private static int RunTrain(TrainOptions opts)
{
var trainingPath = opts.Input;
var testingPath = opts.Test;
string resultFileName = Path.Combine(opts.Output, "ml.result");
MfProblem training = IOHelper.LoadDataFromTextFile(trainingPath);
MfProblem testing = !string.IsNullOrEmpty(testingPath) ? IOHelper.LoadDataFromTextFile(testingPath) : null;
MfModel model = new MfTrainer().Fit(training, testing, new MfTrainerOptions()
{
NumberOfThreads = opts.Threads,
ApproximationRank = opts.Factors,
Eps = opts.Eps,
LambdaRegularization = opts.Lambda,
NonNegativeMatrixFactorization = opts.NonNegativeMatrixFactorization,
NumberOfIterations = opts.NumberOfIterations,
Verbose = opts.Verbose
});
var predictor = new MfPredictor(model);
model.SaveModelToFile(resultFileName);
if (testing != null)
{
MfMetric metrics = predictor.Evaluate(testing);
Console.WriteLine($"RMSE: {metrics.RootMeanSquaredError}");
}
return(0);
}
private static void MainMain()
{
var data = "Data";
var trainingPath = Path.Combine(data, "training.ratings");
var testingPath = Path.Combine(data, "test.ratings");
MfProblem training = IOHelper.LoadDataFromTextFile(trainingPath);
MfProblem testing = IOHelper.LoadDataFromTextFile(testingPath);
Console.WriteLine("Model training started.");
var model = new MfTrainer(new ConsoleLogger()).Fit(training, testing, new MfTrainerOptions()
{
Verbose = true,
LambdaRegularization = 0.2f,
NumberOfThreads = 16,
NumberOfIterations = 8
});
var predictor = new MfPredictor(model);
Console.WriteLine("Prediction calculation started.");
MfMetric metrics = predictor.Evaluate(testing);
Console.WriteLine($"RMSE: {metrics.RootMeanSquaredError}");
Console.WriteLine($"RSquared: {metrics.RSquared}");
Console.WriteLine("Press any key to close..");
Console.ReadKey();
}
private static int RunPredict(PredictOptions opts)
{
string resultFileName = Path.Combine(opts.Output, "ml.result");
string inputFileName = opts.Input;
string modelFileName = opts.Model;
var problem = IOHelper.LoadDataFromTextFile(inputFileName);
var model = IOHelper.LoadModelFromFile(modelFileName);
var predictor = new MfPredictor(model);
using (var predictionWriter = File.CreateText(resultFileName))
{
foreach (var mfNode in problem.R)
{
predictionWriter.WriteLine(Math.Round(predictor.Predict(mfNode.U, mfNode.V), 1));
}
}
return(0);
}
