using Microsoft.ML;
using Microsoft.ML.AutoML;
using Microsoft.ML.Data;
using Microsoft.ML.Trainers.FastTree;
using System;
using System.Collections.Generic;
using System.Linq;
using static Microsoft.ML.DataOperationsCatalog;

namespace AutoML
{
    class Program
    {
        static void Main(string[] args)
        {
            var mlContext = new MLContext(seed: 0);

            var examples = GenerateData(100);

            var dataview = mlContext.Data.LoadFromEnumerable(examples);

            TrainTestData trainTestSplit = mlContext.Data.TrainTestSplit(dataview, testFraction: 0.1, samplingKeyColumnName: null);
            IDataView trainingData = trainTestSplit.TrainSet;
            IDataView testData = trainTestSplit.TestSet;

            ITransformer model = TrainRegresionAutoML(trainingData);
            ReportOnFeatureImportance(mlContext, model, dataview);            

            OutputData prediction = PredictRegresinAutoML<InputData,OutputData>(model, new InputData(){A = 6, B = 6});          
        }

        static ITransformer TrainRegresionAutoML(IDataView trainData)
        {
            var mlContext = new MLContext(seed: 0);

            var settings = new RegressionExperimentSettings
            {
                MaxExperimentTimeInSeconds = 10, // In Second
                OptimizingMetric = RegressionMetric.RSquared,
                CacheDirectory = null
            };

            var experiment = mlContext.Auto().CreateRegressionExperiment(settings);
            
            var model = experiment.Execute(trainData);            


            return model.BestRun.Model;
        }

        private static void ReportOnFeatureImportance(MLContext context, ITransformer model, IDataView data)
        {            
            // Need to cast from the ITransformer interface to gain access to the LastTransformer property.
            var typedModel = (TransformerChain<IPredictionTransformer<object>>)model;
            var modelParams = typedModel.LastTransformer.Model as FastTreeRegressionModelParameters;
            var weights = new VBuffer<float>();
            modelParams.GetFeatureWeights(ref weights);            
        }

        static TDst PredictRegresinAutoML<TSrc, TDst>(ITransformer model, TSrc inputData) 
            where TSrc : class
            where TDst : class, new()
        {
            var mlContext = new MLContext(seed: 0);

            var predictor = mlContext.Model.CreatePredictionEngine<TSrc, TDst>(model);
            return predictor.Predict(inputData);
        }      

        private static IEnumerable<InputData> GenerateData(int count,
            int seed = 0)

        {
            for (int i = 0; i < count; i++)
            {
                for (int ii = 0; ii < count; ii++)
                {
                    yield return new InputData
                    {
                        A = i,
                        B = ii,
                        Value = i * ii
                    };
                }
            }
        }       
    }

    public class InputData
    {     
        public float A { get; set; }
        
        public float B { get; set; }

        [ColumnName("Label")]
        public float Value { get; set; }
    }

    public class OutputData
    {
        [ColumnName("Score")]
        public float Result;
    }

    public class FeatureImportance
    {
        public string Name { get; set; }

        public double RSquaredMean { get; set; }

        public double CorrelationCoefficient { get; set; }
    }
}