LMTNode

Overview | Package | Class | Tree | Deprecated | Index | Help | Weka's home

PREV CLASS NEXT CLASS | FRAMES NO FRAMES
SUMMARY: NESTED | FIELD | CONSTR | METHOD | DETAIL: FIELD | CONSTR | METHOD

## weka.classifiers.trees.lmt
Class LMTNode

    java.lang.Object
      weka.classifiers.Classifier
          weka.classifiers.trees.lmt.LogisticBase
              weka.classifiers.trees.lmt.LMTNode

All Implemented Interfaces:
  java.io.Serializable, java.lang.Cloneable, CapabilitiesHandler, OptionHandler, RevisionHandler, WeightedInstancesHandler

    public class LMTNode

extends LogisticBase

Class for logistic model tree structure.

Version:
  $Revision: 1.8 $
Author:
  Niels Landwehr, Marc Sumner
See Also:
  Serialized Form

Field Summary
`double` | `m_alpha` Alpha-value (for pruning) at the node
`double` | `m_numIncorrectModel` Weighted number of training examples currently misclassified by the logistic model at the node
`double` | `m_numIncorrectTree` Weighted number of training examples currently misclassified by the subtree rooted at the node

Constructor Summary
`LMTNode(ModelSelection modelSelection, int numBoostingIterations, boolean fastRegression, boolean errorOnProbabilities, int minNumInstances, double weightTrimBeta, boolean useAIC)` Constructor for logistic model tree node.

Method Summary
`int` | `assignIDs(int lastID)` Assigns unique IDs to all nodes in the tree
`int` | `assignLeafModelNumbers(int leafCounter)` Assigns numbers to the logistic regression models at the leaves of the tree
`void` | `buildClassifier(Instances data)` Method for building a logistic model tree (only called for the root node).
`void` | `buildTree(Instances data, SimpleLinearRegression[][] higherRegressions, double totalInstanceWeight, double higherNumParameters)` Method for building the tree structure.
`void` | `calculateAlphas()` Updates the alpha field for all nodes.
`void` | `cleanup()` Cleanup in order to save memory.
`double[]` | `distributionForInstance(Instance instance)` Returns the class probabilities for an instance given by the logistic model tree.
`java.lang.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.
`java.util.Vector` | `getNodes()` Return a list of all inner nodes in the tree
`void` | `getNodes(java.util.Vector nodeList)` Fills a list with all inner nodes in the tree
`int` | `getNumInnerNodes()` Method to count the number of inner nodes in the tree
`int` | `getNumLeaves()` Returns the number of leaves in the tree.
`java.lang.String` | `getRevision()` Returns the revision string.
`java.lang.String` | `graph()` Returns graph describing the tree.
`boolean` | `hasModels()` Returns true if the logistic regression model at this node has changed compared to the one at the parent node.
`double[]` | `modelDistributionForInstance(Instance instance)` Returns the class probabilities for an instance according to the logistic model at the node.
`void` | `modelErrors()` Updates the numIncorrectModel field for all nodes.
`java.lang.String` | `modelsToString()` Returns a string describing the logistic regression function at the node.
`int` | `numLeaves()` Returns the number of leaves (normal count).
`int` | `numNodes()` Returns the number of nodes.
`void` | `prune(double alpha)` Prunes a logistic model tree using the CART pruning scheme, given a cost-complexity parameter alpha.
`int` | `prune(double[] alphas, double[] errors, Instances test)` Method for performing one fold in the cross-validation of the cost-complexity parameter.
`java.lang.String` | `toString()` Returns a description of the logistic model tree (tree structure and logistic models)
`void` | `treeErrors()` Updates the numIncorrectTree field for all nodes.

Methods inherited from class weka.classifiers.trees.lmt.LogisticBase
`getMaxIterations, getNumRegressions, getUseAIC, getUsedAttributes, getWeightTrimBeta, percentAttributesUsed, setHeuristicStop, setMaxIterations, setUseAIC, setWeightTrimBeta`

Methods inherited from class weka.classifiers.Classifier
`classifyInstance, debugTipText, forName, getCapabilities, getDebug, getOptions, listOptions, makeCopies, makeCopy, setDebug, setOptions`

Methods inherited from class java.lang.Object
`equals, getClass, hashCode, notify, notifyAll, wait, wait, wait`

Field Detail

### 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 Detail

### LMTNode

    public LMTNode(ModelSelection modelSelection,
                   int numBoostingIterations,
                   boolean fastRegression,
                   boolean errorOnProbabilities,
                   int minNumInstances,
                   double weightTrimBeta,
                   boolean useAIC)

  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 Detail

### buildClassifier

    public void buildClassifier(Instances data)
                         throws java.lang.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.

  Overrides:
    `buildClassifier` in class `LogisticBase`
  Parameters:
    `data` - the data to train with
  Throws:
    `java.lang.Exception` - if something goes wrong

### buildTree

    public void buildTree(Instances data,
                          SimpleLinearRegression[][] higherRegressions,
                          double totalInstanceWeight,
                          double higherNumParameters)
                   throws java.lang.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:
    `java.lang.Exception` - if something goes wrong

### prune

    public void prune(double alpha)
               throws java.lang.Exception

  Prunes a logistic model tree using the CART pruning scheme, given a cost-complexity parameter alpha.

  Parameters:
    `alpha` - the cost-complexity measure
  Throws:
    `java.lang.Exception` - if something goes wrong

### prune

    public int prune(double[] alphas,
                     double[] errors,
                     Instances test)
              throws java.lang.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:
    `java.lang.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

### modelErrors

    public void modelErrors()
                     throws java.lang.Exception

  Updates the numIncorrectModel field for all nodes. This is needed for calculating the alpha-values.

  Throws:
    `java.lang.Exception`

### treeErrors

    public void treeErrors()

  Updates the numIncorrectTree field for all nodes. This is needed for calculating the alpha-values.

### calculateAlphas

    public void calculateAlphas()
                         throws java.lang.Exception

  Updates the alpha field for all nodes.

  Throws:
    `java.lang.Exception`

### getNodes

    public java.util.Vector getNodes()

  Return a list of all inner nodes in the tree

  Returns:
    the list of nodes

### getNodes

    public void getNodes(java.util.Vector 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 java.lang.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:
    `java.lang.Exception`

### distributionForInstance

    public double[] distributionForInstance(Instance instance)
                                     throws java.lang.Exception

  Returns the class probabilities for an instance given by the logistic model tree.

  Overrides:
    `distributionForInstance` in class `LogisticBase`
  Parameters:
    `instance` - the instance
  Returns:
    the array of probabilities
  Throws:
    `java.lang.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 java.lang.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 java.lang.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 java.lang.String modelsToString()

  Returns a string describing the logistic regression function at the node.

### graph

    public java.lang.String graph()
                           throws java.lang.Exception

  Returns graph describing the tree.

  Throws:
    `java.lang.Exception` - if something goes wrong

### cleanup

    public void cleanup()

  Cleanup in order to save memory.

  Overrides:
    `cleanup` in class `LogisticBase`

### getRevision

    public java.lang.String getRevision()

  Returns the revision string.

  Specified by:
    `getRevision` in interface `RevisionHandler`
  Overrides:
    `getRevision` in class `LogisticBase`
  Returns:
    the revision

Overview | Package | Class | Tree | Deprecated | Index | Help | Weka's home

PREV CLASS NEXT CLASS | FRAMES NO FRAMES
SUMMARY: NESTED | FIELD | CONSTR | METHOD | DETAIL: FIELD | CONSTR | METHOD