Source code for FixedMiniBatchTransformer
# 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 pyspark.ml.wrapper import JavaTransformer, JavaEstimator, JavaModel
from pyspark.ml.common import inherit_doc
from mmlspark.Utils import *
[docs]@inherit_doc
class FixedMiniBatchTransformer(ComplexParamsMixin, JavaMLReadable, JavaMLWritable, JavaTransformer):
"""
Args:
batchSize (int): The max size of the buffer
buffered (bool): Whether or not to buffer batches in memory (default: false)
maxBufferSize (int): The max size of the buffer (default: 2147483647)
"""
@keyword_only
def __init__(self, batchSize=None, buffered=False, maxBufferSize=2147483647):
super(FixedMiniBatchTransformer, self).__init__()
self._java_obj = self._new_java_obj("com.microsoft.ml.spark.FixedMiniBatchTransformer")
self.batchSize = Param(self, "batchSize", "batchSize: The max size of the buffer")
self.buffered = Param(self, "buffered", "buffered: Whether or not to buffer batches in memory (default: false)")
self._setDefault(buffered=False)
self.maxBufferSize = Param(self, "maxBufferSize", "maxBufferSize: The max size of the buffer (default: 2147483647)")
self._setDefault(maxBufferSize=2147483647)
if hasattr(self, "_input_kwargs"):
kwargs = self._input_kwargs
else:
kwargs = self.__init__._input_kwargs
self.setParams(**kwargs)
[docs] @keyword_only
def setParams(self, batchSize=None, buffered=False, maxBufferSize=2147483647):
"""
Set the (keyword only) parameters
Args:
batchSize (int): The max size of the buffer
buffered (bool): Whether or not to buffer batches in memory (default: false)
maxBufferSize (int): The max size of the buffer (default: 2147483647)
"""
if hasattr(self, "_input_kwargs"):
kwargs = self._input_kwargs
else:
kwargs = self.__init__._input_kwargs
return self._set(**kwargs)
[docs] def setBatchSize(self, value):
"""
Args:
batchSize (int): The max size of the buffer
"""
self._set(batchSize=value)
return self
[docs] def getBatchSize(self):
"""
Returns:
int: The max size of the buffer
"""
return self.getOrDefault(self.batchSize)
[docs] def setBuffered(self, value):
"""
Args:
buffered (bool): Whether or not to buffer batches in memory (default: false)
"""
self._set(buffered=value)
return self
[docs] def getBuffered(self):
"""
Returns:
bool: Whether or not to buffer batches in memory (default: false)
"""
return self.getOrDefault(self.buffered)
[docs] def setMaxBufferSize(self, value):
"""
Args:
maxBufferSize (int): The max size of the buffer (default: 2147483647)
"""
self._set(maxBufferSize=value)
return self
[docs] def getMaxBufferSize(self):
"""
Returns:
int: The max size of the buffer (default: 2147483647)
"""
return self.getOrDefault(self.maxBufferSize)
[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.FixedMiniBatchTransformer"
@staticmethod
def _from_java(java_stage):
module_name=FixedMiniBatchTransformer.__module__
module_name=module_name.rsplit(".", 1)[0] + ".FixedMiniBatchTransformer"
return from_java(java_stage, module_name)