IGNITE-7451: Make Linear SVM for multi-classification

this closes #3484

examples/src/main/java/org/apache/ignite/examples/ml/svm/SVMBinaryClassificationExample.java

@@ -19,10 +19,7 @@
 
 import java.io.File;
 import java.io.IOException;
-import java.net.URISyntaxException;
-import java.net.URL;
 import java.nio.file.Path;
-import java.nio.file.Paths;
 import org.apache.ignite.Ignite;
 import org.apache.ignite.Ignition;
 import org.apache.ignite.examples.ExampleNodeStartup;
@@ -33,13 +30,13 @@
 import org.apache.ignite.ml.structures.preprocessing.LabeledDatasetLoader;
 import org.apache.ignite.ml.structures.preprocessing.LabellingMachine;
 import org.apache.ignite.ml.structures.preprocessing.Normalizer;
+import org.apache.ignite.ml.svm.SVMLinearBinaryClassificationModel;
 import org.apache.ignite.ml.svm.SVMLinearBinaryClassificationTrainer;
-import org.apache.ignite.ml.svm.SVMLinearClassificationModel;
 import org.apache.ignite.thread.IgniteThread;
 
 /**
  * <p>
- * Example of using {@link org.apache.ignite.ml.svm.SVMLinearClassificationModel} with Titanic dataset.</p>
+ * Example of using {@link org.apache.ignite.ml.svm.SVMLinearBinaryClassificationModel} with Titanic dataset.</p>
  * <p>
  * Note that in this example we cannot guarantee order in which nodes return results of intermediate
  * computations and therefore algorithm can return different results.</p>
@@ -95,10 +92,10 @@ public static void main(String[] args) throws InterruptedException {
                     LabeledDataset train = split.train();
 
                     System.out.println("\n>>> Create new linear binary SVM trainer object.");
-                    Trainer<SVMLinearClassificationModel, LabeledDataset> trainer = new SVMLinearBinaryClassificationTrainer();
+                    Trainer<SVMLinearBinaryClassificationModel, LabeledDataset> trainer = new SVMLinearBinaryClassificationTrainer();
 
                     System.out.println("\n>>> Perform the training to get the model.");
-                    SVMLinearClassificationModel mdl = trainer.train(train);
+                    SVMLinearBinaryClassificationModel mdl = trainer.train(train);
 
                     System.out.println("\n>>> SVM classification model: " + mdl);
 

modules/ml/src/main/java/org/apache/ignite/ml/structures/LabeledDataset.java

@@ -191,10 +191,20 @@ public void setLabel(int idx, double lb) {
 
     /** */
     public static Vector emptyVector(int size, boolean isDistributed) {
-
         if(isDistributed)
             return new SparseDistributedVector(size);
         else
             return new DenseLocalOnHeapVector(size);
     }
+
+    /** Makes copy with new Label objects and old features and Metadata objects. */
+    public LabeledDataset copy(){
+        LabeledDataset res = new LabeledDataset(this.data, this.colSize);
+        res.isDistributed = this.isDistributed;
+        res.meta = this.meta;
+        for (int i = 0; i < rowSize; i++)
+            res.setLabel(i, this.label(i));
+
+        return res;
+    }
 }

modules/ml/src/main/java/org/apache/ignite/ml/svm/SVMLinearClassificationModel.java → modules/ml/src/main/java/org/apache/ignite/ml/svm/SVMLinearBinaryClassificationModel.java

@@ -27,7 +27,7 @@
 /**
  * Base class for SVM linear classification model.
  */
-public class SVMLinearClassificationModel implements Model<Vector, Double>, Exportable<SVMLinearClassificationModel>, Serializable {
+public class SVMLinearBinaryClassificationModel implements Model<Vector, Double>, Exportable<SVMLinearBinaryClassificationModel>, Serializable {
     /** Output label format. -1 and +1 for false value and raw distances from the separating hyperplane otherwise. */
     private boolean isKeepingRawLabels = false;
 
@@ -41,47 +41,51 @@ public class SVMLinearClassificationModel implements Model<Vector, Double>, Expo
     private double intercept;
 
     /** */
-    public SVMLinearClassificationModel(Vector weights, double intercept) {
+    public SVMLinearBinaryClassificationModel(Vector weights, double intercept) {
         this.weights = weights;
         this.intercept = intercept;
     }
 
     /**
      * Set up the output label format.
+     *
      * @param isKeepingRawLabels The parameter value.
      * @return Model with new isKeepingRawLabels parameter value.
      */
-    public SVMLinearClassificationModel withRawLabels(boolean isKeepingRawLabels) {
+    public SVMLinearBinaryClassificationModel withRawLabels(boolean isKeepingRawLabels) {
         this.isKeepingRawLabels = isKeepingRawLabels;
         return this;
     }
 
     /**
      * Set up the threshold.
+     *
      * @param threshold The parameter value.
      * @return Model with new threshold parameter value.
      */
-    public SVMLinearClassificationModel withThreshold(double threshold) {
+    public SVMLinearBinaryClassificationModel withThreshold(double threshold) {
         this.threshold = threshold;
         return this;
     }
 
     /**
      * Set up the weights.
+     *
      * @param weights The parameter value.
      * @return Model with new weights parameter value.
      */
-    public SVMLinearClassificationModel withWeights(Vector weights) {
+    public SVMLinearBinaryClassificationModel withWeights(Vector weights) {
         this.weights = weights;
         return this;
     }
 
     /**
      * Set up the intercept.
+     *
      * @param intercept The parameter value.
      * @return Model with new intercept parameter value.
      */
-    public SVMLinearClassificationModel withIntercept(double intercept) {
+    public SVMLinearBinaryClassificationModel withIntercept(double intercept) {
         this.intercept = intercept;
         return this;
     }
@@ -97,6 +101,7 @@ public SVMLinearClassificationModel withIntercept(double intercept) {
 
     /**
      * Gets the output label format mode.
+     *
      * @return The parameter value.
      */
     public boolean isKeepingRawLabels() {
@@ -105,6 +110,7 @@ public boolean isKeepingRawLabels() {
 
     /**
      * Gets the threshold.
+     *
      * @return The parameter value.
      */
     public double threshold() {
@@ -113,6 +119,7 @@ public double threshold() {
 
     /**
      * Gets the weights.
+     *
      * @return The parameter value.
      */
     public Vector weights() {
@@ -121,14 +128,15 @@ public Vector weights() {
 
     /**
      * Gets the intercept.
+     *
      * @return The parameter value.
      */
     public double intercept() {
         return intercept;
     }
 
     /** {@inheritDoc} */
-    @Override public <P> void saveModel(Exporter<SVMLinearClassificationModel, P> exporter, P path) {
+    @Override public <P> void saveModel(Exporter<SVMLinearBinaryClassificationModel, P> exporter, P path) {
         exporter.save(this, path);
     }
 
@@ -138,7 +146,7 @@ public double intercept() {
             return true;
         if (o == null || getClass() != o.getClass())
             return false;
-        SVMLinearClassificationModel mdl = (SVMLinearClassificationModel)o;
+        SVMLinearBinaryClassificationModel mdl = (SVMLinearBinaryClassificationModel)o;
         return Double.compare(mdl.intercept, intercept) == 0
             && Double.compare(mdl.threshold, threshold) == 0
             && Boolean.compare(mdl.isKeepingRawLabels, isKeepingRawLabels) == 0

modules/ml/src/main/java/org/apache/ignite/ml/svm/SVMLinearBinaryClassificationTrainer.java

@@ -32,7 +32,7 @@
  * and +1 labels for two classes and makes binary classification. </p> The paper about this algorithm could be found
  * here https://arxiv.org/abs/1409.1458.
  */
-public class SVMLinearBinaryClassificationTrainer implements Trainer<SVMLinearClassificationModel, LabeledDataset> {
+public class SVMLinearBinaryClassificationTrainer implements Trainer<SVMLinearBinaryClassificationModel, LabeledDataset> {
     /** Amount of outer SDCA algorithm iterations. */
     private int amountOfIterations = 20;
 
@@ -51,7 +51,7 @@ public class SVMLinearBinaryClassificationTrainer implements Trainer<SVMLinearCl
      * @param data data to build model
      * @return model
      */
-    @Override public SVMLinearClassificationModel train(LabeledDataset data) {
+    @Override public SVMLinearBinaryClassificationModel train(LabeledDataset data) {
         isDistributed = data.isDistributed();
 
         final int weightVectorSizeWithIntercept = data.colSize() + 1;
@@ -62,7 +62,7 @@ public class SVMLinearBinaryClassificationTrainer implements Trainer<SVMLinearCl
             weights = weights.plus(deltaWeights); // creates new vector
         }
 
-        return new SVMLinearClassificationModel(weights.viewPart(1, weights.size() - 1), weights.get(0));
+        return new SVMLinearBinaryClassificationModel(weights.viewPart(1, weights.size() - 1), weights.get(0));
     }
 
     /** */

modules/ml/src/main/java/org/apache/ignite/ml/svm/SVMLinearMultiClassClassificationModel.java

@@ -0,0 +1,89 @@
+/*
+ * Licensed to the Apache Software Foundation (ASF) under one or more
+ * contributor license agreements.  See the NOTICE file distributed with
+ * this work for additional information regarding copyright ownership.
+ * The ASF licenses this file to You 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.
+ */
+
+package org.apache.ignite.ml.svm;
+
+import java.io.Serializable;
+import java.util.HashMap;
+import java.util.Map;
+import java.util.Objects;
+import java.util.TreeMap;
+import org.apache.ignite.ml.Exportable;
+import org.apache.ignite.ml.Exporter;
+import org.apache.ignite.ml.Model;
+import org.apache.ignite.ml.math.Vector;
+
+/** Base class for multi-classification model for set of SVM classifiers. */
+public class SVMLinearMultiClassClassificationModel implements Model<Vector, Double>, Exportable<SVMLinearMultiClassClassificationModel>, Serializable {
+    /** List of models associated with each class. */
+    private Map<Double, SVMLinearBinaryClassificationModel> models;
+
+    /** */
+    public SVMLinearMultiClassClassificationModel() {
+        this.models = new HashMap<>();
+    }
+
+    /** {@inheritDoc} */
+    @Override public Double apply(Vector input) {
+        TreeMap<Double, Double> maxMargins = new TreeMap<>();
+
+        models.forEach((k, v) -> maxMargins.put(input.dot(v.weights()) + v.intercept(), k));
+
+        return maxMargins.lastEntry().getValue();
+    }
+
+    /** {@inheritDoc} */
+    @Override public <P> void saveModel(Exporter<SVMLinearMultiClassClassificationModel, P> exporter, P path) {
+        exporter.save(this, path);
+    }
+
+    /** {@inheritDoc} */
+    @Override public boolean equals(Object o) {
+        if (this == o)
+            return true;
+        if (o == null || getClass() != o.getClass())
+            return false;
+        SVMLinearMultiClassClassificationModel mdl = (SVMLinearMultiClassClassificationModel)o;
+        return Objects.equals(models, mdl.models);
+    }
+
+    /** {@inheritDoc} */
+    @Override public int hashCode() {
+        return Objects.hash(models);
+    }
+
+    /** {@inheritDoc} */
+    @Override public String toString() {
+        StringBuilder wholeStr = new StringBuilder();
+
+        models.forEach((clsLb, mdl) -> {
+            wholeStr.append("The class with label " + clsLb + " has classifier: " + mdl.toString() + System.lineSeparator());
+        });
+
+        return wholeStr.toString();
+    }
+
+    /**
+     * Adds a specific SVM binary classifier to the bunch of same classifiers.
+     *
+     * @param clsLb The class label for the added model.
+     * @param mdl The model.
+     */
+    public void add(double clsLb, SVMLinearBinaryClassificationModel mdl) {
+        models.put(clsLb, mdl);
+    }
+}