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Diffstat (limited to 'python/pyspark/ml/param/shared.py')
-rw-r--r-- | python/pyspark/ml/param/shared.py | 111 |
1 files changed, 111 insertions, 0 deletions
diff --git a/python/pyspark/ml/param/shared.py b/python/pyspark/ml/param/shared.py index 682170aee8..af12181286 100644 --- a/python/pyspark/ml/param/shared.py +++ b/python/pyspark/ml/param/shared.py @@ -459,6 +459,117 @@ class HasHandleInvalid(Params): return self.getOrDefault(self.handleInvalid) +class HasElasticNetParam(Params): + """ + 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.. + """ + + # a placeholder to make it appear in the generated doc + 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.") + + def __init__(self): + super(HasElasticNetParam, self).__init__() + #: param for 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. + self.elasticNetParam = Param(self, "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.") + self._setDefault(elasticNetParam=0.0) + + def setElasticNetParam(self, value): + """ + Sets the value of :py:attr:`elasticNetParam`. + """ + self._paramMap[self.elasticNetParam] = value + return self + + def getElasticNetParam(self): + """ + Gets the value of elasticNetParam or its default value. + """ + return self.getOrDefault(self.elasticNetParam) + + +class HasFitIntercept(Params): + """ + Mixin for param fitIntercept: whether to fit an intercept term.. + """ + + # a placeholder to make it appear in the generated doc + fitIntercept = Param(Params._dummy(), "fitIntercept", "whether to fit an intercept term.") + + def __init__(self): + super(HasFitIntercept, self).__init__() + #: param for whether to fit an intercept term. + self.fitIntercept = Param(self, "fitIntercept", "whether to fit an intercept term.") + self._setDefault(fitIntercept=True) + + def setFitIntercept(self, value): + """ + Sets the value of :py:attr:`fitIntercept`. + """ + self._paramMap[self.fitIntercept] = value + return self + + def getFitIntercept(self): + """ + Gets the value of fitIntercept or its default value. + """ + return self.getOrDefault(self.fitIntercept) + + +class HasStandardization(Params): + """ + Mixin for param standardization: whether to standardize the training features before fitting the model.. + """ + + # a placeholder to make it appear in the generated doc + standardization = Param(Params._dummy(), "standardization", "whether to standardize the training features before fitting the model.") + + def __init__(self): + super(HasStandardization, self).__init__() + #: param for whether to standardize the training features before fitting the model. + self.standardization = Param(self, "standardization", "whether to standardize the training features before fitting the model.") + self._setDefault(standardization=True) + + def setStandardization(self, value): + """ + Sets the value of :py:attr:`standardization`. + """ + self._paramMap[self.standardization] = value + return self + + def getStandardization(self): + """ + Gets the value of standardization or its default value. + """ + return self.getOrDefault(self.standardization) + + +class HasThresholds(Params): + """ + 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. The class with largest value p/t is predicted, where p is the original probability of that class and t is the class' threshold.. + """ + + # a placeholder to make it appear in the generated doc + 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. The class with largest value p/t is predicted, where p is the original probability of that class and t is the class' threshold.") + + def __init__(self): + super(HasThresholds, self).__init__() + #: param for 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. The class with largest value p/t is predicted, where p is the original probability of that class and t is the class' threshold. + self.thresholds = Param(self, "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. The class with largest value p/t is predicted, where p is the original probability of that class and t is the class' threshold.") + + def setThresholds(self, value): + """ + Sets the value of :py:attr:`thresholds`. + """ + self._paramMap[self.thresholds] = value + return self + + def getThresholds(self): + """ + Gets the value of thresholds or its default value. + """ + return self.getOrDefault(self.thresholds) + + class DecisionTreeParams(Params): """ Mixin for Decision Tree parameters. |