default=True) input_flags = parser.parse_args() print(input_flags) KNN_similarity_to_report_list = [ "cosine", "dice", "jaccard", "asymmetric", "tversky" ] dataset_list = ["movielens100k"] for dataset_name in dataset_list: read_data_split_and_search( dataset_name, flag_baselines_tune=input_flags.baseline_tune, flag_DL_article_default=input_flags.DL_article_default, flag_print_results=input_flags.print_results, ) if input_flags.print_results: generate_latex_hyperparameters( result_folder_path="result_experiments/{}/".format( CONFERENCE_NAME), algorithm_name=ALGORITHM_NAME, experiment_subfolder_list=dataset_list, ICM_names_to_report_list=["ICM_genre"], other_algorithm_list=[MCRecML100k_RecommenderWrapper], KNN_similarity_to_report_list=KNN_similarity_to_report_list, split_per_algorithm_type=True)
parser.add_argument('-b', '--baseline_tune', help="Baseline hyperparameter search", type = bool, default = False) parser.add_argument('-a', '--DL_article_default', help="Train the DL model with article hyperparameters", type = bool, default = False) parser.add_argument('-p', '--print_results', help="Print results", type = bool, default = True) parser.add_argument('-m', '--MF_baseline_tune', help="Matrix Factorization hyperparameter search", type = bool, default = False) input_flags = parser.parse_args() print(input_flags) KNN_similarity_to_report_list = ["cosine", "dice", "jaccard", "asymmetric", "tversky"] dataset_list = ["movielens20m", "netflixPrize"] for dataset_name in dataset_list: read_data_split_and_search(dataset_name, flag_baselines_tune=input_flags.baseline_tune, flag_MF_baselines_tune = input_flags.MF_baseline_tune, flag_DL_article_default= input_flags.DL_article_default, flag_print_results = input_flags.print_results, ) if input_flags.print_results: generate_latex_hyperparameters(result_folder_path ="result_experiments/{}/".format(CONFERENCE_NAME), algorithm_name= ALGORITHM_NAME, experiment_subfolder_list = ["{}_cold_user".format(dataset) for dataset in dataset_list], other_algorithm_list = [Mult_VAE_RecommenderWrapper], KNN_similarity_to_report_list = KNN_similarity_to_report_list, split_per_algorithm_type = True)
KNN_similarity_to_report_list = [ "cosine", "dice", "jaccard", "asymmetric", "tversky" ] dataset_list = [ 'movielens1m_original', 'movielens1m_ours', 'tafeng_original', 'tafeng_ours' ] for dataset_name in dataset_list: read_data_split_and_search( dataset_name, flag_baselines_tune=input_flags.baseline_tune, flag_DL_article_default=input_flags.DL_article_default, flag_print_results=input_flags.print_results, ) if input_flags.print_results: generate_latex_hyperparameters( result_folder_path="result_experiments/{}/".format( CONFERENCE_NAME), algorithm_name=ALGORITHM_NAME, experiment_subfolder_list=dataset_list, ICM_names_to_report_list=["ICM_all", "ICM_original"], UCM_names_to_report_list=["UCM_all"], other_algorithm_list=[ DeepCF_RecommenderWrapper, CoupledCF_RecommenderWrapper ], KNN_similarity_to_report_list=KNN_similarity_to_report_list, split_per_algorithm_type=True)
dataset_variant_list = ["a", "t"] train_interactions_list = [1, 10] for dataset_variant in dataset_variant_list: for train_interactions in train_interactions_list: read_data_split_and_search( dataset_variant, train_interactions, flag_baselines_tune=input_flags.baseline_tune, flag_DL_article_default=input_flags.DL_article_default, flag_print_results=input_flags.print_results, ) if input_flags.print_results: generate_latex_hyperparameters( result_folder_path="result_experiments/{}/".format( CONFERENCE_NAME), algorithm_name=ALGORITHM_NAME, experiment_subfolder_list=[ "citeulike_{}_{}".format(dataset_variant, train_interactions) for dataset_variant in dataset_variant_list for train_interactions in train_interactions_list ], ICM_names_to_report_list=["ICM_tokens_TFIDF", "ICM_tokens_bool"], KNN_similarity_to_report_list=KNN_similarity_to_report_list, other_algorithm_list=[CollaborativeDL_Matlab_RecommenderWrapper], split_per_algorithm_type=True)
read_data_split_and_search( dataset_name, cold_start=input_flags.cold_start, flag_baselines_tune=input_flags.baseline_tune, flag_DL_article_default=input_flags.DL_article_default, flag_DL_tune=input_flags.DL_tune, flag_print_results=input_flags.print_results) # mantain compatibility with latex parameteres function if input_flags.cold_start and input_flags.print_results: for n_cold_item in cold_start_items_list: generate_latex_hyperparameters( result_folder_path="result_experiments/{}/".format( CONFERENCE_NAME), algorithm_name="{}_cold_{}".format(ALGORITHM_NAME, n_cold_item), experiment_subfolder_list=dataset_cold_start_list, other_algorithm_list=other_algorithm_list, KNN_similarity_to_report_list=KNN_similarity_to_report_list, split_per_algorithm_type=True) elif not input_flags.cold_start and input_flags.print_results: generate_latex_hyperparameters( result_folder_path="result_experiments/{}/".format( CONFERENCE_NAME), algorithm_name=ALGORITHM_NAME, experiment_subfolder_list=dataset_list, other_algorithm_list=other_algorithm_list, KNN_similarity_to_report_list=KNN_similarity_to_report_list, split_per_algorithm_type=True)