7e759b2d95
### What changes were proposed in this pull request? make CrossValidator/TrainValidateSplit/OneVsRest Reader/Writer support Python backend estimator/model ### Why are the changes needed? Currently, pyspark support third-party library to define python backend estimator/evaluator, i.e., estimator that inherit `Estimator` instead of `JavaEstimator`, and only can be used in pyspark. CrossValidator and TrainValidateSplit support tuning these python backend estimator, but cannot support saving/load, becase CrossValidator and TrainValidateSplit writer implementation is use JavaMLWriter, which require to convert nested estimator and evaluator into java instance. OneVsRest saving/load now only support java backend classifier due to similar issue. ### Does this PR introduce _any_ user-facing change? No ### How was this patch tested? Unit test. Closes #30471 from WeichenXu123/support_pyio_tuning. Authored-by: Weichen Xu <weichen.xu@databricks.com> Signed-off-by: Weichen Xu <weichen.xu@databricks.com>
137 lines
4.5 KiB
Python
137 lines
4.5 KiB
Python
#
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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 Any, Dict, Generic, Optional, Type, TypeVar, Union
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from pyspark import SparkContext as SparkContext, since as since # noqa: F401
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from pyspark.ml.common import inherit_doc as inherit_doc # noqa: F401
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from pyspark.sql import SparkSession as SparkSession
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from pyspark.util import VersionUtils as VersionUtils # noqa: F401
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S = TypeVar("S")
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R = TypeVar("R", bound=MLReadable)
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class Identifiable:
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uid: str
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def __init__(self) -> None: ...
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class BaseReadWrite:
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def __init__(self) -> None: ...
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def session(self, sparkSession: SparkSession) -> Union[MLWriter, MLReader]: ...
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@property
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def sparkSession(self) -> SparkSession: ...
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@property
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def sc(self) -> SparkContext: ...
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class MLWriter(BaseReadWrite):
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shouldOverwrite: bool = ...
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def __init__(self) -> None: ...
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def save(self, path: str) -> None: ...
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def saveImpl(self, path: str) -> None: ...
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def overwrite(self) -> MLWriter: ...
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class GeneralMLWriter(MLWriter):
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source: str
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def format(self, source: str) -> MLWriter: ...
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class JavaMLWriter(MLWriter):
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def __init__(self, instance: JavaMLWritable) -> None: ...
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def save(self, path: str) -> None: ...
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def overwrite(self) -> JavaMLWriter: ...
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def option(self, key: str, value: Any) -> JavaMLWriter: ...
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def session(self, sparkSession: SparkSession) -> JavaMLWriter: ...
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class GeneralJavaMLWriter(JavaMLWriter):
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def __init__(self, instance: MLWritable) -> None: ...
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def format(self, source: str) -> GeneralJavaMLWriter: ...
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class MLWritable:
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def write(self) -> MLWriter: ...
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def save(self, path: str) -> None: ...
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class JavaMLWritable(MLWritable):
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def write(self) -> JavaMLWriter: ...
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class GeneralJavaMLWritable(JavaMLWritable):
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def write(self) -> GeneralJavaMLWriter: ...
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class MLReader(BaseReadWrite, Generic[R]):
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def load(self, path: str) -> R: ...
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class JavaMLReader(MLReader[R]):
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def __init__(self, clazz: Type[JavaMLReadable]) -> None: ...
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def load(self, path: str) -> R: ...
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def session(self, sparkSession: SparkSession) -> JavaMLReader[R]: ...
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class MLReadable(Generic[R]):
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@classmethod
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def read(cls: Type[R]) -> MLReader[R]: ...
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@classmethod
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def load(cls: Type[R], path: str) -> R: ...
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class JavaMLReadable(MLReadable[R]):
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@classmethod
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def read(cls: Type[R]) -> JavaMLReader[R]: ...
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class DefaultParamsWritable(MLWritable):
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def write(self) -> MLWriter: ...
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class DefaultParamsWriter(MLWriter):
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instance: DefaultParamsWritable
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def __init__(self, instance: DefaultParamsWritable) -> None: ...
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def saveImpl(self, path: str) -> None: ...
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@staticmethod
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def saveMetadata(
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instance: DefaultParamsWritable,
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path: str,
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sc: SparkContext,
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extraMetadata: Optional[Dict[str, Any]] = ...,
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paramMap: Optional[Dict[str, Any]] = ...,
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) -> None: ...
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class DefaultParamsReadable(MLReadable[R]):
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@classmethod
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def read(cls: Type[R]) -> MLReader[R]: ...
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class DefaultParamsReader(MLReader[R]):
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cls: Type[R]
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def __init__(self, cls: Type[MLReadable]) -> None: ...
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def load(self, path: str) -> R: ...
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@staticmethod
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def loadMetadata(
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path: str, sc: SparkContext, expectedClassName: str = ...
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) -> Dict[str, Any]: ...
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@staticmethod
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def getAndSetParams(instance: R, metadata: Dict[str, Any]) -> None: ...
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@staticmethod
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def loadParamsInstance(path: str, sc: SparkContext) -> R: ...
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class HasTrainingSummary(Generic[S]):
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@property
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def hasSummary(self) -> bool: ...
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@property
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def summary(self) -> S: ...
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class MetaAlgorithmReadWrite:
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@staticmethod
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def isMetaEstimator(pyInstance: Any) -> bool: ...
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@staticmethod
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def getAllNestedStages(pyInstance: Any) -> list: ...
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@staticmethod
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def getUidMap(instance: Any) -> dict: ...
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