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Prefeaturization of AutoML - not working as expected but keeping for …
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…future reference.
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@mlusiak
mlusiak committed May 23, 2021
1 parent cfce520 commit 5614143
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Showing 2 changed files with 45 additions and 41 deletions.
27 changes: 23 additions & 4 deletions src/Tyres/DataModels/TyreStint.cs
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Original file line number Diff line number Diff line change
@@ -1,4 +1,6 @@
using Microsoft.ML.Data;
using System;
using Microsoft.ML.Data;
using Microsoft.ML.Transforms;

namespace Tyres.DataModels
{
Expand Down Expand Up @@ -26,19 +28,36 @@ public class TyreStint
public float Laps;
}

public class TransformedTyreStint : TyreStint
public class DistanceTyreStint : TyreStint
{
public float Distance { get; set; }
}

public class TyreStintPrediction
{
[ColumnName("Score")]
[ColumnName("Score")]
public float Distance;
}

public class CustomDistanceMapping
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;
}
}
}
59 changes: 22 additions & 37 deletions src/Tyres/Program.cs
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Original file line number Diff line number Diff line change
Expand Up @@ -18,6 +18,7 @@ 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);
Expand All @@ -26,49 +27,33 @@ 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;

// Build data pipeline
var pipeline = mlContext.Transforms.CustomMapping((TyreStint input, CustomDistanceMapping output) => output.Distance = input.Laps * input.TrackLength, contractName: null)
.Append(mlContext.Transforms.CopyColumns(outputColumnName: "Label", inputColumnName: nameof(TransformedTyreStint.Distance)))
.Append(mlContext.Transforms.Categorical.OneHotEncoding(outputColumnName: "TeamEncoded", inputColumnName: nameof(TyreStint.Team)))
.Append(mlContext.Transforms.Categorical.OneHotEncoding(outputColumnName: "CarEncoded", inputColumnName: nameof(TyreStint.Car)))
.Append(mlContext.Transforms.Categorical.OneHotEncoding(outputColumnName: "DriverEncoded", inputColumnName: nameof(TyreStint.Driver)))
.Append(mlContext.Transforms.Categorical.OneHotEncoding(outputColumnName: "CompoundEncoded", inputColumnName: nameof(TyreStint.Compound)))
.Append(mlContext.Transforms.NormalizeMeanVariance(outputColumnName: nameof(TyreStint.AirTemperature)))
.Append(mlContext.Transforms.NormalizeMeanVariance(outputColumnName: nameof(TyreStint.TrackTemperature)))
.Append(mlContext.Transforms.Categorical.OneHotEncoding(outputColumnName: "ReasonEncoded", inputColumnName: nameof(TyreStint.Reason)))
.Append(mlContext.Transforms.Concatenate("Features",
"TeamEncoded", "CarEncoded", "DriverEncoded", "CompoundEncoded", nameof(TyreStint.AirTemperature), nameof(TyreStint.TrackTemperature)));


/*
// Setting the training algorithm
var trainer = mlContext.Regression.Trainers.Sdca(labelColumnName: "Label", featureColumnName: "Features");
var trainingPipeline = pipeline.Append(trainer);
// Training the model
Console.WriteLine("=============== Training the model ===============");
var trainedModel = trainingPipeline.Fit(trainingData);
// Evaluate the model on test data
Console.WriteLine("===== Evaluating Model's accuracy with Test data =====");
var predictions = trainedModel.Transform(testingData);
var metrics = mlContext.Regression.Evaluate(predictions, labelColumnName: "Label", scoreColumnName: "Score");
PrintRegressionMetrics(trainer, metrics);
*/

var experimentTime = 600u;
// Run AutoML experiment
var experimentTime = 60u;
Console.WriteLine("=============== Training the model ===============");
Console.WriteLine($"Running AutoML regression experiment for {experimentTime} seconds...");
var experimentResult = mlContext.Auto()
.CreateRegressionExperiment(experimentTime)
.Execute(trainingData, progressHandler: null, labelColumnName: "Laps");
.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
);


// Print top models found by AutoML
Console.WriteLine();
Expand All @@ -80,23 +65,23 @@ static void Main(string[] args)
var trainedModel = best.Model;
var predictions = trainedModel.Transform(testingData);

var metrics = mlContext.Regression.Evaluate(predictions, labelColumnName: "Laps", scoreColumnName: "Score");
var metrics = mlContext.Regression.Evaluate(predictions, labelColumnName: nameof(DistanceTyreStint.Distance), scoreColumnName: "Score");
TrainingHelper.PrintRegressionMetrics(best.TrainerName, metrics);


// Run sample predictions
var predictionEngine = mlContext.Model.CreatePredictionEngine<TyreStint, TyreStintPrediction>(trainedModel);

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 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 lhPred = predictionEngine.Predict(lh);
var lhLaps = lhPred.Distance / 5412f;

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 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 mvPred = predictionEngine.Predict(mv);
var mvLaps = mvPred.Distance / 5412f;



// Printing predictions for top10 grid places
var top10Monaco = new List<Top10Driver>()
{
new Top10Driver() {Team = "Ferrari", Car = "SF21", Name = "Charles Leclerc", StartingCompound = "C5"},
Expand Down

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