# Buggy code
def validation_curve(
    estimator,
    X,
    y,
    param_name,
    param_range,
    groups=None,
    cv="warn",
    scoring=None,
    n_jobs=None,
    pre_dispatch="all",
    verbose=0,
    error_score="raise-deprecating",
):
    """Validation curve.

    Determine training and test scores for varying parameter values.

    Compute scores for an estimator with different values of a specified
    parameter. This is similar to grid search with one parameter. However, this
    will also compute training scores and is merely a utility for plotting the
    results.

    Read more in the :ref:`User Guide <learning_curve>`.

    Parameters
    ----------
    estimator : object type that implements the "fit" and "predict" methods
        An object of that type which is cloned for each validation.

    X : array-like, shape (n_samples, n_features)
        Training vector, where n_samples is the number of samples and
        n_features is the number of features.

    y : array-like, shape (n_samples) or (n_samples, n_features), optional
        Target relative to X for classification or regression;
        None for unsupervised learning.

    param_name : string
        Name of the parameter that will be varied.

    param_range : array-like, shape (n_values,)
        The values of the parameter that will be evaluated.

    groups : array-like, with shape (n_samples,), optional
        Group labels for the samples used while splitting the dataset into
        train/test set.

    cv : int, cross-validation generator or an iterable, optional
        Determines the cross-validation splitting strategy.
        Possible inputs for cv are:

        - None, to use the default 3-fold cross validation,
        - integer, to specify the number of folds in a `(Stratified)KFold`,
        - An object to be used as a cross-validation generator.
        - An iterable yielding train, test splits.

        For integer/None inputs, if the estimator is a classifier and ``y`` is
        either binary or multiclass, :class:`StratifiedKFold` is used. In all
        other cases, :class:`KFold` is used.

        Refer :ref:`User Guide <cross_validation>` for the various
        cross-validation strategies that can be used here.

        .. versionchanged:: 0.20
            ``cv`` default value if None will change from 3-fold to 5-fold
            in v0.22.

    scoring : string, callable or None, optional, default: None
        A string (see model evaluation documentation) or
        a scorer callable object / function with signature
        ``scorer(estimator, X, y)``.

    n_jobs : integer, optional
        Number of jobs to run in parallel (default 1).

    pre_dispatch : integer or string, optional
        Number of predispatched jobs for parallel execution (default is
        all). The option can reduce the allocated memory. The string can
        be an expression like '2*n_jobs'.

    verbose : integer, optional
        Controls the verbosity: the higher, the more messages.

    error_score : 'raise' | 'raise-deprecating' or numeric
        Value to assign to the score if an error occurs in estimator fitting.
        If set to 'raise', the error is raised.
        If set to 'raise-deprecating', a FutureWarning is printed before the
        error is raised.
        If a numeric value is given, FitFailedWarning is raised. This parameter
        does not affect the refit step, which will always raise the error.
        Default is 'raise-deprecating' but from version 0.22 it will change
        to np.nan.

    Returns
    -------
    train_scores : array, shape (n_ticks, n_cv_folds)
        Scores on training sets.

    test_scores : array, shape (n_ticks, n_cv_folds)
        Scores on test set.

    Notes
    -----
    See :ref:`sphx_glr_auto_examples_model_selection_plot_validation_curve.py`

    """
    X, y, groups = indexable(X, y, groups)

    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)(
            clone(estimator),
            X,
            y,
            scorer,
            train,
            test,
            verbose,
            parameters={param_name: v},
            fit_params=None,
            return_train_score=True,
            error_score=error_score,
        )
        # NOTE do not change order of iteration to allow one time cv splitters
        for train, test in cv.split(X, y, groups)
        for v in param_range
    )
    out = np.asarray(out)
    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]


# Traceback (ValueError)
Unexpected failing examples:
/home/circleci/project/examples/cluster/plot_feature_agglomeration_vs_univariate_selection.py failed leaving traceback:
Traceback (most recent call last):
File "/home/circleci/project/examples/cluster/plot_feature_agglomeration_vs_univariate_selection.py", line 75, in <module>
clf.fit(X, y)  # set the best parameters
File "/home/circleci/project/sklearn/model_selection/_search.py", line 663, in fit
cv.split(X, y, groups)))
File "/home/circleci/project/sklearn/externals/joblib/parallel.py", line 981, in __call__
if self.dispatch_one_batch(iterator):
File "/home/circleci/project/sklearn/externals/joblib/parallel.py", line 818, in dispatch_one_batch
self._pickle_cache)
File "/home/circleci/project/sklearn/externals/joblib/parallel.py", line 253, in __init__
self.items = list(iterator_slice)
File "/home/circleci/project/sklearn/model_selection/_search.py", line 662, in <genexpr>
for parameters, (train, test) in product(candidate_params,
File "/home/circleci/project/sklearn/base.py", line 62, in clone
new_object_params[name] = clone(param, safe=False)
File "/home/circleci/project/sklearn/base.py", line 50, in clone
return estimator_type([clone(e, safe=safe) for e in estimator])
File "/home/circleci/project/sklearn/base.py", line 50, in <listcomp>
return estimator_type([clone(e, safe=safe) for e in estimator])
File "/home/circleci/project/sklearn/base.py", line 50, in clone
return estimator_type([clone(e, safe=safe) for e in estimator])
File "/home/circleci/project/sklearn/base.py", line 50, in <listcomp>
return estimator_type([clone(e, safe=safe) for e in estimator])
File "/home/circleci/project/sklearn/base.py", line 62, in clone
new_object_params[name] = clone(param, safe=False)
File "/home/circleci/project/sklearn/base.py", line 53, in clone
return copy.deepcopy(estimator)
File "/home/circleci/miniconda/envs/testenv/lib/python3.6/copy.py", line 180, in deepcopy
y = _reconstruct(x, memo, *rv)
File "/home/circleci/miniconda/envs/testenv/lib/python3.6/copy.py", line 274, in _reconstruct
y = func(*args)
File "/home/circleci/project/sklearn/externals/joblib/memory.py", line 830, in __init__
location, cachedir))
ValueError: You set both "location='/tmp/tmp1v2mgts1' and "cachedir=False". 'cachedir' has been deprecated in version 0.12 and will be removed in version 0.14.
Please only set "location='/tmp/tmp1v2mgts1'"


/home/circleci/project/examples/compose/plot_compare_reduction.py failed leaving traceback:
Traceback (most recent call last):
File "/home/circleci/project/examples/compose/plot_compare_reduction.py", line 119, in <module>
grid.fit(digits.data, digits.target)
File "/home/circleci/project/sklearn/model_selection/_search.py", line 663, in fit
cv.split(X, y, groups)))
File "/home/circleci/project/sklearn/externals/joblib/parallel.py", line 981, in __call__
if self.dispatch_one_batch(iterator):
File "/home/circleci/project/sklearn/externals/joblib/parallel.py", line 818, in dispatch_one_batch
self._pickle_cache)
File "/home/circleci/project/sklearn/externals/joblib/parallel.py", line 253, in __init__
self.items = list(iterator_slice)
File "/home/circleci/project/sklearn/model_selection/_search.py", line 662, in <genexpr>
for parameters, (train, test) in product(candidate_params,
File "/home/circleci/project/sklearn/base.py", line 62, in clone
new_object_params[name] = clone(param, safe=False)
File "/home/circleci/project/sklearn/base.py", line 53, in clone
return copy.deepcopy(estimator)
File "/home/circleci/miniconda/envs/testenv/lib/python3.6/copy.py", line 180, in deepcopy
y = _reconstruct(x, memo, *rv)
File "/home/circleci/miniconda/envs/testenv/lib/python3.6/copy.py", line 274, in _reconstruct
y = func(*args)
File "/home/circleci/project/sklearn/externals/joblib/memory.py", line 830, in __init__
location, cachedir))
ValueError: You set both "location='/tmp/tmpa408j1cz' and "cachedir=False". 'cachedir' has been deprecated in version 0.12 and will be removed in version 0.14.
Please only set "location='/tmp/tmpa408j1cz'"

# Fixed code
def validation_curve(
    estimator,
    X,
    y,
    param_name,
    param_range,
    groups=None,
    cv="warn",
    scoring=None,
    n_jobs=None,
    pre_dispatch="all",
    verbose=0,
    error_score="raise-deprecating",
):
    """Validation curve.

    Determine training and test scores for varying parameter values.

    Compute scores for an estimator with different values of a specified
    parameter. This is similar to grid search with one parameter. However, this
    will also compute training scores and is merely a utility for plotting the
    results.

    Read more in the :ref:`User Guide <learning_curve>`.

    Parameters
    ----------
    estimator : object type that implements the "fit" and "predict" methods
        An object of that type which is cloned for each validation.

    X : array-like, shape (n_samples, n_features)
        Training vector, where n_samples is the number of samples and
        n_features is the number of features.

    y : array-like, shape (n_samples) or (n_samples, n_features), optional
        Target relative to X for classification or regression;
        None for unsupervised learning.

    param_name : string
        Name of the parameter that will be varied.

    param_range : array-like, shape (n_values,)
        The values of the parameter that will be evaluated.

    groups : array-like, with shape (n_samples,), optional
        Group labels for the samples used while splitting the dataset into
        train/test set.

    cv : int, cross-validation generator or an iterable, optional
        Determines the cross-validation splitting strategy.
        Possible inputs for cv are:

        - None, to use the default 3-fold cross validation,
        - integer, to specify the number of folds in a `(Stratified)KFold`,
        - An object to be used as a cross-validation generator.
        - An iterable yielding train, test splits.

        For integer/None inputs, if the estimator is a classifier and ``y`` is
        either binary or multiclass, :class:`StratifiedKFold` is used. In all
        other cases, :class:`KFold` is used.

        Refer :ref:`User Guide <cross_validation>` for the various
        cross-validation strategies that can be used here.

        .. versionchanged:: 0.20
            ``cv`` default value if None will change from 3-fold to 5-fold
            in v0.22.

    scoring : string, callable or None, optional, default: None
        A string (see model evaluation documentation) or
        a scorer callable object / function with signature
        ``scorer(estimator, X, y)``.

    n_jobs : int or None, optional (default=None)
        Number of jobs to run in parallel.
        ``None`` means 1 unless in a :obj:`joblib.parallel_backend` context.
        ``-1`` means using all processors. See :term:`Glossary <n_jobs>`
        for more details.

    pre_dispatch : integer or string, optional
        Number of predispatched jobs for parallel execution (default is
        all). The option can reduce the allocated memory. The string can
        be an expression like '2*n_jobs'.

    verbose : integer, optional
        Controls the verbosity: the higher, the more messages.

    error_score : 'raise' | 'raise-deprecating' or numeric
        Value to assign to the score if an error occurs in estimator fitting.
        If set to 'raise', the error is raised.
        If set to 'raise-deprecating', a FutureWarning is printed before the
        error is raised.
        If a numeric value is given, FitFailedWarning is raised. This parameter
        does not affect the refit step, which will always raise the error.
        Default is 'raise-deprecating' but from version 0.22 it will change
        to np.nan.

    Returns
    -------
    train_scores : array, shape (n_ticks, n_cv_folds)
        Scores on training sets.

    test_scores : array, shape (n_ticks, n_cv_folds)
        Scores on test set.

    Notes
    -----
    See :ref:`sphx_glr_auto_examples_model_selection_plot_validation_curve.py`

    """
    X, y, groups = indexable(X, y, groups)

    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)(
            clone(estimator),
            X,
            y,
            scorer,
            train,
            test,
            verbose,
            parameters={param_name: v},
            fit_params=None,
            return_train_score=True,
            error_score=error_score,
        )
        # NOTE do not change order of iteration to allow one time cv splitters
        for train, test in cv.split(X, y, groups)
        for v in param_range
    )
    out = np.asarray(out)
    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]