Upload metrics.py

grewtse/evaluators/metrics.py

@@ -75,30 +75,8 @@ def calculate_accuracy(df: pd.DataFrame) -> float:
     return correct / total if total > 0 else 0.0
 
 def calculate_all_metrics(df: pd.DataFrame) -> dict:
-    predictions = get_predictions(df)
-    true_labels = np.ones(len(df), dtype=int)
-
-    # Calculate confusion matrix components
-    tp = np.sum((predictions == 1) & (true_labels == 1))
-    fp = np.sum((predictions == 1) & (true_labels == 0))
-    fn = np.sum((predictions == 0) & (true_labels == 1))
-    tn = np.sum((predictions == 0) & (true_labels == 0))
-
-    total = len(predictions)
-
-    # Calculate metrics
-    accuracy = (tp + tn) / total if total > 0 else 0.0
-    precision = tp / (tp + fp) if (tp + fp) > 0 else 0.0
-    recall = tp / (tp + fn) if (tp + fn) > 0 else 0.0
-    f1 = 2 * (precision * recall) / (precision + recall) if (precision + recall) > 0 else 0.0
+    accuracy = calculate_accuracy(df)
 
     return {
         'accuracy': round(accuracy,2),
-        'precision': round(precision, 2),
-        'recall': round(recall, 2),
-        'f1': round(f1, 2),
-        'true_positives': int(tp),
-        'false_positives': int(fp),
-        'false_negatives': int(fn),
-        'true_negatives': int(tn)
     }