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# DO NOT MODIFY THIS FILE! It was generated by _shared_params_code_gen.py.
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from pyspark . ml . param import *
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class HasMaxIter ( Params ) :
"""
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Mixin for param maxIter : max number of iterations ( > = 0 ) .
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"""
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maxIter = Param ( Params . _dummy ( ) , " maxIter " , " max number of iterations (>= 0). " , typeConverter = TypeConverters . toInt )
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def __init__ ( self ) :
super ( HasMaxIter , self ) . __init__ ( )
def getMaxIter ( self ) :
"""
Gets the value of maxIter or its default value .
"""
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return self . getOrDefault ( self . maxIter )
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class HasRegParam ( Params ) :
"""
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Mixin for param regParam : regularization parameter ( > = 0 ) .
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"""
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regParam = Param ( Params . _dummy ( ) , " regParam " , " regularization parameter (>= 0). " , typeConverter = TypeConverters . toFloat )
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def __init__ ( self ) :
super ( HasRegParam , self ) . __init__ ( )
def getRegParam ( self ) :
"""
Gets the value of regParam or its default value .
"""
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return self . getOrDefault ( self . regParam )
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class HasFeaturesCol ( Params ) :
"""
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Mixin for param featuresCol : features column name .
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"""
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featuresCol = Param ( Params . _dummy ( ) , " featuresCol " , " features column name. " , typeConverter = TypeConverters . toString )
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def __init__ ( self ) :
super ( HasFeaturesCol , self ) . __init__ ( )
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self . _setDefault ( featuresCol = ' features ' )
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def getFeaturesCol ( self ) :
"""
Gets the value of featuresCol or its default value .
"""
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return self . getOrDefault ( self . featuresCol )
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class HasLabelCol ( Params ) :
"""
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Mixin for param labelCol : label column name .
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"""
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labelCol = Param ( Params . _dummy ( ) , " labelCol " , " label column name. " , typeConverter = TypeConverters . toString )
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def __init__ ( self ) :
super ( HasLabelCol , self ) . __init__ ( )
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self . _setDefault ( labelCol = ' label ' )
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def getLabelCol ( self ) :
"""
Gets the value of labelCol or its default value .
"""
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return self . getOrDefault ( self . labelCol )
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class HasPredictionCol ( Params ) :
"""
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Mixin for param predictionCol : prediction column name .
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"""
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predictionCol = Param ( Params . _dummy ( ) , " predictionCol " , " prediction column name. " , typeConverter = TypeConverters . toString )
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def __init__ ( self ) :
super ( HasPredictionCol , self ) . __init__ ( )
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self . _setDefault ( predictionCol = ' prediction ' )
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def getPredictionCol ( self ) :
"""
Gets the value of predictionCol or its default value .
"""
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return self . getOrDefault ( self . predictionCol )
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class HasProbabilityCol ( Params ) :
"""
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Mixin for param probabilityCol : Column name for predicted class conditional probabilities . Note : Not all models output well - calibrated probability estimates ! These probabilities should be treated as confidences , not precise probabilities .
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"""
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probabilityCol = Param ( Params . _dummy ( ) , " probabilityCol " , " Column name for predicted class conditional probabilities. Note: Not all models output well-calibrated probability estimates! These probabilities should be treated as confidences, not precise probabilities. " , typeConverter = TypeConverters . toString )
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def __init__ ( self ) :
super ( HasProbabilityCol , self ) . __init__ ( )
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self . _setDefault ( probabilityCol = ' probability ' )
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def getProbabilityCol ( self ) :
"""
Gets the value of probabilityCol or its default value .
"""
return self . getOrDefault ( self . probabilityCol )
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class HasRawPredictionCol ( Params ) :
"""
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Mixin for param rawPredictionCol : raw prediction ( a . k . a . confidence ) column name .
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"""
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rawPredictionCol = Param ( Params . _dummy ( ) , " rawPredictionCol " , " raw prediction (a.k.a. confidence) column name. " , typeConverter = TypeConverters . toString )
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def __init__ ( self ) :
super ( HasRawPredictionCol , self ) . __init__ ( )
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self . _setDefault ( rawPredictionCol = ' rawPrediction ' )
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def getRawPredictionCol ( self ) :
"""
Gets the value of rawPredictionCol or its default value .
"""
return self . getOrDefault ( self . rawPredictionCol )
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class HasInputCol ( Params ) :
"""
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Mixin for param inputCol : input column name .
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"""
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inputCol = Param ( Params . _dummy ( ) , " inputCol " , " input column name. " , typeConverter = TypeConverters . toString )
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def __init__ ( self ) :
super ( HasInputCol , self ) . __init__ ( )
def getInputCol ( self ) :
"""
Gets the value of inputCol or its default value .
"""
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return self . getOrDefault ( self . inputCol )
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class HasInputCols ( Params ) :
"""
Mixin for param inputCols : input column names .
"""
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inputCols = Param ( Params . _dummy ( ) , " inputCols " , " input column names. " , typeConverter = TypeConverters . toListString )
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def __init__ ( self ) :
super ( HasInputCols , self ) . __init__ ( )
def getInputCols ( self ) :
"""
Gets the value of inputCols or its default value .
"""
return self . getOrDefault ( self . inputCols )
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class HasOutputCol ( Params ) :
"""
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Mixin for param outputCol : output column name .
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"""
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outputCol = Param ( Params . _dummy ( ) , " outputCol " , " output column name. " , typeConverter = TypeConverters . toString )
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def __init__ ( self ) :
super ( HasOutputCol , self ) . __init__ ( )
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self . _setDefault ( outputCol = self . uid + ' __output ' )
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def getOutputCol ( self ) :
"""
Gets the value of outputCol or its default value .
"""
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return self . getOrDefault ( self . outputCol )
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class HasOutputCols ( Params ) :
"""
Mixin for param outputCols : output column names .
"""
outputCols = Param ( Params . _dummy ( ) , " outputCols " , " output column names. " , typeConverter = TypeConverters . toListString )
def __init__ ( self ) :
super ( HasOutputCols , self ) . __init__ ( )
def getOutputCols ( self ) :
"""
Gets the value of outputCols or its default value .
"""
return self . getOrDefault ( self . outputCols )
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class HasNumFeatures ( Params ) :
"""
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Mixin for param numFeatures : Number of features . Should be greater than 0.
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"""
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numFeatures = Param ( Params . _dummy ( ) , " numFeatures " , " Number of features. Should be greater than 0. " , typeConverter = TypeConverters . toInt )
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def __init__ ( self ) :
super ( HasNumFeatures , self ) . __init__ ( )
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self . _setDefault ( numFeatures = 262144 )
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def getNumFeatures ( self ) :
"""
Gets the value of numFeatures or its default value .
"""
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return self . getOrDefault ( self . numFeatures )
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class HasCheckpointInterval ( Params ) :
"""
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Mixin for param checkpointInterval : set checkpoint interval ( > = 1 ) or disable checkpoint ( - 1 ) . E . g . 10 means that the cache will get checkpointed every 10 iterations . Note : this setting will be ignored if the checkpoint directory is not set in the SparkContext .
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"""
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checkpointInterval = Param ( Params . _dummy ( ) , " checkpointInterval " , " set checkpoint interval (>= 1) or disable checkpoint (-1). E.g. 10 means that the cache will get checkpointed every 10 iterations. Note: this setting will be ignored if the checkpoint directory is not set in the SparkContext. " , typeConverter = TypeConverters . toInt )
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def __init__ ( self ) :
super ( HasCheckpointInterval , self ) . __init__ ( )
def getCheckpointInterval ( self ) :
"""
Gets the value of checkpointInterval or its default value .
"""
return self . getOrDefault ( self . checkpointInterval )
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class HasSeed ( Params ) :
"""
Mixin for param seed : random seed .
"""
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seed = Param ( Params . _dummy ( ) , " seed " , " random seed. " , typeConverter = TypeConverters . toInt )
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def __init__ ( self ) :
super ( HasSeed , self ) . __init__ ( )
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self . _setDefault ( seed = hash ( type ( self ) . __name__ ) )
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def getSeed ( self ) :
"""
Gets the value of seed or its default value .
"""
return self . getOrDefault ( self . seed )
class HasTol ( Params ) :
"""
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Mixin for param tol : the convergence tolerance for iterative algorithms ( > = 0 ) .
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"""
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tol = Param ( Params . _dummy ( ) , " tol " , " the convergence tolerance for iterative algorithms (>= 0). " , typeConverter = TypeConverters . toFloat )
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def __init__ ( self ) :
super ( HasTol , self ) . __init__ ( )
def getTol ( self ) :
"""
Gets the value of tol or its default value .
"""
return self . getOrDefault ( self . tol )
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class HasRelativeError ( Params ) :
"""
Mixin for param relativeError : the relative target precision for the approximate quantile algorithm . Must be in the range [ 0 , 1 ]
"""
relativeError = Param ( Params . _dummy ( ) , " relativeError " , " the relative target precision for the approximate quantile algorithm. Must be in the range [0, 1] " , typeConverter = TypeConverters . toFloat )
def __init__ ( self ) :
super ( HasRelativeError , self ) . __init__ ( )
self . _setDefault ( relativeError = 0.001 )
def getRelativeError ( self ) :
"""
Gets the value of relativeError or its default value .
"""
return self . getOrDefault ( self . relativeError )
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class HasStepSize ( Params ) :
"""
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Mixin for param stepSize : Step size to be used for each iteration of optimization ( > = 0 ) .
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"""
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stepSize = Param ( Params . _dummy ( ) , " stepSize " , " Step size to be used for each iteration of optimization (>= 0). " , typeConverter = TypeConverters . toFloat )
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def __init__ ( self ) :
super ( HasStepSize , self ) . __init__ ( )
def getStepSize ( self ) :
"""
Gets the value of stepSize or its default value .
"""
return self . getOrDefault ( self . stepSize )
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class HasHandleInvalid ( Params ) :
"""
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Mixin for param handleInvalid : how to handle invalid entries . Options are skip ( which will filter out rows with bad values ) , or error ( which will throw an error ) . More options may be added later .
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"""
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handleInvalid = Param ( Params . _dummy ( ) , " handleInvalid " , " how to handle invalid entries. Options are skip (which will filter out rows with bad values), or error (which will throw an error). More options may be added later. " , typeConverter = TypeConverters . toString )
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def __init__ ( self ) :
super ( HasHandleInvalid , self ) . __init__ ( )
def getHandleInvalid ( self ) :
"""
Gets the value of handleInvalid or its default value .
"""
return self . getOrDefault ( self . handleInvalid )
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class HasElasticNetParam ( Params ) :
"""
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Mixin for param elasticNetParam : the ElasticNet mixing parameter , in range [ 0 , 1 ] . For alpha = 0 , the penalty is an L2 penalty . For alpha = 1 , it is an L1 penalty .
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"""
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elasticNetParam = Param ( Params . _dummy ( ) , " elasticNetParam " , " the ElasticNet mixing parameter, in range [0, 1]. For alpha = 0, the penalty is an L2 penalty. For alpha = 1, it is an L1 penalty. " , typeConverter = TypeConverters . toFloat )
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def __init__ ( self ) :
super ( HasElasticNetParam , self ) . __init__ ( )
self . _setDefault ( elasticNetParam = 0.0 )
def getElasticNetParam ( self ) :
"""
Gets the value of elasticNetParam or its default value .
"""
return self . getOrDefault ( self . elasticNetParam )
class HasFitIntercept ( Params ) :
"""
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Mixin for param fitIntercept : whether to fit an intercept term .
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"""
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fitIntercept = Param ( Params . _dummy ( ) , " fitIntercept " , " whether to fit an intercept term. " , typeConverter = TypeConverters . toBoolean )
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def __init__ ( self ) :
super ( HasFitIntercept , self ) . __init__ ( )
self . _setDefault ( fitIntercept = True )
def getFitIntercept ( self ) :
"""
Gets the value of fitIntercept or its default value .
"""
return self . getOrDefault ( self . fitIntercept )
class HasStandardization ( Params ) :
"""
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Mixin for param standardization : whether to standardize the training features before fitting the model .
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"""
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standardization = Param ( Params . _dummy ( ) , " standardization " , " whether to standardize the training features before fitting the model. " , typeConverter = TypeConverters . toBoolean )
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def __init__ ( self ) :
super ( HasStandardization , self ) . __init__ ( )
self . _setDefault ( standardization = True )
def getStandardization ( self ) :
"""
Gets the value of standardization or its default value .
"""
return self . getOrDefault ( self . standardization )
class HasThresholds ( Params ) :
"""
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Mixin for param thresholds : Thresholds in multi - class classification to adjust the probability of predicting each class . Array must have length equal to the number of classes , with values > 0 , excepting that at most one value may be 0. The class with largest value p / t is predicted , where p is the original probability of that class and t is the class ' s threshold.
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"""
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thresholds = Param ( Params . _dummy ( ) , " thresholds " , " Thresholds in multi-class classification to adjust the probability of predicting each class. Array must have length equal to the number of classes, with values > 0, excepting that at most one value may be 0. The class with largest value p/t is predicted, where p is the original probability of that class and t is the class ' s threshold. " , typeConverter = TypeConverters . toListFloat )
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def __init__ ( self ) :
super ( HasThresholds , self ) . __init__ ( )
def getThresholds ( self ) :
"""
Gets the value of thresholds or its default value .
"""
return self . getOrDefault ( self . thresholds )
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class HasThreshold ( Params ) :
"""
Mixin for param threshold : threshold in binary classification prediction , in range [ 0 , 1 ]
"""
threshold = Param ( Params . _dummy ( ) , " threshold " , " threshold in binary classification prediction, in range [0, 1] " , typeConverter = TypeConverters . toFloat )
def __init__ ( self ) :
super ( HasThreshold , self ) . __init__ ( )
self . _setDefault ( threshold = 0.5 )
def getThreshold ( self ) :
"""
Gets the value of threshold or its default value .
"""
return self . getOrDefault ( self . threshold )
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class HasWeightCol ( Params ) :
"""
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Mixin for param weightCol : weight column name . If this is not set or empty , we treat all instance weights as 1.0 .
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"""
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weightCol = Param ( Params . _dummy ( ) , " weightCol " , " weight column name. If this is not set or empty, we treat all instance weights as 1.0. " , typeConverter = TypeConverters . toString )
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def __init__ ( self ) :
super ( HasWeightCol , self ) . __init__ ( )
def getWeightCol ( self ) :
"""
Gets the value of weightCol or its default value .
"""
return self . getOrDefault ( self . weightCol )
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class HasSolver ( Params ) :
"""
Mixin for param solver : the solver algorithm for optimization . If this is not set or empty , default value is ' auto ' .
"""
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solver = Param ( Params . _dummy ( ) , " solver " , " the solver algorithm for optimization. If this is not set or empty, default value is ' auto ' . " , typeConverter = TypeConverters . toString )
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def __init__ ( self ) :
super ( HasSolver , self ) . __init__ ( )
self . _setDefault ( solver = ' auto ' )
def getSolver ( self ) :
"""
Gets the value of solver or its default value .
"""
return self . getOrDefault ( self . solver )
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class HasVarianceCol ( Params ) :
"""
Mixin for param varianceCol : column name for the biased sample variance of prediction .
"""
varianceCol = Param ( Params . _dummy ( ) , " varianceCol " , " column name for the biased sample variance of prediction. " , typeConverter = TypeConverters . toString )
def __init__ ( self ) :
super ( HasVarianceCol , self ) . __init__ ( )
def getVarianceCol ( self ) :
"""
Gets the value of varianceCol or its default value .
"""
return self . getOrDefault ( self . varianceCol )
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class HasAggregationDepth ( Params ) :
"""
Mixin for param aggregationDepth : suggested depth for treeAggregate ( > = 2 ) .
"""
aggregationDepth = Param ( Params . _dummy ( ) , " aggregationDepth " , " suggested depth for treeAggregate (>= 2). " , typeConverter = TypeConverters . toInt )
def __init__ ( self ) :
super ( HasAggregationDepth , self ) . __init__ ( )
self . _setDefault ( aggregationDepth = 2 )
def getAggregationDepth ( self ) :
"""
Gets the value of aggregationDepth or its default value .
"""
return self . getOrDefault ( self . aggregationDepth )
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class HasParallelism ( Params ) :
"""
Mixin for param parallelism : the number of threads to use when running parallel algorithms ( > = 1 ) .
"""
parallelism = Param ( Params . _dummy ( ) , " parallelism " , " the number of threads to use when running parallel algorithms (>= 1). " , typeConverter = TypeConverters . toInt )
def __init__ ( self ) :
super ( HasParallelism , self ) . __init__ ( )
self . _setDefault ( parallelism = 1 )
def getParallelism ( self ) :
"""
Gets the value of parallelism or its default value .
"""
return self . getOrDefault ( self . parallelism )
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class HasCollectSubModels ( Params ) :
"""
Mixin for param collectSubModels : Param for whether to collect a list of sub - models trained during tuning . If set to false , then only the single best sub - model will be available after fitting . If set to true , then all sub - models will be available . Warning : For large models , collecting all sub - models can cause OOMs on the Spark driver .
"""
collectSubModels = Param ( Params . _dummy ( ) , " collectSubModels " , " Param for whether to collect a list of sub-models trained during tuning. If set to false, then only the single best sub-model will be available after fitting. If set to true, then all sub-models will be available. Warning: For large models, collecting all sub-models can cause OOMs on the Spark driver. " , typeConverter = TypeConverters . toBoolean )
def __init__ ( self ) :
super ( HasCollectSubModels , self ) . __init__ ( )
self . _setDefault ( collectSubModels = False )
def getCollectSubModels ( self ) :
"""
Gets the value of collectSubModels or its default value .
"""
return self . getOrDefault ( self . collectSubModels )
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class HasLoss ( Params ) :
"""
Mixin for param loss : the loss function to be optimized .
"""
loss = Param ( Params . _dummy ( ) , " loss " , " the loss function to be optimized. " , typeConverter = TypeConverters . toString )
def __init__ ( self ) :
super ( HasLoss , self ) . __init__ ( )
def getLoss ( self ) :
"""
Gets the value of loss or its default value .
"""
return self . getOrDefault ( self . loss )
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class HasDistanceMeasure ( Params ) :
"""
Mixin for param distanceMeasure : the distance measure . Supported options : ' euclidean ' and ' cosine ' .
"""
distanceMeasure = Param ( Params . _dummy ( ) , " distanceMeasure " , " the distance measure. Supported options: ' euclidean ' and ' cosine ' . " , typeConverter = TypeConverters . toString )
def __init__ ( self ) :
super ( HasDistanceMeasure , self ) . __init__ ( )
self . _setDefault ( distanceMeasure = ' euclidean ' )
def getDistanceMeasure ( self ) :
"""
Gets the value of distanceMeasure or its default value .
"""
return self . getOrDefault ( self . distanceMeasure )
class HasValidationIndicatorCol ( Params ) :
"""
Mixin for param validationIndicatorCol : name of the column that indicates whether each row is for training or for validation . False indicates training ; true indicates validation .
"""
validationIndicatorCol = Param ( Params . _dummy ( ) , " validationIndicatorCol " , " name of the column that indicates whether each row is for training or for validation. False indicates training; true indicates validation. " , typeConverter = TypeConverters . toString )
def __init__ ( self ) :
super ( HasValidationIndicatorCol , self ) . __init__ ( )
def getValidationIndicatorCol ( self ) :
"""
Gets the value of validationIndicatorCol or its default value .
"""
return self . getOrDefault ( self . validationIndicatorCol )