print_results_latex_table( result_folder_path=output_folder_path, results_file_prefix_name=ALGORITHM_NAME, dataset_name=dataset_name, metrics_to_report_list=["PRECISION", "RECALL", "NDCG"], cutoffs_to_report_list=[10], ICM_names_to_report_list=ICM_names_to_report_list, other_algorithm_list=[MCRecML100k_RecommenderWrapper]) from functools import partial if __name__ == '__main__': ALGORITHM_NAME = "MCRec" CONFERENCE_NAME = "KDD" dataset_list = ["movielens100k"] for dataset in dataset_list: read_data_split_and_search_MCRec(dataset) print_parameters_latex_table( result_folder_path="result_experiments/{}/".format(CONFERENCE_NAME), results_file_prefix_name=ALGORITHM_NAME, experiment_subfolder_list=dataset_list, ICM_names_to_report_list=["ICM_genre"], other_algorithm_list=[MCRecML100k_RecommenderWrapper])
read_data_split_and_search_SpectralCF(dataset_name, cold_start=cold_start, cold_items=cold_start_items, isKNN_multiprocess=isKNN_multiprocess, isKNN_tune=isKNN_tune, isSpectralCF_train_default=isSpectralCF_train_default, print_results=print_results ) else: for dataset_name in dataset_list: read_data_split_and_search_SpectralCF(dataset_name, cold_start=cold_start, isKNN_multiprocess=isKNN_multiprocess, isKNN_tune=isKNN_tune, isSpectralCF_train_default=isSpectralCF_train_default, print_results=print_results ) # mantain compatibility with latex parameteres function if cold_start and print_results: for n_cold_item in cold_start_items_list: print_parameters_latex_table(result_folder_path = "result_experiments/{}/".format(CONFERENCE_NAME), results_file_prefix_name = "{}_cold_{}".format(ALGORITHM_NAME, n_cold_item), experiment_subfolder_list = dataset_cold_start_list, other_algorithm_list = [SpectralCF_RecommenderWrapper]) elif not cold_start and print_results: print_parameters_latex_table(result_folder_path = "result_experiments/{}/".format(CONFERENCE_NAME), results_file_prefix_name = ALGORITHM_NAME, experiment_subfolder_list = dataset_list, other_algorithm_list = [SpectralCF_RecommenderWrapper])
dataset_name=dataset_name, results_file_prefix_name=ALGORITHM_NAME, other_algorithm_list=[NeuMF_RecommenderWrapper], n_validation_users=n_validation_users, n_test_users=n_test_users, n_decimals=2) print_results_latex_table(result_folder_path=output_folder_path, results_file_prefix_name=ALGORITHM_NAME, dataset_name=dataset_name, metrics_to_report_list=["HIT_RATE", "NDCG"], cutoffs_to_report_list=[1, 5, 10], other_algorithm_list=[NeuMF_RecommenderWrapper]) if __name__ == '__main__': ALGORITHM_NAME = "NeuMF" CONFERENCE_NAME = "WWW" dataset_list = ["movielens1m", "pinterest"] for dataset in dataset_list: read_data_split_and_search_NeuCF(dataset) print_parameters_latex_table( result_folder_path="result_experiments/{}/".format(CONFERENCE_NAME), results_file_prefix_name=ALGORITHM_NAME, experiment_subfolder_list=dataset_list, other_algorithm_list=[NeuMF_RecommenderWrapper])
cutoffs_to_report_list=[50, 100, 150, 200, 250, 300], ICM_names_to_report_list=ICM_names_to_report_list, other_algorithm_list=[CollaborativeVAE_RecommenderWrapper]) if __name__ == '__main__': ALGORITHM_NAME = "CollaborativeVAE" CONFERENCE_NAME = "KDD" 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_CollaborativeVAE( dataset_variant, train_interactions) print_parameters_latex_table( result_folder_path="result_experiments/{}/".format(CONFERENCE_NAME), results_file_prefix_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_title_abstract"], other_algorithm_list=[CollaborativeVAE_RecommenderWrapper])
print_results_latex_table( result_folder_path=output_folder_path, results_file_prefix_name=ALGORITHM_NAME, dataset_name=dataset_name, metrics_to_report_list=["RECALL", "NDCG"], cutoffs_to_report_list=[20, 50, 100], other_algorithm_list=[MultiVAE_RecommenderWrapper]) from functools import partial if __name__ == '__main__': ALGORITHM_NAME = "Mult_VAE" CONFERENCE_NAME = "WWW" dataset_list = ["movielens20m", "netflixPrize"] for dataset in dataset_list: read_data_split_and_search_MultiVAE(dataset) print_parameters_latex_table( result_folder_path="result_experiments/{}/".format(CONFERENCE_NAME), results_file_prefix_name=ALGORITHM_NAME, experiment_subfolder_list=[ "{}_cold_user".format(dataset) for dataset in dataset_list ], other_algorithm_list=[MultiVAE_RecommenderWrapper])