.. 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 :ref:`Go to the end ` to download the full example code. or to run this example in your browser via JupyterLite or 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 Tf-idf-weighted document-term sparse matrix to encode the features and demonstrates various classifiers that can efficiently handle sparse matrices. For document analysis via an unsupervised learning approach, see the example script :ref:`sphx_glr_auto_examples_text_plot_document_clustering.py`. .. GENERATED FROM PYTHON SOURCE LINES 16-21 .. code-block:: Python # Authors: The scikit-learn developers # SPDX-License-Identifier: BSD-3-Clause .. GENERATED FROM PYTHON SOURCE LINES 22-34 Loading and vectorizing the 20 newsgroups text dataset ====================================================== We define a function to load data from :ref:`20newsgroups_dataset`, which comprises around 18,000 newsgroups posts on 20 topics split in two subsets: one for training (or development) and the other one for testing (or for performance evaluation). Note that, by default, the text samples contain some message metadata such as `'headers'`, `'footers'` (signatures) and `'quotes'` to other posts. The `fetch_20newsgroups` function therefore accepts a parameter named `remove` to attempt stripping such information that can make the classification problem "too easy". This is achieved using simple heuristics that are neither perfect nor standard, hence disabled by default. .. GENERATED FROM PYTHON SOURCE LINES 34-117 .. code-block:: Python from time import time from sklearn.datasets import fetch_20newsgroups from sklearn.feature_extraction.text import TfidfVectorizer categories = [ "alt.atheism", "talk.religion.misc", "comp.graphics", "sci.space", ] def size_mb(docs): return sum(len(s.encode("utf-8")) for s in docs) / 1e6 def load_dataset(verbose=False, remove=()): """Load and vectorize the 20 newsgroups dataset.""" 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, ) # order of labels in `target_names` can be different from `categories` target_names = data_train.target_names # split target in a training set and a test set y_train, y_test = data_train.target, data_test.target # Extracting features from the training data using a sparse vectorizer t0 = time() vectorizer = TfidfVectorizer( sublinear_tf=True, max_df=0.5, min_df=5, stop_words="english" ) X_train = vectorizer.fit_transform(data_train.data) duration_train = time() - t0 # Extracting features from the test data using the same vectorizer t0 = time() X_test = vectorizer.transform(data_test.data) duration_test = time() - t0 feature_names = vectorizer.get_feature_names_out() if verbose: # compute size of loaded data data_train_size_mb = size_mb(data_train.data) data_test_size_mb = size_mb(data_test.data) print( f"{len(data_train.data)} documents - " f"{data_train_size_mb:.2f}MB (training set)" ) print(f"{len(data_test.data)} documents - {data_test_size_mb:.2f}MB (test set)") print(f"{len(target_names)} categories") print( f"vectorize training done in {duration_train:.3f}s " f"at {data_train_size_mb / duration_train:.3f}MB/s" ) print(f"n_samples: {X_train.shape[0]}, n_features: {X_train.shape[1]}") print( f"vectorize testing done in {duration_test:.3f}s " f"at {data_test_size_mb / duration_test:.3f}MB/s" ) print(f"n_samples: {X_test.shape[0]}, n_features: {X_test.shape[1]}") return X_train, X_test, y_train, y_test, feature_names, target_names .. GENERATED FROM PYTHON SOURCE LINES 118-132 Analysis of a bag-of-words document classifier ============================================== We will now train a classifier twice, once on the text samples including metadata and once after stripping the metadata. For both cases we will analyze the classification errors on a test set using a confusion matrix and inspect the coefficients that define the classification function of the trained models. Model without metadata stripping -------------------------------- We start by using the custom function `load_dataset` to load the data without metadata stripping. .. GENERATED FROM PYTHON SOURCE LINES 132-137 .. code-block:: Python X_train, X_test, y_train, y_test, feature_names, target_names = load_dataset( verbose=True ) .. rst-class:: sphx-glr-script-out .. code-block:: none 2034 documents - 3.98MB (training set) 1353 documents - 2.87MB (test set) 4 categories vectorize training done in 0.408s at 9.765MB/s n_samples: 2034, n_features: 7831 vectorize testing done in 0.256s at 11.202MB/s n_samples: 1353, n_features: 7831 .. GENERATED FROM PYTHON SOURCE LINES 138-146 Our first model is an instance of the :class:`~sklearn.linear_model.RidgeClassifier` class. This is a linear classification model that uses the mean squared error on {-1, 1} encoded targets, one for each possible class. Contrary to :class:`~sklearn.linear_model.LogisticRegression`, :class:`~sklearn.linear_model.RidgeClassifier` does not provide probabilistic predictions (no `predict_proba` method), but it is often faster to train. .. GENERATED FROM PYTHON SOURCE LINES 146-153 .. code-block:: Python from sklearn.linear_model import RidgeClassifier clf = RidgeClassifier(tol=1e-2, solver="sparse_cg") clf.fit(X_train, y_train) pred = clf.predict(X_test) .. GENERATED FROM PYTHON SOURCE LINES 154-156 We plot the confusion matrix of this classifier to find if there is a pattern in the classification errors. .. GENERATED FROM PYTHON SOURCE LINES 156-169 .. code-block:: Python import matplotlib.pyplot as plt from sklearn.metrics import ConfusionMatrixDisplay fig, ax = plt.subplots(figsize=(10, 5)) ConfusionMatrixDisplay.from_predictions(y_test, pred, ax=ax) ax.xaxis.set_ticklabels(target_names) ax.yaxis.set_ticklabels(target_names) _ = ax.set_title( f"Confusion Matrix for {clf.__class__.__name__}\non the original documents" ) .. image-sg:: /auto_examples/text/images/sphx_glr_plot_document_classification_20newsgroups_001.png :alt: Confusion Matrix for RidgeClassifier on the original documents :srcset: /auto_examples/text/images/sphx_glr_plot_document_classification_20newsgroups_001.png :class: sphx-glr-single-img .. GENERATED FROM PYTHON SOURCE LINES 170-182 The confusion matrix highlights that documents of the `alt.atheism` class are often confused with documents with the class `talk.religion.misc` class and vice-versa which is expected since the topics are semantically related. We also observe that some documents of the `sci.space` class can be misclassified as `comp.graphics` while the converse is much rarer. A manual inspection of those badly classified documents would be required to get some insights on this asymmetry. It could be the case that the vocabulary of the space topic could be more specific than the vocabulary for computer graphics. We can gain a deeper understanding of how this classifier makes its decisions by looking at the words with the highest average feature effects: .. GENERATED FROM PYTHON SOURCE LINES 182-233 .. code-block:: Python import numpy as np import pandas as pd def plot_feature_effects(): # learned coefficients weighted by frequency of appearance average_feature_effects = clf.coef_ * np.asarray(X_train.mean(axis=0)).ravel() for i, label in enumerate(target_names): top5 = np.argsort(average_feature_effects[i])[-5:][::-1] if i == 0: top = pd.DataFrame(feature_names[top5], columns=[label]) top_indices = top5 else: top[label] = feature_names[top5] top_indices = np.concatenate((top_indices, top5), axis=None) top_indices = np.unique(top_indices) predictive_words = feature_names[top_indices] # plot feature effects bar_size = 0.25 padding = 0.75 y_locs = np.arange(len(top_indices)) * (4 * bar_size + padding) fig, ax = plt.subplots(figsize=(10, 8)) for i, label in enumerate(target_names): ax.barh( y_locs + (i - 2) * bar_size, average_feature_effects[i, top_indices], height=bar_size, label=label, ) ax.set( yticks=y_locs, yticklabels=predictive_words, ylim=[ 0 - 4 * bar_size, len(top_indices) * (4 * bar_size + padding) - 4 * bar_size, ], ) ax.legend(loc="lower right") print("top 5 keywords per class:") print(top) return ax _ = plot_feature_effects().set_title("Average feature effect on the original data") .. image-sg:: /auto_examples/text/images/sphx_glr_plot_document_classification_20newsgroups_002.png :alt: Average feature effect on the original data :srcset: /auto_examples/text/images/sphx_glr_plot_document_classification_20newsgroups_002.png :class: sphx-glr-single-img .. rst-class:: sphx-glr-script-out .. code-block:: none top 5 keywords per class: alt.atheism comp.graphics sci.space talk.religion.misc 0 keith graphics space christian 1 god university nasa com 2 atheists thanks orbit god 3 people does moon morality 4 caltech image access people .. GENERATED FROM PYTHON SOURCE LINES 234-245 We can observe that the most predictive words are often strongly positively associated with a single class and negatively associated with all the other classes. Most of those positive associations are quite easy to interpret. However, some words such as `"god"` and `"people"` are positively associated to both `"talk.misc.religion"` and `"alt.atheism"` as those two classes expectedly share some common vocabulary. Notice however that there are also words such as `"christian"` and `"morality"` that are only positively associated with `"talk.misc.religion"`. Furthermore, in this version of the dataset, the word `"caltech"` is one of the top predictive features for atheism due to pollution in the dataset coming from some sort of metadata such as the email addresses of the sender of previous emails in the discussion as can be seen below: .. GENERATED FROM PYTHON SOURCE LINES 245-255 .. code-block:: Python data_train = fetch_20newsgroups( subset="train", categories=categories, shuffle=True, random_state=42 ) for doc in data_train.data: if "caltech" in doc: print(doc) break .. rst-class:: sphx-glr-script-out .. code-block:: none From: livesey@solntze.wpd.sgi.com (Jon Livesey) Subject: Re: Morality? (was Re: , keith@cco.caltech.edu (Keith Allan Schneider) writes: |> livesey@solntze.wpd.sgi.com (Jon Livesey) writes: |> |> >>>Explain to me |> >>>how instinctive acts can be moral acts, and I am happy to listen. |> >>For example, if it were instinctive not to murder... |> > |> >Then not murdering would have no moral significance, since there |> >would be nothing voluntary about it. |> |> See, there you go again, saying that a moral act is only significant |> if it is "voluntary." Why do you think this? If you force me to do something, am I morally responsible for it? |> |> And anyway, humans have the ability to disregard some of their instincts. Well, make up your mind. Is it to be "instinctive not to murder" or not? |> |> >>So, only intelligent beings can be moral, even if the bahavior of other |> >>beings mimics theirs? |> > |> >You are starting to get the point. Mimicry is not necessarily the |> >same as the action being imitated. A Parrot saying "Pretty Polly" |> >isn't necessarily commenting on the pulchritude of Polly. |> |> You are attaching too many things to the term "moral," I think. |> Let's try this: is it "good" that animals of the same species |> don't kill each other. Or, do you think this is right? It's not even correct. Animals of the same species do kill one another. |> |> Or do you think that animals are machines, and that nothing they do |> is either right nor wrong? Sigh. I wonder how many times we have been round this loop. I think that instinctive bahaviour has no moral significance. I am quite prepared to believe that higher animals, such as primates, have the beginnings of a moral sense, since they seem to exhibit self-awareness. |> |> |> >>Animals of the same species could kill each other arbitarily, but |> >>they don't. |> > |> >They do. I and other posters have given you many examples of exactly |> >this, but you seem to have a very short memory. |> |> Those weren't arbitrary killings. They were slayings related to some |> sort of mating ritual or whatnot. So what? Are you trying to say that some killing in animals has a moral significance and some does not? Is this your natural morality> |> |> >>Are you trying to say that this isn't an act of morality because |> >>most animals aren't intelligent enough to think like we do? |> > |> >I'm saying: |> > "There must be the possibility that the organism - it's not |> > just people we are talking about - can consider alternatives." |> > |> >It's right there in the posting you are replying to. |> |> Yes it was, but I still don't understand your distinctions. What |> do you mean by "consider?" Can a small child be moral? How about |> a gorilla? A dolphin? A platypus? Where is the line drawn? Does |> the being need to be self aware? Are you blind? What do you think that this sentence means? "There must be the possibility that the organism - it's not just people we are talking about - can consider alternatives." What would that imply? |> |> What *do* you call the mechanism which seems to prevent animals of |> the same species from (arbitrarily) killing each other? Don't |> you find the fact that they don't at all significant? I find the fact that they do to be significant. jon. .. GENERATED FROM PYTHON SOURCE LINES 256-272 Such headers, signature footers (and quoted metadata from previous messages) can be considered side information that artificially reveals the newsgroup by identifying the registered members and one would rather want our text classifier to only learn from the "main content" of each text document instead of relying on the leaked identity of the writers. Model with metadata stripping ----------------------------- The `remove` option of the 20 newsgroups dataset loader in scikit-learn allows to heuristically attempt to filter out some of this unwanted metadata that makes the classification problem artificially easier. Be aware that such filtering of the text contents is far from perfect. Let us try to leverage this option to train a text classifier that does not rely too much on this kind of metadata to make its decisions: .. GENERATED FROM PYTHON SOURCE LINES 272-293 .. code-block:: Python ( X_train, X_test, y_train, y_test, feature_names, target_names, ) = load_dataset(remove=("headers", "footers", "quotes")) clf = RidgeClassifier(tol=1e-2, solver="sparse_cg") clf.fit(X_train, y_train) pred = clf.predict(X_test) fig, ax = plt.subplots(figsize=(10, 5)) ConfusionMatrixDisplay.from_predictions(y_test, pred, ax=ax) ax.xaxis.set_ticklabels(target_names) ax.yaxis.set_ticklabels(target_names) _ = ax.set_title( f"Confusion Matrix for {clf.__class__.__name__}\non filtered documents" ) .. image-sg:: /auto_examples/text/images/sphx_glr_plot_document_classification_20newsgroups_003.png :alt: Confusion Matrix for RidgeClassifier on filtered documents :srcset: /auto_examples/text/images/sphx_glr_plot_document_classification_20newsgroups_003.png :class: sphx-glr-single-img .. GENERATED FROM PYTHON SOURCE LINES 294-298 By looking at the confusion matrix, it is more evident that the scores of the model trained with metadata were over-optimistic. The classification problem without access to the metadata is less accurate but more representative of the intended text classification problem. .. GENERATED FROM PYTHON SOURCE LINES 298-301 .. code-block:: Python _ = plot_feature_effects().set_title("Average feature effects on filtered documents") .. image-sg:: /auto_examples/text/images/sphx_glr_plot_document_classification_20newsgroups_004.png :alt: Average feature effects on filtered documents :srcset: /auto_examples/text/images/sphx_glr_plot_document_classification_20newsgroups_004.png :class: sphx-glr-single-img .. rst-class:: sphx-glr-script-out .. code-block:: none top 5 keywords per class: alt.atheism comp.graphics sci.space talk.religion.misc 0 don graphics space god 1 people file like christian 2 say thanks nasa jesus 3 religion image orbit christians 4 post does launch wrong .. GENERATED FROM PYTHON SOURCE LINES 302-304 In the next section we keep the dataset without metadata to compare several classifiers. .. GENERATED FROM PYTHON SOURCE LINES 306-315 Benchmarking classifiers ======================== Scikit-learn provides many different kinds of classification algorithms. In this section we will train a selection of those classifiers on the same text classification problem and measure both their generalization performance (accuracy on the test set) and their computation performance (speed), both at training time and testing time. For such purpose we define the following benchmarking utilities: .. GENERATED FROM PYTHON SOURCE LINES 315-350 .. code-block:: Python from sklearn import metrics from sklearn.utils.extmath import density def benchmark(clf, custom_name=False): print("_" * 80) print("Training: ") print(clf) t0 = time() clf.fit(X_train, y_train) train_time = time() - t0 print(f"train time: {train_time:.3}s") t0 = time() pred = clf.predict(X_test) test_time = time() - t0 print(f"test time: {test_time:.3}s") score = metrics.accuracy_score(y_test, pred) print(f"accuracy: {score:.3}") if hasattr(clf, "coef_"): print(f"dimensionality: {clf.coef_.shape[1]}") print(f"density: {density(clf.coef_)}") print() print() if custom_name: clf_descr = str(custom_name) else: clf_descr = clf.__class__.__name__ return clf_descr, score, train_time, test_time .. GENERATED FROM PYTHON SOURCE LINES 351-361 We now train and test the datasets with 8 different classification models and get performance results for each model. The goal of this study is to highlight the computation/accuracy tradeoffs of different types of classifiers for such a multi-class text classification problem. Notice that the most important hyperparameters values were tuned using a grid search procedure not shown in this notebook for the sake of simplicity. See the example script :ref:`sphx_glr_auto_examples_model_selection_plot_grid_search_text_feature_extraction.py` # noqa: E501 for a demo on how such tuning can be done. .. GENERATED FROM PYTHON SOURCE LINES 361-392 .. code-block:: Python from sklearn.ensemble import RandomForestClassifier from sklearn.linear_model import LogisticRegression, SGDClassifier from sklearn.naive_bayes import ComplementNB from sklearn.neighbors import KNeighborsClassifier, NearestCentroid from sklearn.svm import LinearSVC results = [] for clf, name in ( (LogisticRegression(C=5, max_iter=1000), "Logistic Regression"), (RidgeClassifier(alpha=1.0, solver="sparse_cg"), "Ridge Classifier"), (KNeighborsClassifier(n_neighbors=100), "kNN"), (RandomForestClassifier(), "Random Forest"), # L2 penalty Linear SVC (LinearSVC(C=0.1, dual=False, max_iter=1000), "Linear SVC"), # L2 penalty Linear SGD ( SGDClassifier( loss="log_loss", alpha=1e-4, n_iter_no_change=3, early_stopping=True ), "log-loss SGD", ), # NearestCentroid (aka Rocchio classifier) (NearestCentroid(), "NearestCentroid"), # Sparse naive Bayes classifier (ComplementNB(alpha=0.1), "Complement naive Bayes"), ): print("=" * 80) print(name) results.append(benchmark(clf, name)) .. rst-class:: sphx-glr-script-out .. code-block:: none ================================================================================ Logistic Regression ________________________________________________________________________________ Training: LogisticRegression(C=5, max_iter=1000) train time: 0.204s test time: 0.000932s accuracy: 0.772 dimensionality: 5316 density: 1.0 ================================================================================ Ridge Classifier ________________________________________________________________________________ Training: RidgeClassifier(solver='sparse_cg') train time: 0.0356s test time: 0.000883s accuracy: 0.76 dimensionality: 5316 density: 1.0 ================================================================================ kNN ________________________________________________________________________________ Training: KNeighborsClassifier(n_neighbors=100) train time: 0.00123s test time: 0.0798s accuracy: 0.752 ================================================================================ Random Forest ________________________________________________________________________________ Training: RandomForestClassifier() train time: 1.86s test time: 0.0607s accuracy: 0.704 ================================================================================ Linear SVC ________________________________________________________________________________ Training: LinearSVC(C=0.1, dual=False) train time: 0.0307s test time: 0.000678s accuracy: 0.752 dimensionality: 5316 density: 1.0 ================================================================================ log-loss SGD ________________________________________________________________________________ Training: SGDClassifier(early_stopping=True, loss='log_loss', n_iter_no_change=3) train time: 0.033s test time: 0.000718s accuracy: 0.758 dimensionality: 5316 density: 1.0 ================================================================================ NearestCentroid ________________________________________________________________________________ Training: NearestCentroid() train time: 0.485s test time: 0.00265s accuracy: 0.748 ================================================================================ Complement naive Bayes ________________________________________________________________________________ Training: ComplementNB(alpha=0.1) train time: 0.00341s test time: 0.00102s accuracy: 0.779 .. GENERATED FROM PYTHON SOURCE LINES 393-398 Plot accuracy, training and test time of each classifier ======================================================== The scatter plots show the trade-off between the test accuracy and the training and testing time of each classifier. .. GENERATED FROM PYTHON SOURCE LINES 398-428 .. code-block:: Python 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) test_time = np.array(test_time) fig, ax1 = plt.subplots(figsize=(10, 8)) ax1.scatter(score, training_time, s=60) ax1.set( title="Score-training time trade-off", yscale="log", xlabel="test accuracy", ylabel="training time (s)", ) fig, ax2 = plt.subplots(figsize=(10, 8)) ax2.scatter(score, test_time, s=60) ax2.set( title="Score-test time trade-off", yscale="log", xlabel="test accuracy", ylabel="test time (s)", ) for i, txt in enumerate(clf_names): ax1.annotate(txt, (score[i], training_time[i])) ax2.annotate(txt, (score[i], test_time[i])) .. rst-class:: sphx-glr-horizontal * .. image-sg:: /auto_examples/text/images/sphx_glr_plot_document_classification_20newsgroups_005.png :alt: Score-training time trade-off :srcset: /auto_examples/text/images/sphx_glr_plot_document_classification_20newsgroups_005.png :class: sphx-glr-multi-img * .. image-sg:: /auto_examples/text/images/sphx_glr_plot_document_classification_20newsgroups_006.png :alt: Score-test time trade-off :srcset: /auto_examples/text/images/sphx_glr_plot_document_classification_20newsgroups_006.png :class: sphx-glr-multi-img .. GENERATED FROM PYTHON SOURCE LINES 429-451 The naive Bayes model has the best trade-off between score and training/testing time, while Random Forest is both slow to train, expensive to predict and has a comparatively bad accuracy. This is expected: for high-dimensional prediction problems, linear models are often better suited as most problems become linearly separable when the feature space has 10,000 dimensions or more. The difference in training speed and accuracy of the linear models can be explained by the choice of the loss function they optimize and the kind of regularization they use. Be aware that some linear models with the same loss but a different solver or regularization configuration may yield different fitting times and test accuracy. We can observe on the second plot that once trained, all linear models have approximately the same prediction speed which is expected because they all implement the same prediction function. KNeighborsClassifier has a relatively low accuracy and has the highest testing time. The long prediction time is also expected: for each prediction the model has to compute the pairwise distances between the testing sample and each document in the training set, which is computationally expensive. Furthermore, the "curse of dimensionality" harms the ability of this model to yield competitive accuracy in the high dimensional feature space of text classification problems. .. rst-class:: sphx-glr-timing **Total running time of the script:** (0 minutes 7.637 seconds) .. _sphx_glr_download_auto_examples_text_plot_document_classification_20newsgroups.py: .. only:: html .. container:: sphx-glr-footer sphx-glr-footer-example .. container:: binder-badge .. image:: images/binder_badge_logo.svg :target: https://mybinder.org/v2/gh/scikit-learn/scikit-learn/main?urlpath=lab/tree/notebooks/auto_examples/text/plot_document_classification_20newsgroups.ipynb :alt: Launch binder :width: 150 px .. container:: lite-badge .. image:: images/jupyterlite_badge_logo.svg :target: ../../lite/lab/index.html?path=auto_examples/text/plot_document_classification_20newsgroups.ipynb :alt: Launch JupyterLite :width: 150 px .. container:: sphx-glr-download sphx-glr-download-jupyter :download:`Download Jupyter notebook: plot_document_classification_20newsgroups.ipynb ` .. 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-zip :download:`Download zipped: plot_document_classification_20newsgroups.zip ` .. include:: plot_document_classification_20newsgroups.recommendations .. only:: html .. rst-class:: sphx-glr-signature `Gallery generated by Sphinx-Gallery `_