1
2
3
4
5
6
7
8
9
10
11
12
13
14
15
16
17
18
19
20
21
22
23
24
25
26
27
28
29
30
31
32
33
34
35
36
37
38
39
40
41
42
43
44
45
46
47
48
49
50
51
52
53
54
55
56
57
58
59
60
61
62
63
64
65
66
67
68
69
70
71
72
73
74
75
76
77
78
79
80
81
82
83
84
85
86
87
88
89
90
91
92
93
94
95
96
97
98
99
100
101
102
103
104
105
106
107
108
109
110
111
112
113
114
115
116
117
118
119
120
121
122
123
124
125
126
127
128
129
130
131
132
133
134
135
136
137
138
139
140
141
142
|
#
# 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/).
"""
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 >> 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):
sc.stop()
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
sc.stop()
|