examples/src/main/python/ml/bucketizer_example.py

#
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# this work for additional information regarding copyright ownership.
# The ASF licenses this file to You under the Apache License, Version 2.0
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#
#    http://www.apache.org/licenses/LICENSE-2.0
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from pyspark.sql import SparkSession
# $example on$
from pyspark.ml.feature import Bucketizer
# $example off$

if __name__ == "__main__":
    spark = SparkSession\
        .builder\
        .appName("BucketizerExample")\
        .getOrCreate()

    # $example on$
    splits = [-float("inf"), -0.5, 0.0, 0.5, float("inf")]

    data = [(-999.9,), (-0.5,), (-0.3,), (0.0,), (0.2,), (999.9,)]
    dataFrame = spark.createDataFrame(data, ["features"])

    bucketizer = Bucketizer(splits=splits, inputCol="features", outputCol="bucketedFeatures")

    # Transform original data into its bucket index.
    bucketedData = bucketizer.transform(dataFrame)

    print("Bucketizer output with %d buckets" % (len(bucketizer.getSplits()) - 1))
    bucketedData.show()
    # $example off$

    spark.stop()