Examples with MLInput org.opensearch.ml.common.parameter.MLInput used on opensource projects

Example 11 with MLInput

use of org.opensearch.ml.common.parameter.MLInput in project ml-commons by opensearch-project.

the class TrainingITTests method testTrainingWithEmptyDataset.

// Train a model with empty dataset.
public void testTrainingWithEmptyDataset() {
    SearchSourceBuilder searchSourceBuilder = generateSearchSourceBuilder();
    searchSourceBuilder.query(QueryBuilders.matchQuery("noSuchName", ""));
    MLInputDataset inputDataset = new SearchQueryInputDataset(Collections.singletonList(TESTING_INDEX_NAME), searchSourceBuilder);
    MLInput mlInput = MLInput.builder().algorithm(FunctionName.KMEANS).inputDataset(inputDataset).build();
    MLTrainingTaskRequest trainingRequest = new MLTrainingTaskRequest(mlInput, false);
    expectThrows(IllegalArgumentException.class, () -> client().execute(MLTrainingTaskAction.INSTANCE, trainingRequest).actionGet());
}

Example 12 with MLInput

use of org.opensearch.ml.common.parameter.MLInput in project ml-commons by opensearch-project.

the class MLPredictTaskRunner method predict.

private void predict(MLTask mlTask, DataFrame inputDataFrame, MLPredictionTaskRequest request, ActionListener<MLTaskResponse> listener) {
    ActionListener<MLTaskResponse> internalListener = wrappedCleanupListener(listener, mlTask.getTaskId());
    // track ML task count and add ML task into cache
    mlStats.getStat(ML_EXECUTING_TASK_COUNT).increment();
    mlStats.getStat(ML_TOTAL_REQUEST_COUNT).increment();
    mlStats.createCounterStatIfAbsent(requestCountStat(mlTask.getFunctionName(), ActionName.PREDICT)).increment();
    mlTaskManager.add(mlTask);
    // run predict
    if (request.getModelId() != null) {
        // search model by model id.
        try (ThreadContext.StoredContext context = threadPool.getThreadContext().stashContext()) {
            MLInput mlInput = request.getMlInput();
            ActionListener<GetResponse> getResponseListener = ActionListener.wrap(r -> {
                if (r == null || !r.isExists()) {
                    internalListener.onFailure(new ResourceNotFoundException("No model found, please check the modelId."));
                    return;
                }
                Map<String, Object> source = r.getSourceAsMap();
                User requestUser = getUserContext(client);
                User resourceUser = User.parse((String) source.get(USER));
                if (!checkUserPermissions(requestUser, resourceUser, request.getModelId())) {
                    // The backend roles of request user and resource user doesn't have intersection
                    OpenSearchException e = new OpenSearchException("User: " + requestUser.getName() + " does not have permissions to run predict by model: " + request.getModelId());
                    handlePredictFailure(mlTask, internalListener, e, false);
                    return;
                }
                Model model = new Model();
                model.setName((String) source.get(MLModel.MODEL_NAME));
                model.setVersion((Integer) source.get(MLModel.MODEL_VERSION));
                byte[] decoded = Base64.getDecoder().decode((String) source.get(MLModel.MODEL_CONTENT));
                model.setContent(decoded);
                // run predict
                mlTaskManager.updateTaskState(mlTask.getTaskId(), MLTaskState.RUNNING, mlTask.isAsync());
                MLOutput output = MLEngine.predict(mlInput.toBuilder().inputDataset(new DataFrameInputDataset(inputDataFrame)).build(), model);
                if (output instanceof MLPredictionOutput) {
                    ((MLPredictionOutput) output).setStatus(MLTaskState.COMPLETED.name());
                }
                // Once prediction complete, reduce ML_EXECUTING_TASK_COUNT and update task state
                handleAsyncMLTaskComplete(mlTask);
                MLTaskResponse response = MLTaskResponse.builder().output(output).build();
                internalListener.onResponse(response);
            }, e -> {
                log.error("Failed to predict " + mlInput.getAlgorithm() + ", modelId: " + mlTask.getModelId(), e);
                handlePredictFailure(mlTask, internalListener, e, true);
            });
            GetRequest getRequest = new GetRequest(ML_MODEL_INDEX, mlTask.getModelId());
            client.get(getRequest, ActionListener.runBefore(getResponseListener, () -> context.restore()));
        } catch (Exception e) {
            log.error("Failed to get model " + mlTask.getModelId(), e);
            handlePredictFailure(mlTask, internalListener, e, true);
        }
    } else {
        IllegalArgumentException e = new IllegalArgumentException("ModelId is invalid");
        log.error("ModelId is invalid", e);
        handlePredictFailure(mlTask, internalListener, e, false);
    }
}

Example 13 with MLInput

use of org.opensearch.ml.common.parameter.MLInput in project ml-commons by opensearch-project.

the class MachineLearningNodeClientTest method predict_Exception_WithNullDataSet.

@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);
}

Example 14 with MLInput

use of org.opensearch.ml.common.parameter.MLInput in project ml-commons by opensearch-project.

the class MachineLearningNodeClientTest method train_Exception_WithNullDataSet.

@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);
}

Example 15 with MLInput

use of org.opensearch.ml.common.parameter.MLInput in project ml-commons by opensearch-project.

the class MachineLearningClientTest method predict_WithAlgoAndInputData.

@Test
public void predict_WithAlgoAndInputData() {
    MLInput mlInput = MLInput.builder().algorithm(FunctionName.KMEANS).inputDataset(new DataFrameInputDataset(input)).build();
    assertEquals(output, machineLearningClient.predict(null, mlInput).actionGet());
}