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# The ASF licenses this file to You under the Apache License, Version 2.0
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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
>>> import sys
>>> 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])
... ]
>>> categoricalFeaturesInfo = {} # no categorical features
>>> model = DecisionTree.trainClassifier(sc.parallelize(data), numClasses=2,
... categoricalFeaturesInfo=categoricalFeaturesInfo)
>>> sys.stdout.write(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),
... categoricalFeaturesInfo=categoricalFeaturesInfo)
>>> 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
"""
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, "classification",
numClasses, categoricalFeaturesInfoJMap,
impurity, maxDepth, maxBins)
dataBytes.unpersist()
return DecisionTreeModel(sc, model)
@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
"""
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, "regression",
0, 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()
