def _incremental_fit_estimator(estimator, X, y, classes, train, test,
                               train_sizes, scorer, verbose):
    train_scores, test_scores = [], []
    partitions = zip(train_sizes, np.split(train, train_sizes)[:-1])
    for n_train_samples, partial_train in partitions:
        train_subset = train[:n_train_samples]
        X_train, y_train = _safe_split(estimator, X, y, train_subset)
        X_partial_train, y_partial_train = _safe_split(estimator, X, y,
                                                       partial_train)
        X_test, y_test = _safe_split(estimator, X, y, test, train_subset)
        if y_partial_train is None:
            estimator.partial_fit(X_partial_train, classes=classes)
        else:
            estimator.partial_fit(X_partial_train, y_partial_train,
                                  classes=classes)
        train_scores.append(_score(estimator, X_train, y_train, scorer))
        test_scores.append(_score(estimator, X_test, y_test, scorer))
    return np.array((train_scores, test_scores)).T
Train estimator on training subsets incrementally and compute scores.
dataset/ETHPy150Open scikit-learn/scikit-learn/sklearn/model_selection/_validation.py/_incremental_fit_estimator