Source code for

# 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 import *
from pyspark import keyword_only
from import JavaMLReadable, JavaMLWritable
from import running_on_synapse_internal
from import *
from import JavaTransformer, JavaEstimator, JavaModel
from import JavaEvaluator
from import inherit_doc
from import *
from import TypeConverters
from 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("", 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 ""
@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)