Source code for synapse.ml.stages.EnsembleByKey

# 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 EnsembleByKey(ComplexParamsMixin, JavaMLReadable, JavaMLWritable, JavaTransformer): """ Args: colNames (list): Names of the result of each col collapseGroup (bool): Whether to collapse all items in group to one entry cols (list): Cols to ensemble keys (list): Keys to group by strategy (str): How to ensemble the scores, ex: mean vectorDims (dict): the dimensions of any vector columns, used to avoid materialization """ colNames = Param(Params._dummy(), "colNames", "Names of the result of each col", typeConverter=TypeConverters.toListString) collapseGroup = Param(Params._dummy(), "collapseGroup", "Whether to collapse all items in group to one entry", typeConverter=TypeConverters.toBoolean) cols = Param(Params._dummy(), "cols", "Cols to ensemble", typeConverter=TypeConverters.toListString) keys = Param(Params._dummy(), "keys", "Keys to group by", typeConverter=TypeConverters.toListString) strategy = Param(Params._dummy(), "strategy", "How to ensemble the scores, ex: mean", typeConverter=TypeConverters.toString) vectorDims = Param(Params._dummy(), "vectorDims", "the dimensions of any vector columns, used to avoid materialization") @keyword_only def __init__( self, java_obj=None, colNames=None, collapseGroup=True, cols=None, keys=None, strategy="mean", vectorDims=None ): super(EnsembleByKey, self).__init__() if java_obj is None: self._java_obj = self._new_java_obj("com.microsoft.azure.synapse.ml.stages.EnsembleByKey", self.uid) else: self._java_obj = java_obj self._setDefault(collapseGroup=True) self._setDefault(strategy="mean") 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, colNames=None, collapseGroup=True, cols=None, keys=None, strategy="mean", vectorDims=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.EnsembleByKey"
@staticmethod def _from_java(java_stage): module_name=EnsembleByKey.__module__ module_name=module_name.rsplit(".", 1)[0] + ".EnsembleByKey" return from_java(java_stage, module_name)
[docs] def setColNames(self, value): """ Args: colNames: Names of the result of each col """ self._set(colNames=value) return self
[docs] def setCollapseGroup(self, value): """ Args: collapseGroup: Whether to collapse all items in group to one entry """ self._set(collapseGroup=value) return self
[docs] def setCols(self, value): """ Args: cols: Cols to ensemble """ self._set(cols=value) return self
[docs] def setKeys(self, value): """ Args: keys: Keys to group by """ self._set(keys=value) return self
[docs] def setStrategy(self, value): """ Args: strategy: How to ensemble the scores, ex: mean """ self._set(strategy=value) return self
[docs] def setVectorDims(self, value): """ Args: vectorDims: the dimensions of any vector columns, used to avoid materialization """ self._set(vectorDims=value) return self
[docs] def getColNames(self): """ Returns: colNames: Names of the result of each col """ return self.getOrDefault(self.colNames)
[docs] def getCollapseGroup(self): """ Returns: collapseGroup: Whether to collapse all items in group to one entry """ return self.getOrDefault(self.collapseGroup)
[docs] def getCols(self): """ Returns: cols: Cols to ensemble """ return self.getOrDefault(self.cols)
[docs] def getKeys(self): """ Returns: keys: Keys to group by """ return self.getOrDefault(self.keys)
[docs] def getStrategy(self): """ Returns: strategy: How to ensemble the scores, ex: mean """ return self.getOrDefault(self.strategy)
[docs] def getVectorDims(self): """ Returns: vectorDims: the dimensions of any vector columns, used to avoid materialization """ return self.getOrDefault(self.vectorDims)