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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.
#
from __future__ import print_function
from pyspark import SparkContext
# $example on$
from pyspark.mllib.linalg import Matrices, Vectors
from pyspark.mllib.regression import LabeledPoint
from pyspark.mllib.stat import Statistics
# $example off$
if __name__ == "__main__":
sc = SparkContext(appName="HypothesisTestingExample")
# $example on$
vec = Vectors.dense(0.1, 0.15, 0.2, 0.3, 0.25) # a vector composed of the frequencies of events
# compute the goodness of fit. If a second vector to test against
# is not supplied as a parameter, the test runs against a uniform distribution.
goodnessOfFitTestResult = Statistics.chiSqTest(vec)
# summary of the test including the p-value, degrees of freedom,
# test statistic, the method used, and the null hypothesis.
print("%s\n" % goodnessOfFitTestResult)
mat = Matrices.dense(3, 2, [1.0, 3.0, 5.0, 2.0, 4.0, 6.0]) # a contingency matrix
# conduct Pearson's independence test on the input contingency matrix
independenceTestResult = Statistics.chiSqTest(mat)
# summary of the test including the p-value, degrees of freedom,
# test statistic, the method used, and the null hypothesis.
print("%s\n" % independenceTestResult)
obs = sc.parallelize(
[LabeledPoint(1.0, [1.0, 0.0, 3.0]),
LabeledPoint(1.0, [1.0, 2.0, 0.0]),
LabeledPoint(1.0, [-1.0, 0.0, -0.5])]
) # LabeledPoint(feature, label)
# The contingency table is constructed from an RDD of LabeledPoint and used to conduct
# the independence test. Returns an array containing the ChiSquaredTestResult for every feature
# against the label.
featureTestResults = Statistics.chiSqTest(obs)
for i, result in enumerate(featureTestResults):
print("Column %d:\n%s" % (i + 1, result))
# $example off$
sc.stop()
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