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Make it possible to run multi-fidelity opimization with zero ensemble… #90

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Merged
merged 1 commit into from
Mar 27, 2025
Merged

Make it possible to run multi-fidelity opimization with zero ensemble… #90

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136 changes: 69 additions & 67 deletions ensemble/ensemble.py
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Original file line number Diff line number Diff line change
Expand Up @@ -154,6 +154,7 @@ def _ext_ml_info(self):
if 'multilevel' in self.keys_en:
# parse
self.multilevel = {}
self.ML_error_corr = 'none'
for i, opt in enumerate(list(zip(*self.keys_en['multilevel']))[0]):
if opt == 'levels':
self.multilevel['levels'] = [elem for elem in range(
Expand Down Expand Up @@ -708,74 +709,75 @@ def calc_ml_prediction(self, input_state=None):
self.sim.redund_sim.setup_fwd_run(level=level)
self.sim.setup_fwd_run(level=level)
ml_ne = self.multilevel['ne'][level]
# Ensure that we put all the states in a list
list_state = [deepcopy({}) for _ in ml_ne]
for i in ml_ne:
if input_state is None:
for key in self.state[level].keys():
if self.state[level][key].ndim == 1:
list_state[i][key] = deepcopy(self.state[level][key])
elif self.state[level][key].ndim == 2:
list_state[i][key] = deepcopy(self.state[level][key][:, i])
else:
for key in self.state.keys():
if input_state[level][key].ndim == 1:
list_state[i][key] = deepcopy(input_state[level][key])
elif input_state[level][key].ndim == 2:
list_state[i][key] = deepcopy(input_state[level][key][:, i])
if self.aux_input is not None: # several models are used
list_state[i]['aux_input'] = self.aux_input[i]

# Index list of ensemble members
list_member_index = list(ml_ne)

# Run prediction in parallel using p_map
en_pred = p_map(self.sim.run_fwd_sim, list_state,
list_member_index, num_cpus=no_tot_run, disable=self.disable_tqdm)

# List successful runs and crashes
list_crash = [indx for indx, el in enumerate(en_pred) if el is False]
list_success = [indx for indx, el in enumerate(en_pred) if el is not False]
success = True

# Dump all information and print error if all runs have crashed
if not list_success:
self.save()
success = False
if len(list_crash) > 1:
print(
'\n\033[1;31mERROR: All started simulations has failed! We dump all information and exit!\033[1;m')
self.logger.info(
'\n\033[1;31mERROR: All started simulations has failed! We dump all information and exit!\033[1;m')
sys.exit(1)
return success

# Check crashed runs
if list_crash:
# Replace crashed runs with (random) successful runs. If there are more crashed runs than successful once,
# we draw with replacement.
if len(list_crash) < len(list_success):
copy_member = np.random.choice(
list_success, size=len(list_crash), replace=False)
else:
copy_member = np.random.choice(
list_success, size=len(list_crash), replace=True)

# Insert the replaced runs in prediction list
for indx, el in enumerate(copy_member):
print(f'\033[92m--- Ensemble member {list_crash[indx]} failed, has been replaced by ensemble member '
f'{el}! ---\033[92m')
self.logger.info(f'\033[92m--- Ensemble member {list_crash[indx]} failed, has been replaced by '
f'ensemble member {el}! ---\033[92m')
for key in self.state[level].keys():
self.state[level][key][:, list_crash[indx]] = deepcopy(
self.state[level][key][:, el])
en_pred[list_crash[indx]] = deepcopy(en_pred[el])
if ml_ne:
# Ensure that we put all the states in a list
list_state = [deepcopy({}) for _ in ml_ne]
for i in ml_ne:
if input_state is None:
for key in self.state[level].keys():
if self.state[level][key].ndim == 1:
list_state[i][key] = deepcopy(self.state[level][key])
elif self.state[level][key].ndim == 2:
list_state[i][key] = deepcopy(self.state[level][key][:, i])
else:
for key in self.state.keys():
if input_state[level][key].ndim == 1:
list_state[i][key] = deepcopy(input_state[level][key])
elif input_state[level][key].ndim == 2:
list_state[i][key] = deepcopy(input_state[level][key][:, i])
if self.aux_input is not None: # several models are used
list_state[i]['aux_input'] = self.aux_input[i]

# Index list of ensemble members
list_member_index = list(ml_ne)

# Run prediction in parallel using p_map
en_pred = p_map(self.sim.run_fwd_sim, list_state,
list_member_index, num_cpus=no_tot_run, disable=self.disable_tqdm)

# Convert ensemble specific result into pred_data, and filter for NONE data
ml_pred_data.append([{typ: np.concatenate(tuple((el[ind][typ][:, np.newaxis]) for el in en_pred), axis=1)
if any(elem is not None for elem in tuple((el[ind][typ]) for el in en_pred))
else None for typ in en_pred[0][0].keys()} for ind in range(len(en_pred[0]))])
# List successful runs and crashes
list_crash = [indx for indx, el in enumerate(en_pred) if el is False]
list_success = [indx for indx, el in enumerate(en_pred) if el is not False]
success = True

# Dump all information and print error if all runs have crashed
if not list_success:
self.save()
success = False
if len(list_crash) > 1:
print(
'\n\033[1;31mERROR: All started simulations has failed! We dump all information and exit!\033[1;m')
self.logger.info(
'\n\033[1;31mERROR: All started simulations has failed! We dump all information and exit!\033[1;m')
sys.exit(1)
return success

# Check crashed runs
if list_crash:
# Replace crashed runs with (random) successful runs. If there are more crashed runs than successful once,
# we draw with replacement.
if len(list_crash) < len(list_success):
copy_member = np.random.choice(
list_success, size=len(list_crash), replace=False)
else:
copy_member = np.random.choice(
list_success, size=len(list_crash), replace=True)

# Insert the replaced runs in prediction list
for indx, el in enumerate(copy_member):
print(f'\033[92m--- Ensemble member {list_crash[indx]} failed, has been replaced by ensemble member '
f'{el}! ---\033[92m')
self.logger.info(f'\033[92m--- Ensemble member {list_crash[indx]} failed, has been replaced by '
f'ensemble member {el}! ---\033[92m')
for key in self.state[level].keys():
self.state[level][key][:, list_crash[indx]] = deepcopy(
self.state[level][key][:, el])
en_pred[list_crash[indx]] = deepcopy(en_pred[el])

# Convert ensemble specific result into pred_data, and filter for NONE data
ml_pred_data.append([{typ: np.concatenate(tuple((el[ind][typ][:, np.newaxis]) for el in en_pred), axis=1)
if any(elem is not None for elem in tuple((el[ind][typ]) for el in en_pred))
else None for typ in en_pred[0][0].keys()} for ind in range(len(en_pred[0]))])

# loop over time instance first, and the level instance.
self.pred_data = np.array(ml_pred_data).T.tolist()
Expand Down
4 changes: 4 additions & 0 deletions popt/loop/ensemble.py
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Original file line number Diff line number Diff line change
Expand Up @@ -260,6 +260,10 @@ def gradient(self, x, *args, **kwargs):
the generated ensemble ($X$) through the simulator to give an ensemble of objective function values
($J$), and in the end calculate $S$. Note that $S$ is an $N_x \times 1$ vector, where
$N_x$ is length of the control vector and the objective function is scalar.

Note: In the case of multi-fidelity optimization, it is possible to specify 0 members for some of the levels
in order to skip these levels. In that case, cov_wgt should have the same length as the number of levels
that is acutally used.

Parameters
----------
Expand Down