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, Generic, List
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from pyspark.ml._typing import T
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from pyspark.ml.param import *
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class HasMaxIter(Params):
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maxIter: Param[int]
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def __init__(self) -> None: ...
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def getMaxIter(self) -> int: ...
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class HasRegParam(Params):
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regParam: Param[float]
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def __init__(self) -> None: ...
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def getRegParam(self) -> float: ...
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class HasFeaturesCol(Params):
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featuresCol: Param[str]
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def __init__(self) -> None: ...
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def getFeaturesCol(self) -> str: ...
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class HasLabelCol(Params):
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labelCol: Param[str]
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def __init__(self) -> None: ...
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def getLabelCol(self) -> str: ...
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class HasPredictionCol(Params):
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predictionCol: Param[str]
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def __init__(self) -> None: ...
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def getPredictionCol(self) -> str: ...
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class HasProbabilityCol(Params):
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probabilityCol: Param[str]
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def __init__(self) -> None: ...
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def getProbabilityCol(self) -> str: ...
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class HasRawPredictionCol(Params):
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rawPredictionCol: Param[str]
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def __init__(self) -> None: ...
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def getRawPredictionCol(self) -> str: ...
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class HasInputCol(Params):
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inputCol: Param[str]
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def __init__(self) -> None: ...
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def getInputCol(self) -> str: ...
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class HasInputCols(Params):
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inputCols: Param[List[str]]
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def __init__(self) -> None: ...
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def getInputCols(self) -> List[str]: ...
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class HasOutputCol(Params):
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outputCol: Param[str]
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def __init__(self) -> None: ...
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def getOutputCol(self) -> str: ...
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class HasOutputCols(Params):
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outputCols: Param[List[str]]
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def __init__(self) -> None: ...
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def getOutputCols(self) -> List[str]: ...
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class HasNumFeatures(Params):
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numFeatures: Param[int]
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def __init__(self) -> None: ...
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def getNumFeatures(self) -> int: ...
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class HasCheckpointInterval(Params):
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checkpointInterval: Param[int]
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def __init__(self) -> None: ...
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def getCheckpointInterval(self) -> int: ...
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class HasSeed(Params):
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seed: Param[int]
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def __init__(self) -> None: ...
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def getSeed(self) -> int: ...
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class HasTol(Params):
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tol: Param[float]
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def __init__(self) -> None: ...
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def getTol(self) -> float: ...
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class HasRelativeError(Params):
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relativeError: Param[float]
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def __init__(self) -> None: ...
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def getRelativeError(self) -> float: ...
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class HasStepSize(Params):
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stepSize: Param[float]
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def __init__(self) -> None: ...
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def getStepSize(self) -> float: ...
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class HasHandleInvalid(Params):
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handleInvalid: Param[str]
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def __init__(self) -> None: ...
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def getHandleInvalid(self) -> str: ...
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class HasElasticNetParam(Params):
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elasticNetParam: Param[float]
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def __init__(self) -> None: ...
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def getElasticNetParam(self) -> float: ...
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class HasFitIntercept(Params):
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fitIntercept: Param[bool]
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def __init__(self) -> None: ...
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def getFitIntercept(self) -> bool: ...
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class HasStandardization(Params):
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standardization: Param[bool]
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def __init__(self) -> None: ...
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def getStandardization(self) -> bool: ...
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class HasThresholds(Params):
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thresholds: Param[List[float]]
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def __init__(self) -> None: ...
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def getThresholds(self) -> List[float]: ...
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class HasThreshold(Params):
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threshold: Param[float]
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def __init__(self) -> None: ...
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def getThreshold(self) -> float: ...
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class HasWeightCol(Params):
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weightCol: Param[str]
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def __init__(self) -> None: ...
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def getWeightCol(self) -> str: ...
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class HasSolver(Params):
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solver: Param[str]
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def __init__(self) -> None: ...
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def getSolver(self) -> str: ...
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class HasVarianceCol(Params):
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varianceCol: Param[str]
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def __init__(self) -> None: ...
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def getVarianceCol(self) -> str: ...
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class HasAggregationDepth(Params):
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aggregationDepth: Param[int]
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def __init__(self) -> None: ...
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def getAggregationDepth(self) -> int: ...
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class HasParallelism(Params):
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parallelism: Param[int]
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def __init__(self) -> None: ...
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def getParallelism(self) -> int: ...
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class HasCollectSubModels(Params):
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collectSubModels: Param[bool]
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def __init__(self) -> None: ...
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def getCollectSubModels(self) -> bool: ...
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class HasLoss(Params):
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loss: Param[str]
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def __init__(self) -> None: ...
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def getLoss(self) -> str: ...
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class HasValidationIndicatorCol(Params):
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validationIndicatorCol: Param[str]
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def __init__(self) -> None: ...
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def getValidationIndicatorCol(self) -> str: ...
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class HasDistanceMeasure(Params):
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distanceMeasure: Param[str]
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def __init__(self) -> None: ...
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def getDistanceMeasure(self) -> str: ...
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class HasBlockSize(Params):
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blockSize: Param[int]
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def __init__(self) -> None: ...
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def getBlockSize(self) -> int: ...
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2020-11-12 06:14:07 -05:00
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class HasMaxBlockSizeInMB(Params):
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maxBlockSizeInMB: Param[float]
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def __init__(self) -> None: ...
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def getMaxBlockSizeInMB(self) -> float: ...
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