Adds SGD protobuf serialization

Classification/SGD/src/main/java/org/tribuo/classification/sgd/crf/CRFModel.java

@@ -1,5 +1,5 @@
 /*
- * Copyright (c) 2015-2020, 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.
@@ -16,6 +16,8 @@
 
 package org.tribuo.classification.sgd.crf;
 
+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.ImmutableFeatureMap;
@@ -24,10 +26,14 @@
 import org.tribuo.Prediction;
 import org.tribuo.classification.Label;
 import org.tribuo.classification.sequence.ConfidencePredictingSequenceModel;
+import org.tribuo.classification.sgd.protos.CRFModelProto;
+import org.tribuo.impl.ModelDataCarrier;
+import org.tribuo.math.Parameters;
 import org.tribuo.math.la.DenseVector;
 import org.tribuo.math.la.SGDVector;
 import org.tribuo.math.la.SparseVector;
 import org.tribuo.math.la.Tensor;
+import org.tribuo.protos.core.SequenceModelProto;
 import org.tribuo.provenance.ModelProvenance;
 import org.tribuo.sequence.SequenceExample;
 
@@ -59,6 +65,11 @@ public class CRFModel extends ConfidencePredictingSequenceModel {
     private static final Logger logger = Logger.getLogger(CRFModel.class.getName());
     private static final long serialVersionUID = 2L;
 
+    /**
+     * Protobuf serialization version.
+     */
+    public static final int CURRENT_VERSION = 0;
+
     private final CRFParameters parameters;
 
     /**
@@ -87,6 +98,37 @@ public enum ConfidenceType {
         this.confidenceType = ConfidenceType.NONE;
     }
 
+    /**
+     * Deserialization factory.
+     * @param version The serialized object version.
+     * @param className The class name.
+     * @param message The serialized data.
+     */
+    public static CRFModel 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);
+        }
+        CRFModelProto proto = message.unpack(CRFModelProto.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();
+
+        Parameters params = Parameters.deserialize(proto.getParams());
+        if (!(params instanceof CRFParameters)) {
+            throw new IllegalStateException("Invalid protobuf, parameters must be CRFParameters, found " + params.getClass());
+        }
+
+        ConfidenceType confidenceType = ConfidenceType.valueOf(proto.getConfidenceType());
+
+        CRFModel model = new CRFModel(carrier.name(),carrier.provenance(),carrier.featureDomain(),outputDomain,(CRFParameters) params);
+        model.confidenceType = confidenceType;
+        return model;
+    }
+
     /**
      * Sets the inference method used for confidence prediction.
      * If CONSTRAINED_BP uses the constrained belief propagation algorithm from Culotta and McCallum 2004,
@@ -257,6 +299,22 @@ public String generateWeightsString() {
         return buffer.toString();
     }
 
+    @Override
+    public SequenceModelProto serialize() {
+        ModelDataCarrier<Label> carrier = createDataCarrier();
+        CRFModelProto.Builder modelBuilder = CRFModelProto.newBuilder();
+        modelBuilder.setConfidenceType(confidenceType.name());
+        modelBuilder.setMetadata(carrier.serialize());
+        modelBuilder.setParams(parameters.serialize());
+
+        SequenceModelProto.Builder builder = SequenceModelProto.newBuilder();
+        builder.setVersion(CURRENT_VERSION);
+        builder.setClassName(CRFModel.class.getName());
+        builder.setSerializedData(Any.pack(modelBuilder.build()));
+
+        return builder.build();
+    }
+
     /**
      * Converts a {@link SequenceExample} into an array of {@link SparseVector}s suitable for CRF prediction.
      * @deprecated As it's replaced with {@link #convertToVector} which is more flexible.

Classification/SGD/src/main/java/org/tribuo/classification/sgd/fm/FMClassificationModel.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.
@@ -16,16 +16,22 @@
 
 package org.tribuo.classification.sgd.fm;
 
+import com.google.protobuf.Any;
+import com.google.protobuf.InvalidProtocolBufferException;
 import org.tribuo.Example;
 import org.tribuo.ImmutableFeatureMap;
 import org.tribuo.ImmutableOutputInfo;
 import org.tribuo.ONNXExportable;
 import org.tribuo.Prediction;
 import org.tribuo.classification.Label;
+import org.tribuo.classification.sgd.protos.FMClassificationModelProto;
 import org.tribuo.common.sgd.AbstractFMModel;
 import org.tribuo.common.sgd.FMParameters;
+import org.tribuo.impl.ModelDataCarrier;
+import org.tribuo.math.Parameters;
 import org.tribuo.math.la.DenseVector;
 import org.tribuo.math.util.VectorNormalizer;
+import org.tribuo.protos.core.ModelProto;
 import org.tribuo.provenance.ModelProvenance;
 import org.tribuo.util.onnx.ONNXNode;
 
@@ -45,6 +51,11 @@
 public class FMClassificationModel extends AbstractFMModel<Label> implements ONNXExportable {
     private static final long serialVersionUID = 1L;
 
+    /**
+     * Protobuf serialization version.
+     */
+    public static final int CURRENT_VERSION = 0;
+
     private final VectorNormalizer normalizer;
 
     /**
@@ -64,6 +75,35 @@ public class FMClassificationModel extends AbstractFMModel<Label> implements ONN
         this.normalizer = normalizer;
     }
 
+    /**
+     * Deserialization factory.
+     * @param version The serialized object version.
+     * @param className The class name.
+     * @param message The serialized data.
+     */
+    public static FMClassificationModel 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);
+        }
+        FMClassificationModelProto proto = message.unpack(FMClassificationModelProto.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();
+
+        Parameters params = Parameters.deserialize(proto.getParams());
+        if (!(params instanceof FMParameters)) {
+            throw new IllegalStateException("Invalid protobuf, parameters must be FMParameters, found " + params.getClass());
+        }
+
+        VectorNormalizer normalizer = VectorNormalizer.deserialize(proto.getNormalizer());
+
+        return new FMClassificationModel(carrier.name(),carrier.provenance(),carrier.featureDomain(),outputDomain,(FMParameters) params, normalizer, carrier.generatesProbabilities());
+    }
+
     @Override
     public Prediction<Label> predict(Example<Label> example) {
         PredAndActive predTuple = predictSingle(example);
@@ -86,6 +126,22 @@ public Prediction<Label> predict(Example<Label> example) {
         return new Prediction<>(maxLabel, predMap, predTuple.numActiveFeatures, example, generatesProbabilities);
     }
 
+    @Override
+    public ModelProto serialize() {
+        ModelDataCarrier<Label> carrier = createDataCarrier();
+        FMClassificationModelProto.Builder modelBuilder = FMClassificationModelProto.newBuilder();
+        modelBuilder.setMetadata(carrier.serialize());
+        modelBuilder.setParams(modelParameters.serialize());
+        modelBuilder.setNormalizer(normalizer.serialize());
+
+        ModelProto.Builder builder = ModelProto.newBuilder();
+        builder.setVersion(CURRENT_VERSION);
+        builder.setClassName(FMClassificationModel.class.getName());
+        builder.setSerializedData(Any.pack(modelBuilder.build()));
+
+        return builder.build();
+    }
+
     @Override
     protected FMClassificationModel copy(String newName, ModelProvenance newProvenance) {
         return new FMClassificationModel(newName,newProvenance,featureIDMap,outputIDInfo,(FMParameters)modelParameters.copy(),normalizer,generatesProbabilities);

Classification/SGD/src/main/java/org/tribuo/classification/sgd/kernel/KernelSVMModel.java

@@ -1,5 +1,5 @@
 /*
- * Copyright (c) 2015-2020, 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.
@@ -16,6 +16,8 @@
 
 package org.tribuo.classification.sgd.kernel;
 
+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;
@@ -24,10 +26,15 @@
 import org.tribuo.Model;
 import org.tribuo.Prediction;
 import org.tribuo.classification.Label;
+import org.tribuo.classification.sgd.protos.KernelSVMModelProto;
+import org.tribuo.impl.ModelDataCarrier;
 import org.tribuo.math.kernel.Kernel;
 import org.tribuo.math.la.DenseMatrix;
 import org.tribuo.math.la.DenseVector;
 import org.tribuo.math.la.SparseVector;
+import org.tribuo.math.la.Tensor;
+import org.tribuo.math.protos.TensorProto;
+import org.tribuo.protos.core.ModelProto;
 import org.tribuo.provenance.ModelProvenance;
 
 import java.util.Collections;
@@ -49,6 +56,11 @@
 public class KernelSVMModel extends Model<Label> {
     private static final long serialVersionUID = 2L;
 
+    /**
+     * Protobuf serialization version.
+     */
+    public static final int CURRENT_VERSION = 0;
+
     private final Kernel kernel;
     private final SparseVector[] supportVectors;
     private final DenseMatrix weights;
@@ -62,6 +74,58 @@ public class KernelSVMModel extends Model<Label> {
         this.weights = weights;
     }
 
+    /**
+     * Deserialization factory.
+     * @param version The serialized object version.
+     * @param className The class name.
+     * @param message The serialized data.
+     */
+    public static KernelSVMModel 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);
+        }
+        KernelSVMModelProto proto = message.unpack(KernelSVMModelProto.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();
+
+        SparseVector[] supportVectors = new SparseVector[proto.getSupportVectorsCount()];
+        int featureSize = carrier.featureDomain().size() + 1;
+        List<TensorProto> supportProtos = proto.getSupportVectorsList();
+        for (int i = 0; i < supportProtos.size(); i++) {
+            Tensor tensor = Tensor.deserialize(supportProtos.get(i));
+            if (!(tensor instanceof SparseVector)) {
+                throw new IllegalStateException("Invalid protobuf, support vector must be a sparse vector, found " + tensor.getClass());
+            }
+            SparseVector vec = (SparseVector) tensor;
+            if (vec.size() != featureSize) {
+                throw new IllegalStateException("Invalid protobuf, support vector size must equal feature domain size, found " + vec.size() + ", expected " + featureSize);
+            }
+            supportVectors[i] = vec;
+        }
+
+        Tensor weightTensor = Tensor.deserialize(proto.getWeights());
+        if (!(weightTensor instanceof DenseMatrix)) {
+            throw new IllegalStateException("Invalid protobuf, weights must be a dense matrix, found " + weightTensor.getClass());
+        }
+        DenseMatrix weights = (DenseMatrix) weightTensor;
+        if (weights.getDimension1Size() != carrier.outputDomain().size()) {
+            throw new IllegalStateException("Invalid protobuf, weights not the right size, expected " + carrier.outputDomain().size() + ", found " + weights.getDimension1Size());
+        }
+        if (weights.getDimension2Size() != supportVectors.length) {
+            throw new IllegalStateException("Invalid protobuf, weights not the right size, expected " + supportVectors.length + ", found " + weights.getDimension2Size());
+        }
+
+        Kernel kernel = Kernel.deserialize(proto.getKernel());
+
+        return new KernelSVMModel(carrier.name(), carrier.provenance(), carrier.featureDomain(), outputDomain,
+                kernel, supportVectors, weights);
+    }
+
     /**
      * Returns the number of support vectors used.
      * @return The number of support vectors.
@@ -109,6 +173,25 @@ public Optional<Excuse<Label>> getExcuse(Example<Label> example) {
         return Optional.empty();
     }
 
+    @Override
+    public ModelProto serialize() {
+        ModelDataCarrier<Label> carrier = createDataCarrier();
+        KernelSVMModelProto.Builder modelBuilder = KernelSVMModelProto.newBuilder();
+        modelBuilder.setMetadata(carrier.serialize());
+        modelBuilder.setKernel(kernel.serialize());
+        modelBuilder.setWeights(weights.serialize());
+        for (SparseVector v : supportVectors) {
+            modelBuilder.addSupportVectors(v.serialize());
+        }
+
+        ModelProto.Builder builder = ModelProto.newBuilder();
+        builder.setVersion(CURRENT_VERSION);
+        builder.setClassName(KernelSVMModel.class.getName());
+        builder.setSerializedData(Any.pack(modelBuilder.build()));
+
+        return builder.build();
+    }
+
     @Override
     protected KernelSVMModel copy(String newName, ModelProvenance newProvenance) {
         SparseVector[] vectorCopies = new SparseVector[supportVectors.length];