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141f1e0 bc714de 141f1e0 bc714de 141f1e0 bc714de 141f1e0 bc714de 141f1e0 bc714de 141f1e0 bc714de 141f1e0 bc714de 141f1e0 | 1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 16 17 18 19 20 21 22 23 24 25 26 27 28 29 30 31 32 33 34 35 36 37 38 39 40 41 42 43 44 45 46 47 48 49 | import pandas as pd
from sklearn.metrics import accuracy_score, confusion_matrix, f1_score, precision_score, recall_score
def compute_metrics(predictions_df: pd.DataFrame, true_labels: dict[str, float]) -> dict[str, float]:
y_true = []
y_pred = []
for _, row in predictions_df.iterrows():
id_val = str(row["id"]).strip()
if id_val not in true_labels:
continue
true_label = int(true_labels[id_val])
pred_label = int(row["label"])
y_true.append(true_label)
y_pred.append(pred_label)
if len(y_true) == 0:
return {
"accuracy": 0.0,
"f1": 0.0,
"precision": 0.0,
"recall": 0.0,
"tp": 0,
"fp": 0,
"fn": 0,
"tn": 0,
}
accuracy = accuracy_score(y_true, y_pred)
f1 = f1_score(y_true, y_pred, zero_division=0.0)
precision = precision_score(y_true, y_pred, zero_division=0.0)
recall = recall_score(y_true, y_pred, zero_division=0.0)
tn, fp, fn, tp = confusion_matrix(y_true, y_pred).ravel()
return {
"accuracy": float(accuracy),
"f1": float(f1),
"precision": float(precision),
"recall": float(recall),
"tp": int(tp),
"fp": int(fp),
"fn": int(fn),
"tn": int(tn),
}
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