keystoneml.evaluation

MeanAveragePrecisionEvaluator

class MeanAveragePrecisionEvaluator extends Evaluator[DenseVector[Double], Array[Int], DenseVector[Double]] with Serializable

MeanAveragePrecision (MAP) calculator based on MATLAB code from the encoding eval toolkit at http://www.robots.ox.ac.uk/~vgg/software/enceval_toolkit/

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Serializable, Serializable, Evaluator[DenseVector[Double], Array[Int], DenseVector[Double]], AnyRef, Any
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Instance Constructors

  1. new MeanAveragePrecisionEvaluator(numClasses: Int)

    numClasses

    The number of classes in the dataset.

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  10. def evaluate(testPredicted: RDD[DenseVector[Double]], testActual: RDD[Array[Int]]): DenseVector[Double]

    Compute the mean average precision for multi-class classification NOTE: The current implementation is only suitable when we have a small number of classes and data items.

    Compute the mean average precision for multi-class classification NOTE: The current implementation is only suitable when we have a small number of classes and data items.

    testPredicted

    For every test image, this contains a list of scores for each class

    testActual

    For every test image, this contains list of valid labels. Labels are assumed to be class ids.

    returns

    An array containing average precision scores for each class

    Definition Classes
    MeanAveragePrecisionEvaluator → Evaluator

  11. def evaluate(predictions: PipelineDataset[DenseVector[Double]], labels: PipelineDataset[Array[Int]]): DenseVector[Double]

    Definition Classes
    Evaluator

  12. def evaluate(predictions: RDD[DenseVector[Double]], labels: PipelineDataset[Array[Int]]): DenseVector[Double]

    Definition Classes
    Evaluator

  13. def evaluate(predictions: PipelineDataset[DenseVector[Double]], labels: RDD[Array[Int]]): DenseVector[Double]

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    Evaluator

  14. def finalize(): Unit

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Inherited from Evaluator[DenseVector[Double], Array[Int], DenseVector[Double]]

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