private static async Task runTaxiFarePredictionAsync()
{
var modelInput = new List <TaxiFarePrediction.ModelInput>
{
new TaxiFarePrediction.ModelInput
{
VendorId = getTaxiFareInputStringValue(TaxiFareInputType.VendorId),
RateCode = getTaxiFareInputStringValue(TaxiFareInputType.RateCode),
PassengerCount = getTaxiFareInputFloatValue(TaxiFareInputType.PassengerCount),
TripDistance = getTaxiFareInputFloatValue(TaxiFareInputType.TripDistance),
PaymentType = getTaxiFareInputStringValue(TaxiFareInputType.PaymentType)
}
};
var runnerRequest = new TaxiFarePrediction.RunnerRequest
{
ModelInput = modelInput
};
var runnerResponse = await TaxiFarePrediction.ModelRunner.Instance.RunClassificationAsync(runnerRequest);
if (!runnerResponse.Success)
{
Console.WriteLine($"Text sentiment analysis failed: {runnerResponse.Message}");
}
}
/// <summary>
/// Uses ML.NET to predict taxi fare based on input an trained model.
/// </summary>
/// <param name="runnerRequest">Request with input needed for taxi fare prediction.</param>
/// <returns>Taxi fare 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
{
VendorId = p.VendorId,
RateCode = p.RateCode,
PassengerCount = p.PassengerCount,
TripDistance = p.TripDistance,
PaymentType = p.PaymentType
})
.ToList();
var modelOutput = modelBuilder.Predict(trainedModel, modelInput)
.Select(p => new ModelOutput
{
PredictedFareAmount = p.FareAmount
})
.ToList();
return(new RunnerResponse
{
Success = true,
ModelOutput = modelOutput
});
}
catch (Exception ex)
{
return(new RunnerResponse
{
Success = false,
Message = ex.ToExceptionMessage()
});
}
}
