Ordina-ML.Net_Recommender_System

Program.cs

using BookRecommenderShared.Configuration;
using Microsoft.ML;
using Microsoft.ML.Data;
using System;
using System.IO;

namespace BookRecommenderTrainer
{
    class Program
    {
        static void Main(string[] args)
        {
            var mlContext = new MLContext();

            var reader = mlContext.Data.CreateTextReader(new TextLoader.Arguments()
            {
                Separator = ",",
                HasHeader = true,
                /* 
                 * Define the columns to read be from the csv.
                 * The indeces are based on the position in the csv file and will receive a new index.
                 * DataKind is defined as R4 (floating point) instead of integer
                 * Computers are optimized for processing floating point calculations 
                 * so this will not create performance issues but mitigates some possible issues
                 */
                Column = new[]
                {
                    new TextLoader.Column(Labels.Label, DataKind.R4, 1), // new index 0
                    new TextLoader.Column(Labels.User, DataKind.R4, 2),  // new index 1
                    new TextLoader.Column(Labels.BookId, DataKind.R4, 3) // new index 2
                }
            });

            var data = reader.Read(Path.Combine(Environment.CurrentDirectory, Filenames.DataFolder, Filenames.RatingsDataset));
            
            // Split our dataset in 80% training data and 20% testing data to evaluate our model
            var (trainData, testData) = mlContext.BinaryClassification.TrainTestSplit(data, testFraction: 0.2);
        }
    }
}