def sample_triple_time(self, dump=True, load_save=False):
"""
Sample triples or load triples samples from files. Only applicable for basket based Recommender
Returns:
None
"""
sample_file_name = ("triple_" + self.config["dataset"] +
(("_" + str(self.config["percent"] * 100))
if "percent" in self.config else "") +
(("_" + str(self.config["time_step"]))
if "time_step" in self.config else "_10") + "_" +
str(self.config["n_sample"])
if "percent" in self.config else "" + ".csv")
self.process_path = os.path.join(self.config["root_dir"],
self.config["process_dir"])
ensureDir(self.process_path)
sample_file = os.path.join(self.process_path, sample_file_name)
my_sampler = Sampler(
self.train,
sample_file,
self.config["n_sample"],
dump=dump,
load_save=load_save,
)
return my_sampler.sample_by_time(self.config["time_step"])
def get_adj_mat(self):
""" Get the adjacent matrix, if not previously stored then call the function to create
This method is for NGCF model
Return:
Different types of adjacment matrix
"""
self.init_train_items()
process_file_name = (
"ngcf_"
+ self.config["dataset"]
+ "_"
+ self.config["data_split"]
+ (
("_" + str(self.config["percent"] * 100))
if "percent" in self.config
else ""
)
)
self.process_path = os.path.join(
self.config["root_dir"],
self.config["process_dir"],
self.config["dataset"] + "/",
)
process_file_name = os.path.join(self.process_path, process_file_name)
ensureDir(process_file_name)
print(process_file_name)
try:
t1 = time()
adj_mat = sp.load_npz(os.path.join(process_file_name, "s_adj_mat.npz"))
norm_adj_mat = sp.load_npz(
os.path.join(process_file_name, "s_norm_adj_mat.npz")
)
mean_adj_mat = sp.load_npz(
os.path.join(process_file_name, "s_mean_adj_mat.npz")
)
print("already load adj matrix", adj_mat.shape, time() - t1)
except Exception:
adj_mat, norm_adj_mat, mean_adj_mat = self.create_adj_mat()
sp.save_npz(os.path.join(process_file_name, "s_adj_mat.npz"), adj_mat)
sp.save_npz(
os.path.join(process_file_name, "s_norm_adj_mat.npz"), norm_adj_mat
)
sp.save_npz(
os.path.join(process_file_name, "s_mean_adj_mat.npz"), mean_adj_mat
)
return adj_mat, norm_adj_mat, mean_adj_mat
def prepare_env(config):
"""Prepare running environment
- Load parameters from json files.
- Initialize system folders, model name and the paths to be saved.
- Initialize resource monitor.
- Initialize random seed.
- Initialize logging.
Args:
config (dict): Global configs.
"""
# obtain abspath for the project
# You need specified it if it is running in the container.
if "root_dir" not in config:
file_dir = os.path.dirname(os.path.abspath(__file__))
config["root_dir"] = os.path.abspath(os.path.join(file_dir, ".."))
# load config file from json
with open(config["config_file"]) as config_params:
print("loading config file", config["config_file"])
json_config = json.load(config_params)
print(f"Current pid: {os.getpid()}")
# update global parameters with these parameters received from the command line .
json_config.update(config)
config = json_config
# construct unique model run id, which consist of model name, config id and a timestamp
timestamp_str = datetime.now().strftime("%Y%m%d_%H%M%S")
random_str = "".join(
[random.choice(string.ascii_lowercase) for n in range(6)])
config["model_run_id"] = (config["model"] + "_" + config["config_id"] +
"_" + timestamp_str + "_" + random_str)
set_seed(config["seed"] if "seed" in config else 2020)
initialize_folders(config["root_dir"])
# Initialize log file
config["log_file"] = os.path.join(config["root_dir"], config["log_dir"],
config["model_run_id"])
logger.init_std_logger(config["log_file"])
print("python version:", sys.version)
print("pytorch version:", torch.__version__)
# File paths to be saved
config["run_dir"] = os.path.join(config["root_dir"], config["run_dir"],
config["model_run_id"])
print(
"The intermediate running statuses will be reported in folder:",
config["run_dir"],
)
# Model checkpoints paths to be saved
config["model_save_dir"] = os.path.join(config["root_dir"],
config["checkpoint_dir"],
config["model_run_id"])
ensureDir(config["model_save_dir"])
print("Model checkpoint will save in file:", config["model_save_dir"])
config["result_file"] = os.path.join(config["root_dir"],
config["result_dir"],
config["result_file"])
print("Performance result will save in file:", config["result_file"])
# remove comments
print_dict_as_table(config, "Model configs")
return config