examples/src/main/python/ml/logistic_regression_summary_example.py - master - githubmirror/spark

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

65 строк2 KB

HyukjinKwon

[SPARK-32138] Drop Python 2.7, 3.4 and 3.5

14 июл 2020, 05:22

14 июл 2020, 05:224ad9bfd

#
# 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
# distributed under the License is distributed on an "AS IS" BASIS,
# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
# See the License for the specific language governing permissions and
# limitations under the License.
#

"""
An example demonstrating Logistic Regression Summary.
Run with:
  bin/spark-submit examples/src/main/python/ml/logistic_regression_summary_example.py
"""
# $example on$
from pyspark.ml.classification import LogisticRegression
# $example off$
from pyspark.sql import SparkSession

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()