ML

Game1/Game1.csproj

@@ -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" />

Game1/Sources/ML_Joel/DataModel/Input.cs

@@ -0,0 +1,24 @@
+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; }
+    }
+}

Game1/Sources/ML_Joel/DataModel/Output.cs

@@ -0,0 +1,15 @@
+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; }
+    }
+}

Game1/Sources/ML_Joel/Model.cs

@@ -0,0 +1,114 @@
+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);
+        }
+
+    }
+}