.. DO NOT EDIT. .. THIS FILE WAS AUTOMATICALLY GENERATED BY SPHINX-GALLERY. .. TO MAKE CHANGES, EDIT THE SOURCE PYTHON FILE: .. "auto_examples/cluster/plot_kmeans_digits.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_cluster_plot_kmeans_digits.py: =========================================================== A demo of K-Means clustering on the handwritten digits data =========================================================== In this example we compare the various initialization strategies for K-means in terms of runtime and quality of the results. As the ground truth is known here, we also apply different cluster quality metrics to judge the goodness of fit of the cluster labels to the ground truth. Cluster quality metrics evaluated (see :ref:`clustering_evaluation` for definitions and discussions of the metrics): =========== ======================================================== Shorthand full name =========== ======================================================== homo homogeneity score compl completeness score v-meas V measure ARI adjusted Rand index AMI adjusted mutual information silhouette silhouette coefficient =========== ======================================================== .. GENERATED FROM PYTHON SOURCE LINES 29-35 Load the dataset ---------------- We will start by loading the `digits` dataset. This dataset contains handwritten digits from 0 to 9. In the context of clustering, one would like to group images such that the handwritten digits on the image are the same. .. GENERATED FROM PYTHON SOURCE LINES 35-45 .. code-block:: Python import numpy as np from sklearn.datasets import load_digits data, labels = load_digits(return_X_y=True) (n_samples, n_features), n_digits = data.shape, np.unique(labels).size print(f"# digits: {n_digits}; # samples: {n_samples}; # features {n_features}") .. rst-class:: sphx-glr-script-out .. code-block:: none # digits: 10; # samples: 1797; # features 64 .. GENERATED FROM PYTHON SOURCE LINES 46-56 Define our evaluation benchmark ------------------------------- We will first our evaluation benchmark. During this benchmark, we intend to compare different initialization methods for KMeans. Our benchmark will: * create a pipeline which will scale the data using a :class:`~sklearn.preprocessing.StandardScaler`; * train and time the pipeline fitting; * measure the performance of the clustering obtained via different metrics. .. GENERATED FROM PYTHON SOURCE LINES 56-113 .. code-block:: Python from time import time from sklearn import metrics from sklearn.pipeline import make_pipeline from sklearn.preprocessing import StandardScaler def bench_k_means(kmeans, name, data, labels): """Benchmark to evaluate the KMeans initialization methods. Parameters ---------- kmeans : KMeans instance A :class:`~sklearn.cluster.KMeans` instance with the initialization already set. name : str Name given to the strategy. It will be used to show the results in a table. data : ndarray of shape (n_samples, n_features) The data to cluster. labels : ndarray of shape (n_samples,) The labels used to compute the clustering metrics which requires some supervision. """ t0 = time() estimator = make_pipeline(StandardScaler(), kmeans).fit(data) fit_time = time() - t0 results = [name, fit_time, estimator[-1].inertia_] # Define the metrics which require only the true labels and estimator # labels clustering_metrics = [ metrics.homogeneity_score, metrics.completeness_score, metrics.v_measure_score, metrics.adjusted_rand_score, metrics.adjusted_mutual_info_score, ] results += [m(labels, estimator[-1].labels_) for m in clustering_metrics] # The silhouette score requires the full dataset results += [ metrics.silhouette_score( data, estimator[-1].labels_, metric="euclidean", sample_size=300, ) ] # Show the results formatter_result = ( "{:9s}\t{:.3f}s\t{:.0f}\t{:.3f}\t{:.3f}\t{:.3f}\t{:.3f}\t{:.3f}\t{:.3f}" ) print(formatter_result.format(*results)) .. GENERATED FROM PYTHON SOURCE LINES 114-127 Run the benchmark ----------------- We will compare three approaches: * an initialization using `k-means++`. This method is stochastic and we will run the initialization 4 times; * a random initialization. This method is stochastic as well and we will run the initialization 4 times; * an initialization based on a :class:`~sklearn.decomposition.PCA` projection. Indeed, we will use the components of the :class:`~sklearn.decomposition.PCA` to initialize KMeans. This method is deterministic and a single initialization suffice. .. GENERATED FROM PYTHON SOURCE LINES 127-145 .. code-block:: Python from sklearn.cluster import KMeans from sklearn.decomposition import PCA print(82 * "_") print("init\t\ttime\tinertia\thomo\tcompl\tv-meas\tARI\tAMI\tsilhouette") kmeans = KMeans(init="k-means++", n_clusters=n_digits, n_init=4, random_state=0) bench_k_means(kmeans=kmeans, name="k-means++", data=data, labels=labels) kmeans = KMeans(init="random", n_clusters=n_digits, n_init=4, random_state=0) bench_k_means(kmeans=kmeans, name="random", data=data, labels=labels) pca = PCA(n_components=n_digits).fit(data) kmeans = KMeans(init=pca.components_, n_clusters=n_digits, n_init=1) bench_k_means(kmeans=kmeans, name="PCA-based", data=data, labels=labels) print(82 * "_") .. rst-class:: sphx-glr-script-out .. code-block:: none __________________________________________________________________________________ init time inertia homo compl v-meas ARI AMI silhouette k-means++ 0.032s 69545 0.598 0.645 0.621 0.469 0.617 0.152 random 0.036s 69735 0.681 0.723 0.701 0.574 0.698 0.170 PCA-based 0.011s 72686 0.636 0.658 0.647 0.521 0.643 0.142 __________________________________________________________________________________ .. GENERATED FROM PYTHON SOURCE LINES 146-153 Visualize the results on PCA-reduced data ----------------------------------------- :class:`~sklearn.decomposition.PCA` allows to project the data from the original 64-dimensional space into a lower dimensional space. Subsequently, we can use :class:`~sklearn.decomposition.PCA` to project into a 2-dimensional space and plot the data and the clusters in this new space. .. GENERATED FROM PYTHON SOURCE LINES 153-204 .. code-block:: Python import matplotlib.pyplot as plt reduced_data = PCA(n_components=2).fit_transform(data) kmeans = KMeans(init="k-means++", n_clusters=n_digits, n_init=4) kmeans.fit(reduced_data) # Step size of the mesh. Decrease to increase the quality of the VQ. h = 0.02 # point in the mesh [x_min, x_max]x[y_min, y_max]. # Plot the decision boundary. For that, we will assign a color to each x_min, x_max = reduced_data[:, 0].min() - 1, reduced_data[:, 0].max() + 1 y_min, y_max = reduced_data[:, 1].min() - 1, reduced_data[:, 1].max() + 1 xx, yy = np.meshgrid(np.arange(x_min, x_max, h), np.arange(y_min, y_max, h)) # Obtain labels for each point in mesh. Use last trained model. Z = kmeans.predict(np.c_[xx.ravel(), yy.ravel()]) # Put the result into a color plot Z = Z.reshape(xx.shape) plt.figure(1) plt.clf() plt.imshow( Z, interpolation="nearest", extent=(xx.min(), xx.max(), yy.min(), yy.max()), cmap=plt.cm.Paired, aspect="auto", origin="lower", ) plt.plot(reduced_data[:, 0], reduced_data[:, 1], "k.", markersize=2) # Plot the centroids as a white X centroids = kmeans.cluster_centers_ plt.scatter( centroids[:, 0], centroids[:, 1], marker="x", s=169, linewidths=3, color="w", zorder=10, ) plt.title( "K-means clustering on the digits dataset (PCA-reduced data)\n" "Centroids are marked with white cross" ) plt.xlim(x_min, x_max) plt.ylim(y_min, y_max) plt.xticks(()) plt.yticks(()) plt.show() .. image-sg:: /auto_examples/cluster/images/sphx_glr_plot_kmeans_digits_001.png :alt: K-means clustering on the digits dataset (PCA-reduced data) Centroids are marked with white cross :srcset: /auto_examples/cluster/images/sphx_glr_plot_kmeans_digits_001.png :class: sphx-glr-single-img .. rst-class:: sphx-glr-timing **Total running time of the script:** (0 minutes 0.662 seconds) .. _sphx_glr_download_auto_examples_cluster_plot_kmeans_digits.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/cluster/plot_kmeans_digits.ipynb :alt: Launch binder :width: 150 px .. container:: lite-badge .. image:: images/jupyterlite_badge_logo.svg :target: ../../lite/lab/?path=auto_examples/cluster/plot_kmeans_digits.ipynb :alt: Launch JupyterLite :width: 150 px .. container:: sphx-glr-download sphx-glr-download-jupyter :download:`Download Jupyter notebook: plot_kmeans_digits.ipynb ` .. container:: sphx-glr-download sphx-glr-download-python :download:`Download Python source code: plot_kmeans_digits.py ` .. include:: plot_kmeans_digits.recommendations .. only:: html .. rst-class:: sphx-glr-signature `Gallery generated by Sphinx-Gallery `_