def run_calibration(n_rounds, current_period=0, verbose=0):
if not os.path.isdir('../calibrations'):
os.makedirs('../calibrations')
map = Map()
best_score = None
for i in range(n_rounds):
if i % 10 == 0:
print(f'round {i}...')
evaluations = []
array_params, pdict = build_parameters(current_period, verbose)
map.from_arrays(**array_params)
for _ in range(N_PERIODS):
for _ in range(pdict['n_moves_per_period']):
map.make_move()
map.forward_all_cells()
state_ids, state_numbers = map.get_states_numbers()
evaluations.append((state_ids, state_numbers))
score = evaluate(evaluations, DAY, N_PERIODS)
if best_score is None or (
score['hosp']['err'] <= best_score['hosp']['err']
and score['icu']['err'] <= best_score['icu']['err']):
fpath = os.path.join(CALIBRATION_DIR, f'{i}.npy')
to_save = {
'score': score,
'params': array_params,
'rt': map.get_r_factors(),
'pdict': pdict
}
print(f'New best score found: {score}, saved under {fpath}')
print(f"corresponding pdict:\n{pdict}")
best_score = score
np.save(fpath, to_save)
map.save(os.path.join('maps', 'week1'))
def run_calibration(n_rounds, current_period=0, verbose=0):
if not os.path.isdir('../calibrations'):
os.makedirs('../calibrations')
memory_error = False
map = Map()
best_score = None
for i in range(n_rounds):
if i%10 == 0:
print(f'round {i}...')
evaluations = []
array_params, pdict = build_parameters(current_period, verbose)
try:
map.from_arrays(**array_params)
except:
print('Memory error')
memory_error = True
pass
if memory_error:
memory_error = False
continue
for prd in range(N_PERIODS):
for _ in range(pdict['n_moves_per_period']):
map.make_move(prop_cont_factor=pdict['prop_cont_factor'])
map.forward_all_cells()
state_ids, state_numbers = map.get_states_numbers()
# Check if we are already in the good range at mid time
evaluation = (state_ids, state_numbers)
if prd == 6:
ind_asymptomatic = np.where(evaluation[0] == 1)[0][0].astype(np.uint32)
n_asymptomatic = evaluation[1][ind_asymptomatic]
evaluations.append(evaluation)
score, progressions = evaluate_move(evaluations)
if best_score is None or score < best_score:
fpath = os.path.join(CALIBRATION_DIR, f'move_3e_{i}.npy')
to_save = {'score': score, 'params': array_params, 'pdict': pdict}
print(f'New best score found: {score}, saved under {fpath}')
print(f'Corresponding progressions: {progressions}')
print(f'corresponding params:')
pprint(pdict)
best_score = score
np.save(fpath, to_save)
# agents move now `f_unmove`x less than before lockdown
f_unmove = 1
p_moves = np.load(os.path.join(map_path, 'p_moves.npy')).flatten()
n_p_moves = p_moves.shape[0]
p_move = pdict['avg_p_move'] / f_unmove
p_moves = get_p_moves(n_p_moves, p_move)
print(f'DEBUG: mean of p_move: {np.mean(p_moves)}')
res = {}
map = Map()
map.load(map_path)
# map.set_verbose(3)
map.set_p_moves(p_moves)
map.set_unsafeties(unsafeties)
new_hosps = []
for i in range(N_PERIODS):
res[i] = {}
for _ in range(n_moves_per_period):
map.make_move(p_mask=.3)
new_states = map.forward_all_cells(tracing_rate=0)
new_hosp = new_states[new_states == 4].shape[0]
new_hosps.append(new_hosp)
state_ids, state_numbers = map.get_states_numbers()
for j in range(state_ids.shape[0]):
res[i][state_ids[j]] = state_numbers[j]
pprint(res)
print(new_hosps)
new_state_id = 1
print(
f'Injecting {N_INFECTED_AGENTS_START} contaminated agents out of {N_AGENTS} in map'
)
map.change_state_agents(np.array([infected_agent_id]),
np.array([new_state_id]))
stats = {}
t_start = time()
for i in range(N_PERIODS):
print(f'starting period {i}...')
t0 = time()
for j in range(N_MOVES_PER_PERIOD):
t_ = time()
map.make_move()
map.forward_all_cells()
states_ids, state_numbers = map.get_states_numbers()
stats[i] = {
states_ids[k]: state_numbers[k]
for k in range(len(states_ids))
}
if 5 in stats:
print()
print(f'period {i} computed in {time() - t0}s')
print(f'duration: {time() - t_start}s')
print(stats)