Object

com.enriquegrodrigo.spark.crowd.methods

Glad

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object Glad

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

This algorithm only works with types.BinaryAnnotation datasets.

The algorithm returns a types.GladModel, with information about the class true label estimation, the annotator precision, the instances difficulty and the log-likilihood of the model.

Example: 

  1. import com.enriquegrodrigo.spark.crowd.methods.Glad
import com.enriquegrodrigo.spark.crowd.types._
sc.setCheckpointDir("checkpoint")
val annFile = "data/binary-ann.parquet"
val annData = spark.read.parquet(annFile).as[BinaryAnnotation]
//Applying the learning algorithm
val mode = Glad(annData)
//Get MulticlassLabel with the class predictions
val pred = mode.getMu().as[BinarySoftLabel]
//Annotator precision matrices
val annprec = mode.getAnnotatorPrecision()
//Annotator precision matrices
val annprec = mode.getInstanceDifficulty()
//Annotator likelihood
val like = mode.getLogLikelihood()
Version

0.1.5

See also

Whitehill, Jacob, et al. "Whose vote should count more: Optimal integration of labels from labelers of unknown expertise." Advances in neural information processing systems. 2009.

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  4. def apply(dataset: Dataset[BinaryAnnotation], eMIters: Int = 5, eMThreshold: Double = 0.1, gradIters: Int = 30, gradThreshold: Double = 0.5, gradLearningRate: Double = 0.01, alphaPrior: Double = 1, betaPrior: Double = 10): GladModel

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    Apply the Glad Algorithm.

    Apply the Glad Algorithm.

    dataset

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

    eMIters

    Number of iterations for the EM algorithm

    eMThreshold

    LogLikelihood variability threshold for the EM algorithm

    gradIters

    Maximum number of iterations for the GradientDescent algorithm

    gradThreshold

    Threshold for the log likelihood variability for the gradient descent algorithm

    gradLearningRate

    Learning rate for the gradient descent algorithm

    alphaPrior

    First value for all alpha parameters

    betaPrior

    First value for all beta parameters

    Version

    0.1.5

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