# yellowbrick.cluster.silhouette
# Implements visualizers using the silhouette metric for cluster evaluation.
#
# Author: Benjamin Bengfort <bbengfort@districtdatalabs.com>
# Created: Mon Mar 27 10:09:24 2017 -0400
#
# Copyright (C) 2016 District Data Labs
# For license information, see LICENSE.txt
#
# ID: silhouette.py [57b563b] benjamin@bengfort.com $
"""
Implements visualizers that use the silhouette metric for cluster evaluation.
"""
##########################################################################
## Imports
##########################################################################
import numpy as np
from ..style import color_palette
from .base import ClusteringScoreVisualizer
from sklearn.metrics import silhouette_score, silhouette_samples
## Packages for export
__all__ = [
"SilhouetteVisualizer"
]
##########################################################################
## Silhouette Method for K Selection
##########################################################################
[belgeler]class SilhouetteVisualizer(ClusteringScoreVisualizer):
"""
TODO: Document this class!
"""
def __init__(self, model, ax=None, **kwargs):
super(SilhouetteVisualizer, self).__init__(model, ax=ax, **kwargs)
# Visual Properties
# TODO: Fix the color handling
self.colormap = kwargs.get('colormap', 'set1')
self.color = kwargs.get('color', None)
# Required internal properties
self.silhouette_score_ = None
self.silhouette_samples_ = None
self.n_samples = None
self.n_clusters = None
[belgeler] def fit(self, X, y=None, **kwargs):
"""
Fits the model and generates the the silhouette visualization.
TODO: decide to use this method or the score method to draw.
NOTE: Probably this would be better in score, but the standard score
is a little different and I'm not sure how it's used.
"""
# Fit the wrapped estimator
self.estimator.fit(X, y, **kwargs)
# Get the properties of the dataset
self.n_samples = X.shape[0]
self.n_clusters = self.estimator.n_clusters
# Compute the scores of the cluster
labels = self.estimator.predict(X)
self.silhouette_score_ = silhouette_score(X, labels)
self.silhouette_samples_ = silhouette_samples(X, labels)
# Draw the silhouette figure
self.draw(labels)
# Return the estimator
return self
[belgeler] def draw(self, labels):
"""
Draw the silhouettes for each sample and the average score.
Parameters
----------
labels : array-like
An array with the cluster label for each silhouette sample,
usually computed with ``predict()``. Labels are not stored on the
visualizer so that the figure can be redrawn with new data.
"""
# Track the positions of the lines being drawn
y_lower = 10 # The bottom of the silhouette
# Get the colors from the various properties
# TODO: Use resolve_colors instead of this
colors = color_palette(self.colormap, self.n_clusters)
# For each cluster, plot the silhouette scores
for idx in range(self.n_clusters):
# Collect silhouette scores for samples in the current cluster .
values = self.silhouette_samples_[labels == idx]
values.sort()
# Compute the size of the cluster and find upper limit
size = values.shape[0]
y_upper = y_lower + size
color = colors[idx]
self.ax.fill_betweenx(
np.arange(y_lower, y_upper), 0, values,
facecolor=color, edgecolor=color, alpha=0.5
)
# Label the silhouette plots with their cluster numbers
self.ax.text(-0.05, y_lower + 0.5 * size, str(idx))
# Compute the new y_lower for next plot
y_lower = y_upper + 10
# The vertical line for average silhouette score of all the values
self.ax.axvline(
x=self.silhouette_score_, color="red", linestyle="--"
)
return self.ax
[belgeler] def finalize(self):
"""
Prepare the figure for rendering by setting the title and adjusting
the limits on the axes, adding labels and a legend.
"""
# Set the title
self.set_title((
"Silhouette Plot of {} Clustering for {} Samples in {} Centers"
).format(
self.name, self.n_samples, self.n_clusters
))
# Set the X and Y limits
# The silhouette coefficient can range from -1, 1
self.ax.set_xlim([-1, 1])
# The (n_clusters+1)*10 is for inserting blank space between
# silhouette plots of individual clusters, to demarcate them clearly.
self.ax.set_ylim([0, self.n_samples + (self.n_clusters + 1) * 10])
# Set the x and y labels
self.ax.set_xlabel("silhouette coefficient values")
self.ax.set_ylabel("cluster label")
# Set the ticks on the axis object.
self.ax.set_yticks([]) # Clear the yaxis labels / ticks
self.ax.set_xticks(np.linspace(-1,1,11))