适用于<python机器学习与实践>的twenty_newsgroups.py文件内容

路径为：

D:\software\python27\Lib\site-packages\sklearn\datasets

替换twenty_newsgroups.py中的内容如下：

"""Caching loader for the 20 newsgroups text classification datasetThe description of the dataset is available on the official website at:    http://people.csail.mit.edu/jrennie/20Newsgroups/Quoting the introduction:    The 20 Newsgroups data set is a collection of approximately 20,000    newsgroup documents, partitioned (nearly) evenly across 20 different    newsgroups. To the best of my knowledge, it was originally collected    by Ken Lang, probably for his Newsweeder: Learning to filter netnews    paper, though he does not explicitly mention this collection. The 20    newsgroups collection has become a popular data set for experiments    in text applications of machine learning techniques, such as text    classification and text clustering.This dataset loader will download the recommended "by date" variant of thedataset and which features a point in time split between the train andtest sets. The compressed dataset size is around 14 Mb compressed. Onceuncompressed the train set is 52 MB and the test set is 34 MB.The data is downloaded, extracted and cached in the '~/scikit_learn_data'folder.The `fetch_20newsgroups` function will not vectorize the data into numpyarrays but the dataset lists the filenames of the posts and their categoriesas target labels.The `fetch_20newsgroups_tfidf` function will in addition do a simple tf-idfvectorization step."""# Copyright (c) 2011 Olivier Grisel <[e-mail]># License: BSD 3 clauseimport osimport loggingimport tarfileimport pickleimport shutilimport reimport codecsimport numpy as npimport scipy.sparse as spfrom .base import get_data_homefrom .base import Bunchfrom .base import load_filesfrom ..utils import check_random_statefrom ..feature_extraction.text import CountVectorizerfrom ..preprocessing import normalizefrom ..externals import joblib, sixif six.PY3:    from urllib.request import urlopenelse:    from urllib2 import urlopenlogger = logging.getLogger(__name__)URL = ("http://people.csail.mit.edu/jrennie/"       "20Newsgroups/20news-bydate.tar.gz")ARCHIVE_NAME = "20news-bydate.tar.gz"CACHE_NAME = "20news-bydate.pkz"TRAIN_FOLDER = "20news-bydate-train"TEST_FOLDER = "20news-bydate-test"def download_20newsgroups(target_dir, cache_path):    """Download the 20 newsgroups data and stored it as a zipped pickle."""    archive_path = os.path.join(target_dir, ARCHIVE_NAME)    train_path = os.path.join(target_dir, TRAIN_FOLDER)    test_path = os.path.join(target_dir, TEST_FOLDER)    # if not os.path.exists(target_dir):    #     os.makedirs(target_dir)    #    # if os.path.exists(archive_path):    #     # Download is not complete as the .tar.gz file is removed after    #     # download.    #     logger.warn("Download was incomplete, downloading again.")    #     os.remove(archive_path)    # logger.warn("Downloading dataset from %s (14 MB)", URL)    # opener = urlopen(URL)    # open(archive_path, 'wb').write(opener.read())    logger.info("Decompressing %s", archive_path)    tarfile.open(archive_path, "r:gz").extractall(path=target_dir)    os.remove(archive_path)    # Store a zipped pickle    cache = dict(train=load_files(train_path, encoding='latin1'),                 test=load_files(test_path, encoding='latin1'))    compressed_content = codecs.encode(pickle.dumps(cache), 'zlib_codec')    open(cache_path, 'wb').write(compressed_content)    shutil.rmtree(target_dir)    return cachedef strip_newsgroup_header(text):    """    Given text in "news" format, strip the headers, by removing everything    before the first blank line.    """    _before, _blankline, after = text.partition('\n\n')    return after_QUOTE_RE = re.compile(r'(writes in|writes:|wrote:|says:|said:'                       r'|^In article|^Quoted from|^\||^>)')def strip_newsgroup_quoting(text):    """    Given text in "news" format, strip lines beginning with the quote    characters > or |, plus lines that often introduce a quoted section    (for example, because they contain the string 'writes:'.)    """    good_lines = [line for line in text.split('\n')                  if not _QUOTE_RE.search(line)]    return '\n'.join(good_lines)def strip_newsgroup_footer(text):    """    Given text in "news" format, attempt to remove a signature block.    As a rough heuristic, we assume that signatures are set apart by either    a blank line or a line made of hyphens, and that it is the last such line    in the file (disregarding blank lines at the end).    """    lines = text.strip().split('\n')    for line_num in range(len(lines) - 1, -1, -1):        line = lines[line_num]        if line.strip().strip('-') == '':            break    if line_num > 0:        return '\n'.join(lines[:line_num])    else:        return textdef fetch_20newsgroups(data_home=None, subset='train', categories=None,                       shuffle=True, random_state=42,                       remove=(),                       download_if_missing=True):    """Load the filenames and data from the 20 newsgroups dataset.    Parameters    ----------    subset: 'train' or 'test', 'all', optional        Select the dataset to load: 'train' for the training set, 'test'        for the test set, 'all' for both, with shuffled ordering.    data_home: optional, default: None        Specify an download and cache folder for the datasets. If None,        all scikit-learn data is stored in '~/scikit_learn_data' subfolders.    categories: None or collection of string or unicode        If None (default), load all the categories.        If not None, list of category names to load (other categories        ignored).    shuffle: bool, optional        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: numpy random number generator or seed integer        Used to shuffle the dataset.    download_if_missing: optional, True by default        If False, raise an IOError if the data is not locally available        instead of trying to download the data from the source site.    remove: tuple        May contain any subset of ('headers', 'footers', 'quotes'). Each of        these are kinds of text that will be detected and removed from the        newsgroup posts, preventing classifiers from overfitting on        metadata.        'headers' removes newsgroup headers, 'footers' removes blocks at the        ends of posts that look like signatures, and 'quotes' removes lines        that appear to be quoting another post.        'headers' follows an exact standard; the other filters are not always        correct.    """    data_home = get_data_home(data_home=data_home)    cache_path = os.path.join(data_home, CACHE_NAME)    twenty_home = os.path.join(data_home, "20news_home")    cache = None    if os.path.exists(cache_path):        try:            with open(cache_path, 'rb') as f:                compressed_content = f.read()            uncompressed_content = codecs.decode(                compressed_content, 'zlib_codec')            cache = pickle.loads(uncompressed_content)        except Exception as e:            print(80 * '_')            print('Cache loading failed')            print(80 * '_')            print(e)    if cache is None:        if download_if_missing:            cache = download_20newsgroups(target_dir=twenty_home,                                          cache_path=cache_path)        else:            raise IOError('20Newsgroups dataset not found')    if subset in ('train', 'test'):        data = cache[subset]    elif subset == 'all':        data_lst = list()        target = list()        filenames = list()        for subset in ('train', 'test'):            data = cache[subset]            data_lst.extend(data.data)            target.extend(data.target)            filenames.extend(data.filenames)        data.data = data_lst        data.target = np.array(target)        data.filenames = np.array(filenames)        data.description = 'the 20 newsgroups by date dataset'    else:        raise ValueError(            "subset can only be 'train', 'test' or 'all', got '%s'" % subset)    if 'headers' in remove:        data.data = [strip_newsgroup_header(text) for text in data.data]    if 'footers' in remove:        data.data = [strip_newsgroup_footer(text) for text in data.data]    if 'quotes' in remove:        data.data = [strip_newsgroup_quoting(text) for text in data.data]    if categories is not None:        labels = [(data.target_names.index(cat), cat) for cat in categories]        # Sort the categories to have the ordering of the labels        labels.sort()        labels, categories = zip(*labels)        mask = np.in1d(data.target, labels)        data.filenames = data.filenames[mask]        data.target = data.target[mask]        # searchsorted to have continuous labels        data.target = np.searchsorted(labels, data.target)        data.target_names = list(categories)        # Use an object array to shuffle: avoids memory copy        data_lst = np.array(data.data, dtype=object)        data_lst = data_lst[mask]        data.data = data_lst.tolist()    if shuffle:        random_state = check_random_state(random_state)        indices = np.arange(data.target.shape[0])        random_state.shuffle(indices)        data.filenames = data.filenames[indices]        data.target = data.target[indices]        # Use an object array to shuffle: avoids memory copy        data_lst = np.array(data.data, dtype=object)        data_lst = data_lst[indices]        data.data = data_lst.tolist()    return datadef fetch_20newsgroups_vectorized(subset="train", remove=(), data_home=None):    """Load the 20 newsgroups dataset and transform it into tf-idf vectors.    This is a convenience function; the tf-idf transformation is done using the    default settings for `sklearn.feature_extraction.text.Vectorizer`. For more    advanced usage (stopword filtering, n-gram extraction, etc.), combine    fetch_20newsgroups with a custom `Vectorizer` or `CountVectorizer`.    Parameters    ----------    subset: 'train' or 'test', 'all', optional        Select the dataset to load: 'train' for the training set, 'test'        for the test set, 'all' for both, with shuffled ordering.    data_home: optional, default: None        Specify an download and cache folder for the datasets. If None,        all scikit-learn data is stored in '~/scikit_learn_data' subfolders.    remove: tuple        May contain any subset of ('headers', 'footers', 'quotes'). Each of        these are kinds of text that will be detected and removed from the        newsgroup posts, preventing classifiers from overfitting on        metadata.        'headers' removes newsgroup headers, 'footers' removes blocks at the        ends of posts that look like signatures, and 'quotes' removes lines        that appear to be quoting another post.    Returns    -------    bunch : Bunch object        bunch.data: sparse matrix, shape [n_samples, n_features]        bunch.target: array, shape [n_samples]        bunch.target_names: list, length [n_classes]    """    data_home = get_data_home(data_home=data_home)    filebase = '20newsgroup_vectorized'    if remove:        filebase += 'remove-' + ('-'.join(remove))    target_file = os.path.join(data_home, filebase + ".pk")    # we shuffle but use a fixed seed for the memoization    data_train = fetch_20newsgroups(data_home=data_home,                                    subset='train',                                    categories=None,                                    shuffle=True,                                    random_state=12,                                    remove=remove)    data_test = fetch_20newsgroups(data_home=data_home,                                   subset='test',                                   categories=None,                                   shuffle=True,                                   random_state=12,                                   remove=remove)    if os.path.exists(target_file):        X_train, X_test = joblib.load(target_file)    else:        vectorizer = CountVectorizer(dtype=np.int16)        X_train = vectorizer.fit_transform(data_train.data).tocsr()        X_test = vectorizer.transform(data_test.data).tocsr()        joblib.dump((X_train, X_test), target_file, compress=9)    # the data is stored as int16 for compactness    # but normalize needs floats    X_train = X_train.astype(np.float64)    X_test = X_test.astype(np.float64)    normalize(X_train, copy=False)    normalize(X_test, copy=False)    target_names = data_train.target_names    if subset == "train":        data = X_train        target = data_train.target    elif subset == "test":        data = X_test        target = data_test.target    elif subset == "all":        data = sp.vstack((X_train, X_test)).tocsr()        target = np.concatenate((data_train.target, data_test.target))    else:        raise ValueError("%r is not a valid subset: should be one of "                         "['train', 'test', 'all']" % subset)    return Bunch(data=data, target=target, target_names=target_names)