public object Execute(ExecutorContext context)
{
var cmdletContext = context as CmdletContext;
// create request
var request = new Amazon.MachineLearning.Model.UpdateMLModelRequest();
if (cmdletContext.MLModelId != null)
{
request.MLModelId = cmdletContext.MLModelId;
}
if (cmdletContext.MLModelName != null)
{
request.MLModelName = cmdletContext.MLModelName;
}
if (cmdletContext.ScoreThreshold != null)
{
request.ScoreThreshold = cmdletContext.ScoreThreshold.Value;
}
CmdletOutput output;
// issue call
var client = Client ?? CreateClient(_CurrentCredentials, _RegionEndpoint);
try
{
var response = CallAWSServiceOperation(client, request);
object pipelineOutput = null;
pipelineOutput = cmdletContext.Select(response, this);
output = new CmdletOutput
{
PipelineOutput = pipelineOutput,
ServiceResponse = response
};
}
catch (Exception e)
{
output = new CmdletOutput {
ErrorResponse = e
};
}
return(output);
}
/// <summary>
/// Initiates the asynchronous execution of the UpdateMLModel operation.
/// </summary>
///
/// <param name="request">Container for the necessary parameters to execute the UpdateMLModel operation.</param>
/// <param name="cancellationToken">
/// A cancellation token that can be used by other objects or threads to receive notice of cancellation.
/// </param>
/// <returns>The task object representing the asynchronous operation.</returns>
public Task<UpdateMLModelResponse> UpdateMLModelAsync(UpdateMLModelRequest request, System.Threading.CancellationToken cancellationToken = default(CancellationToken))
{
var marshaller = new UpdateMLModelRequestMarshaller();
var unmarshaller = UpdateMLModelResponseUnmarshaller.Instance;
return InvokeAsync<UpdateMLModelRequest,UpdateMLModelResponse>(request, marshaller,
unmarshaller, cancellationToken);
}
/// <summary>
/// Updates the <code>MLModelName</code> and the <code>ScoreThreshold</code> of an <code>MLModel</code>.
///
///
/// <para>
/// You can use the <a>GetMLModel</a> operation to view the contents of the updated data
/// element.
/// </para>
/// </summary>
/// <param name="mlModelId">The ID assigned to the <code>MLModel</code> during creation.</param>
/// <param name="mlModelName">A user-supplied name or description of the <code>MLModel</code>.</param>
/// <param name="scoreThreshold">The <code>ScoreThreshold</code> used in binary classification <code>MLModel</code> that marks the boundary between a positive prediction and a negative prediction. Output values greater than or equal to the <code>ScoreThreshold</code> receive a positive result from the <code>MLModel</code>, such as <code>true</code>. Output values less than the <code>ScoreThreshold</code> receive a negative response from the <code>MLModel</code>, such as <code>false</code>.</param>
/// <param name="cancellationToken">
/// A cancellation token that can be used by other objects or threads to receive notice of cancellation.
/// </param>
///
/// <returns>The response from the UpdateMLModel service method, as returned by MachineLearning.</returns>
/// <exception cref="Amazon.MachineLearning.Model.InternalServerException">
/// An error on the server occurred when trying to process a request.
/// </exception>
/// <exception cref="Amazon.MachineLearning.Model.InvalidInputException">
/// An error on the client occurred. Typically, the cause is an invalid input value.
/// </exception>
/// <exception cref="Amazon.MachineLearning.Model.ResourceNotFoundException">
/// A specified resource cannot be located.
/// </exception>
public Task<UpdateMLModelResponse> UpdateMLModelAsync(string mlModelId, string mlModelName, float scoreThreshold, System.Threading.CancellationToken cancellationToken = default(CancellationToken))
{
var request = new UpdateMLModelRequest();
request.MLModelId = mlModelId;
request.MLModelName = mlModelName;
request.ScoreThreshold = scoreThreshold;
return UpdateMLModelAsync(request, cancellationToken);
}
internal UpdateMLModelResponse UpdateMLModel(UpdateMLModelRequest request)
{
var marshaller = new UpdateMLModelRequestMarshaller();
var unmarshaller = UpdateMLModelResponseUnmarshaller.Instance;
return Invoke<UpdateMLModelRequest,UpdateMLModelResponse>(request, marshaller, unmarshaller);
}
/// <summary>
/// Updates the <code>MLModelName</code> and the <code>ScoreThreshold</code> of an <code>MLModel</code>.
///
///
/// <para>
/// You can use the <code>GetMLModel</code> operation to view the contents of the updated
/// data element.
/// </para>
/// </summary>
/// <param name="mlModelId">The ID assigned to the <code>MLModel</code> during creation.</param>
/// <param name="mlModelName">A user-supplied name or description of the <code>MLModel</code>.</param>
/// <param name="scoreThreshold">The <code>ScoreThreshold</code> used in binary classification <code>MLModel</code> that marks the boundary between a positive prediction and a negative prediction. Output values greater than or equal to the <code>ScoreThreshold</code> receive a positive result from the <code>MLModel</code>, such as <code>true</code>. Output values less than the <code>ScoreThreshold</code> receive a negative response from the <code>MLModel</code>, such as <code>false</code>.</param>
///
/// <returns>The response from the UpdateMLModel service method, as returned by MachineLearning.</returns>
/// <exception cref="Amazon.MachineLearning.Model.InternalServerException">
/// An error on the server occurred when trying to process a request.
/// </exception>
/// <exception cref="Amazon.MachineLearning.Model.InvalidInputException">
/// An error on the client occurred. Typically, the cause is an invalid input value.
/// </exception>
/// <exception cref="Amazon.MachineLearning.Model.ResourceNotFoundException">
/// A specified resource cannot be located.
/// </exception>
public UpdateMLModelResponse UpdateMLModel(string mlModelId, string mlModelName, float scoreThreshold)
{
var request = new UpdateMLModelRequest();
request.MLModelId = mlModelId;
request.MLModelName = mlModelName;
request.ScoreThreshold = scoreThreshold;
return UpdateMLModel(request);
}
private Amazon.MachineLearning.Model.UpdateMLModelResponse CallAWSServiceOperation(IAmazonMachineLearning client, Amazon.MachineLearning.Model.UpdateMLModelRequest request)
{
Utils.Common.WriteVerboseEndpointMessage(this, client.Config, "Amazon Machine Learning", "UpdateMLModel");
try
{
#if DESKTOP
return(client.UpdateMLModel(request));
#elif CORECLR
return(client.UpdateMLModelAsync(request).GetAwaiter().GetResult());
#else
#error "Unknown build edition"
#endif
}
catch (AmazonServiceException exc)
{
var webException = exc.InnerException as System.Net.WebException;
if (webException != null)
{
throw new Exception(Utils.Common.FormatNameResolutionFailureMessage(client.Config, webException.Message), webException);
}
throw;
}
}
/// <summary>
/// Initiates the asynchronous execution of the UpdateMLModel operation.
/// </summary>
///
/// <param name="request">Container for the necessary parameters to execute the UpdateMLModel operation on AmazonMachineLearningClient.</param>
/// <param name="callback">An AsyncCallback delegate that is invoked when the operation completes.</param>
/// <param name="state">A user-defined state object that is passed to the callback procedure. Retrieve this object from within the callback
/// procedure using the AsyncState property.</param>
///
/// <returns>An IAsyncResult that can be used to poll or wait for results, or both; this value is also needed when invoking EndUpdateMLModel
/// operation.</returns>
public IAsyncResult BeginUpdateMLModel(UpdateMLModelRequest request, AsyncCallback callback, object state)
{
var marshaller = new UpdateMLModelRequestMarshaller();
var unmarshaller = UpdateMLModelResponseUnmarshaller.Instance;
return BeginInvoke<UpdateMLModelRequest>(request, marshaller, unmarshaller,
callback, state);
}
