/**
* Create a new Model.
*
* @param model {@link org.wso2.carbon.ml.commons.domain.MLModelData} object
* @return JSON of {@link org.wso2.carbon.ml.commons.domain.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);
return Response.ok(insertedModel).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();
}
}
/**
* Get the model data
*
* @param modelName Name of the model
* @return JSON of {@link org.wso2.carbon.ml.commons.domain.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();
}
}
/**
* 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
* @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) {
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);
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();
}
}
/**
* 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();
}
}
/**
* 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
* @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) {
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);
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();
}
}
/**
* Get all models
*
* @return JSON array of {@link org.wso2.carbon.ml.commons.domain.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();
}
}
/**
* Download the model
*
* @param modelName Name of the model
* @return A {@link org.wso2.carbon.ml.commons.domain.MLModel} as a {@link
* javax.ws.rs.core.StreamingOutput}
*/
@GET
@Path("/{modelName}/export")
@Produces(MediaType.APPLICATION_OCTET_STREAM)
public Response exportModel(@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) {
final MLModel generatedModel = mlModelHandler.retrieveModel(model.getId());
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.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 .",
modelName, 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
* @return A file as a {@link javax.ws.rs.core.StreamingOutput}
*/
@POST
@Path("/predictionStreams")
@Produces(MediaType.APPLICATION_OCTET_STREAM)
@Consumes(MediaType.MULTIPART_FORM_DATA)
public Response streamingPredict(
@Multipart("modelId") long modelId,
@Multipart("dataFormat") String dataFormat,
@Multipart("columnHeader") String columnHeader,
@Multipart("file") InputStream inputStream) {
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.BAD_REQUEST).entity(new MLErrorBean(msg)).build();
}
final String predictions =
mlModelHandler.streamingPredict(
tenantId, userName, modelId, dataFormat, columnHeader, inputStream);
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()))
.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();
}
}