.. note:: :class: sphx-glr-download-link-note Click :ref:`here ` to download the full example code or to run this example in your browser via Binder .. 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. .. code-block:: default # Author: Peter Prettenhofer # Olivier Grisel # Mathieu Blondel # Lars Buitinck # License: BSD 3 clause 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') 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() .. rst-class:: sphx-glr-script-out Out: .. code-block:: none Usage: plot_document_classification_20newsgroups.py [options] Options: -h, --help show this help message and exit --report Print a detailed classification report. --chi2_select=SELECT_CHI2 Select some number of features using a chi-squared test --confusion_matrix Print the confusion matrix. --top10 Print ten most discriminative terms per class for every classifier. --all_categories Whether to use all categories or not. --use_hashing Use a hashing vectorizer. --n_features=N_FEATURES n_features when using the hashing vectorizer. --filtered Remove newsgroup information that is easily overfit: headers, signatures, and quoting. Load data from the training set ------------------------------------ Let's load data from the newsgroups dataset which comprises around 18000 newsgroups posts on 20 topics split in two subsets: one for training (or development) and the other one for testing (or for performance evaluation). .. code-block:: default 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(target_names)) 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] + "..." .. rst-class:: sphx-glr-script-out Out: .. code-block:: none Loading 20 newsgroups dataset for categories: ['alt.atheism', 'talk.religion.misc', 'comp.graphics', 'sci.space'] data loaded 2034 documents - 3.980MB (training set) 1353 documents - 2.867MB (test set) 4 categories Extracting features from the training data using a sparse vectorizer done in 0.494025s at 8.055MB/s n_samples: 2034, n_features: 33809 Extracting features from the test data using the same vectorizer done in 0.375047s at 7.646MB/s n_samples: 1353, n_features: 33809 Benchmark classifiers ------------------------------------ We train and test the datasets with 15 different classification models and get performance results for each model. .. code-block:: default 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="sag"), "Ridge Classifier"), (Perceptron(max_iter=50), "Perceptron"), (PassiveAggressiveClassifier(max_iter=50), "Passive-Aggressive"), (KNeighborsClassifier(n_neighbors=10), "kNN"), (RandomForestClassifier(), "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, max_iter=50, penalty=penalty))) # Train SGD with Elastic Net penalty print('=' * 80) print("Elastic-Net penalty") results.append(benchmark(SGDClassifier(alpha=.0001, max_iter=50, penalty="elasticnet"))) # 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"))]))) .. rst-class:: sphx-glr-script-out Out: .. code-block:: none ================================================================================ Ridge Classifier ________________________________________________________________________________ Training: RidgeClassifier(solver='sag', tol=0.01) /home/circleci/project/sklearn/linear_model/_ridge.py:557: UserWarning: "sag" solver requires many iterations to fit an intercept with sparse inputs. Either set the solver to "auto" or "sparse_cg", or set a low "tol" and a high "max_iter" (especially if inputs are not standardized). warnings.warn( train time: 0.209s test time: 0.003s accuracy: 0.897 dimensionality: 33809 density: 1.000000 ================================================================================ Perceptron ________________________________________________________________________________ Training: Perceptron(max_iter=50) train time: 0.019s test time: 0.002s accuracy: 0.888 dimensionality: 33809 density: 0.255302 ================================================================================ Passive-Aggressive ________________________________________________________________________________ Training: PassiveAggressiveClassifier(max_iter=50) train time: 0.032s test time: 0.002s accuracy: 0.902 dimensionality: 33809 density: 0.692841 ================================================================================ kNN ________________________________________________________________________________ Training: KNeighborsClassifier(n_neighbors=10) train time: 0.003s test time: 0.215s accuracy: 0.858 ================================================================================ Random forest ________________________________________________________________________________ Training: RandomForestClassifier() train time: 1.682s test time: 0.072s accuracy: 0.837 ================================================================================ L2 penalty ________________________________________________________________________________ Training: LinearSVC(dual=False, tol=0.001) train time: 0.075s test time: 0.001s accuracy: 0.900 dimensionality: 33809 density: 1.000000 ________________________________________________________________________________ Training: SGDClassifier(max_iter=50) train time: 0.022s test time: 0.002s accuracy: 0.899 dimensionality: 33809 density: 0.569944 ================================================================================ L1 penalty ________________________________________________________________________________ Training: LinearSVC(dual=False, penalty='l1', tol=0.001) train time: 0.217s test time: 0.001s accuracy: 0.873 dimensionality: 33809 density: 0.005553 ________________________________________________________________________________ Training: SGDClassifier(max_iter=50, penalty='l1') train time: 0.094s test time: 0.002s accuracy: 0.888 dimensionality: 33809 density: 0.022982 ================================================================================ Elastic-Net penalty ________________________________________________________________________________ Training: SGDClassifier(max_iter=50, penalty='elasticnet') train time: 0.123s test time: 0.002s accuracy: 0.902 dimensionality: 33809 density: 0.187502 ================================================================================ NearestCentroid (aka Rocchio classifier) ________________________________________________________________________________ Training: NearestCentroid() train time: 0.005s test time: 0.002s accuracy: 0.855 ================================================================================ Naive Bayes ________________________________________________________________________________ Training: MultinomialNB(alpha=0.01) train time: 0.008s test time: 0.001s accuracy: 0.899 dimensionality: 33809 density: 1.000000 ________________________________________________________________________________ Training: BernoulliNB(alpha=0.01) train time: 0.009s test time: 0.007s accuracy: 0.884 dimensionality: 33809 density: 1.000000 ________________________________________________________________________________ Training: ComplementNB(alpha=0.1) train time: 0.007s test time: 0.001s accuracy: 0.911 dimensionality: 33809 density: 1.000000 ================================================================================ LinearSVC with L1-based feature selection ________________________________________________________________________________ Training: Pipeline(steps=[('feature_selection', SelectFromModel(estimator=LinearSVC(dual=False, penalty='l1', tol=0.001))), ('classification', LinearSVC())]) train time: 0.213s test time: 0.003s accuracy: 0.880 Add plots ------------------------------------ The bar plot indicates the accuracy, training time (normalized) and test time (normalized) of each classifier. .. code-block:: default 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() .. image:: /auto_examples/text/images/sphx_glr_plot_document_classification_20newsgroups_001.png :class: sphx-glr-single-img .. rst-class:: sphx-glr-timing **Total running time of the script:** ( 0 minutes 5.861 seconds) **Estimated memory usage:** 46 MB .. _sphx_glr_download_auto_examples_text_plot_document_classification_20newsgroups.py: .. only :: html .. container:: sphx-glr-footer :class: sphx-glr-footer-example .. container:: binder-badge .. image:: https://mybinder.org/badge_logo.svg :target: https://mybinder.org/v2/gh/scikit-learn/scikit-learn/0.22.X?urlpath=lab/tree/notebooks/auto_examples/text/plot_document_classification_20newsgroups.ipynb :width: 150 px .. container:: sphx-glr-download :download:`Download Python source code: 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