|
#
# 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.
#
import unittest
from pyspark.ml.pipeline import Pipeline
from pyspark.testing.mlutils import MockDataset, MockEstimator, MockTransformer, PySparkTestCase
class PipelineTests(PySparkTestCase):
def test_pipeline(self):
dataset = MockDataset()
estimator0 = MockEstimator()
transformer1 = MockTransformer()
estimator2 = MockEstimator()
transformer3 = MockTransformer()
pipeline = Pipeline(stages=[estimator0, transformer1, estimator2, transformer3])
pipeline_model = pipeline.fit(dataset, {estimator0.fake: 0, transformer1.fake: 1})
model0, transformer1, model2, transformer3 = pipeline_model.stages
self.assertEqual(0, model0.dataset_index)
self.assertEqual(0, model0.getFake())
self.assertEqual(1, transformer1.dataset_index)
self.assertEqual(1, transformer1.getFake())
self.assertEqual(2, dataset.index)
self.assertIsNone(model2.dataset_index, "The last model shouldn't be called in fit.")
self.assertIsNone(transformer3.dataset_index,
"The last transformer shouldn't be called in 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)
def test_identity_pipeline(self):
dataset = MockDataset()
def doTransform(pipeline):
pipeline_model = pipeline.fit(dataset)
return pipeline_model.transform(dataset)
# check that empty pipeline did not perform any transformation
self.assertEqual(dataset.index, doTransform(Pipeline(stages=[])).index)
# check that failure to set stages param will raise KeyError for missing param
self.assertRaises(KeyError, lambda: doTransform(Pipeline()))
if __name__ == "__main__":
from pyspark.ml.tests.test_pipeline import * # noqa: F401
try:
import xmlrunner # type: ignore[import]
testRunner = xmlrunner.XMLTestRunner(output='target/test-reports', verbosity=2)
except ImportError:
testRunner = None
unittest.main(testRunner=testRunner, verbosity=2)
|
