org.apache.spark.ml.odkl

Evaluator

object Evaluator extends Serializable

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Type Members

  1. class EmptyEvaluator extends Evaluator[EmptyEvaluator]

    Used in case when folding is needed, but not the evaluation

  2. class EvaluatingTransformer[M <: ModelWithSummary[M], E <: Evaluator[E]] extends ModelOnlyTransformer[M, EvaluatingTransformer[M, E]] with HasMetricsBlock

    Utility used for transparent injection of the evaluator into training chain.

  3. class PostProcessingEvaluator[E <: Evaluator[E]] extends Evaluator[PostProcessingEvaluator[E]]

  4. class TrainOnlyFilter extends Transformer with HasIsTestCol

    Utility used to filter out test data before passing to estimator.

  5. class TrainTestEvaluator[N <: Evaluator[N]] extends Evaluator[TrainTestEvaluator[N]] with HasIsTestCol

    This is a simple workaround to add kind of grouping by test/train column for evaluators without embedded support for grouping (eg.

Value Members

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

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

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  3. final def ##(): Int

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  4. final def ==(arg0: AnyRef): Boolean

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  5. final def ==(arg0: Any): Boolean

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  6. def addFolds[M <: ModelWithSummary[M]](estimator: SummarizableEstimator[M], folder: FoldsAssigner = new FoldsAssigner()): UnwrappedStage[M, IdentityModelTransformer[M]]

    Adds folds (foldNum column) to the dataset before passing it to the nested estimator.

    Adds folds (foldNum column) to the dataset before passing it to the nested estimator.

    estimator

    Nested predictor for fitting the model

    folder

    Transformer adding folds (by default based on row hash)

    returns

    Estimator returning a model fit by nested predictor on a dataset with extra foldNum column

  7. final def asInstanceOf[T0]: T0

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  8. def clone(): AnyRef

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  9. def crossValidate[M <: ModelWithSummary[M], E <: Evaluator[E]](estimator: SummarizableEstimator[M], evaluator: E, numFolds: Int = 10, parallel: Boolean = false, cacheForks: Boolean = false): SummarizableEstimator[M]

    Performs a cross validation given predictor and evaluator.

    Performs a cross validation given predictor and evaluator. Returns a model with summary blocks extended with foldNum column.

    Split into folds is done based on the hash of entire row.

    estimator

    Nested predictor for fitting the model.

    evaluator

    Evaluator for creating a metric.

    numFolds

    Number of folds for validation (defeult 10)

    parallel

    Whenever to train and evaluate folds in parallel.

    cacheForks

    Whenever to cache forks before iterating

    returns

    Estimator which returns a model fit by the nested predictor on the entire dataset with summary blocks extended with numFolds column.

  10. final def eq(arg0: AnyRef): Boolean

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  11. def equals(arg0: Any): Boolean

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  12. def evaluate[M <: ModelWithSummary[M], E <: Evaluator[E]](estimator: SummarizableEstimator[M], evaluator: E): SummarizableEstimator[M]

    Fit and then evaluate model.

    Fit and then evaluate model. Results of evaluation is stored into a dedicated summary block.

    estimator

    Used to fit the model

    evaluator

    Used to evaluate the model.

    returns

    Estimator which returns a model fit by nested predictor with extra summary block for metrics, produced by evaluator.

  13. def finalize(): Unit

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  14. final def getClass(): Class[_]

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  15. def hashCode(): Int

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  16. final def isInstanceOf[T0]: Boolean

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  17. final def ne(arg0: AnyRef): Boolean

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  18. final def notify(): Unit

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  19. final def notifyAll(): Unit

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  20. final def synchronized[T0](arg0: ⇒ T0): T0

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  21. def toString(): String

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  22. def validateInFolds[M <: ModelWithSummary[M], E <: Evaluator[E]](estimator: SummarizableEstimator[M], evaluator: E, numFolds: Int = 10, parallel: Boolean = false, cacheForks: Boolean = false): CrossValidator[M]

    Performs a cross validation given predictor and evaluator.

    Performs a cross validation given predictor and evaluator. Returns a model with summary blocks extended with foldNum column.

    Split into folds is expected to be done externaly.

    estimator

    Nested predictor for fitting the model.

    evaluator

    Evaluator for creating a metric.

    numFolds

    Number of folds for validation (defeult 10)

    parallel

    Whenever to train and evaluate folds in parallel.

    cacheForks

    Whenever to cache forks before iterating

    returns

    Estimator which returns a model fit by the nested predictor on the entire dataset with summary blocks extended with numFolds column.

  23. final def wait(): Unit

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  24. final def wait(arg0: Long, arg1: Int): Unit

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  25. final def wait(arg0: Long): Unit

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