.. DO NOT EDIT. .. THIS FILE WAS AUTOMATICALLY GENERATED BY SPHINX-GALLERY. .. TO MAKE CHANGES, EDIT THE SOURCE PYTHON FILE: .. "auto_examples/text/plot_document_classification_20newsgroups.py" .. LINE NUMBERS ARE GIVEN BELOW. .. only:: html .. 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. .. GENERATED FROM PYTHON SOURCE LINES 15-96 .. 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. .. GENERATED FROM PYTHON SOURCE LINES 97-102 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). .. GENERATED FROM PYTHON SOURCE LINES 102-198 .. 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_out() 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 is not None: # keep selected feature names feature_names = feature_names[ch2.get_support()] print("done in %fs" % (time() - t0)) print() 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 0.378518s at 10.513MB/s n_samples: 2034, n_features: 33809 Extracting features from the test data using the same vectorizer done in 0.241246s at 11.886MB/s n_samples: 1353, n_features: 33809 .. GENERATED FROM PYTHON SOURCE LINES 199-203 Benchmark classifiers ------------------------------------ We train and test the datasets with 15 different classification models and get performance results for each model. .. GENERATED FROM PYTHON SOURCE LINES 203-296 .. 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:738: 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: 0.145s test time: 0.001s accuracy: 0.898 dimensionality: 33809 density: 1.000000 ================================================================================ Perceptron ________________________________________________________________________________ Training: Perceptron(max_iter=50) train time: 0.015s test time: 0.001s accuracy: 0.888 dimensionality: 33809 density: 0.255302 ================================================================================ Passive-Aggressive ________________________________________________________________________________ Training: PassiveAggressiveClassifier(max_iter=50) train time: 0.026s test time: 0.002s accuracy: 0.902 dimensionality: 33809 density: 0.711867 ================================================================================ kNN ________________________________________________________________________________ Training: KNeighborsClassifier(n_neighbors=10) train time: 0.001s test time: 0.156s accuracy: 0.858 ================================================================================ Random forest ________________________________________________________________________________ Training: RandomForestClassifier() train time: 1.234s test time: 0.072s accuracy: 0.826 ================================================================================ L2 penalty ________________________________________________________________________________ Training: LinearSVC(dual=False, tol=0.001) train time: 0.074s test time: 0.001s accuracy: 0.900 dimensionality: 33809 density: 1.000000 ________________________________________________________________________________ Training: SGDClassifier(max_iter=50) train time: 0.022s test time: 0.001s accuracy: 0.903 dimensionality: 33809 density: 0.579424 ================================================================================ L1 penalty ________________________________________________________________________________ Training: LinearSVC(dual=False, penalty='l1', tol=0.001) train time: 0.168s test time: 0.001s accuracy: 0.873 dimensionality: 33809 density: 0.005553 ________________________________________________________________________________ Training: SGDClassifier(max_iter=50, penalty='l1') train time: 0.088s test time: 0.002s accuracy: 0.880 dimensionality: 33809 density: 0.022509 ================================================================================ Elastic-Net penalty ________________________________________________________________________________ Training: SGDClassifier(max_iter=50, penalty='elasticnet') train time: 0.130s test time: 0.002s accuracy: 0.901 dimensionality: 33809 density: 0.184685 ================================================================================ NearestCentroid (aka Rocchio classifier) ________________________________________________________________________________ Training: NearestCentroid() train time: 0.004s test time: 0.002s accuracy: 0.855 ================================================================================ Naive Bayes ________________________________________________________________________________ Training: MultinomialNB(alpha=0.01) train time: 0.003s test time: 0.001s accuracy: 0.899 /home/circleci/project/sklearn/utils/deprecation.py:103: FutureWarning: Attribute `coef_` was deprecated in version 0.24 and will be removed in 1.1 (renaming of 0.26). warnings.warn(msg, category=FutureWarning) dimensionality: 33809 density: 1.000000 ________________________________________________________________________________ Training: BernoulliNB(alpha=0.01) train time: 0.005s test time: 0.004s accuracy: 0.884 /home/circleci/project/sklearn/utils/deprecation.py:103: FutureWarning: Attribute `coef_` was deprecated in version 0.24 and will be removed in 1.1 (renaming of 0.26). warnings.warn(msg, category=FutureWarning) dimensionality: 33809 density: 1.000000 ________________________________________________________________________________ Training: ComplementNB(alpha=0.1) train time: 0.003s test time: 0.001s accuracy: 0.911 /home/circleci/project/sklearn/utils/deprecation.py:103: FutureWarning: Attribute `coef_` was deprecated in version 0.24 and will be removed in 1.1 (renaming of 0.26). warnings.warn(msg, category=FutureWarning) 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: 0.185s test time: 0.002s accuracy: 0.879 .. GENERATED FROM PYTHON SOURCE LINES 297-301 Add plots ------------------------------------ The bar plot indicates the accuracy, training time (normalized) and test time (normalized) of each classifier. .. GENERATED FROM PYTHON SOURCE LINES 301-325 .. 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-sg:: /auto_examples/text/images/sphx_glr_plot_document_classification_20newsgroups_001.png :alt: Score :srcset: /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 3.564 seconds) .. _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:: images/binder_badge_logo.svg :target: https://mybinder.org/v2/gh/scikit-learn/scikit-learn/1.0.X?urlpath=lab/tree/notebooks/auto_examples/text/plot_document_classification_20newsgroups.ipynb :alt: Launch binder :width: 150 px .. container:: sphx-glr-download sphx-glr-download-python :download:`Download Python source code: plot_document_classification_20newsgroups.py ` .. container:: sphx-glr-download sphx-glr-download-jupyter :download:`Download Jupyter notebook: plot_document_classification_20newsgroups.ipynb ` .. only:: html .. rst-class:: sphx-glr-signature `Gallery generated by Sphinx-Gallery `_