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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 Example.
"""
from __future__ import print_function
from pyspark import SparkContext
# $example on$
from pyspark.mllib.tree import GradientBoostedTrees, GradientBoostedTreesModel
from pyspark.mllib.util import MLUtils
# $example off$
if __name__ == "__main__":
sc = SparkContext(appName="PythonGradientBoostedTreesClassificationExample")
# $example on$
# Load and parse the data file.
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])
# Train a GradientBoostedTrees model.
# Notes: (a) Empty categoricalFeaturesInfo indicates all features are continuous.
# (b) Use more iterations in practice.
model = GradientBoostedTrees.trainClassifier(trainingData,
categoricalFeaturesInfo={}, numIterations=3)
# 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 lp: lp[0] != lp[1]).count() / float(testData.count())
print('Test Error = ' + str(testErr))
print('Learned classification GBT model:')
print(model.toDebugString())
# Save and load model
model.save(sc, "target/tmp/myGradientBoostingClassificationModel")
sameModel = GradientBoostedTreesModel.load(sc,
"target/tmp/myGradientBoostingClassificationModel")
# $example off$
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