Package org.tribuo.regression.rtree

Class IndependentRegressionTreeModel

java.lang.Object
org.tribuo.Model<T>
org.tribuo.SparseModel<T>
org.tribuo.common.tree.TreeModel<Regressor>
org.tribuo.regression.rtree.IndependentRegressionTreeModel
All Implemented Interfaces:
com.oracle.labs.mlrg.olcut.provenance.Provenancable<ModelProvenance>, Serializable
public final class IndependentRegressionTreeModel extends TreeModel<Regressor>
A Model wrapped around a list of decision tree root Nodes used to generate independent predictions for each dimension in a regression.
See Also:

  • Method Details

    • getDepth

      public int getDepth()
      Probes the trees to find the depth.
      Overrides:
      getDepth in class TreeModel<Regressor>
      Returns:
      The maximum depth across the trees.

    • predict

      public Prediction<Regressor> predict(Example<Regressor> example)
      Description copied from class: Model
      Uses the model to predict the output for a single example.

      predict does not mutate the example.

      Throws IllegalArgumentException if the example has no features or no feature overlap with the model.

      Overrides:
      predict in class TreeModel<Regressor>
      Parameters:
      example - the example to predict.
      Returns:
      the result of the prediction.

    • getTopFeatures

      public Map<String,List<com.oracle.labs.mlrg.olcut.util.Pair<String,Double>>> getTopFeatures(int n)
      Description copied from class: Model
      Gets the top n features associated with this model.

      If the model does not produce per output feature lists, it returns a map with a single element with key Model.ALL_OUTPUTS.

      If the model cannot describe it's top features then it returns Collections.emptyMap().

      Overrides:
      getTopFeatures in class TreeModel<Regressor>
      Parameters:
      n - the number of features to return. If this value is less than 0, all features should be returned for each class, unless the model cannot score it's features.
      Returns:
      a map from string outputs to an ordered list of pairs of feature names and weights associated with that feature in the model

    • getExcuse

      public Optional<Excuse<Regressor>> getExcuse(Example<Regressor> example)
      Description copied from class: Model
      Generates an excuse for an example.

      This attempts to explain a classification result. Generating an excuse may be quite an expensive operation.

      This excuse either contains per class information or an entry with key Model.ALL_OUTPUTS.

      The optional is empty if the model does not provide excuses.

      Overrides:
      getExcuse in class TreeModel<Regressor>
      Parameters:
      example - The input example.
      Returns:
      An optional excuse object. The optional is empty if this model does not provide excuses.

    • copy

      protected IndependentRegressionTreeModel copy(String newName, ModelProvenance newProvenance)
      Description copied from class: Model
      Copies a model, replacing its provenance and name with the supplied values.

      Used to provide the provenance removal functionality.

      Overrides:
      copy in class TreeModel<Regressor>
      Parameters:
      newName - The new name.
      newProvenance - The new provenance.
      Returns:
      A copy of the model.

    • getFeatures

      public Set<String> getFeatures()
      Description copied from class: TreeModel
      Returns the set of features which are split on in this tree.
      Overrides:
      getFeatures in class TreeModel<Regressor>
      Returns:
      The feature names used by this tree.

    • toString

      public String toString()
      Overrides:
      toString in class TreeModel<Regressor>

    • getRoots

      public Map<String,Node<Regressor>> getRoots()
      Returns an unmodifiable view on the root node collection.

      The nodes themselves are immutable.

      Returns:
      The root node collection.

    • getRoot

      public Node<Regressor> getRoot()
      Returns null, as this model contains multiple roots, one per regression output dimension.

      Use getRoots() instead.

      Overrides:
      getRoot in class TreeModel<Regressor>
      Returns:
      null.