From e4e4e9ec832dac9c5e4b565758af28a9d17b2e8d Mon Sep 17 00:00:00 2001
From: Yaliang Wu <ylwu@amazon.com>
Date: Thu, 24 Mar 2022 22:18:04 -0700
Subject: [PATCH] use protostuff to serialize/deserialize RCF model

Signed-off-by: Yaliang Wu <ylwu@amazon.com>
---
 ml-algorithms/build.gradle                    |   4 ++
 .../lib/randomcutforest-core-2.0.1.jar        | Bin 224943 -> 234947 bytes
 .../randomcutforest-parkservices-2.0.1.jar    | Bin 72934 -> 72866 bytes
 .../algorithms/rcf/BatchRandomCutForest.java  |  11 ++--
 .../rcf/FixedInTimeRandomCutForest.java       |  11 ++--
 .../algorithms/rcf/RCFModelSerDeSer.java      |  59 +++++++++++++++++
 .../ml/engine/utils/ModelSerDeSer.java        |   9 ++-
 .../opensearch/ml/engine/MLEngineTest.java    |  14 ++--
 .../ml/engine/ModelSerDeSerTest.java          |  43 ++++++------
 .../algorithms/clustering/KMeansTest.java     |   6 +-
 .../algorithms/rcf/RCFModelSerDeSerTest.java  |  61 ++++++++++++++++++
 .../{KMeansHelper.java => MLTestHelper.java}  |  28 ++++++--
 .../ml/action/MLCommonsIntegTestCase.java     |  39 ++++++++++-
 .../action/prediction/PredictionITTests.java  |  53 ++++++++++++++-
 .../org/opensearch/ml/utils/TestData.java     |  43 +++++++++++-
 15 files changed, 322 insertions(+), 59 deletions(-)
 create mode 100644 ml-algorithms/src/main/java/org/opensearch/ml/engine/algorithms/rcf/RCFModelSerDeSer.java
 create mode 100644 ml-algorithms/src/test/java/org/opensearch/ml/engine/algorithms/rcf/RCFModelSerDeSerTest.java
 rename ml-algorithms/src/test/java/org/opensearch/ml/engine/helper/{KMeansHelper.java => MLTestHelper.java} (58%)

diff --git a/ml-algorithms/build.gradle b/ml-algorithms/build.gradle
index 8aef8cf349..bd3e14adf4 100644
--- a/ml-algorithms/build.gradle
+++ b/ml-algorithms/build.gradle
@@ -23,6 +23,10 @@ dependencies {
     implementation group: 'commons-io', name: 'commons-io', version: '2.11.0'
     implementation files('lib/randomcutforest-parkservices-2.0.1.jar')
     implementation files('lib/randomcutforest-core-2.0.1.jar')
+    implementation group: 'io.protostuff', name: 'protostuff-core', version: '1.8.0'
+    implementation group: 'io.protostuff', name: 'protostuff-runtime', version: '1.8.0'
+    implementation group: 'io.protostuff', name: 'protostuff-api', version: '1.8.0'
+    implementation group: 'io.protostuff', name: 'protostuff-collectionschema', version: '1.8.0'
     testImplementation group: 'junit', name: 'junit', version: '4.12'
     testImplementation group: 'org.mockito', name: 'mockito-core', version: '4.4.0'
     testImplementation group: 'org.mockito', name: 'mockito-inline', version: '4.4.0'
diff --git a/ml-algorithms/lib/randomcutforest-core-2.0.1.jar b/ml-algorithms/lib/randomcutforest-core-2.0.1.jar
index 4b0f6b79f06fad347f92f6243b3bfba6cdc00125..0131d21633db692b8e23ad90a6fbeddeb7ab09d3 100644
GIT binary patch
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diff --git a/ml-algorithms/lib/randomcutforest-parkservices-2.0.1.jar b/ml-algorithms/lib/randomcutforest-parkservices-2.0.1.jar
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SrX-jZ_&3N2kpd0ytp7ij9)a)x

diff --git a/ml-algorithms/src/main/java/org/opensearch/ml/engine/algorithms/rcf/BatchRandomCutForest.java b/ml-algorithms/src/main/java/org/opensearch/ml/engine/algorithms/rcf/BatchRandomCutForest.java
index e09514364c..4446e816fd 100644
--- a/ml-algorithms/src/main/java/org/opensearch/ml/engine/algorithms/rcf/BatchRandomCutForest.java
+++ b/ml-algorithms/src/main/java/org/opensearch/ml/engine/algorithms/rcf/BatchRandomCutForest.java
@@ -9,20 +9,19 @@
 import com.amazon.randomcutforest.state.RandomCutForestMapper;
 import com.amazon.randomcutforest.state.RandomCutForestState;
 import lombok.extern.log4j.Log4j2;
+import org.opensearch.ml.common.FunctionName;
+import org.opensearch.ml.common.Model;
 import org.opensearch.ml.common.dataframe.ColumnMeta;
 import org.opensearch.ml.common.dataframe.ColumnValue;
 import org.opensearch.ml.common.dataframe.DataFrame;
 import org.opensearch.ml.common.dataframe.DataFrameBuilder;
 import org.opensearch.ml.common.dataframe.Row;
-import org.opensearch.ml.common.input.parameter.rcf.BatchRCFParams;
-import org.opensearch.ml.common.FunctionName;
 import org.opensearch.ml.common.input.parameter.MLAlgoParams;
+import org.opensearch.ml.common.input.parameter.rcf.BatchRCFParams;
 import org.opensearch.ml.common.output.MLOutput;
 import org.opensearch.ml.common.output.MLPredictionOutput;
-import org.opensearch.ml.common.Model;
 import org.opensearch.ml.engine.TrainAndPredictable;
 import org.opensearch.ml.engine.annotation.Function;
-import org.opensearch.ml.engine.utils.ModelSerDeSer;
 
 import java.util.ArrayList;
 import java.util.HashMap;
@@ -68,7 +67,7 @@ public MLOutput predict(DataFrame dataFrame, Model model) {
         if (model == null) {
             throw new IllegalArgumentException("No model found for batch RCF prediction.");
         }
-        RandomCutForestState state = (RandomCutForestState) ModelSerDeSer.deserialize(model.getContent());
+        RandomCutForestState state = RCFModelSerDeSer.deserializeRCF(model.getContent());
         RandomCutForest forest = rcfMapper.toModel(state);
         List<Map<String, Object>> predictResult = process(dataFrame, forest, 0);
         return MLPredictionOutput.builder().predictionResult(DataFrameBuilder.load(predictResult)).build();
@@ -83,7 +82,7 @@ public Model train(DataFrame dataFrame) {
         model.setName(FunctionName.BATCH_RCF.name());
         model.setVersion(1);
         RandomCutForestState state = rcfMapper.toState(forest);
-        model.setContent(ModelSerDeSer.serialize(state));
+        model.setContent(RCFModelSerDeSer.serializeRCF(state));
         return model;
     }
 
diff --git a/ml-algorithms/src/main/java/org/opensearch/ml/engine/algorithms/rcf/FixedInTimeRandomCutForest.java b/ml-algorithms/src/main/java/org/opensearch/ml/engine/algorithms/rcf/FixedInTimeRandomCutForest.java
index 85ccb9c818..bd881e7beb 100644
--- a/ml-algorithms/src/main/java/org/opensearch/ml/engine/algorithms/rcf/FixedInTimeRandomCutForest.java
+++ b/ml-algorithms/src/main/java/org/opensearch/ml/engine/algorithms/rcf/FixedInTimeRandomCutForest.java
@@ -12,6 +12,8 @@
 import com.amazon.randomcutforest.parkservices.state.ThresholdedRandomCutForestMapper;
 import com.amazon.randomcutforest.parkservices.state.ThresholdedRandomCutForestState;
 import lombok.extern.log4j.Log4j2;
+import org.opensearch.ml.common.FunctionName;
+import org.opensearch.ml.common.Model;
 import org.opensearch.ml.common.dataframe.ColumnMeta;
 import org.opensearch.ml.common.dataframe.ColumnType;
 import org.opensearch.ml.common.dataframe.ColumnValue;
@@ -19,15 +21,12 @@
 import org.opensearch.ml.common.dataframe.DataFrameBuilder;
 import org.opensearch.ml.common.dataframe.Row;
 import org.opensearch.ml.common.exception.MLValidationException;
-import org.opensearch.ml.common.FunctionName;
 import org.opensearch.ml.common.input.parameter.MLAlgoParams;
+import org.opensearch.ml.common.input.parameter.rcf.FitRCFParams;
 import org.opensearch.ml.common.output.MLOutput;
 import org.opensearch.ml.common.output.MLPredictionOutput;
-import org.opensearch.ml.common.Model;
-import org.opensearch.ml.common.input.parameter.rcf.FitRCFParams;
 import org.opensearch.ml.engine.TrainAndPredictable;
 import org.opensearch.ml.engine.annotation.Function;
-import org.opensearch.ml.engine.utils.ModelSerDeSer;
 
 import java.text.DateFormat;
 import java.text.ParseException;
@@ -99,7 +98,7 @@ public MLOutput predict(DataFrame dataFrame, Model model) {
         if (model == null) {
             throw new IllegalArgumentException("No model found for FIT RCF prediction.");
         }
-        ThresholdedRandomCutForestState state = (ThresholdedRandomCutForestState) ModelSerDeSer.deserialize(model.getContent());
+        ThresholdedRandomCutForestState state = RCFModelSerDeSer.deserializeTRCF(model.getContent());
         ThresholdedRandomCutForest forest = trcfMapper.toModel(state);
         List<Map<String, Object>> predictResult = process(dataFrame, forest);
         return MLPredictionOutput.builder().predictionResult(DataFrameBuilder.load(predictResult)).build();
@@ -113,7 +112,7 @@ public Model train(DataFrame dataFrame) {
         model.setName(FunctionName.FIT_RCF.name());
         model.setVersion(1);
         ThresholdedRandomCutForestState state = trcfMapper.toState(forest);
-         model.setContent(ModelSerDeSer.serialize(state));
+        model.setContent(RCFModelSerDeSer.serializeTRCF(state));
         return model;
     }
 
diff --git a/ml-algorithms/src/main/java/org/opensearch/ml/engine/algorithms/rcf/RCFModelSerDeSer.java b/ml-algorithms/src/main/java/org/opensearch/ml/engine/algorithms/rcf/RCFModelSerDeSer.java
new file mode 100644
index 0000000000..8079268297
--- /dev/null
+++ b/ml-algorithms/src/main/java/org/opensearch/ml/engine/algorithms/rcf/RCFModelSerDeSer.java
@@ -0,0 +1,59 @@
+/*
+ * Copyright OpenSearch Contributors
+ * SPDX-License-Identifier: Apache-2.0
+ */
+
+package org.opensearch.ml.engine.algorithms.rcf;
+
+import com.amazon.randomcutforest.parkservices.state.ThresholdedRandomCutForestState;
+import com.amazon.randomcutforest.state.RandomCutForestState;
+import io.protostuff.LinkedBuffer;
+import io.protostuff.ProtostuffIOUtil;
+import io.protostuff.Schema;
+import io.protostuff.runtime.RuntimeSchema;
+import lombok.experimental.UtilityClass;
+
+import java.security.AccessController;
+import java.security.PrivilegedAction;
+
+@UtilityClass
+public class RCFModelSerDeSer {
+    private static final int SERIALIZATION_BUFFER_BYTES = 512;
+    private static final Schema<RandomCutForestState> rcfSchema =
+            AccessController.doPrivileged((PrivilegedAction<Schema<RandomCutForestState>>) () ->
+                    RuntimeSchema.getSchema(RandomCutForestState.class));
+    private static final Schema<ThresholdedRandomCutForestState> trcfSchema =
+            AccessController.doPrivileged((PrivilegedAction<Schema<ThresholdedRandomCutForestState>>) () ->
+                    RuntimeSchema.getSchema(ThresholdedRandomCutForestState.class));
+
+    public static byte[] serializeRCF(RandomCutForestState model) {
+        return serialize(model, rcfSchema);
+    }
+
+    public static byte[] serializeTRCF(ThresholdedRandomCutForestState model) {
+        return serialize(model, trcfSchema);
+    }
+
+    public static RandomCutForestState deserializeRCF(byte[] bytes) {
+        return deserialize(bytes, rcfSchema);
+    }
+
+    public static ThresholdedRandomCutForestState deserializeTRCF(byte[] bytes) {
+        return deserialize(bytes, trcfSchema);
+    }
+
+    private static <T> byte[] serialize(T model, Schema<T> schema) {
+        LinkedBuffer buffer = LinkedBuffer.allocate(SERIALIZATION_BUFFER_BYTES);
+        byte[] bytes = AccessController.doPrivileged((PrivilegedAction<byte[]>) () -> ProtostuffIOUtil.toByteArray(model, schema, buffer));
+        return bytes;
+    }
+
+    private static <T> T deserialize(byte[] bytes, Schema<T> schema) {
+        T model = schema.newMessage();
+        AccessController.doPrivileged((PrivilegedAction<Void>) () -> {
+            ProtostuffIOUtil.mergeFrom(bytes, model, schema);
+            return null;
+        });
+        return model;
+    }
+}
diff --git a/ml-algorithms/src/main/java/org/opensearch/ml/engine/utils/ModelSerDeSer.java b/ml-algorithms/src/main/java/org/opensearch/ml/engine/utils/ModelSerDeSer.java
index 7b7306ebb6..4d477928b8 100644
--- a/ml-algorithms/src/main/java/org/opensearch/ml/engine/utils/ModelSerDeSer.java
+++ b/ml-algorithms/src/main/java/org/opensearch/ml/engine/utils/ModelSerDeSer.java
@@ -6,12 +6,12 @@
 package org.opensearch.ml.engine.utils;
 
 import lombok.experimental.UtilityClass;
+import org.apache.commons.io.serialization.ValidatingObjectInputStream;
 import org.opensearch.ml.engine.exceptions.ModelSerDeSerException;
 
 import java.io.ByteArrayInputStream;
 import java.io.ByteArrayOutputStream;
 import java.io.IOException;
-import java.io.ObjectInputStream;
 import java.io.ObjectOutputStream;
 
 @UtilityClass
@@ -41,8 +41,11 @@ public static byte[] serialize(Object model) {
     }
 
     public static Object deserialize(byte[] modelBin) {
-        try (ObjectInputStream objectInputStream = new ObjectInputStream(new ByteArrayInputStream(modelBin))) {
-            return objectInputStream.readObject();
+        try (ByteArrayInputStream inputStream = new ByteArrayInputStream(modelBin);
+             ValidatingObjectInputStream validatingObjectInputStream = new ValidatingObjectInputStream(inputStream)){
+            // Validate the model class type to avoid deserialization attack.
+            validatingObjectInputStream.accept(ACCEPT_CLASS_PATTERNS);
+            return validatingObjectInputStream.readObject();
         } catch (IOException | ClassNotFoundException e) {
             throw new ModelSerDeSerException("Failed to deserialize model.", e.getCause());
         }
diff --git a/ml-algorithms/src/test/java/org/opensearch/ml/engine/MLEngineTest.java b/ml-algorithms/src/test/java/org/opensearch/ml/engine/MLEngineTest.java
index e9b92f7317..aa154d62bb 100644
--- a/ml-algorithms/src/test/java/org/opensearch/ml/engine/MLEngineTest.java
+++ b/ml-algorithms/src/test/java/org/opensearch/ml/engine/MLEngineTest.java
@@ -30,9 +30,9 @@
 import java.io.IOException;
 import java.util.Arrays;
 
-import static org.opensearch.ml.engine.helper.KMeansHelper.constructKMeansDataFrame;
 import static org.opensearch.ml.engine.helper.LinearRegressionHelper.constructLinearRegressionPredictionDataFrame;
 import static org.opensearch.ml.engine.helper.LinearRegressionHelper.constructLinearRegressionTrainDataFrame;
+import static org.opensearch.ml.engine.helper.MLTestHelper.constructTestDataFrame;
 
 public class MLEngineTest {
     @Rule
@@ -41,7 +41,7 @@ public class MLEngineTest {
     @Test
     public void predictKMeans() {
         Model model = trainKMeansModel();
-        DataFrame predictionDataFrame = constructKMeansDataFrame(10);
+        DataFrame predictionDataFrame = constructTestDataFrame(10);
         MLInputDataset inputDataset = DataFrameInputDataset.builder().dataFrame(predictionDataFrame).build();
         Input mlInput = MLInput.builder().algorithm(FunctionName.KMEANS).inputDataset(inputDataset).build();
         MLPredictionOutput output = (MLPredictionOutput)MLEngine.predict(mlInput, model);
@@ -106,7 +106,7 @@ public void train_EmptyDataFrame() {
         FunctionName algoName = FunctionName.LINEAR_REGRESSION;
         try (MockedStatic<MLEngineClassLoader> loader = Mockito.mockStatic(MLEngineClassLoader.class)) {
             loader.when(() -> MLEngineClassLoader.initInstance(algoName, null, MLAlgoParams.class)).thenReturn(null);
-            MLInputDataset inputDataset = DataFrameInputDataset.builder().dataFrame(constructKMeansDataFrame(0)).build();
+            MLInputDataset inputDataset = DataFrameInputDataset.builder().dataFrame(constructTestDataFrame(0)).build();
             MLEngine.train(MLInput.builder().algorithm(algoName).inputDataset(inputDataset).build());
         }
     }
@@ -118,7 +118,7 @@ public void train_UnsupportedAlgorithm() {
         FunctionName algoName = FunctionName.LINEAR_REGRESSION;
         try (MockedStatic<MLEngineClassLoader> loader = Mockito.mockStatic(MLEngineClassLoader.class)) {
             loader.when(() -> MLEngineClassLoader.initInstance(algoName, null, MLAlgoParams.class)).thenReturn(null);
-            MLInputDataset inputDataset = DataFrameInputDataset.builder().dataFrame(constructKMeansDataFrame(10)).build();
+            MLInputDataset inputDataset = DataFrameInputDataset.builder().dataFrame(constructTestDataFrame(10)).build();
             MLEngine.train(MLInput.builder().algorithm(algoName).inputDataset(inputDataset).build());
         }
     }
@@ -134,7 +134,7 @@ public void predictNullInput() {
     public void predictWithoutAlgoName() {
         exceptionRule.expect(IllegalArgumentException.class);
         exceptionRule.expectMessage("algorithm can't be null");
-        MLInputDataset inputDataset = DataFrameInputDataset.builder().dataFrame(constructKMeansDataFrame(10)).build();
+        MLInputDataset inputDataset = DataFrameInputDataset.builder().dataFrame(constructTestDataFrame(10)).build();
         Input mlInput = MLInput.builder().inputDataset(inputDataset).build();
         MLEngine.predict(mlInput, null);
     }
@@ -165,7 +165,7 @@ public void predictUnsupportedAlgorithm() {
     public void trainAndPredictWithKmeans() {
         int dataSize = 100;
         MLAlgoParams parameters = KMeansParams.builder().build();
-        DataFrame dataFrame = constructKMeansDataFrame(dataSize);
+        DataFrame dataFrame = constructTestDataFrame(dataSize);
         MLInputDataset inputData = new DataFrameInputDataset(dataFrame);
         Input input = new MLInput(FunctionName.KMEANS, parameters, inputData);
         MLPredictionOutput output = (MLPredictionOutput) MLEngine.trainAndPredict(input);
@@ -216,7 +216,7 @@ private Model trainKMeansModel() {
                 .iterations(10)
                 .distanceType(KMeansParams.DistanceType.EUCLIDEAN)
                 .build();
-        DataFrame trainDataFrame = constructKMeansDataFrame(100);
+        DataFrame trainDataFrame = constructTestDataFrame(100);
         MLInputDataset inputDataset = DataFrameInputDataset.builder().dataFrame(trainDataFrame).build();
         Input mlInput = MLInput.builder().algorithm(FunctionName.KMEANS).parameters(parameters).inputDataset(inputDataset).build();
         return MLEngine.train(mlInput);
diff --git a/ml-algorithms/src/test/java/org/opensearch/ml/engine/ModelSerDeSerTest.java b/ml-algorithms/src/test/java/org/opensearch/ml/engine/ModelSerDeSerTest.java
index be317a350a..ebff98035b 100644
--- a/ml-algorithms/src/test/java/org/opensearch/ml/engine/ModelSerDeSerTest.java
+++ b/ml-algorithms/src/test/java/org/opensearch/ml/engine/ModelSerDeSerTest.java
@@ -8,41 +8,40 @@
 import org.junit.Rule;
 import org.junit.Test;
 import org.junit.rules.ExpectedException;
-import org.opensearch.ml.common.input.parameter.clustering.KMeansParams;
 import org.opensearch.ml.common.Model;
+import org.opensearch.ml.common.input.parameter.clustering.KMeansParams;
+import org.opensearch.ml.common.input.parameter.regression.LinearRegressionParams;
 import org.opensearch.ml.engine.algorithms.clustering.KMeans;
-import org.opensearch.ml.engine.exceptions.ModelSerDeSerException;
+import org.opensearch.ml.engine.algorithms.regression.LinearRegression;
 import org.opensearch.ml.engine.utils.ModelSerDeSer;
 import org.tribuo.clustering.kmeans.KMeansModel;
+import org.tribuo.regression.sgd.linear.LinearSGDModel;
 
-import java.util.Arrays;
-
-import static org.junit.Assert.assertFalse;
-import static org.junit.Assert.assertTrue;
-import static org.opensearch.ml.engine.helper.KMeansHelper.constructKMeansDataFrame;
+import static org.junit.Assert.assertNotNull;
+import static org.opensearch.ml.engine.helper.MLTestHelper.constructTestDataFrame;
 
 public class ModelSerDeSerTest {
     @Rule
     public ExpectedException thrown = ExpectedException.none();
 
-    private final Object dummyModel = new Object();
-
-    @Test
-    public void testModelSerDeSerBlocklModel() {
-        thrown.expect(ModelSerDeSerException.class);
-        byte[] modelBin = ModelSerDeSer.serialize(dummyModel);
-        Object model = ModelSerDeSer.deserialize(modelBin);
-        assertTrue(model.equals(dummyModel));
-    }
-
     @Test
     public void testModelSerDeSerKMeans() {
         KMeansParams params = KMeansParams.builder().build();
         KMeans kMeans = new KMeans(params);
-        Model model = kMeans.train(constructKMeansDataFrame(100));
+        Model model = kMeans.train(constructTestDataFrame(100));
+
+        KMeansModel deserializedModel = (KMeansModel) ModelSerDeSer.deserialize(model.getContent());
+        assertNotNull(deserializedModel);
+    }
 
-        KMeansModel kMeansModel = (KMeansModel) ModelSerDeSer.deserialize(model.getContent());
-        byte[] serializedModel = ModelSerDeSer.serialize(kMeansModel);
-        assertFalse(Arrays.equals(serializedModel, model.getContent()));
+    @Test
+    public void testModelSerDeSerLinearRegression() {
+        LinearRegressionParams params = LinearRegressionParams.builder().target("f2").build();
+        LinearRegression linearRegression = new LinearRegression(params);
+        Model model = linearRegression.train(constructTestDataFrame(100));
+
+        LinearSGDModel deserializedModel = (LinearSGDModel) ModelSerDeSer.deserialize(model.getContent());
+        assertNotNull(deserializedModel);
     }
-}
\ No newline at end of file
+
+}
diff --git a/ml-algorithms/src/test/java/org/opensearch/ml/engine/algorithms/clustering/KMeansTest.java b/ml-algorithms/src/test/java/org/opensearch/ml/engine/algorithms/clustering/KMeansTest.java
index 04695e277d..9ef7630f07 100644
--- a/ml-algorithms/src/test/java/org/opensearch/ml/engine/algorithms/clustering/KMeansTest.java
+++ b/ml-algorithms/src/test/java/org/opensearch/ml/engine/algorithms/clustering/KMeansTest.java
@@ -16,7 +16,7 @@
 import org.opensearch.ml.common.output.MLPredictionOutput;
 import org.opensearch.ml.common.Model;
 
-import static org.opensearch.ml.engine.helper.KMeansHelper.constructKMeansDataFrame;
+import static org.opensearch.ml.engine.helper.MLTestHelper.constructTestDataFrame;
 
 
 public class KMeansTest {
@@ -107,11 +107,11 @@ public void constructorWithNegtiveIterations() {
     }
 
     private void constructKMeansPredictionDataFrame() {
-        predictionDataFrame = constructKMeansDataFrame(predictionSize);
+        predictionDataFrame = constructTestDataFrame(predictionSize);
     }
 
     private void constructKMeansTrainDataFrame() {
-        trainDataFrame = constructKMeansDataFrame(trainSize);
+        trainDataFrame = constructTestDataFrame(trainSize);
     }
 
 }
diff --git a/ml-algorithms/src/test/java/org/opensearch/ml/engine/algorithms/rcf/RCFModelSerDeSerTest.java b/ml-algorithms/src/test/java/org/opensearch/ml/engine/algorithms/rcf/RCFModelSerDeSerTest.java
new file mode 100644
index 0000000000..9690009505
--- /dev/null
+++ b/ml-algorithms/src/test/java/org/opensearch/ml/engine/algorithms/rcf/RCFModelSerDeSerTest.java
@@ -0,0 +1,61 @@
+/*
+ * Copyright OpenSearch Contributors
+ * SPDX-License-Identifier: Apache-2.0
+ */
+
+package org.opensearch.ml.engine.algorithms.rcf;
+
+import com.amazon.randomcutforest.RandomCutForest;
+import com.amazon.randomcutforest.parkservices.ThresholdedRandomCutForest;
+import com.amazon.randomcutforest.parkservices.state.ThresholdedRandomCutForestMapper;
+import com.amazon.randomcutforest.parkservices.state.ThresholdedRandomCutForestState;
+import com.amazon.randomcutforest.state.RandomCutForestMapper;
+import com.amazon.randomcutforest.state.RandomCutForestState;
+import org.junit.Rule;
+import org.junit.Test;
+import org.junit.rules.ExpectedException;
+import org.opensearch.ml.common.Model;
+import org.opensearch.ml.common.input.parameter.rcf.BatchRCFParams;
+import org.opensearch.ml.common.input.parameter.rcf.FitRCFParams;
+
+import java.util.Arrays;
+
+import static org.junit.Assert.assertNotNull;
+import static org.junit.Assert.assertTrue;
+import static org.opensearch.ml.engine.helper.MLTestHelper.TIME_FIELD;
+import static org.opensearch.ml.engine.helper.MLTestHelper.constructTestDataFrame;
+
+public class RCFModelSerDeSerTest {
+    @Rule
+    public ExpectedException thrown = ExpectedException.none();
+
+    private final RandomCutForestMapper rcfMapper = new RandomCutForestMapper();
+    private final ThresholdedRandomCutForestMapper trcfMapper = new ThresholdedRandomCutForestMapper();
+
+    @Test
+    public void testModelSerDeSerBatchRCF() {
+        BatchRCFParams params = BatchRCFParams.builder().build();
+        BatchRandomCutForest batchRCF = new BatchRandomCutForest(params);
+        Model model = batchRCF.train(constructTestDataFrame(500));
+
+        RandomCutForestState deserializedState = RCFModelSerDeSer.deserializeRCF(model.getContent());
+        RandomCutForest forest = rcfMapper.toModel(deserializedState);
+        assertNotNull(forest);
+        byte[] serializedModel = RCFModelSerDeSer.serializeRCF(deserializedState);
+        assertTrue(Arrays.equals(serializedModel, model.getContent()));
+    }
+
+    @Test
+    public void testModelSerDeSerFitRCF() {
+        FitRCFParams params = FitRCFParams.builder().timeField(TIME_FIELD).build();
+        FixedInTimeRandomCutForest fitRCF = new FixedInTimeRandomCutForest(params);
+        Model model = fitRCF.train(constructTestDataFrame(500, true));
+
+        ThresholdedRandomCutForestState deserializedState = RCFModelSerDeSer.deserializeTRCF(model.getContent());
+        ThresholdedRandomCutForest forest = trcfMapper.toModel(deserializedState);
+        assertNotNull(forest);
+        byte[] serializedModel = RCFModelSerDeSer.serializeTRCF(deserializedState);
+        assertTrue(Arrays.equals(serializedModel, model.getContent()));
+    }
+
+}
diff --git a/ml-algorithms/src/test/java/org/opensearch/ml/engine/helper/KMeansHelper.java b/ml-algorithms/src/test/java/org/opensearch/ml/engine/helper/MLTestHelper.java
similarity index 58%
rename from ml-algorithms/src/test/java/org/opensearch/ml/engine/helper/KMeansHelper.java
rename to ml-algorithms/src/test/java/org/opensearch/ml/engine/helper/MLTestHelper.java
index 7b45ea7f91..c98d2a0f3e 100644
--- a/ml-algorithms/src/test/java/org/opensearch/ml/engine/helper/KMeansHelper.java
+++ b/ml-algorithms/src/test/java/org/opensearch/ml/engine/helper/MLTestHelper.java
@@ -13,13 +13,27 @@
 import org.opensearch.ml.common.dataframe.DataFrame;
 import org.opensearch.ml.common.dataframe.DataFrameBuilder;
 
+import java.util.ArrayList;
 import java.util.Arrays;
+import java.util.List;
 import java.util.Random;
 
 @UtilityClass
-public class KMeansHelper {
-    public static DataFrame constructKMeansDataFrame(int size) {
-        ColumnMeta[] columnMetas = new ColumnMeta[]{new ColumnMeta("f1", ColumnType.DOUBLE), new ColumnMeta("f2", ColumnType.DOUBLE)};
+public class MLTestHelper {
+
+    public static final String TIME_FIELD = "timestamp";
+    public static DataFrame constructTestDataFrame(int size) {
+        return constructTestDataFrame(size, false);
+    }
+
+    public static DataFrame constructTestDataFrame(int size, boolean addTimeFiled) {
+        List<ColumnMeta> columnMetaList =  new ArrayList<>();
+        columnMetaList.add(new ColumnMeta("f1", ColumnType.DOUBLE));
+        columnMetaList.add(new ColumnMeta("f2", ColumnType.DOUBLE));
+        if (addTimeFiled) {
+            columnMetaList.add(new ColumnMeta(TIME_FIELD, ColumnType.LONG));
+        }
+        ColumnMeta[] columnMetas = columnMetaList.toArray(new ColumnMeta[0]);
         DataFrame dataFrame = DataFrameBuilder.emptyDataFrame(columnMetas);
 
         Random random = new Random(1);
@@ -28,13 +42,19 @@ public static DataFrame constructKMeansDataFrame(int size) {
         MultivariateNormalDistribution g2 = new MultivariateNormalDistribution(new JDKRandomGenerator(random.nextInt()),
                 new double[]{10.0, 10.0}, new double[][]{{2.0, 1.0}, {1.0, 2.0}});
         MultivariateNormalDistribution[] normalDistributions = new MultivariateNormalDistribution[]{g1, g2};
+        long startTime = 1648154137000l;
         for (int i = 0; i < size; ++i) {
             int id = 0;
             if (Math.random() < 0.5) {
                 id = 1;
             }
             double[] sample = normalDistributions[id].sample();
-            dataFrame.appendRow(Arrays.stream(sample).boxed().toArray(Double[]::new));
+            Object[] row = Arrays.stream(sample).boxed().toArray(Double[]::new);
+            if (addTimeFiled) {
+                long timestamp = startTime + 60_000 * i;
+                row = new Object[] {row[0], row[1], timestamp};
+            }
+            dataFrame.appendRow(row);
         }
 
         return dataFrame;
diff --git a/plugin/src/test/java/org/opensearch/ml/action/MLCommonsIntegTestCase.java b/plugin/src/test/java/org/opensearch/ml/action/MLCommonsIntegTestCase.java
index 40dd6b1006..d6f9628579 100644
--- a/plugin/src/test/java/org/opensearch/ml/action/MLCommonsIntegTestCase.java
+++ b/plugin/src/test/java/org/opensearch/ml/action/MLCommonsIntegTestCase.java
@@ -5,6 +5,9 @@
 
 package org.opensearch.ml.action;
 
+import static org.opensearch.ml.utils.TestData.TARGET_FIELD;
+import static org.opensearch.ml.utils.TestData.TIME_FIELD;
+
 import java.util.Collection;
 import java.util.Collections;
 import java.util.Map;
@@ -29,6 +32,8 @@
 import org.opensearch.ml.common.input.parameter.MLAlgoParams;
 import org.opensearch.ml.common.input.parameter.clustering.KMeansParams;
 import org.opensearch.ml.common.input.parameter.rcf.BatchRCFParams;
+import org.opensearch.ml.common.input.parameter.rcf.FitRCFParams;
+import org.opensearch.ml.common.input.parameter.regression.LinearRegressionParams;
 import org.opensearch.ml.common.output.MLPredictionOutput;
 import org.opensearch.ml.common.output.MLTrainingOutput;
 import org.opensearch.ml.common.transport.MLTaskResponse;
@@ -135,6 +140,30 @@ public String trainBatchRCFWithDataFrame(int dataSize, boolean async) {
         return trainModel(FunctionName.BATCH_RCF, BatchRCFParams.builder().build(), inputDataset, async);
     }
 
+    public String trainFitRCFWithDataFrame(int dataSize, boolean async) {
+        MLInputDataset inputDataset = new DataFrameInputDataset(TestData.constructTestDataFrame(dataSize, true));
+        return trainModel(FunctionName.FIT_RCF, FitRCFParams.builder().timeField(TIME_FIELD).build(), inputDataset, async);
+    }
+
+    public LinearRegressionParams getLinearRegressionParams() {
+        return LinearRegressionParams
+            .builder()
+            .objectiveType(LinearRegressionParams.ObjectiveType.SQUARED_LOSS)
+            .optimizerType(LinearRegressionParams.OptimizerType.LINEAR_DECAY_SGD)
+            .learningRate(0.01)
+            .epochs(10)
+            .epsilon(1e-5)
+            .beta1(0.9)
+            .beta2(0.99)
+            .target(TARGET_FIELD)
+            .build();
+    }
+
+    public String trainLinearRegressionWithDataFrame(int dataSize, boolean async) {
+        MLInputDataset inputDataset = new DataFrameInputDataset(TestData.constructTestDataFrameForLinearRegression(dataSize));
+        return trainModel(FunctionName.LINEAR_REGRESSION, getLinearRegressionParams(), inputDataset, async);
+    }
+
     public String trainModel(FunctionName functionName, MLAlgoParams params, MLInputDataset inputDataset, boolean async) {
         MLInput mlInput = MLInput.builder().algorithm(functionName).parameters(params).inputDataset(inputDataset).build();
         MLTrainingTaskRequest trainingRequest = new MLTrainingTaskRequest(mlInput, async);
@@ -155,8 +184,14 @@ public String trainModel(FunctionName functionName, MLAlgoParams params, MLInput
         return id;
     }
 
-    public DataFrame predictAndVerify(String modelId, MLInputDataset inputDataset, FunctionName functionName, int size) {
-        MLInput mlInput = MLInput.builder().algorithm(functionName).inputDataset(inputDataset).build();
+    public DataFrame predictAndVerify(
+        String modelId,
+        MLInputDataset inputDataset,
+        FunctionName functionName,
+        MLAlgoParams parameters,
+        int size
+    ) {
+        MLInput mlInput = MLInput.builder().algorithm(functionName).inputDataset(inputDataset).parameters(parameters).build();
         MLPredictionTaskRequest predictionRequest = new MLPredictionTaskRequest(modelId, mlInput);
         ActionFuture<MLTaskResponse> predictionFuture = client().execute(MLPredictionTaskAction.INSTANCE, predictionRequest);
         MLTaskResponse predictionResponse = predictionFuture.actionGet();
diff --git a/plugin/src/test/java/org/opensearch/ml/action/prediction/PredictionITTests.java b/plugin/src/test/java/org/opensearch/ml/action/prediction/PredictionITTests.java
index 3f5734192c..a6caff2811 100644
--- a/plugin/src/test/java/org/opensearch/ml/action/prediction/PredictionITTests.java
+++ b/plugin/src/test/java/org/opensearch/ml/action/prediction/PredictionITTests.java
@@ -6,6 +6,10 @@
 package org.opensearch.ml.action.prediction;
 
 import static org.opensearch.ml.utils.TestData.IRIS_DATA_SIZE;
+import static org.opensearch.ml.utils.TestData.TIME_FIELD;
+
+import java.util.ArrayList;
+import java.util.List;
 
 import org.apache.lucene.util.LuceneTestCase;
 import org.junit.Before;
@@ -16,10 +20,18 @@
 import org.opensearch.ml.action.MLCommonsIntegTestCase;
 import org.opensearch.ml.common.FunctionName;
 import org.opensearch.ml.common.MLModel;
+import org.opensearch.ml.common.dataframe.ColumnMeta;
+import org.opensearch.ml.common.dataframe.ColumnType;
+import org.opensearch.ml.common.dataframe.ColumnValue;
+import org.opensearch.ml.common.dataframe.DataFrame;
+import org.opensearch.ml.common.dataframe.DefaultDataFrame;
+import org.opensearch.ml.common.dataframe.DoubleValue;
+import org.opensearch.ml.common.dataframe.Row;
 import org.opensearch.ml.common.dataset.DataFrameInputDataset;
 import org.opensearch.ml.common.dataset.MLInputDataset;
 import org.opensearch.ml.common.dataset.SearchQueryInputDataset;
 import org.opensearch.ml.common.input.MLInput;
+import org.opensearch.ml.common.input.parameter.rcf.FitRCFParams;
 import org.opensearch.ml.common.transport.MLTaskResponse;
 import org.opensearch.ml.common.transport.prediction.MLPredictionTaskAction;
 import org.opensearch.ml.common.transport.prediction.MLPredictionTaskRequest;
@@ -33,6 +45,8 @@ public class PredictionITTests extends MLCommonsIntegTestCase {
     private String irisIndexName;
     private String kMeansModelId;
     private String batchRcfModelId;
+    private String fitRcfModelId;
+    private String linearRegressionModelId;
     private int batchRcfDataSize = 100;
 
     @Rule
@@ -50,6 +64,8 @@ public void setUp() throws Exception {
         // assertNotNull(kMeansModel);
 
         batchRcfModelId = trainBatchRCFWithDataFrame(500, false);
+        fitRcfModelId = trainFitRCFWithDataFrame(500, false);
+        linearRegressionModelId = trainLinearRegressionWithDataFrame(100, false);
         MLModel batchRcfModel = getModel(batchRcfModelId);
         assertNotNull(batchRcfModel);
     }
@@ -57,13 +73,13 @@ public void setUp() throws Exception {
     @LuceneTestCase.AwaitsFix(bugUrl = "https://github.com/oracle/tribuo/issues/223")
     public void testPredictionWithSearchInput_KMeans() {
         MLInputDataset inputDataset = new SearchQueryInputDataset(ImmutableList.of(irisIndexName), irisDataQuery());
-        predictAndVerify(kMeansModelId, inputDataset, FunctionName.KMEANS, IRIS_DATA_SIZE);
+        predictAndVerify(kMeansModelId, inputDataset, FunctionName.KMEANS, null, IRIS_DATA_SIZE);
     }
 
     @LuceneTestCase.AwaitsFix(bugUrl = "https://github.com/oracle/tribuo/issues/223")
     public void testPredictionWithDataInput_KMeans() {
         MLInputDataset inputDataset = new DataFrameInputDataset(irisDataFrame());
-        predictAndVerify(kMeansModelId, inputDataset, FunctionName.KMEANS, IRIS_DATA_SIZE);
+        predictAndVerify(kMeansModelId, inputDataset, FunctionName.KMEANS, null, IRIS_DATA_SIZE);
     }
 
     @LuceneTestCase.AwaitsFix(bugUrl = "https://github.com/oracle/tribuo/issues/223")
@@ -89,6 +105,37 @@ public void testPredictionWithEmptyDataset_KMeans() {
 
     public void testPredictionWithDataFrame_BatchRCF() {
         MLInputDataset inputDataset = new DataFrameInputDataset(TestData.constructTestDataFrame(batchRcfDataSize));
-        predictAndVerify(batchRcfModelId, inputDataset, FunctionName.BATCH_RCF, batchRcfDataSize);
+        predictAndVerify(batchRcfModelId, inputDataset, FunctionName.BATCH_RCF, null, batchRcfDataSize);
+    }
+
+    public void testPredictionWithDataFrame_FitRCF() {
+        MLInputDataset inputDataset = new DataFrameInputDataset(TestData.constructTestDataFrame(batchRcfDataSize, true));
+        DataFrame dataFrame = predictAndVerify(
+            fitRcfModelId,
+            inputDataset,
+            FunctionName.FIT_RCF,
+            FitRCFParams.builder().timeField(TIME_FIELD).build(),
+            batchRcfDataSize
+        );
+        System.out.println(dataFrame);
+    }
+
+    public void testPredictionWithDataFrame_LinearRegression() {
+        int size = 1;
+        int feet = 20;
+        ColumnMeta[] columnMetas = new ColumnMeta[] { new ColumnMeta("feet", ColumnType.DOUBLE) };
+        List<Row> rows = new ArrayList<>();
+        rows.add(new Row(new ColumnValue[] { new DoubleValue(feet) }));
+        DataFrame inputDataFrame = new DefaultDataFrame(columnMetas, rows);
+        MLInputDataset inputDataset = new DataFrameInputDataset(inputDataFrame);
+        DataFrame dataFrame = predictAndVerify(
+            linearRegressionModelId,
+            inputDataset,
+            FunctionName.LINEAR_REGRESSION,
+            getLinearRegressionParams(),
+            size
+        );
+        ColumnValue value = dataFrame.getRow(0).getValue(0);
+        assertNotNull(value);
     }
 }
diff --git a/plugin/src/test/java/org/opensearch/ml/utils/TestData.java b/plugin/src/test/java/org/opensearch/ml/utils/TestData.java
index 6aef9620dc..55bf304c26 100644
--- a/plugin/src/test/java/org/opensearch/ml/utils/TestData.java
+++ b/plugin/src/test/java/org/opensearch/ml/utils/TestData.java
@@ -5,23 +5,54 @@
 
 package org.opensearch.ml.utils;
 
+import java.util.ArrayList;
 import java.util.Arrays;
+import java.util.List;
 import java.util.Random;
 
 import org.apache.commons.math3.distribution.MultivariateNormalDistribution;
 import org.apache.commons.math3.random.JDKRandomGenerator;
 import org.opensearch.ml.common.dataframe.ColumnMeta;
 import org.opensearch.ml.common.dataframe.ColumnType;
+import org.opensearch.ml.common.dataframe.ColumnValue;
 import org.opensearch.ml.common.dataframe.DataFrame;
 import org.opensearch.ml.common.dataframe.DataFrameBuilder;
+import org.opensearch.ml.common.dataframe.DefaultDataFrame;
+import org.opensearch.ml.common.dataframe.DoubleValue;
+import org.opensearch.ml.common.dataframe.Row;
 
 import com.google.gson.JsonArray;
 import com.google.gson.JsonObject;
 
 public class TestData {
 
+    public static final String TIME_FIELD = "timestamp";
+    public static final String TARGET_FIELD = "price";
+
     public static DataFrame constructTestDataFrame(int size) {
-        ColumnMeta[] columnMetas = new ColumnMeta[] { new ColumnMeta("f1", ColumnType.DOUBLE), new ColumnMeta("f2", ColumnType.DOUBLE) };
+        return constructTestDataFrame(size, false);
+    }
+
+    public static DataFrame constructTestDataFrameForLinearRegression(int size) {
+        ColumnMeta[] columnMetas = new ColumnMeta[] {
+            new ColumnMeta("feet", ColumnType.DOUBLE),
+            new ColumnMeta(TARGET_FIELD, ColumnType.DOUBLE) };
+
+        List<Row> rows = new ArrayList<>();
+        for (int i = 0; i < size; i++) {
+            rows.add(new Row(new ColumnValue[] { new DoubleValue(i), new DoubleValue(i) }));
+        }
+        return new DefaultDataFrame(columnMetas, rows);
+    }
+
+    public static DataFrame constructTestDataFrame(int size, boolean addTimeFiled) {
+        List<ColumnMeta> columnMetaList = new ArrayList<>();
+        columnMetaList.add(new ColumnMeta("f1", ColumnType.DOUBLE));
+        columnMetaList.add(new ColumnMeta("f2", ColumnType.DOUBLE));
+        if (addTimeFiled) {
+            columnMetaList.add(new ColumnMeta(TIME_FIELD, ColumnType.LONG));
+        }
+        ColumnMeta[] columnMetas = columnMetaList.toArray(new ColumnMeta[0]);
         DataFrame dataFrame = DataFrameBuilder.emptyDataFrame(columnMetas);
 
         Random random = new Random(1);
@@ -36,13 +67,19 @@ public static DataFrame constructTestDataFrame(int size) {
             new double[][] { { 2.0, 1.0 }, { 1.0, 2.0 } }
         );
         MultivariateNormalDistribution[] normalDistributions = new MultivariateNormalDistribution[] { g1, g2 };
+        long startTime = 1648154137000l;
         for (int i = 0; i < size; ++i) {
             int id = 0;
-            if (random.nextDouble() < 0.5) {
+            if (Math.random() < 0.5) {
                 id = 1;
             }
             double[] sample = normalDistributions[id].sample();
-            dataFrame.appendRow(Arrays.stream(sample).boxed().toArray(Double[]::new));
+            Object[] row = Arrays.stream(sample).boxed().toArray(Double[]::new);
+            if (addTimeFiled) {
+                long timestamp = startTime + 60_000 * i;
+                row = new Object[] { row[0], row[1], timestamp };
+            }
+            dataFrame.appendRow(row);
         }
 
         return dataFrame;