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

# -*- coding: utf-8 -*-
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# this work for additional information regarding copyright ownership.
# The ASF licenses this file to You under the Apache License, Version 2.0
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import inspect
import array as pyarray
import unittest

import numpy as np

from pyspark import keyword_only
from pyspark.ml.classification import LogisticRegression
from pyspark.ml.clustering import KMeans
from pyspark.ml.feature import Binarizer, Bucketizer, ElementwiseProduct, IndexToString, \
    MaxAbsScaler, VectorSlicer, Word2Vec
from pyspark.ml.linalg import DenseVector, SparseVector, Vectors
from pyspark.ml.param import Param, Params, TypeConverters
from pyspark.ml.param.shared import HasInputCol, HasMaxIter, HasSeed
from pyspark.ml.wrapper import JavaParams
from pyspark.testing.mlutils import check_params, PySparkTestCase, SparkSessionTestCase


class ParamTypeConversionTests(PySparkTestCase):
    """
    Test that param type conversion happens.
    """

    def test_int(self):
        lr = LogisticRegression(maxIter=5.0)
        self.assertEqual(lr.getMaxIter(), 5)
        self.assertTrue(type(lr.getMaxIter()) == int)
        self.assertRaises(TypeError, lambda: LogisticRegression(maxIter="notAnInt"))
        self.assertRaises(TypeError, lambda: LogisticRegression(maxIter=5.1))

    def test_float(self):
        lr = LogisticRegression(tol=1)
        self.assertEqual(lr.getTol(), 1.0)
        self.assertTrue(type(lr.getTol()) == float)
        self.assertRaises(TypeError, lambda: LogisticRegression(tol="notAFloat"))

    def test_vector(self):
        ewp = ElementwiseProduct(scalingVec=[1, 3])
        self.assertEqual(ewp.getScalingVec(), DenseVector([1.0, 3.0]))
        ewp = ElementwiseProduct(scalingVec=np.array([1.2, 3.4]))
        self.assertEqual(ewp.getScalingVec(), DenseVector([1.2, 3.4]))
        self.assertRaises(TypeError, lambda: ElementwiseProduct(scalingVec=["a", "b"]))

    def test_list(self):
        l = [0, 1]
        for lst_like in [l, np.array(l), DenseVector(l), SparseVector(len(l), range(len(l)), l),
                         pyarray.array('l', l), range(2), tuple(l)]:
            converted = TypeConverters.toList(lst_like)
            self.assertEqual(type(converted), list)
            self.assertListEqual(converted, l)

    def test_list_int(self):
        for indices in [[1.0, 2.0], np.array([1.0, 2.0]), DenseVector([1.0, 2.0]),
                        SparseVector(2, {0: 1.0, 1: 2.0}), range(1, 3), (1.0, 2.0),
                        pyarray.array('d', [1.0, 2.0])]:
            vs = VectorSlicer(indices=indices)
            self.assertListEqual(vs.getIndices(), [1, 2])
            self.assertTrue(all([type(v) == int for v in vs.getIndices()]))
        self.assertRaises(TypeError, lambda: VectorSlicer(indices=["a", "b"]))

    def test_list_float(self):
        b = Bucketizer(splits=[1, 4])
        self.assertEqual(b.getSplits(), [1.0, 4.0])
        self.assertTrue(all([type(v) == float for v in b.getSplits()]))
        self.assertRaises(TypeError, lambda: Bucketizer(splits=["a", 1.0]))

    def test_list_list_float(self):
        b = Bucketizer(splitsArray=[[-0.1, 0.5, 3], [-5, 1.5]])
        self.assertEqual(b.getSplitsArray(), [[-0.1, 0.5, 3.0], [-5.0, 1.5]])
        self.assertTrue(all([type(v) == list for v in b.getSplitsArray()]))
        self.assertTrue(all([type(v) == float for v in b.getSplitsArray()[0]]))
        self.assertTrue(all([type(v) == float for v in b.getSplitsArray()[1]]))
        self.assertRaises(TypeError, lambda: Bucketizer(splitsArray=["a", 1.0]))
        self.assertRaises(TypeError, lambda: Bucketizer(splitsArray=[[-5, 1.5], ["a", 1.0]]))

    def test_list_string(self):
        for labels in [np.array(['a', u'b']), ['a', u'b'], np.array(['a', 'b'])]:
            idx_to_string = IndexToString(labels=labels)
            self.assertListEqual(idx_to_string.getLabels(), ['a', 'b'])
        self.assertRaises(TypeError, lambda: IndexToString(labels=['a', 2]))

    def test_string(self):
        lr = LogisticRegression()
        for col in ['features', u'features', np.str_('features')]:
            lr.setFeaturesCol(col)
            self.assertEqual(lr.getFeaturesCol(), 'features')
        self.assertRaises(TypeError, lambda: LogisticRegression(featuresCol=2.3))

    def test_bool(self):
        self.assertRaises(TypeError, lambda: LogisticRegression(fitIntercept=1))
        self.assertRaises(TypeError, lambda: LogisticRegression(fitIntercept="false"))


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._input_kwargs
        self.setParams(**kwargs)

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

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


class HasThrowableProperty(Params):

    def __init__(self):
        super(HasThrowableProperty, self).__init__()
        self.p = Param(self, "none", "empty param")

    @property
    def test_property(self):
        raise RuntimeError("Test property to raise error when invoked")


class ParamTests(SparkSessionTestCase):

    def test_copy_new_parent(self):
        testParams = TestParams()
        # Copying an instantiated param should fail
        with self.assertRaises(ValueError):
            testParams.maxIter._copy_new_parent(testParams)
        # Copying a dummy param should succeed
        TestParams.maxIter._copy_new_parent(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_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_hasparam(self):
        testParams = TestParams()
        self.assertTrue(all([testParams.hasParam(p.name) for p in testParams.params]))
        self.assertFalse(testParams.hasParam("notAParameter"))
        self.assertTrue(testParams.hasParam(u"maxIter"))

    def test_resolveparam(self):
        testParams = TestParams()
        self.assertEqual(testParams._resolveParam(testParams.maxIter), testParams.maxIter)
        self.assertEqual(testParams._resolveParam("maxIter"), testParams.maxIter)

        self.assertEqual(testParams._resolveParam(u"maxIter"), testParams.maxIter)
        self.assertRaises(AttributeError, lambda: testParams._resolveParam(u"아"))

    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.name))
        self.assertTrue(testParams.hasDefault(maxIter))
        self.assertFalse(testParams.isSet(maxIter))
        self.assertTrue(testParams.isDefined(maxIter))
        self.assertEqual(testParams.getMaxIter(), 10)

        self.assertTrue(testParams.hasParam(inputCol.name))
        self.assertFalse(testParams.hasDefault(inputCol))
        self.assertFalse(testParams.isSet(inputCol))
        self.assertFalse(testParams.isDefined(inputCol))
        with self.assertRaises(KeyError):
            testParams.getInputCol()

        otherParam = Param(Params._dummy(), "otherParam", "Parameter used to test that " +
                           "set raises an error for a non-member parameter.",
                           typeConverter=TypeConverters.toString)
        with self.assertRaises(ValueError):
            testParams.set(otherParam, "value")

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

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

    def test_clear_param(self):
        df = self.spark.createDataFrame([(Vectors.dense([1.0]),), (Vectors.dense([2.0]),)], ["a"])
        maScaler = MaxAbsScaler(inputCol="a", outputCol="scaled")
        model = maScaler.fit(df)
        self.assertTrue(model.isSet(model.outputCol))
        self.assertEqual(model.getOutputCol(), "scaled")
        model.clear(model.outputCol)
        self.assertFalse(model.isSet(model.outputCol))
        self.assertEqual(model.getOutputCol()[:12], 'MaxAbsScaler')
        output = model.transform(df)
        self.assertEqual(model.getOutputCol(), output.schema.names[1])

    def test_kmeans_param(self):
        algo = KMeans()
        self.assertEqual(algo.getInitMode(), "k-means||")
        algo.setK(10)
        self.assertEqual(algo.getK(), 10)
        algo.setInitSteps(10)
        self.assertEqual(algo.getInitSteps(), 10)
        self.assertEqual(algo.getDistanceMeasure(), "euclidean")
        algo.setDistanceMeasure("cosine")
        self.assertEqual(algo.getDistanceMeasure(), "cosine")

    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())

    def test_param_property_error(self):
        param_store = HasThrowableProperty()
        self.assertRaises(RuntimeError, lambda: param_store.test_property)
        params = param_store.params  # should not invoke the property 'test_property'
        self.assertEqual(len(params), 1)

    def test_word2vec_param(self):
        model = Word2Vec().setWindowSize(6)
        # Check windowSize is set properly
        self.assertEqual(model.getWindowSize(), 6)

    def test_copy_param_extras(self):
        tp = TestParams(seed=42)
        extra = {tp.getParam(TestParams.inputCol.name): "copy_input"}
        tp_copy = tp.copy(extra=extra)
        self.assertEqual(tp.uid, tp_copy.uid)
        self.assertEqual(tp.params, tp_copy.params)
        for k, v in extra.items():
            self.assertTrue(tp_copy.isDefined(k))
            self.assertEqual(tp_copy.getOrDefault(k), v)
        copied_no_extra = {}
        for k, v in tp_copy._paramMap.items():
            if k not in extra:
                copied_no_extra[k] = v
        self.assertEqual(tp._paramMap, copied_no_extra)
        self.assertEqual(tp._defaultParamMap, tp_copy._defaultParamMap)
        with self.assertRaises(TypeError):
            tp.copy(extra={"unknown_parameter": None})
        with self.assertRaises(TypeError):
            tp.copy(extra=["must be a dict"])

    def test_logistic_regression_check_thresholds(self):
        self.assertIsInstance(
            LogisticRegression(threshold=0.5, thresholds=[0.5, 0.5]),
            LogisticRegression
        )

        self.assertRaisesRegexp(
            ValueError,
            "Logistic Regression getThreshold found inconsistent.*$",
            LogisticRegression, threshold=0.42, thresholds=[0.5, 0.5]
        )

    def test_preserve_set_state(self):
        dataset = self.spark.createDataFrame([(0.5,)], ["data"])
        binarizer = Binarizer(inputCol="data")
        self.assertFalse(binarizer.isSet("threshold"))
        binarizer.transform(dataset)
        binarizer._transfer_params_from_java()
        self.assertFalse(binarizer.isSet("threshold"),
                         "Params not explicitly set should remain unset after transform")

    def test_default_params_transferred(self):
        dataset = self.spark.createDataFrame([(0.5,)], ["data"])
        binarizer = Binarizer(inputCol="data")
        # intentionally change the pyspark default, but don't set it
        binarizer._defaultParamMap[binarizer.outputCol] = "my_default"
        result = binarizer.transform(dataset).select("my_default").collect()
        self.assertFalse(binarizer.isSet(binarizer.outputCol))
        self.assertEqual(result[0][0], 1.0)


class DefaultValuesTests(PySparkTestCase):
    """
    Test :py:class:`JavaParams` classes to see if their default Param values match
    those in their Scala counterparts.
    """
    def test_java_params(self):
        import re

        import pyspark.ml.feature
        import pyspark.ml.classification
        import pyspark.ml.clustering
        import pyspark.ml.evaluation
        import pyspark.ml.pipeline
        import pyspark.ml.recommendation
        import pyspark.ml.regression

        modules = [pyspark.ml.feature, pyspark.ml.classification, pyspark.ml.clustering,
                   pyspark.ml.evaluation, 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 not name.endswith('Params') \
                        and issubclass(cls, JavaParams) and not inspect.isabstract(cls) \
                        and not re.match("_?Java", name) and name != '_LSH' \
                        and name != '_Selector':
                    check_params(self, cls(), check_params_exist=True)

        # Additional classes that need explicit construction
        from pyspark.ml.feature import CountVectorizerModel, StringIndexerModel
        check_params(self, CountVectorizerModel.from_vocabulary(['a'], 'input'),
                     check_params_exist=True)
        check_params(self, StringIndexerModel.from_labels(['a', 'b'], 'input'),
                     check_params_exist=True)


if __name__ == "__main__":
    from pyspark.ml.tests.test_param import *  # noqa: F401

    try:
        import xmlrunner
        testRunner = xmlrunner.XMLTestRunner(output='target/test-reports', verbosity=2)
    except ImportError:
        testRunner = None
    unittest.main(testRunner=testRunner, verbosity=2)