.. DO NOT EDIT. .. THIS FILE WAS AUTOMATICALLY GENERATED BY SPHINX-GALLERY. .. TO MAKE CHANGES, EDIT THE SOURCE PYTHON FILE: .. "auto_examples/bicluster/plot_bicluster_newsgroups.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_bicluster_plot_bicluster_newsgroups.py: ================================================================ Biclustering documents with the Spectral Co-clustering algorithm ================================================================ This example demonstrates the Spectral Co-clustering algorithm on the twenty newsgroups dataset. The 'comp.os.ms-windows.misc' category is excluded because it contains many posts containing nothing but data. The TF-IDF vectorized posts form a word frequency matrix, which is then biclustered using Dhillon's Spectral Co-Clustering algorithm. The resulting document-word biclusters indicate subsets words used more often in those subsets documents. For a few of the best biclusters, its most common document categories and its ten most important words get printed. The best biclusters are determined by their normalized cut. The best words are determined by comparing their sums inside and outside the bicluster. For comparison, the documents are also clustered using MiniBatchKMeans. The document clusters derived from the biclusters achieve a better V-measure than clusters found by MiniBatchKMeans. .. GENERATED FROM PYTHON SOURCE LINES 25-153 .. rst-class:: sphx-glr-script-out Out: .. code-block:: none Vectorizing... Coclustering... Done in 2.18s. V-measure: 0.4431 MiniBatchKMeans... Done in 7.83s. V-measure: 0.3344 Best biclusters: ---------------- bicluster 0 : 1961 documents, 4388 words categories : 23% talk.politics.guns, 18% talk.politics.misc, 17% sci.med words : gun, geb, guns, banks, gordon, clinton, pitt, cdt, surrender, veal bicluster 1 : 1269 documents, 3558 words categories : 27% soc.religion.christian, 25% talk.politics.mideast, 24% alt.atheism words : god, jesus, christians, sin, objective, kent, belief, christ, faith, moral bicluster 2 : 2201 documents, 2747 words categories : 18% comp.sys.mac.hardware, 17% comp.sys.ibm.pc.hardware, 16% comp.graphics words : voltage, board, dsp, packages, receiver, stereo, shipping, package, compression, image bicluster 3 : 1773 documents, 2620 words categories : 27% rec.motorcycles, 23% rec.autos, 13% misc.forsale words : bike, car, dod, engine, motorcycle, ride, honda, bikes, helmet, bmw bicluster 4 : 201 documents, 1175 words categories : 81% talk.politics.mideast, 10% alt.atheism, 7% soc.religion.christian words : turkish, armenia, armenian, armenians, turks, petch, sera, zuma, argic, gvg47 | .. code-block:: default from collections import defaultdict import operator from time import time import numpy as np from sklearn.cluster import SpectralCoclustering from sklearn.cluster import MiniBatchKMeans from sklearn.datasets import fetch_20newsgroups from sklearn.feature_extraction.text import TfidfVectorizer from sklearn.metrics.cluster import v_measure_score print(__doc__) def number_normalizer(tokens): """ Map all numeric tokens to a placeholder. For many applications, tokens that begin with a number are not directly useful, but the fact that such a token exists can be relevant. By applying this form of dimensionality reduction, some methods may perform better. """ return ("#NUMBER" if token[0].isdigit() else token for token in tokens) class NumberNormalizingVectorizer(TfidfVectorizer): def build_tokenizer(self): tokenize = super().build_tokenizer() return lambda doc: list(number_normalizer(tokenize(doc))) # exclude 'comp.os.ms-windows.misc' categories = ['alt.atheism', 'comp.graphics', 'comp.sys.ibm.pc.hardware', 'comp.sys.mac.hardware', 'comp.windows.x', 'misc.forsale', 'rec.autos', 'rec.motorcycles', 'rec.sport.baseball', 'rec.sport.hockey', 'sci.crypt', 'sci.electronics', 'sci.med', 'sci.space', 'soc.religion.christian', 'talk.politics.guns', 'talk.politics.mideast', 'talk.politics.misc', 'talk.religion.misc'] newsgroups = fetch_20newsgroups(categories=categories) y_true = newsgroups.target vectorizer = NumberNormalizingVectorizer(stop_words='english', min_df=5) cocluster = SpectralCoclustering(n_clusters=len(categories), svd_method='arpack', random_state=0) kmeans = MiniBatchKMeans(n_clusters=len(categories), batch_size=20000, random_state=0) print("Vectorizing...") X = vectorizer.fit_transform(newsgroups.data) print("Coclustering...") start_time = time() cocluster.fit(X) y_cocluster = cocluster.row_labels_ print("Done in {:.2f}s. V-measure: {:.4f}".format( time() - start_time, v_measure_score(y_cocluster, y_true))) print("MiniBatchKMeans...") start_time = time() y_kmeans = kmeans.fit_predict(X) print("Done in {:.2f}s. V-measure: {:.4f}".format( time() - start_time, v_measure_score(y_kmeans, y_true))) feature_names = vectorizer.get_feature_names() document_names = list(newsgroups.target_names[i] for i in newsgroups.target) def bicluster_ncut(i): rows, cols = cocluster.get_indices(i) if not (np.any(rows) and np.any(cols)): import sys return sys.float_info.max row_complement = np.nonzero(np.logical_not(cocluster.rows_[i]))[0] col_complement = np.nonzero(np.logical_not(cocluster.columns_[i]))[0] # Note: the following is identical to X[rows[:, np.newaxis], # cols].sum() but much faster in scipy <= 0.16 weight = X[rows][:, cols].sum() cut = (X[row_complement][:, cols].sum() + X[rows][:, col_complement].sum()) return cut / weight def most_common(d): """Items of a defaultdict(int) with the highest values. Like Counter.most_common in Python >=2.7. """ return sorted(d.items(), key=operator.itemgetter(1), reverse=True) bicluster_ncuts = list(bicluster_ncut(i) for i in range(len(newsgroups.target_names))) best_idx = np.argsort(bicluster_ncuts)[:5] print() print("Best biclusters:") print("----------------") for idx, cluster in enumerate(best_idx): n_rows, n_cols = cocluster.get_shape(cluster) cluster_docs, cluster_words = cocluster.get_indices(cluster) if not len(cluster_docs) or not len(cluster_words): continue # categories counter = defaultdict(int) for i in cluster_docs: counter[document_names[i]] += 1 cat_string = ", ".join("{:.0f}% {}".format(float(c) / n_rows * 100, name) for name, c in most_common(counter)[:3]) # words out_of_cluster_docs = cocluster.row_labels_ != cluster out_of_cluster_docs = np.where(out_of_cluster_docs)[0] word_col = X[:, cluster_words] word_scores = np.array(word_col[cluster_docs, :].sum(axis=0) - word_col[out_of_cluster_docs, :].sum(axis=0)) word_scores = word_scores.ravel() important_words = list(feature_names[cluster_words[i]] for i in word_scores.argsort()[:-11:-1]) print("bicluster {} : {} documents, {} words".format( idx, n_rows, n_cols)) print("categories : {}".format(cat_string)) print("words : {}\n".format(', '.join(important_words))) .. rst-class:: sphx-glr-timing **Total running time of the script:** ( 0 minutes 21.224 seconds) .. _sphx_glr_download_auto_examples_bicluster_plot_bicluster_newsgroups.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/0.24.X?urlpath=lab/tree/notebooks/auto_examples/bicluster/plot_bicluster_newsgroups.ipynb :alt: Launch binder :width: 150 px .. container:: sphx-glr-download sphx-glr-download-python :download:`Download Python source code: plot_bicluster_newsgroups.py ` .. container:: sphx-glr-download sphx-glr-download-jupyter :download:`Download Jupyter notebook: plot_bicluster_newsgroups.ipynb ` .. only:: html .. rst-class:: sphx-glr-signature `Gallery generated by Sphinx-Gallery `_