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[SPARK-5094][MLlib] Add Python API for Gradient Boosted Trees
This PR is implementing the Gradient Boosted Trees for Python API. Author: Kazuki Taniguchi <kazuki.t.1018@gmail.com> Closes #3951 from kazk1018/gbt_for_py and squashes the following commits: 620d247 [Kazuki Taniguchi] [SPARK-5094][MLlib] Add Python API for Gradient Boosted Trees
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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. | ||
| # | ||
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| """ | ||
| Gradient boosted Trees classification and regression using MLlib. | ||
| """ | ||
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| import sys | ||
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| from pyspark.context import SparkContext | ||
| from pyspark.mllib.tree import GradientBoostedTrees | ||
| from pyspark.mllib.util import MLUtils | ||
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| 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()) | ||
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| 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()) | ||
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| if __name__ == "__main__": | ||
| if len(sys.argv) > 1: | ||
| print >> sys.stderr, "Usage: gradient_boosted_trees" | ||
| exit(1) | ||
| sc = SparkContext(appName="PythonGradientBoostedTrees") | ||
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| # 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]) | ||
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| print('\nRunning example of classification using GradientBoostedTrees\n') | ||
| testClassification(trainingData, testData) | ||
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| print('\nRunning example of regression using GradientBoostedTrees\n') | ||
| testRegression(trainingData, testData) | ||
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| sc.stop() |
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