public void Run()
{
Insert2 trainingStatus = m_predictionFramework.GetModelStatus(ProjectModelId);
Console.WriteLine("Training status: {0}", trainingStatus.TrainingStatus);
ToConsole(trainingStatus.ModelInfo);
}
public void Run()
{
Console.WriteLine("Model '{0}' deleted with response '{1}'.", ProjectModelId.ModelId, m_predictionFramework.DeleteTrainedModel(ProjectModelId));
Insert2 insertResponse = m_predictionFramework.TrainRegressionModel(ProjectModelId, m_storageData);
Console.WriteLine("Inserted the training data for the model.");
// Wait until the training is complete
bool trainingRunning = true;
while (trainingRunning)
{
Console.WriteLine("Getting a new training progress status...");
var getResponse = m_predictionFramework.GetModelStatus(ProjectModelId);
Console.WriteLine("Got a new training progress status: {0}", getResponse.TrainingStatus);
switch (getResponse.TrainingStatus)
{
case "RUNNING":
Console.WriteLine("The model training is still in progress, let us wait for {0} ms.", PROGRESS_WAITING_TIME);
Thread.Sleep(PROGRESS_WAITING_TIME);
break;
case "DONE":
Console.WriteLine("The model has been trained successfully.");
ToConsole(getResponse.ModelInfo);
trainingRunning = false;
break;
case "ERROR: TRAINING JOB NOT FOUND":
throw new Exception("the training job was not found.");
case "ERROR: TOO FEW INSTANCES IN DATASET":
throw new Exception("there are too few instances in the dataset.");
default:
throw new ArgumentException("Unknown status (error): " + getResponse.TrainingStatus);
}
}
}