# 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 StratifiedRepartition(ComplexParamsMixin, JavaMLReadable, JavaMLWritable, JavaTransformer):
"""
Args:
labelCol (str): The name of the label column
mode (str): Specify equal to repartition with replacement across all labels, specify original to keep the ratios in the original dataset, or specify mixed to use a heuristic
seed (long): random seed
"""
labelCol = Param(Params._dummy(), "labelCol", "The name of the label column", typeConverter=TypeConverters.toString)
mode = Param(Params._dummy(), "mode", "Specify equal to repartition with replacement across all labels, specify original to keep the ratios in the original dataset, or specify mixed to use a heuristic", typeConverter=TypeConverters.toString)
seed = Param(Params._dummy(), "seed", "random seed")
@keyword_only
def __init__(
self,
java_obj=None,
labelCol=None,
mode="mixed",
seed=1518410069
):
super(StratifiedRepartition, self).__init__()
if java_obj is None:
self._java_obj = self._new_java_obj("com.microsoft.azure.synapse.ml.stages.StratifiedRepartition", self.uid)
else:
self._java_obj = java_obj
self._setDefault(mode="mixed")
self._setDefault(seed=1518410069)
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,
labelCol=None,
mode="mixed",
seed=1518410069
):
"""
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.StratifiedRepartition"
@staticmethod
def _from_java(java_stage):
module_name=StratifiedRepartition.__module__
module_name=module_name.rsplit(".", 1)[0] + ".StratifiedRepartition"
return from_java(java_stage, module_name)
[docs] def setLabelCol(self, value):
"""
Args:
labelCol: The name of the label column
"""
self._set(labelCol=value)
return self
[docs] def setMode(self, value):
"""
Args:
mode: Specify equal to repartition with replacement across all labels, specify original to keep the ratios in the original dataset, or specify mixed to use a heuristic
"""
self._set(mode=value)
return self
[docs] def setSeed(self, value):
"""
Args:
seed: random seed
"""
self._set(seed=value)
return self
[docs] def getLabelCol(self):
"""
Returns:
labelCol: The name of the label column
"""
return self.getOrDefault(self.labelCol)
[docs] def getMode(self):
"""
Returns:
mode: Specify equal to repartition with replacement across all labels, specify original to keep the ratios in the original dataset, or specify mixed to use a heuristic
"""
return self.getOrDefault(self.mode)
[docs] def getSeed(self):
"""
Returns:
seed: random seed
"""
return self.getOrDefault(self.seed)