// TODO add compute_fit again
static void Usage(int exit_code)
{
Console.WriteLine(@"
MyMediaLite KDD Cup 2011 Track 2 tool
usage: KDDTrack2.exe METHOD [ARGUMENTS] [OPTIONS]
use '-' for either TRAINING_FILE or TEST_FILE to read the data from STDIN
methods (plus arguments and their defaults):");
Console.Write(" - ");
Console.WriteLine(string.Join("\n - ", Recommender.List("MyMediaLite.ItemRecommendation")));
Console.WriteLine(@"method ARGUMENTS have the form name=value
general OPTIONS have the form name=value
- random_seed=N set random seed to N
- data_dir=DIR load all files from DIR
- save_model=FILE save computed model to FILE
- load_model=FILE load model from FILE
- prediction_file=FILE write the predictions to FILE ('-' for STDOUT)
- sample_data=BOOL assume the sample data set instead of the real one
- predict_score=BOOL predict scores (double precision) instead of 0/1 decisions
- predict_rated=BOOL instead of predicting what received a good rating, try to predict what received a rating at all
(implies predict_score)
options for finding the right number of iterations (MF methods)
- find_iter=N give out statistics every N iterations
- max_iter=N perform at most N iterations
- epsilon=NUM abort iterations if error is more than best result plus NUM
- err_cutoff=NUM abort if error is above NUM");
Environment.Exit(exit_code);
}
static void Usage(int exit_code)
{
Version version = Assembly.GetEntryAssembly().GetName().Version;
Console.WriteLine("MyMediaLite Rating Prediction {0}.{1:00}", version.Major, version.Minor);
Console.WriteLine(@"
usage: RatingPrediction.exe --training-file=FILE --recommender=METHOD [OPTIONS]
recommenders (plus options and their defaults):");
Console.Write(" - ");
Console.WriteLine(string.Join("\n - ", Recommender.List("MyMediaLite.RatingPrediction")));
Console.WriteLine(@" method ARGUMENTS have the form name=value
general OPTIONS:
--recommender=METHOD set recommender method (default: BiasedMatrixFactorization)
--recommender-options=OPTIONS use OPTIONS as recommender options
--training-file=FILE read training data from FILE
--test-file=FILE read test data from FILE
--help display this usage information and exit
--version display version information and exit
--random-seed=N set random seed to N
--data-dir=DIR load all files from DIR
--user-attributes=FILE file containing user attribute information
--item-attributes=FILE file containing item attribute information
--user-relations=FILE file containing user relation information
--item-relations=FILE file containing item relation information
--save-model=FILE save computed model to FILE
--load-model=FILE load model from FILE
--prediction-file=FILE write the rating predictions to FILE ('-' for STDOUT)
--prediction-line=FORMAT format of the prediction line; {0}, {1}, {2} refer to user ID, item ID,
and predicted rating, respectively; default is {0}\\t{1}\\t{2}
--file-format=ml1m|kddcup2011|default
--rating-type=float|byte|double store ratings as floats or bytes or doubles (default)
--cross-validation=K perform k-fold crossvalidation on the training data
--split-ratio=NUM use a ratio of NUM of the training data for evaluation (simple split)
--online-evaluation perform online evaluation (use every tested rating for incremental training)
--search-hp search for good hyperparameter values (experimental)
options for finding the right number of iterations (iterative methods)
--find-iter=N give out statistics every N iterations
--max-iter=N perform at most N iterations
--epsilon=NUM abort iterations if RMSE is more than best result plus NUM
--rmse-cutoff=NUM abort if RMSE is above NUM
--mae-cutoff=NUM abort if MAE is above NUM
--compute-fit display fit on training data every find_iter iterations");
Environment.Exit(exit_code);
}
static void Usage(int exit_code)
{
Version version = Assembly.GetEntryAssembly().GetName().Version;
Console.WriteLine("MyMediaLite Item Prediction from Implicit Feedback {0}.{1:00}", version.Major, version.Minor);
Console.WriteLine(@"
usage: ItemPrediction.exe --training-file=FILE --recommender=METHOD [OPTIONS]
methods (plus arguments and their defaults):");
Console.Write(" - ");
Console.WriteLine(string.Join("\n - ", Recommender.List("MyMediaLite.ItemRecommendation")));
Console.WriteLine(@" method ARGUMENTS have the form name=value
general OPTIONS:
--recommender=METHOD set recommender method (default: BiasedMatrixFactorization)
--recommender-options=OPTIONS use OPTIONS as recommender options
--training-file=FILE read training data from FILE
--test-file=FILE read test data from FILE
--help display this usage information and exit
--version display version information and exit
--random-seed=N
--data-dir=DIR load all files from DIR
--relevant-items=FILE use the items in FILE (one per line) as candidate items, otherwise all items in the training set
--relevant-users=FILE predict items for users specified in FILE (one user per line)
--user-attributes=FILE file containing user attribute information
--item-attributes=FILE file containing item attribute information
--user-relations=FILE file containing user relation information
--item-relations=FILE file containing item relation information
--save-model=FILE save computed model to FILE
--load-model=FILE load model from FILE
--prediction-file=FILE write ranked predictions to FILE ('-' for STDOUT), one user per line
--predict-items-number=N predict N items per user (needs --predict-items-file)
--test-ratio=NUM evaluate by splitting of a NUM part of the feedback
--online-evaluation perform online evaluation (use every tested user-item combination for incremental training)
--filtered-evaluation perform evaluation filtered by item attribute (expects --item-attributes=FILE)
options for finding the right number of iterations (iterative methods)
--find-iter=N give out statistics every N iterations
--max-iter=N perform at most N iterations
--auc-cutoff=NUM abort if AUC is below NUM
--prec5-cutoff=NUM abort if prec@5 is below NUM
--compute-fit display fit on training data every find_iter iterations");
Environment.Exit(exit_code);
}
static void Usage(int exit_code)
{
Console.WriteLine(@"
MyMediaLite KDD Cup 2011 Track 1 tool
usage: KDDCup.exe METHOD [ARGUMENTS] [OPTIONS]
use '-' for either TRAINING_FILE or TEST_FILE to read the data from STDIN
methods (plus arguments and their defaults):");
Console.Write(" - ");
Console.WriteLine(string.Join("\n - ", Recommender.List("MyMediaLite.RatingPrediction")));
Console.WriteLine(@"method ARGUMENTS have the form name=value
general OPTIONS have the form name=value
- random_seed=N set random seed to N
- data_dir=DIR load all files from DIR
- save_model=FILE save computed model to FILE
- load_model=FILE load model from FILE
- no_eval=BOOL do not evaluate
- prediction_file=FILE write the predictions to FILE ('-' for STDOUT)
- cross_validation=K perform k-fold crossvalidation on the training data
(ignores the test data)
- sample_data=BOOL assume the sample data set instead of the real one
- track2=BOOL perform rating prediction on track 2 data
- good_rating_prob=BOOL try to predict the probability of a good rating (>= 80)
options for finding the right number of iterations (MF methods)
- find_iter=N give out statistics every N iterations
- max_iter=N perform at most N iterations
- epsilon=NUM abort iterations if RMSE is more than best result plus NUM
- rmse_cutoff=NUM abort if RMSE is above NUM
- mae_cutoff=NUM abort if MAE is above NUM
- compute_fit=BOOL display fit on training data every find_iter iterations");
Environment.Exit(exit_code);
}
