protected override void Run(string[] args)
{
if (file_format == RatingFileFormat.KDDCUP_2011)
{
user_mapping = new IdentityMapping();
item_mapping = new IdentityMapping();
}
base.Run(args);
bool do_eval = false;
if (test_ratio > 0 || chronological_split != null)
{
do_eval = true;
}
if (test_file != null && !test_no_ratings)
{
do_eval = true;
}
Console.Error.WriteLine(
string.Format(CultureInfo.InvariantCulture,
"ratings range: [{0}, {1}]", recommender.MinRating, recommender.MaxRating));
if (test_ratio > 0)
{
var split = new RatingsSimpleSplit(training_data, test_ratio);
recommender.Ratings = training_data = split.Train[0];
test_data = split.Test[0];
Console.Error.WriteLine(string.Format(CultureInfo.InvariantCulture, "test ratio {0}", test_ratio));
}
if (chronological_split != null)
{
var split = chronological_split_ratio != -1
? new RatingsChronologicalSplit((ITimedRatings)training_data, chronological_split_ratio)
: new RatingsChronologicalSplit((ITimedRatings)training_data, chronological_split_time);
recommender.Ratings = training_data = split.Train[0];
test_data = split.Test[0];
if (test_ratio != -1)
{
Console.Error.WriteLine(string.Format(CultureInfo.InvariantCulture, "test ratio (chronological) {0}", chronological_split_ratio));
}
else
{
Console.Error.WriteLine(string.Format(CultureInfo.InvariantCulture, "split time {0}", chronological_split_time));
}
}
Console.Write(training_data.Statistics(test_data, user_attributes, item_attributes));
if (find_iter != 0)
{
if (!(recommender is IIterativeModel))
{
Abort("Only iterative recommenders (interface IIterativeModel) support --find-iter=N.");
}
var iterative_recommender = recommender as IIterativeModel;
iterative_recommender.NumIter = num_iter;
Console.WriteLine(recommender);
if (cross_validation > 1)
{
recommender.DoIterativeCrossValidation(cross_validation, max_iter, find_iter);
}
else
{
var eval_stats = new List <double>();
if (load_model_file == null)
{
recommender.Train();
}
if (compute_fit)
{
Console.WriteLine("fit {0} iteration {1}", Render(recommender.Evaluate(training_data)), iterative_recommender.NumIter);
}
Console.WriteLine("{0} iteration {1}", Render(Evaluate()), iterative_recommender.NumIter);
for (int it = (int)iterative_recommender.NumIter + 1; it <= max_iter; it++)
{
TimeSpan time = Wrap.MeasureTime(delegate() {
iterative_recommender.Iterate();
});
training_time_stats.Add(time.TotalSeconds);
if (it % find_iter == 0)
{
if (compute_fit)
{
time = Wrap.MeasureTime(delegate() {
Console.WriteLine("fit {0} iteration {1}", recommender.Evaluate(training_data), it);
});
fit_time_stats.Add(time.TotalSeconds);
}
EvaluationResults results = null;
time = Wrap.MeasureTime(delegate() { results = Evaluate(); });
eval_time_stats.Add(time.TotalSeconds);
eval_stats.Add(results[eval_measures[0]]);
Console.WriteLine("{0} iteration {1}", Render(results), it);
Model.Save(recommender, save_model_file, it);
if (prediction_file != null)
{
recommender.WritePredictions(test_data, prediction_file + "-it-" + it, user_mapping, item_mapping, prediction_line, prediction_header);
}
if (epsilon > 0.0 && results[eval_measures[0]] - eval_stats.Min() > epsilon)
{
Console.Error.WriteLine(string.Format(CultureInfo.InvariantCulture, "{0} >> {1}", results[eval_measures[0]], eval_stats.Min()));
Console.Error.WriteLine("Reached convergence on training/validation data after {0} iterations.", it);
break;
}
if (results[eval_measures[0]] > cutoff)
{
Console.Error.WriteLine("Reached cutoff after {0} iterations.", it);
break;
}
}
} // for
if (max_iter % find_iter != 0)
{
recommender.WritePredictions(test_data, prediction_file, user_mapping, item_mapping, prediction_line, prediction_header);
}
}
}
else
{
TimeSpan seconds;
Console.Write(recommender + " ");
if (load_model_file == null)
{
if (cross_validation > 1)
{
Console.WriteLine();
var results = DoCrossValidation();
Console.Write(Render(results));
do_eval = false;
}
else
{
if (search_hp)
{
double result = new NelderMead("RMSE", recommender).FindMinimum();
Console.Error.WriteLine("estimated quality (on split) {0}", result.ToString(CultureInfo.InvariantCulture));
}
seconds = Wrap.MeasureTime(delegate() { recommender.Train(); });
Console.Write(" training_time " + seconds + " ");
}
}
if (do_eval)
{
if (online_eval)
{
seconds = Wrap.MeasureTime(delegate() { Console.Write(Render(recommender.EvaluateOnline(test_data))); });
}
else
{
seconds = Wrap.MeasureTime(delegate() { Console.Write(Render(Evaluate())); });
}
Console.Write(" testing_time " + seconds);
if (compute_fit)
{
Console.Write("\nfit ");
seconds = Wrap.MeasureTime(delegate() {
Console.Write(Render(recommender.Evaluate(training_data)));
});
Console.Write(" fit_time " + seconds);
}
}
if (prediction_file != null)
{
Console.WriteLine();
seconds = Wrap.MeasureTime(delegate() {
recommender.WritePredictions(test_data, prediction_file, user_mapping, item_mapping, prediction_line, prediction_header);
});
Console.Error.WriteLine("prediction_time " + seconds);
}
Console.WriteLine();
}
Model.Save(recommender, save_model_file);
DisplayStats();
}