def main(args):
progress = WorkSplitter()
progress.section("Tune Parameters")
params = load_yaml(args.grid)
params['models'] = {params['models']: models[params['models']]}
train = load_numpy(path=args.path, name=args.dataset + args.train)
unif_train = load_numpy(path=args.path, name=args.dataset + args.unif_train)
valid = load_numpy(path=args.path, name=args.dataset + args.valid)
hyper_parameter_tuning(train, valid, params, unif_train=unif_train, save_path=args.dataset + args.name,
gpu_on=args.gpu, seed=args.seed, way=args.way, dataset=args.dataset)
def main(args):
params = load_yaml(args.grid)
params['models'] = {params['models']: models[params['models']]}
R_train = load_numpy(path=args.path, name=args.train)
R_valid = load_numpy(path=args.path, name=args.valid)
hyper_parameter_tuning(R_train,
R_valid,
params,
save_path=args.name,
measure=params['similarity'],
gpu_on=args.gpu)
def main(args):
params = load_yaml(args.parameters)
params['models'] = {params['models']: models[params['models']]}
num_users = pd.read_csv(args.data_dir + args.user_col +
'.csv')[args.user_col].nunique()
num_items = pd.read_csv(args.data_dir + args.item_col +
'.csv')[args.item_col].nunique()
df_train = pd.read_csv(args.data_dir + args.train_set)
df_train = df_train[df_train[args.rating_col] == 1]
df_train[args.keyphrase_vector_col] = df_train[
args.keyphrase_vector_col].apply(ast.literal_eval)
df_valid = pd.read_csv(args.data_dir + args.valid_set)
keyphrase_names = pd.read_csv(args.data_dir + args.keyphrase_set)[
args.keyphrase_col].values
if args.explanation:
explanation_parameter_tuning(num_users,
num_items,
args.user_col,
args.item_col,
args.rating_col,
args.keyphrase_vector_col,
df_train,
df_valid,
keyphrase_names,
params,
save_path=args.save_path)
else:
hyper_parameter_tuning(num_users,
num_items,
args.user_col,
args.item_col,
args.rating_col,
args.keyphrase_vector_col,
df_train,
df_valid,
keyphrase_names,
params,
save_path=args.save_path)
def main(args):
params = load_yaml(args.parameters)
params['models'] = {params['models']: models[params['models']]}
R_train = load_numpy(path=args.data_dir, name=args.train_set)
R_valid = load_numpy(path=args.data_dir, name=args.valid_set)
R_train_keyphrase = load_numpy(path=args.data_dir,
name=args.train_keyphrase_set)
R_valid_keyphrase = load_numpy(path=args.data_dir,
name=args.valid_keyphrase_set)
R_train_keyphrase[R_train_keyphrase != 0] = 1
R_valid_keyphrase[R_valid_keyphrase != 0] = 1
hyper_parameter_tuning(R_train,
R_valid,
R_train_keyphrase.todense(),
R_valid_keyphrase,
params,
save_path=args.save_path,
tune_explanation=args.tune_explanation)
def main(args):
params = load_yaml(args.parameters)
params['models'] = {params['models']: models[params['models']]}
R_train = load_numpy(path=args.path, name=args.train)
R_valid = load_numpy(path=args.path, name=args.valid)
hyper_parameter_tuning(R_train, R_valid, params, save_path=args.save_path)