#
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# The ASF licenses this file to You under the Apache License, Version 2.0
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"""
Binary Classification Metrics Example.
"""
from __future__ import print_function
import sys
from pyspark import SparkContext, SQLContext
# $example on$
from pyspark.mllib.classification import LogisticRegressionWithLBFGS
from pyspark.mllib.evaluation import BinaryClassificationMetrics
from pyspark.mllib.util import MLUtils
# $example off$

if __name__ == "__main__":
    sc = SparkContext(appName="BinaryClassificationMetricsExample")
    sqlContext = SQLContext(sc)
    # $example on$
    # Several of the methods available in scala are currently missing from pyspark
    # Load training data in LIBSVM format
    data = MLUtils.loadLibSVMFile(sc, "data/mllib/sample_binary_classification_data.txt")

    # Split data into training (60%) and test (40%)
    training, test = data.randomSplit([0.6, 0.4], seed=11L)
    training.cache()

    # Run training algorithm to build the model
    model = LogisticRegressionWithLBFGS.train(training)

    # Compute raw scores on the test set
    predictionAndLabels = test.map(lambda lp: (float(model.predict(lp.features)), lp.label))

    # Instantiate metrics object
    metrics = BinaryClassificationMetrics(predictionAndLabels)

    # Area under precision-recall curve
    print("Area under PR = %s" % metrics.areaUnderPR)

    # Area under ROC curve
    print("Area under ROC = %s" % metrics.areaUnderROC)
    # $example off$
