com.redhat.et.silex.cluster

ClusteringTreeModel

class ClusteringTreeModel extends Serializable

Enhance a Spark DecisionTreeModel object with methods for Random Forest clustering

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Instance Constructors

  1. new ClusteringTreeModel(self: DecisionTreeModel)

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  15. def nodeIterator: Iterator[Node]

    Return an iterator over the nodes of the decision tree

    Return an iterator over the nodes of the decision tree

    returns

    an iterator over the tree nodes, in breadth first order

  16. final def notify(): Unit

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  17. final def notifyAll(): Unit

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  18. def predictLeafId(features: Vector): Int

    Evaluate a feature vector against a decision tree and return the id of the leaf node it "landed" at

    Evaluate a feature vector against a decision tree and return the id of the leaf node it "landed" at

    features

    The feature vector to evaluate

    returns

    The id of the decision tree leaf node the feature vector reached

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

    Traverse a Spark decision tree 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 a Spark decision tree 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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