Object

com.enriquegrodrigo.spark.crowd.methods

PMTI

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object PMTI

Provides functions for transforming an annotation dataset into a standard label dataset using the modified version of the PM algorithm in the paper Truth Inference in Crowdsourcing: Is the problem solved?.

This algorithm only works with continuous target variables. Thus you need an annotation dataset of types.RealAnnotation:

The algorithm returns a PMTI.PMModel, with information about the class true label estimation and the annotators weight

Example: 

  1. import com.enriquegrodrigo.spark.crowd.methods.PMTI
import com.enriquegrodrigo.spark.crowd.types._
sc.setCheckpointDir("checkpoint")
val annFile = "data/real-ann.parquet"
val annData = spark.read.parquet(annFile)
//Applying the learning algorithm
val mode = PMTI(annData)
//Get MulticlassLabel with the class predictions
val pred = mode.getMu()
//Annotator weights
val annweights = mode.getAnnotatorWeights()
Version

0.2.0

See also

Yudian Zheng, Guoliang Li, Yuanbing Li, Caihua Shan, Reynold Cheng. Truth Inference in Crowdsourcing: Is the Problem Solved? In VLDB 2017, Vol 10, Isuue 5, Pages 541-552, Full Paper, Present in VLDB 2017, Aug 28 - Sep 1, Munich, Germany.

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  1. class PMModel 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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  4. def apply(dataset: Dataset[RealAnnotation], iterations: Int = 5, threshold: Double = 0.1): PMModel

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    Apply the IBCC Algorithm.

    Apply the IBCC Algorithm.

    dataset

    The dataset (spark dataset of types.RealAnnotation)

    iterations

    Iterations of the learning algorithm

    threshold

    Minimum MSE for the algorithm to continue iterating

    Version

    0.2.0

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