examples/src/main/python/ml/robust_scaler_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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# $example on$
from pyspark.ml.feature import RobustScaler
# $example off$
from pyspark.sql import SparkSession

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

    # $example on$
    dataFrame = spark.read.format("libsvm").load("data/mllib/sample_libsvm_data.txt")
    scaler = RobustScaler(inputCol="features", outputCol="scaledFeatures",
                          withScaling=True, withCentering=False,
                          lower=0.25, upper=0.75)

    # Compute summary statistics by fitting the RobustScaler
    scalerModel = scaler.fit(dataFrame)

    # Transform each feature to have unit quantile range.
    scaledData = scalerModel.transform(dataFrame)
    scaledData.show()
    # $example off$

    spark.stop()