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The Waikato Environment for Knowledge Analysis (WEKA), a machine
learning workbench. This version represents the developer version, the
"bleeding edge" of development, you could say. New functionality gets added
to this version.
/*
* 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 3 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, see ruleCollection);
/**
* Get a textual description of this Rule
*
* @param prefix prefix string (typically some number of spaces) to prepend
* @param indent the number of additional spaces to add to the prefix
* @return a description of this Rule as a String
*/
public abstract String toString(String prefix, int indent);
}
/**
* Inner class for representing simple rules
*/
static class SimpleRule extends Rule {
/** For serialization */
private static final long serialVersionUID = -2612893679476049682L;
/** The ID for the rule (optional) */
protected String m_ID;
/** The predicted value when the rule fires (required) */
protected String m_scoreString;
/**
* The predicted value as a number (regression) or index (classification)
* when the rule fires (required)
*/
protected double m_score = Utils.missingValue();
/** The number of training/test instances on which the rule fired (optional) */
protected double m_recordCount = Utils.missingValue();
/**
* The number of training/test instances on which the rule fired and the
* prediction was correct (optional)
*/
protected double m_nbCorrect = Utils.missingValue();
/** The confidence of the rule (optional) */
protected double m_confidence = Utils.missingValue();
/** The score distributions for this rule (if any) */
protected ArrayList m_scoreDistributions =
new ArrayList();
/**
* The relative importance of the rule. May or may not be equal to the
* confidence (optional).
*/
protected double m_weight = Utils.missingValue();
public String toString(String prefix, int indent) {
StringBuffer temp = new StringBuffer();
for (int i = 0; i < indent; i++) {
prefix += " ";
}
temp.append(prefix + "Simple rule: " + m_predicate + "\n");
temp.append(prefix + " => " + m_scoreString + "\n");
if (!Utils.isMissingValue(m_recordCount)) {
temp.append(prefix + " recordCount: " + m_recordCount + "\n");
}
if (!Utils.isMissingValue(m_nbCorrect)) {
temp.append(prefix + " nbCorrect: " + m_nbCorrect + "\n");
}
if (!Utils.isMissingValue(m_confidence)) {
temp.append(prefix + " confidence: " + m_confidence + "\n");
}
if (!Utils.isMissingValue(m_weight)) {
temp.append(prefix + " weight: " + m_weight + "\n");
}
return temp.toString();
}
public String toString() {
return toString("", 0);
}
/**
* Constructor for a simple rule
*
* @param ruleE the XML element holding the simple rule
* @param miningSchema the mining schema to use
* @throws Exception if something goes wrong
*/
public SimpleRule(Element ruleE, MiningSchema miningSchema) throws Exception {
super(ruleE, miningSchema);
String id = ruleE.getAttribute("id");
if (id != null && id.length() > 0) {
m_ID = id;
}
m_scoreString = ruleE.getAttribute("score");
Attribute classAtt = miningSchema.getFieldsAsInstances().classAttribute();
if (classAtt.isNumeric()) {
m_score = Double.parseDouble(m_scoreString);
} else {
if (classAtt.indexOfValue(m_scoreString) < 0) {
throw new Exception("[SimpleRule] class value " + m_scoreString +
"does not exist in class attribute " + classAtt.name());
}
m_score = classAtt.indexOfValue(m_scoreString);
}
String recordCount = ruleE.getAttribute("recordCount");
if (recordCount != null && recordCount.length() > 0) {
m_recordCount = Double.parseDouble(recordCount);
}
String nbCorrect = ruleE.getAttribute("nbCorrect");
if (nbCorrect != null && nbCorrect.length() > 0) {
m_nbCorrect = Double.parseDouble(nbCorrect);
}
String confidence = ruleE.getAttribute("confidence");
if (confidence != null && confidence.length() > 0) {
m_confidence = Double.parseDouble(confidence);
}
String weight = ruleE.getAttribute("weight");
if (weight != null && weight.length() > 0) {
m_weight = Double.parseDouble(weight);
}
// get the ScoreDistributions (if any)
if (miningSchema.getFieldsAsInstances().classAttribute().isNominal()) {
// see if we have any ScoreDistribution entries
NodeList scoreChildren = ruleE.getChildNodes();
for (int i = 0; i < scoreChildren.getLength(); i++) {
Node child = scoreChildren.item(i);
if (child.getNodeType() == Node.ELEMENT_NODE) {
String tagName = ((Element)child).getTagName();
if (tagName.equals("ScoreDistribution")) {
TreeModel.ScoreDistribution newDist =
new TreeModel.ScoreDistribution((Element)child,
miningSchema, m_recordCount);
m_scoreDistributions.add(newDist);
}
}
}
// check that we have as many score distribution elements as there
// are class labels in the data
if (m_scoreDistributions.size() > 0 &&
m_scoreDistributions.size() !=
miningSchema.getFieldsAsInstances().classAttribute().numValues()) {
throw new Exception("[SimpleRule] Number of score distribution elements is "
+ " different than the number of class labels!");
}
//backfit the confidence values (if necessary)
if (Utils.isMissingValue(m_recordCount)) {
double baseCount = 0;
for (TreeModel.ScoreDistribution s : m_scoreDistributions) {
baseCount += s.getRecordCount();
}
for (TreeModel.ScoreDistribution s : m_scoreDistributions) {
s.deriveConfidenceValue(baseCount);
}
}
}
}
/**
* Collect the rule(s) that fire for the supplied incoming instance
*
* @param input a vector of independent and derived independent variables
* @param ruleCollection the array list to add any firing rules into
*/
public void fires(double[] input, ArrayList ruleCollection) {
if (m_predicate.evaluate(input) == TreeModel.Predicate.Eval.TRUE) {
ruleCollection.add(this);
}
}
/**
* Score the incoming instance
*
* @param instance a vector containing the incoming independent and
* derived independent variables
* @param classAtt the class attribute
* @param rsm the rule selection method (ignored by simple rules)
* @return a probability distribution over the class labels or
* the predicted value (in element zero of the array if the class is numeric)
* @throws Exception if something goes wrong
*/
public double[] score(double[] instance, Attribute classAtt)
throws Exception {
double[] preds;
if (classAtt.isNumeric()) {
preds = new double[1];
preds[0] = m_score;
} else {
preds = new double[classAtt.numValues()];
if (m_scoreDistributions.size() > 0) {
for (TreeModel.ScoreDistribution s : m_scoreDistributions) {
preds[s.getClassLabelIndex()] = s.getConfidence();
}
} else if (!Utils.isMissingValue(m_confidence)) {
preds[classAtt.indexOfValue(m_scoreString)] = m_confidence;
} else {
preds[classAtt.indexOfValue(m_scoreString)] = 1.0;
}
}
return preds;
}
/**
* Get the weight of the rule
*
* @return the weight of the rule
*/
public double getWeight() {
return m_weight;
}
/**
* Get the ID of the rule
*
* @return the ID of the rule
*/
public String getID() {
return m_ID;
}
/**
* Get the predicted value of this rule (either a number
* for regression problems or an index of a class label for
* classification problems)
*
* @return the predicted value of this rule
*/
public double getScore() {
return m_score;
}
}
/**
* Inner class representing a compound rule
*/
static class CompoundRule extends Rule {
/** For serialization */
private static final long serialVersionUID = -2853658811459970718L;
/** The child rules of this compound rule */
ArrayList m_childRules = new ArrayList();
public String toString(String prefix, int indent) {
StringBuffer temp = new StringBuffer();
for (int i = 0; i < indent; i++) {
prefix += " ";
}
temp.append(prefix + "Compound rule: " + m_predicate + "\n");
for (Rule r : m_childRules) {
temp.append(r.toString(prefix, indent + 1));
}
return temp.toString();
}
public String toString() {
return toString("", 0);
}
/**
* Constructor.
*
* @param ruleE XML node holding the rule
* @param miningSchema the mining schema to use
* @throws Exception if something goes wrong
*/
public CompoundRule(Element ruleE, MiningSchema miningSchema) throws Exception {
// get the Predicate
super(ruleE, miningSchema);
// get the nested rules
NodeList ruleChildren = ruleE.getChildNodes();
for (int i = 0; i < ruleChildren.getLength(); i++) {
Node child = ruleChildren.item(i);
if (child.getNodeType() == Node.ELEMENT_NODE) {
String tagName = ((Element)child).getTagName();
if (tagName.equals("SimpleRule")) {
Rule childRule = new SimpleRule(((Element)child), miningSchema);
m_childRules.add(childRule);
} else if (tagName.equals("CompoundRule")) {
Rule childRule = new CompoundRule(((Element)child), miningSchema);
m_childRules.add(childRule);
}
}
}
}
/**
* Collect the rule(s) that fire for the supplied incoming instance
*
* @param input a vector of independent and derived independent variables
* @param ruleCollection the array list to add any firing rules into
*/
public void fires(double[] input, ArrayList ruleCollection) {
// evaluate our predicate first
if (m_predicate.evaluate(input) == TreeModel.Predicate.Eval.TRUE) {
// now check the child rules
for (Rule r : m_childRules) {
r.fires(input, ruleCollection);
}
}
}
}
/**
* Inner class representing a set of rules
*/
static class RuleSet implements Serializable {
/** For serialization */
private static final long serialVersionUID = -8718126887943074376L;
enum RuleSelectionMethod {
WEIGHTEDSUM("weightedSum"),
WEIGHTEDMAX("weightedMax"),
FIRSTHIT("firstHit");
private final String m_stringVal;
RuleSelectionMethod(String name) {
m_stringVal = name;
}
public String toString() {
return m_stringVal;
}
}
/**
* The number of training/test cases to which the ruleset was
* applied to generate support and confidence measures for individual
* rules (optional)
*/
private double m_recordCount = Utils.missingValue();
/**
* The number of training/test cases for which the default
* score is correct (optional)
*/
private double m_nbCorrect = Utils.missingValue();
/**
* The default value to predict when no rule in the
* ruleset fires (as a String; optional)
* */
private String m_defaultScore;
/**
* The default value to predict (either a real value or an
* index)
* */
private double m_defaultPrediction = Utils.missingValue();
/**
* The default distribution to predict when no rule in the
* ruleset fires (nominal class only, optional)
*/
private ArrayList m_scoreDistributions =
new ArrayList();
/**
* The default confidence value to return along with a score
* when no rules in the set fire (optional)
*/
private double m_defaultConfidence = Utils.missingValue();
/** The active rule selection method */
private RuleSelectionMethod m_currentMethod;
/** The selection of rule selection methods allowed */
private ArrayList m_availableRuleSelectionMethods =
new ArrayList();
/** The rules contained in the rule set */
private ArrayList m_rules = new ArrayList();
/* (non-Javadoc)
* @see java.lang.Object#toString()
*/
public String toString() {
StringBuffer temp = new StringBuffer();
temp.append("Rule selection method: " + m_currentMethod + "\n");
if (m_defaultScore != null) {
temp.append("Default prediction: " + m_defaultScore + "\n");
if (!Utils.isMissingValue(m_recordCount)) {
temp.append(" recordCount: " + m_recordCount + "\n");
}
if (!Utils.isMissingValue(m_nbCorrect)) {
temp.append(" nbCorrect: " + m_nbCorrect + "\n");
}
if (!Utils.isMissingValue(m_defaultConfidence)) {
temp.append(" defaultConfidence: " + m_defaultConfidence + "\n");
}
temp.append("\n");
}
for (Rule r : m_rules) {
temp.append(r + "\n");
}
return temp.toString();
}
/**
* Constructor for a RuleSet.
*
* @param ruleSetNode the XML node holding the RuleSet
* @param miningSchema the mining schema to use
* @throws Exception if something goes wrong
*/
public RuleSet(Element ruleSetNode, MiningSchema miningSchema)
throws Exception {
String recordCount = ruleSetNode.getAttribute("recordCount");
if (recordCount != null && recordCount.length() > 0) {
m_recordCount = Double.parseDouble(recordCount);
}
String nbCorrect = ruleSetNode.getAttribute("nbCorrect");
if (nbCorrect != null & nbCorrect.length() > 0) {
m_nbCorrect = Double.parseDouble(nbCorrect);
}
String defaultScore = ruleSetNode.getAttribute("defaultScore");
if (defaultScore != null && defaultScore.length() > 0) {
m_defaultScore = defaultScore;
Attribute classAtt = miningSchema.getFieldsAsInstances().classAttribute();
if (classAtt == null) {
throw new Exception("[RuleSet] class attribute not set!");
}
if (classAtt.isNumeric()) {
m_defaultPrediction = Double.parseDouble(defaultScore);
} else {
if (classAtt.indexOfValue(defaultScore) < 0) {
throw new Exception("[RuleSet] class value " + defaultScore +
" not found!");
}
m_defaultPrediction = classAtt.indexOfValue(defaultScore);
}
}
String defaultConfidence = ruleSetNode.getAttribute("defaultConfidence");
if (defaultConfidence != null && defaultConfidence.length() > 0) {
m_defaultConfidence = Double.parseDouble(defaultConfidence);
}
// get the rule selection methods
NodeList selectionNL = ruleSetNode.getElementsByTagName("RuleSelectionMethod");
for (int i = 0; i < selectionNL.getLength(); i++) {
Node selectN = selectionNL.item(i);
if (selectN.getNodeType() == Node.ELEMENT_NODE) {
Element sN = (Element)selectN;
String criterion = sN.getAttribute("criterion");
for (RuleSelectionMethod m : RuleSelectionMethod.values()) {
if (m.toString().equals(criterion)) {
m_availableRuleSelectionMethods.add(m);
if (i == 0) {
// set the default (first specified one)
m_currentMethod = m;
}
}
}
}
}
if (miningSchema.getFieldsAsInstances().classAttribute().isNominal()) {
// see if we have any ScoreDistribution entries
NodeList scoreChildren = ruleSetNode.getChildNodes();
for (int i = 0; i < scoreChildren.getLength(); i++) {
Node child = scoreChildren.item(i);
if (child.getNodeType() == Node.ELEMENT_NODE) {
String tagName = ((Element)child).getTagName();
if (tagName.equals("ScoreDistribution")) {
TreeModel.ScoreDistribution newDist =
new TreeModel.ScoreDistribution((Element)child,
miningSchema, m_recordCount);
m_scoreDistributions.add(newDist);
}
}
}
//backfit the confidence values (if necessary)
if (Utils.isMissingValue(m_recordCount)) {
double baseCount = 0;
for (TreeModel.ScoreDistribution s : m_scoreDistributions) {
baseCount += s.getRecordCount();
}
for (TreeModel.ScoreDistribution s : m_scoreDistributions) {
s.deriveConfidenceValue(baseCount);
}
}
}
// Get the rules in this rule set
NodeList ruleChildren = ruleSetNode.getChildNodes();
for (int i = 0; i < ruleChildren.getLength(); i++) {
Node child = ruleChildren.item(i);
if (child.getNodeType() == Node.ELEMENT_NODE) {
String tagName = ((Element)child).getTagName();
if (tagName.equals("SimpleRule")) {
Rule tempRule = new SimpleRule(((Element)child), miningSchema);
m_rules.add(tempRule);
} else if (tagName.equals("CompoundRule")) {
Rule tempRule = new CompoundRule(((Element)child), miningSchema);
m_rules.add(tempRule);
}
}
}
}
/**
* Score an incoming instance by collecting all rules that fire.
*
* @param instance a vector of incoming attribte and derived field values
* @param classAtt the class attribute
* @return a predicted probability distribution
* @throws Exception is something goes wrong
*/
protected double[] score(double[] instance, Attribute classAtt)
throws Exception {
double[] preds = null;
if (classAtt.isNumeric()) {
preds = new double[1];
} else {
preds = new double[classAtt.numValues()];
}
// holds the rules that fire for this test case
ArrayList firingRules = new ArrayList();
for (Rule r : m_rules) {
r.fires(instance, firingRules);
}
if (firingRules.size() > 0) {
if (m_currentMethod == RuleSelectionMethod.FIRSTHIT) {
preds = firingRules.get(0).score(instance, classAtt);
} else if (m_currentMethod == RuleSelectionMethod.WEIGHTEDMAX) {
double wMax = Double.NEGATIVE_INFINITY;
SimpleRule best = null;
for (SimpleRule s : firingRules) {
if (Utils.isMissingValue(s.getWeight())) {
throw new Exception("[RuleSet] Scoring criterion is WEIGHTEDMAX, but " +
"rule " + s.getID() + " does not have a weight defined!");
}
if (s.getWeight() > wMax) {
wMax = s.getWeight();
best = s;
}
}
if (best == null) {
throw new Exception("[RuleSet] Unable to determine the best rule under " +
"the WEIGHTEDMAX criterion!");
}
preds = best.score(instance, classAtt);
} else if (m_currentMethod == RuleSelectionMethod.WEIGHTEDSUM) {
double sumOfWeights = 0;
for (SimpleRule s : firingRules) {
if (Utils.isMissingValue(s.getWeight())) {
throw new Exception("[RuleSet] Scoring criterion is WEIGHTEDSUM, but " +
"rule " + s.getID() + " does not have a weight defined!");
}
if (classAtt.isNumeric()) {
sumOfWeights += s.getWeight();
preds[0] += (s.getScore() * s.getWeight());
} else {
preds[(int)s.getScore()] += s.getWeight();
}
}
if (classAtt.isNumeric()) {
if (sumOfWeights == 0) {
throw new Exception("[RuleSet] Sum of weights is zero!");
}
preds[0] /= sumOfWeights;
} else {
// array gets normalized in the distributionForInstance() method
}
}
} else {
// default prediction
if (classAtt.isNumeric()) {
preds[0] = m_defaultPrediction;
} else {
if (m_scoreDistributions.size() > 0) {
for (TreeModel.ScoreDistribution s : m_scoreDistributions) {
preds[s.getClassLabelIndex()] = s.getConfidence();
}
} else if (!Utils.isMissingValue(m_defaultConfidence)) {
preds[(int)m_defaultPrediction] = m_defaultConfidence;
} else {
preds[(int)m_defaultPrediction] = 1.0;
}
}
}
return preds;
}
}
/** The mining function */
protected MiningFunction m_functionType = MiningFunction.CLASSIFICATION;
/** The model name (if defined) */
protected String m_modelName;
/** The algorithm name (if defined) */
protected String m_algorithmName;
/** The set of rules */
protected RuleSet m_ruleSet;
/**
* Constructor for a RuleSetModel
*
* @param model the XML element encapsulating the RuleSetModel
* @param dataDictionary the data dictionary to use
* @param miningSchema the mining schema to use
* @throws Exception if something goes wrong
*/
public RuleSetModel(Element model, Instances dataDictionary,
MiningSchema miningSchema) throws Exception {
super(dataDictionary, miningSchema);
if (!getPMMLVersion().equals("3.2")) {
// TODO: might have to throw an exception and only support 3.2
}
String fn = model.getAttribute("functionName");
if (fn.equals("regression")) {
m_functionType = MiningFunction.REGRESSION;
}
String modelName = model.getAttribute("modelName");
if (modelName != null && modelName.length() > 0) {
m_modelName = modelName;
}
String algoName = model.getAttribute("algorithmName");
if (algoName != null && algoName.length() > 0) {
m_algorithmName = algoName;
}
NodeList ruleset = model.getElementsByTagName("RuleSet");
if (ruleset.getLength() == 1) {
Node ruleSetNode = ruleset.item(0);
if (ruleSetNode.getNodeType() == Node.ELEMENT_NODE) {
m_ruleSet = new RuleSet((Element)ruleSetNode, miningSchema);
}
} else {
throw new Exception ("[RuleSetModel] Should only have a single RuleSet!");
}
}
/**
* Classifies the given test instance. The instance has to belong to a
* dataset when it's being classified.
*
* @param inst the instance to be classified
* @return the predicted most likely class for the instance or
* Utils.missingValue() if no prediction is made
* @exception Exception if an error occurred during the prediction
*/
public double[] distributionForInstance(Instance inst) throws Exception {
if (!m_initialized) {
mapToMiningSchema(inst.dataset());
}
double[] preds = null;
if (m_miningSchema.getFieldsAsInstances().classAttribute().isNumeric()) {
preds = new double[1];
} else {
preds = new double[m_miningSchema.getFieldsAsInstances().classAttribute().numValues()];
}
double[] incoming = m_fieldsMap.instanceToSchema(inst, m_miningSchema);
preds = m_ruleSet.score(incoming,
m_miningSchema.getFieldsAsInstances().classAttribute());
if (m_miningSchema.getFieldsAsInstances().classAttribute().isNominal()) {
Utils.normalize(preds);
}
return preds;
}
/**
* Return a textual description of this model.
*
* @return a textual description of this model
*/
public String toString() {
StringBuffer temp = new StringBuffer();
temp.append("PMML version " + getPMMLVersion());
if (!getCreatorApplication().equals("?")) {
temp.append("\nApplication: " + getCreatorApplication());
}
temp.append("\nPMML Model: RuleSetModel");
temp.append("\n\n");
temp.append(m_miningSchema);
if (m_algorithmName != null) {
temp.append("\nAlgorithm: " + m_algorithmName + "\n");
}
temp.append(m_ruleSet);
return temp.toString();
}
/**
* Get the revision string for this class
*
* @return the revision string
*/
public String getRevision() {
return RevisionUtils.extract("$Revision: 8034 $");
}
}
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