-
Notifications
You must be signed in to change notification settings - Fork 0
/
km_gmm_anuran.py
161 lines (128 loc) · 5.54 KB
/
km_gmm_anuran.py
1
2
3
4
5
6
7
8
9
10
11
12
13
14
15
16
17
18
19
20
21
22
23
24
25
26
27
28
29
30
31
32
33
34
35
36
37
38
39
40
41
42
43
44
45
46
47
48
49
50
51
52
53
54
55
56
57
58
59
60
61
62
63
64
65
66
67
68
69
70
71
72
73
74
75
76
77
78
79
80
81
82
83
84
85
86
87
88
89
90
91
92
93
94
95
96
97
98
99
100
101
102
103
104
105
106
107
108
109
110
111
112
113
114
115
116
117
118
119
120
121
122
123
124
125
126
127
128
129
130
131
132
133
134
135
136
137
138
139
140
141
142
143
144
145
146
147
148
149
150
151
152
153
154
155
156
157
158
159
160
161
# -*- coding: utf-8 -*-
"""
Created on Sat Mar 23 14:25:46 2019
@author: DMa
"""
from time import time
import csv
import numpy as np
from sklearn import metrics
from sklearn.preprocessing import MinMaxScaler, OneHotEncoder
from sklearn.cluster import KMeans as KM
from sklearn.mixture import GaussianMixture as GMM
from sklearn.decomposition import PCA
import matplotlib.pyplot as plt
from sklearn.preprocessing import StandardScaler
from collections import defaultdict
#from sklearn.preprocessing import scale
np.random.seed(42)
sample_size = 2000
readFileName = 'Frogs_MFCCs.csv'
with open(readFileName) as df:
df_iter = csv.reader(df, delimiter=',', quotechar='"')
data=[x for x in df_iter]
data = np.array(data)
# skip 1st row and last 4 columns
X = data[1:,0:-4].astype(np.float)
labels = data[1:,-2]
n_digits = len(np.unique(labels))
scaler = StandardScaler()
X_scaled = scaler.fit_transform(X)
#X_test_scaled = scaler.transform(X_test)
# One hot encode target values
one_hot = OneHotEncoder()
y_hot = one_hot.fit_transform(labels.reshape(-1, 1)).todense()
#y_test_hot = one_hot.transform(y_test.reshape(-1, 1)).todense()
#for i in data[1:]: # skip title row
# X = [float(x) for x in i[0:-4]]
## Normalize feature data
#scaler = MinMaxScaler()
#
#X_scaled = scaler.fit_transform(X)
print(82 * '_')
print('init\t\ttime\tinertia\thomo\tcompl\tv-meas\tARI\tAMI\tsilhouette')
def bench_k_means(estimator, name, data):
t0 = time()
estimator.fit(data)
print('%-9s\t%.2fs\t%i\t%.3f\t%.3f\t%.3f\t%.3f\t%.3f\t%.3f'
% (name, (time() - t0), estimator.inertia_,
metrics.homogeneity_score(labels, estimator.labels_),
metrics.completeness_score(labels, estimator.labels_),
metrics.v_measure_score(labels, estimator.labels_),
metrics.adjusted_rand_score(labels, estimator.labels_),
metrics.adjusted_mutual_info_score(labels, estimator.labels_),
metrics.silhouette_score(data, estimator.labels_,
metric='euclidean',
sample_size=sample_size)))
return estimator
clusters = [2,5,10,15,20,25,30,35]
km = KM(random_state=42)
gmm = GMM(random_state=42)
Score = defaultdict(list)
adjMI = defaultdict(list)
S_homog = defaultdict(list)
S_adjMI = defaultdict(list)
S_vm = defaultdict(list)
for k in clusters:
km.set_params(n_clusters=k)
gmm.set_params(n_components=k)
km.fit(X_scaled)
gmm.fit(X_scaled)
Score['km'].append( km.score(X_scaled))
Score['gmm'].append( gmm.score(X_scaled))
S_homog['km'].append(metrics.homogeneity_score(labels, km.predict(X_scaled)))
S_homog['gmm'].append(metrics.homogeneity_score(labels, gmm.predict(X_scaled)))
S_adjMI['km'].append(metrics.adjusted_mutual_info_score(labels, km.predict(X_scaled)))
S_adjMI['gmm'].append(metrics.adjusted_mutual_info_score(labels, gmm.predict(X_scaled)) )
S_vm['km'].append(metrics.v_measure_score(labels, km.predict(X_scaled)))
S_vm['gmm'].append(metrics.v_measure_score(labels, gmm.predict(X_scaled)))
plt.figure(figsize=(9.6, 7.2))
plt.xlabel('Number of clusters')
plt.ylabel('Score value')
plt.title('Score vs. Cluster number for K-mean and Gaussian Mixture (species)')
plt.grid(True)
#plt.legend(['Train', 'Test'], loc='lower right')
for i in ['km', 'gmm']:
plt.plot(clusters, S_homog[i], label= i+' homogeneity score', linewidth=2)
plt.plot(clusters, S_adjMI[i], label= i+' adjusted mutual info score', linewidth=2)
plt.plot(clusters, S_vm[i], label= i+' v measure score', linewidth=2)
plt.legend()
plt.savefig("KM_GMM_Scores_species.png")
#
## #############################################################################
## Visualize the results on PCA-reduced data
#reduced_data = PCA(n_components=15).fit_transform(X_scaled)
##kmeans = KMeans(init='k-means++', n_clusters=n_digits, n_init=10)
##kmeans.fit(reduced_data)
#kmeans=bench_k_means(KMeans(init='k-means++', n_clusters=n_digits, n_init=10),
# name="PCA", data=reduced_data)
def draw_2d():
# Step size of the mesh. Decrease to increase the quality of the VQ.
h = .02 # point in the mesh [x_min, x_max]x[y_min, y_max].
# Plot the decision boundary. For that, we will assign a color to each
x_min, x_max = reduced_data[:, 0].min() - 1, reduced_data[:, 0].max() + 1
y_min, y_max = reduced_data[:, 1].min() - 1, reduced_data[:, 1].max() + 1
xx, yy = np.meshgrid(np.arange(x_min, x_max, h), np.arange(y_min, y_max, h))
# Obtain labels for each point in mesh. Use last trained model.
Z = kmeans.predict(np.c_[xx.ravel(), yy.ravel()])
# Put the result into a color plot
Z = Z.reshape(xx.shape)
plt.figure(1)
plt.clf()
plt.imshow(Z, interpolation='nearest',
extent=(xx.min(), xx.max(), yy.min(), yy.max()),
cmap=plt.cm.Paired,
aspect='auto', origin='lower')
plt.plot(reduced_data[:, 0], reduced_data[:, 1], 'k.', markersize=2)
# Plot the centroids as a white X
centroids = kmeans.cluster_centers_
plt.scatter(centroids[:, 0], centroids[:, 1],
marker='x', s=169, linewidths=3,
color='w', zorder=10)
plt.title('K-means clustering on the digits dataset (PCA-reduced data)\n'
'Centroids are marked with white cross')
plt.xlim(x_min, x_max)
plt.ylim(y_min, y_max)
plt.xticks(())
plt.yticks(())
plt.show()