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

Game1/Content/ML/report_Joel_area

@@ -0,0 +1,2 @@
+Mean Absolute Error: 187.060835104336
+R Squared: 0.913526230109177

Game1/Game1.cs

@@ -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();
 
 
 

Game1/Game1.csproj

@@ -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" />

Game1/Sources/ML/MLModel.cs

@@ -11,11 +11,16 @@ using Microsoft.ML.Trainers.LightGbm;
 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";
+
     /*
-    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";
@@ -23,6 +28,7 @@ class MLModel
    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()

Game1/Sources/ML_Joel/DataModel/InputArea.cs

@@ -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; }
+    }
+}

Game1/Sources/ML_Joel/DataModel/OutputArea.cs

@@ -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; }
+
+    }
+}

Game1/Sources/ML_Joel/Engine.cs

@@ -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 CreateModelArea()
+        {
+            Model.CreateModelArea();
+        }
+
         public static void init()
         {
-            PredictionEngine = MLModel.CreateEngine();
+            PredictionEngine = Model.CreateEngine();
         }
 
-        public static string PredictFertilizer(Crops crop, CropTypes cropTypes)
+        public static void initArea()
         {
-            ModelInput modelInput = new ModelInput
+            PredictionEngineArea = Model.CreateEngineArea();
+        }
+
+        public static float PredictProductionwithRainfall(Crops crop, CropTypes cropTypes)
         {
-                Temperature = crop.getSoilProperties().Temperature,
+            InputArea modelInput = new InputArea
-                Humidity = crop.getSoilProperties().Humidity,
+            {
-                Moisture = crop.getSoilProperties().Moisture,
+                //Season = ,
-                Soil_Type = crop.getSoilProperties().soilType,
+                //Area = crop.getSoilProperties().Area,
-                Crop_Type = cropTypes.CropName,
+                Rainfall = crop.getSoilProperties().Humidity,
-                Nitrogen = crop.getSoilProperties().Nitrogen,
-                Potassium = crop.getSoilProperties().Potassium,
-                Phosporous = crop.getSoilProperties().Phosphorous
             };
 
-            return PredictionEngine.Predict(modelInput).Prediction;
+            return PredictionEngineArea.Predict(modelInput).Score;
         }
     }
 }

Game1/Sources/ML_Joel/Model.cs

@@ -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);
+        }
+
     }
 }