examples/src/main/python/mllib/random_forest_example.py

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"""
Random Forest classification and regression using MLlib.

Note: This example illustrates binary classification.
      For information on multiclass classification, please refer to the decision_tree_runner.py
      example.
"""

import sys

from pyspark.context import SparkContext
from pyspark.mllib.tree import RandomForest
from pyspark.mllib.util import MLUtils


def testClassification(trainingData, testData):
    # Train a RandomForest model.
    #  Empty categoricalFeaturesInfo indicates all features are continuous.
    #  Note: Use larger numTrees in practice.
    #  Setting featureSubsetStrategy="auto" lets the algorithm choose.
    model = RandomForest.trainClassifier(trainingData, numClasses=2,
                                         categoricalFeaturesInfo={},
                                         numTrees=3, featureSubsetStrategy="auto",
                                         impurity='gini', maxDepth=4, maxBins=32)

    # Evaluate model on test instances and compute test error
    predictions = model.predict(testData.map(lambda x: x.features))
    labelsAndPredictions = testData.map(lambda lp: lp.label).zip(predictions)
    testErr = labelsAndPredictions.filter(lambda (v, p): v != p).count()\
        / float(testData.count())
    print('Test Error = ' + str(testErr))
    print('Learned classification forest model:')
    print(model.toDebugString())


def testRegression(trainingData, testData):
    # Train a RandomForest model.
    #  Empty categoricalFeaturesInfo indicates all features are continuous.
    #  Note: Use larger numTrees in practice.
    #  Setting featureSubsetStrategy="auto" lets the algorithm choose.
    model = RandomForest.trainRegressor(trainingData, categoricalFeaturesInfo={},
                                        numTrees=3, featureSubsetStrategy="auto",
                                        impurity='variance', maxDepth=4, maxBins=32)

    # Evaluate model on test instances and compute test error
    predictions = model.predict(testData.map(lambda x: x.features))
    labelsAndPredictions = testData.map(lambda lp: lp.label).zip(predictions)
    testMSE = labelsAndPredictions.map(lambda (v, p): (v - p) * (v - p)).sum()\
        / float(testData.count())
    print('Test Mean Squared Error = ' + str(testMSE))
    print('Learned regression forest model:')
    print(model.toDebugString())


if __name__ == "__main__":
    if len(sys.argv) > 1:
        print >> sys.stderr, "Usage: random_forest_example"
        exit(1)
    sc = SparkContext(appName="PythonRandomForestExample")

    # Load and parse the data file into an RDD of LabeledPoint.
    data = MLUtils.loadLibSVMFile(sc, 'data/mllib/sample_libsvm_data.txt')
    # Split the data into training and test sets (30% held out for testing)
    (trainingData, testData) = data.randomSplit([0.7, 0.3])

    print('\nRunning example of classification using RandomForest\n')
    testClassification(trainingData, testData)

    print('\nRunning example of regression using RandomForest\n')
    testRegression(trainingData, testData)

    sc.stop()