f0f4ba31aaec58f607df632cfbec5fd39802958f,mlxtend/evaluate/learning_curves.py,,plot_learning_curves,#,9

Before Change

        for r in rng:
            model = clf.fit(X_train.ix[:, 0:r], y_train)
            if predict_proba == True:
                y_train_predict = clf.predict_proba(X_train.ix[:, 0:r])
                y_test_predict = clf.predict_proba(X_test.ix[:, 0:r])
            else:
                y_train_predict = clf.predict(X_train.ix[:, 0:r])
                y_test_predict = clf.predict(X_test.ix[:, 0:r])

After Change

    (training_error, test_error): tuple of lists
    

    if scoring != "misclassification error":
        from sklearn import metrics

        scoring_func = {
            "accuracy": metrics.accuracy_score,
            "average_precision": metrics.average_precision_score,
            "f1": metrics.f1_score,
            "f1_micro": metrics.f1_score,
            "f1_macro": metrics.f1_score,
            "f1_weighted": metrics.f1_score,
            "f1_samples": metrics.f1_score,
            "log_loss": metrics.log_loss,
            "precision": metrics.precision_score,
            "recall": metrics.recall_score,
            "roc_auc": metrics.roc_auc_score,
            "adjusted_rand_score": metrics.adjusted_rand_score,
            "mean_absolute_error": metrics.mean_absolute_error,
            "mean_squared_error": metrics.mean_squared_error,
            "median_absolute_error": metrics.median_absolute_error,
            "r2": metrics.r2_score}

        if not scoring in scoring_func.keys():
            raise ArgumentError("scoring must be in", scoring_func.keys())


    else:
        def misclf_err(y_predict, y):
            return  (y_predict != y).sum() / len(y)

        scoring_func = {
            "misclassification error": misclf_err}

    training_errors = []
    test_errors = []

    if kind not in ("training_size", "n_features"):

Instances