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
162
163
164
165
166
167
168
169
170
171
172
173
174
175
176
177
178
179
180
181
182
183
184
185
186
187
188
189
190
191
192
193
194
195
196
197
198
199
200
201
202
203
204
205
206
207
208
209
210
211
212
213
214
215
216
217
218
219
220
221
222
223
224
225
226
227
228
229
230
231
232
233
234
235
236
237
238
239
240
241
242
243
244
245
246
247
248
249
250
251
252
253
254
255
256
257
258
259
260
261
262
263
264
265
266
267
268
269
270
271
272
273
274
275
276
277
278
279
|
# -*- coding: utf-8 -*-
"""
Unit tests for the GenSVM class
"""
from __future__ import division, print_function
import numpy as np
import unittest
import warnings
from sklearn.datasets import load_digits, load_iris
from sklearn.model_selection import train_test_split
from sklearn.preprocessing import maxabs_scale
from gensvm import GenSVM
class GenSVMTestCase(unittest.TestCase):
def test_init_1(self):
""" GENSVM: Sanity check for __init__ """
clf = GenSVM()
self.assertEqual(clf.p, 1.0)
self.assertEqual(clf.lmd, 1e-5)
self.assertEqual(clf.kappa, 0.0)
self.assertEqual(clf.epsilon, 1e-6)
self.assertEqual(clf.weights, "unit")
self.assertEqual(clf.kernel, "linear")
self.assertEqual(clf.gamma, "auto")
self.assertEqual(clf.coef, 1.0)
self.assertEqual(clf.degree, 2.0)
self.assertEqual(clf.kernel_eigen_cutoff, 1e-8)
self.assertEqual(clf.verbose, 0)
self.assertEqual(clf.random_state, None)
self.assertEqual(clf.max_iter, 1e8)
def test_init_invalid_p(self):
""" GENSVM: Check raises for invalid p """
with self.assertRaises(ValueError) as err1:
GenSVM(p=2.6)
exc1 = err1.exception
self.assertEqual(
exc1.args, ("Value for p should be within [1, 2]; got p = 2.6",)
)
with self.assertRaises(ValueError) as err2:
GenSVM(p=0.3)
exc2 = err2.exception
self.assertEqual(
exc2.args, ("Value for p should be within [1, 2]; got p = 0.3",)
)
def test_init_invalid_kappa(self):
""" GENSVM: Check raises for invalid kappa """
with self.assertRaises(ValueError) as err1:
GenSVM(kappa=-1.0)
exc1 = err1.exception
self.assertEqual(
exc1.args,
("Value for kappa should be larger " "than -1; got kappa = -1.0",),
)
def test_init_invalid_lmd(self):
""" GENSVM: Check raises for invalid lmd """
with self.assertRaises(ValueError) as err1:
GenSVM(lmd=-3.0)
exc1 = err1.exception
self.assertEqual(
exc1.args,
("Value for lmd should be larger than 0; got lmd = -3.0",),
)
def test_init_invalid_epsilon(self):
""" GENSVM: Check raises for invalid epsilon """
with self.assertRaises(ValueError) as err1:
GenSVM(epsilon=-1.0)
exc1 = err1.exception
self.assertEqual(
exc1.args,
(
"Value for epsilon should be larger than 0; got "
"epsilon = -1.0",
),
)
def test_init_invalid_gamma(self):
""" GENSVM: Check raises for invalid gamma """
with self.assertRaises(ValueError) as err1:
GenSVM(gamma=0.0)
exc1 = err1.exception
self.assertEqual(exc1.args, ("A gamma value of 0.0 is invalid",))
def test_init_invalid_weights(self):
""" GENSVM: Check raises for invalid weights """
with self.assertRaises(ValueError) as err1:
GenSVM(weights="other")
exc1 = err1.exception
self.assertEqual(
exc1.args,
(
"Unknown weight parameter specified. Should be "
"'unit' or 'group'; got 'other'",
),
)
def test_init_invalid_kernel(self):
""" GENSVM: Check raises for invalid kernel """
with self.assertRaises(ValueError) as err1:
GenSVM(kernel="other")
exc1 = err1.exception
self.assertEqual(
exc1.args,
(
"Unknown kernel specified. Should be "
"'linear', 'rbf', 'poly', or 'sigmoid'; got 'other'",
),
)
def test_invalid_y(self):
""" GENSVM: Check raises for invalid y type """
clf = GenSVM()
X = np.random.random((20, 4))
y = np.random.random((20,))
with self.assertRaises(ValueError) as err:
clf.fit(X, y)
exc = err.exception
self.assertEqual(
exc.args, ("Label type not allowed for GenSVM: 'continuous'",)
)
def test_invalid_seed_V_linear(self):
""" GENSVM: Test for invalid seed_V shape """
clf = GenSVM()
X = np.random.random((20, 4))
y = np.random.randint(1, 4, (20,))
seed_V = np.random.random((10, 2))
with self.assertRaises(ValueError) as err:
clf.fit(X, y, seed_V=seed_V)
exc = err.exception
self.assertEqual(
exc.args, ("Seed V must have shape [5, 2], but has shape [10, 2]",)
)
def test_invalid_seed_V_nonlinear(self):
""" GENSVM: Test for seed_V for nonlinear kernels """
clf = GenSVM(kernel="rbf")
X = np.random.random((20, 4))
y = np.random.randint(1, 4, (20,))
seed_V = np.random.random((5, 2))
with warnings.catch_warnings(record=True) as w:
warnings.simplefilter("always")
clf.fit(X, y, seed_V=seed_V)
self.assertTrue(len(w) == 1)
msg = str(w[0].message)
self.assertEqual(
msg,
(
"Warm starts are only supported for the linear kernel. "
"The seed_V parameter will be ignored."
),
)
def test_fit_predict_strings(self):
""" GENSVM: Test fit and predict with string targets """
digits = load_digits(4)
n_samples = len(digits.images)
X = digits.images.reshape(n_samples, -1)
y = digits.target
labels = np.array(["zero", "one", "two", "three"])
yy = labels[y]
X_train, X_test, y_train, y_test = train_test_split(X, yy)
clf = GenSVM(epsilon=1e-3) # faster testing
clf.fit(X_train, y_train)
y_pred = clf.predict(X_test)
pred_set = set(y_pred)
label_set = set(labels)
self.assertTrue(pred_set.issubset(label_set))
def test_fit_with_seed(self):
""" GENSVM: Test fit with seeding """
# This is based on the unit test for gensvm_train in the C library
n_obs = 10
n_var = 3
n_class = 4
X = np.zeros((n_obs, n_var + 1))
X[0, 0] = 1.0000000000000000
X[0, 1] = 0.8056271362589000
X[0, 2] = 0.4874175854113872
X[0, 3] = 0.4453015882771756
X[1, 0] = 1.0000000000000000
X[1, 1] = 0.7940590105180981
X[1, 2] = 0.1861049005485224
X[1, 3] = 0.8469394287449229
X[2, 0] = 1.0000000000000000
X[2, 1] = 0.0294257611061681
X[2, 2] = 0.0242717976065267
X[2, 3] = 0.5039128672814752
X[3, 0] = 1.0000000000000000
X[3, 1] = 0.1746563833537603
X[3, 2] = 0.9135736087631979
X[3, 3] = 0.5270258081021366
X[4, 0] = 1.0000000000000000
X[4, 1] = 0.0022298761599785
X[4, 2] = 0.3773482059713607
X[4, 3] = 0.8009654729622842
X[5, 0] = 1.0000000000000000
X[5, 1] = 0.6638830667081945
X[5, 2] = 0.6467607601353914
X[5, 3] = 0.0434948735457108
X[6, 0] = 1.0000000000000000
X[6, 1] = 0.0770493004546461
X[6, 2] = 0.3699566427075194
X[6, 3] = 0.7863539761080217
X[7, 0] = 1.0000000000000000
X[7, 1] = 0.2685233952731509
X[7, 2] = 0.8539966432782011
X[7, 3] = 0.0967159557826836
X[8, 0] = 1.0000000000000000
X[8, 1] = 0.1163951898554611
X[8, 2] = 0.7667861436369238
X[8, 3] = 0.5031912600213351
X[9, 0] = 1.0000000000000000
X[9, 1] = 0.2290251898688216
X[9, 2] = 0.4401981048538806
X[9, 3] = 0.0884616753393881
X = X[:, 1:]
y = np.array([2, 1, 3, 2, 3, 2, 4, 1, 3, 4])
seed_V = np.zeros((n_var + 1, n_class - 1))
seed_V[0, 0] = 0.8233234072519983
seed_V[0, 1] = 0.7701104553132680
seed_V[0, 2] = 0.1102697774064020
seed_V[1, 0] = 0.7956168453294307
seed_V[1, 1] = 0.3267543833513200
seed_V[1, 2] = 0.8659836346403005
seed_V[2, 0] = 0.5777227081256917
seed_V[2, 1] = 0.3693175185473680
seed_V[2, 2] = 0.2728942849022845
seed_V[3, 0] = 0.4426030703804438
seed_V[3, 1] = 0.2456426390463990
seed_V[3, 2] = 0.2665038412777220
clf = GenSVM(
p=1.2143,
kappa=0.90298,
lmd=0.00219038,
epsilon=1e-15,
weights="unit",
kernel="linear",
)
clf.fit(X, y, seed_V=seed_V)
V = clf.combined_coef_
eps = 1e-13
self.assertTrue(abs(V[0, 0] - -1.1907736868272805) < eps)
self.assertTrue(abs(V[0, 1] - 1.8651287814979396) < eps)
self.assertTrue(abs(V[0, 2] - 1.7250030581662932) < eps)
self.assertTrue(abs(V[1, 0] - 0.7925100058806183) < eps)
self.assertTrue(abs(V[1, 1] - -3.6093428916761665) < eps)
self.assertTrue(abs(V[1, 2] - -1.3394018960329377) < eps)
self.assertTrue(abs(V[2, 0] - 1.5203132433193016) < eps)
self.assertTrue(abs(V[2, 1] - -1.9118604362643852) < eps)
self.assertTrue(abs(V[2, 2] - -1.7939246097629342) < eps)
self.assertTrue(abs(V[3, 0] - 0.0658817457370326) < eps)
self.assertTrue(abs(V[3, 1] - 0.6547924025329720) < eps)
self.assertTrue(abs(V[3, 2] - -0.6773346708737853) < eps)
if __name__ == "__main__":
unittest.main()
|
