def validation_curve(estimator, X, y, param_name, param_range, labels=None,
                     cv=None, scoring=None, n_jobs=1, pre_dispatch="all",
                     verbose=0):
    X, y, labels = indexable(X, y, labels)

    cv = check_cv(cv, y, classifier=is_classifier(estimator))

    scorer = check_scoring(estimator, scoring=scoring)

    parallel = Parallel(n_jobs=n_jobs, pre_dispatch=pre_dispatch,
                        verbose=verbose)
    out = parallel(delayed(_fit_and_score)(
        estimator, X, y, scorer, train, test, verbose,
        parameters={param_name: v}, fit_params=None, return_train_score=True)
        for train, test in cv.split(X, y, labels) for v in param_range)

    out = np.asarray(out)[:, :2]
    n_params = len(param_range)
    n_cv_folds = out.shape[0] // n_params
    out = out.reshape(n_cv_folds, n_params, 2).transpose((2, 1, 0))

    return out[0], out[1]
Removes the apache config file and reloads apache
dataset/ETHPy150Open scikit-learn/scikit-learn/sklearn/model_selection/_validation.py/validation_curve--crossover with--dataset/ETHPy150Open bmbouter/Opus/opus/lib/deployer/__init__.py/ProjectUndeployer.remove_apache_conf