static void DoTrack1()
{
var rating_predictor_validate = recommender as RatingPredictor;
var rating_predictor_final = rating_predictor_validate.Clone() as RatingPredictor;
rating_predictor_final.Ratings = complete_ratings;
Console.WriteLine("Validation split:");
Utils.DisplayDataStats(training_ratings, validation_ratings, rating_predictor_validate);
Console.WriteLine("Test split:");
Utils.DisplayDataStats(complete_ratings, test_data, rating_predictor_final);
if (find_iter != 0)
{
if (!(recommender is IIterativeModel))
{
Usage("Only iterative recommenders support find_iter.");
}
IIterativeModel iterative_recommender_validate = (MatrixFactorization)rating_predictor_validate;
IIterativeModel iterative_recommender_final = (MatrixFactorization)rating_predictor_final;
Console.WriteLine(recommender.ToString() + " ");
if (load_model_file == string.Empty)
{
iterative_recommender_validate.Train();
iterative_recommender_final.Train();
}
else
{
Recommender.LoadModel(rating_predictor_final, "final-" + load_model_file);
}
if (compute_fit)
{
Console.Write(string.Format(CultureInfo.InvariantCulture, "fit {0:0.#####} ", iterative_recommender_validate.ComputeFit()));
}
MyMediaLite.Eval.Ratings.DisplayResults(MyMediaLite.Eval.Ratings.Evaluate(rating_predictor_validate, validation_ratings));
Console.WriteLine(" " + iterative_recommender_validate.NumIter);
for (int i = (int)iterative_recommender_validate.NumIter + 1; i <= max_iter; i++)
{
TimeSpan time = Utils.MeasureTime(delegate() {
iterative_recommender_validate.Iterate();
iterative_recommender_final.Iterate(); // TODO parallelize this
});
training_time_stats.Add(time.TotalSeconds);
if (i % find_iter == 0)
{
if (compute_fit)
{
double fit = 0;
time = Utils.MeasureTime(delegate() {
fit = iterative_recommender_validate.ComputeFit();
});
fit_time_stats.Add(time.TotalSeconds);
Console.Write(string.Format(CultureInfo.InvariantCulture, "fit {0:0.#####} ", fit));
}
// evaluate and save stats
// TODO parallelize
Dictionary <string, double> results = null;
time = Utils.MeasureTime(delegate() {
results = MyMediaLite.Eval.Ratings.Evaluate(rating_predictor_validate, validation_ratings);
MyMediaLite.Eval.Ratings.DisplayResults(results);
rmse_eval_stats.Add(results["RMSE"]);
Console.WriteLine(" " + i);
});
eval_time_stats.Add(time.TotalSeconds);
// write out model files and predictions
if (save_model_file != string.Empty)
{
Recommender.SaveModel(rating_predictor_validate, save_model_file + "-validate", i);
Recommender.SaveModel(rating_predictor_final, save_model_file, i);
}
if (prediction_file != string.Empty)
{
if (track2)
{
KDDCup.PredictRatingsDouble(rating_predictor_validate, validation_candidates, prediction_file + "-validate-it-" + i);
KDDCup.PredictRatingsDouble(rating_predictor_final, test_data, prediction_file + "-it-" + i);
}
else
{
KDDCup.PredictRatings(rating_predictor_validate, validation_ratings, prediction_file + "-validate-it-" + i);
KDDCup.PredictRatings(rating_predictor_final, test_data, prediction_file + "-it-" + i);
}
}
// check whether we should abort
if (epsilon > 0 && results["RMSE"] > rmse_eval_stats.Min() + epsilon)
{
Console.Error.WriteLine(string.Format(CultureInfo.InvariantCulture, "{0} >> {1}", results["RMSE"], rmse_eval_stats.Min()));
Console.Error.WriteLine("Reached convergence on training/validation data after {0} iterations.", i);
break;
}
if (results["RMSE"] > rmse_cutoff || results["MAE"] > mae_cutoff)
{
Console.Error.WriteLine("Reached cutoff after {0} iterations.", i);
break;
}
}
} // for
DisplayIterationStats();
Recommender.SaveModel(recommender, save_model_file);
}
else
{
TimeSpan seconds;
if (!no_eval)
{
if (load_model_file == string.Empty)
{
Console.Write(recommender.ToString());
if (cross_validation > 0) // TODO cross-validation could also be performed on the complete dataset
{ // TODO support track2
Console.WriteLine();
var split = new RatingCrossValidationSplit(training_ratings, cross_validation);
var results = MyMediaLite.Eval.Ratings.EvaluateOnSplit(rating_predictor_validate, split);
MyMediaLite.Eval.Ratings.DisplayResults(results);
no_eval = true;
rating_predictor_validate.Ratings = training_ratings;
}
else
{
seconds = Utils.MeasureTime(delegate() { recommender.Train(); });
Console.Write(" training_time " + seconds + " ");
Recommender.SaveModel(recommender, save_model_file);
}
}
Console.Write(recommender.ToString() + " ");
seconds = Utils.MeasureTime(
delegate() { MyMediaLite.Eval.Ratings.DisplayResults(MyMediaLite.Eval.Ratings.Evaluate(rating_predictor_validate, validation_ratings)); }
);
Console.Write(" testing_time " + seconds);
}
Console.WriteLine();
if (prediction_file != string.Empty)
{
Console.WriteLine("Prediction for KDD Cup Track 1:");
seconds = Utils.MeasureTime(delegate() { rating_predictor_final.Train(); });
Console.Write(" training_time " + seconds + " ");
if (save_model_file != string.Empty)
{
Recommender.SaveModel(rating_predictor_validate, save_model_file + "-validate");
Recommender.SaveModel(rating_predictor_final, save_model_file);
}
Console.WriteLine();
seconds = Utils.MeasureTime(delegate() {
KDDCup.PredictRatingsDouble(rating_predictor_final, test_data, prediction_file);
if (track2)
{
KDDCup.PredictRatingsDouble(rating_predictor_validate, validation_candidates, prediction_file + "-validate");
}
else
{
KDDCup.PredictRatings(rating_predictor_validate, validation_ratings, prediction_file + "-validate");
}
});
Console.Error.WriteLine("predicting_time " + seconds);
}
}
}