# Buggy code
def pairwise_distances_chunked(
    X,
    Y=None,
    reduce_func=None,
    metric="euclidean",
    n_jobs=None,
    working_memory=None,
    **kwds,
):
    """Generate a distance matrix chunk by chunk with optional reduction

    In cases where not all of a pairwise distance matrix needs to be stored at
    once, this is used to calculate pairwise distances in
    ``working_memory``-sized chunks.  If ``reduce_func`` is given, it is run
    on each chunk and its return values are concatenated into lists, arrays
    or sparse matrices.

    Parameters
    ----------
    X : array [n_samples_a, n_samples_a] if metric == "precomputed", or,
        [n_samples_a, n_features] otherwise
        Array of pairwise distances between samples, or a feature array.

    Y : array [n_samples_b, n_features], optional
        An optional second feature array. Only allowed if
        metric != "precomputed".

    reduce_func : callable, optional
        The function which is applied on each chunk of the distance matrix,
        reducing it to needed values.  ``reduce_func(D_chunk, start)``
        is called repeatedly, where ``D_chunk`` is a contiguous vertical
        slice of the pairwise distance matrix, starting at row ``start``.
        It should return an array, a list, or a sparse matrix of length
        ``D_chunk.shape[0]``, or a tuple of such objects.

        If None, pairwise_distances_chunked returns a generator of vertical
        chunks of the distance matrix.

    metric : string, or callable
        The metric to use when calculating distance between instances in a
        feature array. If metric is a string, it must be one of the options
        allowed by scipy.spatial.distance.pdist for its metric parameter, or
        a metric listed in pairwise.PAIRWISE_DISTANCE_FUNCTIONS.
        If metric is "precomputed", X is assumed to be a distance matrix.
        Alternatively, if metric is a callable function, it is called on each
        pair of instances (rows) and the resulting value recorded. The callable
        should take two arrays from X as input and return a value indicating
        the distance between them.

    n_jobs : int
        The number of jobs to use for the computation. This works by breaking
        down the pairwise matrix into n_jobs even slices and computing them in
        parallel.

        If -1 all CPUs are used. If 1 is given, no parallel computing code is
        used at all, which is useful for debugging. For n_jobs below -1,
        (n_cpus + 1 + n_jobs) are used. Thus for n_jobs = -2, all CPUs but one
        are used.

    working_memory : int, optional
        The sought maximum memory for temporary distance matrix chunks.
        When None (default), the value of
        ``sklearn.get_config()['working_memory']`` is used.

    `**kwds` : optional keyword parameters
        Any further parameters are passed directly to the distance function.
        If using a scipy.spatial.distance metric, the parameters are still
        metric dependent. See the scipy docs for usage examples.

    Yields
    ------
    D_chunk : array or sparse matrix
        A contiguous slice of distance matrix, optionally processed by
        ``reduce_func``.

    Examples
    --------
    Without reduce_func:

    >>> X = np.random.RandomState(0).rand(5, 3)
    >>> D_chunk = next(pairwise_distances_chunked(X))
    >>> D_chunk  # doctest: +ELLIPSIS
    array([[0.  ..., 0.29..., 0.41..., 0.19..., 0.57...],
           [0.29..., 0.  ..., 0.57..., 0.41..., 0.76...],
           [0.41..., 0.57..., 0.  ..., 0.44..., 0.90...],
           [0.19..., 0.41..., 0.44..., 0.  ..., 0.51...],
           [0.57..., 0.76..., 0.90..., 0.51..., 0.  ...]])

    Retrieve all neighbors and average distance within radius r:

    >>> r = .2
    >>> def reduce_func(D_chunk, start):
    ...     neigh = [np.flatnonzero(d < r) for d in D_chunk]
    ...     avg_dist = (D_chunk * (D_chunk < r)).mean(axis=1)
    ...     return neigh, avg_dist
    >>> gen = pairwise_distances_chunked(X, reduce_func=reduce_func)
    >>> neigh, avg_dist = next(gen)
    >>> neigh
    [array([0, 3]), array([1]), array([2]), array([0, 3]), array([4])]
    >>> avg_dist  # doctest: +ELLIPSIS
    array([0.039..., 0.        , 0.        , 0.039..., 0.        ])

    Where r is defined per sample, we need to make use of ``start``:

    >>> r = [.2, .4, .4, .3, .1]
    >>> def reduce_func(D_chunk, start):
    ...     neigh = [np.flatnonzero(d < r[i])
    ...              for i, d in enumerate(D_chunk, start)]
    ...     return neigh
    >>> neigh = next(pairwise_distances_chunked(X, reduce_func=reduce_func))
    >>> neigh
    [array([0, 3]), array([0, 1]), array([2]), array([0, 3]), array([4])]

    Force row-by-row generation by reducing ``working_memory``:

    >>> gen = pairwise_distances_chunked(X, reduce_func=reduce_func,
    ...                                  working_memory=0)
    >>> next(gen)
    [array([0, 3])]
    >>> next(gen)
    [array([0, 1])]
    """
    n_samples_X = _num_samples(X)
    if metric == "precomputed":
        slices = (slice(0, n_samples_X),)
    else:
        if Y is None:
            Y = X
        # We get as many rows as possible within our working_memory budget to
        # store len(Y) distances in each row of output.
        #
        # Note:
        #  - this will get at least 1 row, even if 1 row of distances will
        #    exceed working_memory.
        #  - this does not account for any temporary memory usage while
        #    calculating distances (e.g. difference of vectors in manhattan
        #    distance.
        chunk_n_rows = get_chunk_n_rows(
            row_bytes=8 * _num_samples(Y),
            max_n_rows=n_samples_X,
            working_memory=working_memory,
        )
        slices = gen_batches(n_samples_X, chunk_n_rows)

    for sl in slices:
        if sl.start == 0 and sl.stop == n_samples_X:
            X_chunk = X  # enable optimised paths for X is Y
        else:
            X_chunk = X[sl]
        D_chunk = pairwise_distances(X_chunk, Y, metric=metric, n_jobs=n_jobs, **kwds)
        if reduce_func is not None:
            chunk_size = D_chunk.shape[0]
            D_chunk = reduce_func(D_chunk, sl.start)
            _check_chunk_size(D_chunk, chunk_size)
        yield D_chunk


# 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 pairwise_distances_chunked(
    X,
    Y=None,
    reduce_func=None,
    metric="euclidean",
    n_jobs=None,
    working_memory=None,
    **kwds,
):
    """Generate a distance matrix chunk by chunk with optional reduction

    In cases where not all of a pairwise distance matrix needs to be stored at
    once, this is used to calculate pairwise distances in
    ``working_memory``-sized chunks.  If ``reduce_func`` is given, it is run
    on each chunk and its return values are concatenated into lists, arrays
    or sparse matrices.

    Parameters
    ----------
    X : array [n_samples_a, n_samples_a] if metric == "precomputed", or,
        [n_samples_a, n_features] otherwise
        Array of pairwise distances between samples, or a feature array.

    Y : array [n_samples_b, n_features], optional
        An optional second feature array. Only allowed if
        metric != "precomputed".

    reduce_func : callable, optional
        The function which is applied on each chunk of the distance matrix,
        reducing it to needed values.  ``reduce_func(D_chunk, start)``
        is called repeatedly, where ``D_chunk`` is a contiguous vertical
        slice of the pairwise distance matrix, starting at row ``start``.
        It should return an array, a list, or a sparse matrix of length
        ``D_chunk.shape[0]``, or a tuple of such objects.

        If None, pairwise_distances_chunked returns a generator of vertical
        chunks of the distance matrix.

    metric : string, or callable
        The metric to use when calculating distance between instances in a
        feature array. If metric is a string, it must be one of the options
        allowed by scipy.spatial.distance.pdist for its metric parameter, or
        a metric listed in pairwise.PAIRWISE_DISTANCE_FUNCTIONS.
        If metric is "precomputed", X is assumed to be a distance matrix.
        Alternatively, if metric is a callable function, it is called on each
        pair of instances (rows) and the resulting value recorded. The callable
        should take two arrays from X as input and return a value indicating
        the distance between them.

    n_jobs : int or None, optional (default=None)
        The number of jobs to use for the computation. This works by breaking
        down the pairwise matrix into n_jobs even slices and computing them 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.

    working_memory : int, optional
        The sought maximum memory for temporary distance matrix chunks.
        When None (default), the value of
        ``sklearn.get_config()['working_memory']`` is used.

    `**kwds` : optional keyword parameters
        Any further parameters are passed directly to the distance function.
        If using a scipy.spatial.distance metric, the parameters are still
        metric dependent. See the scipy docs for usage examples.

    Yields
    ------
    D_chunk : array or sparse matrix
        A contiguous slice of distance matrix, optionally processed by
        ``reduce_func``.

    Examples
    --------
    Without reduce_func:

    >>> X = np.random.RandomState(0).rand(5, 3)
    >>> D_chunk = next(pairwise_distances_chunked(X))
    >>> D_chunk  # doctest: +ELLIPSIS
    array([[0.  ..., 0.29..., 0.41..., 0.19..., 0.57...],
           [0.29..., 0.  ..., 0.57..., 0.41..., 0.76...],
           [0.41..., 0.57..., 0.  ..., 0.44..., 0.90...],
           [0.19..., 0.41..., 0.44..., 0.  ..., 0.51...],
           [0.57..., 0.76..., 0.90..., 0.51..., 0.  ...]])

    Retrieve all neighbors and average distance within radius r:

    >>> r = .2
    >>> def reduce_func(D_chunk, start):
    ...     neigh = [np.flatnonzero(d < r) for d in D_chunk]
    ...     avg_dist = (D_chunk * (D_chunk < r)).mean(axis=1)
    ...     return neigh, avg_dist
    >>> gen = pairwise_distances_chunked(X, reduce_func=reduce_func)
    >>> neigh, avg_dist = next(gen)
    >>> neigh
    [array([0, 3]), array([1]), array([2]), array([0, 3]), array([4])]
    >>> avg_dist  # doctest: +ELLIPSIS
    array([0.039..., 0.        , 0.        , 0.039..., 0.        ])

    Where r is defined per sample, we need to make use of ``start``:

    >>> r = [.2, .4, .4, .3, .1]
    >>> def reduce_func(D_chunk, start):
    ...     neigh = [np.flatnonzero(d < r[i])
    ...              for i, d in enumerate(D_chunk, start)]
    ...     return neigh
    >>> neigh = next(pairwise_distances_chunked(X, reduce_func=reduce_func))
    >>> neigh
    [array([0, 3]), array([0, 1]), array([2]), array([0, 3]), array([4])]

    Force row-by-row generation by reducing ``working_memory``:

    >>> gen = pairwise_distances_chunked(X, reduce_func=reduce_func,
    ...                                  working_memory=0)
    >>> next(gen)
    [array([0, 3])]
    >>> next(gen)
    [array([0, 1])]
    """
    n_samples_X = _num_samples(X)
    if metric == "precomputed":
        slices = (slice(0, n_samples_X),)
    else:
        if Y is None:
            Y = X
        # We get as many rows as possible within our working_memory budget to
        # store len(Y) distances in each row of output.
        #
        # Note:
        #  - this will get at least 1 row, even if 1 row of distances will
        #    exceed working_memory.
        #  - this does not account for any temporary memory usage while
        #    calculating distances (e.g. difference of vectors in manhattan
        #    distance.
        chunk_n_rows = get_chunk_n_rows(
            row_bytes=8 * _num_samples(Y),
            max_n_rows=n_samples_X,
            working_memory=working_memory,
        )
        slices = gen_batches(n_samples_X, chunk_n_rows)

    for sl in slices:
        if sl.start == 0 and sl.stop == n_samples_X:
            X_chunk = X  # enable optimised paths for X is Y
        else:
            X_chunk = X[sl]
        D_chunk = pairwise_distances(X_chunk, Y, metric=metric, n_jobs=n_jobs, **kwds)
        if reduce_func is not None:
            chunk_size = D_chunk.shape[0]
            D_chunk = reduce_func(D_chunk, sl.start)
            _check_chunk_size(D_chunk, chunk_size)
        yield D_chunk