path: root/python/pyspark/ml/tests.py

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# The ASF licenses this file to You under the Apache License, Version 2.0
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"""
Unit tests for Spark ML Python APIs.
"""

import sys
try:
    import xmlrunner
except ImportError:
    xmlrunner = None

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, Row
from pyspark.sql.functions import rand
from pyspark.ml.classification import LogisticRegression
from pyspark.ml.evaluation import RegressionEvaluator
from pyspark.ml.param import Param, Params
from pyspark.ml.param.shared import HasMaxIter, HasInputCol, HasSeed
from pyspark.ml.util import keyword_only
from pyspark.ml import Estimator, Model, Pipeline, Transformer
from pyspark.ml.feature import *
from pyspark.ml.tuning import ParamGridBuilder, CrossValidator, CrossValidatorModel
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 ParamTypeConversionTests(PySparkTestCase):
    """
    Test that param type conversion happens.
    """

    def test_int_to_float(self):
        from pyspark.mllib.linalg import Vectors
        df = self.sc.parallelize([
            Row(label=1.0, weight=2.0, features=Vectors.dense(1.0))]).toDF()
        lr = LogisticRegression(elasticNetParam=0)
        lr.fit(df)
        lr.setElasticNetParam(0)
        lr.fit(df)

    def test_invalid_to_float(self):
        from pyspark.mllib.linalg import Vectors
        self.assertRaises(Exception, lambda: LogisticRegression(elasticNetParam="happy"))
        lr = LogisticRegression(elasticNetParam=0)
        self.assertRaises(Exception, lambda: lr.setElasticNetParam("panda"))


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, HasSeed):
    """
    A subclass of Params mixed with HasMaxIter, HasInputCol and HasSeed.
    """
    @keyword_only
    def __init__(self, seed=None):
        super(TestParams, self).__init__()
        self._setDefault(maxIter=10)
        kwargs = self.__init__._input_kwargs
        self.setParams(**kwargs)

    @keyword_only
    def setParams(self, seed=None):
        """
        setParams(self, seed=None)
        Sets params for this test.
        """
        kwargs = self.setParams._input_kwargs
        return self._set(**kwargs)


class OtherTestParams(HasMaxIter, HasInputCol, HasSeed):
    """
    A subclass of Params mixed with HasMaxIter, HasInputCol and HasSeed.
    """
    @keyword_only
    def __init__(self, seed=None):
        super(OtherTestParams, self).__init__()
        self._setDefault(maxIter=10)
        kwargs = self.__init__._input_kwargs
        self.setParams(**kwargs)

    @keyword_only
    def setParams(self, seed=None):
        """
        setParams(self, seed=None)
        Sets params for this test.
        """
        kwargs = self.setParams._input_kwargs
        return self._set(**kwargs)


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
        seed = testParams.seed

        params = testParams.params
        self.assertEqual(params, [inputCol, maxIter, seed])

        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.assertEqual(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()

        # Since the default is normally random, set it to a known number for debug str
        testParams._setDefault(seed=41)
        testParams.setSeed(43)

        self.assertEqual(
            testParams.explainParams(),
            "\n".join(["inputCol: input column name. (undefined)",
                       "maxIter: max number of iterations (>= 0). (default: 10, current: 100)",
                       "seed: random seed. (default: 41, current: 43)"]))

    def test_hasseed(self):
        noSeedSpecd = TestParams()
        withSeedSpecd = TestParams(seed=42)
        other = OtherTestParams()
        # Check that we no longer use 42 as the magic number
        self.assertNotEqual(noSeedSpecd.getSeed(), 42)
        origSeed = noSeedSpecd.getSeed()
        # Check that we only compute the seed once
        self.assertEqual(noSeedSpecd.getSeed(), origSeed)
        # Check that a specified seed is honored
        self.assertEqual(withSeedSpecd.getSeed(), 42)
        # Check that a different class has a different seed
        self.assertNotEqual(other.getSeed(), noSeedSpecd.getSeed())


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)

    def test_ngram(self):
        sqlContext = SQLContext(self.sc)
        dataset = sqlContext.createDataFrame([
            Row(input=["a", "b", "c", "d", "e"])])
        ngram0 = NGram(n=4, inputCol="input", outputCol="output")
        self.assertEqual(ngram0.getN(), 4)
        self.assertEqual(ngram0.getInputCol(), "input")
        self.assertEqual(ngram0.getOutputCol(), "output")
        transformedDF = ngram0.transform(dataset)
        self.assertEqual(transformedDF.head().output, ["a b c d", "b c d e"])

    def test_stopwordsremover(self):
        sqlContext = SQLContext(self.sc)
        dataset = sqlContext.createDataFrame([Row(input=["a", "panda"])])
        stopWordRemover = StopWordsRemover(inputCol="input", outputCol="output")
        # Default
        self.assertEqual(stopWordRemover.getInputCol(), "input")
        transformedDF = stopWordRemover.transform(dataset)
        self.assertEqual(transformedDF.head().output, ["panda"])
        # Custom
        stopwords = ["panda"]
        stopWordRemover.setStopWords(stopwords)
        self.assertEqual(stopWordRemover.getInputCol(), "input")
        self.assertEqual(stopWordRemover.getStopWords(), stopwords)
        transformedDF = stopWordRemover.transform(dataset)
        self.assertEqual(transformedDF.head().output, ["a"])


class HasInducedError(Params):

    def __init__(self):
        super(HasInducedError, self).__init__()
        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_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")


if __name__ == "__main__":
    from pyspark.ml.tests import *
    if xmlrunner:
        unittest.main(testRunner=xmlrunner.XMLTestRunner(output='target/test-reports'))
    else:
        unittest.main()