File size: 3,080 Bytes
d5b51d5 | 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 | #!/usr/bin/env python3
"""Baseline: predict failure_mode from first 3 trajectory iterations."""
from __future__ import annotations
import argparse
import json
from pathlib import Path
ROOT = Path(__file__).resolve().parents[1]
DEFAULT_JSONL = ROOT / "data" / "seed" / "records.jsonl"
def load_records(path: Path) -> list[dict]:
records = []
with path.open(encoding="utf-8") as handle:
for line in handle:
line = line.strip()
if line:
records.append(json.loads(line))
return records
def extract_features(record: dict, window: int = 3) -> list[float]:
trajectory = record["trajectory"][:window]
if len(trajectory) < window:
trajectory = trajectory + [trajectory[-1]] * (window - len(trajectory))
features: list[float] = []
for step in trajectory:
features.extend(
[
step["goal_score"],
step.get("primary_quality", step["goal_score"]),
step["cost_usd"],
step["latency_seconds"],
float(step.get("safety_events", 0)),
float(step.get("human_intervention", False)),
]
)
features.append(record["metadata"]["goal_target"])
features.append(float(record["metadata"]["worker_count"]))
features.append(float(record["metadata"]["evaluator_count"]))
return features
def main(argv: list[str] | None = None) -> int:
parser = argparse.ArgumentParser(description=__doc__)
parser.add_argument("--jsonl", type=Path, default=DEFAULT_JSONL)
parser.add_argument("--window", type=int, default=3)
args = parser.parse_args(argv)
try:
from sklearn.ensemble import RandomForestClassifier
from sklearn.metrics import classification_report
from sklearn.pipeline import Pipeline
from sklearn.preprocessing import StandardScaler
except ImportError as exc:
raise SystemExit("scikit-learn required: pip install -e '.[notebook]'") from exc
records = load_records(args.jsonl)
labeled = [r for r in records if r.get("failure_mode")]
train = [r for r in labeled if r["split"] == "train"]
test = [r for r in labeled if r["split"] == "test"]
if not train or not test:
print("Need labeled train and test splits with failure_mode.")
return 1
x_train = [extract_features(r, args.window) for r in train]
y_train = [r["failure_mode"] for r in train]
x_test = [extract_features(r, args.window) for r in test]
y_test = [r["failure_mode"] for r in test]
model = Pipeline(
[
("scaler", StandardScaler()),
("clf", RandomForestClassifier(n_estimators=200, random_state=42)),
]
)
model.fit(x_train, y_train)
predictions = model.predict(x_test)
print(classification_report(y_test, predictions, zero_division=0))
accuracy = (predictions == y_test).mean()
print(f"Accuracy on test split: {accuracy:.1%} ({len(test)} records)")
return 0
if __name__ == "__main__":
raise SystemExit(main())
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