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<Project Sdk="Microsoft.NET.Sdk">
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<PropertyGroup>
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<OutputType>Exe</OutputType>
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<TargetFramework>netcoreapp3.1</TargetFramework>
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</PropertyGroup>
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<ItemGroup>
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<PackageReference Include="BritishHorseRacingApi" Version="1.0.43" />
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<PackageReference Include="Microsoft.EntityFrameworkCore" Version="3.1.2" />
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<PackageReference Include="Microsoft.EntityFrameworkCore.Relational" Version="3.1.2" />
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<PackageReference Include="Microsoft.EntityFrameworkCore.SqlServer" Version="3.1.2" />
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<PackageReference Include="Microsoft.ML" Version="1.4.0" />
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<PackageReference Include="Microsoft.ML.FastTree" Version="1.4.0" />
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<PackageReference Include="Newtonsoft.Json" Version="12.0.3" />
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<PackageReference Include="SuccessfulCodeTools" Version="1.0.8" />
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<PackageReference Include="System.Data.SqlClient" Version="4.8.1" />
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</ItemGroup>
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<ItemGroup>
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<ProjectReference Include="..\AutoBet.Db\AutoBet.Db.csproj" />
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</ItemGroup>
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</Project>
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using Microsoft.ML;
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using Microsoft.ML.Data;
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using Microsoft.ML.Transforms;
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namespace AutoBet.Learn.Extensions
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{
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public static class EstimatorExtensions
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{
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//Converts string to one hot encoding vectors
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public static EstimatorChain<OneHotEncodingTransformer> EncodeColumn(this IEstimator<ITransformer> pipeline, MLContext mlContext, string column)
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{
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var catergoricalTransforms = mlContext.Transforms.Categorical;
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return pipeline.Append(catergoricalTransforms.OneHotEncoding(outputColumnName: column + "Encoded", inputColumnName: column));
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}
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}
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}
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using Microsoft.ML.Data;
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namespace AutoBet.Learn
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{
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public class HorseOddsResult
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{
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[ColumnName("Score")]
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public float Odds;
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}
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}
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using Microsoft.ML.Data;
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using System;
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namespace AutoBet.Learn
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{
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public class HorseOddsTrainingData
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{
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public string CourseName;
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public int FixtureType;
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public string RaceCriteriaRaceType;
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public float DistanceValue;
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public float RaceClass;
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public float PrizeAmount;
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public string HeadGearWearingCode;
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public int HeadGearWearingSheepSkin;
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public string CourseWinner;
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public string DistanceWinner;
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public string RacehorseName;
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public string TrainerName;
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public string OwnerName;
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public string JockeyName;
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public float WeightsJockeyClaiming;
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public float DecimalOdds;
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public float DivisionSequence;
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public float AgeYears;
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public float DrawnStall;
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public float BhaRating;
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public string WeightValue;
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public float PenaltyValue;
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public int SexType;
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public float FinishTimeInMilliseconds;
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public DateTime RaceDateTime;
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public float RaceYear;
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public float RaceMonth;
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public float RaceDay;
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public float WeightInGrams;
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}
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public class HorseOddsTrainingPrediction
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{
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[ColumnName("Score")]
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public float FinishTimeInMilliseconds;
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}
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}
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using Microsoft.ML;
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using Microsoft.ML.Data;
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using System;
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using System.Data.SqlClient;
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using System.Linq;
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using AutoBet.Learn.Extensions;
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using Microsoft.ML.Trainers;
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using System.Collections.Generic;
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namespace AutoBet.Learn
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{
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public class Program
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{
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static void Main(string[] args)
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{
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Learn();
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Console.WriteLine("Hello World!");
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}
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public static DateTime DateToPredict = DateTime.Now.Date.AddDays(-3);
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private static void Learn()
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{
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var daysAgo = DateToPredict.AddMonths(-3);
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daysAgo = new DateTime(daysAgo.Year, daysAgo.Month, 1);
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while (DateToPredict != daysAgo)
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{
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var mlContext = new MLContext();
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var model = Train(mlContext);
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Evaluate(mlContext, model);
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UpdatePredictions(mlContext, model);
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DateToPredict = DateToPredict.AddDays(-1);
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}
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}
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private static void UpdatePredictions(MLContext mlContext, ITransformer model)
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{
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using (var db = new Db.HorseRacingDataContext())
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{
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var races = db.Races.Where(e => e.RaceDate == DateToPredict.Date).ToList();
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foreach (var race in races)
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{
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var runners = db.Runners.Where(e => e.RaceId == race.RaceId && e.DecimalOdds != null).ToList();
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foreach (var runner in runners)
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{
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var fixture = db.Fixtures.FirstOrDefault(e => e.FixtureId == race.FixtureId);
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if (fixture == null)
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continue;
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var prediction = TestSinglePrediction(mlContext, model, new HorseOddsTrainingData()
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{
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AgeYears = runner.AgeYears,
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CourseName = fixture.CourseName,
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FixtureType = (int)fixture.FixtureType,
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BhaRating = String.IsNullOrWhiteSpace(runner.BhaRating) ? 0 : int.Parse(runner.BhaRating),
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CourseWinner = runner.CourseWinner,
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DecimalOdds = (float)runner.DecimalOdds.Value,
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DistanceValue = race.DistanceValue,
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DistanceWinner = runner.DistanceWinner,
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DivisionSequence = race.DivisionSequence,
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DrawnStall = runner.DrawnStall ?? 0,
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FinishTimeInMilliseconds = 0,
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HeadGearWearingCode = runner.HeadGearWearingCode ?? "Unknown",
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HeadGearWearingSheepSkin = runner.HeadGearWearingSheepSkin,
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JockeyName = runner.JockeyName,
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OwnerName = runner.OwnerName,
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PenaltyValue = runner.PenaltyValue,
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PrizeAmount = race.PrizeAmount,
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RaceClass = race.RaceClass,
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RaceCriteriaRaceType = race.RaceCriteriaRaceType,
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RaceDateTime = race.RaceDate + race.RaceTime,
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RacehorseName = runner.RaceHorseNameWithoutCountry,
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SexType = (int)runner.SexType,
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TrainerName = runner.TrainerName,
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WeightInGrams = (float)runner.WeightInGrams,
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WeightsJockeyClaiming = runner.WeightsJockeyClaiming,
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WeightValue = runner.WeightValue,
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RaceDay = (float)race.RaceDate.Day,
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RaceMonth = (float)race.RaceDate.Month,
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RaceYear = (float)race.RaceDate.Year
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});
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runner.Prediction = prediction;
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}
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}
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db.SaveChanges();
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}
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}
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private static IDataView LoadData(MLContext mlContext, string filter = "")
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{
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var loader = mlContext.Data.CreateDatabaseLoader<HorseOddsTrainingData>();
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var sqlCommand = @"SELECT
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[CourseName]
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,[FixtureType]
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,[RaceCriteriaRaceType]
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,[DistanceValue]
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,[RaceClass]
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,[PrizeAmount]
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,[HeadGearWearingCode]
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,[HeadGearWearingSheepSkin]
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,[CourseWinner]
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,[DistanceWinner]
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,[RacehorseName]
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,[TrainerName]
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,[OwnerName]
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,[JockeyName]
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,[WeightsJockeyClaiming]
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,[DecimalOdds]
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,[DivisionSequence]
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,[AgeYears]
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,[DrawnStall]
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,[BhaRating]
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,[WeightValue]
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,[PenaltyValue]
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,[SexType]
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,ISNULL([FinishTimeInMilliseconds],0) as FinishTimeInMilliseconds
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,[RaceDateTime]
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,[RaceMonth]
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,[RaceYear]
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,[RaceDay]
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,[WeightInGrams]
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FROM [dbo].[ResultsView] WHERE DecimalOdds IS NOT NULL " + filter;
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var dbSource = new DatabaseSource(SqlClientFactory.Instance, Db.HorseRacingDataContext.ConnectionString, sqlCommand);
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var data = loader.Load(dbSource);
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return data;
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}
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public static ITransformer Train(MLContext mlContext)
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{
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var data = LoadData(mlContext, $"AND FinishTimeInMilliseconds IS NOT NULL AND RaceDate < '{DateToPredict.AddDays(-1).ToString("yyyy/MM/dd", null)}'");
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//var trainers = new Dictionary<string, ITrainerEstimator<ISingleFeaturePredictionTransformer<object>, object>>()
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//{
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// { "FastForest", mlContext.Regression.Trainers.FastForest() },
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// { "FastTree", mlContext.Regression.Trainers.FastTree() },
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// { "FastTreeTweedie", mlContext.Regression.Trainers.FastTreeTweedie() },
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// { "LbfgsPoissonRegression", mlContext.Regression.Trainers.LbfgsPoissonRegression() },
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// { "Scda", mlContext.Regression.Trainers.Sdca() }
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//};
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//foreach (var trainer in trainers)
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//{
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// var modelPipeline = DataTransform(mlContext);
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// var pipelineWithTrainer = modelPipeline.Append(trainer.Value);
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// var model = pipelineWithTrainer.Fit(data);
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// Console.WriteLine($">>>>>>> Evaluating algorithm: {trainer.Key} <<<<<<<");
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// Evaluate(mlContext, model);
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//}
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Console.WriteLine($"Loading data");
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var pipeline = DataTransform(mlContext);
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//var pipelineWithTrainer = pipeline.Append(mlContext.Regression.Trainers.LbfgsPoissonRegression());
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var selectedPipelineWithTrainer = pipeline.Append(pipeline.Append(mlContext.Regression.Trainers.LbfgsPoissonRegression()));
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var finalModel = selectedPipelineWithTrainer.Fit(data);
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return finalModel;
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}
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private static EstimatorChain<ColumnConcatenatingTransformer> DataTransform(MLContext mlContext)
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{
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var pipeline = mlContext.Transforms
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.CopyColumns(outputColumnName: "Label", inputColumnName: "FinishTimeInMilliseconds")
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.EncodeColumn(mlContext, "CourseName")
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.EncodeColumn(mlContext, "FixtureType")
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.EncodeColumn(mlContext, "RaceCriteriaRaceType")
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.EncodeColumn(mlContext, "HeadGearWearingCode")
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.EncodeColumn(mlContext, "RacehorseName")
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.EncodeColumn(mlContext, "TrainerName")
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.EncodeColumn(mlContext, "OwnerName")
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.EncodeColumn(mlContext, "JockeyName")
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.EncodeColumn(mlContext, "HeadGearWearingSheepSkin")
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.EncodeColumn(mlContext, "CourseWinner")
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.EncodeColumn(mlContext, "DistanceWinner")
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.EncodeColumn(mlContext, "WeightValue")
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.EncodeColumn(mlContext, "SexType")
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.Append(mlContext.Transforms.Concatenate("Features",
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"CourseNameEncoded",
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"FixtureTypeEncoded",
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"RaceCriteriaRaceTypeEncoded",
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"DistanceValue",
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"RaceClass",
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"PrizeAmount",
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"HeadGearWearingCodeEncoded",
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"HeadGearWearingSheepSkinEncoded",
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"CourseWinnerEncoded",
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"DistanceWinnerEncoded",
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"RacehorseNameEncoded",
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"TrainerNameEncoded",
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"OwnerNameEncoded",
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"JockeyNameEncoded",
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"WeightsJockeyClaiming",
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"DecimalOdds",
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"DivisionSequence",
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"AgeYears",
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"DrawnStall",
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"WeightValueEncoded",
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"PenaltyValue",
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"SexTypeEncoded",
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"RaceMonth",
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"RaceYear",
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"RaceDay"));
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return pipeline;
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}
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private static RegressionMetrics Evaluate(MLContext mlContext, ITransformer model)
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{
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var dataView = LoadData(mlContext, $"AND FinishTimeInMilliseconds IS NOT NULL AND RaceDate = '{DateToPredict.AddDays(-1).ToString("yyyy/MM/dd", null)}'");
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var predictions = model.Transform(dataView);
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var metrics = mlContext.Regression.Evaluate(predictions, "Label", "Score");
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Console.WriteLine();
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var result = $"* Model quality metrics evaluation R2: {metrics.RSquared:0.####} Root Mean Squared Error: {metrics.RootMeanSquaredError:#.####} *";
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var stars = String.Concat(Enumerable.Range(0, result.Length).Select(e => "*"));
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Console.WriteLine(stars);
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Console.WriteLine(result);
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Console.WriteLine(stars);
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Console.WriteLine();
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return metrics;
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}
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private static float TestSinglePrediction(MLContext mlContext, ITransformer model, HorseOddsTrainingData values)
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{
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var predictionFunction = mlContext.Model.CreatePredictionEngine<HorseOddsTrainingData, HorseOddsTrainingPrediction>(model);
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var prediction = predictionFunction.Predict(values);
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Console.WriteLine($"Predicted time for {values.RacehorseName}: {prediction.FinishTimeInMilliseconds}");
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return prediction.FinishTimeInMilliseconds;
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}
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}
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}
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Reference in New Issue
Block a user