137 lines
5.3 KiB
Python
137 lines
5.3 KiB
Python
"""
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=======================================================================================
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Topic extraction with Non-negative Matrix Factorization and Latent Dirichlet Allocation
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=======================================================================================
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This is an example of applying :class:`~sklearn.decomposition.NMF` and
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:class:`~sklearn.decomposition.LatentDirichletAllocation` on a corpus
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of documents and extract additive models of the topic structure of the
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corpus. The output is a plot of topics, each represented as bar plot
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using top few words based on weights.
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Non-negative Matrix Factorization is applied with two different objective
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functions: the Frobenius norm, and the generalized Kullback-Leibler divergence.
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The latter is equivalent to Probabilistic Latent Semantic Indexing.
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The default parameters (n_samples / n_features / n_components) should make
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the example runnable in a couple of tens of seconds. You can try to
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increase the dimensions of the problem, but be aware that the time
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complexity is polynomial in NMF. In LDA, the time complexity is
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proportional to (n_samples * iterations).
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"""
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# Author: Olivier Grisel <olivier.grisel@ensta.org>
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# Lars Buitinck
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# Chyi-Kwei Yau <chyikwei.yau@gmail.com>
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# License: BSD 3 clause
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from time import time
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import matplotlib.pyplot as plt
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from sklearn.feature_extraction.text import TfidfVectorizer, CountVectorizer
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from sklearn.decomposition import NMF, LatentDirichletAllocation
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from sklearn.datasets import fetch_20newsgroups
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n_samples = 2000
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n_features = 1000
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n_components = 10
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n_top_words = 20
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def plot_top_words(model, feature_names, n_top_words, title):
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fig, axes = plt.subplots(2, 5, figsize=(30, 15), sharex=True)
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axes = axes.flatten()
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for topic_idx, topic in enumerate(model.components_):
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top_features_ind = topic.argsort()[:-n_top_words - 1:-1]
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top_features = [feature_names[i] for i in top_features_ind]
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weights = topic[top_features_ind]
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ax = axes[topic_idx]
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ax.barh(top_features, weights, height=0.7)
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ax.set_title(f'Topic {topic_idx +1}',
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fontdict={'fontsize': 30})
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ax.invert_yaxis()
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ax.tick_params(axis='both', which='major', labelsize=20)
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for i in 'top right left'.split():
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ax.spines[i].set_visible(False)
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fig.suptitle(title, fontsize=40)
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plt.subplots_adjust(top=0.90, bottom=0.05, wspace=0.90, hspace=0.3)
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plt.show()
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# Load the 20 newsgroups dataset and vectorize it. We use a few heuristics
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# to filter out useless terms early on: the posts are stripped of headers,
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# footers and quoted replies, and common English words, words occurring in
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# only one document or in at least 95% of the documents are removed.
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print("Loading dataset...")
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t0 = time()
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data, _ = fetch_20newsgroups(shuffle=True, random_state=1,
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remove=('headers', 'footers', 'quotes'),
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return_X_y=True)
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data_samples = data[:n_samples]
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print("done in %0.3fs." % (time() - t0))
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# Use tf-idf features for NMF.
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print("Extracting tf-idf features for NMF...")
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tfidf_vectorizer = TfidfVectorizer(max_df=0.95, min_df=2,
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max_features=n_features,
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stop_words='english')
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t0 = time()
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tfidf = tfidf_vectorizer.fit_transform(data_samples)
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print("done in %0.3fs." % (time() - t0))
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# Use tf (raw term count) features for LDA.
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print("Extracting tf features for LDA...")
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tf_vectorizer = CountVectorizer(max_df=0.95, min_df=2,
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max_features=n_features,
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stop_words='english')
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t0 = time()
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tf = tf_vectorizer.fit_transform(data_samples)
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print("done in %0.3fs." % (time() - t0))
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print()
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# Fit the NMF model
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print("Fitting the NMF model (Frobenius norm) with tf-idf features, "
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"n_samples=%d and n_features=%d..."
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% (n_samples, n_features))
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t0 = time()
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nmf = NMF(n_components=n_components, random_state=1,
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alpha=.1, l1_ratio=.5).fit(tfidf)
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print("done in %0.3fs." % (time() - t0))
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tfidf_feature_names = tfidf_vectorizer.get_feature_names()
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plot_top_words(nmf, tfidf_feature_names, n_top_words,
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'Topics in NMF model (Frobenius norm)')
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# Fit the NMF model
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print('\n' * 2, "Fitting the NMF model (generalized Kullback-Leibler "
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"divergence) with tf-idf features, n_samples=%d and n_features=%d..."
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% (n_samples, n_features))
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t0 = time()
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nmf = NMF(n_components=n_components, random_state=1,
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beta_loss='kullback-leibler', solver='mu', max_iter=1000, alpha=.1,
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l1_ratio=.5).fit(tfidf)
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print("done in %0.3fs." % (time() - t0))
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tfidf_feature_names = tfidf_vectorizer.get_feature_names()
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plot_top_words(nmf, tfidf_feature_names, n_top_words,
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'Topics in NMF model (generalized Kullback-Leibler divergence)')
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print('\n' * 2, "Fitting LDA models with tf features, "
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"n_samples=%d and n_features=%d..."
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% (n_samples, n_features))
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lda = LatentDirichletAllocation(n_components=n_components, max_iter=5,
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learning_method='online',
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learning_offset=50.,
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random_state=0)
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t0 = time()
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lda.fit(tf)
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print("done in %0.3fs." % (time() - t0))
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tf_feature_names = tf_vectorizer.get_feature_names()
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plot_top_words(lda, tf_feature_names, n_top_words, 'Topics in LDA model')
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