.. DO NOT EDIT. .. THIS FILE WAS AUTOMATICALLY GENERATED BY SPHINX-GALLERY. .. TO MAKE CHANGES, EDIT THE SOURCE PYTHON FILE: .. "auto_examples/text/plot_document_clustering.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_clustering.py: ======================================= Clustering text documents using k-means ======================================= This is an example showing how the scikit-learn API can be used to cluster documents by topics using a `Bag of Words approach `_. Two algorithms are demonstrated, namely :class:`~sklearn.cluster.KMeans` and its more scalable variant, :class:`~sklearn.cluster.MiniBatchKMeans`. Additionally, latent semantic analysis is used to reduce dimensionality and discover latent patterns in the data. This example uses two different text vectorizers: a :class:`~sklearn.feature_extraction.text.TfidfVectorizer` and a :class:`~sklearn.feature_extraction.text.HashingVectorizer`. See the example notebook :ref:`sphx_glr_auto_examples_text_plot_hashing_vs_dict_vectorizer.py` for more information on vectorizers and a comparison of their processing times. For document analysis via a supervised learning approach, see the example script :ref:`sphx_glr_auto_examples_text_plot_document_classification_20newsgroups.py`. .. GENERATED FROM PYTHON SOURCE LINES 25-29 .. code-block:: Python # Authors: The scikit-learn developers # SPDX-License-Identifier: BSD-3-Clause .. GENERATED FROM PYTHON SOURCE LINES 30-44 Loading text data ================= We load data from :ref:`20newsgroups_dataset`, which comprises around 18,000 newsgroups posts on 20 topics. For illustrative purposes and to reduce the computational cost, we select a subset of 4 topics only accounting for around 3,400 documents. See the example :ref:`sphx_glr_auto_examples_text_plot_document_classification_20newsgroups.py` to gain intuition on the overlap of such topics. Notice that, by default, the text samples contain some message metadata such as `"headers"`, `"footers"` (signatures) and `"quotes"` to other posts. We use the `remove` parameter from :func:`~sklearn.datasets.fetch_20newsgroups` to strip those features and have a more sensible clustering problem. .. GENERATED FROM PYTHON SOURCE LINES 44-70 .. code-block:: Python import numpy as np from sklearn.datasets import fetch_20newsgroups categories = [ "alt.atheism", "talk.religion.misc", "comp.graphics", "sci.space", ] dataset = fetch_20newsgroups( remove=("headers", "footers", "quotes"), subset="all", categories=categories, shuffle=True, random_state=42, ) labels = dataset.target unique_labels, category_sizes = np.unique(labels, return_counts=True) true_k = unique_labels.shape[0] print(f"{len(dataset.data)} documents - {true_k} categories") .. rst-class:: sphx-glr-script-out .. code-block:: none 3387 documents - 4 categories .. GENERATED FROM PYTHON SOURCE LINES 71-104 Quantifying the quality of clustering results ============================================= In this section we define a function to score different clustering pipelines using several metrics. Clustering algorithms are fundamentally unsupervised learning methods. However, since we happen to have class labels for this specific dataset, it is possible to use evaluation metrics that leverage this "supervised" ground truth information to quantify the quality of the resulting clusters. Examples of such metrics are the following: - homogeneity, which quantifies how much clusters contain only members of a single class; - completeness, which quantifies how much members of a given class are assigned to the same clusters; - V-measure, the harmonic mean of completeness and homogeneity; - Rand-Index, which measures how frequently pairs of data points are grouped consistently according to the result of the clustering algorithm and the ground truth class assignment; - Adjusted Rand-Index, a chance-adjusted Rand-Index such that random cluster assignment have an ARI of 0.0 in expectation. If the ground truth labels are not known, evaluation can only be performed using the model results itself. In that case, the Silhouette Coefficient comes in handy. See :ref:`sphx_glr_auto_examples_cluster_plot_kmeans_silhouette_analysis.py` for an example on how to do it. For more reference, see :ref:`clustering_evaluation`. .. GENERATED FROM PYTHON SOURCE LINES 104-153 .. code-block:: Python from collections import defaultdict from time import time from sklearn import metrics evaluations = [] evaluations_std = [] def fit_and_evaluate(km, X, name=None, n_runs=5): name = km.__class__.__name__ if name is None else name train_times = [] scores = defaultdict(list) for seed in range(n_runs): km.set_params(random_state=seed) t0 = time() km.fit(X) train_times.append(time() - t0) scores["Homogeneity"].append(metrics.homogeneity_score(labels, km.labels_)) scores["Completeness"].append(metrics.completeness_score(labels, km.labels_)) scores["V-measure"].append(metrics.v_measure_score(labels, km.labels_)) scores["Adjusted Rand-Index"].append( metrics.adjusted_rand_score(labels, km.labels_) ) scores["Silhouette Coefficient"].append( metrics.silhouette_score(X, km.labels_, sample_size=2000) ) train_times = np.asarray(train_times) print(f"clustering done in {train_times.mean():.2f} ± {train_times.std():.2f} s ") evaluation = { "estimator": name, "train_time": train_times.mean(), } evaluation_std = { "estimator": name, "train_time": train_times.std(), } for score_name, score_values in scores.items(): mean_score, std_score = np.mean(score_values), np.std(score_values) print(f"{score_name}: {mean_score:.3f} ± {std_score:.3f}") evaluation[score_name] = mean_score evaluation_std[score_name] = std_score evaluations.append(evaluation) evaluations_std.append(evaluation_std) .. GENERATED FROM PYTHON SOURCE LINES 154-181 K-means clustering on text features =================================== Two feature extraction methods are used in this example: - :class:`~sklearn.feature_extraction.text.TfidfVectorizer` uses an in-memory vocabulary (a Python dict) to map the most frequent words to features indices and hence compute a word occurrence frequency (sparse) matrix. The word frequencies are then reweighted using the Inverse Document Frequency (IDF) vector collected feature-wise over the corpus. - :class:`~sklearn.feature_extraction.text.HashingVectorizer` hashes word occurrences to a fixed dimensional space, possibly with collisions. The word count vectors are then normalized to each have l2-norm equal to one (projected to the euclidean unit-sphere) which seems to be important for k-means to work in high dimensional space. Furthermore it is possible to post-process those extracted features using dimensionality reduction. We will explore the impact of those choices on the clustering quality in the following. Feature Extraction using TfidfVectorizer ---------------------------------------- We first benchmark the estimators using a dictionary vectorizer along with an IDF normalization as provided by :class:`~sklearn.feature_extraction.text.TfidfVectorizer`. .. GENERATED FROM PYTHON SOURCE LINES 181-195 .. code-block:: Python from sklearn.feature_extraction.text import TfidfVectorizer vectorizer = TfidfVectorizer( max_df=0.5, min_df=5, stop_words="english", ) t0 = time() X_tfidf = vectorizer.fit_transform(dataset.data) print(f"vectorization done in {time() - t0:.3f} s") print(f"n_samples: {X_tfidf.shape[0]}, n_features: {X_tfidf.shape[1]}") .. rst-class:: sphx-glr-script-out .. code-block:: none vectorization done in 0.379 s n_samples: 3387, n_features: 7929 .. GENERATED FROM PYTHON SOURCE LINES 196-201 After ignoring terms that appear in more than 50% of the documents (as set by `max_df=0.5`) and terms that are not present in at least 5 documents (set by `min_df=5`), the resulting number of unique terms `n_features` is around 8,000. We can additionally quantify the sparsity of the `X_tfidf` matrix as the fraction of non-zero entries divided by the total number of elements. .. GENERATED FROM PYTHON SOURCE LINES 201-204 .. code-block:: Python print(f"{X_tfidf.nnz / np.prod(X_tfidf.shape):.3f}") .. rst-class:: sphx-glr-script-out .. code-block:: none 0.007 .. GENERATED FROM PYTHON SOURCE LINES 205-222 We find that around 0.7% of the entries of the `X_tfidf` matrix are non-zero. .. _kmeans_sparse_high_dim: Clustering sparse data with k-means ----------------------------------- As both :class:`~sklearn.cluster.KMeans` and :class:`~sklearn.cluster.MiniBatchKMeans` optimize a non-convex objective function, their clustering is not guaranteed to be optimal for a given random init. Even further, on sparse high-dimensional data such as text vectorized using the Bag of Words approach, k-means can initialize centroids on extremely isolated data points. Those data points can stay their own centroids all along. The following code illustrates how the previous phenomenon can sometimes lead to highly imbalanced clusters, depending on the random initialization: .. GENERATED FROM PYTHON SOURCE LINES 222-240 .. code-block:: Python from sklearn.cluster import KMeans for seed in range(5): kmeans = KMeans( n_clusters=true_k, max_iter=100, n_init=1, random_state=seed, ).fit(X_tfidf) cluster_ids, cluster_sizes = np.unique(kmeans.labels_, return_counts=True) print(f"Number of elements assigned to each cluster: {cluster_sizes}") print() print( "True number of documents in each category according to the class labels: " f"{category_sizes}" ) .. rst-class:: sphx-glr-script-out .. code-block:: none Number of elements assigned to each cluster: [ 481 675 1785 446] Number of elements assigned to each cluster: [1689 638 480 580] Number of elements assigned to each cluster: [ 1 1 1 3384] Number of elements assigned to each cluster: [1887 311 332 857] Number of elements assigned to each cluster: [ 291 673 1771 652] True number of documents in each category according to the class labels: [799 973 987 628] .. GENERATED FROM PYTHON SOURCE LINES 241-244 To avoid this problem, one possibility is to increase the number of runs with independent random initiations `n_init`. In such case the clustering with the best inertia (objective function of k-means) is chosen. .. GENERATED FROM PYTHON SOURCE LINES 244-253 .. code-block:: Python kmeans = KMeans( n_clusters=true_k, max_iter=100, n_init=5, ) fit_and_evaluate(kmeans, X_tfidf, name="KMeans\non tf-idf vectors") .. rst-class:: sphx-glr-script-out .. code-block:: none clustering done in 0.18 ± 0.04 s Homogeneity: 0.349 ± 0.010 Completeness: 0.398 ± 0.009 V-measure: 0.372 ± 0.009 Adjusted Rand-Index: 0.203 ± 0.017 Silhouette Coefficient: 0.007 ± 0.000 .. GENERATED FROM PYTHON SOURCE LINES 254-275 All those clustering evaluation metrics have a maximum value of 1.0 (for a perfect clustering result). Higher values are better. Values of the Adjusted Rand-Index close to 0.0 correspond to a random labeling. Notice from the scores above that the cluster assignment is indeed well above chance level, but the overall quality can certainly improve. Keep in mind that the class labels may not reflect accurately the document topics and therefore metrics that use labels are not necessarily the best to evaluate the quality of our clustering pipeline. Performing dimensionality reduction using LSA --------------------------------------------- A `n_init=1` can still be used as long as the dimension of the vectorized space is reduced first to make k-means more stable. For such purpose we use :class:`~sklearn.decomposition.TruncatedSVD`, which works on term count/tf-idf matrices. Since SVD results are not normalized, we redo the normalization to improve the :class:`~sklearn.cluster.KMeans` result. Using SVD to reduce the dimensionality of TF-IDF document vectors is often known as `latent semantic analysis `_ (LSA) in the information retrieval and text mining literature. .. GENERATED FROM PYTHON SOURCE LINES 275-288 .. code-block:: Python from sklearn.decomposition import TruncatedSVD from sklearn.pipeline import make_pipeline from sklearn.preprocessing import Normalizer lsa = make_pipeline(TruncatedSVD(n_components=100), Normalizer(copy=False)) t0 = time() X_lsa = lsa.fit_transform(X_tfidf) explained_variance = lsa[0].explained_variance_ratio_.sum() print(f"LSA done in {time() - t0:.3f} s") print(f"Explained variance of the SVD step: {explained_variance * 100:.1f}%") .. rst-class:: sphx-glr-script-out .. code-block:: none LSA done in 0.337 s Explained variance of the SVD step: 18.4% .. GENERATED FROM PYTHON SOURCE LINES 289-292 Using a single initialization means the processing time will be reduced for both :class:`~sklearn.cluster.KMeans` and :class:`~sklearn.cluster.MiniBatchKMeans`. .. GENERATED FROM PYTHON SOURCE LINES 292-301 .. code-block:: Python kmeans = KMeans( n_clusters=true_k, max_iter=100, n_init=1, ) fit_and_evaluate(kmeans, X_lsa, name="KMeans\nwith LSA on tf-idf vectors") .. rst-class:: sphx-glr-script-out .. code-block:: none clustering done in 0.01 ± 0.00 s Homogeneity: 0.393 ± 0.014 Completeness: 0.428 ± 0.014 V-measure: 0.410 ± 0.013 Adjusted Rand-Index: 0.312 ± 0.025 Silhouette Coefficient: 0.030 ± 0.001 .. GENERATED FROM PYTHON SOURCE LINES 302-307 We can observe that clustering on the LSA representation of the document is significantly faster (both because of `n_init=1` and because the dimensionality of the LSA feature space is much smaller). Furthermore, all the clustering evaluation metrics have improved. We repeat the experiment with :class:`~sklearn.cluster.MiniBatchKMeans`. .. GENERATED FROM PYTHON SOURCE LINES 307-323 .. code-block:: Python from sklearn.cluster import MiniBatchKMeans minibatch_kmeans = MiniBatchKMeans( n_clusters=true_k, n_init=1, init_size=1000, batch_size=1000, ) fit_and_evaluate( minibatch_kmeans, X_lsa, name="MiniBatchKMeans\nwith LSA on tf-idf vectors", ) .. rst-class:: sphx-glr-script-out .. code-block:: none clustering done in 0.02 ± 0.00 s Homogeneity: 0.339 ± 0.098 Completeness: 0.384 ± 0.080 V-measure: 0.357 ± 0.089 Adjusted Rand-Index: 0.299 ± 0.131 Silhouette Coefficient: 0.026 ± 0.005 .. GENERATED FROM PYTHON SOURCE LINES 324-332 Top terms per cluster --------------------- Since :class:`~sklearn.feature_extraction.text.TfidfVectorizer` can be inverted we can identify the cluster centers, which provide an intuition of the most influential words **for each cluster**. See the example script :ref:`sphx_glr_auto_examples_text_plot_document_classification_20newsgroups.py` for a comparison with the most predictive words **for each target class**. .. GENERATED FROM PYTHON SOURCE LINES 332-343 .. code-block:: Python original_space_centroids = lsa[0].inverse_transform(kmeans.cluster_centers_) order_centroids = original_space_centroids.argsort()[:, ::-1] terms = vectorizer.get_feature_names_out() for i in range(true_k): print(f"Cluster {i}: ", end="") for ind in order_centroids[i, :10]: print(f"{terms[ind]} ", end="") print() .. rst-class:: sphx-glr-script-out .. code-block:: none Cluster 0: space nasa shuttle launch station program think sci like just Cluster 1: just like time think don know ve does new good Cluster 2: god people don think jesus bible say believe religion christian Cluster 3: thanks graphics image file program files know help looking format .. GENERATED FROM PYTHON SOURCE LINES 344-354 HashingVectorizer ----------------- An alternative vectorization can be done using a :class:`~sklearn.feature_extraction.text.HashingVectorizer` instance, which does not provide IDF weighting as this is a stateless model (the fit method does nothing). When IDF weighting is needed it can be added by pipelining the :class:`~sklearn.feature_extraction.text.HashingVectorizer` output to a :class:`~sklearn.feature_extraction.text.TfidfTransformer` instance. In this case we also add LSA to the pipeline to reduce the dimension and sparcity of the hashed vector space. .. GENERATED FROM PYTHON SOURCE LINES 354-368 .. code-block:: Python from sklearn.feature_extraction.text import HashingVectorizer, TfidfTransformer lsa_vectorizer = make_pipeline( HashingVectorizer(stop_words="english", n_features=50_000), TfidfTransformer(), TruncatedSVD(n_components=100, random_state=0), Normalizer(copy=False), ) t0 = time() X_hashed_lsa = lsa_vectorizer.fit_transform(dataset.data) print(f"vectorization done in {time() - t0:.3f} s") .. rst-class:: sphx-glr-script-out .. code-block:: none vectorization done in 1.611 s .. GENERATED FROM PYTHON SOURCE LINES 369-378 One can observe that the LSA step takes a relatively long time to fit, especially with hashed vectors. The reason is that a hashed space is typically large (set to `n_features=50_000` in this example). One can try lowering the number of features at the expense of having a larger fraction of features with hash collisions as shown in the example notebook :ref:`sphx_glr_auto_examples_text_plot_hashing_vs_dict_vectorizer.py`. We now fit and evaluate the `kmeans` and `minibatch_kmeans` instances on this hashed-lsa-reduced data: .. GENERATED FROM PYTHON SOURCE LINES 378-381 .. code-block:: Python fit_and_evaluate(kmeans, X_hashed_lsa, name="KMeans\nwith LSA on hashed vectors") .. rst-class:: sphx-glr-script-out .. code-block:: none clustering done in 0.02 ± 0.01 s Homogeneity: 0.387 ± 0.011 Completeness: 0.429 ± 0.017 V-measure: 0.407 ± 0.013 Adjusted Rand-Index: 0.328 ± 0.023 Silhouette Coefficient: 0.029 ± 0.001 .. GENERATED FROM PYTHON SOURCE LINES 382-388 .. code-block:: Python fit_and_evaluate( minibatch_kmeans, X_hashed_lsa, name="MiniBatchKMeans\nwith LSA on hashed vectors", ) .. rst-class:: sphx-glr-script-out .. code-block:: none clustering done in 0.02 ± 0.00 s Homogeneity: 0.357 ± 0.043 Completeness: 0.378 ± 0.046 V-measure: 0.367 ± 0.043 Adjusted Rand-Index: 0.322 ± 0.030 Silhouette Coefficient: 0.028 ± 0.004 .. GENERATED FROM PYTHON SOURCE LINES 389-394 Both methods lead to good results that are similar to running the same models on the traditional LSA vectors (without hashing). Clustering evaluation summary ============================== .. GENERATED FROM PYTHON SOURCE LINES 394-414 .. code-block:: Python import matplotlib.pyplot as plt import pandas as pd fig, (ax0, ax1) = plt.subplots(ncols=2, figsize=(16, 6), sharey=True) df = pd.DataFrame(evaluations[::-1]).set_index("estimator") df_std = pd.DataFrame(evaluations_std[::-1]).set_index("estimator") df.drop( ["train_time"], axis="columns", ).plot.barh(ax=ax0, xerr=df_std) ax0.set_xlabel("Clustering scores") ax0.set_ylabel("") df["train_time"].plot.barh(ax=ax1, xerr=df_std["train_time"]) ax1.set_xlabel("Clustering time (s)") plt.tight_layout() .. image-sg:: /auto_examples/text/images/sphx_glr_plot_document_clustering_001.png :alt: plot document clustering :srcset: /auto_examples/text/images/sphx_glr_plot_document_clustering_001.png :class: sphx-glr-single-img .. GENERATED FROM PYTHON SOURCE LINES 415-449 :class:`~sklearn.cluster.KMeans` and :class:`~sklearn.cluster.MiniBatchKMeans` suffer from the phenomenon called the `Curse of Dimensionality `_ for high dimensional datasets such as text data. That is the reason why the overall scores improve when using LSA. Using LSA reduced data also improves the stability and requires lower clustering time, though keep in mind that the LSA step itself takes a long time, especially with hashed vectors. The Silhouette Coefficient is defined between 0 and 1. In all cases we obtain values close to 0 (even if they improve a bit after using LSA) because its definition requires measuring distances, in contrast with other evaluation metrics such as the V-measure and the Adjusted Rand Index which are only based on cluster assignments rather than distances. Notice that strictly speaking, one should not compare the Silhouette Coefficient between spaces of different dimension, due to the different notions of distance they imply. The homogeneity, completeness and hence v-measure metrics do not yield a baseline with regards to random labeling: this means that depending on the number of samples, clusters and ground truth classes, a completely random labeling will not always yield the same values. In particular random labeling won't yield zero scores, especially when the number of clusters is large. This problem can safely be ignored when the number of samples is more than a thousand and the number of clusters is less than 10, which is the case of the present example. For smaller sample sizes or larger number of clusters it is safer to use an adjusted index such as the Adjusted Rand Index (ARI). See the example :ref:`sphx_glr_auto_examples_cluster_plot_adjusted_for_chance_measures.py` for a demo on the effect of random labeling. The size of the error bars show that :class:`~sklearn.cluster.MiniBatchKMeans` is less stable than :class:`~sklearn.cluster.KMeans` for this relatively small dataset. It is more interesting to use when the number of samples is much bigger, but it can come at the expense of a small degradation in clustering quality compared to the traditional k-means algorithm. .. rst-class:: sphx-glr-timing **Total running time of the script:** (0 minutes 7.026 seconds) .. _sphx_glr_download_auto_examples_text_plot_document_clustering.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/1.6.X?urlpath=lab/tree/notebooks/auto_examples/text/plot_document_clustering.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_clustering.ipynb :alt: Launch JupyterLite :width: 150 px .. container:: sphx-glr-download sphx-glr-download-jupyter :download:`Download Jupyter notebook: plot_document_clustering.ipynb ` .. container:: sphx-glr-download sphx-glr-download-python :download:`Download Python source code: plot_document_clustering.py ` .. container:: sphx-glr-download sphx-glr-download-zip :download:`Download zipped: plot_document_clustering.zip ` .. include:: plot_document_clustering.recommendations .. only:: html .. rst-class:: sphx-glr-signature `Gallery generated by Sphinx-Gallery `_