using System;
using System.Linq;
using Microsoft.ML;
using Microsoft.ML.Data;
 
namespace Samples.Dynamic.Trainers.Regression
{
    class LightGbmAdvanced
    {
        // This example requires installation of additional NuGet package
        // for Microsoft.ML.LightGBM
        // at https://www.nuget.org/packages/Microsoft.ML.LightGbm/
        public static void Example()
        {
            // 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();
 
            // Download and load the housing dataset into an IDataView.
            var dataView = Microsoft.ML.SamplesUtils.DatasetUtils
                .LoadHousingRegressionDataset(mlContext);
 
            //////////////////// Data Preview ////////////////////
            /// Only 6 columns are displayed here.
            // 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: 0.1);
 
            // Create the estimator, here we only need LightGbm trainer
            // as data is already processed in a form consumable by the trainer.
            var labelName = "MedianHomeValue";
            var featureNames = dataView.Schema
                .Select(column => column.Name) // Get the column names
                .Where(name => name != labelName) // Drop the Label
                .ToArray();
            var pipeline = mlContext.Transforms.Concatenate("Features",
                               featureNames)
                               .Append(mlContext.Regression.Trainers.LightGbm(
                               labelColumnName: labelName,
                               numberOfLeaves: 4,
                               minimumExampleCountPerLeaf: 6,
                               learningRate: 0.001));
 
            // Fit this pipeline to the training data.
            var model = pipeline.Fit(split.TrainSet);
 
            // Get the feature importance based on the information gain used during
            // training.
            VBuffer<float> weights = default;
            model.LastTransformer.Model.GetFeatureWeights(ref weights);
            var weightsValues = weights.DenseValues().ToArray();
            Console.WriteLine($"weight 0 - {weightsValues[0]}");
            // CrimesPerCapita  (weight 0) = 0.1898361
            Console.WriteLine($"weight 5 - {weightsValues[5]}");
            // RoomsPerDwelling (weight 5) = 1
 
            // Evaluate how the model is doing on the test data.
            var dataWithPredictions = model.Transform(split.TestSet);
            var metrics = mlContext.Regression.Evaluate(
                dataWithPredictions,
                labelColumnName: labelName);
            PrintMetrics(metrics);
 
            // Expected output
            //   L1: 4.97
            //   L2: 51.37
            //   LossFunction: 51.37
            //   RMS: 7.17
            //   RSquared: 0.08
        }
 
        private 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);
        }
    }
}