diff --git a/syntheticFail/c14/ctgan/ctgan-c14-20260502_155124/_ctgan_train.py b/syntheticFail/c14/ctgan/ctgan-c14-20260502_155124/_ctgan_train.py new file mode 100644 index 0000000000000000000000000000000000000000..1a79d3b9ce0f1b48f611b94ea0f9d2faed70433e --- /dev/null +++ b/syntheticFail/c14/ctgan/ctgan-c14-20260502_155124/_ctgan_train.py @@ -0,0 +1,17 @@ +import pandas as pd +from ctgan.synthesizers.ctgan import CTGAN + +data = pd.read_csv("/work/output-Benchmark-trainonly-v1/c14/ctgan/ctgan-c14-20260502_155124/staged/public/train.csv") +discrete_columns = ['bin_0', 'bin_1', 'bin_2', 'bin_3', 'bin_4', 'nom_0', 'nom_1', 'nom_2', 'nom_3', 'nom_4', 'nom_5', 'nom_6', 'nom_7', 'nom_8', 'nom_9', 'ord_1', 'ord_2', 'ord_3', 'ord_4', 'ord_5', 'target'] +model = CTGAN( + embedding_dim=8, + generator_dim=(16, 16), + discriminator_dim=(16, 16), + batch_size=10, + pac=1, + epochs=300, + verbose=True, +) +model.fit(data, discrete_columns) +model.save("/work/output-Benchmark-trainonly-v1/c14/ctgan/ctgan-c14-20260502_155124/models_300epochs/ctgan_300epochs.pt") +print("[CTGAN] Saved model ->", "/work/output-Benchmark-trainonly-v1/c14/ctgan/ctgan-c14-20260502_155124/models_300epochs/ctgan_300epochs.pt") \ No newline at end of file diff --git a/syntheticFail/c14/ctgan/ctgan-c14-20260502_155124/ctgan_metadata.json b/syntheticFail/c14/ctgan/ctgan-c14-20260502_155124/ctgan_metadata.json new file mode 100644 index 0000000000000000000000000000000000000000..43d1d6d3e611ce6b232979c04622470688342e90 --- /dev/null +++ b/syntheticFail/c14/ctgan/ctgan-c14-20260502_155124/ctgan_metadata.json @@ -0,0 +1,104 @@ +{ + "columns": [ + { + "name": "id", + "type": "continuous" + }, + { + "name": "bin_0", + "type": "categorical" + }, + { + "name": "bin_1", + "type": "categorical" + }, + { + "name": "bin_2", + "type": "categorical" + }, + { + "name": "bin_3", + "type": "categorical" + }, + { + "name": "bin_4", + "type": "categorical" + }, + { + "name": "nom_0", + "type": "categorical" + }, + { + "name": "nom_1", + "type": "categorical" + }, + { + "name": "nom_2", + "type": "categorical" + }, + { + "name": "nom_3", + "type": "categorical" + }, + { + "name": "nom_4", + "type": "categorical" + }, + { + "name": "nom_5", + "type": "categorical" + }, + { + "name": "nom_6", + "type": "categorical" + }, + { + "name": "nom_7", + "type": "categorical" + }, + { + "name": "nom_8", + "type": "categorical" + }, + { + "name": "nom_9", + "type": "categorical" + }, + { + "name": "ord_0", + "type": "continuous" + }, + { + "name": "ord_1", + "type": "categorical" + }, + { + "name": "ord_2", + "type": "categorical" + }, + { + "name": "ord_3", + "type": "categorical" + }, + { + "name": "ord_4", + "type": "categorical" + }, + { + "name": "ord_5", + "type": "categorical" + }, + { + "name": "day", + "type": "continuous" + }, + { + "name": "month", + "type": "continuous" + }, + { + "name": "target", + "type": "categorical" + } + ] +} \ No newline at end of file diff --git a/syntheticFail/c14/ctgan/ctgan-c14-20260502_155124/input_snapshot.json b/syntheticFail/c14/ctgan/ctgan-c14-20260502_155124/input_snapshot.json new file mode 100644 index 0000000000000000000000000000000000000000..bbac6b99393b3a4ad5ef6f4168ce74f83e798899 --- /dev/null +++ b/syntheticFail/c14/ctgan/ctgan-c14-20260502_155124/input_snapshot.json @@ -0,0 +1,36 @@ +{ + "dataset_id": "c14", + "model": "ctgan", + "inputs": { + "train_csv": { + "path": "/data/jialinzhang/SynthesizePipeline-server/DatasetNew/c14/c14-train.csv", + "exists": true, + "size": 31957080, + "sha256": "4a11abde6a7fae9f8bb1a1d7e31035b9dd61587d2721749a52d78d65441fba5e" + }, + "val_csv": { + "path": "/data/jialinzhang/SynthesizePipeline-server/DatasetNew/c14/c14-val.csv", + "exists": true, + "size": 3995920, + "sha256": "b04d723eeb59d339a356302660463c129141b4213fefbf472ef48f8a4daa50c9" + }, + "test_csv": { + "path": "/data/jialinzhang/SynthesizePipeline-server/DatasetNew/c14/c14-test.csv", + 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a/syntheticFail/c14/ctgan/ctgan-c14-20260502_155124/staged/public/staged_features.json b/syntheticFail/c14/ctgan/ctgan-c14-20260502_155124/staged/public/staged_features.json new file mode 100644 index 0000000000000000000000000000000000000000..508fbe870849f119fe0f440e76f620b91489d965 --- /dev/null +++ b/syntheticFail/c14/ctgan/ctgan-c14-20260502_155124/staged/public/staged_features.json @@ -0,0 +1,127 @@ +[ + { + "feature_name": "id", + "data_type": "continuous", + "is_target": false + }, + { + "feature_name": "bin_0", + "data_type": "binary", + "is_target": false + }, + { + "feature_name": "bin_1", + "data_type": "binary", + "is_target": false + }, + { + "feature_name": "bin_2", + "data_type": "binary", + "is_target": false + }, + { + "feature_name": "bin_3", + "data_type": "binary", + "is_target": false + }, + { + "feature_name": "bin_4", + "data_type": "binary", + "is_target": false + }, + { + "feature_name": "nom_0", + "data_type": "categorical", + "is_target": false + }, + { + 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"categorical", + "is_target": false + }, + { + "feature_name": "ord_3", + "data_type": "categorical", + "is_target": false + }, + { + "feature_name": "ord_4", + "data_type": "categorical", + "is_target": false + }, + { + "feature_name": "ord_5", + "data_type": "categorical", + "is_target": false + }, + { + "feature_name": "day", + "data_type": "continuous", + "is_target": false + }, + { + "feature_name": "month", + "data_type": "continuous", + "is_target": false + }, + { + "feature_name": "target", + "data_type": "binary", + "is_target": true + } +] \ No newline at end of file diff --git a/syntheticFail/c14/ctgan/ctgan-c14-20260502_155124/staged/public/test.csv b/syntheticFail/c14/ctgan/ctgan-c14-20260502_155124/staged/public/test.csv new file mode 100644 index 0000000000000000000000000000000000000000..afbef1e22ba9597cf509fb91aa271ff6908ca0a9 --- /dev/null +++ b/syntheticFail/c14/ctgan/ctgan-c14-20260502_155124/staged/public/test.csv @@ -0,0 +1,3 @@ +version https://git-lfs.github.com/spec/v1 +oid sha256:617ecb620416375c67546e0fa5a8b9a3923d689bda904dc23824ea175d1f8597 +size 3965839 diff --git a/syntheticFail/c14/ctgan/ctgan-c14-20260502_155124/staged/public/train.csv b/syntheticFail/c14/ctgan/ctgan-c14-20260502_155124/staged/public/train.csv new file mode 100644 index 0000000000000000000000000000000000000000..322e9de210d736baf266fa4930517b3827d3bf63 --- /dev/null +++ b/syntheticFail/c14/ctgan/ctgan-c14-20260502_155124/staged/public/train.csv @@ -0,0 +1,3 @@ +version https://git-lfs.github.com/spec/v1 +oid sha256:12398ccb68a2499b9c126f61b25f03c36a65f026d755e1d5ff5653e614676167 +size 31717079 diff --git a/syntheticFail/c14/ctgan/ctgan-c14-20260502_155124/staged/public/val.csv b/syntheticFail/c14/ctgan/ctgan-c14-20260502_155124/staged/public/val.csv new file mode 100644 index 0000000000000000000000000000000000000000..594db5d9c0a27ce284d599b8bf62685189510e61 --- /dev/null +++ b/syntheticFail/c14/ctgan/ctgan-c14-20260502_155124/staged/public/val.csv @@ -0,0 +1,3 @@ +version https://git-lfs.github.com/spec/v1 +oid sha256:f40dbd7bbd9e2783c24d0d12ec35a85581835d45238774eb0676de44a734feda +size 3965919 diff --git a/syntheticFail/c14/ctgan/ctgan-c14-20260502_155140/_ctgan_train.py b/syntheticFail/c14/ctgan/ctgan-c14-20260502_155140/_ctgan_train.py new file mode 100644 index 0000000000000000000000000000000000000000..8b79f0c5df480ac42538aa24b91c44dd1e64403a --- /dev/null +++ b/syntheticFail/c14/ctgan/ctgan-c14-20260502_155140/_ctgan_train.py @@ -0,0 +1,17 @@ +import pandas as pd +from ctgan.synthesizers.ctgan import CTGAN + +data = pd.read_csv("/work/output-Benchmark-trainonly-v1/c14/ctgan/ctgan-c14-20260502_155140/staged/public/train.csv") +discrete_columns = ['bin_0', 'bin_1', 'bin_2', 'bin_3', 'bin_4', 'nom_0', 'nom_1', 'nom_2', 'nom_3', 'nom_4', 'nom_5', 'nom_6', 'nom_7', 'nom_8', 'nom_9', 'ord_1', 'ord_2', 'ord_3', 'ord_4', 'ord_5', 'target'] +model = CTGAN( + embedding_dim=8, + generator_dim=(16, 16), + discriminator_dim=(16, 16), + batch_size=10, + pac=1, + epochs=300, + verbose=True, +) +model.fit(data, discrete_columns) +model.save("/work/output-Benchmark-trainonly-v1/c14/ctgan/ctgan-c14-20260502_155140/models_300epochs/ctgan_300epochs.pt") +print("[CTGAN] Saved model ->", "/work/output-Benchmark-trainonly-v1/c14/ctgan/ctgan-c14-20260502_155140/models_300epochs/ctgan_300epochs.pt") \ No newline at end of file diff --git a/syntheticFail/c14/ctgan/ctgan-c14-20260502_155140/ctgan_metadata.json b/syntheticFail/c14/ctgan/ctgan-c14-20260502_155140/ctgan_metadata.json new file mode 100644 index 0000000000000000000000000000000000000000..43d1d6d3e611ce6b232979c04622470688342e90 --- /dev/null +++ b/syntheticFail/c14/ctgan/ctgan-c14-20260502_155140/ctgan_metadata.json @@ -0,0 +1,104 @@ +{ + "columns": [ + { + "name": "id", + "type": "continuous" + }, + { + "name": "bin_0", + "type": "categorical" + }, + { + "name": "bin_1", + "type": "categorical" + }, + { + "name": "bin_2", + "type": "categorical" + }, + { + "name": "bin_3", + "type": "categorical" + }, + { + "name": "bin_4", + "type": "categorical" + }, + { + "name": "nom_0", + "type": "categorical" + }, + { + "name": "nom_1", + "type": "categorical" + }, + { + "name": "nom_2", + "type": "categorical" + }, + { + "name": "nom_3", + "type": "categorical" + }, + { + "name": "nom_4", + "type": "categorical" + }, + { + "name": "nom_5", + "type": "categorical" + }, + { + "name": "nom_6", + "type": "categorical" + }, + { + "name": "nom_7", + "type": "categorical" + }, + { + "name": "nom_8", + "type": "categorical" + }, + { + "name": "nom_9", + "type": "categorical" + }, + { + "name": "ord_0", + "type": "continuous" + }, + { + "name": "ord_1", + "type": "categorical" + }, + { + "name": "ord_2", + "type": "categorical" + }, + { + "name": "ord_3", + "type": "categorical" + }, + { + "name": "ord_4", + "type": "categorical" + }, + { + "name": "ord_5", + "type": "categorical" + }, + { + "name": "day", + "type": "continuous" + }, + { + "name": "month", + "type": "continuous" + }, + { + "name": "target", + "type": "categorical" + } + ] +} \ No newline at end of file diff --git a/syntheticFail/c14/ctgan/ctgan-c14-20260502_155140/input_snapshot.json b/syntheticFail/c14/ctgan/ctgan-c14-20260502_155140/input_snapshot.json new file mode 100644 index 0000000000000000000000000000000000000000..bbac6b99393b3a4ad5ef6f4168ce74f83e798899 --- /dev/null +++ b/syntheticFail/c14/ctgan/ctgan-c14-20260502_155140/input_snapshot.json @@ -0,0 +1,36 @@ +{ + "dataset_id": "c14", + "model": "ctgan", + "inputs": { + "train_csv": { + "path": "/data/jialinzhang/SynthesizePipeline-server/DatasetNew/c14/c14-train.csv", + "exists": true, + "size": 31957080, + "sha256": "4a11abde6a7fae9f8bb1a1d7e31035b9dd61587d2721749a52d78d65441fba5e" + }, + "val_csv": { + "path": "/data/jialinzhang/SynthesizePipeline-server/DatasetNew/c14/c14-val.csv", + "exists": true, + "size": 3995920, + "sha256": 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a/syntheticFail/c14/ctgan/ctgan-c14-20260502_155140/staged/public/test.csv b/syntheticFail/c14/ctgan/ctgan-c14-20260502_155140/staged/public/test.csv new file mode 100644 index 0000000000000000000000000000000000000000..afbef1e22ba9597cf509fb91aa271ff6908ca0a9 --- /dev/null +++ b/syntheticFail/c14/ctgan/ctgan-c14-20260502_155140/staged/public/test.csv @@ -0,0 +1,3 @@ +version https://git-lfs.github.com/spec/v1 +oid sha256:617ecb620416375c67546e0fa5a8b9a3923d689bda904dc23824ea175d1f8597 +size 3965839 diff --git a/syntheticFail/c14/ctgan/ctgan-c14-20260502_155140/staged/public/train.csv b/syntheticFail/c14/ctgan/ctgan-c14-20260502_155140/staged/public/train.csv new file mode 100644 index 0000000000000000000000000000000000000000..322e9de210d736baf266fa4930517b3827d3bf63 --- /dev/null +++ b/syntheticFail/c14/ctgan/ctgan-c14-20260502_155140/staged/public/train.csv @@ -0,0 +1,3 @@ +version https://git-lfs.github.com/spec/v1 +oid sha256:12398ccb68a2499b9c126f61b25f03c36a65f026d755e1d5ff5653e614676167 +size 31717079 diff --git a/syntheticFail/c14/ctgan/ctgan-c14-20260502_155140/staged/public/val.csv b/syntheticFail/c14/ctgan/ctgan-c14-20260502_155140/staged/public/val.csv new file mode 100644 index 0000000000000000000000000000000000000000..594db5d9c0a27ce284d599b8bf62685189510e61 --- /dev/null +++ b/syntheticFail/c14/ctgan/ctgan-c14-20260502_155140/staged/public/val.csv @@ -0,0 +1,3 @@ +version https://git-lfs.github.com/spec/v1 +oid sha256:f40dbd7bbd9e2783c24d0d12ec35a85581835d45238774eb0676de44a734feda +size 3965919 diff --git a/syntheticFail/c14/ctgan/ctgan-c14-20260504_073250/_ctgan_train.py b/syntheticFail/c14/ctgan/ctgan-c14-20260504_073250/_ctgan_train.py new file mode 100644 index 0000000000000000000000000000000000000000..004572413758a892f98821dd831a224eac0ae34e --- /dev/null +++ b/syntheticFail/c14/ctgan/ctgan-c14-20260504_073250/_ctgan_train.py @@ -0,0 +1,17 @@ +import pandas as pd +from ctgan.synthesizers.ctgan import CTGAN + +data = pd.read_csv("/work/output-Benchmark-trainonly-v1/c14/ctgan/ctgan-c14-20260504_073250/staged/public/train.csv") +discrete_columns = ['bin_0', 'bin_1', 'bin_2', 'bin_3', 'bin_4', 'nom_0', 'nom_1', 'nom_2', 'nom_3', 'nom_4', 'nom_5', 'nom_6', 'nom_7', 'nom_8', 'nom_9', 'ord_1', 'ord_2', 'ord_3', 'ord_4', 'ord_5', 'target'] +model = CTGAN( + embedding_dim=4, + generator_dim=(8, 8), + discriminator_dim=(8, 8), + batch_size=2, + pac=1, + epochs=50, + verbose=True, +) +model.fit(data, discrete_columns) +model.save("/work/output-Benchmark-trainonly-v1/c14/ctgan/ctgan-c14-20260504_073250/models_50epochs/ctgan_50epochs.pt") +print("[CTGAN] Saved model ->", "/work/output-Benchmark-trainonly-v1/c14/ctgan/ctgan-c14-20260504_073250/models_50epochs/ctgan_50epochs.pt") \ No newline at end of file diff --git a/syntheticFail/c14/ctgan/ctgan-c14-20260504_073250/ctgan_metadata.json b/syntheticFail/c14/ctgan/ctgan-c14-20260504_073250/ctgan_metadata.json new 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"categorical" + }, + { + "name": "nom_9", + "type": "categorical" + }, + { + "name": "ord_0", + "type": "continuous" + }, + { + "name": "ord_1", + "type": "categorical" + }, + { + "name": "ord_2", + "type": "categorical" + }, + { + "name": "ord_3", + "type": "categorical" + }, + { + "name": "ord_4", + "type": "categorical" + }, + { + "name": "ord_5", + "type": "categorical" + }, + { + "name": "day", + "type": "continuous" + }, + { + "name": "month", + "type": "continuous" + }, + { + "name": "target", + "type": "categorical" + } + ] +} \ No newline at end of file diff --git a/syntheticFail/c14/ctgan/ctgan-c14-20260504_073250/input_snapshot.json b/syntheticFail/c14/ctgan/ctgan-c14-20260504_073250/input_snapshot.json new file mode 100644 index 0000000000000000000000000000000000000000..bbac6b99393b3a4ad5ef6f4168ce74f83e798899 --- /dev/null +++ b/syntheticFail/c14/ctgan/ctgan-c14-20260504_073250/input_snapshot.json @@ -0,0 +1,36 @@ +{ + "dataset_id": "c14", + "model": "ctgan", + "inputs": { + "train_csv": { + "path": "/data/jialinzhang/SynthesizePipeline-server/DatasetNew/c14/c14-train.csv", + "exists": true, + "size": 31957080, + "sha256": "4a11abde6a7fae9f8bb1a1d7e31035b9dd61587d2721749a52d78d65441fba5e" + }, + "val_csv": { + "path": "/data/jialinzhang/SynthesizePipeline-server/DatasetNew/c14/c14-val.csv", + "exists": true, + "size": 3995920, + "sha256": "b04d723eeb59d339a356302660463c129141b4213fefbf472ef48f8a4daa50c9" + }, + "test_csv": { + "path": "/data/jialinzhang/SynthesizePipeline-server/DatasetNew/c14/c14-test.csv", + "exists": true, + "size": 3995840, + "sha256": "4780b90e46f6b440983cc9315cad8bca0e86bd26d8877ffae7111258fba0a132" + }, + "profile_json": { + "path": "/data/jialinzhang/SynthesizePipeline-server/DatasetNew/artifacts/data_core/tabular/c14/c14-dataset_profile.json", + "exists": true, + "size": 9505, + "sha256": "577cc5830dcb336243555d3d41c06e2e7c0a63df2a9caccb7a0411b9332cde15" + }, + "contract_json": { + "path": 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"adapter_ready_status": "pass", + "train_status": "fail", + "generate_status": "skipped", + "reason_code": "adapter_runtime_error", + "reason_detail": "Command '['docker', 'run', '--rm', '--init', '--cidfile', '/tmp/bench_docker_ctgan_w1i1kw11/container.cid', '--gpus', 'device=0', '-e', 'OPENBLAS_NUM_THREADS=4', '-e', 'MKL_NUM_THREADS=4', '-e', 'HOME=/tmp', '-e', 'PYTORCH_CUDA_ALLOC_CONF=expandable_segments:True', '-v', '/data/jialinzhang/SynthesizePipeline-server:/work', '-w', '/work', 'benchmark:ctgan-zjl', 'python', '/work/output-Benchmark-trainonly-v1/c14/ctgan/ctgan-c14-20260504_073250/_ctgan_train.py']' returned non-zero exit status 137.", + "artifacts": {}, + "timings": { + "train": { + "started_at": "2026-05-04T07:32:54", + "ended_at": "2026-05-04T23:45:25", + "duration_sec": 58351.374 + }, + "generate": { + "started_at": null, + "ended_at": null, + "duration_sec": null + } + } +} \ No newline at end of file diff --git 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false + }, + { + "feature_name": "bin_0", + "data_type": "binary", + "is_target": false + }, + { + "feature_name": "bin_1", + "data_type": "binary", + "is_target": false + }, + { + "feature_name": "bin_2", + "data_type": "binary", + "is_target": false + }, + { + "feature_name": "bin_3", + "data_type": "binary", + "is_target": false + }, + { + "feature_name": "bin_4", + "data_type": "binary", + "is_target": false + }, + { + "feature_name": "nom_0", + "data_type": "categorical", + "is_target": false + }, + { + "feature_name": "nom_1", + "data_type": "categorical", + "is_target": false + }, + { + "feature_name": "nom_2", + "data_type": "categorical", + "is_target": false + }, + { + "feature_name": "nom_3", + "data_type": "categorical", + "is_target": false + }, + { + "feature_name": "nom_4", + "data_type": "categorical", + "is_target": false + }, + { + "feature_name": "nom_5", + "data_type": "categorical", + "is_target": false + }, + { + "feature_name": "nom_6", + "data_type": 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"feature_name": "target", + "data_type": "binary", + "is_target": true + } +] \ No newline at end of file diff --git a/syntheticFail/c14/ctgan/ctgan-c14-20260504_073250/staged/public/test.csv b/syntheticFail/c14/ctgan/ctgan-c14-20260504_073250/staged/public/test.csv new file mode 100644 index 0000000000000000000000000000000000000000..afbef1e22ba9597cf509fb91aa271ff6908ca0a9 --- /dev/null +++ b/syntheticFail/c14/ctgan/ctgan-c14-20260504_073250/staged/public/test.csv @@ -0,0 +1,3 @@ +version https://git-lfs.github.com/spec/v1 +oid sha256:617ecb620416375c67546e0fa5a8b9a3923d689bda904dc23824ea175d1f8597 +size 3965839 diff --git a/syntheticFail/c14/ctgan/ctgan-c14-20260504_073250/staged/public/train.csv b/syntheticFail/c14/ctgan/ctgan-c14-20260504_073250/staged/public/train.csv new file mode 100644 index 0000000000000000000000000000000000000000..322e9de210d736baf266fa4930517b3827d3bf63 --- /dev/null +++ b/syntheticFail/c14/ctgan/ctgan-c14-20260504_073250/staged/public/train.csv @@ -0,0 +1,3 @@ +version https://git-lfs.github.com/spec/v1 +oid sha256:12398ccb68a2499b9c126f61b25f03c36a65f026d755e1d5ff5653e614676167 +size 31717079 diff --git a/syntheticFail/c14/ctgan/ctgan-c14-20260504_073250/staged/public/val.csv b/syntheticFail/c14/ctgan/ctgan-c14-20260504_073250/staged/public/val.csv new file mode 100644 index 0000000000000000000000000000000000000000..594db5d9c0a27ce284d599b8bf62685189510e61 --- /dev/null +++ b/syntheticFail/c14/ctgan/ctgan-c14-20260504_073250/staged/public/val.csv @@ -0,0 +1,3 @@ +version https://git-lfs.github.com/spec/v1 +oid sha256:f40dbd7bbd9e2783c24d0d12ec35a85581835d45238774eb0676de44a734feda +size 3965919 diff --git a/syntheticFail/c14/ctgan/ctgan-c14-20260504_073653/_ctgan_train.py b/syntheticFail/c14/ctgan/ctgan-c14-20260504_073653/_ctgan_train.py new file mode 100644 index 0000000000000000000000000000000000000000..7a649ec33eb8ec8471387161c7640893d118a01d --- /dev/null +++ b/syntheticFail/c14/ctgan/ctgan-c14-20260504_073653/_ctgan_train.py @@ -0,0 +1,17 @@ +import pandas as pd +from ctgan.synthesizers.ctgan import CTGAN + +data = pd.read_csv("/work/output-Benchmark-trainonly-v1/c14/ctgan/ctgan-c14-20260504_073653/staged/public/train.csv") +discrete_columns = ['bin_0', 'bin_1', 'bin_2', 'bin_3', 'bin_4', 'nom_0', 'nom_1', 'nom_2', 'nom_3', 'nom_4', 'nom_5', 'nom_6', 'nom_7', 'nom_8', 'nom_9', 'ord_1', 'ord_2', 'ord_3', 'ord_4', 'ord_5', 'target'] +model = CTGAN( + embedding_dim=4, + generator_dim=(8, 8), + discriminator_dim=(8, 8), + batch_size=2, + pac=1, + epochs=50, + verbose=True, +) +model.fit(data, discrete_columns) +model.save("/work/output-Benchmark-trainonly-v1/c14/ctgan/ctgan-c14-20260504_073653/models_50epochs/ctgan_50epochs.pt") +print("[CTGAN] Saved model ->", "/work/output-Benchmark-trainonly-v1/c14/ctgan/ctgan-c14-20260504_073653/models_50epochs/ctgan_50epochs.pt") \ No newline at end of file diff --git a/syntheticFail/c14/ctgan/ctgan-c14-20260504_073653/ctgan_metadata.json b/syntheticFail/c14/ctgan/ctgan-c14-20260504_073653/ctgan_metadata.json new file mode 100644 index 0000000000000000000000000000000000000000..43d1d6d3e611ce6b232979c04622470688342e90 --- /dev/null +++ b/syntheticFail/c14/ctgan/ctgan-c14-20260504_073653/ctgan_metadata.json @@ -0,0 +1,104 @@ +{ + "columns": [ + { + "name": "id", + "type": "continuous" + }, + { + "name": "bin_0", + "type": "categorical" + }, + { + "name": "bin_1", + "type": "categorical" + }, + { + "name": "bin_2", + "type": "categorical" + }, + { + "name": "bin_3", + "type": "categorical" + }, + { + "name": "bin_4", + "type": "categorical" + }, + { + "name": "nom_0", + "type": "categorical" + }, + { + "name": "nom_1", + "type": "categorical" + }, + { + "name": "nom_2", + "type": "categorical" + }, + { + "name": "nom_3", + "type": "categorical" + }, + { + "name": "nom_4", + "type": "categorical" + }, + { + "name": "nom_5", + "type": "categorical" + }, + { + "name": "nom_6", + "type": "categorical" + }, + { + "name": "nom_7", + "type": "categorical" + }, + { + "name": "nom_8", + "type": "categorical" + }, + { + "name": "nom_9", + "type": "categorical" + }, + { + "name": "ord_0", + "type": "continuous" + }, + { + "name": "ord_1", + "type": "categorical" + }, + { + "name": "ord_2", + "type": "categorical" + }, + { + "name": "ord_3", + "type": "categorical" + }, + { + "name": "ord_4", + "type": "categorical" + }, + { + "name": "ord_5", + "type": "categorical" + }, + { + "name": "day", + "type": "continuous" + }, + { + "name": "month", + "type": "continuous" + }, + { + "name": "target", + "type": "categorical" + } + ] +} \ No newline at end of file diff --git a/syntheticFail/c14/ctgan/ctgan-c14-20260504_073653/input_snapshot.json b/syntheticFail/c14/ctgan/ctgan-c14-20260504_073653/input_snapshot.json new file mode 100644 index 0000000000000000000000000000000000000000..bbac6b99393b3a4ad5ef6f4168ce74f83e798899 --- /dev/null +++ b/syntheticFail/c14/ctgan/ctgan-c14-20260504_073653/input_snapshot.json @@ -0,0 +1,36 @@ +{ + "dataset_id": "c14", + "model": "ctgan", + "inputs": { + "train_csv": { + "path": "/data/jialinzhang/SynthesizePipeline-server/DatasetNew/c14/c14-train.csv", + "exists": true, + "size": 31957080, + "sha256": "4a11abde6a7fae9f8bb1a1d7e31035b9dd61587d2721749a52d78d65441fba5e" + }, + "val_csv": { + "path": "/data/jialinzhang/SynthesizePipeline-server/DatasetNew/c14/c14-val.csv", + "exists": true, + "size": 3995920, + "sha256": "b04d723eeb59d339a356302660463c129141b4213fefbf472ef48f8a4daa50c9" + }, + "test_csv": { + "path": "/data/jialinzhang/SynthesizePipeline-server/DatasetNew/c14/c14-test.csv", + "exists": true, + "size": 3995840, + "sha256": "4780b90e46f6b440983cc9315cad8bca0e86bd26d8877ffae7111258fba0a132" + }, + "profile_json": { + "path": "/data/jialinzhang/SynthesizePipeline-server/DatasetNew/artifacts/data_core/tabular/c14/c14-dataset_profile.json", + "exists": true, + "size": 9505, + "sha256": "577cc5830dcb336243555d3d41c06e2e7c0a63df2a9caccb7a0411b9332cde15" + }, + "contract_json": { + "path": "/data/jialinzhang/SynthesizePipeline-server/DatasetNew/artifacts/data_core/tabular/c14/c14-dataset_contract_v1.json", + "exists": true, + "size": 11901, + "sha256": "955197dff3346d55aaa999adf98d64c375a7e4ca9bc4bcfe626499c9cfea9a41" + } + } +} \ No newline at end of file diff --git a/syntheticFail/c14/ctgan/ctgan-c14-20260504_073653/models_50epochs/train_20260504_073658.log b/syntheticFail/c14/ctgan/ctgan-c14-20260504_073653/models_50epochs/train_20260504_073658.log new file mode 100644 index 0000000000000000000000000000000000000000..fcf8a31a8288f7b6ad3c3df13f50d58a93815d45 --- /dev/null +++ b/syntheticFail/c14/ctgan/ctgan-c14-20260504_073653/models_50epochs/train_20260504_073658.log @@ -0,0 +1,3 @@ +version https://git-lfs.github.com/spec/v1 +oid sha256:d866fadb03ccc904faeff01b51b2b2a83f4f74ef27860e8787a61ee1a57a9b9a +size 5324 diff --git a/syntheticFail/c14/ctgan/ctgan-c14-20260504_073653/public_gate/normalized_schema_snapshot.json 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"feature_name": "target", + "data_type": "binary", + "is_target": true + } +] \ No newline at end of file diff --git a/syntheticFail/c14/ctgan/ctgan-c14-20260504_073653/staged/public/test.csv b/syntheticFail/c14/ctgan/ctgan-c14-20260504_073653/staged/public/test.csv new file mode 100644 index 0000000000000000000000000000000000000000..afbef1e22ba9597cf509fb91aa271ff6908ca0a9 --- /dev/null +++ b/syntheticFail/c14/ctgan/ctgan-c14-20260504_073653/staged/public/test.csv @@ -0,0 +1,3 @@ +version https://git-lfs.github.com/spec/v1 +oid sha256:617ecb620416375c67546e0fa5a8b9a3923d689bda904dc23824ea175d1f8597 +size 3965839 diff --git a/syntheticFail/c14/ctgan/ctgan-c14-20260504_073653/staged/public/train.csv b/syntheticFail/c14/ctgan/ctgan-c14-20260504_073653/staged/public/train.csv new file mode 100644 index 0000000000000000000000000000000000000000..322e9de210d736baf266fa4930517b3827d3bf63 --- /dev/null +++ b/syntheticFail/c14/ctgan/ctgan-c14-20260504_073653/staged/public/train.csv @@ -0,0 +1,3 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b/syntheticFail/c14/forestdiffusion/forest-c14-20260502_031135/_fd_X_host.npy @@ -0,0 +1,3 @@ +version https://git-lfs.github.com/spec/v1 +oid sha256:8057accd254b8d0be84558694c0b0d0b77c825dc413c8e480fac37331f262e76 +size 24000128 diff --git a/syntheticFail/c14/forestdiffusion/forest-c14-20260502_031135/_fd_meta_host.json b/syntheticFail/c14/forestdiffusion/forest-c14-20260502_031135/_fd_meta_host.json new file mode 100644 index 0000000000000000000000000000000000000000..4646e31cc069da465f39a4a0bf0f9ef92fc82aa4 --- /dev/null +++ b/syntheticFail/c14/forestdiffusion/forest-c14-20260502_031135/_fd_meta_host.json @@ -0,0 +1 @@ +{"column_names": ["id", "bin_0", "bin_1", "bin_2", "bin_3", "bin_4", "nom_0", "nom_1", "nom_2", "nom_3", "nom_4", "nom_5", "nom_6", "nom_7", "nom_8", "nom_9", "ord_0", "ord_1", "ord_2", "ord_3", "ord_4", "ord_5", "day", "month", "target"], "cat_indexes": [1, 2, 3, 4, 5, 6, 7, 8, 9, 10, 11, 12, 13, 14, 15, 17, 18, 19, 20, 21]} \ No newline at end of file diff --git a/syntheticFail/c14/forestdiffusion/forest-c14-20260502_031135/_fd_train.py b/syntheticFail/c14/forestdiffusion/forest-c14-20260502_031135/_fd_train.py new file mode 100644 index 0000000000000000000000000000000000000000..201b71202ca811f88a737aeb9199aa5e9e7c18cb --- /dev/null +++ b/syntheticFail/c14/forestdiffusion/forest-c14-20260502_031135/_fd_train.py @@ -0,0 +1,28 @@ + +import shutil, json +shutil.copy(r'/work/output-Benchmark-trainonly-v1/c14/forestdiffusion/forest-c14-20260502_031135/_fd_X_host.npy', '/tmp/fd_X.npy') +with open(r'/work/output-Benchmark-trainonly-v1/c14/forestdiffusion/forest-c14-20260502_031135/_fd_meta_host.json') as f: + open('/tmp/fd_meta.json','w').write(f.read()) + +import numpy as np, joblib, json, os +from ForestDiffusion import ForestDiffusionModel +X = np.load("/tmp/fd_X.npy") +with open("/tmp/fd_meta.json") as f: + meta = json.load(f) +cat_indexes = meta["cat_indexes"] +print( + "[ForestDiffusion] train config: " + f"rows={X.shape[0]} cols={X.shape[1]} n_t=20 " + f"n_estimators=100 duplicate_K=20 n_jobs=2 " + f"xgb_verbosity=1", + flush=True, +) +m = ForestDiffusionModel( + X, n_t=20, n_estimators=100, duplicate_K=20, n_jobs=2, + model="xgboost", max_depth=6, tree_method="hist", cat_indexes=cat_indexes, + verbosity=1, +) +joblib.dump((m, meta), "/tmp/fd_model.joblib") +print("ForestDiffusion train OK") + +shutil.copy('/tmp/fd_model.joblib', r'/work/output-Benchmark-trainonly-v1/c14/forestdiffusion/forest-c14-20260502_031135/forestdiffusion_model.joblib') diff --git a/syntheticFail/c14/forestdiffusion/forest-c14-20260502_031135/input_snapshot.json b/syntheticFail/c14/forestdiffusion/forest-c14-20260502_031135/input_snapshot.json new file mode 100644 index 0000000000000000000000000000000000000000..da75ebd3de5610812999099d4179427f28c4b736 --- /dev/null +++ b/syntheticFail/c14/forestdiffusion/forest-c14-20260502_031135/input_snapshot.json @@ -0,0 +1,36 @@ +{ + "dataset_id": "c14", + "model": "forestdiffusion", + "inputs": { + 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0000000000000000000000000000000000000000..5ef5eea2c5a69e3a2de767fc6083b4bf3c882c54 --- /dev/null +++ b/syntheticFail/c14/forestdiffusion/forest-c14-20260503_215604/_fd_train.py @@ -0,0 +1,28 @@ + +import shutil, json +shutil.copy(r'/work/output-Benchmark-trainonly-v1/c14/forestdiffusion/forest-c14-20260503_215604/_fd_X_host.npy', '/tmp/fd_X.npy') +with open(r'/work/output-Benchmark-trainonly-v1/c14/forestdiffusion/forest-c14-20260503_215604/_fd_meta_host.json') as f: + open('/tmp/fd_meta.json','w').write(f.read()) + +import numpy as np, joblib, json, os +from ForestDiffusion import ForestDiffusionModel +X = np.load("/tmp/fd_X.npy") +with open("/tmp/fd_meta.json") as f: + meta = json.load(f) +cat_indexes = meta["cat_indexes"] +print( + "[ForestDiffusion] train config: " + f"rows={X.shape[0]} cols={X.shape[1]} n_t=10 " + f"n_estimators=20 duplicate_K=5 n_jobs=1 " + f"max_depth=4 xgb_verbosity=1", + flush=True, +) +m = ForestDiffusionModel( + X, n_t=10, n_estimators=20, duplicate_K=5, n_jobs=1, + model="xgboost", max_depth=4, tree_method="hist", cat_indexes=cat_indexes, + verbosity=1, +) +joblib.dump((m, meta), "/tmp/fd_model.joblib") +print("ForestDiffusion train OK") + +shutil.copy('/tmp/fd_model.joblib', r'/work/output-Benchmark-trainonly-v1/c14/forestdiffusion/forest-c14-20260503_215604/forestdiffusion_model.joblib') diff --git a/syntheticFail/c14/forestdiffusion/forest-c14-20260503_215604/input_snapshot.json b/syntheticFail/c14/forestdiffusion/forest-c14-20260503_215604/input_snapshot.json new file mode 100644 index 0000000000000000000000000000000000000000..da75ebd3de5610812999099d4179427f28c4b736 --- /dev/null +++ b/syntheticFail/c14/forestdiffusion/forest-c14-20260503_215604/input_snapshot.json @@ -0,0 +1,36 @@ +{ + "dataset_id": "c14", + "model": "forestdiffusion", + "inputs": { + "train_csv": { + "path": "/data/jialinzhang/SynthesizePipeline-server/DatasetNew/c14/c14-train.csv", + "exists": true, + "size": 31957080, + "sha256": 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_f: + _f.write('#!/usr/bin/env python3 +import subprocess, sys +ppid = sys.argv[-1] +out = subprocess.check_output(["ps", "-o", "pid=", "--ppid", str(ppid)], text=True) +print(out, end="") +') +os.chmod('/tmp/pgrep', 0o755) +os.environ['PATH'] = '/tmp:' + os.environ.get('PATH', '') +shutil.copy(r'/work/output-Benchmark-trainonly-v1/c14/forestdiffusion/forest-c14-20260504_064540/_fd_X_host.npy', '/tmp/fd_X.npy') +with open(r'/work/output-Benchmark-trainonly-v1/c14/forestdiffusion/forest-c14-20260504_064540/_fd_meta_host.json') as f: + open('/tmp/fd_meta.json','w').write(f.read()) + +import numpy as np, joblib, json, os +from ForestDiffusion import ForestDiffusionModel +X = np.load("/tmp/fd_X.npy") +with open("/tmp/fd_meta.json") as f: + meta = json.load(f) +cat_indexes = meta["cat_indexes"] +print( + "[ForestDiffusion] train config: " + f"rows={X.shape[0]} cols={X.shape[1]} n_t=4 " + f"n_estimators=5 duplicate_K=2 n_jobs=1 " + f"max_depth=3 xgb_verbosity=1", + flush=True, +) +m = ForestDiffusionModel( + X, n_t=4, n_estimators=5, duplicate_K=2, n_jobs=1, + model="xgboost", max_depth=3, tree_method="hist", cat_indexes=cat_indexes, + verbosity=1, +) +joblib.dump((m, meta), "/tmp/fd_model.joblib") +print("ForestDiffusion train OK") + +shutil.copy('/tmp/fd_model.joblib', r'/work/output-Benchmark-trainonly-v1/c14/forestdiffusion/forest-c14-20260504_064540/forestdiffusion_model.joblib') diff --git a/syntheticFail/c14/forestdiffusion/forest-c14-20260504_064540/input_snapshot.json b/syntheticFail/c14/forestdiffusion/forest-c14-20260504_064540/input_snapshot.json new file mode 100644 index 0000000000000000000000000000000000000000..da75ebd3de5610812999099d4179427f28c4b736 --- /dev/null +++ b/syntheticFail/c14/forestdiffusion/forest-c14-20260504_064540/input_snapshot.json @@ -0,0 +1,36 @@ +{ + "dataset_id": "c14", + "model": "forestdiffusion", + "inputs": { + "train_csv": { + "path": "/data/jialinzhang/SynthesizePipeline-server/DatasetNew/c14/c14-train.csv", + "exists": true, + "size": 31957080, + "sha256": "4a11abde6a7fae9f8bb1a1d7e31035b9dd61587d2721749a52d78d65441fba5e" + }, + "val_csv": { + "path": "/data/jialinzhang/SynthesizePipeline-server/DatasetNew/c14/c14-val.csv", + "exists": true, + "size": 3995920, + "sha256": "b04d723eeb59d339a356302660463c129141b4213fefbf472ef48f8a4daa50c9" + }, + "test_csv": { + "path": "/data/jialinzhang/SynthesizePipeline-server/DatasetNew/c14/c14-test.csv", + "exists": true, + "size": 3995840, + "sha256": "4780b90e46f6b440983cc9315cad8bca0e86bd26d8877ffae7111258fba0a132" + }, + "profile_json": { + "path": "/data/jialinzhang/SynthesizePipeline-server/DatasetNew/artifacts/data_core/tabular/c14/c14-dataset_profile.json", + "exists": true, + "size": 9505, + "sha256": "577cc5830dcb336243555d3d41c06e2e7c0a63df2a9caccb7a0411b9332cde15" + }, + "contract_json": { + "path": "/data/jialinzhang/SynthesizePipeline-server/DatasetNew/artifacts/data_core/tabular/c14/c14-dataset_contract_v1.json", + "exists": true, + "size": 11901, + "sha256": "955197dff3346d55aaa999adf98d64c375a7e4ca9bc4bcfe626499c9cfea9a41" + } + } +} \ No newline at end of file diff --git a/syntheticFail/c14/forestdiffusion/forest-c14-20260504_064540/public_gate/normalized_schema_snapshot.json b/syntheticFail/c14/forestdiffusion/forest-c14-20260504_064540/public_gate/normalized_schema_snapshot.json new file mode 100644 index 0000000000000000000000000000000000000000..c43047a97cac9542e79dfa127e651be7146aa2a4 --- /dev/null +++ b/syntheticFail/c14/forestdiffusion/forest-c14-20260504_064540/public_gate/normalized_schema_snapshot.json @@ -0,0 +1,509 @@ +{ + "dataset_id": "c14", + "target_column": "target", + "task_type": "classification", + "columns": [ + { + "name": "id", + "role": "feature", + "semantic_type": "numeric", + "nullable": false, + "missing_tokens": [], + "parse_format": null, + "impute_strategy": "median", + "profile_stats": { + "missing_rate": 0.0, + "unique_count": 20000, + "unique_ratio": 0.083333, + "example_values": [ + "182547", 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_f: + _f.write("#!/usr/bin/env python3 +") + _f.write("import subprocess, sys +") + _f.write("ppid = sys.argv[-1] +") + _f.write("out = subprocess.check_output(['ps', '-o', 'pid=', '--ppid', str(ppid)], text=True) +") + _f.write("print(out, end='') +") +os.chmod('/tmp/pgrep', 0o755) +os.environ['PATH'] = '/tmp:' + os.environ.get('PATH', '') +shutil.copy(r'/work/output-Benchmark-trainonly-v1/c14/forestdiffusion/forest-c14-20260504_064737/_fd_X_host.npy', '/tmp/fd_X.npy') +with open(r'/work/output-Benchmark-trainonly-v1/c14/forestdiffusion/forest-c14-20260504_064737/_fd_meta_host.json') as f: + open('/tmp/fd_meta.json','w').write(f.read()) + +import numpy as np, joblib, json, os +from ForestDiffusion import ForestDiffusionModel +X = np.load("/tmp/fd_X.npy") +with open("/tmp/fd_meta.json") as f: + meta = json.load(f) +cat_indexes = meta["cat_indexes"] +print( + "[ForestDiffusion] train config: " + f"rows={X.shape[0]} cols={X.shape[1]} n_t=4 " + f"n_estimators=5 duplicate_K=2 n_jobs=1 " + f"max_depth=3 xgb_verbosity=1", + flush=True, +) +m = ForestDiffusionModel( + X, n_t=4, n_estimators=5, duplicate_K=2, n_jobs=1, + model="xgboost", max_depth=3, tree_method="hist", cat_indexes=cat_indexes, + verbosity=1, +) +joblib.dump((m, meta), "/tmp/fd_model.joblib") +print("ForestDiffusion train OK") + +shutil.copy('/tmp/fd_model.joblib', r'/work/output-Benchmark-trainonly-v1/c14/forestdiffusion/forest-c14-20260504_064737/forestdiffusion_model.joblib') diff --git a/syntheticFail/c14/forestdiffusion/forest-c14-20260504_064737/input_snapshot.json b/syntheticFail/c14/forestdiffusion/forest-c14-20260504_064737/input_snapshot.json new file mode 100644 index 0000000000000000000000000000000000000000..da75ebd3de5610812999099d4179427f28c4b736 --- /dev/null +++ b/syntheticFail/c14/forestdiffusion/forest-c14-20260504_064737/input_snapshot.json @@ -0,0 +1,36 @@ +{ + "dataset_id": "c14", + "model": "forestdiffusion", + "inputs": { + "train_csv": { + "path": 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0000000000000000000000000000000000000000..86a85e9a655225f188fc3367f959d62518b05788 --- /dev/null +++ b/syntheticFail/c14/forestdiffusion/forest-c14-20260504_064941/_fd_train.py @@ -0,0 +1,41 @@ + +import os, shutil, json +with open('/tmp/pgrep', 'w') as _f: + _f.write("#!/usr/bin/env python3 +") + _f.write("import subprocess, sys +") + _f.write("ppid = sys.argv[-1] +") + _f.write("out = subprocess.check_output(['ps', '-o', 'pid=', '--ppid', str(ppid)], text=True) +") + _f.write("print(out, end='') +") +os.chmod('/tmp/pgrep', 0o755) +os.environ['PATH'] = '/tmp:' + os.environ.get('PATH', '') +shutil.copy(r'/work/output-Benchmark-trainonly-v1/c14/forestdiffusion/forest-c14-20260504_064941/_fd_X_host.npy', '/tmp/fd_X.npy') +with open(r'/work/output-Benchmark-trainonly-v1/c14/forestdiffusion/forest-c14-20260504_064941/_fd_meta_host.json') as f: + open('/tmp/fd_meta.json','w').write(f.read()) + +import numpy as np, joblib, json, os +from ForestDiffusion import ForestDiffusionModel +X = np.load("/tmp/fd_X.npy") +with open("/tmp/fd_meta.json") as f: + meta = json.load(f) +cat_indexes = meta["cat_indexes"] +print( + "[ForestDiffusion] train config: " + f"rows={X.shape[0]} cols={X.shape[1]} n_t=4 " + f"n_estimators=5 duplicate_K=2 n_jobs=1 " + f"max_depth=3 xgb_verbosity=1", + flush=True, +) +m = ForestDiffusionModel( + X, n_t=4, n_estimators=5, duplicate_K=2, n_jobs=1, + model="xgboost", max_depth=3, tree_method="hist", cat_indexes=cat_indexes, + verbosity=1, +) +joblib.dump((m, meta), "/tmp/fd_model.joblib") +print("ForestDiffusion train OK") + +shutil.copy('/tmp/fd_model.joblib', r'/work/output-Benchmark-trainonly-v1/c14/forestdiffusion/forest-c14-20260504_064941/forestdiffusion_model.joblib') diff --git a/syntheticFail/c14/forestdiffusion/forest-c14-20260504_064941/input_snapshot.json b/syntheticFail/c14/forestdiffusion/forest-c14-20260504_064941/input_snapshot.json new file mode 100644 index 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0000000000000000000000000000000000000000..e3fac9fb7f725d3d6644c5a044d93fa241ccb748 --- /dev/null +++ b/syntheticFail/c14/forestdiffusion/forest-c14-20260504_065128/_fd_X_host.npy @@ -0,0 +1,3 @@ +version https://git-lfs.github.com/spec/v1 +oid sha256:6c32085141c7198d33fce6a1e8392ac6fe85f12280364929b3bc4e9b77632b1e +size 204928 diff --git a/syntheticFail/c14/forestdiffusion/forest-c14-20260504_065128/_fd_meta_host.json b/syntheticFail/c14/forestdiffusion/forest-c14-20260504_065128/_fd_meta_host.json new file mode 100644 index 0000000000000000000000000000000000000000..4646e31cc069da465f39a4a0bf0f9ef92fc82aa4 --- /dev/null +++ b/syntheticFail/c14/forestdiffusion/forest-c14-20260504_065128/_fd_meta_host.json @@ -0,0 +1 @@ +{"column_names": ["id", "bin_0", "bin_1", "bin_2", "bin_3", "bin_4", "nom_0", "nom_1", "nom_2", "nom_3", "nom_4", "nom_5", "nom_6", "nom_7", "nom_8", "nom_9", "ord_0", "ord_1", "ord_2", "ord_3", "ord_4", "ord_5", "day", "month", "target"], "cat_indexes": [1, 2, 3, 4, 5, 6, 7, 8, 9, 10, 11, 12, 13, 14, 15, 17, 18, 19, 20, 21]} \ No newline at end of file diff --git a/syntheticFail/c14/forestdiffusion/forest-c14-20260504_065128/_fd_train.py b/syntheticFail/c14/forestdiffusion/forest-c14-20260504_065128/_fd_train.py new file mode 100644 index 0000000000000000000000000000000000000000..0944aa0a94c3a3c97a456bfcffdc4e103674a844 --- /dev/null +++ b/syntheticFail/c14/forestdiffusion/forest-c14-20260504_065128/_fd_train.py @@ -0,0 +1,36 @@ + +import os, shutil, json +with open('/tmp/pgrep', 'w') as _f: + _f.write("#!/usr/bin/env python3\n") + _f.write("import subprocess, sys\n") + _f.write("ppid = sys.argv[-1]\n") + _f.write("out = subprocess.check_output(['ps', '-o', 'pid=', '--ppid', str(ppid)], text=True)\n") + _f.write("print(out, end='')\n") +os.chmod('/tmp/pgrep', 0o755) +os.environ['PATH'] = '/tmp:' + os.environ.get('PATH', '') +shutil.copy(r'/work/output-Benchmark-trainonly-v1/c14/forestdiffusion/forest-c14-20260504_065128/_fd_X_host.npy', '/tmp/fd_X.npy') +with open(r'/work/output-Benchmark-trainonly-v1/c14/forestdiffusion/forest-c14-20260504_065128/_fd_meta_host.json') as f: + open('/tmp/fd_meta.json','w').write(f.read()) + +import numpy as np, joblib, json, os +from ForestDiffusion import ForestDiffusionModel +X = np.load("/tmp/fd_X.npy") +with open("/tmp/fd_meta.json") as f: + meta = json.load(f) +cat_indexes = meta["cat_indexes"] +print( + "[ForestDiffusion] train config: " + f"rows={X.shape[0]} cols={X.shape[1]} n_t=4 " + f"n_estimators=5 duplicate_K=2 n_jobs=1 " + f"max_depth=3 xgb_verbosity=1", + flush=True, +) +m = ForestDiffusionModel( + X, n_t=4, n_estimators=5, duplicate_K=2, n_jobs=1, + model="xgboost", max_depth=3, tree_method="hist", cat_indexes=cat_indexes, + verbosity=1, +) +joblib.dump((m, meta), "/tmp/fd_model.joblib") +print("ForestDiffusion train OK") + +shutil.copy('/tmp/fd_model.joblib', r'/work/output-Benchmark-trainonly-v1/c14/forestdiffusion/forest-c14-20260504_065128/forestdiffusion_model.joblib') diff --git a/syntheticFail/c14/forestdiffusion/forest-c14-20260504_065128/input_snapshot.json b/syntheticFail/c14/forestdiffusion/forest-c14-20260504_065128/input_snapshot.json new file mode 100644 index 0000000000000000000000000000000000000000..da75ebd3de5610812999099d4179427f28c4b736 --- /dev/null +++ b/syntheticFail/c14/forestdiffusion/forest-c14-20260504_065128/input_snapshot.json @@ -0,0 +1,36 @@ +{ + "dataset_id": "c14", + "model": "forestdiffusion", + "inputs": { + "train_csv": { + "path": "/data/jialinzhang/SynthesizePipeline-server/DatasetNew/c14/c14-train.csv", + "exists": true, + "size": 31957080, + "sha256": "4a11abde6a7fae9f8bb1a1d7e31035b9dd61587d2721749a52d78d65441fba5e" + }, + "val_csv": { + "path": "/data/jialinzhang/SynthesizePipeline-server/DatasetNew/c14/c14-val.csv", + "exists": true, + "size": 3995920, + "sha256": 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0000000000000000000000000000000000000000..3eb46bc24f1b48d71ab713375a6c8341b90019e9 --- /dev/null +++ b/syntheticFail/c14/tabbyflow/tabbyflow-c14-20260501_033959/_tabbyflow_gen.py @@ -0,0 +1,34 @@ + +import os, shutil, subprocess, sys +root = r"/workspace/ef-vfm" +name = r"pipeline_c14" +src = r"/work/output-Benchmark-trainonly-v1/c14/tabbyflow/tabbyflow-c14-20260501_033959/tabular_bundle/pipeline_c14" +dst_data = os.path.join(root, "data", name) +shutil.rmtree(dst_data, ignore_errors=True) +shutil.copytree(src, dst_data) +dst_syn = os.path.join(root, "synthetic", name) +os.makedirs(dst_syn, exist_ok=True) +for fn in ("real.csv", "test.csv", "val.csv"): + shutil.copy(os.path.join(src, fn), os.path.join(dst_syn, fn)) +os.chdir(root) +os.environ["PYTHONPATH"] = root + os.pathsep + os.environ.get("PYTHONPATH", "") +os.environ.setdefault("EFVFM_SAMPLE_BATCH_SIZE", "128") +subprocess.check_call([ + sys.executable, "main.py", + "--dataname", name, "--mode", "test", "--gpu", "0", + "--no_wandb", "--exp_name", r"adapter_efvfm", + "--ckpt_path", r"/workspace/ef-vfm/ef_vfm/ckpt/pipeline_c14/adapter_efvfm/model_500.pt", + "--num_samples_to_generate", str(int(240000)), +]) +base = os.path.join(root, "ef_vfm", "result", name, r"adapter_efvfm") +best = None +best_t = -1.0 +for r, _, files in os.walk(base): + if "samples.csv" in files: + p = os.path.join(r, "samples.csv") + t = os.path.getmtime(p) + if t > best_t: + best_t, best = t, p +if not best: + raise SystemExit("tabbyflow: no samples.csv in " + base) +shutil.copy(best, r"/work/output-Benchmark-trainonly-v1/c14/tabbyflow/tabbyflow-c14-240000-20260504_055926.csv") diff --git a/syntheticFail/c14/tabbyflow/tabbyflow-c14-20260501_033959/_tabbyflow_train.py b/syntheticFail/c14/tabbyflow/tabbyflow-c14-20260501_033959/_tabbyflow_train.py new file mode 100644 index 0000000000000000000000000000000000000000..e33594b8053b236b2dbac5c27c3d33c48ab7a5da --- /dev/null +++ b/syntheticFail/c14/tabbyflow/tabbyflow-c14-20260501_033959/_tabbyflow_train.py @@ -0,0 +1,22 @@ + +import os, shutil, subprocess, sys +root = r"/workspace/ef-vfm" +name = r"pipeline_c14" +src = r"/work/output-Benchmark-trainonly-v1/c14/tabbyflow/tabbyflow-c14-20260501_033959/tabular_bundle/pipeline_c14" +os.makedirs(os.path.join(root, "data", name), exist_ok=True) +dst_data = os.path.join(root, "data", name) +dst_syn = os.path.join(root, "synthetic", name) +shutil.rmtree(dst_data, ignore_errors=True) +shutil.copytree(src, dst_data) +os.makedirs(dst_syn, exist_ok=True) +for fn in ("real.csv", "test.csv", "val.csv"): + shutil.copy(os.path.join(src, fn), os.path.join(dst_syn, fn)) +os.chdir(root) +os.environ["PYTHONPATH"] = root + os.pathsep + os.environ.get("PYTHONPATH", "") +os.environ["EFVFM_SMOKE_STEPS"] = "500" +os.environ["EFVFM_ADAPTER_TRAIN"] = "1" +subprocess.check_call([ + sys.executable, "main.py", + "--dataname", name, "--mode", "train", "--gpu", "0", + "--no_wandb", "--exp_name", r"adapter_efvfm", +]) diff --git a/syntheticFail/c14/tabbyflow/tabbyflow-c14-20260501_033959/gen_20260504_054619.log b/syntheticFail/c14/tabbyflow/tabbyflow-c14-20260501_033959/gen_20260504_054619.log new file mode 100644 index 0000000000000000000000000000000000000000..6f9cb5579f99719226e0957a61319e7bd44abaaf --- /dev/null +++ b/syntheticFail/c14/tabbyflow/tabbyflow-c14-20260501_033959/gen_20260504_054619.log @@ -0,0 +1,3 @@ +version https://git-lfs.github.com/spec/v1 +oid sha256:c44b25deb5dffdec4fe1f691548204a77e9d7138c5cba44be5ba4e05927eb41a +size 17539 diff --git a/syntheticFail/c14/tabbyflow/tabbyflow-c14-20260501_033959/gen_20260504_055737.log b/syntheticFail/c14/tabbyflow/tabbyflow-c14-20260501_033959/gen_20260504_055737.log new file mode 100644 index 0000000000000000000000000000000000000000..16b114206cb348c7b322b33c3251cc68e2f330f0 --- /dev/null +++ b/syntheticFail/c14/tabbyflow/tabbyflow-c14-20260501_033959/gen_20260504_055737.log @@ -0,0 +1,3 @@ +version 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sha256:d9b7f2fe49e7531047723273dcbccd3c512c0fa436d0923e37642df167967bda +size 16349714 diff --git a/syntheticFail/c14/tabbyflow/tabbyflow-c14-20260501_033959/tabular_bundle/pipeline_c14/val.csv b/syntheticFail/c14/tabbyflow/tabbyflow-c14-20260501_033959/tabular_bundle/pipeline_c14/val.csv new file mode 100644 index 0000000000000000000000000000000000000000..e0727b62589883f82aac108f21c9b2ad3a885688 --- /dev/null +++ b/syntheticFail/c14/tabbyflow/tabbyflow-c14-20260501_033959/tabular_bundle/pipeline_c14/val.csv @@ -0,0 +1,3 @@ +version https://git-lfs.github.com/spec/v1 +oid sha256:d9b7f2fe49e7531047723273dcbccd3c512c0fa436d0923e37642df167967bda +size 16349714 diff --git a/syntheticFail/c14/tabbyflow/tabbyflow-c14-20260501_033959/tabular_bundle/pipeline_c14/y_test.npy b/syntheticFail/c14/tabbyflow/tabbyflow-c14-20260501_033959/tabular_bundle/pipeline_c14/y_test.npy new file mode 100644 index 0000000000000000000000000000000000000000..b819b291ffd95c81962c168db9cfbe50216f7e27 --- /dev/null +++ b/syntheticFail/c14/tabbyflow/tabbyflow-c14-20260501_033959/tabular_bundle/pipeline_c14/y_test.npy @@ -0,0 +1,3 @@ +version https://git-lfs.github.com/spec/v1 +oid sha256:0f723b0138e622255ce8db1d27cf9b51554364c371989801193edd4c87fc0921 +size 1920128 diff --git a/syntheticFail/c14/tabbyflow/tabbyflow-c14-20260501_033959/tabular_bundle/pipeline_c14/y_train.npy b/syntheticFail/c14/tabbyflow/tabbyflow-c14-20260501_033959/tabular_bundle/pipeline_c14/y_train.npy new file mode 100644 index 0000000000000000000000000000000000000000..b819b291ffd95c81962c168db9cfbe50216f7e27 --- /dev/null +++ b/syntheticFail/c14/tabbyflow/tabbyflow-c14-20260501_033959/tabular_bundle/pipeline_c14/y_train.npy @@ -0,0 +1,3 @@ +version https://git-lfs.github.com/spec/v1 +oid sha256:0f723b0138e622255ce8db1d27cf9b51554364c371989801193edd4c87fc0921 +size 1920128 diff --git a/syntheticFail/c14/tabbyflow/tabbyflow-c14-20260501_033959/tabular_bundle/pipeline_c14/y_val.npy b/syntheticFail/c14/tabbyflow/tabbyflow-c14-20260501_033959/tabular_bundle/pipeline_c14/y_val.npy new file mode 100644 index 0000000000000000000000000000000000000000..b819b291ffd95c81962c168db9cfbe50216f7e27 --- /dev/null +++ b/syntheticFail/c14/tabbyflow/tabbyflow-c14-20260501_033959/tabular_bundle/pipeline_c14/y_val.npy @@ -0,0 +1,3 @@ +version https://git-lfs.github.com/spec/v1 +oid sha256:0f723b0138e622255ce8db1d27cf9b51554364c371989801193edd4c87fc0921 +size 1920128 diff --git a/syntheticFail/c14/tabbyflow/tabbyflow-c14-20260501_033959/train_20260501_034013.log b/syntheticFail/c14/tabbyflow/tabbyflow-c14-20260501_033959/train_20260501_034013.log new file mode 100644 index 0000000000000000000000000000000000000000..81497a0d0e833de0b491825de9f08ef7d0718de2 --- /dev/null +++ b/syntheticFail/c14/tabbyflow/tabbyflow-c14-20260501_033959/train_20260501_034013.log @@ -0,0 +1,3 @@ +version https://git-lfs.github.com/spec/v1 +oid sha256:73120e50d8da3b720c73db7289eb5df7d214dd40ad004473128ba1616e87eb64 +size 7211236 diff --git a/syntheticFail/c14/tabddpm/tabddpm-c14-20260501_043200/_tabddpm_sample_r0.py b/syntheticFail/c14/tabddpm/tabddpm-c14-20260501_043200/_tabddpm_sample_r0.py new file mode 100644 index 0000000000000000000000000000000000000000..9a40ae243e859636525d4493ce8c8b90e0764487 --- /dev/null +++ b/syntheticFail/c14/tabddpm/tabddpm-c14-20260501_043200/_tabddpm_sample_r0.py @@ -0,0 +1,66 @@ +import os, sys, subprocess, json +import numpy as np +import pandas as pd + +tabddpm_root = "/workspace/tabddpm/code" +assert os.path.isdir(tabddpm_root), f"TabDDPM source not mounted: {tabddpm_root}" +env = os.environ.copy() +env["PYTHONPATH"] = tabddpm_root + (os.pathsep + env.get("PYTHONPATH", "")) + +# Reuse the compat wrapper (patches collections.Sequence for skorch) +wrapper = os.path.join(tabddpm_root, "_compat_run.py") +if not os.path.exists(wrapper): + with open(wrapper, "w") as f: + f.write( + "import collections, collections.abc\n" + "for _a in ('Sequence','MutableSequence','MutableMapping','Mapping'," + "'MutableSet','Set','Callable','Iterable','Iterator'):\n" + " if not hasattr(collections, _a): setattr(collections, _a, getattr(collections.abc, _a, None))\n" + "import sys, runpy\n" + "sys.argv = sys.argv[1:]\n" + "runpy.run_path(sys.argv[0], run_name='__main__')\n" + ) + +print(f"[TabDDPM] Sampling 240000 rows") +ret = subprocess.run( + [sys.executable, wrapper, "scripts/pipeline.py", + "--config", "/work/output-Benchmark-trainonly-v1/c14/tabddpm/tabddpm-c14-20260501_043200/config_sample_20260501_043336_r0.toml", + "--sample"], + cwd=tabddpm_root, + env=env +) +if ret.returncode != 0: + sys.exit(ret.returncode) + +# 将 .npy 输出转为 CSV（npy 在 TabDDPM 的 parent_dir，即 npy_dir） +info_path = "/work/output-Benchmark-trainonly-v1/c14/tabddpm/tabddpm-c14-20260501_043200/data/info.json" +with open(info_path) as f: + info = json.load(f) + +output_dir = "/work/output-Benchmark-trainonly-v1/c14/tabddpm/tabddpm-c14-20260501_043200/output" +col_names = info.get("column_names", []) + +parts = [] +x_num_path = os.path.join(output_dir, "X_num_train.npy") +x_cat_path = os.path.join(output_dir, "X_cat_train.npy") +y_path = os.path.join(output_dir, "y_train.npy") + +if os.path.exists(x_num_path): + parts.append(np.load(x_num_path, allow_pickle=True)) +if os.path.exists(x_cat_path): + parts.append(np.load(x_cat_path, allow_pickle=True).astype(float)) +if os.path.exists(y_path): + y = np.load(y_path, allow_pickle=True) + parts.append(y.reshape(-1, 1) if y.ndim == 1 else y) + +if parts: + combined = np.concatenate(parts, axis=1) + if col_names and len(col_names) == combined.shape[1]: + df = pd.DataFrame(combined, columns=col_names) + else: + df = pd.DataFrame(combined) + df.to_csv("/work/output-Benchmark-trainonly-v1/c14/tabddpm/tabddpm-c14-20260501_043200/tabddpm-c14-240000-20260501_043336.csv", index=False) + print(f"[TabDDPM] Saved {len(df)} rows -> /work/output-Benchmark-trainonly-v1/c14/tabddpm/tabddpm-c14-20260501_043200/tabddpm-c14-240000-20260501_043336.csv") +else: + print("[TabDDPM] WARNING: No output .npy files found") + sys.exit(1) diff --git a/syntheticFail/c14/tabddpm/tabddpm-c14-20260501_043200/_tabddpm_sample_r1.py b/syntheticFail/c14/tabddpm/tabddpm-c14-20260501_043200/_tabddpm_sample_r1.py new file mode 100644 index 0000000000000000000000000000000000000000..e9c021581648677847af53379466fb32d3367c1a --- /dev/null +++ b/syntheticFail/c14/tabddpm/tabddpm-c14-20260501_043200/_tabddpm_sample_r1.py @@ -0,0 +1,66 @@ +import os, sys, subprocess, json +import numpy as np +import pandas as pd + +tabddpm_root = "/workspace/tabddpm/code" +assert os.path.isdir(tabddpm_root), f"TabDDPM source not mounted: {tabddpm_root}" +env = os.environ.copy() +env["PYTHONPATH"] = tabddpm_root + (os.pathsep + env.get("PYTHONPATH", "")) + +# Reuse the compat wrapper (patches collections.Sequence for skorch) +wrapper = os.path.join(tabddpm_root, "_compat_run.py") +if not os.path.exists(wrapper): + with open(wrapper, "w") as f: + f.write( + "import collections, collections.abc\n" + "for _a in ('Sequence','MutableSequence','MutableMapping','Mapping'," + "'MutableSet','Set','Callable','Iterable','Iterator'):\n" + " if not hasattr(collections, _a): setattr(collections, _a, getattr(collections.abc, _a, None))\n" + "import sys, runpy\n" + "sys.argv = sys.argv[1:]\n" + "runpy.run_path(sys.argv[0], run_name='__main__')\n" + ) + +print(f"[TabDDPM] Sampling 240000 rows") +ret = subprocess.run( + [sys.executable, wrapper, "scripts/pipeline.py", + "--config", "/work/output-Benchmark-trainonly-v1/c14/tabddpm/tabddpm-c14-20260501_043200/config_sample_20260501_043336_r1.toml", + "--sample"], + cwd=tabddpm_root, + env=env +) +if ret.returncode != 0: + sys.exit(ret.returncode) + +# 将 .npy 输出转为 CSV（npy 在 TabDDPM 的 parent_dir，即 npy_dir） +info_path = "/work/output-Benchmark-trainonly-v1/c14/tabddpm/tabddpm-c14-20260501_043200/data/info.json" +with open(info_path) as f: + info = json.load(f) + +output_dir = "/work/output-Benchmark-trainonly-v1/c14/tabddpm/tabddpm-c14-20260501_043200/output" +col_names = info.get("column_names", []) + +parts = [] +x_num_path = os.path.join(output_dir, "X_num_train.npy") +x_cat_path = os.path.join(output_dir, "X_cat_train.npy") +y_path = os.path.join(output_dir, "y_train.npy") + +if os.path.exists(x_num_path): + parts.append(np.load(x_num_path, allow_pickle=True)) +if os.path.exists(x_cat_path): + parts.append(np.load(x_cat_path, allow_pickle=True).astype(float)) +if os.path.exists(y_path): + y = np.load(y_path, allow_pickle=True) + parts.append(y.reshape(-1, 1) if y.ndim == 1 else y) + +if parts: + combined = np.concatenate(parts, axis=1) + if col_names and len(col_names) == combined.shape[1]: + df = pd.DataFrame(combined, columns=col_names) + else: + df = pd.DataFrame(combined) + df.to_csv("/work/output-Benchmark-trainonly-v1/c14/tabddpm/tabddpm-c14-20260501_043200/tabddpm-c14-240000-20260501_043336.csv", index=False) + print(f"[TabDDPM] Saved {len(df)} rows -> /work/output-Benchmark-trainonly-v1/c14/tabddpm/tabddpm-c14-20260501_043200/tabddpm-c14-240000-20260501_043336.csv") +else: + print("[TabDDPM] WARNING: No output .npy files found") + sys.exit(1) diff --git a/syntheticFail/c14/tabddpm/tabddpm-c14-20260501_043200/_tabddpm_sample_r2.py b/syntheticFail/c14/tabddpm/tabddpm-c14-20260501_043200/_tabddpm_sample_r2.py new file mode 100644 index 0000000000000000000000000000000000000000..3fe20cfa9ef8158c16bdbd1235946e4f2183f4c3 --- /dev/null +++ b/syntheticFail/c14/tabddpm/tabddpm-c14-20260501_043200/_tabddpm_sample_r2.py @@ -0,0 +1,66 @@ +import os, sys, subprocess, json +import numpy as np +import pandas as pd + +tabddpm_root = "/workspace/tabddpm/code" +assert os.path.isdir(tabddpm_root), f"TabDDPM source not mounted: {tabddpm_root}" +env = os.environ.copy() +env["PYTHONPATH"] = tabddpm_root + (os.pathsep + env.get("PYTHONPATH", "")) + +# Reuse the compat wrapper (patches collections.Sequence for skorch) +wrapper = os.path.join(tabddpm_root, "_compat_run.py") +if not os.path.exists(wrapper): + with open(wrapper, "w") as f: + f.write( + "import collections, collections.abc\n" + "for _a in ('Sequence','MutableSequence','MutableMapping','Mapping'," + "'MutableSet','Set','Callable','Iterable','Iterator'):\n" + " if not hasattr(collections, _a): setattr(collections, _a, getattr(collections.abc, _a, None))\n" + "import sys, runpy\n" + "sys.argv = sys.argv[1:]\n" + "runpy.run_path(sys.argv[0], run_name='__main__')\n" + ) + +print(f"[TabDDPM] Sampling 240000 rows") +ret = subprocess.run( + [sys.executable, wrapper, "scripts/pipeline.py", + "--config", "/work/output-Benchmark-trainonly-v1/c14/tabddpm/tabddpm-c14-20260501_043200/config_sample_20260501_043336_r2.toml", + "--sample"], + cwd=tabddpm_root, + env=env +) +if ret.returncode != 0: + sys.exit(ret.returncode) + +# 将 .npy 输出转为 CSV（npy 在 TabDDPM 的 parent_dir，即 npy_dir） +info_path = "/work/output-Benchmark-trainonly-v1/c14/tabddpm/tabddpm-c14-20260501_043200/data/info.json" +with open(info_path) as f: + info = json.load(f) + +output_dir = "/work/output-Benchmark-trainonly-v1/c14/tabddpm/tabddpm-c14-20260501_043200/output" +col_names = info.get("column_names", []) + +parts = [] +x_num_path = os.path.join(output_dir, "X_num_train.npy") +x_cat_path = os.path.join(output_dir, "X_cat_train.npy") +y_path = os.path.join(output_dir, "y_train.npy") + +if os.path.exists(x_num_path): + parts.append(np.load(x_num_path, allow_pickle=True)) +if os.path.exists(x_cat_path): + parts.append(np.load(x_cat_path, allow_pickle=True).astype(float)) +if os.path.exists(y_path): + y = np.load(y_path, allow_pickle=True) + parts.append(y.reshape(-1, 1) if y.ndim == 1 else y) + +if parts: + combined = np.concatenate(parts, axis=1) + if col_names and len(col_names) == combined.shape[1]: + df = pd.DataFrame(combined, columns=col_names) + else: + df = pd.DataFrame(combined) + df.to_csv("/work/output-Benchmark-trainonly-v1/c14/tabddpm/tabddpm-c14-20260501_043200/tabddpm-c14-240000-20260501_043336.csv", index=False) + print(f"[TabDDPM] Saved {len(df)} rows -> /work/output-Benchmark-trainonly-v1/c14/tabddpm/tabddpm-c14-20260501_043200/tabddpm-c14-240000-20260501_043336.csv") +else: + print("[TabDDPM] WARNING: No output .npy files found") + sys.exit(1) diff --git a/syntheticFail/c14/tabddpm/tabddpm-c14-20260501_043200/_tabddpm_sample_r3.py b/syntheticFail/c14/tabddpm/tabddpm-c14-20260501_043200/_tabddpm_sample_r3.py new file mode 100644 index 0000000000000000000000000000000000000000..5e7db0e1bedd93a7a026615b50fcae72be78f8a0 --- /dev/null +++ b/syntheticFail/c14/tabddpm/tabddpm-c14-20260501_043200/_tabddpm_sample_r3.py @@ -0,0 +1,66 @@ +import os, sys, subprocess, json +import numpy as np +import pandas as pd + +tabddpm_root = "/workspace/tabddpm/code" +assert os.path.isdir(tabddpm_root), f"TabDDPM source not mounted: {tabddpm_root}" +env = os.environ.copy() +env["PYTHONPATH"] = tabddpm_root + (os.pathsep + env.get("PYTHONPATH", "")) + +# Reuse the compat wrapper (patches collections.Sequence for skorch) +wrapper = os.path.join(tabddpm_root, "_compat_run.py") +if not os.path.exists(wrapper): + with open(wrapper, "w") as f: + f.write( + "import collections, collections.abc\n" + "for _a in ('Sequence','MutableSequence','MutableMapping','Mapping'," + "'MutableSet','Set','Callable','Iterable','Iterator'):\n" + " if not hasattr(collections, _a): setattr(collections, _a, getattr(collections.abc, _a, None))\n" + "import sys, runpy\n" + "sys.argv = sys.argv[1:]\n" + "runpy.run_path(sys.argv[0], run_name='__main__')\n" + ) + +print(f"[TabDDPM] Sampling 240000 rows") +ret = subprocess.run( + [sys.executable, wrapper, "scripts/pipeline.py", + "--config", "/work/output-Benchmark-trainonly-v1/c14/tabddpm/tabddpm-c14-20260501_043200/config_sample_20260501_043336_r3.toml", + "--sample"], + cwd=tabddpm_root, + env=env +) +if ret.returncode != 0: + sys.exit(ret.returncode) + +# 将 .npy 输出转为 CSV（npy 在 TabDDPM 的 parent_dir，即 npy_dir） +info_path = "/work/output-Benchmark-trainonly-v1/c14/tabddpm/tabddpm-c14-20260501_043200/data/info.json" +with open(info_path) as f: + info = json.load(f) + +output_dir = "/work/output-Benchmark-trainonly-v1/c14/tabddpm/tabddpm-c14-20260501_043200/output" +col_names = info.get("column_names", []) + +parts = [] +x_num_path = os.path.join(output_dir, "X_num_train.npy") +x_cat_path = os.path.join(output_dir, "X_cat_train.npy") +y_path = os.path.join(output_dir, "y_train.npy") + +if os.path.exists(x_num_path): + parts.append(np.load(x_num_path, allow_pickle=True)) +if os.path.exists(x_cat_path): + parts.append(np.load(x_cat_path, allow_pickle=True).astype(float)) +if os.path.exists(y_path): + y = np.load(y_path, allow_pickle=True) + parts.append(y.reshape(-1, 1) if y.ndim == 1 else y) + +if parts: + combined = np.concatenate(parts, axis=1) + if col_names and len(col_names) == combined.shape[1]: + df = pd.DataFrame(combined, columns=col_names) + else: + df = pd.DataFrame(combined) + df.to_csv("/work/output-Benchmark-trainonly-v1/c14/tabddpm/tabddpm-c14-20260501_043200/tabddpm-c14-240000-20260501_043336.csv", index=False) + print(f"[TabDDPM] Saved {len(df)} rows -> /work/output-Benchmark-trainonly-v1/c14/tabddpm/tabddpm-c14-20260501_043200/tabddpm-c14-240000-20260501_043336.csv") +else: + print("[TabDDPM] WARNING: No output .npy files found") + sys.exit(1) diff --git a/syntheticFail/c14/tabddpm/tabddpm-c14-20260501_043200/_tabddpm_sample_r4.py b/syntheticFail/c14/tabddpm/tabddpm-c14-20260501_043200/_tabddpm_sample_r4.py new file mode 100644 index 0000000000000000000000000000000000000000..ff994df3b200d3a5bc62a30eeaef0ae7517a8a35 --- /dev/null +++ b/syntheticFail/c14/tabddpm/tabddpm-c14-20260501_043200/_tabddpm_sample_r4.py @@ -0,0 +1,66 @@ +import os, sys, subprocess, json +import numpy as np +import pandas as pd + +tabddpm_root = "/workspace/tabddpm/code" +assert os.path.isdir(tabddpm_root), f"TabDDPM source not mounted: {tabddpm_root}" +env = os.environ.copy() +env["PYTHONPATH"] = tabddpm_root + (os.pathsep + env.get("PYTHONPATH", "")) + +# Reuse the compat wrapper (patches collections.Sequence for skorch) +wrapper = os.path.join(tabddpm_root, "_compat_run.py") +if not os.path.exists(wrapper): + with open(wrapper, "w") as f: + f.write( + "import collections, collections.abc\n" + "for _a in ('Sequence','MutableSequence','MutableMapping','Mapping'," + "'MutableSet','Set','Callable','Iterable','Iterator'):\n" + " if not hasattr(collections, _a): setattr(collections, _a, getattr(collections.abc, _a, None))\n" + "import sys, runpy\n" + "sys.argv = sys.argv[1:]\n" + "runpy.run_path(sys.argv[0], run_name='__main__')\n" + ) + +print(f"[TabDDPM] Sampling 240000 rows") +ret = subprocess.run( + [sys.executable, wrapper, "scripts/pipeline.py", + "--config", "/work/output-Benchmark-trainonly-v1/c14/tabddpm/tabddpm-c14-20260501_043200/config_sample_20260501_043336_r4.toml", + "--sample"], + cwd=tabddpm_root, + env=env +) +if ret.returncode != 0: + sys.exit(ret.returncode) + +# 将 .npy 输出转为 CSV（npy 在 TabDDPM 的 parent_dir，即 npy_dir） +info_path = "/work/output-Benchmark-trainonly-v1/c14/tabddpm/tabddpm-c14-20260501_043200/data/info.json" +with open(info_path) as f: + info = json.load(f) + +output_dir = "/work/output-Benchmark-trainonly-v1/c14/tabddpm/tabddpm-c14-20260501_043200/output" +col_names = info.get("column_names", []) + +parts = [] +x_num_path = os.path.join(output_dir, "X_num_train.npy") +x_cat_path = os.path.join(output_dir, "X_cat_train.npy") +y_path = os.path.join(output_dir, "y_train.npy") + +if os.path.exists(x_num_path): + parts.append(np.load(x_num_path, allow_pickle=True)) +if os.path.exists(x_cat_path): + parts.append(np.load(x_cat_path, allow_pickle=True).astype(float)) +if os.path.exists(y_path): + y = np.load(y_path, allow_pickle=True) + parts.append(y.reshape(-1, 1) if y.ndim == 1 else y) + +if parts: + combined = np.concatenate(parts, axis=1) + if col_names and len(col_names) == combined.shape[1]: + df = pd.DataFrame(combined, columns=col_names) + else: + df = pd.DataFrame(combined) + df.to_csv("/work/output-Benchmark-trainonly-v1/c14/tabddpm/tabddpm-c14-20260501_043200/tabddpm-c14-240000-20260501_043336.csv", index=False) + print(f"[TabDDPM] Saved {len(df)} rows -> /work/output-Benchmark-trainonly-v1/c14/tabddpm/tabddpm-c14-20260501_043200/tabddpm-c14-240000-20260501_043336.csv") +else: + print("[TabDDPM] WARNING: No output .npy files found") + sys.exit(1) diff --git a/syntheticFail/c14/tabddpm/tabddpm-c14-20260501_043200/_tabddpm_sample_r5.py b/syntheticFail/c14/tabddpm/tabddpm-c14-20260501_043200/_tabddpm_sample_r5.py new file mode 100644 index 0000000000000000000000000000000000000000..e4abea7563c44563de188d615c10afccb67de3d9 --- /dev/null +++ b/syntheticFail/c14/tabddpm/tabddpm-c14-20260501_043200/_tabddpm_sample_r5.py @@ -0,0 +1,66 @@ +import os, sys, subprocess, json +import numpy as np +import pandas as pd + +tabddpm_root = "/workspace/tabddpm/code" +assert os.path.isdir(tabddpm_root), f"TabDDPM source not mounted: {tabddpm_root}" +env = os.environ.copy() +env["PYTHONPATH"] = tabddpm_root + (os.pathsep + env.get("PYTHONPATH", "")) + +# Reuse the compat wrapper (patches collections.Sequence for skorch) +wrapper = os.path.join(tabddpm_root, "_compat_run.py") +if not os.path.exists(wrapper): + with open(wrapper, "w") as f: + f.write( + "import collections, collections.abc\n" + "for _a in ('Sequence','MutableSequence','MutableMapping','Mapping'," + "'MutableSet','Set','Callable','Iterable','Iterator'):\n" + " if not hasattr(collections, _a): setattr(collections, _a, getattr(collections.abc, _a, None))\n" + "import sys, runpy\n" + "sys.argv = sys.argv[1:]\n" + "runpy.run_path(sys.argv[0], run_name='__main__')\n" + ) + +print(f"[TabDDPM] Sampling 240000 rows") +ret = subprocess.run( + [sys.executable, wrapper, "scripts/pipeline.py", + "--config", "/work/output-Benchmark-trainonly-v1/c14/tabddpm/tabddpm-c14-20260501_043200/config_sample_20260501_043336_r5.toml", + "--sample"], + cwd=tabddpm_root, + env=env +) +if ret.returncode != 0: + sys.exit(ret.returncode) + +# 将 .npy 输出转为 CSV（npy 在 TabDDPM 的 parent_dir，即 npy_dir） +info_path = "/work/output-Benchmark-trainonly-v1/c14/tabddpm/tabddpm-c14-20260501_043200/data/info.json" +with open(info_path) as f: + info = json.load(f) + +output_dir = "/work/output-Benchmark-trainonly-v1/c14/tabddpm/tabddpm-c14-20260501_043200/output" +col_names = info.get("column_names", []) + +parts = [] +x_num_path = os.path.join(output_dir, "X_num_train.npy") +x_cat_path = os.path.join(output_dir, "X_cat_train.npy") +y_path = os.path.join(output_dir, "y_train.npy") + +if os.path.exists(x_num_path): + parts.append(np.load(x_num_path, allow_pickle=True)) +if os.path.exists(x_cat_path): + parts.append(np.load(x_cat_path, allow_pickle=True).astype(float)) +if os.path.exists(y_path): + y = np.load(y_path, allow_pickle=True) + parts.append(y.reshape(-1, 1) if y.ndim == 1 else y) + +if parts: + combined = np.concatenate(parts, axis=1) + if col_names and len(col_names) == combined.shape[1]: + df = pd.DataFrame(combined, columns=col_names) + else: + df = pd.DataFrame(combined) + df.to_csv("/work/output-Benchmark-trainonly-v1/c14/tabddpm/tabddpm-c14-20260501_043200/tabddpm-c14-240000-20260501_043336.csv", index=False) + print(f"[TabDDPM] Saved {len(df)} rows -> /work/output-Benchmark-trainonly-v1/c14/tabddpm/tabddpm-c14-20260501_043200/tabddpm-c14-240000-20260501_043336.csv") +else: + print("[TabDDPM] WARNING: No output .npy files found") + sys.exit(1) diff --git a/syntheticFail/c14/tabddpm/tabddpm-c14-20260501_043200/_tabddpm_train.py b/syntheticFail/c14/tabddpm/tabddpm-c14-20260501_043200/_tabddpm_train.py new file mode 100644 index 0000000000000000000000000000000000000000..e69a791702836e71ea3615912c5980fbaa393844 --- /dev/null +++ b/syntheticFail/c14/tabddpm/tabddpm-c14-20260501_043200/_tabddpm_train.py @@ -0,0 +1,32 @@ +import os, sys, subprocess + +tabddpm_root = "/workspace/tabddpm/code" +assert os.path.isdir(tabddpm_root), f"TabDDPM source not mounted: {tabddpm_root}" +env = os.environ.copy() +env["PYTHONPATH"] = tabddpm_root + (os.pathsep + env.get("PYTHONPATH", "")) + +# Write a wrapper that patches collections.Sequence (removed in Python 3.10+) +# before running pipeline.py - needed because skorch uses old API +wrapper = os.path.join(tabddpm_root, "_compat_run.py") +with open(wrapper, "w") as f: + f.write( + "import collections, collections.abc\n" + "for _a in ('Sequence','MutableSequence','MutableMapping','Mapping'," + "'MutableSet','Set','Callable','Iterable','Iterator'):\n" + " if not hasattr(collections, _a): setattr(collections, _a, getattr(collections.abc, _a, None))\n" + "import sys, runpy\n" + "sys.argv = sys.argv[1:]\n" + "runpy.run_path(sys.argv[0], run_name='__main__')\n" + ) + +print(f"[TabDDPM] Training, config=/work/output-Benchmark-trainonly-v1/c14/tabddpm/tabddpm-c14-20260501_043200/config.toml") +ret = subprocess.run( + [sys.executable, wrapper, "scripts/pipeline.py", + "--config", "/work/output-Benchmark-trainonly-v1/c14/tabddpm/tabddpm-c14-20260501_043200/config.toml", + "--train"], + cwd=tabddpm_root, + env=env +) +if ret.returncode != 0: + sys.exit(ret.returncode) +print("[TabDDPM] Training complete") diff --git a/syntheticFail/c14/tabddpm/tabddpm-c14-20260501_043200/config.toml b/syntheticFail/c14/tabddpm/tabddpm-c14-20260501_043200/config.toml new file mode 100644 index 0000000000000000000000000000000000000000..ea55620eb608b36ff67c1722bb856b4f72f87986 --- /dev/null +++ b/syntheticFail/c14/tabddpm/tabddpm-c14-20260501_043200/config.toml @@ -0,0 +1,39 @@ +seed = 0 +parent_dir = "/work/output-Benchmark-trainonly-v1/c14/tabddpm/tabddpm-c14-20260501_043200/output" +real_data_path = "/work/output-Benchmark-trainonly-v1/c14/tabddpm/tabddpm-c14-20260501_043200/data" +model_type = "mlp" +num_numerical_features = 4 +device = "cuda:0" + +[model_params] +d_in = 24 +num_classes = 2 +is_y_cond = true + +[model_params.rtdl_params] +d_layers = [256, 256] +dropout = 0.0 + +[diffusion_params] +num_timesteps = 1000 +gaussian_loss_type = "mse" + +[train.main] +steps = 5000 +lr = 0.001 +weight_decay = 0.0 +batch_size = 256 + +[train.T] +seed = 0 +normalization = "quantile" +num_nan_policy = "__none__" +cat_nan_policy = "__none__" +cat_min_frequency = "__none__" +cat_encoding = "__none__" +y_policy = "default" + +[sample] +num_samples = 1000 +batch_size = 1000 +seed = 0 diff --git a/syntheticFail/c14/tabddpm/tabddpm-c14-20260501_043200/config_sample_20260501_043336_r0.toml b/syntheticFail/c14/tabddpm/tabddpm-c14-20260501_043200/config_sample_20260501_043336_r0.toml new file mode 100644 index 0000000000000000000000000000000000000000..1654f150f7b70b59d0b688b5b1601d526ca508bf --- /dev/null +++ b/syntheticFail/c14/tabddpm/tabddpm-c14-20260501_043200/config_sample_20260501_043336_r0.toml @@ -0,0 +1,39 @@ +seed = 0 +parent_dir = "/work/output-Benchmark-trainonly-v1/c14/tabddpm/tabddpm-c14-20260501_043200/output" +real_data_path = "/work/output-Benchmark-trainonly-v1/c14/tabddpm/tabddpm-c14-20260501_043200/data" +model_type = "mlp" +num_numerical_features = 4 +device = "cuda:0" + +[model_params] +d_in = 24 +num_classes = 2 +is_y_cond = true + +[model_params.rtdl_params] +d_layers = [256, 256] +dropout = 0.0 + +[diffusion_params] +num_timesteps = 1000 +gaussian_loss_type = "mse" + +[train.main] +steps = 5000 +lr = 0.001 +weight_decay = 0.0 +batch_size = 256 + +[train.T] +seed = 0 +normalization = "quantile" +num_nan_policy = "__none__" +cat_nan_policy = "__none__" +cat_min_frequency = "__none__" +cat_encoding = "__none__" +y_policy = "default" + +[sample] +num_samples = 240000 +batch_size = 1000 +seed = 0 diff --git a/syntheticFail/c14/tabddpm/tabddpm-c14-20260501_043200/config_sample_20260501_043336_r1.toml b/syntheticFail/c14/tabddpm/tabddpm-c14-20260501_043200/config_sample_20260501_043336_r1.toml new file mode 100644 index 0000000000000000000000000000000000000000..7c98e6044e4580d608205f73e767e3bdf21e55a2 --- /dev/null +++ b/syntheticFail/c14/tabddpm/tabddpm-c14-20260501_043200/config_sample_20260501_043336_r1.toml @@ -0,0 +1,39 @@ +seed = 0 +parent_dir = "/work/output-Benchmark-trainonly-v1/c14/tabddpm/tabddpm-c14-20260501_043200/output" +real_data_path = "/work/output-Benchmark-trainonly-v1/c14/tabddpm/tabddpm-c14-20260501_043200/data" +model_type = "mlp" +num_numerical_features = 4 +device = "cuda:0" + +[model_params] +d_in = 24 +num_classes = 2 +is_y_cond = true + +[model_params.rtdl_params] +d_layers = [256, 256] +dropout = 0.0 + +[diffusion_params] +num_timesteps = 1000 +gaussian_loss_type = "mse" + +[train.main] +steps = 5000 +lr = 0.001 +weight_decay = 0.0 +batch_size = 256 + +[train.T] +seed = 0 +normalization = "quantile" +num_nan_policy = "__none__" +cat_nan_policy = "__none__" +cat_min_frequency = "__none__" +cat_encoding = "__none__" +y_policy = "default" + +[sample] +num_samples = 240000 +batch_size = 512 +seed = 1 diff --git a/syntheticFail/c14/tabddpm/tabddpm-c14-20260501_043200/config_sample_20260501_043336_r2.toml b/syntheticFail/c14/tabddpm/tabddpm-c14-20260501_043200/config_sample_20260501_043336_r2.toml new file mode 100644 index 0000000000000000000000000000000000000000..d5f2cb47d101181b14cefc11382f79dc7958e932 --- /dev/null +++ b/syntheticFail/c14/tabddpm/tabddpm-c14-20260501_043200/config_sample_20260501_043336_r2.toml @@ -0,0 +1,39 @@ +seed = 0 +parent_dir = "/work/output-Benchmark-trainonly-v1/c14/tabddpm/tabddpm-c14-20260501_043200/output" +real_data_path = "/work/output-Benchmark-trainonly-v1/c14/tabddpm/tabddpm-c14-20260501_043200/data" +model_type = "mlp" +num_numerical_features = 4 +device = "cuda:0" + +[model_params] +d_in = 24 +num_classes = 2 +is_y_cond = true + +[model_params.rtdl_params] +d_layers = [256, 256] +dropout = 0.0 + +[diffusion_params] +num_timesteps = 1000 +gaussian_loss_type = "mse" + +[train.main] +steps = 5000 +lr = 0.001 +weight_decay = 0.0 +batch_size = 256 + +[train.T] +seed = 0 +normalization = "quantile" +num_nan_policy = "__none__" +cat_nan_policy = "__none__" +cat_min_frequency = "__none__" +cat_encoding = "__none__" +y_policy = "default" + +[sample] +num_samples = 240000 +batch_size = 256 +seed = 2 diff --git a/syntheticFail/c14/tabddpm/tabddpm-c14-20260501_043200/config_sample_20260501_043336_r3.toml b/syntheticFail/c14/tabddpm/tabddpm-c14-20260501_043200/config_sample_20260501_043336_r3.toml new file mode 100644 index 0000000000000000000000000000000000000000..f6331123ec89510dfb31eb3a4d1d34f75e39f209 --- /dev/null +++ b/syntheticFail/c14/tabddpm/tabddpm-c14-20260501_043200/config_sample_20260501_043336_r3.toml @@ -0,0 +1,39 @@ +seed = 0 +parent_dir = "/work/output-Benchmark-trainonly-v1/c14/tabddpm/tabddpm-c14-20260501_043200/output" +real_data_path = "/work/output-Benchmark-trainonly-v1/c14/tabddpm/tabddpm-c14-20260501_043200/data" +model_type = "mlp" +num_numerical_features = 4 +device = "cuda:0" + +[model_params] +d_in = 24 +num_classes = 2 +is_y_cond = true + +[model_params.rtdl_params] +d_layers = [256, 256] +dropout = 0.0 + +[diffusion_params] +num_timesteps = 1000 +gaussian_loss_type = "mse" + +[train.main] +steps = 5000 +lr = 0.001 +weight_decay = 0.0 +batch_size = 256 + +[train.T] +seed = 0 +normalization = "quantile" +num_nan_policy = "__none__" +cat_nan_policy = "__none__" +cat_min_frequency = "__none__" +cat_encoding = "__none__" +y_policy = "default" + +[sample] +num_samples = 240000 +batch_size = 128 +seed = 3 diff --git a/syntheticFail/c14/tabddpm/tabddpm-c14-20260501_043200/config_sample_20260501_043336_r4.toml b/syntheticFail/c14/tabddpm/tabddpm-c14-20260501_043200/config_sample_20260501_043336_r4.toml new file mode 100644 index 0000000000000000000000000000000000000000..6bfca62432acb36266464b07788c295063b7ba18 --- /dev/null +++ b/syntheticFail/c14/tabddpm/tabddpm-c14-20260501_043200/config_sample_20260501_043336_r4.toml @@ -0,0 +1,39 @@ +seed = 0 +parent_dir = "/work/output-Benchmark-trainonly-v1/c14/tabddpm/tabddpm-c14-20260501_043200/output" +real_data_path = "/work/output-Benchmark-trainonly-v1/c14/tabddpm/tabddpm-c14-20260501_043200/data" +model_type = "mlp" +num_numerical_features = 4 +device = "cuda:0" + +[model_params] +d_in = 24 +num_classes = 2 +is_y_cond = true + +[model_params.rtdl_params] +d_layers = [256, 256] +dropout = 0.0 + +[diffusion_params] +num_timesteps = 1000 +gaussian_loss_type = "mse" + +[train.main] +steps = 5000 +lr = 0.001 +weight_decay = 0.0 +batch_size = 256 + +[train.T] +seed = 0 +normalization = "quantile" +num_nan_policy = "__none__" +cat_nan_policy = "__none__" +cat_min_frequency = "__none__" +cat_encoding = "__none__" +y_policy = "default" + +[sample] +num_samples = 240000 +batch_size = 64 +seed = 4 diff --git a/syntheticFail/c14/tabddpm/tabddpm-c14-20260501_043200/config_sample_20260501_043336_r5.toml b/syntheticFail/c14/tabddpm/tabddpm-c14-20260501_043200/config_sample_20260501_043336_r5.toml new file mode 100644 index 0000000000000000000000000000000000000000..84fbfc3b3e8dc8265a487dc574165cbf61a4ccf2 --- /dev/null +++ b/syntheticFail/c14/tabddpm/tabddpm-c14-20260501_043200/config_sample_20260501_043336_r5.toml @@ -0,0 +1,39 @@ +seed = 0 +parent_dir = "/work/output-Benchmark-trainonly-v1/c14/tabddpm/tabddpm-c14-20260501_043200/output" +real_data_path = "/work/output-Benchmark-trainonly-v1/c14/tabddpm/tabddpm-c14-20260501_043200/data" +model_type = "mlp" +num_numerical_features = 4 +device = "cuda:0" + +[model_params] +d_in = 24 +num_classes = 2 +is_y_cond = true + +[model_params.rtdl_params] +d_layers = [256, 256] +dropout = 0.0 + +[diffusion_params] +num_timesteps = 1000 +gaussian_loss_type = "mse" + +[train.main] +steps = 5000 +lr = 0.001 +weight_decay = 0.0 +batch_size = 256 + +[train.T] +seed = 0 +normalization = "quantile" +num_nan_policy = "__none__" +cat_nan_policy = "__none__" +cat_min_frequency = "__none__" +cat_encoding = "__none__" +y_policy = "default" + +[sample] +num_samples = 240000 +batch_size = 32 +seed = 5 diff --git a/syntheticFail/c14/tabddpm/tabddpm-c14-20260501_043200/data/X_cat_train.npy b/syntheticFail/c14/tabddpm/tabddpm-c14-20260501_043200/data/X_cat_train.npy new file mode 100644 index 0000000000000000000000000000000000000000..98bd909f2c6e01569b889e7013b55bfd9382349a --- /dev/null +++ b/syntheticFail/c14/tabddpm/tabddpm-c14-20260501_043200/data/X_cat_train.npy @@ -0,0 +1,3 @@ +version https://git-lfs.github.com/spec/v1 +oid sha256:4db5fc039c0a3755f8187753d8ff1576d6ae627c7fdd403f3fa38914bed677b0 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"/data/jialinzhang/SynthesizePipeline-server/output-Benchmark-trainonly-v1/c14/tabddpm/tabddpm-c14-20260501_043200/staged/public/staged_features.json", + "public_gate_report": "/data/jialinzhang/SynthesizePipeline-server/output-Benchmark-trainonly-v1/c14/tabddpm/tabddpm-c14-20260501_043200/public_gate/public_gate_report.json" +} \ No newline at end of file diff --git a/syntheticFail/c14/tabddpm/tabddpm-c14-20260501_043200/train_20260501_043206.log b/syntheticFail/c14/tabddpm/tabddpm-c14-20260501_043200/train_20260501_043206.log new file mode 100644 index 0000000000000000000000000000000000000000..0757368ec385d4c7bcf593485fae7ad8e4d22145 --- /dev/null +++ b/syntheticFail/c14/tabddpm/tabddpm-c14-20260501_043200/train_20260501_043206.log @@ -0,0 +1,3 @@ +version https://git-lfs.github.com/spec/v1 +oid sha256:edbaf0d1cfabc23c5bc56f92e754b398381cfd26d6efd2cb55f8b199e3e08e1f +size 1135 diff --git a/syntheticFail/c14/tabpfgen/tabpfgen-c14-20260503_194849/_tabpfgen_generate.py b/syntheticFail/c14/tabpfgen/tabpfgen-c14-20260503_194849/_tabpfgen_generate.py new file mode 100644 index 0000000000000000000000000000000000000000..92102568376c28da5bd62aaf48e9330fdf520cad --- /dev/null +++ b/syntheticFail/c14/tabpfgen/tabpfgen-c14-20260503_194849/_tabpfgen_generate.py @@ -0,0 +1,100 @@ +import os +import numpy as np +import pandas as pd +import json +from tabpfgen import TabPFGen + +df = pd.read_csv("/work/output-Benchmark-trainonly-v1/c14/tabpfgen/tabpfgen-c14-20260503_194849/staged/public/train.csv") +target_col = "target" + +target_missing = df[target_col].isna() +if target_missing.any(): + dropped = int(target_missing.sum()) + df = df.loc[~target_missing].copy() + print( + f"[TabPFGen] Dropped {dropped} rows with missing target '{target_col}'" + ) +if df.empty: + raise ValueError( + f"[TabPFGen] No rows remain after dropping missing target '{target_col}'" + ) + +feature_cols = [c for c in df.columns if c != target_col] + +cat_encodings = {} +for col in feature_cols: + if df[col].dtype == object or str(df[col].dtype) == 'category': + cats = sorted(df[col].dropna().unique().tolist(), key=str) + cat_map = {v: i for i, v in enumerate(cats)} + df[col] = df[col].map(cat_map).astype(float) + cat_encodings[col] = cats + print(f"[TabPFGen] Label-encoded '{col}' ({len(cats)} categories)") + +target_cats = None +if df[target_col].dtype == object or str(df[target_col].dtype) == 'category': + cats = sorted(df[target_col].dropna().unique().tolist(), key=str) + t_map = {v: i for i, v in enumerate(cats)} + df[target_col] = df[target_col].map(t_map).astype(float) + target_cats = cats + print(f"[TabPFGen] Label-encoded target '{target_col}' ({len(cats)} categories)") + +X = df[feature_cols].values.astype(np.float32) +y = df[target_col].values +target_n = int(240000) + +for i in range(X.shape[1]): + col_vals = X[:, i] + mask = np.isnan(col_vals) + if mask.any(): + mean_val = np.nanmean(col_vals) + X[mask, i] = mean_val if not np.isnan(mean_val) else 0.0 + +# TabPFGen v0.1.x API：仅支持 n_sgld_steps / sgld_* / device。 +# （旧版脚本中的 energy_*_chunk 与上游 TabPFGen 不一致，会导致 TypeError。） +gen = TabPFGen( + n_sgld_steps=1000, + sgld_step_size=0.01, + sgld_noise_scale=0.01, + device="auto", +) + +print(f"[TabPFGen] Generating {target_n} rows via generate_classification") +X_syn, y_syn = gen.generate_classification(X, y, n_samples=target_n) + +syn_df = pd.DataFrame(X_syn, columns=feature_cols) +syn_df[target_col] = y_syn + +for col, cats in cat_encodings.items(): + codes = np.round(syn_df[col].values).astype(int) + codes = np.clip(codes, 0, len(cats) - 1) + syn_df[col] = [cats[c] for c in codes] + +if target_cats is not None: + codes = np.round(syn_df[target_col].values).astype(int) + codes = np.clip(codes, 0, len(target_cats) - 1) + syn_df[target_col] = [target_cats[c] for c in codes] + +if len(syn_df) > target_n: + print(f"[TabPFGen] Trimming rows: {len(syn_df)} -> {target_n}") + syn_df = syn_df.iloc[:target_n].copy() +elif len(syn_df) < target_n: + deficit = target_n - len(syn_df) + print(f"[TabPFGen] Padding rows: {len(syn_df)} -> {target_n} (deficit={deficit})") + if len(syn_df) > 0: + extra = syn_df.sample(n=deficit, replace=True, random_state=42) + syn_df = pd.concat( + [syn_df.reset_index(drop=True), extra.reset_index(drop=True)], + ignore_index=True, + ) + else: + syn_df = df[feature_cols + [target_col]].sample( + n=target_n, replace=True, random_state=42 + ).reset_index(drop=True) + +syn_df = syn_df[list(df.columns)] +if len(syn_df) != target_n: + raise RuntimeError( + f"[TabPFGen] Row alignment failed: got {len(syn_df)}, expected {target_n}" + ) +syn_df.to_csv("/work/output-Benchmark-trainonly-v1/c14/tabpfgen/tabpfgen-c14-20260503_194849/tabpfgen-c14-240000-20260503_194854.csv", index=False) +print(f"[TabPFGen] Saved {len(syn_df)} rows -> /work/output-Benchmark-trainonly-v1/c14/tabpfgen/tabpfgen-c14-20260503_194849/tabpfgen-c14-240000-20260503_194854.csv") diff --git a/syntheticFail/c14/tabpfgen/tabpfgen-c14-20260503_194849/gen_20260503_194854.log b/syntheticFail/c14/tabpfgen/tabpfgen-c14-20260503_194849/gen_20260503_194854.log new file mode 100644 index 0000000000000000000000000000000000000000..1543562c81185b25c9e00cb7dee8d280077eeb81 --- /dev/null +++ b/syntheticFail/c14/tabpfgen/tabpfgen-c14-20260503_194849/gen_20260503_194854.log @@ -0,0 +1,3 @@ +version https://git-lfs.github.com/spec/v1 +oid sha256:4594407e24cf713ee0578742c0389ef9a2bad30839b116ec27625a9703a5a732 +size 3012 diff --git a/syntheticFail/c14/tabpfgen/tabpfgen-c14-20260503_194849/input_snapshot.json b/syntheticFail/c14/tabpfgen/tabpfgen-c14-20260503_194849/input_snapshot.json new file mode 100644 index 0000000000000000000000000000000000000000..7b3f769e8fd8154b95fc0600df1ab16bb5991aec --- /dev/null +++ b/syntheticFail/c14/tabpfgen/tabpfgen-c14-20260503_194849/input_snapshot.json @@ -0,0 +1,36 @@ +{ + "dataset_id": "c14", + "model": "tabpfgen", + "inputs": { + "train_csv": { + 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"/data/jialinzhang/SynthesizePipeline-server/output-Benchmark-trainonly-v1/c14/tabpfgen/tabpfgen-c14-20260503_194849/staged/public/train.csv", + "json_path": "/data/jialinzhang/SynthesizePipeline-server/output-Benchmark-trainonly-v1/c14/tabpfgen/tabpfgen-c14-20260503_194849/staged/public/staged_features.json", + "target_col": "target", + "is_classification": true, + "task_type": "classification", + "n_rows": 240000, + "n_cols": 25 +} \ No newline at end of file diff --git a/syntheticFail/c14/tabpfgen/tabpfgen-c14-20260503_194849/train_20260503_194854.log b/syntheticFail/c14/tabpfgen/tabpfgen-c14-20260503_194849/train_20260503_194854.log new file mode 100644 index 0000000000000000000000000000000000000000..3f649e5646cb58848ae080f21a1913dbd87b7e62 --- /dev/null +++ b/syntheticFail/c14/tabpfgen/tabpfgen-c14-20260503_194849/train_20260503_194854.log @@ -0,0 +1,3 @@ +version https://git-lfs.github.com/spec/v1 +oid sha256:2bf2aaa212c2006039a9ecebf291ad9c2917486844f9c61f6d865f12ddf05065 +size 599 diff --git a/syntheticFail/c14/tabpfgen/tabpfgen-c14-20260503_215535/_tabpfgen_generate.py b/syntheticFail/c14/tabpfgen/tabpfgen-c14-20260503_215535/_tabpfgen_generate.py new file mode 100644 index 0000000000000000000000000000000000000000..ee39e46422a09662e9af8067ddf04c3d1b025e52 --- /dev/null +++ b/syntheticFail/c14/tabpfgen/tabpfgen-c14-20260503_215535/_tabpfgen_generate.py @@ -0,0 +1,115 @@ +import os +import numpy as np +import pandas as pd +import json +from tabpfgen import TabPFGen + +df = pd.read_csv("/work/output-Benchmark-trainonly-v1/c14/tabpfgen/tabpfgen-c14-20260503_215535/staged/public/train.csv") +target_col = "target" + +target_missing = df[target_col].isna() +if target_missing.any(): + dropped = int(target_missing.sum()) + df = df.loc[~target_missing].copy() + print( + f"[TabPFGen] Dropped {dropped} rows with missing target '{target_col}'" + ) +if df.empty: + raise ValueError( + f"[TabPFGen] No rows remain after dropping missing target '{target_col}'" + ) + +feature_cols = [c for c in df.columns if c != target_col] + +cat_encodings = {} +for col in feature_cols: + if df[col].dtype == object or str(df[col].dtype) == 'category': + cats = sorted(df[col].dropna().unique().tolist(), key=str) + cat_map = {v: i for i, v in enumerate(cats)} + df[col] = df[col].map(cat_map).astype(float) + cat_encodings[col] = cats + print(f"[TabPFGen] Label-encoded '{col}' ({len(cats)} categories)") + +target_cats = None +if df[target_col].dtype == object or str(df[target_col].dtype) == 'category': + cats = sorted(df[target_col].dropna().unique().tolist(), key=str) + t_map = {v: i for i, v in enumerate(cats)} + df[target_col] = df[target_col].map(t_map).astype(float) + target_cats = cats + print(f"[TabPFGen] Label-encoded target '{target_col}' ({len(cats)} categories)") + +X = df[feature_cols].values.astype(np.float32) +y = df[target_col].values +target_n = int(240000) + +for i in range(X.shape[1]): + col_vals = X[:, i] + mask = np.isnan(col_vals) + if mask.any(): + mean_val = np.nanmean(col_vals) + X[mask, i] = mean_val if not np.isnan(mean_val) else 0.0 + +chunk_rows = max(1, int(os.environ.get("TABPFGEN_GEN_CHUNK_ROWS", "256"))) +device = (os.environ.get("TABPFGEN_DEVICE") or "auto").strip() or "auto" + +# TabPFGen v0.1.x API：仅支持 n_sgld_steps / sgld_* / device。 +# （旧版脚本中的 energy_*_chunk 与上游 TabPFGen 不一致，会导致 TypeError。） +gen = TabPFGen( + n_sgld_steps=1000, + sgld_step_size=0.01, + sgld_noise_scale=0.01, + device=device, +) + +print(f"[TabPFGen] Generating {target_n} rows via generate_classification (chunk_rows={chunk_rows}, device={device})") +x_parts = [] +y_parts = [] +remaining = target_n +while remaining > 0: + take = min(chunk_rows, remaining) + X_part, y_part = gen.generate_classification(X, y, n_samples=take) + x_parts.append(np.asarray(X_part)) + y_parts.append(np.asarray(y_part)) + remaining -= take + print(f"[TabPFGen] chunk done: take={take}, remaining={remaining}") + +X_syn = np.concatenate(x_parts, axis=0) +y_syn = np.concatenate(y_parts, axis=0) + +syn_df = pd.DataFrame(X_syn, columns=feature_cols) +syn_df[target_col] = y_syn + +for col, cats in cat_encodings.items(): + codes = np.round(syn_df[col].values).astype(int) + codes = np.clip(codes, 0, len(cats) - 1) + syn_df[col] = [cats[c] for c in codes] + +if target_cats is not None: + codes = np.round(syn_df[target_col].values).astype(int) + codes = np.clip(codes, 0, len(target_cats) - 1) + syn_df[target_col] = [target_cats[c] for c in codes] + +if len(syn_df) > target_n: + print(f"[TabPFGen] Trimming rows: {len(syn_df)} -> {target_n}") + syn_df = syn_df.iloc[:target_n].copy() +elif len(syn_df) < target_n: + deficit = target_n - len(syn_df) + print(f"[TabPFGen] Padding rows: {len(syn_df)} -> {target_n} (deficit={deficit})") + if len(syn_df) > 0: + extra = syn_df.sample(n=deficit, replace=True, random_state=42) + syn_df = pd.concat( + [syn_df.reset_index(drop=True), extra.reset_index(drop=True)], + ignore_index=True, + ) + else: + syn_df = df[feature_cols + [target_col]].sample( + n=target_n, replace=True, random_state=42 + ).reset_index(drop=True) + +syn_df = syn_df[list(df.columns)] +if len(syn_df) != target_n: + raise RuntimeError( + f"[TabPFGen] Row alignment failed: got {len(syn_df)}, expected {target_n}" + ) +syn_df.to_csv("/work/output-Benchmark-trainonly-v1/c14/tabpfgen/tabpfgen-c14-20260503_215535/tabpfgen-c14-240000-20260503_215540.csv", index=False) +print(f"[TabPFGen] Saved {len(syn_df)} rows -> /work/output-Benchmark-trainonly-v1/c14/tabpfgen/tabpfgen-c14-20260503_215535/tabpfgen-c14-240000-20260503_215540.csv") diff --git a/syntheticFail/c14/tabpfgen/tabpfgen-c14-20260503_215535/gen_20260503_215540.log b/syntheticFail/c14/tabpfgen/tabpfgen-c14-20260503_215535/gen_20260503_215540.log new file mode 100644 index 0000000000000000000000000000000000000000..87f09580cd8ed2163455c35661098d8fd0fd0750 --- /dev/null +++ b/syntheticFail/c14/tabpfgen/tabpfgen-c14-20260503_215535/gen_20260503_215540.log @@ -0,0 +1,3 @@ +version https://git-lfs.github.com/spec/v1 +oid sha256:143a123f50f677d1cf0db0c0ff8a0a9d86bc3ea2e4ec857a3e3138d72371e645 +size 3655 diff --git a/syntheticFail/c14/tabpfgen/tabpfgen-c14-20260503_215535/input_snapshot.json b/syntheticFail/c14/tabpfgen/tabpfgen-c14-20260503_215535/input_snapshot.json new file mode 100644 index 0000000000000000000000000000000000000000..7b3f769e8fd8154b95fc0600df1ab16bb5991aec --- /dev/null +++ b/syntheticFail/c14/tabpfgen/tabpfgen-c14-20260503_215535/input_snapshot.json @@ -0,0 +1,36 @@ +{ + "dataset_id": "c14", + "model": "tabpfgen", + "inputs": { + "train_csv": { + "path": "/data/jialinzhang/SynthesizePipeline-server/DatasetNew/c14/c14-train.csv", + "exists": true, + "size": 31957080, + "sha256": "4a11abde6a7fae9f8bb1a1d7e31035b9dd61587d2721749a52d78d65441fba5e" + }, + "val_csv": { + "path": "/data/jialinzhang/SynthesizePipeline-server/DatasetNew/c14/c14-val.csv", + "exists": true, + "size": 3995920, + "sha256": "b04d723eeb59d339a356302660463c129141b4213fefbf472ef48f8a4daa50c9" + }, + "test_csv": { + "path": "/data/jialinzhang/SynthesizePipeline-server/DatasetNew/c14/c14-test.csv", + "exists": true, + "size": 3995840, + "sha256": "4780b90e46f6b440983cc9315cad8bca0e86bd26d8877ffae7111258fba0a132" + }, + "profile_json": { + "path": "/data/jialinzhang/SynthesizePipeline-server/DatasetNew/artifacts/data_core/tabular/c14/c14-dataset_profile.json", + "exists": true, + "size": 9505, + "sha256": "577cc5830dcb336243555d3d41c06e2e7c0a63df2a9caccb7a0411b9332cde15" + }, + "contract_json": { + "path": "/data/jialinzhang/SynthesizePipeline-server/DatasetNew/artifacts/data_core/tabular/c14/c14-dataset_contract_v1.json", + "exists": true, + "size": 11901, + "sha256": "955197dff3346d55aaa999adf98d64c375a7e4ca9bc4bcfe626499c9cfea9a41" + } + } +} \ No newline at end of file diff --git a/syntheticFail/c14/tabpfgen/tabpfgen-c14-20260503_215535/public_gate/normalized_schema_snapshot.json 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+ ] + } + }, + { + "name": "ord_3", + "role": "feature", + "semantic_type": "categorical", + "nullable": false, + "missing_tokens": [], + "parse_format": null, + "impute_strategy": "mode", + "profile_stats": { + "missing_rate": 0.0, + "unique_count": 15, + "unique_ratio": 6.3e-05, + "example_values": [ + "g", + "j", + "l", + "f", + "k" + ] + } + }, + { + "name": "ord_4", + "role": "feature", + "semantic_type": "categorical", + "nullable": false, + "missing_tokens": [], + "parse_format": null, + "impute_strategy": "mode", + "profile_stats": { + "missing_rate": 0.0, + "unique_count": 26, + "unique_ratio": 0.000108, + "example_values": [ + "L", + "S", + "K", + "I", + "A" + ] + } + }, + { + "name": "ord_5", + "role": "feature", + "semantic_type": "categorical", + "nullable": false, + "missing_tokens": [], + "parse_format": null, + "impute_strategy": "mode", + "profile_stats": { + "missing_rate": 0.0, + "unique_count": 192, + "unique_ratio": 0.0008, + "example_values": [ + "qo", + "aM", + 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] + } + } + ], + "public_manifest": "/data/jialinzhang/SynthesizePipeline-server/output-Benchmark-trainonly-v1/c14/tabpfgen/tabpfgen-c14-20260504_065221/public_gate/staged_input_manifest.json", + "train_csv": "/data/jialinzhang/SynthesizePipeline-server/output-Benchmark-trainonly-v1/c14/tabpfgen/tabpfgen-c14-20260504_065221/staged/public/train.csv", + "val_csv": "/data/jialinzhang/SynthesizePipeline-server/output-Benchmark-trainonly-v1/c14/tabpfgen/tabpfgen-c14-20260504_065221/staged/public/val.csv", + "test_csv": "/data/jialinzhang/SynthesizePipeline-server/output-Benchmark-trainonly-v1/c14/tabpfgen/tabpfgen-c14-20260504_065221/staged/public/test.csv", + "features_json": "/data/jialinzhang/SynthesizePipeline-server/output-Benchmark-trainonly-v1/c14/tabpfgen/tabpfgen-c14-20260504_065221/staged/public/staged_features.json", + "public_gate_report": "/data/jialinzhang/SynthesizePipeline-server/output-Benchmark-trainonly-v1/c14/tabpfgen/tabpfgen-c14-20260504_065221/public_gate/public_gate_report.json" +} \ No newline at end of file diff --git a/syntheticFail/c14/tabpfgen/tabpfgen-c14-20260504_065221/tabpfgen_meta.json b/syntheticFail/c14/tabpfgen/tabpfgen-c14-20260504_065221/tabpfgen_meta.json new file mode 100644 index 0000000000000000000000000000000000000000..decdf88d2735e271e3c4c82f3fdcc912a8931c58 --- /dev/null +++ b/syntheticFail/c14/tabpfgen/tabpfgen-c14-20260504_065221/tabpfgen_meta.json @@ -0,0 +1,9 @@ +{ + "csv_path": "/data/jialinzhang/SynthesizePipeline-server/output-Benchmark-trainonly-v1/c14/tabpfgen/tabpfgen-c14-20260504_065221/staged/public/train.csv", + "json_path": "/data/jialinzhang/SynthesizePipeline-server/output-Benchmark-trainonly-v1/c14/tabpfgen/tabpfgen-c14-20260504_065221/staged/public/staged_features.json", + "target_col": "target", + "is_classification": true, + "task_type": "classification", + "n_rows": 240000, + "n_cols": 25 +} \ No newline at end of file diff --git a/syntheticFail/c14/tabpfgen/tabpfgen-c14-20260504_065221/train_20260504_065228.log b/syntheticFail/c14/tabpfgen/tabpfgen-c14-20260504_065221/train_20260504_065228.log new file mode 100644 index 0000000000000000000000000000000000000000..f83fda28276ddd2c885eaa920e596ea50a667dfe --- /dev/null +++ b/syntheticFail/c14/tabpfgen/tabpfgen-c14-20260504_065221/train_20260504_065228.log @@ -0,0 +1,3 @@ +version https://git-lfs.github.com/spec/v1 +oid sha256:1c8b90c1b343b7d24209235f904c7640bbfbe4f1bb286310b3341ae39770fe69 +size 601 diff --git a/syntheticFail/c14/tabpfgen/tabpfgen-c14-20260504_065340/_tabpfgen_generate.py b/syntheticFail/c14/tabpfgen/tabpfgen-c14-20260504_065340/_tabpfgen_generate.py new file mode 100644 index 0000000000000000000000000000000000000000..036cd69e3971434e7b457b39ffc5e28f0ec79dc9 --- /dev/null +++ b/syntheticFail/c14/tabpfgen/tabpfgen-c14-20260504_065340/_tabpfgen_generate.py @@ -0,0 +1,122 @@ +import os +import numpy as np +import pandas as pd +import json +from tabpfgen import TabPFGen + +df = pd.read_csv("/work/output-Benchmark-trainonly-v1/c14/tabpfgen/tabpfgen-c14-20260504_065340/staged/public/train.csv") +target_col = "target" + +target_missing = df[target_col].isna() +if target_missing.any(): + dropped = int(target_missing.sum()) + df = df.loc[~target_missing].copy() + print( + f"[TabPFGen] Dropped {dropped} rows with missing target '{target_col}'" + ) +if df.empty: + raise ValueError( + f"[TabPFGen] No rows remain after dropping missing target '{target_col}'" + ) + +feature_cols = [c for c in df.columns if c != target_col] + +cat_encodings = {} +for col in feature_cols: + if df[col].dtype == object or str(df[col].dtype) == 'category': + cats = sorted(df[col].dropna().unique().tolist(), key=str) + cat_map = {v: i for i, v in enumerate(cats)} + df[col] = df[col].map(cat_map).astype(float) + cat_encodings[col] = cats + print(f"[TabPFGen] Label-encoded '{col}' ({len(cats)} categories)") + +target_cats = None +if df[target_col].dtype == object or str(df[target_col].dtype) == 'category': + cats = sorted(df[target_col].dropna().unique().tolist(), key=str) + t_map = {v: i for i, v in enumerate(cats)} + df[target_col] = df[target_col].map(t_map).astype(float) + target_cats = cats + print(f"[TabPFGen] Label-encoded target '{target_col}' ({len(cats)} categories)") + +X = df[feature_cols].values.astype(np.float32) +y = df[target_col].values +fit_rows_cap = max(1, int(os.environ.get("TABPFGEN_FIT_MAX_ROWS", "50000"))) +if len(X) > fit_rows_cap: + rng = np.random.default_rng(42) + idx = np.sort(rng.choice(len(X), size=fit_rows_cap, replace=False)) + X = X[idx] + y = y[idx] + print(f"[TabPFGen] Downsampled fit rows -> {len(X)} (cap={fit_rows_cap})") +target_n = int(128) + +for i in range(X.shape[1]): + col_vals = X[:, i] + mask = np.isnan(col_vals) + if mask.any(): + mean_val = np.nanmean(col_vals) + X[mask, i] = mean_val if not np.isnan(mean_val) else 0.0 + +chunk_rows = max(1, int(os.environ.get("TABPFGEN_GEN_CHUNK_ROWS", "256"))) +device = (os.environ.get("TABPFGEN_DEVICE") or "auto").strip() or "auto" + +# TabPFGen v0.1.x API：仅支持 n_sgld_steps / sgld_* / device。 +# （旧版脚本中的 energy_*_chunk 与上游 TabPFGen 不一致，会导致 TypeError。） +gen = TabPFGen( + n_sgld_steps=1000, + sgld_step_size=0.01, + sgld_noise_scale=0.01, + device=device, +) + +print(f"[TabPFGen] Generating {target_n} rows via generate_classification (chunk_rows={chunk_rows}, device={device})") +x_parts = [] +y_parts = [] +remaining = target_n +while remaining > 0: + take = min(chunk_rows, remaining) + X_part, y_part = gen.generate_classification(X, y, n_samples=take) + x_parts.append(np.asarray(X_part)) + y_parts.append(np.asarray(y_part)) + remaining -= take + print(f"[TabPFGen] chunk done: take={take}, remaining={remaining}") + +X_syn = np.concatenate(x_parts, axis=0) +y_syn = np.concatenate(y_parts, axis=0) + +syn_df = pd.DataFrame(X_syn, columns=feature_cols) +syn_df[target_col] = y_syn + +for col, cats in cat_encodings.items(): + codes = np.round(syn_df[col].values).astype(int) + codes = np.clip(codes, 0, len(cats) - 1) + syn_df[col] = [cats[c] for c in codes] + +if target_cats is not None: + codes = np.round(syn_df[target_col].values).astype(int) + codes = np.clip(codes, 0, len(target_cats) - 1) + syn_df[target_col] = [target_cats[c] for c in codes] + +if len(syn_df) > target_n: + print(f"[TabPFGen] Trimming rows: {len(syn_df)} -> {target_n}") + syn_df = syn_df.iloc[:target_n].copy() +elif len(syn_df) < target_n: + deficit = target_n - len(syn_df) + print(f"[TabPFGen] Padding rows: {len(syn_df)} -> {target_n} (deficit={deficit})") + if len(syn_df) > 0: + extra = syn_df.sample(n=deficit, replace=True, random_state=42) + syn_df = pd.concat( + [syn_df.reset_index(drop=True), extra.reset_index(drop=True)], + ignore_index=True, + ) + else: + syn_df = df[feature_cols + [target_col]].sample( + n=target_n, replace=True, random_state=42 + ).reset_index(drop=True) + +syn_df = syn_df[list(df.columns)] +if len(syn_df) != target_n: + raise RuntimeError( + f"[TabPFGen] Row alignment failed: got {len(syn_df)}, expected {target_n}" + ) +syn_df.to_csv("/work/output-Benchmark-trainonly-v1/c14/tabpfgen/tabpfgen-c14-20260504_065340/tabpfgen-c14-128-20260504_065345.csv", index=False) +print(f"[TabPFGen] Saved {len(syn_df)} rows -> /work/output-Benchmark-trainonly-v1/c14/tabpfgen/tabpfgen-c14-20260504_065340/tabpfgen-c14-128-20260504_065345.csv") diff --git a/syntheticFail/c14/tabpfgen/tabpfgen-c14-20260504_065340/gen_20260504_065345.log b/syntheticFail/c14/tabpfgen/tabpfgen-c14-20260504_065340/gen_20260504_065345.log new file mode 100644 index 0000000000000000000000000000000000000000..046f0dace77b0faffd96935130a55efe94c5506b --- /dev/null +++ b/syntheticFail/c14/tabpfgen/tabpfgen-c14-20260504_065340/gen_20260504_065345.log @@ -0,0 +1,3 @@ +version https://git-lfs.github.com/spec/v1 +oid sha256:2276c5dc5590a08e49b496af053334a4a2de7c5233af0930b749188fa5a12669 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"/data/jialinzhang/SynthesizePipeline-server/output-Benchmark-trainonly-v1/c14/tabpfgen/tabpfgen-c14-20260504_065340/public_gate/public_gate_report.json" +} \ No newline at end of file diff --git a/syntheticFail/c14/tabpfgen/tabpfgen-c14-20260504_065340/tabpfgen_meta.json b/syntheticFail/c14/tabpfgen/tabpfgen-c14-20260504_065340/tabpfgen_meta.json new file mode 100644 index 0000000000000000000000000000000000000000..83871207a2debf16d3e80efb3ceac02a80cd901a --- /dev/null +++ b/syntheticFail/c14/tabpfgen/tabpfgen-c14-20260504_065340/tabpfgen_meta.json @@ -0,0 +1,9 @@ +{ + "csv_path": "/data/jialinzhang/SynthesizePipeline-server/output-Benchmark-trainonly-v1/c14/tabpfgen/tabpfgen-c14-20260504_065340/staged/public/train.csv", + "json_path": "/data/jialinzhang/SynthesizePipeline-server/output-Benchmark-trainonly-v1/c14/tabpfgen/tabpfgen-c14-20260504_065340/staged/public/staged_features.json", + "target_col": "target", + "is_classification": true, + "task_type": "classification", + "n_rows": 240000, + "n_cols": 25 +} \ No newline at end of file diff --git a/syntheticFail/c14/tabpfgen/tabpfgen-c14-20260504_065340/train_20260504_065345.log b/syntheticFail/c14/tabpfgen/tabpfgen-c14-20260504_065340/train_20260504_065345.log new file mode 100644 index 0000000000000000000000000000000000000000..b6df45f0be8937d80bc9ea69b11052922f0bc673 --- /dev/null +++ b/syntheticFail/c14/tabpfgen/tabpfgen-c14-20260504_065340/train_20260504_065345.log @@ -0,0 +1,3 @@ +version https://git-lfs.github.com/spec/v1 +oid sha256:31a72df6c2b2f5f1b7caaa88fdf1650a6f3cda6cf18f51c1af892bdf0f10e0b3 +size 601 diff --git a/syntheticFail/c14/tabpfgen/tabpfgen-c14-20260504_070317/_tabpfgen_generate.py b/syntheticFail/c14/tabpfgen/tabpfgen-c14-20260504_070317/_tabpfgen_generate.py new file mode 100644 index 0000000000000000000000000000000000000000..337b7c942ffbbaa7c51a0d9c08f2cd8901c71779 --- /dev/null +++ b/syntheticFail/c14/tabpfgen/tabpfgen-c14-20260504_070317/_tabpfgen_generate.py @@ -0,0 +1,122 @@ +import os +import numpy as np +import pandas as pd +import json +from tabpfgen import TabPFGen + +df = pd.read_csv("/work/output-Benchmark-trainonly-v1/c14/tabpfgen/tabpfgen-c14-20260504_070317/staged/public/train.csv") +target_col = "target" + +target_missing = df[target_col].isna() +if target_missing.any(): + dropped = int(target_missing.sum()) + df = df.loc[~target_missing].copy() + print( + f"[TabPFGen] Dropped {dropped} rows with missing target '{target_col}'" + ) +if df.empty: + raise ValueError( + f"[TabPFGen] No rows remain after dropping missing target '{target_col}'" + ) + +feature_cols = [c for c in df.columns if c != target_col] + +cat_encodings = {} +for col in feature_cols: + if df[col].dtype == object or str(df[col].dtype) == 'category': + cats = sorted(df[col].dropna().unique().tolist(), key=str) + cat_map = {v: i for i, v in enumerate(cats)} + df[col] = df[col].map(cat_map).astype(float) + cat_encodings[col] = cats + print(f"[TabPFGen] Label-encoded '{col}' ({len(cats)} categories)") + +target_cats = None +if df[target_col].dtype == object or str(df[target_col].dtype) == 'category': + cats = sorted(df[target_col].dropna().unique().tolist(), key=str) + t_map = {v: i for i, v in enumerate(cats)} + df[target_col] = df[target_col].map(t_map).astype(float) + target_cats = cats + print(f"[TabPFGen] Label-encoded target '{target_col}' ({len(cats)} categories)") + +X = df[feature_cols].values.astype(np.float32) +y = df[target_col].values +fit_rows_cap = max(1, int(os.environ.get("TABPFGEN_FIT_MAX_ROWS", "50000"))) +if len(X) > fit_rows_cap: + rng = np.random.default_rng(42) + idx = np.sort(rng.choice(len(X), size=fit_rows_cap, replace=False)) + X = X[idx] + y = y[idx] + print(f"[TabPFGen] Downsampled fit rows -> {len(X)} (cap={fit_rows_cap})") +target_n = int(240000) + +for i in range(X.shape[1]): + col_vals = X[:, i] + mask = np.isnan(col_vals) + if mask.any(): + mean_val = np.nanmean(col_vals) + X[mask, i] = mean_val if not np.isnan(mean_val) else 0.0 + +chunk_rows = max(1, int(os.environ.get("TABPFGEN_GEN_CHUNK_ROWS", "256"))) +device = (os.environ.get("TABPFGEN_DEVICE") or "auto").strip() or "auto" + +# TabPFGen v0.1.x API：仅支持 n_sgld_steps / sgld_* / device。 +# （旧版脚本中的 energy_*_chunk 与上游 TabPFGen 不一致，会导致 TypeError。） +gen = TabPFGen( + n_sgld_steps=1000, + sgld_step_size=0.01, + sgld_noise_scale=0.01, + device=device, +) + +print(f"[TabPFGen] Generating {target_n} rows via generate_classification (chunk_rows={chunk_rows}, device={device})") +x_parts = [] +y_parts = [] +remaining = target_n +while remaining > 0: + take = min(chunk_rows, remaining) + X_part, y_part = gen.generate_classification(X, y, n_samples=take) + x_parts.append(np.asarray(X_part)) + y_parts.append(np.asarray(y_part)) + remaining -= take + print(f"[TabPFGen] chunk done: take={take}, remaining={remaining}") + +X_syn = np.concatenate(x_parts, axis=0) +y_syn = np.concatenate(y_parts, axis=0) + +syn_df = pd.DataFrame(X_syn, columns=feature_cols) +syn_df[target_col] = y_syn + +for col, cats in cat_encodings.items(): + codes = np.round(syn_df[col].values).astype(int) + codes = np.clip(codes, 0, len(cats) - 1) + syn_df[col] = [cats[c] for c in codes] + +if target_cats is not None: + codes = np.round(syn_df[target_col].values).astype(int) + codes = np.clip(codes, 0, len(target_cats) - 1) + syn_df[target_col] = [target_cats[c] for c in codes] + +if len(syn_df) > target_n: + print(f"[TabPFGen] Trimming rows: {len(syn_df)} -> {target_n}") + syn_df = syn_df.iloc[:target_n].copy() +elif len(syn_df) < target_n: + deficit = target_n - len(syn_df) + print(f"[TabPFGen] Padding rows: {len(syn_df)} -> {target_n} (deficit={deficit})") + if len(syn_df) > 0: + extra = syn_df.sample(n=deficit, replace=True, random_state=42) + syn_df = pd.concat( + [syn_df.reset_index(drop=True), extra.reset_index(drop=True)], + ignore_index=True, + ) + else: + syn_df = df[feature_cols + [target_col]].sample( + n=target_n, replace=True, random_state=42 + ).reset_index(drop=True) + +syn_df = syn_df[list(df.columns)] +if len(syn_df) != target_n: + raise RuntimeError( + f"[TabPFGen] Row alignment failed: got {len(syn_df)}, expected {target_n}" + ) +syn_df.to_csv("/work/output-Benchmark-trainonly-v1/c14/tabpfgen/tabpfgen-c14-20260504_070317/tabpfgen-c14-240000-20260504_070323.csv", index=False) +print(f"[TabPFGen] Saved {len(syn_df)} rows -> /work/output-Benchmark-trainonly-v1/c14/tabpfgen/tabpfgen-c14-20260504_070317/tabpfgen-c14-240000-20260504_070323.csv") diff --git a/syntheticFail/c14/tabpfgen/tabpfgen-c14-20260504_070317/gen_20260504_070323.log b/syntheticFail/c14/tabpfgen/tabpfgen-c14-20260504_070317/gen_20260504_070323.log new file mode 100644 index 0000000000000000000000000000000000000000..7369cbf789a55a4103160c7358ba62c635f0e162 --- /dev/null +++ b/syntheticFail/c14/tabpfgen/tabpfgen-c14-20260504_070317/gen_20260504_070323.log @@ -0,0 +1,3 @@ +version https://git-lfs.github.com/spec/v1 +oid sha256:95a0d1510a8f3a95e5a4e2692605b56db01abf65164c7f714a79a17bd68f03e1 +size 3800 diff --git a/syntheticFail/c14/tabpfgen/tabpfgen-c14-20260504_070317/input_snapshot.json b/syntheticFail/c14/tabpfgen/tabpfgen-c14-20260504_070317/input_snapshot.json new file mode 100644 index 0000000000000000000000000000000000000000..7b3f769e8fd8154b95fc0600df1ab16bb5991aec --- /dev/null +++ b/syntheticFail/c14/tabpfgen/tabpfgen-c14-20260504_070317/input_snapshot.json @@ -0,0 +1,36 @@ +{ + "dataset_id": "c14", + "model": "tabpfgen", + "inputs": { + "train_csv": { + "path": "/data/jialinzhang/SynthesizePipeline-server/DatasetNew/c14/c14-train.csv", + "exists": true, + "size": 31957080, + "sha256": "4a11abde6a7fae9f8bb1a1d7e31035b9dd61587d2721749a52d78d65441fba5e" + }, + "val_csv": { + "path": "/data/jialinzhang/SynthesizePipeline-server/DatasetNew/c14/c14-val.csv", + "exists": true, + "size": 3995920, + "sha256": "b04d723eeb59d339a356302660463c129141b4213fefbf472ef48f8a4daa50c9" + }, + "test_csv": { + 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] + } + } + ], + "public_manifest": "/data/jialinzhang/SynthesizePipeline-server/output-Benchmark-trainonly-v1/c14/tabpfgen/tabpfgen-c14-20260504_070317/public_gate/staged_input_manifest.json", + "train_csv": "/data/jialinzhang/SynthesizePipeline-server/output-Benchmark-trainonly-v1/c14/tabpfgen/tabpfgen-c14-20260504_070317/staged/public/train.csv", + "val_csv": "/data/jialinzhang/SynthesizePipeline-server/output-Benchmark-trainonly-v1/c14/tabpfgen/tabpfgen-c14-20260504_070317/staged/public/val.csv", + "test_csv": "/data/jialinzhang/SynthesizePipeline-server/output-Benchmark-trainonly-v1/c14/tabpfgen/tabpfgen-c14-20260504_070317/staged/public/test.csv", + "features_json": "/data/jialinzhang/SynthesizePipeline-server/output-Benchmark-trainonly-v1/c14/tabpfgen/tabpfgen-c14-20260504_070317/staged/public/staged_features.json", + "public_gate_report": "/data/jialinzhang/SynthesizePipeline-server/output-Benchmark-trainonly-v1/c14/tabpfgen/tabpfgen-c14-20260504_070317/public_gate/public_gate_report.json" +} \ No newline at end of file diff --git a/syntheticFail/c14/tabpfgen/tabpfgen-c14-20260504_070317/tabpfgen_meta.json b/syntheticFail/c14/tabpfgen/tabpfgen-c14-20260504_070317/tabpfgen_meta.json new file mode 100644 index 0000000000000000000000000000000000000000..0e77f4fedc902188801cf33d2d91cd753f36eb8e --- /dev/null +++ b/syntheticFail/c14/tabpfgen/tabpfgen-c14-20260504_070317/tabpfgen_meta.json @@ -0,0 +1,9 @@ +{ + "csv_path": "/data/jialinzhang/SynthesizePipeline-server/output-Benchmark-trainonly-v1/c14/tabpfgen/tabpfgen-c14-20260504_070317/staged/public/train.csv", + "json_path": "/data/jialinzhang/SynthesizePipeline-server/output-Benchmark-trainonly-v1/c14/tabpfgen/tabpfgen-c14-20260504_070317/staged/public/staged_features.json", + "target_col": "target", + "is_classification": true, + "task_type": "classification", + "n_rows": 240000, + "n_cols": 25 +} \ No newline at end of file diff --git a/syntheticFail/c14/tabpfgen/tabpfgen-c14-20260504_070317/train_20260504_070323.log b/syntheticFail/c14/tabpfgen/tabpfgen-c14-20260504_070317/train_20260504_070323.log new file mode 100644 index 0000000000000000000000000000000000000000..1628104f177d42b0494b4b3ecb4fa84a8f5ad8f9 --- /dev/null +++ b/syntheticFail/c14/tabpfgen/tabpfgen-c14-20260504_070317/train_20260504_070323.log @@ -0,0 +1,3 @@ +version https://git-lfs.github.com/spec/v1 +oid sha256:88494da9b72a1b3a30d838f7567b0280831c33ca12680af3fc636717fb464f2e +size 601 diff --git a/syntheticFail/c14/tabsyn/tabsyn-c14-20260501_124512/_tabsyn_train.py b/syntheticFail/c14/tabsyn/tabsyn-c14-20260501_124512/_tabsyn_train.py new file mode 100644 index 0000000000000000000000000000000000000000..51725182770656b696415f547c5a68a14c3b7ebd --- /dev/null +++ b/syntheticFail/c14/tabsyn/tabsyn-c14-20260501_124512/_tabsyn_train.py @@ -0,0 +1,65 @@ +import os, sys, subprocess + +work_dir = "/work/output-Benchmark-trainonly-v1/c14/tabsyn/tabsyn-c14-20260501_124512" +dataname = "tabsyn_c14" +tabsyn_root = "/workspace/tabsyn" + +assert os.path.exists(tabsyn_root), f"TabSyn source not mounted: {tabsyn_root}" + +old = os.environ.get("PYTHONPATH", "") +os.environ["PYTHONPATH"] = tabsyn_root + (os.pathsep + old if old else "") +sys.path.insert(0, tabsyn_root) + +os.chdir(tabsyn_root) + +# Symlink data dir into TabSyn data/ +data_link = os.path.join(tabsyn_root, "data", dataname) +data_src = os.path.join(work_dir, "data", dataname) +os.makedirs(os.path.join(tabsyn_root, "data"), exist_ok=True) +if os.path.exists(data_link): + os.remove(data_link) +os.symlink(data_src, data_link) + +env = os.environ.copy() +env.setdefault("TABSYN_RESUME", "1") +env.setdefault("TABSYN_VAE_BATCH_SIZE", "1024") +# Safer defaults for wide tables on Docker: reduce shared-memory pressure in diffusion DataLoader. +env.setdefault("TABSYN_DIFFUSION_NUM_WORKERS", "0") +_te = None +if _te is not None: + env["TABSYN_VAE_EPOCHS"] = str(_te) + env["TABSYN_DIFFUSION_MAX_EPOCHS"] = str(max(_te + 1, 2)) + +# Data preprocessing is done on the host side (_prepare_data_dir) +# which creates .npy files, train/test CSVs, and info.json + +# Step 1: Train VAE (produces latent embeddings) +print(f"[TabSyn] Step 1/2: Training VAE in {tabsyn_root}, dataname={dataname}") +ret = subprocess.run( + [sys.executable, "main.py", + "--dataname", dataname, + "--mode", "train", + "--method", "vae", + "--gpu", "0"], + cwd=tabsyn_root, + env=env +) +if ret.returncode != 0: + print("[TabSyn] VAE training failed") + sys.exit(ret.returncode) + +# Step 2: Train diffusion model on latent space +print(f"[TabSyn] Step 2/2: Training diffusion model") +ret = subprocess.run( + [sys.executable, "main.py", + "--dataname", dataname, + "--mode", "train", + "--method", "tabsyn", + "--gpu", "0"], + cwd=tabsyn_root, + env=env +) +if ret.returncode != 0: + print("[TabSyn] Diffusion training failed") + sys.exit(ret.returncode) +print("[TabSyn] Training complete (VAE + Diffusion)") diff --git a/syntheticFail/c14/tabsyn/tabsyn-c14-20260501_124512/data/tabsyn_c14/X_cat_test.npy b/syntheticFail/c14/tabsyn/tabsyn-c14-20260501_124512/data/tabsyn_c14/X_cat_test.npy new file mode 100644 index 0000000000000000000000000000000000000000..95cc03ccfebc7a71aafef14ec7f38f5844f747fb --- /dev/null +++ b/syntheticFail/c14/tabsyn/tabsyn-c14-20260501_124512/data/tabsyn_c14/X_cat_test.npy @@ -0,0 +1,3 @@ +version https://git-lfs.github.com/spec/v1 +oid sha256:96fb5a86a8b5c99f151626dee541d9024bac427c393b9ed577c13dbcc6b6832d +size 38400128 diff --git a/syntheticFail/c14/tabsyn/tabsyn-c14-20260501_124512/data/tabsyn_c14/X_cat_train.npy b/syntheticFail/c14/tabsyn/tabsyn-c14-20260501_124512/data/tabsyn_c14/X_cat_train.npy new file mode 100644 index 0000000000000000000000000000000000000000..95cc03ccfebc7a71aafef14ec7f38f5844f747fb --- /dev/null +++ b/syntheticFail/c14/tabsyn/tabsyn-c14-20260501_124512/data/tabsyn_c14/X_cat_train.npy @@ -0,0 +1,3 @@ +version https://git-lfs.github.com/spec/v1 +oid sha256:96fb5a86a8b5c99f151626dee541d9024bac427c393b9ed577c13dbcc6b6832d +size 38400128 diff --git a/syntheticFail/c14/tabsyn/tabsyn-c14-20260501_124512/data/tabsyn_c14/X_num_test.npy b/syntheticFail/c14/tabsyn/tabsyn-c14-20260501_124512/data/tabsyn_c14/X_num_test.npy new file mode 100644 index 0000000000000000000000000000000000000000..c3499d15139a46785b9614e79d47ee35f58034ea --- /dev/null +++ b/syntheticFail/c14/tabsyn/tabsyn-c14-20260501_124512/data/tabsyn_c14/X_num_test.npy @@ -0,0 +1,3 @@ +version https://git-lfs.github.com/spec/v1 +oid sha256:3026532dc1e8f61b12f8f829ec618733e69f9da70d40e8234069ff10f3381878 +size 3840128 diff --git a/syntheticFail/c14/tabsyn/tabsyn-c14-20260501_124512/data/tabsyn_c14/X_num_train.npy b/syntheticFail/c14/tabsyn/tabsyn-c14-20260501_124512/data/tabsyn_c14/X_num_train.npy new file mode 100644 index 0000000000000000000000000000000000000000..c3499d15139a46785b9614e79d47ee35f58034ea --- /dev/null +++ b/syntheticFail/c14/tabsyn/tabsyn-c14-20260501_124512/data/tabsyn_c14/X_num_train.npy @@ -0,0 +1,3 @@ +version https://git-lfs.github.com/spec/v1 +oid sha256:3026532dc1e8f61b12f8f829ec618733e69f9da70d40e8234069ff10f3381878 +size 3840128 diff --git a/syntheticFail/c14/tabsyn/tabsyn-c14-20260501_124512/data/tabsyn_c14/info.json b/syntheticFail/c14/tabsyn/tabsyn-c14-20260501_124512/data/tabsyn_c14/info.json new file mode 100644 index 0000000000000000000000000000000000000000..806b5df78295c3beab12baf94740486fa421dd7e --- /dev/null +++ b/syntheticFail/c14/tabsyn/tabsyn-c14-20260501_124512/data/tabsyn_c14/info.json @@ -0,0 +1,236 @@ +{ + "name": "tabsyn_c14", + "task_type": "multiclass", + "n_num_features": 4, + "n_cat_features": 20, + "train_size": 240000, + "num_col_idx": [ + 0, + 16, + 22, + 23 + ], + "cat_col_idx": [ + 1, + 2, + 3, + 4, + 5, + 6, + 7, + 8, + 9, + 10, + 11, + 12, + 13, + 14, + 15, + 17, + 18, + 19, + 20, + 21 + ], + "target_col_idx": [ + 24 + ], + "column_names": [ + 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+ "sdtype": "categorical" + }, + "6": { + "sdtype": "categorical" + }, + "7": { + "sdtype": "categorical" + }, + "8": { + "sdtype": "categorical" + }, + "9": { + "sdtype": "categorical" + }, + "10": { + "sdtype": "categorical" + }, + "11": { + "sdtype": "categorical" + }, + "12": { + "sdtype": "categorical" + }, + "13": { + "sdtype": "categorical" + }, + "14": { + "sdtype": "categorical" + }, + "15": { + "sdtype": "categorical" + }, + "17": { + "sdtype": "categorical" + }, + "18": { + "sdtype": "categorical" + }, + "19": { + "sdtype": "categorical" + }, + "20": { + "sdtype": "categorical" + }, + "21": { + "sdtype": "categorical" + }, + "24": { + "sdtype": "categorical" + } + } + } +} \ No newline at end of file diff --git a/syntheticFail/c14/tabsyn/tabsyn-c14-20260501_124512/data/tabsyn_c14/test.csv b/syntheticFail/c14/tabsyn/tabsyn-c14-20260501_124512/data/tabsyn_c14/test.csv new file mode 100644 index 0000000000000000000000000000000000000000..6d8d9039c359043c08476b37a4617b1c2a2a1acc --- /dev/null +++ b/syntheticFail/c14/tabsyn/tabsyn-c14-20260501_124512/data/tabsyn_c14/test.csv @@ -0,0 +1,3 @@ +version https://git-lfs.github.com/spec/v1 +oid sha256:c5921345765f8af3133fc50bf596097efd9e2f166a6a923b4951b080daf112c4 +size 16124837 diff --git a/syntheticFail/c14/tabsyn/tabsyn-c14-20260501_124512/data/tabsyn_c14/train.csv b/syntheticFail/c14/tabsyn/tabsyn-c14-20260501_124512/data/tabsyn_c14/train.csv new file mode 100644 index 0000000000000000000000000000000000000000..6d8d9039c359043c08476b37a4617b1c2a2a1acc --- /dev/null +++ b/syntheticFail/c14/tabsyn/tabsyn-c14-20260501_124512/data/tabsyn_c14/train.csv @@ -0,0 +1,3 @@ +version https://git-lfs.github.com/spec/v1 +oid sha256:c5921345765f8af3133fc50bf596097efd9e2f166a6a923b4951b080daf112c4 +size 16124837 diff --git a/syntheticFail/c14/tabsyn/tabsyn-c14-20260501_124512/data/tabsyn_c14/y_test.npy b/syntheticFail/c14/tabsyn/tabsyn-c14-20260501_124512/data/tabsyn_c14/y_test.npy new file mode 100644 index 0000000000000000000000000000000000000000..b2d0ed6255ec373762de8f5dced79925f8486956 --- /dev/null +++ b/syntheticFail/c14/tabsyn/tabsyn-c14-20260501_124512/data/tabsyn_c14/y_test.npy @@ -0,0 +1,3 @@ +version https://git-lfs.github.com/spec/v1 +oid sha256:9feaa3857a64a11ee3e12f755d82c94f0e23fcdd5f916c943a48340cb510db33 +size 1920128 diff --git a/syntheticFail/c14/tabsyn/tabsyn-c14-20260501_124512/data/tabsyn_c14/y_train.npy b/syntheticFail/c14/tabsyn/tabsyn-c14-20260501_124512/data/tabsyn_c14/y_train.npy new file mode 100644 index 0000000000000000000000000000000000000000..b2d0ed6255ec373762de8f5dced79925f8486956 --- /dev/null +++ b/syntheticFail/c14/tabsyn/tabsyn-c14-20260501_124512/data/tabsyn_c14/y_train.npy @@ -0,0 +1,3 @@ +version https://git-lfs.github.com/spec/v1 +oid sha256:9feaa3857a64a11ee3e12f755d82c94f0e23fcdd5f916c943a48340cb510db33 +size 1920128 diff --git a/syntheticFail/c14/tabsyn/tabsyn-c14-20260501_124512/input_snapshot.json 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b/syntheticFail/c14/tabsyn/tabsyn-c14-20260501_124512/public_gate/normalized_schema_snapshot.json @@ -0,0 +1,509 @@ +{ + "dataset_id": "c14", + "target_column": "target", + "task_type": "classification", + "columns": [ + { + "name": "id", + "role": "feature", + "semantic_type": "numeric", + "nullable": false, + "missing_tokens": [], + "parse_format": null, + "impute_strategy": "median", + "profile_stats": { + "missing_rate": 0.0, + "unique_count": 20000, + "unique_ratio": 0.083333, + "example_values": [ + "182547", + "28342", + "172611", + "81876", + "110780" + ] + } + }, + { + "name": "bin_0", + "role": "feature", + "semantic_type": "boolean", + "nullable": false, + "missing_tokens": [], + "parse_format": null, + "impute_strategy": "mode", + "profile_stats": { + "missing_rate": 0.0, + "unique_count": 2, + "unique_ratio": 8e-06, + "example_values": [ + "0", + "1" + ] + } + }, + { + "name": "bin_1", + "role": "feature", + "semantic_type": "boolean", + "nullable": false, + 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