Create scorer.py

scorer.py

@@ -0,0 +1,102 @@
+import pandas as pd
+from sklearn.metrics import accuracy_score, precision_score, recall_score, f1_score, confusion_matrix
+
+TARGET_LABEL = "label_shock_boundary_transition"
+
+
+def normalize_label(df):
+    label_cols = [c for c in df.columns if c.startswith("label_")]
+
+    if len(label_cols) == 1:
+        return df.rename(columns={label_cols[0]: "label"})
+
+    if len(label_cols) == 0:
+        raise ValueError(
+            f"No label column found. Expected {TARGET_LABEL} or a single label_* column."
+        )
+
+    raise ValueError(f"Multiple label columns found: {label_cols}")
+
+
+def validate_binary_labels(series, name):
+    unique_values = set(series.dropna().unique())
+    if not unique_values.issubset({0, 1}):
+        raise ValueError(
+            f"{name} must contain only binary values 0/1. Found: {sorted(unique_values)}"
+        )
+
+
+def validate_lengths(y_true, y_pred):
+    if len(y_true) != len(y_pred):
+        raise ValueError(
+            f"Prediction length mismatch. predictions={len(y_pred)} ground_truth={len(y_true)}"
+        )
+
+
+def validate_prediction_columns(preds):
+    allowed = {"scenario_id", "label"}
+    extra_cols = [c for c in preds.columns if c not in allowed]
+    if extra_cols:
+        raise ValueError(
+            f"Predictions file should contain only scenario_id and label columns after normalization. Extra columns found: {extra_cols}"
+        )
+
+
+def score(predictions_path, ground_truth_path):
+    preds = pd.read_csv(predictions_path)
+    truth = pd.read_csv(ground_truth_path)
+
+    truth = normalize_label(truth)
+    preds = normalize_label(preds)
+
+    if "label" not in preds.columns:
+        raise ValueError("Predictions file must contain a label column")
+
+    if "label" not in truth.columns:
+        raise ValueError("Ground truth file must contain a label column")
+
+    validate_prediction_columns(preds)
+
+    y_true = truth["label"]
+    y_pred = preds["label"]
+
+    validate_lengths(y_true, y_pred)
+    validate_binary_labels(y_true, "Ground truth labels")
+    validate_binary_labels(y_pred, "Prediction labels")
+
+    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, labels=[0, 1])
+    tn, fp, fn, tp = cm.ravel()
+
+    false_safe_rate = fn / (fn + tp) if (fn + tp) > 0 else 0.0
+    true_safe_rate = tn / (tn + fp) if (tn + fp) > 0 else 0.0
+    positive_prediction_rate = (tp + fp) / len(y_pred) if len(y_pred) > 0 else 0.0
+
+    return {
+        "target_label": TARGET_LABEL,
+        "n_samples": int(len(y_true)),
+        "accuracy": float(accuracy),
+        "precision": float(precision),
+        "recall_cascade_detection": float(recall),
+        "false_safe_rate": float(false_safe_rate),
+        "true_safe_rate": float(true_safe_rate),
+        "positive_prediction_rate": float(positive_prediction_rate),
+        "f1": float(f1),
+        "confusion_matrix": cm.tolist(),
+    }
+
+
+if __name__ == "__main__":
+    import sys
+
+    if len(sys.argv) != 3:
+        raise ValueError("Usage: python scorer.py <predictions.csv> <ground_truth.csv>")
+
+    predictions_path = sys.argv[1]
+    ground_truth_path = sys.argv[2]
+
+    print(score(predictions_path, ground_truth_path))