Adds protobuf serialization for LibLinear models

AnomalyDetection/LibLinear/src/main/java/org/tribuo/anomaly/liblinear/LibLinearAnomalyModel.java

@@ -1,5 +1,5 @@
 /*
- * Copyright (c) 2021, Oracle and/or its affiliates. All rights reserved.
+ * Copyright (c) 2021, 2022 Oracle and/or its affiliates. All rights reserved.
  *
  * Licensed under the Apache License, Version 2.0 (the "License");
  * you may not use this file except in compliance with the License.
@@ -16,6 +16,8 @@
 
 package org.tribuo.anomaly.liblinear;
 
+import com.google.protobuf.Any;
+import com.google.protobuf.InvalidProtocolBufferException;
 import com.oracle.labs.mlrg.olcut.util.Pair;
 import org.tribuo.Example;
 import org.tribuo.Excuse;
@@ -27,10 +29,15 @@
 import org.tribuo.anomaly.Event;
 import org.tribuo.common.liblinear.LibLinearModel;
 import org.tribuo.common.liblinear.LibLinearTrainer;
+import org.tribuo.common.liblinear.protos.LibLinearModelProto;
+import org.tribuo.impl.ModelDataCarrier;
 import org.tribuo.provenance.ModelProvenance;
 import de.bwaldvogel.liblinear.FeatureNode;
 import de.bwaldvogel.liblinear.Linear;
 
+import java.io.ByteArrayInputStream;
+import java.io.IOException;
+import java.io.ObjectInputStream;
 import java.util.ArrayList;
 import java.util.Collections;
 import java.util.Comparator;
@@ -67,6 +74,42 @@ public class LibLinearAnomalyModel extends LibLinearModel<Event> {
         super(name, description, featureIDMap, outputIDInfo, false, models);
     }
 
+    /**
+     * Deserialization factory.
+     * @param version The serialized object version.
+     * @param className The class name.
+     * @param message The serialized data.
+     */
+    public static LibLinearAnomalyModel deserializeFromProto(int version, String className, Any message) throws InvalidProtocolBufferException {
+        if (version < 0 || version > CURRENT_VERSION) {
+            throw new IllegalArgumentException("Unknown version " + version + ", this class supports at most version " + CURRENT_VERSION);
+        }
+        if (!"org.tribuo.anomaly.liblinear.LibLinearAnomalyModel".equals(className)) {
+            throw new IllegalStateException("Invalid protobuf, this class can only deserialize LibLinearAnomalyModel");
+        }
+        LibLinearModelProto proto = message.unpack(LibLinearModelProto.class);
+
+        ModelDataCarrier<?> carrier = ModelDataCarrier.deserialize(proto.getMetadata());
+        if (!carrier.outputDomain().getOutput(0).getClass().equals(Event.class)) {
+            throw new IllegalStateException("Invalid protobuf, output domain is not an anomaly domain, found " + carrier.outputDomain().getClass());
+        }
+        @SuppressWarnings("unchecked") // guarded by getClass
+        ImmutableOutputInfo<Event> outputDomain = (ImmutableOutputInfo<Event>) carrier.outputDomain();
+
+        if (proto.getModelsCount() != 1) {
+            throw new IllegalStateException("Invalid protobuf, expected 1 model, found " + proto.getModelsCount());
+        }
+        try {
+            ByteArrayInputStream bais = new ByteArrayInputStream(proto.getModels(0).toByteArray());
+            ObjectInputStream ois = new ObjectInputStream(bais);
+            de.bwaldvogel.liblinear.Model model = (de.bwaldvogel.liblinear.Model) ois.readObject();
+            ois.close();
+            return new LibLinearAnomalyModel(carrier.name(),carrier.provenance(),carrier.featureDomain(),outputDomain,Collections.singletonList(model));
+        } catch (IOException | ClassNotFoundException e) {
+            throw new IllegalStateException("Invalid protobuf, failed to deserialize liblinear model", e);
+        }
+    }
+
     @Override
     public Prediction<Event> predict(Example<Event> example) {
         FeatureNode[] features = LibLinearTrainer.exampleToNodes(example, featureIDMap, null);

AnomalyDetection/LibLinear/src/test/java/org/tribuo/anomaly/liblinear/LibLinearAnomalyTrainerTest.java

@@ -1,5 +1,5 @@
 /*
- * Copyright (c) 2021, Oracle and/or its affiliates. All rights reserved.
+ * Copyright (c) 2021, 2022 Oracle and/or its affiliates. All rights reserved.
  *
  * Licensed under the Apache License, Version 2.0 (the "License");
  * you may not use this file except in compliance with the License.
@@ -66,6 +66,7 @@ public void gaussianDataTest() {
 
         // Test serialization
         Helpers.testModelSerialization(model,Event.class);
+        Helpers.testModelProtoSerialization(model,Event.class,testData);
     }
 
 }

Classification/LibLinear/src/main/java/org/tribuo/classification/liblinear/LibLinearClassificationModel.java

@@ -1,5 +1,5 @@
 /*
- * Copyright (c) 2015-2021, Oracle and/or its affiliates. All rights reserved.
+ * Copyright (c) 2015, 2022, Oracle and/or its affiliates. All rights reserved.
  *
  * Licensed under the Apache License, Version 2.0 (the "License");
  * you may not use this file except in compliance with the License.
@@ -17,6 +17,8 @@
 package org.tribuo.classification.liblinear;
 
 import ai.onnx.proto.OnnxMl;
+import com.google.protobuf.Any;
+import com.google.protobuf.InvalidProtocolBufferException;
 import com.oracle.labs.mlrg.olcut.util.Pair;
 import de.bwaldvogel.liblinear.FeatureNode;
 import de.bwaldvogel.liblinear.Linear;
@@ -31,6 +33,8 @@
 import org.tribuo.classification.Label;
 import org.tribuo.common.liblinear.LibLinearModel;
 import org.tribuo.common.liblinear.LibLinearTrainer;
+import org.tribuo.common.liblinear.protos.LibLinearModelProto;
+import org.tribuo.impl.ModelDataCarrier;
 import org.tribuo.provenance.ModelProvenance;
 import org.tribuo.util.onnx.ONNXContext;
 import org.tribuo.util.onnx.ONNXInitializer;
@@ -39,6 +43,9 @@
 import org.tribuo.util.onnx.ONNXPlaceholder;
 import org.tribuo.util.onnx.ONNXRef;
 
+import java.io.ByteArrayInputStream;
+import java.io.IOException;
+import java.io.ObjectInputStream;
 import java.util.ArrayList;
 import java.util.Arrays;
 import java.util.Collections;
@@ -104,6 +111,42 @@ public class LibLinearClassificationModel extends LibLinearModel<Label> implemen
         }
     }
 
+    /**
+     * Deserialization factory.
+     * @param version The serialized object version.
+     * @param className The class name.
+     * @param message The serialized data.
+     */
+    public static LibLinearClassificationModel deserializeFromProto(int version, String className, Any message) throws InvalidProtocolBufferException {
+        if (version < 0 || version > CURRENT_VERSION) {
+            throw new IllegalArgumentException("Unknown version " + version + ", this class supports at most version " + CURRENT_VERSION);
+        }
+        if (!"org.tribuo.classification.liblinear.LibLinearClassificationModel".equals(className)) {
+            throw new IllegalStateException("Invalid protobuf, this class can only deserialize LibLinearClassificationModel");
+        }
+        LibLinearModelProto proto = message.unpack(LibLinearModelProto.class);
+
+        ModelDataCarrier<?> carrier = ModelDataCarrier.deserialize(proto.getMetadata());
+        if (!carrier.outputDomain().getOutput(0).getClass().equals(Label.class)) {
+            throw new IllegalStateException("Invalid protobuf, output domain is not a label domain, found " + carrier.outputDomain().getClass());
+        }
+        @SuppressWarnings("unchecked") // guarded by getClass
+        ImmutableOutputInfo<Label> outputDomain = (ImmutableOutputInfo<Label>) carrier.outputDomain();
+
+        if (proto.getModelsCount() != 1) {
+            throw new IllegalStateException("Invalid protobuf, expected 1 model, found " + proto.getModelsCount());
+        }
+        try {
+            ByteArrayInputStream bais = new ByteArrayInputStream(proto.getModels(0).toByteArray());
+            ObjectInputStream ois = new ObjectInputStream(bais);
+            de.bwaldvogel.liblinear.Model model = (de.bwaldvogel.liblinear.Model) ois.readObject();
+            ois.close();
+            return new LibLinearClassificationModel(carrier.name(),carrier.provenance(),carrier.featureDomain(),outputDomain,Collections.singletonList(model));
+        } catch (IOException | ClassNotFoundException e) {
+            throw new IllegalStateException("Invalid protobuf, failed to deserialize liblinear model", e);
+        }
+    }
+
     @Override
     public Prediction<Label> predict(Example<Label> example) {
         FeatureNode[] features = LibLinearTrainer.exampleToNodes(example, featureIDMap, null);

Classification/LibLinear/src/test/java/org/tribuo/classification/liblinear/TestLibLinearModel.java

@@ -212,6 +212,8 @@ public void testDenseData() {
 
         // Test serialization
         Helpers.testModelSerialization(model,Label.class);
+
+        Helpers.testModelProtoSerialization(model, Label.class, p.getB());
     }
 
     @Test

Common/LibLinear/src/main/java/org/tribuo/common/liblinear/LibLinearModel.java

@@ -1,5 +1,5 @@
 /*
- * Copyright (c) 2015-2021, Oracle and/or its affiliates. All rights reserved.
+ * Copyright (c) 2015, 2022, Oracle and/or its affiliates. All rights reserved.
  *
  * Licensed under the Apache License, Version 2.0 (the "License");
  * you may not use this file except in compliance with the License.
@@ -16,23 +16,36 @@
 
 package org.tribuo.common.liblinear;
 
+import com.google.protobuf.Any;
+import com.google.protobuf.ByteString;
 import de.bwaldvogel.liblinear.Linear;
+import de.bwaldvogel.liblinear.SolverType;
 import org.tribuo.Example;
 import org.tribuo.Excuse;
 import org.tribuo.ImmutableFeatureMap;
 import org.tribuo.ImmutableOutputInfo;
 import org.tribuo.Model;
 import org.tribuo.Output;
+import org.tribuo.common.liblinear.protos.LibLinearModelProto;
+import org.tribuo.common.liblinear.protos.LibLinearProto;
+import org.tribuo.impl.ModelDataCarrier;
+import org.tribuo.protos.core.ModelProto;
 import org.tribuo.provenance.ModelProvenance;
+import org.tribuo.util.Util;
 
+import java.io.ByteArrayOutputStream;
 import java.io.IOException;
+import java.io.ObjectOutputStream;
 import java.io.StringReader;
 import java.io.StringWriter;
+import java.lang.reflect.Field;
 import java.util.ArrayList;
+import java.util.Arrays;
 import java.util.Collections;
 import java.util.List;
 import java.util.Optional;
 import java.util.logging.Logger;
+import java.util.stream.Collectors;
 
 /**
  * A {@link Model} which wraps a LibLinear-java model.
@@ -57,6 +70,11 @@ public abstract class LibLinearModel<T extends Output<T>> extends Model<T> {
 
     private static final Logger logger = Logger.getLogger(LibLinearModel.class.getName());
 
+    /**
+     * Protobuf serialization version.
+     */
+    public static final int CURRENT_VERSION = 0;
+
     /**
      * The list of LibLinear models. Multiple models are used by multi-label and multidimensional regression outputs.
      * <p>
@@ -163,4 +181,97 @@ protected static de.bwaldvogel.liblinear.Model copyModel(de.bwaldvogel.liblinear
      * @return An excuse for this example.
      */
     protected abstract Excuse<T> innerGetExcuse(Example<T> e, double[][] featureWeights);
+
+    @Override
+    public ModelProto serialize() {
+        ModelDataCarrier<T> carrier = createDataCarrier();
+
+        LibLinearModelProto.Builder modelBuilder = LibLinearModelProto.newBuilder();
+        modelBuilder.setMetadata(carrier.serialize());
+        for (de.bwaldvogel.liblinear.Model m : models) {
+            try {
+                ByteArrayOutputStream baos = new ByteArrayOutputStream();
+                ObjectOutputStream oos = new ObjectOutputStream(baos);
+                oos.writeObject(m);
+                oos.close();
+                modelBuilder.addModels(ByteString.copyFrom(baos.toByteArray()));
+            } catch (IOException e) {
+                throw new IllegalStateException("Could not serialize liblinear model to byte array");
+            }
+        }
+
+        ModelProto.Builder builder = ModelProto.newBuilder();
+        builder.setSerializedData(Any.pack(modelBuilder.build()));
+        builder.setClassName(this.getClass().getName());
+        builder.setVersion(CURRENT_VERSION);
+
+        return builder.build();
+    }
+
+    /**
+     * Serialize the LibLinear model into a protobuf.
+     * <p>
+     * Note deserializing {@link LibLinearProto} requires reflective access into {@code de.bwaldvogel.liblinear.Model}
+     * and thus requires additional command line permissions when running with the module system. For the time
+     * being we use Java serialization to a byte array rather than this method and {@code LibLinearProto}.
+     * @param model The model to serialize.
+     * @return The protobuf.
+     */
+    private static LibLinearProto serializeModel(de.bwaldvogel.liblinear.Model model) {
+        LibLinearProto.Builder builder = LibLinearProto.newBuilder();
+
+        builder.setBias(model.getBias());
+        builder.addAllLabel(Arrays.stream(model.getLabels()).boxed().collect(Collectors.toList()));
+        builder.setNrClass(model.getNrClass());
+        builder.setNrFeature(model.getNrFeature());
+        builder.setSolverType(model.getSolverType().name());
+        builder.addAllW(Arrays.stream(model.getFeatureWeights()).boxed().collect(Collectors.toList()));
+        if (model.getSolverType().isOneClass()) {
+            builder.setRho(model.getDecfunRho());
+        }
+
+        return builder.build();
+    }
+
+    /**
+     * Deserialize a LibLinear model from a protobuf.
+     * <p>
+     * Note this method requires reflective access into {@code de.bwaldvogel.liblinear.Model} and thus
+     * requires additional command line permissions when running with the module system. For the time
+     * being we use Java serialization to a byte array rather than this method and {@code LibLinearProto}.
+     * @param proto The protobuf to deserialize.
+     * @return The model.
+     */
+    private static de.bwaldvogel.liblinear.Model deserializeModels(LibLinearProto proto) {
+        de.bwaldvogel.liblinear.Model model = new de.bwaldvogel.liblinear.Model();
+        Class<de.bwaldvogel.liblinear.Model> modelClass = de.bwaldvogel.liblinear.Model.class;
+        setField(modelClass, "bias", model, proto.getBias());
+        setField(modelClass, "label", model, Util.toPrimitiveInt(proto.getLabelList()));
+        setField(modelClass, "nr_class", model, proto.getNrClass());
+        setField(modelClass, "nr_feature", model, proto.getNrFeature());
+        setField(modelClass, "solverType", model, SolverType.valueOf(proto.getSolverType()));
+        setField(modelClass, "w", model, Util.toPrimitiveDouble(proto.getWList()));
+        setField(modelClass, "rho", model, proto.getRho());
+        return model;
+    }
+
+    /**
+     * Sets a field on the supplied object.
+     * <p>
+     * Wraps any exceptions in {@link IllegalStateException}.
+     * @param clazz The class.
+     * @param fieldName The field name to set.
+     * @param host The host object.
+     * @param value The new field value.
+     */
+    private static <U> void setField(Class<U> clazz, String fieldName, U host, Object value) {
+        try {
+            Field biasField = clazz.getField(fieldName);
+            biasField.setAccessible(true);
+            biasField.set(host, value);
+            biasField.setAccessible(false);
+        } catch (NoSuchFieldException | IllegalAccessException e) {
+            throw new IllegalStateException("Failed to write to field " + fieldName, e);
+        }
+    }
 }