511d4929c8
## What changes were proposed in this pull request? The changes proposed were to add train-validation-split to pyspark.ml.tuning. ## How was the this patch tested? This patch was tested through unit tests located in pyspark/ml/test.py. This is my original work and I license it to Spark. Author: JeremyNixon <jnixon2@gmail.com> Closes #11335 from JeremyNixon/tvs_pyspark.
503 lines
18 KiB
Python
503 lines
18 KiB
Python
#
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# Licensed to the Apache Software Foundation (ASF) under one or more
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# contributor license agreements. See the NOTICE file distributed with
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# this work for additional information regarding copyright ownership.
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# The ASF licenses this file to You under the Apache License, Version 2.0
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# (the "License"); you may not use this file except in compliance with
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# the License. You may obtain a copy of the License at
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#
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# http://www.apache.org/licenses/LICENSE-2.0
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#
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# Unless required by applicable law or agreed to in writing, software
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# distributed under the License is distributed on an "AS IS" BASIS,
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# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
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# See the License for the specific language governing permissions and
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# limitations under the License.
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#
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"""
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Unit tests for Spark ML Python APIs.
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"""
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import sys
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try:
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import xmlrunner
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except ImportError:
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xmlrunner = None
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if sys.version_info[:2] <= (2, 6):
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try:
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import unittest2 as unittest
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except ImportError:
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sys.stderr.write('Please install unittest2 to test with Python 2.6 or earlier')
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sys.exit(1)
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else:
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import unittest
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from shutil import rmtree
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import tempfile
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from pyspark.ml import Estimator, Model, Pipeline, Transformer
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from pyspark.ml.classification import LogisticRegression
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from pyspark.ml.clustering import KMeans
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from pyspark.ml.evaluation import RegressionEvaluator
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from pyspark.ml.feature import *
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from pyspark.ml.param import Param, Params
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from pyspark.ml.param.shared import HasMaxIter, HasInputCol, HasSeed
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from pyspark.ml.regression import LinearRegression
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from pyspark.ml.tuning import *
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from pyspark.ml.util import keyword_only
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from pyspark.mllib.linalg import DenseVector
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from pyspark.sql import DataFrame, SQLContext, Row
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from pyspark.sql.functions import rand
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from pyspark.tests import ReusedPySparkTestCase as PySparkTestCase
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class MockDataset(DataFrame):
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def __init__(self):
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self.index = 0
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class HasFake(Params):
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def __init__(self):
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super(HasFake, self).__init__()
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self.fake = Param(self, "fake", "fake param")
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def getFake(self):
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return self.getOrDefault(self.fake)
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class MockTransformer(Transformer, HasFake):
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def __init__(self):
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super(MockTransformer, self).__init__()
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self.dataset_index = None
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def _transform(self, dataset):
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self.dataset_index = dataset.index
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dataset.index += 1
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return dataset
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class MockEstimator(Estimator, HasFake):
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def __init__(self):
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super(MockEstimator, self).__init__()
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self.dataset_index = None
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def _fit(self, dataset):
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self.dataset_index = dataset.index
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model = MockModel()
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self._copyValues(model)
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return model
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class MockModel(MockTransformer, Model, HasFake):
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pass
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class ParamTypeConversionTests(PySparkTestCase):
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"""
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Test that param type conversion happens.
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"""
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def test_int_to_float(self):
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from pyspark.mllib.linalg import Vectors
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df = self.sc.parallelize([
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Row(label=1.0, weight=2.0, features=Vectors.dense(1.0))]).toDF()
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lr = LogisticRegression(elasticNetParam=0)
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lr.fit(df)
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lr.setElasticNetParam(0)
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lr.fit(df)
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def test_invalid_to_float(self):
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from pyspark.mllib.linalg import Vectors
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self.assertRaises(Exception, lambda: LogisticRegression(elasticNetParam="happy"))
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lr = LogisticRegression(elasticNetParam=0)
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self.assertRaises(Exception, lambda: lr.setElasticNetParam("panda"))
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class PipelineTests(PySparkTestCase):
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def test_pipeline(self):
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dataset = MockDataset()
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estimator0 = MockEstimator()
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transformer1 = MockTransformer()
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estimator2 = MockEstimator()
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transformer3 = MockTransformer()
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pipeline = Pipeline(stages=[estimator0, transformer1, estimator2, transformer3])
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pipeline_model = pipeline.fit(dataset, {estimator0.fake: 0, transformer1.fake: 1})
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model0, transformer1, model2, transformer3 = pipeline_model.stages
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self.assertEqual(0, model0.dataset_index)
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self.assertEqual(0, model0.getFake())
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self.assertEqual(1, transformer1.dataset_index)
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self.assertEqual(1, transformer1.getFake())
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self.assertEqual(2, dataset.index)
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self.assertIsNone(model2.dataset_index, "The last model shouldn't be called in fit.")
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self.assertIsNone(transformer3.dataset_index,
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"The last transformer shouldn't be called in fit.")
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dataset = pipeline_model.transform(dataset)
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self.assertEqual(2, model0.dataset_index)
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self.assertEqual(3, transformer1.dataset_index)
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self.assertEqual(4, model2.dataset_index)
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self.assertEqual(5, transformer3.dataset_index)
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self.assertEqual(6, dataset.index)
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class TestParams(HasMaxIter, HasInputCol, HasSeed):
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"""
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A subclass of Params mixed with HasMaxIter, HasInputCol and HasSeed.
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"""
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@keyword_only
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def __init__(self, seed=None):
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super(TestParams, self).__init__()
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self._setDefault(maxIter=10)
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kwargs = self.__init__._input_kwargs
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self.setParams(**kwargs)
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@keyword_only
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def setParams(self, seed=None):
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"""
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setParams(self, seed=None)
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Sets params for this test.
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"""
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kwargs = self.setParams._input_kwargs
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return self._set(**kwargs)
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class OtherTestParams(HasMaxIter, HasInputCol, HasSeed):
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"""
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A subclass of Params mixed with HasMaxIter, HasInputCol and HasSeed.
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"""
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@keyword_only
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def __init__(self, seed=None):
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super(OtherTestParams, self).__init__()
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self._setDefault(maxIter=10)
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kwargs = self.__init__._input_kwargs
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self.setParams(**kwargs)
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@keyword_only
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def setParams(self, seed=None):
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"""
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setParams(self, seed=None)
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Sets params for this test.
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"""
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kwargs = self.setParams._input_kwargs
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return self._set(**kwargs)
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class ParamTests(PySparkTestCase):
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def test_copy_new_parent(self):
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testParams = TestParams()
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# Copying an instantiated param should fail
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with self.assertRaises(ValueError):
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testParams.maxIter._copy_new_parent(testParams)
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# Copying a dummy param should succeed
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TestParams.maxIter._copy_new_parent(testParams)
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maxIter = testParams.maxIter
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self.assertEqual(maxIter.name, "maxIter")
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self.assertEqual(maxIter.doc, "max number of iterations (>= 0).")
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self.assertTrue(maxIter.parent == testParams.uid)
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def test_param(self):
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testParams = TestParams()
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maxIter = testParams.maxIter
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self.assertEqual(maxIter.name, "maxIter")
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self.assertEqual(maxIter.doc, "max number of iterations (>= 0).")
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self.assertTrue(maxIter.parent == testParams.uid)
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def test_hasparam(self):
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testParams = TestParams()
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self.assertTrue(all([testParams.hasParam(p.name) for p in testParams.params]))
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self.assertFalse(testParams.hasParam("notAParameter"))
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def test_params(self):
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testParams = TestParams()
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maxIter = testParams.maxIter
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inputCol = testParams.inputCol
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seed = testParams.seed
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params = testParams.params
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self.assertEqual(params, [inputCol, maxIter, seed])
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self.assertTrue(testParams.hasParam(maxIter.name))
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self.assertTrue(testParams.hasDefault(maxIter))
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self.assertFalse(testParams.isSet(maxIter))
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self.assertTrue(testParams.isDefined(maxIter))
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self.assertEqual(testParams.getMaxIter(), 10)
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testParams.setMaxIter(100)
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self.assertTrue(testParams.isSet(maxIter))
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self.assertEqual(testParams.getMaxIter(), 100)
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self.assertTrue(testParams.hasParam(inputCol.name))
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self.assertFalse(testParams.hasDefault(inputCol))
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self.assertFalse(testParams.isSet(inputCol))
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self.assertFalse(testParams.isDefined(inputCol))
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with self.assertRaises(KeyError):
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testParams.getInputCol()
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# Since the default is normally random, set it to a known number for debug str
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testParams._setDefault(seed=41)
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testParams.setSeed(43)
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self.assertEqual(
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testParams.explainParams(),
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"\n".join(["inputCol: input column name. (undefined)",
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"maxIter: max number of iterations (>= 0). (default: 10, current: 100)",
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"seed: random seed. (default: 41, current: 43)"]))
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def test_kmeans_param(self):
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algo = KMeans()
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self.assertEqual(algo.getInitMode(), "k-means||")
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algo.setK(10)
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self.assertEqual(algo.getK(), 10)
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algo.setInitSteps(10)
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self.assertEqual(algo.getInitSteps(), 10)
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def test_hasseed(self):
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noSeedSpecd = TestParams()
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withSeedSpecd = TestParams(seed=42)
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other = OtherTestParams()
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# Check that we no longer use 42 as the magic number
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self.assertNotEqual(noSeedSpecd.getSeed(), 42)
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origSeed = noSeedSpecd.getSeed()
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# Check that we only compute the seed once
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self.assertEqual(noSeedSpecd.getSeed(), origSeed)
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# Check that a specified seed is honored
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self.assertEqual(withSeedSpecd.getSeed(), 42)
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# Check that a different class has a different seed
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self.assertNotEqual(other.getSeed(), noSeedSpecd.getSeed())
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class FeatureTests(PySparkTestCase):
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def test_binarizer(self):
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b0 = Binarizer()
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self.assertListEqual(b0.params, [b0.inputCol, b0.outputCol, b0.threshold])
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self.assertTrue(all([~b0.isSet(p) for p in b0.params]))
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self.assertTrue(b0.hasDefault(b0.threshold))
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self.assertEqual(b0.getThreshold(), 0.0)
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b0.setParams(inputCol="input", outputCol="output").setThreshold(1.0)
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self.assertTrue(all([b0.isSet(p) for p in b0.params]))
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self.assertEqual(b0.getThreshold(), 1.0)
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self.assertEqual(b0.getInputCol(), "input")
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self.assertEqual(b0.getOutputCol(), "output")
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b0c = b0.copy({b0.threshold: 2.0})
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self.assertEqual(b0c.uid, b0.uid)
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self.assertListEqual(b0c.params, b0.params)
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self.assertEqual(b0c.getThreshold(), 2.0)
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b1 = Binarizer(threshold=2.0, inputCol="input", outputCol="output")
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self.assertNotEqual(b1.uid, b0.uid)
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self.assertEqual(b1.getThreshold(), 2.0)
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self.assertEqual(b1.getInputCol(), "input")
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self.assertEqual(b1.getOutputCol(), "output")
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def test_idf(self):
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sqlContext = SQLContext(self.sc)
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dataset = sqlContext.createDataFrame([
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(DenseVector([1.0, 2.0]),),
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(DenseVector([0.0, 1.0]),),
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(DenseVector([3.0, 0.2]),)], ["tf"])
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idf0 = IDF(inputCol="tf")
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self.assertListEqual(idf0.params, [idf0.inputCol, idf0.minDocFreq, idf0.outputCol])
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idf0m = idf0.fit(dataset, {idf0.outputCol: "idf"})
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self.assertEqual(idf0m.uid, idf0.uid,
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"Model should inherit the UID from its parent estimator.")
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output = idf0m.transform(dataset)
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self.assertIsNotNone(output.head().idf)
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def test_ngram(self):
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sqlContext = SQLContext(self.sc)
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dataset = sqlContext.createDataFrame([
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Row(input=["a", "b", "c", "d", "e"])])
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ngram0 = NGram(n=4, inputCol="input", outputCol="output")
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self.assertEqual(ngram0.getN(), 4)
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self.assertEqual(ngram0.getInputCol(), "input")
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self.assertEqual(ngram0.getOutputCol(), "output")
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transformedDF = ngram0.transform(dataset)
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self.assertEqual(transformedDF.head().output, ["a b c d", "b c d e"])
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def test_stopwordsremover(self):
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sqlContext = SQLContext(self.sc)
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dataset = sqlContext.createDataFrame([Row(input=["a", "panda"])])
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stopWordRemover = StopWordsRemover(inputCol="input", outputCol="output")
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# Default
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self.assertEqual(stopWordRemover.getInputCol(), "input")
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transformedDF = stopWordRemover.transform(dataset)
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self.assertEqual(transformedDF.head().output, ["panda"])
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# Custom
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stopwords = ["panda"]
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stopWordRemover.setStopWords(stopwords)
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self.assertEqual(stopWordRemover.getInputCol(), "input")
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self.assertEqual(stopWordRemover.getStopWords(), stopwords)
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transformedDF = stopWordRemover.transform(dataset)
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self.assertEqual(transformedDF.head().output, ["a"])
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class HasInducedError(Params):
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def __init__(self):
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super(HasInducedError, self).__init__()
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self.inducedError = Param(self, "inducedError",
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"Uniformly-distributed error added to feature")
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def getInducedError(self):
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return self.getOrDefault(self.inducedError)
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class InducedErrorModel(Model, HasInducedError):
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def __init__(self):
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super(InducedErrorModel, self).__init__()
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def _transform(self, dataset):
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return dataset.withColumn("prediction",
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dataset.feature + (rand(0) * self.getInducedError()))
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class InducedErrorEstimator(Estimator, HasInducedError):
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def __init__(self, inducedError=1.0):
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super(InducedErrorEstimator, self).__init__()
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self._set(inducedError=inducedError)
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def _fit(self, dataset):
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model = InducedErrorModel()
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self._copyValues(model)
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return model
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class CrossValidatorTests(PySparkTestCase):
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def test_fit_minimize_metric(self):
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sqlContext = SQLContext(self.sc)
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dataset = sqlContext.createDataFrame([
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(10, 10.0),
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(50, 50.0),
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(100, 100.0),
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(500, 500.0)] * 10,
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["feature", "label"])
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iee = InducedErrorEstimator()
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evaluator = RegressionEvaluator(metricName="rmse")
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grid = (ParamGridBuilder()
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.addGrid(iee.inducedError, [100.0, 0.0, 10000.0])
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.build())
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cv = CrossValidator(estimator=iee, estimatorParamMaps=grid, evaluator=evaluator)
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cvModel = cv.fit(dataset)
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bestModel = cvModel.bestModel
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bestModelMetric = evaluator.evaluate(bestModel.transform(dataset))
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self.assertEqual(0.0, bestModel.getOrDefault('inducedError'),
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"Best model should have zero induced error")
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self.assertEqual(0.0, bestModelMetric, "Best model has RMSE of 0")
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def test_fit_maximize_metric(self):
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sqlContext = SQLContext(self.sc)
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dataset = sqlContext.createDataFrame([
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(10, 10.0),
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(50, 50.0),
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(100, 100.0),
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(500, 500.0)] * 10,
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["feature", "label"])
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iee = InducedErrorEstimator()
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evaluator = RegressionEvaluator(metricName="r2")
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grid = (ParamGridBuilder()
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.addGrid(iee.inducedError, [100.0, 0.0, 10000.0])
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.build())
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cv = CrossValidator(estimator=iee, estimatorParamMaps=grid, evaluator=evaluator)
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cvModel = cv.fit(dataset)
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bestModel = cvModel.bestModel
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bestModelMetric = evaluator.evaluate(bestModel.transform(dataset))
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self.assertEqual(0.0, bestModel.getOrDefault('inducedError'),
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"Best model should have zero induced error")
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self.assertEqual(1.0, bestModelMetric, "Best model has R-squared of 1")
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class TrainValidationSplitTests(PySparkTestCase):
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def test_fit_minimize_metric(self):
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sqlContext = SQLContext(self.sc)
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dataset = sqlContext.createDataFrame([
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(10, 10.0),
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(50, 50.0),
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(100, 100.0),
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(500, 500.0)] * 10,
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["feature", "label"])
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iee = InducedErrorEstimator()
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evaluator = RegressionEvaluator(metricName="rmse")
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grid = (ParamGridBuilder()
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.addGrid(iee.inducedError, [100.0, 0.0, 10000.0])
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.build())
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tvs = TrainValidationSplit(estimator=iee, estimatorParamMaps=grid, evaluator=evaluator)
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tvsModel = tvs.fit(dataset)
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bestModel = tvsModel.bestModel
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bestModelMetric = evaluator.evaluate(bestModel.transform(dataset))
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self.assertEqual(0.0, bestModel.getOrDefault('inducedError'),
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"Best model should have zero induced error")
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self.assertEqual(0.0, bestModelMetric, "Best model has RMSE of 0")
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def test_fit_maximize_metric(self):
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sqlContext = SQLContext(self.sc)
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dataset = sqlContext.createDataFrame([
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(10, 10.0),
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(50, 50.0),
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(100, 100.0),
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(500, 500.0)] * 10,
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["feature", "label"])
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iee = InducedErrorEstimator()
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evaluator = RegressionEvaluator(metricName="r2")
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grid = (ParamGridBuilder()
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.addGrid(iee.inducedError, [100.0, 0.0, 10000.0])
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.build())
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tvs = TrainValidationSplit(estimator=iee, estimatorParamMaps=grid, evaluator=evaluator)
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tvsModel = tvs.fit(dataset)
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bestModel = tvsModel.bestModel
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bestModelMetric = evaluator.evaluate(bestModel.transform(dataset))
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self.assertEqual(0.0, bestModel.getOrDefault('inducedError'),
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"Best model should have zero induced error")
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self.assertEqual(1.0, bestModelMetric, "Best model has R-squared of 1")
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class PersistenceTest(PySparkTestCase):
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def test_linear_regression(self):
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lr = LinearRegression(maxIter=1)
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path = tempfile.mkdtemp()
|
|
lr_path = path + "/lr"
|
|
lr.save(lr_path)
|
|
lr2 = LinearRegression.load(lr_path)
|
|
self.assertEqual(lr2.uid, lr2.maxIter.parent,
|
|
"Loaded LinearRegression instance uid (%s) did not match Param's uid (%s)"
|
|
% (lr2.uid, lr2.maxIter.parent))
|
|
self.assertEqual(lr._defaultParamMap[lr.maxIter], lr2._defaultParamMap[lr2.maxIter],
|
|
"Loaded LinearRegression instance default params did not match " +
|
|
"original defaults")
|
|
try:
|
|
rmtree(path)
|
|
except OSError:
|
|
pass
|
|
|
|
|
|
if __name__ == "__main__":
|
|
from pyspark.ml.tests import *
|
|
if xmlrunner:
|
|
unittest.main(testRunner=xmlrunner.XMLTestRunner(output='target/test-reports'))
|
|
else:
|
|
unittest.main()
|