def main(args): progress = WorkSplitter() table_path = 'tables/' test = load_numpy(path=args.path, name=args.dataset + args.test) df = pd.DataFrame({ 'model': [ 'AutoRec', 'AutoRec', 'AutoRec', 'InitFeatureEmbedAE', 'InitFeatureEmbedAE', 'InitFeatureEmbedAE', 'AlterFeatureEmbedAE', 'ConcatFeatureEmbedAE', 'UnionSampleAE', 'WRSampleAE', 'BatchSampleAE', 'BridgeLabelAE', 'RefineLabelAE', 'DeepAutoRec', 'DeepAutoRec', 'SoftLabelAE', 'HintAE' ], 'way': [ None, 'unif', 'combine', 'user', 'item', 'both', None, None, None, None, None, None, None, None, 'unif', None, None ] }) progress.subsection("Reproduce") frame = [] for idx, row in df.iterrows(): row = row.to_dict() row['metric'] = ['NLL', 'AUC'] row['rank'] = 200 result = execute(test, row, folder=args.model_folder + args.dataset) frame.append(result) results = pd.concat(frame) save_dataframe_csv(results, table_path, args.name)
def main(args): progress = WorkSplitter() table_path = 'tables/' test = load_numpy(path=args.path, name=args.dataset + args.test) df = pd.DataFrame({ 'model': [ "BiasedMF", "BiasedMF", "BiasedMF", "PropensityMF", "InitFeatureEmbedMF", "InitFeatureEmbedMF", "InitFeatureEmbedMF", "AlterFeatureEmbedMF", "ConcatFeatureEmbedMF", "CausalSampleMF", "UnionSampleMF", "WRSampleMF", "BatchSampleMF", "BridgeLabelMF", "RefineLabelMF" ], 'way': [ None, "unif", "combine", None, "user", "item", "both", None, None, None, None, None, None, None, None ] }) progress.subsection("Reproduce") frame = [] for idx, row in df.iterrows(): row = row.to_dict() row['metric'] = ['NLL', 'AUC'] row['rank'] = 10 result = execute(test, row, folder=args.model_folder + args.dataset) frame.append(result) results = pd.concat(frame) save_dataframe_csv(results, table_path, args.name)
def main(args): table_path = load_yaml('config/global.yml', key='path')['tables'] df = find_best_hyperparameters(table_path + args.problem, 'NDCG') R_train = load_numpy(path=args.path, name=args.train) R_valid = load_numpy(path=args.path, name=args.valid) R_test = load_numpy(path=args.path, name=args.test) R_train = R_train + R_valid topK = [5, 10, 15, 20, 50] frame = [] for idx, row in df.iterrows(): start = timeit.default_timer() row = row.to_dict() row['metric'] = ['R-Precision', 'NDCG', 'Precision', 'Recall', "MAP"] row['topK'] = topK result = execute(R_train, R_test, row, models[row['model']], gpu_on=args.gpu) stop = timeit.default_timer() print('Time: ', stop - start) frame.append(result) results = pd.concat(frame) save_dataframe_csv(results, table_path, args.name)
def main(args): progress = WorkSplitter() table_path = 'tables/' test = load_numpy(path=args.path, name=args.dataset + args.test) df = pd.DataFrame({ 'model': [ 'RestrictedBatchSampleMF', 'RestrictedBatchSampleMF', 'RestrictedBatchSampleMF', 'RestrictedBatchSampleMF', 'RestrictedBatchSampleMF' ], 'way': [None, 'head_users', 'tail_users', 'head_items', 'tail_items'] }) progress.subsection("Gain Analysis") frame = [] for idx, row in df.iterrows(): row = row.to_dict() row['metric'] = ['NLL', 'AUC'] row['rank'] = 10 result = execute(test, row, folder=args.model_folder + args.dataset) frame.append(result) results = pd.concat(frame) save_dataframe_csv(results, table_path, args.name)
def main(args): table_path = load_yaml('config/global.yml', key='path')['tables'] df = find_best_hyperparameters(table_path+args.tuning_result_path, 'MAP@10') R_train = load_numpy(path=args.path, name=args.train) R_valid = load_numpy(path=args.path, name=args.valid) R_test = load_numpy(path=args.path, name=args.test) R_train = R_train + R_valid # R_train[(R_train <= 3).nonzero()] = 0 # R_test[(R_test <= 3).nonzero()] = 0 # R_train[(R_train > 3).nonzero()] = 1 # R_test[(R_test > 3).nonzero()] = 1 # import ipdb; ipdb.set_trace() topK = [5, 10, 15, 20, 50] frame = [] for idx, row in df.iterrows(): start = timeit.default_timer() row = row.to_dict() row['metric'] = ['R-Precision', 'NDCG', 'Precision', 'Recall', "MAP"] row['topK'] = topK result = execute(R_train, R_test, row, models[row['model']]) stop = timeit.default_timer() print('Time: ', stop - start) frame.append(result) results = pd.concat(frame) save_dataframe_csv(results, table_path, args.name)