Create scorer.py

scorer.py

@@ -0,0 +1,109 @@
+import csv
+import sys
+from pathlib import Path
+
+
+def load_csv(path):
+    with open(path, "r", encoding="utf-8") as f:
+        return list(csv.DictReader(f))
+
+
+def normalize_binary(value):
+    if isinstance(value, int):
+        return value
+    text = str(value).strip().lower()
+    if text in {"1", "true", "yes", "positive"}:
+        return 1
+    if text in {"0", "false", "no", "negative"}:
+        return 0
+    raise ValueError(f"Unrecognized binary value: {value}")
+
+
+def find_label_column(fieldnames):
+    matches = [c for c in fieldnames if c.startswith("label_")]
+    if len(matches) != 1:
+        raise ValueError(f"Expected exactly one label_ column, found: {matches}")
+    return matches[0]
+
+
+def compute_metrics(y_true, y_pred):
+    tp = sum(1 for t, p in zip(y_true, y_pred) if t == 1 and p == 1)
+    tn = sum(1 for t, p in zip(y_true, y_pred) if t == 0 and p == 0)
+    fp = sum(1 for t, p in zip(y_true, y_pred) if t == 0 and p == 1)
+    fn = sum(1 for t, p in zip(y_true, y_pred) if t == 1 and p == 0)
+
+    total = len(y_true)
+    accuracy = (tp + tn) / total if total else 0.0
+    precision = tp / (tp + fp) if (tp + fp) else 0.0
+    recall_successful_stabilization = tp / (tp + fn) if (tp + fn) else 0.0
+    failed_rescue_rate = fn / (fn + tp) if (fn + tp) else 0.0
+    f1 = (
+        2 * precision * recall_successful_stabilization / (precision + recall_successful_stabilization)
+        if (precision + recall_successful_stabilization)
+        else 0.0
+    )
+
+    return {
+        "accuracy": accuracy,
+        "precision": precision,
+        "recall_successful_stabilization": recall_successful_stabilization,
+        "failed_rescue_rate": failed_rescue_rate,
+        "f1": f1,
+        "primary_metric": "recall_successful_stabilization",
+        "secondary_metric": "failed_rescue_rate",
+        "confusion_matrix": {
+            "true_positives": tp,
+            "false_positives": fp,
+            "true_negatives": tn,
+            "false_negatives": fn,
+        },
+    }
+
+
+def score(reference_path, prediction_path):
+    references = load_csv(reference_path)
+    predictions = load_csv(prediction_path)
+
+    if not references:
+        raise ValueError("Reference file is empty.")
+    if not predictions:
+        raise ValueError("Prediction file is empty.")
+    if len(references) != len(predictions):
+        raise ValueError("Reference and prediction row counts do not match.")
+
+    label_col = find_label_column(references[0].keys())
+
+    y_true = [normalize_binary(row[label_col]) for row in references]
+
+    pred_col = None
+    for candidate in ["prediction", "predicted_label", label_col]:
+        if candidate in predictions[0]:
+            pred_col = candidate
+            break
+
+    if pred_col is None:
+        raise ValueError(
+            "Prediction file must contain one of: prediction, predicted_label, or the label column name."
+        )
+
+    y_pred = [normalize_binary(row[pred_col]) for row in predictions]
+
+    return compute_metrics(y_true, y_pred)
+
+
+if __name__ == "__main__":
+    if len(sys.argv) != 3:
+        print("Usage: python scorer.py <reference.csv> <predictions.csv>", file=sys.stderr)
+        sys.exit(1)
+
+    ref_path = Path(sys.argv[1])
+    pred_path = Path(sys.argv[2])
+
+    try:
+        results = score(ref_path, pred_path)
+    except Exception as e:
+        print(f"Scoring error: {e}", file=sys.stderr)
+        sys.exit(1)
+
+    for key, value in results.items():
+        print(f"{key}: {value}")