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
|
#
# Licensed to the Apache Software Foundation (ASF) under one or more
# contributor license agreements. See the NOTICE file distributed with
# this work for additional information regarding copyright ownership.
# The ASF licenses this file to You under the Apache License, Version 2.0
# (the "License"); you may not use this file except in compliance with
# the License. You may obtain a copy of the License at
#
# http://www.apache.org/licenses/LICENSE-2.0
#
# Unless required by applicable law or agreed to in writing, software
# distributed under the License 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.
#
"""
Unit tests for Spark ML Python APIs.
"""
import sys
if sys.version_info[:2] <= (2, 6):
try:
import unittest2 as unittest
except ImportError:
sys.stderr.write('Please install unittest2 to test with Python 2.6 or earlier')
sys.exit(1)
else:
import unittest
from pyspark.tests import ReusedPySparkTestCase as PySparkTestCase
from pyspark.sql import DataFrame
from pyspark.ml.param import Param
from pyspark.ml.param.shared import HasMaxIter, HasInputCol
from pyspark.ml.pipeline import Estimator, Model, Pipeline, Transformer
class MockDataset(DataFrame):
def __init__(self):
self.index = 0
class MockTransformer(Transformer):
def __init__(self):
super(MockTransformer, self).__init__()
self.fake = Param(self, "fake", "fake")
self.dataset_index = None
self.fake_param_value = None
def transform(self, dataset, params={}):
self.dataset_index = dataset.index
if self.fake in params:
self.fake_param_value = params[self.fake]
dataset.index += 1
return dataset
class MockEstimator(Estimator):
def __init__(self):
super(MockEstimator, self).__init__()
self.fake = Param(self, "fake", "fake")
self.dataset_index = None
self.fake_param_value = None
self.model = None
def fit(self, dataset, params={}):
self.dataset_index = dataset.index
if self.fake in params:
self.fake_param_value = params[self.fake]
model = MockModel()
self.model = model
return model
class MockModel(MockTransformer, Model):
def __init__(self):
super(MockModel, self).__init__()
class PipelineTests(PySparkTestCase):
def test_pipeline(self):
dataset = MockDataset()
estimator0 = MockEstimator()
transformer1 = MockTransformer()
estimator2 = MockEstimator()
transformer3 = MockTransformer()
pipeline = Pipeline() \
.setStages([estimator0, transformer1, estimator2, transformer3])
pipeline_model = pipeline.fit(dataset, {estimator0.fake: 0, transformer1.fake: 1})
self.assertEqual(0, estimator0.dataset_index)
self.assertEqual(0, estimator0.fake_param_value)
model0 = estimator0.model
self.assertEqual(0, model0.dataset_index)
self.assertEqual(1, transformer1.dataset_index)
self.assertEqual(1, transformer1.fake_param_value)
self.assertEqual(2, estimator2.dataset_index)
model2 = estimator2.model
self.assertIsNone(model2.dataset_index, "The model produced by the last estimator should "
"not be called during fit.")
dataset = pipeline_model.transform(dataset)
self.assertEqual(2, model0.dataset_index)
self.assertEqual(3, transformer1.dataset_index)
self.assertEqual(4, model2.dataset_index)
self.assertEqual(5, transformer3.dataset_index)
self.assertEqual(6, dataset.index)
class TestParams(HasMaxIter, HasInputCol):
"""
A subclass of Params mixed with HasMaxIter and HasInputCol.
"""
def __init__(self):
super(TestParams, self).__init__()
self._setDefault(maxIter=10)
class ParamTests(PySparkTestCase):
def test_param(self):
testParams = TestParams()
maxIter = testParams.maxIter
self.assertEqual(maxIter.name, "maxIter")
self.assertEqual(maxIter.doc, "max number of iterations (>= 0)")
self.assertTrue(maxIter.parent is testParams)
def test_params(self):
testParams = TestParams()
maxIter = testParams.maxIter
inputCol = testParams.inputCol
params = testParams.params
self.assertEqual(params, [inputCol, maxIter])
self.assertTrue(testParams.hasDefault(maxIter))
self.assertFalse(testParams.isSet(maxIter))
self.assertTrue(testParams.isDefined(maxIter))
self.assertEqual(testParams.getMaxIter(), 10)
testParams.setMaxIter(100)
self.assertTrue(testParams.isSet(maxIter))
self.assertEquals(testParams.getMaxIter(), 100)
self.assertFalse(testParams.hasDefault(inputCol))
self.assertFalse(testParams.isSet(inputCol))
self.assertFalse(testParams.isDefined(inputCol))
with self.assertRaises(KeyError):
testParams.getInputCol()
self.assertEquals(
testParams.explainParams(),
"\n".join(["inputCol: input column name (undefined)",
"maxIter: max number of iterations (>= 0) (default: 10, current: 100)"]))
if __name__ == "__main__":
unittest.main()
|
