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https://issues.apache.org/jira/browse/SPARK-3568?page=com.atlassian.jira.plugin.system.issuetabpanels:all-tabpanel
]
Shuo Xiang updated SPARK-3568:
------------------------------
Description:
Include common metrics for ranking algorithms
(http://www-nlp.stanford.edu/IR-book/), including:
- Mean Average Precision
- Precision@n: top-n precision
- Discounted cumulative gain (DCG) and NDCG
This implementation attempts to create a new class called *RankingMetrics*
under *org.apache.spark.mllib.evaluation*, which accepts input (prediction and
label pairs) as *RDD[Array[T], Array[T]]*. The following methods will be
implemented:
{code:title=RankingMetrics.scala|borderStyle=solid}
class RankingMetrics[T](predictionAndLabels: RDD[(Array[T], Array[T])]) {
/* Returns the precsion@k for each query */
lazy val precAtK: RDD[Array[Double]]
/**
* @param k the position to compute the truncated precision
* @return the average precision at the first k ranking positions
*/
def precision(k: Int): Double
/* Returns the average precision for each query */
lazy val avePrec: RDD[Double]
/*Returns the mean average precision (MAP) of all the queries*/
lazy val meanAvePrec: Double
/*Returns the normalized discounted cumulative gain for each query */
lazy val ndcgAtK: RDD[Array[Double]]
/**
* @param k the position to compute the truncated ndcg
* @return the average ndcg at the first k ranking positions
*/
def ndcg(k: Int): Double
}
{code}
was:
Include common metrics for ranking algorithms
(http://www-nlp.stanford.edu/IR-book/), including:
- Mean Average Precision
- Precision@n: top-n precision
- Discounted cumulative gain (DCG) and NDCG
This implementation attempts to create a new class called *RankingMetrics*
under *org.apache.spark.mllib.evaluation*, which accepts input (prediction and
label pairs) as *RDD[Array[Double], Array[Double]]*. The following methods will
be implemented:
{code:title=RankingMetrics.scala|borderStyle=solid}
class RankingMetrics(predictionAndLabels: RDD[(Array[Double], Array[Double])]) {
/* Returns the precsion@k for each query */
lazy val precAtK: RDD[Array[Double]]
/* Returns the average precision for each query */
lazy val avePrec: RDD[Double]
/*Returns the mean average precision (MAP) of all the queries*/
lazy val meanAvePrec: Double
/*Returns the normalized discounted cumulative gain for each query */
lazy val ndcg: RDD[Double]
/* Returns the mean NDCG of all the queries */
lazy val meanNdcg: Double
}
{code}
> Add metrics for ranking algorithms
> ----------------------------------
>
> Key: SPARK-3568
> URL: https://issues.apache.org/jira/browse/SPARK-3568
> Project: Spark
> Issue Type: New Feature
> Components: MLlib
> Reporter: Shuo Xiang
> Assignee: Shuo Xiang
>
> Include common metrics for ranking algorithms
> (http://www-nlp.stanford.edu/IR-book/), including:
> - Mean Average Precision
> - Precision@n: top-n precision
> - Discounted cumulative gain (DCG) and NDCG
> This implementation attempts to create a new class called *RankingMetrics*
> under *org.apache.spark.mllib.evaluation*, which accepts input (prediction
> and label pairs) as *RDD[Array[T], Array[T]]*. The following methods will be
> implemented:
> {code:title=RankingMetrics.scala|borderStyle=solid}
> class RankingMetrics[T](predictionAndLabels: RDD[(Array[T], Array[T])]) {
> /* Returns the precsion@k for each query */
> lazy val precAtK: RDD[Array[Double]]
> /**
> * @param k the position to compute the truncated precision
> * @return the average precision at the first k ranking positions
> */
> def precision(k: Int): Double
> /* Returns the average precision for each query */
> lazy val avePrec: RDD[Double]
> /*Returns the mean average precision (MAP) of all the queries*/
> lazy val meanAvePrec: Double
> /*Returns the normalized discounted cumulative gain for each query */
> lazy val ndcgAtK: RDD[Array[Double]]
> /**
> * @param k the position to compute the truncated ndcg
> * @return the average ndcg at the first k ranking positions
> */
> def ndcg(k: Int): Double
> }
> {code}
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