def compute_metrics()

in classification/train_edu_bert.py [0:0]

• 26 lines of code
• 1 McCabe index (conditional complexity)

def compute_metrics(eval_pred):
    precision_metric = evaluate.load("precision")
    recall_metric = evaluate.load("recall")
    f1_metric = evaluate.load("f1")
    accuracy_metric = evaluate.load("accuracy")

    logits, labels = eval_pred
    preds = np.round(logits.squeeze()).clip(0, 5).astype(int)
    labels = np.round(labels.squeeze()).astype(int)
    precision = precision_metric.compute(
        predictions=preds, references=labels, average="macro"
    )["precision"]
    recall = recall_metric.compute(
        predictions=preds, references=labels, average="macro"
    )["recall"]
    f1 = f1_metric.compute(predictions=preds, references=labels, average="macro")["f1"]
    accuracy = accuracy_metric.compute(predictions=preds, references=labels)["accuracy"]

    report = classification_report(labels, preds)
    cm = confusion_matrix(labels, preds)
    print("Validation Report:\n" + report)
    print("Confusion Matrix:\n" + str(cm))

    return {
        "precision": precision,
        "recall": recall,
        "f1_macro": f1,
        "accuracy": accuracy,
    }