Package weka.classifiers.trees.lmt

Class LMTNode

java.lang.Object
weka.classifiers.AbstractClassifier
weka.classifiers.trees.lmt.LogisticBase
weka.classifiers.trees.lmt.LMTNode
All Implemented Interfaces:
Serializable, Cloneable, Classifier, BatchPredictor, CapabilitiesHandler, CapabilitiesIgnorer, CommandlineRunnable, OptionHandler, RevisionHandler, WeightedInstancesHandler
public class LMTNode extends LogisticBase
Class for logistic model tree structure.
Version:
$Revision: 11566 $
Author:
Niels Landwehr, Marc Sumner
See Also:

  • Field Details

    • m_alpha

      public double m_alpha
      Alpha-value (for pruning) at the node

    • m_numIncorrectModel

      public double m_numIncorrectModel
      Weighted number of training examples currently misclassified by the logistic model at the node

    • m_numIncorrectTree

      public double m_numIncorrectTree
      Weighted number of training examples currently misclassified by the subtree rooted at the node

  • Constructor Details

    • LMTNode

      public LMTNode(ModelSelection modelSelection, int numBoostingIterations, boolean fastRegression, boolean errorOnProbabilities, int minNumInstances, double weightTrimBeta, boolean useAIC, NominalToBinary ntb, int numDecimalPlaces)
      Constructor for logistic model tree node.
      Parameters:
      modelSelection - selection method for local splitting model
      numBoostingIterations - sets the numBoostingIterations parameter
      fastRegression - sets the fastRegression parameter
      errorOnProbabilities - Use error on probabilities for stopping criterion of LogitBoost?
      minNumInstances - minimum number of instances at which a node is considered for splitting

  • Method Details

    • buildClassifier

      public void buildClassifier(Instances data) throws Exception
      Method for building a logistic model tree (only called for the root node). Grows an initial logistic model tree and prunes it back using the CART pruning scheme.
      Specified by:
      buildClassifier in interface Classifier
      Overrides:
      buildClassifier in class LogisticBase
      Parameters:
      data - the data to train with
      Throws:
      Exception - if something goes wrong

    • buildTree

      public void buildTree(Instances data, SimpleLinearRegression[][] higherRegressions, double totalInstanceWeight, double higherNumParameters, Instances numericDataHeader) throws Exception
      Method for building the tree structure. Builds a logistic model, splits the node and recursively builds tree for child nodes.
      Parameters:
      data - the training data passed on to this node
      higherRegressions - An array of regression functions produced by LogitBoost at higher levels in the tree. They represent a logistic regression model that is refined locally at this node.
      totalInstanceWeight - the total number of training examples
      higherNumParameters - effective number of parameters in the logistic regression model built in parent nodes
      Throws:
      Exception - if something goes wrong

    • prune

      public void prune(double alpha) throws Exception
      Prunes a logistic model tree using the CART pruning scheme, given a cost-complexity parameter alpha.
      Parameters:
      alpha - the cost-complexity measure
      Throws:
      Exception - if something goes wrong

    • prune

      public int prune(double[] alphas, double[] errors, Instances test) throws Exception
      Method for performing one fold in the cross-validation of the cost-complexity parameter. Generates a sequence of alpha-values with error estimates for the corresponding (partially pruned) trees, given the test set of that fold.
      Parameters:
      alphas - array to hold the generated alpha-values
      errors - array to hold the corresponding error estimates
      test - test set of that fold (to obtain error estimates)
      Throws:
      Exception - if something goes wrong

    • getNumInnerNodes

      public int getNumInnerNodes()
      Method to count the number of inner nodes in the tree
      Returns:
      the number of inner nodes

    • getNumLeaves

      public int getNumLeaves()
      Returns the number of leaves in the tree. Leaves are only counted if their logistic model has changed compared to the one of the parent node.
      Returns:
      the number of leaves

    • treeErrors

      public void treeErrors()
      Updates the numIncorrectTree field for all nodes. This is needed for calculating the alpha-values.

    • calculateAlphas

      public void calculateAlphas() throws Exception
      Updates the alpha field for all nodes.
      Throws:
      Exception

    • getNodes

      public Vector<LMTNode> getNodes()
      Return a list of all inner nodes in the tree
      Returns:
      the list of nodes

    • getNodes

      public void getNodes(Vector<LMTNode> nodeList)
      Fills a list with all inner nodes in the tree
      Parameters:
      nodeList - the list to be filled

    • hasModels

      public boolean hasModels()
      Returns true if the logistic regression model at this node has changed compared to the one at the parent node.
      Returns:
      whether it has changed

    • modelDistributionForInstance

      public double[] modelDistributionForInstance(Instance instance) throws Exception
      Returns the class probabilities for an instance according to the logistic model at the node.
      Parameters:
      instance - the instance
      Returns:
      the array of probabilities
      Throws:
      Exception

    • distributionForInstance

      public double[] distributionForInstance(Instance instance) throws Exception
      Returns the class probabilities for an instance given by the logistic model tree.
      Specified by:
      distributionForInstance in interface Classifier
      Overrides:
      distributionForInstance in class LogisticBase
      Parameters:
      instance - the instance
      Returns:
      the array of probabilities
      Throws:
      Exception - if distribution can't be computed successfully

    • numLeaves

      public int numLeaves()
      Returns the number of leaves (normal count).
      Returns:
      the number of leaves

    • numNodes

      public int numNodes()
      Returns the number of nodes.
      Returns:
      the number of nodes

    • toString

      public String toString()
      Returns a description of the logistic model tree (tree structure and logistic models)
      Overrides:
      toString in class LogisticBase
      Returns:
      describing string

    • getModelParameters

      public String getModelParameters()
      Returns a string describing the number of LogitBoost iterations performed at this node, the total number of LogitBoost iterations performed (including iterations at higher levels in the tree), and the number of training examples at this node.
      Returns:
      the describing string

    • assignIDs

      public int assignIDs(int lastID)
      Assigns unique IDs to all nodes in the tree

    • assignLeafModelNumbers

      public int assignLeafModelNumbers(int leafCounter)
      Assigns numbers to the logistic regression models at the leaves of the tree

    • modelsToString

      public String modelsToString()
      Returns a string describing the logistic regression function at the node.

    • graph

      public String graph() throws Exception
      Returns graph describing the tree.
      Throws:
      Exception - if something goes wrong

    • getRevision

      public String getRevision()
      Returns the revision string.
      Specified by:
      getRevision in interface RevisionHandler
      Overrides:
      getRevision in class LogisticBase
      Returns:
      the revision