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Diffstat (limited to 'examples/src/main/python')
6 files changed, 277 insertions, 0 deletions
diff --git a/examples/src/main/python/mllib/correlations_example.py b/examples/src/main/python/mllib/correlations_example.py new file mode 100644 index 0000000000..66d18f6e5d --- /dev/null +++ b/examples/src/main/python/mllib/correlations_example.py @@ -0,0 +1,48 @@ +# +# 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 + +import numpy as np + +from pyspark import SparkContext +# $example on$ +from pyspark.mllib.stat import Statistics +# $example off$ + +if __name__ == "__main__": + sc = SparkContext(appName="CorrelationsExample") # SparkContext + + # $example on$ + seriesX = sc.parallelize([1.0, 2.0, 3.0, 3.0, 5.0]) # a series + # seriesY must have the same number of partitions and cardinality as seriesX + seriesY = sc.parallelize([11.0, 22.0, 33.0, 33.0, 555.0]) + + # Compute the correlation using Pearson's method. Enter "spearman" for Spearman's method. + # If a method is not specified, Pearson's method will be used by default. + print("Correlation is: " + str(Statistics.corr(seriesX, seriesY, method="pearson"))) + + data = sc.parallelize( + [np.array([1.0, 10.0, 100.0]), np.array([2.0, 20.0, 200.0]), np.array([5.0, 33.0, 366.0])] + ) # an RDD of Vectors + + # calculate the correlation matrix using Pearson's method. Use "spearman" for Spearman's method. + # If a method is not specified, Pearson's method will be used by default. + print(Statistics.corr(data, method="pearson")) + # $example off$ + + sc.stop() diff --git a/examples/src/main/python/mllib/hypothesis_testing_example.py b/examples/src/main/python/mllib/hypothesis_testing_example.py new file mode 100644 index 0000000000..e566ead0d3 --- /dev/null +++ b/examples/src/main/python/mllib/hypothesis_testing_example.py @@ -0,0 +1,65 @@ +# +# 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() diff --git a/examples/src/main/python/mllib/hypothesis_testing_kolmogorov_smirnov_test_example.py b/examples/src/main/python/mllib/hypothesis_testing_kolmogorov_smirnov_test_example.py new file mode 100644 index 0000000000..ef380dee79 --- /dev/null +++ b/examples/src/main/python/mllib/hypothesis_testing_kolmogorov_smirnov_test_example.py @@ -0,0 +1,40 @@ +# +# 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.stat import Statistics +# $example off$ + +if __name__ == "__main__": + sc = SparkContext(appName="HypothesisTestingKolmogorovSmirnovTestExample") + + # $example on$ + parallelData = sc.parallelize([0.1, 0.15, 0.2, 0.3, 0.25]) + + # run a KS test for the sample versus a standard normal distribution + testResult = Statistics.kolmogorovSmirnovTest(parallelData, "norm", 0, 1) + # summary of the test including the p-value, test statistic, and null hypothesis + # if our p-value indicates significance, we can reject the null hypothesis + # Note that the Scala functionality of calling Statistics.kolmogorovSmirnovTest with + # a lambda to calculate the CDF is not made available in the Python API + print(testResult) + # $example off$ + + sc.stop() diff --git a/examples/src/main/python/mllib/kernel_density_estimation_example.py b/examples/src/main/python/mllib/kernel_density_estimation_example.py new file mode 100644 index 0000000000..3e8f7241a4 --- /dev/null +++ b/examples/src/main/python/mllib/kernel_density_estimation_example.py @@ -0,0 +1,44 @@ +# +# 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.stat import KernelDensity +# $example off$ + +if __name__ == "__main__": + sc = SparkContext(appName="KernelDensityEstimationExample") # SparkContext + + # $example on$ + # an RDD of sample data + data = sc.parallelize([1.0, 1.0, 1.0, 2.0, 3.0, 4.0, 5.0, 5.0, 6.0, 7.0, 8.0, 9.0, 9.0]) + + # Construct the density estimator with the sample data and a standard deviation for the Gaussian + # kernels + kd = KernelDensity() + kd.setSample(data) + kd.setBandwidth(3.0) + + # Find density estimates for the given values + densities = kd.estimate([-1.0, 2.0, 5.0]) + # $example off$ + + print(densities) + + sc.stop() diff --git a/examples/src/main/python/mllib/stratified_sampling_example.py b/examples/src/main/python/mllib/stratified_sampling_example.py new file mode 100644 index 0000000000..a13f8f08dd --- /dev/null +++ b/examples/src/main/python/mllib/stratified_sampling_example.py @@ -0,0 +1,38 @@ +# +# 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 + +if __name__ == "__main__": + sc = SparkContext(appName="StratifiedSamplingExample") # SparkContext + + # $example on$ + # an RDD of any key value pairs + data = sc.parallelize([(1, 'a'), (1, 'b'), (2, 'c'), (2, 'd'), (2, 'e'), (3, 'f')]) + + # specify the exact fraction desired from each key as a dictionary + fractions = {1: 0.1, 2: 0.6, 3: 0.3} + + approxSample = data.sampleByKey(False, fractions) + # $example off$ + + for each in approxSample.collect(): + print(each) + + sc.stop() diff --git a/examples/src/main/python/mllib/summary_statistics_example.py b/examples/src/main/python/mllib/summary_statistics_example.py new file mode 100644 index 0000000000..d55d1a2c2d --- /dev/null +++ b/examples/src/main/python/mllib/summary_statistics_example.py @@ -0,0 +1,42 @@ +# +# 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 +# $example on$ +import numpy as np + +from pyspark.mllib.stat import Statistics +# $example off$ + +if __name__ == "__main__": + sc = SparkContext(appName="SummaryStatisticsExample") # SparkContext + + # $example on$ + mat = sc.parallelize( + [np.array([1.0, 10.0, 100.0]), np.array([2.0, 20.0, 200.0]), np.array([3.0, 30.0, 300.0])] + ) # an RDD of Vectors + + # Compute column summary statistics. + summary = Statistics.colStats(mat) + print(summary.mean()) # a dense vector containing the mean value for each column + print(summary.variance()) # column-wise variance + print(summary.numNonzeros()) # number of nonzeros in each column + # $example off$ + + sc.stop() |