# Buggy code
def dbscan(
    X,
    eps=0.5,
    min_samples=5,
    metric="minkowski",
    metric_params=None,
    algorithm="auto",
    leaf_size=30,
    p=2,
    sample_weight=None,
    n_jobs=None,
):
    """Perform DBSCAN clustering from vector array or distance matrix.

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

    Parameters
    ----------
    X : array or sparse (CSR) matrix of shape (n_samples, n_features), or \
            array of shape (n_samples, n_samples)
        A feature array, or array of distances between samples if
        ``metric='precomputed'``.

    eps : float, optional
        The maximum distance between two samples for them to be considered
        as in the same neighborhood.

    min_samples : int, optional
        The number of samples (or total weight) in a neighborhood for a point
        to be considered as a core point. This includes the point itself.

    metric : string, or callable
        The metric to use when calculating distance between instances in a
        feature array. If metric is a string or callable, it must be one of
        the options allowed by :func:`sklearn.metrics.pairwise_distances` for
        its metric parameter.
        If metric is "precomputed", X is assumed to be a distance matrix and
        must be square. X may be a sparse matrix, in which case only "nonzero"
        elements may be considered neighbors for DBSCAN.

    metric_params : dict, optional
        Additional keyword arguments for the metric function.

        .. versionadded:: 0.19

    algorithm : {'auto', 'ball_tree', 'kd_tree', 'brute'}, optional
        The algorithm to be used by the NearestNeighbors module
        to compute pointwise distances and find nearest neighbors.
        See NearestNeighbors module documentation for details.

    leaf_size : int, optional (default = 30)
        Leaf size passed to BallTree or cKDTree. This can affect the speed
        of the construction and query, as well as the memory required
        to store the tree. The optimal value depends
        on the nature of the problem.

    p : float, optional
        The power of the Minkowski metric to be used to calculate distance
        between points.

    sample_weight : array, shape (n_samples,), optional
        Weight of each sample, such that a sample with a weight of at least
        ``min_samples`` is by itself a core sample; a sample with negative
        weight may inhibit its eps-neighbor from being core.
        Note that weights are absolute, and default to 1.

    n_jobs : int, optional (default = 1)
        The number of parallel jobs to run for neighbors search.
        If ``-1``, then the number of jobs is set to the number of CPU cores.

    Returns
    -------
    core_samples : array [n_core_samples]
        Indices of core samples.

    labels : array [n_samples]
        Cluster labels for each point.  Noisy samples are given the label -1.

    See also
    --------
    DBSCAN
        An estimator interface for this clustering algorithm.
    optics
        A similar clustering at multiple values of eps. Our implementation
        is optimized for memory usage.

    Notes
    -----
    For an example, see :ref:`examples/cluster/plot_dbscan.py
    <sphx_glr_auto_examples_cluster_plot_dbscan.py>`.

    This implementation bulk-computes all neighborhood queries, which increases
    the memory complexity to O(n.d) where d is the average number of neighbors,
    while original DBSCAN had memory complexity O(n). It may attract a higher
    memory complexity when querying these nearest neighborhoods, depending
    on the ``algorithm``.

    One way to avoid the query complexity is to pre-compute sparse
    neighborhoods in chunks using
    :func:`NearestNeighbors.radius_neighbors_graph
    <sklearn.neighbors.NearestNeighbors.radius_neighbors_graph>` with
    ``mode='distance'``, then using ``metric='precomputed'`` here.

    Another way to reduce memory and computation time is to remove
    (near-)duplicate points and use ``sample_weight`` instead.

    :func:`cluster.optics` provides a similar clustering with lower memory
    usage.

    References
    ----------
    Ester, M., H. P. Kriegel, J. Sander, and X. Xu, "A Density-Based
    Algorithm for Discovering Clusters in Large Spatial Databases with Noise".
    In: Proceedings of the 2nd International Conference on Knowledge Discovery
    and Data Mining, Portland, OR, AAAI Press, pp. 226-231. 1996
    """
    if not eps > 0.0:
        raise ValueError("eps must be positive.")

    X = check_array(X, accept_sparse="csr")
    if sample_weight is not None:
        sample_weight = np.asarray(sample_weight)
        check_consistent_length(X, sample_weight)

    # Calculate neighborhood for all samples. This leaves the original point
    # in, which needs to be considered later (i.e. point i is in the
    # neighborhood of point i. While True, its useless information)
    if metric == "precomputed" and sparse.issparse(X):
        neighborhoods = np.empty(X.shape[0], dtype=object)
        X.sum_duplicates()  # XXX: modifies X's internals in-place
        X_mask = X.data <= eps
        masked_indices = X.indices.astype(np.intp, copy=False)[X_mask]
        masked_indptr = np.concatenate(([0], np.cumsum(X_mask)))[X.indptr[1:]]

        # insert the diagonal: a point is its own neighbor, but 0 distance
        # means absence from sparse matrix data
        masked_indices = np.insert(masked_indices, masked_indptr, np.arange(X.shape[0]))
        masked_indptr = masked_indptr[:-1] + np.arange(1, X.shape[0])
        # split into rows
        neighborhoods[:] = np.split(masked_indices, masked_indptr)
    else:
        neighbors_model = NearestNeighbors(
            radius=eps,
            algorithm=algorithm,
            leaf_size=leaf_size,
            metric=metric,
            metric_params=metric_params,
            p=p,
            n_jobs=n_jobs,
        )
        neighbors_model.fit(X)
        # This has worst case O(n^2) memory complexity
        neighborhoods = neighbors_model.radius_neighbors(X, eps, return_distance=False)

    if sample_weight is None:
        n_neighbors = np.array([len(neighbors) for neighbors in neighborhoods])
    else:
        n_neighbors = np.array(
            [np.sum(sample_weight[neighbors]) for neighbors in neighborhoods]
        )

    # Initially, all samples are noise.
    labels = np.full(X.shape[0], -1, dtype=np.intp)

    # A list of all core samples found.
    core_samples = np.asarray(n_neighbors >= min_samples, dtype=np.uint8)
    dbscan_inner(core_samples, neighborhoods, labels)
    return np.where(core_samples)[0], labels


# 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 dbscan(
    X,
    eps=0.5,
    min_samples=5,
    metric="minkowski",
    metric_params=None,
    algorithm="auto",
    leaf_size=30,
    p=2,
    sample_weight=None,
    n_jobs=None,
):
    """Perform DBSCAN clustering from vector array or distance matrix.

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

    Parameters
    ----------
    X : array or sparse (CSR) matrix of shape (n_samples, n_features), or \
            array of shape (n_samples, n_samples)
        A feature array, or array of distances between samples if
        ``metric='precomputed'``.

    eps : float, optional
        The maximum distance between two samples for them to be considered
        as in the same neighborhood.

    min_samples : int, optional
        The number of samples (or total weight) in a neighborhood for a point
        to be considered as a core point. This includes the point itself.

    metric : string, or callable
        The metric to use when calculating distance between instances in a
        feature array. If metric is a string or callable, it must be one of
        the options allowed by :func:`sklearn.metrics.pairwise_distances` for
        its metric parameter.
        If metric is "precomputed", X is assumed to be a distance matrix and
        must be square. X may be a sparse matrix, in which case only "nonzero"
        elements may be considered neighbors for DBSCAN.

    metric_params : dict, optional
        Additional keyword arguments for the metric function.

        .. versionadded:: 0.19

    algorithm : {'auto', 'ball_tree', 'kd_tree', 'brute'}, optional
        The algorithm to be used by the NearestNeighbors module
        to compute pointwise distances and find nearest neighbors.
        See NearestNeighbors module documentation for details.

    leaf_size : int, optional (default = 30)
        Leaf size passed to BallTree or cKDTree. This can affect the speed
        of the construction and query, as well as the memory required
        to store the tree. The optimal value depends
        on the nature of the problem.

    p : float, optional
        The power of the Minkowski metric to be used to calculate distance
        between points.

    sample_weight : array, shape (n_samples,), optional
        Weight of each sample, such that a sample with a weight of at least
        ``min_samples`` is by itself a core sample; a sample with negative
        weight may inhibit its eps-neighbor from being core.
        Note that weights are absolute, and default to 1.

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

    Returns
    -------
    core_samples : array [n_core_samples]
        Indices of core samples.

    labels : array [n_samples]
        Cluster labels for each point.  Noisy samples are given the label -1.

    See also
    --------
    DBSCAN
        An estimator interface for this clustering algorithm.
    optics
        A similar clustering at multiple values of eps. Our implementation
        is optimized for memory usage.

    Notes
    -----
    For an example, see :ref:`examples/cluster/plot_dbscan.py
    <sphx_glr_auto_examples_cluster_plot_dbscan.py>`.

    This implementation bulk-computes all neighborhood queries, which increases
    the memory complexity to O(n.d) where d is the average number of neighbors,
    while original DBSCAN had memory complexity O(n). It may attract a higher
    memory complexity when querying these nearest neighborhoods, depending
    on the ``algorithm``.

    One way to avoid the query complexity is to pre-compute sparse
    neighborhoods in chunks using
    :func:`NearestNeighbors.radius_neighbors_graph
    <sklearn.neighbors.NearestNeighbors.radius_neighbors_graph>` with
    ``mode='distance'``, then using ``metric='precomputed'`` here.

    Another way to reduce memory and computation time is to remove
    (near-)duplicate points and use ``sample_weight`` instead.

    :func:`cluster.optics` provides a similar clustering with lower memory
    usage.

    References
    ----------
    Ester, M., H. P. Kriegel, J. Sander, and X. Xu, "A Density-Based
    Algorithm for Discovering Clusters in Large Spatial Databases with Noise".
    In: Proceedings of the 2nd International Conference on Knowledge Discovery
    and Data Mining, Portland, OR, AAAI Press, pp. 226-231. 1996
    """
    if not eps > 0.0:
        raise ValueError("eps must be positive.")

    X = check_array(X, accept_sparse="csr")
    if sample_weight is not None:
        sample_weight = np.asarray(sample_weight)
        check_consistent_length(X, sample_weight)

    # Calculate neighborhood for all samples. This leaves the original point
    # in, which needs to be considered later (i.e. point i is in the
    # neighborhood of point i. While True, its useless information)
    if metric == "precomputed" and sparse.issparse(X):
        neighborhoods = np.empty(X.shape[0], dtype=object)
        X.sum_duplicates()  # XXX: modifies X's internals in-place
        X_mask = X.data <= eps
        masked_indices = X.indices.astype(np.intp, copy=False)[X_mask]
        masked_indptr = np.concatenate(([0], np.cumsum(X_mask)))[X.indptr[1:]]

        # insert the diagonal: a point is its own neighbor, but 0 distance
        # means absence from sparse matrix data
        masked_indices = np.insert(masked_indices, masked_indptr, np.arange(X.shape[0]))
        masked_indptr = masked_indptr[:-1] + np.arange(1, X.shape[0])
        # split into rows
        neighborhoods[:] = np.split(masked_indices, masked_indptr)
    else:
        neighbors_model = NearestNeighbors(
            radius=eps,
            algorithm=algorithm,
            leaf_size=leaf_size,
            metric=metric,
            metric_params=metric_params,
            p=p,
            n_jobs=n_jobs,
        )
        neighbors_model.fit(X)
        # This has worst case O(n^2) memory complexity
        neighborhoods = neighbors_model.radius_neighbors(X, eps, return_distance=False)

    if sample_weight is None:
        n_neighbors = np.array([len(neighbors) for neighbors in neighborhoods])
    else:
        n_neighbors = np.array(
            [np.sum(sample_weight[neighbors]) for neighbors in neighborhoods]
        )

    # Initially, all samples are noise.
    labels = np.full(X.shape[0], -1, dtype=np.intp)

    # A list of all core samples found.
    core_samples = np.asarray(n_neighbors >= min_samples, dtype=np.uint8)
    dbscan_inner(core_samples, neighborhoods, labels)
    return np.where(core_samples)[0], labels