Modified version of sklearn load_files to accept additional parameter 'ignore_list' which accepts list of file names to be omitted from loading. This is useful especially on OS X and POSIX machine where files beginning with '.' For instance on OS X load_files would load even '.DS_Store' file which is not desirable

load_files.py

from os.path import dirname
from os.path import join
from os.path import exists
from os.path import expanduser
from os.path import isdir
from os import listdir
from os import makedirs

from sklearn.datasets.base import Bunch
from sklearn.utils import check_random_state

def load_files(container_path, description=None, categories=None,
               load_content=True, shuffle=True, encoding=None,
               decode_error='strict', random_state=0, ignore_files=None):
    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)) if ignore_files is not None and d not in ignore_files ]
        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 = []
        for filename in filenames:
            with open(filename, 'rb') as f:
                data.append(f.read())
        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)