2015-01-28 20:14:23 -05:00
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#
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# Licensed to the Apache Software Foundation (ASF) under one or more
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# contributor license agreements. See the NOTICE file distributed with
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# this work for additional information regarding copyright ownership.
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# The ASF licenses this file to You under the Apache License, Version 2.0
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# (the "License"); you may not use this file except in compliance with
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# 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, software
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# distributed under the License is distributed on an "AS IS" BASIS,
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# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
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# See the License for the specific language governing permissions and
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# limitations under the License.
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#
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2015-04-16 02:49:42 -04:00
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# DO NOT MODIFY THIS FILE! It was generated by _shared_params_code_gen.py.
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2015-01-28 20:14:23 -05:00
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from pyspark.ml.param import Param, Params
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class HasMaxIter(Params):
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"""
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Mixin for param maxIter: max number of iterations (>= 0).
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"""
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# a placeholder to make it appear in the generated doc
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maxIter = Param(Params._dummy(), "maxIter", "max number of iterations (>= 0)")
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def __init__(self):
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super(HasMaxIter, self).__init__()
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2015-05-10 22:18:32 -04:00
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#: param for max number of iterations (>= 0)
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self.maxIter = Param(self, "maxIter", "max number of iterations (>= 0)")
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if None is not None:
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self._setDefault(maxIter=None)
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def setMaxIter(self, value):
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"""
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Sets the value of :py:attr:`maxIter`.
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"""
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self.paramMap[self.maxIter] = value
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return self
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def getMaxIter(self):
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"""
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Gets the value of maxIter or its default value.
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"""
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return self.getOrDefault(self.maxIter)
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class HasRegParam(Params):
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"""
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Mixin for param regParam: regularization parameter (>= 0).
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"""
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# a placeholder to make it appear in the generated doc
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regParam = Param(Params._dummy(), "regParam", "regularization parameter (>= 0)")
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def __init__(self):
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super(HasRegParam, self).__init__()
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#: param for regularization parameter (>= 0)
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self.regParam = Param(self, "regParam", "regularization parameter (>= 0)")
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if None is not None:
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self._setDefault(regParam=None)
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def setRegParam(self, value):
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"""
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Sets the value of :py:attr:`regParam`.
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"""
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self.paramMap[self.regParam] = value
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return self
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def getRegParam(self):
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"""
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Gets the value of regParam or its default value.
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"""
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return self.getOrDefault(self.regParam)
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class HasFeaturesCol(Params):
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"""
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Mixin for param featuresCol: features column name.
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"""
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# a placeholder to make it appear in the generated doc
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featuresCol = Param(Params._dummy(), "featuresCol", "features column name")
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def __init__(self):
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super(HasFeaturesCol, self).__init__()
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#: param for features column name
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self.featuresCol = Param(self, "featuresCol", "features column name")
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if 'features' is not None:
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self._setDefault(featuresCol='features')
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def setFeaturesCol(self, value):
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"""
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Sets the value of :py:attr:`featuresCol`.
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"""
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self.paramMap[self.featuresCol] = value
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return self
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def getFeaturesCol(self):
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"""
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Gets the value of featuresCol or its default value.
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"""
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return self.getOrDefault(self.featuresCol)
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class HasLabelCol(Params):
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"""
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Mixin for param labelCol: label column name.
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"""
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# a placeholder to make it appear in the generated doc
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labelCol = Param(Params._dummy(), "labelCol", "label column name")
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def __init__(self):
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super(HasLabelCol, self).__init__()
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#: param for label column name
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self.labelCol = Param(self, "labelCol", "label column name")
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if 'label' is not None:
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self._setDefault(labelCol='label')
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def setLabelCol(self, value):
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"""
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Sets the value of :py:attr:`labelCol`.
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"""
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self.paramMap[self.labelCol] = value
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return self
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def getLabelCol(self):
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"""
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Gets the value of labelCol or its default value.
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"""
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return self.getOrDefault(self.labelCol)
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class HasPredictionCol(Params):
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"""
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Mixin for param predictionCol: prediction column name.
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"""
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# a placeholder to make it appear in the generated doc
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predictionCol = Param(Params._dummy(), "predictionCol", "prediction column name")
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def __init__(self):
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super(HasPredictionCol, self).__init__()
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#: param for prediction column name
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self.predictionCol = Param(self, "predictionCol", "prediction column name")
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if 'prediction' is not None:
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self._setDefault(predictionCol='prediction')
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def setPredictionCol(self, value):
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"""
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Sets the value of :py:attr:`predictionCol`.
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"""
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self.paramMap[self.predictionCol] = value
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return self
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def getPredictionCol(self):
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"""
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Gets the value of predictionCol or its default value.
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"""
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return self.getOrDefault(self.predictionCol)
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class HasRawPredictionCol(Params):
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"""
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Mixin for param rawPredictionCol: raw prediction (a.k.a. confidence) column name.
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"""
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# a placeholder to make it appear in the generated doc
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rawPredictionCol = Param(Params._dummy(), "rawPredictionCol", "raw prediction (a.k.a. confidence) column name")
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def __init__(self):
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super(HasRawPredictionCol, self).__init__()
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#: param for raw prediction (a.k.a. confidence) column name
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self.rawPredictionCol = Param(self, "rawPredictionCol", "raw prediction (a.k.a. confidence) column name")
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if 'rawPrediction' is not None:
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self._setDefault(rawPredictionCol='rawPrediction')
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def setRawPredictionCol(self, value):
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"""
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Sets the value of :py:attr:`rawPredictionCol`.
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"""
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self.paramMap[self.rawPredictionCol] = value
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return self
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def getRawPredictionCol(self):
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"""
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Gets the value of rawPredictionCol or its default value.
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"""
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return self.getOrDefault(self.rawPredictionCol)
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class HasInputCol(Params):
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"""
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Mixin for param inputCol: input column name.
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"""
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# a placeholder to make it appear in the generated doc
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inputCol = Param(Params._dummy(), "inputCol", "input column name")
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def __init__(self):
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super(HasInputCol, self).__init__()
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#: param for input column name
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self.inputCol = Param(self, "inputCol", "input column name")
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if None is not None:
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self._setDefault(inputCol=None)
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def setInputCol(self, value):
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"""
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Sets the value of :py:attr:`inputCol`.
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"""
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self.paramMap[self.inputCol] = value
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return self
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def getInputCol(self):
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"""
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Gets the value of inputCol or its default value.
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"""
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return self.getOrDefault(self.inputCol)
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2015-05-07 13:25:41 -04:00
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class HasInputCols(Params):
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"""
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Mixin for param inputCols: input column names.
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"""
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# a placeholder to make it appear in the generated doc
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inputCols = Param(Params._dummy(), "inputCols", "input column names")
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def __init__(self):
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super(HasInputCols, self).__init__()
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#: param for input column names
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self.inputCols = Param(self, "inputCols", "input column names")
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if None is not None:
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self._setDefault(inputCols=None)
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def setInputCols(self, value):
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"""
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Sets the value of :py:attr:`inputCols`.
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"""
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self.paramMap[self.inputCols] = value
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return self
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def getInputCols(self):
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"""
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Gets the value of inputCols or its default value.
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"""
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return self.getOrDefault(self.inputCols)
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class HasOutputCol(Params):
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"""
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Mixin for param outputCol: output column name.
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"""
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# a placeholder to make it appear in the generated doc
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outputCol = Param(Params._dummy(), "outputCol", "output column name")
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def __init__(self):
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super(HasOutputCol, self).__init__()
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#: param for output column name
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self.outputCol = Param(self, "outputCol", "output column name")
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if None is not None:
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self._setDefault(outputCol=None)
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def setOutputCol(self, value):
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"""
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Sets the value of :py:attr:`outputCol`.
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"""
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self.paramMap[self.outputCol] = value
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return self
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def getOutputCol(self):
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"""
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Gets the value of outputCol or its default value.
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"""
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return self.getOrDefault(self.outputCol)
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class HasNumFeatures(Params):
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"""
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Mixin for param numFeatures: number of features.
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"""
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# a placeholder to make it appear in the generated doc
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numFeatures = Param(Params._dummy(), "numFeatures", "number of features")
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def __init__(self):
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super(HasNumFeatures, self).__init__()
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#: param for number of features
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self.numFeatures = Param(self, "numFeatures", "number of features")
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if None is not None:
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self._setDefault(numFeatures=None)
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def setNumFeatures(self, value):
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"""
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Sets the value of :py:attr:`numFeatures`.
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"""
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self.paramMap[self.numFeatures] = value
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return self
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def getNumFeatures(self):
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"""
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Gets the value of numFeatures or its default value.
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"""
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return self.getOrDefault(self.numFeatures)
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2015-05-08 20:24:32 -04:00
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class HasCheckpointInterval(Params):
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"""
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Mixin for param checkpointInterval: checkpoint interval (>= 1).
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"""
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# a placeholder to make it appear in the generated doc
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checkpointInterval = Param(Params._dummy(), "checkpointInterval", "checkpoint interval (>= 1)")
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def __init__(self):
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super(HasCheckpointInterval, self).__init__()
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#: param for checkpoint interval (>= 1)
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self.checkpointInterval = Param(self, "checkpointInterval", "checkpoint interval (>= 1)")
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if None is not None:
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self._setDefault(checkpointInterval=None)
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def setCheckpointInterval(self, value):
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"""
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Sets the value of :py:attr:`checkpointInterval`.
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"""
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self.paramMap[self.checkpointInterval] = value
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return self
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def getCheckpointInterval(self):
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"""
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Gets the value of checkpointInterval or its default value.
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|
"""
|
|
|
|
return self.getOrDefault(self.checkpointInterval)
|
|
|
|
|
|
|
|
|
2015-05-08 14:14:39 -04:00
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class HasSeed(Params):
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|
"""
|
|
|
|
Mixin for param seed: random seed.
|
|
|
|
"""
|
|
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|
# a placeholder to make it appear in the generated doc
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seed = Param(Params._dummy(), "seed", "random seed")
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|
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|
def __init__(self):
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|
super(HasSeed, self).__init__()
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|
#: param for random seed
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|
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|
self.seed = Param(self, "seed", "random seed")
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|
|
if None is not None:
|
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|
self._setDefault(seed=None)
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|
|
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|
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|
def setSeed(self, value):
|
|
|
|
"""
|
|
|
|
Sets the value of :py:attr:`seed`.
|
|
|
|
"""
|
|
|
|
self.paramMap[self.seed] = value
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|
return self
|
|
|
|
|
|
|
|
def getSeed(self):
|
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|
|
"""
|
|
|
|
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
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|
|
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
|
2015-05-10 22:18:32 -04:00
|
|
|
stepSize = Param(Params._dummy(), "stepSize", "Step size to be used for each iteration of optimization.")
|
2015-05-08 14:14:39 -04:00
|
|
|
|
|
|
|
def __init__(self):
|
|
|
|
super(HasStepSize, self).__init__()
|
|
|
|
#: param for Step size to be used for each iteration of optimization.
|
2015-05-10 22:18:32 -04:00
|
|
|
self.stepSize = Param(self, "stepSize", "Step size to be used for each iteration of optimization.")
|
2015-05-08 14:14:39 -04:00
|
|
|
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)
|