#
# Licensed to the Apache Software Foundation (ASF) under one or more
# contributor license agreements. See the NOTICE file distributed with
# this work for additional information regarding copyright ownership.
# The ASF licenses this file to You under the Apache License, Version 2.0
# (the "License"); you may not use this file except in compliance with
# the License. You may obtain a copy of the License at
#
# http://www.apache.org/licenses/LICENSE-2.0
#
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# distributed under the License is distributed on an "AS IS" BASIS,
# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
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"""
Unit tests for Spark ML Python APIs.
"""
import sys
if sys.version_info[:2] <= (2, 6):
try:
import unittest2 as unittest
except ImportError:
sys.stderr.write('Please install unittest2 to test with Python 2.6 or earlier')
sys.exit(1)
else:
import unittest
from pyspark.tests import ReusedPySparkTestCase as PySparkTestCase
from pyspark.sql import DataFrame, SQLContext
from pyspark.ml.param import Param, Params
from pyspark.ml.param.shared import HasMaxIter, HasInputCol
from pyspark.ml import Estimator, Model, Pipeline, Transformer
from pyspark.ml.feature import *
from pyspark.mllib.linalg import DenseVector
class MockDataset(DataFrame):
def __init__(self):
self.index = 0
class HasFake(Params):
def __init__(self):
super(HasFake, self).__init__()
self.fake = Param(self, "fake", "fake param")
def getFake(self):
return self.getOrDefault(self.fake)
class MockTransformer(Transformer, HasFake):
def __init__(self):
super(MockTransformer, self).__init__()
self.dataset_index = None
def _transform(self, dataset):
self.dataset_index = dataset.index
dataset.index += 1
return dataset
class MockEstimator(Estimator, HasFake):
def __init__(self):
super(MockEstimator, self).__init__()
self.dataset_index = None
def _fit(self, dataset):
self.dataset_index = dataset.index
model = MockModel()
self._copyValues(model)
return model
class MockModel(MockTransformer, Model, HasFake):
pass
class PipelineTests(PySparkTestCase):
def test_pipeline(self):
dataset = MockDataset()
estimator0 = MockEstimator()
transformer1 = MockTransformer()
estimator2 = MockEstimator()
transformer3 = MockTransformer()
pipeline = Pipeline(stages=[estimator0, transformer1, estimator2, transformer3])
pipeline_model = pipeline.fit(dataset, {estimator0.fake: 0, transformer1.fake: 1})
model0, transformer1, model2, transformer3 = pipeline_model.stages
self.assertEqual(0, model0.dataset_index)
self.assertEqual(0, model0.getFake())
self.assertEqual(1, transformer1.dataset_index)
self.assertEqual(1, transformer1.getFake())
self.assertEqual(2, dataset.index)
self.assertIsNone(model2.dataset_index, "The last model shouldn't be called in fit.")
self.assertIsNone(transformer3.dataset_index,
"The last transformer shouldn't be called in fit.")
dataset = pipeline_model.transform(dataset)
self.assertEqual(2, model0.dataset_index)
self.assertEqual(3, transformer1.dataset_index)
self.assertEqual(4, model2.dataset_index)
self.assertEqual(5, transformer3.dataset_index)
self.assertEqual(6, dataset.index)
class TestParams(HasMaxIter, HasInputCol):
"""
A subclass of Params mixed with HasMaxIter and HasInputCol.
"""
def __init__(self):
super(TestParams, self).__init__()
self._setDefault(maxIter=10)
class ParamTests(PySparkTestCase):
def test_param(self):
testParams = TestParams()
maxIter = testParams.maxIter
self.assertEqual(maxIter.name, "maxIter")
self.assertEqual(maxIter.doc, "max number of iterations (>= 0)")
self.assertTrue(maxIter.parent == testParams.uid)
def test_params(self):
testParams = TestParams()
maxIter = testParams.maxIter
inputCol = testParams.inputCol
params = testParams.params
self.assertEqual(params, [inputCol, maxIter])
self.assertTrue(testParams.hasParam(maxIter))
self.assertTrue(testParams.hasDefault(maxIter))
self.assertFalse(testParams.isSet(maxIter))
self.assertTrue(testParams.isDefined(maxIter))
self.assertEqual(testParams.getMaxIter(), 10)
testParams.setMaxIter(100)
self.assertTrue(testParams.isSet(maxIter))
self.assertEquals(testParams.getMaxIter(), 100)
self.assertTrue(testParams.hasParam(inputCol))
self.assertFalse(testParams.hasDefault(inputCol))
self.assertFalse(testParams.isSet(inputCol))
self.assertFalse(testParams.isDefined(inputCol))
with self.assertRaises(KeyError):
testParams.getInputCol()
self.assertEquals(
testParams.explainParams(),
"\n".join(["inputCol: input column name (undefined)",
"maxIter: max number of iterations (>= 0) (default: 10, current: 100)"]))
class FeatureTests(PySparkTestCase):
def test_binarizer(self):
b0 = Binarizer()
self.assertListEqual(b0.params, [b0.inputCol, b0.outputCol, b0.threshold])
self.assertTrue(all([~b0.isSet(p) for p in b0.params]))
self.assertTrue(b0.hasDefault(b0.threshold))
self.assertEqual(b0.getThreshold(), 0.0)
b0.setParams(inputCol="input", outputCol="output").setThreshold(1.0)
self.assertTrue(all([b0.isSet(p) for p in b0.params]))
self.assertEqual(b0.getThreshold(), 1.0)
self.assertEqual(b0.getInputCol(), "input")
self.assertEqual(b0.getOutputCol(), "output")
b0c = b0.copy({b0.threshold: 2.0})
self.assertEqual(b0c.uid, b0.uid)
self.assertListEqual(b0c.params, b0.params)
self.assertEqual(b0c.getThreshold(), 2.0)
b1 = Binarizer(threshold=2.0, inputCol="input", outputCol="output")
self.assertNotEqual(b1.uid, b0.uid)
self.assertEqual(b1.getThreshold(), 2.0)
self.assertEqual(b1.getInputCol(), "input")
self.assertEqual(b1.getOutputCol(), "output")
def test_idf(self):
sqlContext = SQLContext(self.sc)
dataset = sqlContext.createDataFrame([
(DenseVector([1.0, 2.0]),),
(DenseVector([0.0, 1.0]),),
(DenseVector([3.0, 0.2]),)], ["tf"])
idf0 = IDF(inputCol="tf")
self.assertListEqual(idf0.params, [idf0.inputCol, idf0.minDocFreq, idf0.outputCol])
idf0m = idf0.fit(dataset, {idf0.outputCol: "idf"})
self.assertEqual(idf0m.uid, idf0.uid,
"Model should inherit the UID from its parent estimator.")
output = idf0m.transform(dataset)
self.assertIsNotNone(output.head().idf)
if __name__ == "__main__":
unittest.main()
