# # 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. # import sys import random from pyspark import RDD, since from pyspark.mllib.common import callMLlibFunc, inherit_doc, JavaModelWrapper from pyspark.mllib.linalg import _convert_to_vector from pyspark.mllib.regression import LabeledPoint from pyspark.mllib.util import JavaLoader, JavaSaveable from typing import Dict, Optional, Tuple, Union, overload, TYPE_CHECKING from pyspark.rdd import RDD if TYPE_CHECKING: from pyspark.mllib._typing import VectorLike __all__ = [ "DecisionTreeModel", "DecisionTree", "RandomForestModel", "RandomForest", "GradientBoostedTreesModel", "GradientBoostedTrees", ] class TreeEnsembleModel(JavaModelWrapper, JavaSaveable): """TreeEnsembleModel .. versionadded:: 1.3.0 """ @overload def predict(self, x: "VectorLike") -> float: ... @overload def predict(self, x: RDD["VectorLike"]) -> RDD[float]: ... def predict(self, x: Union["VectorLike", RDD["VectorLike"]]) -> Union[float, RDD[float]]: """ 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. """ if isinstance(x, RDD): return self.call("predict", x.map(_convert_to_vector)) else: return self.call("predict", _convert_to_vector(x)) @since("1.3.0") def numTrees(self) -> int: """ Get number of trees in ensemble. """ return self.call("numTrees") @since("1.3.0") def totalNumNodes(self) -> int: """ Get total number of nodes, summed over all trees in the ensemble. """ return self.call("totalNumNodes") def __repr__(self) -> str: """Summary of model""" return self._java_model.toString() @since("1.3.0") def toDebugString(self) -> str: """Full model""" return self._java_model.toDebugString() [docs]class DecisionTreeModel(JavaModelWrapper, JavaSaveable, JavaLoader["DecisionTreeModel"]): """ A decision tree model for classification or regression. .. versionadded:: 1.1.0 """ @overload def predict(self, x: "VectorLike") -> float: ... @overload def predict(self, x: RDD["VectorLike"]) -> RDD[float]: ... [docs] def predict(self, x: Union["VectorLike", RDD["VectorLike"]]) -> Union[float, RDD[float]]: """ 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. """ if isinstance(x, RDD): return self.call("predict", x.map(_convert_to_vector)) else: return self.call("predict", _convert_to_vector(x)) [docs] @since("1.1.0") def numNodes(self) -> int: """Get number of nodes in tree, including leaf nodes.""" return self._java_model.numNodes() [docs] @since("1.1.0") def depth(self) -> int: """ 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() def __repr__(self) -> str: """summary of model.""" return self._java_model.toString() [docs] @since("1.2.0") def toDebugString(self) -> str: """full model.""" return self._java_model.toDebugString() @classmethod def _java_loader_class(cls) -> str: return "org.apache.spark.mllib.tree.model.DecisionTreeModel" [docs]class DecisionTree: """ Learning algorithm for a decision tree model for classification or regression. .. versionadded:: 1.1.0 """ @classmethod def _train( cls, data: RDD[LabeledPoint], type: str, numClasses: int, features: Dict[int, int], impurity: str = "gini", maxDepth: int = 5, maxBins: int = 32, minInstancesPerNode: int = 1, minInfoGain: float = 0.0, ) -> DecisionTreeModel: first = data.first() assert isinstance(first, LabeledPoint), "the data should be RDD of LabeledPoint" model = callMLlibFunc( "trainDecisionTreeModel", data, type, numClasses, features, impurity, maxDepth, maxBins, minInstancesPerNode, minInfoGain, ) return DecisionTreeModel(model) [docs] @classmethod def trainClassifier( cls, data: RDD[LabeledPoint], numClasses: int, categoricalFeaturesInfo: Dict[int, int], impurity: str = "gini", maxDepth: int = 5, maxBins: int = 32, minInstancesPerNode: int = 1, minInfoGain: float = 0.0, ) -> DecisionTreeModel: """ 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 >>> 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] """ return cls._train( data, "classification", numClasses, categoricalFeaturesInfo, impurity, maxDepth, maxBins, minInstancesPerNode, minInfoGain, ) [docs] @classmethod @since("1.1.0") def trainRegressor( cls, data: RDD[LabeledPoint], categoricalFeaturesInfo: Dict[int, int], impurity: str = "variance", maxDepth: int = 5, maxBins: int = 32, minInstancesPerNode: int = 1, minInfoGain: float = 0.0, ) -> DecisionTreeModel: """ 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] """ return cls._train( data, "regression", 0, categoricalFeaturesInfo, impurity, maxDepth, maxBins, minInstancesPerNode, minInfoGain, ) [docs]@inherit_doc class RandomForestModel(TreeEnsembleModel, JavaLoader["RandomForestModel"]): """ Represents a random forest model. .. versionadded:: 1.2.0 """ @classmethod def _java_loader_class(cls) -> str: return "org.apache.spark.mllib.tree.model.RandomForestModel" [docs]class RandomForest: """ Learning algorithm for a random forest model for classification or regression. .. versionadded:: 1.2.0 """ supportedFeatureSubsetStrategies: Tuple[str, ...] = ("auto", "all", "sqrt", "log2", "onethird") @classmethod def _train( cls, data: RDD[LabeledPoint], algo: str, numClasses: int, categoricalFeaturesInfo: Dict[int, int], numTrees: int, featureSubsetStrategy: str, impurity: str, maxDepth: int, maxBins: int, seed: Optional[int], ) -> RandomForestModel: first = data.first() assert isinstance(first, LabeledPoint), "the data should be RDD of LabeledPoint" if featureSubsetStrategy not in cls.supportedFeatureSubsetStrategies: raise ValueError("unsupported featureSubsetStrategy: %s" % featureSubsetStrategy) if seed is None: seed = random.randint(0, 1 << 30) model = callMLlibFunc( "trainRandomForestModel", data, algo, numClasses, categoricalFeaturesInfo, numTrees, featureSubsetStrategy, impurity, maxDepth, maxBins, seed, ) return RandomForestModel(model) [docs] @classmethod def trainClassifier( cls, data: RDD[LabeledPoint], numClasses: int, categoricalFeaturesInfo: Dict[int, int], numTrees: int, featureSubsetStrategy: str = "auto", impurity: str = "gini", maxDepth: int = 4, maxBins: int = 32, seed: Optional[int] = None, ) -> RandomForestModel: """ 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 >>> print(model.toDebugString()) TreeEnsembleModel classifier with 3 trees 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 >>> 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] """ return cls._train( data, "classification", numClasses, categoricalFeaturesInfo, numTrees, featureSubsetStrategy, impurity, maxDepth, maxBins, seed, ) [docs] @classmethod def trainRegressor( cls, data: RDD[LabeledPoint], categoricalFeaturesInfo: Dict[int, int], numTrees: int, featureSubsetStrategy: str = "auto", impurity: str = "variance", maxDepth: int = 4, maxBins: int = 32, seed: Optional[int] = None, ) -> RandomForestModel: """ 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] """ return cls._train( data, "regression", 0, categoricalFeaturesInfo, numTrees, featureSubsetStrategy, impurity, maxDepth, maxBins, seed, ) [docs]@inherit_doc class GradientBoostedTreesModel(TreeEnsembleModel, JavaLoader["GradientBoostedTreesModel"]): """ Represents a gradient-boosted tree model. .. versionadded:: 1.3.0 """ @classmethod def _java_loader_class(cls) -> str: return "org.apache.spark.mllib.tree.model.GradientBoostedTreesModel" [docs]class GradientBoostedTrees: """ Learning algorithm for a gradient boosted trees model for classification or regression. .. versionadded:: 1.3.0 """ @classmethod def _train( cls, data: RDD[LabeledPoint], algo: str, categoricalFeaturesInfo: Dict[int, int], loss: str, numIterations: int, learningRate: float, maxDepth: int, maxBins: int, ) -> GradientBoostedTreesModel: first = data.first() assert isinstance(first, LabeledPoint), "the data should be RDD of LabeledPoint" model = callMLlibFunc( "trainGradientBoostedTreesModel", data, algo, categoricalFeaturesInfo, loss, numIterations, learningRate, maxDepth, maxBins, ) return GradientBoostedTreesModel(model) [docs] @classmethod def trainClassifier( cls, data: RDD[LabeledPoint], categoricalFeaturesInfo: Dict[int, int], loss: str = "logLoss", numIterations: int = 100, learningRate: float = 0.1, maxDepth: int = 3, maxBins: int = 32, ) -> GradientBoostedTreesModel: """ 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 >>> 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, ) [docs] @classmethod def trainRegressor( cls, data: RDD[LabeledPoint], categoricalFeaturesInfo: Dict[int, int], loss: str = "leastSquaresError", numIterations: int = 100, learningRate: float = 0.1, maxDepth: int = 3, maxBins: int = 32, ) -> GradientBoostedTreesModel: """ 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] """ return cls._train( data, "regression", categoricalFeaturesInfo, loss, numIterations, learningRate, maxDepth, maxBins, ) def _test() -> None: import doctest globs = globals().copy() from pyspark.sql import SparkSession spark = SparkSession.builder.master("local[4]").appName("mllib.tree tests").getOrCreate() globs["sc"] = spark.sparkContext (failure_count, test_count) = doctest.testmod( globs=globs, optionflags=doctest.ELLIPSIS | doctest.NORMALIZE_WHITESPACE ) spark.stop() if failure_count: sys.exit(-1) if __name__ == "__main__": _test()