186 lines
6.8 KiB
Python
186 lines
6.8 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 Any, List, Optional, Tuple, Type
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from pyspark.ml._typing import ParamMap
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from pyspark.ml import Estimator, Model
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from pyspark.ml.evaluation import Evaluator
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from pyspark.ml.param import Param
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from pyspark.ml.param.shared import HasCollectSubModels, HasParallelism, HasSeed
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from pyspark.ml.util import MLReader, MLReadable, MLWriter, MLWritable
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class ParamGridBuilder:
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def __init__(self) -> None: ...
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def addGrid(self, param: Param, values: List[Any]) -> ParamGridBuilder: ...
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@overload
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def baseOn(self, __args: ParamMap) -> ParamGridBuilder: ...
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@overload
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def baseOn(self, *args: Tuple[Param, Any]) -> ParamGridBuilder: ...
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def build(self) -> List[ParamMap]: ...
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class _ValidatorParams(HasSeed):
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estimator: Param[Estimator]
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estimatorParamMaps: Param[List[ParamMap]]
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evaluator: Param[Evaluator]
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def getEstimator(self) -> Estimator: ...
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def getEstimatorParamMaps(self) -> List[ParamMap]: ...
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def getEvaluator(self) -> Evaluator: ...
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class _CrossValidatorParams(_ValidatorParams):
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numFolds: Param[int]
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foldCol: Param[str]
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def __init__(self, *args: Any): ...
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def getNumFolds(self) -> int: ...
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def getFoldCol(self) -> str: ...
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class CrossValidator(
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Estimator[CrossValidatorModel],
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_CrossValidatorParams,
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HasParallelism,
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HasCollectSubModels,
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MLReadable[CrossValidator],
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MLWritable,
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):
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def __init__(
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self,
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*,
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estimator: Optional[Estimator] = ...,
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estimatorParamMaps: Optional[List[ParamMap]] = ...,
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evaluator: Optional[Evaluator] = ...,
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numFolds: int = ...,
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seed: Optional[int] = ...,
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parallelism: int = ...,
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collectSubModels: bool = ...,
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foldCol: str = ...
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) -> None: ...
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def setParams(
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self,
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*,
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estimator: Optional[Estimator] = ...,
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estimatorParamMaps: Optional[List[ParamMap]] = ...,
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evaluator: Optional[Evaluator] = ...,
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numFolds: int = ...,
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seed: Optional[int] = ...,
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parallelism: int = ...,
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collectSubModels: bool = ...,
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foldCol: str = ...
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) -> CrossValidator: ...
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def setEstimator(self, value: Estimator) -> CrossValidator: ...
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def setEstimatorParamMaps(self, value: List[ParamMap]) -> CrossValidator: ...
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def setEvaluator(self, value: Evaluator) -> CrossValidator: ...
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def setNumFolds(self, value: int) -> CrossValidator: ...
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def setFoldCol(self, value: str) -> CrossValidator: ...
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def setSeed(self, value: int) -> CrossValidator: ...
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def setParallelism(self, value: int) -> CrossValidator: ...
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def setCollectSubModels(self, value: bool) -> CrossValidator: ...
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def copy(self, extra: Optional[ParamMap] = ...) -> CrossValidator: ...
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def write(self) -> MLWriter: ...
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@classmethod
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def read(cls: Type[CrossValidator]) -> MLReader: ...
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class CrossValidatorModel(
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Model, _CrossValidatorParams, MLReadable[CrossValidatorModel], MLWritable
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):
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bestModel: Model
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avgMetrics: List[float]
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subModels: List[List[Model]]
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def __init__(
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self,
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bestModel: Model,
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avgMetrics: List[float] = ...,
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subModels: Optional[List[List[Model]]] = ...,
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) -> None: ...
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def copy(self, extra: Optional[ParamMap] = ...) -> CrossValidatorModel: ...
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def write(self) -> MLWriter: ...
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@classmethod
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def read(cls: Type[CrossValidatorModel]) -> MLReader: ...
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class _TrainValidationSplitParams(_ValidatorParams):
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trainRatio: Param[float]
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def __init__(self, *args: Any): ...
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def getTrainRatio(self) -> float: ...
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class TrainValidationSplit(
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Estimator[TrainValidationSplitModel],
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_TrainValidationSplitParams,
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HasParallelism,
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HasCollectSubModels,
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MLReadable[TrainValidationSplit],
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MLWritable,
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):
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def __init__(
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self,
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*,
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estimator: Optional[Estimator] = ...,
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estimatorParamMaps: Optional[List[ParamMap]] = ...,
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evaluator: Optional[Evaluator] = ...,
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trainRatio: float = ...,
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parallelism: int = ...,
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collectSubModels: bool = ...,
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seed: Optional[int] = ...
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) -> None: ...
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def setParams(
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self,
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*,
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estimator: Optional[Estimator] = ...,
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estimatorParamMaps: Optional[List[ParamMap]] = ...,
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evaluator: Optional[Evaluator] = ...,
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trainRatio: float = ...,
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parallelism: int = ...,
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collectSubModels: bool = ...,
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seed: Optional[int] = ...
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) -> TrainValidationSplit: ...
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def setEstimator(self, value: Estimator) -> TrainValidationSplit: ...
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def setEstimatorParamMaps(self, value: List[ParamMap]) -> TrainValidationSplit: ...
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def setEvaluator(self, value: Evaluator) -> TrainValidationSplit: ...
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def setTrainRatio(self, value: float) -> TrainValidationSplit: ...
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def setSeed(self, value: int) -> TrainValidationSplit: ...
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def setParallelism(self, value: int) -> TrainValidationSplit: ...
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def setCollectSubModels(self, value: bool) -> TrainValidationSplit: ...
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def copy(self, extra: Optional[ParamMap] = ...) -> TrainValidationSplit: ...
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def write(self) -> MLWriter: ...
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@classmethod
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def read(cls: Type[TrainValidationSplit]) -> MLReader: ...
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class TrainValidationSplitModel(
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Model,
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_TrainValidationSplitParams,
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MLReadable[TrainValidationSplitModel],
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MLWritable,
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):
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bestModel: Model
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validationMetrics: List[float]
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subModels: List[Model]
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def __init__(
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self,
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bestModel: Model,
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validationMetrics: List[float] = ...,
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subModels: Optional[List[Model]] = ...,
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) -> None: ...
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def setEstimator(self, value: Estimator) -> TrainValidationSplitModel: ...
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def setEstimatorParamMaps(
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self, value: List[ParamMap]
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) -> TrainValidationSplitModel: ...
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def setEvaluator(self, value: Evaluator) -> TrainValidationSplitModel: ...
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def copy(self, extra: Optional[ParamMap] = ...) -> TrainValidationSplitModel: ...
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def write(self) -> MLWriter: ...
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@classmethod
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def read(cls: Type[TrainValidationSplitModel]) -> MLReader: ...
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