Object

com.enriquegrodrigo.spark.crowd.methods

IBCC

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object IBCC

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

This algorithm only works with multiclass target variables (Datasets of type types.MulticlassAnnotation

The algorithm returns a IBCC.IBCCModel, with information about the class true label estimation, the annotators precision, and the class prior estimation

Example: 

  1. import com.enriquegrodrigo.spark.crowd.methods.IBCC
import com.enriquegrodrigo.spark.crowd.types._
sc.setCheckpointDir("checkpoint")
val annFile = "data/binary-ann.parquet"
val annData = spark.read.parquet(annFile)
//Applying the learning algorithm
val mode = IBCC(annData)
//Get MulticlassLabel with the class predictions
val pred = mode.getMu()
//Annotator precision matrices
val annprec = mode.getAnnotatorPrecision()
//Annotator precision matrices
val classPrior = mode.getClassPrior()
Version

0.2.0

See also

H.-C. Kim and Z. Ghahramani. Bayesian classifier combination. In AISTATS, pages 619–627, 2012.

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  1. class IBCCModel extends AnyRef

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    Model returned by the learning algorithm.

    Model returned by the learning algorithm.

    Version

    0.2.0

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  1. final def !=(arg0: Any): Boolean

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  2. final def ##(): Int

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  4. def apply(dataset: Dataset[MulticlassAnnotation], eMIters: Int = 5, eMThreshold: Double = 0.1, annDirich: Option[Map[String, Array[Array[Double]]]] = None, classDirich: Option[Array[Double]] = None): IBCCModel

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    Apply the IBCC Algorithm.

    Apply the IBCC Algorithm.

    dataset

    The dataset (spark dataset of MulticlassAnnotation

    eMIters

    Number of iterations for the EM algorithm

    eMThreshold

    LogLikelihood variability threshold for the EM algorithm

    annDirich

    Dirichlech prior for annotators. By default, a uniform prior.

    classDirich

    Dirichlech prior for classes. By default, a uniform prior.

    Version

    0.2.0

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