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# | ||
# 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. | ||
# | ||
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""" | ||
Unit tests for Spark ML Python APIs. | ||
""" | ||
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import sys | ||
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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 | ||
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from pyspark.tests import ReusedPySparkTestCase as PySparkTestCase | ||
from pyspark.sql import SchemaRDD | ||
from pyspark.ml import Transformer, Estimator, Model, Pipeline | ||
from pyspark.ml.param import Param | ||
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class MockDataset(SchemaRDD): | ||
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def __init__(self): | ||
self.index = 0 | ||
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class MockTransformer(Transformer): | ||
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def __init__(self): | ||
super(MockTransformer, self).__init__() | ||
self.fake = Param(self, "fake", "fake", None) | ||
self.dataset_index = None | ||
self.fake_param_value = None | ||
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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 | ||
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class MockEstimator(Estimator): | ||
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def __init__(self): | ||
super(MockEstimator, self).__init__() | ||
self.fake = Param(self, "fake", "fake", None) | ||
self.dataset_index = None | ||
self.fake_param_value = None | ||
self.model = None | ||
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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 | ||
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class MockModel(MockTransformer, Model): | ||
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def __init__(self): | ||
super(MockModel, self).__init__() | ||
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class PipelineTests(PySparkTestCase): | ||
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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) | ||
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if __name__ == "__main__": | ||
unittest.main() |