ActiveRule.java

package com.yahoo.labs.samoa.learners.classifiers.rules.common;

/*
 * #%L
 * SAMOA
 * %%
 * Copyright (C) 2013 - 2014 Yahoo! Inc.
 * %%
 * Licensed under the Apache License, Version 2.0 (the "License");
 * you may not use this file except in compliance with the License.
 * You may obtain a copy of the License at
 * 
 *      http://www.apache.org/licenses/LICENSE-2.0
 * 
 * Unless required by applicable law or agreed to in writing, software
 * distributed under the License is distributed on an "AS IS" BASIS,
 * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
 * See the License for the specific language governing permissions and
 * limitations under the License.
 * #L%
 */

import java.io.Serializable;

import com.yahoo.labs.samoa.moa.classifiers.core.conditionaltests.InstanceConditionalTest;
import com.yahoo.labs.samoa.moa.classifiers.core.conditionaltests.NumericAttributeBinaryTest;
import com.yahoo.labs.samoa.moa.classifiers.rules.core.attributeclassobservers.FIMTDDNumericAttributeClassLimitObserver;
import com.yahoo.labs.samoa.moa.classifiers.rules.core.conditionaltests.NumericAttributeBinaryRulePredicate;

/**
 * ActiveRule is a LearningRule that actively update its LearningNode
 * with incoming instances.
 * 
 * @author Anh Thu Vu
 *
 */

public class ActiveRule extends LearningRule {
	
	private static final long serialVersionUID = 1L;

	private double[] statisticsOtherBranchSplit;

	private Builder builder;
	
	private RuleActiveRegressionNode learningNode;
	
	private RuleSplitNode lastUpdatedRuleSplitNode;
	
	/*
	 * Constructor with Builder
	 */
	public ActiveRule() {
		super();
		this.builder = null;
		this.learningNode = null;
		this.ruleNumberID = 0;
	}
	public ActiveRule(Builder builder) {
		super();
		this.setBuilder(builder);
		this.learningNode = newRuleActiveLearningNode(builder);
		//JD - use builder ID to set ruleNumberID
		this.ruleNumberID=builder.id;        
	}

	private RuleActiveRegressionNode newRuleActiveLearningNode(Builder builder) {
		return new RuleActiveRegressionNode(builder);
	}

	/*
	 * Setters & getters
	 */
	public Builder getBuilder() {
		return builder;
	}

	public void setBuilder(Builder builder) {
		this.builder = builder;
	}
	
	@Override
	public RuleRegressionNode getLearningNode() {
		return this.learningNode;
	}

	@Override
	public void setLearningNode(RuleRegressionNode learningNode) {
		this.learningNode = (RuleActiveRegressionNode) learningNode;
	}
	
	public double[] statisticsOtherBranchSplit() {
		return this.statisticsOtherBranchSplit;
	}
	
	public RuleSplitNode getLastUpdatedRuleSplitNode() {
		return this.lastUpdatedRuleSplitNode;
	}

	/*
	 * Builder
	 */
	public static class Builder implements Serializable {

		private static final long serialVersionUID = 1712887264918475622L;
		protected boolean changeDetection;
		protected boolean usePerceptron;
		protected double threshold;
		protected double alpha;
		protected int predictionFunction;
		protected boolean constantLearningRatioDecay;
		protected double learningRatio;

		protected double[] statistics;

		protected FIMTDDNumericAttributeClassLimitObserver numericObserver;
		
		protected double lastTargetMean;

		public int id;

		public Builder() {
		}

		public Builder changeDetection(boolean changeDetection) {
			this.changeDetection = changeDetection;
			return this;
		}

		public Builder threshold(double threshold) {
			this.threshold = threshold;
			return this;
		}

		public Builder alpha(double alpha) {
			this.alpha = alpha;
			return this;
		}

		public Builder predictionFunction(int predictionFunction) {
			this.predictionFunction = predictionFunction;
			return this;
		}

		public Builder statistics(double[] statistics) {
			this.statistics = statistics;
			return this;
		}
		
		public Builder constantLearningRatioDecay(boolean constantLearningRatioDecay) {
			this.constantLearningRatioDecay = constantLearningRatioDecay;
			return this;
		}
		
		public Builder learningRatio(double learningRatio) {
			this.learningRatio = learningRatio;
			return this;
		}
		
		public Builder numericObserver(FIMTDDNumericAttributeClassLimitObserver numericObserver) {
			this.numericObserver = numericObserver;
			return this;
		}

		public Builder id(int id) {
			this.id = id;
			return this;
		}
		public ActiveRule build() {
			return new ActiveRule(this);
		}

	}

	/**
	 *  Try to Expand method.
	 * @param splitConfidence
	 * @param tieThreshold
	 * @return
	 */
	public boolean tryToExpand(double splitConfidence, double tieThreshold) {

		boolean shouldSplit= this.learningNode.tryToExpand(splitConfidence, tieThreshold);
		return shouldSplit;

	}
	
	//JD: Only call after tryToExpand returning true
	public void split()
	{
		//this.statisticsOtherBranchSplit  = this.learningNode.getStatisticsOtherBranchSplit(); 
		//create a split node,
		int splitIndex = this.learningNode.getSplitIndex();
		InstanceConditionalTest st=this.learningNode.getBestSuggestion().splitTest;
		if(st instanceof NumericAttributeBinaryTest) {
			NumericAttributeBinaryTest splitTest = (NumericAttributeBinaryTest) st;
			NumericAttributeBinaryRulePredicate predicate = new NumericAttributeBinaryRulePredicate(
					splitTest.getAttsTestDependsOn()[0], splitTest.getSplitValue(),
					splitIndex + 1);
			lastUpdatedRuleSplitNode = new RuleSplitNode(predicate, this.learningNode.getStatisticsBranchSplit() );
			if (this.nodeListAdd(lastUpdatedRuleSplitNode)) {
				// create a new learning node
				RuleActiveRegressionNode newLearningNode = newRuleActiveLearningNode(this.getBuilder().statistics(this.learningNode.getStatisticsNewRuleActiveLearningNode())); 
				newLearningNode.initialize(this.learningNode);
				this.learningNode = newLearningNode;
			}
		}
		else
			throw new UnsupportedOperationException("AMRules (currently) only supports numerical attributes.");
	}

	
	
//	protected void debug(String string, int level) {
//	if (this.amRules.VerbosityOption.getValue()>=level) {
//		System.out.println(string);
//	}
//}
	
	/**
	 * MOA GUI output
	 */
	@Override
	public void getDescription(StringBuilder sb, int indent) {
	}
}