spark-instrumented-optimizer/python/pyspark/ml/tuning.pyi

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from typing import overload
from typing import Any, List, Optional, Tuple, Type
from pyspark.ml._typing import ParamMap

from pyspark.ml import Estimator, Model
from pyspark.ml.evaluation import Evaluator
from pyspark.ml.param import Param
from pyspark.ml.param.shared import HasCollectSubModels, HasParallelism, HasSeed
from pyspark.ml.util import MLReader, MLReadable, MLWriter, MLWritable

class ParamGridBuilder:
    def __init__(self) -> None: ...
    def addGrid(self, param: Param, values: List[Any]) -> ParamGridBuilder: ...
    @overload
    def baseOn(self, __args: ParamMap) -> ParamGridBuilder: ...
    @overload
    def baseOn(self, *args: Tuple[Param, Any]) -> ParamGridBuilder: ...
    def build(self) -> List[ParamMap]: ...

class _ValidatorParams(HasSeed):
    estimator: Param[Estimator]
    estimatorParamMaps: Param[List[ParamMap]]
    evaluator: Param[Evaluator]
    def getEstimator(self) -> Estimator: ...
    def getEstimatorParamMaps(self) -> List[ParamMap]: ...
    def getEvaluator(self) -> Evaluator: ...

class _CrossValidatorParams(_ValidatorParams):
    numFolds: Param[int]
    foldCol: Param[str]
    def __init__(self, *args: Any): ...
    def getNumFolds(self) -> int: ...
    def getFoldCol(self) -> str: ...

class CrossValidator(
    Estimator[CrossValidatorModel],
    _CrossValidatorParams,
    HasParallelism,
    HasCollectSubModels,
    MLReadable[CrossValidator],
    MLWritable,
):
    def __init__(
        self,
        *,
        estimator: Optional[Estimator] = ...,
        estimatorParamMaps: Optional[List[ParamMap]] = ...,
        evaluator: Optional[Evaluator] = ...,
        numFolds: int = ...,
        seed: Optional[int] = ...,
        parallelism: int = ...,
        collectSubModels: bool = ...,
        foldCol: str = ...
    ) -> None: ...
    def setParams(
        self,
        *,
        estimator: Optional[Estimator] = ...,
        estimatorParamMaps: Optional[List[ParamMap]] = ...,
        evaluator: Optional[Evaluator] = ...,
        numFolds: int = ...,
        seed: Optional[int] = ...,
        parallelism: int = ...,
        collectSubModels: bool = ...,
        foldCol: str = ...
    ) -> CrossValidator: ...
    def setEstimator(self, value: Estimator) -> CrossValidator: ...
    def setEstimatorParamMaps(self, value: List[ParamMap]) -> CrossValidator: ...
    def setEvaluator(self, value: Evaluator) -> CrossValidator: ...
    def setNumFolds(self, value: int) -> CrossValidator: ...
    def setFoldCol(self, value: str) -> CrossValidator: ...
    def setSeed(self, value: int) -> CrossValidator: ...
    def setParallelism(self, value: int) -> CrossValidator: ...
    def setCollectSubModels(self, value: bool) -> CrossValidator: ...
    def copy(self, extra: Optional[ParamMap] = ...) -> CrossValidator: ...
    def write(self) -> MLWriter: ...
    @classmethod
    def read(cls: Type[CrossValidator]) -> MLReader: ...

class CrossValidatorModel(
    Model, _CrossValidatorParams, MLReadable[CrossValidatorModel], MLWritable
):
    bestModel: Model
    avgMetrics: List[float]
    subModels: List[List[Model]]
    def __init__(
        self,
        bestModel: Model,
        avgMetrics: List[float] = ...,
        subModels: Optional[List[List[Model]]] = ...,
    ) -> None: ...
    def copy(self, extra: Optional[ParamMap] = ...) -> CrossValidatorModel: ...
    def write(self) -> MLWriter: ...
    @classmethod
    def read(cls: Type[CrossValidatorModel]) -> MLReader: ...

class _TrainValidationSplitParams(_ValidatorParams):
    trainRatio: Param[float]
    def __init__(self, *args: Any): ...
    def getTrainRatio(self) -> float: ...

class TrainValidationSplit(
    Estimator[TrainValidationSplitModel],
    _TrainValidationSplitParams,
    HasParallelism,
    HasCollectSubModels,
    MLReadable[TrainValidationSplit],
    MLWritable,
):
    def __init__(
        self,
        *,
        estimator: Optional[Estimator] = ...,
        estimatorParamMaps: Optional[List[ParamMap]] = ...,
        evaluator: Optional[Evaluator] = ...,
        trainRatio: float = ...,
        parallelism: int = ...,
        collectSubModels: bool = ...,
        seed: Optional[int] = ...
    ) -> None: ...
    def setParams(
        self,
        *,
        estimator: Optional[Estimator] = ...,
        estimatorParamMaps: Optional[List[ParamMap]] = ...,
        evaluator: Optional[Evaluator] = ...,
        trainRatio: float = ...,
        parallelism: int = ...,
        collectSubModels: bool = ...,
        seed: Optional[int] = ...
    ) -> TrainValidationSplit: ...
    def setEstimator(self, value: Estimator) -> TrainValidationSplit: ...
    def setEstimatorParamMaps(self, value: List[ParamMap]) -> TrainValidationSplit: ...
    def setEvaluator(self, value: Evaluator) -> TrainValidationSplit: ...
    def setTrainRatio(self, value: float) -> TrainValidationSplit: ...
    def setSeed(self, value: int) -> TrainValidationSplit: ...
    def setParallelism(self, value: int) -> TrainValidationSplit: ...
    def setCollectSubModels(self, value: bool) -> TrainValidationSplit: ...
    def copy(self, extra: Optional[ParamMap] = ...) -> TrainValidationSplit: ...
    def write(self) -> MLWriter: ...
    @classmethod
    def read(cls: Type[TrainValidationSplit]) -> MLReader: ...

class TrainValidationSplitModel(
    Model,
    _TrainValidationSplitParams,
    MLReadable[TrainValidationSplitModel],
    MLWritable,
):
    bestModel: Model
    validationMetrics: List[float]
    subModels: List[Model]
    def __init__(
        self,
        bestModel: Model,
        validationMetrics: List[float] = ...,
        subModels: Optional[List[Model]] = ...,
    ) -> None: ...
    def setEstimator(self, value: Estimator) -> TrainValidationSplitModel: ...
    def setEstimatorParamMaps(
        self, value: List[ParamMap]
    ) -> TrainValidationSplitModel: ...
    def setEvaluator(self, value: Evaluator) -> TrainValidationSplitModel: ...
    def copy(self, extra: Optional[ParamMap] = ...) -> TrainValidationSplitModel: ...
    def write(self) -> MLWriter: ...
    @classmethod
    def read(cls: Type[TrainValidationSplitModel]) -> MLReader: ...