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Bryan Cutler authored
## What changes were proposed in this pull request? Improve example outputs to better reflect the functionality that is being presented. This mostly consisted of modifying what was printed at the end of the example, such as calling show() with truncate=False, but sometimes required minor tweaks in the example data to get relevant output. Explicitly set parameters when they are used as part of the example. Fixed Java examples that failed to run because of using old-style MLlib Vectors or problem with schema. Synced examples between different APIs. ## How was this patch tested? Ran each example for Scala, Python, and Java and made sure output was legible on a terminal of width 100. Author: Bryan Cutler <cutlerb@gmail.com> Closes #14308 from BryanCutler/ml-examples-improve-output-SPARK-16260.
Bryan Cutler authored## What changes were proposed in this pull request? Improve example outputs to better reflect the functionality that is being presented. This mostly consisted of modifying what was printed at the end of the example, such as calling show() with truncate=False, but sometimes required minor tweaks in the example data to get relevant output. Explicitly set parameters when they are used as part of the example. Fixed Java examples that failed to run because of using old-style MLlib Vectors or problem with schema. Synced examples between different APIs. ## How was this patch tested? Ran each example for Scala, Python, and Java and made sure output was legible on a terminal of width 100. Author: Bryan Cutler <cutlerb@gmail.com> Closes #14308 from BryanCutler/ml-examples-improve-output-SPARK-16260.
binarizer_example.py 1.49 KiB
#
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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
# (the "License"); you may not use this file except in compliance with
# the License. You may obtain a copy of the License at
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# http://www.apache.org/licenses/LICENSE-2.0
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#
from __future__ import print_function
from pyspark.sql import SparkSession
# $example on$
from pyspark.ml.feature import Binarizer
# $example off$
if __name__ == "__main__":
spark = SparkSession\
.builder\
.appName("BinarizerExample")\
.getOrCreate()
# $example on$
continuousDataFrame = spark.createDataFrame([
(0, 0.1),
(1, 0.8),
(2, 0.2)
], ["id", "feature"])
binarizer = Binarizer(threshold=0.5, inputCol="feature", outputCol="binarized_feature")
binarizedDataFrame = binarizer.transform(continuousDataFrame)
print("Binarizer output with Threshold = %f" % binarizer.getThreshold())
binarizedDataFrame.show()
# $example off$
spark.stop()