forked from aws/sagemaker-xgboost-container
-
Notifications
You must be signed in to change notification settings - Fork 0
/
Copy pathtest_custom_metrics.py
192 lines (138 loc) · 6.79 KB
/
test_custom_metrics.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
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
# Copyright 2019 Amazon.com, Inc. or its affiliates. All Rights Reserved.
#
# Licensed under the Apache License, Version 2.0 (the 'License'). You
# may not use this file except in compliance with the License. A copy of
# the License is located at
#
# http://aws.amazon.com/apache2.0/
#
# or in the 'license' file accompanying this file. This file is
# distributed on an 'AS IS' BASIS, WITHOUT WARRANTIES OR CONDITIONS OF
# ANY KIND, either express or implied. See the License for the specific
# language governing permissions and limitations under the License.
import numpy as np
import xgboost as xgb
from math import log, sqrt
from sagemaker_xgboost_container.metrics.custom_metrics import accuracy, f1, mse, r2, f1_binary, f1_macro, \
precision_macro, precision_micro, recall_macro, recall_micro, mae, rmse, balanced_accuracy, \
precision, recall
binary_train_data = np.random.rand(10, 2)
binary_train_label = np.array([1, 1, 1, 1, 1, 0, 0, 0, 0, 0])
binary_dtrain = xgb.DMatrix(binary_train_data, label=binary_train_label)
# log(x/(1-x)) is the inverse function of sigmoid
binary_preds = np.asarray([log(0.7/0.3) - 0.5] * 10)
binary_preds_logistic = np.asarray([[log(0.1/0.9) - 0.5, log(0.9/0.1) - 0.5]] * 10)
def test_binary_accuracy():
accuracy_name, accuracy_result = accuracy(binary_preds, binary_dtrain)
assert accuracy_name == 'accuracy'
assert accuracy_result == .5
def test_binary_balanced_accuracy():
bal_accuracy_name, bal_accuracy_result = balanced_accuracy(binary_preds, binary_dtrain)
assert bal_accuracy_name == 'balanced_accuracy'
assert bal_accuracy_result == .5
def test_binary_accuracy_logistic():
accuracy_name, accuracy_result = accuracy(binary_preds_logistic, binary_dtrain)
assert accuracy_name == 'accuracy'
assert accuracy_result == .5
def test_binary_f1():
f1_score_name, f1_score_result = f1(binary_preds, binary_dtrain)
assert f1_score_name == 'f1'
assert f1_score_result == 1/3
def test_binary_f1_logistic():
f1_score_name, f1_score_result = f1(binary_preds_logistic, binary_dtrain)
assert f1_score_name == 'f1'
assert f1_score_result == 1/3
def test_binary_f1_binary():
f1_score_name, f1_score_result = f1_binary(binary_preds, binary_dtrain)
assert f1_score_name == 'f1_binary'
assert f1_score_result == 2/3
def test_binary_f1_binary_logistic():
f1_score_name, f1_score_result = f1_binary(binary_preds_logistic, binary_dtrain)
assert f1_score_name == 'f1_binary'
assert f1_score_result == 2/3
def test_binary_precision():
precision_score_name, precision_score_result = precision(binary_preds, binary_dtrain)
assert precision_score_name == 'precision'
assert precision_score_result == .5
def test_binary_recall():
recall_score_name, recall_score_result = recall(binary_preds, binary_dtrain)
assert recall_score_name == 'recall'
assert recall_score_result == 1
multiclass_train_data = np.random.rand(10, 2)
multiclass_train_label = np.array([0, 0, 1, 1, 1, 1, 1, 2, 2, 2])
multiclass_dtrain = xgb.DMatrix(multiclass_train_data, label=multiclass_train_label)
multiclass_preds = np.ones(10)
multiclass_preds_softprob = np.asarray([[0.9, 0.05, 0.05],
[0.4, 0.5, 0.1],
[0.2, 0.1, 0.7],
[0.8, 0.1, 0.1],
[0.6, 0.2, 0.2],
[0.9, 0.08, 0.02],
[0.4, 0.3, 0.3],
[0.5, 0.25, 0.25],
[0.8, 0.1, 0.1],
[0.6, 0.2, 0.2]])
def test_multiclass_accuracy():
accuracy_name, accuracy_result = accuracy(multiclass_preds, multiclass_dtrain)
assert accuracy_name == 'accuracy'
assert accuracy_result == .5
def test_multiclass_balanced_accuracy():
bal_accuracy_name, bal_accuracy_result = balanced_accuracy(multiclass_preds, multiclass_dtrain)
assert bal_accuracy_name == 'balanced_accuracy'
assert bal_accuracy_result == 1/3
def test_multiclass_accuracy_softprob():
accuracy_name, accuracy_result = accuracy(multiclass_preds_softprob, multiclass_dtrain)
assert accuracy_name == 'accuracy'
assert accuracy_result == .1
def test_multiclass_f1():
f1_score_name, f1_score_result = f1(multiclass_preds, multiclass_dtrain)
assert f1_score_name == 'f1'
assert f1_score_result == 2/9
def test_multiclass_f1_softprob():
f1_score_name, f1_score_result = f1(multiclass_preds_softprob, multiclass_dtrain)
assert f1_score_name == 'f1'
assert f1_score_result == 1/15
def test_multiclass_f1_macro():
f1_score_name, f1_score_result = f1_macro(multiclass_preds, multiclass_dtrain)
assert f1_score_name == 'f1_macro'
assert f1_score_result == 2/9
def test_multiclass_precision_macro():
precision_macro_name, precision_macro_result = precision_macro(multiclass_preds, multiclass_dtrain)
assert precision_macro_name == 'precision_macro'
assert precision_macro_result == 1/6
def test_multiclass_precision_micro():
precision_micro_name, precision_micro_result = precision_micro(multiclass_preds, multiclass_dtrain)
assert precision_micro_name == 'precision_micro'
assert precision_micro_result == 1/2
def test_multiclass_recall_macro():
recall_macro_name, recall_macro_result = recall_macro(multiclass_preds, multiclass_dtrain)
assert recall_macro_name == 'recall_macro'
assert recall_macro_result == 1/3
def test_multiclass_recall_micro():
recall_micro_name, recall_micro_result = recall_micro(multiclass_preds, multiclass_dtrain)
assert recall_micro_name == 'recall_micro'
assert recall_micro_result == 1/2
def test_multiclass_f1_macro_softprob():
f1_score_name, f1_score_result = f1_macro(multiclass_preds_softprob, multiclass_dtrain)
assert f1_score_name == 'f1_macro'
assert f1_score_result == 1/15
regression_train_data = np.random.rand(10, 2)
regression_train_label = np.array([1, 1, 1, 1, 1, 0, 0, 0, 0, 0])
regression_dtrain = xgb.DMatrix(regression_train_data, label=regression_train_label)
regression_preds = np.ones(10)
def test_mse():
mse_score_name, mse_score_result = mse(regression_preds, regression_dtrain)
assert mse_score_name == 'mse'
assert mse_score_result == .5
def test_r2():
r2_score_name, r2_score_result = r2(regression_preds, regression_dtrain)
assert r2_score_name == 'r2'
assert r2_score_result == -1
def test_rmse():
rmse_score_name, rmse_score_result = rmse(regression_preds, regression_dtrain)
assert rmse_score_name == 'rmse'
assert rmse_score_result == sqrt(0.5)
def test_mae():
mae_score_name, mae_score_result = mae(regression_preds, regression_dtrain)
assert mae_score_name == 'mae'
assert mae_score_result == .5