public object Execute(ExecutorContext context)
{
var cmdletContext = context as CmdletContext;
// create request
var request = new Amazon.MachineLearning.Model.PredictRequest();
if (cmdletContext.MLModelId != null)
{
request.MLModelId = cmdletContext.MLModelId;
}
if (cmdletContext.PredictEndpoint != null)
{
request.PredictEndpoint = cmdletContext.PredictEndpoint;
}
if (cmdletContext.Record != null)
{
request.Record = cmdletContext.Record;
}
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 Predict operation.
/// </summary>
///
/// <param name="request">Container for the necessary parameters to execute the Predict 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<PredictResponse> PredictAsync(PredictRequest request, System.Threading.CancellationToken cancellationToken = default(CancellationToken))
{
var marshaller = new PredictRequestMarshaller();
var unmarshaller = PredictResponseUnmarshaller.Instance;
return InvokeAsync<PredictRequest,PredictResponse>(request, marshaller,
unmarshaller, cancellationToken);
}
/// <summary>
/// Generates a prediction for the observation using the specified <code>MLModel</code>.
///
/// <note><title>Note</title>
/// <para>
/// Not all response parameters will be populated because this is dependent on the type
/// of requested model.
/// </para>
/// </note>
/// </summary>
/// <param name="mlModelId">A unique identifier of the <code>MLModel</code>.</param>
/// <param name="predictEndpoint">A property of PredictRequest used to execute the Predict service method.</param>
/// <param name="record">A property of PredictRequest used to execute the Predict service method.</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 Predict 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.LimitExceededException">
/// The subscriber exceeded the maximum number of operations. This exception can occur
/// when listing objects such as <code>DataSource</code>.
/// </exception>
/// <exception cref="Amazon.MachineLearning.Model.PredictorNotMountedException">
/// The exception is thrown when a predict request is made to an unmounted <code>MLModel</code>.
/// </exception>
/// <exception cref="Amazon.MachineLearning.Model.ResourceNotFoundException">
/// A specified resource cannot be located.
/// </exception>
public Task<PredictResponse> PredictAsync(string mlModelId, string predictEndpoint, Dictionary<string, string> record, System.Threading.CancellationToken cancellationToken = default(CancellationToken))
{
var request = new PredictRequest();
request.MLModelId = mlModelId;
request.PredictEndpoint = predictEndpoint;
request.Record = record;
return PredictAsync(request, cancellationToken);
}
internal PredictResponse Predict(PredictRequest request)
{
var marshaller = new PredictRequestMarshaller();
var unmarshaller = PredictResponseUnmarshaller.Instance;
return Invoke<PredictRequest,PredictResponse>(request, marshaller, unmarshaller);
}
/// <summary>
/// Generates a prediction for the observation using the specified <code>ML Model</code>.
///
/// <note><title>Note</title>
/// <para>
/// Not all response parameters will be populated. Whether a response parameter is populated
/// depends on the type of model requested.
/// </para>
/// </note>
/// </summary>
/// <param name="mlModelId">A unique identifier of the <code>MLModel</code>.</param>
/// <param name="predictEndpoint">A property of PredictRequest used to execute the Predict service method.</param>
/// <param name="record">A property of PredictRequest used to execute the Predict service method.</param>
///
/// <returns>The response from the Predict 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.LimitExceededException">
/// The subscriber exceeded the maximum number of operations. This exception can occur
/// when listing objects such as <code>DataSource</code>.
/// </exception>
/// <exception cref="Amazon.MachineLearning.Model.PredictorNotMountedException">
/// The exception is thrown when a predict request is made to an unmounted <code>MLModel</code>.
/// </exception>
/// <exception cref="Amazon.MachineLearning.Model.ResourceNotFoundException">
/// A specified resource cannot be located.
/// </exception>
public PredictResponse Predict(string mlModelId, string predictEndpoint, Dictionary<string, string> record)
{
var request = new PredictRequest();
request.MLModelId = mlModelId;
request.PredictEndpoint = predictEndpoint;
request.Record = record;
return Predict(request);
}
/// <summary>
/// Initiates the asynchronous execution of the Predict operation.
/// </summary>
///
/// <param name="request">Container for the necessary parameters to execute the Predict 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 EndPredict
/// operation.</returns>
public IAsyncResult BeginPredict(PredictRequest request, AsyncCallback callback, object state)
{
var marshaller = new PredictRequestMarshaller();
var unmarshaller = PredictResponseUnmarshaller.Instance;
return BeginInvoke<PredictRequest>(request, marshaller, unmarshaller,
callback, state);
}
private Amazon.MachineLearning.Model.PredictResponse CallAWSServiceOperation(IAmazonMachineLearning client, Amazon.MachineLearning.Model.PredictRequest request)
{
Utils.Common.WriteVerboseEndpointMessage(this, client.Config, "Amazon Machine Learning", "Predict");
try
{
#if DESKTOP
return(client.Predict(request));
#elif CORECLR
return(client.PredictAsync(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;
}
}
