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
143
144
145
146
147
148
149
150
151
152
153
154
155
156
157
158
159
160
161
162
163
164
165
166
167
168
169
170
171
172
173
174
175
176
177
178
179
180
181
182
183
184
185
186
187
188
189
190
191
192
193
194
195
196
197
198
199
200
201
202
203
204
205
206
207
208
209
210
211
212
213
214
215
216
217
218
219
220
221
222
223
224
225
|
#
# 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.
#
from py4j.java_collections import MapConverter
from pyspark import SparkContext, RDD
from pyspark.mllib._common import \
_get_unmangled_rdd, _get_unmangled_double_vector_rdd, _serialize_double_vector, \
_deserialize_labeled_point, _get_unmangled_labeled_point_rdd, \
_deserialize_double
from pyspark.mllib.regression import LabeledPoint
from pyspark.serializers import NoOpSerializer
class DecisionTreeModel(object):
"""
A decision tree model for classification or regression.
EXPERIMENTAL: This is an experimental API.
It will probably be modified for Spark v1.2.
"""
def __init__(self, sc, java_model):
"""
:param sc: Spark context
:param java_model: Handle to Java model object
"""
self._sc = sc
self._java_model = java_model
def __del__(self):
self._sc._gateway.detach(self._java_model)
def predict(self, x):
"""
Predict the label of one or more examples.
:param x: Data point (feature vector),
or an RDD of data points (feature vectors).
"""
pythonAPI = self._sc._jvm.PythonMLLibAPI()
if isinstance(x, RDD):
# Bulk prediction
if x.count() == 0:
return self._sc.parallelize([])
dataBytes = _get_unmangled_double_vector_rdd(x, cache=False)
jSerializedPreds = \
pythonAPI.predictDecisionTreeModel(self._java_model,
dataBytes._jrdd)
serializedPreds = RDD(jSerializedPreds, self._sc, NoOpSerializer())
return serializedPreds.map(lambda bytes: _deserialize_double(bytearray(bytes)))
else:
# Assume x is a single data point.
x_ = _serialize_double_vector(x)
return pythonAPI.predictDecisionTreeModel(self._java_model, x_)
def numNodes(self):
return self._java_model.numNodes()
def depth(self):
return self._java_model.depth()
def __str__(self):
return self._java_model.toString()
class DecisionTree(object):
"""
Learning algorithm for a decision tree model
for classification or regression.
EXPERIMENTAL: This is an experimental API.
It will probably be modified for Spark v1.2.
Example usage:
>>> from numpy import array, ndarray
>>> from pyspark.mllib.regression import LabeledPoint
>>> from pyspark.mllib.tree import DecisionTree
>>> from pyspark.mllib.linalg import SparseVector
>>>
>>> data = [
... LabeledPoint(0.0, [0.0]),
... LabeledPoint(1.0, [1.0]),
... LabeledPoint(1.0, [2.0]),
... LabeledPoint(1.0, [3.0])
... ]
>>>
>>> model = DecisionTree.trainClassifier(sc.parallelize(data), numClasses=2)
>>> print(model)
DecisionTreeModel classifier
If (feature 0 <= 0.5)
Predict: 0.0
Else (feature 0 > 0.5)
Predict: 1.0
>>> model.predict(array([1.0])) > 0
True
>>> model.predict(array([0.0])) == 0
True
>>> sparse_data = [
... LabeledPoint(0.0, SparseVector(2, {0: 0.0})),
... LabeledPoint(1.0, SparseVector(2, {1: 1.0})),
... LabeledPoint(0.0, SparseVector(2, {0: 0.0})),
... LabeledPoint(1.0, SparseVector(2, {1: 2.0}))
... ]
>>>
>>> model = DecisionTree.trainRegressor(sc.parallelize(sparse_data))
>>> model.predict(array([0.0, 1.0])) == 1
True
>>> model.predict(array([0.0, 0.0])) == 0
True
>>> model.predict(SparseVector(2, {1: 1.0})) == 1
True
>>> model.predict(SparseVector(2, {1: 0.0})) == 0
True
"""
@staticmethod
def trainClassifier(data, numClasses, categoricalFeaturesInfo={},
impurity="gini", maxDepth=4, maxBins=100):
"""
Train a DecisionTreeModel for classification.
:param data: Training data: RDD of LabeledPoint.
Labels are integers {0,1,...,numClasses}.
:param numClasses: Number of classes for classification.
:param categoricalFeaturesInfo: Map from categorical feature index
to number of categories.
Any feature not in this map
is treated as continuous.
:param impurity: Supported values: "entropy" or "gini"
:param maxDepth: Max depth of tree.
E.g., depth 0 means 1 leaf node.
Depth 1 means 1 internal node + 2 leaf nodes.
:param maxBins: Number of bins used for finding splits at each node.
:return: DecisionTreeModel
"""
return DecisionTree.train(data, "classification", numClasses,
categoricalFeaturesInfo,
impurity, maxDepth, maxBins)
@staticmethod
def trainRegressor(data, categoricalFeaturesInfo={},
impurity="variance", maxDepth=4, maxBins=100):
"""
Train a DecisionTreeModel for regression.
:param data: Training data: RDD of LabeledPoint.
Labels are real numbers.
:param categoricalFeaturesInfo: Map from categorical feature index
to number of categories.
Any feature not in this map
is treated as continuous.
:param impurity: Supported values: "variance"
:param maxDepth: Max depth of tree.
E.g., depth 0 means 1 leaf node.
Depth 1 means 1 internal node + 2 leaf nodes.
:param maxBins: Number of bins used for finding splits at each node.
:return: DecisionTreeModel
"""
return DecisionTree.train(data, "regression", 0,
categoricalFeaturesInfo,
impurity, maxDepth, maxBins)
@staticmethod
def train(data, algo, numClasses, categoricalFeaturesInfo,
impurity, maxDepth, maxBins=100):
"""
Train a DecisionTreeModel for classification or regression.
:param data: Training data: RDD of LabeledPoint.
For classification, labels are integers
{0,1,...,numClasses}.
For regression, labels are real numbers.
:param algo: "classification" or "regression"
:param numClasses: Number of classes for classification.
:param categoricalFeaturesInfo: Map from categorical feature index
to number of categories.
Any feature not in this map
is treated as continuous.
:param impurity: For classification: "entropy" or "gini".
For regression: "variance".
:param maxDepth: Max depth of tree.
E.g., depth 0 means 1 leaf node.
Depth 1 means 1 internal node + 2 leaf nodes.
:param maxBins: Number of bins used for finding splits at each node.
:return: DecisionTreeModel
"""
sc = data.context
dataBytes = _get_unmangled_labeled_point_rdd(data)
categoricalFeaturesInfoJMap = \
MapConverter().convert(categoricalFeaturesInfo,
sc._gateway._gateway_client)
model = sc._jvm.PythonMLLibAPI().trainDecisionTreeModel(
dataBytes._jrdd, algo,
numClasses, categoricalFeaturesInfoJMap,
impurity, maxDepth, maxBins)
dataBytes.unpersist()
return DecisionTreeModel(sc, model)
def _test():
import doctest
globs = globals().copy()
globs['sc'] = SparkContext('local[4]', 'PythonTest', batchSize=2)
(failure_count, test_count) = doctest.testmod(globs=globs, optionflags=doctest.ELLIPSIS)
globs['sc'].stop()
if failure_count:
exit(-1)
if __name__ == "__main__":
_test()
|
