.. note::
    :class: sphx-glr-download-link-note

    Click :ref:`here <sphx_glr_download_auto_examples_text_plot_document_classification_20newsgroups.py>` to download the full example code
.. rst-class:: sphx-glr-example-title

.. _sphx_glr_auto_examples_text_plot_document_classification_20newsgroups.py:


======================================================
Classification of text documents using sparse features
======================================================

This is an example showing how scikit-learn can be used to classify documents
by topics using a bag-of-words approach. This example uses a scipy.sparse
matrix to store the features and demonstrates various classifiers that can
efficiently handle sparse matrices.

The dataset used in this example is the 20 newsgroups dataset. It will be
automatically downloaded, then cached.

The bar plot indicates the accuracy, training time (normalized) and test time
(normalized) of each classifier.




.. code-block:: python


    # Author: Peter Prettenhofer <peter.prettenhofer@gmail.com>
    #         Olivier Grisel <olivier.grisel@ensta.org>
    #         Mathieu Blondel <mathieu@mblondel.org>
    #         Lars Buitinck
    # License: BSD 3 clause

    from __future__ import print_function

    import logging
    import numpy as np
    from optparse import OptionParser
    import sys
    from time import time
    import matplotlib.pyplot as plt

    from sklearn.datasets import fetch_20newsgroups
    from sklearn.feature_extraction.text import TfidfVectorizer
    from sklearn.feature_extraction.text import HashingVectorizer
    from sklearn.feature_selection import SelectFromModel
    from sklearn.feature_selection import SelectKBest, chi2
    from sklearn.linear_model import RidgeClassifier
    from sklearn.pipeline import Pipeline
    from sklearn.svm import LinearSVC
    from sklearn.linear_model import SGDClassifier
    from sklearn.linear_model import Perceptron
    from sklearn.linear_model import PassiveAggressiveClassifier
    from sklearn.naive_bayes import BernoulliNB, ComplementNB, MultinomialNB
    from sklearn.neighbors import KNeighborsClassifier
    from sklearn.neighbors import NearestCentroid
    from sklearn.ensemble import RandomForestClassifier
    from sklearn.utils.extmath import density
    from sklearn import metrics


    # Display progress logs on stdout
    logging.basicConfig(level=logging.INFO,
                        format='%(asctime)s %(levelname)s %(message)s')


    # parse commandline arguments
    op = OptionParser()
    op.add_option("--report",
                  action="store_true", dest="print_report",
                  help="Print a detailed classification report.")
    op.add_option("--chi2_select",
                  action="store", type="int", dest="select_chi2",
                  help="Select some number of features using a chi-squared test")
    op.add_option("--confusion_matrix",
                  action="store_true", dest="print_cm",
                  help="Print the confusion matrix.")
    op.add_option("--top10",
                  action="store_true", dest="print_top10",
                  help="Print ten most discriminative terms per class"
                       " for every classifier.")
    op.add_option("--all_categories",
                  action="store_true", dest="all_categories",
                  help="Whether to use all categories or not.")
    op.add_option("--use_hashing",
                  action="store_true",
                  help="Use a hashing vectorizer.")
    op.add_option("--n_features",
                  action="store", type=int, default=2 ** 16,
                  help="n_features when using the hashing vectorizer.")
    op.add_option("--filtered",
                  action="store_true",
                  help="Remove newsgroup information that is easily overfit: "
                       "headers, signatures, and quoting.")


    def is_interactive():
        return not hasattr(sys.modules['__main__'], '__file__')


    # work-around for Jupyter notebook and IPython console
    argv = [] if is_interactive() else sys.argv[1:]
    (opts, args) = op.parse_args(argv)
    if len(args) > 0:
        op.error("this script takes no arguments.")
        sys.exit(1)

    print(__doc__)
    op.print_help()
    print()


    # #############################################################################
    # Load some categories from the training set
    if opts.all_categories:
        categories = None
    else:
        categories = [
            'alt.atheism',
            'talk.religion.misc',
            'comp.graphics',
            'sci.space',
        ]

    if opts.filtered:
        remove = ('headers', 'footers', 'quotes')
    else:
        remove = ()

    print("Loading 20 newsgroups dataset for categories:")
    print(categories if categories else "all")

    data_train = fetch_20newsgroups(subset='train', categories=categories,
                                    shuffle=True, random_state=42,
                                    remove=remove)

    data_test = fetch_20newsgroups(subset='test', categories=categories,
                                   shuffle=True, random_state=42,
                                   remove=remove)
    print('data loaded')

    # order of labels in `target_names` can be different from `categories`
    target_names = data_train.target_names


    def size_mb(docs):
        return sum(len(s.encode('utf-8')) for s in docs) / 1e6


    data_train_size_mb = size_mb(data_train.data)
    data_test_size_mb = size_mb(data_test.data)

    print("%d documents - %0.3fMB (training set)" % (
        len(data_train.data), data_train_size_mb))
    print("%d documents - %0.3fMB (test set)" % (
        len(data_test.data), data_test_size_mb))
    print("%d categories" % len(categories))
    print()

    # split a training set and a test set
    y_train, y_test = data_train.target, data_test.target

    print("Extracting features from the training data using a sparse vectorizer")
    t0 = time()
    if opts.use_hashing:
        vectorizer = HashingVectorizer(stop_words='english', alternate_sign=False,
                                       n_features=opts.n_features)
        X_train = vectorizer.transform(data_train.data)
    else:
        vectorizer = TfidfVectorizer(sublinear_tf=True, max_df=0.5,
                                     stop_words='english')
        X_train = vectorizer.fit_transform(data_train.data)
    duration = time() - t0
    print("done in %fs at %0.3fMB/s" % (duration, data_train_size_mb / duration))
    print("n_samples: %d, n_features: %d" % X_train.shape)
    print()

    print("Extracting features from the test data using the same vectorizer")
    t0 = time()
    X_test = vectorizer.transform(data_test.data)
    duration = time() - t0
    print("done in %fs at %0.3fMB/s" % (duration, data_test_size_mb / duration))
    print("n_samples: %d, n_features: %d" % X_test.shape)
    print()

    # mapping from integer feature name to original token string
    if opts.use_hashing:
        feature_names = None
    else:
        feature_names = vectorizer.get_feature_names()

    if opts.select_chi2:
        print("Extracting %d best features by a chi-squared test" %
              opts.select_chi2)
        t0 = time()
        ch2 = SelectKBest(chi2, k=opts.select_chi2)
        X_train = ch2.fit_transform(X_train, y_train)
        X_test = ch2.transform(X_test)
        if feature_names:
            # keep selected feature names
            feature_names = [feature_names[i] for i
                             in ch2.get_support(indices=True)]
        print("done in %fs" % (time() - t0))
        print()

    if feature_names:
        feature_names = np.asarray(feature_names)


    def trim(s):
        """Trim string to fit on terminal (assuming 80-column display)"""
        return s if len(s) <= 80 else s[:77] + "..."


    # #############################################################################
    # Benchmark classifiers
    def benchmark(clf):
        print('_' * 80)
        print("Training: ")
        print(clf)
        t0 = time()
        clf.fit(X_train, y_train)
        train_time = time() - t0
        print("train time: %0.3fs" % train_time)

        t0 = time()
        pred = clf.predict(X_test)
        test_time = time() - t0
        print("test time:  %0.3fs" % test_time)

        score = metrics.accuracy_score(y_test, pred)
        print("accuracy:   %0.3f" % score)

        if hasattr(clf, 'coef_'):
            print("dimensionality: %d" % clf.coef_.shape[1])
            print("density: %f" % density(clf.coef_))

            if opts.print_top10 and feature_names is not None:
                print("top 10 keywords per class:")
                for i, label in enumerate(target_names):
                    top10 = np.argsort(clf.coef_[i])[-10:]
                    print(trim("%s: %s" % (label, " ".join(feature_names[top10]))))
            print()

        if opts.print_report:
            print("classification report:")
            print(metrics.classification_report(y_test, pred,
                                                target_names=target_names))

        if opts.print_cm:
            print("confusion matrix:")
            print(metrics.confusion_matrix(y_test, pred))

        print()
        clf_descr = str(clf).split('(')[0]
        return clf_descr, score, train_time, test_time


    results = []
    for clf, name in (
            (RidgeClassifier(tol=1e-2, solver="lsqr"), "Ridge Classifier"),
            (Perceptron(n_iter=50, tol=1e-3), "Perceptron"),
            (PassiveAggressiveClassifier(n_iter=50, tol=1e-3),
             "Passive-Aggressive"),
            (KNeighborsClassifier(n_neighbors=10), "kNN"),
            (RandomForestClassifier(n_estimators=100), "Random forest")):
        print('=' * 80)
        print(name)
        results.append(benchmark(clf))

    for penalty in ["l2", "l1"]:
        print('=' * 80)
        print("%s penalty" % penalty.upper())
        # Train Liblinear model
        results.append(benchmark(LinearSVC(penalty=penalty, dual=False,
                                           tol=1e-3)))

        # Train SGD model
        results.append(benchmark(SGDClassifier(alpha=.0001, n_iter=50,
                                               penalty=penalty,
                                               max_iter=5)))

    # Train SGD with Elastic Net penalty
    print('=' * 80)
    print("Elastic-Net penalty")
    results.append(benchmark(SGDClassifier(alpha=.0001, n_iter=50,
                                           penalty="elasticnet",
                                           max_iter=5)))

    # Train NearestCentroid without threshold
    print('=' * 80)
    print("NearestCentroid (aka Rocchio classifier)")
    results.append(benchmark(NearestCentroid()))

    # Train sparse Naive Bayes classifiers
    print('=' * 80)
    print("Naive Bayes")
    results.append(benchmark(MultinomialNB(alpha=.01)))
    results.append(benchmark(BernoulliNB(alpha=.01)))
    results.append(benchmark(ComplementNB(alpha=.1)))

    print('=' * 80)
    print("LinearSVC with L1-based feature selection")
    # The smaller C, the stronger the regularization.
    # The more regularization, the more sparsity.
    results.append(benchmark(Pipeline([
      ('feature_selection', SelectFromModel(LinearSVC(penalty="l1", dual=False,
                                                      tol=1e-3))),
      ('classification', LinearSVC(penalty="l2"))])))

    # make some plots

    indices = np.arange(len(results))

    results = [[x[i] for x in results] for i in range(4)]

    clf_names, score, training_time, test_time = results
    training_time = np.array(training_time) / np.max(training_time)
    test_time = np.array(test_time) / np.max(test_time)

    plt.figure(figsize=(12, 8))
    plt.title("Score")
    plt.barh(indices, score, .2, label="score", color='navy')
    plt.barh(indices + .3, training_time, .2, label="training time",
             color='c')
    plt.barh(indices + .6, test_time, .2, label="test time", color='darkorange')
    plt.yticks(())
    plt.legend(loc='best')
    plt.subplots_adjust(left=.25)
    plt.subplots_adjust(top=.95)
    plt.subplots_adjust(bottom=.05)

    for i, c in zip(indices, clf_names):
        plt.text(-.3, i, c)

    plt.show()

**Total running time of the script:** ( 0 minutes  0.000 seconds)


.. _sphx_glr_download_auto_examples_text_plot_document_classification_20newsgroups.py:


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