File size: 5,824 Bytes
20c251e | 1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 16 17 18 19 20 21 22 23 24 25 26 27 28 29 30 31 32 33 34 35 36 37 38 39 40 41 42 43 44 45 46 47 48 49 50 51 52 53 54 55 56 57 58 59 60 61 62 63 64 65 66 67 68 69 70 71 72 73 74 75 76 77 78 79 80 81 82 83 84 85 86 87 88 89 90 91 92 93 94 95 96 97 98 99 100 101 102 103 104 105 106 107 108 109 110 111 112 113 114 115 116 117 118 119 120 121 122 123 124 125 126 127 128 129 130 131 132 133 134 135 136 137 138 139 140 141 142 143 144 145 146 147 148 149 150 151 152 153 154 155 156 157 158 159 160 161 162 163 164 165 166 167 168 169 170 171 172 173 174 175 176 177 178 179 180 181 182 183 | from __future__ import annotations
import csv
import subprocess
import sys
from pathlib import Path
from dovla_cil.data.schema import (
CIL_VERSION,
ActionChunk,
CILRecord,
FailureInfo,
RewardInfo,
StructuredEffect,
make_record_id,
)
from dovla_cil.data.sharding import write_cil_shards
from dovla_cil.experiments.reports import (
compute_candidate_stats,
compute_failure_stats,
generate_dataset_report,
load_dataset_summary,
)
from dovla_cil.utils.io import read_json
def test_dataset_report_script_runs_and_creates_files(tmp_path: Path) -> None:
dataset_dir = tmp_path / "dataset"
out_dir = tmp_path / "report"
_write_tiny_dataset(dataset_dir)
result = subprocess.run(
[
sys.executable,
"scripts/report_dataset.py",
"--dataset",
str(dataset_dir),
"--out",
str(out_dir),
"--sample-groups",
"2",
],
check=True,
text=True,
capture_output=True,
)
expected = {
"summary.json",
"candidate_type_counts.csv",
"reward_histogram.png",
"success_by_candidate_type.csv",
"success_by_candidate_type.png",
"regret_histogram.png",
"group_size_distribution.png",
"failure_type_counts.csv",
"failure_type_counts.png",
"examples.md",
}
assert "num records: 3" in result.stdout
assert expected.issubset({path.name for path in out_dir.iterdir()})
def test_report_stats_match_expected_toy_dataset(tmp_path: Path) -> None:
dataset_dir = tmp_path / "dataset"
out_dir = tmp_path / "report"
records = _write_tiny_dataset(dataset_dir)
summary = generate_dataset_report(dataset_dir, out_dir, sample_groups=2, seed=4)
candidate_stats = {
row["candidate_type"]: row for row in compute_candidate_stats(records)
}
failure_stats = {row["failure_type"]: row for row in compute_failure_stats(records)}
summary_file = read_json(out_dir / "summary.json")
candidate_counts = _read_csv(out_dir / "candidate_type_counts.csv")
assert summary["num_records"] == 3
assert summary["num_groups"] == 2
assert summary["candidate_type_counts"] == {"expert": 2, "wrong_target": 1}
assert summary["failure_type_counts"] == {"success": 2, "wrong_target": 1}
assert summary_file["success_rate"] == 2 / 3
assert candidate_stats["expert"]["success_rate"] == 1.0
assert candidate_stats["wrong_target"]["success_rate"] == 0.0
assert failure_stats["wrong_target"]["count"] == 1
assert candidate_counts == [
{"candidate_type": "expert", "count": "2"},
{"candidate_type": "wrong_target", "count": "1"},
]
assert "| record_id | candidate_type | reward.progress |" in (
out_dir / "examples.md"
).read_text(encoding="utf-8")
def test_load_dataset_summary_function(tmp_path: Path) -> None:
dataset_dir = tmp_path / "dataset"
_write_tiny_dataset(dataset_dir)
summary = load_dataset_summary(dataset_dir)
assert summary["num_records"] == 3
assert summary["reward"]["min"] == 0.2
assert summary["reward"]["max"] == 1.0
assert summary["group_size"]["max"] == 2
def _write_tiny_dataset(dataset_dir: Path) -> list[CILRecord]:
records = [
_record("group-a", 0, "expert", 1.0, True, "success", regret=0.0, rank=0),
_record("group-a", 1, "wrong_target", 0.2, False, "wrong_target", regret=0.8, rank=1),
_record("group-b", 0, "expert", 1.0, True, "success", regret=0.0, rank=0),
]
write_cil_shards(
records,
output_dir=dataset_dir,
max_records_per_shard=10,
dataset_name="report_toy",
backend="toy",
k=2,
task_count=1,
seed=5,
)
return records
def _record(
group_id: str,
index: int,
candidate_type: str,
reward_progress: float,
success: bool,
failure_type: str,
*,
regret: float,
rank: int,
) -> CILRecord:
action = ActionChunk(
action_id=f"{candidate_type}-{index}",
representation="semantic",
horizon=1,
values=[{"command": "noop" if not success else "grasp", "object": "red_mug"}],
skill_type="grasp",
metadata={"candidate_type": candidate_type, "intended_target": "red_mug"},
)
return CILRecord(
version=CIL_VERSION,
record_id=make_record_id(group_id, action.action_id, seed=9),
group_id=group_id,
state_hash=f"state-{group_id}",
task_id="toy_report_task",
scene_id=f"scene-{group_id}",
instruction="Pick the red mug.",
instruction_family={"family": "pick"},
observation_ref=None,
observation_inline={"symbolic_state": {"objects": {}}},
action_chunk=action,
next_observation_ref=None,
next_observation_inline={"symbolic_state": {"objects": {}}},
structured_effect=StructuredEffect(
object_pose_delta={"red_mug": [reward_progress, 0.0, 0.0]},
relation_after={"grasped(red_mug)": success},
moved_objects=["red_mug"] if reward_progress > 0 else [],
),
reward=RewardInfo(
progress=reward_progress,
success=success,
terminal_success=success,
dense_components={"progress": reward_progress},
),
regret=regret,
rank_within_group=rank,
candidate_type=candidate_type,
failure=FailureInfo(
type=failure_type,
symbolic_reason=failure_type,
language_explanation=failure_type,
),
)
def _read_csv(path: Path) -> list[dict[str, str]]:
with path.open("r", encoding="utf-8", newline="") as handle:
return list(csv.DictReader(handle))
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