Source code for synapse.ml.featurize.CleanMissingDataModel

# Copyright (C) Microsoft Corporation. All rights reserved.
# Licensed under the MIT License. See LICENSE in project root for information.


import sys
if sys.version >= '3':
    basestring = str

from pyspark import SparkContext, SQLContext
from pyspark.sql import DataFrame
from pyspark.ml.param.shared import *
from pyspark import keyword_only
from pyspark.ml.util import JavaMLReadable, JavaMLWritable
from synapse.ml.core.platform import running_on_synapse_internal
from synapse.ml.core.serialize.java_params_patch import *
from pyspark.ml.wrapper import JavaTransformer, JavaEstimator, JavaModel
from pyspark.ml.evaluation import JavaEvaluator
from pyspark.ml.common import inherit_doc
from synapse.ml.core.schema.Utils import *
from pyspark.ml.param import TypeConverters
from synapse.ml.core.schema.TypeConversionUtils import generateTypeConverter, complexTypeConverter


[docs]@inherit_doc class CleanMissingDataModel(ComplexParamsMixin, JavaMLReadable, JavaMLWritable, JavaModel): """ Args: colsToFill (list): The columns to fill with fillValues (object): what to replace in the columns inputCols (list): The names of the input columns outputCols (list): The names of the output columns """ colsToFill = Param(Params._dummy(), "colsToFill", "The columns to fill with", typeConverter=TypeConverters.toListString) fillValues = Param(Params._dummy(), "fillValues", "what to replace in the columns") inputCols = Param(Params._dummy(), "inputCols", "The names of the input columns", typeConverter=TypeConverters.toListString) outputCols = Param(Params._dummy(), "outputCols", "The names of the output columns", typeConverter=TypeConverters.toListString) @keyword_only def __init__( self, java_obj=None, colsToFill=None, fillValues=None, inputCols=None, outputCols=None ): super(CleanMissingDataModel, self).__init__() if java_obj is None: self._java_obj = self._new_java_obj("com.microsoft.azure.synapse.ml.featurize.CleanMissingDataModel", self.uid) else: self._java_obj = java_obj if hasattr(self, "_input_kwargs"): kwargs = self._input_kwargs else: kwargs = self.__init__._input_kwargs if java_obj is None: for k,v in kwargs.items(): if v is not None: getattr(self, "set" + k[0].upper() + k[1:])(v)
[docs] @keyword_only def setParams( self, colsToFill=None, fillValues=None, inputCols=None, outputCols=None ): """ Set the (keyword only) parameters """ if hasattr(self, "_input_kwargs"): kwargs = self._input_kwargs else: kwargs = self.__init__._input_kwargs return self._set(**kwargs)
[docs] @classmethod def read(cls): """ Returns an MLReader instance for this class. """ return JavaMMLReader(cls)
[docs] @staticmethod def getJavaPackage(): """ Returns package name String. """ return "com.microsoft.azure.synapse.ml.featurize.CleanMissingDataModel"
@staticmethod def _from_java(java_stage): module_name=CleanMissingDataModel.__module__ module_name=module_name.rsplit(".", 1)[0] + ".CleanMissingDataModel" return from_java(java_stage, module_name)
[docs] def setColsToFill(self, value): """ Args: colsToFill: The columns to fill with """ self._set(colsToFill=value) return self
[docs] def setFillValues(self, value): """ Args: fillValues: what to replace in the columns """ self._set(fillValues=value) return self
[docs] def setInputCols(self, value): """ Args: inputCols: The names of the input columns """ self._set(inputCols=value) return self
[docs] def setOutputCols(self, value): """ Args: outputCols: The names of the output columns """ self._set(outputCols=value) return self
[docs] def getColsToFill(self): """ Returns: colsToFill: The columns to fill with """ return self.getOrDefault(self.colsToFill)
[docs] def getFillValues(self): """ Returns: fillValues: what to replace in the columns """ return self.getOrDefault(self.fillValues)
[docs] def getInputCols(self): """ Returns: inputCols: The names of the input columns """ return self.getOrDefault(self.inputCols)
[docs] def getOutputCols(self): """ Returns: outputCols: The names of the output columns """ return self.getOrDefault(self.outputCols)