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# # Licensed to the Apache Software Foundation (ASF) under one or more # contributor license agreements. See the NOTICE file distributed with # this work for additional information regarding copyright ownership. # The ASF licenses this file to You under the Apache License, Version 2.0 # (the "License"); you may not use this file except in compliance with # the License. You may obtain a copy of the License at # # http://www.apache.org/licenses/LICENSE-2.0 # # Unless required by applicable law or agreed to in writing, software # distributed under the License is distributed on an "AS IS" BASIS, # WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. # See the License for the specific language governing permissions and # limitations under the License. #
'RandomForest', 'GradientBoostedTreesModel', 'GradientBoostedTrees']
"""TreeEnsembleModel
.. versionadded:: 1.3.0 """ """ Predict values for a single data point or an RDD of points using the model trained.
.. versionadded:: 1.3.0
Notes ----- In Python, predict cannot currently be used within an RDD transformation or action. Call predict directly on the RDD instead. """
else:
def numTrees(self): """ Get number of trees in ensemble. """
def totalNumNodes(self): """ Get total number of nodes, summed over all trees in the ensemble. """
""" Summary of model """
def toDebugString(self): """ Full model """
""" A decision tree model for classification or regression.
.. versionadded:: 1.1.0 """ """ Predict the label of one or more examples.
.. versionadded:: 1.1.0
Parameters ---------- x : :py:class:`pyspark.mllib.linalg.Vector` or :py:class:`pyspark.RDD` Data point (feature vector), or an RDD of data points (feature vectors).
Notes ----- In Python, predict cannot currently be used within an RDD transformation or action. Call predict directly on the RDD instead. """
else:
def numNodes(self): """Get number of nodes in tree, including leaf nodes.""" return self._java_model.numNodes()
def depth(self): """ Get depth of tree (e.g. depth 0 means 1 leaf node, depth 1 means 1 internal node + 2 leaf nodes). """ return self._java_model.depth()
""" summary of model. """
def toDebugString(self): """ full model. """
def _java_loader_class(cls):
""" Learning algorithm for a decision tree model for classification or regression.
.. versionadded:: 1.1.0 """
minInstancesPerNode=1, minInfoGain=0.0): impurity, maxDepth, maxBins, minInstancesPerNode, minInfoGain)
impurity="gini", maxDepth=5, maxBins=32, minInstancesPerNode=1, minInfoGain=0.0): """ Train a decision tree model for classification.
.. versionadded:: 1.1.0
Parameters ---------- data : :py:class:`pyspark.RDD` Training data: RDD of LabeledPoint. Labels should take values {0, 1, ..., numClasses-1}. numClasses : int Number of classes for classification. categoricalFeaturesInfo : dict 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}. impurity : str, optional Criterion used for information gain calculation. Supported values: "gini" or "entropy". (default: "gini") maxDepth : int, optional Maximum depth of tree (e.g. depth 0 means 1 leaf node, depth 1 means 1 internal node + 2 leaf nodes). (default: 5) maxBins : int, optional Number of bins used for finding splits at each node. (default: 32) minInstancesPerNode : int, optional Minimum number of instances required at child nodes to create the parent split. (default: 1) minInfoGain : float, optional Minimum info gain required to create a split. (default: 0.0)
Returns ------- :py:class:`DecisionTreeModel`
Examples -------- >>> from numpy import array >>> from pyspark.mllib.regression import LabeledPoint >>> from pyspark.mllib.tree import DecisionTree >>> >>> data = [ ... LabeledPoint(0.0, [0.0]), ... LabeledPoint(1.0, [1.0]), ... LabeledPoint(1.0, [2.0]), ... LabeledPoint(1.0, [3.0]) ... ] >>> model = DecisionTree.trainClassifier(sc.parallelize(data), 2, {}) >>> print(model) DecisionTreeModel classifier of depth 1 with 3 nodes
>>> print(model.toDebugString()) DecisionTreeModel classifier of depth 1 with 3 nodes If (feature 0 <= 0.5) Predict: 0.0 Else (feature 0 > 0.5) Predict: 1.0 <BLANKLINE> >>> model.predict(array([1.0])) 1.0 >>> model.predict(array([0.0])) 0.0 >>> rdd = sc.parallelize([[1.0], [0.0]]) >>> model.predict(rdd).collect() [1.0, 0.0] """ impurity, maxDepth, maxBins, minInstancesPerNode, minInfoGain)
impurity="variance", maxDepth=5, maxBins=32, minInstancesPerNode=1, minInfoGain=0.0): """ Train a decision tree model for regression.
Parameters ---------- data : :py:class:`pyspark.RDD` Training data: RDD of LabeledPoint. Labels are real numbers. categoricalFeaturesInfo : dict 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}. impurity : str, optional Criterion used for information gain calculation. The only supported value for regression is "variance". (default: "variance") maxDepth : int, optional Maximum depth of tree (e.g. depth 0 means 1 leaf node, depth 1 means 1 internal node + 2 leaf nodes). (default: 5) maxBins : int, optional Number of bins used for finding splits at each node. (default: 32) minInstancesPerNode : int, optional Minimum number of instances required at child nodes to create the parent split. (default: 1) minInfoGain : float, optional Minimum info gain required to create a split. (default: 0.0)
Returns ------- :py:class:`DecisionTreeModel`
Examples -------- >>> from pyspark.mllib.regression import LabeledPoint >>> from pyspark.mllib.tree import DecisionTree >>> from pyspark.mllib.linalg import SparseVector >>> >>> sparse_data = [ ... LabeledPoint(0.0, SparseVector(2, {0: 0.0})), ... LabeledPoint(1.0, SparseVector(2, {1: 1.0})), ... LabeledPoint(0.0, SparseVector(2, {0: 0.0})), ... LabeledPoint(1.0, SparseVector(2, {1: 2.0})) ... ] >>> >>> model = DecisionTree.trainRegressor(sc.parallelize(sparse_data), {}) >>> model.predict(SparseVector(2, {1: 1.0})) 1.0 >>> model.predict(SparseVector(2, {1: 0.0})) 0.0 >>> rdd = sc.parallelize([[0.0, 1.0], [0.0, 0.0]]) >>> model.predict(rdd).collect() [1.0, 0.0] """ impurity, maxDepth, maxBins, minInstancesPerNode, minInfoGain)
""" Represents a random forest model.
.. versionadded:: 1.2.0 """
def _java_loader_class(cls):
""" Learning algorithm for a random forest model for classification or regression.
.. versionadded:: 1.2.0 """
def _train(cls, data, algo, numClasses, categoricalFeaturesInfo, numTrees, featureSubsetStrategy, impurity, maxDepth, maxBins, seed): raise ValueError("unsupported featureSubsetStrategy: %s" % featureSubsetStrategy) seed = random.randint(0, 1 << 30) categoricalFeaturesInfo, numTrees, featureSubsetStrategy, impurity, maxDepth, maxBins, seed)
featureSubsetStrategy="auto", impurity="gini", maxDepth=4, maxBins=32, seed=None): """ Train a random forest model for binary or multiclass classification.
.. versionadded:: 1.2.0
Parameters ---------- data : :py:class:`pyspark.RDD` Training dataset: RDD of LabeledPoint. Labels should take values {0, 1, ..., numClasses-1}. numClasses : int Number of classes for classification. categoricalFeaturesInfo : dict 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}. numTrees : int Number of trees in the random forest. featureSubsetStrategy : str, optional Number of features to consider for splits at each node. Supported values: "auto", "all", "sqrt", "log2", "onethird". If "auto" is set, this parameter is set based on numTrees: if numTrees == 1, set to "all"; if numTrees > 1 (forest) set to "sqrt". (default: "auto") impurity : str, optional Criterion used for information gain calculation. Supported values: "gini" or "entropy". (default: "gini") maxDepth : int, optional Maximum depth of tree (e.g. depth 0 means 1 leaf node, depth 1 means 1 internal node + 2 leaf nodes). (default: 4) maxBins : int, optional Maximum number of bins used for splitting features. (default: 32) seed : int, Optional Random seed for bootstrapping and choosing feature subsets. Set as None to generate seed based on system time. (default: None)
Returns ------- :py:class:`RandomForestModel` that can be used for prediction.
Examples -------- >>> from pyspark.mllib.regression import LabeledPoint >>> from pyspark.mllib.tree import RandomForest >>> >>> data = [ ... LabeledPoint(0.0, [0.0]), ... LabeledPoint(0.0, [1.0]), ... LabeledPoint(1.0, [2.0]), ... LabeledPoint(1.0, [3.0]) ... ] >>> model = RandomForest.trainClassifier(sc.parallelize(data), 2, {}, 3, seed=42) >>> model.numTrees() 3 >>> model.totalNumNodes() 7 >>> print(model) TreeEnsembleModel classifier with 3 trees <BLANKLINE> >>> print(model.toDebugString()) TreeEnsembleModel classifier with 3 trees <BLANKLINE> Tree 0: Predict: 1.0 Tree 1: If (feature 0 <= 1.5) Predict: 0.0 Else (feature 0 > 1.5) Predict: 1.0 Tree 2: If (feature 0 <= 1.5) Predict: 0.0 Else (feature 0 > 1.5) Predict: 1.0 <BLANKLINE> >>> model.predict([2.0]) 1.0 >>> model.predict([0.0]) 0.0 >>> rdd = sc.parallelize([[3.0], [1.0]]) >>> model.predict(rdd).collect() [1.0, 0.0] """ categoricalFeaturesInfo, numTrees, featureSubsetStrategy, impurity, maxDepth, maxBins, seed)
impurity="variance", maxDepth=4, maxBins=32, seed=None): """ Train a random forest model for regression.
.. versionadded:: 1.2.0
Parameters ---------- data : :py:class:`pyspark.RDD` Training dataset: RDD of LabeledPoint. Labels are real numbers. categoricalFeaturesInfo : dict 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}. numTrees : int Number of trees in the random forest. featureSubsetStrategy : str, optional Number of features to consider for splits at each node. Supported values: "auto", "all", "sqrt", "log2", "onethird". If "auto" is set, this parameter is set based on numTrees:
- if numTrees == 1, set to "all"; - if numTrees > 1 (forest) set to "onethird" for regression.
(default: "auto") impurity : str, optional Criterion used for information gain calculation. The only supported value for regression is "variance". (default: "variance") maxDepth : int, optional Maximum depth of tree (e.g. depth 0 means 1 leaf node, depth 1 means 1 internal node + 2 leaf nodes). (default: 4) maxBins : int, optional Maximum number of bins used for splitting features. (default: 32) seed : int, optional Random seed for bootstrapping and choosing feature subsets. Set as None to generate seed based on system time. (default: None)
Returns ------- :py:class:`RandomForestModel` that can be used for prediction.
Examples -------- >>> from pyspark.mllib.regression import LabeledPoint >>> from pyspark.mllib.tree import RandomForest >>> 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})) ... ] >>> >>> model = RandomForest.trainRegressor(sc.parallelize(sparse_data), {}, 2, seed=42) >>> model.numTrees() 2 >>> model.totalNumNodes() 4 >>> model.predict(SparseVector(2, {1: 1.0})) 1.0 >>> model.predict(SparseVector(2, {0: 1.0})) 0.5 >>> rdd = sc.parallelize([[0.0, 1.0], [1.0, 0.0]]) >>> model.predict(rdd).collect() [1.0, 0.5] """ featureSubsetStrategy, impurity, maxDepth, maxBins, seed)
""" Represents a gradient-boosted tree model.
.. versionadded:: 1.3.0 """
def _java_loader_class(cls):
""" Learning algorithm for a gradient boosted trees model for classification or regression.
.. versionadded:: 1.3.0 """
def _train(cls, data, algo, categoricalFeaturesInfo, loss, numIterations, learningRate, maxDepth, maxBins): loss, numIterations, learningRate, maxDepth, maxBins)
loss="logLoss", numIterations=100, learningRate=0.1, maxDepth=3, maxBins=32): """ Train a gradient-boosted trees model for classification.
.. versionadded:: 1.3.0
Parameters ---------- data : :py:class:`pyspark.RDD` Training dataset: RDD of LabeledPoint. Labels should take values {0, 1}. categoricalFeaturesInfo : dict 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}. loss : str, optional Loss function used for minimization during gradient boosting. Supported values: "logLoss", "leastSquaresError", "leastAbsoluteError". (default: "logLoss") numIterations : int, optional Number of iterations of boosting. (default: 100) learningRate : float, optional Learning rate for shrinking the contribution of each estimator. The learning rate should be between in the interval (0, 1]. (default: 0.1) maxDepth : int, optional Maximum depth of tree (e.g. depth 0 means 1 leaf node, depth 1 means 1 internal node + 2 leaf nodes). (default: 3) maxBins : int, optional Maximum number of bins used for splitting features. DecisionTree requires maxBins >= max categories. (default: 32)
Returns ------- :py:class:`GradientBoostedTreesModel` that can be used for prediction.
Examples -------- >>> 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] """ loss, numIterations, learningRate, maxDepth, maxBins)
loss="leastSquaresError", numIterations=100, learningRate=0.1, maxDepth=3, maxBins=32): """ Train a gradient-boosted trees model for regression.
.. versionadded:: 1.3.0
Parameters ---------- data : Training dataset: RDD of LabeledPoint. Labels are real numbers. categoricalFeaturesInfo : dict 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}. loss : str, optional Loss function used for minimization during gradient boosting. Supported values: "logLoss", "leastSquaresError", "leastAbsoluteError". (default: "leastSquaresError") numIterations : int, optional Number of iterations of boosting. (default: 100) learningRate : float, optional Learning rate for shrinking the contribution of each estimator. The learning rate should be between in the interval (0, 1]. (default: 0.1) maxDepth : int, optional Maximum depth of tree (e.g. depth 0 means 1 leaf node, depth 1 means 1 internal node + 2 leaf nodes). (default: 3) maxBins : int, optional Maximum number of bins used for splitting features. DecisionTree requires maxBins >= max categories. (default: 32)
Returns ------- :py:class:`GradientBoostedTreesModel` that can be used for prediction.
Examples -------- >>> 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] """ loss, numIterations, learningRate, maxDepth, maxBins)
.master("local[4]")\ .appName("mllib.tree tests")\ .getOrCreate() sys.exit(-1)
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