protected override void SetupOptions()
{
options
.Add("prediction-line=", v => prediction_line = v)
.Add("prediction-header=", v => prediction_header = v)
.Add("chronological-split=", v => chronological_split = v)
.Add("rating-type=", (RatingType v) => rating_type = v)
.Add("file-format=", (RatingFileFormat v) => file_format = v)
.Add("search-hp", v => search_hp = v != null)
.Add("test-no-ratings", v => test_no_ratings = v != null);
}
static void Main(string[] args)
{
Assembly assembly = Assembly.GetExecutingAssembly();
Assembly.LoadFile(Path.GetDirectoryName(assembly.Location) + Path.DirectorySeparatorChar + "MyMediaLiteExperimental.dll");
AppDomain.CurrentDomain.UnhandledException += new UnhandledExceptionEventHandler(Handlers.UnhandledExceptionHandler);
Console.CancelKeyPress += new ConsoleCancelEventHandler(AbortHandler);
// recommender arguments
string method = "BiasedMatrixFactorization";
string recommender_options = string.Empty;
// help/version
bool show_help = false;
bool show_version = false;
// arguments for iteration search
int find_iter = 0;
int max_iter = 500;
double epsilon = 0;
double rmse_cutoff = double.MaxValue;
double mae_cutoff = double.MaxValue;
// data arguments
string data_dir = string.Empty;
string user_attributes_file = string.Empty;
string item_attributes_file = string.Empty;
string user_relations_file = string.Empty;
string item_relations_file = string.Empty;
// other arguments
bool online_eval = false;
bool search_hp = false;
string save_model_file = string.Empty;
string load_model_file = string.Empty;
int random_seed = -1;
string prediction_file = string.Empty;
string prediction_line = "{0}\t{1}\t{2}";
int cross_validation = 0;
double split_ratio = 0;
var p = new OptionSet()
{
// string-valued options
{ "training-file=", v => training_file = v },
{ "test-file=", v => test_file = v },
{ "recommender=", v => method = v },
{ "recommender-options=", v => recommender_options += " " + v },
{ "data-dir=", v => data_dir = v },
{ "user-attributes=", v => user_attributes_file = v },
{ "item-attributes=", v => item_attributes_file = v },
{ "user-relations=", v => user_relations_file = v },
{ "item-relations=", v => item_relations_file = v },
{ "save-model=", v => save_model_file = v },
{ "load-model=", v => load_model_file = v },
{ "prediction-file=", v => prediction_file = v },
{ "prediction-line=", v => prediction_line = v },
// integer-valued options
{ "find-iter=", (int v) => find_iter = v },
{ "max-iter=", (int v) => max_iter = v },
{ "random-seed=", (int v) => random_seed = v },
{ "cross-validation=", (int v) => cross_validation = v },
// double-valued options
{ "epsilon=", (double v) => epsilon = v },
{ "rmse-cutoff=", (double v) => rmse_cutoff = v },
{ "mae-cutoff=", (double v) => mae_cutoff = v },
{ "split-ratio=", (double v) => split_ratio = v },
// enum options
{ "rating-type=", (RatingType v) => rating_type = v },
{ "file-format=", (RatingFileFormat v) => file_format = v },
// boolean options
{ "compute-fit", v => compute_fit = v != null },
{ "online-evaluation", v => online_eval = v != null },
{ "search-hp", v => search_hp = v != null },
{ "help", v => show_help = v != null },
{ "version", v => show_version = v != null },
};
IList <string> extra_args = p.Parse(args);
// TODO make sure interaction of --find-iter and --cross-validation works properly
bool no_eval = test_file == null;
if (show_version)
{
ShowVersion();
}
if (show_help)
{
Usage(0);
}
if (extra_args.Count > 0)
{
Usage("Did not understand " + extra_args[0]);
}
if (training_file == null)
{
Usage("Parameter --training-file=FILE is missing.");
}
if (cross_validation != 0 && split_ratio != 0)
{
Usage("--cross-validation=K and --split-ratio=NUM are mutually exclusive.");
}
if (random_seed != -1)
{
MyMediaLite.Util.Random.InitInstance(random_seed);
}
recommender = Recommender.CreateRatingPredictor(method);
if (recommender == null)
{
Usage(string.Format("Unknown method: '{0}'", method));
}
Recommender.Configure(recommender, recommender_options, Usage);
// ID mapping objects
if (file_format == RatingFileFormat.KDDCUP_2011)
{
user_mapping = new IdentityMapping();
item_mapping = new IdentityMapping();
}
// load all the data
LoadData(data_dir, user_attributes_file, item_attributes_file, user_relations_file, item_relations_file, !online_eval);
Console.Error.WriteLine(string.Format(CultureInfo.InvariantCulture, "ratings range: [{0}, {1}]", recommender.MinRating, recommender.MaxRating));
if (split_ratio > 0)
{
var split = new RatingsSimpleSplit(training_data, split_ratio);
recommender.Ratings = split.Train[0];
training_data = split.Train[0];
test_data = split.Test[0];
}
Utils.DisplayDataStats(training_data, test_data, recommender);
if (find_iter != 0)
{
if (!(recommender is IIterativeModel))
{
Usage("Only iterative recommenders support find_iter.");
}
var iterative_recommender = (IIterativeModel)recommender;
Console.WriteLine(recommender.ToString() + " ");
if (load_model_file == string.Empty)
{
recommender.Train();
}
else
{
Recommender.LoadModel(iterative_recommender, load_model_file);
}
if (compute_fit)
{
Console.Write(string.Format(CultureInfo.InvariantCulture, "fit {0,0:0.#####} ", iterative_recommender.ComputeFit()));
}
MyMediaLite.Eval.Ratings.DisplayResults(MyMediaLite.Eval.Ratings.Evaluate(recommender, test_data));
Console.WriteLine(" iteration " + iterative_recommender.NumIter);
for (int i = (int)iterative_recommender.NumIter + 1; i <= max_iter; i++)
{
TimeSpan time = Utils.MeasureTime(delegate() {
iterative_recommender.Iterate();
});
training_time_stats.Add(time.TotalSeconds);
if (i % find_iter == 0)
{
if (compute_fit)
{
double fit = 0;
time = Utils.MeasureTime(delegate() {
fit = iterative_recommender.ComputeFit();
});
fit_time_stats.Add(time.TotalSeconds);
Console.Write(string.Format(CultureInfo.InvariantCulture, "fit {0,0:0.#####} ", fit));
}
Dictionary <string, double> results = null;
time = Utils.MeasureTime(delegate() { results = MyMediaLite.Eval.Ratings.Evaluate(recommender, test_data); });
eval_time_stats.Add(time.TotalSeconds);
MyMediaLite.Eval.Ratings.DisplayResults(results);
rmse_eval_stats.Add(results["RMSE"]);
Console.WriteLine(" iteration " + i);
Recommender.SaveModel(recommender, save_model_file, i);
if (prediction_file != string.Empty)
{
Prediction.WritePredictions(recommender, test_data, user_mapping, item_mapping, prediction_line, prediction_file + "-it-" + i);
}
if (epsilon > 0.0 && 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
DisplayStats();
}
else
{
TimeSpan seconds;
if (load_model_file == string.Empty)
{
if (cross_validation > 0)
{
Console.Write(recommender.ToString());
Console.WriteLine();
var split = new RatingCrossValidationSplit(training_data, cross_validation);
var results = MyMediaLite.Eval.Ratings.EvaluateOnSplit(recommender, split); // TODO if (search_hp)
MyMediaLite.Eval.Ratings.DisplayResults(results);
no_eval = true;
recommender.Ratings = training_data;
}
else
{
if (search_hp)
{
// TODO --search-hp-criterion=RMSE
double result = NelderMead.FindMinimum("RMSE", recommender);
Console.Error.WriteLine("estimated quality (on split) {0}", result.ToString(CultureInfo.InvariantCulture));
// TODO give out hp search time
}
Console.Write(recommender.ToString());
seconds = Utils.MeasureTime(delegate() { recommender.Train(); });
Console.Write(" training_time " + seconds + " ");
}
}
else
{
Recommender.LoadModel(recommender, load_model_file);
Console.Write(recommender.ToString() + " ");
}
if (!no_eval)
{
if (online_eval) // TODO support also for prediction outputs (to allow external evaluation)
{
seconds = Utils.MeasureTime(delegate() { MyMediaLite.Eval.Ratings.DisplayResults(MyMediaLite.Eval.Ratings.EvaluateOnline(recommender, test_data)); });
}
else
{
seconds = Utils.MeasureTime(delegate() { MyMediaLite.Eval.Ratings.DisplayResults(MyMediaLite.Eval.Ratings.Evaluate(recommender, test_data)); });
}
Console.Write(" testing_time " + seconds);
}
if (compute_fit)
{
Console.Write("fit ");
seconds = Utils.MeasureTime(delegate() {
MyMediaLite.Eval.Ratings.DisplayResults(MyMediaLite.Eval.Ratings.Evaluate(recommender, training_data));
});
Console.Write(string.Format(CultureInfo.InvariantCulture, " fit_time {0,0:0.#####} ", seconds));
}
if (prediction_file != string.Empty)
{
seconds = Utils.MeasureTime(delegate() {
Console.WriteLine();
Prediction.WritePredictions(recommender, test_data, user_mapping, item_mapping, prediction_line, prediction_file);
});
Console.Error.Write("predicting_time " + seconds);
}
Console.WriteLine();
Console.Error.WriteLine("memory {0}", Memory.Usage);
}
Recommender.SaveModel(recommender, save_model_file);
}
static void Main(string[] args)
{
Assembly assembly = Assembly.GetExecutingAssembly();
Assembly.LoadFile(Path.GetDirectoryName(assembly.Location) + Path.DirectorySeparatorChar + "MyMediaLiteExperimental.dll");
AppDomain.CurrentDomain.UnhandledException += new UnhandledExceptionEventHandler(Handlers.UnhandledExceptionHandler);
Console.CancelKeyPress += new ConsoleCancelEventHandler(AbortHandler);
// recommender arguments
string method = "BiasedMatrixFactorization";
string recommender_options = string.Empty;
// help/version
bool show_help = false;
bool show_version = false;
// arguments for iteration search
int find_iter = 0;
int max_iter = 500;
double epsilon = 0;
double rmse_cutoff = double.MaxValue;
double mae_cutoff = double.MaxValue;
// data arguments
string data_dir = string.Empty;
string user_attributes_file = string.Empty;
string item_attributes_file = string.Empty;
string user_relations_file = string.Empty;
string item_relations_file = string.Empty;
// other arguments
bool online_eval = false;
bool search_hp = false;
string save_model_file = string.Empty;
string load_model_file = string.Empty;
int random_seed = -1;
string prediction_file = string.Empty;
string prediction_line = "{0}\t{1}\t{2}";
int cross_validation = 0;
double split_ratio = 0;
var p = new OptionSet() {
// string-valued options
{ "training-file=", v => training_file = v },
{ "test-file=", v => test_file = v },
{ "recommender=", v => method = v },
{ "recommender-options=", v => recommender_options += " " + v },
{ "data-dir=", v => data_dir = v },
{ "user-attributes=", v => user_attributes_file = v },
{ "item-attributes=", v => item_attributes_file = v },
{ "user-relations=", v => user_relations_file = v },
{ "item-relations=", v => item_relations_file = v },
{ "save-model=", v => save_model_file = v },
{ "load-model=", v => load_model_file = v },
{ "prediction-file=", v => prediction_file = v },
{ "prediction-line=", v => prediction_line = v },
// integer-valued options
{ "find-iter=", (int v) => find_iter = v },
{ "max-iter=", (int v) => max_iter = v },
{ "random-seed=", (int v) => random_seed = v },
{ "cross-validation=", (int v) => cross_validation = v },
// double-valued options
{ "epsilon=", (double v) => epsilon = v },
{ "rmse-cutoff=", (double v) => rmse_cutoff = v },
{ "mae-cutoff=", (double v) => mae_cutoff = v },
{ "split-ratio=", (double v) => split_ratio = v },
// enum options
{ "rating-type=", (RatingType v) => rating_type = v },
{ "file-format=", (RatingFileFormat v) => file_format = v },
// boolean options
{ "compute-fit", v => compute_fit = v != null },
{ "online-evaluation", v => online_eval = v != null },
{ "search-hp", v => search_hp = v != null },
{ "help", v => show_help = v != null },
{ "version", v => show_version = v != null },
};
IList<string> extra_args = p.Parse(args);
// TODO make sure interaction of --find-iter and --cross-validation works properly
bool no_eval = test_file == null;
if (show_version)
ShowVersion();
if (show_help)
Usage(0);
if (extra_args.Count > 0)
Usage("Did not understand " + extra_args[0]);
if (training_file == null)
Usage("Parameter --training-file=FILE is missing.");
if (cross_validation != 0 && split_ratio != 0)
Usage("--cross-validation=K and --split-ratio=NUM are mutually exclusive.");
if (random_seed != -1)
MyMediaLite.Util.Random.InitInstance(random_seed);
recommender = Recommender.CreateRatingPredictor(method);
if (recommender == null)
Usage(string.Format("Unknown method: '{0}'", method));
Recommender.Configure(recommender, recommender_options, Usage);
// ID mapping objects
if (file_format == RatingFileFormat.KDDCUP_2011)
{
user_mapping = new IdentityMapping();
item_mapping = new IdentityMapping();
}
// load all the data
LoadData(data_dir, user_attributes_file, item_attributes_file, user_relations_file, item_relations_file, !online_eval);
Console.Error.WriteLine(string.Format(CultureInfo.InvariantCulture, "ratings range: [{0}, {1}]", recommender.MinRating, recommender.MaxRating));
if (split_ratio > 0)
{
var split = new RatingsSimpleSplit(training_data, split_ratio);
recommender.Ratings = split.Train[0];
training_data = split.Train[0];
test_data = split.Test[0];
}
Utils.DisplayDataStats(training_data, test_data, recommender);
if (find_iter != 0)
{
if ( !(recommender is IIterativeModel) )
Usage("Only iterative recommenders support find_iter.");
var iterative_recommender = (IIterativeModel) recommender;
Console.WriteLine(recommender.ToString() + " ");
if (load_model_file == string.Empty)
recommender.Train();
else
Recommender.LoadModel(iterative_recommender, load_model_file);
if (compute_fit)
Console.Write(string.Format(CultureInfo.InvariantCulture, "fit {0,0:0.#####} ", iterative_recommender.ComputeFit()));
MyMediaLite.Eval.Ratings.DisplayResults(MyMediaLite.Eval.Ratings.Evaluate(recommender, test_data));
Console.WriteLine(" iteration " + iterative_recommender.NumIter);
for (int i = (int) iterative_recommender.NumIter + 1; i <= max_iter; i++)
{
TimeSpan time = Utils.MeasureTime(delegate() {
iterative_recommender.Iterate();
});
training_time_stats.Add(time.TotalSeconds);
if (i % find_iter == 0)
{
if (compute_fit)
{
double fit = 0;
time = Utils.MeasureTime(delegate() {
fit = iterative_recommender.ComputeFit();
});
fit_time_stats.Add(time.TotalSeconds);
Console.Write(string.Format(CultureInfo.InvariantCulture, "fit {0,0:0.#####} ", fit));
}
Dictionary<string, double> results = null;
time = Utils.MeasureTime(delegate() { results = MyMediaLite.Eval.Ratings.Evaluate(recommender, test_data); });
eval_time_stats.Add(time.TotalSeconds);
MyMediaLite.Eval.Ratings.DisplayResults(results);
rmse_eval_stats.Add(results["RMSE"]);
Console.WriteLine(" iteration " + i);
Recommender.SaveModel(recommender, save_model_file, i);
if (prediction_file != string.Empty)
Prediction.WritePredictions(recommender, test_data, user_mapping, item_mapping, prediction_line, prediction_file + "-it-" + i);
if (epsilon > 0.0 && 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
DisplayStats();
}
else
{
TimeSpan seconds;
if (load_model_file == string.Empty)
{
if (cross_validation > 0)
{
Console.Write(recommender.ToString());
Console.WriteLine();
var split = new RatingCrossValidationSplit(training_data, cross_validation);
var results = MyMediaLite.Eval.Ratings.EvaluateOnSplit(recommender, split); // TODO if (search_hp)
MyMediaLite.Eval.Ratings.DisplayResults(results);
no_eval = true;
recommender.Ratings = training_data;
}
else
{
if (search_hp)
{
// TODO --search-hp-criterion=RMSE
double result = NelderMead.FindMinimum("RMSE", recommender);
Console.Error.WriteLine("estimated quality (on split) {0}", result.ToString(CultureInfo.InvariantCulture));
// TODO give out hp search time
}
Console.Write(recommender.ToString());
seconds = Utils.MeasureTime( delegate() { recommender.Train(); } );
Console.Write(" training_time " + seconds + " ");
}
}
else
{
Recommender.LoadModel(recommender, load_model_file);
Console.Write(recommender.ToString() + " ");
}
if (!no_eval)
{
if (online_eval) // TODO support also for prediction outputs (to allow external evaluation)
seconds = Utils.MeasureTime(delegate() { MyMediaLite.Eval.Ratings.DisplayResults(MyMediaLite.Eval.Ratings.EvaluateOnline(recommender, test_data)); });
else
seconds = Utils.MeasureTime(delegate() { MyMediaLite.Eval.Ratings.DisplayResults(MyMediaLite.Eval.Ratings.Evaluate(recommender, test_data)); });
Console.Write(" testing_time " + seconds);
}
if (compute_fit)
{
Console.Write("fit ");
seconds = Utils.MeasureTime(delegate() {
MyMediaLite.Eval.Ratings.DisplayResults(MyMediaLite.Eval.Ratings.Evaluate(recommender, training_data));
});
Console.Write(string.Format(CultureInfo.InvariantCulture, " fit_time {0,0:0.#####} ", seconds));
}
if (prediction_file != string.Empty)
{
seconds = Utils.MeasureTime(delegate() {
Console.WriteLine();
Prediction.WritePredictions(recommender, test_data, user_mapping, item_mapping, prediction_line, prediction_file);
});
Console.Error.Write("predicting_time " + seconds);
}
Console.WriteLine();
Console.Error.WriteLine("memory {0}", Memory.Usage);
}
Recommender.SaveModel(recommender, save_model_file);
}
static void Main(string[] args)
{
AppDomain.CurrentDomain.UnhandledException += new UnhandledExceptionEventHandler(Handlers.UnhandledExceptionHandler);
Console.CancelKeyPress += new ConsoleCancelEventHandler(AbortHandler);
// recommender arguments
string method = null;
string recommender_options = string.Empty;
// help/version
bool show_help = false;
bool show_version = false;
// arguments for iteration search
int max_iter = 100;
string measure = "RMSE";
double epsilon = 0;
double cutoff = double.MaxValue;
// other arguments
bool search_hp = false;
int random_seed = -1;
string prediction_line = "{0}\t{1}\t{2}";
string prediction_header = null;
var p = new OptionSet() {
// string-valued options
{ "training-file=", v => training_file = v },
{ "test-file=", v => test_file = v },
{ "recommender=", v => method = v },
{ "recommender-options=", v => recommender_options += " " + v },
{ "data-dir=", v => data_dir = v },
{ "user-attributes=", v => user_attributes_file = v },
{ "item-attributes=", v => item_attributes_file = v },
{ "user-relations=", v => user_relations_file = v },
{ "item-relations=", v => item_relations_file = v },
{ "save-model=", v => save_model_file = v },
{ "load-model=", v => load_model_file = v },
{ "save-user-mapping=", v => save_user_mapping_file = v },
{ "save-item-mapping=", v => save_item_mapping_file = v },
{ "load-user-mapping=", v => load_user_mapping_file = v },
{ "load-item-mapping=", v => load_item_mapping_file = v },
{ "prediction-file=", v => prediction_file = v },
{ "prediction-line=", v => prediction_line = v },
{ "prediction-header=", v => prediction_header = v },
{ "chronological-split=", v => chronological_split = v },
{ "measure=", v => measure = v },
// integer-valued options
{ "find-iter=", (int v) => find_iter = v },
{ "max-iter=", (int v) => max_iter = v },
{ "random-seed=", (int v) => random_seed = v },
{ "cross-validation=", (uint v) => cross_validation = v },
// double-valued options
{ "epsilon=", (double v) => epsilon = v },
{ "cutoff=", (double v) => cutoff = v },
{ "test-ratio=", (double v) => test_ratio = v },
// enum options
{ "rating-type=", (RatingType v) => rating_type = v },
{ "file-format=", (RatingFileFormat v) => file_format = v },
// boolean options
{ "compute-fit", v => compute_fit = v != null },
{ "online-evaluation", v => online_eval = v != null },
{ "show-fold-results", v => show_fold_results = v != null },
{ "search-hp", v => search_hp = v != null },
{ "no-id-mapping", v => no_id_mapping = v != null },
{ "help", v => show_help = v != null },
{ "version", v => show_version = v != null },
};
IList<string> extra_args = p.Parse(args);
// ... some more command line parameter actions ...
bool no_eval = true;
if (test_ratio > 0 || test_file != null || chronological_split != null)
no_eval = false;
if (show_version)
ShowVersion();
if (show_help)
Usage(0);
if (random_seed != -1)
MyMediaLite.Util.Random.Seed = random_seed;
// set up recommender
if (load_model_file != null)
recommender = (RatingPredictor) Model.Load(load_model_file);
else if (method != null)
recommender = Recommender.CreateRatingPredictor(method);
else
recommender = Recommender.CreateRatingPredictor("BiasedMatrixFactorization");
// in case something went wrong ...
if (recommender == null && method != null)
Usage(string.Format("Unknown rating prediction method: '{0}'", method));
if (recommender == null && load_model_file != null)
Abort(string.Format("Could not load model from file {0}.", load_model_file));
CheckParameters(extra_args);
recommender.Configure(recommender_options, (string m) => { Console.Error.WriteLine(m); Environment.Exit(-1); });
// ID mapping objects
if (file_format == RatingFileFormat.KDDCUP_2011 || no_id_mapping)
{
user_mapping = new IdentityMapping();
item_mapping = new IdentityMapping();
}
if (load_user_mapping_file != null)
user_mapping = EntityMappingExtensions.LoadMapping(load_user_mapping_file);
if (load_item_mapping_file != null)
item_mapping = EntityMappingExtensions.LoadMapping(load_item_mapping_file);
// load all the data
LoadData(!online_eval);
// if requested, save ID mappings
if (save_user_mapping_file != null)
user_mapping.SaveMapping(save_user_mapping_file);
if (save_item_mapping_file != null)
item_mapping.SaveMapping(save_item_mapping_file);
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.");
Console.WriteLine(recommender.ToString());
if (cross_validation > 1)
{
recommender.DoIterativeCrossValidation(cross_validation, max_iter, find_iter);
}
else
{
var iterative_recommender = (IIterativeModel) recommender;
var eval_stats = new List<double>();
if (load_model_file == null)
recommender.Train();
if (compute_fit)
Console.WriteLine("fit {0} iteration {1}", recommender.Evaluate(training_data), iterative_recommender.NumIter);
Console.WriteLine("{0} iteration {1}", recommender.Evaluate(test_data), 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);
}
RatingPredictionEvaluationResults results = null;
time = Wrap.MeasureTime(delegate() { results = recommender.Evaluate(test_data); });
eval_time_stats.Add(time.TotalSeconds);
eval_stats.Add(results[measure]);
Console.WriteLine("{0} iteration {1}", 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[measure] - eval_stats.Min() > epsilon)
{
Console.Error.WriteLine(string.Format(CultureInfo.InvariantCulture, "{0} >> {1}", results["RMSE"], eval_stats.Min()));
Console.Error.WriteLine("Reached convergence on training/validation data after {0} iterations.", it);
break;
}
if (results[measure] > cutoff)
{
Console.Error.WriteLine("Reached cutoff after {0} iterations.", it);
break;
}
}
} // for
}
}
else
{
TimeSpan seconds;
Console.Write(recommender + " ");
if (load_model_file == null)
{
if (cross_validation > 1)
{
Console.WriteLine();
var results = recommender.DoCrossValidation(cross_validation, compute_fit, show_fold_results);
Console.Write(results);
no_eval = true;
}
else
{
if (search_hp)
{
double result = NelderMead.FindMinimum("RMSE", recommender);
Console.Error.WriteLine("estimated quality (on split) {0}", result.ToString(CultureInfo.InvariantCulture));
}
seconds = Wrap.MeasureTime( delegate() { recommender.Train(); } );
Console.Write(" training_time " + seconds + " ");
}
}
if (!no_eval)
{
if (online_eval)
seconds = Wrap.MeasureTime(delegate() { Console.Write(recommender.EvaluateOnline(test_data)); });
else
seconds = Wrap.MeasureTime(delegate() { Console.Write(recommender.Evaluate(test_data)); });
Console.Write(" testing_time " + seconds);
if (compute_fit)
{
Console.Write("\nfit ");
seconds = Wrap.MeasureTime(delegate() {
Console.Write(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.Write("prediction_time " + seconds);
}
}
Console.WriteLine();
}
Model.Save(recommender, save_model_file);
DisplayStats();
}