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ML

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BOTLester 2020-05-23 20:39:05 +02:00
parent a656d768f6
commit 2a7c06fbd2
4 changed files with 156 additions and 0 deletions
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3
Game1/Game1.csproj
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@ -90,6 +90,9 @@
<Compile Include="Sources\Crops\PerlinNoise.cs" />
<Compile Include="Sources\Crops\SoilProperties.cs" />
<Compile Include="Sources\ML\Engine.cs" />
<Compile Include="Sources\ML_Joel\DataModel\Input.cs" />
<Compile Include="Sources\ML_Joel\DataModel\Output.cs" />
<Compile Include="Sources\ML_Joel\Model.cs" />
<Compile Include="Sources\Objects\DayNightCycle.cs" />
<Compile Include="Sources\Objects\Fertilizer.cs" />
<Compile Include="Sources\Objects\FertilizerHolder.cs" />

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Game1/Sources/ML_Joel/DataModel/Input.cs Normal file
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using System;
using System.Collections.Generic;
using System.Linq;
using System.Text;
using System.Threading.Tasks;
using Microsoft.ML.Data;
namespace Game1.Sources.ML_Joel
{
class Input
{
[ColumnName("Season"), LoadColumn(0)]
public String Season { get; set; }
[ColumnName("Crop"), LoadColumn(1)]
public String Crop { get; set; }
[ColumnName("Rainfall"), LoadColumn(2)]
public float Rainfall { get; set; }
[ColumnName("Production"), LoadColumn(3)]
public float Production { get; set; }
}
}

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Game1/Sources/ML_Joel/DataModel/Output.cs Normal file
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using System;
using System.Collections.Generic;
using System.Linq;
using System.Text;
using System.Threading.Tasks;
using Microsoft.ML.Data;
namespace Game1.Sources.ML_Joel
{
class Output
{
[ColumnName("PredictedLabel")]
public float Prediction { get; set; }
}
}

114
Game1/Sources/ML_Joel/Model.cs Normal file
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using System;
using System.Collections.Generic;
using System.IO;
using System.Linq;
using System.Text;
using System.Threading.Tasks;
using Microsoft.ML;
using Microsoft.ML.Data;
using Microsoft.ML.Trainers.LightGbm;
namespace Game1.Sources.ML_Joel
{
class Model
{
private static MLContext mlContext = new MLContext(seed: 1);
private static string path = "C:/Users/Joel/source/repos/Oskars Repo/Game1/Content/ML/Rainfall.csv";
private static string modelpath = "C:/Users/Joel/source/repos/Oskars Repo/Game1/Content/ML/MLmodel_Joel";
private static string report = "C:/Users/Joel/source/repos/Oskars Repo/Game1/Content/ML/report_Joel";
// Loading data, creatin and saving ML model for smaller dataset (100)
public static void CreateModel()
{
IDataView trainingDataView = mlContext.Data.LoadFromTextFile<ModelInput>(
path: path,
hasHeader: true,
separatorChar: ',',
allowQuoting: true,
allowSparse: false);
ModelInput sample = mlContext.Data.CreateEnumerable<ModelInput>(trainingDataView, false).ElementAt(0);
ITransformer MLModel = BuildAndTrain(mlContext, trainingDataView, sample, report);
SaveModel(mlContext, MLModel, modelpath, trainingDataView.Schema);
}
// Building and training ML model, very small dataset (100 entries)
public static ITransformer BuildAndTrain(MLContext mLContext, IDataView trainingDataView, ModelInput sample, string reportPath)
{
var options = new LightGbmMulticlassTrainer.Options
{
MaximumBinCountPerFeature = 8,
LearningRate = 0.00025,
NumberOfIterations = 40000,
NumberOfLeaves = 10,
LabelColumnName = "Fertilizer_NameF",
FeatureColumnName = "Features",
Booster = new DartBooster.Options()
{
MaximumTreeDepth = 10
}
};
var pipeline = mlContext.Transforms
.Text.FeaturizeText("Soil_TypeF", "Soil_Type")
.Append(mlContext.Transforms.Text.FeaturizeText("Crop_TypeF", "Crop_Type"))
.Append(mlContext.Transforms.Concatenate("Features", "Temperature", "Humidity", "Moisture", "Soil_TypeF", "Crop_TypeF", "Nitrogen", "Potassium", "Phosphorous"))
.Append(mlContext.Transforms.Conversion.MapValueToKey("Fertilizer_NameF", "Fertilizer_Name"), TransformerScope.TrainTest)
.AppendCacheCheckpoint(mLContext)
.Append(mLContext.MulticlassClassification.Trainers.LightGbm(options))
.Append(mlContext.Transforms.Conversion.MapKeyToValue("PredictedLabel", "PredictedLabel"));
Evaluate(mlContext, trainingDataView, pipeline, 10, reportPath, "Fertilizer_NameF");
ITransformer MLModel = pipeline.Fit(trainingDataView);
return MLModel;
}
public static ITransformer TrainModel(MLContext mlContext, IDataView trainingDataView, IEstimator<ITransformer> trainingPipeline)
{
ITransformer model = trainingPipeline.Fit(trainingDataView);
return model;
}
// Evaluate and save results to a text file
public static void Evaluate(MLContext mlContext, IDataView trainingDataView, IEstimator<ITransformer> trainingPipeline, int folds, string reportPath, string labelColumnName)
{
var crossVal = mlContext.MulticlassClassification.CrossValidate(trainingDataView, trainingPipeline, numberOfFolds: folds, labelColumnName: labelColumnName);
var metricsInMultipleFolds = crossVal.Select(r => r.Metrics);
var MicroAccuracyValues = metricsInMultipleFolds.Select(m => m.MicroAccuracy);
var LogLossValues = metricsInMultipleFolds.Select(m => m.LogLoss);
var LogLossReductionValues = metricsInMultipleFolds.Select(m => m.LogLossReduction);
string MicroAccuracyAverage = MicroAccuracyValues.Average().ToString("0.######");
string LogLossAvg = LogLossValues.Average().ToString("0.######");
string LogLossReductionAvg = LogLossReductionValues.Average().ToString("0.######");
var report = File.CreateText(reportPath);
report.Write("Micro Accuracy: " + MicroAccuracyAverage + '\n' + "LogLoss Average: " + LogLossAvg + '\n' + "LogLoss Reduction: " + LogLossReductionAvg, 0, 0);
report.Flush();
report.Close();
}
public static void SaveModel(MLContext mlContext, ITransformer Model, string modelPath, DataViewSchema modelInputSchema)
{
mlContext.Model.Save(Model, modelInputSchema, modelPath);
}
public static ITransformer LoadModel(bool isBig)
{
return mlContext.Model.Load(modelpath, out DataViewSchema inputSchema);
}
public static Microsoft.ML.PredictionEngine<ModelInput, ModelOutput> CreateEngine()
{
ITransformer mlModel = LoadModel(false);
return mlContext.Model.CreatePredictionEngine<ModelInput, ModelOutput>(mlModel);
}
}
}