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PotatoPlan
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Merge branch 'Joel-ML' of https://git.wmi.amu.edu.pl/s425077/PotatoPlan into Joel-ML

This commit is contained in:
Joel 2020-05-24 21:22:26 +02:00
parent 14e0e92f9b c858261c89
commit 10e58f984f
10 changed files with 1048735 additions and 27 deletions
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Game1/Content/ML/MLmodel_Joel_area Normal file
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Game1/Content/ML/Rainfall_area.csv Normal file
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Game1/Content/ML/report_Joel_area Normal file
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@ -0,0 +1,2 @@
Mean Absolute Error: 187.060835104336
R Squared: 0.913526230109177

2
Game1/Game1.cs
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@ -75,7 +75,9 @@ namespace Game1
cropTypesNames[11] = "Wheat";
Engine.init();
Sources.ML_Joel.Engine.initArea();
//Sources.ML_Joel.Engine.CreateModel();
//Sources.ML_Joel.Engine.CreateModelArea();

2
Game1/Game1.csproj
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@ -90,7 +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\InputArea.cs" />
<Compile Include="Sources\ML_Joel\DataModel\Input.cs" />
<Compile Include="Sources\ML_Joel\DataModel\OutputArea.cs" />
<Compile Include="Sources\ML_Joel\DataModel\Output.cs" />
<Compile Include="Sources\ML_Joel\Engine.cs" />
<Compile Include="Sources\ML_Joel\Model.cs" />

28
Game1/Sources/ML/MLModel.cs
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@ -11,18 +11,24 @@ using Microsoft.ML.Trainers.LightGbm;
class MLModel
{
private static MLContext mlContext = new MLContext(seed: 1);
/*
private static string path = "C:/Users/Joel/source/repos/Oskars Repo/Game1/Content/ML/Fertilizer_Prediction.csv";
private static string modelpath = "C:/Users/Joel/source/repos/Oskars Repo/Game1/Content/ML/MLmodel";
private static string report = "C:/Users/Joel/source/repos/Oskars Repo/Game1/Content/ML/report";
*/
private static string pathBig = "C:/Users/Joel/source/repos/Oskars Repo/Game1/Content/ML/BigFertPredict.csv";
private static string modelpathBig = "C:/Users/Joel/source/repos/Oskars Repo/Game1/Content/ML/MLmodelBig";
private static string reportBig = "C:/Users/Joel/source/repos/Oskars Repo/Game1/Content/ML/report_BigModel";
private static string path = "C:/Users/Joel/source/repos/Oskars Repo/Game1/Content/ML/Fertilizer_Prediction.csv";
private static string modelpath = "C:/Users/Joel/source/repos/Oskars Repo/Game1/Content/ML/MLmodel";
private static string report = "C:/Users/Joel/source/repos/Oskars Repo/Game1/Content/ML/report";
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";
/*
private static string pathBig = "C:/Users/Joel/source/repos/Oskars Repo/Game1/Content/ML/BigFertPredict.csv";
private static string modelpathBig = "C:/Users/Joel/source/repos/Oskars Repo/Game1/Content/ML/MLmodelBig";
private static string reportBig = "C:/Users/Joel/source/repos/Oskars Repo/Game1/Content/ML/report_BigModel";
private static string path = "C:/Users/Joel/source/repos/Oskars Repo/Game1/Content/ML/Fertilizer_Prediction.csv";
private static string modelpath = "C:/Users/Joel/source/repos/Oskars Repo/Game1/Content/ML/MLmodel";
private static string report = "C:/Users/Joel/source/repos/Oskars Repo/Game1/Content/ML/report";
*/
// Loading data, creatin and saving ML model for smaller dataset (100)
public static void CreateModel()

27
Game1/Sources/ML_Joel/DataModel/InputArea.cs Normal file
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@ -0,0 +1,27 @@
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 InputArea
{
[ColumnName("Season"), LoadColumn(0)]
public String Season { get; set; }
[ColumnName("Crop"), LoadColumn(1)]
public String Crop { get; set; }
[ColumnName("Area"), LoadColumn(2)]
public float Area { get; set; }
[ColumnName("Rainfall"), LoadColumn(3)]
public float Rainfall { get; set; }
[ColumnName("Production"), LoadColumn(4)]
public float Production { get; set; }
}
}

20
Game1/Sources/ML_Joel/DataModel/OutputArea.cs Normal file
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@ -0,0 +1,20 @@
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 OutputArea
{
//[ColumnName("PredictedLabel")]
public float Prediction { get; set; }
public float Score { get; set; }
//[ColumnName("Score")]
// public float[] Score { get; set; }
}
}

35
Game1/Sources/ML_Joel/Engine.cs
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@ -11,33 +11,40 @@ namespace Game1.Sources.ML_Joel
static class Engine
{
private static MLContext mlContext = new MLContext(seed: 1);
private static PredictionEngine<ModelInput, ModelOutput> PredictionEngine;
private static PredictionEngine<Input, Output> PredictionEngine;
private static PredictionEngine<InputArea, OutputArea> PredictionEngineArea;
public static void CreateModel()
{
Model.CreateModel();
}
public static void init()
public static void CreateModelArea()
{
PredictionEngine = MLModel.CreateEngine();
Model.CreateModelArea();
}
public static string PredictFertilizer(Crops crop, CropTypes cropTypes)
public static void init()
{
ModelInput modelInput = new ModelInput
PredictionEngine = Model.CreateEngine();
}
public static void initArea()
{
PredictionEngineArea = Model.CreateEngineArea();
}
public static float PredictProductionwithRainfall(Crops crop, CropTypes cropTypes)
{
InputArea modelInput = new InputArea
{
Temperature = crop.getSoilProperties().Temperature,
Humidity = crop.getSoilProperties().Humidity,
Moisture = crop.getSoilProperties().Moisture,
Soil_Type = crop.getSoilProperties().soilType,
Crop_Type = cropTypes.CropName,
Nitrogen = crop.getSoilProperties().Nitrogen,
Potassium = crop.getSoilProperties().Potassium,
Phosporous = crop.getSoilProperties().Phosphorous
//Season = ,
//Area = crop.getSoilProperties().Area,
Rainfall = crop.getSoilProperties().Humidity,
};
return PredictionEngine.Predict(modelInput).Prediction;
return PredictionEngineArea.Predict(modelInput).Score;
}
}
}

70
Game1/Sources/ML_Joel/Model.cs
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@ -22,6 +22,10 @@ namespace Game1.Sources.ML_Joel
private static string modelpath = "C:/Users/Oskar/source/repos/PotatoPlanFinal/Game1/Content/ML/MLmodel_Joel";
private static string report = "C:/Users/Oskar/source/repos/PotatoPlanFinal/Game1/Content/ML/report_Joel";
private static string path_area = "C:/Users/Oskar/source/repos/PotatoPlanFinal/Game1/Content/ML/Rainfall_area.csv";
private static string modelpath_area = "C:/Users/Oskar/source/repos/PotatoPlanFinal/Game1/Content/ML/MLmodel_Joel_area";
private static string report_area = "C:/Users/Oskar/source/repos/PotatoPlanFinal/Game1/Content/ML/report_Joel_area";
// Loading data, creatin and saving ML model for smaller dataset (100)
public static void CreateModel()
{
@ -75,6 +79,57 @@ namespace Game1.Sources.ML_Joel
return MLModel;
}
public static void CreateModelArea()
{
IDataView trainingDataView = mlContext.Data.LoadFromTextFile<InputArea>(
path: path_area,
hasHeader: true,
separatorChar: ',',
allowQuoting: true,
allowSparse: false);
var splitData = mlContext.Data.TrainTestSplit(trainingDataView, testFraction: 0.2);
trainingDataView = splitData.TrainSet;
IDataView testDataView = splitData.TestSet;
ITransformer MLModel = BuildAndTrainArea(mlContext, trainingDataView, testDataView, report_area);
SaveModel(mlContext, MLModel, modelpath_area, trainingDataView.Schema);
}
// Building and training ML model
public static ITransformer BuildAndTrainArea(MLContext mLContext, IDataView trainingDataView, IDataView testDataView, string reportPath)
{
var options = new LightGbmRegressionTrainer.Options
{
MaximumBinCountPerFeature = 40,
LearningRate = 0.00020,
NumberOfIterations = 20000,
NumberOfLeaves = 55,
LabelColumnName = "Production",
FeatureColumnName = "Features",
Booster = new DartBooster.Options()
{
MaximumTreeDepth = 10
}
};
var pipeline = mlContext.Transforms
.Text.FeaturizeText("SeasonF", "Season")
.Append(mlContext.Transforms.Text.FeaturizeText("CropF", "Crop"))
.Append(mlContext.Transforms.Concatenate("Features", "SeasonF", "CropF", "Area", "Rainfall"))
.AppendCacheCheckpoint(mLContext)
.Append(mLContext.Regression.Trainers.LightGbm(options));
ITransformer MLModel = pipeline.Fit(trainingDataView);
var testEval = MLModel.Transform(testDataView);
Evaluate(mlContext, testEval, pipeline, 10, reportPath, "Production");
return MLModel;
}
public static ITransformer TrainModel(MLContext mlContext, IDataView trainingDataView, IEstimator<ITransformer> trainingPipeline)
{
ITransformer model = trainingPipeline.Fit(trainingDataView);
@ -98,16 +153,27 @@ namespace Game1.Sources.ML_Joel
mlContext.Model.Save(Model, modelInputSchema, modelPath);
}
public static ITransformer LoadModel(bool isBig)
public static ITransformer LoadModel()
{
return mlContext.Model.Load(modelpath, out DataViewSchema inputSchema);
}
public static Microsoft.ML.PredictionEngine<Input, Output> CreateEngine()
{
ITransformer mlModel = LoadModel(false);
ITransformer mlModel = LoadModel();
return mlContext.Model.CreatePredictionEngine<Input, Output>(mlModel);
}
public static ITransformer LoadModelArea()
{
return mlContext.Model.Load(modelpath_area, out DataViewSchema inputSchema);
}
public static Microsoft.ML.PredictionEngine<InputArea, OutputArea> CreateEngineArea()
{
ITransformer mlModel = LoadModelArea();
return mlContext.Model.CreatePredictionEngine<InputArea, OutputArea>(mlModel);
}
}
}