Feature/proto serialization

build.gradle

@@ -39,7 +39,7 @@ repositories {
 
 dependencies {
     // Protobuf
-    compile group: 'com.google.protobuf', name: 'protobuf-java', version: '3.11.4'
+    compile group: 'com.google.protobuf', name: 'protobuf-javalite', version: '3.11.4'
     // Swagger codegen dependencies
     swaggerCodegen 'io.swagger:swagger-codegen-cli:2.4.2'             // Swagger Codegen V2
     // This dependency is exported to consumers, that is to say found on their compile classpath.
@@ -80,6 +80,11 @@ jacocoTestReport {
         xml.enabled = true
         csv.enabled = false
     }
+    afterEvaluate {
+        classDirectories.setFrom(files(classDirectories.files.collect {
+            fileTree(dir: it, exclude: 'tech/donau/behaiv/proto**')
+        }))
+    }
 }
 
 task proto(type: Exec) {

proto.sh

@@ -1 +1 @@
- protoc -I=. --java_out=./src/main/java ./behaiv.proto
+ protoc -I=. --java_out=lite:./src/main/java ./behaiv.proto

src/main/java/de/dmi3y/behaiv/kernel/BaseKernel.java

@@ -0,0 +1,103 @@
+package de.dmi3y.behaiv.kernel;
+
+import com.fasterxml.jackson.core.type.TypeReference;
+import com.fasterxml.jackson.databind.ObjectMapper;
+import com.google.protobuf.CodedOutputStream;
+import de.dmi3y.behaiv.storage.BehaivStorage;
+import de.dmi3y.behaiv.tools.DataMappingUtils;
+import de.dmi3y.behaiv.tools.Pair;
+import tech.donau.behaiv.proto.PredictionSet;
+
+import java.io.BufferedReader;
+import java.io.BufferedWriter;
+import java.io.File;
+import java.io.FileInputStream;
+import java.io.FileNotFoundException;
+import java.io.FileOutputStream;
+import java.io.FileReader;
+import java.io.FileWriter;
+import java.io.IOException;
+import java.util.ArrayList;
+import java.util.List;
+
+public abstract class BaseKernel implements Kernel {
+
+    protected String id;
+    protected Long treshold = 10L;
+    protected ObjectMapper objectMapper;
+    protected boolean partialFitAllowed = false;
+    protected boolean alwaysKeepData = true;
+
+    public BaseKernel(String id) {
+        this.id = id;
+        objectMapper = new ObjectMapper();
+    }
+
+
+    @Override
+    public void setId(String id) {
+        this.id = id;
+    }
+
+
+    //list<features>, label
+    protected List<Pair<List<Double>, String>> data = new ArrayList<>();
+
+
+    @Override
+    public void fit() {
+        fit(this.data);
+    }
+
+    @Override
+    public void setTreshold(Long treshold) {
+        this.treshold = treshold;
+    }
+
+    @Override
+    public boolean readyToPredict() {
+        return data.size() > treshold;
+    }
+
+    @Override
+    public void update(List<Pair<List<Double>, String>> data) {
+    }
+
+    @Override
+    public boolean isPartialFitAllowed() {
+        return partialFitAllowed;
+    }
+
+    @Override
+    public void updateSingle(List<Double> features, String label) {
+        data.add(new Pair<>(features, label));
+    }
+
+    @Override
+    public boolean isAlwaysKeepData() {
+        return alwaysKeepData;
+    }
+
+    @Override
+    public void setAlwaysKeepData(boolean alwaysKeepData) {
+        this.alwaysKeepData = alwaysKeepData;
+    }
+
+    @Override
+    public void save(BehaivStorage storage) throws IOException {
+        final File dataFile = storage.getDataFile(id);
+        final PredictionSet predictionSet = DataMappingUtils.createPredictionSet(data);
+        try(FileOutputStream fileOutputStream = new FileOutputStream(dataFile)) {
+            predictionSet.writeTo(fileOutputStream);
+        }
+    }
+
+    @Override
+    public void restore(BehaivStorage storage) throws IOException {
+        final File dataFile = storage.getDataFile(id);
+        try (FileInputStream fileInputStream = new FileInputStream(dataFile)) {
+            final PredictionSet predictionSet = PredictionSet.parseFrom(fileInputStream);
+            data = DataMappingUtils.dataFromPredictionSet(predictionSet);
+        }
+    }
+}

src/main/java/de/dmi3y/behaiv/kernel/Kernel.java

@@ -1,95 +1,37 @@
 package de.dmi3y.behaiv.kernel;
 
-import com.fasterxml.jackson.core.type.TypeReference;
-import com.fasterxml.jackson.databind.ObjectMapper;
 import de.dmi3y.behaiv.storage.BehaivStorage;
 import de.dmi3y.behaiv.tools.Pair;
 
-import java.io.BufferedReader;
-import java.io.BufferedWriter;
-import java.io.FileReader;
-import java.io.FileWriter;
 import java.io.IOException;
-import java.util.ArrayList;
 import java.util.List;
 
-public abstract class Kernel {
+public interface Kernel {
+    void setId(String id);
 
-    protected String id;
-    protected Long treshold = 10L;
-    protected ObjectMapper objectMapper;
-    protected boolean partialFitAllowed = false;
-    protected boolean alwaysKeepData = true;
+    boolean isEmpty();
 
-    public Kernel(String id) {
-        this.id = id;
-        objectMapper = new ObjectMapper();
-    }
+    void fit(List<Pair<List<Double>, String>> data);
 
+    void fit();
 
-    public void setId(String id) {
-        this.id = id;
-    }
+    void setTreshold(Long treshold);
 
+    boolean readyToPredict();
 
-    //list<features>, label
-    protected List<Pair<List<Double>, String>> data = new ArrayList<>();
+    void update(List<Pair<List<Double>, String>> data);
 
+    boolean isPartialFitAllowed();
 
-    public abstract boolean isEmpty();
+    void updateSingle(List<Double> features, String label);
 
-    public abstract void fit(List<Pair<List<Double>, String>> data);
+    String predictOne(List<Double> features);
 
-    public void fit() {
-        fit(this.data);
-    }
+    boolean isAlwaysKeepData();
 
-    public void setTreshold(Long treshold) {
-        this.treshold = treshold;
-    }
+    void setAlwaysKeepData(boolean alwaysKeepData);
 
-    public boolean readyToPredict() {
-        return data.size() > treshold;
-    }
+    void save(BehaivStorage storage) throws IOException;
 
-    public void update(List<Pair<List<Double>, String>> data) {
-    }
-
-    public boolean isPartialFitAllowed() {
-        return partialFitAllowed;
-    }
-
-    public void updateSingle(List<Double> features, String label) {
-        data.add(new Pair<>(features, label));
-    }
-
-    public abstract String predictOne(List<Double> features);
-
-    public boolean isAlwaysKeepData() {
-        return alwaysKeepData;
-    }
-
-    public void setAlwaysKeepData(boolean alwaysKeepData) {
-        this.alwaysKeepData = alwaysKeepData;
-    }
-
-    public void save(BehaivStorage storage) throws IOException {
-
-        try (final BufferedWriter writer = new BufferedWriter(new FileWriter(storage.getDataFile(id)))) {
-            writer.write(objectMapper.writeValueAsString(data));
-        }
-    }
-
-    public void restore(BehaivStorage storage) throws IOException {
-        final TypeReference<List<Pair<List<Double>, String>>> typeReference = new TypeReference<List<Pair<List<Double>, String>>>() {
-        };
-        try (final BufferedReader reader = new BufferedReader(new FileReader(storage.getDataFile(id)))) {
-            final String content = reader.readLine();
-            if (content == null || content.isEmpty()) {
-                data = new ArrayList<>();
-            } else {
-                data = objectMapper.readValue(content, typeReference);
-            }
-        }
-    }
+    void restore(BehaivStorage storage) throws IOException;
 }

src/main/java/de/dmi3y/behaiv/kernel/LogisticRegressionKernel.java

@@ -17,7 +17,7 @@
 import static de.dmi3y.behaiv.tools.DataMappingUtils.toDistinctListOfPairValues;
 import static de.dmi3y.behaiv.tools.DataMappingUtils.toInput2dArray;
 
-public class LogisticRegressionKernel extends Kernel {
+public class LogisticRegressionKernel extends BaseKernel {
 
     protected List<String> labels = new ArrayList<>();
     private Random rand;

src/main/java/de/dmi3y/behaiv/tools/DataMappingUtils.java

@@ -1,6 +1,9 @@
 package de.dmi3y.behaiv.tools;
 
 import org.apache.commons.lang3.ArrayUtils;
+import tech.donau.behaiv.proto.Data;
+import tech.donau.behaiv.proto.Prediction;
+import tech.donau.behaiv.proto.PredictionSet;
 
 import java.util.ArrayList;
 import java.util.HashSet;
@@ -14,6 +17,38 @@ private DataMappingUtils() {
         // Unused utility class
     }
 
+    public static PredictionSet createPredictionSet(List<Pair<List<Double>, String>> data) {
+        final PredictionSet.Builder predictionSetBuilder = PredictionSet.newBuilder();
+        for (int i = 0; i < data.size(); i++) {
+            final List<Double> weights = data.get(i).getKey();
+            final ArrayList<Data> row = new ArrayList<>();
+            for (int j = 0; j < weights.size(); j++) {
+                row.add(Data.newBuilder().setKey("key"+j).setValue(weights.get(j)).build());
+            }
+            predictionSetBuilder.addPrediction(
+                    Prediction.newBuilder()
+                            .addAllData(row)
+                            .setLabel(data.get(i).getValue())
+                            .build()
+            );
+        }
+        return predictionSetBuilder.build();
+    }
+
+    public static List<Pair<List<Double>, String>> dataFromPredictionSet(PredictionSet predictionSet) {
+        final ArrayList<Pair<List<Double>, String>> data = new ArrayList<>();
+        for (int i = 0; i < predictionSet.getPredictionCount(); i++) {
+            final Prediction prediction = predictionSet.getPrediction(i);
+            final List<Data> dataList = prediction.getDataList();
+            final String label = prediction.getLabel();
+            final List<Double> entries = new ArrayList<>();
+            for (Data entry : dataList) {
+                entries.add(entry.getValue());
+            }
+            data.add(Pair.create(entries, label));
+        }
+        return data;
+    }
 
     public static List<String> toDistinctListOfPairValues(List<Pair<List<Double>, String>> data) {
         Set<String> setOfValues = new HashSet<>();