com.redhat.et.silex.cluster

ClusteringRandomForestModel

class ClusteringRandomForestModel extends Serializable

Enhance Spark RandomForestModel objects with methods for Random Forest Clustering

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Instance Constructors

  1. new ClusteringRandomForestModel(self: RandomForestModel)

Value Members

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  8. def countFeatureIndexes: Map[Int, Int]

    Traverse the trees in a Random Forest ensemble and count the number of times each feature index appears over all the trees

    Traverse the trees in a Random Forest ensemble and count the number of times each feature index appears over all the trees

    returns

    A mapping from feature index, to the number of times it appeared over the trees in the ensemble

  9. def countFeatures(names: PartialFunction[Int, String]): Map[String, Int]

    Traverse the trees in a Random Forest ensemble and count the number of times each feature appears over all the trees

    Traverse the trees in a Random Forest ensemble and count the number of times each feature appears over all the trees

    names

    A partial function that returns the name of a feature, given its index

    returns

    A mapping from feature name to the number of times it appeared over the trees in the ensemble

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  15. def histFeatureIndexes: Seq[(Int, Int)]

    Traverse the trees in a Random Forest ensemble and count the number of times each feature index appears over all the trees

    Traverse the trees in a Random Forest ensemble and count the number of times each feature index appears over all the trees

    returns

    A sequence of pairs (idx, n) where 'idx' is a feature index and 'n' is the number of times it was used in the ensemble's trees, sorted in descending order of counts.

  16. def histFeatures(names: PartialFunction[Int, String]): Seq[(String, Int)]

    Traverse the trees in a Random Forest ensemble and count the number of times each feature appears over all the trees

    Traverse the trees in a Random Forest ensemble and count the number of times each feature appears over all the trees

    names

    A partial function that returns the name of a feature, given its index

    returns

    A sequence of pairs (name, n) where 'name' is a feature name and 'n' is the number of times it was used in the ensemble's trees, sorted in descending order of counts.

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  21. def predictLeafIds(data: RDD[Vector]): RDD[Vector[Int]]

    Map an RDD of feature vectors to a corresponding RDD of leaf-node id vectors

    Map an RDD of feature vectors to a corresponding RDD of leaf-node id vectors

    data

    The RDD of feature vectors

    returns

    An RDD of leaf-node id vectors

  22. def predictLeafIds(features: Vector): Vector[Int]

    Evaluate an input feature vector and return a vector of the leaf-node ids that the feature vector "landed" at for each tree in the RF ensemble

    Evaluate an input feature vector and return a vector of the leaf-node ids that the feature vector "landed" at for each tree in the RF ensemble

    features

    The feature vector to evaluate

    returns

    vector of leaf node ids, one from each tree in the ensemble

  23. def rules(names: PartialFunction[Int, String], catInfo: PartialFunction[Int, Int]): Map[Double, Seq[Seq[Predicate]]]

    Traverse the trees in a Random Forest ensemble and convert each path from root to a leaf into a "rule" that is a sequence of individual predicates representing the decision made at each internal node.

    Traverse the trees in a Random Forest ensemble and convert each path from root to a leaf into a "rule" that is a sequence of individual predicates representing the decision made at each internal node.

    names

    a partial function that returns name of a feature given its index

    catInfo

    a partial function from feature index to number of categories. If an index is not present then it is assumed to be numeric

    returns

    a map from leaf-node prediction values to a collection of all rules that will yield that value.

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