Source code for synapse.ml.stages.MultiColumnAdapter

# 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
from pyspark.ml import PipelineModel

[docs]@inherit_doc class MultiColumnAdapter(ComplexParamsMixin, JavaMLReadable, JavaMLWritable, JavaEstimator): """ Args: baseStage (object): base pipeline stage to apply to every column inputCols (list): list of column names encoded as a string outputCols (list): list of column names encoded as a string """ baseStage = Param(Params._dummy(), "baseStage", "base pipeline stage to apply to every column") inputCols = Param(Params._dummy(), "inputCols", "list of column names encoded as a string", typeConverter=TypeConverters.toListString) outputCols = Param(Params._dummy(), "outputCols", "list of column names encoded as a string", typeConverter=TypeConverters.toListString) @keyword_only def __init__( self, java_obj=None, baseStage=None, inputCols=None, outputCols=None ): super(MultiColumnAdapter, self).__init__() if java_obj is None: self._java_obj = self._new_java_obj("com.microsoft.azure.synapse.ml.stages.MultiColumnAdapter", 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, baseStage=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.stages.MultiColumnAdapter"
@staticmethod def _from_java(java_stage): module_name=MultiColumnAdapter.__module__ module_name=module_name.rsplit(".", 1)[0] + ".MultiColumnAdapter" return from_java(java_stage, module_name)
[docs] def setBaseStage(self, value): """ Args: baseStage: base pipeline stage to apply to every column """ self._set(baseStage=value) return self
[docs] def setInputCols(self, value): """ Args: inputCols: list of column names encoded as a string """ self._set(inputCols=value) return self
[docs] def setOutputCols(self, value): """ Args: outputCols: list of column names encoded as a string """ self._set(outputCols=value) return self
[docs] def getBaseStage(self): """ Returns: baseStage: base pipeline stage to apply to every column """ return JavaParams._from_java(self._java_obj.getBaseStage())
[docs] def getInputCols(self): """ Returns: inputCols: list of column names encoded as a string """ return self.getOrDefault(self.inputCols)
[docs] def getOutputCols(self): """ Returns: outputCols: list of column names encoded as a string """ return self.getOrDefault(self.outputCols)
def _create_model(self, java_model): try: model = PipelineModel(java_obj=java_model) model._transfer_params_from_java() except TypeError: model = PipelineModel._from_java(java_model) return model def _fit(self, dataset): java_model = self._fit_java(dataset) return self._create_model(java_model)