Create scorer.py

scorer.py

@@ -0,0 +1,78 @@
+import argparse
+from dataclasses import dataclass
+from typing import Dict, Tuple
+
+import pandas as pd
+
+
+LABEL_COL = "label_cascade_event"
+
+
+@dataclass
+class BinaryMetrics:
+    accuracy: float
+    precision: float
+    recall: float
+    f1: float
+    confusion_matrix: Tuple[int, int, int, int]
+
+    def as_dict(self) -> Dict[str, object]:
+        tn, fp, fn, tp = self.confusion_matrix
+        return {
+            "accuracy": self.accuracy,
+            "precision": self.precision,
+            "recall": self.recall,
+            "f1": self.f1,
+            "confusion_matrix": {"tn": tn, "fp": fp, "fn": fn, "tp": tp},
+        }
+
+
+def _safe_div(n: float, d: float) -> float:
+    return float(n) / float(d) if d else 0.0
+
+
+def compute_binary_metrics(y_true, y_pred) -> BinaryMetrics:
+    y_true = [int(x) for x in y_true]
+    y_pred = [int(x) for x in 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)
+
+    accuracy = _safe_div(tp + tn, tp + tn + fp + fn)
+    precision = _safe_div(tp, tp + fp)
+    recall = _safe_div(tp, tp + fn)
+    f1 = _safe_div(2 * precision * recall, precision + recall)
+
+    return BinaryMetrics(
+        accuracy=accuracy,
+        precision=precision,
+        recall=recall,
+        f1=f1,
+        confusion_matrix=(tn, fp, fn, tp),
+    )
+
+
+def load_labels_from_csv(path: str, label_col: str = LABEL_COL):
+    df = pd.read_csv(path)
+    s = df[label_col].astype(str).str.strip().str.lower()
+    s = s.replace({"true": "1", "false": "0"})
+    return s.astype(int).tolist()
+
+
+def main():
+    parser = argparse.ArgumentParser()
+    parser.add_argument("--gold", required=True)
+    parser.add_argument("--pred", required=True)
+    args = parser.parse_args()
+
+    y_true = load_labels_from_csv(args.gold, LABEL_COL)
+    y_pred = load_labels_from_csv(args.pred, LABEL_COL)
+
+    metrics = compute_binary_metrics(y_true, y_pred)
+    print(metrics.as_dict())
+
+
+if __name__ == "__main__":
+    main()