Object

com.enriquegrodrigo.spark.crowd.methods

RaykarBinary

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object RaykarBinary

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

This algorithm only works with types.BinaryAnnotation datasets. There are versions for the types.MulticlassAnnotation (RaykarMulti) and types.RealAnnotation (RaykarCont).

It will return a types.RaykarBinaryModel with information about the estimation of the ground truth for each example, the annotator precision estimation of the model, the weights of the logistic regression model learned and the log-likelihood of the model.

The next example can be found in the examples folders. In it, the user may also find an example of how to add prior confidence on the annotators.

Example: 

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

0.1.5

See also

Raykar, Vikas C., et al. "Learning from crowds." Journal of Machine Learning Research 11.Apr (2010): 1297-1322.

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  4. def apply(dataset: DataFrame, annDataset: Dataset[BinaryAnnotation], eMIters: Int = 5, eMThreshold: Double = 0.001, gradIters: Int = 100, gradThreshold: Double = 0.1, gradLearning: Double = 0.1, a_prior: Option[Array[Array[Double]]] = None, b_prior: Option[Array[Array[Double]]] = None, w_prior: Option[Array[Array[Double]]] = None): RaykarBinaryModel

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    Applies the learning algorithm

    Applies the learning algorithm

    dataset

    the dataset with feature vectors (spark Dataframe).

    annDataset

    the dataset with the annotations (spark Dataset of types.BinaryAnnotation).

    gradIters

    maximum number of iterations for the GradientDescent algorithm

    gradThreshold

    threshold for the log likelihood variability for the gradient descent algorithm

    gradLearning

    learning rate for the gradient descent algorithm

    a_prior

    prior (Beta distribution hyperparameters) for the estimation of the probability that an annotator correctly classifias positive instances

    b_prior

    prior (Beta distribution hyperparameters) for the estimation of the probability that an annotator correctly classify as negative instances

    w_prior

    prior for the weights of the logistic regression model

    returns

    com.enriquegrodrigo.spark.crowd.types.RaykarBinaryModel

    Version

    0.1.5

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