#

# 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

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# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.

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from __future__ import print_function

# $example on$

from pyspark.ml.linalg import Vectors

from pyspark.ml.stat import Correlation

# $example off$

from pyspark.sql import SparkSession

"""

An example for computing correlation matrix.

Run with:

bin/spark-submit examples/src/main/python/ml/correlation_example.py

"""

if __name__ == "__main__":

spark = SparkSession \

.builder \

.appName("CorrelationExample") \

.getOrCreate()

# $example on$

data = [(Vectors.sparse(4, [(0, 1.0), (3, -2.0)]),),

(Vectors.dense([4.0, 5.0, 0.0, 3.0]),),

(Vectors.dense([6.0, 7.0, 0.0, 8.0]),),

(Vectors.sparse(4, [(0, 9.0), (3, 1.0)]),)]

df = spark.createDataFrame(data, ["features"])

r1 = Correlation.corr(df, "features").head()

print("Pearson correlation matrix:\n" + str(r1[0]))

r2 = Correlation.corr(df, "features", "spearman").head()

print("Spearman correlation matrix:\n" + str(r2[0]))

# $example off$

spark.stop()