RankingEvaluator

class RankingEvaluator.RankingEvaluator(itemCol=None, k=10, labelCol='label', metricName='ndcgAt', nItems=-1, predictionCol='prediction', ratingCol=None, userCol=None)[source]

Bases: mmlspark.Utils.ComplexParamsMixin, pyspark.ml.util.JavaMLReadable, pyspark.ml.util.JavaMLWritable, pyspark.ml.evaluation.JavaEvaluator

Parameters:
  • itemCol (str) – Column of items
  • k (int) – number of items (default: 10)
  • labelCol (str) – label column name (default: label)
  • metricName (str) – metric name in evaluation (ndcgAt|map|precisionAtk|recallAtK|diversityAtK|maxDiversity|mrr|fcp) (default: ndcgAt)
  • nItems (long) – number of items (default: -1)
  • predictionCol (str) – prediction column name (default: prediction)
  • ratingCol (str) – Column of ratings
  • userCol (str) – Column of users
getItemCol()[source]
Returns:Column of items
Return type:str
static getJavaPackage()[source]

Returns package name String.

getK()[source]
Returns:number of items (default: 10)
Return type:int
getLabelCol()[source]
Returns:label column name (default: label)
Return type:str
getMetricName()[source]
Returns:metric name in evaluation (ndcgAt|map|precisionAtk|recallAtK|diversityAtK|maxDiversity|mrr|fcp) (default: ndcgAt)
Return type:str
getNItems()[source]
Returns:number of items (default: -1)
Return type:long
getPredictionCol()[source]
Returns:prediction column name (default: prediction)
Return type:str
getRatingCol()[source]
Returns:Column of ratings
Return type:str
getUserCol()[source]
Returns:Column of users
Return type:str
classmethod read()[source]

Returns an MLReader instance for this class.

setItemCol(value)[source]
Parameters:itemCol (str) – Column of items
setK(value)[source]
Parameters:k (int) – number of items (default: 10)
setLabelCol(value)[source]
Parameters:labelCol (str) – label column name (default: label)
setMetricName(value)[source]
Parameters:metricName (str) – metric name in evaluation (ndcgAt|map|precisionAtk|recallAtK|diversityAtK|maxDiversity|mrr|fcp) (default: ndcgAt)
setNItems(value)[source]
Parameters:nItems (long) – number of items (default: -1)
setParams(itemCol=None, k=10, labelCol='label', metricName='ndcgAt', nItems=-1, predictionCol='prediction', ratingCol=None, userCol=None)[source]

Set the (keyword only) parameters

Parameters:
  • itemCol (str) – Column of items
  • k (int) – number of items (default: 10)
  • labelCol (str) – label column name (default: label)
  • metricName (str) – metric name in evaluation (ndcgAt|map|precisionAtk|recallAtK|diversityAtK|maxDiversity|mrr|fcp) (default: ndcgAt)
  • nItems (long) – number of items (default: -1)
  • predictionCol (str) – prediction column name (default: prediction)
  • ratingCol (str) – Column of ratings
  • userCol (str) – Column of users
setPredictionCol(value)[source]
Parameters:predictionCol (str) – prediction column name (default: prediction)
setRatingCol(value)[source]
Parameters:ratingCol (str) – Column of ratings
setUserCol(value)[source]
Parameters:userCol (str) – Column of users