| # |
| # 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 import Pipeline |
| from pyspark.ml.classification import LogisticRegression, OneVsRest |
| from pyspark.ml.feature import VectorAssembler |
| from pyspark.ml.linalg import Vectors |
| from pyspark.ml.util import MetaAlgorithmReadWrite |
| from pyspark.testing.mlutils import SparkSessionTestCase |
| |
| |
| class MetaAlgorithmReadWriteTests(SparkSessionTestCase): |
| def test_getAllNestedStages(self): |
| def _check_uid_set_equal(stages, expected_stages): |
| uids = set(map(lambda x: x.uid, stages)) |
| expected_uids = set(map(lambda x: x.uid, expected_stages)) |
| self.assertEqual(uids, expected_uids) |
| |
| df1 = self.spark.createDataFrame( |
| [ |
| (Vectors.dense([1.0, 2.0]), 1.0), |
| (Vectors.dense([-1.0, -2.0]), 0.0), |
| ], |
| ["features", "label"], |
| ) |
| df2 = self.spark.createDataFrame( |
| [ |
| (1.0, 2.0, 1.0), |
| (1.0, 2.0, 0.0), |
| ], |
| ["a", "b", "label"], |
| ) |
| vs = VectorAssembler(inputCols=["a", "b"], outputCol="features") |
| lr = LogisticRegression() |
| pipeline = Pipeline(stages=[vs, lr]) |
| pipelineModel = pipeline.fit(df2) |
| ova = OneVsRest(classifier=lr) |
| ovaModel = ova.fit(df1) |
| |
| ova_pipeline = Pipeline(stages=[vs, ova]) |
| nested_pipeline = Pipeline(stages=[ova_pipeline]) |
| |
| _check_uid_set_equal( |
| MetaAlgorithmReadWrite.getAllNestedStages(pipeline), [pipeline, vs, lr] |
| ) |
| _check_uid_set_equal( |
| MetaAlgorithmReadWrite.getAllNestedStages(pipelineModel), |
| [pipelineModel] + pipelineModel.stages, |
| ) |
| _check_uid_set_equal(MetaAlgorithmReadWrite.getAllNestedStages(ova), [ova, lr]) |
| _check_uid_set_equal( |
| MetaAlgorithmReadWrite.getAllNestedStages(ovaModel), [ovaModel, lr] + ovaModel.models |
| ) |
| _check_uid_set_equal( |
| MetaAlgorithmReadWrite.getAllNestedStages(nested_pipeline), |
| [nested_pipeline, ova_pipeline, vs, ova, lr], |
| ) |
| |
| |
| if __name__ == "__main__": |
| from pyspark.ml.tests.test_util import * # noqa: F401 |
| |
| try: |
| import xmlrunner |
| |
| testRunner = xmlrunner.XMLTestRunner(output="target/test-reports", verbosity=2) |
| except ImportError: |
| testRunner = None |
| unittest.main(testRunner=testRunner, verbosity=2) |