# Buggy code
def load_files(
    container_path,
    description=None,
    categories=None,
    load_content=True,
    shuffle=True,
    encoding=None,
    charset=None,
    charset_error=None,
    decode_error="strict",
    random_state=0,
):
    """Load text files with categories as subfolder names.

    Individual samples are assumed to be files stored a two levels folder
    structure such as the following:

        container_folder/
            category_1_folder/
                file_1.txt
                file_2.txt
                ...
                file_42.txt
            category_2_folder/
                file_43.txt
                file_44.txt
                ...

    The folder names are used as supervised signal label names. The
    individual file names are not important.

    This function does not try to extract features into a numpy array or
    scipy sparse matrix. In addition, if load_content is false it
    does not try to load the files in memory.

    To use text files in a scikit-learn classification or clustering
    algorithm, you will need to use the `sklearn.feature_extraction.text`
    module to build a feature extraction transformer that suits your
    problem.

    If you set load_content=True, you should also specify the encoding of
    the text using the 'encoding' parameter. For many modern text files,
    'utf-8' will be the correct encoding. If you leave encoding equal to None,
    then the content will be made of bytes instead of Unicode, and you will
    not be able to use most functions in `sklearn.feature_extraction.text`.

    Similar feature extractors should be built for other kind of unstructured
    data input such as images, audio, video, ...

    Parameters
    ----------
    container_path : string or unicode
        Path to the main folder holding one subfolder per category

    description: string or unicode, optional (default=None)
        A paragraph describing the characteristic of the dataset: its source,
        reference, etc.

    categories : A collection of strings or None, optional (default=None)
        If None (default), load all the categories.
        If not None, list of category names to load (other categories ignored).

    load_content : boolean, optional (default=True)
        Whether to load or not the content of the different files. If
        true a 'data' attribute containing the text information is present
        in the data structure returned. If not, a filenames attribute
        gives the path to the files.

    encoding : string or None (default is None)
        If None, do not try to decode the content of the files (e.g. for
        images or other non-text content).
        If not None, encoding to use to decode text files to Unicode if
        load_content is True.

    decode_error: {'strict', 'ignore', 'replace'}, optional
        Instruction on what to do if a byte sequence is given to analyze that
        contains characters not of the given `encoding`. Passed as keyword
        argument 'errors' to bytes.decode.

    shuffle : bool, optional (default=True)
        Whether or not to shuffle the data: might be important for models that
        make the assumption that the samples are independent and identically
        distributed (i.i.d.), such as stochastic gradient descent.

    random_state : int, RandomState instance or None, optional (default=0)
        If int, random_state is the seed used by the random number generator;
        If RandomState instance, random_state is the random number generator;
        If None, the random number generator is the RandomState instance used
        by `np.random`.

    Returns
    -------
    data : Bunch
        Dictionary-like object, the interesting attributes are: either
        data, the raw text data to learn, or 'filenames', the files
        holding it, 'target', the classification labels (integer index),
        'target_names', the meaning of the labels, and 'DESCR', the full
        description of the dataset.
    """
    if charset is not None:
        warnings.warn(
            "The charset parameter is deprecated as of version "
            "0.14 and will be removed in 0.16. Use encode instead.",
            DeprecationWarning,
        )
        encoding = charset

    if charset_error is not None:
        warnings.warn(
            "The charset_error parameter is deprecated as of "
            "version 0.14 and will be removed in 0.16. Use "
            "decode_error instead.",
            DeprecationWarning,
        )
        decode_error = charset_error

    target = []
    target_names = []
    filenames = []

    folders = [
        f for f in sorted(listdir(container_path)) if isdir(join(container_path, f))
    ]

    if categories is not None:
        folders = [f for f in folders if f in categories]

    for label, folder in enumerate(folders):
        target_names.append(folder)
        folder_path = join(container_path, folder)
        documents = [join(folder_path, d) for d in sorted(listdir(folder_path))]
        target.extend(len(documents) * [label])
        filenames.extend(documents)

    # convert to array for fancy indexing
    filenames = np.array(filenames)
    target = np.array(target)

    if shuffle:
        random_state = check_random_state(random_state)
        indices = np.arange(filenames.shape[0])
        random_state.shuffle(indices)
        filenames = filenames[indices]
        target = target[indices]

    if load_content:
        data = [open(filename, "rb").read() for filename in filenames]
        if encoding is not None:
            data = [d.decode(encoding, decode_error) for d in data]
        return Bunch(
            data=data,
            filenames=filenames,
            target_names=target_names,
            target=target,
            DESCR=description,
        )

    return Bunch(
        filenames=filenames, target_names=target_names, target=target, DESCR=description
    )


# Traceback (IndexError)
======================================================================
ERROR: sklearn.tests.test_common.test_regressors_train('RANSACRegressor', <class 'sklearn.linear_model.ransac.RANSACRegr
essor'>, array([[-0.44836249, -0.47282444, -1.20608008, ..., -0.75500806,
----------------------------------------------------------------------
Traceback (most recent call last):
File "C:\Python27\lib\site-packages\nose\case.py", line 197, in runTest
self.test(*self.arg)
File "C:\Python27\lib\site-packages\sklearn\tests\test_common.py", line 850, in check_regressors_train
assert_raises(ValueError, regressor.fit, X, y[:-1])
File "C:\Python27\lib\unittest\case.py", line 473, in assertRaises
callableObj(*args, **kwargs)
File "C:\Python27\lib\site-packages\sklearn\linear_model\ransac.py", line 261, in fit
y_subset = y[subset_idxs]
IndexError: index 199 is out of bounds for axis 0 with size 199

# Fixed code
def load_files(
    container_path,
    description=None,
    categories=None,
    load_content=True,
    shuffle=True,
    encoding=None,
    charset=None,
    charset_error=None,
    decode_error="strict",
    random_state=0,
):
    """Load text files with categories as subfolder names.

    Individual samples are assumed to be files stored a two levels folder
    structure such as the following:

        container_folder/
            category_1_folder/
                file_1.txt
                file_2.txt
                ...
                file_42.txt
            category_2_folder/
                file_43.txt
                file_44.txt
                ...

    The folder names are used as supervised signal label names. The
    individual file names are not important.

    This function does not try to extract features into a numpy array or
    scipy sparse matrix. In addition, if load_content is false it
    does not try to load the files in memory.

    To use text files in a scikit-learn classification or clustering
    algorithm, you will need to use the `sklearn.feature_extraction.text`
    module to build a feature extraction transformer that suits your
    problem.

    If you set load_content=True, you should also specify the encoding of
    the text using the 'encoding' parameter. For many modern text files,
    'utf-8' will be the correct encoding. If you leave encoding equal to None,
    then the content will be made of bytes instead of Unicode, and you will
    not be able to use most functions in `sklearn.feature_extraction.text`.

    Similar feature extractors should be built for other kind of unstructured
    data input such as images, audio, video, ...

    Parameters
    ----------
    container_path : string or unicode
        Path to the main folder holding one subfolder per category

    description: string or unicode, optional (default=None)
        A paragraph describing the characteristic of the dataset: its source,
        reference, etc.

    categories : A collection of strings or None, optional (default=None)
        If None (default), load all the categories.
        If not None, list of category names to load (other categories ignored).

    load_content : boolean, optional (default=True)
        Whether to load or not the content of the different files. If
        true a 'data' attribute containing the text information is present
        in the data structure returned. If not, a filenames attribute
        gives the path to the files.

    encoding : string or None (default is None)
        If None, do not try to decode the content of the files (e.g. for
        images or other non-text content).
        If not None, encoding to use to decode text files to Unicode if
        load_content is True.

    decode_error: {'strict', 'ignore', 'replace'}, optional
        Instruction on what to do if a byte sequence is given to analyze that
        contains characters not of the given `encoding`. Passed as keyword
        argument 'errors' to bytes.decode.

    shuffle : bool, optional (default=True)
        Whether or not to shuffle the data: might be important for models that
        make the assumption that the samples are independent and identically
        distributed (i.i.d.), such as stochastic gradient descent.

    random_state : int, RandomState instance or None, optional (default=0)
        If int, random_state is the seed used by the random number generator;
        If RandomState instance, random_state is the random number generator;
        If None, the random number generator is the RandomState instance used
        by `np.random`.

    Returns
    -------
    data : Bunch
        Dictionary-like object, the interesting attributes are: either
        data, the raw text data to learn, or 'filenames', the files
        holding it, 'target', the classification labels (integer index),
        'target_names', the meaning of the labels, and 'DESCR', the full
        description of the dataset.
    """
    if charset is not None:
        warnings.warn(
            "The charset parameter is deprecated as of version "
            "0.14 and will be removed in 0.16. "
            "Use encoding instead.",
            DeprecationWarning,
        )
        encoding = charset

    if charset_error is not None:
        warnings.warn(
            "The charset_error parameter is deprecated as of "
            "version 0.14 and will be removed in 0.16. Use "
            "decode_error instead.",
            DeprecationWarning,
        )
        decode_error = charset_error

    target = []
    target_names = []
    filenames = []

    folders = [
        f for f in sorted(listdir(container_path)) if isdir(join(container_path, f))
    ]

    if categories is not None:
        folders = [f for f in folders if f in categories]

    for label, folder in enumerate(folders):
        target_names.append(folder)
        folder_path = join(container_path, folder)
        documents = [join(folder_path, d) for d in sorted(listdir(folder_path))]
        target.extend(len(documents) * [label])
        filenames.extend(documents)

    # convert to array for fancy indexing
    filenames = np.array(filenames)
    target = np.array(target)

    if shuffle:
        random_state = check_random_state(random_state)
        indices = np.arange(filenames.shape[0])
        random_state.shuffle(indices)
        filenames = filenames[indices]
        target = target[indices]

    if load_content:
        data = [open(filename, "rb").read() for filename in filenames]
        if encoding is not None:
            data = [d.decode(encoding, decode_error) for d in data]
        return Bunch(
            data=data,
            filenames=filenames,
            target_names=target_names,
            target=target,
            DESCR=description,
        )

    return Bunch(
        filenames=filenames, target_names=target_names, target=target, DESCR=description
    )