spark-instrumented-optimizer/python/pyspark/ml/tests.py

#
# 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
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#    http://www.apache.org/licenses/LICENSE-2.0
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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()