Packages

  • package root
    Definition Classes
    root
  • package org
    Definition Classes
    root
  • package apache
    Definition Classes
    org
  • package spark

    Core Spark functionality.

    Core Spark functionality. org.apache.spark.SparkContext serves as the main entry point to Spark, while org.apache.spark.rdd.RDD is the data type representing a distributed collection, and provides most parallel operations.

    In addition, org.apache.spark.rdd.PairRDDFunctions contains operations available only on RDDs of key-value pairs, such as groupByKey and join; org.apache.spark.rdd.DoubleRDDFunctions contains operations available only on RDDs of Doubles; and org.apache.spark.rdd.SequenceFileRDDFunctions contains operations available on RDDs that can be saved as SequenceFiles. These operations are automatically available on any RDD of the right type (e.g. RDD[(Int, Int)] through implicit conversions.

    Java programmers should reference the org.apache.spark.api.java package for Spark programming APIs in Java.

    Classes and methods marked with Experimental are user-facing features which have not been officially adopted by the Spark project. These are subject to change or removal in minor releases.

    Classes and methods marked with Developer API are intended for advanced users want to extend Spark through lower level interfaces. These are subject to changes or removal in minor releases.

    Definition Classes
    apache
  • package mllib

    RDD-based machine learning APIs (in maintenance mode).

    RDD-based machine learning APIs (in maintenance mode).

    The spark.mllib package is in maintenance mode as of the Spark 2.0.0 release to encourage migration to the DataFrame-based APIs under the org.apache.spark.ml package. While in maintenance mode,

    • no new features in the RDD-based spark.mllib package will be accepted, unless they block implementing new features in the DataFrame-based spark.ml package;
    • bug fixes in the RDD-based APIs will still be accepted.

    The developers will continue adding more features to the DataFrame-based APIs in the 2.x series to reach feature parity with the RDD-based APIs. And once we reach feature parity, this package will be deprecated.

    Definition Classes
    spark
    See also

    SPARK-4591 to track the progress of feature parity

  • package evaluation
    Definition Classes
    mllib
  • BinaryClassificationMetrics
  • MulticlassMetrics
  • MultilabelMetrics
  • RankingMetrics
  • RegressionMetrics

class RankingMetrics[T] extends Logging with Serializable

Evaluator for ranking algorithms.

Java users should use RankingMetrics$.of to create a RankingMetrics instance.

Annotations
@Since( "1.2.0" )
Source
RankingMetrics.scala
Linear Supertypes
Serializable, Serializable, Logging, AnyRef, Any
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  1. RankingMetrics
  2. Serializable
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Visibility
  1. Public
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Instance Constructors

  1. new RankingMetrics(predictionAndLabels: RDD[(Array[T], Array[T])])(implicit arg0: ClassTag[T])

    predictionAndLabels

    an RDD of (predicted ranking, ground truth set) pairs.

Value Members

  1. lazy val meanAveragePrecision: Double

    Returns the mean average precision (MAP) of all the queries.

    Returns the mean average precision (MAP) of all the queries. If a query has an empty ground truth set, the average precision will be zero and a log warning is generated.

    Annotations
    @Since( "1.2.0" )
  2. def meanAveragePrecisionAt(k: Int): Double

    Returns the mean average precision (MAP) at ranking position k of all the queries.

    Returns the mean average precision (MAP) at ranking position k of all the queries. If a query has an empty ground truth set, the average precision will be zero and a log warning is generated.

    k

    the position to compute the truncated precision, must be positive

    returns

    the mean average precision at first k ranking positions

    Annotations
    @Since( "3.0.0" )
  3. def ndcgAt(k: Int): Double

    Compute the average NDCG value of all the queries, truncated at ranking position k.

    Compute the average NDCG value of all the queries, truncated at ranking position k. The discounted cumulative gain at position k is computed as: sumi=1k (2{relevance of ith item} - 1) / log(i + 1), and the NDCG is obtained by dividing the DCG value on the ground truth set. In the current implementation, the relevance value is binary.

    If a query has an empty ground truth set, zero will be used as ndcg together with a log warning.

    See the following paper for detail:

    IR evaluation methods for retrieving highly relevant documents. K. Jarvelin and J. Kekalainen

    k

    the position to compute the truncated ndcg, must be positive

    returns

    the average ndcg at the first k ranking positions

    Annotations
    @Since( "1.2.0" )
  4. def precisionAt(k: Int): Double

    Compute the average precision of all the queries, truncated at ranking position k.

    Compute the average precision of all the queries, truncated at ranking position k.

    If for a query, the ranking algorithm returns n (n is less than k) results, the precision value will be computed as #(relevant items retrieved) / k. This formula also applies when the size of the ground truth set is less than k.

    If a query has an empty ground truth set, zero will be used as precision together with a log warning.

    See the following paper for detail:

    IR evaluation methods for retrieving highly relevant documents. K. Jarvelin and J. Kekalainen

    k

    the position to compute the truncated precision, must be positive

    returns

    the average precision at the first k ranking positions

    Annotations
    @Since( "1.2.0" )
  5. def recallAt(k: Int): Double

    Compute the average recall of all the queries, truncated at ranking position k.

    Compute the average recall of all the queries, truncated at ranking position k.

    If for a query, the ranking algorithm returns n results, the recall value will be computed as #(relevant items retrieved) / #(ground truth set). This formula also applies when the size of the ground truth set is less than k.

    If a query has an empty ground truth set, zero will be used as recall together with a log warning.

    See the following paper for detail:

    IR evaluation methods for retrieving highly relevant documents. K. Jarvelin and J. Kekalainen

    k

    the position to compute the truncated recall, must be positive

    returns

    the average recall at the first k ranking positions

    Annotations
    @Since( "3.0.0" )