# 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.platform import running_on_synapse_internal
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 RankingEvaluator(ComplexParamsMixin, JavaMLReadable, JavaMLWritable, JavaEvaluator):
"""
Args:
itemCol (str): Column of items
k (int): number of items
labelCol (str): label column name
metricName (str): metric name in evaluation (ndcgAt|map|precisionAtk|recallAtK|diversityAtK|maxDiversity|mrr|fcp)
nItems (long): number of items
predictionCol (str): prediction column name
ratingCol (str): Column of ratings
userCol (str): Column of users
"""
itemCol = Param(Params._dummy(), "itemCol", "Column of items", typeConverter=TypeConverters.toString)
k = Param(Params._dummy(), "k", "number of items", typeConverter=TypeConverters.toInt)
labelCol = Param(Params._dummy(), "labelCol", "label column name", typeConverter=TypeConverters.toString)
metricName = Param(Params._dummy(), "metricName", "metric name in evaluation (ndcgAt|map|precisionAtk|recallAtK|diversityAtK|maxDiversity|mrr|fcp)", typeConverter=TypeConverters.toString)
nItems = Param(Params._dummy(), "nItems", "number of items")
predictionCol = Param(Params._dummy(), "predictionCol", "prediction column name", typeConverter=TypeConverters.toString)
ratingCol = Param(Params._dummy(), "ratingCol", "Column of ratings", typeConverter=TypeConverters.toString)
userCol = Param(Params._dummy(), "userCol", "Column of users", typeConverter=TypeConverters.toString)
@keyword_only
def __init__(
self,
java_obj=None,
itemCol=None,
k=10,
labelCol="label",
metricName="ndcgAt",
nItems=-1,
predictionCol="prediction",
ratingCol=None,
userCol=None
):
super(RankingEvaluator, self).__init__()
if java_obj is None:
self._java_obj = self._new_java_obj("com.microsoft.azure.synapse.ml.recommendation.RankingEvaluator", self.uid)
else:
self._java_obj = java_obj
self._setDefault(k=10)
self._setDefault(labelCol="label")
self._setDefault(metricName="ndcgAt")
self._setDefault(nItems=-1)
self._setDefault(predictionCol="prediction")
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,
itemCol=None,
k=10,
labelCol="label",
metricName="ndcgAt",
nItems=-1,
predictionCol="prediction",
ratingCol=None,
userCol=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.recommendation.RankingEvaluator"
@staticmethod
def _from_java(java_stage):
module_name=RankingEvaluator.__module__
module_name=module_name.rsplit(".", 1)[0] + ".RankingEvaluator"
return from_java(java_stage, module_name)
[docs] def setItemCol(self, value):
"""
Args:
itemCol: Column of items
"""
self._set(itemCol=value)
return self
[docs] def setK(self, value):
"""
Args:
k: number of items
"""
self._set(k=value)
return self
[docs] def setLabelCol(self, value):
"""
Args:
labelCol: label column name
"""
self._set(labelCol=value)
return self
[docs] def setMetricName(self, value):
"""
Args:
metricName: metric name in evaluation (ndcgAt|map|precisionAtk|recallAtK|diversityAtK|maxDiversity|mrr|fcp)
"""
self._set(metricName=value)
return self
[docs] def setNItems(self, value):
"""
Args:
nItems: number of items
"""
self._set(nItems=value)
return self
[docs] def setPredictionCol(self, value):
"""
Args:
predictionCol: prediction column name
"""
self._set(predictionCol=value)
return self
[docs] def setRatingCol(self, value):
"""
Args:
ratingCol: Column of ratings
"""
self._set(ratingCol=value)
return self
[docs] def setUserCol(self, value):
"""
Args:
userCol: Column of users
"""
self._set(userCol=value)
return self
[docs] def getItemCol(self):
"""
Returns:
itemCol: Column of items
"""
return self.getOrDefault(self.itemCol)
[docs] def getK(self):
"""
Returns:
k: number of items
"""
return self.getOrDefault(self.k)
[docs] def getLabelCol(self):
"""
Returns:
labelCol: label column name
"""
return self.getOrDefault(self.labelCol)
[docs] def getMetricName(self):
"""
Returns:
metricName: metric name in evaluation (ndcgAt|map|precisionAtk|recallAtK|diversityAtK|maxDiversity|mrr|fcp)
"""
return self.getOrDefault(self.metricName)
[docs] def getNItems(self):
"""
Returns:
nItems: number of items
"""
return self.getOrDefault(self.nItems)
[docs] def getPredictionCol(self):
"""
Returns:
predictionCol: prediction column name
"""
return self.getOrDefault(self.predictionCol)
[docs] def getRatingCol(self):
"""
Returns:
ratingCol: Column of ratings
"""
return self.getOrDefault(self.ratingCol)
[docs] def getUserCol(self):
"""
Returns:
userCol: Column of users
"""
return self.getOrDefault(self.userCol)