fix storePredictions and storeY

.github/workflows/capymoa.yml

@@ -0,0 +1,35 @@
+name: Package Jar for CapyMOA
+
+on:
+  push:
+    branches: [ master, capymoa ]
+  pull_request:
+    branches: [ master, capymoa ]
+
+jobs:
+  build:
+
+    runs-on: ubuntu-latest
+
+    steps:
+    - uses: actions/checkout@v4
+    - name: Set up JDK 17
+      uses: actions/setup-java@v4
+      with:
+        java-version: '17'
+        distribution: 'temurin'
+        cache: maven
+
+    - name: Build with Maven
+      working-directory: ./moa
+      # no tests
+      run: mvn -B package --file pom.xml -DskipTests
+
+    # Upload jar file as artifact
+    - name: Upload artifact
+      uses: actions/upload-artifact@v4
+      with:
+        name: moa-jar
+        path: ./moa/target/moa-*-jar-with-dependencies.jar
+        if-no-files-found: error
+        retention-days: 7

.gitignore

@@ -3,4 +3,34 @@
 *.iml
 *~
 *.bak
+.DS_Store
+.settings
+.project
 
+# MacOS folder metadata
+.DS_Store
+
+# Compiled class file
+*.class
+
+# Log file
+*.log
+
+# BlueJ files
+*.ctxt
+
+# Mobile Tools for Java (J2ME)
+.mtj.tmp/
+
+# Package Files #
+*.jar
+*.war
+*.nar
+*.ear
+*.zip
+*.tar.gz
+*.rar
+
+# virtual machine crash logs, see http://www.java.com/en/download/help/error_hotspot.xml
+hs_err_pid*
+replay_pid*

README.md

@@ -32,3 +32,27 @@ If you want to refer to MOA in a publication, please cite the following JMLR pap
 > MOA: Massive Online Analysis; Journal of Machine Learning Research 11: 1601-1604 
 
 
+## Building MOA for CapyMOA
+
+You can now upload the build artifact from the GitHub Actions workflow. **Make sure to unzip it!**
+
+> These steps assume you have Java installed and maven installed. If you don't
+> have maven installed, you can download it from
+> [here](https://maven.apache.org/download.cgi). You can achieve the same
+> outcome with IntelliJ IDEA by [building moa with the IDE](https://moa.cms.waikato.ac.nz/tutorial-6-building-moa-from-the-source/) (The linked doc is a  little out of date)
+> and [packaging it as a single jar file](https://stackoverflow.com/questions/1082580/how-to-build-jars-from-intellij-idea-properly).
+
+You can compile moa as a single jar file with all dependencies included by running the following command in the `moa` directory:
+```bash
+cd ./moa
+mvn compile assembly:single
+```
+
+If successful, the jar file will be built to a file like this `moa/target/moa-2023.04.1-SNAPSHOT-jar-with-dependencies.jar` with a different date.
+
+One way to verify that the jar file was built correctly is to run the following command:
+```bash
+java -jar ./moa/target/moa-2023.04.1-SNAPSHOT-jar-with-dependencies.jar
+```
+This should start the MOA GUI.
+

moa/pom.xml

@@ -251,14 +251,24 @@
         <artifactId>license-maven-plugin</artifactId>
       </plugin>
 
-      <plugin>
+      <!-- TODO: Commented out because my build was failing -->
+      <!-- <plugin>
         <groupId>com.github.fracpete</groupId>
         <artifactId>latex-maven-plugin</artifactId>
-      </plugin>
+      </plugin> -->
 
       <plugin>
-        <groupId>org.apache.maven.plugins</groupId>
         <artifactId>maven-assembly-plugin</artifactId>
+        <configuration>
+          <archive>
+            <manifest>
+              <mainClass>moa.gui.GUI</mainClass>
+            </manifest>
+          </archive>
+          <descriptorRefs>
+            <descriptorRef>jar-with-dependencies</descriptorRef>
+          </descriptorRefs>
+        </configuration>
       </plugin>
 
       <plugin>

moa/src/main/java/moa/classifiers/AbstractClassifier.java

@@ -95,7 +95,6 @@ public void prepareForUseImpl(TaskMonitor monitor,
             resetLearning();
         }
     }
-
 
     @Override
     public double[] getVotesForInstance(Example<Instance> example){
@@ -105,6 +104,26 @@ public double[] getVotesForInstance(Example<Instance> example){
     @Override
     public abstract double[] getVotesForInstance(Instance inst);
 
+    @Override
+    public double getConfidenceForPrediction(Instance inst, double prediction) {
+        double[] votes = this.getVotesForInstance(inst);
+        double predictionValue = votes[(int) prediction];
+
+        double sum = 0.0;
+        for (double vote : votes)
+            sum += vote;
+
+        // Check if the sum is zero
+        if (sum == 0.0)
+            return 0.0; // Return 0 if sum is zero to avoid division by zero
+        return predictionValue / sum;
+    }
+
+    @Override
+    public double getConfidenceForPrediction(Example<Instance> example, double prediction) {
+        return getConfidenceForPrediction(example.getData(), prediction);
+    }
+
     @Override
     public Prediction getPredictionForInstance(Example<Instance> example){
 		return getPredictionForInstance(example.getData());

moa/src/main/java/moa/classifiers/Classifier.java

@@ -15,7 +15,7 @@
  *
  *    You should have received a copy of the GNU General Public License
  *    along with this program. If not, see <http://www.gnu.org/licenses/>.
- *    
+ *
  */
 package moa.classifiers;
 
@@ -76,7 +76,7 @@ public interface Classifier extends Learner<Example<Instance>> {
      * test instance in each class
      */
     public double[] getVotesForInstance(Instance inst);
-    
+
     /**
      * Sets the reference to the header of the data stream. The header of the
      * data stream is extended from WEKA
@@ -86,7 +86,7 @@ public interface Classifier extends Learner<Example<Instance>> {
      * @param ih the reference to the data stream header
      */
     //public void setModelContext(InstancesHeader ih);
-    
+
     /**
      * Gets the reference to the header of the data stream. The header of the
      * data stream is extended from WEKA
@@ -96,6 +96,8 @@ public interface Classifier extends Learner<Example<Instance>> {
      * @return the reference to the data stream header
      */
     //public InstancesHeader getModelContext();
-    
+
     public Prediction getPredictionForInstance(Instance inst);
+
+    public double getConfidenceForPrediction(Instance inst, double prediction);
 }

moa/src/main/java/moa/classifiers/SemiSupervisedLearner.java

@@ -19,12 +19,16 @@
  */
 package moa.classifiers;
 
+import com.yahoo.labs.samoa.instances.Instance;
+import moa.core.Example;
+import moa.learners.Learner;
+
 /**
- * Learner interface for incremental semi supervised models. It is used only in the GUI Regression Tab. 
- *
- * @author Richard Kirkby (rkirkby@cs.waikato.ac.nz)
- * @version $Revision: 7 $
+ * Updated learner interface for semi-supervised methods.
  */
-public interface SemiSupervisedLearner {
-
+public interface SemiSupervisedLearner extends Learner<Example<Instance>> {
+    // Returns the pseudo-label used. If no pseudo-label was used, then return -1.
+    int trainOnUnlabeledInstance(Instance instance);
+
+    void addInitialWarmupTrainingInstances();
 }

moa/src/main/java/moa/classifiers/core/attributeclassobservers/GaussianNumericAttributeClassObserver.java

@@ -15,7 +15,7 @@
  *
  *    You should have received a copy of the GNU General Public License
  *    along with this program. If not, see <http://www.gnu.org/licenses/>.
- *    
+ *
  */
 package moa.classifiers.core.attributeclassobservers;
 
@@ -81,7 +81,7 @@ public void observeAttributeClass(double attVal, int classVal, double weight) {
 
     @Override
     public double probabilityOfAttributeValueGivenClass(double attVal,
-            int classVal) {
+                                                        int classVal) {
         GaussianEstimator obs = this.attValDistPerClass.get(classVal);
         return obs != null ? obs.probabilityDensity(attVal) : 0.0;
     }
@@ -99,12 +99,24 @@ public AttributeSplitSuggestion getBestEvaluatedSplitSuggestion(
             if ((bestSuggestion == null) || (merit > bestSuggestion.merit)) {
                 bestSuggestion = new AttributeSplitSuggestion(
                         new NumericAttributeBinaryTest(attIndex, splitValue,
-                        true), postSplitDists, merit);
+                                true), postSplitDists, merit);
             }
         }
         return bestSuggestion;
     }
 
+    /* Used by PLASTIC during restructuring when forcing a leaf split becomes necessary */
+    public AttributeSplitSuggestion forceSplit(
+            SplitCriterion criterion, double[] preSplitDist, int attIndex, double threshold) {
+        AttributeSplitSuggestion bestSuggestion = null;
+        double[][] postSplitDists = getClassDistsResultingFromBinarySplit(threshold);
+        double merit = criterion.getMeritOfSplit(preSplitDist,
+                postSplitDists);
+        bestSuggestion = new AttributeSplitSuggestion(
+                new NumericAttributeBinaryTest(attIndex, threshold, true), postSplitDists, merit);
+        return bestSuggestion;
+    }
+
     public double[] getSplitPointSuggestions() {
         Set<Double> suggestedSplitValues = new TreeSet<Double>();
         double minValue = Double.POSITIVE_INFINITY;

moa/src/main/java/moa/classifiers/core/attributeclassobservers/NominalAttributeClassObserver.java

@@ -15,7 +15,7 @@
  *
  *    You should have received a copy of the GNU General Public License
  *    along with this program. If not, see <http://www.gnu.org/licenses/>.
- *    
+ *
  */
 package moa.classifiers.core.attributeclassobservers;
 
@@ -68,7 +68,7 @@ public void observeAttributeClass(double attVal, int classVal, double weight) {
 
     @Override
     public double probabilityOfAttributeValueGivenClass(double attVal,
-            int classVal) {
+                                                        int classVal) {
         DoubleVector obs = this.attValDistPerClass.get(classVal);
         return obs != null ? (obs.getValue((int) attVal) + 1.0)
                 / (obs.sumOfValues() + obs.numValues()) : 0.0;
@@ -109,6 +109,33 @@ public AttributeSplitSuggestion getBestEvaluatedSplitSuggestion(
         return bestSuggestion;
     }
 
+    /* Used by PLASTIC during restructuring when forcing a leaf split becomes necessary */
+    public AttributeSplitSuggestion forceSplit(
+            SplitCriterion criterion, double[] preSplitDist, int attIndex, boolean binary, Double splitValue) {
+        AttributeSplitSuggestion bestSuggestion;
+        int maxAttValsObserved = getMaxAttValsObserved();
+        if (!binary) {
+            double[][] postSplitDists = getClassDistsResultingFromMultiwaySplit(maxAttValsObserved);
+            double merit = criterion.getMeritOfSplit(preSplitDist,
+                    postSplitDists);
+            bestSuggestion = new AttributeSplitSuggestion(
+                    new NominalAttributeMultiwayTest(attIndex), postSplitDists,
+                    merit);
+            return bestSuggestion;
+        }
+        assert splitValue != null: "Split value is null";
+        if (splitValue >= maxAttValsObserved) {
+            return null;
+        }
+        double[][] postSplitDists = getClassDistsResultingFromBinarySplit(splitValue.intValue());
+        double merit = criterion.getMeritOfSplit(preSplitDist,
+                postSplitDists);
+        bestSuggestion = new AttributeSplitSuggestion(
+                new NominalAttributeBinaryTest(attIndex, splitValue.intValue()),
+                postSplitDists, merit);
+        return bestSuggestion;
+    }
+
     public int getMaxAttValsObserved() {
         int maxAttValsObserved = 0;
         for (DoubleVector attValDist : this.attValDistPerClass) {
@@ -157,7 +184,7 @@ public double[][] getClassDistsResultingFromBinarySplit(int valIndex) {
             }
         }
         return new double[][]{equalsDist.getArrayRef(),
-                    notEqualDist.getArrayRef()};
+                notEqualDist.getArrayRef()};
     }
 
     @Override

moa/src/main/java/moa/classifiers/core/conditionaltests/NominalAttributeBinaryTest.java

@@ -15,7 +15,7 @@
  *
  *    You should have received a copy of the GNU General Public License
  *    along with this program. If not, see <http://www.gnu.org/licenses/>.
- *    
+ *
  */
 package moa.classifiers.core.conditionaltests;
 
@@ -48,6 +48,10 @@ public int branchForInstance(Instance inst) {
         return inst.isMissing(instAttIndex) ? -1 : ((int) inst.value(instAttIndex) == this.attValue ? 0 : 1);
     }
 
+    public double getValue() {
+        return attValue;
+    }
+
     @Override
     public String describeConditionForBranch(int branch, InstancesHeader context) {
         if ((branch == 0) || (branch == 1)) {

moa/src/main/java/moa/classifiers/core/conditionaltests/NumericAttributeBinaryTest.java

@@ -15,7 +15,7 @@
  *
  *    You should have received a copy of the GNU General Public License
  *    along with this program. If not, see <http://www.gnu.org/licenses/>.
- *    
+ *
  */
 package moa.classifiers.core.conditionaltests;
 
@@ -39,16 +39,20 @@ public class NumericAttributeBinaryTest extends InstanceConditionalBinaryTest {
     protected boolean equalsPassesTest;
 
     public NumericAttributeBinaryTest(int attIndex, double attValue,
-            boolean equalsPassesTest) {
+                                      boolean equalsPassesTest) {
         this.attIndex = attIndex;
         this.attValue = attValue;
         this.equalsPassesTest = equalsPassesTest;
     }
 
+    public double getValue() {
+        return attValue;
+    }
+
     @Override
     public int branchForInstance(Instance inst) {
         int instAttIndex = this.attIndex ; // < inst.classIndex() ? this.attIndex
-               // : this.attIndex + 1;
+        // : this.attIndex + 1;
         if (inst.isMissing(instAttIndex)) {
             return -1;
         }