#
# 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
#
# Unless required by applicable law or agreed to in writing, software
# distributed under the License is distributed on an "AS IS" BASIS,
# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
# See the License for the specific language governing permissions and
# limitations under the License.
#

import os
import sys
import tempfile

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.common import _to_java_object_rdd
from pyspark.mllib.util import LinearDataGenerator
from pyspark.mllib.util import MLUtils
from pyspark.mllib.linalg import SparseVector, DenseVector, SparseMatrix, Vectors
from pyspark.mllib.random import RandomRDDs
from pyspark.mllib.regression import LabeledPoint
from pyspark.mllib.stat import Statistics
from pyspark.testing.mllibutils import MLlibTestCase


class MLUtilsTests(MLlibTestCase):
    def test_append_bias(self):
        data = [2.0, 2.0, 2.0]
        ret = MLUtils.appendBias(data)
        self.assertEqual(ret[3], 1.0)
        self.assertEqual(type(ret), DenseVector)

    def test_append_bias_with_vector(self):
        data = Vectors.dense([2.0, 2.0, 2.0])
        ret = MLUtils.appendBias(data)
        self.assertEqual(ret[3], 1.0)
        self.assertEqual(type(ret), DenseVector)

    def test_append_bias_with_sp_vector(self):
        data = Vectors.sparse(3, {0: 2.0, 2: 2.0})
        expected = Vectors.sparse(4, {0: 2.0, 2: 2.0, 3: 1.0})
        # Returned value must be SparseVector
        ret = MLUtils.appendBias(data)
        self.assertEqual(ret, expected)
        self.assertEqual(type(ret), SparseVector)

    def test_load_vectors(self):
        import shutil
        data = [
            [1.0, 2.0, 3.0],
            [1.0, 2.0, 3.0]
        ]
        temp_dir = tempfile.mkdtemp()
        load_vectors_path = os.path.join(temp_dir, "test_load_vectors")
        try:
            self.sc.parallelize(data).saveAsTextFile(load_vectors_path)
            ret_rdd = MLUtils.loadVectors(self.sc, load_vectors_path)
            ret = ret_rdd.collect()
            self.assertEqual(len(ret), 2)
            self.assertEqual(ret[0], DenseVector([1.0, 2.0, 3.0]))
            self.assertEqual(ret[1], DenseVector([1.0, 2.0, 3.0]))
        except:
            self.fail()
        finally:
            shutil.rmtree(load_vectors_path)


class LinearDataGeneratorTests(MLlibTestCase):
    def test_dim(self):
        linear_data = LinearDataGenerator.generateLinearInput(
            intercept=0.0, weights=[0.0, 0.0, 0.0],
            xMean=[0.0, 0.0, 0.0], xVariance=[0.33, 0.33, 0.33],
            nPoints=4, seed=0, eps=0.1)
        self.assertEqual(len(linear_data), 4)
        for point in linear_data:
            self.assertEqual(len(point.features), 3)

        linear_data = LinearDataGenerator.generateLinearRDD(
            sc=self.sc, nexamples=6, nfeatures=2, eps=0.1,
            nParts=2, intercept=0.0).collect()
        self.assertEqual(len(linear_data), 6)
        for point in linear_data:
            self.assertEqual(len(point.features), 2)


class SerDeTest(MLlibTestCase):
    def test_to_java_object_rdd(self):  # SPARK-6660
        data = RandomRDDs.uniformRDD(self.sc, 10, 5, seed=0)
        self.assertEqual(_to_java_object_rdd(data).count(), 10)


if __name__ == "__main__":
    from pyspark.mllib.tests.test_util import *

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