01f09b1612
## What changes were proposed in this pull request? General decisions to follow, except where noted: * spark.mllib, pyspark.mllib: Remove all Experimental annotations. Leave DeveloperApi annotations alone. * spark.ml, pyspark.ml ** Annotate Estimator-Model pairs of classes and companion objects the same way. ** For all algorithms marked Experimental with Since tag <= 1.6, remove Experimental annotation. ** For all algorithms marked Experimental with Since tag = 2.0, leave Experimental annotation. * DeveloperApi annotations are left alone, except where noted. * No changes to which types are sealed. Exceptions where I am leaving items Experimental in spark.ml, pyspark.ml, mainly because the items are new: * Model Summary classes * MLWriter, MLReader, MLWritable, MLReadable * Evaluator and subclasses: There is discussion of changes around evaluating multiple metrics at once for efficiency. * RFormula: Its behavior may need to change slightly to match R in edge cases. * AFTSurvivalRegression * MultilayerPerceptronClassifier DeveloperApi changes: * ml.tree.Node, ml.tree.Split, and subclasses should no longer be DeveloperApi ## How was this patch tested? N/A Note to reviewers: * spark.ml.clustering.LDA underwent significant changes (additional methods), so let me know if you want me to leave it Experimental. * Be careful to check for cases where a class should no longer be Experimental but has an Experimental method, val, or other feature. I did not find such cases, but please verify. Author: Joseph K. Bradley <joseph@databricks.com> Closes #14147 from jkbradley/experimental-audit.
661 lines
24 KiB
Python
661 lines
24 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 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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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:
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Data point (feature vector), or an RDD of data points (feature
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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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"""
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Get depth of tree (e.g. depth 0 means 1 leaf node, depth 1
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means 1 internal node + 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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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 decision tree model for classification.
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:param data:
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Training data: RDD of LabeledPoint. Labels should take values
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{0, 1, ..., numClasses-1}.
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:param numClasses:
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Number of classes for classification.
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:param categoricalFeaturesInfo:
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Map storing arity of categorical features. An entry (n -> k)
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indicates that feature n is categorical with k categories
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indexed from 0: {0, 1, ..., k-1}.
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:param impurity:
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Criterion used for information gain calculation.
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Supported values: "gini" or "entropy".
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(default: "gini")
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:param maxDepth:
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Maximum depth of tree (e.g. depth 0 means 1 leaf node, depth 1
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means 1 internal node + 2 leaf nodes).
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(default: 5)
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:param maxBins:
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Number of bins used for finding splits at each node.
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(default: 32)
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:param minInstancesPerNode:
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Minimum number of instances required at child nodes to create
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the parent split.
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(default: 1)
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:param minInfoGain:
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Minimum info gain required to create a split.
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(default: 0.0)
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:return:
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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 decision tree model for regression.
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:param data:
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Training data: RDD of LabeledPoint. Labels are real numbers.
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:param categoricalFeaturesInfo:
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Map storing arity of categorical features. An entry (n -> k)
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indicates that feature n is categorical with k categories
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indexed from 0: {0, 1, ..., k-1}.
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:param impurity:
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Criterion used for information gain calculation.
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The only supported value for regression is "variance".
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(default: "variance")
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:param maxDepth:
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Maximum depth of tree (e.g. depth 0 means 1 leaf node, depth 1
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means 1 internal node + 2 leaf nodes).
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(default: 5)
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:param maxBins:
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Number of bins used for finding splits at each node.
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(default: 32)
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:param minInstancesPerNode:
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Minimum number of instances required at child nodes to create
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the parent split.
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(default: 1)
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:param minInfoGain:
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Minimum info gain required to create a split.
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(default: 0.0)
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:return:
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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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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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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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Train a random forest model for binary or multiclass
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classification.
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:param data:
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Training dataset: RDD of LabeledPoint. Labels should take values
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{0, 1, ..., numClasses-1}.
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:param numClasses:
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Number of classes for classification.
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:param categoricalFeaturesInfo:
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Map storing arity of categorical features. An entry (n -> k)
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indicates that feature n is categorical with k categories
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indexed from 0: {0, 1, ..., k-1}.
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:param numTrees:
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Number of trees in the random forest.
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:param featureSubsetStrategy:
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Number of features to consider for splits at each node.
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Supported values: "auto", "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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(default: "auto")
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:param impurity:
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Criterion used for information gain calculation.
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Supported values: "gini" or "entropy".
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(default: "gini")
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:param maxDepth:
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Maximum depth of tree (e.g. depth 0 means 1 leaf node, depth 1
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means 1 internal node + 2 leaf nodes).
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(default: 4)
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:param maxBins:
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Maximum number of bins used for splitting features.
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(default: 32)
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:param seed:
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Random seed for bootstrapping and choosing feature subsets.
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Set as None to generate seed based on system time.
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(default: None)
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:return:
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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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Train a random forest model for regression.
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:param data:
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Training dataset: RDD of LabeledPoint. Labels are real numbers.
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:param categoricalFeaturesInfo:
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Map storing arity of categorical features. An entry (n -> k)
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indicates that feature n is categorical with k categories
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indexed from 0: {0, 1, ..., k-1}.
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:param numTrees:
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Number of trees in the random forest.
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:param featureSubsetStrategy:
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Number of features to consider for splits at each node.
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Supported values: "auto", "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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(default: "auto")
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:param impurity:
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Criterion used for information gain calculation.
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The only supported value for regression is "variance".
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(default: "variance")
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:param maxDepth:
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Maximum depth of tree (e.g. depth 0 means 1 leaf node, depth 1
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means 1 internal node + 2 leaf nodes).
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(default: 4)
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:param maxBins:
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Maximum number of bins used for splitting features.
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(default: 32)
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:param seed:
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Random seed for bootstrapping and choosing feature subsets.
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Set as None to generate seed based on system time.
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(default: None)
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:return:
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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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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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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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Train a gradient-boosted trees model for classification.
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:param data:
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Training dataset: RDD of LabeledPoint. Labels should take values
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{0, 1}.
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:param categoricalFeaturesInfo:
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Map storing arity of categorical features. An entry (n -> k)
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indicates that feature n is categorical with k categories
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indexed from 0: {0, 1, ..., k-1}.
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:param loss:
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Loss function used for minimization during gradient boosting.
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Supported values: "logLoss", "leastSquaresError",
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"leastAbsoluteError".
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(default: "logLoss")
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:param numIterations:
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Number of iterations of boosting.
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(default: 100)
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:param learningRate:
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Learning rate for shrinking the contribution of each estimator.
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The learning rate should be between in the interval (0, 1].
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(default: 0.1)
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:param maxDepth:
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Maximum depth of tree (e.g. depth 0 means 1 leaf node, depth 1
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means 1 internal node + 2 leaf nodes).
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(default: 3)
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:param maxBins:
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Maximum number of bins used for splitting features. DecisionTree
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requires maxBins >= max categories.
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(default: 32)
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:return:
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GradientBoostedTreesModel 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 GradientBoostedTrees
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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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>>>
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>>> model = GradientBoostedTrees.trainClassifier(sc.parallelize(data), {}, numIterations=10)
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>>> model.numTrees()
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10
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>>> model.totalNumNodes()
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30
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>>> print(model) # it already has newline
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TreeEnsembleModel classifier with 10 trees
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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([[2.0], [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", categoricalFeaturesInfo,
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loss, numIterations, learningRate, maxDepth, maxBins)
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@classmethod
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@since("1.3.0")
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def trainRegressor(cls, data, categoricalFeaturesInfo,
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loss="leastSquaresError", numIterations=100, learningRate=0.1, maxDepth=3,
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maxBins=32):
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"""
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Train a gradient-boosted trees model for regression.
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:param data:
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Training dataset: RDD of LabeledPoint. Labels are real numbers.
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:param categoricalFeaturesInfo:
|
|
Map storing arity of categorical features. An entry (n -> k)
|
|
indicates that feature n is categorical with k categories
|
|
indexed from 0: {0, 1, ..., k-1}.
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:param loss:
|
|
Loss function used for minimization during gradient boosting.
|
|
Supported values: "logLoss", "leastSquaresError",
|
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"leastAbsoluteError".
|
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(default: "leastSquaresError")
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:param numIterations:
|
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Number of iterations of boosting.
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|
(default: 100)
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|
: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 maxDepth:
|
|
Maximum depth of tree (e.g. depth 0 means 1 leaf node, depth 1
|
|
means 1 internal node + 2 leaf nodes).
|
|
(default: 3)
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:param maxBins:
|
|
Maximum number of bins used for splitting features. DecisionTree
|
|
requires maxBins >= max categories.
|
|
(default: 32)
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|
:return:
|
|
GradientBoostedTreesModel 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 GradientBoostedTrees
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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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>>> data = sc.parallelize(sparse_data)
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>>> model = GradientBoostedTrees.trainRegressor(data, {}, numIterations=10)
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>>> model.numTrees()
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10
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>>> model.totalNumNodes()
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12
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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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0.0
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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, 0.0]
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"""
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return cls._train(data, "regression", categoricalFeaturesInfo,
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loss, numIterations, learningRate, maxDepth, maxBins)
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|
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|
def _test():
|
|
import doctest
|
|
globs = globals().copy()
|
|
from pyspark.sql import SparkSession
|
|
spark = SparkSession.builder\
|
|
.master("local[4]")\
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|
.appName("mllib.tree tests")\
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|
.getOrCreate()
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|
globs['sc'] = spark.sparkContext
|
|
(failure_count, test_count) = doctest.testmod(globs=globs, optionflags=doctest.ELLIPSIS)
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|
spark.stop()
|
|
if failure_count:
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|
exit(-1)
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|
|
|
if __name__ == "__main__":
|
|
_test()
|