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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.
#
"""
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.pipeline import Transformer, Estimator, Pipeline
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", None)
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", None)
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, Transformer):
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)
if __name__ == "__main__":
unittest.main()
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