using System;
using Microsoft.ML;
using Microsoft.ML.Data;
using Microsoft.ML.SamplesUtils;
using Microsoft.ML.Trainers;
 
namespace Samples.Dynamic.Trainers.Regression
{
    public static class OrdinaryLeastSquaresWithOptionsAdvanced
    {
        // This example requires installation of additional nuget package
        // for Microsoft.ML.Mkl.Components at
        // "https://www.nuget.org/packages/Microsoft.ML.Mkl.Components/"
        // In this examples we will use the housing price dataset. The goal is to
        // predict median home value. For more details about this dataset, please
        // see https://archive.ics.uci.edu/ml/machine-learning-databases/housing/
        public static void Example()
        {
            // Downloading a regression dataset from
            // github.com/dotnet/machinelearning
            string dataFile = DatasetUtils.GetHousingRegressionDataset();
 
            // Create a new ML context, for ML.NET operations. It can be used for
            // exception tracking and logging, as well as the source of randomness.
            var mlContext = new MLContext(seed: 3);
 
            // Creating a data loader, based on the format of the data
            // The data is tab separated with all numeric columns.
            // The first column being the label and rest are numeric features
            // Here only seven numeric columns are used as features
            var dataView = mlContext.Data.LoadFromTextFile(dataFile,
                new TextLoader.Options
                {
                    Separators = new[] { '\t' },
                    HasHeader = true,
                    Columns = new[]
               {
                    new TextLoader.Column("Label", DataKind.Single, 0),
                    new TextLoader.Column("Features", DataKind.Single, 1, 6)
                }
                });
 
            //////////////////// Data Preview ////////////////////
            // MedianHomeValue    CrimesPerCapita    PercentResidental    PercentNonRetail    CharlesRiver    NitricOxides    RoomsPerDwelling    PercentPre40s
            // 24.00              0.00632            18.00                2.310               0               0.5380          6.5750              65.20
            // 21.60              0.02731            00.00                7.070               0               0.4690          6.4210              78.90
            // 34.70              0.02729            00.00                7.070               0               0.4690          7.1850              61.10
 
            var split = mlContext.Data.TrainTestSplit(dataView, testFraction: 0.2);
 
            // Create the estimator, here we only need OrdinaryLeastSquares trainer 
            // as data is already processed in a form consumable by the trainer
            var pipeline = mlContext.Regression.Trainers.Ols(
                new OlsTrainer.Options()
                {
                    L2Regularization = 0.1f,
                    CalculateStatistics = false
                });
            var model = pipeline.Fit(split.TrainSet);
 
            // Check the weights that the model learned
            var weightsValues = model.Model.Weights;
            Console.WriteLine($"weight 0 - {weightsValues[0]}");
            // CrimesPerCapita  (weight 0) = -0.1783206
            Console.WriteLine($"weight 3 - {weightsValues[3]}");
            // CharlesRiver (weight 1) = 3.118422
            var dataWithPredictions = model.Transform(split.TestSet);
            var metrics = mlContext.Regression.Evaluate(dataWithPredictions);
 
            PrintMetrics(metrics);
 
            // Expected output:
            //   L1: 4.14
            //   L2: 32.35
            //   LossFunction: 32.35
            //   RMS: 5.69
            //   RSquared: 0.56
        }
 
        public static void PrintMetrics(RegressionMetrics metrics)
        {
            Console.WriteLine("Mean Absolute Error: " + metrics.MeanAbsoluteError);
            Console.WriteLine("Mean Squared Error: " + metrics.MeanSquaredError);
            Console.WriteLine(
                "Root Mean Squared Error: " + metrics.RootMeanSquaredError);
 
            Console.WriteLine("RSquared: " + metrics.RSquared);
        }
    }
}