static void Main(string[] args)
{
try
{
var context = new MLContext(seed: 0);
var modelFile = AppDomain.CurrentDomain.BaseDirectory + "MLModel.zip";
var model = context.Model.Load(filePath: modelFile, out DataViewSchema inputSchema);
var predictionEngine = context.Model.CreatePredictionEngine <TaxiFareInput, TaxiFarePrediction>(model);
//CMT,1,1,581,5.4,CSH,16.5
var modelInput = new TaxiFareInput
{
Rate_code = 1,
Passenger_count = 1,
Trip_time_in_secs = 581,
Trip_distance = 5.4F,
Payment_type = "CSH",
Fare_amount = 0
};
var modelOutput = predictionEngine.Predict(modelInput);
Console.WriteLine("Predicted Fare : " + modelOutput.Score.ToString() + ", " +
"Actual Fare : 16.5");
}
catch (Exception exceptionObject)
{
Console.WriteLine("Error Occurred, Details : " + exceptionObject.Message);
}
Console.WriteLine("End of App!");
Console.ReadLine();
}
public TaxiFarePrediction GetTaxiFare(TaxiFareInput taxiFareInput)
{
var validation = taxiFareInput != default(TaxiFareInput) &&
taxiFareInput.Passenger_count >= 1 &&
taxiFareInput.Trip_distance >= 1 &&
taxiFareInput.Trip_time_in_secs >= 1;
if (!validation)
{
throw new ArgumentException("Invalid Taxi Fare Input Specified!");
}
var prediction = predictionEngine.Predict(taxiFareInput);
return(prediction);
}
static void Main(string[] args)
{
// Create single instance of sample data from first line of dataset for model input
TaxiFareInput sampleData = CreateSingleDataSample(DATA_FILEPATH);
// Make a single prediction on the sample data and print results
TaxiFarePrediction predictionResult = ConsumeModel.Predict(sampleData);
Console.WriteLine("Using model to make single prediction -- Comparing actual Fare_amount with predicted Fare_amount from sample data...\n\n");
Console.WriteLine($"vendor_id: {sampleData.Vendor_id}");
Console.WriteLine($"rate_code: {sampleData.Rate_code}");
Console.WriteLine($"passenger_count: {sampleData.Passenger_count}");
Console.WriteLine($"trip_time_in_secs: {sampleData.Trip_time_in_secs}");
Console.WriteLine($"trip_distance: {sampleData.Trip_distance}");
Console.WriteLine($"payment_type: {sampleData.Payment_type}");
Console.WriteLine($"\n\nActual Fare_amount: {sampleData.Fare_amount} \nPredicted Fare_amount: {predictionResult.Score}\n\n");
Console.WriteLine("=============== End of process, hit any key to finish ===============");
Console.ReadKey();
}
// Change this code to create your own sample data
#region CreateSingleDataSample
// Method to load single row of dataset to try a single prediction
private static TaxiFareInput CreateSingleDataSample(string dataFilePath)
{
// Create MLContext
MLContext mlContext = new MLContext();
// Load dataset
IDataView dataView = mlContext.Data.LoadFromTextFile <TaxiFareInput>(
path: dataFilePath,
hasHeader: true,
separatorChar: ',',
allowQuoting: true,
allowSparse: false);
// Use first line of dataset as model input
// You can replace this with new test data (hardcoded or from end-user application)
TaxiFareInput sampleForPrediction = mlContext.Data.CreateEnumerable <TaxiFareInput>(dataView, false)
.First();
return(sampleForPrediction);
}
public IActionResult CalculateTaxiFare([FromBody] TaxiFareInput taxiFareInput)
{
var validation = this.taxiFarePredictionService != default(ITaxiFarePredictionService) &&
taxiFareInput != default(TaxiFareInput) &&
taxiFareInput.Passenger_count >= MIN_PASSENGERS &&
taxiFareInput.Trip_distance >= MIN_TRIP_DISTANCE &&
taxiFareInput.Trip_time_in_secs >= MIN_TRIP_IN_SECS;
if (!validation)
{
return(new BadRequestResult());
}
try
{
var taxiFarePrediction = this.taxiFarePredictionService.GetTaxiFare(taxiFareInput);
return(Ok(taxiFarePrediction));
}
catch (Exception exceptionObject)
{
return(new BadRequestObjectResult(exceptionObject.Message));
}
}
