def run_random_search(verbose,
num_diff_experiments,
num_repeat_experiment,
allow_duplicates=False,
df_path=None,
overwrite=True,
data_to_collect=POSSIBLE_DATA,
MVP_key='waitingTime',
save_model=True):
grid = load_constants('constants/constants-grid.json')
if not allow_duplicates:
_, num_choices = get_num_grid_choices(grid)
num_diff_experiments = min(num_choices, num_diff_experiments)
# Make grid choice generator
grid_choice_gen = grid_choices_random(grid, num_diff_experiments)
for diff_experiment, constants in enumerate(grid_choice_gen):
data_collector_obj = DataCollector(
data_to_collect, MVP_key, constants,
'test' if constants['agent']['agent_type'] == 'rule' else 'eval',
df_path, overwrite if diff_experiment == 0 else False, verbose)
for same_experiment in range(num_repeat_experiment):
print(' --- Running experiment {}.{} / {}.{} --- '.format(
diff_experiment + 1, same_experiment + 1, num_diff_experiments,
num_repeat_experiment))
if save_model:
data_collector_obj.set_save_model_path(
'models/saved_models/random_{}-{}.pt'.format(
diff_experiment + 1, same_experiment + 1))
run_experiment(diff_experiment + 1, same_experiment + 1, constants,
data_collector_obj)
def run_normal(verbose,
num_experiments=1,
df_path=None,
overwrite=True,
data_to_collect=POSSIBLE_DATA,
MVP_key='waitingTime',
save_model=True,
load_model_file=None):
# if loading, then dont save
if load_model_file:
save_model = False
if not df_path:
df_path = 'run-data.xlsx' # def. path
# Load constants
constants = load_constants('constants/constants.json')
data_collector_obj = DataCollector(
data_to_collect, MVP_key, constants,
'test' if constants['agent']['agent_type'] == 'rule' or load_model_file
else 'eval', df_path, overwrite, verbose)
loaded_model = None
if load_model_file:
loaded_model = torch.load('models/saved_models/' + load_model_file)
for exp in range(num_experiments):
print(' --- Running experiment {} / {} --- '.format(
exp + 1, num_experiments))
if save_model:
data_collector_obj.set_save_model_path(
'models/saved_models/normal_{}.pt'.format(exp + 1))
run_experiment(exp + 1,
None,
constants,
data_collector_obj,
loaded_model=loaded_model)
