Custom ml_cross_val_score function from mlfinlab for the Keras Model

ml_cross_val_score.py

from sklearn.metrics import log_loss, f1_score, accuracy_score

def ml_cross_val_score(
        baseline_fn,
        X: pd.DataFrame,
        y: pd.Series,
        cv_gen,
        sample_weight_train: np.ndarray = None,
        sample_weight_score: np.ndarray = None,
        scoring = log_loss):
    # pylint: disable=invalid-name
    # pylint: disable=comparison-with-callable

    # If no sample_weight then broadcast a value of 1 to all samples (full weight).
    if sample_weight_train is None:
        sample_weight_train = np.ones((X.shape[0],))

    if sample_weight_score is None:
        sample_weight_score = np.ones((X.shape[0],))

    # Score model on KFolds
    ret_scores = []
    for train, test in cv_gen.split(X=X, y=y):
        print(f'Fold {len(ret_scores) + 1}')
        X_train, y_train = X.iloc[train, :], y.iloc[train]
        class_weights = compute_class_weight(y_train)
        classifier = baseline_fn(input_dim=X_train.shape[1])
        
        classifier.fit(X_train, y_train, 
                       validation_split=0.2,
                       sample_weight=sample_weight_train[train],
                       class_weight=class_weights, 
                       epochs=100, verbose=2, callbacks=callbacks)
        
        if scoring == log_loss:
            prob = classifier.predict_proba(X.iloc[test, :])
            score = -1 * scoring(y.iloc[test], prob, sample_weight=sample_weight_score[test], labels=classifier.classes_)
        else:
            pred = classifier.predict_classes(X.iloc[test, :])
            score = scoring(y.iloc[test], pred, sample_weight=sample_weight_score[test])
            
        print('Score: ', score)
        ret_scores.append(score)
    return np.array(ret_scores)