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"""
Decision tree classification and regression using MLlib.
"""
import numpy, os, 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 >> sys.stderr, \
"Dataset for classification should have at least 2 classes." + \
" The given dataset had only %d classes." % numClasses
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 >> sys.stderr, \
"Usage: decision_tree_runner [libsvm format data filepath]\n" + \
" Note: This only supports binary classification."
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):
usage()
points = MLUtils.loadLibSVMFile(sc, dataPath)
# Re-index class labels if needed.
(reindexedData, origToNewLabels) = reindexClassLabels(points)
# Train a classifier.
categoricalFeaturesInfo={} # no categorical features
model = DecisionTree.trainClassifier(reindexedData, numClasses=2,
categoricalFeaturesInfo=categoricalFeaturesInfo)
# Print learned tree and stats.
print "Trained DecisionTree for classification:"
print " Model numNodes: %d\n" % model.numNodes()
print " Model depth: %d\n" % model.depth()
print " Training accuracy: %g\n" % getAccuracy(model, reindexedData)
print model