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/Oskar/source/repos/PotatoPlanFinal/Game1/Content/ML/Rainfall.csv";
        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";

        /*
        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";

        private static string path_area = "C:/Users/Joel/source/repos/Oskars Repo/Game1/Content/ML/Rainfall_area.csv";
        private static string modelpath_area = "C:/Users/Joel/source/repos/Oskars Repo/Game1/Content/ML/MLmodel_Joel_area";
        private static string report_area = "C:/Users/Joel/source/repos/Oskars Repo/Game1/Content/ML/report_Joel_area";
        */
        // Loading data, creatin and saving ML model for smaller dataset (100)
        public static void CreateModel()
        {

            IDataView trainingDataView = mlContext.Data.LoadFromTextFile<Input>(
                path: path,
                hasHeader: true,
                separatorChar: ',',
                allowQuoting: true,
                allowSparse: false);

            var splitData = mlContext.Data.TrainTestSplit(trainingDataView, testFraction: 0.2);
            trainingDataView = splitData.TrainSet;
            IDataView testDataView = splitData.TestSet;
            Input sample = mlContext.Data.CreateEnumerable<Input>(trainingDataView, false).ElementAt(0);
            ITransformer MLModel = BuildAndTrain(mlContext, trainingDataView, testDataView, sample, report);
            SaveModel(mlContext, MLModel, modelpath, trainingDataView.Schema);
        }

        // Building and training ML model
        public static ITransformer BuildAndTrain(MLContext mLContext, IDataView trainingDataView, IDataView testDataView, Input sample, 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", "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 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);
            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 eval = mlContext.Regression.Evaluate(trainingDataView, labelColumnName: labelColumnName);

            var report = File.CreateText(reportPath);
            report.Write("Mean Absolute Error: " + eval.MeanAbsoluteError + '\n' + "R Squared: " + eval.RSquared, 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()
        {
             return mlContext.Model.Load(modelpath, out DataViewSchema inputSchema);
        }

        public static Microsoft.ML.PredictionEngine<Input, Output> CreateEngine()
        {
            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);
        }

    }
}