127 lines
4 KiB
Python
127 lines
4 KiB
Python
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#
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# Licensed to the Apache Software Foundation (ASF) under one
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# or more contributor license agreements. See the NOTICE file
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# distributed with this work for additional information
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# regarding copyright ownership. The ASF licenses this file
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# to you under the Apache License, Version 2.0 (the
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# "License"); you may not use this file except in compliance
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# with 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,
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# software distributed under the License is distributed on an
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# "AS IS" BASIS, WITHOUT WARRANTIES OR CONDITIONS OF ANY
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# KIND, either express or implied. See the License for the
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# specific language governing permissions and limitations
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# under the License.
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from typing import overload
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from typing import Dict, Optional, Tuple
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from pyspark.mllib._typing import VectorLike
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from pyspark.rdd import RDD
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from pyspark.mllib.common import JavaModelWrapper
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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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class TreeEnsembleModel(JavaModelWrapper, JavaSaveable):
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@overload
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def predict(self, x: VectorLike) -> float: ...
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@overload
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def predict(self, x: RDD[VectorLike]) -> RDD[VectorLike]: ...
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def numTrees(self) -> int: ...
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def totalNumNodes(self) -> int: ...
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def toDebugString(self) -> str: ...
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class DecisionTreeModel(JavaModelWrapper, JavaSaveable, JavaLoader[DecisionTreeModel]):
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@overload
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def predict(self, x: VectorLike) -> float: ...
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@overload
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def predict(self, x: RDD[VectorLike]) -> RDD[VectorLike]: ...
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def numNodes(self) -> int: ...
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def depth(self) -> int: ...
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def toDebugString(self) -> str: ...
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class DecisionTree:
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@classmethod
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def trainClassifier(
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cls,
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data: RDD[LabeledPoint],
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numClasses: int,
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categoricalFeaturesInfo: Dict[int, int],
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impurity: str = ...,
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maxDepth: int = ...,
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maxBins: int = ...,
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minInstancesPerNode: int = ...,
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minInfoGain: float = ...,
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) -> DecisionTreeModel: ...
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@classmethod
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def trainRegressor(
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cls,
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data: RDD[LabeledPoint],
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categoricalFeaturesInfo: Dict[int, int],
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impurity: str = ...,
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maxDepth: int = ...,
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maxBins: int = ...,
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minInstancesPerNode: int = ...,
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minInfoGain: float = ...,
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) -> DecisionTreeModel: ...
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class RandomForestModel(TreeEnsembleModel, JavaLoader[RandomForestModel]): ...
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class RandomForest:
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supportedFeatureSubsetStrategies: Tuple[str, ...]
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@classmethod
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def trainClassifier(
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cls,
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data: RDD[LabeledPoint],
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numClasses: int,
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categoricalFeaturesInfo: Dict[int, int],
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numTrees: int,
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featureSubsetStrategy: str = ...,
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impurity: str = ...,
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maxDepth: int = ...,
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maxBins: int = ...,
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seed: Optional[int] = ...,
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) -> RandomForestModel: ...
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@classmethod
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def trainRegressor(
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cls,
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data: RDD[LabeledPoint],
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categoricalFeaturesInfo: Dict[int, int],
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numTrees: int,
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featureSubsetStrategy: str = ...,
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impurity: str = ...,
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maxDepth: int = ...,
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maxBins: int = ...,
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seed: Optional[int] = ...,
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) -> RandomForestModel: ...
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class GradientBoostedTreesModel(
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TreeEnsembleModel, JavaLoader[GradientBoostedTreesModel]
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): ...
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class GradientBoostedTrees:
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@classmethod
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def trainClassifier(
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cls,
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data: RDD[LabeledPoint],
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categoricalFeaturesInfo: Dict[int, int],
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loss: str = ...,
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numIterations: int = ...,
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learningRate: float = ...,
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maxDepth: int = ...,
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maxBins: int = ...,
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) -> GradientBoostedTreesModel: ...
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@classmethod
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def trainRegressor(
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cls,
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data: RDD[LabeledPoint],
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categoricalFeaturesInfo: Dict[int, int],
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loss: str = ...,
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numIterations: int = ...,
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learningRate: float = ...,
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maxDepth: int = ...,
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maxBins: int = ...,
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) -> GradientBoostedTreesModel: ...
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