# 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 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 (default: true)
cols (list): Cols to ensemble
keys (list): Keys to group by
strategy (str): How to ensemble the scores, ex: mean (default: mean)
vectorDims (dict): the dimensions of any vector columns, used to avoid materialization
"""
@keyword_only
def __init__(self, colNames=None, collapseGroup=True, cols=None, keys=None, strategy="mean", vectorDims=None):
super(EnsembleByKey, self).__init__()
self._java_obj = self._new_java_obj("com.microsoft.ml.spark.stages.EnsembleByKey")
self.colNames = Param(self, "colNames", "colNames: Names of the result of each col")
self.collapseGroup = Param(self, "collapseGroup", "collapseGroup: Whether to collapse all items in group to one entry (default: true)")
self._setDefault(collapseGroup=True)
self.cols = Param(self, "cols", "cols: Cols to ensemble")
self.keys = Param(self, "keys", "keys: Keys to group by")
self.strategy = Param(self, "strategy", "strategy: How to ensemble the scores, ex: mean (default: mean)")
self._setDefault(strategy="mean")
self.vectorDims = Param(self, "vectorDims", "vectorDims: the dimensions of any vector columns, used to avoid materialization")
if hasattr(self, "_input_kwargs"):
kwargs = self._input_kwargs
else:
kwargs = self.__init__._input_kwargs
self.setParams(**kwargs)
[docs] @keyword_only
def setParams(self, colNames=None, collapseGroup=True, cols=None, keys=None, strategy="mean", vectorDims=None):
"""
Set the (keyword only) parameters
Args:
colNames (list): Names of the result of each col
collapseGroup (bool): Whether to collapse all items in group to one entry (default: true)
cols (list): Cols to ensemble
keys (list): Keys to group by
strategy (str): How to ensemble the scores, ex: mean (default: mean)
vectorDims (dict): the dimensions of any vector columns, used to avoid materialization
"""
if hasattr(self, "_input_kwargs"):
kwargs = self._input_kwargs
else:
kwargs = self.__init__._input_kwargs
return self._set(**kwargs)
[docs] def setColNames(self, value):
"""
Args:
colNames (list): Names of the result of each col
"""
self._set(colNames=value)
return self
[docs] def getColNames(self):
"""
Returns:
list: Names of the result of each col
"""
return self.getOrDefault(self.colNames)
[docs] def setCollapseGroup(self, value):
"""
Args:
collapseGroup (bool): Whether to collapse all items in group to one entry (default: true)
"""
self._set(collapseGroup=value)
return self
[docs] def getCollapseGroup(self):
"""
Returns:
bool: Whether to collapse all items in group to one entry (default: true)
"""
return self.getOrDefault(self.collapseGroup)
[docs] def setCols(self, value):
"""
Args:
cols (list): Cols to ensemble
"""
self._set(cols=value)
return self
[docs] def getCols(self):
"""
Returns:
list: Cols to ensemble
"""
return self.getOrDefault(self.cols)
[docs] def setKeys(self, value):
"""
Args:
keys (list): Keys to group by
"""
self._set(keys=value)
return self
[docs] def getKeys(self):
"""
Returns:
list: Keys to group by
"""
return self.getOrDefault(self.keys)
[docs] def setStrategy(self, value):
"""
Args:
strategy (str): How to ensemble the scores, ex: mean (default: mean)
"""
self._set(strategy=value)
return self
[docs] def getStrategy(self):
"""
Returns:
str: How to ensemble the scores, ex: mean (default: mean)
"""
return self.getOrDefault(self.strategy)
[docs] def setVectorDims(self, value):
"""
Args:
vectorDims (dict): the dimensions of any vector columns, used to avoid materialization
"""
self._set(vectorDims=value)
return self
[docs] def getVectorDims(self):
"""
Returns:
dict: the dimensions of any vector columns, used to avoid materialization
"""
return self.getOrDefault(self.vectorDims)
[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.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)