Create scorer.py

scorer.py

@@ -0,0 +1,53 @@
+import pandas as pd
+from sklearn.metrics import accuracy_score, precision_score, recall_score, f1_score, confusion_matrix
+
+
+def _resolve_target_col(df: pd.DataFrame) -> str:
+    label_cols = [c for c in df.columns if c.startswith("label_")]
+    if len(label_cols) == 1:
+        return label_cols[0]
+
+    raise ValueError(
+        f"Expected exactly one label column starting with 'label_', found: {label_cols}"
+    )
+
+
+def score(solution, submission):
+    if isinstance(solution, str):
+        solution = pd.read_csv(solution)
+
+    if isinstance(submission, str):
+        submission = pd.read_csv(submission)
+
+    target_col = _resolve_target_col(solution)
+
+    if target_col not in submission.columns:
+        submission_label_cols = [c for c in submission.columns if c.startswith("label_")]
+        if len(submission_label_cols) == 1:
+            submission = submission.rename(columns={submission_label_cols[0]: target_col})
+        else:
+            raise ValueError(
+                f"Submission must contain target column '{target_col}' or exactly one label column. "
+                f"Found: {list(submission.columns)}"
+            )
+
+    y_true = solution[target_col]
+    y_pred = submission[target_col]
+
+    accuracy = accuracy_score(y_true, y_pred)
+    precision = precision_score(y_true, y_pred, zero_division=0)
+    recall = recall_score(y_true, y_pred, zero_division=0)
+    f1 = f1_score(y_true, y_pred, zero_division=0)
+    cm = confusion_matrix(y_true, y_pred)
+
+    return {
+        "target_column": target_col,
+        "positive_class": 1,
+        "primary_metric": "recall_cascade_detection",
+        "accuracy": accuracy,
+        "precision": precision,
+        "recall_cascade_detection": recall,
+        "false_safe_rate": 1 - recall,
+        "f1": f1,
+        "confusion_matrix": cm.tolist(),
+    }