// 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); }