GlickoEstimator.training.cs

// This file was auto-generated by ML.NET Model Builder.
using System;
using System.IO;
using System.Collections.Generic;
using System.Linq;
using System.Text;
using System.Threading.Tasks;
using Microsoft.ML.Data;
using Microsoft.ML.Trainers.LightGbm;
using Microsoft.ML.Trainers;
using Microsoft.ML;

namespace Minomuncher
{
    public partial class GlickoEstimator
    {
        public const string RetrainFilePath =  @"C:\Users\CSDotNET\source\repos\Minomuncher\NeuralNetwork\bin\Debug\net8.0\OutputFile.csv";
        public const char RetrainSeparatorChar = ',';
        public const bool RetrainHasHeader =  true;

         /// <summary>
        /// Train a new model with the provided dataset.
        /// </summary>
        /// <param name="outputModelPath">File path for saving the model. Should be similar to "C:\YourPath\ModelName.mlnet"</param>
        /// <param name="inputDataFilePath">Path to the data file for training.</param>
        /// <param name="separatorChar">Separator character for delimited training file.</param>
        /// <param name="hasHeader">Boolean if training file has a header.</param>
        public static void Train(string outputModelPath, string inputDataFilePath = RetrainFilePath, char separatorChar = RetrainSeparatorChar, bool hasHeader = RetrainHasHeader)
        {
            var mlContext = new MLContext();

            var data = LoadIDataViewFromFile(mlContext, inputDataFilePath, separatorChar, hasHeader);
            var model = RetrainModel(mlContext, data);
            SaveModel(mlContext, model, data, outputModelPath);
        }

        /// <summary>
        /// Load an IDataView from a file path.
        /// </summary>
        /// <param name="mlContext">The common context for all ML.NET operations.</param>
        /// <param name="inputDataFilePath">Path to the data file for training.</param>
        /// <param name="separatorChar">Separator character for delimited training file.</param>
        /// <param name="hasHeader">Boolean if training file has a header.</param>
        /// <returns>IDataView with loaded training data.</returns>
        public static IDataView LoadIDataViewFromFile(MLContext mlContext, string inputDataFilePath, char separatorChar, bool hasHeader)
        {
            return mlContext.Data.LoadFromTextFile<ModelInput>(inputDataFilePath, separatorChar, hasHeader);
        }



        /// <summary>
        /// Save a model at the specified path.
        /// </summary>
        /// <param name="mlContext">The common context for all ML.NET operations.</param>
        /// <param name="model">Model to save.</param>
        /// <param name="data">IDataView used to train the model.</param>
        /// <param name="modelSavePath">File path for saving the model. Should be similar to "C:\YourPath\ModelName.mlnet.</param>
        public static void SaveModel(MLContext mlContext, ITransformer model, IDataView data, string modelSavePath)
        {
            // Pull the data schema from the IDataView used for training the model
            DataViewSchema dataViewSchema = data.Schema;

            using (var fs = File.Create(modelSavePath))
            {
                mlContext.Model.Save(model, dataViewSchema, fs);
            }
        }


        /// <summary>
        /// Retrains model using the pipeline generated as part of the training process.
        /// </summary>
        /// <param name="mlContext"></param>
        /// <param name="trainData"></param>
        /// <returns></returns>
        public static ITransformer RetrainModel(MLContext mlContext, IDataView trainData)
        {
            var pipeline = BuildPipeline(mlContext);
            var model = pipeline.Fit(trainData);

            return model;
        }


        /// <summary>
        /// build the pipeline that is used from model builder. Use this function to retrain model.
        /// </summary>
        /// <param name="mlContext"></param>
        /// <returns></returns>
        public static IEstimator<ITransformer> BuildPipeline(MLContext mlContext)
        {
            // Data process configuration with pipeline data transformations
            var pipeline = mlContext.Transforms.ReplaceMissingValues(new []{new InputOutputColumnPair(@"Single", @"Single"),new InputOutputColumnPair(@"Double", @"Double"),new InputOutputColumnPair(@"Triple", @"Triple"),new InputOutputColumnPair(@"Quad", @"Quad"),new InputOutputColumnPair(@"Tspin", @"Tspin"),new InputOutputColumnPair(@"TspinSingle", @"TspinSingle"),new InputOutputColumnPair(@"TspinDouble", @"TspinDouble"),new InputOutputColumnPair(@"TspinTriple", @"TspinTriple"),new InputOutputColumnPair(@"TspinMini", @"TspinMini"),new InputOutputColumnPair(@"TspinMiniSingle", @"TspinMiniSingle"),new InputOutputColumnPair(@"PerfectClear", @"PerfectClear"),new InputOutputColumnPair(@"TEfficiency", @"TEfficiency"),new InputOutputColumnPair(@"IEfficiency", @"IEfficiency"),new InputOutputColumnPair(@"CheeseApl", @"CheeseApl"),new InputOutputColumnPair(@"DownStackAPL", @"DownStackAPL"),new InputOutputColumnPair(@"UpStackAPL", @"UpStackAPL"),new InputOutputColumnPair(@"APL", @"APL"),new InputOutputColumnPair(@"APP", @"APP"),new InputOutputColumnPair(@"KPP", @"KPP"),new InputOutputColumnPair(@"KPS", @"KPS"),new InputOutputColumnPair(@"StackHeight", @"StackHeight"),new InputOutputColumnPair(@"GarbageHeight", @"GarbageHeight"),new InputOutputColumnPair(@"SpikeEfficiency", @"SpikeEfficiency"),new InputOutputColumnPair(@"APM", @"APM"),new InputOutputColumnPair(@"OpenerAPM", @"OpenerAPM"),new InputOutputColumnPair(@"MidGameAPM", @"MidGameAPM"),new InputOutputColumnPair(@"PPS", @"PPS"),new InputOutputColumnPair(@"OpenerPPS", @"OpenerPPS"),new InputOutputColumnPair(@"MidGamePPS", @"MidGamePPS"),new InputOutputColumnPair(@"BTBChainEfficiency", @"BTBChainEfficiency"),new InputOutputColumnPair(@"BTBChain", @"BTBChain"),new InputOutputColumnPair(@"BTBChainAPM", @"BTBChainAPM"),new InputOutputColumnPair(@"BTBChainAttack", @"BTBChainAttack"),new InputOutputColumnPair(@"BTBChainAPP", @"BTBChainAPP"),new InputOutputColumnPair(@"BTBChainEfficiency2", @"BTBChainEfficiency2"),new InputOutputColumnPair(@"ComboChain", @"ComboChain"),new InputOutputColumnPair(@"ComboChainAPM", @"ComboChainAPM"),new InputOutputColumnPair(@"ComboChainAttack", @"ComboChainAttack"),new InputOutputColumnPair(@"ComboChainAPP", @"ComboChainAPP"),new InputOutputColumnPair(@"AverageSpikePotential", @"AverageSpikePotential"),new InputOutputColumnPair(@"AverageDefencePotential", @"AverageDefencePotential"),new InputOutputColumnPair(@"BlockfishScore", @"BlockfishScore"),new InputOutputColumnPair(@"BurstPPS", @"BurstPPS"),new InputOutputColumnPair(@"AttackDelayRate", @"AttackDelayRate"),new InputOutputColumnPair(@"PreAttackDelayRate", @"PreAttackDelayRate")})      
                                    .Append(mlContext.Transforms.Concatenate(@"Features", new []{@"Single",@"Double",@"Triple",@"Quad",@"Tspin",@"TspinSingle",@"TspinDouble",@"TspinTriple",@"TspinMini",@"TspinMiniSingle",@"PerfectClear",@"TEfficiency",@"IEfficiency",@"CheeseApl",@"DownStackAPL",@"UpStackAPL",@"APL",@"APP",@"KPP",@"KPS",@"StackHeight",@"GarbageHeight",@"SpikeEfficiency",@"APM",@"OpenerAPM",@"MidGameAPM",@"PPS",@"OpenerPPS",@"MidGamePPS",@"BTBChainEfficiency",@"BTBChain",@"BTBChainAPM",@"BTBChainAttack",@"BTBChainAPP",@"BTBChainEfficiency2",@"ComboChain",@"ComboChainAPM",@"ComboChainAttack",@"ComboChainAPP",@"AverageSpikePotential",@"AverageDefencePotential",@"BlockfishScore",@"BurstPPS",@"AttackDelayRate",@"PreAttackDelayRate"}))      
                                    .Append(mlContext.Regression.Trainers.LightGbm(new LightGbmRegressionTrainer.Options(){NumberOfLeaves=6077,NumberOfIterations=5351,MinimumExampleCountPerLeaf=32,LearningRate=0.0348779173562721,LabelColumnName=@"Glicko",FeatureColumnName=@"Features",ExampleWeightColumnName=null,Booster=new GradientBooster.Options(){SubsampleFraction=0.00617418716985885,FeatureFraction=0.99999999,L1Regularization=4.75137461226499E-09,L2Regularization=0.212213320351439},MaximumBinCountPerFeature=272}));

            return pipeline;
        }
    }
 }