Object

com.enriquegrodrigo.spark.crowd.methods

MajorityVoting

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object MajorityVoting

Provides functions for transforming an annotation dataset into a standard label dataset using the majority voting approach

This object provides several functions for using majority voting style algorithms over annotations datasets (spark datasets with types com.enriquegrodrigo.spark.crowd.types.BinaryAnnotation, com.enriquegrodrigo.spark.crowd.types.MulticlassAnnotation, or com.enriquegrodrigo.spark.crowd.types.RealAnnotation). For discrete types (com.enriquegrodrigo.spark.crowd.types.BinaryAnnotation, com.enriquegrodrigo.spark.crowd.types.MulticlassAnnotation) the method uses the most frequent class. For continuous types, the mean is used.

The object also provides methods for estimating the probability of a class for the discrete type, computing, for the binary case, the mean of the positive class and, for the multiclass case, the one vs all mean of a class against the others.

Example: 

  1. result: Dataset[BinaryLabel] = MajorityVoting.transformBinary(dataset)
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0.1

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  1. case class ExampleClassCombination(example: Long, clas: Int) extends Product with Serializable

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    Combination example class for complete multiclass probability estimation

    Combination example class for complete multiclass probability estimation

    Version

    0.1

  2. case class ExampleClassFrequency(example: Long, clas: Int, freq: Double) extends Product with Serializable

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    Frequency of a concrete class for example

    Frequency of a concrete class for example

    Version

    0.1

  3. case class ExampleFrequency(example: Long, freq: Double) extends Product with Serializable

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    Number of labels for an example

    Number of labels for an example

    Version

    0.1

  4. class MulticlassMajorityEstimation extends Aggregator[(ExampleClassCombination, MulticlassAnnotation), Double, Double]

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    Obtain multiclass soft probability estimation

    Obtain multiclass soft probability estimation

    Version

    0.1

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  17. def transformBinary(dataset: Dataset[BinaryAnnotation]): Dataset[BinaryLabel]

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    Obtains the most frequent class for BinaryAnnotation datasets

    Obtains the most frequent class for BinaryAnnotation datasets

    dataset

    The annotations dataset to be aggregated

  18. def transformMulticlass(dataset: Dataset[MulticlassAnnotation]): Dataset[MulticlassLabel]

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    Obtain the most frequent class for all examples in the annotation dataset.

    Obtain the most frequent class for all examples in the annotation dataset.

    dataset

    The annotations dataset to be aggregated

  19. def transformReal(dataset: Dataset[RealAnnotation]): Dataset[RealLabel]

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    Obtain the mean of the annotations for a given example.

    Obtain the mean of the annotations for a given example.

    dataset

    The annotations dataset to be aggregated

  20. def transformSoftBinary(dataset: Dataset[BinaryAnnotation]): Dataset[BinarySoftLabel]

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    Obtains probability of the class being positive for BinaryAnnotation datasets

    Obtains probability of the class being positive for BinaryAnnotation datasets

    dataset

    The annotations dataset to be aggregated

  21. def transformSoftMulti(dataset: Dataset[MulticlassAnnotation]): Dataset[MulticlassSoftProb]

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    Obtain a list of datasets resulting of applying transformSoftBinary to each class against the others

    Obtain a list of datasets resulting of applying transformSoftBinary to each class against the others

    dataset

    The annotations dataset to be aggregated

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