public static void Main(string[] args)
{
// load the data
var training_data = RatingData.Read(args[0]);
var test_data = RatingData.Read(args[1]);
// set up the recommender
var recommender = new UserItemBaseline();
recommender.Ratings = training_data;
recommender.Train();
// measure the accuracy on the test data set
var results = recommender.Evaluate(test_data);
Console.WriteLine("RMSE={0} MAE={1}", results["RMSE"], results["MAE"]);
Console.WriteLine(results);
// make a prediction for a certain user and item
Console.WriteLine(recommender.Predict(1, 1));
var bmf = new BiasedMatrixFactorization {Ratings = training_data};
Console.WriteLine(bmf.DoCrossValidation());
}
static void Main(string[] args)
{
var t = new Stopwatch();
//Utils.Converter(@"D:\__testdata\lab10\train.csv");
//return;
t.Restart();
Console.WriteLine("RatingData.Read Begin");
var trainingData = RatingData.Read(TRAIN_DATA_FILE_NAME);
Console.WriteLine("RatingData.Read End {0}", t.Elapsed.TotalSeconds);
//t.Restart();
//Console.WriteLine("RatingData.Read Begin");
//var evalData = RatingData.Read(EVAL_DATA_FILE_NAME);
//Console.WriteLine("RatingData.Read End {0}", t.Elapsed.TotalSeconds);
//http://www.mymedialite.net/examples/datasets.html
// num_factors=120
// bias_reg=0.001
// regularization=0.055
// learn_rate=0.07
// num_iter=100
// bold_driver=true
var recommender = new BiasedMatrixFactorization
{
Ratings = trainingData,
NumIter = 10,
MaxRating = 5,
MinRating = 1,
};
t.Restart();
Console.WriteLine("Training begin");
recommender.Train();
Console.WriteLine("Training end {0}", t.Elapsed.TotalSeconds);
//t.Restart();
//Console.WriteLine("Evaluate begin");
//var resultsb = recommender.Evaluate(evalData);
//Console.WriteLine("Evaluate end {0}", t.Elapsed.TotalSeconds);
Console.WriteLine(recommender.DoCrossValidation());
//Console.WriteLine("RMSE={0} MAE={1}", resultsb["RMSE"], resultsb["MAE"]);
t.Restart();
Console.WriteLine("Result Begin");
var users = recommender.Ratings.AllUsers.ToArray();
var items = recommender.Ratings.AllItems.ToArray();
var ratingAvg = recommender.Ratings.Average;
using(var testIn = new StreamReader(TEST_FILE_NAME))
using (var resultOut = new StreamWriter(RESULTS_FILE_NAME))
{
var count = 0;
string line;
while ((line = testIn.ReadLine()) != null)
{
if (count > 0)
{
var parts = line.Split(new[] {","}, StringSplitOptions.RemoveEmptyEntries);
var userId = int.Parse(parts[0]);
var itemId = int.Parse(parts[1]);
var rating = !users.Contains(userId) || !items.Contains(itemId)
? ratingAvg
: recommender.Predict(userId, itemId);
resultOut.WriteLine("{0}, {1}, {2}", userId, itemId, rating);
}
if (count%10000 == 0)
{
resultOut.Flush();
Console.Write(".");
}
count++;
}
resultOut.Close();
testIn.Close();
}
Console.WriteLine();
Console.WriteLine("Result End {0}", t.Elapsed.TotalSeconds);
t.Stop();
Console.WriteLine("End");
Console.ReadLine();
}
