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

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# 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 __future__ import print_function

# $example on$
from pyspark.ml.classification import LogisticRegression
# $example off$
from pyspark.sql import SparkSession

"""
An example demonstrating Logistic Regression Summary.
Run with:
  bin/spark-submit examples/src/main/python/ml/logistic_regression_summary_example.py
"""

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

    # Load training data
    training = spark.read.format("libsvm").load("data/mllib/sample_libsvm_data.txt")

    lr = LogisticRegression(maxIter=10, regParam=0.3, elasticNetParam=0.8)

    # Fit the model
    lrModel = lr.fit(training)

    # $example on$
    # Extract the summary from the returned LogisticRegressionModel instance trained
    # in the earlier example
    trainingSummary = lrModel.summary

    # Obtain the objective per iteration
    objectiveHistory = trainingSummary.objectiveHistory
    print("objectiveHistory:")
    for objective in objectiveHistory:
        print(objective)

    # Obtain the receiver-operating characteristic as a dataframe and areaUnderROC.
    trainingSummary.roc.show()
    print("areaUnderROC: " + str(trainingSummary.areaUnderROC))

    # Set the model threshold to maximize F-Measure
    fMeasure = trainingSummary.fMeasureByThreshold
    maxFMeasure = fMeasure.groupBy().max('F-Measure').select('max(F-Measure)').head()
    bestThreshold = fMeasure.where(fMeasure['F-Measure'] == maxFMeasure['max(F-Measure)']) \
        .select('threshold').head()['threshold']
    lr.setThreshold(bestThreshold)
    # $example off$

    spark.stop()