# 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.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 *
[docs]@inherit_doc
class ClassBalancer(ComplexParamsMixin, JavaMLReadable, JavaMLWritable, JavaEstimator):
"""
Args:
broadcastJoin (bool): Whether to broadcast the class to weight mapping to the worker (default: true)
inputCol (str): The name of the input column
outputCol (str): The name of the output column (default: weight)
"""
@keyword_only
def __init__(self, broadcastJoin=True, inputCol=None, outputCol="weight"):
super(ClassBalancer, self).__init__()
self._java_obj = self._new_java_obj("com.microsoft.ml.spark.stages.ClassBalancer")
self.broadcastJoin = Param(self, "broadcastJoin", "broadcastJoin: Whether to broadcast the class to weight mapping to the worker (default: true)")
self._setDefault(broadcastJoin=True)
self.inputCol = Param(self, "inputCol", "inputCol: The name of the input column")
self.outputCol = Param(self, "outputCol", "outputCol: The name of the output column (default: weight)")
self._setDefault(outputCol="weight")
if hasattr(self, "_input_kwargs"):
kwargs = self._input_kwargs
else:
kwargs = self.__init__._input_kwargs
self.setParams(**kwargs)
[docs] @keyword_only
def setParams(self, broadcastJoin=True, inputCol=None, outputCol="weight"):
"""
Set the (keyword only) parameters
Args:
broadcastJoin (bool): Whether to broadcast the class to weight mapping to the worker (default: true)
inputCol (str): The name of the input column
outputCol (str): The name of the output column (default: weight)
"""
if hasattr(self, "_input_kwargs"):
kwargs = self._input_kwargs
else:
kwargs = self.__init__._input_kwargs
return self._set(**kwargs)
[docs] def setBroadcastJoin(self, value):
"""
Args:
broadcastJoin (bool): Whether to broadcast the class to weight mapping to the worker (default: true)
"""
self._set(broadcastJoin=value)
return self
[docs] def getBroadcastJoin(self):
"""
Returns:
bool: Whether to broadcast the class to weight mapping to the worker (default: true)
"""
return self.getOrDefault(self.broadcastJoin)
[docs] def setOutputCol(self, value):
"""
Args:
outputCol (str): The name of the output column (default: weight)
"""
self._set(outputCol=value)
return self
[docs] def getOutputCol(self):
"""
Returns:
str: The name of the output column (default: weight)
"""
return self.getOrDefault(self.outputCol)
[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.ClassBalancer"
@staticmethod
def _from_java(java_stage):
module_name=ClassBalancer.__module__
module_name=module_name.rsplit(".", 1)[0] + ".ClassBalancer"
return from_java(java_stage, module_name)
def _create_model(self, java_model):
return ClassBalancerModel(java_model)
[docs]class ClassBalancerModel(ComplexParamsMixin, JavaModel, JavaMLWritable, JavaMLReadable):
"""
Model fitted by :class:`ClassBalancer`.
This class is left empty on purpose.
All necessary methods are exposed through inheritance.
"""
[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.ClassBalancerModel"
@staticmethod
def _from_java(java_stage):
module_name=ClassBalancerModel.__module__
module_name=module_name.rsplit(".", 1)[0] + ".ClassBalancerModel"
return from_java(java_stage, module_name)