private static void DisplayTrainingResults(string name, LearnerMatrix learnerMatrix,
Frame <int, string> testData)
{
Console.WriteLine($"{name}:");
var labels = testData["median_house_value"].Values.ToArray();
var predictions = learnerMatrix.Transform(testData);
var rmse = Math.Sqrt(new SquareLoss(labels).Loss(predictions));
var mae = new AbsoluteLoss(labels.ToJagged()).Loss(predictions.ToJagged()) / labels.Length;
var range = Math.Abs(labels.Max() - labels.Min());
Console.WriteLine($"Label range: {range}");
Console.WriteLine($"RMSE: {rmse} {rmse / range * 100:0.00}%");
Console.WriteLine($"MAE: {mae} {mae / range * 100:0.00}%");
Console.WriteLine();
}
static void Main(string[] args)
{
Console.WriteLine("Loading data....");
var path = Path.GetFullPath(Path.Combine(AppDomain.CurrentDomain.BaseDirectory, @"..\..\..\..\california_housing.csv"));
var housing = Frame.ReadCsv(path, separators: ",");
housing = housing.Where(kv => ((decimal)kv.Value["median_house_value"]) < 500000);
// shuffle row indices
var rnd = new Random();
var indices = Enumerable.Range(0, housing.Rows.KeyCount).OrderBy(v => rnd.NextDouble());
// shuffle the frame using the indices
housing = housing.IndexRowsWith(indices).SortRowsByKey();
// convert the house value range to thousands
housing["median_house_value"] /= 1000;
// create the rooms_per_person feature
housing.AddColumn("rooms_per_person",
(housing["total_rooms"] / housing["population"]).Select(v => v.Value <= 4.0 ? v.Value : 4.0));
// create training, validation, and test frames
var training = housing.Rows[Enumerable.Range(0, 12000)];
var validation = housing.Rows[Enumerable.Range(12000, 2500)];
var test = housing.Rows[Enumerable.Range(14500, 2500)];
//Setup of learner matrix
var columns = new List <string>();
columns.Add("median_income");
columns.Add("rooms_per_person");
var learnerMatrix = new LearnerMatrix(training, columns);
//Verification of results
DisplayTrainingResults("Training data", learnerMatrix, training);
DisplayTrainingResults("Validation data", learnerMatrix, validation);
DisplayTrainingResults("Test data", learnerMatrix, test);
Console.ReadLine();
}
