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MachineLearningNodeClientTest.java
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/*
* Copyright OpenSearch Contributors
* SPDX-License-Identifier: Apache-2.0
*/
package org.opensearch.ml.client;
import static org.junit.Assert.assertEquals;
import static org.junit.Assert.assertFalse;
import static org.junit.Assert.assertTrue;
import static org.junit.Assert.fail;
import static org.mockito.Answers.RETURNS_DEEP_STUBS;
import static org.mockito.ArgumentMatchers.any;
import static org.mockito.ArgumentMatchers.eq;
import static org.mockito.ArgumentMatchers.isA;
import static org.mockito.Mockito.doAnswer;
import static org.mockito.Mockito.verify;
import static org.opensearch.ml.common.CommonValue.MASTER_KEY;
import static org.opensearch.ml.common.input.Constants.ACTION;
import static org.opensearch.ml.common.input.Constants.ALGORITHM;
import static org.opensearch.ml.common.input.Constants.ASYNC;
import static org.opensearch.ml.common.input.Constants.KMEANS;
import static org.opensearch.ml.common.input.Constants.MODELID;
import static org.opensearch.ml.common.input.Constants.PREDICT;
import static org.opensearch.ml.common.input.Constants.RCF;
import static org.opensearch.ml.common.input.Constants.TRAIN;
import static org.opensearch.ml.common.input.Constants.TRAINANDPREDICT;
import java.io.IOException;
import java.util.ArrayList;
import java.util.Arrays;
import java.util.Collections;
import java.util.HashMap;
import java.util.List;
import java.util.Map;
import org.apache.lucene.search.TotalHits;
import org.junit.Before;
import org.junit.Rule;
import org.junit.Test;
import org.junit.rules.ExpectedException;
import org.mockito.ArgumentCaptor;
import org.mockito.InjectMocks;
import org.mockito.Mock;
import org.mockito.MockitoAnnotations;
import org.opensearch.OpenSearchStatusException;
import org.opensearch.action.delete.DeleteResponse;
import org.opensearch.action.search.SearchRequest;
import org.opensearch.action.search.SearchResponse;
import org.opensearch.action.search.ShardSearchFailure;
import org.opensearch.client.node.NodeClient;
import org.opensearch.cluster.ClusterName;
import org.opensearch.common.xcontent.XContentFactory;
import org.opensearch.core.action.ActionListener;
import org.opensearch.core.common.bytes.BytesReference;
import org.opensearch.core.index.Index;
import org.opensearch.core.index.shard.ShardId;
import org.opensearch.core.rest.RestStatus;
import org.opensearch.core.xcontent.ToXContent;
import org.opensearch.core.xcontent.ToXContentObject;
import org.opensearch.core.xcontent.XContentBuilder;
import org.opensearch.ml.common.AccessMode;
import org.opensearch.ml.common.Configuration;
import org.opensearch.ml.common.FunctionName;
import org.opensearch.ml.common.MLAgentType;
import org.opensearch.ml.common.MLConfig;
import org.opensearch.ml.common.MLModel;
import org.opensearch.ml.common.MLTask;
import org.opensearch.ml.common.MLTaskState;
import org.opensearch.ml.common.MLTaskType;
import org.opensearch.ml.common.ToolMetadata;
import org.opensearch.ml.common.agent.MLAgent;
import org.opensearch.ml.common.dataframe.DataFrame;
import org.opensearch.ml.common.dataset.MLInputDataset;
import org.opensearch.ml.common.input.Input;
import org.opensearch.ml.common.input.MLInput;
import org.opensearch.ml.common.input.execute.metricscorrelation.MetricsCorrelationInput;
import org.opensearch.ml.common.model.MLModelConfig;
import org.opensearch.ml.common.model.MLModelFormat;
import org.opensearch.ml.common.model.TextEmbeddingModelConfig;
import org.opensearch.ml.common.output.MLOutput;
import org.opensearch.ml.common.output.MLPredictionOutput;
import org.opensearch.ml.common.output.MLTrainingOutput;
import org.opensearch.ml.common.output.Output;
import org.opensearch.ml.common.output.execute.metrics_correlation.MCorrModelTensor;
import org.opensearch.ml.common.output.execute.metrics_correlation.MCorrModelTensors;
import org.opensearch.ml.common.output.execute.metrics_correlation.MetricsCorrelationOutput;
import org.opensearch.ml.common.transport.MLTaskResponse;
import org.opensearch.ml.common.transport.agent.MLAgentDeleteAction;
import org.opensearch.ml.common.transport.agent.MLAgentDeleteRequest;
import org.opensearch.ml.common.transport.agent.MLRegisterAgentAction;
import org.opensearch.ml.common.transport.agent.MLRegisterAgentRequest;
import org.opensearch.ml.common.transport.agent.MLRegisterAgentResponse;
import org.opensearch.ml.common.transport.config.MLConfigGetAction;
import org.opensearch.ml.common.transport.config.MLConfigGetRequest;
import org.opensearch.ml.common.transport.config.MLConfigGetResponse;
import org.opensearch.ml.common.transport.connector.MLConnectorDeleteAction;
import org.opensearch.ml.common.transport.connector.MLConnectorDeleteRequest;
import org.opensearch.ml.common.transport.connector.MLCreateConnectorAction;
import org.opensearch.ml.common.transport.connector.MLCreateConnectorInput;
import org.opensearch.ml.common.transport.connector.MLCreateConnectorRequest;
import org.opensearch.ml.common.transport.connector.MLCreateConnectorResponse;
import org.opensearch.ml.common.transport.deploy.MLDeployModelAction;
import org.opensearch.ml.common.transport.deploy.MLDeployModelRequest;
import org.opensearch.ml.common.transport.deploy.MLDeployModelResponse;
import org.opensearch.ml.common.transport.execute.MLExecuteTaskAction;
import org.opensearch.ml.common.transport.execute.MLExecuteTaskRequest;
import org.opensearch.ml.common.transport.execute.MLExecuteTaskResponse;
import org.opensearch.ml.common.transport.model.MLModelDeleteAction;
import org.opensearch.ml.common.transport.model.MLModelDeleteRequest;
import org.opensearch.ml.common.transport.model.MLModelGetAction;
import org.opensearch.ml.common.transport.model.MLModelGetRequest;
import org.opensearch.ml.common.transport.model.MLModelGetResponse;
import org.opensearch.ml.common.transport.model.MLModelSearchAction;
import org.opensearch.ml.common.transport.model_group.MLRegisterModelGroupAction;
import org.opensearch.ml.common.transport.model_group.MLRegisterModelGroupInput;
import org.opensearch.ml.common.transport.model_group.MLRegisterModelGroupRequest;
import org.opensearch.ml.common.transport.model_group.MLRegisterModelGroupResponse;
import org.opensearch.ml.common.transport.prediction.MLPredictionTaskAction;
import org.opensearch.ml.common.transport.prediction.MLPredictionTaskRequest;
import org.opensearch.ml.common.transport.register.MLRegisterModelAction;
import org.opensearch.ml.common.transport.register.MLRegisterModelInput;
import org.opensearch.ml.common.transport.register.MLRegisterModelRequest;
import org.opensearch.ml.common.transport.register.MLRegisterModelResponse;
import org.opensearch.ml.common.transport.task.MLTaskDeleteAction;
import org.opensearch.ml.common.transport.task.MLTaskDeleteRequest;
import org.opensearch.ml.common.transport.task.MLTaskGetAction;
import org.opensearch.ml.common.transport.task.MLTaskGetRequest;
import org.opensearch.ml.common.transport.task.MLTaskGetResponse;
import org.opensearch.ml.common.transport.task.MLTaskSearchAction;
import org.opensearch.ml.common.transport.tools.MLGetToolAction;
import org.opensearch.ml.common.transport.tools.MLListToolsAction;
import org.opensearch.ml.common.transport.tools.MLToolGetRequest;
import org.opensearch.ml.common.transport.tools.MLToolGetResponse;
import org.opensearch.ml.common.transport.tools.MLToolsListRequest;
import org.opensearch.ml.common.transport.tools.MLToolsListResponse;
import org.opensearch.ml.common.transport.training.MLTrainingTaskAction;
import org.opensearch.ml.common.transport.training.MLTrainingTaskRequest;
import org.opensearch.ml.common.transport.trainpredict.MLTrainAndPredictionTaskAction;
import org.opensearch.ml.common.transport.undeploy.MLUndeployModelNodesResponse;
import org.opensearch.ml.common.transport.undeploy.MLUndeployModelsAction;
import org.opensearch.ml.common.transport.undeploy.MLUndeployModelsRequest;
import org.opensearch.ml.common.transport.undeploy.MLUndeployModelsResponse;
import org.opensearch.search.SearchHit;
import org.opensearch.search.SearchHits;
import org.opensearch.search.aggregations.InternalAggregations;
import org.opensearch.search.internal.InternalSearchResponse;
import org.opensearch.search.profile.SearchProfileShardResults;
import org.opensearch.search.suggest.Suggest;
public class MachineLearningNodeClientTest {
@Mock(answer = RETURNS_DEEP_STUBS)
NodeClient client;
@Mock
MLInputDataset input;
@Mock
DataFrame output;
@Mock
ActionListener<MLOutput> dataFrameActionListener;
@Mock
ActionListener<MLOutput> trainingActionListener;
@Mock
ActionListener<MLModel> getModelActionListener;
@Mock
ActionListener<DeleteResponse> deleteModelActionListener;
@Mock
ActionListener<SearchResponse> searchModelActionListener;
@Mock
ActionListener<MLTask> getTaskActionListener;
@Mock
ActionListener<DeleteResponse> deleteTaskActionListener;
@Mock
ActionListener<SearchResponse> searchTaskActionListener;
@Mock
ActionListener<MLRegisterModelResponse> registerModelActionListener;
@Mock
ActionListener<MLDeployModelResponse> deployModelActionListener;
@Mock
ActionListener<MLUndeployModelsResponse> undeployModelsActionListener;
@Mock
ActionListener<MLCreateConnectorResponse> createConnectorActionListener;
@Mock
ActionListener<DeleteResponse> deleteConnectorActionListener;
@Mock
ActionListener<MLRegisterModelGroupResponse> registerModelGroupResponseActionListener;
@Mock
ActionListener<MLExecuteTaskResponse> executeTaskResponseActionListener;
@Mock
ActionListener<MLRegisterAgentResponse> registerAgentResponseActionListener;
@Mock
ActionListener<DeleteResponse> deleteAgentActionListener;
@Mock
ActionListener<List<ToolMetadata>> listToolsActionListener;
@Mock
ActionListener<ToolMetadata> getToolActionListener;
@Mock
ActionListener<MLConfig> getMlConfigListener;
@InjectMocks
MachineLearningNodeClient machineLearningNodeClient;
@Rule
public ExpectedException exceptionRule = ExpectedException.none();
@Before
public void setUp() {
MockitoAnnotations.openMocks(this);
}
@Test
public void predict() {
doAnswer(invocation -> {
ActionListener<MLTaskResponse> actionListener = invocation.getArgument(2);
MLPredictionOutput predictionOutput = MLPredictionOutput
.builder()
.status("Success")
.predictionResult(output)
.taskId("taskId")
.build();
actionListener.onResponse(MLTaskResponse.builder().output(predictionOutput).build());
return null;
}).when(client).execute(eq(MLPredictionTaskAction.INSTANCE), any(), any());
ArgumentCaptor<MLOutput> dataFrameArgumentCaptor = ArgumentCaptor.forClass(MLOutput.class);
MLInput mlInput = MLInput.builder().algorithm(FunctionName.KMEANS).inputDataset(input).build();
machineLearningNodeClient.predict(null, mlInput, dataFrameActionListener);
verify(client).execute(eq(MLPredictionTaskAction.INSTANCE), isA(MLPredictionTaskRequest.class), any());
verify(dataFrameActionListener).onResponse(dataFrameArgumentCaptor.capture());
assertEquals(output, ((MLPredictionOutput) dataFrameArgumentCaptor.getValue()).getPredictionResult());
}
@Test
public void execute_train_asyncTask() {
String modelId = "test_model_id";
String status = "InProgress";
doAnswer(invocation -> {
ActionListener<MLTaskResponse> actionListener = invocation.getArgument(2);
MLTrainingOutput output = MLTrainingOutput.builder().status(status).modelId(modelId).build();
actionListener.onResponse(MLTaskResponse.builder().output(output).build());
return null;
}).when(client).execute(eq(MLTrainingTaskAction.INSTANCE), any(), any());
ArgumentCaptor<MLOutput> argumentCaptor = ArgumentCaptor.forClass(MLOutput.class);
Map<String, Object> args = new HashMap<>();
args.put(ACTION, TRAIN);
args.put(ALGORITHM, KMEANS);
args.put(ASYNC, true);
MLInput mlInput = MLInput.builder().algorithm(FunctionName.SAMPLE_ALGO).inputDataset(input).build();
machineLearningNodeClient.run(mlInput, args, trainingActionListener);
verify(client).execute(eq(MLTrainingTaskAction.INSTANCE), isA(MLTrainingTaskRequest.class), any());
verify(trainingActionListener).onResponse(argumentCaptor.capture());
assertEquals(modelId, ((MLTrainingOutput) argumentCaptor.getValue()).getModelId());
assertEquals(status, ((MLTrainingOutput) argumentCaptor.getValue()).getStatus());
}
@Test
public void execute_predict_missing_modelId() {
exceptionRule.expect(IllegalArgumentException.class);
exceptionRule.expectMessage("The model ID is required for prediction.");
Map<String, Object> args = new HashMap<>();
args.put(ACTION, PREDICT);
args.put(ALGORITHM, KMEANS);
MLInput mlInput = MLInput.builder().algorithm(FunctionName.SAMPLE_ALGO).inputDataset(input).build();
machineLearningNodeClient.run(mlInput, args, dataFrameActionListener);
}
@Test
public void predict_Exception_WithNullAlgorithm() {
exceptionRule.expect(IllegalArgumentException.class);
exceptionRule.expectMessage("algorithm can't be null");
MLInput mlInput = MLInput.builder().inputDataset(input).build();
machineLearningNodeClient.predict(null, mlInput, dataFrameActionListener);
}
@Test
public void predict_Exception_WithNullDataSet() {
exceptionRule.expect(IllegalArgumentException.class);
exceptionRule.expectMessage("input data set can't be null");
MLInput mlInput = MLInput.builder().algorithm(FunctionName.KMEANS).build();
machineLearningNodeClient.predict(null, mlInput, dataFrameActionListener);
}
@Test
public void train() {
String modelId = "test_model_id";
String status = "InProgress";
doAnswer(invocation -> {
ActionListener<MLTaskResponse> actionListener = invocation.getArgument(2);
MLTrainingOutput output = MLTrainingOutput.builder().status(status).modelId(modelId).build();
actionListener.onResponse(MLTaskResponse.builder().output(output).build());
return null;
}).when(client).execute(eq(MLTrainingTaskAction.INSTANCE), any(), any());
ArgumentCaptor<MLOutput> argumentCaptor = ArgumentCaptor.forClass(MLOutput.class);
MLInput mlInput = MLInput.builder().algorithm(FunctionName.KMEANS).inputDataset(input).build();
machineLearningNodeClient.train(mlInput, false, trainingActionListener);
verify(client).execute(eq(MLTrainingTaskAction.INSTANCE), isA(MLTrainingTaskRequest.class), any());
verify(trainingActionListener).onResponse(argumentCaptor.capture());
assertEquals(modelId, ((MLTrainingOutput) argumentCaptor.getValue()).getModelId());
assertEquals(status, ((MLTrainingOutput) argumentCaptor.getValue()).getStatus());
}
@Test
public void getModel_withTenantId() {
String modelContent = "test content";
String tenantId = "tenantId";
doAnswer(invocation -> {
ActionListener<MLModelGetResponse> actionListener = invocation.getArgument(2);
MLModel mlModel = MLModel.builder().algorithm(FunctionName.KMEANS).name("test").content(modelContent).build();
MLModelGetResponse output = MLModelGetResponse.builder().mlModel(mlModel).build();
actionListener.onResponse(output);
return null;
}).when(client).execute(eq(MLModelGetAction.INSTANCE), any(), any());
ArgumentCaptor<MLModel> argumentCaptor = ArgumentCaptor.forClass(MLModel.class);
machineLearningNodeClient.getModel("modelId", tenantId, getModelActionListener);
verify(client).execute(eq(MLModelGetAction.INSTANCE), isA(MLModelGetRequest.class), any());
verify(getModelActionListener).onResponse(argumentCaptor.capture());
assertEquals(FunctionName.KMEANS, argumentCaptor.getValue().getAlgorithm());
assertEquals(modelContent, argumentCaptor.getValue().getContent());
}
@Test
public void undeployModels_withNullNodeIds() {
doAnswer(invocation -> {
ActionListener<MLUndeployModelsResponse> actionListener = invocation.getArgument(2);
MLUndeployModelsResponse output = new MLUndeployModelsResponse(
new MLUndeployModelNodesResponse(ClusterName.DEFAULT, Collections.emptyList(), Collections.emptyList())
);
actionListener.onResponse(output);
return null;
}).when(client).execute(eq(MLUndeployModelsAction.INSTANCE), any(), any());
machineLearningNodeClient.undeploy(new String[] { "model1" }, null, undeployModelsActionListener);
verify(client).execute(eq(MLUndeployModelsAction.INSTANCE), isA(MLUndeployModelsRequest.class), any());
}
@Test
public void createConnector_withValidInput() {
doAnswer(invocation -> {
ActionListener<MLCreateConnectorResponse> actionListener = invocation.getArgument(2);
MLCreateConnectorResponse output = new MLCreateConnectorResponse("connectorId");
actionListener.onResponse(output);
return null;
}).when(client).execute(eq(MLCreateConnectorAction.INSTANCE), any(), any());
MLCreateConnectorInput input = MLCreateConnectorInput
.builder()
.name("testConnector")
.protocol("http")
.version("1")
.credential(Map.of("TEST_CREDENTIAL_KEY", "TEST_CREDENTIAL_VALUE"))
.parameters(Map.of("endpoint", "https://example.com"))
.build();
machineLearningNodeClient.createConnector(input, createConnectorActionListener);
verify(client).execute(eq(MLCreateConnectorAction.INSTANCE), isA(MLCreateConnectorRequest.class), any());
}
@Test
public void registerModelGroup_withValidInput() {
doAnswer(invocation -> {
ActionListener<MLRegisterModelGroupResponse> actionListener = invocation.getArgument(2);
MLRegisterModelGroupResponse output = new MLRegisterModelGroupResponse("groupId", "created");
actionListener.onResponse(output);
return null;
}).when(client).execute(eq(MLRegisterModelGroupAction.INSTANCE), any(), any());
MLRegisterModelGroupInput input = MLRegisterModelGroupInput
.builder()
.name("test")
.description("description")
.backendRoles(Arrays.asList("role1", "role2"))
.modelAccessMode(AccessMode.PUBLIC)
.build();
machineLearningNodeClient.registerModelGroup(input, registerModelGroupResponseActionListener);
verify(client).execute(eq(MLRegisterModelGroupAction.INSTANCE), isA(MLRegisterModelGroupRequest.class), any());
}
@Test
public void listTools_withValidRequest() {
doAnswer(invocation -> {
ActionListener<MLToolsListResponse> actionListener = invocation.getArgument(2);
MLToolsListResponse output = MLToolsListResponse
.builder()
.toolMetadata(
Arrays
.asList(
ToolMetadata.builder().name("tool1").description("description1").build(),
ToolMetadata.builder().name("tool2").description("description2").build()
)
)
.build();
actionListener.onResponse(output);
return null;
}).when(client).execute(eq(MLListToolsAction.INSTANCE), any(), any());
machineLearningNodeClient.listTools(listToolsActionListener);
verify(client).execute(eq(MLListToolsAction.INSTANCE), isA(MLToolsListRequest.class), any());
}
@Test
public void listTools_withEmptyResponse() {
doAnswer(invocation -> {
ActionListener<MLToolsListResponse> actionListener = invocation.getArgument(2);
MLToolsListResponse output = MLToolsListResponse.builder().toolMetadata(Collections.emptyList()).build();
actionListener.onResponse(output);
return null;
}).when(client).execute(eq(MLListToolsAction.INSTANCE), any(), any());
ArgumentCaptor<List<ToolMetadata>> argumentCaptor = ArgumentCaptor.forClass(List.class);
machineLearningNodeClient.listTools(listToolsActionListener);
verify(client).execute(eq(MLListToolsAction.INSTANCE), isA(MLToolsListRequest.class), any());
verify(listToolsActionListener).onResponse(argumentCaptor.capture());
List<ToolMetadata> capturedTools = argumentCaptor.getValue();
assertTrue(capturedTools.isEmpty());
}
@Test
public void getTool_withValidToolName() {
doAnswer(invocation -> {
ActionListener<MLToolGetResponse> actionListener = invocation.getArgument(2);
MLToolGetResponse output = MLToolGetResponse
.builder()
.toolMetadata(ToolMetadata.builder().name("tool1").description("description1").build())
.build();
actionListener.onResponse(output);
return null;
}).when(client).execute(eq(MLGetToolAction.INSTANCE), any(), any());
machineLearningNodeClient.getTool("tool1", getToolActionListener);
verify(client).execute(eq(MLGetToolAction.INSTANCE), isA(MLToolGetRequest.class), any());
}
@Test
public void getTool_withValidRequest() {
ToolMetadata toolMetadata = ToolMetadata
.builder()
.name("MathTool")
.description("Use this tool to calculate any math problem.")
.build();
doAnswer(invocation -> {
ActionListener<MLToolGetResponse> actionListener = invocation.getArgument(2);
MLToolGetResponse output = MLToolGetResponse.builder().toolMetadata(toolMetadata).build();
actionListener.onResponse(output);
return null;
}).when(client).execute(eq(MLGetToolAction.INSTANCE), any(), any());
ArgumentCaptor<ToolMetadata> argumentCaptor = ArgumentCaptor.forClass(ToolMetadata.class);
machineLearningNodeClient.getTool("MathTool", getToolActionListener);
verify(client).execute(eq(MLGetToolAction.INSTANCE), isA(MLToolGetRequest.class), any());
verify(getToolActionListener).onResponse(argumentCaptor.capture());
ToolMetadata capturedTool = argumentCaptor.getValue();
assertEquals("MathTool", capturedTool.getName());
assertEquals("Use this tool to calculate any math problem.", capturedTool.getDescription());
}
@Test
public void getTool_withFailureResponse() {
doAnswer(invocation -> {
ActionListener<MLToolGetResponse> actionListener = invocation.getArgument(2);
actionListener.onFailure(new RuntimeException("Test exception"));
return null;
}).when(client).execute(eq(MLGetToolAction.INSTANCE), any(), any());
machineLearningNodeClient.getTool("MathTool", new ActionListener<>() {
@Override
public void onResponse(ToolMetadata toolMetadata) {
fail("Expected failure but got response");
}
@Override
public void onFailure(Exception e) {
assertEquals("Test exception", e.getMessage());
}
});
verify(client).execute(eq(MLGetToolAction.INSTANCE), isA(MLToolGetRequest.class), any());
}
@Test
public void train_withAsync() {
doAnswer(invocation -> {
ActionListener<MLTaskResponse> actionListener = invocation.getArgument(2);
MLTrainingOutput output = MLTrainingOutput.builder().status("InProgress").modelId("modelId").build();
actionListener.onResponse(MLTaskResponse.builder().output(output).build());
return null;
}).when(client).execute(eq(MLTrainingTaskAction.INSTANCE), any(), any());
MLInput mlInput = MLInput.builder().algorithm(FunctionName.KMEANS).inputDataset(input).build();
machineLearningNodeClient.train(mlInput, true, trainingActionListener);
verify(client).execute(eq(MLTrainingTaskAction.INSTANCE), isA(MLTrainingTaskRequest.class), any());
}
@Test
public void deleteModel_withTenantId() {
String modelId = "testModelId";
String tenantId = "tenantId";
doAnswer(invocation -> {
ActionListener<DeleteResponse> actionListener = invocation.getArgument(2);
ShardId shardId = new ShardId(new Index("indexName", "uuid"), 1);
DeleteResponse output = new DeleteResponse(shardId, modelId, 1, 1, 1, true);
actionListener.onResponse(output);
return null;
}).when(client).execute(eq(MLModelDeleteAction.INSTANCE), any(), any());
ArgumentCaptor<DeleteResponse> argumentCaptor = ArgumentCaptor.forClass(DeleteResponse.class);
machineLearningNodeClient.deleteModel(modelId, tenantId, deleteModelActionListener);
verify(client).execute(eq(MLModelDeleteAction.INSTANCE), isA(MLModelDeleteRequest.class), any());
verify(deleteModelActionListener).onResponse(argumentCaptor.capture());
assertEquals(modelId, argumentCaptor.getValue().getId());
}
@Test
public void train_Exception_WithNullDataSet() {
exceptionRule.expect(IllegalArgumentException.class);
exceptionRule.expectMessage("input data set can't be null");
MLInput mlInput = MLInput.builder().algorithm(FunctionName.KMEANS).build();
machineLearningNodeClient.train(mlInput, false, trainingActionListener);
}
@Test
public void train_Exception_WithNullInput() {
exceptionRule.expect(IllegalArgumentException.class);
exceptionRule.expectMessage("ML Input can't be null");
machineLearningNodeClient.train(null, false, trainingActionListener);
}
@Test
public void trainAndPredict() {
doAnswer(invocation -> {
ActionListener<MLTaskResponse> actionListener = invocation.getArgument(2);
MLPredictionOutput predictionOutput = MLPredictionOutput
.builder()
.status(MLTaskState.COMPLETED.name())
.predictionResult(output)
.taskId("taskId")
.build();
actionListener.onResponse(MLTaskResponse.builder().output(predictionOutput).build());
return null;
}).when(client).execute(eq(MLTrainAndPredictionTaskAction.INSTANCE), any(), any());
ArgumentCaptor<MLOutput> argumentCaptor = ArgumentCaptor.forClass(MLOutput.class);
MLInput mlInput = MLInput.builder().algorithm(FunctionName.KMEANS).inputDataset(input).build();
machineLearningNodeClient.trainAndPredict(mlInput, trainingActionListener);
verify(client).execute(eq(MLTrainAndPredictionTaskAction.INSTANCE), isA(MLTrainingTaskRequest.class), any());
verify(trainingActionListener).onResponse(argumentCaptor.capture());
assertEquals(MLTaskState.COMPLETED.name(), ((MLPredictionOutput) argumentCaptor.getValue()).getStatus());
assertEquals(output, ((MLPredictionOutput) argumentCaptor.getValue()).getPredictionResult());
}
@Test
public void execute_predict() {
Map<String, Object> args = new HashMap<>();
args.put(ACTION, PREDICT);
args.put(ALGORITHM, KMEANS);
args.put(MODELID, "123");
execute_predict(args);
}
@Test
public void execute_predict_null_model_id() {
exceptionRule.expect(IllegalArgumentException.class);
exceptionRule.expectMessage("The model ID is required for prediction.");
Map<String, Object> args = new HashMap<>();
args.put(ACTION, PREDICT);
args.put(ALGORITHM, KMEANS);
execute_predict(args);
}
private void execute_predict(Map<String, Object> args) {
doAnswer(invocation -> {
ActionListener<MLTaskResponse> actionListener = invocation.getArgument(2);
MLPredictionOutput predictionOutput = MLPredictionOutput
.builder()
.status("Success")
.predictionResult(output)
.taskId("taskId")
.build();
actionListener.onResponse(MLTaskResponse.builder().output(predictionOutput).build());
return null;
}).when(client).execute(eq(MLPredictionTaskAction.INSTANCE), any(), any());
ArgumentCaptor<MLOutput> dataFrameArgumentCaptor = ArgumentCaptor.forClass(MLOutput.class);
MLInput mlInput = MLInput.builder().algorithm(FunctionName.SAMPLE_ALGO).inputDataset(input).build();
machineLearningNodeClient.run(mlInput, args, dataFrameActionListener);
verify(client).execute(eq(MLPredictionTaskAction.INSTANCE), isA(MLPredictionTaskRequest.class), any());
verify(dataFrameActionListener).onResponse(dataFrameArgumentCaptor.capture());
assertEquals(output, ((MLPredictionOutput) dataFrameArgumentCaptor.getValue()).getPredictionResult());
}
@Test
public void execute_train() {
String modelId = "test_model_id";
String status = "InProgress";
doAnswer(invocation -> {
ActionListener<MLTaskResponse> actionListener = invocation.getArgument(2);
MLTrainingOutput output = MLTrainingOutput.builder().status(status).modelId(modelId).build();
actionListener.onResponse(MLTaskResponse.builder().output(output).build());
return null;
}).when(client).execute(eq(MLTrainingTaskAction.INSTANCE), any(), any());
ArgumentCaptor<MLOutput> argumentCaptor = ArgumentCaptor.forClass(MLOutput.class);
Map<String, Object> args = new HashMap<>();
args.put(ACTION, TRAIN);
args.put(ALGORITHM, KMEANS);
MLInput mlInput = MLInput.builder().algorithm(FunctionName.SAMPLE_ALGO).inputDataset(input).build();
machineLearningNodeClient.run(mlInput, args, trainingActionListener);
verify(client).execute(eq(MLTrainingTaskAction.INSTANCE), isA(MLTrainingTaskRequest.class), any());
verify(trainingActionListener).onResponse(argumentCaptor.capture());
assertEquals(modelId, ((MLTrainingOutput) argumentCaptor.getValue()).getModelId());
assertEquals(status, ((MLTrainingOutput) argumentCaptor.getValue()).getStatus());
}
@Test
public void execute_null_action() {
exceptionRule.expect(IllegalArgumentException.class);
exceptionRule.expectMessage("The parameter action is required.");
Map<String, Object> args = new HashMap<>();
args.put(ALGORITHM, KMEANS);
execute_trainandpredict(args);
}
@Test
public void execute_unsupported_action() {
exceptionRule.expect(IllegalArgumentException.class);
exceptionRule.expectMessage("Unsupported action.");
Map<String, Object> args = new HashMap<>();
args.put(ACTION, "unsupported");
args.put(ALGORITHM, KMEANS);
execute_trainandpredict(args);
}
@Test
public void execute_null_algorithm() {
exceptionRule.expect(IllegalArgumentException.class);
exceptionRule.expectMessage("The parameter algorithm is required.");
Map<String, Object> args = new HashMap<>();
args.put(ACTION, TRAINANDPREDICT);
execute_trainandpredict(args);
}
@Test
public void execute_unsupported_algorithm() {
String algo = "dummyAlgo";
exceptionRule.expect(IllegalArgumentException.class);
exceptionRule.expectMessage(String.format("unsupported algorithm: %s.", algo));
Map<String, Object> args = new HashMap<>();
args.put(ACTION, TRAINANDPREDICT);
args.put(ALGORITHM, algo);
execute_trainandpredict(args);
}
@Test
public void execute_trainandpredict_kmeans() {
Map<String, Object> args = new HashMap<>();
args.put(ACTION, TRAINANDPREDICT);
args.put(ALGORITHM, KMEANS);
execute_trainandpredict(args);
}
@Test
public void execute_trainandpredict_batch_rcf() {
Map<String, Object> args = new HashMap<>();
args.put(ACTION, TRAINANDPREDICT);
args.put(ALGORITHM, RCF);
execute_trainandpredict(args);
}
@Test
public void execute_trainandpredict_fit_rcf() {
Map<String, Object> args = new HashMap<>();
args.put(ACTION, TRAINANDPREDICT);
args.put(ALGORITHM, RCF);
args.put("timeField", "ts");
execute_trainandpredict(args);
}
private void execute_trainandpredict(Map<String, Object> args) {
doAnswer(invocation -> {
ActionListener<MLTaskResponse> actionListener = invocation.getArgument(2);
MLPredictionOutput predictionOutput = MLPredictionOutput
.builder()
.status(MLTaskState.COMPLETED.name())
.predictionResult(output)
.taskId("taskId")
.build();
actionListener.onResponse(MLTaskResponse.builder().output(predictionOutput).build());
return null;
}).when(client).execute(eq(MLTrainAndPredictionTaskAction.INSTANCE), any(), any());
ArgumentCaptor<MLOutput> argumentCaptor = ArgumentCaptor.forClass(MLOutput.class);
MLInput mlInput = MLInput.builder().algorithm(FunctionName.SAMPLE_ALGO).inputDataset(input).build();
machineLearningNodeClient.run(mlInput, args, trainingActionListener);
verify(client).execute(eq(MLTrainAndPredictionTaskAction.INSTANCE), isA(MLTrainingTaskRequest.class), any());
verify(trainingActionListener).onResponse(argumentCaptor.capture());
assertEquals(MLTaskState.COMPLETED.name(), ((MLPredictionOutput) argumentCaptor.getValue()).getStatus());
assertEquals(output, ((MLPredictionOutput) argumentCaptor.getValue()).getPredictionResult());
}
@Test
public void getModel() {
String modelContent = "test content";
doAnswer(invocation -> {
ActionListener<MLModelGetResponse> actionListener = invocation.getArgument(2);
MLModel mlModel = MLModel.builder().algorithm(FunctionName.KMEANS).name("test").content(modelContent).build();
MLModelGetResponse output = MLModelGetResponse.builder().mlModel(mlModel).build();
actionListener.onResponse(output);
return null;
}).when(client).execute(eq(MLModelGetAction.INSTANCE), any(), any());
ArgumentCaptor<MLModel> argumentCaptor = ArgumentCaptor.forClass(MLModel.class);
machineLearningNodeClient.getModel("modelId", getModelActionListener);
verify(client).execute(eq(MLModelGetAction.INSTANCE), isA(MLModelGetRequest.class), any());
verify(getModelActionListener).onResponse(argumentCaptor.capture());
assertEquals(FunctionName.KMEANS, argumentCaptor.getValue().getAlgorithm());
assertEquals(modelContent, argumentCaptor.getValue().getContent());
}
@Test
public void deleteConnector_withTenantId() {
String connectorId = "connectorId";
String tenantId = "tenantId";
doAnswer(invocation -> {
ActionListener<DeleteResponse> actionListener = invocation.getArgument(2);
ShardId shardId = new ShardId(new Index("indexName", "uuid"), 1);
DeleteResponse output = new DeleteResponse(shardId, connectorId, 1, 1, 1, true);
actionListener.onResponse(output);
return null;
}).when(client).execute(eq(MLConnectorDeleteAction.INSTANCE), any(), any());
ArgumentCaptor<DeleteResponse> argumentCaptor = ArgumentCaptor.forClass(DeleteResponse.class);
machineLearningNodeClient.deleteConnector(connectorId, tenantId, deleteConnectorActionListener);
verify(client).execute(eq(MLConnectorDeleteAction.INSTANCE), isA(MLConnectorDeleteRequest.class), any());
verify(deleteConnectorActionListener).onResponse(argumentCaptor.capture());
assertEquals(connectorId, (argumentCaptor.getValue()).getId());
}
@Test
public void deleteModel() {
String modelId = "testModelId";
doAnswer(invocation -> {
ActionListener<DeleteResponse> actionListener = invocation.getArgument(2);
ShardId shardId = new ShardId(new Index("indexName", "uuid"), 1);
DeleteResponse output = new DeleteResponse(shardId, modelId, 1, 1, 1, true);
actionListener.onResponse(output);
return null;
}).when(client).execute(eq(MLModelDeleteAction.INSTANCE), any(), any());
ArgumentCaptor<DeleteResponse> argumentCaptor = ArgumentCaptor.forClass(DeleteResponse.class);
machineLearningNodeClient.deleteModel(modelId, deleteModelActionListener);
verify(client).execute(eq(MLModelDeleteAction.INSTANCE), isA(MLModelDeleteRequest.class), any());
verify(deleteModelActionListener).onResponse(argumentCaptor.capture());
assertEquals(modelId, argumentCaptor.getValue().getId());
}
@Test
public void searchModel() {
String modelContent = "test content";
doAnswer(invocation -> {
ActionListener<SearchResponse> actionListener = invocation.getArgument(2);
MLModel mlModel = MLModel.builder().algorithm(FunctionName.KMEANS).name("test").content(modelContent).build();
SearchResponse output = createSearchResponse(mlModel);
actionListener.onResponse(output);
return null;
}).when(client).execute(eq(MLModelSearchAction.INSTANCE), any(), any());
ArgumentCaptor<SearchResponse> argumentCaptor = ArgumentCaptor.forClass(SearchResponse.class);
SearchRequest searchREquest = new SearchRequest();
machineLearningNodeClient.searchModel(searchREquest, searchModelActionListener);
verify(client).execute(eq(MLModelSearchAction.INSTANCE), isA(SearchRequest.class), any());
verify(searchModelActionListener).onResponse(argumentCaptor.capture());
Map<String, Object> source = argumentCaptor.getValue().getHits().getAt(0).getSourceAsMap();
assertEquals(modelContent, source.get(MLModel.MODEL_CONTENT_FIELD));
}
@Test
public void registerModelGroup() {
String modelGroupId = "modeGroupId";
String status = MLTaskState.CREATED.name();
doAnswer(invocation -> {
ActionListener<MLRegisterModelGroupResponse> actionListener = invocation.getArgument(2);
MLRegisterModelGroupResponse output = new MLRegisterModelGroupResponse(modelGroupId, status);
actionListener.onResponse(output);
return null;
}).when(client).execute(eq(MLRegisterModelGroupAction.INSTANCE), any(), any());
ArgumentCaptor<MLRegisterModelGroupResponse> argumentCaptor = ArgumentCaptor.forClass(MLRegisterModelGroupResponse.class);
List<String> backendRoles = Arrays.asList("IT", "HR");
MLRegisterModelGroupInput mlRegisterModelGroupInput = MLRegisterModelGroupInput
.builder()
.name("test")
.description("description")
.backendRoles(backendRoles)
.modelAccessMode(AccessMode.from("public"))
.isAddAllBackendRoles(false)
.build();
machineLearningNodeClient.registerModelGroup(mlRegisterModelGroupInput, registerModelGroupResponseActionListener);
verify(client).execute(eq(MLRegisterModelGroupAction.INSTANCE), isA(MLRegisterModelGroupRequest.class), any());
verify(registerModelGroupResponseActionListener).onResponse(argumentCaptor.capture());
assertEquals(modelGroupId, (argumentCaptor.getValue().getModelGroupId()));
assertEquals(status, (argumentCaptor.getValue().getStatus()));
}
@Test
public void getTask() {
String taskId = "taskId";
String modelId = "modelId";
doAnswer(invocation -> {
ActionListener<MLTaskGetResponse> actionListener = invocation.getArgument(2);
MLTask mlTask = MLTask.builder().taskId(taskId).modelId(modelId).functionName(FunctionName.KMEANS).build();
MLTaskGetResponse output = MLTaskGetResponse.builder().mlTask(mlTask).build();
actionListener.onResponse(output);
return null;
}).when(client).execute(eq(MLTaskGetAction.INSTANCE), any(), any());
ArgumentCaptor<MLTask> argumentCaptor = ArgumentCaptor.forClass(MLTask.class);
machineLearningNodeClient.getTask(taskId, getTaskActionListener);
verify(client).execute(eq(MLTaskGetAction.INSTANCE), isA(MLTaskGetRequest.class), any());
verify(getTaskActionListener).onResponse(argumentCaptor.capture());
assertEquals(FunctionName.KMEANS, argumentCaptor.getValue().getFunctionName());
assertEquals(modelId, argumentCaptor.getValue().getModelId());
assertEquals(taskId, argumentCaptor.getValue().getTaskId());
}
@Test
public void deleteTask() {
String taskId = "taskId";
doAnswer(invocation -> {
ActionListener<DeleteResponse> actionListener = invocation.getArgument(2);
ShardId shardId = new ShardId(new Index("indexName", "uuid"), 1);
DeleteResponse output = new DeleteResponse(shardId, taskId, 1, 1, 1, true);
actionListener.onResponse(output);
return null;
}).when(client).execute(eq(MLTaskDeleteAction.INSTANCE), any(), any());
ArgumentCaptor<DeleteResponse> argumentCaptor = ArgumentCaptor.forClass(DeleteResponse.class);
machineLearningNodeClient.deleteTask(taskId, deleteTaskActionListener);
verify(client).execute(eq(MLTaskDeleteAction.INSTANCE), isA(MLTaskDeleteRequest.class), any());
verify(deleteTaskActionListener).onResponse(argumentCaptor.capture());
assertEquals(taskId, argumentCaptor.getValue().getId());
}
@Test
public void searchTask() {
String taskId = "taskId";
String modelId = "modelId";
doAnswer(invocation -> {
ActionListener<SearchResponse> actionListener = invocation.getArgument(2);
MLTask mlTask = MLTask.builder().taskId(taskId).modelId(modelId).functionName(FunctionName.KMEANS).build();
SearchResponse output = createSearchResponse(mlTask);
actionListener.onResponse(output);
return null;
}).when(client).execute(eq(MLTaskSearchAction.INSTANCE), any(), any());
ArgumentCaptor<SearchResponse> argumentCaptor = ArgumentCaptor.forClass(SearchResponse.class);
SearchRequest searchREquest = new SearchRequest();
machineLearningNodeClient.searchTask(searchREquest, searchTaskActionListener);
verify(client).execute(eq(MLTaskSearchAction.INSTANCE), isA(SearchRequest.class), any());
verify(searchTaskActionListener).onResponse(argumentCaptor.capture());
assertEquals(1, argumentCaptor.getValue().getHits().getTotalHits().value);
Map<String, Object> source = argumentCaptor.getValue().getHits().getAt(0).getSourceAsMap();
assertEquals(taskId, source.get(MLTask.TASK_ID_FIELD));
assertEquals(modelId, source.get(MLTask.MODEL_ID_FIELD));
}
@Test
public void register() {
String taskId = "taskId";
String status = MLTaskState.CREATED.name();
FunctionName functionName = FunctionName.KMEANS;
doAnswer(invocation -> {
ActionListener<MLRegisterModelResponse> actionListener = invocation.getArgument(2);
MLRegisterModelResponse output = new MLRegisterModelResponse(taskId, status);
actionListener.onResponse(output);
return null;
}).when(client).execute(eq(MLRegisterModelAction.INSTANCE), any(), any());
ArgumentCaptor<MLRegisterModelResponse> argumentCaptor = ArgumentCaptor.forClass(MLRegisterModelResponse.class);
MLModelConfig config = TextEmbeddingModelConfig
.builder()
.modelType("testModelType")
.allConfig("{\"field1\":\"value1\",\"field2\":\"value2\"}")
.frameworkType(TextEmbeddingModelConfig.FrameworkType.SENTENCE_TRANSFORMERS)
.embeddingDimension(100)
.build();
MLRegisterModelInput mlInput = MLRegisterModelInput
.builder()
.functionName(functionName)
.modelName("testModelName")
.version("testModelVersion")
.modelGroupId("modelGroupId")
.url("url")
.modelFormat(MLModelFormat.ONNX)
.modelConfig(config)
.deployModel(true)
.modelNodeIds(new String[] { "modelNodeIds" })
.build();
machineLearningNodeClient.register(mlInput, registerModelActionListener);
verify(client).execute(eq(MLRegisterModelAction.INSTANCE), isA(MLRegisterModelRequest.class), any());
verify(registerModelActionListener).onResponse(argumentCaptor.capture());
assertEquals(taskId, (argumentCaptor.getValue()).getTaskId());
assertEquals(status, (argumentCaptor.getValue()).getStatus());
}
@Test
public void deploy() {
String taskId = "taskId";
String status = MLTaskState.CREATED.name();
MLTaskType mlTaskType = MLTaskType.DEPLOY_MODEL;
String modelId = "modelId";
doAnswer(invocation -> {
ActionListener<MLDeployModelResponse> actionListener = invocation.getArgument(2);
MLDeployModelResponse output = new MLDeployModelResponse(taskId, mlTaskType, status);
actionListener.onResponse(output);
return null;
}).when(client).execute(eq(MLDeployModelAction.INSTANCE), any(), any());
ArgumentCaptor<MLDeployModelResponse> argumentCaptor = ArgumentCaptor.forClass(MLDeployModelResponse.class);
machineLearningNodeClient.deploy(modelId, deployModelActionListener);
verify(client).execute(eq(MLDeployModelAction.INSTANCE), isA(MLDeployModelRequest.class), any());
verify(deployModelActionListener).onResponse(argumentCaptor.capture());
assertEquals(taskId, (argumentCaptor.getValue()).getTaskId());
assertEquals(status, (argumentCaptor.getValue()).getStatus());
}
@Test
public void undeploy() {
ClusterName clusterName = new ClusterName("clusterName");
String[] modelIds = new String[] { "modelId" };
String[] nodeIds = new String[] { "nodeId" };
doAnswer(invocation -> {
ActionListener<MLUndeployModelsResponse> actionListener = invocation.getArgument(2);
MLUndeployModelNodesResponse mlUndeployModelNodesResponse = new MLUndeployModelNodesResponse(
clusterName,
Collections.emptyList(),
Collections.emptyList()
);
MLUndeployModelsResponse output = new MLUndeployModelsResponse(mlUndeployModelNodesResponse);
actionListener.onResponse(output);
return null;
}).when(client).execute(eq(MLUndeployModelsAction.INSTANCE), any(), any());
ArgumentCaptor<MLUndeployModelsResponse> argumentCaptor = ArgumentCaptor.forClass(MLUndeployModelsResponse.class);
machineLearningNodeClient.undeploy(modelIds, nodeIds, undeployModelsActionListener);
verify(client).execute(eq(MLUndeployModelsAction.INSTANCE), isA(MLUndeployModelsRequest.class), any());