.. DO NOT EDIT. .. THIS FILE WAS AUTOMATICALLY GENERATED BY SPHINX-GALLERY. .. TO MAKE CHANGES, EDIT THE SOURCE PYTHON FILE: .. "auto_examples/cluster/plot_adjusted_for_chance_measures.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_cluster_plot_adjusted_for_chance_measures.py: ========================================================== Adjustment for chance in clustering performance evaluation ========================================================== The following plots demonstrate the impact of the number of clusters and number of samples on various clustering performance evaluation metrics. Non-adjusted measures such as the V-Measure show a dependency between the number of clusters and the number of samples: the mean V-Measure of random labeling increases significantly as the number of clusters is closer to the total number of samples used to compute the measure. Adjusted for chance measure such as ARI display some random variations centered around a mean score of 0.0 for any number of samples and clusters. Only adjusted measures can hence safely be used as a consensus index to evaluate the average stability of clustering algorithms for a given value of k on various overlapping sub-samples of the dataset. .. GENERATED FROM PYTHON SOURCE LINES 23-126 .. rst-class:: sphx-glr-horizontal * .. image:: /auto_examples/cluster/images/sphx_glr_plot_adjusted_for_chance_measures_001.png :alt: Clustering measures for 2 random uniform labelings with equal number of clusters :class: sphx-glr-multi-img * .. image:: /auto_examples/cluster/images/sphx_glr_plot_adjusted_for_chance_measures_002.png :alt: Clustering measures for random uniform labeling against reference assignment with 10 classes :class: sphx-glr-multi-img .. rst-class:: sphx-glr-script-out Out: .. code-block:: none Computing adjusted_rand_score for 10 values of n_clusters and n_samples=100 done in 0.028s Computing v_measure_score for 10 values of n_clusters and n_samples=100 done in 0.065s Computing ami_score for 10 values of n_clusters and n_samples=100 done in 0.337s Computing mutual_info_score for 10 values of n_clusters and n_samples=100 done in 0.061s Computing adjusted_rand_score for 10 values of n_clusters and n_samples=1000 done in 0.039s Computing v_measure_score for 10 values of n_clusters and n_samples=1000 done in 0.059s Computing ami_score for 10 values of n_clusters and n_samples=1000 done in 0.267s Computing mutual_info_score for 10 values of n_clusters and n_samples=1000 done in 0.070s | .. code-block:: default print(__doc__) # Author: Olivier Grisel # License: BSD 3 clause import numpy as np import matplotlib.pyplot as plt from time import time from sklearn import metrics def uniform_labelings_scores(score_func, n_samples, n_clusters_range, fixed_n_classes=None, n_runs=5, seed=42): """Compute score for 2 random uniform cluster labelings. Both random labelings have the same number of clusters for each value possible value in ``n_clusters_range``. When fixed_n_classes is not None the first labeling is considered a ground truth class assignment with fixed number of classes. """ random_labels = np.random.RandomState(seed).randint scores = np.zeros((len(n_clusters_range), n_runs)) if fixed_n_classes is not None: labels_a = random_labels(low=0, high=fixed_n_classes, size=n_samples) for i, k in enumerate(n_clusters_range): for j in range(n_runs): if fixed_n_classes is None: labels_a = random_labels(low=0, high=k, size=n_samples) labels_b = random_labels(low=0, high=k, size=n_samples) scores[i, j] = score_func(labels_a, labels_b) return scores def ami_score(U, V): return metrics.adjusted_mutual_info_score(U, V) score_funcs = [ metrics.adjusted_rand_score, metrics.v_measure_score, ami_score, metrics.mutual_info_score, ] # 2 independent random clusterings with equal cluster number n_samples = 100 n_clusters_range = np.linspace(2, n_samples, 10).astype(int) plt.figure(1) plots = [] names = [] for score_func in score_funcs: print("Computing %s for %d values of n_clusters and n_samples=%d" % (score_func.__name__, len(n_clusters_range), n_samples)) t0 = time() scores = uniform_labelings_scores(score_func, n_samples, n_clusters_range) print("done in %0.3fs" % (time() - t0)) plots.append(plt.errorbar( n_clusters_range, np.median(scores, axis=1), scores.std(axis=1))[0]) names.append(score_func.__name__) plt.title("Clustering measures for 2 random uniform labelings\n" "with equal number of clusters") plt.xlabel('Number of clusters (Number of samples is fixed to %d)' % n_samples) plt.ylabel('Score value') plt.legend(plots, names) plt.ylim(bottom=-0.05, top=1.05) # Random labeling with varying n_clusters against ground class labels # with fixed number of clusters n_samples = 1000 n_clusters_range = np.linspace(2, 100, 10).astype(int) n_classes = 10 plt.figure(2) plots = [] names = [] for score_func in score_funcs: print("Computing %s for %d values of n_clusters and n_samples=%d" % (score_func.__name__, len(n_clusters_range), n_samples)) t0 = time() scores = uniform_labelings_scores(score_func, n_samples, n_clusters_range, fixed_n_classes=n_classes) print("done in %0.3fs" % (time() - t0)) plots.append(plt.errorbar( n_clusters_range, scores.mean(axis=1), scores.std(axis=1))[0]) names.append(score_func.__name__) plt.title("Clustering measures for random uniform labeling\n" "against reference assignment with %d classes" % n_classes) plt.xlabel('Number of clusters (Number of samples is fixed to %d)' % n_samples) plt.ylabel('Score value') plt.ylim(bottom=-0.05, top=1.05) plt.legend(plots, names) plt.show() .. rst-class:: sphx-glr-timing **Total running time of the script:** ( 0 minutes 1.165 seconds) .. _sphx_glr_download_auto_examples_cluster_plot_adjusted_for_chance_measures.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/cluster/plot_adjusted_for_chance_measures.ipynb :alt: Launch binder :width: 150 px .. container:: sphx-glr-download sphx-glr-download-python :download:`Download Python source code: plot_adjusted_for_chance_measures.py ` .. container:: sphx-glr-download sphx-glr-download-jupyter :download:`Download Jupyter notebook: plot_adjusted_for_chance_measures.ipynb ` .. only:: html .. rst-class:: sphx-glr-signature `Gallery generated by Sphinx-Gallery `_