PotatoPlan/Game1/Sources/ML/MLModel.cs

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C#
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using System;
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
{
class MLModel
{
private static MLContext mlContext = new MLContext(seed: 1);
private static string path = "C:/Users/Oskar/source/repos/PotatoPlanFinal/Game1/Content/ML/Fertilizer_Prediction.csv";
private static string modelpath = "C:/Users/Oskar/source/repos/PotatoPlanFinal/Game1/Content/ML/MLmodel";
private static string report = "C:/Users/Oskar/source/repos/PotatoPlanFinal/Game1/Content/ML/report";
private static string pathBig = "C:/Users/Oskar/source/repos/PotatoPlanFinal/Game1/Content/ML/BigFertPredict.csv";
private static string modelpathBig = "C:/Users/Oskar/source/repos/PotatoPlanFinal/Game1/Content/ML/MLmodelBig";
private static string reportBig = "C:/Users/Oskar/source/repos/PotatoPlanFinal/Game1/Content/ML/report_BigModel";
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// Loading data, creatin and saving ML model for smaller dataset (100)
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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);
}
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// ... for bigger dataset (1600)
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public static void CreateBigModel()
{
IDataView trainingDataView = mlContext.Data.LoadFromTextFile<BigModelInput>(
path: pathBig,
hasHeader: true,
separatorChar: ',',
allowQuoting: true,
allowSparse: false);
BigModelInput sample = mlContext.Data.CreateEnumerable<BigModelInput>(trainingDataView, false).ElementAt(0);
ITransformer MLModel = BuildAndTrain(mlContext, trainingDataView, sample, reportBig);
SaveModel(mlContext, MLModel, modelpathBig, trainingDataView.Schema);
}
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// Building and training ML model, very small dataset (100 entries)
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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.NormalizeMinMax("Features"))
.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;
}
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//Building and training ML model, moderate size dataset (1600 entries)
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public static ITransformer BuildAndTrain(MLContext mLContext, IDataView trainingDataView, BigModelInput sample, string reportPath)
{
var options = new LightGbmMulticlassTrainer.Options
{
MaximumBinCountPerFeature = 10,
LearningRate = 0.001,
NumberOfIterations = 10000,
NumberOfLeaves = 12,
LabelColumnName = "ClassF",
FeatureColumnName = "Features",
Booster = new DartBooster.Options()
{
MaximumTreeDepth = 12
}
};
var pipeline = mlContext.Transforms
.Concatenate("Features", "Ca", "Mg", "K", "S", "N", "Lime", "C", "P", "Moisture")
.Append(mLContext.Transforms.NormalizeMinMax("Features"))
.Append(mlContext.Transforms.Conversion.MapValueToKey("ClassF", "Class"), TransformerScope.TrainTest)
.AppendCacheCheckpoint(mLContext)
.Append(mLContext.MulticlassClassification.Trainers.LightGbm(options))
.Append(mlContext.Transforms.Conversion.MapKeyToValue("PredictedLabel", "PredictedLabel"));
Evaluate(mlContext, trainingDataView, pipeline, 8, reportPath, "ClassF");
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;
}
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// Evaluate and save results to a text file
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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();
}
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public static void SaveModel(MLContext mlContext, ITransformer Model, string modelPath, DataViewSchema modelInputSchema)
{
mlContext.Model.Save(Model, modelInputSchema, modelPath);
}
public static ITransformer LoadModel(bool isBig)
{
if (isBig)
return mlContext.Model.Load(modelpathBig, out DataViewSchema inputSchema);
else
return mlContext.Model.Load(modelpath, out DataViewSchema inputSchema);
}
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public static Microsoft.ML.PredictionEngine<ModelInput, ModelOutput> CreateEngine()
{
ITransformer mlModel = LoadModel(false);
return mlContext.Model.CreatePredictionEngine<ModelInput, ModelOutput>(mlModel);
}
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}
}