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#
# 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.
#
"""
Gradient boosted Trees classification and regression using MLlib.
"""
import sys
from pyspark.context import SparkContext
from pyspark.mllib.tree import GradientBoostedTrees
from pyspark.mllib.util import MLUtils
def testClassification(trainingData, testData):
# Train a GradientBoostedTrees model.
# Empty categoricalFeaturesInfo indicates all features are continuous.
model = GradientBoostedTrees.trainClassifier(trainingData, categoricalFeaturesInfo={},
numIterations=30, maxDepth=4)
# 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 ensemble model:')
print(model.toDebugString())
def testRegression(trainingData, testData):
# Train a GradientBoostedTrees model.
# Empty categoricalFeaturesInfo indicates all features are continuous.
model = GradientBoostedTrees.trainRegressor(trainingData, categoricalFeaturesInfo={},
numIterations=30, maxDepth=4)
# 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 ensemble model:')
print(model.toDebugString())
if __name__ == "__main__":
if len(sys.argv) > 1:
print >> sys.stderr, "Usage: gradient_boosted_trees"
exit(1)
sc = SparkContext(appName="PythonGradientBoostedTrees")
# 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 GradientBoostedTrees\n')
testClassification(trainingData, testData)
print('\nRunning example of regression using GradientBoostedTrees\n')
testRegression(trainingData, testData)
sc.stop()
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