Implement linear regressor using Weka3

7 files changed, 217 insertions, 33 deletions

diff --git a/Metrics/Metrics-Calculation/ca.mcgill.ecse.dslreasoner.realistic.metrics.calculator/.classpath b/Metrics/Metrics-Calculation/ca.mcgill.ecse.dslreasoner.realistic.metrics.calculator/.classpath
index f4f8357b..006b2686 100644
--- a/Metrics/Metrics-Calculation/ca.mcgill.ecse.dslreasoner.realistic.metrics.calculator/.classpath
+++ b/Metrics/Metrics-Calculation/ca.mcgill.ecse.dslreasoner.realistic.metrics.calculator/.classpath
@@ -6,5 +6,6 @@
         <classpathentry kind="src" path="xtend-gen"/>
         <classpathentry kind="lib" path="C:/Users/chenp/eclipse-workspace/VIATRA-Generator/Metrics/Metrics-Calculation/ca.mcgill.ecse.dslreasoner.realistic.metrics.calculator/lib/commons-math3-3.6.1.jar"/>
         <classpathentry kind="lib" path="C:/Users/chenp/eclipse-workspace/VIATRA-Generator/Metrics/Metrics-Calculation/ca.mcgill.ecse.dslreasoner.realistic.metrics.calculator/lib/commons-math3-3.6.1-javadoc.jar"/>
+        <classpathentry kind="lib" path="C:/Users/chenp/eclipse-workspace/VIATRA-Generator/Metrics/Metrics-Calculation/ca.mcgill.ecse.dslreasoner.realistic.metrics.calculator/lib/weka.jar"/>
         <classpathentry kind="output" path="bin"/>
 </classpath>
diff --git a/Metrics/Metrics-Calculation/ca.mcgill.ecse.dslreasoner.realistic.metrics.calculator/META-INF/MANIFEST.MF b/Metrics/Metrics-Calculation/ca.mcgill.ecse.dslreasoner.realistic.metrics.calculator/META-INF/MANIFEST.MF
index da19e07c..febd4757 100644
--- a/Metrics/Metrics-Calculation/ca.mcgill.ecse.dslreasoner.realistic.metrics.calculator/META-INF/MANIFEST.MF
+++ b/Metrics/Metrics-Calculation/ca.mcgill.ecse.dslreasoner.realistic.metrics.calculator/META-INF/MANIFEST.MF
@@ -15,4 +15,9 @@ Require-Bundle: com.google.guava,
  hu.bme.mit.inf.dslreasoner.domains.yakindu.sgraph;bundle-version="1.0.0",
  hu.bme.mit.inf.dslreasoner.viatrasolver.partialinterpretationlanguage;bundle-version="1.0.0",
  org.eclipse.viatra.dse;bundle-version="0.21.2"
-Export-Package: ca.mcgill.ecse.dslreasoner.realistic.metrics.calculator.app
+Export-Package: ca.mcgill.ecse.dslreasoner.realistic.metrics.calculator.app,
+ ca.mcgill.ecse.dslreasoner.realistic.metrics.calculator.distance,
+ ca.mcgill.ecse.dslreasoner.realistic.metrics.calculator.graph,
+ ca.mcgill.ecse.dslreasoner.realistic.metrics.calculator.io,
+ ca.mcgill.ecse.dslreasoner.realistic.metrics.calculator.metrics,
+ ca.mcgill.ecse.dslreasoner.realistic.metrics.calculator.predictor
diff --git a/Metrics/Metrics-Calculation/ca.mcgill.ecse.dslreasoner.realistic.metrics.calculator/src/ca/mcgill/ecse/dslreasoner/realistic/metrics/calculator/app/PartialInterpretationMetricDistance.xtend b/Metrics/Metrics-Calculation/ca.mcgill.ecse.dslreasoner.realistic.metrics.calculator/src/ca/mcgill/ecse/dslreasoner/realistic/metrics/calculator/app/PartialInterpretationMetricDistance.xtend
index b63451e8..45986ecf 100644
--- a/Metrics/Metrics-Calculation/ca.mcgill.ecse.dslreasoner.realistic.metrics.calculator/src/ca/mcgill/ecse/dslreasoner/realistic/metrics/calculator/app/PartialInterpretationMetricDistance.xtend
+++ b/Metrics/Metrics-Calculation/ca.mcgill.ecse.dslreasoner.realistic.metrics.calculator/src/ca/mcgill/ecse/dslreasoner/realistic/metrics/calculator/app/PartialInterpretationMetricDistance.xtend
@@ -8,26 +8,35 @@ import ca.mcgill.ecse.dslreasoner.realistic.metrics.calculator.metrics.Metric
 import ca.mcgill.ecse.dslreasoner.realistic.metrics.calculator.metrics.MultiplexParticipationCoefficientMetric
 import ca.mcgill.ecse.dslreasoner.realistic.metrics.calculator.metrics.NodeActivityMetric
 import ca.mcgill.ecse.dslreasoner.realistic.metrics.calculator.metrics.OutDegreeMetric
+import ca.mcgill.ecse.dslreasoner.realistic.metrics.calculator.predictor.LinearModel
 import hu.bme.mit.inf.dslreasoner.viatrasolver.partialinterpretationlanguage.partialinterpretation.PartialInterpretation
 import java.util.ArrayList
 import java.util.HashMap
+import java.util.List
 import java.util.Map
-import org.apache.commons.math3.stat.regression.SimpleRegression
-import java.util.stream.DoubleStream.Builder
+import org.apache.commons.math3.stat.regression.OLSMultipleLinearRegression
+import org.eclipse.xtend.lib.annotations.Accessors
 
 class PartialInterpretationMetricDistance {
         
         var KSDistance ks;
         var JSDistance js;
         var Map<Object, StateData> stateAndHistory;
-        var SimpleRegression regression;
+        var OLSMultipleLinearRegression regression;
+        List<StateData> samples;
+        
+        @Accessors(PUBLIC_GETTER)
+        var LinearModel linearModel;
         
         
         new(){
                 ks = new KSDistance(Domain.Yakinduum);
                 js = new JSDistance(Domain.Yakinduum);
-                regression = new SimpleRegression();
+                regression = new OLSMultipleLinearRegression();
+                regression.noIntercept = false;
                 stateAndHistory = new HashMap<Object, StateData>();
+                samples = new ArrayList<StateData>();
+                linearModel = new LinearModel(0.01);
         }
         
         def MetricDistanceGroup calculateMetricDistanceKS(PartialInterpretation partial){
@@ -63,37 +72,76 @@ class PartialInterpretationMetricDistance {
         }
         
         def resetRegression(Object state){
-                regression = new SimpleRegression();
+                samples.clear();
                 
                 if(stateAndHistory.containsKey(state)){
                         var data = stateAndHistory.get(state);
-                        regression.addData(data.numOfNodeFeature, data.value);
                         
-                        while(stateAndHistory.containsKey(data.lastState)){
+                        var curState = state;
+                        
+                        samples.add(data);
+                        
+                        while(stateAndHistory.containsKey(data.lastState) && data.lastState != curState){
+                                curState = data.lastState;
                                 data = stateAndHistory.get(data.lastState);
-                                regression.addData(data.numOfNodeFeature, data.value);
+                                samples.add(data);
+                        }
+                                        
+                        if(samples.size == 0){
+                                println('state: ' + state);
+                                println('last state: ' + data.lastState);
                         }
                 }
+                println("trajectory sample size:" + samples.size)
         }
         
-        def feedData(Object state, int numOfNodes, double value, Object lastState){
-                var data = new StateData(numOfNodes, value, lastState);
+        def feedData(Object state, double[] features, double value, Object lastState){
+                var data = new StateData(features, value, lastState);
                 stateAndHistory.put(state, data);
-                regression.addData(data.numOfNodeFeature, data.value);
+                samples.add(data);
         }
         
-        def getPredictionForNextDataSample(int numOfNodes, double value, int numberOfNodesToPredict){
-                var data = new StateData(numOfNodes, value, null);
-                regression.addData(data.numOfNodeFeature, data.value);
+        def getPredictionForNextDataSample(double[] features, double value, double[] featuresToPredict){        
+                if(samples.size <= 4){
+                        println('OK');
+                }       
+                var data = new StateData(features, value, null);
+                samples.add(data);
+                
+                // create training set from current data                
+                var double[][] xSamples = samples.map[it.features];
+                var double[] ySamples = samples.map[it.value];
+                
                 
-                var prediction = predict(numberOfNodesToPredict);
-                regression.removeData(data.numOfNodeFeature, data.value);
+                regression.newSampleData(ySamples, xSamples);
+                var prediction = predict(featuresToPredict);    
+                
+                //remove the last element just added
+                samples.remove(samples.size - 1);
                 return prediction;
         }
         
-        def predict(int numOfNodes){
-                var data = new StateData(numOfNodes, 0, null);
-                return regression.predict(data.numOfNodeFeature);
+        def private predict(double[] featuresToPredict){
+                var parameters = regression.estimateRegressionParameters();
+                // the regression will add an initial column for 1's, the first parameter is constant term
+                var result = parameters.get(0);
+                for(var i = 0; i < featuresToPredict.length; i++){
+                        result += parameters.get(i+1) * featuresToPredict.get(i);
+                }
+                return result;
+        }
+        
+        def double[] calculateFeature(int step, int violations){
+                var features = newDoubleArrayOfSize(5);
+                //constant term
+                features.set(0, 1);
+                
+                features.set(1, 1.0 / step);
+                features.set(2, violations);
+                features.set(3, Math.pow(violations, 2));
+                features.set(4, Math.pow(violations, 0.5));
+                
+                return features;
         }
 }
 
diff --git a/Metrics/Metrics-Calculation/ca.mcgill.ecse.dslreasoner.realistic.metrics.calculator/src/ca/mcgill/ecse/dslreasoner/realistic/metrics/calculator/app/Test.java b/Metrics/Metrics-Calculation/ca.mcgill.ecse.dslreasoner.realistic.metrics.calculator/src/ca/mcgill/ecse/dslreasoner/realistic/metrics/calculator/app/Test.java
new file mode 100644
index 00000000..f06b377f
--- /dev/null
+++ b/Metrics/Metrics-Calculation/ca.mcgill.ecse.dslreasoner.realistic.metrics.calculator/src/ca/mcgill/ecse/dslreasoner/realistic/metrics/calculator/app/Test.java
@@ -0,0 +1,31 @@
+package ca.mcgill.ecse.dslreasoner.realistic.metrics.calculator.app;
+
+import java.util.ArrayList;
+import java.util.List;
+
+import weka.core.matrix.LinearRegression;
+import weka.core.matrix.Matrix;
+
+public class Test {
+        public static void main(String[] args) {
+                linearRegressionTest();
+        }
+        
+        public static void linearRegressionTest() {
+                double[][] x = {{1,1,2,3}, {1,2,3,4}, {1,3,5,7}, {1,1,5,7}}; 
+                double[] y = {10, 13, 19, 17};
+                double[] valueToPredict = {1,1,1,1}; 
+                Matrix m = new Matrix(x);
+                Matrix n = new Matrix(y, y.length);
+                
+                LinearRegression regression = new LinearRegression(m, n, 0);
+                double[] coef = regression.getCoefficients();
+        
+                //predict
+                double a = 0;
+                for(int i = 0; i < coef.length; i++) {
+                        a += coef[i] * valueToPredict[i];
+                }
+                System.out.println(a);
+        }
+}
diff --git a/Metrics/Metrics-Calculation/ca.mcgill.ecse.dslreasoner.realistic.metrics.calculator/src/ca/mcgill/ecse/dslreasoner/realistic/metrics/calculator/distance/CostDistance.xtend b/Metrics/Metrics-Calculation/ca.mcgill.ecse.dslreasoner.realistic.metrics.calculator/src/ca/mcgill/ecse/dslreasoner/realistic/metrics/calculator/distance/CostDistance.xtend
index ee856201..33d10fa3 100644
--- a/Metrics/Metrics-Calculation/ca.mcgill.ecse.dslreasoner.realistic.metrics.calculator/src/ca/mcgill/ecse/dslreasoner/realistic/metrics/calculator/distance/CostDistance.xtend
+++ b/Metrics/Metrics-Calculation/ca.mcgill.ecse.dslreasoner.realistic.metrics.calculator/src/ca/mcgill/ecse/dslreasoner/realistic/metrics/calculator/distance/CostDistance.xtend
@@ -1,28 +1,21 @@
 package ca.mcgill.ecse.dslreasoner.realistic.metrics.calculator.distance
 
-import org.apache.commons.math3.stat.regression.SimpleRegression
 import org.eclipse.xtend.lib.annotations.Accessors
 
 class CostDistance {
-        
-        var SimpleRegression regression;
-        
-        new(){
-                regression = new SimpleRegression(true);
-        }
-        
+
 }
 
 class StateData{
         @Accessors(PUBLIC_GETTER)
-        var double numOfNodeFeature;
+        var double[] features;
         @Accessors(PUBLIC_GETTER)
         var double value;
         @Accessors(PUBLIC_GETTER)
         var Object lastState;
         
-        new(int numOfNode, double value, Object lastState){
-                this.numOfNodeFeature = 1.0 / numOfNode;
+        new(double[] features, double value, Object lastState){
+                this.features = features;
                 this.value = value
                 this.lastState = lastState;
         }
diff --git a/Metrics/Metrics-Calculation/ca.mcgill.ecse.dslreasoner.realistic.metrics.calculator/src/ca/mcgill/ecse/dslreasoner/realistic/metrics/calculator/io/CsvFileWriter.xtend b/Metrics/Metrics-Calculation/ca.mcgill.ecse.dslreasoner.realistic.metrics.calculator/src/ca/mcgill/ecse/dslreasoner/realistic/metrics/calculator/io/CsvFileWriter.xtend
index 01e3940b..00b38d90 100644
--- a/Metrics/Metrics-Calculation/ca.mcgill.ecse.dslreasoner.realistic.metrics.calculator/src/ca/mcgill/ecse/dslreasoner/realistic/metrics/calculator/io/CsvFileWriter.xtend
+++ b/Metrics/Metrics-Calculation/ca.mcgill.ecse.dslreasoner.realistic.metrics.calculator/src/ca/mcgill/ecse/dslreasoner/realistic/metrics/calculator/io/CsvFileWriter.xtend
@@ -2,19 +2,34 @@ package ca.mcgill.ecse.dslreasoner.realistic.metrics.calculator.io;
 
 import java.io.File
 import java.io.FileNotFoundException
+import java.io.FileOutputStream
 import java.io.PrintWriter
 import java.util.ArrayList
 import java.util.List
 
 class CsvFileWriter {
+        
         def static void write(ArrayList<ArrayList<String>> datas, String uri) {
                 if(datas.size() <= 0) {
                         return;
                 }
-                
+                val PrintWriter writer = new PrintWriter(new File(uri));
+                output(writer, datas, uri);
+        }
+        
+        def static void append(ArrayList<ArrayList<String>> datas, String uri) {
+                if(datas.size() <= 0) {
+                        return;
+                }
+                val PrintWriter writer = new PrintWriter(new FileOutputStream(new File(uri), true));
+                output(writer, datas, uri);
+        }
+        
+        
+        def private static void output(PrintWriter writer, ArrayList<ArrayList<String>> datas, String uri) {
                 //println("Output csv for " + uri);
                 try  {
-                        val PrintWriter writer = new PrintWriter(new File(uri));
+                        
                         val output = new StringBuilder;
                         for(List<String> datarow : datas){
                                 for(var i = 0; i < datarow.size() - 1; i++){
diff --git a/Metrics/Metrics-Calculation/ca.mcgill.ecse.dslreasoner.realistic.metrics.calculator/src/ca/mcgill/ecse/dslreasoner/realistic/metrics/calculator/predictor/LinearModel.xtend b/Metrics/Metrics-Calculation/ca.mcgill.ecse.dslreasoner.realistic.metrics.calculator/src/ca/mcgill/ecse/dslreasoner/realistic/metrics/calculator/predictor/LinearModel.xtend
new file mode 100644
index 00000000..f0ded347
--- /dev/null
+++ b/Metrics/Metrics-Calculation/ca.mcgill.ecse.dslreasoner.realistic.metrics.calculator/src/ca/mcgill/ecse/dslreasoner/realistic/metrics/calculator/predictor/LinearModel.xtend
@@ -0,0 +1,91 @@
+package ca.mcgill.ecse.dslreasoner.realistic.metrics.calculator.predictor
+
+import ca.mcgill.ecse.dslreasoner.realistic.metrics.calculator.distance.StateData
+import java.util.ArrayList
+import java.util.HashMap
+import java.util.List
+import java.util.Map
+import weka.core.matrix.LinearRegression
+import weka.core.matrix.Matrix
+
+class LinearModel {
+        var double ridge;
+        var Map<Object, StateData> stateAndHistory;
+        List<StateData> samples;
+        
+        new(double ridge){
+                this.ridge = ridge;
+                stateAndHistory = new HashMap<Object, StateData>();
+                samples = new ArrayList<StateData>();
+        }
+        
+        /**
+         * reset the current train data for regression to a new trajectory
+         * @param state: the last state of the trajectory
+         */
+        def resetRegression(Object state){
+                samples.clear();
+                
+                if(stateAndHistory.containsKey(state)){
+                        var data = stateAndHistory.get(state);
+                        var curState = state;
+                        
+                        samples.add(data);
+                        
+                        //loop through data until the oldest state in the record
+                        while(stateAndHistory.containsKey(data.lastState) && data.lastState != curState){
+                                curState = data.lastState;
+                                data = stateAndHistory.get(data.lastState);
+                                samples.add(data);
+                        }
+                }
+        }
+        
+        /**
+         * Add a new data point to the current training set
+         * @param state: the state on which the new data point is calculated
+         * @param features: the set of feature value(x)
+         * @param value: the value of the state (y)
+         * @param lastState: the state which transformed to current state, used to record the trajectory
+         */
+        def feedData(Object state, double[] features, double value, Object lastState){
+                var data = new StateData(features, value, lastState);
+                stateAndHistory.put(state, data);
+                samples.add(data);
+        }
+        
+        /**
+         * get prediction for next state, without storing the data point into the training set
+         * @param features: the feature values of current state
+         * @param value: the value of the current state
+         * @param: featuresToPredict: the features of the state wanted to be predected
+         * @return the value of the state to be predicted
+         */
+        def double getPredictionForNextDataSample(double[] features, double value, double[] featuresToPredict){         
+                var data = new StateData(features, value, null);
+                samples.add(data);
+                
+                // create training set from current data                
+                val double[][] xSamples = samples.map[it.features];
+                val double[] ySamples = samples.map[it.value];
+                
+                val x = new Matrix(xSamples);
+                val y = new Matrix(ySamples, ySamples.size());  
+                
+                val regression = new LinearRegression(x, y, ridge);
+                var prediction = predict(regression.coefficients, featuresToPredict);   
+                
+                //remove the last element just added
+                samples.remove(samples.size - 1);
+                return prediction;
+        }
+        
+        def private predict(double[] parameters, double[] featuresToPredict){
+                // the regression will add an initial column for 1's, the first parameter is constant term
+                var result = parameters.get(0);
+                for(var i = 0; i < featuresToPredict.length; i++){
+                        result += parameters.get(i) * featuresToPredict.get(i);
+                }
+                return result;
+        }
+}
\ No newline at end of file