Create scorer.py

scorer.py

@@ -0,0 +1,67 @@
+import sys
+import json
+import pandas as pd
+from sklearn.metrics import accuracy_score, precision_score, recall_score, f1_score, confusion_matrix
+
+def load_csv(path):
+    try:
+        return pd.read_csv(path)
+    except Exception as e:
+        print(f"Error loading {path}: {e}")
+        sys.exit(1)
+
+def main():
+    if len(sys.argv) != 3:
+        print("Usage: python scorer.py predictions.csv data/test.csv")
+        sys.exit(1)
+
+    pred_path = sys.argv[1]
+    truth_path = sys.argv[2]
+
+    pred = load_csv(pred_path)
+    truth = load_csv(truth_path)
+
+    required_pred = {"scenario_id", "prediction"}
+    if not required_pred.issubset(pred.columns):
+        print("Error: predictions.csv must contain scenario_id,prediction")
+        sys.exit(1)
+
+    if "scenario_id" not in truth.columns or "label" not in truth.columns:
+        print("Error: test.csv must contain scenario_id,label")
+        sys.exit(1)
+
+    try:
+        pred["prediction"] = pred["prediction"].astype(float).astype(int)
+        truth["label"] = truth["label"].astype(float).astype(int)
+    except (ValueError, TypeError):
+        print("Error: prediction and label columns must contain 0 or 1.")
+        sys.exit(1)
+
+    merged = truth.merge(pred[["scenario_id", "prediction"]], on="scenario_id", how="inner")
+
+    if len(merged) == 0:
+        print("Error: no matching scenario_id values.")
+        sys.exit(1)
+
+    y_true = merged["label"]
+    y_pred = merged["prediction"]
+
+    cm = confusion_matrix(y_true, y_pred, labels=[0, 1])
+
+    results = {
+        "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),
+        "confusion_matrix": {
+            "tn": int(cm[0][0]),
+            "fp": int(cm[0][1]),
+            "fn": int(cm[1][0]),
+            "tp": int(cm[1][1])
+        }
+    }
+
+    print(json.dumps(results, indent=2))
+
+if __name__ == "__main__":
+    main()