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com.enriquegrodrigo.spark.crowd.methods

CGlad

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object CGlad

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

This algorithm only works with types.BinaryAnnotation datasets.

The algorithm returns a types.CGladModel, with information as the class true label estimation, the annotator precision or the cluster difficulty.

Example: 

  1. import com.enriquegrodrigo.spark.crowd.methods.CGlad
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 = CGlad(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.getClusterDifficulty()
//Cluster for each example
val annprec = mode.getClusters()
Version

0.2.1

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  4. def apply(dataset: Dataset[BinaryAnnotation], eMIters: Int = 10, eMThreshold: Double = 0, gradIters: Int = 100, gradThreshold: Double = 0, gradLearningRate: Double = 0.01, gradDataFraction: Double = 1.0, backtrackingLimit: Double = 0.1, rank: Integer = 8, k: Integer = 32, seed: Long = 1L): CGladModel

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    Apply the CGlad Algorithm.

    Apply the CGlad 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

    gradDataFraction

    fraction of the data used in the stochastic gradient descent optimization

    backtrackingLimit

    minimum value that gradientLearningRate can take in the backtracking mechanism

    rank

    size of vectors in the matrix factorization

    k

    number of clusters

    Version

    0.1.5

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