/// <summary>
/// Uses ML.NET to predict iris flower species based on input an trained model.
/// </summary>
/// <param name="runnerRequest">Request with input needed for iris flower species determination.</param>
/// <returns>Iris flower species prediction result.</returns>
public async Task <RunnerResponse> RunClassificationAsync(RunnerRequest runnerRequest)
{
try
{
var modelBuilder = new ModelBuilder();
var trainedModel = await modelBuilder.TrainAsync();
var modelMetrics = modelBuilder.Evaluate(trainedModel);
var modelInput = runnerRequest.ModelInput
.Select(p => new DataModel
{
SepalLength = p.SepalLength,
SepalWidth = p.SepalWidth,
PetalLength = p.PetalLength,
PetalWidth = p.PetalWidth
})
.ToList();
var modelOutput = modelBuilder.Predict(trainedModel, modelInput)
.Select(p => new ModelOutput
{
PredictedSpecies = p.PredictedLabels
})
.ToList();
return(new RunnerResponse
{
Success = true,
ModelOutput = modelOutput
});
}
catch (Exception ex)
{
return(new RunnerResponse
{
Success = false,
Message = ex.ToExceptionMessage()
});
}
}
private static async Task runIrisFlowerSpeciesDeterminationAsync()
{
var modelInput = new SpeciesDetermination.ModelInput
{
SepalLength = getIrisFlowerInputFloatValue(IrisFlowerInputType.SepalLength),
SepalWidth = getIrisFlowerInputFloatValue(IrisFlowerInputType.SepalWidth),
PetalLength = getIrisFlowerInputFloatValue(IrisFlowerInputType.PetalLength),
PetalWidth = getIrisFlowerInputFloatValue(IrisFlowerInputType.PetalWidth)
};
var runnerRequest = new SpeciesDetermination.RunnerRequest
{
ModelInput = new List <SpeciesDetermination.ModelInput> {
modelInput
}
};
var runnerResponse = await SpeciesDetermination.ModelRunner.Instance.RunClassificationAsync(runnerRequest);
if (!runnerResponse.Success)
{
Console.WriteLine($"Iris flower species determination failed: {runnerResponse.Message}");
}
}
