133 lines
4.8 KiB
Python
133 lines
4.8 KiB
Python
"""
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The :mod:`scikitplot.cluster` module includes plots built specifically for
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scikit-learn clusterer instances e.g. KMeans. You can use your own clusterers,
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but these plots assume specific properties shared by scikit-learn estimators.
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The specific requirements are documented per function.
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"""
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from __future__ import absolute_import, division, print_function, \
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unicode_literals
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import time
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import matplotlib.pyplot as plt
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import numpy as np
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from sklearn.base import clone
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from joblib import Parallel, delayed
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def plot_elbow_curve(clf, X, title='Elbow Plot', cluster_ranges=None, n_jobs=1,
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show_cluster_time=True, ax=None, figsize=None,
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title_fontsize="large", text_fontsize="medium"):
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"""Plots elbow curve of different values of K for KMeans clustering.
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Args:
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clf: Clusterer instance that implements ``fit``,``fit_predict``, and
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``score`` methods, and an ``n_clusters`` hyperparameter.
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e.g. :class:`sklearn.cluster.KMeans` instance
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X (array-like, shape (n_samples, n_features)):
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Data to cluster, where n_samples is the number of samples and
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n_features is the number of features.
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title (string, optional): Title of the generated plot. Defaults to
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"Elbow Plot"
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cluster_ranges (None or :obj:`list` of int, optional): List of
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n_clusters for which to plot the explained variances. Defaults to
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``range(1, 12, 2)``.
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n_jobs (int, optional): Number of jobs to run in parallel. Defaults to
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1.
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show_cluster_time (bool, optional): Include plot of time it took to
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cluster for a particular K.
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ax (:class:`matplotlib.axes.Axes`, optional): The axes upon which to
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plot the curve. If None, the plot is drawn on a new set of axes.
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figsize (2-tuple, optional): Tuple denoting figure size of the plot
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e.g. (6, 6). Defaults to ``None``.
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title_fontsize (string or int, optional): Matplotlib-style fontsizes.
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Use e.g. "small", "medium", "large" or integer-values. Defaults to
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"large".
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text_fontsize (string or int, optional): Matplotlib-style fontsizes.
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Use e.g. "small", "medium", "large" or integer-values. Defaults to
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"medium".
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Returns:
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ax (:class:`matplotlib.axes.Axes`): The axes on which the plot was
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drawn.
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Example:
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>>> import scikitplot as skplt
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>>> kmeans = KMeans(random_state=1)
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>>> skplt.cluster.plot_elbow_curve(kmeans, cluster_ranges=range(1, 30))
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<matplotlib.axes._subplots.AxesSubplot object at 0x7fe967d64490>
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>>> plt.show()
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.. image:: _static/examples/plot_elbow_curve.png
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:align: center
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:alt: Elbow Curve
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"""
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if cluster_ranges is None:
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cluster_ranges = range(1, 12, 2)
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else:
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cluster_ranges = sorted(cluster_ranges)
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if not hasattr(clf, 'n_clusters'):
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raise TypeError('"n_clusters" attribute not in classifier. '
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'Cannot plot elbow method.')
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tuples = Parallel(n_jobs=n_jobs)(delayed(_clone_and_score_clusterer)
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(clf, X, i) for i in cluster_ranges)
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clfs, times = zip(*tuples)
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if ax is None:
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fig, ax = plt.subplots(1, 1, figsize=figsize)
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ax.set_title(title, fontsize=title_fontsize)
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ax.plot(cluster_ranges, np.absolute(clfs), 'b*-')
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ax.grid(True)
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ax.set_xlabel('Number of clusters', fontsize=text_fontsize)
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ax.set_ylabel('Sum of Squared Errors', fontsize=text_fontsize)
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ax.tick_params(labelsize=text_fontsize)
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if show_cluster_time:
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ax2_color = 'green'
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ax2 = ax.twinx()
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ax2.plot(cluster_ranges, times, ':', alpha=0.75, color=ax2_color)
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ax2.set_ylabel('Clustering duration (seconds)',
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color=ax2_color, alpha=0.75,
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fontsize=text_fontsize)
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ax2.tick_params(colors=ax2_color, labelsize=text_fontsize)
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return ax
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def _clone_and_score_clusterer(clf, X, n_clusters):
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"""Clones and scores clusterer instance.
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Args:
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clf: Clusterer instance that implements ``fit``,``fit_predict``, and
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``score`` methods, and an ``n_clusters`` hyperparameter.
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e.g. :class:`sklearn.cluster.KMeans` instance
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X (array-like, shape (n_samples, n_features)):
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Data to cluster, where n_samples is the number of samples and
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n_features is the number of features.
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n_clusters (int): Number of clusters
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Returns:
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score: Score of clusters
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time: Number of seconds it took to fit cluster
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"""
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start = time.time()
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clf = clone(clf)
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setattr(clf, 'n_clusters', n_clusters)
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return clf.fit(X).score(X), time.time() - start
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