Source code for mmlspark.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 mmlspark.core.serialize.java_params_patch import *
from pyspark.ml.wrapper import JavaTransformer, JavaEstimator, JavaModel
from pyspark.ml.common import inherit_doc
from mmlspark.core.schema.Utils import *
from mmlspark.core.schema.TypeConversionUtils import generateTypeConverter, complexTypeConverter

[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 """ @keyword_only def __init__(self, baseStage=None, inputCols=None, outputCols=None): super(MultiColumnAdapter, self).__init__() self._java_obj = self._new_java_obj("com.microsoft.ml.spark.stages.MultiColumnAdapter") self._cache = {} self.baseStage = Param(self, "baseStage", "baseStage: base pipeline stage to apply to every column", generateTypeConverter("baseStage", self._cache, complexTypeConverter)) self.inputCols = Param(self, "inputCols", "inputCols: list of column names encoded as a string") self.outputCols = Param(self, "outputCols", "outputCols: list of column names encoded as a string") if hasattr(self, "_input_kwargs"): kwargs = self._input_kwargs else: kwargs = self.__init__._input_kwargs self.setParams(**kwargs)
[docs] @keyword_only def setParams(self, baseStage=None, inputCols=None, outputCols=None): """ Set the (keyword only) parameters 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 """ if hasattr(self, "_input_kwargs"): kwargs = self._input_kwargs else: kwargs = self.__init__._input_kwargs return self._set(**kwargs)
[docs] def getBaseStage(self): """ Returns: object: base pipeline stage to apply to every column """ return self._cache.get("baseStage", None)
[docs] def getInputCols(self): """ Returns: list: list of column names encoded as a string """ return self.getOrDefault(self.inputCols)
[docs] def getOutputCols(self): """ Returns: list: list of column names encoded as a string """ return self.getOrDefault(self.outputCols)
[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] @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.ml.spark.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) def _create_model(self, java_model): return PipelineModel(java_model)
[docs]class PipelineModel(ComplexParamsMixin, JavaModel, JavaMLWritable, JavaMLReadable): """ Model fitted by :class:`MultiColumnAdapter`. """
[docs] @classmethod def read(cls): """ Returns an MLReader instance for this class. """ return JavaMMLReader(cls)
[docs] @staticmethod def getJavaPackage(): """ Returns package name String. """ return "org.apache.spark.ml.PipelineModel"
@staticmethod def _from_java(java_stage): module_name=PipelineModel.__module__ module_name=module_name.rsplit(".", 1)[0] + ".PipelineModel" return from_java(java_stage, module_name)