidate'] desc=Frequency feature for token 'meeting' in an email message. +- word_freq_original: role=feature, type=numeric_sparse_frequency. tags=['condition_candidate', 'measure', 'rare_pattern_candidate'] desc=Frequency feature for token 'original' in an email message. +- word_freq_project: role=feature, type=numeric_sparse_frequency. tags=['condition_candidate', 'measure', 'rare_pattern_candidate'] desc=Frequency feature for token 'project' in an email message. +- word_freq_re: role=feature, type=numeric_sparse_frequency. tags=['condition_candidate', 'measure', 'rare_pattern_candidate'] desc=Frequency feature for token 're' in an email message. +- word_freq_edu: role=feature, type=numeric_sparse_frequency. tags=['condition_candidate', 'measure', 'rare_pattern_candidate'] desc=Frequency feature for token 'edu' in an email message. +- word_freq_table: role=feature, type=numeric_sparse_frequency. tags=['condition_candidate', 'measure', 'rare_pattern_candidate'] desc=Frequency feature for token 'table' in an email message. +- word_freq_conference: role=feature, type=numeric_sparse_frequency. tags=['condition_candidate', 'measure', 'rare_pattern_candidate'] desc=Frequency feature for token 'conference' in an email message. +- char_freq_%3B: role=feature, type=numeric_sparse_frequency. tags=['condition_candidate', 'measure', 'rare_pattern_candidate'] desc=Character frequency feature for symbol ';'. +- char_freq_%28: role=feature, type=numeric_sparse_frequency. tags=['condition_candidate', 'measure', 'rare_pattern_candidate'] desc=Character frequency feature for symbol '('. +- char_freq_%5B: role=feature, type=numeric_sparse_frequency. tags=['condition_candidate', 'measure', 'rare_pattern_candidate'] desc=Character frequency feature for symbol '['. +- char_freq_%21: role=feature, type=numeric_sparse_frequency. tags=['condition_candidate', 'measure', 'rare_pattern_candidate'] desc=Character frequency feature for symbol '!'. +- char_freq_%24: role=feature, type=numeric_sparse_frequency. tags=['condition_candidate', 'measure', 'rare_pattern_candidate'] desc=Character frequency feature for symbol '$'. +- char_freq_%23: role=feature, type=numeric_sparse_frequency. tags=['condition_candidate', 'measure', 'rare_pattern_candidate'] desc=Character frequency feature for symbol '#'. +- capital_run_length_average: role=feature, type=numeric_heavy_tail. tags=['condition_candidate', 'measure', 'rare_pattern_candidate'] desc=Average length of uninterrupted capital-letter runs. +- capital_run_length_longest: role=feature, type=numeric_heavy_tail. tags=['condition_candidate', 'measure', 'rare_pattern_candidate'] desc=Longest uninterrupted capital-letter run. +- capital_run_length_total: role=feature, type=numeric_heavy_tail. tags=['condition_candidate', 'measure', 'rare_pattern_candidate'] desc=Total number of capital letters in runs. +- class: role=target, type=binary_target. tags=['target_candidate'] desc=Binary spam label (1=spam, 0=non-spam). +- useful_field_combinations: [['word_freq_free', 'word_freq_you', 'class'], ['char_freq_%21', 'capital_run_length_longest', 'class'], ['word_freq_remove', 'word_freq_money', 'class']] +- fields_requiring_caution: ['capital_run_length_average', 'capital_run_length_longest', 'capital_run_length_total'] +- source_url: https://www.openml.org/d/44 + +SQLite schema snapshot: +{ + "table_name": "n1", + "quoted_table_name": "\"n1\"", + "row_count": 4601, + "columns": [ + { + "name": "word_freq_make", + "type": "TEXT", + "notnull": false, + "pk": false + }, + { + "name": "word_freq_address", + "type": "TEXT", + "notnull": false, + "pk": false + }, + { + "name": "word_freq_all", + "type": "TEXT", + "notnull": false, + "pk": false + }, + { + "name": "word_freq_3d", + "type": "TEXT", + "notnull": false, + "pk": false + }, + { + "name": "word_freq_our", + "type": "TEXT", + "notnull": false, + "pk": false + }, + { + "name": "word_freq_over", + "type": "TEXT", + "notnull": false, + "pk": false + }, + { + "name": "word_freq_remove", + "type": "TEXT", + "notnull": false, + "pk": false + }, + { + "name": "word_freq_internet", + "type": "TEXT", + "notnull": false, + "pk": false + }, + { + "name": "word_freq_order", + "type": "TEXT", + "notnull": false, + "pk": false + }, + { + "name": "word_freq_mail", + "type": "TEXT", + "notnull": false, + "pk": false + }, + { + "name": "word_freq_receive", + "type": "TEXT", + "notnull": false, + "pk": false + }, + { + "name": "word_freq_will", + "type": "TEXT", + "notnull": false, + "pk": false + }, + { + "name": "word_freq_people", + "type": "TEXT", + "notnull": false, + "pk": false + }, + { + "name": "word_freq_report", + "type": "TEXT", + "notnull": false, + "pk": false + }, + { + "name": "word_freq_addresses", + "type": "TEXT", + "notnull": false, + "pk": false + }, + { + "name": "word_freq_free", + "type": "TEXT", + "notnull": false, + "pk": false + }, + { + "name": "word_freq_business", + "type": "TEXT", + "notnull": false, + "pk": false + }, + { + "name": "word_freq_email", + "type": "TEXT", + "notnull": false, + "pk": false + }, + { + "name": "word_freq_you", + "type": "TEXT", + "notnull": false, + "pk": false + }, + { + "name": "word_freq_credit", + "type": "TEXT", + "notnull": false, + "pk": false + }, + { + "name": "word_freq_your", + "type": "TEXT", + "notnull": false, + "pk": false + }, + { + "name": "word_freq_font", + "type": "TEXT", + "notnull": false, + "pk": false + }, + { + "name": "word_freq_000", + "type": "TEXT", + "notnull": false, + "pk": false + }, + { + "name": "word_freq_money", + "type": "TEXT", + "notnull": false, + "pk": false + }, + { + "name": "word_freq_hp", + "type": "TEXT", + "notnull": false, + "pk": false + }, + { + "name": "word_freq_hpl", + "type": "TEXT", + "notnull": false, + "pk": false + }, + { + "name": "word_freq_george", + "type": "TEXT", + "notnull": false, + "pk": false + }, + { + "name": "word_freq_650", + "type": "TEXT", + "notnull": false, + "pk": false + }, + { + "name": "word_freq_lab", + "type": "TEXT", + "notnull": false, + "pk": false + }, + { + "name": "word_freq_labs", + "type": "TEXT", + "notnull": false, + "pk": false + }, + { + "name": "word_freq_telnet", + "type": "TEXT", + "notnull": false, + "pk": false + }, + { + "name": "word_freq_857", + "type": "TEXT", + "notnull": false, + "pk": false + }, + { + "name": "word_freq_data", + "type": "TEXT", + "notnull": false, + "pk": false + }, + { + "name": "word_freq_415", + "type": "TEXT", + "notnull": false, + "pk": false + }, + { + "name": "word_freq_85", + "type": "TEXT", + "notnull": false, + "pk": false + }, + { + "name": "word_freq_technology", + "type": "TEXT", + "notnull": false, + "pk": false + }, + { + "name": "word_freq_1999", + "type": "TEXT", + "notnull": false, + "pk": false + }, + { + "name": "word_freq_parts", + "type": "TEXT", + "notnull": false, + "pk": false + }, + { + "name": "word_freq_pm", + "type": "TEXT", + "notnull": false, + "pk": false + }, + { + "name": "word_freq_direct", + "type": "TEXT", + "notnull": false, + "pk": false + }, + { + "name": "word_freq_cs", + "type": "TEXT", + "notnull": false, + "pk": false + }, + { + "name": "word_freq_meeting", + "type": "TEXT", + "notnull": false, + "pk": false + }, + { + "name": "word_freq_original", + "type": "TEXT", + "notnull": false, + "pk": false + }, + { + "name": "word_freq_project", + "type": "TEXT", + "notnull": false, + "pk": false + }, + { + "name": "word_freq_re", + "type": "TEXT", + "notnull": false, + "pk": false + }, + { + "name": "word_freq_edu", + "type": "TEXT", + "notnull": false, + "pk": false + }, + { + "name": "word_freq_table", + "type": "TEXT", + "notnull": false, + "pk": false + }, + { + "name": "word_freq_conference", + "type": "TEXT", + "notnull": false, + "pk": false + }, + { + "name": "char_freq_%3B", + "type": "TEXT", + "notnull": false, + "pk": false + }, + { + "name": "char_freq_%28", + "type": "TEXT", + "notnull": false, + "pk": false + }, + { + "name": "char_freq_%5B", + "type": "TEXT", + "notnull": false, + "pk": false + }, + { + "name": "char_freq_%21", + "type": "TEXT", + "notnull": false, + "pk": false + }, + { + "name": "char_freq_%24", + "type": "TEXT", + "notnull": false, + "pk": false + }, + { + "name": "char_freq_%23", + "type": "TEXT", + "notnull": false, + "pk": false + }, + { + "name": "capital_run_length_average", + "type": "TEXT", + "notnull": false, + "pk": false + }, + { + "name": "capital_run_length_longest", + "type": "TEXT", + "notnull": false, + "pk": false + }, + { + "name": "capital_run_length_total", + "type": "TEXT", + "notnull": false, + "pk": false + }, + { + "name": "class", + "type": "TEXT", + "notnull": false, + "pk": false + } + ], + "sample_rows": [ + { + "word_freq_make": "0", + "word_freq_address": "0.64", + "word_freq_all": "0.64", + "word_freq_3d": "0", + "word_freq_our": "0.32", + "word_freq_over": "0", + "word_freq_remove": "0", + "word_freq_internet": "0", + "word_freq_order": "0", + "word_freq_mail": "0", + "word_freq_receive": "0", + "word_freq_will": "0.64", + "word_freq_people": "0", + "word_freq_report": "0", + "word_freq_addresses": "0", + "word_freq_free": "0.32", + "word_freq_business": "0", + "word_freq_email": "1.29", + "word_freq_you": "1.93", + "word_freq_credit": "0", + "word_freq_your": "0.96", + "word_freq_font": "0", + "word_freq_000": "0", + "word_freq_money": "0", + "word_freq_hp": "0", + "word_freq_hpl": "0", + "word_freq_george": "0", + "word_freq_650": "0", + "word_freq_lab": "0", + "word_freq_labs": "0", + "word_freq_telnet": "0", + "word_freq_857": "0", + "word_freq_data": "0", + "word_freq_415": "0", + "word_freq_85": "0", + "word_freq_technology": "0", + "word_freq_1999": "0", + "word_freq_parts": "0", + "word_freq_pm": "0", + "word_freq_direct": "0", + "word_freq_cs": "0", + "word_freq_meeting": "0", + "word_freq_original": "0", + "word_freq_project": "0", + "word_freq_re": "0", + "word_freq_edu": "0", + "word_freq_table": "0", + "word_freq_conference": "0", + "char_freq_%3B": "0", + "char_freq_%28": "0", + "char_freq_%5B": "0", + "char_freq_%21": "0.778", + "char_freq_%24": "0", + "char_freq_%23": "0", + "capital_run_length_average": "3.756", + "capital_run_length_longest": "61", + "capital_run_length_total": "278", + "class": "1" + }, + { + "word_freq_make": "0.21", + "word_freq_address": "0.28", + "word_freq_all": "0.5", + "word_freq_3d": "0", + "word_freq_our": "0.14", + "word_freq_over": "0.28", + "word_freq_remove": "0.21", + "word_freq_internet": "0.07", + "word_freq_order": "0", + "word_freq_mail": "0.94", + "word_freq_receive": "0.21", + "word_freq_will": "0.79", + "word_freq_people": "0.65", + "word_freq_report": "0.21", + "word_freq_addresses": "0.14", + "word_freq_free": "0.14", + "word_freq_business": "0.07", + "word_freq_email": "0.28", + "word_freq_you": "3.47", + "word_freq_credit": "0", + "word_freq_your": "1.59", + "word_freq_font": "0", + "word_freq_000": "0.43", + "word_freq_money": "0.43", + "word_freq_hp": "0", + "word_freq_hpl": "0", + "word_freq_george": "0", + "word_freq_650": "0", + "word_freq_lab": "0", + "word_freq_labs": "0", + "word_freq_telnet": "0", + "word_freq_857": "0", + "word_freq_data": "0", + "word_freq_415": "0", + "word_freq_85": "0", + "word_freq_technology": "0", + "word_freq_1999": "0.07", + "word_freq_parts": "0", + "word_freq_pm": "0", + "word_freq_direct": "0", + "word_freq_cs": "0", + "word_freq_meeting": "0", + "word_freq_original": "0", + "word_freq_project": "0", + "word_freq_re": "0", + "word_freq_edu": "0", + "word_freq_table": "0", + "word_freq_conference": "0", + "char_freq_%3B": "0", + "char_freq_%28": "0.132", + "char_freq_%5B": "0", + "char_freq_%21": "0.372", + "char_freq_%24": "0.18", + "char_freq_%23": "0.048", + "capital_run_length_average": "5.114", + "capital_run_length_longest": "101", + "capital_run_length_total": "1028", + "class": "1" + }, + { + "word_freq_make": "0.06", + "word_freq_address": "0", + "word_freq_all": "0.71", + "word_freq_3d": "0", + "word_freq_our": "1.23", + "word_freq_over": "0.19", + "word_freq_remove": "0.19", + "word_freq_internet": "0.12", + "word_freq_order": "0.64", + "word_freq_mail": "0.25", + "word_freq_receive": "0.38", + "word_freq_will": "0.45", + "word_freq_people": "0.12", + "word_freq_report": "0", + "word_freq_addresses": "1.75", + "word_freq_free": "0.06", + "word_freq_business": "0.06", + "word_freq_email": "1.03", + "word_freq_you": "1.36", + "word_freq_credit": "0.32", + "word_freq_your": "0.51", + "word_freq_font": "0", + "word_freq_000": "1.16", + "word_freq_money": "0.06", + "word_freq_hp": "0", + "word_freq_hpl": "0", + "word_freq_george": "0", + "word_freq_650": "0", + "word_freq_lab": "0", + "word_freq_labs": "0", + "word_freq_telnet": "0", + "word_freq_857": "0", + "word_freq_data": "0", + "word_freq_415": "0", + "word_freq_85": "0", + "word_freq_technology": "0", + "word_freq_1999": "0", + "word_freq_parts": "0", + "word_freq_pm": "0", + "word_freq_direct": "0.06", + "word_freq_cs": "0", + "word_freq_meeting": "0", + "word_freq_original": "0.12", + "word_freq_project": "0", + "word_freq_re": "0.06", + "word_freq_edu": "0.06", + "word_freq_table": "0", + "word_freq_conference": "0", + "char_freq_%3B": "0.01", + "char_freq_%28": "0.143", + "char_freq_%5B": "0", + "char_freq_%21": "0.276", + "char_freq_%24": "0.184", + "char_freq_%23": "0.01", + "capital_run_length_average": "9.821", + "capital_run_length_longest": "485", + "capital_run_length_total": "2259", + "class": "1" + }, + { + "word_freq_make": "0", + "word_freq_address": "0", + "word_freq_all": "0", + "word_freq_3d": "0", + "word_freq_our": "0.63", + "word_freq_over": "0", + "word_freq_remove": "0.31", + "word_freq_internet": "0.63", + "word_freq_order": "0.31", + "word_freq_mail": "0.63", + "word_freq_receive": "0.31", + "word_freq_will": "0.31", + "word_freq_people": "0.31", + "word_freq_report": "0", + "word_freq_addresses": "0", + "word_freq_free": "0.31", + "word_freq_business": "0", + "word_freq_email": "0", + "word_freq_you": "3.18", + "word_freq_credit": "0", + "word_freq_your": "0.31", + "word_freq_font": "0", + "word_freq_000": "0", + "word_freq_money": "0", + "word_freq_hp": "0", + "word_freq_hpl": "0", + "word_freq_george": "0", + "word_freq_650": "0", + "word_freq_lab": "0", + "word_freq_labs": "0", + "word_freq_telnet": "0", + "word_freq_857": "0", + "word_freq_data": "0", + "word_freq_415": "0", + "word_freq_85": "0", + "word_freq_technology": "0", + "word_freq_1999": "0", + "word_freq_parts": "0", + "word_freq_pm": "0", + "word_freq_direct": "0", + "word_freq_cs": "0", + "word_freq_meeting": "0", + "word_freq_original": "0", + "word_freq_project": "0", + "word_freq_re": "0", + "word_freq_edu": "0", + "word_freq_table": "0", + "word_freq_conference": "0", + "char_freq_%3B": "0", + "char_freq_%28": "0.137", + "char_freq_%5B": "0", + "char_freq_%21": "0.137", + "char_freq_%24": "0", + "char_freq_%23": "0", + "capital_run_length_average": "3.537", + "capital_run_length_longest": "40", + "capital_run_length_total": "191", + "class": "1" + }, + { + "word_freq_make": "0", + "word_freq_address": "0", + "word_freq_all": "0", + "word_freq_3d": "0", + "word_freq_our": "0.63", + "word_freq_over": "0", + "word_freq_remove": "0.31", + "word_freq_internet": "0.63", + "word_freq_order": "0.31", + "word_freq_mail": "0.63", + "word_freq_receive": "0.31", + "word_freq_will": "0.31", + "word_freq_people": "0.31", + "word_freq_report": "0", + "word_freq_addresses": "0", + "word_freq_free": "0.31", + "word_freq_business": "0", + "word_freq_email": "0", + "word_freq_you": "3.18", + "word_freq_credit": "0", + "word_freq_your": "0.31", + "word_freq_font": "0", + "word_freq_000": "0", + "word_freq_money": "0", + "word_freq_hp": "0", + "word_freq_hpl": "0", + "word_freq_george": "0", + "word_freq_650": "0", + "word_freq_lab": "0", + "word_freq_labs": "0", + "word_freq_telnet": "0", + "word_freq_857": "0", + "word_freq_data": "0", + "word_freq_415": "0", + "word_freq_85": "0", + "word_freq_technology": "0", + "word_freq_1999": "0", + "word_freq_parts": "0", + "word_freq_pm": "0", + "word_freq_direct": "0", + "word_freq_cs": "0", + "word_freq_meeting": "0", + "word_freq_original": "0", + "word_freq_project": "0", + "word_freq_re": "0", + "word_freq_edu": "0", + "word_freq_table": "0", + "word_freq_conference": "0", + "char_freq_%3B": "0", + "char_freq_%28": "0.135", + "char_freq_%5B": "0", + "char_freq_%21": "0.135", + "char_freq_%24": "0", + "char_freq_%23": "0", + "capital_run_length_average": "3.537", + "capital_run_length_longest": "40", + "capital_run_length_total": "191", + "class": "1" + } + ] +} + +Shortlisted templates: +[ + { + "template_id": "tpl_tail_low_support_group_count_v2", + "template_name": "Low-Support Group Count", + "primary_family": "tail_rarity_structure", + "portability": "yes", + "sql_skeleton": "SELECT\n {group_col},\n COUNT(*) AS support\nFROM {table}\nGROUP BY {group_col}\nORDER BY support ASC, {group_col}\nLIMIT {top_k};", + "required_roles": [ + "group_col" + ] + } +] + +Problem instance: +{ + "dataset_id": "n1", + "question": "Use template Low-Support Group Count to probe tail_mass_similarity with semantic role rare_extreme_view. Focus on group_col=class.", + "planned_template_id": "tpl_tail_low_support_group_count_v2", + "bindings": { + "group_col": "class", + "top_k": 15, + "top_n": 5, + "num_tiles": 10, + "percentile_value": 0.95, + "z_threshold": 2.0, + "fraction_threshold": 0.05, + "baseline_multiplier": 1.75, + "baseline_fraction": 0.1, + "min_group_size": 5, + "min_support": 4, + "measure_threshold": 0.8, + "time_grain": "month", + "lookback_rows": 3, + "current_period_start": "'2024-01-01'", + "current_period_end": "'2024-04-01'", + "previous_period_start": "'2023-10-01'", + "previous_period_end": "'2024-01-01'", + "drift_ratio_threshold": 0.8 + }, + "can_vary": [], + "must_fix": [], + "runtime_sql_skeleton": "SELECT\n {group_col},\n COUNT(*) AS support\nFROM {table}\nGROUP BY {group_col}\nORDER BY support ASC, {group_col}\nLIMIT {top_k};" +} + +Repair context: +{} diff --git a/Query/sql/v2/runs/v2_cli_20260502_081223_d/n1/artifacts/v2q_n1_50bcf86bb04ee1de/cli/sql_response_attempt_1.raw.txt b/Query/sql/v2/runs/v2_cli_20260502_081223_d/n1/artifacts/v2q_n1_50bcf86bb04ee1de/cli/sql_response_attempt_1.raw.txt new file mode 100644 index 0000000000000000000000000000000000000000..81a53cf3d8cc2accf738681268445f8d0d32562c --- /dev/null +++ b/Query/sql/v2/runs/v2_cli_20260502_081223_d/n1/artifacts/v2q_n1_50bcf86bb04ee1de/cli/sql_response_attempt_1.raw.txt @@ -0,0 +1,4 @@ +{"type":"thread.started","thread_id":"019e40fa-4a25-7842-8085-30c74adc5fdc"} +{"type":"turn.started"} +{"type":"item.completed","item":{"id":"item_0","type":"agent_message","text":"{\"sql\":\"-- template_id: tpl_tail_low_support_group_count_v2\\nSELECT\\n \\\"class\\\",\\n COUNT(*) AS \\\"support\\\"\\nFROM \\\"n1\\\"\\nGROUP BY \\\"class\\\"\\nORDER BY \\\"support\\\" ASC, \\\"class\\\"\\nLIMIT 15;\",\"notes\":\"Used the provided Low-Support Group Count template with group_col bound to \\\"class\\\" and top_k set to 15.\"}"}} +{"type":"turn.completed","usage":{"input_tokens":20323,"cached_input_tokens":12032,"output_tokens":307,"reasoning_output_tokens":214}} diff --git a/Query/sql/v2/runs/v2_cli_20260502_081223_d/n1/artifacts/v2q_n1_50bcf86bb04ee1de/cli/sql_response_attempt_1.txt b/Query/sql/v2/runs/v2_cli_20260502_081223_d/n1/artifacts/v2q_n1_50bcf86bb04ee1de/cli/sql_response_attempt_1.txt new file mode 100644 index 0000000000000000000000000000000000000000..e2a2fe7f3d02f78a517c670e3b80c98e634f6416 --- /dev/null +++ b/Query/sql/v2/runs/v2_cli_20260502_081223_d/n1/artifacts/v2q_n1_50bcf86bb04ee1de/cli/sql_response_attempt_1.txt @@ -0,0 +1 @@ +{"sql":"-- template_id: tpl_tail_low_support_group_count_v2\nSELECT\n \"class\",\n COUNT(*) AS \"support\"\nFROM \"n1\"\nGROUP BY \"class\"\nORDER BY \"support\" ASC, \"class\"\nLIMIT 15;","notes":"Used the provided Low-Support Group Count template with group_col bound to \"class\" and top_k set to 15."} \ No newline at end of file diff --git a/Query/sql/v2/runs/v2_cli_20260502_081223_d/n1/artifacts/v2q_n1_50bcf86bb04ee1de/cli/sql_stderr_attempt_1.txt b/Query/sql/v2/runs/v2_cli_20260502_081223_d/n1/artifacts/v2q_n1_50bcf86bb04ee1de/cli/sql_stderr_attempt_1.txt new file mode 100644 index 0000000000000000000000000000000000000000..e69de29bb2d1d6434b8b29ae775ad8c2e48c5391 diff --git a/Query/sql/v2/runs/v2_cli_20260502_081223_d/n1/artifacts/v2q_n1_53e96775ad03e613/cli/conversation.jsonl b/Query/sql/v2/runs/v2_cli_20260502_081223_d/n1/artifacts/v2q_n1_53e96775ad03e613/cli/conversation.jsonl new file mode 100644 index 0000000000000000000000000000000000000000..39e3acd02e7498302d23d7b852fe9a9068509031 --- /dev/null +++ b/Query/sql/v2/runs/v2_cli_20260502_081223_d/n1/artifacts/v2q_n1_53e96775ad03e613/cli/conversation.jsonl @@ -0,0 +1,2 @@ +{"attempt": 1, "phase": "sql_generation", "role": "user", "content_path": "cli/sql_prompt_attempt_1.txt", "metrics": {"chars": 29358, "bytes_utf8": 29358, "lines": 792, "estimated_tokens": null}} +{"attempt": 1, "phase": "sql_generation", "role": "assistant", "content_path": "cli/sql_response_attempt_1.txt", "raw_content_path": "cli/sql_response_attempt_1.raw.txt", "stderr_path": "cli/sql_stderr_attempt_1.txt", "metrics": {"chars": 383, "bytes_utf8": 383, "lines": 1, "estimated_tokens": null}, "usage": {"input_tokens": 20315, "cached_input_tokens": 12032, "output_tokens": 261, "reasoning_output_tokens": 160}} diff --git a/Query/sql/v2/runs/v2_cli_20260502_081223_d/n1/artifacts/v2q_n1_53e96775ad03e613/cli/session_summary.json b/Query/sql/v2/runs/v2_cli_20260502_081223_d/n1/artifacts/v2q_n1_53e96775ad03e613/cli/session_summary.json new file mode 100644 index 0000000000000000000000000000000000000000..f96a842bee33538423c12f695663cfb491bb34c0 --- /dev/null +++ b/Query/sql/v2/runs/v2_cli_20260502_081223_d/n1/artifacts/v2q_n1_53e96775ad03e613/cli/session_summary.json @@ -0,0 +1,25 @@ +{ + "engine": "v2-cli:codex", + "command": "codex exec --skip-git-repo-check --disable plugins --sandbox read-only --cd \"/data/jialinzhang/SQLagent\" -m gpt-5.4 --json -", + "ai_cli_calls": 1, + "usage_summary": { + "dataset_id": "n1", + "model": "v2-cli:codex", + "run_id": "v2q_n1_53e96775ad03e613", + "api_calls": 0, + "input_tokens": 20315, + "cached_input_tokens": 12032, + "output_tokens": 261, + "total_tokens": 20576, + "cost_usd": 0.0, + "ai_cli_calls": 1, + "estimated_input_tokens": 0, + "estimated_output_tokens": 0, + "estimated_total_tokens": 0, + "usage_source": "ai_cli_json_usage", + "cli_elapsed_ms_total": 16801.39, + "sql_execution_elapsed_ms_total": 4.11, + "conversation_log_path": "/data/jialinzhang/TabQueryBench/sql_workloads/v2_current/runs_and_launches/runs/v2_cli_20260502_081223_d/n1/artifacts/v2q_n1_53e96775ad03e613/cli/conversation.jsonl", + "note": "Executed through a local AI CLI with structured usage metadata." + } +} \ No newline at end of file diff --git a/Query/sql/v2/runs/v2_cli_20260502_081223_d/n1/artifacts/v2q_n1_53e96775ad03e613/cli/sql_attempt_1.metadata.json b/Query/sql/v2/runs/v2_cli_20260502_081223_d/n1/artifacts/v2q_n1_53e96775ad03e613/cli/sql_attempt_1.metadata.json new file mode 100644 index 0000000000000000000000000000000000000000..b5c72d3e7c063cea6a8c878c023a6cf6368ad1f5 --- /dev/null +++ b/Query/sql/v2/runs/v2_cli_20260502_081223_d/n1/artifacts/v2q_n1_53e96775ad03e613/cli/sql_attempt_1.metadata.json @@ -0,0 +1,45 @@ +{ + "attempt": 1, + "phase": "sql_generation", + "command": "codex exec --skip-git-repo-check --disable plugins --sandbox read-only --cd \"/data/jialinzhang/SQLagent\" -m gpt-5.4 --json -", + "started_at": "2026-05-19T15:30:29.509934+00:00", + "ended_at": "2026-05-19T15:30:46.311358+00:00", + "elapsed_ms": 16801.39, + "prompt_metrics": { + "chars": 29358, + "bytes_utf8": 29358, + "lines": 792, + "estimated_tokens": null + }, + "stdout_metrics": { + "chars": 1411, + "bytes_utf8": 1411, + "lines": 6, + "estimated_tokens": null + }, + "stderr_metrics": { + "chars": 0, + "bytes_utf8": 0, + "lines": 0, + "estimated_tokens": null + }, + "parsed_output": { + "format": "jsonl_events", + "text_metrics": { + "chars": 383, + "bytes_utf8": 383, + "lines": 1, + "estimated_tokens": null + }, + "usage": { + "input_tokens": 20315, + "cached_input_tokens": 12032, + "output_tokens": 261, + "reasoning_output_tokens": 160 + } + }, + "prompt_path": "cli/sql_prompt_attempt_1.txt", + "response_path": "cli/sql_response_attempt_1.txt", + "raw_response_path": "cli/sql_response_attempt_1.raw.txt", + "stderr_path": "cli/sql_stderr_attempt_1.txt" +} \ No newline at end of file diff --git a/Query/sql/v2/runs/v2_cli_20260502_081223_d/n1/artifacts/v2q_n1_53e96775ad03e613/cli/sql_prompt_attempt_1.txt b/Query/sql/v2/runs/v2_cli_20260502_081223_d/n1/artifacts/v2q_n1_53e96775ad03e613/cli/sql_prompt_attempt_1.txt new file mode 100644 index 0000000000000000000000000000000000000000..f4fe5d61708d90532ab08c171220927132183280 --- /dev/null +++ b/Query/sql/v2/runs/v2_cli_20260502_081223_d/n1/artifacts/v2q_n1_53e96775ad03e613/cli/sql_prompt_attempt_1.txt @@ -0,0 +1,792 @@ +You are generating one SQLite SELECT query for a single-table SQL QA task. +Return strict JSON only, with this schema: {"sql": "...", "notes": "..."}. +Rules: +- Use only the provided table and columns. +- Do not write INSERT, UPDATE, DELETE, DROP, ALTER, CREATE, PRAGMA, ATTACH, DETACH, or VACUUM. +- Prefer the planned template and bound roles when provided. +- Add a leading SQL comment exactly like: -- template_id: . +- Generate SQLite-compatible SQL. SQLite does not support PERCENTILE_CONT or STDDEV. +- Quote identifiers with double quotes. +- Return no markdown and no extra prose. + +Dataset context: +Dataset context for SQL QA: +- dataset_id: n1 +- dataset_name: Spambase +- table_name: n1 +- table_layout: single-table dataset (do not assume joins). +- row_semantics: One row is one email represented by engineered token/character frequency and capitalization-run features. +- task_type: classification +- target_column: class +- main_row_count: 4601 +- important_fields: +- word_freq_make: role=feature, type=numeric_sparse_frequency. tags=['condition_candidate', 'measure', 'rare_pattern_candidate'] desc=Frequency feature for token 'make' in an email message. +- word_freq_address: role=feature, type=numeric_sparse_frequency. tags=['condition_candidate', 'measure', 'rare_pattern_candidate'] desc=Frequency feature for token 'address' in an email message. +- word_freq_all: role=feature, type=numeric_sparse_frequency. tags=['condition_candidate', 'measure', 'rare_pattern_candidate'] desc=Frequency feature for token 'all' in an email message. +- word_freq_3d: role=feature, type=numeric_sparse_frequency. tags=['condition_candidate', 'measure', 'rare_pattern_candidate'] desc=Frequency feature for token '3d' in an email message. +- word_freq_our: role=feature, type=numeric_sparse_frequency. tags=['condition_candidate', 'measure', 'rare_pattern_candidate'] desc=Frequency feature for token 'our' in an email message. +- word_freq_over: role=feature, type=numeric_sparse_frequency. tags=['condition_candidate', 'measure', 'rare_pattern_candidate'] desc=Frequency feature for token 'over' in an email message. +- word_freq_remove: role=feature, type=numeric_sparse_frequency. tags=['condition_candidate', 'measure', 'rare_pattern_candidate'] desc=Frequency feature for token 'remove' in an email message. +- word_freq_internet: role=feature, type=numeric_sparse_frequency. tags=['condition_candidate', 'measure', 'rare_pattern_candidate'] desc=Frequency feature for token 'internet' in an email message. +- word_freq_order: role=feature, type=numeric_sparse_frequency. tags=['condition_candidate', 'measure', 'rare_pattern_candidate'] desc=Frequency feature for token 'order' in an email message. +- word_freq_mail: role=feature, type=numeric_sparse_frequency. tags=['condition_candidate', 'measure', 'rare_pattern_candidate'] desc=Frequency feature for token 'mail' in an email message. +- word_freq_receive: role=feature, type=numeric_sparse_frequency. tags=['condition_candidate', 'measure', 'rare_pattern_candidate'] desc=Frequency feature for token 'receive' in an email message. +- word_freq_will: role=feature, type=numeric_sparse_frequency. tags=['condition_candidate', 'measure', 'rare_pattern_candidate'] desc=Frequency feature for token 'will' in an email message. +- word_freq_people: role=feature, type=numeric_sparse_frequency. tags=['condition_candidate', 'measure', 'rare_pattern_candidate'] desc=Frequency feature for token 'people' in an email message. +- word_freq_report: role=feature, type=numeric_sparse_frequency. tags=['condition_candidate', 'measure', 'rare_pattern_candidate'] desc=Frequency feature for token 'report' in an email message. +- word_freq_addresses: role=feature, type=numeric_sparse_frequency. tags=['condition_candidate', 'measure', 'rare_pattern_candidate'] desc=Frequency feature for token 'addresses' in an email message. +- word_freq_free: role=feature, type=numeric_sparse_frequency. tags=['condition_candidate', 'measure', 'rare_pattern_candidate'] desc=Frequency feature for token 'free' in an email message. +- word_freq_business: role=feature, type=numeric_sparse_frequency. tags=['condition_candidate', 'measure', 'rare_pattern_candidate'] desc=Frequency feature for token 'business' in an email message. +- word_freq_email: role=feature, type=numeric_sparse_frequency. tags=['condition_candidate', 'measure', 'rare_pattern_candidate'] desc=Frequency feature for token 'email' in an email message. +- word_freq_you: role=feature, type=numeric_sparse_frequency. tags=['condition_candidate', 'measure', 'rare_pattern_candidate'] desc=Frequency feature for token 'you' in an email message. +- word_freq_credit: role=feature, type=numeric_sparse_frequency. tags=['condition_candidate', 'measure', 'rare_pattern_candidate'] desc=Frequency feature for token 'credit' in an email message. +- word_freq_your: role=feature, type=numeric_sparse_frequency. tags=['condition_candidate', 'measure', 'rare_pattern_candidate'] desc=Frequency feature for token 'your' in an email message. +- word_freq_font: role=feature, type=numeric_sparse_frequency. tags=['condition_candidate', 'measure', 'rare_pattern_candidate'] desc=Frequency feature for token 'font' in an email message. +- word_freq_000: role=feature, type=numeric_sparse_frequency. tags=['condition_candidate', 'measure', 'rare_pattern_candidate'] desc=Frequency feature for token '000' in an email message. +- word_freq_money: role=feature, type=numeric_sparse_frequency. tags=['condition_candidate', 'measure', 'rare_pattern_candidate'] desc=Frequency feature for token 'money' in an email message. +- word_freq_hp: role=feature, type=numeric_sparse_frequency. tags=['condition_candidate', 'measure', 'rare_pattern_candidate'] desc=Frequency feature for token 'hp' in an email message. +- word_freq_hpl: role=feature, type=numeric_sparse_frequency. tags=['condition_candidate', 'measure', 'rare_pattern_candidate'] desc=Frequency feature for token 'hpl' in an email message. +- word_freq_george: role=feature, type=numeric_sparse_frequency. tags=['condition_candidate', 'measure', 'rare_pattern_candidate'] desc=Frequency feature for token 'george' in an email message. +- word_freq_650: role=feature, type=numeric_sparse_frequency. tags=['condition_candidate', 'measure', 'rare_pattern_candidate'] desc=Frequency feature for token '650' in an email message. +- word_freq_lab: role=feature, type=numeric_sparse_frequency. tags=['condition_candidate', 'measure', 'rare_pattern_candidate'] desc=Frequency feature for token 'lab' in an email message. +- word_freq_labs: role=feature, type=numeric_sparse_frequency. tags=['condition_candidate', 'measure', 'rare_pattern_candidate'] desc=Frequency feature for token 'labs' in an email message. +- word_freq_telnet: role=feature, type=numeric_sparse_frequency. tags=['condition_candidate', 'measure', 'rare_pattern_candidate'] desc=Frequency feature for token 'telnet' in an email message. +- word_freq_857: role=feature, type=numeric_sparse_frequency. tags=['condition_candidate', 'measure', 'rare_pattern_candidate'] desc=Frequency feature for token '857' in an email message. +- word_freq_data: role=feature, type=numeric_sparse_frequency. tags=['condition_candidate', 'measure', 'rare_pattern_candidate'] desc=Frequency feature for token 'data' in an email message. +- word_freq_415: role=feature, type=numeric_sparse_frequency. tags=['condition_candidate', 'measure', 'rare_pattern_candidate'] desc=Frequency feature for token '415' in an email message. +- word_freq_85: role=feature, type=numeric_sparse_frequency. tags=['condition_candidate', 'measure', 'rare_pattern_candidate'] desc=Frequency feature for token '85' in an email message. +- word_freq_technology: role=feature, type=numeric_sparse_frequency. tags=['condition_candidate', 'measure', 'rare_pattern_candidate'] desc=Frequency feature for token 'technology' in an email message. +- word_freq_1999: role=feature, type=numeric_sparse_frequency. tags=['condition_candidate', 'measure', 'rare_pattern_candidate'] desc=Frequency feature for token '1999' in an email message. +- word_freq_parts: role=feature, type=numeric_sparse_frequency. tags=['condition_candidate', 'measure', 'rare_pattern_candidate'] desc=Frequency feature for token 'parts' in an email message. +- word_freq_pm: role=feature, type=numeric_sparse_frequency. tags=['condition_candidate', 'measure', 'rare_pattern_candidate'] desc=Frequency feature for token 'pm' in an email message. +- word_freq_direct: role=feature, type=numeric_sparse_frequency. tags=['condition_candidate', 'measure', 'rare_pattern_candidate'] desc=Frequency feature for token 'direct' in an email message. +- word_freq_cs: role=feature, type=numeric_sparse_frequency. tags=['condition_candidate', 'measure', 'rare_pattern_candidate'] desc=Frequency feature for token 'cs' in an email message. +- word_freq_meeting: role=feature, type=numeric_sparse_frequency. tags=['condition_candidate', 'measure', 'rare_pattern_candidate'] desc=Frequency feature for token 'meeting' in an email message. +- word_freq_original: role=feature, type=numeric_sparse_frequency. tags=['condition_candidate', 'measure', 'rare_pattern_candidate'] desc=Frequency feature for token 'original' in an email message. +- word_freq_project: role=feature, type=numeric_sparse_frequency. tags=['condition_candidate', 'measure', 'rare_pattern_candidate'] desc=Frequency feature for token 'project' in an email message. +- word_freq_re: role=feature, type=numeric_sparse_frequency. tags=['condition_candidate', 'measure', 'rare_pattern_candidate'] desc=Frequency feature for token 're' in an email message. +- word_freq_edu: role=feature, type=numeric_sparse_frequency. tags=['condition_candidate', 'measure', 'rare_pattern_candidate'] desc=Frequency feature for token 'edu' in an email message. +- word_freq_table: role=feature, type=numeric_sparse_frequency. tags=['condition_candidate', 'measure', 'rare_pattern_candidate'] desc=Frequency feature for token 'table' in an email message. +- word_freq_conference: role=feature, type=numeric_sparse_frequency. tags=['condition_candidate', 'measure', 'rare_pattern_candidate'] desc=Frequency feature for token 'conference' in an email message. +- char_freq_%3B: role=feature, type=numeric_sparse_frequency. tags=['condition_candidate', 'measure', 'rare_pattern_candidate'] desc=Character frequency feature for symbol ';'. +- char_freq_%28: role=feature, type=numeric_sparse_frequency. tags=['condition_candidate', 'measure', 'rare_pattern_candidate'] desc=Character frequency feature for symbol '('. +- char_freq_%5B: role=feature, type=numeric_sparse_frequency. tags=['condition_candidate', 'measure', 'rare_pattern_candidate'] desc=Character frequency feature for symbol '['. +- char_freq_%21: role=feature, type=numeric_sparse_frequency. tags=['condition_candidate', 'measure', 'rare_pattern_candidate'] desc=Character frequency feature for symbol '!'. +- char_freq_%24: role=feature, type=numeric_sparse_frequency. tags=['condition_candidate', 'measure', 'rare_pattern_candidate'] desc=Character frequency feature for symbol '$'. +- char_freq_%23: role=feature, type=numeric_sparse_frequency. tags=['condition_candidate', 'measure', 'rare_pattern_candidate'] desc=Character frequency feature for symbol '#'. +- capital_run_length_average: role=feature, type=numeric_heavy_tail. tags=['condition_candidate', 'measure', 'rare_pattern_candidate'] desc=Average length of uninterrupted capital-letter runs. +- capital_run_length_longest: role=feature, type=numeric_heavy_tail. tags=['condition_candidate', 'measure', 'rare_pattern_candidate'] desc=Longest uninterrupted capital-letter run. +- capital_run_length_total: role=feature, type=numeric_heavy_tail. tags=['condition_candidate', 'measure', 'rare_pattern_candidate'] desc=Total number of capital letters in runs. +- class: role=target, type=binary_target. tags=['target_candidate'] desc=Binary spam label (1=spam, 0=non-spam). +- useful_field_combinations: [['word_freq_free', 'word_freq_you', 'class'], ['char_freq_%21', 'capital_run_length_longest', 'class'], ['word_freq_remove', 'word_freq_money', 'class']] +- fields_requiring_caution: ['capital_run_length_average', 'capital_run_length_longest', 'capital_run_length_total'] +- source_url: https://www.openml.org/d/44 + +SQLite schema snapshot: +{ + "table_name": "n1", + "quoted_table_name": "\"n1\"", + "row_count": 4601, + "columns": [ + { + "name": "word_freq_make", + "type": "TEXT", + "notnull": false, + "pk": false + }, + { + "name": "word_freq_address", + "type": "TEXT", + "notnull": false, + "pk": false + }, + { + "name": "word_freq_all", + "type": "TEXT", + "notnull": false, + "pk": false + }, + { + "name": "word_freq_3d", + "type": "TEXT", + "notnull": false, + "pk": false + }, + { + "name": "word_freq_our", + "type": "TEXT", + "notnull": false, + "pk": false + }, + { + "name": "word_freq_over", + "type": "TEXT", + "notnull": false, + "pk": false + }, + { + "name": "word_freq_remove", + "type": "TEXT", + "notnull": false, + "pk": false + }, + { + "name": "word_freq_internet", + "type": "TEXT", + "notnull": false, + "pk": false + }, + { + "name": "word_freq_order", + "type": "TEXT", + "notnull": false, + "pk": false + }, + { + "name": "word_freq_mail", + "type": "TEXT", + "notnull": false, + "pk": false + }, + { + "name": "word_freq_receive", + "type": "TEXT", + "notnull": false, + "pk": false + }, + { + "name": "word_freq_will", + "type": "TEXT", + "notnull": false, + "pk": false + }, + { + "name": "word_freq_people", + "type": "TEXT", + "notnull": false, + "pk": false + }, + { + "name": "word_freq_report", + "type": "TEXT", + "notnull": false, + "pk": false + }, + { + "name": "word_freq_addresses", + "type": "TEXT", + "notnull": false, + "pk": false + }, + { + "name": "word_freq_free", + "type": "TEXT", + "notnull": false, + "pk": false + }, + { + "name": "word_freq_business", + "type": "TEXT", + "notnull": false, + "pk": false + }, + { + "name": "word_freq_email", + "type": "TEXT", + "notnull": false, + "pk": false + }, + { + "name": "word_freq_you", + "type": "TEXT", + "notnull": false, + "pk": false + }, + { + "name": "word_freq_credit", + "type": "TEXT", + "notnull": false, + "pk": false + }, + { + "name": "word_freq_your", + "type": "TEXT", + "notnull": false, + "pk": false + }, + { + "name": "word_freq_font", + "type": "TEXT", + "notnull": false, + "pk": false + }, + { + "name": "word_freq_000", + "type": "TEXT", + "notnull": false, + "pk": false + }, + { + "name": "word_freq_money", + "type": "TEXT", + "notnull": false, + "pk": false + }, + { + "name": "word_freq_hp", + "type": "TEXT", + "notnull": false, + "pk": false + }, + { + "name": "word_freq_hpl", + "type": "TEXT", + "notnull": false, + "pk": false + }, + { + "name": "word_freq_george", + "type": "TEXT", + "notnull": false, + "pk": false + }, + { + "name": "word_freq_650", + "type": "TEXT", + "notnull": false, + "pk": false + }, + { + "name": "word_freq_lab", + "type": "TEXT", + "notnull": false, + "pk": false + }, + { + "name": "word_freq_labs", + "type": "TEXT", + "notnull": false, + "pk": false + }, + { + "name": "word_freq_telnet", + "type": "TEXT", + "notnull": false, + "pk": false + }, + { + "name": "word_freq_857", + "type": "TEXT", + "notnull": false, + "pk": false + }, + { + "name": "word_freq_data", + "type": "TEXT", + "notnull": false, + "pk": false + }, + { + "name": "word_freq_415", + "type": "TEXT", + "notnull": false, + "pk": false + }, + { + "name": "word_freq_85", + "type": "TEXT", + "notnull": false, + "pk": false + }, + { + "name": "word_freq_technology", + "type": "TEXT", + "notnull": false, + "pk": false + }, + { + "name": "word_freq_1999", + "type": "TEXT", + "notnull": false, + "pk": false + }, + { + "name": "word_freq_parts", + "type": "TEXT", + "notnull": false, + "pk": false + }, + { + "name": "word_freq_pm", + "type": "TEXT", + "notnull": false, + "pk": false + }, + { + "name": "word_freq_direct", + "type": "TEXT", + "notnull": false, + "pk": false + }, + { + "name": "word_freq_cs", + "type": "TEXT", + "notnull": false, + "pk": false + }, + { + "name": "word_freq_meeting", + "type": "TEXT", + "notnull": false, + "pk": false + }, + { + "name": "word_freq_original", + "type": "TEXT", + "notnull": false, + "pk": false + }, + { + "name": "word_freq_project", + "type": "TEXT", + "notnull": false, + "pk": false + }, + { + "name": "word_freq_re", + "type": "TEXT", + "notnull": false, + "pk": false + }, + { + "name": "word_freq_edu", + "type": "TEXT", + "notnull": false, + "pk": false + }, + { + "name": "word_freq_table", + "type": "TEXT", + "notnull": false, + "pk": false + }, + { + "name": "word_freq_conference", + "type": "TEXT", + "notnull": false, + "pk": false + }, + { + "name": "char_freq_%3B", + "type": "TEXT", + "notnull": false, + "pk": false + }, + { + "name": "char_freq_%28", + "type": "TEXT", + "notnull": false, + "pk": false + }, + { + "name": "char_freq_%5B", + "type": "TEXT", + "notnull": false, + "pk": false + }, + { + "name": "char_freq_%21", + "type": "TEXT", + "notnull": false, + "pk": false + }, + { + "name": "char_freq_%24", + "type": "TEXT", + "notnull": false, + "pk": false + }, + { + "name": "char_freq_%23", + "type": "TEXT", + "notnull": false, + "pk": false + }, + { + "name": "capital_run_length_average", + "type": "TEXT", + "notnull": false, + "pk": false + }, + { + "name": "capital_run_length_longest", + "type": "TEXT", + "notnull": false, + "pk": false + }, + { + "name": "capital_run_length_total", + "type": "TEXT", + "notnull": false, + "pk": false + }, + { + "name": "class", + "type": "TEXT", + "notnull": false, + "pk": false + } + ], + "sample_rows": [ + { + "word_freq_make": "0", + "word_freq_address": "0.64", + "word_freq_all": "0.64", + "word_freq_3d": "0", + "word_freq_our": "0.32", + "word_freq_over": "0", + "word_freq_remove": "0", + "word_freq_internet": "0", + "word_freq_order": "0", + "word_freq_mail": "0", + "word_freq_receive": "0", + "word_freq_will": "0.64", + "word_freq_people": "0", + "word_freq_report": "0", + "word_freq_addresses": "0", + "word_freq_free": "0.32", + "word_freq_business": "0", + "word_freq_email": "1.29", + "word_freq_you": "1.93", + "word_freq_credit": "0", + "word_freq_your": "0.96", + "word_freq_font": "0", + "word_freq_000": "0", + "word_freq_money": "0", + "word_freq_hp": "0", + "word_freq_hpl": "0", + "word_freq_george": "0", + "word_freq_650": "0", + "word_freq_lab": "0", + "word_freq_labs": "0", + "word_freq_telnet": "0", + "word_freq_857": "0", + "word_freq_data": "0", + "word_freq_415": "0", + "word_freq_85": "0", + "word_freq_technology": "0", + "word_freq_1999": "0", + "word_freq_parts": "0", + "word_freq_pm": "0", + "word_freq_direct": "0", + "word_freq_cs": "0", + "word_freq_meeting": "0", + "word_freq_original": "0", + "word_freq_project": "0", + "word_freq_re": "0", + "word_freq_edu": "0", + "word_freq_table": "0", + "word_freq_conference": "0", + "char_freq_%3B": "0", + "char_freq_%28": "0", + "char_freq_%5B": "0", + "char_freq_%21": "0.778", + "char_freq_%24": "0", + "char_freq_%23": "0", + "capital_run_length_average": "3.756", + "capital_run_length_longest": "61", + "capital_run_length_total": "278", + "class": "1" + }, + { + "word_freq_make": "0.21", + "word_freq_address": "0.28", + "word_freq_all": "0.5", + "word_freq_3d": "0", + "word_freq_our": "0.14", + "word_freq_over": "0.28", + "word_freq_remove": "0.21", + "word_freq_internet": "0.07", + "word_freq_order": "0", + "word_freq_mail": "0.94", + "word_freq_receive": "0.21", + "word_freq_will": "0.79", + "word_freq_people": "0.65", + "word_freq_report": "0.21", + "word_freq_addresses": "0.14", + "word_freq_free": "0.14", + "word_freq_business": "0.07", + "word_freq_email": "0.28", + "word_freq_you": "3.47", + "word_freq_credit": "0", + "word_freq_your": "1.59", + "word_freq_font": "0", + "word_freq_000": "0.43", + "word_freq_money": "0.43", + "word_freq_hp": "0", + "word_freq_hpl": "0", + "word_freq_george": "0", + "word_freq_650": "0", + "word_freq_lab": "0", + "word_freq_labs": "0", + "word_freq_telnet": "0", + "word_freq_857": "0", + "word_freq_data": "0", + "word_freq_415": "0", + "word_freq_85": "0", + "word_freq_technology": "0", + "word_freq_1999": "0.07", + "word_freq_parts": "0", + "word_freq_pm": "0", + "word_freq_direct": "0", + "word_freq_cs": "0", + "word_freq_meeting": "0", + "word_freq_original": "0", + "word_freq_project": "0", + "word_freq_re": "0", + "word_freq_edu": "0", + "word_freq_table": "0", + "word_freq_conference": "0", + "char_freq_%3B": "0", + "char_freq_%28": "0.132", + "char_freq_%5B": "0", + "char_freq_%21": "0.372", + "char_freq_%24": "0.18", + "char_freq_%23": "0.048", + "capital_run_length_average": "5.114", + "capital_run_length_longest": "101", + "capital_run_length_total": "1028", + "class": "1" + }, + { + "word_freq_make": "0.06", + "word_freq_address": "0", + "word_freq_all": "0.71", + "word_freq_3d": "0", + "word_freq_our": "1.23", + "word_freq_over": "0.19", + "word_freq_remove": "0.19", + "word_freq_internet": "0.12", + "word_freq_order": "0.64", + "word_freq_mail": "0.25", + "word_freq_receive": "0.38", + "word_freq_will": "0.45", + "word_freq_people": "0.12", + "word_freq_report": "0", + "word_freq_addresses": "1.75", + "word_freq_free": "0.06", + "word_freq_business": "0.06", + "word_freq_email": "1.03", + "word_freq_you": "1.36", + "word_freq_credit": "0.32", + "word_freq_your": "0.51", + "word_freq_font": "0", + "word_freq_000": "1.16", + "word_freq_money": "0.06", + "word_freq_hp": "0", + "word_freq_hpl": "0", + "word_freq_george": "0", + "word_freq_650": "0", + "word_freq_lab": "0", + "word_freq_labs": "0", + "word_freq_telnet": "0", + "word_freq_857": "0", + "word_freq_data": "0", + "word_freq_415": "0", + "word_freq_85": "0", + "word_freq_technology": "0", + "word_freq_1999": "0", + "word_freq_parts": "0", + "word_freq_pm": "0", + "word_freq_direct": "0.06", + "word_freq_cs": "0", + "word_freq_meeting": "0", + "word_freq_original": "0.12", + "word_freq_project": "0", + "word_freq_re": "0.06", + "word_freq_edu": "0.06", + "word_freq_table": "0", + "word_freq_conference": "0", + "char_freq_%3B": "0.01", + "char_freq_%28": "0.143", + "char_freq_%5B": "0", + "char_freq_%21": "0.276", + "char_freq_%24": "0.184", + "char_freq_%23": "0.01", + "capital_run_length_average": "9.821", + "capital_run_length_longest": "485", + "capital_run_length_total": "2259", + "class": "1" + }, + { + "word_freq_make": "0", + "word_freq_address": "0", + "word_freq_all": "0", + "word_freq_3d": "0", + "word_freq_our": "0.63", + "word_freq_over": "0", + "word_freq_remove": "0.31", + "word_freq_internet": "0.63", + "word_freq_order": "0.31", + "word_freq_mail": "0.63", + "word_freq_receive": "0.31", + "word_freq_will": "0.31", + "word_freq_people": "0.31", + "word_freq_report": "0", + "word_freq_addresses": "0", + "word_freq_free": "0.31", + "word_freq_business": "0", + "word_freq_email": "0", + "word_freq_you": "3.18", + "word_freq_credit": "0", + "word_freq_your": "0.31", + "word_freq_font": "0", + "word_freq_000": "0", + "word_freq_money": "0", + "word_freq_hp": "0", + "word_freq_hpl": "0", + "word_freq_george": "0", + "word_freq_650": "0", + "word_freq_lab": "0", + "word_freq_labs": "0", + "word_freq_telnet": "0", + "word_freq_857": "0", + "word_freq_data": "0", + "word_freq_415": "0", + "word_freq_85": "0", + "word_freq_technology": "0", + "word_freq_1999": "0", + "word_freq_parts": "0", + "word_freq_pm": "0", + "word_freq_direct": "0", + "word_freq_cs": "0", + "word_freq_meeting": "0", + "word_freq_original": "0", + "word_freq_project": "0", + "word_freq_re": "0", + "word_freq_edu": "0", + "word_freq_table": "0", + "word_freq_conference": "0", + "char_freq_%3B": "0", + "char_freq_%28": "0.137", + "char_freq_%5B": "0", + "char_freq_%21": "0.137", + "char_freq_%24": "0", + "char_freq_%23": "0", + "capital_run_length_average": "3.537", + "capital_run_length_longest": "40", + "capital_run_length_total": "191", + "class": "1" + }, + { + "word_freq_make": "0", + "word_freq_address": "0", + "word_freq_all": "0", + "word_freq_3d": "0", + "word_freq_our": "0.63", + "word_freq_over": "0", + "word_freq_remove": "0.31", + "word_freq_internet": "0.63", + "word_freq_order": "0.31", + "word_freq_mail": "0.63", + "word_freq_receive": "0.31", + "word_freq_will": "0.31", + "word_freq_people": "0.31", + "word_freq_report": "0", + "word_freq_addresses": "0", + "word_freq_free": "0.31", + "word_freq_business": "0", + "word_freq_email": "0", + "word_freq_you": "3.18", + "word_freq_credit": "0", + "word_freq_your": "0.31", + "word_freq_font": "0", + "word_freq_000": "0", + "word_freq_money": "0", + "word_freq_hp": "0", + "word_freq_hpl": "0", + "word_freq_george": "0", + "word_freq_650": "0", + "word_freq_lab": "0", + "word_freq_labs": "0", + "word_freq_telnet": "0", + "word_freq_857": "0", + "word_freq_data": "0", + "word_freq_415": "0", + "word_freq_85": "0", + "word_freq_technology": "0", + "word_freq_1999": "0", + "word_freq_parts": "0", + "word_freq_pm": "0", + "word_freq_direct": "0", + "word_freq_cs": "0", + "word_freq_meeting": "0", + "word_freq_original": "0", + "word_freq_project": "0", + "word_freq_re": "0", + "word_freq_edu": "0", + "word_freq_table": "0", + "word_freq_conference": "0", + "char_freq_%3B": "0", + "char_freq_%28": "0.135", + "char_freq_%5B": "0", + "char_freq_%21": "0.135", + "char_freq_%24": "0", + "char_freq_%23": "0", + "capital_run_length_average": "3.537", + "capital_run_length_longest": "40", + "capital_run_length_total": "191", + "class": "1" + } + ] +} + +Shortlisted templates: +[ + { + "template_id": "tpl_h2o_group_sum", + "template_name": "Grouped Numeric Sum", + "primary_family": "subgroup_structure", + "portability": "partial", + "sql_skeleton": "SELECT {group_col}, SUM({measure_col}) AS total_measure\nFROM {table}\nGROUP BY {group_col}\nORDER BY total_measure DESC;", + "required_roles": [ + "group_col", + "measure_col" + ] + } +] + +Problem instance: +{ + "dataset_id": "n1", + "question": "Use template Grouped Numeric Sum to probe internal_profile_stability with semantic role collapsed_target_view. Focus on group_col=class, measure_col=word_freq_over.", + "planned_template_id": "tpl_h2o_group_sum", + "bindings": { + "group_col": "class", + "measure_col": "word_freq_over", + "top_k": 15, + "top_n": 5, + "num_tiles": 10, + "percentile_value": 0.95, + "z_threshold": 2.0, + "fraction_threshold": 0.05, + "baseline_multiplier": 1.75, + "baseline_fraction": 0.1, + "min_group_size": 5, + "min_support": 4, + "measure_threshold": 0.0, + "time_grain": "month", + "lookback_rows": 3, + "current_period_start": "'2024-01-01'", + "current_period_end": "'2024-04-01'", + "previous_period_start": "'2023-10-01'", + "previous_period_end": "'2024-01-01'", + "drift_ratio_threshold": 0.8 + }, + "can_vary": [], + "must_fix": [], + "runtime_sql_skeleton": "SELECT {group_col}, SUM({measure_col}) AS total_measure\nFROM {table}\nGROUP BY {group_col}\nORDER BY total_measure DESC;" +} + +Repair context: +{} diff --git a/Query/sql/v2/runs/v2_cli_20260502_081223_d/n1/artifacts/v2q_n1_53e96775ad03e613/cli/sql_response_attempt_1.raw.txt b/Query/sql/v2/runs/v2_cli_20260502_081223_d/n1/artifacts/v2q_n1_53e96775ad03e613/cli/sql_response_attempt_1.raw.txt new file mode 100644 index 0000000000000000000000000000000000000000..a534ce8c1dce3cd76f09bd1f6045b9288ee56fd0 --- /dev/null +++ b/Query/sql/v2/runs/v2_cli_20260502_081223_d/n1/artifacts/v2q_n1_53e96775ad03e613/cli/sql_response_attempt_1.raw.txt @@ -0,0 +1,6 @@ +{"type":"thread.started","thread_id":"019e40dc-2512-7ec1-9cbe-fb581f2b3eb2"} +{"type":"turn.started"} +{"type":"error","message":"Reconnecting... 2/5 (stream disconnected before completion: Rate limit reached for gpt-5.4 in organization org-yXK0mBGOR7eXhtGEx8v7e3bf on tokens per min (TPM): Limit 2000000, Used 2000000, Requested 20950. Please try again in 628ms. Visit https://platform.openai.com/account/rate-limits to learn more.)"} +{"type":"error","message":"Reconnecting... 3/5 (stream disconnected before completion: Rate limit reached for gpt-5.4 in organization org-yXK0mBGOR7eXhtGEx8v7e3bf on tokens per min (TPM): Limit 2000000, Used 1999437, Requested 20950. Please try again in 611ms. Visit https://platform.openai.com/account/rate-limits to learn more.)"} +{"type":"item.completed","item":{"id":"item_0","type":"agent_message","text":"{\"sql\":\"-- template_id: tpl_h2o_group_sum\\nSELECT \\\"class\\\", SUM(CAST(\\\"word_freq_over\\\" AS REAL)) AS \\\"total_measure\\\"\\nFROM \\\"n1\\\"\\nGROUP BY \\\"class\\\"\\nORDER BY \\\"total_measure\\\" DESC;\",\"notes\":\"Used the provided template with group_col=\\\"class\\\" and measure_col=\\\"word_freq_over\\\". CAST to REAL is included because the schema snapshot types these numeric-looking fields as TEXT.\"}"}} +{"type":"turn.completed","usage":{"input_tokens":20315,"cached_input_tokens":12032,"output_tokens":261,"reasoning_output_tokens":160}} diff --git a/Query/sql/v2/runs/v2_cli_20260502_081223_d/n1/artifacts/v2q_n1_53e96775ad03e613/cli/sql_response_attempt_1.txt b/Query/sql/v2/runs/v2_cli_20260502_081223_d/n1/artifacts/v2q_n1_53e96775ad03e613/cli/sql_response_attempt_1.txt new file mode 100644 index 0000000000000000000000000000000000000000..903cbc76067fb7316e1dde4c3146cd966967ba50 --- /dev/null +++ b/Query/sql/v2/runs/v2_cli_20260502_081223_d/n1/artifacts/v2q_n1_53e96775ad03e613/cli/sql_response_attempt_1.txt @@ -0,0 +1 @@ +{"sql":"-- template_id: tpl_h2o_group_sum\nSELECT \"class\", SUM(CAST(\"word_freq_over\" AS REAL)) AS \"total_measure\"\nFROM \"n1\"\nGROUP BY \"class\"\nORDER BY \"total_measure\" DESC;","notes":"Used the provided template with group_col=\"class\" and measure_col=\"word_freq_over\". CAST to REAL is included because the schema snapshot types these numeric-looking fields as TEXT."} \ No newline at end of file diff --git a/Query/sql/v2/runs/v2_cli_20260502_081223_d/n1/artifacts/v2q_n1_53e96775ad03e613/cli/sql_stderr_attempt_1.txt b/Query/sql/v2/runs/v2_cli_20260502_081223_d/n1/artifacts/v2q_n1_53e96775ad03e613/cli/sql_stderr_attempt_1.txt new file mode 100644 index 0000000000000000000000000000000000000000..e69de29bb2d1d6434b8b29ae775ad8c2e48c5391 diff --git a/Query/sql/v2/runs/v2_cli_20260502_081223_d/n1/artifacts/v2q_n1_5aeecbc5027a1afb/cli/conversation.jsonl b/Query/sql/v2/runs/v2_cli_20260502_081223_d/n1/artifacts/v2q_n1_5aeecbc5027a1afb/cli/conversation.jsonl new file mode 100644 index 0000000000000000000000000000000000000000..acb245bc89b685d668bfdbf8ef364f4174b94ce3 --- /dev/null +++ b/Query/sql/v2/runs/v2_cli_20260502_081223_d/n1/artifacts/v2q_n1_5aeecbc5027a1afb/cli/conversation.jsonl @@ -0,0 +1,2 @@ +{"attempt": 1, "phase": "sql_generation", "role": "user", "content_path": "cli/sql_prompt_attempt_1.txt", "metrics": {"chars": 29768, "bytes_utf8": 29768, "lines": 794, "estimated_tokens": null}} +{"attempt": 1, "phase": "sql_generation", "role": "assistant", "content_path": "cli/sql_response_attempt_1.txt", "raw_content_path": "cli/sql_response_attempt_1.raw.txt", "stderr_path": "cli/sql_stderr_attempt_1.txt", "metrics": {"chars": 635, "bytes_utf8": 635, "lines": 1, "estimated_tokens": null}, "usage": {"input_tokens": 20435, "cached_input_tokens": 19840, "output_tokens": 425, "reasoning_output_tokens": 246}} diff --git a/Query/sql/v2/runs/v2_cli_20260502_081223_d/n1/artifacts/v2q_n1_5aeecbc5027a1afb/cli/session_summary.json b/Query/sql/v2/runs/v2_cli_20260502_081223_d/n1/artifacts/v2q_n1_5aeecbc5027a1afb/cli/session_summary.json new file mode 100644 index 0000000000000000000000000000000000000000..200fd5471f472bf916ca3b8ea29ca35d994d0ce4 --- /dev/null +++ b/Query/sql/v2/runs/v2_cli_20260502_081223_d/n1/artifacts/v2q_n1_5aeecbc5027a1afb/cli/session_summary.json @@ -0,0 +1,25 @@ +{ + "engine": "v2-cli:codex", + "command": "codex exec --skip-git-repo-check --disable plugins --sandbox read-only --cd \"/data/jialinzhang/SQLagent\" -m gpt-5.4 --json -", + "ai_cli_calls": 1, + "usage_summary": { + "dataset_id": "n1", + "model": "v2-cli:codex", + "run_id": "v2q_n1_5aeecbc5027a1afb", + "api_calls": 0, + "input_tokens": 20435, + "cached_input_tokens": 19840, + "output_tokens": 425, + "total_tokens": 20860, + "cost_usd": 0.0, + "ai_cli_calls": 1, + "estimated_input_tokens": 0, + "estimated_output_tokens": 0, + "estimated_total_tokens": 0, + "usage_source": "ai_cli_json_usage", + "cli_elapsed_ms_total": 13906.97, + "sql_execution_elapsed_ms_total": 3.53, + "conversation_log_path": "/data/jialinzhang/TabQueryBench/sql_workloads/v2_current/runs_and_launches/runs/v2_cli_20260502_081223_d/n1/artifacts/v2q_n1_5aeecbc5027a1afb/cli/conversation.jsonl", + "note": "Executed through a local AI CLI with structured usage metadata." + } +} \ No newline at end of file diff --git a/Query/sql/v2/runs/v2_cli_20260502_081223_d/n1/artifacts/v2q_n1_5aeecbc5027a1afb/cli/sql_attempt_1.metadata.json b/Query/sql/v2/runs/v2_cli_20260502_081223_d/n1/artifacts/v2q_n1_5aeecbc5027a1afb/cli/sql_attempt_1.metadata.json new file mode 100644 index 0000000000000000000000000000000000000000..0328adc0b3f0253dbc9c29fa868566ea131a4588 --- /dev/null +++ b/Query/sql/v2/runs/v2_cli_20260502_081223_d/n1/artifacts/v2q_n1_5aeecbc5027a1afb/cli/sql_attempt_1.metadata.json @@ -0,0 +1,45 @@ +{ + "attempt": 1, + "phase": "sql_generation", + "command": "codex exec --skip-git-repo-check --disable plugins --sandbox read-only --cd \"/data/jialinzhang/SQLagent\" -m gpt-5.4 --json -", + "started_at": "2026-05-19T15:38:38.122513+00:00", + "ended_at": "2026-05-19T15:38:52.029511+00:00", + "elapsed_ms": 13906.97, + "prompt_metrics": { + "chars": 29768, + "bytes_utf8": 29768, + "lines": 794, + "estimated_tokens": null + }, + "stdout_metrics": { + "chars": 1017, + "bytes_utf8": 1017, + "lines": 4, + "estimated_tokens": null + }, + "stderr_metrics": { + "chars": 0, + "bytes_utf8": 0, + "lines": 0, + "estimated_tokens": null + }, + "parsed_output": { + "format": "jsonl_events", + "text_metrics": { + "chars": 635, + "bytes_utf8": 635, + "lines": 1, + "estimated_tokens": null + }, + "usage": { + "input_tokens": 20435, + "cached_input_tokens": 19840, + "output_tokens": 425, + "reasoning_output_tokens": 246 + } + }, + "prompt_path": "cli/sql_prompt_attempt_1.txt", + "response_path": "cli/sql_response_attempt_1.txt", + "raw_response_path": "cli/sql_response_attempt_1.raw.txt", + "stderr_path": "cli/sql_stderr_attempt_1.txt" +} \ No newline at end of file diff --git a/Query/sql/v2/runs/v2_cli_20260502_081223_d/n1/artifacts/v2q_n1_5aeecbc5027a1afb/cli/sql_prompt_attempt_1.txt b/Query/sql/v2/runs/v2_cli_20260502_081223_d/n1/artifacts/v2q_n1_5aeecbc5027a1afb/cli/sql_prompt_attempt_1.txt new file mode 100644 index 0000000000000000000000000000000000000000..94b8352a83c59ed301f7fd0bd974a498aeab5703 --- /dev/null +++ b/Query/sql/v2/runs/v2_cli_20260502_081223_d/n1/artifacts/v2q_n1_5aeecbc5027a1afb/cli/sql_prompt_attempt_1.txt @@ -0,0 +1,794 @@ +You are generating one SQLite SELECT query for a single-table SQL QA task. +Return strict JSON only, with this schema: {"sql": "...", "notes": "..."}. +Rules: +- Use only the provided table and columns. +- Do not write INSERT, UPDATE, DELETE, DROP, ALTER, CREATE, PRAGMA, ATTACH, DETACH, or VACUUM. +- Prefer the planned template and bound roles when provided. +- Add a leading SQL comment exactly like: -- template_id: . +- Generate SQLite-compatible SQL. SQLite does not support PERCENTILE_CONT or STDDEV. +- Quote identifiers with double quotes. +- Return no markdown and no extra prose. + +Dataset context: +Dataset context for SQL QA: +- dataset_id: n1 +- dataset_name: Spambase +- table_name: n1 +- table_layout: single-table dataset (do not assume joins). +- row_semantics: One row is one email represented by engineered token/character frequency and capitalization-run features. +- task_type: classification +- target_column: class +- main_row_count: 4601 +- important_fields: +- word_freq_make: role=feature, type=numeric_sparse_frequency. tags=['condition_candidate', 'measure', 'rare_pattern_candidate'] desc=Frequency feature for token 'make' in an email message. +- word_freq_address: role=feature, type=numeric_sparse_frequency. tags=['condition_candidate', 'measure', 'rare_pattern_candidate'] desc=Frequency feature for token 'address' in an email message. +- word_freq_all: role=feature, type=numeric_sparse_frequency. tags=['condition_candidate', 'measure', 'rare_pattern_candidate'] desc=Frequency feature for token 'all' in an email message. +- word_freq_3d: role=feature, type=numeric_sparse_frequency. tags=['condition_candidate', 'measure', 'rare_pattern_candidate'] desc=Frequency feature for token '3d' in an email message. +- word_freq_our: role=feature, type=numeric_sparse_frequency. tags=['condition_candidate', 'measure', 'rare_pattern_candidate'] desc=Frequency feature for token 'our' in an email message. +- word_freq_over: role=feature, type=numeric_sparse_frequency. tags=['condition_candidate', 'measure', 'rare_pattern_candidate'] desc=Frequency feature for token 'over' in an email message. +- word_freq_remove: role=feature, type=numeric_sparse_frequency. tags=['condition_candidate', 'measure', 'rare_pattern_candidate'] desc=Frequency feature for token 'remove' in an email message. +- word_freq_internet: role=feature, type=numeric_sparse_frequency. tags=['condition_candidate', 'measure', 'rare_pattern_candidate'] desc=Frequency feature for token 'internet' in an email message. +- word_freq_order: role=feature, type=numeric_sparse_frequency. tags=['condition_candidate', 'measure', 'rare_pattern_candidate'] desc=Frequency feature for token 'order' in an email message. +- word_freq_mail: role=feature, type=numeric_sparse_frequency. tags=['condition_candidate', 'measure', 'rare_pattern_candidate'] desc=Frequency feature for token 'mail' in an email message. +- word_freq_receive: role=feature, type=numeric_sparse_frequency. tags=['condition_candidate', 'measure', 'rare_pattern_candidate'] desc=Frequency feature for token 'receive' in an email message. +- word_freq_will: role=feature, type=numeric_sparse_frequency. tags=['condition_candidate', 'measure', 'rare_pattern_candidate'] desc=Frequency feature for token 'will' in an email message. +- word_freq_people: role=feature, type=numeric_sparse_frequency. tags=['condition_candidate', 'measure', 'rare_pattern_candidate'] desc=Frequency feature for token 'people' in an email message. +- word_freq_report: role=feature, type=numeric_sparse_frequency. tags=['condition_candidate', 'measure', 'rare_pattern_candidate'] desc=Frequency feature for token 'report' in an email message. +- word_freq_addresses: role=feature, type=numeric_sparse_frequency. tags=['condition_candidate', 'measure', 'rare_pattern_candidate'] desc=Frequency feature for token 'addresses' in an email message. +- word_freq_free: role=feature, type=numeric_sparse_frequency. tags=['condition_candidate', 'measure', 'rare_pattern_candidate'] desc=Frequency feature for token 'free' in an email message. +- word_freq_business: role=feature, type=numeric_sparse_frequency. tags=['condition_candidate', 'measure', 'rare_pattern_candidate'] desc=Frequency feature for token 'business' in an email message. +- word_freq_email: role=feature, type=numeric_sparse_frequency. tags=['condition_candidate', 'measure', 'rare_pattern_candidate'] desc=Frequency feature for token 'email' in an email message. +- word_freq_you: role=feature, type=numeric_sparse_frequency. tags=['condition_candidate', 'measure', 'rare_pattern_candidate'] desc=Frequency feature for token 'you' in an email message. +- word_freq_credit: role=feature, type=numeric_sparse_frequency. tags=['condition_candidate', 'measure', 'rare_pattern_candidate'] desc=Frequency feature for token 'credit' in an email message. +- word_freq_your: role=feature, type=numeric_sparse_frequency. tags=['condition_candidate', 'measure', 'rare_pattern_candidate'] desc=Frequency feature for token 'your' in an email message. +- word_freq_font: role=feature, type=numeric_sparse_frequency. tags=['condition_candidate', 'measure', 'rare_pattern_candidate'] desc=Frequency feature for token 'font' in an email message. +- word_freq_000: role=feature, type=numeric_sparse_frequency. tags=['condition_candidate', 'measure', 'rare_pattern_candidate'] desc=Frequency feature for token '000' in an email message. +- word_freq_money: role=feature, type=numeric_sparse_frequency. tags=['condition_candidate', 'measure', 'rare_pattern_candidate'] desc=Frequency feature for token 'money' in an email message. +- word_freq_hp: role=feature, type=numeric_sparse_frequency. tags=['condition_candidate', 'measure', 'rare_pattern_candidate'] desc=Frequency feature for token 'hp' in an email message. +- word_freq_hpl: role=feature, type=numeric_sparse_frequency. tags=['condition_candidate', 'measure', 'rare_pattern_candidate'] desc=Frequency feature for token 'hpl' in an email message. +- word_freq_george: role=feature, type=numeric_sparse_frequency. tags=['condition_candidate', 'measure', 'rare_pattern_candidate'] desc=Frequency feature for token 'george' in an email message. +- word_freq_650: role=feature, type=numeric_sparse_frequency. tags=['condition_candidate', 'measure', 'rare_pattern_candidate'] desc=Frequency feature for token '650' in an email message. +- word_freq_lab: role=feature, type=numeric_sparse_frequency. tags=['condition_candidate', 'measure', 'rare_pattern_candidate'] desc=Frequency feature for token 'lab' in an email message. +- word_freq_labs: role=feature, type=numeric_sparse_frequency. tags=['condition_candidate', 'measure', 'rare_pattern_candidate'] desc=Frequency feature for token 'labs' in an email message. +- word_freq_telnet: role=feature, type=numeric_sparse_frequency. tags=['condition_candidate', 'measure', 'rare_pattern_candidate'] desc=Frequency feature for token 'telnet' in an email message. +- word_freq_857: role=feature, type=numeric_sparse_frequency. tags=['condition_candidate', 'measure', 'rare_pattern_candidate'] desc=Frequency feature for token '857' in an email message. +- word_freq_data: role=feature, type=numeric_sparse_frequency. tags=['condition_candidate', 'measure', 'rare_pattern_candidate'] desc=Frequency feature for token 'data' in an email message. +- word_freq_415: role=feature, type=numeric_sparse_frequency. tags=['condition_candidate', 'measure', 'rare_pattern_candidate'] desc=Frequency feature for token '415' in an email message. +- word_freq_85: role=feature, type=numeric_sparse_frequency. tags=['condition_candidate', 'measure', 'rare_pattern_candidate'] desc=Frequency feature for token '85' in an email message. +- word_freq_technology: role=feature, type=numeric_sparse_frequency. tags=['condition_candidate', 'measure', 'rare_pattern_candidate'] desc=Frequency feature for token 'technology' in an email message. +- word_freq_1999: role=feature, type=numeric_sparse_frequency. tags=['condition_candidate', 'measure', 'rare_pattern_candidate'] desc=Frequency feature for token '1999' in an email message. +- word_freq_parts: role=feature, type=numeric_sparse_frequency. tags=['condition_candidate', 'measure', 'rare_pattern_candidate'] desc=Frequency feature for token 'parts' in an email message. +- word_freq_pm: role=feature, type=numeric_sparse_frequency. tags=['condition_candidate', 'measure', 'rare_pattern_candidate'] desc=Frequency feature for token 'pm' in an email message. +- word_freq_direct: role=feature, type=numeric_sparse_frequency. tags=['condition_candidate', 'measure', 'rare_pattern_candidate'] desc=Frequency feature for token 'direct' in an email message. +- word_freq_cs: role=feature, type=numeric_sparse_frequency. tags=['condition_candidate', 'measure', 'rare_pattern_candidate'] desc=Frequency feature for token 'cs' in an email message. +- word_freq_meeting: role=feature, type=numeric_sparse_frequency. tags=['condition_candidate', 'measure', 'rare_pattern_candidate'] desc=Frequency feature for token 'meeting' in an email message. +- word_freq_original: role=feature, type=numeric_sparse_frequency. tags=['condition_candidate', 'measure', 'rare_pattern_candidate'] desc=Frequency feature for token 'original' in an email message. +- word_freq_project: role=feature, type=numeric_sparse_frequency. tags=['condition_candidate', 'measure', 'rare_pattern_candidate'] desc=Frequency feature for token 'project' in an email message. +- word_freq_re: role=feature, type=numeric_sparse_frequency. tags=['condition_candidate', 'measure', 'rare_pattern_candidate'] desc=Frequency feature for token 're' in an email message. +- word_freq_edu: role=feature, type=numeric_sparse_frequency. tags=['condition_candidate', 'measure', 'rare_pattern_candidate'] desc=Frequency feature for token 'edu' in an email message. +- word_freq_table: role=feature, type=numeric_sparse_frequency. tags=['condition_candidate', 'measure', 'rare_pattern_candidate'] desc=Frequency feature for token 'table' in an email message. +- word_freq_conference: role=feature, type=numeric_sparse_frequency. tags=['condition_candidate', 'measure', 'rare_pattern_candidate'] desc=Frequency feature for token 'conference' in an email message. +- char_freq_%3B: role=feature, type=numeric_sparse_frequency. tags=['condition_candidate', 'measure', 'rare_pattern_candidate'] desc=Character frequency feature for symbol ';'. +- char_freq_%28: role=feature, type=numeric_sparse_frequency. tags=['condition_candidate', 'measure', 'rare_pattern_candidate'] desc=Character frequency feature for symbol '('. +- char_freq_%5B: role=feature, type=numeric_sparse_frequency. tags=['condition_candidate', 'measure', 'rare_pattern_candidate'] desc=Character frequency feature for symbol '['. +- char_freq_%21: role=feature, type=numeric_sparse_frequency. tags=['condition_candidate', 'measure', 'rare_pattern_candidate'] desc=Character frequency feature for symbol '!'. +- char_freq_%24: role=feature, type=numeric_sparse_frequency. tags=['condition_candidate', 'measure', 'rare_pattern_candidate'] desc=Character frequency feature for symbol '$'. +- char_freq_%23: role=feature, type=numeric_sparse_frequency. tags=['condition_candidate', 'measure', 'rare_pattern_candidate'] desc=Character frequency feature for symbol '#'. +- capital_run_length_average: role=feature, type=numeric_heavy_tail. tags=['condition_candidate', 'measure', 'rare_pattern_candidate'] desc=Average length of uninterrupted capital-letter runs. +- capital_run_length_longest: role=feature, type=numeric_heavy_tail. tags=['condition_candidate', 'measure', 'rare_pattern_candidate'] desc=Longest uninterrupted capital-letter run. +- capital_run_length_total: role=feature, type=numeric_heavy_tail. tags=['condition_candidate', 'measure', 'rare_pattern_candidate'] desc=Total number of capital letters in runs. +- class: role=target, type=binary_target. tags=['target_candidate'] desc=Binary spam label (1=spam, 0=non-spam). +- useful_field_combinations: [['word_freq_free', 'word_freq_you', 'class'], ['char_freq_%21', 'capital_run_length_longest', 'class'], ['word_freq_remove', 'word_freq_money', 'class']] +- fields_requiring_caution: ['capital_run_length_average', 'capital_run_length_longest', 'capital_run_length_total'] +- source_url: https://www.openml.org/d/44 + +SQLite schema snapshot: +{ + "table_name": "n1", + "quoted_table_name": "\"n1\"", + "row_count": 4601, + "columns": [ + { + "name": "word_freq_make", + "type": "TEXT", + "notnull": false, + "pk": false + }, + { + "name": "word_freq_address", + "type": "TEXT", + "notnull": false, + "pk": false + }, + { + "name": "word_freq_all", + "type": "TEXT", + "notnull": false, + "pk": false + }, + { + "name": "word_freq_3d", + "type": "TEXT", + "notnull": false, + "pk": false + }, + { + "name": "word_freq_our", + "type": "TEXT", + "notnull": false, + "pk": false + }, + { + "name": "word_freq_over", + "type": "TEXT", + "notnull": false, + "pk": false + }, + { + "name": "word_freq_remove", + "type": "TEXT", + "notnull": false, + "pk": false + }, + { + "name": "word_freq_internet", + "type": "TEXT", + "notnull": false, + "pk": false + }, + { + "name": "word_freq_order", + "type": "TEXT", + "notnull": false, + "pk": false + }, + { + "name": "word_freq_mail", + "type": "TEXT", + "notnull": false, + "pk": false + }, + { + "name": "word_freq_receive", + "type": "TEXT", + "notnull": false, + "pk": false + }, + { + "name": "word_freq_will", + "type": "TEXT", + "notnull": false, + "pk": false + }, + { + "name": "word_freq_people", + "type": "TEXT", + "notnull": false, + "pk": false + }, + { + "name": "word_freq_report", + "type": "TEXT", + "notnull": false, + "pk": false + }, + { + "name": "word_freq_addresses", + "type": "TEXT", + "notnull": false, + "pk": false + }, + { + "name": "word_freq_free", + "type": "TEXT", + "notnull": false, + "pk": false + }, + { + "name": "word_freq_business", + "type": "TEXT", + "notnull": false, + "pk": false + }, + { + "name": "word_freq_email", + "type": "TEXT", + "notnull": false, + "pk": false + }, + { + "name": "word_freq_you", + "type": "TEXT", + "notnull": false, + "pk": false + }, + { + "name": "word_freq_credit", + "type": "TEXT", + "notnull": false, + "pk": false + }, + { + "name": "word_freq_your", + "type": "TEXT", + "notnull": false, + "pk": false + }, + { + "name": "word_freq_font", + "type": "TEXT", + "notnull": false, + "pk": false + }, + { + "name": "word_freq_000", + "type": "TEXT", + "notnull": false, + "pk": false + }, + { + "name": "word_freq_money", + "type": "TEXT", + "notnull": false, + "pk": false + }, + { + "name": "word_freq_hp", + "type": "TEXT", + "notnull": false, + "pk": false + }, + { + "name": "word_freq_hpl", + "type": "TEXT", + "notnull": false, + "pk": false + }, + { + "name": "word_freq_george", + "type": "TEXT", + "notnull": false, + "pk": false + }, + { + "name": "word_freq_650", + "type": "TEXT", + "notnull": false, + "pk": false + }, + { + "name": "word_freq_lab", + "type": "TEXT", + "notnull": false, + "pk": false + }, + { + "name": "word_freq_labs", + "type": "TEXT", + "notnull": false, + "pk": false + }, + { + "name": "word_freq_telnet", + "type": "TEXT", + "notnull": false, + "pk": false + }, + { + "name": "word_freq_857", + "type": "TEXT", + "notnull": false, + "pk": false + }, + { + "name": "word_freq_data", + "type": "TEXT", + "notnull": false, + "pk": false + }, + { + "name": "word_freq_415", + "type": "TEXT", + "notnull": false, + "pk": false + }, + { + "name": "word_freq_85", + "type": "TEXT", + "notnull": false, + "pk": false + }, + { + "name": "word_freq_technology", + "type": "TEXT", + "notnull": false, + "pk": false + }, + { + "name": "word_freq_1999", + "type": "TEXT", + "notnull": false, + "pk": false + }, + { + "name": "word_freq_parts", + "type": "TEXT", + "notnull": false, + "pk": false + }, + { + "name": "word_freq_pm", + "type": "TEXT", + "notnull": false, + "pk": false + }, + { + "name": "word_freq_direct", + "type": "TEXT", + "notnull": false, + "pk": false + }, + { + "name": "word_freq_cs", + "type": "TEXT", + "notnull": false, + "pk": false + }, + { + "name": "word_freq_meeting", + "type": "TEXT", + "notnull": false, + "pk": false + }, + { + "name": "word_freq_original", + "type": "TEXT", + "notnull": false, + "pk": false + }, + { + "name": "word_freq_project", + "type": "TEXT", + "notnull": false, + "pk": false + }, + { + "name": "word_freq_re", + "type": "TEXT", + "notnull": false, + "pk": false + }, + { + "name": "word_freq_edu", + "type": "TEXT", + "notnull": false, + "pk": false + }, + { + "name": "word_freq_table", + "type": "TEXT", + "notnull": false, + "pk": false + }, + { + "name": "word_freq_conference", + "type": "TEXT", + "notnull": false, + "pk": false + }, + { + "name": "char_freq_%3B", + "type": "TEXT", + "notnull": false, + "pk": false + }, + { + "name": "char_freq_%28", + "type": "TEXT", + "notnull": false, + "pk": false + }, + { + "name": "char_freq_%5B", + "type": "TEXT", + "notnull": false, + "pk": false + }, + { + "name": "char_freq_%21", + "type": "TEXT", + "notnull": false, + "pk": false + }, + { + "name": "char_freq_%24", + "type": "TEXT", + "notnull": false, + "pk": false + }, + { + "name": "char_freq_%23", + "type": "TEXT", + "notnull": false, + "pk": false + }, + { + "name": "capital_run_length_average", + "type": "TEXT", + "notnull": false, + "pk": false + }, + { + "name": "capital_run_length_longest", + "type": "TEXT", + "notnull": false, + "pk": false + }, + { + "name": "capital_run_length_total", + "type": "TEXT", + "notnull": false, + "pk": false + }, + { + "name": "class", + "type": "TEXT", + "notnull": false, + "pk": false + } + ], + "sample_rows": [ + { + "word_freq_make": "0", + "word_freq_address": "0.64", + "word_freq_all": "0.64", + "word_freq_3d": "0", + "word_freq_our": "0.32", + "word_freq_over": "0", + "word_freq_remove": "0", + "word_freq_internet": "0", + "word_freq_order": "0", + "word_freq_mail": "0", + "word_freq_receive": "0", + "word_freq_will": "0.64", + "word_freq_people": "0", + "word_freq_report": "0", + "word_freq_addresses": "0", + "word_freq_free": "0.32", + "word_freq_business": "0", + "word_freq_email": "1.29", + "word_freq_you": "1.93", + "word_freq_credit": "0", + "word_freq_your": "0.96", + "word_freq_font": "0", + "word_freq_000": "0", + "word_freq_money": "0", + "word_freq_hp": "0", + "word_freq_hpl": "0", + "word_freq_george": "0", + "word_freq_650": "0", + "word_freq_lab": "0", + "word_freq_labs": "0", + "word_freq_telnet": "0", + "word_freq_857": "0", + "word_freq_data": "0", + "word_freq_415": "0", + "word_freq_85": "0", + "word_freq_technology": "0", + "word_freq_1999": "0", + "word_freq_parts": "0", + "word_freq_pm": "0", + "word_freq_direct": "0", + "word_freq_cs": "0", + "word_freq_meeting": "0", + "word_freq_original": "0", + "word_freq_project": "0", + "word_freq_re": "0", + "word_freq_edu": "0", + "word_freq_table": "0", + "word_freq_conference": "0", + "char_freq_%3B": "0", + "char_freq_%28": "0", + "char_freq_%5B": "0", + "char_freq_%21": "0.778", + "char_freq_%24": "0", + "char_freq_%23": "0", + "capital_run_length_average": "3.756", + "capital_run_length_longest": "61", + "capital_run_length_total": "278", + "class": "1" + }, + { + "word_freq_make": "0.21", + "word_freq_address": "0.28", + "word_freq_all": "0.5", + "word_freq_3d": "0", + "word_freq_our": "0.14", + "word_freq_over": "0.28", + "word_freq_remove": "0.21", + "word_freq_internet": "0.07", + "word_freq_order": "0", + "word_freq_mail": "0.94", + "word_freq_receive": "0.21", + "word_freq_will": "0.79", + "word_freq_people": "0.65", + "word_freq_report": "0.21", + "word_freq_addresses": "0.14", + "word_freq_free": "0.14", + "word_freq_business": "0.07", + "word_freq_email": "0.28", + "word_freq_you": "3.47", + "word_freq_credit": "0", + "word_freq_your": "1.59", + "word_freq_font": "0", + "word_freq_000": "0.43", + "word_freq_money": "0.43", + "word_freq_hp": "0", + "word_freq_hpl": "0", + "word_freq_george": "0", + "word_freq_650": "0", + "word_freq_lab": "0", + "word_freq_labs": "0", + "word_freq_telnet": "0", + "word_freq_857": "0", + "word_freq_data": "0", + "word_freq_415": "0", + "word_freq_85": "0", + "word_freq_technology": "0", + "word_freq_1999": "0.07", + "word_freq_parts": "0", + "word_freq_pm": "0", + "word_freq_direct": "0", + "word_freq_cs": "0", + "word_freq_meeting": "0", + "word_freq_original": "0", + "word_freq_project": "0", + "word_freq_re": "0", + "word_freq_edu": "0", + "word_freq_table": "0", + "word_freq_conference": "0", + "char_freq_%3B": "0", + "char_freq_%28": "0.132", + "char_freq_%5B": "0", + "char_freq_%21": "0.372", + "char_freq_%24": "0.18", + "char_freq_%23": "0.048", + "capital_run_length_average": "5.114", + "capital_run_length_longest": "101", + "capital_run_length_total": "1028", + "class": "1" + }, + { + "word_freq_make": "0.06", + "word_freq_address": "0", + "word_freq_all": "0.71", + "word_freq_3d": "0", + "word_freq_our": "1.23", + "word_freq_over": "0.19", + "word_freq_remove": "0.19", + "word_freq_internet": "0.12", + "word_freq_order": "0.64", + "word_freq_mail": "0.25", + "word_freq_receive": "0.38", + "word_freq_will": "0.45", + "word_freq_people": "0.12", + "word_freq_report": "0", + "word_freq_addresses": "1.75", + "word_freq_free": "0.06", + "word_freq_business": "0.06", + "word_freq_email": "1.03", + "word_freq_you": "1.36", + "word_freq_credit": "0.32", + "word_freq_your": "0.51", + "word_freq_font": "0", + "word_freq_000": "1.16", + "word_freq_money": "0.06", + "word_freq_hp": "0", + "word_freq_hpl": "0", + "word_freq_george": "0", + "word_freq_650": "0", + "word_freq_lab": "0", + "word_freq_labs": "0", + "word_freq_telnet": "0", + "word_freq_857": "0", + "word_freq_data": "0", + "word_freq_415": "0", + "word_freq_85": "0", + "word_freq_technology": "0", + "word_freq_1999": "0", + "word_freq_parts": "0", + "word_freq_pm": "0", + "word_freq_direct": "0.06", + "word_freq_cs": "0", + "word_freq_meeting": "0", + "word_freq_original": "0.12", + "word_freq_project": "0", + "word_freq_re": "0.06", + "word_freq_edu": "0.06", + "word_freq_table": "0", + "word_freq_conference": "0", + "char_freq_%3B": "0.01", + "char_freq_%28": "0.143", + "char_freq_%5B": "0", + "char_freq_%21": "0.276", + "char_freq_%24": "0.184", + "char_freq_%23": "0.01", + "capital_run_length_average": "9.821", + "capital_run_length_longest": "485", + "capital_run_length_total": "2259", + "class": "1" + }, + { + "word_freq_make": "0", + "word_freq_address": "0", + "word_freq_all": "0", + "word_freq_3d": "0", + "word_freq_our": "0.63", + "word_freq_over": "0", + "word_freq_remove": "0.31", + "word_freq_internet": "0.63", + "word_freq_order": "0.31", + "word_freq_mail": "0.63", + "word_freq_receive": "0.31", + "word_freq_will": "0.31", + "word_freq_people": "0.31", + "word_freq_report": "0", + "word_freq_addresses": "0", + "word_freq_free": "0.31", + "word_freq_business": "0", + "word_freq_email": "0", + "word_freq_you": "3.18", + "word_freq_credit": "0", + "word_freq_your": "0.31", + "word_freq_font": "0", + "word_freq_000": "0", + "word_freq_money": "0", + "word_freq_hp": "0", + "word_freq_hpl": "0", + "word_freq_george": "0", + "word_freq_650": "0", + "word_freq_lab": "0", + "word_freq_labs": "0", + "word_freq_telnet": "0", + "word_freq_857": "0", + "word_freq_data": "0", + "word_freq_415": "0", + "word_freq_85": "0", + "word_freq_technology": "0", + "word_freq_1999": "0", + "word_freq_parts": "0", + "word_freq_pm": "0", + "word_freq_direct": "0", + "word_freq_cs": "0", + "word_freq_meeting": "0", + "word_freq_original": "0", + "word_freq_project": "0", + "word_freq_re": "0", + "word_freq_edu": "0", + "word_freq_table": "0", + "word_freq_conference": "0", + "char_freq_%3B": "0", + "char_freq_%28": "0.137", + "char_freq_%5B": "0", + "char_freq_%21": "0.137", + "char_freq_%24": "0", + "char_freq_%23": "0", + "capital_run_length_average": "3.537", + "capital_run_length_longest": "40", + "capital_run_length_total": "191", + "class": "1" + }, + { + "word_freq_make": "0", + "word_freq_address": "0", + "word_freq_all": "0", + "word_freq_3d": "0", + "word_freq_our": "0.63", + "word_freq_over": "0", + "word_freq_remove": "0.31", + "word_freq_internet": "0.63", + "word_freq_order": "0.31", + "word_freq_mail": "0.63", + "word_freq_receive": "0.31", + "word_freq_will": "0.31", + "word_freq_people": "0.31", + "word_freq_report": "0", + "word_freq_addresses": "0", + "word_freq_free": "0.31", + "word_freq_business": "0", + "word_freq_email": "0", + "word_freq_you": "3.18", + "word_freq_credit": "0", + "word_freq_your": "0.31", + "word_freq_font": "0", + "word_freq_000": "0", + "word_freq_money": "0", + "word_freq_hp": "0", + "word_freq_hpl": "0", + "word_freq_george": "0", + "word_freq_650": "0", + "word_freq_lab": "0", + "word_freq_labs": "0", + "word_freq_telnet": "0", + "word_freq_857": "0", + "word_freq_data": "0", + "word_freq_415": "0", + "word_freq_85": "0", + "word_freq_technology": "0", + "word_freq_1999": "0", + "word_freq_parts": "0", + "word_freq_pm": "0", + "word_freq_direct": "0", + "word_freq_cs": "0", + "word_freq_meeting": "0", + "word_freq_original": "0", + "word_freq_project": "0", + "word_freq_re": "0", + "word_freq_edu": "0", + "word_freq_table": "0", + "word_freq_conference": "0", + "char_freq_%3B": "0", + "char_freq_%28": "0.135", + "char_freq_%5B": "0", + "char_freq_%21": "0.135", + "char_freq_%24": "0", + "char_freq_%23": "0", + "capital_run_length_average": "3.537", + "capital_run_length_longest": "40", + "capital_run_length_total": "191", + "class": "1" + } + ] +} + +Shortlisted templates: +[ + { + "template_id": "tpl_tpcds_within_group_share", + "template_name": "Within-Group Share of Total", + "primary_family": "conditional_dependency_structure", + "portability": "partial", + "sql_skeleton": "SELECT {group_col}, {item_col},\n SUM({measure_col}) AS total_measure,\n SUM({measure_col}) * 100.0 / SUM(SUM({measure_col})) OVER (PARTITION BY {group_col}) AS share_within_group\nFROM {table}\nGROUP BY {group_col}, {item_col}\nORDER BY share_within_group DESC;", + "required_roles": [ + "group_col", + "item_col", + "measure_col" + ] + } +] + +Problem instance: +{ + "dataset_id": "n1", + "question": "Use template Within-Group Share of Total to probe dependency_strength_similarity with semantic role focused_target_view. Focus on group_col=class, measure_col=word_freq_data.", + "planned_template_id": "tpl_tpcds_within_group_share", + "bindings": { + "group_col": "class", + "measure_col": "word_freq_data", + "item_col": "word_freq_415", + "top_k": 17, + "top_n": 4, + "num_tiles": 10, + "percentile_value": 0.9, + "z_threshold": 2.0, + "fraction_threshold": 0.05, + "baseline_multiplier": 1.75, + "baseline_fraction": 0.1, + "min_group_size": 5, + "min_support": 4, + "measure_threshold": 0.0, + "time_grain": "month", + "lookback_rows": 3, + "current_period_start": "'2024-01-01'", + "current_period_end": "'2024-04-01'", + "previous_period_start": "'2023-10-01'", + "previous_period_end": "'2024-01-01'", + "drift_ratio_threshold": 0.8 + }, + "can_vary": [], + "must_fix": [], + "runtime_sql_skeleton": "SELECT {group_col}, {item_col},\n SUM({measure_col}) AS total_measure,\n SUM({measure_col}) * 100.0 / SUM(SUM({measure_col})) OVER (PARTITION BY {group_col}) AS share_within_group\nFROM {table}\nGROUP BY {group_col}, {item_col}\nORDER BY share_within_group DESC;" +} + +Repair context: +{} diff --git a/Query/sql/v2/runs/v2_cli_20260502_081223_d/n1/artifacts/v2q_n1_5aeecbc5027a1afb/cli/sql_response_attempt_1.raw.txt b/Query/sql/v2/runs/v2_cli_20260502_081223_d/n1/artifacts/v2q_n1_5aeecbc5027a1afb/cli/sql_response_attempt_1.raw.txt new file mode 100644 index 0000000000000000000000000000000000000000..240a6e94e0b054fb757c755750910098fbd0e283 --- /dev/null +++ b/Query/sql/v2/runs/v2_cli_20260502_081223_d/n1/artifacts/v2q_n1_5aeecbc5027a1afb/cli/sql_response_attempt_1.raw.txt @@ -0,0 +1,4 @@ +{"type":"thread.started","thread_id":"019e40e3-99a0-7df1-9d83-d831f0f6e719"} +{"type":"turn.started"} +{"type":"item.completed","item":{"id":"item_0","type":"agent_message","text":"{\"sql\":\"-- template_id: tpl_tpcds_within_group_share\\nSELECT\\n \\\"class\\\",\\n \\\"word_freq_415\\\",\\n SUM(CAST(\\\"word_freq_data\\\" AS REAL)) AS total_measure,\\n SUM(CAST(\\\"word_freq_data\\\" AS REAL)) * 100.0 / SUM(SUM(CAST(\\\"word_freq_data\\\" AS REAL))) OVER (PARTITION BY \\\"class\\\") AS share_within_group\\nFROM \\\"n1\\\"\\nGROUP BY \\\"class\\\", \\\"word_freq_415\\\"\\nORDER BY share_within_group DESC;\",\"notes\":\"Applied the provided Within-Group Share of Total template with group_col=\\\"class\\\", item_col=\\\"word_freq_415\\\", and measure_col=\\\"word_freq_data\\\". CAST to REAL is used because the schema stores these numeric fields as TEXT in SQLite.\"}"}} +{"type":"turn.completed","usage":{"input_tokens":20435,"cached_input_tokens":19840,"output_tokens":425,"reasoning_output_tokens":246}} diff --git a/Query/sql/v2/runs/v2_cli_20260502_081223_d/n1/artifacts/v2q_n1_5aeecbc5027a1afb/cli/sql_response_attempt_1.txt b/Query/sql/v2/runs/v2_cli_20260502_081223_d/n1/artifacts/v2q_n1_5aeecbc5027a1afb/cli/sql_response_attempt_1.txt new file mode 100644 index 0000000000000000000000000000000000000000..4d5c2a3cfd1642c48869cd318b9d727199e696ce --- /dev/null +++ b/Query/sql/v2/runs/v2_cli_20260502_081223_d/n1/artifacts/v2q_n1_5aeecbc5027a1afb/cli/sql_response_attempt_1.txt @@ -0,0 +1 @@ +{"sql":"-- template_id: tpl_tpcds_within_group_share\nSELECT\n \"class\",\n \"word_freq_415\",\n SUM(CAST(\"word_freq_data\" AS REAL)) AS total_measure,\n SUM(CAST(\"word_freq_data\" AS REAL)) * 100.0 / SUM(SUM(CAST(\"word_freq_data\" AS REAL))) OVER (PARTITION BY \"class\") AS share_within_group\nFROM \"n1\"\nGROUP BY \"class\", \"word_freq_415\"\nORDER BY share_within_group DESC;","notes":"Applied the provided Within-Group Share of Total template with group_col=\"class\", item_col=\"word_freq_415\", and measure_col=\"word_freq_data\". CAST to REAL is used because the schema stores these numeric fields as TEXT in SQLite."} \ No newline at end of file diff --git a/Query/sql/v2/runs/v2_cli_20260502_081223_d/n1/artifacts/v2q_n1_5aeecbc5027a1afb/cli/sql_stderr_attempt_1.txt b/Query/sql/v2/runs/v2_cli_20260502_081223_d/n1/artifacts/v2q_n1_5aeecbc5027a1afb/cli/sql_stderr_attempt_1.txt new file mode 100644 index 0000000000000000000000000000000000000000..e69de29bb2d1d6434b8b29ae775ad8c2e48c5391 diff --git a/Query/sql/v2/runs/v2_cli_20260502_081223_d/n1/artifacts/v2q_n1_668298232015b5fd/cli/conversation.jsonl b/Query/sql/v2/runs/v2_cli_20260502_081223_d/n1/artifacts/v2q_n1_668298232015b5fd/cli/conversation.jsonl new file mode 100644 index 0000000000000000000000000000000000000000..e800cd3826d136cc161fb40756e424e101b82a94 --- /dev/null +++ b/Query/sql/v2/runs/v2_cli_20260502_081223_d/n1/artifacts/v2q_n1_668298232015b5fd/cli/conversation.jsonl @@ -0,0 +1,4 @@ +{"attempt": 1, "phase": "sql_generation", "role": "user", "content_path": "cli/sql_prompt_attempt_1.txt", "metrics": {"chars": 29299, "bytes_utf8": 29299, "lines": 790, "estimated_tokens": null}} +{"attempt": 1, "phase": "sql_generation", "role": "assistant", "content_path": "cli/sql_response_attempt_1.txt", "raw_content_path": "cli/sql_response_attempt_1.raw.txt", "stderr_path": "cli/sql_stderr_attempt_1.txt", "metrics": {"chars": 280, "bytes_utf8": 280, "lines": 4, "estimated_tokens": null}, "usage": {}, "status": "failed", "error": "AI CLI command failed with exit code 1: "} +{"attempt": 2, "phase": "sql_generation", "role": "user", "content_path": "cli/sql_prompt_attempt_2.txt", "metrics": {"chars": 29299, "bytes_utf8": 29299, "lines": 790, "estimated_tokens": null}} +{"attempt": 2, "phase": "sql_generation", "role": "assistant", "content_path": "cli/sql_response_attempt_2.txt", "raw_content_path": "cli/sql_response_attempt_2.raw.txt", "stderr_path": "cli/sql_stderr_attempt_2.txt", "metrics": {"chars": 366, "bytes_utf8": 366, "lines": 1, "estimated_tokens": null}, "usage": {"input_tokens": 20306, "cached_input_tokens": 12032, "output_tokens": 237, "reasoning_output_tokens": 132}} diff --git a/Query/sql/v2/runs/v2_cli_20260502_081223_d/n1/artifacts/v2q_n1_668298232015b5fd/cli/session_summary.json b/Query/sql/v2/runs/v2_cli_20260502_081223_d/n1/artifacts/v2q_n1_668298232015b5fd/cli/session_summary.json new file mode 100644 index 0000000000000000000000000000000000000000..154a2c585af876e250cdfd31880b927425bb1ad5 --- /dev/null +++ b/Query/sql/v2/runs/v2_cli_20260502_081223_d/n1/artifacts/v2q_n1_668298232015b5fd/cli/session_summary.json @@ -0,0 +1,25 @@ +{ + "engine": "v2-cli:codex", + "command": "codex exec --skip-git-repo-check --disable plugins --sandbox read-only --cd \"/data/jialinzhang/SQLagent\" -m gpt-5.4 --json -", + "ai_cli_calls": 2, + "usage_summary": { + "dataset_id": "n1", + "model": "v2-cli:codex", + "run_id": "v2q_n1_668298232015b5fd", + "api_calls": 0, + "input_tokens": 20306, + "cached_input_tokens": 12032, + "output_tokens": 237, + "total_tokens": 20543, + "cost_usd": 0.0, + "ai_cli_calls": 2, + "estimated_input_tokens": 0, + "estimated_output_tokens": 0, + "estimated_total_tokens": 0, + "usage_source": "ai_cli_json_usage", + "cli_elapsed_ms_total": 12011.82, + "sql_execution_elapsed_ms_total": 1.08, + "conversation_log_path": "/data/jialinzhang/TabQueryBench/sql_workloads/v2_current/runs_and_launches/runs/v2_cli_20260502_081223_d/n1/artifacts/v2q_n1_668298232015b5fd/cli/conversation.jsonl", + "note": "Executed through a local AI CLI with structured usage metadata." + } +} \ No newline at end of file diff --git a/Query/sql/v2/runs/v2_cli_20260502_081223_d/n1/artifacts/v2q_n1_668298232015b5fd/cli/sql_attempt_1.metadata.json b/Query/sql/v2/runs/v2_cli_20260502_081223_d/n1/artifacts/v2q_n1_668298232015b5fd/cli/sql_attempt_1.metadata.json new file mode 100644 index 0000000000000000000000000000000000000000..3267a7068acafca021e0ec16b4b13be3f60dc6b0 --- /dev/null +++ b/Query/sql/v2/runs/v2_cli_20260502_081223_d/n1/artifacts/v2q_n1_668298232015b5fd/cli/sql_attempt_1.metadata.json @@ -0,0 +1,43 @@ +{ + "attempt": 1, + "phase": "sql_generation", + "command": "codex exec --skip-git-repo-check --disable plugins --sandbox read-only --cd \"/data/jialinzhang/SQLagent\" -m gpt-5.4 --json -", + "started_at": "2026-05-19T16:01:17.448319+00:00", + "ended_at": "2026-05-19T16:01:20.753814+00:00", + "elapsed_ms": 3305.47, + "returncode": 1, + "prompt_metrics": { + "chars": 29299, + "bytes_utf8": 29299, + "lines": 790, + "estimated_tokens": null + }, + "stdout_metrics": { + "chars": 281, + "bytes_utf8": 281, + "lines": 4, + "estimated_tokens": null + }, + "stderr_metrics": { + "chars": 0, + "bytes_utf8": 0, + "lines": 0, + "estimated_tokens": null + }, + "parsed_output": { + "format": "jsonl_events", + "text_metrics": { + "chars": 280, + "bytes_utf8": 280, + "lines": 4, + "estimated_tokens": null + }, + "usage": {} + }, + "status": "failed", + "error": "AI CLI command failed with exit code 1: ", + "prompt_path": "cli/sql_prompt_attempt_1.txt", + "response_path": "cli/sql_response_attempt_1.txt", + "raw_response_path": "cli/sql_response_attempt_1.raw.txt", + "stderr_path": "cli/sql_stderr_attempt_1.txt" +} \ No newline at end of file diff --git a/Query/sql/v2/runs/v2_cli_20260502_081223_d/n1/artifacts/v2q_n1_668298232015b5fd/cli/sql_attempt_2.metadata.json b/Query/sql/v2/runs/v2_cli_20260502_081223_d/n1/artifacts/v2q_n1_668298232015b5fd/cli/sql_attempt_2.metadata.json new file mode 100644 index 0000000000000000000000000000000000000000..ee19cefeb1875fe93c8884912de63ce2fb52e653 --- /dev/null +++ b/Query/sql/v2/runs/v2_cli_20260502_081223_d/n1/artifacts/v2q_n1_668298232015b5fd/cli/sql_attempt_2.metadata.json @@ -0,0 +1,45 @@ +{ + "attempt": 2, + "phase": "sql_generation", + "command": "codex exec --skip-git-repo-check --disable plugins --sandbox read-only --cd \"/data/jialinzhang/SQLagent\" -m gpt-5.4 --json -", + "started_at": "2026-05-19T16:01:21.757321+00:00", + "ended_at": "2026-05-19T16:01:30.463725+00:00", + "elapsed_ms": 8706.35, + "prompt_metrics": { + "chars": 29299, + "bytes_utf8": 29299, + "lines": 790, + "estimated_tokens": null + }, + "stdout_metrics": { + "chars": 710, + "bytes_utf8": 710, + "lines": 4, + "estimated_tokens": null + }, + "stderr_metrics": { + "chars": 0, + "bytes_utf8": 0, + "lines": 0, + "estimated_tokens": null + }, + "parsed_output": { + "format": "jsonl_events", + "text_metrics": { + "chars": 366, + "bytes_utf8": 366, + "lines": 1, + "estimated_tokens": null + }, + "usage": { + "input_tokens": 20306, + "cached_input_tokens": 12032, + "output_tokens": 237, + "reasoning_output_tokens": 132 + } + }, + "prompt_path": "cli/sql_prompt_attempt_2.txt", + "response_path": "cli/sql_response_attempt_2.txt", + "raw_response_path": "cli/sql_response_attempt_2.raw.txt", + "stderr_path": "cli/sql_stderr_attempt_2.txt" +} \ No newline at end of file diff --git a/Query/sql/v2/runs/v2_cli_20260502_081223_d/n1/artifacts/v2q_n1_668298232015b5fd/cli/sql_prompt_attempt_1.txt b/Query/sql/v2/runs/v2_cli_20260502_081223_d/n1/artifacts/v2q_n1_668298232015b5fd/cli/sql_prompt_attempt_1.txt new file mode 100644 index 0000000000000000000000000000000000000000..a6be00f01934c72c7d960f710db540dea46487f8 --- /dev/null +++ b/Query/sql/v2/runs/v2_cli_20260502_081223_d/n1/artifacts/v2q_n1_668298232015b5fd/cli/sql_prompt_attempt_1.txt @@ -0,0 +1,790 @@ +You are generating one SQLite SELECT query for a single-table SQL QA task. +Return strict JSON only, with this schema: {"sql": "...", "notes": "..."}. +Rules: +- Use only the provided table and columns. +- Do not write INSERT, UPDATE, DELETE, DROP, ALTER, CREATE, PRAGMA, ATTACH, DETACH, or VACUUM. +- Prefer the planned template and bound roles when provided. +- Add a leading SQL comment exactly like: -- template_id: . +- Generate SQLite-compatible SQL. SQLite does not support PERCENTILE_CONT or STDDEV. +- Quote identifiers with double quotes. +- Return no markdown and no extra prose. + +Dataset context: +Dataset context for SQL QA: +- dataset_id: n1 +- dataset_name: Spambase +- table_name: n1 +- table_layout: single-table dataset (do not assume joins). +- row_semantics: One row is one email represented by engineered token/character frequency and capitalization-run features. +- task_type: classification +- target_column: class +- main_row_count: 4601 +- important_fields: +- word_freq_make: role=feature, type=numeric_sparse_frequency. tags=['condition_candidate', 'measure', 'rare_pattern_candidate'] desc=Frequency feature for token 'make' in an email message. +- word_freq_address: role=feature, type=numeric_sparse_frequency. tags=['condition_candidate', 'measure', 'rare_pattern_candidate'] desc=Frequency feature for token 'address' in an email message. +- word_freq_all: role=feature, type=numeric_sparse_frequency. tags=['condition_candidate', 'measure', 'rare_pattern_candidate'] desc=Frequency feature for token 'all' in an email message. +- word_freq_3d: role=feature, type=numeric_sparse_frequency. tags=['condition_candidate', 'measure', 'rare_pattern_candidate'] desc=Frequency feature for token '3d' in an email message. +- word_freq_our: role=feature, type=numeric_sparse_frequency. tags=['condition_candidate', 'measure', 'rare_pattern_candidate'] desc=Frequency feature for token 'our' in an email message. +- word_freq_over: role=feature, type=numeric_sparse_frequency. tags=['condition_candidate', 'measure', 'rare_pattern_candidate'] desc=Frequency feature for token 'over' in an email message. +- word_freq_remove: role=feature, type=numeric_sparse_frequency. tags=['condition_candidate', 'measure', 'rare_pattern_candidate'] desc=Frequency feature for token 'remove' in an email message. +- word_freq_internet: role=feature, type=numeric_sparse_frequency. tags=['condition_candidate', 'measure', 'rare_pattern_candidate'] desc=Frequency feature for token 'internet' in an email message. +- word_freq_order: role=feature, type=numeric_sparse_frequency. tags=['condition_candidate', 'measure', 'rare_pattern_candidate'] desc=Frequency feature for token 'order' in an email message. +- word_freq_mail: role=feature, type=numeric_sparse_frequency. tags=['condition_candidate', 'measure', 'rare_pattern_candidate'] desc=Frequency feature for token 'mail' in an email message. +- word_freq_receive: role=feature, type=numeric_sparse_frequency. tags=['condition_candidate', 'measure', 'rare_pattern_candidate'] desc=Frequency feature for token 'receive' in an email message. +- word_freq_will: role=feature, type=numeric_sparse_frequency. tags=['condition_candidate', 'measure', 'rare_pattern_candidate'] desc=Frequency feature for token 'will' in an email message. +- word_freq_people: role=feature, type=numeric_sparse_frequency. tags=['condition_candidate', 'measure', 'rare_pattern_candidate'] desc=Frequency feature for token 'people' in an email message. +- word_freq_report: role=feature, type=numeric_sparse_frequency. tags=['condition_candidate', 'measure', 'rare_pattern_candidate'] desc=Frequency feature for token 'report' in an email message. +- word_freq_addresses: role=feature, type=numeric_sparse_frequency. tags=['condition_candidate', 'measure', 'rare_pattern_candidate'] desc=Frequency feature for token 'addresses' in an email message. +- word_freq_free: role=feature, type=numeric_sparse_frequency. tags=['condition_candidate', 'measure', 'rare_pattern_candidate'] desc=Frequency feature for token 'free' in an email message. +- word_freq_business: role=feature, type=numeric_sparse_frequency. tags=['condition_candidate', 'measure', 'rare_pattern_candidate'] desc=Frequency feature for token 'business' in an email message. +- word_freq_email: role=feature, type=numeric_sparse_frequency. tags=['condition_candidate', 'measure', 'rare_pattern_candidate'] desc=Frequency feature for token 'email' in an email message. +- word_freq_you: role=feature, type=numeric_sparse_frequency. tags=['condition_candidate', 'measure', 'rare_pattern_candidate'] desc=Frequency feature for token 'you' in an email message. +- word_freq_credit: role=feature, type=numeric_sparse_frequency. tags=['condition_candidate', 'measure', 'rare_pattern_candidate'] desc=Frequency feature for token 'credit' in an email message. +- word_freq_your: role=feature, type=numeric_sparse_frequency. tags=['condition_candidate', 'measure', 'rare_pattern_candidate'] desc=Frequency feature for token 'your' in an email message. +- word_freq_font: role=feature, type=numeric_sparse_frequency. tags=['condition_candidate', 'measure', 'rare_pattern_candidate'] desc=Frequency feature for token 'font' in an email message. +- word_freq_000: role=feature, type=numeric_sparse_frequency. tags=['condition_candidate', 'measure', 'rare_pattern_candidate'] desc=Frequency feature for token '000' in an email message. +- word_freq_money: role=feature, type=numeric_sparse_frequency. tags=['condition_candidate', 'measure', 'rare_pattern_candidate'] desc=Frequency feature for token 'money' in an email message. +- word_freq_hp: role=feature, type=numeric_sparse_frequency. tags=['condition_candidate', 'measure', 'rare_pattern_candidate'] desc=Frequency feature for token 'hp' in an email message. +- word_freq_hpl: role=feature, type=numeric_sparse_frequency. tags=['condition_candidate', 'measure', 'rare_pattern_candidate'] desc=Frequency feature for token 'hpl' in an email message. +- word_freq_george: role=feature, type=numeric_sparse_frequency. tags=['condition_candidate', 'measure', 'rare_pattern_candidate'] desc=Frequency feature for token 'george' in an email message. +- word_freq_650: role=feature, type=numeric_sparse_frequency. tags=['condition_candidate', 'measure', 'rare_pattern_candidate'] desc=Frequency feature for token '650' in an email message. +- word_freq_lab: role=feature, type=numeric_sparse_frequency. tags=['condition_candidate', 'measure', 'rare_pattern_candidate'] desc=Frequency feature for token 'lab' in an email message. +- word_freq_labs: role=feature, type=numeric_sparse_frequency. tags=['condition_candidate', 'measure', 'rare_pattern_candidate'] desc=Frequency feature for token 'labs' in an email message. +- word_freq_telnet: role=feature, type=numeric_sparse_frequency. tags=['condition_candidate', 'measure', 'rare_pattern_candidate'] desc=Frequency feature for token 'telnet' in an email message. +- word_freq_857: role=feature, type=numeric_sparse_frequency. tags=['condition_candidate', 'measure', 'rare_pattern_candidate'] desc=Frequency feature for token '857' in an email message. +- word_freq_data: role=feature, type=numeric_sparse_frequency. tags=['condition_candidate', 'measure', 'rare_pattern_candidate'] desc=Frequency feature for token 'data' in an email message. +- word_freq_415: role=feature, type=numeric_sparse_frequency. tags=['condition_candidate', 'measure', 'rare_pattern_candidate'] desc=Frequency feature for token '415' in an email message. +- word_freq_85: role=feature, type=numeric_sparse_frequency. tags=['condition_candidate', 'measure', 'rare_pattern_candidate'] desc=Frequency feature for token '85' in an email message. +- word_freq_technology: role=feature, type=numeric_sparse_frequency. tags=['condition_candidate', 'measure', 'rare_pattern_candidate'] desc=Frequency feature for token 'technology' in an email message. +- word_freq_1999: role=feature, type=numeric_sparse_frequency. tags=['condition_candidate', 'measure', 'rare_pattern_candidate'] desc=Frequency feature for token '1999' in an email message. +- word_freq_parts: role=feature, type=numeric_sparse_frequency. tags=['condition_candidate', 'measure', 'rare_pattern_candidate'] desc=Frequency feature for token 'parts' in an email message. +- word_freq_pm: role=feature, type=numeric_sparse_frequency. tags=['condition_candidate', 'measure', 'rare_pattern_candidate'] desc=Frequency feature for token 'pm' in an email message. +- word_freq_direct: role=feature, type=numeric_sparse_frequency. tags=['condition_candidate', 'measure', 'rare_pattern_candidate'] desc=Frequency feature for token 'direct' in an email message. +- word_freq_cs: role=feature, type=numeric_sparse_frequency. tags=['condition_candidate', 'measure', 'rare_pattern_candidate'] desc=Frequency feature for token 'cs' in an email message. +- word_freq_meeting: role=feature, type=numeric_sparse_frequency. tags=['condition_candidate', 'measure', 'rare_pattern_candidate'] desc=Frequency feature for token 'meeting' in an email message. +- word_freq_original: role=feature, type=numeric_sparse_frequency. tags=['condition_candidate', 'measure', 'rare_pattern_candidate'] desc=Frequency feature for token 'original' in an email message. +- word_freq_project: role=feature, type=numeric_sparse_frequency. tags=['condition_candidate', 'measure', 'rare_pattern_candidate'] desc=Frequency feature for token 'project' in an email message. +- word_freq_re: role=feature, type=numeric_sparse_frequency. tags=['condition_candidate', 'measure', 'rare_pattern_candidate'] desc=Frequency feature for token 're' in an email message. +- word_freq_edu: role=feature, type=numeric_sparse_frequency. tags=['condition_candidate', 'measure', 'rare_pattern_candidate'] desc=Frequency feature for token 'edu' in an email message. +- word_freq_table: role=feature, type=numeric_sparse_frequency. tags=['condition_candidate', 'measure', 'rare_pattern_candidate'] desc=Frequency feature for token 'table' in an email message. +- word_freq_conference: role=feature, type=numeric_sparse_frequency. tags=['condition_candidate', 'measure', 'rare_pattern_candidate'] desc=Frequency feature for token 'conference' in an email message. +- char_freq_%3B: role=feature, type=numeric_sparse_frequency. tags=['condition_candidate', 'measure', 'rare_pattern_candidate'] desc=Character frequency feature for symbol ';'. +- char_freq_%28: role=feature, type=numeric_sparse_frequency. tags=['condition_candidate', 'measure', 'rare_pattern_candidate'] desc=Character frequency feature for symbol '('. +- char_freq_%5B: role=feature, type=numeric_sparse_frequency. tags=['condition_candidate', 'measure', 'rare_pattern_candidate'] desc=Character frequency feature for symbol '['. +- char_freq_%21: role=feature, type=numeric_sparse_frequency. tags=['condition_candidate', 'measure', 'rare_pattern_candidate'] desc=Character frequency feature for symbol '!'. +- char_freq_%24: role=feature, type=numeric_sparse_frequency. tags=['condition_candidate', 'measure', 'rare_pattern_candidate'] desc=Character frequency feature for symbol '$'. +- char_freq_%23: role=feature, type=numeric_sparse_frequency. tags=['condition_candidate', 'measure', 'rare_pattern_candidate'] desc=Character frequency feature for symbol '#'. +- capital_run_length_average: role=feature, type=numeric_heavy_tail. tags=['condition_candidate', 'measure', 'rare_pattern_candidate'] desc=Average length of uninterrupted capital-letter runs. +- capital_run_length_longest: role=feature, type=numeric_heavy_tail. tags=['condition_candidate', 'measure', 'rare_pattern_candidate'] desc=Longest uninterrupted capital-letter run. +- capital_run_length_total: role=feature, type=numeric_heavy_tail. tags=['condition_candidate', 'measure', 'rare_pattern_candidate'] desc=Total number of capital letters in runs. +- class: role=target, type=binary_target. tags=['target_candidate'] desc=Binary spam label (1=spam, 0=non-spam). +- useful_field_combinations: [['word_freq_free', 'word_freq_you', 'class'], ['char_freq_%21', 'capital_run_length_longest', 'class'], ['word_freq_remove', 'word_freq_money', 'class']] +- fields_requiring_caution: ['capital_run_length_average', 'capital_run_length_longest', 'capital_run_length_total'] +- source_url: https://www.openml.org/d/44 + +SQLite schema snapshot: +{ + "table_name": "n1", + "quoted_table_name": "\"n1\"", + "row_count": 4601, + "columns": [ + { + "name": "word_freq_make", + "type": "TEXT", + "notnull": false, + "pk": false + }, + { + "name": "word_freq_address", + "type": "TEXT", + "notnull": false, + "pk": false + }, + { + "name": "word_freq_all", + "type": "TEXT", + "notnull": false, + "pk": false + }, + { + "name": "word_freq_3d", + "type": "TEXT", + "notnull": false, + "pk": false + }, + { + "name": "word_freq_our", + "type": "TEXT", + "notnull": false, + "pk": false + }, + { + "name": "word_freq_over", + "type": "TEXT", + "notnull": false, + "pk": false + }, + { + "name": "word_freq_remove", + "type": "TEXT", + "notnull": false, + "pk": false + }, + { + "name": "word_freq_internet", + "type": "TEXT", + "notnull": false, + "pk": false + }, + { + "name": "word_freq_order", + "type": "TEXT", + "notnull": false, + "pk": false + }, + { + "name": "word_freq_mail", + "type": "TEXT", + "notnull": false, + "pk": false + }, + { + "name": "word_freq_receive", + "type": "TEXT", + "notnull": false, + "pk": false + }, + { + "name": "word_freq_will", + "type": "TEXT", + "notnull": false, + "pk": false + }, + { + "name": "word_freq_people", + "type": "TEXT", + "notnull": false, + "pk": false + }, + { + "name": "word_freq_report", + "type": "TEXT", + "notnull": false, + "pk": false + }, + { + "name": "word_freq_addresses", + "type": "TEXT", + "notnull": false, + "pk": false + }, + { + "name": "word_freq_free", + "type": "TEXT", + "notnull": false, + "pk": false + }, + { + "name": "word_freq_business", + "type": "TEXT", + "notnull": false, + "pk": false + }, + { + "name": "word_freq_email", + "type": "TEXT", + "notnull": false, + "pk": false + }, + { + "name": "word_freq_you", + "type": "TEXT", + "notnull": false, + "pk": false + }, + { + "name": "word_freq_credit", + "type": "TEXT", + "notnull": false, + "pk": false + }, + { + "name": "word_freq_your", + "type": "TEXT", + "notnull": false, + "pk": false + }, + { + "name": "word_freq_font", + "type": "TEXT", + "notnull": false, + "pk": false + }, + { + "name": "word_freq_000", + "type": "TEXT", + "notnull": false, + "pk": false + }, + { + "name": "word_freq_money", + "type": "TEXT", + "notnull": false, + "pk": false + }, + { + "name": "word_freq_hp", + "type": "TEXT", + "notnull": false, + "pk": false + }, + { + "name": "word_freq_hpl", + "type": "TEXT", + "notnull": false, + "pk": false + }, + { + "name": "word_freq_george", + "type": "TEXT", + "notnull": false, + "pk": false + }, + { + "name": "word_freq_650", + "type": "TEXT", + "notnull": false, + "pk": false + }, + { + "name": "word_freq_lab", + "type": "TEXT", + "notnull": false, + "pk": false + }, + { + "name": "word_freq_labs", + "type": "TEXT", + "notnull": false, + "pk": false + }, + { + "name": "word_freq_telnet", + "type": "TEXT", + "notnull": false, + "pk": false + }, + { + "name": "word_freq_857", + "type": "TEXT", + "notnull": false, + "pk": false + }, + { + "name": "word_freq_data", + "type": "TEXT", + "notnull": false, + "pk": false + }, + { + "name": "word_freq_415", + "type": "TEXT", + "notnull": false, + "pk": false + }, + { + "name": "word_freq_85", + "type": "TEXT", + "notnull": false, + "pk": false + }, + { + "name": "word_freq_technology", + "type": "TEXT", + "notnull": false, + "pk": false + }, + { + "name": "word_freq_1999", + "type": "TEXT", + "notnull": false, + "pk": false + }, + { + "name": "word_freq_parts", + "type": "TEXT", + "notnull": false, + "pk": false + }, + { + "name": "word_freq_pm", + "type": "TEXT", + "notnull": false, + "pk": false + }, + { + "name": "word_freq_direct", + "type": "TEXT", + "notnull": false, + "pk": false + }, + { + "name": "word_freq_cs", + "type": "TEXT", + "notnull": false, + "pk": false + }, + { + "name": "word_freq_meeting", + "type": "TEXT", + "notnull": false, + "pk": false + }, + { + "name": "word_freq_original", + "type": "TEXT", + "notnull": false, + "pk": false + }, + { + "name": "word_freq_project", + "type": "TEXT", + "notnull": false, + "pk": false + }, + { + "name": "word_freq_re", + "type": "TEXT", + "notnull": false, + "pk": false + }, + { + "name": "word_freq_edu", + "type": "TEXT", + "notnull": false, + "pk": false + }, + { + "name": "word_freq_table", + "type": "TEXT", + "notnull": false, + "pk": false + }, + { + "name": "word_freq_conference", + "type": "TEXT", + "notnull": false, + "pk": false + }, + { + "name": "char_freq_%3B", + "type": "TEXT", + "notnull": false, + "pk": false + }, + { + "name": "char_freq_%28", + "type": "TEXT", + "notnull": false, + "pk": false + }, + { + "name": "char_freq_%5B", + "type": "TEXT", + "notnull": false, + "pk": false + }, + { + "name": "char_freq_%21", + "type": "TEXT", + "notnull": false, + "pk": false + }, + { + "name": "char_freq_%24", + "type": "TEXT", + "notnull": false, + "pk": false + }, + { + "name": "char_freq_%23", + "type": "TEXT", + "notnull": false, + "pk": false + }, + { + "name": "capital_run_length_average", + "type": "TEXT", + "notnull": false, + "pk": false + }, + { + "name": "capital_run_length_longest", + "type": "TEXT", + "notnull": false, + "pk": false + }, + { + "name": "capital_run_length_total", + "type": "TEXT", + "notnull": false, + "pk": false + }, + { + "name": "class", + "type": "TEXT", + "notnull": false, + "pk": false + } + ], + "sample_rows": [ + { + "word_freq_make": "0", + "word_freq_address": "0.64", + "word_freq_all": "0.64", + "word_freq_3d": "0", + "word_freq_our": "0.32", + "word_freq_over": "0", + "word_freq_remove": "0", + "word_freq_internet": "0", + "word_freq_order": "0", + "word_freq_mail": "0", + "word_freq_receive": "0", + "word_freq_will": "0.64", + "word_freq_people": "0", + "word_freq_report": "0", + "word_freq_addresses": "0", + "word_freq_free": "0.32", + "word_freq_business": "0", + "word_freq_email": "1.29", + "word_freq_you": "1.93", + "word_freq_credit": "0", + "word_freq_your": "0.96", + "word_freq_font": "0", + "word_freq_000": "0", + "word_freq_money": "0", + "word_freq_hp": "0", + "word_freq_hpl": "0", + "word_freq_george": "0", + "word_freq_650": "0", + "word_freq_lab": "0", + "word_freq_labs": "0", + "word_freq_telnet": "0", + "word_freq_857": "0", + "word_freq_data": "0", + "word_freq_415": "0", + "word_freq_85": "0", + "word_freq_technology": "0", + "word_freq_1999": "0", + "word_freq_parts": "0", + "word_freq_pm": "0", + "word_freq_direct": "0", + "word_freq_cs": "0", + "word_freq_meeting": "0", + "word_freq_original": "0", + "word_freq_project": "0", + "word_freq_re": "0", + "word_freq_edu": "0", + "word_freq_table": "0", + "word_freq_conference": "0", + "char_freq_%3B": "0", + "char_freq_%28": "0", + "char_freq_%5B": "0", + "char_freq_%21": "0.778", + "char_freq_%24": "0", + "char_freq_%23": "0", + "capital_run_length_average": "3.756", + "capital_run_length_longest": "61", + "capital_run_length_total": "278", + "class": "1" + }, + { + "word_freq_make": "0.21", + "word_freq_address": "0.28", + "word_freq_all": "0.5", + "word_freq_3d": "0", + "word_freq_our": "0.14", + "word_freq_over": "0.28", + "word_freq_remove": "0.21", + "word_freq_internet": "0.07", + "word_freq_order": "0", + "word_freq_mail": "0.94", + "word_freq_receive": "0.21", + "word_freq_will": "0.79", + "word_freq_people": "0.65", + "word_freq_report": "0.21", + "word_freq_addresses": "0.14", + "word_freq_free": "0.14", + "word_freq_business": "0.07", + "word_freq_email": "0.28", + "word_freq_you": "3.47", + "word_freq_credit": "0", + "word_freq_your": "1.59", + "word_freq_font": "0", + "word_freq_000": "0.43", + "word_freq_money": "0.43", + "word_freq_hp": "0", + "word_freq_hpl": "0", + "word_freq_george": "0", + "word_freq_650": "0", + "word_freq_lab": "0", + "word_freq_labs": "0", + "word_freq_telnet": "0", + "word_freq_857": "0", + "word_freq_data": "0", + "word_freq_415": "0", + "word_freq_85": "0", + "word_freq_technology": "0", + "word_freq_1999": "0.07", + "word_freq_parts": "0", + "word_freq_pm": "0", + "word_freq_direct": "0", + "word_freq_cs": "0", + "word_freq_meeting": "0", + "word_freq_original": "0", + "word_freq_project": "0", + "word_freq_re": "0", + "word_freq_edu": "0", + "word_freq_table": "0", + "word_freq_conference": "0", + "char_freq_%3B": "0", + "char_freq_%28": "0.132", + "char_freq_%5B": "0", + "char_freq_%21": "0.372", + "char_freq_%24": "0.18", + "char_freq_%23": "0.048", + "capital_run_length_average": "5.114", + "capital_run_length_longest": "101", + "capital_run_length_total": "1028", + "class": "1" + }, + { + "word_freq_make": "0.06", + "word_freq_address": "0", + "word_freq_all": "0.71", + "word_freq_3d": "0", + "word_freq_our": "1.23", + "word_freq_over": "0.19", + "word_freq_remove": "0.19", + "word_freq_internet": "0.12", + "word_freq_order": "0.64", + "word_freq_mail": "0.25", + "word_freq_receive": "0.38", + "word_freq_will": "0.45", + "word_freq_people": "0.12", + "word_freq_report": "0", + "word_freq_addresses": "1.75", + "word_freq_free": "0.06", + "word_freq_business": "0.06", + "word_freq_email": "1.03", + "word_freq_you": "1.36", + "word_freq_credit": "0.32", + "word_freq_your": "0.51", + "word_freq_font": "0", + "word_freq_000": "1.16", + "word_freq_money": "0.06", + "word_freq_hp": "0", + "word_freq_hpl": "0", + "word_freq_george": "0", + "word_freq_650": "0", + "word_freq_lab": "0", + "word_freq_labs": "0", + "word_freq_telnet": "0", + "word_freq_857": "0", + "word_freq_data": "0", + "word_freq_415": "0", + "word_freq_85": "0", + "word_freq_technology": "0", + "word_freq_1999": "0", + "word_freq_parts": "0", + "word_freq_pm": "0", + "word_freq_direct": "0.06", + "word_freq_cs": "0", + "word_freq_meeting": "0", + "word_freq_original": "0.12", + "word_freq_project": "0", + "word_freq_re": "0.06", + "word_freq_edu": "0.06", + "word_freq_table": "0", + "word_freq_conference": "0", + "char_freq_%3B": "0.01", + "char_freq_%28": "0.143", + "char_freq_%5B": "0", + "char_freq_%21": "0.276", + "char_freq_%24": "0.184", + "char_freq_%23": "0.01", + "capital_run_length_average": "9.821", + "capital_run_length_longest": "485", + "capital_run_length_total": "2259", + "class": "1" + }, + { + "word_freq_make": "0", + "word_freq_address": "0", + "word_freq_all": "0", + "word_freq_3d": "0", + "word_freq_our": "0.63", + "word_freq_over": "0", + "word_freq_remove": "0.31", + "word_freq_internet": "0.63", + "word_freq_order": "0.31", + "word_freq_mail": "0.63", + "word_freq_receive": "0.31", + "word_freq_will": "0.31", + "word_freq_people": "0.31", + "word_freq_report": "0", + "word_freq_addresses": "0", + "word_freq_free": "0.31", + "word_freq_business": "0", + "word_freq_email": "0", + "word_freq_you": "3.18", + "word_freq_credit": "0", + "word_freq_your": "0.31", + "word_freq_font": "0", + "word_freq_000": "0", + "word_freq_money": "0", + "word_freq_hp": "0", + "word_freq_hpl": "0", + "word_freq_george": "0", + "word_freq_650": "0", + "word_freq_lab": "0", + "word_freq_labs": "0", + "word_freq_telnet": "0", + "word_freq_857": "0", + "word_freq_data": "0", + "word_freq_415": "0", + "word_freq_85": "0", + "word_freq_technology": "0", + "word_freq_1999": "0", + "word_freq_parts": "0", + "word_freq_pm": "0", + "word_freq_direct": "0", + "word_freq_cs": "0", + "word_freq_meeting": "0", + "word_freq_original": "0", + "word_freq_project": "0", + "word_freq_re": "0", + "word_freq_edu": "0", + "word_freq_table": "0", + "word_freq_conference": "0", + "char_freq_%3B": "0", + "char_freq_%28": "0.137", + "char_freq_%5B": "0", + "char_freq_%21": "0.137", + "char_freq_%24": "0", + "char_freq_%23": "0", + "capital_run_length_average": "3.537", + "capital_run_length_longest": "40", + "capital_run_length_total": "191", + "class": "1" + }, + { + "word_freq_make": "0", + "word_freq_address": "0", + "word_freq_all": "0", + "word_freq_3d": "0", + "word_freq_our": "0.63", + "word_freq_over": "0", + "word_freq_remove": "0.31", + "word_freq_internet": "0.63", + "word_freq_order": "0.31", + "word_freq_mail": "0.63", + "word_freq_receive": "0.31", + "word_freq_will": "0.31", + "word_freq_people": "0.31", + "word_freq_report": "0", + "word_freq_addresses": "0", + "word_freq_free": "0.31", + "word_freq_business": "0", + "word_freq_email": "0", + "word_freq_you": "3.18", + "word_freq_credit": "0", + "word_freq_your": "0.31", + "word_freq_font": "0", + "word_freq_000": "0", + "word_freq_money": "0", + "word_freq_hp": "0", + "word_freq_hpl": "0", + "word_freq_george": "0", + "word_freq_650": "0", + "word_freq_lab": "0", + "word_freq_labs": "0", + "word_freq_telnet": "0", + "word_freq_857": "0", + "word_freq_data": "0", + "word_freq_415": "0", + "word_freq_85": "0", + "word_freq_technology": "0", + "word_freq_1999": "0", + "word_freq_parts": "0", + "word_freq_pm": "0", + "word_freq_direct": "0", + "word_freq_cs": "0", + "word_freq_meeting": "0", + "word_freq_original": "0", + "word_freq_project": "0", + "word_freq_re": "0", + "word_freq_edu": "0", + "word_freq_table": "0", + "word_freq_conference": "0", + "char_freq_%3B": "0", + "char_freq_%28": "0.135", + "char_freq_%5B": "0", + "char_freq_%21": "0.135", + "char_freq_%24": "0", + "char_freq_%23": "0", + "capital_run_length_average": "3.537", + "capital_run_length_longest": "40", + "capital_run_length_total": "191", + "class": "1" + } + ] +} + +Shortlisted templates: +[ + { + "template_id": "tpl_threshold_rarity_cdf", + "template_name": "Threshold Rarity CDF", + "primary_family": "tail_rarity_structure", + "portability": "yes", + "sql_skeleton": "SELECT AVG(CASE WHEN {measure_col} <= {measure_threshold} THEN 1 ELSE 0 END) AS empirical_cdf_at_threshold\nFROM {table};", + "required_roles": [ + "measure_col" + ] + } +] + +Problem instance: +{ + "dataset_id": "n1", + "question": "Use template Threshold Rarity CDF to probe tail_set_consistency with semantic role rare_extreme_view. Focus on measure_col=word_freq_make.", + "planned_template_id": "tpl_threshold_rarity_cdf", + "bindings": { + "measure_col": "word_freq_make", + "top_k": 14, + "top_n": 5, + "num_tiles": 10, + "percentile_value": 0.95, + "z_threshold": 2.0, + "fraction_threshold": 0.1, + "baseline_multiplier": 1.5, + "baseline_fraction": 0.1, + "min_group_size": 5, + "min_support": 5, + "measure_threshold": 0.0, + "time_grain": "month", + "lookback_rows": 3, + "current_period_start": "'2024-01-01'", + "current_period_end": "'2024-04-01'", + "previous_period_start": "'2023-10-01'", + "previous_period_end": "'2024-01-01'", + "drift_ratio_threshold": 0.8 + }, + "can_vary": [], + "must_fix": [], + "runtime_sql_skeleton": "SELECT AVG(CASE WHEN {measure_col} <= {measure_threshold} THEN 1 ELSE 0 END) AS empirical_cdf_at_threshold\nFROM {table};" +} + +Repair context: +{} diff --git a/Query/sql/v2/runs/v2_cli_20260502_081223_d/n1/artifacts/v2q_n1_668298232015b5fd/cli/sql_prompt_attempt_2.txt b/Query/sql/v2/runs/v2_cli_20260502_081223_d/n1/artifacts/v2q_n1_668298232015b5fd/cli/sql_prompt_attempt_2.txt new file mode 100644 index 0000000000000000000000000000000000000000..a6be00f01934c72c7d960f710db540dea46487f8 --- /dev/null +++ b/Query/sql/v2/runs/v2_cli_20260502_081223_d/n1/artifacts/v2q_n1_668298232015b5fd/cli/sql_prompt_attempt_2.txt @@ -0,0 +1,790 @@ +You are generating one SQLite SELECT query for a single-table SQL QA task. +Return strict JSON only, with this schema: {"sql": "...", "notes": "..."}. +Rules: +- Use only the provided table and columns. +- Do not write INSERT, UPDATE, DELETE, DROP, ALTER, CREATE, PRAGMA, ATTACH, DETACH, or VACUUM. +- Prefer the planned template and bound roles when provided. +- Add a leading SQL comment exactly like: -- template_id: . +- Generate SQLite-compatible SQL. SQLite does not support PERCENTILE_CONT or STDDEV. +- Quote identifiers with double quotes. +- Return no markdown and no extra prose. + +Dataset context: +Dataset context for SQL QA: +- dataset_id: n1 +- dataset_name: Spambase +- table_name: n1 +- table_layout: single-table dataset (do not assume joins). +- row_semantics: One row is one email represented by engineered token/character frequency and capitalization-run features. +- task_type: classification +- target_column: class +- main_row_count: 4601 +- important_fields: +- word_freq_make: role=feature, type=numeric_sparse_frequency. tags=['condition_candidate', 'measure', 'rare_pattern_candidate'] desc=Frequency feature for token 'make' in an email message. +- word_freq_address: role=feature, type=numeric_sparse_frequency. tags=['condition_candidate', 'measure', 'rare_pattern_candidate'] desc=Frequency feature for token 'address' in an email message. +- word_freq_all: role=feature, type=numeric_sparse_frequency. tags=['condition_candidate', 'measure', 'rare_pattern_candidate'] desc=Frequency feature for token 'all' in an email message. +- word_freq_3d: role=feature, type=numeric_sparse_frequency. tags=['condition_candidate', 'measure', 'rare_pattern_candidate'] desc=Frequency feature for token '3d' in an email message. +- word_freq_our: role=feature, type=numeric_sparse_frequency. tags=['condition_candidate', 'measure', 'rare_pattern_candidate'] desc=Frequency feature for token 'our' in an email message. +- word_freq_over: role=feature, type=numeric_sparse_frequency. tags=['condition_candidate', 'measure', 'rare_pattern_candidate'] desc=Frequency feature for token 'over' in an email message. +- word_freq_remove: role=feature, type=numeric_sparse_frequency. tags=['condition_candidate', 'measure', 'rare_pattern_candidate'] desc=Frequency feature for token 'remove' in an email message. +- word_freq_internet: role=feature, type=numeric_sparse_frequency. tags=['condition_candidate', 'measure', 'rare_pattern_candidate'] desc=Frequency feature for token 'internet' in an email message. +- word_freq_order: role=feature, type=numeric_sparse_frequency. tags=['condition_candidate', 'measure', 'rare_pattern_candidate'] desc=Frequency feature for token 'order' in an email message. +- word_freq_mail: role=feature, type=numeric_sparse_frequency. tags=['condition_candidate', 'measure', 'rare_pattern_candidate'] desc=Frequency feature for token 'mail' in an email message. +- word_freq_receive: role=feature, type=numeric_sparse_frequency. tags=['condition_candidate', 'measure', 'rare_pattern_candidate'] desc=Frequency feature for token 'receive' in an email message. +- word_freq_will: role=feature, type=numeric_sparse_frequency. tags=['condition_candidate', 'measure', 'rare_pattern_candidate'] desc=Frequency feature for token 'will' in an email message. +- word_freq_people: role=feature, type=numeric_sparse_frequency. tags=['condition_candidate', 'measure', 'rare_pattern_candidate'] desc=Frequency feature for token 'people' in an email message. +- word_freq_report: role=feature, type=numeric_sparse_frequency. tags=['condition_candidate', 'measure', 'rare_pattern_candidate'] desc=Frequency feature for token 'report' in an email message. +- word_freq_addresses: role=feature, type=numeric_sparse_frequency. tags=['condition_candidate', 'measure', 'rare_pattern_candidate'] desc=Frequency feature for token 'addresses' in an email message. +- word_freq_free: role=feature, type=numeric_sparse_frequency. tags=['condition_candidate', 'measure', 'rare_pattern_candidate'] desc=Frequency feature for token 'free' in an email message. +- word_freq_business: role=feature, type=numeric_sparse_frequency. tags=['condition_candidate', 'measure', 'rare_pattern_candidate'] desc=Frequency feature for token 'business' in an email message. +- word_freq_email: role=feature, type=numeric_sparse_frequency. tags=['condition_candidate', 'measure', 'rare_pattern_candidate'] desc=Frequency feature for token 'email' in an email message. +- word_freq_you: role=feature, type=numeric_sparse_frequency. tags=['condition_candidate', 'measure', 'rare_pattern_candidate'] desc=Frequency feature for token 'you' in an email message. +- word_freq_credit: role=feature, type=numeric_sparse_frequency. tags=['condition_candidate', 'measure', 'rare_pattern_candidate'] desc=Frequency feature for token 'credit' in an email message. +- word_freq_your: role=feature, type=numeric_sparse_frequency. tags=['condition_candidate', 'measure', 'rare_pattern_candidate'] desc=Frequency feature for token 'your' in an email message. +- word_freq_font: role=feature, type=numeric_sparse_frequency. tags=['condition_candidate', 'measure', 'rare_pattern_candidate'] desc=Frequency feature for token 'font' in an email message. +- word_freq_000: role=feature, type=numeric_sparse_frequency. tags=['condition_candidate', 'measure', 'rare_pattern_candidate'] desc=Frequency feature for token '000' in an email message. +- word_freq_money: role=feature, type=numeric_sparse_frequency. tags=['condition_candidate', 'measure', 'rare_pattern_candidate'] desc=Frequency feature for token 'money' in an email message. +- word_freq_hp: role=feature, type=numeric_sparse_frequency. tags=['condition_candidate', 'measure', 'rare_pattern_candidate'] desc=Frequency feature for token 'hp' in an email message. +- word_freq_hpl: role=feature, type=numeric_sparse_frequency. tags=['condition_candidate', 'measure', 'rare_pattern_candidate'] desc=Frequency feature for token 'hpl' in an email message. +- word_freq_george: role=feature, type=numeric_sparse_frequency. tags=['condition_candidate', 'measure', 'rare_pattern_candidate'] desc=Frequency feature for token 'george' in an email message. +- word_freq_650: role=feature, type=numeric_sparse_frequency. tags=['condition_candidate', 'measure', 'rare_pattern_candidate'] desc=Frequency feature for token '650' in an email message. +- word_freq_lab: role=feature, type=numeric_sparse_frequency. tags=['condition_candidate', 'measure', 'rare_pattern_candidate'] desc=Frequency feature for token 'lab' in an email message. +- word_freq_labs: role=feature, type=numeric_sparse_frequency. tags=['condition_candidate', 'measure', 'rare_pattern_candidate'] desc=Frequency feature for token 'labs' in an email message. +- word_freq_telnet: role=feature, type=numeric_sparse_frequency. tags=['condition_candidate', 'measure', 'rare_pattern_candidate'] desc=Frequency feature for token 'telnet' in an email message. +- word_freq_857: role=feature, type=numeric_sparse_frequency. tags=['condition_candidate', 'measure', 'rare_pattern_candidate'] desc=Frequency feature for token '857' in an email message. +- word_freq_data: role=feature, type=numeric_sparse_frequency. tags=['condition_candidate', 'measure', 'rare_pattern_candidate'] desc=Frequency feature for token 'data' in an email message. +- word_freq_415: role=feature, type=numeric_sparse_frequency. tags=['condition_candidate', 'measure', 'rare_pattern_candidate'] desc=Frequency feature for token '415' in an email message. +- word_freq_85: role=feature, type=numeric_sparse_frequency. tags=['condition_candidate', 'measure', 'rare_pattern_candidate'] desc=Frequency feature for token '85' in an email message. +- word_freq_technology: role=feature, type=numeric_sparse_frequency. tags=['condition_candidate', 'measure', 'rare_pattern_candidate'] desc=Frequency feature for token 'technology' in an email message. +- word_freq_1999: role=feature, type=numeric_sparse_frequency. tags=['condition_candidate', 'measure', 'rare_pattern_candidate'] desc=Frequency feature for token '1999' in an email message. +- word_freq_parts: role=feature, type=numeric_sparse_frequency. tags=['condition_candidate', 'measure', 'rare_pattern_candidate'] desc=Frequency feature for token 'parts' in an email message. +- word_freq_pm: role=feature, type=numeric_sparse_frequency. tags=['condition_candidate', 'measure', 'rare_pattern_candidate'] desc=Frequency feature for token 'pm' in an email message. +- word_freq_direct: role=feature, type=numeric_sparse_frequency. tags=['condition_candidate', 'measure', 'rare_pattern_candidate'] desc=Frequency feature for token 'direct' in an email message. +- word_freq_cs: role=feature, type=numeric_sparse_frequency. tags=['condition_candidate', 'measure', 'rare_pattern_candidate'] desc=Frequency feature for token 'cs' in an email message. +- word_freq_meeting: role=feature, type=numeric_sparse_frequency. tags=['condition_candidate', 'measure', 'rare_pattern_candidate'] desc=Frequency feature for token 'meeting' in an email message. +- word_freq_original: role=feature, type=numeric_sparse_frequency. tags=['condition_candidate', 'measure', 'rare_pattern_candidate'] desc=Frequency feature for token 'original' in an email message. +- word_freq_project: role=feature, type=numeric_sparse_frequency. tags=['condition_candidate', 'measure', 'rare_pattern_candidate'] desc=Frequency feature for token 'project' in an email message. +- word_freq_re: role=feature, type=numeric_sparse_frequency. tags=['condition_candidate', 'measure', 'rare_pattern_candidate'] desc=Frequency feature for token 're' in an email message. +- word_freq_edu: role=feature, type=numeric_sparse_frequency. tags=['condition_candidate', 'measure', 'rare_pattern_candidate'] desc=Frequency feature for token 'edu' in an email message. +- word_freq_table: role=feature, type=numeric_sparse_frequency. tags=['condition_candidate', 'measure', 'rare_pattern_candidate'] desc=Frequency feature for token 'table' in an email message. +- word_freq_conference: role=feature, type=numeric_sparse_frequency. tags=['condition_candidate', 'measure', 'rare_pattern_candidate'] desc=Frequency feature for token 'conference' in an email message. +- char_freq_%3B: role=feature, type=numeric_sparse_frequency. tags=['condition_candidate', 'measure', 'rare_pattern_candidate'] desc=Character frequency feature for symbol ';'. +- char_freq_%28: role=feature, type=numeric_sparse_frequency. tags=['condition_candidate', 'measure', 'rare_pattern_candidate'] desc=Character frequency feature for symbol '('. +- char_freq_%5B: role=feature, type=numeric_sparse_frequency. tags=['condition_candidate', 'measure', 'rare_pattern_candidate'] desc=Character frequency feature for symbol '['. +- char_freq_%21: role=feature, type=numeric_sparse_frequency. tags=['condition_candidate', 'measure', 'rare_pattern_candidate'] desc=Character frequency feature for symbol '!'. +- char_freq_%24: role=feature, type=numeric_sparse_frequency. tags=['condition_candidate', 'measure', 'rare_pattern_candidate'] desc=Character frequency feature for symbol '$'. +- char_freq_%23: role=feature, type=numeric_sparse_frequency. tags=['condition_candidate', 'measure', 'rare_pattern_candidate'] desc=Character frequency feature for symbol '#'. +- capital_run_length_average: role=feature, type=numeric_heavy_tail. tags=['condition_candidate', 'measure', 'rare_pattern_candidate'] desc=Average length of uninterrupted capital-letter runs. +- capital_run_length_longest: role=feature, type=numeric_heavy_tail. tags=['condition_candidate', 'measure', 'rare_pattern_candidate'] desc=Longest uninterrupted capital-letter run. +- capital_run_length_total: role=feature, type=numeric_heavy_tail. tags=['condition_candidate', 'measure', 'rare_pattern_candidate'] desc=Total number of capital letters in runs. +- class: role=target, type=binary_target. tags=['target_candidate'] desc=Binary spam label (1=spam, 0=non-spam). +- useful_field_combinations: [['word_freq_free', 'word_freq_you', 'class'], ['char_freq_%21', 'capital_run_length_longest', 'class'], ['word_freq_remove', 'word_freq_money', 'class']] +- fields_requiring_caution: ['capital_run_length_average', 'capital_run_length_longest', 'capital_run_length_total'] +- source_url: https://www.openml.org/d/44 + +SQLite schema snapshot: +{ + "table_name": "n1", + "quoted_table_name": "\"n1\"", + "row_count": 4601, + "columns": [ + { + "name": "word_freq_make", + "type": "TEXT", + "notnull": false, + "pk": false + }, + { + "name": "word_freq_address", + "type": "TEXT", + "notnull": false, + "pk": false + }, + { + "name": "word_freq_all", + "type": "TEXT", + "notnull": false, + "pk": false + }, + { + "name": "word_freq_3d", + "type": "TEXT", + "notnull": false, + "pk": false + }, + { + "name": "word_freq_our", + "type": "TEXT", + "notnull": false, + "pk": false + }, + { + "name": "word_freq_over", + "type": "TEXT", + "notnull": false, + "pk": false + }, + { + "name": "word_freq_remove", + "type": "TEXT", + "notnull": false, + "pk": false + }, + { + "name": "word_freq_internet", + "type": "TEXT", + "notnull": false, + "pk": false + }, + { + "name": "word_freq_order", + "type": "TEXT", + "notnull": false, + "pk": false + }, + { + "name": "word_freq_mail", + "type": "TEXT", + "notnull": false, + "pk": false + }, + { + "name": "word_freq_receive", + "type": "TEXT", + "notnull": false, + "pk": false + }, + { + "name": "word_freq_will", + "type": "TEXT", + "notnull": false, + "pk": false + }, + { + "name": "word_freq_people", + "type": "TEXT", + "notnull": false, + "pk": false + }, + { + "name": "word_freq_report", + "type": "TEXT", + "notnull": false, + "pk": false + }, + { + "name": "word_freq_addresses", + "type": "TEXT", + "notnull": false, + "pk": false + }, + { + "name": "word_freq_free", + "type": "TEXT", + "notnull": false, + "pk": false + }, + { + "name": "word_freq_business", + "type": "TEXT", + "notnull": false, + "pk": false + }, + { + "name": "word_freq_email", + "type": "TEXT", + "notnull": false, + "pk": false + }, + { + "name": "word_freq_you", + "type": "TEXT", + "notnull": false, + "pk": false + }, + { + "name": "word_freq_credit", + "type": "TEXT", + "notnull": false, + "pk": false + }, + { + "name": "word_freq_your", + "type": "TEXT", + "notnull": false, + "pk": false + }, + { + "name": "word_freq_font", + "type": "TEXT", + "notnull": false, + "pk": false + }, + { + "name": "word_freq_000", + "type": "TEXT", + "notnull": false, + "pk": false + }, + { + "name": "word_freq_money", + "type": "TEXT", + "notnull": false, + "pk": false + }, + { + "name": "word_freq_hp", + "type": "TEXT", + "notnull": false, + "pk": false + }, + { + "name": "word_freq_hpl", + "type": "TEXT", + "notnull": false, + "pk": false + }, + { + "name": "word_freq_george", + "type": "TEXT", + "notnull": false, + "pk": false + }, + { + "name": "word_freq_650", + "type": "TEXT", + "notnull": false, + "pk": false + }, + { + "name": "word_freq_lab", + "type": "TEXT", + "notnull": false, + "pk": false + }, + { + "name": "word_freq_labs", + "type": "TEXT", + "notnull": false, + "pk": false + }, + { + "name": "word_freq_telnet", + "type": "TEXT", + "notnull": false, + "pk": false + }, + { + "name": "word_freq_857", + "type": "TEXT", + "notnull": false, + "pk": false + }, + { + "name": "word_freq_data", + "type": "TEXT", + "notnull": false, + "pk": false + }, + { + "name": "word_freq_415", + "type": "TEXT", + "notnull": false, + "pk": false + }, + { + "name": "word_freq_85", + "type": "TEXT", + "notnull": false, + "pk": false + }, + { + "name": "word_freq_technology", + "type": "TEXT", + "notnull": false, + "pk": false + }, + { + "name": "word_freq_1999", + "type": "TEXT", + "notnull": false, + "pk": false + }, + { + "name": "word_freq_parts", + "type": "TEXT", + "notnull": false, + "pk": false + }, + { + "name": "word_freq_pm", + "type": "TEXT", + "notnull": false, + "pk": false + }, + { + "name": "word_freq_direct", + "type": "TEXT", + "notnull": false, + "pk": false + }, + { + "name": "word_freq_cs", + "type": "TEXT", + "notnull": false, + "pk": false + }, + { + "name": "word_freq_meeting", + "type": "TEXT", + "notnull": false, + "pk": false + }, + { + "name": "word_freq_original", + "type": "TEXT", + "notnull": false, + "pk": false + }, + { + "name": "word_freq_project", + "type": "TEXT", + "notnull": false, + "pk": false + }, + { + "name": "word_freq_re", + "type": "TEXT", + "notnull": false, + "pk": false + }, + { + "name": "word_freq_edu", + "type": "TEXT", + "notnull": false, + "pk": false + }, + { + "name": "word_freq_table", + "type": "TEXT", + "notnull": false, + "pk": false + }, + { + "name": "word_freq_conference", + "type": "TEXT", + "notnull": false, + "pk": false + }, + { + "name": "char_freq_%3B", + "type": "TEXT", + "notnull": false, + "pk": false + }, + { + "name": "char_freq_%28", + "type": "TEXT", + "notnull": false, + "pk": false + }, + { + "name": "char_freq_%5B", + "type": "TEXT", + "notnull": false, + "pk": false + }, + { + "name": "char_freq_%21", + "type": "TEXT", + "notnull": false, + "pk": false + }, + { + "name": "char_freq_%24", + "type": "TEXT", + "notnull": false, + "pk": false + }, + { + "name": "char_freq_%23", + "type": "TEXT", + "notnull": false, + "pk": false + }, + { + "name": "capital_run_length_average", + "type": "TEXT", + "notnull": false, + "pk": false + }, + { + "name": "capital_run_length_longest", + "type": "TEXT", + "notnull": false, + "pk": false + }, + { + "name": "capital_run_length_total", + "type": "TEXT", + "notnull": false, + "pk": false + }, + { + "name": "class", + "type": "TEXT", + "notnull": false, + "pk": false + } + ], + "sample_rows": [ + { + "word_freq_make": "0", + "word_freq_address": "0.64", + "word_freq_all": "0.64", + "word_freq_3d": "0", + "word_freq_our": "0.32", + "word_freq_over": "0", + "word_freq_remove": "0", + "word_freq_internet": "0", + "word_freq_order": "0", + "word_freq_mail": "0", + "word_freq_receive": "0", + "word_freq_will": "0.64", + "word_freq_people": "0", + "word_freq_report": "0", + "word_freq_addresses": "0", + "word_freq_free": "0.32", + "word_freq_business": "0", + "word_freq_email": "1.29", + "word_freq_you": "1.93", + "word_freq_credit": "0", + "word_freq_your": "0.96", + "word_freq_font": "0", + "word_freq_000": "0", + "word_freq_money": "0", + "word_freq_hp": "0", + "word_freq_hpl": "0", + "word_freq_george": "0", + "word_freq_650": "0", + "word_freq_lab": "0", + "word_freq_labs": "0", + "word_freq_telnet": "0", + "word_freq_857": "0", + "word_freq_data": "0", + "word_freq_415": "0", + "word_freq_85": "0", + "word_freq_technology": "0", + "word_freq_1999": "0", + "word_freq_parts": "0", + "word_freq_pm": "0", + "word_freq_direct": "0", + "word_freq_cs": "0", + "word_freq_meeting": "0", + "word_freq_original": "0", + "word_freq_project": "0", + "word_freq_re": "0", + "word_freq_edu": "0", + "word_freq_table": "0", + "word_freq_conference": "0", + "char_freq_%3B": "0", + "char_freq_%28": "0", + "char_freq_%5B": "0", + "char_freq_%21": "0.778", + "char_freq_%24": "0", + "char_freq_%23": "0", + "capital_run_length_average": "3.756", + "capital_run_length_longest": "61", + "capital_run_length_total": "278", + "class": "1" + }, + { + "word_freq_make": "0.21", + "word_freq_address": "0.28", + "word_freq_all": "0.5", + "word_freq_3d": "0", + "word_freq_our": "0.14", + "word_freq_over": "0.28", + "word_freq_remove": "0.21", + "word_freq_internet": "0.07", + "word_freq_order": "0", + "word_freq_mail": "0.94", + "word_freq_receive": "0.21", + "word_freq_will": "0.79", + "word_freq_people": "0.65", + "word_freq_report": "0.21", + "word_freq_addresses": "0.14", + "word_freq_free": "0.14", + "word_freq_business": "0.07", + "word_freq_email": "0.28", + "word_freq_you": "3.47", + "word_freq_credit": "0", + "word_freq_your": "1.59", + "word_freq_font": "0", + "word_freq_000": "0.43", + "word_freq_money": "0.43", + "word_freq_hp": "0", + "word_freq_hpl": "0", + "word_freq_george": "0", + "word_freq_650": "0", + "word_freq_lab": "0", + "word_freq_labs": "0", + "word_freq_telnet": "0", + "word_freq_857": "0", + "word_freq_data": "0", + "word_freq_415": "0", + "word_freq_85": "0", + "word_freq_technology": "0", + "word_freq_1999": "0.07", + "word_freq_parts": "0", + "word_freq_pm": "0", + "word_freq_direct": "0", + "word_freq_cs": "0", + "word_freq_meeting": "0", + "word_freq_original": "0", + "word_freq_project": "0", + "word_freq_re": "0", + "word_freq_edu": "0", + "word_freq_table": "0", + "word_freq_conference": "0", + "char_freq_%3B": "0", + "char_freq_%28": "0.132", + "char_freq_%5B": "0", + "char_freq_%21": "0.372", + "char_freq_%24": "0.18", + "char_freq_%23": "0.048", + "capital_run_length_average": "5.114", + "capital_run_length_longest": "101", + "capital_run_length_total": "1028", + "class": "1" + }, + { + "word_freq_make": "0.06", + "word_freq_address": "0", + "word_freq_all": "0.71", + "word_freq_3d": "0", + "word_freq_our": "1.23", + "word_freq_over": "0.19", + "word_freq_remove": "0.19", + "word_freq_internet": "0.12", + "word_freq_order": "0.64", + "word_freq_mail": "0.25", + "word_freq_receive": "0.38", + "word_freq_will": "0.45", + "word_freq_people": "0.12", + "word_freq_report": "0", + "word_freq_addresses": "1.75", + "word_freq_free": "0.06", + "word_freq_business": "0.06", + "word_freq_email": "1.03", + "word_freq_you": "1.36", + "word_freq_credit": "0.32", + "word_freq_your": "0.51", + "word_freq_font": "0", + "word_freq_000": "1.16", + "word_freq_money": "0.06", + "word_freq_hp": "0", + "word_freq_hpl": "0", + "word_freq_george": "0", + "word_freq_650": "0", + "word_freq_lab": "0", + "word_freq_labs": "0", + "word_freq_telnet": "0", + "word_freq_857": "0", + "word_freq_data": "0", + "word_freq_415": "0", + "word_freq_85": "0", + "word_freq_technology": "0", + "word_freq_1999": "0", + "word_freq_parts": "0", + "word_freq_pm": "0", + "word_freq_direct": "0.06", + "word_freq_cs": "0", + "word_freq_meeting": "0", + "word_freq_original": "0.12", + "word_freq_project": "0", + "word_freq_re": "0.06", + "word_freq_edu": "0.06", + "word_freq_table": "0", + "word_freq_conference": "0", + "char_freq_%3B": "0.01", + "char_freq_%28": "0.143", + "char_freq_%5B": "0", + "char_freq_%21": "0.276", + "char_freq_%24": "0.184", + "char_freq_%23": "0.01", + "capital_run_length_average": "9.821", + "capital_run_length_longest": "485", + "capital_run_length_total": "2259", + "class": "1" + }, + { + "word_freq_make": "0", + "word_freq_address": "0", + "word_freq_all": "0", + "word_freq_3d": "0", + "word_freq_our": "0.63", + "word_freq_over": "0", + "word_freq_remove": "0.31", + "word_freq_internet": "0.63", + "word_freq_order": "0.31", + "word_freq_mail": "0.63", + "word_freq_receive": "0.31", + "word_freq_will": "0.31", + "word_freq_people": "0.31", + "word_freq_report": "0", + "word_freq_addresses": "0", + "word_freq_free": "0.31", + "word_freq_business": "0", + "word_freq_email": "0", + "word_freq_you": "3.18", + "word_freq_credit": "0", + "word_freq_your": "0.31", + "word_freq_font": "0", + "word_freq_000": "0", + "word_freq_money": "0", + "word_freq_hp": "0", + "word_freq_hpl": "0", + "word_freq_george": "0", + "word_freq_650": "0", + "word_freq_lab": "0", + "word_freq_labs": "0", + "word_freq_telnet": "0", + "word_freq_857": "0", + "word_freq_data": "0", + "word_freq_415": "0", + "word_freq_85": "0", + "word_freq_technology": "0", + "word_freq_1999": "0", + "word_freq_parts": "0", + "word_freq_pm": "0", + "word_freq_direct": "0", + "word_freq_cs": "0", + "word_freq_meeting": "0", + "word_freq_original": "0", + "word_freq_project": "0", + "word_freq_re": "0", + "word_freq_edu": "0", + "word_freq_table": "0", + "word_freq_conference": "0", + "char_freq_%3B": "0", + "char_freq_%28": "0.137", + "char_freq_%5B": "0", + "char_freq_%21": "0.137", + "char_freq_%24": "0", + "char_freq_%23": "0", + "capital_run_length_average": "3.537", + "capital_run_length_longest": "40", + "capital_run_length_total": "191", + "class": "1" + }, + { + "word_freq_make": "0", + "word_freq_address": "0", + "word_freq_all": "0", + "word_freq_3d": "0", + "word_freq_our": "0.63", + "word_freq_over": "0", + "word_freq_remove": "0.31", + "word_freq_internet": "0.63", + "word_freq_order": "0.31", + "word_freq_mail": "0.63", + "word_freq_receive": "0.31", + "word_freq_will": "0.31", + "word_freq_people": "0.31", + "word_freq_report": "0", + "word_freq_addresses": "0", + "word_freq_free": "0.31", + "word_freq_business": "0", + "word_freq_email": "0", + "word_freq_you": "3.18", + "word_freq_credit": "0", + "word_freq_your": "0.31", + "word_freq_font": "0", + "word_freq_000": "0", + "word_freq_money": "0", + "word_freq_hp": "0", + "word_freq_hpl": "0", + "word_freq_george": "0", + "word_freq_650": "0", + "word_freq_lab": "0", + "word_freq_labs": "0", + "word_freq_telnet": "0", + "word_freq_857": "0", + "word_freq_data": "0", + "word_freq_415": "0", + "word_freq_85": "0", + "word_freq_technology": "0", + "word_freq_1999": "0", + "word_freq_parts": "0", + "word_freq_pm": "0", + "word_freq_direct": "0", + "word_freq_cs": "0", + "word_freq_meeting": "0", + "word_freq_original": "0", + "word_freq_project": "0", + "word_freq_re": "0", + "word_freq_edu": "0", + "word_freq_table": "0", + "word_freq_conference": "0", + "char_freq_%3B": "0", + "char_freq_%28": "0.135", + "char_freq_%5B": "0", + "char_freq_%21": "0.135", + "char_freq_%24": "0", + "char_freq_%23": "0", + "capital_run_length_average": "3.537", + "capital_run_length_longest": "40", + "capital_run_length_total": "191", + "class": "1" + } + ] +} + +Shortlisted templates: +[ + { + "template_id": "tpl_threshold_rarity_cdf", + "template_name": "Threshold Rarity CDF", + "primary_family": "tail_rarity_structure", + "portability": "yes", + "sql_skeleton": "SELECT AVG(CASE WHEN {measure_col} <= {measure_threshold} THEN 1 ELSE 0 END) AS empirical_cdf_at_threshold\nFROM {table};", + "required_roles": [ + "measure_col" + ] + } +] + +Problem instance: +{ + "dataset_id": "n1", + "question": "Use template Threshold Rarity CDF to probe tail_set_consistency with semantic role rare_extreme_view. Focus on measure_col=word_freq_make.", + "planned_template_id": "tpl_threshold_rarity_cdf", + "bindings": { + "measure_col": "word_freq_make", + "top_k": 14, + "top_n": 5, + "num_tiles": 10, + "percentile_value": 0.95, + "z_threshold": 2.0, + "fraction_threshold": 0.1, + "baseline_multiplier": 1.5, + "baseline_fraction": 0.1, + "min_group_size": 5, + "min_support": 5, + "measure_threshold": 0.0, + "time_grain": "month", + "lookback_rows": 3, + "current_period_start": "'2024-01-01'", + "current_period_end": "'2024-04-01'", + "previous_period_start": "'2023-10-01'", + "previous_period_end": "'2024-01-01'", + "drift_ratio_threshold": 0.8 + }, + "can_vary": [], + "must_fix": [], + "runtime_sql_skeleton": "SELECT AVG(CASE WHEN {measure_col} <= {measure_threshold} THEN 1 ELSE 0 END) AS empirical_cdf_at_threshold\nFROM {table};" +} + +Repair context: +{} diff --git a/Query/sql/v2/runs/v2_cli_20260502_081223_d/n1/artifacts/v2q_n1_668298232015b5fd/cli/sql_response_attempt_1.txt b/Query/sql/v2/runs/v2_cli_20260502_081223_d/n1/artifacts/v2q_n1_668298232015b5fd/cli/sql_response_attempt_1.txt new file mode 100644 index 0000000000000000000000000000000000000000..8c460b3227ea3fe236efc045e1ad432a61db46eb --- /dev/null +++ b/Query/sql/v2/runs/v2_cli_20260502_081223_d/n1/artifacts/v2q_n1_668298232015b5fd/cli/sql_response_attempt_1.txt @@ -0,0 +1,4 @@ +{"type":"thread.started","thread_id":"019e40f8-5794-7c43-b77b-c4845aa58923"} +{"type":"turn.started"} +{"type":"error","message":"Quota exceeded. Check your plan and billing details."} +{"type":"turn.failed","error":{"message":"Quota exceeded. Check your plan and billing details."}} \ No newline at end of file diff --git a/Query/sql/v2/runs/v2_cli_20260502_081223_d/n1/artifacts/v2q_n1_668298232015b5fd/cli/sql_response_attempt_2.txt b/Query/sql/v2/runs/v2_cli_20260502_081223_d/n1/artifacts/v2q_n1_668298232015b5fd/cli/sql_response_attempt_2.txt new file mode 100644 index 0000000000000000000000000000000000000000..6d111c72edd1f01c8dca8cc506fcbc66c62334d4 --- /dev/null +++ b/Query/sql/v2/runs/v2_cli_20260502_081223_d/n1/artifacts/v2q_n1_668298232015b5fd/cli/sql_response_attempt_2.txt @@ -0,0 +1 @@ +{"sql":"-- template_id: tpl_threshold_rarity_cdf\nSELECT AVG(CASE WHEN CAST(\"word_freq_make\" AS REAL) <= 0.0 THEN 1 ELSE 0 END) AS \"empirical_cdf_at_threshold\"\nFROM \"n1\";","notes":"Applied the Threshold Rarity CDF template using measure_col=\"word_freq_make\" and measure_threshold=0.0. CAST to REAL is used because the schema stores feature values as TEXT."} \ No newline at end of file diff --git a/Query/sql/v2/runs/v2_cli_20260502_081223_d/n1/artifacts/v2q_n1_668298232015b5fd/cli/sql_stderr_attempt_2.txt b/Query/sql/v2/runs/v2_cli_20260502_081223_d/n1/artifacts/v2q_n1_668298232015b5fd/cli/sql_stderr_attempt_2.txt new file mode 100644 index 0000000000000000000000000000000000000000..e69de29bb2d1d6434b8b29ae775ad8c2e48c5391 diff --git a/Query/sql/v2/runs/v2_cli_20260502_081223_d/n1/artifacts/v2q_n1_6acc8ab2557e8260/cli/conversation.jsonl b/Query/sql/v2/runs/v2_cli_20260502_081223_d/n1/artifacts/v2q_n1_6acc8ab2557e8260/cli/conversation.jsonl new file mode 100644 index 0000000000000000000000000000000000000000..f01ae717b2de4dd429eca4f316b63cc3b5258726 --- /dev/null +++ b/Query/sql/v2/runs/v2_cli_20260502_081223_d/n1/artifacts/v2q_n1_6acc8ab2557e8260/cli/conversation.jsonl @@ -0,0 +1,2 @@ +{"attempt": 1, "phase": "sql_generation", "role": "user", "content_path": "cli/sql_prompt_attempt_1.txt", "metrics": {"chars": 30144, "bytes_utf8": 30144, "lines": 795, "estimated_tokens": null}} +{"attempt": 1, "phase": "sql_generation", "role": "assistant", "content_path": "cli/sql_response_attempt_1.txt", "raw_content_path": "cli/sql_response_attempt_1.raw.txt", "stderr_path": "cli/sql_stderr_attempt_1.txt", "metrics": {"chars": 817, "bytes_utf8": 817, "lines": 1, "estimated_tokens": null}, "usage": {"input_tokens": 20520, "cached_input_tokens": 19840, "output_tokens": 543, "reasoning_output_tokens": 326}} diff --git a/Query/sql/v2/runs/v2_cli_20260502_081223_d/n1/artifacts/v2q_n1_6acc8ab2557e8260/cli/session_summary.json b/Query/sql/v2/runs/v2_cli_20260502_081223_d/n1/artifacts/v2q_n1_6acc8ab2557e8260/cli/session_summary.json new file mode 100644 index 0000000000000000000000000000000000000000..7806e158cfe50ee7a284139795144a8dae711499 --- /dev/null +++ b/Query/sql/v2/runs/v2_cli_20260502_081223_d/n1/artifacts/v2q_n1_6acc8ab2557e8260/cli/session_summary.json @@ -0,0 +1,25 @@ +{ + "engine": "v2-cli:codex", + "command": "codex exec --skip-git-repo-check --disable plugins --sandbox read-only --cd \"/data/jialinzhang/SQLagent\" -m gpt-5.4 --json -", + "ai_cli_calls": 1, + "usage_summary": { + "dataset_id": "n1", + "model": "v2-cli:codex", + "run_id": "v2q_n1_6acc8ab2557e8260", + "api_calls": 0, + "input_tokens": 20520, + "cached_input_tokens": 19840, + "output_tokens": 543, + "total_tokens": 21063, + "cost_usd": 0.0, + "ai_cli_calls": 1, + "estimated_input_tokens": 0, + "estimated_output_tokens": 0, + "estimated_total_tokens": 0, + "usage_source": "ai_cli_json_usage", + "cli_elapsed_ms_total": 13263.99, + "sql_execution_elapsed_ms_total": 4.0, + "conversation_log_path": "/data/jialinzhang/TabQueryBench/sql_workloads/v2_current/runs_and_launches/runs/v2_cli_20260502_081223_d/n1/artifacts/v2q_n1_6acc8ab2557e8260/cli/conversation.jsonl", + "note": "Executed through a local AI CLI with structured usage metadata." + } +} \ No newline at end of file diff --git a/Query/sql/v2/runs/v2_cli_20260502_081223_d/n1/artifacts/v2q_n1_6acc8ab2557e8260/cli/sql_attempt_1.metadata.json b/Query/sql/v2/runs/v2_cli_20260502_081223_d/n1/artifacts/v2q_n1_6acc8ab2557e8260/cli/sql_attempt_1.metadata.json new file mode 100644 index 0000000000000000000000000000000000000000..043575e8419c14e841e57a70a970288a95adcd49 --- /dev/null +++ b/Query/sql/v2/runs/v2_cli_20260502_081223_d/n1/artifacts/v2q_n1_6acc8ab2557e8260/cli/sql_attempt_1.metadata.json @@ -0,0 +1,45 @@ +{ + "attempt": 1, + "phase": "sql_generation", + "command": "codex exec --skip-git-repo-check --disable plugins --sandbox read-only --cd \"/data/jialinzhang/SQLagent\" -m gpt-5.4 --json -", + "started_at": "2026-05-19T15:39:49.515070+00:00", + "ended_at": "2026-05-19T15:40:02.779089+00:00", + "elapsed_ms": 13263.99, + "prompt_metrics": { + "chars": 30144, + "bytes_utf8": 30144, + "lines": 795, + "estimated_tokens": null + }, + "stdout_metrics": { + "chars": 1186, + "bytes_utf8": 1186, + "lines": 4, + "estimated_tokens": null + }, + "stderr_metrics": { + "chars": 0, + "bytes_utf8": 0, + "lines": 0, + "estimated_tokens": null + }, + "parsed_output": { + "format": "jsonl_events", + "text_metrics": { + "chars": 817, + "bytes_utf8": 817, + "lines": 1, + "estimated_tokens": null + }, + "usage": { + "input_tokens": 20520, + "cached_input_tokens": 19840, + "output_tokens": 543, + "reasoning_output_tokens": 326 + } + }, + "prompt_path": "cli/sql_prompt_attempt_1.txt", + "response_path": "cli/sql_response_attempt_1.txt", + "raw_response_path": "cli/sql_response_attempt_1.raw.txt", + "stderr_path": "cli/sql_stderr_attempt_1.txt" +} \ No newline at end of file diff --git a/Query/sql/v2/runs/v2_cli_20260502_081223_d/n1/artifacts/v2q_n1_6acc8ab2557e8260/cli/sql_prompt_attempt_1.txt b/Query/sql/v2/runs/v2_cli_20260502_081223_d/n1/artifacts/v2q_n1_6acc8ab2557e8260/cli/sql_prompt_attempt_1.txt new file mode 100644 index 0000000000000000000000000000000000000000..ebe779df5af30ff5c3945649ac318b8d7b83d099 --- /dev/null +++ b/Query/sql/v2/runs/v2_cli_20260502_081223_d/n1/artifacts/v2q_n1_6acc8ab2557e8260/cli/sql_prompt_attempt_1.txt @@ -0,0 +1,795 @@ +You are generating one SQLite SELECT query for a single-table SQL QA task. +Return strict JSON only, with this schema: {"sql": "...", "notes": "..."}. +Rules: +- Use only the provided table and columns. +- Do not write INSERT, UPDATE, DELETE, DROP, ALTER, CREATE, PRAGMA, ATTACH, DETACH, or VACUUM. +- Prefer the planned template and bound roles when provided. +- Add a leading SQL comment exactly like: -- template_id: . +- Generate SQLite-compatible SQL. SQLite does not support PERCENTILE_CONT or STDDEV. +- Quote identifiers with double quotes. +- Return no markdown and no extra prose. + +Dataset context: +Dataset context for SQL QA: +- dataset_id: n1 +- dataset_name: Spambase +- table_name: n1 +- table_layout: single-table dataset (do not assume joins). +- row_semantics: One row is one email represented by engineered token/character frequency and capitalization-run features. +- task_type: classification +- target_column: class +- main_row_count: 4601 +- important_fields: +- word_freq_make: role=feature, type=numeric_sparse_frequency. tags=['condition_candidate', 'measure', 'rare_pattern_candidate'] desc=Frequency feature for token 'make' in an email message. +- word_freq_address: role=feature, type=numeric_sparse_frequency. tags=['condition_candidate', 'measure', 'rare_pattern_candidate'] desc=Frequency feature for token 'address' in an email message. +- word_freq_all: role=feature, type=numeric_sparse_frequency. tags=['condition_candidate', 'measure', 'rare_pattern_candidate'] desc=Frequency feature for token 'all' in an email message. +- word_freq_3d: role=feature, type=numeric_sparse_frequency. tags=['condition_candidate', 'measure', 'rare_pattern_candidate'] desc=Frequency feature for token '3d' in an email message. +- word_freq_our: role=feature, type=numeric_sparse_frequency. tags=['condition_candidate', 'measure', 'rare_pattern_candidate'] desc=Frequency feature for token 'our' in an email message. +- word_freq_over: role=feature, type=numeric_sparse_frequency. tags=['condition_candidate', 'measure', 'rare_pattern_candidate'] desc=Frequency feature for token 'over' in an email message. +- word_freq_remove: role=feature, type=numeric_sparse_frequency. tags=['condition_candidate', 'measure', 'rare_pattern_candidate'] desc=Frequency feature for token 'remove' in an email message. +- word_freq_internet: role=feature, type=numeric_sparse_frequency. tags=['condition_candidate', 'measure', 'rare_pattern_candidate'] desc=Frequency feature for token 'internet' in an email message. +- word_freq_order: role=feature, type=numeric_sparse_frequency. tags=['condition_candidate', 'measure', 'rare_pattern_candidate'] desc=Frequency feature for token 'order' in an email message. +- word_freq_mail: role=feature, type=numeric_sparse_frequency. tags=['condition_candidate', 'measure', 'rare_pattern_candidate'] desc=Frequency feature for token 'mail' in an email message. +- word_freq_receive: role=feature, type=numeric_sparse_frequency. tags=['condition_candidate', 'measure', 'rare_pattern_candidate'] desc=Frequency feature for token 'receive' in an email message. +- word_freq_will: role=feature, type=numeric_sparse_frequency. tags=['condition_candidate', 'measure', 'rare_pattern_candidate'] desc=Frequency feature for token 'will' in an email message. +- word_freq_people: role=feature, type=numeric_sparse_frequency. tags=['condition_candidate', 'measure', 'rare_pattern_candidate'] desc=Frequency feature for token 'people' in an email message. +- word_freq_report: role=feature, type=numeric_sparse_frequency. tags=['condition_candidate', 'measure', 'rare_pattern_candidate'] desc=Frequency feature for token 'report' in an email message. +- word_freq_addresses: role=feature, type=numeric_sparse_frequency. tags=['condition_candidate', 'measure', 'rare_pattern_candidate'] desc=Frequency feature for token 'addresses' in an email message. +- word_freq_free: role=feature, type=numeric_sparse_frequency. tags=['condition_candidate', 'measure', 'rare_pattern_candidate'] desc=Frequency feature for token 'free' in an email message. +- word_freq_business: role=feature, type=numeric_sparse_frequency. tags=['condition_candidate', 'measure', 'rare_pattern_candidate'] desc=Frequency feature for token 'business' in an email message. +- word_freq_email: role=feature, type=numeric_sparse_frequency. tags=['condition_candidate', 'measure', 'rare_pattern_candidate'] desc=Frequency feature for token 'email' in an email message. +- word_freq_you: role=feature, type=numeric_sparse_frequency. tags=['condition_candidate', 'measure', 'rare_pattern_candidate'] desc=Frequency feature for token 'you' in an email message. +- word_freq_credit: role=feature, type=numeric_sparse_frequency. tags=['condition_candidate', 'measure', 'rare_pattern_candidate'] desc=Frequency feature for token 'credit' in an email message. +- word_freq_your: role=feature, type=numeric_sparse_frequency. tags=['condition_candidate', 'measure', 'rare_pattern_candidate'] desc=Frequency feature for token 'your' in an email message. +- word_freq_font: role=feature, type=numeric_sparse_frequency. tags=['condition_candidate', 'measure', 'rare_pattern_candidate'] desc=Frequency feature for token 'font' in an email message. +- word_freq_000: role=feature, type=numeric_sparse_frequency. tags=['condition_candidate', 'measure', 'rare_pattern_candidate'] desc=Frequency feature for token '000' in an email message. +- word_freq_money: role=feature, type=numeric_sparse_frequency. tags=['condition_candidate', 'measure', 'rare_pattern_candidate'] desc=Frequency feature for token 'money' in an email message. +- word_freq_hp: role=feature, type=numeric_sparse_frequency. tags=['condition_candidate', 'measure', 'rare_pattern_candidate'] desc=Frequency feature for token 'hp' in an email message. +- word_freq_hpl: role=feature, type=numeric_sparse_frequency. tags=['condition_candidate', 'measure', 'rare_pattern_candidate'] desc=Frequency feature for token 'hpl' in an email message. +- word_freq_george: role=feature, type=numeric_sparse_frequency. tags=['condition_candidate', 'measure', 'rare_pattern_candidate'] desc=Frequency feature for token 'george' in an email message. +- word_freq_650: role=feature, type=numeric_sparse_frequency. tags=['condition_candidate', 'measure', 'rare_pattern_candidate'] desc=Frequency feature for token '650' in an email message. +- word_freq_lab: role=feature, type=numeric_sparse_frequency. tags=['condition_candidate', 'measure', 'rare_pattern_candidate'] desc=Frequency feature for token 'lab' in an email message. +- word_freq_labs: role=feature, type=numeric_sparse_frequency. tags=['condition_candidate', 'measure', 'rare_pattern_candidate'] desc=Frequency feature for token 'labs' in an email message. +- word_freq_telnet: role=feature, type=numeric_sparse_frequency. tags=['condition_candidate', 'measure', 'rare_pattern_candidate'] desc=Frequency feature for token 'telnet' in an email message. +- word_freq_857: role=feature, type=numeric_sparse_frequency. tags=['condition_candidate', 'measure', 'rare_pattern_candidate'] desc=Frequency feature for token '857' in an email message. +- word_freq_data: role=feature, type=numeric_sparse_frequency. tags=['condition_candidate', 'measure', 'rare_pattern_candidate'] desc=Frequency feature for token 'data' in an email message. +- word_freq_415: role=feature, type=numeric_sparse_frequency. tags=['condition_candidate', 'measure', 'rare_pattern_candidate'] desc=Frequency feature for token '415' in an email message. +- word_freq_85: role=feature, type=numeric_sparse_frequency. tags=['condition_candidate', 'measure', 'rare_pattern_candidate'] desc=Frequency feature for token '85' in an email message. +- word_freq_technology: role=feature, type=numeric_sparse_frequency. tags=['condition_candidate', 'measure', 'rare_pattern_candidate'] desc=Frequency feature for token 'technology' in an email message. +- word_freq_1999: role=feature, type=numeric_sparse_frequency. tags=['condition_candidate', 'measure', 'rare_pattern_candidate'] desc=Frequency feature for token '1999' in an email message. +- word_freq_parts: role=feature, type=numeric_sparse_frequency. tags=['condition_candidate', 'measure', 'rare_pattern_candidate'] desc=Frequency feature for token 'parts' in an email message. +- word_freq_pm: role=feature, type=numeric_sparse_frequency. tags=['condition_candidate', 'measure', 'rare_pattern_candidate'] desc=Frequency feature for token 'pm' in an email message. +- word_freq_direct: role=feature, type=numeric_sparse_frequency. tags=['condition_candidate', 'measure', 'rare_pattern_candidate'] desc=Frequency feature for token 'direct' in an email message. +- word_freq_cs: role=feature, type=numeric_sparse_frequency. tags=['condition_candidate', 'measure', 'rare_pattern_candidate'] desc=Frequency feature for token 'cs' in an email message. +- word_freq_meeting: role=feature, type=numeric_sparse_frequency. tags=['condition_candidate', 'measure', 'rare_pattern_candidate'] desc=Frequency feature for token 'meeting' in an email message. +- word_freq_original: role=feature, type=numeric_sparse_frequency. tags=['condition_candidate', 'measure', 'rare_pattern_candidate'] desc=Frequency feature for token 'original' in an email message. +- word_freq_project: role=feature, type=numeric_sparse_frequency. tags=['condition_candidate', 'measure', 'rare_pattern_candidate'] desc=Frequency feature for token 'project' in an email message. +- word_freq_re: role=feature, type=numeric_sparse_frequency. tags=['condition_candidate', 'measure', 'rare_pattern_candidate'] desc=Frequency feature for token 're' in an email message. +- word_freq_edu: role=feature, type=numeric_sparse_frequency. tags=['condition_candidate', 'measure', 'rare_pattern_candidate'] desc=Frequency feature for token 'edu' in an email message. +- word_freq_table: role=feature, type=numeric_sparse_frequency. tags=['condition_candidate', 'measure', 'rare_pattern_candidate'] desc=Frequency feature for token 'table' in an email message. +- word_freq_conference: role=feature, type=numeric_sparse_frequency. tags=['condition_candidate', 'measure', 'rare_pattern_candidate'] desc=Frequency feature for token 'conference' in an email message. +- char_freq_%3B: role=feature, type=numeric_sparse_frequency. tags=['condition_candidate', 'measure', 'rare_pattern_candidate'] desc=Character frequency feature for symbol ';'. +- char_freq_%28: role=feature, type=numeric_sparse_frequency. tags=['condition_candidate', 'measure', 'rare_pattern_candidate'] desc=Character frequency feature for symbol '('. +- char_freq_%5B: role=feature, type=numeric_sparse_frequency. tags=['condition_candidate', 'measure', 'rare_pattern_candidate'] desc=Character frequency feature for symbol '['. +- char_freq_%21: role=feature, type=numeric_sparse_frequency. tags=['condition_candidate', 'measure', 'rare_pattern_candidate'] desc=Character frequency feature for symbol '!'. +- char_freq_%24: role=feature, type=numeric_sparse_frequency. tags=['condition_candidate', 'measure', 'rare_pattern_candidate'] desc=Character frequency feature for symbol '$'. +- char_freq_%23: role=feature, type=numeric_sparse_frequency. tags=['condition_candidate', 'measure', 'rare_pattern_candidate'] desc=Character frequency feature for symbol '#'. +- capital_run_length_average: role=feature, type=numeric_heavy_tail. tags=['condition_candidate', 'measure', 'rare_pattern_candidate'] desc=Average length of uninterrupted capital-letter runs. +- capital_run_length_longest: role=feature, type=numeric_heavy_tail. tags=['condition_candidate', 'measure', 'rare_pattern_candidate'] desc=Longest uninterrupted capital-letter run. +- capital_run_length_total: role=feature, type=numeric_heavy_tail. tags=['condition_candidate', 'measure', 'rare_pattern_candidate'] desc=Total number of capital letters in runs. +- class: role=target, type=binary_target. tags=['target_candidate'] desc=Binary spam label (1=spam, 0=non-spam). +- useful_field_combinations: [['word_freq_free', 'word_freq_you', 'class'], ['char_freq_%21', 'capital_run_length_longest', 'class'], ['word_freq_remove', 'word_freq_money', 'class']] +- fields_requiring_caution: ['capital_run_length_average', 'capital_run_length_longest', 'capital_run_length_total'] +- source_url: https://www.openml.org/d/44 + +SQLite schema snapshot: +{ + "table_name": "n1", + "quoted_table_name": "\"n1\"", + "row_count": 4601, + "columns": [ + { + "name": "word_freq_make", + "type": "TEXT", + "notnull": false, + "pk": false + }, + { + "name": "word_freq_address", + "type": "TEXT", + "notnull": false, + "pk": false + }, + { + "name": "word_freq_all", + "type": "TEXT", + "notnull": false, + "pk": false + }, + { + "name": "word_freq_3d", + "type": "TEXT", + "notnull": false, + "pk": false + }, + { + "name": "word_freq_our", + "type": "TEXT", + "notnull": false, + "pk": false + }, + { + "name": "word_freq_over", + "type": "TEXT", + "notnull": false, + "pk": false + }, + { + "name": "word_freq_remove", + "type": "TEXT", + "notnull": false, + "pk": false + }, + { + "name": "word_freq_internet", + "type": "TEXT", + "notnull": false, + "pk": false + }, + { + "name": "word_freq_order", + "type": "TEXT", + "notnull": false, + "pk": false + }, + { + "name": "word_freq_mail", + "type": "TEXT", + "notnull": false, + "pk": false + }, + { + "name": "word_freq_receive", + "type": "TEXT", + "notnull": false, + "pk": false + }, + { + "name": "word_freq_will", + "type": "TEXT", + "notnull": false, + "pk": false + }, + { + "name": "word_freq_people", + "type": "TEXT", + "notnull": false, + "pk": false + }, + { + "name": "word_freq_report", + "type": "TEXT", + "notnull": false, + "pk": false + }, + { + "name": "word_freq_addresses", + "type": "TEXT", + "notnull": false, + "pk": false + }, + { + "name": "word_freq_free", + "type": "TEXT", + "notnull": false, + "pk": false + }, + { + "name": "word_freq_business", + "type": "TEXT", + "notnull": false, + "pk": false + }, + { + "name": "word_freq_email", + "type": "TEXT", + "notnull": false, + "pk": false + }, + { + "name": "word_freq_you", + "type": "TEXT", + "notnull": false, + "pk": false + }, + { + "name": "word_freq_credit", + "type": "TEXT", + "notnull": false, + "pk": false + }, + { + "name": "word_freq_your", + "type": "TEXT", + "notnull": false, + "pk": false + }, + { + "name": "word_freq_font", + "type": "TEXT", + "notnull": false, + "pk": false + }, + { + "name": "word_freq_000", + "type": "TEXT", + "notnull": false, + "pk": false + }, + { + "name": "word_freq_money", + "type": "TEXT", + "notnull": false, + "pk": false + }, + { + "name": "word_freq_hp", + "type": "TEXT", + "notnull": false, + "pk": false + }, + { + "name": "word_freq_hpl", + "type": "TEXT", + "notnull": false, + "pk": false + }, + { + "name": "word_freq_george", + "type": "TEXT", + "notnull": false, + "pk": false + }, + { + "name": "word_freq_650", + "type": "TEXT", + "notnull": false, + "pk": false + }, + { + "name": "word_freq_lab", + "type": "TEXT", + "notnull": false, + "pk": false + }, + { + "name": "word_freq_labs", + "type": "TEXT", + "notnull": false, + "pk": false + }, + { + "name": "word_freq_telnet", + "type": "TEXT", + "notnull": false, + "pk": false + }, + { + "name": "word_freq_857", + "type": "TEXT", + "notnull": false, + "pk": false + }, + { + "name": "word_freq_data", + "type": "TEXT", + "notnull": false, + "pk": false + }, + { + "name": "word_freq_415", + "type": "TEXT", + "notnull": false, + "pk": false + }, + { + "name": "word_freq_85", + "type": "TEXT", + "notnull": false, + "pk": false + }, + { + "name": "word_freq_technology", + "type": "TEXT", + "notnull": false, + "pk": false + }, + { + "name": "word_freq_1999", + "type": "TEXT", + "notnull": false, + "pk": false + }, + { + "name": "word_freq_parts", + "type": "TEXT", + "notnull": false, + "pk": false + }, + { + "name": "word_freq_pm", + "type": "TEXT", + "notnull": false, + "pk": false + }, + { + "name": "word_freq_direct", + "type": "TEXT", + "notnull": false, + "pk": false + }, + { + "name": "word_freq_cs", + "type": "TEXT", + "notnull": false, + "pk": false + }, + { + "name": "word_freq_meeting", + "type": "TEXT", + "notnull": false, + "pk": false + }, + { + "name": "word_freq_original", + "type": "TEXT", + "notnull": false, + "pk": false + }, + { + "name": "word_freq_project", + "type": "TEXT", + "notnull": false, + "pk": false + }, + { + "name": "word_freq_re", + "type": "TEXT", + "notnull": false, + "pk": false + }, + { + "name": "word_freq_edu", + "type": "TEXT", + "notnull": false, + "pk": false + }, + { + "name": "word_freq_table", + "type": "TEXT", + "notnull": false, + "pk": false + }, + { + "name": "word_freq_conference", + "type": "TEXT", + "notnull": false, + "pk": false + }, + { + "name": "char_freq_%3B", + "type": "TEXT", + "notnull": false, + "pk": false + }, + { + "name": "char_freq_%28", + "type": "TEXT", + "notnull": false, + "pk": false + }, + { + "name": "char_freq_%5B", + "type": "TEXT", + "notnull": false, + "pk": false + }, + { + "name": "char_freq_%21", + "type": "TEXT", + "notnull": false, + "pk": false + }, + { + "name": "char_freq_%24", + "type": "TEXT", + "notnull": false, + "pk": false + }, + { + "name": "char_freq_%23", + "type": "TEXT", + "notnull": false, + "pk": false + }, + { + "name": "capital_run_length_average", + "type": "TEXT", + "notnull": false, + "pk": false + }, + { + "name": "capital_run_length_longest", + "type": "TEXT", + "notnull": false, + "pk": false + }, + { + "name": "capital_run_length_total", + "type": "TEXT", + "notnull": false, + "pk": false + }, + { + "name": "class", + "type": "TEXT", + "notnull": false, + "pk": false + } + ], + "sample_rows": [ + { + "word_freq_make": "0", + "word_freq_address": "0.64", + "word_freq_all": "0.64", + "word_freq_3d": "0", + "word_freq_our": "0.32", + "word_freq_over": "0", + "word_freq_remove": "0", + "word_freq_internet": "0", + "word_freq_order": "0", + "word_freq_mail": "0", + "word_freq_receive": "0", + "word_freq_will": "0.64", + "word_freq_people": "0", + "word_freq_report": "0", + "word_freq_addresses": "0", + "word_freq_free": "0.32", + "word_freq_business": "0", + "word_freq_email": "1.29", + "word_freq_you": "1.93", + "word_freq_credit": "0", + "word_freq_your": "0.96", + "word_freq_font": "0", + "word_freq_000": "0", + "word_freq_money": "0", + "word_freq_hp": "0", + "word_freq_hpl": "0", + "word_freq_george": "0", + "word_freq_650": "0", + "word_freq_lab": "0", + "word_freq_labs": "0", + "word_freq_telnet": "0", + "word_freq_857": "0", + "word_freq_data": "0", + "word_freq_415": "0", + "word_freq_85": "0", + "word_freq_technology": "0", + "word_freq_1999": "0", + "word_freq_parts": "0", + "word_freq_pm": "0", + "word_freq_direct": "0", + "word_freq_cs": "0", + "word_freq_meeting": "0", + "word_freq_original": "0", + "word_freq_project": "0", + "word_freq_re": "0", + "word_freq_edu": "0", + "word_freq_table": "0", + "word_freq_conference": "0", + "char_freq_%3B": "0", + "char_freq_%28": "0", + "char_freq_%5B": "0", + "char_freq_%21": "0.778", + "char_freq_%24": "0", + "char_freq_%23": "0", + "capital_run_length_average": "3.756", + "capital_run_length_longest": "61", + "capital_run_length_total": "278", + "class": "1" + }, + { + "word_freq_make": "0.21", + "word_freq_address": "0.28", + "word_freq_all": "0.5", + "word_freq_3d": "0", + "word_freq_our": "0.14", + "word_freq_over": "0.28", + "word_freq_remove": "0.21", + "word_freq_internet": "0.07", + "word_freq_order": "0", + "word_freq_mail": "0.94", + "word_freq_receive": "0.21", + "word_freq_will": "0.79", + "word_freq_people": "0.65", + "word_freq_report": "0.21", + "word_freq_addresses": "0.14", + "word_freq_free": "0.14", + "word_freq_business": "0.07", + "word_freq_email": "0.28", + "word_freq_you": "3.47", + "word_freq_credit": "0", + "word_freq_your": "1.59", + "word_freq_font": "0", + "word_freq_000": "0.43", + "word_freq_money": "0.43", + "word_freq_hp": "0", + "word_freq_hpl": "0", + "word_freq_george": "0", + "word_freq_650": "0", + "word_freq_lab": "0", + "word_freq_labs": "0", + "word_freq_telnet": "0", + "word_freq_857": "0", + "word_freq_data": "0", + "word_freq_415": "0", + "word_freq_85": "0", + "word_freq_technology": "0", + "word_freq_1999": "0.07", + "word_freq_parts": "0", + "word_freq_pm": "0", + "word_freq_direct": "0", + "word_freq_cs": "0", + "word_freq_meeting": "0", + "word_freq_original": "0", + "word_freq_project": "0", + "word_freq_re": "0", + "word_freq_edu": "0", + "word_freq_table": "0", + "word_freq_conference": "0", + "char_freq_%3B": "0", + "char_freq_%28": "0.132", + "char_freq_%5B": "0", + "char_freq_%21": "0.372", + "char_freq_%24": "0.18", + "char_freq_%23": "0.048", + "capital_run_length_average": "5.114", + "capital_run_length_longest": "101", + "capital_run_length_total": "1028", + "class": "1" + }, + { + "word_freq_make": "0.06", + "word_freq_address": "0", + "word_freq_all": "0.71", + "word_freq_3d": "0", + "word_freq_our": "1.23", + "word_freq_over": "0.19", + "word_freq_remove": "0.19", + "word_freq_internet": "0.12", + "word_freq_order": "0.64", + "word_freq_mail": "0.25", + "word_freq_receive": "0.38", + "word_freq_will": "0.45", + "word_freq_people": "0.12", + "word_freq_report": "0", + "word_freq_addresses": "1.75", + "word_freq_free": "0.06", + "word_freq_business": "0.06", + "word_freq_email": "1.03", + "word_freq_you": "1.36", + "word_freq_credit": "0.32", + "word_freq_your": "0.51", + "word_freq_font": "0", + "word_freq_000": "1.16", + "word_freq_money": "0.06", + "word_freq_hp": "0", + "word_freq_hpl": "0", + "word_freq_george": "0", + "word_freq_650": "0", + "word_freq_lab": "0", + "word_freq_labs": "0", + "word_freq_telnet": "0", + "word_freq_857": "0", + "word_freq_data": "0", + "word_freq_415": "0", + "word_freq_85": "0", + "word_freq_technology": "0", + "word_freq_1999": "0", + "word_freq_parts": "0", + "word_freq_pm": "0", + "word_freq_direct": "0.06", + "word_freq_cs": "0", + "word_freq_meeting": "0", + "word_freq_original": "0.12", + "word_freq_project": "0", + "word_freq_re": "0.06", + "word_freq_edu": "0.06", + "word_freq_table": "0", + "word_freq_conference": "0", + "char_freq_%3B": "0.01", + "char_freq_%28": "0.143", + "char_freq_%5B": "0", + "char_freq_%21": "0.276", + "char_freq_%24": "0.184", + "char_freq_%23": "0.01", + "capital_run_length_average": "9.821", + "capital_run_length_longest": "485", + "capital_run_length_total": "2259", + "class": "1" + }, + { + "word_freq_make": "0", + "word_freq_address": "0", + "word_freq_all": "0", + "word_freq_3d": "0", + "word_freq_our": "0.63", + "word_freq_over": "0", + "word_freq_remove": "0.31", + "word_freq_internet": "0.63", + "word_freq_order": "0.31", + "word_freq_mail": "0.63", + "word_freq_receive": "0.31", + "word_freq_will": "0.31", + "word_freq_people": "0.31", + "word_freq_report": "0", + "word_freq_addresses": "0", + "word_freq_free": "0.31", + "word_freq_business": "0", + "word_freq_email": "0", + "word_freq_you": "3.18", + "word_freq_credit": "0", + "word_freq_your": "0.31", + "word_freq_font": "0", + "word_freq_000": "0", + "word_freq_money": "0", + "word_freq_hp": "0", + "word_freq_hpl": "0", + "word_freq_george": "0", + "word_freq_650": "0", + "word_freq_lab": "0", + "word_freq_labs": "0", + "word_freq_telnet": "0", + "word_freq_857": "0", + "word_freq_data": "0", + "word_freq_415": "0", + "word_freq_85": "0", + "word_freq_technology": "0", + "word_freq_1999": "0", + "word_freq_parts": "0", + "word_freq_pm": "0", + "word_freq_direct": "0", + "word_freq_cs": "0", + "word_freq_meeting": "0", + "word_freq_original": "0", + "word_freq_project": "0", + "word_freq_re": "0", + "word_freq_edu": "0", + "word_freq_table": "0", + "word_freq_conference": "0", + "char_freq_%3B": "0", + "char_freq_%28": "0.137", + "char_freq_%5B": "0", + "char_freq_%21": "0.137", + "char_freq_%24": "0", + "char_freq_%23": "0", + "capital_run_length_average": "3.537", + "capital_run_length_longest": "40", + "capital_run_length_total": "191", + "class": "1" + }, + { + "word_freq_make": "0", + "word_freq_address": "0", + "word_freq_all": "0", + "word_freq_3d": "0", + "word_freq_our": "0.63", + "word_freq_over": "0", + "word_freq_remove": "0.31", + "word_freq_internet": "0.63", + "word_freq_order": "0.31", + "word_freq_mail": "0.63", + "word_freq_receive": "0.31", + "word_freq_will": "0.31", + "word_freq_people": "0.31", + "word_freq_report": "0", + "word_freq_addresses": "0", + "word_freq_free": "0.31", + "word_freq_business": "0", + "word_freq_email": "0", + "word_freq_you": "3.18", + "word_freq_credit": "0", + "word_freq_your": "0.31", + "word_freq_font": "0", + "word_freq_000": "0", + "word_freq_money": "0", + "word_freq_hp": "0", + "word_freq_hpl": "0", + "word_freq_george": "0", + "word_freq_650": "0", + "word_freq_lab": "0", + "word_freq_labs": "0", + "word_freq_telnet": "0", + "word_freq_857": "0", + "word_freq_data": "0", + "word_freq_415": "0", + "word_freq_85": "0", + "word_freq_technology": "0", + "word_freq_1999": "0", + "word_freq_parts": "0", + "word_freq_pm": "0", + "word_freq_direct": "0", + "word_freq_cs": "0", + "word_freq_meeting": "0", + "word_freq_original": "0", + "word_freq_project": "0", + "word_freq_re": "0", + "word_freq_edu": "0", + "word_freq_table": "0", + "word_freq_conference": "0", + "char_freq_%3B": "0", + "char_freq_%28": "0.135", + "char_freq_%5B": "0", + "char_freq_%21": "0.135", + "char_freq_%24": "0", + "char_freq_%23": "0", + "capital_run_length_average": "3.537", + "capital_run_length_longest": "40", + "capital_run_length_total": "191", + "class": "1" + } + ] +} + +Shortlisted templates: +[ + { + "template_id": "tpl_m4_group_ratio_two_conditions", + "template_name": "Grouped Ratio of Two Conditions", + "primary_family": "conditional_dependency_structure", + "portability": "yes", + "sql_skeleton": "WITH grouped AS (\n SELECT {group_col},\n SUM(CASE WHEN {condition_col} = {positive_value} THEN 1 ELSE 0 END) AS numerator_count,\n SUM(CASE WHEN {condition_col} = {negative_value} THEN 1 ELSE 0 END) AS denominator_count\n FROM {table}\n GROUP BY {group_col}\n)\nSELECT {group_col},\n CAST(numerator_count AS FLOAT) / NULLIF(denominator_count, 0) AS condition_ratio\nFROM grouped\nORDER BY condition_ratio DESC;", + "required_roles": [ + "group_col", + "condition_col" + ] + } +] + +Problem instance: +{ + "dataset_id": "n1", + "question": "Use template Grouped Ratio of Two Conditions to probe direction_consistency with semantic role contrastive_conditional_view. Focus on group_col=class, condition_col=class.", + "planned_template_id": "tpl_m4_group_ratio_two_conditions", + "bindings": { + "group_col": "class", + "condition_col": "class", + "condition_value": "0", + "positive_value": "0", + "negative_value": "1", + "top_k": 13, + "top_n": 5, + "num_tiles": 10, + "percentile_value": 0.95, + "z_threshold": 2.0, + "fraction_threshold": 0.1, + "baseline_multiplier": 1.5, + "baseline_fraction": 0.1, + "min_group_size": 5, + "min_support": 5, + "measure_threshold": 0.0, + "time_grain": "month", + "lookback_rows": 3, + "current_period_start": "'2024-01-01'", + "current_period_end": "'2024-04-01'", + "previous_period_start": "'2023-10-01'", + "previous_period_end": "'2024-01-01'", + "drift_ratio_threshold": 0.8 + }, + "can_vary": [], + "must_fix": [], + "runtime_sql_skeleton": "WITH grouped AS (\n SELECT {group_col},\n SUM(CASE WHEN {condition_col} = {positive_value} THEN 1 ELSE 0 END) AS numerator_count,\n SUM(CASE WHEN {condition_col} = {negative_value} THEN 1 ELSE 0 END) AS denominator_count\n FROM {table}\n GROUP BY {group_col}\n)\nSELECT {group_col},\n CAST(numerator_count AS FLOAT) / NULLIF(denominator_count, 0) AS condition_ratio\nFROM grouped\nORDER BY condition_ratio DESC;" +} + +Repair context: +{} diff --git a/Query/sql/v2/runs/v2_cli_20260502_081223_d/n1/artifacts/v2q_n1_6acc8ab2557e8260/cli/sql_response_attempt_1.raw.txt b/Query/sql/v2/runs/v2_cli_20260502_081223_d/n1/artifacts/v2q_n1_6acc8ab2557e8260/cli/sql_response_attempt_1.raw.txt new file mode 100644 index 0000000000000000000000000000000000000000..ce3fc7a341430380dc5261f35b90a0639d2de3c4 --- /dev/null +++ b/Query/sql/v2/runs/v2_cli_20260502_081223_d/n1/artifacts/v2q_n1_6acc8ab2557e8260/cli/sql_response_attempt_1.raw.txt @@ -0,0 +1,4 @@ +{"type":"thread.started","thread_id":"019e40e4-b096-7dc2-b150-72a19153f94b"} +{"type":"turn.started"} +{"type":"item.completed","item":{"id":"item_0","type":"agent_message","text":"{\"sql\":\"-- template_id: tpl_m4_group_ratio_two_conditions\\nWITH grouped AS (\\n SELECT \\\"class\\\",\\n SUM(CASE WHEN \\\"class\\\" = '0' THEN 1 ELSE 0 END) AS numerator_count,\\n SUM(CASE WHEN \\\"class\\\" = '1' THEN 1 ELSE 0 END) AS denominator_count\\n FROM \\\"n1\\\"\\n GROUP BY \\\"class\\\"\\n)\\nSELECT \\\"class\\\",\\n CAST(numerator_count AS FLOAT) / NULLIF(denominator_count, 0) AS condition_ratio\\nFROM grouped\\nORDER BY condition_ratio DESC;\",\"notes\":\"Used the requested template with both group_col and condition_col bound to \\\"class\\\". Because \\\"class\\\" is stored as TEXT in the schema snapshot, the condition values are compared as '0' and '1'. Grouping and conditioning on the same binary column means the '0' group will have a zero denominator and yield NULL, while the '1' group yields 0.0.\"}"}} +{"type":"turn.completed","usage":{"input_tokens":20520,"cached_input_tokens":19840,"output_tokens":543,"reasoning_output_tokens":326}} diff --git a/Query/sql/v2/runs/v2_cli_20260502_081223_d/n1/artifacts/v2q_n1_6acc8ab2557e8260/cli/sql_response_attempt_1.txt b/Query/sql/v2/runs/v2_cli_20260502_081223_d/n1/artifacts/v2q_n1_6acc8ab2557e8260/cli/sql_response_attempt_1.txt new file mode 100644 index 0000000000000000000000000000000000000000..17d3a3d554b4925f50fc398648038c034d0599e2 --- /dev/null +++ b/Query/sql/v2/runs/v2_cli_20260502_081223_d/n1/artifacts/v2q_n1_6acc8ab2557e8260/cli/sql_response_attempt_1.txt @@ -0,0 +1 @@ +{"sql":"-- template_id: tpl_m4_group_ratio_two_conditions\nWITH grouped AS (\n SELECT \"class\",\n SUM(CASE WHEN \"class\" = '0' THEN 1 ELSE 0 END) AS numerator_count,\n SUM(CASE WHEN \"class\" = '1' THEN 1 ELSE 0 END) AS denominator_count\n FROM \"n1\"\n GROUP BY \"class\"\n)\nSELECT \"class\",\n CAST(numerator_count AS FLOAT) / NULLIF(denominator_count, 0) AS condition_ratio\nFROM grouped\nORDER BY condition_ratio DESC;","notes":"Used the requested template with both group_col and condition_col bound to \"class\". Because \"class\" is stored as TEXT in the schema snapshot, the condition values are compared as '0' and '1'. Grouping and conditioning on the same binary column means the '0' group will have a zero denominator and yield NULL, while the '1' group yields 0.0."} \ No newline at end of file diff --git a/Query/sql/v2/runs/v2_cli_20260502_081223_d/n1/artifacts/v2q_n1_6acc8ab2557e8260/cli/sql_stderr_attempt_1.txt b/Query/sql/v2/runs/v2_cli_20260502_081223_d/n1/artifacts/v2q_n1_6acc8ab2557e8260/cli/sql_stderr_attempt_1.txt new file mode 100644 index 0000000000000000000000000000000000000000..e69de29bb2d1d6434b8b29ae775ad8c2e48c5391 diff --git a/Query/sql/v2/runs/v2_cli_20260502_081223_d/n1/artifacts/v2q_n1_70dc9ee9dd88cbbd/cli/sql_attempt_1.metadata.json b/Query/sql/v2/runs/v2_cli_20260502_081223_d/n1/artifacts/v2q_n1_70dc9ee9dd88cbbd/cli/sql_attempt_1.metadata.json new file mode 100644 index 0000000000000000000000000000000000000000..2634ba23b0795560076ce23a184d33e0e0c129ad --- /dev/null +++ b/Query/sql/v2/runs/v2_cli_20260502_081223_d/n1/artifacts/v2q_n1_70dc9ee9dd88cbbd/cli/sql_attempt_1.metadata.json @@ -0,0 +1,43 @@ +{ + "attempt": 1, + "phase": "sql_generation", + "command": "codex exec --skip-git-repo-check --disable plugins --sandbox read-only --cd \"/data/jialinzhang/SQLagent\" -m gpt-5.4 --json -", + "started_at": "2026-05-19T16:05:58.758470+00:00", + "ended_at": "2026-05-19T16:06:01.973800+00:00", + "elapsed_ms": 3215.29, + "returncode": 1, + "prompt_metrics": { + "chars": 29519, + "bytes_utf8": 29519, + "lines": 792, + "estimated_tokens": null + }, + "stdout_metrics": { + "chars": 281, + "bytes_utf8": 281, + "lines": 4, + "estimated_tokens": null + }, + "stderr_metrics": { + "chars": 0, + "bytes_utf8": 0, + "lines": 0, + "estimated_tokens": null + }, + "parsed_output": { + "format": "jsonl_events", + "text_metrics": { + "chars": 280, + "bytes_utf8": 280, + "lines": 4, + "estimated_tokens": null + }, + "usage": {} + }, + "status": "failed", + "error": "AI CLI command failed with exit code 1: ", + "prompt_path": "cli/sql_prompt_attempt_1.txt", + "response_path": "cli/sql_response_attempt_1.txt", + "raw_response_path": "cli/sql_response_attempt_1.raw.txt", + "stderr_path": "cli/sql_stderr_attempt_1.txt" +} \ No newline at end of file diff --git a/Query/sql/v2/runs/v2_cli_20260502_081223_d/n1/artifacts/v2q_n1_70dc9ee9dd88cbbd/cli/sql_attempt_2.metadata.json b/Query/sql/v2/runs/v2_cli_20260502_081223_d/n1/artifacts/v2q_n1_70dc9ee9dd88cbbd/cli/sql_attempt_2.metadata.json new file mode 100644 index 0000000000000000000000000000000000000000..f548955f610f709e7dbc7922e47c67de922aaa02 --- /dev/null +++ b/Query/sql/v2/runs/v2_cli_20260502_081223_d/n1/artifacts/v2q_n1_70dc9ee9dd88cbbd/cli/sql_attempt_2.metadata.json @@ -0,0 +1,43 @@ +{ + "attempt": 2, + "phase": "sql_generation", + "command": "codex exec --skip-git-repo-check --disable plugins --sandbox read-only --cd \"/data/jialinzhang/SQLagent\" -m gpt-5.4 --json -", + "started_at": "2026-05-19T16:06:02.978334+00:00", + "ended_at": "2026-05-19T16:06:06.534296+00:00", + "elapsed_ms": 3555.9, + "returncode": 1, + "prompt_metrics": { + "chars": 29519, + "bytes_utf8": 29519, + "lines": 792, + "estimated_tokens": null + }, + "stdout_metrics": { + "chars": 281, + "bytes_utf8": 281, + "lines": 4, + "estimated_tokens": null + }, + "stderr_metrics": { + "chars": 0, + "bytes_utf8": 0, + "lines": 0, + "estimated_tokens": null + }, + "parsed_output": { + "format": "jsonl_events", + "text_metrics": { + "chars": 280, + "bytes_utf8": 280, + "lines": 4, + "estimated_tokens": null + }, + "usage": {} + }, + "status": "failed", + "error": "AI CLI command failed with exit code 1: ", + "prompt_path": "cli/sql_prompt_attempt_2.txt", + "response_path": "cli/sql_response_attempt_2.txt", + "raw_response_path": "cli/sql_response_attempt_2.raw.txt", + "stderr_path": "cli/sql_stderr_attempt_2.txt" +} \ No newline at end of file diff --git a/Query/sql/v2/runs/v2_cli_20260502_081223_d/n1/artifacts/v2q_n1_70dc9ee9dd88cbbd/cli/sql_prompt_attempt_1.txt b/Query/sql/v2/runs/v2_cli_20260502_081223_d/n1/artifacts/v2q_n1_70dc9ee9dd88cbbd/cli/sql_prompt_attempt_1.txt new file mode 100644 index 0000000000000000000000000000000000000000..c45546903ffb1982dc76517c57eb6f13176edd5b --- /dev/null +++ b/Query/sql/v2/runs/v2_cli_20260502_081223_d/n1/artifacts/v2q_n1_70dc9ee9dd88cbbd/cli/sql_prompt_attempt_1.txt @@ -0,0 +1,792 @@ +You are generating one SQLite SELECT query for a single-table SQL QA task. +Return strict JSON only, with this schema: {"sql": "...", "notes": "..."}. +Rules: +- Use only the provided table and columns. +- Do not write INSERT, UPDATE, DELETE, DROP, ALTER, CREATE, PRAGMA, ATTACH, DETACH, or VACUUM. +- Prefer the planned template and bound roles when provided. +- Add a leading SQL comment exactly like: -- template_id: . +- Generate SQLite-compatible SQL. SQLite does not support PERCENTILE_CONT or STDDEV. +- Quote identifiers with double quotes. +- Return no markdown and no extra prose. + +Dataset context: +Dataset context for SQL QA: +- dataset_id: n1 +- dataset_name: Spambase +- table_name: n1 +- table_layout: single-table dataset (do not assume joins). +- row_semantics: One row is one email represented by engineered token/character frequency and capitalization-run features. +- task_type: classification +- target_column: class +- main_row_count: 4601 +- important_fields: +- word_freq_make: role=feature, type=numeric_sparse_frequency. tags=['condition_candidate', 'measure', 'rare_pattern_candidate'] desc=Frequency feature for token 'make' in an email message. +- word_freq_address: role=feature, type=numeric_sparse_frequency. tags=['condition_candidate', 'measure', 'rare_pattern_candidate'] desc=Frequency feature for token 'address' in an email message. +- word_freq_all: role=feature, type=numeric_sparse_frequency. tags=['condition_candidate', 'measure', 'rare_pattern_candidate'] desc=Frequency feature for token 'all' in an email message. +- word_freq_3d: role=feature, type=numeric_sparse_frequency. tags=['condition_candidate', 'measure', 'rare_pattern_candidate'] desc=Frequency feature for token '3d' in an email message. +- word_freq_our: role=feature, type=numeric_sparse_frequency. tags=['condition_candidate', 'measure', 'rare_pattern_candidate'] desc=Frequency feature for token 'our' in an email message. +- word_freq_over: role=feature, type=numeric_sparse_frequency. tags=['condition_candidate', 'measure', 'rare_pattern_candidate'] desc=Frequency feature for token 'over' in an email message. +- word_freq_remove: role=feature, type=numeric_sparse_frequency. tags=['condition_candidate', 'measure', 'rare_pattern_candidate'] desc=Frequency feature for token 'remove' in an email message. +- word_freq_internet: role=feature, type=numeric_sparse_frequency. tags=['condition_candidate', 'measure', 'rare_pattern_candidate'] desc=Frequency feature for token 'internet' in an email message. +- word_freq_order: role=feature, type=numeric_sparse_frequency. tags=['condition_candidate', 'measure', 'rare_pattern_candidate'] desc=Frequency feature for token 'order' in an email message. +- word_freq_mail: role=feature, type=numeric_sparse_frequency. tags=['condition_candidate', 'measure', 'rare_pattern_candidate'] desc=Frequency feature for token 'mail' in an email message. +- word_freq_receive: role=feature, type=numeric_sparse_frequency. tags=['condition_candidate', 'measure', 'rare_pattern_candidate'] desc=Frequency feature for token 'receive' in an email message. +- word_freq_will: role=feature, type=numeric_sparse_frequency. tags=['condition_candidate', 'measure', 'rare_pattern_candidate'] desc=Frequency feature for token 'will' in an email message. +- word_freq_people: role=feature, type=numeric_sparse_frequency. tags=['condition_candidate', 'measure', 'rare_pattern_candidate'] desc=Frequency feature for token 'people' in an email message. +- word_freq_report: role=feature, type=numeric_sparse_frequency. tags=['condition_candidate', 'measure', 'rare_pattern_candidate'] desc=Frequency feature for token 'report' in an email message. +- word_freq_addresses: role=feature, type=numeric_sparse_frequency. tags=['condition_candidate', 'measure', 'rare_pattern_candidate'] desc=Frequency feature for token 'addresses' in an email message. +- word_freq_free: role=feature, type=numeric_sparse_frequency. tags=['condition_candidate', 'measure', 'rare_pattern_candidate'] desc=Frequency feature for token 'free' in an email message. +- word_freq_business: role=feature, type=numeric_sparse_frequency. tags=['condition_candidate', 'measure', 'rare_pattern_candidate'] desc=Frequency feature for token 'business' in an email message. +- word_freq_email: role=feature, type=numeric_sparse_frequency. tags=['condition_candidate', 'measure', 'rare_pattern_candidate'] desc=Frequency feature for token 'email' in an email message. +- word_freq_you: role=feature, type=numeric_sparse_frequency. tags=['condition_candidate', 'measure', 'rare_pattern_candidate'] desc=Frequency feature for token 'you' in an email message. +- word_freq_credit: role=feature, type=numeric_sparse_frequency. tags=['condition_candidate', 'measure', 'rare_pattern_candidate'] desc=Frequency feature for token 'credit' in an email message. +- word_freq_your: role=feature, type=numeric_sparse_frequency. tags=['condition_candidate', 'measure', 'rare_pattern_candidate'] desc=Frequency feature for token 'your' in an email message. +- word_freq_font: role=feature, type=numeric_sparse_frequency. tags=['condition_candidate', 'measure', 'rare_pattern_candidate'] desc=Frequency feature for token 'font' in an email message. +- word_freq_000: role=feature, type=numeric_sparse_frequency. tags=['condition_candidate', 'measure', 'rare_pattern_candidate'] desc=Frequency feature for token '000' in an email message. +- word_freq_money: role=feature, type=numeric_sparse_frequency. tags=['condition_candidate', 'measure', 'rare_pattern_candidate'] desc=Frequency feature for token 'money' in an email message. +- word_freq_hp: role=feature, type=numeric_sparse_frequency. tags=['condition_candidate', 'measure', 'rare_pattern_candidate'] desc=Frequency feature for token 'hp' in an email message. +- word_freq_hpl: role=feature, type=numeric_sparse_frequency. tags=['condition_candidate', 'measure', 'rare_pattern_candidate'] desc=Frequency feature for token 'hpl' in an email message. +- word_freq_george: role=feature, type=numeric_sparse_frequency. tags=['condition_candidate', 'measure', 'rare_pattern_candidate'] desc=Frequency feature for token 'george' in an email message. +- word_freq_650: role=feature, type=numeric_sparse_frequency. tags=['condition_candidate', 'measure', 'rare_pattern_candidate'] desc=Frequency feature for token '650' in an email message. +- word_freq_lab: role=feature, type=numeric_sparse_frequency. tags=['condition_candidate', 'measure', 'rare_pattern_candidate'] desc=Frequency feature for token 'lab' in an email message. +- word_freq_labs: role=feature, type=numeric_sparse_frequency. tags=['condition_candidate', 'measure', 'rare_pattern_candidate'] desc=Frequency feature for token 'labs' in an email message. +- word_freq_telnet: role=feature, type=numeric_sparse_frequency. tags=['condition_candidate', 'measure', 'rare_pattern_candidate'] desc=Frequency feature for token 'telnet' in an email message. +- word_freq_857: role=feature, type=numeric_sparse_frequency. tags=['condition_candidate', 'measure', 'rare_pattern_candidate'] desc=Frequency feature for token '857' in an email message. +- word_freq_data: role=feature, type=numeric_sparse_frequency. tags=['condition_candidate', 'measure', 'rare_pattern_candidate'] desc=Frequency feature for token 'data' in an email message. +- word_freq_415: role=feature, type=numeric_sparse_frequency. tags=['condition_candidate', 'measure', 'rare_pattern_candidate'] desc=Frequency feature for token '415' in an email message. +- word_freq_85: role=feature, type=numeric_sparse_frequency. tags=['condition_candidate', 'measure', 'rare_pattern_candidate'] desc=Frequency feature for token '85' in an email message. +- word_freq_technology: role=feature, type=numeric_sparse_frequency. tags=['condition_candidate', 'measure', 'rare_pattern_candidate'] desc=Frequency feature for token 'technology' in an email message. +- word_freq_1999: role=feature, type=numeric_sparse_frequency. tags=['condition_candidate', 'measure', 'rare_pattern_candidate'] desc=Frequency feature for token '1999' in an email message. +- word_freq_parts: role=feature, type=numeric_sparse_frequency. tags=['condition_candidate', 'measure', 'rare_pattern_candidate'] desc=Frequency feature for token 'parts' in an email message. +- word_freq_pm: role=feature, type=numeric_sparse_frequency. tags=['condition_candidate', 'measure', 'rare_pattern_candidate'] desc=Frequency feature for token 'pm' in an email message. +- word_freq_direct: role=feature, type=numeric_sparse_frequency. tags=['condition_candidate', 'measure', 'rare_pattern_candidate'] desc=Frequency feature for token 'direct' in an email message. +- word_freq_cs: role=feature, type=numeric_sparse_frequency. tags=['condition_candidate', 'measure', 'rare_pattern_candidate'] desc=Frequency feature for token 'cs' in an email message. +- word_freq_meeting: role=feature, type=numeric_sparse_frequency. tags=['condition_candidate', 'measure', 'rare_pattern_candidate'] desc=Frequency feature for token 'meeting' in an email message. +- word_freq_original: role=feature, type=numeric_sparse_frequency. tags=['condition_candidate', 'measure', 'rare_pattern_candidate'] desc=Frequency feature for token 'original' in an email message. +- word_freq_project: role=feature, type=numeric_sparse_frequency. tags=['condition_candidate', 'measure', 'rare_pattern_candidate'] desc=Frequency feature for token 'project' in an email message. +- word_freq_re: role=feature, type=numeric_sparse_frequency. tags=['condition_candidate', 'measure', 'rare_pattern_candidate'] desc=Frequency feature for token 're' in an email message. +- word_freq_edu: role=feature, type=numeric_sparse_frequency. tags=['condition_candidate', 'measure', 'rare_pattern_candidate'] desc=Frequency feature for token 'edu' in an email message. +- word_freq_table: role=feature, type=numeric_sparse_frequency. tags=['condition_candidate', 'measure', 'rare_pattern_candidate'] desc=Frequency feature for token 'table' in an email message. +- word_freq_conference: role=feature, type=numeric_sparse_frequency. tags=['condition_candidate', 'measure', 'rare_pattern_candidate'] desc=Frequency feature for token 'conference' in an email message. +- char_freq_%3B: role=feature, type=numeric_sparse_frequency. tags=['condition_candidate', 'measure', 'rare_pattern_candidate'] desc=Character frequency feature for symbol ';'. +- char_freq_%28: role=feature, type=numeric_sparse_frequency. tags=['condition_candidate', 'measure', 'rare_pattern_candidate'] desc=Character frequency feature for symbol '('. +- char_freq_%5B: role=feature, type=numeric_sparse_frequency. tags=['condition_candidate', 'measure', 'rare_pattern_candidate'] desc=Character frequency feature for symbol '['. +- char_freq_%21: role=feature, type=numeric_sparse_frequency. tags=['condition_candidate', 'measure', 'rare_pattern_candidate'] desc=Character frequency feature for symbol '!'. +- char_freq_%24: role=feature, type=numeric_sparse_frequency. tags=['condition_candidate', 'measure', 'rare_pattern_candidate'] desc=Character frequency feature for symbol '$'. +- char_freq_%23: role=feature, type=numeric_sparse_frequency. tags=['condition_candidate', 'measure', 'rare_pattern_candidate'] desc=Character frequency feature for symbol '#'. +- capital_run_length_average: role=feature, type=numeric_heavy_tail. tags=['condition_candidate', 'measure', 'rare_pattern_candidate'] desc=Average length of uninterrupted capital-letter runs. +- capital_run_length_longest: role=feature, type=numeric_heavy_tail. tags=['condition_candidate', 'measure', 'rare_pattern_candidate'] desc=Longest uninterrupted capital-letter run. +- capital_run_length_total: role=feature, type=numeric_heavy_tail. tags=['condition_candidate', 'measure', 'rare_pattern_candidate'] desc=Total number of capital letters in runs. +- class: role=target, type=binary_target. tags=['target_candidate'] desc=Binary spam label (1=spam, 0=non-spam). +- useful_field_combinations: [['word_freq_free', 'word_freq_you', 'class'], ['char_freq_%21', 'capital_run_length_longest', 'class'], ['word_freq_remove', 'word_freq_money', 'class']] +- fields_requiring_caution: ['capital_run_length_average', 'capital_run_length_longest', 'capital_run_length_total'] +- source_url: https://www.openml.org/d/44 + +SQLite schema snapshot: +{ + "table_name": "n1", + "quoted_table_name": "\"n1\"", + "row_count": 4601, + "columns": [ + { + "name": "word_freq_make", + "type": "TEXT", + "notnull": false, + "pk": false + }, + { + "name": "word_freq_address", + "type": "TEXT", + "notnull": false, + "pk": false + }, + { + "name": "word_freq_all", + "type": "TEXT", + "notnull": false, + "pk": false + }, + { + "name": "word_freq_3d", + "type": "TEXT", + "notnull": false, + "pk": false + }, + { + "name": "word_freq_our", + "type": "TEXT", + "notnull": false, + "pk": false + }, + { + "name": "word_freq_over", + "type": "TEXT", + "notnull": false, + "pk": false + }, + { + "name": "word_freq_remove", + "type": "TEXT", + "notnull": false, + "pk": false + }, + { + "name": "word_freq_internet", + "type": "TEXT", + "notnull": false, + "pk": false + }, + { + "name": "word_freq_order", + "type": "TEXT", + "notnull": false, + "pk": false + }, + { + "name": "word_freq_mail", + "type": "TEXT", + "notnull": false, + "pk": false + }, + { + "name": "word_freq_receive", + "type": "TEXT", + "notnull": false, + "pk": false + }, + { + "name": "word_freq_will", + "type": "TEXT", + "notnull": false, + "pk": false + }, + { + "name": "word_freq_people", + "type": "TEXT", + "notnull": false, + "pk": false + }, + { + "name": "word_freq_report", + "type": "TEXT", + "notnull": false, + "pk": false + }, + { + "name": "word_freq_addresses", + "type": "TEXT", + "notnull": false, + "pk": false + }, + { + "name": "word_freq_free", + "type": "TEXT", + "notnull": false, + "pk": false + }, + { + "name": "word_freq_business", + "type": "TEXT", + "notnull": false, + "pk": false + }, + { + "name": "word_freq_email", + "type": "TEXT", + "notnull": false, + "pk": false + }, + { + "name": "word_freq_you", + "type": "TEXT", + "notnull": false, + "pk": false + }, + { + "name": "word_freq_credit", + "type": "TEXT", + "notnull": false, + "pk": false + }, + { + "name": "word_freq_your", + "type": "TEXT", + "notnull": false, + "pk": false + }, + { + "name": "word_freq_font", + "type": "TEXT", + "notnull": false, + "pk": false + }, + { + "name": "word_freq_000", + "type": "TEXT", + "notnull": false, + "pk": false + }, + { + "name": "word_freq_money", + "type": "TEXT", + "notnull": false, + "pk": false + }, + { + "name": "word_freq_hp", + "type": "TEXT", + "notnull": false, + "pk": false + }, + { + "name": "word_freq_hpl", + "type": "TEXT", + "notnull": false, + "pk": false + }, + { + "name": "word_freq_george", + "type": "TEXT", + "notnull": false, + "pk": false + }, + { + "name": "word_freq_650", + "type": "TEXT", + "notnull": false, + "pk": false + }, + { + "name": "word_freq_lab", + "type": "TEXT", + "notnull": false, + "pk": false + }, + { + "name": "word_freq_labs", + "type": "TEXT", + "notnull": false, + "pk": false + }, + { + "name": "word_freq_telnet", + "type": "TEXT", + "notnull": false, + "pk": false + }, + { + "name": "word_freq_857", + "type": "TEXT", + "notnull": false, + "pk": false + }, + { + "name": "word_freq_data", + "type": "TEXT", + "notnull": false, + "pk": false + }, + { + "name": "word_freq_415", + "type": "TEXT", + "notnull": false, + "pk": false + }, + { + "name": "word_freq_85", + "type": "TEXT", + "notnull": false, + "pk": false + }, + { + "name": "word_freq_technology", + "type": "TEXT", + "notnull": false, + "pk": false + }, + { + "name": "word_freq_1999", + "type": "TEXT", + "notnull": false, + "pk": false + }, + { + "name": "word_freq_parts", + "type": "TEXT", + "notnull": false, + "pk": false + }, + { + "name": "word_freq_pm", + "type": "TEXT", + "notnull": false, + "pk": false + }, + { + "name": "word_freq_direct", + "type": "TEXT", + "notnull": false, + "pk": false + }, + { + "name": "word_freq_cs", + "type": "TEXT", + "notnull": false, + "pk": false + }, + { + "name": "word_freq_meeting", + "type": "TEXT", + "notnull": false, + "pk": false + }, + { + "name": "word_freq_original", + "type": "TEXT", + "notnull": false, + "pk": false + }, + { + "name": "word_freq_project", + "type": "TEXT", + "notnull": false, + "pk": false + }, + { + "name": "word_freq_re", + "type": "TEXT", + "notnull": false, + "pk": false + }, + { + "name": "word_freq_edu", + "type": "TEXT", + "notnull": false, + "pk": false + }, + { + "name": "word_freq_table", + "type": "TEXT", + "notnull": false, + "pk": false + }, + { + "name": "word_freq_conference", + "type": "TEXT", + "notnull": false, + "pk": false + }, + { + "name": "char_freq_%3B", + "type": "TEXT", + "notnull": false, + "pk": false + }, + { + "name": "char_freq_%28", + "type": "TEXT", + "notnull": false, + "pk": false + }, + { + "name": "char_freq_%5B", + "type": "TEXT", + "notnull": false, + "pk": false + }, + { + "name": "char_freq_%21", + "type": "TEXT", + "notnull": false, + "pk": false + }, + { + "name": "char_freq_%24", + "type": "TEXT", + "notnull": false, + "pk": false + }, + { + "name": "char_freq_%23", + "type": "TEXT", + "notnull": false, + "pk": false + }, + { + "name": "capital_run_length_average", + "type": "TEXT", + "notnull": false, + "pk": false + }, + { + "name": "capital_run_length_longest", + "type": "TEXT", + "notnull": false, + "pk": false + }, + { + "name": "capital_run_length_total", + "type": "TEXT", + "notnull": false, + "pk": false + }, + { + "name": "class", + "type": "TEXT", + "notnull": false, + "pk": false + } + ], + "sample_rows": [ + { + "word_freq_make": "0", + "word_freq_address": "0.64", + "word_freq_all": "0.64", + "word_freq_3d": "0", + "word_freq_our": "0.32", + "word_freq_over": "0", + "word_freq_remove": "0", + "word_freq_internet": "0", + "word_freq_order": "0", + "word_freq_mail": "0", + "word_freq_receive": "0", + "word_freq_will": "0.64", + "word_freq_people": "0", + "word_freq_report": "0", + "word_freq_addresses": "0", + "word_freq_free": "0.32", + "word_freq_business": "0", + "word_freq_email": "1.29", + "word_freq_you": "1.93", + "word_freq_credit": "0", + "word_freq_your": "0.96", + "word_freq_font": "0", + "word_freq_000": "0", + "word_freq_money": "0", + "word_freq_hp": "0", + "word_freq_hpl": "0", + "word_freq_george": "0", + "word_freq_650": "0", + "word_freq_lab": "0", + "word_freq_labs": "0", + "word_freq_telnet": "0", + "word_freq_857": "0", + "word_freq_data": "0", + "word_freq_415": "0", + "word_freq_85": "0", + "word_freq_technology": "0", + "word_freq_1999": "0", + "word_freq_parts": "0", + "word_freq_pm": "0", + "word_freq_direct": "0", + "word_freq_cs": "0", + "word_freq_meeting": "0", + "word_freq_original": "0", + "word_freq_project": "0", + "word_freq_re": "0", + "word_freq_edu": "0", + "word_freq_table": "0", + "word_freq_conference": "0", + "char_freq_%3B": "0", + "char_freq_%28": "0", + "char_freq_%5B": "0", + "char_freq_%21": "0.778", + "char_freq_%24": "0", + "char_freq_%23": "0", + "capital_run_length_average": "3.756", + "capital_run_length_longest": "61", + "capital_run_length_total": "278", + "class": "1" + }, + { + "word_freq_make": "0.21", + "word_freq_address": "0.28", + "word_freq_all": "0.5", + "word_freq_3d": "0", + "word_freq_our": "0.14", + "word_freq_over": "0.28", + "word_freq_remove": "0.21", + "word_freq_internet": "0.07", + "word_freq_order": "0", + "word_freq_mail": "0.94", + "word_freq_receive": "0.21", + "word_freq_will": "0.79", + "word_freq_people": "0.65", + "word_freq_report": "0.21", + "word_freq_addresses": "0.14", + "word_freq_free": "0.14", + "word_freq_business": "0.07", + "word_freq_email": "0.28", + "word_freq_you": "3.47", + "word_freq_credit": "0", + "word_freq_your": "1.59", + "word_freq_font": "0", + "word_freq_000": "0.43", + "word_freq_money": "0.43", + "word_freq_hp": "0", + "word_freq_hpl": "0", + "word_freq_george": "0", + "word_freq_650": "0", + "word_freq_lab": "0", + "word_freq_labs": "0", + "word_freq_telnet": "0", + "word_freq_857": "0", + "word_freq_data": "0", + "word_freq_415": "0", + "word_freq_85": "0", + "word_freq_technology": "0", + "word_freq_1999": "0.07", + "word_freq_parts": "0", + "word_freq_pm": "0", + "word_freq_direct": "0", + "word_freq_cs": "0", + "word_freq_meeting": "0", + "word_freq_original": "0", + "word_freq_project": "0", + "word_freq_re": "0", + "word_freq_edu": "0", + "word_freq_table": "0", + "word_freq_conference": "0", + "char_freq_%3B": "0", + "char_freq_%28": "0.132", + "char_freq_%5B": "0", + "char_freq_%21": "0.372", + "char_freq_%24": "0.18", + "char_freq_%23": "0.048", + "capital_run_length_average": "5.114", + "capital_run_length_longest": "101", + "capital_run_length_total": "1028", + "class": "1" + }, + { + "word_freq_make": "0.06", + "word_freq_address": "0", + "word_freq_all": "0.71", + "word_freq_3d": "0", + "word_freq_our": "1.23", + "word_freq_over": "0.19", + "word_freq_remove": "0.19", + "word_freq_internet": "0.12", + "word_freq_order": "0.64", + "word_freq_mail": "0.25", + "word_freq_receive": "0.38", + "word_freq_will": "0.45", + "word_freq_people": "0.12", + "word_freq_report": "0", + "word_freq_addresses": "1.75", + "word_freq_free": "0.06", + "word_freq_business": "0.06", + "word_freq_email": "1.03", + "word_freq_you": "1.36", + "word_freq_credit": "0.32", + "word_freq_your": "0.51", + "word_freq_font": "0", + "word_freq_000": "1.16", + "word_freq_money": "0.06", + "word_freq_hp": "0", + "word_freq_hpl": "0", + "word_freq_george": "0", + "word_freq_650": "0", + "word_freq_lab": "0", + "word_freq_labs": "0", + "word_freq_telnet": "0", + "word_freq_857": "0", + "word_freq_data": "0", + "word_freq_415": "0", + "word_freq_85": "0", + "word_freq_technology": "0", + "word_freq_1999": "0", + "word_freq_parts": "0", + "word_freq_pm": "0", + "word_freq_direct": "0.06", + "word_freq_cs": "0", + "word_freq_meeting": "0", + "word_freq_original": "0.12", + "word_freq_project": "0", + "word_freq_re": "0.06", + "word_freq_edu": "0.06", + "word_freq_table": "0", + "word_freq_conference": "0", + "char_freq_%3B": "0.01", + "char_freq_%28": "0.143", + "char_freq_%5B": "0", + "char_freq_%21": "0.276", + "char_freq_%24": "0.184", + "char_freq_%23": "0.01", + "capital_run_length_average": "9.821", + "capital_run_length_longest": "485", + "capital_run_length_total": "2259", + "class": "1" + }, + { + "word_freq_make": "0", + "word_freq_address": "0", + "word_freq_all": "0", + "word_freq_3d": "0", + "word_freq_our": "0.63", + "word_freq_over": "0", + "word_freq_remove": "0.31", + "word_freq_internet": "0.63", + "word_freq_order": "0.31", + "word_freq_mail": "0.63", + "word_freq_receive": "0.31", + "word_freq_will": "0.31", + "word_freq_people": "0.31", + "word_freq_report": "0", + "word_freq_addresses": "0", + "word_freq_free": "0.31", + "word_freq_business": "0", + "word_freq_email": "0", + "word_freq_you": "3.18", + "word_freq_credit": "0", + "word_freq_your": "0.31", + "word_freq_font": "0", + "word_freq_000": "0", + "word_freq_money": "0", + "word_freq_hp": "0", + "word_freq_hpl": "0", + "word_freq_george": "0", + "word_freq_650": "0", + "word_freq_lab": "0", + "word_freq_labs": "0", + "word_freq_telnet": "0", + "word_freq_857": "0", + "word_freq_data": "0", + "word_freq_415": "0", + "word_freq_85": "0", + "word_freq_technology": "0", + "word_freq_1999": "0", + "word_freq_parts": "0", + "word_freq_pm": "0", + "word_freq_direct": "0", + "word_freq_cs": "0", + "word_freq_meeting": "0", + "word_freq_original": "0", + "word_freq_project": "0", + "word_freq_re": "0", + "word_freq_edu": "0", + "word_freq_table": "0", + "word_freq_conference": "0", + "char_freq_%3B": "0", + "char_freq_%28": "0.137", + "char_freq_%5B": "0", + "char_freq_%21": "0.137", + "char_freq_%24": "0", + "char_freq_%23": "0", + "capital_run_length_average": "3.537", + "capital_run_length_longest": "40", + "capital_run_length_total": "191", + "class": "1" + }, + { + "word_freq_make": "0", + "word_freq_address": "0", + "word_freq_all": "0", + "word_freq_3d": "0", + "word_freq_our": "0.63", + "word_freq_over": "0", + "word_freq_remove": "0.31", + "word_freq_internet": "0.63", + "word_freq_order": "0.31", + "word_freq_mail": "0.63", + "word_freq_receive": "0.31", + "word_freq_will": "0.31", + "word_freq_people": "0.31", + "word_freq_report": "0", + "word_freq_addresses": "0", + "word_freq_free": "0.31", + "word_freq_business": "0", + "word_freq_email": "0", + "word_freq_you": "3.18", + "word_freq_credit": "0", + "word_freq_your": "0.31", + "word_freq_font": "0", + "word_freq_000": "0", + "word_freq_money": "0", + "word_freq_hp": "0", + "word_freq_hpl": "0", + "word_freq_george": "0", + "word_freq_650": "0", + "word_freq_lab": "0", + "word_freq_labs": "0", + "word_freq_telnet": "0", + "word_freq_857": "0", + "word_freq_data": "0", + "word_freq_415": "0", + "word_freq_85": "0", + "word_freq_technology": "0", + "word_freq_1999": "0", + "word_freq_parts": "0", + "word_freq_pm": "0", + "word_freq_direct": "0", + "word_freq_cs": "0", + "word_freq_meeting": "0", + "word_freq_original": "0", + "word_freq_project": "0", + "word_freq_re": "0", + "word_freq_edu": "0", + "word_freq_table": "0", + "word_freq_conference": "0", + "char_freq_%3B": "0", + "char_freq_%28": "0.135", + "char_freq_%5B": "0", + "char_freq_%21": "0.135", + "char_freq_%24": "0", + "char_freq_%23": "0", + "capital_run_length_average": "3.537", + "capital_run_length_longest": "40", + "capital_run_length_total": "191", + "class": "1" + } + ] +} + +Shortlisted templates: +[ + { + "template_id": "tpl_tpch_thresholded_group_ranking", + "template_name": "Thresholded Group Ranking", + "primary_family": "tail_rarity_structure", + "portability": "partial", + "sql_skeleton": "SELECT {group_col}, SUM({measure_col}) AS total_measure\nFROM {table}\nGROUP BY {group_col}\nHAVING SUM({measure_col}) > {measure_threshold}\nORDER BY total_measure DESC\nLIMIT {top_k};", + "required_roles": [ + "group_col", + "measure_col" + ] + } +] + +Problem instance: +{ + "dataset_id": "n1", + "question": "Use template Thresholded Group Ranking to probe tail_set_consistency with semantic role rare_extreme_view. Focus on group_col=class, measure_col=word_freq_hp.", + "planned_template_id": "tpl_tpch_thresholded_group_ranking", + "bindings": { + "group_col": "class", + "measure_col": "word_freq_hp", + "top_k": 13, + "top_n": 5, + "num_tiles": 10, + "percentile_value": 0.95, + "z_threshold": 2.0, + "fraction_threshold": 0.1, + "baseline_multiplier": 1.5, + "baseline_fraction": 0.1, + "min_group_size": 5, + "min_support": 5, + "measure_threshold": 0.0, + "time_grain": "month", + "lookback_rows": 3, + "current_period_start": "'2024-01-01'", + "current_period_end": "'2024-04-01'", + "previous_period_start": "'2023-10-01'", + "previous_period_end": "'2024-01-01'", + "drift_ratio_threshold": 0.8 + }, + "can_vary": [], + "must_fix": [], + "runtime_sql_skeleton": "SELECT {group_col}, SUM({measure_col}) AS total_measure\nFROM {table}\nGROUP BY {group_col}\nHAVING SUM({measure_col}) > {measure_threshold}\nORDER BY total_measure DESC\nLIMIT {top_k};" +} + +Repair context: +{} diff --git a/Query/sql/v2/runs/v2_cli_20260502_081223_d/n1/artifacts/v2q_n1_70dc9ee9dd88cbbd/cli/sql_prompt_attempt_2.txt b/Query/sql/v2/runs/v2_cli_20260502_081223_d/n1/artifacts/v2q_n1_70dc9ee9dd88cbbd/cli/sql_prompt_attempt_2.txt new file mode 100644 index 0000000000000000000000000000000000000000..c45546903ffb1982dc76517c57eb6f13176edd5b --- /dev/null +++ b/Query/sql/v2/runs/v2_cli_20260502_081223_d/n1/artifacts/v2q_n1_70dc9ee9dd88cbbd/cli/sql_prompt_attempt_2.txt @@ -0,0 +1,792 @@ +You are generating one SQLite SELECT query for a single-table SQL QA task. +Return strict JSON only, with this schema: {"sql": "...", "notes": "..."}. +Rules: +- Use only the provided table and columns. +- Do not write INSERT, UPDATE, DELETE, DROP, ALTER, CREATE, PRAGMA, ATTACH, DETACH, or VACUUM. +- Prefer the planned template and bound roles when provided. +- Add a leading SQL comment exactly like: -- template_id: . +- Generate SQLite-compatible SQL. SQLite does not support PERCENTILE_CONT or STDDEV. +- Quote identifiers with double quotes. +- Return no markdown and no extra prose. + +Dataset context: +Dataset context for SQL QA: +- dataset_id: n1 +- dataset_name: Spambase +- table_name: n1 +- table_layout: single-table dataset (do not assume joins). +- row_semantics: One row is one email represented by engineered token/character frequency and capitalization-run features. +- task_type: classification +- target_column: class +- main_row_count: 4601 +- important_fields: +- word_freq_make: role=feature, type=numeric_sparse_frequency. tags=['condition_candidate', 'measure', 'rare_pattern_candidate'] desc=Frequency feature for token 'make' in an email message. +- word_freq_address: role=feature, type=numeric_sparse_frequency. tags=['condition_candidate', 'measure', 'rare_pattern_candidate'] desc=Frequency feature for token 'address' in an email message. +- word_freq_all: role=feature, type=numeric_sparse_frequency. tags=['condition_candidate', 'measure', 'rare_pattern_candidate'] desc=Frequency feature for token 'all' in an email message. +- word_freq_3d: role=feature, type=numeric_sparse_frequency. tags=['condition_candidate', 'measure', 'rare_pattern_candidate'] desc=Frequency feature for token '3d' in an email message. +- word_freq_our: role=feature, type=numeric_sparse_frequency. tags=['condition_candidate', 'measure', 'rare_pattern_candidate'] desc=Frequency feature for token 'our' in an email message. +- word_freq_over: role=feature, type=numeric_sparse_frequency. tags=['condition_candidate', 'measure', 'rare_pattern_candidate'] desc=Frequency feature for token 'over' in an email message. +- word_freq_remove: role=feature, type=numeric_sparse_frequency. tags=['condition_candidate', 'measure', 'rare_pattern_candidate'] desc=Frequency feature for token 'remove' in an email message. +- word_freq_internet: role=feature, type=numeric_sparse_frequency. tags=['condition_candidate', 'measure', 'rare_pattern_candidate'] desc=Frequency feature for token 'internet' in an email message. +- word_freq_order: role=feature, type=numeric_sparse_frequency. tags=['condition_candidate', 'measure', 'rare_pattern_candidate'] desc=Frequency feature for token 'order' in an email message. +- word_freq_mail: role=feature, type=numeric_sparse_frequency. tags=['condition_candidate', 'measure', 'rare_pattern_candidate'] desc=Frequency feature for token 'mail' in an email message. +- word_freq_receive: role=feature, type=numeric_sparse_frequency. tags=['condition_candidate', 'measure', 'rare_pattern_candidate'] desc=Frequency feature for token 'receive' in an email message. +- word_freq_will: role=feature, type=numeric_sparse_frequency. tags=['condition_candidate', 'measure', 'rare_pattern_candidate'] desc=Frequency feature for token 'will' in an email message. +- word_freq_people: role=feature, type=numeric_sparse_frequency. tags=['condition_candidate', 'measure', 'rare_pattern_candidate'] desc=Frequency feature for token 'people' in an email message. +- word_freq_report: role=feature, type=numeric_sparse_frequency. tags=['condition_candidate', 'measure', 'rare_pattern_candidate'] desc=Frequency feature for token 'report' in an email message. +- word_freq_addresses: role=feature, type=numeric_sparse_frequency. tags=['condition_candidate', 'measure', 'rare_pattern_candidate'] desc=Frequency feature for token 'addresses' in an email message. +- word_freq_free: role=feature, type=numeric_sparse_frequency. tags=['condition_candidate', 'measure', 'rare_pattern_candidate'] desc=Frequency feature for token 'free' in an email message. +- word_freq_business: role=feature, type=numeric_sparse_frequency. tags=['condition_candidate', 'measure', 'rare_pattern_candidate'] desc=Frequency feature for token 'business' in an email message. +- word_freq_email: role=feature, type=numeric_sparse_frequency. tags=['condition_candidate', 'measure', 'rare_pattern_candidate'] desc=Frequency feature for token 'email' in an email message. +- word_freq_you: role=feature, type=numeric_sparse_frequency. tags=['condition_candidate', 'measure', 'rare_pattern_candidate'] desc=Frequency feature for token 'you' in an email message. +- word_freq_credit: role=feature, type=numeric_sparse_frequency. tags=['condition_candidate', 'measure', 'rare_pattern_candidate'] desc=Frequency feature for token 'credit' in an email message. +- word_freq_your: role=feature, type=numeric_sparse_frequency. tags=['condition_candidate', 'measure', 'rare_pattern_candidate'] desc=Frequency feature for token 'your' in an email message. +- word_freq_font: role=feature, type=numeric_sparse_frequency. tags=['condition_candidate', 'measure', 'rare_pattern_candidate'] desc=Frequency feature for token 'font' in an email message. +- word_freq_000: role=feature, type=numeric_sparse_frequency. tags=['condition_candidate', 'measure', 'rare_pattern_candidate'] desc=Frequency feature for token '000' in an email message. +- word_freq_money: role=feature, type=numeric_sparse_frequency. tags=['condition_candidate', 'measure', 'rare_pattern_candidate'] desc=Frequency feature for token 'money' in an email message. +- word_freq_hp: role=feature, type=numeric_sparse_frequency. tags=['condition_candidate', 'measure', 'rare_pattern_candidate'] desc=Frequency feature for token 'hp' in an email message. +- word_freq_hpl: role=feature, type=numeric_sparse_frequency. tags=['condition_candidate', 'measure', 'rare_pattern_candidate'] desc=Frequency feature for token 'hpl' in an email message. +- word_freq_george: role=feature, type=numeric_sparse_frequency. tags=['condition_candidate', 'measure', 'rare_pattern_candidate'] desc=Frequency feature for token 'george' in an email message. +- word_freq_650: role=feature, type=numeric_sparse_frequency. tags=['condition_candidate', 'measure', 'rare_pattern_candidate'] desc=Frequency feature for token '650' in an email message. +- word_freq_lab: role=feature, type=numeric_sparse_frequency. tags=['condition_candidate', 'measure', 'rare_pattern_candidate'] desc=Frequency feature for token 'lab' in an email message. +- word_freq_labs: role=feature, type=numeric_sparse_frequency. tags=['condition_candidate', 'measure', 'rare_pattern_candidate'] desc=Frequency feature for token 'labs' in an email message. +- word_freq_telnet: role=feature, type=numeric_sparse_frequency. tags=['condition_candidate', 'measure', 'rare_pattern_candidate'] desc=Frequency feature for token 'telnet' in an email message. +- word_freq_857: role=feature, type=numeric_sparse_frequency. tags=['condition_candidate', 'measure', 'rare_pattern_candidate'] desc=Frequency feature for token '857' in an email message. +- word_freq_data: role=feature, type=numeric_sparse_frequency. tags=['condition_candidate', 'measure', 'rare_pattern_candidate'] desc=Frequency feature for token 'data' in an email message. +- word_freq_415: role=feature, type=numeric_sparse_frequency. tags=['condition_candidate', 'measure', 'rare_pattern_candidate'] desc=Frequency feature for token '415' in an email message. +- word_freq_85: role=feature, type=numeric_sparse_frequency. tags=['condition_candidate', 'measure', 'rare_pattern_candidate'] desc=Frequency feature for token '85' in an email message. +- word_freq_technology: role=feature, type=numeric_sparse_frequency. tags=['condition_candidate', 'measure', 'rare_pattern_candidate'] desc=Frequency feature for token 'technology' in an email message. +- word_freq_1999: role=feature, type=numeric_sparse_frequency. tags=['condition_candidate', 'measure', 'rare_pattern_candidate'] desc=Frequency feature for token '1999' in an email message. +- word_freq_parts: role=feature, type=numeric_sparse_frequency. tags=['condition_candidate', 'measure', 'rare_pattern_candidate'] desc=Frequency feature for token 'parts' in an email message. +- word_freq_pm: role=feature, type=numeric_sparse_frequency. tags=['condition_candidate', 'measure', 'rare_pattern_candidate'] desc=Frequency feature for token 'pm' in an email message. +- word_freq_direct: role=feature, type=numeric_sparse_frequency. tags=['condition_candidate', 'measure', 'rare_pattern_candidate'] desc=Frequency feature for token 'direct' in an email message. +- word_freq_cs: role=feature, type=numeric_sparse_frequency. tags=['condition_candidate', 'measure', 'rare_pattern_candidate'] desc=Frequency feature for token 'cs' in an email message. +- word_freq_meeting: role=feature, type=numeric_sparse_frequency. tags=['condition_candidate', 'measure', 'rare_pattern_candidate'] desc=Frequency feature for token 'meeting' in an email message. +- word_freq_original: role=feature, type=numeric_sparse_frequency. tags=['condition_candidate', 'measure', 'rare_pattern_candidate'] desc=Frequency feature for token 'original' in an email message. +- word_freq_project: role=feature, type=numeric_sparse_frequency. tags=['condition_candidate', 'measure', 'rare_pattern_candidate'] desc=Frequency feature for token 'project' in an email message. +- word_freq_re: role=feature, type=numeric_sparse_frequency. tags=['condition_candidate', 'measure', 'rare_pattern_candidate'] desc=Frequency feature for token 're' in an email message. +- word_freq_edu: role=feature, type=numeric_sparse_frequency. tags=['condition_candidate', 'measure', 'rare_pattern_candidate'] desc=Frequency feature for token 'edu' in an email message. +- word_freq_table: role=feature, type=numeric_sparse_frequency. tags=['condition_candidate', 'measure', 'rare_pattern_candidate'] desc=Frequency feature for token 'table' in an email message. +- word_freq_conference: role=feature, type=numeric_sparse_frequency. tags=['condition_candidate', 'measure', 'rare_pattern_candidate'] desc=Frequency feature for token 'conference' in an email message. +- char_freq_%3B: role=feature, type=numeric_sparse_frequency. tags=['condition_candidate', 'measure', 'rare_pattern_candidate'] desc=Character frequency feature for symbol ';'. +- char_freq_%28: role=feature, type=numeric_sparse_frequency. tags=['condition_candidate', 'measure', 'rare_pattern_candidate'] desc=Character frequency feature for symbol '('. +- char_freq_%5B: role=feature, type=numeric_sparse_frequency. tags=['condition_candidate', 'measure', 'rare_pattern_candidate'] desc=Character frequency feature for symbol '['. +- char_freq_%21: role=feature, type=numeric_sparse_frequency. tags=['condition_candidate', 'measure', 'rare_pattern_candidate'] desc=Character frequency feature for symbol '!'. +- char_freq_%24: role=feature, type=numeric_sparse_frequency. tags=['condition_candidate', 'measure', 'rare_pattern_candidate'] desc=Character frequency feature for symbol '$'. +- char_freq_%23: role=feature, type=numeric_sparse_frequency. tags=['condition_candidate', 'measure', 'rare_pattern_candidate'] desc=Character frequency feature for symbol '#'. +- capital_run_length_average: role=feature, type=numeric_heavy_tail. tags=['condition_candidate', 'measure', 'rare_pattern_candidate'] desc=Average length of uninterrupted capital-letter runs. +- capital_run_length_longest: role=feature, type=numeric_heavy_tail. tags=['condition_candidate', 'measure', 'rare_pattern_candidate'] desc=Longest uninterrupted capital-letter run. +- capital_run_length_total: role=feature, type=numeric_heavy_tail. tags=['condition_candidate', 'measure', 'rare_pattern_candidate'] desc=Total number of capital letters in runs. +- class: role=target, type=binary_target. tags=['target_candidate'] desc=Binary spam label (1=spam, 0=non-spam). +- useful_field_combinations: [['word_freq_free', 'word_freq_you', 'class'], ['char_freq_%21', 'capital_run_length_longest', 'class'], ['word_freq_remove', 'word_freq_money', 'class']] +- fields_requiring_caution: ['capital_run_length_average', 'capital_run_length_longest', 'capital_run_length_total'] +- source_url: https://www.openml.org/d/44 + +SQLite schema snapshot: +{ + "table_name": "n1", + "quoted_table_name": "\"n1\"", + "row_count": 4601, + "columns": [ + { + "name": "word_freq_make", + "type": "TEXT", + "notnull": false, + "pk": false + }, + { + "name": "word_freq_address", + "type": "TEXT", + "notnull": false, + "pk": false + }, + { + "name": "word_freq_all", + "type": "TEXT", + "notnull": false, + "pk": false + }, + { + "name": "word_freq_3d", + "type": "TEXT", + "notnull": false, + "pk": false + }, + { + "name": "word_freq_our", + "type": "TEXT", + "notnull": false, + "pk": false + }, + { + "name": "word_freq_over", + "type": "TEXT", + "notnull": false, + "pk": false + }, + { + "name": "word_freq_remove", + "type": "TEXT", + "notnull": false, + "pk": false + }, + { + "name": "word_freq_internet", + "type": "TEXT", + "notnull": false, + "pk": false + }, + { + "name": "word_freq_order", + "type": "TEXT", + "notnull": false, + "pk": false + }, + { + "name": "word_freq_mail", + "type": "TEXT", + "notnull": false, + "pk": false + }, + { + "name": "word_freq_receive", + "type": "TEXT", + "notnull": false, + "pk": false + }, + { + "name": "word_freq_will", + "type": "TEXT", + "notnull": false, + "pk": false + }, + { + "name": "word_freq_people", + "type": "TEXT", + "notnull": false, + "pk": false + }, + { + "name": "word_freq_report", + "type": "TEXT", + "notnull": false, + "pk": false + }, + { + "name": "word_freq_addresses", + "type": "TEXT", + "notnull": false, + "pk": false + }, + { + "name": "word_freq_free", + "type": "TEXT", + "notnull": false, + "pk": false + }, + { + "name": "word_freq_business", + "type": "TEXT", + "notnull": false, + "pk": false + }, + { + "name": "word_freq_email", + "type": "TEXT", + "notnull": false, + "pk": false + }, + { + "name": "word_freq_you", + "type": "TEXT", + "notnull": false, + "pk": false + }, + { + "name": "word_freq_credit", + "type": "TEXT", + "notnull": false, + "pk": false + }, + { + "name": "word_freq_your", + "type": "TEXT", + "notnull": false, + "pk": false + }, + { + "name": "word_freq_font", + "type": "TEXT", + "notnull": false, + "pk": false + }, + { + "name": "word_freq_000", + "type": "TEXT", + "notnull": false, + "pk": false + }, + { + "name": "word_freq_money", + "type": "TEXT", + "notnull": false, + "pk": false + }, + { + "name": "word_freq_hp", + "type": "TEXT", + "notnull": false, + "pk": false + }, + { + "name": "word_freq_hpl", + "type": "TEXT", + "notnull": false, + "pk": false + }, + { + "name": "word_freq_george", + "type": "TEXT", + "notnull": false, + "pk": false + }, + { + "name": "word_freq_650", + "type": "TEXT", + "notnull": false, + "pk": false + }, + { + "name": "word_freq_lab", + "type": "TEXT", + "notnull": false, + "pk": false + }, + { + "name": "word_freq_labs", + "type": "TEXT", + "notnull": false, + "pk": false + }, + { + "name": "word_freq_telnet", + "type": "TEXT", + "notnull": false, + "pk": false + }, + { + "name": "word_freq_857", + "type": "TEXT", + "notnull": false, + "pk": false + }, + { + "name": "word_freq_data", + "type": "TEXT", + "notnull": false, + "pk": false + }, + { + "name": "word_freq_415", + "type": "TEXT", + "notnull": false, + "pk": false + }, + { + "name": "word_freq_85", + "type": "TEXT", + "notnull": false, + "pk": false + }, + { + "name": "word_freq_technology", + "type": "TEXT", + "notnull": false, + "pk": false + }, + { + "name": "word_freq_1999", + "type": "TEXT", + "notnull": false, + "pk": false + }, + { + "name": "word_freq_parts", + "type": "TEXT", + "notnull": false, + "pk": false + }, + { + "name": "word_freq_pm", + "type": "TEXT", + "notnull": false, + "pk": false + }, + { + "name": "word_freq_direct", + "type": "TEXT", + "notnull": false, + "pk": false + }, + { + "name": "word_freq_cs", + "type": "TEXT", + "notnull": false, + "pk": false + }, + { + "name": "word_freq_meeting", + "type": "TEXT", + "notnull": false, + "pk": false + }, + { + "name": "word_freq_original", + "type": "TEXT", + "notnull": false, + "pk": false + }, + { + "name": "word_freq_project", + "type": "TEXT", + "notnull": false, + "pk": false + }, + { + "name": "word_freq_re", + "type": "TEXT", + "notnull": false, + "pk": false + }, + { + "name": "word_freq_edu", + "type": "TEXT", + "notnull": false, + "pk": false + }, + { + "name": "word_freq_table", + "type": "TEXT", + "notnull": false, + "pk": false + }, + { + "name": "word_freq_conference", + "type": "TEXT", + "notnull": false, + "pk": false + }, + { + "name": "char_freq_%3B", + "type": "TEXT", + "notnull": false, + "pk": false + }, + { + "name": "char_freq_%28", + "type": "TEXT", + "notnull": false, + "pk": false + }, + { + "name": "char_freq_%5B", + "type": "TEXT", + "notnull": false, + "pk": false + }, + { + "name": "char_freq_%21", + "type": "TEXT", + "notnull": false, + "pk": false + }, + { + "name": "char_freq_%24", + "type": "TEXT", + "notnull": false, + "pk": false + }, + { + "name": "char_freq_%23", + "type": "TEXT", + "notnull": false, + "pk": false + }, + { + "name": "capital_run_length_average", + "type": "TEXT", + "notnull": false, + "pk": false + }, + { + "name": "capital_run_length_longest", + "type": "TEXT", + "notnull": false, + "pk": false + }, + { + "name": "capital_run_length_total", + "type": "TEXT", + "notnull": false, + "pk": false + }, + { + "name": "class", + "type": "TEXT", + "notnull": false, + "pk": false + } + ], + "sample_rows": [ + { + "word_freq_make": "0", + "word_freq_address": "0.64", + "word_freq_all": "0.64", + "word_freq_3d": "0", + "word_freq_our": "0.32", + "word_freq_over": "0", + "word_freq_remove": "0", + "word_freq_internet": "0", + "word_freq_order": "0", + "word_freq_mail": "0", + "word_freq_receive": "0", + "word_freq_will": "0.64", + "word_freq_people": "0", + "word_freq_report": "0", + "word_freq_addresses": "0", + "word_freq_free": "0.32", + "word_freq_business": "0", + "word_freq_email": "1.29", + "word_freq_you": "1.93", + "word_freq_credit": "0", + "word_freq_your": "0.96", + "word_freq_font": "0", + "word_freq_000": "0", + "word_freq_money": "0", + "word_freq_hp": "0", + "word_freq_hpl": "0", + "word_freq_george": "0", + "word_freq_650": "0", + "word_freq_lab": "0", + "word_freq_labs": "0", + "word_freq_telnet": "0", + "word_freq_857": "0", + "word_freq_data": "0", + "word_freq_415": "0", + "word_freq_85": "0", + "word_freq_technology": "0", + "word_freq_1999": "0", + "word_freq_parts": "0", + "word_freq_pm": "0", + "word_freq_direct": "0", + "word_freq_cs": "0", + "word_freq_meeting": "0", + "word_freq_original": "0", + "word_freq_project": "0", + "word_freq_re": "0", + "word_freq_edu": "0", + "word_freq_table": "0", + "word_freq_conference": "0", + "char_freq_%3B": "0", + "char_freq_%28": "0", + "char_freq_%5B": "0", + "char_freq_%21": "0.778", + "char_freq_%24": "0", + "char_freq_%23": "0", + "capital_run_length_average": "3.756", + "capital_run_length_longest": "61", + "capital_run_length_total": "278", + "class": "1" + }, + { + "word_freq_make": "0.21", + "word_freq_address": "0.28", + "word_freq_all": "0.5", + "word_freq_3d": "0", + "word_freq_our": "0.14", + "word_freq_over": "0.28", + "word_freq_remove": "0.21", + "word_freq_internet": "0.07", + "word_freq_order": "0", + "word_freq_mail": "0.94", + "word_freq_receive": "0.21", + "word_freq_will": "0.79", + "word_freq_people": "0.65", + "word_freq_report": "0.21", + "word_freq_addresses": "0.14", + "word_freq_free": "0.14", + "word_freq_business": "0.07", + "word_freq_email": "0.28", + "word_freq_you": "3.47", + "word_freq_credit": "0", + "word_freq_your": "1.59", + "word_freq_font": "0", + "word_freq_000": "0.43", + "word_freq_money": "0.43", + "word_freq_hp": "0", + "word_freq_hpl": "0", + "word_freq_george": "0", + "word_freq_650": "0", + "word_freq_lab": "0", + "word_freq_labs": "0", + "word_freq_telnet": "0", + "word_freq_857": "0", + "word_freq_data": "0", + "word_freq_415": "0", + "word_freq_85": "0", + "word_freq_technology": "0", + "word_freq_1999": "0.07", + "word_freq_parts": "0", + "word_freq_pm": "0", + "word_freq_direct": "0", + "word_freq_cs": "0", + "word_freq_meeting": "0", + "word_freq_original": "0", + "word_freq_project": "0", + "word_freq_re": "0", + "word_freq_edu": "0", + "word_freq_table": "0", + "word_freq_conference": "0", + "char_freq_%3B": "0", + "char_freq_%28": "0.132", + "char_freq_%5B": "0", + "char_freq_%21": "0.372", + "char_freq_%24": "0.18", + "char_freq_%23": "0.048", + "capital_run_length_average": "5.114", + "capital_run_length_longest": "101", + "capital_run_length_total": "1028", + "class": "1" + }, + { + "word_freq_make": "0.06", + "word_freq_address": "0", + "word_freq_all": "0.71", + "word_freq_3d": "0", + "word_freq_our": "1.23", + "word_freq_over": "0.19", + "word_freq_remove": "0.19", + "word_freq_internet": "0.12", + "word_freq_order": "0.64", + "word_freq_mail": "0.25", + "word_freq_receive": "0.38", + "word_freq_will": "0.45", + "word_freq_people": "0.12", + "word_freq_report": "0", + "word_freq_addresses": "1.75", + "word_freq_free": "0.06", + "word_freq_business": "0.06", + "word_freq_email": "1.03", + "word_freq_you": "1.36", + "word_freq_credit": "0.32", + "word_freq_your": "0.51", + "word_freq_font": "0", + "word_freq_000": "1.16", + "word_freq_money": "0.06", + "word_freq_hp": "0", + "word_freq_hpl": "0", + "word_freq_george": "0", + "word_freq_650": "0", + "word_freq_lab": "0", + "word_freq_labs": "0", + "word_freq_telnet": "0", + "word_freq_857": "0", + "word_freq_data": "0", + "word_freq_415": "0", + "word_freq_85": "0", + "word_freq_technology": "0", + "word_freq_1999": "0", + "word_freq_parts": "0", + "word_freq_pm": "0", + "word_freq_direct": "0.06", + "word_freq_cs": "0", + "word_freq_meeting": "0", + "word_freq_original": "0.12", + "word_freq_project": "0", + "word_freq_re": "0.06", + "word_freq_edu": "0.06", + "word_freq_table": "0", + "word_freq_conference": "0", + "char_freq_%3B": "0.01", + "char_freq_%28": "0.143", + "char_freq_%5B": "0", + "char_freq_%21": "0.276", + "char_freq_%24": "0.184", + "char_freq_%23": "0.01", + "capital_run_length_average": "9.821", + "capital_run_length_longest": "485", + "capital_run_length_total": "2259", + "class": "1" + }, + { + "word_freq_make": "0", + "word_freq_address": "0", + "word_freq_all": "0", + "word_freq_3d": "0", + "word_freq_our": "0.63", + "word_freq_over": "0", + "word_freq_remove": "0.31", + "word_freq_internet": "0.63", + "word_freq_order": "0.31", + "word_freq_mail": "0.63", + "word_freq_receive": "0.31", + "word_freq_will": "0.31", + "word_freq_people": "0.31", + "word_freq_report": "0", + "word_freq_addresses": "0", + "word_freq_free": "0.31", + "word_freq_business": "0", + "word_freq_email": "0", + "word_freq_you": "3.18", + "word_freq_credit": "0", + "word_freq_your": "0.31", + "word_freq_font": "0", + "word_freq_000": "0", + "word_freq_money": "0", + "word_freq_hp": "0", + "word_freq_hpl": "0", + "word_freq_george": "0", + "word_freq_650": "0", + "word_freq_lab": "0", + "word_freq_labs": "0", + "word_freq_telnet": "0", + "word_freq_857": "0", + "word_freq_data": "0", + "word_freq_415": "0", + "word_freq_85": "0", + "word_freq_technology": "0", + "word_freq_1999": "0", + "word_freq_parts": "0", + "word_freq_pm": "0", + "word_freq_direct": "0", + "word_freq_cs": "0", + "word_freq_meeting": "0", + "word_freq_original": "0", + "word_freq_project": "0", + "word_freq_re": "0", + "word_freq_edu": "0", + "word_freq_table": "0", + "word_freq_conference": "0", + "char_freq_%3B": "0", + "char_freq_%28": "0.137", + "char_freq_%5B": "0", + "char_freq_%21": "0.137", + "char_freq_%24": "0", + "char_freq_%23": "0", + "capital_run_length_average": "3.537", + "capital_run_length_longest": "40", + "capital_run_length_total": "191", + "class": "1" + }, + { + "word_freq_make": "0", + "word_freq_address": "0", + "word_freq_all": "0", + "word_freq_3d": "0", + "word_freq_our": "0.63", + "word_freq_over": "0", + "word_freq_remove": "0.31", + "word_freq_internet": "0.63", + "word_freq_order": "0.31", + "word_freq_mail": "0.63", + "word_freq_receive": "0.31", + "word_freq_will": "0.31", + "word_freq_people": "0.31", + "word_freq_report": "0", + "word_freq_addresses": "0", + "word_freq_free": "0.31", + "word_freq_business": "0", + "word_freq_email": "0", + "word_freq_you": "3.18", + "word_freq_credit": "0", + "word_freq_your": "0.31", + "word_freq_font": "0", + "word_freq_000": "0", + "word_freq_money": "0", + "word_freq_hp": "0", + "word_freq_hpl": "0", + "word_freq_george": "0", + "word_freq_650": "0", + "word_freq_lab": "0", + "word_freq_labs": "0", + "word_freq_telnet": "0", + "word_freq_857": "0", + "word_freq_data": "0", + "word_freq_415": "0", + "word_freq_85": "0", + "word_freq_technology": "0", + "word_freq_1999": "0", + "word_freq_parts": "0", + "word_freq_pm": "0", + "word_freq_direct": "0", + "word_freq_cs": "0", + "word_freq_meeting": "0", + "word_freq_original": "0", + "word_freq_project": "0", + "word_freq_re": "0", + "word_freq_edu": "0", + "word_freq_table": "0", + "word_freq_conference": "0", + "char_freq_%3B": "0", + "char_freq_%28": "0.135", + "char_freq_%5B": "0", + "char_freq_%21": "0.135", + "char_freq_%24": "0", + "char_freq_%23": "0", + "capital_run_length_average": "3.537", + "capital_run_length_longest": "40", + "capital_run_length_total": "191", + "class": "1" + } + ] +} + +Shortlisted templates: +[ + { + "template_id": "tpl_tpch_thresholded_group_ranking", + "template_name": "Thresholded Group Ranking", + "primary_family": "tail_rarity_structure", + "portability": "partial", + "sql_skeleton": "SELECT {group_col}, SUM({measure_col}) AS total_measure\nFROM {table}\nGROUP BY {group_col}\nHAVING SUM({measure_col}) > {measure_threshold}\nORDER BY total_measure DESC\nLIMIT {top_k};", + "required_roles": [ + "group_col", + "measure_col" + ] + } +] + +Problem instance: +{ + "dataset_id": "n1", + "question": "Use template Thresholded Group Ranking to probe tail_set_consistency with semantic role rare_extreme_view. Focus on group_col=class, measure_col=word_freq_hp.", + "planned_template_id": "tpl_tpch_thresholded_group_ranking", + "bindings": { + "group_col": "class", + "measure_col": "word_freq_hp", + "top_k": 13, + "top_n": 5, + "num_tiles": 10, + "percentile_value": 0.95, + "z_threshold": 2.0, + "fraction_threshold": 0.1, + "baseline_multiplier": 1.5, + "baseline_fraction": 0.1, + "min_group_size": 5, + "min_support": 5, + "measure_threshold": 0.0, + "time_grain": "month", + "lookback_rows": 3, + "current_period_start": "'2024-01-01'", + "current_period_end": "'2024-04-01'", + "previous_period_start": "'2023-10-01'", + "previous_period_end": "'2024-01-01'", + "drift_ratio_threshold": 0.8 + }, + "can_vary": [], + "must_fix": [], + "runtime_sql_skeleton": "SELECT {group_col}, SUM({measure_col}) AS total_measure\nFROM {table}\nGROUP BY {group_col}\nHAVING SUM({measure_col}) > {measure_threshold}\nORDER BY total_measure DESC\nLIMIT {top_k};" +} + +Repair context: +{} diff --git a/Query/sql/v2/runs/v2_cli_20260502_081223_d/n1/artifacts/v2q_n1_70dc9ee9dd88cbbd/cli/sql_response_attempt_1.raw.txt b/Query/sql/v2/runs/v2_cli_20260502_081223_d/n1/artifacts/v2q_n1_70dc9ee9dd88cbbd/cli/sql_response_attempt_1.raw.txt new file mode 100644 index 0000000000000000000000000000000000000000..cf84be9eaf808c76da7caa2ddbf00a114eb4ce2f --- /dev/null +++ b/Query/sql/v2/runs/v2_cli_20260502_081223_d/n1/artifacts/v2q_n1_70dc9ee9dd88cbbd/cli/sql_response_attempt_1.raw.txt @@ -0,0 +1,4 @@ +{"type":"thread.started","thread_id":"019e40fc-a26e-7982-8225-cdcb007cc5cb"} +{"type":"turn.started"} +{"type":"error","message":"Quota exceeded. Check your plan and billing details."} +{"type":"turn.failed","error":{"message":"Quota exceeded. Check your plan and billing details."}} diff --git a/Query/sql/v2/runs/v2_cli_20260502_081223_d/n1/artifacts/v2q_n1_70dc9ee9dd88cbbd/cli/sql_response_attempt_1.txt b/Query/sql/v2/runs/v2_cli_20260502_081223_d/n1/artifacts/v2q_n1_70dc9ee9dd88cbbd/cli/sql_response_attempt_1.txt new file mode 100644 index 0000000000000000000000000000000000000000..48709cbd1a4ac44d2ad4052c7e9d997520339066 --- /dev/null +++ b/Query/sql/v2/runs/v2_cli_20260502_081223_d/n1/artifacts/v2q_n1_70dc9ee9dd88cbbd/cli/sql_response_attempt_1.txt @@ -0,0 +1,4 @@ +{"type":"thread.started","thread_id":"019e40fc-a26e-7982-8225-cdcb007cc5cb"} +{"type":"turn.started"} +{"type":"error","message":"Quota exceeded. Check your plan and billing details."} +{"type":"turn.failed","error":{"message":"Quota exceeded. Check your plan and billing details."}} \ No newline at end of file diff --git a/Query/sql/v2/runs/v2_cli_20260502_081223_d/n1/artifacts/v2q_n1_70dc9ee9dd88cbbd/cli/sql_response_attempt_2.raw.txt b/Query/sql/v2/runs/v2_cli_20260502_081223_d/n1/artifacts/v2q_n1_70dc9ee9dd88cbbd/cli/sql_response_attempt_2.raw.txt new file mode 100644 index 0000000000000000000000000000000000000000..b018dd02426b6b4aa8e76f551245383c57e065bd --- /dev/null +++ b/Query/sql/v2/runs/v2_cli_20260502_081223_d/n1/artifacts/v2q_n1_70dc9ee9dd88cbbd/cli/sql_response_attempt_2.raw.txt @@ -0,0 +1,4 @@ +{"type":"thread.started","thread_id":"019e40fc-b2fc-7ba2-b14b-53d2590c6427"} +{"type":"turn.started"} +{"type":"error","message":"Quota exceeded. Check your plan and billing details."} +{"type":"turn.failed","error":{"message":"Quota exceeded. Check your plan and billing details."}} diff --git a/Query/sql/v2/runs/v2_cli_20260502_081223_d/n1/artifacts/v2q_n1_70dc9ee9dd88cbbd/cli/sql_response_attempt_2.txt b/Query/sql/v2/runs/v2_cli_20260502_081223_d/n1/artifacts/v2q_n1_70dc9ee9dd88cbbd/cli/sql_response_attempt_2.txt new file mode 100644 index 0000000000000000000000000000000000000000..83aa635dfc7cd9e97feb5f56a23ed1f09d6aa9e1 --- /dev/null +++ b/Query/sql/v2/runs/v2_cli_20260502_081223_d/n1/artifacts/v2q_n1_70dc9ee9dd88cbbd/cli/sql_response_attempt_2.txt @@ -0,0 +1,4 @@ +{"type":"thread.started","thread_id":"019e40fc-b2fc-7ba2-b14b-53d2590c6427"} +{"type":"turn.started"} +{"type":"error","message":"Quota exceeded. Check your plan and billing details."} +{"type":"turn.failed","error":{"message":"Quota exceeded. Check your plan and billing details."}} \ No newline at end of file diff --git a/Query/sql/v2/runs/v2_cli_20260502_081223_d/n1/artifacts/v2q_n1_70dc9ee9dd88cbbd/cli/sql_stderr_attempt_1.txt b/Query/sql/v2/runs/v2_cli_20260502_081223_d/n1/artifacts/v2q_n1_70dc9ee9dd88cbbd/cli/sql_stderr_attempt_1.txt new file mode 100644 index 0000000000000000000000000000000000000000..e69de29bb2d1d6434b8b29ae775ad8c2e48c5391 diff --git a/Query/sql/v2/runs/v2_cli_20260502_081223_d/n1/artifacts/v2q_n1_70dc9ee9dd88cbbd/cli/sql_stderr_attempt_2.txt b/Query/sql/v2/runs/v2_cli_20260502_081223_d/n1/artifacts/v2q_n1_70dc9ee9dd88cbbd/cli/sql_stderr_attempt_2.txt new file mode 100644 index 0000000000000000000000000000000000000000..e69de29bb2d1d6434b8b29ae775ad8c2e48c5391 diff --git a/Query/sql/v2/runs/v2_cli_20260502_081223_d/n1/artifacts/v2q_n1_7c2bf462ef7bc57f/cli/conversation.jsonl b/Query/sql/v2/runs/v2_cli_20260502_081223_d/n1/artifacts/v2q_n1_7c2bf462ef7bc57f/cli/conversation.jsonl new file mode 100644 index 0000000000000000000000000000000000000000..c48f1d01dc615d1c5546d1c6771690e86f5ae298 --- /dev/null +++ b/Query/sql/v2/runs/v2_cli_20260502_081223_d/n1/artifacts/v2q_n1_7c2bf462ef7bc57f/cli/conversation.jsonl @@ -0,0 +1,2 @@ +{"attempt": 1, "phase": "sql_generation", "role": "user", "content_path": "cli/sql_prompt_attempt_1.txt", "metrics": {"chars": 29574, "bytes_utf8": 29574, "lines": 795, "estimated_tokens": null}} +{"attempt": 1, "phase": "sql_generation", "role": "assistant", "content_path": "cli/sql_response_attempt_1.txt", "raw_content_path": "cli/sql_response_attempt_1.raw.txt", "stderr_path": "cli/sql_stderr_attempt_1.txt", "metrics": {"chars": 436, "bytes_utf8": 436, "lines": 1, "estimated_tokens": null}, "usage": {"input_tokens": 20372, "cached_input_tokens": 12032, "output_tokens": 336, "reasoning_output_tokens": 220}} diff --git a/Query/sql/v2/runs/v2_cli_20260502_081223_d/n1/artifacts/v2q_n1_7c2bf462ef7bc57f/cli/session_summary.json b/Query/sql/v2/runs/v2_cli_20260502_081223_d/n1/artifacts/v2q_n1_7c2bf462ef7bc57f/cli/session_summary.json new file mode 100644 index 0000000000000000000000000000000000000000..b90ccc8932e2acd94235da10bb9668dd9afcc1cc --- /dev/null +++ b/Query/sql/v2/runs/v2_cli_20260502_081223_d/n1/artifacts/v2q_n1_7c2bf462ef7bc57f/cli/session_summary.json @@ -0,0 +1,25 @@ +{ + "engine": "v2-cli:codex", + "command": "codex exec --skip-git-repo-check --disable plugins --sandbox read-only --cd \"/data/jialinzhang/SQLagent\" -m gpt-5.4 --json -", + "ai_cli_calls": 1, + "usage_summary": { + "dataset_id": "n1", + "model": "v2-cli:codex", + "run_id": "v2q_n1_7c2bf462ef7bc57f", + "api_calls": 0, + "input_tokens": 20372, + "cached_input_tokens": 12032, + "output_tokens": 336, + "total_tokens": 20708, + "cost_usd": 0.0, + "ai_cli_calls": 1, + "estimated_input_tokens": 0, + "estimated_output_tokens": 0, + "estimated_total_tokens": 0, + "usage_source": "ai_cli_json_usage", + "cli_elapsed_ms_total": 9284.27, + "sql_execution_elapsed_ms_total": 4.62, + "conversation_log_path": "/data/jialinzhang/TabQueryBench/sql_workloads/v2_current/runs_and_launches/runs/v2_cli_20260502_081223_d/n1/artifacts/v2q_n1_7c2bf462ef7bc57f/cli/conversation.jsonl", + "note": "Executed through a local AI CLI with structured usage metadata." + } +} \ No newline at end of file diff --git a/Query/sql/v2/runs/v2_cli_20260502_081223_d/n1/artifacts/v2q_n1_7c2bf462ef7bc57f/cli/sql_attempt_1.metadata.json b/Query/sql/v2/runs/v2_cli_20260502_081223_d/n1/artifacts/v2q_n1_7c2bf462ef7bc57f/cli/sql_attempt_1.metadata.json new file mode 100644 index 0000000000000000000000000000000000000000..3dacf30a8915adcaf06db187bdd46c31c62c7191 --- /dev/null +++ b/Query/sql/v2/runs/v2_cli_20260502_081223_d/n1/artifacts/v2q_n1_7c2bf462ef7bc57f/cli/sql_attempt_1.metadata.json @@ -0,0 +1,45 @@ +{ + "attempt": 1, + "phase": "sql_generation", + "command": "codex exec --skip-git-repo-check --disable plugins --sandbox read-only --cd \"/data/jialinzhang/SQLagent\" -m gpt-5.4 --json -", + "started_at": "2026-05-19T15:58:11.506074+00:00", + "ended_at": "2026-05-19T15:58:20.790382+00:00", + "elapsed_ms": 9284.27, + "prompt_metrics": { + "chars": 29574, + "bytes_utf8": 29574, + "lines": 795, + "estimated_tokens": null + }, + "stdout_metrics": { + "chars": 799, + "bytes_utf8": 799, + "lines": 4, + "estimated_tokens": null + }, + "stderr_metrics": { + "chars": 0, + "bytes_utf8": 0, + "lines": 0, + "estimated_tokens": null + }, + "parsed_output": { + "format": "jsonl_events", + "text_metrics": { + "chars": 436, + "bytes_utf8": 436, + "lines": 1, + "estimated_tokens": null + }, + "usage": { + "input_tokens": 20372, + "cached_input_tokens": 12032, + "output_tokens": 336, + "reasoning_output_tokens": 220 + } + }, + "prompt_path": "cli/sql_prompt_attempt_1.txt", + "response_path": "cli/sql_response_attempt_1.txt", + "raw_response_path": "cli/sql_response_attempt_1.raw.txt", + "stderr_path": "cli/sql_stderr_attempt_1.txt" +} \ No newline at end of file diff --git a/Query/sql/v2/runs/v2_cli_20260502_081223_d/n1/artifacts/v2q_n1_7c2bf462ef7bc57f/cli/sql_prompt_attempt_1.txt b/Query/sql/v2/runs/v2_cli_20260502_081223_d/n1/artifacts/v2q_n1_7c2bf462ef7bc57f/cli/sql_prompt_attempt_1.txt new file mode 100644 index 0000000000000000000000000000000000000000..dccac06b49061778a7e5850fb6f33867a90646fb --- /dev/null +++ b/Query/sql/v2/runs/v2_cli_20260502_081223_d/n1/artifacts/v2q_n1_7c2bf462ef7bc57f/cli/sql_prompt_attempt_1.txt @@ -0,0 +1,795 @@ +You are generating one SQLite SELECT query for a single-table SQL QA task. +Return strict JSON only, with this schema: {"sql": "...", "notes": "..."}. +Rules: +- Use only the provided table and columns. +- Do not write INSERT, UPDATE, DELETE, DROP, ALTER, CREATE, PRAGMA, ATTACH, DETACH, or VACUUM. +- Prefer the planned template and bound roles when provided. +- Add a leading SQL comment exactly like: -- template_id: . +- Generate SQLite-compatible SQL. SQLite does not support PERCENTILE_CONT or STDDEV. +- Quote identifiers with double quotes. +- Return no markdown and no extra prose. + +Dataset context: +Dataset context for SQL QA: +- dataset_id: n1 +- dataset_name: Spambase +- table_name: n1 +- table_layout: single-table dataset (do not assume joins). +- row_semantics: One row is one email represented by engineered token/character frequency and capitalization-run features. +- task_type: classification +- target_column: class +- main_row_count: 4601 +- important_fields: +- word_freq_make: role=feature, type=numeric_sparse_frequency. tags=['condition_candidate', 'measure', 'rare_pattern_candidate'] desc=Frequency feature for token 'make' in an email message. +- word_freq_address: role=feature, type=numeric_sparse_frequency. tags=['condition_candidate', 'measure', 'rare_pattern_candidate'] desc=Frequency feature for token 'address' in an email message. +- word_freq_all: role=feature, type=numeric_sparse_frequency. tags=['condition_candidate', 'measure', 'rare_pattern_candidate'] desc=Frequency feature for token 'all' in an email message. +- word_freq_3d: role=feature, type=numeric_sparse_frequency. tags=['condition_candidate', 'measure', 'rare_pattern_candidate'] desc=Frequency feature for token '3d' in an email message. +- word_freq_our: role=feature, type=numeric_sparse_frequency. tags=['condition_candidate', 'measure', 'rare_pattern_candidate'] desc=Frequency feature for token 'our' in an email message. +- word_freq_over: role=feature, type=numeric_sparse_frequency. tags=['condition_candidate', 'measure', 'rare_pattern_candidate'] desc=Frequency feature for token 'over' in an email message. +- word_freq_remove: role=feature, type=numeric_sparse_frequency. tags=['condition_candidate', 'measure', 'rare_pattern_candidate'] desc=Frequency feature for token 'remove' in an email message. +- word_freq_internet: role=feature, type=numeric_sparse_frequency. tags=['condition_candidate', 'measure', 'rare_pattern_candidate'] desc=Frequency feature for token 'internet' in an email message. +- word_freq_order: role=feature, type=numeric_sparse_frequency. tags=['condition_candidate', 'measure', 'rare_pattern_candidate'] desc=Frequency feature for token 'order' in an email message. +- word_freq_mail: role=feature, type=numeric_sparse_frequency. tags=['condition_candidate', 'measure', 'rare_pattern_candidate'] desc=Frequency feature for token 'mail' in an email message. +- word_freq_receive: role=feature, type=numeric_sparse_frequency. tags=['condition_candidate', 'measure', 'rare_pattern_candidate'] desc=Frequency feature for token 'receive' in an email message. +- word_freq_will: role=feature, type=numeric_sparse_frequency. tags=['condition_candidate', 'measure', 'rare_pattern_candidate'] desc=Frequency feature for token 'will' in an email message. +- word_freq_people: role=feature, type=numeric_sparse_frequency. tags=['condition_candidate', 'measure', 'rare_pattern_candidate'] desc=Frequency feature for token 'people' in an email message. +- word_freq_report: role=feature, type=numeric_sparse_frequency. tags=['condition_candidate', 'measure', 'rare_pattern_candidate'] desc=Frequency feature for token 'report' in an email message. +- word_freq_addresses: role=feature, type=numeric_sparse_frequency. tags=['condition_candidate', 'measure', 'rare_pattern_candidate'] desc=Frequency feature for token 'addresses' in an email message. +- word_freq_free: role=feature, type=numeric_sparse_frequency. tags=['condition_candidate', 'measure', 'rare_pattern_candidate'] desc=Frequency feature for token 'free' in an email message. +- word_freq_business: role=feature, type=numeric_sparse_frequency. tags=['condition_candidate', 'measure', 'rare_pattern_candidate'] desc=Frequency feature for token 'business' in an email message. +- word_freq_email: role=feature, type=numeric_sparse_frequency. tags=['condition_candidate', 'measure', 'rare_pattern_candidate'] desc=Frequency feature for token 'email' in an email message. +- word_freq_you: role=feature, type=numeric_sparse_frequency. tags=['condition_candidate', 'measure', 'rare_pattern_candidate'] desc=Frequency feature for token 'you' in an email message. +- word_freq_credit: role=feature, type=numeric_sparse_frequency. tags=['condition_candidate', 'measure', 'rare_pattern_candidate'] desc=Frequency feature for token 'credit' in an email message. +- word_freq_your: role=feature, type=numeric_sparse_frequency. tags=['condition_candidate', 'measure', 'rare_pattern_candidate'] desc=Frequency feature for token 'your' in an email message. +- word_freq_font: role=feature, type=numeric_sparse_frequency. tags=['condition_candidate', 'measure', 'rare_pattern_candidate'] desc=Frequency feature for token 'font' in an email message. +- word_freq_000: role=feature, type=numeric_sparse_frequency. tags=['condition_candidate', 'measure', 'rare_pattern_candidate'] desc=Frequency feature for token '000' in an email message. +- word_freq_money: role=feature, type=numeric_sparse_frequency. tags=['condition_candidate', 'measure', 'rare_pattern_candidate'] desc=Frequency feature for token 'money' in an email message. +- word_freq_hp: role=feature, type=numeric_sparse_frequency. tags=['condition_candidate', 'measure', 'rare_pattern_candidate'] desc=Frequency feature for token 'hp' in an email message. +- word_freq_hpl: role=feature, type=numeric_sparse_frequency. tags=['condition_candidate', 'measure', 'rare_pattern_candidate'] desc=Frequency feature for token 'hpl' in an email message. +- word_freq_george: role=feature, type=numeric_sparse_frequency. tags=['condition_candidate', 'measure', 'rare_pattern_candidate'] desc=Frequency feature for token 'george' in an email message. +- word_freq_650: role=feature, type=numeric_sparse_frequency. tags=['condition_candidate', 'measure', 'rare_pattern_candidate'] desc=Frequency feature for token '650' in an email message. +- word_freq_lab: role=feature, type=numeric_sparse_frequency. tags=['condition_candidate', 'measure', 'rare_pattern_candidate'] desc=Frequency feature for token 'lab' in an email message. +- word_freq_labs: role=feature, type=numeric_sparse_frequency. tags=['condition_candidate', 'measure', 'rare_pattern_candidate'] desc=Frequency feature for token 'labs' in an email message. +- word_freq_telnet: role=feature, type=numeric_sparse_frequency. tags=['condition_candidate', 'measure', 'rare_pattern_candidate'] desc=Frequency feature for token 'telnet' in an email message. +- word_freq_857: role=feature, type=numeric_sparse_frequency. tags=['condition_candidate', 'measure', 'rare_pattern_candidate'] desc=Frequency feature for token '857' in an email message. +- word_freq_data: role=feature, type=numeric_sparse_frequency. tags=['condition_candidate', 'measure', 'rare_pattern_candidate'] desc=Frequency feature for token 'data' in an email message. +- word_freq_415: role=feature, type=numeric_sparse_frequency. tags=['condition_candidate', 'measure', 'rare_pattern_candidate'] desc=Frequency feature for token '415' in an email message. +- word_freq_85: role=feature, type=numeric_sparse_frequency. tags=['condition_candidate', 'measure', 'rare_pattern_candidate'] desc=Frequency feature for token '85' in an email message. +- word_freq_technology: role=feature, type=numeric_sparse_frequency. tags=['condition_candidate', 'measure', 'rare_pattern_candidate'] desc=Frequency feature for token 'technology' in an email message. +- word_freq_1999: role=feature, type=numeric_sparse_frequency. tags=['condition_candidate', 'measure', 'rare_pattern_candidate'] desc=Frequency feature for token '1999' in an email message. +- word_freq_parts: role=feature, type=numeric_sparse_frequency. tags=['condition_candidate', 'measure', 'rare_pattern_candidate'] desc=Frequency feature for token 'parts' in an email message. +- word_freq_pm: role=feature, type=numeric_sparse_frequency. tags=['condition_candidate', 'measure', 'rare_pattern_candidate'] desc=Frequency feature for token 'pm' in an email message. +- word_freq_direct: role=feature, type=numeric_sparse_frequency. tags=['condition_candidate', 'measure', 'rare_pattern_candidate'] desc=Frequency feature for token 'direct' in an email message. +- word_freq_cs: role=feature, type=numeric_sparse_frequency. tags=['condition_candidate', 'measure', 'rare_pattern_candidate'] desc=Frequency feature for token 'cs' in an email message. +- word_freq_meeting: role=feature, type=numeric_sparse_frequency. tags=['condition_candidate', 'measure', 'rare_pattern_candidate'] desc=Frequency feature for token 'meeting' in an email message. +- word_freq_original: role=feature, type=numeric_sparse_frequency. tags=['condition_candidate', 'measure', 'rare_pattern_candidate'] desc=Frequency feature for token 'original' in an email message. +- word_freq_project: role=feature, type=numeric_sparse_frequency. tags=['condition_candidate', 'measure', 'rare_pattern_candidate'] desc=Frequency feature for token 'project' in an email message. +- word_freq_re: role=feature, type=numeric_sparse_frequency. tags=['condition_candidate', 'measure', 'rare_pattern_candidate'] desc=Frequency feature for token 're' in an email message. +- word_freq_edu: role=feature, type=numeric_sparse_frequency. tags=['condition_candidate', 'measure', 'rare_pattern_candidate'] desc=Frequency feature for token 'edu' in an email message. +- word_freq_table: role=feature, type=numeric_sparse_frequency. tags=['condition_candidate', 'measure', 'rare_pattern_candidate'] desc=Frequency feature for token 'table' in an email message. +- word_freq_conference: role=feature, type=numeric_sparse_frequency. tags=['condition_candidate', 'measure', 'rare_pattern_candidate'] desc=Frequency feature for token 'conference' in an email message. +- char_freq_%3B: role=feature, type=numeric_sparse_frequency. tags=['condition_candidate', 'measure', 'rare_pattern_candidate'] desc=Character frequency feature for symbol ';'. +- char_freq_%28: role=feature, type=numeric_sparse_frequency. tags=['condition_candidate', 'measure', 'rare_pattern_candidate'] desc=Character frequency feature for symbol '('. +- char_freq_%5B: role=feature, type=numeric_sparse_frequency. tags=['condition_candidate', 'measure', 'rare_pattern_candidate'] desc=Character frequency feature for symbol '['. +- char_freq_%21: role=feature, type=numeric_sparse_frequency. tags=['condition_candidate', 'measure', 'rare_pattern_candidate'] desc=Character frequency feature for symbol '!'. +- char_freq_%24: role=feature, type=numeric_sparse_frequency. tags=['condition_candidate', 'measure', 'rare_pattern_candidate'] desc=Character frequency feature for symbol '$'. +- char_freq_%23: role=feature, type=numeric_sparse_frequency. tags=['condition_candidate', 'measure', 'rare_pattern_candidate'] desc=Character frequency feature for symbol '#'. +- capital_run_length_average: role=feature, type=numeric_heavy_tail. tags=['condition_candidate', 'measure', 'rare_pattern_candidate'] desc=Average length of uninterrupted capital-letter runs. +- capital_run_length_longest: role=feature, type=numeric_heavy_tail. tags=['condition_candidate', 'measure', 'rare_pattern_candidate'] desc=Longest uninterrupted capital-letter run. +- capital_run_length_total: role=feature, type=numeric_heavy_tail. tags=['condition_candidate', 'measure', 'rare_pattern_candidate'] desc=Total number of capital letters in runs. +- class: role=target, type=binary_target. tags=['target_candidate'] desc=Binary spam label (1=spam, 0=non-spam). +- useful_field_combinations: [['word_freq_free', 'word_freq_you', 'class'], ['char_freq_%21', 'capital_run_length_longest', 'class'], ['word_freq_remove', 'word_freq_money', 'class']] +- fields_requiring_caution: ['capital_run_length_average', 'capital_run_length_longest', 'capital_run_length_total'] +- source_url: https://www.openml.org/d/44 + +SQLite schema snapshot: +{ + "table_name": "n1", + "quoted_table_name": "\"n1\"", + "row_count": 4601, + "columns": [ + { + "name": "word_freq_make", + "type": "TEXT", + "notnull": false, + "pk": false + }, + { + "name": "word_freq_address", + "type": "TEXT", + "notnull": false, + "pk": false + }, + { + "name": "word_freq_all", + "type": "TEXT", + "notnull": false, + "pk": false + }, + { + "name": "word_freq_3d", + "type": "TEXT", + "notnull": false, + "pk": false + }, + { + "name": "word_freq_our", + "type": "TEXT", + "notnull": false, + "pk": false + }, + { + "name": "word_freq_over", + "type": "TEXT", + "notnull": false, + "pk": false + }, + { + "name": "word_freq_remove", + "type": "TEXT", + "notnull": false, + "pk": false + }, + { + "name": "word_freq_internet", + "type": "TEXT", + "notnull": false, + "pk": false + }, + { + "name": "word_freq_order", + "type": "TEXT", + "notnull": false, + "pk": false + }, + { + "name": "word_freq_mail", + "type": "TEXT", + "notnull": false, + "pk": false + }, + { + "name": "word_freq_receive", + "type": "TEXT", + "notnull": false, + "pk": false + }, + { + "name": "word_freq_will", + "type": "TEXT", + "notnull": false, + "pk": false + }, + { + "name": "word_freq_people", + "type": "TEXT", + "notnull": false, + "pk": false + }, + { + "name": "word_freq_report", + "type": "TEXT", + "notnull": false, + "pk": false + }, + { + "name": "word_freq_addresses", + "type": "TEXT", + "notnull": false, + "pk": false + }, + { + "name": "word_freq_free", + "type": "TEXT", + "notnull": false, + "pk": false + }, + { + "name": "word_freq_business", + "type": "TEXT", + "notnull": false, + "pk": false + }, + { + "name": "word_freq_email", + "type": "TEXT", + "notnull": false, + "pk": false + }, + { + "name": "word_freq_you", + "type": "TEXT", + "notnull": false, + "pk": false + }, + { + "name": "word_freq_credit", + "type": "TEXT", + "notnull": false, + "pk": false + }, + { + "name": "word_freq_your", + "type": "TEXT", + "notnull": false, + "pk": false + }, + { + "name": "word_freq_font", + "type": "TEXT", + "notnull": false, + "pk": false + }, + { + "name": "word_freq_000", + "type": "TEXT", + "notnull": false, + "pk": false + }, + { + "name": "word_freq_money", + "type": "TEXT", + "notnull": false, + "pk": false + }, + { + "name": "word_freq_hp", + "type": "TEXT", + "notnull": false, + "pk": false + }, + { + "name": "word_freq_hpl", + "type": "TEXT", + "notnull": false, + "pk": false + }, + { + "name": "word_freq_george", + "type": "TEXT", + "notnull": false, + "pk": false + }, + { + "name": "word_freq_650", + "type": "TEXT", + "notnull": false, + "pk": false + }, + { + "name": "word_freq_lab", + "type": "TEXT", + "notnull": false, + "pk": false + }, + { + "name": "word_freq_labs", + "type": "TEXT", + "notnull": false, + "pk": false + }, + { + "name": "word_freq_telnet", + "type": "TEXT", + "notnull": false, + "pk": false + }, + { + "name": "word_freq_857", + "type": "TEXT", + "notnull": false, + "pk": false + }, + { + "name": "word_freq_data", + "type": "TEXT", + "notnull": false, + "pk": false + }, + { + "name": "word_freq_415", + "type": "TEXT", + "notnull": false, + "pk": false + }, + { + "name": "word_freq_85", + "type": "TEXT", + "notnull": false, + "pk": false + }, + { + "name": "word_freq_technology", + "type": "TEXT", + "notnull": false, + "pk": false + }, + { + "name": "word_freq_1999", + "type": "TEXT", + "notnull": false, + "pk": false + }, + { + "name": "word_freq_parts", + "type": "TEXT", + "notnull": false, + "pk": false + }, + { + "name": "word_freq_pm", + "type": "TEXT", + "notnull": false, + "pk": false + }, + { + "name": "word_freq_direct", + "type": "TEXT", + "notnull": false, + "pk": false + }, + { + "name": "word_freq_cs", + "type": "TEXT", + "notnull": false, + "pk": false + }, + { + "name": "word_freq_meeting", + "type": "TEXT", + "notnull": false, + "pk": false + }, + { + "name": "word_freq_original", + "type": "TEXT", + "notnull": false, + "pk": false + }, + { + "name": "word_freq_project", + "type": "TEXT", + "notnull": false, + "pk": false + }, + { + "name": "word_freq_re", + "type": "TEXT", + "notnull": false, + "pk": false + }, + { + "name": "word_freq_edu", + "type": "TEXT", + "notnull": false, + "pk": false + }, + { + "name": "word_freq_table", + "type": "TEXT", + "notnull": false, + "pk": false + }, + { + "name": "word_freq_conference", + "type": "TEXT", + "notnull": false, + "pk": false + }, + { + "name": "char_freq_%3B", + "type": "TEXT", + "notnull": false, + "pk": false + }, + { + "name": "char_freq_%28", + "type": "TEXT", + "notnull": false, + "pk": false + }, + { + "name": "char_freq_%5B", + "type": "TEXT", + "notnull": false, + "pk": false + }, + { + "name": "char_freq_%21", + "type": "TEXT", + "notnull": false, + "pk": false + }, + { + "name": "char_freq_%24", + "type": "TEXT", + "notnull": false, + "pk": false + }, + { + "name": "char_freq_%23", + "type": "TEXT", + "notnull": false, + "pk": false + }, + { + "name": "capital_run_length_average", + "type": "TEXT", + "notnull": false, + "pk": false + }, + { + "name": "capital_run_length_longest", + "type": "TEXT", + "notnull": false, + "pk": false + }, + { + "name": "capital_run_length_total", + "type": "TEXT", + "notnull": false, + "pk": false + }, + { + "name": "class", + "type": "TEXT", + "notnull": false, + "pk": false + } + ], + "sample_rows": [ + { + "word_freq_make": "0", + "word_freq_address": "0.64", + "word_freq_all": "0.64", + "word_freq_3d": "0", + "word_freq_our": "0.32", + "word_freq_over": "0", + "word_freq_remove": "0", + "word_freq_internet": "0", + "word_freq_order": "0", + "word_freq_mail": "0", + "word_freq_receive": "0", + "word_freq_will": "0.64", + "word_freq_people": "0", + "word_freq_report": "0", + "word_freq_addresses": "0", + "word_freq_free": "0.32", + "word_freq_business": "0", + "word_freq_email": "1.29", + "word_freq_you": "1.93", + "word_freq_credit": "0", + "word_freq_your": "0.96", + "word_freq_font": "0", + "word_freq_000": "0", + "word_freq_money": "0", + "word_freq_hp": "0", + "word_freq_hpl": "0", + "word_freq_george": "0", + "word_freq_650": "0", + "word_freq_lab": "0", + "word_freq_labs": "0", + "word_freq_telnet": "0", + "word_freq_857": "0", + "word_freq_data": "0", + "word_freq_415": "0", + "word_freq_85": "0", + "word_freq_technology": "0", + "word_freq_1999": "0", + "word_freq_parts": "0", + "word_freq_pm": "0", + "word_freq_direct": "0", + "word_freq_cs": "0", + "word_freq_meeting": "0", + "word_freq_original": "0", + "word_freq_project": "0", + "word_freq_re": "0", + "word_freq_edu": "0", + "word_freq_table": "0", + "word_freq_conference": "0", + "char_freq_%3B": "0", + "char_freq_%28": "0", + "char_freq_%5B": "0", + "char_freq_%21": "0.778", + "char_freq_%24": "0", + "char_freq_%23": "0", + "capital_run_length_average": "3.756", + "capital_run_length_longest": "61", + "capital_run_length_total": "278", + "class": "1" + }, + { + "word_freq_make": "0.21", + "word_freq_address": "0.28", + "word_freq_all": "0.5", + "word_freq_3d": "0", + "word_freq_our": "0.14", + "word_freq_over": "0.28", + "word_freq_remove": "0.21", + "word_freq_internet": "0.07", + "word_freq_order": "0", + "word_freq_mail": "0.94", + "word_freq_receive": "0.21", + "word_freq_will": "0.79", + "word_freq_people": "0.65", + "word_freq_report": "0.21", + "word_freq_addresses": "0.14", + "word_freq_free": "0.14", + "word_freq_business": "0.07", + "word_freq_email": "0.28", + "word_freq_you": "3.47", + "word_freq_credit": "0", + "word_freq_your": "1.59", + "word_freq_font": "0", + "word_freq_000": "0.43", + "word_freq_money": "0.43", + "word_freq_hp": "0", + "word_freq_hpl": "0", + "word_freq_george": "0", + "word_freq_650": "0", + "word_freq_lab": "0", + "word_freq_labs": "0", + "word_freq_telnet": "0", + "word_freq_857": "0", + "word_freq_data": "0", + "word_freq_415": "0", + "word_freq_85": "0", + "word_freq_technology": "0", + "word_freq_1999": "0.07", + "word_freq_parts": "0", + "word_freq_pm": "0", + "word_freq_direct": "0", + "word_freq_cs": "0", + "word_freq_meeting": "0", + "word_freq_original": "0", + "word_freq_project": "0", + "word_freq_re": "0", + "word_freq_edu": "0", + "word_freq_table": "0", + "word_freq_conference": "0", + "char_freq_%3B": "0", + "char_freq_%28": "0.132", + "char_freq_%5B": "0", + "char_freq_%21": "0.372", + "char_freq_%24": "0.18", + "char_freq_%23": "0.048", + "capital_run_length_average": "5.114", + "capital_run_length_longest": "101", + "capital_run_length_total": "1028", + "class": "1" + }, + { + "word_freq_make": "0.06", + "word_freq_address": "0", + "word_freq_all": "0.71", + "word_freq_3d": "0", + "word_freq_our": "1.23", + "word_freq_over": "0.19", + "word_freq_remove": "0.19", + "word_freq_internet": "0.12", + "word_freq_order": "0.64", + "word_freq_mail": "0.25", + "word_freq_receive": "0.38", + "word_freq_will": "0.45", + "word_freq_people": "0.12", + "word_freq_report": "0", + "word_freq_addresses": "1.75", + "word_freq_free": "0.06", + "word_freq_business": "0.06", + "word_freq_email": "1.03", + "word_freq_you": "1.36", + "word_freq_credit": "0.32", + "word_freq_your": "0.51", + "word_freq_font": "0", + "word_freq_000": "1.16", + "word_freq_money": "0.06", + "word_freq_hp": "0", + "word_freq_hpl": "0", + "word_freq_george": "0", + "word_freq_650": "0", + "word_freq_lab": "0", + "word_freq_labs": "0", + "word_freq_telnet": "0", + "word_freq_857": "0", + "word_freq_data": "0", + "word_freq_415": "0", + "word_freq_85": "0", + "word_freq_technology": "0", + "word_freq_1999": "0", + "word_freq_parts": "0", + "word_freq_pm": "0", + "word_freq_direct": "0.06", + "word_freq_cs": "0", + "word_freq_meeting": "0", + "word_freq_original": "0.12", + "word_freq_project": "0", + "word_freq_re": "0.06", + "word_freq_edu": "0.06", + "word_freq_table": "0", + "word_freq_conference": "0", + "char_freq_%3B": "0.01", + "char_freq_%28": "0.143", + "char_freq_%5B": "0", + "char_freq_%21": "0.276", + "char_freq_%24": "0.184", + "char_freq_%23": "0.01", + "capital_run_length_average": "9.821", + "capital_run_length_longest": "485", + "capital_run_length_total": "2259", + "class": "1" + }, + { + "word_freq_make": "0", + "word_freq_address": "0", + "word_freq_all": "0", + "word_freq_3d": "0", + "word_freq_our": "0.63", + "word_freq_over": "0", + "word_freq_remove": "0.31", + "word_freq_internet": "0.63", + "word_freq_order": "0.31", + "word_freq_mail": "0.63", + "word_freq_receive": "0.31", + "word_freq_will": "0.31", + "word_freq_people": "0.31", + "word_freq_report": "0", + "word_freq_addresses": "0", + "word_freq_free": "0.31", + "word_freq_business": "0", + "word_freq_email": "0", + "word_freq_you": "3.18", + "word_freq_credit": "0", + "word_freq_your": "0.31", + "word_freq_font": "0", + "word_freq_000": "0", + "word_freq_money": "0", + "word_freq_hp": "0", + "word_freq_hpl": "0", + "word_freq_george": "0", + "word_freq_650": "0", + "word_freq_lab": "0", + "word_freq_labs": "0", + "word_freq_telnet": "0", + "word_freq_857": "0", + "word_freq_data": "0", + "word_freq_415": "0", + "word_freq_85": "0", + "word_freq_technology": "0", + "word_freq_1999": "0", + "word_freq_parts": "0", + "word_freq_pm": "0", + "word_freq_direct": "0", + "word_freq_cs": "0", + "word_freq_meeting": "0", + "word_freq_original": "0", + "word_freq_project": "0", + "word_freq_re": "0", + "word_freq_edu": "0", + "word_freq_table": "0", + "word_freq_conference": "0", + "char_freq_%3B": "0", + "char_freq_%28": "0.137", + "char_freq_%5B": "0", + "char_freq_%21": "0.137", + "char_freq_%24": "0", + "char_freq_%23": "0", + "capital_run_length_average": "3.537", + "capital_run_length_longest": "40", + "capital_run_length_total": "191", + "class": "1" + }, + { + "word_freq_make": "0", + "word_freq_address": "0", + "word_freq_all": "0", + "word_freq_3d": "0", + "word_freq_our": "0.63", + "word_freq_over": "0", + "word_freq_remove": "0.31", + "word_freq_internet": "0.63", + "word_freq_order": "0.31", + "word_freq_mail": "0.63", + "word_freq_receive": "0.31", + "word_freq_will": "0.31", + "word_freq_people": "0.31", + "word_freq_report": "0", + "word_freq_addresses": "0", + "word_freq_free": "0.31", + "word_freq_business": "0", + "word_freq_email": "0", + "word_freq_you": "3.18", + "word_freq_credit": "0", + "word_freq_your": "0.31", + "word_freq_font": "0", + "word_freq_000": "0", + "word_freq_money": "0", + "word_freq_hp": "0", + "word_freq_hpl": "0", + "word_freq_george": "0", + "word_freq_650": "0", + "word_freq_lab": "0", + "word_freq_labs": "0", + "word_freq_telnet": "0", + "word_freq_857": "0", + "word_freq_data": "0", + "word_freq_415": "0", + "word_freq_85": "0", + "word_freq_technology": "0", + "word_freq_1999": "0", + "word_freq_parts": "0", + "word_freq_pm": "0", + "word_freq_direct": "0", + "word_freq_cs": "0", + "word_freq_meeting": "0", + "word_freq_original": "0", + "word_freq_project": "0", + "word_freq_re": "0", + "word_freq_edu": "0", + "word_freq_table": "0", + "word_freq_conference": "0", + "char_freq_%3B": "0", + "char_freq_%28": "0.135", + "char_freq_%5B": "0", + "char_freq_%21": "0.135", + "char_freq_%24": "0", + "char_freq_%23": "0", + "capital_run_length_average": "3.537", + "capital_run_length_longest": "40", + "capital_run_length_total": "191", + "class": "1" + } + ] +} + +Shortlisted templates: +[ + { + "template_id": "tpl_m4_group_condition_rate", + "template_name": "Grouped Condition Rate", + "primary_family": "conditional_dependency_structure", + "portability": "yes", + "sql_skeleton": "SELECT {group_col},\n AVG(CASE WHEN {condition_col} = {condition_value} THEN 1 ELSE 0 END) AS condition_rate\nFROM {table}\nGROUP BY {group_col}\nORDER BY condition_rate DESC;", + "required_roles": [ + "group_col", + "condition_col" + ] + } +] + +Problem instance: +{ + "dataset_id": "n1", + "question": "Use template Grouped Condition Rate to probe direction_consistency with semantic role focused_target_view. Focus on group_col=class, condition_col=class.", + "planned_template_id": "tpl_m4_group_condition_rate", + "bindings": { + "group_col": "class", + "condition_col": "class", + "condition_value": "0", + "positive_value": "0", + "negative_value": "1", + "top_k": 12, + "top_n": 4, + "num_tiles": 10, + "percentile_value": 0.9, + "z_threshold": 2.0, + "fraction_threshold": 0.1, + "baseline_multiplier": 1.5, + "baseline_fraction": 0.1, + "min_group_size": 5, + "min_support": 5, + "measure_threshold": 0.0, + "time_grain": "month", + "lookback_rows": 3, + "current_period_start": "'2024-01-01'", + "current_period_end": "'2024-04-01'", + "previous_period_start": "'2023-10-01'", + "previous_period_end": "'2024-01-01'", + "drift_ratio_threshold": 0.8 + }, + "can_vary": [], + "must_fix": [], + "runtime_sql_skeleton": "SELECT {group_col},\n AVG(CASE WHEN {condition_col} = {condition_value} THEN 1 ELSE 0 END) AS condition_rate\nFROM {table}\nGROUP BY {group_col}\nORDER BY condition_rate DESC;" +} + +Repair context: +{} diff --git a/Query/sql/v2/runs/v2_cli_20260502_081223_d/n1/artifacts/v2q_n1_7c2bf462ef7bc57f/cli/sql_response_attempt_1.raw.txt b/Query/sql/v2/runs/v2_cli_20260502_081223_d/n1/artifacts/v2q_n1_7c2bf462ef7bc57f/cli/sql_response_attempt_1.raw.txt new file mode 100644 index 0000000000000000000000000000000000000000..3dfbf684a0279454dc9697a0bfef1951421e810e --- /dev/null +++ b/Query/sql/v2/runs/v2_cli_20260502_081223_d/n1/artifacts/v2q_n1_7c2bf462ef7bc57f/cli/sql_response_attempt_1.raw.txt @@ -0,0 +1,4 @@ +{"type":"thread.started","thread_id":"019e40f5-8153-7073-8976-6e90b46e7aec"} +{"type":"turn.started"} +{"type":"item.completed","item":{"id":"item_0","type":"agent_message","text":"{\"sql\":\"-- template_id: tpl_m4_group_condition_rate\\nSELECT \\\"class\\\",\\n AVG(CASE WHEN \\\"class\\\" = '0' THEN 1 ELSE 0 END) AS \\\"condition_rate\\\"\\nFROM \\\"n1\\\"\\nGROUP BY \\\"class\\\"\\nORDER BY \\\"condition_rate\\\" DESC;\",\"notes\":\"Used the required Grouped Condition Rate template with both group_col and condition_col bound to \\\"class\\\". Compared against string literal '0' because the schema snapshot shows \\\"class\\\" is stored as TEXT.\"}"}} +{"type":"turn.completed","usage":{"input_tokens":20372,"cached_input_tokens":12032,"output_tokens":336,"reasoning_output_tokens":220}} diff --git a/Query/sql/v2/runs/v2_cli_20260502_081223_d/n1/artifacts/v2q_n1_7c2bf462ef7bc57f/cli/sql_response_attempt_1.txt b/Query/sql/v2/runs/v2_cli_20260502_081223_d/n1/artifacts/v2q_n1_7c2bf462ef7bc57f/cli/sql_response_attempt_1.txt new file mode 100644 index 0000000000000000000000000000000000000000..1ac3f912f6a92ecee46f5eb1faa707a7ced26040 --- /dev/null +++ b/Query/sql/v2/runs/v2_cli_20260502_081223_d/n1/artifacts/v2q_n1_7c2bf462ef7bc57f/cli/sql_response_attempt_1.txt @@ -0,0 +1 @@ +{"sql":"-- template_id: tpl_m4_group_condition_rate\nSELECT \"class\",\n AVG(CASE WHEN \"class\" = '0' THEN 1 ELSE 0 END) AS \"condition_rate\"\nFROM \"n1\"\nGROUP BY \"class\"\nORDER BY \"condition_rate\" DESC;","notes":"Used the required Grouped Condition Rate template with both group_col and condition_col bound to \"class\". Compared against string literal '0' because the schema snapshot shows \"class\" is stored as TEXT."} \ No newline at end of file diff --git a/Query/sql/v2/runs/v2_cli_20260502_081223_d/n1/artifacts/v2q_n1_7c2bf462ef7bc57f/cli/sql_stderr_attempt_1.txt b/Query/sql/v2/runs/v2_cli_20260502_081223_d/n1/artifacts/v2q_n1_7c2bf462ef7bc57f/cli/sql_stderr_attempt_1.txt new file mode 100644 index 0000000000000000000000000000000000000000..e69de29bb2d1d6434b8b29ae775ad8c2e48c5391 diff --git a/Query/sql/v2/runs/v2_cli_20260502_081223_d/n1/artifacts/v2q_n1_8026fc7d1ee92c9c/cli/conversation.jsonl b/Query/sql/v2/runs/v2_cli_20260502_081223_d/n1/artifacts/v2q_n1_8026fc7d1ee92c9c/cli/conversation.jsonl new file mode 100644 index 0000000000000000000000000000000000000000..50b9d45bb88a6fc7ff1c6cd406c4d7abdb7638b0 --- /dev/null +++ b/Query/sql/v2/runs/v2_cli_20260502_081223_d/n1/artifacts/v2q_n1_8026fc7d1ee92c9c/cli/conversation.jsonl @@ -0,0 +1,2 @@ +{"attempt": 1, "phase": "sql_generation", "role": "user", "content_path": "cli/sql_prompt_attempt_1.txt", "metrics": {"chars": 29442, "bytes_utf8": 29442, "lines": 792, "estimated_tokens": null}} +{"attempt": 1, "phase": "sql_generation", "role": "assistant", "content_path": "cli/sql_response_attempt_1.txt", "raw_content_path": "cli/sql_response_attempt_1.raw.txt", "stderr_path": "cli/sql_stderr_attempt_1.txt", "metrics": {"chars": 415, "bytes_utf8": 415, "lines": 1, "estimated_tokens": null}, "usage": {"input_tokens": 20329, "cached_input_tokens": 12032, "output_tokens": 317, "reasoning_output_tokens": 212}} diff --git a/Query/sql/v2/runs/v2_cli_20260502_081223_d/n1/artifacts/v2q_n1_8026fc7d1ee92c9c/cli/session_summary.json b/Query/sql/v2/runs/v2_cli_20260502_081223_d/n1/artifacts/v2q_n1_8026fc7d1ee92c9c/cli/session_summary.json new file mode 100644 index 0000000000000000000000000000000000000000..bbc0894a2fed6e1598a562272d6648b6627dd213 --- /dev/null +++ b/Query/sql/v2/runs/v2_cli_20260502_081223_d/n1/artifacts/v2q_n1_8026fc7d1ee92c9c/cli/session_summary.json @@ -0,0 +1,25 @@ +{ + "engine": "v2-cli:codex", + "command": "codex exec --skip-git-repo-check --disable plugins --sandbox read-only --cd \"/data/jialinzhang/SQLagent\" -m gpt-5.4 --json -", + "ai_cli_calls": 1, + "usage_summary": { + "dataset_id": "n1", + "model": "v2-cli:codex", + "run_id": "v2q_n1_8026fc7d1ee92c9c", + "api_calls": 0, + "input_tokens": 20329, + "cached_input_tokens": 12032, + "output_tokens": 317, + "total_tokens": 20646, + "cost_usd": 0.0, + "ai_cli_calls": 1, + "estimated_input_tokens": 0, + "estimated_output_tokens": 0, + "estimated_total_tokens": 0, + "usage_source": "ai_cli_json_usage", + "cli_elapsed_ms_total": 13873.95, + "sql_execution_elapsed_ms_total": 9.07, + "conversation_log_path": "/data/jialinzhang/TabQueryBench/sql_workloads/v2_current/runs_and_launches/runs/v2_cli_20260502_081223_d/n1/artifacts/v2q_n1_8026fc7d1ee92c9c/cli/conversation.jsonl", + "note": "Executed through a local AI CLI with structured usage metadata." + } +} \ No newline at end of file diff --git a/Query/sql/v2/runs/v2_cli_20260502_081223_d/n1/artifacts/v2q_n1_8026fc7d1ee92c9c/cli/sql_attempt_1.metadata.json b/Query/sql/v2/runs/v2_cli_20260502_081223_d/n1/artifacts/v2q_n1_8026fc7d1ee92c9c/cli/sql_attempt_1.metadata.json new file mode 100644 index 0000000000000000000000000000000000000000..d882da998330bfcf6aca1f61576b83b76d519bde --- /dev/null +++ b/Query/sql/v2/runs/v2_cli_20260502_081223_d/n1/artifacts/v2q_n1_8026fc7d1ee92c9c/cli/sql_attempt_1.metadata.json @@ -0,0 +1,45 @@ +{ + "attempt": 1, + "phase": "sql_generation", + "command": "codex exec --skip-git-repo-check --disable plugins --sandbox read-only --cd \"/data/jialinzhang/SQLagent\" -m gpt-5.4 --json -", + "started_at": "2026-05-19T15:43:03.794882+00:00", + "ended_at": "2026-05-19T15:43:17.668871+00:00", + "elapsed_ms": 13873.95, + "prompt_metrics": { + "chars": 29442, + "bytes_utf8": 29442, + "lines": 792, + "estimated_tokens": null + }, + "stdout_metrics": { + "chars": 1102, + "bytes_utf8": 1102, + "lines": 5, + "estimated_tokens": null + }, + "stderr_metrics": { + "chars": 0, + "bytes_utf8": 0, + "lines": 0, + "estimated_tokens": null + }, + "parsed_output": { + "format": "jsonl_events", + "text_metrics": { + "chars": 415, + "bytes_utf8": 415, + "lines": 1, + "estimated_tokens": null + }, + "usage": { + "input_tokens": 20329, + "cached_input_tokens": 12032, + "output_tokens": 317, + "reasoning_output_tokens": 212 + } + }, + "prompt_path": "cli/sql_prompt_attempt_1.txt", + "response_path": "cli/sql_response_attempt_1.txt", + "raw_response_path": "cli/sql_response_attempt_1.raw.txt", + "stderr_path": "cli/sql_stderr_attempt_1.txt" +} \ No newline at end of file diff --git a/Query/sql/v2/runs/v2_cli_20260502_081223_d/n1/artifacts/v2q_n1_8026fc7d1ee92c9c/cli/sql_prompt_attempt_1.txt b/Query/sql/v2/runs/v2_cli_20260502_081223_d/n1/artifacts/v2q_n1_8026fc7d1ee92c9c/cli/sql_prompt_attempt_1.txt new file mode 100644 index 0000000000000000000000000000000000000000..011b92bf3225ef85851f8a873b383c14ee1a1d2e --- /dev/null +++ b/Query/sql/v2/runs/v2_cli_20260502_081223_d/n1/artifacts/v2q_n1_8026fc7d1ee92c9c/cli/sql_prompt_attempt_1.txt @@ -0,0 +1,792 @@ +You are generating one SQLite SELECT query for a single-table SQL QA task. +Return strict JSON only, with this schema: {"sql": "...", "notes": "..."}. +Rules: +- Use only the provided table and columns. +- Do not write INSERT, UPDATE, DELETE, DROP, ALTER, CREATE, PRAGMA, ATTACH, DETACH, or VACUUM. +- Prefer the planned template and bound roles when provided. +- Add a leading SQL comment exactly like: -- template_id: . +- Generate SQLite-compatible SQL. SQLite does not support PERCENTILE_CONT or STDDEV. +- Quote identifiers with double quotes. +- Return no markdown and no extra prose. + +Dataset context: +Dataset context for SQL QA: +- dataset_id: n1 +- dataset_name: Spambase +- table_name: n1 +- table_layout: single-table dataset (do not assume joins). +- row_semantics: One row is one email represented by engineered token/character frequency and capitalization-run features. +- task_type: classification +- target_column: class +- main_row_count: 4601 +- important_fields: +- word_freq_make: role=feature, type=numeric_sparse_frequency. tags=['condition_candidate', 'measure', 'rare_pattern_candidate'] desc=Frequency feature for token 'make' in an email message. +- word_freq_address: role=feature, type=numeric_sparse_frequency. tags=['condition_candidate', 'measure', 'rare_pattern_candidate'] desc=Frequency feature for token 'address' in an email message. +- word_freq_all: role=feature, type=numeric_sparse_frequency. tags=['condition_candidate', 'measure', 'rare_pattern_candidate'] desc=Frequency feature for token 'all' in an email message. +- word_freq_3d: role=feature, type=numeric_sparse_frequency. tags=['condition_candidate', 'measure', 'rare_pattern_candidate'] desc=Frequency feature for token '3d' in an email message. +- word_freq_our: role=feature, type=numeric_sparse_frequency. tags=['condition_candidate', 'measure', 'rare_pattern_candidate'] desc=Frequency feature for token 'our' in an email message. +- word_freq_over: role=feature, type=numeric_sparse_frequency. tags=['condition_candidate', 'measure', 'rare_pattern_candidate'] desc=Frequency feature for token 'over' in an email message. +- word_freq_remove: role=feature, type=numeric_sparse_frequency. tags=['condition_candidate', 'measure', 'rare_pattern_candidate'] desc=Frequency feature for token 'remove' in an email message. +- word_freq_internet: role=feature, type=numeric_sparse_frequency. tags=['condition_candidate', 'measure', 'rare_pattern_candidate'] desc=Frequency feature for token 'internet' in an email message. +- word_freq_order: role=feature, type=numeric_sparse_frequency. tags=['condition_candidate', 'measure', 'rare_pattern_candidate'] desc=Frequency feature for token 'order' in an email message. +- word_freq_mail: role=feature, type=numeric_sparse_frequency. tags=['condition_candidate', 'measure', 'rare_pattern_candidate'] desc=Frequency feature for token 'mail' in an email message. +- word_freq_receive: role=feature, type=numeric_sparse_frequency. tags=['condition_candidate', 'measure', 'rare_pattern_candidate'] desc=Frequency feature for token 'receive' in an email message. +- word_freq_will: role=feature, type=numeric_sparse_frequency. tags=['condition_candidate', 'measure', 'rare_pattern_candidate'] desc=Frequency feature for token 'will' in an email message. +- word_freq_people: role=feature, type=numeric_sparse_frequency. tags=['condition_candidate', 'measure', 'rare_pattern_candidate'] desc=Frequency feature for token 'people' in an email message. +- word_freq_report: role=feature, type=numeric_sparse_frequency. tags=['condition_candidate', 'measure', 'rare_pattern_candidate'] desc=Frequency feature for token 'report' in an email message. +- word_freq_addresses: role=feature, type=numeric_sparse_frequency. tags=['condition_candidate', 'measure', 'rare_pattern_candidate'] desc=Frequency feature for token 'addresses' in an email message. +- word_freq_free: role=feature, type=numeric_sparse_frequency. tags=['condition_candidate', 'measure', 'rare_pattern_candidate'] desc=Frequency feature for token 'free' in an email message. +- word_freq_business: role=feature, type=numeric_sparse_frequency. tags=['condition_candidate', 'measure', 'rare_pattern_candidate'] desc=Frequency feature for token 'business' in an email message. +- word_freq_email: role=feature, type=numeric_sparse_frequency. tags=['condition_candidate', 'measure', 'rare_pattern_candidate'] desc=Frequency feature for token 'email' in an email message. +- word_freq_you: role=feature, type=numeric_sparse_frequency. tags=['condition_candidate', 'measure', 'rare_pattern_candidate'] desc=Frequency feature for token 'you' in an email message. +- word_freq_credit: role=feature, type=numeric_sparse_frequency. tags=['condition_candidate', 'measure', 'rare_pattern_candidate'] desc=Frequency feature for token 'credit' in an email message. +- word_freq_your: role=feature, type=numeric_sparse_frequency. tags=['condition_candidate', 'measure', 'rare_pattern_candidate'] desc=Frequency feature for token 'your' in an email message. +- word_freq_font: role=feature, type=numeric_sparse_frequency. tags=['condition_candidate', 'measure', 'rare_pattern_candidate'] desc=Frequency feature for token 'font' in an email message. +- word_freq_000: role=feature, type=numeric_sparse_frequency. tags=['condition_candidate', 'measure', 'rare_pattern_candidate'] desc=Frequency feature for token '000' in an email message. +- word_freq_money: role=feature, type=numeric_sparse_frequency. tags=['condition_candidate', 'measure', 'rare_pattern_candidate'] desc=Frequency feature for token 'money' in an email message. +- word_freq_hp: role=feature, type=numeric_sparse_frequency. tags=['condition_candidate', 'measure', 'rare_pattern_candidate'] desc=Frequency feature for token 'hp' in an email message. +- word_freq_hpl: role=feature, type=numeric_sparse_frequency. tags=['condition_candidate', 'measure', 'rare_pattern_candidate'] desc=Frequency feature for token 'hpl' in an email message. +- word_freq_george: role=feature, type=numeric_sparse_frequency. tags=['condition_candidate', 'measure', 'rare_pattern_candidate'] desc=Frequency feature for token 'george' in an email message. +- word_freq_650: role=feature, type=numeric_sparse_frequency. tags=['condition_candidate', 'measure', 'rare_pattern_candidate'] desc=Frequency feature for token '650' in an email message. +- word_freq_lab: role=feature, type=numeric_sparse_frequency. tags=['condition_candidate', 'measure', 'rare_pattern_candidate'] desc=Frequency feature for token 'lab' in an email message. +- word_freq_labs: role=feature, type=numeric_sparse_frequency. tags=['condition_candidate', 'measure', 'rare_pattern_candidate'] desc=Frequency feature for token 'labs' in an email message. +- word_freq_telnet: role=feature, type=numeric_sparse_frequency. tags=['condition_candidate', 'measure', 'rare_pattern_candidate'] desc=Frequency feature for token 'telnet' in an email message. +- word_freq_857: role=feature, type=numeric_sparse_frequency. tags=['condition_candidate', 'measure', 'rare_pattern_candidate'] desc=Frequency feature for token '857' in an email message. +- word_freq_data: role=feature, type=numeric_sparse_frequency. tags=['condition_candidate', 'measure', 'rare_pattern_candidate'] desc=Frequency feature for token 'data' in an email message. +- word_freq_415: role=feature, type=numeric_sparse_frequency. tags=['condition_candidate', 'measure', 'rare_pattern_candidate'] desc=Frequency feature for token '415' in an email message. +- word_freq_85: role=feature, type=numeric_sparse_frequency. tags=['condition_candidate', 'measure', 'rare_pattern_candidate'] desc=Frequency feature for token '85' in an email message. +- word_freq_technology: role=feature, type=numeric_sparse_frequency. tags=['condition_candidate', 'measure', 'rare_pattern_candidate'] desc=Frequency feature for token 'technology' in an email message. +- word_freq_1999: role=feature, type=numeric_sparse_frequency. tags=['condition_candidate', 'measure', 'rare_pattern_candidate'] desc=Frequency feature for token '1999' in an email message. +- word_freq_parts: role=feature, type=numeric_sparse_frequency. tags=['condition_candidate', 'measure', 'rare_pattern_candidate'] desc=Frequency feature for token 'parts' in an email message. +- word_freq_pm: role=feature, type=numeric_sparse_frequency. tags=['condition_candidate', 'measure', 'rare_pattern_candidate'] desc=Frequency feature for token 'pm' in an email message. +- word_freq_direct: role=feature, type=numeric_sparse_frequency. tags=['condition_candidate', 'measure', 'rare_pattern_candidate'] desc=Frequency feature for token 'direct' in an email message. +- word_freq_cs: role=feature, type=numeric_sparse_frequency. tags=['condition_candidate', 'measure', 'rare_pattern_candidate'] desc=Frequency feature for token 'cs' in an email message. +- word_freq_meeting: role=feature, type=numeric_sparse_frequency. tags=['condition_candidate', 'measure', 'rare_pattern_candidate'] desc=Frequency feature for token 'meeting' in an email message. +- word_freq_original: role=feature, type=numeric_sparse_frequency. tags=['condition_candidate', 'measure', 'rare_pattern_candidate'] desc=Frequency feature for token 'original' in an email message. +- word_freq_project: role=feature, type=numeric_sparse_frequency. tags=['condition_candidate', 'measure', 'rare_pattern_candidate'] desc=Frequency feature for token 'project' in an email message. +- word_freq_re: role=feature, type=numeric_sparse_frequency. tags=['condition_candidate', 'measure', 'rare_pattern_candidate'] desc=Frequency feature for token 're' in an email message. +- word_freq_edu: role=feature, type=numeric_sparse_frequency. tags=['condition_candidate', 'measure', 'rare_pattern_candidate'] desc=Frequency feature for token 'edu' in an email message. +- word_freq_table: role=feature, type=numeric_sparse_frequency. tags=['condition_candidate', 'measure', 'rare_pattern_candidate'] desc=Frequency feature for token 'table' in an email message. +- word_freq_conference: role=feature, type=numeric_sparse_frequency. tags=['condition_candidate', 'measure', 'rare_pattern_candidate'] desc=Frequency feature for token 'conference' in an email message. +- char_freq_%3B: role=feature, type=numeric_sparse_frequency. tags=['condition_candidate', 'measure', 'rare_pattern_candidate'] desc=Character frequency feature for symbol ';'. +- char_freq_%28: role=feature, type=numeric_sparse_frequency. tags=['condition_candidate', 'measure', 'rare_pattern_candidate'] desc=Character frequency feature for symbol '('. +- char_freq_%5B: role=feature, type=numeric_sparse_frequency. tags=['condition_candidate', 'measure', 'rare_pattern_candidate'] desc=Character frequency feature for symbol '['. +- char_freq_%21: role=feature, type=numeric_sparse_frequency. tags=['condition_candidate', 'measure', 'rare_pattern_candidate'] desc=Character frequency feature for symbol '!'. +- char_freq_%24: role=feature, type=numeric_sparse_frequency. tags=['condition_candidate', 'measure', 'rare_pattern_candidate'] desc=Character frequency feature for symbol '$'. +- char_freq_%23: role=feature, type=numeric_sparse_frequency. tags=['condition_candidate', 'measure', 'rare_pattern_candidate'] desc=Character frequency feature for symbol '#'. +- capital_run_length_average: role=feature, type=numeric_heavy_tail. tags=['condition_candidate', 'measure', 'rare_pattern_candidate'] desc=Average length of uninterrupted capital-letter runs. +- capital_run_length_longest: role=feature, type=numeric_heavy_tail. tags=['condition_candidate', 'measure', 'rare_pattern_candidate'] desc=Longest uninterrupted capital-letter run. +- capital_run_length_total: role=feature, type=numeric_heavy_tail. tags=['condition_candidate', 'measure', 'rare_pattern_candidate'] desc=Total number of capital letters in runs. +- class: role=target, type=binary_target. tags=['target_candidate'] desc=Binary spam label (1=spam, 0=non-spam). +- useful_field_combinations: [['word_freq_free', 'word_freq_you', 'class'], ['char_freq_%21', 'capital_run_length_longest', 'class'], ['word_freq_remove', 'word_freq_money', 'class']] +- fields_requiring_caution: ['capital_run_length_average', 'capital_run_length_longest', 'capital_run_length_total'] +- source_url: https://www.openml.org/d/44 + +SQLite schema snapshot: +{ + "table_name": "n1", + "quoted_table_name": "\"n1\"", + "row_count": 4601, + "columns": [ + { + "name": "word_freq_make", + "type": "TEXT", + "notnull": false, + "pk": false + }, + { + "name": "word_freq_address", + "type": "TEXT", + "notnull": false, + "pk": false + }, + { + "name": "word_freq_all", + "type": "TEXT", + "notnull": false, + "pk": false + }, + { + "name": "word_freq_3d", + "type": "TEXT", + "notnull": false, + "pk": false + }, + { + "name": "word_freq_our", + "type": "TEXT", + "notnull": false, + "pk": false + }, + { + "name": "word_freq_over", + "type": "TEXT", + "notnull": false, + "pk": false + }, + { + "name": "word_freq_remove", + "type": "TEXT", + "notnull": false, + "pk": false + }, + { + "name": "word_freq_internet", + "type": "TEXT", + "notnull": false, + "pk": false + }, + { + "name": "word_freq_order", + "type": "TEXT", + "notnull": false, + "pk": false + }, + { + "name": "word_freq_mail", + "type": "TEXT", + "notnull": false, + "pk": false + }, + { + "name": "word_freq_receive", + "type": "TEXT", + "notnull": false, + "pk": false + }, + { + "name": "word_freq_will", + "type": "TEXT", + "notnull": false, + "pk": false + }, + { + "name": "word_freq_people", + "type": "TEXT", + "notnull": false, + "pk": false + }, + { + "name": "word_freq_report", + "type": "TEXT", + "notnull": false, + "pk": false + }, + { + "name": "word_freq_addresses", + "type": "TEXT", + "notnull": false, + "pk": false + }, + { + "name": "word_freq_free", + "type": "TEXT", + "notnull": false, + "pk": false + }, + { + "name": "word_freq_business", + "type": "TEXT", + "notnull": false, + "pk": false + }, + { + "name": "word_freq_email", + "type": "TEXT", + "notnull": false, + "pk": false + }, + { + "name": "word_freq_you", + "type": "TEXT", + "notnull": false, + "pk": false + }, + { + "name": "word_freq_credit", + "type": "TEXT", + "notnull": false, + "pk": false + }, + { + "name": "word_freq_your", + "type": "TEXT", + "notnull": false, + "pk": false + }, + { + "name": "word_freq_font", + "type": "TEXT", + "notnull": false, + "pk": false + }, + { + "name": "word_freq_000", + "type": "TEXT", + "notnull": false, + "pk": false + }, + { + "name": "word_freq_money", + "type": "TEXT", + "notnull": false, + "pk": false + }, + { + "name": "word_freq_hp", + "type": "TEXT", + "notnull": false, + "pk": false + }, + { + "name": "word_freq_hpl", + "type": "TEXT", + "notnull": false, + "pk": false + }, + { + "name": "word_freq_george", + "type": "TEXT", + "notnull": false, + "pk": false + }, + { + "name": "word_freq_650", + "type": "TEXT", + "notnull": false, + "pk": false + }, + { + "name": "word_freq_lab", + "type": "TEXT", + "notnull": false, + "pk": false + }, + { + "name": "word_freq_labs", + "type": "TEXT", + "notnull": false, + "pk": false + }, + { + "name": "word_freq_telnet", + "type": "TEXT", + "notnull": false, + "pk": false + }, + { + "name": "word_freq_857", + "type": "TEXT", + "notnull": false, + "pk": false + }, + { + "name": "word_freq_data", + "type": "TEXT", + "notnull": false, + "pk": false + }, + { + "name": "word_freq_415", + "type": "TEXT", + "notnull": false, + "pk": false + }, + { + "name": "word_freq_85", + "type": "TEXT", + "notnull": false, + "pk": false + }, + { + "name": "word_freq_technology", + "type": "TEXT", + "notnull": false, + "pk": false + }, + { + "name": "word_freq_1999", + "type": "TEXT", + "notnull": false, + "pk": false + }, + { + "name": "word_freq_parts", + "type": "TEXT", + "notnull": false, + "pk": false + }, + { + "name": "word_freq_pm", + "type": "TEXT", + "notnull": false, + "pk": false + }, + { + "name": "word_freq_direct", + "type": "TEXT", + "notnull": false, + "pk": false + }, + { + "name": "word_freq_cs", + "type": "TEXT", + "notnull": false, + "pk": false + }, + { + "name": "word_freq_meeting", + "type": "TEXT", + "notnull": false, + "pk": false + }, + { + "name": "word_freq_original", + "type": "TEXT", + "notnull": false, + "pk": false + }, + { + "name": "word_freq_project", + "type": "TEXT", + "notnull": false, + "pk": false + }, + { + "name": "word_freq_re", + "type": "TEXT", + "notnull": false, + "pk": false + }, + { + "name": "word_freq_edu", + "type": "TEXT", + "notnull": false, + "pk": false + }, + { + "name": "word_freq_table", + "type": "TEXT", + "notnull": false, + "pk": false + }, + { + "name": "word_freq_conference", + "type": "TEXT", + "notnull": false, + "pk": false + }, + { + "name": "char_freq_%3B", + "type": "TEXT", + "notnull": false, + "pk": false + }, + { + "name": "char_freq_%28", + "type": "TEXT", + "notnull": false, + "pk": false + }, + { + "name": "char_freq_%5B", + "type": "TEXT", + "notnull": false, + "pk": false + }, + { + "name": "char_freq_%21", + "type": "TEXT", + "notnull": false, + "pk": false + }, + { + "name": "char_freq_%24", + "type": "TEXT", + "notnull": false, + "pk": false + }, + { + "name": "char_freq_%23", + "type": "TEXT", + "notnull": false, + "pk": false + }, + { + "name": "capital_run_length_average", + "type": "TEXT", + "notnull": false, + "pk": false + }, + { + "name": "capital_run_length_longest", + "type": "TEXT", + "notnull": false, + "pk": false + }, + { + "name": "capital_run_length_total", + "type": "TEXT", + "notnull": false, + "pk": false + }, + { + "name": "class", + "type": "TEXT", + "notnull": false, + "pk": false + } + ], + "sample_rows": [ + { + "word_freq_make": "0", + "word_freq_address": "0.64", + "word_freq_all": "0.64", + "word_freq_3d": "0", + "word_freq_our": "0.32", + "word_freq_over": "0", + "word_freq_remove": "0", + "word_freq_internet": "0", + "word_freq_order": "0", + "word_freq_mail": "0", + "word_freq_receive": "0", + "word_freq_will": "0.64", + "word_freq_people": "0", + "word_freq_report": "0", + "word_freq_addresses": "0", + "word_freq_free": "0.32", + "word_freq_business": "0", + "word_freq_email": "1.29", + "word_freq_you": "1.93", + "word_freq_credit": "0", + "word_freq_your": "0.96", + "word_freq_font": "0", + "word_freq_000": "0", + "word_freq_money": "0", + "word_freq_hp": "0", + "word_freq_hpl": "0", + "word_freq_george": "0", + "word_freq_650": "0", + "word_freq_lab": "0", + "word_freq_labs": "0", + "word_freq_telnet": "0", + "word_freq_857": "0", + "word_freq_data": "0", + "word_freq_415": "0", + "word_freq_85": "0", + "word_freq_technology": "0", + "word_freq_1999": "0", + "word_freq_parts": "0", + "word_freq_pm": "0", + "word_freq_direct": "0", + "word_freq_cs": "0", + "word_freq_meeting": "0", + "word_freq_original": "0", + "word_freq_project": "0", + "word_freq_re": "0", + "word_freq_edu": "0", + "word_freq_table": "0", + "word_freq_conference": "0", + "char_freq_%3B": "0", + "char_freq_%28": "0", + "char_freq_%5B": "0", + "char_freq_%21": "0.778", + "char_freq_%24": "0", + "char_freq_%23": "0", + "capital_run_length_average": "3.756", + "capital_run_length_longest": "61", + "capital_run_length_total": "278", + "class": "1" + }, + { + "word_freq_make": "0.21", + "word_freq_address": "0.28", + "word_freq_all": "0.5", + "word_freq_3d": "0", + "word_freq_our": "0.14", + "word_freq_over": "0.28", + "word_freq_remove": "0.21", + "word_freq_internet": "0.07", + "word_freq_order": "0", + "word_freq_mail": "0.94", + "word_freq_receive": "0.21", + "word_freq_will": "0.79", + "word_freq_people": "0.65", + "word_freq_report": "0.21", + "word_freq_addresses": "0.14", + "word_freq_free": "0.14", + "word_freq_business": "0.07", + "word_freq_email": "0.28", + "word_freq_you": "3.47", + "word_freq_credit": "0", + "word_freq_your": "1.59", + "word_freq_font": "0", + "word_freq_000": "0.43", + "word_freq_money": "0.43", + "word_freq_hp": "0", + "word_freq_hpl": "0", + "word_freq_george": "0", + "word_freq_650": "0", + "word_freq_lab": "0", + "word_freq_labs": "0", + "word_freq_telnet": "0", + "word_freq_857": "0", + "word_freq_data": "0", + "word_freq_415": "0", + "word_freq_85": "0", + "word_freq_technology": "0", + "word_freq_1999": "0.07", + "word_freq_parts": "0", + "word_freq_pm": "0", + "word_freq_direct": "0", + "word_freq_cs": "0", + "word_freq_meeting": "0", + "word_freq_original": "0", + "word_freq_project": "0", + "word_freq_re": "0", + "word_freq_edu": "0", + "word_freq_table": "0", + "word_freq_conference": "0", + "char_freq_%3B": "0", + "char_freq_%28": "0.132", + "char_freq_%5B": "0", + "char_freq_%21": "0.372", + "char_freq_%24": "0.18", + "char_freq_%23": "0.048", + "capital_run_length_average": "5.114", + "capital_run_length_longest": "101", + "capital_run_length_total": "1028", + "class": "1" + }, + { + "word_freq_make": "0.06", + "word_freq_address": "0", + "word_freq_all": "0.71", + "word_freq_3d": "0", + "word_freq_our": "1.23", + "word_freq_over": "0.19", + "word_freq_remove": "0.19", + "word_freq_internet": "0.12", + "word_freq_order": "0.64", + "word_freq_mail": "0.25", + "word_freq_receive": "0.38", + "word_freq_will": "0.45", + "word_freq_people": "0.12", + "word_freq_report": "0", + "word_freq_addresses": "1.75", + "word_freq_free": "0.06", + "word_freq_business": "0.06", + "word_freq_email": "1.03", + "word_freq_you": "1.36", + "word_freq_credit": "0.32", + "word_freq_your": "0.51", + "word_freq_font": "0", + "word_freq_000": "1.16", + "word_freq_money": "0.06", + "word_freq_hp": "0", + "word_freq_hpl": "0", + "word_freq_george": "0", + "word_freq_650": "0", + "word_freq_lab": "0", + "word_freq_labs": "0", + "word_freq_telnet": "0", + "word_freq_857": "0", + "word_freq_data": "0", + "word_freq_415": "0", + "word_freq_85": "0", + "word_freq_technology": "0", + "word_freq_1999": "0", + "word_freq_parts": "0", + "word_freq_pm": "0", + "word_freq_direct": "0.06", + "word_freq_cs": "0", + "word_freq_meeting": "0", + "word_freq_original": "0.12", + "word_freq_project": "0", + "word_freq_re": "0.06", + "word_freq_edu": "0.06", + "word_freq_table": "0", + "word_freq_conference": "0", + "char_freq_%3B": "0.01", + "char_freq_%28": "0.143", + "char_freq_%5B": "0", + "char_freq_%21": "0.276", + "char_freq_%24": "0.184", + "char_freq_%23": "0.01", + "capital_run_length_average": "9.821", + "capital_run_length_longest": "485", + "capital_run_length_total": "2259", + "class": "1" + }, + { + "word_freq_make": "0", + "word_freq_address": "0", + "word_freq_all": "0", + "word_freq_3d": "0", + "word_freq_our": "0.63", + "word_freq_over": "0", + "word_freq_remove": "0.31", + "word_freq_internet": "0.63", + "word_freq_order": "0.31", + "word_freq_mail": "0.63", + "word_freq_receive": "0.31", + "word_freq_will": "0.31", + "word_freq_people": "0.31", + "word_freq_report": "0", + "word_freq_addresses": "0", + "word_freq_free": "0.31", + "word_freq_business": "0", + "word_freq_email": "0", + "word_freq_you": "3.18", + "word_freq_credit": "0", + "word_freq_your": "0.31", + "word_freq_font": "0", + "word_freq_000": "0", + "word_freq_money": "0", + "word_freq_hp": "0", + "word_freq_hpl": "0", + "word_freq_george": "0", + "word_freq_650": "0", + "word_freq_lab": "0", + "word_freq_labs": "0", + "word_freq_telnet": "0", + "word_freq_857": "0", + "word_freq_data": "0", + "word_freq_415": "0", + "word_freq_85": "0", + "word_freq_technology": "0", + "word_freq_1999": "0", + "word_freq_parts": "0", + "word_freq_pm": "0", + "word_freq_direct": "0", + "word_freq_cs": "0", + "word_freq_meeting": "0", + "word_freq_original": "0", + "word_freq_project": "0", + "word_freq_re": "0", + "word_freq_edu": "0", + "word_freq_table": "0", + "word_freq_conference": "0", + "char_freq_%3B": "0", + "char_freq_%28": "0.137", + "char_freq_%5B": "0", + "char_freq_%21": "0.137", + "char_freq_%24": "0", + "char_freq_%23": "0", + "capital_run_length_average": "3.537", + "capital_run_length_longest": "40", + "capital_run_length_total": "191", + "class": "1" + }, + { + "word_freq_make": "0", + "word_freq_address": "0", + "word_freq_all": "0", + "word_freq_3d": "0", + "word_freq_our": "0.63", + "word_freq_over": "0", + "word_freq_remove": "0.31", + "word_freq_internet": "0.63", + "word_freq_order": "0.31", + "word_freq_mail": "0.63", + "word_freq_receive": "0.31", + "word_freq_will": "0.31", + "word_freq_people": "0.31", + "word_freq_report": "0", + "word_freq_addresses": "0", + "word_freq_free": "0.31", + "word_freq_business": "0", + "word_freq_email": "0", + "word_freq_you": "3.18", + "word_freq_credit": "0", + "word_freq_your": "0.31", + "word_freq_font": "0", + "word_freq_000": "0", + "word_freq_money": "0", + "word_freq_hp": "0", + "word_freq_hpl": "0", + "word_freq_george": "0", + "word_freq_650": "0", + "word_freq_lab": "0", + "word_freq_labs": "0", + "word_freq_telnet": "0", + "word_freq_857": "0", + "word_freq_data": "0", + "word_freq_415": "0", + "word_freq_85": "0", + "word_freq_technology": "0", + "word_freq_1999": "0", + "word_freq_parts": "0", + "word_freq_pm": "0", + "word_freq_direct": "0", + "word_freq_cs": "0", + "word_freq_meeting": "0", + "word_freq_original": "0", + "word_freq_project": "0", + "word_freq_re": "0", + "word_freq_edu": "0", + "word_freq_table": "0", + "word_freq_conference": "0", + "char_freq_%3B": "0", + "char_freq_%28": "0.135", + "char_freq_%5B": "0", + "char_freq_%21": "0.135", + "char_freq_%24": "0", + "char_freq_%23": "0", + "capital_run_length_average": "3.537", + "capital_run_length_longest": "40", + "capital_run_length_total": "191", + "class": "1" + } + ] +} + +Shortlisted templates: +[ + { + "template_id": "tpl_m4_window_partition_avg", + "template_name": "Window Partition Average", + "primary_family": "conditional_dependency_structure", + "portability": "partial", + "sql_skeleton": "SELECT DISTINCT {group_col},\n AVG({measure_col}) OVER (PARTITION BY {group_col}) AS avg_measure\nFROM {table}\nORDER BY avg_measure DESC;", + "required_roles": [ + "group_col", + "measure_col" + ] + } +] + +Problem instance: +{ + "dataset_id": "n1", + "question": "Use template Window Partition Average to probe slice_level_consistency with semantic role filtered_stable_view. Focus on group_col=class, measure_col=char_freq_%24.", + "planned_template_id": "tpl_m4_window_partition_avg", + "bindings": { + "group_col": "class", + "measure_col": "char_freq_%24", + "top_k": 12, + "top_n": 3, + "num_tiles": 10, + "percentile_value": 0.95, + "z_threshold": 2.0, + "fraction_threshold": 0.1, + "baseline_multiplier": 1.5, + "baseline_fraction": 0.1, + "min_group_size": 5, + "min_support": 5, + "measure_threshold": 0.052, + "time_grain": "month", + "lookback_rows": 3, + "current_period_start": "'2024-01-01'", + "current_period_end": "'2024-04-01'", + "previous_period_start": "'2023-10-01'", + "previous_period_end": "'2024-01-01'", + "drift_ratio_threshold": 0.8 + }, + "can_vary": [], + "must_fix": [], + "runtime_sql_skeleton": "SELECT DISTINCT {group_col},\n AVG({measure_col}) OVER (PARTITION BY {group_col}) AS avg_measure\nFROM {table}\nORDER BY avg_measure DESC;" +} + +Repair context: +{} diff --git a/Query/sql/v2/runs/v2_cli_20260502_081223_d/n1/artifacts/v2q_n1_8026fc7d1ee92c9c/cli/sql_response_attempt_1.raw.txt b/Query/sql/v2/runs/v2_cli_20260502_081223_d/n1/artifacts/v2q_n1_8026fc7d1ee92c9c/cli/sql_response_attempt_1.raw.txt new file mode 100644 index 0000000000000000000000000000000000000000..72026637a1b7ea5c2d593be3eea3313961a72787 --- /dev/null +++ b/Query/sql/v2/runs/v2_cli_20260502_081223_d/n1/artifacts/v2q_n1_8026fc7d1ee92c9c/cli/sql_response_attempt_1.raw.txt @@ -0,0 +1,5 @@ +{"type":"thread.started","thread_id":"019e40e7-a784-7b70-99a9-9843801a6485"} +{"type":"turn.started"} +{"type":"error","message":"Reconnecting... 2/5 (stream disconnected before completion: Rate limit reached for gpt-5.4 in organization org-yXK0mBGOR7eXhtGEx8v7e3bf on tokens per min (TPM): Limit 2000000, Used 1983373, Requested 20964. Please try again in 130ms. Visit https://platform.openai.com/account/rate-limits to learn more.)"} +{"type":"item.completed","item":{"id":"item_0","type":"agent_message","text":"{\"sql\":\"-- template_id: tpl_m4_window_partition_avg\\nSELECT DISTINCT \\\"class\\\",\\n AVG(CAST(\\\"char_freq_%24\\\" AS REAL)) OVER (PARTITION BY \\\"class\\\") AS avg_measure\\nFROM \\\"n1\\\"\\nORDER BY avg_measure DESC;\",\"notes\":\"Used the requested Window Partition Average template with group_col=\\\"class\\\" and measure_col=\\\"char_freq_%24\\\". CAST to REAL is added because the schema stores numeric-looking values as TEXT.\"}"}} +{"type":"turn.completed","usage":{"input_tokens":20329,"cached_input_tokens":12032,"output_tokens":317,"reasoning_output_tokens":212}} diff --git a/Query/sql/v2/runs/v2_cli_20260502_081223_d/n1/artifacts/v2q_n1_8026fc7d1ee92c9c/cli/sql_response_attempt_1.txt b/Query/sql/v2/runs/v2_cli_20260502_081223_d/n1/artifacts/v2q_n1_8026fc7d1ee92c9c/cli/sql_response_attempt_1.txt new file mode 100644 index 0000000000000000000000000000000000000000..8656ed075a247fa27bc94c8dff67c257347fde98 --- /dev/null +++ b/Query/sql/v2/runs/v2_cli_20260502_081223_d/n1/artifacts/v2q_n1_8026fc7d1ee92c9c/cli/sql_response_attempt_1.txt @@ -0,0 +1 @@ +{"sql":"-- template_id: tpl_m4_window_partition_avg\nSELECT DISTINCT \"class\",\n AVG(CAST(\"char_freq_%24\" AS REAL)) OVER (PARTITION BY \"class\") AS avg_measure\nFROM \"n1\"\nORDER BY avg_measure DESC;","notes":"Used the requested Window Partition Average template with group_col=\"class\" and measure_col=\"char_freq_%24\". CAST to REAL is added because the schema stores numeric-looking values as TEXT."} \ No newline at end of file diff --git a/Query/sql/v2/runs/v2_cli_20260502_081223_d/n1/artifacts/v2q_n1_8026fc7d1ee92c9c/cli/sql_stderr_attempt_1.txt b/Query/sql/v2/runs/v2_cli_20260502_081223_d/n1/artifacts/v2q_n1_8026fc7d1ee92c9c/cli/sql_stderr_attempt_1.txt new file mode 100644 index 0000000000000000000000000000000000000000..e69de29bb2d1d6434b8b29ae775ad8c2e48c5391 diff --git a/Query/sql/v2/runs/v2_cli_20260502_081223_d/n1/artifacts/v2q_n1_855ad85365f9223b/cli/conversation.jsonl b/Query/sql/v2/runs/v2_cli_20260502_081223_d/n1/artifacts/v2q_n1_855ad85365f9223b/cli/conversation.jsonl new file mode 100644 index 0000000000000000000000000000000000000000..917f669ca77e14482c4884418092b04dbd09eab5 --- /dev/null +++ b/Query/sql/v2/runs/v2_cli_20260502_081223_d/n1/artifacts/v2q_n1_855ad85365f9223b/cli/conversation.jsonl @@ -0,0 +1,2 @@ +{"attempt": 1, "phase": "sql_generation", "role": "user", "content_path": "cli/sql_prompt_attempt_1.txt", "metrics": {"chars": 29919, "bytes_utf8": 29919, "lines": 792, "estimated_tokens": null}} +{"attempt": 1, "phase": "sql_generation", "role": "assistant", "content_path": "cli/sql_response_attempt_1.txt", "raw_content_path": "cli/sql_response_attempt_1.raw.txt", "stderr_path": "cli/sql_stderr_attempt_1.txt", "metrics": {"chars": 679, "bytes_utf8": 679, "lines": 1, "estimated_tokens": null}, "usage": {"input_tokens": 20453, "cached_input_tokens": 19840, "output_tokens": 383, "reasoning_output_tokens": 192}} diff --git a/Query/sql/v2/runs/v2_cli_20260502_081223_d/n1/artifacts/v2q_n1_855ad85365f9223b/cli/session_summary.json b/Query/sql/v2/runs/v2_cli_20260502_081223_d/n1/artifacts/v2q_n1_855ad85365f9223b/cli/session_summary.json new file mode 100644 index 0000000000000000000000000000000000000000..c870fab69446278716989e1f0dc7d6614ba18bbc --- /dev/null +++ b/Query/sql/v2/runs/v2_cli_20260502_081223_d/n1/artifacts/v2q_n1_855ad85365f9223b/cli/session_summary.json @@ -0,0 +1,25 @@ +{ + "engine": "v2-cli:codex", + "command": "codex exec --skip-git-repo-check --disable plugins --sandbox read-only --cd \"/data/jialinzhang/SQLagent\" -m gpt-5.4 --json -", + "ai_cli_calls": 1, + "usage_summary": { + "dataset_id": "n1", + "model": "v2-cli:codex", + "run_id": "v2q_n1_855ad85365f9223b", + "api_calls": 0, + "input_tokens": 20453, + "cached_input_tokens": 19840, + "output_tokens": 383, + "total_tokens": 20836, + "cost_usd": 0.0, + "ai_cli_calls": 1, + "estimated_input_tokens": 0, + "estimated_output_tokens": 0, + "estimated_total_tokens": 0, + "usage_source": "ai_cli_json_usage", + "cli_elapsed_ms_total": 12068.4, + "sql_execution_elapsed_ms_total": 2.3, + "conversation_log_path": "/data/jialinzhang/TabQueryBench/sql_workloads/v2_current/runs_and_launches/runs/v2_cli_20260502_081223_d/n1/artifacts/v2q_n1_855ad85365f9223b/cli/conversation.jsonl", + "note": "Executed through a local AI CLI with structured usage metadata." + } +} \ No newline at end of file diff --git a/Query/sql/v2/runs/v2_cli_20260502_081223_d/n1/artifacts/v2q_n1_855ad85365f9223b/cli/sql_attempt_1.metadata.json b/Query/sql/v2/runs/v2_cli_20260502_081223_d/n1/artifacts/v2q_n1_855ad85365f9223b/cli/sql_attempt_1.metadata.json new file mode 100644 index 0000000000000000000000000000000000000000..5b83ed007305516e2d832bd91c145a0b88e0bd86 --- /dev/null +++ b/Query/sql/v2/runs/v2_cli_20260502_081223_d/n1/artifacts/v2q_n1_855ad85365f9223b/cli/sql_attempt_1.metadata.json @@ -0,0 +1,45 @@ +{ + "attempt": 1, + "phase": "sql_generation", + "command": "codex exec --skip-git-repo-check --disable plugins --sandbox read-only --cd \"/data/jialinzhang/SQLagent\" -m gpt-5.4 --json -", + "started_at": "2026-05-19T15:46:49.029079+00:00", + "ended_at": "2026-05-19T15:47:01.097508+00:00", + "elapsed_ms": 12068.4, + "prompt_metrics": { + "chars": 29919, + "bytes_utf8": 29919, + "lines": 792, + "estimated_tokens": null + }, + "stdout_metrics": { + "chars": 1094, + "bytes_utf8": 1094, + "lines": 4, + "estimated_tokens": null + }, + "stderr_metrics": { + "chars": 0, + "bytes_utf8": 0, + "lines": 0, + "estimated_tokens": null + }, + "parsed_output": { + "format": "jsonl_events", + "text_metrics": { + "chars": 679, + "bytes_utf8": 679, + "lines": 1, + "estimated_tokens": null + }, + "usage": { + "input_tokens": 20453, + "cached_input_tokens": 19840, + "output_tokens": 383, + "reasoning_output_tokens": 192 + } + }, + "prompt_path": "cli/sql_prompt_attempt_1.txt", + "response_path": "cli/sql_response_attempt_1.txt", + "raw_response_path": "cli/sql_response_attempt_1.raw.txt", + "stderr_path": "cli/sql_stderr_attempt_1.txt" +} \ No newline at end of file diff --git a/Query/sql/v2/runs/v2_cli_20260502_081223_d/n1/artifacts/v2q_n1_855ad85365f9223b/cli/sql_prompt_attempt_1.txt b/Query/sql/v2/runs/v2_cli_20260502_081223_d/n1/artifacts/v2q_n1_855ad85365f9223b/cli/sql_prompt_attempt_1.txt new file mode 100644 index 0000000000000000000000000000000000000000..d02a85a9291e9acf6cd9323ea519e3f0c430fc51 --- /dev/null +++ b/Query/sql/v2/runs/v2_cli_20260502_081223_d/n1/artifacts/v2q_n1_855ad85365f9223b/cli/sql_prompt_attempt_1.txt @@ -0,0 +1,792 @@ +You are generating one SQLite SELECT query for a single-table SQL QA task. +Return strict JSON only, with this schema: {"sql": "...", "notes": "..."}. +Rules: +- Use only the provided table and columns. +- Do not write INSERT, UPDATE, DELETE, DROP, ALTER, CREATE, PRAGMA, ATTACH, DETACH, or VACUUM. +- Prefer the planned template and bound roles when provided. +- Add a leading SQL comment exactly like: -- template_id: . +- Generate SQLite-compatible SQL. SQLite does not support PERCENTILE_CONT or STDDEV. +- Quote identifiers with double quotes. +- Return no markdown and no extra prose. + +Dataset context: +Dataset context for SQL QA: +- dataset_id: n1 +- dataset_name: Spambase +- table_name: n1 +- table_layout: single-table dataset (do not assume joins). +- row_semantics: One row is one email represented by engineered token/character frequency and capitalization-run features. +- task_type: classification +- target_column: class +- main_row_count: 4601 +- important_fields: +- word_freq_make: role=feature, type=numeric_sparse_frequency. tags=['condition_candidate', 'measure', 'rare_pattern_candidate'] desc=Frequency feature for token 'make' in an email message. +- word_freq_address: role=feature, type=numeric_sparse_frequency. tags=['condition_candidate', 'measure', 'rare_pattern_candidate'] desc=Frequency feature for token 'address' in an email message. +- word_freq_all: role=feature, type=numeric_sparse_frequency. tags=['condition_candidate', 'measure', 'rare_pattern_candidate'] desc=Frequency feature for token 'all' in an email message. +- word_freq_3d: role=feature, type=numeric_sparse_frequency. tags=['condition_candidate', 'measure', 'rare_pattern_candidate'] desc=Frequency feature for token '3d' in an email message. +- word_freq_our: role=feature, type=numeric_sparse_frequency. tags=['condition_candidate', 'measure', 'rare_pattern_candidate'] desc=Frequency feature for token 'our' in an email message. +- word_freq_over: role=feature, type=numeric_sparse_frequency. tags=['condition_candidate', 'measure', 'rare_pattern_candidate'] desc=Frequency feature for token 'over' in an email message. +- word_freq_remove: role=feature, type=numeric_sparse_frequency. tags=['condition_candidate', 'measure', 'rare_pattern_candidate'] desc=Frequency feature for token 'remove' in an email message. +- word_freq_internet: role=feature, type=numeric_sparse_frequency. tags=['condition_candidate', 'measure', 'rare_pattern_candidate'] desc=Frequency feature for token 'internet' in an email message. +- word_freq_order: role=feature, type=numeric_sparse_frequency. tags=['condition_candidate', 'measure', 'rare_pattern_candidate'] desc=Frequency feature for token 'order' in an email message. +- word_freq_mail: role=feature, type=numeric_sparse_frequency. tags=['condition_candidate', 'measure', 'rare_pattern_candidate'] desc=Frequency feature for token 'mail' in an email message. +- word_freq_receive: role=feature, type=numeric_sparse_frequency. tags=['condition_candidate', 'measure', 'rare_pattern_candidate'] desc=Frequency feature for token 'receive' in an email message. +- word_freq_will: role=feature, type=numeric_sparse_frequency. tags=['condition_candidate', 'measure', 'rare_pattern_candidate'] desc=Frequency feature for token 'will' in an email message. +- word_freq_people: role=feature, type=numeric_sparse_frequency. tags=['condition_candidate', 'measure', 'rare_pattern_candidate'] desc=Frequency feature for token 'people' in an email message. +- word_freq_report: role=feature, type=numeric_sparse_frequency. tags=['condition_candidate', 'measure', 'rare_pattern_candidate'] desc=Frequency feature for token 'report' in an email message. +- word_freq_addresses: role=feature, type=numeric_sparse_frequency. tags=['condition_candidate', 'measure', 'rare_pattern_candidate'] desc=Frequency feature for token 'addresses' in an email message. +- word_freq_free: role=feature, type=numeric_sparse_frequency. tags=['condition_candidate', 'measure', 'rare_pattern_candidate'] desc=Frequency feature for token 'free' in an email message. +- word_freq_business: role=feature, type=numeric_sparse_frequency. tags=['condition_candidate', 'measure', 'rare_pattern_candidate'] desc=Frequency feature for token 'business' in an email message. +- word_freq_email: role=feature, type=numeric_sparse_frequency. tags=['condition_candidate', 'measure', 'rare_pattern_candidate'] desc=Frequency feature for token 'email' in an email message. +- word_freq_you: role=feature, type=numeric_sparse_frequency. tags=['condition_candidate', 'measure', 'rare_pattern_candidate'] desc=Frequency feature for token 'you' in an email message. +- word_freq_credit: role=feature, type=numeric_sparse_frequency. tags=['condition_candidate', 'measure', 'rare_pattern_candidate'] desc=Frequency feature for token 'credit' in an email message. +- word_freq_your: role=feature, type=numeric_sparse_frequency. tags=['condition_candidate', 'measure', 'rare_pattern_candidate'] desc=Frequency feature for token 'your' in an email message. +- word_freq_font: role=feature, type=numeric_sparse_frequency. tags=['condition_candidate', 'measure', 'rare_pattern_candidate'] desc=Frequency feature for token 'font' in an email message. +- word_freq_000: role=feature, type=numeric_sparse_frequency. tags=['condition_candidate', 'measure', 'rare_pattern_candidate'] desc=Frequency feature for token '000' in an email message. +- word_freq_money: role=feature, type=numeric_sparse_frequency. tags=['condition_candidate', 'measure', 'rare_pattern_candidate'] desc=Frequency feature for token 'money' in an email message. +- word_freq_hp: role=feature, type=numeric_sparse_frequency. tags=['condition_candidate', 'measure', 'rare_pattern_candidate'] desc=Frequency feature for token 'hp' in an email message. +- word_freq_hpl: role=feature, type=numeric_sparse_frequency. tags=['condition_candidate', 'measure', 'rare_pattern_candidate'] desc=Frequency feature for token 'hpl' in an email message. +- word_freq_george: role=feature, type=numeric_sparse_frequency. tags=['condition_candidate', 'measure', 'rare_pattern_candidate'] desc=Frequency feature for token 'george' in an email message. +- word_freq_650: role=feature, type=numeric_sparse_frequency. tags=['condition_candidate', 'measure', 'rare_pattern_candidate'] desc=Frequency feature for token '650' in an email message. +- word_freq_lab: role=feature, type=numeric_sparse_frequency. tags=['condition_candidate', 'measure', 'rare_pattern_candidate'] desc=Frequency feature for token 'lab' in an email message. +- word_freq_labs: role=feature, type=numeric_sparse_frequency. tags=['condition_candidate', 'measure', 'rare_pattern_candidate'] desc=Frequency feature for token 'labs' in an email message. +- word_freq_telnet: role=feature, type=numeric_sparse_frequency. tags=['condition_candidate', 'measure', 'rare_pattern_candidate'] desc=Frequency feature for token 'telnet' in an email message. +- word_freq_857: role=feature, type=numeric_sparse_frequency. tags=['condition_candidate', 'measure', 'rare_pattern_candidate'] desc=Frequency feature for token '857' in an email message. +- word_freq_data: role=feature, type=numeric_sparse_frequency. tags=['condition_candidate', 'measure', 'rare_pattern_candidate'] desc=Frequency feature for token 'data' in an email message. +- word_freq_415: role=feature, type=numeric_sparse_frequency. tags=['condition_candidate', 'measure', 'rare_pattern_candidate'] desc=Frequency feature for token '415' in an email message. +- word_freq_85: role=feature, type=numeric_sparse_frequency. tags=['condition_candidate', 'measure', 'rare_pattern_candidate'] desc=Frequency feature for token '85' in an email message. +- word_freq_technology: role=feature, type=numeric_sparse_frequency. tags=['condition_candidate', 'measure', 'rare_pattern_candidate'] desc=Frequency feature for token 'technology' in an email message. +- word_freq_1999: role=feature, type=numeric_sparse_frequency. tags=['condition_candidate', 'measure', 'rare_pattern_candidate'] desc=Frequency feature for token '1999' in an email message. +- word_freq_parts: role=feature, type=numeric_sparse_frequency. tags=['condition_candidate', 'measure', 'rare_pattern_candidate'] desc=Frequency feature for token 'parts' in an email message. +- word_freq_pm: role=feature, type=numeric_sparse_frequency. tags=['condition_candidate', 'measure', 'rare_pattern_candidate'] desc=Frequency feature for token 'pm' in an email message. +- word_freq_direct: role=feature, type=numeric_sparse_frequency. tags=['condition_candidate', 'measure', 'rare_pattern_candidate'] desc=Frequency feature for token 'direct' in an email message. +- word_freq_cs: role=feature, type=numeric_sparse_frequency. tags=['condition_candidate', 'measure', 'rare_pattern_candidate'] desc=Frequency feature for token 'cs' in an email message. +- word_freq_meeting: role=feature, type=numeric_sparse_frequency. tags=['condition_candidate', 'measure', 'rare_pattern_candidate'] desc=Frequency feature for token 'meeting' in an email message. +- word_freq_original: role=feature, type=numeric_sparse_frequency. tags=['condition_candidate', 'measure', 'rare_pattern_candidate'] desc=Frequency feature for token 'original' in an email message. +- word_freq_project: role=feature, type=numeric_sparse_frequency. tags=['condition_candidate', 'measure', 'rare_pattern_candidate'] desc=Frequency feature for token 'project' in an email message. +- word_freq_re: role=feature, type=numeric_sparse_frequency. tags=['condition_candidate', 'measure', 'rare_pattern_candidate'] desc=Frequency feature for token 're' in an email message. +- word_freq_edu: role=feature, type=numeric_sparse_frequency. tags=['condition_candidate', 'measure', 'rare_pattern_candidate'] desc=Frequency feature for token 'edu' in an email message. +- word_freq_table: role=feature, type=numeric_sparse_frequency. tags=['condition_candidate', 'measure', 'rare_pattern_candidate'] desc=Frequency feature for token 'table' in an email message. +- word_freq_conference: role=feature, type=numeric_sparse_frequency. tags=['condition_candidate', 'measure', 'rare_pattern_candidate'] desc=Frequency feature for token 'conference' in an email message. +- char_freq_%3B: role=feature, type=numeric_sparse_frequency. tags=['condition_candidate', 'measure', 'rare_pattern_candidate'] desc=Character frequency feature for symbol ';'. +- char_freq_%28: role=feature, type=numeric_sparse_frequency. tags=['condition_candidate', 'measure', 'rare_pattern_candidate'] desc=Character frequency feature for symbol '('. +- char_freq_%5B: role=feature, type=numeric_sparse_frequency. tags=['condition_candidate', 'measure', 'rare_pattern_candidate'] desc=Character frequency feature for symbol '['. +- char_freq_%21: role=feature, type=numeric_sparse_frequency. tags=['condition_candidate', 'measure', 'rare_pattern_candidate'] desc=Character frequency feature for symbol '!'. +- char_freq_%24: role=feature, type=numeric_sparse_frequency. tags=['condition_candidate', 'measure', 'rare_pattern_candidate'] desc=Character frequency feature for symbol '$'. +- char_freq_%23: role=feature, type=numeric_sparse_frequency. tags=['condition_candidate', 'measure', 'rare_pattern_candidate'] desc=Character frequency feature for symbol '#'. +- capital_run_length_average: role=feature, type=numeric_heavy_tail. tags=['condition_candidate', 'measure', 'rare_pattern_candidate'] desc=Average length of uninterrupted capital-letter runs. +- capital_run_length_longest: role=feature, type=numeric_heavy_tail. tags=['condition_candidate', 'measure', 'rare_pattern_candidate'] desc=Longest uninterrupted capital-letter run. +- capital_run_length_total: role=feature, type=numeric_heavy_tail. tags=['condition_candidate', 'measure', 'rare_pattern_candidate'] desc=Total number of capital letters in runs. +- class: role=target, type=binary_target. tags=['target_candidate'] desc=Binary spam label (1=spam, 0=non-spam). +- useful_field_combinations: [['word_freq_free', 'word_freq_you', 'class'], ['char_freq_%21', 'capital_run_length_longest', 'class'], ['word_freq_remove', 'word_freq_money', 'class']] +- fields_requiring_caution: ['capital_run_length_average', 'capital_run_length_longest', 'capital_run_length_total'] +- source_url: https://www.openml.org/d/44 + +SQLite schema snapshot: +{ + "table_name": "n1", + "quoted_table_name": "\"n1\"", + "row_count": 4601, + "columns": [ + { + "name": "word_freq_make", + "type": "TEXT", + "notnull": false, + "pk": false + }, + { + "name": "word_freq_address", + "type": "TEXT", + "notnull": false, + "pk": false + }, + { + "name": "word_freq_all", + "type": "TEXT", + "notnull": false, + "pk": false + }, + { + "name": "word_freq_3d", + "type": "TEXT", + "notnull": false, + "pk": false + }, + { + "name": "word_freq_our", + "type": "TEXT", + "notnull": false, + "pk": false + }, + { + "name": "word_freq_over", + "type": "TEXT", + "notnull": false, + "pk": false + }, + { + "name": "word_freq_remove", + "type": "TEXT", + "notnull": false, + "pk": false + }, + { + "name": "word_freq_internet", + "type": "TEXT", + "notnull": false, + "pk": false + }, + { + "name": "word_freq_order", + "type": "TEXT", + "notnull": false, + "pk": false + }, + { + "name": "word_freq_mail", + "type": "TEXT", + "notnull": false, + "pk": false + }, + { + "name": "word_freq_receive", + "type": "TEXT", + "notnull": false, + "pk": false + }, + { + "name": "word_freq_will", + "type": "TEXT", + "notnull": false, + "pk": false + }, + { + "name": "word_freq_people", + "type": "TEXT", + "notnull": false, + "pk": false + }, + { + "name": "word_freq_report", + "type": "TEXT", + "notnull": false, + "pk": false + }, + { + "name": "word_freq_addresses", + "type": "TEXT", + "notnull": false, + "pk": false + }, + { + "name": "word_freq_free", + "type": "TEXT", + "notnull": false, + "pk": false + }, + { + "name": "word_freq_business", + "type": "TEXT", + "notnull": false, + "pk": false + }, + { + "name": "word_freq_email", + "type": "TEXT", + "notnull": false, + "pk": false + }, + { + "name": "word_freq_you", + "type": "TEXT", + "notnull": false, + "pk": false + }, + { + "name": "word_freq_credit", + "type": "TEXT", + "notnull": false, + "pk": false + }, + { + "name": "word_freq_your", + "type": "TEXT", + "notnull": false, + "pk": false + }, + { + "name": "word_freq_font", + "type": "TEXT", + "notnull": false, + "pk": false + }, + { + "name": "word_freq_000", + "type": "TEXT", + "notnull": false, + "pk": false + }, + { + "name": "word_freq_money", + "type": "TEXT", + "notnull": false, + "pk": false + }, + { + "name": "word_freq_hp", + "type": "TEXT", + "notnull": false, + "pk": false + }, + { + "name": "word_freq_hpl", + "type": "TEXT", + "notnull": false, + "pk": false + }, + { + "name": "word_freq_george", + "type": "TEXT", + "notnull": false, + "pk": false + }, + { + "name": "word_freq_650", + "type": "TEXT", + "notnull": false, + "pk": false + }, + { + "name": "word_freq_lab", + "type": "TEXT", + "notnull": false, + "pk": false + }, + { + "name": "word_freq_labs", + "type": "TEXT", + "notnull": false, + "pk": false + }, + { + "name": "word_freq_telnet", + "type": "TEXT", + "notnull": false, + "pk": false + }, + { + "name": "word_freq_857", + "type": "TEXT", + "notnull": false, + "pk": false + }, + { + "name": "word_freq_data", + "type": "TEXT", + "notnull": false, + "pk": false + }, + { + "name": "word_freq_415", + "type": "TEXT", + "notnull": false, + "pk": false + }, + { + "name": "word_freq_85", + "type": "TEXT", + "notnull": false, + "pk": false + }, + { + "name": "word_freq_technology", + "type": "TEXT", + "notnull": false, + "pk": false + }, + { + "name": "word_freq_1999", + "type": "TEXT", + "notnull": false, + "pk": false + }, + { + "name": "word_freq_parts", + "type": "TEXT", + "notnull": false, + "pk": false + }, + { + "name": "word_freq_pm", + "type": "TEXT", + "notnull": false, + "pk": false + }, + { + "name": "word_freq_direct", + "type": "TEXT", + "notnull": false, + "pk": false + }, + { + "name": "word_freq_cs", + "type": "TEXT", + "notnull": false, + "pk": false + }, + { + "name": "word_freq_meeting", + "type": "TEXT", + "notnull": false, + "pk": false + }, + { + "name": "word_freq_original", + "type": "TEXT", + "notnull": false, + "pk": false + }, + { + "name": "word_freq_project", + "type": "TEXT", + "notnull": false, + "pk": false + }, + { + "name": "word_freq_re", + "type": "TEXT", + "notnull": false, + "pk": false + }, + { + "name": "word_freq_edu", + "type": "TEXT", + "notnull": false, + "pk": false + }, + { + "name": "word_freq_table", + "type": "TEXT", + "notnull": false, + "pk": false + }, + { + "name": "word_freq_conference", + "type": "TEXT", + "notnull": false, + "pk": false + }, + { + "name": "char_freq_%3B", + "type": "TEXT", + "notnull": false, + "pk": false + }, + { + "name": "char_freq_%28", + "type": "TEXT", + "notnull": false, + "pk": false + }, + { + "name": "char_freq_%5B", + "type": "TEXT", + "notnull": false, + "pk": false + }, + { + "name": "char_freq_%21", + "type": "TEXT", + "notnull": false, + "pk": false + }, + { + "name": "char_freq_%24", + "type": "TEXT", + "notnull": false, + "pk": false + }, + { + "name": "char_freq_%23", + "type": "TEXT", + "notnull": false, + "pk": false + }, + { + "name": "capital_run_length_average", + "type": "TEXT", + "notnull": false, + "pk": false + }, + { + "name": "capital_run_length_longest", + "type": "TEXT", + "notnull": false, + "pk": false + }, + { + "name": "capital_run_length_total", + "type": "TEXT", + "notnull": false, + "pk": false + }, + { + "name": "class", + "type": "TEXT", + "notnull": false, + "pk": false + } + ], + "sample_rows": [ + { + "word_freq_make": "0", + "word_freq_address": "0.64", + "word_freq_all": "0.64", + "word_freq_3d": "0", + "word_freq_our": "0.32", + "word_freq_over": "0", + "word_freq_remove": "0", + "word_freq_internet": "0", + "word_freq_order": "0", + "word_freq_mail": "0", + "word_freq_receive": "0", + "word_freq_will": "0.64", + "word_freq_people": "0", + "word_freq_report": "0", + "word_freq_addresses": "0", + "word_freq_free": "0.32", + "word_freq_business": "0", + "word_freq_email": "1.29", + "word_freq_you": "1.93", + "word_freq_credit": "0", + "word_freq_your": "0.96", + "word_freq_font": "0", + "word_freq_000": "0", + "word_freq_money": "0", + "word_freq_hp": "0", + "word_freq_hpl": "0", + "word_freq_george": "0", + "word_freq_650": "0", + "word_freq_lab": "0", + "word_freq_labs": "0", + "word_freq_telnet": "0", + "word_freq_857": "0", + "word_freq_data": "0", + "word_freq_415": "0", + "word_freq_85": "0", + "word_freq_technology": "0", + "word_freq_1999": "0", + "word_freq_parts": "0", + "word_freq_pm": "0", + "word_freq_direct": "0", + "word_freq_cs": "0", + "word_freq_meeting": "0", + "word_freq_original": "0", + "word_freq_project": "0", + "word_freq_re": "0", + "word_freq_edu": "0", + "word_freq_table": "0", + "word_freq_conference": "0", + "char_freq_%3B": "0", + "char_freq_%28": "0", + "char_freq_%5B": "0", + "char_freq_%21": "0.778", + "char_freq_%24": "0", + "char_freq_%23": "0", + "capital_run_length_average": "3.756", + "capital_run_length_longest": "61", + "capital_run_length_total": "278", + "class": "1" + }, + { + "word_freq_make": "0.21", + "word_freq_address": "0.28", + "word_freq_all": "0.5", + "word_freq_3d": "0", + "word_freq_our": "0.14", + "word_freq_over": "0.28", + "word_freq_remove": "0.21", + "word_freq_internet": "0.07", + "word_freq_order": "0", + "word_freq_mail": "0.94", + "word_freq_receive": "0.21", + "word_freq_will": "0.79", + "word_freq_people": "0.65", + "word_freq_report": "0.21", + "word_freq_addresses": "0.14", + "word_freq_free": "0.14", + "word_freq_business": "0.07", + "word_freq_email": "0.28", + "word_freq_you": "3.47", + "word_freq_credit": "0", + "word_freq_your": "1.59", + "word_freq_font": "0", + "word_freq_000": "0.43", + "word_freq_money": "0.43", + "word_freq_hp": "0", + "word_freq_hpl": "0", + "word_freq_george": "0", + "word_freq_650": "0", + "word_freq_lab": "0", + "word_freq_labs": "0", + "word_freq_telnet": "0", + "word_freq_857": "0", + "word_freq_data": "0", + "word_freq_415": "0", + "word_freq_85": "0", + "word_freq_technology": "0", + "word_freq_1999": "0.07", + "word_freq_parts": "0", + "word_freq_pm": "0", + "word_freq_direct": "0", + "word_freq_cs": "0", + "word_freq_meeting": "0", + "word_freq_original": "0", + "word_freq_project": "0", + "word_freq_re": "0", + "word_freq_edu": "0", + "word_freq_table": "0", + "word_freq_conference": "0", + "char_freq_%3B": "0", + "char_freq_%28": "0.132", + "char_freq_%5B": "0", + "char_freq_%21": "0.372", + "char_freq_%24": "0.18", + "char_freq_%23": "0.048", + "capital_run_length_average": "5.114", + "capital_run_length_longest": "101", + "capital_run_length_total": "1028", + "class": "1" + }, + { + "word_freq_make": "0.06", + "word_freq_address": "0", + "word_freq_all": "0.71", + "word_freq_3d": "0", + "word_freq_our": "1.23", + "word_freq_over": "0.19", + "word_freq_remove": "0.19", + "word_freq_internet": "0.12", + "word_freq_order": "0.64", + "word_freq_mail": "0.25", + "word_freq_receive": "0.38", + "word_freq_will": "0.45", + "word_freq_people": "0.12", + "word_freq_report": "0", + "word_freq_addresses": "1.75", + "word_freq_free": "0.06", + "word_freq_business": "0.06", + "word_freq_email": "1.03", + "word_freq_you": "1.36", + "word_freq_credit": "0.32", + "word_freq_your": "0.51", + "word_freq_font": "0", + "word_freq_000": "1.16", + "word_freq_money": "0.06", + "word_freq_hp": "0", + "word_freq_hpl": "0", + "word_freq_george": "0", + "word_freq_650": "0", + "word_freq_lab": "0", + "word_freq_labs": "0", + "word_freq_telnet": "0", + "word_freq_857": "0", + "word_freq_data": "0", + "word_freq_415": "0", + "word_freq_85": "0", + "word_freq_technology": "0", + "word_freq_1999": "0", + "word_freq_parts": "0", + "word_freq_pm": "0", + "word_freq_direct": "0.06", + "word_freq_cs": "0", + "word_freq_meeting": "0", + "word_freq_original": "0.12", + "word_freq_project": "0", + "word_freq_re": "0.06", + "word_freq_edu": "0.06", + "word_freq_table": "0", + "word_freq_conference": "0", + "char_freq_%3B": "0.01", + "char_freq_%28": "0.143", + "char_freq_%5B": "0", + "char_freq_%21": "0.276", + "char_freq_%24": "0.184", + "char_freq_%23": "0.01", + "capital_run_length_average": "9.821", + "capital_run_length_longest": "485", + "capital_run_length_total": "2259", + "class": "1" + }, + { + "word_freq_make": "0", + "word_freq_address": "0", + "word_freq_all": "0", + "word_freq_3d": "0", + "word_freq_our": "0.63", + "word_freq_over": "0", + "word_freq_remove": "0.31", + "word_freq_internet": "0.63", + "word_freq_order": "0.31", + "word_freq_mail": "0.63", + "word_freq_receive": "0.31", + "word_freq_will": "0.31", + "word_freq_people": "0.31", + "word_freq_report": "0", + "word_freq_addresses": "0", + "word_freq_free": "0.31", + "word_freq_business": "0", + "word_freq_email": "0", + "word_freq_you": "3.18", + "word_freq_credit": "0", + "word_freq_your": "0.31", + "word_freq_font": "0", + "word_freq_000": "0", + "word_freq_money": "0", + "word_freq_hp": "0", + "word_freq_hpl": "0", + "word_freq_george": "0", + "word_freq_650": "0", + "word_freq_lab": "0", + "word_freq_labs": "0", + "word_freq_telnet": "0", + "word_freq_857": "0", + "word_freq_data": "0", + "word_freq_415": "0", + "word_freq_85": "0", + "word_freq_technology": "0", + "word_freq_1999": "0", + "word_freq_parts": "0", + "word_freq_pm": "0", + "word_freq_direct": "0", + "word_freq_cs": "0", + "word_freq_meeting": "0", + "word_freq_original": "0", + "word_freq_project": "0", + "word_freq_re": "0", + "word_freq_edu": "0", + "word_freq_table": "0", + "word_freq_conference": "0", + "char_freq_%3B": "0", + "char_freq_%28": "0.137", + "char_freq_%5B": "0", + "char_freq_%21": "0.137", + "char_freq_%24": "0", + "char_freq_%23": "0", + "capital_run_length_average": "3.537", + "capital_run_length_longest": "40", + "capital_run_length_total": "191", + "class": "1" + }, + { + "word_freq_make": "0", + "word_freq_address": "0", + "word_freq_all": "0", + "word_freq_3d": "0", + "word_freq_our": "0.63", + "word_freq_over": "0", + "word_freq_remove": "0.31", + "word_freq_internet": "0.63", + "word_freq_order": "0.31", + "word_freq_mail": "0.63", + "word_freq_receive": "0.31", + "word_freq_will": "0.31", + "word_freq_people": "0.31", + "word_freq_report": "0", + "word_freq_addresses": "0", + "word_freq_free": "0.31", + "word_freq_business": "0", + "word_freq_email": "0", + "word_freq_you": "3.18", + "word_freq_credit": "0", + "word_freq_your": "0.31", + "word_freq_font": "0", + "word_freq_000": "0", + "word_freq_money": "0", + "word_freq_hp": "0", + "word_freq_hpl": "0", + "word_freq_george": "0", + "word_freq_650": "0", + "word_freq_lab": "0", + "word_freq_labs": "0", + "word_freq_telnet": "0", + "word_freq_857": "0", + "word_freq_data": "0", + "word_freq_415": "0", + "word_freq_85": "0", + "word_freq_technology": "0", + "word_freq_1999": "0", + "word_freq_parts": "0", + "word_freq_pm": "0", + "word_freq_direct": "0", + "word_freq_cs": "0", + "word_freq_meeting": "0", + "word_freq_original": "0", + "word_freq_project": "0", + "word_freq_re": "0", + "word_freq_edu": "0", + "word_freq_table": "0", + "word_freq_conference": "0", + "char_freq_%3B": "0", + "char_freq_%28": "0.135", + "char_freq_%5B": "0", + "char_freq_%21": "0.135", + "char_freq_%24": "0", + "char_freq_%23": "0", + "capital_run_length_average": "3.537", + "capital_run_length_longest": "40", + "capital_run_length_total": "191", + "class": "1" + } + ] +} + +Shortlisted templates: +[ + { + "template_id": "tpl_tpch_relative_total_threshold", + "template_name": "Relative-to-Total Extreme Threshold", + "primary_family": "tail_rarity_structure", + "portability": "partial", + "sql_skeleton": "WITH grouped AS (\n SELECT {group_col}, SUM({measure_col}) AS group_value\n FROM {table}\n GROUP BY {group_col}\n), total AS (\n SELECT SUM(group_value) AS total_value\n FROM grouped\n)\nSELECT g.{group_col}, g.group_value\nFROM grouped AS g\nCROSS JOIN total AS t\nWHERE g.group_value > t.total_value * {fraction_threshold}\nORDER BY g.group_value DESC;", + "required_roles": [ + "group_col", + "measure_col" + ] + } +] + +Problem instance: +{ + "dataset_id": "n1", + "question": "Use template Relative-to-Total Extreme Threshold to probe tail_mass_similarity with semantic role filtered_stable_view. Focus on group_col=class, measure_col=word_freq_business.", + "planned_template_id": "tpl_tpch_relative_total_threshold", + "bindings": { + "group_col": "class", + "measure_col": "word_freq_business", + "top_k": 13, + "top_n": 4, + "num_tiles": 10, + "percentile_value": 0.9, + "z_threshold": 2.0, + "fraction_threshold": 0.1, + "baseline_multiplier": 1.5, + "baseline_fraction": 0.1, + "min_group_size": 5, + "min_support": 5, + "measure_threshold": 0.0, + "time_grain": "month", + "lookback_rows": 3, + "current_period_start": "'2024-01-01'", + "current_period_end": "'2024-04-01'", + "previous_period_start": "'2023-10-01'", + "previous_period_end": "'2024-01-01'", + "drift_ratio_threshold": 0.8 + }, + "can_vary": [], + "must_fix": [], + "runtime_sql_skeleton": "WITH grouped AS (\n SELECT {group_col}, SUM({measure_col}) AS group_value\n FROM {table}\n GROUP BY {group_col}\n), total AS (\n SELECT SUM(group_value) AS total_value\n FROM grouped\n)\nSELECT g.{group_col}, g.group_value\nFROM grouped AS g\nCROSS JOIN total AS t\nWHERE g.group_value > t.total_value * {fraction_threshold}\nORDER BY g.group_value DESC;" +} + +Repair context: +{} diff --git a/Query/sql/v2/runs/v2_cli_20260502_081223_d/n1/artifacts/v2q_n1_855ad85365f9223b/cli/sql_response_attempt_1.raw.txt b/Query/sql/v2/runs/v2_cli_20260502_081223_d/n1/artifacts/v2q_n1_855ad85365f9223b/cli/sql_response_attempt_1.raw.txt new file mode 100644 index 0000000000000000000000000000000000000000..e2edebb8660d22536d1f04e63deaa4b34156667e --- /dev/null +++ b/Query/sql/v2/runs/v2_cli_20260502_081223_d/n1/artifacts/v2q_n1_855ad85365f9223b/cli/sql_response_attempt_1.raw.txt @@ -0,0 +1,4 @@ +{"type":"thread.started","thread_id":"019e40eb-17c3-7e42-8e8d-ffc6c4e67ad4"} +{"type":"turn.started"} +{"type":"item.completed","item":{"id":"item_0","type":"agent_message","text":"{\"sql\":\"-- template_id: tpl_tpch_relative_total_threshold\\nWITH \\\"grouped\\\" AS (\\n SELECT \\\"class\\\", SUM(CAST(\\\"word_freq_business\\\" AS REAL)) AS \\\"group_value\\\"\\n FROM \\\"n1\\\"\\n GROUP BY \\\"class\\\"\\n), \\\"total\\\" AS (\\n SELECT SUM(\\\"group_value\\\") AS \\\"total_value\\\"\\n FROM \\\"grouped\\\"\\n)\\nSELECT g.\\\"class\\\", g.\\\"group_value\\\"\\nFROM \\\"grouped\\\" AS g\\nCROSS JOIN \\\"total\\\" AS t\\nWHERE g.\\\"group_value\\\" > t.\\\"total_value\\\" * 0.1\\nORDER BY g.\\\"group_value\\\" DESC;\",\"notes\":\"Uses the planned Relative-to-Total Extreme Threshold template with group_col=\\\"class\\\" and measure_col=\\\"word_freq_business\\\". CAST to REAL is included because the schema stores feature values as TEXT.\"}"}} +{"type":"turn.completed","usage":{"input_tokens":20453,"cached_input_tokens":19840,"output_tokens":383,"reasoning_output_tokens":192}} diff --git a/Query/sql/v2/runs/v2_cli_20260502_081223_d/n1/artifacts/v2q_n1_855ad85365f9223b/cli/sql_response_attempt_1.txt b/Query/sql/v2/runs/v2_cli_20260502_081223_d/n1/artifacts/v2q_n1_855ad85365f9223b/cli/sql_response_attempt_1.txt new file mode 100644 index 0000000000000000000000000000000000000000..61ca185dd65422ee0d5e797327bf5815dd78ce34 --- /dev/null +++ b/Query/sql/v2/runs/v2_cli_20260502_081223_d/n1/artifacts/v2q_n1_855ad85365f9223b/cli/sql_response_attempt_1.txt @@ -0,0 +1 @@ +{"sql":"-- template_id: tpl_tpch_relative_total_threshold\nWITH \"grouped\" AS (\n SELECT \"class\", SUM(CAST(\"word_freq_business\" AS REAL)) AS \"group_value\"\n FROM \"n1\"\n GROUP BY \"class\"\n), \"total\" AS (\n SELECT SUM(\"group_value\") AS \"total_value\"\n FROM \"grouped\"\n)\nSELECT g.\"class\", g.\"group_value\"\nFROM \"grouped\" AS g\nCROSS JOIN \"total\" AS t\nWHERE g.\"group_value\" > t.\"total_value\" * 0.1\nORDER BY g.\"group_value\" DESC;","notes":"Uses the planned Relative-to-Total Extreme Threshold template with group_col=\"class\" and measure_col=\"word_freq_business\". CAST to REAL is included because the schema stores feature values as TEXT."} \ No newline at end of file diff --git a/Query/sql/v2/runs/v2_cli_20260502_081223_d/n1/artifacts/v2q_n1_855ad85365f9223b/cli/sql_stderr_attempt_1.txt b/Query/sql/v2/runs/v2_cli_20260502_081223_d/n1/artifacts/v2q_n1_855ad85365f9223b/cli/sql_stderr_attempt_1.txt new file mode 100644 index 0000000000000000000000000000000000000000..e69de29bb2d1d6434b8b29ae775ad8c2e48c5391 diff --git a/Query/sql/v2/runs/v2_cli_20260502_081223_d/n1/artifacts/v2q_n1_855c79c74e5ae2c5/cli/conversation.jsonl b/Query/sql/v2/runs/v2_cli_20260502_081223_d/n1/artifacts/v2q_n1_855c79c74e5ae2c5/cli/conversation.jsonl new file mode 100644 index 0000000000000000000000000000000000000000..5eca526eb8b89c31068dbafd0d3631d77722d49a --- /dev/null +++ b/Query/sql/v2/runs/v2_cli_20260502_081223_d/n1/artifacts/v2q_n1_855c79c74e5ae2c5/cli/conversation.jsonl @@ -0,0 +1,2 @@ +{"attempt": 1, "phase": "sql_generation", "role": "user", "content_path": "cli/sql_prompt_attempt_1.txt", "metrics": {"chars": 29915, "bytes_utf8": 29915, "lines": 792, "estimated_tokens": null}} +{"attempt": 1, "phase": "sql_generation", "role": "assistant", "content_path": "cli/sql_response_attempt_1.txt", "raw_content_path": "cli/sql_response_attempt_1.raw.txt", "stderr_path": "cli/sql_stderr_attempt_1.txt", "metrics": {"chars": 610, "bytes_utf8": 610, "lines": 1, "estimated_tokens": null}, "usage": {"input_tokens": 20453, "cached_input_tokens": 12032, "output_tokens": 676, "reasoning_output_tokens": 516}} diff --git a/Query/sql/v2/runs/v2_cli_20260502_081223_d/n1/artifacts/v2q_n1_855c79c74e5ae2c5/cli/session_summary.json b/Query/sql/v2/runs/v2_cli_20260502_081223_d/n1/artifacts/v2q_n1_855c79c74e5ae2c5/cli/session_summary.json new file mode 100644 index 0000000000000000000000000000000000000000..034f35d9121e8a2174012122de9aae0c9349d6cc --- /dev/null +++ b/Query/sql/v2/runs/v2_cli_20260502_081223_d/n1/artifacts/v2q_n1_855c79c74e5ae2c5/cli/session_summary.json @@ -0,0 +1,25 @@ +{ + "engine": "v2-cli:codex", + "command": "codex exec --skip-git-repo-check --disable plugins --sandbox read-only --cd \"/data/jialinzhang/SQLagent\" -m gpt-5.4 --json -", + "ai_cli_calls": 1, + "usage_summary": { + "dataset_id": "n1", + "model": "v2-cli:codex", + "run_id": "v2q_n1_855c79c74e5ae2c5", + "api_calls": 0, + "input_tokens": 20453, + "cached_input_tokens": 12032, + "output_tokens": 676, + "total_tokens": 21129, + "cost_usd": 0.0, + "ai_cli_calls": 1, + "estimated_input_tokens": 0, + "estimated_output_tokens": 0, + "estimated_total_tokens": 0, + "usage_source": "ai_cli_json_usage", + "cli_elapsed_ms_total": 17197.7, + "sql_execution_elapsed_ms_total": 4.16, + "conversation_log_path": "/data/jialinzhang/TabQueryBench/sql_workloads/v2_current/runs_and_launches/runs/v2_cli_20260502_081223_d/n1/artifacts/v2q_n1_855c79c74e5ae2c5/cli/conversation.jsonl", + "note": "Executed through a local AI CLI with structured usage metadata." + } +} \ No newline at end of file diff --git a/Query/sql/v2/runs/v2_cli_20260502_081223_d/n1/artifacts/v2q_n1_855c79c74e5ae2c5/cli/sql_attempt_1.metadata.json b/Query/sql/v2/runs/v2_cli_20260502_081223_d/n1/artifacts/v2q_n1_855c79c74e5ae2c5/cli/sql_attempt_1.metadata.json new file mode 100644 index 0000000000000000000000000000000000000000..ed5e72eb4dd6822f25e150d560ccbb1f44547215 --- /dev/null +++ b/Query/sql/v2/runs/v2_cli_20260502_081223_d/n1/artifacts/v2q_n1_855c79c74e5ae2c5/cli/sql_attempt_1.metadata.json @@ -0,0 +1,45 @@ +{ + "attempt": 1, + "phase": "sql_generation", + "command": "codex exec --skip-git-repo-check --disable plugins --sandbox read-only --cd \"/data/jialinzhang/SQLagent\" -m gpt-5.4 --json -", + "started_at": "2026-05-19T15:50:35.778318+00:00", + "ended_at": "2026-05-19T15:50:52.976050+00:00", + "elapsed_ms": 17197.7, + "prompt_metrics": { + "chars": 29915, + "bytes_utf8": 29915, + "lines": 792, + "estimated_tokens": null + }, + "stdout_metrics": { + "chars": 977, + "bytes_utf8": 977, + "lines": 4, + "estimated_tokens": null + }, + "stderr_metrics": { + "chars": 0, + "bytes_utf8": 0, + "lines": 0, + "estimated_tokens": null + }, + "parsed_output": { + "format": "jsonl_events", + "text_metrics": { + "chars": 610, + "bytes_utf8": 610, + "lines": 1, + "estimated_tokens": null + }, + "usage": { + "input_tokens": 20453, + "cached_input_tokens": 12032, + "output_tokens": 676, + "reasoning_output_tokens": 516 + } + }, + "prompt_path": "cli/sql_prompt_attempt_1.txt", + "response_path": "cli/sql_response_attempt_1.txt", + "raw_response_path": "cli/sql_response_attempt_1.raw.txt", + "stderr_path": "cli/sql_stderr_attempt_1.txt" +} \ No newline at end of file diff --git a/Query/sql/v2/runs/v2_cli_20260502_081223_d/n1/artifacts/v2q_n1_855c79c74e5ae2c5/cli/sql_prompt_attempt_1.txt b/Query/sql/v2/runs/v2_cli_20260502_081223_d/n1/artifacts/v2q_n1_855c79c74e5ae2c5/cli/sql_prompt_attempt_1.txt new file mode 100644 index 0000000000000000000000000000000000000000..10f0f602e1437b9ef5663a68cf32158c5b07b565 --- /dev/null +++ b/Query/sql/v2/runs/v2_cli_20260502_081223_d/n1/artifacts/v2q_n1_855c79c74e5ae2c5/cli/sql_prompt_attempt_1.txt @@ -0,0 +1,792 @@ +You are generating one SQLite SELECT query for a single-table SQL QA task. +Return strict JSON only, with this schema: {"sql": "...", "notes": "..."}. +Rules: +- Use only the provided table and columns. +- Do not write INSERT, UPDATE, DELETE, DROP, ALTER, CREATE, PRAGMA, ATTACH, DETACH, or VACUUM. +- Prefer the planned template and bound roles when provided. +- Add a leading SQL comment exactly like: -- template_id: . +- Generate SQLite-compatible SQL. SQLite does not support PERCENTILE_CONT or STDDEV. +- Quote identifiers with double quotes. +- Return no markdown and no extra prose. + +Dataset context: +Dataset context for SQL QA: +- dataset_id: n1 +- dataset_name: Spambase +- table_name: n1 +- table_layout: single-table dataset (do not assume joins). +- row_semantics: One row is one email represented by engineered token/character frequency and capitalization-run features. +- task_type: classification +- target_column: class +- main_row_count: 4601 +- important_fields: +- word_freq_make: role=feature, type=numeric_sparse_frequency. tags=['condition_candidate', 'measure', 'rare_pattern_candidate'] desc=Frequency feature for token 'make' in an email message. +- word_freq_address: role=feature, type=numeric_sparse_frequency. tags=['condition_candidate', 'measure', 'rare_pattern_candidate'] desc=Frequency feature for token 'address' in an email message. +- word_freq_all: role=feature, type=numeric_sparse_frequency. tags=['condition_candidate', 'measure', 'rare_pattern_candidate'] desc=Frequency feature for token 'all' in an email message. +- word_freq_3d: role=feature, type=numeric_sparse_frequency. tags=['condition_candidate', 'measure', 'rare_pattern_candidate'] desc=Frequency feature for token '3d' in an email message. +- word_freq_our: role=feature, type=numeric_sparse_frequency. tags=['condition_candidate', 'measure', 'rare_pattern_candidate'] desc=Frequency feature for token 'our' in an email message. +- word_freq_over: role=feature, type=numeric_sparse_frequency. tags=['condition_candidate', 'measure', 'rare_pattern_candidate'] desc=Frequency feature for token 'over' in an email message. +- word_freq_remove: role=feature, type=numeric_sparse_frequency. tags=['condition_candidate', 'measure', 'rare_pattern_candidate'] desc=Frequency feature for token 'remove' in an email message. +- word_freq_internet: role=feature, type=numeric_sparse_frequency. tags=['condition_candidate', 'measure', 'rare_pattern_candidate'] desc=Frequency feature for token 'internet' in an email message. +- word_freq_order: role=feature, type=numeric_sparse_frequency. tags=['condition_candidate', 'measure', 'rare_pattern_candidate'] desc=Frequency feature for token 'order' in an email message. +- word_freq_mail: role=feature, type=numeric_sparse_frequency. tags=['condition_candidate', 'measure', 'rare_pattern_candidate'] desc=Frequency feature for token 'mail' in an email message. +- word_freq_receive: role=feature, type=numeric_sparse_frequency. tags=['condition_candidate', 'measure', 'rare_pattern_candidate'] desc=Frequency feature for token 'receive' in an email message. +- word_freq_will: role=feature, type=numeric_sparse_frequency. tags=['condition_candidate', 'measure', 'rare_pattern_candidate'] desc=Frequency feature for token 'will' in an email message. +- word_freq_people: role=feature, type=numeric_sparse_frequency. tags=['condition_candidate', 'measure', 'rare_pattern_candidate'] desc=Frequency feature for token 'people' in an email message. +- word_freq_report: role=feature, type=numeric_sparse_frequency. tags=['condition_candidate', 'measure', 'rare_pattern_candidate'] desc=Frequency feature for token 'report' in an email message. +- word_freq_addresses: role=feature, type=numeric_sparse_frequency. tags=['condition_candidate', 'measure', 'rare_pattern_candidate'] desc=Frequency feature for token 'addresses' in an email message. +- word_freq_free: role=feature, type=numeric_sparse_frequency. tags=['condition_candidate', 'measure', 'rare_pattern_candidate'] desc=Frequency feature for token 'free' in an email message. +- word_freq_business: role=feature, type=numeric_sparse_frequency. tags=['condition_candidate', 'measure', 'rare_pattern_candidate'] desc=Frequency feature for token 'business' in an email message. +- word_freq_email: role=feature, type=numeric_sparse_frequency. tags=['condition_candidate', 'measure', 'rare_pattern_candidate'] desc=Frequency feature for token 'email' in an email message. +- word_freq_you: role=feature, type=numeric_sparse_frequency. tags=['condition_candidate', 'measure', 'rare_pattern_candidate'] desc=Frequency feature for token 'you' in an email message. +- word_freq_credit: role=feature, type=numeric_sparse_frequency. tags=['condition_candidate', 'measure', 'rare_pattern_candidate'] desc=Frequency feature for token 'credit' in an email message. +- word_freq_your: role=feature, type=numeric_sparse_frequency. tags=['condition_candidate', 'measure', 'rare_pattern_candidate'] desc=Frequency feature for token 'your' in an email message. +- word_freq_font: role=feature, type=numeric_sparse_frequency. tags=['condition_candidate', 'measure', 'rare_pattern_candidate'] desc=Frequency feature for token 'font' in an email message. +- word_freq_000: role=feature, type=numeric_sparse_frequency. tags=['condition_candidate', 'measure', 'rare_pattern_candidate'] desc=Frequency feature for token '000' in an email message. +- word_freq_money: role=feature, type=numeric_sparse_frequency. tags=['condition_candidate', 'measure', 'rare_pattern_candidate'] desc=Frequency feature for token 'money' in an email message. +- word_freq_hp: role=feature, type=numeric_sparse_frequency. tags=['condition_candidate', 'measure', 'rare_pattern_candidate'] desc=Frequency feature for token 'hp' in an email message. +- word_freq_hpl: role=feature, type=numeric_sparse_frequency. tags=['condition_candidate', 'measure', 'rare_pattern_candidate'] desc=Frequency feature for token 'hpl' in an email message. +- word_freq_george: role=feature, type=numeric_sparse_frequency. tags=['condition_candidate', 'measure', 'rare_pattern_candidate'] desc=Frequency feature for token 'george' in an email message. +- word_freq_650: role=feature, type=numeric_sparse_frequency. tags=['condition_candidate', 'measure', 'rare_pattern_candidate'] desc=Frequency feature for token '650' in an email message. +- word_freq_lab: role=feature, type=numeric_sparse_frequency. tags=['condition_candidate', 'measure', 'rare_pattern_candidate'] desc=Frequency feature for token 'lab' in an email message. +- word_freq_labs: role=feature, type=numeric_sparse_frequency. tags=['condition_candidate', 'measure', 'rare_pattern_candidate'] desc=Frequency feature for token 'labs' in an email message. +- word_freq_telnet: role=feature, type=numeric_sparse_frequency. tags=['condition_candidate', 'measure', 'rare_pattern_candidate'] desc=Frequency feature for token 'telnet' in an email message. +- word_freq_857: role=feature, type=numeric_sparse_frequency. tags=['condition_candidate', 'measure', 'rare_pattern_candidate'] desc=Frequency feature for token '857' in an email message. +- word_freq_data: role=feature, type=numeric_sparse_frequency. tags=['condition_candidate', 'measure', 'rare_pattern_candidate'] desc=Frequency feature for token 'data' in an email message. +- word_freq_415: role=feature, type=numeric_sparse_frequency. tags=['condition_candidate', 'measure', 'rare_pattern_candidate'] desc=Frequency feature for token '415' in an email message. +- word_freq_85: role=feature, type=numeric_sparse_frequency. tags=['condition_candidate', 'measure', 'rare_pattern_candidate'] desc=Frequency feature for token '85' in an email message. +- word_freq_technology: role=feature, type=numeric_sparse_frequency. tags=['condition_candidate', 'measure', 'rare_pattern_candidate'] desc=Frequency feature for token 'technology' in an email message. +- word_freq_1999: role=feature, type=numeric_sparse_frequency. tags=['condition_candidate', 'measure', 'rare_pattern_candidate'] desc=Frequency feature for token '1999' in an email message. +- word_freq_parts: role=feature, type=numeric_sparse_frequency. tags=['condition_candidate', 'measure', 'rare_pattern_candidate'] desc=Frequency feature for token 'parts' in an email message. +- word_freq_pm: role=feature, type=numeric_sparse_frequency. tags=['condition_candidate', 'measure', 'rare_pattern_candidate'] desc=Frequency feature for token 'pm' in an email message. +- word_freq_direct: role=feature, type=numeric_sparse_frequency. tags=['condition_candidate', 'measure', 'rare_pattern_candidate'] desc=Frequency feature for token 'direct' in an email message. +- word_freq_cs: role=feature, type=numeric_sparse_frequency. tags=['condition_candidate', 'measure', 'rare_pattern_candidate'] desc=Frequency feature for token 'cs' in an email message. +- word_freq_meeting: role=feature, type=numeric_sparse_frequency. tags=['condition_candidate', 'measure', 'rare_pattern_candidate'] desc=Frequency feature for token 'meeting' in an email message. +- word_freq_original: role=feature, type=numeric_sparse_frequency. tags=['condition_candidate', 'measure', 'rare_pattern_candidate'] desc=Frequency feature for token 'original' in an email message. +- word_freq_project: role=feature, type=numeric_sparse_frequency. tags=['condition_candidate', 'measure', 'rare_pattern_candidate'] desc=Frequency feature for token 'project' in an email message. +- word_freq_re: role=feature, type=numeric_sparse_frequency. tags=['condition_candidate', 'measure', 'rare_pattern_candidate'] desc=Frequency feature for token 're' in an email message. +- word_freq_edu: role=feature, type=numeric_sparse_frequency. tags=['condition_candidate', 'measure', 'rare_pattern_candidate'] desc=Frequency feature for token 'edu' in an email message. +- word_freq_table: role=feature, type=numeric_sparse_frequency. tags=['condition_candidate', 'measure', 'rare_pattern_candidate'] desc=Frequency feature for token 'table' in an email message. +- word_freq_conference: role=feature, type=numeric_sparse_frequency. tags=['condition_candidate', 'measure', 'rare_pattern_candidate'] desc=Frequency feature for token 'conference' in an email message. +- char_freq_%3B: role=feature, type=numeric_sparse_frequency. tags=['condition_candidate', 'measure', 'rare_pattern_candidate'] desc=Character frequency feature for symbol ';'. +- char_freq_%28: role=feature, type=numeric_sparse_frequency. tags=['condition_candidate', 'measure', 'rare_pattern_candidate'] desc=Character frequency feature for symbol '('. +- char_freq_%5B: role=feature, type=numeric_sparse_frequency. tags=['condition_candidate', 'measure', 'rare_pattern_candidate'] desc=Character frequency feature for symbol '['. +- char_freq_%21: role=feature, type=numeric_sparse_frequency. tags=['condition_candidate', 'measure', 'rare_pattern_candidate'] desc=Character frequency feature for symbol '!'. +- char_freq_%24: role=feature, type=numeric_sparse_frequency. tags=['condition_candidate', 'measure', 'rare_pattern_candidate'] desc=Character frequency feature for symbol '$'. +- char_freq_%23: role=feature, type=numeric_sparse_frequency. tags=['condition_candidate', 'measure', 'rare_pattern_candidate'] desc=Character frequency feature for symbol '#'. +- capital_run_length_average: role=feature, type=numeric_heavy_tail. tags=['condition_candidate', 'measure', 'rare_pattern_candidate'] desc=Average length of uninterrupted capital-letter runs. +- capital_run_length_longest: role=feature, type=numeric_heavy_tail. tags=['condition_candidate', 'measure', 'rare_pattern_candidate'] desc=Longest uninterrupted capital-letter run. +- capital_run_length_total: role=feature, type=numeric_heavy_tail. tags=['condition_candidate', 'measure', 'rare_pattern_candidate'] desc=Total number of capital letters in runs. +- class: role=target, type=binary_target. tags=['target_candidate'] desc=Binary spam label (1=spam, 0=non-spam). +- useful_field_combinations: [['word_freq_free', 'word_freq_you', 'class'], ['char_freq_%21', 'capital_run_length_longest', 'class'], ['word_freq_remove', 'word_freq_money', 'class']] +- fields_requiring_caution: ['capital_run_length_average', 'capital_run_length_longest', 'capital_run_length_total'] +- source_url: https://www.openml.org/d/44 + +SQLite schema snapshot: +{ + "table_name": "n1", + "quoted_table_name": "\"n1\"", + "row_count": 4601, + "columns": [ + { + "name": "word_freq_make", + "type": "TEXT", + "notnull": false, + "pk": false + }, + { + "name": "word_freq_address", + "type": "TEXT", + "notnull": false, + "pk": false + }, + { + "name": "word_freq_all", + "type": "TEXT", + "notnull": false, + "pk": false + }, + { + "name": "word_freq_3d", + "type": "TEXT", + "notnull": false, + "pk": false + }, + { + "name": "word_freq_our", + "type": "TEXT", + "notnull": false, + "pk": false + }, + { + "name": "word_freq_over", + "type": "TEXT", + "notnull": false, + "pk": false + }, + { + "name": "word_freq_remove", + "type": "TEXT", + "notnull": false, + "pk": false + }, + { + "name": "word_freq_internet", + "type": "TEXT", + "notnull": false, + "pk": false + }, + { + "name": "word_freq_order", + "type": "TEXT", + "notnull": false, + "pk": false + }, + { + "name": "word_freq_mail", + "type": "TEXT", + "notnull": false, + "pk": false + }, + { + "name": "word_freq_receive", + "type": "TEXT", + "notnull": false, + "pk": false + }, + { + "name": "word_freq_will", + "type": "TEXT", + "notnull": false, + "pk": false + }, + { + "name": "word_freq_people", + "type": "TEXT", + "notnull": false, + "pk": false + }, + { + "name": "word_freq_report", + "type": "TEXT", + "notnull": false, + "pk": false + }, + { + "name": "word_freq_addresses", + "type": "TEXT", + "notnull": false, + "pk": false + }, + { + "name": "word_freq_free", + "type": "TEXT", + "notnull": false, + "pk": false + }, + { + "name": "word_freq_business", + "type": "TEXT", + "notnull": false, + "pk": false + }, + { + "name": "word_freq_email", + "type": "TEXT", + "notnull": false, + "pk": false + }, + { + "name": "word_freq_you", + "type": "TEXT", + "notnull": false, + "pk": false + }, + { + "name": "word_freq_credit", + "type": "TEXT", + "notnull": false, + "pk": false + }, + { + "name": "word_freq_your", + "type": "TEXT", + "notnull": false, + "pk": false + }, + { + "name": "word_freq_font", + "type": "TEXT", + "notnull": false, + "pk": false + }, + { + "name": "word_freq_000", + "type": "TEXT", + "notnull": false, + "pk": false + }, + { + "name": "word_freq_money", + "type": "TEXT", + "notnull": false, + "pk": false + }, + { + "name": "word_freq_hp", + "type": "TEXT", + "notnull": false, + "pk": false + }, + { + "name": "word_freq_hpl", + "type": "TEXT", + "notnull": false, + "pk": false + }, + { + "name": "word_freq_george", + "type": "TEXT", + "notnull": false, + "pk": false + }, + { + "name": "word_freq_650", + "type": "TEXT", + "notnull": false, + "pk": false + }, + { + "name": "word_freq_lab", + "type": "TEXT", + "notnull": false, + "pk": false + }, + { + "name": "word_freq_labs", + "type": "TEXT", + "notnull": false, + "pk": false + }, + { + "name": "word_freq_telnet", + "type": "TEXT", + "notnull": false, + "pk": false + }, + { + "name": "word_freq_857", + "type": "TEXT", + "notnull": false, + "pk": false + }, + { + "name": "word_freq_data", + "type": "TEXT", + "notnull": false, + "pk": false + }, + { + "name": "word_freq_415", + "type": "TEXT", + "notnull": false, + "pk": false + }, + { + "name": "word_freq_85", + "type": "TEXT", + "notnull": false, + "pk": false + }, + { + "name": "word_freq_technology", + "type": "TEXT", + "notnull": false, + "pk": false + }, + { + "name": "word_freq_1999", + "type": "TEXT", + "notnull": false, + "pk": false + }, + { + "name": "word_freq_parts", + "type": "TEXT", + "notnull": false, + "pk": false + }, + { + "name": "word_freq_pm", + "type": "TEXT", + "notnull": false, + "pk": false + }, + { + "name": "word_freq_direct", + "type": "TEXT", + "notnull": false, + "pk": false + }, + { + "name": "word_freq_cs", + "type": "TEXT", + "notnull": false, + "pk": false + }, + { + "name": "word_freq_meeting", + "type": "TEXT", + "notnull": false, + "pk": false + }, + { + "name": "word_freq_original", + "type": "TEXT", + "notnull": false, + "pk": false + }, + { + "name": "word_freq_project", + "type": "TEXT", + "notnull": false, + "pk": false + }, + { + "name": "word_freq_re", + "type": "TEXT", + "notnull": false, + "pk": false + }, + { + "name": "word_freq_edu", + "type": "TEXT", + "notnull": false, + "pk": false + }, + { + "name": "word_freq_table", + "type": "TEXT", + "notnull": false, + "pk": false + }, + { + "name": "word_freq_conference", + "type": "TEXT", + "notnull": false, + "pk": false + }, + { + "name": "char_freq_%3B", + "type": "TEXT", + "notnull": false, + "pk": false + }, + { + "name": "char_freq_%28", + "type": "TEXT", + "notnull": false, + "pk": false + }, + { + "name": "char_freq_%5B", + "type": "TEXT", + "notnull": false, + "pk": false + }, + { + "name": "char_freq_%21", + "type": "TEXT", + "notnull": false, + "pk": false + }, + { + "name": "char_freq_%24", + "type": "TEXT", + "notnull": false, + "pk": false + }, + { + "name": "char_freq_%23", + "type": "TEXT", + "notnull": false, + "pk": false + }, + { + "name": "capital_run_length_average", + "type": "TEXT", + "notnull": false, + "pk": false + }, + { + "name": "capital_run_length_longest", + "type": "TEXT", + "notnull": false, + "pk": false + }, + { + "name": "capital_run_length_total", + "type": "TEXT", + "notnull": false, + "pk": false + }, + { + "name": "class", + "type": "TEXT", + "notnull": false, + "pk": false + } + ], + "sample_rows": [ + { + "word_freq_make": "0", + "word_freq_address": "0.64", + "word_freq_all": "0.64", + "word_freq_3d": "0", + "word_freq_our": "0.32", + "word_freq_over": "0", + "word_freq_remove": "0", + "word_freq_internet": "0", + "word_freq_order": "0", + "word_freq_mail": "0", + "word_freq_receive": "0", + "word_freq_will": "0.64", + "word_freq_people": "0", + "word_freq_report": "0", + "word_freq_addresses": "0", + "word_freq_free": "0.32", + "word_freq_business": "0", + "word_freq_email": "1.29", + "word_freq_you": "1.93", + "word_freq_credit": "0", + "word_freq_your": "0.96", + "word_freq_font": "0", + "word_freq_000": "0", + "word_freq_money": "0", + "word_freq_hp": "0", + "word_freq_hpl": "0", + "word_freq_george": "0", + "word_freq_650": "0", + "word_freq_lab": "0", + "word_freq_labs": "0", + "word_freq_telnet": "0", + "word_freq_857": "0", + "word_freq_data": "0", + "word_freq_415": "0", + "word_freq_85": "0", + "word_freq_technology": "0", + "word_freq_1999": "0", + "word_freq_parts": "0", + "word_freq_pm": "0", + "word_freq_direct": "0", + "word_freq_cs": "0", + "word_freq_meeting": "0", + "word_freq_original": "0", + "word_freq_project": "0", + "word_freq_re": "0", + "word_freq_edu": "0", + "word_freq_table": "0", + "word_freq_conference": "0", + "char_freq_%3B": "0", + "char_freq_%28": "0", + "char_freq_%5B": "0", + "char_freq_%21": "0.778", + "char_freq_%24": "0", + "char_freq_%23": "0", + "capital_run_length_average": "3.756", + "capital_run_length_longest": "61", + "capital_run_length_total": "278", + "class": "1" + }, + { + "word_freq_make": "0.21", + "word_freq_address": "0.28", + "word_freq_all": "0.5", + "word_freq_3d": "0", + "word_freq_our": "0.14", + "word_freq_over": "0.28", + "word_freq_remove": "0.21", + "word_freq_internet": "0.07", + "word_freq_order": "0", + "word_freq_mail": "0.94", + "word_freq_receive": "0.21", + "word_freq_will": "0.79", + "word_freq_people": "0.65", + "word_freq_report": "0.21", + "word_freq_addresses": "0.14", + "word_freq_free": "0.14", + "word_freq_business": "0.07", + "word_freq_email": "0.28", + "word_freq_you": "3.47", + "word_freq_credit": "0", + "word_freq_your": "1.59", + "word_freq_font": "0", + "word_freq_000": "0.43", + "word_freq_money": "0.43", + "word_freq_hp": "0", + "word_freq_hpl": "0", + "word_freq_george": "0", + "word_freq_650": "0", + "word_freq_lab": "0", + "word_freq_labs": "0", + "word_freq_telnet": "0", + "word_freq_857": "0", + "word_freq_data": "0", + "word_freq_415": "0", + "word_freq_85": "0", + "word_freq_technology": "0", + "word_freq_1999": "0.07", + "word_freq_parts": "0", + "word_freq_pm": "0", + "word_freq_direct": "0", + "word_freq_cs": "0", + "word_freq_meeting": "0", + "word_freq_original": "0", + "word_freq_project": "0", + "word_freq_re": "0", + "word_freq_edu": "0", + "word_freq_table": "0", + "word_freq_conference": "0", + "char_freq_%3B": "0", + "char_freq_%28": "0.132", + "char_freq_%5B": "0", + "char_freq_%21": "0.372", + "char_freq_%24": "0.18", + "char_freq_%23": "0.048", + "capital_run_length_average": "5.114", + "capital_run_length_longest": "101", + "capital_run_length_total": "1028", + "class": "1" + }, + { + "word_freq_make": "0.06", + "word_freq_address": "0", + "word_freq_all": "0.71", + "word_freq_3d": "0", + "word_freq_our": "1.23", + "word_freq_over": "0.19", + "word_freq_remove": "0.19", + "word_freq_internet": "0.12", + "word_freq_order": "0.64", + "word_freq_mail": "0.25", + "word_freq_receive": "0.38", + "word_freq_will": "0.45", + "word_freq_people": "0.12", + "word_freq_report": "0", + "word_freq_addresses": "1.75", + "word_freq_free": "0.06", + "word_freq_business": "0.06", + "word_freq_email": "1.03", + "word_freq_you": "1.36", + "word_freq_credit": "0.32", + "word_freq_your": "0.51", + "word_freq_font": "0", + "word_freq_000": "1.16", + "word_freq_money": "0.06", + "word_freq_hp": "0", + "word_freq_hpl": "0", + "word_freq_george": "0", + "word_freq_650": "0", + "word_freq_lab": "0", + "word_freq_labs": "0", + "word_freq_telnet": "0", + "word_freq_857": "0", + "word_freq_data": "0", + "word_freq_415": "0", + "word_freq_85": "0", + "word_freq_technology": "0", + "word_freq_1999": "0", + "word_freq_parts": "0", + "word_freq_pm": "0", + "word_freq_direct": "0.06", + "word_freq_cs": "0", + "word_freq_meeting": "0", + "word_freq_original": "0.12", + "word_freq_project": "0", + "word_freq_re": "0.06", + "word_freq_edu": "0.06", + "word_freq_table": "0", + "word_freq_conference": "0", + "char_freq_%3B": "0.01", + "char_freq_%28": "0.143", + "char_freq_%5B": "0", + "char_freq_%21": "0.276", + "char_freq_%24": "0.184", + "char_freq_%23": "0.01", + "capital_run_length_average": "9.821", + "capital_run_length_longest": "485", + "capital_run_length_total": "2259", + "class": "1" + }, + { + "word_freq_make": "0", + "word_freq_address": "0", + "word_freq_all": "0", + "word_freq_3d": "0", + "word_freq_our": "0.63", + "word_freq_over": "0", + "word_freq_remove": "0.31", + "word_freq_internet": "0.63", + "word_freq_order": "0.31", + "word_freq_mail": "0.63", + "word_freq_receive": "0.31", + "word_freq_will": "0.31", + "word_freq_people": "0.31", + "word_freq_report": "0", + "word_freq_addresses": "0", + "word_freq_free": "0.31", + "word_freq_business": "0", + "word_freq_email": "0", + "word_freq_you": "3.18", + "word_freq_credit": "0", + "word_freq_your": "0.31", + "word_freq_font": "0", + "word_freq_000": "0", + "word_freq_money": "0", + "word_freq_hp": "0", + "word_freq_hpl": "0", + "word_freq_george": "0", + "word_freq_650": "0", + "word_freq_lab": "0", + "word_freq_labs": "0", + "word_freq_telnet": "0", + "word_freq_857": "0", + "word_freq_data": "0", + "word_freq_415": "0", + "word_freq_85": "0", + "word_freq_technology": "0", + "word_freq_1999": "0", + "word_freq_parts": "0", + "word_freq_pm": "0", + "word_freq_direct": "0", + "word_freq_cs": "0", + "word_freq_meeting": "0", + "word_freq_original": "0", + "word_freq_project": "0", + "word_freq_re": "0", + "word_freq_edu": "0", + "word_freq_table": "0", + "word_freq_conference": "0", + "char_freq_%3B": "0", + "char_freq_%28": "0.137", + "char_freq_%5B": "0", + "char_freq_%21": "0.137", + "char_freq_%24": "0", + "char_freq_%23": "0", + "capital_run_length_average": "3.537", + "capital_run_length_longest": "40", + "capital_run_length_total": "191", + "class": "1" + }, + { + "word_freq_make": "0", + "word_freq_address": "0", + "word_freq_all": "0", + "word_freq_3d": "0", + "word_freq_our": "0.63", + "word_freq_over": "0", + "word_freq_remove": "0.31", + "word_freq_internet": "0.63", + "word_freq_order": "0.31", + "word_freq_mail": "0.63", + "word_freq_receive": "0.31", + "word_freq_will": "0.31", + "word_freq_people": "0.31", + "word_freq_report": "0", + "word_freq_addresses": "0", + "word_freq_free": "0.31", + "word_freq_business": "0", + "word_freq_email": "0", + "word_freq_you": "3.18", + "word_freq_credit": "0", + "word_freq_your": "0.31", + "word_freq_font": "0", + "word_freq_000": "0", + "word_freq_money": "0", + "word_freq_hp": "0", + "word_freq_hpl": "0", + "word_freq_george": "0", + "word_freq_650": "0", + "word_freq_lab": "0", + "word_freq_labs": "0", + "word_freq_telnet": "0", + "word_freq_857": "0", + "word_freq_data": "0", + "word_freq_415": "0", + "word_freq_85": "0", + "word_freq_technology": "0", + "word_freq_1999": "0", + "word_freq_parts": "0", + "word_freq_pm": "0", + "word_freq_direct": "0", + "word_freq_cs": "0", + "word_freq_meeting": "0", + "word_freq_original": "0", + "word_freq_project": "0", + "word_freq_re": "0", + "word_freq_edu": "0", + "word_freq_table": "0", + "word_freq_conference": "0", + "char_freq_%3B": "0", + "char_freq_%28": "0.135", + "char_freq_%5B": "0", + "char_freq_%21": "0.135", + "char_freq_%24": "0", + "char_freq_%23": "0", + "capital_run_length_average": "3.537", + "capital_run_length_longest": "40", + "capital_run_length_total": "191", + "class": "1" + } + ] +} + +Shortlisted templates: +[ + { + "template_id": "tpl_tpch_relative_total_threshold", + "template_name": "Relative-to-Total Extreme Threshold", + "primary_family": "tail_rarity_structure", + "portability": "partial", + "sql_skeleton": "WITH grouped AS (\n SELECT {group_col}, SUM({measure_col}) AS group_value\n FROM {table}\n GROUP BY {group_col}\n), total AS (\n SELECT SUM(group_value) AS total_value\n FROM grouped\n)\nSELECT g.{group_col}, g.group_value\nFROM grouped AS g\nCROSS JOIN total AS t\nWHERE g.group_value > t.total_value * {fraction_threshold}\nORDER BY g.group_value DESC;", + "required_roles": [ + "group_col", + "measure_col" + ] + } +] + +Problem instance: +{ + "dataset_id": "n1", + "question": "Use template Relative-to-Total Extreme Threshold to probe tail_mass_similarity with semantic role filtered_stable_view. Focus on group_col=class, measure_col=word_freq_money.", + "planned_template_id": "tpl_tpch_relative_total_threshold", + "bindings": { + "group_col": "class", + "measure_col": "word_freq_money", + "top_k": 15, + "top_n": 4, + "num_tiles": 10, + "percentile_value": 0.9, + "z_threshold": 2.0, + "fraction_threshold": 0.05, + "baseline_multiplier": 1.75, + "baseline_fraction": 0.1, + "min_group_size": 5, + "min_support": 4, + "measure_threshold": 0.0, + "time_grain": "month", + "lookback_rows": 3, + "current_period_start": "'2024-01-01'", + "current_period_end": "'2024-04-01'", + "previous_period_start": "'2023-10-01'", + "previous_period_end": "'2024-01-01'", + "drift_ratio_threshold": 0.8 + }, + "can_vary": [], + "must_fix": [], + "runtime_sql_skeleton": "WITH grouped AS (\n SELECT {group_col}, SUM({measure_col}) AS group_value\n FROM {table}\n GROUP BY {group_col}\n), total AS (\n SELECT SUM(group_value) AS total_value\n FROM grouped\n)\nSELECT g.{group_col}, g.group_value\nFROM grouped AS g\nCROSS JOIN total AS t\nWHERE g.group_value > t.total_value * {fraction_threshold}\nORDER BY g.group_value DESC;" +} + +Repair context: +{} diff --git a/Query/sql/v2/runs/v2_cli_20260502_081223_d/n1/artifacts/v2q_n1_855c79c74e5ae2c5/cli/sql_response_attempt_1.raw.txt b/Query/sql/v2/runs/v2_cli_20260502_081223_d/n1/artifacts/v2q_n1_855c79c74e5ae2c5/cli/sql_response_attempt_1.raw.txt new file mode 100644 index 0000000000000000000000000000000000000000..c6e5e46f77348544f992f954755cc1d250daf09d --- /dev/null +++ b/Query/sql/v2/runs/v2_cli_20260502_081223_d/n1/artifacts/v2q_n1_855c79c74e5ae2c5/cli/sql_response_attempt_1.raw.txt @@ -0,0 +1,4 @@ +{"type":"thread.started","thread_id":"019e40ee-8d0a-7353-b582-c4b02f8cbceb"} +{"type":"turn.started"} +{"type":"item.completed","item":{"id":"item_0","type":"agent_message","text":"{\"sql\":\"-- template_id: tpl_tpch_relative_total_threshold\\nWITH grouped AS (\\n SELECT \\\"class\\\", SUM(CAST(\\\"word_freq_money\\\" AS REAL)) AS group_value\\n FROM \\\"n1\\\"\\n GROUP BY \\\"class\\\"\\n), total AS (\\n SELECT SUM(group_value) AS total_value\\n FROM grouped\\n)\\nSELECT g.\\\"class\\\", g.group_value\\nFROM grouped AS g\\nCROSS JOIN total AS t\\nWHERE g.group_value > t.total_value * 0.05\\nORDER BY g.group_value DESC;\",\"notes\":\"Used the requested template with group_col=\\\"class\\\" and measure_col=\\\"word_freq_money\\\". CAST to REAL is included because the schema stores numeric-looking fields as TEXT.\"}"}} +{"type":"turn.completed","usage":{"input_tokens":20453,"cached_input_tokens":12032,"output_tokens":676,"reasoning_output_tokens":516}} diff --git a/Query/sql/v2/runs/v2_cli_20260502_081223_d/n1/artifacts/v2q_n1_855c79c74e5ae2c5/cli/sql_response_attempt_1.txt b/Query/sql/v2/runs/v2_cli_20260502_081223_d/n1/artifacts/v2q_n1_855c79c74e5ae2c5/cli/sql_response_attempt_1.txt new file mode 100644 index 0000000000000000000000000000000000000000..6a0a8646fdc0a167c1995d656c327c18dd055303 --- /dev/null +++ b/Query/sql/v2/runs/v2_cli_20260502_081223_d/n1/artifacts/v2q_n1_855c79c74e5ae2c5/cli/sql_response_attempt_1.txt @@ -0,0 +1 @@ +{"sql":"-- template_id: tpl_tpch_relative_total_threshold\nWITH grouped AS (\n SELECT \"class\", SUM(CAST(\"word_freq_money\" AS REAL)) AS group_value\n FROM \"n1\"\n GROUP BY \"class\"\n), total AS (\n SELECT SUM(group_value) AS total_value\n FROM grouped\n)\nSELECT g.\"class\", g.group_value\nFROM grouped AS g\nCROSS JOIN total AS t\nWHERE g.group_value > t.total_value * 0.05\nORDER BY g.group_value DESC;","notes":"Used the requested template with group_col=\"class\" and measure_col=\"word_freq_money\". CAST to REAL is included because the schema stores numeric-looking fields as TEXT."} \ No newline at end of file diff --git a/Query/sql/v2/runs/v2_cli_20260502_081223_d/n1/artifacts/v2q_n1_855c79c74e5ae2c5/cli/sql_stderr_attempt_1.txt b/Query/sql/v2/runs/v2_cli_20260502_081223_d/n1/artifacts/v2q_n1_855c79c74e5ae2c5/cli/sql_stderr_attempt_1.txt new file mode 100644 index 0000000000000000000000000000000000000000..e69de29bb2d1d6434b8b29ae775ad8c2e48c5391 diff --git a/Query/sql/v2/runs/v2_cli_20260502_081223_d/n1/artifacts/v2q_n1_867ca70683b596fe/cli/conversation.jsonl b/Query/sql/v2/runs/v2_cli_20260502_081223_d/n1/artifacts/v2q_n1_867ca70683b596fe/cli/conversation.jsonl new file mode 100644 index 0000000000000000000000000000000000000000..c9375fe162a685a3f7331e71faac6d8916a4c3e1 --- /dev/null +++ b/Query/sql/v2/runs/v2_cli_20260502_081223_d/n1/artifacts/v2q_n1_867ca70683b596fe/cli/conversation.jsonl @@ -0,0 +1,4 @@ +{"attempt": 1, "phase": "sql_generation", "role": "user", "content_path": "cli/sql_prompt_attempt_1.txt", "metrics": {"chars": 29298, "bytes_utf8": 29298, "lines": 790, "estimated_tokens": null}} +{"attempt": 1, "phase": "sql_generation", "role": "assistant", "content_path": "cli/sql_response_attempt_1.txt", "raw_content_path": "cli/sql_response_attempt_1.raw.txt", "stderr_path": "cli/sql_stderr_attempt_1.txt", "metrics": {"chars": 280, "bytes_utf8": 280, "lines": 4, "estimated_tokens": null}, "usage": {}, "status": "failed", "error": "AI CLI command failed with exit code 1: "} +{"attempt": 2, "phase": "sql_generation", "role": "user", "content_path": "cli/sql_prompt_attempt_2.txt", "metrics": {"chars": 29298, "bytes_utf8": 29298, "lines": 790, "estimated_tokens": null}} +{"attempt": 2, "phase": "sql_generation", "role": "assistant", "content_path": "cli/sql_response_attempt_2.txt", "raw_content_path": "cli/sql_response_attempt_2.raw.txt", "stderr_path": "cli/sql_stderr_attempt_2.txt", "metrics": {"chars": 401, "bytes_utf8": 401, "lines": 1, "estimated_tokens": null}, "usage": {"input_tokens": 20308, "cached_input_tokens": 12032, "output_tokens": 249, "reasoning_output_tokens": 139}} diff --git a/Query/sql/v2/runs/v2_cli_20260502_081223_d/n1/artifacts/v2q_n1_867ca70683b596fe/cli/session_summary.json b/Query/sql/v2/runs/v2_cli_20260502_081223_d/n1/artifacts/v2q_n1_867ca70683b596fe/cli/session_summary.json new file mode 100644 index 0000000000000000000000000000000000000000..99bd5982e853d3a0c10d5b8e03ee8e1e8f07b81f --- /dev/null +++ b/Query/sql/v2/runs/v2_cli_20260502_081223_d/n1/artifacts/v2q_n1_867ca70683b596fe/cli/session_summary.json @@ -0,0 +1,25 @@ +{ + "engine": "v2-cli:codex", + "command": "codex exec --skip-git-repo-check --disable plugins --sandbox read-only --cd \"/data/jialinzhang/SQLagent\" -m gpt-5.4 --json -", + "ai_cli_calls": 2, + "usage_summary": { + "dataset_id": "n1", + "model": "v2-cli:codex", + "run_id": "v2q_n1_867ca70683b596fe", + "api_calls": 0, + "input_tokens": 20308, + "cached_input_tokens": 12032, + "output_tokens": 249, + "total_tokens": 20557, + "cost_usd": 0.0, + "ai_cli_calls": 2, + "estimated_input_tokens": 0, + "estimated_output_tokens": 0, + "estimated_total_tokens": 0, + "usage_source": "ai_cli_json_usage", + "cli_elapsed_ms_total": 11179.38, + "sql_execution_elapsed_ms_total": 3.28, + "conversation_log_path": "/data/jialinzhang/TabQueryBench/sql_workloads/v2_current/runs_and_launches/runs/v2_cli_20260502_081223_d/n1/artifacts/v2q_n1_867ca70683b596fe/cli/conversation.jsonl", + "note": "Executed through a local AI CLI with structured usage metadata." + } +} \ No newline at end of file diff --git a/Query/sql/v2/runs/v2_cli_20260502_081223_d/n1/artifacts/v2q_n1_867ca70683b596fe/cli/sql_attempt_1.metadata.json b/Query/sql/v2/runs/v2_cli_20260502_081223_d/n1/artifacts/v2q_n1_867ca70683b596fe/cli/sql_attempt_1.metadata.json new file mode 100644 index 0000000000000000000000000000000000000000..1db3dc81d9ef5ef38df138071451d3d675ebd468 --- /dev/null +++ b/Query/sql/v2/runs/v2_cli_20260502_081223_d/n1/artifacts/v2q_n1_867ca70683b596fe/cli/sql_attempt_1.metadata.json @@ -0,0 +1,43 @@ +{ + "attempt": 1, + "phase": "sql_generation", + "command": "codex exec --skip-git-repo-check --disable plugins --sandbox read-only --cd \"/data/jialinzhang/SQLagent\" -m gpt-5.4 --json -", + "started_at": "2026-05-19T16:00:30.775054+00:00", + "ended_at": "2026-05-19T16:00:33.925448+00:00", + "elapsed_ms": 3150.37, + "returncode": 1, + "prompt_metrics": { + "chars": 29298, + "bytes_utf8": 29298, + "lines": 790, + "estimated_tokens": null + }, + "stdout_metrics": { + "chars": 281, + "bytes_utf8": 281, + "lines": 4, + "estimated_tokens": null + }, + "stderr_metrics": { + "chars": 0, + "bytes_utf8": 0, + "lines": 0, + "estimated_tokens": null + }, + "parsed_output": { + "format": "jsonl_events", + "text_metrics": { + "chars": 280, + "bytes_utf8": 280, + "lines": 4, + "estimated_tokens": null + }, + "usage": {} + }, + "status": "failed", + "error": "AI CLI command failed with exit code 1: ", + "prompt_path": "cli/sql_prompt_attempt_1.txt", + "response_path": "cli/sql_response_attempt_1.txt", + "raw_response_path": "cli/sql_response_attempt_1.raw.txt", + "stderr_path": "cli/sql_stderr_attempt_1.txt" +} \ No newline at end of file diff --git a/Query/sql/v2/runs/v2_cli_20260502_081223_d/n1/artifacts/v2q_n1_867ca70683b596fe/cli/sql_attempt_2.metadata.json b/Query/sql/v2/runs/v2_cli_20260502_081223_d/n1/artifacts/v2q_n1_867ca70683b596fe/cli/sql_attempt_2.metadata.json new file mode 100644 index 0000000000000000000000000000000000000000..f9580a1ea7a2e7fc4a26c581255b5eae67e28b80 --- /dev/null +++ b/Query/sql/v2/runs/v2_cli_20260502_081223_d/n1/artifacts/v2q_n1_867ca70683b596fe/cli/sql_attempt_2.metadata.json @@ -0,0 +1,45 @@ +{ + "attempt": 2, + "phase": "sql_generation", + "command": "codex exec --skip-git-repo-check --disable plugins --sandbox read-only --cd \"/data/jialinzhang/SQLagent\" -m gpt-5.4 --json -", + "started_at": "2026-05-19T16:00:34.928666+00:00", + "ended_at": "2026-05-19T16:00:42.957734+00:00", + "elapsed_ms": 8029.01, + "prompt_metrics": { + "chars": 29298, + "bytes_utf8": 29298, + "lines": 790, + "estimated_tokens": null + }, + "stdout_metrics": { + "chars": 741, + "bytes_utf8": 741, + "lines": 4, + "estimated_tokens": null + }, + "stderr_metrics": { + "chars": 0, + "bytes_utf8": 0, + "lines": 0, + "estimated_tokens": null + }, + "parsed_output": { + "format": "jsonl_events", + "text_metrics": { + "chars": 401, + "bytes_utf8": 401, + "lines": 1, + "estimated_tokens": null + }, + "usage": { + "input_tokens": 20308, + "cached_input_tokens": 12032, + "output_tokens": 249, + "reasoning_output_tokens": 139 + } + }, + "prompt_path": "cli/sql_prompt_attempt_2.txt", + "response_path": "cli/sql_response_attempt_2.txt", + "raw_response_path": "cli/sql_response_attempt_2.raw.txt", + "stderr_path": "cli/sql_stderr_attempt_2.txt" +} \ No newline at end of file diff --git a/Query/sql/v2/runs/v2_cli_20260502_081223_d/n1/artifacts/v2q_n1_867ca70683b596fe/cli/sql_prompt_attempt_1.txt b/Query/sql/v2/runs/v2_cli_20260502_081223_d/n1/artifacts/v2q_n1_867ca70683b596fe/cli/sql_prompt_attempt_1.txt new file mode 100644 index 0000000000000000000000000000000000000000..4c3038fe154e6927ddfa56f05e1c7912b1b6b323 --- /dev/null +++ b/Query/sql/v2/runs/v2_cli_20260502_081223_d/n1/artifacts/v2q_n1_867ca70683b596fe/cli/sql_prompt_attempt_1.txt @@ -0,0 +1,790 @@ +You are generating one SQLite SELECT query for a single-table SQL QA task. +Return strict JSON only, with this schema: {"sql": "...", "notes": "..."}. +Rules: +- Use only the provided table and columns. +- Do not write INSERT, UPDATE, DELETE, DROP, ALTER, CREATE, PRAGMA, ATTACH, DETACH, or VACUUM. +- Prefer the planned template and bound roles when provided. +- Add a leading SQL comment exactly like: -- template_id: . +- Generate SQLite-compatible SQL. SQLite does not support PERCENTILE_CONT or STDDEV. +- Quote identifiers with double quotes. +- Return no markdown and no extra prose. + +Dataset context: +Dataset context for SQL QA: +- dataset_id: n1 +- dataset_name: Spambase +- table_name: n1 +- table_layout: single-table dataset (do not assume joins). +- row_semantics: One row is one email represented by engineered token/character frequency and capitalization-run features. +- task_type: classification +- target_column: class +- main_row_count: 4601 +- important_fields: +- word_freq_make: role=feature, type=numeric_sparse_frequency. tags=['condition_candidate', 'measure', 'rare_pattern_candidate'] desc=Frequency feature for token 'make' in an email message. +- word_freq_address: role=feature, type=numeric_sparse_frequency. tags=['condition_candidate', 'measure', 'rare_pattern_candidate'] desc=Frequency feature for token 'address' in an email message. +- word_freq_all: role=feature, type=numeric_sparse_frequency. tags=['condition_candidate', 'measure', 'rare_pattern_candidate'] desc=Frequency feature for token 'all' in an email message. +- word_freq_3d: role=feature, type=numeric_sparse_frequency. tags=['condition_candidate', 'measure', 'rare_pattern_candidate'] desc=Frequency feature for token '3d' in an email message. +- word_freq_our: role=feature, type=numeric_sparse_frequency. tags=['condition_candidate', 'measure', 'rare_pattern_candidate'] desc=Frequency feature for token 'our' in an email message. +- word_freq_over: role=feature, type=numeric_sparse_frequency. tags=['condition_candidate', 'measure', 'rare_pattern_candidate'] desc=Frequency feature for token 'over' in an email message. +- word_freq_remove: role=feature, type=numeric_sparse_frequency. tags=['condition_candidate', 'measure', 'rare_pattern_candidate'] desc=Frequency feature for token 'remove' in an email message. +- word_freq_internet: role=feature, type=numeric_sparse_frequency. tags=['condition_candidate', 'measure', 'rare_pattern_candidate'] desc=Frequency feature for token 'internet' in an email message. +- word_freq_order: role=feature, type=numeric_sparse_frequency. tags=['condition_candidate', 'measure', 'rare_pattern_candidate'] desc=Frequency feature for token 'order' in an email message. +- word_freq_mail: role=feature, type=numeric_sparse_frequency. tags=['condition_candidate', 'measure', 'rare_pattern_candidate'] desc=Frequency feature for token 'mail' in an email message. +- word_freq_receive: role=feature, type=numeric_sparse_frequency. tags=['condition_candidate', 'measure', 'rare_pattern_candidate'] desc=Frequency feature for token 'receive' in an email message. +- word_freq_will: role=feature, type=numeric_sparse_frequency. tags=['condition_candidate', 'measure', 'rare_pattern_candidate'] desc=Frequency feature for token 'will' in an email message. +- word_freq_people: role=feature, type=numeric_sparse_frequency. tags=['condition_candidate', 'measure', 'rare_pattern_candidate'] desc=Frequency feature for token 'people' in an email message. +- word_freq_report: role=feature, type=numeric_sparse_frequency. tags=['condition_candidate', 'measure', 'rare_pattern_candidate'] desc=Frequency feature for token 'report' in an email message. +- word_freq_addresses: role=feature, type=numeric_sparse_frequency. tags=['condition_candidate', 'measure', 'rare_pattern_candidate'] desc=Frequency feature for token 'addresses' in an email message. +- word_freq_free: role=feature, type=numeric_sparse_frequency. tags=['condition_candidate', 'measure', 'rare_pattern_candidate'] desc=Frequency feature for token 'free' in an email message. +- word_freq_business: role=feature, type=numeric_sparse_frequency. tags=['condition_candidate', 'measure', 'rare_pattern_candidate'] desc=Frequency feature for token 'business' in an email message. +- word_freq_email: role=feature, type=numeric_sparse_frequency. tags=['condition_candidate', 'measure', 'rare_pattern_candidate'] desc=Frequency feature for token 'email' in an email message. +- word_freq_you: role=feature, type=numeric_sparse_frequency. tags=['condition_candidate', 'measure', 'rare_pattern_candidate'] desc=Frequency feature for token 'you' in an email message. +- word_freq_credit: role=feature, type=numeric_sparse_frequency. tags=['condition_candidate', 'measure', 'rare_pattern_candidate'] desc=Frequency feature for token 'credit' in an email message. +- word_freq_your: role=feature, type=numeric_sparse_frequency. tags=['condition_candidate', 'measure', 'rare_pattern_candidate'] desc=Frequency feature for token 'your' in an email message. +- word_freq_font: role=feature, type=numeric_sparse_frequency. tags=['condition_candidate', 'measure', 'rare_pattern_candidate'] desc=Frequency feature for token 'font' in an email message. +- word_freq_000: role=feature, type=numeric_sparse_frequency. tags=['condition_candidate', 'measure', 'rare_pattern_candidate'] desc=Frequency feature for token '000' in an email message. +- word_freq_money: role=feature, type=numeric_sparse_frequency. tags=['condition_candidate', 'measure', 'rare_pattern_candidate'] desc=Frequency feature for token 'money' in an email message. +- word_freq_hp: role=feature, type=numeric_sparse_frequency. tags=['condition_candidate', 'measure', 'rare_pattern_candidate'] desc=Frequency feature for token 'hp' in an email message. +- word_freq_hpl: role=feature, type=numeric_sparse_frequency. tags=['condition_candidate', 'measure', 'rare_pattern_candidate'] desc=Frequency feature for token 'hpl' in an email message. +- word_freq_george: role=feature, type=numeric_sparse_frequency. tags=['condition_candidate', 'measure', 'rare_pattern_candidate'] desc=Frequency feature for token 'george' in an email message. +- word_freq_650: role=feature, type=numeric_sparse_frequency. tags=['condition_candidate', 'measure', 'rare_pattern_candidate'] desc=Frequency feature for token '650' in an email message. +- word_freq_lab: role=feature, type=numeric_sparse_frequency. tags=['condition_candidate', 'measure', 'rare_pattern_candidate'] desc=Frequency feature for token 'lab' in an email message. +- word_freq_labs: role=feature, type=numeric_sparse_frequency. tags=['condition_candidate', 'measure', 'rare_pattern_candidate'] desc=Frequency feature for token 'labs' in an email message. +- word_freq_telnet: role=feature, type=numeric_sparse_frequency. tags=['condition_candidate', 'measure', 'rare_pattern_candidate'] desc=Frequency feature for token 'telnet' in an email message. +- word_freq_857: role=feature, type=numeric_sparse_frequency. tags=['condition_candidate', 'measure', 'rare_pattern_candidate'] desc=Frequency feature for token '857' in an email message. +- word_freq_data: role=feature, type=numeric_sparse_frequency. tags=['condition_candidate', 'measure', 'rare_pattern_candidate'] desc=Frequency feature for token 'data' in an email message. +- word_freq_415: role=feature, type=numeric_sparse_frequency. tags=['condition_candidate', 'measure', 'rare_pattern_candidate'] desc=Frequency feature for token '415' in an email message. +- word_freq_85: role=feature, type=numeric_sparse_frequency. tags=['condition_candidate', 'measure', 'rare_pattern_candidate'] desc=Frequency feature for token '85' in an email message. +- word_freq_technology: role=feature, type=numeric_sparse_frequency. tags=['condition_candidate', 'measure', 'rare_pattern_candidate'] desc=Frequency feature for token 'technology' in an email message. +- word_freq_1999: role=feature, type=numeric_sparse_frequency. tags=['condition_candidate', 'measure', 'rare_pattern_candidate'] desc=Frequency feature for token '1999' in an email message. +- word_freq_parts: role=feature, type=numeric_sparse_frequency. tags=['condition_candidate', 'measure', 'rare_pattern_candidate'] desc=Frequency feature for token 'parts' in an email message. +- word_freq_pm: role=feature, type=numeric_sparse_frequency. tags=['condition_candidate', 'measure', 'rare_pattern_candidate'] desc=Frequency feature for token 'pm' in an email message. +- word_freq_direct: role=feature, type=numeric_sparse_frequency. tags=['condition_candidate', 'measure', 'rare_pattern_candidate'] desc=Frequency feature for token 'direct' in an email message. +- word_freq_cs: role=feature, type=numeric_sparse_frequency. tags=['condition_candidate', 'measure', 'rare_pattern_candidate'] desc=Frequency feature for token 'cs' in an email message. +- word_freq_meeting: role=feature, type=numeric_sparse_frequency. tags=['condition_candidate', 'measure', 'rare_pattern_candidate'] desc=Frequency feature for token 'meeting' in an email message. +- word_freq_original: role=feature, type=numeric_sparse_frequency. tags=['condition_candidate', 'measure', 'rare_pattern_candidate'] desc=Frequency feature for token 'original' in an email message. +- word_freq_project: role=feature, type=numeric_sparse_frequency. tags=['condition_candidate', 'measure', 'rare_pattern_candidate'] desc=Frequency feature for token 'project' in an email message. +- word_freq_re: role=feature, type=numeric_sparse_frequency. tags=['condition_candidate', 'measure', 'rare_pattern_candidate'] desc=Frequency feature for token 're' in an email message. +- word_freq_edu: role=feature, type=numeric_sparse_frequency. tags=['condition_candidate', 'measure', 'rare_pattern_candidate'] desc=Frequency feature for token 'edu' in an email message. +- word_freq_table: role=feature, type=numeric_sparse_frequency. tags=['condition_candidate', 'measure', 'rare_pattern_candidate'] desc=Frequency feature for token 'table' in an email message. +- word_freq_conference: role=feature, type=numeric_sparse_frequency. tags=['condition_candidate', 'measure', 'rare_pattern_candidate'] desc=Frequency feature for token 'conference' in an email message. +- char_freq_%3B: role=feature, type=numeric_sparse_frequency. tags=['condition_candidate', 'measure', 'rare_pattern_candidate'] desc=Character frequency feature for symbol ';'. +- char_freq_%28: role=feature, type=numeric_sparse_frequency. tags=['condition_candidate', 'measure', 'rare_pattern_candidate'] desc=Character frequency feature for symbol '('. +- char_freq_%5B: role=feature, type=numeric_sparse_frequency. tags=['condition_candidate', 'measure', 'rare_pattern_candidate'] desc=Character frequency feature for symbol '['. +- char_freq_%21: role=feature, type=numeric_sparse_frequency. tags=['condition_candidate', 'measure', 'rare_pattern_candidate'] desc=Character frequency feature for symbol '!'. +- char_freq_%24: role=feature, type=numeric_sparse_frequency. tags=['condition_candidate', 'measure', 'rare_pattern_candidate'] desc=Character frequency feature for symbol '$'. +- char_freq_%23: role=feature, type=numeric_sparse_frequency. tags=['condition_candidate', 'measure', 'rare_pattern_candidate'] desc=Character frequency feature for symbol '#'. +- capital_run_length_average: role=feature, type=numeric_heavy_tail. tags=['condition_candidate', 'measure', 'rare_pattern_candidate'] desc=Average length of uninterrupted capital-letter runs. +- capital_run_length_longest: role=feature, type=numeric_heavy_tail. tags=['condition_candidate', 'measure', 'rare_pattern_candidate'] desc=Longest uninterrupted capital-letter run. +- capital_run_length_total: role=feature, type=numeric_heavy_tail. tags=['condition_candidate', 'measure', 'rare_pattern_candidate'] desc=Total number of capital letters in runs. +- class: role=target, type=binary_target. tags=['target_candidate'] desc=Binary spam label (1=spam, 0=non-spam). +- useful_field_combinations: [['word_freq_free', 'word_freq_you', 'class'], ['char_freq_%21', 'capital_run_length_longest', 'class'], ['word_freq_remove', 'word_freq_money', 'class']] +- fields_requiring_caution: ['capital_run_length_average', 'capital_run_length_longest', 'capital_run_length_total'] +- source_url: https://www.openml.org/d/44 + +SQLite schema snapshot: +{ + "table_name": "n1", + "quoted_table_name": "\"n1\"", + "row_count": 4601, + "columns": [ + { + "name": "word_freq_make", + "type": "TEXT", + "notnull": false, + "pk": false + }, + { + "name": "word_freq_address", + "type": "TEXT", + "notnull": false, + "pk": false + }, + { + "name": "word_freq_all", + "type": "TEXT", + "notnull": false, + "pk": false + }, + { + "name": "word_freq_3d", + "type": "TEXT", + "notnull": false, + "pk": false + }, + { + "name": "word_freq_our", + "type": "TEXT", + "notnull": false, + "pk": false + }, + { + "name": "word_freq_over", + "type": "TEXT", + "notnull": false, + "pk": false + }, + { + "name": "word_freq_remove", + "type": "TEXT", + "notnull": false, + "pk": false + }, + { + "name": "word_freq_internet", + "type": "TEXT", + "notnull": false, + "pk": false + }, + { + "name": "word_freq_order", + "type": "TEXT", + "notnull": false, + "pk": false + }, + { + "name": "word_freq_mail", + "type": "TEXT", + "notnull": false, + "pk": false + }, + { + "name": "word_freq_receive", + "type": "TEXT", + "notnull": false, + "pk": false + }, + { + "name": "word_freq_will", + "type": "TEXT", + "notnull": false, + "pk": false + }, + { + "name": "word_freq_people", + "type": "TEXT", + "notnull": false, + "pk": false + }, + { + "name": "word_freq_report", + "type": "TEXT", + "notnull": false, + "pk": false + }, + { + "name": "word_freq_addresses", + "type": "TEXT", + "notnull": false, + "pk": false + }, + { + "name": "word_freq_free", + "type": "TEXT", + "notnull": false, + "pk": false + }, + { + "name": "word_freq_business", + "type": "TEXT", + "notnull": false, + "pk": false + }, + { + "name": "word_freq_email", + "type": "TEXT", + "notnull": false, + "pk": false + }, + { + "name": "word_freq_you", + "type": "TEXT", + "notnull": false, + "pk": false + }, + { + "name": "word_freq_credit", + "type": "TEXT", + "notnull": false, + "pk": false + }, + { + "name": "word_freq_your", + "type": "TEXT", + "notnull": false, + "pk": false + }, + { + "name": "word_freq_font", + "type": "TEXT", + "notnull": false, + "pk": false + }, + { + "name": "word_freq_000", + "type": "TEXT", + "notnull": false, + "pk": false + }, + { + "name": "word_freq_money", + "type": "TEXT", + "notnull": false, + "pk": false + }, + { + "name": "word_freq_hp", + "type": "TEXT", + "notnull": false, + "pk": false + }, + { + "name": "word_freq_hpl", + "type": "TEXT", + "notnull": false, + "pk": false + }, + { + "name": "word_freq_george", + "type": "TEXT", + "notnull": false, + "pk": false + }, + { + "name": "word_freq_650", + "type": "TEXT", + "notnull": false, + "pk": false + }, + { + "name": "word_freq_lab", + "type": "TEXT", + "notnull": false, + "pk": false + }, + { + "name": "word_freq_labs", + "type": "TEXT", + "notnull": false, + "pk": false + }, + { + "name": "word_freq_telnet", + "type": "TEXT", + "notnull": false, + "pk": false + }, + { + "name": "word_freq_857", + "type": "TEXT", + "notnull": false, + "pk": false + }, + { + "name": "word_freq_data", + "type": "TEXT", + "notnull": false, + "pk": false + }, + { + "name": "word_freq_415", + "type": "TEXT", + "notnull": false, + "pk": false + }, + { + "name": "word_freq_85", + "type": "TEXT", + "notnull": false, + "pk": false + }, + { + "name": "word_freq_technology", + "type": "TEXT", + "notnull": false, + "pk": false + }, + { + "name": "word_freq_1999", + "type": "TEXT", + "notnull": false, + "pk": false + }, + { + "name": "word_freq_parts", + "type": "TEXT", + "notnull": false, + "pk": false + }, + { + "name": "word_freq_pm", + "type": "TEXT", + "notnull": false, + "pk": false + }, + { + "name": "word_freq_direct", + "type": "TEXT", + "notnull": false, + "pk": false + }, + { + "name": "word_freq_cs", + "type": "TEXT", + "notnull": false, + "pk": false + }, + { + "name": "word_freq_meeting", + "type": "TEXT", + "notnull": false, + "pk": false + }, + { + "name": "word_freq_original", + "type": "TEXT", + "notnull": false, + "pk": false + }, + { + "name": "word_freq_project", + "type": "TEXT", + "notnull": false, + "pk": false + }, + { + "name": "word_freq_re", + "type": "TEXT", + "notnull": false, + "pk": false + }, + { + "name": "word_freq_edu", + "type": "TEXT", + "notnull": false, + "pk": false + }, + { + "name": "word_freq_table", + "type": "TEXT", + "notnull": false, + "pk": false + }, + { + "name": "word_freq_conference", + "type": "TEXT", + "notnull": false, + "pk": false + }, + { + "name": "char_freq_%3B", + "type": "TEXT", + "notnull": false, + "pk": false + }, + { + "name": "char_freq_%28", + "type": "TEXT", + "notnull": false, + "pk": false + }, + { + "name": "char_freq_%5B", + "type": "TEXT", + "notnull": false, + "pk": false + }, + { + "name": "char_freq_%21", + "type": "TEXT", + "notnull": false, + "pk": false + }, + { + "name": "char_freq_%24", + "type": "TEXT", + "notnull": false, + "pk": false + }, + { + "name": "char_freq_%23", + "type": "TEXT", + "notnull": false, + "pk": false + }, + { + "name": "capital_run_length_average", + "type": "TEXT", + "notnull": false, + "pk": false + }, + { + "name": "capital_run_length_longest", + "type": "TEXT", + "notnull": false, + "pk": false + }, + { + "name": "capital_run_length_total", + "type": "TEXT", + "notnull": false, + "pk": false + }, + { + "name": "class", + "type": "TEXT", + "notnull": false, + "pk": false + } + ], + "sample_rows": [ + { + "word_freq_make": "0", + "word_freq_address": "0.64", + "word_freq_all": "0.64", + "word_freq_3d": "0", + "word_freq_our": "0.32", + "word_freq_over": "0", + "word_freq_remove": "0", + "word_freq_internet": "0", + "word_freq_order": "0", + "word_freq_mail": "0", + "word_freq_receive": "0", + "word_freq_will": "0.64", + "word_freq_people": "0", + "word_freq_report": "0", + "word_freq_addresses": "0", + "word_freq_free": "0.32", + "word_freq_business": "0", + "word_freq_email": "1.29", + "word_freq_you": "1.93", + "word_freq_credit": "0", + "word_freq_your": "0.96", + "word_freq_font": "0", + "word_freq_000": "0", + "word_freq_money": "0", + "word_freq_hp": "0", + "word_freq_hpl": "0", + "word_freq_george": "0", + "word_freq_650": "0", + "word_freq_lab": "0", + "word_freq_labs": "0", + "word_freq_telnet": "0", + "word_freq_857": "0", + "word_freq_data": "0", + "word_freq_415": "0", + "word_freq_85": "0", + "word_freq_technology": "0", + "word_freq_1999": "0", + "word_freq_parts": "0", + "word_freq_pm": "0", + "word_freq_direct": "0", + "word_freq_cs": "0", + "word_freq_meeting": "0", + "word_freq_original": "0", + "word_freq_project": "0", + "word_freq_re": "0", + "word_freq_edu": "0", + "word_freq_table": "0", + "word_freq_conference": "0", + "char_freq_%3B": "0", + "char_freq_%28": "0", + "char_freq_%5B": "0", + "char_freq_%21": "0.778", + "char_freq_%24": "0", + "char_freq_%23": "0", + "capital_run_length_average": "3.756", + "capital_run_length_longest": "61", + "capital_run_length_total": "278", + "class": "1" + }, + { + "word_freq_make": "0.21", + "word_freq_address": "0.28", + "word_freq_all": "0.5", + "word_freq_3d": "0", + "word_freq_our": "0.14", + "word_freq_over": "0.28", + "word_freq_remove": "0.21", + "word_freq_internet": "0.07", + "word_freq_order": "0", + "word_freq_mail": "0.94", + "word_freq_receive": "0.21", + "word_freq_will": "0.79", + "word_freq_people": "0.65", + "word_freq_report": "0.21", + "word_freq_addresses": "0.14", + "word_freq_free": "0.14", + "word_freq_business": "0.07", + "word_freq_email": "0.28", + "word_freq_you": "3.47", + "word_freq_credit": "0", + "word_freq_your": "1.59", + "word_freq_font": "0", + "word_freq_000": "0.43", + "word_freq_money": "0.43", + "word_freq_hp": "0", + "word_freq_hpl": "0", + "word_freq_george": "0", + "word_freq_650": "0", + "word_freq_lab": "0", + "word_freq_labs": "0", + "word_freq_telnet": "0", + "word_freq_857": "0", + "word_freq_data": "0", + "word_freq_415": "0", + "word_freq_85": "0", + "word_freq_technology": "0", + "word_freq_1999": "0.07", + "word_freq_parts": "0", + "word_freq_pm": "0", + "word_freq_direct": "0", + "word_freq_cs": "0", + "word_freq_meeting": "0", + "word_freq_original": "0", + "word_freq_project": "0", + "word_freq_re": "0", + "word_freq_edu": "0", + "word_freq_table": "0", + "word_freq_conference": "0", + "char_freq_%3B": "0", + "char_freq_%28": "0.132", + "char_freq_%5B": "0", + "char_freq_%21": "0.372", + "char_freq_%24": "0.18", + "char_freq_%23": "0.048", + "capital_run_length_average": "5.114", + "capital_run_length_longest": "101", + "capital_run_length_total": "1028", + "class": "1" + }, + { + "word_freq_make": "0.06", + "word_freq_address": "0", + "word_freq_all": "0.71", + "word_freq_3d": "0", + "word_freq_our": "1.23", + "word_freq_over": "0.19", + "word_freq_remove": "0.19", + "word_freq_internet": "0.12", + "word_freq_order": "0.64", + "word_freq_mail": "0.25", + "word_freq_receive": "0.38", + "word_freq_will": "0.45", + "word_freq_people": "0.12", + "word_freq_report": "0", + "word_freq_addresses": "1.75", + "word_freq_free": "0.06", + "word_freq_business": "0.06", + "word_freq_email": "1.03", + "word_freq_you": "1.36", + "word_freq_credit": "0.32", + "word_freq_your": "0.51", + "word_freq_font": "0", + "word_freq_000": "1.16", + "word_freq_money": "0.06", + "word_freq_hp": "0", + "word_freq_hpl": "0", + "word_freq_george": "0", + "word_freq_650": "0", + "word_freq_lab": "0", + "word_freq_labs": "0", + "word_freq_telnet": "0", + "word_freq_857": "0", + "word_freq_data": "0", + "word_freq_415": "0", + "word_freq_85": "0", + "word_freq_technology": "0", + "word_freq_1999": "0", + "word_freq_parts": "0", + "word_freq_pm": "0", + "word_freq_direct": "0.06", + "word_freq_cs": "0", + "word_freq_meeting": "0", + "word_freq_original": "0.12", + "word_freq_project": "0", + "word_freq_re": "0.06", + "word_freq_edu": "0.06", + "word_freq_table": "0", + "word_freq_conference": "0", + "char_freq_%3B": "0.01", + "char_freq_%28": "0.143", + "char_freq_%5B": "0", + "char_freq_%21": "0.276", + "char_freq_%24": "0.184", + "char_freq_%23": "0.01", + "capital_run_length_average": "9.821", + "capital_run_length_longest": "485", + "capital_run_length_total": "2259", + "class": "1" + }, + { + "word_freq_make": "0", + "word_freq_address": "0", + "word_freq_all": "0", + "word_freq_3d": "0", + "word_freq_our": "0.63", + "word_freq_over": "0", + "word_freq_remove": "0.31", + "word_freq_internet": "0.63", + "word_freq_order": "0.31", + "word_freq_mail": "0.63", + "word_freq_receive": "0.31", + "word_freq_will": "0.31", + "word_freq_people": "0.31", + "word_freq_report": "0", + "word_freq_addresses": "0", + "word_freq_free": "0.31", + "word_freq_business": "0", + "word_freq_email": "0", + "word_freq_you": "3.18", + "word_freq_credit": "0", + "word_freq_your": "0.31", + "word_freq_font": "0", + "word_freq_000": "0", + "word_freq_money": "0", + "word_freq_hp": "0", + "word_freq_hpl": "0", + "word_freq_george": "0", + "word_freq_650": "0", + "word_freq_lab": "0", + "word_freq_labs": "0", + "word_freq_telnet": "0", + "word_freq_857": "0", + "word_freq_data": "0", + "word_freq_415": "0", + "word_freq_85": "0", + "word_freq_technology": "0", + "word_freq_1999": "0", + "word_freq_parts": "0", + "word_freq_pm": "0", + "word_freq_direct": "0", + "word_freq_cs": "0", + "word_freq_meeting": "0", + "word_freq_original": "0", + "word_freq_project": "0", + "word_freq_re": "0", + "word_freq_edu": "0", + "word_freq_table": "0", + "word_freq_conference": "0", + "char_freq_%3B": "0", + "char_freq_%28": "0.137", + "char_freq_%5B": "0", + "char_freq_%21": "0.137", + "char_freq_%24": "0", + "char_freq_%23": "0", + "capital_run_length_average": "3.537", + "capital_run_length_longest": "40", + "capital_run_length_total": "191", + "class": "1" + }, + { + "word_freq_make": "0", + "word_freq_address": "0", + "word_freq_all": "0", + "word_freq_3d": "0", + "word_freq_our": "0.63", + "word_freq_over": "0", + "word_freq_remove": "0.31", + "word_freq_internet": "0.63", + "word_freq_order": "0.31", + "word_freq_mail": "0.63", + "word_freq_receive": "0.31", + "word_freq_will": "0.31", + "word_freq_people": "0.31", + "word_freq_report": "0", + "word_freq_addresses": "0", + "word_freq_free": "0.31", + "word_freq_business": "0", + "word_freq_email": "0", + "word_freq_you": "3.18", + "word_freq_credit": "0", + "word_freq_your": "0.31", + "word_freq_font": "0", + "word_freq_000": "0", + "word_freq_money": "0", + "word_freq_hp": "0", + "word_freq_hpl": "0", + "word_freq_george": "0", + "word_freq_650": "0", + "word_freq_lab": "0", + "word_freq_labs": "0", + "word_freq_telnet": "0", + "word_freq_857": "0", + "word_freq_data": "0", + "word_freq_415": "0", + "word_freq_85": "0", + "word_freq_technology": "0", + "word_freq_1999": "0", + "word_freq_parts": "0", + "word_freq_pm": "0", + "word_freq_direct": "0", + "word_freq_cs": "0", + "word_freq_meeting": "0", + "word_freq_original": "0", + "word_freq_project": "0", + "word_freq_re": "0", + "word_freq_edu": "0", + "word_freq_table": "0", + "word_freq_conference": "0", + "char_freq_%3B": "0", + "char_freq_%28": "0.135", + "char_freq_%5B": "0", + "char_freq_%21": "0.135", + "char_freq_%24": "0", + "char_freq_%23": "0", + "capital_run_length_average": "3.537", + "capital_run_length_longest": "40", + "capital_run_length_total": "191", + "class": "1" + } + ] +} + +Shortlisted templates: +[ + { + "template_id": "tpl_threshold_rarity_cdf", + "template_name": "Threshold Rarity CDF", + "primary_family": "tail_rarity_structure", + "portability": "yes", + "sql_skeleton": "SELECT AVG(CASE WHEN {measure_col} <= {measure_threshold} THEN 1 ELSE 0 END) AS empirical_cdf_at_threshold\nFROM {table};", + "required_roles": [ + "measure_col" + ] + } +] + +Problem instance: +{ + "dataset_id": "n1", + "question": "Use template Threshold Rarity CDF to probe tail_set_consistency with semantic role rare_extreme_view. Focus on measure_col=char_freq_%24.", + "planned_template_id": "tpl_threshold_rarity_cdf", + "bindings": { + "measure_col": "char_freq_%24", + "top_k": 14, + "top_n": 4, + "num_tiles": 10, + "percentile_value": 0.9, + "z_threshold": 2.0, + "fraction_threshold": 0.1, + "baseline_multiplier": 1.5, + "baseline_fraction": 0.1, + "min_group_size": 5, + "min_support": 5, + "measure_threshold": 0.052, + "time_grain": "month", + "lookback_rows": 3, + "current_period_start": "'2024-01-01'", + "current_period_end": "'2024-04-01'", + "previous_period_start": "'2023-10-01'", + "previous_period_end": "'2024-01-01'", + "drift_ratio_threshold": 0.8 + }, + "can_vary": [], + "must_fix": [], + "runtime_sql_skeleton": "SELECT AVG(CASE WHEN {measure_col} <= {measure_threshold} THEN 1 ELSE 0 END) AS empirical_cdf_at_threshold\nFROM {table};" +} + +Repair context: +{} diff --git a/Query/sql/v2/runs/v2_cli_20260502_081223_d/n1/artifacts/v2q_n1_867ca70683b596fe/cli/sql_prompt_attempt_2.txt b/Query/sql/v2/runs/v2_cli_20260502_081223_d/n1/artifacts/v2q_n1_867ca70683b596fe/cli/sql_prompt_attempt_2.txt new file mode 100644 index 0000000000000000000000000000000000000000..4c3038fe154e6927ddfa56f05e1c7912b1b6b323 --- /dev/null +++ b/Query/sql/v2/runs/v2_cli_20260502_081223_d/n1/artifacts/v2q_n1_867ca70683b596fe/cli/sql_prompt_attempt_2.txt @@ -0,0 +1,790 @@ +You are generating one SQLite SELECT query for a single-table SQL QA task. +Return strict JSON only, with this schema: {"sql": "...", "notes": "..."}. +Rules: +- Use only the provided table and columns. +- Do not write INSERT, UPDATE, DELETE, DROP, ALTER, CREATE, PRAGMA, ATTACH, DETACH, or VACUUM. +- Prefer the planned template and bound roles when provided. +- Add a leading SQL comment exactly like: -- template_id: . +- Generate SQLite-compatible SQL. SQLite does not support PERCENTILE_CONT or STDDEV. +- Quote identifiers with double quotes. +- Return no markdown and no extra prose. + +Dataset context: +Dataset context for SQL QA: +- dataset_id: n1 +- dataset_name: Spambase +- table_name: n1 +- table_layout: single-table dataset (do not assume joins). +- row_semantics: One row is one email represented by engineered token/character frequency and capitalization-run features. +- task_type: classification +- target_column: class +- main_row_count: 4601 +- important_fields: +- word_freq_make: role=feature, type=numeric_sparse_frequency. tags=['condition_candidate', 'measure', 'rare_pattern_candidate'] desc=Frequency feature for token 'make' in an email message. +- word_freq_address: role=feature, type=numeric_sparse_frequency. tags=['condition_candidate', 'measure', 'rare_pattern_candidate'] desc=Frequency feature for token 'address' in an email message. +- word_freq_all: role=feature, type=numeric_sparse_frequency. tags=['condition_candidate', 'measure', 'rare_pattern_candidate'] desc=Frequency feature for token 'all' in an email message. +- word_freq_3d: role=feature, type=numeric_sparse_frequency. tags=['condition_candidate', 'measure', 'rare_pattern_candidate'] desc=Frequency feature for token '3d' in an email message. +- word_freq_our: role=feature, type=numeric_sparse_frequency. tags=['condition_candidate', 'measure', 'rare_pattern_candidate'] desc=Frequency feature for token 'our' in an email message. +- word_freq_over: role=feature, type=numeric_sparse_frequency. tags=['condition_candidate', 'measure', 'rare_pattern_candidate'] desc=Frequency feature for token 'over' in an email message. +- word_freq_remove: role=feature, type=numeric_sparse_frequency. tags=['condition_candidate', 'measure', 'rare_pattern_candidate'] desc=Frequency feature for token 'remove' in an email message. +- word_freq_internet: role=feature, type=numeric_sparse_frequency. tags=['condition_candidate', 'measure', 'rare_pattern_candidate'] desc=Frequency feature for token 'internet' in an email message. +- word_freq_order: role=feature, type=numeric_sparse_frequency. tags=['condition_candidate', 'measure', 'rare_pattern_candidate'] desc=Frequency feature for token 'order' in an email message. +- word_freq_mail: role=feature, type=numeric_sparse_frequency. tags=['condition_candidate', 'measure', 'rare_pattern_candidate'] desc=Frequency feature for token 'mail' in an email message. +- word_freq_receive: role=feature, type=numeric_sparse_frequency. tags=['condition_candidate', 'measure', 'rare_pattern_candidate'] desc=Frequency feature for token 'receive' in an email message. +- word_freq_will: role=feature, type=numeric_sparse_frequency. tags=['condition_candidate', 'measure', 'rare_pattern_candidate'] desc=Frequency feature for token 'will' in an email message. +- word_freq_people: role=feature, type=numeric_sparse_frequency. tags=['condition_candidate', 'measure', 'rare_pattern_candidate'] desc=Frequency feature for token 'people' in an email message. +- word_freq_report: role=feature, type=numeric_sparse_frequency. tags=['condition_candidate', 'measure', 'rare_pattern_candidate'] desc=Frequency feature for token 'report' in an email message. +- word_freq_addresses: role=feature, type=numeric_sparse_frequency. tags=['condition_candidate', 'measure', 'rare_pattern_candidate'] desc=Frequency feature for token 'addresses' in an email message. +- word_freq_free: role=feature, type=numeric_sparse_frequency. tags=['condition_candidate', 'measure', 'rare_pattern_candidate'] desc=Frequency feature for token 'free' in an email message. +- word_freq_business: role=feature, type=numeric_sparse_frequency. tags=['condition_candidate', 'measure', 'rare_pattern_candidate'] desc=Frequency feature for token 'business' in an email message. +- word_freq_email: role=feature, type=numeric_sparse_frequency. tags=['condition_candidate', 'measure', 'rare_pattern_candidate'] desc=Frequency feature for token 'email' in an email message. +- word_freq_you: role=feature, type=numeric_sparse_frequency. tags=['condition_candidate', 'measure', 'rare_pattern_candidate'] desc=Frequency feature for token 'you' in an email message. +- word_freq_credit: role=feature, type=numeric_sparse_frequency. tags=['condition_candidate', 'measure', 'rare_pattern_candidate'] desc=Frequency feature for token 'credit' in an email message. +- word_freq_your: role=feature, type=numeric_sparse_frequency. tags=['condition_candidate', 'measure', 'rare_pattern_candidate'] desc=Frequency feature for token 'your' in an email message. +- word_freq_font: role=feature, type=numeric_sparse_frequency. tags=['condition_candidate', 'measure', 'rare_pattern_candidate'] desc=Frequency feature for token 'font' in an email message. +- word_freq_000: role=feature, type=numeric_sparse_frequency. tags=['condition_candidate', 'measure', 'rare_pattern_candidate'] desc=Frequency feature for token '000' in an email message. +- word_freq_money: role=feature, type=numeric_sparse_frequency. tags=['condition_candidate', 'measure', 'rare_pattern_candidate'] desc=Frequency feature for token 'money' in an email message. +- word_freq_hp: role=feature, type=numeric_sparse_frequency. tags=['condition_candidate', 'measure', 'rare_pattern_candidate'] desc=Frequency feature for token 'hp' in an email message. +- word_freq_hpl: role=feature, type=numeric_sparse_frequency. tags=['condition_candidate', 'measure', 'rare_pattern_candidate'] desc=Frequency feature for token 'hpl' in an email message. +- word_freq_george: role=feature, type=numeric_sparse_frequency. tags=['condition_candidate', 'measure', 'rare_pattern_candidate'] desc=Frequency feature for token 'george' in an email message. +- word_freq_650: role=feature, type=numeric_sparse_frequency. tags=['condition_candidate', 'measure', 'rare_pattern_candidate'] desc=Frequency feature for token '650' in an email message. +- word_freq_lab: role=feature, type=numeric_sparse_frequency. tags=['condition_candidate', 'measure', 'rare_pattern_candidate'] desc=Frequency feature for token 'lab' in an email message. +- word_freq_labs: role=feature, type=numeric_sparse_frequency. tags=['condition_candidate', 'measure', 'rare_pattern_candidate'] desc=Frequency feature for token 'labs' in an email message. +- word_freq_telnet: role=feature, type=numeric_sparse_frequency. tags=['condition_candidate', 'measure', 'rare_pattern_candidate'] desc=Frequency feature for token 'telnet' in an email message. +- word_freq_857: role=feature, type=numeric_sparse_frequency. tags=['condition_candidate', 'measure', 'rare_pattern_candidate'] desc=Frequency feature for token '857' in an email message. +- word_freq_data: role=feature, type=numeric_sparse_frequency. tags=['condition_candidate', 'measure', 'rare_pattern_candidate'] desc=Frequency feature for token 'data' in an email message. +- word_freq_415: role=feature, type=numeric_sparse_frequency. tags=['condition_candidate', 'measure', 'rare_pattern_candidate'] desc=Frequency feature for token '415' in an email message. +- word_freq_85: role=feature, type=numeric_sparse_frequency. tags=['condition_candidate', 'measure', 'rare_pattern_candidate'] desc=Frequency feature for token '85' in an email message. +- word_freq_technology: role=feature, type=numeric_sparse_frequency. tags=['condition_candidate', 'measure', 'rare_pattern_candidate'] desc=Frequency feature for token 'technology' in an email message. +- word_freq_1999: role=feature, type=numeric_sparse_frequency. tags=['condition_candidate', 'measure', 'rare_pattern_candidate'] desc=Frequency feature for token '1999' in an email message. +- word_freq_parts: role=feature, type=numeric_sparse_frequency. tags=['condition_candidate', 'measure', 'rare_pattern_candidate'] desc=Frequency feature for token 'parts' in an email message. +- word_freq_pm: role=feature, type=numeric_sparse_frequency. tags=['condition_candidate', 'measure', 'rare_pattern_candidate'] desc=Frequency feature for token 'pm' in an email message. +- word_freq_direct: role=feature, type=numeric_sparse_frequency. tags=['condition_candidate', 'measure', 'rare_pattern_candidate'] desc=Frequency feature for token 'direct' in an email message. +- word_freq_cs: role=feature, type=numeric_sparse_frequency. tags=['condition_candidate', 'measure', 'rare_pattern_candidate'] desc=Frequency feature for token 'cs' in an email message. +- word_freq_meeting: role=feature, type=numeric_sparse_frequency. tags=['condition_candidate', 'measure', 'rare_pattern_candidate'] desc=Frequency feature for token 'meeting' in an email message. +- word_freq_original: role=feature, type=numeric_sparse_frequency. tags=['condition_candidate', 'measure', 'rare_pattern_candidate'] desc=Frequency feature for token 'original' in an email message. +- word_freq_project: role=feature, type=numeric_sparse_frequency. tags=['condition_candidate', 'measure', 'rare_pattern_candidate'] desc=Frequency feature for token 'project' in an email message. +- word_freq_re: role=feature, type=numeric_sparse_frequency. tags=['condition_candidate', 'measure', 'rare_pattern_candidate'] desc=Frequency feature for token 're' in an email message. +- word_freq_edu: role=feature, type=numeric_sparse_frequency. tags=['condition_candidate', 'measure', 'rare_pattern_candidate'] desc=Frequency feature for token 'edu' in an email message. +- word_freq_table: role=feature, type=numeric_sparse_frequency. tags=['condition_candidate', 'measure', 'rare_pattern_candidate'] desc=Frequency feature for token 'table' in an email message. +- word_freq_conference: role=feature, type=numeric_sparse_frequency. tags=['condition_candidate', 'measure', 'rare_pattern_candidate'] desc=Frequency feature for token 'conference' in an email message. +- char_freq_%3B: role=feature, type=numeric_sparse_frequency. tags=['condition_candidate', 'measure', 'rare_pattern_candidate'] desc=Character frequency feature for symbol ';'. +- char_freq_%28: role=feature, type=numeric_sparse_frequency. tags=['condition_candidate', 'measure', 'rare_pattern_candidate'] desc=Character frequency feature for symbol '('. +- char_freq_%5B: role=feature, type=numeric_sparse_frequency. tags=['condition_candidate', 'measure', 'rare_pattern_candidate'] desc=Character frequency feature for symbol '['. +- char_freq_%21: role=feature, type=numeric_sparse_frequency. tags=['condition_candidate', 'measure', 'rare_pattern_candidate'] desc=Character frequency feature for symbol '!'. +- char_freq_%24: role=feature, type=numeric_sparse_frequency. tags=['condition_candidate', 'measure', 'rare_pattern_candidate'] desc=Character frequency feature for symbol '$'. +- char_freq_%23: role=feature, type=numeric_sparse_frequency. tags=['condition_candidate', 'measure', 'rare_pattern_candidate'] desc=Character frequency feature for symbol '#'. +- capital_run_length_average: role=feature, type=numeric_heavy_tail. tags=['condition_candidate', 'measure', 'rare_pattern_candidate'] desc=Average length of uninterrupted capital-letter runs. +- capital_run_length_longest: role=feature, type=numeric_heavy_tail. tags=['condition_candidate', 'measure', 'rare_pattern_candidate'] desc=Longest uninterrupted capital-letter run. +- capital_run_length_total: role=feature, type=numeric_heavy_tail. tags=['condition_candidate', 'measure', 'rare_pattern_candidate'] desc=Total number of capital letters in runs. +- class: role=target, type=binary_target. tags=['target_candidate'] desc=Binary spam label (1=spam, 0=non-spam). +- useful_field_combinations: [['word_freq_free', 'word_freq_you', 'class'], ['char_freq_%21', 'capital_run_length_longest', 'class'], ['word_freq_remove', 'word_freq_money', 'class']] +- fields_requiring_caution: ['capital_run_length_average', 'capital_run_length_longest', 'capital_run_length_total'] +- source_url: https://www.openml.org/d/44 + +SQLite schema snapshot: +{ + "table_name": "n1", + "quoted_table_name": "\"n1\"", + "row_count": 4601, + "columns": [ + { + "name": "word_freq_make", + "type": "TEXT", + "notnull": false, + "pk": false + }, + { + "name": "word_freq_address", + "type": "TEXT", + "notnull": false, + "pk": false + }, + { + "name": "word_freq_all", + "type": "TEXT", + "notnull": false, + "pk": false + }, + { + "name": "word_freq_3d", + "type": "TEXT", + "notnull": false, + "pk": false + }, + { + "name": "word_freq_our", + "type": "TEXT", + "notnull": false, + "pk": false + }, + { + "name": "word_freq_over", + "type": "TEXT", + "notnull": false, + "pk": false + }, + { + "name": "word_freq_remove", + "type": "TEXT", + "notnull": false, + "pk": false + }, + { + "name": "word_freq_internet", + "type": "TEXT", + "notnull": false, + "pk": false + }, + { + "name": "word_freq_order", + "type": "TEXT", + "notnull": false, + "pk": false + }, + { + "name": "word_freq_mail", + "type": "TEXT", + "notnull": false, + "pk": false + }, + { + "name": "word_freq_receive", + "type": "TEXT", + "notnull": false, + "pk": false + }, + { + "name": "word_freq_will", + "type": "TEXT", + "notnull": false, + "pk": false + }, + { + "name": "word_freq_people", + "type": "TEXT", + "notnull": false, + "pk": false + }, + { + "name": "word_freq_report", + "type": "TEXT", + "notnull": false, + "pk": false + }, + { + "name": "word_freq_addresses", + "type": "TEXT", + "notnull": false, + "pk": false + }, + { + "name": "word_freq_free", + "type": "TEXT", + "notnull": false, + "pk": false + }, + { + "name": "word_freq_business", + "type": "TEXT", + "notnull": false, + "pk": false + }, + { + "name": "word_freq_email", + "type": "TEXT", + "notnull": false, + "pk": false + }, + { + "name": "word_freq_you", + "type": "TEXT", + "notnull": false, + "pk": false + }, + { + "name": "word_freq_credit", + "type": "TEXT", + "notnull": false, + "pk": false + }, + { + "name": "word_freq_your", + "type": "TEXT", + "notnull": false, + "pk": false + }, + { + "name": "word_freq_font", + "type": "TEXT", + "notnull": false, + "pk": false + }, + { + "name": "word_freq_000", + "type": "TEXT", + "notnull": false, + "pk": false + }, + { + "name": "word_freq_money", + "type": "TEXT", + "notnull": false, + "pk": false + }, + { + "name": "word_freq_hp", + "type": "TEXT", + "notnull": false, + "pk": false + }, + { + "name": "word_freq_hpl", + "type": "TEXT", + "notnull": false, + "pk": false + }, + { + "name": "word_freq_george", + "type": "TEXT", + "notnull": false, + "pk": false + }, + { + "name": "word_freq_650", + "type": "TEXT", + "notnull": false, + "pk": false + }, + { + "name": "word_freq_lab", + "type": "TEXT", + "notnull": false, + "pk": false + }, + { + "name": "word_freq_labs", + "type": "TEXT", + "notnull": false, + "pk": false + }, + { + "name": "word_freq_telnet", + "type": "TEXT", + "notnull": false, + "pk": false + }, + { + "name": "word_freq_857", + "type": "TEXT", + "notnull": false, + "pk": false + }, + { + "name": "word_freq_data", + "type": "TEXT", + "notnull": false, + "pk": false + }, + { + "name": "word_freq_415", + "type": "TEXT", + "notnull": false, + "pk": false + }, + { + "name": "word_freq_85", + "type": "TEXT", + "notnull": false, + "pk": false + }, + { + "name": "word_freq_technology", + "type": "TEXT", + "notnull": false, + "pk": false + }, + { + "name": "word_freq_1999", + "type": "TEXT", + "notnull": false, + "pk": false + }, + { + "name": "word_freq_parts", + "type": "TEXT", + "notnull": false, + "pk": false + }, + { + "name": "word_freq_pm", + "type": "TEXT", + "notnull": false, + "pk": false + }, + { + "name": "word_freq_direct", + "type": "TEXT", + "notnull": false, + "pk": false + }, + { + "name": "word_freq_cs", + "type": "TEXT", + "notnull": false, + "pk": false + }, + { + "name": "word_freq_meeting", + "type": "TEXT", + "notnull": false, + "pk": false + }, + { + "name": "word_freq_original", + "type": "TEXT", + "notnull": false, + "pk": false + }, + { + "name": "word_freq_project", + "type": "TEXT", + "notnull": false, + "pk": false + }, + { + "name": "word_freq_re", + "type": "TEXT", + "notnull": false, + "pk": false + }, + { + "name": "word_freq_edu", + "type": "TEXT", + "notnull": false, + "pk": false + }, + { + "name": "word_freq_table", + "type": "TEXT", + "notnull": false, + "pk": false + }, + { + "name": "word_freq_conference", + "type": "TEXT", + "notnull": false, + "pk": false + }, + { + "name": "char_freq_%3B", + "type": "TEXT", + "notnull": false, + "pk": false + }, + { + "name": "char_freq_%28", + "type": "TEXT", + "notnull": false, + "pk": false + }, + { + "name": "char_freq_%5B", + "type": "TEXT", + "notnull": false, + "pk": false + }, + { + "name": "char_freq_%21", + "type": "TEXT", + "notnull": false, + "pk": false + }, + { + "name": "char_freq_%24", + "type": "TEXT", + "notnull": false, + "pk": false + }, + { + "name": "char_freq_%23", + "type": "TEXT", + "notnull": false, + "pk": false + }, + { + "name": "capital_run_length_average", + "type": "TEXT", + "notnull": false, + "pk": false + }, + { + "name": "capital_run_length_longest", + "type": "TEXT", + "notnull": false, + "pk": false + }, + { + "name": "capital_run_length_total", + "type": "TEXT", + "notnull": false, + "pk": false + }, + { + "name": "class", + "type": "TEXT", + "notnull": false, + "pk": false + } + ], + "sample_rows": [ + { + "word_freq_make": "0", + "word_freq_address": "0.64", + "word_freq_all": "0.64", + "word_freq_3d": "0", + "word_freq_our": "0.32", + "word_freq_over": "0", + "word_freq_remove": "0", + "word_freq_internet": "0", + "word_freq_order": "0", + "word_freq_mail": "0", + "word_freq_receive": "0", + "word_freq_will": "0.64", + "word_freq_people": "0", + "word_freq_report": "0", + "word_freq_addresses": "0", + "word_freq_free": "0.32", + "word_freq_business": "0", + "word_freq_email": "1.29", + "word_freq_you": "1.93", + "word_freq_credit": "0", + "word_freq_your": "0.96", + "word_freq_font": "0", + "word_freq_000": "0", + "word_freq_money": "0", + "word_freq_hp": "0", + "word_freq_hpl": "0", + "word_freq_george": "0", + "word_freq_650": "0", + "word_freq_lab": "0", + "word_freq_labs": "0", + "word_freq_telnet": "0", + "word_freq_857": "0", + "word_freq_data": "0", + "word_freq_415": "0", + "word_freq_85": "0", + "word_freq_technology": "0", + "word_freq_1999": "0", + "word_freq_parts": "0", + "word_freq_pm": "0", + "word_freq_direct": "0", + "word_freq_cs": "0", + "word_freq_meeting": "0", + "word_freq_original": "0", + "word_freq_project": "0", + "word_freq_re": "0", + "word_freq_edu": "0", + "word_freq_table": "0", + "word_freq_conference": "0", + "char_freq_%3B": "0", + "char_freq_%28": "0", + "char_freq_%5B": "0", + "char_freq_%21": "0.778", + "char_freq_%24": "0", + "char_freq_%23": "0", + "capital_run_length_average": "3.756", + "capital_run_length_longest": "61", + "capital_run_length_total": "278", + "class": "1" + }, + { + "word_freq_make": "0.21", + "word_freq_address": "0.28", + "word_freq_all": "0.5", + "word_freq_3d": "0", + "word_freq_our": "0.14", + "word_freq_over": "0.28", + "word_freq_remove": "0.21", + "word_freq_internet": "0.07", + "word_freq_order": "0", + "word_freq_mail": "0.94", + "word_freq_receive": "0.21", + "word_freq_will": "0.79", + "word_freq_people": "0.65", + "word_freq_report": "0.21", + "word_freq_addresses": "0.14", + "word_freq_free": "0.14", + "word_freq_business": "0.07", + "word_freq_email": "0.28", + "word_freq_you": "3.47", + "word_freq_credit": "0", + "word_freq_your": "1.59", + "word_freq_font": "0", + "word_freq_000": "0.43", + "word_freq_money": "0.43", + "word_freq_hp": "0", + "word_freq_hpl": "0", + "word_freq_george": "0", + "word_freq_650": "0", + "word_freq_lab": "0", + "word_freq_labs": "0", + "word_freq_telnet": "0", + "word_freq_857": "0", + "word_freq_data": "0", + "word_freq_415": "0", + "word_freq_85": "0", + "word_freq_technology": "0", + "word_freq_1999": "0.07", + "word_freq_parts": "0", + "word_freq_pm": "0", + "word_freq_direct": "0", + "word_freq_cs": "0", + "word_freq_meeting": "0", + "word_freq_original": "0", + "word_freq_project": "0", + "word_freq_re": "0", + "word_freq_edu": "0", + "word_freq_table": "0", + "word_freq_conference": "0", + "char_freq_%3B": "0", + "char_freq_%28": "0.132", + "char_freq_%5B": "0", + "char_freq_%21": "0.372", + "char_freq_%24": "0.18", + "char_freq_%23": "0.048", + "capital_run_length_average": "5.114", + "capital_run_length_longest": "101", + "capital_run_length_total": "1028", + "class": "1" + }, + { + "word_freq_make": "0.06", + "word_freq_address": "0", + "word_freq_all": "0.71", + "word_freq_3d": "0", + "word_freq_our": "1.23", + "word_freq_over": "0.19", + "word_freq_remove": "0.19", + "word_freq_internet": "0.12", + "word_freq_order": "0.64", + "word_freq_mail": "0.25", + "word_freq_receive": "0.38", + "word_freq_will": "0.45", + "word_freq_people": "0.12", + "word_freq_report": "0", + "word_freq_addresses": "1.75", + "word_freq_free": "0.06", + "word_freq_business": "0.06", + "word_freq_email": "1.03", + "word_freq_you": "1.36", + "word_freq_credit": "0.32", + "word_freq_your": "0.51", + "word_freq_font": "0", + "word_freq_000": "1.16", + "word_freq_money": "0.06", + "word_freq_hp": "0", + "word_freq_hpl": "0", + "word_freq_george": "0", + "word_freq_650": "0", + "word_freq_lab": "0", + "word_freq_labs": "0", + "word_freq_telnet": "0", + "word_freq_857": "0", + "word_freq_data": "0", + "word_freq_415": "0", + "word_freq_85": "0", + "word_freq_technology": "0", + "word_freq_1999": "0", + "word_freq_parts": "0", + "word_freq_pm": "0", + "word_freq_direct": "0.06", + "word_freq_cs": "0", + "word_freq_meeting": "0", + "word_freq_original": "0.12", + "word_freq_project": "0", + "word_freq_re": "0.06", + "word_freq_edu": "0.06", + "word_freq_table": "0", + "word_freq_conference": "0", + "char_freq_%3B": "0.01", + "char_freq_%28": "0.143", + "char_freq_%5B": "0", + "char_freq_%21": "0.276", + "char_freq_%24": "0.184", + "char_freq_%23": "0.01", + "capital_run_length_average": "9.821", + "capital_run_length_longest": "485", + "capital_run_length_total": "2259", + "class": "1" + }, + { + "word_freq_make": "0", + "word_freq_address": "0", + "word_freq_all": "0", + "word_freq_3d": "0", + "word_freq_our": "0.63", + "word_freq_over": "0", + "word_freq_remove": "0.31", + "word_freq_internet": "0.63", + "word_freq_order": "0.31", + "word_freq_mail": "0.63", + "word_freq_receive": "0.31", + "word_freq_will": "0.31", + "word_freq_people": "0.31", + "word_freq_report": "0", + "word_freq_addresses": "0", + "word_freq_free": "0.31", + "word_freq_business": "0", + "word_freq_email": "0", + "word_freq_you": "3.18", + "word_freq_credit": "0", + "word_freq_your": "0.31", + "word_freq_font": "0", + "word_freq_000": "0", + "word_freq_money": "0", + "word_freq_hp": "0", + "word_freq_hpl": "0", + "word_freq_george": "0", + "word_freq_650": "0", + "word_freq_lab": "0", + "word_freq_labs": "0", + "word_freq_telnet": "0", + "word_freq_857": "0", + "word_freq_data": "0", + "word_freq_415": "0", + "word_freq_85": "0", + "word_freq_technology": "0", + "word_freq_1999": "0", + "word_freq_parts": "0", + "word_freq_pm": "0", + "word_freq_direct": "0", + "word_freq_cs": "0", + "word_freq_meeting": "0", + "word_freq_original": "0", + "word_freq_project": "0", + "word_freq_re": "0", + "word_freq_edu": "0", + "word_freq_table": "0", + "word_freq_conference": "0", + "char_freq_%3B": "0", + "char_freq_%28": "0.137", + "char_freq_%5B": "0", + "char_freq_%21": "0.137", + "char_freq_%24": "0", + "char_freq_%23": "0", + "capital_run_length_average": "3.537", + "capital_run_length_longest": "40", + "capital_run_length_total": "191", + "class": "1" + }, + { + "word_freq_make": "0", + "word_freq_address": "0", + "word_freq_all": "0", + "word_freq_3d": "0", + "word_freq_our": "0.63", + "word_freq_over": "0", + "word_freq_remove": "0.31", + "word_freq_internet": "0.63", + "word_freq_order": "0.31", + "word_freq_mail": "0.63", + "word_freq_receive": "0.31", + "word_freq_will": "0.31", + "word_freq_people": "0.31", + "word_freq_report": "0", + "word_freq_addresses": "0", + "word_freq_free": "0.31", + "word_freq_business": "0", + "word_freq_email": "0", + "word_freq_you": "3.18", + "word_freq_credit": "0", + "word_freq_your": "0.31", + "word_freq_font": "0", + "word_freq_000": "0", + "word_freq_money": "0", + "word_freq_hp": "0", + "word_freq_hpl": "0", + "word_freq_george": "0", + "word_freq_650": "0", + "word_freq_lab": "0", + "word_freq_labs": "0", + "word_freq_telnet": "0", + "word_freq_857": "0", + "word_freq_data": "0", + "word_freq_415": "0", + "word_freq_85": "0", + "word_freq_technology": "0", + "word_freq_1999": "0", + "word_freq_parts": "0", + "word_freq_pm": "0", + "word_freq_direct": "0", + "word_freq_cs": "0", + "word_freq_meeting": "0", + "word_freq_original": "0", + "word_freq_project": "0", + "word_freq_re": "0", + "word_freq_edu": "0", + "word_freq_table": "0", + "word_freq_conference": "0", + "char_freq_%3B": "0", + "char_freq_%28": "0.135", + "char_freq_%5B": "0", + "char_freq_%21": "0.135", + "char_freq_%24": "0", + "char_freq_%23": "0", + "capital_run_length_average": "3.537", + "capital_run_length_longest": "40", + "capital_run_length_total": "191", + "class": "1" + } + ] +} + +Shortlisted templates: +[ + { + "template_id": "tpl_threshold_rarity_cdf", + "template_name": "Threshold Rarity CDF", + "primary_family": "tail_rarity_structure", + "portability": "yes", + "sql_skeleton": "SELECT AVG(CASE WHEN {measure_col} <= {measure_threshold} THEN 1 ELSE 0 END) AS empirical_cdf_at_threshold\nFROM {table};", + "required_roles": [ + "measure_col" + ] + } +] + +Problem instance: +{ + "dataset_id": "n1", + "question": "Use template Threshold Rarity CDF to probe tail_set_consistency with semantic role rare_extreme_view. Focus on measure_col=char_freq_%24.", + "planned_template_id": "tpl_threshold_rarity_cdf", + "bindings": { + "measure_col": "char_freq_%24", + "top_k": 14, + "top_n": 4, + "num_tiles": 10, + "percentile_value": 0.9, + "z_threshold": 2.0, + "fraction_threshold": 0.1, + "baseline_multiplier": 1.5, + "baseline_fraction": 0.1, + "min_group_size": 5, + "min_support": 5, + "measure_threshold": 0.052, + "time_grain": "month", + "lookback_rows": 3, + "current_period_start": "'2024-01-01'", + "current_period_end": "'2024-04-01'", + "previous_period_start": "'2023-10-01'", + "previous_period_end": "'2024-01-01'", + "drift_ratio_threshold": 0.8 + }, + "can_vary": [], + "must_fix": [], + "runtime_sql_skeleton": "SELECT AVG(CASE WHEN {measure_col} <= {measure_threshold} THEN 1 ELSE 0 END) AS empirical_cdf_at_threshold\nFROM {table};" +} + +Repair context: +{} diff --git a/Query/sql/v2/runs/v2_cli_20260502_081223_d/n1/artifacts/v2q_n1_867ca70683b596fe/cli/sql_response_attempt_1.raw.txt b/Query/sql/v2/runs/v2_cli_20260502_081223_d/n1/artifacts/v2q_n1_867ca70683b596fe/cli/sql_response_attempt_1.raw.txt new file mode 100644 index 0000000000000000000000000000000000000000..3266fec6c9bd877b766d1e8868a67764776e6e82 --- /dev/null +++ b/Query/sql/v2/runs/v2_cli_20260502_081223_d/n1/artifacts/v2q_n1_867ca70683b596fe/cli/sql_response_attempt_1.raw.txt @@ -0,0 +1,4 @@ +{"type":"thread.started","thread_id":"019e40f7-a13d-7051-b8a8-7c163d071828"} +{"type":"turn.started"} +{"type":"error","message":"Quota exceeded. Check your plan and billing details."} +{"type":"turn.failed","error":{"message":"Quota exceeded. Check your plan and billing details."}} diff --git a/Query/sql/v2/runs/v2_cli_20260502_081223_d/n1/artifacts/v2q_n1_867ca70683b596fe/cli/sql_response_attempt_1.txt b/Query/sql/v2/runs/v2_cli_20260502_081223_d/n1/artifacts/v2q_n1_867ca70683b596fe/cli/sql_response_attempt_1.txt new file mode 100644 index 0000000000000000000000000000000000000000..bde0fa4f56d389227843d35f9fbc68a14035762a --- /dev/null +++ b/Query/sql/v2/runs/v2_cli_20260502_081223_d/n1/artifacts/v2q_n1_867ca70683b596fe/cli/sql_response_attempt_1.txt @@ -0,0 +1,4 @@ +{"type":"thread.started","thread_id":"019e40f7-a13d-7051-b8a8-7c163d071828"} +{"type":"turn.started"} +{"type":"error","message":"Quota exceeded. Check your plan and billing details."} +{"type":"turn.failed","error":{"message":"Quota exceeded. Check your plan and billing details."}} \ No newline at end of file diff --git a/Query/sql/v2/runs/v2_cli_20260502_081223_d/n1/artifacts/v2q_n1_867ca70683b596fe/cli/sql_response_attempt_2.raw.txt b/Query/sql/v2/runs/v2_cli_20260502_081223_d/n1/artifacts/v2q_n1_867ca70683b596fe/cli/sql_response_attempt_2.raw.txt new file mode 100644 index 0000000000000000000000000000000000000000..981f8cce66fbf200e4e9e44d7cc96283736214bc --- /dev/null +++ b/Query/sql/v2/runs/v2_cli_20260502_081223_d/n1/artifacts/v2q_n1_867ca70683b596fe/cli/sql_response_attempt_2.raw.txt @@ -0,0 +1,4 @@ +{"type":"thread.started","thread_id":"019e40f7-b1a3-7d51-8a3b-dad8a87f14a7"} +{"type":"turn.started"} +{"type":"item.completed","item":{"id":"item_0","type":"agent_message","text":"{\"sql\":\"-- template_id: tpl_threshold_rarity_cdf\\nSELECT AVG(CASE WHEN CAST(\\\"char_freq_%24\\\" AS REAL) <= 0.052 THEN 1 ELSE 0 END) AS empirical_cdf_at_threshold\\nFROM \\\"n1\\\";\",\"notes\":\"Used the planned Threshold Rarity CDF template with measure_col bound to \\\"char_freq_%24\\\" and measure_threshold fixed at 0.052. Cast to REAL because the schema snapshot shows TEXT storage for this numeric feature.\"}"}} +{"type":"turn.completed","usage":{"input_tokens":20308,"cached_input_tokens":12032,"output_tokens":249,"reasoning_output_tokens":139}} diff --git a/Query/sql/v2/runs/v2_cli_20260502_081223_d/n1/artifacts/v2q_n1_867ca70683b596fe/cli/sql_response_attempt_2.txt b/Query/sql/v2/runs/v2_cli_20260502_081223_d/n1/artifacts/v2q_n1_867ca70683b596fe/cli/sql_response_attempt_2.txt new file mode 100644 index 0000000000000000000000000000000000000000..e0876871825f1399f3e213b58fafcac6b1258ba1 --- /dev/null +++ b/Query/sql/v2/runs/v2_cli_20260502_081223_d/n1/artifacts/v2q_n1_867ca70683b596fe/cli/sql_response_attempt_2.txt @@ -0,0 +1 @@ +{"sql":"-- template_id: tpl_threshold_rarity_cdf\nSELECT AVG(CASE WHEN CAST(\"char_freq_%24\" AS REAL) <= 0.052 THEN 1 ELSE 0 END) AS empirical_cdf_at_threshold\nFROM \"n1\";","notes":"Used the planned Threshold Rarity CDF template with measure_col bound to \"char_freq_%24\" and measure_threshold fixed at 0.052. Cast to REAL because the schema snapshot shows TEXT storage for this numeric feature."} \ No newline at end of file diff --git a/Query/sql/v2/runs/v2_cli_20260502_081223_d/n1/artifacts/v2q_n1_867ca70683b596fe/cli/sql_stderr_attempt_1.txt b/Query/sql/v2/runs/v2_cli_20260502_081223_d/n1/artifacts/v2q_n1_867ca70683b596fe/cli/sql_stderr_attempt_1.txt new file mode 100644 index 0000000000000000000000000000000000000000..e69de29bb2d1d6434b8b29ae775ad8c2e48c5391 diff --git a/Query/sql/v2/runs/v2_cli_20260502_081223_d/n1/artifacts/v2q_n1_867ca70683b596fe/cli/sql_stderr_attempt_2.txt b/Query/sql/v2/runs/v2_cli_20260502_081223_d/n1/artifacts/v2q_n1_867ca70683b596fe/cli/sql_stderr_attempt_2.txt new file mode 100644 index 0000000000000000000000000000000000000000..e69de29bb2d1d6434b8b29ae775ad8c2e48c5391 diff --git a/Query/sql/v2/runs/v2_cli_20260502_081223_d/n1/artifacts/v2q_n1_88854a375f8b78b2/cli/conversation.jsonl b/Query/sql/v2/runs/v2_cli_20260502_081223_d/n1/artifacts/v2q_n1_88854a375f8b78b2/cli/conversation.jsonl new file mode 100644 index 0000000000000000000000000000000000000000..c55307c2c64237511d29e86e2f14c4f56a8f88b8 --- /dev/null +++ b/Query/sql/v2/runs/v2_cli_20260502_081223_d/n1/artifacts/v2q_n1_88854a375f8b78b2/cli/conversation.jsonl @@ -0,0 +1,2 @@ +{"attempt": 1, "phase": "sql_generation", "role": "user", "content_path": "cli/sql_prompt_attempt_1.txt", "metrics": {"chars": 29467, "bytes_utf8": 29467, "lines": 792, "estimated_tokens": null}} +{"attempt": 1, "phase": "sql_generation", "role": "assistant", "content_path": "cli/sql_response_attempt_1.txt", "raw_content_path": "cli/sql_response_attempt_1.raw.txt", "stderr_path": "cli/sql_stderr_attempt_1.txt", "metrics": {"chars": 434, "bytes_utf8": 434, "lines": 1, "estimated_tokens": null}, "usage": {"input_tokens": 20328, "cached_input_tokens": 12032, "output_tokens": 353, "reasoning_output_tokens": 249}} diff --git a/Query/sql/v2/runs/v2_cli_20260502_081223_d/n1/artifacts/v2q_n1_88854a375f8b78b2/cli/session_summary.json b/Query/sql/v2/runs/v2_cli_20260502_081223_d/n1/artifacts/v2q_n1_88854a375f8b78b2/cli/session_summary.json new file mode 100644 index 0000000000000000000000000000000000000000..93991b856a0cc4e95c69e9066ee20e8a64041c04 --- /dev/null +++ b/Query/sql/v2/runs/v2_cli_20260502_081223_d/n1/artifacts/v2q_n1_88854a375f8b78b2/cli/session_summary.json @@ -0,0 +1,25 @@ +{ + "engine": "v2-cli:codex", + "command": "codex exec --skip-git-repo-check --disable plugins --sandbox read-only --cd \"/data/jialinzhang/SQLagent\" -m gpt-5.4 --json -", + "ai_cli_calls": 1, + "usage_summary": { + "dataset_id": "n1", + "model": "v2-cli:codex", + "run_id": "v2q_n1_88854a375f8b78b2", + "api_calls": 0, + "input_tokens": 20328, + "cached_input_tokens": 12032, + "output_tokens": 353, + "total_tokens": 20681, + "cost_usd": 0.0, + "ai_cli_calls": 1, + "estimated_input_tokens": 0, + "estimated_output_tokens": 0, + "estimated_total_tokens": 0, + "usage_source": "ai_cli_json_usage", + "cli_elapsed_ms_total": 9032.9, + "sql_execution_elapsed_ms_total": 9.42, + "conversation_log_path": "/data/jialinzhang/TabQueryBench/sql_workloads/v2_current/runs_and_launches/runs/v2_cli_20260502_081223_d/n1/artifacts/v2q_n1_88854a375f8b78b2/cli/conversation.jsonl", + "note": "Executed through a local AI CLI with structured usage metadata." + } +} \ No newline at end of file diff --git a/Query/sql/v2/runs/v2_cli_20260502_081223_d/n1/artifacts/v2q_n1_88854a375f8b78b2/cli/sql_attempt_1.metadata.json b/Query/sql/v2/runs/v2_cli_20260502_081223_d/n1/artifacts/v2q_n1_88854a375f8b78b2/cli/sql_attempt_1.metadata.json new file mode 100644 index 0000000000000000000000000000000000000000..2b402ac9035787252024a762ec12ca1bb1dc9961 --- /dev/null +++ b/Query/sql/v2/runs/v2_cli_20260502_081223_d/n1/artifacts/v2q_n1_88854a375f8b78b2/cli/sql_attempt_1.metadata.json @@ -0,0 +1,45 @@ +{ + "attempt": 1, + "phase": "sql_generation", + "command": "codex exec --skip-git-repo-check --disable plugins --sandbox read-only --cd \"/data/jialinzhang/SQLagent\" -m gpt-5.4 --json -", + "started_at": "2026-05-19T15:44:04.133611+00:00", + "ended_at": "2026-05-19T15:44:13.166532+00:00", + "elapsed_ms": 9032.9, + "prompt_metrics": { + "chars": 29467, + "bytes_utf8": 29467, + "lines": 792, + "estimated_tokens": null + }, + "stdout_metrics": { + "chars": 788, + "bytes_utf8": 788, + "lines": 4, + "estimated_tokens": null + }, + "stderr_metrics": { + "chars": 0, + "bytes_utf8": 0, + "lines": 0, + "estimated_tokens": null + }, + "parsed_output": { + "format": "jsonl_events", + "text_metrics": { + "chars": 434, + "bytes_utf8": 434, + "lines": 1, + "estimated_tokens": null + }, + "usage": { + "input_tokens": 20328, + "cached_input_tokens": 12032, + "output_tokens": 353, + "reasoning_output_tokens": 249 + } + }, + "prompt_path": "cli/sql_prompt_attempt_1.txt", + "response_path": "cli/sql_response_attempt_1.txt", + "raw_response_path": "cli/sql_response_attempt_1.raw.txt", + "stderr_path": "cli/sql_stderr_attempt_1.txt" +} \ No newline at end of file diff --git a/Query/sql/v2/runs/v2_cli_20260502_081223_d/n1/artifacts/v2q_n1_88854a375f8b78b2/cli/sql_prompt_attempt_1.txt b/Query/sql/v2/runs/v2_cli_20260502_081223_d/n1/artifacts/v2q_n1_88854a375f8b78b2/cli/sql_prompt_attempt_1.txt new file mode 100644 index 0000000000000000000000000000000000000000..91f5460c825feeeaf302f9393606583a278fec4a --- /dev/null +++ b/Query/sql/v2/runs/v2_cli_20260502_081223_d/n1/artifacts/v2q_n1_88854a375f8b78b2/cli/sql_prompt_attempt_1.txt @@ -0,0 +1,792 @@ +You are generating one SQLite SELECT query for a single-table SQL QA task. +Return strict JSON only, with this schema: {"sql": "...", "notes": "..."}. +Rules: +- Use only the provided table and columns. +- Do not write INSERT, UPDATE, DELETE, DROP, ALTER, CREATE, PRAGMA, ATTACH, DETACH, or VACUUM. +- Prefer the planned template and bound roles when provided. +- Add a leading SQL comment exactly like: -- template_id: . +- Generate SQLite-compatible SQL. SQLite does not support PERCENTILE_CONT or STDDEV. +- Quote identifiers with double quotes. +- Return no markdown and no extra prose. + +Dataset context: +Dataset context for SQL QA: +- dataset_id: n1 +- dataset_name: Spambase +- table_name: n1 +- table_layout: single-table dataset (do not assume joins). +- row_semantics: One row is one email represented by engineered token/character frequency and capitalization-run features. +- task_type: classification +- target_column: class +- main_row_count: 4601 +- important_fields: +- word_freq_make: role=feature, type=numeric_sparse_frequency. tags=['condition_candidate', 'measure', 'rare_pattern_candidate'] desc=Frequency feature for token 'make' in an email message. +- word_freq_address: role=feature, type=numeric_sparse_frequency. tags=['condition_candidate', 'measure', 'rare_pattern_candidate'] desc=Frequency feature for token 'address' in an email message. +- word_freq_all: role=feature, type=numeric_sparse_frequency. tags=['condition_candidate', 'measure', 'rare_pattern_candidate'] desc=Frequency feature for token 'all' in an email message. +- word_freq_3d: role=feature, type=numeric_sparse_frequency. tags=['condition_candidate', 'measure', 'rare_pattern_candidate'] desc=Frequency feature for token '3d' in an email message. +- word_freq_our: role=feature, type=numeric_sparse_frequency. tags=['condition_candidate', 'measure', 'rare_pattern_candidate'] desc=Frequency feature for token 'our' in an email message. +- word_freq_over: role=feature, type=numeric_sparse_frequency. tags=['condition_candidate', 'measure', 'rare_pattern_candidate'] desc=Frequency feature for token 'over' in an email message. +- word_freq_remove: role=feature, type=numeric_sparse_frequency. tags=['condition_candidate', 'measure', 'rare_pattern_candidate'] desc=Frequency feature for token 'remove' in an email message. +- word_freq_internet: role=feature, type=numeric_sparse_frequency. tags=['condition_candidate', 'measure', 'rare_pattern_candidate'] desc=Frequency feature for token 'internet' in an email message. +- word_freq_order: role=feature, type=numeric_sparse_frequency. tags=['condition_candidate', 'measure', 'rare_pattern_candidate'] desc=Frequency feature for token 'order' in an email message. +- word_freq_mail: role=feature, type=numeric_sparse_frequency. tags=['condition_candidate', 'measure', 'rare_pattern_candidate'] desc=Frequency feature for token 'mail' in an email message. +- word_freq_receive: role=feature, type=numeric_sparse_frequency. tags=['condition_candidate', 'measure', 'rare_pattern_candidate'] desc=Frequency feature for token 'receive' in an email message. +- word_freq_will: role=feature, type=numeric_sparse_frequency. tags=['condition_candidate', 'measure', 'rare_pattern_candidate'] desc=Frequency feature for token 'will' in an email message. +- word_freq_people: role=feature, type=numeric_sparse_frequency. tags=['condition_candidate', 'measure', 'rare_pattern_candidate'] desc=Frequency feature for token 'people' in an email message. +- word_freq_report: role=feature, type=numeric_sparse_frequency. tags=['condition_candidate', 'measure', 'rare_pattern_candidate'] desc=Frequency feature for token 'report' in an email message. +- word_freq_addresses: role=feature, type=numeric_sparse_frequency. tags=['condition_candidate', 'measure', 'rare_pattern_candidate'] desc=Frequency feature for token 'addresses' in an email message. +- word_freq_free: role=feature, type=numeric_sparse_frequency. tags=['condition_candidate', 'measure', 'rare_pattern_candidate'] desc=Frequency feature for token 'free' in an email message. +- word_freq_business: role=feature, type=numeric_sparse_frequency. tags=['condition_candidate', 'measure', 'rare_pattern_candidate'] desc=Frequency feature for token 'business' in an email message. +- word_freq_email: role=feature, type=numeric_sparse_frequency. tags=['condition_candidate', 'measure', 'rare_pattern_candidate'] desc=Frequency feature for token 'email' in an email message. +- word_freq_you: role=feature, type=numeric_sparse_frequency. tags=['condition_candidate', 'measure', 'rare_pattern_candidate'] desc=Frequency feature for token 'you' in an email message. +- word_freq_credit: role=feature, type=numeric_sparse_frequency. tags=['condition_candidate', 'measure', 'rare_pattern_candidate'] desc=Frequency feature for token 'credit' in an email message. +- word_freq_your: role=feature, type=numeric_sparse_frequency. tags=['condition_candidate', 'measure', 'rare_pattern_candidate'] desc=Frequency feature for token 'your' in an email message. +- word_freq_font: role=feature, type=numeric_sparse_frequency. tags=['condition_candidate', 'measure', 'rare_pattern_candidate'] desc=Frequency feature for token 'font' in an email message. +- word_freq_000: role=feature, type=numeric_sparse_frequency. tags=['condition_candidate', 'measure', 'rare_pattern_candidate'] desc=Frequency feature for token '000' in an email message. +- word_freq_money: role=feature, type=numeric_sparse_frequency. tags=['condition_candidate', 'measure', 'rare_pattern_candidate'] desc=Frequency feature for token 'money' in an email message. +- word_freq_hp: role=feature, type=numeric_sparse_frequency. tags=['condition_candidate', 'measure', 'rare_pattern_candidate'] desc=Frequency feature for token 'hp' in an email message. +- word_freq_hpl: role=feature, type=numeric_sparse_frequency. tags=['condition_candidate', 'measure', 'rare_pattern_candidate'] desc=Frequency feature for token 'hpl' in an email message. +- word_freq_george: role=feature, type=numeric_sparse_frequency. tags=['condition_candidate', 'measure', 'rare_pattern_candidate'] desc=Frequency feature for token 'george' in an email message. +- word_freq_650: role=feature, type=numeric_sparse_frequency. tags=['condition_candidate', 'measure', 'rare_pattern_candidate'] desc=Frequency feature for token '650' in an email message. +- word_freq_lab: role=feature, type=numeric_sparse_frequency. tags=['condition_candidate', 'measure', 'rare_pattern_candidate'] desc=Frequency feature for token 'lab' in an email message. +- word_freq_labs: role=feature, type=numeric_sparse_frequency. tags=['condition_candidate', 'measure', 'rare_pattern_candidate'] desc=Frequency feature for token 'labs' in an email message. +- word_freq_telnet: role=feature, type=numeric_sparse_frequency. tags=['condition_candidate', 'measure', 'rare_pattern_candidate'] desc=Frequency feature for token 'telnet' in an email message. +- word_freq_857: role=feature, type=numeric_sparse_frequency. tags=['condition_candidate', 'measure', 'rare_pattern_candidate'] desc=Frequency feature for token '857' in an email message. +- word_freq_data: role=feature, type=numeric_sparse_frequency. tags=['condition_candidate', 'measure', 'rare_pattern_candidate'] desc=Frequency feature for token 'data' in an email message. +- word_freq_415: role=feature, type=numeric_sparse_frequency. tags=['condition_candidate', 'measure', 'rare_pattern_candidate'] desc=Frequency feature for token '415' in an email message. +- word_freq_85: role=feature, type=numeric_sparse_frequency. tags=['condition_candidate', 'measure', 'rare_pattern_candidate'] desc=Frequency feature for token '85' in an email message. +- word_freq_technology: role=feature, type=numeric_sparse_frequency. tags=['condition_candidate', 'measure', 'rare_pattern_candidate'] desc=Frequency feature for token 'technology' in an email message. +- word_freq_1999: role=feature, type=numeric_sparse_frequency. tags=['condition_candidate', 'measure', 'rare_pattern_candidate'] desc=Frequency feature for token '1999' in an email message. +- word_freq_parts: role=feature, type=numeric_sparse_frequency. tags=['condition_candidate', 'measure', 'rare_pattern_candidate'] desc=Frequency feature for token 'parts' in an email message. +- word_freq_pm: role=feature, type=numeric_sparse_frequency. tags=['condition_candidate', 'measure', 'rare_pattern_candidate'] desc=Frequency feature for token 'pm' in an email message. +- word_freq_direct: role=feature, type=numeric_sparse_frequency. tags=['condition_candidate', 'measure', 'rare_pattern_candidate'] desc=Frequency feature for token 'direct' in an email message. +- word_freq_cs: role=feature, type=numeric_sparse_frequency. tags=['condition_candidate', 'measure', 'rare_pattern_candidate'] desc=Frequency feature for token 'cs' in an email message. +- word_freq_meeting: role=feature, type=numeric_sparse_frequency. tags=['condition_candidate', 'measure', 'rare_pattern_candidate'] desc=Frequency feature for token 'meeting' in an email message. +- word_freq_original: role=feature, type=numeric_sparse_frequency. tags=['condition_candidate', 'measure', 'rare_pattern_candidate'] desc=Frequency feature for token 'original' in an email message. +- word_freq_project: role=feature, type=numeric_sparse_frequency. tags=['condition_candidate', 'measure', 'rare_pattern_candidate'] desc=Frequency feature for token 'project' in an email message. +- word_freq_re: role=feature, type=numeric_sparse_frequency. tags=['condition_candidate', 'measure', 'rare_pattern_candidate'] desc=Frequency feature for token 're' in an email message. +- word_freq_edu: role=feature, type=numeric_sparse_frequency. tags=['condition_candidate', 'measure', 'rare_pattern_candidate'] desc=Frequency feature for token 'edu' in an email message. +- word_freq_table: role=feature, type=numeric_sparse_frequency. tags=['condition_candidate', 'measure', 'rare_pattern_candidate'] desc=Frequency feature for token 'table' in an email message. +- word_freq_conference: role=feature, type=numeric_sparse_frequency. tags=['condition_candidate', 'measure', 'rare_pattern_candidate'] desc=Frequency feature for token 'conference' in an email message. +- char_freq_%3B: role=feature, type=numeric_sparse_frequency. tags=['condition_candidate', 'measure', 'rare_pattern_candidate'] desc=Character frequency feature for symbol ';'. +- char_freq_%28: role=feature, type=numeric_sparse_frequency. tags=['condition_candidate', 'measure', 'rare_pattern_candidate'] desc=Character frequency feature for symbol '('. +- char_freq_%5B: role=feature, type=numeric_sparse_frequency. tags=['condition_candidate', 'measure', 'rare_pattern_candidate'] desc=Character frequency feature for symbol '['. +- char_freq_%21: role=feature, type=numeric_sparse_frequency. tags=['condition_candidate', 'measure', 'rare_pattern_candidate'] desc=Character frequency feature for symbol '!'. +- char_freq_%24: role=feature, type=numeric_sparse_frequency. tags=['condition_candidate', 'measure', 'rare_pattern_candidate'] desc=Character frequency feature for symbol '$'. +- char_freq_%23: role=feature, type=numeric_sparse_frequency. tags=['condition_candidate', 'measure', 'rare_pattern_candidate'] desc=Character frequency feature for symbol '#'. +- capital_run_length_average: role=feature, type=numeric_heavy_tail. tags=['condition_candidate', 'measure', 'rare_pattern_candidate'] desc=Average length of uninterrupted capital-letter runs. +- capital_run_length_longest: role=feature, type=numeric_heavy_tail. tags=['condition_candidate', 'measure', 'rare_pattern_candidate'] desc=Longest uninterrupted capital-letter run. +- capital_run_length_total: role=feature, type=numeric_heavy_tail. tags=['condition_candidate', 'measure', 'rare_pattern_candidate'] desc=Total number of capital letters in runs. +- class: role=target, type=binary_target. tags=['target_candidate'] desc=Binary spam label (1=spam, 0=non-spam). +- useful_field_combinations: [['word_freq_free', 'word_freq_you', 'class'], ['char_freq_%21', 'capital_run_length_longest', 'class'], ['word_freq_remove', 'word_freq_money', 'class']] +- fields_requiring_caution: ['capital_run_length_average', 'capital_run_length_longest', 'capital_run_length_total'] +- source_url: https://www.openml.org/d/44 + +SQLite schema snapshot: +{ + "table_name": "n1", + "quoted_table_name": "\"n1\"", + "row_count": 4601, + "columns": [ + { + "name": "word_freq_make", + "type": "TEXT", + "notnull": false, + "pk": false + }, + { + "name": "word_freq_address", + "type": "TEXT", + "notnull": false, + "pk": false + }, + { + "name": "word_freq_all", + "type": "TEXT", + "notnull": false, + "pk": false + }, + { + "name": "word_freq_3d", + "type": "TEXT", + "notnull": false, + "pk": false + }, + { + "name": "word_freq_our", + "type": "TEXT", + "notnull": false, + "pk": false + }, + { + "name": "word_freq_over", + "type": "TEXT", + "notnull": false, + "pk": false + }, + { + "name": "word_freq_remove", + "type": "TEXT", + "notnull": false, + "pk": false + }, + { + "name": "word_freq_internet", + "type": "TEXT", + "notnull": false, + "pk": false + }, + { + "name": "word_freq_order", + "type": "TEXT", + "notnull": false, + "pk": false + }, + { + "name": "word_freq_mail", + "type": "TEXT", + "notnull": false, + "pk": false + }, + { + "name": "word_freq_receive", + "type": "TEXT", + "notnull": false, + "pk": false + }, + { + "name": "word_freq_will", + "type": "TEXT", + "notnull": false, + "pk": false + }, + { + "name": "word_freq_people", + "type": "TEXT", + "notnull": false, + "pk": false + }, + { + "name": "word_freq_report", + "type": "TEXT", + "notnull": false, + "pk": false + }, + { + "name": "word_freq_addresses", + "type": "TEXT", + "notnull": false, + "pk": false + }, + { + "name": "word_freq_free", + "type": "TEXT", + "notnull": false, + "pk": false + }, + { + "name": "word_freq_business", + "type": "TEXT", + "notnull": false, + "pk": false + }, + { + "name": "word_freq_email", + "type": "TEXT", + "notnull": false, + "pk": false + }, + { + "name": "word_freq_you", + "type": "TEXT", + "notnull": false, + "pk": false + }, + { + "name": "word_freq_credit", + "type": "TEXT", + "notnull": false, + "pk": false + }, + { + "name": "word_freq_your", + "type": "TEXT", + "notnull": false, + "pk": false + }, + { + "name": "word_freq_font", + "type": "TEXT", + "notnull": false, + "pk": false + }, + { + "name": "word_freq_000", + "type": "TEXT", + "notnull": false, + "pk": false + }, + { + "name": "word_freq_money", + "type": "TEXT", + "notnull": false, + "pk": false + }, + { + "name": "word_freq_hp", + "type": "TEXT", + "notnull": false, + "pk": false + }, + { + "name": "word_freq_hpl", + "type": "TEXT", + "notnull": false, + "pk": false + }, + { + "name": "word_freq_george", + "type": "TEXT", + "notnull": false, + "pk": false + }, + { + "name": "word_freq_650", + "type": "TEXT", + "notnull": false, + "pk": false + }, + { + "name": "word_freq_lab", + "type": "TEXT", + "notnull": false, + "pk": false + }, + { + "name": "word_freq_labs", + "type": "TEXT", + "notnull": false, + "pk": false + }, + { + "name": "word_freq_telnet", + "type": "TEXT", + "notnull": false, + "pk": false + }, + { + "name": "word_freq_857", + "type": "TEXT", + "notnull": false, + "pk": false + }, + { + "name": "word_freq_data", + "type": "TEXT", + "notnull": false, + "pk": false + }, + { + "name": "word_freq_415", + "type": "TEXT", + "notnull": false, + "pk": false + }, + { + "name": "word_freq_85", + "type": "TEXT", + "notnull": false, + "pk": false + }, + { + "name": "word_freq_technology", + "type": "TEXT", + "notnull": false, + "pk": false + }, + { + "name": "word_freq_1999", + "type": "TEXT", + "notnull": false, + "pk": false + }, + { + "name": "word_freq_parts", + "type": "TEXT", + "notnull": false, + "pk": false + }, + { + "name": "word_freq_pm", + "type": "TEXT", + "notnull": false, + "pk": false + }, + { + "name": "word_freq_direct", + "type": "TEXT", + "notnull": false, + "pk": false + }, + { + "name": "word_freq_cs", + "type": "TEXT", + "notnull": false, + "pk": false + }, + { + "name": "word_freq_meeting", + "type": "TEXT", + "notnull": false, + "pk": false + }, + { + "name": "word_freq_original", + "type": "TEXT", + "notnull": false, + "pk": false + }, + { + "name": "word_freq_project", + "type": "TEXT", + "notnull": false, + "pk": false + }, + { + "name": "word_freq_re", + "type": "TEXT", + "notnull": false, + "pk": false + }, + { + "name": "word_freq_edu", + "type": "TEXT", + "notnull": false, + "pk": false + }, + { + "name": "word_freq_table", + "type": "TEXT", + "notnull": false, + "pk": false + }, + { + "name": "word_freq_conference", + "type": "TEXT", + "notnull": false, + "pk": false + }, + { + "name": "char_freq_%3B", + "type": "TEXT", + "notnull": false, + "pk": false + }, + { + "name": "char_freq_%28", + "type": "TEXT", + "notnull": false, + "pk": false + }, + { + "name": "char_freq_%5B", + "type": "TEXT", + "notnull": false, + "pk": false + }, + { + "name": "char_freq_%21", + "type": "TEXT", + "notnull": false, + "pk": false + }, + { + "name": "char_freq_%24", + "type": "TEXT", + "notnull": false, + "pk": false + }, + { + "name": "char_freq_%23", + "type": "TEXT", + "notnull": false, + "pk": false + }, + { + "name": "capital_run_length_average", + "type": "TEXT", + "notnull": false, + "pk": false + }, + { + "name": "capital_run_length_longest", + "type": "TEXT", + "notnull": false, + "pk": false + }, + { + "name": "capital_run_length_total", + "type": "TEXT", + "notnull": false, + "pk": false + }, + { + "name": "class", + "type": "TEXT", + "notnull": false, + "pk": false + } + ], + "sample_rows": [ + { + "word_freq_make": "0", + "word_freq_address": "0.64", + "word_freq_all": "0.64", + "word_freq_3d": "0", + "word_freq_our": "0.32", + "word_freq_over": "0", + "word_freq_remove": "0", + "word_freq_internet": "0", + "word_freq_order": "0", + "word_freq_mail": "0", + "word_freq_receive": "0", + "word_freq_will": "0.64", + "word_freq_people": "0", + "word_freq_report": "0", + "word_freq_addresses": "0", + "word_freq_free": "0.32", + "word_freq_business": "0", + "word_freq_email": "1.29", + "word_freq_you": "1.93", + "word_freq_credit": "0", + "word_freq_your": "0.96", + "word_freq_font": "0", + "word_freq_000": "0", + "word_freq_money": "0", + "word_freq_hp": "0", + "word_freq_hpl": "0", + "word_freq_george": "0", + "word_freq_650": "0", + "word_freq_lab": "0", + "word_freq_labs": "0", + "word_freq_telnet": "0", + "word_freq_857": "0", + "word_freq_data": "0", + "word_freq_415": "0", + "word_freq_85": "0", + "word_freq_technology": "0", + "word_freq_1999": "0", + "word_freq_parts": "0", + "word_freq_pm": "0", + "word_freq_direct": "0", + "word_freq_cs": "0", + "word_freq_meeting": "0", + "word_freq_original": "0", + "word_freq_project": "0", + "word_freq_re": "0", + "word_freq_edu": "0", + "word_freq_table": "0", + "word_freq_conference": "0", + "char_freq_%3B": "0", + "char_freq_%28": "0", + "char_freq_%5B": "0", + "char_freq_%21": "0.778", + "char_freq_%24": "0", + "char_freq_%23": "0", + "capital_run_length_average": "3.756", + "capital_run_length_longest": "61", + "capital_run_length_total": "278", + "class": "1" + }, + { + "word_freq_make": "0.21", + "word_freq_address": "0.28", + "word_freq_all": "0.5", + "word_freq_3d": "0", + "word_freq_our": "0.14", + "word_freq_over": "0.28", + "word_freq_remove": "0.21", + "word_freq_internet": "0.07", + "word_freq_order": "0", + "word_freq_mail": "0.94", + "word_freq_receive": "0.21", + "word_freq_will": "0.79", + "word_freq_people": "0.65", + "word_freq_report": "0.21", + "word_freq_addresses": "0.14", + "word_freq_free": "0.14", + "word_freq_business": "0.07", + "word_freq_email": "0.28", + "word_freq_you": "3.47", + "word_freq_credit": "0", + "word_freq_your": "1.59", + "word_freq_font": "0", + "word_freq_000": "0.43", + "word_freq_money": "0.43", + "word_freq_hp": "0", + "word_freq_hpl": "0", + "word_freq_george": "0", + "word_freq_650": "0", + "word_freq_lab": "0", + "word_freq_labs": "0", + "word_freq_telnet": "0", + "word_freq_857": "0", + "word_freq_data": "0", + "word_freq_415": "0", + "word_freq_85": "0", + "word_freq_technology": "0", + "word_freq_1999": "0.07", + "word_freq_parts": "0", + "word_freq_pm": "0", + "word_freq_direct": "0", + "word_freq_cs": "0", + "word_freq_meeting": "0", + "word_freq_original": "0", + "word_freq_project": "0", + "word_freq_re": "0", + "word_freq_edu": "0", + "word_freq_table": "0", + "word_freq_conference": "0", + "char_freq_%3B": "0", + "char_freq_%28": "0.132", + "char_freq_%5B": "0", + "char_freq_%21": "0.372", + "char_freq_%24": "0.18", + "char_freq_%23": "0.048", + "capital_run_length_average": "5.114", + "capital_run_length_longest": "101", + "capital_run_length_total": "1028", + "class": "1" + }, + { + "word_freq_make": "0.06", + "word_freq_address": "0", + "word_freq_all": "0.71", + "word_freq_3d": "0", + "word_freq_our": "1.23", + "word_freq_over": "0.19", + "word_freq_remove": "0.19", + "word_freq_internet": "0.12", + "word_freq_order": "0.64", + "word_freq_mail": "0.25", + "word_freq_receive": "0.38", + "word_freq_will": "0.45", + "word_freq_people": "0.12", + "word_freq_report": "0", + "word_freq_addresses": "1.75", + "word_freq_free": "0.06", + "word_freq_business": "0.06", + "word_freq_email": "1.03", + "word_freq_you": "1.36", + "word_freq_credit": "0.32", + "word_freq_your": "0.51", + "word_freq_font": "0", + "word_freq_000": "1.16", + "word_freq_money": "0.06", + "word_freq_hp": "0", + "word_freq_hpl": "0", + "word_freq_george": "0", + "word_freq_650": "0", + "word_freq_lab": "0", + "word_freq_labs": "0", + "word_freq_telnet": "0", + "word_freq_857": "0", + "word_freq_data": "0", + "word_freq_415": "0", + "word_freq_85": "0", + "word_freq_technology": "0", + "word_freq_1999": "0", + "word_freq_parts": "0", + "word_freq_pm": "0", + "word_freq_direct": "0.06", + "word_freq_cs": "0", + "word_freq_meeting": "0", + "word_freq_original": "0.12", + "word_freq_project": "0", + "word_freq_re": "0.06", + "word_freq_edu": "0.06", + "word_freq_table": "0", + "word_freq_conference": "0", + "char_freq_%3B": "0.01", + "char_freq_%28": "0.143", + "char_freq_%5B": "0", + "char_freq_%21": "0.276", + "char_freq_%24": "0.184", + "char_freq_%23": "0.01", + "capital_run_length_average": "9.821", + "capital_run_length_longest": "485", + "capital_run_length_total": "2259", + "class": "1" + }, + { + "word_freq_make": "0", + "word_freq_address": "0", + "word_freq_all": "0", + "word_freq_3d": "0", + "word_freq_our": "0.63", + "word_freq_over": "0", + "word_freq_remove": "0.31", + "word_freq_internet": "0.63", + "word_freq_order": "0.31", + "word_freq_mail": "0.63", + "word_freq_receive": "0.31", + "word_freq_will": "0.31", + "word_freq_people": "0.31", + "word_freq_report": "0", + "word_freq_addresses": "0", + "word_freq_free": "0.31", + "word_freq_business": "0", + "word_freq_email": "0", + "word_freq_you": "3.18", + "word_freq_credit": "0", + "word_freq_your": "0.31", + "word_freq_font": "0", + "word_freq_000": "0", + "word_freq_money": "0", + "word_freq_hp": "0", + "word_freq_hpl": "0", + "word_freq_george": "0", + "word_freq_650": "0", + "word_freq_lab": "0", + "word_freq_labs": "0", + "word_freq_telnet": "0", + "word_freq_857": "0", + "word_freq_data": "0", + "word_freq_415": "0", + "word_freq_85": "0", + "word_freq_technology": "0", + "word_freq_1999": "0", + "word_freq_parts": "0", + "word_freq_pm": "0", + "word_freq_direct": "0", + "word_freq_cs": "0", + "word_freq_meeting": "0", + "word_freq_original": "0", + "word_freq_project": "0", + "word_freq_re": "0", + "word_freq_edu": "0", + "word_freq_table": "0", + "word_freq_conference": "0", + "char_freq_%3B": "0", + "char_freq_%28": "0.137", + "char_freq_%5B": "0", + "char_freq_%21": "0.137", + "char_freq_%24": "0", + "char_freq_%23": "0", + "capital_run_length_average": "3.537", + "capital_run_length_longest": "40", + "capital_run_length_total": "191", + "class": "1" + }, + { + "word_freq_make": "0", + "word_freq_address": "0", + "word_freq_all": "0", + "word_freq_3d": "0", + "word_freq_our": "0.63", + "word_freq_over": "0", + "word_freq_remove": "0.31", + "word_freq_internet": "0.63", + "word_freq_order": "0.31", + "word_freq_mail": "0.63", + "word_freq_receive": "0.31", + "word_freq_will": "0.31", + "word_freq_people": "0.31", + "word_freq_report": "0", + "word_freq_addresses": "0", + "word_freq_free": "0.31", + "word_freq_business": "0", + "word_freq_email": "0", + "word_freq_you": "3.18", + "word_freq_credit": "0", + "word_freq_your": "0.31", + "word_freq_font": "0", + "word_freq_000": "0", + "word_freq_money": "0", + "word_freq_hp": "0", + "word_freq_hpl": "0", + "word_freq_george": "0", + "word_freq_650": "0", + "word_freq_lab": "0", + "word_freq_labs": "0", + "word_freq_telnet": "0", + "word_freq_857": "0", + "word_freq_data": "0", + "word_freq_415": "0", + "word_freq_85": "0", + "word_freq_technology": "0", + "word_freq_1999": "0", + "word_freq_parts": "0", + "word_freq_pm": "0", + "word_freq_direct": "0", + "word_freq_cs": "0", + "word_freq_meeting": "0", + "word_freq_original": "0", + "word_freq_project": "0", + "word_freq_re": "0", + "word_freq_edu": "0", + "word_freq_table": "0", + "word_freq_conference": "0", + "char_freq_%3B": "0", + "char_freq_%28": "0.135", + "char_freq_%5B": "0", + "char_freq_%21": "0.135", + "char_freq_%24": "0", + "char_freq_%23": "0", + "capital_run_length_average": "3.537", + "capital_run_length_longest": "40", + "capital_run_length_total": "191", + "class": "1" + } + ] +} + +Shortlisted templates: +[ + { + "template_id": "tpl_m4_window_partition_avg", + "template_name": "Window Partition Average", + "primary_family": "conditional_dependency_structure", + "portability": "partial", + "sql_skeleton": "SELECT DISTINCT {group_col},\n AVG({measure_col}) OVER (PARTITION BY {group_col}) AS avg_measure\nFROM {table}\nORDER BY avg_measure DESC;", + "required_roles": [ + "group_col", + "measure_col" + ] + } +] + +Problem instance: +{ + "dataset_id": "n1", + "question": "Use template Window Partition Average to probe slice_level_consistency with semantic role ranked_signal_view. Focus on group_col=class, measure_col=capital_run_length_average.", + "planned_template_id": "tpl_m4_window_partition_avg", + "bindings": { + "group_col": "class", + "measure_col": "capital_run_length_average", + "top_k": 19, + "top_n": 6, + "num_tiles": 10, + "percentile_value": 0.9, + "z_threshold": 2.0, + "fraction_threshold": 0.05, + "baseline_multiplier": 1.75, + "baseline_fraction": 0.1, + "min_group_size": 5, + "min_support": 4, + "measure_threshold": 3.025, + "time_grain": "month", + "lookback_rows": 3, + "current_period_start": "'2024-01-01'", + "current_period_end": "'2024-04-01'", + "previous_period_start": "'2023-10-01'", + "previous_period_end": "'2024-01-01'", + "drift_ratio_threshold": 0.8 + }, + "can_vary": [], + "must_fix": [], + "runtime_sql_skeleton": "SELECT DISTINCT {group_col},\n AVG({measure_col}) OVER (PARTITION BY {group_col}) AS avg_measure\nFROM {table}\nORDER BY avg_measure DESC;" +} + +Repair context: +{} diff --git a/Query/sql/v2/runs/v2_cli_20260502_081223_d/n1/artifacts/v2q_n1_88854a375f8b78b2/cli/sql_response_attempt_1.raw.txt b/Query/sql/v2/runs/v2_cli_20260502_081223_d/n1/artifacts/v2q_n1_88854a375f8b78b2/cli/sql_response_attempt_1.raw.txt new file mode 100644 index 0000000000000000000000000000000000000000..19f0a79cf751fdc0489fe341749066597cae8d35 --- /dev/null +++ b/Query/sql/v2/runs/v2_cli_20260502_081223_d/n1/artifacts/v2q_n1_88854a375f8b78b2/cli/sql_response_attempt_1.raw.txt @@ -0,0 +1,4 @@ +{"type":"thread.started","thread_id":"019e40e8-9335-7221-b3cc-d833b4f07cf9"} +{"type":"turn.started"} +{"type":"item.completed","item":{"id":"item_0","type":"agent_message","text":"{\"sql\":\"-- template_id: tpl_m4_window_partition_avg\\nSELECT DISTINCT \\\"class\\\",\\n AVG(CAST(\\\"capital_run_length_average\\\" AS REAL)) OVER (PARTITION BY \\\"class\\\") AS avg_measure\\nFROM \\\"n1\\\"\\nORDER BY avg_measure DESC;\",\"notes\":\"Uses the provided Window Partition Average template with group_col=\\\"class\\\" and measure_col=\\\"capital_run_length_average\\\". CAST to REAL is applied because the schema stores feature values as TEXT.\"}"}} +{"type":"turn.completed","usage":{"input_tokens":20328,"cached_input_tokens":12032,"output_tokens":353,"reasoning_output_tokens":249}} diff --git a/Query/sql/v2/runs/v2_cli_20260502_081223_d/n1/artifacts/v2q_n1_88854a375f8b78b2/cli/sql_response_attempt_1.txt b/Query/sql/v2/runs/v2_cli_20260502_081223_d/n1/artifacts/v2q_n1_88854a375f8b78b2/cli/sql_response_attempt_1.txt new file mode 100644 index 0000000000000000000000000000000000000000..a5168fc02e2d54d3e1001fd30a2857d92d24aaa4 --- /dev/null +++ b/Query/sql/v2/runs/v2_cli_20260502_081223_d/n1/artifacts/v2q_n1_88854a375f8b78b2/cli/sql_response_attempt_1.txt @@ -0,0 +1 @@ +{"sql":"-- template_id: tpl_m4_window_partition_avg\nSELECT DISTINCT \"class\",\n AVG(CAST(\"capital_run_length_average\" AS REAL)) OVER (PARTITION BY \"class\") AS avg_measure\nFROM \"n1\"\nORDER BY avg_measure DESC;","notes":"Uses the provided Window Partition Average template with group_col=\"class\" and measure_col=\"capital_run_length_average\". CAST to REAL is applied because the schema stores feature values as TEXT."} \ No newline at end of file diff --git a/Query/sql/v2/runs/v2_cli_20260502_081223_d/n1/artifacts/v2q_n1_88854a375f8b78b2/cli/sql_stderr_attempt_1.txt b/Query/sql/v2/runs/v2_cli_20260502_081223_d/n1/artifacts/v2q_n1_88854a375f8b78b2/cli/sql_stderr_attempt_1.txt new file mode 100644 index 0000000000000000000000000000000000000000..e69de29bb2d1d6434b8b29ae775ad8c2e48c5391 diff --git a/Query/sql/v2/runs/v2_cli_20260502_081223_d/n1/artifacts/v2q_n1_894c352c250c7153/cli/sql_attempt_1.metadata.json b/Query/sql/v2/runs/v2_cli_20260502_081223_d/n1/artifacts/v2q_n1_894c352c250c7153/cli/sql_attempt_1.metadata.json new file mode 100644 index 0000000000000000000000000000000000000000..521fbc367af04382a717fe3ab76961cfdef527f3 --- /dev/null +++ b/Query/sql/v2/runs/v2_cli_20260502_081223_d/n1/artifacts/v2q_n1_894c352c250c7153/cli/sql_attempt_1.metadata.json @@ -0,0 +1,43 @@ +{ + "attempt": 1, + "phase": "sql_generation", + "command": "codex exec --skip-git-repo-check --disable plugins --sandbox read-only --cd \"/data/jialinzhang/SQLagent\" -m gpt-5.4 --json -", + "started_at": "2026-05-19T15:55:40.121919+00:00", + "ended_at": "2026-05-19T15:55:42.978041+00:00", + "elapsed_ms": 2856.09, + "returncode": 1, + "prompt_metrics": { + "chars": 29533, + "bytes_utf8": 29533, + "lines": 792, + "estimated_tokens": null + }, + "stdout_metrics": { + "chars": 281, + "bytes_utf8": 281, + "lines": 4, + "estimated_tokens": null + }, + "stderr_metrics": { + "chars": 0, + "bytes_utf8": 0, + "lines": 0, + "estimated_tokens": null + }, + "parsed_output": { + "format": "jsonl_events", + "text_metrics": { + "chars": 280, + "bytes_utf8": 280, + "lines": 4, + "estimated_tokens": null + }, + "usage": {} + }, + "status": "failed", + "error": "AI CLI command failed with exit code 1: ", + "prompt_path": "cli/sql_prompt_attempt_1.txt", + "response_path": "cli/sql_response_attempt_1.txt", + "raw_response_path": "cli/sql_response_attempt_1.raw.txt", + "stderr_path": "cli/sql_stderr_attempt_1.txt" +} \ No newline at end of file diff --git a/Query/sql/v2/runs/v2_cli_20260502_081223_d/n1/artifacts/v2q_n1_894c352c250c7153/cli/sql_attempt_2.metadata.json b/Query/sql/v2/runs/v2_cli_20260502_081223_d/n1/artifacts/v2q_n1_894c352c250c7153/cli/sql_attempt_2.metadata.json new file mode 100644 index 0000000000000000000000000000000000000000..7a2922b18010bba9f855ee06cbe0211448b07351 --- /dev/null +++ b/Query/sql/v2/runs/v2_cli_20260502_081223_d/n1/artifacts/v2q_n1_894c352c250c7153/cli/sql_attempt_2.metadata.json @@ -0,0 +1,43 @@ +{ + "attempt": 2, + "phase": "sql_generation", + "command": "codex exec --skip-git-repo-check --disable plugins --sandbox read-only --cd \"/data/jialinzhang/SQLagent\" -m gpt-5.4 --json -", + "started_at": "2026-05-19T15:55:43.980966+00:00", + "ended_at": "2026-05-19T15:55:47.677775+00:00", + "elapsed_ms": 3696.78, + "returncode": 1, + "prompt_metrics": { + "chars": 29533, + "bytes_utf8": 29533, + "lines": 792, + "estimated_tokens": null + }, + "stdout_metrics": { + "chars": 281, + "bytes_utf8": 281, + "lines": 4, + "estimated_tokens": null + }, + "stderr_metrics": { + "chars": 0, + "bytes_utf8": 0, + "lines": 0, + "estimated_tokens": null + }, + "parsed_output": { + "format": "jsonl_events", + "text_metrics": { + "chars": 280, + "bytes_utf8": 280, + "lines": 4, + "estimated_tokens": null + }, + "usage": {} + }, + "status": "failed", + "error": "AI CLI command failed with exit code 1: ", + "prompt_path": "cli/sql_prompt_attempt_2.txt", + "response_path": "cli/sql_response_attempt_2.txt", + "raw_response_path": "cli/sql_response_attempt_2.raw.txt", + "stderr_path": "cli/sql_stderr_attempt_2.txt" +} \ No newline at end of file diff --git a/Query/sql/v2/runs/v2_cli_20260502_081223_d/n1/artifacts/v2q_n1_894c352c250c7153/cli/sql_prompt_attempt_1.txt b/Query/sql/v2/runs/v2_cli_20260502_081223_d/n1/artifacts/v2q_n1_894c352c250c7153/cli/sql_prompt_attempt_1.txt new file mode 100644 index 0000000000000000000000000000000000000000..c3c4b66237220b2356deeb15712fb26d5e901424 --- /dev/null +++ b/Query/sql/v2/runs/v2_cli_20260502_081223_d/n1/artifacts/v2q_n1_894c352c250c7153/cli/sql_prompt_attempt_1.txt @@ -0,0 +1,792 @@ +You are generating one SQLite SELECT query for a single-table SQL QA task. +Return strict JSON only, with this schema: {"sql": "...", "notes": "..."}. +Rules: +- Use only the provided table and columns. +- Do not write INSERT, UPDATE, DELETE, DROP, ALTER, CREATE, PRAGMA, ATTACH, DETACH, or VACUUM. +- Prefer the planned template and bound roles when provided. +- Add a leading SQL comment exactly like: -- template_id: . +- Generate SQLite-compatible SQL. SQLite does not support PERCENTILE_CONT or STDDEV. +- Quote identifiers with double quotes. +- Return no markdown and no extra prose. + +Dataset context: +Dataset context for SQL QA: +- dataset_id: n1 +- dataset_name: Spambase +- table_name: n1 +- table_layout: single-table dataset (do not assume joins). +- row_semantics: One row is one email represented by engineered token/character frequency and capitalization-run features. +- task_type: classification +- target_column: class +- main_row_count: 4601 +- important_fields: +- word_freq_make: role=feature, type=numeric_sparse_frequency. tags=['condition_candidate', 'measure', 'rare_pattern_candidate'] desc=Frequency feature for token 'make' in an email message. +- word_freq_address: role=feature, type=numeric_sparse_frequency. tags=['condition_candidate', 'measure', 'rare_pattern_candidate'] desc=Frequency feature for token 'address' in an email message. +- word_freq_all: role=feature, type=numeric_sparse_frequency. tags=['condition_candidate', 'measure', 'rare_pattern_candidate'] desc=Frequency feature for token 'all' in an email message. +- word_freq_3d: role=feature, type=numeric_sparse_frequency. tags=['condition_candidate', 'measure', 'rare_pattern_candidate'] desc=Frequency feature for token '3d' in an email message. +- word_freq_our: role=feature, type=numeric_sparse_frequency. tags=['condition_candidate', 'measure', 'rare_pattern_candidate'] desc=Frequency feature for token 'our' in an email message. +- word_freq_over: role=feature, type=numeric_sparse_frequency. tags=['condition_candidate', 'measure', 'rare_pattern_candidate'] desc=Frequency feature for token 'over' in an email message. +- word_freq_remove: role=feature, type=numeric_sparse_frequency. tags=['condition_candidate', 'measure', 'rare_pattern_candidate'] desc=Frequency feature for token 'remove' in an email message. +- word_freq_internet: role=feature, type=numeric_sparse_frequency. tags=['condition_candidate', 'measure', 'rare_pattern_candidate'] desc=Frequency feature for token 'internet' in an email message. +- word_freq_order: role=feature, type=numeric_sparse_frequency. tags=['condition_candidate', 'measure', 'rare_pattern_candidate'] desc=Frequency feature for token 'order' in an email message. +- word_freq_mail: role=feature, type=numeric_sparse_frequency. tags=['condition_candidate', 'measure', 'rare_pattern_candidate'] desc=Frequency feature for token 'mail' in an email message. +- word_freq_receive: role=feature, type=numeric_sparse_frequency. tags=['condition_candidate', 'measure', 'rare_pattern_candidate'] desc=Frequency feature for token 'receive' in an email message. +- word_freq_will: role=feature, type=numeric_sparse_frequency. tags=['condition_candidate', 'measure', 'rare_pattern_candidate'] desc=Frequency feature for token 'will' in an email message. +- word_freq_people: role=feature, type=numeric_sparse_frequency. tags=['condition_candidate', 'measure', 'rare_pattern_candidate'] desc=Frequency feature for token 'people' in an email message. +- word_freq_report: role=feature, type=numeric_sparse_frequency. tags=['condition_candidate', 'measure', 'rare_pattern_candidate'] desc=Frequency feature for token 'report' in an email message. +- word_freq_addresses: role=feature, type=numeric_sparse_frequency. tags=['condition_candidate', 'measure', 'rare_pattern_candidate'] desc=Frequency feature for token 'addresses' in an email message. +- word_freq_free: role=feature, type=numeric_sparse_frequency. tags=['condition_candidate', 'measure', 'rare_pattern_candidate'] desc=Frequency feature for token 'free' in an email message. +- word_freq_business: role=feature, type=numeric_sparse_frequency. tags=['condition_candidate', 'measure', 'rare_pattern_candidate'] desc=Frequency feature for token 'business' in an email message. +- word_freq_email: role=feature, type=numeric_sparse_frequency. tags=['condition_candidate', 'measure', 'rare_pattern_candidate'] desc=Frequency feature for token 'email' in an email message. +- word_freq_you: role=feature, type=numeric_sparse_frequency. tags=['condition_candidate', 'measure', 'rare_pattern_candidate'] desc=Frequency feature for token 'you' in an email message. +- word_freq_credit: role=feature, type=numeric_sparse_frequency. tags=['condition_candidate', 'measure', 'rare_pattern_candidate'] desc=Frequency feature for token 'credit' in an email message. +- word_freq_your: role=feature, type=numeric_sparse_frequency. tags=['condition_candidate', 'measure', 'rare_pattern_candidate'] desc=Frequency feature for token 'your' in an email message. +- word_freq_font: role=feature, type=numeric_sparse_frequency. tags=['condition_candidate', 'measure', 'rare_pattern_candidate'] desc=Frequency feature for token 'font' in an email message. +- word_freq_000: role=feature, type=numeric_sparse_frequency. tags=['condition_candidate', 'measure', 'rare_pattern_candidate'] desc=Frequency feature for token '000' in an email message. +- word_freq_money: role=feature, type=numeric_sparse_frequency. tags=['condition_candidate', 'measure', 'rare_pattern_candidate'] desc=Frequency feature for token 'money' in an email message. +- word_freq_hp: role=feature, type=numeric_sparse_frequency. tags=['condition_candidate', 'measure', 'rare_pattern_candidate'] desc=Frequency feature for token 'hp' in an email message. +- word_freq_hpl: role=feature, type=numeric_sparse_frequency. tags=['condition_candidate', 'measure', 'rare_pattern_candidate'] desc=Frequency feature for token 'hpl' in an email message. +- word_freq_george: role=feature, type=numeric_sparse_frequency. tags=['condition_candidate', 'measure', 'rare_pattern_candidate'] desc=Frequency feature for token 'george' in an email message. +- word_freq_650: role=feature, type=numeric_sparse_frequency. tags=['condition_candidate', 'measure', 'rare_pattern_candidate'] desc=Frequency feature for token '650' in an email message. +- word_freq_lab: role=feature, type=numeric_sparse_frequency. tags=['condition_candidate', 'measure', 'rare_pattern_candidate'] desc=Frequency feature for token 'lab' in an email message. +- word_freq_labs: role=feature, type=numeric_sparse_frequency. tags=['condition_candidate', 'measure', 'rare_pattern_candidate'] desc=Frequency feature for token 'labs' in an email message. +- word_freq_telnet: role=feature, type=numeric_sparse_frequency. tags=['condition_candidate', 'measure', 'rare_pattern_candidate'] desc=Frequency feature for token 'telnet' in an email message. +- word_freq_857: role=feature, type=numeric_sparse_frequency. tags=['condition_candidate', 'measure', 'rare_pattern_candidate'] desc=Frequency feature for token '857' in an email message. +- word_freq_data: role=feature, type=numeric_sparse_frequency. tags=['condition_candidate', 'measure', 'rare_pattern_candidate'] desc=Frequency feature for token 'data' in an email message. +- word_freq_415: role=feature, type=numeric_sparse_frequency. tags=['condition_candidate', 'measure', 'rare_pattern_candidate'] desc=Frequency feature for token '415' in an email message. +- word_freq_85: role=feature, type=numeric_sparse_frequency. tags=['condition_candidate', 'measure', 'rare_pattern_candidate'] desc=Frequency feature for token '85' in an email message. +- word_freq_technology: role=feature, type=numeric_sparse_frequency. tags=['condition_candidate', 'measure', 'rare_pattern_candidate'] desc=Frequency feature for token 'technology' in an email message. +- word_freq_1999: role=feature, type=numeric_sparse_frequency. tags=['condition_candidate', 'measure', 'rare_pattern_candidate'] desc=Frequency feature for token '1999' in an email message. +- word_freq_parts: role=feature, type=numeric_sparse_frequency. tags=['condition_candidate', 'measure', 'rare_pattern_candidate'] desc=Frequency feature for token 'parts' in an email message. +- word_freq_pm: role=feature, type=numeric_sparse_frequency. tags=['condition_candidate', 'measure', 'rare_pattern_candidate'] desc=Frequency feature for token 'pm' in an email message. +- word_freq_direct: role=feature, type=numeric_sparse_frequency. tags=['condition_candidate', 'measure', 'rare_pattern_candidate'] desc=Frequency feature for token 'direct' in an email message. +- word_freq_cs: role=feature, type=numeric_sparse_frequency. tags=['condition_candidate', 'measure', 'rare_pattern_candidate'] desc=Frequency feature for token 'cs' in an email message. +- word_freq_meeting: role=feature, type=numeric_sparse_frequency. tags=['condition_candidate', 'measure', 'rare_pattern_candidate'] desc=Frequency feature for token 'meeting' in an email message. +- word_freq_original: role=feature, type=numeric_sparse_frequency. tags=['condition_candidate', 'measure', 'rare_pattern_candidate'] desc=Frequency feature for token 'original' in an email message. +- word_freq_project: role=feature, type=numeric_sparse_frequency. tags=['condition_candidate', 'measure', 'rare_pattern_candidate'] desc=Frequency feature for token 'project' in an email message. +- word_freq_re: role=feature, type=numeric_sparse_frequency. tags=['condition_candidate', 'measure', 'rare_pattern_candidate'] desc=Frequency feature for token 're' in an email message. +- word_freq_edu: role=feature, type=numeric_sparse_frequency. tags=['condition_candidate', 'measure', 'rare_pattern_candidate'] desc=Frequency feature for token 'edu' in an email message. +- word_freq_table: role=feature, type=numeric_sparse_frequency. tags=['condition_candidate', 'measure', 'rare_pattern_candidate'] desc=Frequency feature for token 'table' in an email message. +- word_freq_conference: role=feature, type=numeric_sparse_frequency. tags=['condition_candidate', 'measure', 'rare_pattern_candidate'] desc=Frequency feature for token 'conference' in an email message. +- char_freq_%3B: role=feature, type=numeric_sparse_frequency. tags=['condition_candidate', 'measure', 'rare_pattern_candidate'] desc=Character frequency feature for symbol ';'. +- char_freq_%28: role=feature, type=numeric_sparse_frequency. tags=['condition_candidate', 'measure', 'rare_pattern_candidate'] desc=Character frequency feature for symbol '('. +- char_freq_%5B: role=feature, type=numeric_sparse_frequency. tags=['condition_candidate', 'measure', 'rare_pattern_candidate'] desc=Character frequency feature for symbol '['. +- char_freq_%21: role=feature, type=numeric_sparse_frequency. tags=['condition_candidate', 'measure', 'rare_pattern_candidate'] desc=Character frequency feature for symbol '!'. +- char_freq_%24: role=feature, type=numeric_sparse_frequency. tags=['condition_candidate', 'measure', 'rare_pattern_candidate'] desc=Character frequency feature for symbol '$'. +- char_freq_%23: role=feature, type=numeric_sparse_frequency. tags=['condition_candidate', 'measure', 'rare_pattern_candidate'] desc=Character frequency feature for symbol '#'. +- capital_run_length_average: role=feature, type=numeric_heavy_tail. tags=['condition_candidate', 'measure', 'rare_pattern_candidate'] desc=Average length of uninterrupted capital-letter runs. +- capital_run_length_longest: role=feature, type=numeric_heavy_tail. tags=['condition_candidate', 'measure', 'rare_pattern_candidate'] desc=Longest uninterrupted capital-letter run. +- capital_run_length_total: role=feature, type=numeric_heavy_tail. tags=['condition_candidate', 'measure', 'rare_pattern_candidate'] desc=Total number of capital letters in runs. +- class: role=target, type=binary_target. tags=['target_candidate'] desc=Binary spam label (1=spam, 0=non-spam). +- useful_field_combinations: [['word_freq_free', 'word_freq_you', 'class'], ['char_freq_%21', 'capital_run_length_longest', 'class'], ['word_freq_remove', 'word_freq_money', 'class']] +- fields_requiring_caution: ['capital_run_length_average', 'capital_run_length_longest', 'capital_run_length_total'] +- source_url: https://www.openml.org/d/44 + +SQLite schema snapshot: +{ + "table_name": "n1", + "quoted_table_name": "\"n1\"", + "row_count": 4601, + "columns": [ + { + "name": "word_freq_make", + "type": "TEXT", + "notnull": false, + "pk": false + }, + { + "name": "word_freq_address", + "type": "TEXT", + "notnull": false, + "pk": false + }, + { + "name": "word_freq_all", + "type": "TEXT", + "notnull": false, + "pk": false + }, + { + "name": "word_freq_3d", + "type": "TEXT", + "notnull": false, + "pk": false + }, + { + "name": "word_freq_our", + "type": "TEXT", + "notnull": false, + "pk": false + }, + { + "name": "word_freq_over", + "type": "TEXT", + "notnull": false, + "pk": false + }, + { + "name": "word_freq_remove", + "type": "TEXT", + "notnull": false, + "pk": false + }, + { + "name": "word_freq_internet", + "type": "TEXT", + "notnull": false, + "pk": false + }, + { + "name": "word_freq_order", + "type": "TEXT", + "notnull": false, + "pk": false + }, + { + "name": "word_freq_mail", + "type": "TEXT", + "notnull": false, + "pk": false + }, + { + "name": "word_freq_receive", + "type": "TEXT", + "notnull": false, + "pk": false + }, + { + "name": "word_freq_will", + "type": "TEXT", + "notnull": false, + "pk": false + }, + { + "name": "word_freq_people", + "type": "TEXT", + "notnull": false, + "pk": false + }, + { + "name": "word_freq_report", + "type": "TEXT", + "notnull": false, + "pk": false + }, + { + "name": "word_freq_addresses", + "type": "TEXT", + "notnull": false, + "pk": false + }, + { + "name": "word_freq_free", + "type": "TEXT", + "notnull": false, + "pk": false + }, + { + "name": "word_freq_business", + "type": "TEXT", + "notnull": false, + "pk": false + }, + { + "name": "word_freq_email", + "type": "TEXT", + "notnull": false, + "pk": false + }, + { + "name": "word_freq_you", + "type": "TEXT", + "notnull": false, + "pk": false + }, + { + "name": "word_freq_credit", + "type": "TEXT", + "notnull": false, + "pk": false + }, + { + "name": "word_freq_your", + "type": "TEXT", + "notnull": false, + "pk": false + }, + { + "name": "word_freq_font", + "type": "TEXT", + "notnull": false, + "pk": false + }, + { + "name": "word_freq_000", + "type": "TEXT", + "notnull": false, + "pk": false + }, + { + "name": "word_freq_money", + "type": "TEXT", + "notnull": false, + "pk": false + }, + { + "name": "word_freq_hp", + "type": "TEXT", + "notnull": false, + "pk": false + }, + { + "name": "word_freq_hpl", + "type": "TEXT", + "notnull": false, + "pk": false + }, + { + "name": "word_freq_george", + "type": "TEXT", + "notnull": false, + "pk": false + }, + { + "name": "word_freq_650", + "type": "TEXT", + "notnull": false, + "pk": false + }, + { + "name": "word_freq_lab", + "type": "TEXT", + "notnull": false, + "pk": false + }, + { + "name": "word_freq_labs", + "type": "TEXT", + "notnull": false, + "pk": false + }, + { + "name": "word_freq_telnet", + "type": "TEXT", + "notnull": false, + "pk": false + }, + { + "name": "word_freq_857", + "type": "TEXT", + "notnull": false, + "pk": false + }, + { + "name": "word_freq_data", + "type": "TEXT", + "notnull": false, + "pk": false + }, + { + "name": "word_freq_415", + "type": "TEXT", + "notnull": false, + "pk": false + }, + { + "name": "word_freq_85", + "type": "TEXT", + "notnull": false, + "pk": false + }, + { + "name": "word_freq_technology", + "type": "TEXT", + "notnull": false, + "pk": false + }, + { + "name": "word_freq_1999", + "type": "TEXT", + "notnull": false, + "pk": false + }, + { + "name": "word_freq_parts", + "type": "TEXT", + "notnull": false, + "pk": false + }, + { + "name": "word_freq_pm", + "type": "TEXT", + "notnull": false, + "pk": false + }, + { + "name": "word_freq_direct", + "type": "TEXT", + "notnull": false, + "pk": false + }, + { + "name": "word_freq_cs", + "type": "TEXT", + "notnull": false, + "pk": false + }, + { + "name": "word_freq_meeting", + "type": "TEXT", + "notnull": false, + "pk": false + }, + { + "name": "word_freq_original", + "type": "TEXT", + "notnull": false, + "pk": false + }, + { + "name": "word_freq_project", + "type": "TEXT", + "notnull": false, + "pk": false + }, + { + "name": "word_freq_re", + "type": "TEXT", + "notnull": false, + "pk": false + }, + { + "name": "word_freq_edu", + "type": "TEXT", + "notnull": false, + "pk": false + }, + { + "name": "word_freq_table", + "type": "TEXT", + "notnull": false, + "pk": false + }, + { + "name": "word_freq_conference", + "type": "TEXT", + "notnull": false, + "pk": false + }, + { + "name": "char_freq_%3B", + "type": "TEXT", + "notnull": false, + "pk": false + }, + { + "name": "char_freq_%28", + "type": "TEXT", + "notnull": false, + "pk": false + }, + { + "name": "char_freq_%5B", + "type": "TEXT", + "notnull": false, + "pk": false + }, + { + "name": "char_freq_%21", + "type": "TEXT", + "notnull": false, + "pk": false + }, + { + "name": "char_freq_%24", + "type": "TEXT", + "notnull": false, + "pk": false + }, + { + "name": "char_freq_%23", + "type": "TEXT", + "notnull": false, + "pk": false + }, + { + "name": "capital_run_length_average", + "type": "TEXT", + "notnull": false, + "pk": false + }, + { + "name": "capital_run_length_longest", + "type": "TEXT", + "notnull": false, + "pk": false + }, + { + "name": "capital_run_length_total", + "type": "TEXT", + "notnull": false, + "pk": false + }, + { + "name": "class", + "type": "TEXT", + "notnull": false, + "pk": false + } + ], + "sample_rows": [ + { + "word_freq_make": "0", + "word_freq_address": "0.64", + "word_freq_all": "0.64", + "word_freq_3d": "0", + "word_freq_our": "0.32", + "word_freq_over": "0", + "word_freq_remove": "0", + "word_freq_internet": "0", + "word_freq_order": "0", + "word_freq_mail": "0", + "word_freq_receive": "0", + "word_freq_will": "0.64", + "word_freq_people": "0", + "word_freq_report": "0", + "word_freq_addresses": "0", + "word_freq_free": "0.32", + "word_freq_business": "0", + "word_freq_email": "1.29", + "word_freq_you": "1.93", + "word_freq_credit": "0", + "word_freq_your": "0.96", + "word_freq_font": "0", + "word_freq_000": "0", + "word_freq_money": "0", + "word_freq_hp": "0", + "word_freq_hpl": "0", + "word_freq_george": "0", + "word_freq_650": "0", + "word_freq_lab": "0", + "word_freq_labs": "0", + "word_freq_telnet": "0", + "word_freq_857": "0", + "word_freq_data": "0", + "word_freq_415": "0", + "word_freq_85": "0", + "word_freq_technology": "0", + "word_freq_1999": "0", + "word_freq_parts": "0", + "word_freq_pm": "0", + "word_freq_direct": "0", + "word_freq_cs": "0", + "word_freq_meeting": "0", + "word_freq_original": "0", + "word_freq_project": "0", + "word_freq_re": "0", + "word_freq_edu": "0", + "word_freq_table": "0", + "word_freq_conference": "0", + "char_freq_%3B": "0", + "char_freq_%28": "0", + "char_freq_%5B": "0", + "char_freq_%21": "0.778", + "char_freq_%24": "0", + "char_freq_%23": "0", + "capital_run_length_average": "3.756", + "capital_run_length_longest": "61", + "capital_run_length_total": "278", + "class": "1" + }, + { + "word_freq_make": "0.21", + "word_freq_address": "0.28", + "word_freq_all": "0.5", + "word_freq_3d": "0", + "word_freq_our": "0.14", + "word_freq_over": "0.28", + "word_freq_remove": "0.21", + "word_freq_internet": "0.07", + "word_freq_order": "0", + "word_freq_mail": "0.94", + "word_freq_receive": "0.21", + "word_freq_will": "0.79", + "word_freq_people": "0.65", + "word_freq_report": "0.21", + "word_freq_addresses": "0.14", + "word_freq_free": "0.14", + "word_freq_business": "0.07", + "word_freq_email": "0.28", + "word_freq_you": "3.47", + "word_freq_credit": "0", + "word_freq_your": "1.59", + "word_freq_font": "0", + "word_freq_000": "0.43", + "word_freq_money": "0.43", + "word_freq_hp": "0", + "word_freq_hpl": "0", + "word_freq_george": "0", + "word_freq_650": "0", + "word_freq_lab": "0", + "word_freq_labs": "0", + "word_freq_telnet": "0", + "word_freq_857": "0", + "word_freq_data": "0", + "word_freq_415": "0", + "word_freq_85": "0", + "word_freq_technology": "0", + "word_freq_1999": "0.07", + "word_freq_parts": "0", + "word_freq_pm": "0", + "word_freq_direct": "0", + "word_freq_cs": "0", + "word_freq_meeting": "0", + "word_freq_original": "0", + "word_freq_project": "0", + "word_freq_re": "0", + "word_freq_edu": "0", + "word_freq_table": "0", + "word_freq_conference": "0", + "char_freq_%3B": "0", + "char_freq_%28": "0.132", + "char_freq_%5B": "0", + "char_freq_%21": "0.372", + "char_freq_%24": "0.18", + "char_freq_%23": "0.048", + "capital_run_length_average": "5.114", + "capital_run_length_longest": "101", + "capital_run_length_total": "1028", + "class": "1" + }, + { + "word_freq_make": "0.06", + "word_freq_address": "0", + "word_freq_all": "0.71", + "word_freq_3d": "0", + "word_freq_our": "1.23", + "word_freq_over": "0.19", + "word_freq_remove": "0.19", + "word_freq_internet": "0.12", + "word_freq_order": "0.64", + "word_freq_mail": "0.25", + "word_freq_receive": "0.38", + "word_freq_will": "0.45", + "word_freq_people": "0.12", + "word_freq_report": "0", + "word_freq_addresses": "1.75", + "word_freq_free": "0.06", + "word_freq_business": "0.06", + "word_freq_email": "1.03", + "word_freq_you": "1.36", + "word_freq_credit": "0.32", + "word_freq_your": "0.51", + "word_freq_font": "0", + "word_freq_000": "1.16", + "word_freq_money": "0.06", + "word_freq_hp": "0", + "word_freq_hpl": "0", + "word_freq_george": "0", + "word_freq_650": "0", + "word_freq_lab": "0", + "word_freq_labs": "0", + "word_freq_telnet": "0", + "word_freq_857": "0", + "word_freq_data": "0", + "word_freq_415": "0", + "word_freq_85": "0", + "word_freq_technology": "0", + "word_freq_1999": "0", + "word_freq_parts": "0", + "word_freq_pm": "0", + "word_freq_direct": "0.06", + "word_freq_cs": "0", + "word_freq_meeting": "0", + "word_freq_original": "0.12", + "word_freq_project": "0", + "word_freq_re": "0.06", + "word_freq_edu": "0.06", + "word_freq_table": "0", + "word_freq_conference": "0", + "char_freq_%3B": "0.01", + "char_freq_%28": "0.143", + "char_freq_%5B": "0", + "char_freq_%21": "0.276", + "char_freq_%24": "0.184", + "char_freq_%23": "0.01", + "capital_run_length_average": "9.821", + "capital_run_length_longest": "485", + "capital_run_length_total": "2259", + "class": "1" + }, + { + "word_freq_make": "0", + "word_freq_address": "0", + "word_freq_all": "0", + "word_freq_3d": "0", + "word_freq_our": "0.63", + "word_freq_over": "0", + "word_freq_remove": "0.31", + "word_freq_internet": "0.63", + "word_freq_order": "0.31", + "word_freq_mail": "0.63", + "word_freq_receive": "0.31", + "word_freq_will": "0.31", + "word_freq_people": "0.31", + "word_freq_report": "0", + "word_freq_addresses": "0", + "word_freq_free": "0.31", + "word_freq_business": "0", + "word_freq_email": "0", + "word_freq_you": "3.18", + "word_freq_credit": "0", + "word_freq_your": "0.31", + "word_freq_font": "0", + "word_freq_000": "0", + "word_freq_money": "0", + "word_freq_hp": "0", + "word_freq_hpl": "0", + "word_freq_george": "0", + "word_freq_650": "0", + "word_freq_lab": "0", + "word_freq_labs": "0", + "word_freq_telnet": "0", + "word_freq_857": "0", + "word_freq_data": "0", + "word_freq_415": "0", + "word_freq_85": "0", + "word_freq_technology": "0", + "word_freq_1999": "0", + "word_freq_parts": "0", + "word_freq_pm": "0", + "word_freq_direct": "0", + "word_freq_cs": "0", + "word_freq_meeting": "0", + "word_freq_original": "0", + "word_freq_project": "0", + "word_freq_re": "0", + "word_freq_edu": "0", + "word_freq_table": "0", + "word_freq_conference": "0", + "char_freq_%3B": "0", + "char_freq_%28": "0.137", + "char_freq_%5B": "0", + "char_freq_%21": "0.137", + "char_freq_%24": "0", + "char_freq_%23": "0", + "capital_run_length_average": "3.537", + "capital_run_length_longest": "40", + "capital_run_length_total": "191", + "class": "1" + }, + { + "word_freq_make": "0", + "word_freq_address": "0", + "word_freq_all": "0", + "word_freq_3d": "0", + "word_freq_our": "0.63", + "word_freq_over": "0", + "word_freq_remove": "0.31", + "word_freq_internet": "0.63", + "word_freq_order": "0.31", + "word_freq_mail": "0.63", + "word_freq_receive": "0.31", + "word_freq_will": "0.31", + "word_freq_people": "0.31", + "word_freq_report": "0", + "word_freq_addresses": "0", + "word_freq_free": "0.31", + "word_freq_business": "0", + "word_freq_email": "0", + "word_freq_you": "3.18", + "word_freq_credit": "0", + "word_freq_your": "0.31", + "word_freq_font": "0", + "word_freq_000": "0", + "word_freq_money": "0", + "word_freq_hp": "0", + "word_freq_hpl": "0", + "word_freq_george": "0", + "word_freq_650": "0", + "word_freq_lab": "0", + "word_freq_labs": "0", + "word_freq_telnet": "0", + "word_freq_857": "0", + "word_freq_data": "0", + "word_freq_415": "0", + "word_freq_85": "0", + "word_freq_technology": "0", + "word_freq_1999": "0", + "word_freq_parts": "0", + "word_freq_pm": "0", + "word_freq_direct": "0", + "word_freq_cs": "0", + "word_freq_meeting": "0", + "word_freq_original": "0", + "word_freq_project": "0", + "word_freq_re": "0", + "word_freq_edu": "0", + "word_freq_table": "0", + "word_freq_conference": "0", + "char_freq_%3B": "0", + "char_freq_%28": "0.135", + "char_freq_%5B": "0", + "char_freq_%21": "0.135", + "char_freq_%24": "0", + "char_freq_%23": "0", + "capital_run_length_average": "3.537", + "capital_run_length_longest": "40", + "capital_run_length_total": "191", + "class": "1" + } + ] +} + +Shortlisted templates: +[ + { + "template_id": "tpl_grouped_percentile_point", + "template_name": "Grouped Percentile Point", + "primary_family": "tail_rarity_structure", + "portability": "yes", + "sql_skeleton": "SELECT {group_col},\n PERCENTILE_CONT({percentile_value}) WITHIN GROUP (ORDER BY {measure_col}) AS percentile_measure\nFROM {table}\nGROUP BY {group_col}\nORDER BY percentile_measure DESC;", + "required_roles": [ + "group_col", + "measure_col" + ] + } +] + +Problem instance: +{ + "dataset_id": "n1", + "question": "Use template Grouped Percentile Point to probe tail_concentration_consistency with semantic role ranked_signal_view. Focus on group_col=class, measure_col=word_freq_data.", + "planned_template_id": "tpl_grouped_percentile_point", + "bindings": { + "group_col": "class", + "measure_col": "word_freq_data", + "top_k": 14, + "top_n": 4, + "num_tiles": 10, + "percentile_value": 0.9, + "z_threshold": 2.0, + "fraction_threshold": 0.1, + "baseline_multiplier": 1.5, + "baseline_fraction": 0.1, + "min_group_size": 5, + "min_support": 5, + "measure_threshold": 0.0, + "time_grain": "month", + "lookback_rows": 3, + "current_period_start": "'2024-01-01'", + "current_period_end": "'2024-04-01'", + "previous_period_start": "'2023-10-01'", + "previous_period_end": "'2024-01-01'", + "drift_ratio_threshold": 0.8 + }, + "can_vary": [], + "must_fix": [], + "runtime_sql_skeleton": "SELECT {group_col},\n PERCENTILE_CONT({percentile_value}) WITHIN GROUP (ORDER BY {measure_col}) AS percentile_measure\nFROM {table}\nGROUP BY {group_col}\nORDER BY percentile_measure DESC;" +} + +Repair context: +{} diff --git a/Query/sql/v2/runs/v2_cli_20260502_081223_d/n1/artifacts/v2q_n1_894c352c250c7153/cli/sql_prompt_attempt_2.txt b/Query/sql/v2/runs/v2_cli_20260502_081223_d/n1/artifacts/v2q_n1_894c352c250c7153/cli/sql_prompt_attempt_2.txt new file mode 100644 index 0000000000000000000000000000000000000000..c3c4b66237220b2356deeb15712fb26d5e901424 --- /dev/null +++ b/Query/sql/v2/runs/v2_cli_20260502_081223_d/n1/artifacts/v2q_n1_894c352c250c7153/cli/sql_prompt_attempt_2.txt @@ -0,0 +1,792 @@ +You are generating one SQLite SELECT query for a single-table SQL QA task. +Return strict JSON only, with this schema: {"sql": "...", "notes": "..."}. +Rules: +- Use only the provided table and columns. +- Do not write INSERT, UPDATE, DELETE, DROP, ALTER, CREATE, PRAGMA, ATTACH, DETACH, or VACUUM. +- Prefer the planned template and bound roles when provided. +- Add a leading SQL comment exactly like: -- template_id: . +- Generate SQLite-compatible SQL. SQLite does not support PERCENTILE_CONT or STDDEV. +- Quote identifiers with double quotes. +- Return no markdown and no extra prose. + +Dataset context: +Dataset context for SQL QA: +- dataset_id: n1 +- dataset_name: Spambase +- table_name: n1 +- table_layout: single-table dataset (do not assume joins). +- row_semantics: One row is one email represented by engineered token/character frequency and capitalization-run features. +- task_type: classification +- target_column: class +- main_row_count: 4601 +- important_fields: +- word_freq_make: role=feature, type=numeric_sparse_frequency. tags=['condition_candidate', 'measure', 'rare_pattern_candidate'] desc=Frequency feature for token 'make' in an email message. +- word_freq_address: role=feature, type=numeric_sparse_frequency. tags=['condition_candidate', 'measure', 'rare_pattern_candidate'] desc=Frequency feature for token 'address' in an email message. +- word_freq_all: role=feature, type=numeric_sparse_frequency. tags=['condition_candidate', 'measure', 'rare_pattern_candidate'] desc=Frequency feature for token 'all' in an email message. +- word_freq_3d: role=feature, type=numeric_sparse_frequency. tags=['condition_candidate', 'measure', 'rare_pattern_candidate'] desc=Frequency feature for token '3d' in an email message. +- word_freq_our: role=feature, type=numeric_sparse_frequency. tags=['condition_candidate', 'measure', 'rare_pattern_candidate'] desc=Frequency feature for token 'our' in an email message. +- word_freq_over: role=feature, type=numeric_sparse_frequency. tags=['condition_candidate', 'measure', 'rare_pattern_candidate'] desc=Frequency feature for token 'over' in an email message. +- word_freq_remove: role=feature, type=numeric_sparse_frequency. tags=['condition_candidate', 'measure', 'rare_pattern_candidate'] desc=Frequency feature for token 'remove' in an email message. +- word_freq_internet: role=feature, type=numeric_sparse_frequency. tags=['condition_candidate', 'measure', 'rare_pattern_candidate'] desc=Frequency feature for token 'internet' in an email message. +- word_freq_order: role=feature, type=numeric_sparse_frequency. tags=['condition_candidate', 'measure', 'rare_pattern_candidate'] desc=Frequency feature for token 'order' in an email message. +- word_freq_mail: role=feature, type=numeric_sparse_frequency. tags=['condition_candidate', 'measure', 'rare_pattern_candidate'] desc=Frequency feature for token 'mail' in an email message. +- word_freq_receive: role=feature, type=numeric_sparse_frequency. tags=['condition_candidate', 'measure', 'rare_pattern_candidate'] desc=Frequency feature for token 'receive' in an email message. +- word_freq_will: role=feature, type=numeric_sparse_frequency. tags=['condition_candidate', 'measure', 'rare_pattern_candidate'] desc=Frequency feature for token 'will' in an email message. +- word_freq_people: role=feature, type=numeric_sparse_frequency. tags=['condition_candidate', 'measure', 'rare_pattern_candidate'] desc=Frequency feature for token 'people' in an email message. +- word_freq_report: role=feature, type=numeric_sparse_frequency. tags=['condition_candidate', 'measure', 'rare_pattern_candidate'] desc=Frequency feature for token 'report' in an email message. +- word_freq_addresses: role=feature, type=numeric_sparse_frequency. tags=['condition_candidate', 'measure', 'rare_pattern_candidate'] desc=Frequency feature for token 'addresses' in an email message. +- word_freq_free: role=feature, type=numeric_sparse_frequency. tags=['condition_candidate', 'measure', 'rare_pattern_candidate'] desc=Frequency feature for token 'free' in an email message. +- word_freq_business: role=feature, type=numeric_sparse_frequency. tags=['condition_candidate', 'measure', 'rare_pattern_candidate'] desc=Frequency feature for token 'business' in an email message. +- word_freq_email: role=feature, type=numeric_sparse_frequency. tags=['condition_candidate', 'measure', 'rare_pattern_candidate'] desc=Frequency feature for token 'email' in an email message. +- word_freq_you: role=feature, type=numeric_sparse_frequency. tags=['condition_candidate', 'measure', 'rare_pattern_candidate'] desc=Frequency feature for token 'you' in an email message. +- word_freq_credit: role=feature, type=numeric_sparse_frequency. tags=['condition_candidate', 'measure', 'rare_pattern_candidate'] desc=Frequency feature for token 'credit' in an email message. +- word_freq_your: role=feature, type=numeric_sparse_frequency. tags=['condition_candidate', 'measure', 'rare_pattern_candidate'] desc=Frequency feature for token 'your' in an email message. +- word_freq_font: role=feature, type=numeric_sparse_frequency. tags=['condition_candidate', 'measure', 'rare_pattern_candidate'] desc=Frequency feature for token 'font' in an email message. +- word_freq_000: role=feature, type=numeric_sparse_frequency. tags=['condition_candidate', 'measure', 'rare_pattern_candidate'] desc=Frequency feature for token '000' in an email message. +- word_freq_money: role=feature, type=numeric_sparse_frequency. tags=['condition_candidate', 'measure', 'rare_pattern_candidate'] desc=Frequency feature for token 'money' in an email message. +- word_freq_hp: role=feature, type=numeric_sparse_frequency. tags=['condition_candidate', 'measure', 'rare_pattern_candidate'] desc=Frequency feature for token 'hp' in an email message. +- word_freq_hpl: role=feature, type=numeric_sparse_frequency. tags=['condition_candidate', 'measure', 'rare_pattern_candidate'] desc=Frequency feature for token 'hpl' in an email message. +- word_freq_george: role=feature, type=numeric_sparse_frequency. tags=['condition_candidate', 'measure', 'rare_pattern_candidate'] desc=Frequency feature for token 'george' in an email message. +- word_freq_650: role=feature, type=numeric_sparse_frequency. tags=['condition_candidate', 'measure', 'rare_pattern_candidate'] desc=Frequency feature for token '650' in an email message. +- word_freq_lab: role=feature, type=numeric_sparse_frequency. tags=['condition_candidate', 'measure', 'rare_pattern_candidate'] desc=Frequency feature for token 'lab' in an email message. +- word_freq_labs: role=feature, type=numeric_sparse_frequency. tags=['condition_candidate', 'measure', 'rare_pattern_candidate'] desc=Frequency feature for token 'labs' in an email message. +- word_freq_telnet: role=feature, type=numeric_sparse_frequency. tags=['condition_candidate', 'measure', 'rare_pattern_candidate'] desc=Frequency feature for token 'telnet' in an email message. +- word_freq_857: role=feature, type=numeric_sparse_frequency. tags=['condition_candidate', 'measure', 'rare_pattern_candidate'] desc=Frequency feature for token '857' in an email message. +- word_freq_data: role=feature, type=numeric_sparse_frequency. tags=['condition_candidate', 'measure', 'rare_pattern_candidate'] desc=Frequency feature for token 'data' in an email message. +- word_freq_415: role=feature, type=numeric_sparse_frequency. tags=['condition_candidate', 'measure', 'rare_pattern_candidate'] desc=Frequency feature for token '415' in an email message. +- word_freq_85: role=feature, type=numeric_sparse_frequency. tags=['condition_candidate', 'measure', 'rare_pattern_candidate'] desc=Frequency feature for token '85' in an email message. +- word_freq_technology: role=feature, type=numeric_sparse_frequency. tags=['condition_candidate', 'measure', 'rare_pattern_candidate'] desc=Frequency feature for token 'technology' in an email message. +- word_freq_1999: role=feature, type=numeric_sparse_frequency. tags=['condition_candidate', 'measure', 'rare_pattern_candidate'] desc=Frequency feature for token '1999' in an email message. +- word_freq_parts: role=feature, type=numeric_sparse_frequency. tags=['condition_candidate', 'measure', 'rare_pattern_candidate'] desc=Frequency feature for token 'parts' in an email message. +- word_freq_pm: role=feature, type=numeric_sparse_frequency. tags=['condition_candidate', 'measure', 'rare_pattern_candidate'] desc=Frequency feature for token 'pm' in an email message. +- word_freq_direct: role=feature, type=numeric_sparse_frequency. tags=['condition_candidate', 'measure', 'rare_pattern_candidate'] desc=Frequency feature for token 'direct' in an email message. +- word_freq_cs: role=feature, type=numeric_sparse_frequency. tags=['condition_candidate', 'measure', 'rare_pattern_candidate'] desc=Frequency feature for token 'cs' in an email message. +- word_freq_meeting: role=feature, type=numeric_sparse_frequency. tags=['condition_candidate', 'measure', 'rare_pattern_candidate'] desc=Frequency feature for token 'meeting' in an email message. +- word_freq_original: role=feature, type=numeric_sparse_frequency. tags=['condition_candidate', 'measure', 'rare_pattern_candidate'] desc=Frequency feature for token 'original' in an email message. +- word_freq_project: role=feature, type=numeric_sparse_frequency. tags=['condition_candidate', 'measure', 'rare_pattern_candidate'] desc=Frequency feature for token 'project' in an email message. +- word_freq_re: role=feature, type=numeric_sparse_frequency. tags=['condition_candidate', 'measure', 'rare_pattern_candidate'] desc=Frequency feature for token 're' in an email message. +- word_freq_edu: role=feature, type=numeric_sparse_frequency. tags=['condition_candidate', 'measure', 'rare_pattern_candidate'] desc=Frequency feature for token 'edu' in an email message. +- word_freq_table: role=feature, type=numeric_sparse_frequency. tags=['condition_candidate', 'measure', 'rare_pattern_candidate'] desc=Frequency feature for token 'table' in an email message. +- word_freq_conference: role=feature, type=numeric_sparse_frequency. tags=['condition_candidate', 'measure', 'rare_pattern_candidate'] desc=Frequency feature for token 'conference' in an email message. +- char_freq_%3B: role=feature, type=numeric_sparse_frequency. tags=['condition_candidate', 'measure', 'rare_pattern_candidate'] desc=Character frequency feature for symbol ';'. +- char_freq_%28: role=feature, type=numeric_sparse_frequency. tags=['condition_candidate', 'measure', 'rare_pattern_candidate'] desc=Character frequency feature for symbol '('. +- char_freq_%5B: role=feature, type=numeric_sparse_frequency. tags=['condition_candidate', 'measure', 'rare_pattern_candidate'] desc=Character frequency feature for symbol '['. +- char_freq_%21: role=feature, type=numeric_sparse_frequency. tags=['condition_candidate', 'measure', 'rare_pattern_candidate'] desc=Character frequency feature for symbol '!'. +- char_freq_%24: role=feature, type=numeric_sparse_frequency. tags=['condition_candidate', 'measure', 'rare_pattern_candidate'] desc=Character frequency feature for symbol '$'. +- char_freq_%23: role=feature, type=numeric_sparse_frequency. tags=['condition_candidate', 'measure', 'rare_pattern_candidate'] desc=Character frequency feature for symbol '#'. +- capital_run_length_average: role=feature, type=numeric_heavy_tail. tags=['condition_candidate', 'measure', 'rare_pattern_candidate'] desc=Average length of uninterrupted capital-letter runs. +- capital_run_length_longest: role=feature, type=numeric_heavy_tail. tags=['condition_candidate', 'measure', 'rare_pattern_candidate'] desc=Longest uninterrupted capital-letter run. +- capital_run_length_total: role=feature, type=numeric_heavy_tail. tags=['condition_candidate', 'measure', 'rare_pattern_candidate'] desc=Total number of capital letters in runs. +- class: role=target, type=binary_target. tags=['target_candidate'] desc=Binary spam label (1=spam, 0=non-spam). +- useful_field_combinations: [['word_freq_free', 'word_freq_you', 'class'], ['char_freq_%21', 'capital_run_length_longest', 'class'], ['word_freq_remove', 'word_freq_money', 'class']] +- fields_requiring_caution: ['capital_run_length_average', 'capital_run_length_longest', 'capital_run_length_total'] +- source_url: https://www.openml.org/d/44 + +SQLite schema snapshot: +{ + "table_name": "n1", + "quoted_table_name": "\"n1\"", + "row_count": 4601, + "columns": [ + { + "name": "word_freq_make", + "type": "TEXT", + "notnull": false, + "pk": false + }, + { + "name": "word_freq_address", + "type": "TEXT", + "notnull": false, + "pk": false + }, + { + "name": "word_freq_all", + "type": "TEXT", + "notnull": false, + "pk": false + }, + { + "name": "word_freq_3d", + "type": "TEXT", + "notnull": false, + "pk": false + }, + { + "name": "word_freq_our", + "type": "TEXT", + "notnull": false, + "pk": false + }, + { + "name": "word_freq_over", + "type": "TEXT", + "notnull": false, + "pk": false + }, + { + "name": "word_freq_remove", + "type": "TEXT", + "notnull": false, + "pk": false + }, + { + "name": "word_freq_internet", + "type": "TEXT", + "notnull": false, + "pk": false + }, + { + "name": "word_freq_order", + "type": "TEXT", + "notnull": false, + "pk": false + }, + { + "name": "word_freq_mail", + "type": "TEXT", + "notnull": false, + "pk": false + }, + { + "name": "word_freq_receive", + "type": "TEXT", + "notnull": false, + "pk": false + }, + { + "name": "word_freq_will", + "type": "TEXT", + "notnull": false, + "pk": false + }, + { + "name": "word_freq_people", + "type": "TEXT", + "notnull": false, + "pk": false + }, + { + "name": "word_freq_report", + "type": "TEXT", + "notnull": false, + "pk": false + }, + { + "name": "word_freq_addresses", + "type": "TEXT", + "notnull": false, + "pk": false + }, + { + "name": "word_freq_free", + "type": "TEXT", + "notnull": false, + "pk": false + }, + { + "name": "word_freq_business", + "type": "TEXT", + "notnull": false, + "pk": false + }, + { + "name": "word_freq_email", + "type": "TEXT", + "notnull": false, + "pk": false + }, + { + "name": "word_freq_you", + "type": "TEXT", + "notnull": false, + "pk": false + }, + { + "name": "word_freq_credit", + "type": "TEXT", + "notnull": false, + "pk": false + }, + { + "name": "word_freq_your", + "type": "TEXT", + "notnull": false, + "pk": false + }, + { + "name": "word_freq_font", + "type": "TEXT", + "notnull": false, + "pk": false + }, + { + "name": "word_freq_000", + "type": "TEXT", + "notnull": false, + "pk": false + }, + { + "name": "word_freq_money", + "type": "TEXT", + "notnull": false, + "pk": false + }, + { + "name": "word_freq_hp", + "type": "TEXT", + "notnull": false, + "pk": false + }, + { + "name": "word_freq_hpl", + "type": "TEXT", + "notnull": false, + "pk": false + }, + { + "name": "word_freq_george", + "type": "TEXT", + "notnull": false, + "pk": false + }, + { + "name": "word_freq_650", + "type": "TEXT", + "notnull": false, + "pk": false + }, + { + "name": "word_freq_lab", + "type": "TEXT", + "notnull": false, + "pk": false + }, + { + "name": "word_freq_labs", + "type": "TEXT", + "notnull": false, + "pk": false + }, + { + "name": "word_freq_telnet", + "type": "TEXT", + "notnull": false, + "pk": false + }, + { + "name": "word_freq_857", + "type": "TEXT", + "notnull": false, + "pk": false + }, + { + "name": "word_freq_data", + "type": "TEXT", + "notnull": false, + "pk": false + }, + { + "name": "word_freq_415", + "type": "TEXT", + "notnull": false, + "pk": false + }, + { + "name": "word_freq_85", + "type": "TEXT", + "notnull": false, + "pk": false + }, + { + "name": "word_freq_technology", + "type": "TEXT", + "notnull": false, + "pk": false + }, + { + "name": "word_freq_1999", + "type": "TEXT", + "notnull": false, + "pk": false + }, + { + "name": "word_freq_parts", + "type": "TEXT", + "notnull": false, + "pk": false + }, + { + "name": "word_freq_pm", + "type": "TEXT", + "notnull": false, + "pk": false + }, + { + "name": "word_freq_direct", + "type": "TEXT", + "notnull": false, + "pk": false + }, + { + "name": "word_freq_cs", + "type": "TEXT", + "notnull": false, + "pk": false + }, + { + "name": "word_freq_meeting", + "type": "TEXT", + "notnull": false, + "pk": false + }, + { + "name": "word_freq_original", + "type": "TEXT", + "notnull": false, + "pk": false + }, + { + "name": "word_freq_project", + "type": "TEXT", + "notnull": false, + "pk": false + }, + { + "name": "word_freq_re", + "type": "TEXT", + "notnull": false, + "pk": false + }, + { + "name": "word_freq_edu", + "type": "TEXT", + "notnull": false, + "pk": false + }, + { + "name": "word_freq_table", + "type": "TEXT", + "notnull": false, + "pk": false + }, + { + "name": "word_freq_conference", + "type": "TEXT", + "notnull": false, + "pk": false + }, + { + "name": "char_freq_%3B", + "type": "TEXT", + "notnull": false, + "pk": false + }, + { + "name": "char_freq_%28", + "type": "TEXT", + "notnull": false, + "pk": false + }, + { + "name": "char_freq_%5B", + "type": "TEXT", + "notnull": false, + "pk": false + }, + { + "name": "char_freq_%21", + "type": "TEXT", + "notnull": false, + "pk": false + }, + { + "name": "char_freq_%24", + "type": "TEXT", + "notnull": false, + "pk": false + }, + { + "name": "char_freq_%23", + "type": "TEXT", + "notnull": false, + "pk": false + }, + { + "name": "capital_run_length_average", + "type": "TEXT", + "notnull": false, + "pk": false + }, + { + "name": "capital_run_length_longest", + "type": "TEXT", + "notnull": false, + "pk": false + }, + { + "name": "capital_run_length_total", + "type": "TEXT", + "notnull": false, + "pk": false + }, + { + "name": "class", + "type": "TEXT", + "notnull": false, + "pk": false + } + ], + "sample_rows": [ + { + "word_freq_make": "0", + "word_freq_address": "0.64", + "word_freq_all": "0.64", + "word_freq_3d": "0", + "word_freq_our": "0.32", + "word_freq_over": "0", + "word_freq_remove": "0", + "word_freq_internet": "0", + "word_freq_order": "0", + "word_freq_mail": "0", + "word_freq_receive": "0", + "word_freq_will": "0.64", + "word_freq_people": "0", + "word_freq_report": "0", + "word_freq_addresses": "0", + "word_freq_free": "0.32", + "word_freq_business": "0", + "word_freq_email": "1.29", + "word_freq_you": "1.93", + "word_freq_credit": "0", + "word_freq_your": "0.96", + "word_freq_font": "0", + "word_freq_000": "0", + "word_freq_money": "0", + "word_freq_hp": "0", + "word_freq_hpl": "0", + "word_freq_george": "0", + "word_freq_650": "0", + "word_freq_lab": "0", + "word_freq_labs": "0", + "word_freq_telnet": "0", + "word_freq_857": "0", + "word_freq_data": "0", + "word_freq_415": "0", + "word_freq_85": "0", + "word_freq_technology": "0", + "word_freq_1999": "0", + "word_freq_parts": "0", + "word_freq_pm": "0", + "word_freq_direct": "0", + "word_freq_cs": "0", + "word_freq_meeting": "0", + "word_freq_original": "0", + "word_freq_project": "0", + "word_freq_re": "0", + "word_freq_edu": "0", + "word_freq_table": "0", + "word_freq_conference": "0", + "char_freq_%3B": "0", + "char_freq_%28": "0", + "char_freq_%5B": "0", + "char_freq_%21": "0.778", + "char_freq_%24": "0", + "char_freq_%23": "0", + "capital_run_length_average": "3.756", + "capital_run_length_longest": "61", + "capital_run_length_total": "278", + "class": "1" + }, + { + "word_freq_make": "0.21", + "word_freq_address": "0.28", + "word_freq_all": "0.5", + "word_freq_3d": "0", + "word_freq_our": "0.14", + "word_freq_over": "0.28", + "word_freq_remove": "0.21", + "word_freq_internet": "0.07", + "word_freq_order": "0", + "word_freq_mail": "0.94", + "word_freq_receive": "0.21", + "word_freq_will": "0.79", + "word_freq_people": "0.65", + "word_freq_report": "0.21", + "word_freq_addresses": "0.14", + "word_freq_free": "0.14", + "word_freq_business": "0.07", + "word_freq_email": "0.28", + "word_freq_you": "3.47", + "word_freq_credit": "0", + "word_freq_your": "1.59", + "word_freq_font": "0", + "word_freq_000": "0.43", + "word_freq_money": "0.43", + "word_freq_hp": "0", + "word_freq_hpl": "0", + "word_freq_george": "0", + "word_freq_650": "0", + "word_freq_lab": "0", + "word_freq_labs": "0", + "word_freq_telnet": "0", + "word_freq_857": "0", + "word_freq_data": "0", + "word_freq_415": "0", + "word_freq_85": "0", + "word_freq_technology": "0", + "word_freq_1999": "0.07", + "word_freq_parts": "0", + "word_freq_pm": "0", + "word_freq_direct": "0", + "word_freq_cs": "0", + "word_freq_meeting": "0", + "word_freq_original": "0", + "word_freq_project": "0", + "word_freq_re": "0", + "word_freq_edu": "0", + "word_freq_table": "0", + "word_freq_conference": "0", + "char_freq_%3B": "0", + "char_freq_%28": "0.132", + "char_freq_%5B": "0", + "char_freq_%21": "0.372", + "char_freq_%24": "0.18", + "char_freq_%23": "0.048", + "capital_run_length_average": "5.114", + "capital_run_length_longest": "101", + "capital_run_length_total": "1028", + "class": "1" + }, + { + "word_freq_make": "0.06", + "word_freq_address": "0", + "word_freq_all": "0.71", + "word_freq_3d": "0", + "word_freq_our": "1.23", + "word_freq_over": "0.19", + "word_freq_remove": "0.19", + "word_freq_internet": "0.12", + "word_freq_order": "0.64", + "word_freq_mail": "0.25", + "word_freq_receive": "0.38", + "word_freq_will": "0.45", + "word_freq_people": "0.12", + "word_freq_report": "0", + "word_freq_addresses": "1.75", + "word_freq_free": "0.06", + "word_freq_business": "0.06", + "word_freq_email": "1.03", + "word_freq_you": "1.36", + "word_freq_credit": "0.32", + "word_freq_your": "0.51", + "word_freq_font": "0", + "word_freq_000": "1.16", + "word_freq_money": "0.06", + "word_freq_hp": "0", + "word_freq_hpl": "0", + "word_freq_george": "0", + "word_freq_650": "0", + "word_freq_lab": "0", + "word_freq_labs": "0", + "word_freq_telnet": "0", + "word_freq_857": "0", + "word_freq_data": "0", + "word_freq_415": "0", + "word_freq_85": "0", + "word_freq_technology": "0", + "word_freq_1999": "0", + "word_freq_parts": "0", + "word_freq_pm": "0", + "word_freq_direct": "0.06", + "word_freq_cs": "0", + "word_freq_meeting": "0", + "word_freq_original": "0.12", + "word_freq_project": "0", + "word_freq_re": "0.06", + "word_freq_edu": "0.06", + "word_freq_table": "0", + "word_freq_conference": "0", + "char_freq_%3B": "0.01", + "char_freq_%28": "0.143", + "char_freq_%5B": "0", + "char_freq_%21": "0.276", + "char_freq_%24": "0.184", + "char_freq_%23": "0.01", + "capital_run_length_average": "9.821", + "capital_run_length_longest": "485", + "capital_run_length_total": "2259", + "class": "1" + }, + { + "word_freq_make": "0", + "word_freq_address": "0", + "word_freq_all": "0", + "word_freq_3d": "0", + "word_freq_our": "0.63", + "word_freq_over": "0", + "word_freq_remove": "0.31", + "word_freq_internet": "0.63", + "word_freq_order": "0.31", + "word_freq_mail": "0.63", + "word_freq_receive": "0.31", + "word_freq_will": "0.31", + "word_freq_people": "0.31", + "word_freq_report": "0", + "word_freq_addresses": "0", + "word_freq_free": "0.31", + "word_freq_business": "0", + "word_freq_email": "0", + "word_freq_you": "3.18", + "word_freq_credit": "0", + "word_freq_your": "0.31", + "word_freq_font": "0", + "word_freq_000": "0", + "word_freq_money": "0", + "word_freq_hp": "0", + "word_freq_hpl": "0", + "word_freq_george": "0", + "word_freq_650": "0", + "word_freq_lab": "0", + "word_freq_labs": "0", + "word_freq_telnet": "0", + "word_freq_857": "0", + "word_freq_data": "0", + "word_freq_415": "0", + "word_freq_85": "0", + "word_freq_technology": "0", + "word_freq_1999": "0", + "word_freq_parts": "0", + "word_freq_pm": "0", + "word_freq_direct": "0", + "word_freq_cs": "0", + "word_freq_meeting": "0", + "word_freq_original": "0", + "word_freq_project": "0", + "word_freq_re": "0", + "word_freq_edu": "0", + "word_freq_table": "0", + "word_freq_conference": "0", + "char_freq_%3B": "0", + "char_freq_%28": "0.137", + "char_freq_%5B": "0", + "char_freq_%21": "0.137", + "char_freq_%24": "0", + "char_freq_%23": "0", + "capital_run_length_average": "3.537", + "capital_run_length_longest": "40", + "capital_run_length_total": "191", + "class": "1" + }, + { + "word_freq_make": "0", + "word_freq_address": "0", + "word_freq_all": "0", + "word_freq_3d": "0", + "word_freq_our": "0.63", + "word_freq_over": "0", + "word_freq_remove": "0.31", + "word_freq_internet": "0.63", + "word_freq_order": "0.31", + "word_freq_mail": "0.63", + "word_freq_receive": "0.31", + "word_freq_will": "0.31", + "word_freq_people": "0.31", + "word_freq_report": "0", + "word_freq_addresses": "0", + "word_freq_free": "0.31", + "word_freq_business": "0", + "word_freq_email": "0", + "word_freq_you": "3.18", + "word_freq_credit": "0", + "word_freq_your": "0.31", + "word_freq_font": "0", + "word_freq_000": "0", + "word_freq_money": "0", + "word_freq_hp": "0", + "word_freq_hpl": "0", + "word_freq_george": "0", + "word_freq_650": "0", + "word_freq_lab": "0", + "word_freq_labs": "0", + "word_freq_telnet": "0", + "word_freq_857": "0", + "word_freq_data": "0", + "word_freq_415": "0", + "word_freq_85": "0", + "word_freq_technology": "0", + "word_freq_1999": "0", + "word_freq_parts": "0", + "word_freq_pm": "0", + "word_freq_direct": "0", + "word_freq_cs": "0", + "word_freq_meeting": "0", + "word_freq_original": "0", + "word_freq_project": "0", + "word_freq_re": "0", + "word_freq_edu": "0", + "word_freq_table": "0", + "word_freq_conference": "0", + "char_freq_%3B": "0", + "char_freq_%28": "0.135", + "char_freq_%5B": "0", + "char_freq_%21": "0.135", + "char_freq_%24": "0", + "char_freq_%23": "0", + "capital_run_length_average": "3.537", + "capital_run_length_longest": "40", + "capital_run_length_total": "191", + "class": "1" + } + ] +} + +Shortlisted templates: +[ + { + "template_id": "tpl_grouped_percentile_point", + "template_name": "Grouped Percentile Point", + "primary_family": "tail_rarity_structure", + "portability": "yes", + "sql_skeleton": "SELECT {group_col},\n PERCENTILE_CONT({percentile_value}) WITHIN GROUP (ORDER BY {measure_col}) AS percentile_measure\nFROM {table}\nGROUP BY {group_col}\nORDER BY percentile_measure DESC;", + "required_roles": [ + "group_col", + "measure_col" + ] + } +] + +Problem instance: +{ + "dataset_id": "n1", + "question": "Use template Grouped Percentile Point to probe tail_concentration_consistency with semantic role ranked_signal_view. Focus on group_col=class, measure_col=word_freq_data.", + "planned_template_id": "tpl_grouped_percentile_point", + "bindings": { + "group_col": "class", + "measure_col": "word_freq_data", + "top_k": 14, + "top_n": 4, + "num_tiles": 10, + "percentile_value": 0.9, + "z_threshold": 2.0, + "fraction_threshold": 0.1, + "baseline_multiplier": 1.5, + "baseline_fraction": 0.1, + "min_group_size": 5, + "min_support": 5, + "measure_threshold": 0.0, + "time_grain": "month", + "lookback_rows": 3, + "current_period_start": "'2024-01-01'", + "current_period_end": "'2024-04-01'", + "previous_period_start": "'2023-10-01'", + "previous_period_end": "'2024-01-01'", + "drift_ratio_threshold": 0.8 + }, + "can_vary": [], + "must_fix": [], + "runtime_sql_skeleton": "SELECT {group_col},\n PERCENTILE_CONT({percentile_value}) WITHIN GROUP (ORDER BY {measure_col}) AS percentile_measure\nFROM {table}\nGROUP BY {group_col}\nORDER BY percentile_measure DESC;" +} + +Repair context: +{} diff --git a/Query/sql/v2/runs/v2_cli_20260502_081223_d/n1/artifacts/v2q_n1_894c352c250c7153/cli/sql_response_attempt_1.raw.txt b/Query/sql/v2/runs/v2_cli_20260502_081223_d/n1/artifacts/v2q_n1_894c352c250c7153/cli/sql_response_attempt_1.raw.txt new file mode 100644 index 0000000000000000000000000000000000000000..6bdebb20486aee43e47d94b38e783bb5218bd9e4 --- /dev/null +++ b/Query/sql/v2/runs/v2_cli_20260502_081223_d/n1/artifacts/v2q_n1_894c352c250c7153/cli/sql_response_attempt_1.raw.txt @@ -0,0 +1,4 @@ +{"type":"thread.started","thread_id":"019e40f3-3203-74e1-ac26-5050ade0205e"} +{"type":"turn.started"} +{"type":"error","message":"Quota exceeded. Check your plan and billing details."} +{"type":"turn.failed","error":{"message":"Quota exceeded. Check your plan and billing details."}} diff --git a/Query/sql/v2/runs/v2_cli_20260502_081223_d/n1/artifacts/v2q_n1_894c352c250c7153/cli/sql_response_attempt_1.txt b/Query/sql/v2/runs/v2_cli_20260502_081223_d/n1/artifacts/v2q_n1_894c352c250c7153/cli/sql_response_attempt_1.txt new file mode 100644 index 0000000000000000000000000000000000000000..2030b4af0df309dc7d85fc3058f2e589a799fd45 --- /dev/null +++ b/Query/sql/v2/runs/v2_cli_20260502_081223_d/n1/artifacts/v2q_n1_894c352c250c7153/cli/sql_response_attempt_1.txt @@ -0,0 +1,4 @@ +{"type":"thread.started","thread_id":"019e40f3-3203-74e1-ac26-5050ade0205e"} +{"type":"turn.started"} +{"type":"error","message":"Quota exceeded. Check your plan and billing details."} +{"type":"turn.failed","error":{"message":"Quota exceeded. Check your plan and billing details."}} \ No newline at end of file diff --git a/Query/sql/v2/runs/v2_cli_20260502_081223_d/n1/artifacts/v2q_n1_894c352c250c7153/cli/sql_response_attempt_2.raw.txt b/Query/sql/v2/runs/v2_cli_20260502_081223_d/n1/artifacts/v2q_n1_894c352c250c7153/cli/sql_response_attempt_2.raw.txt new file mode 100644 index 0000000000000000000000000000000000000000..d00e8b04fef0e72ee2fcf0662c000724768e6ef8 --- /dev/null +++ b/Query/sql/v2/runs/v2_cli_20260502_081223_d/n1/artifacts/v2q_n1_894c352c250c7153/cli/sql_response_attempt_2.raw.txt @@ -0,0 +1,4 @@ +{"type":"thread.started","thread_id":"019e40f3-4117-7802-a50e-cb290cdfe3d0"} +{"type":"turn.started"} +{"type":"error","message":"Quota exceeded. Check your plan and billing details."} +{"type":"turn.failed","error":{"message":"Quota exceeded. Check your plan and billing details."}} diff --git a/Query/sql/v2/runs/v2_cli_20260502_081223_d/n1/artifacts/v2q_n1_894c352c250c7153/cli/sql_response_attempt_2.txt b/Query/sql/v2/runs/v2_cli_20260502_081223_d/n1/artifacts/v2q_n1_894c352c250c7153/cli/sql_response_attempt_2.txt new file mode 100644 index 0000000000000000000000000000000000000000..95a4a3f41b9aaee2677ce8f48705eef679596f2e --- /dev/null +++ b/Query/sql/v2/runs/v2_cli_20260502_081223_d/n1/artifacts/v2q_n1_894c352c250c7153/cli/sql_response_attempt_2.txt @@ -0,0 +1,4 @@ +{"type":"thread.started","thread_id":"019e40f3-4117-7802-a50e-cb290cdfe3d0"} +{"type":"turn.started"} +{"type":"error","message":"Quota exceeded. Check your plan and billing details."} +{"type":"turn.failed","error":{"message":"Quota exceeded. Check your plan and billing details."}} \ No newline at end of file diff --git a/Query/sql/v2/runs/v2_cli_20260502_081223_d/n1/artifacts/v2q_n1_894c352c250c7153/cli/sql_stderr_attempt_1.txt b/Query/sql/v2/runs/v2_cli_20260502_081223_d/n1/artifacts/v2q_n1_894c352c250c7153/cli/sql_stderr_attempt_1.txt new file mode 100644 index 0000000000000000000000000000000000000000..e69de29bb2d1d6434b8b29ae775ad8c2e48c5391 diff --git a/Query/sql/v2/runs/v2_cli_20260502_081223_d/n1/artifacts/v2q_n1_894c352c250c7153/cli/sql_stderr_attempt_2.txt b/Query/sql/v2/runs/v2_cli_20260502_081223_d/n1/artifacts/v2q_n1_894c352c250c7153/cli/sql_stderr_attempt_2.txt new file mode 100644 index 0000000000000000000000000000000000000000..e69de29bb2d1d6434b8b29ae775ad8c2e48c5391 diff --git a/Query/sql/v2/runs/v2_cli_20260502_081223_d/n1/artifacts/v2q_n1_8c91771173682483/cli/conversation.jsonl b/Query/sql/v2/runs/v2_cli_20260502_081223_d/n1/artifacts/v2q_n1_8c91771173682483/cli/conversation.jsonl new file mode 100644 index 0000000000000000000000000000000000000000..8681c5d4038e36f8d56567f8e73e37a50a60562e --- /dev/null +++ b/Query/sql/v2/runs/v2_cli_20260502_081223_d/n1/artifacts/v2q_n1_8c91771173682483/cli/conversation.jsonl @@ -0,0 +1,2 @@ +{"attempt": 1, "phase": "sql_generation", "role": "user", "content_path": "cli/sql_prompt_attempt_1.txt", "metrics": {"chars": 29439, "bytes_utf8": 29439, "lines": 792, "estimated_tokens": null}} +{"attempt": 1, "phase": "sql_generation", "role": "assistant", "content_path": "cli/sql_response_attempt_1.txt", "raw_content_path": "cli/sql_response_attempt_1.raw.txt", "stderr_path": "cli/sql_stderr_attempt_1.txt", "metrics": {"chars": 415, "bytes_utf8": 415, "lines": 1, "estimated_tokens": null}, "usage": {"input_tokens": 20328, "cached_input_tokens": 19840, "output_tokens": 339, "reasoning_output_tokens": 234}} diff --git a/Query/sql/v2/runs/v2_cli_20260502_081223_d/n1/artifacts/v2q_n1_8c91771173682483/cli/session_summary.json b/Query/sql/v2/runs/v2_cli_20260502_081223_d/n1/artifacts/v2q_n1_8c91771173682483/cli/session_summary.json new file mode 100644 index 0000000000000000000000000000000000000000..709cb7a58186151989f301d16d617d71b47d7b49 --- /dev/null +++ b/Query/sql/v2/runs/v2_cli_20260502_081223_d/n1/artifacts/v2q_n1_8c91771173682483/cli/session_summary.json @@ -0,0 +1,25 @@ +{ + "engine": "v2-cli:codex", + "command": "codex exec --skip-git-repo-check --disable plugins --sandbox read-only --cd \"/data/jialinzhang/SQLagent\" -m gpt-5.4 --json -", + "ai_cli_calls": 1, + "usage_summary": { + "dataset_id": "n1", + "model": "v2-cli:codex", + "run_id": "v2q_n1_8c91771173682483", + "api_calls": 0, + "input_tokens": 20328, + "cached_input_tokens": 19840, + "output_tokens": 339, + "total_tokens": 20667, + "cost_usd": 0.0, + "ai_cli_calls": 1, + "estimated_input_tokens": 0, + "estimated_output_tokens": 0, + "estimated_total_tokens": 0, + "usage_source": "ai_cli_json_usage", + "cli_elapsed_ms_total": 14687.4, + "sql_execution_elapsed_ms_total": 7.85, + "conversation_log_path": "/data/jialinzhang/TabQueryBench/sql_workloads/v2_current/runs_and_launches/runs/v2_cli_20260502_081223_d/n1/artifacts/v2q_n1_8c91771173682483/cli/conversation.jsonl", + "note": "Executed through a local AI CLI with structured usage metadata." + } +} \ No newline at end of file diff --git a/Query/sql/v2/runs/v2_cli_20260502_081223_d/n1/artifacts/v2q_n1_8c91771173682483/cli/sql_attempt_1.metadata.json b/Query/sql/v2/runs/v2_cli_20260502_081223_d/n1/artifacts/v2q_n1_8c91771173682483/cli/sql_attempt_1.metadata.json new file mode 100644 index 0000000000000000000000000000000000000000..f9747cf8e015ed891af9b57f7d46ff3b95337563 --- /dev/null +++ b/Query/sql/v2/runs/v2_cli_20260502_081223_d/n1/artifacts/v2q_n1_8c91771173682483/cli/sql_attempt_1.metadata.json @@ -0,0 +1,45 @@ +{ + "attempt": 1, + "phase": "sql_generation", + "command": "codex exec --skip-git-repo-check --disable plugins --sandbox read-only --cd \"/data/jialinzhang/SQLagent\" -m gpt-5.4 --json -", + "started_at": "2026-05-19T15:43:17.683777+00:00", + "ended_at": "2026-05-19T15:43:32.371208+00:00", + "elapsed_ms": 14687.4, + "prompt_metrics": { + "chars": 29439, + "bytes_utf8": 29439, + "lines": 792, + "estimated_tokens": null + }, + "stdout_metrics": { + "chars": 1102, + "bytes_utf8": 1102, + "lines": 5, + "estimated_tokens": null + }, + "stderr_metrics": { + "chars": 0, + "bytes_utf8": 0, + "lines": 0, + "estimated_tokens": null + }, + "parsed_output": { + "format": "jsonl_events", + "text_metrics": { + "chars": 415, + "bytes_utf8": 415, + "lines": 1, + "estimated_tokens": null + }, + "usage": { + "input_tokens": 20328, + "cached_input_tokens": 19840, + "output_tokens": 339, + "reasoning_output_tokens": 234 + } + }, + "prompt_path": "cli/sql_prompt_attempt_1.txt", + "response_path": "cli/sql_response_attempt_1.txt", + "raw_response_path": "cli/sql_response_attempt_1.raw.txt", + "stderr_path": "cli/sql_stderr_attempt_1.txt" +} \ No newline at end of file diff --git a/Query/sql/v2/runs/v2_cli_20260502_081223_d/n1/artifacts/v2q_n1_8c91771173682483/cli/sql_prompt_attempt_1.txt b/Query/sql/v2/runs/v2_cli_20260502_081223_d/n1/artifacts/v2q_n1_8c91771173682483/cli/sql_prompt_attempt_1.txt new file mode 100644 index 0000000000000000000000000000000000000000..2ff9f3a0eff0081c354294fe9324ff681fcc9228 --- /dev/null +++ b/Query/sql/v2/runs/v2_cli_20260502_081223_d/n1/artifacts/v2q_n1_8c91771173682483/cli/sql_prompt_attempt_1.txt @@ -0,0 +1,792 @@ +You are generating one SQLite SELECT query for a single-table SQL QA task. +Return strict JSON only, with this schema: {"sql": "...", "notes": "..."}. +Rules: +- Use only the provided table and columns. +- Do not write INSERT, UPDATE, DELETE, DROP, ALTER, CREATE, PRAGMA, ATTACH, DETACH, or VACUUM. +- Prefer the planned template and bound roles when provided. +- Add a leading SQL comment exactly like: -- template_id: . +- Generate SQLite-compatible SQL. SQLite does not support PERCENTILE_CONT or STDDEV. +- Quote identifiers with double quotes. +- Return no markdown and no extra prose. + +Dataset context: +Dataset context for SQL QA: +- dataset_id: n1 +- dataset_name: Spambase +- table_name: n1 +- table_layout: single-table dataset (do not assume joins). +- row_semantics: One row is one email represented by engineered token/character frequency and capitalization-run features. +- task_type: classification +- target_column: class +- main_row_count: 4601 +- important_fields: +- word_freq_make: role=feature, type=numeric_sparse_frequency. tags=['condition_candidate', 'measure', 'rare_pattern_candidate'] desc=Frequency feature for token 'make' in an email message. +- word_freq_address: role=feature, type=numeric_sparse_frequency. tags=['condition_candidate', 'measure', 'rare_pattern_candidate'] desc=Frequency feature for token 'address' in an email message. +- word_freq_all: role=feature, type=numeric_sparse_frequency. tags=['condition_candidate', 'measure', 'rare_pattern_candidate'] desc=Frequency feature for token 'all' in an email message. +- word_freq_3d: role=feature, type=numeric_sparse_frequency. tags=['condition_candidate', 'measure', 'rare_pattern_candidate'] desc=Frequency feature for token '3d' in an email message. +- word_freq_our: role=feature, type=numeric_sparse_frequency. tags=['condition_candidate', 'measure', 'rare_pattern_candidate'] desc=Frequency feature for token 'our' in an email message. +- word_freq_over: role=feature, type=numeric_sparse_frequency. tags=['condition_candidate', 'measure', 'rare_pattern_candidate'] desc=Frequency feature for token 'over' in an email message. +- word_freq_remove: role=feature, type=numeric_sparse_frequency. tags=['condition_candidate', 'measure', 'rare_pattern_candidate'] desc=Frequency feature for token 'remove' in an email message. +- word_freq_internet: role=feature, type=numeric_sparse_frequency. tags=['condition_candidate', 'measure', 'rare_pattern_candidate'] desc=Frequency feature for token 'internet' in an email message. +- word_freq_order: role=feature, type=numeric_sparse_frequency. tags=['condition_candidate', 'measure', 'rare_pattern_candidate'] desc=Frequency feature for token 'order' in an email message. +- word_freq_mail: role=feature, type=numeric_sparse_frequency. tags=['condition_candidate', 'measure', 'rare_pattern_candidate'] desc=Frequency feature for token 'mail' in an email message. +- word_freq_receive: role=feature, type=numeric_sparse_frequency. tags=['condition_candidate', 'measure', 'rare_pattern_candidate'] desc=Frequency feature for token 'receive' in an email message. +- word_freq_will: role=feature, type=numeric_sparse_frequency. tags=['condition_candidate', 'measure', 'rare_pattern_candidate'] desc=Frequency feature for token 'will' in an email message. +- word_freq_people: role=feature, type=numeric_sparse_frequency. tags=['condition_candidate', 'measure', 'rare_pattern_candidate'] desc=Frequency feature for token 'people' in an email message. +- word_freq_report: role=feature, type=numeric_sparse_frequency. tags=['condition_candidate', 'measure', 'rare_pattern_candidate'] desc=Frequency feature for token 'report' in an email message. +- word_freq_addresses: role=feature, type=numeric_sparse_frequency. tags=['condition_candidate', 'measure', 'rare_pattern_candidate'] desc=Frequency feature for token 'addresses' in an email message. +- word_freq_free: role=feature, type=numeric_sparse_frequency. tags=['condition_candidate', 'measure', 'rare_pattern_candidate'] desc=Frequency feature for token 'free' in an email message. +- word_freq_business: role=feature, type=numeric_sparse_frequency. tags=['condition_candidate', 'measure', 'rare_pattern_candidate'] desc=Frequency feature for token 'business' in an email message. +- word_freq_email: role=feature, type=numeric_sparse_frequency. tags=['condition_candidate', 'measure', 'rare_pattern_candidate'] desc=Frequency feature for token 'email' in an email message. +- word_freq_you: role=feature, type=numeric_sparse_frequency. tags=['condition_candidate', 'measure', 'rare_pattern_candidate'] desc=Frequency feature for token 'you' in an email message. +- word_freq_credit: role=feature, type=numeric_sparse_frequency. tags=['condition_candidate', 'measure', 'rare_pattern_candidate'] desc=Frequency feature for token 'credit' in an email message. +- word_freq_your: role=feature, type=numeric_sparse_frequency. tags=['condition_candidate', 'measure', 'rare_pattern_candidate'] desc=Frequency feature for token 'your' in an email message. +- word_freq_font: role=feature, type=numeric_sparse_frequency. tags=['condition_candidate', 'measure', 'rare_pattern_candidate'] desc=Frequency feature for token 'font' in an email message. +- word_freq_000: role=feature, type=numeric_sparse_frequency. tags=['condition_candidate', 'measure', 'rare_pattern_candidate'] desc=Frequency feature for token '000' in an email message. +- word_freq_money: role=feature, type=numeric_sparse_frequency. tags=['condition_candidate', 'measure', 'rare_pattern_candidate'] desc=Frequency feature for token 'money' in an email message. +- word_freq_hp: role=feature, type=numeric_sparse_frequency. tags=['condition_candidate', 'measure', 'rare_pattern_candidate'] desc=Frequency feature for token 'hp' in an email message. +- word_freq_hpl: role=feature, type=numeric_sparse_frequency. tags=['condition_candidate', 'measure', 'rare_pattern_candidate'] desc=Frequency feature for token 'hpl' in an email message. +- word_freq_george: role=feature, type=numeric_sparse_frequency. tags=['condition_candidate', 'measure', 'rare_pattern_candidate'] desc=Frequency feature for token 'george' in an email message. +- word_freq_650: role=feature, type=numeric_sparse_frequency. tags=['condition_candidate', 'measure', 'rare_pattern_candidate'] desc=Frequency feature for token '650' in an email message. +- word_freq_lab: role=feature, type=numeric_sparse_frequency. tags=['condition_candidate', 'measure', 'rare_pattern_candidate'] desc=Frequency feature for token 'lab' in an email message. +- word_freq_labs: role=feature, type=numeric_sparse_frequency. tags=['condition_candidate', 'measure', 'rare_pattern_candidate'] desc=Frequency feature for token 'labs' in an email message. +- word_freq_telnet: role=feature, type=numeric_sparse_frequency. tags=['condition_candidate', 'measure', 'rare_pattern_candidate'] desc=Frequency feature for token 'telnet' in an email message. +- word_freq_857: role=feature, type=numeric_sparse_frequency. tags=['condition_candidate', 'measure', 'rare_pattern_candidate'] desc=Frequency feature for token '857' in an email message. +- word_freq_data: role=feature, type=numeric_sparse_frequency. tags=['condition_candidate', 'measure', 'rare_pattern_candidate'] desc=Frequency feature for token 'data' in an email message. +- word_freq_415: role=feature, type=numeric_sparse_frequency. tags=['condition_candidate', 'measure', 'rare_pattern_candidate'] desc=Frequency feature for token '415' in an email message. +- word_freq_85: role=feature, type=numeric_sparse_frequency. tags=['condition_candidate', 'measure', 'rare_pattern_candidate'] desc=Frequency feature for token '85' in an email message. +- word_freq_technology: role=feature, type=numeric_sparse_frequency. tags=['condition_candidate', 'measure', 'rare_pattern_candidate'] desc=Frequency feature for token 'technology' in an email message. +- word_freq_1999: role=feature, type=numeric_sparse_frequency. tags=['condition_candidate', 'measure', 'rare_pattern_candidate'] desc=Frequency feature for token '1999' in an email message. +- word_freq_parts: role=feature, type=numeric_sparse_frequency. tags=['condition_candidate', 'measure', 'rare_pattern_candidate'] desc=Frequency feature for token 'parts' in an email message. +- word_freq_pm: role=feature, type=numeric_sparse_frequency. tags=['condition_candidate', 'measure', 'rare_pattern_candidate'] desc=Frequency feature for token 'pm' in an email message. +- word_freq_direct: role=feature, type=numeric_sparse_frequency. tags=['condition_candidate', 'measure', 'rare_pattern_candidate'] desc=Frequency feature for token 'direct' in an email message. +- word_freq_cs: role=feature, type=numeric_sparse_frequency. tags=['condition_candidate', 'measure', 'rare_pattern_candidate'] desc=Frequency feature for token 'cs' in an email message. +- word_freq_meeting: role=feature, type=numeric_sparse_frequency. tags=['condition_candidate', 'measure', 'rare_pattern_candidate'] desc=Frequency feature for token 'meeting' in an email message. +- word_freq_original: role=feature, type=numeric_sparse_frequency. tags=['condition_candidate', 'measure', 'rare_pattern_candidate'] desc=Frequency feature for token 'original' in an email message. +- word_freq_project: role=feature, type=numeric_sparse_frequency. tags=['condition_candidate', 'measure', 'rare_pattern_candidate'] desc=Frequency feature for token 'project' in an email message. +- word_freq_re: role=feature, type=numeric_sparse_frequency. tags=['condition_candidate', 'measure', 'rare_pattern_candidate'] desc=Frequency feature for token 're' in an email message. +- word_freq_edu: role=feature, type=numeric_sparse_frequency. tags=['condition_candidate', 'measure', 'rare_pattern_candidate'] desc=Frequency feature for token 'edu' in an email message. +- word_freq_table: role=feature, type=numeric_sparse_frequency. tags=['condition_candidate', 'measure', 'rare_pattern_candidate'] desc=Frequency feature for token 'table' in an email message. +- word_freq_conference: role=feature, type=numeric_sparse_frequency. tags=['condition_candidate', 'measure', 'rare_pattern_candidate'] desc=Frequency feature for token 'conference' in an email message. +- char_freq_%3B: role=feature, type=numeric_sparse_frequency. tags=['condition_candidate', 'measure', 'rare_pattern_candidate'] desc=Character frequency feature for symbol ';'. +- char_freq_%28: role=feature, type=numeric_sparse_frequency. tags=['condition_candidate', 'measure', 'rare_pattern_candidate'] desc=Character frequency feature for symbol '('. +- char_freq_%5B: role=feature, type=numeric_sparse_frequency. tags=['condition_candidate', 'measure', 'rare_pattern_candidate'] desc=Character frequency feature for symbol '['. +- char_freq_%21: role=feature, type=numeric_sparse_frequency. tags=['condition_candidate', 'measure', 'rare_pattern_candidate'] desc=Character frequency feature for symbol '!'. +- char_freq_%24: role=feature, type=numeric_sparse_frequency. tags=['condition_candidate', 'measure', 'rare_pattern_candidate'] desc=Character frequency feature for symbol '$'. +- char_freq_%23: role=feature, type=numeric_sparse_frequency. tags=['condition_candidate', 'measure', 'rare_pattern_candidate'] desc=Character frequency feature for symbol '#'. +- capital_run_length_average: role=feature, type=numeric_heavy_tail. tags=['condition_candidate', 'measure', 'rare_pattern_candidate'] desc=Average length of uninterrupted capital-letter runs. +- capital_run_length_longest: role=feature, type=numeric_heavy_tail. tags=['condition_candidate', 'measure', 'rare_pattern_candidate'] desc=Longest uninterrupted capital-letter run. +- capital_run_length_total: role=feature, type=numeric_heavy_tail. tags=['condition_candidate', 'measure', 'rare_pattern_candidate'] desc=Total number of capital letters in runs. +- class: role=target, type=binary_target. tags=['target_candidate'] desc=Binary spam label (1=spam, 0=non-spam). +- useful_field_combinations: [['word_freq_free', 'word_freq_you', 'class'], ['char_freq_%21', 'capital_run_length_longest', 'class'], ['word_freq_remove', 'word_freq_money', 'class']] +- fields_requiring_caution: ['capital_run_length_average', 'capital_run_length_longest', 'capital_run_length_total'] +- source_url: https://www.openml.org/d/44 + +SQLite schema snapshot: +{ + "table_name": "n1", + "quoted_table_name": "\"n1\"", + "row_count": 4601, + "columns": [ + { + "name": "word_freq_make", + "type": "TEXT", + "notnull": false, + "pk": false + }, + { + "name": "word_freq_address", + "type": "TEXT", + "notnull": false, + "pk": false + }, + { + "name": "word_freq_all", + "type": "TEXT", + "notnull": false, + "pk": false + }, + { + "name": "word_freq_3d", + "type": "TEXT", + "notnull": false, + "pk": false + }, + { + "name": "word_freq_our", + "type": "TEXT", + "notnull": false, + "pk": false + }, + { + "name": "word_freq_over", + "type": "TEXT", + "notnull": false, + "pk": false + }, + { + "name": "word_freq_remove", + "type": "TEXT", + "notnull": false, + "pk": false + }, + { + "name": "word_freq_internet", + "type": "TEXT", + "notnull": false, + "pk": false + }, + { + "name": "word_freq_order", + "type": "TEXT", + "notnull": false, + "pk": false + }, + { + "name": "word_freq_mail", + "type": "TEXT", + "notnull": false, + "pk": false + }, + { + "name": "word_freq_receive", + "type": "TEXT", + "notnull": false, + "pk": false + }, + { + "name": "word_freq_will", + "type": "TEXT", + "notnull": false, + "pk": false + }, + { + "name": "word_freq_people", + "type": "TEXT", + "notnull": false, + "pk": false + }, + { + "name": "word_freq_report", + "type": "TEXT", + "notnull": false, + "pk": false + }, + { + "name": "word_freq_addresses", + "type": "TEXT", + "notnull": false, + "pk": false + }, + { + "name": "word_freq_free", + "type": "TEXT", + "notnull": false, + "pk": false + }, + { + "name": "word_freq_business", + "type": "TEXT", + "notnull": false, + "pk": false + }, + { + "name": "word_freq_email", + "type": "TEXT", + "notnull": false, + "pk": false + }, + { + "name": "word_freq_you", + "type": "TEXT", + "notnull": false, + "pk": false + }, + { + "name": "word_freq_credit", + "type": "TEXT", + "notnull": false, + "pk": false + }, + { + "name": "word_freq_your", + "type": "TEXT", + "notnull": false, + "pk": false + }, + { + "name": "word_freq_font", + "type": "TEXT", + "notnull": false, + "pk": false + }, + { + "name": "word_freq_000", + "type": "TEXT", + "notnull": false, + "pk": false + }, + { + "name": "word_freq_money", + "type": "TEXT", + "notnull": false, + "pk": false + }, + { + "name": "word_freq_hp", + "type": "TEXT", + "notnull": false, + "pk": false + }, + { + "name": "word_freq_hpl", + "type": "TEXT", + "notnull": false, + "pk": false + }, + { + "name": "word_freq_george", + "type": "TEXT", + "notnull": false, + "pk": false + }, + { + "name": "word_freq_650", + "type": "TEXT", + "notnull": false, + "pk": false + }, + { + "name": "word_freq_lab", + "type": "TEXT", + "notnull": false, + "pk": false + }, + { + "name": "word_freq_labs", + "type": "TEXT", + "notnull": false, + "pk": false + }, + { + "name": "word_freq_telnet", + "type": "TEXT", + "notnull": false, + "pk": false + }, + { + "name": "word_freq_857", + "type": "TEXT", + "notnull": false, + "pk": false + }, + { + "name": "word_freq_data", + "type": "TEXT", + "notnull": false, + "pk": false + }, + { + "name": "word_freq_415", + "type": "TEXT", + "notnull": false, + "pk": false + }, + { + "name": "word_freq_85", + "type": "TEXT", + "notnull": false, + "pk": false + }, + { + "name": "word_freq_technology", + "type": "TEXT", + "notnull": false, + "pk": false + }, + { + "name": "word_freq_1999", + "type": "TEXT", + "notnull": false, + "pk": false + }, + { + "name": "word_freq_parts", + "type": "TEXT", + "notnull": false, + "pk": false + }, + { + "name": "word_freq_pm", + "type": "TEXT", + "notnull": false, + "pk": false + }, + { + "name": "word_freq_direct", + "type": "TEXT", + "notnull": false, + "pk": false + }, + { + "name": "word_freq_cs", + "type": "TEXT", + "notnull": false, + "pk": false + }, + { + "name": "word_freq_meeting", + "type": "TEXT", + "notnull": false, + "pk": false + }, + { + "name": "word_freq_original", + "type": "TEXT", + "notnull": false, + "pk": false + }, + { + "name": "word_freq_project", + "type": "TEXT", + "notnull": false, + "pk": false + }, + { + "name": "word_freq_re", + "type": "TEXT", + "notnull": false, + "pk": false + }, + { + "name": "word_freq_edu", + "type": "TEXT", + "notnull": false, + "pk": false + }, + { + "name": "word_freq_table", + "type": "TEXT", + "notnull": false, + "pk": false + }, + { + "name": "word_freq_conference", + "type": "TEXT", + "notnull": false, + "pk": false + }, + { + "name": "char_freq_%3B", + "type": "TEXT", + "notnull": false, + "pk": false + }, + { + "name": "char_freq_%28", + "type": "TEXT", + "notnull": false, + "pk": false + }, + { + "name": "char_freq_%5B", + "type": "TEXT", + "notnull": false, + "pk": false + }, + { + "name": "char_freq_%21", + "type": "TEXT", + "notnull": false, + "pk": false + }, + { + "name": "char_freq_%24", + "type": "TEXT", + "notnull": false, + "pk": false + }, + { + "name": "char_freq_%23", + "type": "TEXT", + "notnull": false, + "pk": false + }, + { + "name": "capital_run_length_average", + "type": "TEXT", + "notnull": false, + "pk": false + }, + { + "name": "capital_run_length_longest", + "type": "TEXT", + "notnull": false, + "pk": false + }, + { + "name": "capital_run_length_total", + "type": "TEXT", + "notnull": false, + "pk": false + }, + { + "name": "class", + "type": "TEXT", + "notnull": false, + "pk": false + } + ], + "sample_rows": [ + { + "word_freq_make": "0", + "word_freq_address": "0.64", + "word_freq_all": "0.64", + "word_freq_3d": "0", + "word_freq_our": "0.32", + "word_freq_over": "0", + "word_freq_remove": "0", + "word_freq_internet": "0", + "word_freq_order": "0", + "word_freq_mail": "0", + "word_freq_receive": "0", + "word_freq_will": "0.64", + "word_freq_people": "0", + "word_freq_report": "0", + "word_freq_addresses": "0", + "word_freq_free": "0.32", + "word_freq_business": "0", + "word_freq_email": "1.29", + "word_freq_you": "1.93", + "word_freq_credit": "0", + "word_freq_your": "0.96", + "word_freq_font": "0", + "word_freq_000": "0", + "word_freq_money": "0", + "word_freq_hp": "0", + "word_freq_hpl": "0", + "word_freq_george": "0", + "word_freq_650": "0", + "word_freq_lab": "0", + "word_freq_labs": "0", + "word_freq_telnet": "0", + "word_freq_857": "0", + "word_freq_data": "0", + "word_freq_415": "0", + "word_freq_85": "0", + "word_freq_technology": "0", + "word_freq_1999": "0", + "word_freq_parts": "0", + "word_freq_pm": "0", + "word_freq_direct": "0", + "word_freq_cs": "0", + "word_freq_meeting": "0", + "word_freq_original": "0", + "word_freq_project": "0", + "word_freq_re": "0", + "word_freq_edu": "0", + "word_freq_table": "0", + "word_freq_conference": "0", + "char_freq_%3B": "0", + "char_freq_%28": "0", + "char_freq_%5B": "0", + "char_freq_%21": "0.778", + "char_freq_%24": "0", + "char_freq_%23": "0", + "capital_run_length_average": "3.756", + "capital_run_length_longest": "61", + "capital_run_length_total": "278", + "class": "1" + }, + { + "word_freq_make": "0.21", + "word_freq_address": "0.28", + "word_freq_all": "0.5", + "word_freq_3d": "0", + "word_freq_our": "0.14", + "word_freq_over": "0.28", + "word_freq_remove": "0.21", + "word_freq_internet": "0.07", + "word_freq_order": "0", + "word_freq_mail": "0.94", + "word_freq_receive": "0.21", + "word_freq_will": "0.79", + "word_freq_people": "0.65", + "word_freq_report": "0.21", + "word_freq_addresses": "0.14", + "word_freq_free": "0.14", + "word_freq_business": "0.07", + "word_freq_email": "0.28", + "word_freq_you": "3.47", + "word_freq_credit": "0", + "word_freq_your": "1.59", + "word_freq_font": "0", + "word_freq_000": "0.43", + "word_freq_money": "0.43", + "word_freq_hp": "0", + "word_freq_hpl": "0", + "word_freq_george": "0", + "word_freq_650": "0", + "word_freq_lab": "0", + "word_freq_labs": "0", + "word_freq_telnet": "0", + "word_freq_857": "0", + "word_freq_data": "0", + "word_freq_415": "0", + "word_freq_85": "0", + "word_freq_technology": "0", + "word_freq_1999": "0.07", + "word_freq_parts": "0", + "word_freq_pm": "0", + "word_freq_direct": "0", + "word_freq_cs": "0", + "word_freq_meeting": "0", + "word_freq_original": "0", + "word_freq_project": "0", + "word_freq_re": "0", + "word_freq_edu": "0", + "word_freq_table": "0", + "word_freq_conference": "0", + "char_freq_%3B": "0", + "char_freq_%28": "0.132", + "char_freq_%5B": "0", + "char_freq_%21": "0.372", + "char_freq_%24": "0.18", + "char_freq_%23": "0.048", + "capital_run_length_average": "5.114", + "capital_run_length_longest": "101", + "capital_run_length_total": "1028", + "class": "1" + }, + { + "word_freq_make": "0.06", + "word_freq_address": "0", + "word_freq_all": "0.71", + "word_freq_3d": "0", + "word_freq_our": "1.23", + "word_freq_over": "0.19", + "word_freq_remove": "0.19", + "word_freq_internet": "0.12", + "word_freq_order": "0.64", + "word_freq_mail": "0.25", + "word_freq_receive": "0.38", + "word_freq_will": "0.45", + "word_freq_people": "0.12", + "word_freq_report": "0", + "word_freq_addresses": "1.75", + "word_freq_free": "0.06", + "word_freq_business": "0.06", + "word_freq_email": "1.03", + "word_freq_you": "1.36", + "word_freq_credit": "0.32", + "word_freq_your": "0.51", + "word_freq_font": "0", + "word_freq_000": "1.16", + "word_freq_money": "0.06", + "word_freq_hp": "0", + "word_freq_hpl": "0", + "word_freq_george": "0", + "word_freq_650": "0", + "word_freq_lab": "0", + "word_freq_labs": "0", + "word_freq_telnet": "0", + "word_freq_857": "0", + "word_freq_data": "0", + "word_freq_415": "0", + "word_freq_85": "0", + "word_freq_technology": "0", + "word_freq_1999": "0", + "word_freq_parts": "0", + "word_freq_pm": "0", + "word_freq_direct": "0.06", + "word_freq_cs": "0", + "word_freq_meeting": "0", + "word_freq_original": "0.12", + "word_freq_project": "0", + "word_freq_re": "0.06", + "word_freq_edu": "0.06", + "word_freq_table": "0", + "word_freq_conference": "0", + "char_freq_%3B": "0.01", + "char_freq_%28": "0.143", + "char_freq_%5B": "0", + "char_freq_%21": "0.276", + "char_freq_%24": "0.184", + "char_freq_%23": "0.01", + "capital_run_length_average": "9.821", + "capital_run_length_longest": "485", + "capital_run_length_total": "2259", + "class": "1" + }, + { + "word_freq_make": "0", + "word_freq_address": "0", + "word_freq_all": "0", + "word_freq_3d": "0", + "word_freq_our": "0.63", + "word_freq_over": "0", + "word_freq_remove": "0.31", + "word_freq_internet": "0.63", + "word_freq_order": "0.31", + "word_freq_mail": "0.63", + "word_freq_receive": "0.31", + "word_freq_will": "0.31", + "word_freq_people": "0.31", + "word_freq_report": "0", + "word_freq_addresses": "0", + "word_freq_free": "0.31", + "word_freq_business": "0", + "word_freq_email": "0", + "word_freq_you": "3.18", + "word_freq_credit": "0", + "word_freq_your": "0.31", + "word_freq_font": "0", + "word_freq_000": "0", + "word_freq_money": "0", + "word_freq_hp": "0", + "word_freq_hpl": "0", + "word_freq_george": "0", + "word_freq_650": "0", + "word_freq_lab": "0", + "word_freq_labs": "0", + "word_freq_telnet": "0", + "word_freq_857": "0", + "word_freq_data": "0", + "word_freq_415": "0", + "word_freq_85": "0", + "word_freq_technology": "0", + "word_freq_1999": "0", + "word_freq_parts": "0", + "word_freq_pm": "0", + "word_freq_direct": "0", + "word_freq_cs": "0", + "word_freq_meeting": "0", + "word_freq_original": "0", + "word_freq_project": "0", + "word_freq_re": "0", + "word_freq_edu": "0", + "word_freq_table": "0", + "word_freq_conference": "0", + "char_freq_%3B": "0", + "char_freq_%28": "0.137", + "char_freq_%5B": "0", + "char_freq_%21": "0.137", + "char_freq_%24": "0", + "char_freq_%23": "0", + "capital_run_length_average": "3.537", + "capital_run_length_longest": "40", + "capital_run_length_total": "191", + "class": "1" + }, + { + "word_freq_make": "0", + "word_freq_address": "0", + "word_freq_all": "0", + "word_freq_3d": "0", + "word_freq_our": "0.63", + "word_freq_over": "0", + "word_freq_remove": "0.31", + "word_freq_internet": "0.63", + "word_freq_order": "0.31", + "word_freq_mail": "0.63", + "word_freq_receive": "0.31", + "word_freq_will": "0.31", + "word_freq_people": "0.31", + "word_freq_report": "0", + "word_freq_addresses": "0", + "word_freq_free": "0.31", + "word_freq_business": "0", + "word_freq_email": "0", + "word_freq_you": "3.18", + "word_freq_credit": "0", + "word_freq_your": "0.31", + "word_freq_font": "0", + "word_freq_000": "0", + "word_freq_money": "0", + "word_freq_hp": "0", + "word_freq_hpl": "0", + "word_freq_george": "0", + "word_freq_650": "0", + "word_freq_lab": "0", + "word_freq_labs": "0", + "word_freq_telnet": "0", + "word_freq_857": "0", + "word_freq_data": "0", + "word_freq_415": "0", + "word_freq_85": "0", + "word_freq_technology": "0", + "word_freq_1999": "0", + "word_freq_parts": "0", + "word_freq_pm": "0", + "word_freq_direct": "0", + "word_freq_cs": "0", + "word_freq_meeting": "0", + "word_freq_original": "0", + "word_freq_project": "0", + "word_freq_re": "0", + "word_freq_edu": "0", + "word_freq_table": "0", + "word_freq_conference": "0", + "char_freq_%3B": "0", + "char_freq_%28": "0.135", + "char_freq_%5B": "0", + "char_freq_%21": "0.135", + "char_freq_%24": "0", + "char_freq_%23": "0", + "capital_run_length_average": "3.537", + "capital_run_length_longest": "40", + "capital_run_length_total": "191", + "class": "1" + } + ] +} + +Shortlisted templates: +[ + { + "template_id": "tpl_m4_window_partition_avg", + "template_name": "Window Partition Average", + "primary_family": "conditional_dependency_structure", + "portability": "partial", + "sql_skeleton": "SELECT DISTINCT {group_col},\n AVG({measure_col}) OVER (PARTITION BY {group_col}) AS avg_measure\nFROM {table}\nORDER BY avg_measure DESC;", + "required_roles": [ + "group_col", + "measure_col" + ] + } +] + +Problem instance: +{ + "dataset_id": "n1", + "question": "Use template Window Partition Average to probe slice_level_consistency with semantic role ranked_signal_view. Focus on group_col=class, measure_col=char_freq_%24.", + "planned_template_id": "tpl_m4_window_partition_avg", + "bindings": { + "group_col": "class", + "measure_col": "char_freq_%24", + "top_k": 17, + "top_n": 4, + "num_tiles": 10, + "percentile_value": 0.9, + "z_threshold": 2.0, + "fraction_threshold": 0.05, + "baseline_multiplier": 1.75, + "baseline_fraction": 0.1, + "min_group_size": 5, + "min_support": 4, + "measure_threshold": 0.0, + "time_grain": "month", + "lookback_rows": 3, + "current_period_start": "'2024-01-01'", + "current_period_end": "'2024-04-01'", + "previous_period_start": "'2023-10-01'", + "previous_period_end": "'2024-01-01'", + "drift_ratio_threshold": 0.8 + }, + "can_vary": [], + "must_fix": [], + "runtime_sql_skeleton": "SELECT DISTINCT {group_col},\n AVG({measure_col}) OVER (PARTITION BY {group_col}) AS avg_measure\nFROM {table}\nORDER BY avg_measure DESC;" +} + +Repair context: +{} diff --git a/Query/sql/v2/runs/v2_cli_20260502_081223_d/n1/artifacts/v2q_n1_8c91771173682483/cli/sql_response_attempt_1.raw.txt b/Query/sql/v2/runs/v2_cli_20260502_081223_d/n1/artifacts/v2q_n1_8c91771173682483/cli/sql_response_attempt_1.raw.txt new file mode 100644 index 0000000000000000000000000000000000000000..db72a41121eb26caea69325149b4046452c02c67 --- /dev/null +++ b/Query/sql/v2/runs/v2_cli_20260502_081223_d/n1/artifacts/v2q_n1_8c91771173682483/cli/sql_response_attempt_1.raw.txt @@ -0,0 +1,5 @@ +{"type":"thread.started","thread_id":"019e40e7-ddd3-7471-ade5-a14ead3f4321"} +{"type":"turn.started"} +{"type":"error","message":"Reconnecting... 2/5 (stream disconnected before completion: Rate limit reached for gpt-5.4 in organization org-yXK0mBGOR7eXhtGEx8v7e3bf on tokens per min (TPM): Limit 2000000, Used 1992077, Requested 20963. Please try again in 391ms. Visit https://platform.openai.com/account/rate-limits to learn more.)"} +{"type":"item.completed","item":{"id":"item_0","type":"agent_message","text":"{\"sql\":\"-- template_id: tpl_m4_window_partition_avg\\nSELECT DISTINCT \\\"class\\\",\\n AVG(CAST(\\\"char_freq_%24\\\" AS REAL)) OVER (PARTITION BY \\\"class\\\") AS avg_measure\\nFROM \\\"n1\\\"\\nORDER BY avg_measure DESC;\",\"notes\":\"Uses the requested Window Partition Average template with group_col=\\\"class\\\" and measure_col=\\\"char_freq_%24\\\". CAST to REAL is added because the schema stores numeric-looking values as TEXT.\"}"}} +{"type":"turn.completed","usage":{"input_tokens":20328,"cached_input_tokens":19840,"output_tokens":339,"reasoning_output_tokens":234}} diff --git a/Query/sql/v2/runs/v2_cli_20260502_081223_d/n1/artifacts/v2q_n1_8c91771173682483/cli/sql_response_attempt_1.txt b/Query/sql/v2/runs/v2_cli_20260502_081223_d/n1/artifacts/v2q_n1_8c91771173682483/cli/sql_response_attempt_1.txt new file mode 100644 index 0000000000000000000000000000000000000000..0fc705601907efaef77b6cf6734b434e466d61b1 --- /dev/null +++ b/Query/sql/v2/runs/v2_cli_20260502_081223_d/n1/artifacts/v2q_n1_8c91771173682483/cli/sql_response_attempt_1.txt @@ -0,0 +1 @@ +{"sql":"-- template_id: tpl_m4_window_partition_avg\nSELECT DISTINCT \"class\",\n AVG(CAST(\"char_freq_%24\" AS REAL)) OVER (PARTITION BY \"class\") AS avg_measure\nFROM \"n1\"\nORDER BY avg_measure DESC;","notes":"Uses the requested Window Partition Average template with group_col=\"class\" and measure_col=\"char_freq_%24\". CAST to REAL is added because the schema stores numeric-looking values as TEXT."} \ No newline at end of file diff --git a/Query/sql/v2/runs/v2_cli_20260502_081223_d/n1/artifacts/v2q_n1_8c91771173682483/cli/sql_stderr_attempt_1.txt b/Query/sql/v2/runs/v2_cli_20260502_081223_d/n1/artifacts/v2q_n1_8c91771173682483/cli/sql_stderr_attempt_1.txt new file mode 100644 index 0000000000000000000000000000000000000000..e69de29bb2d1d6434b8b29ae775ad8c2e48c5391 diff --git a/Query/sql/v2/runs/v2_cli_20260502_081223_d/n1/artifacts/v2q_n1_8d4076cdf75ee6c9/cli/sql_attempt_1.metadata.json b/Query/sql/v2/runs/v2_cli_20260502_081223_d/n1/artifacts/v2q_n1_8d4076cdf75ee6c9/cli/sql_attempt_1.metadata.json new file mode 100644 index 0000000000000000000000000000000000000000..859bdc347f27a1e30d0e78991c37a11300714ce5 --- /dev/null +++ b/Query/sql/v2/runs/v2_cli_20260502_081223_d/n1/artifacts/v2q_n1_8d4076cdf75ee6c9/cli/sql_attempt_1.metadata.json @@ -0,0 +1,43 @@ +{ + "attempt": 1, + "phase": "sql_generation", + "command": "codex exec --skip-git-repo-check --disable plugins --sandbox read-only --cd \"/data/jialinzhang/SQLagent\" -m gpt-5.4 --json -", + "started_at": "2026-05-19T16:02:46.838285+00:00", + "ended_at": "2026-05-19T16:02:49.957485+00:00", + "elapsed_ms": 3119.17, + "returncode": 1, + "prompt_metrics": { + "chars": 29336, + "bytes_utf8": 29336, + "lines": 790, + "estimated_tokens": null + }, + "stdout_metrics": { + "chars": 281, + "bytes_utf8": 281, + "lines": 4, + "estimated_tokens": null + }, + "stderr_metrics": { + "chars": 0, + "bytes_utf8": 0, + "lines": 0, + "estimated_tokens": null + }, + "parsed_output": { + "format": "jsonl_events", + "text_metrics": { + "chars": 280, + "bytes_utf8": 280, + "lines": 4, + "estimated_tokens": null + }, + "usage": {} + }, + "status": "failed", + "error": "AI CLI command failed with exit code 1: ", + "prompt_path": "cli/sql_prompt_attempt_1.txt", + "response_path": "cli/sql_response_attempt_1.txt", + "raw_response_path": "cli/sql_response_attempt_1.raw.txt", + "stderr_path": "cli/sql_stderr_attempt_1.txt" +} \ No newline at end of file diff --git a/Query/sql/v2/runs/v2_cli_20260502_081223_d/n1/artifacts/v2q_n1_8d4076cdf75ee6c9/cli/sql_attempt_2.metadata.json b/Query/sql/v2/runs/v2_cli_20260502_081223_d/n1/artifacts/v2q_n1_8d4076cdf75ee6c9/cli/sql_attempt_2.metadata.json new file mode 100644 index 0000000000000000000000000000000000000000..a69012c97eaa25fb96e5558174d84f2d75769edc --- /dev/null +++ b/Query/sql/v2/runs/v2_cli_20260502_081223_d/n1/artifacts/v2q_n1_8d4076cdf75ee6c9/cli/sql_attempt_2.metadata.json @@ -0,0 +1,43 @@ +{ + "attempt": 2, + "phase": "sql_generation", + "command": "codex exec --skip-git-repo-check --disable plugins --sandbox read-only --cd \"/data/jialinzhang/SQLagent\" -m gpt-5.4 --json -", + "started_at": "2026-05-19T16:02:50.959486+00:00", + "ended_at": "2026-05-19T16:02:53.997966+00:00", + "elapsed_ms": 3038.44, + "returncode": 1, + "prompt_metrics": { + "chars": 29336, + "bytes_utf8": 29336, + "lines": 790, + "estimated_tokens": null + }, + "stdout_metrics": { + "chars": 281, + "bytes_utf8": 281, + "lines": 4, + "estimated_tokens": null + }, + "stderr_metrics": { + "chars": 0, + "bytes_utf8": 0, + "lines": 0, + "estimated_tokens": null + }, + "parsed_output": { + "format": "jsonl_events", + "text_metrics": { + "chars": 280, + "bytes_utf8": 280, + "lines": 4, + "estimated_tokens": null + }, + "usage": {} + }, + "status": "failed", + "error": "AI CLI command failed with exit code 1: ", + "prompt_path": "cli/sql_prompt_attempt_2.txt", + "response_path": "cli/sql_response_attempt_2.txt", + "raw_response_path": "cli/sql_response_attempt_2.raw.txt", + "stderr_path": "cli/sql_stderr_attempt_2.txt" +} \ No newline at end of file diff --git a/Query/sql/v2/runs/v2_cli_20260502_081223_d/n1/artifacts/v2q_n1_8d4076cdf75ee6c9/cli/sql_prompt_attempt_1.txt b/Query/sql/v2/runs/v2_cli_20260502_081223_d/n1/artifacts/v2q_n1_8d4076cdf75ee6c9/cli/sql_prompt_attempt_1.txt new file mode 100644 index 0000000000000000000000000000000000000000..678102ba2dfa028be68057287b101131a31d0fb5 --- /dev/null +++ b/Query/sql/v2/runs/v2_cli_20260502_081223_d/n1/artifacts/v2q_n1_8d4076cdf75ee6c9/cli/sql_prompt_attempt_1.txt @@ -0,0 +1,790 @@ +You are generating one SQLite SELECT query for a single-table SQL QA task. +Return strict JSON only, with this schema: {"sql": "...", "notes": "..."}. +Rules: +- Use only the provided table and columns. +- Do not write INSERT, UPDATE, DELETE, DROP, ALTER, CREATE, PRAGMA, ATTACH, DETACH, or VACUUM. +- Prefer the planned template and bound roles when provided. +- Add a leading SQL comment exactly like: -- template_id: . +- Generate SQLite-compatible SQL. SQLite does not support PERCENTILE_CONT or STDDEV. +- Quote identifiers with double quotes. +- Return no markdown and no extra prose. + +Dataset context: +Dataset context for SQL QA: +- dataset_id: n1 +- dataset_name: Spambase +- table_name: n1 +- table_layout: single-table dataset (do not assume joins). +- row_semantics: One row is one email represented by engineered token/character frequency and capitalization-run features. +- task_type: classification +- target_column: class +- main_row_count: 4601 +- important_fields: +- word_freq_make: role=feature, type=numeric_sparse_frequency. tags=['condition_candidate', 'measure', 'rare_pattern_candidate'] desc=Frequency feature for token 'make' in an email message. +- word_freq_address: role=feature, type=numeric_sparse_frequency. tags=['condition_candidate', 'measure', 'rare_pattern_candidate'] desc=Frequency feature for token 'address' in an email message. +- word_freq_all: role=feature, type=numeric_sparse_frequency. tags=['condition_candidate', 'measure', 'rare_pattern_candidate'] desc=Frequency feature for token 'all' in an email message. +- word_freq_3d: role=feature, type=numeric_sparse_frequency. tags=['condition_candidate', 'measure', 'rare_pattern_candidate'] desc=Frequency feature for token '3d' in an email message. +- word_freq_our: role=feature, type=numeric_sparse_frequency. tags=['condition_candidate', 'measure', 'rare_pattern_candidate'] desc=Frequency feature for token 'our' in an email message. +- word_freq_over: role=feature, type=numeric_sparse_frequency. tags=['condition_candidate', 'measure', 'rare_pattern_candidate'] desc=Frequency feature for token 'over' in an email message. +- word_freq_remove: role=feature, type=numeric_sparse_frequency. tags=['condition_candidate', 'measure', 'rare_pattern_candidate'] desc=Frequency feature for token 'remove' in an email message. +- word_freq_internet: role=feature, type=numeric_sparse_frequency. tags=['condition_candidate', 'measure', 'rare_pattern_candidate'] desc=Frequency feature for token 'internet' in an email message. +- word_freq_order: role=feature, type=numeric_sparse_frequency. tags=['condition_candidate', 'measure', 'rare_pattern_candidate'] desc=Frequency feature for token 'order' in an email message. +- word_freq_mail: role=feature, type=numeric_sparse_frequency. tags=['condition_candidate', 'measure', 'rare_pattern_candidate'] desc=Frequency feature for token 'mail' in an email message. +- word_freq_receive: role=feature, type=numeric_sparse_frequency. tags=['condition_candidate', 'measure', 'rare_pattern_candidate'] desc=Frequency feature for token 'receive' in an email message. +- word_freq_will: role=feature, type=numeric_sparse_frequency. tags=['condition_candidate', 'measure', 'rare_pattern_candidate'] desc=Frequency feature for token 'will' in an email message. +- word_freq_people: role=feature, type=numeric_sparse_frequency. tags=['condition_candidate', 'measure', 'rare_pattern_candidate'] desc=Frequency feature for token 'people' in an email message. +- word_freq_report: role=feature, type=numeric_sparse_frequency. tags=['condition_candidate', 'measure', 'rare_pattern_candidate'] desc=Frequency feature for token 'report' in an email message. +- word_freq_addresses: role=feature, type=numeric_sparse_frequency. tags=['condition_candidate', 'measure', 'rare_pattern_candidate'] desc=Frequency feature for token 'addresses' in an email message. +- word_freq_free: role=feature, type=numeric_sparse_frequency. tags=['condition_candidate', 'measure', 'rare_pattern_candidate'] desc=Frequency feature for token 'free' in an email message. +- word_freq_business: role=feature, type=numeric_sparse_frequency. tags=['condition_candidate', 'measure', 'rare_pattern_candidate'] desc=Frequency feature for token 'business' in an email message. +- word_freq_email: role=feature, type=numeric_sparse_frequency. tags=['condition_candidate', 'measure', 'rare_pattern_candidate'] desc=Frequency feature for token 'email' in an email message. +- word_freq_you: role=feature, type=numeric_sparse_frequency. tags=['condition_candidate', 'measure', 'rare_pattern_candidate'] desc=Frequency feature for token 'you' in an email message. +- word_freq_credit: role=feature, type=numeric_sparse_frequency. tags=['condition_candidate', 'measure', 'rare_pattern_candidate'] desc=Frequency feature for token 'credit' in an email message. +- word_freq_your: role=feature, type=numeric_sparse_frequency. tags=['condition_candidate', 'measure', 'rare_pattern_candidate'] desc=Frequency feature for token 'your' in an email message. +- word_freq_font: role=feature, type=numeric_sparse_frequency. tags=['condition_candidate', 'measure', 'rare_pattern_candidate'] desc=Frequency feature for token 'font' in an email message. +- word_freq_000: role=feature, type=numeric_sparse_frequency. tags=['condition_candidate', 'measure', 'rare_pattern_candidate'] desc=Frequency feature for token '000' in an email message. +- word_freq_money: role=feature, type=numeric_sparse_frequency. tags=['condition_candidate', 'measure', 'rare_pattern_candidate'] desc=Frequency feature for token 'money' in an email message. +- word_freq_hp: role=feature, type=numeric_sparse_frequency. tags=['condition_candidate', 'measure', 'rare_pattern_candidate'] desc=Frequency feature for token 'hp' in an email message. +- word_freq_hpl: role=feature, type=numeric_sparse_frequency. tags=['condition_candidate', 'measure', 'rare_pattern_candidate'] desc=Frequency feature for token 'hpl' in an email message. +- word_freq_george: role=feature, type=numeric_sparse_frequency. tags=['condition_candidate', 'measure', 'rare_pattern_candidate'] desc=Frequency feature for token 'george' in an email message. +- word_freq_650: role=feature, type=numeric_sparse_frequency. tags=['condition_candidate', 'measure', 'rare_pattern_candidate'] desc=Frequency feature for token '650' in an email message. +- word_freq_lab: role=feature, type=numeric_sparse_frequency. tags=['condition_candidate', 'measure', 'rare_pattern_candidate'] desc=Frequency feature for token 'lab' in an email message. +- word_freq_labs: role=feature, type=numeric_sparse_frequency. tags=['condition_candidate', 'measure', 'rare_pattern_candidate'] desc=Frequency feature for token 'labs' in an email message. +- word_freq_telnet: role=feature, type=numeric_sparse_frequency. tags=['condition_candidate', 'measure', 'rare_pattern_candidate'] desc=Frequency feature for token 'telnet' in an email message. +- word_freq_857: role=feature, type=numeric_sparse_frequency. tags=['condition_candidate', 'measure', 'rare_pattern_candidate'] desc=Frequency feature for token '857' in an email message. +- word_freq_data: role=feature, type=numeric_sparse_frequency. tags=['condition_candidate', 'measure', 'rare_pattern_candidate'] desc=Frequency feature for token 'data' in an email message. +- word_freq_415: role=feature, type=numeric_sparse_frequency. tags=['condition_candidate', 'measure', 'rare_pattern_candidate'] desc=Frequency feature for token '415' in an email message. +- word_freq_85: role=feature, type=numeric_sparse_frequency. tags=['condition_candidate', 'measure', 'rare_pattern_candidate'] desc=Frequency feature for token '85' in an email message. +- word_freq_technology: role=feature, type=numeric_sparse_frequency. tags=['condition_candidate', 'measure', 'rare_pattern_candidate'] desc=Frequency feature for token 'technology' in an email message. +- word_freq_1999: role=feature, type=numeric_sparse_frequency. tags=['condition_candidate', 'measure', 'rare_pattern_candidate'] desc=Frequency feature for token '1999' in an email message. +- word_freq_parts: role=feature, type=numeric_sparse_frequency. tags=['condition_candidate', 'measure', 'rare_pattern_candidate'] desc=Frequency feature for token 'parts' in an email message. +- word_freq_pm: role=feature, type=numeric_sparse_frequency. tags=['condition_candidate', 'measure', 'rare_pattern_candidate'] desc=Frequency feature for token 'pm' in an email message. +- word_freq_direct: role=feature, type=numeric_sparse_frequency. tags=['condition_candidate', 'measure', 'rare_pattern_candidate'] desc=Frequency feature for token 'direct' in an email message. +- word_freq_cs: role=feature, type=numeric_sparse_frequency. tags=['condition_candidate', 'measure', 'rare_pattern_candidate'] desc=Frequency feature for token 'cs' in an email message. +- word_freq_meeting: role=feature, type=numeric_sparse_frequency. tags=['condition_candidate', 'measure', 'rare_pattern_candidate'] desc=Frequency feature for token 'meeting' in an email message. +- word_freq_original: role=feature, type=numeric_sparse_frequency. tags=['condition_candidate', 'measure', 'rare_pattern_candidate'] desc=Frequency feature for token 'original' in an email message. +- word_freq_project: role=feature, type=numeric_sparse_frequency. tags=['condition_candidate', 'measure', 'rare_pattern_candidate'] desc=Frequency feature for token 'project' in an email message. +- word_freq_re: role=feature, type=numeric_sparse_frequency. tags=['condition_candidate', 'measure', 'rare_pattern_candidate'] desc=Frequency feature for token 're' in an email message. +- word_freq_edu: role=feature, type=numeric_sparse_frequency. tags=['condition_candidate', 'measure', 'rare_pattern_candidate'] desc=Frequency feature for token 'edu' in an email message. +- word_freq_table: role=feature, type=numeric_sparse_frequency. tags=['condition_candidate', 'measure', 'rare_pattern_candidate'] desc=Frequency feature for token 'table' in an email message. +- word_freq_conference: role=feature, type=numeric_sparse_frequency. tags=['condition_candidate', 'measure', 'rare_pattern_candidate'] desc=Frequency feature for token 'conference' in an email message. +- char_freq_%3B: role=feature, type=numeric_sparse_frequency. tags=['condition_candidate', 'measure', 'rare_pattern_candidate'] desc=Character frequency feature for symbol ';'. +- char_freq_%28: role=feature, type=numeric_sparse_frequency. tags=['condition_candidate', 'measure', 'rare_pattern_candidate'] desc=Character frequency feature for symbol '('. +- char_freq_%5B: role=feature, type=numeric_sparse_frequency. tags=['condition_candidate', 'measure', 'rare_pattern_candidate'] desc=Character frequency feature for symbol '['. +- char_freq_%21: role=feature, type=numeric_sparse_frequency. tags=['condition_candidate', 'measure', 'rare_pattern_candidate'] desc=Character frequency feature for symbol '!'. +- char_freq_%24: role=feature, type=numeric_sparse_frequency. tags=['condition_candidate', 'measure', 'rare_pattern_candidate'] desc=Character frequency feature for symbol '$'. +- char_freq_%23: role=feature, type=numeric_sparse_frequency. tags=['condition_candidate', 'measure', 'rare_pattern_candidate'] desc=Character frequency feature for symbol '#'. +- capital_run_length_average: role=feature, type=numeric_heavy_tail. tags=['condition_candidate', 'measure', 'rare_pattern_candidate'] desc=Average length of uninterrupted capital-letter runs. +- capital_run_length_longest: role=feature, type=numeric_heavy_tail. tags=['condition_candidate', 'measure', 'rare_pattern_candidate'] desc=Longest uninterrupted capital-letter run. +- capital_run_length_total: role=feature, type=numeric_heavy_tail. tags=['condition_candidate', 'measure', 'rare_pattern_candidate'] desc=Total number of capital letters in runs. +- class: role=target, type=binary_target. tags=['target_candidate'] desc=Binary spam label (1=spam, 0=non-spam). +- useful_field_combinations: [['word_freq_free', 'word_freq_you', 'class'], ['char_freq_%21', 'capital_run_length_longest', 'class'], ['word_freq_remove', 'word_freq_money', 'class']] +- fields_requiring_caution: ['capital_run_length_average', 'capital_run_length_longest', 'capital_run_length_total'] +- source_url: https://www.openml.org/d/44 + +SQLite schema snapshot: +{ + "table_name": "n1", + "quoted_table_name": "\"n1\"", + "row_count": 4601, + "columns": [ + { + "name": "word_freq_make", + "type": "TEXT", + "notnull": false, + "pk": false + }, + { + "name": "word_freq_address", + "type": "TEXT", + "notnull": false, + "pk": false + }, + { + "name": "word_freq_all", + "type": "TEXT", + "notnull": false, + "pk": false + }, + { + "name": "word_freq_3d", + "type": "TEXT", + "notnull": false, + "pk": false + }, + { + "name": "word_freq_our", + "type": "TEXT", + "notnull": false, + "pk": false + }, + { + "name": "word_freq_over", + "type": "TEXT", + "notnull": false, + "pk": false + }, + { + "name": "word_freq_remove", + "type": "TEXT", + "notnull": false, + "pk": false + }, + { + "name": "word_freq_internet", + "type": "TEXT", + "notnull": false, + "pk": false + }, + { + "name": "word_freq_order", + "type": "TEXT", + "notnull": false, + "pk": false + }, + { + "name": "word_freq_mail", + "type": "TEXT", + "notnull": false, + "pk": false + }, + { + "name": "word_freq_receive", + "type": "TEXT", + "notnull": false, + "pk": false + }, + { + "name": "word_freq_will", + "type": "TEXT", + "notnull": false, + "pk": false + }, + { + "name": "word_freq_people", + "type": "TEXT", + "notnull": false, + "pk": false + }, + { + "name": "word_freq_report", + "type": "TEXT", + "notnull": false, + "pk": false + }, + { + "name": "word_freq_addresses", + "type": "TEXT", + "notnull": false, + "pk": false + }, + { + "name": "word_freq_free", + "type": "TEXT", + "notnull": false, + "pk": false + }, + { + "name": "word_freq_business", + "type": "TEXT", + "notnull": false, + "pk": false + }, + { + "name": "word_freq_email", + "type": "TEXT", + "notnull": false, + "pk": false + }, + { + "name": "word_freq_you", + "type": "TEXT", + "notnull": false, + "pk": false + }, + { + "name": "word_freq_credit", + "type": "TEXT", + "notnull": false, + "pk": false + }, + { + "name": "word_freq_your", + "type": "TEXT", + "notnull": false, + "pk": false + }, + { + "name": "word_freq_font", + "type": "TEXT", + "notnull": false, + "pk": false + }, + { + "name": "word_freq_000", + "type": "TEXT", + "notnull": false, + "pk": false + }, + { + "name": "word_freq_money", + "type": "TEXT", + "notnull": false, + "pk": false + }, + { + "name": "word_freq_hp", + "type": "TEXT", + "notnull": false, + "pk": false + }, + { + "name": "word_freq_hpl", + "type": "TEXT", + "notnull": false, + "pk": false + }, + { + "name": "word_freq_george", + "type": "TEXT", + "notnull": false, + "pk": false + }, + { + "name": "word_freq_650", + "type": "TEXT", + "notnull": false, + "pk": false + }, + { + "name": "word_freq_lab", + "type": "TEXT", + "notnull": false, + "pk": false + }, + { + "name": "word_freq_labs", + "type": "TEXT", + "notnull": false, + "pk": false + }, + { + "name": "word_freq_telnet", + "type": "TEXT", + "notnull": false, + "pk": false + }, + { + "name": "word_freq_857", + "type": "TEXT", + "notnull": false, + "pk": false + }, + { + "name": "word_freq_data", + "type": "TEXT", + "notnull": false, + "pk": false + }, + { + "name": "word_freq_415", + "type": "TEXT", + "notnull": false, + "pk": false + }, + { + "name": "word_freq_85", + "type": "TEXT", + "notnull": false, + "pk": false + }, + { + "name": "word_freq_technology", + "type": "TEXT", + "notnull": false, + "pk": false + }, + { + "name": "word_freq_1999", + "type": "TEXT", + "notnull": false, + "pk": false + }, + { + "name": "word_freq_parts", + "type": "TEXT", + "notnull": false, + "pk": false + }, + { + "name": "word_freq_pm", + "type": "TEXT", + "notnull": false, + "pk": false + }, + { + "name": "word_freq_direct", + "type": "TEXT", + "notnull": false, + "pk": false + }, + { + "name": "word_freq_cs", + "type": "TEXT", + "notnull": false, + "pk": false + }, + { + "name": "word_freq_meeting", + "type": "TEXT", + "notnull": false, + "pk": false + }, + { + "name": "word_freq_original", + "type": "TEXT", + "notnull": false, + "pk": false + }, + { + "name": "word_freq_project", + "type": "TEXT", + "notnull": false, + "pk": false + }, + { + "name": "word_freq_re", + "type": "TEXT", + "notnull": false, + "pk": false + }, + { + "name": "word_freq_edu", + "type": "TEXT", + "notnull": false, + "pk": false + }, + { + "name": "word_freq_table", + "type": "TEXT", + "notnull": false, + "pk": false + }, + { + "name": "word_freq_conference", + "type": "TEXT", + "notnull": false, + "pk": false + }, + { + "name": "char_freq_%3B", + "type": "TEXT", + "notnull": false, + "pk": false + }, + { + "name": "char_freq_%28", + "type": "TEXT", + "notnull": false, + "pk": false + }, + { + "name": "char_freq_%5B", + "type": "TEXT", + "notnull": false, + "pk": false + }, + { + "name": "char_freq_%21", + "type": "TEXT", + "notnull": false, + "pk": false + }, + { + "name": "char_freq_%24", + "type": "TEXT", + "notnull": false, + "pk": false + }, + { + "name": "char_freq_%23", + "type": "TEXT", + "notnull": false, + "pk": false + }, + { + "name": "capital_run_length_average", + "type": "TEXT", + "notnull": false, + "pk": false + }, + { + "name": "capital_run_length_longest", + "type": "TEXT", + "notnull": false, + "pk": false + }, + { + "name": "capital_run_length_total", + "type": "TEXT", + "notnull": false, + "pk": false + }, + { + "name": "class", + "type": "TEXT", + "notnull": false, + "pk": false + } + ], + "sample_rows": [ + { + "word_freq_make": "0", + "word_freq_address": "0.64", + "word_freq_all": "0.64", + "word_freq_3d": "0", + "word_freq_our": "0.32", + "word_freq_over": "0", + "word_freq_remove": "0", + "word_freq_internet": "0", + "word_freq_order": "0", + "word_freq_mail": "0", + "word_freq_receive": "0", + "word_freq_will": "0.64", + "word_freq_people": "0", + "word_freq_report": "0", + "word_freq_addresses": "0", + "word_freq_free": "0.32", + "word_freq_business": "0", + "word_freq_email": "1.29", + "word_freq_you": "1.93", + "word_freq_credit": "0", + "word_freq_your": "0.96", + "word_freq_font": "0", + "word_freq_000": "0", + "word_freq_money": "0", + "word_freq_hp": "0", + "word_freq_hpl": "0", + "word_freq_george": "0", + "word_freq_650": "0", + "word_freq_lab": "0", + "word_freq_labs": "0", + "word_freq_telnet": "0", + "word_freq_857": "0", + "word_freq_data": "0", + "word_freq_415": "0", + "word_freq_85": "0", + "word_freq_technology": "0", + "word_freq_1999": "0", + "word_freq_parts": "0", + "word_freq_pm": "0", + "word_freq_direct": "0", + "word_freq_cs": "0", + "word_freq_meeting": "0", + "word_freq_original": "0", + "word_freq_project": "0", + "word_freq_re": "0", + "word_freq_edu": "0", + "word_freq_table": "0", + "word_freq_conference": "0", + "char_freq_%3B": "0", + "char_freq_%28": "0", + "char_freq_%5B": "0", + "char_freq_%21": "0.778", + "char_freq_%24": "0", + "char_freq_%23": "0", + "capital_run_length_average": "3.756", + "capital_run_length_longest": "61", + "capital_run_length_total": "278", + "class": "1" + }, + { + "word_freq_make": "0.21", + "word_freq_address": "0.28", + "word_freq_all": "0.5", + "word_freq_3d": "0", + "word_freq_our": "0.14", + "word_freq_over": "0.28", + "word_freq_remove": "0.21", + "word_freq_internet": "0.07", + "word_freq_order": "0", + "word_freq_mail": "0.94", + "word_freq_receive": "0.21", + "word_freq_will": "0.79", + "word_freq_people": "0.65", + "word_freq_report": "0.21", + "word_freq_addresses": "0.14", + "word_freq_free": "0.14", + "word_freq_business": "0.07", + "word_freq_email": "0.28", + "word_freq_you": "3.47", + "word_freq_credit": "0", + "word_freq_your": "1.59", + "word_freq_font": "0", + "word_freq_000": "0.43", + "word_freq_money": "0.43", + "word_freq_hp": "0", + "word_freq_hpl": "0", + "word_freq_george": "0", + "word_freq_650": "0", + "word_freq_lab": "0", + "word_freq_labs": "0", + "word_freq_telnet": "0", + "word_freq_857": "0", + "word_freq_data": "0", + "word_freq_415": "0", + "word_freq_85": "0", + "word_freq_technology": "0", + "word_freq_1999": "0.07", + "word_freq_parts": "0", + "word_freq_pm": "0", + "word_freq_direct": "0", + "word_freq_cs": "0", + "word_freq_meeting": "0", + "word_freq_original": "0", + "word_freq_project": "0", + "word_freq_re": "0", + "word_freq_edu": "0", + "word_freq_table": "0", + "word_freq_conference": "0", + "char_freq_%3B": "0", + "char_freq_%28": "0.132", + "char_freq_%5B": "0", + "char_freq_%21": "0.372", + "char_freq_%24": "0.18", + "char_freq_%23": "0.048", + "capital_run_length_average": "5.114", + "capital_run_length_longest": "101", + "capital_run_length_total": "1028", + "class": "1" + }, + { + "word_freq_make": "0.06", + "word_freq_address": "0", + "word_freq_all": "0.71", + "word_freq_3d": "0", + "word_freq_our": "1.23", + "word_freq_over": "0.19", + "word_freq_remove": "0.19", + "word_freq_internet": "0.12", + "word_freq_order": "0.64", + "word_freq_mail": "0.25", + "word_freq_receive": "0.38", + "word_freq_will": "0.45", + "word_freq_people": "0.12", + "word_freq_report": "0", + "word_freq_addresses": "1.75", + "word_freq_free": "0.06", + "word_freq_business": "0.06", + "word_freq_email": "1.03", + "word_freq_you": "1.36", + "word_freq_credit": "0.32", + "word_freq_your": "0.51", + "word_freq_font": "0", + "word_freq_000": "1.16", + "word_freq_money": "0.06", + "word_freq_hp": "0", + "word_freq_hpl": "0", + "word_freq_george": "0", + "word_freq_650": "0", + "word_freq_lab": "0", + "word_freq_labs": "0", + "word_freq_telnet": "0", + "word_freq_857": "0", + "word_freq_data": "0", + "word_freq_415": "0", + "word_freq_85": "0", + "word_freq_technology": "0", + "word_freq_1999": "0", + "word_freq_parts": "0", + "word_freq_pm": "0", + "word_freq_direct": "0.06", + "word_freq_cs": "0", + "word_freq_meeting": "0", + "word_freq_original": "0.12", + "word_freq_project": "0", + "word_freq_re": "0.06", + "word_freq_edu": "0.06", + "word_freq_table": "0", + "word_freq_conference": "0", + "char_freq_%3B": "0.01", + "char_freq_%28": "0.143", + "char_freq_%5B": "0", + "char_freq_%21": "0.276", + "char_freq_%24": "0.184", + "char_freq_%23": "0.01", + "capital_run_length_average": "9.821", + "capital_run_length_longest": "485", + "capital_run_length_total": "2259", + "class": "1" + }, + { + "word_freq_make": "0", + "word_freq_address": "0", + "word_freq_all": "0", + "word_freq_3d": "0", + "word_freq_our": "0.63", + "word_freq_over": "0", + "word_freq_remove": "0.31", + "word_freq_internet": "0.63", + "word_freq_order": "0.31", + "word_freq_mail": "0.63", + "word_freq_receive": "0.31", + "word_freq_will": "0.31", + "word_freq_people": "0.31", + "word_freq_report": "0", + "word_freq_addresses": "0", + "word_freq_free": "0.31", + "word_freq_business": "0", + "word_freq_email": "0", + "word_freq_you": "3.18", + "word_freq_credit": "0", + "word_freq_your": "0.31", + "word_freq_font": "0", + "word_freq_000": "0", + "word_freq_money": "0", + "word_freq_hp": "0", + "word_freq_hpl": "0", + "word_freq_george": "0", + "word_freq_650": "0", + "word_freq_lab": "0", + "word_freq_labs": "0", + "word_freq_telnet": "0", + "word_freq_857": "0", + "word_freq_data": "0", + "word_freq_415": "0", + "word_freq_85": "0", + "word_freq_technology": "0", + "word_freq_1999": "0", + "word_freq_parts": "0", + "word_freq_pm": "0", + "word_freq_direct": "0", + "word_freq_cs": "0", + "word_freq_meeting": "0", + "word_freq_original": "0", + "word_freq_project": "0", + "word_freq_re": "0", + "word_freq_edu": "0", + "word_freq_table": "0", + "word_freq_conference": "0", + "char_freq_%3B": "0", + "char_freq_%28": "0.137", + "char_freq_%5B": "0", + "char_freq_%21": "0.137", + "char_freq_%24": "0", + "char_freq_%23": "0", + "capital_run_length_average": "3.537", + "capital_run_length_longest": "40", + "capital_run_length_total": "191", + "class": "1" + }, + { + "word_freq_make": "0", + "word_freq_address": "0", + "word_freq_all": "0", + "word_freq_3d": "0", + "word_freq_our": "0.63", + "word_freq_over": "0", + "word_freq_remove": "0.31", + "word_freq_internet": "0.63", + "word_freq_order": "0.31", + "word_freq_mail": "0.63", + "word_freq_receive": "0.31", + "word_freq_will": "0.31", + "word_freq_people": "0.31", + "word_freq_report": "0", + "word_freq_addresses": "0", + "word_freq_free": "0.31", + "word_freq_business": "0", + "word_freq_email": "0", + "word_freq_you": "3.18", + "word_freq_credit": "0", + "word_freq_your": "0.31", + "word_freq_font": "0", + "word_freq_000": "0", + "word_freq_money": "0", + "word_freq_hp": "0", + "word_freq_hpl": "0", + "word_freq_george": "0", + "word_freq_650": "0", + "word_freq_lab": "0", + "word_freq_labs": "0", + "word_freq_telnet": "0", + "word_freq_857": "0", + "word_freq_data": "0", + "word_freq_415": "0", + "word_freq_85": "0", + "word_freq_technology": "0", + "word_freq_1999": "0", + "word_freq_parts": "0", + "word_freq_pm": "0", + "word_freq_direct": "0", + "word_freq_cs": "0", + "word_freq_meeting": "0", + "word_freq_original": "0", + "word_freq_project": "0", + "word_freq_re": "0", + "word_freq_edu": "0", + "word_freq_table": "0", + "word_freq_conference": "0", + "char_freq_%3B": "0", + "char_freq_%28": "0.135", + "char_freq_%5B": "0", + "char_freq_%21": "0.135", + "char_freq_%24": "0", + "char_freq_%23": "0", + "capital_run_length_average": "3.537", + "capital_run_length_longest": "40", + "capital_run_length_total": "191", + "class": "1" + } + ] +} + +Shortlisted templates: +[ + { + "template_id": "tpl_tail_low_support_group_count_v2", + "template_name": "Low-Support Group Count", + "primary_family": "tail_rarity_structure", + "portability": "yes", + "sql_skeleton": "SELECT\n {group_col},\n COUNT(*) AS support\nFROM {table}\nGROUP BY {group_col}\nORDER BY support ASC, {group_col}\nLIMIT {top_k};", + "required_roles": [ + "group_col" + ] + } +] + +Problem instance: +{ + "dataset_id": "n1", + "question": "Use template Low-Support Group Count to probe tail_mass_similarity with semantic role rare_extreme_view. Focus on group_col=class.", + "planned_template_id": "tpl_tail_low_support_group_count_v2", + "bindings": { + "group_col": "class", + "top_k": 18, + "top_n": 7, + "num_tiles": 10, + "percentile_value": 0.95, + "z_threshold": 2.0, + "fraction_threshold": 0.05, + "baseline_multiplier": 1.75, + "baseline_fraction": 0.1, + "min_group_size": 5, + "min_support": 4, + "measure_threshold": 0.16, + "time_grain": "month", + "lookback_rows": 3, + "current_period_start": "'2024-01-01'", + "current_period_end": "'2024-04-01'", + "previous_period_start": "'2023-10-01'", + "previous_period_end": "'2024-01-01'", + "drift_ratio_threshold": 0.8 + }, + "can_vary": [], + "must_fix": [], + "runtime_sql_skeleton": "SELECT\n {group_col},\n COUNT(*) AS support\nFROM {table}\nGROUP BY {group_col}\nORDER BY support ASC, {group_col}\nLIMIT {top_k};" +} + +Repair context: +{} diff --git a/Query/sql/v2/runs/v2_cli_20260502_081223_d/n1/artifacts/v2q_n1_8d4076cdf75ee6c9/cli/sql_prompt_attempt_2.txt b/Query/sql/v2/runs/v2_cli_20260502_081223_d/n1/artifacts/v2q_n1_8d4076cdf75ee6c9/cli/sql_prompt_attempt_2.txt new file mode 100644 index 0000000000000000000000000000000000000000..678102ba2dfa028be68057287b101131a31d0fb5 --- /dev/null +++ b/Query/sql/v2/runs/v2_cli_20260502_081223_d/n1/artifacts/v2q_n1_8d4076cdf75ee6c9/cli/sql_prompt_attempt_2.txt @@ -0,0 +1,790 @@ +You are generating one SQLite SELECT query for a single-table SQL QA task. +Return strict JSON only, with this schema: {"sql": "...", "notes": "..."}. +Rules: +- Use only the provided table and columns. +- Do not write INSERT, UPDATE, DELETE, DROP, ALTER, CREATE, PRAGMA, ATTACH, DETACH, or VACUUM. +- Prefer the planned template and bound roles when provided. +- Add a leading SQL comment exactly like: -- template_id: . +- Generate SQLite-compatible SQL. SQLite does not support PERCENTILE_CONT or STDDEV. +- Quote identifiers with double quotes. +- Return no markdown and no extra prose. + +Dataset context: +Dataset context for SQL QA: +- dataset_id: n1 +- dataset_name: Spambase +- table_name: n1 +- table_layout: single-table dataset (do not assume joins). +- row_semantics: One row is one email represented by engineered token/character frequency and capitalization-run features. +- task_type: classification +- target_column: class +- main_row_count: 4601 +- important_fields: +- word_freq_make: role=feature, type=numeric_sparse_frequency. tags=['condition_candidate', 'measure', 'rare_pattern_candidate'] desc=Frequency feature for token 'make' in an email message. +- word_freq_address: role=feature, type=numeric_sparse_frequency. tags=['condition_candidate', 'measure', 'rare_pattern_candidate'] desc=Frequency feature for token 'address' in an email message. +- word_freq_all: role=feature, type=numeric_sparse_frequency. tags=['condition_candidate', 'measure', 'rare_pattern_candidate'] desc=Frequency feature for token 'all' in an email message. +- word_freq_3d: role=feature, type=numeric_sparse_frequency. tags=['condition_candidate', 'measure', 'rare_pattern_candidate'] desc=Frequency feature for token '3d' in an email message. +- word_freq_our: role=feature, type=numeric_sparse_frequency. tags=['condition_candidate', 'measure', 'rare_pattern_candidate'] desc=Frequency feature for token 'our' in an email message. +- word_freq_over: role=feature, type=numeric_sparse_frequency. tags=['condition_candidate', 'measure', 'rare_pattern_candidate'] desc=Frequency feature for token 'over' in an email message. +- word_freq_remove: role=feature, type=numeric_sparse_frequency. tags=['condition_candidate', 'measure', 'rare_pattern_candidate'] desc=Frequency feature for token 'remove' in an email message. +- word_freq_internet: role=feature, type=numeric_sparse_frequency. tags=['condition_candidate', 'measure', 'rare_pattern_candidate'] desc=Frequency feature for token 'internet' in an email message. +- word_freq_order: role=feature, type=numeric_sparse_frequency. tags=['condition_candidate', 'measure', 'rare_pattern_candidate'] desc=Frequency feature for token 'order' in an email message. +- word_freq_mail: role=feature, type=numeric_sparse_frequency. tags=['condition_candidate', 'measure', 'rare_pattern_candidate'] desc=Frequency feature for token 'mail' in an email message. +- word_freq_receive: role=feature, type=numeric_sparse_frequency. tags=['condition_candidate', 'measure', 'rare_pattern_candidate'] desc=Frequency feature for token 'receive' in an email message. +- word_freq_will: role=feature, type=numeric_sparse_frequency. tags=['condition_candidate', 'measure', 'rare_pattern_candidate'] desc=Frequency feature for token 'will' in an email message. +- word_freq_people: role=feature, type=numeric_sparse_frequency. tags=['condition_candidate', 'measure', 'rare_pattern_candidate'] desc=Frequency feature for token 'people' in an email message. +- word_freq_report: role=feature, type=numeric_sparse_frequency. tags=['condition_candidate', 'measure', 'rare_pattern_candidate'] desc=Frequency feature for token 'report' in an email message. +- word_freq_addresses: role=feature, type=numeric_sparse_frequency. tags=['condition_candidate', 'measure', 'rare_pattern_candidate'] desc=Frequency feature for token 'addresses' in an email message. +- word_freq_free: role=feature, type=numeric_sparse_frequency. tags=['condition_candidate', 'measure', 'rare_pattern_candidate'] desc=Frequency feature for token 'free' in an email message. +- word_freq_business: role=feature, type=numeric_sparse_frequency. tags=['condition_candidate', 'measure', 'rare_pattern_candidate'] desc=Frequency feature for token 'business' in an email message. +- word_freq_email: role=feature, type=numeric_sparse_frequency. tags=['condition_candidate', 'measure', 'rare_pattern_candidate'] desc=Frequency feature for token 'email' in an email message. +- word_freq_you: role=feature, type=numeric_sparse_frequency. tags=['condition_candidate', 'measure', 'rare_pattern_candidate'] desc=Frequency feature for token 'you' in an email message. +- word_freq_credit: role=feature, type=numeric_sparse_frequency. tags=['condition_candidate', 'measure', 'rare_pattern_candidate'] desc=Frequency feature for token 'credit' in an email message. +- word_freq_your: role=feature, type=numeric_sparse_frequency. tags=['condition_candidate', 'measure', 'rare_pattern_candidate'] desc=Frequency feature for token 'your' in an email message. +- word_freq_font: role=feature, type=numeric_sparse_frequency. tags=['condition_candidate', 'measure', 'rare_pattern_candidate'] desc=Frequency feature for token 'font' in an email message. +- word_freq_000: role=feature, type=numeric_sparse_frequency. tags=['condition_candidate', 'measure', 'rare_pattern_candidate'] desc=Frequency feature for token '000' in an email message. +- word_freq_money: role=feature, type=numeric_sparse_frequency. tags=['condition_candidate', 'measure', 'rare_pattern_candidate'] desc=Frequency feature for token 'money' in an email message. +- word_freq_hp: role=feature, type=numeric_sparse_frequency. tags=['condition_candidate', 'measure', 'rare_pattern_candidate'] desc=Frequency feature for token 'hp' in an email message. +- word_freq_hpl: role=feature, type=numeric_sparse_frequency. tags=['condition_candidate', 'measure', 'rare_pattern_candidate'] desc=Frequency feature for token 'hpl' in an email message. +- word_freq_george: role=feature, type=numeric_sparse_frequency. tags=['condition_candidate', 'measure', 'rare_pattern_candidate'] desc=Frequency feature for token 'george' in an email message. +- word_freq_650: role=feature, type=numeric_sparse_frequency. tags=['condition_candidate', 'measure', 'rare_pattern_candidate'] desc=Frequency feature for token '650' in an email message. +- word_freq_lab: role=feature, type=numeric_sparse_frequency. tags=['condition_candidate', 'measure', 'rare_pattern_candidate'] desc=Frequency feature for token 'lab' in an email message. +- word_freq_labs: role=feature, type=numeric_sparse_frequency. tags=['condition_candidate', 'measure', 'rare_pattern_candidate'] desc=Frequency feature for token 'labs' in an email message. +- word_freq_telnet: role=feature, type=numeric_sparse_frequency. tags=['condition_candidate', 'measure', 'rare_pattern_candidate'] desc=Frequency feature for token 'telnet' in an email message. +- word_freq_857: role=feature, type=numeric_sparse_frequency. tags=['condition_candidate', 'measure', 'rare_pattern_candidate'] desc=Frequency feature for token '857' in an email message. +- word_freq_data: role=feature, type=numeric_sparse_frequency. tags=['condition_candidate', 'measure', 'rare_pattern_candidate'] desc=Frequency feature for token 'data' in an email message. +- word_freq_415: role=feature, type=numeric_sparse_frequency. tags=['condition_candidate', 'measure', 'rare_pattern_candidate'] desc=Frequency feature for token '415' in an email message. +- word_freq_85: role=feature, type=numeric_sparse_frequency. tags=['condition_candidate', 'measure', 'rare_pattern_candidate'] desc=Frequency feature for token '85' in an email message. +- word_freq_technology: role=feature, type=numeric_sparse_frequency. tags=['condition_candidate', 'measure', 'rare_pattern_candidate'] desc=Frequency feature for token 'technology' in an email message. +- word_freq_1999: role=feature, type=numeric_sparse_frequency. tags=['condition_candidate', 'measure', 'rare_pattern_candidate'] desc=Frequency feature for token '1999' in an email message. +- word_freq_parts: role=feature, type=numeric_sparse_frequency. tags=['condition_candidate', 'measure', 'rare_pattern_candidate'] desc=Frequency feature for token 'parts' in an email message. +- word_freq_pm: role=feature, type=numeric_sparse_frequency. tags=['condition_candidate', 'measure', 'rare_pattern_candidate'] desc=Frequency feature for token 'pm' in an email message. +- word_freq_direct: role=feature, type=numeric_sparse_frequency. tags=['condition_candidate', 'measure', 'rare_pattern_candidate'] desc=Frequency feature for token 'direct' in an email message. +- word_freq_cs: role=feature, type=numeric_sparse_frequency. tags=['condition_candidate', 'measure', 'rare_pattern_candidate'] desc=Frequency feature for token 'cs' in an email message. +- word_freq_meeting: role=feature, type=numeric_sparse_frequency. tags=['condition_candidate', 'measure', 'rare_pattern_candidate'] desc=Frequency feature for token 'meeting' in an email message. +- word_freq_original: role=feature, type=numeric_sparse_frequency. tags=['condition_candidate', 'measure', 'rare_pattern_candidate'] desc=Frequency feature for token 'original' in an email message. +- word_freq_project: role=feature, type=numeric_sparse_frequency. tags=['condition_candidate', 'measure', 'rare_pattern_candidate'] desc=Frequency feature for token 'project' in an email message. +- word_freq_re: role=feature, type=numeric_sparse_frequency. tags=['condition_candidate', 'measure', 'rare_pattern_candidate'] desc=Frequency feature for token 're' in an email message. +- word_freq_edu: role=feature, type=numeric_sparse_frequency. tags=['condition_candidate', 'measure', 'rare_pattern_candidate'] desc=Frequency feature for token 'edu' in an email message. +- word_freq_table: role=feature, type=numeric_sparse_frequency. tags=['condition_candidate', 'measure', 'rare_pattern_candidate'] desc=Frequency feature for token 'table' in an email message. +- word_freq_conference: role=feature, type=numeric_sparse_frequency. tags=['condition_candidate', 'measure', 'rare_pattern_candidate'] desc=Frequency feature for token 'conference' in an email message. +- char_freq_%3B: role=feature, type=numeric_sparse_frequency. tags=['condition_candidate', 'measure', 'rare_pattern_candidate'] desc=Character frequency feature for symbol ';'. +- char_freq_%28: role=feature, type=numeric_sparse_frequency. tags=['condition_candidate', 'measure', 'rare_pattern_candidate'] desc=Character frequency feature for symbol '('. +- char_freq_%5B: role=feature, type=numeric_sparse_frequency. tags=['condition_candidate', 'measure', 'rare_pattern_candidate'] desc=Character frequency feature for symbol '['. +- char_freq_%21: role=feature, type=numeric_sparse_frequency. tags=['condition_candidate', 'measure', 'rare_pattern_candidate'] desc=Character frequency feature for symbol '!'. +- char_freq_%24: role=feature, type=numeric_sparse_frequency. tags=['condition_candidate', 'measure', 'rare_pattern_candidate'] desc=Character frequency feature for symbol '$'. +- char_freq_%23: role=feature, type=numeric_sparse_frequency. tags=['condition_candidate', 'measure', 'rare_pattern_candidate'] desc=Character frequency feature for symbol '#'. +- capital_run_length_average: role=feature, type=numeric_heavy_tail. tags=['condition_candidate', 'measure', 'rare_pattern_candidate'] desc=Average length of uninterrupted capital-letter runs. +- capital_run_length_longest: role=feature, type=numeric_heavy_tail. tags=['condition_candidate', 'measure', 'rare_pattern_candidate'] desc=Longest uninterrupted capital-letter run. +- capital_run_length_total: role=feature, type=numeric_heavy_tail. tags=['condition_candidate', 'measure', 'rare_pattern_candidate'] desc=Total number of capital letters in runs. +- class: role=target, type=binary_target. tags=['target_candidate'] desc=Binary spam label (1=spam, 0=non-spam). +- useful_field_combinations: [['word_freq_free', 'word_freq_you', 'class'], ['char_freq_%21', 'capital_run_length_longest', 'class'], ['word_freq_remove', 'word_freq_money', 'class']] +- fields_requiring_caution: ['capital_run_length_average', 'capital_run_length_longest', 'capital_run_length_total'] +- source_url: https://www.openml.org/d/44 + +SQLite schema snapshot: +{ + "table_name": "n1", + "quoted_table_name": "\"n1\"", + "row_count": 4601, + "columns": [ + { + "name": "word_freq_make", + "type": "TEXT", + "notnull": false, + "pk": false + }, + { + "name": "word_freq_address", + "type": "TEXT", + "notnull": false, + "pk": false + }, + { + "name": "word_freq_all", + "type": "TEXT", + "notnull": false, + "pk": false + }, + { + "name": "word_freq_3d", + "type": "TEXT", + "notnull": false, + "pk": false + }, + { + "name": "word_freq_our", + "type": "TEXT", + "notnull": false, + "pk": false + }, + { + "name": "word_freq_over", + "type": "TEXT", + "notnull": false, + "pk": false + }, + { + "name": "word_freq_remove", + "type": "TEXT", + "notnull": false, + "pk": false + }, + { + "name": "word_freq_internet", + "type": "TEXT", + "notnull": false, + "pk": false + }, + { + "name": "word_freq_order", + "type": "TEXT", + "notnull": false, + "pk": false + }, + { + "name": "word_freq_mail", + "type": "TEXT", + "notnull": false, + "pk": false + }, + { + "name": "word_freq_receive", + "type": "TEXT", + "notnull": false, + "pk": false + }, + { + "name": "word_freq_will", + "type": "TEXT", + "notnull": false, + "pk": false + }, + { + "name": "word_freq_people", + "type": "TEXT", + "notnull": false, + "pk": false + }, + { + "name": "word_freq_report", + "type": "TEXT", + "notnull": false, + "pk": false + }, + { + "name": "word_freq_addresses", + "type": "TEXT", + "notnull": false, + "pk": false + }, + { + "name": "word_freq_free", + "type": "TEXT", + "notnull": false, + "pk": false + }, + { + "name": "word_freq_business", + "type": "TEXT", + "notnull": false, + "pk": false + }, + { + "name": "word_freq_email", + "type": "TEXT", + "notnull": false, + "pk": false + }, + { + "name": "word_freq_you", + "type": "TEXT", + "notnull": false, + "pk": false + }, + { + "name": "word_freq_credit", + "type": "TEXT", + "notnull": false, + "pk": false + }, + { + "name": "word_freq_your", + "type": "TEXT", + "notnull": false, + "pk": false + }, + { + "name": "word_freq_font", + "type": "TEXT", + "notnull": false, + "pk": false + }, + { + "name": "word_freq_000", + "type": "TEXT", + "notnull": false, + "pk": false + }, + { + "name": "word_freq_money", + "type": "TEXT", + "notnull": false, + "pk": false + }, + { + "name": "word_freq_hp", + "type": "TEXT", + "notnull": false, + "pk": false + }, + { + "name": "word_freq_hpl", + "type": "TEXT", + "notnull": false, + "pk": false + }, + { + "name": "word_freq_george", + "type": "TEXT", + "notnull": false, + "pk": false + }, + { + "name": "word_freq_650", + "type": "TEXT", + "notnull": false, + "pk": false + }, + { + "name": "word_freq_lab", + "type": "TEXT", + "notnull": false, + "pk": false + }, + { + "name": "word_freq_labs", + "type": "TEXT", + "notnull": false, + "pk": false + }, + { + "name": "word_freq_telnet", + "type": "TEXT", + "notnull": false, + "pk": false + }, + { + "name": "word_freq_857", + "type": "TEXT", + "notnull": false, + "pk": false + }, + { + "name": "word_freq_data", + "type": "TEXT", + "notnull": false, + "pk": false + }, + { + "name": "word_freq_415", + "type": "TEXT", + "notnull": false, + "pk": false + }, + { + "name": "word_freq_85", + "type": "TEXT", + "notnull": false, + "pk": false + }, + { + "name": "word_freq_technology", + "type": "TEXT", + "notnull": false, + "pk": false + }, + { + "name": "word_freq_1999", + "type": "TEXT", + "notnull": false, + "pk": false + }, + { + "name": "word_freq_parts", + "type": "TEXT", + "notnull": false, + "pk": false + }, + { + "name": "word_freq_pm", + "type": "TEXT", + "notnull": false, + "pk": false + }, + { + "name": "word_freq_direct", + "type": "TEXT", + "notnull": false, + "pk": false + }, + { + "name": "word_freq_cs", + "type": "TEXT", + "notnull": false, + "pk": false + }, + { + "name": "word_freq_meeting", + "type": "TEXT", + "notnull": false, + "pk": false + }, + { + "name": "word_freq_original", + "type": "TEXT", + "notnull": false, + "pk": false + }, + { + "name": "word_freq_project", + "type": "TEXT", + "notnull": false, + "pk": false + }, + { + "name": "word_freq_re", + "type": "TEXT", + "notnull": false, + "pk": false + }, + { + "name": "word_freq_edu", + "type": "TEXT", + "notnull": false, + "pk": false + }, + { + "name": "word_freq_table", + "type": "TEXT", + "notnull": false, + "pk": false + }, + { + "name": "word_freq_conference", + "type": "TEXT", + "notnull": false, + "pk": false + }, + { + "name": "char_freq_%3B", + "type": "TEXT", + "notnull": false, + "pk": false + }, + { + "name": "char_freq_%28", + "type": "TEXT", + "notnull": false, + "pk": false + }, + { + "name": "char_freq_%5B", + "type": "TEXT", + "notnull": false, + "pk": false + }, + { + "name": "char_freq_%21", + "type": "TEXT", + "notnull": false, + "pk": false + }, + { + "name": "char_freq_%24", + "type": "TEXT", + "notnull": false, + "pk": false + }, + { + "name": "char_freq_%23", + "type": "TEXT", + "notnull": false, + "pk": false + }, + { + "name": "capital_run_length_average", + "type": "TEXT", + "notnull": false, + "pk": false + }, + { + "name": "capital_run_length_longest", + "type": "TEXT", + "notnull": false, + "pk": false + }, + { + "name": "capital_run_length_total", + "type": "TEXT", + "notnull": false, + "pk": false + }, + { + "name": "class", + "type": "TEXT", + "notnull": false, + "pk": false + } + ], + "sample_rows": [ + { + "word_freq_make": "0", + "word_freq_address": "0.64", + "word_freq_all": "0.64", + "word_freq_3d": "0", + "word_freq_our": "0.32", + "word_freq_over": "0", + "word_freq_remove": "0", + "word_freq_internet": "0", + "word_freq_order": "0", + "word_freq_mail": "0", + "word_freq_receive": "0", + "word_freq_will": "0.64", + "word_freq_people": "0", + "word_freq_report": "0", + "word_freq_addresses": "0", + "word_freq_free": "0.32", + "word_freq_business": "0", + "word_freq_email": "1.29", + "word_freq_you": "1.93", + "word_freq_credit": "0", + "word_freq_your": "0.96", + "word_freq_font": "0", + "word_freq_000": "0", + "word_freq_money": "0", + "word_freq_hp": "0", + "word_freq_hpl": "0", + "word_freq_george": "0", + "word_freq_650": "0", + "word_freq_lab": "0", + "word_freq_labs": "0", + "word_freq_telnet": "0", + "word_freq_857": "0", + "word_freq_data": "0", + "word_freq_415": "0", + "word_freq_85": "0", + "word_freq_technology": "0", + "word_freq_1999": "0", + "word_freq_parts": "0", + "word_freq_pm": "0", + "word_freq_direct": "0", + "word_freq_cs": "0", + "word_freq_meeting": "0", + "word_freq_original": "0", + "word_freq_project": "0", + "word_freq_re": "0", + "word_freq_edu": "0", + "word_freq_table": "0", + "word_freq_conference": "0", + "char_freq_%3B": "0", + "char_freq_%28": "0", + "char_freq_%5B": "0", + "char_freq_%21": "0.778", + "char_freq_%24": "0", + "char_freq_%23": "0", + "capital_run_length_average": "3.756", + "capital_run_length_longest": "61", + "capital_run_length_total": "278", + "class": "1" + }, + { + "word_freq_make": "0.21", + "word_freq_address": "0.28", + "word_freq_all": "0.5", + "word_freq_3d": "0", + "word_freq_our": "0.14", + "word_freq_over": "0.28", + "word_freq_remove": "0.21", + "word_freq_internet": "0.07", + "word_freq_order": "0", + "word_freq_mail": "0.94", + "word_freq_receive": "0.21", + "word_freq_will": "0.79", + "word_freq_people": "0.65", + "word_freq_report": "0.21", + "word_freq_addresses": "0.14", + "word_freq_free": "0.14", + "word_freq_business": "0.07", + "word_freq_email": "0.28", + "word_freq_you": "3.47", + "word_freq_credit": "0", + "word_freq_your": "1.59", + "word_freq_font": "0", + "word_freq_000": "0.43", + "word_freq_money": "0.43", + "word_freq_hp": "0", + "word_freq_hpl": "0", + "word_freq_george": "0", + "word_freq_650": "0", + "word_freq_lab": "0", + "word_freq_labs": "0", + "word_freq_telnet": "0", + "word_freq_857": "0", + "word_freq_data": "0", + "word_freq_415": "0", + "word_freq_85": "0", + "word_freq_technology": "0", + "word_freq_1999": "0.07", + "word_freq_parts": "0", + "word_freq_pm": "0", + "word_freq_direct": "0", + "word_freq_cs": "0", + "word_freq_meeting": "0", + "word_freq_original": "0", + "word_freq_project": "0", + "word_freq_re": "0", + "word_freq_edu": "0", + "word_freq_table": "0", + "word_freq_conference": "0", + "char_freq_%3B": "0", + "char_freq_%28": "0.132", + "char_freq_%5B": "0", + "char_freq_%21": "0.372", + "char_freq_%24": "0.18", + "char_freq_%23": "0.048", + "capital_run_length_average": "5.114", + "capital_run_length_longest": "101", + "capital_run_length_total": "1028", + "class": "1" + }, + { + "word_freq_make": "0.06", + "word_freq_address": "0", + "word_freq_all": "0.71", + "word_freq_3d": "0", + "word_freq_our": "1.23", + "word_freq_over": "0.19", + "word_freq_remove": "0.19", + "word_freq_internet": "0.12", + "word_freq_order": "0.64", + "word_freq_mail": "0.25", + "word_freq_receive": "0.38", + "word_freq_will": "0.45", + "word_freq_people": "0.12", + "word_freq_report": "0", + "word_freq_addresses": "1.75", + "word_freq_free": "0.06", + "word_freq_business": "0.06", + "word_freq_email": "1.03", + "word_freq_you": "1.36", + "word_freq_credit": "0.32", + "word_freq_your": "0.51", + "word_freq_font": "0", + "word_freq_000": "1.16", + "word_freq_money": "0.06", + "word_freq_hp": "0", + "word_freq_hpl": "0", + "word_freq_george": "0", + "word_freq_650": "0", + "word_freq_lab": "0", + "word_freq_labs": "0", + "word_freq_telnet": "0", + "word_freq_857": "0", + "word_freq_data": "0", + "word_freq_415": "0", + "word_freq_85": "0", + "word_freq_technology": "0", + "word_freq_1999": "0", + "word_freq_parts": "0", + "word_freq_pm": "0", + "word_freq_direct": "0.06", + "word_freq_cs": "0", + "word_freq_meeting": "0", + "word_freq_original": "0.12", + "word_freq_project": "0", + "word_freq_re": "0.06", + "word_freq_edu": "0.06", + "word_freq_table": "0", + "word_freq_conference": "0", + "char_freq_%3B": "0.01", + "char_freq_%28": "0.143", + "char_freq_%5B": "0", + "char_freq_%21": "0.276", + "char_freq_%24": "0.184", + "char_freq_%23": "0.01", + "capital_run_length_average": "9.821", + "capital_run_length_longest": "485", + "capital_run_length_total": "2259", + "class": "1" + }, + { + "word_freq_make": "0", + "word_freq_address": "0", + "word_freq_all": "0", + "word_freq_3d": "0", + "word_freq_our": "0.63", + "word_freq_over": "0", + "word_freq_remove": "0.31", + "word_freq_internet": "0.63", + "word_freq_order": "0.31", + "word_freq_mail": "0.63", + "word_freq_receive": "0.31", + "word_freq_will": "0.31", + "word_freq_people": "0.31", + "word_freq_report": "0", + "word_freq_addresses": "0", + "word_freq_free": "0.31", + "word_freq_business": "0", + "word_freq_email": "0", + "word_freq_you": "3.18", + "word_freq_credit": "0", + "word_freq_your": "0.31", + "word_freq_font": "0", + "word_freq_000": "0", + "word_freq_money": "0", + "word_freq_hp": "0", + "word_freq_hpl": "0", + "word_freq_george": "0", + "word_freq_650": "0", + "word_freq_lab": "0", + "word_freq_labs": "0", + "word_freq_telnet": "0", + "word_freq_857": "0", + "word_freq_data": "0", + "word_freq_415": "0", + "word_freq_85": "0", + "word_freq_technology": "0", + "word_freq_1999": "0", + "word_freq_parts": "0", + "word_freq_pm": "0", + "word_freq_direct": "0", + "word_freq_cs": "0", + "word_freq_meeting": "0", + "word_freq_original": "0", + "word_freq_project": "0", + "word_freq_re": "0", + "word_freq_edu": "0", + "word_freq_table": "0", + "word_freq_conference": "0", + "char_freq_%3B": "0", + "char_freq_%28": "0.137", + "char_freq_%5B": "0", + "char_freq_%21": "0.137", + "char_freq_%24": "0", + "char_freq_%23": "0", + "capital_run_length_average": "3.537", + "capital_run_length_longest": "40", + "capital_run_length_total": "191", + "class": "1" + }, + { + "word_freq_make": "0", + "word_freq_address": "0", + "word_freq_all": "0", + "word_freq_3d": "0", + "word_freq_our": "0.63", + "word_freq_over": "0", + "word_freq_remove": "0.31", + "word_freq_internet": "0.63", + "word_freq_order": "0.31", + "word_freq_mail": "0.63", + "word_freq_receive": "0.31", + "word_freq_will": "0.31", + "word_freq_people": "0.31", + "word_freq_report": "0", + "word_freq_addresses": "0", + "word_freq_free": "0.31", + "word_freq_business": "0", + "word_freq_email": "0", + "word_freq_you": "3.18", + "word_freq_credit": "0", + "word_freq_your": "0.31", + "word_freq_font": "0", + "word_freq_000": "0", + "word_freq_money": "0", + "word_freq_hp": "0", + "word_freq_hpl": "0", + "word_freq_george": "0", + "word_freq_650": "0", + "word_freq_lab": "0", + "word_freq_labs": "0", + "word_freq_telnet": "0", + "word_freq_857": "0", + "word_freq_data": "0", + "word_freq_415": "0", + "word_freq_85": "0", + "word_freq_technology": "0", + "word_freq_1999": "0", + "word_freq_parts": "0", + "word_freq_pm": "0", + "word_freq_direct": "0", + "word_freq_cs": "0", + "word_freq_meeting": "0", + "word_freq_original": "0", + "word_freq_project": "0", + "word_freq_re": "0", + "word_freq_edu": "0", + "word_freq_table": "0", + "word_freq_conference": "0", + "char_freq_%3B": "0", + "char_freq_%28": "0.135", + "char_freq_%5B": "0", + "char_freq_%21": "0.135", + "char_freq_%24": "0", + "char_freq_%23": "0", + "capital_run_length_average": "3.537", + "capital_run_length_longest": "40", + "capital_run_length_total": "191", + "class": "1" + } + ] +} + +Shortlisted templates: +[ + { + "template_id": "tpl_tail_low_support_group_count_v2", + "template_name": "Low-Support Group Count", + "primary_family": "tail_rarity_structure", + "portability": "yes", + "sql_skeleton": "SELECT\n {group_col},\n COUNT(*) AS support\nFROM {table}\nGROUP BY {group_col}\nORDER BY support ASC, {group_col}\nLIMIT {top_k};", + "required_roles": [ + "group_col" + ] + } +] + +Problem instance: +{ + "dataset_id": "n1", + "question": "Use template Low-Support Group Count to probe tail_mass_similarity with semantic role rare_extreme_view. Focus on group_col=class.", + "planned_template_id": "tpl_tail_low_support_group_count_v2", + "bindings": { + "group_col": "class", + "top_k": 18, + "top_n": 7, + "num_tiles": 10, + "percentile_value": 0.95, + "z_threshold": 2.0, + "fraction_threshold": 0.05, + "baseline_multiplier": 1.75, + "baseline_fraction": 0.1, + "min_group_size": 5, + "min_support": 4, + "measure_threshold": 0.16, + "time_grain": "month", + "lookback_rows": 3, + "current_period_start": "'2024-01-01'", + "current_period_end": "'2024-04-01'", + "previous_period_start": "'2023-10-01'", + "previous_period_end": "'2024-01-01'", + "drift_ratio_threshold": 0.8 + }, + "can_vary": [], + "must_fix": [], + "runtime_sql_skeleton": "SELECT\n {group_col},\n COUNT(*) AS support\nFROM {table}\nGROUP BY {group_col}\nORDER BY support ASC, {group_col}\nLIMIT {top_k};" +} + +Repair context: +{} diff --git a/Query/sql/v2/runs/v2_cli_20260502_081223_d/n1/artifacts/v2q_n1_8d4076cdf75ee6c9/cli/sql_response_attempt_1.raw.txt b/Query/sql/v2/runs/v2_cli_20260502_081223_d/n1/artifacts/v2q_n1_8d4076cdf75ee6c9/cli/sql_response_attempt_1.raw.txt new file mode 100644 index 0000000000000000000000000000000000000000..dcf4502752251b4478d468e5fa9862e73cbaac33 --- /dev/null +++ b/Query/sql/v2/runs/v2_cli_20260502_081223_d/n1/artifacts/v2q_n1_8d4076cdf75ee6c9/cli/sql_response_attempt_1.raw.txt @@ -0,0 +1,4 @@ +{"type":"thread.started","thread_id":"019e40f9-b4be-7901-8f1d-c41db6f1bb04"} +{"type":"turn.started"} +{"type":"error","message":"Quota exceeded. Check your plan and billing details."} +{"type":"turn.failed","error":{"message":"Quota exceeded. Check your plan and billing details."}} diff --git a/Query/sql/v2/runs/v2_cli_20260502_081223_d/n1/artifacts/v2q_n1_8d4076cdf75ee6c9/cli/sql_response_attempt_1.txt b/Query/sql/v2/runs/v2_cli_20260502_081223_d/n1/artifacts/v2q_n1_8d4076cdf75ee6c9/cli/sql_response_attempt_1.txt new file mode 100644 index 0000000000000000000000000000000000000000..3c13cb56be611727cd563794417d9dd46e28a9bc --- /dev/null +++ b/Query/sql/v2/runs/v2_cli_20260502_081223_d/n1/artifacts/v2q_n1_8d4076cdf75ee6c9/cli/sql_response_attempt_1.txt @@ -0,0 +1,4 @@ +{"type":"thread.started","thread_id":"019e40f9-b4be-7901-8f1d-c41db6f1bb04"} +{"type":"turn.started"} +{"type":"error","message":"Quota exceeded. Check your plan and billing details."} +{"type":"turn.failed","error":{"message":"Quota exceeded. Check your plan and billing details."}} \ No newline at end of file diff --git a/Query/sql/v2/runs/v2_cli_20260502_081223_d/n1/artifacts/v2q_n1_8d4076cdf75ee6c9/cli/sql_response_attempt_2.raw.txt b/Query/sql/v2/runs/v2_cli_20260502_081223_d/n1/artifacts/v2q_n1_8d4076cdf75ee6c9/cli/sql_response_attempt_2.raw.txt new file mode 100644 index 0000000000000000000000000000000000000000..422e093940dce5d012cfcc2668f29cee9fb77a29 --- /dev/null +++ b/Query/sql/v2/runs/v2_cli_20260502_081223_d/n1/artifacts/v2q_n1_8d4076cdf75ee6c9/cli/sql_response_attempt_2.raw.txt @@ -0,0 +1,4 @@ +{"type":"thread.started","thread_id":"019e40f9-c4e5-7ca0-8646-8b1c25a9530c"} +{"type":"turn.started"} +{"type":"error","message":"Quota exceeded. Check your plan and billing details."} +{"type":"turn.failed","error":{"message":"Quota exceeded. Check your plan and billing details."}} diff --git a/Query/sql/v2/runs/v2_cli_20260502_081223_d/n1/artifacts/v2q_n1_8d4076cdf75ee6c9/cli/sql_response_attempt_2.txt b/Query/sql/v2/runs/v2_cli_20260502_081223_d/n1/artifacts/v2q_n1_8d4076cdf75ee6c9/cli/sql_response_attempt_2.txt new file mode 100644 index 0000000000000000000000000000000000000000..52100059463ba8b49896e614e6bc3db94d205b2b --- /dev/null +++ b/Query/sql/v2/runs/v2_cli_20260502_081223_d/n1/artifacts/v2q_n1_8d4076cdf75ee6c9/cli/sql_response_attempt_2.txt @@ -0,0 +1,4 @@ +{"type":"thread.started","thread_id":"019e40f9-c4e5-7ca0-8646-8b1c25a9530c"} +{"type":"turn.started"} +{"type":"error","message":"Quota exceeded. Check your plan and billing details."} +{"type":"turn.failed","error":{"message":"Quota exceeded. Check your plan and billing details."}} \ No newline at end of file diff --git a/Query/sql/v2/runs/v2_cli_20260502_081223_d/n1/artifacts/v2q_n1_8d4076cdf75ee6c9/cli/sql_stderr_attempt_1.txt b/Query/sql/v2/runs/v2_cli_20260502_081223_d/n1/artifacts/v2q_n1_8d4076cdf75ee6c9/cli/sql_stderr_attempt_1.txt new file mode 100644 index 0000000000000000000000000000000000000000..e69de29bb2d1d6434b8b29ae775ad8c2e48c5391 diff --git a/Query/sql/v2/runs/v2_cli_20260502_081223_d/n1/artifacts/v2q_n1_8d4076cdf75ee6c9/cli/sql_stderr_attempt_2.txt b/Query/sql/v2/runs/v2_cli_20260502_081223_d/n1/artifacts/v2q_n1_8d4076cdf75ee6c9/cli/sql_stderr_attempt_2.txt new file mode 100644 index 0000000000000000000000000000000000000000..e69de29bb2d1d6434b8b29ae775ad8c2e48c5391 diff --git a/Query/sql/v2/runs/v2_cli_20260502_081223_d/n1/artifacts/v2q_n1_947c549aff46407a/cli/conversation.jsonl b/Query/sql/v2/runs/v2_cli_20260502_081223_d/n1/artifacts/v2q_n1_947c549aff46407a/cli/conversation.jsonl new file mode 100644 index 0000000000000000000000000000000000000000..52750b98b962f692398d5c4f9a372dc7b15e8641 --- /dev/null +++ b/Query/sql/v2/runs/v2_cli_20260502_081223_d/n1/artifacts/v2q_n1_947c549aff46407a/cli/conversation.jsonl @@ -0,0 +1,2 @@ +{"attempt": 1, "phase": "sql_generation", "role": "user", "content_path": "cli/sql_prompt_attempt_1.txt", "metrics": {"chars": 29366, "bytes_utf8": 29366, "lines": 792, "estimated_tokens": null}} +{"attempt": 1, "phase": "sql_generation", "role": "assistant", "content_path": "cli/sql_response_attempt_1.txt", "raw_content_path": "cli/sql_response_attempt_1.raw.txt", "stderr_path": "cli/sql_stderr_attempt_1.txt", "metrics": {"chars": 377, "bytes_utf8": 377, "lines": 1, "estimated_tokens": null}, "usage": {"input_tokens": 20317, "cached_input_tokens": 12032, "output_tokens": 308, "reasoning_output_tokens": 207}} diff --git a/Query/sql/v2/runs/v2_cli_20260502_081223_d/n1/artifacts/v2q_n1_947c549aff46407a/cli/session_summary.json b/Query/sql/v2/runs/v2_cli_20260502_081223_d/n1/artifacts/v2q_n1_947c549aff46407a/cli/session_summary.json new file mode 100644 index 0000000000000000000000000000000000000000..42ae72b66b83e37b4bff2dae937edec9cc94f44d --- /dev/null +++ b/Query/sql/v2/runs/v2_cli_20260502_081223_d/n1/artifacts/v2q_n1_947c549aff46407a/cli/session_summary.json @@ -0,0 +1,25 @@ +{ + "engine": "v2-cli:codex", + "command": "codex exec --skip-git-repo-check --disable plugins --sandbox read-only --cd \"/data/jialinzhang/SQLagent\" -m gpt-5.4 --json -", + "ai_cli_calls": 1, + "usage_summary": { + "dataset_id": "n1", + "model": "v2-cli:codex", + "run_id": "v2q_n1_947c549aff46407a", + "api_calls": 0, + "input_tokens": 20317, + "cached_input_tokens": 12032, + "output_tokens": 308, + "total_tokens": 20625, + "cost_usd": 0.0, + "ai_cli_calls": 1, + "estimated_input_tokens": 0, + "estimated_output_tokens": 0, + "estimated_total_tokens": 0, + "usage_source": "ai_cli_json_usage", + "cli_elapsed_ms_total": 9077.15, + "sql_execution_elapsed_ms_total": 5.02, + "conversation_log_path": "/data/jialinzhang/TabQueryBench/sql_workloads/v2_current/runs_and_launches/runs/v2_cli_20260502_081223_d/n1/artifacts/v2q_n1_947c549aff46407a/cli/conversation.jsonl", + "note": "Executed through a local AI CLI with structured usage metadata." + } +} \ No newline at end of file diff --git a/Query/sql/v2/runs/v2_cli_20260502_081223_d/n1/artifacts/v2q_n1_947c549aff46407a/cli/sql_attempt_1.metadata.json b/Query/sql/v2/runs/v2_cli_20260502_081223_d/n1/artifacts/v2q_n1_947c549aff46407a/cli/sql_attempt_1.metadata.json new file mode 100644 index 0000000000000000000000000000000000000000..9f6d8757287a01a144c1c3b4b8a6ccaf12328dd8 --- /dev/null +++ b/Query/sql/v2/runs/v2_cli_20260502_081223_d/n1/artifacts/v2q_n1_947c549aff46407a/cli/sql_attempt_1.metadata.json @@ -0,0 +1,45 @@ +{ + "attempt": 1, + "phase": "sql_generation", + "command": "codex exec --skip-git-repo-check --disable plugins --sandbox read-only --cd \"/data/jialinzhang/SQLagent\" -m gpt-5.4 --json -", + "started_at": "2026-05-19T15:32:03.026892+00:00", + "ended_at": "2026-05-19T15:32:12.104092+00:00", + "elapsed_ms": 9077.15, + "prompt_metrics": { + "chars": 29366, + "bytes_utf8": 29366, + "lines": 792, + "estimated_tokens": null + }, + "stdout_metrics": { + "chars": 739, + "bytes_utf8": 739, + "lines": 4, + "estimated_tokens": null + }, + "stderr_metrics": { + "chars": 0, + "bytes_utf8": 0, + "lines": 0, + "estimated_tokens": null + }, + "parsed_output": { + "format": "jsonl_events", + "text_metrics": { + "chars": 377, + "bytes_utf8": 377, + "lines": 1, + "estimated_tokens": null + }, + "usage": { + "input_tokens": 20317, + "cached_input_tokens": 12032, + "output_tokens": 308, + "reasoning_output_tokens": 207 + } + }, + "prompt_path": "cli/sql_prompt_attempt_1.txt", + "response_path": "cli/sql_response_attempt_1.txt", + "raw_response_path": "cli/sql_response_attempt_1.raw.txt", + "stderr_path": "cli/sql_stderr_attempt_1.txt" +} \ No newline at end of file diff --git a/Query/sql/v2/runs/v2_cli_20260502_081223_d/n1/artifacts/v2q_n1_947c549aff46407a/cli/sql_prompt_attempt_1.txt b/Query/sql/v2/runs/v2_cli_20260502_081223_d/n1/artifacts/v2q_n1_947c549aff46407a/cli/sql_prompt_attempt_1.txt new file mode 100644 index 0000000000000000000000000000000000000000..c244e1c638bccfe68aa21e7329a75b9a6e7f818a --- /dev/null +++ b/Query/sql/v2/runs/v2_cli_20260502_081223_d/n1/artifacts/v2q_n1_947c549aff46407a/cli/sql_prompt_attempt_1.txt @@ -0,0 +1,792 @@ +You are generating one SQLite SELECT query for a single-table SQL QA task. +Return strict JSON only, with this schema: {"sql": "...", "notes": "..."}. +Rules: +- Use only the provided table and columns. +- Do not write INSERT, UPDATE, DELETE, DROP, ALTER, CREATE, PRAGMA, ATTACH, DETACH, or VACUUM. +- Prefer the planned template and bound roles when provided. +- Add a leading SQL comment exactly like: -- template_id: . +- Generate SQLite-compatible SQL. SQLite does not support PERCENTILE_CONT or STDDEV. +- Quote identifiers with double quotes. +- Return no markdown and no extra prose. + +Dataset context: +Dataset context for SQL QA: +- dataset_id: n1 +- dataset_name: Spambase +- table_name: n1 +- table_layout: single-table dataset (do not assume joins). +- row_semantics: One row is one email represented by engineered token/character frequency and capitalization-run features. +- task_type: classification +- target_column: class +- main_row_count: 4601 +- important_fields: +- word_freq_make: role=feature, type=numeric_sparse_frequency. tags=['condition_candidate', 'measure', 'rare_pattern_candidate'] desc=Frequency feature for token 'make' in an email message. +- word_freq_address: role=feature, type=numeric_sparse_frequency. tags=['condition_candidate', 'measure', 'rare_pattern_candidate'] desc=Frequency feature for token 'address' in an email message. +- word_freq_all: role=feature, type=numeric_sparse_frequency. tags=['condition_candidate', 'measure', 'rare_pattern_candidate'] desc=Frequency feature for token 'all' in an email message. +- word_freq_3d: role=feature, type=numeric_sparse_frequency. tags=['condition_candidate', 'measure', 'rare_pattern_candidate'] desc=Frequency feature for token '3d' in an email message. +- word_freq_our: role=feature, type=numeric_sparse_frequency. tags=['condition_candidate', 'measure', 'rare_pattern_candidate'] desc=Frequency feature for token 'our' in an email message. +- word_freq_over: role=feature, type=numeric_sparse_frequency. tags=['condition_candidate', 'measure', 'rare_pattern_candidate'] desc=Frequency feature for token 'over' in an email message. +- word_freq_remove: role=feature, type=numeric_sparse_frequency. tags=['condition_candidate', 'measure', 'rare_pattern_candidate'] desc=Frequency feature for token 'remove' in an email message. +- word_freq_internet: role=feature, type=numeric_sparse_frequency. tags=['condition_candidate', 'measure', 'rare_pattern_candidate'] desc=Frequency feature for token 'internet' in an email message. +- word_freq_order: role=feature, type=numeric_sparse_frequency. tags=['condition_candidate', 'measure', 'rare_pattern_candidate'] desc=Frequency feature for token 'order' in an email message. +- word_freq_mail: role=feature, type=numeric_sparse_frequency. tags=['condition_candidate', 'measure', 'rare_pattern_candidate'] desc=Frequency feature for token 'mail' in an email message. +- word_freq_receive: role=feature, type=numeric_sparse_frequency. tags=['condition_candidate', 'measure', 'rare_pattern_candidate'] desc=Frequency feature for token 'receive' in an email message. +- word_freq_will: role=feature, type=numeric_sparse_frequency. tags=['condition_candidate', 'measure', 'rare_pattern_candidate'] desc=Frequency feature for token 'will' in an email message. +- word_freq_people: role=feature, type=numeric_sparse_frequency. tags=['condition_candidate', 'measure', 'rare_pattern_candidate'] desc=Frequency feature for token 'people' in an email message. +- word_freq_report: role=feature, type=numeric_sparse_frequency. tags=['condition_candidate', 'measure', 'rare_pattern_candidate'] desc=Frequency feature for token 'report' in an email message. +- word_freq_addresses: role=feature, type=numeric_sparse_frequency. tags=['condition_candidate', 'measure', 'rare_pattern_candidate'] desc=Frequency feature for token 'addresses' in an email message. +- word_freq_free: role=feature, type=numeric_sparse_frequency. tags=['condition_candidate', 'measure', 'rare_pattern_candidate'] desc=Frequency feature for token 'free' in an email message. +- word_freq_business: role=feature, type=numeric_sparse_frequency. tags=['condition_candidate', 'measure', 'rare_pattern_candidate'] desc=Frequency feature for token 'business' in an email message. +- word_freq_email: role=feature, type=numeric_sparse_frequency. tags=['condition_candidate', 'measure', 'rare_pattern_candidate'] desc=Frequency feature for token 'email' in an email message. +- word_freq_you: role=feature, type=numeric_sparse_frequency. tags=['condition_candidate', 'measure', 'rare_pattern_candidate'] desc=Frequency feature for token 'you' in an email message. +- word_freq_credit: role=feature, type=numeric_sparse_frequency. tags=['condition_candidate', 'measure', 'rare_pattern_candidate'] desc=Frequency feature for token 'credit' in an email message. +- word_freq_your: role=feature, type=numeric_sparse_frequency. tags=['condition_candidate', 'measure', 'rare_pattern_candidate'] desc=Frequency feature for token 'your' in an email message. +- word_freq_font: role=feature, type=numeric_sparse_frequency. tags=['condition_candidate', 'measure', 'rare_pattern_candidate'] desc=Frequency feature for token 'font' in an email message. +- word_freq_000: role=feature, type=numeric_sparse_frequency. tags=['condition_candidate', 'measure', 'rare_pattern_candidate'] desc=Frequency feature for token '000' in an email message. +- word_freq_money: role=feature, type=numeric_sparse_frequency. tags=['condition_candidate', 'measure', 'rare_pattern_candidate'] desc=Frequency feature for token 'money' in an email message. +- word_freq_hp: role=feature, type=numeric_sparse_frequency. tags=['condition_candidate', 'measure', 'rare_pattern_candidate'] desc=Frequency feature for token 'hp' in an email message. +- word_freq_hpl: role=feature, type=numeric_sparse_frequency. tags=['condition_candidate', 'measure', 'rare_pattern_candidate'] desc=Frequency feature for token 'hpl' in an email message. +- word_freq_george: role=feature, type=numeric_sparse_frequency. tags=['condition_candidate', 'measure', 'rare_pattern_candidate'] desc=Frequency feature for token 'george' in an email message. +- word_freq_650: role=feature, type=numeric_sparse_frequency. tags=['condition_candidate', 'measure', 'rare_pattern_candidate'] desc=Frequency feature for token '650' in an email message. +- word_freq_lab: role=feature, type=numeric_sparse_frequency. tags=['condition_candidate', 'measure', 'rare_pattern_candidate'] desc=Frequency feature for token 'lab' in an email message. +- word_freq_labs: role=feature, type=numeric_sparse_frequency. tags=['condition_candidate', 'measure', 'rare_pattern_candidate'] desc=Frequency feature for token 'labs' in an email message. +- word_freq_telnet: role=feature, type=numeric_sparse_frequency. tags=['condition_candidate', 'measure', 'rare_pattern_candidate'] desc=Frequency feature for token 'telnet' in an email message. +- word_freq_857: role=feature, type=numeric_sparse_frequency. tags=['condition_candidate', 'measure', 'rare_pattern_candidate'] desc=Frequency feature for token '857' in an email message. +- word_freq_data: role=feature, type=numeric_sparse_frequency. tags=['condition_candidate', 'measure', 'rare_pattern_candidate'] desc=Frequency feature for token 'data' in an email message. +- word_freq_415: role=feature, type=numeric_sparse_frequency. tags=['condition_candidate', 'measure', 'rare_pattern_candidate'] desc=Frequency feature for token '415' in an email message. +- word_freq_85: role=feature, type=numeric_sparse_frequency. tags=['condition_candidate', 'measure', 'rare_pattern_candidate'] desc=Frequency feature for token '85' in an email message. +- word_freq_technology: role=feature, type=numeric_sparse_frequency. tags=['condition_candidate', 'measure', 'rare_pattern_candidate'] desc=Frequency feature for token 'technology' in an email message. +- word_freq_1999: role=feature, type=numeric_sparse_frequency. tags=['condition_candidate', 'measure', 'rare_pattern_candidate'] desc=Frequency feature for token '1999' in an email message. +- word_freq_parts: role=feature, type=numeric_sparse_frequency. tags=['condition_candidate', 'measure', 'rare_pattern_candidate'] desc=Frequency feature for token 'parts' in an email message. +- word_freq_pm: role=feature, type=numeric_sparse_frequency. tags=['condition_candidate', 'measure', 'rare_pattern_candidate'] desc=Frequency feature for token 'pm' in an email message. +- word_freq_direct: role=feature, type=numeric_sparse_frequency. tags=['condition_candidate', 'measure', 'rare_pattern_candidate'] desc=Frequency feature for token 'direct' in an email message. +- word_freq_cs: role=feature, type=numeric_sparse_frequency. tags=['condition_candidate', 'measure', 'rare_pattern_candidate'] desc=Frequency feature for token 'cs' in an email message. +- word_freq_meeting: role=feature, type=numeric_sparse_frequency. tags=['condition_candidate', 'measure', 'rare_pattern_candidate'] desc=Frequency feature for token 'meeting' in an email message. +- word_freq_original: role=feature, type=numeric_sparse_frequency. tags=['condition_candidate', 'measure', 'rare_pattern_candidate'] desc=Frequency feature for token 'original' in an email message. +- word_freq_project: role=feature, type=numeric_sparse_frequency. tags=['condition_candidate', 'measure', 'rare_pattern_candidate'] desc=Frequency feature for token 'project' in an email message. +- word_freq_re: role=feature, type=numeric_sparse_frequency. tags=['condition_candidate', 'measure', 'rare_pattern_candidate'] desc=Frequency feature for token 're' in an email message. +- word_freq_edu: role=feature, type=numeric_sparse_frequency. tags=['condition_candidate', 'measure', 'rare_pattern_candidate'] desc=Frequency feature for token 'edu' in an email message. +- word_freq_table: role=feature, type=numeric_sparse_frequency. tags=['condition_candidate', 'measure', 'rare_pattern_candidate'] desc=Frequency feature for token 'table' in an email message. +- word_freq_conference: role=feature, type=numeric_sparse_frequency. tags=['condition_candidate', 'measure', 'rare_pattern_candidate'] desc=Frequency feature for token 'conference' in an email message. +- char_freq_%3B: role=feature, type=numeric_sparse_frequency. tags=['condition_candidate', 'measure', 'rare_pattern_candidate'] desc=Character frequency feature for symbol ';'. +- char_freq_%28: role=feature, type=numeric_sparse_frequency. tags=['condition_candidate', 'measure', 'rare_pattern_candidate'] desc=Character frequency feature for symbol '('. +- char_freq_%5B: role=feature, type=numeric_sparse_frequency. tags=['condition_candidate', 'measure', 'rare_pattern_candidate'] desc=Character frequency feature for symbol '['. +- char_freq_%21: role=feature, type=numeric_sparse_frequency. tags=['condition_candidate', 'measure', 'rare_pattern_candidate'] desc=Character frequency feature for symbol '!'. +- char_freq_%24: role=feature, type=numeric_sparse_frequency. tags=['condition_candidate', 'measure', 'rare_pattern_candidate'] desc=Character frequency feature for symbol '$'. +- char_freq_%23: role=feature, type=numeric_sparse_frequency. tags=['condition_candidate', 'measure', 'rare_pattern_candidate'] desc=Character frequency feature for symbol '#'. +- capital_run_length_average: role=feature, type=numeric_heavy_tail. tags=['condition_candidate', 'measure', 'rare_pattern_candidate'] desc=Average length of uninterrupted capital-letter runs. +- capital_run_length_longest: role=feature, type=numeric_heavy_tail. tags=['condition_candidate', 'measure', 'rare_pattern_candidate'] desc=Longest uninterrupted capital-letter run. +- capital_run_length_total: role=feature, type=numeric_heavy_tail. tags=['condition_candidate', 'measure', 'rare_pattern_candidate'] desc=Total number of capital letters in runs. +- class: role=target, type=binary_target. tags=['target_candidate'] desc=Binary spam label (1=spam, 0=non-spam). +- useful_field_combinations: [['word_freq_free', 'word_freq_you', 'class'], ['char_freq_%21', 'capital_run_length_longest', 'class'], ['word_freq_remove', 'word_freq_money', 'class']] +- fields_requiring_caution: ['capital_run_length_average', 'capital_run_length_longest', 'capital_run_length_total'] +- source_url: https://www.openml.org/d/44 + +SQLite schema snapshot: +{ + "table_name": "n1", + "quoted_table_name": "\"n1\"", + "row_count": 4601, + "columns": [ + { + "name": "word_freq_make", + "type": "TEXT", + "notnull": false, + "pk": false + }, + { + "name": "word_freq_address", + "type": "TEXT", + "notnull": false, + "pk": false + }, + { + "name": "word_freq_all", + "type": "TEXT", + "notnull": false, + "pk": false + }, + { + "name": "word_freq_3d", + "type": "TEXT", + "notnull": false, + "pk": false + }, + { + "name": "word_freq_our", + "type": "TEXT", + "notnull": false, + "pk": false + }, + { + "name": "word_freq_over", + "type": "TEXT", + "notnull": false, + "pk": false + }, + { + "name": "word_freq_remove", + "type": "TEXT", + "notnull": false, + "pk": false + }, + { + "name": "word_freq_internet", + "type": "TEXT", + "notnull": false, + "pk": false + }, + { + "name": "word_freq_order", + "type": "TEXT", + "notnull": false, + "pk": false + }, + { + "name": "word_freq_mail", + "type": "TEXT", + "notnull": false, + "pk": false + }, + { + "name": "word_freq_receive", + "type": "TEXT", + "notnull": false, + "pk": false + }, + { + "name": "word_freq_will", + "type": "TEXT", + "notnull": false, + "pk": false + }, + { + "name": "word_freq_people", + "type": "TEXT", + "notnull": false, + "pk": false + }, + { + "name": "word_freq_report", + "type": "TEXT", + "notnull": false, + "pk": false + }, + { + "name": "word_freq_addresses", + "type": "TEXT", + "notnull": false, + "pk": false + }, + { + "name": "word_freq_free", + "type": "TEXT", + "notnull": false, + "pk": false + }, + { + "name": "word_freq_business", + "type": "TEXT", + "notnull": false, + "pk": false + }, + { + "name": "word_freq_email", + "type": "TEXT", + "notnull": false, + "pk": false + }, + { + "name": "word_freq_you", + "type": "TEXT", + "notnull": false, + "pk": false + }, + { + "name": "word_freq_credit", + "type": "TEXT", + "notnull": false, + "pk": false + }, + { + "name": "word_freq_your", + "type": "TEXT", + "notnull": false, + "pk": false + }, + { + "name": "word_freq_font", + "type": "TEXT", + "notnull": false, + "pk": false + }, + { + "name": "word_freq_000", + "type": "TEXT", + "notnull": false, + "pk": false + }, + { + "name": "word_freq_money", + "type": "TEXT", + "notnull": false, + "pk": false + }, + { + "name": "word_freq_hp", + "type": "TEXT", + "notnull": false, + "pk": false + }, + { + "name": "word_freq_hpl", + "type": "TEXT", + "notnull": false, + "pk": false + }, + { + "name": "word_freq_george", + "type": "TEXT", + "notnull": false, + "pk": false + }, + { + "name": "word_freq_650", + "type": "TEXT", + "notnull": false, + "pk": false + }, + { + "name": "word_freq_lab", + "type": "TEXT", + "notnull": false, + "pk": false + }, + { + "name": "word_freq_labs", + "type": "TEXT", + "notnull": false, + "pk": false + }, + { + "name": "word_freq_telnet", + "type": "TEXT", + "notnull": false, + "pk": false + }, + { + "name": "word_freq_857", + "type": "TEXT", + "notnull": false, + "pk": false + }, + { + "name": "word_freq_data", + "type": "TEXT", + "notnull": false, + "pk": false + }, + { + "name": "word_freq_415", + "type": "TEXT", + "notnull": false, + "pk": false + }, + { + "name": "word_freq_85", + "type": "TEXT", + "notnull": false, + "pk": false + }, + { + "name": "word_freq_technology", + "type": "TEXT", + "notnull": false, + "pk": false + }, + { + "name": "word_freq_1999", + "type": "TEXT", + "notnull": false, + "pk": false + }, + { + "name": "word_freq_parts", + "type": "TEXT", + "notnull": false, + "pk": false + }, + { + "name": "word_freq_pm", + "type": "TEXT", + "notnull": false, + "pk": false + }, + { + "name": "word_freq_direct", + "type": "TEXT", + "notnull": false, + "pk": false + }, + { + "name": "word_freq_cs", + "type": "TEXT", + "notnull": false, + "pk": false + }, + { + "name": "word_freq_meeting", + "type": "TEXT", + "notnull": false, + "pk": false + }, + { + "name": "word_freq_original", + "type": "TEXT", + "notnull": false, + "pk": false + }, + { + "name": "word_freq_project", + "type": "TEXT", + "notnull": false, + "pk": false + }, + { + "name": "word_freq_re", + "type": "TEXT", + "notnull": false, + "pk": false + }, + { + "name": "word_freq_edu", + "type": "TEXT", + "notnull": false, + "pk": false + }, + { + "name": "word_freq_table", + "type": "TEXT", + "notnull": false, + "pk": false + }, + { + "name": "word_freq_conference", + "type": "TEXT", + "notnull": false, + "pk": false + }, + { + "name": "char_freq_%3B", + "type": "TEXT", + "notnull": false, + "pk": false + }, + { + "name": "char_freq_%28", + "type": "TEXT", + "notnull": false, + "pk": false + }, + { + "name": "char_freq_%5B", + "type": "TEXT", + "notnull": false, + "pk": false + }, + { + "name": "char_freq_%21", + "type": "TEXT", + "notnull": false, + "pk": false + }, + { + "name": "char_freq_%24", + "type": "TEXT", + "notnull": false, + "pk": false + }, + { + "name": "char_freq_%23", + "type": "TEXT", + "notnull": false, + "pk": false + }, + { + "name": "capital_run_length_average", + "type": "TEXT", + "notnull": false, + "pk": false + }, + { + "name": "capital_run_length_longest", + "type": "TEXT", + "notnull": false, + "pk": false + }, + { + "name": "capital_run_length_total", + "type": "TEXT", + "notnull": false, + "pk": false + }, + { + "name": "class", + "type": "TEXT", + "notnull": false, + "pk": false + } + ], + "sample_rows": [ + { + "word_freq_make": "0", + "word_freq_address": "0.64", + "word_freq_all": "0.64", + "word_freq_3d": "0", + "word_freq_our": "0.32", + "word_freq_over": "0", + "word_freq_remove": "0", + "word_freq_internet": "0", + "word_freq_order": "0", + "word_freq_mail": "0", + "word_freq_receive": "0", + "word_freq_will": "0.64", + "word_freq_people": "0", + "word_freq_report": "0", + "word_freq_addresses": "0", + "word_freq_free": "0.32", + "word_freq_business": "0", + "word_freq_email": "1.29", + "word_freq_you": "1.93", + "word_freq_credit": "0", + "word_freq_your": "0.96", + "word_freq_font": "0", + "word_freq_000": "0", + "word_freq_money": "0", + "word_freq_hp": "0", + "word_freq_hpl": "0", + "word_freq_george": "0", + "word_freq_650": "0", + "word_freq_lab": "0", + "word_freq_labs": "0", + "word_freq_telnet": "0", + "word_freq_857": "0", + "word_freq_data": "0", + "word_freq_415": "0", + "word_freq_85": "0", + "word_freq_technology": "0", + "word_freq_1999": "0", + "word_freq_parts": "0", + "word_freq_pm": "0", + "word_freq_direct": "0", + "word_freq_cs": "0", + "word_freq_meeting": "0", + "word_freq_original": "0", + "word_freq_project": "0", + "word_freq_re": "0", + "word_freq_edu": "0", + "word_freq_table": "0", + "word_freq_conference": "0", + "char_freq_%3B": "0", + "char_freq_%28": "0", + "char_freq_%5B": "0", + "char_freq_%21": "0.778", + "char_freq_%24": "0", + "char_freq_%23": "0", + "capital_run_length_average": "3.756", + "capital_run_length_longest": "61", + "capital_run_length_total": "278", + "class": "1" + }, + { + "word_freq_make": "0.21", + "word_freq_address": "0.28", + "word_freq_all": "0.5", + "word_freq_3d": "0", + "word_freq_our": "0.14", + "word_freq_over": "0.28", + "word_freq_remove": "0.21", + "word_freq_internet": "0.07", + "word_freq_order": "0", + "word_freq_mail": "0.94", + "word_freq_receive": "0.21", + "word_freq_will": "0.79", + "word_freq_people": "0.65", + "word_freq_report": "0.21", + "word_freq_addresses": "0.14", + "word_freq_free": "0.14", + "word_freq_business": "0.07", + "word_freq_email": "0.28", + "word_freq_you": "3.47", + "word_freq_credit": "0", + "word_freq_your": "1.59", + "word_freq_font": "0", + "word_freq_000": "0.43", + "word_freq_money": "0.43", + "word_freq_hp": "0", + "word_freq_hpl": "0", + "word_freq_george": "0", + "word_freq_650": "0", + "word_freq_lab": "0", + "word_freq_labs": "0", + "word_freq_telnet": "0", + "word_freq_857": "0", + "word_freq_data": "0", + "word_freq_415": "0", + "word_freq_85": "0", + "word_freq_technology": "0", + "word_freq_1999": "0.07", + "word_freq_parts": "0", + "word_freq_pm": "0", + "word_freq_direct": "0", + "word_freq_cs": "0", + "word_freq_meeting": "0", + "word_freq_original": "0", + "word_freq_project": "0", + "word_freq_re": "0", + "word_freq_edu": "0", + "word_freq_table": "0", + "word_freq_conference": "0", + "char_freq_%3B": "0", + "char_freq_%28": "0.132", + "char_freq_%5B": "0", + "char_freq_%21": "0.372", + "char_freq_%24": "0.18", + "char_freq_%23": "0.048", + "capital_run_length_average": "5.114", + "capital_run_length_longest": "101", + "capital_run_length_total": "1028", + "class": "1" + }, + { + "word_freq_make": "0.06", + "word_freq_address": "0", + "word_freq_all": "0.71", + "word_freq_3d": "0", + "word_freq_our": "1.23", + "word_freq_over": "0.19", + "word_freq_remove": "0.19", + "word_freq_internet": "0.12", + "word_freq_order": "0.64", + "word_freq_mail": "0.25", + "word_freq_receive": "0.38", + "word_freq_will": "0.45", + "word_freq_people": "0.12", + "word_freq_report": "0", + "word_freq_addresses": "1.75", + "word_freq_free": "0.06", + "word_freq_business": "0.06", + "word_freq_email": "1.03", + "word_freq_you": "1.36", + "word_freq_credit": "0.32", + "word_freq_your": "0.51", + "word_freq_font": "0", + "word_freq_000": "1.16", + "word_freq_money": "0.06", + "word_freq_hp": "0", + "word_freq_hpl": "0", + "word_freq_george": "0", + "word_freq_650": "0", + "word_freq_lab": "0", + "word_freq_labs": "0", + "word_freq_telnet": "0", + "word_freq_857": "0", + "word_freq_data": "0", + "word_freq_415": "0", + "word_freq_85": "0", + "word_freq_technology": "0", + "word_freq_1999": "0", + "word_freq_parts": "0", + "word_freq_pm": "0", + "word_freq_direct": "0.06", + "word_freq_cs": "0", + "word_freq_meeting": "0", + "word_freq_original": "0.12", + "word_freq_project": "0", + "word_freq_re": "0.06", + "word_freq_edu": "0.06", + "word_freq_table": "0", + "word_freq_conference": "0", + "char_freq_%3B": "0.01", + "char_freq_%28": "0.143", + "char_freq_%5B": "0", + "char_freq_%21": "0.276", + "char_freq_%24": "0.184", + "char_freq_%23": "0.01", + "capital_run_length_average": "9.821", + "capital_run_length_longest": "485", + "capital_run_length_total": "2259", + "class": "1" + }, + { + "word_freq_make": "0", + "word_freq_address": "0", + "word_freq_all": "0", + "word_freq_3d": "0", + "word_freq_our": "0.63", + "word_freq_over": "0", + "word_freq_remove": "0.31", + "word_freq_internet": "0.63", + "word_freq_order": "0.31", + "word_freq_mail": "0.63", + "word_freq_receive": "0.31", + "word_freq_will": "0.31", + "word_freq_people": "0.31", + "word_freq_report": "0", + "word_freq_addresses": "0", + "word_freq_free": "0.31", + "word_freq_business": "0", + "word_freq_email": "0", + "word_freq_you": "3.18", + "word_freq_credit": "0", + "word_freq_your": "0.31", + "word_freq_font": "0", + "word_freq_000": "0", + "word_freq_money": "0", + "word_freq_hp": "0", + "word_freq_hpl": "0", + "word_freq_george": "0", + "word_freq_650": "0", + "word_freq_lab": "0", + "word_freq_labs": "0", + "word_freq_telnet": "0", + "word_freq_857": "0", + "word_freq_data": "0", + "word_freq_415": "0", + "word_freq_85": "0", + "word_freq_technology": "0", + "word_freq_1999": "0", + "word_freq_parts": "0", + "word_freq_pm": "0", + "word_freq_direct": "0", + "word_freq_cs": "0", + "word_freq_meeting": "0", + "word_freq_original": "0", + "word_freq_project": "0", + "word_freq_re": "0", + "word_freq_edu": "0", + "word_freq_table": "0", + "word_freq_conference": "0", + "char_freq_%3B": "0", + "char_freq_%28": "0.137", + "char_freq_%5B": "0", + "char_freq_%21": "0.137", + "char_freq_%24": "0", + "char_freq_%23": "0", + "capital_run_length_average": "3.537", + "capital_run_length_longest": "40", + "capital_run_length_total": "191", + "class": "1" + }, + { + "word_freq_make": "0", + "word_freq_address": "0", + "word_freq_all": "0", + "word_freq_3d": "0", + "word_freq_our": "0.63", + "word_freq_over": "0", + "word_freq_remove": "0.31", + "word_freq_internet": "0.63", + "word_freq_order": "0.31", + "word_freq_mail": "0.63", + "word_freq_receive": "0.31", + "word_freq_will": "0.31", + "word_freq_people": "0.31", + "word_freq_report": "0", + "word_freq_addresses": "0", + "word_freq_free": "0.31", + "word_freq_business": "0", + "word_freq_email": "0", + "word_freq_you": "3.18", + "word_freq_credit": "0", + "word_freq_your": "0.31", + "word_freq_font": "0", + "word_freq_000": "0", + "word_freq_money": "0", + "word_freq_hp": "0", + "word_freq_hpl": "0", + "word_freq_george": "0", + "word_freq_650": "0", + "word_freq_lab": "0", + "word_freq_labs": "0", + "word_freq_telnet": "0", + "word_freq_857": "0", + "word_freq_data": "0", + "word_freq_415": "0", + "word_freq_85": "0", + "word_freq_technology": "0", + "word_freq_1999": "0", + "word_freq_parts": "0", + "word_freq_pm": "0", + "word_freq_direct": "0", + "word_freq_cs": "0", + "word_freq_meeting": "0", + "word_freq_original": "0", + "word_freq_project": "0", + "word_freq_re": "0", + "word_freq_edu": "0", + "word_freq_table": "0", + "word_freq_conference": "0", + "char_freq_%3B": "0", + "char_freq_%28": "0.135", + "char_freq_%5B": "0", + "char_freq_%21": "0.135", + "char_freq_%24": "0", + "char_freq_%23": "0", + "capital_run_length_average": "3.537", + "capital_run_length_longest": "40", + "capital_run_length_total": "191", + "class": "1" + } + ] +} + +Shortlisted templates: +[ + { + "template_id": "tpl_h2o_group_sum", + "template_name": "Grouped Numeric Sum", + "primary_family": "subgroup_structure", + "portability": "partial", + "sql_skeleton": "SELECT {group_col}, SUM({measure_col}) AS total_measure\nFROM {table}\nGROUP BY {group_col}\nORDER BY total_measure DESC;", + "required_roles": [ + "group_col", + "measure_col" + ] + } +] + +Problem instance: +{ + "dataset_id": "n1", + "question": "Use template Grouped Numeric Sum to probe internal_profile_stability with semantic role collapsed_target_view. Focus on group_col=class, measure_col=word_freq_internet.", + "planned_template_id": "tpl_h2o_group_sum", + "bindings": { + "group_col": "class", + "measure_col": "word_freq_internet", + "top_k": 17, + "top_n": 7, + "num_tiles": 10, + "percentile_value": 0.95, + "z_threshold": 2.0, + "fraction_threshold": 0.05, + "baseline_multiplier": 1.75, + "baseline_fraction": 0.1, + "min_group_size": 5, + "min_support": 4, + "measure_threshold": 0.0, + "time_grain": "month", + "lookback_rows": 3, + "current_period_start": "'2024-01-01'", + "current_period_end": "'2024-04-01'", + "previous_period_start": "'2023-10-01'", + "previous_period_end": "'2024-01-01'", + "drift_ratio_threshold": 0.8 + }, + "can_vary": [], + "must_fix": [], + "runtime_sql_skeleton": "SELECT {group_col}, SUM({measure_col}) AS total_measure\nFROM {table}\nGROUP BY {group_col}\nORDER BY total_measure DESC;" +} + +Repair context: +{} diff --git a/Query/sql/v2/runs/v2_cli_20260502_081223_d/n1/artifacts/v2q_n1_947c549aff46407a/cli/sql_response_attempt_1.raw.txt b/Query/sql/v2/runs/v2_cli_20260502_081223_d/n1/artifacts/v2q_n1_947c549aff46407a/cli/sql_response_attempt_1.raw.txt new file mode 100644 index 0000000000000000000000000000000000000000..4ed0c8e77090cf696035581d4d69e6ca0a4ce79b --- /dev/null +++ b/Query/sql/v2/runs/v2_cli_20260502_081223_d/n1/artifacts/v2q_n1_947c549aff46407a/cli/sql_response_attempt_1.raw.txt @@ -0,0 +1,4 @@ +{"type":"thread.started","thread_id":"019e40dd-9263-7c23-aa14-bb0b8346c971"} +{"type":"turn.started"} +{"type":"item.completed","item":{"id":"item_0","type":"agent_message","text":"{\"sql\":\"-- template_id: tpl_h2o_group_sum\\nSELECT \\\"class\\\", SUM(CAST(\\\"word_freq_internet\\\" AS REAL)) AS \\\"total_measure\\\"\\nFROM \\\"n1\\\"\\nGROUP BY \\\"class\\\"\\nORDER BY \\\"total_measure\\\" DESC;\",\"notes\":\"Applied the grouped numeric sum template with \\\"class\\\" as the grouping column and cast \\\"word_freq_internet\\\" to REAL because the schema snapshot shows it is stored as TEXT.\"}"}} +{"type":"turn.completed","usage":{"input_tokens":20317,"cached_input_tokens":12032,"output_tokens":308,"reasoning_output_tokens":207}} diff --git a/Query/sql/v2/runs/v2_cli_20260502_081223_d/n1/artifacts/v2q_n1_947c549aff46407a/cli/sql_response_attempt_1.txt b/Query/sql/v2/runs/v2_cli_20260502_081223_d/n1/artifacts/v2q_n1_947c549aff46407a/cli/sql_response_attempt_1.txt new file mode 100644 index 0000000000000000000000000000000000000000..2f73104f7700562eb9b3e479a38cfd63d8a323f0 --- /dev/null +++ b/Query/sql/v2/runs/v2_cli_20260502_081223_d/n1/artifacts/v2q_n1_947c549aff46407a/cli/sql_response_attempt_1.txt @@ -0,0 +1 @@ +{"sql":"-- template_id: tpl_h2o_group_sum\nSELECT \"class\", SUM(CAST(\"word_freq_internet\" AS REAL)) AS \"total_measure\"\nFROM \"n1\"\nGROUP BY \"class\"\nORDER BY \"total_measure\" DESC;","notes":"Applied the grouped numeric sum template with \"class\" as the grouping column and cast \"word_freq_internet\" to REAL because the schema snapshot shows it is stored as TEXT."} \ No newline at end of file diff --git a/Query/sql/v2/runs/v2_cli_20260502_081223_d/n1/artifacts/v2q_n1_947c549aff46407a/cli/sql_stderr_attempt_1.txt b/Query/sql/v2/runs/v2_cli_20260502_081223_d/n1/artifacts/v2q_n1_947c549aff46407a/cli/sql_stderr_attempt_1.txt new file mode 100644 index 0000000000000000000000000000000000000000..e69de29bb2d1d6434b8b29ae775ad8c2e48c5391 diff --git a/Query/sql/v2/runs/v2_cli_20260502_081223_d/n1/artifacts/v2q_n1_9c4ca2499b13991c/cli/conversation.jsonl b/Query/sql/v2/runs/v2_cli_20260502_081223_d/n1/artifacts/v2q_n1_9c4ca2499b13991c/cli/conversation.jsonl new file mode 100644 index 0000000000000000000000000000000000000000..1c636ccc7b8c5161b584b4cf54532c6c02ca9a59 --- /dev/null +++ b/Query/sql/v2/runs/v2_cli_20260502_081223_d/n1/artifacts/v2q_n1_9c4ca2499b13991c/cli/conversation.jsonl @@ -0,0 +1,2 @@ +{"attempt": 1, "phase": "sql_generation", "role": "user", "content_path": "cli/sql_prompt_attempt_1.txt", "metrics": {"chars": 29535, "bytes_utf8": 29535, "lines": 792, "estimated_tokens": null}} +{"attempt": 1, "phase": "sql_generation", "role": "assistant", "content_path": "cli/sql_response_attempt_1.txt", "raw_content_path": "cli/sql_response_attempt_1.raw.txt", "stderr_path": "cli/sql_stderr_attempt_1.txt", "metrics": {"chars": 1738, "bytes_utf8": 1738, "lines": 1, "estimated_tokens": null}, "usage": {"input_tokens": 20355, "cached_input_tokens": 19840, "output_tokens": 2681, "reasoning_output_tokens": 2070}} diff --git a/Query/sql/v2/runs/v2_cli_20260502_081223_d/n1/artifacts/v2q_n1_9c4ca2499b13991c/cli/session_summary.json b/Query/sql/v2/runs/v2_cli_20260502_081223_d/n1/artifacts/v2q_n1_9c4ca2499b13991c/cli/session_summary.json new file mode 100644 index 0000000000000000000000000000000000000000..d4bf3387eecb7dd8331e188dfaa7a8c41bf7b486 --- /dev/null +++ b/Query/sql/v2/runs/v2_cli_20260502_081223_d/n1/artifacts/v2q_n1_9c4ca2499b13991c/cli/session_summary.json @@ -0,0 +1,25 @@ +{ + "engine": "v2-cli:codex", + "command": "codex exec --skip-git-repo-check --disable plugins --sandbox read-only --cd \"/data/jialinzhang/SQLagent\" -m gpt-5.4 --json -", + "ai_cli_calls": 1, + "usage_summary": { + "dataset_id": "n1", + "model": "v2-cli:codex", + "run_id": "v2q_n1_9c4ca2499b13991c", + "api_calls": 0, + "input_tokens": 20355, + "cached_input_tokens": 19840, + "output_tokens": 2681, + "total_tokens": 23036, + "cost_usd": 0.0, + "ai_cli_calls": 1, + "estimated_input_tokens": 0, + "estimated_output_tokens": 0, + "estimated_total_tokens": 0, + "usage_source": "ai_cli_json_usage", + "cli_elapsed_ms_total": 37942.51, + "sql_execution_elapsed_ms_total": 33.52, + "conversation_log_path": "/data/jialinzhang/TabQueryBench/sql_workloads/v2_current/runs_and_launches/runs/v2_cli_20260502_081223_d/n1/artifacts/v2q_n1_9c4ca2499b13991c/cli/conversation.jsonl", + "note": "Executed through a local AI CLI with structured usage metadata." + } +} \ No newline at end of file diff --git a/Query/sql/v2/runs/v2_cli_20260502_081223_d/n1/artifacts/v2q_n1_9c4ca2499b13991c/cli/sql_attempt_1.metadata.json b/Query/sql/v2/runs/v2_cli_20260502_081223_d/n1/artifacts/v2q_n1_9c4ca2499b13991c/cli/sql_attempt_1.metadata.json new file mode 100644 index 0000000000000000000000000000000000000000..9760ae77312ce1584cc90e51627e664ea1eab258 --- /dev/null +++ b/Query/sql/v2/runs/v2_cli_20260502_081223_d/n1/artifacts/v2q_n1_9c4ca2499b13991c/cli/sql_attempt_1.metadata.json @@ -0,0 +1,45 @@ +{ + "attempt": 1, + "phase": "sql_generation", + "command": "codex exec --skip-git-repo-check --disable plugins --sandbox read-only --cd \"/data/jialinzhang/SQLagent\" -m gpt-5.4 --json -", + "started_at": "2026-05-19T15:53:08.622751+00:00", + "ended_at": "2026-05-19T15:53:46.565295+00:00", + "elapsed_ms": 37942.51, + "prompt_metrics": { + "chars": 29535, + "bytes_utf8": 29535, + "lines": 792, + "estimated_tokens": null + }, + "stdout_metrics": { + "chars": 2329, + "bytes_utf8": 2329, + "lines": 4, + "estimated_tokens": null + }, + "stderr_metrics": { + "chars": 0, + "bytes_utf8": 0, + "lines": 0, + "estimated_tokens": null + }, + "parsed_output": { + "format": "jsonl_events", + "text_metrics": { + "chars": 1738, + "bytes_utf8": 1738, + "lines": 1, + "estimated_tokens": null + }, + "usage": { + "input_tokens": 20355, + "cached_input_tokens": 19840, + "output_tokens": 2681, + "reasoning_output_tokens": 2070 + } + }, + "prompt_path": "cli/sql_prompt_attempt_1.txt", + "response_path": "cli/sql_response_attempt_1.txt", + "raw_response_path": "cli/sql_response_attempt_1.raw.txt", + "stderr_path": "cli/sql_stderr_attempt_1.txt" +} \ No newline at end of file diff --git a/Query/sql/v2/runs/v2_cli_20260502_081223_d/n1/artifacts/v2q_n1_9c4ca2499b13991c/cli/sql_prompt_attempt_1.txt b/Query/sql/v2/runs/v2_cli_20260502_081223_d/n1/artifacts/v2q_n1_9c4ca2499b13991c/cli/sql_prompt_attempt_1.txt new file mode 100644 index 0000000000000000000000000000000000000000..5b54d54f250693ac498489fe13411875047a4632 --- /dev/null +++ b/Query/sql/v2/runs/v2_cli_20260502_081223_d/n1/artifacts/v2q_n1_9c4ca2499b13991c/cli/sql_prompt_attempt_1.txt @@ -0,0 +1,792 @@ +You are generating one SQLite SELECT query for a single-table SQL QA task. +Return strict JSON only, with this schema: {"sql": "...", "notes": "..."}. +Rules: +- Use only the provided table and columns. +- Do not write INSERT, UPDATE, DELETE, DROP, ALTER, CREATE, PRAGMA, ATTACH, DETACH, or VACUUM. +- Prefer the planned template and bound roles when provided. +- Add a leading SQL comment exactly like: -- template_id: . +- Generate SQLite-compatible SQL. SQLite does not support PERCENTILE_CONT or STDDEV. +- Quote identifiers with double quotes. +- Return no markdown and no extra prose. + +Dataset context: +Dataset context for SQL QA: +- dataset_id: n1 +- dataset_name: Spambase +- table_name: n1 +- table_layout: single-table dataset (do not assume joins). +- row_semantics: One row is one email represented by engineered token/character frequency and capitalization-run features. +- task_type: classification +- target_column: class +- main_row_count: 4601 +- important_fields: +- word_freq_make: role=feature, type=numeric_sparse_frequency. tags=['condition_candidate', 'measure', 'rare_pattern_candidate'] desc=Frequency feature for token 'make' in an email message. +- word_freq_address: role=feature, type=numeric_sparse_frequency. tags=['condition_candidate', 'measure', 'rare_pattern_candidate'] desc=Frequency feature for token 'address' in an email message. +- word_freq_all: role=feature, type=numeric_sparse_frequency. tags=['condition_candidate', 'measure', 'rare_pattern_candidate'] desc=Frequency feature for token 'all' in an email message. +- word_freq_3d: role=feature, type=numeric_sparse_frequency. tags=['condition_candidate', 'measure', 'rare_pattern_candidate'] desc=Frequency feature for token '3d' in an email message. +- word_freq_our: role=feature, type=numeric_sparse_frequency. tags=['condition_candidate', 'measure', 'rare_pattern_candidate'] desc=Frequency feature for token 'our' in an email message. +- word_freq_over: role=feature, type=numeric_sparse_frequency. tags=['condition_candidate', 'measure', 'rare_pattern_candidate'] desc=Frequency feature for token 'over' in an email message. +- word_freq_remove: role=feature, type=numeric_sparse_frequency. tags=['condition_candidate', 'measure', 'rare_pattern_candidate'] desc=Frequency feature for token 'remove' in an email message. +- word_freq_internet: role=feature, type=numeric_sparse_frequency. tags=['condition_candidate', 'measure', 'rare_pattern_candidate'] desc=Frequency feature for token 'internet' in an email message. +- word_freq_order: role=feature, type=numeric_sparse_frequency. tags=['condition_candidate', 'measure', 'rare_pattern_candidate'] desc=Frequency feature for token 'order' in an email message. +- word_freq_mail: role=feature, type=numeric_sparse_frequency. tags=['condition_candidate', 'measure', 'rare_pattern_candidate'] desc=Frequency feature for token 'mail' in an email message. +- word_freq_receive: role=feature, type=numeric_sparse_frequency. tags=['condition_candidate', 'measure', 'rare_pattern_candidate'] desc=Frequency feature for token 'receive' in an email message. +- word_freq_will: role=feature, type=numeric_sparse_frequency. tags=['condition_candidate', 'measure', 'rare_pattern_candidate'] desc=Frequency feature for token 'will' in an email message. +- word_freq_people: role=feature, type=numeric_sparse_frequency. tags=['condition_candidate', 'measure', 'rare_pattern_candidate'] desc=Frequency feature for token 'people' in an email message. +- word_freq_report: role=feature, type=numeric_sparse_frequency. tags=['condition_candidate', 'measure', 'rare_pattern_candidate'] desc=Frequency feature for token 'report' in an email message. +- word_freq_addresses: role=feature, type=numeric_sparse_frequency. tags=['condition_candidate', 'measure', 'rare_pattern_candidate'] desc=Frequency feature for token 'addresses' in an email message. +- word_freq_free: role=feature, type=numeric_sparse_frequency. tags=['condition_candidate', 'measure', 'rare_pattern_candidate'] desc=Frequency feature for token 'free' in an email message. +- word_freq_business: role=feature, type=numeric_sparse_frequency. tags=['condition_candidate', 'measure', 'rare_pattern_candidate'] desc=Frequency feature for token 'business' in an email message. +- word_freq_email: role=feature, type=numeric_sparse_frequency. tags=['condition_candidate', 'measure', 'rare_pattern_candidate'] desc=Frequency feature for token 'email' in an email message. +- word_freq_you: role=feature, type=numeric_sparse_frequency. tags=['condition_candidate', 'measure', 'rare_pattern_candidate'] desc=Frequency feature for token 'you' in an email message. +- word_freq_credit: role=feature, type=numeric_sparse_frequency. tags=['condition_candidate', 'measure', 'rare_pattern_candidate'] desc=Frequency feature for token 'credit' in an email message. +- word_freq_your: role=feature, type=numeric_sparse_frequency. tags=['condition_candidate', 'measure', 'rare_pattern_candidate'] desc=Frequency feature for token 'your' in an email message. +- word_freq_font: role=feature, type=numeric_sparse_frequency. tags=['condition_candidate', 'measure', 'rare_pattern_candidate'] desc=Frequency feature for token 'font' in an email message. +- word_freq_000: role=feature, type=numeric_sparse_frequency. tags=['condition_candidate', 'measure', 'rare_pattern_candidate'] desc=Frequency feature for token '000' in an email message. +- word_freq_money: role=feature, type=numeric_sparse_frequency. tags=['condition_candidate', 'measure', 'rare_pattern_candidate'] desc=Frequency feature for token 'money' in an email message. +- word_freq_hp: role=feature, type=numeric_sparse_frequency. tags=['condition_candidate', 'measure', 'rare_pattern_candidate'] desc=Frequency feature for token 'hp' in an email message. +- word_freq_hpl: role=feature, type=numeric_sparse_frequency. tags=['condition_candidate', 'measure', 'rare_pattern_candidate'] desc=Frequency feature for token 'hpl' in an email message. +- word_freq_george: role=feature, type=numeric_sparse_frequency. tags=['condition_candidate', 'measure', 'rare_pattern_candidate'] desc=Frequency feature for token 'george' in an email message. +- word_freq_650: role=feature, type=numeric_sparse_frequency. tags=['condition_candidate', 'measure', 'rare_pattern_candidate'] desc=Frequency feature for token '650' in an email message. +- word_freq_lab: role=feature, type=numeric_sparse_frequency. tags=['condition_candidate', 'measure', 'rare_pattern_candidate'] desc=Frequency feature for token 'lab' in an email message. +- word_freq_labs: role=feature, type=numeric_sparse_frequency. tags=['condition_candidate', 'measure', 'rare_pattern_candidate'] desc=Frequency feature for token 'labs' in an email message. +- word_freq_telnet: role=feature, type=numeric_sparse_frequency. tags=['condition_candidate', 'measure', 'rare_pattern_candidate'] desc=Frequency feature for token 'telnet' in an email message. +- word_freq_857: role=feature, type=numeric_sparse_frequency. tags=['condition_candidate', 'measure', 'rare_pattern_candidate'] desc=Frequency feature for token '857' in an email message. +- word_freq_data: role=feature, type=numeric_sparse_frequency. tags=['condition_candidate', 'measure', 'rare_pattern_candidate'] desc=Frequency feature for token 'data' in an email message. +- word_freq_415: role=feature, type=numeric_sparse_frequency. tags=['condition_candidate', 'measure', 'rare_pattern_candidate'] desc=Frequency feature for token '415' in an email message. +- word_freq_85: role=feature, type=numeric_sparse_frequency. tags=['condition_candidate', 'measure', 'rare_pattern_candidate'] desc=Frequency feature for token '85' in an email message. +- word_freq_technology: role=feature, type=numeric_sparse_frequency. tags=['condition_candidate', 'measure', 'rare_pattern_candidate'] desc=Frequency feature for token 'technology' in an email message. +- word_freq_1999: role=feature, type=numeric_sparse_frequency. tags=['condition_candidate', 'measure', 'rare_pattern_candidate'] desc=Frequency feature for token '1999' in an email message. +- word_freq_parts: role=feature, type=numeric_sparse_frequency. tags=['condition_candidate', 'measure', 'rare_pattern_candidate'] desc=Frequency feature for token 'parts' in an email message. +- word_freq_pm: role=feature, type=numeric_sparse_frequency. tags=['condition_candidate', 'measure', 'rare_pattern_candidate'] desc=Frequency feature for token 'pm' in an email message. +- word_freq_direct: role=feature, type=numeric_sparse_frequency. tags=['condition_candidate', 'measure', 'rare_pattern_candidate'] desc=Frequency feature for token 'direct' in an email message. +- word_freq_cs: role=feature, type=numeric_sparse_frequency. tags=['condition_candidate', 'measure', 'rare_pattern_candidate'] desc=Frequency feature for token 'cs' in an email message. +- word_freq_meeting: role=feature, type=numeric_sparse_frequency. tags=['condition_candidate', 'measure', 'rare_pattern_candidate'] desc=Frequency feature for token 'meeting' in an email message. +- word_freq_original: role=feature, type=numeric_sparse_frequency. tags=['condition_candidate', 'measure', 'rare_pattern_candidate'] desc=Frequency feature for token 'original' in an email message. +- word_freq_project: role=feature, type=numeric_sparse_frequency. tags=['condition_candidate', 'measure', 'rare_pattern_candidate'] desc=Frequency feature for token 'project' in an email message. +- word_freq_re: role=feature, type=numeric_sparse_frequency. tags=['condition_candidate', 'measure', 'rare_pattern_candidate'] desc=Frequency feature for token 're' in an email message. +- word_freq_edu: role=feature, type=numeric_sparse_frequency. tags=['condition_candidate', 'measure', 'rare_pattern_candidate'] desc=Frequency feature for token 'edu' in an email message. +- word_freq_table: role=feature, type=numeric_sparse_frequency. tags=['condition_candidate', 'measure', 'rare_pattern_candidate'] desc=Frequency feature for token 'table' in an email message. +- word_freq_conference: role=feature, type=numeric_sparse_frequency. tags=['condition_candidate', 'measure', 'rare_pattern_candidate'] desc=Frequency feature for token 'conference' in an email message. +- char_freq_%3B: role=feature, type=numeric_sparse_frequency. tags=['condition_candidate', 'measure', 'rare_pattern_candidate'] desc=Character frequency feature for symbol ';'. +- char_freq_%28: role=feature, type=numeric_sparse_frequency. tags=['condition_candidate', 'measure', 'rare_pattern_candidate'] desc=Character frequency feature for symbol '('. +- char_freq_%5B: role=feature, type=numeric_sparse_frequency. tags=['condition_candidate', 'measure', 'rare_pattern_candidate'] desc=Character frequency feature for symbol '['. +- char_freq_%21: role=feature, type=numeric_sparse_frequency. tags=['condition_candidate', 'measure', 'rare_pattern_candidate'] desc=Character frequency feature for symbol '!'. +- char_freq_%24: role=feature, type=numeric_sparse_frequency. tags=['condition_candidate', 'measure', 'rare_pattern_candidate'] desc=Character frequency feature for symbol '$'. +- char_freq_%23: role=feature, type=numeric_sparse_frequency. tags=['condition_candidate', 'measure', 'rare_pattern_candidate'] desc=Character frequency feature for symbol '#'. +- capital_run_length_average: role=feature, type=numeric_heavy_tail. tags=['condition_candidate', 'measure', 'rare_pattern_candidate'] desc=Average length of uninterrupted capital-letter runs. +- capital_run_length_longest: role=feature, type=numeric_heavy_tail. tags=['condition_candidate', 'measure', 'rare_pattern_candidate'] desc=Longest uninterrupted capital-letter run. +- capital_run_length_total: role=feature, type=numeric_heavy_tail. tags=['condition_candidate', 'measure', 'rare_pattern_candidate'] desc=Total number of capital letters in runs. +- class: role=target, type=binary_target. tags=['target_candidate'] desc=Binary spam label (1=spam, 0=non-spam). +- useful_field_combinations: [['word_freq_free', 'word_freq_you', 'class'], ['char_freq_%21', 'capital_run_length_longest', 'class'], ['word_freq_remove', 'word_freq_money', 'class']] +- fields_requiring_caution: ['capital_run_length_average', 'capital_run_length_longest', 'capital_run_length_total'] +- source_url: https://www.openml.org/d/44 + +SQLite schema snapshot: +{ + "table_name": "n1", + "quoted_table_name": "\"n1\"", + "row_count": 4601, + "columns": [ + { + "name": "word_freq_make", + "type": "TEXT", + "notnull": false, + "pk": false + }, + { + "name": "word_freq_address", + "type": "TEXT", + "notnull": false, + "pk": false + }, + { + "name": "word_freq_all", + "type": "TEXT", + "notnull": false, + "pk": false + }, + { + "name": "word_freq_3d", + "type": "TEXT", + "notnull": false, + "pk": false + }, + { + "name": "word_freq_our", + "type": "TEXT", + "notnull": false, + "pk": false + }, + { + "name": "word_freq_over", + "type": "TEXT", + "notnull": false, + "pk": false + }, + { + "name": "word_freq_remove", + "type": "TEXT", + "notnull": false, + "pk": false + }, + { + "name": "word_freq_internet", + "type": "TEXT", + "notnull": false, + "pk": false + }, + { + "name": "word_freq_order", + "type": "TEXT", + "notnull": false, + "pk": false + }, + { + "name": "word_freq_mail", + "type": "TEXT", + "notnull": false, + "pk": false + }, + { + "name": "word_freq_receive", + "type": "TEXT", + "notnull": false, + "pk": false + }, + { + "name": "word_freq_will", + "type": "TEXT", + "notnull": false, + "pk": false + }, + { + "name": "word_freq_people", + "type": "TEXT", + "notnull": false, + "pk": false + }, + { + "name": "word_freq_report", + "type": "TEXT", + "notnull": false, + "pk": false + }, + { + "name": "word_freq_addresses", + "type": "TEXT", + "notnull": false, + "pk": false + }, + { + "name": "word_freq_free", + "type": "TEXT", + "notnull": false, + "pk": false + }, + { + "name": "word_freq_business", + "type": "TEXT", + "notnull": false, + "pk": false + }, + { + "name": "word_freq_email", + "type": "TEXT", + "notnull": false, + "pk": false + }, + { + "name": "word_freq_you", + "type": "TEXT", + "notnull": false, + "pk": false + }, + { + "name": "word_freq_credit", + "type": "TEXT", + "notnull": false, + "pk": false + }, + { + "name": "word_freq_your", + "type": "TEXT", + "notnull": false, + "pk": false + }, + { + "name": "word_freq_font", + "type": "TEXT", + "notnull": false, + "pk": false + }, + { + "name": "word_freq_000", + "type": "TEXT", + "notnull": false, + "pk": false + }, + { + "name": "word_freq_money", + "type": "TEXT", + "notnull": false, + "pk": false + }, + { + "name": "word_freq_hp", + "type": "TEXT", + "notnull": false, + "pk": false + }, + { + "name": "word_freq_hpl", + "type": "TEXT", + "notnull": false, + "pk": false + }, + { + "name": "word_freq_george", + "type": "TEXT", + "notnull": false, + "pk": false + }, + { + "name": "word_freq_650", + "type": "TEXT", + "notnull": false, + "pk": false + }, + { + "name": "word_freq_lab", + "type": "TEXT", + "notnull": false, + "pk": false + }, + { + "name": "word_freq_labs", + "type": "TEXT", + "notnull": false, + "pk": false + }, + { + "name": "word_freq_telnet", + "type": "TEXT", + "notnull": false, + "pk": false + }, + { + "name": "word_freq_857", + "type": "TEXT", + "notnull": false, + "pk": false + }, + { + "name": "word_freq_data", + "type": "TEXT", + "notnull": false, + "pk": false + }, + { + "name": "word_freq_415", + "type": "TEXT", + "notnull": false, + "pk": false + }, + { + "name": "word_freq_85", + "type": "TEXT", + "notnull": false, + "pk": false + }, + { + "name": "word_freq_technology", + "type": "TEXT", + "notnull": false, + "pk": false + }, + { + "name": "word_freq_1999", + "type": "TEXT", + "notnull": false, + "pk": false + }, + { + "name": "word_freq_parts", + "type": "TEXT", + "notnull": false, + "pk": false + }, + { + "name": "word_freq_pm", + "type": "TEXT", + "notnull": false, + "pk": false + }, + { + "name": "word_freq_direct", + "type": "TEXT", + "notnull": false, + "pk": false + }, + { + "name": "word_freq_cs", + "type": "TEXT", + "notnull": false, + "pk": false + }, + { + "name": "word_freq_meeting", + "type": "TEXT", + "notnull": false, + "pk": false + }, + { + "name": "word_freq_original", + "type": "TEXT", + "notnull": false, + "pk": false + }, + { + "name": "word_freq_project", + "type": "TEXT", + "notnull": false, + "pk": false + }, + { + "name": "word_freq_re", + "type": "TEXT", + "notnull": false, + "pk": false + }, + { + "name": "word_freq_edu", + "type": "TEXT", + "notnull": false, + "pk": false + }, + { + "name": "word_freq_table", + "type": "TEXT", + "notnull": false, + "pk": false + }, + { + "name": "word_freq_conference", + "type": "TEXT", + "notnull": false, + "pk": false + }, + { + "name": "char_freq_%3B", + "type": "TEXT", + "notnull": false, + "pk": false + }, + { + "name": "char_freq_%28", + "type": "TEXT", + "notnull": false, + "pk": false + }, + { + "name": "char_freq_%5B", + "type": "TEXT", + "notnull": false, + "pk": false + }, + { + "name": "char_freq_%21", + "type": "TEXT", + "notnull": false, + "pk": false + }, + { + "name": "char_freq_%24", + "type": "TEXT", + "notnull": false, + "pk": false + }, + { + "name": "char_freq_%23", + "type": "TEXT", + "notnull": false, + "pk": false + }, + { + "name": "capital_run_length_average", + "type": "TEXT", + "notnull": false, + "pk": false + }, + { + "name": "capital_run_length_longest", + "type": "TEXT", + "notnull": false, + "pk": false + }, + { + "name": "capital_run_length_total", + "type": "TEXT", + "notnull": false, + "pk": false + }, + { + "name": "class", + "type": "TEXT", + "notnull": false, + "pk": false + } + ], + "sample_rows": [ + { + "word_freq_make": "0", + "word_freq_address": "0.64", + "word_freq_all": "0.64", + "word_freq_3d": "0", + "word_freq_our": "0.32", + "word_freq_over": "0", + "word_freq_remove": "0", + "word_freq_internet": "0", + "word_freq_order": "0", + "word_freq_mail": "0", + "word_freq_receive": "0", + "word_freq_will": "0.64", + "word_freq_people": "0", + "word_freq_report": "0", + "word_freq_addresses": "0", + "word_freq_free": "0.32", + "word_freq_business": "0", + "word_freq_email": "1.29", + "word_freq_you": "1.93", + "word_freq_credit": "0", + "word_freq_your": "0.96", + "word_freq_font": "0", + "word_freq_000": "0", + "word_freq_money": "0", + "word_freq_hp": "0", + "word_freq_hpl": "0", + "word_freq_george": "0", + "word_freq_650": "0", + "word_freq_lab": "0", + "word_freq_labs": "0", + "word_freq_telnet": "0", + "word_freq_857": "0", + "word_freq_data": "0", + "word_freq_415": "0", + "word_freq_85": "0", + "word_freq_technology": "0", + "word_freq_1999": "0", + "word_freq_parts": "0", + "word_freq_pm": "0", + "word_freq_direct": "0", + "word_freq_cs": "0", + "word_freq_meeting": "0", + "word_freq_original": "0", + "word_freq_project": "0", + "word_freq_re": "0", + "word_freq_edu": "0", + "word_freq_table": "0", + "word_freq_conference": "0", + "char_freq_%3B": "0", + "char_freq_%28": "0", + "char_freq_%5B": "0", + "char_freq_%21": "0.778", + "char_freq_%24": "0", + "char_freq_%23": "0", + "capital_run_length_average": "3.756", + "capital_run_length_longest": "61", + "capital_run_length_total": "278", + "class": "1" + }, + { + "word_freq_make": "0.21", + "word_freq_address": "0.28", + "word_freq_all": "0.5", + "word_freq_3d": "0", + "word_freq_our": "0.14", + "word_freq_over": "0.28", + "word_freq_remove": "0.21", + "word_freq_internet": "0.07", + "word_freq_order": "0", + "word_freq_mail": "0.94", + "word_freq_receive": "0.21", + "word_freq_will": "0.79", + "word_freq_people": "0.65", + "word_freq_report": "0.21", + "word_freq_addresses": "0.14", + "word_freq_free": "0.14", + "word_freq_business": "0.07", + "word_freq_email": "0.28", + "word_freq_you": "3.47", + "word_freq_credit": "0", + "word_freq_your": "1.59", + "word_freq_font": "0", + "word_freq_000": "0.43", + "word_freq_money": "0.43", + "word_freq_hp": "0", + "word_freq_hpl": "0", + "word_freq_george": "0", + "word_freq_650": "0", + "word_freq_lab": "0", + "word_freq_labs": "0", + "word_freq_telnet": "0", + "word_freq_857": "0", + "word_freq_data": "0", + "word_freq_415": "0", + "word_freq_85": "0", + "word_freq_technology": "0", + "word_freq_1999": "0.07", + "word_freq_parts": "0", + "word_freq_pm": "0", + "word_freq_direct": "0", + "word_freq_cs": "0", + "word_freq_meeting": "0", + "word_freq_original": "0", + "word_freq_project": "0", + "word_freq_re": "0", + "word_freq_edu": "0", + "word_freq_table": "0", + "word_freq_conference": "0", + "char_freq_%3B": "0", + "char_freq_%28": "0.132", + "char_freq_%5B": "0", + "char_freq_%21": "0.372", + "char_freq_%24": "0.18", + "char_freq_%23": "0.048", + "capital_run_length_average": "5.114", + "capital_run_length_longest": "101", + "capital_run_length_total": "1028", + "class": "1" + }, + { + "word_freq_make": "0.06", + "word_freq_address": "0", + "word_freq_all": "0.71", + "word_freq_3d": "0", + "word_freq_our": "1.23", + "word_freq_over": "0.19", + "word_freq_remove": "0.19", + "word_freq_internet": "0.12", + "word_freq_order": "0.64", + "word_freq_mail": "0.25", + "word_freq_receive": "0.38", + "word_freq_will": "0.45", + "word_freq_people": "0.12", + "word_freq_report": "0", + "word_freq_addresses": "1.75", + "word_freq_free": "0.06", + "word_freq_business": "0.06", + "word_freq_email": "1.03", + "word_freq_you": "1.36", + "word_freq_credit": "0.32", + "word_freq_your": "0.51", + "word_freq_font": "0", + "word_freq_000": "1.16", + "word_freq_money": "0.06", + "word_freq_hp": "0", + "word_freq_hpl": "0", + "word_freq_george": "0", + "word_freq_650": "0", + "word_freq_lab": "0", + "word_freq_labs": "0", + "word_freq_telnet": "0", + "word_freq_857": "0", + "word_freq_data": "0", + "word_freq_415": "0", + "word_freq_85": "0", + "word_freq_technology": "0", + "word_freq_1999": "0", + "word_freq_parts": "0", + "word_freq_pm": "0", + "word_freq_direct": "0.06", + "word_freq_cs": "0", + "word_freq_meeting": "0", + "word_freq_original": "0.12", + "word_freq_project": "0", + "word_freq_re": "0.06", + "word_freq_edu": "0.06", + "word_freq_table": "0", + "word_freq_conference": "0", + "char_freq_%3B": "0.01", + "char_freq_%28": "0.143", + "char_freq_%5B": "0", + "char_freq_%21": "0.276", + "char_freq_%24": "0.184", + "char_freq_%23": "0.01", + "capital_run_length_average": "9.821", + "capital_run_length_longest": "485", + "capital_run_length_total": "2259", + "class": "1" + }, + { + "word_freq_make": "0", + "word_freq_address": "0", + "word_freq_all": "0", + "word_freq_3d": "0", + "word_freq_our": "0.63", + "word_freq_over": "0", + "word_freq_remove": "0.31", + "word_freq_internet": "0.63", + "word_freq_order": "0.31", + "word_freq_mail": "0.63", + "word_freq_receive": "0.31", + "word_freq_will": "0.31", + "word_freq_people": "0.31", + "word_freq_report": "0", + "word_freq_addresses": "0", + "word_freq_free": "0.31", + "word_freq_business": "0", + "word_freq_email": "0", + "word_freq_you": "3.18", + "word_freq_credit": "0", + "word_freq_your": "0.31", + "word_freq_font": "0", + "word_freq_000": "0", + "word_freq_money": "0", + "word_freq_hp": "0", + "word_freq_hpl": "0", + "word_freq_george": "0", + "word_freq_650": "0", + "word_freq_lab": "0", + "word_freq_labs": "0", + "word_freq_telnet": "0", + "word_freq_857": "0", + "word_freq_data": "0", + "word_freq_415": "0", + "word_freq_85": "0", + "word_freq_technology": "0", + "word_freq_1999": "0", + "word_freq_parts": "0", + "word_freq_pm": "0", + "word_freq_direct": "0", + "word_freq_cs": "0", + "word_freq_meeting": "0", + "word_freq_original": "0", + "word_freq_project": "0", + "word_freq_re": "0", + "word_freq_edu": "0", + "word_freq_table": "0", + "word_freq_conference": "0", + "char_freq_%3B": "0", + "char_freq_%28": "0.137", + "char_freq_%5B": "0", + "char_freq_%21": "0.137", + "char_freq_%24": "0", + "char_freq_%23": "0", + "capital_run_length_average": "3.537", + "capital_run_length_longest": "40", + "capital_run_length_total": "191", + "class": "1" + }, + { + "word_freq_make": "0", + "word_freq_address": "0", + "word_freq_all": "0", + "word_freq_3d": "0", + "word_freq_our": "0.63", + "word_freq_over": "0", + "word_freq_remove": "0.31", + "word_freq_internet": "0.63", + "word_freq_order": "0.31", + "word_freq_mail": "0.63", + "word_freq_receive": "0.31", + "word_freq_will": "0.31", + "word_freq_people": "0.31", + "word_freq_report": "0", + "word_freq_addresses": "0", + "word_freq_free": "0.31", + "word_freq_business": "0", + "word_freq_email": "0", + "word_freq_you": "3.18", + "word_freq_credit": "0", + "word_freq_your": "0.31", + "word_freq_font": "0", + "word_freq_000": "0", + "word_freq_money": "0", + "word_freq_hp": "0", + "word_freq_hpl": "0", + "word_freq_george": "0", + "word_freq_650": "0", + "word_freq_lab": "0", + "word_freq_labs": "0", + "word_freq_telnet": "0", + "word_freq_857": "0", + "word_freq_data": "0", + "word_freq_415": "0", + "word_freq_85": "0", + "word_freq_technology": "0", + "word_freq_1999": "0", + "word_freq_parts": "0", + "word_freq_pm": "0", + "word_freq_direct": "0", + "word_freq_cs": "0", + "word_freq_meeting": "0", + "word_freq_original": "0", + "word_freq_project": "0", + "word_freq_re": "0", + "word_freq_edu": "0", + "word_freq_table": "0", + "word_freq_conference": "0", + "char_freq_%3B": "0", + "char_freq_%28": "0.135", + "char_freq_%5B": "0", + "char_freq_%21": "0.135", + "char_freq_%24": "0", + "char_freq_%23": "0", + "capital_run_length_average": "3.537", + "capital_run_length_longest": "40", + "capital_run_length_total": "191", + "class": "1" + } + ] +} + +Shortlisted templates: +[ + { + "template_id": "tpl_grouped_percentile_point", + "template_name": "Grouped Percentile Point", + "primary_family": "tail_rarity_structure", + "portability": "yes", + "sql_skeleton": "SELECT {group_col},\n PERCENTILE_CONT({percentile_value}) WITHIN GROUP (ORDER BY {measure_col}) AS percentile_measure\nFROM {table}\nGROUP BY {group_col}\nORDER BY percentile_measure DESC;", + "required_roles": [ + "group_col", + "measure_col" + ] + } +] + +Problem instance: +{ + "dataset_id": "n1", + "question": "Use template Grouped Percentile Point to probe tail_concentration_consistency with semantic role ranked_signal_view. Focus on group_col=class, measure_col=word_freq_labs.", + "planned_template_id": "tpl_grouped_percentile_point", + "bindings": { + "group_col": "class", + "measure_col": "word_freq_labs", + "top_k": 16, + "top_n": 6, + "num_tiles": 10, + "percentile_value": 0.9, + "z_threshold": 2.0, + "fraction_threshold": 0.05, + "baseline_multiplier": 1.75, + "baseline_fraction": 0.1, + "min_group_size": 5, + "min_support": 4, + "measure_threshold": 0.0, + "time_grain": "month", + "lookback_rows": 3, + "current_period_start": "'2024-01-01'", + "current_period_end": "'2024-04-01'", + "previous_period_start": "'2023-10-01'", + "previous_period_end": "'2024-01-01'", + "drift_ratio_threshold": 0.8 + }, + "can_vary": [], + "must_fix": [], + "runtime_sql_skeleton": "SELECT {group_col},\n PERCENTILE_CONT({percentile_value}) WITHIN GROUP (ORDER BY {measure_col}) AS percentile_measure\nFROM {table}\nGROUP BY {group_col}\nORDER BY percentile_measure DESC;" +} + +Repair context: +{} diff --git a/Query/sql/v2/runs/v2_cli_20260502_081223_d/n1/artifacts/v2q_n1_9c4ca2499b13991c/cli/sql_response_attempt_1.raw.txt b/Query/sql/v2/runs/v2_cli_20260502_081223_d/n1/artifacts/v2q_n1_9c4ca2499b13991c/cli/sql_response_attempt_1.raw.txt new file mode 100644 index 0000000000000000000000000000000000000000..8773921b2e1938e7b4f9fbdf61231a054ddf4fbf --- /dev/null +++ b/Query/sql/v2/runs/v2_cli_20260502_081223_d/n1/artifacts/v2q_n1_9c4ca2499b13991c/cli/sql_response_attempt_1.raw.txt @@ -0,0 +1,4 @@ +{"type":"thread.started","thread_id":"019e40f0-e20b-79f1-8329-bf1ed374d8b7"} +{"type":"turn.started"} +{"type":"item.completed","item":{"id":"item_0","type":"agent_message","text":"{\"sql\":\"-- template_id: tpl_grouped_percentile_point\\nWITH \\\"ordered\\\" AS (\\n SELECT\\n \\\"class\\\" AS \\\"class\\\",\\n CAST(\\\"word_freq_labs\\\" AS REAL) AS \\\"measure\\\",\\n ROW_NUMBER() OVER (\\n PARTITION BY \\\"class\\\"\\n ORDER BY CAST(\\\"word_freq_labs\\\" AS REAL)\\n ) AS \\\"rn\\\",\\n COUNT(*) OVER (\\n PARTITION BY \\\"class\\\"\\n ) AS \\\"cnt\\\"\\n FROM \\\"n1\\\"\\n WHERE \\\"word_freq_labs\\\" IS NOT NULL\\n),\\n\\\"positions\\\" AS (\\n SELECT DISTINCT\\n \\\"class\\\",\\n ((\\\"cnt\\\" - 1) * 0.9) + 1.0 AS \\\"pos\\\",\\n CAST((((\\\"cnt\\\" - 1) * 0.9) + 1.0) AS INTEGER) AS \\\"lo_rn\\\",\\n CASE\\n WHEN (((\\\"cnt\\\" - 1) * 0.9) + 1.0) = CAST((((\\\"cnt\\\" - 1) * 0.9) + 1.0) AS INTEGER)\\n THEN CAST((((\\\"cnt\\\" - 1) * 0.9) + 1.0) AS INTEGER)\\n ELSE CAST((((\\\"cnt\\\" - 1) * 0.9) + 1.0) AS INTEGER) + 1\\n END AS \\\"hi_rn\\\"\\n FROM \\\"ordered\\\"\\n)\\nSELECT\\n p.\\\"class\\\" AS \\\"class\\\",\\n CASE\\n WHEN p.\\\"lo_rn\\\" = p.\\\"hi_rn\\\" THEN\\n MAX(CASE WHEN o.\\\"rn\\\" = p.\\\"lo_rn\\\" THEN o.\\\"measure\\\" END)\\n ELSE\\n MAX(CASE WHEN o.\\\"rn\\\" = p.\\\"lo_rn\\\" THEN o.\\\"measure\\\" END) +\\n (p.\\\"pos\\\" - p.\\\"lo_rn\\\") * (\\n MAX(CASE WHEN o.\\\"rn\\\" = p.\\\"hi_rn\\\" THEN o.\\\"measure\\\" END) -\\n MAX(CASE WHEN o.\\\"rn\\\" = p.\\\"lo_rn\\\" THEN o.\\\"measure\\\" END)\\n )\\n END AS \\\"percentile_measure\\\"\\nFROM \\\"positions\\\" AS p\\nJOIN \\\"ordered\\\" AS o\\n ON o.\\\"class\\\" = p.\\\"class\\\"\\nGROUP BY\\n p.\\\"class\\\",\\n p.\\\"pos\\\",\\n p.\\\"lo_rn\\\",\\n p.\\\"hi_rn\\\"\\nORDER BY \\\"percentile_measure\\\" DESC;\",\"notes\":\"SQLite does not support PERCENTILE_CONT, so this query emulates the continuous 90th percentile of \\\"word_freq_labs\\\" within each \\\"class\\\" using window functions and linear interpolation after casting the TEXT measure to REAL.\"}"}} +{"type":"turn.completed","usage":{"input_tokens":20355,"cached_input_tokens":19840,"output_tokens":2681,"reasoning_output_tokens":2070}} diff --git a/Query/sql/v2/runs/v2_cli_20260502_081223_d/n1/artifacts/v2q_n1_9c4ca2499b13991c/cli/sql_response_attempt_1.txt b/Query/sql/v2/runs/v2_cli_20260502_081223_d/n1/artifacts/v2q_n1_9c4ca2499b13991c/cli/sql_response_attempt_1.txt new file mode 100644 index 0000000000000000000000000000000000000000..8098f4013bb516a1f0d28586fa866541d46c923f --- /dev/null +++ b/Query/sql/v2/runs/v2_cli_20260502_081223_d/n1/artifacts/v2q_n1_9c4ca2499b13991c/cli/sql_response_attempt_1.txt @@ -0,0 +1 @@ +{"sql":"-- template_id: tpl_grouped_percentile_point\nWITH \"ordered\" AS (\n SELECT\n \"class\" AS \"class\",\n CAST(\"word_freq_labs\" AS REAL) AS \"measure\",\n ROW_NUMBER() OVER (\n PARTITION BY \"class\"\n ORDER BY CAST(\"word_freq_labs\" AS REAL)\n ) AS \"rn\",\n COUNT(*) OVER (\n PARTITION BY \"class\"\n ) AS \"cnt\"\n FROM \"n1\"\n WHERE \"word_freq_labs\" IS NOT NULL\n),\n\"positions\" AS (\n SELECT DISTINCT\n \"class\",\n ((\"cnt\" - 1) * 0.9) + 1.0 AS \"pos\",\n CAST((((\"cnt\" - 1) * 0.9) + 1.0) AS INTEGER) AS \"lo_rn\",\n CASE\n WHEN (((\"cnt\" - 1) * 0.9) + 1.0) = CAST((((\"cnt\" - 1) * 0.9) + 1.0) AS INTEGER)\n THEN CAST((((\"cnt\" - 1) * 0.9) + 1.0) AS INTEGER)\n ELSE CAST((((\"cnt\" - 1) * 0.9) + 1.0) AS INTEGER) + 1\n END AS \"hi_rn\"\n FROM \"ordered\"\n)\nSELECT\n p.\"class\" AS \"class\",\n CASE\n WHEN p.\"lo_rn\" = p.\"hi_rn\" THEN\n MAX(CASE WHEN o.\"rn\" = p.\"lo_rn\" THEN o.\"measure\" END)\n ELSE\n MAX(CASE WHEN o.\"rn\" = p.\"lo_rn\" THEN o.\"measure\" END) +\n (p.\"pos\" - p.\"lo_rn\") * (\n MAX(CASE WHEN o.\"rn\" = p.\"hi_rn\" THEN o.\"measure\" END) -\n MAX(CASE WHEN o.\"rn\" = p.\"lo_rn\" THEN o.\"measure\" END)\n )\n END AS \"percentile_measure\"\nFROM \"positions\" AS p\nJOIN \"ordered\" AS o\n ON o.\"class\" = p.\"class\"\nGROUP BY\n p.\"class\",\n p.\"pos\",\n p.\"lo_rn\",\n p.\"hi_rn\"\nORDER BY \"percentile_measure\" DESC;","notes":"SQLite does not support PERCENTILE_CONT, so this query emulates the continuous 90th percentile of \"word_freq_labs\" within each \"class\" using window functions and linear interpolation after casting the TEXT measure to REAL."} \ No newline at end of file diff --git a/Query/sql/v2/runs/v2_cli_20260502_081223_d/n1/artifacts/v2q_n1_9c4ca2499b13991c/cli/sql_stderr_attempt_1.txt b/Query/sql/v2/runs/v2_cli_20260502_081223_d/n1/artifacts/v2q_n1_9c4ca2499b13991c/cli/sql_stderr_attempt_1.txt new file mode 100644 index 0000000000000000000000000000000000000000..e69de29bb2d1d6434b8b29ae775ad8c2e48c5391 diff --git a/Query/sql/v2/runs/v2_cli_20260502_081223_d/n1/artifacts/v2q_n1_9e31f6545f16bec8/cli/sql_attempt_1.metadata.json b/Query/sql/v2/runs/v2_cli_20260502_081223_d/n1/artifacts/v2q_n1_9e31f6545f16bec8/cli/sql_attempt_1.metadata.json new file mode 100644 index 0000000000000000000000000000000000000000..172c9cc77f307a0133896701291635a34cc65c08 --- /dev/null +++ b/Query/sql/v2/runs/v2_cli_20260502_081223_d/n1/artifacts/v2q_n1_9e31f6545f16bec8/cli/sql_attempt_1.metadata.json @@ -0,0 +1,43 @@ +{ + "attempt": 1, + "phase": "sql_generation", + "command": "codex exec --skip-git-repo-check --disable plugins --sandbox read-only --cd \"/data/jialinzhang/SQLagent\" -m gpt-5.4 --json -", + "started_at": "2026-05-19T16:00:01.059299+00:00", + "ended_at": "2026-05-19T16:00:04.118190+00:00", + "elapsed_ms": 3058.86, + "returncode": 1, + "prompt_metrics": { + "chars": 29588, + "bytes_utf8": 29588, + "lines": 795, + "estimated_tokens": null + }, + "stdout_metrics": { + "chars": 281, + "bytes_utf8": 281, + "lines": 4, + "estimated_tokens": null + }, + "stderr_metrics": { + "chars": 0, + "bytes_utf8": 0, + "lines": 0, + "estimated_tokens": null + }, + "parsed_output": { + "format": "jsonl_events", + "text_metrics": { + "chars": 280, + "bytes_utf8": 280, + "lines": 4, + "estimated_tokens": null + }, + "usage": {} + }, + "status": "failed", + "error": "AI CLI command failed with exit code 1: ", + "prompt_path": "cli/sql_prompt_attempt_1.txt", + "response_path": "cli/sql_response_attempt_1.txt", + "raw_response_path": "cli/sql_response_attempt_1.raw.txt", + "stderr_path": "cli/sql_stderr_attempt_1.txt" +} \ No newline at end of file diff --git a/Query/sql/v2/runs/v2_cli_20260502_081223_d/n1/artifacts/v2q_n1_9e31f6545f16bec8/cli/sql_attempt_2.metadata.json b/Query/sql/v2/runs/v2_cli_20260502_081223_d/n1/artifacts/v2q_n1_9e31f6545f16bec8/cli/sql_attempt_2.metadata.json new file mode 100644 index 0000000000000000000000000000000000000000..6bba0afefc3c35bc11faf2f2dc49f21282cfd9cc --- /dev/null +++ b/Query/sql/v2/runs/v2_cli_20260502_081223_d/n1/artifacts/v2q_n1_9e31f6545f16bec8/cli/sql_attempt_2.metadata.json @@ -0,0 +1,43 @@ +{ + "attempt": 2, + "phase": "sql_generation", + "command": "codex exec --skip-git-repo-check --disable plugins --sandbox read-only --cd \"/data/jialinzhang/SQLagent\" -m gpt-5.4 --json -", + "started_at": "2026-05-19T16:00:05.121670+00:00", + "ended_at": "2026-05-19T16:00:08.579983+00:00", + "elapsed_ms": 3458.26, + "returncode": 1, + "prompt_metrics": { + "chars": 29588, + "bytes_utf8": 29588, + "lines": 795, + "estimated_tokens": null + }, + "stdout_metrics": { + "chars": 281, + "bytes_utf8": 281, + "lines": 4, + "estimated_tokens": null + }, + "stderr_metrics": { + "chars": 0, + "bytes_utf8": 0, + "lines": 0, + "estimated_tokens": null + }, + "parsed_output": { + "format": "jsonl_events", + "text_metrics": { + "chars": 280, + "bytes_utf8": 280, + "lines": 4, + "estimated_tokens": null + }, + "usage": {} + }, + "status": "failed", + "error": "AI CLI command failed with exit code 1: ", + "prompt_path": "cli/sql_prompt_attempt_2.txt", + "response_path": "cli/sql_response_attempt_2.txt", + "raw_response_path": "cli/sql_response_attempt_2.raw.txt", + "stderr_path": "cli/sql_stderr_attempt_2.txt" +} \ No newline at end of file diff --git a/Query/sql/v2/runs/v2_cli_20260502_081223_d/n1/artifacts/v2q_n1_9e31f6545f16bec8/cli/sql_prompt_attempt_1.txt b/Query/sql/v2/runs/v2_cli_20260502_081223_d/n1/artifacts/v2q_n1_9e31f6545f16bec8/cli/sql_prompt_attempt_1.txt new file mode 100644 index 0000000000000000000000000000000000000000..c1379fa397d9bb0da34d0af5aa9eec25c31326c9 --- /dev/null +++ b/Query/sql/v2/runs/v2_cli_20260502_081223_d/n1/artifacts/v2q_n1_9e31f6545f16bec8/cli/sql_prompt_attempt_1.txt @@ -0,0 +1,795 @@ +You are generating one SQLite SELECT query for a single-table SQL QA task. +Return strict JSON only, with this schema: {"sql": "...", "notes": "..."}. +Rules: +- Use only the provided table and columns. +- Do not write INSERT, UPDATE, DELETE, DROP, ALTER, CREATE, PRAGMA, ATTACH, DETACH, or VACUUM. +- Prefer the planned template and bound roles when provided. +- Add a leading SQL comment exactly like: -- template_id: . +- Generate SQLite-compatible SQL. SQLite does not support PERCENTILE_CONT or STDDEV. +- Quote identifiers with double quotes. +- Return no markdown and no extra prose. + +Dataset context: +Dataset context for SQL QA: +- dataset_id: n1 +- dataset_name: Spambase +- table_name: n1 +- table_layout: single-table dataset (do not assume joins). +- row_semantics: One row is one email represented by engineered token/character frequency and capitalization-run features. +- task_type: classification +- target_column: class +- main_row_count: 4601 +- important_fields: +- word_freq_make: role=feature, type=numeric_sparse_frequency. tags=['condition_candidate', 'measure', 'rare_pattern_candidate'] desc=Frequency feature for token 'make' in an email message. +- word_freq_address: role=feature, type=numeric_sparse_frequency. tags=['condition_candidate', 'measure', 'rare_pattern_candidate'] desc=Frequency feature for token 'address' in an email message. +- word_freq_all: role=feature, type=numeric_sparse_frequency. tags=['condition_candidate', 'measure', 'rare_pattern_candidate'] desc=Frequency feature for token 'all' in an email message. +- word_freq_3d: role=feature, type=numeric_sparse_frequency. tags=['condition_candidate', 'measure', 'rare_pattern_candidate'] desc=Frequency feature for token '3d' in an email message. +- word_freq_our: role=feature, type=numeric_sparse_frequency. tags=['condition_candidate', 'measure', 'rare_pattern_candidate'] desc=Frequency feature for token 'our' in an email message. +- word_freq_over: role=feature, type=numeric_sparse_frequency. tags=['condition_candidate', 'measure', 'rare_pattern_candidate'] desc=Frequency feature for token 'over' in an email message. +- word_freq_remove: role=feature, type=numeric_sparse_frequency. tags=['condition_candidate', 'measure', 'rare_pattern_candidate'] desc=Frequency feature for token 'remove' in an email message. +- word_freq_internet: role=feature, type=numeric_sparse_frequency. tags=['condition_candidate', 'measure', 'rare_pattern_candidate'] desc=Frequency feature for token 'internet' in an email message. +- word_freq_order: role=feature, type=numeric_sparse_frequency. tags=['condition_candidate', 'measure', 'rare_pattern_candidate'] desc=Frequency feature for token 'order' in an email message. +- word_freq_mail: role=feature, type=numeric_sparse_frequency. tags=['condition_candidate', 'measure', 'rare_pattern_candidate'] desc=Frequency feature for token 'mail' in an email message. +- word_freq_receive: role=feature, type=numeric_sparse_frequency. tags=['condition_candidate', 'measure', 'rare_pattern_candidate'] desc=Frequency feature for token 'receive' in an email message. +- word_freq_will: role=feature, type=numeric_sparse_frequency. tags=['condition_candidate', 'measure', 'rare_pattern_candidate'] desc=Frequency feature for token 'will' in an email message. +- word_freq_people: role=feature, type=numeric_sparse_frequency. tags=['condition_candidate', 'measure', 'rare_pattern_candidate'] desc=Frequency feature for token 'people' in an email message. +- word_freq_report: role=feature, type=numeric_sparse_frequency. tags=['condition_candidate', 'measure', 'rare_pattern_candidate'] desc=Frequency feature for token 'report' in an email message. +- word_freq_addresses: role=feature, type=numeric_sparse_frequency. tags=['condition_candidate', 'measure', 'rare_pattern_candidate'] desc=Frequency feature for token 'addresses' in an email message. +- word_freq_free: role=feature, type=numeric_sparse_frequency. tags=['condition_candidate', 'measure', 'rare_pattern_candidate'] desc=Frequency feature for token 'free' in an email message. +- word_freq_business: role=feature, type=numeric_sparse_frequency. tags=['condition_candidate', 'measure', 'rare_pattern_candidate'] desc=Frequency feature for token 'business' in an email message. +- word_freq_email: role=feature, type=numeric_sparse_frequency. tags=['condition_candidate', 'measure', 'rare_pattern_candidate'] desc=Frequency feature for token 'email' in an email message. +- word_freq_you: role=feature, type=numeric_sparse_frequency. tags=['condition_candidate', 'measure', 'rare_pattern_candidate'] desc=Frequency feature for token 'you' in an email message. +- word_freq_credit: role=feature, type=numeric_sparse_frequency. tags=['condition_candidate', 'measure', 'rare_pattern_candidate'] desc=Frequency feature for token 'credit' in an email message. +- word_freq_your: role=feature, type=numeric_sparse_frequency. tags=['condition_candidate', 'measure', 'rare_pattern_candidate'] desc=Frequency feature for token 'your' in an email message. +- word_freq_font: role=feature, type=numeric_sparse_frequency. tags=['condition_candidate', 'measure', 'rare_pattern_candidate'] desc=Frequency feature for token 'font' in an email message. +- word_freq_000: role=feature, type=numeric_sparse_frequency. tags=['condition_candidate', 'measure', 'rare_pattern_candidate'] desc=Frequency feature for token '000' in an email message. +- word_freq_money: role=feature, type=numeric_sparse_frequency. tags=['condition_candidate', 'measure', 'rare_pattern_candidate'] desc=Frequency feature for token 'money' in an email message. +- word_freq_hp: role=feature, type=numeric_sparse_frequency. tags=['condition_candidate', 'measure', 'rare_pattern_candidate'] desc=Frequency feature for token 'hp' in an email message. +- word_freq_hpl: role=feature, type=numeric_sparse_frequency. tags=['condition_candidate', 'measure', 'rare_pattern_candidate'] desc=Frequency feature for token 'hpl' in an email message. +- word_freq_george: role=feature, type=numeric_sparse_frequency. tags=['condition_candidate', 'measure', 'rare_pattern_candidate'] desc=Frequency feature for token 'george' in an email message. +- word_freq_650: role=feature, type=numeric_sparse_frequency. tags=['condition_candidate', 'measure', 'rare_pattern_candidate'] desc=Frequency feature for token '650' in an email message. +- word_freq_lab: role=feature, type=numeric_sparse_frequency. tags=['condition_candidate', 'measure', 'rare_pattern_candidate'] desc=Frequency feature for token 'lab' in an email message. +- word_freq_labs: role=feature, type=numeric_sparse_frequency. tags=['condition_candidate', 'measure', 'rare_pattern_candidate'] desc=Frequency feature for token 'labs' in an email message. +- word_freq_telnet: role=feature, type=numeric_sparse_frequency. tags=['condition_candidate', 'measure', 'rare_pattern_candidate'] desc=Frequency feature for token 'telnet' in an email message. +- word_freq_857: role=feature, type=numeric_sparse_frequency. tags=['condition_candidate', 'measure', 'rare_pattern_candidate'] desc=Frequency feature for token '857' in an email message. +- word_freq_data: role=feature, type=numeric_sparse_frequency. tags=['condition_candidate', 'measure', 'rare_pattern_candidate'] desc=Frequency feature for token 'data' in an email message. +- word_freq_415: role=feature, type=numeric_sparse_frequency. tags=['condition_candidate', 'measure', 'rare_pattern_candidate'] desc=Frequency feature for token '415' in an email message. +- word_freq_85: role=feature, type=numeric_sparse_frequency. tags=['condition_candidate', 'measure', 'rare_pattern_candidate'] desc=Frequency feature for token '85' in an email message. +- word_freq_technology: role=feature, type=numeric_sparse_frequency. tags=['condition_candidate', 'measure', 'rare_pattern_candidate'] desc=Frequency feature for token 'technology' in an email message. +- word_freq_1999: role=feature, type=numeric_sparse_frequency. tags=['condition_candidate', 'measure', 'rare_pattern_candidate'] desc=Frequency feature for token '1999' in an email message. +- word_freq_parts: role=feature, type=numeric_sparse_frequency. tags=['condition_candidate', 'measure', 'rare_pattern_candidate'] desc=Frequency feature for token 'parts' in an email message. +- word_freq_pm: role=feature, type=numeric_sparse_frequency. tags=['condition_candidate', 'measure', 'rare_pattern_candidate'] desc=Frequency feature for token 'pm' in an email message. +- word_freq_direct: role=feature, type=numeric_sparse_frequency. tags=['condition_candidate', 'measure', 'rare_pattern_candidate'] desc=Frequency feature for token 'direct' in an email message. +- word_freq_cs: role=feature, type=numeric_sparse_frequency. tags=['condition_candidate', 'measure', 'rare_pattern_candidate'] desc=Frequency feature for token 'cs' in an email message. +- word_freq_meeting: role=feature, type=numeric_sparse_frequency. tags=['condition_candidate', 'measure', 'rare_pattern_candidate'] desc=Frequency feature for token 'meeting' in an email message. +- word_freq_original: role=feature, type=numeric_sparse_frequency. tags=['condition_candidate', 'measure', 'rare_pattern_candidate'] desc=Frequency feature for token 'original' in an email message. +- word_freq_project: role=feature, type=numeric_sparse_frequency. tags=['condition_candidate', 'measure', 'rare_pattern_candidate'] desc=Frequency feature for token 'project' in an email message. +- word_freq_re: role=feature, type=numeric_sparse_frequency. tags=['condition_candidate', 'measure', 'rare_pattern_candidate'] desc=Frequency feature for token 're' in an email message. +- word_freq_edu: role=feature, type=numeric_sparse_frequency. tags=['condition_candidate', 'measure', 'rare_pattern_candidate'] desc=Frequency feature for token 'edu' in an email message. +- word_freq_table: role=feature, type=numeric_sparse_frequency. tags=['condition_candidate', 'measure', 'rare_pattern_candidate'] desc=Frequency feature for token 'table' in an email message. +- word_freq_conference: role=feature, type=numeric_sparse_frequency. tags=['condition_candidate', 'measure', 'rare_pattern_candidate'] desc=Frequency feature for token 'conference' in an email message. +- char_freq_%3B: role=feature, type=numeric_sparse_frequency. tags=['condition_candidate', 'measure', 'rare_pattern_candidate'] desc=Character frequency feature for symbol ';'. +- char_freq_%28: role=feature, type=numeric_sparse_frequency. tags=['condition_candidate', 'measure', 'rare_pattern_candidate'] desc=Character frequency feature for symbol '('. +- char_freq_%5B: role=feature, type=numeric_sparse_frequency. tags=['condition_candidate', 'measure', 'rare_pattern_candidate'] desc=Character frequency feature for symbol '['. +- char_freq_%21: role=feature, type=numeric_sparse_frequency. tags=['condition_candidate', 'measure', 'rare_pattern_candidate'] desc=Character frequency feature for symbol '!'. +- char_freq_%24: role=feature, type=numeric_sparse_frequency. tags=['condition_candidate', 'measure', 'rare_pattern_candidate'] desc=Character frequency feature for symbol '$'. +- char_freq_%23: role=feature, type=numeric_sparse_frequency. tags=['condition_candidate', 'measure', 'rare_pattern_candidate'] desc=Character frequency feature for symbol '#'. +- capital_run_length_average: role=feature, type=numeric_heavy_tail. tags=['condition_candidate', 'measure', 'rare_pattern_candidate'] desc=Average length of uninterrupted capital-letter runs. +- capital_run_length_longest: role=feature, type=numeric_heavy_tail. tags=['condition_candidate', 'measure', 'rare_pattern_candidate'] desc=Longest uninterrupted capital-letter run. +- capital_run_length_total: role=feature, type=numeric_heavy_tail. tags=['condition_candidate', 'measure', 'rare_pattern_candidate'] desc=Total number of capital letters in runs. +- class: role=target, type=binary_target. tags=['target_candidate'] desc=Binary spam label (1=spam, 0=non-spam). +- useful_field_combinations: [['word_freq_free', 'word_freq_you', 'class'], ['char_freq_%21', 'capital_run_length_longest', 'class'], ['word_freq_remove', 'word_freq_money', 'class']] +- fields_requiring_caution: ['capital_run_length_average', 'capital_run_length_longest', 'capital_run_length_total'] +- source_url: https://www.openml.org/d/44 + +SQLite schema snapshot: +{ + "table_name": "n1", + "quoted_table_name": "\"n1\"", + "row_count": 4601, + "columns": [ + { + "name": "word_freq_make", + "type": "TEXT", + "notnull": false, + "pk": false + }, + { + "name": "word_freq_address", + "type": "TEXT", + "notnull": false, + "pk": false + }, + { + "name": "word_freq_all", + "type": "TEXT", + "notnull": false, + "pk": false + }, + { + "name": "word_freq_3d", + "type": "TEXT", + "notnull": false, + "pk": false + }, + { + "name": "word_freq_our", + "type": "TEXT", + "notnull": false, + "pk": false + }, + { + "name": "word_freq_over", + "type": "TEXT", + "notnull": false, + "pk": false + }, + { + "name": "word_freq_remove", + "type": "TEXT", + "notnull": false, + "pk": false + }, + { + "name": "word_freq_internet", + "type": "TEXT", + "notnull": false, + "pk": false + }, + { + "name": "word_freq_order", + "type": "TEXT", + "notnull": false, + "pk": false + }, + { + "name": "word_freq_mail", + "type": "TEXT", + "notnull": false, + "pk": false + }, + { + "name": "word_freq_receive", + "type": "TEXT", + "notnull": false, + "pk": false + }, + { + "name": "word_freq_will", + "type": "TEXT", + "notnull": false, + "pk": false + }, + { + "name": "word_freq_people", + "type": "TEXT", + "notnull": false, + "pk": false + }, + { + "name": "word_freq_report", + "type": "TEXT", + "notnull": false, + "pk": false + }, + { + "name": "word_freq_addresses", + "type": "TEXT", + "notnull": false, + "pk": false + }, + { + "name": "word_freq_free", + "type": "TEXT", + "notnull": false, + "pk": false + }, + { + "name": "word_freq_business", + "type": "TEXT", + "notnull": false, + "pk": false + }, + { + "name": "word_freq_email", + "type": "TEXT", + "notnull": false, + "pk": false + }, + { + "name": "word_freq_you", + "type": "TEXT", + "notnull": false, + "pk": false + }, + { + "name": "word_freq_credit", + "type": "TEXT", + "notnull": false, + "pk": false + }, + { + "name": "word_freq_your", + "type": "TEXT", + "notnull": false, + "pk": false + }, + { + "name": "word_freq_font", + "type": "TEXT", + "notnull": false, + "pk": false + }, + { + "name": "word_freq_000", + "type": "TEXT", + "notnull": false, + "pk": false + }, + { + "name": "word_freq_money", + "type": "TEXT", + "notnull": false, + "pk": false + }, + { + "name": "word_freq_hp", + "type": "TEXT", + "notnull": false, + "pk": false + }, + { + "name": "word_freq_hpl", + "type": "TEXT", + "notnull": false, + "pk": false + }, + { + "name": "word_freq_george", + "type": "TEXT", + "notnull": false, + "pk": false + }, + { + "name": "word_freq_650", + "type": "TEXT", + "notnull": false, + "pk": false + }, + { + "name": "word_freq_lab", + "type": "TEXT", + "notnull": false, + "pk": false + }, + { + "name": "word_freq_labs", + "type": "TEXT", + "notnull": false, + "pk": false + }, + { + "name": "word_freq_telnet", + "type": "TEXT", + "notnull": false, + "pk": false + }, + { + "name": "word_freq_857", + "type": "TEXT", + "notnull": false, + "pk": false + }, + { + "name": "word_freq_data", + "type": "TEXT", + "notnull": false, + "pk": false + }, + { + "name": "word_freq_415", + "type": "TEXT", + "notnull": false, + "pk": false + }, + { + "name": "word_freq_85", + "type": "TEXT", + "notnull": false, + "pk": false + }, + { + "name": "word_freq_technology", + "type": "TEXT", + "notnull": false, + "pk": false + }, + { + "name": "word_freq_1999", + "type": "TEXT", + "notnull": false, + "pk": false + }, + { + "name": "word_freq_parts", + "type": "TEXT", + "notnull": false, + "pk": false + }, + { + "name": "word_freq_pm", + "type": "TEXT", + "notnull": false, + "pk": false + }, + { + "name": "word_freq_direct", + "type": "TEXT", + "notnull": false, + "pk": false + }, + { + "name": "word_freq_cs", + "type": "TEXT", + "notnull": false, + "pk": false + }, + { + "name": "word_freq_meeting", + "type": "TEXT", + "notnull": false, + "pk": false + }, + { + "name": "word_freq_original", + "type": "TEXT", + "notnull": false, + "pk": false + }, + { + "name": "word_freq_project", + "type": "TEXT", + "notnull": false, + "pk": false + }, + { + "name": "word_freq_re", + "type": "TEXT", + "notnull": false, + "pk": false + }, + { + "name": "word_freq_edu", + "type": "TEXT", + "notnull": false, + "pk": false + }, + { + "name": "word_freq_table", + "type": "TEXT", + "notnull": false, + "pk": false + }, + { + "name": "word_freq_conference", + "type": "TEXT", + "notnull": false, + "pk": false + }, + { + "name": "char_freq_%3B", + "type": "TEXT", + "notnull": false, + "pk": false + }, + { + "name": "char_freq_%28", + "type": "TEXT", + "notnull": false, + "pk": false + }, + { + "name": "char_freq_%5B", + "type": "TEXT", + "notnull": false, + "pk": false + }, + { + "name": "char_freq_%21", + "type": "TEXT", + "notnull": false, + "pk": false + }, + { + "name": "char_freq_%24", + "type": "TEXT", + "notnull": false, + "pk": false + }, + { + "name": "char_freq_%23", + "type": "TEXT", + "notnull": false, + "pk": false + }, + { + "name": "capital_run_length_average", + "type": "TEXT", + "notnull": false, + "pk": false + }, + { + "name": "capital_run_length_longest", + "type": "TEXT", + "notnull": false, + "pk": false + }, + { + "name": "capital_run_length_total", + "type": "TEXT", + "notnull": false, + "pk": false + }, + { + "name": "class", + "type": "TEXT", + "notnull": false, + "pk": false + } + ], + "sample_rows": [ + { + "word_freq_make": "0", + "word_freq_address": "0.64", + "word_freq_all": "0.64", + "word_freq_3d": "0", + "word_freq_our": "0.32", + "word_freq_over": "0", + "word_freq_remove": "0", + "word_freq_internet": "0", + "word_freq_order": "0", + "word_freq_mail": "0", + "word_freq_receive": "0", + "word_freq_will": "0.64", + "word_freq_people": "0", + "word_freq_report": "0", + "word_freq_addresses": "0", + "word_freq_free": "0.32", + "word_freq_business": "0", + "word_freq_email": "1.29", + "word_freq_you": "1.93", + "word_freq_credit": "0", + "word_freq_your": "0.96", + "word_freq_font": "0", + "word_freq_000": "0", + "word_freq_money": "0", + "word_freq_hp": "0", + "word_freq_hpl": "0", + "word_freq_george": "0", + "word_freq_650": "0", + "word_freq_lab": "0", + "word_freq_labs": "0", + "word_freq_telnet": "0", + "word_freq_857": "0", + "word_freq_data": "0", + "word_freq_415": "0", + "word_freq_85": "0", + "word_freq_technology": "0", + "word_freq_1999": "0", + "word_freq_parts": "0", + "word_freq_pm": "0", + "word_freq_direct": "0", + "word_freq_cs": "0", + "word_freq_meeting": "0", + "word_freq_original": "0", + "word_freq_project": "0", + "word_freq_re": "0", + "word_freq_edu": "0", + "word_freq_table": "0", + "word_freq_conference": "0", + "char_freq_%3B": "0", + "char_freq_%28": "0", + "char_freq_%5B": "0", + "char_freq_%21": "0.778", + "char_freq_%24": "0", + "char_freq_%23": "0", + "capital_run_length_average": "3.756", + "capital_run_length_longest": "61", + "capital_run_length_total": "278", + "class": "1" + }, + { + "word_freq_make": "0.21", + "word_freq_address": "0.28", + "word_freq_all": "0.5", + "word_freq_3d": "0", + "word_freq_our": "0.14", + "word_freq_over": "0.28", + "word_freq_remove": "0.21", + "word_freq_internet": "0.07", + "word_freq_order": "0", + "word_freq_mail": "0.94", + "word_freq_receive": "0.21", + "word_freq_will": "0.79", + "word_freq_people": "0.65", + "word_freq_report": "0.21", + "word_freq_addresses": "0.14", + "word_freq_free": "0.14", + "word_freq_business": "0.07", + "word_freq_email": "0.28", + "word_freq_you": "3.47", + "word_freq_credit": "0", + "word_freq_your": "1.59", + "word_freq_font": "0", + "word_freq_000": "0.43", + "word_freq_money": "0.43", + "word_freq_hp": "0", + "word_freq_hpl": "0", + "word_freq_george": "0", + "word_freq_650": "0", + "word_freq_lab": "0", + "word_freq_labs": "0", + "word_freq_telnet": "0", + "word_freq_857": "0", + "word_freq_data": "0", + "word_freq_415": "0", + "word_freq_85": "0", + "word_freq_technology": "0", + "word_freq_1999": "0.07", + "word_freq_parts": "0", + "word_freq_pm": "0", + "word_freq_direct": "0", + "word_freq_cs": "0", + "word_freq_meeting": "0", + "word_freq_original": "0", + "word_freq_project": "0", + "word_freq_re": "0", + "word_freq_edu": "0", + "word_freq_table": "0", + "word_freq_conference": "0", + "char_freq_%3B": "0", + "char_freq_%28": "0.132", + "char_freq_%5B": "0", + "char_freq_%21": "0.372", + "char_freq_%24": "0.18", + "char_freq_%23": "0.048", + "capital_run_length_average": "5.114", + "capital_run_length_longest": "101", + "capital_run_length_total": "1028", + "class": "1" + }, + { + "word_freq_make": "0.06", + "word_freq_address": "0", + "word_freq_all": "0.71", + "word_freq_3d": "0", + "word_freq_our": "1.23", + "word_freq_over": "0.19", + "word_freq_remove": "0.19", + "word_freq_internet": "0.12", + "word_freq_order": "0.64", + "word_freq_mail": "0.25", + "word_freq_receive": "0.38", + "word_freq_will": "0.45", + "word_freq_people": "0.12", + "word_freq_report": "0", + "word_freq_addresses": "1.75", + "word_freq_free": "0.06", + "word_freq_business": "0.06", + "word_freq_email": "1.03", + "word_freq_you": "1.36", + "word_freq_credit": "0.32", + "word_freq_your": "0.51", + "word_freq_font": "0", + "word_freq_000": "1.16", + "word_freq_money": "0.06", + "word_freq_hp": "0", + "word_freq_hpl": "0", + "word_freq_george": "0", + "word_freq_650": "0", + "word_freq_lab": "0", + "word_freq_labs": "0", + "word_freq_telnet": "0", + "word_freq_857": "0", + "word_freq_data": "0", + "word_freq_415": "0", + "word_freq_85": "0", + "word_freq_technology": "0", + "word_freq_1999": "0", + "word_freq_parts": "0", + "word_freq_pm": "0", + "word_freq_direct": "0.06", + "word_freq_cs": "0", + "word_freq_meeting": "0", + "word_freq_original": "0.12", + "word_freq_project": "0", + "word_freq_re": "0.06", + "word_freq_edu": "0.06", + "word_freq_table": "0", + "word_freq_conference": "0", + "char_freq_%3B": "0.01", + "char_freq_%28": "0.143", + "char_freq_%5B": "0", + "char_freq_%21": "0.276", + "char_freq_%24": "0.184", + "char_freq_%23": "0.01", + "capital_run_length_average": "9.821", + "capital_run_length_longest": "485", + "capital_run_length_total": "2259", + "class": "1" + }, + { + "word_freq_make": "0", + "word_freq_address": "0", + "word_freq_all": "0", + "word_freq_3d": "0", + "word_freq_our": "0.63", + "word_freq_over": "0", + "word_freq_remove": "0.31", + "word_freq_internet": "0.63", + "word_freq_order": "0.31", + "word_freq_mail": "0.63", + "word_freq_receive": "0.31", + "word_freq_will": "0.31", + "word_freq_people": "0.31", + "word_freq_report": "0", + "word_freq_addresses": "0", + "word_freq_free": "0.31", + "word_freq_business": "0", + "word_freq_email": "0", + "word_freq_you": "3.18", + "word_freq_credit": "0", + "word_freq_your": "0.31", + "word_freq_font": "0", + "word_freq_000": "0", + "word_freq_money": "0", + "word_freq_hp": "0", + "word_freq_hpl": "0", + "word_freq_george": "0", + "word_freq_650": "0", + "word_freq_lab": "0", + "word_freq_labs": "0", + "word_freq_telnet": "0", + "word_freq_857": "0", + "word_freq_data": "0", + "word_freq_415": "0", + "word_freq_85": "0", + "word_freq_technology": "0", + "word_freq_1999": "0", + "word_freq_parts": "0", + "word_freq_pm": "0", + "word_freq_direct": "0", + "word_freq_cs": "0", + "word_freq_meeting": "0", + "word_freq_original": "0", + "word_freq_project": "0", + "word_freq_re": "0", + "word_freq_edu": "0", + "word_freq_table": "0", + "word_freq_conference": "0", + "char_freq_%3B": "0", + "char_freq_%28": "0.137", + "char_freq_%5B": "0", + "char_freq_%21": "0.137", + "char_freq_%24": "0", + "char_freq_%23": "0", + "capital_run_length_average": "3.537", + "capital_run_length_longest": "40", + "capital_run_length_total": "191", + "class": "1" + }, + { + "word_freq_make": "0", + "word_freq_address": "0", + "word_freq_all": "0", + "word_freq_3d": "0", + "word_freq_our": "0.63", + "word_freq_over": "0", + "word_freq_remove": "0.31", + "word_freq_internet": "0.63", + "word_freq_order": "0.31", + "word_freq_mail": "0.63", + "word_freq_receive": "0.31", + "word_freq_will": "0.31", + "word_freq_people": "0.31", + "word_freq_report": "0", + "word_freq_addresses": "0", + "word_freq_free": "0.31", + "word_freq_business": "0", + "word_freq_email": "0", + "word_freq_you": "3.18", + "word_freq_credit": "0", + "word_freq_your": "0.31", + "word_freq_font": "0", + "word_freq_000": "0", + "word_freq_money": "0", + "word_freq_hp": "0", + "word_freq_hpl": "0", + "word_freq_george": "0", + "word_freq_650": "0", + "word_freq_lab": "0", + "word_freq_labs": "0", + "word_freq_telnet": "0", + "word_freq_857": "0", + "word_freq_data": "0", + "word_freq_415": "0", + "word_freq_85": "0", + "word_freq_technology": "0", + "word_freq_1999": "0", + "word_freq_parts": "0", + "word_freq_pm": "0", + "word_freq_direct": "0", + "word_freq_cs": "0", + "word_freq_meeting": "0", + "word_freq_original": "0", + "word_freq_project": "0", + "word_freq_re": "0", + "word_freq_edu": "0", + "word_freq_table": "0", + "word_freq_conference": "0", + "char_freq_%3B": "0", + "char_freq_%28": "0.135", + "char_freq_%5B": "0", + "char_freq_%21": "0.135", + "char_freq_%24": "0", + "char_freq_%23": "0", + "capital_run_length_average": "3.537", + "capital_run_length_longest": "40", + "capital_run_length_total": "191", + "class": "1" + } + ] +} + +Shortlisted templates: +[ + { + "template_id": "tpl_m4_group_condition_rate", + "template_name": "Grouped Condition Rate", + "primary_family": "conditional_dependency_structure", + "portability": "yes", + "sql_skeleton": "SELECT {group_col},\n AVG(CASE WHEN {condition_col} = {condition_value} THEN 1 ELSE 0 END) AS condition_rate\nFROM {table}\nGROUP BY {group_col}\nORDER BY condition_rate DESC;", + "required_roles": [ + "group_col", + "condition_col" + ] + } +] + +Problem instance: +{ + "dataset_id": "n1", + "question": "Use template Grouped Condition Rate to probe dependency_strength_similarity with semantic role within_group_proportion. Focus on group_col=class, condition_col=class.", + "planned_template_id": "tpl_m4_group_condition_rate", + "bindings": { + "group_col": "class", + "condition_col": "class", + "condition_value": "0", + "positive_value": "0", + "negative_value": "1", + "top_k": 12, + "top_n": 5, + "num_tiles": 10, + "percentile_value": 0.95, + "z_threshold": 2.0, + "fraction_threshold": 0.1, + "baseline_multiplier": 1.5, + "baseline_fraction": 0.1, + "min_group_size": 5, + "min_support": 5, + "measure_threshold": 0.0, + "time_grain": "month", + "lookback_rows": 3, + "current_period_start": "'2024-01-01'", + "current_period_end": "'2024-04-01'", + "previous_period_start": "'2023-10-01'", + "previous_period_end": "'2024-01-01'", + "drift_ratio_threshold": 0.8 + }, + "can_vary": [], + "must_fix": [], + "runtime_sql_skeleton": "SELECT {group_col},\n AVG(CASE WHEN {condition_col} = {condition_value} THEN 1 ELSE 0 END) AS condition_rate\nFROM {table}\nGROUP BY {group_col}\nORDER BY condition_rate DESC;" +} + +Repair context: +{} diff --git a/Query/sql/v2/runs/v2_cli_20260502_081223_d/n1/artifacts/v2q_n1_9e31f6545f16bec8/cli/sql_prompt_attempt_2.txt b/Query/sql/v2/runs/v2_cli_20260502_081223_d/n1/artifacts/v2q_n1_9e31f6545f16bec8/cli/sql_prompt_attempt_2.txt new file mode 100644 index 0000000000000000000000000000000000000000..c1379fa397d9bb0da34d0af5aa9eec25c31326c9 --- /dev/null +++ b/Query/sql/v2/runs/v2_cli_20260502_081223_d/n1/artifacts/v2q_n1_9e31f6545f16bec8/cli/sql_prompt_attempt_2.txt @@ -0,0 +1,795 @@ +You are generating one SQLite SELECT query for a single-table SQL QA task. +Return strict JSON only, with this schema: {"sql": "...", "notes": "..."}. +Rules: +- Use only the provided table and columns. +- Do not write INSERT, UPDATE, DELETE, DROP, ALTER, CREATE, PRAGMA, ATTACH, DETACH, or VACUUM. +- Prefer the planned template and bound roles when provided. +- Add a leading SQL comment exactly like: -- template_id: . +- Generate SQLite-compatible SQL. SQLite does not support PERCENTILE_CONT or STDDEV. +- Quote identifiers with double quotes. +- Return no markdown and no extra prose. + +Dataset context: +Dataset context for SQL QA: +- dataset_id: n1 +- dataset_name: Spambase +- table_name: n1 +- table_layout: single-table dataset (do not assume joins). +- row_semantics: One row is one email represented by engineered token/character frequency and capitalization-run features. +- task_type: classification +- target_column: class +- main_row_count: 4601 +- important_fields: +- word_freq_make: role=feature, type=numeric_sparse_frequency. tags=['condition_candidate', 'measure', 'rare_pattern_candidate'] desc=Frequency feature for token 'make' in an email message. +- word_freq_address: role=feature, type=numeric_sparse_frequency. tags=['condition_candidate', 'measure', 'rare_pattern_candidate'] desc=Frequency feature for token 'address' in an email message. +- word_freq_all: role=feature, type=numeric_sparse_frequency. tags=['condition_candidate', 'measure', 'rare_pattern_candidate'] desc=Frequency feature for token 'all' in an email message. +- word_freq_3d: role=feature, type=numeric_sparse_frequency. tags=['condition_candidate', 'measure', 'rare_pattern_candidate'] desc=Frequency feature for token '3d' in an email message. +- word_freq_our: role=feature, type=numeric_sparse_frequency. tags=['condition_candidate', 'measure', 'rare_pattern_candidate'] desc=Frequency feature for token 'our' in an email message. +- word_freq_over: role=feature, type=numeric_sparse_frequency. tags=['condition_candidate', 'measure', 'rare_pattern_candidate'] desc=Frequency feature for token 'over' in an email message. +- word_freq_remove: role=feature, type=numeric_sparse_frequency. tags=['condition_candidate', 'measure', 'rare_pattern_candidate'] desc=Frequency feature for token 'remove' in an email message. +- word_freq_internet: role=feature, type=numeric_sparse_frequency. tags=['condition_candidate', 'measure', 'rare_pattern_candidate'] desc=Frequency feature for token 'internet' in an email message. +- word_freq_order: role=feature, type=numeric_sparse_frequency. tags=['condition_candidate', 'measure', 'rare_pattern_candidate'] desc=Frequency feature for token 'order' in an email message. +- word_freq_mail: role=feature, type=numeric_sparse_frequency. tags=['condition_candidate', 'measure', 'rare_pattern_candidate'] desc=Frequency feature for token 'mail' in an email message. +- word_freq_receive: role=feature, type=numeric_sparse_frequency. tags=['condition_candidate', 'measure', 'rare_pattern_candidate'] desc=Frequency feature for token 'receive' in an email message. +- word_freq_will: role=feature, type=numeric_sparse_frequency. tags=['condition_candidate', 'measure', 'rare_pattern_candidate'] desc=Frequency feature for token 'will' in an email message. +- word_freq_people: role=feature, type=numeric_sparse_frequency. tags=['condition_candidate', 'measure', 'rare_pattern_candidate'] desc=Frequency feature for token 'people' in an email message. +- word_freq_report: role=feature, type=numeric_sparse_frequency. tags=['condition_candidate', 'measure', 'rare_pattern_candidate'] desc=Frequency feature for token 'report' in an email message. +- word_freq_addresses: role=feature, type=numeric_sparse_frequency. tags=['condition_candidate', 'measure', 'rare_pattern_candidate'] desc=Frequency feature for token 'addresses' in an email message. +- word_freq_free: role=feature, type=numeric_sparse_frequency. tags=['condition_candidate', 'measure', 'rare_pattern_candidate'] desc=Frequency feature for token 'free' in an email message. +- word_freq_business: role=feature, type=numeric_sparse_frequency. tags=['condition_candidate', 'measure', 'rare_pattern_candidate'] desc=Frequency feature for token 'business' in an email message. +- word_freq_email: role=feature, type=numeric_sparse_frequency. tags=['condition_candidate', 'measure', 'rare_pattern_candidate'] desc=Frequency feature for token 'email' in an email message. +- word_freq_you: role=feature, type=numeric_sparse_frequency. tags=['condition_candidate', 'measure', 'rare_pattern_candidate'] desc=Frequency feature for token 'you' in an email message. +- word_freq_credit: role=feature, type=numeric_sparse_frequency. tags=['condition_candidate', 'measure', 'rare_pattern_candidate'] desc=Frequency feature for token 'credit' in an email message. +- word_freq_your: role=feature, type=numeric_sparse_frequency. tags=['condition_candidate', 'measure', 'rare_pattern_candidate'] desc=Frequency feature for token 'your' in an email message. +- word_freq_font: role=feature, type=numeric_sparse_frequency. tags=['condition_candidate', 'measure', 'rare_pattern_candidate'] desc=Frequency feature for token 'font' in an email message. +- word_freq_000: role=feature, type=numeric_sparse_frequency. tags=['condition_candidate', 'measure', 'rare_pattern_candidate'] desc=Frequency feature for token '000' in an email message. +- word_freq_money: role=feature, type=numeric_sparse_frequency. tags=['condition_candidate', 'measure', 'rare_pattern_candidate'] desc=Frequency feature for token 'money' in an email message. +- word_freq_hp: role=feature, type=numeric_sparse_frequency. tags=['condition_candidate', 'measure', 'rare_pattern_candidate'] desc=Frequency feature for token 'hp' in an email message. +- word_freq_hpl: role=feature, type=numeric_sparse_frequency. tags=['condition_candidate', 'measure', 'rare_pattern_candidate'] desc=Frequency feature for token 'hpl' in an email message. +- word_freq_george: role=feature, type=numeric_sparse_frequency. tags=['condition_candidate', 'measure', 'rare_pattern_candidate'] desc=Frequency feature for token 'george' in an email message. +- word_freq_650: role=feature, type=numeric_sparse_frequency. tags=['condition_candidate', 'measure', 'rare_pattern_candidate'] desc=Frequency feature for token '650' in an email message. +- word_freq_lab: role=feature, type=numeric_sparse_frequency. tags=['condition_candidate', 'measure', 'rare_pattern_candidate'] desc=Frequency feature for token 'lab' in an email message. +- word_freq_labs: role=feature, type=numeric_sparse_frequency. tags=['condition_candidate', 'measure', 'rare_pattern_candidate'] desc=Frequency feature for token 'labs' in an email message. +- word_freq_telnet: role=feature, type=numeric_sparse_frequency. tags=['condition_candidate', 'measure', 'rare_pattern_candidate'] desc=Frequency feature for token 'telnet' in an email message. +- word_freq_857: role=feature, type=numeric_sparse_frequency. tags=['condition_candidate', 'measure', 'rare_pattern_candidate'] desc=Frequency feature for token '857' in an email message. +- word_freq_data: role=feature, type=numeric_sparse_frequency. tags=['condition_candidate', 'measure', 'rare_pattern_candidate'] desc=Frequency feature for token 'data' in an email message. +- word_freq_415: role=feature, type=numeric_sparse_frequency. tags=['condition_candidate', 'measure', 'rare_pattern_candidate'] desc=Frequency feature for token '415' in an email message. +- word_freq_85: role=feature, type=numeric_sparse_frequency. tags=['condition_candidate', 'measure', 'rare_pattern_candidate'] desc=Frequency feature for token '85' in an email message. +- word_freq_technology: role=feature, type=numeric_sparse_frequency. tags=['condition_candidate', 'measure', 'rare_pattern_candidate'] desc=Frequency feature for token 'technology' in an email message. +- word_freq_1999: role=feature, type=numeric_sparse_frequency. tags=['condition_candidate', 'measure', 'rare_pattern_candidate'] desc=Frequency feature for token '1999' in an email message. +- word_freq_parts: role=feature, type=numeric_sparse_frequency. tags=['condition_candidate', 'measure', 'rare_pattern_candidate'] desc=Frequency feature for token 'parts' in an email message. +- word_freq_pm: role=feature, type=numeric_sparse_frequency. tags=['condition_candidate', 'measure', 'rare_pattern_candidate'] desc=Frequency feature for token 'pm' in an email message. +- word_freq_direct: role=feature, type=numeric_sparse_frequency. tags=['condition_candidate', 'measure', 'rare_pattern_candidate'] desc=Frequency feature for token 'direct' in an email message. +- word_freq_cs: role=feature, type=numeric_sparse_frequency. tags=['condition_candidate', 'measure', 'rare_pattern_candidate'] desc=Frequency feature for token 'cs' in an email message. +- word_freq_meeting: role=feature, type=numeric_sparse_frequency. tags=['condition_candidate', 'measure', 'rare_pattern_candidate'] desc=Frequency feature for token 'meeting' in an email message. +- word_freq_original: role=feature, type=numeric_sparse_frequency. tags=['condition_candidate', 'measure', 'rare_pattern_candidate'] desc=Frequency feature for token 'original' in an email message. +- word_freq_project: role=feature, type=numeric_sparse_frequency. tags=['condition_candidate', 'measure', 'rare_pattern_candidate'] desc=Frequency feature for token 'project' in an email message. +- word_freq_re: role=feature, type=numeric_sparse_frequency. tags=['condition_candidate', 'measure', 'rare_pattern_candidate'] desc=Frequency feature for token 're' in an email message. +- word_freq_edu: role=feature, type=numeric_sparse_frequency. tags=['condition_candidate', 'measure', 'rare_pattern_candidate'] desc=Frequency feature for token 'edu' in an email message. +- word_freq_table: role=feature, type=numeric_sparse_frequency. tags=['condition_candidate', 'measure', 'rare_pattern_candidate'] desc=Frequency feature for token 'table' in an email message. +- word_freq_conference: role=feature, type=numeric_sparse_frequency. tags=['condition_candidate', 'measure', 'rare_pattern_candidate'] desc=Frequency feature for token 'conference' in an email message. +- char_freq_%3B: role=feature, type=numeric_sparse_frequency. tags=['condition_candidate', 'measure', 'rare_pattern_candidate'] desc=Character frequency feature for symbol ';'. +- char_freq_%28: role=feature, type=numeric_sparse_frequency. tags=['condition_candidate', 'measure', 'rare_pattern_candidate'] desc=Character frequency feature for symbol '('. +- char_freq_%5B: role=feature, type=numeric_sparse_frequency. tags=['condition_candidate', 'measure', 'rare_pattern_candidate'] desc=Character frequency feature for symbol '['. +- char_freq_%21: role=feature, type=numeric_sparse_frequency. tags=['condition_candidate', 'measure', 'rare_pattern_candidate'] desc=Character frequency feature for symbol '!'. +- char_freq_%24: role=feature, type=numeric_sparse_frequency. tags=['condition_candidate', 'measure', 'rare_pattern_candidate'] desc=Character frequency feature for symbol '$'. +- char_freq_%23: role=feature, type=numeric_sparse_frequency. tags=['condition_candidate', 'measure', 'rare_pattern_candidate'] desc=Character frequency feature for symbol '#'. +- capital_run_length_average: role=feature, type=numeric_heavy_tail. tags=['condition_candidate', 'measure', 'rare_pattern_candidate'] desc=Average length of uninterrupted capital-letter runs. +- capital_run_length_longest: role=feature, type=numeric_heavy_tail. tags=['condition_candidate', 'measure', 'rare_pattern_candidate'] desc=Longest uninterrupted capital-letter run. +- capital_run_length_total: role=feature, type=numeric_heavy_tail. tags=['condition_candidate', 'measure', 'rare_pattern_candidate'] desc=Total number of capital letters in runs. +- class: role=target, type=binary_target. tags=['target_candidate'] desc=Binary spam label (1=spam, 0=non-spam). +- useful_field_combinations: [['word_freq_free', 'word_freq_you', 'class'], ['char_freq_%21', 'capital_run_length_longest', 'class'], ['word_freq_remove', 'word_freq_money', 'class']] +- fields_requiring_caution: ['capital_run_length_average', 'capital_run_length_longest', 'capital_run_length_total'] +- source_url: https://www.openml.org/d/44 + +SQLite schema snapshot: +{ + "table_name": "n1", + "quoted_table_name": "\"n1\"", + "row_count": 4601, + "columns": [ + { + "name": "word_freq_make", + "type": "TEXT", + "notnull": false, + "pk": false + }, + { + "name": "word_freq_address", + "type": "TEXT", + "notnull": false, + "pk": false + }, + { + "name": "word_freq_all", + "type": "TEXT", + "notnull": false, + "pk": false + }, + { + "name": "word_freq_3d", + "type": "TEXT", + "notnull": false, + "pk": false + }, + { + "name": "word_freq_our", + "type": "TEXT", + "notnull": false, + "pk": false + }, + { + "name": "word_freq_over", + "type": "TEXT", + "notnull": false, + "pk": false + }, + { + "name": "word_freq_remove", + "type": "TEXT", + "notnull": false, + "pk": false + }, + { + "name": "word_freq_internet", + "type": "TEXT", + "notnull": false, + "pk": false + }, + { + "name": "word_freq_order", + "type": "TEXT", + "notnull": false, + "pk": false + }, + { + "name": "word_freq_mail", + "type": "TEXT", + "notnull": false, + "pk": false + }, + { + "name": "word_freq_receive", + "type": "TEXT", + "notnull": false, + "pk": false + }, + { + "name": "word_freq_will", + "type": "TEXT", + "notnull": false, + "pk": false + }, + { + "name": "word_freq_people", + "type": "TEXT", + "notnull": false, + "pk": false + }, + { + "name": "word_freq_report", + "type": "TEXT", + "notnull": false, + "pk": false + }, + { + "name": "word_freq_addresses", + "type": "TEXT", + "notnull": false, + "pk": false + }, + { + "name": "word_freq_free", + "type": "TEXT", + "notnull": false, + "pk": false + }, + { + "name": "word_freq_business", + "type": "TEXT", + "notnull": false, + "pk": false + }, + { + "name": "word_freq_email", + "type": "TEXT", + "notnull": false, + "pk": false + }, + { + "name": "word_freq_you", + "type": "TEXT", + "notnull": false, + "pk": false + }, + { + "name": "word_freq_credit", + "type": "TEXT", + "notnull": false, + "pk": false + }, + { + "name": "word_freq_your", + "type": "TEXT", + "notnull": false, + "pk": false + }, + { + "name": "word_freq_font", + "type": "TEXT", + "notnull": false, + "pk": false + }, + { + "name": "word_freq_000", + "type": "TEXT", + "notnull": false, + "pk": false + }, + { + "name": "word_freq_money", + "type": "TEXT", + "notnull": false, + "pk": false + }, + { + "name": "word_freq_hp", + "type": "TEXT", + "notnull": false, + "pk": false + }, + { + "name": "word_freq_hpl", + "type": "TEXT", + "notnull": false, + "pk": false + }, + { + "name": "word_freq_george", + "type": "TEXT", + "notnull": false, + "pk": false + }, + { + "name": "word_freq_650", + "type": "TEXT", + "notnull": false, + "pk": false + }, + { + "name": "word_freq_lab", + "type": "TEXT", + "notnull": false, + "pk": false + }, + { + "name": "word_freq_labs", + "type": "TEXT", + "notnull": false, + "pk": false + }, + { + "name": "word_freq_telnet", + "type": "TEXT", + "notnull": false, + "pk": false + }, + { + "name": "word_freq_857", + "type": "TEXT", + "notnull": false, + "pk": false + }, + { + "name": "word_freq_data", + "type": "TEXT", + "notnull": false, + "pk": false + }, + { + "name": "word_freq_415", + "type": "TEXT", + "notnull": false, + "pk": false + }, + { + "name": "word_freq_85", + "type": "TEXT", + "notnull": false, + "pk": false + }, + { + "name": "word_freq_technology", + "type": "TEXT", + "notnull": false, + "pk": false + }, + { + "name": "word_freq_1999", + "type": "TEXT", + "notnull": false, + "pk": false + }, + { + "name": "word_freq_parts", + "type": "TEXT", + "notnull": false, + "pk": false + }, + { + "name": "word_freq_pm", + "type": "TEXT", + "notnull": false, + "pk": false + }, + { + "name": "word_freq_direct", + "type": "TEXT", + "notnull": false, + "pk": false + }, + { + "name": "word_freq_cs", + "type": "TEXT", + "notnull": false, + "pk": false + }, + { + "name": "word_freq_meeting", + "type": "TEXT", + "notnull": false, + "pk": false + }, + { + "name": "word_freq_original", + "type": "TEXT", + "notnull": false, + "pk": false + }, + { + "name": "word_freq_project", + "type": "TEXT", + "notnull": false, + "pk": false + }, + { + "name": "word_freq_re", + "type": "TEXT", + "notnull": false, + "pk": false + }, + { + "name": "word_freq_edu", + "type": "TEXT", + "notnull": false, + "pk": false + }, + { + "name": "word_freq_table", + "type": "TEXT", + "notnull": false, + "pk": false + }, + { + "name": "word_freq_conference", + "type": "TEXT", + "notnull": false, + "pk": false + }, + { + "name": "char_freq_%3B", + "type": "TEXT", + "notnull": false, + "pk": false + }, + { + "name": "char_freq_%28", + "type": "TEXT", + "notnull": false, + "pk": false + }, + { + "name": "char_freq_%5B", + "type": "TEXT", + "notnull": false, + "pk": false + }, + { + "name": "char_freq_%21", + "type": "TEXT", + "notnull": false, + "pk": false + }, + { + "name": "char_freq_%24", + "type": "TEXT", + "notnull": false, + "pk": false + }, + { + "name": "char_freq_%23", + "type": "TEXT", + "notnull": false, + "pk": false + }, + { + "name": "capital_run_length_average", + "type": "TEXT", + "notnull": false, + "pk": false + }, + { + "name": "capital_run_length_longest", + "type": "TEXT", + "notnull": false, + "pk": false + }, + { + "name": "capital_run_length_total", + "type": "TEXT", + "notnull": false, + "pk": false + }, + { + "name": "class", + "type": "TEXT", + "notnull": false, + "pk": false + } + ], + "sample_rows": [ + { + "word_freq_make": "0", + "word_freq_address": "0.64", + "word_freq_all": "0.64", + "word_freq_3d": "0", + "word_freq_our": "0.32", + "word_freq_over": "0", + "word_freq_remove": "0", + "word_freq_internet": "0", + "word_freq_order": "0", + "word_freq_mail": "0", + "word_freq_receive": "0", + "word_freq_will": "0.64", + "word_freq_people": "0", + "word_freq_report": "0", + "word_freq_addresses": "0", + "word_freq_free": "0.32", + "word_freq_business": "0", + "word_freq_email": "1.29", + "word_freq_you": "1.93", + "word_freq_credit": "0", + "word_freq_your": "0.96", + "word_freq_font": "0", + "word_freq_000": "0", + "word_freq_money": "0", + "word_freq_hp": "0", + "word_freq_hpl": "0", + "word_freq_george": "0", + "word_freq_650": "0", + "word_freq_lab": "0", + "word_freq_labs": "0", + "word_freq_telnet": "0", + "word_freq_857": "0", + "word_freq_data": "0", + "word_freq_415": "0", + "word_freq_85": "0", + "word_freq_technology": "0", + "word_freq_1999": "0", + "word_freq_parts": "0", + "word_freq_pm": "0", + "word_freq_direct": "0", + "word_freq_cs": "0", + "word_freq_meeting": "0", + "word_freq_original": "0", + "word_freq_project": "0", + "word_freq_re": "0", + "word_freq_edu": "0", + "word_freq_table": "0", + "word_freq_conference": "0", + "char_freq_%3B": "0", + "char_freq_%28": "0", + "char_freq_%5B": "0", + "char_freq_%21": "0.778", + "char_freq_%24": "0", + "char_freq_%23": "0", + "capital_run_length_average": "3.756", + "capital_run_length_longest": "61", + "capital_run_length_total": "278", + "class": "1" + }, + { + "word_freq_make": "0.21", + "word_freq_address": "0.28", + "word_freq_all": "0.5", + "word_freq_3d": "0", + "word_freq_our": "0.14", + "word_freq_over": "0.28", + "word_freq_remove": "0.21", + "word_freq_internet": "0.07", + "word_freq_order": "0", + "word_freq_mail": "0.94", + "word_freq_receive": "0.21", + "word_freq_will": "0.79", + "word_freq_people": "0.65", + "word_freq_report": "0.21", + "word_freq_addresses": "0.14", + "word_freq_free": "0.14", + "word_freq_business": "0.07", + "word_freq_email": "0.28", + "word_freq_you": "3.47", + "word_freq_credit": "0", + "word_freq_your": "1.59", + "word_freq_font": "0", + "word_freq_000": "0.43", + "word_freq_money": "0.43", + "word_freq_hp": "0", + "word_freq_hpl": "0", + "word_freq_george": "0", + "word_freq_650": "0", + "word_freq_lab": "0", + "word_freq_labs": "0", + "word_freq_telnet": "0", + "word_freq_857": "0", + "word_freq_data": "0", + "word_freq_415": "0", + "word_freq_85": "0", + "word_freq_technology": "0", + "word_freq_1999": "0.07", + "word_freq_parts": "0", + "word_freq_pm": "0", + "word_freq_direct": "0", + "word_freq_cs": "0", + "word_freq_meeting": "0", + "word_freq_original": "0", + "word_freq_project": "0", + "word_freq_re": "0", + "word_freq_edu": "0", + "word_freq_table": "0", + "word_freq_conference": "0", + "char_freq_%3B": "0", + "char_freq_%28": "0.132", + "char_freq_%5B": "0", + "char_freq_%21": "0.372", + "char_freq_%24": "0.18", + "char_freq_%23": "0.048", + "capital_run_length_average": "5.114", + "capital_run_length_longest": "101", + "capital_run_length_total": "1028", + "class": "1" + }, + { + "word_freq_make": "0.06", + "word_freq_address": "0", + "word_freq_all": "0.71", + "word_freq_3d": "0", + "word_freq_our": "1.23", + "word_freq_over": "0.19", + "word_freq_remove": "0.19", + "word_freq_internet": "0.12", + "word_freq_order": "0.64", + "word_freq_mail": "0.25", + "word_freq_receive": "0.38", + "word_freq_will": "0.45", + "word_freq_people": "0.12", + "word_freq_report": "0", + "word_freq_addresses": "1.75", + "word_freq_free": "0.06", + "word_freq_business": "0.06", + "word_freq_email": "1.03", + "word_freq_you": "1.36", + "word_freq_credit": "0.32", + "word_freq_your": "0.51", + "word_freq_font": "0", + "word_freq_000": "1.16", + "word_freq_money": "0.06", + "word_freq_hp": "0", + "word_freq_hpl": "0", + "word_freq_george": "0", + "word_freq_650": "0", + "word_freq_lab": "0", + "word_freq_labs": "0", + "word_freq_telnet": "0", + "word_freq_857": "0", + "word_freq_data": "0", + "word_freq_415": "0", + "word_freq_85": "0", + "word_freq_technology": "0", + "word_freq_1999": "0", + "word_freq_parts": "0", + "word_freq_pm": "0", + "word_freq_direct": "0.06", + "word_freq_cs": "0", + "word_freq_meeting": "0", + "word_freq_original": "0.12", + "word_freq_project": "0", + "word_freq_re": "0.06", + "word_freq_edu": "0.06", + "word_freq_table": "0", + "word_freq_conference": "0", + "char_freq_%3B": "0.01", + "char_freq_%28": "0.143", + "char_freq_%5B": "0", + "char_freq_%21": "0.276", + "char_freq_%24": "0.184", + "char_freq_%23": "0.01", + "capital_run_length_average": "9.821", + "capital_run_length_longest": "485", + "capital_run_length_total": "2259", + "class": "1" + }, + { + "word_freq_make": "0", + "word_freq_address": "0", + "word_freq_all": "0", + "word_freq_3d": "0", + "word_freq_our": "0.63", + "word_freq_over": "0", + "word_freq_remove": "0.31", + "word_freq_internet": "0.63", + "word_freq_order": "0.31", + "word_freq_mail": "0.63", + "word_freq_receive": "0.31", + "word_freq_will": "0.31", + "word_freq_people": "0.31", + "word_freq_report": "0", + "word_freq_addresses": "0", + "word_freq_free": "0.31", + "word_freq_business": "0", + "word_freq_email": "0", + "word_freq_you": "3.18", + "word_freq_credit": "0", + "word_freq_your": "0.31", + "word_freq_font": "0", + "word_freq_000": "0", + "word_freq_money": "0", + "word_freq_hp": "0", + "word_freq_hpl": "0", + "word_freq_george": "0", + "word_freq_650": "0", + "word_freq_lab": "0", + "word_freq_labs": "0", + "word_freq_telnet": "0", + "word_freq_857": "0", + "word_freq_data": "0", + "word_freq_415": "0", + "word_freq_85": "0", + "word_freq_technology": "0", + "word_freq_1999": "0", + "word_freq_parts": "0", + "word_freq_pm": "0", + "word_freq_direct": "0", + "word_freq_cs": "0", + "word_freq_meeting": "0", + "word_freq_original": "0", + "word_freq_project": "0", + "word_freq_re": "0", + "word_freq_edu": "0", + "word_freq_table": "0", + "word_freq_conference": "0", + "char_freq_%3B": "0", + "char_freq_%28": "0.137", + "char_freq_%5B": "0", + "char_freq_%21": "0.137", + "char_freq_%24": "0", + "char_freq_%23": "0", + "capital_run_length_average": "3.537", + "capital_run_length_longest": "40", + "capital_run_length_total": "191", + "class": "1" + }, + { + "word_freq_make": "0", + "word_freq_address": "0", + "word_freq_all": "0", + "word_freq_3d": "0", + "word_freq_our": "0.63", + "word_freq_over": "0", + "word_freq_remove": "0.31", + "word_freq_internet": "0.63", + "word_freq_order": "0.31", + "word_freq_mail": "0.63", + "word_freq_receive": "0.31", + "word_freq_will": "0.31", + "word_freq_people": "0.31", + "word_freq_report": "0", + "word_freq_addresses": "0", + "word_freq_free": "0.31", + "word_freq_business": "0", + "word_freq_email": "0", + "word_freq_you": "3.18", + "word_freq_credit": "0", + "word_freq_your": "0.31", + "word_freq_font": "0", + "word_freq_000": "0", + "word_freq_money": "0", + "word_freq_hp": "0", + "word_freq_hpl": "0", + "word_freq_george": "0", + "word_freq_650": "0", + "word_freq_lab": "0", + "word_freq_labs": "0", + "word_freq_telnet": "0", + "word_freq_857": "0", + "word_freq_data": "0", + "word_freq_415": "0", + "word_freq_85": "0", + "word_freq_technology": "0", + "word_freq_1999": "0", + "word_freq_parts": "0", + "word_freq_pm": "0", + "word_freq_direct": "0", + "word_freq_cs": "0", + "word_freq_meeting": "0", + "word_freq_original": "0", + "word_freq_project": "0", + "word_freq_re": "0", + "word_freq_edu": "0", + "word_freq_table": "0", + "word_freq_conference": "0", + "char_freq_%3B": "0", + "char_freq_%28": "0.135", + "char_freq_%5B": "0", + "char_freq_%21": "0.135", + "char_freq_%24": "0", + "char_freq_%23": "0", + "capital_run_length_average": "3.537", + "capital_run_length_longest": "40", + "capital_run_length_total": "191", + "class": "1" + } + ] +} + +Shortlisted templates: +[ + { + "template_id": "tpl_m4_group_condition_rate", + "template_name": "Grouped Condition Rate", + "primary_family": "conditional_dependency_structure", + "portability": "yes", + "sql_skeleton": "SELECT {group_col},\n AVG(CASE WHEN {condition_col} = {condition_value} THEN 1 ELSE 0 END) AS condition_rate\nFROM {table}\nGROUP BY {group_col}\nORDER BY condition_rate DESC;", + "required_roles": [ + "group_col", + "condition_col" + ] + } +] + +Problem instance: +{ + "dataset_id": "n1", + "question": "Use template Grouped Condition Rate to probe dependency_strength_similarity with semantic role within_group_proportion. Focus on group_col=class, condition_col=class.", + "planned_template_id": "tpl_m4_group_condition_rate", + "bindings": { + "group_col": "class", + "condition_col": "class", + "condition_value": "0", + "positive_value": "0", + "negative_value": "1", + "top_k": 12, + "top_n": 5, + "num_tiles": 10, + "percentile_value": 0.95, + "z_threshold": 2.0, + "fraction_threshold": 0.1, + "baseline_multiplier": 1.5, + "baseline_fraction": 0.1, + "min_group_size": 5, + "min_support": 5, + "measure_threshold": 0.0, + "time_grain": "month", + "lookback_rows": 3, + "current_period_start": "'2024-01-01'", + "current_period_end": "'2024-04-01'", + "previous_period_start": "'2023-10-01'", + "previous_period_end": "'2024-01-01'", + "drift_ratio_threshold": 0.8 + }, + "can_vary": [], + "must_fix": [], + "runtime_sql_skeleton": "SELECT {group_col},\n AVG(CASE WHEN {condition_col} = {condition_value} THEN 1 ELSE 0 END) AS condition_rate\nFROM {table}\nGROUP BY {group_col}\nORDER BY condition_rate DESC;" +} + +Repair context: +{} diff --git a/Query/sql/v2/runs/v2_cli_20260502_081223_d/n1/artifacts/v2q_n1_9e31f6545f16bec8/cli/sql_response_attempt_1.raw.txt b/Query/sql/v2/runs/v2_cli_20260502_081223_d/n1/artifacts/v2q_n1_9e31f6545f16bec8/cli/sql_response_attempt_1.raw.txt new file mode 100644 index 0000000000000000000000000000000000000000..e077ce6402aded21393b7cec18eb8dd09b542e5d --- /dev/null +++ b/Query/sql/v2/runs/v2_cli_20260502_081223_d/n1/artifacts/v2q_n1_9e31f6545f16bec8/cli/sql_response_attempt_1.raw.txt @@ -0,0 +1,4 @@ +{"type":"thread.started","thread_id":"019e40f7-2d4f-7352-b3f8-5684d250ac17"} +{"type":"turn.started"} +{"type":"error","message":"Quota exceeded. Check your plan and billing details."} +{"type":"turn.failed","error":{"message":"Quota exceeded. Check your plan and billing details."}} diff --git a/Query/sql/v2/runs/v2_cli_20260502_081223_d/n1/artifacts/v2q_n1_9e31f6545f16bec8/cli/sql_response_attempt_1.txt b/Query/sql/v2/runs/v2_cli_20260502_081223_d/n1/artifacts/v2q_n1_9e31f6545f16bec8/cli/sql_response_attempt_1.txt new file mode 100644 index 0000000000000000000000000000000000000000..b3885b0144c41c63e4199a6d21176d87ae7887f3 --- /dev/null +++ b/Query/sql/v2/runs/v2_cli_20260502_081223_d/n1/artifacts/v2q_n1_9e31f6545f16bec8/cli/sql_response_attempt_1.txt @@ -0,0 +1,4 @@ +{"type":"thread.started","thread_id":"019e40f7-2d4f-7352-b3f8-5684d250ac17"} +{"type":"turn.started"} +{"type":"error","message":"Quota exceeded. Check your plan and billing details."} +{"type":"turn.failed","error":{"message":"Quota exceeded. Check your plan and billing details."}} \ No newline at end of file diff --git a/Query/sql/v2/runs/v2_cli_20260502_081223_d/n1/artifacts/v2q_n1_9e31f6545f16bec8/cli/sql_response_attempt_2.raw.txt b/Query/sql/v2/runs/v2_cli_20260502_081223_d/n1/artifacts/v2q_n1_9e31f6545f16bec8/cli/sql_response_attempt_2.raw.txt new file mode 100644 index 0000000000000000000000000000000000000000..e4bf3334a80adb9efcda0ccca8ef5e0ef5801a69 --- /dev/null +++ b/Query/sql/v2/runs/v2_cli_20260502_081223_d/n1/artifacts/v2q_n1_9e31f6545f16bec8/cli/sql_response_attempt_2.raw.txt @@ -0,0 +1,4 @@ +{"type":"thread.started","thread_id":"019e40f7-3cfd-7620-bd6e-43a79ac7cd73"} +{"type":"turn.started"} +{"type":"error","message":"Quota exceeded. Check your plan and billing details."} +{"type":"turn.failed","error":{"message":"Quota exceeded. Check your plan and billing details."}} diff --git a/Query/sql/v2/runs/v2_cli_20260502_081223_d/n1/artifacts/v2q_n1_9e31f6545f16bec8/cli/sql_response_attempt_2.txt b/Query/sql/v2/runs/v2_cli_20260502_081223_d/n1/artifacts/v2q_n1_9e31f6545f16bec8/cli/sql_response_attempt_2.txt new file mode 100644 index 0000000000000000000000000000000000000000..dd397e9a5d2b25b2e2b7a5b29acc4473bc7ba064 --- /dev/null +++ b/Query/sql/v2/runs/v2_cli_20260502_081223_d/n1/artifacts/v2q_n1_9e31f6545f16bec8/cli/sql_response_attempt_2.txt @@ -0,0 +1,4 @@ +{"type":"thread.started","thread_id":"019e40f7-3cfd-7620-bd6e-43a79ac7cd73"} +{"type":"turn.started"} +{"type":"error","message":"Quota exceeded. Check your plan and billing details."} +{"type":"turn.failed","error":{"message":"Quota exceeded. Check your plan and billing details."}} \ No newline at end of file diff --git a/Query/sql/v2/runs/v2_cli_20260502_081223_d/n1/artifacts/v2q_n1_9e31f6545f16bec8/cli/sql_stderr_attempt_1.txt b/Query/sql/v2/runs/v2_cli_20260502_081223_d/n1/artifacts/v2q_n1_9e31f6545f16bec8/cli/sql_stderr_attempt_1.txt new file mode 100644 index 0000000000000000000000000000000000000000..e69de29bb2d1d6434b8b29ae775ad8c2e48c5391 diff --git a/Query/sql/v2/runs/v2_cli_20260502_081223_d/n1/artifacts/v2q_n1_9e31f6545f16bec8/cli/sql_stderr_attempt_2.txt b/Query/sql/v2/runs/v2_cli_20260502_081223_d/n1/artifacts/v2q_n1_9e31f6545f16bec8/cli/sql_stderr_attempt_2.txt new file mode 100644 index 0000000000000000000000000000000000000000..e69de29bb2d1d6434b8b29ae775ad8c2e48c5391 diff --git a/Query/sql/v2/runs/v2_cli_20260502_081223_d/n1/artifacts/v2q_n1_a02fa97aecaa7989/cli/conversation.jsonl b/Query/sql/v2/runs/v2_cli_20260502_081223_d/n1/artifacts/v2q_n1_a02fa97aecaa7989/cli/conversation.jsonl new file mode 100644 index 0000000000000000000000000000000000000000..dc9c5defdc82659ba9fe4569065c11cca13cf210 --- /dev/null +++ b/Query/sql/v2/runs/v2_cli_20260502_081223_d/n1/artifacts/v2q_n1_a02fa97aecaa7989/cli/conversation.jsonl @@ -0,0 +1,2 @@ +{"attempt": 1, "phase": "sql_generation", "role": "user", "content_path": "cli/sql_prompt_attempt_1.txt", "metrics": {"chars": 30144, "bytes_utf8": 30144, "lines": 795, "estimated_tokens": null}} +{"attempt": 1, "phase": "sql_generation", "role": "assistant", "content_path": "cli/sql_response_attempt_1.txt", "raw_content_path": "cli/sql_response_attempt_1.raw.txt", "stderr_path": "cli/sql_stderr_attempt_1.txt", "metrics": {"chars": 649, "bytes_utf8": 649, "lines": 1, "estimated_tokens": null}, "usage": {"input_tokens": 20520, "cached_input_tokens": 12032, "output_tokens": 494, "reasoning_output_tokens": 320}} diff --git a/Query/sql/v2/runs/v2_cli_20260502_081223_d/n1/artifacts/v2q_n1_a02fa97aecaa7989/cli/session_summary.json b/Query/sql/v2/runs/v2_cli_20260502_081223_d/n1/artifacts/v2q_n1_a02fa97aecaa7989/cli/session_summary.json new file mode 100644 index 0000000000000000000000000000000000000000..5272610f0bbba9bed6b0dc2454234ecc0ee69b40 --- /dev/null +++ b/Query/sql/v2/runs/v2_cli_20260502_081223_d/n1/artifacts/v2q_n1_a02fa97aecaa7989/cli/session_summary.json @@ -0,0 +1,25 @@ +{ + "engine": "v2-cli:codex", + "command": "codex exec --skip-git-repo-check --disable plugins --sandbox read-only --cd \"/data/jialinzhang/SQLagent\" -m gpt-5.4 --json -", + "ai_cli_calls": 1, + "usage_summary": { + "dataset_id": "n1", + "model": "v2-cli:codex", + "run_id": "v2q_n1_a02fa97aecaa7989", + "api_calls": 0, + "input_tokens": 20520, + "cached_input_tokens": 12032, + "output_tokens": 494, + "total_tokens": 21014, + "cost_usd": 0.0, + "ai_cli_calls": 1, + "estimated_input_tokens": 0, + "estimated_output_tokens": 0, + "estimated_total_tokens": 0, + "usage_source": "ai_cli_json_usage", + "cli_elapsed_ms_total": 25966.21, + "sql_execution_elapsed_ms_total": 2.37, + "conversation_log_path": "/data/jialinzhang/TabQueryBench/sql_workloads/v2_current/runs_and_launches/runs/v2_cli_20260502_081223_d/n1/artifacts/v2q_n1_a02fa97aecaa7989/cli/conversation.jsonl", + "note": "Executed through a local AI CLI with structured usage metadata." + } +} \ No newline at end of file diff --git a/Query/sql/v2/runs/v2_cli_20260502_081223_d/n1/artifacts/v2q_n1_a02fa97aecaa7989/cli/sql_attempt_1.metadata.json b/Query/sql/v2/runs/v2_cli_20260502_081223_d/n1/artifacts/v2q_n1_a02fa97aecaa7989/cli/sql_attempt_1.metadata.json new file mode 100644 index 0000000000000000000000000000000000000000..f8f004f56b6c22fc58b85d11930dccb635837a3f --- /dev/null +++ b/Query/sql/v2/runs/v2_cli_20260502_081223_d/n1/artifacts/v2q_n1_a02fa97aecaa7989/cli/sql_attempt_1.metadata.json @@ -0,0 +1,45 @@ +{ + "attempt": 1, + "phase": "sql_generation", + "command": "codex exec --skip-git-repo-check --disable plugins --sandbox read-only --cd \"/data/jialinzhang/SQLagent\" -m gpt-5.4 --json -", + "started_at": "2026-05-19T15:40:49.559324+00:00", + "ended_at": "2026-05-19T15:41:15.525573+00:00", + "elapsed_ms": 25966.21, + "prompt_metrics": { + "chars": 30144, + "bytes_utf8": 30144, + "lines": 795, + "estimated_tokens": null + }, + "stdout_metrics": { + "chars": 2346, + "bytes_utf8": 2346, + "lines": 8, + "estimated_tokens": null + }, + "stderr_metrics": { + "chars": 0, + "bytes_utf8": 0, + "lines": 0, + "estimated_tokens": null + }, + "parsed_output": { + "format": "jsonl_events", + "text_metrics": { + "chars": 649, + "bytes_utf8": 649, + "lines": 1, + "estimated_tokens": null + }, + "usage": { + "input_tokens": 20520, + "cached_input_tokens": 12032, + "output_tokens": 494, + "reasoning_output_tokens": 320 + } + }, + "prompt_path": "cli/sql_prompt_attempt_1.txt", + "response_path": "cli/sql_response_attempt_1.txt", + "raw_response_path": "cli/sql_response_attempt_1.raw.txt", + "stderr_path": "cli/sql_stderr_attempt_1.txt" +} \ No newline at end of file diff --git a/Query/sql/v2/runs/v2_cli_20260502_081223_d/n1/artifacts/v2q_n1_a02fa97aecaa7989/cli/sql_prompt_attempt_1.txt b/Query/sql/v2/runs/v2_cli_20260502_081223_d/n1/artifacts/v2q_n1_a02fa97aecaa7989/cli/sql_prompt_attempt_1.txt new file mode 100644 index 0000000000000000000000000000000000000000..f7cb2ee6e39ad9312962e63338c8e34b6cd9654c --- /dev/null +++ b/Query/sql/v2/runs/v2_cli_20260502_081223_d/n1/artifacts/v2q_n1_a02fa97aecaa7989/cli/sql_prompt_attempt_1.txt @@ -0,0 +1,795 @@ +You are generating one SQLite SELECT query for a single-table SQL QA task. +Return strict JSON only, with this schema: {"sql": "...", "notes": "..."}. +Rules: +- Use only the provided table and columns. +- Do not write INSERT, UPDATE, DELETE, DROP, ALTER, CREATE, PRAGMA, ATTACH, DETACH, or VACUUM. +- Prefer the planned template and bound roles when provided. +- Add a leading SQL comment exactly like: -- template_id: . +- Generate SQLite-compatible SQL. SQLite does not support PERCENTILE_CONT or STDDEV. +- Quote identifiers with double quotes. +- Return no markdown and no extra prose. + +Dataset context: +Dataset context for SQL QA: +- dataset_id: n1 +- dataset_name: Spambase +- table_name: n1 +- table_layout: single-table dataset (do not assume joins). +- row_semantics: One row is one email represented by engineered token/character frequency and capitalization-run features. +- task_type: classification +- target_column: class +- main_row_count: 4601 +- important_fields: +- word_freq_make: role=feature, type=numeric_sparse_frequency. tags=['condition_candidate', 'measure', 'rare_pattern_candidate'] desc=Frequency feature for token 'make' in an email message. +- word_freq_address: role=feature, type=numeric_sparse_frequency. tags=['condition_candidate', 'measure', 'rare_pattern_candidate'] desc=Frequency feature for token 'address' in an email message. +- word_freq_all: role=feature, type=numeric_sparse_frequency. tags=['condition_candidate', 'measure', 'rare_pattern_candidate'] desc=Frequency feature for token 'all' in an email message. +- word_freq_3d: role=feature, type=numeric_sparse_frequency. tags=['condition_candidate', 'measure', 'rare_pattern_candidate'] desc=Frequency feature for token '3d' in an email message. +- word_freq_our: role=feature, type=numeric_sparse_frequency. tags=['condition_candidate', 'measure', 'rare_pattern_candidate'] desc=Frequency feature for token 'our' in an email message. +- word_freq_over: role=feature, type=numeric_sparse_frequency. tags=['condition_candidate', 'measure', 'rare_pattern_candidate'] desc=Frequency feature for token 'over' in an email message. +- word_freq_remove: role=feature, type=numeric_sparse_frequency. tags=['condition_candidate', 'measure', 'rare_pattern_candidate'] desc=Frequency feature for token 'remove' in an email message. +- word_freq_internet: role=feature, type=numeric_sparse_frequency. tags=['condition_candidate', 'measure', 'rare_pattern_candidate'] desc=Frequency feature for token 'internet' in an email message. +- word_freq_order: role=feature, type=numeric_sparse_frequency. tags=['condition_candidate', 'measure', 'rare_pattern_candidate'] desc=Frequency feature for token 'order' in an email message. +- word_freq_mail: role=feature, type=numeric_sparse_frequency. tags=['condition_candidate', 'measure', 'rare_pattern_candidate'] desc=Frequency feature for token 'mail' in an email message. +- word_freq_receive: role=feature, type=numeric_sparse_frequency. tags=['condition_candidate', 'measure', 'rare_pattern_candidate'] desc=Frequency feature for token 'receive' in an email message. +- word_freq_will: role=feature, type=numeric_sparse_frequency. tags=['condition_candidate', 'measure', 'rare_pattern_candidate'] desc=Frequency feature for token 'will' in an email message. +- word_freq_people: role=feature, type=numeric_sparse_frequency. tags=['condition_candidate', 'measure', 'rare_pattern_candidate'] desc=Frequency feature for token 'people' in an email message. +- word_freq_report: role=feature, type=numeric_sparse_frequency. tags=['condition_candidate', 'measure', 'rare_pattern_candidate'] desc=Frequency feature for token 'report' in an email message. +- word_freq_addresses: role=feature, type=numeric_sparse_frequency. tags=['condition_candidate', 'measure', 'rare_pattern_candidate'] desc=Frequency feature for token 'addresses' in an email message. +- word_freq_free: role=feature, type=numeric_sparse_frequency. tags=['condition_candidate', 'measure', 'rare_pattern_candidate'] desc=Frequency feature for token 'free' in an email message. +- word_freq_business: role=feature, type=numeric_sparse_frequency. tags=['condition_candidate', 'measure', 'rare_pattern_candidate'] desc=Frequency feature for token 'business' in an email message. +- word_freq_email: role=feature, type=numeric_sparse_frequency. tags=['condition_candidate', 'measure', 'rare_pattern_candidate'] desc=Frequency feature for token 'email' in an email message. +- word_freq_you: role=feature, type=numeric_sparse_frequency. tags=['condition_candidate', 'measure', 'rare_pattern_candidate'] desc=Frequency feature for token 'you' in an email message. +- word_freq_credit: role=feature, type=numeric_sparse_frequency. tags=['condition_candidate', 'measure', 'rare_pattern_candidate'] desc=Frequency feature for token 'credit' in an email message. +- word_freq_your: role=feature, type=numeric_sparse_frequency. tags=['condition_candidate', 'measure', 'rare_pattern_candidate'] desc=Frequency feature for token 'your' in an email message. +- word_freq_font: role=feature, type=numeric_sparse_frequency. tags=['condition_candidate', 'measure', 'rare_pattern_candidate'] desc=Frequency feature for token 'font' in an email message. +- word_freq_000: role=feature, type=numeric_sparse_frequency. tags=['condition_candidate', 'measure', 'rare_pattern_candidate'] desc=Frequency feature for token '000' in an email message. +- word_freq_money: role=feature, type=numeric_sparse_frequency. tags=['condition_candidate', 'measure', 'rare_pattern_candidate'] desc=Frequency feature for token 'money' in an email message. +- word_freq_hp: role=feature, type=numeric_sparse_frequency. tags=['condition_candidate', 'measure', 'rare_pattern_candidate'] desc=Frequency feature for token 'hp' in an email message. +- word_freq_hpl: role=feature, type=numeric_sparse_frequency. tags=['condition_candidate', 'measure', 'rare_pattern_candidate'] desc=Frequency feature for token 'hpl' in an email message. +- word_freq_george: role=feature, type=numeric_sparse_frequency. tags=['condition_candidate', 'measure', 'rare_pattern_candidate'] desc=Frequency feature for token 'george' in an email message. +- word_freq_650: role=feature, type=numeric_sparse_frequency. tags=['condition_candidate', 'measure', 'rare_pattern_candidate'] desc=Frequency feature for token '650' in an email message. +- word_freq_lab: role=feature, type=numeric_sparse_frequency. tags=['condition_candidate', 'measure', 'rare_pattern_candidate'] desc=Frequency feature for token 'lab' in an email message. +- word_freq_labs: role=feature, type=numeric_sparse_frequency. tags=['condition_candidate', 'measure', 'rare_pattern_candidate'] desc=Frequency feature for token 'labs' in an email message. +- word_freq_telnet: role=feature, type=numeric_sparse_frequency. tags=['condition_candidate', 'measure', 'rare_pattern_candidate'] desc=Frequency feature for token 'telnet' in an email message. +- word_freq_857: role=feature, type=numeric_sparse_frequency. tags=['condition_candidate', 'measure', 'rare_pattern_candidate'] desc=Frequency feature for token '857' in an email message. +- word_freq_data: role=feature, type=numeric_sparse_frequency. tags=['condition_candidate', 'measure', 'rare_pattern_candidate'] desc=Frequency feature for token 'data' in an email message. +- word_freq_415: role=feature, type=numeric_sparse_frequency. tags=['condition_candidate', 'measure', 'rare_pattern_candidate'] desc=Frequency feature for token '415' in an email message. +- word_freq_85: role=feature, type=numeric_sparse_frequency. tags=['condition_candidate', 'measure', 'rare_pattern_candidate'] desc=Frequency feature for token '85' in an email message. +- word_freq_technology: role=feature, type=numeric_sparse_frequency. tags=['condition_candidate', 'measure', 'rare_pattern_candidate'] desc=Frequency feature for token 'technology' in an email message. +- word_freq_1999: role=feature, type=numeric_sparse_frequency. tags=['condition_candidate', 'measure', 'rare_pattern_candidate'] desc=Frequency feature for token '1999' in an email message. +- word_freq_parts: role=feature, type=numeric_sparse_frequency. tags=['condition_candidate', 'measure', 'rare_pattern_candidate'] desc=Frequency feature for token 'parts' in an email message. +- word_freq_pm: role=feature, type=numeric_sparse_frequency. tags=['condition_candidate', 'measure', 'rare_pattern_candidate'] desc=Frequency feature for token 'pm' in an email message. +- word_freq_direct: role=feature, type=numeric_sparse_frequency. tags=['condition_candidate', 'measure', 'rare_pattern_candidate'] desc=Frequency feature for token 'direct' in an email message. +- word_freq_cs: role=feature, type=numeric_sparse_frequency. tags=['condition_candidate', 'measure', 'rare_pattern_candidate'] desc=Frequency feature for token 'cs' in an email message. +- word_freq_meeting: role=feature, type=numeric_sparse_frequency. tags=['condition_candidate', 'measure', 'rare_pattern_candidate'] desc=Frequency feature for token 'meeting' in an email message. +- word_freq_original: role=feature, type=numeric_sparse_frequency. tags=['condition_candidate', 'measure', 'rare_pattern_candidate'] desc=Frequency feature for token 'original' in an email message. +- word_freq_project: role=feature, type=numeric_sparse_frequency. tags=['condition_candidate', 'measure', 'rare_pattern_candidate'] desc=Frequency feature for token 'project' in an email message. +- word_freq_re: role=feature, type=numeric_sparse_frequency. tags=['condition_candidate', 'measure', 'rare_pattern_candidate'] desc=Frequency feature for token 're' in an email message. +- word_freq_edu: role=feature, type=numeric_sparse_frequency. tags=['condition_candidate', 'measure', 'rare_pattern_candidate'] desc=Frequency feature for token 'edu' in an email message. +- word_freq_table: role=feature, type=numeric_sparse_frequency. tags=['condition_candidate', 'measure', 'rare_pattern_candidate'] desc=Frequency feature for token 'table' in an email message. +- word_freq_conference: role=feature, type=numeric_sparse_frequency. tags=['condition_candidate', 'measure', 'rare_pattern_candidate'] desc=Frequency feature for token 'conference' in an email message. +- char_freq_%3B: role=feature, type=numeric_sparse_frequency. tags=['condition_candidate', 'measure', 'rare_pattern_candidate'] desc=Character frequency feature for symbol ';'. +- char_freq_%28: role=feature, type=numeric_sparse_frequency. tags=['condition_candidate', 'measure', 'rare_pattern_candidate'] desc=Character frequency feature for symbol '('. +- char_freq_%5B: role=feature, type=numeric_sparse_frequency. tags=['condition_candidate', 'measure', 'rare_pattern_candidate'] desc=Character frequency feature for symbol '['. +- char_freq_%21: role=feature, type=numeric_sparse_frequency. tags=['condition_candidate', 'measure', 'rare_pattern_candidate'] desc=Character frequency feature for symbol '!'. +- char_freq_%24: role=feature, type=numeric_sparse_frequency. tags=['condition_candidate', 'measure', 'rare_pattern_candidate'] desc=Character frequency feature for symbol '$'. +- char_freq_%23: role=feature, type=numeric_sparse_frequency. tags=['condition_candidate', 'measure', 'rare_pattern_candidate'] desc=Character frequency feature for symbol '#'. +- capital_run_length_average: role=feature, type=numeric_heavy_tail. tags=['condition_candidate', 'measure', 'rare_pattern_candidate'] desc=Average length of uninterrupted capital-letter runs. +- capital_run_length_longest: role=feature, type=numeric_heavy_tail. tags=['condition_candidate', 'measure', 'rare_pattern_candidate'] desc=Longest uninterrupted capital-letter run. +- capital_run_length_total: role=feature, type=numeric_heavy_tail. tags=['condition_candidate', 'measure', 'rare_pattern_candidate'] desc=Total number of capital letters in runs. +- class: role=target, type=binary_target. tags=['target_candidate'] desc=Binary spam label (1=spam, 0=non-spam). +- useful_field_combinations: [['word_freq_free', 'word_freq_you', 'class'], ['char_freq_%21', 'capital_run_length_longest', 'class'], ['word_freq_remove', 'word_freq_money', 'class']] +- fields_requiring_caution: ['capital_run_length_average', 'capital_run_length_longest', 'capital_run_length_total'] +- source_url: https://www.openml.org/d/44 + +SQLite schema snapshot: +{ + "table_name": "n1", + "quoted_table_name": "\"n1\"", + "row_count": 4601, + "columns": [ + { + "name": "word_freq_make", + "type": "TEXT", + "notnull": false, + "pk": false + }, + { + "name": "word_freq_address", + "type": "TEXT", + "notnull": false, + "pk": false + }, + { + "name": "word_freq_all", + "type": "TEXT", + "notnull": false, + "pk": false + }, + { + "name": "word_freq_3d", + "type": "TEXT", + "notnull": false, + "pk": false + }, + { + "name": "word_freq_our", + "type": "TEXT", + "notnull": false, + "pk": false + }, + { + "name": "word_freq_over", + "type": "TEXT", + "notnull": false, + "pk": false + }, + { + "name": "word_freq_remove", + "type": "TEXT", + "notnull": false, + "pk": false + }, + { + "name": "word_freq_internet", + "type": "TEXT", + "notnull": false, + "pk": false + }, + { + "name": "word_freq_order", + "type": "TEXT", + "notnull": false, + "pk": false + }, + { + "name": "word_freq_mail", + "type": "TEXT", + "notnull": false, + "pk": false + }, + { + "name": "word_freq_receive", + "type": "TEXT", + "notnull": false, + "pk": false + }, + { + "name": "word_freq_will", + "type": "TEXT", + "notnull": false, + "pk": false + }, + { + "name": "word_freq_people", + "type": "TEXT", + "notnull": false, + "pk": false + }, + { + "name": "word_freq_report", + "type": "TEXT", + "notnull": false, + "pk": false + }, + { + "name": "word_freq_addresses", + "type": "TEXT", + "notnull": false, + "pk": false + }, + { + "name": "word_freq_free", + "type": "TEXT", + "notnull": false, + "pk": false + }, + { + "name": "word_freq_business", + "type": "TEXT", + "notnull": false, + "pk": false + }, + { + "name": "word_freq_email", + "type": "TEXT", + "notnull": false, + "pk": false + }, + { + "name": "word_freq_you", + "type": "TEXT", + "notnull": false, + "pk": false + }, + { + "name": "word_freq_credit", + "type": "TEXT", + "notnull": false, + "pk": false + }, + { + "name": "word_freq_your", + "type": "TEXT", + "notnull": false, + "pk": false + }, + { + "name": "word_freq_font", + "type": "TEXT", + "notnull": false, + "pk": false + }, + { + "name": "word_freq_000", + "type": "TEXT", + "notnull": false, + "pk": false + }, + { + "name": "word_freq_money", + "type": "TEXT", + "notnull": false, + "pk": false + }, + { + "name": "word_freq_hp", + "type": "TEXT", + "notnull": false, + "pk": false + }, + { + "name": "word_freq_hpl", + "type": "TEXT", + "notnull": false, + "pk": false + }, + { + "name": "word_freq_george", + "type": "TEXT", + "notnull": false, + "pk": false + }, + { + "name": "word_freq_650", + "type": "TEXT", + "notnull": false, + "pk": false + }, + { + "name": "word_freq_lab", + "type": "TEXT", + "notnull": false, + "pk": false + }, + { + "name": "word_freq_labs", + "type": "TEXT", + "notnull": false, + "pk": false + }, + { + "name": "word_freq_telnet", + "type": "TEXT", + "notnull": false, + "pk": false + }, + { + "name": "word_freq_857", + "type": "TEXT", + "notnull": false, + "pk": false + }, + { + "name": "word_freq_data", + "type": "TEXT", + "notnull": false, + "pk": false + }, + { + "name": "word_freq_415", + "type": "TEXT", + "notnull": false, + "pk": false + }, + { + "name": "word_freq_85", + "type": "TEXT", + "notnull": false, + "pk": false + }, + { + "name": "word_freq_technology", + "type": "TEXT", + "notnull": false, + "pk": false + }, + { + "name": "word_freq_1999", + "type": "TEXT", + "notnull": false, + "pk": false + }, + { + "name": "word_freq_parts", + "type": "TEXT", + "notnull": false, + "pk": false + }, + { + "name": "word_freq_pm", + "type": "TEXT", + "notnull": false, + "pk": false + }, + { + "name": "word_freq_direct", + "type": "TEXT", + "notnull": false, + "pk": false + }, + { + "name": "word_freq_cs", + "type": "TEXT", + "notnull": false, + "pk": false + }, + { + "name": "word_freq_meeting", + "type": "TEXT", + "notnull": false, + "pk": false + }, + { + "name": "word_freq_original", + "type": "TEXT", + "notnull": false, + "pk": false + }, + { + "name": "word_freq_project", + "type": "TEXT", + "notnull": false, + "pk": false + }, + { + "name": "word_freq_re", + "type": "TEXT", + "notnull": false, + "pk": false + }, + { + "name": "word_freq_edu", + "type": "TEXT", + "notnull": false, + "pk": false + }, + { + "name": "word_freq_table", + "type": "TEXT", + "notnull": false, + "pk": false + }, + { + "name": "word_freq_conference", + "type": "TEXT", + "notnull": false, + "pk": false + }, + { + "name": "char_freq_%3B", + "type": "TEXT", + "notnull": false, + "pk": false + }, + { + "name": "char_freq_%28", + "type": "TEXT", + "notnull": false, + "pk": false + }, + { + "name": "char_freq_%5B", + "type": "TEXT", + "notnull": false, + "pk": false + }, + { + "name": "char_freq_%21", + "type": "TEXT", + "notnull": false, + "pk": false + }, + { + "name": "char_freq_%24", + "type": "TEXT", + "notnull": false, + "pk": false + }, + { + "name": "char_freq_%23", + "type": "TEXT", + "notnull": false, + "pk": false + }, + { + "name": "capital_run_length_average", + "type": "TEXT", + "notnull": false, + "pk": false + }, + { + "name": "capital_run_length_longest", + "type": "TEXT", + "notnull": false, + "pk": false + }, + { + "name": "capital_run_length_total", + "type": "TEXT", + "notnull": false, + "pk": false + }, + { + "name": "class", + "type": "TEXT", + "notnull": false, + "pk": false + } + ], + "sample_rows": [ + { + "word_freq_make": "0", + "word_freq_address": "0.64", + "word_freq_all": "0.64", + "word_freq_3d": "0", + "word_freq_our": "0.32", + "word_freq_over": "0", + "word_freq_remove": "0", + "word_freq_internet": "0", + "word_freq_order": "0", + "word_freq_mail": "0", + "word_freq_receive": "0", + "word_freq_will": "0.64", + "word_freq_people": "0", + "word_freq_report": "0", + "word_freq_addresses": "0", + "word_freq_free": "0.32", + "word_freq_business": "0", + "word_freq_email": "1.29", + "word_freq_you": "1.93", + "word_freq_credit": "0", + "word_freq_your": "0.96", + "word_freq_font": "0", + "word_freq_000": "0", + "word_freq_money": "0", + "word_freq_hp": "0", + "word_freq_hpl": "0", + "word_freq_george": "0", + "word_freq_650": "0", + "word_freq_lab": "0", + "word_freq_labs": "0", + "word_freq_telnet": "0", + "word_freq_857": "0", + "word_freq_data": "0", + "word_freq_415": "0", + "word_freq_85": "0", + "word_freq_technology": "0", + "word_freq_1999": "0", + "word_freq_parts": "0", + "word_freq_pm": "0", + "word_freq_direct": "0", + "word_freq_cs": "0", + "word_freq_meeting": "0", + "word_freq_original": "0", + "word_freq_project": "0", + "word_freq_re": "0", + "word_freq_edu": "0", + "word_freq_table": "0", + "word_freq_conference": "0", + "char_freq_%3B": "0", + "char_freq_%28": "0", + "char_freq_%5B": "0", + "char_freq_%21": "0.778", + "char_freq_%24": "0", + "char_freq_%23": "0", + "capital_run_length_average": "3.756", + "capital_run_length_longest": "61", + "capital_run_length_total": "278", + "class": "1" + }, + { + "word_freq_make": "0.21", + "word_freq_address": "0.28", + "word_freq_all": "0.5", + "word_freq_3d": "0", + "word_freq_our": "0.14", + "word_freq_over": "0.28", + "word_freq_remove": "0.21", + "word_freq_internet": "0.07", + "word_freq_order": "0", + "word_freq_mail": "0.94", + "word_freq_receive": "0.21", + "word_freq_will": "0.79", + "word_freq_people": "0.65", + "word_freq_report": "0.21", + "word_freq_addresses": "0.14", + "word_freq_free": "0.14", + "word_freq_business": "0.07", + "word_freq_email": "0.28", + "word_freq_you": "3.47", + "word_freq_credit": "0", + "word_freq_your": "1.59", + "word_freq_font": "0", + "word_freq_000": "0.43", + "word_freq_money": "0.43", + "word_freq_hp": "0", + "word_freq_hpl": "0", + "word_freq_george": "0", + "word_freq_650": "0", + "word_freq_lab": "0", + "word_freq_labs": "0", + "word_freq_telnet": "0", + "word_freq_857": "0", + "word_freq_data": "0", + "word_freq_415": "0", + "word_freq_85": "0", + "word_freq_technology": "0", + "word_freq_1999": "0.07", + "word_freq_parts": "0", + "word_freq_pm": "0", + "word_freq_direct": "0", + "word_freq_cs": "0", + "word_freq_meeting": "0", + "word_freq_original": "0", + "word_freq_project": "0", + "word_freq_re": "0", + "word_freq_edu": "0", + "word_freq_table": "0", + "word_freq_conference": "0", + "char_freq_%3B": "0", + "char_freq_%28": "0.132", + "char_freq_%5B": "0", + "char_freq_%21": "0.372", + "char_freq_%24": "0.18", + "char_freq_%23": "0.048", + "capital_run_length_average": "5.114", + "capital_run_length_longest": "101", + "capital_run_length_total": "1028", + "class": "1" + }, + { + "word_freq_make": "0.06", + "word_freq_address": "0", + "word_freq_all": "0.71", + "word_freq_3d": "0", + "word_freq_our": "1.23", + "word_freq_over": "0.19", + "word_freq_remove": "0.19", + "word_freq_internet": "0.12", + "word_freq_order": "0.64", + "word_freq_mail": "0.25", + "word_freq_receive": "0.38", + "word_freq_will": "0.45", + "word_freq_people": "0.12", + "word_freq_report": "0", + "word_freq_addresses": "1.75", + "word_freq_free": "0.06", + "word_freq_business": "0.06", + "word_freq_email": "1.03", + "word_freq_you": "1.36", + "word_freq_credit": "0.32", + "word_freq_your": "0.51", + "word_freq_font": "0", + "word_freq_000": "1.16", + "word_freq_money": "0.06", + "word_freq_hp": "0", + "word_freq_hpl": "0", + "word_freq_george": "0", + "word_freq_650": "0", + "word_freq_lab": "0", + "word_freq_labs": "0", + "word_freq_telnet": "0", + "word_freq_857": "0", + "word_freq_data": "0", + "word_freq_415": "0", + "word_freq_85": "0", + "word_freq_technology": "0", + "word_freq_1999": "0", + "word_freq_parts": "0", + "word_freq_pm": "0", + "word_freq_direct": "0.06", + "word_freq_cs": "0", + "word_freq_meeting": "0", + "word_freq_original": "0.12", + "word_freq_project": "0", + "word_freq_re": "0.06", + "word_freq_edu": "0.06", + "word_freq_table": "0", + "word_freq_conference": "0", + "char_freq_%3B": "0.01", + "char_freq_%28": "0.143", + "char_freq_%5B": "0", + "char_freq_%21": "0.276", + "char_freq_%24": "0.184", + "char_freq_%23": "0.01", + "capital_run_length_average": "9.821", + "capital_run_length_longest": "485", + "capital_run_length_total": "2259", + "class": "1" + }, + { + "word_freq_make": "0", + "word_freq_address": "0", + "word_freq_all": "0", + "word_freq_3d": "0", + "word_freq_our": "0.63", + "word_freq_over": "0", + "word_freq_remove": "0.31", + "word_freq_internet": "0.63", + "word_freq_order": "0.31", + "word_freq_mail": "0.63", + "word_freq_receive": "0.31", + "word_freq_will": "0.31", + "word_freq_people": "0.31", + "word_freq_report": "0", + "word_freq_addresses": "0", + "word_freq_free": "0.31", + "word_freq_business": "0", + "word_freq_email": "0", + "word_freq_you": "3.18", + "word_freq_credit": "0", + "word_freq_your": "0.31", + "word_freq_font": "0", + "word_freq_000": "0", + "word_freq_money": "0", + "word_freq_hp": "0", + "word_freq_hpl": "0", + "word_freq_george": "0", + "word_freq_650": "0", + "word_freq_lab": "0", + "word_freq_labs": "0", + "word_freq_telnet": "0", + "word_freq_857": "0", + "word_freq_data": "0", + "word_freq_415": "0", + "word_freq_85": "0", + "word_freq_technology": "0", + "word_freq_1999": "0", + "word_freq_parts": "0", + "word_freq_pm": "0", + "word_freq_direct": "0", + "word_freq_cs": "0", + "word_freq_meeting": "0", + "word_freq_original": "0", + "word_freq_project": "0", + "word_freq_re": "0", + "word_freq_edu": "0", + "word_freq_table": "0", + "word_freq_conference": "0", + "char_freq_%3B": "0", + "char_freq_%28": "0.137", + "char_freq_%5B": "0", + "char_freq_%21": "0.137", + "char_freq_%24": "0", + "char_freq_%23": "0", + "capital_run_length_average": "3.537", + "capital_run_length_longest": "40", + "capital_run_length_total": "191", + "class": "1" + }, + { + "word_freq_make": "0", + "word_freq_address": "0", + "word_freq_all": "0", + "word_freq_3d": "0", + "word_freq_our": "0.63", + "word_freq_over": "0", + "word_freq_remove": "0.31", + "word_freq_internet": "0.63", + "word_freq_order": "0.31", + "word_freq_mail": "0.63", + "word_freq_receive": "0.31", + "word_freq_will": "0.31", + "word_freq_people": "0.31", + "word_freq_report": "0", + "word_freq_addresses": "0", + "word_freq_free": "0.31", + "word_freq_business": "0", + "word_freq_email": "0", + "word_freq_you": "3.18", + "word_freq_credit": "0", + "word_freq_your": "0.31", + "word_freq_font": "0", + "word_freq_000": "0", + "word_freq_money": "0", + "word_freq_hp": "0", + "word_freq_hpl": "0", + "word_freq_george": "0", + "word_freq_650": "0", + "word_freq_lab": "0", + "word_freq_labs": "0", + "word_freq_telnet": "0", + "word_freq_857": "0", + "word_freq_data": "0", + "word_freq_415": "0", + "word_freq_85": "0", + "word_freq_technology": "0", + "word_freq_1999": "0", + "word_freq_parts": "0", + "word_freq_pm": "0", + "word_freq_direct": "0", + "word_freq_cs": "0", + "word_freq_meeting": "0", + "word_freq_original": "0", + "word_freq_project": "0", + "word_freq_re": "0", + "word_freq_edu": "0", + "word_freq_table": "0", + "word_freq_conference": "0", + "char_freq_%3B": "0", + "char_freq_%28": "0.135", + "char_freq_%5B": "0", + "char_freq_%21": "0.135", + "char_freq_%24": "0", + "char_freq_%23": "0", + "capital_run_length_average": "3.537", + "capital_run_length_longest": "40", + "capital_run_length_total": "191", + "class": "1" + } + ] +} + +Shortlisted templates: +[ + { + "template_id": "tpl_m4_group_ratio_two_conditions", + "template_name": "Grouped Ratio of Two Conditions", + "primary_family": "conditional_dependency_structure", + "portability": "yes", + "sql_skeleton": "WITH grouped AS (\n SELECT {group_col},\n SUM(CASE WHEN {condition_col} = {positive_value} THEN 1 ELSE 0 END) AS numerator_count,\n SUM(CASE WHEN {condition_col} = {negative_value} THEN 1 ELSE 0 END) AS denominator_count\n FROM {table}\n GROUP BY {group_col}\n)\nSELECT {group_col},\n CAST(numerator_count AS FLOAT) / NULLIF(denominator_count, 0) AS condition_ratio\nFROM grouped\nORDER BY condition_ratio DESC;", + "required_roles": [ + "group_col", + "condition_col" + ] + } +] + +Problem instance: +{ + "dataset_id": "n1", + "question": "Use template Grouped Ratio of Two Conditions to probe direction_consistency with semantic role contrastive_conditional_view. Focus on group_col=class, condition_col=class.", + "planned_template_id": "tpl_m4_group_ratio_two_conditions", + "bindings": { + "group_col": "class", + "condition_col": "class", + "condition_value": "0", + "positive_value": "0", + "negative_value": "1", + "top_k": 12, + "top_n": 5, + "num_tiles": 10, + "percentile_value": 0.95, + "z_threshold": 2.0, + "fraction_threshold": 0.1, + "baseline_multiplier": 1.5, + "baseline_fraction": 0.1, + "min_group_size": 5, + "min_support": 5, + "measure_threshold": 0.0, + "time_grain": "month", + "lookback_rows": 3, + "current_period_start": "'2024-01-01'", + "current_period_end": "'2024-04-01'", + "previous_period_start": "'2023-10-01'", + "previous_period_end": "'2024-01-01'", + "drift_ratio_threshold": 0.8 + }, + "can_vary": [], + "must_fix": [], + "runtime_sql_skeleton": "WITH grouped AS (\n SELECT {group_col},\n SUM(CASE WHEN {condition_col} = {positive_value} THEN 1 ELSE 0 END) AS numerator_count,\n SUM(CASE WHEN {condition_col} = {negative_value} THEN 1 ELSE 0 END) AS denominator_count\n FROM {table}\n GROUP BY {group_col}\n)\nSELECT {group_col},\n CAST(numerator_count AS FLOAT) / NULLIF(denominator_count, 0) AS condition_ratio\nFROM grouped\nORDER BY condition_ratio DESC;" +} + +Repair context: +{} diff --git a/Query/sql/v2/runs/v2_cli_20260502_081223_d/n1/artifacts/v2q_n1_a02fa97aecaa7989/cli/sql_response_attempt_1.raw.txt b/Query/sql/v2/runs/v2_cli_20260502_081223_d/n1/artifacts/v2q_n1_a02fa97aecaa7989/cli/sql_response_attempt_1.raw.txt new file mode 100644 index 0000000000000000000000000000000000000000..19c792d4778e31b1ebd3c8667f663275936d03a5 --- /dev/null +++ b/Query/sql/v2/runs/v2_cli_20260502_081223_d/n1/artifacts/v2q_n1_a02fa97aecaa7989/cli/sql_response_attempt_1.raw.txt @@ -0,0 +1,8 @@ +{"type":"thread.started","thread_id":"019e40e5-9b29-7e33-9afa-03475ae09302"} +{"type":"turn.started"} +{"type":"error","message":"Reconnecting... 2/5 (stream disconnected before completion: Rate limit reached for gpt-5.4 in organization org-yXK0mBGOR7eXhtGEx8v7e3bf on tokens per min (TPM): Limit 2000000, Used 1996777, Requested 21155. Please try again in 537ms. Visit https://platform.openai.com/account/rate-limits to learn more.)"} +{"type":"error","message":"Reconnecting... 3/5 (stream disconnected before completion: Rate limit reached for gpt-5.4 in organization org-yXK0mBGOR7eXhtGEx8v7e3bf on tokens per min (TPM): Limit 2000000, Used 1991205, Requested 21155. Please try again in 370ms. Visit https://platform.openai.com/account/rate-limits to learn more.)"} +{"type":"error","message":"Reconnecting... 4/5 (stream disconnected before completion: Rate limit reached for gpt-5.4 in organization org-yXK0mBGOR7eXhtGEx8v7e3bf on tokens per min (TPM): Limit 2000000, Used 1985528, Requested 21155. Please try again in 200ms. Visit https://platform.openai.com/account/rate-limits to learn more.)"} +{"type":"error","message":"Reconnecting... 5/5 (stream disconnected before completion: Rate limit reached for gpt-5.4 in organization org-yXK0mBGOR7eXhtGEx8v7e3bf on tokens per min (TPM): Limit 2000000, Used 1989926, Requested 21155. Please try again in 332ms. Visit https://platform.openai.com/account/rate-limits to learn more.)"} +{"type":"item.completed","item":{"id":"item_0","type":"agent_message","text":"{\"sql\":\"-- template_id: tpl_m4_group_ratio_two_conditions\\nWITH grouped AS (\\n SELECT \\\"class\\\",\\n SUM(CASE WHEN \\\"class\\\" = '0' THEN 1 ELSE 0 END) AS numerator_count,\\n SUM(CASE WHEN \\\"class\\\" = '1' THEN 1 ELSE 0 END) AS denominator_count\\n FROM \\\"n1\\\"\\n GROUP BY \\\"class\\\"\\n)\\nSELECT \\\"class\\\",\\n CAST(numerator_count AS FLOAT) / NULLIF(denominator_count, 0) AS condition_ratio\\nFROM grouped\\nORDER BY condition_ratio DESC;\",\"notes\":\"Applied the provided grouped-ratio template with both group_col and condition_col bound to \\\"class\\\", using '0' as the numerator condition and '1' as the denominator condition.\"}"}} +{"type":"turn.completed","usage":{"input_tokens":20520,"cached_input_tokens":12032,"output_tokens":494,"reasoning_output_tokens":320}} diff --git a/Query/sql/v2/runs/v2_cli_20260502_081223_d/n1/artifacts/v2q_n1_a02fa97aecaa7989/cli/sql_response_attempt_1.txt b/Query/sql/v2/runs/v2_cli_20260502_081223_d/n1/artifacts/v2q_n1_a02fa97aecaa7989/cli/sql_response_attempt_1.txt new file mode 100644 index 0000000000000000000000000000000000000000..913da3b2c16e79b3414b7f6f1eeb69eb291c82ea --- /dev/null +++ b/Query/sql/v2/runs/v2_cli_20260502_081223_d/n1/artifacts/v2q_n1_a02fa97aecaa7989/cli/sql_response_attempt_1.txt @@ -0,0 +1 @@ +{"sql":"-- template_id: tpl_m4_group_ratio_two_conditions\nWITH grouped AS (\n SELECT \"class\",\n SUM(CASE WHEN \"class\" = '0' THEN 1 ELSE 0 END) AS numerator_count,\n SUM(CASE WHEN \"class\" = '1' THEN 1 ELSE 0 END) AS denominator_count\n FROM \"n1\"\n GROUP BY \"class\"\n)\nSELECT \"class\",\n CAST(numerator_count AS FLOAT) / NULLIF(denominator_count, 0) AS condition_ratio\nFROM grouped\nORDER BY condition_ratio DESC;","notes":"Applied the provided grouped-ratio template with both group_col and condition_col bound to \"class\", using '0' as the numerator condition and '1' as the denominator condition."} \ No newline at end of file diff --git a/Query/sql/v2/runs/v2_cli_20260502_081223_d/n1/artifacts/v2q_n1_a02fa97aecaa7989/cli/sql_stderr_attempt_1.txt b/Query/sql/v2/runs/v2_cli_20260502_081223_d/n1/artifacts/v2q_n1_a02fa97aecaa7989/cli/sql_stderr_attempt_1.txt new file mode 100644 index 0000000000000000000000000000000000000000..e69de29bb2d1d6434b8b29ae775ad8c2e48c5391 diff --git a/Query/sql/v2/runs/v2_cli_20260502_081223_d/n1/artifacts/v2q_n1_a0df9d7ff6afb3fc/cli/conversation.jsonl b/Query/sql/v2/runs/v2_cli_20260502_081223_d/n1/artifacts/v2q_n1_a0df9d7ff6afb3fc/cli/conversation.jsonl new file mode 100644 index 0000000000000000000000000000000000000000..7dca105c63d9ac62d47bea1c2e7a916b6417877f --- /dev/null +++ b/Query/sql/v2/runs/v2_cli_20260502_081223_d/n1/artifacts/v2q_n1_a0df9d7ff6afb3fc/cli/conversation.jsonl @@ -0,0 +1,2 @@ +{"attempt": 1, "phase": "sql_generation", "role": "user", "content_path": "cli/sql_prompt_attempt_1.txt", "metrics": {"chars": 29763, "bytes_utf8": 29763, "lines": 794, "estimated_tokens": null}} +{"attempt": 1, "phase": "sql_generation", "role": "assistant", "content_path": "cli/sql_response_attempt_1.txt", "raw_content_path": "cli/sql_response_attempt_1.raw.txt", "stderr_path": "cli/sql_stderr_attempt_1.txt", "metrics": {"chars": 596, "bytes_utf8": 596, "lines": 1, "estimated_tokens": null}, "usage": {"input_tokens": 20437, "cached_input_tokens": 19840, "output_tokens": 631, "reasoning_output_tokens": 460}} diff --git a/Query/sql/v2/runs/v2_cli_20260502_081223_d/n1/artifacts/v2q_n1_a0df9d7ff6afb3fc/cli/session_summary.json b/Query/sql/v2/runs/v2_cli_20260502_081223_d/n1/artifacts/v2q_n1_a0df9d7ff6afb3fc/cli/session_summary.json new file mode 100644 index 0000000000000000000000000000000000000000..965bddafe722b1c8dd92c9d6442eaa8bc185a2cd --- /dev/null +++ b/Query/sql/v2/runs/v2_cli_20260502_081223_d/n1/artifacts/v2q_n1_a0df9d7ff6afb3fc/cli/session_summary.json @@ -0,0 +1,25 @@ +{ + "engine": "v2-cli:codex", + "command": "codex exec --skip-git-repo-check --disable plugins --sandbox read-only --cd \"/data/jialinzhang/SQLagent\" -m gpt-5.4 --json -", + "ai_cli_calls": 1, + "usage_summary": { + "dataset_id": "n1", + "model": "v2-cli:codex", + "run_id": "v2q_n1_a0df9d7ff6afb3fc", + "api_calls": 0, + "input_tokens": 20437, + "cached_input_tokens": 19840, + "output_tokens": 631, + "total_tokens": 21068, + "cost_usd": 0.0, + "ai_cli_calls": 1, + "estimated_input_tokens": 0, + "estimated_output_tokens": 0, + "estimated_total_tokens": 0, + "usage_source": "ai_cli_json_usage", + "cli_elapsed_ms_total": 12913.01, + "sql_execution_elapsed_ms_total": 4.15, + "conversation_log_path": "/data/jialinzhang/TabQueryBench/sql_workloads/v2_current/runs_and_launches/runs/v2_cli_20260502_081223_d/n1/artifacts/v2q_n1_a0df9d7ff6afb3fc/cli/conversation.jsonl", + "note": "Executed through a local AI CLI with structured usage metadata." + } +} \ No newline at end of file diff --git a/Query/sql/v2/runs/v2_cli_20260502_081223_d/n1/artifacts/v2q_n1_a0df9d7ff6afb3fc/cli/sql_attempt_1.metadata.json b/Query/sql/v2/runs/v2_cli_20260502_081223_d/n1/artifacts/v2q_n1_a0df9d7ff6afb3fc/cli/sql_attempt_1.metadata.json new file mode 100644 index 0000000000000000000000000000000000000000..4b34cc69ced2938307402fe3cd9facbfcb6f80ee --- /dev/null +++ b/Query/sql/v2/runs/v2_cli_20260502_081223_d/n1/artifacts/v2q_n1_a0df9d7ff6afb3fc/cli/sql_attempt_1.metadata.json @@ -0,0 +1,45 @@ +{ + "attempt": 1, + "phase": "sql_generation", + "command": "codex exec --skip-git-repo-check --disable plugins --sandbox read-only --cd \"/data/jialinzhang/SQLagent\" -m gpt-5.4 --json -", + "started_at": "2026-05-19T15:38:52.037653+00:00", + "ended_at": "2026-05-19T15:39:04.950685+00:00", + "elapsed_ms": 12913.01, + "prompt_metrics": { + "chars": 29763, + "bytes_utf8": 29763, + "lines": 794, + "estimated_tokens": null + }, + "stdout_metrics": { + "chars": 976, + "bytes_utf8": 976, + "lines": 4, + "estimated_tokens": null + }, + "stderr_metrics": { + "chars": 0, + "bytes_utf8": 0, + "lines": 0, + "estimated_tokens": null + }, + "parsed_output": { + "format": "jsonl_events", + "text_metrics": { + "chars": 596, + "bytes_utf8": 596, + "lines": 1, + "estimated_tokens": null + }, + "usage": { + "input_tokens": 20437, + "cached_input_tokens": 19840, + "output_tokens": 631, + "reasoning_output_tokens": 460 + } + }, + "prompt_path": "cli/sql_prompt_attempt_1.txt", + "response_path": "cli/sql_response_attempt_1.txt", + "raw_response_path": "cli/sql_response_attempt_1.raw.txt", + "stderr_path": "cli/sql_stderr_attempt_1.txt" +} \ No newline at end of file diff --git a/Query/sql/v2/runs/v2_cli_20260502_081223_d/n1/artifacts/v2q_n1_a0df9d7ff6afb3fc/cli/sql_prompt_attempt_1.txt b/Query/sql/v2/runs/v2_cli_20260502_081223_d/n1/artifacts/v2q_n1_a0df9d7ff6afb3fc/cli/sql_prompt_attempt_1.txt new file mode 100644 index 0000000000000000000000000000000000000000..5f94f10f060334bd7c9bd62a11ba5ca6d7adabb9 --- /dev/null +++ b/Query/sql/v2/runs/v2_cli_20260502_081223_d/n1/artifacts/v2q_n1_a0df9d7ff6afb3fc/cli/sql_prompt_attempt_1.txt @@ -0,0 +1,794 @@ +You are generating one SQLite SELECT query for a single-table SQL QA task. +Return strict JSON only, with this schema: {"sql": "...", "notes": "..."}. +Rules: +- Use only the provided table and columns. +- Do not write INSERT, UPDATE, DELETE, DROP, ALTER, CREATE, PRAGMA, ATTACH, DETACH, or VACUUM. +- Prefer the planned template and bound roles when provided. +- Add a leading SQL comment exactly like: -- template_id: . +- Generate SQLite-compatible SQL. SQLite does not support PERCENTILE_CONT or STDDEV. +- Quote identifiers with double quotes. +- Return no markdown and no extra prose. + +Dataset context: +Dataset context for SQL QA: +- dataset_id: n1 +- dataset_name: Spambase +- table_name: n1 +- table_layout: single-table dataset (do not assume joins). +- row_semantics: One row is one email represented by engineered token/character frequency and capitalization-run features. +- task_type: classification +- target_column: class +- main_row_count: 4601 +- important_fields: +- word_freq_make: role=feature, type=numeric_sparse_frequency. tags=['condition_candidate', 'measure', 'rare_pattern_candidate'] desc=Frequency feature for token 'make' in an email message. +- word_freq_address: role=feature, type=numeric_sparse_frequency. tags=['condition_candidate', 'measure', 'rare_pattern_candidate'] desc=Frequency feature for token 'address' in an email message. +- word_freq_all: role=feature, type=numeric_sparse_frequency. tags=['condition_candidate', 'measure', 'rare_pattern_candidate'] desc=Frequency feature for token 'all' in an email message. +- word_freq_3d: role=feature, type=numeric_sparse_frequency. tags=['condition_candidate', 'measure', 'rare_pattern_candidate'] desc=Frequency feature for token '3d' in an email message. +- word_freq_our: role=feature, type=numeric_sparse_frequency. tags=['condition_candidate', 'measure', 'rare_pattern_candidate'] desc=Frequency feature for token 'our' in an email message. +- word_freq_over: role=feature, type=numeric_sparse_frequency. tags=['condition_candidate', 'measure', 'rare_pattern_candidate'] desc=Frequency feature for token 'over' in an email message. +- word_freq_remove: role=feature, type=numeric_sparse_frequency. tags=['condition_candidate', 'measure', 'rare_pattern_candidate'] desc=Frequency feature for token 'remove' in an email message. +- word_freq_internet: role=feature, type=numeric_sparse_frequency. tags=['condition_candidate', 'measure', 'rare_pattern_candidate'] desc=Frequency feature for token 'internet' in an email message. +- word_freq_order: role=feature, type=numeric_sparse_frequency. tags=['condition_candidate', 'measure', 'rare_pattern_candidate'] desc=Frequency feature for token 'order' in an email message. +- word_freq_mail: role=feature, type=numeric_sparse_frequency. tags=['condition_candidate', 'measure', 'rare_pattern_candidate'] desc=Frequency feature for token 'mail' in an email message. +- word_freq_receive: role=feature, type=numeric_sparse_frequency. tags=['condition_candidate', 'measure', 'rare_pattern_candidate'] desc=Frequency feature for token 'receive' in an email message. +- word_freq_will: role=feature, type=numeric_sparse_frequency. tags=['condition_candidate', 'measure', 'rare_pattern_candidate'] desc=Frequency feature for token 'will' in an email message. +- word_freq_people: role=feature, type=numeric_sparse_frequency. tags=['condition_candidate', 'measure', 'rare_pattern_candidate'] desc=Frequency feature for token 'people' in an email message. +- word_freq_report: role=feature, type=numeric_sparse_frequency. tags=['condition_candidate', 'measure', 'rare_pattern_candidate'] desc=Frequency feature for token 'report' in an email message. +- word_freq_addresses: role=feature, type=numeric_sparse_frequency. tags=['condition_candidate', 'measure', 'rare_pattern_candidate'] desc=Frequency feature for token 'addresses' in an email message. +- word_freq_free: role=feature, type=numeric_sparse_frequency. tags=['condition_candidate', 'measure', 'rare_pattern_candidate'] desc=Frequency feature for token 'free' in an email message. +- word_freq_business: role=feature, type=numeric_sparse_frequency. tags=['condition_candidate', 'measure', 'rare_pattern_candidate'] desc=Frequency feature for token 'business' in an email message. +- word_freq_email: role=feature, type=numeric_sparse_frequency. tags=['condition_candidate', 'measure', 'rare_pattern_candidate'] desc=Frequency feature for token 'email' in an email message. +- word_freq_you: role=feature, type=numeric_sparse_frequency. tags=['condition_candidate', 'measure', 'rare_pattern_candidate'] desc=Frequency feature for token 'you' in an email message. +- word_freq_credit: role=feature, type=numeric_sparse_frequency. tags=['condition_candidate', 'measure', 'rare_pattern_candidate'] desc=Frequency feature for token 'credit' in an email message. +- word_freq_your: role=feature, type=numeric_sparse_frequency. tags=['condition_candidate', 'measure', 'rare_pattern_candidate'] desc=Frequency feature for token 'your' in an email message. +- word_freq_font: role=feature, type=numeric_sparse_frequency. tags=['condition_candidate', 'measure', 'rare_pattern_candidate'] desc=Frequency feature for token 'font' in an email message. +- word_freq_000: role=feature, type=numeric_sparse_frequency. tags=['condition_candidate', 'measure', 'rare_pattern_candidate'] desc=Frequency feature for token '000' in an email message. +- word_freq_money: role=feature, type=numeric_sparse_frequency. tags=['condition_candidate', 'measure', 'rare_pattern_candidate'] desc=Frequency feature for token 'money' in an email message. +- word_freq_hp: role=feature, type=numeric_sparse_frequency. tags=['condition_candidate', 'measure', 'rare_pattern_candidate'] desc=Frequency feature for token 'hp' in an email message. +- word_freq_hpl: role=feature, type=numeric_sparse_frequency. tags=['condition_candidate', 'measure', 'rare_pattern_candidate'] desc=Frequency feature for token 'hpl' in an email message. +- word_freq_george: role=feature, type=numeric_sparse_frequency. tags=['condition_candidate', 'measure', 'rare_pattern_candidate'] desc=Frequency feature for token 'george' in an email message. +- word_freq_650: role=feature, type=numeric_sparse_frequency. tags=['condition_candidate', 'measure', 'rare_pattern_candidate'] desc=Frequency feature for token '650' in an email message. +- word_freq_lab: role=feature, type=numeric_sparse_frequency. tags=['condition_candidate', 'measure', 'rare_pattern_candidate'] desc=Frequency feature for token 'lab' in an email message. +- word_freq_labs: role=feature, type=numeric_sparse_frequency. tags=['condition_candidate', 'measure', 'rare_pattern_candidate'] desc=Frequency feature for token 'labs' in an email message. +- word_freq_telnet: role=feature, type=numeric_sparse_frequency. tags=['condition_candidate', 'measure', 'rare_pattern_candidate'] desc=Frequency feature for token 'telnet' in an email message. +- word_freq_857: role=feature, type=numeric_sparse_frequency. tags=['condition_candidate', 'measure', 'rare_pattern_candidate'] desc=Frequency feature for token '857' in an email message. +- word_freq_data: role=feature, type=numeric_sparse_frequency. tags=['condition_candidate', 'measure', 'rare_pattern_candidate'] desc=Frequency feature for token 'data' in an email message. +- word_freq_415: role=feature, type=numeric_sparse_frequency. tags=['condition_candidate', 'measure', 'rare_pattern_candidate'] desc=Frequency feature for token '415' in an email message. +- word_freq_85: role=feature, type=numeric_sparse_frequency. tags=['condition_candidate', 'measure', 'rare_pattern_candidate'] desc=Frequency feature for token '85' in an email message. +- word_freq_technology: role=feature, type=numeric_sparse_frequency. tags=['condition_candidate', 'measure', 'rare_pattern_candidate'] desc=Frequency feature for token 'technology' in an email message. +- word_freq_1999: role=feature, type=numeric_sparse_frequency. tags=['condition_candidate', 'measure', 'rare_pattern_candidate'] desc=Frequency feature for token '1999' in an email message. +- word_freq_parts: role=feature, type=numeric_sparse_frequency. tags=['condition_candidate', 'measure', 'rare_pattern_candidate'] desc=Frequency feature for token 'parts' in an email message. +- word_freq_pm: role=feature, type=numeric_sparse_frequency. tags=['condition_candidate', 'measure', 'rare_pattern_candidate'] desc=Frequency feature for token 'pm' in an email message. +- word_freq_direct: role=feature, type=numeric_sparse_frequency. tags=['condition_candidate', 'measure', 'rare_pattern_candidate'] desc=Frequency feature for token 'direct' in an email message. +- word_freq_cs: role=feature, type=numeric_sparse_frequency. tags=['condition_candidate', 'measure', 'rare_pattern_candidate'] desc=Frequency feature for token 'cs' in an email message. +- word_freq_meeting: role=feature, type=numeric_sparse_frequency. tags=['condition_candidate', 'measure', 'rare_pattern_candidate'] desc=Frequency feature for token 'meeting' in an email message. +- word_freq_original: role=feature, type=numeric_sparse_frequency. tags=['condition_candidate', 'measure', 'rare_pattern_candidate'] desc=Frequency feature for token 'original' in an email message. +- word_freq_project: role=feature, type=numeric_sparse_frequency. tags=['condition_candidate', 'measure', 'rare_pattern_candidate'] desc=Frequency feature for token 'project' in an email message. +- word_freq_re: role=feature, type=numeric_sparse_frequency. tags=['condition_candidate', 'measure', 'rare_pattern_candidate'] desc=Frequency feature for token 're' in an email message. +- word_freq_edu: role=feature, type=numeric_sparse_frequency. tags=['condition_candidate', 'measure', 'rare_pattern_candidate'] desc=Frequency feature for token 'edu' in an email message. +- word_freq_table: role=feature, type=numeric_sparse_frequency. tags=['condition_candidate', 'measure', 'rare_pattern_candidate'] desc=Frequency feature for token 'table' in an email message. +- word_freq_conference: role=feature, type=numeric_sparse_frequency. tags=['condition_candidate', 'measure', 'rare_pattern_candidate'] desc=Frequency feature for token 'conference' in an email message. +- char_freq_%3B: role=feature, type=numeric_sparse_frequency. tags=['condition_candidate', 'measure', 'rare_pattern_candidate'] desc=Character frequency feature for symbol ';'. +- char_freq_%28: role=feature, type=numeric_sparse_frequency. tags=['condition_candidate', 'measure', 'rare_pattern_candidate'] desc=Character frequency feature for symbol '('. +- char_freq_%5B: role=feature, type=numeric_sparse_frequency. tags=['condition_candidate', 'measure', 'rare_pattern_candidate'] desc=Character frequency feature for symbol '['. +- char_freq_%21: role=feature, type=numeric_sparse_frequency. tags=['condition_candidate', 'measure', 'rare_pattern_candidate'] desc=Character frequency feature for symbol '!'. +- char_freq_%24: role=feature, type=numeric_sparse_frequency. tags=['condition_candidate', 'measure', 'rare_pattern_candidate'] desc=Character frequency feature for symbol '$'. +- char_freq_%23: role=feature, type=numeric_sparse_frequency. tags=['condition_candidate', 'measure', 'rare_pattern_candidate'] desc=Character frequency feature for symbol '#'. +- capital_run_length_average: role=feature, type=numeric_heavy_tail. tags=['condition_candidate', 'measure', 'rare_pattern_candidate'] desc=Average length of uninterrupted capital-letter runs. +- capital_run_length_longest: role=feature, type=numeric_heavy_tail. tags=['condition_candidate', 'measure', 'rare_pattern_candidate'] desc=Longest uninterrupted capital-letter run. +- capital_run_length_total: role=feature, type=numeric_heavy_tail. tags=['condition_candidate', 'measure', 'rare_pattern_candidate'] desc=Total number of capital letters in runs. +- class: role=target, type=binary_target. tags=['target_candidate'] desc=Binary spam label (1=spam, 0=non-spam). +- useful_field_combinations: [['word_freq_free', 'word_freq_you', 'class'], ['char_freq_%21', 'capital_run_length_longest', 'class'], ['word_freq_remove', 'word_freq_money', 'class']] +- fields_requiring_caution: ['capital_run_length_average', 'capital_run_length_longest', 'capital_run_length_total'] +- source_url: https://www.openml.org/d/44 + +SQLite schema snapshot: +{ + "table_name": "n1", + "quoted_table_name": "\"n1\"", + "row_count": 4601, + "columns": [ + { + "name": "word_freq_make", + "type": "TEXT", + "notnull": false, + "pk": false + }, + { + "name": "word_freq_address", + "type": "TEXT", + "notnull": false, + "pk": false + }, + { + "name": "word_freq_all", + "type": "TEXT", + "notnull": false, + "pk": false + }, + { + "name": "word_freq_3d", + "type": "TEXT", + "notnull": false, + "pk": false + }, + { + "name": "word_freq_our", + "type": "TEXT", + "notnull": false, + "pk": false + }, + { + "name": "word_freq_over", + "type": "TEXT", + "notnull": false, + "pk": false + }, + { + "name": "word_freq_remove", + "type": "TEXT", + "notnull": false, + "pk": false + }, + { + "name": "word_freq_internet", + "type": "TEXT", + "notnull": false, + "pk": false + }, + { + "name": "word_freq_order", + "type": "TEXT", + "notnull": false, + "pk": false + }, + { + "name": "word_freq_mail", + "type": "TEXT", + "notnull": false, + "pk": false + }, + { + "name": "word_freq_receive", + "type": "TEXT", + "notnull": false, + "pk": false + }, + { + "name": "word_freq_will", + "type": "TEXT", + "notnull": false, + "pk": false + }, + { + "name": "word_freq_people", + "type": "TEXT", + "notnull": false, + "pk": false + }, + { + "name": "word_freq_report", + "type": "TEXT", + "notnull": false, + "pk": false + }, + { + "name": "word_freq_addresses", + "type": "TEXT", + "notnull": false, + "pk": false + }, + { + "name": "word_freq_free", + "type": "TEXT", + "notnull": false, + "pk": false + }, + { + "name": "word_freq_business", + "type": "TEXT", + "notnull": false, + "pk": false + }, + { + "name": "word_freq_email", + "type": "TEXT", + "notnull": false, + "pk": false + }, + { + "name": "word_freq_you", + "type": "TEXT", + "notnull": false, + "pk": false + }, + { + "name": "word_freq_credit", + "type": "TEXT", + "notnull": false, + "pk": false + }, + { + "name": "word_freq_your", + "type": "TEXT", + "notnull": false, + "pk": false + }, + { + "name": "word_freq_font", + "type": "TEXT", + "notnull": false, + "pk": false + }, + { + "name": "word_freq_000", + "type": "TEXT", + "notnull": false, + "pk": false + }, + { + "name": "word_freq_money", + "type": "TEXT", + "notnull": false, + "pk": false + }, + { + "name": "word_freq_hp", + "type": "TEXT", + "notnull": false, + "pk": false + }, + { + "name": "word_freq_hpl", + "type": "TEXT", + "notnull": false, + "pk": false + }, + { + "name": "word_freq_george", + "type": "TEXT", + "notnull": false, + "pk": false + }, + { + "name": "word_freq_650", + "type": "TEXT", + "notnull": false, + "pk": false + }, + { + "name": "word_freq_lab", + "type": "TEXT", + "notnull": false, + "pk": false + }, + { + "name": "word_freq_labs", + "type": "TEXT", + "notnull": false, + "pk": false + }, + { + "name": "word_freq_telnet", + "type": "TEXT", + "notnull": false, + "pk": false + }, + { + "name": "word_freq_857", + "type": "TEXT", + "notnull": false, + "pk": false + }, + { + "name": "word_freq_data", + "type": "TEXT", + "notnull": false, + "pk": false + }, + { + "name": "word_freq_415", + "type": "TEXT", + "notnull": false, + "pk": false + }, + { + "name": "word_freq_85", + "type": "TEXT", + "notnull": false, + "pk": false + }, + { + "name": "word_freq_technology", + "type": "TEXT", + "notnull": false, + "pk": false + }, + { + "name": "word_freq_1999", + "type": "TEXT", + "notnull": false, + "pk": false + }, + { + "name": "word_freq_parts", + "type": "TEXT", + "notnull": false, + "pk": false + }, + { + "name": "word_freq_pm", + "type": "TEXT", + "notnull": false, + "pk": false + }, + { + "name": "word_freq_direct", + "type": "TEXT", + "notnull": false, + "pk": false + }, + { + "name": "word_freq_cs", + "type": "TEXT", + "notnull": false, + "pk": false + }, + { + "name": "word_freq_meeting", + "type": "TEXT", + "notnull": false, + "pk": false + }, + { + "name": "word_freq_original", + "type": "TEXT", + "notnull": false, + "pk": false + }, + { + "name": "word_freq_project", + "type": "TEXT", + "notnull": false, + "pk": false + }, + { + "name": "word_freq_re", + "type": "TEXT", + "notnull": false, + "pk": false + }, + { + "name": "word_freq_edu", + "type": "TEXT", + "notnull": false, + "pk": false + }, + { + "name": "word_freq_table", + "type": "TEXT", + "notnull": false, + "pk": false + }, + { + "name": "word_freq_conference", + "type": "TEXT", + "notnull": false, + "pk": false + }, + { + "name": "char_freq_%3B", + "type": "TEXT", + "notnull": false, + "pk": false + }, + { + "name": "char_freq_%28", + "type": "TEXT", + "notnull": false, + "pk": false + }, + { + "name": "char_freq_%5B", + "type": "TEXT", + "notnull": false, + "pk": false + }, + { + "name": "char_freq_%21", + "type": "TEXT", + "notnull": false, + "pk": false + }, + { + "name": "char_freq_%24", + "type": "TEXT", + "notnull": false, + "pk": false + }, + { + "name": "char_freq_%23", + "type": "TEXT", + "notnull": false, + "pk": false + }, + { + "name": "capital_run_length_average", + "type": "TEXT", + "notnull": false, + "pk": false + }, + { + "name": "capital_run_length_longest", + "type": "TEXT", + "notnull": false, + "pk": false + }, + { + "name": "capital_run_length_total", + "type": "TEXT", + "notnull": false, + "pk": false + }, + { + "name": "class", + "type": "TEXT", + "notnull": false, + "pk": false + } + ], + "sample_rows": [ + { + "word_freq_make": "0", + "word_freq_address": "0.64", + "word_freq_all": "0.64", + "word_freq_3d": "0", + "word_freq_our": "0.32", + "word_freq_over": "0", + "word_freq_remove": "0", + "word_freq_internet": "0", + "word_freq_order": "0", + "word_freq_mail": "0", + "word_freq_receive": "0", + "word_freq_will": "0.64", + "word_freq_people": "0", + "word_freq_report": "0", + "word_freq_addresses": "0", + "word_freq_free": "0.32", + "word_freq_business": "0", + "word_freq_email": "1.29", + "word_freq_you": "1.93", + "word_freq_credit": "0", + "word_freq_your": "0.96", + "word_freq_font": "0", + "word_freq_000": "0", + "word_freq_money": "0", + "word_freq_hp": "0", + "word_freq_hpl": "0", + "word_freq_george": "0", + "word_freq_650": "0", + "word_freq_lab": "0", + "word_freq_labs": "0", + "word_freq_telnet": "0", + "word_freq_857": "0", + "word_freq_data": "0", + "word_freq_415": "0", + "word_freq_85": "0", + "word_freq_technology": "0", + "word_freq_1999": "0", + "word_freq_parts": "0", + "word_freq_pm": "0", + "word_freq_direct": "0", + "word_freq_cs": "0", + "word_freq_meeting": "0", + "word_freq_original": "0", + "word_freq_project": "0", + "word_freq_re": "0", + "word_freq_edu": "0", + "word_freq_table": "0", + "word_freq_conference": "0", + "char_freq_%3B": "0", + "char_freq_%28": "0", + "char_freq_%5B": "0", + "char_freq_%21": "0.778", + "char_freq_%24": "0", + "char_freq_%23": "0", + "capital_run_length_average": "3.756", + "capital_run_length_longest": "61", + "capital_run_length_total": "278", + "class": "1" + }, + { + "word_freq_make": "0.21", + "word_freq_address": "0.28", + "word_freq_all": "0.5", + "word_freq_3d": "0", + "word_freq_our": "0.14", + "word_freq_over": "0.28", + "word_freq_remove": "0.21", + "word_freq_internet": "0.07", + "word_freq_order": "0", + "word_freq_mail": "0.94", + "word_freq_receive": "0.21", + "word_freq_will": "0.79", + "word_freq_people": "0.65", + "word_freq_report": "0.21", + "word_freq_addresses": "0.14", + "word_freq_free": "0.14", + "word_freq_business": "0.07", + "word_freq_email": "0.28", + "word_freq_you": "3.47", + "word_freq_credit": "0", + "word_freq_your": "1.59", + "word_freq_font": "0", + "word_freq_000": "0.43", + "word_freq_money": "0.43", + "word_freq_hp": "0", + "word_freq_hpl": "0", + "word_freq_george": "0", + "word_freq_650": "0", + "word_freq_lab": "0", + "word_freq_labs": "0", + "word_freq_telnet": "0", + "word_freq_857": "0", + "word_freq_data": "0", + "word_freq_415": "0", + "word_freq_85": "0", + "word_freq_technology": "0", + "word_freq_1999": "0.07", + "word_freq_parts": "0", + "word_freq_pm": "0", + "word_freq_direct": "0", + "word_freq_cs": "0", + "word_freq_meeting": "0", + "word_freq_original": "0", + "word_freq_project": "0", + "word_freq_re": "0", + "word_freq_edu": "0", + "word_freq_table": "0", + "word_freq_conference": "0", + "char_freq_%3B": "0", + "char_freq_%28": "0.132", + "char_freq_%5B": "0", + "char_freq_%21": "0.372", + "char_freq_%24": "0.18", + "char_freq_%23": "0.048", + "capital_run_length_average": "5.114", + "capital_run_length_longest": "101", + "capital_run_length_total": "1028", + "class": "1" + }, + { + "word_freq_make": "0.06", + "word_freq_address": "0", + "word_freq_all": "0.71", + "word_freq_3d": "0", + "word_freq_our": "1.23", + "word_freq_over": "0.19", + "word_freq_remove": "0.19", + "word_freq_internet": "0.12", + "word_freq_order": "0.64", + "word_freq_mail": "0.25", + "word_freq_receive": "0.38", + "word_freq_will": "0.45", + "word_freq_people": "0.12", + "word_freq_report": "0", + "word_freq_addresses": "1.75", + "word_freq_free": "0.06", + "word_freq_business": "0.06", + "word_freq_email": "1.03", + "word_freq_you": "1.36", + "word_freq_credit": "0.32", + "word_freq_your": "0.51", + "word_freq_font": "0", + "word_freq_000": "1.16", + "word_freq_money": "0.06", + "word_freq_hp": "0", + "word_freq_hpl": "0", + "word_freq_george": "0", + "word_freq_650": "0", + "word_freq_lab": "0", + "word_freq_labs": "0", + "word_freq_telnet": "0", + "word_freq_857": "0", + "word_freq_data": "0", + "word_freq_415": "0", + "word_freq_85": "0", + "word_freq_technology": "0", + "word_freq_1999": "0", + "word_freq_parts": "0", + "word_freq_pm": "0", + "word_freq_direct": "0.06", + "word_freq_cs": "0", + "word_freq_meeting": "0", + "word_freq_original": "0.12", + "word_freq_project": "0", + "word_freq_re": "0.06", + "word_freq_edu": "0.06", + "word_freq_table": "0", + "word_freq_conference": "0", + "char_freq_%3B": "0.01", + "char_freq_%28": "0.143", + "char_freq_%5B": "0", + "char_freq_%21": "0.276", + "char_freq_%24": "0.184", + "char_freq_%23": "0.01", + "capital_run_length_average": "9.821", + "capital_run_length_longest": "485", + "capital_run_length_total": "2259", + "class": "1" + }, + { + "word_freq_make": "0", + "word_freq_address": "0", + "word_freq_all": "0", + "word_freq_3d": "0", + "word_freq_our": "0.63", + "word_freq_over": "0", + "word_freq_remove": "0.31", + "word_freq_internet": "0.63", + "word_freq_order": "0.31", + "word_freq_mail": "0.63", + "word_freq_receive": "0.31", + "word_freq_will": "0.31", + "word_freq_people": "0.31", + "word_freq_report": "0", + "word_freq_addresses": "0", + "word_freq_free": "0.31", + "word_freq_business": "0", + "word_freq_email": "0", + "word_freq_you": "3.18", + "word_freq_credit": "0", + "word_freq_your": "0.31", + "word_freq_font": "0", + "word_freq_000": "0", + "word_freq_money": "0", + "word_freq_hp": "0", + "word_freq_hpl": "0", + "word_freq_george": "0", + "word_freq_650": "0", + "word_freq_lab": "0", + "word_freq_labs": "0", + "word_freq_telnet": "0", + "word_freq_857": "0", + "word_freq_data": "0", + "word_freq_415": "0", + "word_freq_85": "0", + "word_freq_technology": "0", + "word_freq_1999": "0", + "word_freq_parts": "0", + "word_freq_pm": "0", + "word_freq_direct": "0", + "word_freq_cs": "0", + "word_freq_meeting": "0", + "word_freq_original": "0", + "word_freq_project": "0", + "word_freq_re": "0", + "word_freq_edu": "0", + "word_freq_table": "0", + "word_freq_conference": "0", + "char_freq_%3B": "0", + "char_freq_%28": "0.137", + "char_freq_%5B": "0", + "char_freq_%21": "0.137", + "char_freq_%24": "0", + "char_freq_%23": "0", + "capital_run_length_average": "3.537", + "capital_run_length_longest": "40", + "capital_run_length_total": "191", + "class": "1" + }, + { + "word_freq_make": "0", + "word_freq_address": "0", + "word_freq_all": "0", + "word_freq_3d": "0", + "word_freq_our": "0.63", + "word_freq_over": "0", + "word_freq_remove": "0.31", + "word_freq_internet": "0.63", + "word_freq_order": "0.31", + "word_freq_mail": "0.63", + "word_freq_receive": "0.31", + "word_freq_will": "0.31", + "word_freq_people": "0.31", + "word_freq_report": "0", + "word_freq_addresses": "0", + "word_freq_free": "0.31", + "word_freq_business": "0", + "word_freq_email": "0", + "word_freq_you": "3.18", + "word_freq_credit": "0", + "word_freq_your": "0.31", + "word_freq_font": "0", + "word_freq_000": "0", + "word_freq_money": "0", + "word_freq_hp": "0", + "word_freq_hpl": "0", + "word_freq_george": "0", + "word_freq_650": "0", + "word_freq_lab": "0", + "word_freq_labs": "0", + "word_freq_telnet": "0", + "word_freq_857": "0", + "word_freq_data": "0", + "word_freq_415": "0", + "word_freq_85": "0", + "word_freq_technology": "0", + "word_freq_1999": "0", + "word_freq_parts": "0", + "word_freq_pm": "0", + "word_freq_direct": "0", + "word_freq_cs": "0", + "word_freq_meeting": "0", + "word_freq_original": "0", + "word_freq_project": "0", + "word_freq_re": "0", + "word_freq_edu": "0", + "word_freq_table": "0", + "word_freq_conference": "0", + "char_freq_%3B": "0", + "char_freq_%28": "0.135", + "char_freq_%5B": "0", + "char_freq_%21": "0.135", + "char_freq_%24": "0", + "char_freq_%23": "0", + "capital_run_length_average": "3.537", + "capital_run_length_longest": "40", + "capital_run_length_total": "191", + "class": "1" + } + ] +} + +Shortlisted templates: +[ + { + "template_id": "tpl_tpcds_within_group_share", + "template_name": "Within-Group Share of Total", + "primary_family": "conditional_dependency_structure", + "portability": "partial", + "sql_skeleton": "SELECT {group_col}, {item_col},\n SUM({measure_col}) AS total_measure,\n SUM({measure_col}) * 100.0 / SUM(SUM({measure_col})) OVER (PARTITION BY {group_col}) AS share_within_group\nFROM {table}\nGROUP BY {group_col}, {item_col}\nORDER BY share_within_group DESC;", + "required_roles": [ + "group_col", + "item_col", + "measure_col" + ] + } +] + +Problem instance: +{ + "dataset_id": "n1", + "question": "Use template Within-Group Share of Total to probe dependency_strength_similarity with semantic role focused_target_view. Focus on group_col=class, measure_col=word_freq_415.", + "planned_template_id": "tpl_tpcds_within_group_share", + "bindings": { + "group_col": "class", + "measure_col": "word_freq_415", + "item_col": "word_freq_85", + "top_k": 13, + "top_n": 4, + "num_tiles": 10, + "percentile_value": 0.9, + "z_threshold": 2.0, + "fraction_threshold": 0.1, + "baseline_multiplier": 1.5, + "baseline_fraction": 0.1, + "min_group_size": 5, + "min_support": 5, + "measure_threshold": 0.0, + "time_grain": "month", + "lookback_rows": 3, + "current_period_start": "'2024-01-01'", + "current_period_end": "'2024-04-01'", + "previous_period_start": "'2023-10-01'", + "previous_period_end": "'2024-01-01'", + "drift_ratio_threshold": 0.8 + }, + "can_vary": [], + "must_fix": [], + "runtime_sql_skeleton": "SELECT {group_col}, {item_col},\n SUM({measure_col}) AS total_measure,\n SUM({measure_col}) * 100.0 / SUM(SUM({measure_col})) OVER (PARTITION BY {group_col}) AS share_within_group\nFROM {table}\nGROUP BY {group_col}, {item_col}\nORDER BY share_within_group DESC;" +} + +Repair context: +{} diff --git a/Query/sql/v2/runs/v2_cli_20260502_081223_d/n1/artifacts/v2q_n1_a0df9d7ff6afb3fc/cli/sql_response_attempt_1.raw.txt b/Query/sql/v2/runs/v2_cli_20260502_081223_d/n1/artifacts/v2q_n1_a0df9d7ff6afb3fc/cli/sql_response_attempt_1.raw.txt new file mode 100644 index 0000000000000000000000000000000000000000..16a08ee2ae7e4bef89424a21d23a11141e882a06 --- /dev/null +++ b/Query/sql/v2/runs/v2_cli_20260502_081223_d/n1/artifacts/v2q_n1_a0df9d7ff6afb3fc/cli/sql_response_attempt_1.raw.txt @@ -0,0 +1,4 @@ +{"type":"thread.started","thread_id":"019e40e3-d00d-7053-847d-838a3e75c805"} +{"type":"turn.started"} +{"type":"item.completed","item":{"id":"item_0","type":"agent_message","text":"{\"sql\":\"-- template_id: tpl_tpcds_within_group_share\\nSELECT \\\"class\\\", \\\"word_freq_85\\\",\\n SUM(CAST(\\\"word_freq_415\\\" AS REAL)) AS total_measure,\\n SUM(CAST(\\\"word_freq_415\\\" AS REAL)) * 100.0 / SUM(SUM(CAST(\\\"word_freq_415\\\" AS REAL))) OVER (PARTITION BY \\\"class\\\") AS share_within_group\\nFROM \\\"n1\\\"\\nGROUP BY \\\"class\\\", \\\"word_freq_85\\\"\\nORDER BY share_within_group DESC;\",\"notes\":\"Used the planned template with group_col=\\\"class\\\", item_col=\\\"word_freq_85\\\", and measure_col=\\\"word_freq_415\\\". Cast the measure to REAL because the schema stores numeric-looking fields as TEXT.\"}"}} +{"type":"turn.completed","usage":{"input_tokens":20437,"cached_input_tokens":19840,"output_tokens":631,"reasoning_output_tokens":460}} diff --git a/Query/sql/v2/runs/v2_cli_20260502_081223_d/n1/artifacts/v2q_n1_a0df9d7ff6afb3fc/cli/sql_response_attempt_1.txt b/Query/sql/v2/runs/v2_cli_20260502_081223_d/n1/artifacts/v2q_n1_a0df9d7ff6afb3fc/cli/sql_response_attempt_1.txt new file mode 100644 index 0000000000000000000000000000000000000000..b76e26851d19b0c4f8e534f7cd168495abaf9388 --- /dev/null +++ b/Query/sql/v2/runs/v2_cli_20260502_081223_d/n1/artifacts/v2q_n1_a0df9d7ff6afb3fc/cli/sql_response_attempt_1.txt @@ -0,0 +1 @@ +{"sql":"-- template_id: tpl_tpcds_within_group_share\nSELECT \"class\", \"word_freq_85\",\n SUM(CAST(\"word_freq_415\" AS REAL)) AS total_measure,\n SUM(CAST(\"word_freq_415\" AS REAL)) * 100.0 / SUM(SUM(CAST(\"word_freq_415\" AS REAL))) OVER (PARTITION BY \"class\") AS share_within_group\nFROM \"n1\"\nGROUP BY \"class\", \"word_freq_85\"\nORDER BY share_within_group DESC;","notes":"Used the planned template with group_col=\"class\", item_col=\"word_freq_85\", and measure_col=\"word_freq_415\". Cast the measure to REAL because the schema stores numeric-looking fields as TEXT."} \ No newline at end of file diff --git a/Query/sql/v2/runs/v2_cli_20260502_081223_d/n1/artifacts/v2q_n1_a0df9d7ff6afb3fc/cli/sql_stderr_attempt_1.txt b/Query/sql/v2/runs/v2_cli_20260502_081223_d/n1/artifacts/v2q_n1_a0df9d7ff6afb3fc/cli/sql_stderr_attempt_1.txt new file mode 100644 index 0000000000000000000000000000000000000000..e69de29bb2d1d6434b8b29ae775ad8c2e48c5391 diff --git a/Query/sql/v2/runs/v2_cli_20260502_081223_d/n1/artifacts/v2q_n1_a4cf422837fa3733/cli/conversation.jsonl b/Query/sql/v2/runs/v2_cli_20260502_081223_d/n1/artifacts/v2q_n1_a4cf422837fa3733/cli/conversation.jsonl new file mode 100644 index 0000000000000000000000000000000000000000..bdd705a0f5334d66333d250bfcdb0b448c30016f --- /dev/null +++ b/Query/sql/v2/runs/v2_cli_20260502_081223_d/n1/artifacts/v2q_n1_a4cf422837fa3733/cli/conversation.jsonl @@ -0,0 +1,2 @@ +{"attempt": 1, "phase": "sql_generation", "role": "user", "content_path": "cli/sql_prompt_attempt_1.txt", "metrics": {"chars": 29351, "bytes_utf8": 29351, "lines": 792, "estimated_tokens": null}} +{"attempt": 1, "phase": "sql_generation", "role": "assistant", "content_path": "cli/sql_response_attempt_1.txt", "raw_content_path": "cli/sql_response_attempt_1.raw.txt", "stderr_path": "cli/sql_stderr_attempt_1.txt", "metrics": {"chars": 366, "bytes_utf8": 366, "lines": 1, "estimated_tokens": null}, "usage": {"input_tokens": 20319, "cached_input_tokens": 12288, "output_tokens": 332, "reasoning_output_tokens": 230}} diff --git a/Query/sql/v2/runs/v2_cli_20260502_081223_d/n1/artifacts/v2q_n1_a4cf422837fa3733/cli/session_summary.json b/Query/sql/v2/runs/v2_cli_20260502_081223_d/n1/artifacts/v2q_n1_a4cf422837fa3733/cli/session_summary.json new file mode 100644 index 0000000000000000000000000000000000000000..fa727c3a4e8da63a5d2995d8d7e0e31e336553da --- /dev/null +++ b/Query/sql/v2/runs/v2_cli_20260502_081223_d/n1/artifacts/v2q_n1_a4cf422837fa3733/cli/session_summary.json @@ -0,0 +1,25 @@ +{ + "engine": "v2-cli:codex", + "command": "codex exec --skip-git-repo-check --disable plugins --sandbox read-only --cd \"/data/jialinzhang/SQLagent\" -m gpt-5.4 --json -", + "ai_cli_calls": 1, + "usage_summary": { + "dataset_id": "n1", + "model": "v2-cli:codex", + "run_id": "v2q_n1_a4cf422837fa3733", + "api_calls": 0, + "input_tokens": 20319, + "cached_input_tokens": 12288, + "output_tokens": 332, + "total_tokens": 20651, + "cost_usd": 0.0, + "ai_cli_calls": 1, + "estimated_input_tokens": 0, + "estimated_output_tokens": 0, + "estimated_total_tokens": 0, + "usage_source": "ai_cli_json_usage", + "cli_elapsed_ms_total": 8910.25, + "sql_execution_elapsed_ms_total": 2.18, + "conversation_log_path": "/data/jialinzhang/TabQueryBench/sql_workloads/v2_current/runs_and_launches/runs/v2_cli_20260502_081223_d/n1/artifacts/v2q_n1_a4cf422837fa3733/cli/conversation.jsonl", + "note": "Executed through a local AI CLI with structured usage metadata." + } +} \ No newline at end of file diff --git a/Query/sql/v2/runs/v2_cli_20260502_081223_d/n1/artifacts/v2q_n1_a4cf422837fa3733/cli/sql_attempt_1.metadata.json b/Query/sql/v2/runs/v2_cli_20260502_081223_d/n1/artifacts/v2q_n1_a4cf422837fa3733/cli/sql_attempt_1.metadata.json new file mode 100644 index 0000000000000000000000000000000000000000..0738a7bf39648907bccdd5dbd5edbae63a2d9f98 --- /dev/null +++ b/Query/sql/v2/runs/v2_cli_20260502_081223_d/n1/artifacts/v2q_n1_a4cf422837fa3733/cli/sql_attempt_1.metadata.json @@ -0,0 +1,45 @@ +{ + "attempt": 1, + "phase": "sql_generation", + "command": "codex exec --skip-git-repo-check --disable plugins --sandbox read-only --cd \"/data/jialinzhang/SQLagent\" -m gpt-5.4 --json -", + "started_at": "2026-05-19T15:29:14.742684+00:00", + "ended_at": "2026-05-19T15:29:23.652970+00:00", + "elapsed_ms": 8910.25, + "prompt_metrics": { + "chars": 29351, + "bytes_utf8": 29351, + "lines": 792, + "estimated_tokens": null + }, + "stdout_metrics": { + "chars": 720, + "bytes_utf8": 720, + "lines": 4, + "estimated_tokens": null + }, + "stderr_metrics": { + "chars": 0, + "bytes_utf8": 0, + "lines": 0, + "estimated_tokens": null + }, + "parsed_output": { + "format": "jsonl_events", + "text_metrics": { + "chars": 366, + "bytes_utf8": 366, + "lines": 1, + "estimated_tokens": null + }, + "usage": { + "input_tokens": 20319, + "cached_input_tokens": 12288, + "output_tokens": 332, + "reasoning_output_tokens": 230 + } + }, + "prompt_path": "cli/sql_prompt_attempt_1.txt", + "response_path": "cli/sql_response_attempt_1.txt", + "raw_response_path": "cli/sql_response_attempt_1.raw.txt", + "stderr_path": "cli/sql_stderr_attempt_1.txt" +} \ No newline at end of file diff --git a/Query/sql/v2/runs/v2_cli_20260502_081223_d/n1/artifacts/v2q_n1_a4cf422837fa3733/cli/sql_prompt_attempt_1.txt b/Query/sql/v2/runs/v2_cli_20260502_081223_d/n1/artifacts/v2q_n1_a4cf422837fa3733/cli/sql_prompt_attempt_1.txt new file mode 100644 index 0000000000000000000000000000000000000000..0aa031c306e454525bba760477b9310ae187d0ea --- /dev/null +++ b/Query/sql/v2/runs/v2_cli_20260502_081223_d/n1/artifacts/v2q_n1_a4cf422837fa3733/cli/sql_prompt_attempt_1.txt @@ -0,0 +1,792 @@ +You are generating one SQLite SELECT query for a single-table SQL QA task. +Return strict JSON only, with this schema: {"sql": "...", "notes": "..."}. +Rules: +- Use only the provided table and columns. +- Do not write INSERT, UPDATE, DELETE, DROP, ALTER, CREATE, PRAGMA, ATTACH, DETACH, or VACUUM. +- Prefer the planned template and bound roles when provided. +- Add a leading SQL comment exactly like: -- template_id: . +- Generate SQLite-compatible SQL. SQLite does not support PERCENTILE_CONT or STDDEV. +- Quote identifiers with double quotes. +- Return no markdown and no extra prose. + +Dataset context: +Dataset context for SQL QA: +- dataset_id: n1 +- dataset_name: Spambase +- table_name: n1 +- table_layout: single-table dataset (do not assume joins). +- row_semantics: One row is one email represented by engineered token/character frequency and capitalization-run features. +- task_type: classification +- target_column: class +- main_row_count: 4601 +- important_fields: +- word_freq_make: role=feature, type=numeric_sparse_frequency. tags=['condition_candidate', 'measure', 'rare_pattern_candidate'] desc=Frequency feature for token 'make' in an email message. +- word_freq_address: role=feature, type=numeric_sparse_frequency. tags=['condition_candidate', 'measure', 'rare_pattern_candidate'] desc=Frequency feature for token 'address' in an email message. +- word_freq_all: role=feature, type=numeric_sparse_frequency. tags=['condition_candidate', 'measure', 'rare_pattern_candidate'] desc=Frequency feature for token 'all' in an email message. +- word_freq_3d: role=feature, type=numeric_sparse_frequency. tags=['condition_candidate', 'measure', 'rare_pattern_candidate'] desc=Frequency feature for token '3d' in an email message. +- word_freq_our: role=feature, type=numeric_sparse_frequency. tags=['condition_candidate', 'measure', 'rare_pattern_candidate'] desc=Frequency feature for token 'our' in an email message. +- word_freq_over: role=feature, type=numeric_sparse_frequency. tags=['condition_candidate', 'measure', 'rare_pattern_candidate'] desc=Frequency feature for token 'over' in an email message. +- word_freq_remove: role=feature, type=numeric_sparse_frequency. tags=['condition_candidate', 'measure', 'rare_pattern_candidate'] desc=Frequency feature for token 'remove' in an email message. +- word_freq_internet: role=feature, type=numeric_sparse_frequency. tags=['condition_candidate', 'measure', 'rare_pattern_candidate'] desc=Frequency feature for token 'internet' in an email message. +- word_freq_order: role=feature, type=numeric_sparse_frequency. tags=['condition_candidate', 'measure', 'rare_pattern_candidate'] desc=Frequency feature for token 'order' in an email message. +- word_freq_mail: role=feature, type=numeric_sparse_frequency. tags=['condition_candidate', 'measure', 'rare_pattern_candidate'] desc=Frequency feature for token 'mail' in an email message. +- word_freq_receive: role=feature, type=numeric_sparse_frequency. tags=['condition_candidate', 'measure', 'rare_pattern_candidate'] desc=Frequency feature for token 'receive' in an email message. +- word_freq_will: role=feature, type=numeric_sparse_frequency. tags=['condition_candidate', 'measure', 'rare_pattern_candidate'] desc=Frequency feature for token 'will' in an email message. +- word_freq_people: role=feature, type=numeric_sparse_frequency. tags=['condition_candidate', 'measure', 'rare_pattern_candidate'] desc=Frequency feature for token 'people' in an email message. +- word_freq_report: role=feature, type=numeric_sparse_frequency. tags=['condition_candidate', 'measure', 'rare_pattern_candidate'] desc=Frequency feature for token 'report' in an email message. +- word_freq_addresses: role=feature, type=numeric_sparse_frequency. tags=['condition_candidate', 'measure', 'rare_pattern_candidate'] desc=Frequency feature for token 'addresses' in an email message. +- word_freq_free: role=feature, type=numeric_sparse_frequency. tags=['condition_candidate', 'measure', 'rare_pattern_candidate'] desc=Frequency feature for token 'free' in an email message. +- word_freq_business: role=feature, type=numeric_sparse_frequency. tags=['condition_candidate', 'measure', 'rare_pattern_candidate'] desc=Frequency feature for token 'business' in an email message. +- word_freq_email: role=feature, type=numeric_sparse_frequency. tags=['condition_candidate', 'measure', 'rare_pattern_candidate'] desc=Frequency feature for token 'email' in an email message. +- word_freq_you: role=feature, type=numeric_sparse_frequency. tags=['condition_candidate', 'measure', 'rare_pattern_candidate'] desc=Frequency feature for token 'you' in an email message. +- word_freq_credit: role=feature, type=numeric_sparse_frequency. tags=['condition_candidate', 'measure', 'rare_pattern_candidate'] desc=Frequency feature for token 'credit' in an email message. +- word_freq_your: role=feature, type=numeric_sparse_frequency. tags=['condition_candidate', 'measure', 'rare_pattern_candidate'] desc=Frequency feature for token 'your' in an email message. +- word_freq_font: role=feature, type=numeric_sparse_frequency. tags=['condition_candidate', 'measure', 'rare_pattern_candidate'] desc=Frequency feature for token 'font' in an email message. +- word_freq_000: role=feature, type=numeric_sparse_frequency. tags=['condition_candidate', 'measure', 'rare_pattern_candidate'] desc=Frequency feature for token '000' in an email message. +- word_freq_money: role=feature, type=numeric_sparse_frequency. tags=['condition_candidate', 'measure', 'rare_pattern_candidate'] desc=Frequency feature for token 'money' in an email message. +- word_freq_hp: role=feature, type=numeric_sparse_frequency. tags=['condition_candidate', 'measure', 'rare_pattern_candidate'] desc=Frequency feature for token 'hp' in an email message. +- word_freq_hpl: role=feature, type=numeric_sparse_frequency. tags=['condition_candidate', 'measure', 'rare_pattern_candidate'] desc=Frequency feature for token 'hpl' in an email message. +- word_freq_george: role=feature, type=numeric_sparse_frequency. tags=['condition_candidate', 'measure', 'rare_pattern_candidate'] desc=Frequency feature for token 'george' in an email message. +- word_freq_650: role=feature, type=numeric_sparse_frequency. tags=['condition_candidate', 'measure', 'rare_pattern_candidate'] desc=Frequency feature for token '650' in an email message. +- word_freq_lab: role=feature, type=numeric_sparse_frequency. tags=['condition_candidate', 'measure', 'rare_pattern_candidate'] desc=Frequency feature for token 'lab' in an email message. +- word_freq_labs: role=feature, type=numeric_sparse_frequency. tags=['condition_candidate', 'measure', 'rare_pattern_candidate'] desc=Frequency feature for token 'labs' in an email message. +- word_freq_telnet: role=feature, type=numeric_sparse_frequency. tags=['condition_candidate', 'measure', 'rare_pattern_candidate'] desc=Frequency feature for token 'telnet' in an email message. +- word_freq_857: role=feature, type=numeric_sparse_frequency. tags=['condition_candidate', 'measure', 'rare_pattern_candidate'] desc=Frequency feature for token '857' in an email message. +- word_freq_data: role=feature, type=numeric_sparse_frequency. tags=['condition_candidate', 'measure', 'rare_pattern_candidate'] desc=Frequency feature for token 'data' in an email message. +- word_freq_415: role=feature, type=numeric_sparse_frequency. tags=['condition_candidate', 'measure', 'rare_pattern_candidate'] desc=Frequency feature for token '415' in an email message. +- word_freq_85: role=feature, type=numeric_sparse_frequency. tags=['condition_candidate', 'measure', 'rare_pattern_candidate'] desc=Frequency feature for token '85' in an email message. +- word_freq_technology: role=feature, type=numeric_sparse_frequency. tags=['condition_candidate', 'measure', 'rare_pattern_candidate'] desc=Frequency feature for token 'technology' in an email message. +- word_freq_1999: role=feature, type=numeric_sparse_frequency. tags=['condition_candidate', 'measure', 'rare_pattern_candidate'] desc=Frequency feature for token '1999' in an email message. +- word_freq_parts: role=feature, type=numeric_sparse_frequency. tags=['condition_candidate', 'measure', 'rare_pattern_candidate'] desc=Frequency feature for token 'parts' in an email message. +- word_freq_pm: role=feature, type=numeric_sparse_frequency. tags=['condition_candidate', 'measure', 'rare_pattern_candidate'] desc=Frequency feature for token 'pm' in an email message. +- word_freq_direct: role=feature, type=numeric_sparse_frequency. tags=['condition_candidate', 'measure', 'rare_pattern_candidate'] desc=Frequency feature for token 'direct' in an email message. +- word_freq_cs: role=feature, type=numeric_sparse_frequency. tags=['condition_candidate', 'measure', 'rare_pattern_candidate'] desc=Frequency feature for token 'cs' in an email message. +- word_freq_meeting: role=feature, type=numeric_sparse_frequency. tags=['condition_candidate', 'measure', 'rare_pattern_candidate'] desc=Frequency feature for token 'meeting' in an email message. +- word_freq_original: role=feature, type=numeric_sparse_frequency. tags=['condition_candidate', 'measure', 'rare_pattern_candidate'] desc=Frequency feature for token 'original' in an email message. +- word_freq_project: role=feature, type=numeric_sparse_frequency. tags=['condition_candidate', 'measure', 'rare_pattern_candidate'] desc=Frequency feature for token 'project' in an email message. +- word_freq_re: role=feature, type=numeric_sparse_frequency. tags=['condition_candidate', 'measure', 'rare_pattern_candidate'] desc=Frequency feature for token 're' in an email message. +- word_freq_edu: role=feature, type=numeric_sparse_frequency. tags=['condition_candidate', 'measure', 'rare_pattern_candidate'] desc=Frequency feature for token 'edu' in an email message. +- word_freq_table: role=feature, type=numeric_sparse_frequency. tags=['condition_candidate', 'measure', 'rare_pattern_candidate'] desc=Frequency feature for token 'table' in an email message. +- word_freq_conference: role=feature, type=numeric_sparse_frequency. tags=['condition_candidate', 'measure', 'rare_pattern_candidate'] desc=Frequency feature for token 'conference' in an email message. +- char_freq_%3B: role=feature, type=numeric_sparse_frequency. tags=['condition_candidate', 'measure', 'rare_pattern_candidate'] desc=Character frequency feature for symbol ';'. +- char_freq_%28: role=feature, type=numeric_sparse_frequency. tags=['condition_candidate', 'measure', 'rare_pattern_candidate'] desc=Character frequency feature for symbol '('. +- char_freq_%5B: role=feature, type=numeric_sparse_frequency. tags=['condition_candidate', 'measure', 'rare_pattern_candidate'] desc=Character frequency feature for symbol '['. +- char_freq_%21: role=feature, type=numeric_sparse_frequency. tags=['condition_candidate', 'measure', 'rare_pattern_candidate'] desc=Character frequency feature for symbol '!'. +- char_freq_%24: role=feature, type=numeric_sparse_frequency. tags=['condition_candidate', 'measure', 'rare_pattern_candidate'] desc=Character frequency feature for symbol '$'. +- char_freq_%23: role=feature, type=numeric_sparse_frequency. tags=['condition_candidate', 'measure', 'rare_pattern_candidate'] desc=Character frequency feature for symbol '#'. +- capital_run_length_average: role=feature, type=numeric_heavy_tail. tags=['condition_candidate', 'measure', 'rare_pattern_candidate'] desc=Average length of uninterrupted capital-letter runs. +- capital_run_length_longest: role=feature, type=numeric_heavy_tail. tags=['condition_candidate', 'measure', 'rare_pattern_candidate'] desc=Longest uninterrupted capital-letter run. +- capital_run_length_total: role=feature, type=numeric_heavy_tail. tags=['condition_candidate', 'measure', 'rare_pattern_candidate'] desc=Total number of capital letters in runs. +- class: role=target, type=binary_target. tags=['target_candidate'] desc=Binary spam label (1=spam, 0=non-spam). +- useful_field_combinations: [['word_freq_free', 'word_freq_you', 'class'], ['char_freq_%21', 'capital_run_length_longest', 'class'], ['word_freq_remove', 'word_freq_money', 'class']] +- fields_requiring_caution: ['capital_run_length_average', 'capital_run_length_longest', 'capital_run_length_total'] +- source_url: https://www.openml.org/d/44 + +SQLite schema snapshot: +{ + "table_name": "n1", + "quoted_table_name": "\"n1\"", + "row_count": 4601, + "columns": [ + { + "name": "word_freq_make", + "type": "TEXT", + "notnull": false, + "pk": false + }, + { + "name": "word_freq_address", + "type": "TEXT", + "notnull": false, + "pk": false + }, + { + "name": "word_freq_all", + "type": "TEXT", + "notnull": false, + "pk": false + }, + { + "name": "word_freq_3d", + "type": "TEXT", + "notnull": false, + "pk": false + }, + { + "name": "word_freq_our", + "type": "TEXT", + "notnull": false, + "pk": false + }, + { + "name": "word_freq_over", + "type": "TEXT", + "notnull": false, + "pk": false + }, + { + "name": "word_freq_remove", + "type": "TEXT", + "notnull": false, + "pk": false + }, + { + "name": "word_freq_internet", + "type": "TEXT", + "notnull": false, + "pk": false + }, + { + "name": "word_freq_order", + "type": "TEXT", + "notnull": false, + "pk": false + }, + { + "name": "word_freq_mail", + "type": "TEXT", + "notnull": false, + "pk": false + }, + { + "name": "word_freq_receive", + "type": "TEXT", + "notnull": false, + "pk": false + }, + { + "name": "word_freq_will", + "type": "TEXT", + "notnull": false, + "pk": false + }, + { + "name": "word_freq_people", + "type": "TEXT", + "notnull": false, + "pk": false + }, + { + "name": "word_freq_report", + "type": "TEXT", + "notnull": false, + "pk": false + }, + { + "name": "word_freq_addresses", + "type": "TEXT", + "notnull": false, + "pk": false + }, + { + "name": "word_freq_free", + "type": "TEXT", + "notnull": false, + "pk": false + }, + { + "name": "word_freq_business", + "type": "TEXT", + "notnull": false, + "pk": false + }, + { + "name": "word_freq_email", + "type": "TEXT", + "notnull": false, + "pk": false + }, + { + "name": "word_freq_you", + "type": "TEXT", + "notnull": false, + "pk": false + }, + { + "name": "word_freq_credit", + "type": "TEXT", + "notnull": false, + "pk": false + }, + { + "name": "word_freq_your", + "type": "TEXT", + "notnull": false, + "pk": false + }, + { + "name": "word_freq_font", + "type": "TEXT", + "notnull": false, + "pk": false + }, + { + "name": "word_freq_000", + "type": "TEXT", + "notnull": false, + "pk": false + }, + { + "name": "word_freq_money", + "type": "TEXT", + "notnull": false, + "pk": false + }, + { + "name": "word_freq_hp", + "type": "TEXT", + "notnull": false, + "pk": false + }, + { + "name": "word_freq_hpl", + "type": "TEXT", + "notnull": false, + "pk": false + }, + { + "name": "word_freq_george", + "type": "TEXT", + "notnull": false, + "pk": false + }, + { + "name": "word_freq_650", + "type": "TEXT", + "notnull": false, + "pk": false + }, + { + "name": "word_freq_lab", + "type": "TEXT", + "notnull": false, + "pk": false + }, + { + "name": "word_freq_labs", + "type": "TEXT", + "notnull": false, + "pk": false + }, + { + "name": "word_freq_telnet", + "type": "TEXT", + "notnull": false, + "pk": false + }, + { + "name": "word_freq_857", + "type": "TEXT", + "notnull": false, + "pk": false + }, + { + "name": "word_freq_data", + "type": "TEXT", + "notnull": false, + "pk": false + }, + { + "name": "word_freq_415", + "type": "TEXT", + "notnull": false, + "pk": false + }, + { + "name": "word_freq_85", + "type": "TEXT", + "notnull": false, + "pk": false + }, + { + "name": "word_freq_technology", + "type": "TEXT", + "notnull": false, + "pk": false + }, + { + "name": "word_freq_1999", + "type": "TEXT", + "notnull": false, + "pk": false + }, + { + "name": "word_freq_parts", + "type": "TEXT", + "notnull": false, + "pk": false + }, + { + "name": "word_freq_pm", + "type": "TEXT", + "notnull": false, + "pk": false + }, + { + "name": "word_freq_direct", + "type": "TEXT", + "notnull": false, + "pk": false + }, + { + "name": "word_freq_cs", + "type": "TEXT", + "notnull": false, + "pk": false + }, + { + "name": "word_freq_meeting", + "type": "TEXT", + "notnull": false, + "pk": false + }, + { + "name": "word_freq_original", + "type": "TEXT", + "notnull": false, + "pk": false + }, + { + "name": "word_freq_project", + "type": "TEXT", + "notnull": false, + "pk": false + }, + { + "name": "word_freq_re", + "type": "TEXT", + "notnull": false, + "pk": false + }, + { + "name": "word_freq_edu", + "type": "TEXT", + "notnull": false, + "pk": false + }, + { + "name": "word_freq_table", + "type": "TEXT", + "notnull": false, + "pk": false + }, + { + "name": "word_freq_conference", + "type": "TEXT", + "notnull": false, + "pk": false + }, + { + "name": "char_freq_%3B", + "type": "TEXT", + "notnull": false, + "pk": false + }, + { + "name": "char_freq_%28", + "type": "TEXT", + "notnull": false, + "pk": false + }, + { + "name": "char_freq_%5B", + "type": "TEXT", + "notnull": false, + "pk": false + }, + { + "name": "char_freq_%21", + "type": "TEXT", + "notnull": false, + "pk": false + }, + { + "name": "char_freq_%24", + "type": "TEXT", + "notnull": false, + "pk": false + }, + { + "name": "char_freq_%23", + "type": "TEXT", + "notnull": false, + "pk": false + }, + { + "name": "capital_run_length_average", + "type": "TEXT", + "notnull": false, + "pk": false + }, + { + "name": "capital_run_length_longest", + "type": "TEXT", + "notnull": false, + "pk": false + }, + { + "name": "capital_run_length_total", + "type": "TEXT", + "notnull": false, + "pk": false + }, + { + "name": "class", + "type": "TEXT", + "notnull": false, + "pk": false + } + ], + "sample_rows": [ + { + "word_freq_make": "0", + "word_freq_address": "0.64", + "word_freq_all": "0.64", + "word_freq_3d": "0", + "word_freq_our": "0.32", + "word_freq_over": "0", + "word_freq_remove": "0", + "word_freq_internet": "0", + "word_freq_order": "0", + "word_freq_mail": "0", + "word_freq_receive": "0", + "word_freq_will": "0.64", + "word_freq_people": "0", + "word_freq_report": "0", + "word_freq_addresses": "0", + "word_freq_free": "0.32", + "word_freq_business": "0", + "word_freq_email": "1.29", + "word_freq_you": "1.93", + "word_freq_credit": "0", + "word_freq_your": "0.96", + "word_freq_font": "0", + "word_freq_000": "0", + "word_freq_money": "0", + "word_freq_hp": "0", + "word_freq_hpl": "0", + "word_freq_george": "0", + "word_freq_650": "0", + "word_freq_lab": "0", + "word_freq_labs": "0", + "word_freq_telnet": "0", + "word_freq_857": "0", + "word_freq_data": "0", + "word_freq_415": "0", + "word_freq_85": "0", + "word_freq_technology": "0", + "word_freq_1999": "0", + "word_freq_parts": "0", + "word_freq_pm": "0", + "word_freq_direct": "0", + "word_freq_cs": "0", + "word_freq_meeting": "0", + "word_freq_original": "0", + "word_freq_project": "0", + "word_freq_re": "0", + "word_freq_edu": "0", + "word_freq_table": "0", + "word_freq_conference": "0", + "char_freq_%3B": "0", + "char_freq_%28": "0", + "char_freq_%5B": "0", + "char_freq_%21": "0.778", + "char_freq_%24": "0", + "char_freq_%23": "0", + "capital_run_length_average": "3.756", + "capital_run_length_longest": "61", + "capital_run_length_total": "278", + "class": "1" + }, + { + "word_freq_make": "0.21", + "word_freq_address": "0.28", + "word_freq_all": "0.5", + "word_freq_3d": "0", + "word_freq_our": "0.14", + "word_freq_over": "0.28", + "word_freq_remove": "0.21", + "word_freq_internet": "0.07", + "word_freq_order": "0", + "word_freq_mail": "0.94", + "word_freq_receive": "0.21", + "word_freq_will": "0.79", + "word_freq_people": "0.65", + "word_freq_report": "0.21", + "word_freq_addresses": "0.14", + "word_freq_free": "0.14", + "word_freq_business": "0.07", + "word_freq_email": "0.28", + "word_freq_you": "3.47", + "word_freq_credit": "0", + "word_freq_your": "1.59", + "word_freq_font": "0", + "word_freq_000": "0.43", + "word_freq_money": "0.43", + "word_freq_hp": "0", + "word_freq_hpl": "0", + "word_freq_george": "0", + "word_freq_650": "0", + "word_freq_lab": "0", + "word_freq_labs": "0", + "word_freq_telnet": "0", + "word_freq_857": "0", + "word_freq_data": "0", + "word_freq_415": "0", + "word_freq_85": "0", + "word_freq_technology": "0", + "word_freq_1999": "0.07", + "word_freq_parts": "0", + "word_freq_pm": "0", + "word_freq_direct": "0", + "word_freq_cs": "0", + "word_freq_meeting": "0", + "word_freq_original": "0", + "word_freq_project": "0", + "word_freq_re": "0", + "word_freq_edu": "0", + "word_freq_table": "0", + "word_freq_conference": "0", + "char_freq_%3B": "0", + "char_freq_%28": "0.132", + "char_freq_%5B": "0", + "char_freq_%21": "0.372", + "char_freq_%24": "0.18", + "char_freq_%23": "0.048", + "capital_run_length_average": "5.114", + "capital_run_length_longest": "101", + "capital_run_length_total": "1028", + "class": "1" + }, + { + "word_freq_make": "0.06", + "word_freq_address": "0", + "word_freq_all": "0.71", + "word_freq_3d": "0", + "word_freq_our": "1.23", + "word_freq_over": "0.19", + "word_freq_remove": "0.19", + "word_freq_internet": "0.12", + "word_freq_order": "0.64", + "word_freq_mail": "0.25", + "word_freq_receive": "0.38", + "word_freq_will": "0.45", + "word_freq_people": "0.12", + "word_freq_report": "0", + "word_freq_addresses": "1.75", + "word_freq_free": "0.06", + "word_freq_business": "0.06", + "word_freq_email": "1.03", + "word_freq_you": "1.36", + "word_freq_credit": "0.32", + "word_freq_your": "0.51", + "word_freq_font": "0", + "word_freq_000": "1.16", + "word_freq_money": "0.06", + "word_freq_hp": "0", + "word_freq_hpl": "0", + "word_freq_george": "0", + "word_freq_650": "0", + "word_freq_lab": "0", + "word_freq_labs": "0", + "word_freq_telnet": "0", + "word_freq_857": "0", + "word_freq_data": "0", + "word_freq_415": "0", + "word_freq_85": "0", + "word_freq_technology": "0", + "word_freq_1999": "0", + "word_freq_parts": "0", + "word_freq_pm": "0", + "word_freq_direct": "0.06", + "word_freq_cs": "0", + "word_freq_meeting": "0", + "word_freq_original": "0.12", + "word_freq_project": "0", + "word_freq_re": "0.06", + "word_freq_edu": "0.06", + "word_freq_table": "0", + "word_freq_conference": "0", + "char_freq_%3B": "0.01", + "char_freq_%28": "0.143", + "char_freq_%5B": "0", + "char_freq_%21": "0.276", + "char_freq_%24": "0.184", + "char_freq_%23": "0.01", + "capital_run_length_average": "9.821", + "capital_run_length_longest": "485", + "capital_run_length_total": "2259", + "class": "1" + }, + { + "word_freq_make": "0", + "word_freq_address": "0", + "word_freq_all": "0", + "word_freq_3d": "0", + "word_freq_our": "0.63", + "word_freq_over": "0", + "word_freq_remove": "0.31", + "word_freq_internet": "0.63", + "word_freq_order": "0.31", + "word_freq_mail": "0.63", + "word_freq_receive": "0.31", + "word_freq_will": "0.31", + "word_freq_people": "0.31", + "word_freq_report": "0", + "word_freq_addresses": "0", + "word_freq_free": "0.31", + "word_freq_business": "0", + "word_freq_email": "0", + "word_freq_you": "3.18", + "word_freq_credit": "0", + "word_freq_your": "0.31", + "word_freq_font": "0", + "word_freq_000": "0", + "word_freq_money": "0", + "word_freq_hp": "0", + "word_freq_hpl": "0", + "word_freq_george": "0", + "word_freq_650": "0", + "word_freq_lab": "0", + "word_freq_labs": "0", + "word_freq_telnet": "0", + "word_freq_857": "0", + "word_freq_data": "0", + "word_freq_415": "0", + "word_freq_85": "0", + "word_freq_technology": "0", + "word_freq_1999": "0", + "word_freq_parts": "0", + "word_freq_pm": "0", + "word_freq_direct": "0", + "word_freq_cs": "0", + "word_freq_meeting": "0", + "word_freq_original": "0", + "word_freq_project": "0", + "word_freq_re": "0", + "word_freq_edu": "0", + "word_freq_table": "0", + "word_freq_conference": "0", + "char_freq_%3B": "0", + "char_freq_%28": "0.137", + "char_freq_%5B": "0", + "char_freq_%21": "0.137", + "char_freq_%24": "0", + "char_freq_%23": "0", + "capital_run_length_average": "3.537", + "capital_run_length_longest": "40", + "capital_run_length_total": "191", + "class": "1" + }, + { + "word_freq_make": "0", + "word_freq_address": "0", + "word_freq_all": "0", + "word_freq_3d": "0", + "word_freq_our": "0.63", + "word_freq_over": "0", + "word_freq_remove": "0.31", + "word_freq_internet": "0.63", + "word_freq_order": "0.31", + "word_freq_mail": "0.63", + "word_freq_receive": "0.31", + "word_freq_will": "0.31", + "word_freq_people": "0.31", + "word_freq_report": "0", + "word_freq_addresses": "0", + "word_freq_free": "0.31", + "word_freq_business": "0", + "word_freq_email": "0", + "word_freq_you": "3.18", + "word_freq_credit": "0", + "word_freq_your": "0.31", + "word_freq_font": "0", + "word_freq_000": "0", + "word_freq_money": "0", + "word_freq_hp": "0", + "word_freq_hpl": "0", + "word_freq_george": "0", + "word_freq_650": "0", + "word_freq_lab": "0", + "word_freq_labs": "0", + "word_freq_telnet": "0", + "word_freq_857": "0", + "word_freq_data": "0", + "word_freq_415": "0", + "word_freq_85": "0", + "word_freq_technology": "0", + "word_freq_1999": "0", + "word_freq_parts": "0", + "word_freq_pm": "0", + "word_freq_direct": "0", + "word_freq_cs": "0", + "word_freq_meeting": "0", + "word_freq_original": "0", + "word_freq_project": "0", + "word_freq_re": "0", + "word_freq_edu": "0", + "word_freq_table": "0", + "word_freq_conference": "0", + "char_freq_%3B": "0", + "char_freq_%28": "0.135", + "char_freq_%5B": "0", + "char_freq_%21": "0.135", + "char_freq_%24": "0", + "char_freq_%23": "0", + "capital_run_length_average": "3.537", + "capital_run_length_longest": "40", + "capital_run_length_total": "191", + "class": "1" + } + ] +} + +Shortlisted templates: +[ + { + "template_id": "tpl_h2o_group_sum", + "template_name": "Grouped Numeric Sum", + "primary_family": "subgroup_structure", + "portability": "partial", + "sql_skeleton": "SELECT {group_col}, SUM({measure_col}) AS total_measure\nFROM {table}\nGROUP BY {group_col}\nORDER BY total_measure DESC;", + "required_roles": [ + "group_col", + "measure_col" + ] + } +] + +Problem instance: +{ + "dataset_id": "n1", + "question": "Use template Grouped Numeric Sum to probe internal_profile_stability with semantic role collapsed_target_view. Focus on group_col=class, measure_col=word_freq_3d.", + "planned_template_id": "tpl_h2o_group_sum", + "bindings": { + "group_col": "class", + "measure_col": "word_freq_3d", + "top_k": 13, + "top_n": 6, + "num_tiles": 10, + "percentile_value": 0.9, + "z_threshold": 2.0, + "fraction_threshold": 0.1, + "baseline_multiplier": 1.5, + "baseline_fraction": 0.1, + "min_group_size": 5, + "min_support": 5, + "measure_threshold": 0.0, + "time_grain": "month", + "lookback_rows": 3, + "current_period_start": "'2024-01-01'", + "current_period_end": "'2024-04-01'", + "previous_period_start": "'2023-10-01'", + "previous_period_end": "'2024-01-01'", + "drift_ratio_threshold": 0.8 + }, + "can_vary": [], + "must_fix": [], + "runtime_sql_skeleton": "SELECT {group_col}, SUM({measure_col}) AS total_measure\nFROM {table}\nGROUP BY {group_col}\nORDER BY total_measure DESC;" +} + +Repair context: +{} diff --git a/Query/sql/v2/runs/v2_cli_20260502_081223_d/n1/artifacts/v2q_n1_a4cf422837fa3733/cli/sql_response_attempt_1.raw.txt b/Query/sql/v2/runs/v2_cli_20260502_081223_d/n1/artifacts/v2q_n1_a4cf422837fa3733/cli/sql_response_attempt_1.raw.txt new file mode 100644 index 0000000000000000000000000000000000000000..83b84d3e80d413572ecfae6ebd41d64ff43a4681 --- /dev/null +++ b/Query/sql/v2/runs/v2_cli_20260502_081223_d/n1/artifacts/v2q_n1_a4cf422837fa3733/cli/sql_response_attempt_1.raw.txt @@ -0,0 +1,4 @@ +{"type":"thread.started","thread_id":"019e40db-00ec-7ba1-b643-40bd64d02462"} +{"type":"turn.started"} +{"type":"item.completed","item":{"id":"item_0","type":"agent_message","text":"{\"sql\":\"-- template_id: tpl_h2o_group_sum\\nSELECT \\\"class\\\", SUM(CAST(\\\"word_freq_3d\\\" AS REAL)) AS total_measure\\nFROM \\\"n1\\\"\\nGROUP BY \\\"class\\\"\\nORDER BY total_measure DESC;\",\"notes\":\"Applied the planned Grouped Numeric Sum template with group_col=\\\"class\\\" and measure_col=\\\"word_freq_3d\\\". The measure is cast to REAL because the schema stores values as TEXT.\"}"}} +{"type":"turn.completed","usage":{"input_tokens":20319,"cached_input_tokens":12288,"output_tokens":332,"reasoning_output_tokens":230}} diff --git a/Query/sql/v2/runs/v2_cli_20260502_081223_d/n1/artifacts/v2q_n1_a4cf422837fa3733/cli/sql_response_attempt_1.txt b/Query/sql/v2/runs/v2_cli_20260502_081223_d/n1/artifacts/v2q_n1_a4cf422837fa3733/cli/sql_response_attempt_1.txt new file mode 100644 index 0000000000000000000000000000000000000000..a2b003c4e378cc519a956c3ee9e97f5f6835ce8b --- /dev/null +++ b/Query/sql/v2/runs/v2_cli_20260502_081223_d/n1/artifacts/v2q_n1_a4cf422837fa3733/cli/sql_response_attempt_1.txt @@ -0,0 +1 @@ +{"sql":"-- template_id: tpl_h2o_group_sum\nSELECT \"class\", SUM(CAST(\"word_freq_3d\" AS REAL)) AS total_measure\nFROM \"n1\"\nGROUP BY \"class\"\nORDER BY total_measure DESC;","notes":"Applied the planned Grouped Numeric Sum template with group_col=\"class\" and measure_col=\"word_freq_3d\". The measure is cast to REAL because the schema stores values as TEXT."} \ No newline at end of file diff --git a/Query/sql/v2/runs/v2_cli_20260502_081223_d/n1/artifacts/v2q_n1_a4cf422837fa3733/cli/sql_stderr_attempt_1.txt b/Query/sql/v2/runs/v2_cli_20260502_081223_d/n1/artifacts/v2q_n1_a4cf422837fa3733/cli/sql_stderr_attempt_1.txt new file mode 100644 index 0000000000000000000000000000000000000000..e69de29bb2d1d6434b8b29ae775ad8c2e48c5391 diff --git a/Query/sql/v2/runs/v2_cli_20260502_081223_d/n1/artifacts/v2q_n1_a6e7250181274940/cli/conversation.jsonl b/Query/sql/v2/runs/v2_cli_20260502_081223_d/n1/artifacts/v2q_n1_a6e7250181274940/cli/conversation.jsonl new file mode 100644 index 0000000000000000000000000000000000000000..a57031a05f2a91e25e565d71324c0e4a5ec95f29 --- /dev/null +++ b/Query/sql/v2/runs/v2_cli_20260502_081223_d/n1/artifacts/v2q_n1_a6e7250181274940/cli/conversation.jsonl @@ -0,0 +1,2 @@ +{"attempt": 1, "phase": "sql_generation", "role": "user", "content_path": "cli/sql_prompt_attempt_1.txt", "metrics": {"chars": 29588, "bytes_utf8": 29588, "lines": 795, "estimated_tokens": null}} +{"attempt": 1, "phase": "sql_generation", "role": "assistant", "content_path": "cli/sql_response_attempt_1.txt", "raw_content_path": "cli/sql_response_attempt_1.raw.txt", "stderr_path": "cli/sql_stderr_attempt_1.txt", "metrics": {"chars": 416, "bytes_utf8": 416, "lines": 1, "estimated_tokens": null}, "usage": {"input_tokens": 20373, "cached_input_tokens": 19840, "output_tokens": 290, "reasoning_output_tokens": 181}} diff --git a/Query/sql/v2/runs/v2_cli_20260502_081223_d/n1/artifacts/v2q_n1_a6e7250181274940/cli/session_summary.json b/Query/sql/v2/runs/v2_cli_20260502_081223_d/n1/artifacts/v2q_n1_a6e7250181274940/cli/session_summary.json new file mode 100644 index 0000000000000000000000000000000000000000..d2e6ece480b5a09ed23a2f6ac0f60bab263297db --- /dev/null +++ b/Query/sql/v2/runs/v2_cli_20260502_081223_d/n1/artifacts/v2q_n1_a6e7250181274940/cli/session_summary.json @@ -0,0 +1,25 @@ +{ + "engine": "v2-cli:codex", + "command": "codex exec --skip-git-repo-check --disable plugins --sandbox read-only --cd \"/data/jialinzhang/SQLagent\" -m gpt-5.4 --json -", + "ai_cli_calls": 1, + "usage_summary": { + "dataset_id": "n1", + "model": "v2-cli:codex", + "run_id": "v2q_n1_a6e7250181274940", + "api_calls": 0, + "input_tokens": 20373, + "cached_input_tokens": 19840, + "output_tokens": 290, + "total_tokens": 20663, + "cost_usd": 0.0, + "ai_cli_calls": 1, + "estimated_input_tokens": 0, + "estimated_output_tokens": 0, + "estimated_total_tokens": 0, + "usage_source": "ai_cli_json_usage", + "cli_elapsed_ms_total": 10995.98, + "sql_execution_elapsed_ms_total": 2.0, + "conversation_log_path": "/data/jialinzhang/TabQueryBench/sql_workloads/v2_current/runs_and_launches/runs/v2_cli_20260502_081223_d/n1/artifacts/v2q_n1_a6e7250181274940/cli/conversation.jsonl", + "note": "Executed through a local AI CLI with structured usage metadata." + } +} \ No newline at end of file diff --git a/Query/sql/v2/runs/v2_cli_20260502_081223_d/n1/artifacts/v2q_n1_a6e7250181274940/cli/sql_attempt_1.metadata.json b/Query/sql/v2/runs/v2_cli_20260502_081223_d/n1/artifacts/v2q_n1_a6e7250181274940/cli/sql_attempt_1.metadata.json new file mode 100644 index 0000000000000000000000000000000000000000..1296cbb021dbc790e17b329acb028d73931d0fc3 --- /dev/null +++ b/Query/sql/v2/runs/v2_cli_20260502_081223_d/n1/artifacts/v2q_n1_a6e7250181274940/cli/sql_attempt_1.metadata.json @@ -0,0 +1,45 @@ +{ + "attempt": 1, + "phase": "sql_generation", + "command": "codex exec --skip-git-repo-check --disable plugins --sandbox read-only --cd \"/data/jialinzhang/SQLagent\" -m gpt-5.4 --json -", + "started_at": "2026-05-19T15:59:13.528837+00:00", + "ended_at": "2026-05-19T15:59:24.524847+00:00", + "elapsed_ms": 10995.98, + "prompt_metrics": { + "chars": 29588, + "bytes_utf8": 29588, + "lines": 795, + "estimated_tokens": null + }, + "stdout_metrics": { + "chars": 771, + "bytes_utf8": 771, + "lines": 4, + "estimated_tokens": null + }, + "stderr_metrics": { + "chars": 0, + "bytes_utf8": 0, + "lines": 0, + "estimated_tokens": null + }, + "parsed_output": { + "format": "jsonl_events", + "text_metrics": { + "chars": 416, + "bytes_utf8": 416, + "lines": 1, + "estimated_tokens": null + }, + "usage": { + "input_tokens": 20373, + "cached_input_tokens": 19840, + "output_tokens": 290, + "reasoning_output_tokens": 181 + } + }, + "prompt_path": "cli/sql_prompt_attempt_1.txt", + "response_path": "cli/sql_response_attempt_1.txt", + "raw_response_path": "cli/sql_response_attempt_1.raw.txt", + "stderr_path": "cli/sql_stderr_attempt_1.txt" +} \ No newline at end of file diff --git a/Query/sql/v2/runs/v2_cli_20260502_081223_d/n1/artifacts/v2q_n1_a6e7250181274940/cli/sql_prompt_attempt_1.txt b/Query/sql/v2/runs/v2_cli_20260502_081223_d/n1/artifacts/v2q_n1_a6e7250181274940/cli/sql_prompt_attempt_1.txt new file mode 100644 index 0000000000000000000000000000000000000000..9c2e106c5bdb2c8303fcd2d8d235420c605aed05 --- /dev/null +++ b/Query/sql/v2/runs/v2_cli_20260502_081223_d/n1/artifacts/v2q_n1_a6e7250181274940/cli/sql_prompt_attempt_1.txt @@ -0,0 +1,795 @@ +You are generating one SQLite SELECT query for a single-table SQL QA task. +Return strict JSON only, with this schema: {"sql": "...", "notes": "..."}. +Rules: +- Use only the provided table and columns. +- Do not write INSERT, UPDATE, DELETE, DROP, ALTER, CREATE, PRAGMA, ATTACH, DETACH, or VACUUM. +- Prefer the planned template and bound roles when provided. +- Add a leading SQL comment exactly like: -- template_id: . +- Generate SQLite-compatible SQL. SQLite does not support PERCENTILE_CONT or STDDEV. +- Quote identifiers with double quotes. +- Return no markdown and no extra prose. + +Dataset context: +Dataset context for SQL QA: +- dataset_id: n1 +- dataset_name: Spambase +- table_name: n1 +- table_layout: single-table dataset (do not assume joins). +- row_semantics: One row is one email represented by engineered token/character frequency and capitalization-run features. +- task_type: classification +- target_column: class +- main_row_count: 4601 +- important_fields: +- word_freq_make: role=feature, type=numeric_sparse_frequency. tags=['condition_candidate', 'measure', 'rare_pattern_candidate'] desc=Frequency feature for token 'make' in an email message. +- word_freq_address: role=feature, type=numeric_sparse_frequency. tags=['condition_candidate', 'measure', 'rare_pattern_candidate'] desc=Frequency feature for token 'address' in an email message. +- word_freq_all: role=feature, type=numeric_sparse_frequency. tags=['condition_candidate', 'measure', 'rare_pattern_candidate'] desc=Frequency feature for token 'all' in an email message. +- word_freq_3d: role=feature, type=numeric_sparse_frequency. tags=['condition_candidate', 'measure', 'rare_pattern_candidate'] desc=Frequency feature for token '3d' in an email message. +- word_freq_our: role=feature, type=numeric_sparse_frequency. tags=['condition_candidate', 'measure', 'rare_pattern_candidate'] desc=Frequency feature for token 'our' in an email message. +- word_freq_over: role=feature, type=numeric_sparse_frequency. tags=['condition_candidate', 'measure', 'rare_pattern_candidate'] desc=Frequency feature for token 'over' in an email message. +- word_freq_remove: role=feature, type=numeric_sparse_frequency. tags=['condition_candidate', 'measure', 'rare_pattern_candidate'] desc=Frequency feature for token 'remove' in an email message. +- word_freq_internet: role=feature, type=numeric_sparse_frequency. tags=['condition_candidate', 'measure', 'rare_pattern_candidate'] desc=Frequency feature for token 'internet' in an email message. +- word_freq_order: role=feature, type=numeric_sparse_frequency. tags=['condition_candidate', 'measure', 'rare_pattern_candidate'] desc=Frequency feature for token 'order' in an email message. +- word_freq_mail: role=feature, type=numeric_sparse_frequency. tags=['condition_candidate', 'measure', 'rare_pattern_candidate'] desc=Frequency feature for token 'mail' in an email message. +- word_freq_receive: role=feature, type=numeric_sparse_frequency. tags=['condition_candidate', 'measure', 'rare_pattern_candidate'] desc=Frequency feature for token 'receive' in an email message. +- word_freq_will: role=feature, type=numeric_sparse_frequency. tags=['condition_candidate', 'measure', 'rare_pattern_candidate'] desc=Frequency feature for token 'will' in an email message. +- word_freq_people: role=feature, type=numeric_sparse_frequency. tags=['condition_candidate', 'measure', 'rare_pattern_candidate'] desc=Frequency feature for token 'people' in an email message. +- word_freq_report: role=feature, type=numeric_sparse_frequency. tags=['condition_candidate', 'measure', 'rare_pattern_candidate'] desc=Frequency feature for token 'report' in an email message. +- word_freq_addresses: role=feature, type=numeric_sparse_frequency. tags=['condition_candidate', 'measure', 'rare_pattern_candidate'] desc=Frequency feature for token 'addresses' in an email message. +- word_freq_free: role=feature, type=numeric_sparse_frequency. tags=['condition_candidate', 'measure', 'rare_pattern_candidate'] desc=Frequency feature for token 'free' in an email message. +- word_freq_business: role=feature, type=numeric_sparse_frequency. tags=['condition_candidate', 'measure', 'rare_pattern_candidate'] desc=Frequency feature for token 'business' in an email message. +- word_freq_email: role=feature, type=numeric_sparse_frequency. tags=['condition_candidate', 'measure', 'rare_pattern_candidate'] desc=Frequency feature for token 'email' in an email message. +- word_freq_you: role=feature, type=numeric_sparse_frequency. tags=['condition_candidate', 'measure', 'rare_pattern_candidate'] desc=Frequency feature for token 'you' in an email message. +- word_freq_credit: role=feature, type=numeric_sparse_frequency. tags=['condition_candidate', 'measure', 'rare_pattern_candidate'] desc=Frequency feature for token 'credit' in an email message. +- word_freq_your: role=feature, type=numeric_sparse_frequency. tags=['condition_candidate', 'measure', 'rare_pattern_candidate'] desc=Frequency feature for token 'your' in an email message. +- word_freq_font: role=feature, type=numeric_sparse_frequency. tags=['condition_candidate', 'measure', 'rare_pattern_candidate'] desc=Frequency feature for token 'font' in an email message. +- word_freq_000: role=feature, type=numeric_sparse_frequency. tags=['condition_candidate', 'measure', 'rare_pattern_candidate'] desc=Frequency feature for token '000' in an email message. +- word_freq_money: role=feature, type=numeric_sparse_frequency. tags=['condition_candidate', 'measure', 'rare_pattern_candidate'] desc=Frequency feature for token 'money' in an email message. +- word_freq_hp: role=feature, type=numeric_sparse_frequency. tags=['condition_candidate', 'measure', 'rare_pattern_candidate'] desc=Frequency feature for token 'hp' in an email message. +- word_freq_hpl: role=feature, type=numeric_sparse_frequency. tags=['condition_candidate', 'measure', 'rare_pattern_candidate'] desc=Frequency feature for token 'hpl' in an email message. +- word_freq_george: role=feature, type=numeric_sparse_frequency. tags=['condition_candidate', 'measure', 'rare_pattern_candidate'] desc=Frequency feature for token 'george' in an email message. +- word_freq_650: role=feature, type=numeric_sparse_frequency. tags=['condition_candidate', 'measure', 'rare_pattern_candidate'] desc=Frequency feature for token '650' in an email message. +- word_freq_lab: role=feature, type=numeric_sparse_frequency. tags=['condition_candidate', 'measure', 'rare_pattern_candidate'] desc=Frequency feature for token 'lab' in an email message. +- word_freq_labs: role=feature, type=numeric_sparse_frequency. tags=['condition_candidate', 'measure', 'rare_pattern_candidate'] desc=Frequency feature for token 'labs' in an email message. +- word_freq_telnet: role=feature, type=numeric_sparse_frequency. tags=['condition_candidate', 'measure', 'rare_pattern_candidate'] desc=Frequency feature for token 'telnet' in an email message. +- word_freq_857: role=feature, type=numeric_sparse_frequency. tags=['condition_candidate', 'measure', 'rare_pattern_candidate'] desc=Frequency feature for token '857' in an email message. +- word_freq_data: role=feature, type=numeric_sparse_frequency. tags=['condition_candidate', 'measure', 'rare_pattern_candidate'] desc=Frequency feature for token 'data' in an email message. +- word_freq_415: role=feature, type=numeric_sparse_frequency. tags=['condition_candidate', 'measure', 'rare_pattern_candidate'] desc=Frequency feature for token '415' in an email message. +- word_freq_85: role=feature, type=numeric_sparse_frequency. tags=['condition_candidate', 'measure', 'rare_pattern_candidate'] desc=Frequency feature for token '85' in an email message. +- word_freq_technology: role=feature, type=numeric_sparse_frequency. tags=['condition_candidate', 'measure', 'rare_pattern_candidate'] desc=Frequency feature for token 'technology' in an email message. +- word_freq_1999: role=feature, type=numeric_sparse_frequency. tags=['condition_candidate', 'measure', 'rare_pattern_candidate'] desc=Frequency feature for token '1999' in an email message. +- word_freq_parts: role=feature, type=numeric_sparse_frequency. tags=['condition_candidate', 'measure', 'rare_pattern_candidate'] desc=Frequency feature for token 'parts' in an email message. +- word_freq_pm: role=feature, type=numeric_sparse_frequency. tags=['condition_candidate', 'measure', 'rare_pattern_candidate'] desc=Frequency feature for token 'pm' in an email message. +- word_freq_direct: role=feature, type=numeric_sparse_frequency. tags=['condition_candidate', 'measure', 'rare_pattern_candidate'] desc=Frequency feature for token 'direct' in an email message. +- word_freq_cs: role=feature, type=numeric_sparse_frequency. tags=['condition_candidate', 'measure', 'rare_pattern_candidate'] desc=Frequency feature for token 'cs' in an email message. +- word_freq_meeting: role=feature, type=numeric_sparse_frequency. tags=['condition_candidate', 'measure', 'rare_pattern_candidate'] desc=Frequency feature for token 'meeting' in an email message. +- word_freq_original: role=feature, type=numeric_sparse_frequency. tags=['condition_candidate', 'measure', 'rare_pattern_candidate'] desc=Frequency feature for token 'original' in an email message. +- word_freq_project: role=feature, type=numeric_sparse_frequency. tags=['condition_candidate', 'measure', 'rare_pattern_candidate'] desc=Frequency feature for token 'project' in an email message. +- word_freq_re: role=feature, type=numeric_sparse_frequency. tags=['condition_candidate', 'measure', 'rare_pattern_candidate'] desc=Frequency feature for token 're' in an email message. +- word_freq_edu: role=feature, type=numeric_sparse_frequency. tags=['condition_candidate', 'measure', 'rare_pattern_candidate'] desc=Frequency feature for token 'edu' in an email message. +- word_freq_table: role=feature, type=numeric_sparse_frequency. tags=['condition_candidate', 'measure', 'rare_pattern_candidate'] desc=Frequency feature for token 'table' in an email message. +- word_freq_conference: role=feature, type=numeric_sparse_frequency. tags=['condition_candidate', 'measure', 'rare_pattern_candidate'] desc=Frequency feature for token 'conference' in an email message. +- char_freq_%3B: role=feature, type=numeric_sparse_frequency. tags=['condition_candidate', 'measure', 'rare_pattern_candidate'] desc=Character frequency feature for symbol ';'. +- char_freq_%28: role=feature, type=numeric_sparse_frequency. tags=['condition_candidate', 'measure', 'rare_pattern_candidate'] desc=Character frequency feature for symbol '('. +- char_freq_%5B: role=feature, type=numeric_sparse_frequency. tags=['condition_candidate', 'measure', 'rare_pattern_candidate'] desc=Character frequency feature for symbol '['. +- char_freq_%21: role=feature, type=numeric_sparse_frequency. tags=['condition_candidate', 'measure', 'rare_pattern_candidate'] desc=Character frequency feature for symbol '!'. +- char_freq_%24: role=feature, type=numeric_sparse_frequency. tags=['condition_candidate', 'measure', 'rare_pattern_candidate'] desc=Character frequency feature for symbol '$'. +- char_freq_%23: role=feature, type=numeric_sparse_frequency. tags=['condition_candidate', 'measure', 'rare_pattern_candidate'] desc=Character frequency feature for symbol '#'. +- capital_run_length_average: role=feature, type=numeric_heavy_tail. tags=['condition_candidate', 'measure', 'rare_pattern_candidate'] desc=Average length of uninterrupted capital-letter runs. +- capital_run_length_longest: role=feature, type=numeric_heavy_tail. tags=['condition_candidate', 'measure', 'rare_pattern_candidate'] desc=Longest uninterrupted capital-letter run. +- capital_run_length_total: role=feature, type=numeric_heavy_tail. tags=['condition_candidate', 'measure', 'rare_pattern_candidate'] desc=Total number of capital letters in runs. +- class: role=target, type=binary_target. tags=['target_candidate'] desc=Binary spam label (1=spam, 0=non-spam). +- useful_field_combinations: [['word_freq_free', 'word_freq_you', 'class'], ['char_freq_%21', 'capital_run_length_longest', 'class'], ['word_freq_remove', 'word_freq_money', 'class']] +- fields_requiring_caution: ['capital_run_length_average', 'capital_run_length_longest', 'capital_run_length_total'] +- source_url: https://www.openml.org/d/44 + +SQLite schema snapshot: +{ + "table_name": "n1", + "quoted_table_name": "\"n1\"", + "row_count": 4601, + "columns": [ + { + "name": "word_freq_make", + "type": "TEXT", + "notnull": false, + "pk": false + }, + { + "name": "word_freq_address", + "type": "TEXT", + "notnull": false, + "pk": false + }, + { + "name": "word_freq_all", + "type": "TEXT", + "notnull": false, + "pk": false + }, + { + "name": "word_freq_3d", + "type": "TEXT", + "notnull": false, + "pk": false + }, + { + "name": "word_freq_our", + "type": "TEXT", + "notnull": false, + "pk": false + }, + { + "name": "word_freq_over", + "type": "TEXT", + "notnull": false, + "pk": false + }, + { + "name": "word_freq_remove", + "type": "TEXT", + "notnull": false, + "pk": false + }, + { + "name": "word_freq_internet", + "type": "TEXT", + "notnull": false, + "pk": false + }, + { + "name": "word_freq_order", + "type": "TEXT", + "notnull": false, + "pk": false + }, + { + "name": "word_freq_mail", + "type": "TEXT", + "notnull": false, + "pk": false + }, + { + "name": "word_freq_receive", + "type": "TEXT", + "notnull": false, + "pk": false + }, + { + "name": "word_freq_will", + "type": "TEXT", + "notnull": false, + "pk": false + }, + { + "name": "word_freq_people", + "type": "TEXT", + "notnull": false, + "pk": false + }, + { + "name": "word_freq_report", + "type": "TEXT", + "notnull": false, + "pk": false + }, + { + "name": "word_freq_addresses", + "type": "TEXT", + "notnull": false, + "pk": false + }, + { + "name": "word_freq_free", + "type": "TEXT", + "notnull": false, + "pk": false + }, + { + "name": "word_freq_business", + "type": "TEXT", + "notnull": false, + "pk": false + }, + { + "name": "word_freq_email", + "type": "TEXT", + "notnull": false, + "pk": false + }, + { + "name": "word_freq_you", + "type": "TEXT", + "notnull": false, + "pk": false + }, + { + "name": "word_freq_credit", + "type": "TEXT", + "notnull": false, + "pk": false + }, + { + "name": "word_freq_your", + "type": "TEXT", + "notnull": false, + "pk": false + }, + { + "name": "word_freq_font", + "type": "TEXT", + "notnull": false, + "pk": false + }, + { + "name": "word_freq_000", + "type": "TEXT", + "notnull": false, + "pk": false + }, + { + "name": "word_freq_money", + "type": "TEXT", + "notnull": false, + "pk": false + }, + { + "name": "word_freq_hp", + "type": "TEXT", + "notnull": false, + "pk": false + }, + { + "name": "word_freq_hpl", + "type": "TEXT", + "notnull": false, + "pk": false + }, + { + "name": "word_freq_george", + "type": "TEXT", + "notnull": false, + "pk": false + }, + { + "name": "word_freq_650", + "type": "TEXT", + "notnull": false, + "pk": false + }, + { + "name": "word_freq_lab", + "type": "TEXT", + "notnull": false, + "pk": false + }, + { + "name": "word_freq_labs", + "type": "TEXT", + "notnull": false, + "pk": false + }, + { + "name": "word_freq_telnet", + "type": "TEXT", + "notnull": false, + "pk": false + }, + { + "name": "word_freq_857", + "type": "TEXT", + "notnull": false, + "pk": false + }, + { + "name": "word_freq_data", + "type": "TEXT", + "notnull": false, + "pk": false + }, + { + "name": "word_freq_415", + "type": "TEXT", + "notnull": false, + "pk": false + }, + { + "name": "word_freq_85", + "type": "TEXT", + "notnull": false, + "pk": false + }, + { + "name": "word_freq_technology", + "type": "TEXT", + "notnull": false, + "pk": false + }, + { + "name": "word_freq_1999", + "type": "TEXT", + "notnull": false, + "pk": false + }, + { + "name": "word_freq_parts", + "type": "TEXT", + "notnull": false, + "pk": false + }, + { + "name": "word_freq_pm", + "type": "TEXT", + "notnull": false, + "pk": false + }, + { + "name": "word_freq_direct", + "type": "TEXT", + "notnull": false, + "pk": false + }, + { + "name": "word_freq_cs", + "type": "TEXT", + "notnull": false, + "pk": false + }, + { + "name": "word_freq_meeting", + "type": "TEXT", + "notnull": false, + "pk": false + }, + { + "name": "word_freq_original", + "type": "TEXT", + "notnull": false, + "pk": false + }, + { + "name": "word_freq_project", + "type": "TEXT", + "notnull": false, + "pk": false + }, + { + "name": "word_freq_re", + "type": "TEXT", + "notnull": false, + "pk": false + }, + { + "name": "word_freq_edu", + "type": "TEXT", + "notnull": false, + "pk": false + }, + { + "name": "word_freq_table", + "type": "TEXT", + "notnull": false, + "pk": false + }, + { + "name": "word_freq_conference", + "type": "TEXT", + "notnull": false, + "pk": false + }, + { + "name": "char_freq_%3B", + "type": "TEXT", + "notnull": false, + "pk": false + }, + { + "name": "char_freq_%28", + "type": "TEXT", + "notnull": false, + "pk": false + }, + { + "name": "char_freq_%5B", + "type": "TEXT", + "notnull": false, + "pk": false + }, + { + "name": "char_freq_%21", + "type": "TEXT", + "notnull": false, + "pk": false + }, + { + "name": "char_freq_%24", + "type": "TEXT", + "notnull": false, + "pk": false + }, + { + "name": "char_freq_%23", + "type": "TEXT", + "notnull": false, + "pk": false + }, + { + "name": "capital_run_length_average", + "type": "TEXT", + "notnull": false, + "pk": false + }, + { + "name": "capital_run_length_longest", + "type": "TEXT", + "notnull": false, + "pk": false + }, + { + "name": "capital_run_length_total", + "type": "TEXT", + "notnull": false, + "pk": false + }, + { + "name": "class", + "type": "TEXT", + "notnull": false, + "pk": false + } + ], + "sample_rows": [ + { + "word_freq_make": "0", + "word_freq_address": "0.64", + "word_freq_all": "0.64", + "word_freq_3d": "0", + "word_freq_our": "0.32", + "word_freq_over": "0", + "word_freq_remove": "0", + "word_freq_internet": "0", + "word_freq_order": "0", + "word_freq_mail": "0", + "word_freq_receive": "0", + "word_freq_will": "0.64", + "word_freq_people": "0", + "word_freq_report": "0", + "word_freq_addresses": "0", + "word_freq_free": "0.32", + "word_freq_business": "0", + "word_freq_email": "1.29", + "word_freq_you": "1.93", + "word_freq_credit": "0", + "word_freq_your": "0.96", + "word_freq_font": "0", + "word_freq_000": "0", + "word_freq_money": "0", + "word_freq_hp": "0", + "word_freq_hpl": "0", + "word_freq_george": "0", + "word_freq_650": "0", + "word_freq_lab": "0", + "word_freq_labs": "0", + "word_freq_telnet": "0", + "word_freq_857": "0", + "word_freq_data": "0", + "word_freq_415": "0", + "word_freq_85": "0", + "word_freq_technology": "0", + "word_freq_1999": "0", + "word_freq_parts": "0", + "word_freq_pm": "0", + "word_freq_direct": "0", + "word_freq_cs": "0", + "word_freq_meeting": "0", + "word_freq_original": "0", + "word_freq_project": "0", + "word_freq_re": "0", + "word_freq_edu": "0", + "word_freq_table": "0", + "word_freq_conference": "0", + "char_freq_%3B": "0", + "char_freq_%28": "0", + "char_freq_%5B": "0", + "char_freq_%21": "0.778", + "char_freq_%24": "0", + "char_freq_%23": "0", + "capital_run_length_average": "3.756", + "capital_run_length_longest": "61", + "capital_run_length_total": "278", + "class": "1" + }, + { + "word_freq_make": "0.21", + "word_freq_address": "0.28", + "word_freq_all": "0.5", + "word_freq_3d": "0", + "word_freq_our": "0.14", + "word_freq_over": "0.28", + "word_freq_remove": "0.21", + "word_freq_internet": "0.07", + "word_freq_order": "0", + "word_freq_mail": "0.94", + "word_freq_receive": "0.21", + "word_freq_will": "0.79", + "word_freq_people": "0.65", + "word_freq_report": "0.21", + "word_freq_addresses": "0.14", + "word_freq_free": "0.14", + "word_freq_business": "0.07", + "word_freq_email": "0.28", + "word_freq_you": "3.47", + "word_freq_credit": "0", + "word_freq_your": "1.59", + "word_freq_font": "0", + "word_freq_000": "0.43", + "word_freq_money": "0.43", + "word_freq_hp": "0", + "word_freq_hpl": "0", + "word_freq_george": "0", + "word_freq_650": "0", + "word_freq_lab": "0", + "word_freq_labs": "0", + "word_freq_telnet": "0", + "word_freq_857": "0", + "word_freq_data": "0", + "word_freq_415": "0", + "word_freq_85": "0", + "word_freq_technology": "0", + "word_freq_1999": "0.07", + "word_freq_parts": "0", + "word_freq_pm": "0", + "word_freq_direct": "0", + "word_freq_cs": "0", + "word_freq_meeting": "0", + "word_freq_original": "0", + "word_freq_project": "0", + "word_freq_re": "0", + "word_freq_edu": "0", + "word_freq_table": "0", + "word_freq_conference": "0", + "char_freq_%3B": "0", + "char_freq_%28": "0.132", + "char_freq_%5B": "0", + "char_freq_%21": "0.372", + "char_freq_%24": "0.18", + "char_freq_%23": "0.048", + "capital_run_length_average": "5.114", + "capital_run_length_longest": "101", + "capital_run_length_total": "1028", + "class": "1" + }, + { + "word_freq_make": "0.06", + "word_freq_address": "0", + "word_freq_all": "0.71", + "word_freq_3d": "0", + "word_freq_our": "1.23", + "word_freq_over": "0.19", + "word_freq_remove": "0.19", + "word_freq_internet": "0.12", + "word_freq_order": "0.64", + "word_freq_mail": "0.25", + "word_freq_receive": "0.38", + "word_freq_will": "0.45", + "word_freq_people": "0.12", + "word_freq_report": "0", + "word_freq_addresses": "1.75", + "word_freq_free": "0.06", + "word_freq_business": "0.06", + "word_freq_email": "1.03", + "word_freq_you": "1.36", + "word_freq_credit": "0.32", + "word_freq_your": "0.51", + "word_freq_font": "0", + "word_freq_000": "1.16", + "word_freq_money": "0.06", + "word_freq_hp": "0", + "word_freq_hpl": "0", + "word_freq_george": "0", + "word_freq_650": "0", + "word_freq_lab": "0", + "word_freq_labs": "0", + "word_freq_telnet": "0", + "word_freq_857": "0", + "word_freq_data": "0", + "word_freq_415": "0", + "word_freq_85": "0", + "word_freq_technology": "0", + "word_freq_1999": "0", + "word_freq_parts": "0", + "word_freq_pm": "0", + "word_freq_direct": "0.06", + "word_freq_cs": "0", + "word_freq_meeting": "0", + "word_freq_original": "0.12", + "word_freq_project": "0", + "word_freq_re": "0.06", + "word_freq_edu": "0.06", + "word_freq_table": "0", + "word_freq_conference": "0", + "char_freq_%3B": "0.01", + "char_freq_%28": "0.143", + "char_freq_%5B": "0", + "char_freq_%21": "0.276", + "char_freq_%24": "0.184", + "char_freq_%23": "0.01", + "capital_run_length_average": "9.821", + "capital_run_length_longest": "485", + "capital_run_length_total": "2259", + "class": "1" + }, + { + "word_freq_make": "0", + "word_freq_address": "0", + "word_freq_all": "0", + "word_freq_3d": "0", + "word_freq_our": "0.63", + "word_freq_over": "0", + "word_freq_remove": "0.31", + "word_freq_internet": "0.63", + "word_freq_order": "0.31", + "word_freq_mail": "0.63", + "word_freq_receive": "0.31", + "word_freq_will": "0.31", + "word_freq_people": "0.31", + "word_freq_report": "0", + "word_freq_addresses": "0", + "word_freq_free": "0.31", + "word_freq_business": "0", + "word_freq_email": "0", + "word_freq_you": "3.18", + "word_freq_credit": "0", + "word_freq_your": "0.31", + "word_freq_font": "0", + "word_freq_000": "0", + "word_freq_money": "0", + "word_freq_hp": "0", + "word_freq_hpl": "0", + "word_freq_george": "0", + "word_freq_650": "0", + "word_freq_lab": "0", + "word_freq_labs": "0", + "word_freq_telnet": "0", + "word_freq_857": "0", + "word_freq_data": "0", + "word_freq_415": "0", + "word_freq_85": "0", + "word_freq_technology": "0", + "word_freq_1999": "0", + "word_freq_parts": "0", + "word_freq_pm": "0", + "word_freq_direct": "0", + "word_freq_cs": "0", + "word_freq_meeting": "0", + "word_freq_original": "0", + "word_freq_project": "0", + "word_freq_re": "0", + "word_freq_edu": "0", + "word_freq_table": "0", + "word_freq_conference": "0", + "char_freq_%3B": "0", + "char_freq_%28": "0.137", + "char_freq_%5B": "0", + "char_freq_%21": "0.137", + "char_freq_%24": "0", + "char_freq_%23": "0", + "capital_run_length_average": "3.537", + "capital_run_length_longest": "40", + "capital_run_length_total": "191", + "class": "1" + }, + { + "word_freq_make": "0", + "word_freq_address": "0", + "word_freq_all": "0", + "word_freq_3d": "0", + "word_freq_our": "0.63", + "word_freq_over": "0", + "word_freq_remove": "0.31", + "word_freq_internet": "0.63", + "word_freq_order": "0.31", + "word_freq_mail": "0.63", + "word_freq_receive": "0.31", + "word_freq_will": "0.31", + "word_freq_people": "0.31", + "word_freq_report": "0", + "word_freq_addresses": "0", + "word_freq_free": "0.31", + "word_freq_business": "0", + "word_freq_email": "0", + "word_freq_you": "3.18", + "word_freq_credit": "0", + "word_freq_your": "0.31", + "word_freq_font": "0", + "word_freq_000": "0", + "word_freq_money": "0", + "word_freq_hp": "0", + "word_freq_hpl": "0", + "word_freq_george": "0", + "word_freq_650": "0", + "word_freq_lab": "0", + "word_freq_labs": "0", + "word_freq_telnet": "0", + "word_freq_857": "0", + "word_freq_data": "0", + "word_freq_415": "0", + "word_freq_85": "0", + "word_freq_technology": "0", + "word_freq_1999": "0", + "word_freq_parts": "0", + "word_freq_pm": "0", + "word_freq_direct": "0", + "word_freq_cs": "0", + "word_freq_meeting": "0", + "word_freq_original": "0", + "word_freq_project": "0", + "word_freq_re": "0", + "word_freq_edu": "0", + "word_freq_table": "0", + "word_freq_conference": "0", + "char_freq_%3B": "0", + "char_freq_%28": "0.135", + "char_freq_%5B": "0", + "char_freq_%21": "0.135", + "char_freq_%24": "0", + "char_freq_%23": "0", + "capital_run_length_average": "3.537", + "capital_run_length_longest": "40", + "capital_run_length_total": "191", + "class": "1" + } + ] +} + +Shortlisted templates: +[ + { + "template_id": "tpl_m4_group_condition_rate", + "template_name": "Grouped Condition Rate", + "primary_family": "conditional_dependency_structure", + "portability": "yes", + "sql_skeleton": "SELECT {group_col},\n AVG(CASE WHEN {condition_col} = {condition_value} THEN 1 ELSE 0 END) AS condition_rate\nFROM {table}\nGROUP BY {group_col}\nORDER BY condition_rate DESC;", + "required_roles": [ + "group_col", + "condition_col" + ] + } +] + +Problem instance: +{ + "dataset_id": "n1", + "question": "Use template Grouped Condition Rate to probe dependency_strength_similarity with semantic role within_group_proportion. Focus on group_col=class, condition_col=class.", + "planned_template_id": "tpl_m4_group_condition_rate", + "bindings": { + "group_col": "class", + "condition_col": "class", + "condition_value": "0", + "positive_value": "0", + "negative_value": "1", + "top_k": 10, + "top_n": 3, + "num_tiles": 10, + "percentile_value": 0.95, + "z_threshold": 2.0, + "fraction_threshold": 0.1, + "baseline_multiplier": 1.5, + "baseline_fraction": 0.1, + "min_group_size": 5, + "min_support": 5, + "measure_threshold": 0.0, + "time_grain": "month", + "lookback_rows": 3, + "current_period_start": "'2024-01-01'", + "current_period_end": "'2024-04-01'", + "previous_period_start": "'2023-10-01'", + "previous_period_end": "'2024-01-01'", + "drift_ratio_threshold": 0.8 + }, + "can_vary": [], + "must_fix": [], + "runtime_sql_skeleton": "SELECT {group_col},\n AVG(CASE WHEN {condition_col} = {condition_value} THEN 1 ELSE 0 END) AS condition_rate\nFROM {table}\nGROUP BY {group_col}\nORDER BY condition_rate DESC;" +} + +Repair context: +{} diff --git a/Query/sql/v2/runs/v2_cli_20260502_081223_d/n1/artifacts/v2q_n1_a6e7250181274940/cli/sql_response_attempt_1.raw.txt b/Query/sql/v2/runs/v2_cli_20260502_081223_d/n1/artifacts/v2q_n1_a6e7250181274940/cli/sql_response_attempt_1.raw.txt new file mode 100644 index 0000000000000000000000000000000000000000..6e30ac5de813ce54aeb4037c1a7e586dd6ac9b17 --- /dev/null +++ b/Query/sql/v2/runs/v2_cli_20260502_081223_d/n1/artifacts/v2q_n1_a6e7250181274940/cli/sql_response_attempt_1.raw.txt @@ -0,0 +1,4 @@ +{"type":"thread.started","thread_id":"019e40f6-738e-75e2-a979-8c134a5091ba"} +{"type":"turn.started"} +{"type":"item.completed","item":{"id":"item_0","type":"agent_message","text":"{\"sql\":\"-- template_id: tpl_m4_group_condition_rate\\nSELECT \\\"class\\\",\\n AVG(CASE WHEN \\\"class\\\" = '0' THEN 1 ELSE 0 END) AS condition_rate\\nFROM \\\"n1\\\"\\nGROUP BY \\\"class\\\"\\nORDER BY condition_rate DESC;\",\"notes\":\"Applied the provided Grouped Condition Rate template with \\\"class\\\" as both the grouping column and condition column; compared against text value '0' because the schema stores \\\"class\\\" as TEXT.\"}"}} +{"type":"turn.completed","usage":{"input_tokens":20373,"cached_input_tokens":19840,"output_tokens":290,"reasoning_output_tokens":181}} diff --git a/Query/sql/v2/runs/v2_cli_20260502_081223_d/n1/artifacts/v2q_n1_a6e7250181274940/cli/sql_response_attempt_1.txt b/Query/sql/v2/runs/v2_cli_20260502_081223_d/n1/artifacts/v2q_n1_a6e7250181274940/cli/sql_response_attempt_1.txt new file mode 100644 index 0000000000000000000000000000000000000000..1b40d5eccabf979ebe6ae1208986c63133e4c011 --- /dev/null +++ b/Query/sql/v2/runs/v2_cli_20260502_081223_d/n1/artifacts/v2q_n1_a6e7250181274940/cli/sql_response_attempt_1.txt @@ -0,0 +1 @@ +{"sql":"-- template_id: tpl_m4_group_condition_rate\nSELECT \"class\",\n AVG(CASE WHEN \"class\" = '0' THEN 1 ELSE 0 END) AS condition_rate\nFROM \"n1\"\nGROUP BY \"class\"\nORDER BY condition_rate DESC;","notes":"Applied the provided Grouped Condition Rate template with \"class\" as both the grouping column and condition column; compared against text value '0' because the schema stores \"class\" as TEXT."} \ No newline at end of file diff --git a/Query/sql/v2/runs/v2_cli_20260502_081223_d/n1/artifacts/v2q_n1_a6e7250181274940/cli/sql_stderr_attempt_1.txt b/Query/sql/v2/runs/v2_cli_20260502_081223_d/n1/artifacts/v2q_n1_a6e7250181274940/cli/sql_stderr_attempt_1.txt new file mode 100644 index 0000000000000000000000000000000000000000..e69de29bb2d1d6434b8b29ae775ad8c2e48c5391 diff --git a/Query/sql/v2/runs/v2_cli_20260502_081223_d/n1/artifacts/v2q_n1_a6edaf12833dab15/cli/conversation.jsonl b/Query/sql/v2/runs/v2_cli_20260502_081223_d/n1/artifacts/v2q_n1_a6edaf12833dab15/cli/conversation.jsonl new file mode 100644 index 0000000000000000000000000000000000000000..03ea345e0e8c792e2b9fc10ca25275a31b29b614 --- /dev/null +++ b/Query/sql/v2/runs/v2_cli_20260502_081223_d/n1/artifacts/v2q_n1_a6edaf12833dab15/cli/conversation.jsonl @@ -0,0 +1,2 @@ +{"attempt": 1, "phase": "sql_generation", "role": "user", "content_path": "cli/sql_prompt_attempt_1.txt", "metrics": {"chars": 29770, "bytes_utf8": 29770, "lines": 794, "estimated_tokens": null}} +{"attempt": 1, "phase": "sql_generation", "role": "assistant", "content_path": "cli/sql_response_attempt_1.txt", "raw_content_path": "cli/sql_response_attempt_1.raw.txt", "stderr_path": "cli/sql_stderr_attempt_1.txt", "metrics": {"chars": 659, "bytes_utf8": 659, "lines": 1, "estimated_tokens": null}, "usage": {"input_tokens": 20436, "cached_input_tokens": 19840, "output_tokens": 696, "reasoning_output_tokens": 516}} diff --git a/Query/sql/v2/runs/v2_cli_20260502_081223_d/n1/artifacts/v2q_n1_a6edaf12833dab15/cli/session_summary.json b/Query/sql/v2/runs/v2_cli_20260502_081223_d/n1/artifacts/v2q_n1_a6edaf12833dab15/cli/session_summary.json new file mode 100644 index 0000000000000000000000000000000000000000..acaaa7ddf4e04df6380ccefaed319655eb684044 --- /dev/null +++ b/Query/sql/v2/runs/v2_cli_20260502_081223_d/n1/artifacts/v2q_n1_a6edaf12833dab15/cli/session_summary.json @@ -0,0 +1,25 @@ +{ + "engine": "v2-cli:codex", + "command": "codex exec --skip-git-repo-check --disable plugins --sandbox read-only --cd \"/data/jialinzhang/SQLagent\" -m gpt-5.4 --json -", + "ai_cli_calls": 1, + "usage_summary": { + "dataset_id": "n1", + "model": "v2-cli:codex", + "run_id": "v2q_n1_a6edaf12833dab15", + "api_calls": 0, + "input_tokens": 20436, + "cached_input_tokens": 19840, + "output_tokens": 696, + "total_tokens": 21132, + "cost_usd": 0.0, + "ai_cli_calls": 1, + "estimated_input_tokens": 0, + "estimated_output_tokens": 0, + "estimated_total_tokens": 0, + "usage_source": "ai_cli_json_usage", + "cli_elapsed_ms_total": 13696.31, + "sql_execution_elapsed_ms_total": 6.69, + "conversation_log_path": "/data/jialinzhang/TabQueryBench/sql_workloads/v2_current/runs_and_launches/runs/v2_cli_20260502_081223_d/n1/artifacts/v2q_n1_a6edaf12833dab15/cli/conversation.jsonl", + "note": "Executed through a local AI CLI with structured usage metadata." + } +} \ No newline at end of file diff --git a/Query/sql/v2/runs/v2_cli_20260502_081223_d/n1/artifacts/v2q_n1_a6edaf12833dab15/cli/sql_attempt_1.metadata.json b/Query/sql/v2/runs/v2_cli_20260502_081223_d/n1/artifacts/v2q_n1_a6edaf12833dab15/cli/sql_attempt_1.metadata.json new file mode 100644 index 0000000000000000000000000000000000000000..e0a98d8110e34b40fc8388a87fe965bba33a2003 --- /dev/null +++ b/Query/sql/v2/runs/v2_cli_20260502_081223_d/n1/artifacts/v2q_n1_a6edaf12833dab15/cli/sql_attempt_1.metadata.json @@ -0,0 +1,45 @@ +{ + "attempt": 1, + "phase": "sql_generation", + "command": "codex exec --skip-git-repo-check --disable plugins --sandbox read-only --cd \"/data/jialinzhang/SQLagent\" -m gpt-5.4 --json -", + "started_at": "2026-05-19T15:35:57.334427+00:00", + "ended_at": "2026-05-19T15:36:11.030779+00:00", + "elapsed_ms": 13696.31, + "prompt_metrics": { + "chars": 29770, + "bytes_utf8": 29770, + "lines": 794, + "estimated_tokens": null + }, + "stdout_metrics": { + "chars": 1051, + "bytes_utf8": 1051, + "lines": 4, + "estimated_tokens": null + }, + "stderr_metrics": { + "chars": 0, + "bytes_utf8": 0, + "lines": 0, + "estimated_tokens": null + }, + "parsed_output": { + "format": "jsonl_events", + "text_metrics": { + "chars": 659, + "bytes_utf8": 659, + "lines": 1, + "estimated_tokens": null + }, + "usage": { + "input_tokens": 20436, + "cached_input_tokens": 19840, + "output_tokens": 696, + "reasoning_output_tokens": 516 + } + }, + "prompt_path": "cli/sql_prompt_attempt_1.txt", + "response_path": "cli/sql_response_attempt_1.txt", + "raw_response_path": "cli/sql_response_attempt_1.raw.txt", + "stderr_path": "cli/sql_stderr_attempt_1.txt" +} \ No newline at end of file diff --git a/Query/sql/v2/runs/v2_cli_20260502_081223_d/n1/artifacts/v2q_n1_a6edaf12833dab15/cli/sql_prompt_attempt_1.txt b/Query/sql/v2/runs/v2_cli_20260502_081223_d/n1/artifacts/v2q_n1_a6edaf12833dab15/cli/sql_prompt_attempt_1.txt new file mode 100644 index 0000000000000000000000000000000000000000..c970c82a9719033505bf99f278e9996068658a29 --- /dev/null +++ b/Query/sql/v2/runs/v2_cli_20260502_081223_d/n1/artifacts/v2q_n1_a6edaf12833dab15/cli/sql_prompt_attempt_1.txt @@ -0,0 +1,794 @@ +You are generating one SQLite SELECT query for a single-table SQL QA task. +Return strict JSON only, with this schema: {"sql": "...", "notes": "..."}. +Rules: +- Use only the provided table and columns. +- Do not write INSERT, UPDATE, DELETE, DROP, ALTER, CREATE, PRAGMA, ATTACH, DETACH, or VACUUM. +- Prefer the planned template and bound roles when provided. +- Add a leading SQL comment exactly like: -- template_id: . +- Generate SQLite-compatible SQL. SQLite does not support PERCENTILE_CONT or STDDEV. +- Quote identifiers with double quotes. +- Return no markdown and no extra prose. + +Dataset context: +Dataset context for SQL QA: +- dataset_id: n1 +- dataset_name: Spambase +- table_name: n1 +- table_layout: single-table dataset (do not assume joins). +- row_semantics: One row is one email represented by engineered token/character frequency and capitalization-run features. +- task_type: classification +- target_column: class +- main_row_count: 4601 +- important_fields: +- word_freq_make: role=feature, type=numeric_sparse_frequency. tags=['condition_candidate', 'measure', 'rare_pattern_candidate'] desc=Frequency feature for token 'make' in an email message. +- word_freq_address: role=feature, type=numeric_sparse_frequency. tags=['condition_candidate', 'measure', 'rare_pattern_candidate'] desc=Frequency feature for token 'address' in an email message. +- word_freq_all: role=feature, type=numeric_sparse_frequency. tags=['condition_candidate', 'measure', 'rare_pattern_candidate'] desc=Frequency feature for token 'all' in an email message. +- word_freq_3d: role=feature, type=numeric_sparse_frequency. tags=['condition_candidate', 'measure', 'rare_pattern_candidate'] desc=Frequency feature for token '3d' in an email message. +- word_freq_our: role=feature, type=numeric_sparse_frequency. tags=['condition_candidate', 'measure', 'rare_pattern_candidate'] desc=Frequency feature for token 'our' in an email message. +- word_freq_over: role=feature, type=numeric_sparse_frequency. tags=['condition_candidate', 'measure', 'rare_pattern_candidate'] desc=Frequency feature for token 'over' in an email message. +- word_freq_remove: role=feature, type=numeric_sparse_frequency. tags=['condition_candidate', 'measure', 'rare_pattern_candidate'] desc=Frequency feature for token 'remove' in an email message. +- word_freq_internet: role=feature, type=numeric_sparse_frequency. tags=['condition_candidate', 'measure', 'rare_pattern_candidate'] desc=Frequency feature for token 'internet' in an email message. +- word_freq_order: role=feature, type=numeric_sparse_frequency. tags=['condition_candidate', 'measure', 'rare_pattern_candidate'] desc=Frequency feature for token 'order' in an email message. +- word_freq_mail: role=feature, type=numeric_sparse_frequency. tags=['condition_candidate', 'measure', 'rare_pattern_candidate'] desc=Frequency feature for token 'mail' in an email message. +- word_freq_receive: role=feature, type=numeric_sparse_frequency. tags=['condition_candidate', 'measure', 'rare_pattern_candidate'] desc=Frequency feature for token 'receive' in an email message. +- word_freq_will: role=feature, type=numeric_sparse_frequency. tags=['condition_candidate', 'measure', 'rare_pattern_candidate'] desc=Frequency feature for token 'will' in an email message. +- word_freq_people: role=feature, type=numeric_sparse_frequency. tags=['condition_candidate', 'measure', 'rare_pattern_candidate'] desc=Frequency feature for token 'people' in an email message. +- word_freq_report: role=feature, type=numeric_sparse_frequency. tags=['condition_candidate', 'measure', 'rare_pattern_candidate'] desc=Frequency feature for token 'report' in an email message. +- word_freq_addresses: role=feature, type=numeric_sparse_frequency. tags=['condition_candidate', 'measure', 'rare_pattern_candidate'] desc=Frequency feature for token 'addresses' in an email message. +- word_freq_free: role=feature, type=numeric_sparse_frequency. tags=['condition_candidate', 'measure', 'rare_pattern_candidate'] desc=Frequency feature for token 'free' in an email message. +- word_freq_business: role=feature, type=numeric_sparse_frequency. tags=['condition_candidate', 'measure', 'rare_pattern_candidate'] desc=Frequency feature for token 'business' in an email message. +- word_freq_email: role=feature, type=numeric_sparse_frequency. tags=['condition_candidate', 'measure', 'rare_pattern_candidate'] desc=Frequency feature for token 'email' in an email message. +- word_freq_you: role=feature, type=numeric_sparse_frequency. tags=['condition_candidate', 'measure', 'rare_pattern_candidate'] desc=Frequency feature for token 'you' in an email message. +- word_freq_credit: role=feature, type=numeric_sparse_frequency. tags=['condition_candidate', 'measure', 'rare_pattern_candidate'] desc=Frequency feature for token 'credit' in an email message. +- word_freq_your: role=feature, type=numeric_sparse_frequency. tags=['condition_candidate', 'measure', 'rare_pattern_candidate'] desc=Frequency feature for token 'your' in an email message. +- word_freq_font: role=feature, type=numeric_sparse_frequency. tags=['condition_candidate', 'measure', 'rare_pattern_candidate'] desc=Frequency feature for token 'font' in an email message. +- word_freq_000: role=feature, type=numeric_sparse_frequency. tags=['condition_candidate', 'measure', 'rare_pattern_candidate'] desc=Frequency feature for token '000' in an email message. +- word_freq_money: role=feature, type=numeric_sparse_frequency. tags=['condition_candidate', 'measure', 'rare_pattern_candidate'] desc=Frequency feature for token 'money' in an email message. +- word_freq_hp: role=feature, type=numeric_sparse_frequency. tags=['condition_candidate', 'measure', 'rare_pattern_candidate'] desc=Frequency feature for token 'hp' in an email message. +- word_freq_hpl: role=feature, type=numeric_sparse_frequency. tags=['condition_candidate', 'measure', 'rare_pattern_candidate'] desc=Frequency feature for token 'hpl' in an email message. +- word_freq_george: role=feature, type=numeric_sparse_frequency. tags=['condition_candidate', 'measure', 'rare_pattern_candidate'] desc=Frequency feature for token 'george' in an email message. +- word_freq_650: role=feature, type=numeric_sparse_frequency. tags=['condition_candidate', 'measure', 'rare_pattern_candidate'] desc=Frequency feature for token '650' in an email message. +- word_freq_lab: role=feature, type=numeric_sparse_frequency. tags=['condition_candidate', 'measure', 'rare_pattern_candidate'] desc=Frequency feature for token 'lab' in an email message. +- word_freq_labs: role=feature, type=numeric_sparse_frequency. tags=['condition_candidate', 'measure', 'rare_pattern_candidate'] desc=Frequency feature for token 'labs' in an email message. +- word_freq_telnet: role=feature, type=numeric_sparse_frequency. tags=['condition_candidate', 'measure', 'rare_pattern_candidate'] desc=Frequency feature for token 'telnet' in an email message. +- word_freq_857: role=feature, type=numeric_sparse_frequency. tags=['condition_candidate', 'measure', 'rare_pattern_candidate'] desc=Frequency feature for token '857' in an email message. +- word_freq_data: role=feature, type=numeric_sparse_frequency. tags=['condition_candidate', 'measure', 'rare_pattern_candidate'] desc=Frequency feature for token 'data' in an email message. +- word_freq_415: role=feature, type=numeric_sparse_frequency. tags=['condition_candidate', 'measure', 'rare_pattern_candidate'] desc=Frequency feature for token '415' in an email message. +- word_freq_85: role=feature, type=numeric_sparse_frequency. tags=['condition_candidate', 'measure', 'rare_pattern_candidate'] desc=Frequency feature for token '85' in an email message. +- word_freq_technology: role=feature, type=numeric_sparse_frequency. tags=['condition_candidate', 'measure', 'rare_pattern_candidate'] desc=Frequency feature for token 'technology' in an email message. +- word_freq_1999: role=feature, type=numeric_sparse_frequency. tags=['condition_candidate', 'measure', 'rare_pattern_candidate'] desc=Frequency feature for token '1999' in an email message. +- word_freq_parts: role=feature, type=numeric_sparse_frequency. tags=['condition_candidate', 'measure', 'rare_pattern_candidate'] desc=Frequency feature for token 'parts' in an email message. +- word_freq_pm: role=feature, type=numeric_sparse_frequency. tags=['condition_candidate', 'measure', 'rare_pattern_candidate'] desc=Frequency feature for token 'pm' in an email message. +- word_freq_direct: role=feature, type=numeric_sparse_frequency. tags=['condition_candidate', 'measure', 'rare_pattern_candidate'] desc=Frequency feature for token 'direct' in an email message. +- word_freq_cs: role=feature, type=numeric_sparse_frequency. tags=['condition_candidate', 'measure', 'rare_pattern_candidate'] desc=Frequency feature for token 'cs' in an email message. +- word_freq_meeting: role=feature, type=numeric_sparse_frequency. tags=['condition_candidate', 'measure', 'rare_pattern_candidate'] desc=Frequency feature for token 'meeting' in an email message. +- word_freq_original: role=feature, type=numeric_sparse_frequency. tags=['condition_candidate', 'measure', 'rare_pattern_candidate'] desc=Frequency feature for token 'original' in an email message. +- word_freq_project: role=feature, type=numeric_sparse_frequency. tags=['condition_candidate', 'measure', 'rare_pattern_candidate'] desc=Frequency feature for token 'project' in an email message. +- word_freq_re: role=feature, type=numeric_sparse_frequency. tags=['condition_candidate', 'measure', 'rare_pattern_candidate'] desc=Frequency feature for token 're' in an email message. +- word_freq_edu: role=feature, type=numeric_sparse_frequency. tags=['condition_candidate', 'measure', 'rare_pattern_candidate'] desc=Frequency feature for token 'edu' in an email message. +- word_freq_table: role=feature, type=numeric_sparse_frequency. tags=['condition_candidate', 'measure', 'rare_pattern_candidate'] desc=Frequency feature for token 'table' in an email message. +- word_freq_conference: role=feature, type=numeric_sparse_frequency. tags=['condition_candidate', 'measure', 'rare_pattern_candidate'] desc=Frequency feature for token 'conference' in an email message. +- char_freq_%3B: role=feature, type=numeric_sparse_frequency. tags=['condition_candidate', 'measure', 'rare_pattern_candidate'] desc=Character frequency feature for symbol ';'. +- char_freq_%28: role=feature, type=numeric_sparse_frequency. tags=['condition_candidate', 'measure', 'rare_pattern_candidate'] desc=Character frequency feature for symbol '('. +- char_freq_%5B: role=feature, type=numeric_sparse_frequency. tags=['condition_candidate', 'measure', 'rare_pattern_candidate'] desc=Character frequency feature for symbol '['. +- char_freq_%21: role=feature, type=numeric_sparse_frequency. tags=['condition_candidate', 'measure', 'rare_pattern_candidate'] desc=Character frequency feature for symbol '!'. +- char_freq_%24: role=feature, type=numeric_sparse_frequency. tags=['condition_candidate', 'measure', 'rare_pattern_candidate'] desc=Character frequency feature for symbol '$'. +- char_freq_%23: role=feature, type=numeric_sparse_frequency. tags=['condition_candidate', 'measure', 'rare_pattern_candidate'] desc=Character frequency feature for symbol '#'. +- capital_run_length_average: role=feature, type=numeric_heavy_tail. tags=['condition_candidate', 'measure', 'rare_pattern_candidate'] desc=Average length of uninterrupted capital-letter runs. +- capital_run_length_longest: role=feature, type=numeric_heavy_tail. tags=['condition_candidate', 'measure', 'rare_pattern_candidate'] desc=Longest uninterrupted capital-letter run. +- capital_run_length_total: role=feature, type=numeric_heavy_tail. tags=['condition_candidate', 'measure', 'rare_pattern_candidate'] desc=Total number of capital letters in runs. +- class: role=target, type=binary_target. tags=['target_candidate'] desc=Binary spam label (1=spam, 0=non-spam). +- useful_field_combinations: [['word_freq_free', 'word_freq_you', 'class'], ['char_freq_%21', 'capital_run_length_longest', 'class'], ['word_freq_remove', 'word_freq_money', 'class']] +- fields_requiring_caution: ['capital_run_length_average', 'capital_run_length_longest', 'capital_run_length_total'] +- source_url: https://www.openml.org/d/44 + +SQLite schema snapshot: +{ + "table_name": "n1", + "quoted_table_name": "\"n1\"", + "row_count": 4601, + "columns": [ + { + "name": "word_freq_make", + "type": "TEXT", + "notnull": false, + "pk": false + }, + { + "name": "word_freq_address", + "type": "TEXT", + "notnull": false, + "pk": false + }, + { + "name": "word_freq_all", + "type": "TEXT", + "notnull": false, + "pk": false + }, + { + "name": "word_freq_3d", + "type": "TEXT", + "notnull": false, + "pk": false + }, + { + "name": "word_freq_our", + "type": "TEXT", + "notnull": false, + "pk": false + }, + { + "name": "word_freq_over", + "type": "TEXT", + "notnull": false, + "pk": false + }, + { + "name": "word_freq_remove", + "type": "TEXT", + "notnull": false, + "pk": false + }, + { + "name": "word_freq_internet", + "type": "TEXT", + "notnull": false, + "pk": false + }, + { + "name": "word_freq_order", + "type": "TEXT", + "notnull": false, + "pk": false + }, + { + "name": "word_freq_mail", + "type": "TEXT", + "notnull": false, + "pk": false + }, + { + "name": "word_freq_receive", + "type": "TEXT", + "notnull": false, + "pk": false + }, + { + "name": "word_freq_will", + "type": "TEXT", + "notnull": false, + "pk": false + }, + { + "name": "word_freq_people", + "type": "TEXT", + "notnull": false, + "pk": false + }, + { + "name": "word_freq_report", + "type": "TEXT", + "notnull": false, + "pk": false + }, + { + "name": "word_freq_addresses", + "type": "TEXT", + "notnull": false, + "pk": false + }, + { + "name": "word_freq_free", + "type": "TEXT", + "notnull": false, + "pk": false + }, + { + "name": "word_freq_business", + "type": "TEXT", + "notnull": false, + "pk": false + }, + { + "name": "word_freq_email", + "type": "TEXT", + "notnull": false, + "pk": false + }, + { + "name": "word_freq_you", + "type": "TEXT", + "notnull": false, + "pk": false + }, + { + "name": "word_freq_credit", + "type": "TEXT", + "notnull": false, + "pk": false + }, + { + "name": "word_freq_your", + "type": "TEXT", + "notnull": false, + "pk": false + }, + { + "name": "word_freq_font", + "type": "TEXT", + "notnull": false, + "pk": false + }, + { + "name": "word_freq_000", + "type": "TEXT", + "notnull": false, + "pk": false + }, + { + "name": "word_freq_money", + "type": "TEXT", + "notnull": false, + "pk": false + }, + { + "name": "word_freq_hp", + "type": "TEXT", + "notnull": false, + "pk": false + }, + { + "name": "word_freq_hpl", + "type": "TEXT", + "notnull": false, + "pk": false + }, + { + "name": "word_freq_george", + "type": "TEXT", + "notnull": false, + "pk": false + }, + { + "name": "word_freq_650", + "type": "TEXT", + "notnull": false, + "pk": false + }, + { + "name": "word_freq_lab", + "type": "TEXT", + "notnull": false, + "pk": false + }, + { + "name": "word_freq_labs", + "type": "TEXT", + "notnull": false, + "pk": false + }, + { + "name": "word_freq_telnet", + "type": "TEXT", + "notnull": false, + "pk": false + }, + { + "name": "word_freq_857", + "type": "TEXT", + "notnull": false, + "pk": false + }, + { + "name": "word_freq_data", + "type": "TEXT", + "notnull": false, + "pk": false + }, + { + "name": "word_freq_415", + "type": "TEXT", + "notnull": false, + "pk": false + }, + { + "name": "word_freq_85", + "type": "TEXT", + "notnull": false, + "pk": false + }, + { + "name": "word_freq_technology", + "type": "TEXT", + "notnull": false, + "pk": false + }, + { + "name": "word_freq_1999", + "type": "TEXT", + "notnull": false, + "pk": false + }, + { + "name": "word_freq_parts", + "type": "TEXT", + "notnull": false, + "pk": false + }, + { + "name": "word_freq_pm", + "type": "TEXT", + "notnull": false, + "pk": false + }, + { + "name": "word_freq_direct", + "type": "TEXT", + "notnull": false, + "pk": false + }, + { + "name": "word_freq_cs", + "type": "TEXT", + "notnull": false, + "pk": false + }, + { + "name": "word_freq_meeting", + "type": "TEXT", + "notnull": false, + "pk": false + }, + { + "name": "word_freq_original", + "type": "TEXT", + "notnull": false, + "pk": false + }, + { + "name": "word_freq_project", + "type": "TEXT", + "notnull": false, + "pk": false + }, + { + "name": "word_freq_re", + "type": "TEXT", + "notnull": false, + "pk": false + }, + { + "name": "word_freq_edu", + "type": "TEXT", + "notnull": false, + "pk": false + }, + { + "name": "word_freq_table", + "type": "TEXT", + "notnull": false, + "pk": false + }, + { + "name": "word_freq_conference", + "type": "TEXT", + "notnull": false, + "pk": false + }, + { + "name": "char_freq_%3B", + "type": "TEXT", + "notnull": false, + "pk": false + }, + { + "name": "char_freq_%28", + "type": "TEXT", + "notnull": false, + "pk": false + }, + { + "name": "char_freq_%5B", + "type": "TEXT", + "notnull": false, + "pk": false + }, + { + "name": "char_freq_%21", + "type": "TEXT", + "notnull": false, + "pk": false + }, + { + "name": "char_freq_%24", + "type": "TEXT", + "notnull": false, + "pk": false + }, + { + "name": "char_freq_%23", + "type": "TEXT", + "notnull": false, + "pk": false + }, + { + "name": "capital_run_length_average", + "type": "TEXT", + "notnull": false, + "pk": false + }, + { + "name": "capital_run_length_longest", + "type": "TEXT", + "notnull": false, + "pk": false + }, + { + "name": "capital_run_length_total", + "type": "TEXT", + "notnull": false, + "pk": false + }, + { + "name": "class", + "type": "TEXT", + "notnull": false, + "pk": false + } + ], + "sample_rows": [ + { + "word_freq_make": "0", + "word_freq_address": "0.64", + "word_freq_all": "0.64", + "word_freq_3d": "0", + "word_freq_our": "0.32", + "word_freq_over": "0", + "word_freq_remove": "0", + "word_freq_internet": "0", + "word_freq_order": "0", + "word_freq_mail": "0", + "word_freq_receive": "0", + "word_freq_will": "0.64", + "word_freq_people": "0", + "word_freq_report": "0", + "word_freq_addresses": "0", + "word_freq_free": "0.32", + "word_freq_business": "0", + "word_freq_email": "1.29", + "word_freq_you": "1.93", + "word_freq_credit": "0", + "word_freq_your": "0.96", + "word_freq_font": "0", + "word_freq_000": "0", + "word_freq_money": "0", + "word_freq_hp": "0", + "word_freq_hpl": "0", + "word_freq_george": "0", + "word_freq_650": "0", + "word_freq_lab": "0", + "word_freq_labs": "0", + "word_freq_telnet": "0", + "word_freq_857": "0", + "word_freq_data": "0", + "word_freq_415": "0", + "word_freq_85": "0", + "word_freq_technology": "0", + "word_freq_1999": "0", + "word_freq_parts": "0", + "word_freq_pm": "0", + "word_freq_direct": "0", + "word_freq_cs": "0", + "word_freq_meeting": "0", + "word_freq_original": "0", + "word_freq_project": "0", + "word_freq_re": "0", + "word_freq_edu": "0", + "word_freq_table": "0", + "word_freq_conference": "0", + "char_freq_%3B": "0", + "char_freq_%28": "0", + "char_freq_%5B": "0", + "char_freq_%21": "0.778", + "char_freq_%24": "0", + "char_freq_%23": "0", + "capital_run_length_average": "3.756", + "capital_run_length_longest": "61", + "capital_run_length_total": "278", + "class": "1" + }, + { + "word_freq_make": "0.21", + "word_freq_address": "0.28", + "word_freq_all": "0.5", + "word_freq_3d": "0", + "word_freq_our": "0.14", + "word_freq_over": "0.28", + "word_freq_remove": "0.21", + "word_freq_internet": "0.07", + "word_freq_order": "0", + "word_freq_mail": "0.94", + "word_freq_receive": "0.21", + "word_freq_will": "0.79", + "word_freq_people": "0.65", + "word_freq_report": "0.21", + "word_freq_addresses": "0.14", + "word_freq_free": "0.14", + "word_freq_business": "0.07", + "word_freq_email": "0.28", + "word_freq_you": "3.47", + "word_freq_credit": "0", + "word_freq_your": "1.59", + "word_freq_font": "0", + "word_freq_000": "0.43", + "word_freq_money": "0.43", + "word_freq_hp": "0", + "word_freq_hpl": "0", + "word_freq_george": "0", + "word_freq_650": "0", + "word_freq_lab": "0", + "word_freq_labs": "0", + "word_freq_telnet": "0", + "word_freq_857": "0", + "word_freq_data": "0", + "word_freq_415": "0", + "word_freq_85": "0", + "word_freq_technology": "0", + "word_freq_1999": "0.07", + "word_freq_parts": "0", + "word_freq_pm": "0", + "word_freq_direct": "0", + "word_freq_cs": "0", + "word_freq_meeting": "0", + "word_freq_original": "0", + "word_freq_project": "0", + "word_freq_re": "0", + "word_freq_edu": "0", + "word_freq_table": "0", + "word_freq_conference": "0", + "char_freq_%3B": "0", + "char_freq_%28": "0.132", + "char_freq_%5B": "0", + "char_freq_%21": "0.372", + "char_freq_%24": "0.18", + "char_freq_%23": "0.048", + "capital_run_length_average": "5.114", + "capital_run_length_longest": "101", + "capital_run_length_total": "1028", + "class": "1" + }, + { + "word_freq_make": "0.06", + "word_freq_address": "0", + "word_freq_all": "0.71", + "word_freq_3d": "0", + "word_freq_our": "1.23", + "word_freq_over": "0.19", + "word_freq_remove": "0.19", + "word_freq_internet": "0.12", + "word_freq_order": "0.64", + "word_freq_mail": "0.25", + "word_freq_receive": "0.38", + "word_freq_will": "0.45", + "word_freq_people": "0.12", + "word_freq_report": "0", + "word_freq_addresses": "1.75", + "word_freq_free": "0.06", + "word_freq_business": "0.06", + "word_freq_email": "1.03", + "word_freq_you": "1.36", + "word_freq_credit": "0.32", + "word_freq_your": "0.51", + "word_freq_font": "0", + "word_freq_000": "1.16", + "word_freq_money": "0.06", + "word_freq_hp": "0", + "word_freq_hpl": "0", + "word_freq_george": "0", + "word_freq_650": "0", + "word_freq_lab": "0", + "word_freq_labs": "0", + "word_freq_telnet": "0", + "word_freq_857": "0", + "word_freq_data": "0", + "word_freq_415": "0", + "word_freq_85": "0", + "word_freq_technology": "0", + "word_freq_1999": "0", + "word_freq_parts": "0", + "word_freq_pm": "0", + "word_freq_direct": "0.06", + "word_freq_cs": "0", + "word_freq_meeting": "0", + "word_freq_original": "0.12", + "word_freq_project": "0", + "word_freq_re": "0.06", + "word_freq_edu": "0.06", + "word_freq_table": "0", + "word_freq_conference": "0", + "char_freq_%3B": "0.01", + "char_freq_%28": "0.143", + "char_freq_%5B": "0", + "char_freq_%21": "0.276", + "char_freq_%24": "0.184", + "char_freq_%23": "0.01", + "capital_run_length_average": "9.821", + "capital_run_length_longest": "485", + "capital_run_length_total": "2259", + "class": "1" + }, + { + "word_freq_make": "0", + "word_freq_address": "0", + "word_freq_all": "0", + "word_freq_3d": "0", + "word_freq_our": "0.63", + "word_freq_over": "0", + "word_freq_remove": "0.31", + "word_freq_internet": "0.63", + "word_freq_order": "0.31", + "word_freq_mail": "0.63", + "word_freq_receive": "0.31", + "word_freq_will": "0.31", + "word_freq_people": "0.31", + "word_freq_report": "0", + "word_freq_addresses": "0", + "word_freq_free": "0.31", + "word_freq_business": "0", + "word_freq_email": "0", + "word_freq_you": "3.18", + "word_freq_credit": "0", + "word_freq_your": "0.31", + "word_freq_font": "0", + "word_freq_000": "0", + "word_freq_money": "0", + "word_freq_hp": "0", + "word_freq_hpl": "0", + "word_freq_george": "0", + "word_freq_650": "0", + "word_freq_lab": "0", + "word_freq_labs": "0", + "word_freq_telnet": "0", + "word_freq_857": "0", + "word_freq_data": "0", + "word_freq_415": "0", + "word_freq_85": "0", + "word_freq_technology": "0", + "word_freq_1999": "0", + "word_freq_parts": "0", + "word_freq_pm": "0", + "word_freq_direct": "0", + "word_freq_cs": "0", + "word_freq_meeting": "0", + "word_freq_original": "0", + "word_freq_project": "0", + "word_freq_re": "0", + "word_freq_edu": "0", + "word_freq_table": "0", + "word_freq_conference": "0", + "char_freq_%3B": "0", + "char_freq_%28": "0.137", + "char_freq_%5B": "0", + "char_freq_%21": "0.137", + "char_freq_%24": "0", + "char_freq_%23": "0", + "capital_run_length_average": "3.537", + "capital_run_length_longest": "40", + "capital_run_length_total": "191", + "class": "1" + }, + { + "word_freq_make": "0", + "word_freq_address": "0", + "word_freq_all": "0", + "word_freq_3d": "0", + "word_freq_our": "0.63", + "word_freq_over": "0", + "word_freq_remove": "0.31", + "word_freq_internet": "0.63", + "word_freq_order": "0.31", + "word_freq_mail": "0.63", + "word_freq_receive": "0.31", + "word_freq_will": "0.31", + "word_freq_people": "0.31", + "word_freq_report": "0", + "word_freq_addresses": "0", + "word_freq_free": "0.31", + "word_freq_business": "0", + "word_freq_email": "0", + "word_freq_you": "3.18", + "word_freq_credit": "0", + "word_freq_your": "0.31", + "word_freq_font": "0", + "word_freq_000": "0", + "word_freq_money": "0", + "word_freq_hp": "0", + "word_freq_hpl": "0", + "word_freq_george": "0", + "word_freq_650": "0", + "word_freq_lab": "0", + "word_freq_labs": "0", + "word_freq_telnet": "0", + "word_freq_857": "0", + "word_freq_data": "0", + "word_freq_415": "0", + "word_freq_85": "0", + "word_freq_technology": "0", + "word_freq_1999": "0", + "word_freq_parts": "0", + "word_freq_pm": "0", + "word_freq_direct": "0", + "word_freq_cs": "0", + "word_freq_meeting": "0", + "word_freq_original": "0", + "word_freq_project": "0", + "word_freq_re": "0", + "word_freq_edu": "0", + "word_freq_table": "0", + "word_freq_conference": "0", + "char_freq_%3B": "0", + "char_freq_%28": "0.135", + "char_freq_%5B": "0", + "char_freq_%21": "0.135", + "char_freq_%24": "0", + "char_freq_%23": "0", + "capital_run_length_average": "3.537", + "capital_run_length_longest": "40", + "capital_run_length_total": "191", + "class": "1" + } + ] +} + +Shortlisted templates: +[ + { + "template_id": "tpl_tpcds_within_group_share", + "template_name": "Within-Group Share of Total", + "primary_family": "conditional_dependency_structure", + "portability": "partial", + "sql_skeleton": "SELECT {group_col}, {item_col},\n SUM({measure_col}) AS total_measure,\n SUM({measure_col}) * 100.0 / SUM(SUM({measure_col})) OVER (PARTITION BY {group_col}) AS share_within_group\nFROM {table}\nGROUP BY {group_col}, {item_col}\nORDER BY share_within_group DESC;", + "required_roles": [ + "group_col", + "item_col", + "measure_col" + ] + } +] + +Problem instance: +{ + "dataset_id": "n1", + "question": "Use template Within-Group Share of Total to probe dependency_strength_similarity with semantic role within_group_proportion. Focus on group_col=class, measure_col=word_freq_lab.", + "planned_template_id": "tpl_tpcds_within_group_share", + "bindings": { + "group_col": "class", + "measure_col": "word_freq_lab", + "item_col": "word_freq_labs", + "top_k": 13, + "top_n": 3, + "num_tiles": 10, + "percentile_value": 0.95, + "z_threshold": 2.0, + "fraction_threshold": 0.1, + "baseline_multiplier": 1.5, + "baseline_fraction": 0.1, + "min_group_size": 5, + "min_support": 5, + "measure_threshold": 0.0, + "time_grain": "month", + "lookback_rows": 3, + "current_period_start": "'2024-01-01'", + "current_period_end": "'2024-04-01'", + "previous_period_start": "'2023-10-01'", + "previous_period_end": "'2024-01-01'", + "drift_ratio_threshold": 0.8 + }, + "can_vary": [], + "must_fix": [], + "runtime_sql_skeleton": "SELECT {group_col}, {item_col},\n SUM({measure_col}) AS total_measure,\n SUM({measure_col}) * 100.0 / SUM(SUM({measure_col})) OVER (PARTITION BY {group_col}) AS share_within_group\nFROM {table}\nGROUP BY {group_col}, {item_col}\nORDER BY share_within_group DESC;" +} + +Repair context: +{} diff --git a/Query/sql/v2/runs/v2_cli_20260502_081223_d/n1/artifacts/v2q_n1_a6edaf12833dab15/cli/sql_response_attempt_1.raw.txt b/Query/sql/v2/runs/v2_cli_20260502_081223_d/n1/artifacts/v2q_n1_a6edaf12833dab15/cli/sql_response_attempt_1.raw.txt new file mode 100644 index 0000000000000000000000000000000000000000..8f1e93f247ab9fe8899930a5eac5cf54e102eea0 --- /dev/null +++ b/Query/sql/v2/runs/v2_cli_20260502_081223_d/n1/artifacts/v2q_n1_a6edaf12833dab15/cli/sql_response_attempt_1.raw.txt @@ -0,0 +1,4 @@ +{"type":"thread.started","thread_id":"019e40e1-25ab-7970-a0ab-7d18ed611eb0"} +{"type":"turn.started"} +{"type":"item.completed","item":{"id":"item_0","type":"agent_message","text":"{\"sql\":\"-- template_id: tpl_tpcds_within_group_share\\nSELECT \\\"class\\\", \\\"word_freq_labs\\\",\\n SUM(CAST(\\\"word_freq_lab\\\" AS REAL)) AS \\\"total_measure\\\",\\n SUM(CAST(\\\"word_freq_lab\\\" AS REAL)) * 100.0 / SUM(SUM(CAST(\\\"word_freq_lab\\\" AS REAL))) OVER (PARTITION BY \\\"class\\\") AS \\\"share_within_group\\\"\\nFROM \\\"n1\\\"\\nGROUP BY \\\"class\\\", \\\"word_freq_labs\\\"\\nORDER BY \\\"share_within_group\\\" DESC;\",\"notes\":\"Used the provided Within-Group Share of Total template with bindings group_col=\\\"class\\\", item_col=\\\"word_freq_labs\\\", and measure_col=\\\"word_freq_lab\\\". Cast the measure column to REAL because the schema stores numeric-looking values as TEXT.\"}"}} +{"type":"turn.completed","usage":{"input_tokens":20436,"cached_input_tokens":19840,"output_tokens":696,"reasoning_output_tokens":516}} diff --git a/Query/sql/v2/runs/v2_cli_20260502_081223_d/n1/artifacts/v2q_n1_a6edaf12833dab15/cli/sql_response_attempt_1.txt b/Query/sql/v2/runs/v2_cli_20260502_081223_d/n1/artifacts/v2q_n1_a6edaf12833dab15/cli/sql_response_attempt_1.txt new file mode 100644 index 0000000000000000000000000000000000000000..fa53609fe9b1e9e648a19e001d179a3cee99def2 --- /dev/null +++ b/Query/sql/v2/runs/v2_cli_20260502_081223_d/n1/artifacts/v2q_n1_a6edaf12833dab15/cli/sql_response_attempt_1.txt @@ -0,0 +1 @@ +{"sql":"-- template_id: tpl_tpcds_within_group_share\nSELECT \"class\", \"word_freq_labs\",\n SUM(CAST(\"word_freq_lab\" AS REAL)) AS \"total_measure\",\n SUM(CAST(\"word_freq_lab\" AS REAL)) * 100.0 / SUM(SUM(CAST(\"word_freq_lab\" AS REAL))) OVER (PARTITION BY \"class\") AS \"share_within_group\"\nFROM \"n1\"\nGROUP BY \"class\", \"word_freq_labs\"\nORDER BY \"share_within_group\" DESC;","notes":"Used the provided Within-Group Share of Total template with bindings group_col=\"class\", item_col=\"word_freq_labs\", and measure_col=\"word_freq_lab\". Cast the measure column to REAL because the schema stores numeric-looking values as TEXT."} \ No newline at end of file diff --git a/Query/sql/v2/runs/v2_cli_20260502_081223_d/n1/artifacts/v2q_n1_a6edaf12833dab15/cli/sql_stderr_attempt_1.txt b/Query/sql/v2/runs/v2_cli_20260502_081223_d/n1/artifacts/v2q_n1_a6edaf12833dab15/cli/sql_stderr_attempt_1.txt new file mode 100644 index 0000000000000000000000000000000000000000..e69de29bb2d1d6434b8b29ae775ad8c2e48c5391 diff --git a/Query/sql/v2/runs/v2_cli_20260502_081223_d/n1/artifacts/v2q_n1_aede3038f7b5c4f1/cli/conversation.jsonl b/Query/sql/v2/runs/v2_cli_20260502_081223_d/n1/artifacts/v2q_n1_aede3038f7b5c4f1/cli/conversation.jsonl new file mode 100644 index 0000000000000000000000000000000000000000..ae5e231b45b99784245d31a0a80c3caecb4d1a34 --- /dev/null +++ b/Query/sql/v2/runs/v2_cli_20260502_081223_d/n1/artifacts/v2q_n1_aede3038f7b5c4f1/cli/conversation.jsonl @@ -0,0 +1,2 @@ +{"attempt": 1, "phase": "sql_generation", "role": "user", "content_path": "cli/sql_prompt_attempt_1.txt", "metrics": {"chars": 30144, "bytes_utf8": 30144, "lines": 795, "estimated_tokens": null}} +{"attempt": 1, "phase": "sql_generation", "role": "assistant", "content_path": "cli/sql_response_attempt_1.txt", "raw_content_path": "cli/sql_response_attempt_1.raw.txt", "stderr_path": "cli/sql_stderr_attempt_1.txt", "metrics": {"chars": 656, "bytes_utf8": 656, "lines": 1, "estimated_tokens": null}, "usage": {"input_tokens": 20520, "cached_input_tokens": 19840, "output_tokens": 696, "reasoning_output_tokens": 516}} diff --git a/Query/sql/v2/runs/v2_cli_20260502_081223_d/n1/artifacts/v2q_n1_aede3038f7b5c4f1/cli/session_summary.json b/Query/sql/v2/runs/v2_cli_20260502_081223_d/n1/artifacts/v2q_n1_aede3038f7b5c4f1/cli/session_summary.json new file mode 100644 index 0000000000000000000000000000000000000000..1f10edb22a1c5b4fe9b5ca4b321b6c8d21378fe0 --- /dev/null +++ b/Query/sql/v2/runs/v2_cli_20260502_081223_d/n1/artifacts/v2q_n1_aede3038f7b5c4f1/cli/session_summary.json @@ -0,0 +1,25 @@ +{ + "engine": "v2-cli:codex", + "command": "codex exec --skip-git-repo-check --disable plugins --sandbox read-only --cd \"/data/jialinzhang/SQLagent\" -m gpt-5.4 --json -", + "ai_cli_calls": 1, + "usage_summary": { + "dataset_id": "n1", + "model": "v2-cli:codex", + "run_id": "v2q_n1_aede3038f7b5c4f1", + "api_calls": 0, + "input_tokens": 20520, + "cached_input_tokens": 19840, + "output_tokens": 696, + "total_tokens": 21216, + "cost_usd": 0.0, + "ai_cli_calls": 1, + "estimated_input_tokens": 0, + "estimated_output_tokens": 0, + "estimated_total_tokens": 0, + "usage_source": "ai_cli_json_usage", + "cli_elapsed_ms_total": 17171.56, + "sql_execution_elapsed_ms_total": 2.79, + "conversation_log_path": "/data/jialinzhang/TabQueryBench/sql_workloads/v2_current/runs_and_launches/runs/v2_cli_20260502_081223_d/n1/artifacts/v2q_n1_aede3038f7b5c4f1/cli/conversation.jsonl", + "note": "Executed through a local AI CLI with structured usage metadata." + } +} \ No newline at end of file diff --git a/Query/sql/v2/runs/v2_cli_20260502_081223_d/n1/artifacts/v2q_n1_aede3038f7b5c4f1/cli/sql_attempt_1.metadata.json b/Query/sql/v2/runs/v2_cli_20260502_081223_d/n1/artifacts/v2q_n1_aede3038f7b5c4f1/cli/sql_attempt_1.metadata.json new file mode 100644 index 0000000000000000000000000000000000000000..04f72efd268ee023aa9492e2730f66fb98e1f834 --- /dev/null +++ b/Query/sql/v2/runs/v2_cli_20260502_081223_d/n1/artifacts/v2q_n1_aede3038f7b5c4f1/cli/sql_attempt_1.metadata.json @@ -0,0 +1,45 @@ +{ + "attempt": 1, + "phase": "sql_generation", + "command": "codex exec --skip-git-repo-check --disable plugins --sandbox read-only --cd \"/data/jialinzhang/SQLagent\" -m gpt-5.4 --json -", + "started_at": "2026-05-19T15:39:16.925782+00:00", + "ended_at": "2026-05-19T15:39:34.097393+00:00", + "elapsed_ms": 17171.56, + "prompt_metrics": { + "chars": 30144, + "bytes_utf8": 30144, + "lines": 795, + "estimated_tokens": null + }, + "stdout_metrics": { + "chars": 1025, + "bytes_utf8": 1025, + "lines": 4, + "estimated_tokens": null + }, + "stderr_metrics": { + "chars": 0, + "bytes_utf8": 0, + "lines": 0, + "estimated_tokens": null + }, + "parsed_output": { + "format": "jsonl_events", + "text_metrics": { + "chars": 656, + "bytes_utf8": 656, + "lines": 1, + "estimated_tokens": null + }, + "usage": { + "input_tokens": 20520, + "cached_input_tokens": 19840, + "output_tokens": 696, + "reasoning_output_tokens": 516 + } + }, + "prompt_path": "cli/sql_prompt_attempt_1.txt", + "response_path": "cli/sql_response_attempt_1.txt", + "raw_response_path": "cli/sql_response_attempt_1.raw.txt", + "stderr_path": "cli/sql_stderr_attempt_1.txt" +} \ No newline at end of file diff --git a/Query/sql/v2/runs/v2_cli_20260502_081223_d/n1/artifacts/v2q_n1_aede3038f7b5c4f1/cli/sql_prompt_attempt_1.txt b/Query/sql/v2/runs/v2_cli_20260502_081223_d/n1/artifacts/v2q_n1_aede3038f7b5c4f1/cli/sql_prompt_attempt_1.txt new file mode 100644 index 0000000000000000000000000000000000000000..8ae9cb9bba75533463a5eeb1472adb92652c3909 --- /dev/null +++ b/Query/sql/v2/runs/v2_cli_20260502_081223_d/n1/artifacts/v2q_n1_aede3038f7b5c4f1/cli/sql_prompt_attempt_1.txt @@ -0,0 +1,795 @@ +You are generating one SQLite SELECT query for a single-table SQL QA task. +Return strict JSON only, with this schema: {"sql": "...", "notes": "..."}. +Rules: +- Use only the provided table and columns. +- Do not write INSERT, UPDATE, DELETE, DROP, ALTER, CREATE, PRAGMA, ATTACH, DETACH, or VACUUM. +- Prefer the planned template and bound roles when provided. +- Add a leading SQL comment exactly like: -- template_id: . +- Generate SQLite-compatible SQL. SQLite does not support PERCENTILE_CONT or STDDEV. +- Quote identifiers with double quotes. +- Return no markdown and no extra prose. + +Dataset context: +Dataset context for SQL QA: +- dataset_id: n1 +- dataset_name: Spambase +- table_name: n1 +- table_layout: single-table dataset (do not assume joins). +- row_semantics: One row is one email represented by engineered token/character frequency and capitalization-run features. +- task_type: classification +- target_column: class +- main_row_count: 4601 +- important_fields: +- word_freq_make: role=feature, type=numeric_sparse_frequency. tags=['condition_candidate', 'measure', 'rare_pattern_candidate'] desc=Frequency feature for token 'make' in an email message. +- word_freq_address: role=feature, type=numeric_sparse_frequency. tags=['condition_candidate', 'measure', 'rare_pattern_candidate'] desc=Frequency feature for token 'address' in an email message. +- word_freq_all: role=feature, type=numeric_sparse_frequency. tags=['condition_candidate', 'measure', 'rare_pattern_candidate'] desc=Frequency feature for token 'all' in an email message. +- word_freq_3d: role=feature, type=numeric_sparse_frequency. tags=['condition_candidate', 'measure', 'rare_pattern_candidate'] desc=Frequency feature for token '3d' in an email message. +- word_freq_our: role=feature, type=numeric_sparse_frequency. tags=['condition_candidate', 'measure', 'rare_pattern_candidate'] desc=Frequency feature for token 'our' in an email message. +- word_freq_over: role=feature, type=numeric_sparse_frequency. tags=['condition_candidate', 'measure', 'rare_pattern_candidate'] desc=Frequency feature for token 'over' in an email message. +- word_freq_remove: role=feature, type=numeric_sparse_frequency. tags=['condition_candidate', 'measure', 'rare_pattern_candidate'] desc=Frequency feature for token 'remove' in an email message. +- word_freq_internet: role=feature, type=numeric_sparse_frequency. tags=['condition_candidate', 'measure', 'rare_pattern_candidate'] desc=Frequency feature for token 'internet' in an email message. +- word_freq_order: role=feature, type=numeric_sparse_frequency. tags=['condition_candidate', 'measure', 'rare_pattern_candidate'] desc=Frequency feature for token 'order' in an email message. +- word_freq_mail: role=feature, type=numeric_sparse_frequency. tags=['condition_candidate', 'measure', 'rare_pattern_candidate'] desc=Frequency feature for token 'mail' in an email message. +- word_freq_receive: role=feature, type=numeric_sparse_frequency. tags=['condition_candidate', 'measure', 'rare_pattern_candidate'] desc=Frequency feature for token 'receive' in an email message. +- word_freq_will: role=feature, type=numeric_sparse_frequency. tags=['condition_candidate', 'measure', 'rare_pattern_candidate'] desc=Frequency feature for token 'will' in an email message. +- word_freq_people: role=feature, type=numeric_sparse_frequency. tags=['condition_candidate', 'measure', 'rare_pattern_candidate'] desc=Frequency feature for token 'people' in an email message. +- word_freq_report: role=feature, type=numeric_sparse_frequency. tags=['condition_candidate', 'measure', 'rare_pattern_candidate'] desc=Frequency feature for token 'report' in an email message. +- word_freq_addresses: role=feature, type=numeric_sparse_frequency. tags=['condition_candidate', 'measure', 'rare_pattern_candidate'] desc=Frequency feature for token 'addresses' in an email message. +- word_freq_free: role=feature, type=numeric_sparse_frequency. tags=['condition_candidate', 'measure', 'rare_pattern_candidate'] desc=Frequency feature for token 'free' in an email message. +- word_freq_business: role=feature, type=numeric_sparse_frequency. tags=['condition_candidate', 'measure', 'rare_pattern_candidate'] desc=Frequency feature for token 'business' in an email message. +- word_freq_email: role=feature, type=numeric_sparse_frequency. tags=['condition_candidate', 'measure', 'rare_pattern_candidate'] desc=Frequency feature for token 'email' in an email message. +- word_freq_you: role=feature, type=numeric_sparse_frequency. tags=['condition_candidate', 'measure', 'rare_pattern_candidate'] desc=Frequency feature for token 'you' in an email message. +- word_freq_credit: role=feature, type=numeric_sparse_frequency. tags=['condition_candidate', 'measure', 'rare_pattern_candidate'] desc=Frequency feature for token 'credit' in an email message. +- word_freq_your: role=feature, type=numeric_sparse_frequency. tags=['condition_candidate', 'measure', 'rare_pattern_candidate'] desc=Frequency feature for token 'your' in an email message. +- word_freq_font: role=feature, type=numeric_sparse_frequency. tags=['condition_candidate', 'measure', 'rare_pattern_candidate'] desc=Frequency feature for token 'font' in an email message. +- word_freq_000: role=feature, type=numeric_sparse_frequency. tags=['condition_candidate', 'measure', 'rare_pattern_candidate'] desc=Frequency feature for token '000' in an email message. +- word_freq_money: role=feature, type=numeric_sparse_frequency. tags=['condition_candidate', 'measure', 'rare_pattern_candidate'] desc=Frequency feature for token 'money' in an email message. +- word_freq_hp: role=feature, type=numeric_sparse_frequency. tags=['condition_candidate', 'measure', 'rare_pattern_candidate'] desc=Frequency feature for token 'hp' in an email message. +- word_freq_hpl: role=feature, type=numeric_sparse_frequency. tags=['condition_candidate', 'measure', 'rare_pattern_candidate'] desc=Frequency feature for token 'hpl' in an email message. +- word_freq_george: role=feature, type=numeric_sparse_frequency. tags=['condition_candidate', 'measure', 'rare_pattern_candidate'] desc=Frequency feature for token 'george' in an email message. +- word_freq_650: role=feature, type=numeric_sparse_frequency. tags=['condition_candidate', 'measure', 'rare_pattern_candidate'] desc=Frequency feature for token '650' in an email message. +- word_freq_lab: role=feature, type=numeric_sparse_frequency. tags=['condition_candidate', 'measure', 'rare_pattern_candidate'] desc=Frequency feature for token 'lab' in an email message. +- word_freq_labs: role=feature, type=numeric_sparse_frequency. tags=['condition_candidate', 'measure', 'rare_pattern_candidate'] desc=Frequency feature for token 'labs' in an email message. +- word_freq_telnet: role=feature, type=numeric_sparse_frequency. tags=['condition_candidate', 'measure', 'rare_pattern_candidate'] desc=Frequency feature for token 'telnet' in an email message. +- word_freq_857: role=feature, type=numeric_sparse_frequency. tags=['condition_candidate', 'measure', 'rare_pattern_candidate'] desc=Frequency feature for token '857' in an email message. +- word_freq_data: role=feature, type=numeric_sparse_frequency. tags=['condition_candidate', 'measure', 'rare_pattern_candidate'] desc=Frequency feature for token 'data' in an email message. +- word_freq_415: role=feature, type=numeric_sparse_frequency. tags=['condition_candidate', 'measure', 'rare_pattern_candidate'] desc=Frequency feature for token '415' in an email message. +- word_freq_85: role=feature, type=numeric_sparse_frequency. tags=['condition_candidate', 'measure', 'rare_pattern_candidate'] desc=Frequency feature for token '85' in an email message. +- word_freq_technology: role=feature, type=numeric_sparse_frequency. tags=['condition_candidate', 'measure', 'rare_pattern_candidate'] desc=Frequency feature for token 'technology' in an email message. +- word_freq_1999: role=feature, type=numeric_sparse_frequency. tags=['condition_candidate', 'measure', 'rare_pattern_candidate'] desc=Frequency feature for token '1999' in an email message. +- word_freq_parts: role=feature, type=numeric_sparse_frequency. tags=['condition_candidate', 'measure', 'rare_pattern_candidate'] desc=Frequency feature for token 'parts' in an email message. +- word_freq_pm: role=feature, type=numeric_sparse_frequency. tags=['condition_candidate', 'measure', 'rare_pattern_candidate'] desc=Frequency feature for token 'pm' in an email message. +- word_freq_direct: role=feature, type=numeric_sparse_frequency. tags=['condition_candidate', 'measure', 'rare_pattern_candidate'] desc=Frequency feature for token 'direct' in an email message. +- word_freq_cs: role=feature, type=numeric_sparse_frequency. tags=['condition_candidate', 'measure', 'rare_pattern_candidate'] desc=Frequency feature for token 'cs' in an email message. +- word_freq_meeting: role=feature, type=numeric_sparse_frequency. tags=['condition_candidate', 'measure', 'rare_pattern_candidate'] desc=Frequency feature for token 'meeting' in an email message. +- word_freq_original: role=feature, type=numeric_sparse_frequency. tags=['condition_candidate', 'measure', 'rare_pattern_candidate'] desc=Frequency feature for token 'original' in an email message. +- word_freq_project: role=feature, type=numeric_sparse_frequency. tags=['condition_candidate', 'measure', 'rare_pattern_candidate'] desc=Frequency feature for token 'project' in an email message. +- word_freq_re: role=feature, type=numeric_sparse_frequency. tags=['condition_candidate', 'measure', 'rare_pattern_candidate'] desc=Frequency feature for token 're' in an email message. +- word_freq_edu: role=feature, type=numeric_sparse_frequency. tags=['condition_candidate', 'measure', 'rare_pattern_candidate'] desc=Frequency feature for token 'edu' in an email message. +- word_freq_table: role=feature, type=numeric_sparse_frequency. tags=['condition_candidate', 'measure', 'rare_pattern_candidate'] desc=Frequency feature for token 'table' in an email message. +- word_freq_conference: role=feature, type=numeric_sparse_frequency. tags=['condition_candidate', 'measure', 'rare_pattern_candidate'] desc=Frequency feature for token 'conference' in an email message. +- char_freq_%3B: role=feature, type=numeric_sparse_frequency. tags=['condition_candidate', 'measure', 'rare_pattern_candidate'] desc=Character frequency feature for symbol ';'. +- char_freq_%28: role=feature, type=numeric_sparse_frequency. tags=['condition_candidate', 'measure', 'rare_pattern_candidate'] desc=Character frequency feature for symbol '('. +- char_freq_%5B: role=feature, type=numeric_sparse_frequency. tags=['condition_candidate', 'measure', 'rare_pattern_candidate'] desc=Character frequency feature for symbol '['. +- char_freq_%21: role=feature, type=numeric_sparse_frequency. tags=['condition_candidate', 'measure', 'rare_pattern_candidate'] desc=Character frequency feature for symbol '!'. +- char_freq_%24: role=feature, type=numeric_sparse_frequency. tags=['condition_candidate', 'measure', 'rare_pattern_candidate'] desc=Character frequency feature for symbol '$'. +- char_freq_%23: role=feature, type=numeric_sparse_frequency. tags=['condition_candidate', 'measure', 'rare_pattern_candidate'] desc=Character frequency feature for symbol '#'. +- capital_run_length_average: role=feature, type=numeric_heavy_tail. tags=['condition_candidate', 'measure', 'rare_pattern_candidate'] desc=Average length of uninterrupted capital-letter runs. +- capital_run_length_longest: role=feature, type=numeric_heavy_tail. tags=['condition_candidate', 'measure', 'rare_pattern_candidate'] desc=Longest uninterrupted capital-letter run. +- capital_run_length_total: role=feature, type=numeric_heavy_tail. tags=['condition_candidate', 'measure', 'rare_pattern_candidate'] desc=Total number of capital letters in runs. +- class: role=target, type=binary_target. tags=['target_candidate'] desc=Binary spam label (1=spam, 0=non-spam). +- useful_field_combinations: [['word_freq_free', 'word_freq_you', 'class'], ['char_freq_%21', 'capital_run_length_longest', 'class'], ['word_freq_remove', 'word_freq_money', 'class']] +- fields_requiring_caution: ['capital_run_length_average', 'capital_run_length_longest', 'capital_run_length_total'] +- source_url: https://www.openml.org/d/44 + +SQLite schema snapshot: +{ + "table_name": "n1", + "quoted_table_name": "\"n1\"", + "row_count": 4601, + "columns": [ + { + "name": "word_freq_make", + "type": "TEXT", + "notnull": false, + "pk": false + }, + { + "name": "word_freq_address", + "type": "TEXT", + "notnull": false, + "pk": false + }, + { + "name": "word_freq_all", + "type": "TEXT", + "notnull": false, + "pk": false + }, + { + "name": "word_freq_3d", + "type": "TEXT", + "notnull": false, + "pk": false + }, + { + "name": "word_freq_our", + "type": "TEXT", + "notnull": false, + "pk": false + }, + { + "name": "word_freq_over", + "type": "TEXT", + "notnull": false, + "pk": false + }, + { + "name": "word_freq_remove", + "type": "TEXT", + "notnull": false, + "pk": false + }, + { + "name": "word_freq_internet", + "type": "TEXT", + "notnull": false, + "pk": false + }, + { + "name": "word_freq_order", + "type": "TEXT", + "notnull": false, + "pk": false + }, + { + "name": "word_freq_mail", + "type": "TEXT", + "notnull": false, + "pk": false + }, + { + "name": "word_freq_receive", + "type": "TEXT", + "notnull": false, + "pk": false + }, + { + "name": "word_freq_will", + "type": "TEXT", + "notnull": false, + "pk": false + }, + { + "name": "word_freq_people", + "type": "TEXT", + "notnull": false, + "pk": false + }, + { + "name": "word_freq_report", + "type": "TEXT", + "notnull": false, + "pk": false + }, + { + "name": "word_freq_addresses", + "type": "TEXT", + "notnull": false, + "pk": false + }, + { + "name": "word_freq_free", + "type": "TEXT", + "notnull": false, + "pk": false + }, + { + "name": "word_freq_business", + "type": "TEXT", + "notnull": false, + "pk": false + }, + { + "name": "word_freq_email", + "type": "TEXT", + "notnull": false, + "pk": false + }, + { + "name": "word_freq_you", + "type": "TEXT", + "notnull": false, + "pk": false + }, + { + "name": "word_freq_credit", + "type": "TEXT", + "notnull": false, + "pk": false + }, + { + "name": "word_freq_your", + "type": "TEXT", + "notnull": false, + "pk": false + }, + { + "name": "word_freq_font", + "type": "TEXT", + "notnull": false, + "pk": false + }, + { + "name": "word_freq_000", + "type": "TEXT", + "notnull": false, + "pk": false + }, + { + "name": "word_freq_money", + "type": "TEXT", + "notnull": false, + "pk": false + }, + { + "name": "word_freq_hp", + "type": "TEXT", + "notnull": false, + "pk": false + }, + { + "name": "word_freq_hpl", + "type": "TEXT", + "notnull": false, + "pk": false + }, + { + "name": "word_freq_george", + "type": "TEXT", + "notnull": false, + "pk": false + }, + { + "name": "word_freq_650", + "type": "TEXT", + "notnull": false, + "pk": false + }, + { + "name": "word_freq_lab", + "type": "TEXT", + "notnull": false, + "pk": false + }, + { + "name": "word_freq_labs", + "type": "TEXT", + "notnull": false, + "pk": false + }, + { + "name": "word_freq_telnet", + "type": "TEXT", + "notnull": false, + "pk": false + }, + { + "name": "word_freq_857", + "type": "TEXT", + "notnull": false, + "pk": false + }, + { + "name": "word_freq_data", + "type": "TEXT", + "notnull": false, + "pk": false + }, + { + "name": "word_freq_415", + "type": "TEXT", + "notnull": false, + "pk": false + }, + { + "name": "word_freq_85", + "type": "TEXT", + "notnull": false, + "pk": false + }, + { + "name": "word_freq_technology", + "type": "TEXT", + "notnull": false, + "pk": false + }, + { + "name": "word_freq_1999", + "type": "TEXT", + "notnull": false, + "pk": false + }, + { + "name": "word_freq_parts", + "type": "TEXT", + "notnull": false, + "pk": false + }, + { + "name": "word_freq_pm", + "type": "TEXT", + "notnull": false, + "pk": false + }, + { + "name": "word_freq_direct", + "type": "TEXT", + "notnull": false, + "pk": false + }, + { + "name": "word_freq_cs", + "type": "TEXT", + "notnull": false, + "pk": false + }, + { + "name": "word_freq_meeting", + "type": "TEXT", + "notnull": false, + "pk": false + }, + { + "name": "word_freq_original", + "type": "TEXT", + "notnull": false, + "pk": false + }, + { + "name": "word_freq_project", + "type": "TEXT", + "notnull": false, + "pk": false + }, + { + "name": "word_freq_re", + "type": "TEXT", + "notnull": false, + "pk": false + }, + { + "name": "word_freq_edu", + "type": "TEXT", + "notnull": false, + "pk": false + }, + { + "name": "word_freq_table", + "type": "TEXT", + "notnull": false, + "pk": false + }, + { + "name": "word_freq_conference", + "type": "TEXT", + "notnull": false, + "pk": false + }, + { + "name": "char_freq_%3B", + "type": "TEXT", + "notnull": false, + "pk": false + }, + { + "name": "char_freq_%28", + "type": "TEXT", + "notnull": false, + "pk": false + }, + { + "name": "char_freq_%5B", + "type": "TEXT", + "notnull": false, + "pk": false + }, + { + "name": "char_freq_%21", + "type": "TEXT", + "notnull": false, + "pk": false + }, + { + "name": "char_freq_%24", + "type": "TEXT", + "notnull": false, + "pk": false + }, + { + "name": "char_freq_%23", + "type": "TEXT", + "notnull": false, + "pk": false + }, + { + "name": "capital_run_length_average", + "type": "TEXT", + "notnull": false, + "pk": false + }, + { + "name": "capital_run_length_longest", + "type": "TEXT", + "notnull": false, + "pk": false + }, + { + "name": "capital_run_length_total", + "type": "TEXT", + "notnull": false, + "pk": false + }, + { + "name": "class", + "type": "TEXT", + "notnull": false, + "pk": false + } + ], + "sample_rows": [ + { + "word_freq_make": "0", + "word_freq_address": "0.64", + "word_freq_all": "0.64", + "word_freq_3d": "0", + "word_freq_our": "0.32", + "word_freq_over": "0", + "word_freq_remove": "0", + "word_freq_internet": "0", + "word_freq_order": "0", + "word_freq_mail": "0", + "word_freq_receive": "0", + "word_freq_will": "0.64", + "word_freq_people": "0", + "word_freq_report": "0", + "word_freq_addresses": "0", + "word_freq_free": "0.32", + "word_freq_business": "0", + "word_freq_email": "1.29", + "word_freq_you": "1.93", + "word_freq_credit": "0", + "word_freq_your": "0.96", + "word_freq_font": "0", + "word_freq_000": "0", + "word_freq_money": "0", + "word_freq_hp": "0", + "word_freq_hpl": "0", + "word_freq_george": "0", + "word_freq_650": "0", + "word_freq_lab": "0", + "word_freq_labs": "0", + "word_freq_telnet": "0", + "word_freq_857": "0", + "word_freq_data": "0", + "word_freq_415": "0", + "word_freq_85": "0", + "word_freq_technology": "0", + "word_freq_1999": "0", + "word_freq_parts": "0", + "word_freq_pm": "0", + "word_freq_direct": "0", + "word_freq_cs": "0", + "word_freq_meeting": "0", + "word_freq_original": "0", + "word_freq_project": "0", + "word_freq_re": "0", + "word_freq_edu": "0", + "word_freq_table": "0", + "word_freq_conference": "0", + "char_freq_%3B": "0", + "char_freq_%28": "0", + "char_freq_%5B": "0", + "char_freq_%21": "0.778", + "char_freq_%24": "0", + "char_freq_%23": "0", + "capital_run_length_average": "3.756", + "capital_run_length_longest": "61", + "capital_run_length_total": "278", + "class": "1" + }, + { + "word_freq_make": "0.21", + "word_freq_address": "0.28", + "word_freq_all": "0.5", + "word_freq_3d": "0", + "word_freq_our": "0.14", + "word_freq_over": "0.28", + "word_freq_remove": "0.21", + "word_freq_internet": "0.07", + "word_freq_order": "0", + "word_freq_mail": "0.94", + "word_freq_receive": "0.21", + "word_freq_will": "0.79", + "word_freq_people": "0.65", + "word_freq_report": "0.21", + "word_freq_addresses": "0.14", + "word_freq_free": "0.14", + "word_freq_business": "0.07", + "word_freq_email": "0.28", + "word_freq_you": "3.47", + "word_freq_credit": "0", + "word_freq_your": "1.59", + "word_freq_font": "0", + "word_freq_000": "0.43", + "word_freq_money": "0.43", + "word_freq_hp": "0", + "word_freq_hpl": "0", + "word_freq_george": "0", + "word_freq_650": "0", + "word_freq_lab": "0", + "word_freq_labs": "0", + "word_freq_telnet": "0", + "word_freq_857": "0", + "word_freq_data": "0", + "word_freq_415": "0", + "word_freq_85": "0", + "word_freq_technology": "0", + "word_freq_1999": "0.07", + "word_freq_parts": "0", + "word_freq_pm": "0", + "word_freq_direct": "0", + "word_freq_cs": "0", + "word_freq_meeting": "0", + "word_freq_original": "0", + "word_freq_project": "0", + "word_freq_re": "0", + "word_freq_edu": "0", + "word_freq_table": "0", + "word_freq_conference": "0", + "char_freq_%3B": "0", + "char_freq_%28": "0.132", + "char_freq_%5B": "0", + "char_freq_%21": "0.372", + "char_freq_%24": "0.18", + "char_freq_%23": "0.048", + "capital_run_length_average": "5.114", + "capital_run_length_longest": "101", + "capital_run_length_total": "1028", + "class": "1" + }, + { + "word_freq_make": "0.06", + "word_freq_address": "0", + "word_freq_all": "0.71", + "word_freq_3d": "0", + "word_freq_our": "1.23", + "word_freq_over": "0.19", + "word_freq_remove": "0.19", + "word_freq_internet": "0.12", + "word_freq_order": "0.64", + "word_freq_mail": "0.25", + "word_freq_receive": "0.38", + "word_freq_will": "0.45", + "word_freq_people": "0.12", + "word_freq_report": "0", + "word_freq_addresses": "1.75", + "word_freq_free": "0.06", + "word_freq_business": "0.06", + "word_freq_email": "1.03", + "word_freq_you": "1.36", + "word_freq_credit": "0.32", + "word_freq_your": "0.51", + "word_freq_font": "0", + "word_freq_000": "1.16", + "word_freq_money": "0.06", + "word_freq_hp": "0", + "word_freq_hpl": "0", + "word_freq_george": "0", + "word_freq_650": "0", + "word_freq_lab": "0", + "word_freq_labs": "0", + "word_freq_telnet": "0", + "word_freq_857": "0", + "word_freq_data": "0", + "word_freq_415": "0", + "word_freq_85": "0", + "word_freq_technology": "0", + "word_freq_1999": "0", + "word_freq_parts": "0", + "word_freq_pm": "0", + "word_freq_direct": "0.06", + "word_freq_cs": "0", + "word_freq_meeting": "0", + "word_freq_original": "0.12", + "word_freq_project": "0", + "word_freq_re": "0.06", + "word_freq_edu": "0.06", + "word_freq_table": "0", + "word_freq_conference": "0", + "char_freq_%3B": "0.01", + "char_freq_%28": "0.143", + "char_freq_%5B": "0", + "char_freq_%21": "0.276", + "char_freq_%24": "0.184", + "char_freq_%23": "0.01", + "capital_run_length_average": "9.821", + "capital_run_length_longest": "485", + "capital_run_length_total": "2259", + "class": "1" + }, + { + "word_freq_make": "0", + "word_freq_address": "0", + "word_freq_all": "0", + "word_freq_3d": "0", + "word_freq_our": "0.63", + "word_freq_over": "0", + "word_freq_remove": "0.31", + "word_freq_internet": "0.63", + "word_freq_order": "0.31", + "word_freq_mail": "0.63", + "word_freq_receive": "0.31", + "word_freq_will": "0.31", + "word_freq_people": "0.31", + "word_freq_report": "0", + "word_freq_addresses": "0", + "word_freq_free": "0.31", + "word_freq_business": "0", + "word_freq_email": "0", + "word_freq_you": "3.18", + "word_freq_credit": "0", + "word_freq_your": "0.31", + "word_freq_font": "0", + "word_freq_000": "0", + "word_freq_money": "0", + "word_freq_hp": "0", + "word_freq_hpl": "0", + "word_freq_george": "0", + "word_freq_650": "0", + "word_freq_lab": "0", + "word_freq_labs": "0", + "word_freq_telnet": "0", + "word_freq_857": "0", + "word_freq_data": "0", + "word_freq_415": "0", + "word_freq_85": "0", + "word_freq_technology": "0", + "word_freq_1999": "0", + "word_freq_parts": "0", + "word_freq_pm": "0", + "word_freq_direct": "0", + "word_freq_cs": "0", + "word_freq_meeting": "0", + "word_freq_original": "0", + "word_freq_project": "0", + "word_freq_re": "0", + "word_freq_edu": "0", + "word_freq_table": "0", + "word_freq_conference": "0", + "char_freq_%3B": "0", + "char_freq_%28": "0.137", + "char_freq_%5B": "0", + "char_freq_%21": "0.137", + "char_freq_%24": "0", + "char_freq_%23": "0", + "capital_run_length_average": "3.537", + "capital_run_length_longest": "40", + "capital_run_length_total": "191", + "class": "1" + }, + { + "word_freq_make": "0", + "word_freq_address": "0", + "word_freq_all": "0", + "word_freq_3d": "0", + "word_freq_our": "0.63", + "word_freq_over": "0", + "word_freq_remove": "0.31", + "word_freq_internet": "0.63", + "word_freq_order": "0.31", + "word_freq_mail": "0.63", + "word_freq_receive": "0.31", + "word_freq_will": "0.31", + "word_freq_people": "0.31", + "word_freq_report": "0", + "word_freq_addresses": "0", + "word_freq_free": "0.31", + "word_freq_business": "0", + "word_freq_email": "0", + "word_freq_you": "3.18", + "word_freq_credit": "0", + "word_freq_your": "0.31", + "word_freq_font": "0", + "word_freq_000": "0", + "word_freq_money": "0", + "word_freq_hp": "0", + "word_freq_hpl": "0", + "word_freq_george": "0", + "word_freq_650": "0", + "word_freq_lab": "0", + "word_freq_labs": "0", + "word_freq_telnet": "0", + "word_freq_857": "0", + "word_freq_data": "0", + "word_freq_415": "0", + "word_freq_85": "0", + "word_freq_technology": "0", + "word_freq_1999": "0", + "word_freq_parts": "0", + "word_freq_pm": "0", + "word_freq_direct": "0", + "word_freq_cs": "0", + "word_freq_meeting": "0", + "word_freq_original": "0", + "word_freq_project": "0", + "word_freq_re": "0", + "word_freq_edu": "0", + "word_freq_table": "0", + "word_freq_conference": "0", + "char_freq_%3B": "0", + "char_freq_%28": "0.135", + "char_freq_%5B": "0", + "char_freq_%21": "0.135", + "char_freq_%24": "0", + "char_freq_%23": "0", + "capital_run_length_average": "3.537", + "capital_run_length_longest": "40", + "capital_run_length_total": "191", + "class": "1" + } + ] +} + +Shortlisted templates: +[ + { + "template_id": "tpl_m4_group_ratio_two_conditions", + "template_name": "Grouped Ratio of Two Conditions", + "primary_family": "conditional_dependency_structure", + "portability": "yes", + "sql_skeleton": "WITH grouped AS (\n SELECT {group_col},\n SUM(CASE WHEN {condition_col} = {positive_value} THEN 1 ELSE 0 END) AS numerator_count,\n SUM(CASE WHEN {condition_col} = {negative_value} THEN 1 ELSE 0 END) AS denominator_count\n FROM {table}\n GROUP BY {group_col}\n)\nSELECT {group_col},\n CAST(numerator_count AS FLOAT) / NULLIF(denominator_count, 0) AS condition_ratio\nFROM grouped\nORDER BY condition_ratio DESC;", + "required_roles": [ + "group_col", + "condition_col" + ] + } +] + +Problem instance: +{ + "dataset_id": "n1", + "question": "Use template Grouped Ratio of Two Conditions to probe direction_consistency with semantic role contrastive_conditional_view. Focus on group_col=class, condition_col=class.", + "planned_template_id": "tpl_m4_group_ratio_two_conditions", + "bindings": { + "group_col": "class", + "condition_col": "class", + "condition_value": "0", + "positive_value": "0", + "negative_value": "1", + "top_k": 11, + "top_n": 3, + "num_tiles": 10, + "percentile_value": 0.95, + "z_threshold": 2.0, + "fraction_threshold": 0.1, + "baseline_multiplier": 1.5, + "baseline_fraction": 0.1, + "min_group_size": 5, + "min_support": 5, + "measure_threshold": 0.0, + "time_grain": "month", + "lookback_rows": 3, + "current_period_start": "'2024-01-01'", + "current_period_end": "'2024-04-01'", + "previous_period_start": "'2023-10-01'", + "previous_period_end": "'2024-01-01'", + "drift_ratio_threshold": 0.8 + }, + "can_vary": [], + "must_fix": [], + "runtime_sql_skeleton": "WITH grouped AS (\n SELECT {group_col},\n SUM(CASE WHEN {condition_col} = {positive_value} THEN 1 ELSE 0 END) AS numerator_count,\n SUM(CASE WHEN {condition_col} = {negative_value} THEN 1 ELSE 0 END) AS denominator_count\n FROM {table}\n GROUP BY {group_col}\n)\nSELECT {group_col},\n CAST(numerator_count AS FLOAT) / NULLIF(denominator_count, 0) AS condition_ratio\nFROM grouped\nORDER BY condition_ratio DESC;" +} + +Repair context: +{} diff --git a/Query/sql/v2/runs/v2_cli_20260502_081223_d/n1/artifacts/v2q_n1_aede3038f7b5c4f1/cli/sql_response_attempt_1.raw.txt b/Query/sql/v2/runs/v2_cli_20260502_081223_d/n1/artifacts/v2q_n1_aede3038f7b5c4f1/cli/sql_response_attempt_1.raw.txt new file mode 100644 index 0000000000000000000000000000000000000000..73221e1092eac832354321f045b2197ac0a726c2 --- /dev/null +++ b/Query/sql/v2/runs/v2_cli_20260502_081223_d/n1/artifacts/v2q_n1_aede3038f7b5c4f1/cli/sql_response_attempt_1.raw.txt @@ -0,0 +1,4 @@ +{"type":"thread.started","thread_id":"019e40e4-3142-7e13-a587-09b8b44f8d9c"} +{"type":"turn.started"} +{"type":"item.completed","item":{"id":"item_0","type":"agent_message","text":"{\"sql\":\"-- template_id: tpl_m4_group_ratio_two_conditions\\nWITH grouped AS (\\n SELECT \\\"class\\\",\\n SUM(CASE WHEN \\\"class\\\" = '0' THEN 1 ELSE 0 END) AS numerator_count,\\n SUM(CASE WHEN \\\"class\\\" = '1' THEN 1 ELSE 0 END) AS denominator_count\\n FROM \\\"n1\\\"\\n GROUP BY \\\"class\\\"\\n)\\nSELECT \\\"class\\\",\\n CAST(numerator_count AS FLOAT) / NULLIF(denominator_count, 0) AS condition_ratio\\nFROM grouped\\nORDER BY condition_ratio DESC;\",\"notes\":\"Applied the provided grouped-ratio template with group_col=\\\"class\\\" and condition_col=\\\"class\\\". Since the schema stores values as TEXT, the class labels are compared as '0' and '1'.\"}"}} +{"type":"turn.completed","usage":{"input_tokens":20520,"cached_input_tokens":19840,"output_tokens":696,"reasoning_output_tokens":516}} diff --git a/Query/sql/v2/runs/v2_cli_20260502_081223_d/n1/artifacts/v2q_n1_aede3038f7b5c4f1/cli/sql_response_attempt_1.txt b/Query/sql/v2/runs/v2_cli_20260502_081223_d/n1/artifacts/v2q_n1_aede3038f7b5c4f1/cli/sql_response_attempt_1.txt new file mode 100644 index 0000000000000000000000000000000000000000..2b500ee3e63c4251f214f1524067434c8eb64749 --- /dev/null +++ b/Query/sql/v2/runs/v2_cli_20260502_081223_d/n1/artifacts/v2q_n1_aede3038f7b5c4f1/cli/sql_response_attempt_1.txt @@ -0,0 +1 @@ +{"sql":"-- template_id: tpl_m4_group_ratio_two_conditions\nWITH grouped AS (\n SELECT \"class\",\n SUM(CASE WHEN \"class\" = '0' THEN 1 ELSE 0 END) AS numerator_count,\n SUM(CASE WHEN \"class\" = '1' THEN 1 ELSE 0 END) AS denominator_count\n FROM \"n1\"\n GROUP BY \"class\"\n)\nSELECT \"class\",\n CAST(numerator_count AS FLOAT) / NULLIF(denominator_count, 0) AS condition_ratio\nFROM grouped\nORDER BY condition_ratio DESC;","notes":"Applied the provided grouped-ratio template with group_col=\"class\" and condition_col=\"class\". Since the schema stores values as TEXT, the class labels are compared as '0' and '1'."} \ No newline at end of file diff --git a/Query/sql/v2/runs/v2_cli_20260502_081223_d/n1/artifacts/v2q_n1_aede3038f7b5c4f1/cli/sql_stderr_attempt_1.txt b/Query/sql/v2/runs/v2_cli_20260502_081223_d/n1/artifacts/v2q_n1_aede3038f7b5c4f1/cli/sql_stderr_attempt_1.txt new file mode 100644 index 0000000000000000000000000000000000000000..e69de29bb2d1d6434b8b29ae775ad8c2e48c5391 diff --git a/Query/sql/v2/runs/v2_cli_20260502_081223_d/n1/artifacts/v2q_n1_b3c3ef60f48c6167/cli/conversation.jsonl b/Query/sql/v2/runs/v2_cli_20260502_081223_d/n1/artifacts/v2q_n1_b3c3ef60f48c6167/cli/conversation.jsonl new file mode 100644 index 0000000000000000000000000000000000000000..b7f5ad7cdac4a89f56199de39f8697dc662b6a0e --- /dev/null +++ b/Query/sql/v2/runs/v2_cli_20260502_081223_d/n1/artifacts/v2q_n1_b3c3ef60f48c6167/cli/conversation.jsonl @@ -0,0 +1,2 @@ +{"attempt": 1, "phase": "sql_generation", "role": "user", "content_path": "cli/sql_prompt_attempt_1.txt", "metrics": {"chars": 29915, "bytes_utf8": 29915, "lines": 792, "estimated_tokens": null}} +{"attempt": 1, "phase": "sql_generation", "role": "assistant", "content_path": "cli/sql_response_attempt_1.txt", "raw_content_path": "cli/sql_response_attempt_1.raw.txt", "stderr_path": "cli/sql_stderr_attempt_1.txt", "metrics": {"chars": 636, "bytes_utf8": 636, "lines": 1, "estimated_tokens": null}, "usage": {"input_tokens": 20453, "cached_input_tokens": 19840, "output_tokens": 303, "reasoning_output_tokens": 138}} diff --git a/Query/sql/v2/runs/v2_cli_20260502_081223_d/n1/artifacts/v2q_n1_b3c3ef60f48c6167/cli/session_summary.json b/Query/sql/v2/runs/v2_cli_20260502_081223_d/n1/artifacts/v2q_n1_b3c3ef60f48c6167/cli/session_summary.json new file mode 100644 index 0000000000000000000000000000000000000000..72cbebf83418630769bae05397112547cd2b1b97 --- /dev/null +++ b/Query/sql/v2/runs/v2_cli_20260502_081223_d/n1/artifacts/v2q_n1_b3c3ef60f48c6167/cli/session_summary.json @@ -0,0 +1,25 @@ +{ + "engine": "v2-cli:codex", + "command": "codex exec --skip-git-repo-check --disable plugins --sandbox read-only --cd \"/data/jialinzhang/SQLagent\" -m gpt-5.4 --json -", + "ai_cli_calls": 1, + "usage_summary": { + "dataset_id": "n1", + "model": "v2-cli:codex", + "run_id": "v2q_n1_b3c3ef60f48c6167", + "api_calls": 0, + "input_tokens": 20453, + "cached_input_tokens": 19840, + "output_tokens": 303, + "total_tokens": 20756, + "cost_usd": 0.0, + "ai_cli_calls": 1, + "estimated_input_tokens": 0, + "estimated_output_tokens": 0, + "estimated_total_tokens": 0, + "usage_source": "ai_cli_json_usage", + "cli_elapsed_ms_total": 14584.69, + "sql_execution_elapsed_ms_total": 2.71, + "conversation_log_path": "/data/jialinzhang/TabQueryBench/sql_workloads/v2_current/runs_and_launches/runs/v2_cli_20260502_081223_d/n1/artifacts/v2q_n1_b3c3ef60f48c6167/cli/conversation.jsonl", + "note": "Executed through a local AI CLI with structured usage metadata." + } +} \ No newline at end of file diff --git a/Query/sql/v2/runs/v2_cli_20260502_081223_d/n1/artifacts/v2q_n1_b3c3ef60f48c6167/cli/sql_attempt_1.metadata.json b/Query/sql/v2/runs/v2_cli_20260502_081223_d/n1/artifacts/v2q_n1_b3c3ef60f48c6167/cli/sql_attempt_1.metadata.json new file mode 100644 index 0000000000000000000000000000000000000000..1a30c171269ac78bdd923290176ac56c4f6c1df0 --- /dev/null +++ b/Query/sql/v2/runs/v2_cli_20260502_081223_d/n1/artifacts/v2q_n1_b3c3ef60f48c6167/cli/sql_attempt_1.metadata.json @@ -0,0 +1,45 @@ +{ + "attempt": 1, + "phase": "sql_generation", + "command": "codex exec --skip-git-repo-check --disable plugins --sandbox read-only --cd \"/data/jialinzhang/SQLagent\" -m gpt-5.4 --json -", + "started_at": "2026-05-19T15:47:34.037160+00:00", + "ended_at": "2026-05-19T15:47:48.621896+00:00", + "elapsed_ms": 14584.69, + "prompt_metrics": { + "chars": 29915, + "bytes_utf8": 29915, + "lines": 792, + "estimated_tokens": null + }, + "stdout_metrics": { + "chars": 1336, + "bytes_utf8": 1336, + "lines": 5, + "estimated_tokens": null + }, + "stderr_metrics": { + "chars": 0, + "bytes_utf8": 0, + "lines": 0, + "estimated_tokens": null + }, + "parsed_output": { + "format": "jsonl_events", + "text_metrics": { + "chars": 636, + "bytes_utf8": 636, + "lines": 1, + "estimated_tokens": null + }, + "usage": { + "input_tokens": 20453, + "cached_input_tokens": 19840, + "output_tokens": 303, + "reasoning_output_tokens": 138 + } + }, + "prompt_path": "cli/sql_prompt_attempt_1.txt", + "response_path": "cli/sql_response_attempt_1.txt", + "raw_response_path": "cli/sql_response_attempt_1.raw.txt", + "stderr_path": "cli/sql_stderr_attempt_1.txt" +} \ No newline at end of file diff --git a/Query/sql/v2/runs/v2_cli_20260502_081223_d/n1/artifacts/v2q_n1_b3c3ef60f48c6167/cli/sql_prompt_attempt_1.txt b/Query/sql/v2/runs/v2_cli_20260502_081223_d/n1/artifacts/v2q_n1_b3c3ef60f48c6167/cli/sql_prompt_attempt_1.txt new file mode 100644 index 0000000000000000000000000000000000000000..e0628acf707895de829e6dc0989c68d2b3981ce9 --- /dev/null +++ b/Query/sql/v2/runs/v2_cli_20260502_081223_d/n1/artifacts/v2q_n1_b3c3ef60f48c6167/cli/sql_prompt_attempt_1.txt @@ -0,0 +1,792 @@ +You are generating one SQLite SELECT query for a single-table SQL QA task. +Return strict JSON only, with this schema: {"sql": "...", "notes": "..."}. +Rules: +- Use only the provided table and columns. +- Do not write INSERT, UPDATE, DELETE, DROP, ALTER, CREATE, PRAGMA, ATTACH, DETACH, or VACUUM. +- Prefer the planned template and bound roles when provided. +- Add a leading SQL comment exactly like: -- template_id: . +- Generate SQLite-compatible SQL. SQLite does not support PERCENTILE_CONT or STDDEV. +- Quote identifiers with double quotes. +- Return no markdown and no extra prose. + +Dataset context: +Dataset context for SQL QA: +- dataset_id: n1 +- dataset_name: Spambase +- table_name: n1 +- table_layout: single-table dataset (do not assume joins). +- row_semantics: One row is one email represented by engineered token/character frequency and capitalization-run features. +- task_type: classification +- target_column: class +- main_row_count: 4601 +- important_fields: +- word_freq_make: role=feature, type=numeric_sparse_frequency. tags=['condition_candidate', 'measure', 'rare_pattern_candidate'] desc=Frequency feature for token 'make' in an email message. +- word_freq_address: role=feature, type=numeric_sparse_frequency. tags=['condition_candidate', 'measure', 'rare_pattern_candidate'] desc=Frequency feature for token 'address' in an email message. +- word_freq_all: role=feature, type=numeric_sparse_frequency. tags=['condition_candidate', 'measure', 'rare_pattern_candidate'] desc=Frequency feature for token 'all' in an email message. +- word_freq_3d: role=feature, type=numeric_sparse_frequency. tags=['condition_candidate', 'measure', 'rare_pattern_candidate'] desc=Frequency feature for token '3d' in an email message. +- word_freq_our: role=feature, type=numeric_sparse_frequency. tags=['condition_candidate', 'measure', 'rare_pattern_candidate'] desc=Frequency feature for token 'our' in an email message. +- word_freq_over: role=feature, type=numeric_sparse_frequency. tags=['condition_candidate', 'measure', 'rare_pattern_candidate'] desc=Frequency feature for token 'over' in an email message. +- word_freq_remove: role=feature, type=numeric_sparse_frequency. tags=['condition_candidate', 'measure', 'rare_pattern_candidate'] desc=Frequency feature for token 'remove' in an email message. +- word_freq_internet: role=feature, type=numeric_sparse_frequency. tags=['condition_candidate', 'measure', 'rare_pattern_candidate'] desc=Frequency feature for token 'internet' in an email message. +- word_freq_order: role=feature, type=numeric_sparse_frequency. tags=['condition_candidate', 'measure', 'rare_pattern_candidate'] desc=Frequency feature for token 'order' in an email message. +- word_freq_mail: role=feature, type=numeric_sparse_frequency. tags=['condition_candidate', 'measure', 'rare_pattern_candidate'] desc=Frequency feature for token 'mail' in an email message. +- word_freq_receive: role=feature, type=numeric_sparse_frequency. tags=['condition_candidate', 'measure', 'rare_pattern_candidate'] desc=Frequency feature for token 'receive' in an email message. +- word_freq_will: role=feature, type=numeric_sparse_frequency. tags=['condition_candidate', 'measure', 'rare_pattern_candidate'] desc=Frequency feature for token 'will' in an email message. +- word_freq_people: role=feature, type=numeric_sparse_frequency. tags=['condition_candidate', 'measure', 'rare_pattern_candidate'] desc=Frequency feature for token 'people' in an email message. +- word_freq_report: role=feature, type=numeric_sparse_frequency. tags=['condition_candidate', 'measure', 'rare_pattern_candidate'] desc=Frequency feature for token 'report' in an email message. +- word_freq_addresses: role=feature, type=numeric_sparse_frequency. tags=['condition_candidate', 'measure', 'rare_pattern_candidate'] desc=Frequency feature for token 'addresses' in an email message. +- word_freq_free: role=feature, type=numeric_sparse_frequency. tags=['condition_candidate', 'measure', 'rare_pattern_candidate'] desc=Frequency feature for token 'free' in an email message. +- word_freq_business: role=feature, type=numeric_sparse_frequency. tags=['condition_candidate', 'measure', 'rare_pattern_candidate'] desc=Frequency feature for token 'business' in an email message. +- word_freq_email: role=feature, type=numeric_sparse_frequency. tags=['condition_candidate', 'measure', 'rare_pattern_candidate'] desc=Frequency feature for token 'email' in an email message. +- word_freq_you: role=feature, type=numeric_sparse_frequency. tags=['condition_candidate', 'measure', 'rare_pattern_candidate'] desc=Frequency feature for token 'you' in an email message. +- word_freq_credit: role=feature, type=numeric_sparse_frequency. tags=['condition_candidate', 'measure', 'rare_pattern_candidate'] desc=Frequency feature for token 'credit' in an email message. +- word_freq_your: role=feature, type=numeric_sparse_frequency. tags=['condition_candidate', 'measure', 'rare_pattern_candidate'] desc=Frequency feature for token 'your' in an email message. +- word_freq_font: role=feature, type=numeric_sparse_frequency. tags=['condition_candidate', 'measure', 'rare_pattern_candidate'] desc=Frequency feature for token 'font' in an email message. +- word_freq_000: role=feature, type=numeric_sparse_frequency. tags=['condition_candidate', 'measure', 'rare_pattern_candidate'] desc=Frequency feature for token '000' in an email message. +- word_freq_money: role=feature, type=numeric_sparse_frequency. tags=['condition_candidate', 'measure', 'rare_pattern_candidate'] desc=Frequency feature for token 'money' in an email message. +- word_freq_hp: role=feature, type=numeric_sparse_frequency. tags=['condition_candidate', 'measure', 'rare_pattern_candidate'] desc=Frequency feature for token 'hp' in an email message. +- word_freq_hpl: role=feature, type=numeric_sparse_frequency. tags=['condition_candidate', 'measure', 'rare_pattern_candidate'] desc=Frequency feature for token 'hpl' in an email message. +- word_freq_george: role=feature, type=numeric_sparse_frequency. tags=['condition_candidate', 'measure', 'rare_pattern_candidate'] desc=Frequency feature for token 'george' in an email message. +- word_freq_650: role=feature, type=numeric_sparse_frequency. tags=['condition_candidate', 'measure', 'rare_pattern_candidate'] desc=Frequency feature for token '650' in an email message. +- word_freq_lab: role=feature, type=numeric_sparse_frequency. tags=['condition_candidate', 'measure', 'rare_pattern_candidate'] desc=Frequency feature for token 'lab' in an email message. +- word_freq_labs: role=feature, type=numeric_sparse_frequency. tags=['condition_candidate', 'measure', 'rare_pattern_candidate'] desc=Frequency feature for token 'labs' in an email message. +- word_freq_telnet: role=feature, type=numeric_sparse_frequency. tags=['condition_candidate', 'measure', 'rare_pattern_candidate'] desc=Frequency feature for token 'telnet' in an email message. +- word_freq_857: role=feature, type=numeric_sparse_frequency. tags=['condition_candidate', 'measure', 'rare_pattern_candidate'] desc=Frequency feature for token '857' in an email message. +- word_freq_data: role=feature, type=numeric_sparse_frequency. tags=['condition_candidate', 'measure', 'rare_pattern_candidate'] desc=Frequency feature for token 'data' in an email message. +- word_freq_415: role=feature, type=numeric_sparse_frequency. tags=['condition_candidate', 'measure', 'rare_pattern_candidate'] desc=Frequency feature for token '415' in an email message. +- word_freq_85: role=feature, type=numeric_sparse_frequency. tags=['condition_candidate', 'measure', 'rare_pattern_candidate'] desc=Frequency feature for token '85' in an email message. +- word_freq_technology: role=feature, type=numeric_sparse_frequency. tags=['condition_candidate', 'measure', 'rare_pattern_candidate'] desc=Frequency feature for token 'technology' in an email message. +- word_freq_1999: role=feature, type=numeric_sparse_frequency. tags=['condition_candidate', 'measure', 'rare_pattern_candidate'] desc=Frequency feature for token '1999' in an email message. +- word_freq_parts: role=feature, type=numeric_sparse_frequency. tags=['condition_candidate', 'measure', 'rare_pattern_candidate'] desc=Frequency feature for token 'parts' in an email message. +- word_freq_pm: role=feature, type=numeric_sparse_frequency. tags=['condition_candidate', 'measure', 'rare_pattern_candidate'] desc=Frequency feature for token 'pm' in an email message. +- word_freq_direct: role=feature, type=numeric_sparse_frequency. tags=['condition_candidate', 'measure', 'rare_pattern_candidate'] desc=Frequency feature for token 'direct' in an email message. +- word_freq_cs: role=feature, type=numeric_sparse_frequency. tags=['condition_candidate', 'measure', 'rare_pattern_candidate'] desc=Frequency feature for token 'cs' in an email message. +- word_freq_meeting: role=feature, type=numeric_sparse_frequency. tags=['condition_candidate', 'measure', 'rare_pattern_candidate'] desc=Frequency feature for token 'meeting' in an email message. +- word_freq_original: role=feature, type=numeric_sparse_frequency. tags=['condition_candidate', 'measure', 'rare_pattern_candidate'] desc=Frequency feature for token 'original' in an email message. +- word_freq_project: role=feature, type=numeric_sparse_frequency. tags=['condition_candidate', 'measure', 'rare_pattern_candidate'] desc=Frequency feature for token 'project' in an email message. +- word_freq_re: role=feature, type=numeric_sparse_frequency. tags=['condition_candidate', 'measure', 'rare_pattern_candidate'] desc=Frequency feature for token 're' in an email message. +- word_freq_edu: role=feature, type=numeric_sparse_frequency. tags=['condition_candidate', 'measure', 'rare_pattern_candidate'] desc=Frequency feature for token 'edu' in an email message. +- word_freq_table: role=feature, type=numeric_sparse_frequency. tags=['condition_candidate', 'measure', 'rare_pattern_candidate'] desc=Frequency feature for token 'table' in an email message. +- word_freq_conference: role=feature, type=numeric_sparse_frequency. tags=['condition_candidate', 'measure', 'rare_pattern_candidate'] desc=Frequency feature for token 'conference' in an email message. +- char_freq_%3B: role=feature, type=numeric_sparse_frequency. tags=['condition_candidate', 'measure', 'rare_pattern_candidate'] desc=Character frequency feature for symbol ';'. +- char_freq_%28: role=feature, type=numeric_sparse_frequency. tags=['condition_candidate', 'measure', 'rare_pattern_candidate'] desc=Character frequency feature for symbol '('. +- char_freq_%5B: role=feature, type=numeric_sparse_frequency. tags=['condition_candidate', 'measure', 'rare_pattern_candidate'] desc=Character frequency feature for symbol '['. +- char_freq_%21: role=feature, type=numeric_sparse_frequency. tags=['condition_candidate', 'measure', 'rare_pattern_candidate'] desc=Character frequency feature for symbol '!'. +- char_freq_%24: role=feature, type=numeric_sparse_frequency. tags=['condition_candidate', 'measure', 'rare_pattern_candidate'] desc=Character frequency feature for symbol '$'. +- char_freq_%23: role=feature, type=numeric_sparse_frequency. tags=['condition_candidate', 'measure', 'rare_pattern_candidate'] desc=Character frequency feature for symbol '#'. +- capital_run_length_average: role=feature, type=numeric_heavy_tail. tags=['condition_candidate', 'measure', 'rare_pattern_candidate'] desc=Average length of uninterrupted capital-letter runs. +- capital_run_length_longest: role=feature, type=numeric_heavy_tail. tags=['condition_candidate', 'measure', 'rare_pattern_candidate'] desc=Longest uninterrupted capital-letter run. +- capital_run_length_total: role=feature, type=numeric_heavy_tail. tags=['condition_candidate', 'measure', 'rare_pattern_candidate'] desc=Total number of capital letters in runs. +- class: role=target, type=binary_target. tags=['target_candidate'] desc=Binary spam label (1=spam, 0=non-spam). +- useful_field_combinations: [['word_freq_free', 'word_freq_you', 'class'], ['char_freq_%21', 'capital_run_length_longest', 'class'], ['word_freq_remove', 'word_freq_money', 'class']] +- fields_requiring_caution: ['capital_run_length_average', 'capital_run_length_longest', 'capital_run_length_total'] +- source_url: https://www.openml.org/d/44 + +SQLite schema snapshot: +{ + "table_name": "n1", + "quoted_table_name": "\"n1\"", + "row_count": 4601, + "columns": [ + { + "name": "word_freq_make", + "type": "TEXT", + "notnull": false, + "pk": false + }, + { + "name": "word_freq_address", + "type": "TEXT", + "notnull": false, + "pk": false + }, + { + "name": "word_freq_all", + "type": "TEXT", + "notnull": false, + "pk": false + }, + { + "name": "word_freq_3d", + "type": "TEXT", + "notnull": false, + "pk": false + }, + { + "name": "word_freq_our", + "type": "TEXT", + "notnull": false, + "pk": false + }, + { + "name": "word_freq_over", + "type": "TEXT", + "notnull": false, + "pk": false + }, + { + "name": "word_freq_remove", + "type": "TEXT", + "notnull": false, + "pk": false + }, + { + "name": "word_freq_internet", + "type": "TEXT", + "notnull": false, + "pk": false + }, + { + "name": "word_freq_order", + "type": "TEXT", + "notnull": false, + "pk": false + }, + { + "name": "word_freq_mail", + "type": "TEXT", + "notnull": false, + "pk": false + }, + { + "name": "word_freq_receive", + "type": "TEXT", + "notnull": false, + "pk": false + }, + { + "name": "word_freq_will", + "type": "TEXT", + "notnull": false, + "pk": false + }, + { + "name": "word_freq_people", + "type": "TEXT", + "notnull": false, + "pk": false + }, + { + "name": "word_freq_report", + "type": "TEXT", + "notnull": false, + "pk": false + }, + { + "name": "word_freq_addresses", + "type": "TEXT", + "notnull": false, + "pk": false + }, + { + "name": "word_freq_free", + "type": "TEXT", + "notnull": false, + "pk": false + }, + { + "name": "word_freq_business", + "type": "TEXT", + "notnull": false, + "pk": false + }, + { + "name": "word_freq_email", + "type": "TEXT", + "notnull": false, + "pk": false + }, + { + "name": "word_freq_you", + "type": "TEXT", + "notnull": false, + "pk": false + }, + { + "name": "word_freq_credit", + "type": "TEXT", + "notnull": false, + "pk": false + }, + { + "name": "word_freq_your", + "type": "TEXT", + "notnull": false, + "pk": false + }, + { + "name": "word_freq_font", + "type": "TEXT", + "notnull": false, + "pk": false + }, + { + "name": "word_freq_000", + "type": "TEXT", + "notnull": false, + "pk": false + }, + { + "name": "word_freq_money", + "type": "TEXT", + "notnull": false, + "pk": false + }, + { + "name": "word_freq_hp", + "type": "TEXT", + "notnull": false, + "pk": false + }, + { + "name": "word_freq_hpl", + "type": "TEXT", + "notnull": false, + "pk": false + }, + { + "name": "word_freq_george", + "type": "TEXT", + "notnull": false, + "pk": false + }, + { + "name": "word_freq_650", + "type": "TEXT", + "notnull": false, + "pk": false + }, + { + "name": "word_freq_lab", + "type": "TEXT", + "notnull": false, + "pk": false + }, + { + "name": "word_freq_labs", + "type": "TEXT", + "notnull": false, + "pk": false + }, + { + "name": "word_freq_telnet", + "type": "TEXT", + "notnull": false, + "pk": false + }, + { + "name": "word_freq_857", + "type": "TEXT", + "notnull": false, + "pk": false + }, + { + "name": "word_freq_data", + "type": "TEXT", + "notnull": false, + "pk": false + }, + { + "name": "word_freq_415", + "type": "TEXT", + "notnull": false, + "pk": false + }, + { + "name": "word_freq_85", + "type": "TEXT", + "notnull": false, + "pk": false + }, + { + "name": "word_freq_technology", + "type": "TEXT", + "notnull": false, + "pk": false + }, + { + "name": "word_freq_1999", + "type": "TEXT", + "notnull": false, + "pk": false + }, + { + "name": "word_freq_parts", + "type": "TEXT", + "notnull": false, + "pk": false + }, + { + "name": "word_freq_pm", + "type": "TEXT", + "notnull": false, + "pk": false + }, + { + "name": "word_freq_direct", + "type": "TEXT", + "notnull": false, + "pk": false + }, + { + "name": "word_freq_cs", + "type": "TEXT", + "notnull": false, + "pk": false + }, + { + "name": "word_freq_meeting", + "type": "TEXT", + "notnull": false, + "pk": false + }, + { + "name": "word_freq_original", + "type": "TEXT", + "notnull": false, + "pk": false + }, + { + "name": "word_freq_project", + "type": "TEXT", + "notnull": false, + "pk": false + }, + { + "name": "word_freq_re", + "type": "TEXT", + "notnull": false, + "pk": false + }, + { + "name": "word_freq_edu", + "type": "TEXT", + "notnull": false, + "pk": false + }, + { + "name": "word_freq_table", + "type": "TEXT", + "notnull": false, + "pk": false + }, + { + "name": "word_freq_conference", + "type": "TEXT", + "notnull": false, + "pk": false + }, + { + "name": "char_freq_%3B", + "type": "TEXT", + "notnull": false, + "pk": false + }, + { + "name": "char_freq_%28", + "type": "TEXT", + "notnull": false, + "pk": false + }, + { + "name": "char_freq_%5B", + "type": "TEXT", + "notnull": false, + "pk": false + }, + { + "name": "char_freq_%21", + "type": "TEXT", + "notnull": false, + "pk": false + }, + { + "name": "char_freq_%24", + "type": "TEXT", + "notnull": false, + "pk": false + }, + { + "name": "char_freq_%23", + "type": "TEXT", + "notnull": false, + "pk": false + }, + { + "name": "capital_run_length_average", + "type": "TEXT", + "notnull": false, + "pk": false + }, + { + "name": "capital_run_length_longest", + "type": "TEXT", + "notnull": false, + "pk": false + }, + { + "name": "capital_run_length_total", + "type": "TEXT", + "notnull": false, + "pk": false + }, + { + "name": "class", + "type": "TEXT", + "notnull": false, + "pk": false + } + ], + "sample_rows": [ + { + "word_freq_make": "0", + "word_freq_address": "0.64", + "word_freq_all": "0.64", + "word_freq_3d": "0", + "word_freq_our": "0.32", + "word_freq_over": "0", + "word_freq_remove": "0", + "word_freq_internet": "0", + "word_freq_order": "0", + "word_freq_mail": "0", + "word_freq_receive": "0", + "word_freq_will": "0.64", + "word_freq_people": "0", + "word_freq_report": "0", + "word_freq_addresses": "0", + "word_freq_free": "0.32", + "word_freq_business": "0", + "word_freq_email": "1.29", + "word_freq_you": "1.93", + "word_freq_credit": "0", + "word_freq_your": "0.96", + "word_freq_font": "0", + "word_freq_000": "0", + "word_freq_money": "0", + "word_freq_hp": "0", + "word_freq_hpl": "0", + "word_freq_george": "0", + "word_freq_650": "0", + "word_freq_lab": "0", + "word_freq_labs": "0", + "word_freq_telnet": "0", + "word_freq_857": "0", + "word_freq_data": "0", + "word_freq_415": "0", + "word_freq_85": "0", + "word_freq_technology": "0", + "word_freq_1999": "0", + "word_freq_parts": "0", + "word_freq_pm": "0", + "word_freq_direct": "0", + "word_freq_cs": "0", + "word_freq_meeting": "0", + "word_freq_original": "0", + "word_freq_project": "0", + "word_freq_re": "0", + "word_freq_edu": "0", + "word_freq_table": "0", + "word_freq_conference": "0", + "char_freq_%3B": "0", + "char_freq_%28": "0", + "char_freq_%5B": "0", + "char_freq_%21": "0.778", + "char_freq_%24": "0", + "char_freq_%23": "0", + "capital_run_length_average": "3.756", + "capital_run_length_longest": "61", + "capital_run_length_total": "278", + "class": "1" + }, + { + "word_freq_make": "0.21", + "word_freq_address": "0.28", + "word_freq_all": "0.5", + "word_freq_3d": "0", + "word_freq_our": "0.14", + "word_freq_over": "0.28", + "word_freq_remove": "0.21", + "word_freq_internet": "0.07", + "word_freq_order": "0", + "word_freq_mail": "0.94", + "word_freq_receive": "0.21", + "word_freq_will": "0.79", + "word_freq_people": "0.65", + "word_freq_report": "0.21", + "word_freq_addresses": "0.14", + "word_freq_free": "0.14", + "word_freq_business": "0.07", + "word_freq_email": "0.28", + "word_freq_you": "3.47", + "word_freq_credit": "0", + "word_freq_your": "1.59", + "word_freq_font": "0", + "word_freq_000": "0.43", + "word_freq_money": "0.43", + "word_freq_hp": "0", + "word_freq_hpl": "0", + "word_freq_george": "0", + "word_freq_650": "0", + "word_freq_lab": "0", + "word_freq_labs": "0", + "word_freq_telnet": "0", + "word_freq_857": "0", + "word_freq_data": "0", + "word_freq_415": "0", + "word_freq_85": "0", + "word_freq_technology": "0", + "word_freq_1999": "0.07", + "word_freq_parts": "0", + "word_freq_pm": "0", + "word_freq_direct": "0", + "word_freq_cs": "0", + "word_freq_meeting": "0", + "word_freq_original": "0", + "word_freq_project": "0", + "word_freq_re": "0", + "word_freq_edu": "0", + "word_freq_table": "0", + "word_freq_conference": "0", + "char_freq_%3B": "0", + "char_freq_%28": "0.132", + "char_freq_%5B": "0", + "char_freq_%21": "0.372", + "char_freq_%24": "0.18", + "char_freq_%23": "0.048", + "capital_run_length_average": "5.114", + "capital_run_length_longest": "101", + "capital_run_length_total": "1028", + "class": "1" + }, + { + "word_freq_make": "0.06", + "word_freq_address": "0", + "word_freq_all": "0.71", + "word_freq_3d": "0", + "word_freq_our": "1.23", + "word_freq_over": "0.19", + "word_freq_remove": "0.19", + "word_freq_internet": "0.12", + "word_freq_order": "0.64", + "word_freq_mail": "0.25", + "word_freq_receive": "0.38", + "word_freq_will": "0.45", + "word_freq_people": "0.12", + "word_freq_report": "0", + "word_freq_addresses": "1.75", + "word_freq_free": "0.06", + "word_freq_business": "0.06", + "word_freq_email": "1.03", + "word_freq_you": "1.36", + "word_freq_credit": "0.32", + "word_freq_your": "0.51", + "word_freq_font": "0", + "word_freq_000": "1.16", + "word_freq_money": "0.06", + "word_freq_hp": "0", + "word_freq_hpl": "0", + "word_freq_george": "0", + "word_freq_650": "0", + "word_freq_lab": "0", + "word_freq_labs": "0", + "word_freq_telnet": "0", + "word_freq_857": "0", + "word_freq_data": "0", + "word_freq_415": "0", + "word_freq_85": "0", + "word_freq_technology": "0", + "word_freq_1999": "0", + "word_freq_parts": "0", + "word_freq_pm": "0", + "word_freq_direct": "0.06", + "word_freq_cs": "0", + "word_freq_meeting": "0", + "word_freq_original": "0.12", + "word_freq_project": "0", + "word_freq_re": "0.06", + "word_freq_edu": "0.06", + "word_freq_table": "0", + "word_freq_conference": "0", + "char_freq_%3B": "0.01", + "char_freq_%28": "0.143", + "char_freq_%5B": "0", + "char_freq_%21": "0.276", + "char_freq_%24": "0.184", + "char_freq_%23": "0.01", + "capital_run_length_average": "9.821", + "capital_run_length_longest": "485", + "capital_run_length_total": "2259", + "class": "1" + }, + { + "word_freq_make": "0", + "word_freq_address": "0", + "word_freq_all": "0", + "word_freq_3d": "0", + "word_freq_our": "0.63", + "word_freq_over": "0", + "word_freq_remove": "0.31", + "word_freq_internet": "0.63", + "word_freq_order": "0.31", + "word_freq_mail": "0.63", + "word_freq_receive": "0.31", + "word_freq_will": "0.31", + "word_freq_people": "0.31", + "word_freq_report": "0", + "word_freq_addresses": "0", + "word_freq_free": "0.31", + "word_freq_business": "0", + "word_freq_email": "0", + "word_freq_you": "3.18", + "word_freq_credit": "0", + "word_freq_your": "0.31", + "word_freq_font": "0", + "word_freq_000": "0", + "word_freq_money": "0", + "word_freq_hp": "0", + "word_freq_hpl": "0", + "word_freq_george": "0", + "word_freq_650": "0", + "word_freq_lab": "0", + "word_freq_labs": "0", + "word_freq_telnet": "0", + "word_freq_857": "0", + "word_freq_data": "0", + "word_freq_415": "0", + "word_freq_85": "0", + "word_freq_technology": "0", + "word_freq_1999": "0", + "word_freq_parts": "0", + "word_freq_pm": "0", + "word_freq_direct": "0", + "word_freq_cs": "0", + "word_freq_meeting": "0", + "word_freq_original": "0", + "word_freq_project": "0", + "word_freq_re": "0", + "word_freq_edu": "0", + "word_freq_table": "0", + "word_freq_conference": "0", + "char_freq_%3B": "0", + "char_freq_%28": "0.137", + "char_freq_%5B": "0", + "char_freq_%21": "0.137", + "char_freq_%24": "0", + "char_freq_%23": "0", + "capital_run_length_average": "3.537", + "capital_run_length_longest": "40", + "capital_run_length_total": "191", + "class": "1" + }, + { + "word_freq_make": "0", + "word_freq_address": "0", + "word_freq_all": "0", + "word_freq_3d": "0", + "word_freq_our": "0.63", + "word_freq_over": "0", + "word_freq_remove": "0.31", + "word_freq_internet": "0.63", + "word_freq_order": "0.31", + "word_freq_mail": "0.63", + "word_freq_receive": "0.31", + "word_freq_will": "0.31", + "word_freq_people": "0.31", + "word_freq_report": "0", + "word_freq_addresses": "0", + "word_freq_free": "0.31", + "word_freq_business": "0", + "word_freq_email": "0", + "word_freq_you": "3.18", + "word_freq_credit": "0", + "word_freq_your": "0.31", + "word_freq_font": "0", + "word_freq_000": "0", + "word_freq_money": "0", + "word_freq_hp": "0", + "word_freq_hpl": "0", + "word_freq_george": "0", + "word_freq_650": "0", + "word_freq_lab": "0", + "word_freq_labs": "0", + "word_freq_telnet": "0", + "word_freq_857": "0", + "word_freq_data": "0", + "word_freq_415": "0", + "word_freq_85": "0", + "word_freq_technology": "0", + "word_freq_1999": "0", + "word_freq_parts": "0", + "word_freq_pm": "0", + "word_freq_direct": "0", + "word_freq_cs": "0", + "word_freq_meeting": "0", + "word_freq_original": "0", + "word_freq_project": "0", + "word_freq_re": "0", + "word_freq_edu": "0", + "word_freq_table": "0", + "word_freq_conference": "0", + "char_freq_%3B": "0", + "char_freq_%28": "0.135", + "char_freq_%5B": "0", + "char_freq_%21": "0.135", + "char_freq_%24": "0", + "char_freq_%23": "0", + "capital_run_length_average": "3.537", + "capital_run_length_longest": "40", + "capital_run_length_total": "191", + "class": "1" + } + ] +} + +Shortlisted templates: +[ + { + "template_id": "tpl_tpch_relative_total_threshold", + "template_name": "Relative-to-Total Extreme Threshold", + "primary_family": "tail_rarity_structure", + "portability": "partial", + "sql_skeleton": "WITH grouped AS (\n SELECT {group_col}, SUM({measure_col}) AS group_value\n FROM {table}\n GROUP BY {group_col}\n), total AS (\n SELECT SUM(group_value) AS total_value\n FROM grouped\n)\nSELECT g.{group_col}, g.group_value\nFROM grouped AS g\nCROSS JOIN total AS t\nWHERE g.group_value > t.total_value * {fraction_threshold}\nORDER BY g.group_value DESC;", + "required_roles": [ + "group_col", + "measure_col" + ] + } +] + +Problem instance: +{ + "dataset_id": "n1", + "question": "Use template Relative-to-Total Extreme Threshold to probe tail_mass_similarity with semantic role filtered_stable_view. Focus on group_col=class, measure_col=word_freq_email.", + "planned_template_id": "tpl_tpch_relative_total_threshold", + "bindings": { + "group_col": "class", + "measure_col": "word_freq_email", + "top_k": 19, + "top_n": 6, + "num_tiles": 10, + "percentile_value": 0.9, + "z_threshold": 2.0, + "fraction_threshold": 0.05, + "baseline_multiplier": 1.75, + "baseline_fraction": 0.1, + "min_group_size": 5, + "min_support": 4, + "measure_threshold": 0.0, + "time_grain": "month", + "lookback_rows": 3, + "current_period_start": "'2024-01-01'", + "current_period_end": "'2024-04-01'", + "previous_period_start": "'2023-10-01'", + "previous_period_end": "'2024-01-01'", + "drift_ratio_threshold": 0.8 + }, + "can_vary": [], + "must_fix": [], + "runtime_sql_skeleton": "WITH grouped AS (\n SELECT {group_col}, SUM({measure_col}) AS group_value\n FROM {table}\n GROUP BY {group_col}\n), total AS (\n SELECT SUM(group_value) AS total_value\n FROM grouped\n)\nSELECT g.{group_col}, g.group_value\nFROM grouped AS g\nCROSS JOIN total AS t\nWHERE g.group_value > t.total_value * {fraction_threshold}\nORDER BY g.group_value DESC;" +} + +Repair context: +{} diff --git a/Query/sql/v2/runs/v2_cli_20260502_081223_d/n1/artifacts/v2q_n1_b3c3ef60f48c6167/cli/sql_response_attempt_1.raw.txt b/Query/sql/v2/runs/v2_cli_20260502_081223_d/n1/artifacts/v2q_n1_b3c3ef60f48c6167/cli/sql_response_attempt_1.raw.txt new file mode 100644 index 0000000000000000000000000000000000000000..10b65ca96efca99c9475ed3fe49e7022d2ed6fbb --- /dev/null +++ b/Query/sql/v2/runs/v2_cli_20260502_081223_d/n1/artifacts/v2q_n1_b3c3ef60f48c6167/cli/sql_response_attempt_1.raw.txt @@ -0,0 +1,5 @@ +{"type":"thread.started","thread_id":"019e40eb-c7ac-77d3-86c6-794804b1ffe4"} +{"type":"turn.started"} +{"type":"error","message":"Reconnecting... 2/5 (stream disconnected before completion: Rate limit reached for gpt-5.4 in organization org-yXK0mBGOR7eXhtGEx8v7e3bf on tokens per min (TPM): Limit 2000000, Used 1995716, Requested 21088. Please try again in 504ms. Visit https://platform.openai.com/account/rate-limits to learn more.)"} +{"type":"item.completed","item":{"id":"item_0","type":"agent_message","text":"{\"sql\":\"-- template_id: tpl_tpch_relative_total_threshold\\nWITH grouped AS (\\n SELECT \\\"class\\\", SUM(CAST(\\\"word_freq_email\\\" AS REAL)) AS group_value\\n FROM \\\"n1\\\"\\n GROUP BY \\\"class\\\"\\n), total AS (\\n SELECT SUM(group_value) AS total_value\\n FROM grouped\\n)\\nSELECT g.\\\"class\\\", g.group_value\\nFROM grouped AS g\\nCROSS JOIN total AS t\\nWHERE g.group_value > t.total_value * 0.05\\nORDER BY g.group_value DESC;\",\"notes\":\"Applied the required Relative-to-Total Extreme Threshold template with group_col=\\\"class\\\" and measure_col=\\\"word_freq_email\\\". CAST to REAL is used because the schema stores feature values as TEXT.\"}"}} +{"type":"turn.completed","usage":{"input_tokens":20453,"cached_input_tokens":19840,"output_tokens":303,"reasoning_output_tokens":138}} diff --git a/Query/sql/v2/runs/v2_cli_20260502_081223_d/n1/artifacts/v2q_n1_b3c3ef60f48c6167/cli/sql_response_attempt_1.txt b/Query/sql/v2/runs/v2_cli_20260502_081223_d/n1/artifacts/v2q_n1_b3c3ef60f48c6167/cli/sql_response_attempt_1.txt new file mode 100644 index 0000000000000000000000000000000000000000..bf23faa0c4d908a2c52e1825b4032cf2dcb270b0 --- /dev/null +++ b/Query/sql/v2/runs/v2_cli_20260502_081223_d/n1/artifacts/v2q_n1_b3c3ef60f48c6167/cli/sql_response_attempt_1.txt @@ -0,0 +1 @@ +{"sql":"-- template_id: tpl_tpch_relative_total_threshold\nWITH grouped AS (\n SELECT \"class\", SUM(CAST(\"word_freq_email\" AS REAL)) AS group_value\n FROM \"n1\"\n GROUP BY \"class\"\n), total AS (\n SELECT SUM(group_value) AS total_value\n FROM grouped\n)\nSELECT g.\"class\", g.group_value\nFROM grouped AS g\nCROSS JOIN total AS t\nWHERE g.group_value > t.total_value * 0.05\nORDER BY g.group_value DESC;","notes":"Applied the required Relative-to-Total Extreme Threshold template with group_col=\"class\" and measure_col=\"word_freq_email\". CAST to REAL is used because the schema stores feature values as TEXT."} \ No newline at end of file diff --git a/Query/sql/v2/runs/v2_cli_20260502_081223_d/n1/artifacts/v2q_n1_b3c3ef60f48c6167/cli/sql_stderr_attempt_1.txt b/Query/sql/v2/runs/v2_cli_20260502_081223_d/n1/artifacts/v2q_n1_b3c3ef60f48c6167/cli/sql_stderr_attempt_1.txt new file mode 100644 index 0000000000000000000000000000000000000000..e69de29bb2d1d6434b8b29ae775ad8c2e48c5391 diff --git a/Query/sql/v2/runs/v2_cli_20260502_081223_d/n1/artifacts/v2q_n1_b4a50b6eca8328dc/cli/conversation.jsonl b/Query/sql/v2/runs/v2_cli_20260502_081223_d/n1/artifacts/v2q_n1_b4a50b6eca8328dc/cli/conversation.jsonl new file mode 100644 index 0000000000000000000000000000000000000000..901795240b3d52f052a6412dea3fef29302a4e6c --- /dev/null +++ b/Query/sql/v2/runs/v2_cli_20260502_081223_d/n1/artifacts/v2q_n1_b4a50b6eca8328dc/cli/conversation.jsonl @@ -0,0 +1,4 @@ +{"attempt": 1, "phase": "sql_generation", "role": "user", "content_path": "cli/sql_prompt_attempt_1.txt", "metrics": {"chars": 29585, "bytes_utf8": 29585, "lines": 795, "estimated_tokens": null}} +{"attempt": 1, "phase": "sql_generation", "role": "assistant", "content_path": "cli/sql_response_attempt_1.txt", "raw_content_path": "cli/sql_response_attempt_1.raw.txt", "stderr_path": "cli/sql_stderr_attempt_1.txt", "metrics": {"chars": 280, "bytes_utf8": 280, "lines": 4, "estimated_tokens": null}, "usage": {}, "status": "failed", "error": "AI CLI command failed with exit code 1: "} +{"attempt": 2, "phase": "sql_generation", "role": "user", "content_path": "cli/sql_prompt_attempt_2.txt", "metrics": {"chars": 29585, "bytes_utf8": 29585, "lines": 795, "estimated_tokens": null}} +{"attempt": 2, "phase": "sql_generation", "role": "assistant", "content_path": "cli/sql_response_attempt_2.txt", "raw_content_path": "cli/sql_response_attempt_2.raw.txt", "stderr_path": "cli/sql_stderr_attempt_2.txt", "metrics": {"chars": 503, "bytes_utf8": 503, "lines": 1, "estimated_tokens": null}, "usage": {"input_tokens": 20372, "cached_input_tokens": 12032, "output_tokens": 329, "reasoning_output_tokens": 191}} diff --git a/Query/sql/v2/runs/v2_cli_20260502_081223_d/n1/artifacts/v2q_n1_b4a50b6eca8328dc/cli/session_summary.json b/Query/sql/v2/runs/v2_cli_20260502_081223_d/n1/artifacts/v2q_n1_b4a50b6eca8328dc/cli/session_summary.json new file mode 100644 index 0000000000000000000000000000000000000000..8677d6cb8a749fa5f78e31e63c143e13aa1dd984 --- /dev/null +++ b/Query/sql/v2/runs/v2_cli_20260502_081223_d/n1/artifacts/v2q_n1_b4a50b6eca8328dc/cli/session_summary.json @@ -0,0 +1,25 @@ +{ + "engine": "v2-cli:codex", + "command": "codex exec --skip-git-repo-check --disable plugins --sandbox read-only --cd \"/data/jialinzhang/SQLagent\" -m gpt-5.4 --json -", + "ai_cli_calls": 2, + "usage_summary": { + "dataset_id": "n1", + "model": "v2-cli:codex", + "run_id": "v2q_n1_b4a50b6eca8328dc", + "api_calls": 0, + "input_tokens": 20372, + "cached_input_tokens": 12032, + "output_tokens": 329, + "total_tokens": 20701, + "cost_usd": 0.0, + "ai_cli_calls": 2, + "estimated_input_tokens": 0, + "estimated_output_tokens": 0, + "estimated_total_tokens": 0, + "usage_source": "ai_cli_json_usage", + "cli_elapsed_ms_total": 13273.09, + "sql_execution_elapsed_ms_total": 3.19, + "conversation_log_path": "/data/jialinzhang/TabQueryBench/sql_workloads/v2_current/runs_and_launches/runs/v2_cli_20260502_081223_d/n1/artifacts/v2q_n1_b4a50b6eca8328dc/cli/conversation.jsonl", + "note": "Executed through a local AI CLI with structured usage metadata." + } +} \ No newline at end of file diff --git a/Query/sql/v2/runs/v2_cli_20260502_081223_d/n1/artifacts/v2q_n1_b4a50b6eca8328dc/cli/sql_attempt_1.metadata.json b/Query/sql/v2/runs/v2_cli_20260502_081223_d/n1/artifacts/v2q_n1_b4a50b6eca8328dc/cli/sql_attempt_1.metadata.json new file mode 100644 index 0000000000000000000000000000000000000000..7ec45cdea34b5e9e490607462c4667b4aa70124f --- /dev/null +++ b/Query/sql/v2/runs/v2_cli_20260502_081223_d/n1/artifacts/v2q_n1_b4a50b6eca8328dc/cli/sql_attempt_1.metadata.json @@ -0,0 +1,43 @@ +{ + "attempt": 1, + "phase": "sql_generation", + "command": "codex exec --skip-git-repo-check --disable plugins --sandbox read-only --cd \"/data/jialinzhang/SQLagent\" -m gpt-5.4 --json -", + "started_at": "2026-05-19T16:00:08.587190+00:00", + "ended_at": "2026-05-19T16:00:12.060393+00:00", + "elapsed_ms": 3473.17, + "returncode": 1, + "prompt_metrics": { + "chars": 29585, + "bytes_utf8": 29585, + "lines": 795, + "estimated_tokens": null + }, + "stdout_metrics": { + "chars": 281, + "bytes_utf8": 281, + "lines": 4, + "estimated_tokens": null + }, + "stderr_metrics": { + "chars": 0, + "bytes_utf8": 0, + "lines": 0, + "estimated_tokens": null + }, + "parsed_output": { + "format": "jsonl_events", + "text_metrics": { + "chars": 280, + "bytes_utf8": 280, + "lines": 4, + "estimated_tokens": null + }, + "usage": {} + }, + "status": "failed", + "error": "AI CLI command failed with exit code 1: ", + "prompt_path": "cli/sql_prompt_attempt_1.txt", + "response_path": "cli/sql_response_attempt_1.txt", + "raw_response_path": "cli/sql_response_attempt_1.raw.txt", + "stderr_path": "cli/sql_stderr_attempt_1.txt" +} \ No newline at end of file diff --git a/Query/sql/v2/runs/v2_cli_20260502_081223_d/n1/artifacts/v2q_n1_b4a50b6eca8328dc/cli/sql_attempt_2.metadata.json b/Query/sql/v2/runs/v2_cli_20260502_081223_d/n1/artifacts/v2q_n1_b4a50b6eca8328dc/cli/sql_attempt_2.metadata.json new file mode 100644 index 0000000000000000000000000000000000000000..c87fca7aca51e10c8559ed859953d216ced3de35 --- /dev/null +++ b/Query/sql/v2/runs/v2_cli_20260502_081223_d/n1/artifacts/v2q_n1_b4a50b6eca8328dc/cli/sql_attempt_2.metadata.json @@ -0,0 +1,45 @@ +{ + "attempt": 2, + "phase": "sql_generation", + "command": "codex exec --skip-git-repo-check --disable plugins --sandbox read-only --cd \"/data/jialinzhang/SQLagent\" -m gpt-5.4 --json -", + "started_at": "2026-05-19T16:00:13.064384+00:00", + "ended_at": "2026-05-19T16:00:22.864357+00:00", + "elapsed_ms": 9799.92, + "prompt_metrics": { + "chars": 29585, + "bytes_utf8": 29585, + "lines": 795, + "estimated_tokens": null + }, + "stdout_metrics": { + "chars": 862, + "bytes_utf8": 862, + "lines": 4, + "estimated_tokens": null + }, + "stderr_metrics": { + "chars": 0, + "bytes_utf8": 0, + "lines": 0, + "estimated_tokens": null + }, + "parsed_output": { + "format": "jsonl_events", + "text_metrics": { + "chars": 503, + "bytes_utf8": 503, + "lines": 1, + "estimated_tokens": null + }, + "usage": { + "input_tokens": 20372, + "cached_input_tokens": 12032, + "output_tokens": 329, + "reasoning_output_tokens": 191 + } + }, + "prompt_path": "cli/sql_prompt_attempt_2.txt", + "response_path": "cli/sql_response_attempt_2.txt", + "raw_response_path": "cli/sql_response_attempt_2.raw.txt", + "stderr_path": "cli/sql_stderr_attempt_2.txt" +} \ No newline at end of file diff --git a/Query/sql/v2/runs/v2_cli_20260502_081223_d/n1/artifacts/v2q_n1_b4a50b6eca8328dc/cli/sql_prompt_attempt_1.txt b/Query/sql/v2/runs/v2_cli_20260502_081223_d/n1/artifacts/v2q_n1_b4a50b6eca8328dc/cli/sql_prompt_attempt_1.txt new file mode 100644 index 0000000000000000000000000000000000000000..eb74b711cc96d421f83c13b88d6d64844cac4b32 --- /dev/null +++ b/Query/sql/v2/runs/v2_cli_20260502_081223_d/n1/artifacts/v2q_n1_b4a50b6eca8328dc/cli/sql_prompt_attempt_1.txt @@ -0,0 +1,795 @@ +You are generating one SQLite SELECT query for a single-table SQL QA task. +Return strict JSON only, with this schema: {"sql": "...", "notes": "..."}. +Rules: +- Use only the provided table and columns. +- Do not write INSERT, UPDATE, DELETE, DROP, ALTER, CREATE, PRAGMA, ATTACH, DETACH, or VACUUM. +- Prefer the planned template and bound roles when provided. +- Add a leading SQL comment exactly like: -- template_id: . +- Generate SQLite-compatible SQL. SQLite does not support PERCENTILE_CONT or STDDEV. +- Quote identifiers with double quotes. +- Return no markdown and no extra prose. + +Dataset context: +Dataset context for SQL QA: +- dataset_id: n1 +- dataset_name: Spambase +- table_name: n1 +- table_layout: single-table dataset (do not assume joins). +- row_semantics: One row is one email represented by engineered token/character frequency and capitalization-run features. +- task_type: classification +- target_column: class +- main_row_count: 4601 +- important_fields: +- word_freq_make: role=feature, type=numeric_sparse_frequency. tags=['condition_candidate', 'measure', 'rare_pattern_candidate'] desc=Frequency feature for token 'make' in an email message. +- word_freq_address: role=feature, type=numeric_sparse_frequency. tags=['condition_candidate', 'measure', 'rare_pattern_candidate'] desc=Frequency feature for token 'address' in an email message. +- word_freq_all: role=feature, type=numeric_sparse_frequency. tags=['condition_candidate', 'measure', 'rare_pattern_candidate'] desc=Frequency feature for token 'all' in an email message. +- word_freq_3d: role=feature, type=numeric_sparse_frequency. tags=['condition_candidate', 'measure', 'rare_pattern_candidate'] desc=Frequency feature for token '3d' in an email message. +- word_freq_our: role=feature, type=numeric_sparse_frequency. tags=['condition_candidate', 'measure', 'rare_pattern_candidate'] desc=Frequency feature for token 'our' in an email message. +- word_freq_over: role=feature, type=numeric_sparse_frequency. tags=['condition_candidate', 'measure', 'rare_pattern_candidate'] desc=Frequency feature for token 'over' in an email message. +- word_freq_remove: role=feature, type=numeric_sparse_frequency. tags=['condition_candidate', 'measure', 'rare_pattern_candidate'] desc=Frequency feature for token 'remove' in an email message. +- word_freq_internet: role=feature, type=numeric_sparse_frequency. tags=['condition_candidate', 'measure', 'rare_pattern_candidate'] desc=Frequency feature for token 'internet' in an email message. +- word_freq_order: role=feature, type=numeric_sparse_frequency. tags=['condition_candidate', 'measure', 'rare_pattern_candidate'] desc=Frequency feature for token 'order' in an email message. +- word_freq_mail: role=feature, type=numeric_sparse_frequency. tags=['condition_candidate', 'measure', 'rare_pattern_candidate'] desc=Frequency feature for token 'mail' in an email message. +- word_freq_receive: role=feature, type=numeric_sparse_frequency. tags=['condition_candidate', 'measure', 'rare_pattern_candidate'] desc=Frequency feature for token 'receive' in an email message. +- word_freq_will: role=feature, type=numeric_sparse_frequency. tags=['condition_candidate', 'measure', 'rare_pattern_candidate'] desc=Frequency feature for token 'will' in an email message. +- word_freq_people: role=feature, type=numeric_sparse_frequency. tags=['condition_candidate', 'measure', 'rare_pattern_candidate'] desc=Frequency feature for token 'people' in an email message. +- word_freq_report: role=feature, type=numeric_sparse_frequency. tags=['condition_candidate', 'measure', 'rare_pattern_candidate'] desc=Frequency feature for token 'report' in an email message. +- word_freq_addresses: role=feature, type=numeric_sparse_frequency. tags=['condition_candidate', 'measure', 'rare_pattern_candidate'] desc=Frequency feature for token 'addresses' in an email message. +- word_freq_free: role=feature, type=numeric_sparse_frequency. tags=['condition_candidate', 'measure', 'rare_pattern_candidate'] desc=Frequency feature for token 'free' in an email message. +- word_freq_business: role=feature, type=numeric_sparse_frequency. tags=['condition_candidate', 'measure', 'rare_pattern_candidate'] desc=Frequency feature for token 'business' in an email message. +- word_freq_email: role=feature, type=numeric_sparse_frequency. tags=['condition_candidate', 'measure', 'rare_pattern_candidate'] desc=Frequency feature for token 'email' in an email message. +- word_freq_you: role=feature, type=numeric_sparse_frequency. tags=['condition_candidate', 'measure', 'rare_pattern_candidate'] desc=Frequency feature for token 'you' in an email message. +- word_freq_credit: role=feature, type=numeric_sparse_frequency. tags=['condition_candidate', 'measure', 'rare_pattern_candidate'] desc=Frequency feature for token 'credit' in an email message. +- word_freq_your: role=feature, type=numeric_sparse_frequency. tags=['condition_candidate', 'measure', 'rare_pattern_candidate'] desc=Frequency feature for token 'your' in an email message. +- word_freq_font: role=feature, type=numeric_sparse_frequency. tags=['condition_candidate', 'measure', 'rare_pattern_candidate'] desc=Frequency feature for token 'font' in an email message. +- word_freq_000: role=feature, type=numeric_sparse_frequency. tags=['condition_candidate', 'measure', 'rare_pattern_candidate'] desc=Frequency feature for token '000' in an email message. +- word_freq_money: role=feature, type=numeric_sparse_frequency. tags=['condition_candidate', 'measure', 'rare_pattern_candidate'] desc=Frequency feature for token 'money' in an email message. +- word_freq_hp: role=feature, type=numeric_sparse_frequency. tags=['condition_candidate', 'measure', 'rare_pattern_candidate'] desc=Frequency feature for token 'hp' in an email message. +- word_freq_hpl: role=feature, type=numeric_sparse_frequency. tags=['condition_candidate', 'measure', 'rare_pattern_candidate'] desc=Frequency feature for token 'hpl' in an email message. +- word_freq_george: role=feature, type=numeric_sparse_frequency. tags=['condition_candidate', 'measure', 'rare_pattern_candidate'] desc=Frequency feature for token 'george' in an email message. +- word_freq_650: role=feature, type=numeric_sparse_frequency. tags=['condition_candidate', 'measure', 'rare_pattern_candidate'] desc=Frequency feature for token '650' in an email message. +- word_freq_lab: role=feature, type=numeric_sparse_frequency. tags=['condition_candidate', 'measure', 'rare_pattern_candidate'] desc=Frequency feature for token 'lab' in an email message. +- word_freq_labs: role=feature, type=numeric_sparse_frequency. tags=['condition_candidate', 'measure', 'rare_pattern_candidate'] desc=Frequency feature for token 'labs' in an email message. +- word_freq_telnet: role=feature, type=numeric_sparse_frequency. tags=['condition_candidate', 'measure', 'rare_pattern_candidate'] desc=Frequency feature for token 'telnet' in an email message. +- word_freq_857: role=feature, type=numeric_sparse_frequency. tags=['condition_candidate', 'measure', 'rare_pattern_candidate'] desc=Frequency feature for token '857' in an email message. +- word_freq_data: role=feature, type=numeric_sparse_frequency. tags=['condition_candidate', 'measure', 'rare_pattern_candidate'] desc=Frequency feature for token 'data' in an email message. +- word_freq_415: role=feature, type=numeric_sparse_frequency. tags=['condition_candidate', 'measure', 'rare_pattern_candidate'] desc=Frequency feature for token '415' in an email message. +- word_freq_85: role=feature, type=numeric_sparse_frequency. tags=['condition_candidate', 'measure', 'rare_pattern_candidate'] desc=Frequency feature for token '85' in an email message. +- word_freq_technology: role=feature, type=numeric_sparse_frequency. tags=['condition_candidate', 'measure', 'rare_pattern_candidate'] desc=Frequency feature for token 'technology' in an email message. +- word_freq_1999: role=feature, type=numeric_sparse_frequency. tags=['condition_candidate', 'measure', 'rare_pattern_candidate'] desc=Frequency feature for token '1999' in an email message. +- word_freq_parts: role=feature, type=numeric_sparse_frequency. tags=['condition_candidate', 'measure', 'rare_pattern_candidate'] desc=Frequency feature for token 'parts' in an email message. +- word_freq_pm: role=feature, type=numeric_sparse_frequency. tags=['condition_candidate', 'measure', 'rare_pattern_candidate'] desc=Frequency feature for token 'pm' in an email message. +- word_freq_direct: role=feature, type=numeric_sparse_frequency. tags=['condition_candidate', 'measure', 'rare_pattern_candidate'] desc=Frequency feature for token 'direct' in an email message. +- word_freq_cs: role=feature, type=numeric_sparse_frequency. tags=['condition_candidate', 'measure', 'rare_pattern_candidate'] desc=Frequency feature for token 'cs' in an email message. +- word_freq_meeting: role=feature, type=numeric_sparse_frequency. tags=['condition_candidate', 'measure', 'rare_pattern_candidate'] desc=Frequency feature for token 'meeting' in an email message. +- word_freq_original: role=feature, type=numeric_sparse_frequency. tags=['condition_candidate', 'measure', 'rare_pattern_candidate'] desc=Frequency feature for token 'original' in an email message. +- word_freq_project: role=feature, type=numeric_sparse_frequency. tags=['condition_candidate', 'measure', 'rare_pattern_candidate'] desc=Frequency feature for token 'project' in an email message. +- word_freq_re: role=feature, type=numeric_sparse_frequency. tags=['condition_candidate', 'measure', 'rare_pattern_candidate'] desc=Frequency feature for token 're' in an email message. +- word_freq_edu: role=feature, type=numeric_sparse_frequency. tags=['condition_candidate', 'measure', 'rare_pattern_candidate'] desc=Frequency feature for token 'edu' in an email message. +- word_freq_table: role=feature, type=numeric_sparse_frequency. tags=['condition_candidate', 'measure', 'rare_pattern_candidate'] desc=Frequency feature for token 'table' in an email message. +- word_freq_conference: role=feature, type=numeric_sparse_frequency. tags=['condition_candidate', 'measure', 'rare_pattern_candidate'] desc=Frequency feature for token 'conference' in an email message. +- char_freq_%3B: role=feature, type=numeric_sparse_frequency. tags=['condition_candidate', 'measure', 'rare_pattern_candidate'] desc=Character frequency feature for symbol ';'. +- char_freq_%28: role=feature, type=numeric_sparse_frequency. tags=['condition_candidate', 'measure', 'rare_pattern_candidate'] desc=Character frequency feature for symbol '('. +- char_freq_%5B: role=feature, type=numeric_sparse_frequency. tags=['condition_candidate', 'measure', 'rare_pattern_candidate'] desc=Character frequency feature for symbol '['. +- char_freq_%21: role=feature, type=numeric_sparse_frequency. tags=['condition_candidate', 'measure', 'rare_pattern_candidate'] desc=Character frequency feature for symbol '!'. +- char_freq_%24: role=feature, type=numeric_sparse_frequency. tags=['condition_candidate', 'measure', 'rare_pattern_candidate'] desc=Character frequency feature for symbol '$'. +- char_freq_%23: role=feature, type=numeric_sparse_frequency. tags=['condition_candidate', 'measure', 'rare_pattern_candidate'] desc=Character frequency feature for symbol '#'. +- capital_run_length_average: role=feature, type=numeric_heavy_tail. tags=['condition_candidate', 'measure', 'rare_pattern_candidate'] desc=Average length of uninterrupted capital-letter runs. +- capital_run_length_longest: role=feature, type=numeric_heavy_tail. tags=['condition_candidate', 'measure', 'rare_pattern_candidate'] desc=Longest uninterrupted capital-letter run. +- capital_run_length_total: role=feature, type=numeric_heavy_tail. tags=['condition_candidate', 'measure', 'rare_pattern_candidate'] desc=Total number of capital letters in runs. +- class: role=target, type=binary_target. tags=['target_candidate'] desc=Binary spam label (1=spam, 0=non-spam). +- useful_field_combinations: [['word_freq_free', 'word_freq_you', 'class'], ['char_freq_%21', 'capital_run_length_longest', 'class'], ['word_freq_remove', 'word_freq_money', 'class']] +- fields_requiring_caution: ['capital_run_length_average', 'capital_run_length_longest', 'capital_run_length_total'] +- source_url: https://www.openml.org/d/44 + +SQLite schema snapshot: +{ + "table_name": "n1", + "quoted_table_name": "\"n1\"", + "row_count": 4601, + "columns": [ + { + "name": "word_freq_make", + "type": "TEXT", + "notnull": false, + "pk": false + }, + { + "name": "word_freq_address", + "type": "TEXT", + "notnull": false, + "pk": false + }, + { + "name": "word_freq_all", + "type": "TEXT", + "notnull": false, + "pk": false + }, + { + "name": "word_freq_3d", + "type": "TEXT", + "notnull": false, + "pk": false + }, + { + "name": "word_freq_our", + "type": "TEXT", + "notnull": false, + "pk": false + }, + { + "name": "word_freq_over", + "type": "TEXT", + "notnull": false, + "pk": false + }, + { + "name": "word_freq_remove", + "type": "TEXT", + "notnull": false, + "pk": false + }, + { + "name": "word_freq_internet", + "type": "TEXT", + "notnull": false, + "pk": false + }, + { + "name": "word_freq_order", + "type": "TEXT", + "notnull": false, + "pk": false + }, + { + "name": "word_freq_mail", + "type": "TEXT", + "notnull": false, + "pk": false + }, + { + "name": "word_freq_receive", + "type": "TEXT", + "notnull": false, + "pk": false + }, + { + "name": "word_freq_will", + "type": "TEXT", + "notnull": false, + "pk": false + }, + { + "name": "word_freq_people", + "type": "TEXT", + "notnull": false, + "pk": false + }, + { + "name": "word_freq_report", + "type": "TEXT", + "notnull": false, + "pk": false + }, + { + "name": "word_freq_addresses", + "type": "TEXT", + "notnull": false, + "pk": false + }, + { + "name": "word_freq_free", + "type": "TEXT", + "notnull": false, + "pk": false + }, + { + "name": "word_freq_business", + "type": "TEXT", + "notnull": false, + "pk": false + }, + { + "name": "word_freq_email", + "type": "TEXT", + "notnull": false, + "pk": false + }, + { + "name": "word_freq_you", + "type": "TEXT", + "notnull": false, + "pk": false + }, + { + "name": "word_freq_credit", + "type": "TEXT", + "notnull": false, + "pk": false + }, + { + "name": "word_freq_your", + "type": "TEXT", + "notnull": false, + "pk": false + }, + { + "name": "word_freq_font", + "type": "TEXT", + "notnull": false, + "pk": false + }, + { + "name": "word_freq_000", + "type": "TEXT", + "notnull": false, + "pk": false + }, + { + "name": "word_freq_money", + "type": "TEXT", + "notnull": false, + "pk": false + }, + { + "name": "word_freq_hp", + "type": "TEXT", + "notnull": false, + "pk": false + }, + { + "name": "word_freq_hpl", + "type": "TEXT", + "notnull": false, + "pk": false + }, + { + "name": "word_freq_george", + "type": "TEXT", + "notnull": false, + "pk": false + }, + { + "name": "word_freq_650", + "type": "TEXT", + "notnull": false, + "pk": false + }, + { + "name": "word_freq_lab", + "type": "TEXT", + "notnull": false, + "pk": false + }, + { + "name": "word_freq_labs", + "type": "TEXT", + "notnull": false, + "pk": false + }, + { + "name": "word_freq_telnet", + "type": "TEXT", + "notnull": false, + "pk": false + }, + { + "name": "word_freq_857", + "type": "TEXT", + "notnull": false, + "pk": false + }, + { + "name": "word_freq_data", + "type": "TEXT", + "notnull": false, + "pk": false + }, + { + "name": "word_freq_415", + "type": "TEXT", + "notnull": false, + "pk": false + }, + { + "name": "word_freq_85", + "type": "TEXT", + "notnull": false, + "pk": false + }, + { + "name": "word_freq_technology", + "type": "TEXT", + "notnull": false, + "pk": false + }, + { + "name": "word_freq_1999", + "type": "TEXT", + "notnull": false, + "pk": false + }, + { + "name": "word_freq_parts", + "type": "TEXT", + "notnull": false, + "pk": false + }, + { + "name": "word_freq_pm", + "type": "TEXT", + "notnull": false, + "pk": false + }, + { + "name": "word_freq_direct", + "type": "TEXT", + "notnull": false, + "pk": false + }, + { + "name": "word_freq_cs", + "type": "TEXT", + "notnull": false, + "pk": false + }, + { + "name": "word_freq_meeting", + "type": "TEXT", + "notnull": false, + "pk": false + }, + { + "name": "word_freq_original", + "type": "TEXT", + "notnull": false, + "pk": false + }, + { + "name": "word_freq_project", + "type": "TEXT", + "notnull": false, + "pk": false + }, + { + "name": "word_freq_re", + "type": "TEXT", + "notnull": false, + "pk": false + }, + { + "name": "word_freq_edu", + "type": "TEXT", + "notnull": false, + "pk": false + }, + { + "name": "word_freq_table", + "type": "TEXT", + "notnull": false, + "pk": false + }, + { + "name": "word_freq_conference", + "type": "TEXT", + "notnull": false, + "pk": false + }, + { + "name": "char_freq_%3B", + "type": "TEXT", + "notnull": false, + "pk": false + }, + { + "name": "char_freq_%28", + "type": "TEXT", + "notnull": false, + "pk": false + }, + { + "name": "char_freq_%5B", + "type": "TEXT", + "notnull": false, + "pk": false + }, + { + "name": "char_freq_%21", + "type": "TEXT", + "notnull": false, + "pk": false + }, + { + "name": "char_freq_%24", + "type": "TEXT", + "notnull": false, + "pk": false + }, + { + "name": "char_freq_%23", + "type": "TEXT", + "notnull": false, + "pk": false + }, + { + "name": "capital_run_length_average", + "type": "TEXT", + "notnull": false, + "pk": false + }, + { + "name": "capital_run_length_longest", + "type": "TEXT", + "notnull": false, + "pk": false + }, + { + "name": "capital_run_length_total", + "type": "TEXT", + "notnull": false, + "pk": false + }, + { + "name": "class", + "type": "TEXT", + "notnull": false, + "pk": false + } + ], + "sample_rows": [ + { + "word_freq_make": "0", + "word_freq_address": "0.64", + "word_freq_all": "0.64", + "word_freq_3d": "0", + "word_freq_our": "0.32", + "word_freq_over": "0", + "word_freq_remove": "0", + "word_freq_internet": "0", + "word_freq_order": "0", + "word_freq_mail": "0", + "word_freq_receive": "0", + "word_freq_will": "0.64", + "word_freq_people": "0", + "word_freq_report": "0", + "word_freq_addresses": "0", + "word_freq_free": "0.32", + "word_freq_business": "0", + "word_freq_email": "1.29", + "word_freq_you": "1.93", + "word_freq_credit": "0", + "word_freq_your": "0.96", + "word_freq_font": "0", + "word_freq_000": "0", + "word_freq_money": "0", + "word_freq_hp": "0", + "word_freq_hpl": "0", + "word_freq_george": "0", + "word_freq_650": "0", + "word_freq_lab": "0", + "word_freq_labs": "0", + "word_freq_telnet": "0", + "word_freq_857": "0", + "word_freq_data": "0", + "word_freq_415": "0", + "word_freq_85": "0", + "word_freq_technology": "0", + "word_freq_1999": "0", + "word_freq_parts": "0", + "word_freq_pm": "0", + "word_freq_direct": "0", + "word_freq_cs": "0", + "word_freq_meeting": "0", + "word_freq_original": "0", + "word_freq_project": "0", + "word_freq_re": "0", + "word_freq_edu": "0", + "word_freq_table": "0", + "word_freq_conference": "0", + "char_freq_%3B": "0", + "char_freq_%28": "0", + "char_freq_%5B": "0", + "char_freq_%21": "0.778", + "char_freq_%24": "0", + "char_freq_%23": "0", + "capital_run_length_average": "3.756", + "capital_run_length_longest": "61", + "capital_run_length_total": "278", + "class": "1" + }, + { + "word_freq_make": "0.21", + "word_freq_address": "0.28", + "word_freq_all": "0.5", + "word_freq_3d": "0", + "word_freq_our": "0.14", + "word_freq_over": "0.28", + "word_freq_remove": "0.21", + "word_freq_internet": "0.07", + "word_freq_order": "0", + "word_freq_mail": "0.94", + "word_freq_receive": "0.21", + "word_freq_will": "0.79", + "word_freq_people": "0.65", + "word_freq_report": "0.21", + "word_freq_addresses": "0.14", + "word_freq_free": "0.14", + "word_freq_business": "0.07", + "word_freq_email": "0.28", + "word_freq_you": "3.47", + "word_freq_credit": "0", + "word_freq_your": "1.59", + "word_freq_font": "0", + "word_freq_000": "0.43", + "word_freq_money": "0.43", + "word_freq_hp": "0", + "word_freq_hpl": "0", + "word_freq_george": "0", + "word_freq_650": "0", + "word_freq_lab": "0", + "word_freq_labs": "0", + "word_freq_telnet": "0", + "word_freq_857": "0", + "word_freq_data": "0", + "word_freq_415": "0", + "word_freq_85": "0", + "word_freq_technology": "0", + "word_freq_1999": "0.07", + "word_freq_parts": "0", + "word_freq_pm": "0", + "word_freq_direct": "0", + "word_freq_cs": "0", + "word_freq_meeting": "0", + "word_freq_original": "0", + "word_freq_project": "0", + "word_freq_re": "0", + "word_freq_edu": "0", + "word_freq_table": "0", + "word_freq_conference": "0", + "char_freq_%3B": "0", + "char_freq_%28": "0.132", + "char_freq_%5B": "0", + "char_freq_%21": "0.372", + "char_freq_%24": "0.18", + "char_freq_%23": "0.048", + "capital_run_length_average": "5.114", + "capital_run_length_longest": "101", + "capital_run_length_total": "1028", + "class": "1" + }, + { + "word_freq_make": "0.06", + "word_freq_address": "0", + "word_freq_all": "0.71", + "word_freq_3d": "0", + "word_freq_our": "1.23", + "word_freq_over": "0.19", + "word_freq_remove": "0.19", + "word_freq_internet": "0.12", + "word_freq_order": "0.64", + "word_freq_mail": "0.25", + "word_freq_receive": "0.38", + "word_freq_will": "0.45", + "word_freq_people": "0.12", + "word_freq_report": "0", + "word_freq_addresses": "1.75", + "word_freq_free": "0.06", + "word_freq_business": "0.06", + "word_freq_email": "1.03", + "word_freq_you": "1.36", + "word_freq_credit": "0.32", + "word_freq_your": "0.51", + "word_freq_font": "0", + "word_freq_000": "1.16", + "word_freq_money": "0.06", + "word_freq_hp": "0", + "word_freq_hpl": "0", + "word_freq_george": "0", + "word_freq_650": "0", + "word_freq_lab": "0", + "word_freq_labs": "0", + "word_freq_telnet": "0", + "word_freq_857": "0", + "word_freq_data": "0", + "word_freq_415": "0", + "word_freq_85": "0", + "word_freq_technology": "0", + "word_freq_1999": "0", + "word_freq_parts": "0", + "word_freq_pm": "0", + "word_freq_direct": "0.06", + "word_freq_cs": "0", + "word_freq_meeting": "0", + "word_freq_original": "0.12", + "word_freq_project": "0", + "word_freq_re": "0.06", + "word_freq_edu": "0.06", + "word_freq_table": "0", + "word_freq_conference": "0", + "char_freq_%3B": "0.01", + "char_freq_%28": "0.143", + "char_freq_%5B": "0", + "char_freq_%21": "0.276", + "char_freq_%24": "0.184", + "char_freq_%23": "0.01", + "capital_run_length_average": "9.821", + "capital_run_length_longest": "485", + "capital_run_length_total": "2259", + "class": "1" + }, + { + "word_freq_make": "0", + "word_freq_address": "0", + "word_freq_all": "0", + "word_freq_3d": "0", + "word_freq_our": "0.63", + "word_freq_over": "0", + "word_freq_remove": "0.31", + "word_freq_internet": "0.63", + "word_freq_order": "0.31", + "word_freq_mail": "0.63", + "word_freq_receive": "0.31", + "word_freq_will": "0.31", + "word_freq_people": "0.31", + "word_freq_report": "0", + "word_freq_addresses": "0", + "word_freq_free": "0.31", + "word_freq_business": "0", + "word_freq_email": "0", + "word_freq_you": "3.18", + "word_freq_credit": "0", + "word_freq_your": "0.31", + "word_freq_font": "0", + "word_freq_000": "0", + "word_freq_money": "0", + "word_freq_hp": "0", + "word_freq_hpl": "0", + "word_freq_george": "0", + "word_freq_650": "0", + "word_freq_lab": "0", + "word_freq_labs": "0", + "word_freq_telnet": "0", + "word_freq_857": "0", + "word_freq_data": "0", + "word_freq_415": "0", + "word_freq_85": "0", + "word_freq_technology": "0", + "word_freq_1999": "0", + "word_freq_parts": "0", + "word_freq_pm": "0", + "word_freq_direct": "0", + "word_freq_cs": "0", + "word_freq_meeting": "0", + "word_freq_original": "0", + "word_freq_project": "0", + "word_freq_re": "0", + "word_freq_edu": "0", + "word_freq_table": "0", + "word_freq_conference": "0", + "char_freq_%3B": "0", + "char_freq_%28": "0.137", + "char_freq_%5B": "0", + "char_freq_%21": "0.137", + "char_freq_%24": "0", + "char_freq_%23": "0", + "capital_run_length_average": "3.537", + "capital_run_length_longest": "40", + "capital_run_length_total": "191", + "class": "1" + }, + { + "word_freq_make": "0", + "word_freq_address": "0", + "word_freq_all": "0", + "word_freq_3d": "0", + "word_freq_our": "0.63", + "word_freq_over": "0", + "word_freq_remove": "0.31", + "word_freq_internet": "0.63", + "word_freq_order": "0.31", + "word_freq_mail": "0.63", + "word_freq_receive": "0.31", + "word_freq_will": "0.31", + "word_freq_people": "0.31", + "word_freq_report": "0", + "word_freq_addresses": "0", + "word_freq_free": "0.31", + "word_freq_business": "0", + "word_freq_email": "0", + "word_freq_you": "3.18", + "word_freq_credit": "0", + "word_freq_your": "0.31", + "word_freq_font": "0", + "word_freq_000": "0", + "word_freq_money": "0", + "word_freq_hp": "0", + "word_freq_hpl": "0", + "word_freq_george": "0", + "word_freq_650": "0", + "word_freq_lab": "0", + "word_freq_labs": "0", + "word_freq_telnet": "0", + "word_freq_857": "0", + "word_freq_data": "0", + "word_freq_415": "0", + "word_freq_85": "0", + "word_freq_technology": "0", + "word_freq_1999": "0", + "word_freq_parts": "0", + "word_freq_pm": "0", + "word_freq_direct": "0", + "word_freq_cs": "0", + "word_freq_meeting": "0", + "word_freq_original": "0", + "word_freq_project": "0", + "word_freq_re": "0", + "word_freq_edu": "0", + "word_freq_table": "0", + "word_freq_conference": "0", + "char_freq_%3B": "0", + "char_freq_%28": "0.135", + "char_freq_%5B": "0", + "char_freq_%21": "0.135", + "char_freq_%24": "0", + "char_freq_%23": "0", + "capital_run_length_average": "3.537", + "capital_run_length_longest": "40", + "capital_run_length_total": "191", + "class": "1" + } + ] +} + +Shortlisted templates: +[ + { + "template_id": "tpl_m4_group_condition_rate", + "template_name": "Grouped Condition Rate", + "primary_family": "conditional_dependency_structure", + "portability": "yes", + "sql_skeleton": "SELECT {group_col},\n AVG(CASE WHEN {condition_col} = {condition_value} THEN 1 ELSE 0 END) AS condition_rate\nFROM {table}\nGROUP BY {group_col}\nORDER BY condition_rate DESC;", + "required_roles": [ + "group_col", + "condition_col" + ] + } +] + +Problem instance: +{ + "dataset_id": "n1", + "question": "Use template Grouped Condition Rate to probe dependency_strength_similarity with semantic role focused_target_view. Focus on group_col=class, condition_col=class.", + "planned_template_id": "tpl_m4_group_condition_rate", + "bindings": { + "group_col": "class", + "condition_col": "class", + "condition_value": "1", + "positive_value": "0", + "negative_value": "1", + "top_k": 17, + "top_n": 6, + "num_tiles": 10, + "percentile_value": 0.9, + "z_threshold": 2.0, + "fraction_threshold": 0.05, + "baseline_multiplier": 1.75, + "baseline_fraction": 0.1, + "min_group_size": 5, + "min_support": 4, + "measure_threshold": 0.0, + "time_grain": "month", + "lookback_rows": 3, + "current_period_start": "'2024-01-01'", + "current_period_end": "'2024-04-01'", + "previous_period_start": "'2023-10-01'", + "previous_period_end": "'2024-01-01'", + "drift_ratio_threshold": 0.8 + }, + "can_vary": [], + "must_fix": [], + "runtime_sql_skeleton": "SELECT {group_col},\n AVG(CASE WHEN {condition_col} = {condition_value} THEN 1 ELSE 0 END) AS condition_rate\nFROM {table}\nGROUP BY {group_col}\nORDER BY condition_rate DESC;" +} + +Repair context: +{} diff --git a/Query/sql/v2/runs/v2_cli_20260502_081223_d/n1/artifacts/v2q_n1_b4a50b6eca8328dc/cli/sql_prompt_attempt_2.txt b/Query/sql/v2/runs/v2_cli_20260502_081223_d/n1/artifacts/v2q_n1_b4a50b6eca8328dc/cli/sql_prompt_attempt_2.txt new file mode 100644 index 0000000000000000000000000000000000000000..eb74b711cc96d421f83c13b88d6d64844cac4b32 --- /dev/null +++ b/Query/sql/v2/runs/v2_cli_20260502_081223_d/n1/artifacts/v2q_n1_b4a50b6eca8328dc/cli/sql_prompt_attempt_2.txt @@ -0,0 +1,795 @@ +You are generating one SQLite SELECT query for a single-table SQL QA task. +Return strict JSON only, with this schema: {"sql": "...", "notes": "..."}. +Rules: +- Use only the provided table and columns. +- Do not write INSERT, UPDATE, DELETE, DROP, ALTER, CREATE, PRAGMA, ATTACH, DETACH, or VACUUM. +- Prefer the planned template and bound roles when provided. +- Add a leading SQL comment exactly like: -- template_id: . +- Generate SQLite-compatible SQL. SQLite does not support PERCENTILE_CONT or STDDEV. +- Quote identifiers with double quotes. +- Return no markdown and no extra prose. + +Dataset context: +Dataset context for SQL QA: +- dataset_id: n1 +- dataset_name: Spambase +- table_name: n1 +- table_layout: single-table dataset (do not assume joins). +- row_semantics: One row is one email represented by engineered token/character frequency and capitalization-run features. +- task_type: classification +- target_column: class +- main_row_count: 4601 +- important_fields: +- word_freq_make: role=feature, type=numeric_sparse_frequency. tags=['condition_candidate', 'measure', 'rare_pattern_candidate'] desc=Frequency feature for token 'make' in an email message. +- word_freq_address: role=feature, type=numeric_sparse_frequency. tags=['condition_candidate', 'measure', 'rare_pattern_candidate'] desc=Frequency feature for token 'address' in an email message. +- word_freq_all: role=feature, type=numeric_sparse_frequency. tags=['condition_candidate', 'measure', 'rare_pattern_candidate'] desc=Frequency feature for token 'all' in an email message. +- word_freq_3d: role=feature, type=numeric_sparse_frequency. tags=['condition_candidate', 'measure', 'rare_pattern_candidate'] desc=Frequency feature for token '3d' in an email message. +- word_freq_our: role=feature, type=numeric_sparse_frequency. tags=['condition_candidate', 'measure', 'rare_pattern_candidate'] desc=Frequency feature for token 'our' in an email message. +- word_freq_over: role=feature, type=numeric_sparse_frequency. tags=['condition_candidate', 'measure', 'rare_pattern_candidate'] desc=Frequency feature for token 'over' in an email message. +- word_freq_remove: role=feature, type=numeric_sparse_frequency. tags=['condition_candidate', 'measure', 'rare_pattern_candidate'] desc=Frequency feature for token 'remove' in an email message. +- word_freq_internet: role=feature, type=numeric_sparse_frequency. tags=['condition_candidate', 'measure', 'rare_pattern_candidate'] desc=Frequency feature for token 'internet' in an email message. +- word_freq_order: role=feature, type=numeric_sparse_frequency. tags=['condition_candidate', 'measure', 'rare_pattern_candidate'] desc=Frequency feature for token 'order' in an email message. +- word_freq_mail: role=feature, type=numeric_sparse_frequency. tags=['condition_candidate', 'measure', 'rare_pattern_candidate'] desc=Frequency feature for token 'mail' in an email message. +- word_freq_receive: role=feature, type=numeric_sparse_frequency. tags=['condition_candidate', 'measure', 'rare_pattern_candidate'] desc=Frequency feature for token 'receive' in an email message. +- word_freq_will: role=feature, type=numeric_sparse_frequency. tags=['condition_candidate', 'measure', 'rare_pattern_candidate'] desc=Frequency feature for token 'will' in an email message. +- word_freq_people: role=feature, type=numeric_sparse_frequency. tags=['condition_candidate', 'measure', 'rare_pattern_candidate'] desc=Frequency feature for token 'people' in an email message. +- word_freq_report: role=feature, type=numeric_sparse_frequency. tags=['condition_candidate', 'measure', 'rare_pattern_candidate'] desc=Frequency feature for token 'report' in an email message. +- word_freq_addresses: role=feature, type=numeric_sparse_frequency. tags=['condition_candidate', 'measure', 'rare_pattern_candidate'] desc=Frequency feature for token 'addresses' in an email message. +- word_freq_free: role=feature, type=numeric_sparse_frequency. tags=['condition_candidate', 'measure', 'rare_pattern_candidate'] desc=Frequency feature for token 'free' in an email message. +- word_freq_business: role=feature, type=numeric_sparse_frequency. tags=['condition_candidate', 'measure', 'rare_pattern_candidate'] desc=Frequency feature for token 'business' in an email message. +- word_freq_email: role=feature, type=numeric_sparse_frequency. tags=['condition_candidate', 'measure', 'rare_pattern_candidate'] desc=Frequency feature for token 'email' in an email message. +- word_freq_you: role=feature, type=numeric_sparse_frequency. tags=['condition_candidate', 'measure', 'rare_pattern_candidate'] desc=Frequency feature for token 'you' in an email message. +- word_freq_credit: role=feature, type=numeric_sparse_frequency. tags=['condition_candidate', 'measure', 'rare_pattern_candidate'] desc=Frequency feature for token 'credit' in an email message. +- word_freq_your: role=feature, type=numeric_sparse_frequency. tags=['condition_candidate', 'measure', 'rare_pattern_candidate'] desc=Frequency feature for token 'your' in an email message. +- word_freq_font: role=feature, type=numeric_sparse_frequency. tags=['condition_candidate', 'measure', 'rare_pattern_candidate'] desc=Frequency feature for token 'font' in an email message. +- word_freq_000: role=feature, type=numeric_sparse_frequency. tags=['condition_candidate', 'measure', 'rare_pattern_candidate'] desc=Frequency feature for token '000' in an email message. +- word_freq_money: role=feature, type=numeric_sparse_frequency. tags=['condition_candidate', 'measure', 'rare_pattern_candidate'] desc=Frequency feature for token 'money' in an email message. +- word_freq_hp: role=feature, type=numeric_sparse_frequency. tags=['condition_candidate', 'measure', 'rare_pattern_candidate'] desc=Frequency feature for token 'hp' in an email message. +- word_freq_hpl: role=feature, type=numeric_sparse_frequency. tags=['condition_candidate', 'measure', 'rare_pattern_candidate'] desc=Frequency feature for token 'hpl' in an email message. +- word_freq_george: role=feature, type=numeric_sparse_frequency. tags=['condition_candidate', 'measure', 'rare_pattern_candidate'] desc=Frequency feature for token 'george' in an email message. +- word_freq_650: role=feature, type=numeric_sparse_frequency. tags=['condition_candidate', 'measure', 'rare_pattern_candidate'] desc=Frequency feature for token '650' in an email message. +- word_freq_lab: role=feature, type=numeric_sparse_frequency. tags=['condition_candidate', 'measure', 'rare_pattern_candidate'] desc=Frequency feature for token 'lab' in an email message. +- word_freq_labs: role=feature, type=numeric_sparse_frequency. tags=['condition_candidate', 'measure', 'rare_pattern_candidate'] desc=Frequency feature for token 'labs' in an email message. +- word_freq_telnet: role=feature, type=numeric_sparse_frequency. tags=['condition_candidate', 'measure', 'rare_pattern_candidate'] desc=Frequency feature for token 'telnet' in an email message. +- word_freq_857: role=feature, type=numeric_sparse_frequency. tags=['condition_candidate', 'measure', 'rare_pattern_candidate'] desc=Frequency feature for token '857' in an email message. +- word_freq_data: role=feature, type=numeric_sparse_frequency. tags=['condition_candidate', 'measure', 'rare_pattern_candidate'] desc=Frequency feature for token 'data' in an email message. +- word_freq_415: role=feature, type=numeric_sparse_frequency. tags=['condition_candidate', 'measure', 'rare_pattern_candidate'] desc=Frequency feature for token '415' in an email message. +- word_freq_85: role=feature, type=numeric_sparse_frequency. tags=['condition_candidate', 'measure', 'rare_pattern_candidate'] desc=Frequency feature for token '85' in an email message. +- word_freq_technology: role=feature, type=numeric_sparse_frequency. tags=['condition_candidate', 'measure', 'rare_pattern_candidate'] desc=Frequency feature for token 'technology' in an email message. +- word_freq_1999: role=feature, type=numeric_sparse_frequency. tags=['condition_candidate', 'measure', 'rare_pattern_candidate'] desc=Frequency feature for token '1999' in an email message. +- word_freq_parts: role=feature, type=numeric_sparse_frequency. tags=['condition_candidate', 'measure', 'rare_pattern_candidate'] desc=Frequency feature for token 'parts' in an email message. +- word_freq_pm: role=feature, type=numeric_sparse_frequency. tags=['condition_candidate', 'measure', 'rare_pattern_candidate'] desc=Frequency feature for token 'pm' in an email message. +- word_freq_direct: role=feature, type=numeric_sparse_frequency. tags=['condition_candidate', 'measure', 'rare_pattern_candidate'] desc=Frequency feature for token 'direct' in an email message. +- word_freq_cs: role=feature, type=numeric_sparse_frequency. tags=['condition_candidate', 'measure', 'rare_pattern_candidate'] desc=Frequency feature for token 'cs' in an email message. +- word_freq_meeting: role=feature, type=numeric_sparse_frequency. tags=['condition_candidate', 'measure', 'rare_pattern_candidate'] desc=Frequency feature for token 'meeting' in an email message. +- word_freq_original: role=feature, type=numeric_sparse_frequency. tags=['condition_candidate', 'measure', 'rare_pattern_candidate'] desc=Frequency feature for token 'original' in an email message. +- word_freq_project: role=feature, type=numeric_sparse_frequency. tags=['condition_candidate', 'measure', 'rare_pattern_candidate'] desc=Frequency feature for token 'project' in an email message. +- word_freq_re: role=feature, type=numeric_sparse_frequency. tags=['condition_candidate', 'measure', 'rare_pattern_candidate'] desc=Frequency feature for token 're' in an email message. +- word_freq_edu: role=feature, type=numeric_sparse_frequency. tags=['condition_candidate', 'measure', 'rare_pattern_candidate'] desc=Frequency feature for token 'edu' in an email message. +- word_freq_table: role=feature, type=numeric_sparse_frequency. tags=['condition_candidate', 'measure', 'rare_pattern_candidate'] desc=Frequency feature for token 'table' in an email message. +- word_freq_conference: role=feature, type=numeric_sparse_frequency. tags=['condition_candidate', 'measure', 'rare_pattern_candidate'] desc=Frequency feature for token 'conference' in an email message. +- char_freq_%3B: role=feature, type=numeric_sparse_frequency. tags=['condition_candidate', 'measure', 'rare_pattern_candidate'] desc=Character frequency feature for symbol ';'. +- char_freq_%28: role=feature, type=numeric_sparse_frequency. tags=['condition_candidate', 'measure', 'rare_pattern_candidate'] desc=Character frequency feature for symbol '('. +- char_freq_%5B: role=feature, type=numeric_sparse_frequency. tags=['condition_candidate', 'measure', 'rare_pattern_candidate'] desc=Character frequency feature for symbol '['. +- char_freq_%21: role=feature, type=numeric_sparse_frequency. tags=['condition_candidate', 'measure', 'rare_pattern_candidate'] desc=Character frequency feature for symbol '!'. +- char_freq_%24: role=feature, type=numeric_sparse_frequency. tags=['condition_candidate', 'measure', 'rare_pattern_candidate'] desc=Character frequency feature for symbol '$'. +- char_freq_%23: role=feature, type=numeric_sparse_frequency. tags=['condition_candidate', 'measure', 'rare_pattern_candidate'] desc=Character frequency feature for symbol '#'. +- capital_run_length_average: role=feature, type=numeric_heavy_tail. tags=['condition_candidate', 'measure', 'rare_pattern_candidate'] desc=Average length of uninterrupted capital-letter runs. +- capital_run_length_longest: role=feature, type=numeric_heavy_tail. tags=['condition_candidate', 'measure', 'rare_pattern_candidate'] desc=Longest uninterrupted capital-letter run. +- capital_run_length_total: role=feature, type=numeric_heavy_tail. tags=['condition_candidate', 'measure', 'rare_pattern_candidate'] desc=Total number of capital letters in runs. +- class: role=target, type=binary_target. tags=['target_candidate'] desc=Binary spam label (1=spam, 0=non-spam). +- useful_field_combinations: [['word_freq_free', 'word_freq_you', 'class'], ['char_freq_%21', 'capital_run_length_longest', 'class'], ['word_freq_remove', 'word_freq_money', 'class']] +- fields_requiring_caution: ['capital_run_length_average', 'capital_run_length_longest', 'capital_run_length_total'] +- source_url: https://www.openml.org/d/44 + +SQLite schema snapshot: +{ + "table_name": "n1", + "quoted_table_name": "\"n1\"", + "row_count": 4601, + "columns": [ + { + "name": "word_freq_make", + "type": "TEXT", + "notnull": false, + "pk": false + }, + { + "name": "word_freq_address", + "type": "TEXT", + "notnull": false, + "pk": false + }, + { + "name": "word_freq_all", + "type": "TEXT", + "notnull": false, + "pk": false + }, + { + "name": "word_freq_3d", + "type": "TEXT", + "notnull": false, + "pk": false + }, + { + "name": "word_freq_our", + "type": "TEXT", + "notnull": false, + "pk": false + }, + { + "name": "word_freq_over", + "type": "TEXT", + "notnull": false, + "pk": false + }, + { + "name": "word_freq_remove", + "type": "TEXT", + "notnull": false, + "pk": false + }, + { + "name": "word_freq_internet", + "type": "TEXT", + "notnull": false, + "pk": false + }, + { + "name": "word_freq_order", + "type": "TEXT", + "notnull": false, + "pk": false + }, + { + "name": "word_freq_mail", + "type": "TEXT", + "notnull": false, + "pk": false + }, + { + "name": "word_freq_receive", + "type": "TEXT", + "notnull": false, + "pk": false + }, + { + "name": "word_freq_will", + "type": "TEXT", + "notnull": false, + "pk": false + }, + { + "name": "word_freq_people", + "type": "TEXT", + "notnull": false, + "pk": false + }, + { + "name": "word_freq_report", + "type": "TEXT", + "notnull": false, + "pk": false + }, + { + "name": "word_freq_addresses", + "type": "TEXT", + "notnull": false, + "pk": false + }, + { + "name": "word_freq_free", + "type": "TEXT", + "notnull": false, + "pk": false + }, + { + "name": "word_freq_business", + "type": "TEXT", + "notnull": false, + "pk": false + }, + { + "name": "word_freq_email", + "type": "TEXT", + "notnull": false, + "pk": false + }, + { + "name": "word_freq_you", + "type": "TEXT", + "notnull": false, + "pk": false + }, + { + "name": "word_freq_credit", + "type": "TEXT", + "notnull": false, + "pk": false + }, + { + "name": "word_freq_your", + "type": "TEXT", + "notnull": false, + "pk": false + }, + { + "name": "word_freq_font", + "type": "TEXT", + "notnull": false, + "pk": false + }, + { + "name": "word_freq_000", + "type": "TEXT", + "notnull": false, + "pk": false + }, + { + "name": "word_freq_money", + "type": "TEXT", + "notnull": false, + "pk": false + }, + { + "name": "word_freq_hp", + "type": "TEXT", + "notnull": false, + "pk": false + }, + { + "name": "word_freq_hpl", + "type": "TEXT", + "notnull": false, + "pk": false + }, + { + "name": "word_freq_george", + "type": "TEXT", + "notnull": false, + "pk": false + }, + { + "name": "word_freq_650", + "type": "TEXT", + "notnull": false, + "pk": false + }, + { + "name": "word_freq_lab", + "type": "TEXT", + "notnull": false, + "pk": false + }, + { + "name": "word_freq_labs", + "type": "TEXT", + "notnull": false, + "pk": false + }, + { + "name": "word_freq_telnet", + "type": "TEXT", + "notnull": false, + "pk": false + }, + { + "name": "word_freq_857", + "type": "TEXT", + "notnull": false, + "pk": false + }, + { + "name": "word_freq_data", + "type": "TEXT", + "notnull": false, + "pk": false + }, + { + "name": "word_freq_415", + "type": "TEXT", + "notnull": false, + "pk": false + }, + { + "name": "word_freq_85", + "type": "TEXT", + "notnull": false, + "pk": false + }, + { + "name": "word_freq_technology", + "type": "TEXT", + "notnull": false, + "pk": false + }, + { + "name": "word_freq_1999", + "type": "TEXT", + "notnull": false, + "pk": false + }, + { + "name": "word_freq_parts", + "type": "TEXT", + "notnull": false, + "pk": false + }, + { + "name": "word_freq_pm", + "type": "TEXT", + "notnull": false, + "pk": false + }, + { + "name": "word_freq_direct", + "type": "TEXT", + "notnull": false, + "pk": false + }, + { + "name": "word_freq_cs", + "type": "TEXT", + "notnull": false, + "pk": false + }, + { + "name": "word_freq_meeting", + "type": "TEXT", + "notnull": false, + "pk": false + }, + { + "name": "word_freq_original", + "type": "TEXT", + "notnull": false, + "pk": false + }, + { + "name": "word_freq_project", + "type": "TEXT", + "notnull": false, + "pk": false + }, + { + "name": "word_freq_re", + "type": "TEXT", + "notnull": false, + "pk": false + }, + { + "name": "word_freq_edu", + "type": "TEXT", + "notnull": false, + "pk": false + }, + { + "name": "word_freq_table", + "type": "TEXT", + "notnull": false, + "pk": false + }, + { + "name": "word_freq_conference", + "type": "TEXT", + "notnull": false, + "pk": false + }, + { + "name": "char_freq_%3B", + "type": "TEXT", + "notnull": false, + "pk": false + }, + { + "name": "char_freq_%28", + "type": "TEXT", + "notnull": false, + "pk": false + }, + { + "name": "char_freq_%5B", + "type": "TEXT", + "notnull": false, + "pk": false + }, + { + "name": "char_freq_%21", + "type": "TEXT", + "notnull": false, + "pk": false + }, + { + "name": "char_freq_%24", + "type": "TEXT", + "notnull": false, + "pk": false + }, + { + "name": "char_freq_%23", + "type": "TEXT", + "notnull": false, + "pk": false + }, + { + "name": "capital_run_length_average", + "type": "TEXT", + "notnull": false, + "pk": false + }, + { + "name": "capital_run_length_longest", + "type": "TEXT", + "notnull": false, + "pk": false + }, + { + "name": "capital_run_length_total", + "type": "TEXT", + "notnull": false, + "pk": false + }, + { + "name": "class", + "type": "TEXT", + "notnull": false, + "pk": false + } + ], + "sample_rows": [ + { + "word_freq_make": "0", + "word_freq_address": "0.64", + "word_freq_all": "0.64", + "word_freq_3d": "0", + "word_freq_our": "0.32", + "word_freq_over": "0", + "word_freq_remove": "0", + "word_freq_internet": "0", + "word_freq_order": "0", + "word_freq_mail": "0", + "word_freq_receive": "0", + "word_freq_will": "0.64", + "word_freq_people": "0", + "word_freq_report": "0", + "word_freq_addresses": "0", + "word_freq_free": "0.32", + "word_freq_business": "0", + "word_freq_email": "1.29", + "word_freq_you": "1.93", + "word_freq_credit": "0", + "word_freq_your": "0.96", + "word_freq_font": "0", + "word_freq_000": "0", + "word_freq_money": "0", + "word_freq_hp": "0", + "word_freq_hpl": "0", + "word_freq_george": "0", + "word_freq_650": "0", + "word_freq_lab": "0", + "word_freq_labs": "0", + "word_freq_telnet": "0", + "word_freq_857": "0", + "word_freq_data": "0", + "word_freq_415": "0", + "word_freq_85": "0", + "word_freq_technology": "0", + "word_freq_1999": "0", + "word_freq_parts": "0", + "word_freq_pm": "0", + "word_freq_direct": "0", + "word_freq_cs": "0", + "word_freq_meeting": "0", + "word_freq_original": "0", + "word_freq_project": "0", + "word_freq_re": "0", + "word_freq_edu": "0", + "word_freq_table": "0", + "word_freq_conference": "0", + "char_freq_%3B": "0", + "char_freq_%28": "0", + "char_freq_%5B": "0", + "char_freq_%21": "0.778", + "char_freq_%24": "0", + "char_freq_%23": "0", + "capital_run_length_average": "3.756", + "capital_run_length_longest": "61", + "capital_run_length_total": "278", + "class": "1" + }, + { + "word_freq_make": "0.21", + "word_freq_address": "0.28", + "word_freq_all": "0.5", + "word_freq_3d": "0", + "word_freq_our": "0.14", + "word_freq_over": "0.28", + "word_freq_remove": "0.21", + "word_freq_internet": "0.07", + "word_freq_order": "0", + "word_freq_mail": "0.94", + "word_freq_receive": "0.21", + "word_freq_will": "0.79", + "word_freq_people": "0.65", + "word_freq_report": "0.21", + "word_freq_addresses": "0.14", + "word_freq_free": "0.14", + "word_freq_business": "0.07", + "word_freq_email": "0.28", + "word_freq_you": "3.47", + "word_freq_credit": "0", + "word_freq_your": "1.59", + "word_freq_font": "0", + "word_freq_000": "0.43", + "word_freq_money": "0.43", + "word_freq_hp": "0", + "word_freq_hpl": "0", + "word_freq_george": "0", + "word_freq_650": "0", + "word_freq_lab": "0", + "word_freq_labs": "0", + "word_freq_telnet": "0", + "word_freq_857": "0", + "word_freq_data": "0", + "word_freq_415": "0", + "word_freq_85": "0", + "word_freq_technology": "0", + "word_freq_1999": "0.07", + "word_freq_parts": "0", + "word_freq_pm": "0", + "word_freq_direct": "0", + "word_freq_cs": "0", + "word_freq_meeting": "0", + "word_freq_original": "0", + "word_freq_project": "0", + "word_freq_re": "0", + "word_freq_edu": "0", + "word_freq_table": "0", + "word_freq_conference": "0", + "char_freq_%3B": "0", + "char_freq_%28": "0.132", + "char_freq_%5B": "0", + "char_freq_%21": "0.372", + "char_freq_%24": "0.18", + "char_freq_%23": "0.048", + "capital_run_length_average": "5.114", + "capital_run_length_longest": "101", + "capital_run_length_total": "1028", + "class": "1" + }, + { + "word_freq_make": "0.06", + "word_freq_address": "0", + "word_freq_all": "0.71", + "word_freq_3d": "0", + "word_freq_our": "1.23", + "word_freq_over": "0.19", + "word_freq_remove": "0.19", + "word_freq_internet": "0.12", + "word_freq_order": "0.64", + "word_freq_mail": "0.25", + "word_freq_receive": "0.38", + "word_freq_will": "0.45", + "word_freq_people": "0.12", + "word_freq_report": "0", + "word_freq_addresses": "1.75", + "word_freq_free": "0.06", + "word_freq_business": "0.06", + "word_freq_email": "1.03", + "word_freq_you": "1.36", + "word_freq_credit": "0.32", + "word_freq_your": "0.51", + "word_freq_font": "0", + "word_freq_000": "1.16", + "word_freq_money": "0.06", + "word_freq_hp": "0", + "word_freq_hpl": "0", + "word_freq_george": "0", + "word_freq_650": "0", + "word_freq_lab": "0", + "word_freq_labs": "0", + "word_freq_telnet": "0", + "word_freq_857": "0", + "word_freq_data": "0", + "word_freq_415": "0", + "word_freq_85": "0", + "word_freq_technology": "0", + "word_freq_1999": "0", + "word_freq_parts": "0", + "word_freq_pm": "0", + "word_freq_direct": "0.06", + "word_freq_cs": "0", + "word_freq_meeting": "0", + "word_freq_original": "0.12", + "word_freq_project": "0", + "word_freq_re": "0.06", + "word_freq_edu": "0.06", + "word_freq_table": "0", + "word_freq_conference": "0", + "char_freq_%3B": "0.01", + "char_freq_%28": "0.143", + "char_freq_%5B": "0", + "char_freq_%21": "0.276", + "char_freq_%24": "0.184", + "char_freq_%23": "0.01", + "capital_run_length_average": "9.821", + "capital_run_length_longest": "485", + "capital_run_length_total": "2259", + "class": "1" + }, + { + "word_freq_make": "0", + "word_freq_address": "0", + "word_freq_all": "0", + "word_freq_3d": "0", + "word_freq_our": "0.63", + "word_freq_over": "0", + "word_freq_remove": "0.31", + "word_freq_internet": "0.63", + "word_freq_order": "0.31", + "word_freq_mail": "0.63", + "word_freq_receive": "0.31", + "word_freq_will": "0.31", + "word_freq_people": "0.31", + "word_freq_report": "0", + "word_freq_addresses": "0", + "word_freq_free": "0.31", + "word_freq_business": "0", + "word_freq_email": "0", + "word_freq_you": "3.18", + "word_freq_credit": "0", + "word_freq_your": "0.31", + "word_freq_font": "0", + "word_freq_000": "0", + "word_freq_money": "0", + "word_freq_hp": "0", + "word_freq_hpl": "0", + "word_freq_george": "0", + "word_freq_650": "0", + "word_freq_lab": "0", + "word_freq_labs": "0", + "word_freq_telnet": "0", + "word_freq_857": "0", + "word_freq_data": "0", + "word_freq_415": "0", + "word_freq_85": "0", + "word_freq_technology": "0", + "word_freq_1999": "0", + "word_freq_parts": "0", + "word_freq_pm": "0", + "word_freq_direct": "0", + "word_freq_cs": "0", + "word_freq_meeting": "0", + "word_freq_original": "0", + "word_freq_project": "0", + "word_freq_re": "0", + "word_freq_edu": "0", + "word_freq_table": "0", + "word_freq_conference": "0", + "char_freq_%3B": "0", + "char_freq_%28": "0.137", + "char_freq_%5B": "0", + "char_freq_%21": "0.137", + "char_freq_%24": "0", + "char_freq_%23": "0", + "capital_run_length_average": "3.537", + "capital_run_length_longest": "40", + "capital_run_length_total": "191", + "class": "1" + }, + { + "word_freq_make": "0", + "word_freq_address": "0", + "word_freq_all": "0", + "word_freq_3d": "0", + "word_freq_our": "0.63", + "word_freq_over": "0", + "word_freq_remove": "0.31", + "word_freq_internet": "0.63", + "word_freq_order": "0.31", + "word_freq_mail": "0.63", + "word_freq_receive": "0.31", + "word_freq_will": "0.31", + "word_freq_people": "0.31", + "word_freq_report": "0", + "word_freq_addresses": "0", + "word_freq_free": "0.31", + "word_freq_business": "0", + "word_freq_email": "0", + "word_freq_you": "3.18", + "word_freq_credit": "0", + "word_freq_your": "0.31", + "word_freq_font": "0", + "word_freq_000": "0", + "word_freq_money": "0", + "word_freq_hp": "0", + "word_freq_hpl": "0", + "word_freq_george": "0", + "word_freq_650": "0", + "word_freq_lab": "0", + "word_freq_labs": "0", + "word_freq_telnet": "0", + "word_freq_857": "0", + "word_freq_data": "0", + "word_freq_415": "0", + "word_freq_85": "0", + "word_freq_technology": "0", + "word_freq_1999": "0", + "word_freq_parts": "0", + "word_freq_pm": "0", + "word_freq_direct": "0", + "word_freq_cs": "0", + "word_freq_meeting": "0", + "word_freq_original": "0", + "word_freq_project": "0", + "word_freq_re": "0", + "word_freq_edu": "0", + "word_freq_table": "0", + "word_freq_conference": "0", + "char_freq_%3B": "0", + "char_freq_%28": "0.135", + "char_freq_%5B": "0", + "char_freq_%21": "0.135", + "char_freq_%24": "0", + "char_freq_%23": "0", + "capital_run_length_average": "3.537", + "capital_run_length_longest": "40", + "capital_run_length_total": "191", + "class": "1" + } + ] +} + +Shortlisted templates: +[ + { + "template_id": "tpl_m4_group_condition_rate", + "template_name": "Grouped Condition Rate", + "primary_family": "conditional_dependency_structure", + "portability": "yes", + "sql_skeleton": "SELECT {group_col},\n AVG(CASE WHEN {condition_col} = {condition_value} THEN 1 ELSE 0 END) AS condition_rate\nFROM {table}\nGROUP BY {group_col}\nORDER BY condition_rate DESC;", + "required_roles": [ + "group_col", + "condition_col" + ] + } +] + +Problem instance: +{ + "dataset_id": "n1", + "question": "Use template Grouped Condition Rate to probe dependency_strength_similarity with semantic role focused_target_view. Focus on group_col=class, condition_col=class.", + "planned_template_id": "tpl_m4_group_condition_rate", + "bindings": { + "group_col": "class", + "condition_col": "class", + "condition_value": "1", + "positive_value": "0", + "negative_value": "1", + "top_k": 17, + "top_n": 6, + "num_tiles": 10, + "percentile_value": 0.9, + "z_threshold": 2.0, + "fraction_threshold": 0.05, + "baseline_multiplier": 1.75, + "baseline_fraction": 0.1, + "min_group_size": 5, + "min_support": 4, + "measure_threshold": 0.0, + "time_grain": "month", + "lookback_rows": 3, + "current_period_start": "'2024-01-01'", + "current_period_end": "'2024-04-01'", + "previous_period_start": "'2023-10-01'", + "previous_period_end": "'2024-01-01'", + "drift_ratio_threshold": 0.8 + }, + "can_vary": [], + "must_fix": [], + "runtime_sql_skeleton": "SELECT {group_col},\n AVG(CASE WHEN {condition_col} = {condition_value} THEN 1 ELSE 0 END) AS condition_rate\nFROM {table}\nGROUP BY {group_col}\nORDER BY condition_rate DESC;" +} + +Repair context: +{} diff --git a/Query/sql/v2/runs/v2_cli_20260502_081223_d/n1/artifacts/v2q_n1_b4a50b6eca8328dc/cli/sql_response_attempt_1.raw.txt b/Query/sql/v2/runs/v2_cli_20260502_081223_d/n1/artifacts/v2q_n1_b4a50b6eca8328dc/cli/sql_response_attempt_1.raw.txt new file mode 100644 index 0000000000000000000000000000000000000000..9b91f4a6c65cf8c1f3359ca61a7a88bfc432fc5b --- /dev/null +++ b/Query/sql/v2/runs/v2_cli_20260502_081223_d/n1/artifacts/v2q_n1_b4a50b6eca8328dc/cli/sql_response_attempt_1.raw.txt @@ -0,0 +1,4 @@ +{"type":"thread.started","thread_id":"019e40f7-4a99-7380-84f9-6d6ccb26e348"} +{"type":"turn.started"} +{"type":"error","message":"Quota exceeded. Check your plan and billing details."} +{"type":"turn.failed","error":{"message":"Quota exceeded. Check your plan and billing details."}} diff --git a/Query/sql/v2/runs/v2_cli_20260502_081223_d/n1/artifacts/v2q_n1_b4a50b6eca8328dc/cli/sql_response_attempt_1.txt b/Query/sql/v2/runs/v2_cli_20260502_081223_d/n1/artifacts/v2q_n1_b4a50b6eca8328dc/cli/sql_response_attempt_1.txt new file mode 100644 index 0000000000000000000000000000000000000000..88195e1c8c17495c07c6e15b28e22eb3b960d8d4 --- /dev/null +++ b/Query/sql/v2/runs/v2_cli_20260502_081223_d/n1/artifacts/v2q_n1_b4a50b6eca8328dc/cli/sql_response_attempt_1.txt @@ -0,0 +1,4 @@ +{"type":"thread.started","thread_id":"019e40f7-4a99-7380-84f9-6d6ccb26e348"} +{"type":"turn.started"} +{"type":"error","message":"Quota exceeded. Check your plan and billing details."} +{"type":"turn.failed","error":{"message":"Quota exceeded. Check your plan and billing details."}} \ No newline at end of file diff --git a/Query/sql/v2/runs/v2_cli_20260502_081223_d/n1/artifacts/v2q_n1_b4a50b6eca8328dc/cli/sql_response_attempt_2.raw.txt b/Query/sql/v2/runs/v2_cli_20260502_081223_d/n1/artifacts/v2q_n1_b4a50b6eca8328dc/cli/sql_response_attempt_2.raw.txt new file mode 100644 index 0000000000000000000000000000000000000000..8f7bb481eecf8c2efb17a7e96b2a31220d5afb3f --- /dev/null +++ b/Query/sql/v2/runs/v2_cli_20260502_081223_d/n1/artifacts/v2q_n1_b4a50b6eca8328dc/cli/sql_response_attempt_2.raw.txt @@ -0,0 +1,4 @@ +{"type":"thread.started","thread_id":"019e40f7-5bff-7a53-9678-58c2040ad3d1"} +{"type":"turn.started"} +{"type":"item.completed","item":{"id":"item_0","type":"agent_message","text":"{\"sql\":\"-- template_id: tpl_m4_group_condition_rate\\nSELECT \\\"class\\\",\\n AVG(CASE WHEN \\\"class\\\" = '1' THEN 1 ELSE 0 END) AS condition_rate\\nFROM \\\"n1\\\"\\nGROUP BY \\\"class\\\"\\nORDER BY condition_rate DESC;\",\"notes\":\"Applied the provided Grouped Condition Rate template with group_col=\\\"class\\\" and condition_col=\\\"class\\\" on single table \\\"n1\\\". Since the grouping column and condition column are the same, the result is deterministic by class group: class='1' yields 1.0 and class='0' yields 0.0.\"}"}} +{"type":"turn.completed","usage":{"input_tokens":20372,"cached_input_tokens":12032,"output_tokens":329,"reasoning_output_tokens":191}} diff --git a/Query/sql/v2/runs/v2_cli_20260502_081223_d/n1/artifacts/v2q_n1_b4a50b6eca8328dc/cli/sql_response_attempt_2.txt b/Query/sql/v2/runs/v2_cli_20260502_081223_d/n1/artifacts/v2q_n1_b4a50b6eca8328dc/cli/sql_response_attempt_2.txt new file mode 100644 index 0000000000000000000000000000000000000000..2fe9fb5af456a88b1506f636514eb1ea4c8b29f5 --- /dev/null +++ b/Query/sql/v2/runs/v2_cli_20260502_081223_d/n1/artifacts/v2q_n1_b4a50b6eca8328dc/cli/sql_response_attempt_2.txt @@ -0,0 +1 @@ +{"sql":"-- template_id: tpl_m4_group_condition_rate\nSELECT \"class\",\n AVG(CASE WHEN \"class\" = '1' THEN 1 ELSE 0 END) AS condition_rate\nFROM \"n1\"\nGROUP BY \"class\"\nORDER BY condition_rate DESC;","notes":"Applied the provided Grouped Condition Rate template with group_col=\"class\" and condition_col=\"class\" on single table \"n1\". Since the grouping column and condition column are the same, the result is deterministic by class group: class='1' yields 1.0 and class='0' yields 0.0."} \ No newline at end of file diff --git a/Query/sql/v2/runs/v2_cli_20260502_081223_d/n1/artifacts/v2q_n1_b4a50b6eca8328dc/cli/sql_stderr_attempt_1.txt b/Query/sql/v2/runs/v2_cli_20260502_081223_d/n1/artifacts/v2q_n1_b4a50b6eca8328dc/cli/sql_stderr_attempt_1.txt new file mode 100644 index 0000000000000000000000000000000000000000..e69de29bb2d1d6434b8b29ae775ad8c2e48c5391 diff --git a/Query/sql/v2/runs/v2_cli_20260502_081223_d/n1/artifacts/v2q_n1_b4a50b6eca8328dc/cli/sql_stderr_attempt_2.txt b/Query/sql/v2/runs/v2_cli_20260502_081223_d/n1/artifacts/v2q_n1_b4a50b6eca8328dc/cli/sql_stderr_attempt_2.txt new file mode 100644 index 0000000000000000000000000000000000000000..e69de29bb2d1d6434b8b29ae775ad8c2e48c5391 diff --git a/Query/sql/v2/runs/v2_cli_20260502_081223_d/n1/artifacts/v2q_n1_b554c2be063e07f0/cli/conversation.jsonl b/Query/sql/v2/runs/v2_cli_20260502_081223_d/n1/artifacts/v2q_n1_b554c2be063e07f0/cli/conversation.jsonl new file mode 100644 index 0000000000000000000000000000000000000000..c1275c550ea860b11dafce189c4303a82324386d --- /dev/null +++ b/Query/sql/v2/runs/v2_cli_20260502_081223_d/n1/artifacts/v2q_n1_b554c2be063e07f0/cli/conversation.jsonl @@ -0,0 +1,2 @@ +{"attempt": 1, "phase": "sql_generation", "role": "user", "content_path": "cli/sql_prompt_attempt_1.txt", "metrics": {"chars": 29356, "bytes_utf8": 29356, "lines": 792, "estimated_tokens": null}} +{"attempt": 1, "phase": "sql_generation", "role": "assistant", "content_path": "cli/sql_response_attempt_1.txt", "raw_content_path": "cli/sql_response_attempt_1.raw.txt", "stderr_path": "cli/sql_stderr_attempt_1.txt", "metrics": {"chars": 395, "bytes_utf8": 395, "lines": 1, "estimated_tokens": null}, "usage": {"input_tokens": 20315, "cached_input_tokens": 11264, "output_tokens": 368, "reasoning_output_tokens": 265}} diff --git a/Query/sql/v2/runs/v2_cli_20260502_081223_d/n1/artifacts/v2q_n1_b554c2be063e07f0/cli/session_summary.json b/Query/sql/v2/runs/v2_cli_20260502_081223_d/n1/artifacts/v2q_n1_b554c2be063e07f0/cli/session_summary.json new file mode 100644 index 0000000000000000000000000000000000000000..cf040f0b0cf57ffef5c52e606ee69a675468cbe3 --- /dev/null +++ b/Query/sql/v2/runs/v2_cli_20260502_081223_d/n1/artifacts/v2q_n1_b554c2be063e07f0/cli/session_summary.json @@ -0,0 +1,25 @@ +{ + "engine": "v2-cli:codex", + "command": "codex exec --skip-git-repo-check --disable plugins --sandbox read-only --cd \"/data/jialinzhang/SQLagent\" -m gpt-5.4 --json -", + "ai_cli_calls": 1, + "usage_summary": { + "dataset_id": "n1", + "model": "v2-cli:codex", + "run_id": "v2q_n1_b554c2be063e07f0", + "api_calls": 0, + "input_tokens": 20315, + "cached_input_tokens": 11264, + "output_tokens": 368, + "total_tokens": 20683, + "cost_usd": 0.0, + "ai_cli_calls": 1, + "estimated_input_tokens": 0, + "estimated_output_tokens": 0, + "estimated_total_tokens": 0, + "usage_source": "ai_cli_json_usage", + "cli_elapsed_ms_total": 9988.57, + "sql_execution_elapsed_ms_total": 2.19, + "conversation_log_path": "/data/jialinzhang/TabQueryBench/sql_workloads/v2_current/runs_and_launches/runs/v2_cli_20260502_081223_d/n1/artifacts/v2q_n1_b554c2be063e07f0/cli/conversation.jsonl", + "note": "Executed through a local AI CLI with structured usage metadata." + } +} \ No newline at end of file diff --git a/Query/sql/v2/runs/v2_cli_20260502_081223_d/n1/artifacts/v2q_n1_b554c2be063e07f0/cli/sql_attempt_1.metadata.json b/Query/sql/v2/runs/v2_cli_20260502_081223_d/n1/artifacts/v2q_n1_b554c2be063e07f0/cli/sql_attempt_1.metadata.json new file mode 100644 index 0000000000000000000000000000000000000000..d74a1d005d4002cd1848a2e6fc5c44bc3c6f5cf4 --- /dev/null +++ b/Query/sql/v2/runs/v2_cli_20260502_081223_d/n1/artifacts/v2q_n1_b554c2be063e07f0/cli/sql_attempt_1.metadata.json @@ -0,0 +1,45 @@ +{ + "attempt": 1, + "phase": "sql_generation", + "command": "codex exec --skip-git-repo-check --disable plugins --sandbox read-only --cd \"/data/jialinzhang/SQLagent\" -m gpt-5.4 --json -", + "started_at": "2026-05-19T15:28:18.348566+00:00", + "ended_at": "2026-05-19T15:28:28.337162+00:00", + "elapsed_ms": 9988.57, + "prompt_metrics": { + "chars": 29356, + "bytes_utf8": 29356, + "lines": 792, + "estimated_tokens": null + }, + "stdout_metrics": { + "chars": 757, + "bytes_utf8": 757, + "lines": 4, + "estimated_tokens": null + }, + "stderr_metrics": { + "chars": 0, + "bytes_utf8": 0, + "lines": 0, + "estimated_tokens": null + }, + "parsed_output": { + "format": "jsonl_events", + "text_metrics": { + "chars": 395, + "bytes_utf8": 395, + "lines": 1, + "estimated_tokens": null + }, + "usage": { + "input_tokens": 20315, + "cached_input_tokens": 11264, + "output_tokens": 368, + "reasoning_output_tokens": 265 + } + }, + "prompt_path": "cli/sql_prompt_attempt_1.txt", + "response_path": "cli/sql_response_attempt_1.txt", + "raw_response_path": "cli/sql_response_attempt_1.raw.txt", + "stderr_path": "cli/sql_stderr_attempt_1.txt" +} \ No newline at end of file diff --git a/Query/sql/v2/runs/v2_cli_20260502_081223_d/n1/artifacts/v2q_n1_b554c2be063e07f0/cli/sql_prompt_attempt_1.txt b/Query/sql/v2/runs/v2_cli_20260502_081223_d/n1/artifacts/v2q_n1_b554c2be063e07f0/cli/sql_prompt_attempt_1.txt new file mode 100644 index 0000000000000000000000000000000000000000..6b4fa71a32cbbdcd0c00c1abda6bf9439e6f0170 --- /dev/null +++ b/Query/sql/v2/runs/v2_cli_20260502_081223_d/n1/artifacts/v2q_n1_b554c2be063e07f0/cli/sql_prompt_attempt_1.txt @@ -0,0 +1,792 @@ +You are generating one SQLite SELECT query for a single-table SQL QA task. +Return strict JSON only, with this schema: {"sql": "...", "notes": "..."}. +Rules: +- Use only the provided table and columns. +- Do not write INSERT, UPDATE, DELETE, DROP, ALTER, CREATE, PRAGMA, ATTACH, DETACH, or VACUUM. +- Prefer the planned template and bound roles when provided. +- Add a leading SQL comment exactly like: -- template_id: . +- Generate SQLite-compatible SQL. SQLite does not support PERCENTILE_CONT or STDDEV. +- Quote identifiers with double quotes. +- Return no markdown and no extra prose. + +Dataset context: +Dataset context for SQL QA: +- dataset_id: n1 +- dataset_name: Spambase +- table_name: n1 +- table_layout: single-table dataset (do not assume joins). +- row_semantics: One row is one email represented by engineered token/character frequency and capitalization-run features. +- task_type: classification +- target_column: class +- main_row_count: 4601 +- important_fields: +- word_freq_make: role=feature, type=numeric_sparse_frequency. tags=['condition_candidate', 'measure', 'rare_pattern_candidate'] desc=Frequency feature for token 'make' in an email message. +- word_freq_address: role=feature, type=numeric_sparse_frequency. tags=['condition_candidate', 'measure', 'rare_pattern_candidate'] desc=Frequency feature for token 'address' in an email message. +- word_freq_all: role=feature, type=numeric_sparse_frequency. tags=['condition_candidate', 'measure', 'rare_pattern_candidate'] desc=Frequency feature for token 'all' in an email message. +- word_freq_3d: role=feature, type=numeric_sparse_frequency. tags=['condition_candidate', 'measure', 'rare_pattern_candidate'] desc=Frequency feature for token '3d' in an email message. +- word_freq_our: role=feature, type=numeric_sparse_frequency. tags=['condition_candidate', 'measure', 'rare_pattern_candidate'] desc=Frequency feature for token 'our' in an email message. +- word_freq_over: role=feature, type=numeric_sparse_frequency. tags=['condition_candidate', 'measure', 'rare_pattern_candidate'] desc=Frequency feature for token 'over' in an email message. +- word_freq_remove: role=feature, type=numeric_sparse_frequency. tags=['condition_candidate', 'measure', 'rare_pattern_candidate'] desc=Frequency feature for token 'remove' in an email message. +- word_freq_internet: role=feature, type=numeric_sparse_frequency. tags=['condition_candidate', 'measure', 'rare_pattern_candidate'] desc=Frequency feature for token 'internet' in an email message. +- word_freq_order: role=feature, type=numeric_sparse_frequency. tags=['condition_candidate', 'measure', 'rare_pattern_candidate'] desc=Frequency feature for token 'order' in an email message. +- word_freq_mail: role=feature, type=numeric_sparse_frequency. tags=['condition_candidate', 'measure', 'rare_pattern_candidate'] desc=Frequency feature for token 'mail' in an email message. +- word_freq_receive: role=feature, type=numeric_sparse_frequency. tags=['condition_candidate', 'measure', 'rare_pattern_candidate'] desc=Frequency feature for token 'receive' in an email message. +- word_freq_will: role=feature, type=numeric_sparse_frequency. tags=['condition_candidate', 'measure', 'rare_pattern_candidate'] desc=Frequency feature for token 'will' in an email message. +- word_freq_people: role=feature, type=numeric_sparse_frequency. tags=['condition_candidate', 'measure', 'rare_pattern_candidate'] desc=Frequency feature for token 'people' in an email message. +- word_freq_report: role=feature, type=numeric_sparse_frequency. tags=['condition_candidate', 'measure', 'rare_pattern_candidate'] desc=Frequency feature for token 'report' in an email message. +- word_freq_addresses: role=feature, type=numeric_sparse_frequency. tags=['condition_candidate', 'measure', 'rare_pattern_candidate'] desc=Frequency feature for token 'addresses' in an email message. +- word_freq_free: role=feature, type=numeric_sparse_frequency. tags=['condition_candidate', 'measure', 'rare_pattern_candidate'] desc=Frequency feature for token 'free' in an email message. +- word_freq_business: role=feature, type=numeric_sparse_frequency. tags=['condition_candidate', 'measure', 'rare_pattern_candidate'] desc=Frequency feature for token 'business' in an email message. +- word_freq_email: role=feature, type=numeric_sparse_frequency. tags=['condition_candidate', 'measure', 'rare_pattern_candidate'] desc=Frequency feature for token 'email' in an email message. +- word_freq_you: role=feature, type=numeric_sparse_frequency. tags=['condition_candidate', 'measure', 'rare_pattern_candidate'] desc=Frequency feature for token 'you' in an email message. +- word_freq_credit: role=feature, type=numeric_sparse_frequency. tags=['condition_candidate', 'measure', 'rare_pattern_candidate'] desc=Frequency feature for token 'credit' in an email message. +- word_freq_your: role=feature, type=numeric_sparse_frequency. tags=['condition_candidate', 'measure', 'rare_pattern_candidate'] desc=Frequency feature for token 'your' in an email message. +- word_freq_font: role=feature, type=numeric_sparse_frequency. tags=['condition_candidate', 'measure', 'rare_pattern_candidate'] desc=Frequency feature for token 'font' in an email message. +- word_freq_000: role=feature, type=numeric_sparse_frequency. tags=['condition_candidate', 'measure', 'rare_pattern_candidate'] desc=Frequency feature for token '000' in an email message. +- word_freq_money: role=feature, type=numeric_sparse_frequency. tags=['condition_candidate', 'measure', 'rare_pattern_candidate'] desc=Frequency feature for token 'money' in an email message. +- word_freq_hp: role=feature, type=numeric_sparse_frequency. tags=['condition_candidate', 'measure', 'rare_pattern_candidate'] desc=Frequency feature for token 'hp' in an email message. +- word_freq_hpl: role=feature, type=numeric_sparse_frequency. tags=['condition_candidate', 'measure', 'rare_pattern_candidate'] desc=Frequency feature for token 'hpl' in an email message. +- word_freq_george: role=feature, type=numeric_sparse_frequency. tags=['condition_candidate', 'measure', 'rare_pattern_candidate'] desc=Frequency feature for token 'george' in an email message. +- word_freq_650: role=feature, type=numeric_sparse_frequency. tags=['condition_candidate', 'measure', 'rare_pattern_candidate'] desc=Frequency feature for token '650' in an email message. +- word_freq_lab: role=feature, type=numeric_sparse_frequency. tags=['condition_candidate', 'measure', 'rare_pattern_candidate'] desc=Frequency feature for token 'lab' in an email message. +- word_freq_labs: role=feature, type=numeric_sparse_frequency. tags=['condition_candidate', 'measure', 'rare_pattern_candidate'] desc=Frequency feature for token 'labs' in an email message. +- word_freq_telnet: role=feature, type=numeric_sparse_frequency. tags=['condition_candidate', 'measure', 'rare_pattern_candidate'] desc=Frequency feature for token 'telnet' in an email message. +- word_freq_857: role=feature, type=numeric_sparse_frequency. tags=['condition_candidate', 'measure', 'rare_pattern_candidate'] desc=Frequency feature for token '857' in an email message. +- word_freq_data: role=feature, type=numeric_sparse_frequency. tags=['condition_candidate', 'measure', 'rare_pattern_candidate'] desc=Frequency feature for token 'data' in an email message. +- word_freq_415: role=feature, type=numeric_sparse_frequency. tags=['condition_candidate', 'measure', 'rare_pattern_candidate'] desc=Frequency feature for token '415' in an email message. +- word_freq_85: role=feature, type=numeric_sparse_frequency. tags=['condition_candidate', 'measure', 'rare_pattern_candidate'] desc=Frequency feature for token '85' in an email message. +- word_freq_technology: role=feature, type=numeric_sparse_frequency. tags=['condition_candidate', 'measure', 'rare_pattern_candidate'] desc=Frequency feature for token 'technology' in an email message. +- word_freq_1999: role=feature, type=numeric_sparse_frequency. tags=['condition_candidate', 'measure', 'rare_pattern_candidate'] desc=Frequency feature for token '1999' in an email message. +- word_freq_parts: role=feature, type=numeric_sparse_frequency. tags=['condition_candidate', 'measure', 'rare_pattern_candidate'] desc=Frequency feature for token 'parts' in an email message. +- word_freq_pm: role=feature, type=numeric_sparse_frequency. tags=['condition_candidate', 'measure', 'rare_pattern_candidate'] desc=Frequency feature for token 'pm' in an email message. +- word_freq_direct: role=feature, type=numeric_sparse_frequency. tags=['condition_candidate', 'measure', 'rare_pattern_candidate'] desc=Frequency feature for token 'direct' in an email message. +- word_freq_cs: role=feature, type=numeric_sparse_frequency. tags=['condition_candidate', 'measure', 'rare_pattern_candidate'] desc=Frequency feature for token 'cs' in an email message. +- word_freq_meeting: role=feature, type=numeric_sparse_frequency. tags=['condition_candidate', 'measure', 'rare_pattern_candidate'] desc=Frequency feature for token 'meeting' in an email message. +- word_freq_original: role=feature, type=numeric_sparse_frequency. tags=['condition_candidate', 'measure', 'rare_pattern_candidate'] desc=Frequency feature for token 'original' in an email message. +- word_freq_project: role=feature, type=numeric_sparse_frequency. tags=['condition_candidate', 'measure', 'rare_pattern_candidate'] desc=Frequency feature for token 'project' in an email message. +- word_freq_re: role=feature, type=numeric_sparse_frequency. tags=['condition_candidate', 'measure', 'rare_pattern_candidate'] desc=Frequency feature for token 're' in an email message. +- word_freq_edu: role=feature, type=numeric_sparse_frequency. tags=['condition_candidate', 'measure', 'rare_pattern_candidate'] desc=Frequency feature for token 'edu' in an email message. +- word_freq_table: role=feature, type=numeric_sparse_frequency. tags=['condition_candidate', 'measure', 'rare_pattern_candidate'] desc=Frequency feature for token 'table' in an email message. +- word_freq_conference: role=feature, type=numeric_sparse_frequency. tags=['condition_candidate', 'measure', 'rare_pattern_candidate'] desc=Frequency feature for token 'conference' in an email message. +- char_freq_%3B: role=feature, type=numeric_sparse_frequency. tags=['condition_candidate', 'measure', 'rare_pattern_candidate'] desc=Character frequency feature for symbol ';'. +- char_freq_%28: role=feature, type=numeric_sparse_frequency. tags=['condition_candidate', 'measure', 'rare_pattern_candidate'] desc=Character frequency feature for symbol '('. +- char_freq_%5B: role=feature, type=numeric_sparse_frequency. tags=['condition_candidate', 'measure', 'rare_pattern_candidate'] desc=Character frequency feature for symbol '['. +- char_freq_%21: role=feature, type=numeric_sparse_frequency. tags=['condition_candidate', 'measure', 'rare_pattern_candidate'] desc=Character frequency feature for symbol '!'. +- char_freq_%24: role=feature, type=numeric_sparse_frequency. tags=['condition_candidate', 'measure', 'rare_pattern_candidate'] desc=Character frequency feature for symbol '$'. +- char_freq_%23: role=feature, type=numeric_sparse_frequency. tags=['condition_candidate', 'measure', 'rare_pattern_candidate'] desc=Character frequency feature for symbol '#'. +- capital_run_length_average: role=feature, type=numeric_heavy_tail. tags=['condition_candidate', 'measure', 'rare_pattern_candidate'] desc=Average length of uninterrupted capital-letter runs. +- capital_run_length_longest: role=feature, type=numeric_heavy_tail. tags=['condition_candidate', 'measure', 'rare_pattern_candidate'] desc=Longest uninterrupted capital-letter run. +- capital_run_length_total: role=feature, type=numeric_heavy_tail. tags=['condition_candidate', 'measure', 'rare_pattern_candidate'] desc=Total number of capital letters in runs. +- class: role=target, type=binary_target. tags=['target_candidate'] desc=Binary spam label (1=spam, 0=non-spam). +- useful_field_combinations: [['word_freq_free', 'word_freq_you', 'class'], ['char_freq_%21', 'capital_run_length_longest', 'class'], ['word_freq_remove', 'word_freq_money', 'class']] +- fields_requiring_caution: ['capital_run_length_average', 'capital_run_length_longest', 'capital_run_length_total'] +- source_url: https://www.openml.org/d/44 + +SQLite schema snapshot: +{ + "table_name": "n1", + "quoted_table_name": "\"n1\"", + "row_count": 4601, + "columns": [ + { + "name": "word_freq_make", + "type": "TEXT", + "notnull": false, + "pk": false + }, + { + "name": "word_freq_address", + "type": "TEXT", + "notnull": false, + "pk": false + }, + { + "name": "word_freq_all", + "type": "TEXT", + "notnull": false, + "pk": false + }, + { + "name": "word_freq_3d", + "type": "TEXT", + "notnull": false, + "pk": false + }, + { + "name": "word_freq_our", + "type": "TEXT", + "notnull": false, + "pk": false + }, + { + "name": "word_freq_over", + "type": "TEXT", + "notnull": false, + "pk": false + }, + { + "name": "word_freq_remove", + "type": "TEXT", + "notnull": false, + "pk": false + }, + { + "name": "word_freq_internet", + "type": "TEXT", + "notnull": false, + "pk": false + }, + { + "name": "word_freq_order", + "type": "TEXT", + "notnull": false, + "pk": false + }, + { + "name": "word_freq_mail", + "type": "TEXT", + "notnull": false, + "pk": false + }, + { + "name": "word_freq_receive", + "type": "TEXT", + "notnull": false, + "pk": false + }, + { + "name": "word_freq_will", + "type": "TEXT", + "notnull": false, + "pk": false + }, + { + "name": "word_freq_people", + "type": "TEXT", + "notnull": false, + "pk": false + }, + { + "name": "word_freq_report", + "type": "TEXT", + "notnull": false, + "pk": false + }, + { + "name": "word_freq_addresses", + "type": "TEXT", + "notnull": false, + "pk": false + }, + { + "name": "word_freq_free", + "type": "TEXT", + "notnull": false, + "pk": false + }, + { + "name": "word_freq_business", + "type": "TEXT", + "notnull": false, + "pk": false + }, + { + "name": "word_freq_email", + "type": "TEXT", + "notnull": false, + "pk": false + }, + { + "name": "word_freq_you", + "type": "TEXT", + "notnull": false, + "pk": false + }, + { + "name": "word_freq_credit", + "type": "TEXT", + "notnull": false, + "pk": false + }, + { + "name": "word_freq_your", + "type": "TEXT", + "notnull": false, + "pk": false + }, + { + "name": "word_freq_font", + "type": "TEXT", + "notnull": false, + "pk": false + }, + { + "name": "word_freq_000", + "type": "TEXT", + "notnull": false, + "pk": false + }, + { + "name": "word_freq_money", + "type": "TEXT", + "notnull": false, + "pk": false + }, + { + "name": "word_freq_hp", + "type": "TEXT", + "notnull": false, + "pk": false + }, + { + "name": "word_freq_hpl", + "type": "TEXT", + "notnull": false, + "pk": false + }, + { + "name": "word_freq_george", + "type": "TEXT", + "notnull": false, + "pk": false + }, + { + "name": "word_freq_650", + "type": "TEXT", + "notnull": false, + "pk": false + }, + { + "name": "word_freq_lab", + "type": "TEXT", + "notnull": false, + "pk": false + }, + { + "name": "word_freq_labs", + "type": "TEXT", + "notnull": false, + "pk": false + }, + { + "name": "word_freq_telnet", + "type": "TEXT", + "notnull": false, + "pk": false + }, + { + "name": "word_freq_857", + "type": "TEXT", + "notnull": false, + "pk": false + }, + { + "name": "word_freq_data", + "type": "TEXT", + "notnull": false, + "pk": false + }, + { + "name": "word_freq_415", + "type": "TEXT", + "notnull": false, + "pk": false + }, + { + "name": "word_freq_85", + "type": "TEXT", + "notnull": false, + "pk": false + }, + { + "name": "word_freq_technology", + "type": "TEXT", + "notnull": false, + "pk": false + }, + { + "name": "word_freq_1999", + "type": "TEXT", + "notnull": false, + "pk": false + }, + { + "name": "word_freq_parts", + "type": "TEXT", + "notnull": false, + "pk": false + }, + { + "name": "word_freq_pm", + "type": "TEXT", + "notnull": false, + "pk": false + }, + { + "name": "word_freq_direct", + "type": "TEXT", + "notnull": false, + "pk": false + }, + { + "name": "word_freq_cs", + "type": "TEXT", + "notnull": false, + "pk": false + }, + { + "name": "word_freq_meeting", + "type": "TEXT", + "notnull": false, + "pk": false + }, + { + "name": "word_freq_original", + "type": "TEXT", + "notnull": false, + "pk": false + }, + { + "name": "word_freq_project", + "type": "TEXT", + "notnull": false, + "pk": false + }, + { + "name": "word_freq_re", + "type": "TEXT", + "notnull": false, + "pk": false + }, + { + "name": "word_freq_edu", + "type": "TEXT", + "notnull": false, + "pk": false + }, + { + "name": "word_freq_table", + "type": "TEXT", + "notnull": false, + "pk": false + }, + { + "name": "word_freq_conference", + "type": "TEXT", + "notnull": false, + "pk": false + }, + { + "name": "char_freq_%3B", + "type": "TEXT", + "notnull": false, + "pk": false + }, + { + "name": "char_freq_%28", + "type": "TEXT", + "notnull": false, + "pk": false + }, + { + "name": "char_freq_%5B", + "type": "TEXT", + "notnull": false, + "pk": false + }, + { + "name": "char_freq_%21", + "type": "TEXT", + "notnull": false, + "pk": false + }, + { + "name": "char_freq_%24", + "type": "TEXT", + "notnull": false, + "pk": false + }, + { + "name": "char_freq_%23", + "type": "TEXT", + "notnull": false, + "pk": false + }, + { + "name": "capital_run_length_average", + "type": "TEXT", + "notnull": false, + "pk": false + }, + { + "name": "capital_run_length_longest", + "type": "TEXT", + "notnull": false, + "pk": false + }, + { + "name": "capital_run_length_total", + "type": "TEXT", + "notnull": false, + "pk": false + }, + { + "name": "class", + "type": "TEXT", + "notnull": false, + "pk": false + } + ], + "sample_rows": [ + { + "word_freq_make": "0", + "word_freq_address": "0.64", + "word_freq_all": "0.64", + "word_freq_3d": "0", + "word_freq_our": "0.32", + "word_freq_over": "0", + "word_freq_remove": "0", + "word_freq_internet": "0", + "word_freq_order": "0", + "word_freq_mail": "0", + "word_freq_receive": "0", + "word_freq_will": "0.64", + "word_freq_people": "0", + "word_freq_report": "0", + "word_freq_addresses": "0", + "word_freq_free": "0.32", + "word_freq_business": "0", + "word_freq_email": "1.29", + "word_freq_you": "1.93", + "word_freq_credit": "0", + "word_freq_your": "0.96", + "word_freq_font": "0", + "word_freq_000": "0", + "word_freq_money": "0", + "word_freq_hp": "0", + "word_freq_hpl": "0", + "word_freq_george": "0", + "word_freq_650": "0", + "word_freq_lab": "0", + "word_freq_labs": "0", + "word_freq_telnet": "0", + "word_freq_857": "0", + "word_freq_data": "0", + "word_freq_415": "0", + "word_freq_85": "0", + "word_freq_technology": "0", + "word_freq_1999": "0", + "word_freq_parts": "0", + "word_freq_pm": "0", + "word_freq_direct": "0", + "word_freq_cs": "0", + "word_freq_meeting": "0", + "word_freq_original": "0", + "word_freq_project": "0", + "word_freq_re": "0", + "word_freq_edu": "0", + "word_freq_table": "0", + "word_freq_conference": "0", + "char_freq_%3B": "0", + "char_freq_%28": "0", + "char_freq_%5B": "0", + "char_freq_%21": "0.778", + "char_freq_%24": "0", + "char_freq_%23": "0", + "capital_run_length_average": "3.756", + "capital_run_length_longest": "61", + "capital_run_length_total": "278", + "class": "1" + }, + { + "word_freq_make": "0.21", + "word_freq_address": "0.28", + "word_freq_all": "0.5", + "word_freq_3d": "0", + "word_freq_our": "0.14", + "word_freq_over": "0.28", + "word_freq_remove": "0.21", + "word_freq_internet": "0.07", + "word_freq_order": "0", + "word_freq_mail": "0.94", + "word_freq_receive": "0.21", + "word_freq_will": "0.79", + "word_freq_people": "0.65", + "word_freq_report": "0.21", + "word_freq_addresses": "0.14", + "word_freq_free": "0.14", + "word_freq_business": "0.07", + "word_freq_email": "0.28", + "word_freq_you": "3.47", + "word_freq_credit": "0", + "word_freq_your": "1.59", + "word_freq_font": "0", + "word_freq_000": "0.43", + "word_freq_money": "0.43", + "word_freq_hp": "0", + "word_freq_hpl": "0", + "word_freq_george": "0", + "word_freq_650": "0", + "word_freq_lab": "0", + "word_freq_labs": "0", + "word_freq_telnet": "0", + "word_freq_857": "0", + "word_freq_data": "0", + "word_freq_415": "0", + "word_freq_85": "0", + "word_freq_technology": "0", + "word_freq_1999": "0.07", + "word_freq_parts": "0", + "word_freq_pm": "0", + "word_freq_direct": "0", + "word_freq_cs": "0", + "word_freq_meeting": "0", + "word_freq_original": "0", + "word_freq_project": "0", + "word_freq_re": "0", + "word_freq_edu": "0", + "word_freq_table": "0", + "word_freq_conference": "0", + "char_freq_%3B": "0", + "char_freq_%28": "0.132", + "char_freq_%5B": "0", + "char_freq_%21": "0.372", + "char_freq_%24": "0.18", + "char_freq_%23": "0.048", + "capital_run_length_average": "5.114", + "capital_run_length_longest": "101", + "capital_run_length_total": "1028", + "class": "1" + }, + { + "word_freq_make": "0.06", + "word_freq_address": "0", + "word_freq_all": "0.71", + "word_freq_3d": "0", + "word_freq_our": "1.23", + "word_freq_over": "0.19", + "word_freq_remove": "0.19", + "word_freq_internet": "0.12", + "word_freq_order": "0.64", + "word_freq_mail": "0.25", + "word_freq_receive": "0.38", + "word_freq_will": "0.45", + "word_freq_people": "0.12", + "word_freq_report": "0", + "word_freq_addresses": "1.75", + "word_freq_free": "0.06", + "word_freq_business": "0.06", + "word_freq_email": "1.03", + "word_freq_you": "1.36", + "word_freq_credit": "0.32", + "word_freq_your": "0.51", + "word_freq_font": "0", + "word_freq_000": "1.16", + "word_freq_money": "0.06", + "word_freq_hp": "0", + "word_freq_hpl": "0", + "word_freq_george": "0", + "word_freq_650": "0", + "word_freq_lab": "0", + "word_freq_labs": "0", + "word_freq_telnet": "0", + "word_freq_857": "0", + "word_freq_data": "0", + "word_freq_415": "0", + "word_freq_85": "0", + "word_freq_technology": "0", + "word_freq_1999": "0", + "word_freq_parts": "0", + "word_freq_pm": "0", + "word_freq_direct": "0.06", + "word_freq_cs": "0", + "word_freq_meeting": "0", + "word_freq_original": "0.12", + "word_freq_project": "0", + "word_freq_re": "0.06", + "word_freq_edu": "0.06", + "word_freq_table": "0", + "word_freq_conference": "0", + "char_freq_%3B": "0.01", + "char_freq_%28": "0.143", + "char_freq_%5B": "0", + "char_freq_%21": "0.276", + "char_freq_%24": "0.184", + "char_freq_%23": "0.01", + "capital_run_length_average": "9.821", + "capital_run_length_longest": "485", + "capital_run_length_total": "2259", + "class": "1" + }, + { + "word_freq_make": "0", + "word_freq_address": "0", + "word_freq_all": "0", + "word_freq_3d": "0", + "word_freq_our": "0.63", + "word_freq_over": "0", + "word_freq_remove": "0.31", + "word_freq_internet": "0.63", + "word_freq_order": "0.31", + "word_freq_mail": "0.63", + "word_freq_receive": "0.31", + "word_freq_will": "0.31", + "word_freq_people": "0.31", + "word_freq_report": "0", + "word_freq_addresses": "0", + "word_freq_free": "0.31", + "word_freq_business": "0", + "word_freq_email": "0", + "word_freq_you": "3.18", + "word_freq_credit": "0", + "word_freq_your": "0.31", + "word_freq_font": "0", + "word_freq_000": "0", + "word_freq_money": "0", + "word_freq_hp": "0", + "word_freq_hpl": "0", + "word_freq_george": "0", + "word_freq_650": "0", + "word_freq_lab": "0", + "word_freq_labs": "0", + "word_freq_telnet": "0", + "word_freq_857": "0", + "word_freq_data": "0", + "word_freq_415": "0", + "word_freq_85": "0", + "word_freq_technology": "0", + "word_freq_1999": "0", + "word_freq_parts": "0", + "word_freq_pm": "0", + "word_freq_direct": "0", + "word_freq_cs": "0", + "word_freq_meeting": "0", + "word_freq_original": "0", + "word_freq_project": "0", + "word_freq_re": "0", + "word_freq_edu": "0", + "word_freq_table": "0", + "word_freq_conference": "0", + "char_freq_%3B": "0", + "char_freq_%28": "0.137", + "char_freq_%5B": "0", + "char_freq_%21": "0.137", + "char_freq_%24": "0", + "char_freq_%23": "0", + "capital_run_length_average": "3.537", + "capital_run_length_longest": "40", + "capital_run_length_total": "191", + "class": "1" + }, + { + "word_freq_make": "0", + "word_freq_address": "0", + "word_freq_all": "0", + "word_freq_3d": "0", + "word_freq_our": "0.63", + "word_freq_over": "0", + "word_freq_remove": "0.31", + "word_freq_internet": "0.63", + "word_freq_order": "0.31", + "word_freq_mail": "0.63", + "word_freq_receive": "0.31", + "word_freq_will": "0.31", + "word_freq_people": "0.31", + "word_freq_report": "0", + "word_freq_addresses": "0", + "word_freq_free": "0.31", + "word_freq_business": "0", + "word_freq_email": "0", + "word_freq_you": "3.18", + "word_freq_credit": "0", + "word_freq_your": "0.31", + "word_freq_font": "0", + "word_freq_000": "0", + "word_freq_money": "0", + "word_freq_hp": "0", + "word_freq_hpl": "0", + "word_freq_george": "0", + "word_freq_650": "0", + "word_freq_lab": "0", + "word_freq_labs": "0", + "word_freq_telnet": "0", + "word_freq_857": "0", + "word_freq_data": "0", + "word_freq_415": "0", + "word_freq_85": "0", + "word_freq_technology": "0", + "word_freq_1999": "0", + "word_freq_parts": "0", + "word_freq_pm": "0", + "word_freq_direct": "0", + "word_freq_cs": "0", + "word_freq_meeting": "0", + "word_freq_original": "0", + "word_freq_project": "0", + "word_freq_re": "0", + "word_freq_edu": "0", + "word_freq_table": "0", + "word_freq_conference": "0", + "char_freq_%3B": "0", + "char_freq_%28": "0.135", + "char_freq_%5B": "0", + "char_freq_%21": "0.135", + "char_freq_%24": "0", + "char_freq_%23": "0", + "capital_run_length_average": "3.537", + "capital_run_length_longest": "40", + "capital_run_length_total": "191", + "class": "1" + } + ] +} + +Shortlisted templates: +[ + { + "template_id": "tpl_h2o_group_sum", + "template_name": "Grouped Numeric Sum", + "primary_family": "subgroup_structure", + "portability": "partial", + "sql_skeleton": "SELECT {group_col}, SUM({measure_col}) AS total_measure\nFROM {table}\nGROUP BY {group_col}\nORDER BY total_measure DESC;", + "required_roles": [ + "group_col", + "measure_col" + ] + } +] + +Problem instance: +{ + "dataset_id": "n1", + "question": "Use template Grouped Numeric Sum to probe internal_profile_stability with semantic role collapsed_target_view. Focus on group_col=class, measure_col=word_freq_make.", + "planned_template_id": "tpl_h2o_group_sum", + "bindings": { + "group_col": "class", + "measure_col": "word_freq_make", + "top_k": 10, + "top_n": 3, + "num_tiles": 10, + "percentile_value": 0.95, + "z_threshold": 2.0, + "fraction_threshold": 0.1, + "baseline_multiplier": 1.5, + "baseline_fraction": 0.1, + "min_group_size": 5, + "min_support": 5, + "measure_threshold": 0.0, + "time_grain": "month", + "lookback_rows": 3, + "current_period_start": "'2024-01-01'", + "current_period_end": "'2024-04-01'", + "previous_period_start": "'2023-10-01'", + "previous_period_end": "'2024-01-01'", + "drift_ratio_threshold": 0.8 + }, + "can_vary": [], + "must_fix": [], + "runtime_sql_skeleton": "SELECT {group_col}, SUM({measure_col}) AS total_measure\nFROM {table}\nGROUP BY {group_col}\nORDER BY total_measure DESC;" +} + +Repair context: +{} diff --git a/Query/sql/v2/runs/v2_cli_20260502_081223_d/n1/artifacts/v2q_n1_b554c2be063e07f0/cli/sql_response_attempt_1.raw.txt b/Query/sql/v2/runs/v2_cli_20260502_081223_d/n1/artifacts/v2q_n1_b554c2be063e07f0/cli/sql_response_attempt_1.raw.txt new file mode 100644 index 0000000000000000000000000000000000000000..75ce3098c5183674aa1d2f8a5aaded0920c24b69 --- /dev/null +++ b/Query/sql/v2/runs/v2_cli_20260502_081223_d/n1/artifacts/v2q_n1_b554c2be063e07f0/cli/sql_response_attempt_1.raw.txt @@ -0,0 +1,4 @@ +{"type":"thread.started","thread_id":"019e40da-2511-7f60-a243-3d0f0db95f68"} +{"type":"turn.started"} +{"type":"item.completed","item":{"id":"item_0","type":"agent_message","text":"{\"sql\":\"-- template_id: tpl_h2o_group_sum\\nSELECT \\\"class\\\", SUM(CAST(\\\"word_freq_make\\\" AS REAL)) AS \\\"total_measure\\\"\\nFROM \\\"n1\\\"\\nGROUP BY \\\"class\\\"\\nORDER BY \\\"total_measure\\\" DESC;\",\"notes\":\"Uses the planned grouped numeric sum template with group_col=\\\"class\\\" and measure_col=\\\"word_freq_make\\\". CAST to REAL is included because the schema snapshot stores this numeric feature as TEXT.\"}"}} +{"type":"turn.completed","usage":{"input_tokens":20315,"cached_input_tokens":11264,"output_tokens":368,"reasoning_output_tokens":265}} diff --git a/Query/sql/v2/runs/v2_cli_20260502_081223_d/n1/artifacts/v2q_n1_b554c2be063e07f0/cli/sql_response_attempt_1.txt b/Query/sql/v2/runs/v2_cli_20260502_081223_d/n1/artifacts/v2q_n1_b554c2be063e07f0/cli/sql_response_attempt_1.txt new file mode 100644 index 0000000000000000000000000000000000000000..7a5f6689da944650f11889011f0a79e8dcf8b405 --- /dev/null +++ b/Query/sql/v2/runs/v2_cli_20260502_081223_d/n1/artifacts/v2q_n1_b554c2be063e07f0/cli/sql_response_attempt_1.txt @@ -0,0 +1 @@ +{"sql":"-- template_id: tpl_h2o_group_sum\nSELECT \"class\", SUM(CAST(\"word_freq_make\" AS REAL)) AS \"total_measure\"\nFROM \"n1\"\nGROUP BY \"class\"\nORDER BY \"total_measure\" DESC;","notes":"Uses the planned grouped numeric sum template with group_col=\"class\" and measure_col=\"word_freq_make\". CAST to REAL is included because the schema snapshot stores this numeric feature as TEXT."} \ No newline at end of file diff --git a/Query/sql/v2/runs/v2_cli_20260502_081223_d/n1/artifacts/v2q_n1_b554c2be063e07f0/cli/sql_stderr_attempt_1.txt b/Query/sql/v2/runs/v2_cli_20260502_081223_d/n1/artifacts/v2q_n1_b554c2be063e07f0/cli/sql_stderr_attempt_1.txt new file mode 100644 index 0000000000000000000000000000000000000000..e69de29bb2d1d6434b8b29ae775ad8c2e48c5391 diff --git a/Query/sql/v2/runs/v2_cli_20260502_081223_d/n1/artifacts/v2q_n1_babf667d12d619da/cli/conversation.jsonl b/Query/sql/v2/runs/v2_cli_20260502_081223_d/n1/artifacts/v2q_n1_babf667d12d619da/cli/conversation.jsonl new file mode 100644 index 0000000000000000000000000000000000000000..7550d9549e0d94681497a45831e6cf71d40585c5 --- /dev/null +++ b/Query/sql/v2/runs/v2_cli_20260502_081223_d/n1/artifacts/v2q_n1_babf667d12d619da/cli/conversation.jsonl @@ -0,0 +1,4 @@ +{"attempt": 1, "phase": "sql_generation", "role": "user", "content_path": "cli/sql_prompt_attempt_1.txt", "metrics": {"chars": 29527, "bytes_utf8": 29527, "lines": 792, "estimated_tokens": null}} +{"attempt": 1, "phase": "sql_generation", "role": "assistant", "content_path": "cli/sql_response_attempt_1.txt", "raw_content_path": "cli/sql_response_attempt_1.raw.txt", "stderr_path": "cli/sql_stderr_attempt_1.txt", "metrics": {"chars": 280, "bytes_utf8": 280, "lines": 4, "estimated_tokens": null}, "usage": {}, "status": "failed", "error": "AI CLI command failed with exit code 1: "} +{"attempt": 2, "phase": "sql_generation", "role": "user", "content_path": "cli/sql_prompt_attempt_2.txt", "metrics": {"chars": 29527, "bytes_utf8": 29527, "lines": 792, "estimated_tokens": null}} +{"attempt": 2, "phase": "sql_generation", "role": "assistant", "content_path": "cli/sql_response_attempt_2.txt", "raw_content_path": "cli/sql_response_attempt_2.raw.txt", "stderr_path": "cli/sql_stderr_attempt_2.txt", "metrics": {"chars": 478, "bytes_utf8": 478, "lines": 1, "estimated_tokens": null}, "usage": {"input_tokens": 20359, "cached_input_tokens": 19840, "output_tokens": 435, "reasoning_output_tokens": 306}} diff --git a/Query/sql/v2/runs/v2_cli_20260502_081223_d/n1/artifacts/v2q_n1_babf667d12d619da/cli/session_summary.json b/Query/sql/v2/runs/v2_cli_20260502_081223_d/n1/artifacts/v2q_n1_babf667d12d619da/cli/session_summary.json new file mode 100644 index 0000000000000000000000000000000000000000..6300f28f455d66a5017e3ff64cb9f392b351e29d --- /dev/null +++ b/Query/sql/v2/runs/v2_cli_20260502_081223_d/n1/artifacts/v2q_n1_babf667d12d619da/cli/session_summary.json @@ -0,0 +1,25 @@ +{ + "engine": "v2-cli:codex", + "command": "codex exec --skip-git-repo-check --disable plugins --sandbox read-only --cd \"/data/jialinzhang/SQLagent\" -m gpt-5.4 --json -", + "ai_cli_calls": 2, + "usage_summary": { + "dataset_id": "n1", + "model": "v2-cli:codex", + "run_id": "v2q_n1_babf667d12d619da", + "api_calls": 0, + "input_tokens": 20359, + "cached_input_tokens": 19840, + "output_tokens": 435, + "total_tokens": 20794, + "cost_usd": 0.0, + "ai_cli_calls": 2, + "estimated_input_tokens": 0, + "estimated_output_tokens": 0, + "estimated_total_tokens": 0, + "usage_source": "ai_cli_json_usage", + "cli_elapsed_ms_total": 14412.49, + "sql_execution_elapsed_ms_total": 3.89, + "conversation_log_path": "/data/jialinzhang/TabQueryBench/sql_workloads/v2_current/runs_and_launches/runs/v2_cli_20260502_081223_d/n1/artifacts/v2q_n1_babf667d12d619da/cli/conversation.jsonl", + "note": "Executed through a local AI CLI with structured usage metadata." + } +} \ No newline at end of file diff --git a/Query/sql/v2/runs/v2_cli_20260502_081223_d/n1/artifacts/v2q_n1_babf667d12d619da/cli/sql_attempt_1.metadata.json b/Query/sql/v2/runs/v2_cli_20260502_081223_d/n1/artifacts/v2q_n1_babf667d12d619da/cli/sql_attempt_1.metadata.json new file mode 100644 index 0000000000000000000000000000000000000000..7700bfed6b1f0fc5e48f8e88eb2f9a7b5f08bebc --- /dev/null +++ b/Query/sql/v2/runs/v2_cli_20260502_081223_d/n1/artifacts/v2q_n1_babf667d12d619da/cli/sql_attempt_1.metadata.json @@ -0,0 +1,43 @@ +{ + "attempt": 1, + "phase": "sql_generation", + "command": "codex exec --skip-git-repo-check --disable plugins --sandbox read-only --cd \"/data/jialinzhang/SQLagent\" -m gpt-5.4 --json -", + "started_at": "2026-05-19T16:05:30.031350+00:00", + "ended_at": "2026-05-19T16:05:34.148116+00:00", + "elapsed_ms": 4116.74, + "returncode": 1, + "prompt_metrics": { + "chars": 29527, + "bytes_utf8": 29527, + "lines": 792, + "estimated_tokens": null + }, + "stdout_metrics": { + "chars": 281, + "bytes_utf8": 281, + "lines": 4, + "estimated_tokens": null + }, + "stderr_metrics": { + "chars": 0, + "bytes_utf8": 0, + "lines": 0, + "estimated_tokens": null + }, + "parsed_output": { + "format": "jsonl_events", + "text_metrics": { + "chars": 280, + "bytes_utf8": 280, + "lines": 4, + "estimated_tokens": null + }, + "usage": {} + }, + "status": "failed", + "error": "AI CLI command failed with exit code 1: ", + "prompt_path": "cli/sql_prompt_attempt_1.txt", + "response_path": "cli/sql_response_attempt_1.txt", + "raw_response_path": "cli/sql_response_attempt_1.raw.txt", + "stderr_path": "cli/sql_stderr_attempt_1.txt" +} \ No newline at end of file diff --git a/Query/sql/v2/runs/v2_cli_20260502_081223_d/n1/artifacts/v2q_n1_babf667d12d619da/cli/sql_attempt_2.metadata.json b/Query/sql/v2/runs/v2_cli_20260502_081223_d/n1/artifacts/v2q_n1_babf667d12d619da/cli/sql_attempt_2.metadata.json new file mode 100644 index 0000000000000000000000000000000000000000..b5af8c069ecc5f653100d86c4e887310bab10da9 --- /dev/null +++ b/Query/sql/v2/runs/v2_cli_20260502_081223_d/n1/artifacts/v2q_n1_babf667d12d619da/cli/sql_attempt_2.metadata.json @@ -0,0 +1,45 @@ +{ + "attempt": 2, + "phase": "sql_generation", + "command": "codex exec --skip-git-repo-check --disable plugins --sandbox read-only --cd \"/data/jialinzhang/SQLagent\" -m gpt-5.4 --json -", + "started_at": "2026-05-19T16:05:35.150508+00:00", + "ended_at": "2026-05-19T16:05:45.446301+00:00", + "elapsed_ms": 10295.75, + "prompt_metrics": { + "chars": 29527, + "bytes_utf8": 29527, + "lines": 792, + "estimated_tokens": null + }, + "stdout_metrics": { + "chars": 846, + "bytes_utf8": 846, + "lines": 4, + "estimated_tokens": null + }, + "stderr_metrics": { + "chars": 0, + "bytes_utf8": 0, + "lines": 0, + "estimated_tokens": null + }, + "parsed_output": { + "format": "jsonl_events", + "text_metrics": { + "chars": 478, + "bytes_utf8": 478, + "lines": 1, + "estimated_tokens": null + }, + "usage": { + "input_tokens": 20359, + "cached_input_tokens": 19840, + "output_tokens": 435, + "reasoning_output_tokens": 306 + } + }, + "prompt_path": "cli/sql_prompt_attempt_2.txt", + "response_path": "cli/sql_response_attempt_2.txt", + "raw_response_path": "cli/sql_response_attempt_2.raw.txt", + "stderr_path": "cli/sql_stderr_attempt_2.txt" +} \ No newline at end of file diff --git a/Query/sql/v2/runs/v2_cli_20260502_081223_d/n1/artifacts/v2q_n1_babf667d12d619da/cli/sql_prompt_attempt_1.txt b/Query/sql/v2/runs/v2_cli_20260502_081223_d/n1/artifacts/v2q_n1_babf667d12d619da/cli/sql_prompt_attempt_1.txt new file mode 100644 index 0000000000000000000000000000000000000000..5a6b00422e82c1ae72015f3800352115529ac60e --- /dev/null +++ b/Query/sql/v2/runs/v2_cli_20260502_081223_d/n1/artifacts/v2q_n1_babf667d12d619da/cli/sql_prompt_attempt_1.txt @@ -0,0 +1,792 @@ +You are generating one SQLite SELECT query for a single-table SQL QA task. +Return strict JSON only, with this schema: {"sql": "...", "notes": "..."}. +Rules: +- Use only the provided table and columns. +- Do not write INSERT, UPDATE, DELETE, DROP, ALTER, CREATE, PRAGMA, ATTACH, DETACH, or VACUUM. +- Prefer the planned template and bound roles when provided. +- Add a leading SQL comment exactly like: -- template_id: . +- Generate SQLite-compatible SQL. SQLite does not support PERCENTILE_CONT or STDDEV. +- Quote identifiers with double quotes. +- Return no markdown and no extra prose. + +Dataset context: +Dataset context for SQL QA: +- dataset_id: n1 +- dataset_name: Spambase +- table_name: n1 +- table_layout: single-table dataset (do not assume joins). +- row_semantics: One row is one email represented by engineered token/character frequency and capitalization-run features. +- task_type: classification +- target_column: class +- main_row_count: 4601 +- important_fields: +- word_freq_make: role=feature, type=numeric_sparse_frequency. tags=['condition_candidate', 'measure', 'rare_pattern_candidate'] desc=Frequency feature for token 'make' in an email message. +- word_freq_address: role=feature, type=numeric_sparse_frequency. tags=['condition_candidate', 'measure', 'rare_pattern_candidate'] desc=Frequency feature for token 'address' in an email message. +- word_freq_all: role=feature, type=numeric_sparse_frequency. tags=['condition_candidate', 'measure', 'rare_pattern_candidate'] desc=Frequency feature for token 'all' in an email message. +- word_freq_3d: role=feature, type=numeric_sparse_frequency. tags=['condition_candidate', 'measure', 'rare_pattern_candidate'] desc=Frequency feature for token '3d' in an email message. +- word_freq_our: role=feature, type=numeric_sparse_frequency. tags=['condition_candidate', 'measure', 'rare_pattern_candidate'] desc=Frequency feature for token 'our' in an email message. +- word_freq_over: role=feature, type=numeric_sparse_frequency. tags=['condition_candidate', 'measure', 'rare_pattern_candidate'] desc=Frequency feature for token 'over' in an email message. +- word_freq_remove: role=feature, type=numeric_sparse_frequency. tags=['condition_candidate', 'measure', 'rare_pattern_candidate'] desc=Frequency feature for token 'remove' in an email message. +- word_freq_internet: role=feature, type=numeric_sparse_frequency. tags=['condition_candidate', 'measure', 'rare_pattern_candidate'] desc=Frequency feature for token 'internet' in an email message. +- word_freq_order: role=feature, type=numeric_sparse_frequency. tags=['condition_candidate', 'measure', 'rare_pattern_candidate'] desc=Frequency feature for token 'order' in an email message. +- word_freq_mail: role=feature, type=numeric_sparse_frequency. tags=['condition_candidate', 'measure', 'rare_pattern_candidate'] desc=Frequency feature for token 'mail' in an email message. +- word_freq_receive: role=feature, type=numeric_sparse_frequency. tags=['condition_candidate', 'measure', 'rare_pattern_candidate'] desc=Frequency feature for token 'receive' in an email message. +- word_freq_will: role=feature, type=numeric_sparse_frequency. tags=['condition_candidate', 'measure', 'rare_pattern_candidate'] desc=Frequency feature for token 'will' in an email message. +- word_freq_people: role=feature, type=numeric_sparse_frequency. tags=['condition_candidate', 'measure', 'rare_pattern_candidate'] desc=Frequency feature for token 'people' in an email message. +- word_freq_report: role=feature, type=numeric_sparse_frequency. tags=['condition_candidate', 'measure', 'rare_pattern_candidate'] desc=Frequency feature for token 'report' in an email message. +- word_freq_addresses: role=feature, type=numeric_sparse_frequency. tags=['condition_candidate', 'measure', 'rare_pattern_candidate'] desc=Frequency feature for token 'addresses' in an email message. +- word_freq_free: role=feature, type=numeric_sparse_frequency. tags=['condition_candidate', 'measure', 'rare_pattern_candidate'] desc=Frequency feature for token 'free' in an email message. +- word_freq_business: role=feature, type=numeric_sparse_frequency. tags=['condition_candidate', 'measure', 'rare_pattern_candidate'] desc=Frequency feature for token 'business' in an email message. +- word_freq_email: role=feature, type=numeric_sparse_frequency. tags=['condition_candidate', 'measure', 'rare_pattern_candidate'] desc=Frequency feature for token 'email' in an email message. +- word_freq_you: role=feature, type=numeric_sparse_frequency. tags=['condition_candidate', 'measure', 'rare_pattern_candidate'] desc=Frequency feature for token 'you' in an email message. +- word_freq_credit: role=feature, type=numeric_sparse_frequency. tags=['condition_candidate', 'measure', 'rare_pattern_candidate'] desc=Frequency feature for token 'credit' in an email message. +- word_freq_your: role=feature, type=numeric_sparse_frequency. tags=['condition_candidate', 'measure', 'rare_pattern_candidate'] desc=Frequency feature for token 'your' in an email message. +- word_freq_font: role=feature, type=numeric_sparse_frequency. tags=['condition_candidate', 'measure', 'rare_pattern_candidate'] desc=Frequency feature for token 'font' in an email message. +- word_freq_000: role=feature, type=numeric_sparse_frequency. tags=['condition_candidate', 'measure', 'rare_pattern_candidate'] desc=Frequency feature for token '000' in an email message. +- word_freq_money: role=feature, type=numeric_sparse_frequency. tags=['condition_candidate', 'measure', 'rare_pattern_candidate'] desc=Frequency feature for token 'money' in an email message. +- word_freq_hp: role=feature, type=numeric_sparse_frequency. tags=['condition_candidate', 'measure', 'rare_pattern_candidate'] desc=Frequency feature for token 'hp' in an email message. +- word_freq_hpl: role=feature, type=numeric_sparse_frequency. tags=['condition_candidate', 'measure', 'rare_pattern_candidate'] desc=Frequency feature for token 'hpl' in an email message. +- word_freq_george: role=feature, type=numeric_sparse_frequency. tags=['condition_candidate', 'measure', 'rare_pattern_candidate'] desc=Frequency feature for token 'george' in an email message. +- word_freq_650: role=feature, type=numeric_sparse_frequency. tags=['condition_candidate', 'measure', 'rare_pattern_candidate'] desc=Frequency feature for token '650' in an email message. +- word_freq_lab: role=feature, type=numeric_sparse_frequency. tags=['condition_candidate', 'measure', 'rare_pattern_candidate'] desc=Frequency feature for token 'lab' in an email message. +- word_freq_labs: role=feature, type=numeric_sparse_frequency. tags=['condition_candidate', 'measure', 'rare_pattern_candidate'] desc=Frequency feature for token 'labs' in an email message. +- word_freq_telnet: role=feature, type=numeric_sparse_frequency. tags=['condition_candidate', 'measure', 'rare_pattern_candidate'] desc=Frequency feature for token 'telnet' in an email message. +- word_freq_857: role=feature, type=numeric_sparse_frequency. tags=['condition_candidate', 'measure', 'rare_pattern_candidate'] desc=Frequency feature for token '857' in an email message. +- word_freq_data: role=feature, type=numeric_sparse_frequency. tags=['condition_candidate', 'measure', 'rare_pattern_candidate'] desc=Frequency feature for token 'data' in an email message. +- word_freq_415: role=feature, type=numeric_sparse_frequency. tags=['condition_candidate', 'measure', 'rare_pattern_candidate'] desc=Frequency feature for token '415' in an email message. +- word_freq_85: role=feature, type=numeric_sparse_frequency. tags=['condition_candidate', 'measure', 'rare_pattern_candidate'] desc=Frequency feature for token '85' in an email message. +- word_freq_technology: role=feature, type=numeric_sparse_frequency. tags=['condition_candidate', 'measure', 'rare_pattern_candidate'] desc=Frequency feature for token 'technology' in an email message. +- word_freq_1999: role=feature, type=numeric_sparse_frequency. tags=['condition_candidate', 'measure', 'rare_pattern_candidate'] desc=Frequency feature for token '1999' in an email message. +- word_freq_parts: role=feature, type=numeric_sparse_frequency. tags=['condition_candidate', 'measure', 'rare_pattern_candidate'] desc=Frequency feature for token 'parts' in an email message. +- word_freq_pm: role=feature, type=numeric_sparse_frequency. tags=['condition_candidate', 'measure', 'rare_pattern_candidate'] desc=Frequency feature for token 'pm' in an email message. +- word_freq_direct: role=feature, type=numeric_sparse_frequency. tags=['condition_candidate', 'measure', 'rare_pattern_candidate'] desc=Frequency feature for token 'direct' in an email message. +- word_freq_cs: role=feature, type=numeric_sparse_frequency. tags=['condition_candidate', 'measure', 'rare_pattern_candidate'] desc=Frequency feature for token 'cs' in an email message. +- word_freq_meeting: role=feature, type=numeric_sparse_frequency. tags=['condition_candidate', 'measure', 'rare_pattern_candidate'] desc=Frequency feature for token 'meeting' in an email message. +- word_freq_original: role=feature, type=numeric_sparse_frequency. tags=['condition_candidate', 'measure', 'rare_pattern_candidate'] desc=Frequency feature for token 'original' in an email message. +- word_freq_project: role=feature, type=numeric_sparse_frequency. tags=['condition_candidate', 'measure', 'rare_pattern_candidate'] desc=Frequency feature for token 'project' in an email message. +- word_freq_re: role=feature, type=numeric_sparse_frequency. tags=['condition_candidate', 'measure', 'rare_pattern_candidate'] desc=Frequency feature for token 're' in an email message. +- word_freq_edu: role=feature, type=numeric_sparse_frequency. tags=['condition_candidate', 'measure', 'rare_pattern_candidate'] desc=Frequency feature for token 'edu' in an email message. +- word_freq_table: role=feature, type=numeric_sparse_frequency. tags=['condition_candidate', 'measure', 'rare_pattern_candidate'] desc=Frequency feature for token 'table' in an email message. +- word_freq_conference: role=feature, type=numeric_sparse_frequency. tags=['condition_candidate', 'measure', 'rare_pattern_candidate'] desc=Frequency feature for token 'conference' in an email message. +- char_freq_%3B: role=feature, type=numeric_sparse_frequency. tags=['condition_candidate', 'measure', 'rare_pattern_candidate'] desc=Character frequency feature for symbol ';'. +- char_freq_%28: role=feature, type=numeric_sparse_frequency. tags=['condition_candidate', 'measure', 'rare_pattern_candidate'] desc=Character frequency feature for symbol '('. +- char_freq_%5B: role=feature, type=numeric_sparse_frequency. tags=['condition_candidate', 'measure', 'rare_pattern_candidate'] desc=Character frequency feature for symbol '['. +- char_freq_%21: role=feature, type=numeric_sparse_frequency. tags=['condition_candidate', 'measure', 'rare_pattern_candidate'] desc=Character frequency feature for symbol '!'. +- char_freq_%24: role=feature, type=numeric_sparse_frequency. tags=['condition_candidate', 'measure', 'rare_pattern_candidate'] desc=Character frequency feature for symbol '$'. +- char_freq_%23: role=feature, type=numeric_sparse_frequency. tags=['condition_candidate', 'measure', 'rare_pattern_candidate'] desc=Character frequency feature for symbol '#'. +- capital_run_length_average: role=feature, type=numeric_heavy_tail. tags=['condition_candidate', 'measure', 'rare_pattern_candidate'] desc=Average length of uninterrupted capital-letter runs. +- capital_run_length_longest: role=feature, type=numeric_heavy_tail. tags=['condition_candidate', 'measure', 'rare_pattern_candidate'] desc=Longest uninterrupted capital-letter run. +- capital_run_length_total: role=feature, type=numeric_heavy_tail. tags=['condition_candidate', 'measure', 'rare_pattern_candidate'] desc=Total number of capital letters in runs. +- class: role=target, type=binary_target. tags=['target_candidate'] desc=Binary spam label (1=spam, 0=non-spam). +- useful_field_combinations: [['word_freq_free', 'word_freq_you', 'class'], ['char_freq_%21', 'capital_run_length_longest', 'class'], ['word_freq_remove', 'word_freq_money', 'class']] +- fields_requiring_caution: ['capital_run_length_average', 'capital_run_length_longest', 'capital_run_length_total'] +- source_url: https://www.openml.org/d/44 + +SQLite schema snapshot: +{ + "table_name": "n1", + "quoted_table_name": "\"n1\"", + "row_count": 4601, + "columns": [ + { + "name": "word_freq_make", + "type": "TEXT", + "notnull": false, + "pk": false + }, + { + "name": "word_freq_address", + "type": "TEXT", + "notnull": false, + "pk": false + }, + { + "name": "word_freq_all", + "type": "TEXT", + "notnull": false, + "pk": false + }, + { + "name": "word_freq_3d", + "type": "TEXT", + "notnull": false, + "pk": false + }, + { + "name": "word_freq_our", + "type": "TEXT", + "notnull": false, + "pk": false + }, + { + "name": "word_freq_over", + "type": "TEXT", + "notnull": false, + "pk": false + }, + { + "name": "word_freq_remove", + "type": "TEXT", + "notnull": false, + "pk": false + }, + { + "name": "word_freq_internet", + "type": "TEXT", + "notnull": false, + "pk": false + }, + { + "name": "word_freq_order", + "type": "TEXT", + "notnull": false, + "pk": false + }, + { + "name": "word_freq_mail", + "type": "TEXT", + "notnull": false, + "pk": false + }, + { + "name": "word_freq_receive", + "type": "TEXT", + "notnull": false, + "pk": false + }, + { + "name": "word_freq_will", + "type": "TEXT", + "notnull": false, + "pk": false + }, + { + "name": "word_freq_people", + "type": "TEXT", + "notnull": false, + "pk": false + }, + { + "name": "word_freq_report", + "type": "TEXT", + "notnull": false, + "pk": false + }, + { + "name": "word_freq_addresses", + "type": "TEXT", + "notnull": false, + "pk": false + }, + { + "name": "word_freq_free", + "type": "TEXT", + "notnull": false, + "pk": false + }, + { + "name": "word_freq_business", + "type": "TEXT", + "notnull": false, + "pk": false + }, + { + "name": "word_freq_email", + "type": "TEXT", + "notnull": false, + "pk": false + }, + { + "name": "word_freq_you", + "type": "TEXT", + "notnull": false, + "pk": false + }, + { + "name": "word_freq_credit", + "type": "TEXT", + "notnull": false, + "pk": false + }, + { + "name": "word_freq_your", + "type": "TEXT", + "notnull": false, + "pk": false + }, + { + "name": "word_freq_font", + "type": "TEXT", + "notnull": false, + "pk": false + }, + { + "name": "word_freq_000", + "type": "TEXT", + "notnull": false, + "pk": false + }, + { + "name": "word_freq_money", + "type": "TEXT", + "notnull": false, + "pk": false + }, + { + "name": "word_freq_hp", + "type": "TEXT", + "notnull": false, + "pk": false + }, + { + "name": "word_freq_hpl", + "type": "TEXT", + "notnull": false, + "pk": false + }, + { + "name": "word_freq_george", + "type": "TEXT", + "notnull": false, + "pk": false + }, + { + "name": "word_freq_650", + "type": "TEXT", + "notnull": false, + "pk": false + }, + { + "name": "word_freq_lab", + "type": "TEXT", + "notnull": false, + "pk": false + }, + { + "name": "word_freq_labs", + "type": "TEXT", + "notnull": false, + "pk": false + }, + { + "name": "word_freq_telnet", + "type": "TEXT", + "notnull": false, + "pk": false + }, + { + "name": "word_freq_857", + "type": "TEXT", + "notnull": false, + "pk": false + }, + { + "name": "word_freq_data", + "type": "TEXT", + "notnull": false, + "pk": false + }, + { + "name": "word_freq_415", + "type": "TEXT", + "notnull": false, + "pk": false + }, + { + "name": "word_freq_85", + "type": "TEXT", + "notnull": false, + "pk": false + }, + { + "name": "word_freq_technology", + "type": "TEXT", + "notnull": false, + "pk": false + }, + { + "name": "word_freq_1999", + "type": "TEXT", + "notnull": false, + "pk": false + }, + { + "name": "word_freq_parts", + "type": "TEXT", + "notnull": false, + "pk": false + }, + { + "name": "word_freq_pm", + "type": "TEXT", + "notnull": false, + "pk": false + }, + { + "name": "word_freq_direct", + "type": "TEXT", + "notnull": false, + "pk": false + }, + { + "name": "word_freq_cs", + "type": "TEXT", + "notnull": false, + "pk": false + }, + { + "name": "word_freq_meeting", + "type": "TEXT", + "notnull": false, + "pk": false + }, + { + "name": "word_freq_original", + "type": "TEXT", + "notnull": false, + "pk": false + }, + { + "name": "word_freq_project", + "type": "TEXT", + "notnull": false, + "pk": false + }, + { + "name": "word_freq_re", + "type": "TEXT", + "notnull": false, + "pk": false + }, + { + "name": "word_freq_edu", + "type": "TEXT", + "notnull": false, + "pk": false + }, + { + "name": "word_freq_table", + "type": "TEXT", + "notnull": false, + "pk": false + }, + { + "name": "word_freq_conference", + "type": "TEXT", + "notnull": false, + "pk": false + }, + { + "name": "char_freq_%3B", + "type": "TEXT", + "notnull": false, + "pk": false + }, + { + "name": "char_freq_%28", + "type": "TEXT", + "notnull": false, + "pk": false + }, + { + "name": "char_freq_%5B", + "type": "TEXT", + "notnull": false, + "pk": false + }, + { + "name": "char_freq_%21", + "type": "TEXT", + "notnull": false, + "pk": false + }, + { + "name": "char_freq_%24", + "type": "TEXT", + "notnull": false, + "pk": false + }, + { + "name": "char_freq_%23", + "type": "TEXT", + "notnull": false, + "pk": false + }, + { + "name": "capital_run_length_average", + "type": "TEXT", + "notnull": false, + "pk": false + }, + { + "name": "capital_run_length_longest", + "type": "TEXT", + "notnull": false, + "pk": false + }, + { + "name": "capital_run_length_total", + "type": "TEXT", + "notnull": false, + "pk": false + }, + { + "name": "class", + "type": "TEXT", + "notnull": false, + "pk": false + } + ], + "sample_rows": [ + { + "word_freq_make": "0", + "word_freq_address": "0.64", + "word_freq_all": "0.64", + "word_freq_3d": "0", + "word_freq_our": "0.32", + "word_freq_over": "0", + "word_freq_remove": "0", + "word_freq_internet": "0", + "word_freq_order": "0", + "word_freq_mail": "0", + "word_freq_receive": "0", + "word_freq_will": "0.64", + "word_freq_people": "0", + "word_freq_report": "0", + "word_freq_addresses": "0", + "word_freq_free": "0.32", + "word_freq_business": "0", + "word_freq_email": "1.29", + "word_freq_you": "1.93", + "word_freq_credit": "0", + "word_freq_your": "0.96", + "word_freq_font": "0", + "word_freq_000": "0", + "word_freq_money": "0", + "word_freq_hp": "0", + "word_freq_hpl": "0", + "word_freq_george": "0", + "word_freq_650": "0", + "word_freq_lab": "0", + "word_freq_labs": "0", + "word_freq_telnet": "0", + "word_freq_857": "0", + "word_freq_data": "0", + "word_freq_415": "0", + "word_freq_85": "0", + "word_freq_technology": "0", + "word_freq_1999": "0", + "word_freq_parts": "0", + "word_freq_pm": "0", + "word_freq_direct": "0", + "word_freq_cs": "0", + "word_freq_meeting": "0", + "word_freq_original": "0", + "word_freq_project": "0", + "word_freq_re": "0", + "word_freq_edu": "0", + "word_freq_table": "0", + "word_freq_conference": "0", + "char_freq_%3B": "0", + "char_freq_%28": "0", + "char_freq_%5B": "0", + "char_freq_%21": "0.778", + "char_freq_%24": "0", + "char_freq_%23": "0", + "capital_run_length_average": "3.756", + "capital_run_length_longest": "61", + "capital_run_length_total": "278", + "class": "1" + }, + { + "word_freq_make": "0.21", + "word_freq_address": "0.28", + "word_freq_all": "0.5", + "word_freq_3d": "0", + "word_freq_our": "0.14", + "word_freq_over": "0.28", + "word_freq_remove": "0.21", + "word_freq_internet": "0.07", + "word_freq_order": "0", + "word_freq_mail": "0.94", + "word_freq_receive": "0.21", + "word_freq_will": "0.79", + "word_freq_people": "0.65", + "word_freq_report": "0.21", + "word_freq_addresses": "0.14", + "word_freq_free": "0.14", + "word_freq_business": "0.07", + "word_freq_email": "0.28", + "word_freq_you": "3.47", + "word_freq_credit": "0", + "word_freq_your": "1.59", + "word_freq_font": "0", + "word_freq_000": "0.43", + "word_freq_money": "0.43", + "word_freq_hp": "0", + "word_freq_hpl": "0", + "word_freq_george": "0", + "word_freq_650": "0", + "word_freq_lab": "0", + "word_freq_labs": "0", + "word_freq_telnet": "0", + "word_freq_857": "0", + "word_freq_data": "0", + "word_freq_415": "0", + "word_freq_85": "0", + "word_freq_technology": "0", + "word_freq_1999": "0.07", + "word_freq_parts": "0", + "word_freq_pm": "0", + "word_freq_direct": "0", + "word_freq_cs": "0", + "word_freq_meeting": "0", + "word_freq_original": "0", + "word_freq_project": "0", + "word_freq_re": "0", + "word_freq_edu": "0", + "word_freq_table": "0", + "word_freq_conference": "0", + "char_freq_%3B": "0", + "char_freq_%28": "0.132", + "char_freq_%5B": "0", + "char_freq_%21": "0.372", + "char_freq_%24": "0.18", + "char_freq_%23": "0.048", + "capital_run_length_average": "5.114", + "capital_run_length_longest": "101", + "capital_run_length_total": "1028", + "class": "1" + }, + { + "word_freq_make": "0.06", + "word_freq_address": "0", + "word_freq_all": "0.71", + "word_freq_3d": "0", + "word_freq_our": "1.23", + "word_freq_over": "0.19", + "word_freq_remove": "0.19", + "word_freq_internet": "0.12", + "word_freq_order": "0.64", + "word_freq_mail": "0.25", + "word_freq_receive": "0.38", + "word_freq_will": "0.45", + "word_freq_people": "0.12", + "word_freq_report": "0", + "word_freq_addresses": "1.75", + "word_freq_free": "0.06", + "word_freq_business": "0.06", + "word_freq_email": "1.03", + "word_freq_you": "1.36", + "word_freq_credit": "0.32", + "word_freq_your": "0.51", + "word_freq_font": "0", + "word_freq_000": "1.16", + "word_freq_money": "0.06", + "word_freq_hp": "0", + "word_freq_hpl": "0", + "word_freq_george": "0", + "word_freq_650": "0", + "word_freq_lab": "0", + "word_freq_labs": "0", + "word_freq_telnet": "0", + "word_freq_857": "0", + "word_freq_data": "0", + "word_freq_415": "0", + "word_freq_85": "0", + "word_freq_technology": "0", + "word_freq_1999": "0", + "word_freq_parts": "0", + "word_freq_pm": "0", + "word_freq_direct": "0.06", + "word_freq_cs": "0", + "word_freq_meeting": "0", + "word_freq_original": "0.12", + "word_freq_project": "0", + "word_freq_re": "0.06", + "word_freq_edu": "0.06", + "word_freq_table": "0", + "word_freq_conference": "0", + "char_freq_%3B": "0.01", + "char_freq_%28": "0.143", + "char_freq_%5B": "0", + "char_freq_%21": "0.276", + "char_freq_%24": "0.184", + "char_freq_%23": "0.01", + "capital_run_length_average": "9.821", + "capital_run_length_longest": "485", + "capital_run_length_total": "2259", + "class": "1" + }, + { + "word_freq_make": "0", + "word_freq_address": "0", + "word_freq_all": "0", + "word_freq_3d": "0", + "word_freq_our": "0.63", + "word_freq_over": "0", + "word_freq_remove": "0.31", + "word_freq_internet": "0.63", + "word_freq_order": "0.31", + "word_freq_mail": "0.63", + "word_freq_receive": "0.31", + "word_freq_will": "0.31", + "word_freq_people": "0.31", + "word_freq_report": "0", + "word_freq_addresses": "0", + "word_freq_free": "0.31", + "word_freq_business": "0", + "word_freq_email": "0", + "word_freq_you": "3.18", + "word_freq_credit": "0", + "word_freq_your": "0.31", + "word_freq_font": "0", + "word_freq_000": "0", + "word_freq_money": "0", + "word_freq_hp": "0", + "word_freq_hpl": "0", + "word_freq_george": "0", + "word_freq_650": "0", + "word_freq_lab": "0", + "word_freq_labs": "0", + "word_freq_telnet": "0", + "word_freq_857": "0", + "word_freq_data": "0", + "word_freq_415": "0", + "word_freq_85": "0", + "word_freq_technology": "0", + "word_freq_1999": "0", + "word_freq_parts": "0", + "word_freq_pm": "0", + "word_freq_direct": "0", + "word_freq_cs": "0", + "word_freq_meeting": "0", + "word_freq_original": "0", + "word_freq_project": "0", + "word_freq_re": "0", + "word_freq_edu": "0", + "word_freq_table": "0", + "word_freq_conference": "0", + "char_freq_%3B": "0", + "char_freq_%28": "0.137", + "char_freq_%5B": "0", + "char_freq_%21": "0.137", + "char_freq_%24": "0", + "char_freq_%23": "0", + "capital_run_length_average": "3.537", + "capital_run_length_longest": "40", + "capital_run_length_total": "191", + "class": "1" + }, + { + "word_freq_make": "0", + "word_freq_address": "0", + "word_freq_all": "0", + "word_freq_3d": "0", + "word_freq_our": "0.63", + "word_freq_over": "0", + "word_freq_remove": "0.31", + "word_freq_internet": "0.63", + "word_freq_order": "0.31", + "word_freq_mail": "0.63", + "word_freq_receive": "0.31", + "word_freq_will": "0.31", + "word_freq_people": "0.31", + "word_freq_report": "0", + "word_freq_addresses": "0", + "word_freq_free": "0.31", + "word_freq_business": "0", + "word_freq_email": "0", + "word_freq_you": "3.18", + "word_freq_credit": "0", + "word_freq_your": "0.31", + "word_freq_font": "0", + "word_freq_000": "0", + "word_freq_money": "0", + "word_freq_hp": "0", + "word_freq_hpl": "0", + "word_freq_george": "0", + "word_freq_650": "0", + "word_freq_lab": "0", + "word_freq_labs": "0", + "word_freq_telnet": "0", + "word_freq_857": "0", + "word_freq_data": "0", + "word_freq_415": "0", + "word_freq_85": "0", + "word_freq_technology": "0", + "word_freq_1999": "0", + "word_freq_parts": "0", + "word_freq_pm": "0", + "word_freq_direct": "0", + "word_freq_cs": "0", + "word_freq_meeting": "0", + "word_freq_original": "0", + "word_freq_project": "0", + "word_freq_re": "0", + "word_freq_edu": "0", + "word_freq_table": "0", + "word_freq_conference": "0", + "char_freq_%3B": "0", + "char_freq_%28": "0.135", + "char_freq_%5B": "0", + "char_freq_%21": "0.135", + "char_freq_%24": "0", + "char_freq_%23": "0", + "capital_run_length_average": "3.537", + "capital_run_length_longest": "40", + "capital_run_length_total": "191", + "class": "1" + } + ] +} + +Shortlisted templates: +[ + { + "template_id": "tpl_tpch_thresholded_group_ranking", + "template_name": "Thresholded Group Ranking", + "primary_family": "tail_rarity_structure", + "portability": "partial", + "sql_skeleton": "SELECT {group_col}, SUM({measure_col}) AS total_measure\nFROM {table}\nGROUP BY {group_col}\nHAVING SUM({measure_col}) > {measure_threshold}\nORDER BY total_measure DESC\nLIMIT {top_k};", + "required_roles": [ + "group_col", + "measure_col" + ] + } +] + +Problem instance: +{ + "dataset_id": "n1", + "question": "Use template Thresholded Group Ranking to probe tail_mass_similarity with semantic role filtered_stable_view. Focus on group_col=class, measure_col=word_freq_money.", + "planned_template_id": "tpl_tpch_thresholded_group_ranking", + "bindings": { + "group_col": "class", + "measure_col": "word_freq_money", + "top_k": 12, + "top_n": 4, + "num_tiles": 10, + "percentile_value": 0.9, + "z_threshold": 2.0, + "fraction_threshold": 0.1, + "baseline_multiplier": 1.5, + "baseline_fraction": 0.1, + "min_group_size": 5, + "min_support": 5, + "measure_threshold": 0.0, + "time_grain": "month", + "lookback_rows": 3, + "current_period_start": "'2024-01-01'", + "current_period_end": "'2024-04-01'", + "previous_period_start": "'2023-10-01'", + "previous_period_end": "'2024-01-01'", + "drift_ratio_threshold": 0.8 + }, + "can_vary": [], + "must_fix": [], + "runtime_sql_skeleton": "SELECT {group_col}, SUM({measure_col}) AS total_measure\nFROM {table}\nGROUP BY {group_col}\nHAVING SUM({measure_col}) > {measure_threshold}\nORDER BY total_measure DESC\nLIMIT {top_k};" +} + +Repair context: +{} diff --git a/Query/sql/v2/runs/v2_cli_20260502_081223_d/n1/artifacts/v2q_n1_babf667d12d619da/cli/sql_prompt_attempt_2.txt b/Query/sql/v2/runs/v2_cli_20260502_081223_d/n1/artifacts/v2q_n1_babf667d12d619da/cli/sql_prompt_attempt_2.txt new file mode 100644 index 0000000000000000000000000000000000000000..5a6b00422e82c1ae72015f3800352115529ac60e --- /dev/null +++ b/Query/sql/v2/runs/v2_cli_20260502_081223_d/n1/artifacts/v2q_n1_babf667d12d619da/cli/sql_prompt_attempt_2.txt @@ -0,0 +1,792 @@ +You are generating one SQLite SELECT query for a single-table SQL QA task. +Return strict JSON only, with this schema: {"sql": "...", "notes": "..."}. +Rules: +- Use only the provided table and columns. +- Do not write INSERT, UPDATE, DELETE, DROP, ALTER, CREATE, PRAGMA, ATTACH, DETACH, or VACUUM. +- Prefer the planned template and bound roles when provided. +- Add a leading SQL comment exactly like: -- template_id: . +- Generate SQLite-compatible SQL. SQLite does not support PERCENTILE_CONT or STDDEV. +- Quote identifiers with double quotes. +- Return no markdown and no extra prose. + +Dataset context: +Dataset context for SQL QA: +- dataset_id: n1 +- dataset_name: Spambase +- table_name: n1 +- table_layout: single-table dataset (do not assume joins). +- row_semantics: One row is one email represented by engineered token/character frequency and capitalization-run features. +- task_type: classification +- target_column: class +- main_row_count: 4601 +- important_fields: +- word_freq_make: role=feature, type=numeric_sparse_frequency. tags=['condition_candidate', 'measure', 'rare_pattern_candidate'] desc=Frequency feature for token 'make' in an email message. +- word_freq_address: role=feature, type=numeric_sparse_frequency. tags=['condition_candidate', 'measure', 'rare_pattern_candidate'] desc=Frequency feature for token 'address' in an email message. +- word_freq_all: role=feature, type=numeric_sparse_frequency. tags=['condition_candidate', 'measure', 'rare_pattern_candidate'] desc=Frequency feature for token 'all' in an email message. +- word_freq_3d: role=feature, type=numeric_sparse_frequency. tags=['condition_candidate', 'measure', 'rare_pattern_candidate'] desc=Frequency feature for token '3d' in an email message. +- word_freq_our: role=feature, type=numeric_sparse_frequency. tags=['condition_candidate', 'measure', 'rare_pattern_candidate'] desc=Frequency feature for token 'our' in an email message. +- word_freq_over: role=feature, type=numeric_sparse_frequency. tags=['condition_candidate', 'measure', 'rare_pattern_candidate'] desc=Frequency feature for token 'over' in an email message. +- word_freq_remove: role=feature, type=numeric_sparse_frequency. tags=['condition_candidate', 'measure', 'rare_pattern_candidate'] desc=Frequency feature for token 'remove' in an email message. +- word_freq_internet: role=feature, type=numeric_sparse_frequency. tags=['condition_candidate', 'measure', 'rare_pattern_candidate'] desc=Frequency feature for token 'internet' in an email message. +- word_freq_order: role=feature, type=numeric_sparse_frequency. tags=['condition_candidate', 'measure', 'rare_pattern_candidate'] desc=Frequency feature for token 'order' in an email message. +- word_freq_mail: role=feature, type=numeric_sparse_frequency. tags=['condition_candidate', 'measure', 'rare_pattern_candidate'] desc=Frequency feature for token 'mail' in an email message. +- word_freq_receive: role=feature, type=numeric_sparse_frequency. tags=['condition_candidate', 'measure', 'rare_pattern_candidate'] desc=Frequency feature for token 'receive' in an email message. +- word_freq_will: role=feature, type=numeric_sparse_frequency. tags=['condition_candidate', 'measure', 'rare_pattern_candidate'] desc=Frequency feature for token 'will' in an email message. +- word_freq_people: role=feature, type=numeric_sparse_frequency. tags=['condition_candidate', 'measure', 'rare_pattern_candidate'] desc=Frequency feature for token 'people' in an email message. +- word_freq_report: role=feature, type=numeric_sparse_frequency. tags=['condition_candidate', 'measure', 'rare_pattern_candidate'] desc=Frequency feature for token 'report' in an email message. +- word_freq_addresses: role=feature, type=numeric_sparse_frequency. tags=['condition_candidate', 'measure', 'rare_pattern_candidate'] desc=Frequency feature for token 'addresses' in an email message. +- word_freq_free: role=feature, type=numeric_sparse_frequency. tags=['condition_candidate', 'measure', 'rare_pattern_candidate'] desc=Frequency feature for token 'free' in an email message. +- word_freq_business: role=feature, type=numeric_sparse_frequency. tags=['condition_candidate', 'measure', 'rare_pattern_candidate'] desc=Frequency feature for token 'business' in an email message. +- word_freq_email: role=feature, type=numeric_sparse_frequency. tags=['condition_candidate', 'measure', 'rare_pattern_candidate'] desc=Frequency feature for token 'email' in an email message. +- word_freq_you: role=feature, type=numeric_sparse_frequency. tags=['condition_candidate', 'measure', 'rare_pattern_candidate'] desc=Frequency feature for token 'you' in an email message. +- word_freq_credit: role=feature, type=numeric_sparse_frequency. tags=['condition_candidate', 'measure', 'rare_pattern_candidate'] desc=Frequency feature for token 'credit' in an email message. +- word_freq_your: role=feature, type=numeric_sparse_frequency. tags=['condition_candidate', 'measure', 'rare_pattern_candidate'] desc=Frequency feature for token 'your' in an email message. +- word_freq_font: role=feature, type=numeric_sparse_frequency. tags=['condition_candidate', 'measure', 'rare_pattern_candidate'] desc=Frequency feature for token 'font' in an email message. +- word_freq_000: role=feature, type=numeric_sparse_frequency. tags=['condition_candidate', 'measure', 'rare_pattern_candidate'] desc=Frequency feature for token '000' in an email message. +- word_freq_money: role=feature, type=numeric_sparse_frequency. tags=['condition_candidate', 'measure', 'rare_pattern_candidate'] desc=Frequency feature for token 'money' in an email message. +- word_freq_hp: role=feature, type=numeric_sparse_frequency. tags=['condition_candidate', 'measure', 'rare_pattern_candidate'] desc=Frequency feature for token 'hp' in an email message. +- word_freq_hpl: role=feature, type=numeric_sparse_frequency. tags=['condition_candidate', 'measure', 'rare_pattern_candidate'] desc=Frequency feature for token 'hpl' in an email message. +- word_freq_george: role=feature, type=numeric_sparse_frequency. tags=['condition_candidate', 'measure', 'rare_pattern_candidate'] desc=Frequency feature for token 'george' in an email message. +- word_freq_650: role=feature, type=numeric_sparse_frequency. tags=['condition_candidate', 'measure', 'rare_pattern_candidate'] desc=Frequency feature for token '650' in an email message. +- word_freq_lab: role=feature, type=numeric_sparse_frequency. tags=['condition_candidate', 'measure', 'rare_pattern_candidate'] desc=Frequency feature for token 'lab' in an email message. +- word_freq_labs: role=feature, type=numeric_sparse_frequency. tags=['condition_candidate', 'measure', 'rare_pattern_candidate'] desc=Frequency feature for token 'labs' in an email message. +- word_freq_telnet: role=feature, type=numeric_sparse_frequency. tags=['condition_candidate', 'measure', 'rare_pattern_candidate'] desc=Frequency feature for token 'telnet' in an email message. +- word_freq_857: role=feature, type=numeric_sparse_frequency. tags=['condition_candidate', 'measure', 'rare_pattern_candidate'] desc=Frequency feature for token '857' in an email message. +- word_freq_data: role=feature, type=numeric_sparse_frequency. tags=['condition_candidate', 'measure', 'rare_pattern_candidate'] desc=Frequency feature for token 'data' in an email message. +- word_freq_415: role=feature, type=numeric_sparse_frequency. tags=['condition_candidate', 'measure', 'rare_pattern_candidate'] desc=Frequency feature for token '415' in an email message. +- word_freq_85: role=feature, type=numeric_sparse_frequency. tags=['condition_candidate', 'measure', 'rare_pattern_candidate'] desc=Frequency feature for token '85' in an email message. +- word_freq_technology: role=feature, type=numeric_sparse_frequency. tags=['condition_candidate', 'measure', 'rare_pattern_candidate'] desc=Frequency feature for token 'technology' in an email message. +- word_freq_1999: role=feature, type=numeric_sparse_frequency. tags=['condition_candidate', 'measure', 'rare_pattern_candidate'] desc=Frequency feature for token '1999' in an email message. +- word_freq_parts: role=feature, type=numeric_sparse_frequency. tags=['condition_candidate', 'measure', 'rare_pattern_candidate'] desc=Frequency feature for token 'parts' in an email message. +- word_freq_pm: role=feature, type=numeric_sparse_frequency. tags=['condition_candidate', 'measure', 'rare_pattern_candidate'] desc=Frequency feature for token 'pm' in an email message. +- word_freq_direct: role=feature, type=numeric_sparse_frequency. tags=['condition_candidate', 'measure', 'rare_pattern_candidate'] desc=Frequency feature for token 'direct' in an email message. +- word_freq_cs: role=feature, type=numeric_sparse_frequency. tags=['condition_candidate', 'measure', 'rare_pattern_candidate'] desc=Frequency feature for token 'cs' in an email message. +- word_freq_meeting: role=feature, type=numeric_sparse_frequency. tags=['condition_candidate', 'measure', 'rare_pattern_candidate'] desc=Frequency feature for token 'meeting' in an email message. +- word_freq_original: role=feature, type=numeric_sparse_frequency. tags=['condition_candidate', 'measure', 'rare_pattern_candidate'] desc=Frequency feature for token 'original' in an email message. +- word_freq_project: role=feature, type=numeric_sparse_frequency. tags=['condition_candidate', 'measure', 'rare_pattern_candidate'] desc=Frequency feature for token 'project' in an email message. +- word_freq_re: role=feature, type=numeric_sparse_frequency. tags=['condition_candidate', 'measure', 'rare_pattern_candidate'] desc=Frequency feature for token 're' in an email message. +- word_freq_edu: role=feature, type=numeric_sparse_frequency. tags=['condition_candidate', 'measure', 'rare_pattern_candidate'] desc=Frequency feature for token 'edu' in an email message. +- word_freq_table: role=feature, type=numeric_sparse_frequency. tags=['condition_candidate', 'measure', 'rare_pattern_candidate'] desc=Frequency feature for token 'table' in an email message. +- word_freq_conference: role=feature, type=numeric_sparse_frequency. tags=['condition_candidate', 'measure', 'rare_pattern_candidate'] desc=Frequency feature for token 'conference' in an email message. +- char_freq_%3B: role=feature, type=numeric_sparse_frequency. tags=['condition_candidate', 'measure', 'rare_pattern_candidate'] desc=Character frequency feature for symbol ';'. +- char_freq_%28: role=feature, type=numeric_sparse_frequency. tags=['condition_candidate', 'measure', 'rare_pattern_candidate'] desc=Character frequency feature for symbol '('. +- char_freq_%5B: role=feature, type=numeric_sparse_frequency. tags=['condition_candidate', 'measure', 'rare_pattern_candidate'] desc=Character frequency feature for symbol '['. +- char_freq_%21: role=feature, type=numeric_sparse_frequency. tags=['condition_candidate', 'measure', 'rare_pattern_candidate'] desc=Character frequency feature for symbol '!'. +- char_freq_%24: role=feature, type=numeric_sparse_frequency. tags=['condition_candidate', 'measure', 'rare_pattern_candidate'] desc=Character frequency feature for symbol '$'. +- char_freq_%23: role=feature, type=numeric_sparse_frequency. tags=['condition_candidate', 'measure', 'rare_pattern_candidate'] desc=Character frequency feature for symbol '#'. +- capital_run_length_average: role=feature, type=numeric_heavy_tail. tags=['condition_candidate', 'measure', 'rare_pattern_candidate'] desc=Average length of uninterrupted capital-letter runs. +- capital_run_length_longest: role=feature, type=numeric_heavy_tail. tags=['condition_candidate', 'measure', 'rare_pattern_candidate'] desc=Longest uninterrupted capital-letter run. +- capital_run_length_total: role=feature, type=numeric_heavy_tail. tags=['condition_candidate', 'measure', 'rare_pattern_candidate'] desc=Total number of capital letters in runs. +- class: role=target, type=binary_target. tags=['target_candidate'] desc=Binary spam label (1=spam, 0=non-spam). +- useful_field_combinations: [['word_freq_free', 'word_freq_you', 'class'], ['char_freq_%21', 'capital_run_length_longest', 'class'], ['word_freq_remove', 'word_freq_money', 'class']] +- fields_requiring_caution: ['capital_run_length_average', 'capital_run_length_longest', 'capital_run_length_total'] +- source_url: https://www.openml.org/d/44 + +SQLite schema snapshot: +{ + "table_name": "n1", + "quoted_table_name": "\"n1\"", + "row_count": 4601, + "columns": [ + { + "name": "word_freq_make", + "type": "TEXT", + "notnull": false, + "pk": false + }, + { + "name": "word_freq_address", + "type": "TEXT", + "notnull": false, + "pk": false + }, + { + "name": "word_freq_all", + "type": "TEXT", + "notnull": false, + "pk": false + }, + { + "name": "word_freq_3d", + "type": "TEXT", + "notnull": false, + "pk": false + }, + { + "name": "word_freq_our", + "type": "TEXT", + "notnull": false, + "pk": false + }, + { + "name": "word_freq_over", + "type": "TEXT", + "notnull": false, + "pk": false + }, + { + "name": "word_freq_remove", + "type": "TEXT", + "notnull": false, + "pk": false + }, + { + "name": "word_freq_internet", + "type": "TEXT", + "notnull": false, + "pk": false + }, + { + "name": "word_freq_order", + "type": "TEXT", + "notnull": false, + "pk": false + }, + { + "name": "word_freq_mail", + "type": "TEXT", + "notnull": false, + "pk": false + }, + { + "name": "word_freq_receive", + "type": "TEXT", + "notnull": false, + "pk": false + }, + { + "name": "word_freq_will", + "type": "TEXT", + "notnull": false, + "pk": false + }, + { + "name": "word_freq_people", + "type": "TEXT", + "notnull": false, + "pk": false + }, + { + "name": "word_freq_report", + "type": "TEXT", + "notnull": false, + "pk": false + }, + { + "name": "word_freq_addresses", + "type": "TEXT", + "notnull": false, + "pk": false + }, + { + "name": "word_freq_free", + "type": "TEXT", + "notnull": false, + "pk": false + }, + { + "name": "word_freq_business", + "type": "TEXT", + "notnull": false, + "pk": false + }, + { + "name": "word_freq_email", + "type": "TEXT", + "notnull": false, + "pk": false + }, + { + "name": "word_freq_you", + "type": "TEXT", + "notnull": false, + "pk": false + }, + { + "name": "word_freq_credit", + "type": "TEXT", + "notnull": false, + "pk": false + }, + { + "name": "word_freq_your", + "type": "TEXT", + "notnull": false, + "pk": false + }, + { + "name": "word_freq_font", + "type": "TEXT", + "notnull": false, + "pk": false + }, + { + "name": "word_freq_000", + "type": "TEXT", + "notnull": false, + "pk": false + }, + { + "name": "word_freq_money", + "type": "TEXT", + "notnull": false, + "pk": false + }, + { + "name": "word_freq_hp", + "type": "TEXT", + "notnull": false, + "pk": false + }, + { + "name": "word_freq_hpl", + "type": "TEXT", + "notnull": false, + "pk": false + }, + { + "name": "word_freq_george", + "type": "TEXT", + "notnull": false, + "pk": false + }, + { + "name": "word_freq_650", + "type": "TEXT", + "notnull": false, + "pk": false + }, + { + "name": "word_freq_lab", + "type": "TEXT", + "notnull": false, + "pk": false + }, + { + "name": "word_freq_labs", + "type": "TEXT", + "notnull": false, + "pk": false + }, + { + "name": "word_freq_telnet", + "type": "TEXT", + "notnull": false, + "pk": false + }, + { + "name": "word_freq_857", + "type": "TEXT", + "notnull": false, + "pk": false + }, + { + "name": "word_freq_data", + "type": "TEXT", + "notnull": false, + "pk": false + }, + { + "name": "word_freq_415", + "type": "TEXT", + "notnull": false, + "pk": false + }, + { + "name": "word_freq_85", + "type": "TEXT", + "notnull": false, + "pk": false + }, + { + "name": "word_freq_technology", + "type": "TEXT", + "notnull": false, + "pk": false + }, + { + "name": "word_freq_1999", + "type": "TEXT", + "notnull": false, + "pk": false + }, + { + "name": "word_freq_parts", + "type": "TEXT", + "notnull": false, + "pk": false + }, + { + "name": "word_freq_pm", + "type": "TEXT", + "notnull": false, + "pk": false + }, + { + "name": "word_freq_direct", + "type": "TEXT", + "notnull": false, + "pk": false + }, + { + "name": "word_freq_cs", + "type": "TEXT", + "notnull": false, + "pk": false + }, + { + "name": "word_freq_meeting", + "type": "TEXT", + "notnull": false, + "pk": false + }, + { + "name": "word_freq_original", + "type": "TEXT", + "notnull": false, + "pk": false + }, + { + "name": "word_freq_project", + "type": "TEXT", + "notnull": false, + "pk": false + }, + { + "name": "word_freq_re", + "type": "TEXT", + "notnull": false, + "pk": false + }, + { + "name": "word_freq_edu", + "type": "TEXT", + "notnull": false, + "pk": false + }, + { + "name": "word_freq_table", + "type": "TEXT", + "notnull": false, + "pk": false + }, + { + "name": "word_freq_conference", + "type": "TEXT", + "notnull": false, + "pk": false + }, + { + "name": "char_freq_%3B", + "type": "TEXT", + "notnull": false, + "pk": false + }, + { + "name": "char_freq_%28", + "type": "TEXT", + "notnull": false, + "pk": false + }, + { + "name": "char_freq_%5B", + "type": "TEXT", + "notnull": false, + "pk": false + }, + { + "name": "char_freq_%21", + "type": "TEXT", + "notnull": false, + "pk": false + }, + { + "name": "char_freq_%24", + "type": "TEXT", + "notnull": false, + "pk": false + }, + { + "name": "char_freq_%23", + "type": "TEXT", + "notnull": false, + "pk": false + }, + { + "name": "capital_run_length_average", + "type": "TEXT", + "notnull": false, + "pk": false + }, + { + "name": "capital_run_length_longest", + "type": "TEXT", + "notnull": false, + "pk": false + }, + { + "name": "capital_run_length_total", + "type": "TEXT", + "notnull": false, + "pk": false + }, + { + "name": "class", + "type": "TEXT", + "notnull": false, + "pk": false + } + ], + "sample_rows": [ + { + "word_freq_make": "0", + "word_freq_address": "0.64", + "word_freq_all": "0.64", + "word_freq_3d": "0", + "word_freq_our": "0.32", + "word_freq_over": "0", + "word_freq_remove": "0", + "word_freq_internet": "0", + "word_freq_order": "0", + "word_freq_mail": "0", + "word_freq_receive": "0", + "word_freq_will": "0.64", + "word_freq_people": "0", + "word_freq_report": "0", + "word_freq_addresses": "0", + "word_freq_free": "0.32", + "word_freq_business": "0", + "word_freq_email": "1.29", + "word_freq_you": "1.93", + "word_freq_credit": "0", + "word_freq_your": "0.96", + "word_freq_font": "0", + "word_freq_000": "0", + "word_freq_money": "0", + "word_freq_hp": "0", + "word_freq_hpl": "0", + "word_freq_george": "0", + "word_freq_650": "0", + "word_freq_lab": "0", + "word_freq_labs": "0", + "word_freq_telnet": "0", + "word_freq_857": "0", + "word_freq_data": "0", + "word_freq_415": "0", + "word_freq_85": "0", + "word_freq_technology": "0", + "word_freq_1999": "0", + "word_freq_parts": "0", + "word_freq_pm": "0", + "word_freq_direct": "0", + "word_freq_cs": "0", + "word_freq_meeting": "0", + "word_freq_original": "0", + "word_freq_project": "0", + "word_freq_re": "0", + "word_freq_edu": "0", + "word_freq_table": "0", + "word_freq_conference": "0", + "char_freq_%3B": "0", + "char_freq_%28": "0", + "char_freq_%5B": "0", + "char_freq_%21": "0.778", + "char_freq_%24": "0", + "char_freq_%23": "0", + "capital_run_length_average": "3.756", + "capital_run_length_longest": "61", + "capital_run_length_total": "278", + "class": "1" + }, + { + "word_freq_make": "0.21", + "word_freq_address": "0.28", + "word_freq_all": "0.5", + "word_freq_3d": "0", + "word_freq_our": "0.14", + "word_freq_over": "0.28", + "word_freq_remove": "0.21", + "word_freq_internet": "0.07", + "word_freq_order": "0", + "word_freq_mail": "0.94", + "word_freq_receive": "0.21", + "word_freq_will": "0.79", + "word_freq_people": "0.65", + "word_freq_report": "0.21", + "word_freq_addresses": "0.14", + "word_freq_free": "0.14", + "word_freq_business": "0.07", + "word_freq_email": "0.28", + "word_freq_you": "3.47", + "word_freq_credit": "0", + "word_freq_your": "1.59", + "word_freq_font": "0", + "word_freq_000": "0.43", + "word_freq_money": "0.43", + "word_freq_hp": "0", + "word_freq_hpl": "0", + "word_freq_george": "0", + "word_freq_650": "0", + "word_freq_lab": "0", + "word_freq_labs": "0", + "word_freq_telnet": "0", + "word_freq_857": "0", + "word_freq_data": "0", + "word_freq_415": "0", + "word_freq_85": "0", + "word_freq_technology": "0", + "word_freq_1999": "0.07", + "word_freq_parts": "0", + "word_freq_pm": "0", + "word_freq_direct": "0", + "word_freq_cs": "0", + "word_freq_meeting": "0", + "word_freq_original": "0", + "word_freq_project": "0", + "word_freq_re": "0", + "word_freq_edu": "0", + "word_freq_table": "0", + "word_freq_conference": "0", + "char_freq_%3B": "0", + "char_freq_%28": "0.132", + "char_freq_%5B": "0", + "char_freq_%21": "0.372", + "char_freq_%24": "0.18", + "char_freq_%23": "0.048", + "capital_run_length_average": "5.114", + "capital_run_length_longest": "101", + "capital_run_length_total": "1028", + "class": "1" + }, + { + "word_freq_make": "0.06", + "word_freq_address": "0", + "word_freq_all": "0.71", + "word_freq_3d": "0", + "word_freq_our": "1.23", + "word_freq_over": "0.19", + "word_freq_remove": "0.19", + "word_freq_internet": "0.12", + "word_freq_order": "0.64", + "word_freq_mail": "0.25", + "word_freq_receive": "0.38", + "word_freq_will": "0.45", + "word_freq_people": "0.12", + "word_freq_report": "0", + "word_freq_addresses": "1.75", + "word_freq_free": "0.06", + "word_freq_business": "0.06", + "word_freq_email": "1.03", + "word_freq_you": "1.36", + "word_freq_credit": "0.32", + "word_freq_your": "0.51", + "word_freq_font": "0", + "word_freq_000": "1.16", + "word_freq_money": "0.06", + "word_freq_hp": "0", + "word_freq_hpl": "0", + "word_freq_george": "0", + "word_freq_650": "0", + "word_freq_lab": "0", + "word_freq_labs": "0", + "word_freq_telnet": "0", + "word_freq_857": "0", + "word_freq_data": "0", + "word_freq_415": "0", + "word_freq_85": "0", + "word_freq_technology": "0", + "word_freq_1999": "0", + "word_freq_parts": "0", + "word_freq_pm": "0", + "word_freq_direct": "0.06", + "word_freq_cs": "0", + "word_freq_meeting": "0", + "word_freq_original": "0.12", + "word_freq_project": "0", + "word_freq_re": "0.06", + "word_freq_edu": "0.06", + "word_freq_table": "0", + "word_freq_conference": "0", + "char_freq_%3B": "0.01", + "char_freq_%28": "0.143", + "char_freq_%5B": "0", + "char_freq_%21": "0.276", + "char_freq_%24": "0.184", + "char_freq_%23": "0.01", + "capital_run_length_average": "9.821", + "capital_run_length_longest": "485", + "capital_run_length_total": "2259", + "class": "1" + }, + { + "word_freq_make": "0", + "word_freq_address": "0", + "word_freq_all": "0", + "word_freq_3d": "0", + "word_freq_our": "0.63", + "word_freq_over": "0", + "word_freq_remove": "0.31", + "word_freq_internet": "0.63", + "word_freq_order": "0.31", + "word_freq_mail": "0.63", + "word_freq_receive": "0.31", + "word_freq_will": "0.31", + "word_freq_people": "0.31", + "word_freq_report": "0", + "word_freq_addresses": "0", + "word_freq_free": "0.31", + "word_freq_business": "0", + "word_freq_email": "0", + "word_freq_you": "3.18", + "word_freq_credit": "0", + "word_freq_your": "0.31", + "word_freq_font": "0", + "word_freq_000": "0", + "word_freq_money": "0", + "word_freq_hp": "0", + "word_freq_hpl": "0", + "word_freq_george": "0", + "word_freq_650": "0", + "word_freq_lab": "0", + "word_freq_labs": "0", + "word_freq_telnet": "0", + "word_freq_857": "0", + "word_freq_data": "0", + "word_freq_415": "0", + "word_freq_85": "0", + "word_freq_technology": "0", + "word_freq_1999": "0", + "word_freq_parts": "0", + "word_freq_pm": "0", + "word_freq_direct": "0", + "word_freq_cs": "0", + "word_freq_meeting": "0", + "word_freq_original": "0", + "word_freq_project": "0", + "word_freq_re": "0", + "word_freq_edu": "0", + "word_freq_table": "0", + "word_freq_conference": "0", + "char_freq_%3B": "0", + "char_freq_%28": "0.137", + "char_freq_%5B": "0", + "char_freq_%21": "0.137", + "char_freq_%24": "0", + "char_freq_%23": "0", + "capital_run_length_average": "3.537", + "capital_run_length_longest": "40", + "capital_run_length_total": "191", + "class": "1" + }, + { + "word_freq_make": "0", + "word_freq_address": "0", + "word_freq_all": "0", + "word_freq_3d": "0", + "word_freq_our": "0.63", + "word_freq_over": "0", + "word_freq_remove": "0.31", + "word_freq_internet": "0.63", + "word_freq_order": "0.31", + "word_freq_mail": "0.63", + "word_freq_receive": "0.31", + "word_freq_will": "0.31", + "word_freq_people": "0.31", + "word_freq_report": "0", + "word_freq_addresses": "0", + "word_freq_free": "0.31", + "word_freq_business": "0", + "word_freq_email": "0", + "word_freq_you": "3.18", + "word_freq_credit": "0", + "word_freq_your": "0.31", + "word_freq_font": "0", + "word_freq_000": "0", + "word_freq_money": "0", + "word_freq_hp": "0", + "word_freq_hpl": "0", + "word_freq_george": "0", + "word_freq_650": "0", + "word_freq_lab": "0", + "word_freq_labs": "0", + "word_freq_telnet": "0", + "word_freq_857": "0", + "word_freq_data": "0", + "word_freq_415": "0", + "word_freq_85": "0", + "word_freq_technology": "0", + "word_freq_1999": "0", + "word_freq_parts": "0", + "word_freq_pm": "0", + "word_freq_direct": "0", + "word_freq_cs": "0", + "word_freq_meeting": "0", + "word_freq_original": "0", + "word_freq_project": "0", + "word_freq_re": "0", + "word_freq_edu": "0", + "word_freq_table": "0", + "word_freq_conference": "0", + "char_freq_%3B": "0", + "char_freq_%28": "0.135", + "char_freq_%5B": "0", + "char_freq_%21": "0.135", + "char_freq_%24": "0", + "char_freq_%23": "0", + "capital_run_length_average": "3.537", + "capital_run_length_longest": "40", + "capital_run_length_total": "191", + "class": "1" + } + ] +} + +Shortlisted templates: +[ + { + "template_id": "tpl_tpch_thresholded_group_ranking", + "template_name": "Thresholded Group Ranking", + "primary_family": "tail_rarity_structure", + "portability": "partial", + "sql_skeleton": "SELECT {group_col}, SUM({measure_col}) AS total_measure\nFROM {table}\nGROUP BY {group_col}\nHAVING SUM({measure_col}) > {measure_threshold}\nORDER BY total_measure DESC\nLIMIT {top_k};", + "required_roles": [ + "group_col", + "measure_col" + ] + } +] + +Problem instance: +{ + "dataset_id": "n1", + "question": "Use template Thresholded Group Ranking to probe tail_mass_similarity with semantic role filtered_stable_view. Focus on group_col=class, measure_col=word_freq_money.", + "planned_template_id": "tpl_tpch_thresholded_group_ranking", + "bindings": { + "group_col": "class", + "measure_col": "word_freq_money", + "top_k": 12, + "top_n": 4, + "num_tiles": 10, + "percentile_value": 0.9, + "z_threshold": 2.0, + "fraction_threshold": 0.1, + "baseline_multiplier": 1.5, + "baseline_fraction": 0.1, + "min_group_size": 5, + "min_support": 5, + "measure_threshold": 0.0, + "time_grain": "month", + "lookback_rows": 3, + "current_period_start": "'2024-01-01'", + "current_period_end": "'2024-04-01'", + "previous_period_start": "'2023-10-01'", + "previous_period_end": "'2024-01-01'", + "drift_ratio_threshold": 0.8 + }, + "can_vary": [], + "must_fix": [], + "runtime_sql_skeleton": "SELECT {group_col}, SUM({measure_col}) AS total_measure\nFROM {table}\nGROUP BY {group_col}\nHAVING SUM({measure_col}) > {measure_threshold}\nORDER BY total_measure DESC\nLIMIT {top_k};" +} + +Repair context: +{} diff --git a/Query/sql/v2/runs/v2_cli_20260502_081223_d/n1/artifacts/v2q_n1_babf667d12d619da/cli/sql_response_attempt_1.raw.txt b/Query/sql/v2/runs/v2_cli_20260502_081223_d/n1/artifacts/v2q_n1_babf667d12d619da/cli/sql_response_attempt_1.raw.txt new file mode 100644 index 0000000000000000000000000000000000000000..6287ba4d5f3e06b97142989b749db7751f0c68d1 --- /dev/null +++ b/Query/sql/v2/runs/v2_cli_20260502_081223_d/n1/artifacts/v2q_n1_babf667d12d619da/cli/sql_response_attempt_1.raw.txt @@ -0,0 +1,4 @@ +{"type":"thread.started","thread_id":"019e40fc-323b-7520-985d-9aa540f92ea7"} +{"type":"turn.started"} +{"type":"error","message":"Quota exceeded. Check your plan and billing details."} +{"type":"turn.failed","error":{"message":"Quota exceeded. Check your plan and billing details."}} diff --git a/Query/sql/v2/runs/v2_cli_20260502_081223_d/n1/artifacts/v2q_n1_babf667d12d619da/cli/sql_response_attempt_1.txt b/Query/sql/v2/runs/v2_cli_20260502_081223_d/n1/artifacts/v2q_n1_babf667d12d619da/cli/sql_response_attempt_1.txt new file mode 100644 index 0000000000000000000000000000000000000000..307e01484b79851af30ab0a4f52d5c0bc4901869 --- /dev/null +++ b/Query/sql/v2/runs/v2_cli_20260502_081223_d/n1/artifacts/v2q_n1_babf667d12d619da/cli/sql_response_attempt_1.txt @@ -0,0 +1,4 @@ +{"type":"thread.started","thread_id":"019e40fc-323b-7520-985d-9aa540f92ea7"} +{"type":"turn.started"} +{"type":"error","message":"Quota exceeded. Check your plan and billing details."} +{"type":"turn.failed","error":{"message":"Quota exceeded. Check your plan and billing details."}} \ No newline at end of file diff --git a/Query/sql/v2/runs/v2_cli_20260502_081223_d/n1/artifacts/v2q_n1_babf667d12d619da/cli/sql_response_attempt_2.raw.txt b/Query/sql/v2/runs/v2_cli_20260502_081223_d/n1/artifacts/v2q_n1_babf667d12d619da/cli/sql_response_attempt_2.raw.txt new file mode 100644 index 0000000000000000000000000000000000000000..9e5a17f74c7b6f327093dd140e40df47b24192f4 --- /dev/null +++ b/Query/sql/v2/runs/v2_cli_20260502_081223_d/n1/artifacts/v2q_n1_babf667d12d619da/cli/sql_response_attempt_2.raw.txt @@ -0,0 +1,4 @@ +{"type":"thread.started","thread_id":"019e40fc-4646-7690-98ea-490b77eb216b"} +{"type":"turn.started"} +{"type":"item.completed","item":{"id":"item_0","type":"agent_message","text":"{\"sql\":\"-- template_id: tpl_tpch_thresholded_group_ranking\\nSELECT \\\"class\\\", SUM(CAST(\\\"word_freq_money\\\" AS REAL)) AS \\\"total_measure\\\"\\nFROM \\\"n1\\\"\\nGROUP BY \\\"class\\\"\\nHAVING SUM(CAST(\\\"word_freq_money\\\" AS REAL)) > 0.0\\nORDER BY \\\"total_measure\\\" DESC\\nLIMIT 12;\",\"notes\":\"Used the planned Thresholded Group Ranking template with group_col=\\\"class\\\" and measure_col=\\\"word_freq_money\\\". Casted the measure to REAL because the schema stores numeric-looking values as TEXT.\"}"}} +{"type":"turn.completed","usage":{"input_tokens":20359,"cached_input_tokens":19840,"output_tokens":435,"reasoning_output_tokens":306}} diff --git a/Query/sql/v2/runs/v2_cli_20260502_081223_d/n1/artifacts/v2q_n1_babf667d12d619da/cli/sql_response_attempt_2.txt b/Query/sql/v2/runs/v2_cli_20260502_081223_d/n1/artifacts/v2q_n1_babf667d12d619da/cli/sql_response_attempt_2.txt new file mode 100644 index 0000000000000000000000000000000000000000..037ad74630a3d3072c3fbf4c18d66dd0b238e5c4 --- /dev/null +++ b/Query/sql/v2/runs/v2_cli_20260502_081223_d/n1/artifacts/v2q_n1_babf667d12d619da/cli/sql_response_attempt_2.txt @@ -0,0 +1 @@ +{"sql":"-- template_id: tpl_tpch_thresholded_group_ranking\nSELECT \"class\", SUM(CAST(\"word_freq_money\" AS REAL)) AS \"total_measure\"\nFROM \"n1\"\nGROUP BY \"class\"\nHAVING SUM(CAST(\"word_freq_money\" AS REAL)) > 0.0\nORDER BY \"total_measure\" DESC\nLIMIT 12;","notes":"Used the planned Thresholded Group Ranking template with group_col=\"class\" and measure_col=\"word_freq_money\". Casted the measure to REAL because the schema stores numeric-looking values as TEXT."} \ No newline at end of file diff --git a/Query/sql/v2/runs/v2_cli_20260502_081223_d/n1/artifacts/v2q_n1_babf667d12d619da/cli/sql_stderr_attempt_1.txt b/Query/sql/v2/runs/v2_cli_20260502_081223_d/n1/artifacts/v2q_n1_babf667d12d619da/cli/sql_stderr_attempt_1.txt new file mode 100644 index 0000000000000000000000000000000000000000..e69de29bb2d1d6434b8b29ae775ad8c2e48c5391 diff --git a/Query/sql/v2/runs/v2_cli_20260502_081223_d/n1/artifacts/v2q_n1_babf667d12d619da/cli/sql_stderr_attempt_2.txt b/Query/sql/v2/runs/v2_cli_20260502_081223_d/n1/artifacts/v2q_n1_babf667d12d619da/cli/sql_stderr_attempt_2.txt new file mode 100644 index 0000000000000000000000000000000000000000..e69de29bb2d1d6434b8b29ae775ad8c2e48c5391 diff --git a/Query/sql/v2/runs/v2_cli_20260502_081223_d/n1/artifacts/v2q_n1_bf0b21a004175eea/cli/conversation.jsonl b/Query/sql/v2/runs/v2_cli_20260502_081223_d/n1/artifacts/v2q_n1_bf0b21a004175eea/cli/conversation.jsonl new file mode 100644 index 0000000000000000000000000000000000000000..7afca773fd56c66ca21e39718e3fd3d6bc46ccc9 --- /dev/null +++ b/Query/sql/v2/runs/v2_cli_20260502_081223_d/n1/artifacts/v2q_n1_bf0b21a004175eea/cli/conversation.jsonl @@ -0,0 +1,2 @@ +{"attempt": 1, "phase": "sql_generation", "role": "user", "content_path": "cli/sql_prompt_attempt_1.txt", "metrics": {"chars": 29254, "bytes_utf8": 29254, "lines": 790, "estimated_tokens": null}} +{"attempt": 1, "phase": "sql_generation", "role": "assistant", "content_path": "cli/sql_response_attempt_1.txt", "raw_content_path": "cli/sql_response_attempt_1.raw.txt", "stderr_path": "cli/sql_stderr_attempt_1.txt", "metrics": {"chars": 295, "bytes_utf8": 295, "lines": 1, "estimated_tokens": null}, "usage": {"input_tokens": 20286, "cached_input_tokens": 12032, "output_tokens": 300, "reasoning_output_tokens": 221}} diff --git a/Query/sql/v2/runs/v2_cli_20260502_081223_d/n1/artifacts/v2q_n1_bf0b21a004175eea/cli/session_summary.json b/Query/sql/v2/runs/v2_cli_20260502_081223_d/n1/artifacts/v2q_n1_bf0b21a004175eea/cli/session_summary.json new file mode 100644 index 0000000000000000000000000000000000000000..ecf2b4bbab7a2dc22e87c0cbf952278f62d789cc --- /dev/null +++ b/Query/sql/v2/runs/v2_cli_20260502_081223_d/n1/artifacts/v2q_n1_bf0b21a004175eea/cli/session_summary.json @@ -0,0 +1,25 @@ +{ + "engine": "v2-cli:codex", + "command": "codex exec --skip-git-repo-check --disable plugins --sandbox read-only --cd \"/data/jialinzhang/SQLagent\" -m gpt-5.4 --json -", + "ai_cli_calls": 1, + "usage_summary": { + "dataset_id": "n1", + "model": "v2-cli:codex", + "run_id": "v2q_n1_bf0b21a004175eea", + "api_calls": 0, + "input_tokens": 20286, + "cached_input_tokens": 12032, + "output_tokens": 300, + "total_tokens": 20586, + "cost_usd": 0.0, + "ai_cli_calls": 1, + "estimated_input_tokens": 0, + "estimated_output_tokens": 0, + "estimated_total_tokens": 0, + "usage_source": "ai_cli_json_usage", + "cli_elapsed_ms_total": 8892.79, + "sql_execution_elapsed_ms_total": 3.51, + "conversation_log_path": "/data/jialinzhang/TabQueryBench/sql_workloads/v2_current/runs_and_launches/runs/v2_cli_20260502_081223_d/n1/artifacts/v2q_n1_bf0b21a004175eea/cli/conversation.jsonl", + "note": "Executed through a local AI CLI with structured usage metadata." + } +} \ No newline at end of file diff --git a/Query/sql/v2/runs/v2_cli_20260502_081223_d/n1/artifacts/v2q_n1_bf0b21a004175eea/cli/sql_attempt_1.metadata.json b/Query/sql/v2/runs/v2_cli_20260502_081223_d/n1/artifacts/v2q_n1_bf0b21a004175eea/cli/sql_attempt_1.metadata.json new file mode 100644 index 0000000000000000000000000000000000000000..56edfdcc484a4c74e6857fc84295b15b46d9a195 --- /dev/null +++ b/Query/sql/v2/runs/v2_cli_20260502_081223_d/n1/artifacts/v2q_n1_bf0b21a004175eea/cli/sql_attempt_1.metadata.json @@ -0,0 +1,45 @@ +{ + "attempt": 1, + "phase": "sql_generation", + "command": "codex exec --skip-git-repo-check --disable plugins --sandbox read-only --cd \"/data/jialinzhang/SQLagent\" -m gpt-5.4 --json -", + "started_at": "2026-05-19T15:33:02.516303+00:00", + "ended_at": "2026-05-19T15:33:11.409129+00:00", + "elapsed_ms": 8892.79, + "prompt_metrics": { + "chars": 29254, + "bytes_utf8": 29254, + "lines": 790, + "estimated_tokens": null + }, + "stdout_metrics": { + "chars": 649, + "bytes_utf8": 649, + "lines": 4, + "estimated_tokens": null + }, + "stderr_metrics": { + "chars": 0, + "bytes_utf8": 0, + "lines": 0, + "estimated_tokens": null + }, + "parsed_output": { + "format": "jsonl_events", + "text_metrics": { + "chars": 295, + "bytes_utf8": 295, + "lines": 1, + "estimated_tokens": null + }, + "usage": { + "input_tokens": 20286, + "cached_input_tokens": 12032, + "output_tokens": 300, + "reasoning_output_tokens": 221 + } + }, + "prompt_path": "cli/sql_prompt_attempt_1.txt", + "response_path": "cli/sql_response_attempt_1.txt", + "raw_response_path": "cli/sql_response_attempt_1.raw.txt", + "stderr_path": "cli/sql_stderr_attempt_1.txt" +} \ No newline at end of file diff --git a/Query/sql/v2/runs/v2_cli_20260502_081223_d/n1/artifacts/v2q_n1_bf0b21a004175eea/cli/sql_prompt_attempt_1.txt b/Query/sql/v2/runs/v2_cli_20260502_081223_d/n1/artifacts/v2q_n1_bf0b21a004175eea/cli/sql_prompt_attempt_1.txt new file mode 100644 index 0000000000000000000000000000000000000000..7b660cf515e7e32e6498d7bf286e8b57b656bc03 --- /dev/null +++ b/Query/sql/v2/runs/v2_cli_20260502_081223_d/n1/artifacts/v2q_n1_bf0b21a004175eea/cli/sql_prompt_attempt_1.txt @@ -0,0 +1,790 @@ +You are generating one SQLite SELECT query for a single-table SQL QA task. +Return strict JSON only, with this schema: {"sql": "...", "notes": "..."}. +Rules: +- Use only the provided table and columns. +- Do not write INSERT, UPDATE, DELETE, DROP, ALTER, CREATE, PRAGMA, ATTACH, DETACH, or VACUUM. +- Prefer the planned template and bound roles when provided. +- Add a leading SQL comment exactly like: -- template_id: . +- Generate SQLite-compatible SQL. SQLite does not support PERCENTILE_CONT or STDDEV. +- Quote identifiers with double quotes. +- Return no markdown and no extra prose. + +Dataset context: +Dataset context for SQL QA: +- dataset_id: n1 +- dataset_name: Spambase +- table_name: n1 +- table_layout: single-table dataset (do not assume joins). +- row_semantics: One row is one email represented by engineered token/character frequency and capitalization-run features. +- task_type: classification +- target_column: class +- main_row_count: 4601 +- important_fields: +- word_freq_make: role=feature, type=numeric_sparse_frequency. tags=['condition_candidate', 'measure', 'rare_pattern_candidate'] desc=Frequency feature for token 'make' in an email message. +- word_freq_address: role=feature, type=numeric_sparse_frequency. tags=['condition_candidate', 'measure', 'rare_pattern_candidate'] desc=Frequency feature for token 'address' in an email message. +- word_freq_all: role=feature, type=numeric_sparse_frequency. tags=['condition_candidate', 'measure', 'rare_pattern_candidate'] desc=Frequency feature for token 'all' in an email message. +- word_freq_3d: role=feature, type=numeric_sparse_frequency. tags=['condition_candidate', 'measure', 'rare_pattern_candidate'] desc=Frequency feature for token '3d' in an email message. +- word_freq_our: role=feature, type=numeric_sparse_frequency. tags=['condition_candidate', 'measure', 'rare_pattern_candidate'] desc=Frequency feature for token 'our' in an email message. +- word_freq_over: role=feature, type=numeric_sparse_frequency. tags=['condition_candidate', 'measure', 'rare_pattern_candidate'] desc=Frequency feature for token 'over' in an email message. +- word_freq_remove: role=feature, type=numeric_sparse_frequency. tags=['condition_candidate', 'measure', 'rare_pattern_candidate'] desc=Frequency feature for token 'remove' in an email message. +- word_freq_internet: role=feature, type=numeric_sparse_frequency. tags=['condition_candidate', 'measure', 'rare_pattern_candidate'] desc=Frequency feature for token 'internet' in an email message. +- word_freq_order: role=feature, type=numeric_sparse_frequency. tags=['condition_candidate', 'measure', 'rare_pattern_candidate'] desc=Frequency feature for token 'order' in an email message. +- word_freq_mail: role=feature, type=numeric_sparse_frequency. tags=['condition_candidate', 'measure', 'rare_pattern_candidate'] desc=Frequency feature for token 'mail' in an email message. +- word_freq_receive: role=feature, type=numeric_sparse_frequency. tags=['condition_candidate', 'measure', 'rare_pattern_candidate'] desc=Frequency feature for token 'receive' in an email message. +- word_freq_will: role=feature, type=numeric_sparse_frequency. tags=['condition_candidate', 'measure', 'rare_pattern_candidate'] desc=Frequency feature for token 'will' in an email message. +- word_freq_people: role=feature, type=numeric_sparse_frequency. tags=['condition_candidate', 'measure', 'rare_pattern_candidate'] desc=Frequency feature for token 'people' in an email message. +- word_freq_report: role=feature, type=numeric_sparse_frequency. tags=['condition_candidate', 'measure', 'rare_pattern_candidate'] desc=Frequency feature for token 'report' in an email message. +- word_freq_addresses: role=feature, type=numeric_sparse_frequency. tags=['condition_candidate', 'measure', 'rare_pattern_candidate'] desc=Frequency feature for token 'addresses' in an email message. +- word_freq_free: role=feature, type=numeric_sparse_frequency. tags=['condition_candidate', 'measure', 'rare_pattern_candidate'] desc=Frequency feature for token 'free' in an email message. +- word_freq_business: role=feature, type=numeric_sparse_frequency. tags=['condition_candidate', 'measure', 'rare_pattern_candidate'] desc=Frequency feature for token 'business' in an email message. +- word_freq_email: role=feature, type=numeric_sparse_frequency. tags=['condition_candidate', 'measure', 'rare_pattern_candidate'] desc=Frequency feature for token 'email' in an email message. +- word_freq_you: role=feature, type=numeric_sparse_frequency. tags=['condition_candidate', 'measure', 'rare_pattern_candidate'] desc=Frequency feature for token 'you' in an email message. +- word_freq_credit: role=feature, type=numeric_sparse_frequency. tags=['condition_candidate', 'measure', 'rare_pattern_candidate'] desc=Frequency feature for token 'credit' in an email message. +- word_freq_your: role=feature, type=numeric_sparse_frequency. tags=['condition_candidate', 'measure', 'rare_pattern_candidate'] desc=Frequency feature for token 'your' in an email message. +- word_freq_font: role=feature, type=numeric_sparse_frequency. tags=['condition_candidate', 'measure', 'rare_pattern_candidate'] desc=Frequency feature for token 'font' in an email message. +- word_freq_000: role=feature, type=numeric_sparse_frequency. tags=['condition_candidate', 'measure', 'rare_pattern_candidate'] desc=Frequency feature for token '000' in an email message. +- word_freq_money: role=feature, type=numeric_sparse_frequency. tags=['condition_candidate', 'measure', 'rare_pattern_candidate'] desc=Frequency feature for token 'money' in an email message. +- word_freq_hp: role=feature, type=numeric_sparse_frequency. tags=['condition_candidate', 'measure', 'rare_pattern_candidate'] desc=Frequency feature for token 'hp' in an email message. +- word_freq_hpl: role=feature, type=numeric_sparse_frequency. tags=['condition_candidate', 'measure', 'rare_pattern_candidate'] desc=Frequency feature for token 'hpl' in an email message. +- word_freq_george: role=feature, type=numeric_sparse_frequency. tags=['condition_candidate', 'measure', 'rare_pattern_candidate'] desc=Frequency feature for token 'george' in an email message. +- word_freq_650: role=feature, type=numeric_sparse_frequency. tags=['condition_candidate', 'measure', 'rare_pattern_candidate'] desc=Frequency feature for token '650' in an email message. +- word_freq_lab: role=feature, type=numeric_sparse_frequency. tags=['condition_candidate', 'measure', 'rare_pattern_candidate'] desc=Frequency feature for token 'lab' in an email message. +- word_freq_labs: role=feature, type=numeric_sparse_frequency. tags=['condition_candidate', 'measure', 'rare_pattern_candidate'] desc=Frequency feature for token 'labs' in an email message. +- word_freq_telnet: role=feature, type=numeric_sparse_frequency. tags=['condition_candidate', 'measure', 'rare_pattern_candidate'] desc=Frequency feature for token 'telnet' in an email message. +- word_freq_857: role=feature, type=numeric_sparse_frequency. tags=['condition_candidate', 'measure', 'rare_pattern_candidate'] desc=Frequency feature for token '857' in an email message. +- word_freq_data: role=feature, type=numeric_sparse_frequency. tags=['condition_candidate', 'measure', 'rare_pattern_candidate'] desc=Frequency feature for token 'data' in an email message. +- word_freq_415: role=feature, type=numeric_sparse_frequency. tags=['condition_candidate', 'measure', 'rare_pattern_candidate'] desc=Frequency feature for token '415' in an email message. +- word_freq_85: role=feature, type=numeric_sparse_frequency. tags=['condition_candidate', 'measure', 'rare_pattern_candidate'] desc=Frequency feature for token '85' in an email message. +- word_freq_technology: role=feature, type=numeric_sparse_frequency. tags=['condition_candidate', 'measure', 'rare_pattern_candidate'] desc=Frequency feature for token 'technology' in an email message. +- word_freq_1999: role=feature, type=numeric_sparse_frequency. tags=['condition_candidate', 'measure', 'rare_pattern_candidate'] desc=Frequency feature for token '1999' in an email message. +- word_freq_parts: role=feature, type=numeric_sparse_frequency. tags=['condition_candidate', 'measure', 'rare_pattern_candidate'] desc=Frequency feature for token 'parts' in an email message. +- word_freq_pm: role=feature, type=numeric_sparse_frequency. tags=['condition_candidate', 'measure', 'rare_pattern_candidate'] desc=Frequency feature for token 'pm' in an email message. +- word_freq_direct: role=feature, type=numeric_sparse_frequency. tags=['condition_candidate', 'measure', 'rare_pattern_candidate'] desc=Frequency feature for token 'direct' in an email message. +- word_freq_cs: role=feature, type=numeric_sparse_frequency. tags=['condition_candidate', 'measure', 'rare_pattern_candidate'] desc=Frequency feature for token 'cs' in an email message. +- word_freq_meeting: role=feature, type=numeric_sparse_frequency. tags=['condition_candidate', 'measure', 'rare_pattern_candidate'] desc=Frequency feature for token 'meeting' in an email message. +- word_freq_original: role=feature, type=numeric_sparse_frequency. tags=['condition_candidate', 'measure', 'rare_pattern_candidate'] desc=Frequency feature for token 'original' in an email message. +- word_freq_project: role=feature, type=numeric_sparse_frequency. tags=['condition_candidate', 'measure', 'rare_pattern_candidate'] desc=Frequency feature for token 'project' in an email message. +- word_freq_re: role=feature, type=numeric_sparse_frequency. tags=['condition_candidate', 'measure', 'rare_pattern_candidate'] desc=Frequency feature for token 're' in an email message. +- word_freq_edu: role=feature, type=numeric_sparse_frequency. tags=['condition_candidate', 'measure', 'rare_pattern_candidate'] desc=Frequency feature for token 'edu' in an email message. +- word_freq_table: role=feature, type=numeric_sparse_frequency. tags=['condition_candidate', 'measure', 'rare_pattern_candidate'] desc=Frequency feature for token 'table' in an email message. +- word_freq_conference: role=feature, type=numeric_sparse_frequency. tags=['condition_candidate', 'measure', 'rare_pattern_candidate'] desc=Frequency feature for token 'conference' in an email message. +- char_freq_%3B: role=feature, type=numeric_sparse_frequency. tags=['condition_candidate', 'measure', 'rare_pattern_candidate'] desc=Character frequency feature for symbol ';'. +- char_freq_%28: role=feature, type=numeric_sparse_frequency. tags=['condition_candidate', 'measure', 'rare_pattern_candidate'] desc=Character frequency feature for symbol '('. +- char_freq_%5B: role=feature, type=numeric_sparse_frequency. tags=['condition_candidate', 'measure', 'rare_pattern_candidate'] desc=Character frequency feature for symbol '['. +- char_freq_%21: role=feature, type=numeric_sparse_frequency. tags=['condition_candidate', 'measure', 'rare_pattern_candidate'] desc=Character frequency feature for symbol '!'. +- char_freq_%24: role=feature, type=numeric_sparse_frequency. tags=['condition_candidate', 'measure', 'rare_pattern_candidate'] desc=Character frequency feature for symbol '$'. +- char_freq_%23: role=feature, type=numeric_sparse_frequency. tags=['condition_candidate', 'measure', 'rare_pattern_candidate'] desc=Character frequency feature for symbol '#'. +- capital_run_length_average: role=feature, type=numeric_heavy_tail. tags=['condition_candidate', 'measure', 'rare_pattern_candidate'] desc=Average length of uninterrupted capital-letter runs. +- capital_run_length_longest: role=feature, type=numeric_heavy_tail. tags=['condition_candidate', 'measure', 'rare_pattern_candidate'] desc=Longest uninterrupted capital-letter run. +- capital_run_length_total: role=feature, type=numeric_heavy_tail. tags=['condition_candidate', 'measure', 'rare_pattern_candidate'] desc=Total number of capital letters in runs. +- class: role=target, type=binary_target. tags=['target_candidate'] desc=Binary spam label (1=spam, 0=non-spam). +- useful_field_combinations: [['word_freq_free', 'word_freq_you', 'class'], ['char_freq_%21', 'capital_run_length_longest', 'class'], ['word_freq_remove', 'word_freq_money', 'class']] +- fields_requiring_caution: ['capital_run_length_average', 'capital_run_length_longest', 'capital_run_length_total'] +- source_url: https://www.openml.org/d/44 + +SQLite schema snapshot: +{ + "table_name": "n1", + "quoted_table_name": "\"n1\"", + "row_count": 4601, + "columns": [ + { + "name": "word_freq_make", + "type": "TEXT", + "notnull": false, + "pk": false + }, + { + "name": "word_freq_address", + "type": "TEXT", + "notnull": false, + "pk": false + }, + { + "name": "word_freq_all", + "type": "TEXT", + "notnull": false, + "pk": false + }, + { + "name": "word_freq_3d", + "type": "TEXT", + "notnull": false, + "pk": false + }, + { + "name": "word_freq_our", + "type": "TEXT", + "notnull": false, + "pk": false + }, + { + "name": "word_freq_over", + "type": "TEXT", + "notnull": false, + "pk": false + }, + { + "name": "word_freq_remove", + "type": "TEXT", + "notnull": false, + "pk": false + }, + { + "name": "word_freq_internet", + "type": "TEXT", + "notnull": false, + "pk": false + }, + { + "name": "word_freq_order", + "type": "TEXT", + "notnull": false, + "pk": false + }, + { + "name": "word_freq_mail", + "type": "TEXT", + "notnull": false, + "pk": false + }, + { + "name": "word_freq_receive", + "type": "TEXT", + "notnull": false, + "pk": false + }, + { + "name": "word_freq_will", + "type": "TEXT", + "notnull": false, + "pk": false + }, + { + "name": "word_freq_people", + "type": "TEXT", + "notnull": false, + "pk": false + }, + { + "name": "word_freq_report", + "type": "TEXT", + "notnull": false, + "pk": false + }, + { + "name": "word_freq_addresses", + "type": "TEXT", + "notnull": false, + "pk": false + }, + { + "name": "word_freq_free", + "type": "TEXT", + "notnull": false, + "pk": false + }, + { + "name": "word_freq_business", + "type": "TEXT", + "notnull": false, + "pk": false + }, + { + "name": "word_freq_email", + "type": "TEXT", + "notnull": false, + "pk": false + }, + { + "name": "word_freq_you", + "type": "TEXT", + "notnull": false, + "pk": false + }, + { + "name": "word_freq_credit", + "type": "TEXT", + "notnull": false, + "pk": false + }, + { + "name": "word_freq_your", + "type": "TEXT", + "notnull": false, + "pk": false + }, + { + "name": "word_freq_font", + "type": "TEXT", + "notnull": false, + "pk": false + }, + { + "name": "word_freq_000", + "type": "TEXT", + "notnull": false, + "pk": false + }, + { + "name": "word_freq_money", + "type": "TEXT", + "notnull": false, + "pk": false + }, + { + "name": "word_freq_hp", + "type": "TEXT", + "notnull": false, + "pk": false + }, + { + "name": "word_freq_hpl", + "type": "TEXT", + "notnull": false, + "pk": false + }, + { + "name": "word_freq_george", + "type": "TEXT", + "notnull": false, + "pk": false + }, + { + "name": "word_freq_650", + "type": "TEXT", + "notnull": false, + "pk": false + }, + { + "name": "word_freq_lab", + "type": "TEXT", + "notnull": false, + "pk": false + }, + { + "name": "word_freq_labs", + "type": "TEXT", + "notnull": false, + "pk": false + }, + { + "name": "word_freq_telnet", + "type": "TEXT", + "notnull": false, + "pk": false + }, + { + "name": "word_freq_857", + "type": "TEXT", + "notnull": false, + "pk": false + }, + { + "name": "word_freq_data", + "type": "TEXT", + "notnull": false, + "pk": false + }, + { + "name": "word_freq_415", + "type": "TEXT", + "notnull": false, + "pk": false + }, + { + "name": "word_freq_85", + "type": "TEXT", + "notnull": false, + "pk": false + }, + { + "name": "word_freq_technology", + "type": "TEXT", + "notnull": false, + "pk": false + }, + { + "name": "word_freq_1999", + "type": "TEXT", + "notnull": false, + "pk": false + }, + { + "name": "word_freq_parts", + "type": "TEXT", + "notnull": false, + "pk": false + }, + { + "name": "word_freq_pm", + "type": "TEXT", + "notnull": false, + "pk": false + }, + { + "name": "word_freq_direct", + "type": "TEXT", + "notnull": false, + "pk": false + }, + { + "name": "word_freq_cs", + "type": "TEXT", + "notnull": false, + "pk": false + }, + { + "name": "word_freq_meeting", + "type": "TEXT", + "notnull": false, + "pk": false + }, + { + "name": "word_freq_original", + "type": "TEXT", + "notnull": false, + "pk": false + }, + { + "name": "word_freq_project", + "type": "TEXT", + "notnull": false, + "pk": false + }, + { + "name": "word_freq_re", + "type": "TEXT", + "notnull": false, + "pk": false + }, + { + "name": "word_freq_edu", + "type": "TEXT", + "notnull": false, + "pk": false + }, + { + "name": "word_freq_table", + "type": "TEXT", + "notnull": false, + "pk": false + }, + { + "name": "word_freq_conference", + "type": "TEXT", + "notnull": false, + "pk": false + }, + { + "name": "char_freq_%3B", + "type": "TEXT", + "notnull": false, + "pk": false + }, + { + "name": "char_freq_%28", + "type": "TEXT", + "notnull": false, + "pk": false + }, + { + "name": "char_freq_%5B", + "type": "TEXT", + "notnull": false, + "pk": false + }, + { + "name": "char_freq_%21", + "type": "TEXT", + "notnull": false, + "pk": false + }, + { + "name": "char_freq_%24", + "type": "TEXT", + "notnull": false, + "pk": false + }, + { + "name": "char_freq_%23", + "type": "TEXT", + "notnull": false, + "pk": false + }, + { + "name": "capital_run_length_average", + "type": "TEXT", + "notnull": false, + "pk": false + }, + { + "name": "capital_run_length_longest", + "type": "TEXT", + "notnull": false, + "pk": false + }, + { + "name": "capital_run_length_total", + "type": "TEXT", + "notnull": false, + "pk": false + }, + { + "name": "class", + "type": "TEXT", + "notnull": false, + "pk": false + } + ], + "sample_rows": [ + { + "word_freq_make": "0", + "word_freq_address": "0.64", + "word_freq_all": "0.64", + "word_freq_3d": "0", + "word_freq_our": "0.32", + "word_freq_over": "0", + "word_freq_remove": "0", + "word_freq_internet": "0", + "word_freq_order": "0", + "word_freq_mail": "0", + "word_freq_receive": "0", + "word_freq_will": "0.64", + "word_freq_people": "0", + "word_freq_report": "0", + "word_freq_addresses": "0", + "word_freq_free": "0.32", + "word_freq_business": "0", + "word_freq_email": "1.29", + "word_freq_you": "1.93", + "word_freq_credit": "0", + "word_freq_your": "0.96", + "word_freq_font": "0", + "word_freq_000": "0", + "word_freq_money": "0", + "word_freq_hp": "0", + "word_freq_hpl": "0", + "word_freq_george": "0", + "word_freq_650": "0", + "word_freq_lab": "0", + "word_freq_labs": "0", + "word_freq_telnet": "0", + "word_freq_857": "0", + "word_freq_data": "0", + "word_freq_415": "0", + "word_freq_85": "0", + "word_freq_technology": "0", + "word_freq_1999": "0", + "word_freq_parts": "0", + "word_freq_pm": "0", + "word_freq_direct": "0", + "word_freq_cs": "0", + "word_freq_meeting": "0", + "word_freq_original": "0", + "word_freq_project": "0", + "word_freq_re": "0", + "word_freq_edu": "0", + "word_freq_table": "0", + "word_freq_conference": "0", + "char_freq_%3B": "0", + "char_freq_%28": "0", + "char_freq_%5B": "0", + "char_freq_%21": "0.778", + "char_freq_%24": "0", + "char_freq_%23": "0", + "capital_run_length_average": "3.756", + "capital_run_length_longest": "61", + "capital_run_length_total": "278", + "class": "1" + }, + { + "word_freq_make": "0.21", + "word_freq_address": "0.28", + "word_freq_all": "0.5", + "word_freq_3d": "0", + "word_freq_our": "0.14", + "word_freq_over": "0.28", + "word_freq_remove": "0.21", + "word_freq_internet": "0.07", + "word_freq_order": "0", + "word_freq_mail": "0.94", + "word_freq_receive": "0.21", + "word_freq_will": "0.79", + "word_freq_people": "0.65", + "word_freq_report": "0.21", + "word_freq_addresses": "0.14", + "word_freq_free": "0.14", + "word_freq_business": "0.07", + "word_freq_email": "0.28", + "word_freq_you": "3.47", + "word_freq_credit": "0", + "word_freq_your": "1.59", + "word_freq_font": "0", + "word_freq_000": "0.43", + "word_freq_money": "0.43", + "word_freq_hp": "0", + "word_freq_hpl": "0", + "word_freq_george": "0", + "word_freq_650": "0", + "word_freq_lab": "0", + "word_freq_labs": "0", + "word_freq_telnet": "0", + "word_freq_857": "0", + "word_freq_data": "0", + "word_freq_415": "0", + "word_freq_85": "0", + "word_freq_technology": "0", + "word_freq_1999": "0.07", + "word_freq_parts": "0", + "word_freq_pm": "0", + "word_freq_direct": "0", + "word_freq_cs": "0", + "word_freq_meeting": "0", + "word_freq_original": "0", + "word_freq_project": "0", + "word_freq_re": "0", + "word_freq_edu": "0", + "word_freq_table": "0", + "word_freq_conference": "0", + "char_freq_%3B": "0", + "char_freq_%28": "0.132", + "char_freq_%5B": "0", + "char_freq_%21": "0.372", + "char_freq_%24": "0.18", + "char_freq_%23": "0.048", + "capital_run_length_average": "5.114", + "capital_run_length_longest": "101", + "capital_run_length_total": "1028", + "class": "1" + }, + { + "word_freq_make": "0.06", + "word_freq_address": "0", + "word_freq_all": "0.71", + "word_freq_3d": "0", + "word_freq_our": "1.23", + "word_freq_over": "0.19", + "word_freq_remove": "0.19", + "word_freq_internet": "0.12", + "word_freq_order": "0.64", + "word_freq_mail": "0.25", + "word_freq_receive": "0.38", + "word_freq_will": "0.45", + "word_freq_people": "0.12", + "word_freq_report": "0", + "word_freq_addresses": "1.75", + "word_freq_free": "0.06", + "word_freq_business": "0.06", + "word_freq_email": "1.03", + "word_freq_you": "1.36", + "word_freq_credit": "0.32", + "word_freq_your": "0.51", + "word_freq_font": "0", + "word_freq_000": "1.16", + "word_freq_money": "0.06", + "word_freq_hp": "0", + "word_freq_hpl": "0", + "word_freq_george": "0", + "word_freq_650": "0", + "word_freq_lab": "0", + "word_freq_labs": "0", + "word_freq_telnet": "0", + "word_freq_857": "0", + "word_freq_data": "0", + "word_freq_415": "0", + "word_freq_85": "0", + "word_freq_technology": "0", + "word_freq_1999": "0", + "word_freq_parts": "0", + "word_freq_pm": "0", + "word_freq_direct": "0.06", + "word_freq_cs": "0", + "word_freq_meeting": "0", + "word_freq_original": "0.12", + "word_freq_project": "0", + "word_freq_re": "0.06", + "word_freq_edu": "0.06", + "word_freq_table": "0", + "word_freq_conference": "0", + "char_freq_%3B": "0.01", + "char_freq_%28": "0.143", + "char_freq_%5B": "0", + "char_freq_%21": "0.276", + "char_freq_%24": "0.184", + "char_freq_%23": "0.01", + "capital_run_length_average": "9.821", + "capital_run_length_longest": "485", + "capital_run_length_total": "2259", + "class": "1" + }, + { + "word_freq_make": "0", + "word_freq_address": "0", + "word_freq_all": "0", + "word_freq_3d": "0", + "word_freq_our": "0.63", + "word_freq_over": "0", + "word_freq_remove": "0.31", + "word_freq_internet": "0.63", + "word_freq_order": "0.31", + "word_freq_mail": "0.63", + "word_freq_receive": "0.31", + "word_freq_will": "0.31", + "word_freq_people": "0.31", + "word_freq_report": "0", + "word_freq_addresses": "0", + "word_freq_free": "0.31", + "word_freq_business": "0", + "word_freq_email": "0", + "word_freq_you": "3.18", + "word_freq_credit": "0", + "word_freq_your": "0.31", + "word_freq_font": "0", + "word_freq_000": "0", + "word_freq_money": "0", + "word_freq_hp": "0", + "word_freq_hpl": "0", + "word_freq_george": "0", + "word_freq_650": "0", + "word_freq_lab": "0", + "word_freq_labs": "0", + "word_freq_telnet": "0", + "word_freq_857": "0", + "word_freq_data": "0", + "word_freq_415": "0", + "word_freq_85": "0", + "word_freq_technology": "0", + "word_freq_1999": "0", + "word_freq_parts": "0", + "word_freq_pm": "0", + "word_freq_direct": "0", + "word_freq_cs": "0", + "word_freq_meeting": "0", + "word_freq_original": "0", + "word_freq_project": "0", + "word_freq_re": "0", + "word_freq_edu": "0", + "word_freq_table": "0", + "word_freq_conference": "0", + "char_freq_%3B": "0", + "char_freq_%28": "0.137", + "char_freq_%5B": "0", + "char_freq_%21": "0.137", + "char_freq_%24": "0", + "char_freq_%23": "0", + "capital_run_length_average": "3.537", + "capital_run_length_longest": "40", + "capital_run_length_total": "191", + "class": "1" + }, + { + "word_freq_make": "0", + "word_freq_address": "0", + "word_freq_all": "0", + "word_freq_3d": "0", + "word_freq_our": "0.63", + "word_freq_over": "0", + "word_freq_remove": "0.31", + "word_freq_internet": "0.63", + "word_freq_order": "0.31", + "word_freq_mail": "0.63", + "word_freq_receive": "0.31", + "word_freq_will": "0.31", + "word_freq_people": "0.31", + "word_freq_report": "0", + "word_freq_addresses": "0", + "word_freq_free": "0.31", + "word_freq_business": "0", + "word_freq_email": "0", + "word_freq_you": "3.18", + "word_freq_credit": "0", + "word_freq_your": "0.31", + "word_freq_font": "0", + "word_freq_000": "0", + "word_freq_money": "0", + "word_freq_hp": "0", + "word_freq_hpl": "0", + "word_freq_george": "0", + "word_freq_650": "0", + "word_freq_lab": "0", + "word_freq_labs": "0", + "word_freq_telnet": "0", + "word_freq_857": "0", + "word_freq_data": "0", + "word_freq_415": "0", + "word_freq_85": "0", + "word_freq_technology": "0", + "word_freq_1999": "0", + "word_freq_parts": "0", + "word_freq_pm": "0", + "word_freq_direct": "0", + "word_freq_cs": "0", + "word_freq_meeting": "0", + "word_freq_original": "0", + "word_freq_project": "0", + "word_freq_re": "0", + "word_freq_edu": "0", + "word_freq_table": "0", + "word_freq_conference": "0", + "char_freq_%3B": "0", + "char_freq_%28": "0.135", + "char_freq_%5B": "0", + "char_freq_%21": "0.135", + "char_freq_%24": "0", + "char_freq_%23": "0", + "capital_run_length_average": "3.537", + "capital_run_length_longest": "40", + "capital_run_length_total": "191", + "class": "1" + } + ] +} + +Shortlisted templates: +[ + { + "template_id": "tpl_clickbench_group_count", + "template_name": "Grouped Count by Category", + "primary_family": "subgroup_structure", + "portability": "yes", + "sql_skeleton": "SELECT {group_col}, COUNT(*) AS row_count\nFROM {table}\nGROUP BY {group_col}\nORDER BY row_count DESC;", + "required_roles": [ + "group_col" + ] + } +] + +Problem instance: +{ + "dataset_id": "n1", + "question": "Use template Grouped Count by Category to probe subgroup_size_stability with semantic role count_distribution. Focus on group_col=class.", + "planned_template_id": "tpl_clickbench_group_count", + "bindings": { + "group_col": "class", + "top_k": 11, + "top_n": 3, + "num_tiles": 10, + "percentile_value": 0.95, + "z_threshold": 2.0, + "fraction_threshold": 0.1, + "baseline_multiplier": 1.5, + "baseline_fraction": 0.1, + "min_group_size": 5, + "min_support": 5, + "measure_threshold": 0.0, + "time_grain": "month", + "lookback_rows": 3, + "current_period_start": "'2024-01-01'", + "current_period_end": "'2024-04-01'", + "previous_period_start": "'2023-10-01'", + "previous_period_end": "'2024-01-01'", + "drift_ratio_threshold": 0.8 + }, + "can_vary": [], + "must_fix": [], + "runtime_sql_skeleton": "SELECT {group_col}, COUNT(*) AS row_count\nFROM {table}\nGROUP BY {group_col}\nORDER BY row_count DESC;" +} + +Repair context: +{} diff --git a/Query/sql/v2/runs/v2_cli_20260502_081223_d/n1/artifacts/v2q_n1_bf0b21a004175eea/cli/sql_response_attempt_1.raw.txt b/Query/sql/v2/runs/v2_cli_20260502_081223_d/n1/artifacts/v2q_n1_bf0b21a004175eea/cli/sql_response_attempt_1.raw.txt new file mode 100644 index 0000000000000000000000000000000000000000..6d5c3c01b6faa5bce999505ce4355fcd0ee16b68 --- /dev/null +++ b/Query/sql/v2/runs/v2_cli_20260502_081223_d/n1/artifacts/v2q_n1_bf0b21a004175eea/cli/sql_response_attempt_1.raw.txt @@ -0,0 +1,4 @@ +{"type":"thread.started","thread_id":"019e40de-7ac3-7260-9725-076a8b7dd7b3"} +{"type":"turn.started"} +{"type":"item.completed","item":{"id":"item_0","type":"agent_message","text":"{\"sql\":\"-- template_id: tpl_clickbench_group_count\\nSELECT \\\"class\\\", COUNT(*) AS \\\"row_count\\\"\\nFROM \\\"n1\\\"\\nGROUP BY \\\"class\\\"\\nORDER BY \\\"row_count\\\" DESC;\",\"notes\":\"Uses the Grouped Count by Category template with \\\"class\\\" as the grouping column to return the count distribution by class.\"}"}} +{"type":"turn.completed","usage":{"input_tokens":20286,"cached_input_tokens":12032,"output_tokens":300,"reasoning_output_tokens":221}} diff --git a/Query/sql/v2/runs/v2_cli_20260502_081223_d/n1/artifacts/v2q_n1_bf0b21a004175eea/cli/sql_response_attempt_1.txt b/Query/sql/v2/runs/v2_cli_20260502_081223_d/n1/artifacts/v2q_n1_bf0b21a004175eea/cli/sql_response_attempt_1.txt new file mode 100644 index 0000000000000000000000000000000000000000..94b18d6d78481375c841a9f9be2a2ca6a065a93e --- /dev/null +++ b/Query/sql/v2/runs/v2_cli_20260502_081223_d/n1/artifacts/v2q_n1_bf0b21a004175eea/cli/sql_response_attempt_1.txt @@ -0,0 +1 @@ +{"sql":"-- template_id: tpl_clickbench_group_count\nSELECT \"class\", COUNT(*) AS \"row_count\"\nFROM \"n1\"\nGROUP BY \"class\"\nORDER BY \"row_count\" DESC;","notes":"Uses the Grouped Count by Category template with \"class\" as the grouping column to return the count distribution by class."} \ No newline at end of file diff --git a/Query/sql/v2/runs/v2_cli_20260502_081223_d/n1/artifacts/v2q_n1_bf0b21a004175eea/cli/sql_stderr_attempt_1.txt b/Query/sql/v2/runs/v2_cli_20260502_081223_d/n1/artifacts/v2q_n1_bf0b21a004175eea/cli/sql_stderr_attempt_1.txt new file mode 100644 index 0000000000000000000000000000000000000000..e69de29bb2d1d6434b8b29ae775ad8c2e48c5391 diff --git a/Query/sql/v2/runs/v2_cli_20260502_081223_d/n1/artifacts/v2q_n1_ce2d6910ad4d519f/cli/conversation.jsonl b/Query/sql/v2/runs/v2_cli_20260502_081223_d/n1/artifacts/v2q_n1_ce2d6910ad4d519f/cli/conversation.jsonl new file mode 100644 index 0000000000000000000000000000000000000000..582fc5af15af503000043021b56544f4e0a87eca --- /dev/null +++ b/Query/sql/v2/runs/v2_cli_20260502_081223_d/n1/artifacts/v2q_n1_ce2d6910ad4d519f/cli/conversation.jsonl @@ -0,0 +1,2 @@ +{"attempt": 1, "phase": "sql_generation", "role": "user", "content_path": "cli/sql_prompt_attempt_1.txt", "metrics": {"chars": 29537, "bytes_utf8": 29537, "lines": 792, "estimated_tokens": null}} +{"attempt": 1, "phase": "sql_generation", "role": "assistant", "content_path": "cli/sql_response_attempt_1.txt", "raw_content_path": "cli/sql_response_attempt_1.raw.txt", "stderr_path": "cli/sql_stderr_attempt_1.txt", "metrics": {"chars": 1791, "bytes_utf8": 1791, "lines": 1, "estimated_tokens": null}, "usage": {"input_tokens": 20355, "cached_input_tokens": 19840, "output_tokens": 2620, "reasoning_output_tokens": 1976}} diff --git a/Query/sql/v2/runs/v2_cli_20260502_081223_d/n1/artifacts/v2q_n1_ce2d6910ad4d519f/cli/session_summary.json b/Query/sql/v2/runs/v2_cli_20260502_081223_d/n1/artifacts/v2q_n1_ce2d6910ad4d519f/cli/session_summary.json new file mode 100644 index 0000000000000000000000000000000000000000..7520de47180a96aeaa65da14e80e83f73d1137c5 --- /dev/null +++ b/Query/sql/v2/runs/v2_cli_20260502_081223_d/n1/artifacts/v2q_n1_ce2d6910ad4d519f/cli/session_summary.json @@ -0,0 +1,25 @@ +{ + "engine": "v2-cli:codex", + "command": "codex exec --skip-git-repo-check --disable plugins --sandbox read-only --cd \"/data/jialinzhang/SQLagent\" -m gpt-5.4 --json -", + "ai_cli_calls": 1, + "usage_summary": { + "dataset_id": "n1", + "model": "v2-cli:codex", + "run_id": "v2q_n1_ce2d6910ad4d519f", + "api_calls": 0, + "input_tokens": 20355, + "cached_input_tokens": 19840, + "output_tokens": 2620, + "total_tokens": 22975, + "cost_usd": 0.0, + "ai_cli_calls": 1, + "estimated_input_tokens": 0, + "estimated_output_tokens": 0, + "estimated_total_tokens": 0, + "usage_source": "ai_cli_json_usage", + "cli_elapsed_ms_total": 34658.36, + "sql_execution_elapsed_ms_total": 25.19, + "conversation_log_path": "/data/jialinzhang/TabQueryBench/sql_workloads/v2_current/runs_and_launches/runs/v2_cli_20260502_081223_d/n1/artifacts/v2q_n1_ce2d6910ad4d519f/cli/conversation.jsonl", + "note": "Executed through a local AI CLI with structured usage metadata." + } +} \ No newline at end of file diff --git a/Query/sql/v2/runs/v2_cli_20260502_081223_d/n1/artifacts/v2q_n1_ce2d6910ad4d519f/cli/sql_attempt_1.metadata.json b/Query/sql/v2/runs/v2_cli_20260502_081223_d/n1/artifacts/v2q_n1_ce2d6910ad4d519f/cli/sql_attempt_1.metadata.json new file mode 100644 index 0000000000000000000000000000000000000000..923468c947fc071ff5999ea9c8e0b77775d47cf0 --- /dev/null +++ b/Query/sql/v2/runs/v2_cli_20260502_081223_d/n1/artifacts/v2q_n1_ce2d6910ad4d519f/cli/sql_attempt_1.metadata.json @@ -0,0 +1,45 @@ +{ + "attempt": 1, + "phase": "sql_generation", + "command": "codex exec --skip-git-repo-check --disable plugins --sandbox read-only --cd \"/data/jialinzhang/SQLagent\" -m gpt-5.4 --json -", + "started_at": "2026-05-19T15:53:46.606527+00:00", + "ended_at": "2026-05-19T15:54:21.264914+00:00", + "elapsed_ms": 34658.36, + "prompt_metrics": { + "chars": 29537, + "bytes_utf8": 29537, + "lines": 792, + "estimated_tokens": null + }, + "stdout_metrics": { + "chars": 2491, + "bytes_utf8": 2491, + "lines": 4, + "estimated_tokens": null + }, + "stderr_metrics": { + "chars": 0, + "bytes_utf8": 0, + "lines": 0, + "estimated_tokens": null + }, + "parsed_output": { + "format": "jsonl_events", + "text_metrics": { + "chars": 1791, + "bytes_utf8": 1791, + "lines": 1, + "estimated_tokens": null + }, + "usage": { + "input_tokens": 20355, + "cached_input_tokens": 19840, + "output_tokens": 2620, + "reasoning_output_tokens": 1976 + } + }, + "prompt_path": "cli/sql_prompt_attempt_1.txt", + "response_path": "cli/sql_response_attempt_1.txt", + "raw_response_path": "cli/sql_response_attempt_1.raw.txt", + "stderr_path": "cli/sql_stderr_attempt_1.txt" +} \ No newline at end of file diff --git a/Query/sql/v2/runs/v2_cli_20260502_081223_d/n1/artifacts/v2q_n1_ce2d6910ad4d519f/cli/sql_prompt_attempt_1.txt b/Query/sql/v2/runs/v2_cli_20260502_081223_d/n1/artifacts/v2q_n1_ce2d6910ad4d519f/cli/sql_prompt_attempt_1.txt new file mode 100644 index 0000000000000000000000000000000000000000..c042df84c33e1282b891f03c15cbb04eaba022e1 --- /dev/null +++ b/Query/sql/v2/runs/v2_cli_20260502_081223_d/n1/artifacts/v2q_n1_ce2d6910ad4d519f/cli/sql_prompt_attempt_1.txt @@ -0,0 +1,792 @@ +You are generating one SQLite SELECT query for a single-table SQL QA task. +Return strict JSON only, with this schema: {"sql": "...", "notes": "..."}. +Rules: +- Use only the provided table and columns. +- Do not write INSERT, UPDATE, DELETE, DROP, ALTER, CREATE, PRAGMA, ATTACH, DETACH, or VACUUM. +- Prefer the planned template and bound roles when provided. +- Add a leading SQL comment exactly like: -- template_id: . +- Generate SQLite-compatible SQL. SQLite does not support PERCENTILE_CONT or STDDEV. +- Quote identifiers with double quotes. +- Return no markdown and no extra prose. + +Dataset context: +Dataset context for SQL QA: +- dataset_id: n1 +- dataset_name: Spambase +- table_name: n1 +- table_layout: single-table dataset (do not assume joins). +- row_semantics: One row is one email represented by engineered token/character frequency and capitalization-run features. +- task_type: classification +- target_column: class +- main_row_count: 4601 +- important_fields: +- word_freq_make: role=feature, type=numeric_sparse_frequency. tags=['condition_candidate', 'measure', 'rare_pattern_candidate'] desc=Frequency feature for token 'make' in an email message. +- word_freq_address: role=feature, type=numeric_sparse_frequency. tags=['condition_candidate', 'measure', 'rare_pattern_candidate'] desc=Frequency feature for token 'address' in an email message. +- word_freq_all: role=feature, type=numeric_sparse_frequency. tags=['condition_candidate', 'measure', 'rare_pattern_candidate'] desc=Frequency feature for token 'all' in an email message. +- word_freq_3d: role=feature, type=numeric_sparse_frequency. tags=['condition_candidate', 'measure', 'rare_pattern_candidate'] desc=Frequency feature for token '3d' in an email message. +- word_freq_our: role=feature, type=numeric_sparse_frequency. tags=['condition_candidate', 'measure', 'rare_pattern_candidate'] desc=Frequency feature for token 'our' in an email message. +- word_freq_over: role=feature, type=numeric_sparse_frequency. tags=['condition_candidate', 'measure', 'rare_pattern_candidate'] desc=Frequency feature for token 'over' in an email message. +- word_freq_remove: role=feature, type=numeric_sparse_frequency. tags=['condition_candidate', 'measure', 'rare_pattern_candidate'] desc=Frequency feature for token 'remove' in an email message. +- word_freq_internet: role=feature, type=numeric_sparse_frequency. tags=['condition_candidate', 'measure', 'rare_pattern_candidate'] desc=Frequency feature for token 'internet' in an email message. +- word_freq_order: role=feature, type=numeric_sparse_frequency. tags=['condition_candidate', 'measure', 'rare_pattern_candidate'] desc=Frequency feature for token 'order' in an email message. +- word_freq_mail: role=feature, type=numeric_sparse_frequency. tags=['condition_candidate', 'measure', 'rare_pattern_candidate'] desc=Frequency feature for token 'mail' in an email message. +- word_freq_receive: role=feature, type=numeric_sparse_frequency. tags=['condition_candidate', 'measure', 'rare_pattern_candidate'] desc=Frequency feature for token 'receive' in an email message. +- word_freq_will: role=feature, type=numeric_sparse_frequency. tags=['condition_candidate', 'measure', 'rare_pattern_candidate'] desc=Frequency feature for token 'will' in an email message. +- word_freq_people: role=feature, type=numeric_sparse_frequency. tags=['condition_candidate', 'measure', 'rare_pattern_candidate'] desc=Frequency feature for token 'people' in an email message. +- word_freq_report: role=feature, type=numeric_sparse_frequency. tags=['condition_candidate', 'measure', 'rare_pattern_candidate'] desc=Frequency feature for token 'report' in an email message. +- word_freq_addresses: role=feature, type=numeric_sparse_frequency. tags=['condition_candidate', 'measure', 'rare_pattern_candidate'] desc=Frequency feature for token 'addresses' in an email message. +- word_freq_free: role=feature, type=numeric_sparse_frequency. tags=['condition_candidate', 'measure', 'rare_pattern_candidate'] desc=Frequency feature for token 'free' in an email message. +- word_freq_business: role=feature, type=numeric_sparse_frequency. tags=['condition_candidate', 'measure', 'rare_pattern_candidate'] desc=Frequency feature for token 'business' in an email message. +- word_freq_email: role=feature, type=numeric_sparse_frequency. tags=['condition_candidate', 'measure', 'rare_pattern_candidate'] desc=Frequency feature for token 'email' in an email message. +- word_freq_you: role=feature, type=numeric_sparse_frequency. tags=['condition_candidate', 'measure', 'rare_pattern_candidate'] desc=Frequency feature for token 'you' in an email message. +- word_freq_credit: role=feature, type=numeric_sparse_frequency. tags=['condition_candidate', 'measure', 'rare_pattern_candidate'] desc=Frequency feature for token 'credit' in an email message. +- word_freq_your: role=feature, type=numeric_sparse_frequency. tags=['condition_candidate', 'measure', 'rare_pattern_candidate'] desc=Frequency feature for token 'your' in an email message. +- word_freq_font: role=feature, type=numeric_sparse_frequency. tags=['condition_candidate', 'measure', 'rare_pattern_candidate'] desc=Frequency feature for token 'font' in an email message. +- word_freq_000: role=feature, type=numeric_sparse_frequency. tags=['condition_candidate', 'measure', 'rare_pattern_candidate'] desc=Frequency feature for token '000' in an email message. +- word_freq_money: role=feature, type=numeric_sparse_frequency. tags=['condition_candidate', 'measure', 'rare_pattern_candidate'] desc=Frequency feature for token 'money' in an email message. +- word_freq_hp: role=feature, type=numeric_sparse_frequency. tags=['condition_candidate', 'measure', 'rare_pattern_candidate'] desc=Frequency feature for token 'hp' in an email message. +- word_freq_hpl: role=feature, type=numeric_sparse_frequency. tags=['condition_candidate', 'measure', 'rare_pattern_candidate'] desc=Frequency feature for token 'hpl' in an email message. +- word_freq_george: role=feature, type=numeric_sparse_frequency. tags=['condition_candidate', 'measure', 'rare_pattern_candidate'] desc=Frequency feature for token 'george' in an email message. +- word_freq_650: role=feature, type=numeric_sparse_frequency. tags=['condition_candidate', 'measure', 'rare_pattern_candidate'] desc=Frequency feature for token '650' in an email message. +- word_freq_lab: role=feature, type=numeric_sparse_frequency. tags=['condition_candidate', 'measure', 'rare_pattern_candidate'] desc=Frequency feature for token 'lab' in an email message. +- word_freq_labs: role=feature, type=numeric_sparse_frequency. tags=['condition_candidate', 'measure', 'rare_pattern_candidate'] desc=Frequency feature for token 'labs' in an email message. +- word_freq_telnet: role=feature, type=numeric_sparse_frequency. tags=['condition_candidate', 'measure', 'rare_pattern_candidate'] desc=Frequency feature for token 'telnet' in an email message. +- word_freq_857: role=feature, type=numeric_sparse_frequency. tags=['condition_candidate', 'measure', 'rare_pattern_candidate'] desc=Frequency feature for token '857' in an email message. +- word_freq_data: role=feature, type=numeric_sparse_frequency. tags=['condition_candidate', 'measure', 'rare_pattern_candidate'] desc=Frequency feature for token 'data' in an email message. +- word_freq_415: role=feature, type=numeric_sparse_frequency. tags=['condition_candidate', 'measure', 'rare_pattern_candidate'] desc=Frequency feature for token '415' in an email message. +- word_freq_85: role=feature, type=numeric_sparse_frequency. tags=['condition_candidate', 'measure', 'rare_pattern_candidate'] desc=Frequency feature for token '85' in an email message. +- word_freq_technology: role=feature, type=numeric_sparse_frequency. tags=['condition_candidate', 'measure', 'rare_pattern_candidate'] desc=Frequency feature for token 'technology' in an email message. +- word_freq_1999: role=feature, type=numeric_sparse_frequency. tags=['condition_candidate', 'measure', 'rare_pattern_candidate'] desc=Frequency feature for token '1999' in an email message. +- word_freq_parts: role=feature, type=numeric_sparse_frequency. tags=['condition_candidate', 'measure', 'rare_pattern_candidate'] desc=Frequency feature for token 'parts' in an email message. +- word_freq_pm: role=feature, type=numeric_sparse_frequency. tags=['condition_candidate', 'measure', 'rare_pattern_candidate'] desc=Frequency feature for token 'pm' in an email message. +- word_freq_direct: role=feature, type=numeric_sparse_frequency. tags=['condition_candidate', 'measure', 'rare_pattern_candidate'] desc=Frequency feature for token 'direct' in an email message. +- word_freq_cs: role=feature, type=numeric_sparse_frequency. tags=['condition_candidate', 'measure', 'rare_pattern_candidate'] desc=Frequency feature for token 'cs' in an email message. +- word_freq_meeting: role=feature, type=numeric_sparse_frequency. tags=['condition_candidate', 'measure', 'rare_pattern_candidate'] desc=Frequency feature for token 'meeting' in an email message. +- word_freq_original: role=feature, type=numeric_sparse_frequency. tags=['condition_candidate', 'measure', 'rare_pattern_candidate'] desc=Frequency feature for token 'original' in an email message. +- word_freq_project: role=feature, type=numeric_sparse_frequency. tags=['condition_candidate', 'measure', 'rare_pattern_candidate'] desc=Frequency feature for token 'project' in an email message. +- word_freq_re: role=feature, type=numeric_sparse_frequency. tags=['condition_candidate', 'measure', 'rare_pattern_candidate'] desc=Frequency feature for token 're' in an email message. +- word_freq_edu: role=feature, type=numeric_sparse_frequency. tags=['condition_candidate', 'measure', 'rare_pattern_candidate'] desc=Frequency feature for token 'edu' in an email message. +- word_freq_table: role=feature, type=numeric_sparse_frequency. tags=['condition_candidate', 'measure', 'rare_pattern_candidate'] desc=Frequency feature for token 'table' in an email message. +- word_freq_conference: role=feature, type=numeric_sparse_frequency. tags=['condition_candidate', 'measure', 'rare_pattern_candidate'] desc=Frequency feature for token 'conference' in an email message. +- char_freq_%3B: role=feature, type=numeric_sparse_frequency. tags=['condition_candidate', 'measure', 'rare_pattern_candidate'] desc=Character frequency feature for symbol ';'. +- char_freq_%28: role=feature, type=numeric_sparse_frequency. tags=['condition_candidate', 'measure', 'rare_pattern_candidate'] desc=Character frequency feature for symbol '('. +- char_freq_%5B: role=feature, type=numeric_sparse_frequency. tags=['condition_candidate', 'measure', 'rare_pattern_candidate'] desc=Character frequency feature for symbol '['. +- char_freq_%21: role=feature, type=numeric_sparse_frequency. tags=['condition_candidate', 'measure', 'rare_pattern_candidate'] desc=Character frequency feature for symbol '!'. +- char_freq_%24: role=feature, type=numeric_sparse_frequency. tags=['condition_candidate', 'measure', 'rare_pattern_candidate'] desc=Character frequency feature for symbol '$'. +- char_freq_%23: role=feature, type=numeric_sparse_frequency. tags=['condition_candidate', 'measure', 'rare_pattern_candidate'] desc=Character frequency feature for symbol '#'. +- capital_run_length_average: role=feature, type=numeric_heavy_tail. tags=['condition_candidate', 'measure', 'rare_pattern_candidate'] desc=Average length of uninterrupted capital-letter runs. +- capital_run_length_longest: role=feature, type=numeric_heavy_tail. tags=['condition_candidate', 'measure', 'rare_pattern_candidate'] desc=Longest uninterrupted capital-letter run. +- capital_run_length_total: role=feature, type=numeric_heavy_tail. tags=['condition_candidate', 'measure', 'rare_pattern_candidate'] desc=Total number of capital letters in runs. +- class: role=target, type=binary_target. tags=['target_candidate'] desc=Binary spam label (1=spam, 0=non-spam). +- useful_field_combinations: [['word_freq_free', 'word_freq_you', 'class'], ['char_freq_%21', 'capital_run_length_longest', 'class'], ['word_freq_remove', 'word_freq_money', 'class']] +- fields_requiring_caution: ['capital_run_length_average', 'capital_run_length_longest', 'capital_run_length_total'] +- source_url: https://www.openml.org/d/44 + +SQLite schema snapshot: +{ + "table_name": "n1", + "quoted_table_name": "\"n1\"", + "row_count": 4601, + "columns": [ + { + "name": "word_freq_make", + "type": "TEXT", + "notnull": false, + "pk": false + }, + { + "name": "word_freq_address", + "type": "TEXT", + "notnull": false, + "pk": false + }, + { + "name": "word_freq_all", + "type": "TEXT", + "notnull": false, + "pk": false + }, + { + "name": "word_freq_3d", + "type": "TEXT", + "notnull": false, + "pk": false + }, + { + "name": "word_freq_our", + "type": "TEXT", + "notnull": false, + "pk": false + }, + { + "name": "word_freq_over", + "type": "TEXT", + "notnull": false, + "pk": false + }, + { + "name": "word_freq_remove", + "type": "TEXT", + "notnull": false, + "pk": false + }, + { + "name": "word_freq_internet", + "type": "TEXT", + "notnull": false, + "pk": false + }, + { + "name": "word_freq_order", + "type": "TEXT", + "notnull": false, + "pk": false + }, + { + "name": "word_freq_mail", + "type": "TEXT", + "notnull": false, + "pk": false + }, + { + "name": "word_freq_receive", + "type": "TEXT", + "notnull": false, + "pk": false + }, + { + "name": "word_freq_will", + "type": "TEXT", + "notnull": false, + "pk": false + }, + { + "name": "word_freq_people", + "type": "TEXT", + "notnull": false, + "pk": false + }, + { + "name": "word_freq_report", + "type": "TEXT", + "notnull": false, + "pk": false + }, + { + "name": "word_freq_addresses", + "type": "TEXT", + "notnull": false, + "pk": false + }, + { + "name": "word_freq_free", + "type": "TEXT", + "notnull": false, + "pk": false + }, + { + "name": "word_freq_business", + "type": "TEXT", + "notnull": false, + "pk": false + }, + { + "name": "word_freq_email", + "type": "TEXT", + "notnull": false, + "pk": false + }, + { + "name": "word_freq_you", + "type": "TEXT", + "notnull": false, + "pk": false + }, + { + "name": "word_freq_credit", + "type": "TEXT", + "notnull": false, + "pk": false + }, + { + "name": "word_freq_your", + "type": "TEXT", + "notnull": false, + "pk": false + }, + { + "name": "word_freq_font", + "type": "TEXT", + "notnull": false, + "pk": false + }, + { + "name": "word_freq_000", + "type": "TEXT", + "notnull": false, + "pk": false + }, + { + "name": "word_freq_money", + "type": "TEXT", + "notnull": false, + "pk": false + }, + { + "name": "word_freq_hp", + "type": "TEXT", + "notnull": false, + "pk": false + }, + { + "name": "word_freq_hpl", + "type": "TEXT", + "notnull": false, + "pk": false + }, + { + "name": "word_freq_george", + "type": "TEXT", + "notnull": false, + "pk": false + }, + { + "name": "word_freq_650", + "type": "TEXT", + "notnull": false, + "pk": false + }, + { + "name": "word_freq_lab", + "type": "TEXT", + "notnull": false, + "pk": false + }, + { + "name": "word_freq_labs", + "type": "TEXT", + "notnull": false, + "pk": false + }, + { + "name": "word_freq_telnet", + "type": "TEXT", + "notnull": false, + "pk": false + }, + { + "name": "word_freq_857", + "type": "TEXT", + "notnull": false, + "pk": false + }, + { + "name": "word_freq_data", + "type": "TEXT", + "notnull": false, + "pk": false + }, + { + "name": "word_freq_415", + "type": "TEXT", + "notnull": false, + "pk": false + }, + { + "name": "word_freq_85", + "type": "TEXT", + "notnull": false, + "pk": false + }, + { + "name": "word_freq_technology", + "type": "TEXT", + "notnull": false, + "pk": false + }, + { + "name": "word_freq_1999", + "type": "TEXT", + "notnull": false, + "pk": false + }, + { + "name": "word_freq_parts", + "type": "TEXT", + "notnull": false, + "pk": false + }, + { + "name": "word_freq_pm", + "type": "TEXT", + "notnull": false, + "pk": false + }, + { + "name": "word_freq_direct", + "type": "TEXT", + "notnull": false, + "pk": false + }, + { + "name": "word_freq_cs", + "type": "TEXT", + "notnull": false, + "pk": false + }, + { + "name": "word_freq_meeting", + "type": "TEXT", + "notnull": false, + "pk": false + }, + { + "name": "word_freq_original", + "type": "TEXT", + "notnull": false, + "pk": false + }, + { + "name": "word_freq_project", + "type": "TEXT", + "notnull": false, + "pk": false + }, + { + "name": "word_freq_re", + "type": "TEXT", + "notnull": false, + "pk": false + }, + { + "name": "word_freq_edu", + "type": "TEXT", + "notnull": false, + "pk": false + }, + { + "name": "word_freq_table", + "type": "TEXT", + "notnull": false, + "pk": false + }, + { + "name": "word_freq_conference", + "type": "TEXT", + "notnull": false, + "pk": false + }, + { + "name": "char_freq_%3B", + "type": "TEXT", + "notnull": false, + "pk": false + }, + { + "name": "char_freq_%28", + "type": "TEXT", + "notnull": false, + "pk": false + }, + { + "name": "char_freq_%5B", + "type": "TEXT", + "notnull": false, + "pk": false + }, + { + "name": "char_freq_%21", + "type": "TEXT", + "notnull": false, + "pk": false + }, + { + "name": "char_freq_%24", + "type": "TEXT", + "notnull": false, + "pk": false + }, + { + "name": "char_freq_%23", + "type": "TEXT", + "notnull": false, + "pk": false + }, + { + "name": "capital_run_length_average", + "type": "TEXT", + "notnull": false, + "pk": false + }, + { + "name": "capital_run_length_longest", + "type": "TEXT", + "notnull": false, + "pk": false + }, + { + "name": "capital_run_length_total", + "type": "TEXT", + "notnull": false, + "pk": false + }, + { + "name": "class", + "type": "TEXT", + "notnull": false, + "pk": false + } + ], + "sample_rows": [ + { + "word_freq_make": "0", + "word_freq_address": "0.64", + "word_freq_all": "0.64", + "word_freq_3d": "0", + "word_freq_our": "0.32", + "word_freq_over": "0", + "word_freq_remove": "0", + "word_freq_internet": "0", + "word_freq_order": "0", + "word_freq_mail": "0", + "word_freq_receive": "0", + "word_freq_will": "0.64", + "word_freq_people": "0", + "word_freq_report": "0", + "word_freq_addresses": "0", + "word_freq_free": "0.32", + "word_freq_business": "0", + "word_freq_email": "1.29", + "word_freq_you": "1.93", + "word_freq_credit": "0", + "word_freq_your": "0.96", + "word_freq_font": "0", + "word_freq_000": "0", + "word_freq_money": "0", + "word_freq_hp": "0", + "word_freq_hpl": "0", + "word_freq_george": "0", + "word_freq_650": "0", + "word_freq_lab": "0", + "word_freq_labs": "0", + "word_freq_telnet": "0", + "word_freq_857": "0", + "word_freq_data": "0", + "word_freq_415": "0", + "word_freq_85": "0", + "word_freq_technology": "0", + "word_freq_1999": "0", + "word_freq_parts": "0", + "word_freq_pm": "0", + "word_freq_direct": "0", + "word_freq_cs": "0", + "word_freq_meeting": "0", + "word_freq_original": "0", + "word_freq_project": "0", + "word_freq_re": "0", + "word_freq_edu": "0", + "word_freq_table": "0", + "word_freq_conference": "0", + "char_freq_%3B": "0", + "char_freq_%28": "0", + "char_freq_%5B": "0", + "char_freq_%21": "0.778", + "char_freq_%24": "0", + "char_freq_%23": "0", + "capital_run_length_average": "3.756", + "capital_run_length_longest": "61", + "capital_run_length_total": "278", + "class": "1" + }, + { + "word_freq_make": "0.21", + "word_freq_address": "0.28", + "word_freq_all": "0.5", + "word_freq_3d": "0", + "word_freq_our": "0.14", + "word_freq_over": "0.28", + "word_freq_remove": "0.21", + "word_freq_internet": "0.07", + "word_freq_order": "0", + "word_freq_mail": "0.94", + "word_freq_receive": "0.21", + "word_freq_will": "0.79", + "word_freq_people": "0.65", + "word_freq_report": "0.21", + "word_freq_addresses": "0.14", + "word_freq_free": "0.14", + "word_freq_business": "0.07", + "word_freq_email": "0.28", + "word_freq_you": "3.47", + "word_freq_credit": "0", + "word_freq_your": "1.59", + "word_freq_font": "0", + "word_freq_000": "0.43", + "word_freq_money": "0.43", + "word_freq_hp": "0", + "word_freq_hpl": "0", + "word_freq_george": "0", + "word_freq_650": "0", + "word_freq_lab": "0", + "word_freq_labs": "0", + "word_freq_telnet": "0", + "word_freq_857": "0", + "word_freq_data": "0", + "word_freq_415": "0", + "word_freq_85": "0", + "word_freq_technology": "0", + "word_freq_1999": "0.07", + "word_freq_parts": "0", + "word_freq_pm": "0", + "word_freq_direct": "0", + "word_freq_cs": "0", + "word_freq_meeting": "0", + "word_freq_original": "0", + "word_freq_project": "0", + "word_freq_re": "0", + "word_freq_edu": "0", + "word_freq_table": "0", + "word_freq_conference": "0", + "char_freq_%3B": "0", + "char_freq_%28": "0.132", + "char_freq_%5B": "0", + "char_freq_%21": "0.372", + "char_freq_%24": "0.18", + "char_freq_%23": "0.048", + "capital_run_length_average": "5.114", + "capital_run_length_longest": "101", + "capital_run_length_total": "1028", + "class": "1" + }, + { + "word_freq_make": "0.06", + "word_freq_address": "0", + "word_freq_all": "0.71", + "word_freq_3d": "0", + "word_freq_our": "1.23", + "word_freq_over": "0.19", + "word_freq_remove": "0.19", + "word_freq_internet": "0.12", + "word_freq_order": "0.64", + "word_freq_mail": "0.25", + "word_freq_receive": "0.38", + "word_freq_will": "0.45", + "word_freq_people": "0.12", + "word_freq_report": "0", + "word_freq_addresses": "1.75", + "word_freq_free": "0.06", + "word_freq_business": "0.06", + "word_freq_email": "1.03", + "word_freq_you": "1.36", + "word_freq_credit": "0.32", + "word_freq_your": "0.51", + "word_freq_font": "0", + "word_freq_000": "1.16", + "word_freq_money": "0.06", + "word_freq_hp": "0", + "word_freq_hpl": "0", + "word_freq_george": "0", + "word_freq_650": "0", + "word_freq_lab": "0", + "word_freq_labs": "0", + "word_freq_telnet": "0", + "word_freq_857": "0", + "word_freq_data": "0", + "word_freq_415": "0", + "word_freq_85": "0", + "word_freq_technology": "0", + "word_freq_1999": "0", + "word_freq_parts": "0", + "word_freq_pm": "0", + "word_freq_direct": "0.06", + "word_freq_cs": "0", + "word_freq_meeting": "0", + "word_freq_original": "0.12", + "word_freq_project": "0", + "word_freq_re": "0.06", + "word_freq_edu": "0.06", + "word_freq_table": "0", + "word_freq_conference": "0", + "char_freq_%3B": "0.01", + "char_freq_%28": "0.143", + "char_freq_%5B": "0", + "char_freq_%21": "0.276", + "char_freq_%24": "0.184", + "char_freq_%23": "0.01", + "capital_run_length_average": "9.821", + "capital_run_length_longest": "485", + "capital_run_length_total": "2259", + "class": "1" + }, + { + "word_freq_make": "0", + "word_freq_address": "0", + "word_freq_all": "0", + "word_freq_3d": "0", + "word_freq_our": "0.63", + "word_freq_over": "0", + "word_freq_remove": "0.31", + "word_freq_internet": "0.63", + "word_freq_order": "0.31", + "word_freq_mail": "0.63", + "word_freq_receive": "0.31", + "word_freq_will": "0.31", + "word_freq_people": "0.31", + "word_freq_report": "0", + "word_freq_addresses": "0", + "word_freq_free": "0.31", + "word_freq_business": "0", + "word_freq_email": "0", + "word_freq_you": "3.18", + "word_freq_credit": "0", + "word_freq_your": "0.31", + "word_freq_font": "0", + "word_freq_000": "0", + "word_freq_money": "0", + "word_freq_hp": "0", + "word_freq_hpl": "0", + "word_freq_george": "0", + "word_freq_650": "0", + "word_freq_lab": "0", + "word_freq_labs": "0", + "word_freq_telnet": "0", + "word_freq_857": "0", + "word_freq_data": "0", + "word_freq_415": "0", + "word_freq_85": "0", + "word_freq_technology": "0", + "word_freq_1999": "0", + "word_freq_parts": "0", + "word_freq_pm": "0", + "word_freq_direct": "0", + "word_freq_cs": "0", + "word_freq_meeting": "0", + "word_freq_original": "0", + "word_freq_project": "0", + "word_freq_re": "0", + "word_freq_edu": "0", + "word_freq_table": "0", + "word_freq_conference": "0", + "char_freq_%3B": "0", + "char_freq_%28": "0.137", + "char_freq_%5B": "0", + "char_freq_%21": "0.137", + "char_freq_%24": "0", + "char_freq_%23": "0", + "capital_run_length_average": "3.537", + "capital_run_length_longest": "40", + "capital_run_length_total": "191", + "class": "1" + }, + { + "word_freq_make": "0", + "word_freq_address": "0", + "word_freq_all": "0", + "word_freq_3d": "0", + "word_freq_our": "0.63", + "word_freq_over": "0", + "word_freq_remove": "0.31", + "word_freq_internet": "0.63", + "word_freq_order": "0.31", + "word_freq_mail": "0.63", + "word_freq_receive": "0.31", + "word_freq_will": "0.31", + "word_freq_people": "0.31", + "word_freq_report": "0", + "word_freq_addresses": "0", + "word_freq_free": "0.31", + "word_freq_business": "0", + "word_freq_email": "0", + "word_freq_you": "3.18", + "word_freq_credit": "0", + "word_freq_your": "0.31", + "word_freq_font": "0", + "word_freq_000": "0", + "word_freq_money": "0", + "word_freq_hp": "0", + "word_freq_hpl": "0", + "word_freq_george": "0", + "word_freq_650": "0", + "word_freq_lab": "0", + "word_freq_labs": "0", + "word_freq_telnet": "0", + "word_freq_857": "0", + "word_freq_data": "0", + "word_freq_415": "0", + "word_freq_85": "0", + "word_freq_technology": "0", + "word_freq_1999": "0", + "word_freq_parts": "0", + "word_freq_pm": "0", + "word_freq_direct": "0", + "word_freq_cs": "0", + "word_freq_meeting": "0", + "word_freq_original": "0", + "word_freq_project": "0", + "word_freq_re": "0", + "word_freq_edu": "0", + "word_freq_table": "0", + "word_freq_conference": "0", + "char_freq_%3B": "0", + "char_freq_%28": "0.135", + "char_freq_%5B": "0", + "char_freq_%21": "0.135", + "char_freq_%24": "0", + "char_freq_%23": "0", + "capital_run_length_average": "3.537", + "capital_run_length_longest": "40", + "capital_run_length_total": "191", + "class": "1" + } + ] +} + +Shortlisted templates: +[ + { + "template_id": "tpl_grouped_percentile_point", + "template_name": "Grouped Percentile Point", + "primary_family": "tail_rarity_structure", + "portability": "yes", + "sql_skeleton": "SELECT {group_col},\n PERCENTILE_CONT({percentile_value}) WITHIN GROUP (ORDER BY {measure_col}) AS percentile_measure\nFROM {table}\nGROUP BY {group_col}\nORDER BY percentile_measure DESC;", + "required_roles": [ + "group_col", + "measure_col" + ] + } +] + +Problem instance: +{ + "dataset_id": "n1", + "question": "Use template Grouped Percentile Point to probe tail_concentration_consistency with semantic role ranked_signal_view. Focus on group_col=class, measure_col=word_freq_telnet.", + "planned_template_id": "tpl_grouped_percentile_point", + "bindings": { + "group_col": "class", + "measure_col": "word_freq_telnet", + "top_k": 12, + "top_n": 6, + "num_tiles": 10, + "percentile_value": 0.9, + "z_threshold": 2.0, + "fraction_threshold": 0.1, + "baseline_multiplier": 1.5, + "baseline_fraction": 0.1, + "min_group_size": 5, + "min_support": 5, + "measure_threshold": 0.0, + "time_grain": "month", + "lookback_rows": 3, + "current_period_start": "'2024-01-01'", + "current_period_end": "'2024-04-01'", + "previous_period_start": "'2023-10-01'", + "previous_period_end": "'2024-01-01'", + "drift_ratio_threshold": 0.8 + }, + "can_vary": [], + "must_fix": [], + "runtime_sql_skeleton": "SELECT {group_col},\n PERCENTILE_CONT({percentile_value}) WITHIN GROUP (ORDER BY {measure_col}) AS percentile_measure\nFROM {table}\nGROUP BY {group_col}\nORDER BY percentile_measure DESC;" +} + +Repair context: +{} diff --git a/Query/sql/v2/runs/v2_cli_20260502_081223_d/n1/artifacts/v2q_n1_ce2d6910ad4d519f/cli/sql_response_attempt_1.raw.txt b/Query/sql/v2/runs/v2_cli_20260502_081223_d/n1/artifacts/v2q_n1_ce2d6910ad4d519f/cli/sql_response_attempt_1.raw.txt new file mode 100644 index 0000000000000000000000000000000000000000..bb02659c81fe0ea4f49496d9a1a585a6a9ab9c6c --- /dev/null +++ b/Query/sql/v2/runs/v2_cli_20260502_081223_d/n1/artifacts/v2q_n1_ce2d6910ad4d519f/cli/sql_response_attempt_1.raw.txt @@ -0,0 +1,4 @@ +{"type":"thread.started","thread_id":"019e40f1-768d-78a3-8c81-ba673ca11611"} +{"type":"turn.started"} +{"type":"item.completed","item":{"id":"item_0","type":"agent_message","text":"{\"sql\":\"-- template_id: tpl_grouped_percentile_point\\nWITH \\\"ordered\\\" AS (\\n SELECT\\n \\\"class\\\",\\n CAST(\\\"word_freq_telnet\\\" AS REAL) AS \\\"measure\\\",\\n ROW_NUMBER() OVER (\\n PARTITION BY \\\"class\\\"\\n ORDER BY CAST(\\\"word_freq_telnet\\\" AS REAL)\\n ) AS \\\"pos\\\",\\n COUNT(*) OVER (\\n PARTITION BY \\\"class\\\"\\n ) AS \\\"n\\\"\\n FROM \\\"n1\\\"\\n WHERE \\\"class\\\" IS NOT NULL\\n AND \\\"word_freq_telnet\\\" IS NOT NULL\\n),\\n\\\"params\\\" AS (\\n SELECT\\n \\\"class\\\",\\n \\\"n\\\",\\n (1.0 + 0.9 * (\\\"n\\\" - 1)) AS \\\"rn\\\",\\n CAST((1.0 + 0.9 * (\\\"n\\\" - 1)) AS INTEGER) AS \\\"frn\\\",\\n CASE\\n WHEN (1.0 + 0.9 * (\\\"n\\\" - 1)) = CAST((1.0 + 0.9 * (\\\"n\\\" - 1)) AS INTEGER)\\n THEN CAST((1.0 + 0.9 * (\\\"n\\\" - 1)) AS INTEGER)\\n ELSE CAST((1.0 + 0.9 * (\\\"n\\\" - 1)) AS INTEGER) + 1\\n END AS \\\"crn\\\"\\n FROM \\\"ordered\\\"\\n GROUP BY \\\"class\\\", \\\"n\\\"\\n)\\nSELECT\\n \\\"p\\\".\\\"class\\\",\\n CASE\\n WHEN \\\"p\\\".\\\"frn\\\" = \\\"p\\\".\\\"crn\\\" THEN\\n MAX(CASE WHEN \\\"o\\\".\\\"pos\\\" = \\\"p\\\".\\\"frn\\\" THEN \\\"o\\\".\\\"measure\\\" END)\\n ELSE\\n (\\\"p\\\".\\\"crn\\\" - \\\"p\\\".\\\"rn\\\") * MAX(CASE WHEN \\\"o\\\".\\\"pos\\\" = \\\"p\\\".\\\"frn\\\" THEN \\\"o\\\".\\\"measure\\\" END) +\\n (\\\"p\\\".\\\"rn\\\" - \\\"p\\\".\\\"frn\\\") * MAX(CASE WHEN \\\"o\\\".\\\"pos\\\" = \\\"p\\\".\\\"crn\\\" THEN \\\"o\\\".\\\"measure\\\" END)\\n END AS \\\"percentile_measure\\\"\\nFROM \\\"params\\\" AS \\\"p\\\"\\nJOIN \\\"ordered\\\" AS \\\"o\\\"\\n ON \\\"o\\\".\\\"class\\\" = \\\"p\\\".\\\"class\\\"\\nGROUP BY\\n \\\"p\\\".\\\"class\\\",\\n \\\"p\\\".\\\"n\\\",\\n \\\"p\\\".\\\"rn\\\",\\n \\\"p\\\".\\\"frn\\\",\\n \\\"p\\\".\\\"crn\\\"\\nORDER BY \\\"percentile_measure\\\" DESC;\",\"notes\":\"SQLite does not support PERCENTILE_CONT, so this query emulates the 90th percentile per \\\"class\\\" with window functions and linear interpolation. \\\"word_freq_telnet\\\" is CAST to REAL because the schema stores numeric-looking values as TEXT.\"}"}} +{"type":"turn.completed","usage":{"input_tokens":20355,"cached_input_tokens":19840,"output_tokens":2620,"reasoning_output_tokens":1976}} diff --git a/Query/sql/v2/runs/v2_cli_20260502_081223_d/n1/artifacts/v2q_n1_ce2d6910ad4d519f/cli/sql_response_attempt_1.txt b/Query/sql/v2/runs/v2_cli_20260502_081223_d/n1/artifacts/v2q_n1_ce2d6910ad4d519f/cli/sql_response_attempt_1.txt new file mode 100644 index 0000000000000000000000000000000000000000..956e270e59f5745c0dfa8fed3f4669fb62d4c4bf --- /dev/null +++ b/Query/sql/v2/runs/v2_cli_20260502_081223_d/n1/artifacts/v2q_n1_ce2d6910ad4d519f/cli/sql_response_attempt_1.txt @@ -0,0 +1 @@ +{"sql":"-- template_id: tpl_grouped_percentile_point\nWITH \"ordered\" AS (\n SELECT\n \"class\",\n CAST(\"word_freq_telnet\" AS REAL) AS \"measure\",\n ROW_NUMBER() OVER (\n PARTITION BY \"class\"\n ORDER BY CAST(\"word_freq_telnet\" AS REAL)\n ) AS \"pos\",\n COUNT(*) OVER (\n PARTITION BY \"class\"\n ) AS \"n\"\n FROM \"n1\"\n WHERE \"class\" IS NOT NULL\n AND \"word_freq_telnet\" IS NOT NULL\n),\n\"params\" AS (\n SELECT\n \"class\",\n \"n\",\n (1.0 + 0.9 * (\"n\" - 1)) AS \"rn\",\n CAST((1.0 + 0.9 * (\"n\" - 1)) AS INTEGER) AS \"frn\",\n CASE\n WHEN (1.0 + 0.9 * (\"n\" - 1)) = CAST((1.0 + 0.9 * (\"n\" - 1)) AS INTEGER)\n THEN CAST((1.0 + 0.9 * (\"n\" - 1)) AS INTEGER)\n ELSE CAST((1.0 + 0.9 * (\"n\" - 1)) AS INTEGER) + 1\n END AS \"crn\"\n FROM \"ordered\"\n GROUP BY \"class\", \"n\"\n)\nSELECT\n \"p\".\"class\",\n CASE\n WHEN \"p\".\"frn\" = \"p\".\"crn\" THEN\n MAX(CASE WHEN \"o\".\"pos\" = \"p\".\"frn\" THEN \"o\".\"measure\" END)\n ELSE\n (\"p\".\"crn\" - \"p\".\"rn\") * MAX(CASE WHEN \"o\".\"pos\" = \"p\".\"frn\" THEN \"o\".\"measure\" END) +\n (\"p\".\"rn\" - \"p\".\"frn\") * MAX(CASE WHEN \"o\".\"pos\" = \"p\".\"crn\" THEN \"o\".\"measure\" END)\n END AS \"percentile_measure\"\nFROM \"params\" AS \"p\"\nJOIN \"ordered\" AS \"o\"\n ON \"o\".\"class\" = \"p\".\"class\"\nGROUP BY\n \"p\".\"class\",\n \"p\".\"n\",\n \"p\".\"rn\",\n \"p\".\"frn\",\n \"p\".\"crn\"\nORDER BY \"percentile_measure\" DESC;","notes":"SQLite does not support PERCENTILE_CONT, so this query emulates the 90th percentile per \"class\" with window functions and linear interpolation. \"word_freq_telnet\" is CAST to REAL because the schema stores numeric-looking values as TEXT."} \ No newline at end of file diff --git a/Query/sql/v2/runs/v2_cli_20260502_081223_d/n1/artifacts/v2q_n1_ce2d6910ad4d519f/cli/sql_stderr_attempt_1.txt b/Query/sql/v2/runs/v2_cli_20260502_081223_d/n1/artifacts/v2q_n1_ce2d6910ad4d519f/cli/sql_stderr_attempt_1.txt new file mode 100644 index 0000000000000000000000000000000000000000..e69de29bb2d1d6434b8b29ae775ad8c2e48c5391 diff --git a/Query/sql/v2/runs/v2_cli_20260502_081223_d/n1/artifacts/v2q_n1_cff363bb65d6bf4b/cli/conversation.jsonl b/Query/sql/v2/runs/v2_cli_20260502_081223_d/n1/artifacts/v2q_n1_cff363bb65d6bf4b/cli/conversation.jsonl new file mode 100644 index 0000000000000000000000000000000000000000..699daa5250d96c6da81b9e6be8aac6404962c761 --- /dev/null +++ b/Query/sql/v2/runs/v2_cli_20260502_081223_d/n1/artifacts/v2q_n1_cff363bb65d6bf4b/cli/conversation.jsonl @@ -0,0 +1,2 @@ +{"attempt": 1, "phase": "sql_generation", "role": "user", "content_path": "cli/sql_prompt_attempt_1.txt", "metrics": {"chars": 29439, "bytes_utf8": 29439, "lines": 792, "estimated_tokens": null}} +{"attempt": 1, "phase": "sql_generation", "role": "assistant", "content_path": "cli/sql_response_attempt_1.txt", "raw_content_path": "cli/sql_response_attempt_1.raw.txt", "stderr_path": "cli/sql_stderr_attempt_1.txt", "metrics": {"chars": 442, "bytes_utf8": 442, "lines": 1, "estimated_tokens": null}, "usage": {"input_tokens": 20330, "cached_input_tokens": 19840, "output_tokens": 511, "reasoning_output_tokens": 395}} diff --git a/Query/sql/v2/runs/v2_cli_20260502_081223_d/n1/artifacts/v2q_n1_cff363bb65d6bf4b/cli/session_summary.json b/Query/sql/v2/runs/v2_cli_20260502_081223_d/n1/artifacts/v2q_n1_cff363bb65d6bf4b/cli/session_summary.json new file mode 100644 index 0000000000000000000000000000000000000000..2dabc6df062f42126fafcc723777773cf7f69f93 --- /dev/null +++ b/Query/sql/v2/runs/v2_cli_20260502_081223_d/n1/artifacts/v2q_n1_cff363bb65d6bf4b/cli/session_summary.json @@ -0,0 +1,25 @@ +{ + "engine": "v2-cli:codex", + "command": "codex exec --skip-git-repo-check --disable plugins --sandbox read-only --cd \"/data/jialinzhang/SQLagent\" -m gpt-5.4 --json -", + "ai_cli_calls": 1, + "usage_summary": { + "dataset_id": "n1", + "model": "v2-cli:codex", + "run_id": "v2q_n1_cff363bb65d6bf4b", + "api_calls": 0, + "input_tokens": 20330, + "cached_input_tokens": 19840, + "output_tokens": 511, + "total_tokens": 20841, + "cost_usd": 0.0, + "ai_cli_calls": 1, + "estimated_input_tokens": 0, + "estimated_output_tokens": 0, + "estimated_total_tokens": 0, + "usage_source": "ai_cli_json_usage", + "cli_elapsed_ms_total": 13980.04, + "sql_execution_elapsed_ms_total": 8.75, + "conversation_log_path": "/data/jialinzhang/TabQueryBench/sql_workloads/v2_current/runs_and_launches/runs/v2_cli_20260502_081223_d/n1/artifacts/v2q_n1_cff363bb65d6bf4b/cli/conversation.jsonl", + "note": "Executed through a local AI CLI with structured usage metadata." + } +} \ No newline at end of file diff --git a/Query/sql/v2/runs/v2_cli_20260502_081223_d/n1/artifacts/v2q_n1_cff363bb65d6bf4b/cli/sql_attempt_1.metadata.json b/Query/sql/v2/runs/v2_cli_20260502_081223_d/n1/artifacts/v2q_n1_cff363bb65d6bf4b/cli/sql_attempt_1.metadata.json new file mode 100644 index 0000000000000000000000000000000000000000..f3683a9f6eb3fc5cad69f3e04ed74a6784136b53 --- /dev/null +++ b/Query/sql/v2/runs/v2_cli_20260502_081223_d/n1/artifacts/v2q_n1_cff363bb65d6bf4b/cli/sql_attempt_1.metadata.json @@ -0,0 +1,45 @@ +{ + "attempt": 1, + "phase": "sql_generation", + "command": "codex exec --skip-git-repo-check --disable plugins --sandbox read-only --cd \"/data/jialinzhang/SQLagent\" -m gpt-5.4 --json -", + "started_at": "2026-05-19T15:42:27.597240+00:00", + "ended_at": "2026-05-19T15:42:41.577314+00:00", + "elapsed_ms": 13980.04, + "prompt_metrics": { + "chars": 29439, + "bytes_utf8": 29439, + "lines": 792, + "estimated_tokens": null + }, + "stdout_metrics": { + "chars": 805, + "bytes_utf8": 805, + "lines": 4, + "estimated_tokens": null + }, + "stderr_metrics": { + "chars": 0, + "bytes_utf8": 0, + "lines": 0, + "estimated_tokens": null + }, + "parsed_output": { + "format": "jsonl_events", + "text_metrics": { + "chars": 442, + "bytes_utf8": 442, + "lines": 1, + "estimated_tokens": null + }, + "usage": { + "input_tokens": 20330, + "cached_input_tokens": 19840, + "output_tokens": 511, + "reasoning_output_tokens": 395 + } + }, + "prompt_path": "cli/sql_prompt_attempt_1.txt", + "response_path": "cli/sql_response_attempt_1.txt", + "raw_response_path": "cli/sql_response_attempt_1.raw.txt", + "stderr_path": "cli/sql_stderr_attempt_1.txt" +} \ No newline at end of file diff --git a/Query/sql/v2/runs/v2_cli_20260502_081223_d/n1/artifacts/v2q_n1_cff363bb65d6bf4b/cli/sql_prompt_attempt_1.txt b/Query/sql/v2/runs/v2_cli_20260502_081223_d/n1/artifacts/v2q_n1_cff363bb65d6bf4b/cli/sql_prompt_attempt_1.txt new file mode 100644 index 0000000000000000000000000000000000000000..5ff50eef546d7d5a20ce7397366b8907b185c085 --- /dev/null +++ b/Query/sql/v2/runs/v2_cli_20260502_081223_d/n1/artifacts/v2q_n1_cff363bb65d6bf4b/cli/sql_prompt_attempt_1.txt @@ -0,0 +1,792 @@ +You are generating one SQLite SELECT query for a single-table SQL QA task. +Return strict JSON only, with this schema: {"sql": "...", "notes": "..."}. +Rules: +- Use only the provided table and columns. +- Do not write INSERT, UPDATE, DELETE, DROP, ALTER, CREATE, PRAGMA, ATTACH, DETACH, or VACUUM. +- Prefer the planned template and bound roles when provided. +- Add a leading SQL comment exactly like: -- template_id: . +- Generate SQLite-compatible SQL. SQLite does not support PERCENTILE_CONT or STDDEV. +- Quote identifiers with double quotes. +- Return no markdown and no extra prose. + +Dataset context: +Dataset context for SQL QA: +- dataset_id: n1 +- dataset_name: Spambase +- table_name: n1 +- table_layout: single-table dataset (do not assume joins). +- row_semantics: One row is one email represented by engineered token/character frequency and capitalization-run features. +- task_type: classification +- target_column: class +- main_row_count: 4601 +- important_fields: +- word_freq_make: role=feature, type=numeric_sparse_frequency. tags=['condition_candidate', 'measure', 'rare_pattern_candidate'] desc=Frequency feature for token 'make' in an email message. +- word_freq_address: role=feature, type=numeric_sparse_frequency. tags=['condition_candidate', 'measure', 'rare_pattern_candidate'] desc=Frequency feature for token 'address' in an email message. +- word_freq_all: role=feature, type=numeric_sparse_frequency. tags=['condition_candidate', 'measure', 'rare_pattern_candidate'] desc=Frequency feature for token 'all' in an email message. +- word_freq_3d: role=feature, type=numeric_sparse_frequency. tags=['condition_candidate', 'measure', 'rare_pattern_candidate'] desc=Frequency feature for token '3d' in an email message. +- word_freq_our: role=feature, type=numeric_sparse_frequency. tags=['condition_candidate', 'measure', 'rare_pattern_candidate'] desc=Frequency feature for token 'our' in an email message. +- word_freq_over: role=feature, type=numeric_sparse_frequency. tags=['condition_candidate', 'measure', 'rare_pattern_candidate'] desc=Frequency feature for token 'over' in an email message. +- word_freq_remove: role=feature, type=numeric_sparse_frequency. tags=['condition_candidate', 'measure', 'rare_pattern_candidate'] desc=Frequency feature for token 'remove' in an email message. +- word_freq_internet: role=feature, type=numeric_sparse_frequency. tags=['condition_candidate', 'measure', 'rare_pattern_candidate'] desc=Frequency feature for token 'internet' in an email message. +- word_freq_order: role=feature, type=numeric_sparse_frequency. tags=['condition_candidate', 'measure', 'rare_pattern_candidate'] desc=Frequency feature for token 'order' in an email message. +- word_freq_mail: role=feature, type=numeric_sparse_frequency. tags=['condition_candidate', 'measure', 'rare_pattern_candidate'] desc=Frequency feature for token 'mail' in an email message. +- word_freq_receive: role=feature, type=numeric_sparse_frequency. tags=['condition_candidate', 'measure', 'rare_pattern_candidate'] desc=Frequency feature for token 'receive' in an email message. +- word_freq_will: role=feature, type=numeric_sparse_frequency. tags=['condition_candidate', 'measure', 'rare_pattern_candidate'] desc=Frequency feature for token 'will' in an email message. +- word_freq_people: role=feature, type=numeric_sparse_frequency. tags=['condition_candidate', 'measure', 'rare_pattern_candidate'] desc=Frequency feature for token 'people' in an email message. +- word_freq_report: role=feature, type=numeric_sparse_frequency. tags=['condition_candidate', 'measure', 'rare_pattern_candidate'] desc=Frequency feature for token 'report' in an email message. +- word_freq_addresses: role=feature, type=numeric_sparse_frequency. tags=['condition_candidate', 'measure', 'rare_pattern_candidate'] desc=Frequency feature for token 'addresses' in an email message. +- word_freq_free: role=feature, type=numeric_sparse_frequency. tags=['condition_candidate', 'measure', 'rare_pattern_candidate'] desc=Frequency feature for token 'free' in an email message. +- word_freq_business: role=feature, type=numeric_sparse_frequency. tags=['condition_candidate', 'measure', 'rare_pattern_candidate'] desc=Frequency feature for token 'business' in an email message. +- word_freq_email: role=feature, type=numeric_sparse_frequency. tags=['condition_candidate', 'measure', 'rare_pattern_candidate'] desc=Frequency feature for token 'email' in an email message. +- word_freq_you: role=feature, type=numeric_sparse_frequency. tags=['condition_candidate', 'measure', 'rare_pattern_candidate'] desc=Frequency feature for token 'you' in an email message. +- word_freq_credit: role=feature, type=numeric_sparse_frequency. tags=['condition_candidate', 'measure', 'rare_pattern_candidate'] desc=Frequency feature for token 'credit' in an email message. +- word_freq_your: role=feature, type=numeric_sparse_frequency. tags=['condition_candidate', 'measure', 'rare_pattern_candidate'] desc=Frequency feature for token 'your' in an email message. +- word_freq_font: role=feature, type=numeric_sparse_frequency. tags=['condition_candidate', 'measure', 'rare_pattern_candidate'] desc=Frequency feature for token 'font' in an email message. +- word_freq_000: role=feature, type=numeric_sparse_frequency. tags=['condition_candidate', 'measure', 'rare_pattern_candidate'] desc=Frequency feature for token '000' in an email message. +- word_freq_money: role=feature, type=numeric_sparse_frequency. tags=['condition_candidate', 'measure', 'rare_pattern_candidate'] desc=Frequency feature for token 'money' in an email message. +- word_freq_hp: role=feature, type=numeric_sparse_frequency. tags=['condition_candidate', 'measure', 'rare_pattern_candidate'] desc=Frequency feature for token 'hp' in an email message. +- word_freq_hpl: role=feature, type=numeric_sparse_frequency. tags=['condition_candidate', 'measure', 'rare_pattern_candidate'] desc=Frequency feature for token 'hpl' in an email message. +- word_freq_george: role=feature, type=numeric_sparse_frequency. tags=['condition_candidate', 'measure', 'rare_pattern_candidate'] desc=Frequency feature for token 'george' in an email message. +- word_freq_650: role=feature, type=numeric_sparse_frequency. tags=['condition_candidate', 'measure', 'rare_pattern_candidate'] desc=Frequency feature for token '650' in an email message. +- word_freq_lab: role=feature, type=numeric_sparse_frequency. tags=['condition_candidate', 'measure', 'rare_pattern_candidate'] desc=Frequency feature for token 'lab' in an email message. +- word_freq_labs: role=feature, type=numeric_sparse_frequency. tags=['condition_candidate', 'measure', 'rare_pattern_candidate'] desc=Frequency feature for token 'labs' in an email message. +- word_freq_telnet: role=feature, type=numeric_sparse_frequency. tags=['condition_candidate', 'measure', 'rare_pattern_candidate'] desc=Frequency feature for token 'telnet' in an email message. +- word_freq_857: role=feature, type=numeric_sparse_frequency. tags=['condition_candidate', 'measure', 'rare_pattern_candidate'] desc=Frequency feature for token '857' in an email message. +- word_freq_data: role=feature, type=numeric_sparse_frequency. tags=['condition_candidate', 'measure', 'rare_pattern_candidate'] desc=Frequency feature for token 'data' in an email message. +- word_freq_415: role=feature, type=numeric_sparse_frequency. tags=['condition_candidate', 'measure', 'rare_pattern_candidate'] desc=Frequency feature for token '415' in an email message. +- word_freq_85: role=feature, type=numeric_sparse_frequency. tags=['condition_candidate', 'measure', 'rare_pattern_candidate'] desc=Frequency feature for token '85' in an email message. +- word_freq_technology: role=feature, type=numeric_sparse_frequency. tags=['condition_candidate', 'measure', 'rare_pattern_candidate'] desc=Frequency feature for token 'technology' in an email message. +- word_freq_1999: role=feature, type=numeric_sparse_frequency. tags=['condition_candidate', 'measure', 'rare_pattern_candidate'] desc=Frequency feature for token '1999' in an email message. +- word_freq_parts: role=feature, type=numeric_sparse_frequency. tags=['condition_candidate', 'measure', 'rare_pattern_candidate'] desc=Frequency feature for token 'parts' in an email message. +- word_freq_pm: role=feature, type=numeric_sparse_frequency. tags=['condition_candidate', 'measure', 'rare_pattern_candidate'] desc=Frequency feature for token 'pm' in an email message. +- word_freq_direct: role=feature, type=numeric_sparse_frequency. tags=['condition_candidate', 'measure', 'rare_pattern_candidate'] desc=Frequency feature for token 'direct' in an email message. +- word_freq_cs: role=feature, type=numeric_sparse_frequency. tags=['condition_candidate', 'measure', 'rare_pattern_candidate'] desc=Frequency feature for token 'cs' in an email message. +- word_freq_meeting: role=feature, type=numeric_sparse_frequency. tags=['condition_candidate', 'measure', 'rare_pattern_candidate'] desc=Frequency feature for token 'meeting' in an email message. +- word_freq_original: role=feature, type=numeric_sparse_frequency. tags=['condition_candidate', 'measure', 'rare_pattern_candidate'] desc=Frequency feature for token 'original' in an email message. +- word_freq_project: role=feature, type=numeric_sparse_frequency. tags=['condition_candidate', 'measure', 'rare_pattern_candidate'] desc=Frequency feature for token 'project' in an email message. +- word_freq_re: role=feature, type=numeric_sparse_frequency. tags=['condition_candidate', 'measure', 'rare_pattern_candidate'] desc=Frequency feature for token 're' in an email message. +- word_freq_edu: role=feature, type=numeric_sparse_frequency. tags=['condition_candidate', 'measure', 'rare_pattern_candidate'] desc=Frequency feature for token 'edu' in an email message. +- word_freq_table: role=feature, type=numeric_sparse_frequency. tags=['condition_candidate', 'measure', 'rare_pattern_candidate'] desc=Frequency feature for token 'table' in an email message. +- word_freq_conference: role=feature, type=numeric_sparse_frequency. tags=['condition_candidate', 'measure', 'rare_pattern_candidate'] desc=Frequency feature for token 'conference' in an email message. +- char_freq_%3B: role=feature, type=numeric_sparse_frequency. tags=['condition_candidate', 'measure', 'rare_pattern_candidate'] desc=Character frequency feature for symbol ';'. +- char_freq_%28: role=feature, type=numeric_sparse_frequency. tags=['condition_candidate', 'measure', 'rare_pattern_candidate'] desc=Character frequency feature for symbol '('. +- char_freq_%5B: role=feature, type=numeric_sparse_frequency. tags=['condition_candidate', 'measure', 'rare_pattern_candidate'] desc=Character frequency feature for symbol '['. +- char_freq_%21: role=feature, type=numeric_sparse_frequency. tags=['condition_candidate', 'measure', 'rare_pattern_candidate'] desc=Character frequency feature for symbol '!'. +- char_freq_%24: role=feature, type=numeric_sparse_frequency. tags=['condition_candidate', 'measure', 'rare_pattern_candidate'] desc=Character frequency feature for symbol '$'. +- char_freq_%23: role=feature, type=numeric_sparse_frequency. tags=['condition_candidate', 'measure', 'rare_pattern_candidate'] desc=Character frequency feature for symbol '#'. +- capital_run_length_average: role=feature, type=numeric_heavy_tail. tags=['condition_candidate', 'measure', 'rare_pattern_candidate'] desc=Average length of uninterrupted capital-letter runs. +- capital_run_length_longest: role=feature, type=numeric_heavy_tail. tags=['condition_candidate', 'measure', 'rare_pattern_candidate'] desc=Longest uninterrupted capital-letter run. +- capital_run_length_total: role=feature, type=numeric_heavy_tail. tags=['condition_candidate', 'measure', 'rare_pattern_candidate'] desc=Total number of capital letters in runs. +- class: role=target, type=binary_target. tags=['target_candidate'] desc=Binary spam label (1=spam, 0=non-spam). +- useful_field_combinations: [['word_freq_free', 'word_freq_you', 'class'], ['char_freq_%21', 'capital_run_length_longest', 'class'], ['word_freq_remove', 'word_freq_money', 'class']] +- fields_requiring_caution: ['capital_run_length_average', 'capital_run_length_longest', 'capital_run_length_total'] +- source_url: https://www.openml.org/d/44 + +SQLite schema snapshot: +{ + "table_name": "n1", + "quoted_table_name": "\"n1\"", + "row_count": 4601, + "columns": [ + { + "name": "word_freq_make", + "type": "TEXT", + "notnull": false, + "pk": false + }, + { + "name": "word_freq_address", + "type": "TEXT", + "notnull": false, + "pk": false + }, + { + "name": "word_freq_all", + "type": "TEXT", + "notnull": false, + "pk": false + }, + { + "name": "word_freq_3d", + "type": "TEXT", + "notnull": false, + "pk": false + }, + { + "name": "word_freq_our", + "type": "TEXT", + "notnull": false, + "pk": false + }, + { + "name": "word_freq_over", + "type": "TEXT", + "notnull": false, + "pk": false + }, + { + "name": "word_freq_remove", + "type": "TEXT", + "notnull": false, + "pk": false + }, + { + "name": "word_freq_internet", + "type": "TEXT", + "notnull": false, + "pk": false + }, + { + "name": "word_freq_order", + "type": "TEXT", + "notnull": false, + "pk": false + }, + { + "name": "word_freq_mail", + "type": "TEXT", + "notnull": false, + "pk": false + }, + { + "name": "word_freq_receive", + "type": "TEXT", + "notnull": false, + "pk": false + }, + { + "name": "word_freq_will", + "type": "TEXT", + "notnull": false, + "pk": false + }, + { + "name": "word_freq_people", + "type": "TEXT", + "notnull": false, + "pk": false + }, + { + "name": "word_freq_report", + "type": "TEXT", + "notnull": false, + "pk": false + }, + { + "name": "word_freq_addresses", + "type": "TEXT", + "notnull": false, + "pk": false + }, + { + "name": "word_freq_free", + "type": "TEXT", + "notnull": false, + "pk": false + }, + { + "name": "word_freq_business", + "type": "TEXT", + "notnull": false, + "pk": false + }, + { + "name": "word_freq_email", + "type": "TEXT", + "notnull": false, + "pk": false + }, + { + "name": "word_freq_you", + "type": "TEXT", + "notnull": false, + "pk": false + }, + { + "name": "word_freq_credit", + "type": "TEXT", + "notnull": false, + "pk": false + }, + { + "name": "word_freq_your", + "type": "TEXT", + "notnull": false, + "pk": false + }, + { + "name": "word_freq_font", + "type": "TEXT", + "notnull": false, + "pk": false + }, + { + "name": "word_freq_000", + "type": "TEXT", + "notnull": false, + "pk": false + }, + { + "name": "word_freq_money", + "type": "TEXT", + "notnull": false, + "pk": false + }, + { + "name": "word_freq_hp", + "type": "TEXT", + "notnull": false, + "pk": false + }, + { + "name": "word_freq_hpl", + "type": "TEXT", + "notnull": false, + "pk": false + }, + { + "name": "word_freq_george", + "type": "TEXT", + "notnull": false, + "pk": false + }, + { + "name": "word_freq_650", + "type": "TEXT", + "notnull": false, + "pk": false + }, + { + "name": "word_freq_lab", + "type": "TEXT", + "notnull": false, + "pk": false + }, + { + "name": "word_freq_labs", + "type": "TEXT", + "notnull": false, + "pk": false + }, + { + "name": "word_freq_telnet", + "type": "TEXT", + "notnull": false, + "pk": false + }, + { + "name": "word_freq_857", + "type": "TEXT", + "notnull": false, + "pk": false + }, + { + "name": "word_freq_data", + "type": "TEXT", + "notnull": false, + "pk": false + }, + { + "name": "word_freq_415", + "type": "TEXT", + "notnull": false, + "pk": false + }, + { + "name": "word_freq_85", + "type": "TEXT", + "notnull": false, + "pk": false + }, + { + "name": "word_freq_technology", + "type": "TEXT", + "notnull": false, + "pk": false + }, + { + "name": "word_freq_1999", + "type": "TEXT", + "notnull": false, + "pk": false + }, + { + "name": "word_freq_parts", + "type": "TEXT", + "notnull": false, + "pk": false + }, + { + "name": "word_freq_pm", + "type": "TEXT", + "notnull": false, + "pk": false + }, + { + "name": "word_freq_direct", + "type": "TEXT", + "notnull": false, + "pk": false + }, + { + "name": "word_freq_cs", + "type": "TEXT", + "notnull": false, + "pk": false + }, + { + "name": "word_freq_meeting", + "type": "TEXT", + "notnull": false, + "pk": false + }, + { + "name": "word_freq_original", + "type": "TEXT", + "notnull": false, + "pk": false + }, + { + "name": "word_freq_project", + "type": "TEXT", + "notnull": false, + "pk": false + }, + { + "name": "word_freq_re", + "type": "TEXT", + "notnull": false, + "pk": false + }, + { + "name": "word_freq_edu", + "type": "TEXT", + "notnull": false, + "pk": false + }, + { + "name": "word_freq_table", + "type": "TEXT", + "notnull": false, + "pk": false + }, + { + "name": "word_freq_conference", + "type": "TEXT", + "notnull": false, + "pk": false + }, + { + "name": "char_freq_%3B", + "type": "TEXT", + "notnull": false, + "pk": false + }, + { + "name": "char_freq_%28", + "type": "TEXT", + "notnull": false, + "pk": false + }, + { + "name": "char_freq_%5B", + "type": "TEXT", + "notnull": false, + "pk": false + }, + { + "name": "char_freq_%21", + "type": "TEXT", + "notnull": false, + "pk": false + }, + { + "name": "char_freq_%24", + "type": "TEXT", + "notnull": false, + "pk": false + }, + { + "name": "char_freq_%23", + "type": "TEXT", + "notnull": false, + "pk": false + }, + { + "name": "capital_run_length_average", + "type": "TEXT", + "notnull": false, + "pk": false + }, + { + "name": "capital_run_length_longest", + "type": "TEXT", + "notnull": false, + "pk": false + }, + { + "name": "capital_run_length_total", + "type": "TEXT", + "notnull": false, + "pk": false + }, + { + "name": "class", + "type": "TEXT", + "notnull": false, + "pk": false + } + ], + "sample_rows": [ + { + "word_freq_make": "0", + "word_freq_address": "0.64", + "word_freq_all": "0.64", + "word_freq_3d": "0", + "word_freq_our": "0.32", + "word_freq_over": "0", + "word_freq_remove": "0", + "word_freq_internet": "0", + "word_freq_order": "0", + "word_freq_mail": "0", + "word_freq_receive": "0", + "word_freq_will": "0.64", + "word_freq_people": "0", + "word_freq_report": "0", + "word_freq_addresses": "0", + "word_freq_free": "0.32", + "word_freq_business": "0", + "word_freq_email": "1.29", + "word_freq_you": "1.93", + "word_freq_credit": "0", + "word_freq_your": "0.96", + "word_freq_font": "0", + "word_freq_000": "0", + "word_freq_money": "0", + "word_freq_hp": "0", + "word_freq_hpl": "0", + "word_freq_george": "0", + "word_freq_650": "0", + "word_freq_lab": "0", + "word_freq_labs": "0", + "word_freq_telnet": "0", + "word_freq_857": "0", + "word_freq_data": "0", + "word_freq_415": "0", + "word_freq_85": "0", + "word_freq_technology": "0", + "word_freq_1999": "0", + "word_freq_parts": "0", + "word_freq_pm": "0", + "word_freq_direct": "0", + "word_freq_cs": "0", + "word_freq_meeting": "0", + "word_freq_original": "0", + "word_freq_project": "0", + "word_freq_re": "0", + "word_freq_edu": "0", + "word_freq_table": "0", + "word_freq_conference": "0", + "char_freq_%3B": "0", + "char_freq_%28": "0", + "char_freq_%5B": "0", + "char_freq_%21": "0.778", + "char_freq_%24": "0", + "char_freq_%23": "0", + "capital_run_length_average": "3.756", + "capital_run_length_longest": "61", + "capital_run_length_total": "278", + "class": "1" + }, + { + "word_freq_make": "0.21", + "word_freq_address": "0.28", + "word_freq_all": "0.5", + "word_freq_3d": "0", + "word_freq_our": "0.14", + "word_freq_over": "0.28", + "word_freq_remove": "0.21", + "word_freq_internet": "0.07", + "word_freq_order": "0", + "word_freq_mail": "0.94", + "word_freq_receive": "0.21", + "word_freq_will": "0.79", + "word_freq_people": "0.65", + "word_freq_report": "0.21", + "word_freq_addresses": "0.14", + "word_freq_free": "0.14", + "word_freq_business": "0.07", + "word_freq_email": "0.28", + "word_freq_you": "3.47", + "word_freq_credit": "0", + "word_freq_your": "1.59", + "word_freq_font": "0", + "word_freq_000": "0.43", + "word_freq_money": "0.43", + "word_freq_hp": "0", + "word_freq_hpl": "0", + "word_freq_george": "0", + "word_freq_650": "0", + "word_freq_lab": "0", + "word_freq_labs": "0", + "word_freq_telnet": "0", + "word_freq_857": "0", + "word_freq_data": "0", + "word_freq_415": "0", + "word_freq_85": "0", + "word_freq_technology": "0", + "word_freq_1999": "0.07", + "word_freq_parts": "0", + "word_freq_pm": "0", + "word_freq_direct": "0", + "word_freq_cs": "0", + "word_freq_meeting": "0", + "word_freq_original": "0", + "word_freq_project": "0", + "word_freq_re": "0", + "word_freq_edu": "0", + "word_freq_table": "0", + "word_freq_conference": "0", + "char_freq_%3B": "0", + "char_freq_%28": "0.132", + "char_freq_%5B": "0", + "char_freq_%21": "0.372", + "char_freq_%24": "0.18", + "char_freq_%23": "0.048", + "capital_run_length_average": "5.114", + "capital_run_length_longest": "101", + "capital_run_length_total": "1028", + "class": "1" + }, + { + "word_freq_make": "0.06", + "word_freq_address": "0", + "word_freq_all": "0.71", + "word_freq_3d": "0", + "word_freq_our": "1.23", + "word_freq_over": "0.19", + "word_freq_remove": "0.19", + "word_freq_internet": "0.12", + "word_freq_order": "0.64", + "word_freq_mail": "0.25", + "word_freq_receive": "0.38", + "word_freq_will": "0.45", + "word_freq_people": "0.12", + "word_freq_report": "0", + "word_freq_addresses": "1.75", + "word_freq_free": "0.06", + "word_freq_business": "0.06", + "word_freq_email": "1.03", + "word_freq_you": "1.36", + "word_freq_credit": "0.32", + "word_freq_your": "0.51", + "word_freq_font": "0", + "word_freq_000": "1.16", + "word_freq_money": "0.06", + "word_freq_hp": "0", + "word_freq_hpl": "0", + "word_freq_george": "0", + "word_freq_650": "0", + "word_freq_lab": "0", + "word_freq_labs": "0", + "word_freq_telnet": "0", + "word_freq_857": "0", + "word_freq_data": "0", + "word_freq_415": "0", + "word_freq_85": "0", + "word_freq_technology": "0", + "word_freq_1999": "0", + "word_freq_parts": "0", + "word_freq_pm": "0", + "word_freq_direct": "0.06", + "word_freq_cs": "0", + "word_freq_meeting": "0", + "word_freq_original": "0.12", + "word_freq_project": "0", + "word_freq_re": "0.06", + "word_freq_edu": "0.06", + "word_freq_table": "0", + "word_freq_conference": "0", + "char_freq_%3B": "0.01", + "char_freq_%28": "0.143", + "char_freq_%5B": "0", + "char_freq_%21": "0.276", + "char_freq_%24": "0.184", + "char_freq_%23": "0.01", + "capital_run_length_average": "9.821", + "capital_run_length_longest": "485", + "capital_run_length_total": "2259", + "class": "1" + }, + { + "word_freq_make": "0", + "word_freq_address": "0", + "word_freq_all": "0", + "word_freq_3d": "0", + "word_freq_our": "0.63", + "word_freq_over": "0", + "word_freq_remove": "0.31", + "word_freq_internet": "0.63", + "word_freq_order": "0.31", + "word_freq_mail": "0.63", + "word_freq_receive": "0.31", + "word_freq_will": "0.31", + "word_freq_people": "0.31", + "word_freq_report": "0", + "word_freq_addresses": "0", + "word_freq_free": "0.31", + "word_freq_business": "0", + "word_freq_email": "0", + "word_freq_you": "3.18", + "word_freq_credit": "0", + "word_freq_your": "0.31", + "word_freq_font": "0", + "word_freq_000": "0", + "word_freq_money": "0", + "word_freq_hp": "0", + "word_freq_hpl": "0", + "word_freq_george": "0", + "word_freq_650": "0", + "word_freq_lab": "0", + "word_freq_labs": "0", + "word_freq_telnet": "0", + "word_freq_857": "0", + "word_freq_data": "0", + "word_freq_415": "0", + "word_freq_85": "0", + "word_freq_technology": "0", + "word_freq_1999": "0", + "word_freq_parts": "0", + "word_freq_pm": "0", + "word_freq_direct": "0", + "word_freq_cs": "0", + "word_freq_meeting": "0", + "word_freq_original": "0", + "word_freq_project": "0", + "word_freq_re": "0", + "word_freq_edu": "0", + "word_freq_table": "0", + "word_freq_conference": "0", + "char_freq_%3B": "0", + "char_freq_%28": "0.137", + "char_freq_%5B": "0", + "char_freq_%21": "0.137", + "char_freq_%24": "0", + "char_freq_%23": "0", + "capital_run_length_average": "3.537", + "capital_run_length_longest": "40", + "capital_run_length_total": "191", + "class": "1" + }, + { + "word_freq_make": "0", + "word_freq_address": "0", + "word_freq_all": "0", + "word_freq_3d": "0", + "word_freq_our": "0.63", + "word_freq_over": "0", + "word_freq_remove": "0.31", + "word_freq_internet": "0.63", + "word_freq_order": "0.31", + "word_freq_mail": "0.63", + "word_freq_receive": "0.31", + "word_freq_will": "0.31", + "word_freq_people": "0.31", + "word_freq_report": "0", + "word_freq_addresses": "0", + "word_freq_free": "0.31", + "word_freq_business": "0", + "word_freq_email": "0", + "word_freq_you": "3.18", + "word_freq_credit": "0", + "word_freq_your": "0.31", + "word_freq_font": "0", + "word_freq_000": "0", + "word_freq_money": "0", + "word_freq_hp": "0", + "word_freq_hpl": "0", + "word_freq_george": "0", + "word_freq_650": "0", + "word_freq_lab": "0", + "word_freq_labs": "0", + "word_freq_telnet": "0", + "word_freq_857": "0", + "word_freq_data": "0", + "word_freq_415": "0", + "word_freq_85": "0", + "word_freq_technology": "0", + "word_freq_1999": "0", + "word_freq_parts": "0", + "word_freq_pm": "0", + "word_freq_direct": "0", + "word_freq_cs": "0", + "word_freq_meeting": "0", + "word_freq_original": "0", + "word_freq_project": "0", + "word_freq_re": "0", + "word_freq_edu": "0", + "word_freq_table": "0", + "word_freq_conference": "0", + "char_freq_%3B": "0", + "char_freq_%28": "0.135", + "char_freq_%5B": "0", + "char_freq_%21": "0.135", + "char_freq_%24": "0", + "char_freq_%23": "0", + "capital_run_length_average": "3.537", + "capital_run_length_longest": "40", + "capital_run_length_total": "191", + "class": "1" + } + ] +} + +Shortlisted templates: +[ + { + "template_id": "tpl_m4_window_partition_avg", + "template_name": "Window Partition Average", + "primary_family": "conditional_dependency_structure", + "portability": "partial", + "sql_skeleton": "SELECT DISTINCT {group_col},\n AVG({measure_col}) OVER (PARTITION BY {group_col}) AS avg_measure\nFROM {table}\nORDER BY avg_measure DESC;", + "required_roles": [ + "group_col", + "measure_col" + ] + } +] + +Problem instance: +{ + "dataset_id": "n1", + "question": "Use template Window Partition Average to probe slice_level_consistency with semantic role ranked_signal_view. Focus on group_col=class, measure_col=char_freq_%5B.", + "planned_template_id": "tpl_m4_window_partition_avg", + "bindings": { + "group_col": "class", + "measure_col": "char_freq_%5B", + "top_k": 15, + "top_n": 6, + "num_tiles": 10, + "percentile_value": 0.9, + "z_threshold": 2.0, + "fraction_threshold": 0.05, + "baseline_multiplier": 1.75, + "baseline_fraction": 0.1, + "min_group_size": 5, + "min_support": 4, + "measure_threshold": 0.0, + "time_grain": "month", + "lookback_rows": 3, + "current_period_start": "'2024-01-01'", + "current_period_end": "'2024-04-01'", + "previous_period_start": "'2023-10-01'", + "previous_period_end": "'2024-01-01'", + "drift_ratio_threshold": 0.8 + }, + "can_vary": [], + "must_fix": [], + "runtime_sql_skeleton": "SELECT DISTINCT {group_col},\n AVG({measure_col}) OVER (PARTITION BY {group_col}) AS avg_measure\nFROM {table}\nORDER BY avg_measure DESC;" +} + +Repair context: +{} diff --git a/Query/sql/v2/runs/v2_cli_20260502_081223_d/n1/artifacts/v2q_n1_cff363bb65d6bf4b/cli/sql_response_attempt_1.raw.txt b/Query/sql/v2/runs/v2_cli_20260502_081223_d/n1/artifacts/v2q_n1_cff363bb65d6bf4b/cli/sql_response_attempt_1.raw.txt new file mode 100644 index 0000000000000000000000000000000000000000..952d0e3cfc9c4d198cc892767674f19592938cd6 --- /dev/null +++ b/Query/sql/v2/runs/v2_cli_20260502_081223_d/n1/artifacts/v2q_n1_cff363bb65d6bf4b/cli/sql_response_attempt_1.raw.txt @@ -0,0 +1,4 @@ +{"type":"thread.started","thread_id":"019e40e7-1a01-7483-b6b2-6b1947613801"} +{"type":"turn.started"} +{"type":"item.completed","item":{"id":"item_0","type":"agent_message","text":"{\"sql\":\"-- template_id: tpl_m4_window_partition_avg\\nSELECT DISTINCT\\n \\\"class\\\",\\n AVG(CAST(\\\"char_freq_%5B\\\" AS REAL)) OVER (PARTITION BY \\\"class\\\") AS \\\"avg_measure\\\"\\nFROM \\\"n1\\\"\\nORDER BY \\\"avg_measure\\\" DESC;\",\"notes\":\"Used the provided Window Partition Average template with group_col=\\\"class\\\" and measure_col=\\\"char_freq_%5B\\\". CAST to REAL is included because the schema snapshot shows numeric features stored as TEXT in SQLite.\"}"}} +{"type":"turn.completed","usage":{"input_tokens":20330,"cached_input_tokens":19840,"output_tokens":511,"reasoning_output_tokens":395}} diff --git a/Query/sql/v2/runs/v2_cli_20260502_081223_d/n1/artifacts/v2q_n1_cff363bb65d6bf4b/cli/sql_response_attempt_1.txt b/Query/sql/v2/runs/v2_cli_20260502_081223_d/n1/artifacts/v2q_n1_cff363bb65d6bf4b/cli/sql_response_attempt_1.txt new file mode 100644 index 0000000000000000000000000000000000000000..1ca04b5c8d8c53423a3cdc8adbeeb5f84105f418 --- /dev/null +++ b/Query/sql/v2/runs/v2_cli_20260502_081223_d/n1/artifacts/v2q_n1_cff363bb65d6bf4b/cli/sql_response_attempt_1.txt @@ -0,0 +1 @@ +{"sql":"-- template_id: tpl_m4_window_partition_avg\nSELECT DISTINCT\n \"class\",\n AVG(CAST(\"char_freq_%5B\" AS REAL)) OVER (PARTITION BY \"class\") AS \"avg_measure\"\nFROM \"n1\"\nORDER BY \"avg_measure\" DESC;","notes":"Used the provided Window Partition Average template with group_col=\"class\" and measure_col=\"char_freq_%5B\". CAST to REAL is included because the schema snapshot shows numeric features stored as TEXT in SQLite."} \ No newline at end of file diff --git a/Query/sql/v2/runs/v2_cli_20260502_081223_d/n1/artifacts/v2q_n1_cff363bb65d6bf4b/cli/sql_stderr_attempt_1.txt b/Query/sql/v2/runs/v2_cli_20260502_081223_d/n1/artifacts/v2q_n1_cff363bb65d6bf4b/cli/sql_stderr_attempt_1.txt new file mode 100644 index 0000000000000000000000000000000000000000..e69de29bb2d1d6434b8b29ae775ad8c2e48c5391 diff --git a/Query/sql/v2/runs/v2_cli_20260502_081223_d/n1/artifacts/v2q_n1_d298d05ffbfff4b2/cli/conversation.jsonl b/Query/sql/v2/runs/v2_cli_20260502_081223_d/n1/artifacts/v2q_n1_d298d05ffbfff4b2/cli/conversation.jsonl new file mode 100644 index 0000000000000000000000000000000000000000..8c81481e57e0864aebfd63fef24635e6db27f3f1 --- /dev/null +++ b/Query/sql/v2/runs/v2_cli_20260502_081223_d/n1/artifacts/v2q_n1_d298d05ffbfff4b2/cli/conversation.jsonl @@ -0,0 +1,2 @@ +{"attempt": 1, "phase": "sql_generation", "role": "user", "content_path": "cli/sql_prompt_attempt_1.txt", "metrics": {"chars": 29324, "bytes_utf8": 29324, "lines": 790, "estimated_tokens": null}} +{"attempt": 1, "phase": "sql_generation", "role": "assistant", "content_path": "cli/sql_response_attempt_1.txt", "raw_content_path": "cli/sql_response_attempt_1.raw.txt", "stderr_path": "cli/sql_stderr_attempt_1.txt", "metrics": {"chars": 414, "bytes_utf8": 414, "lines": 1, "estimated_tokens": null}, "usage": {"input_tokens": 20310, "cached_input_tokens": 12032, "output_tokens": 443, "reasoning_output_tokens": 332}} diff --git a/Query/sql/v2/runs/v2_cli_20260502_081223_d/n1/artifacts/v2q_n1_d298d05ffbfff4b2/cli/session_summary.json b/Query/sql/v2/runs/v2_cli_20260502_081223_d/n1/artifacts/v2q_n1_d298d05ffbfff4b2/cli/session_summary.json new file mode 100644 index 0000000000000000000000000000000000000000..80e56e0d20033d95e2d9ac2c28e2ee025a961da4 --- /dev/null +++ b/Query/sql/v2/runs/v2_cli_20260502_081223_d/n1/artifacts/v2q_n1_d298d05ffbfff4b2/cli/session_summary.json @@ -0,0 +1,25 @@ +{ + "engine": "v2-cli:codex", + "command": "codex exec --skip-git-repo-check --disable plugins --sandbox read-only --cd \"/data/jialinzhang/SQLagent\" -m gpt-5.4 --json -", + "ai_cli_calls": 1, + "usage_summary": { + "dataset_id": "n1", + "model": "v2-cli:codex", + "run_id": "v2q_n1_d298d05ffbfff4b2", + "api_calls": 0, + "input_tokens": 20310, + "cached_input_tokens": 12032, + "output_tokens": 443, + "total_tokens": 20753, + "cost_usd": 0.0, + "ai_cli_calls": 1, + "estimated_input_tokens": 0, + "estimated_output_tokens": 0, + "estimated_total_tokens": 0, + "usage_source": "ai_cli_json_usage", + "cli_elapsed_ms_total": 10991.62, + "sql_execution_elapsed_ms_total": 2.47, + "conversation_log_path": "/data/jialinzhang/TabQueryBench/sql_workloads/v2_current/runs_and_launches/runs/v2_cli_20260502_081223_d/n1/artifacts/v2q_n1_d298d05ffbfff4b2/cli/conversation.jsonl", + "note": "Executed through a local AI CLI with structured usage metadata." + } +} \ No newline at end of file diff --git a/Query/sql/v2/runs/v2_cli_20260502_081223_d/n1/artifacts/v2q_n1_d298d05ffbfff4b2/cli/sql_attempt_1.metadata.json b/Query/sql/v2/runs/v2_cli_20260502_081223_d/n1/artifacts/v2q_n1_d298d05ffbfff4b2/cli/sql_attempt_1.metadata.json new file mode 100644 index 0000000000000000000000000000000000000000..3b1bcd29e4bff4311c9ebd4112567aaed5794f5c --- /dev/null +++ b/Query/sql/v2/runs/v2_cli_20260502_081223_d/n1/artifacts/v2q_n1_d298d05ffbfff4b2/cli/sql_attempt_1.metadata.json @@ -0,0 +1,45 @@ +{ + "attempt": 1, + "phase": "sql_generation", + "command": "codex exec --skip-git-repo-check --disable plugins --sandbox read-only --cd \"/data/jialinzhang/SQLagent\" -m gpt-5.4 --json -", + "started_at": "2026-05-19T16:00:57.879978+00:00", + "ended_at": "2026-05-19T16:01:08.871653+00:00", + "elapsed_ms": 10991.62, + "prompt_metrics": { + "chars": 29324, + "bytes_utf8": 29324, + "lines": 790, + "estimated_tokens": null + }, + "stdout_metrics": { + "chars": 754, + "bytes_utf8": 754, + "lines": 4, + "estimated_tokens": null + }, + "stderr_metrics": { + "chars": 0, + "bytes_utf8": 0, + "lines": 0, + "estimated_tokens": null + }, + "parsed_output": { + "format": "jsonl_events", + "text_metrics": { + "chars": 414, + "bytes_utf8": 414, + "lines": 1, + "estimated_tokens": null + }, + "usage": { + "input_tokens": 20310, + "cached_input_tokens": 12032, + "output_tokens": 443, + "reasoning_output_tokens": 332 + } + }, + "prompt_path": "cli/sql_prompt_attempt_1.txt", + "response_path": "cli/sql_response_attempt_1.txt", + "raw_response_path": "cli/sql_response_attempt_1.raw.txt", + "stderr_path": "cli/sql_stderr_attempt_1.txt" +} \ No newline at end of file diff --git a/Query/sql/v2/runs/v2_cli_20260502_081223_d/n1/artifacts/v2q_n1_d298d05ffbfff4b2/cli/sql_prompt_attempt_1.txt b/Query/sql/v2/runs/v2_cli_20260502_081223_d/n1/artifacts/v2q_n1_d298d05ffbfff4b2/cli/sql_prompt_attempt_1.txt new file mode 100644 index 0000000000000000000000000000000000000000..91b86889b4ad275081d2b8c0d3563445c4c7f032 --- /dev/null +++ b/Query/sql/v2/runs/v2_cli_20260502_081223_d/n1/artifacts/v2q_n1_d298d05ffbfff4b2/cli/sql_prompt_attempt_1.txt @@ -0,0 +1,790 @@ +You are generating one SQLite SELECT query for a single-table SQL QA task. +Return strict JSON only, with this schema: {"sql": "...", "notes": "..."}. +Rules: +- Use only the provided table and columns. +- Do not write INSERT, UPDATE, DELETE, DROP, ALTER, CREATE, PRAGMA, ATTACH, DETACH, or VACUUM. +- Prefer the planned template and bound roles when provided. +- Add a leading SQL comment exactly like: -- template_id: . +- Generate SQLite-compatible SQL. SQLite does not support PERCENTILE_CONT or STDDEV. +- Quote identifiers with double quotes. +- Return no markdown and no extra prose. + +Dataset context: +Dataset context for SQL QA: +- dataset_id: n1 +- dataset_name: Spambase +- table_name: n1 +- table_layout: single-table dataset (do not assume joins). +- row_semantics: One row is one email represented by engineered token/character frequency and capitalization-run features. +- task_type: classification +- target_column: class +- main_row_count: 4601 +- important_fields: +- word_freq_make: role=feature, type=numeric_sparse_frequency. tags=['condition_candidate', 'measure', 'rare_pattern_candidate'] desc=Frequency feature for token 'make' in an email message. +- word_freq_address: role=feature, type=numeric_sparse_frequency. tags=['condition_candidate', 'measure', 'rare_pattern_candidate'] desc=Frequency feature for token 'address' in an email message. +- word_freq_all: role=feature, type=numeric_sparse_frequency. tags=['condition_candidate', 'measure', 'rare_pattern_candidate'] desc=Frequency feature for token 'all' in an email message. +- word_freq_3d: role=feature, type=numeric_sparse_frequency. tags=['condition_candidate', 'measure', 'rare_pattern_candidate'] desc=Frequency feature for token '3d' in an email message. +- word_freq_our: role=feature, type=numeric_sparse_frequency. tags=['condition_candidate', 'measure', 'rare_pattern_candidate'] desc=Frequency feature for token 'our' in an email message. +- word_freq_over: role=feature, type=numeric_sparse_frequency. tags=['condition_candidate', 'measure', 'rare_pattern_candidate'] desc=Frequency feature for token 'over' in an email message. +- word_freq_remove: role=feature, type=numeric_sparse_frequency. tags=['condition_candidate', 'measure', 'rare_pattern_candidate'] desc=Frequency feature for token 'remove' in an email message. +- word_freq_internet: role=feature, type=numeric_sparse_frequency. tags=['condition_candidate', 'measure', 'rare_pattern_candidate'] desc=Frequency feature for token 'internet' in an email message. +- word_freq_order: role=feature, type=numeric_sparse_frequency. tags=['condition_candidate', 'measure', 'rare_pattern_candidate'] desc=Frequency feature for token 'order' in an email message. +- word_freq_mail: role=feature, type=numeric_sparse_frequency. tags=['condition_candidate', 'measure', 'rare_pattern_candidate'] desc=Frequency feature for token 'mail' in an email message. +- word_freq_receive: role=feature, type=numeric_sparse_frequency. tags=['condition_candidate', 'measure', 'rare_pattern_candidate'] desc=Frequency feature for token 'receive' in an email message. +- word_freq_will: role=feature, type=numeric_sparse_frequency. tags=['condition_candidate', 'measure', 'rare_pattern_candidate'] desc=Frequency feature for token 'will' in an email message. +- word_freq_people: role=feature, type=numeric_sparse_frequency. tags=['condition_candidate', 'measure', 'rare_pattern_candidate'] desc=Frequency feature for token 'people' in an email message. +- word_freq_report: role=feature, type=numeric_sparse_frequency. tags=['condition_candidate', 'measure', 'rare_pattern_candidate'] desc=Frequency feature for token 'report' in an email message. +- word_freq_addresses: role=feature, type=numeric_sparse_frequency. tags=['condition_candidate', 'measure', 'rare_pattern_candidate'] desc=Frequency feature for token 'addresses' in an email message. +- word_freq_free: role=feature, type=numeric_sparse_frequency. tags=['condition_candidate', 'measure', 'rare_pattern_candidate'] desc=Frequency feature for token 'free' in an email message. +- word_freq_business: role=feature, type=numeric_sparse_frequency. tags=['condition_candidate', 'measure', 'rare_pattern_candidate'] desc=Frequency feature for token 'business' in an email message. +- word_freq_email: role=feature, type=numeric_sparse_frequency. tags=['condition_candidate', 'measure', 'rare_pattern_candidate'] desc=Frequency feature for token 'email' in an email message. +- word_freq_you: role=feature, type=numeric_sparse_frequency. tags=['condition_candidate', 'measure', 'rare_pattern_candidate'] desc=Frequency feature for token 'you' in an email message. +- word_freq_credit: role=feature, type=numeric_sparse_frequency. tags=['condition_candidate', 'measure', 'rare_pattern_candidate'] desc=Frequency feature for token 'credit' in an email message. +- word_freq_your: role=feature, type=numeric_sparse_frequency. tags=['condition_candidate', 'measure', 'rare_pattern_candidate'] desc=Frequency feature for token 'your' in an email message. +- word_freq_font: role=feature, type=numeric_sparse_frequency. tags=['condition_candidate', 'measure', 'rare_pattern_candidate'] desc=Frequency feature for token 'font' in an email message. +- word_freq_000: role=feature, type=numeric_sparse_frequency. tags=['condition_candidate', 'measure', 'rare_pattern_candidate'] desc=Frequency feature for token '000' in an email message. +- word_freq_money: role=feature, type=numeric_sparse_frequency. tags=['condition_candidate', 'measure', 'rare_pattern_candidate'] desc=Frequency feature for token 'money' in an email message. +- word_freq_hp: role=feature, type=numeric_sparse_frequency. tags=['condition_candidate', 'measure', 'rare_pattern_candidate'] desc=Frequency feature for token 'hp' in an email message. +- word_freq_hpl: role=feature, type=numeric_sparse_frequency. tags=['condition_candidate', 'measure', 'rare_pattern_candidate'] desc=Frequency feature for token 'hpl' in an email message. +- word_freq_george: role=feature, type=numeric_sparse_frequency. tags=['condition_candidate', 'measure', 'rare_pattern_candidate'] desc=Frequency feature for token 'george' in an email message. +- word_freq_650: role=feature, type=numeric_sparse_frequency. tags=['condition_candidate', 'measure', 'rare_pattern_candidate'] desc=Frequency feature for token '650' in an email message. +- word_freq_lab: role=feature, type=numeric_sparse_frequency. tags=['condition_candidate', 'measure', 'rare_pattern_candidate'] desc=Frequency feature for token 'lab' in an email message. +- word_freq_labs: role=feature, type=numeric_sparse_frequency. tags=['condition_candidate', 'measure', 'rare_pattern_candidate'] desc=Frequency feature for token 'labs' in an email message. +- word_freq_telnet: role=feature, type=numeric_sparse_frequency. tags=['condition_candidate', 'measure', 'rare_pattern_candidate'] desc=Frequency feature for token 'telnet' in an email message. +- word_freq_857: role=feature, type=numeric_sparse_frequency. tags=['condition_candidate', 'measure', 'rare_pattern_candidate'] desc=Frequency feature for token '857' in an email message. +- word_freq_data: role=feature, type=numeric_sparse_frequency. tags=['condition_candidate', 'measure', 'rare_pattern_candidate'] desc=Frequency feature for token 'data' in an email message. +- word_freq_415: role=feature, type=numeric_sparse_frequency. tags=['condition_candidate', 'measure', 'rare_pattern_candidate'] desc=Frequency feature for token '415' in an email message. +- word_freq_85: role=feature, type=numeric_sparse_frequency. tags=['condition_candidate', 'measure', 'rare_pattern_candidate'] desc=Frequency feature for token '85' in an email message. +- word_freq_technology: role=feature, type=numeric_sparse_frequency. tags=['condition_candidate', 'measure', 'rare_pattern_candidate'] desc=Frequency feature for token 'technology' in an email message. +- word_freq_1999: role=feature, type=numeric_sparse_frequency. tags=['condition_candidate', 'measure', 'rare_pattern_candidate'] desc=Frequency feature for token '1999' in an email message. +- word_freq_parts: role=feature, type=numeric_sparse_frequency. tags=['condition_candidate', 'measure', 'rare_pattern_candidate'] desc=Frequency feature for token 'parts' in an email message. +- word_freq_pm: role=feature, type=numeric_sparse_frequency. tags=['condition_candidate', 'measure', 'rare_pattern_candidate'] desc=Frequency feature for token 'pm' in an email message. +- word_freq_direct: role=feature, type=numeric_sparse_frequency. tags=['condition_candidate', 'measure', 'rare_pattern_candidate'] desc=Frequency feature for token 'direct' in an email message. +- word_freq_cs: role=feature, type=numeric_sparse_frequency. tags=['condition_candidate', 'measure', 'rare_pattern_candidate'] desc=Frequency feature for token 'cs' in an email message. +- word_freq_meeting: role=feature, type=numeric_sparse_frequency. tags=['condition_candidate', 'measure', 'rare_pattern_candidate'] desc=Frequency feature for token 'meeting' in an email message. +- word_freq_original: role=feature, type=numeric_sparse_frequency. tags=['condition_candidate', 'measure', 'rare_pattern_candidate'] desc=Frequency feature for token 'original' in an email message. +- word_freq_project: role=feature, type=numeric_sparse_frequency. tags=['condition_candidate', 'measure', 'rare_pattern_candidate'] desc=Frequency feature for token 'project' in an email message. +- word_freq_re: role=feature, type=numeric_sparse_frequency. tags=['condition_candidate', 'measure', 'rare_pattern_candidate'] desc=Frequency feature for token 're' in an email message. +- word_freq_edu: role=feature, type=numeric_sparse_frequency. tags=['condition_candidate', 'measure', 'rare_pattern_candidate'] desc=Frequency feature for token 'edu' in an email message. +- word_freq_table: role=feature, type=numeric_sparse_frequency. tags=['condition_candidate', 'measure', 'rare_pattern_candidate'] desc=Frequency feature for token 'table' in an email message. +- word_freq_conference: role=feature, type=numeric_sparse_frequency. tags=['condition_candidate', 'measure', 'rare_pattern_candidate'] desc=Frequency feature for token 'conference' in an email message. +- char_freq_%3B: role=feature, type=numeric_sparse_frequency. tags=['condition_candidate', 'measure', 'rare_pattern_candidate'] desc=Character frequency feature for symbol ';'. +- char_freq_%28: role=feature, type=numeric_sparse_frequency. tags=['condition_candidate', 'measure', 'rare_pattern_candidate'] desc=Character frequency feature for symbol '('. +- char_freq_%5B: role=feature, type=numeric_sparse_frequency. tags=['condition_candidate', 'measure', 'rare_pattern_candidate'] desc=Character frequency feature for symbol '['. +- char_freq_%21: role=feature, type=numeric_sparse_frequency. tags=['condition_candidate', 'measure', 'rare_pattern_candidate'] desc=Character frequency feature for symbol '!'. +- char_freq_%24: role=feature, type=numeric_sparse_frequency. tags=['condition_candidate', 'measure', 'rare_pattern_candidate'] desc=Character frequency feature for symbol '$'. +- char_freq_%23: role=feature, type=numeric_sparse_frequency. tags=['condition_candidate', 'measure', 'rare_pattern_candidate'] desc=Character frequency feature for symbol '#'. +- capital_run_length_average: role=feature, type=numeric_heavy_tail. tags=['condition_candidate', 'measure', 'rare_pattern_candidate'] desc=Average length of uninterrupted capital-letter runs. +- capital_run_length_longest: role=feature, type=numeric_heavy_tail. tags=['condition_candidate', 'measure', 'rare_pattern_candidate'] desc=Longest uninterrupted capital-letter run. +- capital_run_length_total: role=feature, type=numeric_heavy_tail. tags=['condition_candidate', 'measure', 'rare_pattern_candidate'] desc=Total number of capital letters in runs. +- class: role=target, type=binary_target. tags=['target_candidate'] desc=Binary spam label (1=spam, 0=non-spam). +- useful_field_combinations: [['word_freq_free', 'word_freq_you', 'class'], ['char_freq_%21', 'capital_run_length_longest', 'class'], ['word_freq_remove', 'word_freq_money', 'class']] +- fields_requiring_caution: ['capital_run_length_average', 'capital_run_length_longest', 'capital_run_length_total'] +- source_url: https://www.openml.org/d/44 + +SQLite schema snapshot: +{ + "table_name": "n1", + "quoted_table_name": "\"n1\"", + "row_count": 4601, + "columns": [ + { + "name": "word_freq_make", + "type": "TEXT", + "notnull": false, + "pk": false + }, + { + "name": "word_freq_address", + "type": "TEXT", + "notnull": false, + "pk": false + }, + { + "name": "word_freq_all", + "type": "TEXT", + "notnull": false, + "pk": false + }, + { + "name": "word_freq_3d", + "type": "TEXT", + "notnull": false, + "pk": false + }, + { + "name": "word_freq_our", + "type": "TEXT", + "notnull": false, + "pk": false + }, + { + "name": "word_freq_over", + "type": "TEXT", + "notnull": false, + "pk": false + }, + { + "name": "word_freq_remove", + "type": "TEXT", + "notnull": false, + "pk": false + }, + { + "name": "word_freq_internet", + "type": "TEXT", + "notnull": false, + "pk": false + }, + { + "name": "word_freq_order", + "type": "TEXT", + "notnull": false, + "pk": false + }, + { + "name": "word_freq_mail", + "type": "TEXT", + "notnull": false, + "pk": false + }, + { + "name": "word_freq_receive", + "type": "TEXT", + "notnull": false, + "pk": false + }, + { + "name": "word_freq_will", + "type": "TEXT", + "notnull": false, + "pk": false + }, + { + "name": "word_freq_people", + "type": "TEXT", + "notnull": false, + "pk": false + }, + { + "name": "word_freq_report", + "type": "TEXT", + "notnull": false, + "pk": false + }, + { + "name": "word_freq_addresses", + "type": "TEXT", + "notnull": false, + "pk": false + }, + { + "name": "word_freq_free", + "type": "TEXT", + "notnull": false, + "pk": false + }, + { + "name": "word_freq_business", + "type": "TEXT", + "notnull": false, + "pk": false + }, + { + "name": "word_freq_email", + "type": "TEXT", + "notnull": false, + "pk": false + }, + { + "name": "word_freq_you", + "type": "TEXT", + "notnull": false, + "pk": false + }, + { + "name": "word_freq_credit", + "type": "TEXT", + "notnull": false, + "pk": false + }, + { + "name": "word_freq_your", + "type": "TEXT", + "notnull": false, + "pk": false + }, + { + "name": "word_freq_font", + "type": "TEXT", + "notnull": false, + "pk": false + }, + { + "name": "word_freq_000", + "type": "TEXT", + "notnull": false, + "pk": false + }, + { + "name": "word_freq_money", + "type": "TEXT", + "notnull": false, + "pk": false + }, + { + "name": "word_freq_hp", + "type": "TEXT", + "notnull": false, + "pk": false + }, + { + "name": "word_freq_hpl", + "type": "TEXT", + "notnull": false, + "pk": false + }, + { + "name": "word_freq_george", + "type": "TEXT", + "notnull": false, + "pk": false + }, + { + "name": "word_freq_650", + "type": "TEXT", + "notnull": false, + "pk": false + }, + { + "name": "word_freq_lab", + "type": "TEXT", + "notnull": false, + "pk": false + }, + { + "name": "word_freq_labs", + "type": "TEXT", + "notnull": false, + "pk": false + }, + { + "name": "word_freq_telnet", + "type": "TEXT", + "notnull": false, + "pk": false + }, + { + "name": "word_freq_857", + "type": "TEXT", + "notnull": false, + "pk": false + }, + { + "name": "word_freq_data", + "type": "TEXT", + "notnull": false, + "pk": false + }, + { + "name": "word_freq_415", + "type": "TEXT", + "notnull": false, + "pk": false + }, + { + "name": "word_freq_85", + "type": "TEXT", + "notnull": false, + "pk": false + }, + { + "name": "word_freq_technology", + "type": "TEXT", + "notnull": false, + "pk": false + }, + { + "name": "word_freq_1999", + "type": "TEXT", + "notnull": false, + "pk": false + }, + { + "name": "word_freq_parts", + "type": "TEXT", + "notnull": false, + "pk": false + }, + { + "name": "word_freq_pm", + "type": "TEXT", + "notnull": false, + "pk": false + }, + { + "name": "word_freq_direct", + "type": "TEXT", + "notnull": false, + "pk": false + }, + { + "name": "word_freq_cs", + "type": "TEXT", + "notnull": false, + "pk": false + }, + { + "name": "word_freq_meeting", + "type": "TEXT", + "notnull": false, + "pk": false + }, + { + "name": "word_freq_original", + "type": "TEXT", + "notnull": false, + "pk": false + }, + { + "name": "word_freq_project", + "type": "TEXT", + "notnull": false, + "pk": false + }, + { + "name": "word_freq_re", + "type": "TEXT", + "notnull": false, + "pk": false + }, + { + "name": "word_freq_edu", + "type": "TEXT", + "notnull": false, + "pk": false + }, + { + "name": "word_freq_table", + "type": "TEXT", + "notnull": false, + "pk": false + }, + { + "name": "word_freq_conference", + "type": "TEXT", + "notnull": false, + "pk": false + }, + { + "name": "char_freq_%3B", + "type": "TEXT", + "notnull": false, + "pk": false + }, + { + "name": "char_freq_%28", + "type": "TEXT", + "notnull": false, + "pk": false + }, + { + "name": "char_freq_%5B", + "type": "TEXT", + "notnull": false, + "pk": false + }, + { + "name": "char_freq_%21", + "type": "TEXT", + "notnull": false, + "pk": false + }, + { + "name": "char_freq_%24", + "type": "TEXT", + "notnull": false, + "pk": false + }, + { + "name": "char_freq_%23", + "type": "TEXT", + "notnull": false, + "pk": false + }, + { + "name": "capital_run_length_average", + "type": "TEXT", + "notnull": false, + "pk": false + }, + { + "name": "capital_run_length_longest", + "type": "TEXT", + "notnull": false, + "pk": false + }, + { + "name": "capital_run_length_total", + "type": "TEXT", + "notnull": false, + "pk": false + }, + { + "name": "class", + "type": "TEXT", + "notnull": false, + "pk": false + } + ], + "sample_rows": [ + { + "word_freq_make": "0", + "word_freq_address": "0.64", + "word_freq_all": "0.64", + "word_freq_3d": "0", + "word_freq_our": "0.32", + "word_freq_over": "0", + "word_freq_remove": "0", + "word_freq_internet": "0", + "word_freq_order": "0", + "word_freq_mail": "0", + "word_freq_receive": "0", + "word_freq_will": "0.64", + "word_freq_people": "0", + "word_freq_report": "0", + "word_freq_addresses": "0", + "word_freq_free": "0.32", + "word_freq_business": "0", + "word_freq_email": "1.29", + "word_freq_you": "1.93", + "word_freq_credit": "0", + "word_freq_your": "0.96", + "word_freq_font": "0", + "word_freq_000": "0", + "word_freq_money": "0", + "word_freq_hp": "0", + "word_freq_hpl": "0", + "word_freq_george": "0", + "word_freq_650": "0", + "word_freq_lab": "0", + "word_freq_labs": "0", + "word_freq_telnet": "0", + "word_freq_857": "0", + "word_freq_data": "0", + "word_freq_415": "0", + "word_freq_85": "0", + "word_freq_technology": "0", + "word_freq_1999": "0", + "word_freq_parts": "0", + "word_freq_pm": "0", + "word_freq_direct": "0", + "word_freq_cs": "0", + "word_freq_meeting": "0", + "word_freq_original": "0", + "word_freq_project": "0", + "word_freq_re": "0", + "word_freq_edu": "0", + "word_freq_table": "0", + "word_freq_conference": "0", + "char_freq_%3B": "0", + "char_freq_%28": "0", + "char_freq_%5B": "0", + "char_freq_%21": "0.778", + "char_freq_%24": "0", + "char_freq_%23": "0", + "capital_run_length_average": "3.756", + "capital_run_length_longest": "61", + "capital_run_length_total": "278", + "class": "1" + }, + { + "word_freq_make": "0.21", + "word_freq_address": "0.28", + "word_freq_all": "0.5", + "word_freq_3d": "0", + "word_freq_our": "0.14", + "word_freq_over": "0.28", + "word_freq_remove": "0.21", + "word_freq_internet": "0.07", + "word_freq_order": "0", + "word_freq_mail": "0.94", + "word_freq_receive": "0.21", + "word_freq_will": "0.79", + "word_freq_people": "0.65", + "word_freq_report": "0.21", + "word_freq_addresses": "0.14", + "word_freq_free": "0.14", + "word_freq_business": "0.07", + "word_freq_email": "0.28", + "word_freq_you": "3.47", + "word_freq_credit": "0", + "word_freq_your": "1.59", + "word_freq_font": "0", + "word_freq_000": "0.43", + "word_freq_money": "0.43", + "word_freq_hp": "0", + "word_freq_hpl": "0", + "word_freq_george": "0", + "word_freq_650": "0", + "word_freq_lab": "0", + "word_freq_labs": "0", + "word_freq_telnet": "0", + "word_freq_857": "0", + "word_freq_data": "0", + "word_freq_415": "0", + "word_freq_85": "0", + "word_freq_technology": "0", + "word_freq_1999": "0.07", + "word_freq_parts": "0", + "word_freq_pm": "0", + "word_freq_direct": "0", + "word_freq_cs": "0", + "word_freq_meeting": "0", + "word_freq_original": "0", + "word_freq_project": "0", + "word_freq_re": "0", + "word_freq_edu": "0", + "word_freq_table": "0", + "word_freq_conference": "0", + "char_freq_%3B": "0", + "char_freq_%28": "0.132", + "char_freq_%5B": "0", + "char_freq_%21": "0.372", + "char_freq_%24": "0.18", + "char_freq_%23": "0.048", + "capital_run_length_average": "5.114", + "capital_run_length_longest": "101", + "capital_run_length_total": "1028", + "class": "1" + }, + { + "word_freq_make": "0.06", + "word_freq_address": "0", + "word_freq_all": "0.71", + "word_freq_3d": "0", + "word_freq_our": "1.23", + "word_freq_over": "0.19", + "word_freq_remove": "0.19", + "word_freq_internet": "0.12", + "word_freq_order": "0.64", + "word_freq_mail": "0.25", + "word_freq_receive": "0.38", + "word_freq_will": "0.45", + "word_freq_people": "0.12", + "word_freq_report": "0", + "word_freq_addresses": "1.75", + "word_freq_free": "0.06", + "word_freq_business": "0.06", + "word_freq_email": "1.03", + "word_freq_you": "1.36", + "word_freq_credit": "0.32", + "word_freq_your": "0.51", + "word_freq_font": "0", + "word_freq_000": "1.16", + "word_freq_money": "0.06", + "word_freq_hp": "0", + "word_freq_hpl": "0", + "word_freq_george": "0", + "word_freq_650": "0", + "word_freq_lab": "0", + "word_freq_labs": "0", + "word_freq_telnet": "0", + "word_freq_857": "0", + "word_freq_data": "0", + "word_freq_415": "0", + "word_freq_85": "0", + "word_freq_technology": "0", + "word_freq_1999": "0", + "word_freq_parts": "0", + "word_freq_pm": "0", + "word_freq_direct": "0.06", + "word_freq_cs": "0", + "word_freq_meeting": "0", + "word_freq_original": "0.12", + "word_freq_project": "0", + "word_freq_re": "0.06", + "word_freq_edu": "0.06", + "word_freq_table": "0", + "word_freq_conference": "0", + "char_freq_%3B": "0.01", + "char_freq_%28": "0.143", + "char_freq_%5B": "0", + "char_freq_%21": "0.276", + "char_freq_%24": "0.184", + "char_freq_%23": "0.01", + "capital_run_length_average": "9.821", + "capital_run_length_longest": "485", + "capital_run_length_total": "2259", + "class": "1" + }, + { + "word_freq_make": "0", + "word_freq_address": "0", + "word_freq_all": "0", + "word_freq_3d": "0", + "word_freq_our": "0.63", + "word_freq_over": "0", + "word_freq_remove": "0.31", + "word_freq_internet": "0.63", + "word_freq_order": "0.31", + "word_freq_mail": "0.63", + "word_freq_receive": "0.31", + "word_freq_will": "0.31", + "word_freq_people": "0.31", + "word_freq_report": "0", + "word_freq_addresses": "0", + "word_freq_free": "0.31", + "word_freq_business": "0", + "word_freq_email": "0", + "word_freq_you": "3.18", + "word_freq_credit": "0", + "word_freq_your": "0.31", + "word_freq_font": "0", + "word_freq_000": "0", + "word_freq_money": "0", + "word_freq_hp": "0", + "word_freq_hpl": "0", + "word_freq_george": "0", + "word_freq_650": "0", + "word_freq_lab": "0", + "word_freq_labs": "0", + "word_freq_telnet": "0", + "word_freq_857": "0", + "word_freq_data": "0", + "word_freq_415": "0", + "word_freq_85": "0", + "word_freq_technology": "0", + "word_freq_1999": "0", + "word_freq_parts": "0", + "word_freq_pm": "0", + "word_freq_direct": "0", + "word_freq_cs": "0", + "word_freq_meeting": "0", + "word_freq_original": "0", + "word_freq_project": "0", + "word_freq_re": "0", + "word_freq_edu": "0", + "word_freq_table": "0", + "word_freq_conference": "0", + "char_freq_%3B": "0", + "char_freq_%28": "0.137", + "char_freq_%5B": "0", + "char_freq_%21": "0.137", + "char_freq_%24": "0", + "char_freq_%23": "0", + "capital_run_length_average": "3.537", + "capital_run_length_longest": "40", + "capital_run_length_total": "191", + "class": "1" + }, + { + "word_freq_make": "0", + "word_freq_address": "0", + "word_freq_all": "0", + "word_freq_3d": "0", + "word_freq_our": "0.63", + "word_freq_over": "0", + "word_freq_remove": "0.31", + "word_freq_internet": "0.63", + "word_freq_order": "0.31", + "word_freq_mail": "0.63", + "word_freq_receive": "0.31", + "word_freq_will": "0.31", + "word_freq_people": "0.31", + "word_freq_report": "0", + "word_freq_addresses": "0", + "word_freq_free": "0.31", + "word_freq_business": "0", + "word_freq_email": "0", + "word_freq_you": "3.18", + "word_freq_credit": "0", + "word_freq_your": "0.31", + "word_freq_font": "0", + "word_freq_000": "0", + "word_freq_money": "0", + "word_freq_hp": "0", + "word_freq_hpl": "0", + "word_freq_george": "0", + "word_freq_650": "0", + "word_freq_lab": "0", + "word_freq_labs": "0", + "word_freq_telnet": "0", + "word_freq_857": "0", + "word_freq_data": "0", + "word_freq_415": "0", + "word_freq_85": "0", + "word_freq_technology": "0", + "word_freq_1999": "0", + "word_freq_parts": "0", + "word_freq_pm": "0", + "word_freq_direct": "0", + "word_freq_cs": "0", + "word_freq_meeting": "0", + "word_freq_original": "0", + "word_freq_project": "0", + "word_freq_re": "0", + "word_freq_edu": "0", + "word_freq_table": "0", + "word_freq_conference": "0", + "char_freq_%3B": "0", + "char_freq_%28": "0.135", + "char_freq_%5B": "0", + "char_freq_%21": "0.135", + "char_freq_%24": "0", + "char_freq_%23": "0", + "capital_run_length_average": "3.537", + "capital_run_length_longest": "40", + "capital_run_length_total": "191", + "class": "1" + } + ] +} + +Shortlisted templates: +[ + { + "template_id": "tpl_threshold_rarity_cdf", + "template_name": "Threshold Rarity CDF", + "primary_family": "tail_rarity_structure", + "portability": "yes", + "sql_skeleton": "SELECT AVG(CASE WHEN {measure_col} <= {measure_threshold} THEN 1 ELSE 0 END) AS empirical_cdf_at_threshold\nFROM {table};", + "required_roles": [ + "measure_col" + ] + } +] + +Problem instance: +{ + "dataset_id": "n1", + "question": "Use template Threshold Rarity CDF to probe tail_set_consistency with semantic role rare_extreme_view. Focus on measure_col=capital_run_length_longest.", + "planned_template_id": "tpl_threshold_rarity_cdf", + "bindings": { + "measure_col": "capital_run_length_longest", + "top_k": 12, + "top_n": 3, + "num_tiles": 10, + "percentile_value": 0.95, + "z_threshold": 2.0, + "fraction_threshold": 0.1, + "baseline_multiplier": 1.5, + "baseline_fraction": 0.1, + "min_group_size": 5, + "min_support": 5, + "measure_threshold": 43.0, + "time_grain": "month", + "lookback_rows": 3, + "current_period_start": "'2024-01-01'", + "current_period_end": "'2024-04-01'", + "previous_period_start": "'2023-10-01'", + "previous_period_end": "'2024-01-01'", + "drift_ratio_threshold": 0.8 + }, + "can_vary": [], + "must_fix": [], + "runtime_sql_skeleton": "SELECT AVG(CASE WHEN {measure_col} <= {measure_threshold} THEN 1 ELSE 0 END) AS empirical_cdf_at_threshold\nFROM {table};" +} + +Repair context: +{} diff --git a/Query/sql/v2/runs/v2_cli_20260502_081223_d/n1/artifacts/v2q_n1_d298d05ffbfff4b2/cli/sql_response_attempt_1.raw.txt b/Query/sql/v2/runs/v2_cli_20260502_081223_d/n1/artifacts/v2q_n1_d298d05ffbfff4b2/cli/sql_response_attempt_1.raw.txt new file mode 100644 index 0000000000000000000000000000000000000000..67edc48de54de01b81d20a6262a1b22820cd7f50 --- /dev/null +++ b/Query/sql/v2/runs/v2_cli_20260502_081223_d/n1/artifacts/v2q_n1_d298d05ffbfff4b2/cli/sql_response_attempt_1.raw.txt @@ -0,0 +1,4 @@ +{"type":"thread.started","thread_id":"019e40f8-0b44-7330-a76d-c2b83b660d1c"} +{"type":"turn.started"} +{"type":"item.completed","item":{"id":"item_0","type":"agent_message","text":"{\"sql\":\"-- template_id: tpl_threshold_rarity_cdf\\nSELECT AVG(CASE WHEN CAST(NULLIF(\\\"capital_run_length_longest\\\", '') AS REAL) <= 43.0 THEN 1 ELSE 0 END) AS empirical_cdf_at_threshold\\nFROM \\\"n1\\\";\",\"notes\":\"Applied the Threshold Rarity CDF template using measure_col=\\\"capital_run_length_longest\\\" and measure_threshold=43.0. The measure column is cast from TEXT to REAL to ensure numeric comparison in SQLite.\"}"}} +{"type":"turn.completed","usage":{"input_tokens":20310,"cached_input_tokens":12032,"output_tokens":443,"reasoning_output_tokens":332}} diff --git a/Query/sql/v2/runs/v2_cli_20260502_081223_d/n1/artifacts/v2q_n1_d298d05ffbfff4b2/cli/sql_response_attempt_1.txt b/Query/sql/v2/runs/v2_cli_20260502_081223_d/n1/artifacts/v2q_n1_d298d05ffbfff4b2/cli/sql_response_attempt_1.txt new file mode 100644 index 0000000000000000000000000000000000000000..22d76a3abc2fdc3291e92a662161bbd9a64c7060 --- /dev/null +++ b/Query/sql/v2/runs/v2_cli_20260502_081223_d/n1/artifacts/v2q_n1_d298d05ffbfff4b2/cli/sql_response_attempt_1.txt @@ -0,0 +1 @@ +{"sql":"-- template_id: tpl_threshold_rarity_cdf\nSELECT AVG(CASE WHEN CAST(NULLIF(\"capital_run_length_longest\", '') AS REAL) <= 43.0 THEN 1 ELSE 0 END) AS empirical_cdf_at_threshold\nFROM \"n1\";","notes":"Applied the Threshold Rarity CDF template using measure_col=\"capital_run_length_longest\" and measure_threshold=43.0. The measure column is cast from TEXT to REAL to ensure numeric comparison in SQLite."} \ No newline at end of file diff --git a/Query/sql/v2/runs/v2_cli_20260502_081223_d/n1/artifacts/v2q_n1_d298d05ffbfff4b2/cli/sql_stderr_attempt_1.txt b/Query/sql/v2/runs/v2_cli_20260502_081223_d/n1/artifacts/v2q_n1_d298d05ffbfff4b2/cli/sql_stderr_attempt_1.txt new file mode 100644 index 0000000000000000000000000000000000000000..e69de29bb2d1d6434b8b29ae775ad8c2e48c5391 diff --git a/Query/sql/v2/runs/v2_cli_20260502_081223_d/n1/artifacts/v2q_n1_d896d5a1ee904606/cli/conversation.jsonl b/Query/sql/v2/runs/v2_cli_20260502_081223_d/n1/artifacts/v2q_n1_d896d5a1ee904606/cli/conversation.jsonl new file mode 100644 index 0000000000000000000000000000000000000000..541518ed55fbbcc3db4783eb0efa090bbffd3238 --- /dev/null +++ b/Query/sql/v2/runs/v2_cli_20260502_081223_d/n1/artifacts/v2q_n1_d896d5a1ee904606/cli/conversation.jsonl @@ -0,0 +1,2 @@ +{"attempt": 1, "phase": "sql_generation", "role": "user", "content_path": "cli/sql_prompt_attempt_1.txt", "metrics": {"chars": 29529, "bytes_utf8": 29529, "lines": 792, "estimated_tokens": null}} +{"attempt": 1, "phase": "sql_generation", "role": "assistant", "content_path": "cli/sql_response_attempt_1.txt", "raw_content_path": "cli/sql_response_attempt_1.raw.txt", "stderr_path": "cli/sql_stderr_attempt_1.txt", "metrics": {"chars": 463, "bytes_utf8": 463, "lines": 1, "estimated_tokens": null}, "usage": {"input_tokens": 20359, "cached_input_tokens": 12032, "output_tokens": 275, "reasoning_output_tokens": 152}} diff --git a/Query/sql/v2/runs/v2_cli_20260502_081223_d/n1/artifacts/v2q_n1_d896d5a1ee904606/cli/session_summary.json b/Query/sql/v2/runs/v2_cli_20260502_081223_d/n1/artifacts/v2q_n1_d896d5a1ee904606/cli/session_summary.json new file mode 100644 index 0000000000000000000000000000000000000000..d6837edf00a7c490ccb424cb49e70b55e4da97e9 --- /dev/null +++ b/Query/sql/v2/runs/v2_cli_20260502_081223_d/n1/artifacts/v2q_n1_d896d5a1ee904606/cli/session_summary.json @@ -0,0 +1,25 @@ +{ + "engine": "v2-cli:codex", + "command": "codex exec --skip-git-repo-check --disable plugins --sandbox read-only --cd \"/data/jialinzhang/SQLagent\" -m gpt-5.4 --json -", + "ai_cli_calls": 1, + "usage_summary": { + "dataset_id": "n1", + "model": "v2-cli:codex", + "run_id": "v2q_n1_d896d5a1ee904606", + "api_calls": 0, + "input_tokens": 20359, + "cached_input_tokens": 12032, + "output_tokens": 275, + "total_tokens": 20634, + "cost_usd": 0.0, + "ai_cli_calls": 1, + "estimated_input_tokens": 0, + "estimated_output_tokens": 0, + "estimated_total_tokens": 0, + "usage_source": "ai_cli_json_usage", + "cli_elapsed_ms_total": 10406.36, + "sql_execution_elapsed_ms_total": 4.21, + "conversation_log_path": "/data/jialinzhang/TabQueryBench/sql_workloads/v2_current/runs_and_launches/runs/v2_cli_20260502_081223_d/n1/artifacts/v2q_n1_d896d5a1ee904606/cli/conversation.jsonl", + "note": "Executed through a local AI CLI with structured usage metadata." + } +} \ No newline at end of file diff --git a/Query/sql/v2/runs/v2_cli_20260502_081223_d/n1/artifacts/v2q_n1_d896d5a1ee904606/cli/sql_attempt_1.metadata.json b/Query/sql/v2/runs/v2_cli_20260502_081223_d/n1/artifacts/v2q_n1_d896d5a1ee904606/cli/sql_attempt_1.metadata.json new file mode 100644 index 0000000000000000000000000000000000000000..c8396b0a947d8186efb38d156d1e1dfe8d63fb47 --- /dev/null +++ b/Query/sql/v2/runs/v2_cli_20260502_081223_d/n1/artifacts/v2q_n1_d896d5a1ee904606/cli/sql_attempt_1.metadata.json @@ -0,0 +1,45 @@ +{ + "attempt": 1, + "phase": "sql_generation", + "command": "codex exec --skip-git-repo-check --disable plugins --sandbox read-only --cd \"/data/jialinzhang/SQLagent\" -m gpt-5.4 --json -", + "started_at": "2026-05-19T16:04:08.503261+00:00", + "ended_at": "2026-05-19T16:04:18.909655+00:00", + "elapsed_ms": 10406.36, + "prompt_metrics": { + "chars": 29529, + "bytes_utf8": 29529, + "lines": 792, + "estimated_tokens": null + }, + "stdout_metrics": { + "chars": 823, + "bytes_utf8": 823, + "lines": 4, + "estimated_tokens": null + }, + "stderr_metrics": { + "chars": 0, + "bytes_utf8": 0, + "lines": 0, + "estimated_tokens": null + }, + "parsed_output": { + "format": "jsonl_events", + "text_metrics": { + "chars": 463, + "bytes_utf8": 463, + "lines": 1, + "estimated_tokens": null + }, + "usage": { + "input_tokens": 20359, + "cached_input_tokens": 12032, + "output_tokens": 275, + "reasoning_output_tokens": 152 + } + }, + "prompt_path": "cli/sql_prompt_attempt_1.txt", + "response_path": "cli/sql_response_attempt_1.txt", + "raw_response_path": "cli/sql_response_attempt_1.raw.txt", + "stderr_path": "cli/sql_stderr_attempt_1.txt" +} \ No newline at end of file diff --git a/Query/sql/v2/runs/v2_cli_20260502_081223_d/n1/artifacts/v2q_n1_d896d5a1ee904606/cli/sql_prompt_attempt_1.txt b/Query/sql/v2/runs/v2_cli_20260502_081223_d/n1/artifacts/v2q_n1_d896d5a1ee904606/cli/sql_prompt_attempt_1.txt new file mode 100644 index 0000000000000000000000000000000000000000..f4a864ffba46d185ef8ba20644581a75deb3f380 --- /dev/null +++ b/Query/sql/v2/runs/v2_cli_20260502_081223_d/n1/artifacts/v2q_n1_d896d5a1ee904606/cli/sql_prompt_attempt_1.txt @@ -0,0 +1,792 @@ +You are generating one SQLite SELECT query for a single-table SQL QA task. +Return strict JSON only, with this schema: {"sql": "...", "notes": "..."}. +Rules: +- Use only the provided table and columns. +- Do not write INSERT, UPDATE, DELETE, DROP, ALTER, CREATE, PRAGMA, ATTACH, DETACH, or VACUUM. +- Prefer the planned template and bound roles when provided. +- Add a leading SQL comment exactly like: -- template_id: . +- Generate SQLite-compatible SQL. SQLite does not support PERCENTILE_CONT or STDDEV. +- Quote identifiers with double quotes. +- Return no markdown and no extra prose. + +Dataset context: +Dataset context for SQL QA: +- dataset_id: n1 +- dataset_name: Spambase +- table_name: n1 +- table_layout: single-table dataset (do not assume joins). +- row_semantics: One row is one email represented by engineered token/character frequency and capitalization-run features. +- task_type: classification +- target_column: class +- main_row_count: 4601 +- important_fields: +- word_freq_make: role=feature, type=numeric_sparse_frequency. tags=['condition_candidate', 'measure', 'rare_pattern_candidate'] desc=Frequency feature for token 'make' in an email message. +- word_freq_address: role=feature, type=numeric_sparse_frequency. tags=['condition_candidate', 'measure', 'rare_pattern_candidate'] desc=Frequency feature for token 'address' in an email message. +- word_freq_all: role=feature, type=numeric_sparse_frequency. tags=['condition_candidate', 'measure', 'rare_pattern_candidate'] desc=Frequency feature for token 'all' in an email message. +- word_freq_3d: role=feature, type=numeric_sparse_frequency. tags=['condition_candidate', 'measure', 'rare_pattern_candidate'] desc=Frequency feature for token '3d' in an email message. +- word_freq_our: role=feature, type=numeric_sparse_frequency. tags=['condition_candidate', 'measure', 'rare_pattern_candidate'] desc=Frequency feature for token 'our' in an email message. +- word_freq_over: role=feature, type=numeric_sparse_frequency. tags=['condition_candidate', 'measure', 'rare_pattern_candidate'] desc=Frequency feature for token 'over' in an email message. +- word_freq_remove: role=feature, type=numeric_sparse_frequency. tags=['condition_candidate', 'measure', 'rare_pattern_candidate'] desc=Frequency feature for token 'remove' in an email message. +- word_freq_internet: role=feature, type=numeric_sparse_frequency. tags=['condition_candidate', 'measure', 'rare_pattern_candidate'] desc=Frequency feature for token 'internet' in an email message. +- word_freq_order: role=feature, type=numeric_sparse_frequency. tags=['condition_candidate', 'measure', 'rare_pattern_candidate'] desc=Frequency feature for token 'order' in an email message. +- word_freq_mail: role=feature, type=numeric_sparse_frequency. tags=['condition_candidate', 'measure', 'rare_pattern_candidate'] desc=Frequency feature for token 'mail' in an email message. +- word_freq_receive: role=feature, type=numeric_sparse_frequency. tags=['condition_candidate', 'measure', 'rare_pattern_candidate'] desc=Frequency feature for token 'receive' in an email message. +- word_freq_will: role=feature, type=numeric_sparse_frequency. tags=['condition_candidate', 'measure', 'rare_pattern_candidate'] desc=Frequency feature for token 'will' in an email message. +- word_freq_people: role=feature, type=numeric_sparse_frequency. tags=['condition_candidate', 'measure', 'rare_pattern_candidate'] desc=Frequency feature for token 'people' in an email message. +- word_freq_report: role=feature, type=numeric_sparse_frequency. tags=['condition_candidate', 'measure', 'rare_pattern_candidate'] desc=Frequency feature for token 'report' in an email message. +- word_freq_addresses: role=feature, type=numeric_sparse_frequency. tags=['condition_candidate', 'measure', 'rare_pattern_candidate'] desc=Frequency feature for token 'addresses' in an email message. +- word_freq_free: role=feature, type=numeric_sparse_frequency. tags=['condition_candidate', 'measure', 'rare_pattern_candidate'] desc=Frequency feature for token 'free' in an email message. +- word_freq_business: role=feature, type=numeric_sparse_frequency. tags=['condition_candidate', 'measure', 'rare_pattern_candidate'] desc=Frequency feature for token 'business' in an email message. +- word_freq_email: role=feature, type=numeric_sparse_frequency. tags=['condition_candidate', 'measure', 'rare_pattern_candidate'] desc=Frequency feature for token 'email' in an email message. +- word_freq_you: role=feature, type=numeric_sparse_frequency. tags=['condition_candidate', 'measure', 'rare_pattern_candidate'] desc=Frequency feature for token 'you' in an email message. +- word_freq_credit: role=feature, type=numeric_sparse_frequency. tags=['condition_candidate', 'measure', 'rare_pattern_candidate'] desc=Frequency feature for token 'credit' in an email message. +- word_freq_your: role=feature, type=numeric_sparse_frequency. tags=['condition_candidate', 'measure', 'rare_pattern_candidate'] desc=Frequency feature for token 'your' in an email message. +- word_freq_font: role=feature, type=numeric_sparse_frequency. tags=['condition_candidate', 'measure', 'rare_pattern_candidate'] desc=Frequency feature for token 'font' in an email message. +- word_freq_000: role=feature, type=numeric_sparse_frequency. tags=['condition_candidate', 'measure', 'rare_pattern_candidate'] desc=Frequency feature for token '000' in an email message. +- word_freq_money: role=feature, type=numeric_sparse_frequency. tags=['condition_candidate', 'measure', 'rare_pattern_candidate'] desc=Frequency feature for token 'money' in an email message. +- word_freq_hp: role=feature, type=numeric_sparse_frequency. tags=['condition_candidate', 'measure', 'rare_pattern_candidate'] desc=Frequency feature for token 'hp' in an email message. +- word_freq_hpl: role=feature, type=numeric_sparse_frequency. tags=['condition_candidate', 'measure', 'rare_pattern_candidate'] desc=Frequency feature for token 'hpl' in an email message. +- word_freq_george: role=feature, type=numeric_sparse_frequency. tags=['condition_candidate', 'measure', 'rare_pattern_candidate'] desc=Frequency feature for token 'george' in an email message. +- word_freq_650: role=feature, type=numeric_sparse_frequency. tags=['condition_candidate', 'measure', 'rare_pattern_candidate'] desc=Frequency feature for token '650' in an email message. +- word_freq_lab: role=feature, type=numeric_sparse_frequency. tags=['condition_candidate', 'measure', 'rare_pattern_candidate'] desc=Frequency feature for token 'lab' in an email message. +- word_freq_labs: role=feature, type=numeric_sparse_frequency. tags=['condition_candidate', 'measure', 'rare_pattern_candidate'] desc=Frequency feature for token 'labs' in an email message. +- word_freq_telnet: role=feature, type=numeric_sparse_frequency. tags=['condition_candidate', 'measure', 'rare_pattern_candidate'] desc=Frequency feature for token 'telnet' in an email message. +- word_freq_857: role=feature, type=numeric_sparse_frequency. tags=['condition_candidate', 'measure', 'rare_pattern_candidate'] desc=Frequency feature for token '857' in an email message. +- word_freq_data: role=feature, type=numeric_sparse_frequency. tags=['condition_candidate', 'measure', 'rare_pattern_candidate'] desc=Frequency feature for token 'data' in an email message. +- word_freq_415: role=feature, type=numeric_sparse_frequency. tags=['condition_candidate', 'measure', 'rare_pattern_candidate'] desc=Frequency feature for token '415' in an email message. +- word_freq_85: role=feature, type=numeric_sparse_frequency. tags=['condition_candidate', 'measure', 'rare_pattern_candidate'] desc=Frequency feature for token '85' in an email message. +- word_freq_technology: role=feature, type=numeric_sparse_frequency. tags=['condition_candidate', 'measure', 'rare_pattern_candidate'] desc=Frequency feature for token 'technology' in an email message. +- word_freq_1999: role=feature, type=numeric_sparse_frequency. tags=['condition_candidate', 'measure', 'rare_pattern_candidate'] desc=Frequency feature for token '1999' in an email message. +- word_freq_parts: role=feature, type=numeric_sparse_frequency. tags=['condition_candidate', 'measure', 'rare_pattern_candidate'] desc=Frequency feature for token 'parts' in an email message. +- word_freq_pm: role=feature, type=numeric_sparse_frequency. tags=['condition_candidate', 'measure', 'rare_pattern_candidate'] desc=Frequency feature for token 'pm' in an email message. +- word_freq_direct: role=feature, type=numeric_sparse_frequency. tags=['condition_candidate', 'measure', 'rare_pattern_candidate'] desc=Frequency feature for token 'direct' in an email message. +- word_freq_cs: role=feature, type=numeric_sparse_frequency. tags=['condition_candidate', 'measure', 'rare_pattern_candidate'] desc=Frequency feature for token 'cs' in an email message. +- word_freq_meeting: role=feature, type=numeric_sparse_frequency. tags=['condition_candidate', 'measure', 'rare_pattern_candidate'] desc=Frequency feature for token 'meeting' in an email message. +- word_freq_original: role=feature, type=numeric_sparse_frequency. tags=['condition_candidate', 'measure', 'rare_pattern_candidate'] desc=Frequency feature for token 'original' in an email message. +- word_freq_project: role=feature, type=numeric_sparse_frequency. tags=['condition_candidate', 'measure', 'rare_pattern_candidate'] desc=Frequency feature for token 'project' in an email message. +- word_freq_re: role=feature, type=numeric_sparse_frequency. tags=['condition_candidate', 'measure', 'rare_pattern_candidate'] desc=Frequency feature for token 're' in an email message. +- word_freq_edu: role=feature, type=numeric_sparse_frequency. tags=['condition_candidate', 'measure', 'rare_pattern_candidate'] desc=Frequency feature for token 'edu' in an email message. +- word_freq_table: role=feature, type=numeric_sparse_frequency. tags=['condition_candidate', 'measure', 'rare_pattern_candidate'] desc=Frequency feature for token 'table' in an email message. +- word_freq_conference: role=feature, type=numeric_sparse_frequency. tags=['condition_candidate', 'measure', 'rare_pattern_candidate'] desc=Frequency feature for token 'conference' in an email message. +- char_freq_%3B: role=feature, type=numeric_sparse_frequency. tags=['condition_candidate', 'measure', 'rare_pattern_candidate'] desc=Character frequency feature for symbol ';'. +- char_freq_%28: role=feature, type=numeric_sparse_frequency. tags=['condition_candidate', 'measure', 'rare_pattern_candidate'] desc=Character frequency feature for symbol '('. +- char_freq_%5B: role=feature, type=numeric_sparse_frequency. tags=['condition_candidate', 'measure', 'rare_pattern_candidate'] desc=Character frequency feature for symbol '['. +- char_freq_%21: role=feature, type=numeric_sparse_frequency. tags=['condition_candidate', 'measure', 'rare_pattern_candidate'] desc=Character frequency feature for symbol '!'. +- char_freq_%24: role=feature, type=numeric_sparse_frequency. tags=['condition_candidate', 'measure', 'rare_pattern_candidate'] desc=Character frequency feature for symbol '$'. +- char_freq_%23: role=feature, type=numeric_sparse_frequency. tags=['condition_candidate', 'measure', 'rare_pattern_candidate'] desc=Character frequency feature for symbol '#'. +- capital_run_length_average: role=feature, type=numeric_heavy_tail. tags=['condition_candidate', 'measure', 'rare_pattern_candidate'] desc=Average length of uninterrupted capital-letter runs. +- capital_run_length_longest: role=feature, type=numeric_heavy_tail. tags=['condition_candidate', 'measure', 'rare_pattern_candidate'] desc=Longest uninterrupted capital-letter run. +- capital_run_length_total: role=feature, type=numeric_heavy_tail. tags=['condition_candidate', 'measure', 'rare_pattern_candidate'] desc=Total number of capital letters in runs. +- class: role=target, type=binary_target. tags=['target_candidate'] desc=Binary spam label (1=spam, 0=non-spam). +- useful_field_combinations: [['word_freq_free', 'word_freq_you', 'class'], ['char_freq_%21', 'capital_run_length_longest', 'class'], ['word_freq_remove', 'word_freq_money', 'class']] +- fields_requiring_caution: ['capital_run_length_average', 'capital_run_length_longest', 'capital_run_length_total'] +- source_url: https://www.openml.org/d/44 + +SQLite schema snapshot: +{ + "table_name": "n1", + "quoted_table_name": "\"n1\"", + "row_count": 4601, + "columns": [ + { + "name": "word_freq_make", + "type": "TEXT", + "notnull": false, + "pk": false + }, + { + "name": "word_freq_address", + "type": "TEXT", + "notnull": false, + "pk": false + }, + { + "name": "word_freq_all", + "type": "TEXT", + "notnull": false, + "pk": false + }, + { + "name": "word_freq_3d", + "type": "TEXT", + "notnull": false, + "pk": false + }, + { + "name": "word_freq_our", + "type": "TEXT", + "notnull": false, + "pk": false + }, + { + "name": "word_freq_over", + "type": "TEXT", + "notnull": false, + "pk": false + }, + { + "name": "word_freq_remove", + "type": "TEXT", + "notnull": false, + "pk": false + }, + { + "name": "word_freq_internet", + "type": "TEXT", + "notnull": false, + "pk": false + }, + { + "name": "word_freq_order", + "type": "TEXT", + "notnull": false, + "pk": false + }, + { + "name": "word_freq_mail", + "type": "TEXT", + "notnull": false, + "pk": false + }, + { + "name": "word_freq_receive", + "type": "TEXT", + "notnull": false, + "pk": false + }, + { + "name": "word_freq_will", + "type": "TEXT", + "notnull": false, + "pk": false + }, + { + "name": "word_freq_people", + "type": "TEXT", + "notnull": false, + "pk": false + }, + { + "name": "word_freq_report", + "type": "TEXT", + "notnull": false, + "pk": false + }, + { + "name": "word_freq_addresses", + "type": "TEXT", + "notnull": false, + "pk": false + }, + { + "name": "word_freq_free", + "type": "TEXT", + "notnull": false, + "pk": false + }, + { + "name": "word_freq_business", + "type": "TEXT", + "notnull": false, + "pk": false + }, + { + "name": "word_freq_email", + "type": "TEXT", + "notnull": false, + "pk": false + }, + { + "name": "word_freq_you", + "type": "TEXT", + "notnull": false, + "pk": false + }, + { + "name": "word_freq_credit", + "type": "TEXT", + "notnull": false, + "pk": false + }, + { + "name": "word_freq_your", + "type": "TEXT", + "notnull": false, + "pk": false + }, + { + "name": "word_freq_font", + "type": "TEXT", + "notnull": false, + "pk": false + }, + { + "name": "word_freq_000", + "type": "TEXT", + "notnull": false, + "pk": false + }, + { + "name": "word_freq_money", + "type": "TEXT", + "notnull": false, + "pk": false + }, + { + "name": "word_freq_hp", + "type": "TEXT", + "notnull": false, + "pk": false + }, + { + "name": "word_freq_hpl", + "type": "TEXT", + "notnull": false, + "pk": false + }, + { + "name": "word_freq_george", + "type": "TEXT", + "notnull": false, + "pk": false + }, + { + "name": "word_freq_650", + "type": "TEXT", + "notnull": false, + "pk": false + }, + { + "name": "word_freq_lab", + "type": "TEXT", + "notnull": false, + "pk": false + }, + { + "name": "word_freq_labs", + "type": "TEXT", + "notnull": false, + "pk": false + }, + { + "name": "word_freq_telnet", + "type": "TEXT", + "notnull": false, + "pk": false + }, + { + "name": "word_freq_857", + "type": "TEXT", + "notnull": false, + "pk": false + }, + { + "name": "word_freq_data", + "type": "TEXT", + "notnull": false, + "pk": false + }, + { + "name": "word_freq_415", + "type": "TEXT", + "notnull": false, + "pk": false + }, + { + "name": "word_freq_85", + "type": "TEXT", + "notnull": false, + "pk": false + }, + { + "name": "word_freq_technology", + "type": "TEXT", + "notnull": false, + "pk": false + }, + { + "name": "word_freq_1999", + "type": "TEXT", + "notnull": false, + "pk": false + }, + { + "name": "word_freq_parts", + "type": "TEXT", + "notnull": false, + "pk": false + }, + { + "name": "word_freq_pm", + "type": "TEXT", + "notnull": false, + "pk": false + }, + { + "name": "word_freq_direct", + "type": "TEXT", + "notnull": false, + "pk": false + }, + { + "name": "word_freq_cs", + "type": "TEXT", + "notnull": false, + "pk": false + }, + { + "name": "word_freq_meeting", + "type": "TEXT", + "notnull": false, + "pk": false + }, + { + "name": "word_freq_original", + "type": "TEXT", + "notnull": false, + "pk": false + }, + { + "name": "word_freq_project", + "type": "TEXT", + "notnull": false, + "pk": false + }, + { + "name": "word_freq_re", + "type": "TEXT", + "notnull": false, + "pk": false + }, + { + "name": "word_freq_edu", + "type": "TEXT", + "notnull": false, + "pk": false + }, + { + "name": "word_freq_table", + "type": "TEXT", + "notnull": false, + "pk": false + }, + { + "name": "word_freq_conference", + "type": "TEXT", + "notnull": false, + "pk": false + }, + { + "name": "char_freq_%3B", + "type": "TEXT", + "notnull": false, + "pk": false + }, + { + "name": "char_freq_%28", + "type": "TEXT", + "notnull": false, + "pk": false + }, + { + "name": "char_freq_%5B", + "type": "TEXT", + "notnull": false, + "pk": false + }, + { + "name": "char_freq_%21", + "type": "TEXT", + "notnull": false, + "pk": false + }, + { + "name": "char_freq_%24", + "type": "TEXT", + "notnull": false, + "pk": false + }, + { + "name": "char_freq_%23", + "type": "TEXT", + "notnull": false, + "pk": false + }, + { + "name": "capital_run_length_average", + "type": "TEXT", + "notnull": false, + "pk": false + }, + { + "name": "capital_run_length_longest", + "type": "TEXT", + "notnull": false, + "pk": false + }, + { + "name": "capital_run_length_total", + "type": "TEXT", + "notnull": false, + "pk": false + }, + { + "name": "class", + "type": "TEXT", + "notnull": false, + "pk": false + } + ], + "sample_rows": [ + { + "word_freq_make": "0", + "word_freq_address": "0.64", + "word_freq_all": "0.64", + "word_freq_3d": "0", + "word_freq_our": "0.32", + "word_freq_over": "0", + "word_freq_remove": "0", + "word_freq_internet": "0", + "word_freq_order": "0", + "word_freq_mail": "0", + "word_freq_receive": "0", + "word_freq_will": "0.64", + "word_freq_people": "0", + "word_freq_report": "0", + "word_freq_addresses": "0", + "word_freq_free": "0.32", + "word_freq_business": "0", + "word_freq_email": "1.29", + "word_freq_you": "1.93", + "word_freq_credit": "0", + "word_freq_your": "0.96", + "word_freq_font": "0", + "word_freq_000": "0", + "word_freq_money": "0", + "word_freq_hp": "0", + "word_freq_hpl": "0", + "word_freq_george": "0", + "word_freq_650": "0", + "word_freq_lab": "0", + "word_freq_labs": "0", + "word_freq_telnet": "0", + "word_freq_857": "0", + "word_freq_data": "0", + "word_freq_415": "0", + "word_freq_85": "0", + "word_freq_technology": "0", + "word_freq_1999": "0", + "word_freq_parts": "0", + "word_freq_pm": "0", + "word_freq_direct": "0", + "word_freq_cs": "0", + "word_freq_meeting": "0", + "word_freq_original": "0", + "word_freq_project": "0", + "word_freq_re": "0", + "word_freq_edu": "0", + "word_freq_table": "0", + "word_freq_conference": "0", + "char_freq_%3B": "0", + "char_freq_%28": "0", + "char_freq_%5B": "0", + "char_freq_%21": "0.778", + "char_freq_%24": "0", + "char_freq_%23": "0", + "capital_run_length_average": "3.756", + "capital_run_length_longest": "61", + "capital_run_length_total": "278", + "class": "1" + }, + { + "word_freq_make": "0.21", + "word_freq_address": "0.28", + "word_freq_all": "0.5", + "word_freq_3d": "0", + "word_freq_our": "0.14", + "word_freq_over": "0.28", + "word_freq_remove": "0.21", + "word_freq_internet": "0.07", + "word_freq_order": "0", + "word_freq_mail": "0.94", + "word_freq_receive": "0.21", + "word_freq_will": "0.79", + "word_freq_people": "0.65", + "word_freq_report": "0.21", + "word_freq_addresses": "0.14", + "word_freq_free": "0.14", + "word_freq_business": "0.07", + "word_freq_email": "0.28", + "word_freq_you": "3.47", + "word_freq_credit": "0", + "word_freq_your": "1.59", + "word_freq_font": "0", + "word_freq_000": "0.43", + "word_freq_money": "0.43", + "word_freq_hp": "0", + "word_freq_hpl": "0", + "word_freq_george": "0", + "word_freq_650": "0", + "word_freq_lab": "0", + "word_freq_labs": "0", + "word_freq_telnet": "0", + "word_freq_857": "0", + "word_freq_data": "0", + "word_freq_415": "0", + "word_freq_85": "0", + "word_freq_technology": "0", + "word_freq_1999": "0.07", + "word_freq_parts": "0", + "word_freq_pm": "0", + "word_freq_direct": "0", + "word_freq_cs": "0", + "word_freq_meeting": "0", + "word_freq_original": "0", + "word_freq_project": "0", + "word_freq_re": "0", + "word_freq_edu": "0", + "word_freq_table": "0", + "word_freq_conference": "0", + "char_freq_%3B": "0", + "char_freq_%28": "0.132", + "char_freq_%5B": "0", + "char_freq_%21": "0.372", + "char_freq_%24": "0.18", + "char_freq_%23": "0.048", + "capital_run_length_average": "5.114", + "capital_run_length_longest": "101", + "capital_run_length_total": "1028", + "class": "1" + }, + { + "word_freq_make": "0.06", + "word_freq_address": "0", + "word_freq_all": "0.71", + "word_freq_3d": "0", + "word_freq_our": "1.23", + "word_freq_over": "0.19", + "word_freq_remove": "0.19", + "word_freq_internet": "0.12", + "word_freq_order": "0.64", + "word_freq_mail": "0.25", + "word_freq_receive": "0.38", + "word_freq_will": "0.45", + "word_freq_people": "0.12", + "word_freq_report": "0", + "word_freq_addresses": "1.75", + "word_freq_free": "0.06", + "word_freq_business": "0.06", + "word_freq_email": "1.03", + "word_freq_you": "1.36", + "word_freq_credit": "0.32", + "word_freq_your": "0.51", + "word_freq_font": "0", + "word_freq_000": "1.16", + "word_freq_money": "0.06", + "word_freq_hp": "0", + "word_freq_hpl": "0", + "word_freq_george": "0", + "word_freq_650": "0", + "word_freq_lab": "0", + "word_freq_labs": "0", + "word_freq_telnet": "0", + "word_freq_857": "0", + "word_freq_data": "0", + "word_freq_415": "0", + "word_freq_85": "0", + "word_freq_technology": "0", + "word_freq_1999": "0", + "word_freq_parts": "0", + "word_freq_pm": "0", + "word_freq_direct": "0.06", + "word_freq_cs": "0", + "word_freq_meeting": "0", + "word_freq_original": "0.12", + "word_freq_project": "0", + "word_freq_re": "0.06", + "word_freq_edu": "0.06", + "word_freq_table": "0", + "word_freq_conference": "0", + "char_freq_%3B": "0.01", + "char_freq_%28": "0.143", + "char_freq_%5B": "0", + "char_freq_%21": "0.276", + "char_freq_%24": "0.184", + "char_freq_%23": "0.01", + "capital_run_length_average": "9.821", + "capital_run_length_longest": "485", + "capital_run_length_total": "2259", + "class": "1" + }, + { + "word_freq_make": "0", + "word_freq_address": "0", + "word_freq_all": "0", + "word_freq_3d": "0", + "word_freq_our": "0.63", + "word_freq_over": "0", + "word_freq_remove": "0.31", + "word_freq_internet": "0.63", + "word_freq_order": "0.31", + "word_freq_mail": "0.63", + "word_freq_receive": "0.31", + "word_freq_will": "0.31", + "word_freq_people": "0.31", + "word_freq_report": "0", + "word_freq_addresses": "0", + "word_freq_free": "0.31", + "word_freq_business": "0", + "word_freq_email": "0", + "word_freq_you": "3.18", + "word_freq_credit": "0", + "word_freq_your": "0.31", + "word_freq_font": "0", + "word_freq_000": "0", + "word_freq_money": "0", + "word_freq_hp": "0", + "word_freq_hpl": "0", + "word_freq_george": "0", + "word_freq_650": "0", + "word_freq_lab": "0", + "word_freq_labs": "0", + "word_freq_telnet": "0", + "word_freq_857": "0", + "word_freq_data": "0", + "word_freq_415": "0", + "word_freq_85": "0", + "word_freq_technology": "0", + "word_freq_1999": "0", + "word_freq_parts": "0", + "word_freq_pm": "0", + "word_freq_direct": "0", + "word_freq_cs": "0", + "word_freq_meeting": "0", + "word_freq_original": "0", + "word_freq_project": "0", + "word_freq_re": "0", + "word_freq_edu": "0", + "word_freq_table": "0", + "word_freq_conference": "0", + "char_freq_%3B": "0", + "char_freq_%28": "0.137", + "char_freq_%5B": "0", + "char_freq_%21": "0.137", + "char_freq_%24": "0", + "char_freq_%23": "0", + "capital_run_length_average": "3.537", + "capital_run_length_longest": "40", + "capital_run_length_total": "191", + "class": "1" + }, + { + "word_freq_make": "0", + "word_freq_address": "0", + "word_freq_all": "0", + "word_freq_3d": "0", + "word_freq_our": "0.63", + "word_freq_over": "0", + "word_freq_remove": "0.31", + "word_freq_internet": "0.63", + "word_freq_order": "0.31", + "word_freq_mail": "0.63", + "word_freq_receive": "0.31", + "word_freq_will": "0.31", + "word_freq_people": "0.31", + "word_freq_report": "0", + "word_freq_addresses": "0", + "word_freq_free": "0.31", + "word_freq_business": "0", + "word_freq_email": "0", + "word_freq_you": "3.18", + "word_freq_credit": "0", + "word_freq_your": "0.31", + "word_freq_font": "0", + "word_freq_000": "0", + "word_freq_money": "0", + "word_freq_hp": "0", + "word_freq_hpl": "0", + "word_freq_george": "0", + "word_freq_650": "0", + "word_freq_lab": "0", + "word_freq_labs": "0", + "word_freq_telnet": "0", + "word_freq_857": "0", + "word_freq_data": "0", + "word_freq_415": "0", + "word_freq_85": "0", + "word_freq_technology": "0", + "word_freq_1999": "0", + "word_freq_parts": "0", + "word_freq_pm": "0", + "word_freq_direct": "0", + "word_freq_cs": "0", + "word_freq_meeting": "0", + "word_freq_original": "0", + "word_freq_project": "0", + "word_freq_re": "0", + "word_freq_edu": "0", + "word_freq_table": "0", + "word_freq_conference": "0", + "char_freq_%3B": "0", + "char_freq_%28": "0.135", + "char_freq_%5B": "0", + "char_freq_%21": "0.135", + "char_freq_%24": "0", + "char_freq_%23": "0", + "capital_run_length_average": "3.537", + "capital_run_length_longest": "40", + "capital_run_length_total": "191", + "class": "1" + } + ] +} + +Shortlisted templates: +[ + { + "template_id": "tpl_tpch_thresholded_group_ranking", + "template_name": "Thresholded Group Ranking", + "primary_family": "tail_rarity_structure", + "portability": "partial", + "sql_skeleton": "SELECT {group_col}, SUM({measure_col}) AS total_measure\nFROM {table}\nGROUP BY {group_col}\nHAVING SUM({measure_col}) > {measure_threshold}\nORDER BY total_measure DESC\nLIMIT {top_k};", + "required_roles": [ + "group_col", + "measure_col" + ] + } +] + +Problem instance: +{ + "dataset_id": "n1", + "question": "Use template Thresholded Group Ranking to probe tail_mass_similarity with semantic role filtered_stable_view. Focus on group_col=class, measure_col=word_freq_credit.", + "planned_template_id": "tpl_tpch_thresholded_group_ranking", + "bindings": { + "group_col": "class", + "measure_col": "word_freq_credit", + "top_k": 13, + "top_n": 4, + "num_tiles": 10, + "percentile_value": 0.9, + "z_threshold": 2.0, + "fraction_threshold": 0.1, + "baseline_multiplier": 1.5, + "baseline_fraction": 0.1, + "min_group_size": 5, + "min_support": 5, + "measure_threshold": 0.0, + "time_grain": "month", + "lookback_rows": 3, + "current_period_start": "'2024-01-01'", + "current_period_end": "'2024-04-01'", + "previous_period_start": "'2023-10-01'", + "previous_period_end": "'2024-01-01'", + "drift_ratio_threshold": 0.8 + }, + "can_vary": [], + "must_fix": [], + "runtime_sql_skeleton": "SELECT {group_col}, SUM({measure_col}) AS total_measure\nFROM {table}\nGROUP BY {group_col}\nHAVING SUM({measure_col}) > {measure_threshold}\nORDER BY total_measure DESC\nLIMIT {top_k};" +} + +Repair context: +{} diff --git a/Query/sql/v2/runs/v2_cli_20260502_081223_d/n1/artifacts/v2q_n1_d896d5a1ee904606/cli/sql_response_attempt_1.raw.txt b/Query/sql/v2/runs/v2_cli_20260502_081223_d/n1/artifacts/v2q_n1_d896d5a1ee904606/cli/sql_response_attempt_1.raw.txt new file mode 100644 index 0000000000000000000000000000000000000000..e5ad691420aa29f544ac141b8db04d9ff0ba8841 --- /dev/null +++ b/Query/sql/v2/runs/v2_cli_20260502_081223_d/n1/artifacts/v2q_n1_d896d5a1ee904606/cli/sql_response_attempt_1.raw.txt @@ -0,0 +1,4 @@ +{"type":"thread.started","thread_id":"019e40fa-f3c2-7683-acb5-7aa913960d87"} +{"type":"turn.started"} +{"type":"item.completed","item":{"id":"item_0","type":"agent_message","text":"{\"sql\":\"-- template_id: tpl_tpch_thresholded_group_ranking\\nSELECT \\\"class\\\", SUM(CAST(\\\"word_freq_credit\\\" AS REAL)) AS total_measure\\nFROM \\\"n1\\\"\\nGROUP BY \\\"class\\\"\\nHAVING SUM(CAST(\\\"word_freq_credit\\\" AS REAL)) > 0.0\\nORDER BY total_measure DESC\\nLIMIT 13;\",\"notes\":\"Applied the provided Thresholded Group Ranking template with group_col=\\\"class\\\" and measure_col=\\\"word_freq_credit\\\". CAST to REAL is used because the schema stores feature values as TEXT.\"}"}} +{"type":"turn.completed","usage":{"input_tokens":20359,"cached_input_tokens":12032,"output_tokens":275,"reasoning_output_tokens":152}} diff --git a/Query/sql/v2/runs/v2_cli_20260502_081223_d/n1/artifacts/v2q_n1_d896d5a1ee904606/cli/sql_response_attempt_1.txt b/Query/sql/v2/runs/v2_cli_20260502_081223_d/n1/artifacts/v2q_n1_d896d5a1ee904606/cli/sql_response_attempt_1.txt new file mode 100644 index 0000000000000000000000000000000000000000..d490d996f08bdb88df0278516d148ae52ace9c5c --- /dev/null +++ b/Query/sql/v2/runs/v2_cli_20260502_081223_d/n1/artifacts/v2q_n1_d896d5a1ee904606/cli/sql_response_attempt_1.txt @@ -0,0 +1 @@ +{"sql":"-- template_id: tpl_tpch_thresholded_group_ranking\nSELECT \"class\", SUM(CAST(\"word_freq_credit\" AS REAL)) AS total_measure\nFROM \"n1\"\nGROUP BY \"class\"\nHAVING SUM(CAST(\"word_freq_credit\" AS REAL)) > 0.0\nORDER BY total_measure DESC\nLIMIT 13;","notes":"Applied the provided Thresholded Group Ranking template with group_col=\"class\" and measure_col=\"word_freq_credit\". CAST to REAL is used because the schema stores feature values as TEXT."} \ No newline at end of file diff --git a/Query/sql/v2/runs/v2_cli_20260502_081223_d/n1/artifacts/v2q_n1_d896d5a1ee904606/cli/sql_stderr_attempt_1.txt b/Query/sql/v2/runs/v2_cli_20260502_081223_d/n1/artifacts/v2q_n1_d896d5a1ee904606/cli/sql_stderr_attempt_1.txt new file mode 100644 index 0000000000000000000000000000000000000000..e69de29bb2d1d6434b8b29ae775ad8c2e48c5391 diff --git a/Query/sql/v2/runs/v2_cli_20260502_081223_d/n1/artifacts/v2q_n1_da46b022f55b7a37/cli/conversation.jsonl b/Query/sql/v2/runs/v2_cli_20260502_081223_d/n1/artifacts/v2q_n1_da46b022f55b7a37/cli/conversation.jsonl new file mode 100644 index 0000000000000000000000000000000000000000..d2b2b286f80d180312f3be46ef8fa7dc47914a51 --- /dev/null +++ b/Query/sql/v2/runs/v2_cli_20260502_081223_d/n1/artifacts/v2q_n1_da46b022f55b7a37/cli/conversation.jsonl @@ -0,0 +1,2 @@ +{"attempt": 1, "phase": "sql_generation", "role": "user", "content_path": "cli/sql_prompt_attempt_1.txt", "metrics": {"chars": 29913, "bytes_utf8": 29913, "lines": 792, "estimated_tokens": null}} +{"attempt": 1, "phase": "sql_generation", "role": "assistant", "content_path": "cli/sql_response_attempt_1.txt", "raw_content_path": "cli/sql_response_attempt_1.raw.txt", "stderr_path": "cli/sql_stderr_attempt_1.txt", "metrics": {"chars": 669, "bytes_utf8": 669, "lines": 1, "estimated_tokens": null}, "usage": {"input_tokens": 20453, "cached_input_tokens": 19840, "output_tokens": 540, "reasoning_output_tokens": 367}} diff --git a/Query/sql/v2/runs/v2_cli_20260502_081223_d/n1/artifacts/v2q_n1_da46b022f55b7a37/cli/session_summary.json b/Query/sql/v2/runs/v2_cli_20260502_081223_d/n1/artifacts/v2q_n1_da46b022f55b7a37/cli/session_summary.json new file mode 100644 index 0000000000000000000000000000000000000000..d435ff42e4f06ee8e007fcbfbdf036256e706ec2 --- /dev/null +++ b/Query/sql/v2/runs/v2_cli_20260502_081223_d/n1/artifacts/v2q_n1_da46b022f55b7a37/cli/session_summary.json @@ -0,0 +1,25 @@ +{ + "engine": "v2-cli:codex", + "command": "codex exec --skip-git-repo-check --disable plugins --sandbox read-only --cd \"/data/jialinzhang/SQLagent\" -m gpt-5.4 --json -", + "ai_cli_calls": 1, + "usage_summary": { + "dataset_id": "n1", + "model": "v2-cli:codex", + "run_id": "v2q_n1_da46b022f55b7a37", + "api_calls": 0, + "input_tokens": 20453, + "cached_input_tokens": 19840, + "output_tokens": 540, + "total_tokens": 20993, + "cost_usd": 0.0, + "ai_cli_calls": 1, + "estimated_input_tokens": 0, + "estimated_output_tokens": 0, + "estimated_total_tokens": 0, + "usage_source": "ai_cli_json_usage", + "cli_elapsed_ms_total": 11684.87, + "sql_execution_elapsed_ms_total": 2.2, + "conversation_log_path": "/data/jialinzhang/TabQueryBench/sql_workloads/v2_current/runs_and_launches/runs/v2_cli_20260502_081223_d/n1/artifacts/v2q_n1_da46b022f55b7a37/cli/conversation.jsonl", + "note": "Executed through a local AI CLI with structured usage metadata." + } +} \ No newline at end of file diff --git a/Query/sql/v2/runs/v2_cli_20260502_081223_d/n1/artifacts/v2q_n1_da46b022f55b7a37/cli/sql_attempt_1.metadata.json b/Query/sql/v2/runs/v2_cli_20260502_081223_d/n1/artifacts/v2q_n1_da46b022f55b7a37/cli/sql_attempt_1.metadata.json new file mode 100644 index 0000000000000000000000000000000000000000..861e3dc620ead8a822a06aadb42f775a09d095d5 --- /dev/null +++ b/Query/sql/v2/runs/v2_cli_20260502_081223_d/n1/artifacts/v2q_n1_da46b022f55b7a37/cli/sql_attempt_1.metadata.json @@ -0,0 +1,45 @@ +{ + "attempt": 1, + "phase": "sql_generation", + "command": "codex exec --skip-git-repo-check --disable plugins --sandbox read-only --cd \"/data/jialinzhang/SQLagent\" -m gpt-5.4 --json -", + "started_at": "2026-05-19T15:49:22.067982+00:00", + "ended_at": "2026-05-19T15:49:33.752898+00:00", + "elapsed_ms": 11684.87, + "prompt_metrics": { + "chars": 29913, + "bytes_utf8": 29913, + "lines": 792, + "estimated_tokens": null + }, + "stdout_metrics": { + "chars": 1040, + "bytes_utf8": 1040, + "lines": 4, + "estimated_tokens": null + }, + "stderr_metrics": { + "chars": 0, + "bytes_utf8": 0, + "lines": 0, + "estimated_tokens": null + }, + "parsed_output": { + "format": "jsonl_events", + "text_metrics": { + "chars": 669, + "bytes_utf8": 669, + "lines": 1, + "estimated_tokens": null + }, + "usage": { + "input_tokens": 20453, + "cached_input_tokens": 19840, + "output_tokens": 540, + "reasoning_output_tokens": 367 + } + }, + "prompt_path": "cli/sql_prompt_attempt_1.txt", + "response_path": "cli/sql_response_attempt_1.txt", + "raw_response_path": "cli/sql_response_attempt_1.raw.txt", + "stderr_path": "cli/sql_stderr_attempt_1.txt" +} \ No newline at end of file diff --git a/Query/sql/v2/runs/v2_cli_20260502_081223_d/n1/artifacts/v2q_n1_da46b022f55b7a37/cli/sql_prompt_attempt_1.txt b/Query/sql/v2/runs/v2_cli_20260502_081223_d/n1/artifacts/v2q_n1_da46b022f55b7a37/cli/sql_prompt_attempt_1.txt new file mode 100644 index 0000000000000000000000000000000000000000..0129bbf420fe2507a6540904448bdbf183100568 --- /dev/null +++ b/Query/sql/v2/runs/v2_cli_20260502_081223_d/n1/artifacts/v2q_n1_da46b022f55b7a37/cli/sql_prompt_attempt_1.txt @@ -0,0 +1,792 @@ +You are generating one SQLite SELECT query for a single-table SQL QA task. +Return strict JSON only, with this schema: {"sql": "...", "notes": "..."}. +Rules: +- Use only the provided table and columns. +- Do not write INSERT, UPDATE, DELETE, DROP, ALTER, CREATE, PRAGMA, ATTACH, DETACH, or VACUUM. +- Prefer the planned template and bound roles when provided. +- Add a leading SQL comment exactly like: -- template_id: . +- Generate SQLite-compatible SQL. SQLite does not support PERCENTILE_CONT or STDDEV. +- Quote identifiers with double quotes. +- Return no markdown and no extra prose. + +Dataset context: +Dataset context for SQL QA: +- dataset_id: n1 +- dataset_name: Spambase +- table_name: n1 +- table_layout: single-table dataset (do not assume joins). +- row_semantics: One row is one email represented by engineered token/character frequency and capitalization-run features. +- task_type: classification +- target_column: class +- main_row_count: 4601 +- important_fields: +- word_freq_make: role=feature, type=numeric_sparse_frequency. tags=['condition_candidate', 'measure', 'rare_pattern_candidate'] desc=Frequency feature for token 'make' in an email message. +- word_freq_address: role=feature, type=numeric_sparse_frequency. tags=['condition_candidate', 'measure', 'rare_pattern_candidate'] desc=Frequency feature for token 'address' in an email message. +- word_freq_all: role=feature, type=numeric_sparse_frequency. tags=['condition_candidate', 'measure', 'rare_pattern_candidate'] desc=Frequency feature for token 'all' in an email message. +- word_freq_3d: role=feature, type=numeric_sparse_frequency. tags=['condition_candidate', 'measure', 'rare_pattern_candidate'] desc=Frequency feature for token '3d' in an email message. +- word_freq_our: role=feature, type=numeric_sparse_frequency. tags=['condition_candidate', 'measure', 'rare_pattern_candidate'] desc=Frequency feature for token 'our' in an email message. +- word_freq_over: role=feature, type=numeric_sparse_frequency. tags=['condition_candidate', 'measure', 'rare_pattern_candidate'] desc=Frequency feature for token 'over' in an email message. +- word_freq_remove: role=feature, type=numeric_sparse_frequency. tags=['condition_candidate', 'measure', 'rare_pattern_candidate'] desc=Frequency feature for token 'remove' in an email message. +- word_freq_internet: role=feature, type=numeric_sparse_frequency. tags=['condition_candidate', 'measure', 'rare_pattern_candidate'] desc=Frequency feature for token 'internet' in an email message. +- word_freq_order: role=feature, type=numeric_sparse_frequency. tags=['condition_candidate', 'measure', 'rare_pattern_candidate'] desc=Frequency feature for token 'order' in an email message. +- word_freq_mail: role=feature, type=numeric_sparse_frequency. tags=['condition_candidate', 'measure', 'rare_pattern_candidate'] desc=Frequency feature for token 'mail' in an email message. +- word_freq_receive: role=feature, type=numeric_sparse_frequency. tags=['condition_candidate', 'measure', 'rare_pattern_candidate'] desc=Frequency feature for token 'receive' in an email message. +- word_freq_will: role=feature, type=numeric_sparse_frequency. tags=['condition_candidate', 'measure', 'rare_pattern_candidate'] desc=Frequency feature for token 'will' in an email message. +- word_freq_people: role=feature, type=numeric_sparse_frequency. tags=['condition_candidate', 'measure', 'rare_pattern_candidate'] desc=Frequency feature for token 'people' in an email message. +- word_freq_report: role=feature, type=numeric_sparse_frequency. tags=['condition_candidate', 'measure', 'rare_pattern_candidate'] desc=Frequency feature for token 'report' in an email message. +- word_freq_addresses: role=feature, type=numeric_sparse_frequency. tags=['condition_candidate', 'measure', 'rare_pattern_candidate'] desc=Frequency feature for token 'addresses' in an email message. +- word_freq_free: role=feature, type=numeric_sparse_frequency. tags=['condition_candidate', 'measure', 'rare_pattern_candidate'] desc=Frequency feature for token 'free' in an email message. +- word_freq_business: role=feature, type=numeric_sparse_frequency. tags=['condition_candidate', 'measure', 'rare_pattern_candidate'] desc=Frequency feature for token 'business' in an email message. +- word_freq_email: role=feature, type=numeric_sparse_frequency. tags=['condition_candidate', 'measure', 'rare_pattern_candidate'] desc=Frequency feature for token 'email' in an email message. +- word_freq_you: role=feature, type=numeric_sparse_frequency. tags=['condition_candidate', 'measure', 'rare_pattern_candidate'] desc=Frequency feature for token 'you' in an email message. +- word_freq_credit: role=feature, type=numeric_sparse_frequency. tags=['condition_candidate', 'measure', 'rare_pattern_candidate'] desc=Frequency feature for token 'credit' in an email message. +- word_freq_your: role=feature, type=numeric_sparse_frequency. tags=['condition_candidate', 'measure', 'rare_pattern_candidate'] desc=Frequency feature for token 'your' in an email message. +- word_freq_font: role=feature, type=numeric_sparse_frequency. tags=['condition_candidate', 'measure', 'rare_pattern_candidate'] desc=Frequency feature for token 'font' in an email message. +- word_freq_000: role=feature, type=numeric_sparse_frequency. tags=['condition_candidate', 'measure', 'rare_pattern_candidate'] desc=Frequency feature for token '000' in an email message. +- word_freq_money: role=feature, type=numeric_sparse_frequency. tags=['condition_candidate', 'measure', 'rare_pattern_candidate'] desc=Frequency feature for token 'money' in an email message. +- word_freq_hp: role=feature, type=numeric_sparse_frequency. tags=['condition_candidate', 'measure', 'rare_pattern_candidate'] desc=Frequency feature for token 'hp' in an email message. +- word_freq_hpl: role=feature, type=numeric_sparse_frequency. tags=['condition_candidate', 'measure', 'rare_pattern_candidate'] desc=Frequency feature for token 'hpl' in an email message. +- word_freq_george: role=feature, type=numeric_sparse_frequency. tags=['condition_candidate', 'measure', 'rare_pattern_candidate'] desc=Frequency feature for token 'george' in an email message. +- word_freq_650: role=feature, type=numeric_sparse_frequency. tags=['condition_candidate', 'measure', 'rare_pattern_candidate'] desc=Frequency feature for token '650' in an email message. +- word_freq_lab: role=feature, type=numeric_sparse_frequency. tags=['condition_candidate', 'measure', 'rare_pattern_candidate'] desc=Frequency feature for token 'lab' in an email message. +- word_freq_labs: role=feature, type=numeric_sparse_frequency. tags=['condition_candidate', 'measure', 'rare_pattern_candidate'] desc=Frequency feature for token 'labs' in an email message. +- word_freq_telnet: role=feature, type=numeric_sparse_frequency. tags=['condition_candidate', 'measure', 'rare_pattern_candidate'] desc=Frequency feature for token 'telnet' in an email message. +- word_freq_857: role=feature, type=numeric_sparse_frequency. tags=['condition_candidate', 'measure', 'rare_pattern_candidate'] desc=Frequency feature for token '857' in an email message. +- word_freq_data: role=feature, type=numeric_sparse_frequency. tags=['condition_candidate', 'measure', 'rare_pattern_candidate'] desc=Frequency feature for token 'data' in an email message. +- word_freq_415: role=feature, type=numeric_sparse_frequency. tags=['condition_candidate', 'measure', 'rare_pattern_candidate'] desc=Frequency feature for token '415' in an email message. +- word_freq_85: role=feature, type=numeric_sparse_frequency. tags=['condition_candidate', 'measure', 'rare_pattern_candidate'] desc=Frequency feature for token '85' in an email message. +- word_freq_technology: role=feature, type=numeric_sparse_frequency. tags=['condition_candidate', 'measure', 'rare_pattern_candidate'] desc=Frequency feature for token 'technology' in an email message. +- word_freq_1999: role=feature, type=numeric_sparse_frequency. tags=['condition_candidate', 'measure', 'rare_pattern_candidate'] desc=Frequency feature for token '1999' in an email message. +- word_freq_parts: role=feature, type=numeric_sparse_frequency. tags=['condition_candidate', 'measure', 'rare_pattern_candidate'] desc=Frequency feature for token 'parts' in an email message. +- word_freq_pm: role=feature, type=numeric_sparse_frequency. tags=['condition_candidate', 'measure', 'rare_pattern_candidate'] desc=Frequency feature for token 'pm' in an email message. +- word_freq_direct: role=feature, type=numeric_sparse_frequency. tags=['condition_candidate', 'measure', 'rare_pattern_candidate'] desc=Frequency feature for token 'direct' in an email message. +- word_freq_cs: role=feature, type=numeric_sparse_frequency. tags=['condition_candidate', 'measure', 'rare_pattern_candidate'] desc=Frequency feature for token 'cs' in an email message. +- word_freq_meeting: role=feature, type=numeric_sparse_frequency. tags=['condition_candidate', 'measure', 'rare_pattern_candidate'] desc=Frequency feature for token 'meeting' in an email message. +- word_freq_original: role=feature, type=numeric_sparse_frequency. tags=['condition_candidate', 'measure', 'rare_pattern_candidate'] desc=Frequency feature for token 'original' in an email message. +- word_freq_project: role=feature, type=numeric_sparse_frequency. tags=['condition_candidate', 'measure', 'rare_pattern_candidate'] desc=Frequency feature for token 'project' in an email message. +- word_freq_re: role=feature, type=numeric_sparse_frequency. tags=['condition_candidate', 'measure', 'rare_pattern_candidate'] desc=Frequency feature for token 're' in an email message. +- word_freq_edu: role=feature, type=numeric_sparse_frequency. tags=['condition_candidate', 'measure', 'rare_pattern_candidate'] desc=Frequency feature for token 'edu' in an email message. +- word_freq_table: role=feature, type=numeric_sparse_frequency. tags=['condition_candidate', 'measure', 'rare_pattern_candidate'] desc=Frequency feature for token 'table' in an email message. +- word_freq_conference: role=feature, type=numeric_sparse_frequency. tags=['condition_candidate', 'measure', 'rare_pattern_candidate'] desc=Frequency feature for token 'conference' in an email message. +- char_freq_%3B: role=feature, type=numeric_sparse_frequency. tags=['condition_candidate', 'measure', 'rare_pattern_candidate'] desc=Character frequency feature for symbol ';'. +- char_freq_%28: role=feature, type=numeric_sparse_frequency. tags=['condition_candidate', 'measure', 'rare_pattern_candidate'] desc=Character frequency feature for symbol '('. +- char_freq_%5B: role=feature, type=numeric_sparse_frequency. tags=['condition_candidate', 'measure', 'rare_pattern_candidate'] desc=Character frequency feature for symbol '['. +- char_freq_%21: role=feature, type=numeric_sparse_frequency. tags=['condition_candidate', 'measure', 'rare_pattern_candidate'] desc=Character frequency feature for symbol '!'. +- char_freq_%24: role=feature, type=numeric_sparse_frequency. tags=['condition_candidate', 'measure', 'rare_pattern_candidate'] desc=Character frequency feature for symbol '$'. +- char_freq_%23: role=feature, type=numeric_sparse_frequency. tags=['condition_candidate', 'measure', 'rare_pattern_candidate'] desc=Character frequency feature for symbol '#'. +- capital_run_length_average: role=feature, type=numeric_heavy_tail. tags=['condition_candidate', 'measure', 'rare_pattern_candidate'] desc=Average length of uninterrupted capital-letter runs. +- capital_run_length_longest: role=feature, type=numeric_heavy_tail. tags=['condition_candidate', 'measure', 'rare_pattern_candidate'] desc=Longest uninterrupted capital-letter run. +- capital_run_length_total: role=feature, type=numeric_heavy_tail. tags=['condition_candidate', 'measure', 'rare_pattern_candidate'] desc=Total number of capital letters in runs. +- class: role=target, type=binary_target. tags=['target_candidate'] desc=Binary spam label (1=spam, 0=non-spam). +- useful_field_combinations: [['word_freq_free', 'word_freq_you', 'class'], ['char_freq_%21', 'capital_run_length_longest', 'class'], ['word_freq_remove', 'word_freq_money', 'class']] +- fields_requiring_caution: ['capital_run_length_average', 'capital_run_length_longest', 'capital_run_length_total'] +- source_url: https://www.openml.org/d/44 + +SQLite schema snapshot: +{ + "table_name": "n1", + "quoted_table_name": "\"n1\"", + "row_count": 4601, + "columns": [ + { + "name": "word_freq_make", + "type": "TEXT", + "notnull": false, + "pk": false + }, + { + "name": "word_freq_address", + "type": "TEXT", + "notnull": false, + "pk": false + }, + { + "name": "word_freq_all", + "type": "TEXT", + "notnull": false, + "pk": false + }, + { + "name": "word_freq_3d", + "type": "TEXT", + "notnull": false, + "pk": false + }, + { + "name": "word_freq_our", + "type": "TEXT", + "notnull": false, + "pk": false + }, + { + "name": "word_freq_over", + "type": "TEXT", + "notnull": false, + "pk": false + }, + { + "name": "word_freq_remove", + "type": "TEXT", + "notnull": false, + "pk": false + }, + { + "name": "word_freq_internet", + "type": "TEXT", + "notnull": false, + "pk": false + }, + { + "name": "word_freq_order", + "type": "TEXT", + "notnull": false, + "pk": false + }, + { + "name": "word_freq_mail", + "type": "TEXT", + "notnull": false, + "pk": false + }, + { + "name": "word_freq_receive", + "type": "TEXT", + "notnull": false, + "pk": false + }, + { + "name": "word_freq_will", + "type": "TEXT", + "notnull": false, + "pk": false + }, + { + "name": "word_freq_people", + "type": "TEXT", + "notnull": false, + "pk": false + }, + { + "name": "word_freq_report", + "type": "TEXT", + "notnull": false, + "pk": false + }, + { + "name": "word_freq_addresses", + "type": "TEXT", + "notnull": false, + "pk": false + }, + { + "name": "word_freq_free", + "type": "TEXT", + "notnull": false, + "pk": false + }, + { + "name": "word_freq_business", + "type": "TEXT", + "notnull": false, + "pk": false + }, + { + "name": "word_freq_email", + "type": "TEXT", + "notnull": false, + "pk": false + }, + { + "name": "word_freq_you", + "type": "TEXT", + "notnull": false, + "pk": false + }, + { + "name": "word_freq_credit", + "type": "TEXT", + "notnull": false, + "pk": false + }, + { + "name": "word_freq_your", + "type": "TEXT", + "notnull": false, + "pk": false + }, + { + "name": "word_freq_font", + "type": "TEXT", + "notnull": false, + "pk": false + }, + { + "name": "word_freq_000", + "type": "TEXT", + "notnull": false, + "pk": false + }, + { + "name": "word_freq_money", + "type": "TEXT", + "notnull": false, + "pk": false + }, + { + "name": "word_freq_hp", + "type": "TEXT", + "notnull": false, + "pk": false + }, + { + "name": "word_freq_hpl", + "type": "TEXT", + "notnull": false, + "pk": false + }, + { + "name": "word_freq_george", + "type": "TEXT", + "notnull": false, + "pk": false + }, + { + "name": "word_freq_650", + "type": "TEXT", + "notnull": false, + "pk": false + }, + { + "name": "word_freq_lab", + "type": "TEXT", + "notnull": false, + "pk": false + }, + { + "name": "word_freq_labs", + "type": "TEXT", + "notnull": false, + "pk": false + }, + { + "name": "word_freq_telnet", + "type": "TEXT", + "notnull": false, + "pk": false + }, + { + "name": "word_freq_857", + "type": "TEXT", + "notnull": false, + "pk": false + }, + { + "name": "word_freq_data", + "type": "TEXT", + "notnull": false, + "pk": false + }, + { + "name": "word_freq_415", + "type": "TEXT", + "notnull": false, + "pk": false + }, + { + "name": "word_freq_85", + "type": "TEXT", + "notnull": false, + "pk": false + }, + { + "name": "word_freq_technology", + "type": "TEXT", + "notnull": false, + "pk": false + }, + { + "name": "word_freq_1999", + "type": "TEXT", + "notnull": false, + "pk": false + }, + { + "name": "word_freq_parts", + "type": "TEXT", + "notnull": false, + "pk": false + }, + { + "name": "word_freq_pm", + "type": "TEXT", + "notnull": false, + "pk": false + }, + { + "name": "word_freq_direct", + "type": "TEXT", + "notnull": false, + "pk": false + }, + { + "name": "word_freq_cs", + "type": "TEXT", + "notnull": false, + "pk": false + }, + { + "name": "word_freq_meeting", + "type": "TEXT", + "notnull": false, + "pk": false + }, + { + "name": "word_freq_original", + "type": "TEXT", + "notnull": false, + "pk": false + }, + { + "name": "word_freq_project", + "type": "TEXT", + "notnull": false, + "pk": false + }, + { + "name": "word_freq_re", + "type": "TEXT", + "notnull": false, + "pk": false + }, + { + "name": "word_freq_edu", + "type": "TEXT", + "notnull": false, + "pk": false + }, + { + "name": "word_freq_table", + "type": "TEXT", + "notnull": false, + "pk": false + }, + { + "name": "word_freq_conference", + "type": "TEXT", + "notnull": false, + "pk": false + }, + { + "name": "char_freq_%3B", + "type": "TEXT", + "notnull": false, + "pk": false + }, + { + "name": "char_freq_%28", + "type": "TEXT", + "notnull": false, + "pk": false + }, + { + "name": "char_freq_%5B", + "type": "TEXT", + "notnull": false, + "pk": false + }, + { + "name": "char_freq_%21", + "type": "TEXT", + "notnull": false, + "pk": false + }, + { + "name": "char_freq_%24", + "type": "TEXT", + "notnull": false, + "pk": false + }, + { + "name": "char_freq_%23", + "type": "TEXT", + "notnull": false, + "pk": false + }, + { + "name": "capital_run_length_average", + "type": "TEXT", + "notnull": false, + "pk": false + }, + { + "name": "capital_run_length_longest", + "type": "TEXT", + "notnull": false, + "pk": false + }, + { + "name": "capital_run_length_total", + "type": "TEXT", + "notnull": false, + "pk": false + }, + { + "name": "class", + "type": "TEXT", + "notnull": false, + "pk": false + } + ], + "sample_rows": [ + { + "word_freq_make": "0", + "word_freq_address": "0.64", + "word_freq_all": "0.64", + "word_freq_3d": "0", + "word_freq_our": "0.32", + "word_freq_over": "0", + "word_freq_remove": "0", + "word_freq_internet": "0", + "word_freq_order": "0", + "word_freq_mail": "0", + "word_freq_receive": "0", + "word_freq_will": "0.64", + "word_freq_people": "0", + "word_freq_report": "0", + "word_freq_addresses": "0", + "word_freq_free": "0.32", + "word_freq_business": "0", + "word_freq_email": "1.29", + "word_freq_you": "1.93", + "word_freq_credit": "0", + "word_freq_your": "0.96", + "word_freq_font": "0", + "word_freq_000": "0", + "word_freq_money": "0", + "word_freq_hp": "0", + "word_freq_hpl": "0", + "word_freq_george": "0", + "word_freq_650": "0", + "word_freq_lab": "0", + "word_freq_labs": "0", + "word_freq_telnet": "0", + "word_freq_857": "0", + "word_freq_data": "0", + "word_freq_415": "0", + "word_freq_85": "0", + "word_freq_technology": "0", + "word_freq_1999": "0", + "word_freq_parts": "0", + "word_freq_pm": "0", + "word_freq_direct": "0", + "word_freq_cs": "0", + "word_freq_meeting": "0", + "word_freq_original": "0", + "word_freq_project": "0", + "word_freq_re": "0", + "word_freq_edu": "0", + "word_freq_table": "0", + "word_freq_conference": "0", + "char_freq_%3B": "0", + "char_freq_%28": "0", + "char_freq_%5B": "0", + "char_freq_%21": "0.778", + "char_freq_%24": "0", + "char_freq_%23": "0", + "capital_run_length_average": "3.756", + "capital_run_length_longest": "61", + "capital_run_length_total": "278", + "class": "1" + }, + { + "word_freq_make": "0.21", + "word_freq_address": "0.28", + "word_freq_all": "0.5", + "word_freq_3d": "0", + "word_freq_our": "0.14", + "word_freq_over": "0.28", + "word_freq_remove": "0.21", + "word_freq_internet": "0.07", + "word_freq_order": "0", + "word_freq_mail": "0.94", + "word_freq_receive": "0.21", + "word_freq_will": "0.79", + "word_freq_people": "0.65", + "word_freq_report": "0.21", + "word_freq_addresses": "0.14", + "word_freq_free": "0.14", + "word_freq_business": "0.07", + "word_freq_email": "0.28", + "word_freq_you": "3.47", + "word_freq_credit": "0", + "word_freq_your": "1.59", + "word_freq_font": "0", + "word_freq_000": "0.43", + "word_freq_money": "0.43", + "word_freq_hp": "0", + "word_freq_hpl": "0", + "word_freq_george": "0", + "word_freq_650": "0", + "word_freq_lab": "0", + "word_freq_labs": "0", + "word_freq_telnet": "0", + "word_freq_857": "0", + "word_freq_data": "0", + "word_freq_415": "0", + "word_freq_85": "0", + "word_freq_technology": "0", + "word_freq_1999": "0.07", + "word_freq_parts": "0", + "word_freq_pm": "0", + "word_freq_direct": "0", + "word_freq_cs": "0", + "word_freq_meeting": "0", + "word_freq_original": "0", + "word_freq_project": "0", + "word_freq_re": "0", + "word_freq_edu": "0", + "word_freq_table": "0", + "word_freq_conference": "0", + "char_freq_%3B": "0", + "char_freq_%28": "0.132", + "char_freq_%5B": "0", + "char_freq_%21": "0.372", + "char_freq_%24": "0.18", + "char_freq_%23": "0.048", + "capital_run_length_average": "5.114", + "capital_run_length_longest": "101", + "capital_run_length_total": "1028", + "class": "1" + }, + { + "word_freq_make": "0.06", + "word_freq_address": "0", + "word_freq_all": "0.71", + "word_freq_3d": "0", + "word_freq_our": "1.23", + "word_freq_over": "0.19", + "word_freq_remove": "0.19", + "word_freq_internet": "0.12", + "word_freq_order": "0.64", + "word_freq_mail": "0.25", + "word_freq_receive": "0.38", + "word_freq_will": "0.45", + "word_freq_people": "0.12", + "word_freq_report": "0", + "word_freq_addresses": "1.75", + "word_freq_free": "0.06", + "word_freq_business": "0.06", + "word_freq_email": "1.03", + "word_freq_you": "1.36", + "word_freq_credit": "0.32", + "word_freq_your": "0.51", + "word_freq_font": "0", + "word_freq_000": "1.16", + "word_freq_money": "0.06", + "word_freq_hp": "0", + "word_freq_hpl": "0", + "word_freq_george": "0", + "word_freq_650": "0", + "word_freq_lab": "0", + "word_freq_labs": "0", + "word_freq_telnet": "0", + "word_freq_857": "0", + "word_freq_data": "0", + "word_freq_415": "0", + "word_freq_85": "0", + "word_freq_technology": "0", + "word_freq_1999": "0", + "word_freq_parts": "0", + "word_freq_pm": "0", + "word_freq_direct": "0.06", + "word_freq_cs": "0", + "word_freq_meeting": "0", + "word_freq_original": "0.12", + "word_freq_project": "0", + "word_freq_re": "0.06", + "word_freq_edu": "0.06", + "word_freq_table": "0", + "word_freq_conference": "0", + "char_freq_%3B": "0.01", + "char_freq_%28": "0.143", + "char_freq_%5B": "0", + "char_freq_%21": "0.276", + "char_freq_%24": "0.184", + "char_freq_%23": "0.01", + "capital_run_length_average": "9.821", + "capital_run_length_longest": "485", + "capital_run_length_total": "2259", + "class": "1" + }, + { + "word_freq_make": "0", + "word_freq_address": "0", + "word_freq_all": "0", + "word_freq_3d": "0", + "word_freq_our": "0.63", + "word_freq_over": "0", + "word_freq_remove": "0.31", + "word_freq_internet": "0.63", + "word_freq_order": "0.31", + "word_freq_mail": "0.63", + "word_freq_receive": "0.31", + "word_freq_will": "0.31", + "word_freq_people": "0.31", + "word_freq_report": "0", + "word_freq_addresses": "0", + "word_freq_free": "0.31", + "word_freq_business": "0", + "word_freq_email": "0", + "word_freq_you": "3.18", + "word_freq_credit": "0", + "word_freq_your": "0.31", + "word_freq_font": "0", + "word_freq_000": "0", + "word_freq_money": "0", + "word_freq_hp": "0", + "word_freq_hpl": "0", + "word_freq_george": "0", + "word_freq_650": "0", + "word_freq_lab": "0", + "word_freq_labs": "0", + "word_freq_telnet": "0", + "word_freq_857": "0", + "word_freq_data": "0", + "word_freq_415": "0", + "word_freq_85": "0", + "word_freq_technology": "0", + "word_freq_1999": "0", + "word_freq_parts": "0", + "word_freq_pm": "0", + "word_freq_direct": "0", + "word_freq_cs": "0", + "word_freq_meeting": "0", + "word_freq_original": "0", + "word_freq_project": "0", + "word_freq_re": "0", + "word_freq_edu": "0", + "word_freq_table": "0", + "word_freq_conference": "0", + "char_freq_%3B": "0", + "char_freq_%28": "0.137", + "char_freq_%5B": "0", + "char_freq_%21": "0.137", + "char_freq_%24": "0", + "char_freq_%23": "0", + "capital_run_length_average": "3.537", + "capital_run_length_longest": "40", + "capital_run_length_total": "191", + "class": "1" + }, + { + "word_freq_make": "0", + "word_freq_address": "0", + "word_freq_all": "0", + "word_freq_3d": "0", + "word_freq_our": "0.63", + "word_freq_over": "0", + "word_freq_remove": "0.31", + "word_freq_internet": "0.63", + "word_freq_order": "0.31", + "word_freq_mail": "0.63", + "word_freq_receive": "0.31", + "word_freq_will": "0.31", + "word_freq_people": "0.31", + "word_freq_report": "0", + "word_freq_addresses": "0", + "word_freq_free": "0.31", + "word_freq_business": "0", + "word_freq_email": "0", + "word_freq_you": "3.18", + "word_freq_credit": "0", + "word_freq_your": "0.31", + "word_freq_font": "0", + "word_freq_000": "0", + "word_freq_money": "0", + "word_freq_hp": "0", + "word_freq_hpl": "0", + "word_freq_george": "0", + "word_freq_650": "0", + "word_freq_lab": "0", + "word_freq_labs": "0", + "word_freq_telnet": "0", + "word_freq_857": "0", + "word_freq_data": "0", + "word_freq_415": "0", + "word_freq_85": "0", + "word_freq_technology": "0", + "word_freq_1999": "0", + "word_freq_parts": "0", + "word_freq_pm": "0", + "word_freq_direct": "0", + "word_freq_cs": "0", + "word_freq_meeting": "0", + "word_freq_original": "0", + "word_freq_project": "0", + "word_freq_re": "0", + "word_freq_edu": "0", + "word_freq_table": "0", + "word_freq_conference": "0", + "char_freq_%3B": "0", + "char_freq_%28": "0.135", + "char_freq_%5B": "0", + "char_freq_%21": "0.135", + "char_freq_%24": "0", + "char_freq_%23": "0", + "capital_run_length_average": "3.537", + "capital_run_length_longest": "40", + "capital_run_length_total": "191", + "class": "1" + } + ] +} + +Shortlisted templates: +[ + { + "template_id": "tpl_tpch_relative_total_threshold", + "template_name": "Relative-to-Total Extreme Threshold", + "primary_family": "tail_rarity_structure", + "portability": "partial", + "sql_skeleton": "WITH grouped AS (\n SELECT {group_col}, SUM({measure_col}) AS group_value\n FROM {table}\n GROUP BY {group_col}\n), total AS (\n SELECT SUM(group_value) AS total_value\n FROM grouped\n)\nSELECT g.{group_col}, g.group_value\nFROM grouped AS g\nCROSS JOIN total AS t\nWHERE g.group_value > t.total_value * {fraction_threshold}\nORDER BY g.group_value DESC;", + "required_roles": [ + "group_col", + "measure_col" + ] + } +] + +Problem instance: +{ + "dataset_id": "n1", + "question": "Use template Relative-to-Total Extreme Threshold to probe tail_mass_similarity with semantic role filtered_stable_view. Focus on group_col=class, measure_col=word_freq_font.", + "planned_template_id": "tpl_tpch_relative_total_threshold", + "bindings": { + "group_col": "class", + "measure_col": "word_freq_font", + "top_k": 18, + "top_n": 6, + "num_tiles": 10, + "percentile_value": 0.9, + "z_threshold": 2.0, + "fraction_threshold": 0.05, + "baseline_multiplier": 1.75, + "baseline_fraction": 0.1, + "min_group_size": 5, + "min_support": 4, + "measure_threshold": 0.0, + "time_grain": "month", + "lookback_rows": 3, + "current_period_start": "'2024-01-01'", + "current_period_end": "'2024-04-01'", + "previous_period_start": "'2023-10-01'", + "previous_period_end": "'2024-01-01'", + "drift_ratio_threshold": 0.8 + }, + "can_vary": [], + "must_fix": [], + "runtime_sql_skeleton": "WITH grouped AS (\n SELECT {group_col}, SUM({measure_col}) AS group_value\n FROM {table}\n GROUP BY {group_col}\n), total AS (\n SELECT SUM(group_value) AS total_value\n FROM grouped\n)\nSELECT g.{group_col}, g.group_value\nFROM grouped AS g\nCROSS JOIN total AS t\nWHERE g.group_value > t.total_value * {fraction_threshold}\nORDER BY g.group_value DESC;" +} + +Repair context: +{} diff --git a/Query/sql/v2/runs/v2_cli_20260502_081223_d/n1/artifacts/v2q_n1_da46b022f55b7a37/cli/sql_response_attempt_1.raw.txt b/Query/sql/v2/runs/v2_cli_20260502_081223_d/n1/artifacts/v2q_n1_da46b022f55b7a37/cli/sql_response_attempt_1.raw.txt new file mode 100644 index 0000000000000000000000000000000000000000..c53c7f1caca48f3ba7873e354419ef59ff5b89e5 --- /dev/null +++ b/Query/sql/v2/runs/v2_cli_20260502_081223_d/n1/artifacts/v2q_n1_da46b022f55b7a37/cli/sql_response_attempt_1.raw.txt @@ -0,0 +1,4 @@ +{"type":"thread.started","thread_id":"019e40ed-6d34-70a0-9353-55f98936c0f9"} +{"type":"turn.started"} +{"type":"item.completed","item":{"id":"item_0","type":"agent_message","text":"{\"sql\":\"-- template_id: tpl_tpch_relative_total_threshold\\nWITH grouped AS (\\n SELECT \\\"class\\\", SUM(CAST(\\\"word_freq_font\\\" AS REAL)) AS group_value\\n FROM \\\"n1\\\"\\n GROUP BY \\\"class\\\"\\n), total AS (\\n SELECT SUM(group_value) AS total_value\\n FROM grouped\\n)\\nSELECT g.\\\"class\\\", g.group_value\\nFROM grouped AS g\\nCROSS JOIN total AS t\\nWHERE g.group_value > t.total_value * 0.05\\nORDER BY g.group_value DESC;\",\"notes\":\"Applied the provided Relative-to-Total Extreme Threshold template with fixed bindings group_col=\\\"class\\\" and measure_col=\\\"word_freq_font\\\". CAST(\\\"word_freq_font\\\" AS REAL) is used because the schema stores numeric values as TEXT.\"}"}} +{"type":"turn.completed","usage":{"input_tokens":20453,"cached_input_tokens":19840,"output_tokens":540,"reasoning_output_tokens":367}} diff --git a/Query/sql/v2/runs/v2_cli_20260502_081223_d/n1/artifacts/v2q_n1_da46b022f55b7a37/cli/sql_response_attempt_1.txt b/Query/sql/v2/runs/v2_cli_20260502_081223_d/n1/artifacts/v2q_n1_da46b022f55b7a37/cli/sql_response_attempt_1.txt new file mode 100644 index 0000000000000000000000000000000000000000..32dc7e0bac61d2133230a378be7babfbc12b47db --- /dev/null +++ b/Query/sql/v2/runs/v2_cli_20260502_081223_d/n1/artifacts/v2q_n1_da46b022f55b7a37/cli/sql_response_attempt_1.txt @@ -0,0 +1 @@ +{"sql":"-- template_id: tpl_tpch_relative_total_threshold\nWITH grouped AS (\n SELECT \"class\", SUM(CAST(\"word_freq_font\" AS REAL)) AS group_value\n FROM \"n1\"\n GROUP BY \"class\"\n), total AS (\n SELECT SUM(group_value) AS total_value\n FROM grouped\n)\nSELECT g.\"class\", g.group_value\nFROM grouped AS g\nCROSS JOIN total AS t\nWHERE g.group_value > t.total_value * 0.05\nORDER BY g.group_value DESC;","notes":"Applied the provided Relative-to-Total Extreme Threshold template with fixed bindings group_col=\"class\" and measure_col=\"word_freq_font\". CAST(\"word_freq_font\" AS REAL) is used because the schema stores numeric values as TEXT."} \ No newline at end of file diff --git a/Query/sql/v2/runs/v2_cli_20260502_081223_d/n1/artifacts/v2q_n1_da46b022f55b7a37/cli/sql_stderr_attempt_1.txt b/Query/sql/v2/runs/v2_cli_20260502_081223_d/n1/artifacts/v2q_n1_da46b022f55b7a37/cli/sql_stderr_attempt_1.txt new file mode 100644 index 0000000000000000000000000000000000000000..e69de29bb2d1d6434b8b29ae775ad8c2e48c5391 diff --git a/Query/sql/v2/runs/v2_cli_20260502_081223_d/n1/artifacts/v2q_n1_dad817b1a18d3020/cli/conversation.jsonl b/Query/sql/v2/runs/v2_cli_20260502_081223_d/n1/artifacts/v2q_n1_dad817b1a18d3020/cli/conversation.jsonl new file mode 100644 index 0000000000000000000000000000000000000000..8a19f37a724d554e76d7ca6cc4f3cb5ea7ec4054 --- /dev/null +++ b/Query/sql/v2/runs/v2_cli_20260502_081223_d/n1/artifacts/v2q_n1_dad817b1a18d3020/cli/conversation.jsonl @@ -0,0 +1,2 @@ +{"attempt": 1, "phase": "sql_generation", "role": "user", "content_path": "cli/sql_prompt_attempt_1.txt", "metrics": {"chars": 29770, "bytes_utf8": 29770, "lines": 794, "estimated_tokens": null}} +{"attempt": 1, "phase": "sql_generation", "role": "assistant", "content_path": "cli/sql_response_attempt_1.txt", "raw_content_path": "cli/sql_response_attempt_1.raw.txt", "stderr_path": "cli/sql_stderr_attempt_1.txt", "metrics": {"chars": 621, "bytes_utf8": 621, "lines": 1, "estimated_tokens": null}, "usage": {"input_tokens": 20438, "cached_input_tokens": 12032, "output_tokens": 621, "reasoning_output_tokens": 444}} diff --git a/Query/sql/v2/runs/v2_cli_20260502_081223_d/n1/artifacts/v2q_n1_dad817b1a18d3020/cli/session_summary.json b/Query/sql/v2/runs/v2_cli_20260502_081223_d/n1/artifacts/v2q_n1_dad817b1a18d3020/cli/session_summary.json new file mode 100644 index 0000000000000000000000000000000000000000..9e7122332fa279cdfb3bdb7a14e8d29358f67f13 --- /dev/null +++ b/Query/sql/v2/runs/v2_cli_20260502_081223_d/n1/artifacts/v2q_n1_dad817b1a18d3020/cli/session_summary.json @@ -0,0 +1,25 @@ +{ + "engine": "v2-cli:codex", + "command": "codex exec --skip-git-repo-check --disable plugins --sandbox read-only --cd \"/data/jialinzhang/SQLagent\" -m gpt-5.4 --json -", + "ai_cli_calls": 1, + "usage_summary": { + "dataset_id": "n1", + "model": "v2-cli:codex", + "run_id": "v2q_n1_dad817b1a18d3020", + "api_calls": 0, + "input_tokens": 20438, + "cached_input_tokens": 12032, + "output_tokens": 621, + "total_tokens": 21059, + "cost_usd": 0.0, + "ai_cli_calls": 1, + "estimated_input_tokens": 0, + "estimated_output_tokens": 0, + "estimated_total_tokens": 0, + "usage_source": "ai_cli_json_usage", + "cli_elapsed_ms_total": 11945.66, + "sql_execution_elapsed_ms_total": 9.44, + "conversation_log_path": "/data/jialinzhang/TabQueryBench/sql_workloads/v2_current/runs_and_launches/runs/v2_cli_20260502_081223_d/n1/artifacts/v2q_n1_dad817b1a18d3020/cli/conversation.jsonl", + "note": "Executed through a local AI CLI with structured usage metadata." + } +} \ No newline at end of file diff --git a/Query/sql/v2/runs/v2_cli_20260502_081223_d/n1/artifacts/v2q_n1_dad817b1a18d3020/cli/sql_attempt_1.metadata.json b/Query/sql/v2/runs/v2_cli_20260502_081223_d/n1/artifacts/v2q_n1_dad817b1a18d3020/cli/sql_attempt_1.metadata.json new file mode 100644 index 0000000000000000000000000000000000000000..d61f272ec1e2cbb5d937e7aeccea97ce89117da2 --- /dev/null +++ b/Query/sql/v2/runs/v2_cli_20260502_081223_d/n1/artifacts/v2q_n1_dad817b1a18d3020/cli/sql_attempt_1.metadata.json @@ -0,0 +1,45 @@ +{ + "attempt": 1, + "phase": "sql_generation", + "command": "codex exec --skip-git-repo-check --disable plugins --sandbox read-only --cd \"/data/jialinzhang/SQLagent\" -m gpt-5.4 --json -", + "started_at": "2026-05-19T15:39:04.959664+00:00", + "ended_at": "2026-05-19T15:39:16.905353+00:00", + "elapsed_ms": 11945.66, + "prompt_metrics": { + "chars": 29770, + "bytes_utf8": 29770, + "lines": 794, + "estimated_tokens": null + }, + "stdout_metrics": { + "chars": 1001, + "bytes_utf8": 1001, + "lines": 4, + "estimated_tokens": null + }, + "stderr_metrics": { + "chars": 0, + "bytes_utf8": 0, + "lines": 0, + "estimated_tokens": null + }, + "parsed_output": { + "format": "jsonl_events", + "text_metrics": { + "chars": 621, + "bytes_utf8": 621, + "lines": 1, + "estimated_tokens": null + }, + "usage": { + "input_tokens": 20438, + "cached_input_tokens": 12032, + "output_tokens": 621, + "reasoning_output_tokens": 444 + } + }, + "prompt_path": "cli/sql_prompt_attempt_1.txt", + "response_path": "cli/sql_response_attempt_1.txt", + "raw_response_path": "cli/sql_response_attempt_1.raw.txt", + "stderr_path": "cli/sql_stderr_attempt_1.txt" +} \ No newline at end of file diff --git a/Query/sql/v2/runs/v2_cli_20260502_081223_d/n1/artifacts/v2q_n1_dad817b1a18d3020/cli/sql_prompt_attempt_1.txt b/Query/sql/v2/runs/v2_cli_20260502_081223_d/n1/artifacts/v2q_n1_dad817b1a18d3020/cli/sql_prompt_attempt_1.txt new file mode 100644 index 0000000000000000000000000000000000000000..feccaf40b450f807e2476ebbcb8f679cd0abd9a4 --- /dev/null +++ b/Query/sql/v2/runs/v2_cli_20260502_081223_d/n1/artifacts/v2q_n1_dad817b1a18d3020/cli/sql_prompt_attempt_1.txt @@ -0,0 +1,794 @@ +You are generating one SQLite SELECT query for a single-table SQL QA task. +Return strict JSON only, with this schema: {"sql": "...", "notes": "..."}. +Rules: +- Use only the provided table and columns. +- Do not write INSERT, UPDATE, DELETE, DROP, ALTER, CREATE, PRAGMA, ATTACH, DETACH, or VACUUM. +- Prefer the planned template and bound roles when provided. +- Add a leading SQL comment exactly like: -- template_id: . +- Generate SQLite-compatible SQL. SQLite does not support PERCENTILE_CONT or STDDEV. +- Quote identifiers with double quotes. +- Return no markdown and no extra prose. + +Dataset context: +Dataset context for SQL QA: +- dataset_id: n1 +- dataset_name: Spambase +- table_name: n1 +- table_layout: single-table dataset (do not assume joins). +- row_semantics: One row is one email represented by engineered token/character frequency and capitalization-run features. +- task_type: classification +- target_column: class +- main_row_count: 4601 +- important_fields: +- word_freq_make: role=feature, type=numeric_sparse_frequency. tags=['condition_candidate', 'measure', 'rare_pattern_candidate'] desc=Frequency feature for token 'make' in an email message. +- word_freq_address: role=feature, type=numeric_sparse_frequency. tags=['condition_candidate', 'measure', 'rare_pattern_candidate'] desc=Frequency feature for token 'address' in an email message. +- word_freq_all: role=feature, type=numeric_sparse_frequency. tags=['condition_candidate', 'measure', 'rare_pattern_candidate'] desc=Frequency feature for token 'all' in an email message. +- word_freq_3d: role=feature, type=numeric_sparse_frequency. tags=['condition_candidate', 'measure', 'rare_pattern_candidate'] desc=Frequency feature for token '3d' in an email message. +- word_freq_our: role=feature, type=numeric_sparse_frequency. tags=['condition_candidate', 'measure', 'rare_pattern_candidate'] desc=Frequency feature for token 'our' in an email message. +- word_freq_over: role=feature, type=numeric_sparse_frequency. tags=['condition_candidate', 'measure', 'rare_pattern_candidate'] desc=Frequency feature for token 'over' in an email message. +- word_freq_remove: role=feature, type=numeric_sparse_frequency. tags=['condition_candidate', 'measure', 'rare_pattern_candidate'] desc=Frequency feature for token 'remove' in an email message. +- word_freq_internet: role=feature, type=numeric_sparse_frequency. tags=['condition_candidate', 'measure', 'rare_pattern_candidate'] desc=Frequency feature for token 'internet' in an email message. +- word_freq_order: role=feature, type=numeric_sparse_frequency. tags=['condition_candidate', 'measure', 'rare_pattern_candidate'] desc=Frequency feature for token 'order' in an email message. +- word_freq_mail: role=feature, type=numeric_sparse_frequency. tags=['condition_candidate', 'measure', 'rare_pattern_candidate'] desc=Frequency feature for token 'mail' in an email message. +- word_freq_receive: role=feature, type=numeric_sparse_frequency. tags=['condition_candidate', 'measure', 'rare_pattern_candidate'] desc=Frequency feature for token 'receive' in an email message. +- word_freq_will: role=feature, type=numeric_sparse_frequency. tags=['condition_candidate', 'measure', 'rare_pattern_candidate'] desc=Frequency feature for token 'will' in an email message. +- word_freq_people: role=feature, type=numeric_sparse_frequency. tags=['condition_candidate', 'measure', 'rare_pattern_candidate'] desc=Frequency feature for token 'people' in an email message. +- word_freq_report: role=feature, type=numeric_sparse_frequency. tags=['condition_candidate', 'measure', 'rare_pattern_candidate'] desc=Frequency feature for token 'report' in an email message. +- word_freq_addresses: role=feature, type=numeric_sparse_frequency. tags=['condition_candidate', 'measure', 'rare_pattern_candidate'] desc=Frequency feature for token 'addresses' in an email message. +- word_freq_free: role=feature, type=numeric_sparse_frequency. tags=['condition_candidate', 'measure', 'rare_pattern_candidate'] desc=Frequency feature for token 'free' in an email message. +- word_freq_business: role=feature, type=numeric_sparse_frequency. tags=['condition_candidate', 'measure', 'rare_pattern_candidate'] desc=Frequency feature for token 'business' in an email message. +- word_freq_email: role=feature, type=numeric_sparse_frequency. tags=['condition_candidate', 'measure', 'rare_pattern_candidate'] desc=Frequency feature for token 'email' in an email message. +- word_freq_you: role=feature, type=numeric_sparse_frequency. tags=['condition_candidate', 'measure', 'rare_pattern_candidate'] desc=Frequency feature for token 'you' in an email message. +- word_freq_credit: role=feature, type=numeric_sparse_frequency. tags=['condition_candidate', 'measure', 'rare_pattern_candidate'] desc=Frequency feature for token 'credit' in an email message. +- word_freq_your: role=feature, type=numeric_sparse_frequency. tags=['condition_candidate', 'measure', 'rare_pattern_candidate'] desc=Frequency feature for token 'your' in an email message. +- word_freq_font: role=feature, type=numeric_sparse_frequency. tags=['condition_candidate', 'measure', 'rare_pattern_candidate'] desc=Frequency feature for token 'font' in an email message. +- word_freq_000: role=feature, type=numeric_sparse_frequency. tags=['condition_candidate', 'measure', 'rare_pattern_candidate'] desc=Frequency feature for token '000' in an email message. +- word_freq_money: role=feature, type=numeric_sparse_frequency. tags=['condition_candidate', 'measure', 'rare_pattern_candidate'] desc=Frequency feature for token 'money' in an email message. +- word_freq_hp: role=feature, type=numeric_sparse_frequency. tags=['condition_candidate', 'measure', 'rare_pattern_candidate'] desc=Frequency feature for token 'hp' in an email message. +- word_freq_hpl: role=feature, type=numeric_sparse_frequency. tags=['condition_candidate', 'measure', 'rare_pattern_candidate'] desc=Frequency feature for token 'hpl' in an email message. +- word_freq_george: role=feature, type=numeric_sparse_frequency. tags=['condition_candidate', 'measure', 'rare_pattern_candidate'] desc=Frequency feature for token 'george' in an email message. +- word_freq_650: role=feature, type=numeric_sparse_frequency. tags=['condition_candidate', 'measure', 'rare_pattern_candidate'] desc=Frequency feature for token '650' in an email message. +- word_freq_lab: role=feature, type=numeric_sparse_frequency. tags=['condition_candidate', 'measure', 'rare_pattern_candidate'] desc=Frequency feature for token 'lab' in an email message. +- word_freq_labs: role=feature, type=numeric_sparse_frequency. tags=['condition_candidate', 'measure', 'rare_pattern_candidate'] desc=Frequency feature for token 'labs' in an email message. +- word_freq_telnet: role=feature, type=numeric_sparse_frequency. tags=['condition_candidate', 'measure', 'rare_pattern_candidate'] desc=Frequency feature for token 'telnet' in an email message. +- word_freq_857: role=feature, type=numeric_sparse_frequency. tags=['condition_candidate', 'measure', 'rare_pattern_candidate'] desc=Frequency feature for token '857' in an email message. +- word_freq_data: role=feature, type=numeric_sparse_frequency. tags=['condition_candidate', 'measure', 'rare_pattern_candidate'] desc=Frequency feature for token 'data' in an email message. +- word_freq_415: role=feature, type=numeric_sparse_frequency. tags=['condition_candidate', 'measure', 'rare_pattern_candidate'] desc=Frequency feature for token '415' in an email message. +- word_freq_85: role=feature, type=numeric_sparse_frequency. tags=['condition_candidate', 'measure', 'rare_pattern_candidate'] desc=Frequency feature for token '85' in an email message. +- word_freq_technology: role=feature, type=numeric_sparse_frequency. tags=['condition_candidate', 'measure', 'rare_pattern_candidate'] desc=Frequency feature for token 'technology' in an email message. +- word_freq_1999: role=feature, type=numeric_sparse_frequency. tags=['condition_candidate', 'measure', 'rare_pattern_candidate'] desc=Frequency feature for token '1999' in an email message. +- word_freq_parts: role=feature, type=numeric_sparse_frequency. tags=['condition_candidate', 'measure', 'rare_pattern_candidate'] desc=Frequency feature for token 'parts' in an email message. +- word_freq_pm: role=feature, type=numeric_sparse_frequency. tags=['condition_candidate', 'measure', 'rare_pattern_candidate'] desc=Frequency feature for token 'pm' in an email message. +- word_freq_direct: role=feature, type=numeric_sparse_frequency. tags=['condition_candidate', 'measure', 'rare_pattern_candidate'] desc=Frequency feature for token 'direct' in an email message. +- word_freq_cs: role=feature, type=numeric_sparse_frequency. tags=['condition_candidate', 'measure', 'rare_pattern_candidate'] desc=Frequency feature for token 'cs' in an email message. +- word_freq_meeting: role=feature, type=numeric_sparse_frequency. tags=['condition_candidate', 'measure', 'rare_pattern_candidate'] desc=Frequency feature for token 'meeting' in an email message. +- word_freq_original: role=feature, type=numeric_sparse_frequency. tags=['condition_candidate', 'measure', 'rare_pattern_candidate'] desc=Frequency feature for token 'original' in an email message. +- word_freq_project: role=feature, type=numeric_sparse_frequency. tags=['condition_candidate', 'measure', 'rare_pattern_candidate'] desc=Frequency feature for token 'project' in an email message. +- word_freq_re: role=feature, type=numeric_sparse_frequency. tags=['condition_candidate', 'measure', 'rare_pattern_candidate'] desc=Frequency feature for token 're' in an email message. +- word_freq_edu: role=feature, type=numeric_sparse_frequency. tags=['condition_candidate', 'measure', 'rare_pattern_candidate'] desc=Frequency feature for token 'edu' in an email message. +- word_freq_table: role=feature, type=numeric_sparse_frequency. tags=['condition_candidate', 'measure', 'rare_pattern_candidate'] desc=Frequency feature for token 'table' in an email message. +- word_freq_conference: role=feature, type=numeric_sparse_frequency. tags=['condition_candidate', 'measure', 'rare_pattern_candidate'] desc=Frequency feature for token 'conference' in an email message. +- char_freq_%3B: role=feature, type=numeric_sparse_frequency. tags=['condition_candidate', 'measure', 'rare_pattern_candidate'] desc=Character frequency feature for symbol ';'. +- char_freq_%28: role=feature, type=numeric_sparse_frequency. tags=['condition_candidate', 'measure', 'rare_pattern_candidate'] desc=Character frequency feature for symbol '('. +- char_freq_%5B: role=feature, type=numeric_sparse_frequency. tags=['condition_candidate', 'measure', 'rare_pattern_candidate'] desc=Character frequency feature for symbol '['. +- char_freq_%21: role=feature, type=numeric_sparse_frequency. tags=['condition_candidate', 'measure', 'rare_pattern_candidate'] desc=Character frequency feature for symbol '!'. +- char_freq_%24: role=feature, type=numeric_sparse_frequency. tags=['condition_candidate', 'measure', 'rare_pattern_candidate'] desc=Character frequency feature for symbol '$'. +- char_freq_%23: role=feature, type=numeric_sparse_frequency. tags=['condition_candidate', 'measure', 'rare_pattern_candidate'] desc=Character frequency feature for symbol '#'. +- capital_run_length_average: role=feature, type=numeric_heavy_tail. tags=['condition_candidate', 'measure', 'rare_pattern_candidate'] desc=Average length of uninterrupted capital-letter runs. +- capital_run_length_longest: role=feature, type=numeric_heavy_tail. tags=['condition_candidate', 'measure', 'rare_pattern_candidate'] desc=Longest uninterrupted capital-letter run. +- capital_run_length_total: role=feature, type=numeric_heavy_tail. tags=['condition_candidate', 'measure', 'rare_pattern_candidate'] desc=Total number of capital letters in runs. +- class: role=target, type=binary_target. tags=['target_candidate'] desc=Binary spam label (1=spam, 0=non-spam). +- useful_field_combinations: [['word_freq_free', 'word_freq_you', 'class'], ['char_freq_%21', 'capital_run_length_longest', 'class'], ['word_freq_remove', 'word_freq_money', 'class']] +- fields_requiring_caution: ['capital_run_length_average', 'capital_run_length_longest', 'capital_run_length_total'] +- source_url: https://www.openml.org/d/44 + +SQLite schema snapshot: +{ + "table_name": "n1", + "quoted_table_name": "\"n1\"", + "row_count": 4601, + "columns": [ + { + "name": "word_freq_make", + "type": "TEXT", + "notnull": false, + "pk": false + }, + { + "name": "word_freq_address", + "type": "TEXT", + "notnull": false, + "pk": false + }, + { + "name": "word_freq_all", + "type": "TEXT", + "notnull": false, + "pk": false + }, + { + "name": "word_freq_3d", + "type": "TEXT", + "notnull": false, + "pk": false + }, + { + "name": "word_freq_our", + "type": "TEXT", + "notnull": false, + "pk": false + }, + { + "name": "word_freq_over", + "type": "TEXT", + "notnull": false, + "pk": false + }, + { + "name": "word_freq_remove", + "type": "TEXT", + "notnull": false, + "pk": false + }, + { + "name": "word_freq_internet", + "type": "TEXT", + "notnull": false, + "pk": false + }, + { + "name": "word_freq_order", + "type": "TEXT", + "notnull": false, + "pk": false + }, + { + "name": "word_freq_mail", + "type": "TEXT", + "notnull": false, + "pk": false + }, + { + "name": "word_freq_receive", + "type": "TEXT", + "notnull": false, + "pk": false + }, + { + "name": "word_freq_will", + "type": "TEXT", + "notnull": false, + "pk": false + }, + { + "name": "word_freq_people", + "type": "TEXT", + "notnull": false, + "pk": false + }, + { + "name": "word_freq_report", + "type": "TEXT", + "notnull": false, + "pk": false + }, + { + "name": "word_freq_addresses", + "type": "TEXT", + "notnull": false, + "pk": false + }, + { + "name": "word_freq_free", + "type": "TEXT", + "notnull": false, + "pk": false + }, + { + "name": "word_freq_business", + "type": "TEXT", + "notnull": false, + "pk": false + }, + { + "name": "word_freq_email", + "type": "TEXT", + "notnull": false, + "pk": false + }, + { + "name": "word_freq_you", + "type": "TEXT", + "notnull": false, + "pk": false + }, + { + "name": "word_freq_credit", + "type": "TEXT", + "notnull": false, + "pk": false + }, + { + "name": "word_freq_your", + "type": "TEXT", + "notnull": false, + "pk": false + }, + { + "name": "word_freq_font", + "type": "TEXT", + "notnull": false, + "pk": false + }, + { + "name": "word_freq_000", + "type": "TEXT", + "notnull": false, + "pk": false + }, + { + "name": "word_freq_money", + "type": "TEXT", + "notnull": false, + "pk": false + }, + { + "name": "word_freq_hp", + "type": "TEXT", + "notnull": false, + "pk": false + }, + { + "name": "word_freq_hpl", + "type": "TEXT", + "notnull": false, + "pk": false + }, + { + "name": "word_freq_george", + "type": "TEXT", + "notnull": false, + "pk": false + }, + { + "name": "word_freq_650", + "type": "TEXT", + "notnull": false, + "pk": false + }, + { + "name": "word_freq_lab", + "type": "TEXT", + "notnull": false, + "pk": false + }, + { + "name": "word_freq_labs", + "type": "TEXT", + "notnull": false, + "pk": false + }, + { + "name": "word_freq_telnet", + "type": "TEXT", + "notnull": false, + "pk": false + }, + { + "name": "word_freq_857", + "type": "TEXT", + "notnull": false, + "pk": false + }, + { + "name": "word_freq_data", + "type": "TEXT", + "notnull": false, + "pk": false + }, + { + "name": "word_freq_415", + "type": "TEXT", + "notnull": false, + "pk": false + }, + { + "name": "word_freq_85", + "type": "TEXT", + "notnull": false, + "pk": false + }, + { + "name": "word_freq_technology", + "type": "TEXT", + "notnull": false, + "pk": false + }, + { + "name": "word_freq_1999", + "type": "TEXT", + "notnull": false, + "pk": false + }, + { + "name": "word_freq_parts", + "type": "TEXT", + "notnull": false, + "pk": false + }, + { + "name": "word_freq_pm", + "type": "TEXT", + "notnull": false, + "pk": false + }, + { + "name": "word_freq_direct", + "type": "TEXT", + "notnull": false, + "pk": false + }, + { + "name": "word_freq_cs", + "type": "TEXT", + "notnull": false, + "pk": false + }, + { + "name": "word_freq_meeting", + "type": "TEXT", + "notnull": false, + "pk": false + }, + { + "name": "word_freq_original", + "type": "TEXT", + "notnull": false, + "pk": false + }, + { + "name": "word_freq_project", + "type": "TEXT", + "notnull": false, + "pk": false + }, + { + "name": "word_freq_re", + "type": "TEXT", + "notnull": false, + "pk": false + }, + { + "name": "word_freq_edu", + "type": "TEXT", + "notnull": false, + "pk": false + }, + { + "name": "word_freq_table", + "type": "TEXT", + "notnull": false, + "pk": false + }, + { + "name": "word_freq_conference", + "type": "TEXT", + "notnull": false, + "pk": false + }, + { + "name": "char_freq_%3B", + "type": "TEXT", + "notnull": false, + "pk": false + }, + { + "name": "char_freq_%28", + "type": "TEXT", + "notnull": false, + "pk": false + }, + { + "name": "char_freq_%5B", + "type": "TEXT", + "notnull": false, + "pk": false + }, + { + "name": "char_freq_%21", + "type": "TEXT", + "notnull": false, + "pk": false + }, + { + "name": "char_freq_%24", + "type": "TEXT", + "notnull": false, + "pk": false + }, + { + "name": "char_freq_%23", + "type": "TEXT", + "notnull": false, + "pk": false + }, + { + "name": "capital_run_length_average", + "type": "TEXT", + "notnull": false, + "pk": false + }, + { + "name": "capital_run_length_longest", + "type": "TEXT", + "notnull": false, + "pk": false + }, + { + "name": "capital_run_length_total", + "type": "TEXT", + "notnull": false, + "pk": false + }, + { + "name": "class", + "type": "TEXT", + "notnull": false, + "pk": false + } + ], + "sample_rows": [ + { + "word_freq_make": "0", + "word_freq_address": "0.64", + "word_freq_all": "0.64", + "word_freq_3d": "0", + "word_freq_our": "0.32", + "word_freq_over": "0", + "word_freq_remove": "0", + "word_freq_internet": "0", + "word_freq_order": "0", + "word_freq_mail": "0", + "word_freq_receive": "0", + "word_freq_will": "0.64", + "word_freq_people": "0", + "word_freq_report": "0", + "word_freq_addresses": "0", + "word_freq_free": "0.32", + "word_freq_business": "0", + "word_freq_email": "1.29", + "word_freq_you": "1.93", + "word_freq_credit": "0", + "word_freq_your": "0.96", + "word_freq_font": "0", + "word_freq_000": "0", + "word_freq_money": "0", + "word_freq_hp": "0", + "word_freq_hpl": "0", + "word_freq_george": "0", + "word_freq_650": "0", + "word_freq_lab": "0", + "word_freq_labs": "0", + "word_freq_telnet": "0", + "word_freq_857": "0", + "word_freq_data": "0", + "word_freq_415": "0", + "word_freq_85": "0", + "word_freq_technology": "0", + "word_freq_1999": "0", + "word_freq_parts": "0", + "word_freq_pm": "0", + "word_freq_direct": "0", + "word_freq_cs": "0", + "word_freq_meeting": "0", + "word_freq_original": "0", + "word_freq_project": "0", + "word_freq_re": "0", + "word_freq_edu": "0", + "word_freq_table": "0", + "word_freq_conference": "0", + "char_freq_%3B": "0", + "char_freq_%28": "0", + "char_freq_%5B": "0", + "char_freq_%21": "0.778", + "char_freq_%24": "0", + "char_freq_%23": "0", + "capital_run_length_average": "3.756", + "capital_run_length_longest": "61", + "capital_run_length_total": "278", + "class": "1" + }, + { + "word_freq_make": "0.21", + "word_freq_address": "0.28", + "word_freq_all": "0.5", + "word_freq_3d": "0", + "word_freq_our": "0.14", + "word_freq_over": "0.28", + "word_freq_remove": "0.21", + "word_freq_internet": "0.07", + "word_freq_order": "0", + "word_freq_mail": "0.94", + "word_freq_receive": "0.21", + "word_freq_will": "0.79", + "word_freq_people": "0.65", + "word_freq_report": "0.21", + "word_freq_addresses": "0.14", + "word_freq_free": "0.14", + "word_freq_business": "0.07", + "word_freq_email": "0.28", + "word_freq_you": "3.47", + "word_freq_credit": "0", + "word_freq_your": "1.59", + "word_freq_font": "0", + "word_freq_000": "0.43", + "word_freq_money": "0.43", + "word_freq_hp": "0", + "word_freq_hpl": "0", + "word_freq_george": "0", + "word_freq_650": "0", + "word_freq_lab": "0", + "word_freq_labs": "0", + "word_freq_telnet": "0", + "word_freq_857": "0", + "word_freq_data": "0", + "word_freq_415": "0", + "word_freq_85": "0", + "word_freq_technology": "0", + "word_freq_1999": "0.07", + "word_freq_parts": "0", + "word_freq_pm": "0", + "word_freq_direct": "0", + "word_freq_cs": "0", + "word_freq_meeting": "0", + "word_freq_original": "0", + "word_freq_project": "0", + "word_freq_re": "0", + "word_freq_edu": "0", + "word_freq_table": "0", + "word_freq_conference": "0", + "char_freq_%3B": "0", + "char_freq_%28": "0.132", + "char_freq_%5B": "0", + "char_freq_%21": "0.372", + "char_freq_%24": "0.18", + "char_freq_%23": "0.048", + "capital_run_length_average": "5.114", + "capital_run_length_longest": "101", + "capital_run_length_total": "1028", + "class": "1" + }, + { + "word_freq_make": "0.06", + "word_freq_address": "0", + "word_freq_all": "0.71", + "word_freq_3d": "0", + "word_freq_our": "1.23", + "word_freq_over": "0.19", + "word_freq_remove": "0.19", + "word_freq_internet": "0.12", + "word_freq_order": "0.64", + "word_freq_mail": "0.25", + "word_freq_receive": "0.38", + "word_freq_will": "0.45", + "word_freq_people": "0.12", + "word_freq_report": "0", + "word_freq_addresses": "1.75", + "word_freq_free": "0.06", + "word_freq_business": "0.06", + "word_freq_email": "1.03", + "word_freq_you": "1.36", + "word_freq_credit": "0.32", + "word_freq_your": "0.51", + "word_freq_font": "0", + "word_freq_000": "1.16", + "word_freq_money": "0.06", + "word_freq_hp": "0", + "word_freq_hpl": "0", + "word_freq_george": "0", + "word_freq_650": "0", + "word_freq_lab": "0", + "word_freq_labs": "0", + "word_freq_telnet": "0", + "word_freq_857": "0", + "word_freq_data": "0", + "word_freq_415": "0", + "word_freq_85": "0", + "word_freq_technology": "0", + "word_freq_1999": "0", + "word_freq_parts": "0", + "word_freq_pm": "0", + "word_freq_direct": "0.06", + "word_freq_cs": "0", + "word_freq_meeting": "0", + "word_freq_original": "0.12", + "word_freq_project": "0", + "word_freq_re": "0.06", + "word_freq_edu": "0.06", + "word_freq_table": "0", + "word_freq_conference": "0", + "char_freq_%3B": "0.01", + "char_freq_%28": "0.143", + "char_freq_%5B": "0", + "char_freq_%21": "0.276", + "char_freq_%24": "0.184", + "char_freq_%23": "0.01", + "capital_run_length_average": "9.821", + "capital_run_length_longest": "485", + "capital_run_length_total": "2259", + "class": "1" + }, + { + "word_freq_make": "0", + "word_freq_address": "0", + "word_freq_all": "0", + "word_freq_3d": "0", + "word_freq_our": "0.63", + "word_freq_over": "0", + "word_freq_remove": "0.31", + "word_freq_internet": "0.63", + "word_freq_order": "0.31", + "word_freq_mail": "0.63", + "word_freq_receive": "0.31", + "word_freq_will": "0.31", + "word_freq_people": "0.31", + "word_freq_report": "0", + "word_freq_addresses": "0", + "word_freq_free": "0.31", + "word_freq_business": "0", + "word_freq_email": "0", + "word_freq_you": "3.18", + "word_freq_credit": "0", + "word_freq_your": "0.31", + "word_freq_font": "0", + "word_freq_000": "0", + "word_freq_money": "0", + "word_freq_hp": "0", + "word_freq_hpl": "0", + "word_freq_george": "0", + "word_freq_650": "0", + "word_freq_lab": "0", + "word_freq_labs": "0", + "word_freq_telnet": "0", + "word_freq_857": "0", + "word_freq_data": "0", + "word_freq_415": "0", + "word_freq_85": "0", + "word_freq_technology": "0", + "word_freq_1999": "0", + "word_freq_parts": "0", + "word_freq_pm": "0", + "word_freq_direct": "0", + "word_freq_cs": "0", + "word_freq_meeting": "0", + "word_freq_original": "0", + "word_freq_project": "0", + "word_freq_re": "0", + "word_freq_edu": "0", + "word_freq_table": "0", + "word_freq_conference": "0", + "char_freq_%3B": "0", + "char_freq_%28": "0.137", + "char_freq_%5B": "0", + "char_freq_%21": "0.137", + "char_freq_%24": "0", + "char_freq_%23": "0", + "capital_run_length_average": "3.537", + "capital_run_length_longest": "40", + "capital_run_length_total": "191", + "class": "1" + }, + { + "word_freq_make": "0", + "word_freq_address": "0", + "word_freq_all": "0", + "word_freq_3d": "0", + "word_freq_our": "0.63", + "word_freq_over": "0", + "word_freq_remove": "0.31", + "word_freq_internet": "0.63", + "word_freq_order": "0.31", + "word_freq_mail": "0.63", + "word_freq_receive": "0.31", + "word_freq_will": "0.31", + "word_freq_people": "0.31", + "word_freq_report": "0", + "word_freq_addresses": "0", + "word_freq_free": "0.31", + "word_freq_business": "0", + "word_freq_email": "0", + "word_freq_you": "3.18", + "word_freq_credit": "0", + "word_freq_your": "0.31", + "word_freq_font": "0", + "word_freq_000": "0", + "word_freq_money": "0", + "word_freq_hp": "0", + "word_freq_hpl": "0", + "word_freq_george": "0", + "word_freq_650": "0", + "word_freq_lab": "0", + "word_freq_labs": "0", + "word_freq_telnet": "0", + "word_freq_857": "0", + "word_freq_data": "0", + "word_freq_415": "0", + "word_freq_85": "0", + "word_freq_technology": "0", + "word_freq_1999": "0", + "word_freq_parts": "0", + "word_freq_pm": "0", + "word_freq_direct": "0", + "word_freq_cs": "0", + "word_freq_meeting": "0", + "word_freq_original": "0", + "word_freq_project": "0", + "word_freq_re": "0", + "word_freq_edu": "0", + "word_freq_table": "0", + "word_freq_conference": "0", + "char_freq_%3B": "0", + "char_freq_%28": "0.135", + "char_freq_%5B": "0", + "char_freq_%21": "0.135", + "char_freq_%24": "0", + "char_freq_%23": "0", + "capital_run_length_average": "3.537", + "capital_run_length_longest": "40", + "capital_run_length_total": "191", + "class": "1" + } + ] +} + +Shortlisted templates: +[ + { + "template_id": "tpl_tpcds_within_group_share", + "template_name": "Within-Group Share of Total", + "primary_family": "conditional_dependency_structure", + "portability": "partial", + "sql_skeleton": "SELECT {group_col}, {item_col},\n SUM({measure_col}) AS total_measure,\n SUM({measure_col}) * 100.0 / SUM(SUM({measure_col})) OVER (PARTITION BY {group_col}) AS share_within_group\nFROM {table}\nGROUP BY {group_col}, {item_col}\nORDER BY share_within_group DESC;", + "required_roles": [ + "group_col", + "item_col", + "measure_col" + ] + } +] + +Problem instance: +{ + "dataset_id": "n1", + "question": "Use template Within-Group Share of Total to probe dependency_strength_similarity with semantic role within_group_proportion. Focus on group_col=class, measure_col=word_freq_415.", + "planned_template_id": "tpl_tpcds_within_group_share", + "bindings": { + "group_col": "class", + "measure_col": "word_freq_415", + "item_col": "word_freq_85", + "top_k": 18, + "top_n": 5, + "num_tiles": 10, + "percentile_value": 0.95, + "z_threshold": 2.0, + "fraction_threshold": 0.05, + "baseline_multiplier": 1.75, + "baseline_fraction": 0.1, + "min_group_size": 5, + "min_support": 4, + "measure_threshold": 0.0, + "time_grain": "month", + "lookback_rows": 3, + "current_period_start": "'2024-01-01'", + "current_period_end": "'2024-04-01'", + "previous_period_start": "'2023-10-01'", + "previous_period_end": "'2024-01-01'", + "drift_ratio_threshold": 0.8 + }, + "can_vary": [], + "must_fix": [], + "runtime_sql_skeleton": "SELECT {group_col}, {item_col},\n SUM({measure_col}) AS total_measure,\n SUM({measure_col}) * 100.0 / SUM(SUM({measure_col})) OVER (PARTITION BY {group_col}) AS share_within_group\nFROM {table}\nGROUP BY {group_col}, {item_col}\nORDER BY share_within_group DESC;" +} + +Repair context: +{} diff --git a/Query/sql/v2/runs/v2_cli_20260502_081223_d/n1/artifacts/v2q_n1_dad817b1a18d3020/cli/sql_response_attempt_1.raw.txt b/Query/sql/v2/runs/v2_cli_20260502_081223_d/n1/artifacts/v2q_n1_dad817b1a18d3020/cli/sql_response_attempt_1.raw.txt new file mode 100644 index 0000000000000000000000000000000000000000..da73f7f000991cb8134eb8bca18ccaa96449e012 --- /dev/null +++ b/Query/sql/v2/runs/v2_cli_20260502_081223_d/n1/artifacts/v2q_n1_dad817b1a18d3020/cli/sql_response_attempt_1.raw.txt @@ -0,0 +1,4 @@ +{"type":"thread.started","thread_id":"019e40e4-028d-7302-80d3-9ac38e548ca9"} +{"type":"turn.started"} +{"type":"item.completed","item":{"id":"item_0","type":"agent_message","text":"{\"sql\":\"-- template_id: tpl_tpcds_within_group_share\\nSELECT \\\"class\\\", \\\"word_freq_85\\\",\\n SUM(CAST(\\\"word_freq_415\\\" AS REAL)) AS total_measure,\\n SUM(CAST(\\\"word_freq_415\\\" AS REAL)) * 100.0 / SUM(SUM(CAST(\\\"word_freq_415\\\" AS REAL))) OVER (PARTITION BY \\\"class\\\") AS share_within_group\\nFROM \\\"n1\\\"\\nGROUP BY \\\"class\\\", \\\"word_freq_85\\\"\\nORDER BY share_within_group DESC;\",\"notes\":\"Applied the Within-Group Share of Total template with group_col=\\\"class\\\", item_col=\\\"word_freq_85\\\", and measure_col=\\\"word_freq_415\\\". Cast the measure from TEXT to REAL because the schema stores numeric fields as TEXT.\"}"}} +{"type":"turn.completed","usage":{"input_tokens":20438,"cached_input_tokens":12032,"output_tokens":621,"reasoning_output_tokens":444}} diff --git a/Query/sql/v2/runs/v2_cli_20260502_081223_d/n1/artifacts/v2q_n1_dad817b1a18d3020/cli/sql_response_attempt_1.txt b/Query/sql/v2/runs/v2_cli_20260502_081223_d/n1/artifacts/v2q_n1_dad817b1a18d3020/cli/sql_response_attempt_1.txt new file mode 100644 index 0000000000000000000000000000000000000000..fe73d5ec9e61dd661d05d7f04d6b0540f4996dc7 --- /dev/null +++ b/Query/sql/v2/runs/v2_cli_20260502_081223_d/n1/artifacts/v2q_n1_dad817b1a18d3020/cli/sql_response_attempt_1.txt @@ -0,0 +1 @@ +{"sql":"-- template_id: tpl_tpcds_within_group_share\nSELECT \"class\", \"word_freq_85\",\n SUM(CAST(\"word_freq_415\" AS REAL)) AS total_measure,\n SUM(CAST(\"word_freq_415\" AS REAL)) * 100.0 / SUM(SUM(CAST(\"word_freq_415\" AS REAL))) OVER (PARTITION BY \"class\") AS share_within_group\nFROM \"n1\"\nGROUP BY \"class\", \"word_freq_85\"\nORDER BY share_within_group DESC;","notes":"Applied the Within-Group Share of Total template with group_col=\"class\", item_col=\"word_freq_85\", and measure_col=\"word_freq_415\". Cast the measure from TEXT to REAL because the schema stores numeric fields as TEXT."} \ No newline at end of file diff --git a/Query/sql/v2/runs/v2_cli_20260502_081223_d/n1/artifacts/v2q_n1_dad817b1a18d3020/cli/sql_stderr_attempt_1.txt b/Query/sql/v2/runs/v2_cli_20260502_081223_d/n1/artifacts/v2q_n1_dad817b1a18d3020/cli/sql_stderr_attempt_1.txt new file mode 100644 index 0000000000000000000000000000000000000000..e69de29bb2d1d6434b8b29ae775ad8c2e48c5391 diff --git a/Query/sql/v2/runs/v2_cli_20260502_081223_d/n1/artifacts/v2q_n1_db01aa23845b4486/cli/sql_attempt_1.metadata.json b/Query/sql/v2/runs/v2_cli_20260502_081223_d/n1/artifacts/v2q_n1_db01aa23845b4486/cli/sql_attempt_1.metadata.json new file mode 100644 index 0000000000000000000000000000000000000000..415cb480bc985a6910fbeb2fc6550c3171f2cab8 --- /dev/null +++ b/Query/sql/v2/runs/v2_cli_20260502_081223_d/n1/artifacts/v2q_n1_db01aa23845b4486/cli/sql_attempt_1.metadata.json @@ -0,0 +1,43 @@ +{ + "attempt": 1, + "phase": "sql_generation", + "command": "codex exec --skip-git-repo-check --disable plugins --sandbox read-only --cd \"/data/jialinzhang/SQLagent\" -m gpt-5.4 --json -", + "started_at": "2026-05-19T16:06:15.864261+00:00", + "ended_at": "2026-05-19T16:06:18.800740+00:00", + "elapsed_ms": 2936.45, + "returncode": 1, + "prompt_metrics": { + "chars": 29523, + "bytes_utf8": 29523, + "lines": 792, + "estimated_tokens": null + }, + "stdout_metrics": { + "chars": 281, + "bytes_utf8": 281, + "lines": 4, + "estimated_tokens": null + }, + "stderr_metrics": { + "chars": 0, + "bytes_utf8": 0, + "lines": 0, + "estimated_tokens": null + }, + "parsed_output": { + "format": "jsonl_events", + "text_metrics": { + "chars": 280, + "bytes_utf8": 280, + "lines": 4, + "estimated_tokens": null + }, + "usage": {} + }, + "status": "failed", + "error": "AI CLI command failed with exit code 1: ", + "prompt_path": "cli/sql_prompt_attempt_1.txt", + "response_path": "cli/sql_response_attempt_1.txt", + "raw_response_path": "cli/sql_response_attempt_1.raw.txt", + "stderr_path": "cli/sql_stderr_attempt_1.txt" +} \ No newline at end of file diff --git a/Query/sql/v2/runs/v2_cli_20260502_081223_d/n1/artifacts/v2q_n1_db01aa23845b4486/cli/sql_attempt_2.metadata.json b/Query/sql/v2/runs/v2_cli_20260502_081223_d/n1/artifacts/v2q_n1_db01aa23845b4486/cli/sql_attempt_2.metadata.json new file mode 100644 index 0000000000000000000000000000000000000000..9df580ef0754cbe88d37a51de28f1f16561ab6e0 --- /dev/null +++ b/Query/sql/v2/runs/v2_cli_20260502_081223_d/n1/artifacts/v2q_n1_db01aa23845b4486/cli/sql_attempt_2.metadata.json @@ -0,0 +1,43 @@ +{ + "attempt": 2, + "phase": "sql_generation", + "command": "codex exec --skip-git-repo-check --disable plugins --sandbox read-only --cd \"/data/jialinzhang/SQLagent\" -m gpt-5.4 --json -", + "started_at": "2026-05-19T16:06:19.803869+00:00", + "ended_at": "2026-05-19T16:06:23.189183+00:00", + "elapsed_ms": 3385.27, + "returncode": 1, + "prompt_metrics": { + "chars": 29523, + "bytes_utf8": 29523, + "lines": 792, + "estimated_tokens": null + }, + "stdout_metrics": { + "chars": 281, + "bytes_utf8": 281, + "lines": 4, + "estimated_tokens": null + }, + "stderr_metrics": { + "chars": 0, + "bytes_utf8": 0, + "lines": 0, + "estimated_tokens": null + }, + "parsed_output": { + "format": "jsonl_events", + "text_metrics": { + "chars": 280, + "bytes_utf8": 280, + "lines": 4, + "estimated_tokens": null + }, + "usage": {} + }, + "status": "failed", + "error": "AI CLI command failed with exit code 1: ", + "prompt_path": "cli/sql_prompt_attempt_2.txt", + "response_path": "cli/sql_response_attempt_2.txt", + "raw_response_path": "cli/sql_response_attempt_2.raw.txt", + "stderr_path": "cli/sql_stderr_attempt_2.txt" +} \ No newline at end of file diff --git a/Query/sql/v2/runs/v2_cli_20260502_081223_d/n1/artifacts/v2q_n1_db01aa23845b4486/cli/sql_prompt_attempt_1.txt b/Query/sql/v2/runs/v2_cli_20260502_081223_d/n1/artifacts/v2q_n1_db01aa23845b4486/cli/sql_prompt_attempt_1.txt new file mode 100644 index 0000000000000000000000000000000000000000..b240a37043750bb7141998ea1bc1978d5b4e4398 --- /dev/null +++ b/Query/sql/v2/runs/v2_cli_20260502_081223_d/n1/artifacts/v2q_n1_db01aa23845b4486/cli/sql_prompt_attempt_1.txt @@ -0,0 +1,792 @@ +You are generating one SQLite SELECT query for a single-table SQL QA task. +Return strict JSON only, with this schema: {"sql": "...", "notes": "..."}. +Rules: +- Use only the provided table and columns. +- Do not write INSERT, UPDATE, DELETE, DROP, ALTER, CREATE, PRAGMA, ATTACH, DETACH, or VACUUM. +- Prefer the planned template and bound roles when provided. +- Add a leading SQL comment exactly like: -- template_id: . +- Generate SQLite-compatible SQL. SQLite does not support PERCENTILE_CONT or STDDEV. +- Quote identifiers with double quotes. +- Return no markdown and no extra prose. + +Dataset context: +Dataset context for SQL QA: +- dataset_id: n1 +- dataset_name: Spambase +- table_name: n1 +- table_layout: single-table dataset (do not assume joins). +- row_semantics: One row is one email represented by engineered token/character frequency and capitalization-run features. +- task_type: classification +- target_column: class +- main_row_count: 4601 +- important_fields: +- word_freq_make: role=feature, type=numeric_sparse_frequency. tags=['condition_candidate', 'measure', 'rare_pattern_candidate'] desc=Frequency feature for token 'make' in an email message. +- word_freq_address: role=feature, type=numeric_sparse_frequency. tags=['condition_candidate', 'measure', 'rare_pattern_candidate'] desc=Frequency feature for token 'address' in an email message. +- word_freq_all: role=feature, type=numeric_sparse_frequency. tags=['condition_candidate', 'measure', 'rare_pattern_candidate'] desc=Frequency feature for token 'all' in an email message. +- word_freq_3d: role=feature, type=numeric_sparse_frequency. tags=['condition_candidate', 'measure', 'rare_pattern_candidate'] desc=Frequency feature for token '3d' in an email message. +- word_freq_our: role=feature, type=numeric_sparse_frequency. tags=['condition_candidate', 'measure', 'rare_pattern_candidate'] desc=Frequency feature for token 'our' in an email message. +- word_freq_over: role=feature, type=numeric_sparse_frequency. tags=['condition_candidate', 'measure', 'rare_pattern_candidate'] desc=Frequency feature for token 'over' in an email message. +- word_freq_remove: role=feature, type=numeric_sparse_frequency. tags=['condition_candidate', 'measure', 'rare_pattern_candidate'] desc=Frequency feature for token 'remove' in an email message. +- word_freq_internet: role=feature, type=numeric_sparse_frequency. tags=['condition_candidate', 'measure', 'rare_pattern_candidate'] desc=Frequency feature for token 'internet' in an email message. +- word_freq_order: role=feature, type=numeric_sparse_frequency. tags=['condition_candidate', 'measure', 'rare_pattern_candidate'] desc=Frequency feature for token 'order' in an email message. +- word_freq_mail: role=feature, type=numeric_sparse_frequency. tags=['condition_candidate', 'measure', 'rare_pattern_candidate'] desc=Frequency feature for token 'mail' in an email message. +- word_freq_receive: role=feature, type=numeric_sparse_frequency. tags=['condition_candidate', 'measure', 'rare_pattern_candidate'] desc=Frequency feature for token 'receive' in an email message. +- word_freq_will: role=feature, type=numeric_sparse_frequency. tags=['condition_candidate', 'measure', 'rare_pattern_candidate'] desc=Frequency feature for token 'will' in an email message. +- word_freq_people: role=feature, type=numeric_sparse_frequency. tags=['condition_candidate', 'measure', 'rare_pattern_candidate'] desc=Frequency feature for token 'people' in an email message. +- word_freq_report: role=feature, type=numeric_sparse_frequency. tags=['condition_candidate', 'measure', 'rare_pattern_candidate'] desc=Frequency feature for token 'report' in an email message. +- word_freq_addresses: role=feature, type=numeric_sparse_frequency. tags=['condition_candidate', 'measure', 'rare_pattern_candidate'] desc=Frequency feature for token 'addresses' in an email message. +- word_freq_free: role=feature, type=numeric_sparse_frequency. tags=['condition_candidate', 'measure', 'rare_pattern_candidate'] desc=Frequency feature for token 'free' in an email message. +- word_freq_business: role=feature, type=numeric_sparse_frequency. tags=['condition_candidate', 'measure', 'rare_pattern_candidate'] desc=Frequency feature for token 'business' in an email message. +- word_freq_email: role=feature, type=numeric_sparse_frequency. tags=['condition_candidate', 'measure', 'rare_pattern_candidate'] desc=Frequency feature for token 'email' in an email message. +- word_freq_you: role=feature, type=numeric_sparse_frequency. tags=['condition_candidate', 'measure', 'rare_pattern_candidate'] desc=Frequency feature for token 'you' in an email message. +- word_freq_credit: role=feature, type=numeric_sparse_frequency. tags=['condition_candidate', 'measure', 'rare_pattern_candidate'] desc=Frequency feature for token 'credit' in an email message. +- word_freq_your: role=feature, type=numeric_sparse_frequency. tags=['condition_candidate', 'measure', 'rare_pattern_candidate'] desc=Frequency feature for token 'your' in an email message. +- word_freq_font: role=feature, type=numeric_sparse_frequency. tags=['condition_candidate', 'measure', 'rare_pattern_candidate'] desc=Frequency feature for token 'font' in an email message. +- word_freq_000: role=feature, type=numeric_sparse_frequency. tags=['condition_candidate', 'measure', 'rare_pattern_candidate'] desc=Frequency feature for token '000' in an email message. +- word_freq_money: role=feature, type=numeric_sparse_frequency. tags=['condition_candidate', 'measure', 'rare_pattern_candidate'] desc=Frequency feature for token 'money' in an email message. +- word_freq_hp: role=feature, type=numeric_sparse_frequency. tags=['condition_candidate', 'measure', 'rare_pattern_candidate'] desc=Frequency feature for token 'hp' in an email message. +- word_freq_hpl: role=feature, type=numeric_sparse_frequency. tags=['condition_candidate', 'measure', 'rare_pattern_candidate'] desc=Frequency feature for token 'hpl' in an email message. +- word_freq_george: role=feature, type=numeric_sparse_frequency. tags=['condition_candidate', 'measure', 'rare_pattern_candidate'] desc=Frequency feature for token 'george' in an email message. +- word_freq_650: role=feature, type=numeric_sparse_frequency. tags=['condition_candidate', 'measure', 'rare_pattern_candidate'] desc=Frequency feature for token '650' in an email message. +- word_freq_lab: role=feature, type=numeric_sparse_frequency. tags=['condition_candidate', 'measure', 'rare_pattern_candidate'] desc=Frequency feature for token 'lab' in an email message. +- word_freq_labs: role=feature, type=numeric_sparse_frequency. tags=['condition_candidate', 'measure', 'rare_pattern_candidate'] desc=Frequency feature for token 'labs' in an email message. +- word_freq_telnet: role=feature, type=numeric_sparse_frequency. tags=['condition_candidate', 'measure', 'rare_pattern_candidate'] desc=Frequency feature for token 'telnet' in an email message. +- word_freq_857: role=feature, type=numeric_sparse_frequency. tags=['condition_candidate', 'measure', 'rare_pattern_candidate'] desc=Frequency feature for token '857' in an email message. +- word_freq_data: role=feature, type=numeric_sparse_frequency. tags=['condition_candidate', 'measure', 'rare_pattern_candidate'] desc=Frequency feature for token 'data' in an email message. +- word_freq_415: role=feature, type=numeric_sparse_frequency. tags=['condition_candidate', 'measure', 'rare_pattern_candidate'] desc=Frequency feature for token '415' in an email message. +- word_freq_85: role=feature, type=numeric_sparse_frequency. tags=['condition_candidate', 'measure', 'rare_pattern_candidate'] desc=Frequency feature for token '85' in an email message. +- word_freq_technology: role=feature, type=numeric_sparse_frequency. tags=['condition_candidate', 'measure', 'rare_pattern_candidate'] desc=Frequency feature for token 'technology' in an email message. +- word_freq_1999: role=feature, type=numeric_sparse_frequency. tags=['condition_candidate', 'measure', 'rare_pattern_candidate'] desc=Frequency feature for token '1999' in an email message. +- word_freq_parts: role=feature, type=numeric_sparse_frequency. tags=['condition_candidate', 'measure', 'rare_pattern_candidate'] desc=Frequency feature for token 'parts' in an email message. +- word_freq_pm: role=feature, type=numeric_sparse_frequency. tags=['condition_candidate', 'measure', 'rare_pattern_candidate'] desc=Frequency feature for token 'pm' in an email message. +- word_freq_direct: role=feature, type=numeric_sparse_frequency. tags=['condition_candidate', 'measure', 'rare_pattern_candidate'] desc=Frequency feature for token 'direct' in an email message. +- word_freq_cs: role=feature, type=numeric_sparse_frequency. tags=['condition_candidate', 'measure', 'rare_pattern_candidate'] desc=Frequency feature for token 'cs' in an email message. +- word_freq_meeting: role=feature, type=numeric_sparse_frequency. tags=['condition_candidate', 'measure', 'rare_pattern_candidate'] desc=Frequency feature for token 'meeting' in an email message. +- word_freq_original: role=feature, type=numeric_sparse_frequency. tags=['condition_candidate', 'measure', 'rare_pattern_candidate'] desc=Frequency feature for token 'original' in an email message. +- word_freq_project: role=feature, type=numeric_sparse_frequency. tags=['condition_candidate', 'measure', 'rare_pattern_candidate'] desc=Frequency feature for token 'project' in an email message. +- word_freq_re: role=feature, type=numeric_sparse_frequency. tags=['condition_candidate', 'measure', 'rare_pattern_candidate'] desc=Frequency feature for token 're' in an email message. +- word_freq_edu: role=feature, type=numeric_sparse_frequency. tags=['condition_candidate', 'measure', 'rare_pattern_candidate'] desc=Frequency feature for token 'edu' in an email message. +- word_freq_table: role=feature, type=numeric_sparse_frequency. tags=['condition_candidate', 'measure', 'rare_pattern_candidate'] desc=Frequency feature for token 'table' in an email message. +- word_freq_conference: role=feature, type=numeric_sparse_frequency. tags=['condition_candidate', 'measure', 'rare_pattern_candidate'] desc=Frequency feature for token 'conference' in an email message. +- char_freq_%3B: role=feature, type=numeric_sparse_frequency. tags=['condition_candidate', 'measure', 'rare_pattern_candidate'] desc=Character frequency feature for symbol ';'. +- char_freq_%28: role=feature, type=numeric_sparse_frequency. tags=['condition_candidate', 'measure', 'rare_pattern_candidate'] desc=Character frequency feature for symbol '('. +- char_freq_%5B: role=feature, type=numeric_sparse_frequency. tags=['condition_candidate', 'measure', 'rare_pattern_candidate'] desc=Character frequency feature for symbol '['. +- char_freq_%21: role=feature, type=numeric_sparse_frequency. tags=['condition_candidate', 'measure', 'rare_pattern_candidate'] desc=Character frequency feature for symbol '!'. +- char_freq_%24: role=feature, type=numeric_sparse_frequency. tags=['condition_candidate', 'measure', 'rare_pattern_candidate'] desc=Character frequency feature for symbol '$'. +- char_freq_%23: role=feature, type=numeric_sparse_frequency. tags=['condition_candidate', 'measure', 'rare_pattern_candidate'] desc=Character frequency feature for symbol '#'. +- capital_run_length_average: role=feature, type=numeric_heavy_tail. tags=['condition_candidate', 'measure', 'rare_pattern_candidate'] desc=Average length of uninterrupted capital-letter runs. +- capital_run_length_longest: role=feature, type=numeric_heavy_tail. tags=['condition_candidate', 'measure', 'rare_pattern_candidate'] desc=Longest uninterrupted capital-letter run. +- capital_run_length_total: role=feature, type=numeric_heavy_tail. tags=['condition_candidate', 'measure', 'rare_pattern_candidate'] desc=Total number of capital letters in runs. +- class: role=target, type=binary_target. tags=['target_candidate'] desc=Binary spam label (1=spam, 0=non-spam). +- useful_field_combinations: [['word_freq_free', 'word_freq_you', 'class'], ['char_freq_%21', 'capital_run_length_longest', 'class'], ['word_freq_remove', 'word_freq_money', 'class']] +- fields_requiring_caution: ['capital_run_length_average', 'capital_run_length_longest', 'capital_run_length_total'] +- source_url: https://www.openml.org/d/44 + +SQLite schema snapshot: +{ + "table_name": "n1", + "quoted_table_name": "\"n1\"", + "row_count": 4601, + "columns": [ + { + "name": "word_freq_make", + "type": "TEXT", + "notnull": false, + "pk": false + }, + { + "name": "word_freq_address", + "type": "TEXT", + "notnull": false, + "pk": false + }, + { + "name": "word_freq_all", + "type": "TEXT", + "notnull": false, + "pk": false + }, + { + "name": "word_freq_3d", + "type": "TEXT", + "notnull": false, + "pk": false + }, + { + "name": "word_freq_our", + "type": "TEXT", + "notnull": false, + "pk": false + }, + { + "name": "word_freq_over", + "type": "TEXT", + "notnull": false, + "pk": false + }, + { + "name": "word_freq_remove", + "type": "TEXT", + "notnull": false, + "pk": false + }, + { + "name": "word_freq_internet", + "type": "TEXT", + "notnull": false, + "pk": false + }, + { + "name": "word_freq_order", + "type": "TEXT", + "notnull": false, + "pk": false + }, + { + "name": "word_freq_mail", + "type": "TEXT", + "notnull": false, + "pk": false + }, + { + "name": "word_freq_receive", + "type": "TEXT", + "notnull": false, + "pk": false + }, + { + "name": "word_freq_will", + "type": "TEXT", + "notnull": false, + "pk": false + }, + { + "name": "word_freq_people", + "type": "TEXT", + "notnull": false, + "pk": false + }, + { + "name": "word_freq_report", + "type": "TEXT", + "notnull": false, + "pk": false + }, + { + "name": "word_freq_addresses", + "type": "TEXT", + "notnull": false, + "pk": false + }, + { + "name": "word_freq_free", + "type": "TEXT", + "notnull": false, + "pk": false + }, + { + "name": "word_freq_business", + "type": "TEXT", + "notnull": false, + "pk": false + }, + { + "name": "word_freq_email", + "type": "TEXT", + "notnull": false, + "pk": false + }, + { + "name": "word_freq_you", + "type": "TEXT", + "notnull": false, + "pk": false + }, + { + "name": "word_freq_credit", + "type": "TEXT", + "notnull": false, + "pk": false + }, + { + "name": "word_freq_your", + "type": "TEXT", + "notnull": false, + "pk": false + }, + { + "name": "word_freq_font", + "type": "TEXT", + "notnull": false, + "pk": false + }, + { + "name": "word_freq_000", + "type": "TEXT", + "notnull": false, + "pk": false + }, + { + "name": "word_freq_money", + "type": "TEXT", + "notnull": false, + "pk": false + }, + { + "name": "word_freq_hp", + "type": "TEXT", + "notnull": false, + "pk": false + }, + { + "name": "word_freq_hpl", + "type": "TEXT", + "notnull": false, + "pk": false + }, + { + "name": "word_freq_george", + "type": "TEXT", + "notnull": false, + "pk": false + }, + { + "name": "word_freq_650", + "type": "TEXT", + "notnull": false, + "pk": false + }, + { + "name": "word_freq_lab", + "type": "TEXT", + "notnull": false, + "pk": false + }, + { + "name": "word_freq_labs", + "type": "TEXT", + "notnull": false, + "pk": false + }, + { + "name": "word_freq_telnet", + "type": "TEXT", + "notnull": false, + "pk": false + }, + { + "name": "word_freq_857", + "type": "TEXT", + "notnull": false, + "pk": false + }, + { + "name": "word_freq_data", + "type": "TEXT", + "notnull": false, + "pk": false + }, + { + "name": "word_freq_415", + "type": "TEXT", + "notnull": false, + "pk": false + }, + { + "name": "word_freq_85", + "type": "TEXT", + "notnull": false, + "pk": false + }, + { + "name": "word_freq_technology", + "type": "TEXT", + "notnull": false, + "pk": false + }, + { + "name": "word_freq_1999", + "type": "TEXT", + "notnull": false, + "pk": false + }, + { + "name": "word_freq_parts", + "type": "TEXT", + "notnull": false, + "pk": false + }, + { + "name": "word_freq_pm", + "type": "TEXT", + "notnull": false, + "pk": false + }, + { + "name": "word_freq_direct", + "type": "TEXT", + "notnull": false, + "pk": false + }, + { + "name": "word_freq_cs", + "type": "TEXT", + "notnull": false, + "pk": false + }, + { + "name": "word_freq_meeting", + "type": "TEXT", + "notnull": false, + "pk": false + }, + { + "name": "word_freq_original", + "type": "TEXT", + "notnull": false, + "pk": false + }, + { + "name": "word_freq_project", + "type": "TEXT", + "notnull": false, + "pk": false + }, + { + "name": "word_freq_re", + "type": "TEXT", + "notnull": false, + "pk": false + }, + { + "name": "word_freq_edu", + "type": "TEXT", + "notnull": false, + "pk": false + }, + { + "name": "word_freq_table", + "type": "TEXT", + "notnull": false, + "pk": false + }, + { + "name": "word_freq_conference", + "type": "TEXT", + "notnull": false, + "pk": false + }, + { + "name": "char_freq_%3B", + "type": "TEXT", + "notnull": false, + "pk": false + }, + { + "name": "char_freq_%28", + "type": "TEXT", + "notnull": false, + "pk": false + }, + { + "name": "char_freq_%5B", + "type": "TEXT", + "notnull": false, + "pk": false + }, + { + "name": "char_freq_%21", + "type": "TEXT", + "notnull": false, + "pk": false + }, + { + "name": "char_freq_%24", + "type": "TEXT", + "notnull": false, + "pk": false + }, + { + "name": "char_freq_%23", + "type": "TEXT", + "notnull": false, + "pk": false + }, + { + "name": "capital_run_length_average", + "type": "TEXT", + "notnull": false, + "pk": false + }, + { + "name": "capital_run_length_longest", + "type": "TEXT", + "notnull": false, + "pk": false + }, + { + "name": "capital_run_length_total", + "type": "TEXT", + "notnull": false, + "pk": false + }, + { + "name": "class", + "type": "TEXT", + "notnull": false, + "pk": false + } + ], + "sample_rows": [ + { + "word_freq_make": "0", + "word_freq_address": "0.64", + "word_freq_all": "0.64", + "word_freq_3d": "0", + "word_freq_our": "0.32", + "word_freq_over": "0", + "word_freq_remove": "0", + "word_freq_internet": "0", + "word_freq_order": "0", + "word_freq_mail": "0", + "word_freq_receive": "0", + "word_freq_will": "0.64", + "word_freq_people": "0", + "word_freq_report": "0", + "word_freq_addresses": "0", + "word_freq_free": "0.32", + "word_freq_business": "0", + "word_freq_email": "1.29", + "word_freq_you": "1.93", + "word_freq_credit": "0", + "word_freq_your": "0.96", + "word_freq_font": "0", + "word_freq_000": "0", + "word_freq_money": "0", + "word_freq_hp": "0", + "word_freq_hpl": "0", + "word_freq_george": "0", + "word_freq_650": "0", + "word_freq_lab": "0", + "word_freq_labs": "0", + "word_freq_telnet": "0", + "word_freq_857": "0", + "word_freq_data": "0", + "word_freq_415": "0", + "word_freq_85": "0", + "word_freq_technology": "0", + "word_freq_1999": "0", + "word_freq_parts": "0", + "word_freq_pm": "0", + "word_freq_direct": "0", + "word_freq_cs": "0", + "word_freq_meeting": "0", + "word_freq_original": "0", + "word_freq_project": "0", + "word_freq_re": "0", + "word_freq_edu": "0", + "word_freq_table": "0", + "word_freq_conference": "0", + "char_freq_%3B": "0", + "char_freq_%28": "0", + "char_freq_%5B": "0", + "char_freq_%21": "0.778", + "char_freq_%24": "0", + "char_freq_%23": "0", + "capital_run_length_average": "3.756", + "capital_run_length_longest": "61", + "capital_run_length_total": "278", + "class": "1" + }, + { + "word_freq_make": "0.21", + "word_freq_address": "0.28", + "word_freq_all": "0.5", + "word_freq_3d": "0", + "word_freq_our": "0.14", + "word_freq_over": "0.28", + "word_freq_remove": "0.21", + "word_freq_internet": "0.07", + "word_freq_order": "0", + "word_freq_mail": "0.94", + "word_freq_receive": "0.21", + "word_freq_will": "0.79", + "word_freq_people": "0.65", + "word_freq_report": "0.21", + "word_freq_addresses": "0.14", + "word_freq_free": "0.14", + "word_freq_business": "0.07", + "word_freq_email": "0.28", + "word_freq_you": "3.47", + "word_freq_credit": "0", + "word_freq_your": "1.59", + "word_freq_font": "0", + "word_freq_000": "0.43", + "word_freq_money": "0.43", + "word_freq_hp": "0", + "word_freq_hpl": "0", + "word_freq_george": "0", + "word_freq_650": "0", + "word_freq_lab": "0", + "word_freq_labs": "0", + "word_freq_telnet": "0", + "word_freq_857": "0", + "word_freq_data": "0", + "word_freq_415": "0", + "word_freq_85": "0", + "word_freq_technology": "0", + "word_freq_1999": "0.07", + "word_freq_parts": "0", + "word_freq_pm": "0", + "word_freq_direct": "0", + "word_freq_cs": "0", + "word_freq_meeting": "0", + "word_freq_original": "0", + "word_freq_project": "0", + "word_freq_re": "0", + "word_freq_edu": "0", + "word_freq_table": "0", + "word_freq_conference": "0", + "char_freq_%3B": "0", + "char_freq_%28": "0.132", + "char_freq_%5B": "0", + "char_freq_%21": "0.372", + "char_freq_%24": "0.18", + "char_freq_%23": "0.048", + "capital_run_length_average": "5.114", + "capital_run_length_longest": "101", + "capital_run_length_total": "1028", + "class": "1" + }, + { + "word_freq_make": "0.06", + "word_freq_address": "0", + "word_freq_all": "0.71", + "word_freq_3d": "0", + "word_freq_our": "1.23", + "word_freq_over": "0.19", + "word_freq_remove": "0.19", + "word_freq_internet": "0.12", + "word_freq_order": "0.64", + "word_freq_mail": "0.25", + "word_freq_receive": "0.38", + "word_freq_will": "0.45", + "word_freq_people": "0.12", + "word_freq_report": "0", + "word_freq_addresses": "1.75", + "word_freq_free": "0.06", + "word_freq_business": "0.06", + "word_freq_email": "1.03", + "word_freq_you": "1.36", + "word_freq_credit": "0.32", + "word_freq_your": "0.51", + "word_freq_font": "0", + "word_freq_000": "1.16", + "word_freq_money": "0.06", + "word_freq_hp": "0", + "word_freq_hpl": "0", + "word_freq_george": "0", + "word_freq_650": "0", + "word_freq_lab": "0", + "word_freq_labs": "0", + "word_freq_telnet": "0", + "word_freq_857": "0", + "word_freq_data": "0", + "word_freq_415": "0", + "word_freq_85": "0", + "word_freq_technology": "0", + "word_freq_1999": "0", + "word_freq_parts": "0", + "word_freq_pm": "0", + "word_freq_direct": "0.06", + "word_freq_cs": "0", + "word_freq_meeting": "0", + "word_freq_original": "0.12", + "word_freq_project": "0", + "word_freq_re": "0.06", + "word_freq_edu": "0.06", + "word_freq_table": "0", + "word_freq_conference": "0", + "char_freq_%3B": "0.01", + "char_freq_%28": "0.143", + "char_freq_%5B": "0", + "char_freq_%21": "0.276", + "char_freq_%24": "0.184", + "char_freq_%23": "0.01", + "capital_run_length_average": "9.821", + "capital_run_length_longest": "485", + "capital_run_length_total": "2259", + "class": "1" + }, + { + "word_freq_make": "0", + "word_freq_address": "0", + "word_freq_all": "0", + "word_freq_3d": "0", + "word_freq_our": "0.63", + "word_freq_over": "0", + "word_freq_remove": "0.31", + "word_freq_internet": "0.63", + "word_freq_order": "0.31", + "word_freq_mail": "0.63", + "word_freq_receive": "0.31", + "word_freq_will": "0.31", + "word_freq_people": "0.31", + "word_freq_report": "0", + "word_freq_addresses": "0", + "word_freq_free": "0.31", + "word_freq_business": "0", + "word_freq_email": "0", + "word_freq_you": "3.18", + "word_freq_credit": "0", + "word_freq_your": "0.31", + "word_freq_font": "0", + "word_freq_000": "0", + "word_freq_money": "0", + "word_freq_hp": "0", + "word_freq_hpl": "0", + "word_freq_george": "0", + "word_freq_650": "0", + "word_freq_lab": "0", + "word_freq_labs": "0", + "word_freq_telnet": "0", + "word_freq_857": "0", + "word_freq_data": "0", + "word_freq_415": "0", + "word_freq_85": "0", + "word_freq_technology": "0", + "word_freq_1999": "0", + "word_freq_parts": "0", + "word_freq_pm": "0", + "word_freq_direct": "0", + "word_freq_cs": "0", + "word_freq_meeting": "0", + "word_freq_original": "0", + "word_freq_project": "0", + "word_freq_re": "0", + "word_freq_edu": "0", + "word_freq_table": "0", + "word_freq_conference": "0", + "char_freq_%3B": "0", + "char_freq_%28": "0.137", + "char_freq_%5B": "0", + "char_freq_%21": "0.137", + "char_freq_%24": "0", + "char_freq_%23": "0", + "capital_run_length_average": "3.537", + "capital_run_length_longest": "40", + "capital_run_length_total": "191", + "class": "1" + }, + { + "word_freq_make": "0", + "word_freq_address": "0", + "word_freq_all": "0", + "word_freq_3d": "0", + "word_freq_our": "0.63", + "word_freq_over": "0", + "word_freq_remove": "0.31", + "word_freq_internet": "0.63", + "word_freq_order": "0.31", + "word_freq_mail": "0.63", + "word_freq_receive": "0.31", + "word_freq_will": "0.31", + "word_freq_people": "0.31", + "word_freq_report": "0", + "word_freq_addresses": "0", + "word_freq_free": "0.31", + "word_freq_business": "0", + "word_freq_email": "0", + "word_freq_you": "3.18", + "word_freq_credit": "0", + "word_freq_your": "0.31", + "word_freq_font": "0", + "word_freq_000": "0", + "word_freq_money": "0", + "word_freq_hp": "0", + "word_freq_hpl": "0", + "word_freq_george": "0", + "word_freq_650": "0", + "word_freq_lab": "0", + "word_freq_labs": "0", + "word_freq_telnet": "0", + "word_freq_857": "0", + "word_freq_data": "0", + "word_freq_415": "0", + "word_freq_85": "0", + "word_freq_technology": "0", + "word_freq_1999": "0", + "word_freq_parts": "0", + "word_freq_pm": "0", + "word_freq_direct": "0", + "word_freq_cs": "0", + "word_freq_meeting": "0", + "word_freq_original": "0", + "word_freq_project": "0", + "word_freq_re": "0", + "word_freq_edu": "0", + "word_freq_table": "0", + "word_freq_conference": "0", + "char_freq_%3B": "0", + "char_freq_%28": "0.135", + "char_freq_%5B": "0", + "char_freq_%21": "0.135", + "char_freq_%24": "0", + "char_freq_%23": "0", + "capital_run_length_average": "3.537", + "capital_run_length_longest": "40", + "capital_run_length_total": "191", + "class": "1" + } + ] +} + +Shortlisted templates: +[ + { + "template_id": "tpl_tpch_thresholded_group_ranking", + "template_name": "Thresholded Group Ranking", + "primary_family": "tail_rarity_structure", + "portability": "partial", + "sql_skeleton": "SELECT {group_col}, SUM({measure_col}) AS total_measure\nFROM {table}\nGROUP BY {group_col}\nHAVING SUM({measure_col}) > {measure_threshold}\nORDER BY total_measure DESC\nLIMIT {top_k};", + "required_roles": [ + "group_col", + "measure_col" + ] + } +] + +Problem instance: +{ + "dataset_id": "n1", + "question": "Use template Thresholded Group Ranking to probe tail_mass_similarity with semantic role filtered_stable_view. Focus on group_col=class, measure_col=word_freq_hpl.", + "planned_template_id": "tpl_tpch_thresholded_group_ranking", + "bindings": { + "group_col": "class", + "measure_col": "word_freq_hpl", + "top_k": 14, + "top_n": 6, + "num_tiles": 10, + "percentile_value": 0.9, + "z_threshold": 2.0, + "fraction_threshold": 0.1, + "baseline_multiplier": 1.5, + "baseline_fraction": 0.1, + "min_group_size": 5, + "min_support": 5, + "measure_threshold": 0.0, + "time_grain": "month", + "lookback_rows": 3, + "current_period_start": "'2024-01-01'", + "current_period_end": "'2024-04-01'", + "previous_period_start": "'2023-10-01'", + "previous_period_end": "'2024-01-01'", + "drift_ratio_threshold": 0.8 + }, + "can_vary": [], + "must_fix": [], + "runtime_sql_skeleton": "SELECT {group_col}, SUM({measure_col}) AS total_measure\nFROM {table}\nGROUP BY {group_col}\nHAVING SUM({measure_col}) > {measure_threshold}\nORDER BY total_measure DESC\nLIMIT {top_k};" +} + +Repair context: +{} diff --git a/Query/sql/v2/runs/v2_cli_20260502_081223_d/n1/artifacts/v2q_n1_db01aa23845b4486/cli/sql_prompt_attempt_2.txt b/Query/sql/v2/runs/v2_cli_20260502_081223_d/n1/artifacts/v2q_n1_db01aa23845b4486/cli/sql_prompt_attempt_2.txt new file mode 100644 index 0000000000000000000000000000000000000000..b240a37043750bb7141998ea1bc1978d5b4e4398 --- /dev/null +++ b/Query/sql/v2/runs/v2_cli_20260502_081223_d/n1/artifacts/v2q_n1_db01aa23845b4486/cli/sql_prompt_attempt_2.txt @@ -0,0 +1,792 @@ +You are generating one SQLite SELECT query for a single-table SQL QA task. +Return strict JSON only, with this schema: {"sql": "...", "notes": "..."}. +Rules: +- Use only the provided table and columns. +- Do not write INSERT, UPDATE, DELETE, DROP, ALTER, CREATE, PRAGMA, ATTACH, DETACH, or VACUUM. +- Prefer the planned template and bound roles when provided. +- Add a leading SQL comment exactly like: -- template_id: . +- Generate SQLite-compatible SQL. SQLite does not support PERCENTILE_CONT or STDDEV. +- Quote identifiers with double quotes. +- Return no markdown and no extra prose. + +Dataset context: +Dataset context for SQL QA: +- dataset_id: n1 +- dataset_name: Spambase +- table_name: n1 +- table_layout: single-table dataset (do not assume joins). +- row_semantics: One row is one email represented by engineered token/character frequency and capitalization-run features. +- task_type: classification +- target_column: class +- main_row_count: 4601 +- important_fields: +- word_freq_make: role=feature, type=numeric_sparse_frequency. tags=['condition_candidate', 'measure', 'rare_pattern_candidate'] desc=Frequency feature for token 'make' in an email message. +- word_freq_address: role=feature, type=numeric_sparse_frequency. tags=['condition_candidate', 'measure', 'rare_pattern_candidate'] desc=Frequency feature for token 'address' in an email message. +- word_freq_all: role=feature, type=numeric_sparse_frequency. tags=['condition_candidate', 'measure', 'rare_pattern_candidate'] desc=Frequency feature for token 'all' in an email message. +- word_freq_3d: role=feature, type=numeric_sparse_frequency. tags=['condition_candidate', 'measure', 'rare_pattern_candidate'] desc=Frequency feature for token '3d' in an email message. +- word_freq_our: role=feature, type=numeric_sparse_frequency. tags=['condition_candidate', 'measure', 'rare_pattern_candidate'] desc=Frequency feature for token 'our' in an email message. +- word_freq_over: role=feature, type=numeric_sparse_frequency. tags=['condition_candidate', 'measure', 'rare_pattern_candidate'] desc=Frequency feature for token 'over' in an email message. +- word_freq_remove: role=feature, type=numeric_sparse_frequency. tags=['condition_candidate', 'measure', 'rare_pattern_candidate'] desc=Frequency feature for token 'remove' in an email message. +- word_freq_internet: role=feature, type=numeric_sparse_frequency. tags=['condition_candidate', 'measure', 'rare_pattern_candidate'] desc=Frequency feature for token 'internet' in an email message. +- word_freq_order: role=feature, type=numeric_sparse_frequency. tags=['condition_candidate', 'measure', 'rare_pattern_candidate'] desc=Frequency feature for token 'order' in an email message. +- word_freq_mail: role=feature, type=numeric_sparse_frequency. tags=['condition_candidate', 'measure', 'rare_pattern_candidate'] desc=Frequency feature for token 'mail' in an email message. +- word_freq_receive: role=feature, type=numeric_sparse_frequency. tags=['condition_candidate', 'measure', 'rare_pattern_candidate'] desc=Frequency feature for token 'receive' in an email message. +- word_freq_will: role=feature, type=numeric_sparse_frequency. tags=['condition_candidate', 'measure', 'rare_pattern_candidate'] desc=Frequency feature for token 'will' in an email message. +- word_freq_people: role=feature, type=numeric_sparse_frequency. tags=['condition_candidate', 'measure', 'rare_pattern_candidate'] desc=Frequency feature for token 'people' in an email message. +- word_freq_report: role=feature, type=numeric_sparse_frequency. tags=['condition_candidate', 'measure', 'rare_pattern_candidate'] desc=Frequency feature for token 'report' in an email message. +- word_freq_addresses: role=feature, type=numeric_sparse_frequency. tags=['condition_candidate', 'measure', 'rare_pattern_candidate'] desc=Frequency feature for token 'addresses' in an email message. +- word_freq_free: role=feature, type=numeric_sparse_frequency. tags=['condition_candidate', 'measure', 'rare_pattern_candidate'] desc=Frequency feature for token 'free' in an email message. +- word_freq_business: role=feature, type=numeric_sparse_frequency. tags=['condition_candidate', 'measure', 'rare_pattern_candidate'] desc=Frequency feature for token 'business' in an email message. +- word_freq_email: role=feature, type=numeric_sparse_frequency. tags=['condition_candidate', 'measure', 'rare_pattern_candidate'] desc=Frequency feature for token 'email' in an email message. +- word_freq_you: role=feature, type=numeric_sparse_frequency. tags=['condition_candidate', 'measure', 'rare_pattern_candidate'] desc=Frequency feature for token 'you' in an email message. +- word_freq_credit: role=feature, type=numeric_sparse_frequency. tags=['condition_candidate', 'measure', 'rare_pattern_candidate'] desc=Frequency feature for token 'credit' in an email message. +- word_freq_your: role=feature, type=numeric_sparse_frequency. tags=['condition_candidate', 'measure', 'rare_pattern_candidate'] desc=Frequency feature for token 'your' in an email message. +- word_freq_font: role=feature, type=numeric_sparse_frequency. tags=['condition_candidate', 'measure', 'rare_pattern_candidate'] desc=Frequency feature for token 'font' in an email message. +- word_freq_000: role=feature, type=numeric_sparse_frequency. tags=['condition_candidate', 'measure', 'rare_pattern_candidate'] desc=Frequency feature for token '000' in an email message. +- word_freq_money: role=feature, type=numeric_sparse_frequency. tags=['condition_candidate', 'measure', 'rare_pattern_candidate'] desc=Frequency feature for token 'money' in an email message. +- word_freq_hp: role=feature, type=numeric_sparse_frequency. tags=['condition_candidate', 'measure', 'rare_pattern_candidate'] desc=Frequency feature for token 'hp' in an email message. +- word_freq_hpl: role=feature, type=numeric_sparse_frequency. tags=['condition_candidate', 'measure', 'rare_pattern_candidate'] desc=Frequency feature for token 'hpl' in an email message. +- word_freq_george: role=feature, type=numeric_sparse_frequency. tags=['condition_candidate', 'measure', 'rare_pattern_candidate'] desc=Frequency feature for token 'george' in an email message. +- word_freq_650: role=feature, type=numeric_sparse_frequency. tags=['condition_candidate', 'measure', 'rare_pattern_candidate'] desc=Frequency feature for token '650' in an email message. +- word_freq_lab: role=feature, type=numeric_sparse_frequency. tags=['condition_candidate', 'measure', 'rare_pattern_candidate'] desc=Frequency feature for token 'lab' in an email message. +- word_freq_labs: role=feature, type=numeric_sparse_frequency. tags=['condition_candidate', 'measure', 'rare_pattern_candidate'] desc=Frequency feature for token 'labs' in an email message. +- word_freq_telnet: role=feature, type=numeric_sparse_frequency. tags=['condition_candidate', 'measure', 'rare_pattern_candidate'] desc=Frequency feature for token 'telnet' in an email message. +- word_freq_857: role=feature, type=numeric_sparse_frequency. tags=['condition_candidate', 'measure', 'rare_pattern_candidate'] desc=Frequency feature for token '857' in an email message. +- word_freq_data: role=feature, type=numeric_sparse_frequency. tags=['condition_candidate', 'measure', 'rare_pattern_candidate'] desc=Frequency feature for token 'data' in an email message. +- word_freq_415: role=feature, type=numeric_sparse_frequency. tags=['condition_candidate', 'measure', 'rare_pattern_candidate'] desc=Frequency feature for token '415' in an email message. +- word_freq_85: role=feature, type=numeric_sparse_frequency. tags=['condition_candidate', 'measure', 'rare_pattern_candidate'] desc=Frequency feature for token '85' in an email message. +- word_freq_technology: role=feature, type=numeric_sparse_frequency. tags=['condition_candidate', 'measure', 'rare_pattern_candidate'] desc=Frequency feature for token 'technology' in an email message. +- word_freq_1999: role=feature, type=numeric_sparse_frequency. tags=['condition_candidate', 'measure', 'rare_pattern_candidate'] desc=Frequency feature for token '1999' in an email message. +- word_freq_parts: role=feature, type=numeric_sparse_frequency. tags=['condition_candidate', 'measure', 'rare_pattern_candidate'] desc=Frequency feature for token 'parts' in an email message. +- word_freq_pm: role=feature, type=numeric_sparse_frequency. tags=['condition_candidate', 'measure', 'rare_pattern_candidate'] desc=Frequency feature for token 'pm' in an email message. +- word_freq_direct: role=feature, type=numeric_sparse_frequency. tags=['condition_candidate', 'measure', 'rare_pattern_candidate'] desc=Frequency feature for token 'direct' in an email message. +- word_freq_cs: role=feature, type=numeric_sparse_frequency. tags=['condition_candidate', 'measure', 'rare_pattern_candidate'] desc=Frequency feature for token 'cs' in an email message. +- word_freq_meeting: role=feature, type=numeric_sparse_frequency. tags=['condition_candidate', 'measure', 'rare_pattern_candidate'] desc=Frequency feature for token 'meeting' in an email message. +- word_freq_original: role=feature, type=numeric_sparse_frequency. tags=['condition_candidate', 'measure', 'rare_pattern_candidate'] desc=Frequency feature for token 'original' in an email message. +- word_freq_project: role=feature, type=numeric_sparse_frequency. tags=['condition_candidate', 'measure', 'rare_pattern_candidate'] desc=Frequency feature for token 'project' in an email message. +- word_freq_re: role=feature, type=numeric_sparse_frequency. tags=['condition_candidate', 'measure', 'rare_pattern_candidate'] desc=Frequency feature for token 're' in an email message. +- word_freq_edu: role=feature, type=numeric_sparse_frequency. tags=['condition_candidate', 'measure', 'rare_pattern_candidate'] desc=Frequency feature for token 'edu' in an email message. +- word_freq_table: role=feature, type=numeric_sparse_frequency. tags=['condition_candidate', 'measure', 'rare_pattern_candidate'] desc=Frequency feature for token 'table' in an email message. +- word_freq_conference: role=feature, type=numeric_sparse_frequency. tags=['condition_candidate', 'measure', 'rare_pattern_candidate'] desc=Frequency feature for token 'conference' in an email message. +- char_freq_%3B: role=feature, type=numeric_sparse_frequency. tags=['condition_candidate', 'measure', 'rare_pattern_candidate'] desc=Character frequency feature for symbol ';'. +- char_freq_%28: role=feature, type=numeric_sparse_frequency. tags=['condition_candidate', 'measure', 'rare_pattern_candidate'] desc=Character frequency feature for symbol '('. +- char_freq_%5B: role=feature, type=numeric_sparse_frequency. tags=['condition_candidate', 'measure', 'rare_pattern_candidate'] desc=Character frequency feature for symbol '['. +- char_freq_%21: role=feature, type=numeric_sparse_frequency. tags=['condition_candidate', 'measure', 'rare_pattern_candidate'] desc=Character frequency feature for symbol '!'. +- char_freq_%24: role=feature, type=numeric_sparse_frequency. tags=['condition_candidate', 'measure', 'rare_pattern_candidate'] desc=Character frequency feature for symbol '$'. +- char_freq_%23: role=feature, type=numeric_sparse_frequency. tags=['condition_candidate', 'measure', 'rare_pattern_candidate'] desc=Character frequency feature for symbol '#'. +- capital_run_length_average: role=feature, type=numeric_heavy_tail. tags=['condition_candidate', 'measure', 'rare_pattern_candidate'] desc=Average length of uninterrupted capital-letter runs. +- capital_run_length_longest: role=feature, type=numeric_heavy_tail. tags=['condition_candidate', 'measure', 'rare_pattern_candidate'] desc=Longest uninterrupted capital-letter run. +- capital_run_length_total: role=feature, type=numeric_heavy_tail. tags=['condition_candidate', 'measure', 'rare_pattern_candidate'] desc=Total number of capital letters in runs. +- class: role=target, type=binary_target. tags=['target_candidate'] desc=Binary spam label (1=spam, 0=non-spam). +- useful_field_combinations: [['word_freq_free', 'word_freq_you', 'class'], ['char_freq_%21', 'capital_run_length_longest', 'class'], ['word_freq_remove', 'word_freq_money', 'class']] +- fields_requiring_caution: ['capital_run_length_average', 'capital_run_length_longest', 'capital_run_length_total'] +- source_url: https://www.openml.org/d/44 + +SQLite schema snapshot: +{ + "table_name": "n1", + "quoted_table_name": "\"n1\"", + "row_count": 4601, + "columns": [ + { + "name": "word_freq_make", + "type": "TEXT", + "notnull": false, + "pk": false + }, + { + "name": "word_freq_address", + "type": "TEXT", + "notnull": false, + "pk": false + }, + { + "name": "word_freq_all", + "type": "TEXT", + "notnull": false, + "pk": false + }, + { + "name": "word_freq_3d", + "type": "TEXT", + "notnull": false, + "pk": false + }, + { + "name": "word_freq_our", + "type": "TEXT", + "notnull": false, + "pk": false + }, + { + "name": "word_freq_over", + "type": "TEXT", + "notnull": false, + "pk": false + }, + { + "name": "word_freq_remove", + "type": "TEXT", + "notnull": false, + "pk": false + }, + { + "name": "word_freq_internet", + "type": "TEXT", + "notnull": false, + "pk": false + }, + { + "name": "word_freq_order", + "type": "TEXT", + "notnull": false, + "pk": false + }, + { + "name": "word_freq_mail", + "type": "TEXT", + "notnull": false, + "pk": false + }, + { + "name": "word_freq_receive", + "type": "TEXT", + "notnull": false, + "pk": false + }, + { + "name": "word_freq_will", + "type": "TEXT", + "notnull": false, + "pk": false + }, + { + "name": "word_freq_people", + "type": "TEXT", + "notnull": false, + "pk": false + }, + { + "name": "word_freq_report", + "type": "TEXT", + "notnull": false, + "pk": false + }, + { + "name": "word_freq_addresses", + "type": "TEXT", + "notnull": false, + "pk": false + }, + { + "name": "word_freq_free", + "type": "TEXT", + "notnull": false, + "pk": false + }, + { + "name": "word_freq_business", + "type": "TEXT", + "notnull": false, + "pk": false + }, + { + "name": "word_freq_email", + "type": "TEXT", + "notnull": false, + "pk": false + }, + { + "name": "word_freq_you", + "type": "TEXT", + "notnull": false, + "pk": false + }, + { + "name": "word_freq_credit", + "type": "TEXT", + "notnull": false, + "pk": false + }, + { + "name": "word_freq_your", + "type": "TEXT", + "notnull": false, + "pk": false + }, + { + "name": "word_freq_font", + "type": "TEXT", + "notnull": false, + "pk": false + }, + { + "name": "word_freq_000", + "type": "TEXT", + "notnull": false, + "pk": false + }, + { + "name": "word_freq_money", + "type": "TEXT", + "notnull": false, + "pk": false + }, + { + "name": "word_freq_hp", + "type": "TEXT", + "notnull": false, + "pk": false + }, + { + "name": "word_freq_hpl", + "type": "TEXT", + "notnull": false, + "pk": false + }, + { + "name": "word_freq_george", + "type": "TEXT", + "notnull": false, + "pk": false + }, + { + "name": "word_freq_650", + "type": "TEXT", + "notnull": false, + "pk": false + }, + { + "name": "word_freq_lab", + "type": "TEXT", + "notnull": false, + "pk": false + }, + { + "name": "word_freq_labs", + "type": "TEXT", + "notnull": false, + "pk": false + }, + { + "name": "word_freq_telnet", + "type": "TEXT", + "notnull": false, + "pk": false + }, + { + "name": "word_freq_857", + "type": "TEXT", + "notnull": false, + "pk": false + }, + { + "name": "word_freq_data", + "type": "TEXT", + "notnull": false, + "pk": false + }, + { + "name": "word_freq_415", + "type": "TEXT", + "notnull": false, + "pk": false + }, + { + "name": "word_freq_85", + "type": "TEXT", + "notnull": false, + "pk": false + }, + { + "name": "word_freq_technology", + "type": "TEXT", + "notnull": false, + "pk": false + }, + { + "name": "word_freq_1999", + "type": "TEXT", + "notnull": false, + "pk": false + }, + { + "name": "word_freq_parts", + "type": "TEXT", + "notnull": false, + "pk": false + }, + { + "name": "word_freq_pm", + "type": "TEXT", + "notnull": false, + "pk": false + }, + { + "name": "word_freq_direct", + "type": "TEXT", + "notnull": false, + "pk": false + }, + { + "name": "word_freq_cs", + "type": "TEXT", + "notnull": false, + "pk": false + }, + { + "name": "word_freq_meeting", + "type": "TEXT", + "notnull": false, + "pk": false + }, + { + "name": "word_freq_original", + "type": "TEXT", + "notnull": false, + "pk": false + }, + { + "name": "word_freq_project", + "type": "TEXT", + "notnull": false, + "pk": false + }, + { + "name": "word_freq_re", + "type": "TEXT", + "notnull": false, + "pk": false + }, + { + "name": "word_freq_edu", + "type": "TEXT", + "notnull": false, + "pk": false + }, + { + "name": "word_freq_table", + "type": "TEXT", + "notnull": false, + "pk": false + }, + { + "name": "word_freq_conference", + "type": "TEXT", + "notnull": false, + "pk": false + }, + { + "name": "char_freq_%3B", + "type": "TEXT", + "notnull": false, + "pk": false + }, + { + "name": "char_freq_%28", + "type": "TEXT", + "notnull": false, + "pk": false + }, + { + "name": "char_freq_%5B", + "type": "TEXT", + "notnull": false, + "pk": false + }, + { + "name": "char_freq_%21", + "type": "TEXT", + "notnull": false, + "pk": false + }, + { + "name": "char_freq_%24", + "type": "TEXT", + "notnull": false, + "pk": false + }, + { + "name": "char_freq_%23", + "type": "TEXT", + "notnull": false, + "pk": false + }, + { + "name": "capital_run_length_average", + "type": "TEXT", + "notnull": false, + "pk": false + }, + { + "name": "capital_run_length_longest", + "type": "TEXT", + "notnull": false, + "pk": false + }, + { + "name": "capital_run_length_total", + "type": "TEXT", + "notnull": false, + "pk": false + }, + { + "name": "class", + "type": "TEXT", + "notnull": false, + "pk": false + } + ], + "sample_rows": [ + { + "word_freq_make": "0", + "word_freq_address": "0.64", + "word_freq_all": "0.64", + "word_freq_3d": "0", + "word_freq_our": "0.32", + "word_freq_over": "0", + "word_freq_remove": "0", + "word_freq_internet": "0", + "word_freq_order": "0", + "word_freq_mail": "0", + "word_freq_receive": "0", + "word_freq_will": "0.64", + "word_freq_people": "0", + "word_freq_report": "0", + "word_freq_addresses": "0", + "word_freq_free": "0.32", + "word_freq_business": "0", + "word_freq_email": "1.29", + "word_freq_you": "1.93", + "word_freq_credit": "0", + "word_freq_your": "0.96", + "word_freq_font": "0", + "word_freq_000": "0", + "word_freq_money": "0", + "word_freq_hp": "0", + "word_freq_hpl": "0", + "word_freq_george": "0", + "word_freq_650": "0", + "word_freq_lab": "0", + "word_freq_labs": "0", + "word_freq_telnet": "0", + "word_freq_857": "0", + "word_freq_data": "0", + "word_freq_415": "0", + "word_freq_85": "0", + "word_freq_technology": "0", + "word_freq_1999": "0", + "word_freq_parts": "0", + "word_freq_pm": "0", + "word_freq_direct": "0", + "word_freq_cs": "0", + "word_freq_meeting": "0", + "word_freq_original": "0", + "word_freq_project": "0", + "word_freq_re": "0", + "word_freq_edu": "0", + "word_freq_table": "0", + "word_freq_conference": "0", + "char_freq_%3B": "0", + "char_freq_%28": "0", + "char_freq_%5B": "0", + "char_freq_%21": "0.778", + "char_freq_%24": "0", + "char_freq_%23": "0", + "capital_run_length_average": "3.756", + "capital_run_length_longest": "61", + "capital_run_length_total": "278", + "class": "1" + }, + { + "word_freq_make": "0.21", + "word_freq_address": "0.28", + "word_freq_all": "0.5", + "word_freq_3d": "0", + "word_freq_our": "0.14", + "word_freq_over": "0.28", + "word_freq_remove": "0.21", + "word_freq_internet": "0.07", + "word_freq_order": "0", + "word_freq_mail": "0.94", + "word_freq_receive": "0.21", + "word_freq_will": "0.79", + "word_freq_people": "0.65", + "word_freq_report": "0.21", + "word_freq_addresses": "0.14", + "word_freq_free": "0.14", + "word_freq_business": "0.07", + "word_freq_email": "0.28", + "word_freq_you": "3.47", + "word_freq_credit": "0", + "word_freq_your": "1.59", + "word_freq_font": "0", + "word_freq_000": "0.43", + "word_freq_money": "0.43", + "word_freq_hp": "0", + "word_freq_hpl": "0", + "word_freq_george": "0", + "word_freq_650": "0", + "word_freq_lab": "0", + "word_freq_labs": "0", + "word_freq_telnet": "0", + "word_freq_857": "0", + "word_freq_data": "0", + "word_freq_415": "0", + "word_freq_85": "0", + "word_freq_technology": "0", + "word_freq_1999": "0.07", + "word_freq_parts": "0", + "word_freq_pm": "0", + "word_freq_direct": "0", + "word_freq_cs": "0", + "word_freq_meeting": "0", + "word_freq_original": "0", + "word_freq_project": "0", + "word_freq_re": "0", + "word_freq_edu": "0", + "word_freq_table": "0", + "word_freq_conference": "0", + "char_freq_%3B": "0", + "char_freq_%28": "0.132", + "char_freq_%5B": "0", + "char_freq_%21": "0.372", + "char_freq_%24": "0.18", + "char_freq_%23": "0.048", + "capital_run_length_average": "5.114", + "capital_run_length_longest": "101", + "capital_run_length_total": "1028", + "class": "1" + }, + { + "word_freq_make": "0.06", + "word_freq_address": "0", + "word_freq_all": "0.71", + "word_freq_3d": "0", + "word_freq_our": "1.23", + "word_freq_over": "0.19", + "word_freq_remove": "0.19", + "word_freq_internet": "0.12", + "word_freq_order": "0.64", + "word_freq_mail": "0.25", + "word_freq_receive": "0.38", + "word_freq_will": "0.45", + "word_freq_people": "0.12", + "word_freq_report": "0", + "word_freq_addresses": "1.75", + "word_freq_free": "0.06", + "word_freq_business": "0.06", + "word_freq_email": "1.03", + "word_freq_you": "1.36", + "word_freq_credit": "0.32", + "word_freq_your": "0.51", + "word_freq_font": "0", + "word_freq_000": "1.16", + "word_freq_money": "0.06", + "word_freq_hp": "0", + "word_freq_hpl": "0", + "word_freq_george": "0", + "word_freq_650": "0", + "word_freq_lab": "0", + "word_freq_labs": "0", + "word_freq_telnet": "0", + "word_freq_857": "0", + "word_freq_data": "0", + "word_freq_415": "0", + "word_freq_85": "0", + "word_freq_technology": "0", + "word_freq_1999": "0", + "word_freq_parts": "0", + "word_freq_pm": "0", + "word_freq_direct": "0.06", + "word_freq_cs": "0", + "word_freq_meeting": "0", + "word_freq_original": "0.12", + "word_freq_project": "0", + "word_freq_re": "0.06", + "word_freq_edu": "0.06", + "word_freq_table": "0", + "word_freq_conference": "0", + "char_freq_%3B": "0.01", + "char_freq_%28": "0.143", + "char_freq_%5B": "0", + "char_freq_%21": "0.276", + "char_freq_%24": "0.184", + "char_freq_%23": "0.01", + "capital_run_length_average": "9.821", + "capital_run_length_longest": "485", + "capital_run_length_total": "2259", + "class": "1" + }, + { + "word_freq_make": "0", + "word_freq_address": "0", + "word_freq_all": "0", + "word_freq_3d": "0", + "word_freq_our": "0.63", + "word_freq_over": "0", + "word_freq_remove": "0.31", + "word_freq_internet": "0.63", + "word_freq_order": "0.31", + "word_freq_mail": "0.63", + "word_freq_receive": "0.31", + "word_freq_will": "0.31", + "word_freq_people": "0.31", + "word_freq_report": "0", + "word_freq_addresses": "0", + "word_freq_free": "0.31", + "word_freq_business": "0", + "word_freq_email": "0", + "word_freq_you": "3.18", + "word_freq_credit": "0", + "word_freq_your": "0.31", + "word_freq_font": "0", + "word_freq_000": "0", + "word_freq_money": "0", + "word_freq_hp": "0", + "word_freq_hpl": "0", + "word_freq_george": "0", + "word_freq_650": "0", + "word_freq_lab": "0", + "word_freq_labs": "0", + "word_freq_telnet": "0", + "word_freq_857": "0", + "word_freq_data": "0", + "word_freq_415": "0", + "word_freq_85": "0", + "word_freq_technology": "0", + "word_freq_1999": "0", + "word_freq_parts": "0", + "word_freq_pm": "0", + "word_freq_direct": "0", + "word_freq_cs": "0", + "word_freq_meeting": "0", + "word_freq_original": "0", + "word_freq_project": "0", + "word_freq_re": "0", + "word_freq_edu": "0", + "word_freq_table": "0", + "word_freq_conference": "0", + "char_freq_%3B": "0", + "char_freq_%28": "0.137", + "char_freq_%5B": "0", + "char_freq_%21": "0.137", + "char_freq_%24": "0", + "char_freq_%23": "0", + "capital_run_length_average": "3.537", + "capital_run_length_longest": "40", + "capital_run_length_total": "191", + "class": "1" + }, + { + "word_freq_make": "0", + "word_freq_address": "0", + "word_freq_all": "0", + "word_freq_3d": "0", + "word_freq_our": "0.63", + "word_freq_over": "0", + "word_freq_remove": "0.31", + "word_freq_internet": "0.63", + "word_freq_order": "0.31", + "word_freq_mail": "0.63", + "word_freq_receive": "0.31", + "word_freq_will": "0.31", + "word_freq_people": "0.31", + "word_freq_report": "0", + "word_freq_addresses": "0", + "word_freq_free": "0.31", + "word_freq_business": "0", + "word_freq_email": "0", + "word_freq_you": "3.18", + "word_freq_credit": "0", + "word_freq_your": "0.31", + "word_freq_font": "0", + "word_freq_000": "0", + "word_freq_money": "0", + "word_freq_hp": "0", + "word_freq_hpl": "0", + "word_freq_george": "0", + "word_freq_650": "0", + "word_freq_lab": "0", + "word_freq_labs": "0", + "word_freq_telnet": "0", + "word_freq_857": "0", + "word_freq_data": "0", + "word_freq_415": "0", + "word_freq_85": "0", + "word_freq_technology": "0", + "word_freq_1999": "0", + "word_freq_parts": "0", + "word_freq_pm": "0", + "word_freq_direct": "0", + "word_freq_cs": "0", + "word_freq_meeting": "0", + "word_freq_original": "0", + "word_freq_project": "0", + "word_freq_re": "0", + "word_freq_edu": "0", + "word_freq_table": "0", + "word_freq_conference": "0", + "char_freq_%3B": "0", + "char_freq_%28": "0.135", + "char_freq_%5B": "0", + "char_freq_%21": "0.135", + "char_freq_%24": "0", + "char_freq_%23": "0", + "capital_run_length_average": "3.537", + "capital_run_length_longest": "40", + "capital_run_length_total": "191", + "class": "1" + } + ] +} + +Shortlisted templates: +[ + { + "template_id": "tpl_tpch_thresholded_group_ranking", + "template_name": "Thresholded Group Ranking", + "primary_family": "tail_rarity_structure", + "portability": "partial", + "sql_skeleton": "SELECT {group_col}, SUM({measure_col}) AS total_measure\nFROM {table}\nGROUP BY {group_col}\nHAVING SUM({measure_col}) > {measure_threshold}\nORDER BY total_measure DESC\nLIMIT {top_k};", + "required_roles": [ + "group_col", + "measure_col" + ] + } +] + +Problem instance: +{ + "dataset_id": "n1", + "question": "Use template Thresholded Group Ranking to probe tail_mass_similarity with semantic role filtered_stable_view. Focus on group_col=class, measure_col=word_freq_hpl.", + "planned_template_id": "tpl_tpch_thresholded_group_ranking", + "bindings": { + "group_col": "class", + "measure_col": "word_freq_hpl", + "top_k": 14, + "top_n": 6, + "num_tiles": 10, + "percentile_value": 0.9, + "z_threshold": 2.0, + "fraction_threshold": 0.1, + "baseline_multiplier": 1.5, + "baseline_fraction": 0.1, + "min_group_size": 5, + "min_support": 5, + "measure_threshold": 0.0, + "time_grain": "month", + "lookback_rows": 3, + "current_period_start": "'2024-01-01'", + "current_period_end": "'2024-04-01'", + "previous_period_start": "'2023-10-01'", + "previous_period_end": "'2024-01-01'", + "drift_ratio_threshold": 0.8 + }, + "can_vary": [], + "must_fix": [], + "runtime_sql_skeleton": "SELECT {group_col}, SUM({measure_col}) AS total_measure\nFROM {table}\nGROUP BY {group_col}\nHAVING SUM({measure_col}) > {measure_threshold}\nORDER BY total_measure DESC\nLIMIT {top_k};" +} + +Repair context: +{} diff --git a/Query/sql/v2/runs/v2_cli_20260502_081223_d/n1/artifacts/v2q_n1_db01aa23845b4486/cli/sql_response_attempt_1.raw.txt b/Query/sql/v2/runs/v2_cli_20260502_081223_d/n1/artifacts/v2q_n1_db01aa23845b4486/cli/sql_response_attempt_1.raw.txt new file mode 100644 index 0000000000000000000000000000000000000000..8218738cf682f76df89224d326a7739767815412 --- /dev/null +++ b/Query/sql/v2/runs/v2_cli_20260502_081223_d/n1/artifacts/v2q_n1_db01aa23845b4486/cli/sql_response_attempt_1.raw.txt @@ -0,0 +1,4 @@ +{"type":"thread.started","thread_id":"019e40fc-e53a-7d92-a8e0-3f41caceffa3"} +{"type":"turn.started"} +{"type":"error","message":"Quota exceeded. Check your plan and billing details."} +{"type":"turn.failed","error":{"message":"Quota exceeded. Check your plan and billing details."}} diff --git a/Query/sql/v2/runs/v2_cli_20260502_081223_d/n1/artifacts/v2q_n1_db01aa23845b4486/cli/sql_response_attempt_1.txt b/Query/sql/v2/runs/v2_cli_20260502_081223_d/n1/artifacts/v2q_n1_db01aa23845b4486/cli/sql_response_attempt_1.txt new file mode 100644 index 0000000000000000000000000000000000000000..dc4d68302e53893233127e0776811f413de422b0 --- /dev/null +++ b/Query/sql/v2/runs/v2_cli_20260502_081223_d/n1/artifacts/v2q_n1_db01aa23845b4486/cli/sql_response_attempt_1.txt @@ -0,0 +1,4 @@ +{"type":"thread.started","thread_id":"019e40fc-e53a-7d92-a8e0-3f41caceffa3"} +{"type":"turn.started"} +{"type":"error","message":"Quota exceeded. Check your plan and billing details."} +{"type":"turn.failed","error":{"message":"Quota exceeded. Check your plan and billing details."}} \ No newline at end of file diff --git a/Query/sql/v2/runs/v2_cli_20260502_081223_d/n1/artifacts/v2q_n1_db01aa23845b4486/cli/sql_response_attempt_2.raw.txt b/Query/sql/v2/runs/v2_cli_20260502_081223_d/n1/artifacts/v2q_n1_db01aa23845b4486/cli/sql_response_attempt_2.raw.txt new file mode 100644 index 0000000000000000000000000000000000000000..2c4131429417dd7bba4caf2ae00a82e5a7b460e8 --- /dev/null +++ b/Query/sql/v2/runs/v2_cli_20260502_081223_d/n1/artifacts/v2q_n1_db01aa23845b4486/cli/sql_response_attempt_2.raw.txt @@ -0,0 +1,4 @@ +{"type":"thread.started","thread_id":"019e40fc-f49f-7393-89df-66eb9f582201"} +{"type":"turn.started"} +{"type":"error","message":"Quota exceeded. Check your plan and billing details."} +{"type":"turn.failed","error":{"message":"Quota exceeded. Check your plan and billing details."}} diff --git a/Query/sql/v2/runs/v2_cli_20260502_081223_d/n1/artifacts/v2q_n1_db01aa23845b4486/cli/sql_response_attempt_2.txt b/Query/sql/v2/runs/v2_cli_20260502_081223_d/n1/artifacts/v2q_n1_db01aa23845b4486/cli/sql_response_attempt_2.txt new file mode 100644 index 0000000000000000000000000000000000000000..b651a47359103e66b7d90c45dbfac73053ce4427 --- /dev/null +++ b/Query/sql/v2/runs/v2_cli_20260502_081223_d/n1/artifacts/v2q_n1_db01aa23845b4486/cli/sql_response_attempt_2.txt @@ -0,0 +1,4 @@ +{"type":"thread.started","thread_id":"019e40fc-f49f-7393-89df-66eb9f582201"} +{"type":"turn.started"} +{"type":"error","message":"Quota exceeded. Check your plan and billing details."} +{"type":"turn.failed","error":{"message":"Quota exceeded. Check your plan and billing details."}} \ No newline at end of file diff --git a/Query/sql/v2/runs/v2_cli_20260502_081223_d/n1/artifacts/v2q_n1_db01aa23845b4486/cli/sql_stderr_attempt_1.txt b/Query/sql/v2/runs/v2_cli_20260502_081223_d/n1/artifacts/v2q_n1_db01aa23845b4486/cli/sql_stderr_attempt_1.txt new file mode 100644 index 0000000000000000000000000000000000000000..e69de29bb2d1d6434b8b29ae775ad8c2e48c5391 diff --git a/Query/sql/v2/runs/v2_cli_20260502_081223_d/n1/artifacts/v2q_n1_db01aa23845b4486/cli/sql_stderr_attempt_2.txt b/Query/sql/v2/runs/v2_cli_20260502_081223_d/n1/artifacts/v2q_n1_db01aa23845b4486/cli/sql_stderr_attempt_2.txt new file mode 100644 index 0000000000000000000000000000000000000000..e69de29bb2d1d6434b8b29ae775ad8c2e48c5391 diff --git a/Query/sql/v2/runs/v2_cli_20260502_081223_d/n1/artifacts/v2q_n1_dbd55b7c80d6e376/cli/conversation.jsonl b/Query/sql/v2/runs/v2_cli_20260502_081223_d/n1/artifacts/v2q_n1_dbd55b7c80d6e376/cli/conversation.jsonl new file mode 100644 index 0000000000000000000000000000000000000000..0a30d3d7df338cda80b6d72f8abb9678f5efa5a1 --- /dev/null +++ b/Query/sql/v2/runs/v2_cli_20260502_081223_d/n1/artifacts/v2q_n1_dbd55b7c80d6e376/cli/conversation.jsonl @@ -0,0 +1,2 @@ +{"attempt": 1, "phase": "sql_generation", "role": "user", "content_path": "cli/sql_prompt_attempt_1.txt", "metrics": {"chars": 29765, "bytes_utf8": 29765, "lines": 794, "estimated_tokens": null}} +{"attempt": 1, "phase": "sql_generation", "role": "assistant", "content_path": "cli/sql_response_attempt_1.txt", "raw_content_path": "cli/sql_response_attempt_1.raw.txt", "stderr_path": "cli/sql_stderr_attempt_1.txt", "metrics": {"chars": 613, "bytes_utf8": 613, "lines": 1, "estimated_tokens": null}, "usage": {"input_tokens": 20436, "cached_input_tokens": 19840, "output_tokens": 813, "reasoning_output_tokens": 628}} diff --git a/Query/sql/v2/runs/v2_cli_20260502_081223_d/n1/artifacts/v2q_n1_dbd55b7c80d6e376/cli/session_summary.json b/Query/sql/v2/runs/v2_cli_20260502_081223_d/n1/artifacts/v2q_n1_dbd55b7c80d6e376/cli/session_summary.json new file mode 100644 index 0000000000000000000000000000000000000000..76ce36e490fc9264ac447a2619a02655fbf16170 --- /dev/null +++ b/Query/sql/v2/runs/v2_cli_20260502_081223_d/n1/artifacts/v2q_n1_dbd55b7c80d6e376/cli/session_summary.json @@ -0,0 +1,25 @@ +{ + "engine": "v2-cli:codex", + "command": "codex exec --skip-git-repo-check --disable plugins --sandbox read-only --cd \"/data/jialinzhang/SQLagent\" -m gpt-5.4 --json -", + "ai_cli_calls": 1, + "usage_summary": { + "dataset_id": "n1", + "model": "v2-cli:codex", + "run_id": "v2q_n1_dbd55b7c80d6e376", + "api_calls": 0, + "input_tokens": 20436, + "cached_input_tokens": 19840, + "output_tokens": 813, + "total_tokens": 21249, + "cost_usd": 0.0, + "ai_cli_calls": 1, + "estimated_input_tokens": 0, + "estimated_output_tokens": 0, + "estimated_total_tokens": 0, + "usage_source": "ai_cli_json_usage", + "cli_elapsed_ms_total": 14748.45, + "sql_execution_elapsed_ms_total": 9.44, + "conversation_log_path": "/data/jialinzhang/TabQueryBench/sql_workloads/v2_current/runs_and_launches/runs/v2_cli_20260502_081223_d/n1/artifacts/v2q_n1_dbd55b7c80d6e376/cli/conversation.jsonl", + "note": "Executed through a local AI CLI with structured usage metadata." + } +} \ No newline at end of file diff --git a/Query/sql/v2/runs/v2_cli_20260502_081223_d/n1/artifacts/v2q_n1_dbd55b7c80d6e376/cli/sql_attempt_1.metadata.json b/Query/sql/v2/runs/v2_cli_20260502_081223_d/n1/artifacts/v2q_n1_dbd55b7c80d6e376/cli/sql_attempt_1.metadata.json new file mode 100644 index 0000000000000000000000000000000000000000..5c3ee027f9d98911913ea0daa7fbbc80efa754d0 --- /dev/null +++ b/Query/sql/v2/runs/v2_cli_20260502_081223_d/n1/artifacts/v2q_n1_dbd55b7c80d6e376/cli/sql_attempt_1.metadata.json @@ -0,0 +1,45 @@ +{ + "attempt": 1, + "phase": "sql_generation", + "command": "codex exec --skip-git-repo-check --disable plugins --sandbox read-only --cd \"/data/jialinzhang/SQLagent\" -m gpt-5.4 --json -", + "started_at": "2026-05-19T15:37:51.335364+00:00", + "ended_at": "2026-05-19T15:38:06.083850+00:00", + "elapsed_ms": 14748.45, + "prompt_metrics": { + "chars": 29765, + "bytes_utf8": 29765, + "lines": 794, + "estimated_tokens": null + }, + "stdout_metrics": { + "chars": 998, + "bytes_utf8": 998, + "lines": 4, + "estimated_tokens": null + }, + "stderr_metrics": { + "chars": 0, + "bytes_utf8": 0, + "lines": 0, + "estimated_tokens": null + }, + "parsed_output": { + "format": "jsonl_events", + "text_metrics": { + "chars": 613, + "bytes_utf8": 613, + "lines": 1, + "estimated_tokens": null + }, + "usage": { + "input_tokens": 20436, + "cached_input_tokens": 19840, + "output_tokens": 813, + "reasoning_output_tokens": 628 + } + }, + "prompt_path": "cli/sql_prompt_attempt_1.txt", + "response_path": "cli/sql_response_attempt_1.txt", + "raw_response_path": "cli/sql_response_attempt_1.raw.txt", + "stderr_path": "cli/sql_stderr_attempt_1.txt" +} \ No newline at end of file diff --git a/Query/sql/v2/runs/v2_cli_20260502_081223_d/n1/artifacts/v2q_n1_dbd55b7c80d6e376/cli/sql_prompt_attempt_1.txt b/Query/sql/v2/runs/v2_cli_20260502_081223_d/n1/artifacts/v2q_n1_dbd55b7c80d6e376/cli/sql_prompt_attempt_1.txt new file mode 100644 index 0000000000000000000000000000000000000000..3232516eb1f699f1003f5844d93467f5a1920ad7 --- /dev/null +++ b/Query/sql/v2/runs/v2_cli_20260502_081223_d/n1/artifacts/v2q_n1_dbd55b7c80d6e376/cli/sql_prompt_attempt_1.txt @@ -0,0 +1,794 @@ +You are generating one SQLite SELECT query for a single-table SQL QA task. +Return strict JSON only, with this schema: {"sql": "...", "notes": "..."}. +Rules: +- Use only the provided table and columns. +- Do not write INSERT, UPDATE, DELETE, DROP, ALTER, CREATE, PRAGMA, ATTACH, DETACH, or VACUUM. +- Prefer the planned template and bound roles when provided. +- Add a leading SQL comment exactly like: -- template_id: . +- Generate SQLite-compatible SQL. SQLite does not support PERCENTILE_CONT or STDDEV. +- Quote identifiers with double quotes. +- Return no markdown and no extra prose. + +Dataset context: +Dataset context for SQL QA: +- dataset_id: n1 +- dataset_name: Spambase +- table_name: n1 +- table_layout: single-table dataset (do not assume joins). +- row_semantics: One row is one email represented by engineered token/character frequency and capitalization-run features. +- task_type: classification +- target_column: class +- main_row_count: 4601 +- important_fields: +- word_freq_make: role=feature, type=numeric_sparse_frequency. tags=['condition_candidate', 'measure', 'rare_pattern_candidate'] desc=Frequency feature for token 'make' in an email message. +- word_freq_address: role=feature, type=numeric_sparse_frequency. tags=['condition_candidate', 'measure', 'rare_pattern_candidate'] desc=Frequency feature for token 'address' in an email message. +- word_freq_all: role=feature, type=numeric_sparse_frequency. tags=['condition_candidate', 'measure', 'rare_pattern_candidate'] desc=Frequency feature for token 'all' in an email message. +- word_freq_3d: role=feature, type=numeric_sparse_frequency. tags=['condition_candidate', 'measure', 'rare_pattern_candidate'] desc=Frequency feature for token '3d' in an email message. +- word_freq_our: role=feature, type=numeric_sparse_frequency. tags=['condition_candidate', 'measure', 'rare_pattern_candidate'] desc=Frequency feature for token 'our' in an email message. +- word_freq_over: role=feature, type=numeric_sparse_frequency. tags=['condition_candidate', 'measure', 'rare_pattern_candidate'] desc=Frequency feature for token 'over' in an email message. +- word_freq_remove: role=feature, type=numeric_sparse_frequency. tags=['condition_candidate', 'measure', 'rare_pattern_candidate'] desc=Frequency feature for token 'remove' in an email message. +- word_freq_internet: role=feature, type=numeric_sparse_frequency. tags=['condition_candidate', 'measure', 'rare_pattern_candidate'] desc=Frequency feature for token 'internet' in an email message. +- word_freq_order: role=feature, type=numeric_sparse_frequency. tags=['condition_candidate', 'measure', 'rare_pattern_candidate'] desc=Frequency feature for token 'order' in an email message. +- word_freq_mail: role=feature, type=numeric_sparse_frequency. tags=['condition_candidate', 'measure', 'rare_pattern_candidate'] desc=Frequency feature for token 'mail' in an email message. +- word_freq_receive: role=feature, type=numeric_sparse_frequency. tags=['condition_candidate', 'measure', 'rare_pattern_candidate'] desc=Frequency feature for token 'receive' in an email message. +- word_freq_will: role=feature, type=numeric_sparse_frequency. tags=['condition_candidate', 'measure', 'rare_pattern_candidate'] desc=Frequency feature for token 'will' in an email message. +- word_freq_people: role=feature, type=numeric_sparse_frequency. tags=['condition_candidate', 'measure', 'rare_pattern_candidate'] desc=Frequency feature for token 'people' in an email message. +- word_freq_report: role=feature, type=numeric_sparse_frequency. tags=['condition_candidate', 'measure', 'rare_pattern_candidate'] desc=Frequency feature for token 'report' in an email message. +- word_freq_addresses: role=feature, type=numeric_sparse_frequency. tags=['condition_candidate', 'measure', 'rare_pattern_candidate'] desc=Frequency feature for token 'addresses' in an email message. +- word_freq_free: role=feature, type=numeric_sparse_frequency. tags=['condition_candidate', 'measure', 'rare_pattern_candidate'] desc=Frequency feature for token 'free' in an email message. +- word_freq_business: role=feature, type=numeric_sparse_frequency. tags=['condition_candidate', 'measure', 'rare_pattern_candidate'] desc=Frequency feature for token 'business' in an email message. +- word_freq_email: role=feature, type=numeric_sparse_frequency. tags=['condition_candidate', 'measure', 'rare_pattern_candidate'] desc=Frequency feature for token 'email' in an email message. +- word_freq_you: role=feature, type=numeric_sparse_frequency. tags=['condition_candidate', 'measure', 'rare_pattern_candidate'] desc=Frequency feature for token 'you' in an email message. +- word_freq_credit: role=feature, type=numeric_sparse_frequency. tags=['condition_candidate', 'measure', 'rare_pattern_candidate'] desc=Frequency feature for token 'credit' in an email message. +- word_freq_your: role=feature, type=numeric_sparse_frequency. tags=['condition_candidate', 'measure', 'rare_pattern_candidate'] desc=Frequency feature for token 'your' in an email message. +- word_freq_font: role=feature, type=numeric_sparse_frequency. tags=['condition_candidate', 'measure', 'rare_pattern_candidate'] desc=Frequency feature for token 'font' in an email message. +- word_freq_000: role=feature, type=numeric_sparse_frequency. tags=['condition_candidate', 'measure', 'rare_pattern_candidate'] desc=Frequency feature for token '000' in an email message. +- word_freq_money: role=feature, type=numeric_sparse_frequency. tags=['condition_candidate', 'measure', 'rare_pattern_candidate'] desc=Frequency feature for token 'money' in an email message. +- word_freq_hp: role=feature, type=numeric_sparse_frequency. tags=['condition_candidate', 'measure', 'rare_pattern_candidate'] desc=Frequency feature for token 'hp' in an email message. +- word_freq_hpl: role=feature, type=numeric_sparse_frequency. tags=['condition_candidate', 'measure', 'rare_pattern_candidate'] desc=Frequency feature for token 'hpl' in an email message. +- word_freq_george: role=feature, type=numeric_sparse_frequency. tags=['condition_candidate', 'measure', 'rare_pattern_candidate'] desc=Frequency feature for token 'george' in an email message. +- word_freq_650: role=feature, type=numeric_sparse_frequency. tags=['condition_candidate', 'measure', 'rare_pattern_candidate'] desc=Frequency feature for token '650' in an email message. +- word_freq_lab: role=feature, type=numeric_sparse_frequency. tags=['condition_candidate', 'measure', 'rare_pattern_candidate'] desc=Frequency feature for token 'lab' in an email message. +- word_freq_labs: role=feature, type=numeric_sparse_frequency. tags=['condition_candidate', 'measure', 'rare_pattern_candidate'] desc=Frequency feature for token 'labs' in an email message. +- word_freq_telnet: role=feature, type=numeric_sparse_frequency. tags=['condition_candidate', 'measure', 'rare_pattern_candidate'] desc=Frequency feature for token 'telnet' in an email message. +- word_freq_857: role=feature, type=numeric_sparse_frequency. tags=['condition_candidate', 'measure', 'rare_pattern_candidate'] desc=Frequency feature for token '857' in an email message. +- word_freq_data: role=feature, type=numeric_sparse_frequency. tags=['condition_candidate', 'measure', 'rare_pattern_candidate'] desc=Frequency feature for token 'data' in an email message. +- word_freq_415: role=feature, type=numeric_sparse_frequency. tags=['condition_candidate', 'measure', 'rare_pattern_candidate'] desc=Frequency feature for token '415' in an email message. +- word_freq_85: role=feature, type=numeric_sparse_frequency. tags=['condition_candidate', 'measure', 'rare_pattern_candidate'] desc=Frequency feature for token '85' in an email message. +- word_freq_technology: role=feature, type=numeric_sparse_frequency. tags=['condition_candidate', 'measure', 'rare_pattern_candidate'] desc=Frequency feature for token 'technology' in an email message. +- word_freq_1999: role=feature, type=numeric_sparse_frequency. tags=['condition_candidate', 'measure', 'rare_pattern_candidate'] desc=Frequency feature for token '1999' in an email message. +- word_freq_parts: role=feature, type=numeric_sparse_frequency. tags=['condition_candidate', 'measure', 'rare_pattern_candidate'] desc=Frequency feature for token 'parts' in an email message. +- word_freq_pm: role=feature, type=numeric_sparse_frequency. tags=['condition_candidate', 'measure', 'rare_pattern_candidate'] desc=Frequency feature for token 'pm' in an email message. +- word_freq_direct: role=feature, type=numeric_sparse_frequency. tags=['condition_candidate', 'measure', 'rare_pattern_candidate'] desc=Frequency feature for token 'direct' in an email message. +- word_freq_cs: role=feature, type=numeric_sparse_frequency. tags=['condition_candidate', 'measure', 'rare_pattern_candidate'] desc=Frequency feature for token 'cs' in an email message. +- word_freq_meeting: role=feature, type=numeric_sparse_frequency. tags=['condition_candidate', 'measure', 'rare_pattern_candidate'] desc=Frequency feature for token 'meeting' in an email message. +- word_freq_original: role=feature, type=numeric_sparse_frequency. tags=['condition_candidate', 'measure', 'rare_pattern_candidate'] desc=Frequency feature for token 'original' in an email message. +- word_freq_project: role=feature, type=numeric_sparse_frequency. tags=['condition_candidate', 'measure', 'rare_pattern_candidate'] desc=Frequency feature for token 'project' in an email message. +- word_freq_re: role=feature, type=numeric_sparse_frequency. tags=['condition_candidate', 'measure', 'rare_pattern_candidate'] desc=Frequency feature for token 're' in an email message. +- word_freq_edu: role=feature, type=numeric_sparse_frequency. tags=['condition_candidate', 'measure', 'rare_pattern_candidate'] desc=Frequency feature for token 'edu' in an email message. +- word_freq_table: role=feature, type=numeric_sparse_frequency. tags=['condition_candidate', 'measure', 'rare_pattern_candidate'] desc=Frequency feature for token 'table' in an email message. +- word_freq_conference: role=feature, type=numeric_sparse_frequency. tags=['condition_candidate', 'measure', 'rare_pattern_candidate'] desc=Frequency feature for token 'conference' in an email message. +- char_freq_%3B: role=feature, type=numeric_sparse_frequency. tags=['condition_candidate', 'measure', 'rare_pattern_candidate'] desc=Character frequency feature for symbol ';'. +- char_freq_%28: role=feature, type=numeric_sparse_frequency. tags=['condition_candidate', 'measure', 'rare_pattern_candidate'] desc=Character frequency feature for symbol '('. +- char_freq_%5B: role=feature, type=numeric_sparse_frequency. tags=['condition_candidate', 'measure', 'rare_pattern_candidate'] desc=Character frequency feature for symbol '['. +- char_freq_%21: role=feature, type=numeric_sparse_frequency. tags=['condition_candidate', 'measure', 'rare_pattern_candidate'] desc=Character frequency feature for symbol '!'. +- char_freq_%24: role=feature, type=numeric_sparse_frequency. tags=['condition_candidate', 'measure', 'rare_pattern_candidate'] desc=Character frequency feature for symbol '$'. +- char_freq_%23: role=feature, type=numeric_sparse_frequency. tags=['condition_candidate', 'measure', 'rare_pattern_candidate'] desc=Character frequency feature for symbol '#'. +- capital_run_length_average: role=feature, type=numeric_heavy_tail. tags=['condition_candidate', 'measure', 'rare_pattern_candidate'] desc=Average length of uninterrupted capital-letter runs. +- capital_run_length_longest: role=feature, type=numeric_heavy_tail. tags=['condition_candidate', 'measure', 'rare_pattern_candidate'] desc=Longest uninterrupted capital-letter run. +- capital_run_length_total: role=feature, type=numeric_heavy_tail. tags=['condition_candidate', 'measure', 'rare_pattern_candidate'] desc=Total number of capital letters in runs. +- class: role=target, type=binary_target. tags=['target_candidate'] desc=Binary spam label (1=spam, 0=non-spam). +- useful_field_combinations: [['word_freq_free', 'word_freq_you', 'class'], ['char_freq_%21', 'capital_run_length_longest', 'class'], ['word_freq_remove', 'word_freq_money', 'class']] +- fields_requiring_caution: ['capital_run_length_average', 'capital_run_length_longest', 'capital_run_length_total'] +- source_url: https://www.openml.org/d/44 + +SQLite schema snapshot: +{ + "table_name": "n1", + "quoted_table_name": "\"n1\"", + "row_count": 4601, + "columns": [ + { + "name": "word_freq_make", + "type": "TEXT", + "notnull": false, + "pk": false + }, + { + "name": "word_freq_address", + "type": "TEXT", + "notnull": false, + "pk": false + }, + { + "name": "word_freq_all", + "type": "TEXT", + "notnull": false, + "pk": false + }, + { + "name": "word_freq_3d", + "type": "TEXT", + "notnull": false, + "pk": false + }, + { + "name": "word_freq_our", + "type": "TEXT", + "notnull": false, + "pk": false + }, + { + "name": "word_freq_over", + "type": "TEXT", + "notnull": false, + "pk": false + }, + { + "name": "word_freq_remove", + "type": "TEXT", + "notnull": false, + "pk": false + }, + { + "name": "word_freq_internet", + "type": "TEXT", + "notnull": false, + "pk": false + }, + { + "name": "word_freq_order", + "type": "TEXT", + "notnull": false, + "pk": false + }, + { + "name": "word_freq_mail", + "type": "TEXT", + "notnull": false, + "pk": false + }, + { + "name": "word_freq_receive", + "type": "TEXT", + "notnull": false, + "pk": false + }, + { + "name": "word_freq_will", + "type": "TEXT", + "notnull": false, + "pk": false + }, + { + "name": "word_freq_people", + "type": "TEXT", + "notnull": false, + "pk": false + }, + { + "name": "word_freq_report", + "type": "TEXT", + "notnull": false, + "pk": false + }, + { + "name": "word_freq_addresses", + "type": "TEXT", + "notnull": false, + "pk": false + }, + { + "name": "word_freq_free", + "type": "TEXT", + "notnull": false, + "pk": false + }, + { + "name": "word_freq_business", + "type": "TEXT", + "notnull": false, + "pk": false + }, + { + "name": "word_freq_email", + "type": "TEXT", + "notnull": false, + "pk": false + }, + { + "name": "word_freq_you", + "type": "TEXT", + "notnull": false, + "pk": false + }, + { + "name": "word_freq_credit", + "type": "TEXT", + "notnull": false, + "pk": false + }, + { + "name": "word_freq_your", + "type": "TEXT", + "notnull": false, + "pk": false + }, + { + "name": "word_freq_font", + "type": "TEXT", + "notnull": false, + "pk": false + }, + { + "name": "word_freq_000", + "type": "TEXT", + "notnull": false, + "pk": false + }, + { + "name": "word_freq_money", + "type": "TEXT", + "notnull": false, + "pk": false + }, + { + "name": "word_freq_hp", + "type": "TEXT", + "notnull": false, + "pk": false + }, + { + "name": "word_freq_hpl", + "type": "TEXT", + "notnull": false, + "pk": false + }, + { + "name": "word_freq_george", + "type": "TEXT", + "notnull": false, + "pk": false + }, + { + "name": "word_freq_650", + "type": "TEXT", + "notnull": false, + "pk": false + }, + { + "name": "word_freq_lab", + "type": "TEXT", + "notnull": false, + "pk": false + }, + { + "name": "word_freq_labs", + "type": "TEXT", + "notnull": false, + "pk": false + }, + { + "name": "word_freq_telnet", + "type": "TEXT", + "notnull": false, + "pk": false + }, + { + "name": "word_freq_857", + "type": "TEXT", + "notnull": false, + "pk": false + }, + { + "name": "word_freq_data", + "type": "TEXT", + "notnull": false, + "pk": false + }, + { + "name": "word_freq_415", + "type": "TEXT", + "notnull": false, + "pk": false + }, + { + "name": "word_freq_85", + "type": "TEXT", + "notnull": false, + "pk": false + }, + { + "name": "word_freq_technology", + "type": "TEXT", + "notnull": false, + "pk": false + }, + { + "name": "word_freq_1999", + "type": "TEXT", + "notnull": false, + "pk": false + }, + { + "name": "word_freq_parts", + "type": "TEXT", + "notnull": false, + "pk": false + }, + { + "name": "word_freq_pm", + "type": "TEXT", + "notnull": false, + "pk": false + }, + { + "name": "word_freq_direct", + "type": "TEXT", + "notnull": false, + "pk": false + }, + { + "name": "word_freq_cs", + "type": "TEXT", + "notnull": false, + "pk": false + }, + { + "name": "word_freq_meeting", + "type": "TEXT", + "notnull": false, + "pk": false + }, + { + "name": "word_freq_original", + "type": "TEXT", + "notnull": false, + "pk": false + }, + { + "name": "word_freq_project", + "type": "TEXT", + "notnull": false, + "pk": false + }, + { + "name": "word_freq_re", + "type": "TEXT", + "notnull": false, + "pk": false + }, + { + "name": "word_freq_edu", + "type": "TEXT", + "notnull": false, + "pk": false + }, + { + "name": "word_freq_table", + "type": "TEXT", + "notnull": false, + "pk": false + }, + { + "name": "word_freq_conference", + "type": "TEXT", + "notnull": false, + "pk": false + }, + { + "name": "char_freq_%3B", + "type": "TEXT", + "notnull": false, + "pk": false + }, + { + "name": "char_freq_%28", + "type": "TEXT", + "notnull": false, + "pk": false + }, + { + "name": "char_freq_%5B", + "type": "TEXT", + "notnull": false, + "pk": false + }, + { + "name": "char_freq_%21", + "type": "TEXT", + "notnull": false, + "pk": false + }, + { + "name": "char_freq_%24", + "type": "TEXT", + "notnull": false, + "pk": false + }, + { + "name": "char_freq_%23", + "type": "TEXT", + "notnull": false, + "pk": false + }, + { + "name": "capital_run_length_average", + "type": "TEXT", + "notnull": false, + "pk": false + }, + { + "name": "capital_run_length_longest", + "type": "TEXT", + "notnull": false, + "pk": false + }, + { + "name": "capital_run_length_total", + "type": "TEXT", + "notnull": false, + "pk": false + }, + { + "name": "class", + "type": "TEXT", + "notnull": false, + "pk": false + } + ], + "sample_rows": [ + { + "word_freq_make": "0", + "word_freq_address": "0.64", + "word_freq_all": "0.64", + "word_freq_3d": "0", + "word_freq_our": "0.32", + "word_freq_over": "0", + "word_freq_remove": "0", + "word_freq_internet": "0", + "word_freq_order": "0", + "word_freq_mail": "0", + "word_freq_receive": "0", + "word_freq_will": "0.64", + "word_freq_people": "0", + "word_freq_report": "0", + "word_freq_addresses": "0", + "word_freq_free": "0.32", + "word_freq_business": "0", + "word_freq_email": "1.29", + "word_freq_you": "1.93", + "word_freq_credit": "0", + "word_freq_your": "0.96", + "word_freq_font": "0", + "word_freq_000": "0", + "word_freq_money": "0", + "word_freq_hp": "0", + "word_freq_hpl": "0", + "word_freq_george": "0", + "word_freq_650": "0", + "word_freq_lab": "0", + "word_freq_labs": "0", + "word_freq_telnet": "0", + "word_freq_857": "0", + "word_freq_data": "0", + "word_freq_415": "0", + "word_freq_85": "0", + "word_freq_technology": "0", + "word_freq_1999": "0", + "word_freq_parts": "0", + "word_freq_pm": "0", + "word_freq_direct": "0", + "word_freq_cs": "0", + "word_freq_meeting": "0", + "word_freq_original": "0", + "word_freq_project": "0", + "word_freq_re": "0", + "word_freq_edu": "0", + "word_freq_table": "0", + "word_freq_conference": "0", + "char_freq_%3B": "0", + "char_freq_%28": "0", + "char_freq_%5B": "0", + "char_freq_%21": "0.778", + "char_freq_%24": "0", + "char_freq_%23": "0", + "capital_run_length_average": "3.756", + "capital_run_length_longest": "61", + "capital_run_length_total": "278", + "class": "1" + }, + { + "word_freq_make": "0.21", + "word_freq_address": "0.28", + "word_freq_all": "0.5", + "word_freq_3d": "0", + "word_freq_our": "0.14", + "word_freq_over": "0.28", + "word_freq_remove": "0.21", + "word_freq_internet": "0.07", + "word_freq_order": "0", + "word_freq_mail": "0.94", + "word_freq_receive": "0.21", + "word_freq_will": "0.79", + "word_freq_people": "0.65", + "word_freq_report": "0.21", + "word_freq_addresses": "0.14", + "word_freq_free": "0.14", + "word_freq_business": "0.07", + "word_freq_email": "0.28", + "word_freq_you": "3.47", + "word_freq_credit": "0", + "word_freq_your": "1.59", + "word_freq_font": "0", + "word_freq_000": "0.43", + "word_freq_money": "0.43", + "word_freq_hp": "0", + "word_freq_hpl": "0", + "word_freq_george": "0", + "word_freq_650": "0", + "word_freq_lab": "0", + "word_freq_labs": "0", + "word_freq_telnet": "0", + "word_freq_857": "0", + "word_freq_data": "0", + "word_freq_415": "0", + "word_freq_85": "0", + "word_freq_technology": "0", + "word_freq_1999": "0.07", + "word_freq_parts": "0", + "word_freq_pm": "0", + "word_freq_direct": "0", + "word_freq_cs": "0", + "word_freq_meeting": "0", + "word_freq_original": "0", + "word_freq_project": "0", + "word_freq_re": "0", + "word_freq_edu": "0", + "word_freq_table": "0", + "word_freq_conference": "0", + "char_freq_%3B": "0", + "char_freq_%28": "0.132", + "char_freq_%5B": "0", + "char_freq_%21": "0.372", + "char_freq_%24": "0.18", + "char_freq_%23": "0.048", + "capital_run_length_average": "5.114", + "capital_run_length_longest": "101", + "capital_run_length_total": "1028", + "class": "1" + }, + { + "word_freq_make": "0.06", + "word_freq_address": "0", + "word_freq_all": "0.71", + "word_freq_3d": "0", + "word_freq_our": "1.23", + "word_freq_over": "0.19", + "word_freq_remove": "0.19", + "word_freq_internet": "0.12", + "word_freq_order": "0.64", + "word_freq_mail": "0.25", + "word_freq_receive": "0.38", + "word_freq_will": "0.45", + "word_freq_people": "0.12", + "word_freq_report": "0", + "word_freq_addresses": "1.75", + "word_freq_free": "0.06", + "word_freq_business": "0.06", + "word_freq_email": "1.03", + "word_freq_you": "1.36", + "word_freq_credit": "0.32", + "word_freq_your": "0.51", + "word_freq_font": "0", + "word_freq_000": "1.16", + "word_freq_money": "0.06", + "word_freq_hp": "0", + "word_freq_hpl": "0", + "word_freq_george": "0", + "word_freq_650": "0", + "word_freq_lab": "0", + "word_freq_labs": "0", + "word_freq_telnet": "0", + "word_freq_857": "0", + "word_freq_data": "0", + "word_freq_415": "0", + "word_freq_85": "0", + "word_freq_technology": "0", + "word_freq_1999": "0", + "word_freq_parts": "0", + "word_freq_pm": "0", + "word_freq_direct": "0.06", + "word_freq_cs": "0", + "word_freq_meeting": "0", + "word_freq_original": "0.12", + "word_freq_project": "0", + "word_freq_re": "0.06", + "word_freq_edu": "0.06", + "word_freq_table": "0", + "word_freq_conference": "0", + "char_freq_%3B": "0.01", + "char_freq_%28": "0.143", + "char_freq_%5B": "0", + "char_freq_%21": "0.276", + "char_freq_%24": "0.184", + "char_freq_%23": "0.01", + "capital_run_length_average": "9.821", + "capital_run_length_longest": "485", + "capital_run_length_total": "2259", + "class": "1" + }, + { + "word_freq_make": "0", + "word_freq_address": "0", + "word_freq_all": "0", + "word_freq_3d": "0", + "word_freq_our": "0.63", + "word_freq_over": "0", + "word_freq_remove": "0.31", + "word_freq_internet": "0.63", + "word_freq_order": "0.31", + "word_freq_mail": "0.63", + "word_freq_receive": "0.31", + "word_freq_will": "0.31", + "word_freq_people": "0.31", + "word_freq_report": "0", + "word_freq_addresses": "0", + "word_freq_free": "0.31", + "word_freq_business": "0", + "word_freq_email": "0", + "word_freq_you": "3.18", + "word_freq_credit": "0", + "word_freq_your": "0.31", + "word_freq_font": "0", + "word_freq_000": "0", + "word_freq_money": "0", + "word_freq_hp": "0", + "word_freq_hpl": "0", + "word_freq_george": "0", + "word_freq_650": "0", + "word_freq_lab": "0", + "word_freq_labs": "0", + "word_freq_telnet": "0", + "word_freq_857": "0", + "word_freq_data": "0", + "word_freq_415": "0", + "word_freq_85": "0", + "word_freq_technology": "0", + "word_freq_1999": "0", + "word_freq_parts": "0", + "word_freq_pm": "0", + "word_freq_direct": "0", + "word_freq_cs": "0", + "word_freq_meeting": "0", + "word_freq_original": "0", + "word_freq_project": "0", + "word_freq_re": "0", + "word_freq_edu": "0", + "word_freq_table": "0", + "word_freq_conference": "0", + "char_freq_%3B": "0", + "char_freq_%28": "0.137", + "char_freq_%5B": "0", + "char_freq_%21": "0.137", + "char_freq_%24": "0", + "char_freq_%23": "0", + "capital_run_length_average": "3.537", + "capital_run_length_longest": "40", + "capital_run_length_total": "191", + "class": "1" + }, + { + "word_freq_make": "0", + "word_freq_address": "0", + "word_freq_all": "0", + "word_freq_3d": "0", + "word_freq_our": "0.63", + "word_freq_over": "0", + "word_freq_remove": "0.31", + "word_freq_internet": "0.63", + "word_freq_order": "0.31", + "word_freq_mail": "0.63", + "word_freq_receive": "0.31", + "word_freq_will": "0.31", + "word_freq_people": "0.31", + "word_freq_report": "0", + "word_freq_addresses": "0", + "word_freq_free": "0.31", + "word_freq_business": "0", + "word_freq_email": "0", + "word_freq_you": "3.18", + "word_freq_credit": "0", + "word_freq_your": "0.31", + "word_freq_font": "0", + "word_freq_000": "0", + "word_freq_money": "0", + "word_freq_hp": "0", + "word_freq_hpl": "0", + "word_freq_george": "0", + "word_freq_650": "0", + "word_freq_lab": "0", + "word_freq_labs": "0", + "word_freq_telnet": "0", + "word_freq_857": "0", + "word_freq_data": "0", + "word_freq_415": "0", + "word_freq_85": "0", + "word_freq_technology": "0", + "word_freq_1999": "0", + "word_freq_parts": "0", + "word_freq_pm": "0", + "word_freq_direct": "0", + "word_freq_cs": "0", + "word_freq_meeting": "0", + "word_freq_original": "0", + "word_freq_project": "0", + "word_freq_re": "0", + "word_freq_edu": "0", + "word_freq_table": "0", + "word_freq_conference": "0", + "char_freq_%3B": "0", + "char_freq_%28": "0.135", + "char_freq_%5B": "0", + "char_freq_%21": "0.135", + "char_freq_%24": "0", + "char_freq_%23": "0", + "capital_run_length_average": "3.537", + "capital_run_length_longest": "40", + "capital_run_length_total": "191", + "class": "1" + } + ] +} + +Shortlisted templates: +[ + { + "template_id": "tpl_tpcds_within_group_share", + "template_name": "Within-Group Share of Total", + "primary_family": "conditional_dependency_structure", + "portability": "partial", + "sql_skeleton": "SELECT {group_col}, {item_col},\n SUM({measure_col}) AS total_measure,\n SUM({measure_col}) * 100.0 / SUM(SUM({measure_col})) OVER (PARTITION BY {group_col}) AS share_within_group\nFROM {table}\nGROUP BY {group_col}, {item_col}\nORDER BY share_within_group DESC;", + "required_roles": [ + "group_col", + "item_col", + "measure_col" + ] + } +] + +Problem instance: +{ + "dataset_id": "n1", + "question": "Use template Within-Group Share of Total to probe dependency_strength_similarity with semantic role focused_target_view. Focus on group_col=class, measure_col=word_freq_857.", + "planned_template_id": "tpl_tpcds_within_group_share", + "bindings": { + "group_col": "class", + "measure_col": "word_freq_857", + "item_col": "word_freq_data", + "top_k": 11, + "top_n": 6, + "num_tiles": 10, + "percentile_value": 0.9, + "z_threshold": 2.0, + "fraction_threshold": 0.1, + "baseline_multiplier": 1.5, + "baseline_fraction": 0.1, + "min_group_size": 5, + "min_support": 5, + "measure_threshold": 0.0, + "time_grain": "month", + "lookback_rows": 3, + "current_period_start": "'2024-01-01'", + "current_period_end": "'2024-04-01'", + "previous_period_start": "'2023-10-01'", + "previous_period_end": "'2024-01-01'", + "drift_ratio_threshold": 0.8 + }, + "can_vary": [], + "must_fix": [], + "runtime_sql_skeleton": "SELECT {group_col}, {item_col},\n SUM({measure_col}) AS total_measure,\n SUM({measure_col}) * 100.0 / SUM(SUM({measure_col})) OVER (PARTITION BY {group_col}) AS share_within_group\nFROM {table}\nGROUP BY {group_col}, {item_col}\nORDER BY share_within_group DESC;" +} + +Repair context: +{} diff --git a/Query/sql/v2/runs/v2_cli_20260502_081223_d/n1/artifacts/v2q_n1_dbd55b7c80d6e376/cli/sql_response_attempt_1.raw.txt b/Query/sql/v2/runs/v2_cli_20260502_081223_d/n1/artifacts/v2q_n1_dbd55b7c80d6e376/cli/sql_response_attempt_1.raw.txt new file mode 100644 index 0000000000000000000000000000000000000000..cb2d9c56c45d7ba917b7548740f2154d146b36eb --- /dev/null +++ b/Query/sql/v2/runs/v2_cli_20260502_081223_d/n1/artifacts/v2q_n1_dbd55b7c80d6e376/cli/sql_response_attempt_1.raw.txt @@ -0,0 +1,4 @@ +{"type":"thread.started","thread_id":"019e40e2-e2f0-76c2-92b8-32aaacee2464"} +{"type":"turn.started"} +{"type":"item.completed","item":{"id":"item_0","type":"agent_message","text":"{\"sql\":\"-- template_id: tpl_tpcds_within_group_share\\nSELECT\\n \\\"class\\\",\\n \\\"word_freq_data\\\",\\n SUM(CAST(\\\"word_freq_857\\\" AS REAL)) AS total_measure,\\n SUM(CAST(\\\"word_freq_857\\\" AS REAL)) * 100.0 / NULLIF(\\n SUM(SUM(CAST(\\\"word_freq_857\\\" AS REAL))) OVER (PARTITION BY \\\"class\\\"),\\n 0\\n ) AS share_within_group\\nFROM \\\"n1\\\"\\nGROUP BY \\\"class\\\", \\\"word_freq_data\\\"\\nORDER BY share_within_group DESC;\",\"notes\":\"Uses the planned template with group_col=\\\"class\\\", item_col=\\\"word_freq_data\\\", and measure_col=\\\"word_freq_857\\\". CAST to REAL is applied because the schema stores these fields as TEXT.\"}"}} +{"type":"turn.completed","usage":{"input_tokens":20436,"cached_input_tokens":19840,"output_tokens":813,"reasoning_output_tokens":628}} diff --git a/Query/sql/v2/runs/v2_cli_20260502_081223_d/n1/artifacts/v2q_n1_dbd55b7c80d6e376/cli/sql_response_attempt_1.txt b/Query/sql/v2/runs/v2_cli_20260502_081223_d/n1/artifacts/v2q_n1_dbd55b7c80d6e376/cli/sql_response_attempt_1.txt new file mode 100644 index 0000000000000000000000000000000000000000..dc32d32f69618f6716ab1ff03eeee569c82f8c85 --- /dev/null +++ b/Query/sql/v2/runs/v2_cli_20260502_081223_d/n1/artifacts/v2q_n1_dbd55b7c80d6e376/cli/sql_response_attempt_1.txt @@ -0,0 +1 @@ +{"sql":"-- template_id: tpl_tpcds_within_group_share\nSELECT\n \"class\",\n \"word_freq_data\",\n SUM(CAST(\"word_freq_857\" AS REAL)) AS total_measure,\n SUM(CAST(\"word_freq_857\" AS REAL)) * 100.0 / NULLIF(\n SUM(SUM(CAST(\"word_freq_857\" AS REAL))) OVER (PARTITION BY \"class\"),\n 0\n ) AS share_within_group\nFROM \"n1\"\nGROUP BY \"class\", \"word_freq_data\"\nORDER BY share_within_group DESC;","notes":"Uses the planned template with group_col=\"class\", item_col=\"word_freq_data\", and measure_col=\"word_freq_857\". CAST to REAL is applied because the schema stores these fields as TEXT."} \ No newline at end of file diff --git a/Query/sql/v2/runs/v2_cli_20260502_081223_d/n1/artifacts/v2q_n1_dbd55b7c80d6e376/cli/sql_stderr_attempt_1.txt b/Query/sql/v2/runs/v2_cli_20260502_081223_d/n1/artifacts/v2q_n1_dbd55b7c80d6e376/cli/sql_stderr_attempt_1.txt new file mode 100644 index 0000000000000000000000000000000000000000..e69de29bb2d1d6434b8b29ae775ad8c2e48c5391 diff --git a/Query/sql/v2/runs/v2_cli_20260502_081223_d/n1/artifacts/v2q_n1_df35e0cdf57f0221/cli/sql_attempt_1.metadata.json b/Query/sql/v2/runs/v2_cli_20260502_081223_d/n1/artifacts/v2q_n1_df35e0cdf57f0221/cli/sql_attempt_1.metadata.json new file mode 100644 index 0000000000000000000000000000000000000000..9ffd26a699698dce1fe656faf35c36b085960757 --- /dev/null +++ b/Query/sql/v2/runs/v2_cli_20260502_081223_d/n1/artifacts/v2q_n1_df35e0cdf57f0221/cli/sql_attempt_1.metadata.json @@ -0,0 +1,43 @@ +{ + "attempt": 1, + "phase": "sql_generation", + "command": "codex exec --skip-git-repo-check --disable plugins --sandbox read-only --cd \"/data/jialinzhang/SQLagent\" -m gpt-5.4 --json -", + "started_at": "2026-05-19T16:03:33.754336+00:00", + "ended_at": "2026-05-19T16:03:36.516268+00:00", + "elapsed_ms": 2761.91, + "returncode": 1, + "prompt_metrics": { + "chars": 29333, + "bytes_utf8": 29333, + "lines": 790, + "estimated_tokens": null + }, + "stdout_metrics": { + "chars": 281, + "bytes_utf8": 281, + "lines": 4, + "estimated_tokens": null + }, + "stderr_metrics": { + "chars": 0, + "bytes_utf8": 0, + "lines": 0, + "estimated_tokens": null + }, + "parsed_output": { + "format": "jsonl_events", + "text_metrics": { + "chars": 280, + "bytes_utf8": 280, + "lines": 4, + "estimated_tokens": null + }, + "usage": {} + }, + "status": "failed", + "error": "AI CLI command failed with exit code 1: ", + "prompt_path": "cli/sql_prompt_attempt_1.txt", + "response_path": "cli/sql_response_attempt_1.txt", + "raw_response_path": "cli/sql_response_attempt_1.raw.txt", + "stderr_path": "cli/sql_stderr_attempt_1.txt" +} \ No newline at end of file diff --git a/Query/sql/v2/runs/v2_cli_20260502_081223_d/n1/artifacts/v2q_n1_df35e0cdf57f0221/cli/sql_attempt_2.metadata.json b/Query/sql/v2/runs/v2_cli_20260502_081223_d/n1/artifacts/v2q_n1_df35e0cdf57f0221/cli/sql_attempt_2.metadata.json new file mode 100644 index 0000000000000000000000000000000000000000..5a6e20dc2732d976567a00f52902581b5473c470 --- /dev/null +++ b/Query/sql/v2/runs/v2_cli_20260502_081223_d/n1/artifacts/v2q_n1_df35e0cdf57f0221/cli/sql_attempt_2.metadata.json @@ -0,0 +1,43 @@ +{ + "attempt": 2, + "phase": "sql_generation", + "command": "codex exec --skip-git-repo-check --disable plugins --sandbox read-only --cd \"/data/jialinzhang/SQLagent\" -m gpt-5.4 --json -", + "started_at": "2026-05-19T16:03:37.519398+00:00", + "ended_at": "2026-05-19T16:03:40.562847+00:00", + "elapsed_ms": 3043.4, + "returncode": 1, + "prompt_metrics": { + "chars": 29333, + "bytes_utf8": 29333, + "lines": 790, + "estimated_tokens": null + }, + "stdout_metrics": { + "chars": 281, + "bytes_utf8": 281, + "lines": 4, + "estimated_tokens": null + }, + "stderr_metrics": { + "chars": 0, + "bytes_utf8": 0, + "lines": 0, + "estimated_tokens": null + }, + "parsed_output": { + "format": "jsonl_events", + "text_metrics": { + "chars": 280, + "bytes_utf8": 280, + "lines": 4, + "estimated_tokens": null + }, + "usage": {} + }, + "status": "failed", + "error": "AI CLI command failed with exit code 1: ", + "prompt_path": "cli/sql_prompt_attempt_2.txt", + "response_path": "cli/sql_response_attempt_2.txt", + "raw_response_path": "cli/sql_response_attempt_2.raw.txt", + "stderr_path": "cli/sql_stderr_attempt_2.txt" +} \ No newline at end of file diff --git a/Query/sql/v2/runs/v2_cli_20260502_081223_d/n1/artifacts/v2q_n1_df35e0cdf57f0221/cli/sql_prompt_attempt_1.txt b/Query/sql/v2/runs/v2_cli_20260502_081223_d/n1/artifacts/v2q_n1_df35e0cdf57f0221/cli/sql_prompt_attempt_1.txt new file mode 100644 index 0000000000000000000000000000000000000000..fb1812214b9ceb244d428c10e78c92d4f65028e6 --- /dev/null +++ b/Query/sql/v2/runs/v2_cli_20260502_081223_d/n1/artifacts/v2q_n1_df35e0cdf57f0221/cli/sql_prompt_attempt_1.txt @@ -0,0 +1,790 @@ +You are generating one SQLite SELECT query for a single-table SQL QA task. +Return strict JSON only, with this schema: {"sql": "...", "notes": "..."}. +Rules: +- Use only the provided table and columns. +- Do not write INSERT, UPDATE, DELETE, DROP, ALTER, CREATE, PRAGMA, ATTACH, DETACH, or VACUUM. +- Prefer the planned template and bound roles when provided. +- Add a leading SQL comment exactly like: -- template_id: . +- Generate SQLite-compatible SQL. SQLite does not support PERCENTILE_CONT or STDDEV. +- Quote identifiers with double quotes. +- Return no markdown and no extra prose. + +Dataset context: +Dataset context for SQL QA: +- dataset_id: n1 +- dataset_name: Spambase +- table_name: n1 +- table_layout: single-table dataset (do not assume joins). +- row_semantics: One row is one email represented by engineered token/character frequency and capitalization-run features. +- task_type: classification +- target_column: class +- main_row_count: 4601 +- important_fields: +- word_freq_make: role=feature, type=numeric_sparse_frequency. tags=['condition_candidate', 'measure', 'rare_pattern_candidate'] desc=Frequency feature for token 'make' in an email message. +- word_freq_address: role=feature, type=numeric_sparse_frequency. tags=['condition_candidate', 'measure', 'rare_pattern_candidate'] desc=Frequency feature for token 'address' in an email message. +- word_freq_all: role=feature, type=numeric_sparse_frequency. tags=['condition_candidate', 'measure', 'rare_pattern_candidate'] desc=Frequency feature for token 'all' in an email message. +- word_freq_3d: role=feature, type=numeric_sparse_frequency. tags=['condition_candidate', 'measure', 'rare_pattern_candidate'] desc=Frequency feature for token '3d' in an email message. +- word_freq_our: role=feature, type=numeric_sparse_frequency. tags=['condition_candidate', 'measure', 'rare_pattern_candidate'] desc=Frequency feature for token 'our' in an email message. +- word_freq_over: role=feature, type=numeric_sparse_frequency. tags=['condition_candidate', 'measure', 'rare_pattern_candidate'] desc=Frequency feature for token 'over' in an email message. +- word_freq_remove: role=feature, type=numeric_sparse_frequency. tags=['condition_candidate', 'measure', 'rare_pattern_candidate'] desc=Frequency feature for token 'remove' in an email message. +- word_freq_internet: role=feature, type=numeric_sparse_frequency. tags=['condition_candidate', 'measure', 'rare_pattern_candidate'] desc=Frequency feature for token 'internet' in an email message. +- word_freq_order: role=feature, type=numeric_sparse_frequency. tags=['condition_candidate', 'measure', 'rare_pattern_candidate'] desc=Frequency feature for token 'order' in an email message. +- word_freq_mail: role=feature, type=numeric_sparse_frequency. tags=['condition_candidate', 'measure', 'rare_pattern_candidate'] desc=Frequency feature for token 'mail' in an email message. +- word_freq_receive: role=feature, type=numeric_sparse_frequency. tags=['condition_candidate', 'measure', 'rare_pattern_candidate'] desc=Frequency feature for token 'receive' in an email message. +- word_freq_will: role=feature, type=numeric_sparse_frequency. tags=['condition_candidate', 'measure', 'rare_pattern_candidate'] desc=Frequency feature for token 'will' in an email message. +- word_freq_people: role=feature, type=numeric_sparse_frequency. tags=['condition_candidate', 'measure', 'rare_pattern_candidate'] desc=Frequency feature for token 'people' in an email message. +- word_freq_report: role=feature, type=numeric_sparse_frequency. tags=['condition_candidate', 'measure', 'rare_pattern_candidate'] desc=Frequency feature for token 'report' in an email message. +- word_freq_addresses: role=feature, type=numeric_sparse_frequency. tags=['condition_candidate', 'measure', 'rare_pattern_candidate'] desc=Frequency feature for token 'addresses' in an email message. +- word_freq_free: role=feature, type=numeric_sparse_frequency. tags=['condition_candidate', 'measure', 'rare_pattern_candidate'] desc=Frequency feature for token 'free' in an email message. +- word_freq_business: role=feature, type=numeric_sparse_frequency. tags=['condition_candidate', 'measure', 'rare_pattern_candidate'] desc=Frequency feature for token 'business' in an email message. +- word_freq_email: role=feature, type=numeric_sparse_frequency. tags=['condition_candidate', 'measure', 'rare_pattern_candidate'] desc=Frequency feature for token 'email' in an email message. +- word_freq_you: role=feature, type=numeric_sparse_frequency. tags=['condition_candidate', 'measure', 'rare_pattern_candidate'] desc=Frequency feature for token 'you' in an email message. +- word_freq_credit: role=feature, type=numeric_sparse_frequency. tags=['condition_candidate', 'measure', 'rare_pattern_candidate'] desc=Frequency feature for token 'credit' in an email message. +- word_freq_your: role=feature, type=numeric_sparse_frequency. tags=['condition_candidate', 'measure', 'rare_pattern_candidate'] desc=Frequency feature for token 'your' in an email message. +- word_freq_font: role=feature, type=numeric_sparse_frequency. tags=['condition_candidate', 'measure', 'rare_pattern_candidate'] desc=Frequency feature for token 'font' in an email message. +- word_freq_000: role=feature, type=numeric_sparse_frequency. tags=['condition_candidate', 'measure', 'rare_pattern_candidate'] desc=Frequency feature for token '000' in an email message. +- word_freq_money: role=feature, type=numeric_sparse_frequency. tags=['condition_candidate', 'measure', 'rare_pattern_candidate'] desc=Frequency feature for token 'money' in an email message. +- word_freq_hp: role=feature, type=numeric_sparse_frequency. tags=['condition_candidate', 'measure', 'rare_pattern_candidate'] desc=Frequency feature for token 'hp' in an email message. +- word_freq_hpl: role=feature, type=numeric_sparse_frequency. tags=['condition_candidate', 'measure', 'rare_pattern_candidate'] desc=Frequency feature for token 'hpl' in an email message. +- word_freq_george: role=feature, type=numeric_sparse_frequency. tags=['condition_candidate', 'measure', 'rare_pattern_candidate'] desc=Frequency feature for token 'george' in an email message. +- word_freq_650: role=feature, type=numeric_sparse_frequency. tags=['condition_candidate', 'measure', 'rare_pattern_candidate'] desc=Frequency feature for token '650' in an email message. +- word_freq_lab: role=feature, type=numeric_sparse_frequency. tags=['condition_candidate', 'measure', 'rare_pattern_candidate'] desc=Frequency feature for token 'lab' in an email message. +- word_freq_labs: role=feature, type=numeric_sparse_frequency. tags=['condition_candidate', 'measure', 'rare_pattern_candidate'] desc=Frequency feature for token 'labs' in an email message. +- word_freq_telnet: role=feature, type=numeric_sparse_frequency. tags=['condition_candidate', 'measure', 'rare_pattern_candidate'] desc=Frequency feature for token 'telnet' in an email message. +- word_freq_857: role=feature, type=numeric_sparse_frequency. tags=['condition_candidate', 'measure', 'rare_pattern_candidate'] desc=Frequency feature for token '857' in an email message. +- word_freq_data: role=feature, type=numeric_sparse_frequency. tags=['condition_candidate', 'measure', 'rare_pattern_candidate'] desc=Frequency feature for token 'data' in an email message. +- word_freq_415: role=feature, type=numeric_sparse_frequency. tags=['condition_candidate', 'measure', 'rare_pattern_candidate'] desc=Frequency feature for token '415' in an email message. +- word_freq_85: role=feature, type=numeric_sparse_frequency. tags=['condition_candidate', 'measure', 'rare_pattern_candidate'] desc=Frequency feature for token '85' in an email message. +- word_freq_technology: role=feature, type=numeric_sparse_frequency. tags=['condition_candidate', 'measure', 'rare_pattern_candidate'] desc=Frequency feature for token 'technology' in an email message. +- word_freq_1999: role=feature, type=numeric_sparse_frequency. tags=['condition_candidate', 'measure', 'rare_pattern_candidate'] desc=Frequency feature for token '1999' in an email message. +- word_freq_parts: role=feature, type=numeric_sparse_frequency. tags=['condition_candidate', 'measure', 'rare_pattern_candidate'] desc=Frequency feature for token 'parts' in an email message. +- word_freq_pm: role=feature, type=numeric_sparse_frequency. tags=['condition_candidate', 'measure', 'rare_pattern_candidate'] desc=Frequency feature for token 'pm' in an email message. +- word_freq_direct: role=feature, type=numeric_sparse_frequency. tags=['condition_candidate', 'measure', 'rare_pattern_candidate'] desc=Frequency feature for token 'direct' in an email message. +- word_freq_cs: role=feature, type=numeric_sparse_frequency. tags=['condition_candidate', 'measure', 'rare_pattern_candidate'] desc=Frequency feature for token 'cs' in an email message. +- word_freq_meeting: role=feature, type=numeric_sparse_frequency. tags=['condition_candidate', 'measure', 'rare_pattern_candidate'] desc=Frequency feature for token 'meeting' in an email message. +- word_freq_original: role=feature, type=numeric_sparse_frequency. tags=['condition_candidate', 'measure', 'rare_pattern_candidate'] desc=Frequency feature for token 'original' in an email message. +- word_freq_project: role=feature, type=numeric_sparse_frequency. tags=['condition_candidate', 'measure', 'rare_pattern_candidate'] desc=Frequency feature for token 'project' in an email message. +- word_freq_re: role=feature, type=numeric_sparse_frequency. tags=['condition_candidate', 'measure', 'rare_pattern_candidate'] desc=Frequency feature for token 're' in an email message. +- word_freq_edu: role=feature, type=numeric_sparse_frequency. tags=['condition_candidate', 'measure', 'rare_pattern_candidate'] desc=Frequency feature for token 'edu' in an email message. +- word_freq_table: role=feature, type=numeric_sparse_frequency. tags=['condition_candidate', 'measure', 'rare_pattern_candidate'] desc=Frequency feature for token 'table' in an email message. +- word_freq_conference: role=feature, type=numeric_sparse_frequency. tags=['condition_candidate', 'measure', 'rare_pattern_candidate'] desc=Frequency feature for token 'conference' in an email message. +- char_freq_%3B: role=feature, type=numeric_sparse_frequency. tags=['condition_candidate', 'measure', 'rare_pattern_candidate'] desc=Character frequency feature for symbol ';'. +- char_freq_%28: role=feature, type=numeric_sparse_frequency. tags=['condition_candidate', 'measure', 'rare_pattern_candidate'] desc=Character frequency feature for symbol '('. +- char_freq_%5B: role=feature, type=numeric_sparse_frequency. tags=['condition_candidate', 'measure', 'rare_pattern_candidate'] desc=Character frequency feature for symbol '['. +- char_freq_%21: role=feature, type=numeric_sparse_frequency. tags=['condition_candidate', 'measure', 'rare_pattern_candidate'] desc=Character frequency feature for symbol '!'. +- char_freq_%24: role=feature, type=numeric_sparse_frequency. tags=['condition_candidate', 'measure', 'rare_pattern_candidate'] desc=Character frequency feature for symbol '$'. +- char_freq_%23: role=feature, type=numeric_sparse_frequency. tags=['condition_candidate', 'measure', 'rare_pattern_candidate'] desc=Character frequency feature for symbol '#'. +- capital_run_length_average: role=feature, type=numeric_heavy_tail. tags=['condition_candidate', 'measure', 'rare_pattern_candidate'] desc=Average length of uninterrupted capital-letter runs. +- capital_run_length_longest: role=feature, type=numeric_heavy_tail. tags=['condition_candidate', 'measure', 'rare_pattern_candidate'] desc=Longest uninterrupted capital-letter run. +- capital_run_length_total: role=feature, type=numeric_heavy_tail. tags=['condition_candidate', 'measure', 'rare_pattern_candidate'] desc=Total number of capital letters in runs. +- class: role=target, type=binary_target. tags=['target_candidate'] desc=Binary spam label (1=spam, 0=non-spam). +- useful_field_combinations: [['word_freq_free', 'word_freq_you', 'class'], ['char_freq_%21', 'capital_run_length_longest', 'class'], ['word_freq_remove', 'word_freq_money', 'class']] +- fields_requiring_caution: ['capital_run_length_average', 'capital_run_length_longest', 'capital_run_length_total'] +- source_url: https://www.openml.org/d/44 + +SQLite schema snapshot: +{ + "table_name": "n1", + "quoted_table_name": "\"n1\"", + "row_count": 4601, + "columns": [ + { + "name": "word_freq_make", + "type": "TEXT", + "notnull": false, + "pk": false + }, + { + "name": "word_freq_address", + "type": "TEXT", + "notnull": false, + "pk": false + }, + { + "name": "word_freq_all", + "type": "TEXT", + "notnull": false, + "pk": false + }, + { + "name": "word_freq_3d", + "type": "TEXT", + "notnull": false, + "pk": false + }, + { + "name": "word_freq_our", + "type": "TEXT", + "notnull": false, + "pk": false + }, + { + "name": "word_freq_over", + "type": "TEXT", + "notnull": false, + "pk": false + }, + { + "name": "word_freq_remove", + "type": "TEXT", + "notnull": false, + "pk": false + }, + { + "name": "word_freq_internet", + "type": "TEXT", + "notnull": false, + "pk": false + }, + { + "name": "word_freq_order", + "type": "TEXT", + "notnull": false, + "pk": false + }, + { + "name": "word_freq_mail", + "type": "TEXT", + "notnull": false, + "pk": false + }, + { + "name": "word_freq_receive", + "type": "TEXT", + "notnull": false, + "pk": false + }, + { + "name": "word_freq_will", + "type": "TEXT", + "notnull": false, + "pk": false + }, + { + "name": "word_freq_people", + "type": "TEXT", + "notnull": false, + "pk": false + }, + { + "name": "word_freq_report", + "type": "TEXT", + "notnull": false, + "pk": false + }, + { + "name": "word_freq_addresses", + "type": "TEXT", + "notnull": false, + "pk": false + }, + { + "name": "word_freq_free", + "type": "TEXT", + "notnull": false, + "pk": false + }, + { + "name": "word_freq_business", + "type": "TEXT", + "notnull": false, + "pk": false + }, + { + "name": "word_freq_email", + "type": "TEXT", + "notnull": false, + "pk": false + }, + { + "name": "word_freq_you", + "type": "TEXT", + "notnull": false, + "pk": false + }, + { + "name": "word_freq_credit", + "type": "TEXT", + "notnull": false, + "pk": false + }, + { + "name": "word_freq_your", + "type": "TEXT", + "notnull": false, + "pk": false + }, + { + "name": "word_freq_font", + "type": "TEXT", + "notnull": false, + "pk": false + }, + { + "name": "word_freq_000", + "type": "TEXT", + "notnull": false, + "pk": false + }, + { + "name": "word_freq_money", + "type": "TEXT", + "notnull": false, + "pk": false + }, + { + "name": "word_freq_hp", + "type": "TEXT", + "notnull": false, + "pk": false + }, + { + "name": "word_freq_hpl", + "type": "TEXT", + "notnull": false, + "pk": false + }, + { + "name": "word_freq_george", + "type": "TEXT", + "notnull": false, + "pk": false + }, + { + "name": "word_freq_650", + "type": "TEXT", + "notnull": false, + "pk": false + }, + { + "name": "word_freq_lab", + "type": "TEXT", + "notnull": false, + "pk": false + }, + { + "name": "word_freq_labs", + "type": "TEXT", + "notnull": false, + "pk": false + }, + { + "name": "word_freq_telnet", + "type": "TEXT", + "notnull": false, + "pk": false + }, + { + "name": "word_freq_857", + "type": "TEXT", + "notnull": false, + "pk": false + }, + { + "name": "word_freq_data", + "type": "TEXT", + "notnull": false, + "pk": false + }, + { + "name": "word_freq_415", + "type": "TEXT", + "notnull": false, + "pk": false + }, + { + "name": "word_freq_85", + "type": "TEXT", + "notnull": false, + "pk": false + }, + { + "name": "word_freq_technology", + "type": "TEXT", + "notnull": false, + "pk": false + }, + { + "name": "word_freq_1999", + "type": "TEXT", + "notnull": false, + "pk": false + }, + { + "name": "word_freq_parts", + "type": "TEXT", + "notnull": false, + "pk": false + }, + { + "name": "word_freq_pm", + "type": "TEXT", + "notnull": false, + "pk": false + }, + { + "name": "word_freq_direct", + "type": "TEXT", + "notnull": false, + "pk": false + }, + { + "name": "word_freq_cs", + "type": "TEXT", + "notnull": false, + "pk": false + }, + { + "name": "word_freq_meeting", + "type": "TEXT", + "notnull": false, + "pk": false + }, + { + "name": "word_freq_original", + "type": "TEXT", + "notnull": false, + "pk": false + }, + { + "name": "word_freq_project", + "type": "TEXT", + "notnull": false, + "pk": false + }, + { + "name": "word_freq_re", + "type": "TEXT", + "notnull": false, + "pk": false + }, + { + "name": "word_freq_edu", + "type": "TEXT", + "notnull": false, + "pk": false + }, + { + "name": "word_freq_table", + "type": "TEXT", + "notnull": false, + "pk": false + }, + { + "name": "word_freq_conference", + "type": "TEXT", + "notnull": false, + "pk": false + }, + { + "name": "char_freq_%3B", + "type": "TEXT", + "notnull": false, + "pk": false + }, + { + "name": "char_freq_%28", + "type": "TEXT", + "notnull": false, + "pk": false + }, + { + "name": "char_freq_%5B", + "type": "TEXT", + "notnull": false, + "pk": false + }, + { + "name": "char_freq_%21", + "type": "TEXT", + "notnull": false, + "pk": false + }, + { + "name": "char_freq_%24", + "type": "TEXT", + "notnull": false, + "pk": false + }, + { + "name": "char_freq_%23", + "type": "TEXT", + "notnull": false, + "pk": false + }, + { + "name": "capital_run_length_average", + "type": "TEXT", + "notnull": false, + "pk": false + }, + { + "name": "capital_run_length_longest", + "type": "TEXT", + "notnull": false, + "pk": false + }, + { + "name": "capital_run_length_total", + "type": "TEXT", + "notnull": false, + "pk": false + }, + { + "name": "class", + "type": "TEXT", + "notnull": false, + "pk": false + } + ], + "sample_rows": [ + { + "word_freq_make": "0", + "word_freq_address": "0.64", + "word_freq_all": "0.64", + "word_freq_3d": "0", + "word_freq_our": "0.32", + "word_freq_over": "0", + "word_freq_remove": "0", + "word_freq_internet": "0", + "word_freq_order": "0", + "word_freq_mail": "0", + "word_freq_receive": "0", + "word_freq_will": "0.64", + "word_freq_people": "0", + "word_freq_report": "0", + "word_freq_addresses": "0", + "word_freq_free": "0.32", + "word_freq_business": "0", + "word_freq_email": "1.29", + "word_freq_you": "1.93", + "word_freq_credit": "0", + "word_freq_your": "0.96", + "word_freq_font": "0", + "word_freq_000": "0", + "word_freq_money": "0", + "word_freq_hp": "0", + "word_freq_hpl": "0", + "word_freq_george": "0", + "word_freq_650": "0", + "word_freq_lab": "0", + "word_freq_labs": "0", + "word_freq_telnet": "0", + "word_freq_857": "0", + "word_freq_data": "0", + "word_freq_415": "0", + "word_freq_85": "0", + "word_freq_technology": "0", + "word_freq_1999": "0", + "word_freq_parts": "0", + "word_freq_pm": "0", + "word_freq_direct": "0", + "word_freq_cs": "0", + "word_freq_meeting": "0", + "word_freq_original": "0", + "word_freq_project": "0", + "word_freq_re": "0", + "word_freq_edu": "0", + "word_freq_table": "0", + "word_freq_conference": "0", + "char_freq_%3B": "0", + "char_freq_%28": "0", + "char_freq_%5B": "0", + "char_freq_%21": "0.778", + "char_freq_%24": "0", + "char_freq_%23": "0", + "capital_run_length_average": "3.756", + "capital_run_length_longest": "61", + "capital_run_length_total": "278", + "class": "1" + }, + { + "word_freq_make": "0.21", + "word_freq_address": "0.28", + "word_freq_all": "0.5", + "word_freq_3d": "0", + "word_freq_our": "0.14", + "word_freq_over": "0.28", + "word_freq_remove": "0.21", + "word_freq_internet": "0.07", + "word_freq_order": "0", + "word_freq_mail": "0.94", + "word_freq_receive": "0.21", + "word_freq_will": "0.79", + "word_freq_people": "0.65", + "word_freq_report": "0.21", + "word_freq_addresses": "0.14", + "word_freq_free": "0.14", + "word_freq_business": "0.07", + "word_freq_email": "0.28", + "word_freq_you": "3.47", + "word_freq_credit": "0", + "word_freq_your": "1.59", + "word_freq_font": "0", + "word_freq_000": "0.43", + "word_freq_money": "0.43", + "word_freq_hp": "0", + "word_freq_hpl": "0", + "word_freq_george": "0", + "word_freq_650": "0", + "word_freq_lab": "0", + "word_freq_labs": "0", + "word_freq_telnet": "0", + "word_freq_857": "0", + "word_freq_data": "0", + "word_freq_415": "0", + "word_freq_85": "0", + "word_freq_technology": "0", + "word_freq_1999": "0.07", + "word_freq_parts": "0", + "word_freq_pm": "0", + "word_freq_direct": "0", + "word_freq_cs": "0", + "word_freq_meeting": "0", + "word_freq_original": "0", + "word_freq_project": "0", + "word_freq_re": "0", + "word_freq_edu": "0", + "word_freq_table": "0", + "word_freq_conference": "0", + "char_freq_%3B": "0", + "char_freq_%28": "0.132", + "char_freq_%5B": "0", + "char_freq_%21": "0.372", + "char_freq_%24": "0.18", + "char_freq_%23": "0.048", + "capital_run_length_average": "5.114", + "capital_run_length_longest": "101", + "capital_run_length_total": "1028", + "class": "1" + }, + { + "word_freq_make": "0.06", + "word_freq_address": "0", + "word_freq_all": "0.71", + "word_freq_3d": "0", + "word_freq_our": "1.23", + "word_freq_over": "0.19", + "word_freq_remove": "0.19", + "word_freq_internet": "0.12", + "word_freq_order": "0.64", + "word_freq_mail": "0.25", + "word_freq_receive": "0.38", + "word_freq_will": "0.45", + "word_freq_people": "0.12", + "word_freq_report": "0", + "word_freq_addresses": "1.75", + "word_freq_free": "0.06", + "word_freq_business": "0.06", + "word_freq_email": "1.03", + "word_freq_you": "1.36", + "word_freq_credit": "0.32", + "word_freq_your": "0.51", + "word_freq_font": "0", + "word_freq_000": "1.16", + "word_freq_money": "0.06", + "word_freq_hp": "0", + "word_freq_hpl": "0", + "word_freq_george": "0", + "word_freq_650": "0", + "word_freq_lab": "0", + "word_freq_labs": "0", + "word_freq_telnet": "0", + "word_freq_857": "0", + "word_freq_data": "0", + "word_freq_415": "0", + "word_freq_85": "0", + "word_freq_technology": "0", + "word_freq_1999": "0", + "word_freq_parts": "0", + "word_freq_pm": "0", + "word_freq_direct": "0.06", + "word_freq_cs": "0", + "word_freq_meeting": "0", + "word_freq_original": "0.12", + "word_freq_project": "0", + "word_freq_re": "0.06", + "word_freq_edu": "0.06", + "word_freq_table": "0", + "word_freq_conference": "0", + "char_freq_%3B": "0.01", + "char_freq_%28": "0.143", + "char_freq_%5B": "0", + "char_freq_%21": "0.276", + "char_freq_%24": "0.184", + "char_freq_%23": "0.01", + "capital_run_length_average": "9.821", + "capital_run_length_longest": "485", + "capital_run_length_total": "2259", + "class": "1" + }, + { + "word_freq_make": "0", + "word_freq_address": "0", + "word_freq_all": "0", + "word_freq_3d": "0", + "word_freq_our": "0.63", + "word_freq_over": "0", + "word_freq_remove": "0.31", + "word_freq_internet": "0.63", + "word_freq_order": "0.31", + "word_freq_mail": "0.63", + "word_freq_receive": "0.31", + "word_freq_will": "0.31", + "word_freq_people": "0.31", + "word_freq_report": "0", + "word_freq_addresses": "0", + "word_freq_free": "0.31", + "word_freq_business": "0", + "word_freq_email": "0", + "word_freq_you": "3.18", + "word_freq_credit": "0", + "word_freq_your": "0.31", + "word_freq_font": "0", + "word_freq_000": "0", + "word_freq_money": "0", + "word_freq_hp": "0", + "word_freq_hpl": "0", + "word_freq_george": "0", + "word_freq_650": "0", + "word_freq_lab": "0", + "word_freq_labs": "0", + "word_freq_telnet": "0", + "word_freq_857": "0", + "word_freq_data": "0", + "word_freq_415": "0", + "word_freq_85": "0", + "word_freq_technology": "0", + "word_freq_1999": "0", + "word_freq_parts": "0", + "word_freq_pm": "0", + "word_freq_direct": "0", + "word_freq_cs": "0", + "word_freq_meeting": "0", + "word_freq_original": "0", + "word_freq_project": "0", + "word_freq_re": "0", + "word_freq_edu": "0", + "word_freq_table": "0", + "word_freq_conference": "0", + "char_freq_%3B": "0", + "char_freq_%28": "0.137", + "char_freq_%5B": "0", + "char_freq_%21": "0.137", + "char_freq_%24": "0", + "char_freq_%23": "0", + "capital_run_length_average": "3.537", + "capital_run_length_longest": "40", + "capital_run_length_total": "191", + "class": "1" + }, + { + "word_freq_make": "0", + "word_freq_address": "0", + "word_freq_all": "0", + "word_freq_3d": "0", + "word_freq_our": "0.63", + "word_freq_over": "0", + "word_freq_remove": "0.31", + "word_freq_internet": "0.63", + "word_freq_order": "0.31", + "word_freq_mail": "0.63", + "word_freq_receive": "0.31", + "word_freq_will": "0.31", + "word_freq_people": "0.31", + "word_freq_report": "0", + "word_freq_addresses": "0", + "word_freq_free": "0.31", + "word_freq_business": "0", + "word_freq_email": "0", + "word_freq_you": "3.18", + "word_freq_credit": "0", + "word_freq_your": "0.31", + "word_freq_font": "0", + "word_freq_000": "0", + "word_freq_money": "0", + "word_freq_hp": "0", + "word_freq_hpl": "0", + "word_freq_george": "0", + "word_freq_650": "0", + "word_freq_lab": "0", + "word_freq_labs": "0", + "word_freq_telnet": "0", + "word_freq_857": "0", + "word_freq_data": "0", + "word_freq_415": "0", + "word_freq_85": "0", + "word_freq_technology": "0", + "word_freq_1999": "0", + "word_freq_parts": "0", + "word_freq_pm": "0", + "word_freq_direct": "0", + "word_freq_cs": "0", + "word_freq_meeting": "0", + "word_freq_original": "0", + "word_freq_project": "0", + "word_freq_re": "0", + "word_freq_edu": "0", + "word_freq_table": "0", + "word_freq_conference": "0", + "char_freq_%3B": "0", + "char_freq_%28": "0.135", + "char_freq_%5B": "0", + "char_freq_%21": "0.135", + "char_freq_%24": "0", + "char_freq_%23": "0", + "capital_run_length_average": "3.537", + "capital_run_length_longest": "40", + "capital_run_length_total": "191", + "class": "1" + } + ] +} + +Shortlisted templates: +[ + { + "template_id": "tpl_tail_low_support_group_count_v2", + "template_name": "Low-Support Group Count", + "primary_family": "tail_rarity_structure", + "portability": "yes", + "sql_skeleton": "SELECT\n {group_col},\n COUNT(*) AS support\nFROM {table}\nGROUP BY {group_col}\nORDER BY support ASC, {group_col}\nLIMIT {top_k};", + "required_roles": [ + "group_col" + ] + } +] + +Problem instance: +{ + "dataset_id": "n1", + "question": "Use template Low-Support Group Count to probe tail_set_consistency with semantic role rare_extreme_view. Focus on group_col=class.", + "planned_template_id": "tpl_tail_low_support_group_count_v2", + "bindings": { + "group_col": "class", + "top_k": 11, + "top_n": 5, + "num_tiles": 10, + "percentile_value": 0.95, + "z_threshold": 2.0, + "fraction_threshold": 0.1, + "baseline_multiplier": 1.5, + "baseline_fraction": 0.1, + "min_group_size": 5, + "min_support": 5, + "measure_threshold": 0.0, + "time_grain": "month", + "lookback_rows": 3, + "current_period_start": "'2024-01-01'", + "current_period_end": "'2024-04-01'", + "previous_period_start": "'2023-10-01'", + "previous_period_end": "'2024-01-01'", + "drift_ratio_threshold": 0.8 + }, + "can_vary": [], + "must_fix": [], + "runtime_sql_skeleton": "SELECT\n {group_col},\n COUNT(*) AS support\nFROM {table}\nGROUP BY {group_col}\nORDER BY support ASC, {group_col}\nLIMIT {top_k};" +} + +Repair context: +{} diff --git a/Query/sql/v2/runs/v2_cli_20260502_081223_d/n1/artifacts/v2q_n1_df35e0cdf57f0221/cli/sql_prompt_attempt_2.txt b/Query/sql/v2/runs/v2_cli_20260502_081223_d/n1/artifacts/v2q_n1_df35e0cdf57f0221/cli/sql_prompt_attempt_2.txt new file mode 100644 index 0000000000000000000000000000000000000000..fb1812214b9ceb244d428c10e78c92d4f65028e6 --- /dev/null +++ b/Query/sql/v2/runs/v2_cli_20260502_081223_d/n1/artifacts/v2q_n1_df35e0cdf57f0221/cli/sql_prompt_attempt_2.txt @@ -0,0 +1,790 @@ +You are generating one SQLite SELECT query for a single-table SQL QA task. +Return strict JSON only, with this schema: {"sql": "...", "notes": "..."}. +Rules: +- Use only the provided table and columns. +- Do not write INSERT, UPDATE, DELETE, DROP, ALTER, CREATE, PRAGMA, ATTACH, DETACH, or VACUUM. +- Prefer the planned template and bound roles when provided. +- Add a leading SQL comment exactly like: -- template_id: . +- Generate SQLite-compatible SQL. SQLite does not support PERCENTILE_CONT or STDDEV. +- Quote identifiers with double quotes. +- Return no markdown and no extra prose. + +Dataset context: +Dataset context for SQL QA: +- dataset_id: n1 +- dataset_name: Spambase +- table_name: n1 +- table_layout: single-table dataset (do not assume joins). +- row_semantics: One row is one email represented by engineered token/character frequency and capitalization-run features. +- task_type: classification +- target_column: class +- main_row_count: 4601 +- important_fields: +- word_freq_make: role=feature, type=numeric_sparse_frequency. tags=['condition_candidate', 'measure', 'rare_pattern_candidate'] desc=Frequency feature for token 'make' in an email message. +- word_freq_address: role=feature, type=numeric_sparse_frequency. tags=['condition_candidate', 'measure', 'rare_pattern_candidate'] desc=Frequency feature for token 'address' in an email message. +- word_freq_all: role=feature, type=numeric_sparse_frequency. tags=['condition_candidate', 'measure', 'rare_pattern_candidate'] desc=Frequency feature for token 'all' in an email message. +- word_freq_3d: role=feature, type=numeric_sparse_frequency. tags=['condition_candidate', 'measure', 'rare_pattern_candidate'] desc=Frequency feature for token '3d' in an email message. +- word_freq_our: role=feature, type=numeric_sparse_frequency. tags=['condition_candidate', 'measure', 'rare_pattern_candidate'] desc=Frequency feature for token 'our' in an email message. +- word_freq_over: role=feature, type=numeric_sparse_frequency. tags=['condition_candidate', 'measure', 'rare_pattern_candidate'] desc=Frequency feature for token 'over' in an email message. +- word_freq_remove: role=feature, type=numeric_sparse_frequency. tags=['condition_candidate', 'measure', 'rare_pattern_candidate'] desc=Frequency feature for token 'remove' in an email message. +- word_freq_internet: role=feature, type=numeric_sparse_frequency. tags=['condition_candidate', 'measure', 'rare_pattern_candidate'] desc=Frequency feature for token 'internet' in an email message. +- word_freq_order: role=feature, type=numeric_sparse_frequency. tags=['condition_candidate', 'measure', 'rare_pattern_candidate'] desc=Frequency feature for token 'order' in an email message. +- word_freq_mail: role=feature, type=numeric_sparse_frequency. tags=['condition_candidate', 'measure', 'rare_pattern_candidate'] desc=Frequency feature for token 'mail' in an email message. +- word_freq_receive: role=feature, type=numeric_sparse_frequency. tags=['condition_candidate', 'measure', 'rare_pattern_candidate'] desc=Frequency feature for token 'receive' in an email message. +- word_freq_will: role=feature, type=numeric_sparse_frequency. tags=['condition_candidate', 'measure', 'rare_pattern_candidate'] desc=Frequency feature for token 'will' in an email message. +- word_freq_people: role=feature, type=numeric_sparse_frequency. tags=['condition_candidate', 'measure', 'rare_pattern_candidate'] desc=Frequency feature for token 'people' in an email message. +- word_freq_report: role=feature, type=numeric_sparse_frequency. tags=['condition_candidate', 'measure', 'rare_pattern_candidate'] desc=Frequency feature for token 'report' in an email message. +- word_freq_addresses: role=feature, type=numeric_sparse_frequency. tags=['condition_candidate', 'measure', 'rare_pattern_candidate'] desc=Frequency feature for token 'addresses' in an email message. +- word_freq_free: role=feature, type=numeric_sparse_frequency. tags=['condition_candidate', 'measure', 'rare_pattern_candidate'] desc=Frequency feature for token 'free' in an email message. +- word_freq_business: role=feature, type=numeric_sparse_frequency. tags=['condition_candidate', 'measure', 'rare_pattern_candidate'] desc=Frequency feature for token 'business' in an email message. +- word_freq_email: role=feature, type=numeric_sparse_frequency. tags=['condition_candidate', 'measure', 'rare_pattern_candidate'] desc=Frequency feature for token 'email' in an email message. +- word_freq_you: role=feature, type=numeric_sparse_frequency. tags=['condition_candidate', 'measure', 'rare_pattern_candidate'] desc=Frequency feature for token 'you' in an email message. +- word_freq_credit: role=feature, type=numeric_sparse_frequency. tags=['condition_candidate', 'measure', 'rare_pattern_candidate'] desc=Frequency feature for token 'credit' in an email message. +- word_freq_your: role=feature, type=numeric_sparse_frequency. tags=['condition_candidate', 'measure', 'rare_pattern_candidate'] desc=Frequency feature for token 'your' in an email message. +- word_freq_font: role=feature, type=numeric_sparse_frequency. tags=['condition_candidate', 'measure', 'rare_pattern_candidate'] desc=Frequency feature for token 'font' in an email message. +- word_freq_000: role=feature, type=numeric_sparse_frequency. tags=['condition_candidate', 'measure', 'rare_pattern_candidate'] desc=Frequency feature for token '000' in an email message. +- word_freq_money: role=feature, type=numeric_sparse_frequency. tags=['condition_candidate', 'measure', 'rare_pattern_candidate'] desc=Frequency feature for token 'money' in an email message. +- word_freq_hp: role=feature, type=numeric_sparse_frequency. tags=['condition_candidate', 'measure', 'rare_pattern_candidate'] desc=Frequency feature for token 'hp' in an email message. +- word_freq_hpl: role=feature, type=numeric_sparse_frequency. tags=['condition_candidate', 'measure', 'rare_pattern_candidate'] desc=Frequency feature for token 'hpl' in an email message. +- word_freq_george: role=feature, type=numeric_sparse_frequency. tags=['condition_candidate', 'measure', 'rare_pattern_candidate'] desc=Frequency feature for token 'george' in an email message. +- word_freq_650: role=feature, type=numeric_sparse_frequency. tags=['condition_candidate', 'measure', 'rare_pattern_candidate'] desc=Frequency feature for token '650' in an email message. +- word_freq_lab: role=feature, type=numeric_sparse_frequency. tags=['condition_candidate', 'measure', 'rare_pattern_candidate'] desc=Frequency feature for token 'lab' in an email message. +- word_freq_labs: role=feature, type=numeric_sparse_frequency. tags=['condition_candidate', 'measure', 'rare_pattern_candidate'] desc=Frequency feature for token 'labs' in an email message. +- word_freq_telnet: role=feature, type=numeric_sparse_frequency. tags=['condition_candidate', 'measure', 'rare_pattern_candidate'] desc=Frequency feature for token 'telnet' in an email message. +- word_freq_857: role=feature, type=numeric_sparse_frequency. tags=['condition_candidate', 'measure', 'rare_pattern_candidate'] desc=Frequency feature for token '857' in an email message. +- word_freq_data: role=feature, type=numeric_sparse_frequency. tags=['condition_candidate', 'measure', 'rare_pattern_candidate'] desc=Frequency feature for token 'data' in an email message. +- word_freq_415: role=feature, type=numeric_sparse_frequency. tags=['condition_candidate', 'measure', 'rare_pattern_candidate'] desc=Frequency feature for token '415' in an email message. +- word_freq_85: role=feature, type=numeric_sparse_frequency. tags=['condition_candidate', 'measure', 'rare_pattern_candidate'] desc=Frequency feature for token '85' in an email message. +- word_freq_technology: role=feature, type=numeric_sparse_frequency. tags=['condition_candidate', 'measure', 'rare_pattern_candidate'] desc=Frequency feature for token 'technology' in an email message. +- word_freq_1999: role=feature, type=numeric_sparse_frequency. tags=['condition_candidate', 'measure', 'rare_pattern_candidate'] desc=Frequency feature for token '1999' in an email message. +- word_freq_parts: role=feature, type=numeric_sparse_frequency. tags=['condition_candidate', 'measure', 'rare_pattern_candidate'] desc=Frequency feature for token 'parts' in an email message. +- word_freq_pm: role=feature, type=numeric_sparse_frequency. tags=['condition_candidate', 'measure', 'rare_pattern_candidate'] desc=Frequency feature for token 'pm' in an email message. +- word_freq_direct: role=feature, type=numeric_sparse_frequency. tags=['condition_candidate', 'measure', 'rare_pattern_candidate'] desc=Frequency feature for token 'direct' in an email message. +- word_freq_cs: role=feature, type=numeric_sparse_frequency. tags=['condition_candidate', 'measure', 'rare_pattern_candidate'] desc=Frequency feature for token 'cs' in an email message. +- word_freq_meeting: role=feature, type=numeric_sparse_frequency. tags=['condition_candidate', 'measure', 'rare_pattern_candidate'] desc=Frequency feature for token 'meeting' in an email message. +- word_freq_original: role=feature, type=numeric_sparse_frequency. tags=['condition_candidate', 'measure', 'rare_pattern_candidate'] desc=Frequency feature for token 'original' in an email message. +- word_freq_project: role=feature, type=numeric_sparse_frequency. tags=['condition_candidate', 'measure', 'rare_pattern_candidate'] desc=Frequency feature for token 'project' in an email message. +- word_freq_re: role=feature, type=numeric_sparse_frequency. tags=['condition_candidate', 'measure', 'rare_pattern_candidate'] desc=Frequency feature for token 're' in an email message. +- word_freq_edu: role=feature, type=numeric_sparse_frequency. tags=['condition_candidate', 'measure', 'rare_pattern_candidate'] desc=Frequency feature for token 'edu' in an email message. +- word_freq_table: role=feature, type=numeric_sparse_frequency. tags=['condition_candidate', 'measure', 'rare_pattern_candidate'] desc=Frequency feature for token 'table' in an email message. +- word_freq_conference: role=feature, type=numeric_sparse_frequency. tags=['condition_candidate', 'measure', 'rare_pattern_candidate'] desc=Frequency feature for token 'conference' in an email message. +- char_freq_%3B: role=feature, type=numeric_sparse_frequency. tags=['condition_candidate', 'measure', 'rare_pattern_candidate'] desc=Character frequency feature for symbol ';'. +- char_freq_%28: role=feature, type=numeric_sparse_frequency. tags=['condition_candidate', 'measure', 'rare_pattern_candidate'] desc=Character frequency feature for symbol '('. +- char_freq_%5B: role=feature, type=numeric_sparse_frequency. tags=['condition_candidate', 'measure', 'rare_pattern_candidate'] desc=Character frequency feature for symbol '['. +- char_freq_%21: role=feature, type=numeric_sparse_frequency. tags=['condition_candidate', 'measure', 'rare_pattern_candidate'] desc=Character frequency feature for symbol '!'. +- char_freq_%24: role=feature, type=numeric_sparse_frequency. tags=['condition_candidate', 'measure', 'rare_pattern_candidate'] desc=Character frequency feature for symbol '$'. +- char_freq_%23: role=feature, type=numeric_sparse_frequency. tags=['condition_candidate', 'measure', 'rare_pattern_candidate'] desc=Character frequency feature for symbol '#'. +- capital_run_length_average: role=feature, type=numeric_heavy_tail. tags=['condition_candidate', 'measure', 'rare_pattern_candidate'] desc=Average length of uninterrupted capital-letter runs. +- capital_run_length_longest: role=feature, type=numeric_heavy_tail. tags=['condition_candidate', 'measure', 'rare_pattern_candidate'] desc=Longest uninterrupted capital-letter run. +- capital_run_length_total: role=feature, type=numeric_heavy_tail. tags=['condition_candidate', 'measure', 'rare_pattern_candidate'] desc=Total number of capital letters in runs. +- class: role=target, type=binary_target. tags=['target_candidate'] desc=Binary spam label (1=spam, 0=non-spam). +- useful_field_combinations: [['word_freq_free', 'word_freq_you', 'class'], ['char_freq_%21', 'capital_run_length_longest', 'class'], ['word_freq_remove', 'word_freq_money', 'class']] +- fields_requiring_caution: ['capital_run_length_average', 'capital_run_length_longest', 'capital_run_length_total'] +- source_url: https://www.openml.org/d/44 + +SQLite schema snapshot: +{ + "table_name": "n1", + "quoted_table_name": "\"n1\"", + "row_count": 4601, + "columns": [ + { + "name": "word_freq_make", + "type": "TEXT", + "notnull": false, + "pk": false + }, + { + "name": "word_freq_address", + "type": "TEXT", + "notnull": false, + "pk": false + }, + { + "name": "word_freq_all", + "type": "TEXT", + "notnull": false, + "pk": false + }, + { + "name": "word_freq_3d", + "type": "TEXT", + "notnull": false, + "pk": false + }, + { + "name": "word_freq_our", + "type": "TEXT", + "notnull": false, + "pk": false + }, + { + "name": "word_freq_over", + "type": "TEXT", + "notnull": false, + "pk": false + }, + { + "name": "word_freq_remove", + "type": "TEXT", + "notnull": false, + "pk": false + }, + { + "name": "word_freq_internet", + "type": "TEXT", + "notnull": false, + "pk": false + }, + { + "name": "word_freq_order", + "type": "TEXT", + "notnull": false, + "pk": false + }, + { + "name": "word_freq_mail", + "type": "TEXT", + "notnull": false, + "pk": false + }, + { + "name": "word_freq_receive", + "type": "TEXT", + "notnull": false, + "pk": false + }, + { + "name": "word_freq_will", + "type": "TEXT", + "notnull": false, + "pk": false + }, + { + "name": "word_freq_people", + "type": "TEXT", + "notnull": false, + "pk": false + }, + { + "name": "word_freq_report", + "type": "TEXT", + "notnull": false, + "pk": false + }, + { + "name": "word_freq_addresses", + "type": "TEXT", + "notnull": false, + "pk": false + }, + { + "name": "word_freq_free", + "type": "TEXT", + "notnull": false, + "pk": false + }, + { + "name": "word_freq_business", + "type": "TEXT", + "notnull": false, + "pk": false + }, + { + "name": "word_freq_email", + "type": "TEXT", + "notnull": false, + "pk": false + }, + { + "name": "word_freq_you", + "type": "TEXT", + "notnull": false, + "pk": false + }, + { + "name": "word_freq_credit", + "type": "TEXT", + "notnull": false, + "pk": false + }, + { + "name": "word_freq_your", + "type": "TEXT", + "notnull": false, + "pk": false + }, + { + "name": "word_freq_font", + "type": "TEXT", + "notnull": false, + "pk": false + }, + { + "name": "word_freq_000", + "type": "TEXT", + "notnull": false, + "pk": false + }, + { + "name": "word_freq_money", + "type": "TEXT", + "notnull": false, + "pk": false + }, + { + "name": "word_freq_hp", + "type": "TEXT", + "notnull": false, + "pk": false + }, + { + "name": "word_freq_hpl", + "type": "TEXT", + "notnull": false, + "pk": false + }, + { + "name": "word_freq_george", + "type": "TEXT", + "notnull": false, + "pk": false + }, + { + "name": "word_freq_650", + "type": "TEXT", + "notnull": false, + "pk": false + }, + { + "name": "word_freq_lab", + "type": "TEXT", + "notnull": false, + "pk": false + }, + { + "name": "word_freq_labs", + "type": "TEXT", + "notnull": false, + "pk": false + }, + { + "name": "word_freq_telnet", + "type": "TEXT", + "notnull": false, + "pk": false + }, + { + "name": "word_freq_857", + "type": "TEXT", + "notnull": false, + "pk": false + }, + { + "name": "word_freq_data", + "type": "TEXT", + "notnull": false, + "pk": false + }, + { + "name": "word_freq_415", + "type": "TEXT", + "notnull": false, + "pk": false + }, + { + "name": "word_freq_85", + "type": "TEXT", + "notnull": false, + "pk": false + }, + { + "name": "word_freq_technology", + "type": "TEXT", + "notnull": false, + "pk": false + }, + { + "name": "word_freq_1999", + "type": "TEXT", + "notnull": false, + "pk": false + }, + { + "name": "word_freq_parts", + "type": "TEXT", + "notnull": false, + "pk": false + }, + { + "name": "word_freq_pm", + "type": "TEXT", + "notnull": false, + "pk": false + }, + { + "name": "word_freq_direct", + "type": "TEXT", + "notnull": false, + "pk": false + }, + { + "name": "word_freq_cs", + "type": "TEXT", + "notnull": false, + "pk": false + }, + { + "name": "word_freq_meeting", + "type": "TEXT", + "notnull": false, + "pk": false + }, + { + "name": "word_freq_original", + "type": "TEXT", + "notnull": false, + "pk": false + }, + { + "name": "word_freq_project", + "type": "TEXT", + "notnull": false, + "pk": false + }, + { + "name": "word_freq_re", + "type": "TEXT", + "notnull": false, + "pk": false + }, + { + "name": "word_freq_edu", + "type": "TEXT", + "notnull": false, + "pk": false + }, + { + "name": "word_freq_table", + "type": "TEXT", + "notnull": false, + "pk": false + }, + { + "name": "word_freq_conference", + "type": "TEXT", + "notnull": false, + "pk": false + }, + { + "name": "char_freq_%3B", + "type": "TEXT", + "notnull": false, + "pk": false + }, + { + "name": "char_freq_%28", + "type": "TEXT", + "notnull": false, + "pk": false + }, + { + "name": "char_freq_%5B", + "type": "TEXT", + "notnull": false, + "pk": false + }, + { + "name": "char_freq_%21", + "type": "TEXT", + "notnull": false, + "pk": false + }, + { + "name": "char_freq_%24", + "type": "TEXT", + "notnull": false, + "pk": false + }, + { + "name": "char_freq_%23", + "type": "TEXT", + "notnull": false, + "pk": false + }, + { + "name": "capital_run_length_average", + "type": "TEXT", + "notnull": false, + "pk": false + }, + { + "name": "capital_run_length_longest", + "type": "TEXT", + "notnull": false, + "pk": false + }, + { + "name": "capital_run_length_total", + "type": "TEXT", + "notnull": false, + "pk": false + }, + { + "name": "class", + "type": "TEXT", + "notnull": false, + "pk": false + } + ], + "sample_rows": [ + { + "word_freq_make": "0", + "word_freq_address": "0.64", + "word_freq_all": "0.64", + "word_freq_3d": "0", + "word_freq_our": "0.32", + "word_freq_over": "0", + "word_freq_remove": "0", + "word_freq_internet": "0", + "word_freq_order": "0", + "word_freq_mail": "0", + "word_freq_receive": "0", + "word_freq_will": "0.64", + "word_freq_people": "0", + "word_freq_report": "0", + "word_freq_addresses": "0", + "word_freq_free": "0.32", + "word_freq_business": "0", + "word_freq_email": "1.29", + "word_freq_you": "1.93", + "word_freq_credit": "0", + "word_freq_your": "0.96", + "word_freq_font": "0", + "word_freq_000": "0", + "word_freq_money": "0", + "word_freq_hp": "0", + "word_freq_hpl": "0", + "word_freq_george": "0", + "word_freq_650": "0", + "word_freq_lab": "0", + "word_freq_labs": "0", + "word_freq_telnet": "0", + "word_freq_857": "0", + "word_freq_data": "0", + "word_freq_415": "0", + "word_freq_85": "0", + "word_freq_technology": "0", + "word_freq_1999": "0", + "word_freq_parts": "0", + "word_freq_pm": "0", + "word_freq_direct": "0", + "word_freq_cs": "0", + "word_freq_meeting": "0", + "word_freq_original": "0", + "word_freq_project": "0", + "word_freq_re": "0", + "word_freq_edu": "0", + "word_freq_table": "0", + "word_freq_conference": "0", + "char_freq_%3B": "0", + "char_freq_%28": "0", + "char_freq_%5B": "0", + "char_freq_%21": "0.778", + "char_freq_%24": "0", + "char_freq_%23": "0", + "capital_run_length_average": "3.756", + "capital_run_length_longest": "61", + "capital_run_length_total": "278", + "class": "1" + }, + { + "word_freq_make": "0.21", + "word_freq_address": "0.28", + "word_freq_all": "0.5", + "word_freq_3d": "0", + "word_freq_our": "0.14", + "word_freq_over": "0.28", + "word_freq_remove": "0.21", + "word_freq_internet": "0.07", + "word_freq_order": "0", + "word_freq_mail": "0.94", + "word_freq_receive": "0.21", + "word_freq_will": "0.79", + "word_freq_people": "0.65", + "word_freq_report": "0.21", + "word_freq_addresses": "0.14", + "word_freq_free": "0.14", + "word_freq_business": "0.07", + "word_freq_email": "0.28", + "word_freq_you": "3.47", + "word_freq_credit": "0", + "word_freq_your": "1.59", + "word_freq_font": "0", + "word_freq_000": "0.43", + "word_freq_money": "0.43", + "word_freq_hp": "0", + "word_freq_hpl": "0", + "word_freq_george": "0", + "word_freq_650": "0", + "word_freq_lab": "0", + "word_freq_labs": "0", + "word_freq_telnet": "0", + "word_freq_857": "0", + "word_freq_data": "0", + "word_freq_415": "0", + "word_freq_85": "0", + "word_freq_technology": "0", + "word_freq_1999": "0.07", + "word_freq_parts": "0", + "word_freq_pm": "0", + "word_freq_direct": "0", + "word_freq_cs": "0", + "word_freq_meeting": "0", + "word_freq_original": "0", + "word_freq_project": "0", + "word_freq_re": "0", + "word_freq_edu": "0", + "word_freq_table": "0", + "word_freq_conference": "0", + "char_freq_%3B": "0", + "char_freq_%28": "0.132", + "char_freq_%5B": "0", + "char_freq_%21": "0.372", + "char_freq_%24": "0.18", + "char_freq_%23": "0.048", + "capital_run_length_average": "5.114", + "capital_run_length_longest": "101", + "capital_run_length_total": "1028", + "class": "1" + }, + { + "word_freq_make": "0.06", + "word_freq_address": "0", + "word_freq_all": "0.71", + "word_freq_3d": "0", + "word_freq_our": "1.23", + "word_freq_over": "0.19", + "word_freq_remove": "0.19", + "word_freq_internet": "0.12", + "word_freq_order": "0.64", + "word_freq_mail": "0.25", + "word_freq_receive": "0.38", + "word_freq_will": "0.45", + "word_freq_people": "0.12", + "word_freq_report": "0", + "word_freq_addresses": "1.75", + "word_freq_free": "0.06", + "word_freq_business": "0.06", + "word_freq_email": "1.03", + "word_freq_you": "1.36", + "word_freq_credit": "0.32", + "word_freq_your": "0.51", + "word_freq_font": "0", + "word_freq_000": "1.16", + "word_freq_money": "0.06", + "word_freq_hp": "0", + "word_freq_hpl": "0", + "word_freq_george": "0", + "word_freq_650": "0", + "word_freq_lab": "0", + "word_freq_labs": "0", + "word_freq_telnet": "0", + "word_freq_857": "0", + "word_freq_data": "0", + "word_freq_415": "0", + "word_freq_85": "0", + "word_freq_technology": "0", + "word_freq_1999": "0", + "word_freq_parts": "0", + "word_freq_pm": "0", + "word_freq_direct": "0.06", + "word_freq_cs": "0", + "word_freq_meeting": "0", + "word_freq_original": "0.12", + "word_freq_project": "0", + "word_freq_re": "0.06", + "word_freq_edu": "0.06", + "word_freq_table": "0", + "word_freq_conference": "0", + "char_freq_%3B": "0.01", + "char_freq_%28": "0.143", + "char_freq_%5B": "0", + "char_freq_%21": "0.276", + "char_freq_%24": "0.184", + "char_freq_%23": "0.01", + "capital_run_length_average": "9.821", + "capital_run_length_longest": "485", + "capital_run_length_total": "2259", + "class": "1" + }, + { + "word_freq_make": "0", + "word_freq_address": "0", + "word_freq_all": "0", + "word_freq_3d": "0", + "word_freq_our": "0.63", + "word_freq_over": "0", + "word_freq_remove": "0.31", + "word_freq_internet": "0.63", + "word_freq_order": "0.31", + "word_freq_mail": "0.63", + "word_freq_receive": "0.31", + "word_freq_will": "0.31", + "word_freq_people": "0.31", + "word_freq_report": "0", + "word_freq_addresses": "0", + "word_freq_free": "0.31", + "word_freq_business": "0", + "word_freq_email": "0", + "word_freq_you": "3.18", + "word_freq_credit": "0", + "word_freq_your": "0.31", + "word_freq_font": "0", + "word_freq_000": "0", + "word_freq_money": "0", + "word_freq_hp": "0", + "word_freq_hpl": "0", + "word_freq_george": "0", + "word_freq_650": "0", + "word_freq_lab": "0", + "word_freq_labs": "0", + "word_freq_telnet": "0", + "word_freq_857": "0", + "word_freq_data": "0", + "word_freq_415": "0", + "word_freq_85": "0", + "word_freq_technology": "0", + "word_freq_1999": "0", + "word_freq_parts": "0", + "word_freq_pm": "0", + "word_freq_direct": "0", + "word_freq_cs": "0", + "word_freq_meeting": "0", + "word_freq_original": "0", + "word_freq_project": "0", + "word_freq_re": "0", + "word_freq_edu": "0", + "word_freq_table": "0", + "word_freq_conference": "0", + "char_freq_%3B": "0", + "char_freq_%28": "0.137", + "char_freq_%5B": "0", + "char_freq_%21": "0.137", + "char_freq_%24": "0", + "char_freq_%23": "0", + "capital_run_length_average": "3.537", + "capital_run_length_longest": "40", + "capital_run_length_total": "191", + "class": "1" + }, + { + "word_freq_make": "0", + "word_freq_address": "0", + "word_freq_all": "0", + "word_freq_3d": "0", + "word_freq_our": "0.63", + "word_freq_over": "0", + "word_freq_remove": "0.31", + "word_freq_internet": "0.63", + "word_freq_order": "0.31", + "word_freq_mail": "0.63", + "word_freq_receive": "0.31", + "word_freq_will": "0.31", + "word_freq_people": "0.31", + "word_freq_report": "0", + "word_freq_addresses": "0", + "word_freq_free": "0.31", + "word_freq_business": "0", + "word_freq_email": "0", + "word_freq_you": "3.18", + "word_freq_credit": "0", + "word_freq_your": "0.31", + "word_freq_font": "0", + "word_freq_000": "0", + "word_freq_money": "0", + "word_freq_hp": "0", + "word_freq_hpl": "0", + "word_freq_george": "0", + "word_freq_650": "0", + "word_freq_lab": "0", + "word_freq_labs": "0", + "word_freq_telnet": "0", + "word_freq_857": "0", + "word_freq_data": "0", + "word_freq_415": "0", + "word_freq_85": "0", + "word_freq_technology": "0", + "word_freq_1999": "0", + "word_freq_parts": "0", + "word_freq_pm": "0", + "word_freq_direct": "0", + "word_freq_cs": "0", + "word_freq_meeting": "0", + "word_freq_original": "0", + "word_freq_project": "0", + "word_freq_re": "0", + "word_freq_edu": "0", + "word_freq_table": "0", + "word_freq_conference": "0", + "char_freq_%3B": "0", + "char_freq_%28": "0.135", + "char_freq_%5B": "0", + "char_freq_%21": "0.135", + "char_freq_%24": "0", + "char_freq_%23": "0", + "capital_run_length_average": "3.537", + "capital_run_length_longest": "40", + "capital_run_length_total": "191", + "class": "1" + } + ] +} + +Shortlisted templates: +[ + { + "template_id": "tpl_tail_low_support_group_count_v2", + "template_name": "Low-Support Group Count", + "primary_family": "tail_rarity_structure", + "portability": "yes", + "sql_skeleton": "SELECT\n {group_col},\n COUNT(*) AS support\nFROM {table}\nGROUP BY {group_col}\nORDER BY support ASC, {group_col}\nLIMIT {top_k};", + "required_roles": [ + "group_col" + ] + } +] + +Problem instance: +{ + "dataset_id": "n1", + "question": "Use template Low-Support Group Count to probe tail_set_consistency with semantic role rare_extreme_view. Focus on group_col=class.", + "planned_template_id": "tpl_tail_low_support_group_count_v2", + "bindings": { + "group_col": "class", + "top_k": 11, + "top_n": 5, + "num_tiles": 10, + "percentile_value": 0.95, + "z_threshold": 2.0, + "fraction_threshold": 0.1, + "baseline_multiplier": 1.5, + "baseline_fraction": 0.1, + "min_group_size": 5, + "min_support": 5, + "measure_threshold": 0.0, + "time_grain": "month", + "lookback_rows": 3, + "current_period_start": "'2024-01-01'", + "current_period_end": "'2024-04-01'", + "previous_period_start": "'2023-10-01'", + "previous_period_end": "'2024-01-01'", + "drift_ratio_threshold": 0.8 + }, + "can_vary": [], + "must_fix": [], + "runtime_sql_skeleton": "SELECT\n {group_col},\n COUNT(*) AS support\nFROM {table}\nGROUP BY {group_col}\nORDER BY support ASC, {group_col}\nLIMIT {top_k};" +} + +Repair context: +{} diff --git a/Query/sql/v2/runs/v2_cli_20260502_081223_d/n1/artifacts/v2q_n1_df35e0cdf57f0221/cli/sql_response_attempt_1.raw.txt b/Query/sql/v2/runs/v2_cli_20260502_081223_d/n1/artifacts/v2q_n1_df35e0cdf57f0221/cli/sql_response_attempt_1.raw.txt new file mode 100644 index 0000000000000000000000000000000000000000..575f95333da3e4b7aa67db895ed4fcba55712f19 --- /dev/null +++ b/Query/sql/v2/runs/v2_cli_20260502_081223_d/n1/artifacts/v2q_n1_df35e0cdf57f0221/cli/sql_response_attempt_1.raw.txt @@ -0,0 +1,4 @@ +{"type":"thread.started","thread_id":"019e40fa-6c0d-7b21-9bf0-6175a5685077"} +{"type":"turn.started"} +{"type":"error","message":"Quota exceeded. Check your plan and billing details."} +{"type":"turn.failed","error":{"message":"Quota exceeded. Check your plan and billing details."}} diff --git a/Query/sql/v2/runs/v2_cli_20260502_081223_d/n1/artifacts/v2q_n1_df35e0cdf57f0221/cli/sql_response_attempt_1.txt b/Query/sql/v2/runs/v2_cli_20260502_081223_d/n1/artifacts/v2q_n1_df35e0cdf57f0221/cli/sql_response_attempt_1.txt new file mode 100644 index 0000000000000000000000000000000000000000..123f5a6a94cc5f5258d931950b20b20beea948cc --- /dev/null +++ b/Query/sql/v2/runs/v2_cli_20260502_081223_d/n1/artifacts/v2q_n1_df35e0cdf57f0221/cli/sql_response_attempt_1.txt @@ -0,0 +1,4 @@ +{"type":"thread.started","thread_id":"019e40fa-6c0d-7b21-9bf0-6175a5685077"} +{"type":"turn.started"} +{"type":"error","message":"Quota exceeded. Check your plan and billing details."} +{"type":"turn.failed","error":{"message":"Quota exceeded. Check your plan and billing details."}} \ No newline at end of file diff --git a/Query/sql/v2/runs/v2_cli_20260502_081223_d/n1/artifacts/v2q_n1_df35e0cdf57f0221/cli/sql_response_attempt_2.raw.txt b/Query/sql/v2/runs/v2_cli_20260502_081223_d/n1/artifacts/v2q_n1_df35e0cdf57f0221/cli/sql_response_attempt_2.raw.txt new file mode 100644 index 0000000000000000000000000000000000000000..d7dd32d75e49ea85fa412e812a68345ec020f553 --- /dev/null +++ b/Query/sql/v2/runs/v2_cli_20260502_081223_d/n1/artifacts/v2q_n1_df35e0cdf57f0221/cli/sql_response_attempt_2.raw.txt @@ -0,0 +1,4 @@ +{"type":"thread.started","thread_id":"019e40fa-7ab5-7ac3-9517-92338750e78c"} +{"type":"turn.started"} +{"type":"error","message":"Quota exceeded. Check your plan and billing details."} +{"type":"turn.failed","error":{"message":"Quota exceeded. Check your plan and billing details."}} diff --git a/Query/sql/v2/runs/v2_cli_20260502_081223_d/n1/artifacts/v2q_n1_df35e0cdf57f0221/cli/sql_response_attempt_2.txt b/Query/sql/v2/runs/v2_cli_20260502_081223_d/n1/artifacts/v2q_n1_df35e0cdf57f0221/cli/sql_response_attempt_2.txt new file mode 100644 index 0000000000000000000000000000000000000000..a6979fe4211c7a4266db2d26eeac40311fe3d6ab --- /dev/null +++ b/Query/sql/v2/runs/v2_cli_20260502_081223_d/n1/artifacts/v2q_n1_df35e0cdf57f0221/cli/sql_response_attempt_2.txt @@ -0,0 +1,4 @@ +{"type":"thread.started","thread_id":"019e40fa-7ab5-7ac3-9517-92338750e78c"} +{"type":"turn.started"} +{"type":"error","message":"Quota exceeded. Check your plan and billing details."} +{"type":"turn.failed","error":{"message":"Quota exceeded. Check your plan and billing details."}} \ No newline at end of file diff --git a/Query/sql/v2/runs/v2_cli_20260502_081223_d/n1/artifacts/v2q_n1_df35e0cdf57f0221/cli/sql_stderr_attempt_1.txt b/Query/sql/v2/runs/v2_cli_20260502_081223_d/n1/artifacts/v2q_n1_df35e0cdf57f0221/cli/sql_stderr_attempt_1.txt new file mode 100644 index 0000000000000000000000000000000000000000..e69de29bb2d1d6434b8b29ae775ad8c2e48c5391 diff --git a/Query/sql/v2/runs/v2_cli_20260502_081223_d/n1/artifacts/v2q_n1_df35e0cdf57f0221/cli/sql_stderr_attempt_2.txt b/Query/sql/v2/runs/v2_cli_20260502_081223_d/n1/artifacts/v2q_n1_df35e0cdf57f0221/cli/sql_stderr_attempt_2.txt new file mode 100644 index 0000000000000000000000000000000000000000..e69de29bb2d1d6434b8b29ae775ad8c2e48c5391 diff --git a/Query/sql/v2/runs/v2_cli_20260502_081223_d/n1/artifacts/v2q_n1_e097e72cc97ff981/cli/sql_attempt_1.metadata.json b/Query/sql/v2/runs/v2_cli_20260502_081223_d/n1/artifacts/v2q_n1_e097e72cc97ff981/cli/sql_attempt_1.metadata.json new file mode 100644 index 0000000000000000000000000000000000000000..e81cdcf8720649ad38b3041b8dba1cf0ff1c9b6c --- /dev/null +++ b/Query/sql/v2/runs/v2_cli_20260502_081223_d/n1/artifacts/v2q_n1_e097e72cc97ff981/cli/sql_attempt_1.metadata.json @@ -0,0 +1,43 @@ +{ + "attempt": 1, + "phase": "sql_generation", + "command": "codex exec --skip-git-repo-check --disable plugins --sandbox read-only --cd \"/data/jialinzhang/SQLagent\" -m gpt-5.4 --json -", + "started_at": "2026-05-19T16:02:03.679550+00:00", + "ended_at": "2026-05-19T16:02:06.641817+00:00", + "elapsed_ms": 2962.24, + "returncode": 1, + "prompt_metrics": { + "chars": 29335, + "bytes_utf8": 29335, + "lines": 790, + "estimated_tokens": null + }, + "stdout_metrics": { + "chars": 281, + "bytes_utf8": 281, + "lines": 4, + "estimated_tokens": null + }, + "stderr_metrics": { + "chars": 0, + "bytes_utf8": 0, + "lines": 0, + "estimated_tokens": null + }, + "parsed_output": { + "format": "jsonl_events", + "text_metrics": { + "chars": 280, + "bytes_utf8": 280, + "lines": 4, + "estimated_tokens": null + }, + "usage": {} + }, + "status": "failed", + "error": "AI CLI command failed with exit code 1: ", + "prompt_path": "cli/sql_prompt_attempt_1.txt", + "response_path": "cli/sql_response_attempt_1.txt", + "raw_response_path": "cli/sql_response_attempt_1.raw.txt", + "stderr_path": "cli/sql_stderr_attempt_1.txt" +} \ No newline at end of file diff --git a/Query/sql/v2/runs/v2_cli_20260502_081223_d/n1/artifacts/v2q_n1_e097e72cc97ff981/cli/sql_attempt_2.metadata.json b/Query/sql/v2/runs/v2_cli_20260502_081223_d/n1/artifacts/v2q_n1_e097e72cc97ff981/cli/sql_attempt_2.metadata.json new file mode 100644 index 0000000000000000000000000000000000000000..548942d36b645482383e1cb071b2575d6e14b536 --- /dev/null +++ b/Query/sql/v2/runs/v2_cli_20260502_081223_d/n1/artifacts/v2q_n1_e097e72cc97ff981/cli/sql_attempt_2.metadata.json @@ -0,0 +1,43 @@ +{ + "attempt": 2, + "phase": "sql_generation", + "command": "codex exec --skip-git-repo-check --disable plugins --sandbox read-only --cd \"/data/jialinzhang/SQLagent\" -m gpt-5.4 --json -", + "started_at": "2026-05-19T16:02:07.645130+00:00", + "ended_at": "2026-05-19T16:02:10.928528+00:00", + "elapsed_ms": 3283.35, + "returncode": 1, + "prompt_metrics": { + "chars": 29335, + "bytes_utf8": 29335, + "lines": 790, + "estimated_tokens": null + }, + "stdout_metrics": { + "chars": 281, + "bytes_utf8": 281, + "lines": 4, + "estimated_tokens": null + }, + "stderr_metrics": { + "chars": 0, + "bytes_utf8": 0, + "lines": 0, + "estimated_tokens": null + }, + "parsed_output": { + "format": "jsonl_events", + "text_metrics": { + "chars": 280, + "bytes_utf8": 280, + "lines": 4, + "estimated_tokens": null + }, + "usage": {} + }, + "status": "failed", + "error": "AI CLI command failed with exit code 1: ", + "prompt_path": "cli/sql_prompt_attempt_2.txt", + "response_path": "cli/sql_response_attempt_2.txt", + "raw_response_path": "cli/sql_response_attempt_2.raw.txt", + "stderr_path": "cli/sql_stderr_attempt_2.txt" +} \ No newline at end of file diff --git a/Query/sql/v2/runs/v2_cli_20260502_081223_d/n1/artifacts/v2q_n1_e097e72cc97ff981/cli/sql_prompt_attempt_1.txt b/Query/sql/v2/runs/v2_cli_20260502_081223_d/n1/artifacts/v2q_n1_e097e72cc97ff981/cli/sql_prompt_attempt_1.txt new file mode 100644 index 0000000000000000000000000000000000000000..658a50569c7457a2f9255b001038f2cc2b7d8482 --- /dev/null +++ b/Query/sql/v2/runs/v2_cli_20260502_081223_d/n1/artifacts/v2q_n1_e097e72cc97ff981/cli/sql_prompt_attempt_1.txt @@ -0,0 +1,790 @@ +You are generating one SQLite SELECT query for a single-table SQL QA task. +Return strict JSON only, with this schema: {"sql": "...", "notes": "..."}. +Rules: +- Use only the provided table and columns. +- Do not write INSERT, UPDATE, DELETE, DROP, ALTER, CREATE, PRAGMA, ATTACH, DETACH, or VACUUM. +- Prefer the planned template and bound roles when provided. +- Add a leading SQL comment exactly like: -- template_id: . +- Generate SQLite-compatible SQL. SQLite does not support PERCENTILE_CONT or STDDEV. +- Quote identifiers with double quotes. +- Return no markdown and no extra prose. + +Dataset context: +Dataset context for SQL QA: +- dataset_id: n1 +- dataset_name: Spambase +- table_name: n1 +- table_layout: single-table dataset (do not assume joins). +- row_semantics: One row is one email represented by engineered token/character frequency and capitalization-run features. +- task_type: classification +- target_column: class +- main_row_count: 4601 +- important_fields: +- word_freq_make: role=feature, type=numeric_sparse_frequency. tags=['condition_candidate', 'measure', 'rare_pattern_candidate'] desc=Frequency feature for token 'make' in an email message. +- word_freq_address: role=feature, type=numeric_sparse_frequency. tags=['condition_candidate', 'measure', 'rare_pattern_candidate'] desc=Frequency feature for token 'address' in an email message. +- word_freq_all: role=feature, type=numeric_sparse_frequency. tags=['condition_candidate', 'measure', 'rare_pattern_candidate'] desc=Frequency feature for token 'all' in an email message. +- word_freq_3d: role=feature, type=numeric_sparse_frequency. tags=['condition_candidate', 'measure', 'rare_pattern_candidate'] desc=Frequency feature for token '3d' in an email message. +- word_freq_our: role=feature, type=numeric_sparse_frequency. tags=['condition_candidate', 'measure', 'rare_pattern_candidate'] desc=Frequency feature for token 'our' in an email message. +- word_freq_over: role=feature, type=numeric_sparse_frequency. tags=['condition_candidate', 'measure', 'rare_pattern_candidate'] desc=Frequency feature for token 'over' in an email message. +- word_freq_remove: role=feature, type=numeric_sparse_frequency. tags=['condition_candidate', 'measure', 'rare_pattern_candidate'] desc=Frequency feature for token 'remove' in an email message. +- word_freq_internet: role=feature, type=numeric_sparse_frequency. tags=['condition_candidate', 'measure', 'rare_pattern_candidate'] desc=Frequency feature for token 'internet' in an email message. +- word_freq_order: role=feature, type=numeric_sparse_frequency. tags=['condition_candidate', 'measure', 'rare_pattern_candidate'] desc=Frequency feature for token 'order' in an email message. +- word_freq_mail: role=feature, type=numeric_sparse_frequency. tags=['condition_candidate', 'measure', 'rare_pattern_candidate'] desc=Frequency feature for token 'mail' in an email message. +- word_freq_receive: role=feature, type=numeric_sparse_frequency. tags=['condition_candidate', 'measure', 'rare_pattern_candidate'] desc=Frequency feature for token 'receive' in an email message. +- word_freq_will: role=feature, type=numeric_sparse_frequency. tags=['condition_candidate', 'measure', 'rare_pattern_candidate'] desc=Frequency feature for token 'will' in an email message. +- word_freq_people: role=feature, type=numeric_sparse_frequency. tags=['condition_candidate', 'measure', 'rare_pattern_candidate'] desc=Frequency feature for token 'people' in an email message. +- word_freq_report: role=feature, type=numeric_sparse_frequency. tags=['condition_candidate', 'measure', 'rare_pattern_candidate'] desc=Frequency feature for token 'report' in an email message. +- word_freq_addresses: role=feature, type=numeric_sparse_frequency. tags=['condition_candidate', 'measure', 'rare_pattern_candidate'] desc=Frequency feature for token 'addresses' in an email message. +- word_freq_free: role=feature, type=numeric_sparse_frequency. tags=['condition_candidate', 'measure', 'rare_pattern_candidate'] desc=Frequency feature for token 'free' in an email message. +- word_freq_business: role=feature, type=numeric_sparse_frequency. tags=['condition_candidate', 'measure', 'rare_pattern_candidate'] desc=Frequency feature for token 'business' in an email message. +- word_freq_email: role=feature, type=numeric_sparse_frequency. tags=['condition_candidate', 'measure', 'rare_pattern_candidate'] desc=Frequency feature for token 'email' in an email message. +- word_freq_you: role=feature, type=numeric_sparse_frequency. tags=['condition_candidate', 'measure', 'rare_pattern_candidate'] desc=Frequency feature for token 'you' in an email message. +- word_freq_credit: role=feature, type=numeric_sparse_frequency. tags=['condition_candidate', 'measure', 'rare_pattern_candidate'] desc=Frequency feature for token 'credit' in an email message. +- word_freq_your: role=feature, type=numeric_sparse_frequency. tags=['condition_candidate', 'measure', 'rare_pattern_candidate'] desc=Frequency feature for token 'your' in an email message. +- word_freq_font: role=feature, type=numeric_sparse_frequency. tags=['condition_candidate', 'measure', 'rare_pattern_candidate'] desc=Frequency feature for token 'font' in an email message. +- word_freq_000: role=feature, type=numeric_sparse_frequency. tags=['condition_candidate', 'measure', 'rare_pattern_candidate'] desc=Frequency feature for token '000' in an email message. +- word_freq_money: role=feature, type=numeric_sparse_frequency. tags=['condition_candidate', 'measure', 'rare_pattern_candidate'] desc=Frequency feature for token 'money' in an email message. +- word_freq_hp: role=feature, type=numeric_sparse_frequency. tags=['condition_candidate', 'measure', 'rare_pattern_candidate'] desc=Frequency feature for token 'hp' in an email message. +- word_freq_hpl: role=feature, type=numeric_sparse_frequency. tags=['condition_candidate', 'measure', 'rare_pattern_candidate'] desc=Frequency feature for token 'hpl' in an email message. +- word_freq_george: role=feature, type=numeric_sparse_frequency. tags=['condition_candidate', 'measure', 'rare_pattern_candidate'] desc=Frequency feature for token 'george' in an email message. +- word_freq_650: role=feature, type=numeric_sparse_frequency. tags=['condition_candidate', 'measure', 'rare_pattern_candidate'] desc=Frequency feature for token '650' in an email message. +- word_freq_lab: role=feature, type=numeric_sparse_frequency. tags=['condition_candidate', 'measure', 'rare_pattern_candidate'] desc=Frequency feature for token 'lab' in an email message. +- word_freq_labs: role=feature, type=numeric_sparse_frequency. tags=['condition_candidate', 'measure', 'rare_pattern_candidate'] desc=Frequency feature for token 'labs' in an email message. +- word_freq_telnet: role=feature, type=numeric_sparse_frequency. tags=['condition_candidate', 'measure', 'rare_pattern_candidate'] desc=Frequency feature for token 'telnet' in an email message. +- word_freq_857: role=feature, type=numeric_sparse_frequency. tags=['condition_candidate', 'measure', 'rare_pattern_candidate'] desc=Frequency feature for token '857' in an email message. +- word_freq_data: role=feature, type=numeric_sparse_frequency. tags=['condition_candidate', 'measure', 'rare_pattern_candidate'] desc=Frequency feature for token 'data' in an email message. +- word_freq_415: role=feature, type=numeric_sparse_frequency. tags=['condition_candidate', 'measure', 'rare_pattern_candidate'] desc=Frequency feature for token '415' in an email message. +- word_freq_85: role=feature, type=numeric_sparse_frequency. tags=['condition_candidate', 'measure', 'rare_pattern_candidate'] desc=Frequency feature for token '85' in an email message. +- word_freq_technology: role=feature, type=numeric_sparse_frequency. tags=['condition_candidate', 'measure', 'rare_pattern_candidate'] desc=Frequency feature for token 'technology' in an email message. +- word_freq_1999: role=feature, type=numeric_sparse_frequency. tags=['condition_candidate', 'measure', 'rare_pattern_candidate'] desc=Frequency feature for token '1999' in an email message. +- word_freq_parts: role=feature, type=numeric_sparse_frequency. tags=['condition_candidate', 'measure', 'rare_pattern_candidate'] desc=Frequency feature for token 'parts' in an email message. +- word_freq_pm: role=feature, type=numeric_sparse_frequency. tags=['condition_candidate', 'measure', 'rare_pattern_candidate'] desc=Frequency feature for token 'pm' in an email message. +- word_freq_direct: role=feature, type=numeric_sparse_frequency. tags=['condition_candidate', 'measure', 'rare_pattern_candidate'] desc=Frequency feature for token 'direct' in an email message. +- word_freq_cs: role=feature, type=numeric_sparse_frequency. tags=['condition_candidate', 'measure', 'rare_pattern_candidate'] desc=Frequency feature for token 'cs' in an email message. +- word_freq_meeting: role=feature, type=numeric_sparse_frequency. tags=['condition_candidate', 'measure', 'rare_pattern_candidate'] desc=Frequency feature for token 'meeting' in an email message. +- word_freq_original: role=feature, type=numeric_sparse_frequency. tags=['condition_candidate', 'measure', 'rare_pattern_candidate'] desc=Frequency feature for token 'original' in an email message. +- word_freq_project: role=feature, type=numeric_sparse_frequency. tags=['condition_candidate', 'measure', 'rare_pattern_candidate'] desc=Frequency feature for token 'project' in an email message. +- word_freq_re: role=feature, type=numeric_sparse_frequency. tags=['condition_candidate', 'measure', 'rare_pattern_candidate'] desc=Frequency feature for token 're' in an email message. +- word_freq_edu: role=feature, type=numeric_sparse_frequency. tags=['condition_candidate', 'measure', 'rare_pattern_candidate'] desc=Frequency feature for token 'edu' in an email message. +- word_freq_table: role=feature, type=numeric_sparse_frequency. tags=['condition_candidate', 'measure', 'rare_pattern_candidate'] desc=Frequency feature for token 'table' in an email message. +- word_freq_conference: role=feature, type=numeric_sparse_frequency. tags=['condition_candidate', 'measure', 'rare_pattern_candidate'] desc=Frequency feature for token 'conference' in an email message. +- char_freq_%3B: role=feature, type=numeric_sparse_frequency. tags=['condition_candidate', 'measure', 'rare_pattern_candidate'] desc=Character frequency feature for symbol ';'. +- char_freq_%28: role=feature, type=numeric_sparse_frequency. tags=['condition_candidate', 'measure', 'rare_pattern_candidate'] desc=Character frequency feature for symbol '('. +- char_freq_%5B: role=feature, type=numeric_sparse_frequency. tags=['condition_candidate', 'measure', 'rare_pattern_candidate'] desc=Character frequency feature for symbol '['. +- char_freq_%21: role=feature, type=numeric_sparse_frequency. tags=['condition_candidate', 'measure', 'rare_pattern_candidate'] desc=Character frequency feature for symbol '!'. +- char_freq_%24: role=feature, type=numeric_sparse_frequency. tags=['condition_candidate', 'measure', 'rare_pattern_candidate'] desc=Character frequency feature for symbol '$'. +- char_freq_%23: role=feature, type=numeric_sparse_frequency. tags=['condition_candidate', 'measure', 'rare_pattern_candidate'] desc=Character frequency feature for symbol '#'. +- capital_run_length_average: role=feature, type=numeric_heavy_tail. tags=['condition_candidate', 'measure', 'rare_pattern_candidate'] desc=Average length of uninterrupted capital-letter runs. +- capital_run_length_longest: role=feature, type=numeric_heavy_tail. tags=['condition_candidate', 'measure', 'rare_pattern_candidate'] desc=Longest uninterrupted capital-letter run. +- capital_run_length_total: role=feature, type=numeric_heavy_tail. tags=['condition_candidate', 'measure', 'rare_pattern_candidate'] desc=Total number of capital letters in runs. +- class: role=target, type=binary_target. tags=['target_candidate'] desc=Binary spam label (1=spam, 0=non-spam). +- useful_field_combinations: [['word_freq_free', 'word_freq_you', 'class'], ['char_freq_%21', 'capital_run_length_longest', 'class'], ['word_freq_remove', 'word_freq_money', 'class']] +- fields_requiring_caution: ['capital_run_length_average', 'capital_run_length_longest', 'capital_run_length_total'] +- source_url: https://www.openml.org/d/44 + +SQLite schema snapshot: +{ + "table_name": "n1", + "quoted_table_name": "\"n1\"", + "row_count": 4601, + "columns": [ + { + "name": "word_freq_make", + "type": "TEXT", + "notnull": false, + "pk": false + }, + { + "name": "word_freq_address", + "type": "TEXT", + "notnull": false, + "pk": false + }, + { + "name": "word_freq_all", + "type": "TEXT", + "notnull": false, + "pk": false + }, + { + "name": "word_freq_3d", + "type": "TEXT", + "notnull": false, + "pk": false + }, + { + "name": "word_freq_our", + "type": "TEXT", + "notnull": false, + "pk": false + }, + { + "name": "word_freq_over", + "type": "TEXT", + "notnull": false, + "pk": false + }, + { + "name": "word_freq_remove", + "type": "TEXT", + "notnull": false, + "pk": false + }, + { + "name": "word_freq_internet", + "type": "TEXT", + "notnull": false, + "pk": false + }, + { + "name": "word_freq_order", + "type": "TEXT", + "notnull": false, + "pk": false + }, + { + "name": "word_freq_mail", + "type": "TEXT", + "notnull": false, + "pk": false + }, + { + "name": "word_freq_receive", + "type": "TEXT", + "notnull": false, + "pk": false + }, + { + "name": "word_freq_will", + "type": "TEXT", + "notnull": false, + "pk": false + }, + { + "name": "word_freq_people", + "type": "TEXT", + "notnull": false, + "pk": false + }, + { + "name": "word_freq_report", + "type": "TEXT", + "notnull": false, + "pk": false + }, + { + "name": "word_freq_addresses", + "type": "TEXT", + "notnull": false, + "pk": false + }, + { + "name": "word_freq_free", + "type": "TEXT", + "notnull": false, + "pk": false + }, + { + "name": "word_freq_business", + "type": "TEXT", + "notnull": false, + "pk": false + }, + { + "name": "word_freq_email", + "type": "TEXT", + "notnull": false, + "pk": false + }, + { + "name": "word_freq_you", + "type": "TEXT", + "notnull": false, + "pk": false + }, + { + "name": "word_freq_credit", + "type": "TEXT", + "notnull": false, + "pk": false + }, + { + "name": "word_freq_your", + "type": "TEXT", + "notnull": false, + "pk": false + }, + { + "name": "word_freq_font", + "type": "TEXT", + "notnull": false, + "pk": false + }, + { + "name": "word_freq_000", + "type": "TEXT", + "notnull": false, + "pk": false + }, + { + "name": "word_freq_money", + "type": "TEXT", + "notnull": false, + "pk": false + }, + { + "name": "word_freq_hp", + "type": "TEXT", + "notnull": false, + "pk": false + }, + { + "name": "word_freq_hpl", + "type": "TEXT", + "notnull": false, + "pk": false + }, + { + "name": "word_freq_george", + "type": "TEXT", + "notnull": false, + "pk": false + }, + { + "name": "word_freq_650", + "type": "TEXT", + "notnull": false, + "pk": false + }, + { + "name": "word_freq_lab", + "type": "TEXT", + "notnull": false, + "pk": false + }, + { + "name": "word_freq_labs", + "type": "TEXT", + "notnull": false, + "pk": false + }, + { + "name": "word_freq_telnet", + "type": "TEXT", + "notnull": false, + "pk": false + }, + { + "name": "word_freq_857", + "type": "TEXT", + "notnull": false, + "pk": false + }, + { + "name": "word_freq_data", + "type": "TEXT", + "notnull": false, + "pk": false + }, + { + "name": "word_freq_415", + "type": "TEXT", + "notnull": false, + "pk": false + }, + { + "name": "word_freq_85", + "type": "TEXT", + "notnull": false, + "pk": false + }, + { + "name": "word_freq_technology", + "type": "TEXT", + "notnull": false, + "pk": false + }, + { + "name": "word_freq_1999", + "type": "TEXT", + "notnull": false, + "pk": false + }, + { + "name": "word_freq_parts", + "type": "TEXT", + "notnull": false, + "pk": false + }, + { + "name": "word_freq_pm", + "type": "TEXT", + "notnull": false, + "pk": false + }, + { + "name": "word_freq_direct", + "type": "TEXT", + "notnull": false, + "pk": false + }, + { + "name": "word_freq_cs", + "type": "TEXT", + "notnull": false, + "pk": false + }, + { + "name": "word_freq_meeting", + "type": "TEXT", + "notnull": false, + "pk": false + }, + { + "name": "word_freq_original", + "type": "TEXT", + "notnull": false, + "pk": false + }, + { + "name": "word_freq_project", + "type": "TEXT", + "notnull": false, + "pk": false + }, + { + "name": "word_freq_re", + "type": "TEXT", + "notnull": false, + "pk": false + }, + { + "name": "word_freq_edu", + "type": "TEXT", + "notnull": false, + "pk": false + }, + { + "name": "word_freq_table", + "type": "TEXT", + "notnull": false, + "pk": false + }, + { + "name": "word_freq_conference", + "type": "TEXT", + "notnull": false, + "pk": false + }, + { + "name": "char_freq_%3B", + "type": "TEXT", + "notnull": false, + "pk": false + }, + { + "name": "char_freq_%28", + "type": "TEXT", + "notnull": false, + "pk": false + }, + { + "name": "char_freq_%5B", + "type": "TEXT", + "notnull": false, + "pk": false + }, + { + "name": "char_freq_%21", + "type": "TEXT", + "notnull": false, + "pk": false + }, + { + "name": "char_freq_%24", + "type": "TEXT", + "notnull": false, + "pk": false + }, + { + "name": "char_freq_%23", + "type": "TEXT", + "notnull": false, + "pk": false + }, + { + "name": "capital_run_length_average", + "type": "TEXT", + "notnull": false, + "pk": false + }, + { + "name": "capital_run_length_longest", + "type": "TEXT", + "notnull": false, + "pk": false + }, + { + "name": "capital_run_length_total", + "type": "TEXT", + "notnull": false, + "pk": false + }, + { + "name": "class", + "type": "TEXT", + "notnull": false, + "pk": false + } + ], + "sample_rows": [ + { + "word_freq_make": "0", + "word_freq_address": "0.64", + "word_freq_all": "0.64", + "word_freq_3d": "0", + "word_freq_our": "0.32", + "word_freq_over": "0", + "word_freq_remove": "0", + "word_freq_internet": "0", + "word_freq_order": "0", + "word_freq_mail": "0", + "word_freq_receive": "0", + "word_freq_will": "0.64", + "word_freq_people": "0", + "word_freq_report": "0", + "word_freq_addresses": "0", + "word_freq_free": "0.32", + "word_freq_business": "0", + "word_freq_email": "1.29", + "word_freq_you": "1.93", + "word_freq_credit": "0", + "word_freq_your": "0.96", + "word_freq_font": "0", + "word_freq_000": "0", + "word_freq_money": "0", + "word_freq_hp": "0", + "word_freq_hpl": "0", + "word_freq_george": "0", + "word_freq_650": "0", + "word_freq_lab": "0", + "word_freq_labs": "0", + "word_freq_telnet": "0", + "word_freq_857": "0", + "word_freq_data": "0", + "word_freq_415": "0", + "word_freq_85": "0", + "word_freq_technology": "0", + "word_freq_1999": "0", + "word_freq_parts": "0", + "word_freq_pm": "0", + "word_freq_direct": "0", + "word_freq_cs": "0", + "word_freq_meeting": "0", + "word_freq_original": "0", + "word_freq_project": "0", + "word_freq_re": "0", + "word_freq_edu": "0", + "word_freq_table": "0", + "word_freq_conference": "0", + "char_freq_%3B": "0", + "char_freq_%28": "0", + "char_freq_%5B": "0", + "char_freq_%21": "0.778", + "char_freq_%24": "0", + "char_freq_%23": "0", + "capital_run_length_average": "3.756", + "capital_run_length_longest": "61", + "capital_run_length_total": "278", + "class": "1" + }, + { + "word_freq_make": "0.21", + "word_freq_address": "0.28", + "word_freq_all": "0.5", + "word_freq_3d": "0", + "word_freq_our": "0.14", + "word_freq_over": "0.28", + "word_freq_remove": "0.21", + "word_freq_internet": "0.07", + "word_freq_order": "0", + "word_freq_mail": "0.94", + "word_freq_receive": "0.21", + "word_freq_will": "0.79", + "word_freq_people": "0.65", + "word_freq_report": "0.21", + "word_freq_addresses": "0.14", + "word_freq_free": "0.14", + "word_freq_business": "0.07", + "word_freq_email": "0.28", + "word_freq_you": "3.47", + "word_freq_credit": "0", + "word_freq_your": "1.59", + "word_freq_font": "0", + "word_freq_000": "0.43", + "word_freq_money": "0.43", + "word_freq_hp": "0", + "word_freq_hpl": "0", + "word_freq_george": "0", + "word_freq_650": "0", + "word_freq_lab": "0", + "word_freq_labs": "0", + "word_freq_telnet": "0", + "word_freq_857": "0", + "word_freq_data": "0", + "word_freq_415": "0", + "word_freq_85": "0", + "word_freq_technology": "0", + "word_freq_1999": "0.07", + "word_freq_parts": "0", + "word_freq_pm": "0", + "word_freq_direct": "0", + "word_freq_cs": "0", + "word_freq_meeting": "0", + "word_freq_original": "0", + "word_freq_project": "0", + "word_freq_re": "0", + "word_freq_edu": "0", + "word_freq_table": "0", + "word_freq_conference": "0", + "char_freq_%3B": "0", + "char_freq_%28": "0.132", + "char_freq_%5B": "0", + "char_freq_%21": "0.372", + "char_freq_%24": "0.18", + "char_freq_%23": "0.048", + "capital_run_length_average": "5.114", + "capital_run_length_longest": "101", + "capital_run_length_total": "1028", + "class": "1" + }, + { + "word_freq_make": "0.06", + "word_freq_address": "0", + "word_freq_all": "0.71", + "word_freq_3d": "0", + "word_freq_our": "1.23", + "word_freq_over": "0.19", + "word_freq_remove": "0.19", + "word_freq_internet": "0.12", + "word_freq_order": "0.64", + "word_freq_mail": "0.25", + "word_freq_receive": "0.38", + "word_freq_will": "0.45", + "word_freq_people": "0.12", + "word_freq_report": "0", + "word_freq_addresses": "1.75", + "word_freq_free": "0.06", + "word_freq_business": "0.06", + "word_freq_email": "1.03", + "word_freq_you": "1.36", + "word_freq_credit": "0.32", + "word_freq_your": "0.51", + "word_freq_font": "0", + "word_freq_000": "1.16", + "word_freq_money": "0.06", + "word_freq_hp": "0", + "word_freq_hpl": "0", + "word_freq_george": "0", + "word_freq_650": "0", + "word_freq_lab": "0", + "word_freq_labs": "0", + "word_freq_telnet": "0", + "word_freq_857": "0", + "word_freq_data": "0", + "word_freq_415": "0", + "word_freq_85": "0", + "word_freq_technology": "0", + "word_freq_1999": "0", + "word_freq_parts": "0", + "word_freq_pm": "0", + "word_freq_direct": "0.06", + "word_freq_cs": "0", + "word_freq_meeting": "0", + "word_freq_original": "0.12", + "word_freq_project": "0", + "word_freq_re": "0.06", + "word_freq_edu": "0.06", + "word_freq_table": "0", + "word_freq_conference": "0", + "char_freq_%3B": "0.01", + "char_freq_%28": "0.143", + "char_freq_%5B": "0", + "char_freq_%21": "0.276", + "char_freq_%24": "0.184", + "char_freq_%23": "0.01", + "capital_run_length_average": "9.821", + "capital_run_length_longest": "485", + "capital_run_length_total": "2259", + "class": "1" + }, + { + "word_freq_make": "0", + "word_freq_address": "0", + "word_freq_all": "0", + "word_freq_3d": "0", + "word_freq_our": "0.63", + "word_freq_over": "0", + "word_freq_remove": "0.31", + "word_freq_internet": "0.63", + "word_freq_order": "0.31", + "word_freq_mail": "0.63", + "word_freq_receive": "0.31", + "word_freq_will": "0.31", + "word_freq_people": "0.31", + "word_freq_report": "0", + "word_freq_addresses": "0", + "word_freq_free": "0.31", + "word_freq_business": "0", + "word_freq_email": "0", + "word_freq_you": "3.18", + "word_freq_credit": "0", + "word_freq_your": "0.31", + "word_freq_font": "0", + "word_freq_000": "0", + "word_freq_money": "0", + "word_freq_hp": "0", + "word_freq_hpl": "0", + "word_freq_george": "0", + "word_freq_650": "0", + "word_freq_lab": "0", + "word_freq_labs": "0", + "word_freq_telnet": "0", + "word_freq_857": "0", + "word_freq_data": "0", + "word_freq_415": "0", + "word_freq_85": "0", + "word_freq_technology": "0", + "word_freq_1999": "0", + "word_freq_parts": "0", + "word_freq_pm": "0", + "word_freq_direct": "0", + "word_freq_cs": "0", + "word_freq_meeting": "0", + "word_freq_original": "0", + "word_freq_project": "0", + "word_freq_re": "0", + "word_freq_edu": "0", + "word_freq_table": "0", + "word_freq_conference": "0", + "char_freq_%3B": "0", + "char_freq_%28": "0.137", + "char_freq_%5B": "0", + "char_freq_%21": "0.137", + "char_freq_%24": "0", + "char_freq_%23": "0", + "capital_run_length_average": "3.537", + "capital_run_length_longest": "40", + "capital_run_length_total": "191", + "class": "1" + }, + { + "word_freq_make": "0", + "word_freq_address": "0", + "word_freq_all": "0", + "word_freq_3d": "0", + "word_freq_our": "0.63", + "word_freq_over": "0", + "word_freq_remove": "0.31", + "word_freq_internet": "0.63", + "word_freq_order": "0.31", + "word_freq_mail": "0.63", + "word_freq_receive": "0.31", + "word_freq_will": "0.31", + "word_freq_people": "0.31", + "word_freq_report": "0", + "word_freq_addresses": "0", + "word_freq_free": "0.31", + "word_freq_business": "0", + "word_freq_email": "0", + "word_freq_you": "3.18", + "word_freq_credit": "0", + "word_freq_your": "0.31", + "word_freq_font": "0", + "word_freq_000": "0", + "word_freq_money": "0", + "word_freq_hp": "0", + "word_freq_hpl": "0", + "word_freq_george": "0", + "word_freq_650": "0", + "word_freq_lab": "0", + "word_freq_labs": "0", + "word_freq_telnet": "0", + "word_freq_857": "0", + "word_freq_data": "0", + "word_freq_415": "0", + "word_freq_85": "0", + "word_freq_technology": "0", + "word_freq_1999": "0", + "word_freq_parts": "0", + "word_freq_pm": "0", + "word_freq_direct": "0", + "word_freq_cs": "0", + "word_freq_meeting": "0", + "word_freq_original": "0", + "word_freq_project": "0", + "word_freq_re": "0", + "word_freq_edu": "0", + "word_freq_table": "0", + "word_freq_conference": "0", + "char_freq_%3B": "0", + "char_freq_%28": "0.135", + "char_freq_%5B": "0", + "char_freq_%21": "0.135", + "char_freq_%24": "0", + "char_freq_%23": "0", + "capital_run_length_average": "3.537", + "capital_run_length_longest": "40", + "capital_run_length_total": "191", + "class": "1" + } + ] +} + +Shortlisted templates: +[ + { + "template_id": "tpl_tail_low_support_group_count_v2", + "template_name": "Low-Support Group Count", + "primary_family": "tail_rarity_structure", + "portability": "yes", + "sql_skeleton": "SELECT\n {group_col},\n COUNT(*) AS support\nFROM {table}\nGROUP BY {group_col}\nORDER BY support ASC, {group_col}\nLIMIT {top_k};", + "required_roles": [ + "group_col" + ] + } +] + +Problem instance: +{ + "dataset_id": "n1", + "question": "Use template Low-Support Group Count to probe tail_mass_similarity with semantic role rare_extreme_view. Focus on group_col=class.", + "planned_template_id": "tpl_tail_low_support_group_count_v2", + "bindings": { + "group_col": "class", + "top_k": 16, + "top_n": 5, + "num_tiles": 10, + "percentile_value": 0.95, + "z_threshold": 2.0, + "fraction_threshold": 0.05, + "baseline_multiplier": 1.75, + "baseline_fraction": 0.1, + "min_group_size": 5, + "min_support": 4, + "measure_threshold": 0.0, + "time_grain": "month", + "lookback_rows": 3, + "current_period_start": "'2024-01-01'", + "current_period_end": "'2024-04-01'", + "previous_period_start": "'2023-10-01'", + "previous_period_end": "'2024-01-01'", + "drift_ratio_threshold": 0.8 + }, + "can_vary": [], + "must_fix": [], + "runtime_sql_skeleton": "SELECT\n {group_col},\n COUNT(*) AS support\nFROM {table}\nGROUP BY {group_col}\nORDER BY support ASC, {group_col}\nLIMIT {top_k};" +} + +Repair context: +{} diff --git a/Query/sql/v2/runs/v2_cli_20260502_081223_d/n1/artifacts/v2q_n1_e097e72cc97ff981/cli/sql_prompt_attempt_2.txt b/Query/sql/v2/runs/v2_cli_20260502_081223_d/n1/artifacts/v2q_n1_e097e72cc97ff981/cli/sql_prompt_attempt_2.txt new file mode 100644 index 0000000000000000000000000000000000000000..658a50569c7457a2f9255b001038f2cc2b7d8482 --- /dev/null +++ b/Query/sql/v2/runs/v2_cli_20260502_081223_d/n1/artifacts/v2q_n1_e097e72cc97ff981/cli/sql_prompt_attempt_2.txt @@ -0,0 +1,790 @@ +You are generating one SQLite SELECT query for a single-table SQL QA task. +Return strict JSON only, with this schema: {"sql": "...", "notes": "..."}. +Rules: +- Use only the provided table and columns. +- Do not write INSERT, UPDATE, DELETE, DROP, ALTER, CREATE, PRAGMA, ATTACH, DETACH, or VACUUM. +- Prefer the planned template and bound roles when provided. +- Add a leading SQL comment exactly like: -- template_id: . +- Generate SQLite-compatible SQL. SQLite does not support PERCENTILE_CONT or STDDEV. +- Quote identifiers with double quotes. +- Return no markdown and no extra prose. + +Dataset context: +Dataset context for SQL QA: +- dataset_id: n1 +- dataset_name: Spambase +- table_name: n1 +- table_layout: single-table dataset (do not assume joins). +- row_semantics: One row is one email represented by engineered token/character frequency and capitalization-run features. +- task_type: classification +- target_column: class +- main_row_count: 4601 +- important_fields: +- word_freq_make: role=feature, type=numeric_sparse_frequency. tags=['condition_candidate', 'measure', 'rare_pattern_candidate'] desc=Frequency feature for token 'make' in an email message. +- word_freq_address: role=feature, type=numeric_sparse_frequency. tags=['condition_candidate', 'measure', 'rare_pattern_candidate'] desc=Frequency feature for token 'address' in an email message. +- word_freq_all: role=feature, type=numeric_sparse_frequency. tags=['condition_candidate', 'measure', 'rare_pattern_candidate'] desc=Frequency feature for token 'all' in an email message. +- word_freq_3d: role=feature, type=numeric_sparse_frequency. tags=['condition_candidate', 'measure', 'rare_pattern_candidate'] desc=Frequency feature for token '3d' in an email message. +- word_freq_our: role=feature, type=numeric_sparse_frequency. tags=['condition_candidate', 'measure', 'rare_pattern_candidate'] desc=Frequency feature for token 'our' in an email message. +- word_freq_over: role=feature, type=numeric_sparse_frequency. tags=['condition_candidate', 'measure', 'rare_pattern_candidate'] desc=Frequency feature for token 'over' in an email message. +- word_freq_remove: role=feature, type=numeric_sparse_frequency. tags=['condition_candidate', 'measure', 'rare_pattern_candidate'] desc=Frequency feature for token 'remove' in an email message. +- word_freq_internet: role=feature, type=numeric_sparse_frequency. tags=['condition_candidate', 'measure', 'rare_pattern_candidate'] desc=Frequency feature for token 'internet' in an email message. +- word_freq_order: role=feature, type=numeric_sparse_frequency. tags=['condition_candidate', 'measure', 'rare_pattern_candidate'] desc=Frequency feature for token 'order' in an email message. +- word_freq_mail: role=feature, type=numeric_sparse_frequency. tags=['condition_candidate', 'measure', 'rare_pattern_candidate'] desc=Frequency feature for token 'mail' in an email message. +- word_freq_receive: role=feature, type=numeric_sparse_frequency. tags=['condition_candidate', 'measure', 'rare_pattern_candidate'] desc=Frequency feature for token 'receive' in an email message. +- word_freq_will: role=feature, type=numeric_sparse_frequency. tags=['condition_candidate', 'measure', 'rare_pattern_candidate'] desc=Frequency feature for token 'will' in an email message. +- word_freq_people: role=feature, type=numeric_sparse_frequency. tags=['condition_candidate', 'measure', 'rare_pattern_candidate'] desc=Frequency feature for token 'people' in an email message. +- word_freq_report: role=feature, type=numeric_sparse_frequency. tags=['condition_candidate', 'measure', 'rare_pattern_candidate'] desc=Frequency feature for token 'report' in an email message. +- word_freq_addresses: role=feature, type=numeric_sparse_frequency. tags=['condition_candidate', 'measure', 'rare_pattern_candidate'] desc=Frequency feature for token 'addresses' in an email message. +- word_freq_free: role=feature, type=numeric_sparse_frequency. tags=['condition_candidate', 'measure', 'rare_pattern_candidate'] desc=Frequency feature for token 'free' in an email message. +- word_freq_business: role=feature, type=numeric_sparse_frequency. tags=['condition_candidate', 'measure', 'rare_pattern_candidate'] desc=Frequency feature for token 'business' in an email message. +- word_freq_email: role=feature, type=numeric_sparse_frequency. tags=['condition_candidate', 'measure', 'rare_pattern_candidate'] desc=Frequency feature for token 'email' in an email message. +- word_freq_you: role=feature, type=numeric_sparse_frequency. tags=['condition_candidate', 'measure', 'rare_pattern_candidate'] desc=Frequency feature for token 'you' in an email message. +- word_freq_credit: role=feature, type=numeric_sparse_frequency. tags=['condition_candidate', 'measure', 'rare_pattern_candidate'] desc=Frequency feature for token 'credit' in an email message. +- word_freq_your: role=feature, type=numeric_sparse_frequency. tags=['condition_candidate', 'measure', 'rare_pattern_candidate'] desc=Frequency feature for token 'your' in an email message. +- word_freq_font: role=feature, type=numeric_sparse_frequency. tags=['condition_candidate', 'measure', 'rare_pattern_candidate'] desc=Frequency feature for token 'font' in an email message. +- word_freq_000: role=feature, type=numeric_sparse_frequency. tags=['condition_candidate', 'measure', 'rare_pattern_candidate'] desc=Frequency feature for token '000' in an email message. +- word_freq_money: role=feature, type=numeric_sparse_frequency. tags=['condition_candidate', 'measure', 'rare_pattern_candidate'] desc=Frequency feature for token 'money' in an email message. +- word_freq_hp: role=feature, type=numeric_sparse_frequency. tags=['condition_candidate', 'measure', 'rare_pattern_candidate'] desc=Frequency feature for token 'hp' in an email message. +- word_freq_hpl: role=feature, type=numeric_sparse_frequency. tags=['condition_candidate', 'measure', 'rare_pattern_candidate'] desc=Frequency feature for token 'hpl' in an email message. +- word_freq_george: role=feature, type=numeric_sparse_frequency. tags=['condition_candidate', 'measure', 'rare_pattern_candidate'] desc=Frequency feature for token 'george' in an email message. +- word_freq_650: role=feature, type=numeric_sparse_frequency. tags=['condition_candidate', 'measure', 'rare_pattern_candidate'] desc=Frequency feature for token '650' in an email message. +- word_freq_lab: role=feature, type=numeric_sparse_frequency. tags=['condition_candidate', 'measure', 'rare_pattern_candidate'] desc=Frequency feature for token 'lab' in an email message. +- word_freq_labs: role=feature, type=numeric_sparse_frequency. tags=['condition_candidate', 'measure', 'rare_pattern_candidate'] desc=Frequency feature for token 'labs' in an email message. +- word_freq_telnet: role=feature, type=numeric_sparse_frequency. tags=['condition_candidate', 'measure', 'rare_pattern_candidate'] desc=Frequency feature for token 'telnet' in an email message. +- word_freq_857: role=feature, type=numeric_sparse_frequency. tags=['condition_candidate', 'measure', 'rare_pattern_candidate'] desc=Frequency feature for token '857' in an email message. +- word_freq_data: role=feature, type=numeric_sparse_frequency. tags=['condition_candidate', 'measure', 'rare_pattern_candidate'] desc=Frequency feature for token 'data' in an email message. +- word_freq_415: role=feature, type=numeric_sparse_frequency. tags=['condition_candidate', 'measure', 'rare_pattern_candidate'] desc=Frequency feature for token '415' in an email message. +- word_freq_85: role=feature, type=numeric_sparse_frequency. tags=['condition_candidate', 'measure', 'rare_pattern_candidate'] desc=Frequency feature for token '85' in an email message. +- word_freq_technology: role=feature, type=numeric_sparse_frequency. tags=['condition_candidate', 'measure', 'rare_pattern_candidate'] desc=Frequency feature for token 'technology' in an email message. +- word_freq_1999: role=feature, type=numeric_sparse_frequency. tags=['condition_candidate', 'measure', 'rare_pattern_candidate'] desc=Frequency feature for token '1999' in an email message. +- word_freq_parts: role=feature, type=numeric_sparse_frequency. tags=['condition_candidate', 'measure', 'rare_pattern_candidate'] desc=Frequency feature for token 'parts' in an email message. +- word_freq_pm: role=feature, type=numeric_sparse_frequency. tags=['condition_candidate', 'measure', 'rare_pattern_candidate'] desc=Frequency feature for token 'pm' in an email message. +- word_freq_direct: role=feature, type=numeric_sparse_frequency. tags=['condition_candidate', 'measure', 'rare_pattern_candidate'] desc=Frequency feature for token 'direct' in an email message. +- word_freq_cs: role=feature, type=numeric_sparse_frequency. tags=['condition_candidate', 'measure', 'rare_pattern_candidate'] desc=Frequency feature for token 'cs' in an email message. +- word_freq_meeting: role=feature, type=numeric_sparse_frequency. tags=['condition_candidate', 'measure', 'rare_pattern_candidate'] desc=Frequency feature for token 'meeting' in an email message. +- word_freq_original: role=feature, type=numeric_sparse_frequency. tags=['condition_candidate', 'measure', 'rare_pattern_candidate'] desc=Frequency feature for token 'original' in an email message. +- word_freq_project: role=feature, type=numeric_sparse_frequency. tags=['condition_candidate', 'measure', 'rare_pattern_candidate'] desc=Frequency feature for token 'project' in an email message. +- word_freq_re: role=feature, type=numeric_sparse_frequency. tags=['condition_candidate', 'measure', 'rare_pattern_candidate'] desc=Frequency feature for token 're' in an email message. +- word_freq_edu: role=feature, type=numeric_sparse_frequency. tags=['condition_candidate', 'measure', 'rare_pattern_candidate'] desc=Frequency feature for token 'edu' in an email message. +- word_freq_table: role=feature, type=numeric_sparse_frequency. tags=['condition_candidate', 'measure', 'rare_pattern_candidate'] desc=Frequency feature for token 'table' in an email message. +- word_freq_conference: role=feature, type=numeric_sparse_frequency. tags=['condition_candidate', 'measure', 'rare_pattern_candidate'] desc=Frequency feature for token 'conference' in an email message. +- char_freq_%3B: role=feature, type=numeric_sparse_frequency. tags=['condition_candidate', 'measure', 'rare_pattern_candidate'] desc=Character frequency feature for symbol ';'. +- char_freq_%28: role=feature, type=numeric_sparse_frequency. tags=['condition_candidate', 'measure', 'rare_pattern_candidate'] desc=Character frequency feature for symbol '('. +- char_freq_%5B: role=feature, type=numeric_sparse_frequency. tags=['condition_candidate', 'measure', 'rare_pattern_candidate'] desc=Character frequency feature for symbol '['. +- char_freq_%21: role=feature, type=numeric_sparse_frequency. tags=['condition_candidate', 'measure', 'rare_pattern_candidate'] desc=Character frequency feature for symbol '!'. +- char_freq_%24: role=feature, type=numeric_sparse_frequency. tags=['condition_candidate', 'measure', 'rare_pattern_candidate'] desc=Character frequency feature for symbol '$'. +- char_freq_%23: role=feature, type=numeric_sparse_frequency. tags=['condition_candidate', 'measure', 'rare_pattern_candidate'] desc=Character frequency feature for symbol '#'. +- capital_run_length_average: role=feature, type=numeric_heavy_tail. tags=['condition_candidate', 'measure', 'rare_pattern_candidate'] desc=Average length of uninterrupted capital-letter runs. +- capital_run_length_longest: role=feature, type=numeric_heavy_tail. tags=['condition_candidate', 'measure', 'rare_pattern_candidate'] desc=Longest uninterrupted capital-letter run. +- capital_run_length_total: role=feature, type=numeric_heavy_tail. tags=['condition_candidate', 'measure', 'rare_pattern_candidate'] desc=Total number of capital letters in runs. +- class: role=target, type=binary_target. tags=['target_candidate'] desc=Binary spam label (1=spam, 0=non-spam). +- useful_field_combinations: [['word_freq_free', 'word_freq_you', 'class'], ['char_freq_%21', 'capital_run_length_longest', 'class'], ['word_freq_remove', 'word_freq_money', 'class']] +- fields_requiring_caution: ['capital_run_length_average', 'capital_run_length_longest', 'capital_run_length_total'] +- source_url: https://www.openml.org/d/44 + +SQLite schema snapshot: +{ + "table_name": "n1", + "quoted_table_name": "\"n1\"", + "row_count": 4601, + "columns": [ + { + "name": "word_freq_make", + "type": "TEXT", + "notnull": false, + "pk": false + }, + { + "name": "word_freq_address", + "type": "TEXT", + "notnull": false, + "pk": false + }, + { + "name": "word_freq_all", + "type": "TEXT", + "notnull": false, + "pk": false + }, + { + "name": "word_freq_3d", + "type": "TEXT", + "notnull": false, + "pk": false + }, + { + "name": "word_freq_our", + "type": "TEXT", + "notnull": false, + "pk": false + }, + { + "name": "word_freq_over", + "type": "TEXT", + "notnull": false, + "pk": false + }, + { + "name": "word_freq_remove", + "type": "TEXT", + "notnull": false, + "pk": false + }, + { + "name": "word_freq_internet", + "type": "TEXT", + "notnull": false, + "pk": false + }, + { + "name": "word_freq_order", + "type": "TEXT", + "notnull": false, + "pk": false + }, + { + "name": "word_freq_mail", + "type": "TEXT", + "notnull": false, + "pk": false + }, + { + "name": "word_freq_receive", + "type": "TEXT", + "notnull": false, + "pk": false + }, + { + "name": "word_freq_will", + "type": "TEXT", + "notnull": false, + "pk": false + }, + { + "name": "word_freq_people", + "type": "TEXT", + "notnull": false, + "pk": false + }, + { + "name": "word_freq_report", + "type": "TEXT", + "notnull": false, + "pk": false + }, + { + "name": "word_freq_addresses", + "type": "TEXT", + "notnull": false, + "pk": false + }, + { + "name": "word_freq_free", + "type": "TEXT", + "notnull": false, + "pk": false + }, + { + "name": "word_freq_business", + "type": "TEXT", + "notnull": false, + "pk": false + }, + { + "name": "word_freq_email", + "type": "TEXT", + "notnull": false, + "pk": false + }, + { + "name": "word_freq_you", + "type": "TEXT", + "notnull": false, + "pk": false + }, + { + "name": "word_freq_credit", + "type": "TEXT", + "notnull": false, + "pk": false + }, + { + "name": "word_freq_your", + "type": "TEXT", + "notnull": false, + "pk": false + }, + { + "name": "word_freq_font", + "type": "TEXT", + "notnull": false, + "pk": false + }, + { + "name": "word_freq_000", + "type": "TEXT", + "notnull": false, + "pk": false + }, + { + "name": "word_freq_money", + "type": "TEXT", + "notnull": false, + "pk": false + }, + { + "name": "word_freq_hp", + "type": "TEXT", + "notnull": false, + "pk": false + }, + { + "name": "word_freq_hpl", + "type": "TEXT", + "notnull": false, + "pk": false + }, + { + "name": "word_freq_george", + "type": "TEXT", + "notnull": false, + "pk": false + }, + { + "name": "word_freq_650", + "type": "TEXT", + "notnull": false, + "pk": false + }, + { + "name": "word_freq_lab", + "type": "TEXT", + "notnull": false, + "pk": false + }, + { + "name": "word_freq_labs", + "type": "TEXT", + "notnull": false, + "pk": false + }, + { + "name": "word_freq_telnet", + "type": "TEXT", + "notnull": false, + "pk": false + }, + { + "name": "word_freq_857", + "type": "TEXT", + "notnull": false, + "pk": false + }, + { + "name": "word_freq_data", + "type": "TEXT", + "notnull": false, + "pk": false + }, + { + "name": "word_freq_415", + "type": "TEXT", + "notnull": false, + "pk": false + }, + { + "name": "word_freq_85", + "type": "TEXT", + "notnull": false, + "pk": false + }, + { + "name": "word_freq_technology", + "type": "TEXT", + "notnull": false, + "pk": false + }, + { + "name": "word_freq_1999", + "type": "TEXT", + "notnull": false, + "pk": false + }, + { + "name": "word_freq_parts", + "type": "TEXT", + "notnull": false, + "pk": false + }, + { + "name": "word_freq_pm", + "type": "TEXT", + "notnull": false, + "pk": false + }, + { + "name": "word_freq_direct", + "type": "TEXT", + "notnull": false, + "pk": false + }, + { + "name": "word_freq_cs", + "type": "TEXT", + "notnull": false, + "pk": false + }, + { + "name": "word_freq_meeting", + "type": "TEXT", + "notnull": false, + "pk": false + }, + { + "name": "word_freq_original", + "type": "TEXT", + "notnull": false, + "pk": false + }, + { + "name": "word_freq_project", + "type": "TEXT", + "notnull": false, + "pk": false + }, + { + "name": "word_freq_re", + "type": "TEXT", + "notnull": false, + "pk": false + }, + { + "name": "word_freq_edu", + "type": "TEXT", + "notnull": false, + "pk": false + }, + { + "name": "word_freq_table", + "type": "TEXT", + "notnull": false, + "pk": false + }, + { + "name": "word_freq_conference", + "type": "TEXT", + "notnull": false, + "pk": false + }, + { + "name": "char_freq_%3B", + "type": "TEXT", + "notnull": false, + "pk": false + }, + { + "name": "char_freq_%28", + "type": "TEXT", + "notnull": false, + "pk": false + }, + { + "name": "char_freq_%5B", + "type": "TEXT", + "notnull": false, + "pk": false + }, + { + "name": "char_freq_%21", + "type": "TEXT", + "notnull": false, + "pk": false + }, + { + "name": "char_freq_%24", + "type": "TEXT", + "notnull": false, + "pk": false + }, + { + "name": "char_freq_%23", + "type": "TEXT", + "notnull": false, + "pk": false + }, + { + "name": "capital_run_length_average", + "type": "TEXT", + "notnull": false, + "pk": false + }, + { + "name": "capital_run_length_longest", + "type": "TEXT", + "notnull": false, + "pk": false + }, + { + "name": "capital_run_length_total", + "type": "TEXT", + "notnull": false, + "pk": false + }, + { + "name": "class", + "type": "TEXT", + "notnull": false, + "pk": false + } + ], + "sample_rows": [ + { + "word_freq_make": "0", + "word_freq_address": "0.64", + "word_freq_all": "0.64", + "word_freq_3d": "0", + "word_freq_our": "0.32", + "word_freq_over": "0", + "word_freq_remove": "0", + "word_freq_internet": "0", + "word_freq_order": "0", + "word_freq_mail": "0", + "word_freq_receive": "0", + "word_freq_will": "0.64", + "word_freq_people": "0", + "word_freq_report": "0", + "word_freq_addresses": "0", + "word_freq_free": "0.32", + "word_freq_business": "0", + "word_freq_email": "1.29", + "word_freq_you": "1.93", + "word_freq_credit": "0", + "word_freq_your": "0.96", + "word_freq_font": "0", + "word_freq_000": "0", + "word_freq_money": "0", + "word_freq_hp": "0", + "word_freq_hpl": "0", + "word_freq_george": "0", + "word_freq_650": "0", + "word_freq_lab": "0", + "word_freq_labs": "0", + "word_freq_telnet": "0", + "word_freq_857": "0", + "word_freq_data": "0", + "word_freq_415": "0", + "word_freq_85": "0", + "word_freq_technology": "0", + "word_freq_1999": "0", + "word_freq_parts": "0", + "word_freq_pm": "0", + "word_freq_direct": "0", + "word_freq_cs": "0", + "word_freq_meeting": "0", + "word_freq_original": "0", + "word_freq_project": "0", + "word_freq_re": "0", + "word_freq_edu": "0", + "word_freq_table": "0", + "word_freq_conference": "0", + "char_freq_%3B": "0", + "char_freq_%28": "0", + "char_freq_%5B": "0", + "char_freq_%21": "0.778", + "char_freq_%24": "0", + "char_freq_%23": "0", + "capital_run_length_average": "3.756", + "capital_run_length_longest": "61", + "capital_run_length_total": "278", + "class": "1" + }, + { + "word_freq_make": "0.21", + "word_freq_address": "0.28", + "word_freq_all": "0.5", + "word_freq_3d": "0", + "word_freq_our": "0.14", + "word_freq_over": "0.28", + "word_freq_remove": "0.21", + "word_freq_internet": "0.07", + "word_freq_order": "0", + "word_freq_mail": "0.94", + "word_freq_receive": "0.21", + "word_freq_will": "0.79", + "word_freq_people": "0.65", + "word_freq_report": "0.21", + "word_freq_addresses": "0.14", + "word_freq_free": "0.14", + "word_freq_business": "0.07", + "word_freq_email": "0.28", + "word_freq_you": "3.47", + "word_freq_credit": "0", + "word_freq_your": "1.59", + "word_freq_font": "0", + "word_freq_000": "0.43", + "word_freq_money": "0.43", + "word_freq_hp": "0", + "word_freq_hpl": "0", + "word_freq_george": "0", + "word_freq_650": "0", + "word_freq_lab": "0", + "word_freq_labs": "0", + "word_freq_telnet": "0", + "word_freq_857": "0", + "word_freq_data": "0", + "word_freq_415": "0", + "word_freq_85": "0", + "word_freq_technology": "0", + "word_freq_1999": "0.07", + "word_freq_parts": "0", + "word_freq_pm": "0", + "word_freq_direct": "0", + "word_freq_cs": "0", + "word_freq_meeting": "0", + "word_freq_original": "0", + "word_freq_project": "0", + "word_freq_re": "0", + "word_freq_edu": "0", + "word_freq_table": "0", + "word_freq_conference": "0", + "char_freq_%3B": "0", + "char_freq_%28": "0.132", + "char_freq_%5B": "0", + "char_freq_%21": "0.372", + "char_freq_%24": "0.18", + "char_freq_%23": "0.048", + "capital_run_length_average": "5.114", + "capital_run_length_longest": "101", + "capital_run_length_total": "1028", + "class": "1" + }, + { + "word_freq_make": "0.06", + "word_freq_address": "0", + "word_freq_all": "0.71", + "word_freq_3d": "0", + "word_freq_our": "1.23", + "word_freq_over": "0.19", + "word_freq_remove": "0.19", + "word_freq_internet": "0.12", + "word_freq_order": "0.64", + "word_freq_mail": "0.25", + "word_freq_receive": "0.38", + "word_freq_will": "0.45", + "word_freq_people": "0.12", + "word_freq_report": "0", + "word_freq_addresses": "1.75", + "word_freq_free": "0.06", + "word_freq_business": "0.06", + "word_freq_email": "1.03", + "word_freq_you": "1.36", + "word_freq_credit": "0.32", + "word_freq_your": "0.51", + "word_freq_font": "0", + "word_freq_000": "1.16", + "word_freq_money": "0.06", + "word_freq_hp": "0", + "word_freq_hpl": "0", + "word_freq_george": "0", + "word_freq_650": "0", + "word_freq_lab": "0", + "word_freq_labs": "0", + "word_freq_telnet": "0", + "word_freq_857": "0", + "word_freq_data": "0", + "word_freq_415": "0", + "word_freq_85": "0", + "word_freq_technology": "0", + "word_freq_1999": "0", + "word_freq_parts": "0", + "word_freq_pm": "0", + "word_freq_direct": "0.06", + "word_freq_cs": "0", + "word_freq_meeting": "0", + "word_freq_original": "0.12", + "word_freq_project": "0", + "word_freq_re": "0.06", + "word_freq_edu": "0.06", + "word_freq_table": "0", + "word_freq_conference": "0", + "char_freq_%3B": "0.01", + "char_freq_%28": "0.143", + "char_freq_%5B": "0", + "char_freq_%21": "0.276", + "char_freq_%24": "0.184", + "char_freq_%23": "0.01", + "capital_run_length_average": "9.821", + "capital_run_length_longest": "485", + "capital_run_length_total": "2259", + "class": "1" + }, + { + "word_freq_make": "0", + "word_freq_address": "0", + "word_freq_all": "0", + "word_freq_3d": "0", + "word_freq_our": "0.63", + "word_freq_over": "0", + "word_freq_remove": "0.31", + "word_freq_internet": "0.63", + "word_freq_order": "0.31", + "word_freq_mail": "0.63", + "word_freq_receive": "0.31", + "word_freq_will": "0.31", + "word_freq_people": "0.31", + "word_freq_report": "0", + "word_freq_addresses": "0", + "word_freq_free": "0.31", + "word_freq_business": "0", + "word_freq_email": "0", + "word_freq_you": "3.18", + "word_freq_credit": "0", + "word_freq_your": "0.31", + "word_freq_font": "0", + "word_freq_000": "0", + "word_freq_money": "0", + "word_freq_hp": "0", + "word_freq_hpl": "0", + "word_freq_george": "0", + "word_freq_650": "0", + "word_freq_lab": "0", + "word_freq_labs": "0", + "word_freq_telnet": "0", + "word_freq_857": "0", + "word_freq_data": "0", + "word_freq_415": "0", + "word_freq_85": "0", + "word_freq_technology": "0", + "word_freq_1999": "0", + "word_freq_parts": "0", + "word_freq_pm": "0", + "word_freq_direct": "0", + "word_freq_cs": "0", + "word_freq_meeting": "0", + "word_freq_original": "0", + "word_freq_project": "0", + "word_freq_re": "0", + "word_freq_edu": "0", + "word_freq_table": "0", + "word_freq_conference": "0", + "char_freq_%3B": "0", + "char_freq_%28": "0.137", + "char_freq_%5B": "0", + "char_freq_%21": "0.137", + "char_freq_%24": "0", + "char_freq_%23": "0", + "capital_run_length_average": "3.537", + "capital_run_length_longest": "40", + "capital_run_length_total": "191", + "class": "1" + }, + { + "word_freq_make": "0", + "word_freq_address": "0", + "word_freq_all": "0", + "word_freq_3d": "0", + "word_freq_our": "0.63", + "word_freq_over": "0", + "word_freq_remove": "0.31", + "word_freq_internet": "0.63", + "word_freq_order": "0.31", + "word_freq_mail": "0.63", + "word_freq_receive": "0.31", + "word_freq_will": "0.31", + "word_freq_people": "0.31", + "word_freq_report": "0", + "word_freq_addresses": "0", + "word_freq_free": "0.31", + "word_freq_business": "0", + "word_freq_email": "0", + "word_freq_you": "3.18", + "word_freq_credit": "0", + "word_freq_your": "0.31", + "word_freq_font": "0", + "word_freq_000": "0", + "word_freq_money": "0", + "word_freq_hp": "0", + "word_freq_hpl": "0", + "word_freq_george": "0", + "word_freq_650": "0", + "word_freq_lab": "0", + "word_freq_labs": "0", + "word_freq_telnet": "0", + "word_freq_857": "0", + "word_freq_data": "0", + "word_freq_415": "0", + "word_freq_85": "0", + "word_freq_technology": "0", + "word_freq_1999": "0", + "word_freq_parts": "0", + "word_freq_pm": "0", + "word_freq_direct": "0", + "word_freq_cs": "0", + "word_freq_meeting": "0", + "word_freq_original": "0", + "word_freq_project": "0", + "word_freq_re": "0", + "word_freq_edu": "0", + "word_freq_table": "0", + "word_freq_conference": "0", + "char_freq_%3B": "0", + "char_freq_%28": "0.135", + "char_freq_%5B": "0", + "char_freq_%21": "0.135", + "char_freq_%24": "0", + "char_freq_%23": "0", + "capital_run_length_average": "3.537", + "capital_run_length_longest": "40", + "capital_run_length_total": "191", + "class": "1" + } + ] +} + +Shortlisted templates: +[ + { + "template_id": "tpl_tail_low_support_group_count_v2", + "template_name": "Low-Support Group Count", + "primary_family": "tail_rarity_structure", + "portability": "yes", + "sql_skeleton": "SELECT\n {group_col},\n COUNT(*) AS support\nFROM {table}\nGROUP BY {group_col}\nORDER BY support ASC, {group_col}\nLIMIT {top_k};", + "required_roles": [ + "group_col" + ] + } +] + +Problem instance: +{ + "dataset_id": "n1", + "question": "Use template Low-Support Group Count to probe tail_mass_similarity with semantic role rare_extreme_view. Focus on group_col=class.", + "planned_template_id": "tpl_tail_low_support_group_count_v2", + "bindings": { + "group_col": "class", + "top_k": 16, + "top_n": 5, + "num_tiles": 10, + "percentile_value": 0.95, + "z_threshold": 2.0, + "fraction_threshold": 0.05, + "baseline_multiplier": 1.75, + "baseline_fraction": 0.1, + "min_group_size": 5, + "min_support": 4, + "measure_threshold": 0.0, + "time_grain": "month", + "lookback_rows": 3, + "current_period_start": "'2024-01-01'", + "current_period_end": "'2024-04-01'", + "previous_period_start": "'2023-10-01'", + "previous_period_end": "'2024-01-01'", + "drift_ratio_threshold": 0.8 + }, + "can_vary": [], + "must_fix": [], + "runtime_sql_skeleton": "SELECT\n {group_col},\n COUNT(*) AS support\nFROM {table}\nGROUP BY {group_col}\nORDER BY support ASC, {group_col}\nLIMIT {top_k};" +} + +Repair context: +{} diff --git a/Query/sql/v2/runs/v2_cli_20260502_081223_d/n1/artifacts/v2q_n1_e097e72cc97ff981/cli/sql_response_attempt_1.raw.txt b/Query/sql/v2/runs/v2_cli_20260502_081223_d/n1/artifacts/v2q_n1_e097e72cc97ff981/cli/sql_response_attempt_1.raw.txt new file mode 100644 index 0000000000000000000000000000000000000000..aef6f0e38fd6171202932fef5ff2a0e62c13fbd6 --- /dev/null +++ b/Query/sql/v2/runs/v2_cli_20260502_081223_d/n1/artifacts/v2q_n1_e097e72cc97ff981/cli/sql_response_attempt_1.raw.txt @@ -0,0 +1,4 @@ +{"type":"thread.started","thread_id":"019e40f9-0c4a-77d2-a52d-de319bbcea7d"} +{"type":"turn.started"} +{"type":"error","message":"Quota exceeded. Check your plan and billing details."} +{"type":"turn.failed","error":{"message":"Quota exceeded. Check your plan and billing details."}} diff --git a/Query/sql/v2/runs/v2_cli_20260502_081223_d/n1/artifacts/v2q_n1_e097e72cc97ff981/cli/sql_response_attempt_1.txt b/Query/sql/v2/runs/v2_cli_20260502_081223_d/n1/artifacts/v2q_n1_e097e72cc97ff981/cli/sql_response_attempt_1.txt new file mode 100644 index 0000000000000000000000000000000000000000..c629f1e163eaf609c9966f5348a0f1869ab8a1a4 --- /dev/null +++ b/Query/sql/v2/runs/v2_cli_20260502_081223_d/n1/artifacts/v2q_n1_e097e72cc97ff981/cli/sql_response_attempt_1.txt @@ -0,0 +1,4 @@ +{"type":"thread.started","thread_id":"019e40f9-0c4a-77d2-a52d-de319bbcea7d"} +{"type":"turn.started"} +{"type":"error","message":"Quota exceeded. Check your plan and billing details."} +{"type":"turn.failed","error":{"message":"Quota exceeded. Check your plan and billing details."}} \ No newline at end of file diff --git a/Query/sql/v2/runs/v2_cli_20260502_081223_d/n1/artifacts/v2q_n1_e097e72cc97ff981/cli/sql_response_attempt_2.raw.txt b/Query/sql/v2/runs/v2_cli_20260502_081223_d/n1/artifacts/v2q_n1_e097e72cc97ff981/cli/sql_response_attempt_2.raw.txt new file mode 100644 index 0000000000000000000000000000000000000000..28db493093f5ab05b7cc29c209076bc79b94edb0 --- /dev/null +++ b/Query/sql/v2/runs/v2_cli_20260502_081223_d/n1/artifacts/v2q_n1_e097e72cc97ff981/cli/sql_response_attempt_2.raw.txt @@ -0,0 +1,4 @@ +{"type":"thread.started","thread_id":"019e40f9-1bbb-7852-8ab2-24ba00608dd8"} +{"type":"turn.started"} +{"type":"error","message":"Quota exceeded. Check your plan and billing details."} +{"type":"turn.failed","error":{"message":"Quota exceeded. Check your plan and billing details."}} diff --git a/Query/sql/v2/runs/v2_cli_20260502_081223_d/n1/artifacts/v2q_n1_e097e72cc97ff981/cli/sql_response_attempt_2.txt b/Query/sql/v2/runs/v2_cli_20260502_081223_d/n1/artifacts/v2q_n1_e097e72cc97ff981/cli/sql_response_attempt_2.txt new file mode 100644 index 0000000000000000000000000000000000000000..c0cb7679c566d905e20e9c6697d0dc680b923cec --- /dev/null +++ b/Query/sql/v2/runs/v2_cli_20260502_081223_d/n1/artifacts/v2q_n1_e097e72cc97ff981/cli/sql_response_attempt_2.txt @@ -0,0 +1,4 @@ +{"type":"thread.started","thread_id":"019e40f9-1bbb-7852-8ab2-24ba00608dd8"} +{"type":"turn.started"} +{"type":"error","message":"Quota exceeded. Check your plan and billing details."} +{"type":"turn.failed","error":{"message":"Quota exceeded. Check your plan and billing details."}} \ No newline at end of file diff --git a/Query/sql/v2/runs/v2_cli_20260502_081223_d/n1/artifacts/v2q_n1_e097e72cc97ff981/cli/sql_stderr_attempt_1.txt b/Query/sql/v2/runs/v2_cli_20260502_081223_d/n1/artifacts/v2q_n1_e097e72cc97ff981/cli/sql_stderr_attempt_1.txt new file mode 100644 index 0000000000000000000000000000000000000000..e69de29bb2d1d6434b8b29ae775ad8c2e48c5391 diff --git a/Query/sql/v2/runs/v2_cli_20260502_081223_d/n1/artifacts/v2q_n1_e097e72cc97ff981/cli/sql_stderr_attempt_2.txt b/Query/sql/v2/runs/v2_cli_20260502_081223_d/n1/artifacts/v2q_n1_e097e72cc97ff981/cli/sql_stderr_attempt_2.txt new file mode 100644 index 0000000000000000000000000000000000000000..e69de29bb2d1d6434b8b29ae775ad8c2e48c5391 diff --git a/Query/sql/v2/runs/v2_cli_20260502_081223_d/n1/artifacts/v2q_n1_e681cd9511e354f1/cli/conversation.jsonl b/Query/sql/v2/runs/v2_cli_20260502_081223_d/n1/artifacts/v2q_n1_e681cd9511e354f1/cli/conversation.jsonl new file mode 100644 index 0000000000000000000000000000000000000000..abddec9771f332bcb91cd8bd9942db8234691e42 --- /dev/null +++ b/Query/sql/v2/runs/v2_cli_20260502_081223_d/n1/artifacts/v2q_n1_e681cd9511e354f1/cli/conversation.jsonl @@ -0,0 +1,2 @@ +{"attempt": 1, "phase": "sql_generation", "role": "user", "content_path": "cli/sql_prompt_attempt_1.txt", "metrics": {"chars": 29775, "bytes_utf8": 29775, "lines": 794, "estimated_tokens": null}} +{"attempt": 1, "phase": "sql_generation", "role": "assistant", "content_path": "cli/sql_response_attempt_1.txt", "raw_content_path": "cli/sql_response_attempt_1.raw.txt", "stderr_path": "cli/sql_stderr_attempt_1.txt", "metrics": {"chars": 639, "bytes_utf8": 639, "lines": 1, "estimated_tokens": null}, "usage": {"input_tokens": 20438, "cached_input_tokens": 19840, "output_tokens": 701, "reasoning_output_tokens": 516}} diff --git a/Query/sql/v2/runs/v2_cli_20260502_081223_d/n1/artifacts/v2q_n1_e681cd9511e354f1/cli/session_summary.json b/Query/sql/v2/runs/v2_cli_20260502_081223_d/n1/artifacts/v2q_n1_e681cd9511e354f1/cli/session_summary.json new file mode 100644 index 0000000000000000000000000000000000000000..9aad8464ba601c79894accafd5b2effbb6f3525a --- /dev/null +++ b/Query/sql/v2/runs/v2_cli_20260502_081223_d/n1/artifacts/v2q_n1_e681cd9511e354f1/cli/session_summary.json @@ -0,0 +1,25 @@ +{ + "engine": "v2-cli:codex", + "command": "codex exec --skip-git-repo-check --disable plugins --sandbox read-only --cd \"/data/jialinzhang/SQLagent\" -m gpt-5.4 --json -", + "ai_cli_calls": 1, + "usage_summary": { + "dataset_id": "n1", + "model": "v2-cli:codex", + "run_id": "v2q_n1_e681cd9511e354f1", + "api_calls": 0, + "input_tokens": 20438, + "cached_input_tokens": 19840, + "output_tokens": 701, + "total_tokens": 21139, + "cost_usd": 0.0, + "ai_cli_calls": 1, + "estimated_input_tokens": 0, + "estimated_output_tokens": 0, + "estimated_total_tokens": 0, + "usage_source": "ai_cli_json_usage", + "cli_elapsed_ms_total": 19245.86, + "sql_execution_elapsed_ms_total": 8.13, + "conversation_log_path": "/data/jialinzhang/TabQueryBench/sql_workloads/v2_current/runs_and_launches/runs/v2_cli_20260502_081223_d/n1/artifacts/v2q_n1_e681cd9511e354f1/cli/conversation.jsonl", + "note": "Executed through a local AI CLI with structured usage metadata." + } +} \ No newline at end of file diff --git a/Query/sql/v2/runs/v2_cli_20260502_081223_d/n1/artifacts/v2q_n1_e681cd9511e354f1/cli/sql_attempt_1.metadata.json b/Query/sql/v2/runs/v2_cli_20260502_081223_d/n1/artifacts/v2q_n1_e681cd9511e354f1/cli/sql_attempt_1.metadata.json new file mode 100644 index 0000000000000000000000000000000000000000..1ad6a21c7707f1a1e377ab4c2ba95f3fe959ccd0 --- /dev/null +++ b/Query/sql/v2/runs/v2_cli_20260502_081223_d/n1/artifacts/v2q_n1_e681cd9511e354f1/cli/sql_attempt_1.metadata.json @@ -0,0 +1,45 @@ +{ + "attempt": 1, + "phase": "sql_generation", + "command": "codex exec --skip-git-repo-check --disable plugins --sandbox read-only --cd \"/data/jialinzhang/SQLagent\" -m gpt-5.4 --json -", + "started_at": "2026-05-19T15:37:13.523441+00:00", + "ended_at": "2026-05-19T15:37:32.769345+00:00", + "elapsed_ms": 19245.86, + "prompt_metrics": { + "chars": 29775, + "bytes_utf8": 29775, + "lines": 794, + "estimated_tokens": null + }, + "stdout_metrics": { + "chars": 1021, + "bytes_utf8": 1021, + "lines": 4, + "estimated_tokens": null + }, + "stderr_metrics": { + "chars": 0, + "bytes_utf8": 0, + "lines": 0, + "estimated_tokens": null + }, + "parsed_output": { + "format": "jsonl_events", + "text_metrics": { + "chars": 639, + "bytes_utf8": 639, + "lines": 1, + "estimated_tokens": null + }, + "usage": { + "input_tokens": 20438, + "cached_input_tokens": 19840, + "output_tokens": 701, + "reasoning_output_tokens": 516 + } + }, + "prompt_path": "cli/sql_prompt_attempt_1.txt", + "response_path": "cli/sql_response_attempt_1.txt", + "raw_response_path": "cli/sql_response_attempt_1.raw.txt", + "stderr_path": "cli/sql_stderr_attempt_1.txt" +} \ No newline at end of file diff --git a/Query/sql/v2/runs/v2_cli_20260502_081223_d/n1/artifacts/v2q_n1_e681cd9511e354f1/cli/sql_prompt_attempt_1.txt b/Query/sql/v2/runs/v2_cli_20260502_081223_d/n1/artifacts/v2q_n1_e681cd9511e354f1/cli/sql_prompt_attempt_1.txt new file mode 100644 index 0000000000000000000000000000000000000000..479097af424010388618fc54d0969f45969be60c --- /dev/null +++ b/Query/sql/v2/runs/v2_cli_20260502_081223_d/n1/artifacts/v2q_n1_e681cd9511e354f1/cli/sql_prompt_attempt_1.txt @@ -0,0 +1,794 @@ +You are generating one SQLite SELECT query for a single-table SQL QA task. +Return strict JSON only, with this schema: {"sql": "...", "notes": "..."}. +Rules: +- Use only the provided table and columns. +- Do not write INSERT, UPDATE, DELETE, DROP, ALTER, CREATE, PRAGMA, ATTACH, DETACH, or VACUUM. +- Prefer the planned template and bound roles when provided. +- Add a leading SQL comment exactly like: -- template_id: . +- Generate SQLite-compatible SQL. SQLite does not support PERCENTILE_CONT or STDDEV. +- Quote identifiers with double quotes. +- Return no markdown and no extra prose. + +Dataset context: +Dataset context for SQL QA: +- dataset_id: n1 +- dataset_name: Spambase +- table_name: n1 +- table_layout: single-table dataset (do not assume joins). +- row_semantics: One row is one email represented by engineered token/character frequency and capitalization-run features. +- task_type: classification +- target_column: class +- main_row_count: 4601 +- important_fields: +- word_freq_make: role=feature, type=numeric_sparse_frequency. tags=['condition_candidate', 'measure', 'rare_pattern_candidate'] desc=Frequency feature for token 'make' in an email message. +- word_freq_address: role=feature, type=numeric_sparse_frequency. tags=['condition_candidate', 'measure', 'rare_pattern_candidate'] desc=Frequency feature for token 'address' in an email message. +- word_freq_all: role=feature, type=numeric_sparse_frequency. tags=['condition_candidate', 'measure', 'rare_pattern_candidate'] desc=Frequency feature for token 'all' in an email message. +- word_freq_3d: role=feature, type=numeric_sparse_frequency. tags=['condition_candidate', 'measure', 'rare_pattern_candidate'] desc=Frequency feature for token '3d' in an email message. +- word_freq_our: role=feature, type=numeric_sparse_frequency. tags=['condition_candidate', 'measure', 'rare_pattern_candidate'] desc=Frequency feature for token 'our' in an email message. +- word_freq_over: role=feature, type=numeric_sparse_frequency. tags=['condition_candidate', 'measure', 'rare_pattern_candidate'] desc=Frequency feature for token 'over' in an email message. +- word_freq_remove: role=feature, type=numeric_sparse_frequency. tags=['condition_candidate', 'measure', 'rare_pattern_candidate'] desc=Frequency feature for token 'remove' in an email message. +- word_freq_internet: role=feature, type=numeric_sparse_frequency. tags=['condition_candidate', 'measure', 'rare_pattern_candidate'] desc=Frequency feature for token 'internet' in an email message. +- word_freq_order: role=feature, type=numeric_sparse_frequency. tags=['condition_candidate', 'measure', 'rare_pattern_candidate'] desc=Frequency feature for token 'order' in an email message. +- word_freq_mail: role=feature, type=numeric_sparse_frequency. tags=['condition_candidate', 'measure', 'rare_pattern_candidate'] desc=Frequency feature for token 'mail' in an email message. +- word_freq_receive: role=feature, type=numeric_sparse_frequency. tags=['condition_candidate', 'measure', 'rare_pattern_candidate'] desc=Frequency feature for token 'receive' in an email message. +- word_freq_will: role=feature, type=numeric_sparse_frequency. tags=['condition_candidate', 'measure', 'rare_pattern_candidate'] desc=Frequency feature for token 'will' in an email message. +- word_freq_people: role=feature, type=numeric_sparse_frequency. tags=['condition_candidate', 'measure', 'rare_pattern_candidate'] desc=Frequency feature for token 'people' in an email message. +- word_freq_report: role=feature, type=numeric_sparse_frequency. tags=['condition_candidate', 'measure', 'rare_pattern_candidate'] desc=Frequency feature for token 'report' in an email message. +- word_freq_addresses: role=feature, type=numeric_sparse_frequency. tags=['condition_candidate', 'measure', 'rare_pattern_candidate'] desc=Frequency feature for token 'addresses' in an email message. +- word_freq_free: role=feature, type=numeric_sparse_frequency. tags=['condition_candidate', 'measure', 'rare_pattern_candidate'] desc=Frequency feature for token 'free' in an email message. +- word_freq_business: role=feature, type=numeric_sparse_frequency. tags=['condition_candidate', 'measure', 'rare_pattern_candidate'] desc=Frequency feature for token 'business' in an email message. +- word_freq_email: role=feature, type=numeric_sparse_frequency. tags=['condition_candidate', 'measure', 'rare_pattern_candidate'] desc=Frequency feature for token 'email' in an email message. +- word_freq_you: role=feature, type=numeric_sparse_frequency. tags=['condition_candidate', 'measure', 'rare_pattern_candidate'] desc=Frequency feature for token 'you' in an email message. +- word_freq_credit: role=feature, type=numeric_sparse_frequency. tags=['condition_candidate', 'measure', 'rare_pattern_candidate'] desc=Frequency feature for token 'credit' in an email message. +- word_freq_your: role=feature, type=numeric_sparse_frequency. tags=['condition_candidate', 'measure', 'rare_pattern_candidate'] desc=Frequency feature for token 'your' in an email message. +- word_freq_font: role=feature, type=numeric_sparse_frequency. tags=['condition_candidate', 'measure', 'rare_pattern_candidate'] desc=Frequency feature for token 'font' in an email message. +- word_freq_000: role=feature, type=numeric_sparse_frequency. tags=['condition_candidate', 'measure', 'rare_pattern_candidate'] desc=Frequency feature for token '000' in an email message. +- word_freq_money: role=feature, type=numeric_sparse_frequency. tags=['condition_candidate', 'measure', 'rare_pattern_candidate'] desc=Frequency feature for token 'money' in an email message. +- word_freq_hp: role=feature, type=numeric_sparse_frequency. tags=['condition_candidate', 'measure', 'rare_pattern_candidate'] desc=Frequency feature for token 'hp' in an email message. +- word_freq_hpl: role=feature, type=numeric_sparse_frequency. tags=['condition_candidate', 'measure', 'rare_pattern_candidate'] desc=Frequency feature for token 'hpl' in an email message. +- word_freq_george: role=feature, type=numeric_sparse_frequency. tags=['condition_candidate', 'measure', 'rare_pattern_candidate'] desc=Frequency feature for token 'george' in an email message. +- word_freq_650: role=feature, type=numeric_sparse_frequency. tags=['condition_candidate', 'measure', 'rare_pattern_candidate'] desc=Frequency feature for token '650' in an email message. +- word_freq_lab: role=feature, type=numeric_sparse_frequency. tags=['condition_candidate', 'measure', 'rare_pattern_candidate'] desc=Frequency feature for token 'lab' in an email message. +- word_freq_labs: role=feature, type=numeric_sparse_frequency. tags=['condition_candidate', 'measure', 'rare_pattern_candidate'] desc=Frequency feature for token 'labs' in an email message. +- word_freq_telnet: role=feature, type=numeric_sparse_frequency. tags=['condition_candidate', 'measure', 'rare_pattern_candidate'] desc=Frequency feature for token 'telnet' in an email message. +- word_freq_857: role=feature, type=numeric_sparse_frequency. tags=['condition_candidate', 'measure', 'rare_pattern_candidate'] desc=Frequency feature for token '857' in an email message. +- word_freq_data: role=feature, type=numeric_sparse_frequency. tags=['condition_candidate', 'measure', 'rare_pattern_candidate'] desc=Frequency feature for token 'data' in an email message. +- word_freq_415: role=feature, type=numeric_sparse_frequency. tags=['condition_candidate', 'measure', 'rare_pattern_candidate'] desc=Frequency feature for token '415' in an email message. +- word_freq_85: role=feature, type=numeric_sparse_frequency. tags=['condition_candidate', 'measure', 'rare_pattern_candidate'] desc=Frequency feature for token '85' in an email message. +- word_freq_technology: role=feature, type=numeric_sparse_frequency. tags=['condition_candidate', 'measure', 'rare_pattern_candidate'] desc=Frequency feature for token 'technology' in an email message. +- word_freq_1999: role=feature, type=numeric_sparse_frequency. tags=['condition_candidate', 'measure', 'rare_pattern_candidate'] desc=Frequency feature for token '1999' in an email message. +- word_freq_parts: role=feature, type=numeric_sparse_frequency. tags=['condition_candidate', 'measure', 'rare_pattern_candidate'] desc=Frequency feature for token 'parts' in an email message. +- word_freq_pm: role=feature, type=numeric_sparse_frequency. tags=['condition_candidate', 'measure', 'rare_pattern_candidate'] desc=Frequency feature for token 'pm' in an email message. +- word_freq_direct: role=feature, type=numeric_sparse_frequency. tags=['condition_candidate', 'measure', 'rare_pattern_candidate'] desc=Frequency feature for token 'direct' in an email message. +- word_freq_cs: role=feature, type=numeric_sparse_frequency. tags=['condition_candidate', 'measure', 'rare_pattern_candidate'] desc=Frequency feature for token 'cs' in an email message. +- word_freq_meeting: role=feature, type=numeric_sparse_frequency. tags=['condition_candidate', 'measure', 'rare_pattern_candidate'] desc=Frequency feature for token 'meeting' in an email message. +- word_freq_original: role=feature, type=numeric_sparse_frequency. tags=['condition_candidate', 'measure', 'rare_pattern_candidate'] desc=Frequency feature for token 'original' in an email message. +- word_freq_project: role=feature, type=numeric_sparse_frequency. tags=['condition_candidate', 'measure', 'rare_pattern_candidate'] desc=Frequency feature for token 'project' in an email message. +- word_freq_re: role=feature, type=numeric_sparse_frequency. tags=['condition_candidate', 'measure', 'rare_pattern_candidate'] desc=Frequency feature for token 're' in an email message. +- word_freq_edu: role=feature, type=numeric_sparse_frequency. tags=['condition_candidate', 'measure', 'rare_pattern_candidate'] desc=Frequency feature for token 'edu' in an email message. +- word_freq_table: role=feature, type=numeric_sparse_frequency. tags=['condition_candidate', 'measure', 'rare_pattern_candidate'] desc=Frequency feature for token 'table' in an email message. +- word_freq_conference: role=feature, type=numeric_sparse_frequency. tags=['condition_candidate', 'measure', 'rare_pattern_candidate'] desc=Frequency feature for token 'conference' in an email message. +- char_freq_%3B: role=feature, type=numeric_sparse_frequency. tags=['condition_candidate', 'measure', 'rare_pattern_candidate'] desc=Character frequency feature for symbol ';'. +- char_freq_%28: role=feature, type=numeric_sparse_frequency. tags=['condition_candidate', 'measure', 'rare_pattern_candidate'] desc=Character frequency feature for symbol '('. +- char_freq_%5B: role=feature, type=numeric_sparse_frequency. tags=['condition_candidate', 'measure', 'rare_pattern_candidate'] desc=Character frequency feature for symbol '['. +- char_freq_%21: role=feature, type=numeric_sparse_frequency. tags=['condition_candidate', 'measure', 'rare_pattern_candidate'] desc=Character frequency feature for symbol '!'. +- char_freq_%24: role=feature, type=numeric_sparse_frequency. tags=['condition_candidate', 'measure', 'rare_pattern_candidate'] desc=Character frequency feature for symbol '$'. +- char_freq_%23: role=feature, type=numeric_sparse_frequency. tags=['condition_candidate', 'measure', 'rare_pattern_candidate'] desc=Character frequency feature for symbol '#'. +- capital_run_length_average: role=feature, type=numeric_heavy_tail. tags=['condition_candidate', 'measure', 'rare_pattern_candidate'] desc=Average length of uninterrupted capital-letter runs. +- capital_run_length_longest: role=feature, type=numeric_heavy_tail. tags=['condition_candidate', 'measure', 'rare_pattern_candidate'] desc=Longest uninterrupted capital-letter run. +- capital_run_length_total: role=feature, type=numeric_heavy_tail. tags=['condition_candidate', 'measure', 'rare_pattern_candidate'] desc=Total number of capital letters in runs. +- class: role=target, type=binary_target. tags=['target_candidate'] desc=Binary spam label (1=spam, 0=non-spam). +- useful_field_combinations: [['word_freq_free', 'word_freq_you', 'class'], ['char_freq_%21', 'capital_run_length_longest', 'class'], ['word_freq_remove', 'word_freq_money', 'class']] +- fields_requiring_caution: ['capital_run_length_average', 'capital_run_length_longest', 'capital_run_length_total'] +- source_url: https://www.openml.org/d/44 + +SQLite schema snapshot: +{ + "table_name": "n1", + "quoted_table_name": "\"n1\"", + "row_count": 4601, + "columns": [ + { + "name": "word_freq_make", + "type": "TEXT", + "notnull": false, + "pk": false + }, + { + "name": "word_freq_address", + "type": "TEXT", + "notnull": false, + "pk": false + }, + { + "name": "word_freq_all", + "type": "TEXT", + "notnull": false, + "pk": false + }, + { + "name": "word_freq_3d", + "type": "TEXT", + "notnull": false, + "pk": false + }, + { + "name": "word_freq_our", + "type": "TEXT", + "notnull": false, + "pk": false + }, + { + "name": "word_freq_over", + "type": "TEXT", + "notnull": false, + "pk": false + }, + { + "name": "word_freq_remove", + "type": "TEXT", + "notnull": false, + "pk": false + }, + { + "name": "word_freq_internet", + "type": "TEXT", + "notnull": false, + "pk": false + }, + { + "name": "word_freq_order", + "type": "TEXT", + "notnull": false, + "pk": false + }, + { + "name": "word_freq_mail", + "type": "TEXT", + "notnull": false, + "pk": false + }, + { + "name": "word_freq_receive", + "type": "TEXT", + "notnull": false, + "pk": false + }, + { + "name": "word_freq_will", + "type": "TEXT", + "notnull": false, + "pk": false + }, + { + "name": "word_freq_people", + "type": "TEXT", + "notnull": false, + "pk": false + }, + { + "name": "word_freq_report", + "type": "TEXT", + "notnull": false, + "pk": false + }, + { + "name": "word_freq_addresses", + "type": "TEXT", + "notnull": false, + "pk": false + }, + { + "name": "word_freq_free", + "type": "TEXT", + "notnull": false, + "pk": false + }, + { + "name": "word_freq_business", + "type": "TEXT", + "notnull": false, + "pk": false + }, + { + "name": "word_freq_email", + "type": "TEXT", + "notnull": false, + "pk": false + }, + { + "name": "word_freq_you", + "type": "TEXT", + "notnull": false, + "pk": false + }, + { + "name": "word_freq_credit", + "type": "TEXT", + "notnull": false, + "pk": false + }, + { + "name": "word_freq_your", + "type": "TEXT", + "notnull": false, + "pk": false + }, + { + "name": "word_freq_font", + "type": "TEXT", + "notnull": false, + "pk": false + }, + { + "name": "word_freq_000", + "type": "TEXT", + "notnull": false, + "pk": false + }, + { + "name": "word_freq_money", + "type": "TEXT", + "notnull": false, + "pk": false + }, + { + "name": "word_freq_hp", + "type": "TEXT", + "notnull": false, + "pk": false + }, + { + "name": "word_freq_hpl", + "type": "TEXT", + "notnull": false, + "pk": false + }, + { + "name": "word_freq_george", + "type": "TEXT", + "notnull": false, + "pk": false + }, + { + "name": "word_freq_650", + "type": "TEXT", + "notnull": false, + "pk": false + }, + { + "name": "word_freq_lab", + "type": "TEXT", + "notnull": false, + "pk": false + }, + { + "name": "word_freq_labs", + "type": "TEXT", + "notnull": false, + "pk": false + }, + { + "name": "word_freq_telnet", + "type": "TEXT", + "notnull": false, + "pk": false + }, + { + "name": "word_freq_857", + "type": "TEXT", + "notnull": false, + "pk": false + }, + { + "name": "word_freq_data", + "type": "TEXT", + "notnull": false, + "pk": false + }, + { + "name": "word_freq_415", + "type": "TEXT", + "notnull": false, + "pk": false + }, + { + "name": "word_freq_85", + "type": "TEXT", + "notnull": false, + "pk": false + }, + { + "name": "word_freq_technology", + "type": "TEXT", + "notnull": false, + "pk": false + }, + { + "name": "word_freq_1999", + "type": "TEXT", + "notnull": false, + "pk": false + }, + { + "name": "word_freq_parts", + "type": "TEXT", + "notnull": false, + "pk": false + }, + { + "name": "word_freq_pm", + "type": "TEXT", + "notnull": false, + "pk": false + }, + { + "name": "word_freq_direct", + "type": "TEXT", + "notnull": false, + "pk": false + }, + { + "name": "word_freq_cs", + "type": "TEXT", + "notnull": false, + "pk": false + }, + { + "name": "word_freq_meeting", + "type": "TEXT", + "notnull": false, + "pk": false + }, + { + "name": "word_freq_original", + "type": "TEXT", + "notnull": false, + "pk": false + }, + { + "name": "word_freq_project", + "type": "TEXT", + "notnull": false, + "pk": false + }, + { + "name": "word_freq_re", + "type": "TEXT", + "notnull": false, + "pk": false + }, + { + "name": "word_freq_edu", + "type": "TEXT", + "notnull": false, + "pk": false + }, + { + "name": "word_freq_table", + "type": "TEXT", + "notnull": false, + "pk": false + }, + { + "name": "word_freq_conference", + "type": "TEXT", + "notnull": false, + "pk": false + }, + { + "name": "char_freq_%3B", + "type": "TEXT", + "notnull": false, + "pk": false + }, + { + "name": "char_freq_%28", + "type": "TEXT", + "notnull": false, + "pk": false + }, + { + "name": "char_freq_%5B", + "type": "TEXT", + "notnull": false, + "pk": false + }, + { + "name": "char_freq_%21", + "type": "TEXT", + "notnull": false, + "pk": false + }, + { + "name": "char_freq_%24", + "type": "TEXT", + "notnull": false, + "pk": false + }, + { + "name": "char_freq_%23", + "type": "TEXT", + "notnull": false, + "pk": false + }, + { + "name": "capital_run_length_average", + "type": "TEXT", + "notnull": false, + "pk": false + }, + { + "name": "capital_run_length_longest", + "type": "TEXT", + "notnull": false, + "pk": false + }, + { + "name": "capital_run_length_total", + "type": "TEXT", + "notnull": false, + "pk": false + }, + { + "name": "class", + "type": "TEXT", + "notnull": false, + "pk": false + } + ], + "sample_rows": [ + { + "word_freq_make": "0", + "word_freq_address": "0.64", + "word_freq_all": "0.64", + "word_freq_3d": "0", + "word_freq_our": "0.32", + "word_freq_over": "0", + "word_freq_remove": "0", + "word_freq_internet": "0", + "word_freq_order": "0", + "word_freq_mail": "0", + "word_freq_receive": "0", + "word_freq_will": "0.64", + "word_freq_people": "0", + "word_freq_report": "0", + "word_freq_addresses": "0", + "word_freq_free": "0.32", + "word_freq_business": "0", + "word_freq_email": "1.29", + "word_freq_you": "1.93", + "word_freq_credit": "0", + "word_freq_your": "0.96", + "word_freq_font": "0", + "word_freq_000": "0", + "word_freq_money": "0", + "word_freq_hp": "0", + "word_freq_hpl": "0", + "word_freq_george": "0", + "word_freq_650": "0", + "word_freq_lab": "0", + "word_freq_labs": "0", + "word_freq_telnet": "0", + "word_freq_857": "0", + "word_freq_data": "0", + "word_freq_415": "0", + "word_freq_85": "0", + "word_freq_technology": "0", + "word_freq_1999": "0", + "word_freq_parts": "0", + "word_freq_pm": "0", + "word_freq_direct": "0", + "word_freq_cs": "0", + "word_freq_meeting": "0", + "word_freq_original": "0", + "word_freq_project": "0", + "word_freq_re": "0", + "word_freq_edu": "0", + "word_freq_table": "0", + "word_freq_conference": "0", + "char_freq_%3B": "0", + "char_freq_%28": "0", + "char_freq_%5B": "0", + "char_freq_%21": "0.778", + "char_freq_%24": "0", + "char_freq_%23": "0", + "capital_run_length_average": "3.756", + "capital_run_length_longest": "61", + "capital_run_length_total": "278", + "class": "1" + }, + { + "word_freq_make": "0.21", + "word_freq_address": "0.28", + "word_freq_all": "0.5", + "word_freq_3d": "0", + "word_freq_our": "0.14", + "word_freq_over": "0.28", + "word_freq_remove": "0.21", + "word_freq_internet": "0.07", + "word_freq_order": "0", + "word_freq_mail": "0.94", + "word_freq_receive": "0.21", + "word_freq_will": "0.79", + "word_freq_people": "0.65", + "word_freq_report": "0.21", + "word_freq_addresses": "0.14", + "word_freq_free": "0.14", + "word_freq_business": "0.07", + "word_freq_email": "0.28", + "word_freq_you": "3.47", + "word_freq_credit": "0", + "word_freq_your": "1.59", + "word_freq_font": "0", + "word_freq_000": "0.43", + "word_freq_money": "0.43", + "word_freq_hp": "0", + "word_freq_hpl": "0", + "word_freq_george": "0", + "word_freq_650": "0", + "word_freq_lab": "0", + "word_freq_labs": "0", + "word_freq_telnet": "0", + "word_freq_857": "0", + "word_freq_data": "0", + "word_freq_415": "0", + "word_freq_85": "0", + "word_freq_technology": "0", + "word_freq_1999": "0.07", + "word_freq_parts": "0", + "word_freq_pm": "0", + "word_freq_direct": "0", + "word_freq_cs": "0", + "word_freq_meeting": "0", + "word_freq_original": "0", + "word_freq_project": "0", + "word_freq_re": "0", + "word_freq_edu": "0", + "word_freq_table": "0", + "word_freq_conference": "0", + "char_freq_%3B": "0", + "char_freq_%28": "0.132", + "char_freq_%5B": "0", + "char_freq_%21": "0.372", + "char_freq_%24": "0.18", + "char_freq_%23": "0.048", + "capital_run_length_average": "5.114", + "capital_run_length_longest": "101", + "capital_run_length_total": "1028", + "class": "1" + }, + { + "word_freq_make": "0.06", + "word_freq_address": "0", + "word_freq_all": "0.71", + "word_freq_3d": "0", + "word_freq_our": "1.23", + "word_freq_over": "0.19", + "word_freq_remove": "0.19", + "word_freq_internet": "0.12", + "word_freq_order": "0.64", + "word_freq_mail": "0.25", + "word_freq_receive": "0.38", + "word_freq_will": "0.45", + "word_freq_people": "0.12", + "word_freq_report": "0", + "word_freq_addresses": "1.75", + "word_freq_free": "0.06", + "word_freq_business": "0.06", + "word_freq_email": "1.03", + "word_freq_you": "1.36", + "word_freq_credit": "0.32", + "word_freq_your": "0.51", + "word_freq_font": "0", + "word_freq_000": "1.16", + "word_freq_money": "0.06", + "word_freq_hp": "0", + "word_freq_hpl": "0", + "word_freq_george": "0", + "word_freq_650": "0", + "word_freq_lab": "0", + "word_freq_labs": "0", + "word_freq_telnet": "0", + "word_freq_857": "0", + "word_freq_data": "0", + "word_freq_415": "0", + "word_freq_85": "0", + "word_freq_technology": "0", + "word_freq_1999": "0", + "word_freq_parts": "0", + "word_freq_pm": "0", + "word_freq_direct": "0.06", + "word_freq_cs": "0", + "word_freq_meeting": "0", + "word_freq_original": "0.12", + "word_freq_project": "0", + "word_freq_re": "0.06", + "word_freq_edu": "0.06", + "word_freq_table": "0", + "word_freq_conference": "0", + "char_freq_%3B": "0.01", + "char_freq_%28": "0.143", + "char_freq_%5B": "0", + "char_freq_%21": "0.276", + "char_freq_%24": "0.184", + "char_freq_%23": "0.01", + "capital_run_length_average": "9.821", + "capital_run_length_longest": "485", + "capital_run_length_total": "2259", + "class": "1" + }, + { + "word_freq_make": "0", + "word_freq_address": "0", + "word_freq_all": "0", + "word_freq_3d": "0", + "word_freq_our": "0.63", + "word_freq_over": "0", + "word_freq_remove": "0.31", + "word_freq_internet": "0.63", + "word_freq_order": "0.31", + "word_freq_mail": "0.63", + "word_freq_receive": "0.31", + "word_freq_will": "0.31", + "word_freq_people": "0.31", + "word_freq_report": "0", + "word_freq_addresses": "0", + "word_freq_free": "0.31", + "word_freq_business": "0", + "word_freq_email": "0", + "word_freq_you": "3.18", + "word_freq_credit": "0", + "word_freq_your": "0.31", + "word_freq_font": "0", + "word_freq_000": "0", + "word_freq_money": "0", + "word_freq_hp": "0", + "word_freq_hpl": "0", + "word_freq_george": "0", + "word_freq_650": "0", + "word_freq_lab": "0", + "word_freq_labs": "0", + "word_freq_telnet": "0", + "word_freq_857": "0", + "word_freq_data": "0", + "word_freq_415": "0", + "word_freq_85": "0", + "word_freq_technology": "0", + "word_freq_1999": "0", + "word_freq_parts": "0", + "word_freq_pm": "0", + "word_freq_direct": "0", + "word_freq_cs": "0", + "word_freq_meeting": "0", + "word_freq_original": "0", + "word_freq_project": "0", + "word_freq_re": "0", + "word_freq_edu": "0", + "word_freq_table": "0", + "word_freq_conference": "0", + "char_freq_%3B": "0", + "char_freq_%28": "0.137", + "char_freq_%5B": "0", + "char_freq_%21": "0.137", + "char_freq_%24": "0", + "char_freq_%23": "0", + "capital_run_length_average": "3.537", + "capital_run_length_longest": "40", + "capital_run_length_total": "191", + "class": "1" + }, + { + "word_freq_make": "0", + "word_freq_address": "0", + "word_freq_all": "0", + "word_freq_3d": "0", + "word_freq_our": "0.63", + "word_freq_over": "0", + "word_freq_remove": "0.31", + "word_freq_internet": "0.63", + "word_freq_order": "0.31", + "word_freq_mail": "0.63", + "word_freq_receive": "0.31", + "word_freq_will": "0.31", + "word_freq_people": "0.31", + "word_freq_report": "0", + "word_freq_addresses": "0", + "word_freq_free": "0.31", + "word_freq_business": "0", + "word_freq_email": "0", + "word_freq_you": "3.18", + "word_freq_credit": "0", + "word_freq_your": "0.31", + "word_freq_font": "0", + "word_freq_000": "0", + "word_freq_money": "0", + "word_freq_hp": "0", + "word_freq_hpl": "0", + "word_freq_george": "0", + "word_freq_650": "0", + "word_freq_lab": "0", + "word_freq_labs": "0", + "word_freq_telnet": "0", + "word_freq_857": "0", + "word_freq_data": "0", + "word_freq_415": "0", + "word_freq_85": "0", + "word_freq_technology": "0", + "word_freq_1999": "0", + "word_freq_parts": "0", + "word_freq_pm": "0", + "word_freq_direct": "0", + "word_freq_cs": "0", + "word_freq_meeting": "0", + "word_freq_original": "0", + "word_freq_project": "0", + "word_freq_re": "0", + "word_freq_edu": "0", + "word_freq_table": "0", + "word_freq_conference": "0", + "char_freq_%3B": "0", + "char_freq_%28": "0.135", + "char_freq_%5B": "0", + "char_freq_%21": "0.135", + "char_freq_%24": "0", + "char_freq_%23": "0", + "capital_run_length_average": "3.537", + "capital_run_length_longest": "40", + "capital_run_length_total": "191", + "class": "1" + } + ] +} + +Shortlisted templates: +[ + { + "template_id": "tpl_tpcds_within_group_share", + "template_name": "Within-Group Share of Total", + "primary_family": "conditional_dependency_structure", + "portability": "partial", + "sql_skeleton": "SELECT {group_col}, {item_col},\n SUM({measure_col}) AS total_measure,\n SUM({measure_col}) * 100.0 / SUM(SUM({measure_col})) OVER (PARTITION BY {group_col}) AS share_within_group\nFROM {table}\nGROUP BY {group_col}, {item_col}\nORDER BY share_within_group DESC;", + "required_roles": [ + "group_col", + "item_col", + "measure_col" + ] + } +] + +Problem instance: +{ + "dataset_id": "n1", + "question": "Use template Within-Group Share of Total to probe dependency_strength_similarity with semantic role within_group_proportion. Focus on group_col=class, measure_col=word_freq_telnet.", + "planned_template_id": "tpl_tpcds_within_group_share", + "bindings": { + "group_col": "class", + "measure_col": "word_freq_telnet", + "item_col": "word_freq_857", + "top_k": 10, + "top_n": 5, + "num_tiles": 10, + "percentile_value": 0.95, + "z_threshold": 2.0, + "fraction_threshold": 0.1, + "baseline_multiplier": 1.5, + "baseline_fraction": 0.1, + "min_group_size": 5, + "min_support": 5, + "measure_threshold": 0.0, + "time_grain": "month", + "lookback_rows": 3, + "current_period_start": "'2024-01-01'", + "current_period_end": "'2024-04-01'", + "previous_period_start": "'2023-10-01'", + "previous_period_end": "'2024-01-01'", + "drift_ratio_threshold": 0.8 + }, + "can_vary": [], + "must_fix": [], + "runtime_sql_skeleton": "SELECT {group_col}, {item_col},\n SUM({measure_col}) AS total_measure,\n SUM({measure_col}) * 100.0 / SUM(SUM({measure_col})) OVER (PARTITION BY {group_col}) AS share_within_group\nFROM {table}\nGROUP BY {group_col}, {item_col}\nORDER BY share_within_group DESC;" +} + +Repair context: +{} diff --git a/Query/sql/v2/runs/v2_cli_20260502_081223_d/n1/artifacts/v2q_n1_e681cd9511e354f1/cli/sql_response_attempt_1.raw.txt b/Query/sql/v2/runs/v2_cli_20260502_081223_d/n1/artifacts/v2q_n1_e681cd9511e354f1/cli/sql_response_attempt_1.raw.txt new file mode 100644 index 0000000000000000000000000000000000000000..002f2ce178588220e0328f6e25d81ab39ad05f4c --- /dev/null +++ b/Query/sql/v2/runs/v2_cli_20260502_081223_d/n1/artifacts/v2q_n1_e681cd9511e354f1/cli/sql_response_attempt_1.raw.txt @@ -0,0 +1,4 @@ +{"type":"thread.started","thread_id":"019e40e2-4f3c-7d70-ae39-0e5a6dc41beb"} +{"type":"turn.started"} +{"type":"item.completed","item":{"id":"item_0","type":"agent_message","text":"{\"sql\":\"-- template_id: tpl_tpcds_within_group_share\\nSELECT\\n \\\"class\\\",\\n \\\"word_freq_857\\\",\\n SUM(CAST(\\\"word_freq_telnet\\\" AS REAL)) AS total_measure,\\n SUM(CAST(\\\"word_freq_telnet\\\" AS REAL)) * 100.0 / NULLIF(SUM(SUM(CAST(\\\"word_freq_telnet\\\" AS REAL))) OVER (PARTITION BY \\\"class\\\"), 0) AS share_within_group\\nFROM \\\"n1\\\"\\nGROUP BY \\\"class\\\", \\\"word_freq_857\\\"\\nORDER BY share_within_group DESC;\",\"notes\":\"Used the required template bindings with group_col=\\\"class\\\", item_col=\\\"word_freq_857\\\", and measure_col=\\\"word_freq_telnet\\\". Cast the measure from TEXT to REAL because the SQLite schema stores numeric features as TEXT.\"}"}} +{"type":"turn.completed","usage":{"input_tokens":20438,"cached_input_tokens":19840,"output_tokens":701,"reasoning_output_tokens":516}} diff --git a/Query/sql/v2/runs/v2_cli_20260502_081223_d/n1/artifacts/v2q_n1_e681cd9511e354f1/cli/sql_response_attempt_1.txt b/Query/sql/v2/runs/v2_cli_20260502_081223_d/n1/artifacts/v2q_n1_e681cd9511e354f1/cli/sql_response_attempt_1.txt new file mode 100644 index 0000000000000000000000000000000000000000..22ed39d314ca4e02f82b11b0bf6e622c5ce264a0 --- /dev/null +++ b/Query/sql/v2/runs/v2_cli_20260502_081223_d/n1/artifacts/v2q_n1_e681cd9511e354f1/cli/sql_response_attempt_1.txt @@ -0,0 +1 @@ +{"sql":"-- template_id: tpl_tpcds_within_group_share\nSELECT\n \"class\",\n \"word_freq_857\",\n SUM(CAST(\"word_freq_telnet\" AS REAL)) AS total_measure,\n SUM(CAST(\"word_freq_telnet\" AS REAL)) * 100.0 / NULLIF(SUM(SUM(CAST(\"word_freq_telnet\" AS REAL))) OVER (PARTITION BY \"class\"), 0) AS share_within_group\nFROM \"n1\"\nGROUP BY \"class\", \"word_freq_857\"\nORDER BY share_within_group DESC;","notes":"Used the required template bindings with group_col=\"class\", item_col=\"word_freq_857\", and measure_col=\"word_freq_telnet\". Cast the measure from TEXT to REAL because the SQLite schema stores numeric features as TEXT."} \ No newline at end of file diff --git a/Query/sql/v2/runs/v2_cli_20260502_081223_d/n1/artifacts/v2q_n1_e681cd9511e354f1/cli/sql_stderr_attempt_1.txt b/Query/sql/v2/runs/v2_cli_20260502_081223_d/n1/artifacts/v2q_n1_e681cd9511e354f1/cli/sql_stderr_attempt_1.txt new file mode 100644 index 0000000000000000000000000000000000000000..e69de29bb2d1d6434b8b29ae775ad8c2e48c5391 diff --git a/Query/sql/v2/runs/v2_cli_20260502_081223_d/n1/artifacts/v2q_n1_e73d9afbaad490eb/cli/conversation.jsonl b/Query/sql/v2/runs/v2_cli_20260502_081223_d/n1/artifacts/v2q_n1_e73d9afbaad490eb/cli/conversation.jsonl new file mode 100644 index 0000000000000000000000000000000000000000..54f827c389aab3037bd8c339d606cecced3e9694 --- /dev/null +++ b/Query/sql/v2/runs/v2_cli_20260502_081223_d/n1/artifacts/v2q_n1_e73d9afbaad490eb/cli/conversation.jsonl @@ -0,0 +1,2 @@ +{"attempt": 1, "phase": "sql_generation", "role": "user", "content_path": "cli/sql_prompt_attempt_1.txt", "metrics": {"chars": 29912, "bytes_utf8": 29912, "lines": 792, "estimated_tokens": null}} +{"attempt": 1, "phase": "sql_generation", "role": "assistant", "content_path": "cli/sql_response_attempt_1.txt", "raw_content_path": "cli/sql_response_attempt_1.raw.txt", "stderr_path": "cli/sql_stderr_attempt_1.txt", "metrics": {"chars": 669, "bytes_utf8": 669, "lines": 1, "estimated_tokens": null}, "usage": {"input_tokens": 20451, "cached_input_tokens": 12032, "output_tokens": 504, "reasoning_output_tokens": 312}} diff --git a/Query/sql/v2/runs/v2_cli_20260502_081223_d/n1/artifacts/v2q_n1_e73d9afbaad490eb/cli/session_summary.json b/Query/sql/v2/runs/v2_cli_20260502_081223_d/n1/artifacts/v2q_n1_e73d9afbaad490eb/cli/session_summary.json new file mode 100644 index 0000000000000000000000000000000000000000..99936b3505fd073963beabbdab90291c78f79f8b --- /dev/null +++ b/Query/sql/v2/runs/v2_cli_20260502_081223_d/n1/artifacts/v2q_n1_e73d9afbaad490eb/cli/session_summary.json @@ -0,0 +1,25 @@ +{ + "engine": "v2-cli:codex", + "command": "codex exec --skip-git-repo-check --disable plugins --sandbox read-only --cd \"/data/jialinzhang/SQLagent\" -m gpt-5.4 --json -", + "ai_cli_calls": 1, + "usage_summary": { + "dataset_id": "n1", + "model": "v2-cli:codex", + "run_id": "v2q_n1_e73d9afbaad490eb", + "api_calls": 0, + "input_tokens": 20451, + "cached_input_tokens": 12032, + "output_tokens": 504, + "total_tokens": 20955, + "cost_usd": 0.0, + "ai_cli_calls": 1, + "estimated_input_tokens": 0, + "estimated_output_tokens": 0, + "estimated_total_tokens": 0, + "usage_source": "ai_cli_json_usage", + "cli_elapsed_ms_total": 19024.65, + "sql_execution_elapsed_ms_total": 6.02, + "conversation_log_path": "/data/jialinzhang/TabQueryBench/sql_workloads/v2_current/runs_and_launches/runs/v2_cli_20260502_081223_d/n1/artifacts/v2q_n1_e73d9afbaad490eb/cli/conversation.jsonl", + "note": "Executed through a local AI CLI with structured usage metadata." + } +} \ No newline at end of file diff --git a/Query/sql/v2/runs/v2_cli_20260502_081223_d/n1/artifacts/v2q_n1_e73d9afbaad490eb/cli/sql_attempt_1.metadata.json b/Query/sql/v2/runs/v2_cli_20260502_081223_d/n1/artifacts/v2q_n1_e73d9afbaad490eb/cli/sql_attempt_1.metadata.json new file mode 100644 index 0000000000000000000000000000000000000000..20accbb2181aafc030b7d2ce855fa013297e85a5 --- /dev/null +++ b/Query/sql/v2/runs/v2_cli_20260502_081223_d/n1/artifacts/v2q_n1_e73d9afbaad490eb/cli/sql_attempt_1.metadata.json @@ -0,0 +1,45 @@ +{ + "attempt": 1, + "phase": "sql_generation", + "command": "codex exec --skip-git-repo-check --disable plugins --sandbox read-only --cd \"/data/jialinzhang/SQLagent\" -m gpt-5.4 --json -", + "started_at": "2026-05-19T15:47:14.994095+00:00", + "ended_at": "2026-05-19T15:47:34.018799+00:00", + "elapsed_ms": 19024.65, + "prompt_metrics": { + "chars": 29912, + "bytes_utf8": 29912, + "lines": 792, + "estimated_tokens": null + }, + "stdout_metrics": { + "chars": 1754, + "bytes_utf8": 1754, + "lines": 6, + "estimated_tokens": null + }, + "stderr_metrics": { + "chars": 0, + "bytes_utf8": 0, + "lines": 0, + "estimated_tokens": null + }, + "parsed_output": { + "format": "jsonl_events", + "text_metrics": { + "chars": 669, + "bytes_utf8": 669, + "lines": 1, + "estimated_tokens": null + }, + "usage": { + "input_tokens": 20451, + "cached_input_tokens": 12032, + "output_tokens": 504, + "reasoning_output_tokens": 312 + } + }, + "prompt_path": "cli/sql_prompt_attempt_1.txt", + "response_path": "cli/sql_response_attempt_1.txt", + "raw_response_path": "cli/sql_response_attempt_1.raw.txt", + "stderr_path": "cli/sql_stderr_attempt_1.txt" +} \ No newline at end of file diff --git a/Query/sql/v2/runs/v2_cli_20260502_081223_d/n1/artifacts/v2q_n1_e73d9afbaad490eb/cli/sql_prompt_attempt_1.txt b/Query/sql/v2/runs/v2_cli_20260502_081223_d/n1/artifacts/v2q_n1_e73d9afbaad490eb/cli/sql_prompt_attempt_1.txt new file mode 100644 index 0000000000000000000000000000000000000000..429dea4104cc0dfe24c736e35c5481e529dcf0b3 --- /dev/null +++ b/Query/sql/v2/runs/v2_cli_20260502_081223_d/n1/artifacts/v2q_n1_e73d9afbaad490eb/cli/sql_prompt_attempt_1.txt @@ -0,0 +1,792 @@ +You are generating one SQLite SELECT query for a single-table SQL QA task. +Return strict JSON only, with this schema: {"sql": "...", "notes": "..."}. +Rules: +- Use only the provided table and columns. +- Do not write INSERT, UPDATE, DELETE, DROP, ALTER, CREATE, PRAGMA, ATTACH, DETACH, or VACUUM. +- Prefer the planned template and bound roles when provided. +- Add a leading SQL comment exactly like: -- template_id: . +- Generate SQLite-compatible SQL. SQLite does not support PERCENTILE_CONT or STDDEV. +- Quote identifiers with double quotes. +- Return no markdown and no extra prose. + +Dataset context: +Dataset context for SQL QA: +- dataset_id: n1 +- dataset_name: Spambase +- table_name: n1 +- table_layout: single-table dataset (do not assume joins). +- row_semantics: One row is one email represented by engineered token/character frequency and capitalization-run features. +- task_type: classification +- target_column: class +- main_row_count: 4601 +- important_fields: +- word_freq_make: role=feature, type=numeric_sparse_frequency. tags=['condition_candidate', 'measure', 'rare_pattern_candidate'] desc=Frequency feature for token 'make' in an email message. +- word_freq_address: role=feature, type=numeric_sparse_frequency. tags=['condition_candidate', 'measure', 'rare_pattern_candidate'] desc=Frequency feature for token 'address' in an email message. +- word_freq_all: role=feature, type=numeric_sparse_frequency. tags=['condition_candidate', 'measure', 'rare_pattern_candidate'] desc=Frequency feature for token 'all' in an email message. +- word_freq_3d: role=feature, type=numeric_sparse_frequency. tags=['condition_candidate', 'measure', 'rare_pattern_candidate'] desc=Frequency feature for token '3d' in an email message. +- word_freq_our: role=feature, type=numeric_sparse_frequency. tags=['condition_candidate', 'measure', 'rare_pattern_candidate'] desc=Frequency feature for token 'our' in an email message. +- word_freq_over: role=feature, type=numeric_sparse_frequency. tags=['condition_candidate', 'measure', 'rare_pattern_candidate'] desc=Frequency feature for token 'over' in an email message. +- word_freq_remove: role=feature, type=numeric_sparse_frequency. tags=['condition_candidate', 'measure', 'rare_pattern_candidate'] desc=Frequency feature for token 'remove' in an email message. +- word_freq_internet: role=feature, type=numeric_sparse_frequency. tags=['condition_candidate', 'measure', 'rare_pattern_candidate'] desc=Frequency feature for token 'internet' in an email message. +- word_freq_order: role=feature, type=numeric_sparse_frequency. tags=['condition_candidate', 'measure', 'rare_pattern_candidate'] desc=Frequency feature for token 'order' in an email message. +- word_freq_mail: role=feature, type=numeric_sparse_frequency. tags=['condition_candidate', 'measure', 'rare_pattern_candidate'] desc=Frequency feature for token 'mail' in an email message. +- word_freq_receive: role=feature, type=numeric_sparse_frequency. tags=['condition_candidate', 'measure', 'rare_pattern_candidate'] desc=Frequency feature for token 'receive' in an email message. +- word_freq_will: role=feature, type=numeric_sparse_frequency. tags=['condition_candidate', 'measure', 'rare_pattern_candidate'] desc=Frequency feature for token 'will' in an email message. +- word_freq_people: role=feature, type=numeric_sparse_frequency. tags=['condition_candidate', 'measure', 'rare_pattern_candidate'] desc=Frequency feature for token 'people' in an email message. +- word_freq_report: role=feature, type=numeric_sparse_frequency. tags=['condition_candidate', 'measure', 'rare_pattern_candidate'] desc=Frequency feature for token 'report' in an email message. +- word_freq_addresses: role=feature, type=numeric_sparse_frequency. tags=['condition_candidate', 'measure', 'rare_pattern_candidate'] desc=Frequency feature for token 'addresses' in an email message. +- word_freq_free: role=feature, type=numeric_sparse_frequency. tags=['condition_candidate', 'measure', 'rare_pattern_candidate'] desc=Frequency feature for token 'free' in an email message. +- word_freq_business: role=feature, type=numeric_sparse_frequency. tags=['condition_candidate', 'measure', 'rare_pattern_candidate'] desc=Frequency feature for token 'business' in an email message. +- word_freq_email: role=feature, type=numeric_sparse_frequency. tags=['condition_candidate', 'measure', 'rare_pattern_candidate'] desc=Frequency feature for token 'email' in an email message. +- word_freq_you: role=feature, type=numeric_sparse_frequency. tags=['condition_candidate', 'measure', 'rare_pattern_candidate'] desc=Frequency feature for token 'you' in an email message. +- word_freq_credit: role=feature, type=numeric_sparse_frequency. tags=['condition_candidate', 'measure', 'rare_pattern_candidate'] desc=Frequency feature for token 'credit' in an email message. +- word_freq_your: role=feature, type=numeric_sparse_frequency. tags=['condition_candidate', 'measure', 'rare_pattern_candidate'] desc=Frequency feature for token 'your' in an email message. +- word_freq_font: role=feature, type=numeric_sparse_frequency. tags=['condition_candidate', 'measure', 'rare_pattern_candidate'] desc=Frequency feature for token 'font' in an email message. +- word_freq_000: role=feature, type=numeric_sparse_frequency. tags=['condition_candidate', 'measure', 'rare_pattern_candidate'] desc=Frequency feature for token '000' in an email message. +- word_freq_money: role=feature, type=numeric_sparse_frequency. tags=['condition_candidate', 'measure', 'rare_pattern_candidate'] desc=Frequency feature for token 'money' in an email message. +- word_freq_hp: role=feature, type=numeric_sparse_frequency. tags=['condition_candidate', 'measure', 'rare_pattern_candidate'] desc=Frequency feature for token 'hp' in an email message. +- word_freq_hpl: role=feature, type=numeric_sparse_frequency. tags=['condition_candidate', 'measure', 'rare_pattern_candidate'] desc=Frequency feature for token 'hpl' in an email message. +- word_freq_george: role=feature, type=numeric_sparse_frequency. tags=['condition_candidate', 'measure', 'rare_pattern_candidate'] desc=Frequency feature for token 'george' in an email message. +- word_freq_650: role=feature, type=numeric_sparse_frequency. tags=['condition_candidate', 'measure', 'rare_pattern_candidate'] desc=Frequency feature for token '650' in an email message. +- word_freq_lab: role=feature, type=numeric_sparse_frequency. tags=['condition_candidate', 'measure', 'rare_pattern_candidate'] desc=Frequency feature for token 'lab' in an email message. +- word_freq_labs: role=feature, type=numeric_sparse_frequency. tags=['condition_candidate', 'measure', 'rare_pattern_candidate'] desc=Frequency feature for token 'labs' in an email message. +- word_freq_telnet: role=feature, type=numeric_sparse_frequency. tags=['condition_candidate', 'measure', 'rare_pattern_candidate'] desc=Frequency feature for token 'telnet' in an email message. +- word_freq_857: role=feature, type=numeric_sparse_frequency. tags=['condition_candidate', 'measure', 'rare_pattern_candidate'] desc=Frequency feature for token '857' in an email message. +- word_freq_data: role=feature, type=numeric_sparse_frequency. tags=['condition_candidate', 'measure', 'rare_pattern_candidate'] desc=Frequency feature for token 'data' in an email message. +- word_freq_415: role=feature, type=numeric_sparse_frequency. tags=['condition_candidate', 'measure', 'rare_pattern_candidate'] desc=Frequency feature for token '415' in an email message. +- word_freq_85: role=feature, type=numeric_sparse_frequency. tags=['condition_candidate', 'measure', 'rare_pattern_candidate'] desc=Frequency feature for token '85' in an email message. +- word_freq_technology: role=feature, type=numeric_sparse_frequency. tags=['condition_candidate', 'measure', 'rare_pattern_candidate'] desc=Frequency feature for token 'technology' in an email message. +- word_freq_1999: role=feature, type=numeric_sparse_frequency. tags=['condition_candidate', 'measure', 'rare_pattern_candidate'] desc=Frequency feature for token '1999' in an email message. +- word_freq_parts: role=feature, type=numeric_sparse_frequency. tags=['condition_candidate', 'measure', 'rare_pattern_candidate'] desc=Frequency feature for token 'parts' in an email message. +- word_freq_pm: role=feature, type=numeric_sparse_frequency. tags=['condition_candidate', 'measure', 'rare_pattern_candidate'] desc=Frequency feature for token 'pm' in an email message. +- word_freq_direct: role=feature, type=numeric_sparse_frequency. tags=['condition_candidate', 'measure', 'rare_pattern_candidate'] desc=Frequency feature for token 'direct' in an email message. +- word_freq_cs: role=feature, type=numeric_sparse_frequency. tags=['condition_candidate', 'measure', 'rare_pattern_candidate'] desc=Frequency feature for token 'cs' in an email message. +- word_freq_meeting: role=feature, type=numeric_sparse_frequency. tags=['condition_candidate', 'measure', 'rare_pattern_candidate'] desc=Frequency feature for token 'meeting' in an email message. +- word_freq_original: role=feature, type=numeric_sparse_frequency. tags=['condition_candidate', 'measure', 'rare_pattern_candidate'] desc=Frequency feature for token 'original' in an email message. +- word_freq_project: role=feature, type=numeric_sparse_frequency. tags=['condition_candidate', 'measure', 'rare_pattern_candidate'] desc=Frequency feature for token 'project' in an email message. +- word_freq_re: role=feature, type=numeric_sparse_frequency. tags=['condition_candidate', 'measure', 'rare_pattern_candidate'] desc=Frequency feature for token 're' in an email message. +- word_freq_edu: role=feature, type=numeric_sparse_frequency. tags=['condition_candidate', 'measure', 'rare_pattern_candidate'] desc=Frequency feature for token 'edu' in an email message. +- word_freq_table: role=feature, type=numeric_sparse_frequency. tags=['condition_candidate', 'measure', 'rare_pattern_candidate'] desc=Frequency feature for token 'table' in an email message. +- word_freq_conference: role=feature, type=numeric_sparse_frequency. tags=['condition_candidate', 'measure', 'rare_pattern_candidate'] desc=Frequency feature for token 'conference' in an email message. +- char_freq_%3B: role=feature, type=numeric_sparse_frequency. tags=['condition_candidate', 'measure', 'rare_pattern_candidate'] desc=Character frequency feature for symbol ';'. +- char_freq_%28: role=feature, type=numeric_sparse_frequency. tags=['condition_candidate', 'measure', 'rare_pattern_candidate'] desc=Character frequency feature for symbol '('. +- char_freq_%5B: role=feature, type=numeric_sparse_frequency. tags=['condition_candidate', 'measure', 'rare_pattern_candidate'] desc=Character frequency feature for symbol '['. +- char_freq_%21: role=feature, type=numeric_sparse_frequency. tags=['condition_candidate', 'measure', 'rare_pattern_candidate'] desc=Character frequency feature for symbol '!'. +- char_freq_%24: role=feature, type=numeric_sparse_frequency. tags=['condition_candidate', 'measure', 'rare_pattern_candidate'] desc=Character frequency feature for symbol '$'. +- char_freq_%23: role=feature, type=numeric_sparse_frequency. tags=['condition_candidate', 'measure', 'rare_pattern_candidate'] desc=Character frequency feature for symbol '#'. +- capital_run_length_average: role=feature, type=numeric_heavy_tail. tags=['condition_candidate', 'measure', 'rare_pattern_candidate'] desc=Average length of uninterrupted capital-letter runs. +- capital_run_length_longest: role=feature, type=numeric_heavy_tail. tags=['condition_candidate', 'measure', 'rare_pattern_candidate'] desc=Longest uninterrupted capital-letter run. +- capital_run_length_total: role=feature, type=numeric_heavy_tail. tags=['condition_candidate', 'measure', 'rare_pattern_candidate'] desc=Total number of capital letters in runs. +- class: role=target, type=binary_target. tags=['target_candidate'] desc=Binary spam label (1=spam, 0=non-spam). +- useful_field_combinations: [['word_freq_free', 'word_freq_you', 'class'], ['char_freq_%21', 'capital_run_length_longest', 'class'], ['word_freq_remove', 'word_freq_money', 'class']] +- fields_requiring_caution: ['capital_run_length_average', 'capital_run_length_longest', 'capital_run_length_total'] +- source_url: https://www.openml.org/d/44 + +SQLite schema snapshot: +{ + "table_name": "n1", + "quoted_table_name": "\"n1\"", + "row_count": 4601, + "columns": [ + { + "name": "word_freq_make", + "type": "TEXT", + "notnull": false, + "pk": false + }, + { + "name": "word_freq_address", + "type": "TEXT", + "notnull": false, + "pk": false + }, + { + "name": "word_freq_all", + "type": "TEXT", + "notnull": false, + "pk": false + }, + { + "name": "word_freq_3d", + "type": "TEXT", + "notnull": false, + "pk": false + }, + { + "name": "word_freq_our", + "type": "TEXT", + "notnull": false, + "pk": false + }, + { + "name": "word_freq_over", + "type": "TEXT", + "notnull": false, + "pk": false + }, + { + "name": "word_freq_remove", + "type": "TEXT", + "notnull": false, + "pk": false + }, + { + "name": "word_freq_internet", + "type": "TEXT", + "notnull": false, + "pk": false + }, + { + "name": "word_freq_order", + "type": "TEXT", + "notnull": false, + "pk": false + }, + { + "name": "word_freq_mail", + "type": "TEXT", + "notnull": false, + "pk": false + }, + { + "name": "word_freq_receive", + "type": "TEXT", + "notnull": false, + "pk": false + }, + { + "name": "word_freq_will", + "type": "TEXT", + "notnull": false, + "pk": false + }, + { + "name": "word_freq_people", + "type": "TEXT", + "notnull": false, + "pk": false + }, + { + "name": "word_freq_report", + "type": "TEXT", + "notnull": false, + "pk": false + }, + { + "name": "word_freq_addresses", + "type": "TEXT", + "notnull": false, + "pk": false + }, + { + "name": "word_freq_free", + "type": "TEXT", + "notnull": false, + "pk": false + }, + { + "name": "word_freq_business", + "type": "TEXT", + "notnull": false, + "pk": false + }, + { + "name": "word_freq_email", + "type": "TEXT", + "notnull": false, + "pk": false + }, + { + "name": "word_freq_you", + "type": "TEXT", + "notnull": false, + "pk": false + }, + { + "name": "word_freq_credit", + "type": "TEXT", + "notnull": false, + "pk": false + }, + { + "name": "word_freq_your", + "type": "TEXT", + "notnull": false, + "pk": false + }, + { + "name": "word_freq_font", + "type": "TEXT", + "notnull": false, + "pk": false + }, + { + "name": "word_freq_000", + "type": "TEXT", + "notnull": false, + "pk": false + }, + { + "name": "word_freq_money", + "type": "TEXT", + "notnull": false, + "pk": false + }, + { + "name": "word_freq_hp", + "type": "TEXT", + "notnull": false, + "pk": false + }, + { + "name": "word_freq_hpl", + "type": "TEXT", + "notnull": false, + "pk": false + }, + { + "name": "word_freq_george", + "type": "TEXT", + "notnull": false, + "pk": false + }, + { + "name": "word_freq_650", + "type": "TEXT", + "notnull": false, + "pk": false + }, + { + "name": "word_freq_lab", + "type": "TEXT", + "notnull": false, + "pk": false + }, + { + "name": "word_freq_labs", + "type": "TEXT", + "notnull": false, + "pk": false + }, + { + "name": "word_freq_telnet", + "type": "TEXT", + "notnull": false, + "pk": false + }, + { + "name": "word_freq_857", + "type": "TEXT", + "notnull": false, + "pk": false + }, + { + "name": "word_freq_data", + "type": "TEXT", + "notnull": false, + "pk": false + }, + { + "name": "word_freq_415", + "type": "TEXT", + "notnull": false, + "pk": false + }, + { + "name": "word_freq_85", + "type": "TEXT", + "notnull": false, + "pk": false + }, + { + "name": "word_freq_technology", + "type": "TEXT", + "notnull": false, + "pk": false + }, + { + "name": "word_freq_1999", + "type": "TEXT", + "notnull": false, + "pk": false + }, + { + "name": "word_freq_parts", + "type": "TEXT", + "notnull": false, + "pk": false + }, + { + "name": "word_freq_pm", + "type": "TEXT", + "notnull": false, + "pk": false + }, + { + "name": "word_freq_direct", + "type": "TEXT", + "notnull": false, + "pk": false + }, + { + "name": "word_freq_cs", + "type": "TEXT", + "notnull": false, + "pk": false + }, + { + "name": "word_freq_meeting", + "type": "TEXT", + "notnull": false, + "pk": false + }, + { + "name": "word_freq_original", + "type": "TEXT", + "notnull": false, + "pk": false + }, + { + "name": "word_freq_project", + "type": "TEXT", + "notnull": false, + "pk": false + }, + { + "name": "word_freq_re", + "type": "TEXT", + "notnull": false, + "pk": false + }, + { + "name": "word_freq_edu", + "type": "TEXT", + "notnull": false, + "pk": false + }, + { + "name": "word_freq_table", + "type": "TEXT", + "notnull": false, + "pk": false + }, + { + "name": "word_freq_conference", + "type": "TEXT", + "notnull": false, + "pk": false + }, + { + "name": "char_freq_%3B", + "type": "TEXT", + "notnull": false, + "pk": false + }, + { + "name": "char_freq_%28", + "type": "TEXT", + "notnull": false, + "pk": false + }, + { + "name": "char_freq_%5B", + "type": "TEXT", + "notnull": false, + "pk": false + }, + { + "name": "char_freq_%21", + "type": "TEXT", + "notnull": false, + "pk": false + }, + { + "name": "char_freq_%24", + "type": "TEXT", + "notnull": false, + "pk": false + }, + { + "name": "char_freq_%23", + "type": "TEXT", + "notnull": false, + "pk": false + }, + { + "name": "capital_run_length_average", + "type": "TEXT", + "notnull": false, + "pk": false + }, + { + "name": "capital_run_length_longest", + "type": "TEXT", + "notnull": false, + "pk": false + }, + { + "name": "capital_run_length_total", + "type": "TEXT", + "notnull": false, + "pk": false + }, + { + "name": "class", + "type": "TEXT", + "notnull": false, + "pk": false + } + ], + "sample_rows": [ + { + "word_freq_make": "0", + "word_freq_address": "0.64", + "word_freq_all": "0.64", + "word_freq_3d": "0", + "word_freq_our": "0.32", + "word_freq_over": "0", + "word_freq_remove": "0", + "word_freq_internet": "0", + "word_freq_order": "0", + "word_freq_mail": "0", + "word_freq_receive": "0", + "word_freq_will": "0.64", + "word_freq_people": "0", + "word_freq_report": "0", + "word_freq_addresses": "0", + "word_freq_free": "0.32", + "word_freq_business": "0", + "word_freq_email": "1.29", + "word_freq_you": "1.93", + "word_freq_credit": "0", + "word_freq_your": "0.96", + "word_freq_font": "0", + "word_freq_000": "0", + "word_freq_money": "0", + "word_freq_hp": "0", + "word_freq_hpl": "0", + "word_freq_george": "0", + "word_freq_650": "0", + "word_freq_lab": "0", + "word_freq_labs": "0", + "word_freq_telnet": "0", + "word_freq_857": "0", + "word_freq_data": "0", + "word_freq_415": "0", + "word_freq_85": "0", + "word_freq_technology": "0", + "word_freq_1999": "0", + "word_freq_parts": "0", + "word_freq_pm": "0", + "word_freq_direct": "0", + "word_freq_cs": "0", + "word_freq_meeting": "0", + "word_freq_original": "0", + "word_freq_project": "0", + "word_freq_re": "0", + "word_freq_edu": "0", + "word_freq_table": "0", + "word_freq_conference": "0", + "char_freq_%3B": "0", + "char_freq_%28": "0", + "char_freq_%5B": "0", + "char_freq_%21": "0.778", + "char_freq_%24": "0", + "char_freq_%23": "0", + "capital_run_length_average": "3.756", + "capital_run_length_longest": "61", + "capital_run_length_total": "278", + "class": "1" + }, + { + "word_freq_make": "0.21", + "word_freq_address": "0.28", + "word_freq_all": "0.5", + "word_freq_3d": "0", + "word_freq_our": "0.14", + "word_freq_over": "0.28", + "word_freq_remove": "0.21", + "word_freq_internet": "0.07", + "word_freq_order": "0", + "word_freq_mail": "0.94", + "word_freq_receive": "0.21", + "word_freq_will": "0.79", + "word_freq_people": "0.65", + "word_freq_report": "0.21", + "word_freq_addresses": "0.14", + "word_freq_free": "0.14", + "word_freq_business": "0.07", + "word_freq_email": "0.28", + "word_freq_you": "3.47", + "word_freq_credit": "0", + "word_freq_your": "1.59", + "word_freq_font": "0", + "word_freq_000": "0.43", + "word_freq_money": "0.43", + "word_freq_hp": "0", + "word_freq_hpl": "0", + "word_freq_george": "0", + "word_freq_650": "0", + "word_freq_lab": "0", + "word_freq_labs": "0", + "word_freq_telnet": "0", + "word_freq_857": "0", + "word_freq_data": "0", + "word_freq_415": "0", + "word_freq_85": "0", + "word_freq_technology": "0", + "word_freq_1999": "0.07", + "word_freq_parts": "0", + "word_freq_pm": "0", + "word_freq_direct": "0", + "word_freq_cs": "0", + "word_freq_meeting": "0", + "word_freq_original": "0", + "word_freq_project": "0", + "word_freq_re": "0", + "word_freq_edu": "0", + "word_freq_table": "0", + "word_freq_conference": "0", + "char_freq_%3B": "0", + "char_freq_%28": "0.132", + "char_freq_%5B": "0", + "char_freq_%21": "0.372", + "char_freq_%24": "0.18", + "char_freq_%23": "0.048", + "capital_run_length_average": "5.114", + "capital_run_length_longest": "101", + "capital_run_length_total": "1028", + "class": "1" + }, + { + "word_freq_make": "0.06", + "word_freq_address": "0", + "word_freq_all": "0.71", + "word_freq_3d": "0", + "word_freq_our": "1.23", + "word_freq_over": "0.19", + "word_freq_remove": "0.19", + "word_freq_internet": "0.12", + "word_freq_order": "0.64", + "word_freq_mail": "0.25", + "word_freq_receive": "0.38", + "word_freq_will": "0.45", + "word_freq_people": "0.12", + "word_freq_report": "0", + "word_freq_addresses": "1.75", + "word_freq_free": "0.06", + "word_freq_business": "0.06", + "word_freq_email": "1.03", + "word_freq_you": "1.36", + "word_freq_credit": "0.32", + "word_freq_your": "0.51", + "word_freq_font": "0", + "word_freq_000": "1.16", + "word_freq_money": "0.06", + "word_freq_hp": "0", + "word_freq_hpl": "0", + "word_freq_george": "0", + "word_freq_650": "0", + "word_freq_lab": "0", + "word_freq_labs": "0", + "word_freq_telnet": "0", + "word_freq_857": "0", + "word_freq_data": "0", + "word_freq_415": "0", + "word_freq_85": "0", + "word_freq_technology": "0", + "word_freq_1999": "0", + "word_freq_parts": "0", + "word_freq_pm": "0", + "word_freq_direct": "0.06", + "word_freq_cs": "0", + "word_freq_meeting": "0", + "word_freq_original": "0.12", + "word_freq_project": "0", + "word_freq_re": "0.06", + "word_freq_edu": "0.06", + "word_freq_table": "0", + "word_freq_conference": "0", + "char_freq_%3B": "0.01", + "char_freq_%28": "0.143", + "char_freq_%5B": "0", + "char_freq_%21": "0.276", + "char_freq_%24": "0.184", + "char_freq_%23": "0.01", + "capital_run_length_average": "9.821", + "capital_run_length_longest": "485", + "capital_run_length_total": "2259", + "class": "1" + }, + { + "word_freq_make": "0", + "word_freq_address": "0", + "word_freq_all": "0", + "word_freq_3d": "0", + "word_freq_our": "0.63", + "word_freq_over": "0", + "word_freq_remove": "0.31", + "word_freq_internet": "0.63", + "word_freq_order": "0.31", + "word_freq_mail": "0.63", + "word_freq_receive": "0.31", + "word_freq_will": "0.31", + "word_freq_people": "0.31", + "word_freq_report": "0", + "word_freq_addresses": "0", + "word_freq_free": "0.31", + "word_freq_business": "0", + "word_freq_email": "0", + "word_freq_you": "3.18", + "word_freq_credit": "0", + "word_freq_your": "0.31", + "word_freq_font": "0", + "word_freq_000": "0", + "word_freq_money": "0", + "word_freq_hp": "0", + "word_freq_hpl": "0", + "word_freq_george": "0", + "word_freq_650": "0", + "word_freq_lab": "0", + "word_freq_labs": "0", + "word_freq_telnet": "0", + "word_freq_857": "0", + "word_freq_data": "0", + "word_freq_415": "0", + "word_freq_85": "0", + "word_freq_technology": "0", + "word_freq_1999": "0", + "word_freq_parts": "0", + "word_freq_pm": "0", + "word_freq_direct": "0", + "word_freq_cs": "0", + "word_freq_meeting": "0", + "word_freq_original": "0", + "word_freq_project": "0", + "word_freq_re": "0", + "word_freq_edu": "0", + "word_freq_table": "0", + "word_freq_conference": "0", + "char_freq_%3B": "0", + "char_freq_%28": "0.137", + "char_freq_%5B": "0", + "char_freq_%21": "0.137", + "char_freq_%24": "0", + "char_freq_%23": "0", + "capital_run_length_average": "3.537", + "capital_run_length_longest": "40", + "capital_run_length_total": "191", + "class": "1" + }, + { + "word_freq_make": "0", + "word_freq_address": "0", + "word_freq_all": "0", + "word_freq_3d": "0", + "word_freq_our": "0.63", + "word_freq_over": "0", + "word_freq_remove": "0.31", + "word_freq_internet": "0.63", + "word_freq_order": "0.31", + "word_freq_mail": "0.63", + "word_freq_receive": "0.31", + "word_freq_will": "0.31", + "word_freq_people": "0.31", + "word_freq_report": "0", + "word_freq_addresses": "0", + "word_freq_free": "0.31", + "word_freq_business": "0", + "word_freq_email": "0", + "word_freq_you": "3.18", + "word_freq_credit": "0", + "word_freq_your": "0.31", + "word_freq_font": "0", + "word_freq_000": "0", + "word_freq_money": "0", + "word_freq_hp": "0", + "word_freq_hpl": "0", + "word_freq_george": "0", + "word_freq_650": "0", + "word_freq_lab": "0", + "word_freq_labs": "0", + "word_freq_telnet": "0", + "word_freq_857": "0", + "word_freq_data": "0", + "word_freq_415": "0", + "word_freq_85": "0", + "word_freq_technology": "0", + "word_freq_1999": "0", + "word_freq_parts": "0", + "word_freq_pm": "0", + "word_freq_direct": "0", + "word_freq_cs": "0", + "word_freq_meeting": "0", + "word_freq_original": "0", + "word_freq_project": "0", + "word_freq_re": "0", + "word_freq_edu": "0", + "word_freq_table": "0", + "word_freq_conference": "0", + "char_freq_%3B": "0", + "char_freq_%28": "0.135", + "char_freq_%5B": "0", + "char_freq_%21": "0.135", + "char_freq_%24": "0", + "char_freq_%23": "0", + "capital_run_length_average": "3.537", + "capital_run_length_longest": "40", + "capital_run_length_total": "191", + "class": "1" + } + ] +} + +Shortlisted templates: +[ + { + "template_id": "tpl_tpch_relative_total_threshold", + "template_name": "Relative-to-Total Extreme Threshold", + "primary_family": "tail_rarity_structure", + "portability": "partial", + "sql_skeleton": "WITH grouped AS (\n SELECT {group_col}, SUM({measure_col}) AS group_value\n FROM {table}\n GROUP BY {group_col}\n), total AS (\n SELECT SUM(group_value) AS total_value\n FROM grouped\n)\nSELECT g.{group_col}, g.group_value\nFROM grouped AS g\nCROSS JOIN total AS t\nWHERE g.group_value > t.total_value * {fraction_threshold}\nORDER BY g.group_value DESC;", + "required_roles": [ + "group_col", + "measure_col" + ] + } +] + +Problem instance: +{ + "dataset_id": "n1", + "question": "Use template Relative-to-Total Extreme Threshold to probe tail_mass_similarity with semantic role count_distribution. Focus on group_col=class, measure_col=word_freq_email.", + "planned_template_id": "tpl_tpch_relative_total_threshold", + "bindings": { + "group_col": "class", + "measure_col": "word_freq_email", + "top_k": 14, + "top_n": 5, + "num_tiles": 10, + "percentile_value": 0.95, + "z_threshold": 2.0, + "fraction_threshold": 0.1, + "baseline_multiplier": 1.5, + "baseline_fraction": 0.1, + "min_group_size": 5, + "min_support": 5, + "measure_threshold": 0.0, + "time_grain": "month", + "lookback_rows": 3, + "current_period_start": "'2024-01-01'", + "current_period_end": "'2024-04-01'", + "previous_period_start": "'2023-10-01'", + "previous_period_end": "'2024-01-01'", + "drift_ratio_threshold": 0.8 + }, + "can_vary": [], + "must_fix": [], + "runtime_sql_skeleton": "WITH grouped AS (\n SELECT {group_col}, SUM({measure_col}) AS group_value\n FROM {table}\n GROUP BY {group_col}\n), total AS (\n SELECT SUM(group_value) AS total_value\n FROM grouped\n)\nSELECT g.{group_col}, g.group_value\nFROM grouped AS g\nCROSS JOIN total AS t\nWHERE g.group_value > t.total_value * {fraction_threshold}\nORDER BY g.group_value DESC;" +} + +Repair context: +{} diff --git a/Query/sql/v2/runs/v2_cli_20260502_081223_d/n1/artifacts/v2q_n1_e73d9afbaad490eb/cli/sql_response_attempt_1.raw.txt b/Query/sql/v2/runs/v2_cli_20260502_081223_d/n1/artifacts/v2q_n1_e73d9afbaad490eb/cli/sql_response_attempt_1.raw.txt new file mode 100644 index 0000000000000000000000000000000000000000..4873d522c794c1fbc0b01d3e24a2e211992388f8 --- /dev/null +++ b/Query/sql/v2/runs/v2_cli_20260502_081223_d/n1/artifacts/v2q_n1_e73d9afbaad490eb/cli/sql_response_attempt_1.raw.txt @@ -0,0 +1,6 @@ +{"type":"thread.started","thread_id":"019e40eb-7d33-7ea3-892e-61e7cc89cfa9"} +{"type":"turn.started"} +{"type":"error","message":"Reconnecting... 2/5 (stream disconnected before completion: Rate limit reached for gpt-5.4 in organization org-yXK0mBGOR7eXhtGEx8v7e3bf on tokens per min (TPM): Limit 2000000, Used 1994802, Requested 21086. Please try again in 476ms. Visit https://platform.openai.com/account/rate-limits to learn more.)"} +{"type":"error","message":"Reconnecting... 3/5 (stream disconnected before completion: Rate limit reached for gpt-5.4 in organization org-yXK0mBGOR7eXhtGEx8v7e3bf on tokens per min (TPM): Limit 2000000, Used 1987611, Requested 21086. Please try again in 260ms. Visit https://platform.openai.com/account/rate-limits to learn more.)"} +{"type":"item.completed","item":{"id":"item_0","type":"agent_message","text":"{\"sql\":\"-- template_id: tpl_tpch_relative_total_threshold\\nWITH \\\"grouped\\\" AS (\\n SELECT \\\"class\\\", SUM(CAST(\\\"word_freq_email\\\" AS REAL)) AS \\\"group_value\\\"\\n FROM \\\"n1\\\"\\n GROUP BY \\\"class\\\"\\n), \\\"total\\\" AS (\\n SELECT SUM(\\\"group_value\\\") AS \\\"total_value\\\"\\n FROM \\\"grouped\\\"\\n)\\nSELECT g.\\\"class\\\", g.\\\"group_value\\\"\\nFROM \\\"grouped\\\" AS g\\nCROSS JOIN \\\"total\\\" AS t\\nWHERE g.\\\"group_value\\\" > t.\\\"total_value\\\" * 0.1\\nORDER BY g.\\\"group_value\\\" DESC;\",\"notes\":\"Uses the requested Relative-to-Total Extreme Threshold template with group_col=\\\"class\\\" and measure_col=\\\"word_freq_email\\\". Casts \\\"word_freq_email\\\" from TEXT to REAL for numeric aggregation.\"}"}} +{"type":"turn.completed","usage":{"input_tokens":20451,"cached_input_tokens":12032,"output_tokens":504,"reasoning_output_tokens":312}} diff --git a/Query/sql/v2/runs/v2_cli_20260502_081223_d/n1/artifacts/v2q_n1_e73d9afbaad490eb/cli/sql_response_attempt_1.txt b/Query/sql/v2/runs/v2_cli_20260502_081223_d/n1/artifacts/v2q_n1_e73d9afbaad490eb/cli/sql_response_attempt_1.txt new file mode 100644 index 0000000000000000000000000000000000000000..b8d36359cb31d6da5aacbf2d55a84fc248f5f6a6 --- /dev/null +++ b/Query/sql/v2/runs/v2_cli_20260502_081223_d/n1/artifacts/v2q_n1_e73d9afbaad490eb/cli/sql_response_attempt_1.txt @@ -0,0 +1 @@ +{"sql":"-- template_id: tpl_tpch_relative_total_threshold\nWITH \"grouped\" AS (\n SELECT \"class\", SUM(CAST(\"word_freq_email\" AS REAL)) AS \"group_value\"\n FROM \"n1\"\n GROUP BY \"class\"\n), \"total\" AS (\n SELECT SUM(\"group_value\") AS \"total_value\"\n FROM \"grouped\"\n)\nSELECT g.\"class\", g.\"group_value\"\nFROM \"grouped\" AS g\nCROSS JOIN \"total\" AS t\nWHERE g.\"group_value\" > t.\"total_value\" * 0.1\nORDER BY g.\"group_value\" DESC;","notes":"Uses the requested Relative-to-Total Extreme Threshold template with group_col=\"class\" and measure_col=\"word_freq_email\". Casts \"word_freq_email\" from TEXT to REAL for numeric aggregation."} \ No newline at end of file diff --git a/Query/sql/v2/runs/v2_cli_20260502_081223_d/n1/artifacts/v2q_n1_e73d9afbaad490eb/cli/sql_stderr_attempt_1.txt b/Query/sql/v2/runs/v2_cli_20260502_081223_d/n1/artifacts/v2q_n1_e73d9afbaad490eb/cli/sql_stderr_attempt_1.txt new file mode 100644 index 0000000000000000000000000000000000000000..e69de29bb2d1d6434b8b29ae775ad8c2e48c5391 diff --git a/Query/sql/v2/runs/v2_cli_20260502_081223_d/n1/artifacts/v2q_n1_efa184a83eb5d8d4/cli/conversation.jsonl b/Query/sql/v2/runs/v2_cli_20260502_081223_d/n1/artifacts/v2q_n1_efa184a83eb5d8d4/cli/conversation.jsonl new file mode 100644 index 0000000000000000000000000000000000000000..37836346c7e649093692c2c41dc8f786cd5e656c --- /dev/null +++ b/Query/sql/v2/runs/v2_cli_20260502_081223_d/n1/artifacts/v2q_n1_efa184a83eb5d8d4/cli/conversation.jsonl @@ -0,0 +1,2 @@ +{"attempt": 1, "phase": "sql_generation", "role": "user", "content_path": "cli/sql_prompt_attempt_1.txt", "metrics": {"chars": 29254, "bytes_utf8": 29254, "lines": 790, "estimated_tokens": null}} +{"attempt": 1, "phase": "sql_generation", "role": "assistant", "content_path": "cli/sql_response_attempt_1.txt", "raw_content_path": "cli/sql_response_attempt_1.raw.txt", "stderr_path": "cli/sql_stderr_attempt_1.txt", "metrics": {"chars": 271, "bytes_utf8": 271, "lines": 1, "estimated_tokens": null}, "usage": {"input_tokens": 20286, "cached_input_tokens": 12032, "output_tokens": 210, "reasoning_output_tokens": 135}} diff --git a/Query/sql/v2/runs/v2_cli_20260502_081223_d/n1/artifacts/v2q_n1_efa184a83eb5d8d4/cli/session_summary.json b/Query/sql/v2/runs/v2_cli_20260502_081223_d/n1/artifacts/v2q_n1_efa184a83eb5d8d4/cli/session_summary.json new file mode 100644 index 0000000000000000000000000000000000000000..98ff39afda26bf100ccc3e380f8c680b7cbc5ee7 --- /dev/null +++ b/Query/sql/v2/runs/v2_cli_20260502_081223_d/n1/artifacts/v2q_n1_efa184a83eb5d8d4/cli/session_summary.json @@ -0,0 +1,25 @@ +{ + "engine": "v2-cli:codex", + "command": "codex exec --skip-git-repo-check --disable plugins --sandbox read-only --cd \"/data/jialinzhang/SQLagent\" -m gpt-5.4 --json -", + "ai_cli_calls": 1, + "usage_summary": { + "dataset_id": "n1", + "model": "v2-cli:codex", + "run_id": "v2q_n1_efa184a83eb5d8d4", + "api_calls": 0, + "input_tokens": 20286, + "cached_input_tokens": 12032, + "output_tokens": 210, + "total_tokens": 20496, + "cost_usd": 0.0, + "ai_cli_calls": 1, + "estimated_input_tokens": 0, + "estimated_output_tokens": 0, + "estimated_total_tokens": 0, + "usage_source": "ai_cli_json_usage", + "cli_elapsed_ms_total": 7603.11, + "sql_execution_elapsed_ms_total": 2.75, + "conversation_log_path": "/data/jialinzhang/TabQueryBench/sql_workloads/v2_current/runs_and_launches/runs/v2_cli_20260502_081223_d/n1/artifacts/v2q_n1_efa184a83eb5d8d4/cli/conversation.jsonl", + "note": "Executed through a local AI CLI with structured usage metadata." + } +} \ No newline at end of file diff --git a/Query/sql/v2/runs/v2_cli_20260502_081223_d/n1/artifacts/v2q_n1_efa184a83eb5d8d4/cli/sql_attempt_1.metadata.json b/Query/sql/v2/runs/v2_cli_20260502_081223_d/n1/artifacts/v2q_n1_efa184a83eb5d8d4/cli/sql_attempt_1.metadata.json new file mode 100644 index 0000000000000000000000000000000000000000..2dad116ad2182c9eb29aee51c65414e37f84c1e1 --- /dev/null +++ b/Query/sql/v2/runs/v2_cli_20260502_081223_d/n1/artifacts/v2q_n1_efa184a83eb5d8d4/cli/sql_attempt_1.metadata.json @@ -0,0 +1,45 @@ +{ + "attempt": 1, + "phase": "sql_generation", + "command": "codex exec --skip-git-repo-check --disable plugins --sandbox read-only --cd \"/data/jialinzhang/SQLagent\" -m gpt-5.4 --json -", + "started_at": "2026-05-19T15:32:36.525572+00:00", + "ended_at": "2026-05-19T15:32:44.128723+00:00", + "elapsed_ms": 7603.11, + "prompt_metrics": { + "chars": 29254, + "bytes_utf8": 29254, + "lines": 790, + "estimated_tokens": null + }, + "stdout_metrics": { + "chars": 625, + "bytes_utf8": 625, + "lines": 4, + "estimated_tokens": null + }, + "stderr_metrics": { + "chars": 0, + "bytes_utf8": 0, + "lines": 0, + "estimated_tokens": null + }, + "parsed_output": { + "format": "jsonl_events", + "text_metrics": { + "chars": 271, + "bytes_utf8": 271, + "lines": 1, + "estimated_tokens": null + }, + "usage": { + "input_tokens": 20286, + "cached_input_tokens": 12032, + "output_tokens": 210, + "reasoning_output_tokens": 135 + } + }, + "prompt_path": "cli/sql_prompt_attempt_1.txt", + "response_path": "cli/sql_response_attempt_1.txt", + "raw_response_path": "cli/sql_response_attempt_1.raw.txt", + "stderr_path": "cli/sql_stderr_attempt_1.txt" +} \ No newline at end of file diff --git a/Query/sql/v2/runs/v2_cli_20260502_081223_d/n1/artifacts/v2q_n1_efa184a83eb5d8d4/cli/sql_prompt_attempt_1.txt b/Query/sql/v2/runs/v2_cli_20260502_081223_d/n1/artifacts/v2q_n1_efa184a83eb5d8d4/cli/sql_prompt_attempt_1.txt new file mode 100644 index 0000000000000000000000000000000000000000..6744fcc9975771c73e1550d0e341a32b9966c5f4 --- /dev/null +++ b/Query/sql/v2/runs/v2_cli_20260502_081223_d/n1/artifacts/v2q_n1_efa184a83eb5d8d4/cli/sql_prompt_attempt_1.txt @@ -0,0 +1,790 @@ +You are generating one SQLite SELECT query for a single-table SQL QA task. +Return strict JSON only, with this schema: {"sql": "...", "notes": "..."}. +Rules: +- Use only the provided table and columns. +- Do not write INSERT, UPDATE, DELETE, DROP, ALTER, CREATE, PRAGMA, ATTACH, DETACH, or VACUUM. +- Prefer the planned template and bound roles when provided. +- Add a leading SQL comment exactly like: -- template_id: . +- Generate SQLite-compatible SQL. SQLite does not support PERCENTILE_CONT or STDDEV. +- Quote identifiers with double quotes. +- Return no markdown and no extra prose. + +Dataset context: +Dataset context for SQL QA: +- dataset_id: n1 +- dataset_name: Spambase +- table_name: n1 +- table_layout: single-table dataset (do not assume joins). +- row_semantics: One row is one email represented by engineered token/character frequency and capitalization-run features. +- task_type: classification +- target_column: class +- main_row_count: 4601 +- important_fields: +- word_freq_make: role=feature, type=numeric_sparse_frequency. tags=['condition_candidate', 'measure', 'rare_pattern_candidate'] desc=Frequency feature for token 'make' in an email message. +- word_freq_address: role=feature, type=numeric_sparse_frequency. tags=['condition_candidate', 'measure', 'rare_pattern_candidate'] desc=Frequency feature for token 'address' in an email message. +- word_freq_all: role=feature, type=numeric_sparse_frequency. tags=['condition_candidate', 'measure', 'rare_pattern_candidate'] desc=Frequency feature for token 'all' in an email message. +- word_freq_3d: role=feature, type=numeric_sparse_frequency. tags=['condition_candidate', 'measure', 'rare_pattern_candidate'] desc=Frequency feature for token '3d' in an email message. +- word_freq_our: role=feature, type=numeric_sparse_frequency. tags=['condition_candidate', 'measure', 'rare_pattern_candidate'] desc=Frequency feature for token 'our' in an email message. +- word_freq_over: role=feature, type=numeric_sparse_frequency. tags=['condition_candidate', 'measure', 'rare_pattern_candidate'] desc=Frequency feature for token 'over' in an email message. +- word_freq_remove: role=feature, type=numeric_sparse_frequency. tags=['condition_candidate', 'measure', 'rare_pattern_candidate'] desc=Frequency feature for token 'remove' in an email message. +- word_freq_internet: role=feature, type=numeric_sparse_frequency. tags=['condition_candidate', 'measure', 'rare_pattern_candidate'] desc=Frequency feature for token 'internet' in an email message. +- word_freq_order: role=feature, type=numeric_sparse_frequency. tags=['condition_candidate', 'measure', 'rare_pattern_candidate'] desc=Frequency feature for token 'order' in an email message. +- word_freq_mail: role=feature, type=numeric_sparse_frequency. tags=['condition_candidate', 'measure', 'rare_pattern_candidate'] desc=Frequency feature for token 'mail' in an email message. +- word_freq_receive: role=feature, type=numeric_sparse_frequency. tags=['condition_candidate', 'measure', 'rare_pattern_candidate'] desc=Frequency feature for token 'receive' in an email message. +- word_freq_will: role=feature, type=numeric_sparse_frequency. tags=['condition_candidate', 'measure', 'rare_pattern_candidate'] desc=Frequency feature for token 'will' in an email message. +- word_freq_people: role=feature, type=numeric_sparse_frequency. tags=['condition_candidate', 'measure', 'rare_pattern_candidate'] desc=Frequency feature for token 'people' in an email message. +- word_freq_report: role=feature, type=numeric_sparse_frequency. tags=['condition_candidate', 'measure', 'rare_pattern_candidate'] desc=Frequency feature for token 'report' in an email message. +- word_freq_addresses: role=feature, type=numeric_sparse_frequency. tags=['condition_candidate', 'measure', 'rare_pattern_candidate'] desc=Frequency feature for token 'addresses' in an email message. +- word_freq_free: role=feature, type=numeric_sparse_frequency. tags=['condition_candidate', 'measure', 'rare_pattern_candidate'] desc=Frequency feature for token 'free' in an email message. +- word_freq_business: role=feature, type=numeric_sparse_frequency. tags=['condition_candidate', 'measure', 'rare_pattern_candidate'] desc=Frequency feature for token 'business' in an email message. +- word_freq_email: role=feature, type=numeric_sparse_frequency. tags=['condition_candidate', 'measure', 'rare_pattern_candidate'] desc=Frequency feature for token 'email' in an email message. +- word_freq_you: role=feature, type=numeric_sparse_frequency. tags=['condition_candidate', 'measure', 'rare_pattern_candidate'] desc=Frequency feature for token 'you' in an email message. +- word_freq_credit: role=feature, type=numeric_sparse_frequency. tags=['condition_candidate', 'measure', 'rare_pattern_candidate'] desc=Frequency feature for token 'credit' in an email message. +- word_freq_your: role=feature, type=numeric_sparse_frequency. tags=['condition_candidate', 'measure', 'rare_pattern_candidate'] desc=Frequency feature for token 'your' in an email message. +- word_freq_font: role=feature, type=numeric_sparse_frequency. tags=['condition_candidate', 'measure', 'rare_pattern_candidate'] desc=Frequency feature for token 'font' in an email message. +- word_freq_000: role=feature, type=numeric_sparse_frequency. tags=['condition_candidate', 'measure', 'rare_pattern_candidate'] desc=Frequency feature for token '000' in an email message. +- word_freq_money: role=feature, type=numeric_sparse_frequency. tags=['condition_candidate', 'measure', 'rare_pattern_candidate'] desc=Frequency feature for token 'money' in an email message. +- word_freq_hp: role=feature, type=numeric_sparse_frequency. tags=['condition_candidate', 'measure', 'rare_pattern_candidate'] desc=Frequency feature for token 'hp' in an email message. +- word_freq_hpl: role=feature, type=numeric_sparse_frequency. tags=['condition_candidate', 'measure', 'rare_pattern_candidate'] desc=Frequency feature for token 'hpl' in an email message. +- word_freq_george: role=feature, type=numeric_sparse_frequency. tags=['condition_candidate', 'measure', 'rare_pattern_candidate'] desc=Frequency feature for token 'george' in an email message. +- word_freq_650: role=feature, type=numeric_sparse_frequency. tags=['condition_candidate', 'measure', 'rare_pattern_candidate'] desc=Frequency feature for token '650' in an email message. +- word_freq_lab: role=feature, type=numeric_sparse_frequency. tags=['condition_candidate', 'measure', 'rare_pattern_candidate'] desc=Frequency feature for token 'lab' in an email message. +- word_freq_labs: role=feature, type=numeric_sparse_frequency. tags=['condition_candidate', 'measure', 'rare_pattern_candidate'] desc=Frequency feature for token 'labs' in an email message. +- word_freq_telnet: role=feature, type=numeric_sparse_frequency. tags=['condition_candidate', 'measure', 'rare_pattern_candidate'] desc=Frequency feature for token 'telnet' in an email message. +- word_freq_857: role=feature, type=numeric_sparse_frequency. tags=['condition_candidate', 'measure', 'rare_pattern_candidate'] desc=Frequency feature for token '857' in an email message. +- word_freq_data: role=feature, type=numeric_sparse_frequency. tags=['condition_candidate', 'measure', 'rare_pattern_candidate'] desc=Frequency feature for token 'data' in an email message. +- word_freq_415: role=feature, type=numeric_sparse_frequency. tags=['condition_candidate', 'measure', 'rare_pattern_candidate'] desc=Frequency feature for token '415' in an email message. +- word_freq_85: role=feature, type=numeric_sparse_frequency. tags=['condition_candidate', 'measure', 'rare_pattern_candidate'] desc=Frequency feature for token '85' in an email message. +- word_freq_technology: role=feature, type=numeric_sparse_frequency. tags=['condition_candidate', 'measure', 'rare_pattern_candidate'] desc=Frequency feature for token 'technology' in an email message. +- word_freq_1999: role=feature, type=numeric_sparse_frequency. tags=['condition_candidate', 'measure', 'rare_pattern_candidate'] desc=Frequency feature for token '1999' in an email message. +- word_freq_parts: role=feature, type=numeric_sparse_frequency. tags=['condition_candidate', 'measure', 'rare_pattern_candidate'] desc=Frequency feature for token 'parts' in an email message. +- word_freq_pm: role=feature, type=numeric_sparse_frequency. tags=['condition_candidate', 'measure', 'rare_pattern_candidate'] desc=Frequency feature for token 'pm' in an email message. +- word_freq_direct: role=feature, type=numeric_sparse_frequency. tags=['condition_candidate', 'measure', 'rare_pattern_candidate'] desc=Frequency feature for token 'direct' in an email message. +- word_freq_cs: role=feature, type=numeric_sparse_frequency. tags=['condition_candidate', 'measure', 'rare_pattern_candidate'] desc=Frequency feature for token 'cs' in an email message. +- word_freq_meeting: role=feature, type=numeric_sparse_frequency. tags=['condition_candidate', 'measure', 'rare_pattern_candidate'] desc=Frequency feature for token 'meeting' in an email message. +- word_freq_original: role=feature, type=numeric_sparse_frequency. tags=['condition_candidate', 'measure', 'rare_pattern_candidate'] desc=Frequency feature for token 'original' in an email message. +- word_freq_project: role=feature, type=numeric_sparse_frequency. tags=['condition_candidate', 'measure', 'rare_pattern_candidate'] desc=Frequency feature for token 'project' in an email message. +- word_freq_re: role=feature, type=numeric_sparse_frequency. tags=['condition_candidate', 'measure', 'rare_pattern_candidate'] desc=Frequency feature for token 're' in an email message. +- word_freq_edu: role=feature, type=numeric_sparse_frequency. tags=['condition_candidate', 'measure', 'rare_pattern_candidate'] desc=Frequency feature for token 'edu' in an email message. +- word_freq_table: role=feature, type=numeric_sparse_frequency. tags=['condition_candidate', 'measure', 'rare_pattern_candidate'] desc=Frequency feature for token 'table' in an email message. +- word_freq_conference: role=feature, type=numeric_sparse_frequency. tags=['condition_candidate', 'measure', 'rare_pattern_candidate'] desc=Frequency feature for token 'conference' in an email message. +- char_freq_%3B: role=feature, type=numeric_sparse_frequency. tags=['condition_candidate', 'measure', 'rare_pattern_candidate'] desc=Character frequency feature for symbol ';'. +- char_freq_%28: role=feature, type=numeric_sparse_frequency. tags=['condition_candidate', 'measure', 'rare_pattern_candidate'] desc=Character frequency feature for symbol '('. +- char_freq_%5B: role=feature, type=numeric_sparse_frequency. tags=['condition_candidate', 'measure', 'rare_pattern_candidate'] desc=Character frequency feature for symbol '['. +- char_freq_%21: role=feature, type=numeric_sparse_frequency. tags=['condition_candidate', 'measure', 'rare_pattern_candidate'] desc=Character frequency feature for symbol '!'. +- char_freq_%24: role=feature, type=numeric_sparse_frequency. tags=['condition_candidate', 'measure', 'rare_pattern_candidate'] desc=Character frequency feature for symbol '$'. +- char_freq_%23: role=feature, type=numeric_sparse_frequency. tags=['condition_candidate', 'measure', 'rare_pattern_candidate'] desc=Character frequency feature for symbol '#'. +- capital_run_length_average: role=feature, type=numeric_heavy_tail. tags=['condition_candidate', 'measure', 'rare_pattern_candidate'] desc=Average length of uninterrupted capital-letter runs. +- capital_run_length_longest: role=feature, type=numeric_heavy_tail. tags=['condition_candidate', 'measure', 'rare_pattern_candidate'] desc=Longest uninterrupted capital-letter run. +- capital_run_length_total: role=feature, type=numeric_heavy_tail. tags=['condition_candidate', 'measure', 'rare_pattern_candidate'] desc=Total number of capital letters in runs. +- class: role=target, type=binary_target. tags=['target_candidate'] desc=Binary spam label (1=spam, 0=non-spam). +- useful_field_combinations: [['word_freq_free', 'word_freq_you', 'class'], ['char_freq_%21', 'capital_run_length_longest', 'class'], ['word_freq_remove', 'word_freq_money', 'class']] +- fields_requiring_caution: ['capital_run_length_average', 'capital_run_length_longest', 'capital_run_length_total'] +- source_url: https://www.openml.org/d/44 + +SQLite schema snapshot: +{ + "table_name": "n1", + "quoted_table_name": "\"n1\"", + "row_count": 4601, + "columns": [ + { + "name": "word_freq_make", + "type": "TEXT", + "notnull": false, + "pk": false + }, + { + "name": "word_freq_address", + "type": "TEXT", + "notnull": false, + "pk": false + }, + { + "name": "word_freq_all", + "type": "TEXT", + "notnull": false, + "pk": false + }, + { + "name": "word_freq_3d", + "type": "TEXT", + "notnull": false, + "pk": false + }, + { + "name": "word_freq_our", + "type": "TEXT", + "notnull": false, + "pk": false + }, + { + "name": "word_freq_over", + "type": "TEXT", + "notnull": false, + "pk": false + }, + { + "name": "word_freq_remove", + "type": "TEXT", + "notnull": false, + "pk": false + }, + { + "name": "word_freq_internet", + "type": "TEXT", + "notnull": false, + "pk": false + }, + { + "name": "word_freq_order", + "type": "TEXT", + "notnull": false, + "pk": false + }, + { + "name": "word_freq_mail", + "type": "TEXT", + "notnull": false, + "pk": false + }, + { + "name": "word_freq_receive", + "type": "TEXT", + "notnull": false, + "pk": false + }, + { + "name": "word_freq_will", + "type": "TEXT", + "notnull": false, + "pk": false + }, + { + "name": "word_freq_people", + "type": "TEXT", + "notnull": false, + "pk": false + }, + { + "name": "word_freq_report", + "type": "TEXT", + "notnull": false, + "pk": false + }, + { + "name": "word_freq_addresses", + "type": "TEXT", + "notnull": false, + "pk": false + }, + { + "name": "word_freq_free", + "type": "TEXT", + "notnull": false, + "pk": false + }, + { + "name": "word_freq_business", + "type": "TEXT", + "notnull": false, + "pk": false + }, + { + "name": "word_freq_email", + "type": "TEXT", + "notnull": false, + "pk": false + }, + { + "name": "word_freq_you", + "type": "TEXT", + "notnull": false, + "pk": false + }, + { + "name": "word_freq_credit", + "type": "TEXT", + "notnull": false, + "pk": false + }, + { + "name": "word_freq_your", + "type": "TEXT", + "notnull": false, + "pk": false + }, + { + "name": "word_freq_font", + "type": "TEXT", + "notnull": false, + "pk": false + }, + { + "name": "word_freq_000", + "type": "TEXT", + "notnull": false, + "pk": false + }, + { + "name": "word_freq_money", + "type": "TEXT", + "notnull": false, + "pk": false + }, + { + "name": "word_freq_hp", + "type": "TEXT", + "notnull": false, + "pk": false + }, + { + "name": "word_freq_hpl", + "type": "TEXT", + "notnull": false, + "pk": false + }, + { + "name": "word_freq_george", + "type": "TEXT", + "notnull": false, + "pk": false + }, + { + "name": "word_freq_650", + "type": "TEXT", + "notnull": false, + "pk": false + }, + { + "name": "word_freq_lab", + "type": "TEXT", + "notnull": false, + "pk": false + }, + { + "name": "word_freq_labs", + "type": "TEXT", + "notnull": false, + "pk": false + }, + { + "name": "word_freq_telnet", + "type": "TEXT", + "notnull": false, + "pk": false + }, + { + "name": "word_freq_857", + "type": "TEXT", + "notnull": false, + "pk": false + }, + { + "name": "word_freq_data", + "type": "TEXT", + "notnull": false, + "pk": false + }, + { + "name": "word_freq_415", + "type": "TEXT", + "notnull": false, + "pk": false + }, + { + "name": "word_freq_85", + "type": "TEXT", + "notnull": false, + "pk": false + }, + { + "name": "word_freq_technology", + "type": "TEXT", + "notnull": false, + "pk": false + }, + { + "name": "word_freq_1999", + "type": "TEXT", + "notnull": false, + "pk": false + }, + { + "name": "word_freq_parts", + "type": "TEXT", + "notnull": false, + "pk": false + }, + { + "name": "word_freq_pm", + "type": "TEXT", + "notnull": false, + "pk": false + }, + { + "name": "word_freq_direct", + "type": "TEXT", + "notnull": false, + "pk": false + }, + { + "name": "word_freq_cs", + "type": "TEXT", + "notnull": false, + "pk": false + }, + { + "name": "word_freq_meeting", + "type": "TEXT", + "notnull": false, + "pk": false + }, + { + "name": "word_freq_original", + "type": "TEXT", + "notnull": false, + "pk": false + }, + { + "name": "word_freq_project", + "type": "TEXT", + "notnull": false, + "pk": false + }, + { + "name": "word_freq_re", + "type": "TEXT", + "notnull": false, + "pk": false + }, + { + "name": "word_freq_edu", + "type": "TEXT", + "notnull": false, + "pk": false + }, + { + "name": "word_freq_table", + "type": "TEXT", + "notnull": false, + "pk": false + }, + { + "name": "word_freq_conference", + "type": "TEXT", + "notnull": false, + "pk": false + }, + { + "name": "char_freq_%3B", + "type": "TEXT", + "notnull": false, + "pk": false + }, + { + "name": "char_freq_%28", + "type": "TEXT", + "notnull": false, + "pk": false + }, + { + "name": "char_freq_%5B", + "type": "TEXT", + "notnull": false, + "pk": false + }, + { + "name": "char_freq_%21", + "type": "TEXT", + "notnull": false, + "pk": false + }, + { + "name": "char_freq_%24", + "type": "TEXT", + "notnull": false, + "pk": false + }, + { + "name": "char_freq_%23", + "type": "TEXT", + "notnull": false, + "pk": false + }, + { + "name": "capital_run_length_average", + "type": "TEXT", + "notnull": false, + "pk": false + }, + { + "name": "capital_run_length_longest", + "type": "TEXT", + "notnull": false, + "pk": false + }, + { + "name": "capital_run_length_total", + "type": "TEXT", + "notnull": false, + "pk": false + }, + { + "name": "class", + "type": "TEXT", + "notnull": false, + "pk": false + } + ], + "sample_rows": [ + { + "word_freq_make": "0", + "word_freq_address": "0.64", + "word_freq_all": "0.64", + "word_freq_3d": "0", + "word_freq_our": "0.32", + "word_freq_over": "0", + "word_freq_remove": "0", + "word_freq_internet": "0", + "word_freq_order": "0", + "word_freq_mail": "0", + "word_freq_receive": "0", + "word_freq_will": "0.64", + "word_freq_people": "0", + "word_freq_report": "0", + "word_freq_addresses": "0", + "word_freq_free": "0.32", + "word_freq_business": "0", + "word_freq_email": "1.29", + "word_freq_you": "1.93", + "word_freq_credit": "0", + "word_freq_your": "0.96", + "word_freq_font": "0", + "word_freq_000": "0", + "word_freq_money": "0", + "word_freq_hp": "0", + "word_freq_hpl": "0", + "word_freq_george": "0", + "word_freq_650": "0", + "word_freq_lab": "0", + "word_freq_labs": "0", + "word_freq_telnet": "0", + "word_freq_857": "0", + "word_freq_data": "0", + "word_freq_415": "0", + "word_freq_85": "0", + "word_freq_technology": "0", + "word_freq_1999": "0", + "word_freq_parts": "0", + "word_freq_pm": "0", + "word_freq_direct": "0", + "word_freq_cs": "0", + "word_freq_meeting": "0", + "word_freq_original": "0", + "word_freq_project": "0", + "word_freq_re": "0", + "word_freq_edu": "0", + "word_freq_table": "0", + "word_freq_conference": "0", + "char_freq_%3B": "0", + "char_freq_%28": "0", + "char_freq_%5B": "0", + "char_freq_%21": "0.778", + "char_freq_%24": "0", + "char_freq_%23": "0", + "capital_run_length_average": "3.756", + "capital_run_length_longest": "61", + "capital_run_length_total": "278", + "class": "1" + }, + { + "word_freq_make": "0.21", + "word_freq_address": "0.28", + "word_freq_all": "0.5", + "word_freq_3d": "0", + "word_freq_our": "0.14", + "word_freq_over": "0.28", + "word_freq_remove": "0.21", + "word_freq_internet": "0.07", + "word_freq_order": "0", + "word_freq_mail": "0.94", + "word_freq_receive": "0.21", + "word_freq_will": "0.79", + "word_freq_people": "0.65", + "word_freq_report": "0.21", + "word_freq_addresses": "0.14", + "word_freq_free": "0.14", + "word_freq_business": "0.07", + "word_freq_email": "0.28", + "word_freq_you": "3.47", + "word_freq_credit": "0", + "word_freq_your": "1.59", + "word_freq_font": "0", + "word_freq_000": "0.43", + "word_freq_money": "0.43", + "word_freq_hp": "0", + "word_freq_hpl": "0", + "word_freq_george": "0", + "word_freq_650": "0", + "word_freq_lab": "0", + "word_freq_labs": "0", + "word_freq_telnet": "0", + "word_freq_857": "0", + "word_freq_data": "0", + "word_freq_415": "0", + "word_freq_85": "0", + "word_freq_technology": "0", + "word_freq_1999": "0.07", + "word_freq_parts": "0", + "word_freq_pm": "0", + "word_freq_direct": "0", + "word_freq_cs": "0", + "word_freq_meeting": "0", + "word_freq_original": "0", + "word_freq_project": "0", + "word_freq_re": "0", + "word_freq_edu": "0", + "word_freq_table": "0", + "word_freq_conference": "0", + "char_freq_%3B": "0", + "char_freq_%28": "0.132", + "char_freq_%5B": "0", + "char_freq_%21": "0.372", + "char_freq_%24": "0.18", + "char_freq_%23": "0.048", + "capital_run_length_average": "5.114", + "capital_run_length_longest": "101", + "capital_run_length_total": "1028", + "class": "1" + }, + { + "word_freq_make": "0.06", + "word_freq_address": "0", + "word_freq_all": "0.71", + "word_freq_3d": "0", + "word_freq_our": "1.23", + "word_freq_over": "0.19", + "word_freq_remove": "0.19", + "word_freq_internet": "0.12", + "word_freq_order": "0.64", + "word_freq_mail": "0.25", + "word_freq_receive": "0.38", + "word_freq_will": "0.45", + "word_freq_people": "0.12", + "word_freq_report": "0", + "word_freq_addresses": "1.75", + "word_freq_free": "0.06", + "word_freq_business": "0.06", + "word_freq_email": "1.03", + "word_freq_you": "1.36", + "word_freq_credit": "0.32", + "word_freq_your": "0.51", + "word_freq_font": "0", + "word_freq_000": "1.16", + "word_freq_money": "0.06", + "word_freq_hp": "0", + "word_freq_hpl": "0", + "word_freq_george": "0", + "word_freq_650": "0", + "word_freq_lab": "0", + "word_freq_labs": "0", + "word_freq_telnet": "0", + "word_freq_857": "0", + "word_freq_data": "0", + "word_freq_415": "0", + "word_freq_85": "0", + "word_freq_technology": "0", + "word_freq_1999": "0", + "word_freq_parts": "0", + "word_freq_pm": "0", + "word_freq_direct": "0.06", + "word_freq_cs": "0", + "word_freq_meeting": "0", + "word_freq_original": "0.12", + "word_freq_project": "0", + "word_freq_re": "0.06", + "word_freq_edu": "0.06", + "word_freq_table": "0", + "word_freq_conference": "0", + "char_freq_%3B": "0.01", + "char_freq_%28": "0.143", + "char_freq_%5B": "0", + "char_freq_%21": "0.276", + "char_freq_%24": "0.184", + "char_freq_%23": "0.01", + "capital_run_length_average": "9.821", + "capital_run_length_longest": "485", + "capital_run_length_total": "2259", + "class": "1" + }, + { + "word_freq_make": "0", + "word_freq_address": "0", + "word_freq_all": "0", + "word_freq_3d": "0", + "word_freq_our": "0.63", + "word_freq_over": "0", + "word_freq_remove": "0.31", + "word_freq_internet": "0.63", + "word_freq_order": "0.31", + "word_freq_mail": "0.63", + "word_freq_receive": "0.31", + "word_freq_will": "0.31", + "word_freq_people": "0.31", + "word_freq_report": "0", + "word_freq_addresses": "0", + "word_freq_free": "0.31", + "word_freq_business": "0", + "word_freq_email": "0", + "word_freq_you": "3.18", + "word_freq_credit": "0", + "word_freq_your": "0.31", + "word_freq_font": "0", + "word_freq_000": "0", + "word_freq_money": "0", + "word_freq_hp": "0", + "word_freq_hpl": "0", + "word_freq_george": "0", + "word_freq_650": "0", + "word_freq_lab": "0", + "word_freq_labs": "0", + "word_freq_telnet": "0", + "word_freq_857": "0", + "word_freq_data": "0", + "word_freq_415": "0", + "word_freq_85": "0", + "word_freq_technology": "0", + "word_freq_1999": "0", + "word_freq_parts": "0", + "word_freq_pm": "0", + "word_freq_direct": "0", + "word_freq_cs": "0", + "word_freq_meeting": "0", + "word_freq_original": "0", + "word_freq_project": "0", + "word_freq_re": "0", + "word_freq_edu": "0", + "word_freq_table": "0", + "word_freq_conference": "0", + "char_freq_%3B": "0", + "char_freq_%28": "0.137", + "char_freq_%5B": "0", + "char_freq_%21": "0.137", + "char_freq_%24": "0", + "char_freq_%23": "0", + "capital_run_length_average": "3.537", + "capital_run_length_longest": "40", + "capital_run_length_total": "191", + "class": "1" + }, + { + "word_freq_make": "0", + "word_freq_address": "0", + "word_freq_all": "0", + "word_freq_3d": "0", + "word_freq_our": "0.63", + "word_freq_over": "0", + "word_freq_remove": "0.31", + "word_freq_internet": "0.63", + "word_freq_order": "0.31", + "word_freq_mail": "0.63", + "word_freq_receive": "0.31", + "word_freq_will": "0.31", + "word_freq_people": "0.31", + "word_freq_report": "0", + "word_freq_addresses": "0", + "word_freq_free": "0.31", + "word_freq_business": "0", + "word_freq_email": "0", + "word_freq_you": "3.18", + "word_freq_credit": "0", + "word_freq_your": "0.31", + "word_freq_font": "0", + "word_freq_000": "0", + "word_freq_money": "0", + "word_freq_hp": "0", + "word_freq_hpl": "0", + "word_freq_george": "0", + "word_freq_650": "0", + "word_freq_lab": "0", + "word_freq_labs": "0", + "word_freq_telnet": "0", + "word_freq_857": "0", + "word_freq_data": "0", + "word_freq_415": "0", + "word_freq_85": "0", + "word_freq_technology": "0", + "word_freq_1999": "0", + "word_freq_parts": "0", + "word_freq_pm": "0", + "word_freq_direct": "0", + "word_freq_cs": "0", + "word_freq_meeting": "0", + "word_freq_original": "0", + "word_freq_project": "0", + "word_freq_re": "0", + "word_freq_edu": "0", + "word_freq_table": "0", + "word_freq_conference": "0", + "char_freq_%3B": "0", + "char_freq_%28": "0.135", + "char_freq_%5B": "0", + "char_freq_%21": "0.135", + "char_freq_%24": "0", + "char_freq_%23": "0", + "capital_run_length_average": "3.537", + "capital_run_length_longest": "40", + "capital_run_length_total": "191", + "class": "1" + } + ] +} + +Shortlisted templates: +[ + { + "template_id": "tpl_clickbench_group_count", + "template_name": "Grouped Count by Category", + "primary_family": "subgroup_structure", + "portability": "yes", + "sql_skeleton": "SELECT {group_col}, COUNT(*) AS row_count\nFROM {table}\nGROUP BY {group_col}\nORDER BY row_count DESC;", + "required_roles": [ + "group_col" + ] + } +] + +Problem instance: +{ + "dataset_id": "n1", + "question": "Use template Grouped Count by Category to probe subgroup_size_stability with semantic role count_distribution. Focus on group_col=class.", + "planned_template_id": "tpl_clickbench_group_count", + "bindings": { + "group_col": "class", + "top_k": 14, + "top_n": 5, + "num_tiles": 10, + "percentile_value": 0.95, + "z_threshold": 2.0, + "fraction_threshold": 0.1, + "baseline_multiplier": 1.5, + "baseline_fraction": 0.1, + "min_group_size": 5, + "min_support": 5, + "measure_threshold": 0.0, + "time_grain": "month", + "lookback_rows": 3, + "current_period_start": "'2024-01-01'", + "current_period_end": "'2024-04-01'", + "previous_period_start": "'2023-10-01'", + "previous_period_end": "'2024-01-01'", + "drift_ratio_threshold": 0.8 + }, + "can_vary": [], + "must_fix": [], + "runtime_sql_skeleton": "SELECT {group_col}, COUNT(*) AS row_count\nFROM {table}\nGROUP BY {group_col}\nORDER BY row_count DESC;" +} + +Repair context: +{} diff --git a/Query/sql/v2/runs/v2_cli_20260502_081223_d/n1/artifacts/v2q_n1_efa184a83eb5d8d4/cli/sql_response_attempt_1.raw.txt b/Query/sql/v2/runs/v2_cli_20260502_081223_d/n1/artifacts/v2q_n1_efa184a83eb5d8d4/cli/sql_response_attempt_1.raw.txt new file mode 100644 index 0000000000000000000000000000000000000000..5b928e941b7f55c2489c37d787f91c30e3813ad3 --- /dev/null +++ b/Query/sql/v2/runs/v2_cli_20260502_081223_d/n1/artifacts/v2q_n1_efa184a83eb5d8d4/cli/sql_response_attempt_1.raw.txt @@ -0,0 +1,4 @@ +{"type":"thread.started","thread_id":"019e40de-1543-76b1-bf81-2a976968ea58"} +{"type":"turn.started"} +{"type":"item.completed","item":{"id":"item_0","type":"agent_message","text":"{\"sql\":\"-- template_id: tpl_clickbench_group_count\\nSELECT \\\"class\\\", COUNT(*) AS \\\"row_count\\\"\\nFROM \\\"n1\\\"\\nGROUP BY \\\"class\\\"\\nORDER BY \\\"row_count\\\" DESC;\",\"notes\":\"Applies the grouped count template with group_col bound to \\\"class\\\" to show the class distribution.\"}"}} +{"type":"turn.completed","usage":{"input_tokens":20286,"cached_input_tokens":12032,"output_tokens":210,"reasoning_output_tokens":135}} diff --git a/Query/sql/v2/runs/v2_cli_20260502_081223_d/n1/artifacts/v2q_n1_efa184a83eb5d8d4/cli/sql_response_attempt_1.txt b/Query/sql/v2/runs/v2_cli_20260502_081223_d/n1/artifacts/v2q_n1_efa184a83eb5d8d4/cli/sql_response_attempt_1.txt new file mode 100644 index 0000000000000000000000000000000000000000..ad8826b753a3513f8a07789abdf02ed02a1cc88e --- /dev/null +++ b/Query/sql/v2/runs/v2_cli_20260502_081223_d/n1/artifacts/v2q_n1_efa184a83eb5d8d4/cli/sql_response_attempt_1.txt @@ -0,0 +1 @@ +{"sql":"-- template_id: tpl_clickbench_group_count\nSELECT \"class\", COUNT(*) AS \"row_count\"\nFROM \"n1\"\nGROUP BY \"class\"\nORDER BY \"row_count\" DESC;","notes":"Applies the grouped count template with group_col bound to \"class\" to show the class distribution."} \ No newline at end of file diff --git a/Query/sql/v2/runs/v2_cli_20260502_081223_d/n1/artifacts/v2q_n1_efa184a83eb5d8d4/cli/sql_stderr_attempt_1.txt b/Query/sql/v2/runs/v2_cli_20260502_081223_d/n1/artifacts/v2q_n1_efa184a83eb5d8d4/cli/sql_stderr_attempt_1.txt new file mode 100644 index 0000000000000000000000000000000000000000..e69de29bb2d1d6434b8b29ae775ad8c2e48c5391 diff --git a/Query/sql/v2/runs/v2_cli_20260502_081223_d/n1/artifacts/v2q_n1_f28548e301caf223/cli/sql_attempt_1.metadata.json b/Query/sql/v2/runs/v2_cli_20260502_081223_d/n1/artifacts/v2q_n1_f28548e301caf223/cli/sql_attempt_1.metadata.json new file mode 100644 index 0000000000000000000000000000000000000000..da1ea3dfe7866e22d8f22014ffd8cb4f5419ec8a --- /dev/null +++ b/Query/sql/v2/runs/v2_cli_20260502_081223_d/n1/artifacts/v2q_n1_f28548e301caf223/cli/sql_attempt_1.metadata.json @@ -0,0 +1,43 @@ +{ + "attempt": 1, + "phase": "sql_generation", + "command": "codex exec --skip-git-repo-check --disable plugins --sandbox read-only --cd \"/data/jialinzhang/SQLagent\" -m gpt-5.4 --json -", + "started_at": "2026-05-19T16:01:55.128125+00:00", + "ended_at": "2026-05-19T16:01:59.698190+00:00", + "elapsed_ms": 4570.04, + "returncode": 1, + "prompt_metrics": { + "chars": 29333, + "bytes_utf8": 29333, + "lines": 790, + "estimated_tokens": null + }, + "stdout_metrics": { + "chars": 281, + "bytes_utf8": 281, + "lines": 4, + "estimated_tokens": null + }, + "stderr_metrics": { + "chars": 0, + "bytes_utf8": 0, + "lines": 0, + "estimated_tokens": null + }, + "parsed_output": { + "format": "jsonl_events", + "text_metrics": { + "chars": 280, + "bytes_utf8": 280, + "lines": 4, + "estimated_tokens": null + }, + "usage": {} + }, + "status": "failed", + "error": "AI CLI command failed with exit code 1: ", + "prompt_path": "cli/sql_prompt_attempt_1.txt", + "response_path": "cli/sql_response_attempt_1.txt", + "raw_response_path": "cli/sql_response_attempt_1.raw.txt", + "stderr_path": "cli/sql_stderr_attempt_1.txt" +} \ No newline at end of file diff --git a/Query/sql/v2/runs/v2_cli_20260502_081223_d/n1/artifacts/v2q_n1_f28548e301caf223/cli/sql_attempt_2.metadata.json b/Query/sql/v2/runs/v2_cli_20260502_081223_d/n1/artifacts/v2q_n1_f28548e301caf223/cli/sql_attempt_2.metadata.json new file mode 100644 index 0000000000000000000000000000000000000000..f829b7419e577643dc4d721ec046aa1ea730b20b --- /dev/null +++ b/Query/sql/v2/runs/v2_cli_20260502_081223_d/n1/artifacts/v2q_n1_f28548e301caf223/cli/sql_attempt_2.metadata.json @@ -0,0 +1,43 @@ +{ + "attempt": 2, + "phase": "sql_generation", + "command": "codex exec --skip-git-repo-check --disable plugins --sandbox read-only --cd \"/data/jialinzhang/SQLagent\" -m gpt-5.4 --json -", + "started_at": "2026-05-19T16:02:00.700356+00:00", + "ended_at": "2026-05-19T16:02:03.676182+00:00", + "elapsed_ms": 2975.79, + "returncode": 1, + "prompt_metrics": { + "chars": 29333, + "bytes_utf8": 29333, + "lines": 790, + "estimated_tokens": null + }, + "stdout_metrics": { + "chars": 281, + "bytes_utf8": 281, + "lines": 4, + "estimated_tokens": null + }, + "stderr_metrics": { + "chars": 0, + "bytes_utf8": 0, + "lines": 0, + "estimated_tokens": null + }, + "parsed_output": { + "format": "jsonl_events", + "text_metrics": { + "chars": 280, + "bytes_utf8": 280, + "lines": 4, + "estimated_tokens": null + }, + "usage": {} + }, + "status": "failed", + "error": "AI CLI command failed with exit code 1: ", + "prompt_path": "cli/sql_prompt_attempt_2.txt", + "response_path": "cli/sql_response_attempt_2.txt", + "raw_response_path": "cli/sql_response_attempt_2.raw.txt", + "stderr_path": "cli/sql_stderr_attempt_2.txt" +} \ No newline at end of file diff --git a/Query/sql/v2/runs/v2_cli_20260502_081223_d/n1/artifacts/v2q_n1_f28548e301caf223/cli/sql_prompt_attempt_1.txt b/Query/sql/v2/runs/v2_cli_20260502_081223_d/n1/artifacts/v2q_n1_f28548e301caf223/cli/sql_prompt_attempt_1.txt new file mode 100644 index 0000000000000000000000000000000000000000..06e95b69db8a1207956c4c4feee3ec6b07a14fa4 --- /dev/null +++ b/Query/sql/v2/runs/v2_cli_20260502_081223_d/n1/artifacts/v2q_n1_f28548e301caf223/cli/sql_prompt_attempt_1.txt @@ -0,0 +1,790 @@ +You are generating one SQLite SELECT query for a single-table SQL QA task. +Return strict JSON only, with this schema: {"sql": "...", "notes": "..."}. +Rules: +- Use only the provided table and columns. +- Do not write INSERT, UPDATE, DELETE, DROP, ALTER, CREATE, PRAGMA, ATTACH, DETACH, or VACUUM. +- Prefer the planned template and bound roles when provided. +- Add a leading SQL comment exactly like: -- template_id: . +- Generate SQLite-compatible SQL. SQLite does not support PERCENTILE_CONT or STDDEV. +- Quote identifiers with double quotes. +- Return no markdown and no extra prose. + +Dataset context: +Dataset context for SQL QA: +- dataset_id: n1 +- dataset_name: Spambase +- table_name: n1 +- table_layout: single-table dataset (do not assume joins). +- row_semantics: One row is one email represented by engineered token/character frequency and capitalization-run features. +- task_type: classification +- target_column: class +- main_row_count: 4601 +- important_fields: +- word_freq_make: role=feature, type=numeric_sparse_frequency. tags=['condition_candidate', 'measure', 'rare_pattern_candidate'] desc=Frequency feature for token 'make' in an email message. +- word_freq_address: role=feature, type=numeric_sparse_frequency. tags=['condition_candidate', 'measure', 'rare_pattern_candidate'] desc=Frequency feature for token 'address' in an email message. +- word_freq_all: role=feature, type=numeric_sparse_frequency. tags=['condition_candidate', 'measure', 'rare_pattern_candidate'] desc=Frequency feature for token 'all' in an email message. +- word_freq_3d: role=feature, type=numeric_sparse_frequency. tags=['condition_candidate', 'measure', 'rare_pattern_candidate'] desc=Frequency feature for token '3d' in an email message. +- word_freq_our: role=feature, type=numeric_sparse_frequency. tags=['condition_candidate', 'measure', 'rare_pattern_candidate'] desc=Frequency feature for token 'our' in an email message. +- word_freq_over: role=feature, type=numeric_sparse_frequency. tags=['condition_candidate', 'measure', 'rare_pattern_candidate'] desc=Frequency feature for token 'over' in an email message. +- word_freq_remove: role=feature, type=numeric_sparse_frequency. tags=['condition_candidate', 'measure', 'rare_pattern_candidate'] desc=Frequency feature for token 'remove' in an email message. +- word_freq_internet: role=feature, type=numeric_sparse_frequency. tags=['condition_candidate', 'measure', 'rare_pattern_candidate'] desc=Frequency feature for token 'internet' in an email message. +- word_freq_order: role=feature, type=numeric_sparse_frequency. tags=['condition_candidate', 'measure', 'rare_pattern_candidate'] desc=Frequency feature for token 'order' in an email message. +- word_freq_mail: role=feature, type=numeric_sparse_frequency. tags=['condition_candidate', 'measure', 'rare_pattern_candidate'] desc=Frequency feature for token 'mail' in an email message. +- word_freq_receive: role=feature, type=numeric_sparse_frequency. tags=['condition_candidate', 'measure', 'rare_pattern_candidate'] desc=Frequency feature for token 'receive' in an email message. +- word_freq_will: role=feature, type=numeric_sparse_frequency. tags=['condition_candidate', 'measure', 'rare_pattern_candidate'] desc=Frequency feature for token 'will' in an email message. +- word_freq_people: role=feature, type=numeric_sparse_frequency. tags=['condition_candidate', 'measure', 'rare_pattern_candidate'] desc=Frequency feature for token 'people' in an email message. +- word_freq_report: role=feature, type=numeric_sparse_frequency. tags=['condition_candidate', 'measure', 'rare_pattern_candidate'] desc=Frequency feature for token 'report' in an email message. +- word_freq_addresses: role=feature, type=numeric_sparse_frequency. tags=['condition_candidate', 'measure', 'rare_pattern_candidate'] desc=Frequency feature for token 'addresses' in an email message. +- word_freq_free: role=feature, type=numeric_sparse_frequency. tags=['condition_candidate', 'measure', 'rare_pattern_candidate'] desc=Frequency feature for token 'free' in an email message. +- word_freq_business: role=feature, type=numeric_sparse_frequency. tags=['condition_candidate', 'measure', 'rare_pattern_candidate'] desc=Frequency feature for token 'business' in an email message. +- word_freq_email: role=feature, type=numeric_sparse_frequency. tags=['condition_candidate', 'measure', 'rare_pattern_candidate'] desc=Frequency feature for token 'email' in an email message. +- word_freq_you: role=feature, type=numeric_sparse_frequency. tags=['condition_candidate', 'measure', 'rare_pattern_candidate'] desc=Frequency feature for token 'you' in an email message. +- word_freq_credit: role=feature, type=numeric_sparse_frequency. tags=['condition_candidate', 'measure', 'rare_pattern_candidate'] desc=Frequency feature for token 'credit' in an email message. +- word_freq_your: role=feature, type=numeric_sparse_frequency. tags=['condition_candidate', 'measure', 'rare_pattern_candidate'] desc=Frequency feature for token 'your' in an email message. +- word_freq_font: role=feature, type=numeric_sparse_frequency. tags=['condition_candidate', 'measure', 'rare_pattern_candidate'] desc=Frequency feature for token 'font' in an email message. +- word_freq_000: role=feature, type=numeric_sparse_frequency. tags=['condition_candidate', 'measure', 'rare_pattern_candidate'] desc=Frequency feature for token '000' in an email message. +- word_freq_money: role=feature, type=numeric_sparse_frequency. tags=['condition_candidate', 'measure', 'rare_pattern_candidate'] desc=Frequency feature for token 'money' in an email message. +- word_freq_hp: role=feature, type=numeric_sparse_frequency. tags=['condition_candidate', 'measure', 'rare_pattern_candidate'] desc=Frequency feature for token 'hp' in an email message. +- word_freq_hpl: role=feature, type=numeric_sparse_frequency. tags=['condition_candidate', 'measure', 'rare_pattern_candidate'] desc=Frequency feature for token 'hpl' in an email message. +- word_freq_george: role=feature, type=numeric_sparse_frequency. tags=['condition_candidate', 'measure', 'rare_pattern_candidate'] desc=Frequency feature for token 'george' in an email message. +- word_freq_650: role=feature, type=numeric_sparse_frequency. tags=['condition_candidate', 'measure', 'rare_pattern_candidate'] desc=Frequency feature for token '650' in an email message. +- word_freq_lab: role=feature, type=numeric_sparse_frequency. tags=['condition_candidate', 'measure', 'rare_pattern_candidate'] desc=Frequency feature for token 'lab' in an email message. +- word_freq_labs: role=feature, type=numeric_sparse_frequency. tags=['condition_candidate', 'measure', 'rare_pattern_candidate'] desc=Frequency feature for token 'labs' in an email message. +- word_freq_telnet: role=feature, type=numeric_sparse_frequency. tags=['condition_candidate', 'measure', 'rare_pattern_candidate'] desc=Frequency feature for token 'telnet' in an email message. +- word_freq_857: role=feature, type=numeric_sparse_frequency. tags=['condition_candidate', 'measure', 'rare_pattern_candidate'] desc=Frequency feature for token '857' in an email message. +- word_freq_data: role=feature, type=numeric_sparse_frequency. tags=['condition_candidate', 'measure', 'rare_pattern_candidate'] desc=Frequency feature for token 'data' in an email message. +- word_freq_415: role=feature, type=numeric_sparse_frequency. tags=['condition_candidate', 'measure', 'rare_pattern_candidate'] desc=Frequency feature for token '415' in an email message. +- word_freq_85: role=feature, type=numeric_sparse_frequency. tags=['condition_candidate', 'measure', 'rare_pattern_candidate'] desc=Frequency feature for token '85' in an email message. +- word_freq_technology: role=feature, type=numeric_sparse_frequency. tags=['condition_candidate', 'measure', 'rare_pattern_candidate'] desc=Frequency feature for token 'technology' in an email message. +- word_freq_1999: role=feature, type=numeric_sparse_frequency. tags=['condition_candidate', 'measure', 'rare_pattern_candidate'] desc=Frequency feature for token '1999' in an email message. +- word_freq_parts: role=feature, type=numeric_sparse_frequency. tags=['condition_candidate', 'measure', 'rare_pattern_candidate'] desc=Frequency feature for token 'parts' in an email message. +- word_freq_pm: role=feature, type=numeric_sparse_frequency. tags=['condition_candidate', 'measure', 'rare_pattern_candidate'] desc=Frequency feature for token 'pm' in an email message. +- word_freq_direct: role=feature, type=numeric_sparse_frequency. tags=['condition_candidate', 'measure', 'rare_pattern_candidate'] desc=Frequency feature for token 'direct' in an email message. +- word_freq_cs: role=feature, type=numeric_sparse_frequency. tags=['condition_candidate', 'measure', 'rare_pattern_candidate'] desc=Frequency feature for token 'cs' in an email message. +- word_freq_meeting: role=feature, type=numeric_sparse_frequency. tags=['condition_candidate', 'measure', 'rare_pattern_candidate'] desc=Frequency feature for token 'meeting' in an email message. +- word_freq_original: role=feature, type=numeric_sparse_frequency. tags=['condition_candidate', 'measure', 'rare_pattern_candidate'] desc=Frequency feature for token 'original' in an email message. +- word_freq_project: role=feature, type=numeric_sparse_frequency. tags=['condition_candidate', 'measure', 'rare_pattern_candidate'] desc=Frequency feature for token 'project' in an email message. +- word_freq_re: role=feature, type=numeric_sparse_frequency. tags=['condition_candidate', 'measure', 'rare_pattern_candidate'] desc=Frequency feature for token 're' in an email message. +- word_freq_edu: role=feature, type=numeric_sparse_frequency. tags=['condition_candidate', 'measure', 'rare_pattern_candidate'] desc=Frequency feature for token 'edu' in an email message. +- word_freq_table: role=feature, type=numeric_sparse_frequency. tags=['condition_candidate', 'measure', 'rare_pattern_candidate'] desc=Frequency feature for token 'table' in an email message. +- word_freq_conference: role=feature, type=numeric_sparse_frequency. tags=['condition_candidate', 'measure', 'rare_pattern_candidate'] desc=Frequency feature for token 'conference' in an email message. +- char_freq_%3B: role=feature, type=numeric_sparse_frequency. tags=['condition_candidate', 'measure', 'rare_pattern_candidate'] desc=Character frequency feature for symbol ';'. +- char_freq_%28: role=feature, type=numeric_sparse_frequency. tags=['condition_candidate', 'measure', 'rare_pattern_candidate'] desc=Character frequency feature for symbol '('. +- char_freq_%5B: role=feature, type=numeric_sparse_frequency. tags=['condition_candidate', 'measure', 'rare_pattern_candidate'] desc=Character frequency feature for symbol '['. +- char_freq_%21: role=feature, type=numeric_sparse_frequency. tags=['condition_candidate', 'measure', 'rare_pattern_candidate'] desc=Character frequency feature for symbol '!'. +- char_freq_%24: role=feature, type=numeric_sparse_frequency. tags=['condition_candidate', 'measure', 'rare_pattern_candidate'] desc=Character frequency feature for symbol '$'. +- char_freq_%23: role=feature, type=numeric_sparse_frequency. tags=['condition_candidate', 'measure', 'rare_pattern_candidate'] desc=Character frequency feature for symbol '#'. +- capital_run_length_average: role=feature, type=numeric_heavy_tail. tags=['condition_candidate', 'measure', 'rare_pattern_candidate'] desc=Average length of uninterrupted capital-letter runs. +- capital_run_length_longest: role=feature, type=numeric_heavy_tail. tags=['condition_candidate', 'measure', 'rare_pattern_candidate'] desc=Longest uninterrupted capital-letter run. +- capital_run_length_total: role=feature, type=numeric_heavy_tail. tags=['condition_candidate', 'measure', 'rare_pattern_candidate'] desc=Total number of capital letters in runs. +- class: role=target, type=binary_target. tags=['target_candidate'] desc=Binary spam label (1=spam, 0=non-spam). +- useful_field_combinations: [['word_freq_free', 'word_freq_you', 'class'], ['char_freq_%21', 'capital_run_length_longest', 'class'], ['word_freq_remove', 'word_freq_money', 'class']] +- fields_requiring_caution: ['capital_run_length_average', 'capital_run_length_longest', 'capital_run_length_total'] +- source_url: https://www.openml.org/d/44 + +SQLite schema snapshot: +{ + "table_name": "n1", + "quoted_table_name": "\"n1\"", + "row_count": 4601, + "columns": [ + { + "name": "word_freq_make", + "type": "TEXT", + "notnull": false, + "pk": false + }, + { + "name": "word_freq_address", + "type": "TEXT", + "notnull": false, + "pk": false + }, + { + "name": "word_freq_all", + "type": "TEXT", + "notnull": false, + "pk": false + }, + { + "name": "word_freq_3d", + "type": "TEXT", + "notnull": false, + "pk": false + }, + { + "name": "word_freq_our", + "type": "TEXT", + "notnull": false, + "pk": false + }, + { + "name": "word_freq_over", + "type": "TEXT", + "notnull": false, + "pk": false + }, + { + "name": "word_freq_remove", + "type": "TEXT", + "notnull": false, + "pk": false + }, + { + "name": "word_freq_internet", + "type": "TEXT", + "notnull": false, + "pk": false + }, + { + "name": "word_freq_order", + "type": "TEXT", + "notnull": false, + "pk": false + }, + { + "name": "word_freq_mail", + "type": "TEXT", + "notnull": false, + "pk": false + }, + { + "name": "word_freq_receive", + "type": "TEXT", + "notnull": false, + "pk": false + }, + { + "name": "word_freq_will", + "type": "TEXT", + "notnull": false, + "pk": false + }, + { + "name": "word_freq_people", + "type": "TEXT", + "notnull": false, + "pk": false + }, + { + "name": "word_freq_report", + "type": "TEXT", + "notnull": false, + "pk": false + }, + { + "name": "word_freq_addresses", + "type": "TEXT", + "notnull": false, + "pk": false + }, + { + "name": "word_freq_free", + "type": "TEXT", + "notnull": false, + "pk": false + }, + { + "name": "word_freq_business", + "type": "TEXT", + "notnull": false, + "pk": false + }, + { + "name": "word_freq_email", + "type": "TEXT", + "notnull": false, + "pk": false + }, + { + "name": "word_freq_you", + "type": "TEXT", + "notnull": false, + "pk": false + }, + { + "name": "word_freq_credit", + "type": "TEXT", + "notnull": false, + "pk": false + }, + { + "name": "word_freq_your", + "type": "TEXT", + "notnull": false, + "pk": false + }, + { + "name": "word_freq_font", + "type": "TEXT", + "notnull": false, + "pk": false + }, + { + "name": "word_freq_000", + "type": "TEXT", + "notnull": false, + "pk": false + }, + { + "name": "word_freq_money", + "type": "TEXT", + "notnull": false, + "pk": false + }, + { + "name": "word_freq_hp", + "type": "TEXT", + "notnull": false, + "pk": false + }, + { + "name": "word_freq_hpl", + "type": "TEXT", + "notnull": false, + "pk": false + }, + { + "name": "word_freq_george", + "type": "TEXT", + "notnull": false, + "pk": false + }, + { + "name": "word_freq_650", + "type": "TEXT", + "notnull": false, + "pk": false + }, + { + "name": "word_freq_lab", + "type": "TEXT", + "notnull": false, + "pk": false + }, + { + "name": "word_freq_labs", + "type": "TEXT", + "notnull": false, + "pk": false + }, + { + "name": "word_freq_telnet", + "type": "TEXT", + "notnull": false, + "pk": false + }, + { + "name": "word_freq_857", + "type": "TEXT", + "notnull": false, + "pk": false + }, + { + "name": "word_freq_data", + "type": "TEXT", + "notnull": false, + "pk": false + }, + { + "name": "word_freq_415", + "type": "TEXT", + "notnull": false, + "pk": false + }, + { + "name": "word_freq_85", + "type": "TEXT", + "notnull": false, + "pk": false + }, + { + "name": "word_freq_technology", + "type": "TEXT", + "notnull": false, + "pk": false + }, + { + "name": "word_freq_1999", + "type": "TEXT", + "notnull": false, + "pk": false + }, + { + "name": "word_freq_parts", + "type": "TEXT", + "notnull": false, + "pk": false + }, + { + "name": "word_freq_pm", + "type": "TEXT", + "notnull": false, + "pk": false + }, + { + "name": "word_freq_direct", + "type": "TEXT", + "notnull": false, + "pk": false + }, + { + "name": "word_freq_cs", + "type": "TEXT", + "notnull": false, + "pk": false + }, + { + "name": "word_freq_meeting", + "type": "TEXT", + "notnull": false, + "pk": false + }, + { + "name": "word_freq_original", + "type": "TEXT", + "notnull": false, + "pk": false + }, + { + "name": "word_freq_project", + "type": "TEXT", + "notnull": false, + "pk": false + }, + { + "name": "word_freq_re", + "type": "TEXT", + "notnull": false, + "pk": false + }, + { + "name": "word_freq_edu", + "type": "TEXT", + "notnull": false, + "pk": false + }, + { + "name": "word_freq_table", + "type": "TEXT", + "notnull": false, + "pk": false + }, + { + "name": "word_freq_conference", + "type": "TEXT", + "notnull": false, + "pk": false + }, + { + "name": "char_freq_%3B", + "type": "TEXT", + "notnull": false, + "pk": false + }, + { + "name": "char_freq_%28", + "type": "TEXT", + "notnull": false, + "pk": false + }, + { + "name": "char_freq_%5B", + "type": "TEXT", + "notnull": false, + "pk": false + }, + { + "name": "char_freq_%21", + "type": "TEXT", + "notnull": false, + "pk": false + }, + { + "name": "char_freq_%24", + "type": "TEXT", + "notnull": false, + "pk": false + }, + { + "name": "char_freq_%23", + "type": "TEXT", + "notnull": false, + "pk": false + }, + { + "name": "capital_run_length_average", + "type": "TEXT", + "notnull": false, + "pk": false + }, + { + "name": "capital_run_length_longest", + "type": "TEXT", + "notnull": false, + "pk": false + }, + { + "name": "capital_run_length_total", + "type": "TEXT", + "notnull": false, + "pk": false + }, + { + "name": "class", + "type": "TEXT", + "notnull": false, + "pk": false + } + ], + "sample_rows": [ + { + "word_freq_make": "0", + "word_freq_address": "0.64", + "word_freq_all": "0.64", + "word_freq_3d": "0", + "word_freq_our": "0.32", + "word_freq_over": "0", + "word_freq_remove": "0", + "word_freq_internet": "0", + "word_freq_order": "0", + "word_freq_mail": "0", + "word_freq_receive": "0", + "word_freq_will": "0.64", + "word_freq_people": "0", + "word_freq_report": "0", + "word_freq_addresses": "0", + "word_freq_free": "0.32", + "word_freq_business": "0", + "word_freq_email": "1.29", + "word_freq_you": "1.93", + "word_freq_credit": "0", + "word_freq_your": "0.96", + "word_freq_font": "0", + "word_freq_000": "0", + "word_freq_money": "0", + "word_freq_hp": "0", + "word_freq_hpl": "0", + "word_freq_george": "0", + "word_freq_650": "0", + "word_freq_lab": "0", + "word_freq_labs": "0", + "word_freq_telnet": "0", + "word_freq_857": "0", + "word_freq_data": "0", + "word_freq_415": "0", + "word_freq_85": "0", + "word_freq_technology": "0", + "word_freq_1999": "0", + "word_freq_parts": "0", + "word_freq_pm": "0", + "word_freq_direct": "0", + "word_freq_cs": "0", + "word_freq_meeting": "0", + "word_freq_original": "0", + "word_freq_project": "0", + "word_freq_re": "0", + "word_freq_edu": "0", + "word_freq_table": "0", + "word_freq_conference": "0", + "char_freq_%3B": "0", + "char_freq_%28": "0", + "char_freq_%5B": "0", + "char_freq_%21": "0.778", + "char_freq_%24": "0", + "char_freq_%23": "0", + "capital_run_length_average": "3.756", + "capital_run_length_longest": "61", + "capital_run_length_total": "278", + "class": "1" + }, + { + "word_freq_make": "0.21", + "word_freq_address": "0.28", + "word_freq_all": "0.5", + "word_freq_3d": "0", + "word_freq_our": "0.14", + "word_freq_over": "0.28", + "word_freq_remove": "0.21", + "word_freq_internet": "0.07", + "word_freq_order": "0", + "word_freq_mail": "0.94", + "word_freq_receive": "0.21", + "word_freq_will": "0.79", + "word_freq_people": "0.65", + "word_freq_report": "0.21", + "word_freq_addresses": "0.14", + "word_freq_free": "0.14", + "word_freq_business": "0.07", + "word_freq_email": "0.28", + "word_freq_you": "3.47", + "word_freq_credit": "0", + "word_freq_your": "1.59", + "word_freq_font": "0", + "word_freq_000": "0.43", + "word_freq_money": "0.43", + "word_freq_hp": "0", + "word_freq_hpl": "0", + "word_freq_george": "0", + "word_freq_650": "0", + "word_freq_lab": "0", + "word_freq_labs": "0", + "word_freq_telnet": "0", + "word_freq_857": "0", + "word_freq_data": "0", + "word_freq_415": "0", + "word_freq_85": "0", + "word_freq_technology": "0", + "word_freq_1999": "0.07", + "word_freq_parts": "0", + "word_freq_pm": "0", + "word_freq_direct": "0", + "word_freq_cs": "0", + "word_freq_meeting": "0", + "word_freq_original": "0", + "word_freq_project": "0", + "word_freq_re": "0", + "word_freq_edu": "0", + "word_freq_table": "0", + "word_freq_conference": "0", + "char_freq_%3B": "0", + "char_freq_%28": "0.132", + "char_freq_%5B": "0", + "char_freq_%21": "0.372", + "char_freq_%24": "0.18", + "char_freq_%23": "0.048", + "capital_run_length_average": "5.114", + "capital_run_length_longest": "101", + "capital_run_length_total": "1028", + "class": "1" + }, + { + "word_freq_make": "0.06", + "word_freq_address": "0", + "word_freq_all": "0.71", + "word_freq_3d": "0", + "word_freq_our": "1.23", + "word_freq_over": "0.19", + "word_freq_remove": "0.19", + "word_freq_internet": "0.12", + "word_freq_order": "0.64", + "word_freq_mail": "0.25", + "word_freq_receive": "0.38", + "word_freq_will": "0.45", + "word_freq_people": "0.12", + "word_freq_report": "0", + "word_freq_addresses": "1.75", + "word_freq_free": "0.06", + "word_freq_business": "0.06", + "word_freq_email": "1.03", + "word_freq_you": "1.36", + "word_freq_credit": "0.32", + "word_freq_your": "0.51", + "word_freq_font": "0", + "word_freq_000": "1.16", + "word_freq_money": "0.06", + "word_freq_hp": "0", + "word_freq_hpl": "0", + "word_freq_george": "0", + "word_freq_650": "0", + "word_freq_lab": "0", + "word_freq_labs": "0", + "word_freq_telnet": "0", + "word_freq_857": "0", + "word_freq_data": "0", + "word_freq_415": "0", + "word_freq_85": "0", + "word_freq_technology": "0", + "word_freq_1999": "0", + "word_freq_parts": "0", + "word_freq_pm": "0", + "word_freq_direct": "0.06", + "word_freq_cs": "0", + "word_freq_meeting": "0", + "word_freq_original": "0.12", + "word_freq_project": "0", + "word_freq_re": "0.06", + "word_freq_edu": "0.06", + "word_freq_table": "0", + "word_freq_conference": "0", + "char_freq_%3B": "0.01", + "char_freq_%28": "0.143", + "char_freq_%5B": "0", + "char_freq_%21": "0.276", + "char_freq_%24": "0.184", + "char_freq_%23": "0.01", + "capital_run_length_average": "9.821", + "capital_run_length_longest": "485", + "capital_run_length_total": "2259", + "class": "1" + }, + { + "word_freq_make": "0", + "word_freq_address": "0", + "word_freq_all": "0", + "word_freq_3d": "0", + "word_freq_our": "0.63", + "word_freq_over": "0", + "word_freq_remove": "0.31", + "word_freq_internet": "0.63", + "word_freq_order": "0.31", + "word_freq_mail": "0.63", + "word_freq_receive": "0.31", + "word_freq_will": "0.31", + "word_freq_people": "0.31", + "word_freq_report": "0", + "word_freq_addresses": "0", + "word_freq_free": "0.31", + "word_freq_business": "0", + "word_freq_email": "0", + "word_freq_you": "3.18", + "word_freq_credit": "0", + "word_freq_your": "0.31", + "word_freq_font": "0", + "word_freq_000": "0", + "word_freq_money": "0", + "word_freq_hp": "0", + "word_freq_hpl": "0", + "word_freq_george": "0", + "word_freq_650": "0", + "word_freq_lab": "0", + "word_freq_labs": "0", + "word_freq_telnet": "0", + "word_freq_857": "0", + "word_freq_data": "0", + "word_freq_415": "0", + "word_freq_85": "0", + "word_freq_technology": "0", + "word_freq_1999": "0", + "word_freq_parts": "0", + "word_freq_pm": "0", + "word_freq_direct": "0", + "word_freq_cs": "0", + "word_freq_meeting": "0", + "word_freq_original": "0", + "word_freq_project": "0", + "word_freq_re": "0", + "word_freq_edu": "0", + "word_freq_table": "0", + "word_freq_conference": "0", + "char_freq_%3B": "0", + "char_freq_%28": "0.137", + "char_freq_%5B": "0", + "char_freq_%21": "0.137", + "char_freq_%24": "0", + "char_freq_%23": "0", + "capital_run_length_average": "3.537", + "capital_run_length_longest": "40", + "capital_run_length_total": "191", + "class": "1" + }, + { + "word_freq_make": "0", + "word_freq_address": "0", + "word_freq_all": "0", + "word_freq_3d": "0", + "word_freq_our": "0.63", + "word_freq_over": "0", + "word_freq_remove": "0.31", + "word_freq_internet": "0.63", + "word_freq_order": "0.31", + "word_freq_mail": "0.63", + "word_freq_receive": "0.31", + "word_freq_will": "0.31", + "word_freq_people": "0.31", + "word_freq_report": "0", + "word_freq_addresses": "0", + "word_freq_free": "0.31", + "word_freq_business": "0", + "word_freq_email": "0", + "word_freq_you": "3.18", + "word_freq_credit": "0", + "word_freq_your": "0.31", + "word_freq_font": "0", + "word_freq_000": "0", + "word_freq_money": "0", + "word_freq_hp": "0", + "word_freq_hpl": "0", + "word_freq_george": "0", + "word_freq_650": "0", + "word_freq_lab": "0", + "word_freq_labs": "0", + "word_freq_telnet": "0", + "word_freq_857": "0", + "word_freq_data": "0", + "word_freq_415": "0", + "word_freq_85": "0", + "word_freq_technology": "0", + "word_freq_1999": "0", + "word_freq_parts": "0", + "word_freq_pm": "0", + "word_freq_direct": "0", + "word_freq_cs": "0", + "word_freq_meeting": "0", + "word_freq_original": "0", + "word_freq_project": "0", + "word_freq_re": "0", + "word_freq_edu": "0", + "word_freq_table": "0", + "word_freq_conference": "0", + "char_freq_%3B": "0", + "char_freq_%28": "0.135", + "char_freq_%5B": "0", + "char_freq_%21": "0.135", + "char_freq_%24": "0", + "char_freq_%23": "0", + "capital_run_length_average": "3.537", + "capital_run_length_longest": "40", + "capital_run_length_total": "191", + "class": "1" + } + ] +} + +Shortlisted templates: +[ + { + "template_id": "tpl_tail_low_support_group_count_v2", + "template_name": "Low-Support Group Count", + "primary_family": "tail_rarity_structure", + "portability": "yes", + "sql_skeleton": "SELECT\n {group_col},\n COUNT(*) AS support\nFROM {table}\nGROUP BY {group_col}\nORDER BY support ASC, {group_col}\nLIMIT {top_k};", + "required_roles": [ + "group_col" + ] + } +] + +Problem instance: +{ + "dataset_id": "n1", + "question": "Use template Low-Support Group Count to probe tail_mass_similarity with semantic role count_distribution. Focus on group_col=class.", + "planned_template_id": "tpl_tail_low_support_group_count_v2", + "bindings": { + "group_col": "class", + "top_k": 11, + "top_n": 4, + "num_tiles": 10, + "percentile_value": 0.9, + "z_threshold": 2.0, + "fraction_threshold": 0.1, + "baseline_multiplier": 1.5, + "baseline_fraction": 0.1, + "min_group_size": 5, + "min_support": 5, + "measure_threshold": 0.0, + "time_grain": "month", + "lookback_rows": 3, + "current_period_start": "'2024-01-01'", + "current_period_end": "'2024-04-01'", + "previous_period_start": "'2023-10-01'", + "previous_period_end": "'2024-01-01'", + "drift_ratio_threshold": 0.8 + }, + "can_vary": [], + "must_fix": [], + "runtime_sql_skeleton": "SELECT\n {group_col},\n COUNT(*) AS support\nFROM {table}\nGROUP BY {group_col}\nORDER BY support ASC, {group_col}\nLIMIT {top_k};" +} + +Repair context: +{} diff --git a/Query/sql/v2/runs/v2_cli_20260502_081223_d/n1/artifacts/v2q_n1_f28548e301caf223/cli/sql_prompt_attempt_2.txt b/Query/sql/v2/runs/v2_cli_20260502_081223_d/n1/artifacts/v2q_n1_f28548e301caf223/cli/sql_prompt_attempt_2.txt new file mode 100644 index 0000000000000000000000000000000000000000..06e95b69db8a1207956c4c4feee3ec6b07a14fa4 --- /dev/null +++ b/Query/sql/v2/runs/v2_cli_20260502_081223_d/n1/artifacts/v2q_n1_f28548e301caf223/cli/sql_prompt_attempt_2.txt @@ -0,0 +1,790 @@ +You are generating one SQLite SELECT query for a single-table SQL QA task. +Return strict JSON only, with this schema: {"sql": "...", "notes": "..."}. +Rules: +- Use only the provided table and columns. +- Do not write INSERT, UPDATE, DELETE, DROP, ALTER, CREATE, PRAGMA, ATTACH, DETACH, or VACUUM. +- Prefer the planned template and bound roles when provided. +- Add a leading SQL comment exactly like: -- template_id: . +- Generate SQLite-compatible SQL. SQLite does not support PERCENTILE_CONT or STDDEV. +- Quote identifiers with double quotes. +- Return no markdown and no extra prose. + +Dataset context: +Dataset context for SQL QA: +- dataset_id: n1 +- dataset_name: Spambase +- table_name: n1 +- table_layout: single-table dataset (do not assume joins). +- row_semantics: One row is one email represented by engineered token/character frequency and capitalization-run features. +- task_type: classification +- target_column: class +- main_row_count: 4601 +- important_fields: +- word_freq_make: role=feature, type=numeric_sparse_frequency. tags=['condition_candidate', 'measure', 'rare_pattern_candidate'] desc=Frequency feature for token 'make' in an email message. +- word_freq_address: role=feature, type=numeric_sparse_frequency. tags=['condition_candidate', 'measure', 'rare_pattern_candidate'] desc=Frequency feature for token 'address' in an email message. +- word_freq_all: role=feature, type=numeric_sparse_frequency. tags=['condition_candidate', 'measure', 'rare_pattern_candidate'] desc=Frequency feature for token 'all' in an email message. +- word_freq_3d: role=feature, type=numeric_sparse_frequency. tags=['condition_candidate', 'measure', 'rare_pattern_candidate'] desc=Frequency feature for token '3d' in an email message. +- word_freq_our: role=feature, type=numeric_sparse_frequency. tags=['condition_candidate', 'measure', 'rare_pattern_candidate'] desc=Frequency feature for token 'our' in an email message. +- word_freq_over: role=feature, type=numeric_sparse_frequency. tags=['condition_candidate', 'measure', 'rare_pattern_candidate'] desc=Frequency feature for token 'over' in an email message. +- word_freq_remove: role=feature, type=numeric_sparse_frequency. tags=['condition_candidate', 'measure', 'rare_pattern_candidate'] desc=Frequency feature for token 'remove' in an email message. +- word_freq_internet: role=feature, type=numeric_sparse_frequency. tags=['condition_candidate', 'measure', 'rare_pattern_candidate'] desc=Frequency feature for token 'internet' in an email message. +- word_freq_order: role=feature, type=numeric_sparse_frequency. tags=['condition_candidate', 'measure', 'rare_pattern_candidate'] desc=Frequency feature for token 'order' in an email message. +- word_freq_mail: role=feature, type=numeric_sparse_frequency. tags=['condition_candidate', 'measure', 'rare_pattern_candidate'] desc=Frequency feature for token 'mail' in an email message. +- word_freq_receive: role=feature, type=numeric_sparse_frequency. tags=['condition_candidate', 'measure', 'rare_pattern_candidate'] desc=Frequency feature for token 'receive' in an email message. +- word_freq_will: role=feature, type=numeric_sparse_frequency. tags=['condition_candidate', 'measure', 'rare_pattern_candidate'] desc=Frequency feature for token 'will' in an email message. +- word_freq_people: role=feature, type=numeric_sparse_frequency. tags=['condition_candidate', 'measure', 'rare_pattern_candidate'] desc=Frequency feature for token 'people' in an email message. +- word_freq_report: role=feature, type=numeric_sparse_frequency. tags=['condition_candidate', 'measure', 'rare_pattern_candidate'] desc=Frequency feature for token 'report' in an email message. +- word_freq_addresses: role=feature, type=numeric_sparse_frequency. tags=['condition_candidate', 'measure', 'rare_pattern_candidate'] desc=Frequency feature for token 'addresses' in an email message. +- word_freq_free: role=feature, type=numeric_sparse_frequency. tags=['condition_candidate', 'measure', 'rare_pattern_candidate'] desc=Frequency feature for token 'free' in an email message. +- word_freq_business: role=feature, type=numeric_sparse_frequency. tags=['condition_candidate', 'measure', 'rare_pattern_candidate'] desc=Frequency feature for token 'business' in an email message. +- word_freq_email: role=feature, type=numeric_sparse_frequency. tags=['condition_candidate', 'measure', 'rare_pattern_candidate'] desc=Frequency feature for token 'email' in an email message. +- word_freq_you: role=feature, type=numeric_sparse_frequency. tags=['condition_candidate', 'measure', 'rare_pattern_candidate'] desc=Frequency feature for token 'you' in an email message. +- word_freq_credit: role=feature, type=numeric_sparse_frequency. tags=['condition_candidate', 'measure', 'rare_pattern_candidate'] desc=Frequency feature for token 'credit' in an email message. +- word_freq_your: role=feature, type=numeric_sparse_frequency. tags=['condition_candidate', 'measure', 'rare_pattern_candidate'] desc=Frequency feature for token 'your' in an email message. +- word_freq_font: role=feature, type=numeric_sparse_frequency. tags=['condition_candidate', 'measure', 'rare_pattern_candidate'] desc=Frequency feature for token 'font' in an email message. +- word_freq_000: role=feature, type=numeric_sparse_frequency. tags=['condition_candidate', 'measure', 'rare_pattern_candidate'] desc=Frequency feature for token '000' in an email message. +- word_freq_money: role=feature, type=numeric_sparse_frequency. tags=['condition_candidate', 'measure', 'rare_pattern_candidate'] desc=Frequency feature for token 'money' in an email message. +- word_freq_hp: role=feature, type=numeric_sparse_frequency. tags=['condition_candidate', 'measure', 'rare_pattern_candidate'] desc=Frequency feature for token 'hp' in an email message. +- word_freq_hpl: role=feature, type=numeric_sparse_frequency. tags=['condition_candidate', 'measure', 'rare_pattern_candidate'] desc=Frequency feature for token 'hpl' in an email message. +- word_freq_george: role=feature, type=numeric_sparse_frequency. tags=['condition_candidate', 'measure', 'rare_pattern_candidate'] desc=Frequency feature for token 'george' in an email message. +- word_freq_650: role=feature, type=numeric_sparse_frequency. tags=['condition_candidate', 'measure', 'rare_pattern_candidate'] desc=Frequency feature for token '650' in an email message. +- word_freq_lab: role=feature, type=numeric_sparse_frequency. tags=['condition_candidate', 'measure', 'rare_pattern_candidate'] desc=Frequency feature for token 'lab' in an email message. +- word_freq_labs: role=feature, type=numeric_sparse_frequency. tags=['condition_candidate', 'measure', 'rare_pattern_candidate'] desc=Frequency feature for token 'labs' in an email message. +- word_freq_telnet: role=feature, type=numeric_sparse_frequency. tags=['condition_candidate', 'measure', 'rare_pattern_candidate'] desc=Frequency feature for token 'telnet' in an email message. +- word_freq_857: role=feature, type=numeric_sparse_frequency. tags=['condition_candidate', 'measure', 'rare_pattern_candidate'] desc=Frequency feature for token '857' in an email message. +- word_freq_data: role=feature, type=numeric_sparse_frequency. tags=['condition_candidate', 'measure', 'rare_pattern_candidate'] desc=Frequency feature for token 'data' in an email message. +- word_freq_415: role=feature, type=numeric_sparse_frequency. tags=['condition_candidate', 'measure', 'rare_pattern_candidate'] desc=Frequency feature for token '415' in an email message. +- word_freq_85: role=feature, type=numeric_sparse_frequency. tags=['condition_candidate', 'measure', 'rare_pattern_candidate'] desc=Frequency feature for token '85' in an email message. +- word_freq_technology: role=feature, type=numeric_sparse_frequency. tags=['condition_candidate', 'measure', 'rare_pattern_candidate'] desc=Frequency feature for token 'technology' in an email message. +- word_freq_1999: role=feature, type=numeric_sparse_frequency. tags=['condition_candidate', 'measure', 'rare_pattern_candidate'] desc=Frequency feature for token '1999' in an email message. +- word_freq_parts: role=feature, type=numeric_sparse_frequency. tags=['condition_candidate', 'measure', 'rare_pattern_candidate'] desc=Frequency feature for token 'parts' in an email message. +- word_freq_pm: role=feature, type=numeric_sparse_frequency. tags=['condition_candidate', 'measure', 'rare_pattern_candidate'] desc=Frequency feature for token 'pm' in an email message. +- word_freq_direct: role=feature, type=numeric_sparse_frequency. tags=['condition_candidate', 'measure', 'rare_pattern_candidate'] desc=Frequency feature for token 'direct' in an email message. +- word_freq_cs: role=feature, type=numeric_sparse_frequency. tags=['condition_candidate', 'measure', 'rare_pattern_candidate'] desc=Frequency feature for token 'cs' in an email message. +- word_freq_meeting: role=feature, type=numeric_sparse_frequency. tags=['condition_candidate', 'measure', 'rare_pattern_candidate'] desc=Frequency feature for token 'meeting' in an email message. +- word_freq_original: role=feature, type=numeric_sparse_frequency. tags=['condition_candidate', 'measure', 'rare_pattern_candidate'] desc=Frequency feature for token 'original' in an email message. +- word_freq_project: role=feature, type=numeric_sparse_frequency. tags=['condition_candidate', 'measure', 'rare_pattern_candidate'] desc=Frequency feature for token 'project' in an email message. +- word_freq_re: role=feature, type=numeric_sparse_frequency. tags=['condition_candidate', 'measure', 'rare_pattern_candidate'] desc=Frequency feature for token 're' in an email message. +- word_freq_edu: role=feature, type=numeric_sparse_frequency. tags=['condition_candidate', 'measure', 'rare_pattern_candidate'] desc=Frequency feature for token 'edu' in an email message. +- word_freq_table: role=feature, type=numeric_sparse_frequency. tags=['condition_candidate', 'measure', 'rare_pattern_candidate'] desc=Frequency feature for token 'table' in an email message. +- word_freq_conference: role=feature, type=numeric_sparse_frequency. tags=['condition_candidate', 'measure', 'rare_pattern_candidate'] desc=Frequency feature for token 'conference' in an email message. +- char_freq_%3B: role=feature, type=numeric_sparse_frequency. tags=['condition_candidate', 'measure', 'rare_pattern_candidate'] desc=Character frequency feature for symbol ';'. +- char_freq_%28: role=feature, type=numeric_sparse_frequency. tags=['condition_candidate', 'measure', 'rare_pattern_candidate'] desc=Character frequency feature for symbol '('. +- char_freq_%5B: role=feature, type=numeric_sparse_frequency. tags=['condition_candidate', 'measure', 'rare_pattern_candidate'] desc=Character frequency feature for symbol '['. +- char_freq_%21: role=feature, type=numeric_sparse_frequency. tags=['condition_candidate', 'measure', 'rare_pattern_candidate'] desc=Character frequency feature for symbol '!'. +- char_freq_%24: role=feature, type=numeric_sparse_frequency. tags=['condition_candidate', 'measure', 'rare_pattern_candidate'] desc=Character frequency feature for symbol '$'. +- char_freq_%23: role=feature, type=numeric_sparse_frequency. tags=['condition_candidate', 'measure', 'rare_pattern_candidate'] desc=Character frequency feature for symbol '#'. +- capital_run_length_average: role=feature, type=numeric_heavy_tail. tags=['condition_candidate', 'measure', 'rare_pattern_candidate'] desc=Average length of uninterrupted capital-letter runs. +- capital_run_length_longest: role=feature, type=numeric_heavy_tail. tags=['condition_candidate', 'measure', 'rare_pattern_candidate'] desc=Longest uninterrupted capital-letter run. +- capital_run_length_total: role=feature, type=numeric_heavy_tail. tags=['condition_candidate', 'measure', 'rare_pattern_candidate'] desc=Total number of capital letters in runs. +- class: role=target, type=binary_target. tags=['target_candidate'] desc=Binary spam label (1=spam, 0=non-spam). +- useful_field_combinations: [['word_freq_free', 'word_freq_you', 'class'], ['char_freq_%21', 'capital_run_length_longest', 'class'], ['word_freq_remove', 'word_freq_money', 'class']] +- fields_requiring_caution: ['capital_run_length_average', 'capital_run_length_longest', 'capital_run_length_total'] +- source_url: https://www.openml.org/d/44 + +SQLite schema snapshot: +{ + "table_name": "n1", + "quoted_table_name": "\"n1\"", + "row_count": 4601, + "columns": [ + { + "name": "word_freq_make", + "type": "TEXT", + "notnull": false, + "pk": false + }, + { + "name": "word_freq_address", + "type": "TEXT", + "notnull": false, + "pk": false + }, + { + "name": "word_freq_all", + "type": "TEXT", + "notnull": false, + "pk": false + }, + { + "name": "word_freq_3d", + "type": "TEXT", + "notnull": false, + "pk": false + }, + { + "name": "word_freq_our", + "type": "TEXT", + "notnull": false, + "pk": false + }, + { + "name": "word_freq_over", + "type": "TEXT", + "notnull": false, + "pk": false + }, + { + "name": "word_freq_remove", + "type": "TEXT", + "notnull": false, + "pk": false + }, + { + "name": "word_freq_internet", + "type": "TEXT", + "notnull": false, + "pk": false + }, + { + "name": "word_freq_order", + "type": "TEXT", + "notnull": false, + "pk": false + }, + { + "name": "word_freq_mail", + "type": "TEXT", + "notnull": false, + "pk": false + }, + { + "name": "word_freq_receive", + "type": "TEXT", + "notnull": false, + "pk": false + }, + { + "name": "word_freq_will", + "type": "TEXT", + "notnull": false, + "pk": false + }, + { + "name": "word_freq_people", + "type": "TEXT", + "notnull": false, + "pk": false + }, + { + "name": "word_freq_report", + "type": "TEXT", + "notnull": false, + "pk": false + }, + { + "name": "word_freq_addresses", + "type": "TEXT", + "notnull": false, + "pk": false + }, + { + "name": "word_freq_free", + "type": "TEXT", + "notnull": false, + "pk": false + }, + { + "name": "word_freq_business", + "type": "TEXT", + "notnull": false, + "pk": false + }, + { + "name": "word_freq_email", + "type": "TEXT", + "notnull": false, + "pk": false + }, + { + "name": "word_freq_you", + "type": "TEXT", + "notnull": false, + "pk": false + }, + { + "name": "word_freq_credit", + "type": "TEXT", + "notnull": false, + "pk": false + }, + { + "name": "word_freq_your", + "type": "TEXT", + "notnull": false, + "pk": false + }, + { + "name": "word_freq_font", + "type": "TEXT", + "notnull": false, + "pk": false + }, + { + "name": "word_freq_000", + "type": "TEXT", + "notnull": false, + "pk": false + }, + { + "name": "word_freq_money", + "type": "TEXT", + "notnull": false, + "pk": false + }, + { + "name": "word_freq_hp", + "type": "TEXT", + "notnull": false, + "pk": false + }, + { + "name": "word_freq_hpl", + "type": "TEXT", + "notnull": false, + "pk": false + }, + { + "name": "word_freq_george", + "type": "TEXT", + "notnull": false, + "pk": false + }, + { + "name": "word_freq_650", + "type": "TEXT", + "notnull": false, + "pk": false + }, + { + "name": "word_freq_lab", + "type": "TEXT", + "notnull": false, + "pk": false + }, + { + "name": "word_freq_labs", + "type": "TEXT", + "notnull": false, + "pk": false + }, + { + "name": "word_freq_telnet", + "type": "TEXT", + "notnull": false, + "pk": false + }, + { + "name": "word_freq_857", + "type": "TEXT", + "notnull": false, + "pk": false + }, + { + "name": "word_freq_data", + "type": "TEXT", + "notnull": false, + "pk": false + }, + { + "name": "word_freq_415", + "type": "TEXT", + "notnull": false, + "pk": false + }, + { + "name": "word_freq_85", + "type": "TEXT", + "notnull": false, + "pk": false + }, + { + "name": "word_freq_technology", + "type": "TEXT", + "notnull": false, + "pk": false + }, + { + "name": "word_freq_1999", + "type": "TEXT", + "notnull": false, + "pk": false + }, + { + "name": "word_freq_parts", + "type": "TEXT", + "notnull": false, + "pk": false + }, + { + "name": "word_freq_pm", + "type": "TEXT", + "notnull": false, + "pk": false + }, + { + "name": "word_freq_direct", + "type": "TEXT", + "notnull": false, + "pk": false + }, + { + "name": "word_freq_cs", + "type": "TEXT", + "notnull": false, + "pk": false + }, + { + "name": "word_freq_meeting", + "type": "TEXT", + "notnull": false, + "pk": false + }, + { + "name": "word_freq_original", + "type": "TEXT", + "notnull": false, + "pk": false + }, + { + "name": "word_freq_project", + "type": "TEXT", + "notnull": false, + "pk": false + }, + { + "name": "word_freq_re", + "type": "TEXT", + "notnull": false, + "pk": false + }, + { + "name": "word_freq_edu", + "type": "TEXT", + "notnull": false, + "pk": false + }, + { + "name": "word_freq_table", + "type": "TEXT", + "notnull": false, + "pk": false + }, + { + "name": "word_freq_conference", + "type": "TEXT", + "notnull": false, + "pk": false + }, + { + "name": "char_freq_%3B", + "type": "TEXT", + "notnull": false, + "pk": false + }, + { + "name": "char_freq_%28", + "type": "TEXT", + "notnull": false, + "pk": false + }, + { + "name": "char_freq_%5B", + "type": "TEXT", + "notnull": false, + "pk": false + }, + { + "name": "char_freq_%21", + "type": "TEXT", + "notnull": false, + "pk": false + }, + { + "name": "char_freq_%24", + "type": "TEXT", + "notnull": false, + "pk": false + }, + { + "name": "char_freq_%23", + "type": "TEXT", + "notnull": false, + "pk": false + }, + { + "name": "capital_run_length_average", + "type": "TEXT", + "notnull": false, + "pk": false + }, + { + "name": "capital_run_length_longest", + "type": "TEXT", + "notnull": false, + "pk": false + }, + { + "name": "capital_run_length_total", + "type": "TEXT", + "notnull": false, + "pk": false + }, + { + "name": "class", + "type": "TEXT", + "notnull": false, + "pk": false + } + ], + "sample_rows": [ + { + "word_freq_make": "0", + "word_freq_address": "0.64", + "word_freq_all": "0.64", + "word_freq_3d": "0", + "word_freq_our": "0.32", + "word_freq_over": "0", + "word_freq_remove": "0", + "word_freq_internet": "0", + "word_freq_order": "0", + "word_freq_mail": "0", + "word_freq_receive": "0", + "word_freq_will": "0.64", + "word_freq_people": "0", + "word_freq_report": "0", + "word_freq_addresses": "0", + "word_freq_free": "0.32", + "word_freq_business": "0", + "word_freq_email": "1.29", + "word_freq_you": "1.93", + "word_freq_credit": "0", + "word_freq_your": "0.96", + "word_freq_font": "0", + "word_freq_000": "0", + "word_freq_money": "0", + "word_freq_hp": "0", + "word_freq_hpl": "0", + "word_freq_george": "0", + "word_freq_650": "0", + "word_freq_lab": "0", + "word_freq_labs": "0", + "word_freq_telnet": "0", + "word_freq_857": "0", + "word_freq_data": "0", + "word_freq_415": "0", + "word_freq_85": "0", + "word_freq_technology": "0", + "word_freq_1999": "0", + "word_freq_parts": "0", + "word_freq_pm": "0", + "word_freq_direct": "0", + "word_freq_cs": "0", + "word_freq_meeting": "0", + "word_freq_original": "0", + "word_freq_project": "0", + "word_freq_re": "0", + "word_freq_edu": "0", + "word_freq_table": "0", + "word_freq_conference": "0", + "char_freq_%3B": "0", + "char_freq_%28": "0", + "char_freq_%5B": "0", + "char_freq_%21": "0.778", + "char_freq_%24": "0", + "char_freq_%23": "0", + "capital_run_length_average": "3.756", + "capital_run_length_longest": "61", + "capital_run_length_total": "278", + "class": "1" + }, + { + "word_freq_make": "0.21", + "word_freq_address": "0.28", + "word_freq_all": "0.5", + "word_freq_3d": "0", + "word_freq_our": "0.14", + "word_freq_over": "0.28", + "word_freq_remove": "0.21", + "word_freq_internet": "0.07", + "word_freq_order": "0", + "word_freq_mail": "0.94", + "word_freq_receive": "0.21", + "word_freq_will": "0.79", + "word_freq_people": "0.65", + "word_freq_report": "0.21", + "word_freq_addresses": "0.14", + "word_freq_free": "0.14", + "word_freq_business": "0.07", + "word_freq_email": "0.28", + "word_freq_you": "3.47", + "word_freq_credit": "0", + "word_freq_your": "1.59", + "word_freq_font": "0", + "word_freq_000": "0.43", + "word_freq_money": "0.43", + "word_freq_hp": "0", + "word_freq_hpl": "0", + "word_freq_george": "0", + "word_freq_650": "0", + "word_freq_lab": "0", + "word_freq_labs": "0", + "word_freq_telnet": "0", + "word_freq_857": "0", + "word_freq_data": "0", + "word_freq_415": "0", + "word_freq_85": "0", + "word_freq_technology": "0", + "word_freq_1999": "0.07", + "word_freq_parts": "0", + "word_freq_pm": "0", + "word_freq_direct": "0", + "word_freq_cs": "0", + "word_freq_meeting": "0", + "word_freq_original": "0", + "word_freq_project": "0", + "word_freq_re": "0", + "word_freq_edu": "0", + "word_freq_table": "0", + "word_freq_conference": "0", + "char_freq_%3B": "0", + "char_freq_%28": "0.132", + "char_freq_%5B": "0", + "char_freq_%21": "0.372", + "char_freq_%24": "0.18", + "char_freq_%23": "0.048", + "capital_run_length_average": "5.114", + "capital_run_length_longest": "101", + "capital_run_length_total": "1028", + "class": "1" + }, + { + "word_freq_make": "0.06", + "word_freq_address": "0", + "word_freq_all": "0.71", + "word_freq_3d": "0", + "word_freq_our": "1.23", + "word_freq_over": "0.19", + "word_freq_remove": "0.19", + "word_freq_internet": "0.12", + "word_freq_order": "0.64", + "word_freq_mail": "0.25", + "word_freq_receive": "0.38", + "word_freq_will": "0.45", + "word_freq_people": "0.12", + "word_freq_report": "0", + "word_freq_addresses": "1.75", + "word_freq_free": "0.06", + "word_freq_business": "0.06", + "word_freq_email": "1.03", + "word_freq_you": "1.36", + "word_freq_credit": "0.32", + "word_freq_your": "0.51", + "word_freq_font": "0", + "word_freq_000": "1.16", + "word_freq_money": "0.06", + "word_freq_hp": "0", + "word_freq_hpl": "0", + "word_freq_george": "0", + "word_freq_650": "0", + "word_freq_lab": "0", + "word_freq_labs": "0", + "word_freq_telnet": "0", + "word_freq_857": "0", + "word_freq_data": "0", + "word_freq_415": "0", + "word_freq_85": "0", + "word_freq_technology": "0", + "word_freq_1999": "0", + "word_freq_parts": "0", + "word_freq_pm": "0", + "word_freq_direct": "0.06", + "word_freq_cs": "0", + "word_freq_meeting": "0", + "word_freq_original": "0.12", + "word_freq_project": "0", + "word_freq_re": "0.06", + "word_freq_edu": "0.06", + "word_freq_table": "0", + "word_freq_conference": "0", + "char_freq_%3B": "0.01", + "char_freq_%28": "0.143", + "char_freq_%5B": "0", + "char_freq_%21": "0.276", + "char_freq_%24": "0.184", + "char_freq_%23": "0.01", + "capital_run_length_average": "9.821", + "capital_run_length_longest": "485", + "capital_run_length_total": "2259", + "class": "1" + }, + { + "word_freq_make": "0", + "word_freq_address": "0", + "word_freq_all": "0", + "word_freq_3d": "0", + "word_freq_our": "0.63", + "word_freq_over": "0", + "word_freq_remove": "0.31", + "word_freq_internet": "0.63", + "word_freq_order": "0.31", + "word_freq_mail": "0.63", + "word_freq_receive": "0.31", + "word_freq_will": "0.31", + "word_freq_people": "0.31", + "word_freq_report": "0", + "word_freq_addresses": "0", + "word_freq_free": "0.31", + "word_freq_business": "0", + "word_freq_email": "0", + "word_freq_you": "3.18", + "word_freq_credit": "0", + "word_freq_your": "0.31", + "word_freq_font": "0", + "word_freq_000": "0", + "word_freq_money": "0", + "word_freq_hp": "0", + "word_freq_hpl": "0", + "word_freq_george": "0", + "word_freq_650": "0", + "word_freq_lab": "0", + "word_freq_labs": "0", + "word_freq_telnet": "0", + "word_freq_857": "0", + "word_freq_data": "0", + "word_freq_415": "0", + "word_freq_85": "0", + "word_freq_technology": "0", + "word_freq_1999": "0", + "word_freq_parts": "0", + "word_freq_pm": "0", + "word_freq_direct": "0", + "word_freq_cs": "0", + "word_freq_meeting": "0", + "word_freq_original": "0", + "word_freq_project": "0", + "word_freq_re": "0", + "word_freq_edu": "0", + "word_freq_table": "0", + "word_freq_conference": "0", + "char_freq_%3B": "0", + "char_freq_%28": "0.137", + "char_freq_%5B": "0", + "char_freq_%21": "0.137", + "char_freq_%24": "0", + "char_freq_%23": "0", + "capital_run_length_average": "3.537", + "capital_run_length_longest": "40", + "capital_run_length_total": "191", + "class": "1" + }, + { + "word_freq_make": "0", + "word_freq_address": "0", + "word_freq_all": "0", + "word_freq_3d": "0", + "word_freq_our": "0.63", + "word_freq_over": "0", + "word_freq_remove": "0.31", + "word_freq_internet": "0.63", + "word_freq_order": "0.31", + "word_freq_mail": "0.63", + "word_freq_receive": "0.31", + "word_freq_will": "0.31", + "word_freq_people": "0.31", + "word_freq_report": "0", + "word_freq_addresses": "0", + "word_freq_free": "0.31", + "word_freq_business": "0", + "word_freq_email": "0", + "word_freq_you": "3.18", + "word_freq_credit": "0", + "word_freq_your": "0.31", + "word_freq_font": "0", + "word_freq_000": "0", + "word_freq_money": "0", + "word_freq_hp": "0", + "word_freq_hpl": "0", + "word_freq_george": "0", + "word_freq_650": "0", + "word_freq_lab": "0", + "word_freq_labs": "0", + "word_freq_telnet": "0", + "word_freq_857": "0", + "word_freq_data": "0", + "word_freq_415": "0", + "word_freq_85": "0", + "word_freq_technology": "0", + "word_freq_1999": "0", + "word_freq_parts": "0", + "word_freq_pm": "0", + "word_freq_direct": "0", + "word_freq_cs": "0", + "word_freq_meeting": "0", + "word_freq_original": "0", + "word_freq_project": "0", + "word_freq_re": "0", + "word_freq_edu": "0", + "word_freq_table": "0", + "word_freq_conference": "0", + "char_freq_%3B": "0", + "char_freq_%28": "0.135", + "char_freq_%5B": "0", + "char_freq_%21": "0.135", + "char_freq_%24": "0", + "char_freq_%23": "0", + "capital_run_length_average": "3.537", + "capital_run_length_longest": "40", + "capital_run_length_total": "191", + "class": "1" + } + ] +} + +Shortlisted templates: +[ + { + "template_id": "tpl_tail_low_support_group_count_v2", + "template_name": "Low-Support Group Count", + "primary_family": "tail_rarity_structure", + "portability": "yes", + "sql_skeleton": "SELECT\n {group_col},\n COUNT(*) AS support\nFROM {table}\nGROUP BY {group_col}\nORDER BY support ASC, {group_col}\nLIMIT {top_k};", + "required_roles": [ + "group_col" + ] + } +] + +Problem instance: +{ + "dataset_id": "n1", + "question": "Use template Low-Support Group Count to probe tail_mass_similarity with semantic role count_distribution. Focus on group_col=class.", + "planned_template_id": "tpl_tail_low_support_group_count_v2", + "bindings": { + "group_col": "class", + "top_k": 11, + "top_n": 4, + "num_tiles": 10, + "percentile_value": 0.9, + "z_threshold": 2.0, + "fraction_threshold": 0.1, + "baseline_multiplier": 1.5, + "baseline_fraction": 0.1, + "min_group_size": 5, + "min_support": 5, + "measure_threshold": 0.0, + "time_grain": "month", + "lookback_rows": 3, + "current_period_start": "'2024-01-01'", + "current_period_end": "'2024-04-01'", + "previous_period_start": "'2023-10-01'", + "previous_period_end": "'2024-01-01'", + "drift_ratio_threshold": 0.8 + }, + "can_vary": [], + "must_fix": [], + "runtime_sql_skeleton": "SELECT\n {group_col},\n COUNT(*) AS support\nFROM {table}\nGROUP BY {group_col}\nORDER BY support ASC, {group_col}\nLIMIT {top_k};" +} + +Repair context: +{} diff --git a/Query/sql/v2/runs/v2_cli_20260502_081223_d/n1/artifacts/v2q_n1_f28548e301caf223/cli/sql_response_attempt_1.raw.txt b/Query/sql/v2/runs/v2_cli_20260502_081223_d/n1/artifacts/v2q_n1_f28548e301caf223/cli/sql_response_attempt_1.raw.txt new file mode 100644 index 0000000000000000000000000000000000000000..d552ef1001fb1b55b8a13fc5d63bac3b05f70c8c --- /dev/null +++ b/Query/sql/v2/runs/v2_cli_20260502_081223_d/n1/artifacts/v2q_n1_f28548e301caf223/cli/sql_response_attempt_1.raw.txt @@ -0,0 +1,4 @@ +{"type":"thread.started","thread_id":"019e40f8-eaa9-7573-b34b-eb120a8c75d4"} +{"type":"turn.started"} +{"type":"error","message":"Quota exceeded. Check your plan and billing details."} +{"type":"turn.failed","error":{"message":"Quota exceeded. Check your plan and billing details."}} diff --git a/Query/sql/v2/runs/v2_cli_20260502_081223_d/n1/artifacts/v2q_n1_f28548e301caf223/cli/sql_response_attempt_1.txt b/Query/sql/v2/runs/v2_cli_20260502_081223_d/n1/artifacts/v2q_n1_f28548e301caf223/cli/sql_response_attempt_1.txt new file mode 100644 index 0000000000000000000000000000000000000000..866af92702c0f85b48af10bc99e5ad4ed18cf823 --- /dev/null +++ b/Query/sql/v2/runs/v2_cli_20260502_081223_d/n1/artifacts/v2q_n1_f28548e301caf223/cli/sql_response_attempt_1.txt @@ -0,0 +1,4 @@ +{"type":"thread.started","thread_id":"019e40f8-eaa9-7573-b34b-eb120a8c75d4"} +{"type":"turn.started"} +{"type":"error","message":"Quota exceeded. Check your plan and billing details."} +{"type":"turn.failed","error":{"message":"Quota exceeded. Check your plan and billing details."}} \ No newline at end of file diff --git a/Query/sql/v2/runs/v2_cli_20260502_081223_d/n1/artifacts/v2q_n1_f28548e301caf223/cli/sql_response_attempt_2.raw.txt b/Query/sql/v2/runs/v2_cli_20260502_081223_d/n1/artifacts/v2q_n1_f28548e301caf223/cli/sql_response_attempt_2.raw.txt new file mode 100644 index 0000000000000000000000000000000000000000..0324bf786ab45311f32061bf8d95032018727a85 --- /dev/null +++ b/Query/sql/v2/runs/v2_cli_20260502_081223_d/n1/artifacts/v2q_n1_f28548e301caf223/cli/sql_response_attempt_2.raw.txt @@ -0,0 +1,4 @@ +{"type":"thread.started","thread_id":"019e40f9-008e-7952-ac3f-ad0202086c20"} +{"type":"turn.started"} +{"type":"error","message":"Quota exceeded. Check your plan and billing details."} +{"type":"turn.failed","error":{"message":"Quota exceeded. Check your plan and billing details."}} diff --git a/Query/sql/v2/runs/v2_cli_20260502_081223_d/n1/artifacts/v2q_n1_f28548e301caf223/cli/sql_response_attempt_2.txt b/Query/sql/v2/runs/v2_cli_20260502_081223_d/n1/artifacts/v2q_n1_f28548e301caf223/cli/sql_response_attempt_2.txt new file mode 100644 index 0000000000000000000000000000000000000000..c0797eb4a2cb91dbd96f8566c3eb7f09ff16e8b6 --- /dev/null +++ b/Query/sql/v2/runs/v2_cli_20260502_081223_d/n1/artifacts/v2q_n1_f28548e301caf223/cli/sql_response_attempt_2.txt @@ -0,0 +1,4 @@ +{"type":"thread.started","thread_id":"019e40f9-008e-7952-ac3f-ad0202086c20"} +{"type":"turn.started"} +{"type":"error","message":"Quota exceeded. Check your plan and billing details."} +{"type":"turn.failed","error":{"message":"Quota exceeded. Check your plan and billing details."}} \ No newline at end of file diff --git a/Query/sql/v2/runs/v2_cli_20260502_081223_d/n1/artifacts/v2q_n1_f28548e301caf223/cli/sql_stderr_attempt_1.txt b/Query/sql/v2/runs/v2_cli_20260502_081223_d/n1/artifacts/v2q_n1_f28548e301caf223/cli/sql_stderr_attempt_1.txt new file mode 100644 index 0000000000000000000000000000000000000000..e69de29bb2d1d6434b8b29ae775ad8c2e48c5391 diff --git a/Query/sql/v2/runs/v2_cli_20260502_081223_d/n1/artifacts/v2q_n1_f28548e301caf223/cli/sql_stderr_attempt_2.txt b/Query/sql/v2/runs/v2_cli_20260502_081223_d/n1/artifacts/v2q_n1_f28548e301caf223/cli/sql_stderr_attempt_2.txt new file mode 100644 index 0000000000000000000000000000000000000000..e69de29bb2d1d6434b8b29ae775ad8c2e48c5391 diff --git a/Query/sql/v2/runs/v2_cli_20260502_081223_d/n1/artifacts/v2q_n1_f327357bd231f2f8/cli/conversation.jsonl b/Query/sql/v2/runs/v2_cli_20260502_081223_d/n1/artifacts/v2q_n1_f327357bd231f2f8/cli/conversation.jsonl new file mode 100644 index 0000000000000000000000000000000000000000..cf74be574a41901fddcd2fbcceb8229038f33e6b --- /dev/null +++ b/Query/sql/v2/runs/v2_cli_20260502_081223_d/n1/artifacts/v2q_n1_f327357bd231f2f8/cli/conversation.jsonl @@ -0,0 +1,2 @@ +{"attempt": 1, "phase": "sql_generation", "role": "user", "content_path": "cli/sql_prompt_attempt_1.txt", "metrics": {"chars": 29767, "bytes_utf8": 29767, "lines": 794, "estimated_tokens": null}} +{"attempt": 1, "phase": "sql_generation", "role": "assistant", "content_path": "cli/sql_response_attempt_1.txt", "raw_content_path": "cli/sql_response_attempt_1.raw.txt", "stderr_path": "cli/sql_stderr_attempt_1.txt", "metrics": {"chars": 626, "bytes_utf8": 626, "lines": 1, "estimated_tokens": null}, "usage": {"input_tokens": 20436, "cached_input_tokens": 12032, "output_tokens": 606, "reasoning_output_tokens": 428}} diff --git a/Query/sql/v2/runs/v2_cli_20260502_081223_d/n1/artifacts/v2q_n1_f327357bd231f2f8/cli/session_summary.json b/Query/sql/v2/runs/v2_cli_20260502_081223_d/n1/artifacts/v2q_n1_f327357bd231f2f8/cli/session_summary.json new file mode 100644 index 0000000000000000000000000000000000000000..35831b03321ae44ef9f36200fc75a037c4552542 --- /dev/null +++ b/Query/sql/v2/runs/v2_cli_20260502_081223_d/n1/artifacts/v2q_n1_f327357bd231f2f8/cli/session_summary.json @@ -0,0 +1,25 @@ +{ + "engine": "v2-cli:codex", + "command": "codex exec --skip-git-repo-check --disable plugins --sandbox read-only --cd \"/data/jialinzhang/SQLagent\" -m gpt-5.4 --json -", + "ai_cli_calls": 1, + "usage_summary": { + "dataset_id": "n1", + "model": "v2-cli:codex", + "run_id": "v2q_n1_f327357bd231f2f8", + "api_calls": 0, + "input_tokens": 20436, + "cached_input_tokens": 12032, + "output_tokens": 606, + "total_tokens": 21042, + "cost_usd": 0.0, + "ai_cli_calls": 1, + "estimated_input_tokens": 0, + "estimated_output_tokens": 0, + "estimated_total_tokens": 0, + "usage_source": "ai_cli_json_usage", + "cli_elapsed_ms_total": 11353.46, + "sql_execution_elapsed_ms_total": 10.18, + "conversation_log_path": "/data/jialinzhang/TabQueryBench/sql_workloads/v2_current/runs_and_launches/runs/v2_cli_20260502_081223_d/n1/artifacts/v2q_n1_f327357bd231f2f8/cli/conversation.jsonl", + "note": "Executed through a local AI CLI with structured usage metadata." + } +} \ No newline at end of file diff --git a/Query/sql/v2/runs/v2_cli_20260502_081223_d/n1/artifacts/v2q_n1_f327357bd231f2f8/cli/sql_attempt_1.metadata.json b/Query/sql/v2/runs/v2_cli_20260502_081223_d/n1/artifacts/v2q_n1_f327357bd231f2f8/cli/sql_attempt_1.metadata.json new file mode 100644 index 0000000000000000000000000000000000000000..5a014094d49ebc9c0edf5cc54cf5997731586764 --- /dev/null +++ b/Query/sql/v2/runs/v2_cli_20260502_081223_d/n1/artifacts/v2q_n1_f327357bd231f2f8/cli/sql_attempt_1.metadata.json @@ -0,0 +1,45 @@ +{ + "attempt": 1, + "phase": "sql_generation", + "command": "codex exec --skip-git-repo-check --disable plugins --sandbox read-only --cd \"/data/jialinzhang/SQLagent\" -m gpt-5.4 --json -", + "started_at": "2026-05-19T15:33:57.507132+00:00", + "ended_at": "2026-05-19T15:34:08.860637+00:00", + "elapsed_ms": 11353.46, + "prompt_metrics": { + "chars": 29767, + "bytes_utf8": 29767, + "lines": 794, + "estimated_tokens": null + }, + "stdout_metrics": { + "chars": 1008, + "bytes_utf8": 1008, + "lines": 4, + "estimated_tokens": null + }, + "stderr_metrics": { + "chars": 0, + "bytes_utf8": 0, + "lines": 0, + "estimated_tokens": null + }, + "parsed_output": { + "format": "jsonl_events", + "text_metrics": { + "chars": 626, + "bytes_utf8": 626, + "lines": 1, + "estimated_tokens": null + }, + "usage": { + "input_tokens": 20436, + "cached_input_tokens": 12032, + "output_tokens": 606, + "reasoning_output_tokens": 428 + } + }, + "prompt_path": "cli/sql_prompt_attempt_1.txt", + "response_path": "cli/sql_response_attempt_1.txt", + "raw_response_path": "cli/sql_response_attempt_1.raw.txt", + "stderr_path": "cli/sql_stderr_attempt_1.txt" +} \ No newline at end of file diff --git a/Query/sql/v2/runs/v2_cli_20260502_081223_d/n1/artifacts/v2q_n1_f327357bd231f2f8/cli/sql_prompt_attempt_1.txt b/Query/sql/v2/runs/v2_cli_20260502_081223_d/n1/artifacts/v2q_n1_f327357bd231f2f8/cli/sql_prompt_attempt_1.txt new file mode 100644 index 0000000000000000000000000000000000000000..8f347b4fbf4905e28d594d4579c1b36ed749f68e --- /dev/null +++ b/Query/sql/v2/runs/v2_cli_20260502_081223_d/n1/artifacts/v2q_n1_f327357bd231f2f8/cli/sql_prompt_attempt_1.txt @@ -0,0 +1,794 @@ +You are generating one SQLite SELECT query for a single-table SQL QA task. +Return strict JSON only, with this schema: {"sql": "...", "notes": "..."}. +Rules: +- Use only the provided table and columns. +- Do not write INSERT, UPDATE, DELETE, DROP, ALTER, CREATE, PRAGMA, ATTACH, DETACH, or VACUUM. +- Prefer the planned template and bound roles when provided. +- Add a leading SQL comment exactly like: -- template_id: . +- Generate SQLite-compatible SQL. SQLite does not support PERCENTILE_CONT or STDDEV. +- Quote identifiers with double quotes. +- Return no markdown and no extra prose. + +Dataset context: +Dataset context for SQL QA: +- dataset_id: n1 +- dataset_name: Spambase +- table_name: n1 +- table_layout: single-table dataset (do not assume joins). +- row_semantics: One row is one email represented by engineered token/character frequency and capitalization-run features. +- task_type: classification +- target_column: class +- main_row_count: 4601 +- important_fields: +- word_freq_make: role=feature, type=numeric_sparse_frequency. tags=['condition_candidate', 'measure', 'rare_pattern_candidate'] desc=Frequency feature for token 'make' in an email message. +- word_freq_address: role=feature, type=numeric_sparse_frequency. tags=['condition_candidate', 'measure', 'rare_pattern_candidate'] desc=Frequency feature for token 'address' in an email message. +- word_freq_all: role=feature, type=numeric_sparse_frequency. tags=['condition_candidate', 'measure', 'rare_pattern_candidate'] desc=Frequency feature for token 'all' in an email message. +- word_freq_3d: role=feature, type=numeric_sparse_frequency. tags=['condition_candidate', 'measure', 'rare_pattern_candidate'] desc=Frequency feature for token '3d' in an email message. +- word_freq_our: role=feature, type=numeric_sparse_frequency. tags=['condition_candidate', 'measure', 'rare_pattern_candidate'] desc=Frequency feature for token 'our' in an email message. +- word_freq_over: role=feature, type=numeric_sparse_frequency. tags=['condition_candidate', 'measure', 'rare_pattern_candidate'] desc=Frequency feature for token 'over' in an email message. +- word_freq_remove: role=feature, type=numeric_sparse_frequency. tags=['condition_candidate', 'measure', 'rare_pattern_candidate'] desc=Frequency feature for token 'remove' in an email message. +- word_freq_internet: role=feature, type=numeric_sparse_frequency. tags=['condition_candidate', 'measure', 'rare_pattern_candidate'] desc=Frequency feature for token 'internet' in an email message. +- word_freq_order: role=feature, type=numeric_sparse_frequency. tags=['condition_candidate', 'measure', 'rare_pattern_candidate'] desc=Frequency feature for token 'order' in an email message. +- word_freq_mail: role=feature, type=numeric_sparse_frequency. tags=['condition_candidate', 'measure', 'rare_pattern_candidate'] desc=Frequency feature for token 'mail' in an email message. +- word_freq_receive: role=feature, type=numeric_sparse_frequency. tags=['condition_candidate', 'measure', 'rare_pattern_candidate'] desc=Frequency feature for token 'receive' in an email message. +- word_freq_will: role=feature, type=numeric_sparse_frequency. tags=['condition_candidate', 'measure', 'rare_pattern_candidate'] desc=Frequency feature for token 'will' in an email message. +- word_freq_people: role=feature, type=numeric_sparse_frequency. tags=['condition_candidate', 'measure', 'rare_pattern_candidate'] desc=Frequency feature for token 'people' in an email message. +- word_freq_report: role=feature, type=numeric_sparse_frequency. tags=['condition_candidate', 'measure', 'rare_pattern_candidate'] desc=Frequency feature for token 'report' in an email message. +- word_freq_addresses: role=feature, type=numeric_sparse_frequency. tags=['condition_candidate', 'measure', 'rare_pattern_candidate'] desc=Frequency feature for token 'addresses' in an email message. +- word_freq_free: role=feature, type=numeric_sparse_frequency. tags=['condition_candidate', 'measure', 'rare_pattern_candidate'] desc=Frequency feature for token 'free' in an email message. +- word_freq_business: role=feature, type=numeric_sparse_frequency. tags=['condition_candidate', 'measure', 'rare_pattern_candidate'] desc=Frequency feature for token 'business' in an email message. +- word_freq_email: role=feature, type=numeric_sparse_frequency. tags=['condition_candidate', 'measure', 'rare_pattern_candidate'] desc=Frequency feature for token 'email' in an email message. +- word_freq_you: role=feature, type=numeric_sparse_frequency. tags=['condition_candidate', 'measure', 'rare_pattern_candidate'] desc=Frequency feature for token 'you' in an email message. +- word_freq_credit: role=feature, type=numeric_sparse_frequency. tags=['condition_candidate', 'measure', 'rare_pattern_candidate'] desc=Frequency feature for token 'credit' in an email message. +- word_freq_your: role=feature, type=numeric_sparse_frequency. tags=['condition_candidate', 'measure', 'rare_pattern_candidate'] desc=Frequency feature for token 'your' in an email message. +- word_freq_font: role=feature, type=numeric_sparse_frequency. tags=['condition_candidate', 'measure', 'rare_pattern_candidate'] desc=Frequency feature for token 'font' in an email message. +- word_freq_000: role=feature, type=numeric_sparse_frequency. tags=['condition_candidate', 'measure', 'rare_pattern_candidate'] desc=Frequency feature for token '000' in an email message. +- word_freq_money: role=feature, type=numeric_sparse_frequency. tags=['condition_candidate', 'measure', 'rare_pattern_candidate'] desc=Frequency feature for token 'money' in an email message. +- word_freq_hp: role=feature, type=numeric_sparse_frequency. tags=['condition_candidate', 'measure', 'rare_pattern_candidate'] desc=Frequency feature for token 'hp' in an email message. +- word_freq_hpl: role=feature, type=numeric_sparse_frequency. tags=['condition_candidate', 'measure', 'rare_pattern_candidate'] desc=Frequency feature for token 'hpl' in an email message. +- word_freq_george: role=feature, type=numeric_sparse_frequency. tags=['condition_candidate', 'measure', 'rare_pattern_candidate'] desc=Frequency feature for token 'george' in an email message. +- word_freq_650: role=feature, type=numeric_sparse_frequency. tags=['condition_candidate', 'measure', 'rare_pattern_candidate'] desc=Frequency feature for token '650' in an email message. +- word_freq_lab: role=feature, type=numeric_sparse_frequency. tags=['condition_candidate', 'measure', 'rare_pattern_candidate'] desc=Frequency feature for token 'lab' in an email message. +- word_freq_labs: role=feature, type=numeric_sparse_frequency. tags=['condition_candidate', 'measure', 'rare_pattern_candidate'] desc=Frequency feature for token 'labs' in an email message. +- word_freq_telnet: role=feature, type=numeric_sparse_frequency. tags=['condition_candidate', 'measure', 'rare_pattern_candidate'] desc=Frequency feature for token 'telnet' in an email message. +- word_freq_857: role=feature, type=numeric_sparse_frequency. tags=['condition_candidate', 'measure', 'rare_pattern_candidate'] desc=Frequency feature for token '857' in an email message. +- word_freq_data: role=feature, type=numeric_sparse_frequency. tags=['condition_candidate', 'measure', 'rare_pattern_candidate'] desc=Frequency feature for token 'data' in an email message. +- word_freq_415: role=feature, type=numeric_sparse_frequency. tags=['condition_candidate', 'measure', 'rare_pattern_candidate'] desc=Frequency feature for token '415' in an email message. +- word_freq_85: role=feature, type=numeric_sparse_frequency. tags=['condition_candidate', 'measure', 'rare_pattern_candidate'] desc=Frequency feature for token '85' in an email message. +- word_freq_technology: role=feature, type=numeric_sparse_frequency. tags=['condition_candidate', 'measure', 'rare_pattern_candidate'] desc=Frequency feature for token 'technology' in an email message. +- word_freq_1999: role=feature, type=numeric_sparse_frequency. tags=['condition_candidate', 'measure', 'rare_pattern_candidate'] desc=Frequency feature for token '1999' in an email message. +- word_freq_parts: role=feature, type=numeric_sparse_frequency. tags=['condition_candidate', 'measure', 'rare_pattern_candidate'] desc=Frequency feature for token 'parts' in an email message. +- word_freq_pm: role=feature, type=numeric_sparse_frequency. tags=['condition_candidate', 'measure', 'rare_pattern_candidate'] desc=Frequency feature for token 'pm' in an email message. +- word_freq_direct: role=feature, type=numeric_sparse_frequency. tags=['condition_candidate', 'measure', 'rare_pattern_candidate'] desc=Frequency feature for token 'direct' in an email message. +- word_freq_cs: role=feature, type=numeric_sparse_frequency. tags=['condition_candidate', 'measure', 'rare_pattern_candidate'] desc=Frequency feature for token 'cs' in an email message. +- word_freq_meeting: role=feature, type=numeric_sparse_frequency. tags=['condition_candidate', 'measure', 'rare_pattern_candidate'] desc=Frequency feature for token 'meeting' in an email message. +- word_freq_original: role=feature, type=numeric_sparse_frequency. tags=['condition_candidate', 'measure', 'rare_pattern_candidate'] desc=Frequency feature for token 'original' in an email message. +- word_freq_project: role=feature, type=numeric_sparse_frequency. tags=['condition_candidate', 'measure', 'rare_pattern_candidate'] desc=Frequency feature for token 'project' in an email message. +- word_freq_re: role=feature, type=numeric_sparse_frequency. tags=['condition_candidate', 'measure', 'rare_pattern_candidate'] desc=Frequency feature for token 're' in an email message. +- word_freq_edu: role=feature, type=numeric_sparse_frequency. tags=['condition_candidate', 'measure', 'rare_pattern_candidate'] desc=Frequency feature for token 'edu' in an email message. +- word_freq_table: role=feature, type=numeric_sparse_frequency. tags=['condition_candidate', 'measure', 'rare_pattern_candidate'] desc=Frequency feature for token 'table' in an email message. +- word_freq_conference: role=feature, type=numeric_sparse_frequency. tags=['condition_candidate', 'measure', 'rare_pattern_candidate'] desc=Frequency feature for token 'conference' in an email message. +- char_freq_%3B: role=feature, type=numeric_sparse_frequency. tags=['condition_candidate', 'measure', 'rare_pattern_candidate'] desc=Character frequency feature for symbol ';'. +- char_freq_%28: role=feature, type=numeric_sparse_frequency. tags=['condition_candidate', 'measure', 'rare_pattern_candidate'] desc=Character frequency feature for symbol '('. +- char_freq_%5B: role=feature, type=numeric_sparse_frequency. tags=['condition_candidate', 'measure', 'rare_pattern_candidate'] desc=Character frequency feature for symbol '['. +- char_freq_%21: role=feature, type=numeric_sparse_frequency. tags=['condition_candidate', 'measure', 'rare_pattern_candidate'] desc=Character frequency feature for symbol '!'. +- char_freq_%24: role=feature, type=numeric_sparse_frequency. tags=['condition_candidate', 'measure', 'rare_pattern_candidate'] desc=Character frequency feature for symbol '$'. +- char_freq_%23: role=feature, type=numeric_sparse_frequency. tags=['condition_candidate', 'measure', 'rare_pattern_candidate'] desc=Character frequency feature for symbol '#'. +- capital_run_length_average: role=feature, type=numeric_heavy_tail. tags=['condition_candidate', 'measure', 'rare_pattern_candidate'] desc=Average length of uninterrupted capital-letter runs. +- capital_run_length_longest: role=feature, type=numeric_heavy_tail. tags=['condition_candidate', 'measure', 'rare_pattern_candidate'] desc=Longest uninterrupted capital-letter run. +- capital_run_length_total: role=feature, type=numeric_heavy_tail. tags=['condition_candidate', 'measure', 'rare_pattern_candidate'] desc=Total number of capital letters in runs. +- class: role=target, type=binary_target. tags=['target_candidate'] desc=Binary spam label (1=spam, 0=non-spam). +- useful_field_combinations: [['word_freq_free', 'word_freq_you', 'class'], ['char_freq_%21', 'capital_run_length_longest', 'class'], ['word_freq_remove', 'word_freq_money', 'class']] +- fields_requiring_caution: ['capital_run_length_average', 'capital_run_length_longest', 'capital_run_length_total'] +- source_url: https://www.openml.org/d/44 + +SQLite schema snapshot: +{ + "table_name": "n1", + "quoted_table_name": "\"n1\"", + "row_count": 4601, + "columns": [ + { + "name": "word_freq_make", + "type": "TEXT", + "notnull": false, + "pk": false + }, + { + "name": "word_freq_address", + "type": "TEXT", + "notnull": false, + "pk": false + }, + { + "name": "word_freq_all", + "type": "TEXT", + "notnull": false, + "pk": false + }, + { + "name": "word_freq_3d", + "type": "TEXT", + "notnull": false, + "pk": false + }, + { + "name": "word_freq_our", + "type": "TEXT", + "notnull": false, + "pk": false + }, + { + "name": "word_freq_over", + "type": "TEXT", + "notnull": false, + "pk": false + }, + { + "name": "word_freq_remove", + "type": "TEXT", + "notnull": false, + "pk": false + }, + { + "name": "word_freq_internet", + "type": "TEXT", + "notnull": false, + "pk": false + }, + { + "name": "word_freq_order", + "type": "TEXT", + "notnull": false, + "pk": false + }, + { + "name": "word_freq_mail", + "type": "TEXT", + "notnull": false, + "pk": false + }, + { + "name": "word_freq_receive", + "type": "TEXT", + "notnull": false, + "pk": false + }, + { + "name": "word_freq_will", + "type": "TEXT", + "notnull": false, + "pk": false + }, + { + "name": "word_freq_people", + "type": "TEXT", + "notnull": false, + "pk": false + }, + { + "name": "word_freq_report", + "type": "TEXT", + "notnull": false, + "pk": false + }, + { + "name": "word_freq_addresses", + "type": "TEXT", + "notnull": false, + "pk": false + }, + { + "name": "word_freq_free", + "type": "TEXT", + "notnull": false, + "pk": false + }, + { + "name": "word_freq_business", + "type": "TEXT", + "notnull": false, + "pk": false + }, + { + "name": "word_freq_email", + "type": "TEXT", + "notnull": false, + "pk": false + }, + { + "name": "word_freq_you", + "type": "TEXT", + "notnull": false, + "pk": false + }, + { + "name": "word_freq_credit", + "type": "TEXT", + "notnull": false, + "pk": false + }, + { + "name": "word_freq_your", + "type": "TEXT", + "notnull": false, + "pk": false + }, + { + "name": "word_freq_font", + "type": "TEXT", + "notnull": false, + "pk": false + }, + { + "name": "word_freq_000", + "type": "TEXT", + "notnull": false, + "pk": false + }, + { + "name": "word_freq_money", + "type": "TEXT", + "notnull": false, + "pk": false + }, + { + "name": "word_freq_hp", + "type": "TEXT", + "notnull": false, + "pk": false + }, + { + "name": "word_freq_hpl", + "type": "TEXT", + "notnull": false, + "pk": false + }, + { + "name": "word_freq_george", + "type": "TEXT", + "notnull": false, + "pk": false + }, + { + "name": "word_freq_650", + "type": "TEXT", + "notnull": false, + "pk": false + }, + { + "name": "word_freq_lab", + "type": "TEXT", + "notnull": false, + "pk": false + }, + { + "name": "word_freq_labs", + "type": "TEXT", + "notnull": false, + "pk": false + }, + { + "name": "word_freq_telnet", + "type": "TEXT", + "notnull": false, + "pk": false + }, + { + "name": "word_freq_857", + "type": "TEXT", + "notnull": false, + "pk": false + }, + { + "name": "word_freq_data", + "type": "TEXT", + "notnull": false, + "pk": false + }, + { + "name": "word_freq_415", + "type": "TEXT", + "notnull": false, + "pk": false + }, + { + "name": "word_freq_85", + "type": "TEXT", + "notnull": false, + "pk": false + }, + { + "name": "word_freq_technology", + "type": "TEXT", + "notnull": false, + "pk": false + }, + { + "name": "word_freq_1999", + "type": "TEXT", + "notnull": false, + "pk": false + }, + { + "name": "word_freq_parts", + "type": "TEXT", + "notnull": false, + "pk": false + }, + { + "name": "word_freq_pm", + "type": "TEXT", + "notnull": false, + "pk": false + }, + { + "name": "word_freq_direct", + "type": "TEXT", + "notnull": false, + "pk": false + }, + { + "name": "word_freq_cs", + "type": "TEXT", + "notnull": false, + "pk": false + }, + { + "name": "word_freq_meeting", + "type": "TEXT", + "notnull": false, + "pk": false + }, + { + "name": "word_freq_original", + "type": "TEXT", + "notnull": false, + "pk": false + }, + { + "name": "word_freq_project", + "type": "TEXT", + "notnull": false, + "pk": false + }, + { + "name": "word_freq_re", + "type": "TEXT", + "notnull": false, + "pk": false + }, + { + "name": "word_freq_edu", + "type": "TEXT", + "notnull": false, + "pk": false + }, + { + "name": "word_freq_table", + "type": "TEXT", + "notnull": false, + "pk": false + }, + { + "name": "word_freq_conference", + "type": "TEXT", + "notnull": false, + "pk": false + }, + { + "name": "char_freq_%3B", + "type": "TEXT", + "notnull": false, + "pk": false + }, + { + "name": "char_freq_%28", + "type": "TEXT", + "notnull": false, + "pk": false + }, + { + "name": "char_freq_%5B", + "type": "TEXT", + "notnull": false, + "pk": false + }, + { + "name": "char_freq_%21", + "type": "TEXT", + "notnull": false, + "pk": false + }, + { + "name": "char_freq_%24", + "type": "TEXT", + "notnull": false, + "pk": false + }, + { + "name": "char_freq_%23", + "type": "TEXT", + "notnull": false, + "pk": false + }, + { + "name": "capital_run_length_average", + "type": "TEXT", + "notnull": false, + "pk": false + }, + { + "name": "capital_run_length_longest", + "type": "TEXT", + "notnull": false, + "pk": false + }, + { + "name": "capital_run_length_total", + "type": "TEXT", + "notnull": false, + "pk": false + }, + { + "name": "class", + "type": "TEXT", + "notnull": false, + "pk": false + } + ], + "sample_rows": [ + { + "word_freq_make": "0", + "word_freq_address": "0.64", + "word_freq_all": "0.64", + "word_freq_3d": "0", + "word_freq_our": "0.32", + "word_freq_over": "0", + "word_freq_remove": "0", + "word_freq_internet": "0", + "word_freq_order": "0", + "word_freq_mail": "0", + "word_freq_receive": "0", + "word_freq_will": "0.64", + "word_freq_people": "0", + "word_freq_report": "0", + "word_freq_addresses": "0", + "word_freq_free": "0.32", + "word_freq_business": "0", + "word_freq_email": "1.29", + "word_freq_you": "1.93", + "word_freq_credit": "0", + "word_freq_your": "0.96", + "word_freq_font": "0", + "word_freq_000": "0", + "word_freq_money": "0", + "word_freq_hp": "0", + "word_freq_hpl": "0", + "word_freq_george": "0", + "word_freq_650": "0", + "word_freq_lab": "0", + "word_freq_labs": "0", + "word_freq_telnet": "0", + "word_freq_857": "0", + "word_freq_data": "0", + "word_freq_415": "0", + "word_freq_85": "0", + "word_freq_technology": "0", + "word_freq_1999": "0", + "word_freq_parts": "0", + "word_freq_pm": "0", + "word_freq_direct": "0", + "word_freq_cs": "0", + "word_freq_meeting": "0", + "word_freq_original": "0", + "word_freq_project": "0", + "word_freq_re": "0", + "word_freq_edu": "0", + "word_freq_table": "0", + "word_freq_conference": "0", + "char_freq_%3B": "0", + "char_freq_%28": "0", + "char_freq_%5B": "0", + "char_freq_%21": "0.778", + "char_freq_%24": "0", + "char_freq_%23": "0", + "capital_run_length_average": "3.756", + "capital_run_length_longest": "61", + "capital_run_length_total": "278", + "class": "1" + }, + { + "word_freq_make": "0.21", + "word_freq_address": "0.28", + "word_freq_all": "0.5", + "word_freq_3d": "0", + "word_freq_our": "0.14", + "word_freq_over": "0.28", + "word_freq_remove": "0.21", + "word_freq_internet": "0.07", + "word_freq_order": "0", + "word_freq_mail": "0.94", + "word_freq_receive": "0.21", + "word_freq_will": "0.79", + "word_freq_people": "0.65", + "word_freq_report": "0.21", + "word_freq_addresses": "0.14", + "word_freq_free": "0.14", + "word_freq_business": "0.07", + "word_freq_email": "0.28", + "word_freq_you": "3.47", + "word_freq_credit": "0", + "word_freq_your": "1.59", + "word_freq_font": "0", + "word_freq_000": "0.43", + "word_freq_money": "0.43", + "word_freq_hp": "0", + "word_freq_hpl": "0", + "word_freq_george": "0", + "word_freq_650": "0", + "word_freq_lab": "0", + "word_freq_labs": "0", + "word_freq_telnet": "0", + "word_freq_857": "0", + "word_freq_data": "0", + "word_freq_415": "0", + "word_freq_85": "0", + "word_freq_technology": "0", + "word_freq_1999": "0.07", + "word_freq_parts": "0", + "word_freq_pm": "0", + "word_freq_direct": "0", + "word_freq_cs": "0", + "word_freq_meeting": "0", + "word_freq_original": "0", + "word_freq_project": "0", + "word_freq_re": "0", + "word_freq_edu": "0", + "word_freq_table": "0", + "word_freq_conference": "0", + "char_freq_%3B": "0", + "char_freq_%28": "0.132", + "char_freq_%5B": "0", + "char_freq_%21": "0.372", + "char_freq_%24": "0.18", + "char_freq_%23": "0.048", + "capital_run_length_average": "5.114", + "capital_run_length_longest": "101", + "capital_run_length_total": "1028", + "class": "1" + }, + { + "word_freq_make": "0.06", + "word_freq_address": "0", + "word_freq_all": "0.71", + "word_freq_3d": "0", + "word_freq_our": "1.23", + "word_freq_over": "0.19", + "word_freq_remove": "0.19", + "word_freq_internet": "0.12", + "word_freq_order": "0.64", + "word_freq_mail": "0.25", + "word_freq_receive": "0.38", + "word_freq_will": "0.45", + "word_freq_people": "0.12", + "word_freq_report": "0", + "word_freq_addresses": "1.75", + "word_freq_free": "0.06", + "word_freq_business": "0.06", + "word_freq_email": "1.03", + "word_freq_you": "1.36", + "word_freq_credit": "0.32", + "word_freq_your": "0.51", + "word_freq_font": "0", + "word_freq_000": "1.16", + "word_freq_money": "0.06", + "word_freq_hp": "0", + "word_freq_hpl": "0", + "word_freq_george": "0", + "word_freq_650": "0", + "word_freq_lab": "0", + "word_freq_labs": "0", + "word_freq_telnet": "0", + "word_freq_857": "0", + "word_freq_data": "0", + "word_freq_415": "0", + "word_freq_85": "0", + "word_freq_technology": "0", + "word_freq_1999": "0", + "word_freq_parts": "0", + "word_freq_pm": "0", + "word_freq_direct": "0.06", + "word_freq_cs": "0", + "word_freq_meeting": "0", + "word_freq_original": "0.12", + "word_freq_project": "0", + "word_freq_re": "0.06", + "word_freq_edu": "0.06", + "word_freq_table": "0", + "word_freq_conference": "0", + "char_freq_%3B": "0.01", + "char_freq_%28": "0.143", + "char_freq_%5B": "0", + "char_freq_%21": "0.276", + "char_freq_%24": "0.184", + "char_freq_%23": "0.01", + "capital_run_length_average": "9.821", + "capital_run_length_longest": "485", + "capital_run_length_total": "2259", + "class": "1" + }, + { + "word_freq_make": "0", + "word_freq_address": "0", + "word_freq_all": "0", + "word_freq_3d": "0", + "word_freq_our": "0.63", + "word_freq_over": "0", + "word_freq_remove": "0.31", + "word_freq_internet": "0.63", + "word_freq_order": "0.31", + "word_freq_mail": "0.63", + "word_freq_receive": "0.31", + "word_freq_will": "0.31", + "word_freq_people": "0.31", + "word_freq_report": "0", + "word_freq_addresses": "0", + "word_freq_free": "0.31", + "word_freq_business": "0", + "word_freq_email": "0", + "word_freq_you": "3.18", + "word_freq_credit": "0", + "word_freq_your": "0.31", + "word_freq_font": "0", + "word_freq_000": "0", + "word_freq_money": "0", + "word_freq_hp": "0", + "word_freq_hpl": "0", + "word_freq_george": "0", + "word_freq_650": "0", + "word_freq_lab": "0", + "word_freq_labs": "0", + "word_freq_telnet": "0", + "word_freq_857": "0", + "word_freq_data": "0", + "word_freq_415": "0", + "word_freq_85": "0", + "word_freq_technology": "0", + "word_freq_1999": "0", + "word_freq_parts": "0", + "word_freq_pm": "0", + "word_freq_direct": "0", + "word_freq_cs": "0", + "word_freq_meeting": "0", + "word_freq_original": "0", + "word_freq_project": "0", + "word_freq_re": "0", + "word_freq_edu": "0", + "word_freq_table": "0", + "word_freq_conference": "0", + "char_freq_%3B": "0", + "char_freq_%28": "0.137", + "char_freq_%5B": "0", + "char_freq_%21": "0.137", + "char_freq_%24": "0", + "char_freq_%23": "0", + "capital_run_length_average": "3.537", + "capital_run_length_longest": "40", + "capital_run_length_total": "191", + "class": "1" + }, + { + "word_freq_make": "0", + "word_freq_address": "0", + "word_freq_all": "0", + "word_freq_3d": "0", + "word_freq_our": "0.63", + "word_freq_over": "0", + "word_freq_remove": "0.31", + "word_freq_internet": "0.63", + "word_freq_order": "0.31", + "word_freq_mail": "0.63", + "word_freq_receive": "0.31", + "word_freq_will": "0.31", + "word_freq_people": "0.31", + "word_freq_report": "0", + "word_freq_addresses": "0", + "word_freq_free": "0.31", + "word_freq_business": "0", + "word_freq_email": "0", + "word_freq_you": "3.18", + "word_freq_credit": "0", + "word_freq_your": "0.31", + "word_freq_font": "0", + "word_freq_000": "0", + "word_freq_money": "0", + "word_freq_hp": "0", + "word_freq_hpl": "0", + "word_freq_george": "0", + "word_freq_650": "0", + "word_freq_lab": "0", + "word_freq_labs": "0", + "word_freq_telnet": "0", + "word_freq_857": "0", + "word_freq_data": "0", + "word_freq_415": "0", + "word_freq_85": "0", + "word_freq_technology": "0", + "word_freq_1999": "0", + "word_freq_parts": "0", + "word_freq_pm": "0", + "word_freq_direct": "0", + "word_freq_cs": "0", + "word_freq_meeting": "0", + "word_freq_original": "0", + "word_freq_project": "0", + "word_freq_re": "0", + "word_freq_edu": "0", + "word_freq_table": "0", + "word_freq_conference": "0", + "char_freq_%3B": "0", + "char_freq_%28": "0.135", + "char_freq_%5B": "0", + "char_freq_%21": "0.135", + "char_freq_%24": "0", + "char_freq_%23": "0", + "capital_run_length_average": "3.537", + "capital_run_length_longest": "40", + "capital_run_length_total": "191", + "class": "1" + } + ] +} + +Shortlisted templates: +[ + { + "template_id": "tpl_tpcds_within_group_share", + "template_name": "Within-Group Share of Total", + "primary_family": "conditional_dependency_structure", + "portability": "partial", + "sql_skeleton": "SELECT {group_col}, {item_col},\n SUM({measure_col}) AS total_measure,\n SUM({measure_col}) * 100.0 / SUM(SUM({measure_col})) OVER (PARTITION BY {group_col}) AS share_within_group\nFROM {table}\nGROUP BY {group_col}, {item_col}\nORDER BY share_within_group DESC;", + "required_roles": [ + "group_col", + "item_col", + "measure_col" + ] + } +] + +Problem instance: +{ + "dataset_id": "n1", + "question": "Use template Within-Group Share of Total to probe dependency_strength_similarity with semantic role within_group_proportion. Focus on group_col=class, measure_col=word_freq_hp.", + "planned_template_id": "tpl_tpcds_within_group_share", + "bindings": { + "group_col": "class", + "measure_col": "word_freq_hp", + "item_col": "word_freq_hpl", + "top_k": 14, + "top_n": 3, + "num_tiles": 10, + "percentile_value": 0.95, + "z_threshold": 2.0, + "fraction_threshold": 0.1, + "baseline_multiplier": 1.5, + "baseline_fraction": 0.1, + "min_group_size": 5, + "min_support": 5, + "measure_threshold": 0.0, + "time_grain": "month", + "lookback_rows": 3, + "current_period_start": "'2024-01-01'", + "current_period_end": "'2024-04-01'", + "previous_period_start": "'2023-10-01'", + "previous_period_end": "'2024-01-01'", + "drift_ratio_threshold": 0.8 + }, + "can_vary": [], + "must_fix": [], + "runtime_sql_skeleton": "SELECT {group_col}, {item_col},\n SUM({measure_col}) AS total_measure,\n SUM({measure_col}) * 100.0 / SUM(SUM({measure_col})) OVER (PARTITION BY {group_col}) AS share_within_group\nFROM {table}\nGROUP BY {group_col}, {item_col}\nORDER BY share_within_group DESC;" +} + +Repair context: +{} diff --git a/Query/sql/v2/runs/v2_cli_20260502_081223_d/n1/artifacts/v2q_n1_f327357bd231f2f8/cli/sql_response_attempt_1.raw.txt b/Query/sql/v2/runs/v2_cli_20260502_081223_d/n1/artifacts/v2q_n1_f327357bd231f2f8/cli/sql_response_attempt_1.raw.txt new file mode 100644 index 0000000000000000000000000000000000000000..51edc3c495455646a0e97a746a250a6c90e5ac9d --- /dev/null +++ b/Query/sql/v2/runs/v2_cli_20260502_081223_d/n1/artifacts/v2q_n1_f327357bd231f2f8/cli/sql_response_attempt_1.raw.txt @@ -0,0 +1,4 @@ +{"type":"thread.started","thread_id":"019e40df-5182-7013-aa06-f978e8b73fd3"} +{"type":"turn.started"} +{"type":"item.completed","item":{"id":"item_0","type":"agent_message","text":"{\"sql\":\"-- template_id: tpl_tpcds_within_group_share\\nSELECT\\n \\\"class\\\",\\n \\\"word_freq_hpl\\\",\\n SUM(CAST(\\\"word_freq_hp\\\" AS REAL)) AS total_measure,\\n SUM(CAST(\\\"word_freq_hp\\\" AS REAL)) * 100.0 / SUM(SUM(CAST(\\\"word_freq_hp\\\" AS REAL))) OVER (PARTITION BY \\\"class\\\") AS share_within_group\\nFROM \\\"n1\\\"\\nGROUP BY \\\"class\\\", \\\"word_freq_hpl\\\"\\nORDER BY share_within_group DESC;\",\"notes\":\"Used the planned Within-Group Share of Total template with group_col=\\\"class\\\", item_col=\\\"word_freq_hpl\\\", and measure_col=\\\"word_freq_hp\\\". Cast the measure to REAL because the SQLite schema stores these frequency fields as TEXT.\"}"}} +{"type":"turn.completed","usage":{"input_tokens":20436,"cached_input_tokens":12032,"output_tokens":606,"reasoning_output_tokens":428}} diff --git a/Query/sql/v2/runs/v2_cli_20260502_081223_d/n1/artifacts/v2q_n1_f327357bd231f2f8/cli/sql_response_attempt_1.txt b/Query/sql/v2/runs/v2_cli_20260502_081223_d/n1/artifacts/v2q_n1_f327357bd231f2f8/cli/sql_response_attempt_1.txt new file mode 100644 index 0000000000000000000000000000000000000000..086104138e034523eff295b80e54cedbd1b7eb0d --- /dev/null +++ b/Query/sql/v2/runs/v2_cli_20260502_081223_d/n1/artifacts/v2q_n1_f327357bd231f2f8/cli/sql_response_attempt_1.txt @@ -0,0 +1 @@ +{"sql":"-- template_id: tpl_tpcds_within_group_share\nSELECT\n \"class\",\n \"word_freq_hpl\",\n SUM(CAST(\"word_freq_hp\" AS REAL)) AS total_measure,\n SUM(CAST(\"word_freq_hp\" AS REAL)) * 100.0 / SUM(SUM(CAST(\"word_freq_hp\" AS REAL))) OVER (PARTITION BY \"class\") AS share_within_group\nFROM \"n1\"\nGROUP BY \"class\", \"word_freq_hpl\"\nORDER BY share_within_group DESC;","notes":"Used the planned Within-Group Share of Total template with group_col=\"class\", item_col=\"word_freq_hpl\", and measure_col=\"word_freq_hp\". Cast the measure to REAL because the SQLite schema stores these frequency fields as TEXT."} \ No newline at end of file diff --git a/Query/sql/v2/runs/v2_cli_20260502_081223_d/n1/artifacts/v2q_n1_f327357bd231f2f8/cli/sql_stderr_attempt_1.txt b/Query/sql/v2/runs/v2_cli_20260502_081223_d/n1/artifacts/v2q_n1_f327357bd231f2f8/cli/sql_stderr_attempt_1.txt new file mode 100644 index 0000000000000000000000000000000000000000..e69de29bb2d1d6434b8b29ae775ad8c2e48c5391 diff --git a/Query/sql/v2/runs/v2_cli_20260502_081223_d/n1/artifacts/v2q_n1_f72544bb07dd6eb1/cli/sql_attempt_1.metadata.json b/Query/sql/v2/runs/v2_cli_20260502_081223_d/n1/artifacts/v2q_n1_f72544bb07dd6eb1/cli/sql_attempt_1.metadata.json new file mode 100644 index 0000000000000000000000000000000000000000..78e54e25a2ef01854cca64c2bacf44270d144c89 --- /dev/null +++ b/Query/sql/v2/runs/v2_cli_20260502_081223_d/n1/artifacts/v2q_n1_f72544bb07dd6eb1/cli/sql_attempt_1.metadata.json @@ -0,0 +1,43 @@ +{ + "attempt": 1, + "phase": "sql_generation", + "command": "codex exec --skip-git-repo-check --disable plugins --sandbox read-only --cd \"/data/jialinzhang/SQLagent\" -m gpt-5.4 --json -", + "started_at": "2026-05-19T16:04:33.539079+00:00", + "ended_at": "2026-05-19T16:04:40.012554+00:00", + "elapsed_ms": 6473.45, + "returncode": 1, + "prompt_metrics": { + "chars": 29528, + "bytes_utf8": 29528, + "lines": 792, + "estimated_tokens": null + }, + "stdout_metrics": { + "chars": 281, + "bytes_utf8": 281, + "lines": 4, + "estimated_tokens": null + }, + "stderr_metrics": { + "chars": 0, + "bytes_utf8": 0, + "lines": 0, + "estimated_tokens": null + }, + "parsed_output": { + "format": "jsonl_events", + "text_metrics": { + "chars": 280, + "bytes_utf8": 280, + "lines": 4, + "estimated_tokens": null + }, + "usage": {} + }, + "status": "failed", + "error": "AI CLI command failed with exit code 1: ", + "prompt_path": "cli/sql_prompt_attempt_1.txt", + "response_path": "cli/sql_response_attempt_1.txt", + "raw_response_path": "cli/sql_response_attempt_1.raw.txt", + "stderr_path": "cli/sql_stderr_attempt_1.txt" +} \ No newline at end of file diff --git a/Query/sql/v2/runs/v2_cli_20260502_081223_d/n1/artifacts/v2q_n1_f72544bb07dd6eb1/cli/sql_attempt_2.metadata.json b/Query/sql/v2/runs/v2_cli_20260502_081223_d/n1/artifacts/v2q_n1_f72544bb07dd6eb1/cli/sql_attempt_2.metadata.json new file mode 100644 index 0000000000000000000000000000000000000000..1d37cfdf49570962745bc85253c7b77eca24621a --- /dev/null +++ b/Query/sql/v2/runs/v2_cli_20260502_081223_d/n1/artifacts/v2q_n1_f72544bb07dd6eb1/cli/sql_attempt_2.metadata.json @@ -0,0 +1,43 @@ +{ + "attempt": 2, + "phase": "sql_generation", + "command": "codex exec --skip-git-repo-check --disable plugins --sandbox read-only --cd \"/data/jialinzhang/SQLagent\" -m gpt-5.4 --json -", + "started_at": "2026-05-19T16:04:41.014863+00:00", + "ended_at": "2026-05-19T16:04:43.930542+00:00", + "elapsed_ms": 2915.65, + "returncode": 1, + "prompt_metrics": { + "chars": 29528, + "bytes_utf8": 29528, + "lines": 792, + "estimated_tokens": null + }, + "stdout_metrics": { + "chars": 281, + "bytes_utf8": 281, + "lines": 4, + "estimated_tokens": null + }, + "stderr_metrics": { + "chars": 0, + "bytes_utf8": 0, + "lines": 0, + "estimated_tokens": null + }, + "parsed_output": { + "format": "jsonl_events", + "text_metrics": { + "chars": 280, + "bytes_utf8": 280, + "lines": 4, + "estimated_tokens": null + }, + "usage": {} + }, + "status": "failed", + "error": "AI CLI command failed with exit code 1: ", + "prompt_path": "cli/sql_prompt_attempt_2.txt", + "response_path": "cli/sql_response_attempt_2.txt", + "raw_response_path": "cli/sql_response_attempt_2.raw.txt", + "stderr_path": "cli/sql_stderr_attempt_2.txt" +} \ No newline at end of file diff --git a/Query/sql/v2/runs/v2_cli_20260502_081223_d/n1/artifacts/v2q_n1_f72544bb07dd6eb1/cli/sql_prompt_attempt_1.txt b/Query/sql/v2/runs/v2_cli_20260502_081223_d/n1/artifacts/v2q_n1_f72544bb07dd6eb1/cli/sql_prompt_attempt_1.txt new file mode 100644 index 0000000000000000000000000000000000000000..1cb7ad2d12569f9de986d97d040c1aa3814ad7e8 --- /dev/null +++ b/Query/sql/v2/runs/v2_cli_20260502_081223_d/n1/artifacts/v2q_n1_f72544bb07dd6eb1/cli/sql_prompt_attempt_1.txt @@ -0,0 +1,792 @@ +You are generating one SQLite SELECT query for a single-table SQL QA task. +Return strict JSON only, with this schema: {"sql": "...", "notes": "..."}. +Rules: +- Use only the provided table and columns. +- Do not write INSERT, UPDATE, DELETE, DROP, ALTER, CREATE, PRAGMA, ATTACH, DETACH, or VACUUM. +- Prefer the planned template and bound roles when provided. +- Add a leading SQL comment exactly like: -- template_id: . +- Generate SQLite-compatible SQL. SQLite does not support PERCENTILE_CONT or STDDEV. +- Quote identifiers with double quotes. +- Return no markdown and no extra prose. + +Dataset context: +Dataset context for SQL QA: +- dataset_id: n1 +- dataset_name: Spambase +- table_name: n1 +- table_layout: single-table dataset (do not assume joins). +- row_semantics: One row is one email represented by engineered token/character frequency and capitalization-run features. +- task_type: classification +- target_column: class +- main_row_count: 4601 +- important_fields: +- word_freq_make: role=feature, type=numeric_sparse_frequency. tags=['condition_candidate', 'measure', 'rare_pattern_candidate'] desc=Frequency feature for token 'make' in an email message. +- word_freq_address: role=feature, type=numeric_sparse_frequency. tags=['condition_candidate', 'measure', 'rare_pattern_candidate'] desc=Frequency feature for token 'address' in an email message. +- word_freq_all: role=feature, type=numeric_sparse_frequency. tags=['condition_candidate', 'measure', 'rare_pattern_candidate'] desc=Frequency feature for token 'all' in an email message. +- word_freq_3d: role=feature, type=numeric_sparse_frequency. tags=['condition_candidate', 'measure', 'rare_pattern_candidate'] desc=Frequency feature for token '3d' in an email message. +- word_freq_our: role=feature, type=numeric_sparse_frequency. tags=['condition_candidate', 'measure', 'rare_pattern_candidate'] desc=Frequency feature for token 'our' in an email message. +- word_freq_over: role=feature, type=numeric_sparse_frequency. tags=['condition_candidate', 'measure', 'rare_pattern_candidate'] desc=Frequency feature for token 'over' in an email message. +- word_freq_remove: role=feature, type=numeric_sparse_frequency. tags=['condition_candidate', 'measure', 'rare_pattern_candidate'] desc=Frequency feature for token 'remove' in an email message. +- word_freq_internet: role=feature, type=numeric_sparse_frequency. tags=['condition_candidate', 'measure', 'rare_pattern_candidate'] desc=Frequency feature for token 'internet' in an email message. +- word_freq_order: role=feature, type=numeric_sparse_frequency. tags=['condition_candidate', 'measure', 'rare_pattern_candidate'] desc=Frequency feature for token 'order' in an email message. +- word_freq_mail: role=feature, type=numeric_sparse_frequency. tags=['condition_candidate', 'measure', 'rare_pattern_candidate'] desc=Frequency feature for token 'mail' in an email message. +- word_freq_receive: role=feature, type=numeric_sparse_frequency. tags=['condition_candidate', 'measure', 'rare_pattern_candidate'] desc=Frequency feature for token 'receive' in an email message. +- word_freq_will: role=feature, type=numeric_sparse_frequency. tags=['condition_candidate', 'measure', 'rare_pattern_candidate'] desc=Frequency feature for token 'will' in an email message. +- word_freq_people: role=feature, type=numeric_sparse_frequency. tags=['condition_candidate', 'measure', 'rare_pattern_candidate'] desc=Frequency feature for token 'people' in an email message. +- word_freq_report: role=feature, type=numeric_sparse_frequency. tags=['condition_candidate', 'measure', 'rare_pattern_candidate'] desc=Frequency feature for token 'report' in an email message. +- word_freq_addresses: role=feature, type=numeric_sparse_frequency. tags=['condition_candidate', 'measure', 'rare_pattern_candidate'] desc=Frequency feature for token 'addresses' in an email message. +- word_freq_free: role=feature, type=numeric_sparse_frequency. tags=['condition_candidate', 'measure', 'rare_pattern_candidate'] desc=Frequency feature for token 'free' in an email message. +- word_freq_business: role=feature, type=numeric_sparse_frequency. tags=['condition_candidate', 'measure', 'rare_pattern_candidate'] desc=Frequency feature for token 'business' in an email message. +- word_freq_email: role=feature, type=numeric_sparse_frequency. tags=['condition_candidate', 'measure', 'rare_pattern_candidate'] desc=Frequency feature for token 'email' in an email message. +- word_freq_you: role=feature, type=numeric_sparse_frequency. tags=['condition_candidate', 'measure', 'rare_pattern_candidate'] desc=Frequency feature for token 'you' in an email message. +- word_freq_credit: role=feature, type=numeric_sparse_frequency. tags=['condition_candidate', 'measure', 'rare_pattern_candidate'] desc=Frequency feature for token 'credit' in an email message. +- word_freq_your: role=feature, type=numeric_sparse_frequency. tags=['condition_candidate', 'measure', 'rare_pattern_candidate'] desc=Frequency feature for token 'your' in an email message. +- word_freq_font: role=feature, type=numeric_sparse_frequency. tags=['condition_candidate', 'measure', 'rare_pattern_candidate'] desc=Frequency feature for token 'font' in an email message. +- word_freq_000: role=feature, type=numeric_sparse_frequency. tags=['condition_candidate', 'measure', 'rare_pattern_candidate'] desc=Frequency feature for token '000' in an email message. +- word_freq_money: role=feature, type=numeric_sparse_frequency. tags=['condition_candidate', 'measure', 'rare_pattern_candidate'] desc=Frequency feature for token 'money' in an email message. +- word_freq_hp: role=feature, type=numeric_sparse_frequency. tags=['condition_candidate', 'measure', 'rare_pattern_candidate'] desc=Frequency feature for token 'hp' in an email message. +- word_freq_hpl: role=feature, type=numeric_sparse_frequency. tags=['condition_candidate', 'measure', 'rare_pattern_candidate'] desc=Frequency feature for token 'hpl' in an email message. +- word_freq_george: role=feature, type=numeric_sparse_frequency. tags=['condition_candidate', 'measure', 'rare_pattern_candidate'] desc=Frequency feature for token 'george' in an email message. +- word_freq_650: role=feature, type=numeric_sparse_frequency. tags=['condition_candidate', 'measure', 'rare_pattern_candidate'] desc=Frequency feature for token '650' in an email message. +- word_freq_lab: role=feature, type=numeric_sparse_frequency. tags=['condition_candidate', 'measure', 'rare_pattern_candidate'] desc=Frequency feature for token 'lab' in an email message. +- word_freq_labs: role=feature, type=numeric_sparse_frequency. tags=['condition_candidate', 'measure', 'rare_pattern_candidate'] desc=Frequency feature for token 'labs' in an email message. +- word_freq_telnet: role=feature, type=numeric_sparse_frequency. tags=['condition_candidate', 'measure', 'rare_pattern_candidate'] desc=Frequency feature for token 'telnet' in an email message. +- word_freq_857: role=feature, type=numeric_sparse_frequency. tags=['condition_candidate', 'measure', 'rare_pattern_candidate'] desc=Frequency feature for token '857' in an email message. +- word_freq_data: role=feature, type=numeric_sparse_frequency. tags=['condition_candidate', 'measure', 'rare_pattern_candidate'] desc=Frequency feature for token 'data' in an email message. +- word_freq_415: role=feature, type=numeric_sparse_frequency. tags=['condition_candidate', 'measure', 'rare_pattern_candidate'] desc=Frequency feature for token '415' in an email message. +- word_freq_85: role=feature, type=numeric_sparse_frequency. tags=['condition_candidate', 'measure', 'rare_pattern_candidate'] desc=Frequency feature for token '85' in an email message. +- word_freq_technology: role=feature, type=numeric_sparse_frequency. tags=['condition_candidate', 'measure', 'rare_pattern_candidate'] desc=Frequency feature for token 'technology' in an email message. +- word_freq_1999: role=feature, type=numeric_sparse_frequency. tags=['condition_candidate', 'measure', 'rare_pattern_candidate'] desc=Frequency feature for token '1999' in an email message. +- word_freq_parts: role=feature, type=numeric_sparse_frequency. tags=['condition_candidate', 'measure', 'rare_pattern_candidate'] desc=Frequency feature for token 'parts' in an email message. +- word_freq_pm: role=feature, type=numeric_sparse_frequency. tags=['condition_candidate', 'measure', 'rare_pattern_candidate'] desc=Frequency feature for token 'pm' in an email message. +- word_freq_direct: role=feature, type=numeric_sparse_frequency. tags=['condition_candidate', 'measure', 'rare_pattern_candidate'] desc=Frequency feature for token 'direct' in an email message. +- word_freq_cs: role=feature, type=numeric_sparse_frequency. tags=['condition_candidate', 'measure', 'rare_pattern_candidate'] desc=Frequency feature for token 'cs' in an email message. +- word_freq_meeting: role=feature, type=numeric_sparse_frequency. tags=['condition_candidate', 'measure', 'rare_pattern_candidate'] desc=Frequency feature for token 'meeting' in an email message. +- word_freq_original: role=feature, type=numeric_sparse_frequency. tags=['condition_candidate', 'measure', 'rare_pattern_candidate'] desc=Frequency feature for token 'original' in an email message. +- word_freq_project: role=feature, type=numeric_sparse_frequency. tags=['condition_candidate', 'measure', 'rare_pattern_candidate'] desc=Frequency feature for token 'project' in an email message. +- word_freq_re: role=feature, type=numeric_sparse_frequency. tags=['condition_candidate', 'measure', 'rare_pattern_candidate'] desc=Frequency feature for token 're' in an email message. +- word_freq_edu: role=feature, type=numeric_sparse_frequency. tags=['condition_candidate', 'measure', 'rare_pattern_candidate'] desc=Frequency feature for token 'edu' in an email message. +- word_freq_table: role=feature, type=numeric_sparse_frequency. tags=['condition_candidate', 'measure', 'rare_pattern_candidate'] desc=Frequency feature for token 'table' in an email message. +- word_freq_conference: role=feature, type=numeric_sparse_frequency. tags=['condition_candidate', 'measure', 'rare_pattern_candidate'] desc=Frequency feature for token 'conference' in an email message. +- char_freq_%3B: role=feature, type=numeric_sparse_frequency. tags=['condition_candidate', 'measure', 'rare_pattern_candidate'] desc=Character frequency feature for symbol ';'. +- char_freq_%28: role=feature, type=numeric_sparse_frequency. tags=['condition_candidate', 'measure', 'rare_pattern_candidate'] desc=Character frequency feature for symbol '('. +- char_freq_%5B: role=feature, type=numeric_sparse_frequency. tags=['condition_candidate', 'measure', 'rare_pattern_candidate'] desc=Character frequency feature for symbol '['. +- char_freq_%21: role=feature, type=numeric_sparse_frequency. tags=['condition_candidate', 'measure', 'rare_pattern_candidate'] desc=Character frequency feature for symbol '!'. +- char_freq_%24: role=feature, type=numeric_sparse_frequency. tags=['condition_candidate', 'measure', 'rare_pattern_candidate'] desc=Character frequency feature for symbol '$'. +- char_freq_%23: role=feature, type=numeric_sparse_frequency. tags=['condition_candidate', 'measure', 'rare_pattern_candidate'] desc=Character frequency feature for symbol '#'. +- capital_run_length_average: role=feature, type=numeric_heavy_tail. tags=['condition_candidate', 'measure', 'rare_pattern_candidate'] desc=Average length of uninterrupted capital-letter runs. +- capital_run_length_longest: role=feature, type=numeric_heavy_tail. tags=['condition_candidate', 'measure', 'rare_pattern_candidate'] desc=Longest uninterrupted capital-letter run. +- capital_run_length_total: role=feature, type=numeric_heavy_tail. tags=['condition_candidate', 'measure', 'rare_pattern_candidate'] desc=Total number of capital letters in runs. +- class: role=target, type=binary_target. tags=['target_candidate'] desc=Binary spam label (1=spam, 0=non-spam). +- useful_field_combinations: [['word_freq_free', 'word_freq_you', 'class'], ['char_freq_%21', 'capital_run_length_longest', 'class'], ['word_freq_remove', 'word_freq_money', 'class']] +- fields_requiring_caution: ['capital_run_length_average', 'capital_run_length_longest', 'capital_run_length_total'] +- source_url: https://www.openml.org/d/44 + +SQLite schema snapshot: +{ + "table_name": "n1", + "quoted_table_name": "\"n1\"", + "row_count": 4601, + "columns": [ + { + "name": "word_freq_make", + "type": "TEXT", + "notnull": false, + "pk": false + }, + { + "name": "word_freq_address", + "type": "TEXT", + "notnull": false, + "pk": false + }, + { + "name": "word_freq_all", + "type": "TEXT", + "notnull": false, + "pk": false + }, + { + "name": "word_freq_3d", + "type": "TEXT", + "notnull": false, + "pk": false + }, + { + "name": "word_freq_our", + "type": "TEXT", + "notnull": false, + "pk": false + }, + { + "name": "word_freq_over", + "type": "TEXT", + "notnull": false, + "pk": false + }, + { + "name": "word_freq_remove", + "type": "TEXT", + "notnull": false, + "pk": false + }, + { + "name": "word_freq_internet", + "type": "TEXT", + "notnull": false, + "pk": false + }, + { + "name": "word_freq_order", + "type": "TEXT", + "notnull": false, + "pk": false + }, + { + "name": "word_freq_mail", + "type": "TEXT", + "notnull": false, + "pk": false + }, + { + "name": "word_freq_receive", + "type": "TEXT", + "notnull": false, + "pk": false + }, + { + "name": "word_freq_will", + "type": "TEXT", + "notnull": false, + "pk": false + }, + { + "name": "word_freq_people", + "type": "TEXT", + "notnull": false, + "pk": false + }, + { + "name": "word_freq_report", + "type": "TEXT", + "notnull": false, + "pk": false + }, + { + "name": "word_freq_addresses", + "type": "TEXT", + "notnull": false, + "pk": false + }, + { + "name": "word_freq_free", + "type": "TEXT", + "notnull": false, + "pk": false + }, + { + "name": "word_freq_business", + "type": "TEXT", + "notnull": false, + "pk": false + }, + { + "name": "word_freq_email", + "type": "TEXT", + "notnull": false, + "pk": false + }, + { + "name": "word_freq_you", + "type": "TEXT", + "notnull": false, + "pk": false + }, + { + "name": "word_freq_credit", + "type": "TEXT", + "notnull": false, + "pk": false + }, + { + "name": "word_freq_your", + "type": "TEXT", + "notnull": false, + "pk": false + }, + { + "name": "word_freq_font", + "type": "TEXT", + "notnull": false, + "pk": false + }, + { + "name": "word_freq_000", + "type": "TEXT", + "notnull": false, + "pk": false + }, + { + "name": "word_freq_money", + "type": "TEXT", + "notnull": false, + "pk": false + }, + { + "name": "word_freq_hp", + "type": "TEXT", + "notnull": false, + "pk": false + }, + { + "name": "word_freq_hpl", + "type": "TEXT", + "notnull": false, + "pk": false + }, + { + "name": "word_freq_george", + "type": "TEXT", + "notnull": false, + "pk": false + }, + { + "name": "word_freq_650", + "type": "TEXT", + "notnull": false, + "pk": false + }, + { + "name": "word_freq_lab", + "type": "TEXT", + "notnull": false, + "pk": false + }, + { + "name": "word_freq_labs", + "type": "TEXT", + "notnull": false, + "pk": false + }, + { + "name": "word_freq_telnet", + "type": "TEXT", + "notnull": false, + "pk": false + }, + { + "name": "word_freq_857", + "type": "TEXT", + "notnull": false, + "pk": false + }, + { + "name": "word_freq_data", + "type": "TEXT", + "notnull": false, + "pk": false + }, + { + "name": "word_freq_415", + "type": "TEXT", + "notnull": false, + "pk": false + }, + { + "name": "word_freq_85", + "type": "TEXT", + "notnull": false, + "pk": false + }, + { + "name": "word_freq_technology", + "type": "TEXT", + "notnull": false, + "pk": false + }, + { + "name": "word_freq_1999", + "type": "TEXT", + "notnull": false, + "pk": false + }, + { + "name": "word_freq_parts", + "type": "TEXT", + "notnull": false, + "pk": false + }, + { + "name": "word_freq_pm", + "type": "TEXT", + "notnull": false, + "pk": false + }, + { + "name": "word_freq_direct", + "type": "TEXT", + "notnull": false, + "pk": false + }, + { + "name": "word_freq_cs", + "type": "TEXT", + "notnull": false, + "pk": false + }, + { + "name": "word_freq_meeting", + "type": "TEXT", + "notnull": false, + "pk": false + }, + { + "name": "word_freq_original", + "type": "TEXT", + "notnull": false, + "pk": false + }, + { + "name": "word_freq_project", + "type": "TEXT", + "notnull": false, + "pk": false + }, + { + "name": "word_freq_re", + "type": "TEXT", + "notnull": false, + "pk": false + }, + { + "name": "word_freq_edu", + "type": "TEXT", + "notnull": false, + "pk": false + }, + { + "name": "word_freq_table", + "type": "TEXT", + "notnull": false, + "pk": false + }, + { + "name": "word_freq_conference", + "type": "TEXT", + "notnull": false, + "pk": false + }, + { + "name": "char_freq_%3B", + "type": "TEXT", + "notnull": false, + "pk": false + }, + { + "name": "char_freq_%28", + "type": "TEXT", + "notnull": false, + "pk": false + }, + { + "name": "char_freq_%5B", + "type": "TEXT", + "notnull": false, + "pk": false + }, + { + "name": "char_freq_%21", + "type": "TEXT", + "notnull": false, + "pk": false + }, + { + "name": "char_freq_%24", + "type": "TEXT", + "notnull": false, + "pk": false + }, + { + "name": "char_freq_%23", + "type": "TEXT", + "notnull": false, + "pk": false + }, + { + "name": "capital_run_length_average", + "type": "TEXT", + "notnull": false, + "pk": false + }, + { + "name": "capital_run_length_longest", + "type": "TEXT", + "notnull": false, + "pk": false + }, + { + "name": "capital_run_length_total", + "type": "TEXT", + "notnull": false, + "pk": false + }, + { + "name": "class", + "type": "TEXT", + "notnull": false, + "pk": false + } + ], + "sample_rows": [ + { + "word_freq_make": "0", + "word_freq_address": "0.64", + "word_freq_all": "0.64", + "word_freq_3d": "0", + "word_freq_our": "0.32", + "word_freq_over": "0", + "word_freq_remove": "0", + "word_freq_internet": "0", + "word_freq_order": "0", + "word_freq_mail": "0", + "word_freq_receive": "0", + "word_freq_will": "0.64", + "word_freq_people": "0", + "word_freq_report": "0", + "word_freq_addresses": "0", + "word_freq_free": "0.32", + "word_freq_business": "0", + "word_freq_email": "1.29", + "word_freq_you": "1.93", + "word_freq_credit": "0", + "word_freq_your": "0.96", + "word_freq_font": "0", + "word_freq_000": "0", + "word_freq_money": "0", + "word_freq_hp": "0", + "word_freq_hpl": "0", + "word_freq_george": "0", + "word_freq_650": "0", + "word_freq_lab": "0", + "word_freq_labs": "0", + "word_freq_telnet": "0", + "word_freq_857": "0", + "word_freq_data": "0", + "word_freq_415": "0", + "word_freq_85": "0", + "word_freq_technology": "0", + "word_freq_1999": "0", + "word_freq_parts": "0", + "word_freq_pm": "0", + "word_freq_direct": "0", + "word_freq_cs": "0", + "word_freq_meeting": "0", + "word_freq_original": "0", + "word_freq_project": "0", + "word_freq_re": "0", + "word_freq_edu": "0", + "word_freq_table": "0", + "word_freq_conference": "0", + "char_freq_%3B": "0", + "char_freq_%28": "0", + "char_freq_%5B": "0", + "char_freq_%21": "0.778", + "char_freq_%24": "0", + "char_freq_%23": "0", + "capital_run_length_average": "3.756", + "capital_run_length_longest": "61", + "capital_run_length_total": "278", + "class": "1" + }, + { + "word_freq_make": "0.21", + "word_freq_address": "0.28", + "word_freq_all": "0.5", + "word_freq_3d": "0", + "word_freq_our": "0.14", + "word_freq_over": "0.28", + "word_freq_remove": "0.21", + "word_freq_internet": "0.07", + "word_freq_order": "0", + "word_freq_mail": "0.94", + "word_freq_receive": "0.21", + "word_freq_will": "0.79", + "word_freq_people": "0.65", + "word_freq_report": "0.21", + "word_freq_addresses": "0.14", + "word_freq_free": "0.14", + "word_freq_business": "0.07", + "word_freq_email": "0.28", + "word_freq_you": "3.47", + "word_freq_credit": "0", + "word_freq_your": "1.59", + "word_freq_font": "0", + "word_freq_000": "0.43", + "word_freq_money": "0.43", + "word_freq_hp": "0", + "word_freq_hpl": "0", + "word_freq_george": "0", + "word_freq_650": "0", + "word_freq_lab": "0", + "word_freq_labs": "0", + "word_freq_telnet": "0", + "word_freq_857": "0", + "word_freq_data": "0", + "word_freq_415": "0", + "word_freq_85": "0", + "word_freq_technology": "0", + "word_freq_1999": "0.07", + "word_freq_parts": "0", + "word_freq_pm": "0", + "word_freq_direct": "0", + "word_freq_cs": "0", + "word_freq_meeting": "0", + "word_freq_original": "0", + "word_freq_project": "0", + "word_freq_re": "0", + "word_freq_edu": "0", + "word_freq_table": "0", + "word_freq_conference": "0", + "char_freq_%3B": "0", + "char_freq_%28": "0.132", + "char_freq_%5B": "0", + "char_freq_%21": "0.372", + "char_freq_%24": "0.18", + "char_freq_%23": "0.048", + "capital_run_length_average": "5.114", + "capital_run_length_longest": "101", + "capital_run_length_total": "1028", + "class": "1" + }, + { + "word_freq_make": "0.06", + "word_freq_address": "0", + "word_freq_all": "0.71", + "word_freq_3d": "0", + "word_freq_our": "1.23", + "word_freq_over": "0.19", + "word_freq_remove": "0.19", + "word_freq_internet": "0.12", + "word_freq_order": "0.64", + "word_freq_mail": "0.25", + "word_freq_receive": "0.38", + "word_freq_will": "0.45", + "word_freq_people": "0.12", + "word_freq_report": "0", + "word_freq_addresses": "1.75", + "word_freq_free": "0.06", + "word_freq_business": "0.06", + "word_freq_email": "1.03", + "word_freq_you": "1.36", + "word_freq_credit": "0.32", + "word_freq_your": "0.51", + "word_freq_font": "0", + "word_freq_000": "1.16", + "word_freq_money": "0.06", + "word_freq_hp": "0", + "word_freq_hpl": "0", + "word_freq_george": "0", + "word_freq_650": "0", + "word_freq_lab": "0", + "word_freq_labs": "0", + "word_freq_telnet": "0", + "word_freq_857": "0", + "word_freq_data": "0", + "word_freq_415": "0", + "word_freq_85": "0", + "word_freq_technology": "0", + "word_freq_1999": "0", + "word_freq_parts": "0", + "word_freq_pm": "0", + "word_freq_direct": "0.06", + "word_freq_cs": "0", + "word_freq_meeting": "0", + "word_freq_original": "0.12", + "word_freq_project": "0", + "word_freq_re": "0.06", + "word_freq_edu": "0.06", + "word_freq_table": "0", + "word_freq_conference": "0", + "char_freq_%3B": "0.01", + "char_freq_%28": "0.143", + "char_freq_%5B": "0", + "char_freq_%21": "0.276", + "char_freq_%24": "0.184", + "char_freq_%23": "0.01", + "capital_run_length_average": "9.821", + "capital_run_length_longest": "485", + "capital_run_length_total": "2259", + "class": "1" + }, + { + "word_freq_make": "0", + "word_freq_address": "0", + "word_freq_all": "0", + "word_freq_3d": "0", + "word_freq_our": "0.63", + "word_freq_over": "0", + "word_freq_remove": "0.31", + "word_freq_internet": "0.63", + "word_freq_order": "0.31", + "word_freq_mail": "0.63", + "word_freq_receive": "0.31", + "word_freq_will": "0.31", + "word_freq_people": "0.31", + "word_freq_report": "0", + "word_freq_addresses": "0", + "word_freq_free": "0.31", + "word_freq_business": "0", + "word_freq_email": "0", + "word_freq_you": "3.18", + "word_freq_credit": "0", + "word_freq_your": "0.31", + "word_freq_font": "0", + "word_freq_000": "0", + "word_freq_money": "0", + "word_freq_hp": "0", + "word_freq_hpl": "0", + "word_freq_george": "0", + "word_freq_650": "0", + "word_freq_lab": "0", + "word_freq_labs": "0", + "word_freq_telnet": "0", + "word_freq_857": "0", + "word_freq_data": "0", + "word_freq_415": "0", + "word_freq_85": "0", + "word_freq_technology": "0", + "word_freq_1999": "0", + "word_freq_parts": "0", + "word_freq_pm": "0", + "word_freq_direct": "0", + "word_freq_cs": "0", + "word_freq_meeting": "0", + "word_freq_original": "0", + "word_freq_project": "0", + "word_freq_re": "0", + "word_freq_edu": "0", + "word_freq_table": "0", + "word_freq_conference": "0", + "char_freq_%3B": "0", + "char_freq_%28": "0.137", + "char_freq_%5B": "0", + "char_freq_%21": "0.137", + "char_freq_%24": "0", + "char_freq_%23": "0", + "capital_run_length_average": "3.537", + "capital_run_length_longest": "40", + "capital_run_length_total": "191", + "class": "1" + }, + { + "word_freq_make": "0", + "word_freq_address": "0", + "word_freq_all": "0", + "word_freq_3d": "0", + "word_freq_our": "0.63", + "word_freq_over": "0", + "word_freq_remove": "0.31", + "word_freq_internet": "0.63", + "word_freq_order": "0.31", + "word_freq_mail": "0.63", + "word_freq_receive": "0.31", + "word_freq_will": "0.31", + "word_freq_people": "0.31", + "word_freq_report": "0", + "word_freq_addresses": "0", + "word_freq_free": "0.31", + "word_freq_business": "0", + "word_freq_email": "0", + "word_freq_you": "3.18", + "word_freq_credit": "0", + "word_freq_your": "0.31", + "word_freq_font": "0", + "word_freq_000": "0", + "word_freq_money": "0", + "word_freq_hp": "0", + "word_freq_hpl": "0", + "word_freq_george": "0", + "word_freq_650": "0", + "word_freq_lab": "0", + "word_freq_labs": "0", + "word_freq_telnet": "0", + "word_freq_857": "0", + "word_freq_data": "0", + "word_freq_415": "0", + "word_freq_85": "0", + "word_freq_technology": "0", + "word_freq_1999": "0", + "word_freq_parts": "0", + "word_freq_pm": "0", + "word_freq_direct": "0", + "word_freq_cs": "0", + "word_freq_meeting": "0", + "word_freq_original": "0", + "word_freq_project": "0", + "word_freq_re": "0", + "word_freq_edu": "0", + "word_freq_table": "0", + "word_freq_conference": "0", + "char_freq_%3B": "0", + "char_freq_%28": "0.135", + "char_freq_%5B": "0", + "char_freq_%21": "0.135", + "char_freq_%24": "0", + "char_freq_%23": "0", + "capital_run_length_average": "3.537", + "capital_run_length_longest": "40", + "capital_run_length_total": "191", + "class": "1" + } + ] +} + +Shortlisted templates: +[ + { + "template_id": "tpl_tpch_thresholded_group_ranking", + "template_name": "Thresholded Group Ranking", + "primary_family": "tail_rarity_structure", + "portability": "partial", + "sql_skeleton": "SELECT {group_col}, SUM({measure_col}) AS total_measure\nFROM {table}\nGROUP BY {group_col}\nHAVING SUM({measure_col}) > {measure_threshold}\nORDER BY total_measure DESC\nLIMIT {top_k};", + "required_roles": [ + "group_col", + "measure_col" + ] + } +] + +Problem instance: +{ + "dataset_id": "n1", + "question": "Use template Thresholded Group Ranking to probe tail_set_consistency with semantic role filtered_stable_view. Focus on group_col=class, measure_col=word_freq_your.", + "planned_template_id": "tpl_tpch_thresholded_group_ranking", + "bindings": { + "group_col": "class", + "measure_col": "word_freq_your", + "top_k": 19, + "top_n": 6, + "num_tiles": 10, + "percentile_value": 0.9, + "z_threshold": 2.0, + "fraction_threshold": 0.05, + "baseline_multiplier": 1.75, + "baseline_fraction": 0.1, + "min_group_size": 5, + "min_support": 4, + "measure_threshold": 0.94, + "time_grain": "month", + "lookback_rows": 3, + "current_period_start": "'2024-01-01'", + "current_period_end": "'2024-04-01'", + "previous_period_start": "'2023-10-01'", + "previous_period_end": "'2024-01-01'", + "drift_ratio_threshold": 0.8 + }, + "can_vary": [], + "must_fix": [], + "runtime_sql_skeleton": "SELECT {group_col}, SUM({measure_col}) AS total_measure\nFROM {table}\nGROUP BY {group_col}\nHAVING SUM({measure_col}) > {measure_threshold}\nORDER BY total_measure DESC\nLIMIT {top_k};" +} + +Repair context: +{} diff --git a/Query/sql/v2/runs/v2_cli_20260502_081223_d/n1/artifacts/v2q_n1_f72544bb07dd6eb1/cli/sql_prompt_attempt_2.txt b/Query/sql/v2/runs/v2_cli_20260502_081223_d/n1/artifacts/v2q_n1_f72544bb07dd6eb1/cli/sql_prompt_attempt_2.txt new file mode 100644 index 0000000000000000000000000000000000000000..1cb7ad2d12569f9de986d97d040c1aa3814ad7e8 --- /dev/null +++ b/Query/sql/v2/runs/v2_cli_20260502_081223_d/n1/artifacts/v2q_n1_f72544bb07dd6eb1/cli/sql_prompt_attempt_2.txt @@ -0,0 +1,792 @@ +You are generating one SQLite SELECT query for a single-table SQL QA task. +Return strict JSON only, with this schema: {"sql": "...", "notes": "..."}. +Rules: +- Use only the provided table and columns. +- Do not write INSERT, UPDATE, DELETE, DROP, ALTER, CREATE, PRAGMA, ATTACH, DETACH, or VACUUM. +- Prefer the planned template and bound roles when provided. +- Add a leading SQL comment exactly like: -- template_id: . +- Generate SQLite-compatible SQL. SQLite does not support PERCENTILE_CONT or STDDEV. +- Quote identifiers with double quotes. +- Return no markdown and no extra prose. + +Dataset context: +Dataset context for SQL QA: +- dataset_id: n1 +- dataset_name: Spambase +- table_name: n1 +- table_layout: single-table dataset (do not assume joins). +- row_semantics: One row is one email represented by engineered token/character frequency and capitalization-run features. +- task_type: classification +- target_column: class +- main_row_count: 4601 +- important_fields: +- word_freq_make: role=feature, type=numeric_sparse_frequency. tags=['condition_candidate', 'measure', 'rare_pattern_candidate'] desc=Frequency feature for token 'make' in an email message. +- word_freq_address: role=feature, type=numeric_sparse_frequency. tags=['condition_candidate', 'measure', 'rare_pattern_candidate'] desc=Frequency feature for token 'address' in an email message. +- word_freq_all: role=feature, type=numeric_sparse_frequency. tags=['condition_candidate', 'measure', 'rare_pattern_candidate'] desc=Frequency feature for token 'all' in an email message. +- word_freq_3d: role=feature, type=numeric_sparse_frequency. tags=['condition_candidate', 'measure', 'rare_pattern_candidate'] desc=Frequency feature for token '3d' in an email message. +- word_freq_our: role=feature, type=numeric_sparse_frequency. tags=['condition_candidate', 'measure', 'rare_pattern_candidate'] desc=Frequency feature for token 'our' in an email message. +- word_freq_over: role=feature, type=numeric_sparse_frequency. tags=['condition_candidate', 'measure', 'rare_pattern_candidate'] desc=Frequency feature for token 'over' in an email message. +- word_freq_remove: role=feature, type=numeric_sparse_frequency. tags=['condition_candidate', 'measure', 'rare_pattern_candidate'] desc=Frequency feature for token 'remove' in an email message. +- word_freq_internet: role=feature, type=numeric_sparse_frequency. tags=['condition_candidate', 'measure', 'rare_pattern_candidate'] desc=Frequency feature for token 'internet' in an email message. +- word_freq_order: role=feature, type=numeric_sparse_frequency. tags=['condition_candidate', 'measure', 'rare_pattern_candidate'] desc=Frequency feature for token 'order' in an email message. +- word_freq_mail: role=feature, type=numeric_sparse_frequency. tags=['condition_candidate', 'measure', 'rare_pattern_candidate'] desc=Frequency feature for token 'mail' in an email message. +- word_freq_receive: role=feature, type=numeric_sparse_frequency. tags=['condition_candidate', 'measure', 'rare_pattern_candidate'] desc=Frequency feature for token 'receive' in an email message. +- word_freq_will: role=feature, type=numeric_sparse_frequency. tags=['condition_candidate', 'measure', 'rare_pattern_candidate'] desc=Frequency feature for token 'will' in an email message. +- word_freq_people: role=feature, type=numeric_sparse_frequency. tags=['condition_candidate', 'measure', 'rare_pattern_candidate'] desc=Frequency feature for token 'people' in an email message. +- word_freq_report: role=feature, type=numeric_sparse_frequency. tags=['condition_candidate', 'measure', 'rare_pattern_candidate'] desc=Frequency feature for token 'report' in an email message. +- word_freq_addresses: role=feature, type=numeric_sparse_frequency. tags=['condition_candidate', 'measure', 'rare_pattern_candidate'] desc=Frequency feature for token 'addresses' in an email message. +- word_freq_free: role=feature, type=numeric_sparse_frequency. tags=['condition_candidate', 'measure', 'rare_pattern_candidate'] desc=Frequency feature for token 'free' in an email message. +- word_freq_business: role=feature, type=numeric_sparse_frequency. tags=['condition_candidate', 'measure', 'rare_pattern_candidate'] desc=Frequency feature for token 'business' in an email message. +- word_freq_email: role=feature, type=numeric_sparse_frequency. tags=['condition_candidate', 'measure', 'rare_pattern_candidate'] desc=Frequency feature for token 'email' in an email message. +- word_freq_you: role=feature, type=numeric_sparse_frequency. tags=['condition_candidate', 'measure', 'rare_pattern_candidate'] desc=Frequency feature for token 'you' in an email message. +- word_freq_credit: role=feature, type=numeric_sparse_frequency. tags=['condition_candidate', 'measure', 'rare_pattern_candidate'] desc=Frequency feature for token 'credit' in an email message. +- word_freq_your: role=feature, type=numeric_sparse_frequency. tags=['condition_candidate', 'measure', 'rare_pattern_candidate'] desc=Frequency feature for token 'your' in an email message. +- word_freq_font: role=feature, type=numeric_sparse_frequency. tags=['condition_candidate', 'measure', 'rare_pattern_candidate'] desc=Frequency feature for token 'font' in an email message. +- word_freq_000: role=feature, type=numeric_sparse_frequency. tags=['condition_candidate', 'measure', 'rare_pattern_candidate'] desc=Frequency feature for token '000' in an email message. +- word_freq_money: role=feature, type=numeric_sparse_frequency. tags=['condition_candidate', 'measure', 'rare_pattern_candidate'] desc=Frequency feature for token 'money' in an email message. +- word_freq_hp: role=feature, type=numeric_sparse_frequency. tags=['condition_candidate', 'measure', 'rare_pattern_candidate'] desc=Frequency feature for token 'hp' in an email message. +- word_freq_hpl: role=feature, type=numeric_sparse_frequency. tags=['condition_candidate', 'measure', 'rare_pattern_candidate'] desc=Frequency feature for token 'hpl' in an email message. +- word_freq_george: role=feature, type=numeric_sparse_frequency. tags=['condition_candidate', 'measure', 'rare_pattern_candidate'] desc=Frequency feature for token 'george' in an email message. +- word_freq_650: role=feature, type=numeric_sparse_frequency. tags=['condition_candidate', 'measure', 'rare_pattern_candidate'] desc=Frequency feature for token '650' in an email message. +- word_freq_lab: role=feature, type=numeric_sparse_frequency. tags=['condition_candidate', 'measure', 'rare_pattern_candidate'] desc=Frequency feature for token 'lab' in an email message. +- word_freq_labs: role=feature, type=numeric_sparse_frequency. tags=['condition_candidate', 'measure', 'rare_pattern_candidate'] desc=Frequency feature for token 'labs' in an email message. +- word_freq_telnet: role=feature, type=numeric_sparse_frequency. tags=['condition_candidate', 'measure', 'rare_pattern_candidate'] desc=Frequency feature for token 'telnet' in an email message. +- word_freq_857: role=feature, type=numeric_sparse_frequency. tags=['condition_candidate', 'measure', 'rare_pattern_candidate'] desc=Frequency feature for token '857' in an email message. +- word_freq_data: role=feature, type=numeric_sparse_frequency. tags=['condition_candidate', 'measure', 'rare_pattern_candidate'] desc=Frequency feature for token 'data' in an email message. +- word_freq_415: role=feature, type=numeric_sparse_frequency. tags=['condition_candidate', 'measure', 'rare_pattern_candidate'] desc=Frequency feature for token '415' in an email message. +- word_freq_85: role=feature, type=numeric_sparse_frequency. tags=['condition_candidate', 'measure', 'rare_pattern_candidate'] desc=Frequency feature for token '85' in an email message. +- word_freq_technology: role=feature, type=numeric_sparse_frequency. tags=['condition_candidate', 'measure', 'rare_pattern_candidate'] desc=Frequency feature for token 'technology' in an email message. +- word_freq_1999: role=feature, type=numeric_sparse_frequency. tags=['condition_candidate', 'measure', 'rare_pattern_candidate'] desc=Frequency feature for token '1999' in an email message. +- word_freq_parts: role=feature, type=numeric_sparse_frequency. tags=['condition_candidate', 'measure', 'rare_pattern_candidate'] desc=Frequency feature for token 'parts' in an email message. +- word_freq_pm: role=feature, type=numeric_sparse_frequency. tags=['condition_candidate', 'measure', 'rare_pattern_candidate'] desc=Frequency feature for token 'pm' in an email message. +- word_freq_direct: role=feature, type=numeric_sparse_frequency. tags=['condition_candidate', 'measure', 'rare_pattern_candidate'] desc=Frequency feature for token 'direct' in an email message. +- word_freq_cs: role=feature, type=numeric_sparse_frequency. tags=['condition_candidate', 'measure', 'rare_pattern_candidate'] desc=Frequency feature for token 'cs' in an email message. +- word_freq_meeting: role=feature, type=numeric_sparse_frequency. tags=['condition_candidate', 'measure', 'rare_pattern_candidate'] desc=Frequency feature for token 'meeting' in an email message. +- word_freq_original: role=feature, type=numeric_sparse_frequency. tags=['condition_candidate', 'measure', 'rare_pattern_candidate'] desc=Frequency feature for token 'original' in an email message. +- word_freq_project: role=feature, type=numeric_sparse_frequency. tags=['condition_candidate', 'measure', 'rare_pattern_candidate'] desc=Frequency feature for token 'project' in an email message. +- word_freq_re: role=feature, type=numeric_sparse_frequency. tags=['condition_candidate', 'measure', 'rare_pattern_candidate'] desc=Frequency feature for token 're' in an email message. +- word_freq_edu: role=feature, type=numeric_sparse_frequency. tags=['condition_candidate', 'measure', 'rare_pattern_candidate'] desc=Frequency feature for token 'edu' in an email message. +- word_freq_table: role=feature, type=numeric_sparse_frequency. tags=['condition_candidate', 'measure', 'rare_pattern_candidate'] desc=Frequency feature for token 'table' in an email message. +- word_freq_conference: role=feature, type=numeric_sparse_frequency. tags=['condition_candidate', 'measure', 'rare_pattern_candidate'] desc=Frequency feature for token 'conference' in an email message. +- char_freq_%3B: role=feature, type=numeric_sparse_frequency. tags=['condition_candidate', 'measure', 'rare_pattern_candidate'] desc=Character frequency feature for symbol ';'. +- char_freq_%28: role=feature, type=numeric_sparse_frequency. tags=['condition_candidate', 'measure', 'rare_pattern_candidate'] desc=Character frequency feature for symbol '('. +- char_freq_%5B: role=feature, type=numeric_sparse_frequency. tags=['condition_candidate', 'measure', 'rare_pattern_candidate'] desc=Character frequency feature for symbol '['. +- char_freq_%21: role=feature, type=numeric_sparse_frequency. tags=['condition_candidate', 'measure', 'rare_pattern_candidate'] desc=Character frequency feature for symbol '!'. +- char_freq_%24: role=feature, type=numeric_sparse_frequency. tags=['condition_candidate', 'measure', 'rare_pattern_candidate'] desc=Character frequency feature for symbol '$'. +- char_freq_%23: role=feature, type=numeric_sparse_frequency. tags=['condition_candidate', 'measure', 'rare_pattern_candidate'] desc=Character frequency feature for symbol '#'. +- capital_run_length_average: role=feature, type=numeric_heavy_tail. tags=['condition_candidate', 'measure', 'rare_pattern_candidate'] desc=Average length of uninterrupted capital-letter runs. +- capital_run_length_longest: role=feature, type=numeric_heavy_tail. tags=['condition_candidate', 'measure', 'rare_pattern_candidate'] desc=Longest uninterrupted capital-letter run. +- capital_run_length_total: role=feature, type=numeric_heavy_tail. tags=['condition_candidate', 'measure', 'rare_pattern_candidate'] desc=Total number of capital letters in runs. +- class: role=target, type=binary_target. tags=['target_candidate'] desc=Binary spam label (1=spam, 0=non-spam). +- useful_field_combinations: [['word_freq_free', 'word_freq_you', 'class'], ['char_freq_%21', 'capital_run_length_longest', 'class'], ['word_freq_remove', 'word_freq_money', 'class']] +- fields_requiring_caution: ['capital_run_length_average', 'capital_run_length_longest', 'capital_run_length_total'] +- source_url: https://www.openml.org/d/44 + +SQLite schema snapshot: +{ + "table_name": "n1", + "quoted_table_name": "\"n1\"", + "row_count": 4601, + "columns": [ + { + "name": "word_freq_make", + "type": "TEXT", + "notnull": false, + "pk": false + }, + { + "name": "word_freq_address", + "type": "TEXT", + "notnull": false, + "pk": false + }, + { + "name": "word_freq_all", + "type": "TEXT", + "notnull": false, + "pk": false + }, + { + "name": "word_freq_3d", + "type": "TEXT", + "notnull": false, + "pk": false + }, + { + "name": "word_freq_our", + "type": "TEXT", + "notnull": false, + "pk": false + }, + { + "name": "word_freq_over", + "type": "TEXT", + "notnull": false, + "pk": false + }, + { + "name": "word_freq_remove", + "type": "TEXT", + "notnull": false, + "pk": false + }, + { + "name": "word_freq_internet", + "type": "TEXT", + "notnull": false, + "pk": false + }, + { + "name": "word_freq_order", + "type": "TEXT", + "notnull": false, + "pk": false + }, + { + "name": "word_freq_mail", + "type": "TEXT", + "notnull": false, + "pk": false + }, + { + "name": "word_freq_receive", + "type": "TEXT", + "notnull": false, + "pk": false + }, + { + "name": "word_freq_will", + "type": "TEXT", + "notnull": false, + "pk": false + }, + { + "name": "word_freq_people", + "type": "TEXT", + "notnull": false, + "pk": false + }, + { + "name": "word_freq_report", + "type": "TEXT", + "notnull": false, + "pk": false + }, + { + "name": "word_freq_addresses", + "type": "TEXT", + "notnull": false, + "pk": false + }, + { + "name": "word_freq_free", + "type": "TEXT", + "notnull": false, + "pk": false + }, + { + "name": "word_freq_business", + "type": "TEXT", + "notnull": false, + "pk": false + }, + { + "name": "word_freq_email", + "type": "TEXT", + "notnull": false, + "pk": false + }, + { + "name": "word_freq_you", + "type": "TEXT", + "notnull": false, + "pk": false + }, + { + "name": "word_freq_credit", + "type": "TEXT", + "notnull": false, + "pk": false + }, + { + "name": "word_freq_your", + "type": "TEXT", + "notnull": false, + "pk": false + }, + { + "name": "word_freq_font", + "type": "TEXT", + "notnull": false, + "pk": false + }, + { + "name": "word_freq_000", + "type": "TEXT", + "notnull": false, + "pk": false + }, + { + "name": "word_freq_money", + "type": "TEXT", + "notnull": false, + "pk": false + }, + { + "name": "word_freq_hp", + "type": "TEXT", + "notnull": false, + "pk": false + }, + { + "name": "word_freq_hpl", + "type": "TEXT", + "notnull": false, + "pk": false + }, + { + "name": "word_freq_george", + "type": "TEXT", + "notnull": false, + "pk": false + }, + { + "name": "word_freq_650", + "type": "TEXT", + "notnull": false, + "pk": false + }, + { + "name": "word_freq_lab", + "type": "TEXT", + "notnull": false, + "pk": false + }, + { + "name": "word_freq_labs", + "type": "TEXT", + "notnull": false, + "pk": false + }, + { + "name": "word_freq_telnet", + "type": "TEXT", + "notnull": false, + "pk": false + }, + { + "name": "word_freq_857", + "type": "TEXT", + "notnull": false, + "pk": false + }, + { + "name": "word_freq_data", + "type": "TEXT", + "notnull": false, + "pk": false + }, + { + "name": "word_freq_415", + "type": "TEXT", + "notnull": false, + "pk": false + }, + { + "name": "word_freq_85", + "type": "TEXT", + "notnull": false, + "pk": false + }, + { + "name": "word_freq_technology", + "type": "TEXT", + "notnull": false, + "pk": false + }, + { + "name": "word_freq_1999", + "type": "TEXT", + "notnull": false, + "pk": false + }, + { + "name": "word_freq_parts", + "type": "TEXT", + "notnull": false, + "pk": false + }, + { + "name": "word_freq_pm", + "type": "TEXT", + "notnull": false, + "pk": false + }, + { + "name": "word_freq_direct", + "type": "TEXT", + "notnull": false, + "pk": false + }, + { + "name": "word_freq_cs", + "type": "TEXT", + "notnull": false, + "pk": false + }, + { + "name": "word_freq_meeting", + "type": "TEXT", + "notnull": false, + "pk": false + }, + { + "name": "word_freq_original", + "type": "TEXT", + "notnull": false, + "pk": false + }, + { + "name": "word_freq_project", + "type": "TEXT", + "notnull": false, + "pk": false + }, + { + "name": "word_freq_re", + "type": "TEXT", + "notnull": false, + "pk": false + }, + { + "name": "word_freq_edu", + "type": "TEXT", + "notnull": false, + "pk": false + }, + { + "name": "word_freq_table", + "type": "TEXT", + "notnull": false, + "pk": false + }, + { + "name": "word_freq_conference", + "type": "TEXT", + "notnull": false, + "pk": false + }, + { + "name": "char_freq_%3B", + "type": "TEXT", + "notnull": false, + "pk": false + }, + { + "name": "char_freq_%28", + "type": "TEXT", + "notnull": false, + "pk": false + }, + { + "name": "char_freq_%5B", + "type": "TEXT", + "notnull": false, + "pk": false + }, + { + "name": "char_freq_%21", + "type": "TEXT", + "notnull": false, + "pk": false + }, + { + "name": "char_freq_%24", + "type": "TEXT", + "notnull": false, + "pk": false + }, + { + "name": "char_freq_%23", + "type": "TEXT", + "notnull": false, + "pk": false + }, + { + "name": "capital_run_length_average", + "type": "TEXT", + "notnull": false, + "pk": false + }, + { + "name": "capital_run_length_longest", + "type": "TEXT", + "notnull": false, + "pk": false + }, + { + "name": "capital_run_length_total", + "type": "TEXT", + "notnull": false, + "pk": false + }, + { + "name": "class", + "type": "TEXT", + "notnull": false, + "pk": false + } + ], + "sample_rows": [ + { + "word_freq_make": "0", + "word_freq_address": "0.64", + "word_freq_all": "0.64", + "word_freq_3d": "0", + "word_freq_our": "0.32", + "word_freq_over": "0", + "word_freq_remove": "0", + "word_freq_internet": "0", + "word_freq_order": "0", + "word_freq_mail": "0", + "word_freq_receive": "0", + "word_freq_will": "0.64", + "word_freq_people": "0", + "word_freq_report": "0", + "word_freq_addresses": "0", + "word_freq_free": "0.32", + "word_freq_business": "0", + "word_freq_email": "1.29", + "word_freq_you": "1.93", + "word_freq_credit": "0", + "word_freq_your": "0.96", + "word_freq_font": "0", + "word_freq_000": "0", + "word_freq_money": "0", + "word_freq_hp": "0", + "word_freq_hpl": "0", + "word_freq_george": "0", + "word_freq_650": "0", + "word_freq_lab": "0", + "word_freq_labs": "0", + "word_freq_telnet": "0", + "word_freq_857": "0", + "word_freq_data": "0", + "word_freq_415": "0", + "word_freq_85": "0", + "word_freq_technology": "0", + "word_freq_1999": "0", + "word_freq_parts": "0", + "word_freq_pm": "0", + "word_freq_direct": "0", + "word_freq_cs": "0", + "word_freq_meeting": "0", + "word_freq_original": "0", + "word_freq_project": "0", + "word_freq_re": "0", + "word_freq_edu": "0", + "word_freq_table": "0", + "word_freq_conference": "0", + "char_freq_%3B": "0", + "char_freq_%28": "0", + "char_freq_%5B": "0", + "char_freq_%21": "0.778", + "char_freq_%24": "0", + "char_freq_%23": "0", + "capital_run_length_average": "3.756", + "capital_run_length_longest": "61", + "capital_run_length_total": "278", + "class": "1" + }, + { + "word_freq_make": "0.21", + "word_freq_address": "0.28", + "word_freq_all": "0.5", + "word_freq_3d": "0", + "word_freq_our": "0.14", + "word_freq_over": "0.28", + "word_freq_remove": "0.21", + "word_freq_internet": "0.07", + "word_freq_order": "0", + "word_freq_mail": "0.94", + "word_freq_receive": "0.21", + "word_freq_will": "0.79", + "word_freq_people": "0.65", + "word_freq_report": "0.21", + "word_freq_addresses": "0.14", + "word_freq_free": "0.14", + "word_freq_business": "0.07", + "word_freq_email": "0.28", + "word_freq_you": "3.47", + "word_freq_credit": "0", + "word_freq_your": "1.59", + "word_freq_font": "0", + "word_freq_000": "0.43", + "word_freq_money": "0.43", + "word_freq_hp": "0", + "word_freq_hpl": "0", + "word_freq_george": "0", + "word_freq_650": "0", + "word_freq_lab": "0", + "word_freq_labs": "0", + "word_freq_telnet": "0", + "word_freq_857": "0", + "word_freq_data": "0", + "word_freq_415": "0", + "word_freq_85": "0", + "word_freq_technology": "0", + "word_freq_1999": "0.07", + "word_freq_parts": "0", + "word_freq_pm": "0", + "word_freq_direct": "0", + "word_freq_cs": "0", + "word_freq_meeting": "0", + "word_freq_original": "0", + "word_freq_project": "0", + "word_freq_re": "0", + "word_freq_edu": "0", + "word_freq_table": "0", + "word_freq_conference": "0", + "char_freq_%3B": "0", + "char_freq_%28": "0.132", + "char_freq_%5B": "0", + "char_freq_%21": "0.372", + "char_freq_%24": "0.18", + "char_freq_%23": "0.048", + "capital_run_length_average": "5.114", + "capital_run_length_longest": "101", + "capital_run_length_total": "1028", + "class": "1" + }, + { + "word_freq_make": "0.06", + "word_freq_address": "0", + "word_freq_all": "0.71", + "word_freq_3d": "0", + "word_freq_our": "1.23", + "word_freq_over": "0.19", + "word_freq_remove": "0.19", + "word_freq_internet": "0.12", + "word_freq_order": "0.64", + "word_freq_mail": "0.25", + "word_freq_receive": "0.38", + "word_freq_will": "0.45", + "word_freq_people": "0.12", + "word_freq_report": "0", + "word_freq_addresses": "1.75", + "word_freq_free": "0.06", + "word_freq_business": "0.06", + "word_freq_email": "1.03", + "word_freq_you": "1.36", + "word_freq_credit": "0.32", + "word_freq_your": "0.51", + "word_freq_font": "0", + "word_freq_000": "1.16", + "word_freq_money": "0.06", + "word_freq_hp": "0", + "word_freq_hpl": "0", + "word_freq_george": "0", + "word_freq_650": "0", + "word_freq_lab": "0", + "word_freq_labs": "0", + "word_freq_telnet": "0", + "word_freq_857": "0", + "word_freq_data": "0", + "word_freq_415": "0", + "word_freq_85": "0", + "word_freq_technology": "0", + "word_freq_1999": "0", + "word_freq_parts": "0", + "word_freq_pm": "0", + "word_freq_direct": "0.06", + "word_freq_cs": "0", + "word_freq_meeting": "0", + "word_freq_original": "0.12", + "word_freq_project": "0", + "word_freq_re": "0.06", + "word_freq_edu": "0.06", + "word_freq_table": "0", + "word_freq_conference": "0", + "char_freq_%3B": "0.01", + "char_freq_%28": "0.143", + "char_freq_%5B": "0", + "char_freq_%21": "0.276", + "char_freq_%24": "0.184", + "char_freq_%23": "0.01", + "capital_run_length_average": "9.821", + "capital_run_length_longest": "485", + "capital_run_length_total": "2259", + "class": "1" + }, + { + "word_freq_make": "0", + "word_freq_address": "0", + "word_freq_all": "0", + "word_freq_3d": "0", + "word_freq_our": "0.63", + "word_freq_over": "0", + "word_freq_remove": "0.31", + "word_freq_internet": "0.63", + "word_freq_order": "0.31", + "word_freq_mail": "0.63", + "word_freq_receive": "0.31", + "word_freq_will": "0.31", + "word_freq_people": "0.31", + "word_freq_report": "0", + "word_freq_addresses": "0", + "word_freq_free": "0.31", + "word_freq_business": "0", + "word_freq_email": "0", + "word_freq_you": "3.18", + "word_freq_credit": "0", + "word_freq_your": "0.31", + "word_freq_font": "0", + "word_freq_000": "0", + "word_freq_money": "0", + "word_freq_hp": "0", + "word_freq_hpl": "0", + "word_freq_george": "0", + "word_freq_650": "0", + "word_freq_lab": "0", + "word_freq_labs": "0", + "word_freq_telnet": "0", + "word_freq_857": "0", + "word_freq_data": "0", + "word_freq_415": "0", + "word_freq_85": "0", + "word_freq_technology": "0", + "word_freq_1999": "0", + "word_freq_parts": "0", + "word_freq_pm": "0", + "word_freq_direct": "0", + "word_freq_cs": "0", + "word_freq_meeting": "0", + "word_freq_original": "0", + "word_freq_project": "0", + "word_freq_re": "0", + "word_freq_edu": "0", + "word_freq_table": "0", + "word_freq_conference": "0", + "char_freq_%3B": "0", + "char_freq_%28": "0.137", + "char_freq_%5B": "0", + "char_freq_%21": "0.137", + "char_freq_%24": "0", + "char_freq_%23": "0", + "capital_run_length_average": "3.537", + "capital_run_length_longest": "40", + "capital_run_length_total": "191", + "class": "1" + }, + { + "word_freq_make": "0", + "word_freq_address": "0", + "word_freq_all": "0", + "word_freq_3d": "0", + "word_freq_our": "0.63", + "word_freq_over": "0", + "word_freq_remove": "0.31", + "word_freq_internet": "0.63", + "word_freq_order": "0.31", + "word_freq_mail": "0.63", + "word_freq_receive": "0.31", + "word_freq_will": "0.31", + "word_freq_people": "0.31", + "word_freq_report": "0", + "word_freq_addresses": "0", + "word_freq_free": "0.31", + "word_freq_business": "0", + "word_freq_email": "0", + "word_freq_you": "3.18", + "word_freq_credit": "0", + "word_freq_your": "0.31", + "word_freq_font": "0", + "word_freq_000": "0", + "word_freq_money": "0", + "word_freq_hp": "0", + "word_freq_hpl": "0", + "word_freq_george": "0", + "word_freq_650": "0", + "word_freq_lab": "0", + "word_freq_labs": "0", + "word_freq_telnet": "0", + "word_freq_857": "0", + "word_freq_data": "0", + "word_freq_415": "0", + "word_freq_85": "0", + "word_freq_technology": "0", + "word_freq_1999": "0", + "word_freq_parts": "0", + "word_freq_pm": "0", + "word_freq_direct": "0", + "word_freq_cs": "0", + "word_freq_meeting": "0", + "word_freq_original": "0", + "word_freq_project": "0", + "word_freq_re": "0", + "word_freq_edu": "0", + "word_freq_table": "0", + "word_freq_conference": "0", + "char_freq_%3B": "0", + "char_freq_%28": "0.135", + "char_freq_%5B": "0", + "char_freq_%21": "0.135", + "char_freq_%24": "0", + "char_freq_%23": "0", + "capital_run_length_average": "3.537", + "capital_run_length_longest": "40", + "capital_run_length_total": "191", + "class": "1" + } + ] +} + +Shortlisted templates: +[ + { + "template_id": "tpl_tpch_thresholded_group_ranking", + "template_name": "Thresholded Group Ranking", + "primary_family": "tail_rarity_structure", + "portability": "partial", + "sql_skeleton": "SELECT {group_col}, SUM({measure_col}) AS total_measure\nFROM {table}\nGROUP BY {group_col}\nHAVING SUM({measure_col}) > {measure_threshold}\nORDER BY total_measure DESC\nLIMIT {top_k};", + "required_roles": [ + "group_col", + "measure_col" + ] + } +] + +Problem instance: +{ + "dataset_id": "n1", + "question": "Use template Thresholded Group Ranking to probe tail_set_consistency with semantic role filtered_stable_view. Focus on group_col=class, measure_col=word_freq_your.", + "planned_template_id": "tpl_tpch_thresholded_group_ranking", + "bindings": { + "group_col": "class", + "measure_col": "word_freq_your", + "top_k": 19, + "top_n": 6, + "num_tiles": 10, + "percentile_value": 0.9, + "z_threshold": 2.0, + "fraction_threshold": 0.05, + "baseline_multiplier": 1.75, + "baseline_fraction": 0.1, + "min_group_size": 5, + "min_support": 4, + "measure_threshold": 0.94, + "time_grain": "month", + "lookback_rows": 3, + "current_period_start": "'2024-01-01'", + "current_period_end": "'2024-04-01'", + "previous_period_start": "'2023-10-01'", + "previous_period_end": "'2024-01-01'", + "drift_ratio_threshold": 0.8 + }, + "can_vary": [], + "must_fix": [], + "runtime_sql_skeleton": "SELECT {group_col}, SUM({measure_col}) AS total_measure\nFROM {table}\nGROUP BY {group_col}\nHAVING SUM({measure_col}) > {measure_threshold}\nORDER BY total_measure DESC\nLIMIT {top_k};" +} + +Repair context: +{} diff --git a/Query/sql/v2/runs/v2_cli_20260502_081223_d/n1/artifacts/v2q_n1_f72544bb07dd6eb1/cli/sql_response_attempt_1.raw.txt b/Query/sql/v2/runs/v2_cli_20260502_081223_d/n1/artifacts/v2q_n1_f72544bb07dd6eb1/cli/sql_response_attempt_1.raw.txt new file mode 100644 index 0000000000000000000000000000000000000000..26d344403f429b573867e1acc34b32361eda123d --- /dev/null +++ b/Query/sql/v2/runs/v2_cli_20260502_081223_d/n1/artifacts/v2q_n1_f72544bb07dd6eb1/cli/sql_response_attempt_1.raw.txt @@ -0,0 +1,4 @@ +{"type":"thread.started","thread_id":"019e40fb-5593-7ed1-97a1-52362d9306fc"} +{"type":"turn.started"} +{"type":"error","message":"Quota exceeded. Check your plan and billing details."} +{"type":"turn.failed","error":{"message":"Quota exceeded. Check your plan and billing details."}} diff --git a/Query/sql/v2/runs/v2_cli_20260502_081223_d/n1/artifacts/v2q_n1_f72544bb07dd6eb1/cli/sql_response_attempt_1.txt b/Query/sql/v2/runs/v2_cli_20260502_081223_d/n1/artifacts/v2q_n1_f72544bb07dd6eb1/cli/sql_response_attempt_1.txt new file mode 100644 index 0000000000000000000000000000000000000000..d5e0b73e690553030ee0a1971a2a84aee751491f --- /dev/null +++ b/Query/sql/v2/runs/v2_cli_20260502_081223_d/n1/artifacts/v2q_n1_f72544bb07dd6eb1/cli/sql_response_attempt_1.txt @@ -0,0 +1,4 @@ +{"type":"thread.started","thread_id":"019e40fb-5593-7ed1-97a1-52362d9306fc"} +{"type":"turn.started"} +{"type":"error","message":"Quota exceeded. Check your plan and billing details."} +{"type":"turn.failed","error":{"message":"Quota exceeded. Check your plan and billing details."}} \ No newline at end of file diff --git a/Query/sql/v2/runs/v2_cli_20260502_081223_d/n1/artifacts/v2q_n1_f72544bb07dd6eb1/cli/sql_response_attempt_2.raw.txt b/Query/sql/v2/runs/v2_cli_20260502_081223_d/n1/artifacts/v2q_n1_f72544bb07dd6eb1/cli/sql_response_attempt_2.raw.txt new file mode 100644 index 0000000000000000000000000000000000000000..ee66c9b7b9ad34790c7e879841e5acf2810150c0 --- /dev/null +++ b/Query/sql/v2/runs/v2_cli_20260502_081223_d/n1/artifacts/v2q_n1_f72544bb07dd6eb1/cli/sql_response_attempt_2.raw.txt @@ -0,0 +1,4 @@ +{"type":"thread.started","thread_id":"019e40fb-72d2-7092-ad24-3395898b7360"} +{"type":"turn.started"} +{"type":"error","message":"Quota exceeded. Check your plan and billing details."} +{"type":"turn.failed","error":{"message":"Quota exceeded. Check your plan and billing details."}} diff --git a/Query/sql/v2/runs/v2_cli_20260502_081223_d/n1/artifacts/v2q_n1_f72544bb07dd6eb1/cli/sql_response_attempt_2.txt b/Query/sql/v2/runs/v2_cli_20260502_081223_d/n1/artifacts/v2q_n1_f72544bb07dd6eb1/cli/sql_response_attempt_2.txt new file mode 100644 index 0000000000000000000000000000000000000000..aca3e3a5309544d63cebc799a0fc9ae4d8b29798 --- /dev/null +++ b/Query/sql/v2/runs/v2_cli_20260502_081223_d/n1/artifacts/v2q_n1_f72544bb07dd6eb1/cli/sql_response_attempt_2.txt @@ -0,0 +1,4 @@ +{"type":"thread.started","thread_id":"019e40fb-72d2-7092-ad24-3395898b7360"} +{"type":"turn.started"} +{"type":"error","message":"Quota exceeded. Check your plan and billing details."} +{"type":"turn.failed","error":{"message":"Quota exceeded. Check your plan and billing details."}} \ No newline at end of file diff --git a/Query/sql/v2/runs/v2_cli_20260502_081223_d/n1/artifacts/v2q_n1_f72544bb07dd6eb1/cli/sql_stderr_attempt_1.txt b/Query/sql/v2/runs/v2_cli_20260502_081223_d/n1/artifacts/v2q_n1_f72544bb07dd6eb1/cli/sql_stderr_attempt_1.txt new file mode 100644 index 0000000000000000000000000000000000000000..e69de29bb2d1d6434b8b29ae775ad8c2e48c5391 diff --git a/Query/sql/v2/runs/v2_cli_20260502_081223_d/n1/artifacts/v2q_n1_f72544bb07dd6eb1/cli/sql_stderr_attempt_2.txt b/Query/sql/v2/runs/v2_cli_20260502_081223_d/n1/artifacts/v2q_n1_f72544bb07dd6eb1/cli/sql_stderr_attempt_2.txt new file mode 100644 index 0000000000000000000000000000000000000000..e69de29bb2d1d6434b8b29ae775ad8c2e48c5391 diff --git a/Query/sql/v2/runs/v2_cli_20260502_081223_d/n1/artifacts/v2q_n1_f7ac14b1e161e4d5/cli/conversation.jsonl b/Query/sql/v2/runs/v2_cli_20260502_081223_d/n1/artifacts/v2q_n1_f7ac14b1e161e4d5/cli/conversation.jsonl new file mode 100644 index 0000000000000000000000000000000000000000..a3c5c4ac469581651a7a62045b6604a53566645a --- /dev/null +++ b/Query/sql/v2/runs/v2_cli_20260502_081223_d/n1/artifacts/v2q_n1_f7ac14b1e161e4d5/cli/conversation.jsonl @@ -0,0 +1,2 @@ +{"attempt": 1, "phase": "sql_generation", "role": "user", "content_path": "cli/sql_prompt_attempt_1.txt", "metrics": {"chars": 29523, "bytes_utf8": 29523, "lines": 792, "estimated_tokens": null}} +{"attempt": 1, "phase": "sql_generation", "role": "assistant", "content_path": "cli/sql_response_attempt_1.txt", "raw_content_path": "cli/sql_response_attempt_1.raw.txt", "stderr_path": "cli/sql_stderr_attempt_1.txt", "metrics": {"chars": 469, "bytes_utf8": 469, "lines": 1, "estimated_tokens": null}, "usage": {"input_tokens": 20360, "cached_input_tokens": 19840, "output_tokens": 377, "reasoning_output_tokens": 249}} diff --git a/Query/sql/v2/runs/v2_cli_20260502_081223_d/n1/artifacts/v2q_n1_f7ac14b1e161e4d5/cli/session_summary.json b/Query/sql/v2/runs/v2_cli_20260502_081223_d/n1/artifacts/v2q_n1_f7ac14b1e161e4d5/cli/session_summary.json new file mode 100644 index 0000000000000000000000000000000000000000..e5e1394e7f4801f607863670908fe847daefe782 --- /dev/null +++ b/Query/sql/v2/runs/v2_cli_20260502_081223_d/n1/artifacts/v2q_n1_f7ac14b1e161e4d5/cli/session_summary.json @@ -0,0 +1,25 @@ +{ + "engine": "v2-cli:codex", + "command": "codex exec --skip-git-repo-check --disable plugins --sandbox read-only --cd \"/data/jialinzhang/SQLagent\" -m gpt-5.4 --json -", + "ai_cli_calls": 1, + "usage_summary": { + "dataset_id": "n1", + "model": "v2-cli:codex", + "run_id": "v2q_n1_f7ac14b1e161e4d5", + "api_calls": 0, + "input_tokens": 20360, + "cached_input_tokens": 19840, + "output_tokens": 377, + "total_tokens": 20737, + "cost_usd": 0.0, + "ai_cli_calls": 1, + "estimated_input_tokens": 0, + "estimated_output_tokens": 0, + "estimated_total_tokens": 0, + "usage_source": "ai_cli_json_usage", + "cli_elapsed_ms_total": 9312.99, + "sql_execution_elapsed_ms_total": 3.79, + "conversation_log_path": "/data/jialinzhang/TabQueryBench/sql_workloads/v2_current/runs_and_launches/runs/v2_cli_20260502_081223_d/n1/artifacts/v2q_n1_f7ac14b1e161e4d5/cli/conversation.jsonl", + "note": "Executed through a local AI CLI with structured usage metadata." + } +} \ No newline at end of file diff --git a/Query/sql/v2/runs/v2_cli_20260502_081223_d/n1/artifacts/v2q_n1_f7ac14b1e161e4d5/cli/sql_attempt_1.metadata.json b/Query/sql/v2/runs/v2_cli_20260502_081223_d/n1/artifacts/v2q_n1_f7ac14b1e161e4d5/cli/sql_attempt_1.metadata.json new file mode 100644 index 0000000000000000000000000000000000000000..3834b09434016c39e69e97b6caadf024462f613c --- /dev/null +++ b/Query/sql/v2/runs/v2_cli_20260502_081223_d/n1/artifacts/v2q_n1_f7ac14b1e161e4d5/cli/sql_attempt_1.metadata.json @@ -0,0 +1,45 @@ +{ + "attempt": 1, + "phase": "sql_generation", + "command": "codex exec --skip-git-repo-check --disable plugins --sandbox read-only --cd \"/data/jialinzhang/SQLagent\" -m gpt-5.4 --json -", + "started_at": "2026-05-19T16:06:06.540113+00:00", + "ended_at": "2026-05-19T16:06:15.853144+00:00", + "elapsed_ms": 9312.99, + "prompt_metrics": { + "chars": 29523, + "bytes_utf8": 29523, + "lines": 792, + "estimated_tokens": null + }, + "stdout_metrics": { + "chars": 837, + "bytes_utf8": 837, + "lines": 4, + "estimated_tokens": null + }, + "stderr_metrics": { + "chars": 0, + "bytes_utf8": 0, + "lines": 0, + "estimated_tokens": null + }, + "parsed_output": { + "format": "jsonl_events", + "text_metrics": { + "chars": 469, + "bytes_utf8": 469, + "lines": 1, + "estimated_tokens": null + }, + "usage": { + "input_tokens": 20360, + "cached_input_tokens": 19840, + "output_tokens": 377, + "reasoning_output_tokens": 249 + } + }, + "prompt_path": "cli/sql_prompt_attempt_1.txt", + "response_path": "cli/sql_response_attempt_1.txt", + "raw_response_path": "cli/sql_response_attempt_1.raw.txt", + "stderr_path": "cli/sql_stderr_attempt_1.txt" +} \ No newline at end of file diff --git a/Query/sql/v2/runs/v2_cli_20260502_081223_d/n1/artifacts/v2q_n1_f7ac14b1e161e4d5/cli/sql_prompt_attempt_1.txt b/Query/sql/v2/runs/v2_cli_20260502_081223_d/n1/artifacts/v2q_n1_f7ac14b1e161e4d5/cli/sql_prompt_attempt_1.txt new file mode 100644 index 0000000000000000000000000000000000000000..45c47d2df49b343244388e5208116ba4433bb239 --- /dev/null +++ b/Query/sql/v2/runs/v2_cli_20260502_081223_d/n1/artifacts/v2q_n1_f7ac14b1e161e4d5/cli/sql_prompt_attempt_1.txt @@ -0,0 +1,792 @@ +You are generating one SQLite SELECT query for a single-table SQL QA task. +Return strict JSON only, with this schema: {"sql": "...", "notes": "..."}. +Rules: +- Use only the provided table and columns. +- Do not write INSERT, UPDATE, DELETE, DROP, ALTER, CREATE, PRAGMA, ATTACH, DETACH, or VACUUM. +- Prefer the planned template and bound roles when provided. +- Add a leading SQL comment exactly like: -- template_id: . +- Generate SQLite-compatible SQL. SQLite does not support PERCENTILE_CONT or STDDEV. +- Quote identifiers with double quotes. +- Return no markdown and no extra prose. + +Dataset context: +Dataset context for SQL QA: +- dataset_id: n1 +- dataset_name: Spambase +- table_name: n1 +- table_layout: single-table dataset (do not assume joins). +- row_semantics: One row is one email represented by engineered token/character frequency and capitalization-run features. +- task_type: classification +- target_column: class +- main_row_count: 4601 +- important_fields: +- word_freq_make: role=feature, type=numeric_sparse_frequency. tags=['condition_candidate', 'measure', 'rare_pattern_candidate'] desc=Frequency feature for token 'make' in an email message. +- word_freq_address: role=feature, type=numeric_sparse_frequency. tags=['condition_candidate', 'measure', 'rare_pattern_candidate'] desc=Frequency feature for token 'address' in an email message. +- word_freq_all: role=feature, type=numeric_sparse_frequency. tags=['condition_candidate', 'measure', 'rare_pattern_candidate'] desc=Frequency feature for token 'all' in an email message. +- word_freq_3d: role=feature, type=numeric_sparse_frequency. tags=['condition_candidate', 'measure', 'rare_pattern_candidate'] desc=Frequency feature for token '3d' in an email message. +- word_freq_our: role=feature, type=numeric_sparse_frequency. tags=['condition_candidate', 'measure', 'rare_pattern_candidate'] desc=Frequency feature for token 'our' in an email message. +- word_freq_over: role=feature, type=numeric_sparse_frequency. tags=['condition_candidate', 'measure', 'rare_pattern_candidate'] desc=Frequency feature for token 'over' in an email message. +- word_freq_remove: role=feature, type=numeric_sparse_frequency. tags=['condition_candidate', 'measure', 'rare_pattern_candidate'] desc=Frequency feature for token 'remove' in an email message. +- word_freq_internet: role=feature, type=numeric_sparse_frequency. tags=['condition_candidate', 'measure', 'rare_pattern_candidate'] desc=Frequency feature for token 'internet' in an email message. +- word_freq_order: role=feature, type=numeric_sparse_frequency. tags=['condition_candidate', 'measure', 'rare_pattern_candidate'] desc=Frequency feature for token 'order' in an email message. +- word_freq_mail: role=feature, type=numeric_sparse_frequency. tags=['condition_candidate', 'measure', 'rare_pattern_candidate'] desc=Frequency feature for token 'mail' in an email message. +- word_freq_receive: role=feature, type=numeric_sparse_frequency. tags=['condition_candidate', 'measure', 'rare_pattern_candidate'] desc=Frequency feature for token 'receive' in an email message. +- word_freq_will: role=feature, type=numeric_sparse_frequency. tags=['condition_candidate', 'measure', 'rare_pattern_candidate'] desc=Frequency feature for token 'will' in an email message. +- word_freq_people: role=feature, type=numeric_sparse_frequency. tags=['condition_candidate', 'measure', 'rare_pattern_candidate'] desc=Frequency feature for token 'people' in an email message. +- word_freq_report: role=feature, type=numeric_sparse_frequency. tags=['condition_candidate', 'measure', 'rare_pattern_candidate'] desc=Frequency feature for token 'report' in an email message. +- word_freq_addresses: role=feature, type=numeric_sparse_frequency. tags=['condition_candidate', 'measure', 'rare_pattern_candidate'] desc=Frequency feature for token 'addresses' in an email message. +- word_freq_free: role=feature, type=numeric_sparse_frequency. tags=['condition_candidate', 'measure', 'rare_pattern_candidate'] desc=Frequency feature for token 'free' in an email message. +- word_freq_business: role=feature, type=numeric_sparse_frequency. tags=['condition_candidate', 'measure', 'rare_pattern_candidate'] desc=Frequency feature for token 'business' in an email message. +- word_freq_email: role=feature, type=numeric_sparse_frequency. tags=['condition_candidate', 'measure', 'rare_pattern_candidate'] desc=Frequency feature for token 'email' in an email message. +- word_freq_you: role=feature, type=numeric_sparse_frequency. tags=['condition_candidate', 'measure', 'rare_pattern_candidate'] desc=Frequency feature for token 'you' in an email message. +- word_freq_credit: role=feature, type=numeric_sparse_frequency. tags=['condition_candidate', 'measure', 'rare_pattern_candidate'] desc=Frequency feature for token 'credit' in an email message. +- word_freq_your: role=feature, type=numeric_sparse_frequency. tags=['condition_candidate', 'measure', 'rare_pattern_candidate'] desc=Frequency feature for token 'your' in an email message. +- word_freq_font: role=feature, type=numeric_sparse_frequency. tags=['condition_candidate', 'measure', 'rare_pattern_candidate'] desc=Frequency feature for token 'font' in an email message. +- word_freq_000: role=feature, type=numeric_sparse_frequency. tags=['condition_candidate', 'measure', 'rare_pattern_candidate'] desc=Frequency feature for token '000' in an email message. +- word_freq_money: role=feature, type=numeric_sparse_frequency. tags=['condition_candidate', 'measure', 'rare_pattern_candidate'] desc=Frequency feature for token 'money' in an email message. +- word_freq_hp: role=feature, type=numeric_sparse_frequency. tags=['condition_candidate', 'measure', 'rare_pattern_candidate'] desc=Frequency feature for token 'hp' in an email message. +- word_freq_hpl: role=feature, type=numeric_sparse_frequency. tags=['condition_candidate', 'measure', 'rare_pattern_candidate'] desc=Frequency feature for token 'hpl' in an email message. +- word_freq_george: role=feature, type=numeric_sparse_frequency. tags=['condition_candidate', 'measure', 'rare_pattern_candidate'] desc=Frequency feature for token 'george' in an email message. +- word_freq_650: role=feature, type=numeric_sparse_frequency. tags=['condition_candidate', 'measure', 'rare_pattern_candidate'] desc=Frequency feature for token '650' in an email message. +- word_freq_lab: role=feature, type=numeric_sparse_frequency. tags=['condition_candidate', 'measure', 'rare_pattern_candidate'] desc=Frequency feature for token 'lab' in an email message. +- word_freq_labs: role=feature, type=numeric_sparse_frequency. tags=['condition_candidate', 'measure', 'rare_pattern_candidate'] desc=Frequency feature for token 'labs' in an email message. +- word_freq_telnet: role=feature, type=numeric_sparse_frequency. tags=['condition_candidate', 'measure', 'rare_pattern_candidate'] desc=Frequency feature for token 'telnet' in an email message. +- word_freq_857: role=feature, type=numeric_sparse_frequency. tags=['condition_candidate', 'measure', 'rare_pattern_candidate'] desc=Frequency feature for token '857' in an email message. +- word_freq_data: role=feature, type=numeric_sparse_frequency. tags=['condition_candidate', 'measure', 'rare_pattern_candidate'] desc=Frequency feature for token 'data' in an email message. +- word_freq_415: role=feature, type=numeric_sparse_frequency. tags=['condition_candidate', 'measure', 'rare_pattern_candidate'] desc=Frequency feature for token '415' in an email message. +- word_freq_85: role=feature, type=numeric_sparse_frequency. tags=['condition_candidate', 'measure', 'rare_pattern_candidate'] desc=Frequency feature for token '85' in an email message. +- word_freq_technology: role=feature, type=numeric_sparse_frequency. tags=['condition_candidate', 'measure', 'rare_pattern_candidate'] desc=Frequency feature for token 'technology' in an email message. +- word_freq_1999: role=feature, type=numeric_sparse_frequency. tags=['condition_candidate', 'measure', 'rare_pattern_candidate'] desc=Frequency feature for token '1999' in an email message. +- word_freq_parts: role=feature, type=numeric_sparse_frequency. tags=['condition_candidate', 'measure', 'rare_pattern_candidate'] desc=Frequency feature for token 'parts' in an email message. +- word_freq_pm: role=feature, type=numeric_sparse_frequency. tags=['condition_candidate', 'measure', 'rare_pattern_candidate'] desc=Frequency feature for token 'pm' in an email message. +- word_freq_direct: role=feature, type=numeric_sparse_frequency. tags=['condition_candidate', 'measure', 'rare_pattern_candidate'] desc=Frequency feature for token 'direct' in an email message. +- word_freq_cs: role=feature, type=numeric_sparse_frequency. tags=['condition_candidate', 'measure', 'rare_pattern_candidate'] desc=Frequency feature for token 'cs' in an email message. +- word_freq_meeting: role=feature, type=numeric_sparse_frequency. tags=['condition_candidate', 'measure', 'rare_pattern_candidate'] desc=Frequency feature for token 'meeting' in an email message. +- word_freq_original: role=feature, type=numeric_sparse_frequency. tags=['condition_candidate', 'measure', 'rare_pattern_candidate'] desc=Frequency feature for token 'original' in an email message. +- word_freq_project: role=feature, type=numeric_sparse_frequency. tags=['condition_candidate', 'measure', 'rare_pattern_candidate'] desc=Frequency feature for token 'project' in an email message. +- word_freq_re: role=feature, type=numeric_sparse_frequency. tags=['condition_candidate', 'measure', 'rare_pattern_candidate'] desc=Frequency feature for token 're' in an email message. +- word_freq_edu: role=feature, type=numeric_sparse_frequency. tags=['condition_candidate', 'measure', 'rare_pattern_candidate'] desc=Frequency feature for token 'edu' in an email message. +- word_freq_table: role=feature, type=numeric_sparse_frequency. tags=['condition_candidate', 'measure', 'rare_pattern_candidate'] desc=Frequency feature for token 'table' in an email message. +- word_freq_conference: role=feature, type=numeric_sparse_frequency. tags=['condition_candidate', 'measure', 'rare_pattern_candidate'] desc=Frequency feature for token 'conference' in an email message. +- char_freq_%3B: role=feature, type=numeric_sparse_frequency. tags=['condition_candidate', 'measure', 'rare_pattern_candidate'] desc=Character frequency feature for symbol ';'. +- char_freq_%28: role=feature, type=numeric_sparse_frequency. tags=['condition_candidate', 'measure', 'rare_pattern_candidate'] desc=Character frequency feature for symbol '('. +- char_freq_%5B: role=feature, type=numeric_sparse_frequency. tags=['condition_candidate', 'measure', 'rare_pattern_candidate'] desc=Character frequency feature for symbol '['. +- char_freq_%21: role=feature, type=numeric_sparse_frequency. tags=['condition_candidate', 'measure', 'rare_pattern_candidate'] desc=Character frequency feature for symbol '!'. +- char_freq_%24: role=feature, type=numeric_sparse_frequency. tags=['condition_candidate', 'measure', 'rare_pattern_candidate'] desc=Character frequency feature for symbol '$'. +- char_freq_%23: role=feature, type=numeric_sparse_frequency. tags=['condition_candidate', 'measure', 'rare_pattern_candidate'] desc=Character frequency feature for symbol '#'. +- capital_run_length_average: role=feature, type=numeric_heavy_tail. tags=['condition_candidate', 'measure', 'rare_pattern_candidate'] desc=Average length of uninterrupted capital-letter runs. +- capital_run_length_longest: role=feature, type=numeric_heavy_tail. tags=['condition_candidate', 'measure', 'rare_pattern_candidate'] desc=Longest uninterrupted capital-letter run. +- capital_run_length_total: role=feature, type=numeric_heavy_tail. tags=['condition_candidate', 'measure', 'rare_pattern_candidate'] desc=Total number of capital letters in runs. +- class: role=target, type=binary_target. tags=['target_candidate'] desc=Binary spam label (1=spam, 0=non-spam). +- useful_field_combinations: [['word_freq_free', 'word_freq_you', 'class'], ['char_freq_%21', 'capital_run_length_longest', 'class'], ['word_freq_remove', 'word_freq_money', 'class']] +- fields_requiring_caution: ['capital_run_length_average', 'capital_run_length_longest', 'capital_run_length_total'] +- source_url: https://www.openml.org/d/44 + +SQLite schema snapshot: +{ + "table_name": "n1", + "quoted_table_name": "\"n1\"", + "row_count": 4601, + "columns": [ + { + "name": "word_freq_make", + "type": "TEXT", + "notnull": false, + "pk": false + }, + { + "name": "word_freq_address", + "type": "TEXT", + "notnull": false, + "pk": false + }, + { + "name": "word_freq_all", + "type": "TEXT", + "notnull": false, + "pk": false + }, + { + "name": "word_freq_3d", + "type": "TEXT", + "notnull": false, + "pk": false + }, + { + "name": "word_freq_our", + "type": "TEXT", + "notnull": false, + "pk": false + }, + { + "name": "word_freq_over", + "type": "TEXT", + "notnull": false, + "pk": false + }, + { + "name": "word_freq_remove", + "type": "TEXT", + "notnull": false, + "pk": false + }, + { + "name": "word_freq_internet", + "type": "TEXT", + "notnull": false, + "pk": false + }, + { + "name": "word_freq_order", + "type": "TEXT", + "notnull": false, + "pk": false + }, + { + "name": "word_freq_mail", + "type": "TEXT", + "notnull": false, + "pk": false + }, + { + "name": "word_freq_receive", + "type": "TEXT", + "notnull": false, + "pk": false + }, + { + "name": "word_freq_will", + "type": "TEXT", + "notnull": false, + "pk": false + }, + { + "name": "word_freq_people", + "type": "TEXT", + "notnull": false, + "pk": false + }, + { + "name": "word_freq_report", + "type": "TEXT", + "notnull": false, + "pk": false + }, + { + "name": "word_freq_addresses", + "type": "TEXT", + "notnull": false, + "pk": false + }, + { + "name": "word_freq_free", + "type": "TEXT", + "notnull": false, + "pk": false + }, + { + "name": "word_freq_business", + "type": "TEXT", + "notnull": false, + "pk": false + }, + { + "name": "word_freq_email", + "type": "TEXT", + "notnull": false, + "pk": false + }, + { + "name": "word_freq_you", + "type": "TEXT", + "notnull": false, + "pk": false + }, + { + "name": "word_freq_credit", + "type": "TEXT", + "notnull": false, + "pk": false + }, + { + "name": "word_freq_your", + "type": "TEXT", + "notnull": false, + "pk": false + }, + { + "name": "word_freq_font", + "type": "TEXT", + "notnull": false, + "pk": false + }, + { + "name": "word_freq_000", + "type": "TEXT", + "notnull": false, + "pk": false + }, + { + "name": "word_freq_money", + "type": "TEXT", + "notnull": false, + "pk": false + }, + { + "name": "word_freq_hp", + "type": "TEXT", + "notnull": false, + "pk": false + }, + { + "name": "word_freq_hpl", + "type": "TEXT", + "notnull": false, + "pk": false + }, + { + "name": "word_freq_george", + "type": "TEXT", + "notnull": false, + "pk": false + }, + { + "name": "word_freq_650", + "type": "TEXT", + "notnull": false, + "pk": false + }, + { + "name": "word_freq_lab", + "type": "TEXT", + "notnull": false, + "pk": false + }, + { + "name": "word_freq_labs", + "type": "TEXT", + "notnull": false, + "pk": false + }, + { + "name": "word_freq_telnet", + "type": "TEXT", + "notnull": false, + "pk": false + }, + { + "name": "word_freq_857", + "type": "TEXT", + "notnull": false, + "pk": false + }, + { + "name": "word_freq_data", + "type": "TEXT", + "notnull": false, + "pk": false + }, + { + "name": "word_freq_415", + "type": "TEXT", + "notnull": false, + "pk": false + }, + { + "name": "word_freq_85", + "type": "TEXT", + "notnull": false, + "pk": false + }, + { + "name": "word_freq_technology", + "type": "TEXT", + "notnull": false, + "pk": false + }, + { + "name": "word_freq_1999", + "type": "TEXT", + "notnull": false, + "pk": false + }, + { + "name": "word_freq_parts", + "type": "TEXT", + "notnull": false, + "pk": false + }, + { + "name": "word_freq_pm", + "type": "TEXT", + "notnull": false, + "pk": false + }, + { + "name": "word_freq_direct", + "type": "TEXT", + "notnull": false, + "pk": false + }, + { + "name": "word_freq_cs", + "type": "TEXT", + "notnull": false, + "pk": false + }, + { + "name": "word_freq_meeting", + "type": "TEXT", + "notnull": false, + "pk": false + }, + { + "name": "word_freq_original", + "type": "TEXT", + "notnull": false, + "pk": false + }, + { + "name": "word_freq_project", + "type": "TEXT", + "notnull": false, + "pk": false + }, + { + "name": "word_freq_re", + "type": "TEXT", + "notnull": false, + "pk": false + }, + { + "name": "word_freq_edu", + "type": "TEXT", + "notnull": false, + "pk": false + }, + { + "name": "word_freq_table", + "type": "TEXT", + "notnull": false, + "pk": false + }, + { + "name": "word_freq_conference", + "type": "TEXT", + "notnull": false, + "pk": false + }, + { + "name": "char_freq_%3B", + "type": "TEXT", + "notnull": false, + "pk": false + }, + { + "name": "char_freq_%28", + "type": "TEXT", + "notnull": false, + "pk": false + }, + { + "name": "char_freq_%5B", + "type": "TEXT", + "notnull": false, + "pk": false + }, + { + "name": "char_freq_%21", + "type": "TEXT", + "notnull": false, + "pk": false + }, + { + "name": "char_freq_%24", + "type": "TEXT", + "notnull": false, + "pk": false + }, + { + "name": "char_freq_%23", + "type": "TEXT", + "notnull": false, + "pk": false + }, + { + "name": "capital_run_length_average", + "type": "TEXT", + "notnull": false, + "pk": false + }, + { + "name": "capital_run_length_longest", + "type": "TEXT", + "notnull": false, + "pk": false + }, + { + "name": "capital_run_length_total", + "type": "TEXT", + "notnull": false, + "pk": false + }, + { + "name": "class", + "type": "TEXT", + "notnull": false, + "pk": false + } + ], + "sample_rows": [ + { + "word_freq_make": "0", + "word_freq_address": "0.64", + "word_freq_all": "0.64", + "word_freq_3d": "0", + "word_freq_our": "0.32", + "word_freq_over": "0", + "word_freq_remove": "0", + "word_freq_internet": "0", + "word_freq_order": "0", + "word_freq_mail": "0", + "word_freq_receive": "0", + "word_freq_will": "0.64", + "word_freq_people": "0", + "word_freq_report": "0", + "word_freq_addresses": "0", + "word_freq_free": "0.32", + "word_freq_business": "0", + "word_freq_email": "1.29", + "word_freq_you": "1.93", + "word_freq_credit": "0", + "word_freq_your": "0.96", + "word_freq_font": "0", + "word_freq_000": "0", + "word_freq_money": "0", + "word_freq_hp": "0", + "word_freq_hpl": "0", + "word_freq_george": "0", + "word_freq_650": "0", + "word_freq_lab": "0", + "word_freq_labs": "0", + "word_freq_telnet": "0", + "word_freq_857": "0", + "word_freq_data": "0", + "word_freq_415": "0", + "word_freq_85": "0", + "word_freq_technology": "0", + "word_freq_1999": "0", + "word_freq_parts": "0", + "word_freq_pm": "0", + "word_freq_direct": "0", + "word_freq_cs": "0", + "word_freq_meeting": "0", + "word_freq_original": "0", + "word_freq_project": "0", + "word_freq_re": "0", + "word_freq_edu": "0", + "word_freq_table": "0", + "word_freq_conference": "0", + "char_freq_%3B": "0", + "char_freq_%28": "0", + "char_freq_%5B": "0", + "char_freq_%21": "0.778", + "char_freq_%24": "0", + "char_freq_%23": "0", + "capital_run_length_average": "3.756", + "capital_run_length_longest": "61", + "capital_run_length_total": "278", + "class": "1" + }, + { + "word_freq_make": "0.21", + "word_freq_address": "0.28", + "word_freq_all": "0.5", + "word_freq_3d": "0", + "word_freq_our": "0.14", + "word_freq_over": "0.28", + "word_freq_remove": "0.21", + "word_freq_internet": "0.07", + "word_freq_order": "0", + "word_freq_mail": "0.94", + "word_freq_receive": "0.21", + "word_freq_will": "0.79", + "word_freq_people": "0.65", + "word_freq_report": "0.21", + "word_freq_addresses": "0.14", + "word_freq_free": "0.14", + "word_freq_business": "0.07", + "word_freq_email": "0.28", + "word_freq_you": "3.47", + "word_freq_credit": "0", + "word_freq_your": "1.59", + "word_freq_font": "0", + "word_freq_000": "0.43", + "word_freq_money": "0.43", + "word_freq_hp": "0", + "word_freq_hpl": "0", + "word_freq_george": "0", + "word_freq_650": "0", + "word_freq_lab": "0", + "word_freq_labs": "0", + "word_freq_telnet": "0", + "word_freq_857": "0", + "word_freq_data": "0", + "word_freq_415": "0", + "word_freq_85": "0", + "word_freq_technology": "0", + "word_freq_1999": "0.07", + "word_freq_parts": "0", + "word_freq_pm": "0", + "word_freq_direct": "0", + "word_freq_cs": "0", + "word_freq_meeting": "0", + "word_freq_original": "0", + "word_freq_project": "0", + "word_freq_re": "0", + "word_freq_edu": "0", + "word_freq_table": "0", + "word_freq_conference": "0", + "char_freq_%3B": "0", + "char_freq_%28": "0.132", + "char_freq_%5B": "0", + "char_freq_%21": "0.372", + "char_freq_%24": "0.18", + "char_freq_%23": "0.048", + "capital_run_length_average": "5.114", + "capital_run_length_longest": "101", + "capital_run_length_total": "1028", + "class": "1" + }, + { + "word_freq_make": "0.06", + "word_freq_address": "0", + "word_freq_all": "0.71", + "word_freq_3d": "0", + "word_freq_our": "1.23", + "word_freq_over": "0.19", + "word_freq_remove": "0.19", + "word_freq_internet": "0.12", + "word_freq_order": "0.64", + "word_freq_mail": "0.25", + "word_freq_receive": "0.38", + "word_freq_will": "0.45", + "word_freq_people": "0.12", + "word_freq_report": "0", + "word_freq_addresses": "1.75", + "word_freq_free": "0.06", + "word_freq_business": "0.06", + "word_freq_email": "1.03", + "word_freq_you": "1.36", + "word_freq_credit": "0.32", + "word_freq_your": "0.51", + "word_freq_font": "0", + "word_freq_000": "1.16", + "word_freq_money": "0.06", + "word_freq_hp": "0", + "word_freq_hpl": "0", + "word_freq_george": "0", + "word_freq_650": "0", + "word_freq_lab": "0", + "word_freq_labs": "0", + "word_freq_telnet": "0", + "word_freq_857": "0", + "word_freq_data": "0", + "word_freq_415": "0", + "word_freq_85": "0", + "word_freq_technology": "0", + "word_freq_1999": "0", + "word_freq_parts": "0", + "word_freq_pm": "0", + "word_freq_direct": "0.06", + "word_freq_cs": "0", + "word_freq_meeting": "0", + "word_freq_original": "0.12", + "word_freq_project": "0", + "word_freq_re": "0.06", + "word_freq_edu": "0.06", + "word_freq_table": "0", + "word_freq_conference": "0", + "char_freq_%3B": "0.01", + "char_freq_%28": "0.143", + "char_freq_%5B": "0", + "char_freq_%21": "0.276", + "char_freq_%24": "0.184", + "char_freq_%23": "0.01", + "capital_run_length_average": "9.821", + "capital_run_length_longest": "485", + "capital_run_length_total": "2259", + "class": "1" + }, + { + "word_freq_make": "0", + "word_freq_address": "0", + "word_freq_all": "0", + "word_freq_3d": "0", + "word_freq_our": "0.63", + "word_freq_over": "0", + "word_freq_remove": "0.31", + "word_freq_internet": "0.63", + "word_freq_order": "0.31", + "word_freq_mail": "0.63", + "word_freq_receive": "0.31", + "word_freq_will": "0.31", + "word_freq_people": "0.31", + "word_freq_report": "0", + "word_freq_addresses": "0", + "word_freq_free": "0.31", + "word_freq_business": "0", + "word_freq_email": "0", + "word_freq_you": "3.18", + "word_freq_credit": "0", + "word_freq_your": "0.31", + "word_freq_font": "0", + "word_freq_000": "0", + "word_freq_money": "0", + "word_freq_hp": "0", + "word_freq_hpl": "0", + "word_freq_george": "0", + "word_freq_650": "0", + "word_freq_lab": "0", + "word_freq_labs": "0", + "word_freq_telnet": "0", + "word_freq_857": "0", + "word_freq_data": "0", + "word_freq_415": "0", + "word_freq_85": "0", + "word_freq_technology": "0", + "word_freq_1999": "0", + "word_freq_parts": "0", + "word_freq_pm": "0", + "word_freq_direct": "0", + "word_freq_cs": "0", + "word_freq_meeting": "0", + "word_freq_original": "0", + "word_freq_project": "0", + "word_freq_re": "0", + "word_freq_edu": "0", + "word_freq_table": "0", + "word_freq_conference": "0", + "char_freq_%3B": "0", + "char_freq_%28": "0.137", + "char_freq_%5B": "0", + "char_freq_%21": "0.137", + "char_freq_%24": "0", + "char_freq_%23": "0", + "capital_run_length_average": "3.537", + "capital_run_length_longest": "40", + "capital_run_length_total": "191", + "class": "1" + }, + { + "word_freq_make": "0", + "word_freq_address": "0", + "word_freq_all": "0", + "word_freq_3d": "0", + "word_freq_our": "0.63", + "word_freq_over": "0", + "word_freq_remove": "0.31", + "word_freq_internet": "0.63", + "word_freq_order": "0.31", + "word_freq_mail": "0.63", + "word_freq_receive": "0.31", + "word_freq_will": "0.31", + "word_freq_people": "0.31", + "word_freq_report": "0", + "word_freq_addresses": "0", + "word_freq_free": "0.31", + "word_freq_business": "0", + "word_freq_email": "0", + "word_freq_you": "3.18", + "word_freq_credit": "0", + "word_freq_your": "0.31", + "word_freq_font": "0", + "word_freq_000": "0", + "word_freq_money": "0", + "word_freq_hp": "0", + "word_freq_hpl": "0", + "word_freq_george": "0", + "word_freq_650": "0", + "word_freq_lab": "0", + "word_freq_labs": "0", + "word_freq_telnet": "0", + "word_freq_857": "0", + "word_freq_data": "0", + "word_freq_415": "0", + "word_freq_85": "0", + "word_freq_technology": "0", + "word_freq_1999": "0", + "word_freq_parts": "0", + "word_freq_pm": "0", + "word_freq_direct": "0", + "word_freq_cs": "0", + "word_freq_meeting": "0", + "word_freq_original": "0", + "word_freq_project": "0", + "word_freq_re": "0", + "word_freq_edu": "0", + "word_freq_table": "0", + "word_freq_conference": "0", + "char_freq_%3B": "0", + "char_freq_%28": "0.135", + "char_freq_%5B": "0", + "char_freq_%21": "0.135", + "char_freq_%24": "0", + "char_freq_%23": "0", + "capital_run_length_average": "3.537", + "capital_run_length_longest": "40", + "capital_run_length_total": "191", + "class": "1" + } + ] +} + +Shortlisted templates: +[ + { + "template_id": "tpl_tpch_thresholded_group_ranking", + "template_name": "Thresholded Group Ranking", + "primary_family": "tail_rarity_structure", + "portability": "partial", + "sql_skeleton": "SELECT {group_col}, SUM({measure_col}) AS total_measure\nFROM {table}\nGROUP BY {group_col}\nHAVING SUM({measure_col}) > {measure_threshold}\nORDER BY total_measure DESC\nLIMIT {top_k};", + "required_roles": [ + "group_col", + "measure_col" + ] + } +] + +Problem instance: +{ + "dataset_id": "n1", + "question": "Use template Thresholded Group Ranking to probe tail_set_consistency with semantic role filtered_stable_view. Focus on group_col=class, measure_col=word_freq_hp.", + "planned_template_id": "tpl_tpch_thresholded_group_ranking", + "bindings": { + "group_col": "class", + "measure_col": "word_freq_hp", + "top_k": 18, + "top_n": 6, + "num_tiles": 10, + "percentile_value": 0.9, + "z_threshold": 2.0, + "fraction_threshold": 0.05, + "baseline_multiplier": 1.75, + "baseline_fraction": 0.1, + "min_group_size": 5, + "min_support": 4, + "measure_threshold": 0.0, + "time_grain": "month", + "lookback_rows": 3, + "current_period_start": "'2024-01-01'", + "current_period_end": "'2024-04-01'", + "previous_period_start": "'2023-10-01'", + "previous_period_end": "'2024-01-01'", + "drift_ratio_threshold": 0.8 + }, + "can_vary": [], + "must_fix": [], + "runtime_sql_skeleton": "SELECT {group_col}, SUM({measure_col}) AS total_measure\nFROM {table}\nGROUP BY {group_col}\nHAVING SUM({measure_col}) > {measure_threshold}\nORDER BY total_measure DESC\nLIMIT {top_k};" +} + +Repair context: +{} diff --git a/Query/sql/v2/runs/v2_cli_20260502_081223_d/n1/artifacts/v2q_n1_f7ac14b1e161e4d5/cli/sql_response_attempt_1.raw.txt b/Query/sql/v2/runs/v2_cli_20260502_081223_d/n1/artifacts/v2q_n1_f7ac14b1e161e4d5/cli/sql_response_attempt_1.raw.txt new file mode 100644 index 0000000000000000000000000000000000000000..d5d87fc89688df4d344b0cc1d5329785e571006f --- /dev/null +++ b/Query/sql/v2/runs/v2_cli_20260502_081223_d/n1/artifacts/v2q_n1_f7ac14b1e161e4d5/cli/sql_response_attempt_1.raw.txt @@ -0,0 +1,4 @@ +{"type":"thread.started","thread_id":"019e40fc-c0eb-7a03-ba29-df66251dff91"} +{"type":"turn.started"} +{"type":"item.completed","item":{"id":"item_0","type":"agent_message","text":"{\"sql\":\"-- template_id: tpl_tpch_thresholded_group_ranking\\nSELECT \\\"class\\\", SUM(CAST(\\\"word_freq_hp\\\" AS REAL)) AS \\\"total_measure\\\"\\nFROM \\\"n1\\\"\\nGROUP BY \\\"class\\\"\\nHAVING SUM(CAST(\\\"word_freq_hp\\\" AS REAL)) > 0.0\\nORDER BY \\\"total_measure\\\" DESC\\nLIMIT 18;\",\"notes\":\"Used the provided Thresholded Group Ranking template with group_col=\\\"class\\\" and measure_col=\\\"word_freq_hp\\\". Cast measure values to REAL because the schema snapshot types this feature as TEXT.\"}"}} +{"type":"turn.completed","usage":{"input_tokens":20360,"cached_input_tokens":19840,"output_tokens":377,"reasoning_output_tokens":249}} diff --git a/Query/sql/v2/runs/v2_cli_20260502_081223_d/n1/artifacts/v2q_n1_f7ac14b1e161e4d5/cli/sql_response_attempt_1.txt b/Query/sql/v2/runs/v2_cli_20260502_081223_d/n1/artifacts/v2q_n1_f7ac14b1e161e4d5/cli/sql_response_attempt_1.txt new file mode 100644 index 0000000000000000000000000000000000000000..6e145008fdccd02f18cb7f3fd2ec7d9c3f3d4b49 --- /dev/null +++ b/Query/sql/v2/runs/v2_cli_20260502_081223_d/n1/artifacts/v2q_n1_f7ac14b1e161e4d5/cli/sql_response_attempt_1.txt @@ -0,0 +1 @@ +{"sql":"-- template_id: tpl_tpch_thresholded_group_ranking\nSELECT \"class\", SUM(CAST(\"word_freq_hp\" AS REAL)) AS \"total_measure\"\nFROM \"n1\"\nGROUP BY \"class\"\nHAVING SUM(CAST(\"word_freq_hp\" AS REAL)) > 0.0\nORDER BY \"total_measure\" DESC\nLIMIT 18;","notes":"Used the provided Thresholded Group Ranking template with group_col=\"class\" and measure_col=\"word_freq_hp\". Cast measure values to REAL because the schema snapshot types this feature as TEXT."} \ No newline at end of file diff --git a/Query/sql/v2/runs/v2_cli_20260502_081223_d/n1/artifacts/v2q_n1_f7ac14b1e161e4d5/cli/sql_stderr_attempt_1.txt b/Query/sql/v2/runs/v2_cli_20260502_081223_d/n1/artifacts/v2q_n1_f7ac14b1e161e4d5/cli/sql_stderr_attempt_1.txt new file mode 100644 index 0000000000000000000000000000000000000000..e69de29bb2d1d6434b8b29ae775ad8c2e48c5391 diff --git a/Query/sql/v2/runs/v2_cli_20260502_081223_d/n1/artifacts/v2q_n1_fb31e8dde7866159/cli/conversation.jsonl b/Query/sql/v2/runs/v2_cli_20260502_081223_d/n1/artifacts/v2q_n1_fb31e8dde7866159/cli/conversation.jsonl new file mode 100644 index 0000000000000000000000000000000000000000..301e9fbb584cd83b842fb2f61fabb7bd48f5ccd8 --- /dev/null +++ b/Query/sql/v2/runs/v2_cli_20260502_081223_d/n1/artifacts/v2q_n1_fb31e8dde7866159/cli/conversation.jsonl @@ -0,0 +1,2 @@ +{"attempt": 1, "phase": "sql_generation", "role": "user", "content_path": "cli/sql_prompt_attempt_1.txt", "metrics": {"chars": 29462, "bytes_utf8": 29462, "lines": 792, "estimated_tokens": null}} +{"attempt": 1, "phase": "sql_generation", "role": "assistant", "content_path": "cli/sql_response_attempt_1.txt", "raw_content_path": "cli/sql_response_attempt_1.raw.txt", "stderr_path": "cli/sql_stderr_attempt_1.txt", "metrics": {"chars": 444, "bytes_utf8": 444, "lines": 1, "estimated_tokens": null}, "usage": {"input_tokens": 20329, "cached_input_tokens": 12032, "output_tokens": 256, "reasoning_output_tokens": 149}} diff --git a/Query/sql/v2/runs/v2_cli_20260502_081223_d/n1/artifacts/v2q_n1_fb31e8dde7866159/cli/session_summary.json b/Query/sql/v2/runs/v2_cli_20260502_081223_d/n1/artifacts/v2q_n1_fb31e8dde7866159/cli/session_summary.json new file mode 100644 index 0000000000000000000000000000000000000000..f0c81b6617bf4e63b5bdd52cfd1b6f4d81bbad6e --- /dev/null +++ b/Query/sql/v2/runs/v2_cli_20260502_081223_d/n1/artifacts/v2q_n1_fb31e8dde7866159/cli/session_summary.json @@ -0,0 +1,25 @@ +{ + "engine": "v2-cli:codex", + "command": "codex exec --skip-git-repo-check --disable plugins --sandbox read-only --cd \"/data/jialinzhang/SQLagent\" -m gpt-5.4 --json -", + "ai_cli_calls": 1, + "usage_summary": { + "dataset_id": "n1", + "model": "v2-cli:codex", + "run_id": "v2q_n1_fb31e8dde7866159", + "api_calls": 0, + "input_tokens": 20329, + "cached_input_tokens": 12032, + "output_tokens": 256, + "total_tokens": 20585, + "cost_usd": 0.0, + "ai_cli_calls": 1, + "estimated_input_tokens": 0, + "estimated_output_tokens": 0, + "estimated_total_tokens": 0, + "usage_source": "ai_cli_json_usage", + "cli_elapsed_ms_total": 8520.13, + "sql_execution_elapsed_ms_total": 19.5, + "conversation_log_path": "/data/jialinzhang/TabQueryBench/sql_workloads/v2_current/runs_and_launches/runs/v2_cli_20260502_081223_d/n1/artifacts/v2q_n1_fb31e8dde7866159/cli/conversation.jsonl", + "note": "Executed through a local AI CLI with structured usage metadata." + } +} \ No newline at end of file diff --git a/Query/sql/v2/runs/v2_cli_20260502_081223_d/n1/artifacts/v2q_n1_fb31e8dde7866159/cli/sql_attempt_1.metadata.json b/Query/sql/v2/runs/v2_cli_20260502_081223_d/n1/artifacts/v2q_n1_fb31e8dde7866159/cli/sql_attempt_1.metadata.json new file mode 100644 index 0000000000000000000000000000000000000000..7beab9ccf329648a31ef4308eb16233823b83d8a --- /dev/null +++ b/Query/sql/v2/runs/v2_cli_20260502_081223_d/n1/artifacts/v2q_n1_fb31e8dde7866159/cli/sql_attempt_1.metadata.json @@ -0,0 +1,45 @@ +{ + "attempt": 1, + "phase": "sql_generation", + "command": "codex exec --skip-git-repo-check --disable plugins --sandbox read-only --cd \"/data/jialinzhang/SQLagent\" -m gpt-5.4 --json -", + "started_at": "2026-05-19T15:44:13.181054+00:00", + "ended_at": "2026-05-19T15:44:21.701220+00:00", + "elapsed_ms": 8520.13, + "prompt_metrics": { + "chars": 29462, + "bytes_utf8": 29462, + "lines": 792, + "estimated_tokens": null + }, + "stdout_metrics": { + "chars": 798, + "bytes_utf8": 798, + "lines": 4, + "estimated_tokens": null + }, + "stderr_metrics": { + "chars": 0, + "bytes_utf8": 0, + "lines": 0, + "estimated_tokens": null + }, + "parsed_output": { + "format": "jsonl_events", + "text_metrics": { + "chars": 444, + "bytes_utf8": 444, + "lines": 1, + "estimated_tokens": null + }, + "usage": { + "input_tokens": 20329, + "cached_input_tokens": 12032, + "output_tokens": 256, + "reasoning_output_tokens": 149 + } + }, + "prompt_path": "cli/sql_prompt_attempt_1.txt", + "response_path": "cli/sql_response_attempt_1.txt", + "raw_response_path": "cli/sql_response_attempt_1.raw.txt", + "stderr_path": "cli/sql_stderr_attempt_1.txt" +} \ No newline at end of file diff --git a/Query/sql/v2/runs/v2_cli_20260502_081223_d/n1/artifacts/v2q_n1_fb31e8dde7866159/cli/sql_prompt_attempt_1.txt b/Query/sql/v2/runs/v2_cli_20260502_081223_d/n1/artifacts/v2q_n1_fb31e8dde7866159/cli/sql_prompt_attempt_1.txt new file mode 100644 index 0000000000000000000000000000000000000000..90f1d00ed158794b8f668bc330626822b9bdb7ec --- /dev/null +++ b/Query/sql/v2/runs/v2_cli_20260502_081223_d/n1/artifacts/v2q_n1_fb31e8dde7866159/cli/sql_prompt_attempt_1.txt @@ -0,0 +1,792 @@ +You are generating one SQLite SELECT query for a single-table SQL QA task. +Return strict JSON only, with this schema: {"sql": "...", "notes": "..."}. +Rules: +- Use only the provided table and columns. +- Do not write INSERT, UPDATE, DELETE, DROP, ALTER, CREATE, PRAGMA, ATTACH, DETACH, or VACUUM. +- Prefer the planned template and bound roles when provided. +- Add a leading SQL comment exactly like: -- template_id: . +- Generate SQLite-compatible SQL. SQLite does not support PERCENTILE_CONT or STDDEV. +- Quote identifiers with double quotes. +- Return no markdown and no extra prose. + +Dataset context: +Dataset context for SQL QA: +- dataset_id: n1 +- dataset_name: Spambase +- table_name: n1 +- table_layout: single-table dataset (do not assume joins). +- row_semantics: One row is one email represented by engineered token/character frequency and capitalization-run features. +- task_type: classification +- target_column: class +- main_row_count: 4601 +- important_fields: +- word_freq_make: role=feature, type=numeric_sparse_frequency. tags=['condition_candidate', 'measure', 'rare_pattern_candidate'] desc=Frequency feature for token 'make' in an email message. +- word_freq_address: role=feature, type=numeric_sparse_frequency. tags=['condition_candidate', 'measure', 'rare_pattern_candidate'] desc=Frequency feature for token 'address' in an email message. +- word_freq_all: role=feature, type=numeric_sparse_frequency. tags=['condition_candidate', 'measure', 'rare_pattern_candidate'] desc=Frequency feature for token 'all' in an email message. +- word_freq_3d: role=feature, type=numeric_sparse_frequency. tags=['condition_candidate', 'measure', 'rare_pattern_candidate'] desc=Frequency feature for token '3d' in an email message. +- word_freq_our: role=feature, type=numeric_sparse_frequency. tags=['condition_candidate', 'measure', 'rare_pattern_candidate'] desc=Frequency feature for token 'our' in an email message. +- word_freq_over: role=feature, type=numeric_sparse_frequency. tags=['condition_candidate', 'measure', 'rare_pattern_candidate'] desc=Frequency feature for token 'over' in an email message. +- word_freq_remove: role=feature, type=numeric_sparse_frequency. tags=['condition_candidate', 'measure', 'rare_pattern_candidate'] desc=Frequency feature for token 'remove' in an email message. +- word_freq_internet: role=feature, type=numeric_sparse_frequency. tags=['condition_candidate', 'measure', 'rare_pattern_candidate'] desc=Frequency feature for token 'internet' in an email message. +- word_freq_order: role=feature, type=numeric_sparse_frequency. tags=['condition_candidate', 'measure', 'rare_pattern_candidate'] desc=Frequency feature for token 'order' in an email message. +- word_freq_mail: role=feature, type=numeric_sparse_frequency. tags=['condition_candidate', 'measure', 'rare_pattern_candidate'] desc=Frequency feature for token 'mail' in an email message. +- word_freq_receive: role=feature, type=numeric_sparse_frequency. tags=['condition_candidate', 'measure', 'rare_pattern_candidate'] desc=Frequency feature for token 'receive' in an email message. +- word_freq_will: role=feature, type=numeric_sparse_frequency. tags=['condition_candidate', 'measure', 'rare_pattern_candidate'] desc=Frequency feature for token 'will' in an email message. +- word_freq_people: role=feature, type=numeric_sparse_frequency. tags=['condition_candidate', 'measure', 'rare_pattern_candidate'] desc=Frequency feature for token 'people' in an email message. +- word_freq_report: role=feature, type=numeric_sparse_frequency. tags=['condition_candidate', 'measure', 'rare_pattern_candidate'] desc=Frequency feature for token 'report' in an email message. +- word_freq_addresses: role=feature, type=numeric_sparse_frequency. tags=['condition_candidate', 'measure', 'rare_pattern_candidate'] desc=Frequency feature for token 'addresses' in an email message. +- word_freq_free: role=feature, type=numeric_sparse_frequency. tags=['condition_candidate', 'measure', 'rare_pattern_candidate'] desc=Frequency feature for token 'free' in an email message. +- word_freq_business: role=feature, type=numeric_sparse_frequency. tags=['condition_candidate', 'measure', 'rare_pattern_candidate'] desc=Frequency feature for token 'business' in an email message. +- word_freq_email: role=feature, type=numeric_sparse_frequency. tags=['condition_candidate', 'measure', 'rare_pattern_candidate'] desc=Frequency feature for token 'email' in an email message. +- word_freq_you: role=feature, type=numeric_sparse_frequency. tags=['condition_candidate', 'measure', 'rare_pattern_candidate'] desc=Frequency feature for token 'you' in an email message. +- word_freq_credit: role=feature, type=numeric_sparse_frequency. tags=['condition_candidate', 'measure', 'rare_pattern_candidate'] desc=Frequency feature for token 'credit' in an email message. +- word_freq_your: role=feature, type=numeric_sparse_frequency. tags=['condition_candidate', 'measure', 'rare_pattern_candidate'] desc=Frequency feature for token 'your' in an email message. +- word_freq_font: role=feature, type=numeric_sparse_frequency. tags=['condition_candidate', 'measure', 'rare_pattern_candidate'] desc=Frequency feature for token 'font' in an email message. +- word_freq_000: role=feature, type=numeric_sparse_frequency. tags=['condition_candidate', 'measure', 'rare_pattern_candidate'] desc=Frequency feature for token '000' in an email message. +- word_freq_money: role=feature, type=numeric_sparse_frequency. tags=['condition_candidate', 'measure', 'rare_pattern_candidate'] desc=Frequency feature for token 'money' in an email message. +- word_freq_hp: role=feature, type=numeric_sparse_frequency. tags=['condition_candidate', 'measure', 'rare_pattern_candidate'] desc=Frequency feature for token 'hp' in an email message. +- word_freq_hpl: role=feature, type=numeric_sparse_frequency. tags=['condition_candidate', 'measure', 'rare_pattern_candidate'] desc=Frequency feature for token 'hpl' in an email message. +- word_freq_george: role=feature, type=numeric_sparse_frequency. tags=['condition_candidate', 'measure', 'rare_pattern_candidate'] desc=Frequency feature for token 'george' in an email message. +- word_freq_650: role=feature, type=numeric_sparse_frequency. tags=['condition_candidate', 'measure', 'rare_pattern_candidate'] desc=Frequency feature for token '650' in an email message. +- word_freq_lab: role=feature, type=numeric_sparse_frequency. tags=['condition_candidate', 'measure', 'rare_pattern_candidate'] desc=Frequency feature for token 'lab' in an email message. +- word_freq_labs: role=feature, type=numeric_sparse_frequency. tags=['condition_candidate', 'measure', 'rare_pattern_candidate'] desc=Frequency feature for token 'labs' in an email message. +- word_freq_telnet: role=feature, type=numeric_sparse_frequency. tags=['condition_candidate', 'measure', 'rare_pattern_candidate'] desc=Frequency feature for token 'telnet' in an email message. +- word_freq_857: role=feature, type=numeric_sparse_frequency. tags=['condition_candidate', 'measure', 'rare_pattern_candidate'] desc=Frequency feature for token '857' in an email message. +- word_freq_data: role=feature, type=numeric_sparse_frequency. tags=['condition_candidate', 'measure', 'rare_pattern_candidate'] desc=Frequency feature for token 'data' in an email message. +- word_freq_415: role=feature, type=numeric_sparse_frequency. tags=['condition_candidate', 'measure', 'rare_pattern_candidate'] desc=Frequency feature for token '415' in an email message. +- word_freq_85: role=feature, type=numeric_sparse_frequency. tags=['condition_candidate', 'measure', 'rare_pattern_candidate'] desc=Frequency feature for token '85' in an email message. +- word_freq_technology: role=feature, type=numeric_sparse_frequency. tags=['condition_candidate', 'measure', 'rare_pattern_candidate'] desc=Frequency feature for token 'technology' in an email message. +- word_freq_1999: role=feature, type=numeric_sparse_frequency. tags=['condition_candidate', 'measure', 'rare_pattern_candidate'] desc=Frequency feature for token '1999' in an email message. +- word_freq_parts: role=feature, type=numeric_sparse_frequency. tags=['condition_candidate', 'measure', 'rare_pattern_candidate'] desc=Frequency feature for token 'parts' in an email message. +- word_freq_pm: role=feature, type=numeric_sparse_frequency. tags=['condition_candidate', 'measure', 'rare_pattern_candidate'] desc=Frequency feature for token 'pm' in an email message. +- word_freq_direct: role=feature, type=numeric_sparse_frequency. tags=['condition_candidate', 'measure', 'rare_pattern_candidate'] desc=Frequency feature for token 'direct' in an email message. +- word_freq_cs: role=feature, type=numeric_sparse_frequency. tags=['condition_candidate', 'measure', 'rare_pattern_candidate'] desc=Frequency feature for token 'cs' in an email message. +- word_freq_meeting: role=feature, type=numeric_sparse_frequency. tags=['condition_candidate', 'measure', 'rare_pattern_candidate'] desc=Frequency feature for token 'meeting' in an email message. +- word_freq_original: role=feature, type=numeric_sparse_frequency. tags=['condition_candidate', 'measure', 'rare_pattern_candidate'] desc=Frequency feature for token 'original' in an email message. +- word_freq_project: role=feature, type=numeric_sparse_frequency. tags=['condition_candidate', 'measure', 'rare_pattern_candidate'] desc=Frequency feature for token 'project' in an email message. +- word_freq_re: role=feature, type=numeric_sparse_frequency. tags=['condition_candidate', 'measure', 'rare_pattern_candidate'] desc=Frequency feature for token 're' in an email message. +- word_freq_edu: role=feature, type=numeric_sparse_frequency. tags=['condition_candidate', 'measure', 'rare_pattern_candidate'] desc=Frequency feature for token 'edu' in an email message. +- word_freq_table: role=feature, type=numeric_sparse_frequency. tags=['condition_candidate', 'measure', 'rare_pattern_candidate'] desc=Frequency feature for token 'table' in an email message. +- word_freq_conference: role=feature, type=numeric_sparse_frequency. tags=['condition_candidate', 'measure', 'rare_pattern_candidate'] desc=Frequency feature for token 'conference' in an email message. +- char_freq_%3B: role=feature, type=numeric_sparse_frequency. tags=['condition_candidate', 'measure', 'rare_pattern_candidate'] desc=Character frequency feature for symbol ';'. +- char_freq_%28: role=feature, type=numeric_sparse_frequency. tags=['condition_candidate', 'measure', 'rare_pattern_candidate'] desc=Character frequency feature for symbol '('. +- char_freq_%5B: role=feature, type=numeric_sparse_frequency. tags=['condition_candidate', 'measure', 'rare_pattern_candidate'] desc=Character frequency feature for symbol '['. +- char_freq_%21: role=feature, type=numeric_sparse_frequency. tags=['condition_candidate', 'measure', 'rare_pattern_candidate'] desc=Character frequency feature for symbol '!'. +- char_freq_%24: role=feature, type=numeric_sparse_frequency. tags=['condition_candidate', 'measure', 'rare_pattern_candidate'] desc=Character frequency feature for symbol '$'. +- char_freq_%23: role=feature, type=numeric_sparse_frequency. tags=['condition_candidate', 'measure', 'rare_pattern_candidate'] desc=Character frequency feature for symbol '#'. +- capital_run_length_average: role=feature, type=numeric_heavy_tail. tags=['condition_candidate', 'measure', 'rare_pattern_candidate'] desc=Average length of uninterrupted capital-letter runs. +- capital_run_length_longest: role=feature, type=numeric_heavy_tail. tags=['condition_candidate', 'measure', 'rare_pattern_candidate'] desc=Longest uninterrupted capital-letter run. +- capital_run_length_total: role=feature, type=numeric_heavy_tail. tags=['condition_candidate', 'measure', 'rare_pattern_candidate'] desc=Total number of capital letters in runs. +- class: role=target, type=binary_target. tags=['target_candidate'] desc=Binary spam label (1=spam, 0=non-spam). +- useful_field_combinations: [['word_freq_free', 'word_freq_you', 'class'], ['char_freq_%21', 'capital_run_length_longest', 'class'], ['word_freq_remove', 'word_freq_money', 'class']] +- fields_requiring_caution: ['capital_run_length_average', 'capital_run_length_longest', 'capital_run_length_total'] +- source_url: https://www.openml.org/d/44 + +SQLite schema snapshot: +{ + "table_name": "n1", + "quoted_table_name": "\"n1\"", + "row_count": 4601, + "columns": [ + { + "name": "word_freq_make", + "type": "TEXT", + "notnull": false, + "pk": false + }, + { + "name": "word_freq_address", + "type": "TEXT", + "notnull": false, + "pk": false + }, + { + "name": "word_freq_all", + "type": "TEXT", + "notnull": false, + "pk": false + }, + { + "name": "word_freq_3d", + "type": "TEXT", + "notnull": false, + "pk": false + }, + { + "name": "word_freq_our", + "type": "TEXT", + "notnull": false, + "pk": false + }, + { + "name": "word_freq_over", + "type": "TEXT", + "notnull": false, + "pk": false + }, + { + "name": "word_freq_remove", + "type": "TEXT", + "notnull": false, + "pk": false + }, + { + "name": "word_freq_internet", + "type": "TEXT", + "notnull": false, + "pk": false + }, + { + "name": "word_freq_order", + "type": "TEXT", + "notnull": false, + "pk": false + }, + { + "name": "word_freq_mail", + "type": "TEXT", + "notnull": false, + "pk": false + }, + { + "name": "word_freq_receive", + "type": "TEXT", + "notnull": false, + "pk": false + }, + { + "name": "word_freq_will", + "type": "TEXT", + "notnull": false, + "pk": false + }, + { + "name": "word_freq_people", + "type": "TEXT", + "notnull": false, + "pk": false + }, + { + "name": "word_freq_report", + "type": "TEXT", + "notnull": false, + "pk": false + }, + { + "name": "word_freq_addresses", + "type": "TEXT", + "notnull": false, + "pk": false + }, + { + "name": "word_freq_free", + "type": "TEXT", + "notnull": false, + "pk": false + }, + { + "name": "word_freq_business", + "type": "TEXT", + "notnull": false, + "pk": false + }, + { + "name": "word_freq_email", + "type": "TEXT", + "notnull": false, + "pk": false + }, + { + "name": "word_freq_you", + "type": "TEXT", + "notnull": false, + "pk": false + }, + { + "name": "word_freq_credit", + "type": "TEXT", + "notnull": false, + "pk": false + }, + { + "name": "word_freq_your", + "type": "TEXT", + "notnull": false, + "pk": false + }, + { + "name": "word_freq_font", + "type": "TEXT", + "notnull": false, + "pk": false + }, + { + "name": "word_freq_000", + "type": "TEXT", + "notnull": false, + "pk": false + }, + { + "name": "word_freq_money", + "type": "TEXT", + "notnull": false, + "pk": false + }, + { + "name": "word_freq_hp", + "type": "TEXT", + "notnull": false, + "pk": false + }, + { + "name": "word_freq_hpl", + "type": "TEXT", + "notnull": false, + "pk": false + }, + { + "name": "word_freq_george", + "type": "TEXT", + "notnull": false, + "pk": false + }, + { + "name": "word_freq_650", + "type": "TEXT", + "notnull": false, + "pk": false + }, + { + "name": "word_freq_lab", + "type": "TEXT", + "notnull": false, + "pk": false + }, + { + "name": "word_freq_labs", + "type": "TEXT", + "notnull": false, + "pk": false + }, + { + "name": "word_freq_telnet", + "type": "TEXT", + "notnull": false, + "pk": false + }, + { + "name": "word_freq_857", + "type": "TEXT", + "notnull": false, + "pk": false + }, + { + "name": "word_freq_data", + "type": "TEXT", + "notnull": false, + "pk": false + }, + { + "name": "word_freq_415", + "type": "TEXT", + "notnull": false, + "pk": false + }, + { + "name": "word_freq_85", + "type": "TEXT", + "notnull": false, + "pk": false + }, + { + "name": "word_freq_technology", + "type": "TEXT", + "notnull": false, + "pk": false + }, + { + "name": "word_freq_1999", + "type": "TEXT", + "notnull": false, + "pk": false + }, + { + "name": "word_freq_parts", + "type": "TEXT", + "notnull": false, + "pk": false + }, + { + "name": "word_freq_pm", + "type": "TEXT", + "notnull": false, + "pk": false + }, + { + "name": "word_freq_direct", + "type": "TEXT", + "notnull": false, + "pk": false + }, + { + "name": "word_freq_cs", + "type": "TEXT", + "notnull": false, + "pk": false + }, + { + "name": "word_freq_meeting", + "type": "TEXT", + "notnull": false, + "pk": false + }, + { + "name": "word_freq_original", + "type": "TEXT", + "notnull": false, + "pk": false + }, + { + "name": "word_freq_project", + "type": "TEXT", + "notnull": false, + "pk": false + }, + { + "name": "word_freq_re", + "type": "TEXT", + "notnull": false, + "pk": false + }, + { + "name": "word_freq_edu", + "type": "TEXT", + "notnull": false, + "pk": false + }, + { + "name": "word_freq_table", + "type": "TEXT", + "notnull": false, + "pk": false + }, + { + "name": "word_freq_conference", + "type": "TEXT", + "notnull": false, + "pk": false + }, + { + "name": "char_freq_%3B", + "type": "TEXT", + "notnull": false, + "pk": false + }, + { + "name": "char_freq_%28", + "type": "TEXT", + "notnull": false, + "pk": false + }, + { + "name": "char_freq_%5B", + "type": "TEXT", + "notnull": false, + "pk": false + }, + { + "name": "char_freq_%21", + "type": "TEXT", + "notnull": false, + "pk": false + }, + { + "name": "char_freq_%24", + "type": "TEXT", + "notnull": false, + "pk": false + }, + { + "name": "char_freq_%23", + "type": "TEXT", + "notnull": false, + "pk": false + }, + { + "name": "capital_run_length_average", + "type": "TEXT", + "notnull": false, + "pk": false + }, + { + "name": "capital_run_length_longest", + "type": "TEXT", + "notnull": false, + "pk": false + }, + { + "name": "capital_run_length_total", + "type": "TEXT", + "notnull": false, + "pk": false + }, + { + "name": "class", + "type": "TEXT", + "notnull": false, + "pk": false + } + ], + "sample_rows": [ + { + "word_freq_make": "0", + "word_freq_address": "0.64", + "word_freq_all": "0.64", + "word_freq_3d": "0", + "word_freq_our": "0.32", + "word_freq_over": "0", + "word_freq_remove": "0", + "word_freq_internet": "0", + "word_freq_order": "0", + "word_freq_mail": "0", + "word_freq_receive": "0", + "word_freq_will": "0.64", + "word_freq_people": "0", + "word_freq_report": "0", + "word_freq_addresses": "0", + "word_freq_free": "0.32", + "word_freq_business": "0", + "word_freq_email": "1.29", + "word_freq_you": "1.93", + "word_freq_credit": "0", + "word_freq_your": "0.96", + "word_freq_font": "0", + "word_freq_000": "0", + "word_freq_money": "0", + "word_freq_hp": "0", + "word_freq_hpl": "0", + "word_freq_george": "0", + "word_freq_650": "0", + "word_freq_lab": "0", + "word_freq_labs": "0", + "word_freq_telnet": "0", + "word_freq_857": "0", + "word_freq_data": "0", + "word_freq_415": "0", + "word_freq_85": "0", + "word_freq_technology": "0", + "word_freq_1999": "0", + "word_freq_parts": "0", + "word_freq_pm": "0", + "word_freq_direct": "0", + "word_freq_cs": "0", + "word_freq_meeting": "0", + "word_freq_original": "0", + "word_freq_project": "0", + "word_freq_re": "0", + "word_freq_edu": "0", + "word_freq_table": "0", + "word_freq_conference": "0", + "char_freq_%3B": "0", + "char_freq_%28": "0", + "char_freq_%5B": "0", + "char_freq_%21": "0.778", + "char_freq_%24": "0", + "char_freq_%23": "0", + "capital_run_length_average": "3.756", + "capital_run_length_longest": "61", + "capital_run_length_total": "278", + "class": "1" + }, + { + "word_freq_make": "0.21", + "word_freq_address": "0.28", + "word_freq_all": "0.5", + "word_freq_3d": "0", + "word_freq_our": "0.14", + "word_freq_over": "0.28", + "word_freq_remove": "0.21", + "word_freq_internet": "0.07", + "word_freq_order": "0", + "word_freq_mail": "0.94", + "word_freq_receive": "0.21", + "word_freq_will": "0.79", + "word_freq_people": "0.65", + "word_freq_report": "0.21", + "word_freq_addresses": "0.14", + "word_freq_free": "0.14", + "word_freq_business": "0.07", + "word_freq_email": "0.28", + "word_freq_you": "3.47", + "word_freq_credit": "0", + "word_freq_your": "1.59", + "word_freq_font": "0", + "word_freq_000": "0.43", + "word_freq_money": "0.43", + "word_freq_hp": "0", + "word_freq_hpl": "0", + "word_freq_george": "0", + "word_freq_650": "0", + "word_freq_lab": "0", + "word_freq_labs": "0", + "word_freq_telnet": "0", + "word_freq_857": "0", + "word_freq_data": "0", + "word_freq_415": "0", + "word_freq_85": "0", + "word_freq_technology": "0", + "word_freq_1999": "0.07", + "word_freq_parts": "0", + "word_freq_pm": "0", + "word_freq_direct": "0", + "word_freq_cs": "0", + "word_freq_meeting": "0", + "word_freq_original": "0", + "word_freq_project": "0", + "word_freq_re": "0", + "word_freq_edu": "0", + "word_freq_table": "0", + "word_freq_conference": "0", + "char_freq_%3B": "0", + "char_freq_%28": "0.132", + "char_freq_%5B": "0", + "char_freq_%21": "0.372", + "char_freq_%24": "0.18", + "char_freq_%23": "0.048", + "capital_run_length_average": "5.114", + "capital_run_length_longest": "101", + "capital_run_length_total": "1028", + "class": "1" + }, + { + "word_freq_make": "0.06", + "word_freq_address": "0", + "word_freq_all": "0.71", + "word_freq_3d": "0", + "word_freq_our": "1.23", + "word_freq_over": "0.19", + "word_freq_remove": "0.19", + "word_freq_internet": "0.12", + "word_freq_order": "0.64", + "word_freq_mail": "0.25", + "word_freq_receive": "0.38", + "word_freq_will": "0.45", + "word_freq_people": "0.12", + "word_freq_report": "0", + "word_freq_addresses": "1.75", + "word_freq_free": "0.06", + "word_freq_business": "0.06", + "word_freq_email": "1.03", + "word_freq_you": "1.36", + "word_freq_credit": "0.32", + "word_freq_your": "0.51", + "word_freq_font": "0", + "word_freq_000": "1.16", + "word_freq_money": "0.06", + "word_freq_hp": "0", + "word_freq_hpl": "0", + "word_freq_george": "0", + "word_freq_650": "0", + "word_freq_lab": "0", + "word_freq_labs": "0", + "word_freq_telnet": "0", + "word_freq_857": "0", + "word_freq_data": "0", + "word_freq_415": "0", + "word_freq_85": "0", + "word_freq_technology": "0", + "word_freq_1999": "0", + "word_freq_parts": "0", + "word_freq_pm": "0", + "word_freq_direct": "0.06", + "word_freq_cs": "0", + "word_freq_meeting": "0", + "word_freq_original": "0.12", + "word_freq_project": "0", + "word_freq_re": "0.06", + "word_freq_edu": "0.06", + "word_freq_table": "0", + "word_freq_conference": "0", + "char_freq_%3B": "0.01", + "char_freq_%28": "0.143", + "char_freq_%5B": "0", + "char_freq_%21": "0.276", + "char_freq_%24": "0.184", + "char_freq_%23": "0.01", + "capital_run_length_average": "9.821", + "capital_run_length_longest": "485", + "capital_run_length_total": "2259", + "class": "1" + }, + { + "word_freq_make": "0", + "word_freq_address": "0", + "word_freq_all": "0", + "word_freq_3d": "0", + "word_freq_our": "0.63", + "word_freq_over": "0", + "word_freq_remove": "0.31", + "word_freq_internet": "0.63", + "word_freq_order": "0.31", + "word_freq_mail": "0.63", + "word_freq_receive": "0.31", + "word_freq_will": "0.31", + "word_freq_people": "0.31", + "word_freq_report": "0", + "word_freq_addresses": "0", + "word_freq_free": "0.31", + "word_freq_business": "0", + "word_freq_email": "0", + "word_freq_you": "3.18", + "word_freq_credit": "0", + "word_freq_your": "0.31", + "word_freq_font": "0", + "word_freq_000": "0", + "word_freq_money": "0", + "word_freq_hp": "0", + "word_freq_hpl": "0", + "word_freq_george": "0", + "word_freq_650": "0", + "word_freq_lab": "0", + "word_freq_labs": "0", + "word_freq_telnet": "0", + "word_freq_857": "0", + "word_freq_data": "0", + "word_freq_415": "0", + "word_freq_85": "0", + "word_freq_technology": "0", + "word_freq_1999": "0", + "word_freq_parts": "0", + "word_freq_pm": "0", + "word_freq_direct": "0", + "word_freq_cs": "0", + "word_freq_meeting": "0", + "word_freq_original": "0", + "word_freq_project": "0", + "word_freq_re": "0", + "word_freq_edu": "0", + "word_freq_table": "0", + "word_freq_conference": "0", + "char_freq_%3B": "0", + "char_freq_%28": "0.137", + "char_freq_%5B": "0", + "char_freq_%21": "0.137", + "char_freq_%24": "0", + "char_freq_%23": "0", + "capital_run_length_average": "3.537", + "capital_run_length_longest": "40", + "capital_run_length_total": "191", + "class": "1" + }, + { + "word_freq_make": "0", + "word_freq_address": "0", + "word_freq_all": "0", + "word_freq_3d": "0", + "word_freq_our": "0.63", + "word_freq_over": "0", + "word_freq_remove": "0.31", + "word_freq_internet": "0.63", + "word_freq_order": "0.31", + "word_freq_mail": "0.63", + "word_freq_receive": "0.31", + "word_freq_will": "0.31", + "word_freq_people": "0.31", + "word_freq_report": "0", + "word_freq_addresses": "0", + "word_freq_free": "0.31", + "word_freq_business": "0", + "word_freq_email": "0", + "word_freq_you": "3.18", + "word_freq_credit": "0", + "word_freq_your": "0.31", + "word_freq_font": "0", + "word_freq_000": "0", + "word_freq_money": "0", + "word_freq_hp": "0", + "word_freq_hpl": "0", + "word_freq_george": "0", + "word_freq_650": "0", + "word_freq_lab": "0", + "word_freq_labs": "0", + "word_freq_telnet": "0", + "word_freq_857": "0", + "word_freq_data": "0", + "word_freq_415": "0", + "word_freq_85": "0", + "word_freq_technology": "0", + "word_freq_1999": "0", + "word_freq_parts": "0", + "word_freq_pm": "0", + "word_freq_direct": "0", + "word_freq_cs": "0", + "word_freq_meeting": "0", + "word_freq_original": "0", + "word_freq_project": "0", + "word_freq_re": "0", + "word_freq_edu": "0", + "word_freq_table": "0", + "word_freq_conference": "0", + "char_freq_%3B": "0", + "char_freq_%28": "0.135", + "char_freq_%5B": "0", + "char_freq_%21": "0.135", + "char_freq_%24": "0", + "char_freq_%23": "0", + "capital_run_length_average": "3.537", + "capital_run_length_longest": "40", + "capital_run_length_total": "191", + "class": "1" + } + ] +} + +Shortlisted templates: +[ + { + "template_id": "tpl_m4_window_partition_avg", + "template_name": "Window Partition Average", + "primary_family": "conditional_dependency_structure", + "portability": "partial", + "sql_skeleton": "SELECT DISTINCT {group_col},\n AVG({measure_col}) OVER (PARTITION BY {group_col}) AS avg_measure\nFROM {table}\nORDER BY avg_measure DESC;", + "required_roles": [ + "group_col", + "measure_col" + ] + } +] + +Problem instance: +{ + "dataset_id": "n1", + "question": "Use template Window Partition Average to probe direction_consistency with semantic role ranked_signal_view. Focus on group_col=class, measure_col=capital_run_length_longest.", + "planned_template_id": "tpl_m4_window_partition_avg", + "bindings": { + "group_col": "class", + "measure_col": "capital_run_length_longest", + "top_k": 10, + "top_n": 6, + "num_tiles": 10, + "percentile_value": 0.9, + "z_threshold": 2.0, + "fraction_threshold": 0.1, + "baseline_multiplier": 1.5, + "baseline_fraction": 0.1, + "min_group_size": 5, + "min_support": 5, + "measure_threshold": 43.0, + "time_grain": "month", + "lookback_rows": 3, + "current_period_start": "'2024-01-01'", + "current_period_end": "'2024-04-01'", + "previous_period_start": "'2023-10-01'", + "previous_period_end": "'2024-01-01'", + "drift_ratio_threshold": 0.8 + }, + "can_vary": [], + "must_fix": [], + "runtime_sql_skeleton": "SELECT DISTINCT {group_col},\n AVG({measure_col}) OVER (PARTITION BY {group_col}) AS avg_measure\nFROM {table}\nORDER BY avg_measure DESC;" +} + +Repair context: +{} diff --git a/Query/sql/v2/runs/v2_cli_20260502_081223_d/n1/artifacts/v2q_n1_fb31e8dde7866159/cli/sql_response_attempt_1.raw.txt b/Query/sql/v2/runs/v2_cli_20260502_081223_d/n1/artifacts/v2q_n1_fb31e8dde7866159/cli/sql_response_attempt_1.raw.txt new file mode 100644 index 0000000000000000000000000000000000000000..2e04e6d3a31d99f72fee590860658fef5ee56b1c --- /dev/null +++ b/Query/sql/v2/runs/v2_cli_20260502_081223_d/n1/artifacts/v2q_n1_fb31e8dde7866159/cli/sql_response_attempt_1.raw.txt @@ -0,0 +1,4 @@ +{"type":"thread.started","thread_id":"019e40e8-b689-7183-9639-71589d610b1f"} +{"type":"turn.started"} +{"type":"item.completed","item":{"id":"item_0","type":"agent_message","text":"{\"sql\":\"-- template_id: tpl_m4_window_partition_avg\\nSELECT DISTINCT \\\"class\\\",\\n AVG(CAST(\\\"capital_run_length_longest\\\" AS REAL)) OVER (PARTITION BY \\\"class\\\") AS avg_measure\\nFROM \\\"n1\\\"\\nORDER BY avg_measure DESC;\",\"notes\":\"Used the requested Window Partition Average template with group_col=\\\"class\\\" and measure_col=\\\"capital_run_length_longest\\\". CAST to REAL is included because the schema stores numeric-looking fields as TEXT.\"}"}} +{"type":"turn.completed","usage":{"input_tokens":20329,"cached_input_tokens":12032,"output_tokens":256,"reasoning_output_tokens":149}} diff --git a/Query/sql/v2/runs/v2_cli_20260502_081223_d/n1/artifacts/v2q_n1_fb31e8dde7866159/cli/sql_response_attempt_1.txt b/Query/sql/v2/runs/v2_cli_20260502_081223_d/n1/artifacts/v2q_n1_fb31e8dde7866159/cli/sql_response_attempt_1.txt new file mode 100644 index 0000000000000000000000000000000000000000..a6f31ff7b27a703a96f52c3430f9c95adb482322 --- /dev/null +++ b/Query/sql/v2/runs/v2_cli_20260502_081223_d/n1/artifacts/v2q_n1_fb31e8dde7866159/cli/sql_response_attempt_1.txt @@ -0,0 +1 @@ +{"sql":"-- template_id: tpl_m4_window_partition_avg\nSELECT DISTINCT \"class\",\n AVG(CAST(\"capital_run_length_longest\" AS REAL)) OVER (PARTITION BY \"class\") AS avg_measure\nFROM \"n1\"\nORDER BY avg_measure DESC;","notes":"Used the requested Window Partition Average template with group_col=\"class\" and measure_col=\"capital_run_length_longest\". CAST to REAL is included because the schema stores numeric-looking fields as TEXT."} \ No newline at end of file diff --git a/Query/sql/v2/runs/v2_cli_20260502_081223_d/n1/artifacts/v2q_n1_fb31e8dde7866159/cli/sql_stderr_attempt_1.txt b/Query/sql/v2/runs/v2_cli_20260502_081223_d/n1/artifacts/v2q_n1_fb31e8dde7866159/cli/sql_stderr_attempt_1.txt new file mode 100644 index 0000000000000000000000000000000000000000..e69de29bb2d1d6434b8b29ae775ad8c2e48c5391