weka.classifiers.trees.lmt.LMTNode Maven / Gradle / Ivy
Go to download
Show more of this group Show more artifacts with this name
Show all versions of weka-stable Show documentation
Show all versions of weka-stable Show documentation
The Waikato Environment for Knowledge Analysis (WEKA), a machine
learning workbench. This is the stable version. Apart from bugfixes, this version
does not receive any other updates.
/*
* This program is free software; you can redistribute it and/or modify
* it under the terms of the GNU General Public License as published by
* the Free Software Foundation; either version 2 of the License, or
* (at your option) any later version.
*
* This program is distributed in the hope that it will be useful,
* but WITHOUT ANY WARRANTY; without even the implied warranty of
* MERCHANTABILITY or FITNESS FOR A PARTICULAR PURPOSE. See the
* GNU General Public License for more details.
*
* You should have received a copy of the GNU General Public License
* along with this program; if not, write to the Free Software
* Foundation, Inc., 675 Mass Ave, Cambridge, MA 02139, USA.
*/
/*
* LMTNode.java
* Copyright (C) 2003 University of Waikato, Hamilton, New Zealand
*
*/
package weka.classifiers.trees.lmt;
import weka.classifiers.Evaluation;
import weka.classifiers.functions.SimpleLinearRegression;
import weka.classifiers.trees.j48.ClassifierSplitModel;
import weka.classifiers.trees.j48.ModelSelection;
import weka.core.Instance;
import weka.core.Instances;
import weka.core.RevisionHandler;
import weka.core.RevisionUtils;
import weka.core.Utils;
import weka.filters.Filter;
import weka.filters.supervised.attribute.NominalToBinary;
import java.util.Collections;
import java.util.Comparator;
import java.util.Vector;
/**
* Auxiliary class for list of LMTNodes
*/
class CompareNode
implements Comparator, RevisionHandler {
/**
* Compares its two arguments for order.
*
* @param o1 first object
* @param o2 second object
* @return a negative integer, zero, or a positive integer as the first
* argument is less than, equal to, or greater than the second.
*/
public int compare(Object o1, Object o2) {
if ( ((LMTNode)o1).m_alpha < ((LMTNode)o2).m_alpha) return -1;
if ( ((LMTNode)o1).m_alpha > ((LMTNode)o2).m_alpha) return 1;
return 0;
}
/**
* Returns the revision string.
*
* @return the revision
*/
public String getRevision() {
return RevisionUtils.extract("$Revision: 1.8 $");
}
}
/**
* Class for logistic model tree structure.
*
*
* @author Niels Landwehr
* @author Marc Sumner
* @version $Revision: 1.8 $
*/
public class LMTNode
extends LogisticBase {
/** for serialization */
static final long serialVersionUID = 1862737145870398755L;
/** Total number of training instances. */
protected double m_totalInstanceWeight;
/** Node id*/
protected int m_id;
/** ID of logistic model at leaf*/
protected int m_leafModelNum;
/** Alpha-value (for pruning) at the node*/
public double m_alpha;
/** Weighted number of training examples currently misclassified by the logistic model at the node*/
public double m_numIncorrectModel;
/** Weighted number of training examples currently misclassified by the subtree rooted at the node*/
public double m_numIncorrectTree;
/**minimum number of instances at which a node is considered for splitting*/
protected int m_minNumInstances;
/**ModelSelection object (for splitting)*/
protected ModelSelection m_modelSelection;
/**Filter to convert nominal attributes to binary*/
protected NominalToBinary m_nominalToBinary;
/**Simple regression functions fit by LogitBoost at higher levels in the tree*/
protected SimpleLinearRegression[][] m_higherRegressions;
/**Number of simple regression functions fit by LogitBoost at higher levels in the tree*/
protected int m_numHigherRegressions = 0;
/**Number of folds for CART pruning*/
protected static int m_numFoldsPruning = 5;
/**Use heuristic that determines the number of LogitBoost iterations only once in the beginning? */
protected boolean m_fastRegression;
/**Number of instances at the node*/
protected int m_numInstances;
/**The ClassifierSplitModel (for splitting)*/
protected ClassifierSplitModel m_localModel;
/**Array of children of the node*/
protected LMTNode[] m_sons;
/**True if node is leaf*/
protected boolean m_isLeaf;
/**
* Constructor for logistic model tree node.
*
* @param modelSelection selection method for local splitting model
* @param numBoostingIterations sets the numBoostingIterations parameter
* @param fastRegression sets the fastRegression parameter
* @param errorOnProbabilities Use error on probabilities for stopping criterion of LogitBoost?
* @param minNumInstances minimum number of instances at which a node is considered for splitting
*/
public LMTNode(ModelSelection modelSelection, int numBoostingIterations,
boolean fastRegression,
boolean errorOnProbabilities, int minNumInstances,
double weightTrimBeta, boolean useAIC) {
m_modelSelection = modelSelection;
m_fixedNumIterations = numBoostingIterations;
m_fastRegression = fastRegression;
m_errorOnProbabilities = errorOnProbabilities;
m_minNumInstances = minNumInstances;
m_maxIterations = 200;
setWeightTrimBeta(weightTrimBeta);
setUseAIC(useAIC);
}
/**
* 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.
*
* @param data the data to train with
* @throws Exception if something goes wrong
*/
public void buildClassifier(Instances data) throws Exception{
//heuristic to avoid cross-validating the number of LogitBoost iterations
//at every node: build standalone logistic model and take its optimum number
//of iteration everywhere in the tree.
if (m_fastRegression && (m_fixedNumIterations < 0)) m_fixedNumIterations = tryLogistic(data);
//Need to cross-validate alpha-parameter for CART-pruning
Instances cvData = new Instances(data);
cvData.stratify(m_numFoldsPruning);
double[][] alphas = new double[m_numFoldsPruning][];
double[][] errors = new double[m_numFoldsPruning][];
for (int i = 0; i < m_numFoldsPruning; i++) {
//for every fold, grow tree on training set...
Instances train = cvData.trainCV(m_numFoldsPruning, i);
Instances test = cvData.testCV(m_numFoldsPruning, i);
buildTree(train, null, train.numInstances() , 0);
int numNodes = getNumInnerNodes();
alphas[i] = new double[numNodes + 2];
errors[i] = new double[numNodes + 2];
//... then prune back and log alpha-values and errors on test set
prune(alphas[i], errors[i], test);
}
//build tree using all the data
buildTree(data, null, data.numInstances(), 0);
int numNodes = getNumInnerNodes();
double[] treeAlphas = new double[numNodes + 2];
//prune back and log alpha-values
int iterations = prune(treeAlphas, null, null);
double[] treeErrors = new double[numNodes + 2];
for (int i = 0; i <= iterations; i++){
//compute midpoint alphas
double alpha = Math.sqrt(treeAlphas[i] * treeAlphas[i+1]);
double error = 0;
//compute error estimate for final trees from the midpoint-alphas and the error estimates gotten in
//the cross-validation
for (int k = 0; k < m_numFoldsPruning; k++) {
int l = 0;
while (alphas[k][l] <= alpha) l++;
error += errors[k][l - 1];
}
treeErrors[i] = error;
}
//find best alpha
int best = -1;
double bestError = Double.MAX_VALUE;
for (int i = iterations; i >= 0; i--) {
if (treeErrors[i] < bestError) {
bestError = treeErrors[i];
best = i;
}
}
double bestAlpha = Math.sqrt(treeAlphas[best] * treeAlphas[best + 1]);
//"unprune" final tree (faster than regrowing it)
unprune();
//CART-prune it with best alpha
prune(bestAlpha);
cleanup();
}
/**
* Method for building the tree structure.
* Builds a logistic model, splits the node and recursively builds tree for child nodes.
* @param data the training data passed on to this node
* @param 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.
* @param totalInstanceWeight the total number of training examples
* @param higherNumParameters effective number of parameters in the logistic regression model built
* in parent nodes
* @throws Exception if something goes wrong
*/
public void buildTree(Instances data, SimpleLinearRegression[][] higherRegressions,
double totalInstanceWeight, double higherNumParameters) throws Exception{
//save some stuff
m_totalInstanceWeight = totalInstanceWeight;
m_train = new Instances(data);
m_isLeaf = true;
m_sons = null;
m_numInstances = m_train.numInstances();
m_numClasses = m_train.numClasses();
//init
m_numericData = getNumericData(m_train);
m_numericDataHeader = new Instances(m_numericData, 0);
m_regressions = initRegressions();
m_numRegressions = 0;
if (higherRegressions != null) m_higherRegressions = higherRegressions;
else m_higherRegressions = new SimpleLinearRegression[m_numClasses][0];
m_numHigherRegressions = m_higherRegressions[0].length;
m_numParameters = higherNumParameters;
//build logistic model
if (m_numInstances >= m_numFoldsBoosting) {
if (m_fixedNumIterations > 0){
performBoosting(m_fixedNumIterations);
} else if (getUseAIC()) {
performBoostingInfCriterion();
} else {
performBoostingCV();
}
}
m_numParameters += m_numRegressions;
//only keep the simple regression functions that correspond to the selected number of LogitBoost iterations
m_regressions = selectRegressions(m_regressions);
boolean grow;
//split node if more than minNumInstances...
if (m_numInstances > m_minNumInstances) {
//split node: either splitting on class value (a la C4.5) or splitting on residuals
if (m_modelSelection instanceof ResidualModelSelection) {
//need ps/Ys/Zs/weights
double[][] probs = getProbs(getFs(m_numericData));
double[][] trainYs = getYs(m_train);
double[][] dataZs = getZs(probs, trainYs);
double[][] dataWs = getWs(probs, trainYs);
m_localModel = ((ResidualModelSelection)m_modelSelection).selectModel(m_train, dataZs, dataWs);
} else {
m_localModel = m_modelSelection.selectModel(m_train);
}
//... and valid split found
grow = (m_localModel.numSubsets() > 1);
} else {
grow = false;
}
if (grow) {
//create and build children of node
m_isLeaf = false;
Instances[] localInstances = m_localModel.split(m_train);
m_sons = new LMTNode[m_localModel.numSubsets()];
for (int i = 0; i < m_sons.length; i++) {
m_sons[i] = new LMTNode(m_modelSelection, m_fixedNumIterations,
m_fastRegression,
m_errorOnProbabilities,m_minNumInstances,
getWeightTrimBeta(), getUseAIC());
//the "higherRegressions" (partial logistic model fit at higher levels in the tree) passed
//on to the children are the "higherRegressions" at this node plus the regressions added
//at this node (m_regressions).
m_sons[i].buildTree(localInstances[i],
mergeArrays(m_regressions, m_higherRegressions), m_totalInstanceWeight, m_numParameters);
localInstances[i] = null;
}
}
}
/**
* Prunes a logistic model tree using the CART pruning scheme, given a
* cost-complexity parameter alpha.
*
* @param alpha the cost-complexity measure
* @throws Exception if something goes wrong
*/
public void prune(double alpha) throws Exception {
Vector nodeList;
CompareNode comparator = new CompareNode();
//determine training error of logistic models and subtrees, and calculate alpha-values from them
modelErrors();
treeErrors();
calculateAlphas();
//get list of all inner nodes in the tree
nodeList = getNodes();
boolean prune = (nodeList.size() > 0);
while (prune) {
//select node with minimum alpha
LMTNode nodeToPrune = (LMTNode)Collections.min(nodeList,comparator);
//want to prune if its alpha is smaller than alpha
if (nodeToPrune.m_alpha > alpha) break;
nodeToPrune.m_isLeaf = true;
nodeToPrune.m_sons = null;
//update tree errors and alphas
treeErrors();
calculateAlphas();
nodeList = getNodes();
prune = (nodeList.size() > 0);
}
}
/**
* 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.
* @param alphas array to hold the generated alpha-values
* @param errors array to hold the corresponding error estimates
* @param test test set of that fold (to obtain error estimates)
* @throws Exception if something goes wrong
*/
public int prune(double[] alphas, double[] errors, Instances test) throws Exception {
Vector nodeList;
CompareNode comparator = new CompareNode();
//determine training error of logistic models and subtrees, and calculate alpha-values from them
modelErrors();
treeErrors();
calculateAlphas();
//get list of all inner nodes in the tree
nodeList = getNodes();
boolean prune = (nodeList.size() > 0);
//alpha_0 is always zero (unpruned tree)
alphas[0] = 0;
Evaluation eval;
//error of unpruned tree
if (errors != null) {
eval = new Evaluation(test);
eval.evaluateModel(this, test);
errors[0] = eval.errorRate();
}
int iteration = 0;
while (prune) {
iteration++;
//get node with minimum alpha
LMTNode nodeToPrune = (LMTNode)Collections.min(nodeList,comparator);
nodeToPrune.m_isLeaf = true;
//Do not set m_sons null, want to unprune
//get alpha-value of node
alphas[iteration] = nodeToPrune.m_alpha;
//log error
if (errors != null) {
eval = new Evaluation(test);
eval.evaluateModel(this, test);
errors[iteration] = eval.errorRate();
}
//update errors/alphas
treeErrors();
calculateAlphas();
nodeList = getNodes();
prune = (nodeList.size() > 0);
}
//set last alpha 1 to indicate end
alphas[iteration + 1] = 1.0;
return iteration;
}
/**
*Method to "unprune" a logistic model tree.
*Sets all leaf-fields to false.
*Faster than re-growing the tree because the logistic models do not have to be fit again.
*/
protected void unprune() {
if (m_sons != null) {
m_isLeaf = false;
for (int i = 0; i < m_sons.length; i++) m_sons[i].unprune();
}
}
/**
*Determines the optimum number of LogitBoost iterations to perform by building a standalone logistic
*regression function on the training data. Used for the heuristic that avoids cross-validating this
*number again at every node.
*@param data training instances for the logistic model
*@throws Exception if something goes wrong
*/
protected int tryLogistic(Instances data) throws Exception{
//convert nominal attributes
Instances filteredData = new Instances(data);
NominalToBinary nominalToBinary = new NominalToBinary();
nominalToBinary.setInputFormat(filteredData);
filteredData = Filter.useFilter(filteredData, nominalToBinary);
LogisticBase logistic = new LogisticBase(0,true,m_errorOnProbabilities);
//limit LogitBoost to 200 iterations (speed)
logistic.setMaxIterations(200);
logistic.setWeightTrimBeta(getWeightTrimBeta()); // Not in Marc's code. Added by Eibe.
logistic.setUseAIC(getUseAIC());
logistic.buildClassifier(filteredData);
//return best number of iterations
return logistic.getNumRegressions();
}
/**
* Method to count the number of inner nodes in the tree
* @return the number of inner nodes
*/
public int getNumInnerNodes(){
if (m_isLeaf) return 0;
int numNodes = 1;
for (int i = 0; i < m_sons.length; i++) numNodes += m_sons[i].getNumInnerNodes();
return numNodes;
}
/**
* 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.
* @return the number of leaves
*/
public int getNumLeaves(){
int numLeaves;
if (!m_isLeaf) {
numLeaves = 0;
int numEmptyLeaves = 0;
for (int i = 0; i < m_sons.length; i++) {
numLeaves += m_sons[i].getNumLeaves();
if (m_sons[i].m_isLeaf && !m_sons[i].hasModels()) numEmptyLeaves++;
}
if (numEmptyLeaves > 1) {
numLeaves -= (numEmptyLeaves - 1);
}
} else {
numLeaves = 1;
}
return numLeaves;
}
/**
*Updates the numIncorrectModel field for all nodes. This is needed for calculating the alpha-values.
*/
public void modelErrors() throws Exception{
Evaluation eval = new Evaluation(m_train);
if (!m_isLeaf) {
m_isLeaf = true;
eval.evaluateModel(this, m_train);
m_isLeaf = false;
m_numIncorrectModel = eval.incorrect();
for (int i = 0; i < m_sons.length; i++) m_sons[i].modelErrors();
} else {
eval.evaluateModel(this, m_train);
m_numIncorrectModel = eval.incorrect();
}
}
/**
*Updates the numIncorrectTree field for all nodes. This is needed for calculating the alpha-values.
*/
public void treeErrors(){
if (m_isLeaf) {
m_numIncorrectTree = m_numIncorrectModel;
} else {
m_numIncorrectTree = 0;
for (int i = 0; i < m_sons.length; i++) {
m_sons[i].treeErrors();
m_numIncorrectTree += m_sons[i].m_numIncorrectTree;
}
}
}
/**
*Updates the alpha field for all nodes.
*/
public void calculateAlphas() throws Exception {
if (!m_isLeaf) {
double errorDiff = m_numIncorrectModel - m_numIncorrectTree;
if (errorDiff <= 0) {
//split increases training error (should not normally happen).
//prune it instantly.
m_isLeaf = true;
m_sons = null;
m_alpha = Double.MAX_VALUE;
} else {
//compute alpha
errorDiff /= m_totalInstanceWeight;
m_alpha = errorDiff / (double)(getNumLeaves() - 1);
for (int i = 0; i < m_sons.length; i++) m_sons[i].calculateAlphas();
}
} else {
//alpha = infinite for leaves (do not want to prune)
m_alpha = Double.MAX_VALUE;
}
}
/**
* Merges two arrays of regression functions into one
* @param a1 one array
* @param a2 the other array
*
* @return an array that contains all entries from both input arrays
*/
protected SimpleLinearRegression[][] mergeArrays(SimpleLinearRegression[][] a1,
SimpleLinearRegression[][] a2){
int numModels1 = a1[0].length;
int numModels2 = a2[0].length;
SimpleLinearRegression[][] result =
new SimpleLinearRegression[m_numClasses][numModels1 + numModels2];
for (int i = 0; i < m_numClasses; i++)
for (int j = 0; j < numModels1; j++) {
result[i][j] = a1[i][j];
}
for (int i = 0; i < m_numClasses; i++)
for (int j = 0; j < numModels2; j++) result[i][j+numModels1] = a2[i][j];
return result;
}
/**
* Return a list of all inner nodes in the tree
* @return the list of nodes
*/
public Vector getNodes(){
Vector nodeList = new Vector();
getNodes(nodeList);
return nodeList;
}
/**
* Fills a list with all inner nodes in the tree
*
* @param nodeList the list to be filled
*/
public void getNodes(Vector nodeList) {
if (!m_isLeaf) {
nodeList.add(this);
for (int i = 0; i < m_sons.length; i++) m_sons[i].getNodes(nodeList);
}
}
/**
* Returns a numeric version of a set of instances.
* All nominal attributes are replaced by binary ones, and the class variable is replaced
* by a pseudo-class variable that is used by LogitBoost.
*/
protected Instances getNumericData(Instances train) throws Exception{
Instances filteredData = new Instances(train);
m_nominalToBinary = new NominalToBinary();
m_nominalToBinary.setInputFormat(filteredData);
filteredData = Filter.useFilter(filteredData, m_nominalToBinary);
return super.getNumericData(filteredData);
}
/**
* Computes the F-values of LogitBoost for an instance from the current logistic model at the node
* Note that this also takes into account the (partial) logistic model fit at higher levels in
* the tree.
* @param instance the instance
* @return the array of F-values
*/
protected double[] getFs(Instance instance) throws Exception{
double [] pred = new double [m_numClasses];
//Need to take into account partial model fit at higher levels in the tree (m_higherRegressions)
//and the part of the model fit at this node (m_regressions).
//Fs from m_regressions (use method of LogisticBase)
double [] instanceFs = super.getFs(instance);
//Fs from m_higherRegressions
for (int i = 0; i < m_numHigherRegressions; i++) {
double predSum = 0;
for (int j = 0; j < m_numClasses; j++) {
pred[j] = m_higherRegressions[j][i].classifyInstance(instance);
predSum += pred[j];
}
predSum /= m_numClasses;
for (int j = 0; j < m_numClasses; j++) {
instanceFs[j] += (pred[j] - predSum) * (m_numClasses - 1)
/ m_numClasses;
}
}
return instanceFs;
}
/**
*Returns true if the logistic regression model at this node has changed compared to the
*one at the parent node.
*@return whether it has changed
*/
public boolean hasModels() {
return (m_numRegressions > 0);
}
/**
* Returns the class probabilities for an instance according to the logistic model at the node.
* @param instance the instance
* @return the array of probabilities
*/
public double[] modelDistributionForInstance(Instance instance) throws Exception {
//make copy and convert nominal attributes
instance = (Instance)instance.copy();
m_nominalToBinary.input(instance);
instance = m_nominalToBinary.output();
//saet numeric pseudo-class
instance.setDataset(m_numericDataHeader);
return probs(getFs(instance));
}
/**
* Returns the class probabilities for an instance given by the logistic model tree.
* @param instance the instance
* @return the array of probabilities
*/
public double[] distributionForInstance(Instance instance) throws Exception {
double[] probs;
if (m_isLeaf) {
//leaf: use logistic model
probs = modelDistributionForInstance(instance);
} else {
//sort into appropiate child node
int branch = m_localModel.whichSubset(instance);
probs = m_sons[branch].distributionForInstance(instance);
}
return probs;
}
/**
* Returns the number of leaves (normal count).
* @return the number of leaves
*/
public int numLeaves() {
if (m_isLeaf) return 1;
int numLeaves = 0;
for (int i = 0; i < m_sons.length; i++) numLeaves += m_sons[i].numLeaves();
return numLeaves;
}
/**
* Returns the number of nodes.
* @return the number of nodes
*/
public int numNodes() {
if (m_isLeaf) return 1;
int numNodes = 1;
for (int i = 0; i < m_sons.length; i++) numNodes += m_sons[i].numNodes();
return numNodes;
}
/**
* Returns a description of the logistic model tree (tree structure and logistic models)
* @return describing string
*/
public String toString(){
//assign numbers to logistic regression functions at leaves
assignLeafModelNumbers(0);
try{
StringBuffer text = new StringBuffer();
if (m_isLeaf) {
text.append(": ");
text.append("LM_"+m_leafModelNum+":"+getModelParameters());
} else {
dumpTree(0,text);
}
text.append("\n\nNumber of Leaves : \t"+numLeaves()+"\n");
text.append("\nSize of the Tree : \t"+numNodes()+"\n");
//This prints logistic models after the tree, comment out if only tree should be printed
text.append(modelsToString());
return text.toString();
} catch (Exception e){
return "Can't print logistic model tree";
}
}
/**
* 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.
* @return the describing string
*/
public String getModelParameters(){
StringBuffer text = new StringBuffer();
int numModels = m_numRegressions+m_numHigherRegressions;
text.append(m_numRegressions+"/"+numModels+" ("+m_numInstances+")");
return text.toString();
}
/**
* Help method for printing tree structure.
*
* @throws Exception if something goes wrong
*/
protected void dumpTree(int depth,StringBuffer text)
throws Exception {
for (int i = 0; i < m_sons.length; i++) {
text.append("\n");
for (int j = 0; j < depth; j++)
text.append("| ");
text.append(m_localModel.leftSide(m_train));
text.append(m_localModel.rightSide(i, m_train));
if (m_sons[i].m_isLeaf) {
text.append(": ");
text.append("LM_"+m_sons[i].m_leafModelNum+":"+m_sons[i].getModelParameters());
}else
m_sons[i].dumpTree(depth+1,text);
}
}
/**
* Assigns unique IDs to all nodes in the tree
*/
public int assignIDs(int lastID) {
int currLastID = lastID + 1;
m_id = currLastID;
if (m_sons != null) {
for (int i = 0; i < m_sons.length; i++) {
currLastID = m_sons[i].assignIDs(currLastID);
}
}
return currLastID;
}
/**
* Assigns numbers to the logistic regression models at the leaves of the tree
*/
public int assignLeafModelNumbers(int leafCounter) {
if (!m_isLeaf) {
m_leafModelNum = 0;
for (int i = 0; i < m_sons.length; i++){
leafCounter = m_sons[i].assignLeafModelNumbers(leafCounter);
}
} else {
leafCounter++;
m_leafModelNum = leafCounter;
}
return leafCounter;
}
/**
* Returns an array containing the coefficients of the logistic regression function at this node.
* @return the array of coefficients, first dimension is the class, second the attribute.
*/
protected double[][] getCoefficients(){
//Need to take into account partial model fit at higher levels in the tree (m_higherRegressions)
//and the part of the model fit at this node (m_regressions).
//get coefficients from m_regressions: use method of LogisticBase
double[][] coefficients = super.getCoefficients();
//get coefficients from m_higherRegressions:
double constFactor = (double)(m_numClasses - 1) / (double)m_numClasses; // (J - 1)/J
for (int j = 0; j < m_numClasses; j++) {
for (int i = 0; i < m_numHigherRegressions; i++) {
double slope = m_higherRegressions[j][i].getSlope();
double intercept = m_higherRegressions[j][i].getIntercept();
int attribute = m_higherRegressions[j][i].getAttributeIndex();
coefficients[j][0] += constFactor * intercept;
coefficients[j][attribute + 1] += constFactor * slope;
}
}
return coefficients;
}
/**
* Returns a string describing the logistic regression function at the node.
*/
public String modelsToString(){
StringBuffer text = new StringBuffer();
if (m_isLeaf) {
text.append("LM_"+m_leafModelNum+":"+super.toString());
} else {
for (int i = 0; i < m_sons.length; i++) {
text.append("\n"+m_sons[i].modelsToString());
}
}
return text.toString();
}
/**
* Returns graph describing the tree.
*
* @throws Exception if something goes wrong
*/
public String graph() throws Exception {
StringBuffer text = new StringBuffer();
assignIDs(-1);
assignLeafModelNumbers(0);
text.append("digraph LMTree {\n");
if (m_isLeaf) {
text.append("N" + m_id + " [label=\"LM_"+m_leafModelNum+":"+getModelParameters()+"\" " +
"shape=box style=filled");
text.append("]\n");
}else {
text.append("N" + m_id
+ " [label=\"" +
Utils.backQuoteChars(m_localModel.leftSide(m_train)) + "\" ");
text.append("]\n");
graphTree(text);
}
return text.toString() +"}\n";
}
/**
* Helper function for graph description of tree
*
* @throws Exception if something goes wrong
*/
private void graphTree(StringBuffer text) throws Exception {
for (int i = 0; i < m_sons.length; i++) {
text.append("N" + m_id
+ "->" +
"N" + m_sons[i].m_id +
" [label=\"" + Utils.backQuoteChars(m_localModel.rightSide(i,m_train).trim()) +
"\"]\n");
if (m_sons[i].m_isLeaf) {
text.append("N" +m_sons[i].m_id + " [label=\"LM_"+m_sons[i].m_leafModelNum+":"+
m_sons[i].getModelParameters()+"\" " + "shape=box style=filled");
text.append("]\n");
} else {
text.append("N" + m_sons[i].m_id +
" [label=\""+ Utils.backQuoteChars(m_sons[i].m_localModel.leftSide(m_train)) +
"\" ");
text.append("]\n");
m_sons[i].graphTree(text);
}
}
}
/**
* Cleanup in order to save memory.
*/
public void cleanup() {
super.cleanup();
if (!m_isLeaf) {
for (int i = 0; i < m_sons.length; i++) m_sons[i].cleanup();
}
}
/**
* Returns the revision string.
*
* @return the revision
*/
public String getRevision() {
return RevisionUtils.extract("$Revision: 1.8 $");
}
}
© 2015 - 2025 Weber Informatics LLC | Privacy Policy