613 lines
23 KiB
Python
613 lines
23 KiB
Python
#
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# Licensed to the Apache Software Foundation (ASF) under one or more
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# contributor license agreements. See the NOTICE file distributed with
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# this work for additional information regarding copyright ownership.
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# The ASF licenses this file to You under the Apache License, Version 2.0
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# (the "License"); you may not use this file except in compliance with
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# the License. You may obtain a copy of the License at
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#
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# http://www.apache.org/licenses/LICENSE-2.0
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#
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# Unless required by applicable law or agreed to in writing, software
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# distributed under the License is distributed on an "AS IS" BASIS,
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# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
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# See the License for the specific language governing permissions and
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# limitations under the License.
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#
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from __future__ import absolute_import
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import random
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from pyspark import SparkContext, RDD, since
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from pyspark.mllib.common import callMLlibFunc, inherit_doc, JavaModelWrapper
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from pyspark.mllib.linalg import _convert_to_vector
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from pyspark.mllib.regression import LabeledPoint
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from pyspark.mllib.util import JavaLoader, JavaSaveable
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__all__ = ['DecisionTreeModel', 'DecisionTree', 'RandomForestModel',
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'RandomForest', 'GradientBoostedTreesModel', 'GradientBoostedTrees']
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class TreeEnsembleModel(JavaModelWrapper, JavaSaveable):
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"""TreeEnsembleModel
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.. versionadded:: 1.3.0
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"""
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@since("1.3.0")
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def predict(self, x):
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"""
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Predict values for a single data point or an RDD of points using
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the model trained.
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Note: In Python, predict cannot currently be used within an RDD
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transformation or action.
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Call predict directly on the RDD instead.
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"""
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if isinstance(x, RDD):
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return self.call("predict", x.map(_convert_to_vector))
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else:
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return self.call("predict", _convert_to_vector(x))
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@since("1.3.0")
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def numTrees(self):
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"""
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Get number of trees in ensemble.
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"""
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return self.call("numTrees")
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@since("1.3.0")
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def totalNumNodes(self):
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"""
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Get total number of nodes, summed over all trees in the
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ensemble.
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"""
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return self.call("totalNumNodes")
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def __repr__(self):
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""" Summary of model """
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return self._java_model.toString()
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@since("1.3.0")
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def toDebugString(self):
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""" Full model """
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return self._java_model.toDebugString()
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class DecisionTreeModel(JavaModelWrapper, JavaSaveable, JavaLoader):
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"""
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.. note:: Experimental
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A decision tree model for classification or regression.
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.. versionadded:: 1.1.0
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"""
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@since("1.1.0")
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def predict(self, x):
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"""
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Predict the label of one or more examples.
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Note: In Python, predict cannot currently be used within an RDD
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transformation or action.
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Call predict directly on the RDD instead.
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:param x: Data point (feature vector),
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or an RDD of data points (feature vectors).
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"""
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if isinstance(x, RDD):
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return self.call("predict", x.map(_convert_to_vector))
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else:
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return self.call("predict", _convert_to_vector(x))
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@since("1.1.0")
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def numNodes(self):
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"""Get number of nodes in tree, including leaf nodes."""
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return self._java_model.numNodes()
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@since("1.1.0")
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def depth(self):
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"""Get depth of tree.
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E.g.: Depth 0 means 1 leaf node. Depth 1 means 1 internal node and 2 leaf nodes.
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"""
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return self._java_model.depth()
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def __repr__(self):
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""" summary of model. """
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return self._java_model.toString()
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@since("1.2.0")
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def toDebugString(self):
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""" full model. """
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return self._java_model.toDebugString()
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@classmethod
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def _java_loader_class(cls):
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return "org.apache.spark.mllib.tree.model.DecisionTreeModel"
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class DecisionTree(object):
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"""
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.. note:: Experimental
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Learning algorithm for a decision tree model for classification or
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regression.
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.. versionadded:: 1.1.0
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"""
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@classmethod
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def _train(cls, data, type, numClasses, features, impurity="gini", maxDepth=5, maxBins=32,
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minInstancesPerNode=1, minInfoGain=0.0):
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first = data.first()
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assert isinstance(first, LabeledPoint), "the data should be RDD of LabeledPoint"
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model = callMLlibFunc("trainDecisionTreeModel", data, type, numClasses, features,
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impurity, maxDepth, maxBins, minInstancesPerNode, minInfoGain)
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return DecisionTreeModel(model)
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@classmethod
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@since("1.1.0")
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def trainClassifier(cls, data, numClasses, categoricalFeaturesInfo,
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impurity="gini", maxDepth=5, maxBins=32, minInstancesPerNode=1,
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minInfoGain=0.0):
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"""
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Train a DecisionTreeModel for classification.
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:param data: Training data: RDD of LabeledPoint.
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Labels are integers {0,1,...,numClasses}.
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:param numClasses: Number of classes for classification.
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:param categoricalFeaturesInfo: Map from categorical feature index
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to number of categories.
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Any feature not in this map
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is treated as continuous.
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:param impurity: Supported values: "entropy" or "gini"
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:param maxDepth: Max depth of tree.
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E.g., depth 0 means 1 leaf node.
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Depth 1 means 1 internal node + 2 leaf nodes.
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:param maxBins: Number of bins used for finding splits at each node.
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:param minInstancesPerNode: Min number of instances required at child
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nodes to create the parent split
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:param minInfoGain: Min info gain required to create a split
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:return: DecisionTreeModel
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Example usage:
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>>> from numpy import array
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>>> from pyspark.mllib.regression import LabeledPoint
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>>> from pyspark.mllib.tree import DecisionTree
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>>>
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>>> data = [
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... LabeledPoint(0.0, [0.0]),
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... LabeledPoint(1.0, [1.0]),
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... LabeledPoint(1.0, [2.0]),
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... LabeledPoint(1.0, [3.0])
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... ]
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>>> model = DecisionTree.trainClassifier(sc.parallelize(data), 2, {})
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>>> print(model)
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DecisionTreeModel classifier of depth 1 with 3 nodes
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>>> print(model.toDebugString())
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DecisionTreeModel classifier of depth 1 with 3 nodes
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If (feature 0 <= 0.0)
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Predict: 0.0
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Else (feature 0 > 0.0)
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Predict: 1.0
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<BLANKLINE>
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>>> model.predict(array([1.0]))
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1.0
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>>> model.predict(array([0.0]))
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0.0
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>>> rdd = sc.parallelize([[1.0], [0.0]])
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>>> model.predict(rdd).collect()
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[1.0, 0.0]
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"""
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return cls._train(data, "classification", numClasses, categoricalFeaturesInfo,
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impurity, maxDepth, maxBins, minInstancesPerNode, minInfoGain)
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@classmethod
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@since("1.1.0")
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def trainRegressor(cls, data, categoricalFeaturesInfo,
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impurity="variance", maxDepth=5, maxBins=32, minInstancesPerNode=1,
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minInfoGain=0.0):
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"""
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Train a DecisionTreeModel for regression.
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:param data: Training data: RDD of LabeledPoint.
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Labels are real numbers.
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:param categoricalFeaturesInfo: Map from categorical feature
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index to number of categories.
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Any feature not in this map is treated as continuous.
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:param impurity: Supported values: "variance"
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:param maxDepth: Max depth of tree.
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E.g., depth 0 means 1 leaf node.
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Depth 1 means 1 internal node + 2 leaf nodes.
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:param maxBins: Number of bins used for finding splits at each
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node.
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:param minInstancesPerNode: Min number of instances required at
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child nodes to create the parent split
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:param minInfoGain: Min info gain required to create a split
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:return: DecisionTreeModel
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Example usage:
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>>> from pyspark.mllib.regression import LabeledPoint
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>>> from pyspark.mllib.tree import DecisionTree
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>>> from pyspark.mllib.linalg import SparseVector
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>>>
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>>> sparse_data = [
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... LabeledPoint(0.0, SparseVector(2, {0: 0.0})),
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... LabeledPoint(1.0, SparseVector(2, {1: 1.0})),
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... LabeledPoint(0.0, SparseVector(2, {0: 0.0})),
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... LabeledPoint(1.0, SparseVector(2, {1: 2.0}))
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... ]
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>>>
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>>> model = DecisionTree.trainRegressor(sc.parallelize(sparse_data), {})
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>>> model.predict(SparseVector(2, {1: 1.0}))
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1.0
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>>> model.predict(SparseVector(2, {1: 0.0}))
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0.0
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>>> rdd = sc.parallelize([[0.0, 1.0], [0.0, 0.0]])
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>>> model.predict(rdd).collect()
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[1.0, 0.0]
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"""
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return cls._train(data, "regression", 0, categoricalFeaturesInfo,
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impurity, maxDepth, maxBins, minInstancesPerNode, minInfoGain)
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@inherit_doc
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class RandomForestModel(TreeEnsembleModel, JavaLoader):
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"""
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.. note:: Experimental
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Represents a random forest model.
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.. versionadded:: 1.2.0
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"""
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@classmethod
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def _java_loader_class(cls):
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return "org.apache.spark.mllib.tree.model.RandomForestModel"
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class RandomForest(object):
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"""
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.. note:: Experimental
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Learning algorithm for a random forest model for classification or
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regression.
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.. versionadded:: 1.2.0
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"""
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supportedFeatureSubsetStrategies = ("auto", "all", "sqrt", "log2", "onethird")
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@classmethod
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def _train(cls, data, algo, numClasses, categoricalFeaturesInfo, numTrees,
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featureSubsetStrategy, impurity, maxDepth, maxBins, seed):
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first = data.first()
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assert isinstance(first, LabeledPoint), "the data should be RDD of LabeledPoint"
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if featureSubsetStrategy not in cls.supportedFeatureSubsetStrategies:
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raise ValueError("unsupported featureSubsetStrategy: %s" % featureSubsetStrategy)
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if seed is None:
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seed = random.randint(0, 1 << 30)
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model = callMLlibFunc("trainRandomForestModel", data, algo, numClasses,
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categoricalFeaturesInfo, numTrees, featureSubsetStrategy, impurity,
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maxDepth, maxBins, seed)
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return RandomForestModel(model)
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@classmethod
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@since("1.2.0")
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def trainClassifier(cls, data, numClasses, categoricalFeaturesInfo, numTrees,
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featureSubsetStrategy="auto", impurity="gini", maxDepth=4, maxBins=32,
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seed=None):
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"""
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Method to train a decision tree model for binary or multiclass
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classification.
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:param data: Training dataset: RDD of LabeledPoint. Labels
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should take values {0, 1, ..., numClasses-1}.
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:param numClasses: number of classes for classification.
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:param categoricalFeaturesInfo: Map storing arity of categorical
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features. E.g., an entry (n -> k) indicates that
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feature n is categorical with k categories indexed
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from 0: {0, 1, ..., k-1}.
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:param numTrees: Number of trees in the random forest.
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:param featureSubsetStrategy: Number of features to consider for
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splits at each node.
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Supported: "auto" (default), "all", "sqrt", "log2", "onethird".
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If "auto" is set, this parameter is set based on numTrees:
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if numTrees == 1, set to "all";
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if numTrees > 1 (forest) set to "sqrt".
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:param impurity: Criterion used for information gain calculation.
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Supported values: "gini" (recommended) or "entropy".
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:param maxDepth: Maximum depth of the tree.
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E.g., depth 0 means 1 leaf node; depth 1 means
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1 internal node + 2 leaf nodes. (default: 4)
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:param maxBins: maximum number of bins used for splitting
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features
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(default: 32)
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:param seed: Random seed for bootstrapping and choosing feature
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subsets.
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:return: RandomForestModel that can be used for prediction
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Example usage:
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>>> from pyspark.mllib.regression import LabeledPoint
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>>> from pyspark.mllib.tree import RandomForest
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>>>
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>>> data = [
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... LabeledPoint(0.0, [0.0]),
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... LabeledPoint(0.0, [1.0]),
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... LabeledPoint(1.0, [2.0]),
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... LabeledPoint(1.0, [3.0])
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... ]
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>>> model = RandomForest.trainClassifier(sc.parallelize(data), 2, {}, 3, seed=42)
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>>> model.numTrees()
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3
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>>> model.totalNumNodes()
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7
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>>> print(model)
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TreeEnsembleModel classifier with 3 trees
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<BLANKLINE>
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>>> print(model.toDebugString())
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TreeEnsembleModel classifier with 3 trees
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<BLANKLINE>
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Tree 0:
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Predict: 1.0
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Tree 1:
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If (feature 0 <= 1.0)
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Predict: 0.0
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Else (feature 0 > 1.0)
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Predict: 1.0
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Tree 2:
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If (feature 0 <= 1.0)
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Predict: 0.0
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Else (feature 0 > 1.0)
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Predict: 1.0
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<BLANKLINE>
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>>> model.predict([2.0])
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1.0
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>>> model.predict([0.0])
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0.0
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>>> rdd = sc.parallelize([[3.0], [1.0]])
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>>> model.predict(rdd).collect()
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[1.0, 0.0]
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"""
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return cls._train(data, "classification", numClasses,
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categoricalFeaturesInfo, numTrees, featureSubsetStrategy, impurity,
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maxDepth, maxBins, seed)
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@classmethod
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@since("1.2.0")
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def trainRegressor(cls, data, categoricalFeaturesInfo, numTrees, featureSubsetStrategy="auto",
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impurity="variance", maxDepth=4, maxBins=32, seed=None):
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"""
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Method to train a decision tree model for regression.
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:param data: Training dataset: RDD of LabeledPoint. Labels are
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real numbers.
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:param categoricalFeaturesInfo: Map storing arity of categorical
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features. E.g., an entry (n -> k) indicates that feature
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n is categorical with k categories indexed from 0:
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{0, 1, ..., k-1}.
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:param numTrees: Number of trees in the random forest.
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:param featureSubsetStrategy: Number of features to consider for
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splits at each node.
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Supported: "auto" (default), "all", "sqrt", "log2", "onethird".
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If "auto" is set, this parameter is set based on numTrees:
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if numTrees == 1, set to "all";
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if numTrees > 1 (forest) set to "onethird" for regression.
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:param impurity: Criterion used for information gain
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calculation.
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Supported values: "variance".
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:param maxDepth: Maximum depth of the tree. E.g., depth 0 means
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1 leaf node; depth 1 means 1 internal node + 2 leaf
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nodes. (default: 4)
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:param maxBins: maximum number of bins used for splitting
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features (default: 32)
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:param seed: Random seed for bootstrapping and choosing feature
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subsets.
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:return: RandomForestModel that can be used for prediction
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Example usage:
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>>> from pyspark.mllib.regression import LabeledPoint
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>>> from pyspark.mllib.tree import RandomForest
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>>> from pyspark.mllib.linalg import SparseVector
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>>>
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>>> sparse_data = [
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... LabeledPoint(0.0, SparseVector(2, {0: 1.0})),
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... LabeledPoint(1.0, SparseVector(2, {1: 1.0})),
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... LabeledPoint(0.0, SparseVector(2, {0: 1.0})),
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... LabeledPoint(1.0, SparseVector(2, {1: 2.0}))
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... ]
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>>>
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>>> model = RandomForest.trainRegressor(sc.parallelize(sparse_data), {}, 2, seed=42)
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>>> model.numTrees()
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2
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>>> model.totalNumNodes()
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4
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>>> model.predict(SparseVector(2, {1: 1.0}))
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1.0
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>>> model.predict(SparseVector(2, {0: 1.0}))
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0.5
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>>> rdd = sc.parallelize([[0.0, 1.0], [1.0, 0.0]])
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>>> model.predict(rdd).collect()
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[1.0, 0.5]
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"""
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return cls._train(data, "regression", 0, categoricalFeaturesInfo, numTrees,
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featureSubsetStrategy, impurity, maxDepth, maxBins, seed)
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@inherit_doc
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class GradientBoostedTreesModel(TreeEnsembleModel, JavaLoader):
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"""
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.. note:: Experimental
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Represents a gradient-boosted tree model.
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.. versionadded:: 1.3.0
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"""
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@classmethod
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def _java_loader_class(cls):
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return "org.apache.spark.mllib.tree.model.GradientBoostedTreesModel"
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class GradientBoostedTrees(object):
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"""
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.. note:: Experimental
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Learning algorithm for a gradient boosted trees model for
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classification or regression.
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.. versionadded:: 1.3.0
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"""
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@classmethod
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def _train(cls, data, algo, categoricalFeaturesInfo,
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loss, numIterations, learningRate, maxDepth, maxBins):
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first = data.first()
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assert isinstance(first, LabeledPoint), "the data should be RDD of LabeledPoint"
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model = callMLlibFunc("trainGradientBoostedTreesModel", data, algo, categoricalFeaturesInfo,
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loss, numIterations, learningRate, maxDepth, maxBins)
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return GradientBoostedTreesModel(model)
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@classmethod
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@since("1.3.0")
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def trainClassifier(cls, data, categoricalFeaturesInfo,
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loss="logLoss", numIterations=100, learningRate=0.1, maxDepth=3,
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maxBins=32):
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"""
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|
Method to train a gradient-boosted trees model for
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classification.
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:param data: Training dataset: RDD of LabeledPoint.
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Labels should take values {0, 1}.
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:param categoricalFeaturesInfo: Map storing arity of categorical
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features. E.g., an entry (n -> k) indicates that feature
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n is categorical with k categories indexed from 0:
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{0, 1, ..., k-1}.
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:param loss: Loss function used for minimization during gradient
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boosting. Supported: {"logLoss" (default),
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"leastSquaresError", "leastAbsoluteError"}.
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:param numIterations: Number of iterations of boosting.
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(default: 100)
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:param learningRate: Learning rate for shrinking the
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contribution of each estimator. The learning rate
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should be between in the interval (0, 1].
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(default: 0.1)
|
|
:param maxDepth: Maximum depth of the tree. E.g., depth 0 means
|
|
1 leaf node; depth 1 means 1 internal node + 2 leaf
|
|
nodes. (default: 3)
|
|
:param maxBins: maximum number of bins used for splitting
|
|
features (default: 32) DecisionTree requires maxBins >= max categories
|
|
:return: GradientBoostedTreesModel that can be used for
|
|
prediction
|
|
|
|
Example usage:
|
|
|
|
>>> from pyspark.mllib.regression import LabeledPoint
|
|
>>> from pyspark.mllib.tree import GradientBoostedTrees
|
|
>>>
|
|
>>> data = [
|
|
... LabeledPoint(0.0, [0.0]),
|
|
... LabeledPoint(0.0, [1.0]),
|
|
... LabeledPoint(1.0, [2.0]),
|
|
... LabeledPoint(1.0, [3.0])
|
|
... ]
|
|
>>>
|
|
>>> model = GradientBoostedTrees.trainClassifier(sc.parallelize(data), {}, numIterations=10)
|
|
>>> model.numTrees()
|
|
10
|
|
>>> model.totalNumNodes()
|
|
30
|
|
>>> print(model) # it already has newline
|
|
TreeEnsembleModel classifier with 10 trees
|
|
<BLANKLINE>
|
|
>>> model.predict([2.0])
|
|
1.0
|
|
>>> model.predict([0.0])
|
|
0.0
|
|
>>> rdd = sc.parallelize([[2.0], [0.0]])
|
|
>>> model.predict(rdd).collect()
|
|
[1.0, 0.0]
|
|
"""
|
|
return cls._train(data, "classification", categoricalFeaturesInfo,
|
|
loss, numIterations, learningRate, maxDepth, maxBins)
|
|
|
|
@classmethod
|
|
@since("1.3.0")
|
|
def trainRegressor(cls, data, categoricalFeaturesInfo,
|
|
loss="leastSquaresError", numIterations=100, learningRate=0.1, maxDepth=3,
|
|
maxBins=32):
|
|
"""
|
|
Method to train a gradient-boosted trees model for regression.
|
|
|
|
:param data: Training dataset: RDD of LabeledPoint. Labels are
|
|
real numbers.
|
|
:param categoricalFeaturesInfo: Map storing arity of categorical
|
|
features. E.g., an entry (n -> k) indicates that feature
|
|
n is categorical with k categories indexed from 0:
|
|
{0, 1, ..., k-1}.
|
|
:param loss: Loss function used for minimization during gradient
|
|
boosting. Supported: {"logLoss" (default),
|
|
"leastSquaresError", "leastAbsoluteError"}.
|
|
:param numIterations: Number of iterations of boosting.
|
|
(default: 100)
|
|
:param learningRate: Learning rate for shrinking the
|
|
contribution of each estimator. The learning rate
|
|
should be between in the interval (0, 1].
|
|
(default: 0.1)
|
|
:param maxBins: maximum number of bins used for splitting
|
|
features (default: 32) DecisionTree requires maxBins >= max categories
|
|
:param maxDepth: Maximum depth of the tree. E.g., depth 0 means
|
|
1 leaf node; depth 1 means 1 internal node + 2 leaf
|
|
nodes. (default: 3)
|
|
:return: GradientBoostedTreesModel that can be used for
|
|
prediction
|
|
|
|
Example usage:
|
|
|
|
>>> from pyspark.mllib.regression import LabeledPoint
|
|
>>> from pyspark.mllib.tree import GradientBoostedTrees
|
|
>>> from pyspark.mllib.linalg import SparseVector
|
|
>>>
|
|
>>> sparse_data = [
|
|
... LabeledPoint(0.0, SparseVector(2, {0: 1.0})),
|
|
... LabeledPoint(1.0, SparseVector(2, {1: 1.0})),
|
|
... LabeledPoint(0.0, SparseVector(2, {0: 1.0})),
|
|
... LabeledPoint(1.0, SparseVector(2, {1: 2.0}))
|
|
... ]
|
|
>>>
|
|
>>> data = sc.parallelize(sparse_data)
|
|
>>> model = GradientBoostedTrees.trainRegressor(data, {}, numIterations=10)
|
|
>>> model.numTrees()
|
|
10
|
|
>>> model.totalNumNodes()
|
|
12
|
|
>>> model.predict(SparseVector(2, {1: 1.0}))
|
|
1.0
|
|
>>> model.predict(SparseVector(2, {0: 1.0}))
|
|
0.0
|
|
>>> rdd = sc.parallelize([[0.0, 1.0], [1.0, 0.0]])
|
|
>>> model.predict(rdd).collect()
|
|
[1.0, 0.0]
|
|
"""
|
|
return cls._train(data, "regression", categoricalFeaturesInfo,
|
|
loss, numIterations, learningRate, maxDepth, maxBins)
|
|
|
|
|
|
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()
|