tune class name; tune how to add new doc

Signed-off-by: Yaliang Wu <ylwu@amazon.com>
opensearch-project · Nov 23, 2021 · 54bf452 · 54bf452
1 parent db67aa2
commit 54bf452
Show file tree

Hide file tree

Showing 27 changed files with 131 additions and 137 deletions.
diff --git a/client/src/main/java/org/opensearch/ml/client/MachineLearningClient.java b/client/src/main/java/org/opensearch/ml/client/MachineLearningClient.java
@@ -65,9 +65,9 @@ default ActionFuture<MLOutput> train(MLInput mlInput) {
     void train(MLInput mlInput, ActionListener<MLOutput> listener);
 
     /**
-     * Execute ML algorithm.
+     * Execute function and return ActionFuture.
      * @param input input data
-     * @return output
+     * @return ActionFuture of output
      */
     default ActionFuture<Output> execute(Input input) {
         PlainActionFuture<Output> actionFuture = PlainActionFuture.newFuture();
@@ -76,7 +76,7 @@ default ActionFuture<Output> execute(Input input) {
     }
 
     /**
-     * Execute ML algorithm
+     * Execute function and return output in listener
      * @param input input data
      * @param listener action listener
      */

diff --git a/client/src/test/java/org/opensearch/ml/client/MachineLearningClientTest.java b/client/src/test/java/org/opensearch/ml/client/MachineLearningClientTest.java
@@ -22,11 +22,10 @@
 import org.opensearch.common.xcontent.XContentBuilder;
 import org.opensearch.ml.common.dataframe.DataFrame;
 import org.opensearch.ml.common.parameter.Input;
-import org.opensearch.ml.common.parameter.MLAlgoName;
+import org.opensearch.ml.common.parameter.FunctionName;
 import org.opensearch.ml.common.parameter.MLAlgoParams;
 import org.opensearch.ml.common.parameter.MLInput;
 import org.opensearch.ml.common.parameter.MLOutput;
-import org.opensearch.ml.common.parameter.MLOutputType;
 import org.opensearch.ml.common.parameter.MLTrainingOutput;
 import org.opensearch.ml.common.parameter.Output;
 
@@ -93,7 +92,7 @@ public XContentBuilder toXContent(XContentBuilder builder, Params params) throws
     @Test
     public void predict_WithAlgoAndInputData() {
         MLInput mlInput = MLInput.builder()
-                .algorithm(MLAlgoName.KMEANS)
+                .algorithm(FunctionName.KMEANS)
                 .dataFrame(input)
                 .build();
         assertEquals(output, machineLearningClient.predict(null, mlInput).actionGet());
@@ -102,7 +101,7 @@ public void predict_WithAlgoAndInputData() {
     @Test
     public void predict_WithAlgoAndParametersAndInputData() {
         MLInput mlInput = MLInput.builder()
-                .algorithm(MLAlgoName.KMEANS)
+                .algorithm(FunctionName.KMEANS)
                 .parameters(mlParameters)
                 .dataFrame(input)
                 .build();
@@ -112,7 +111,7 @@ public void predict_WithAlgoAndParametersAndInputData() {
     @Test
     public void predict_WithAlgoAndParametersAndInputDataAndModelId() {
         MLInput mlInput = MLInput.builder()
-                .algorithm(MLAlgoName.KMEANS)
+                .algorithm(FunctionName.KMEANS)
                 .parameters(mlParameters)
                 .dataFrame(input)
                 .build();
@@ -122,7 +121,7 @@ public void predict_WithAlgoAndParametersAndInputDataAndModelId() {
     @Test
     public void predict_WithAlgoAndInputDataAndListener() {
         MLInput mlInput = MLInput.builder()
-                .algorithm(MLAlgoName.KMEANS)
+                .algorithm(FunctionName.KMEANS)
                 .dataFrame(input)
                 .build();
         ArgumentCaptor<MLOutput> dataFrameArgumentCaptor = ArgumentCaptor.forClass(MLOutput.class);
@@ -134,7 +133,7 @@ public void predict_WithAlgoAndInputDataAndListener() {
     @Test
     public void predict_WithAlgoAndInputDataAndParametersAndListener() {
         MLInput mlInput = MLInput.builder()
-                .algorithm(MLAlgoName.KMEANS)
+                .algorithm(FunctionName.KMEANS)
                 .parameters(mlParameters)
                 .dataFrame(input)
                 .build();
@@ -147,7 +146,7 @@ public void predict_WithAlgoAndInputDataAndParametersAndListener() {
     @Test
     public void train() {
         MLInput mlInput = MLInput.builder()
-                .algorithm(MLAlgoName.KMEANS)
+                .algorithm(FunctionName.KMEANS)
                 .parameters(mlParameters)
                 .dataFrame(input)
                 .build();

diff --git a/client/src/test/java/org/opensearch/ml/client/MachineLearningNodeClientTest.java b/client/src/test/java/org/opensearch/ml/client/MachineLearningNodeClientTest.java
@@ -23,7 +23,7 @@
 import org.opensearch.client.node.NodeClient;
 import org.opensearch.ml.common.dataframe.DataFrame;
 import org.opensearch.ml.common.dataset.MLInputDataset;
-import org.opensearch.ml.common.parameter.MLAlgoName;
+import org.opensearch.ml.common.parameter.FunctionName;
 import org.opensearch.ml.common.parameter.MLInput;
 import org.opensearch.ml.common.parameter.MLOutput;
 import org.opensearch.ml.common.parameter.MLPredictionOutput;
@@ -88,7 +88,7 @@ public void predict() {
 
         ArgumentCaptor<MLOutput> dataFrameArgumentCaptor = ArgumentCaptor.forClass(MLOutput.class);
         MLInput mlInput = MLInput.builder()
-                .algorithm(MLAlgoName.KMEANS)
+                .algorithm(FunctionName.KMEANS)
                 .inputDataset(input)
                 .build();
         machineLearningNodeClient.predict(null, mlInput, dataFrameActionListener);
@@ -114,7 +114,7 @@ public void predict_Exception_WithNullDataSet() {
         exceptionRule.expect(IllegalArgumentException.class);
         exceptionRule.expectMessage("input data set can't be null");
         MLInput mlInput = MLInput.builder()
-                .algorithm(MLAlgoName.KMEANS)
+                .algorithm(FunctionName.KMEANS)
                 .build();
         machineLearningNodeClient.predict(null, mlInput, dataFrameActionListener);
     }
@@ -137,7 +137,7 @@ public void train() {
 
         ArgumentCaptor<MLOutput> argumentCaptor = ArgumentCaptor.forClass(MLOutput.class);
         MLInput mlInput = MLInput.builder()
-                .algorithm(MLAlgoName.KMEANS)
+                .algorithm(FunctionName.KMEANS)
                 .inputDataset(input)
                 .build();
         machineLearningNodeClient.train(mlInput, trainingActionListener);
@@ -164,7 +164,7 @@ public void train_Exception_WithNullDataSet() {
         exceptionRule.expect(IllegalArgumentException.class);
         exceptionRule.expectMessage("input data set can't be null");
         MLInput mlInput = MLInput.builder()
-                .algorithm(MLAlgoName.KMEANS)
+                .algorithm(FunctionName.KMEANS)
                 .build();
         machineLearningNodeClient.train(mlInput, trainingActionListener);
     }

diff --git a/common/src/main/java/org/opensearch/ml/common/MLCommonsClassLoader.java b/common/src/main/java/org/opensearch/ml/common/MLCommonsClassLoader.java
@@ -20,7 +20,7 @@
 import org.opensearch.common.xcontent.XContentType;
 import org.opensearch.ml.common.dataset.MLInputDataType;
 import org.opensearch.ml.common.parameter.Input;
-import org.opensearch.ml.common.parameter.MLAlgoName;
+import org.opensearch.ml.common.parameter.FunctionName;
 import org.opensearch.ml.common.parameter.MLAlgoParams;
 import org.opensearch.ml.common.parameter.MLOutputType;
 
@@ -57,15 +57,15 @@ public static void loadClassMapping(Class<?> resource, String configFile) {
                 if (currentToken == XContentParser.Token.FIELD_NAME) {
                     String key = parser.currentName();
                     if ("ml_algo_param_class".equals(key)) {
-                        parseMLAlgoParams(parser, parameterClassMap, k -> MLAlgoName.fromString(k));
+                        parseMLAlgoParams(parser, parameterClassMap, k -> FunctionName.fromString(k));
                     } else if ("ml_input_data_set_class".equals(key)) {
                         parseMLAlgoParams(parser, parameterClassMap, k -> MLInputDataType.fromString(k));
                     } else if ("ml_output_class".equals(key)) {
                         parseMLAlgoParams(parser, parameterClassMap, k -> MLOutputType.fromString(k));
                     } else if ("ml_algo_class".equals(key)) {
-                        parseMLAlgoParams(parser, mlAlgoClassMap, k -> MLAlgoName.fromString(k));
+                        parseMLAlgoParams(parser, mlAlgoClassMap, k -> FunctionName.fromString(k));
                     } else if ("executable_function_class".equals(key)) {
-                        parseMLAlgoParams(parser, mlAlgoClassMap, k -> MLAlgoName.fromString(k));
+                        parseMLAlgoParams(parser, mlAlgoClassMap, k -> FunctionName.fromString(k));
                     }
                 } else {
                     parser.nextToken();

diff --git a/common/src/main/java/org/opensearch/ml/common/dataframe/DataFrameType.java b/common/src/main/java/org/opensearch/ml/common/dataframe/DataFrameType.java
@@ -15,7 +15,7 @@
 import lombok.Getter;
 
 public enum DataFrameType {
-    DEFAULT("DEFAULT");
+    DEFAULT("default");
 
     @Getter
     private final String name;

diff --git a/...earch/ml/common/parameter/MLAlgoName.java → ...rch/ml/common/parameter/FunctionName.java b/...earch/ml/common/parameter/MLAlgoName.java → ...rch/ml/common/parameter/FunctionName.java
@@ -2,7 +2,7 @@
 
 import lombok.Getter;
 
-public enum MLAlgoName {
+public enum FunctionName {
     LINEAR_REGRESSION("linear_regression"),
     KMEANS("kmeans"),
     SAMPLE_ALGO("sample_algo"),
@@ -11,16 +11,16 @@ public enum MLAlgoName {
     @Getter
     private final String name;
 
-    MLAlgoName(String name) {
+    FunctionName(String name) {
         this.name = name;
     }
 
     public String toString() {
         return name;
     }
 
-    public static MLAlgoName fromString(String name){
-        for(MLAlgoName e : MLAlgoName.values()){
+    public static FunctionName fromString(String name){
+        for(FunctionName e : FunctionName.values()){
             if(e.name.equals(name)) return e;
         }
         return null;

diff --git a/common/src/main/java/org/opensearch/ml/common/parameter/Input.java b/common/src/main/java/org/opensearch/ml/common/parameter/Input.java
@@ -16,5 +16,5 @@
 
 public interface Input extends ToXContentObject, Writeable {
 
-    MLAlgoName getFunctionName();
+    FunctionName getFunctionName();
 }
diff --git a/common/src/main/java/org/opensearch/ml/common/parameter/KMeansParams.java b/common/src/main/java/org/opensearch/ml/common/parameter/KMeansParams.java
@@ -17,7 +17,7 @@
 @Data
 public class KMeansParams implements MLAlgoParams {
 
-    public static final String PARSE_FIELD_NAME = "kmeans";
+    public static final String PARSE_FIELD_NAME = FunctionName.KMEANS.getName();
     public static final NamedXContentRegistry.Entry XCONTENT_REGISTRY = new NamedXContentRegistry.Entry(
             MLAlgoParams.class,
             new ParseField(PARSE_FIELD_NAME),

diff --git a/common/src/main/java/org/opensearch/ml/common/parameter/LinearRegressionParams.java b/common/src/main/java/org/opensearch/ml/common/parameter/LinearRegressionParams.java
@@ -17,7 +17,7 @@
 @Data
 public class LinearRegressionParams implements MLAlgoParams {
 
-    public static final String PARSE_FIELD_NAME = "linear_regression";
+    public static final String PARSE_FIELD_NAME = FunctionName.LINEAR_REGRESSION.getName();
     public static final NamedXContentRegistry.Entry XCONTENT_REGISTRY = new NamedXContentRegistry.Entry(
             MLAlgoParams.class,
             new ParseField(PARSE_FIELD_NAME),

diff --git a/common/src/main/java/org/opensearch/ml/common/parameter/LocalSampleCalculatorInput.java b/common/src/main/java/org/opensearch/ml/common/parameter/LocalSampleCalculatorInput.java
@@ -28,7 +28,7 @@
 
 @Getter
 public class LocalSampleCalculatorInput implements Input {
-    public static final String PARSE_FIELD_NAME = "local_sample_calculator";
+    public static final String PARSE_FIELD_NAME = FunctionName.LOCAL_SAMPLE_CALCULATOR.getName();
     public static final NamedXContentRegistry.Entry XCONTENT_REGISTRY = new NamedXContentRegistry.Entry(
             Input.class,
             new ParseField(PARSE_FIELD_NAME),
@@ -81,8 +81,8 @@ public LocalSampleCalculatorInput(String operation, List<Double> inputData) {
     }
 
     @Override
-    public MLAlgoName getFunctionName() {
-        return MLAlgoName.LOCAL_SAMPLE_CALCULATOR;
+    public FunctionName getFunctionName() {
+        return FunctionName.LOCAL_SAMPLE_CALCULATOR;
     }
 
     public LocalSampleCalculatorInput(StreamInput in) throws IOException {

diff --git a/common/src/main/java/org/opensearch/ml/common/parameter/MLInput.java b/common/src/main/java/org/opensearch/ml/common/parameter/MLInput.java
@@ -46,7 +46,7 @@ public class MLInput implements Input {
     public static final String INPUT_DATA_FIELD = "input_data";
 
     // Algorithm name
-    private MLAlgoName algorithm;
+    private FunctionName algorithm;
     // ML algorithm parameters
     private MLAlgoParams parameters;
     // Input data to train model, run trained model to predict or run ML algorithms(no-model-based) directly.
@@ -55,7 +55,7 @@ public class MLInput implements Input {
     private int version = 1;
 
     @Builder
-    public MLInput(MLAlgoName algorithm, MLAlgoParams parameters, SearchSourceBuilder searchSourceBuilder, List<String> sourceIndices, DataFrame dataFrame, MLInputDataset inputDataset) {
+    public MLInput(FunctionName algorithm, MLAlgoParams parameters, SearchSourceBuilder searchSourceBuilder, List<String> sourceIndices, DataFrame dataFrame, MLInputDataset inputDataset) {
         this.algorithm = algorithm;
         this.parameters = parameters;
         if (inputDataset != null) {
@@ -66,7 +66,7 @@ public MLInput(MLAlgoName algorithm, MLAlgoParams parameters, SearchSourceBuilde
     }
 
     public MLInput(StreamInput in) throws IOException {
-        this.algorithm = in.readEnum(MLAlgoName.class);
+        this.algorithm = in.readEnum(FunctionName.class);
         if (in.readBoolean()) {
             this.parameters = MLCommonsClassLoader.initInstance(algorithm, in, StreamInput.class);
         }
@@ -120,7 +120,7 @@ public XContentBuilder toXContent(XContentBuilder builder, Params params) throws
     }
 
     public static MLInput parse(XContentParser parser, String algorithmName) throws IOException {
-        MLAlgoName algorithm = MLAlgoName.fromString(algorithmName);
+        FunctionName algorithm = FunctionName.fromString(algorithmName);
         MLAlgoParams mlParameters = null;
         SearchSourceBuilder searchSourceBuilder = null;
         List<String> sourceIndices = new ArrayList<>();
@@ -166,7 +166,7 @@ private MLInputDataset createInputDataSet(SearchSourceBuilder searchSourceBuilde
     }
 
     @Override
-    public MLAlgoName getFunctionName() {
+    public FunctionName getFunctionName() {
         return this.algorithm;
     }
 }
diff --git a/common/src/main/java/org/opensearch/ml/common/parameter/SampleAlgoParams.java b/common/src/main/java/org/opensearch/ml/common/parameter/SampleAlgoParams.java
@@ -25,8 +25,7 @@
 
 @Data
 public class SampleAlgoParams implements MLAlgoParams {
-
-    public static final String PARSE_FIELD_NAME = "sample_algo";
+    public static final String PARSE_FIELD_NAME = FunctionName.SAMPLE_ALGO.getName();
     public static final NamedXContentRegistry.Entry XCONTENT_REGISTRY = new NamedXContentRegistry.Entry(
             MLAlgoParams.class,
             new ParseField(PARSE_FIELD_NAME),

diff --git a/common/src/test/java/org/opensearch/ml/common/MLCommonsClassLoaderTests.java b/common/src/test/java/org/opensearch/ml/common/MLCommonsClassLoaderTests.java
@@ -17,8 +17,7 @@
 import org.junit.rules.ExpectedException;
 import org.opensearch.common.io.stream.BytesStreamOutput;
 import org.opensearch.common.io.stream.StreamInput;
-import org.opensearch.ml.common.MLCommonsClassLoader;
-import org.opensearch.ml.common.parameter.MLAlgoName;
+import org.opensearch.ml.common.parameter.FunctionName;
 import org.opensearch.ml.common.parameter.MLAlgoParams;
 import org.opensearch.ml.common.parameter.SampleAlgoParams;
 
@@ -48,13 +47,13 @@ public void setUp() throws IOException {
 
     @Test
     public void testClassLoader_SampleAlgoParams() {
-        SampleAlgoParams sampleAlgoParams = MLCommonsClassLoader.initInstance(MLAlgoName.SAMPLE_ALGO, streamInput, StreamInput.class);
+        SampleAlgoParams sampleAlgoParams = MLCommonsClassLoader.initInstance(FunctionName.SAMPLE_ALGO, streamInput, StreamInput.class);
         assertEquals(params.getSampleParam(), sampleAlgoParams.getSampleParam());
     }
 
     @Test
     public void testClassLoader_Return_MLAlgoParams() {
-        MLAlgoParams mlAlgoParams = MLCommonsClassLoader.initInstance(MLAlgoName.SAMPLE_ALGO, streamInput, StreamInput.class);
+        MLAlgoParams mlAlgoParams = MLCommonsClassLoader.initInstance(FunctionName.SAMPLE_ALGO, streamInput, StreamInput.class);
         assertTrue(mlAlgoParams instanceof SampleAlgoParams);
         assertEquals(params.getSampleParam(), ((SampleAlgoParams)mlAlgoParams).getSampleParam());
     }

diff --git a/.../test/java/org/opensearch/ml/common/transport/prediction/MLPredictionTaskRequestTest.java b/.../test/java/org/opensearch/ml/common/transport/prediction/MLPredictionTaskRequestTest.java
@@ -34,7 +34,7 @@
 import org.opensearch.ml.common.dataset.MLInputDataset;
 import org.opensearch.ml.common.dataset.SearchQueryInputDataset;
 import org.opensearch.ml.common.parameter.KMeansParams;
-import org.opensearch.ml.common.parameter.MLAlgoName;
+import org.opensearch.ml.common.parameter.FunctionName;
 import org.opensearch.ml.common.parameter.MLInput;
 import org.opensearch.search.builder.SearchSourceBuilder;
 
@@ -50,7 +50,7 @@ public class MLPredictionTaskRequestTest {
     @Before
     public void setUp() {
         mlInput = MLInput.builder()
-                .algorithm(MLAlgoName.KMEANS)
+                .algorithm(FunctionName.KMEANS)
                 .parameters(KMeansParams.builder().centroids(1).build())
                 .dataFrame(DataFrameBuilder.load(Collections.singletonList(new HashMap<String, Object>() {{
                     put("key1", 2.0D);
@@ -68,7 +68,7 @@ public void writeTo_Success() throws IOException {
         BytesStreamOutput bytesStreamOutput = new BytesStreamOutput();
         request.writeTo(bytesStreamOutput);
         request = new MLPredictionTaskRequest(bytesStreamOutput.bytes().streamInput());
-        assertEquals(MLAlgoName.KMEANS, request.getMlInput().getAlgorithm());
+        assertEquals(FunctionName.KMEANS, request.getMlInput().getAlgorithm());
         KMeansParams params = (KMeansParams)request.getMlInput().getParameters();
         assertEquals(1, params.getCentroids().intValue());
         MLInputDataset inputDataset = request.getMlInput().getInputDataset();

diff --git a/.../src/test/java/org/opensearch/ml/common/transport/training/MLTrainingTaskRequestTest.java b/.../src/test/java/org/opensearch/ml/common/transport/training/MLTrainingTaskRequestTest.java
@@ -21,7 +21,7 @@
 import org.opensearch.ml.common.dataframe.DataFrameBuilder;
 import org.opensearch.ml.common.dataset.MLInputDataType;
 import org.opensearch.ml.common.parameter.KMeansParams;
-import org.opensearch.ml.common.parameter.MLAlgoName;
+import org.opensearch.ml.common.parameter.FunctionName;
 import org.opensearch.ml.common.parameter.MLInput;
 
 import java.io.IOException;
@@ -41,7 +41,7 @@ public class MLTrainingTaskRequestTest {
     @Before
     public void setUp() {
         mlInput = MLInput.builder()
-                .algorithm(MLAlgoName.KMEANS)
+                .algorithm(FunctionName.KMEANS)
                 .parameters(KMeansParams.builder().centroids(1).build())
                 .dataFrame(DataFrameBuilder.load(Collections.singletonList(new HashMap<String, Object>() {{
                     put("key1", 2.0D);
@@ -75,7 +75,7 @@ public void writeTo() throws IOException {
         BytesStreamOutput bytesStreamOutput = new BytesStreamOutput();
         request.writeTo(bytesStreamOutput);
         request = new MLTrainingTaskRequest(bytesStreamOutput.bytes().streamInput());
-        assertEquals(MLAlgoName.KMEANS, request.getMlInput().getAlgorithm());
+        assertEquals(FunctionName.KMEANS, request.getMlInput().getAlgorithm());
         assertEquals(1, ((KMeansParams) request.getMlInput().getParameters()).getCentroids().intValue());
         assertEquals(MLInputDataType.DATA_FRAME, request.getMlInput().getInputDataset().getInputDataType());
     }