#308 Predict prices using regression with ML.NET using C#

Data file: Download the and place to Data folder two files: taxi-fare-test.csv and taxi-fare-train.csv

TaxiTrip.cs

// <Snippet1>
using Microsoft.ML.Data;
// </Snippet1>

namespace TaxiFarePrediction
{
    // <Snippet2>
    public class TaxiTrip
    {
        [LoadColumn(0)]
        public string VendorId;

        [LoadColumn(1)]
        public string RateCode;

        [LoadColumn(2)]
        public float PassengerCount;

        [LoadColumn(3)]
        public float TripTime;

        [LoadColumn(4)]
        public float TripDistance;

        [LoadColumn(5)]
        public string PaymentType;

        [LoadColumn(6)]
        public float FareAmount;
    }

    public class TaxiTripFarePrediction
    {
        [ColumnName("Score")]
        public float FareAmount;
    }
    // </Snippet2>
}

Program.cs

//Install Microsoft.ML
//Install Microsoft.ML.FastTree
// <Snippet1>
using System;
using System.IO;
using Microsoft.ML;
// </Snippet1>

namespace TaxiFarePrediction
{
    class Program
    {
        // <Snippet2>
        static readonly string _trainDataPath = Path.Combine(Environment.CurrentDirectory, "Data", "taxi-fare-train.csv");
        static readonly string _testDataPath = Path.Combine(Environment.CurrentDirectory, "Data", "taxi-fare-test.csv");
        static readonly string _modelPath = Path.Combine(Environment.CurrentDirectory, "Data", "Model.zip");
        // </Snippet2>

        static void Main(string[] args)
        {
            Console.WriteLine(Environment.CurrentDirectory);

            // <Snippet3>
            MLContext mlContext = new MLContext(seed: 0);
            // </Snippet3>

            // <Snippet5>
            var model = Train(mlContext, _trainDataPath);
            // </Snippet5>

            // <Snippet14>
            Evaluate(mlContext, model);
            // </Snippet14>

            // <Snippet20>
            TestSinglePrediction(mlContext, model);
            // </Snippet20>
        }

        public static ITransformer Train(MLContext mlContext, string dataPath)
        {
            // <Snippet6>
            IDataView dataView = mlContext.Data.LoadFromTextFile<TaxiTrip>(dataPath, hasHeader: true, separatorChar: ',');
            // </Snippet6>

            // <Snippet7>
            var pipeline = mlContext.Transforms.CopyColumns(outputColumnName: "Label", inputColumnName: "FareAmount")
                    // </Snippet7>
                    // <Snippet8>
                    .Append(mlContext.Transforms.Categorical.OneHotEncoding(outputColumnName: "VendorIdEncoded", inputColumnName: "VendorId"))
                    .Append(mlContext.Transforms.Categorical.OneHotEncoding(outputColumnName: "RateCodeEncoded", inputColumnName: "RateCode"))
                    .Append(mlContext.Transforms.Categorical.OneHotEncoding(outputColumnName: "PaymentTypeEncoded", inputColumnName: "PaymentType"))
                    // </Snippet8>
                    // <Snippet9>
                    .Append(mlContext.Transforms.Concatenate("Features", "VendorIdEncoded", "RateCodeEncoded", "PassengerCount", "TripDistance", "PaymentTypeEncoded"))
                    // </Snippet9>
                    // <Snippet10>
                    .Append(mlContext.Regression.Trainers.FastTree());
            // </Snippet10>

            Console.WriteLine("=============== Create and Train the Model ===============");

            // <Snippet11>
            var model = pipeline.Fit(dataView);
            // </Snippet11>

            Console.WriteLine("=============== End of training ===============");
            Console.WriteLine();
            // <Snippet12>
            return model;
            // </Snippet12>
        }

        private static void Evaluate(MLContext mlContext, ITransformer model)
        {
            // <Snippet15>
            IDataView dataView = mlContext.Data.LoadFromTextFile<TaxiTrip>(_testDataPath, hasHeader: true, separatorChar: ',');
            // </Snippet15>

            // <Snippet16>
            var predictions = model.Transform(dataView);
            // </Snippet16>
            // <Snippet17>
            var metrics = mlContext.Regression.Evaluate(predictions, "Label", "Score");
            // </Snippet17>

            Console.WriteLine();
            Console.WriteLine($"*************************************************");
            Console.WriteLine($"*       Model quality metrics evaluation         ");
            Console.WriteLine($"*------------------------------------------------");
            // <Snippet18>
            Console.WriteLine($"*       RSquared Score:      {metrics.RSquared:0.##}");
            // </Snippet18>
            // <Snippet19>
            Console.WriteLine($"*       Root Mean Squared Error:      {metrics.RootMeanSquaredError:#.##}");
            // </Snippet19>
            Console.WriteLine($"*************************************************");
        }

        private static void TestSinglePrediction(MLContext mlContext, ITransformer model)
        {
            //Prediction test
            // Create prediction function and make prediction.
            // <Snippet22>
            var predictionFunction = mlContext.Model.CreatePredictionEngine<TaxiTrip, TaxiTripFarePrediction>(model);
            // </Snippet22>
            //Sample:
            //vendor_id,rate_code,passenger_count,trip_time_in_secs,trip_distance,payment_type,fare_amount
            //VTS,1,1,1140,3.75,CRD,15.5
            // <Snippet23>
            var taxiTripSample = new TaxiTrip()
            {
                VendorId = "VTS",
                RateCode = "1",
                PassengerCount = 1,
                TripTime = 1140,
                TripDistance = 3.75f,
                PaymentType = "CRD",
                FareAmount = 0 // To predict. Actual/Observed = 15.5
            };
            // </Snippet23>
            // <Snippet24>
            var prediction = predictionFunction.Predict(taxiTripSample);
            // </Snippet24>
            // <Snippet25>
            Console.WriteLine($"**********************************************************************");
            Console.WriteLine($"Predicted fare: {prediction.FareAmount:0.####}, actual fare: 15.5");
            Console.WriteLine($"**********************************************************************");
            // </Snippet25>
        }
    }
}

Output:

*************************************************
*       Model quality metrics evaluation
*------------------------------------------------
*       RSquared Score:      0.71
*       Root Mean Squared Error:      5.86
*************************************************
**********************************************************************
Predicted fare: 14.7698, actual fare: 15.5
**********************************************************************

https://github.com/gantovnik/wordpress_examples/tree/main/ex308