#
# Licensed to the Apache Software Foundation (ASF) under one or more
# contributor license agreements. See the NOTICE file distributed with
# this work for additional information regarding copyright ownership.
# The ASF licenses this file to You under the Apache License, Version 2.0
# (the "License"); you may not use this file except in compliance with
# the License. You may obtain a copy of the License at
#
# http://www.apache.org/licenses/LICENSE-2.0
#
# Unless required by applicable law or agreed to in writing, software
# distributed under the License is distributed on an "AS IS" BASIS,
# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
# See the License for the specific language governing permissions and
# limitations under the License.
#
# DO NOT MODIFY THIS FILE! It was generated by _shared_params_code_gen.py.
from pyspark.ml.param import Param, Params
class HasMaxIter(Params):
"""
Mixin for param maxIter: max number of iterations.
"""
# a placeholder to make it appear in the generated doc
maxIter = Param(Params._dummy(), "maxIter", "max number of iterations")
def __init__(self):
super(HasMaxIter, self).__init__()
#: param for max number of iterations
self.maxIter = Param(self, "maxIter", "max number of iterations")
if None is not None:
self._setDefault(maxIter=None)
def setMaxIter(self, value):
"""
Sets the value of :py:attr:`maxIter`.
"""
self.paramMap[self.maxIter] = value
return self
def getMaxIter(self):
"""
Gets the value of maxIter or its default value.
"""
return self.getOrDefault(self.maxIter)
class HasRegParam(Params):
"""
Mixin for param regParam: regularization constant.
"""
# a placeholder to make it appear in the generated doc
regParam = Param(Params._dummy(), "regParam", "regularization constant")
def __init__(self):
super(HasRegParam, self).__init__()
#: param for regularization constant
self.regParam = Param(self, "regParam", "regularization constant")
if None is not None:
self._setDefault(regParam=None)
def setRegParam(self, value):
"""
Sets the value of :py:attr:`regParam`.
"""
self.paramMap[self.regParam] = value
return self
def getRegParam(self):
"""
Gets the value of regParam or its default value.
"""
return self.getOrDefault(self.regParam)
class HasFeaturesCol(Params):
"""
Mixin for param featuresCol: features column name.
"""
# a placeholder to make it appear in the generated doc
featuresCol = Param(Params._dummy(), "featuresCol", "features column name")
def __init__(self):
super(HasFeaturesCol, self).__init__()
#: param for features column name
self.featuresCol = Param(self, "featuresCol", "features column name")
if 'features' is not None:
self._setDefault(featuresCol='features')
def setFeaturesCol(self, value):
"""
Sets the value of :py:attr:`featuresCol`.
"""
self.paramMap[self.featuresCol] = value
return self
def getFeaturesCol(self):
"""
Gets the value of featuresCol or its default value.
"""
return self.getOrDefault(self.featuresCol)
class HasLabelCol(Params):
"""
Mixin for param labelCol: label column name.
"""
# a placeholder to make it appear in the generated doc
labelCol = Param(Params._dummy(), "labelCol", "label column name")
def __init__(self):
super(HasLabelCol, self).__init__()
#: param for label column name
self.labelCol = Param(self, "labelCol", "label column name")
if 'label' is not None:
self._setDefault(labelCol='label')
def setLabelCol(self, value):
"""
Sets the value of :py:attr:`labelCol`.
"""
self.paramMap[self.labelCol] = value
return self
def getLabelCol(self):
"""
Gets the value of labelCol or its default value.
"""
return self.getOrDefault(self.labelCol)
class HasPredictionCol(Params):
"""
Mixin for param predictionCol: prediction column name.
"""
# a placeholder to make it appear in the generated doc
predictionCol = Param(Params._dummy(), "predictionCol", "prediction column name")
def __init__(self):
super(HasPredictionCol, self).__init__()
#: param for prediction column name
self.predictionCol = Param(self, "predictionCol", "prediction column name")
if 'prediction' is not None:
self._setDefault(predictionCol='prediction')
def setPredictionCol(self, value):
"""
Sets the value of :py:attr:`predictionCol`.
"""
self.paramMap[self.predictionCol] = value
return self
def getPredictionCol(self):
"""
Gets the value of predictionCol or its default value.
"""
return self.getOrDefault(self.predictionCol)
class HasRawPredictionCol(Params):
"""
Mixin for param rawPredictionCol: raw prediction column name.
"""
# a placeholder to make it appear in the generated doc
rawPredictionCol = Param(Params._dummy(), "rawPredictionCol", "raw prediction column name")
def __init__(self):
super(HasRawPredictionCol, self).__init__()
#: param for raw prediction column name
self.rawPredictionCol = Param(self, "rawPredictionCol", "raw prediction column name")
if 'rawPrediction' is not None:
self._setDefault(rawPredictionCol='rawPrediction')
def setRawPredictionCol(self, value):
"""
Sets the value of :py:attr:`rawPredictionCol`.
"""
self.paramMap[self.rawPredictionCol] = value
return self
def getRawPredictionCol(self):
"""
Gets the value of rawPredictionCol or its default value.
"""
return self.getOrDefault(self.rawPredictionCol)
class HasInputCol(Params):
"""
Mixin for param inputCol: input column name.
"""
# a placeholder to make it appear in the generated doc
inputCol = Param(Params._dummy(), "inputCol", "input column name")
def __init__(self):
super(HasInputCol, self).__init__()
#: param for input column name
self.inputCol = Param(self, "inputCol", "input column name")
if None is not None:
self._setDefault(inputCol=None)
def setInputCol(self, value):
"""
Sets the value of :py:attr:`inputCol`.
"""
self.paramMap[self.inputCol] = value
return self
def getInputCol(self):
"""
Gets the value of inputCol or its default value.
"""
return self.getOrDefault(self.inputCol)
class HasInputCols(Params):
"""
Mixin for param inputCols: input column names.
"""
# a placeholder to make it appear in the generated doc
inputCols = Param(Params._dummy(), "inputCols", "input column names")
def __init__(self):
super(HasInputCols, self).__init__()
#: param for input column names
self.inputCols = Param(self, "inputCols", "input column names")
if None is not None:
self._setDefault(inputCols=None)
def setInputCols(self, value):
"""
Sets the value of :py:attr:`inputCols`.
"""
self.paramMap[self.inputCols] = value
return self
def getInputCols(self):
"""
Gets the value of inputCols or its default value.
"""
return self.getOrDefault(self.inputCols)
class HasOutputCol(Params):
"""
Mixin for param outputCol: output column name.
"""
# a placeholder to make it appear in the generated doc
outputCol = Param(Params._dummy(), "outputCol", "output column name")
def __init__(self):
super(HasOutputCol, self).__init__()
#: param for output column name
self.outputCol = Param(self, "outputCol", "output column name")
if None is not None:
self._setDefault(outputCol=None)
def setOutputCol(self, value):
"""
Sets the value of :py:attr:`outputCol`.
"""
self.paramMap[self.outputCol] = value
return self
def getOutputCol(self):
"""
Gets the value of outputCol or its default value.
"""
return self.getOrDefault(self.outputCol)
class HasNumFeatures(Params):
"""
Mixin for param numFeatures: number of features.
"""
# a placeholder to make it appear in the generated doc
numFeatures = Param(Params._dummy(), "numFeatures", "number of features")
def __init__(self):
super(HasNumFeatures, self).__init__()
#: param for number of features
self.numFeatures = Param(self, "numFeatures", "number of features")
if None is not None:
self._setDefault(numFeatures=None)
def setNumFeatures(self, value):
"""
Sets the value of :py:attr:`numFeatures`.
"""
self.paramMap[self.numFeatures] = value
return self
def getNumFeatures(self):
"""
Gets the value of numFeatures or its default value.
"""
return self.getOrDefault(self.numFeatures)
class HasSeed(Params):
"""
Mixin for param seed: random seed.
"""
# a placeholder to make it appear in the generated doc
seed = Param(Params._dummy(), "seed", "random seed")
def __init__(self):
super(HasSeed, self).__init__()
#: param for random seed
self.seed = Param(self, "seed", "random seed")
if None is not None:
self._setDefault(seed=None)
def setSeed(self, value):
"""
Sets the value of :py:attr:`seed`.
"""
self.paramMap[self.seed] = value
return self
def getSeed(self):
"""
Gets the value of seed or its default value.
"""
return self.getOrDefault(self.seed)
class HasTol(Params):
"""
Mixin for param tol: the convergence tolerance for iterative algorithms.
"""
# a placeholder to make it appear in the generated doc
tol = Param(Params._dummy(), "tol", "the convergence tolerance for iterative algorithms")
def __init__(self):
super(HasTol, self).__init__()
#: param for the convergence tolerance for iterative algorithms
self.tol = Param(self, "tol", "the convergence tolerance for iterative algorithms")
if None is not None:
self._setDefault(tol=None)
def setTol(self, value):
"""
Sets the value of :py:attr:`tol`.
"""
self.paramMap[self.tol] = value
return self
def getTol(self):
"""
Gets the value of tol or its default value.
"""
return self.getOrDefault(self.tol)
class HasStepSize(Params):
"""
Mixin for param stepSize: Step size to be used for each iteration of optimization..
"""
# a placeholder to make it appear in the generated doc
stepSize = Param(Params._dummy(), "stepSize",
"Step size to be used for each iteration of optimization.")
def __init__(self):
super(HasStepSize, self).__init__()
#: param for Step size to be used for each iteration of optimization.
self.stepSize = Param(self, "stepSize",
"Step size to be used for each iteration of optimization.")
if None is not None:
self._setDefault(stepSize=None)
def setStepSize(self, value):
"""
Sets the value of :py:attr:`stepSize`.
"""
self.paramMap[self.stepSize] = value
return self
def getStepSize(self):
"""
Gets the value of stepSize or its default value.
"""
return self.getOrDefault(self.stepSize)