.. DO NOT EDIT. .. THIS FILE WAS AUTOMATICALLY GENERATED BY SPHINX-GALLERY. .. TO MAKE CHANGES, EDIT THE SOURCE PYTHON FILE: .. "auto_examples/applications/plot_topics_extraction_with_nmf_lda.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 Binder .. rst-class:: sphx-glr-example-title .. _sphx_glr_auto_examples_applications_plot_topics_extraction_with_nmf_lda.py: ======================================================================================= 使用非负矩阵分解和潜在狄利克雷分配进行主题提取 ======================================================================================= 这是一个应用 :class:`~sklearn.decomposition.NMF` 和 :class:`~sklearn.decomposition.LatentDirichletAllocation` 在文档语料库上提取语料库主题结构的加性模型的示例。输出是一个主题的图，每个主题用基于权重的前几个词表示为条形图。 非负矩阵分解应用了两种不同的目标函数：Frobenius 范数和广义 Kullback-Leibler 散度。后者等同于概率潜在语义索引。 默认参数（n_samples / n_features / n_components）应使示例在几十秒内可运行。您可以尝试增加问题的维度，但请注意，在 NMF 中时间复杂度是多项式的。在 LDA 中，时间复杂度与 (n_samples * 迭代次数) 成正比。 .. GENERATED FROM PYTHON SOURCE LINES 13-212 .. rst-class:: sphx-glr-horizontal * .. image-sg:: /auto_examples/applications/images/sphx_glr_plot_topics_extraction_with_nmf_lda_001.png :alt: Topics in NMF model (Frobenius norm), Topic 1, Topic 2, Topic 3, Topic 4, Topic 5, Topic 6, Topic 7, Topic 8, Topic 9, Topic 10 :srcset: /auto_examples/applications/images/sphx_glr_plot_topics_extraction_with_nmf_lda_001.png :class: sphx-glr-multi-img * .. image-sg:: /auto_examples/applications/images/sphx_glr_plot_topics_extraction_with_nmf_lda_002.png :alt: Topics in NMF model (generalized Kullback-Leibler divergence), Topic 1, Topic 2, Topic 3, Topic 4, Topic 5, Topic 6, Topic 7, Topic 8, Topic 9, Topic 10 :srcset: /auto_examples/applications/images/sphx_glr_plot_topics_extraction_with_nmf_lda_002.png :class: sphx-glr-multi-img * .. image-sg:: /auto_examples/applications/images/sphx_glr_plot_topics_extraction_with_nmf_lda_003.png :alt: Topics in MiniBatchNMF model (Frobenius norm), Topic 1, Topic 2, Topic 3, Topic 4, Topic 5, Topic 6, Topic 7, Topic 8, Topic 9, Topic 10 :srcset: /auto_examples/applications/images/sphx_glr_plot_topics_extraction_with_nmf_lda_003.png :class: sphx-glr-multi-img * .. image-sg:: /auto_examples/applications/images/sphx_glr_plot_topics_extraction_with_nmf_lda_004.png :alt: Topics in MiniBatchNMF model (generalized Kullback-Leibler divergence), Topic 1, Topic 2, Topic 3, Topic 4, Topic 5, Topic 6, Topic 7, Topic 8, Topic 9, Topic 10 :srcset: /auto_examples/applications/images/sphx_glr_plot_topics_extraction_with_nmf_lda_004.png :class: sphx-glr-multi-img * .. image-sg:: /auto_examples/applications/images/sphx_glr_plot_topics_extraction_with_nmf_lda_005.png :alt: Topics in LDA model, Topic 1, Topic 2, Topic 3, Topic 4, Topic 5, Topic 6, Topic 7, Topic 8, Topic 9, Topic 10 :srcset: /auto_examples/applications/images/sphx_glr_plot_topics_extraction_with_nmf_lda_005.png :class: sphx-glr-multi-img .. rst-class:: sphx-glr-script-out .. code-block:: none Loading dataset... done in 0.704s. Extracting tf-idf features for NMF... done in 0.146s. Extracting tf features for LDA... done in 0.133s. Fitting the NMF model (Frobenius norm) with tf-idf features, n_samples=2000 and n_features=1000... done in 0.110s. Fitting the NMF model (generalized Kullback-Leibler divergence) with tf-idf features, n_samples=2000 and n_features=1000... done in 0.551s. Fitting the MiniBatchNMF model (Frobenius norm) with tf-idf features, n_samples=2000 and n_features=1000, batch_size=128... done in 0.062s. Fitting the MiniBatchNMF model (generalized Kullback-Leibler divergence) with tf-idf features, n_samples=2000 and n_features=1000, batch_size=128... done in 0.098s. Fitting LDA models with tf features, n_samples=2000 and n_features=1000... done in 1.496s. | .. code-block:: Python # 作者：scikit-learn 开发者 # SPDX-License-Identifier: BSD-3-Clause from time import time import matplotlib.pyplot as plt from sklearn.datasets import fetch_20newsgroups from sklearn.decomposition import NMF, LatentDirichletAllocation, MiniBatchNMF from sklearn.feature_extraction.text import CountVectorizer, TfidfVectorizer n_samples = 2000 n_features = 1000 n_components = 10 n_top_words = 20 batch_size = 128 init = "nndsvda" def plot_top_words(model, feature_names, n_top_words, title): fig, axes = plt.subplots(2, 5, figsize=(30, 15), sharex=True) axes = axes.flatten() for topic_idx, topic in enumerate(model.components_): top_features_ind = topic.argsort()[-n_top_words:] top_features = feature_names[top_features_ind] weights = topic[top_features_ind] ax = axes[topic_idx] ax.barh(top_features, weights, height=0.7) ax.set_title(f"Topic {topic_idx +1}", fontdict={"fontsize": 30}) ax.tick_params(axis="both", which="major", labelsize=20) for i in "top right left".split(): ax.spines[i].set_visible(False) fig.suptitle(title, fontsize=40) plt.subplots_adjust(top=0.90, bottom=0.05, wspace=0.90, hspace=0.3) plt.show() # 加载20个新闻组数据集并对其进行向量化。我们使用了一些启发式方法来提前过滤掉无用的术语：帖子被去除了标题、页脚和引用的回复，常见的英语单词、仅出现在一个文档中或至少出现在95%文档中的单词也被移除。 print("Loading dataset...") t0 = time() data, _ = fetch_20newsgroups( shuffle=True, random_state=1, remove=("headers", "footers", "quotes"), return_X_y=True, ) data_samples = data[:n_samples] print("done in %0.3fs." % (time() - t0)) # 使用tf-idf特征进行NMF。 print("Extracting tf-idf features for NMF...") tfidf_vectorizer = TfidfVectorizer( max_df=0.95, min_df=2, max_features=n_features, stop_words="english" ) t0 = time() tfidf = tfidf_vectorizer.fit_transform(data_samples) print("done in %0.3fs." % (time() - t0)) # 使用 tf（原始词频）特征进行 LDA。 print("Extracting tf features for LDA...") tf_vectorizer = CountVectorizer( max_df=0.95, min_df=2, max_features=n_features, stop_words="english" ) t0 = time() tf = tf_vectorizer.fit_transform(data_samples) print("done in %0.3fs." % (time() - t0)) print() # 拟合NMF模型 print( "Fitting the NMF model (Frobenius norm) with tf-idf features, " "n_samples=%d and n_features=%d..." % (n_samples, n_features) ) t0 = time() nmf = NMF( n_components=n_components, random_state=1, init=init, beta_loss="frobenius", alpha_W=0.00005, alpha_H=0.00005, l1_ratio=1, ).fit(tfidf) print("done in %0.3fs." % (time() - t0)) tfidf_feature_names = tfidf_vectorizer.get_feature_names_out() plot_top_words( nmf, tfidf_feature_names, n_top_words, "Topics in NMF model (Frobenius norm)" ) # 拟合NMF模型 print( "\n" * 2, "Fitting the NMF model (generalized Kullback-Leibler " "divergence) with tf-idf features, n_samples=%d and n_features=%d..." % (n_samples, n_features), ) t0 = time() nmf = NMF( n_components=n_components, random_state=1, init=init, beta_loss="kullback-leibler", solver="mu", max_iter=1000, alpha_W=0.00005, alpha_H=0.00005, l1_ratio=0.5, ).fit(tfidf) print("done in %0.3fs." % (time() - t0)) tfidf_feature_names = tfidf_vectorizer.get_feature_names_out() plot_top_words( nmf, tfidf_feature_names, n_top_words, "Topics in NMF model (generalized Kullback-Leibler divergence)", ) # 拟合 MiniBatchNMF 模型 print( "\n" * 2, "Fitting the MiniBatchNMF model (Frobenius norm) with tf-idf " "features, n_samples=%d and n_features=%d, batch_size=%d..." % (n_samples, n_features, batch_size), ) t0 = time() mbnmf = MiniBatchNMF( n_components=n_components, random_state=1, batch_size=batch_size, init=init, beta_loss="frobenius", alpha_W=0.00005, alpha_H=0.00005, l1_ratio=0.5, ).fit(tfidf) print("done in %0.3fs." % (time() - t0)) tfidf_feature_names = tfidf_vectorizer.get_feature_names_out() plot_top_words( mbnmf, tfidf_feature_names, n_top_words, "Topics in MiniBatchNMF model (Frobenius norm)", ) # 拟合 MiniBatchNMF 模型 print( "\n" * 2, "Fitting the MiniBatchNMF model (generalized Kullback-Leibler " "divergence) with tf-idf features, n_samples=%d and n_features=%d, " "batch_size=%d..." % (n_samples, n_features, batch_size), ) t0 = time() mbnmf = MiniBatchNMF( n_components=n_components, random_state=1, batch_size=batch_size, init=init, beta_loss="kullback-leibler", alpha_W=0.00005, alpha_H=0.00005, l1_ratio=0.5, ).fit(tfidf) print("done in %0.3fs." % (time() - t0)) tfidf_feature_names = tfidf_vectorizer.get_feature_names_out() plot_top_words( mbnmf, tfidf_feature_names, n_top_words, "Topics in MiniBatchNMF model (generalized Kullback-Leibler divergence)", ) print( "\n" * 2, "Fitting LDA models with tf features, n_samples=%d and n_features=%d..." % (n_samples, n_features), ) lda = LatentDirichletAllocation( n_components=n_components, max_iter=5, learning_method="online", learning_offset=50.0, random_state=0, ) t0 = time() lda.fit(tf) print("done in %0.3fs." % (time() - t0)) tf_feature_names = tf_vectorizer.get_feature_names_out() plot_top_words(lda, tf_feature_names, n_top_words, "Topics in LDA model") .. rst-class:: sphx-glr-timing **Total running time of the script:** (0 minutes 5.812 seconds) .. _sphx_glr_download_auto_examples_applications_plot_topics_extraction_with_nmf_lda.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/applications/plot_topics_extraction_with_nmf_lda.ipynb :alt: Launch binder :width: 150 px .. container:: sphx-glr-download sphx-glr-download-jupyter :download:`Download Jupyter notebook: plot_topics_extraction_with_nmf_lda.ipynb ` .. container:: sphx-glr-download sphx-glr-download-python :download:`Download Python source code: plot_topics_extraction_with_nmf_lda.py ` .. container:: sphx-glr-download sphx-glr-download-zip :download:`Download zipped: plot_topics_extraction_with_nmf_lda.zip ` .. include:: plot_topics_extraction_with_nmf_lda.recommendations .. only:: html .. rst-class:: sphx-glr-signature `Gallery generated by Sphinx-Gallery `_