2020-09-24 01:15:36 -04:00
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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 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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[SPARK-33592] Fix: Pyspark ML Validator params in estimatorParamMaps may be lost after saving and reloading
### What changes were proposed in this pull request?
Fix: Pyspark ML Validator params in estimatorParamMaps may be lost after saving and reloading
When saving validator estimatorParamMaps, will check all nested stages in tuned estimator to get correct param parent.
Two typical cases to manually test:
~~~python
tokenizer = Tokenizer(inputCol="text", outputCol="words")
hashingTF = HashingTF(inputCol=tokenizer.getOutputCol(), outputCol="features")
lr = LogisticRegression()
pipeline = Pipeline(stages=[tokenizer, hashingTF, lr])
paramGrid = ParamGridBuilder() \
.addGrid(hashingTF.numFeatures, [10, 100]) \
.addGrid(lr.maxIter, [100, 200]) \
.build()
tvs = TrainValidationSplit(estimator=pipeline,
estimatorParamMaps=paramGrid,
evaluator=MulticlassClassificationEvaluator())
tvs.save(tvsPath)
loadedTvs = TrainValidationSplit.load(tvsPath)
# check `loadedTvs.getEstimatorParamMaps()` restored correctly.
~~~
~~~python
lr = LogisticRegression()
ova = OneVsRest(classifier=lr)
grid = ParamGridBuilder().addGrid(lr.maxIter, [100, 200]).build()
evaluator = MulticlassClassificationEvaluator()
tvs = TrainValidationSplit(estimator=ova, estimatorParamMaps=grid, evaluator=evaluator)
tvs.save(tvsPath)
loadedTvs = TrainValidationSplit.load(tvsPath)
# check `loadedTvs.getEstimatorParamMaps()` restored correctly.
~~~
### Why are the changes needed?
Bug fix.
### Does this PR introduce _any_ user-facing change?
No
### How was this patch tested?
Unit test.
Closes #30539 from WeichenXu123/fix_tuning_param_maps_io.
Authored-by: Weichen Xu <weichen.xu@databricks.com>
Signed-off-by: Ruifeng Zheng <ruifengz@foxmail.com>
2020-11-30 20:36:42 -05:00
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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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2020-12-03 19:35:50 -05:00
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@staticmethod
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def getUidMap(instance: Any) -> dict: ...
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