# SecuML
# Copyright (C) 2016-2019 ANSSI
#
# SecuML is free software; you can redistribute it and/or modify
# it under the terms of the GNU General Public License as published by
# the Free Software Foundation; either version 2 of the License, or
# (at your option) any later version.
#
# SecuML is distributed in the hope that it will be useful,
# but WITHOUT ANY WARRANTY; without even the implied warranty of
# MERCHANTABILITY or FITNESS FOR A PARTICULAR PURPOSE. See the
# GNU General Public License for more details.
#
# You should have received a copy of the GNU General Public License along
# with SecuML. If not, see <http://www.gnu.org/licenses/>.
import matplotlib.pyplot as plt
import numpy as np
import os.path as path
from sklearn.metrics import silhouette_samples
from secuml.core.tools.color import colors
class Silhouette(object):
def __init__(self, instances):
self.instances = instances
self.distances = None
def gen_eval(self, output_dir, assigned_clusters, quick=False):
if quick:
self.silhouette_avg = 0
return
if self.distances is not None:
self.silhouette_values = silhouette_samples(self.distances,
assigned_clusters,
metric='precomputed')
else:
features = self.instances.features.get_values()
self.silhouette_values = silhouette_samples(features,
assigned_clusters)
self.silhouette_avg = np.mean(self.silhouette_values)
self.dispaly_silhouette(output_dir, assigned_clusters)
# Code from a scikit-learn example:
# Selecting the number of clusters with silhouette analysis on KMeans
# clustering
def dispaly_silhouette(self, output_dir, assigned_clusters):
num_clusters = len(set(assigned_clusters))
plt.clf()
y_lower = 10
all_colors = colors(num_clusters)
for i in range(num_clusters):
# Aggregate the silhouette scores for samples belonging to
# cluster i, and sort them
selection = assigned_clusters == i
ith_cluster_silhouette_values = self.silhouette_values[selection]
ith_cluster_silhouette_values.sort()
size_cluster_i = ith_cluster_silhouette_values.shape[0]
y_upper = y_lower + size_cluster_i
color = all_colors[i]
plt.fill_betweenx(np.arange(y_lower, y_upper),
0, ith_cluster_silhouette_values,
facecolor=color, edgecolor=color, alpha=0.7)
# Label the silhouette plots with their cluster numbers at the
# middle
plt.text(-0.05, y_lower + 0.5 * size_cluster_i, str(i))
# Compute the new y_lower for next plot
y_lower = y_upper + 10 # 10 for the 0 samples
plt.title('The silhouette plot for the various clusters.')
plt.xlabel('The silhouette coefficient values')
plt.ylabel('Cluster label')
# The vertical line for average silhoutte score of all the values
plt.axvline(x=self.silhouette_avg, color='red', linestyle='--')
plt.yticks([]) # Clear the yaxis labels / ticks
plt.xticks([-0.1, 0, 0.2, 0.4, 0.6, 0.8, 1])
plt.savefig(path.join(output_dir, 'silhouette.png'))
plt.clf()
def to_json(self):
obj = {}
obj['silhouette_avg'] = self.silhouette_avg
return obj
