diff options
Diffstat (limited to 'python/pyspark/ml/regression.py')
-rw-r--r-- | python/pyspark/ml/regression.py | 42 |
1 files changed, 11 insertions, 31 deletions
diff --git a/python/pyspark/ml/regression.py b/python/pyspark/ml/regression.py index 44f60a7695..a9503608b7 100644 --- a/python/pyspark/ml/regression.py +++ b/python/pyspark/ml/regression.py @@ -28,7 +28,8 @@ __all__ = ['DecisionTreeRegressor', 'DecisionTreeRegressionModel', 'GBTRegressor @inherit_doc class LinearRegression(JavaEstimator, HasFeaturesCol, HasLabelCol, HasPredictionCol, HasMaxIter, - HasRegParam, HasTol): + HasRegParam, HasTol, HasElasticNetParam, HasFitIntercept, + HasStandardization): """ Linear regression. @@ -63,38 +64,30 @@ class LinearRegression(JavaEstimator, HasFeaturesCol, HasLabelCol, HasPrediction TypeError: Method setParams forces keyword arguments. """ - # 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.") - @keyword_only def __init__(self, featuresCol="features", labelCol="label", predictionCol="prediction", - maxIter=100, regParam=0.0, elasticNetParam=0.0, tol=1e-6): + maxIter=100, regParam=0.0, elasticNetParam=0.0, tol=1e-6, fitIntercept=True, + standardization=True): """ __init__(self, featuresCol="features", labelCol="label", predictionCol="prediction", \ - maxIter=100, regParam=0.0, elasticNetParam=0.0, tol=1e-6) + maxIter=100, regParam=0.0, elasticNetParam=0.0, tol=1e-6, fitIntercept=True, \ + standardization=True) """ super(LinearRegression, self).__init__() self._java_obj = self._new_java_obj( "org.apache.spark.ml.regression.LinearRegression", self.uid) - #: 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(maxIter=100, regParam=0.0, elasticNetParam=0.0, tol=1e-6) + self._setDefault(maxIter=100, regParam=0.0, tol=1e-6) kwargs = self.__init__._input_kwargs self.setParams(**kwargs) @keyword_only def setParams(self, featuresCol="features", labelCol="label", predictionCol="prediction", - maxIter=100, regParam=0.0, elasticNetParam=0.0, tol=1e-6): + maxIter=100, regParam=0.0, elasticNetParam=0.0, tol=1e-6, fitIntercept=True, + standardization=True): """ setParams(self, featuresCol="features", labelCol="label", predictionCol="prediction", \ - maxIter=100, regParam=0.0, elasticNetParam=0.0, tol=1e-6) + maxIter=100, regParam=0.0, elasticNetParam=0.0, tol=1e-6, fitIntercept=True, \ + standardization=True) Sets params for linear regression. """ kwargs = self.setParams._input_kwargs @@ -103,19 +96,6 @@ class LinearRegression(JavaEstimator, HasFeaturesCol, HasLabelCol, HasPrediction def _create_model(self, java_model): return LinearRegressionModel(java_model) - 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 LinearRegressionModel(JavaModel): """ |