/
test_test_evaluator.py
147 lines (129 loc) · 5.45 KB
/
test_test_evaluator.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
# -*- encoding: utf-8 -*-
import copy
import json
import multiprocessing
import os
import shutil
import sys
import unittest
import unittest.mock
import numpy as np
from smac.tae.execute_ta_run import StatusType
this_directory = os.path.dirname(__file__)
sys.path.append(this_directory)
from evaluation_util import get_dataset_getters, BaseEvaluatorTest, \
get_multiclass_classification_datamanager
from autosklearn.constants import *
from autosklearn.evaluation.test_evaluator import TestEvaluator, eval_t
# Otherwise nosetests thinks this is a test to run...
from autosklearn.evaluation.util import read_queue
from autosklearn.util.pipeline import get_configuration_space
from autosklearn.util import Backend
from autosklearn.metrics import accuracy, r2, f1_macro
N_TEST_RUNS = 3
class Dummy(object):
pass
class TestEvaluator_Test(BaseEvaluatorTest, unittest.TestCase):
_multiprocess_can_split_ = True
def test_datasets(self):
for getter in get_dataset_getters():
testname = '%s_%s' % (os.path.basename(__file__).
replace('.pyc', '').replace('.py', ''),
getter.__name__)
with self.subTest(testname):
backend_mock = unittest.mock.Mock(spec=Backend)
backend_mock.get_model_dir.return_value = 'dutirapbdxvltcrpbdlcatepdeau'
D = getter()
D_ = copy.deepcopy(D)
y = D.data['Y_train']
if len(y.shape) == 2 and y.shape[1] == 1:
D_.data['Y_train'] = y.flatten()
backend_mock.load_datamanager.return_value = D_
metric_lookup = {MULTILABEL_CLASSIFICATION: f1_macro,
BINARY_CLASSIFICATION: accuracy,
MULTICLASS_CLASSIFICATION: accuracy,
REGRESSION: r2}
queue_ = multiprocessing.Queue()
evaluator = TestEvaluator(
backend_mock,
queue_,
metric=metric_lookup[D.info['task']]
)
evaluator.fit_predict_and_loss()
rval = read_queue(evaluator.queue)
self.assertEqual(len(rval), 1)
self.assertEqual(len(rval[0]), 3)
self.assertTrue(np.isfinite(rval[0]['loss']))
class FunctionsTest(unittest.TestCase):
def setUp(self):
self.queue = multiprocessing.Queue()
self.configuration = get_configuration_space(
{'task': MULTICLASS_CLASSIFICATION,
'is_sparse': False}).get_default_configuration()
self.data = get_multiclass_classification_datamanager()
self.tmp_dir = os.path.join(os.path.dirname(__file__),
'.test_cv_functions')
self.backend = unittest.mock.Mock(spec=Backend)
self.backend.load_datamanager.return_value = self.data
self.dataset_name = json.dumps({'task_id': 'test'})
def tearDown(self):
try:
shutil.rmtree(self.tmp_dir)
except Exception:
pass
def test_eval_test(self):
eval_t(queue=self.queue,
backend=self.backend,
config=self.configuration,
metric=accuracy,
seed=1, num_run=1,
all_scoring_functions=False,
output_y_hat_optimization=False,
include=None,
exclude=None,
disable_file_output=False,
instance=self.dataset_name
)
rval = read_queue(self.queue)
self.assertEqual(len(rval), 1)
self.assertAlmostEqual(rval[0]['loss'], 0.08)
self.assertEqual(rval[0]['status'], StatusType.SUCCESS)
self.assertNotIn('bac_metric', rval[0]['additional_run_info'])
def test_eval_test_all_loss_functions(self):
eval_t(
queue=self.queue,
backend=self.backend,
config=self.configuration,
metric=accuracy,
seed=1, num_run=1,
all_scoring_functions=True,
output_y_hat_optimization=False,
include=None,
exclude=None,
disable_file_output=False,
instance=self.dataset_name,
)
rval = read_queue(self.queue)
self.assertEqual(len(rval), 1)
fixture = {'accuracy': 0.08,
'balanced_accuracy': 0.05555555555555547,
'f1_macro': 0.06734006734006737,
'f1_micro': 0.08,
'f1_weighted': 0.07919191919191915,
'log_loss': 1.128776115477085,
'pac_score': 0.187005982641133,
'precision_macro': 0.06666666666666676,
'precision_micro': 0.08,
'precision_weighted': 0.064,
'recall_macro': 0.05555555555555547,
'recall_micro': 0.08,
'recall_weighted': 0.08,
'num_run': -1}
additional_run_info = rval[0]['additional_run_info']
for key, value in fixture.items():
self.assertAlmostEqual(additional_run_info[key], fixture[key], msg=key)
self.assertEqual(len(additional_run_info), len(fixture) + 1,
msg=sorted(additional_run_info.items()))
self.assertIn('duration', additional_run_info)
self.assertAlmostEqual(rval[0]['loss'], 0.08)
self.assertEqual(rval[0]['status'], StatusType.SUCCESS)