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#
# 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 array as pyarray
import unittest
from numpy import array
from pyspark.mllib.linalg import Vectors, Matrices
from pyspark.mllib.random import RandomRDDs
from pyspark.mllib.regression import LabeledPoint
from pyspark.mllib.stat import Statistics
from pyspark.sql.utils import IllegalArgumentException
from pyspark.testing.mllibutils import MLlibTestCase
class StatTests(MLlibTestCase):
# 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())
def test_col_norms(self):
data = RandomRDDs.normalVectorRDD(self.sc, 1000, 10, 10)
summary = Statistics.colStats(data)
self.assertEqual(10, len(summary.normL1()))
self.assertEqual(10, len(summary.normL2()))
data2 = self.sc.parallelize(range(10)).map(lambda x: Vectors.dense(x))
summary2 = Statistics.colStats(data2)
self.assertEqual(array([45.0]), summary2.normL1())
import math
expectedNormL2 = math.sqrt(sum(map(lambda x: x*x, range(10))))
self.assertTrue(math.fabs(summary2.normL2()[0] - expectedNormL2) < 1e-14)
class ChiSqTestTests(MLlibTestCase):
def test_goodness_of_fit(self):
from numpy import inf
observed = Vectors.dense([4, 6, 5])
pearson = Statistics.chiSqTest(observed)
# Validated against the R command `chisq.test(c(4, 6, 5), p=c(1/3, 1/3, 1/3))`
self.assertEqual(pearson.statistic, 0.4)
self.assertEqual(pearson.degreesOfFreedom, 2)
self.assertAlmostEqual(pearson.pValue, 0.8187, 4)
# Different expected and observed sum
observed1 = Vectors.dense([21, 38, 43, 80])
expected1 = Vectors.dense([3, 5, 7, 20])
pearson1 = Statistics.chiSqTest(observed1, expected1)
# Results validated against the R command
# `chisq.test(c(21, 38, 43, 80), p=c(3/35, 1/7, 1/5, 4/7))`
self.assertAlmostEqual(pearson1.statistic, 14.1429, 4)
self.assertEqual(pearson1.degreesOfFreedom, 3)
self.assertAlmostEqual(pearson1.pValue, 0.002717, 4)
# Vectors with different sizes
observed3 = Vectors.dense([1.0, 2.0, 3.0])
expected3 = Vectors.dense([1.0, 2.0, 3.0, 4.0])
self.assertRaises(ValueError, Statistics.chiSqTest, observed3, expected3)
# Negative counts in observed
neg_obs = Vectors.dense([1.0, 2.0, 3.0, -4.0])
self.assertRaises(IllegalArgumentException, Statistics.chiSqTest, neg_obs, expected1)
# Count = 0.0 in expected but not observed
zero_expected = Vectors.dense([1.0, 0.0, 3.0])
pearson_inf = Statistics.chiSqTest(observed, zero_expected)
self.assertEqual(pearson_inf.statistic, inf)
self.assertEqual(pearson_inf.degreesOfFreedom, 2)
self.assertEqual(pearson_inf.pValue, 0.0)
# 0.0 in expected and observed simultaneously
zero_observed = Vectors.dense([2.0, 0.0, 1.0])
self.assertRaises(
IllegalArgumentException, Statistics.chiSqTest, zero_observed, zero_expected)
def test_matrix_independence(self):
data = [40.0, 24.0, 29.0, 56.0, 32.0, 42.0, 31.0, 10.0, 0.0, 30.0, 15.0, 12.0]
chi = Statistics.chiSqTest(Matrices.dense(3, 4, data))
# Results validated against R command
# `chisq.test(rbind(c(40, 56, 31, 30),c(24, 32, 10, 15), c(29, 42, 0, 12)))`
self.assertAlmostEqual(chi.statistic, 21.9958, 4)
self.assertEqual(chi.degreesOfFreedom, 6)
self.assertAlmostEqual(chi.pValue, 0.001213, 4)
# Negative counts
neg_counts = Matrices.dense(2, 2, [4.0, 5.0, 3.0, -3.0])
self.assertRaises(IllegalArgumentException, Statistics.chiSqTest, neg_counts)
# Row sum = 0.0
row_zero = Matrices.dense(2, 2, [0.0, 1.0, 0.0, 2.0])
self.assertRaises(IllegalArgumentException, Statistics.chiSqTest, row_zero)
# Column sum = 0.0
col_zero = Matrices.dense(2, 2, [0.0, 0.0, 2.0, 2.0])
self.assertRaises(IllegalArgumentException, Statistics.chiSqTest, col_zero)
def test_chi_sq_pearson(self):
data = [
LabeledPoint(0.0, Vectors.dense([0.5, 10.0])),
LabeledPoint(0.0, Vectors.dense([1.5, 20.0])),
LabeledPoint(1.0, Vectors.dense([1.5, 30.0])),
LabeledPoint(0.0, Vectors.dense([3.5, 30.0])),
LabeledPoint(0.0, Vectors.dense([3.5, 40.0])),
LabeledPoint(1.0, Vectors.dense([3.5, 40.0]))
]
for numParts in [2, 4, 6, 8]:
chi = Statistics.chiSqTest(self.sc.parallelize(data, numParts))
feature1 = chi[0]
self.assertEqual(feature1.statistic, 0.75)
self.assertEqual(feature1.degreesOfFreedom, 2)
self.assertAlmostEqual(feature1.pValue, 0.6873, 4)
feature2 = chi[1]
self.assertEqual(feature2.statistic, 1.5)
self.assertEqual(feature2.degreesOfFreedom, 3)
self.assertAlmostEqual(feature2.pValue, 0.6823, 4)
def test_right_number_of_results(self):
num_cols = 1001
sparse_data = [
LabeledPoint(0.0, Vectors.sparse(num_cols, [(100, 2.0)])),
LabeledPoint(0.1, Vectors.sparse(num_cols, [(200, 1.0)]))
]
chi = Statistics.chiSqTest(self.sc.parallelize(sparse_data))
self.assertEqual(len(chi), num_cols)
self.assertIsNotNone(chi[1000])
class KolmogorovSmirnovTest(MLlibTestCase):
def test_R_implementation_equivalence(self):
data = self.sc.parallelize([
1.1626852897838, -0.585924465893051, 1.78546500331661, -1.33259371048501,
-0.446566766553219, 0.569606122374976, -2.88971761441412, -0.869018343326555,
-0.461702683149641, -0.555540910137444, -0.0201353678515895, -0.150382224136063,
-0.628126755843964, 1.32322085193283, -1.52135057001199, -0.437427868856691,
0.970577579543399, 0.0282226444247749, -0.0857821886527593, 0.389214404984942
])
model = Statistics.kolmogorovSmirnovTest(data, "norm")
self.assertAlmostEqual(model.statistic, 0.189, 3)
self.assertAlmostEqual(model.pValue, 0.422, 3)
model = Statistics.kolmogorovSmirnovTest(data, "norm", 0, 1)
self.assertAlmostEqual(model.statistic, 0.189, 3)
self.assertAlmostEqual(model.pValue, 0.422, 3)
if __name__ == "__main__":
from pyspark.mllib.tests.test_stat import * # noqa: F401
try:
import xmlrunner # type: ignore[import]
testRunner = xmlrunner.XMLTestRunner(output='target/test-reports', verbosity=2)
except ImportError:
testRunner = None
unittest.main(testRunner=testRunner, verbosity=2)
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