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
|
import unittest
from unittest.mock import Mock, patch
import numpy as np
from Orange.base import Model
from Orange.classification import LogisticRegressionLearner
from Orange.classification.calibration import \
ThresholdLearner, ThresholdClassifier, \
CalibratedLearner, CalibratedClassifier
from Orange.data import Table
class TestThresholdClassifier(unittest.TestCase):
def setUp(self):
probs1 = np.array([0.3, 0.5, 0.2, 0.8, 0.9, 0]).reshape(-1, 1)
self.probs = np.hstack((1 - probs1, probs1))
base_model = Mock(return_value=self.probs)
base_model.domain.class_var.is_discrete = True
base_model.domain.class_var.values = ["a", "b"]
self.model = ThresholdClassifier(base_model, 0.5)
self.data = Mock()
def test_threshold(self):
vals = self.model(self.data)
np.testing.assert_equal(vals, [0, 1, 0, 1, 1, 0])
self.model.threshold = 0.8
vals = self.model(self.data)
np.testing.assert_equal(vals, [0, 0, 0, 1, 1, 0])
self.model.threshold = 0
vals = self.model(self.data)
np.testing.assert_equal(vals, [1] * 6)
def test_return_types(self):
vals = self.model(self.data, ret=Model.Value)
np.testing.assert_equal(vals, [0, 1, 0, 1, 1, 0])
vals = self.model(self.data)
np.testing.assert_equal(vals, [0, 1, 0, 1, 1, 0])
probs = self.model(self.data, ret=Model.Probs)
np.testing.assert_equal(probs, self.probs)
vals, probs = self.model(self.data, ret=Model.ValueProbs)
np.testing.assert_equal(vals, [0, 1, 0, 1, 1, 0])
np.testing.assert_equal(probs, self.probs)
def test_nans(self):
self.probs[1, :] = np.nan
vals, probs = self.model(self.data, ret=Model.ValueProbs)
np.testing.assert_equal(vals, [0, np.nan, 0, 1, 1, 0])
np.testing.assert_equal(probs, self.probs)
def test_non_binary_base(self):
base_model = Mock()
base_model.domain.class_var.is_discrete = True
base_model.domain.class_var.values = ["a"]
self.assertRaises(ValueError, ThresholdClassifier, base_model, 0.5)
base_model.domain.class_var.values = ["a", "b", "c"]
self.assertRaises(ValueError, ThresholdClassifier, base_model, 0.5)
base_model.domain.class_var = Mock()
base_model.domain.class_var.is_discrete = False
self.assertRaises(ValueError, ThresholdClassifier, base_model, 0.5)
def test_np_data(self):
"""
Test ThresholdModel with numpy data.
When passing numpy data to model they should be already
transformed to models domain since model do not know how to do it.
"""
data = Table('heart_disease')
base_learner = LogisticRegressionLearner()
model = ThresholdLearner(base_learner)(data)
res = model(model.data_to_model_domain(data).X)
self.assertTupleEqual((len(data),), res.shape)
class TestThresholdLearner(unittest.TestCase):
@patch("Orange.evaluation.performance_curves.Curves.from_results")
@patch("Orange.classification.calibration.TestOnTrainingData")
def test_fit_storage(self, test_on_training, curves_from_results):
curves_from_results.return_value = curves = Mock()
curves.probs = np.array([0.1, 0.15, 0.3, 0.45, 0.6, 0.8])
curves.ca = lambda: np.array([0.1, 0.7, 0.4, 0.4, 0.3, 0.1])
curves.f1 = lambda: np.array([0.1, 0.2, 0.4, 0.4, 0.3, 0.1])
model = Mock()
model.domain.class_var.is_discrete = True
model.domain.class_var.values = ("a", "b")
data = Table("heart_disease")
learner = Mock()
test_on_training.return_value = tot = Mock()
res = Mock()
res.models = np.array([[model]])
tot.return_value = res
thresh_learner = ThresholdLearner(
base_learner=learner,
threshold_criterion=ThresholdLearner.OptimizeCA)
thresh_model = thresh_learner(data)
self.assertEqual(thresh_model.threshold, 0.15)
args, _ = tot.call_args # pylint: disable=unpacking-non-sequence
self.assertEqual(len(args), 2)
self.assertIs(args[0], data)
self.assertIs(args[1][0], learner)
_, kwargs = test_on_training.call_args
self.assertEqual(len(args[1]), 1)
self.assertEqual(kwargs, {"store_models": 1})
thresh_learner = ThresholdLearner(
base_learner=learner,
threshold_criterion=ThresholdLearner.OptimizeF1)
thresh_model = thresh_learner(data)
self.assertEqual(thresh_model.threshold, 0.45)
def test_non_binary_class(self):
thresh_learner = ThresholdLearner(
base_learner=Mock(),
threshold_criterion=ThresholdLearner.OptimizeF1)
data = Mock()
data.domain.class_var.is_discrete = True
data.domain.class_var.values = ["a"]
self.assertRaises(ValueError, thresh_learner.fit_storage, data)
data.domain.class_var.values = ["a", "b", "c"]
self.assertRaises(ValueError, thresh_learner.fit_storage, data)
data.domain.class_var = Mock()
data.domain.class_var.is_discrete = False
self.assertRaises(ValueError, thresh_learner.fit_storage, data)
class TestCalibratedClassifier(unittest.TestCase):
def setUp(self):
probs1 = np.array([0.3, 0.5, 0.2, 0.8, 0.9, 0]).reshape(-1, 1)
self.probs = np.hstack((1 - probs1, probs1))
base_model = Mock(return_value=self.probs)
base_model.domain.class_var.is_discrete = True
base_model.domain.class_var.values = ["a", "b"]
self.model = CalibratedClassifier(base_model, None)
self.data = Mock()
def test_call(self):
calprobs = np.arange(self.probs.size).reshape(self.probs.shape)
calprobs = calprobs / np.sum(calprobs, axis=1)[:, None]
calprobs[-1] = [0.7, 0.3]
self.model.calibrated_probs = Mock(return_value=calprobs)
probs = self.model(self.data, ret=Model.Probs)
self.model.calibrated_probs.assert_called_with(self.probs)
np.testing.assert_almost_equal(probs, calprobs)
vals = self.model(self.data, ret=Model.Value)
np.testing.assert_almost_equal(vals, [1, 1, 1, 1, 1, 0])
vals, probs = self.model(self.data, ret=Model.ValueProbs)
np.testing.assert_almost_equal(probs, calprobs)
np.testing.assert_almost_equal(vals, [1, 1, 1, 1, 1, 0])
def test_calibrated_probs(self):
self.model.calibrators = None
calprobs = self.model.calibrated_probs(self.probs)
np.testing.assert_equal(calprobs, self.probs)
self.assertIsNot(calprobs, self.probs)
calibrator = Mock()
calibrator.predict = lambda x: x**2
self.model.calibrators = [calibrator] * 2
calprobs = self.model.calibrated_probs(self.probs)
expprobs = self.probs ** 2 / np.sum(self.probs ** 2, axis=1)[:, None]
np.testing.assert_almost_equal(calprobs, expprobs)
self.probs[1] = 0
self.probs[2] = np.nan
expprobs[1] = 0.5
expprobs[2] = np.nan
calprobs = self.model.calibrated_probs(self.probs)
np.testing.assert_almost_equal(calprobs, expprobs)
def test_np_data(self):
"""
Test CalibratedClassifier with numpy data.
When passing numpy data to model they should be already
transformed to models domain since model do not know how to do it.
"""
data = Table('heart_disease')
base_learner = LogisticRegressionLearner()
model = CalibratedLearner(base_learner)(data)
res = model(model.data_to_model_domain(data).X)
self.assertTupleEqual((len(data),), res.shape)
class TestCalibratedLearner(unittest.TestCase):
@patch("Orange.classification.calibration._SigmoidCalibration.fit")
@patch("Orange.classification.calibration.TestOnTrainingData")
def test_fit_storage(self, test_on_training, sigmoid_fit):
data = Table("heart_disease")
learner = Mock()
model = Mock()
model.domain.class_var.is_discrete = True
model.domain.class_var.values = ("a", "b")
test_on_training.return_value = tot = Mock()
res = Mock()
res.models = np.array([[model]])
res.probabilities = np.arange(20, dtype=float).reshape(1, 5, 4)
tot.return_value = res
sigmoid_fit.return_value = Mock()
cal_learner = CalibratedLearner(
base_learner=learner, calibration_method=CalibratedLearner.Sigmoid)
cal_model = cal_learner(data)
self.assertIs(cal_model.base_model, model)
self.assertEqual(cal_model.calibrators, [sigmoid_fit.return_value] * 4)
args, _ = tot.call_args # pylint: disable=unpacking-non-sequence
self.assertEqual(len(args), 2)
self.assertIs(args[0], data)
self.assertIs(args[1][0], learner)
self.assertEqual(len(args[1]), 1)
_, kwargs = test_on_training.call_args
self.assertEqual(kwargs, {"store_models": 1})
for call, cls_probs in zip(sigmoid_fit.call_args_list,
res.probabilities[0].T):
np.testing.assert_equal(call[0][0], cls_probs)
if __name__ == "__main__":
unittest.main()
|
