Added CreateEngine() in ML

Game1/Sources/ML/MLModel.cs

@@ -1,17 +1,11 @@
 using System;
 using System.IO;
-using System.Collections.Generic;
-using System.Diagnostics;
-using System.ComponentModel;
 using System.Linq;
 using System.Text;
 using System.Threading.Tasks;
 using Microsoft.ML;
 using Microsoft.ML.Data;
 using Microsoft.ML.Trainers.LightGbm;
-using Microsoft.Xna.Framework;
-using Microsoft.Xna.Framework.Content;
-using Game1;
 
 namespace Game1.Sources.ML
 {
@@ -25,6 +19,7 @@ namespace Game1.Sources.ML
         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";
 
+        // Loading data, creatin and saving ML model for smaller dataset (100)
         public static void CreateModel()
         {
             
@@ -40,6 +35,7 @@ namespace Game1.Sources.ML
             SaveModel(mlContext, MLModel, modelpath, trainingDataView.Schema);
         }
 
+        // ... for bigger dataset (1600)
         public static void CreateBigModel()
         {
             
@@ -55,6 +51,7 @@ namespace Game1.Sources.ML
             SaveModel(mlContext, MLModel, modelpathBig, trainingDataView.Schema);
         }
 
+        // Building and training ML model, very small dataset (100 entries)
         public static ITransformer BuildAndTrain(MLContext mLContext, IDataView trainingDataView, ModelInput sample, string reportPath)
         {
 
@@ -84,25 +81,12 @@ namespace Game1.Sources.ML
                     .Append(mlContext.Transforms.Conversion.MapKeyToValue("PredictedLabel", "PredictedLabel"));
 
             Evaluate(mlContext, trainingDataView, pipeline, 10, reportPath, "Fertilizer_NameF");
-
             ITransformer MLModel = pipeline.Fit(trainingDataView);
-            var predEng = mlContext.Model.CreatePredictionEngine<ModelInput, ModelOutput>(MLModel);
-            ModelOutput predResult = predEng.Predict(sample);
-            ModelOutput predResult2 = predEng.Predict(new ModelInput()
-            {
-                Temperature = 24,
-                Humidity = 59,
-                Moisture = 57,
-                Soil_Type = "Loamy",
-                Crop_Type = "Sugarcane",
-                Nitrogen = 34,
-                Potassium = 0,
-                Phosporous = 3
-            });
 
             return MLModel;
         }
 
+        //Building and training ML model, moderate size dataset (1600 entries)
         public static ITransformer BuildAndTrain(MLContext mLContext, IDataView trainingDataView, BigModelInput sample, string reportPath)
         {
 
@@ -130,10 +114,7 @@ namespace Game1.Sources.ML
                     .Append(mlContext.Transforms.Conversion.MapKeyToValue("PredictedLabel", "PredictedLabel"));
 
             Evaluate(mlContext, trainingDataView, pipeline, 8, reportPath, "ClassF");
-
             ITransformer MLModel = pipeline.Fit(trainingDataView);
-            var predEng = mlContext.Model.CreatePredictionEngine<BigModelInput, BigModelOutput>(MLModel);
-            BigModelOutput predResult = predEng.Predict(sample);
 
             return MLModel;
         }
@@ -144,6 +125,7 @@ namespace Game1.Sources.ML
             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);
@@ -163,6 +145,7 @@ namespace Game1.Sources.ML
             report.Close();
         }
 
+
         public static void SaveModel(MLContext mlContext, ITransformer Model, string modelPath, DataViewSchema modelInputSchema)
         {
             mlContext.Model.Save(Model, modelInputSchema, modelPath);
@@ -176,5 +159,11 @@ namespace Game1.Sources.ML
                 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);
+        }
+
     }
 }