Java tutorial
/* * Copyright (c) 2015, WSO2 Inc. (http://www.wso2.org) All Rights Reserved. * * Licensed under the Apache License, Version 2.0 (the "License"); * you may not use this file except in compliance with the License. * You may obtain a copy of the License at * * http://www.apache.org/licenses/LICENSE-2.0 * * Unless required by applicable law or agreed to in writing, software * distributed under the License is distributed on an "AS IS" BASIS, * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. * See the License for the specific language governing permissions and * limitations under the License. */ package org.wso2.carbon.ml.rest.api; import java.io.*; import java.nio.charset.StandardCharsets; import java.util.ArrayList; import java.util.List; import javax.ws.rs.*; import javax.ws.rs.core.MediaType; import javax.ws.rs.core.Response; import javax.ws.rs.core.StreamingOutput; import com.owlike.genson.Genson; import org.apache.commons.logging.Log; import org.apache.commons.logging.LogFactory; import org.apache.cxf.jaxrs.ext.multipart.Multipart; import org.apache.hadoop.fs.InvalidRequestException; import org.apache.http.HttpHeaders; import org.codehaus.jackson.map.ObjectMapper; import org.json.JSONArray; import org.json.JSONObject; import org.wso2.carbon.context.PrivilegedCarbonContext; import org.wso2.carbon.ml.commons.constants.MLConstants; import org.wso2.carbon.ml.commons.domain.MLModel; import org.wso2.carbon.ml.commons.domain.MLModelData; import org.wso2.carbon.ml.commons.domain.MLStorage; import org.wso2.carbon.ml.commons.domain.ModelSummary; import org.wso2.carbon.ml.core.exceptions.MLModelBuilderException; import org.wso2.carbon.ml.core.exceptions.MLModelHandlerException; import org.wso2.carbon.ml.core.exceptions.MLModelPublisherException; import org.wso2.carbon.ml.core.impl.MLModelHandler; import org.wso2.carbon.ml.commons.domain.config.MLAlgorithm; import org.wso2.carbon.ml.core.exceptions.MLPmmlExportException; import org.wso2.carbon.ml.core.utils.MLCoreServiceValueHolder; import org.wso2.carbon.ml.core.utils.MLUtils; import org.wso2.carbon.ml.rest.api.model.MLErrorBean; import org.wso2.carbon.ml.rest.api.model.MLResponseBean; import org.wso2.carbon.ml.rest.api.neuralNetworks.FeedForwardNetwork; import org.wso2.carbon.ml.rest.api.neuralNetworks.HiddenLayerDetails; import org.wso2.carbon.ml.rest.api.neuralNetworks.OutputLayerDetails; /** * This class is to handle REST verbs GET , POST and DELETE. */ @Path("/models") public class ModelApiV20 extends MLRestAPI { private static final Log logger = LogFactory.getLog(ModelApiV20.class); private MLModelHandler mlModelHandler; public ModelApiV20() { mlModelHandler = new MLModelHandler(); } @OPTIONS public Response options() { return Response.ok().header(HttpHeaders.ALLOW, "GET POST DELETE").build(); } /** * Create a new Model. * * @param model {@link MLModelData} object * @return JSON of {@link MLModelData} object */ @POST @Produces("application/json") @Consumes("application/json") public Response createModel(MLModelData model) { if (model.getAnalysisId() == 0 || model.getVersionSetId() == 0) { logger.error("Required parameters missing"); return Response.status(Response.Status.BAD_REQUEST).entity("Required parameters missing").build(); } PrivilegedCarbonContext carbonContext = PrivilegedCarbonContext.getThreadLocalCarbonContext(); try { int tenantId = carbonContext.getTenantId(); String userName = carbonContext.getUsername(); model.setTenantId(tenantId); model.setUserName(userName); MLModelData insertedModel = mlModelHandler.createModel(model); //hide null json fields in response String[] fieldsToHide = { MLConstants.ML_MODEL_DATA_ID, MLConstants.ML_MODEL_DATA_CREATED_TIME, MLConstants.ML_MODEL_DATA_DATASET_VERSION, MLConstants.ML_MODEL_DATA_ERROR, MLConstants.ML_MODEL_DATA_MODEL_SUMMARY }; Genson.Builder builder = new Genson.Builder(); for (int i = 0; i < fieldsToHide.length; i++) { builder = builder.exclude(fieldsToHide[i], MLModelData.class); } Genson genson = builder.create(); String insertedModelJson = genson.serialize(insertedModel); return Response.ok(insertedModelJson).build(); } catch (MLModelHandlerException e) { String msg = MLUtils.getErrorMsg("Error occurred while creating a model : " + model, e); logger.error(msg, e); return Response.status(Response.Status.INTERNAL_SERVER_ERROR).entity(new MLErrorBean(e.getMessage())) .build(); } } /** * Create a new model storage * * @param modelId Unique id of the model * @param storage {@link MLStorage} object */ @POST @Path("/{modelId}/storages") @Produces("application/json") @Consumes("application/json") public Response addStorage(@PathParam("modelId") long modelId, MLStorage storage) { PrivilegedCarbonContext carbonContext = PrivilegedCarbonContext.getThreadLocalCarbonContext(); int tenantId = carbonContext.getTenantId(); String userName = carbonContext.getUsername(); try { mlModelHandler.addStorage(modelId, storage); return Response.ok().build(); } catch (MLModelHandlerException e) { String msg = MLUtils.getErrorMsg(String.format( "Error occurred while adding storage for the model [id] %s of tenant [id] %s and [user] %s .", modelId, tenantId, userName), e); logger.error(msg, e); return Response.status(Response.Status.INTERNAL_SERVER_ERROR).entity(new MLErrorBean(e.getMessage())) .build(); } } /** * Build the model * * @param modelId Unique id of the model to be built. */ @POST @Path("/{modelId}") @Produces("application/json") @Consumes("application/json") public Response buildModel(@PathParam("modelId") long modelId) { PrivilegedCarbonContext carbonContext = PrivilegedCarbonContext.getThreadLocalCarbonContext(); int tenantId = carbonContext.getTenantId(); String userName = carbonContext.getUsername(); try { mlModelHandler.buildModel(tenantId, userName, modelId); return Response.ok().build(); } catch (MLModelHandlerException e) { String msg = MLUtils.getErrorMsg(String.format( "Error occurred while building the model [id] %s of tenant [id] %s and [user] %s .", modelId, tenantId, userName), e); logger.error(msg, e); return Response.status(Response.Status.INTERNAL_SERVER_ERROR).entity(new MLErrorBean(e.getMessage())) .build(); } catch (MLModelBuilderException e) { String msg = MLUtils.getErrorMsg(String.format( "Error occurred while building the model [id] %s of tenant [id] %s and [user] %s .", modelId, tenantId, userName), e); logger.error(msg, e); return Response.status(Response.Status.INTERNAL_SERVER_ERROR).entity(new MLErrorBean(e.getMessage())) .build(); } } /** * Publish the model to ML registry * * @param modelId Unique id of the model to be published * @return JSON of {@link MLResponseBean} containing the published location of the model */ @POST @Path("/{modelId}/publish") @Produces("application/json") @Consumes("application/json") public Response publishModel(@PathParam("modelId") long modelId, @QueryParam("mode") String mode) { PrivilegedCarbonContext carbonContext = PrivilegedCarbonContext.getThreadLocalCarbonContext(); int tenantId = carbonContext.getTenantId(); String userName = carbonContext.getUsername(); boolean isPMMLSupported = false; try { MLModelData model = mlModelHandler.getModel(tenantId, userName, modelId); // check pmml support if (model != null) { final MLModel generatedModel = mlModelHandler.retrieveModel(model.getId()); String algorithmName = generatedModel.getAlgorithmName(); List<MLAlgorithm> mlAlgorithms = MLCoreServiceValueHolder.getInstance().getAlgorithms(); for (MLAlgorithm mlAlgorithm : mlAlgorithms) { if (algorithmName.equals(mlAlgorithm.getName()) && mlAlgorithm.getPmmlExportable()) { isPMMLSupported = true; break; } } if (isPMMLSupported && (mode == null || mode.equals(MLConstants.ML_MODEL_FORMAT_PMML))) { String registryPath = mlModelHandler.publishModel(tenantId, userName, modelId, MLModelHandler.Format.PMML); return Response.ok(new MLResponseBean(registryPath)).build(); } else if (mode == null || mode.equals(MLConstants.ML_MODEL_FORMAT_SERIALIZED)) { String registryPath = mlModelHandler.publishModel(tenantId, userName, modelId, MLModelHandler.Format.SERIALIZED); return Response.ok(new MLResponseBean(registryPath)).build(); } else { return Response.status(Response.Status.BAD_REQUEST).build(); } } else { return Response.status(Response.Status.NOT_FOUND).build(); } } catch (InvalidRequestException e) { String msg = MLUtils.getErrorMsg(String.format( "Error occurred while publishing the model [id] %s of tenant [id] %s and [user] %s .", modelId, tenantId, userName), e); logger.error(msg, e); return Response.status(Response.Status.BAD_REQUEST).entity(new MLErrorBean(e.getMessage())).build(); } catch (MLModelPublisherException | MLModelHandlerException | MLPmmlExportException e) { String msg = MLUtils.getErrorMsg(String.format( "Error occurred while publishing the model [id] %s of tenant [id] %s and [user] %s .", modelId, tenantId, userName), e); logger.error(msg, e); return Response.status(Response.Status.INTERNAL_SERVER_ERROR).entity(new MLErrorBean(e.getMessage())) .build(); } } /** * Predict using a file and return as a list of predicted values. * * @param modelId Unique id of the model * @param dataFormat Data format of the file (CSV or TSV) * @param inputStream File input stream generated from the file used for predictions * @param percentile a threshold value used to identified cluster boundaries * @param skipDecoding whether the decoding should not be done (true or false) * @return JSON array of predictions */ @POST @Path("/predict") @Produces(MediaType.APPLICATION_JSON) @Consumes(MediaType.MULTIPART_FORM_DATA) public Response predict(@Multipart("modelId") long modelId, @Multipart("dataFormat") String dataFormat, @Multipart("file") InputStream inputStream, @QueryParam("percentile") double percentile, @QueryParam("skipDecoding") boolean skipDecoding) { PrivilegedCarbonContext carbonContext = PrivilegedCarbonContext.getThreadLocalCarbonContext(); int tenantId = carbonContext.getTenantId(); String userName = carbonContext.getUsername(); try { // validate input parameters // if it is a file upload, check whether the file is sent if (inputStream == null || inputStream.available() == 0) { String msg = String.format( "Error occurred while reading the file for model [id] %s of tenant [id] %s and [user] %s .", modelId, tenantId, userName); logger.error(msg); return Response.status(Response.Status.INTERNAL_SERVER_ERROR).entity(new MLErrorBean(msg)).build(); } List<?> predictions = mlModelHandler.predict(tenantId, userName, modelId, dataFormat, inputStream, percentile, skipDecoding); return Response.ok(predictions).build(); } catch (IOException e) { String msg = MLUtils.getErrorMsg(String.format( "Error occurred while reading the file for model [id] %s of tenant [id] %s and [user] %s.", modelId, tenantId, userName), e); logger.error(msg, e); return Response.status(Response.Status.BAD_REQUEST).entity(new MLErrorBean(e.getMessage())).build(); } catch (MLModelHandlerException e) { String msg = MLUtils.getErrorMsg(String.format( "Error occurred while predicting from model [id] %s of tenant [id] %s and [user] %s.", modelId, tenantId, userName), e); logger.error(msg, e); return Response.status(Response.Status.INTERNAL_SERVER_ERROR).entity(new MLErrorBean(e.getMessage())) .build(); } } /** * Predict using a file and return predictions as a CSV. * * @param modelId Unique id of the model * @param dataFormat Data format of the file (CSV or TSV) * @param columnHeader Whether the file contains the column header as the first row (YES or NO) * @param inputStream Input stream generated from the file used for predictions * @param percentile a threshold value used to identified cluster boundaries * @param skipDecoding whether the decoding should not be done (true or false) * @return A file as a {@link StreamingOutput} */ @POST @Path("/predictionStreams") @Produces(MediaType.APPLICATION_OCTET_STREAM) @Consumes(MediaType.MULTIPART_FORM_DATA) public Response streamingPredqict(@Multipart("modelId") long modelId, @Multipart("dataFormat") String dataFormat, @Multipart("columnHeader") String columnHeader, @Multipart("file") InputStream inputStream, @QueryParam("percentile") double percentile, @QueryParam("skipDecoding") boolean skipDecoding) { PrivilegedCarbonContext carbonContext = PrivilegedCarbonContext.getThreadLocalCarbonContext(); int tenantId = carbonContext.getTenantId(); String userName = carbonContext.getUsername(); try { // validate input parameters // if it is a file upload, check whether the file is sent if (inputStream == null || inputStream.available() == 0) { String msg = String.format( "No file found to predict with model [id] %s of tenant [id] %s and [user] %s .", modelId, tenantId, userName); logger.error(msg); return Response.status(Response.Status.BAD_REQUEST).entity(new MLErrorBean(msg)) .type(MediaType.APPLICATION_JSON).build(); } final String predictions = mlModelHandler.streamingPredict(tenantId, userName, modelId, dataFormat, columnHeader, inputStream, percentile, skipDecoding); StreamingOutput stream = new StreamingOutput() { @Override public void write(OutputStream outputStream) throws IOException { Writer writer = new BufferedWriter( new OutputStreamWriter(outputStream, StandardCharsets.UTF_8)); writer.write(predictions); writer.flush(); writer.close(); } }; return Response.ok(stream).header("Content-disposition", "attachment; filename=Predictions_" + modelId + "_" + MLUtils.getDate() + MLConstants.CSV) .build(); } catch (IOException e) { String msg = MLUtils.getErrorMsg(String.format( "Error occurred while reading the file for model [id] %s of tenant [id] %s and [user] %s.", modelId, tenantId, userName), e); logger.error(msg, e); return Response.status(Response.Status.BAD_REQUEST).entity(new MLErrorBean(e.getMessage())) .type(MediaType.APPLICATION_JSON).build(); } catch (MLModelHandlerException e) { String msg = MLUtils.getErrorMsg(String.format( "Error occurred while predicting from model [id] %s of tenant [id] %s and [user] %s.", modelId, tenantId, userName), e); logger.error(msg, e); return Response.status(Response.Status.INTERNAL_SERVER_ERROR).entity(new MLErrorBean(e.getMessage())) .type(MediaType.APPLICATION_JSON).build(); } } /** * Make predictions using a model * * @param modelId Unique id of the model * @param data List of string arrays containing the feature values used for predictions * @param percentile a threshold value used to identified cluster boundaries * @param skipDecoding whether the decoding should not be done (true or false) * @return JSON array of predicted values */ @POST @Path("/{modelId}/predict") @Produces("application/json") @Consumes("application/json") public Response predict(@PathParam("modelId") long modelId, List<String[]> data, @QueryParam("percentile") double percentile, @QueryParam("skipDecoding") boolean skipDecoding) { PrivilegedCarbonContext carbonContext = PrivilegedCarbonContext.getThreadLocalCarbonContext(); int tenantId = carbonContext.getTenantId(); String userName = carbonContext.getUsername(); try { long t1 = System.currentTimeMillis(); List<?> predictions = mlModelHandler.predict(tenantId, userName, modelId, data, percentile, skipDecoding); logger.info(String.format("Prediction from model [id] %s finished in %s seconds.", modelId, (System.currentTimeMillis() - t1) / 1000.0)); return Response.ok(predictions).build(); } catch (MLModelHandlerException e) { String msg = MLUtils.getErrorMsg(String.format( "Error occurred while predicting from model [id] %s of tenant [id] %s and [user] %s.", modelId, tenantId, userName), e); logger.error(msg, e); return Response.status(Response.Status.INTERNAL_SERVER_ERROR).entity(new MLErrorBean(e.getMessage())) .build(); } } /** * Get the model data * * @param modelName Name of the model * @return JSON of {@link MLModelData} object */ @GET @Path("/{modelName}") @Produces("application/json") public Response getModel(@PathParam("modelName") String modelName) { PrivilegedCarbonContext carbonContext = PrivilegedCarbonContext.getThreadLocalCarbonContext(); int tenantId = carbonContext.getTenantId(); String userName = carbonContext.getUsername(); try { MLModelData model = mlModelHandler.getModel(tenantId, userName, modelName); if (model == null) { return Response.status(Response.Status.NOT_FOUND).build(); } return Response.ok(model).build(); } catch (MLModelHandlerException e) { String msg = MLUtils.getErrorMsg(String.format( "Error occurred while retrieving a model [name] %s of tenant [id] %s and [user] %s .", modelName, tenantId, userName), e); logger.error(msg, e); return Response.status(Response.Status.INTERNAL_SERVER_ERROR).entity(new MLErrorBean(e.getMessage())) .build(); } } /** * Get all models * * @return JSON array of {@link MLModelData} objects */ @GET @Produces("application/json") public Response getAllModels() { PrivilegedCarbonContext carbonContext = PrivilegedCarbonContext.getThreadLocalCarbonContext(); int tenantId = carbonContext.getTenantId(); String userName = carbonContext.getUsername(); try { List<MLModelData> models = mlModelHandler.getAllModels(tenantId, userName); return Response.ok(models).build(); } catch (MLModelHandlerException e) { String msg = MLUtils.getErrorMsg( String.format("Error occurred while retrieving all models of tenant [id] %s and [user] %s .", tenantId, userName), e); logger.error(msg, e); return Response.status(Response.Status.INTERNAL_SERVER_ERROR).entity(new MLErrorBean(e.getMessage())) .build(); } } /** * Delete a model * * @param modelId Unique id of the model */ @DELETE @Path("/{modelId}") @Produces("application/json") public Response deleteModel(@PathParam("modelId") long modelId) { PrivilegedCarbonContext carbonContext = PrivilegedCarbonContext.getThreadLocalCarbonContext(); int tenantId = carbonContext.getTenantId(); String userName = carbonContext.getUsername(); try { mlModelHandler.deleteModel(tenantId, userName, modelId); auditLog.info(String.format("User [name] %s of tenant [id] %s deleted a model [id] %s ", userName, tenantId, modelId)); return Response.ok().build(); } catch (MLModelHandlerException e) { String msg = MLUtils.getErrorMsg( String.format("Error occurred while deleting a model [id] %s of tenant [id] %s and [user] %s .", modelId, tenantId, userName), e); logger.error(msg, e); auditLog.error(msg, e); return Response.status(Response.Status.INTERNAL_SERVER_ERROR).entity(new MLErrorBean(e.getMessage())) .build(); } } /** * Get the model summary * * @param modelId Unique id of the model * @return JSON of {@link ModelSummary} object */ @GET @Path("/{modelId}/summary") @Produces("application/json") @Consumes("application/json") public Response getModelSummary(@PathParam("modelId") long modelId) { PrivilegedCarbonContext carbonContext = PrivilegedCarbonContext.getThreadLocalCarbonContext(); int tenantId = carbonContext.getTenantId(); String userName = carbonContext.getUsername(); try { ModelSummary modelSummary = mlModelHandler.getModelSummary(modelId); return Response.ok(modelSummary).build(); } catch (MLModelHandlerException e) { String msg = MLUtils.getErrorMsg(String.format( "Error occurred while retrieving summary of the model [id] %s of tenant [id] %s and [user] %s .", modelId, tenantId, userName), e); logger.error(msg, e); return Response.status(Response.Status.INTERNAL_SERVER_ERROR).entity(new MLErrorBean(e.getMessage())) .build(); } } /** * Download the model * * @param modelId Name of the model * @return A {@link MLModel} as a {@link StreamingOutput} */ @GET @Path("/{modelId}/export") public Response exportModel(@PathParam("modelId") long modelId, @QueryParam("mode") String mode) { PrivilegedCarbonContext carbonContext = PrivilegedCarbonContext.getThreadLocalCarbonContext(); int tenantId = carbonContext.getTenantId(); String userName = carbonContext.getUsername(); boolean isPMMLSupported = false; try { MLModelData model = mlModelHandler.getModel(tenantId, userName, modelId); if (model != null) { String modelName = model.getName(); final MLModel generatedModel = mlModelHandler.retrieveModel(model.getId()); // check pmml support String algorithmName = generatedModel.getAlgorithmName(); List<MLAlgorithm> mlAlgorithms = MLCoreServiceValueHolder.getInstance().getAlgorithms(); for (MLAlgorithm mlAlgorithm : mlAlgorithms) { if (algorithmName.equals(mlAlgorithm.getName()) && mlAlgorithm.getPmmlExportable()) { isPMMLSupported = true; break; } } if (isPMMLSupported && (mode == null || mode.equals(MLConstants.ML_MODEL_FORMAT_PMML))) { final String pmmlModel = mlModelHandler.exportAsPMML(generatedModel); logger.info(String.format("Successfully exported model [id] %s into pmml format", modelId)); return Response.ok(pmmlModel) .header("Content-disposition", "attachment; filename=" + modelName + "PMML.xml") .build(); } else if (mode == null || mode.equals(MLConstants.ML_MODEL_FORMAT_SERIALIZED)) { StreamingOutput stream = new StreamingOutput() { public void write(OutputStream outputStream) throws IOException { ObjectOutputStream out = new ObjectOutputStream(outputStream); out.writeObject(generatedModel); } }; return Response.ok(stream).header("Content-disposition", "attachment; filename=" + modelName) .build(); } else { return Response.status(Response.Status.BAD_REQUEST).build(); } } else { return Response.status(Response.Status.NOT_FOUND).build(); } } catch (MLModelHandlerException e) { String msg = MLUtils.getErrorMsg(String.format( "Error occurred while retrieving model [name] %s of tenant [id] %s and [user] %s .", modelId, tenantId, userName), e); logger.error(msg, e); return Response.status(Response.Status.INTERNAL_SERVER_ERROR).entity(new MLErrorBean(e.getMessage())) .build(); } catch (MLPmmlExportException e) { String msg = MLUtils.getErrorMsg(String.format( "Error occurred while exporting to pmml model [name] %s of tenant [id] %s and [user] %s .", modelId, tenantId, userName), e); logger.error(msg, e); return Response.status(Response.Status.INTERNAL_SERVER_ERROR).entity(new MLErrorBean(e.getMessage())) .build(); } } /** * Get a list of recommended products for a given user using the given model. * * @param modelId id of the recommendation model to be used. * @param userId id of the user. * @param noOfProducts number of recommendations required. * @return an array of product recommendations. */ @GET @Path("/{modelId}/product-recommendations") @Produces("application/json") public Response getProductRecommendations(@PathParam("modelId") long modelId, @QueryParam("user-id") int userId, @QueryParam("no-of-products") int noOfProducts) { PrivilegedCarbonContext carbonContext = PrivilegedCarbonContext.getThreadLocalCarbonContext(); int tenantId = carbonContext.getTenantId(); String userName = carbonContext.getUsername(); try { List<?> recommendations = mlModelHandler.getProductRecommendations(tenantId, userName, modelId, userId, noOfProducts); return Response.ok(recommendations).build(); } catch (MLModelHandlerException e) { String msg = MLUtils.getErrorMsg(String.format( "Error occurred while getting recommendations from model [id] %s of tenant [id] %s and [user] %s.", modelId, tenantId, userName), e); logger.error(msg, e); return Response.status(Response.Status.INTERNAL_SERVER_ERROR).entity(new MLErrorBean(e.getMessage())) .build(); } } /** * Get a list of recommended users for a given product using the given model. * * @param modelId id of the recommendation model to be used. * @param productId id of the product. * @param noOfUsers number of recommendations required. * @return an array of user recommendations. */ @GET @Path("/{modelId}/user-recommendations") @Produces("application/json") public Response getUserRecommendations(@PathParam("modelId") long modelId, @QueryParam("product-id") int productId, @QueryParam("no-of-users") int noOfUsers) { PrivilegedCarbonContext carbonContext = PrivilegedCarbonContext.getThreadLocalCarbonContext(); int tenantId = carbonContext.getTenantId(); String userName = carbonContext.getUsername(); try { List<?> recommendations = mlModelHandler.getUserRecommendations(tenantId, userName, modelId, productId, noOfUsers); return Response.ok(recommendations).build(); } catch (MLModelHandlerException e) { String msg = MLUtils.getErrorMsg(String.format( "Error occurred while getting recommendations from model [id] %s of tenant [id] %s and [user] %s.", modelId, tenantId, userName), e); logger.error(msg, e); return Response.status(Response.Status.INTERNAL_SERVER_ERROR).entity(new MLErrorBean(e.getMessage())) .build(); } } /** * Create a model for Neural networks. * * @return JSON of the performance/evaluation statistics of the trained model */ @POST @Path("/neural-network") @Consumes("application/json") @Produces("application/json") public Response getNeuralNetwork(String networkDetails) { try { String statistics = ""; //read json object JSONObject networkDetail = new JSONObject(networkDetails); //convert the json data to pass to the respective neyral network class String networkName = networkDetail.getString("networkName"); long seed = networkDetail.getLong("seed"); double learningRate = networkDetail.getDouble("learningRate"); int bachSize = networkDetail.getInt("batchSize"); double nepoches = networkDetail.getDouble("nepoches"); int iterations = networkDetail.getInt("iteration"); String optimizationAlgorithms = networkDetail.getString("optimizationAlgorithms"); String updater = networkDetail.getString("updater"); double momentum = networkDetail.getDouble("momentum"); boolean pretrain = networkDetail.getBoolean("pretrain"); boolean backprop = networkDetail.getBoolean("backprop"); int noHiddenLayers = networkDetail.getInt("hiddenlayerno"); int inputLayerNodes = networkDetail.getInt("inputlayernodes"); int datasetId = networkDetail.getInt("datasetId"); int versionId = networkDetail.getInt("versionID"); int analysisId = networkDetail.getInt("analysisID"); JSONArray jsonArrayHiddenDetails = networkDetail.getJSONArray("hiddenlayerDetails"); JSONArray jsonArrayOutputDetails = networkDetail.getJSONArray("outputlayerDetails"); List<HiddenLayerDetails> hiddenLayerList = new ArrayList<>(); List<OutputLayerDetails> outputLayerList = new ArrayList<>(); for (int i = 0; i < jsonArrayHiddenDetails.length(); i++) { JSONObject hiddenJSONObject = jsonArrayHiddenDetails.getJSONObject(i); int hiddenNodes = hiddenJSONObject.getInt("hiddenlayernodes"); String hiddenWeightInit = hiddenJSONObject.getString("hiddenlayerweightinit"); String hiddenActivation = hiddenJSONObject.getString("hiddenlayeractivation"); hiddenLayerList.add(new HiddenLayerDetails(hiddenNodes, hiddenWeightInit, hiddenActivation)); } for (int j = 0; j < jsonArrayOutputDetails.length(); j++) { JSONObject outputJSONObject = jsonArrayOutputDetails.getJSONObject(j); int outputNodes = outputJSONObject.getInt("outputlayernodes"); String outputWeightInit = outputJSONObject.getString("outputlayerweightinit"); String outputActivation = outputJSONObject.getString("outputlayeractivation"); String outputLossFunction = outputJSONObject.getString("outputlaterlossfunction"); outputLayerList.add(new OutputLayerDetails(outputNodes, outputWeightInit, outputActivation, outputLossFunction)); } //make FeedForwardNetwork class object FeedForwardNetwork net = new FeedForwardNetwork(); //Call createFeedForwardNetwork method statistics = net.createFeedForwardNetwork(seed, learningRate, bachSize, nepoches, iterations, optimizationAlgorithms, updater, momentum, pretrain, backprop, noHiddenLayers, inputLayerNodes, datasetId, versionId, analysisId, hiddenLayerList, outputLayerList); ObjectMapper objectMapper = new ObjectMapper(); Object statJson = objectMapper.readValue(objectMapper.writeValueAsString(statistics), Object.class); logger.info("API Response " + statJson.toString()); return Response.ok(statJson).build(); } catch (Exception e) { String msg = MLUtils.getErrorMsg("Error occurred in the server side!!!", e); logger.error(msg, e); return Response.status(Response.Status.INTERNAL_SERVER_ERROR).entity(new MLErrorBean(e.getMessage())) .build(); } } }