def load(self, output_dir):
if not exists(output_dir):
return
self.c_s = np.load(join(output_dir, C_S_TABLE_FILE_NAME) + BIN_EXT)['a'] if \
exists(join(output_dir, C_S_TABLE_FILE_NAME) + BIN_EXT) else \
read_table_csv(join(output_dir, C_S_TABLE_FILE_NAME) + TEXT_EXT, dtype=np.uint32, has_header=True)
self.c_sa = np.load(join(output_dir, C_SA_TABLE_FILE_NAME) + BIN_EXT)['a'] if \
exists(join(output_dir, C_SA_TABLE_FILE_NAME) + BIN_EXT) else \
read_table_csv(join(output_dir, C_SA_TABLE_FILE_NAME) + TEXT_EXT, dtype=np.uint32, has_header=True)
if exists(join(output_dir, C_SAS_TABLE_FILE_NAME) + BIN_EXT):
self.c_sas = np.load(
join(output_dir, C_SAS_TABLE_FILE_NAME) + BIN_EXT)['a']
else:
read_3d_table_csv(self.c_sas,
join(output_dir, C_SAS_TABLE_FILE_NAME) +
TEXT_EXT,
dtype=np.uint32,
has_header=True)
self.r_sa = np.load(join(output_dir, R_SA_TABLE_FILE_NAME) + BIN_EXT)['a'] if \
exists(join(output_dir, R_SA_TABLE_FILE_NAME) + BIN_EXT) else \
read_table_csv(join(output_dir, R_SA_TABLE_FILE_NAME) + TEXT_EXT, dtype=np.float, has_header=True)
self.t_s = np.load(join(output_dir, T_S_TABLE_FILE_NAME) + BIN_EXT)['a'] if \
exists(join(output_dir, T_S_TABLE_FILE_NAME) + BIN_EXT) else \
read_table_csv(join(output_dir, T_S_TABLE_FILE_NAME) + TEXT_EXT, dtype=np.int32, has_header=True)
self.t_sa = np.load(join(output_dir, T_SA_TABLE_FILE_NAME) + BIN_EXT)['a'] if \
exists(join(output_dir, T_SA_TABLE_FILE_NAME) + BIN_EXT) else \
read_table_csv(join(output_dir, T_SA_TABLE_FILE_NAME) + TEXT_EXT, dtype=np.int32, has_header=True)
self.t = np.max(self.t_s)
def load(self, output_dir):
if not exists(output_dir):
return
super().load(output_dir)
self.q = np.load(join(output_dir, Q_TABLE_FILE_NAME) + BIN_EXT)['a'] if \
exists(join(output_dir, Q_TABLE_FILE_NAME) + BIN_EXT) else \
read_table_csv(join(output_dir, Q_TABLE_FILE_NAME), has_header=True)
self.dq = np.load(join(output_dir, DQ_TABLE_FILE_NAME) + BIN_EXT)['a'] if \
exists(join(output_dir, DQ_TABLE_FILE_NAME) + BIN_EXT) else \
read_table_csv(join(output_dir, DQ_TABLE_FILE_NAME), has_header=True)
def load(self,
name,
file_path,
stat_t=StatType.mean,
binary=False,
delimiter=','):
"""
Loads the data regarding a variable previously-saved by a stats collector from a file. Adds the variable sample
data per trial to the collection of variables in this collector.
:param str name: the name of the variable that we want to load.
:param str file_path: the path to the file.
:param int stat_t: the type of statistic of the variable to be loaded.
:param bool binary: whether to load a binary file. If False, a CSV text file will be loaded.
:param str delimiter: the delimiter for the fields in the CSV file, if binary is False.
:rtype: bool
:return: whether the data was successfully loaded.
"""
# tries to load files in different formats
data = None
if binary:
if exists('{}.npz'.format(file_path)):
data = np.load('{}.npz'.format(file_path))['a']
elif exists('{}.npy'.format(file_path)):
data = np.load('{}.npy'.format(file_path))
elif exists('{}.csv'.format(file_path)):
data = read_table_csv('{}.csv'.format(file_path),
delimiter,
has_header=True).T
if data is None:
return False
# sets variable data if loaded correctly
self._vars[name] = data
self._counts[name] = np.count_nonzero(~np.isnan(data), axis=1)
self._stat_types[name] = stat_t
return True
raise ValueError('Full analysis not found: {}'.format(file_name))
full_analysis.set_helper(helper)
# creates and load the agent's tables
agent, exploration_strategy = create_agent(helper, DEF_AGENT_TYPE, None)
agent.load(agent_dir)
# loads the agent's behavior
behavior_tracker = BehaviorTracker(0)
behavior_tracker.load(agent_dir)
# tries to load recorded episodes from results dir
rec_episodes_file = join(agent_dir, 'rec_episodes.csv')
if not exists(rec_episodes_file):
raise ValueError('Recorded episodes file not found: {}'.format(rec_episodes_file))
recorded_episodes = read_table_csv(rec_episodes_file, dtype=int)
# creates output dir if needed
output_dir = sys.argv[5] if len(sys.argv) > 5 else get_explanation_output_dir(agent_dir, explanation_t)
if not exists(output_dir):
makedirs(output_dir)
elif CLEAN_DIR:
rmtree(output_dir)
makedirs(output_dir)
# saves / copies configs to file
config.save_json(join(output_dir, 'config.json'))
# register environment in Gym according to env config
helper.register_gym_environment(False, 0, False)