#
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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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"""
Fuller unit tests for Python MLlib.
"""

import sys
import array as pyarray

from numpy import array, array_equal

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.mllib.linalg import Vector, SparseVector, DenseVector, VectorUDT, _convert_to_vector
from pyspark.mllib.regression import LabeledPoint
from pyspark.mllib.random import RandomRDDs
from pyspark.mllib.stat import Statistics
from pyspark.serializers import PickleSerializer
from pyspark.sql import SQLContext
from pyspark.tests import ReusedPySparkTestCase as PySparkTestCase

_have_scipy = False
try:
    import scipy.sparse
    _have_scipy = True
except:
    # No SciPy, but that's okay, we'll skip those tests
    pass

ser = PickleSerializer()


def _squared_distance(a, b):
    if isinstance(a, Vector):
        return a.squared_distance(b)
    else:
        return b.squared_distance(a)


class VectorTests(PySparkTestCase):

    def _test_serialize(self, v):
        jvec = self.sc._jvm.SerDe.loads(bytearray(ser.dumps(v)))
        nv = ser.loads(str(self.sc._jvm.SerDe.dumps(jvec)))
        self.assertEqual(v, nv)
        vs = [v] * 100
        jvecs = self.sc._jvm.SerDe.loads(bytearray(ser.dumps(vs)))
        nvs = ser.loads(str(self.sc._jvm.SerDe.dumps(jvecs)))
        self.assertEqual(vs, nvs)

    def test_serialize(self):
        self._test_serialize(DenseVector(range(10)))
        self._test_serialize(DenseVector(array([1., 2., 3., 4.])))
        self._test_serialize(DenseVector(pyarray.array('d', range(10))))
        self._test_serialize(SparseVector(4, {1: 1, 3: 2}))

    def test_dot(self):
        sv = SparseVector(4, {1: 1, 3: 2})
        dv = DenseVector(array([1., 2., 3., 4.]))
        lst = DenseVector([1, 2, 3, 4])
        mat = array([[1., 2., 3., 4.],
                     [1., 2., 3., 4.],
                     [1., 2., 3., 4.],
                     [1., 2., 3., 4.]])
        self.assertEquals(10.0, sv.dot(dv))
        self.assertTrue(array_equal(array([3., 6., 9., 12.]), sv.dot(mat)))
        self.assertEquals(30.0, dv.dot(dv))
        self.assertTrue(array_equal(array([10., 20., 30., 40.]), dv.dot(mat)))
        self.assertEquals(30.0, lst.dot(dv))
        self.assertTrue(array_equal(array([10., 20., 30., 40.]), lst.dot(mat)))

    def test_squared_distance(self):
        sv = SparseVector(4, {1: 1, 3: 2})
        dv = DenseVector(array([1., 2., 3., 4.]))
        lst = DenseVector([4, 3, 2, 1])
        self.assertEquals(15.0, _squared_distance(sv, dv))
        self.assertEquals(25.0, _squared_distance(sv, lst))
        self.assertEquals(20.0, _squared_distance(dv, lst))
        self.assertEquals(15.0, _squared_distance(dv, sv))
        self.assertEquals(25.0, _squared_distance(lst, sv))
        self.assertEquals(20.0, _squared_distance(lst, dv))
        self.assertEquals(0.0, _squared_distance(sv, sv))
        self.assertEquals(0.0, _squared_distance(dv, dv))
        self.assertEquals(0.0, _squared_distance(lst, lst))


class ListTests(PySparkTestCase):

    """
    Test MLlib algorithms on plain lists, to make sure they're passed through
    as NumPy arrays.
    """

    def test_clustering(self):
        from pyspark.mllib.clustering import KMeans
        data = [
            [0, 1.1],
            [0, 1.2],
            [1.1, 0],
            [1.2, 0],
        ]
        clusters = KMeans.train(self.sc.parallelize(data), 2, initializationMode="k-means||")
        self.assertEquals(clusters.predict(data[0]), clusters.predict(data[1]))
        self.assertEquals(clusters.predict(data[2]), clusters.predict(data[3]))

    def test_classification(self):
        from pyspark.mllib.classification import LogisticRegressionWithSGD, SVMWithSGD, NaiveBayes
        from pyspark.mllib.tree import DecisionTree
        data = [
            LabeledPoint(0.0, [1, 0, 0]),
            LabeledPoint(1.0, [0, 1, 1]),
            LabeledPoint(0.0, [2, 0, 0]),
            LabeledPoint(1.0, [0, 2, 1])
        ]
        rdd = self.sc.parallelize(data)
        features = [p.features.tolist() for p in data]

        lr_model = LogisticRegressionWithSGD.train(rdd)
        self.assertTrue(lr_model.predict(features[0]) <= 0)
        self.assertTrue(lr_model.predict(features[1]) > 0)
        self.assertTrue(lr_model.predict(features[2]) <= 0)
        self.assertTrue(lr_model.predict(features[3]) > 0)

        svm_model = SVMWithSGD.train(rdd)
        self.assertTrue(svm_model.predict(features[0]) <= 0)
        self.assertTrue(svm_model.predict(features[1]) > 0)
        self.assertTrue(svm_model.predict(features[2]) <= 0)
        self.assertTrue(svm_model.predict(features[3]) > 0)

        nb_model = NaiveBayes.train(rdd)
        self.assertTrue(nb_model.predict(features[0]) <= 0)
        self.assertTrue(nb_model.predict(features[1]) > 0)
        self.assertTrue(nb_model.predict(features[2]) <= 0)
        self.assertTrue(nb_model.predict(features[3]) > 0)

        categoricalFeaturesInfo = {0: 3}  # feature 0 has 3 categories
        dt_model = \
            DecisionTree.trainClassifier(rdd, numClasses=2,
                                         categoricalFeaturesInfo=categoricalFeaturesInfo)
        self.assertTrue(dt_model.predict(features[0]) <= 0)
        self.assertTrue(dt_model.predict(features[1]) > 0)
        self.assertTrue(dt_model.predict(features[2]) <= 0)
        self.assertTrue(dt_model.predict(features[3]) > 0)

    def test_regression(self):
        from pyspark.mllib.regression import LinearRegressionWithSGD, LassoWithSGD, \
            RidgeRegressionWithSGD
        from pyspark.mllib.tree import DecisionTree
        data = [
            LabeledPoint(-1.0, [0, -1]),
            LabeledPoint(1.0, [0, 1]),
            LabeledPoint(-1.0, [0, -2]),
            LabeledPoint(1.0, [0, 2])
        ]
        rdd = self.sc.parallelize(data)
        features = [p.features.tolist() for p in data]

        lr_model = LinearRegressionWithSGD.train(rdd)
        self.assertTrue(lr_model.predict(features[0]) <= 0)
        self.assertTrue(lr_model.predict(features[1]) > 0)
        self.assertTrue(lr_model.predict(features[2]) <= 0)
        self.assertTrue(lr_model.predict(features[3]) > 0)

        lasso_model = LassoWithSGD.train(rdd)
        self.assertTrue(lasso_model.predict(features[0]) <= 0)
        self.assertTrue(lasso_model.predict(features[1]) > 0)
        self.assertTrue(lasso_model.predict(features[2]) <= 0)
        self.assertTrue(lasso_model.predict(features[3]) > 0)

        rr_model = RidgeRegressionWithSGD.train(rdd)
        self.assertTrue(rr_model.predict(features[0]) <= 0)
        self.assertTrue(rr_model.predict(features[1]) > 0)
        self.assertTrue(rr_model.predict(features[2]) <= 0)
        self.assertTrue(rr_model.predict(features[3]) > 0)

        categoricalFeaturesInfo = {0: 2}  # feature 0 has 2 categories
        dt_model = \
            DecisionTree.trainRegressor(rdd, categoricalFeaturesInfo=categoricalFeaturesInfo)
        self.assertTrue(dt_model.predict(features[0]) <= 0)
        self.assertTrue(dt_model.predict(features[1]) > 0)
        self.assertTrue(dt_model.predict(features[2]) <= 0)
        self.assertTrue(dt_model.predict(features[3]) > 0)


class StatTests(PySparkTestCase):
    # SPARK-4023
    def test_col_with_different_rdds(self):
        # numpy
        data = RandomRDDs.normalVectorRDD(self.sc, 1000, 10, 10)
        summary = Statistics.colStats(data)
        self.assertEqual(1000, summary.count())
        # array
        data = self.sc.parallelize([range(10)] * 10)
        summary = Statistics.colStats(data)
        self.assertEqual(10, summary.count())
        # array
        data = self.sc.parallelize([pyarray.array("d", range(10))] * 10)
        summary = Statistics.colStats(data)
        self.assertEqual(10, summary.count())


class VectorUDTTests(PySparkTestCase):

    dv0 = DenseVector([])
    dv1 = DenseVector([1.0, 2.0])
    sv0 = SparseVector(2, [], [])
    sv1 = SparseVector(2, [1], [2.0])
    udt = VectorUDT()

    def test_json_schema(self):
        self.assertEqual(VectorUDT.fromJson(self.udt.jsonValue()), self.udt)

    def test_serialization(self):
        for v in [self.dv0, self.dv1, self.sv0, self.sv1]:
            self.assertEqual(v, self.udt.deserialize(self.udt.serialize(v)))

    def test_infer_schema(self):
        sqlCtx = SQLContext(self.sc)
        rdd = self.sc.parallelize([LabeledPoint(1.0, self.dv1), LabeledPoint(0.0, self.sv1)])
        srdd = sqlCtx.inferSchema(rdd)
        schema = srdd.schema()
        field = [f for f in schema.fields if f.name == "features"][0]
        self.assertEqual(field.dataType, self.udt)
        vectors = srdd.map(lambda p: p.features).collect()
        self.assertEqual(len(vectors), 2)
        for v in vectors:
            if isinstance(v, SparseVector):
                self.assertEqual(v, self.sv1)
            elif isinstance(v, DenseVector):
                self.assertEqual(v, self.dv1)
            else:
                raise ValueError("expecting a vector but got %r of type %r" % (v, type(v)))


@unittest.skipIf(not _have_scipy, "SciPy not installed")
class SciPyTests(PySparkTestCase):

    """
    Test both vector operations and MLlib algorithms with SciPy sparse matrices,
    if SciPy is available.
    """

    def test_serialize(self):
        from scipy.sparse import lil_matrix
        lil = lil_matrix((4, 1))
        lil[1, 0] = 1
        lil[3, 0] = 2
        sv = SparseVector(4, {1: 1, 3: 2})
        self.assertEquals(sv, _convert_to_vector(lil))
        self.assertEquals(sv, _convert_to_vector(lil.tocsc()))
        self.assertEquals(sv, _convert_to_vector(lil.tocoo()))
        self.assertEquals(sv, _convert_to_vector(lil.tocsr()))
        self.assertEquals(sv, _convert_to_vector(lil.todok()))

        def serialize(l):
            return ser.loads(ser.dumps(_convert_to_vector(l)))
        self.assertEquals(sv, serialize(lil))
        self.assertEquals(sv, serialize(lil.tocsc()))
        self.assertEquals(sv, serialize(lil.tocsr()))
        self.assertEquals(sv, serialize(lil.todok()))

    def test_dot(self):
        from scipy.sparse import lil_matrix
        lil = lil_matrix((4, 1))
        lil[1, 0] = 1
        lil[3, 0] = 2
        dv = DenseVector(array([1., 2., 3., 4.]))
        self.assertEquals(10.0, dv.dot(lil))

    def test_squared_distance(self):
        from scipy.sparse import lil_matrix
        lil = lil_matrix((4, 1))
        lil[1, 0] = 3
        lil[3, 0] = 2
        dv = DenseVector(array([1., 2., 3., 4.]))
        sv = SparseVector(4, {0: 1, 1: 2, 2: 3, 3: 4})
        self.assertEquals(15.0, dv.squared_distance(lil))
        self.assertEquals(15.0, sv.squared_distance(lil))

    def scipy_matrix(self, size, values):
        """Create a column SciPy matrix from a dictionary of values"""
        from scipy.sparse import lil_matrix
        lil = lil_matrix((size, 1))
        for key, value in values.items():
            lil[key, 0] = value
        return lil

    def test_clustering(self):
        from pyspark.mllib.clustering import KMeans
        data = [
            self.scipy_matrix(3, {1: 1.0}),
            self.scipy_matrix(3, {1: 1.1}),
            self.scipy_matrix(3, {2: 1.0}),
            self.scipy_matrix(3, {2: 1.1})
        ]
        clusters = KMeans.train(self.sc.parallelize(data), 2, initializationMode="k-means||")
        self.assertEquals(clusters.predict(data[0]), clusters.predict(data[1]))
        self.assertEquals(clusters.predict(data[2]), clusters.predict(data[3]))

    def test_classification(self):
        from pyspark.mllib.classification import LogisticRegressionWithSGD, SVMWithSGD, NaiveBayes
        from pyspark.mllib.tree import DecisionTree
        data = [
            LabeledPoint(0.0, self.scipy_matrix(2, {0: 1.0})),
            LabeledPoint(1.0, self.scipy_matrix(2, {1: 1.0})),
            LabeledPoint(0.0, self.scipy_matrix(2, {0: 2.0})),
            LabeledPoint(1.0, self.scipy_matrix(2, {1: 2.0}))
        ]
        rdd = self.sc.parallelize(data)
        features = [p.features for p in data]

        lr_model = LogisticRegressionWithSGD.train(rdd)
        self.assertTrue(lr_model.predict(features[0]) <= 0)
        self.assertTrue(lr_model.predict(features[1]) > 0)
        self.assertTrue(lr_model.predict(features[2]) <= 0)
        self.assertTrue(lr_model.predict(features[3]) > 0)

        svm_model = SVMWithSGD.train(rdd)
        self.assertTrue(svm_model.predict(features[0]) <= 0)
        self.assertTrue(svm_model.predict(features[1]) > 0)
        self.assertTrue(svm_model.predict(features[2]) <= 0)
        self.assertTrue(svm_model.predict(features[3]) > 0)

        nb_model = NaiveBayes.train(rdd)
        self.assertTrue(nb_model.predict(features[0]) <= 0)
        self.assertTrue(nb_model.predict(features[1]) > 0)
        self.assertTrue(nb_model.predict(features[2]) <= 0)
        self.assertTrue(nb_model.predict(features[3]) > 0)

        categoricalFeaturesInfo = {0: 3}  # feature 0 has 3 categories
        dt_model = DecisionTree.trainClassifier(rdd, numClasses=2,
                                                categoricalFeaturesInfo=categoricalFeaturesInfo)
        self.assertTrue(dt_model.predict(features[0]) <= 0)
        self.assertTrue(dt_model.predict(features[1]) > 0)
        self.assertTrue(dt_model.predict(features[2]) <= 0)
        self.assertTrue(dt_model.predict(features[3]) > 0)

    def test_regression(self):
        from pyspark.mllib.regression import LinearRegressionWithSGD, LassoWithSGD, \
            RidgeRegressionWithSGD
        from pyspark.mllib.tree import DecisionTree
        data = [
            LabeledPoint(-1.0, self.scipy_matrix(2, {1: -1.0})),
            LabeledPoint(1.0, self.scipy_matrix(2, {1: 1.0})),
            LabeledPoint(-1.0, self.scipy_matrix(2, {1: -2.0})),
            LabeledPoint(1.0, self.scipy_matrix(2, {1: 2.0}))
        ]
        rdd = self.sc.parallelize(data)
        features = [p.features for p in data]

        lr_model = LinearRegressionWithSGD.train(rdd)
        self.assertTrue(lr_model.predict(features[0]) <= 0)
        self.assertTrue(lr_model.predict(features[1]) > 0)
        self.assertTrue(lr_model.predict(features[2]) <= 0)
        self.assertTrue(lr_model.predict(features[3]) > 0)

        lasso_model = LassoWithSGD.train(rdd)
        self.assertTrue(lasso_model.predict(features[0]) <= 0)
        self.assertTrue(lasso_model.predict(features[1]) > 0)
        self.assertTrue(lasso_model.predict(features[2]) <= 0)
        self.assertTrue(lasso_model.predict(features[3]) > 0)

        rr_model = RidgeRegressionWithSGD.train(rdd)
        self.assertTrue(rr_model.predict(features[0]) <= 0)
        self.assertTrue(rr_model.predict(features[1]) > 0)
        self.assertTrue(rr_model.predict(features[2]) <= 0)
        self.assertTrue(rr_model.predict(features[3]) > 0)

        categoricalFeaturesInfo = {0: 2}  # feature 0 has 2 categories
        dt_model = DecisionTree.trainRegressor(rdd, categoricalFeaturesInfo=categoricalFeaturesInfo)
        self.assertTrue(dt_model.predict(features[0]) <= 0)
        self.assertTrue(dt_model.predict(features[1]) > 0)
        self.assertTrue(dt_model.predict(features[2]) <= 0)
        self.assertTrue(dt_model.predict(features[3]) > 0)


if __name__ == "__main__":
    if not _have_scipy:
        print "NOTE: Skipping SciPy tests as it does not seem to be installed"
    unittest.main()
    if not _have_scipy:
        print "NOTE: SciPy tests were skipped as it does not seem to be installed"