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Diffstat (limited to 'examples/src/main/python/mllib/decision_tree_runner.py')
-rwxr-xr-x | examples/src/main/python/mllib/decision_tree_runner.py | 144 |
1 files changed, 0 insertions, 144 deletions
diff --git a/examples/src/main/python/mllib/decision_tree_runner.py b/examples/src/main/python/mllib/decision_tree_runner.py deleted file mode 100755 index 513ed8fd51..0000000000 --- a/examples/src/main/python/mllib/decision_tree_runner.py +++ /dev/null @@ -1,144 +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. -# - -""" -Decision tree classification and regression using MLlib. - -This example requires NumPy (http://www.numpy.org/). -""" -from __future__ import print_function - -import numpy -import os -import sys - -from operator import add - -from pyspark import SparkContext -from pyspark.mllib.regression import LabeledPoint -from pyspark.mllib.tree import DecisionTree -from pyspark.mllib.util import MLUtils - - -def getAccuracy(dtModel, data): - """ - Return accuracy of DecisionTreeModel on the given RDD[LabeledPoint]. - """ - seqOp = (lambda acc, x: acc + (x[0] == x[1])) - predictions = dtModel.predict(data.map(lambda x: x.features)) - truth = data.map(lambda p: p.label) - trainCorrect = predictions.zip(truth).aggregate(0, seqOp, add) - if data.count() == 0: - return 0 - return trainCorrect / (0.0 + data.count()) - - -def getMSE(dtModel, data): - """ - Return mean squared error (MSE) of DecisionTreeModel on the given - RDD[LabeledPoint]. - """ - seqOp = (lambda acc, x: acc + numpy.square(x[0] - x[1])) - predictions = dtModel.predict(data.map(lambda x: x.features)) - truth = data.map(lambda p: p.label) - trainMSE = predictions.zip(truth).aggregate(0, seqOp, add) - if data.count() == 0: - return 0 - return trainMSE / (0.0 + data.count()) - - -def reindexClassLabels(data): - """ - Re-index class labels in a dataset to the range {0,...,numClasses-1}. - If all labels in that range already appear at least once, - then the returned RDD is the same one (without a mapping). - Note: If a label simply does not appear in the data, - the index will not include it. - Be aware of this when reindexing subsampled data. - :param data: RDD of LabeledPoint where labels are integer values - denoting labels for a classification problem. - :return: Pair (reindexedData, origToNewLabels) where - reindexedData is an RDD of LabeledPoint with labels in - the range {0,...,numClasses-1}, and - origToNewLabels is a dictionary mapping original labels - to new labels. - """ - # classCounts: class --> # examples in class - classCounts = data.map(lambda x: x.label).countByValue() - numExamples = sum(classCounts.values()) - sortedClasses = sorted(classCounts.keys()) - numClasses = len(classCounts) - # origToNewLabels: class --> index in 0,...,numClasses-1 - if (numClasses < 2): - print("Dataset for classification should have at least 2 classes." - " The given dataset had only %d classes." % numClasses, file=sys.stderr) - exit(1) - origToNewLabels = dict([(sortedClasses[i], i) for i in range(0, numClasses)]) - - print("numClasses = %d" % numClasses) - print("Per-class example fractions, counts:") - print("Class\tFrac\tCount") - for c in sortedClasses: - frac = classCounts[c] / (numExamples + 0.0) - print("%g\t%g\t%d" % (c, frac, classCounts[c])) - - if (sortedClasses[0] == 0 and sortedClasses[-1] == numClasses - 1): - return (data, origToNewLabels) - else: - reindexedData = \ - data.map(lambda x: LabeledPoint(origToNewLabels[x.label], x.features)) - return (reindexedData, origToNewLabels) - - -def usage(): - print("Usage: decision_tree_runner [libsvm format data filepath]", file=sys.stderr) - exit(1) - - -if __name__ == "__main__": - if len(sys.argv) > 2: - usage() - sc = SparkContext(appName="PythonDT") - - # Load data. - dataPath = 'data/mllib/sample_libsvm_data.txt' - if len(sys.argv) == 2: - dataPath = sys.argv[1] - if not os.path.isfile(dataPath): - sc.stop() - usage() - points = MLUtils.loadLibSVMFile(sc, dataPath) - - # Re-index class labels if needed. - (reindexedData, origToNewLabels) = reindexClassLabels(points) - numClasses = len(origToNewLabels) - - # Train a classifier. - categoricalFeaturesInfo = {} # no categorical features - model = DecisionTree.trainClassifier(reindexedData, numClasses=numClasses, - categoricalFeaturesInfo=categoricalFeaturesInfo) - # Print learned tree and stats. - print("Trained DecisionTree for classification:") - print(" Model numNodes: %d" % model.numNodes()) - print(" Model depth: %d" % model.depth()) - print(" Training accuracy: %g" % getAccuracy(model, reindexedData)) - if model.numNodes() < 20: - print(model.toDebugString()) - else: - print(model) - - sc.stop() |