a6428292f7
## What changes were proposed in this pull request? This PR is an update for [https://github.com/apache/spark/pull/12738] which: * Adds a generic unit test for JavaParams wrappers in pyspark.ml for checking default Param values vs. the defaults in the Scala side * Various fixes for bugs found * This includes changing classes taking weightCol to treat unset and empty String Param values the same way. Defaults changed: * Scala * LogisticRegression: weightCol defaults to not set (instead of empty string) * StringIndexer: labels default to not set (instead of empty array) * GeneralizedLinearRegression: * maxIter always defaults to 25 (simpler than defaulting to 25 for a particular solver) * weightCol defaults to not set (instead of empty string) * LinearRegression: weightCol defaults to not set (instead of empty string) * Python * MultilayerPerceptron: layers default to not set (instead of [1,1]) * ChiSqSelector: numTopFeatures defaults to 50 (instead of not set) ## How was this patch tested? Generic unit test. Manually tested that unit test by changing defaults and verifying that broke the test. Author: Joseph K. Bradley <joseph@databricks.com> Author: yinxusen <yinxusen@gmail.com> Closes #12816 from jkbradley/yinxusen-SPARK-14931.
1083 lines
43 KiB
Python
1083 lines
43 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 array
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import sys
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if sys.version > '3':
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xrange = range
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basestring = str
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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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import numpy as np
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import inspect
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from pyspark import keyword_only
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from pyspark.ml import Estimator, Model, Pipeline, PipelineModel, Transformer
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from pyspark.ml.classification import *
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from pyspark.ml.clustering import *
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from pyspark.ml.evaluation import BinaryClassificationEvaluator, RegressionEvaluator
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from pyspark.ml.feature import *
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from pyspark.ml.param import Param, Params, TypeConverters
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from pyspark.ml.param.shared import HasMaxIter, HasInputCol, HasSeed
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from pyspark.ml.recommendation import ALS
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from pyspark.ml.regression import LinearRegression, DecisionTreeRegressor
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from pyspark.ml.tuning import *
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from pyspark.ml.wrapper import JavaParams
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from pyspark.mllib.common import _java2py
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from pyspark.mllib.linalg import Vectors, DenseVector, SparseVector
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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.sql.utils import IllegalArgumentException
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from pyspark.storagelevel import *
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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(self):
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lr = LogisticRegression(maxIter=5.0)
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self.assertEqual(lr.getMaxIter(), 5)
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self.assertTrue(type(lr.getMaxIter()) == int)
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self.assertRaises(TypeError, lambda: LogisticRegression(maxIter="notAnInt"))
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self.assertRaises(TypeError, lambda: LogisticRegression(maxIter=5.1))
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def test_float(self):
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lr = LogisticRegression(tol=1)
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self.assertEqual(lr.getTol(), 1.0)
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self.assertTrue(type(lr.getTol()) == float)
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self.assertRaises(TypeError, lambda: LogisticRegression(tol="notAFloat"))
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def test_vector(self):
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ewp = ElementwiseProduct(scalingVec=[1, 3])
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self.assertEqual(ewp.getScalingVec(), DenseVector([1.0, 3.0]))
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ewp = ElementwiseProduct(scalingVec=np.array([1.2, 3.4]))
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self.assertEqual(ewp.getScalingVec(), DenseVector([1.2, 3.4]))
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self.assertRaises(TypeError, lambda: ElementwiseProduct(scalingVec=["a", "b"]))
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def test_list(self):
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l = [0, 1]
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for lst_like in [l, np.array(l), DenseVector(l), SparseVector(len(l), range(len(l)), l),
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array.array('l', l), xrange(2), tuple(l)]:
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converted = TypeConverters.toList(lst_like)
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self.assertEqual(type(converted), list)
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self.assertListEqual(converted, l)
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def test_list_int(self):
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for indices in [[1.0, 2.0], np.array([1.0, 2.0]), DenseVector([1.0, 2.0]),
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SparseVector(2, {0: 1.0, 1: 2.0}), xrange(1, 3), (1.0, 2.0),
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array.array('d', [1.0, 2.0])]:
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vs = VectorSlicer(indices=indices)
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self.assertListEqual(vs.getIndices(), [1, 2])
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self.assertTrue(all([type(v) == int for v in vs.getIndices()]))
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self.assertRaises(TypeError, lambda: VectorSlicer(indices=["a", "b"]))
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def test_list_float(self):
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b = Bucketizer(splits=[1, 4])
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self.assertEqual(b.getSplits(), [1.0, 4.0])
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self.assertTrue(all([type(v) == float for v in b.getSplits()]))
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self.assertRaises(TypeError, lambda: Bucketizer(splits=["a", 1.0]))
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def test_list_string(self):
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for labels in [np.array(['a', u'b']), ['a', u'b'], np.array(['a', 'b'])]:
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idx_to_string = IndexToString(labels=labels)
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self.assertListEqual(idx_to_string.getLabels(), ['a', 'b'])
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self.assertRaises(TypeError, lambda: IndexToString(labels=['a', 2]))
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def test_string(self):
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lr = LogisticRegression()
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for col in ['features', u'features', np.str_('features')]:
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lr.setFeaturesCol(col)
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self.assertEqual(lr.getFeaturesCol(), 'features')
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self.assertRaises(TypeError, lambda: LogisticRegression(featuresCol=2.3))
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def test_bool(self):
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self.assertRaises(TypeError, lambda: LogisticRegression(fitIntercept=1))
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self.assertRaises(TypeError, lambda: LogisticRegression(fitIntercept="false"))
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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 HasThrowableProperty(Params):
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def __init__(self):
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super(HasThrowableProperty, self).__init__()
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self.p = Param(self, "none", "empty param")
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@property
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def test_property(self):
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raise RuntimeError("Test property to raise error when invoked")
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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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def test_param_property_error(self):
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param_store = HasThrowableProperty()
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self.assertRaises(RuntimeError, lambda: param_store.test_property)
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params = param_store.params # should not invoke the property 'test_property'
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self.assertEqual(len(params), 1)
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def test_word2vec_param(self):
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model = Word2Vec().setWindowSize(6)
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# Check windowSize is set properly
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self.assertEqual(model.getWindowSize(), 6)
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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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self.assertEqual(type(stopWordRemover.getStopWords()), list)
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self.assertTrue(isinstance(stopWordRemover.getStopWords()[0], basestring))
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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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def test_count_vectorizer_with_binary(self):
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sqlContext = SQLContext(self.sc)
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dataset = sqlContext.createDataFrame([
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(0, "a a a b b c".split(' '), SparseVector(3, {0: 1.0, 1: 1.0, 2: 1.0}),),
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(1, "a a".split(' '), SparseVector(3, {0: 1.0}),),
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(2, "a b".split(' '), SparseVector(3, {0: 1.0, 1: 1.0}),),
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(3, "c".split(' '), SparseVector(3, {2: 1.0}),)], ["id", "words", "expected"])
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cv = CountVectorizer(binary=True, inputCol="words", outputCol="features")
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model = cv.fit(dataset)
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transformedList = model.transform(dataset).select("features", "expected").collect()
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for r in transformedList:
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feature, expected = r
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self.assertEqual(feature, expected)
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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",
|
|
"Uniformly-distributed error added to feature")
|
|
|
|
def getInducedError(self):
|
|
return self.getOrDefault(self.inducedError)
|
|
|
|
|
|
class InducedErrorModel(Model, HasInducedError):
|
|
|
|
def __init__(self):
|
|
super(InducedErrorModel, self).__init__()
|
|
|
|
def _transform(self, dataset):
|
|
return dataset.withColumn("prediction",
|
|
dataset.feature + (rand(0) * self.getInducedError()))
|
|
|
|
|
|
class InducedErrorEstimator(Estimator, HasInducedError):
|
|
|
|
def __init__(self, inducedError=1.0):
|
|
super(InducedErrorEstimator, self).__init__()
|
|
self._set(inducedError=inducedError)
|
|
|
|
def _fit(self, dataset):
|
|
model = InducedErrorModel()
|
|
self._copyValues(model)
|
|
return model
|
|
|
|
|
|
class CrossValidatorTests(PySparkTestCase):
|
|
|
|
def test_copy(self):
|
|
sqlContext = SQLContext(self.sc)
|
|
dataset = sqlContext.createDataFrame([
|
|
(10, 10.0),
|
|
(50, 50.0),
|
|
(100, 100.0),
|
|
(500, 500.0)] * 10,
|
|
["feature", "label"])
|
|
|
|
iee = InducedErrorEstimator()
|
|
evaluator = RegressionEvaluator(metricName="rmse")
|
|
|
|
grid = (ParamGridBuilder()
|
|
.addGrid(iee.inducedError, [100.0, 0.0, 10000.0])
|
|
.build())
|
|
cv = CrossValidator(estimator=iee, estimatorParamMaps=grid, evaluator=evaluator)
|
|
cvCopied = cv.copy()
|
|
self.assertEqual(cv.getEstimator().uid, cvCopied.getEstimator().uid)
|
|
|
|
cvModel = cv.fit(dataset)
|
|
cvModelCopied = cvModel.copy()
|
|
for index in range(len(cvModel.avgMetrics)):
|
|
self.assertTrue(abs(cvModel.avgMetrics[index] - cvModelCopied.avgMetrics[index])
|
|
< 0.0001)
|
|
|
|
def test_fit_minimize_metric(self):
|
|
sqlContext = SQLContext(self.sc)
|
|
dataset = sqlContext.createDataFrame([
|
|
(10, 10.0),
|
|
(50, 50.0),
|
|
(100, 100.0),
|
|
(500, 500.0)] * 10,
|
|
["feature", "label"])
|
|
|
|
iee = InducedErrorEstimator()
|
|
evaluator = RegressionEvaluator(metricName="rmse")
|
|
|
|
grid = (ParamGridBuilder()
|
|
.addGrid(iee.inducedError, [100.0, 0.0, 10000.0])
|
|
.build())
|
|
cv = CrossValidator(estimator=iee, estimatorParamMaps=grid, evaluator=evaluator)
|
|
cvModel = cv.fit(dataset)
|
|
bestModel = cvModel.bestModel
|
|
bestModelMetric = evaluator.evaluate(bestModel.transform(dataset))
|
|
|
|
self.assertEqual(0.0, bestModel.getOrDefault('inducedError'),
|
|
"Best model should have zero induced error")
|
|
self.assertEqual(0.0, bestModelMetric, "Best model has RMSE of 0")
|
|
|
|
def test_fit_maximize_metric(self):
|
|
sqlContext = SQLContext(self.sc)
|
|
dataset = sqlContext.createDataFrame([
|
|
(10, 10.0),
|
|
(50, 50.0),
|
|
(100, 100.0),
|
|
(500, 500.0)] * 10,
|
|
["feature", "label"])
|
|
|
|
iee = InducedErrorEstimator()
|
|
evaluator = RegressionEvaluator(metricName="r2")
|
|
|
|
grid = (ParamGridBuilder()
|
|
.addGrid(iee.inducedError, [100.0, 0.0, 10000.0])
|
|
.build())
|
|
cv = CrossValidator(estimator=iee, estimatorParamMaps=grid, evaluator=evaluator)
|
|
cvModel = cv.fit(dataset)
|
|
bestModel = cvModel.bestModel
|
|
bestModelMetric = evaluator.evaluate(bestModel.transform(dataset))
|
|
|
|
self.assertEqual(0.0, bestModel.getOrDefault('inducedError'),
|
|
"Best model should have zero induced error")
|
|
self.assertEqual(1.0, bestModelMetric, "Best model has R-squared of 1")
|
|
|
|
def test_save_load(self):
|
|
# This tests saving and loading the trained model only.
|
|
# Save/load for CrossValidator will be added later: SPARK-13786
|
|
temp_path = tempfile.mkdtemp()
|
|
sqlContext = SQLContext(self.sc)
|
|
dataset = sqlContext.createDataFrame(
|
|
[(Vectors.dense([0.0]), 0.0),
|
|
(Vectors.dense([0.4]), 1.0),
|
|
(Vectors.dense([0.5]), 0.0),
|
|
(Vectors.dense([0.6]), 1.0),
|
|
(Vectors.dense([1.0]), 1.0)] * 10,
|
|
["features", "label"])
|
|
lr = LogisticRegression()
|
|
grid = ParamGridBuilder().addGrid(lr.maxIter, [0, 1]).build()
|
|
evaluator = BinaryClassificationEvaluator()
|
|
cv = CrossValidator(estimator=lr, estimatorParamMaps=grid, evaluator=evaluator)
|
|
cvModel = cv.fit(dataset)
|
|
lrModel = cvModel.bestModel
|
|
|
|
cvModelPath = temp_path + "/cvModel"
|
|
lrModel.save(cvModelPath)
|
|
loadedLrModel = LogisticRegressionModel.load(cvModelPath)
|
|
self.assertEqual(loadedLrModel.uid, lrModel.uid)
|
|
self.assertEqual(loadedLrModel.intercept, lrModel.intercept)
|
|
|
|
|
|
class TrainValidationSplitTests(PySparkTestCase):
|
|
|
|
def test_fit_minimize_metric(self):
|
|
sqlContext = SQLContext(self.sc)
|
|
dataset = sqlContext.createDataFrame([
|
|
(10, 10.0),
|
|
(50, 50.0),
|
|
(100, 100.0),
|
|
(500, 500.0)] * 10,
|
|
["feature", "label"])
|
|
|
|
iee = InducedErrorEstimator()
|
|
evaluator = RegressionEvaluator(metricName="rmse")
|
|
|
|
grid = (ParamGridBuilder()
|
|
.addGrid(iee.inducedError, [100.0, 0.0, 10000.0])
|
|
.build())
|
|
tvs = TrainValidationSplit(estimator=iee, estimatorParamMaps=grid, evaluator=evaluator)
|
|
tvsModel = tvs.fit(dataset)
|
|
bestModel = tvsModel.bestModel
|
|
bestModelMetric = evaluator.evaluate(bestModel.transform(dataset))
|
|
|
|
self.assertEqual(0.0, bestModel.getOrDefault('inducedError'),
|
|
"Best model should have zero induced error")
|
|
self.assertEqual(0.0, bestModelMetric, "Best model has RMSE of 0")
|
|
|
|
def test_fit_maximize_metric(self):
|
|
sqlContext = SQLContext(self.sc)
|
|
dataset = sqlContext.createDataFrame([
|
|
(10, 10.0),
|
|
(50, 50.0),
|
|
(100, 100.0),
|
|
(500, 500.0)] * 10,
|
|
["feature", "label"])
|
|
|
|
iee = InducedErrorEstimator()
|
|
evaluator = RegressionEvaluator(metricName="r2")
|
|
|
|
grid = (ParamGridBuilder()
|
|
.addGrid(iee.inducedError, [100.0, 0.0, 10000.0])
|
|
.build())
|
|
tvs = TrainValidationSplit(estimator=iee, estimatorParamMaps=grid, evaluator=evaluator)
|
|
tvsModel = tvs.fit(dataset)
|
|
bestModel = tvsModel.bestModel
|
|
bestModelMetric = evaluator.evaluate(bestModel.transform(dataset))
|
|
|
|
self.assertEqual(0.0, bestModel.getOrDefault('inducedError'),
|
|
"Best model should have zero induced error")
|
|
self.assertEqual(1.0, bestModelMetric, "Best model has R-squared of 1")
|
|
|
|
def test_save_load(self):
|
|
# This tests saving and loading the trained model only.
|
|
# Save/load for TrainValidationSplit will be added later: SPARK-13786
|
|
temp_path = tempfile.mkdtemp()
|
|
sqlContext = SQLContext(self.sc)
|
|
dataset = sqlContext.createDataFrame(
|
|
[(Vectors.dense([0.0]), 0.0),
|
|
(Vectors.dense([0.4]), 1.0),
|
|
(Vectors.dense([0.5]), 0.0),
|
|
(Vectors.dense([0.6]), 1.0),
|
|
(Vectors.dense([1.0]), 1.0)] * 10,
|
|
["features", "label"])
|
|
lr = LogisticRegression()
|
|
grid = ParamGridBuilder().addGrid(lr.maxIter, [0, 1]).build()
|
|
evaluator = BinaryClassificationEvaluator()
|
|
tvs = TrainValidationSplit(estimator=lr, estimatorParamMaps=grid, evaluator=evaluator)
|
|
tvsModel = tvs.fit(dataset)
|
|
lrModel = tvsModel.bestModel
|
|
|
|
tvsModelPath = temp_path + "/tvsModel"
|
|
lrModel.save(tvsModelPath)
|
|
loadedLrModel = LogisticRegressionModel.load(tvsModelPath)
|
|
self.assertEqual(loadedLrModel.uid, lrModel.uid)
|
|
self.assertEqual(loadedLrModel.intercept, lrModel.intercept)
|
|
|
|
|
|
class PersistenceTest(PySparkTestCase):
|
|
|
|
def test_linear_regression(self):
|
|
lr = LinearRegression(maxIter=1)
|
|
path = tempfile.mkdtemp()
|
|
lr_path = path + "/lr"
|
|
lr.save(lr_path)
|
|
lr2 = LinearRegression.load(lr_path)
|
|
self.assertEqual(lr.uid, lr2.uid)
|
|
self.assertEqual(type(lr.uid), type(lr2.uid))
|
|
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
|
|
|
|
def test_logistic_regression(self):
|
|
lr = LogisticRegression(maxIter=1)
|
|
path = tempfile.mkdtemp()
|
|
lr_path = path + "/logreg"
|
|
lr.save(lr_path)
|
|
lr2 = LogisticRegression.load(lr_path)
|
|
self.assertEqual(lr2.uid, lr2.maxIter.parent,
|
|
"Loaded LogisticRegression 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 LogisticRegression instance default params did not match " +
|
|
"original defaults")
|
|
try:
|
|
rmtree(path)
|
|
except OSError:
|
|
pass
|
|
|
|
def _compare_pipelines(self, m1, m2):
|
|
"""
|
|
Compare 2 ML types, asserting that they are equivalent.
|
|
This currently supports:
|
|
- basic types
|
|
- Pipeline, PipelineModel
|
|
This checks:
|
|
- uid
|
|
- type
|
|
- Param values and parents
|
|
"""
|
|
self.assertEqual(m1.uid, m2.uid)
|
|
self.assertEqual(type(m1), type(m2))
|
|
if isinstance(m1, JavaParams):
|
|
self.assertEqual(len(m1.params), len(m2.params))
|
|
for p in m1.params:
|
|
self.assertEqual(m1.getOrDefault(p), m2.getOrDefault(p))
|
|
self.assertEqual(p.parent, m2.getParam(p.name).parent)
|
|
elif isinstance(m1, Pipeline):
|
|
self.assertEqual(len(m1.getStages()), len(m2.getStages()))
|
|
for s1, s2 in zip(m1.getStages(), m2.getStages()):
|
|
self._compare_pipelines(s1, s2)
|
|
elif isinstance(m1, PipelineModel):
|
|
self.assertEqual(len(m1.stages), len(m2.stages))
|
|
for s1, s2 in zip(m1.stages, m2.stages):
|
|
self._compare_pipelines(s1, s2)
|
|
else:
|
|
raise RuntimeError("_compare_pipelines does not yet support type: %s" % type(m1))
|
|
|
|
def test_pipeline_persistence(self):
|
|
"""
|
|
Pipeline[HashingTF, PCA]
|
|
"""
|
|
sqlContext = SQLContext(self.sc)
|
|
temp_path = tempfile.mkdtemp()
|
|
|
|
try:
|
|
df = sqlContext.createDataFrame([(["a", "b", "c"],), (["c", "d", "e"],)], ["words"])
|
|
tf = HashingTF(numFeatures=10, inputCol="words", outputCol="features")
|
|
pca = PCA(k=2, inputCol="features", outputCol="pca_features")
|
|
pl = Pipeline(stages=[tf, pca])
|
|
model = pl.fit(df)
|
|
|
|
pipeline_path = temp_path + "/pipeline"
|
|
pl.save(pipeline_path)
|
|
loaded_pipeline = Pipeline.load(pipeline_path)
|
|
self._compare_pipelines(pl, loaded_pipeline)
|
|
|
|
model_path = temp_path + "/pipeline-model"
|
|
model.save(model_path)
|
|
loaded_model = PipelineModel.load(model_path)
|
|
self._compare_pipelines(model, loaded_model)
|
|
finally:
|
|
try:
|
|
rmtree(temp_path)
|
|
except OSError:
|
|
pass
|
|
|
|
def test_nested_pipeline_persistence(self):
|
|
"""
|
|
Pipeline[HashingTF, Pipeline[PCA]]
|
|
"""
|
|
sqlContext = SQLContext(self.sc)
|
|
temp_path = tempfile.mkdtemp()
|
|
|
|
try:
|
|
df = sqlContext.createDataFrame([(["a", "b", "c"],), (["c", "d", "e"],)], ["words"])
|
|
tf = HashingTF(numFeatures=10, inputCol="words", outputCol="features")
|
|
pca = PCA(k=2, inputCol="features", outputCol="pca_features")
|
|
p0 = Pipeline(stages=[pca])
|
|
pl = Pipeline(stages=[tf, p0])
|
|
model = pl.fit(df)
|
|
|
|
pipeline_path = temp_path + "/pipeline"
|
|
pl.save(pipeline_path)
|
|
loaded_pipeline = Pipeline.load(pipeline_path)
|
|
self._compare_pipelines(pl, loaded_pipeline)
|
|
|
|
model_path = temp_path + "/pipeline-model"
|
|
model.save(model_path)
|
|
loaded_model = PipelineModel.load(model_path)
|
|
self._compare_pipelines(model, loaded_model)
|
|
finally:
|
|
try:
|
|
rmtree(temp_path)
|
|
except OSError:
|
|
pass
|
|
|
|
def test_decisiontree_classifier(self):
|
|
dt = DecisionTreeClassifier(maxDepth=1)
|
|
path = tempfile.mkdtemp()
|
|
dtc_path = path + "/dtc"
|
|
dt.save(dtc_path)
|
|
dt2 = DecisionTreeClassifier.load(dtc_path)
|
|
self.assertEqual(dt2.uid, dt2.maxDepth.parent,
|
|
"Loaded DecisionTreeClassifier instance uid (%s) "
|
|
"did not match Param's uid (%s)"
|
|
% (dt2.uid, dt2.maxDepth.parent))
|
|
self.assertEqual(dt._defaultParamMap[dt.maxDepth], dt2._defaultParamMap[dt2.maxDepth],
|
|
"Loaded DecisionTreeClassifier instance default params did not match " +
|
|
"original defaults")
|
|
try:
|
|
rmtree(path)
|
|
except OSError:
|
|
pass
|
|
|
|
def test_decisiontree_regressor(self):
|
|
dt = DecisionTreeRegressor(maxDepth=1)
|
|
path = tempfile.mkdtemp()
|
|
dtr_path = path + "/dtr"
|
|
dt.save(dtr_path)
|
|
dt2 = DecisionTreeClassifier.load(dtr_path)
|
|
self.assertEqual(dt2.uid, dt2.maxDepth.parent,
|
|
"Loaded DecisionTreeRegressor instance uid (%s) "
|
|
"did not match Param's uid (%s)"
|
|
% (dt2.uid, dt2.maxDepth.parent))
|
|
self.assertEqual(dt._defaultParamMap[dt.maxDepth], dt2._defaultParamMap[dt2.maxDepth],
|
|
"Loaded DecisionTreeRegressor instance default params did not match " +
|
|
"original defaults")
|
|
try:
|
|
rmtree(path)
|
|
except OSError:
|
|
pass
|
|
|
|
|
|
class LDATest(PySparkTestCase):
|
|
|
|
def _compare(self, m1, m2):
|
|
"""
|
|
Temp method for comparing instances.
|
|
TODO: Replace with generic implementation once SPARK-14706 is merged.
|
|
"""
|
|
self.assertEqual(m1.uid, m2.uid)
|
|
self.assertEqual(type(m1), type(m2))
|
|
self.assertEqual(len(m1.params), len(m2.params))
|
|
for p in m1.params:
|
|
if m1.isDefined(p):
|
|
self.assertEqual(m1.getOrDefault(p), m2.getOrDefault(p))
|
|
self.assertEqual(p.parent, m2.getParam(p.name).parent)
|
|
if isinstance(m1, LDAModel):
|
|
self.assertEqual(m1.vocabSize(), m2.vocabSize())
|
|
self.assertEqual(m1.topicsMatrix(), m2.topicsMatrix())
|
|
|
|
def test_persistence(self):
|
|
# Test save/load for LDA, LocalLDAModel, DistributedLDAModel.
|
|
sqlContext = SQLContext(self.sc)
|
|
df = sqlContext.createDataFrame([
|
|
[1, Vectors.dense([0.0, 1.0])],
|
|
[2, Vectors.sparse(2, {0: 1.0})],
|
|
], ["id", "features"])
|
|
# Fit model
|
|
lda = LDA(k=2, seed=1, optimizer="em")
|
|
distributedModel = lda.fit(df)
|
|
self.assertTrue(distributedModel.isDistributed())
|
|
localModel = distributedModel.toLocal()
|
|
self.assertFalse(localModel.isDistributed())
|
|
# Define paths
|
|
path = tempfile.mkdtemp()
|
|
lda_path = path + "/lda"
|
|
dist_model_path = path + "/distLDAModel"
|
|
local_model_path = path + "/localLDAModel"
|
|
# Test LDA
|
|
lda.save(lda_path)
|
|
lda2 = LDA.load(lda_path)
|
|
self._compare(lda, lda2)
|
|
# Test DistributedLDAModel
|
|
distributedModel.save(dist_model_path)
|
|
distributedModel2 = DistributedLDAModel.load(dist_model_path)
|
|
self._compare(distributedModel, distributedModel2)
|
|
# Test LocalLDAModel
|
|
localModel.save(local_model_path)
|
|
localModel2 = LocalLDAModel.load(local_model_path)
|
|
self._compare(localModel, localModel2)
|
|
# Clean up
|
|
try:
|
|
rmtree(path)
|
|
except OSError:
|
|
pass
|
|
|
|
|
|
class TrainingSummaryTest(PySparkTestCase):
|
|
|
|
def test_linear_regression_summary(self):
|
|
from pyspark.mllib.linalg import Vectors
|
|
sqlContext = SQLContext(self.sc)
|
|
df = sqlContext.createDataFrame([(1.0, 2.0, Vectors.dense(1.0)),
|
|
(0.0, 2.0, Vectors.sparse(1, [], []))],
|
|
["label", "weight", "features"])
|
|
lr = LinearRegression(maxIter=5, regParam=0.0, solver="normal", weightCol="weight",
|
|
fitIntercept=False)
|
|
model = lr.fit(df)
|
|
self.assertTrue(model.hasSummary)
|
|
s = model.summary
|
|
# test that api is callable and returns expected types
|
|
self.assertGreater(s.totalIterations, 0)
|
|
self.assertTrue(isinstance(s.predictions, DataFrame))
|
|
self.assertEqual(s.predictionCol, "prediction")
|
|
self.assertEqual(s.labelCol, "label")
|
|
self.assertEqual(s.featuresCol, "features")
|
|
objHist = s.objectiveHistory
|
|
self.assertTrue(isinstance(objHist, list) and isinstance(objHist[0], float))
|
|
self.assertAlmostEqual(s.explainedVariance, 0.25, 2)
|
|
self.assertAlmostEqual(s.meanAbsoluteError, 0.0)
|
|
self.assertAlmostEqual(s.meanSquaredError, 0.0)
|
|
self.assertAlmostEqual(s.rootMeanSquaredError, 0.0)
|
|
self.assertAlmostEqual(s.r2, 1.0, 2)
|
|
self.assertTrue(isinstance(s.residuals, DataFrame))
|
|
self.assertEqual(s.numInstances, 2)
|
|
devResiduals = s.devianceResiduals
|
|
self.assertTrue(isinstance(devResiduals, list) and isinstance(devResiduals[0], float))
|
|
coefStdErr = s.coefficientStandardErrors
|
|
self.assertTrue(isinstance(coefStdErr, list) and isinstance(coefStdErr[0], float))
|
|
tValues = s.tValues
|
|
self.assertTrue(isinstance(tValues, list) and isinstance(tValues[0], float))
|
|
pValues = s.pValues
|
|
self.assertTrue(isinstance(pValues, list) and isinstance(pValues[0], float))
|
|
# test evaluation (with training dataset) produces a summary with same values
|
|
# one check is enough to verify a summary is returned, Scala version runs full test
|
|
sameSummary = model.evaluate(df)
|
|
self.assertAlmostEqual(sameSummary.explainedVariance, s.explainedVariance)
|
|
|
|
def test_logistic_regression_summary(self):
|
|
from pyspark.mllib.linalg import Vectors
|
|
sqlContext = SQLContext(self.sc)
|
|
df = sqlContext.createDataFrame([(1.0, 2.0, Vectors.dense(1.0)),
|
|
(0.0, 2.0, Vectors.sparse(1, [], []))],
|
|
["label", "weight", "features"])
|
|
lr = LogisticRegression(maxIter=5, regParam=0.01, weightCol="weight", fitIntercept=False)
|
|
model = lr.fit(df)
|
|
self.assertTrue(model.hasSummary)
|
|
s = model.summary
|
|
# test that api is callable and returns expected types
|
|
self.assertTrue(isinstance(s.predictions, DataFrame))
|
|
self.assertEqual(s.probabilityCol, "probability")
|
|
self.assertEqual(s.labelCol, "label")
|
|
self.assertEqual(s.featuresCol, "features")
|
|
objHist = s.objectiveHistory
|
|
self.assertTrue(isinstance(objHist, list) and isinstance(objHist[0], float))
|
|
self.assertGreater(s.totalIterations, 0)
|
|
self.assertTrue(isinstance(s.roc, DataFrame))
|
|
self.assertAlmostEqual(s.areaUnderROC, 1.0, 2)
|
|
self.assertTrue(isinstance(s.pr, DataFrame))
|
|
self.assertTrue(isinstance(s.fMeasureByThreshold, DataFrame))
|
|
self.assertTrue(isinstance(s.precisionByThreshold, DataFrame))
|
|
self.assertTrue(isinstance(s.recallByThreshold, DataFrame))
|
|
# test evaluation (with training dataset) produces a summary with same values
|
|
# one check is enough to verify a summary is returned, Scala version runs full test
|
|
sameSummary = model.evaluate(df)
|
|
self.assertAlmostEqual(sameSummary.areaUnderROC, s.areaUnderROC)
|
|
|
|
|
|
class OneVsRestTests(PySparkTestCase):
|
|
|
|
def test_copy(self):
|
|
sqlContext = SQLContext(self.sc)
|
|
df = sqlContext.createDataFrame([(0.0, Vectors.dense(1.0, 0.8)),
|
|
(1.0, Vectors.sparse(2, [], [])),
|
|
(2.0, Vectors.dense(0.5, 0.5))],
|
|
["label", "features"])
|
|
lr = LogisticRegression(maxIter=5, regParam=0.01)
|
|
ovr = OneVsRest(classifier=lr)
|
|
ovr1 = ovr.copy({lr.maxIter: 10})
|
|
self.assertEqual(ovr.getClassifier().getMaxIter(), 5)
|
|
self.assertEqual(ovr1.getClassifier().getMaxIter(), 10)
|
|
model = ovr.fit(df)
|
|
model1 = model.copy({model.predictionCol: "indexed"})
|
|
self.assertEqual(model1.getPredictionCol(), "indexed")
|
|
|
|
def test_output_columns(self):
|
|
sqlContext = SQLContext(self.sc)
|
|
df = sqlContext.createDataFrame([(0.0, Vectors.dense(1.0, 0.8)),
|
|
(1.0, Vectors.sparse(2, [], [])),
|
|
(2.0, Vectors.dense(0.5, 0.5))],
|
|
["label", "features"])
|
|
lr = LogisticRegression(maxIter=5, regParam=0.01)
|
|
ovr = OneVsRest(classifier=lr)
|
|
model = ovr.fit(df)
|
|
output = model.transform(df)
|
|
self.assertEqual(output.columns, ["label", "features", "prediction"])
|
|
|
|
def test_save_load(self):
|
|
temp_path = tempfile.mkdtemp()
|
|
sqlContext = SQLContext(self.sc)
|
|
df = sqlContext.createDataFrame([(0.0, Vectors.dense(1.0, 0.8)),
|
|
(1.0, Vectors.sparse(2, [], [])),
|
|
(2.0, Vectors.dense(0.5, 0.5))],
|
|
["label", "features"])
|
|
lr = LogisticRegression(maxIter=5, regParam=0.01)
|
|
ovr = OneVsRest(classifier=lr)
|
|
model = ovr.fit(df)
|
|
ovrPath = temp_path + "/ovr"
|
|
ovr.save(ovrPath)
|
|
loadedOvr = OneVsRest.load(ovrPath)
|
|
self.assertEqual(loadedOvr.getFeaturesCol(), ovr.getFeaturesCol())
|
|
self.assertEqual(loadedOvr.getLabelCol(), ovr.getLabelCol())
|
|
self.assertEqual(loadedOvr.getClassifier().uid, ovr.getClassifier().uid)
|
|
modelPath = temp_path + "/ovrModel"
|
|
model.save(modelPath)
|
|
loadedModel = OneVsRestModel.load(modelPath)
|
|
for m, n in zip(model.models, loadedModel.models):
|
|
self.assertEqual(m.uid, n.uid)
|
|
|
|
|
|
class HashingTFTest(PySparkTestCase):
|
|
|
|
def test_apply_binary_term_freqs(self):
|
|
sqlContext = SQLContext(self.sc)
|
|
|
|
df = sqlContext.createDataFrame([(0, ["a", "a", "b", "c", "c", "c"])], ["id", "words"])
|
|
n = 10
|
|
hashingTF = HashingTF()
|
|
hashingTF.setInputCol("words").setOutputCol("features").setNumFeatures(n).setBinary(True)
|
|
output = hashingTF.transform(df)
|
|
features = output.select("features").first().features.toArray()
|
|
expected = Vectors.dense([1.0, 1.0, 1.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0]).toArray()
|
|
for i in range(0, n):
|
|
self.assertAlmostEqual(features[i], expected[i], 14, "Error at " + str(i) +
|
|
": expected " + str(expected[i]) + ", got " + str(features[i]))
|
|
|
|
|
|
class ALSTest(PySparkTestCase):
|
|
|
|
def test_storage_levels(self):
|
|
sqlContext = SQLContext(self.sc)
|
|
df = sqlContext.createDataFrame(
|
|
[(0, 0, 4.0), (0, 1, 2.0), (1, 1, 3.0), (1, 2, 4.0), (2, 1, 1.0), (2, 2, 5.0)],
|
|
["user", "item", "rating"])
|
|
als = ALS().setMaxIter(1).setRank(1)
|
|
# test default params
|
|
als.fit(df)
|
|
self.assertEqual(als.getIntermediateStorageLevel(), "MEMORY_AND_DISK")
|
|
self.assertEqual(als._java_obj.getIntermediateStorageLevel(), "MEMORY_AND_DISK")
|
|
self.assertEqual(als.getFinalStorageLevel(), "MEMORY_AND_DISK")
|
|
self.assertEqual(als._java_obj.getFinalStorageLevel(), "MEMORY_AND_DISK")
|
|
# test non-default params
|
|
als.setIntermediateStorageLevel("MEMORY_ONLY_2")
|
|
als.setFinalStorageLevel("DISK_ONLY")
|
|
als.fit(df)
|
|
self.assertEqual(als.getIntermediateStorageLevel(), "MEMORY_ONLY_2")
|
|
self.assertEqual(als._java_obj.getIntermediateStorageLevel(), "MEMORY_ONLY_2")
|
|
self.assertEqual(als.getFinalStorageLevel(), "DISK_ONLY")
|
|
self.assertEqual(als._java_obj.getFinalStorageLevel(), "DISK_ONLY")
|
|
|
|
|
|
class DefaultValuesTests(PySparkTestCase):
|
|
"""
|
|
Test :py:class:`JavaParams` classes to see if their default Param values match
|
|
those in their Scala counterparts.
|
|
"""
|
|
|
|
def check_params(self, py_stage):
|
|
if not hasattr(py_stage, "_to_java"):
|
|
return
|
|
java_stage = py_stage._to_java()
|
|
if java_stage is None:
|
|
return
|
|
for p in py_stage.params:
|
|
java_param = java_stage.getParam(p.name)
|
|
py_has_default = py_stage.hasDefault(p)
|
|
java_has_default = java_stage.hasDefault(java_param)
|
|
self.assertEqual(py_has_default, java_has_default,
|
|
"Default value mismatch of param %s for Params %s"
|
|
% (p.name, str(py_stage)))
|
|
if py_has_default:
|
|
if p.name == "seed":
|
|
return # Random seeds between Spark and PySpark are different
|
|
java_default =\
|
|
_java2py(self.sc, java_stage.clear(java_param).getOrDefault(java_param))
|
|
py_stage._clear(p)
|
|
py_default = py_stage.getOrDefault(p)
|
|
self.assertEqual(java_default, py_default,
|
|
"Java default %s != python default %s of param %s for Params %s"
|
|
% (str(java_default), str(py_default), p.name, str(py_stage)))
|
|
|
|
def test_java_params(self):
|
|
import pyspark.ml.feature
|
|
import pyspark.ml.classification
|
|
import pyspark.ml.clustering
|
|
import pyspark.ml.pipeline
|
|
import pyspark.ml.recommendation
|
|
import pyspark.ml.regression
|
|
modules = [pyspark.ml.feature, pyspark.ml.classification, pyspark.ml.clustering,
|
|
pyspark.ml.pipeline, pyspark.ml.recommendation, pyspark.ml.regression]
|
|
for module in modules:
|
|
for name, cls in inspect.getmembers(module, inspect.isclass):
|
|
if not name.endswith('Model') and issubclass(cls, JavaParams)\
|
|
and not inspect.isabstract(cls):
|
|
self.check_params(cls())
|
|
|
|
|
|
if __name__ == "__main__":
|
|
from pyspark.ml.tests import *
|
|
if xmlrunner:
|
|
unittest.main(testRunner=xmlrunner.XMLTestRunner(output='target/test-reports'))
|
|
else:
|
|
unittest.main()
|