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