Object

com.enriquegrodrigo.spark.crowd.methods

DawidSkene

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object DawidSkene

Provides functions for transforming an annotation dataset into a standard label dataset using the DawidSkene algorithm.

This algorithm only works with types.MulticlassAnnotation datasets although one can easily use it for types.BinaryAnnotation through Spark Dataset as method

It returns a types.DawidSkeneModel with information about the estimation of the true class, as well as the annotator quality and the log-likelihood obtained by the model.

Example: 

  1. import com.enriquegrodrigo.spark.crowd.methods.DawidSkene
import com.enriquegrodrigo.spark.crowd.types._
val exampleFile = "data/multi-ann.parquet"
val exampleData = spark.read.parquet(exampleFile).as[MulticlassAnnotation]
//Applying the learning algorithm
val mode = DawidSkene(exampleData)
//Get MulticlassLabel with the class predictions
val pred = mode.getMu().as[MulticlassLabel]
//Annotator precision matrices
val annprec = mode.getAnnotatorPrecision()
//Annotator likelihood
val like = mode.getLogLikelihood()
Version

0.1.5

See also

Dawid, Alexander Philip, and Allan M. Skene. "Maximum likelihood estimation of observer error-rates using the EM algorithm." Applied statistics (1979): 20-28.

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  4. def apply(dataset: Dataset[MulticlassAnnotation], eMIters: Int = 10, eMThreshold: Double = 0.001): DawidSkeneModel

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    Applies learning algorithm.

    Applies learning algorithm.

    dataset

    The dataset over which the algorithm will execute (spark Dataset of type types.MulticlassAnnotation

    eMIters

    Number of iterations for the EM algorith

    eMThreshold

    LogLikelihood variability threshold for the EM algorithm

    returns

    types.DawidSkeneModel

    Version

    0.2.0

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