a2fc48c28c
## What changes were proposed in this pull request? This PR breaks down the large mllib/tests.py file that contains all Python MLlib unit tests into several smaller test files to be easier to read and maintain. The tests are broken down as follows: ``` pyspark ├── __init__.py ... ├── mllib │ ├── __init__.py ... │ ├── tests │ │ ├── __init__.py │ │ ├── test_algorithms.py │ │ ├── test_feature.py │ │ ├── test_linalg.py │ │ ├── test_stat.py │ │ ├── test_streaming_algorithms.py │ │ └── test_util.py ... ├── testing ... │ ├── mllibutils.py ... ``` ## How was this patch tested? Ran tests manually by module to ensure test count was the same, and ran `python/run-tests --modules=pyspark-mllib` to verify all passing with Python 2.7 and Python 3.6. Also installed scipy to include optional tests in test_linalg. Closes #23056 from BryanCutler/python-test-breakup-mllib-SPARK-26034. Authored-by: Bryan Cutler <cutlerb@gmail.com> Signed-off-by: hyukjinkwon <gurwls223@apache.org>
116 lines
4.1 KiB
Python
116 lines
4.1 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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import os
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import sys
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import tempfile
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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 pyspark.mllib.common import _to_java_object_rdd
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from pyspark.mllib.util import LinearDataGenerator
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from pyspark.mllib.util import MLUtils
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from pyspark.mllib.linalg import SparseVector, DenseVector, SparseMatrix, Vectors
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from pyspark.mllib.random import RandomRDDs
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from pyspark.mllib.regression import LabeledPoint
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from pyspark.mllib.stat import Statistics
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from pyspark.testing.mllibutils import MLlibTestCase
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class MLUtilsTests(MLlibTestCase):
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def test_append_bias(self):
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data = [2.0, 2.0, 2.0]
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ret = MLUtils.appendBias(data)
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self.assertEqual(ret[3], 1.0)
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self.assertEqual(type(ret), DenseVector)
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def test_append_bias_with_vector(self):
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data = Vectors.dense([2.0, 2.0, 2.0])
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ret = MLUtils.appendBias(data)
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self.assertEqual(ret[3], 1.0)
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self.assertEqual(type(ret), DenseVector)
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def test_append_bias_with_sp_vector(self):
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data = Vectors.sparse(3, {0: 2.0, 2: 2.0})
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expected = Vectors.sparse(4, {0: 2.0, 2: 2.0, 3: 1.0})
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# Returned value must be SparseVector
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ret = MLUtils.appendBias(data)
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self.assertEqual(ret, expected)
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self.assertEqual(type(ret), SparseVector)
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def test_load_vectors(self):
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import shutil
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data = [
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[1.0, 2.0, 3.0],
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[1.0, 2.0, 3.0]
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]
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temp_dir = tempfile.mkdtemp()
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load_vectors_path = os.path.join(temp_dir, "test_load_vectors")
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try:
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self.sc.parallelize(data).saveAsTextFile(load_vectors_path)
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ret_rdd = MLUtils.loadVectors(self.sc, load_vectors_path)
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ret = ret_rdd.collect()
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self.assertEqual(len(ret), 2)
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self.assertEqual(ret[0], DenseVector([1.0, 2.0, 3.0]))
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self.assertEqual(ret[1], DenseVector([1.0, 2.0, 3.0]))
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except:
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self.fail()
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finally:
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shutil.rmtree(load_vectors_path)
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class LinearDataGeneratorTests(MLlibTestCase):
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def test_dim(self):
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linear_data = LinearDataGenerator.generateLinearInput(
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intercept=0.0, weights=[0.0, 0.0, 0.0],
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xMean=[0.0, 0.0, 0.0], xVariance=[0.33, 0.33, 0.33],
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nPoints=4, seed=0, eps=0.1)
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self.assertEqual(len(linear_data), 4)
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for point in linear_data:
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self.assertEqual(len(point.features), 3)
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linear_data = LinearDataGenerator.generateLinearRDD(
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sc=self.sc, nexamples=6, nfeatures=2, eps=0.1,
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nParts=2, intercept=0.0).collect()
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self.assertEqual(len(linear_data), 6)
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for point in linear_data:
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self.assertEqual(len(point.features), 2)
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class SerDeTest(MLlibTestCase):
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def test_to_java_object_rdd(self): # SPARK-6660
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data = RandomRDDs.uniformRDD(self.sc, 10, 5, seed=0)
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self.assertEqual(_to_java_object_rdd(data).count(), 10)
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if __name__ == "__main__":
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from pyspark.mllib.tests.test_util import *
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try:
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import xmlrunner
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testRunner = xmlrunner.XMLTestRunner(output='target/test-reports')
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except ImportError:
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testRunner = None
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unittest.main(testRunner=testRunner, verbosity=2)
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