def add_data(self, queried_images):
for query in queried_images:
self.training_data, self.parent_dataset = bootstrap_ensemble(
self.parent_dataset,
self.training_data,
query,
n_ensembles=self.ensemble,
)
return self.parent_dataset, self.training_data
def __init__(self, learner_params, trainer, training_data, parent_calc,
base_calc, ensemble):
super().__init__(learner_params, trainer, training_data, parent_calc,
base_calc)
self.ensemble = ensemble
assert isinstance(ensemble, int) and ensemble > 1, "Invalid ensemble!"
self.training_data, self.parent_dataset = bootstrap_ensemble(
self.training_data, n_ensembles=ensemble)
self.parent_calls = 0
def add_data(self, queried_images):
for query in queried_images:
# bug here maybe?: self.training_data and self.parent_dataset
# are being overwritten in every iteration.
self.training_data, self.parent_dataset = bootstrap_ensemble(
self.parent_dataset,
self.training_data,
query,
n_ensembles=self.ensemble,
)
return self.parent_dataset, self.training_data
def init_training_data(self):
"""
Prepare the training data by attaching delta values for training.
"""
raw_data = self.parent_dataset
sp_raw_data = convert_to_singlepoint(raw_data)
parent_ref_image = sp_raw_data[0].copy()
base_ref_image = compute_with_calc(sp_raw_data[:1], self.base_calc)[0]
self.refs = [parent_ref_image, base_ref_image]
self.delta_sub_calc = DeltaCalc(self.calcs, "sub", self.refs)
self.ensemble_sets, self.parent_dataset = bootstrap_ensemble(
compute_with_calc(sp_raw_data, self.delta_sub_calc))
def __init__(self, learner_params, parent_dataset, parent_calc, base_calc,
trainer, n_ensembles, n_cores):
Calculator.__init__(self)
self.n_ensembles = n_ensembles
self.parent_calc = parent_calc
self.base_calc = base_calc
self.calcs = [parent_calc, base_calc]
self.trainer = trainer
self.learner_params = learner_params
self.n_cores = n_cores
self.ensemble_sets, self.parent_dataset = bootstrap_ensemble(
parent_dataset, n_ensembles=n_ensembles)
self.init_training_data()
self.ensemble_calc = make_ensemble(self.ensemble_sets, self.trainer,
self.base_calc, self.refs,
self.n_cores)
self.uncertain_tol = learner_params["uncertain_tol"]
self.parent_calls = 0
self.init_training_data()
def calculate(self, atoms, properties, system_changes):
Calculator.calculate(self, atoms, properties, system_changes)
energy_pred = self.ensemble_calc.get_potential_energy(atoms)
force_pred = self.ensemble_calc.get_forces(atoms)
uncertainty = atoms.info["uncertainty"][0]
db = connect('dft_calls.db')
cwd = os.getcwd()
if uncertainty >= self.uncertain_tol:
print('DFT required')
new_data = atoms.copy()
new_data.set_calculator(copy.copy(self.parent_calc))
# os.makedirs("./temp", exist_ok=True)
# os.chdir("./temp")
energy_pred = new_data.get_potential_energy(apply_constraint=False)
force_pred = new_data.get_forces(apply_constraint=False)
new_data.set_calculator(
sp(atoms=new_data, energy=energy_pred, forces=force_pred))
# os.chdir(cwd)
# os.system("rm -rf ./temp")
energy_list.append(energy_pred)
db.write(new_data)
self.ensemble_sets, self.parent_dataset = bootstrap_ensemble(
self.parent_dataset, self.ensemble_sets, new_data=new_data)
self.ensemble_calc = make_ensemble(self.ensemble_sets,
self.trainer, self.base_calc,
self.refs, self.n_cores)
self.parent_calls += 1
else:
db.write(None)
self.results["energy"] = energy_pred
self.results["forces"] = force_pred