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Diffstat (limited to 'examples/src/main/python/mllib/random_forest_example.py')
-rwxr-xr-x | examples/src/main/python/mllib/random_forest_example.py | 90 |
1 files changed, 0 insertions, 90 deletions
diff --git a/examples/src/main/python/mllib/random_forest_example.py b/examples/src/main/python/mllib/random_forest_example.py deleted file mode 100755 index 4cfdad868c..0000000000 --- a/examples/src/main/python/mllib/random_forest_example.py +++ /dev/null @@ -1,90 +0,0 @@ -# -# 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. -# - -""" -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. -""" -from __future__ import print_function - -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[0] != v_p[1]).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_p1: (v_p1[0] - v_p1[1]) * (v_p1[0] - v_p1[1]))\ - .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("Usage: random_forest_example", file=sys.stderr) - 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() |