Working with the Distance baked into dataset

src/Tyres/DataModels/TyreStint.cs

@@ -24,40 +24,13 @@ public class TyreStint
         public float TrackTemperature;
         [LoadColumn(10)]
         public string Reason;
-        [LoadColumn(11)]
-        public float Laps;
-    }
-
-    public class DistanceTyreStint : TyreStint
-    {
-        public float Distance { get; set; }
+        [LoadColumn(12)]
+        public float Distance;
     }
 
     public class TyreStintPrediction
     {
         [ColumnName("Score")]
         public float Distance;
     }
-
-    public class LapsToDistanceInput
-    {
-        public float TrackLength { get; set; }
-        public float Laps { get; set; }
-    }
-
-    public class LapsToDistanceOutput
-    {
-        public float Distance { get; set; }
-    }
-
-    [CustomMappingFactoryAttribute(nameof(CustomMappings.DistanceMapping))]
-    public class CustomMappings : CustomMappingFactory<LapsToDistanceInput, LapsToDistanceOutput>
-    {
-        public static void DistanceMapping(LapsToDistanceInput input, LapsToDistanceOutput output) => output.Distance = input.Laps * input.TrackLength;
-
-        public override Action<LapsToDistanceInput, LapsToDistanceOutput> GetMapping()
-        {
-            return DistanceMapping;
-        }
-    }
 }

src/Tyres/DomainModels/Track.cs

@@ -5,7 +5,7 @@ namespace Tyres.DomainModels
     public class Track
     {
         public string Name { get; set; }
-        public float Distance { get; set; }
+        public float TrackLength { get; set; }
     }
 
     public class Race

src/Tyres/Program.cs

@@ -18,7 +18,6 @@ public class Program
         static void Main(string[] args)
         {
             var mlContext = new MLContext(seed: 0);
-            mlContext.ComponentCatalog.RegisterAssembly(typeof(CustomMappings).Assembly);
 
             // Load data
             var data = mlContext.Data.LoadFromTextFile<TyreStint>(DatasetsLocation, ';', true);
@@ -27,36 +26,29 @@ static void Main(string[] args)
             var filtered = mlContext.Data.FilterByCustomPredicate(data, (TyreStint row) => !(row.Reason.Equals("Pit Stop") || row.Reason.Equals("Race Finish")) );
             var debug = mlContext.Data.CreateEnumerable<TyreStint>(filtered, reuseRowObject: false).Count();
 
-            // Transforming to add distancefeature
-            var customTransformer = mlContext.Transforms.CustomMapping<LapsToDistanceInput, LapsToDistanceOutput>(CustomMappings.DistanceMapping, "LapsToDistance");
-            var transformed = customTransformer.Fit(filtered).Transform(filtered);
-            var debug2 = mlContext.Data.CreateEnumerable<DistanceTyreStint>(transformed, reuseRowObject: false).ToList();
-
             // Divide dataset into training and testing data
             var split = mlContext.Data.TrainTestSplit(filtered, testFraction: 0.1);
             var trainingData = split.TrainSet;
             var testingData = split.TestSet;
 
 
             // Run AutoML experiment
-            var experimentTime = 60u;
+            var experimentTime = 900u;
             Console.WriteLine("=============== Training the model ===============");
             Console.WriteLine($"Running AutoML regression experiment for {experimentTime} seconds...");
             var experimentResult = mlContext.Auto()
                 .CreateRegressionExperiment(experimentTime)
                 .Execute(trainingData, testingData,
                     columnInformation: new ColumnInformation()
                     {
-                        CategoricalColumnNames = { nameof(DistanceTyreStint.Team), nameof(DistanceTyreStint.Car),  nameof(DistanceTyreStint.Driver), nameof(DistanceTyreStint.Compound), nameof(DistanceTyreStint.Reason) },
-                        NumericColumnNames = { nameof(DistanceTyreStint.AirTemperature), nameof(DistanceTyreStint.TrackTemperature) },
-                        LabelColumnName = nameof(DistanceTyreStint.Distance)
-                    },
-                    preFeaturizer: customTransformer
-                    );
+                        CategoricalColumnNames = { nameof(TyreStint.Team), nameof(TyreStint.Car),  nameof(TyreStint.Driver), nameof(TyreStint.Compound), nameof(TyreStint.Reason) },
+                        NumericColumnNames = { nameof(TyreStint.AirTemperature), nameof(TyreStint.TrackTemperature) },
+                        LabelColumnName = nameof(TyreStint.Distance)
+                    }
+                );
 
 
             // Print top models found by AutoML
-            Console.WriteLine();
             TrainingHelper.PrintTopModels(experimentResult);
 
             Console.WriteLine("===== Evaluating model's accuracy with test data =====");
@@ -65,18 +57,18 @@ static void Main(string[] args)
             var trainedModel = best.Model;
             var predictions = trainedModel.Transform(testingData);
 
-            var metrics = mlContext.Regression.Evaluate(predictions, labelColumnName: nameof(DistanceTyreStint.Distance), scoreColumnName: "Score");
+            var metrics = mlContext.Regression.Evaluate(predictions, labelColumnName: nameof(TyreStint.Distance), scoreColumnName: "Score");
             TrainingHelper.PrintRegressionMetrics(best.TrainerName, metrics);
 
 
             // Run sample predictions
             var predictionEngine = mlContext.Model.CreatePredictionEngine<TyreStint, TyreStintPrediction>(trainedModel);
 
-            var lh = new DistanceTyreStint() { Track = "Bahrain International Circuit", TrackLength = 5412f, Team = "Mercedes", Car = "W12", Driver = "Lewis Hamilton", Compound = "C3", AirTemperature = 20.5f, TrackTemperature = 28.3f, Reason = "Pit Stop" };
+            var lh = new TyreStint() { Track = "Bahrain International Circuit", TrackLength = 5412f, Team = "Mercedes", Car = "W12", Driver = "Lewis Hamilton", Compound = "C3", AirTemperature = 20.5f, TrackTemperature = 28.3f, Reason = "Pit Stop" };
             var lhPred = predictionEngine.Predict(lh);
             var lhLaps = lhPred.Distance / 5412f;
 
-            var mv = new DistanceTyreStint() { Track = "Bahrain International Circuit", TrackLength = 5412f, Team = "Red Bull", Car = "RB16B", Driver = "Max Verstappen", Compound = "C3", AirTemperature = 20.5f, TrackTemperature = 28.3f, Reason = "Pit Stop" };
+            var mv = new TyreStint() { Track = "Bahrain International Circuit", TrackLength = 5412f, Team = "Red Bull", Car = "RB16B", Driver = "Max Verstappen", Compound = "C3", AirTemperature = 20.5f, TrackTemperature = 28.3f, Reason = "Pit Stop" };
             var mvPred = predictionEngine.Predict(mv);
             var mvLaps = mvPred.Distance / 5412f;
 
@@ -97,7 +89,7 @@ static void Main(string[] args)
                 new Top10Driver() {Team = "Renault / Alpine", Car = "A521", Name = "Esteban Ocon", StartingCompound = "C5"},
             };
 
-            DataHelper.PrintPredictionTable(predictionEngine, "Monaco", 25.0f, 43.7f, top10Monaco);
+            DataHelper.PrintPredictionTable(predictionEngine, "Monaco", 20.0f, 35.0f, top10Monaco);
         }
     }
 }

src/Tyres/StaticData/Season2021.cs

@@ -33,11 +33,11 @@ public static class Season2021
 
         public static Dictionary<string, Track> Tracks = new Dictionary<string, Track>()
         {
-            { "Bahrain", new Track() {Name = "Bahrain International Circuit", Distance = 5412f} },
-            { "Imola", new Track() {Name = "Imola", Distance = 4909f } },
-            { "Portimão", new Track() {Name = "Portimão", Distance = 4653f } },
-            { "Catalunya", new Track() {Name = "Catalunya", Distance = 4675f } },
-            { "Monaco", new Track() {Name = "Monaco", Distance = 3337f } }
+            { "Bahrain", new Track() {Name = "Bahrain International Circuit", TrackLength = 5412f} },
+            { "Imola", new Track() {Name = "Imola", TrackLength = 4909f } },
+            { "Portimão", new Track() {Name = "Portimão", TrackLength = 4653f } },
+            { "Catalunya", new Track() {Name = "Catalunya", TrackLength = 4675f } },
+            { "Monaco", new Track() {Name = "Monaco", TrackLength = 3337f } }
         };
     }
 }

src/Tyres/ViewHelpers/DataHelper.cs

@@ -11,14 +11,16 @@ public class DataHelper
     {
         public static void PrintPredictionTable(PredictionEngine<TyreStint, TyreStintPrediction> predictionEngine, string trackName, float airTemperature, float trackTemperature, List<Top10Driver> drivers)
         {
-            Console.WriteLine(Season2021.Tracks[trackName].Name);
+            var monaco = Season2021.Tracks[trackName];
+
+            Console.WriteLine(monaco.Name);
             Console.WriteLine("==========");
             foreach (var d in drivers)
             {
                 var prediction = predictionEngine.Predict(new TyreStint()
                 {
-                    Track = Season2021.Tracks[trackName].Name,
-                    TrackLength = Season2021.Tracks[trackName].Distance,
+                    Track = monaco.Name,
+                    TrackLength = monaco.TrackLength,
                     Team = d.Team,
                     Car = d.Car,
                     Driver = d.Name,
@@ -27,7 +29,7 @@ public static void PrintPredictionTable(PredictionEngine<TyreStint, TyreStintPre
                     TrackTemperature = trackTemperature,
                     Reason = "Pit Stop"
                 });
-                Console.WriteLine($"| {d.Name} | {d.StartingCompound} | {prediction.Distance } |  |  |");
+                Console.WriteLine($"| {d.Name} | {d.StartingCompound} | {prediction.Distance / monaco.TrackLength } |  |  |");
             }
             Console.WriteLine("");
         }

src/Tyres/ViewHelpers/TrainingHelper.cs

@@ -9,6 +9,7 @@ public class TrainingHelper
     {
         public static void PrintTopModels(ExperimentResult<RegressionMetrics> experimentResult)
         {
+            Console.WriteLine();
             // Get top few runs ranked by R-Squared.
             // R-Squared is a metric to maximize, so OrderByDescending() is correct.
             // For RMSE and other regression metrics, OrderByAscending() is correct.