# 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.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)