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Create scorer.py
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import argparse
import json
import sys
from datetime import datetime, timezone
import pandas as pd
from sklearn.metrics import accuracy_score, precision_score, recall_score, f1_score, confusion_matrix
REQUIRED_PREDICTION_COLUMNS = {"scenario_id", "prediction"}
REQUIRED_TRUTH_COLUMNS = {"scenario_id", "label"}
def load_csv(path):
try:
return pd.read_csv(path)
except Exception as exc:
raise ValueError(f"Could not read CSV file: {path}. Error: {exc}")
def validate_columns(df, required, name):
missing = required - set(df.columns)
if missing:
raise ValueError(f"{name} is missing required columns: {sorted(missing)}")
def normalize_binary(series, column_name):
try:
values = series.astype(int)
except Exception:
raise ValueError(f"{column_name} must contain binary values 0 or 1")
invalid = sorted(set(values.dropna().unique()) - {0, 1})
if invalid:
raise ValueError(f"{column_name} contains non-binary values: {invalid}")
return values
def score(predictions_path, truth_path):
preds = load_csv(predictions_path)
truth = load_csv(truth_path)
validate_columns(preds, REQUIRED_PREDICTION_COLUMNS, "predictions")
validate_columns(truth, REQUIRED_TRUTH_COLUMNS, "truth")
preds = preds[["scenario_id", "prediction"]].copy()
truth = truth[["scenario_id", "label"]].copy()
merged = truth.merge(preds, on="scenario_id", how="left", indicator=True)
missing = merged[merged["_merge"] == "left_only"]["scenario_id"].tolist()
if missing:
raise ValueError(f"Missing predictions for scenario_id values: {missing}")
extra = preds[~preds["scenario_id"].isin(truth["scenario_id"])]["scenario_id"].tolist()
if extra:
raise ValueError(f"Predictions contain unknown scenario_id values: {extra}")
y_true = normalize_binary(merged["label"], "label")
y_pred = normalize_binary(merged["prediction"], "prediction")
return {
"scorer_version": "v0.1",
"timestamp_utc": datetime.now(timezone.utc).isoformat(),
"num_examples": int(len(y_true)),
"accuracy": float(accuracy_score(y_true, y_pred)),
"precision": float(precision_score(y_true, y_pred, zero_division=0)),
"recall": float(recall_score(y_true, y_pred, zero_division=0)),
"f1": float(f1_score(y_true, y_pred, zero_division=0)),
"confusion_matrix_labels": [0, 1],
"confusion_matrix": confusion_matrix(y_true, y_pred, labels=[0, 1]).tolist(),
}
def main():
parser = argparse.ArgumentParser()
parser.add_argument("--predictions", required=True, help="Path to predictions CSV")
parser.add_argument("--truth", default="data/test.csv", help="Path to truth CSV")
parser.add_argument("--output", default=None, help="Optional path to write JSON results")
args = parser.parse_args()
try:
results = score(args.predictions, args.truth)
except Exception as exc:
print(json.dumps({"error": str(exc)}, indent=2))
sys.exit(1)
print(json.dumps(results, indent=2))
if args.output:
with open(args.output, "w", encoding="utf-8") as f:
json.dump(results, f, indent=2)
if __name__ == "__main__":
main()