def test_parameter(self):
modelname = "SW_StillingerWeber_1985_Si__MO_405512056662_005"
model = KIMModel(modelname)
# parameters
params = model.get_model_params()
sigma = params["sigma"][0]
A = params["A"][0]
# optimizing parameters
# B will not be optimized, only providing initial guess
model.set_opt_params(sigma=[["default"]],
B=[["default", "fix"]],
A=[["default"]])
calc = Calculator(model)
x0 = calc.get_opt_params()
assert x0[0] == sigma
assert x0[1] == A
assert len(x0) == 2
assert model.get_num_opt_params() == 2
x1 = [i + 0.1 for i in x0]
calc.update_model_params(x1)
assert params["sigma"][0] == sigma + 0.1
assert params["A"][0] == A + 0.1
def test_lj():
model = LennardJones()
# set params directly
model.set_fitting_params(sigma=[[1.1, "fix"]], epsilon=[[2.1, None, 3.0]])
model.update_model_params()
# model.echo_model_params()
# model.echo_fitting_params()
# set params by reading from file (the same as set params directly)
# fname = 'tmp_lj.params'
# write_tmp_params(fname)
# model.read_fitting_params(fname)
# delete_tmp_params(fname)
calc = Calculator(model)
dset = Dataset(order_by_species=False)
fname = "./configs_extxyz/MoS2/MoS2_energy_forces_stress.xyz"
dset.read(fname)
configs = dset.get_configs()
energy_forces_stress(calc, configs, True, False, False)
energy_forces_stress(calc, configs, True, True, False)
energy_forces_stress(calc, configs, True, True, True)
# params relation callback
model.set_params_relation_callback(params_relation)
x0 = calc.get_opt_params()
calc.update_opt_params(x0)
sigma = model.get_model_params("sigma")
epsilon = model.get_model_params("epsilon")
assert np.allclose(sigma * 2, epsilon)
def init():
model = KIMModel(model_name="SW_StillingerWeber_1985_Si__MO_405512056662_006")
# Cannot set them all by calling this function only once, because the assertion
# depends on order
model.set_opt_params(A=[[5.0]])
model.set_opt_params(B=[["default"]])
model.set_opt_params(sigma=[[2.0951, "fix"]])
model.set_opt_params(gamma=[[1.5]])
path = Path(__file__).parent.joinpath("configs_extxyz/Si_4")
tset = Dataset(path)
configs = tset.get_configs()
calc = Calculator(model)
calc.create(configs, use_energy=True, use_forces=True)
loss = Loss(calc, residual_fn=residual_fn, nprocs=1)
return loss
def test_compute(self):
test_file_path = Path(__file__).parents[1].joinpath("configs_extxyz")
tset = Dataset(test_file_path.joinpath("Si_4"))
configs = tset.get_configs()
modelname = "SW_StillingerWeber_1985_Si__MO_405512056662_005"
model = KIMModel(modelname)
# calculator
calc = Calculator(model)
compute_arguments = calc.create(configs)
for i, ca in enumerate(compute_arguments):
calc.compute(ca)
energy = calc.get_energy(ca)
forces = calc.get_forces(ca)[:3]
assert energy == pytest.approx(ref_energies[i], 1e-6)
assert np.allclose(forces, ref_forces[i])
"""
Compute the root-mean-square error (RMSE) of a model prediction and reference values in
the dataset.
"""
from kliff.analyzers import EnergyForcesRMSE
from kliff.calculators import Calculator
from kliff.dataset import Dataset
from kliff.models import KIMModel
from kliff.utils import download_dataset
model = KIMModel(model_name="SW_StillingerWeber_1985_Si__MO_405512056662_005")
# load the trained model back
# model.load("kliff_model.yaml")
dataset_path = download_dataset(dataset_name="Si_training_set_4_configs")
tset = Dataset(dataset_path)
configs = tset.get_configs()
calc = Calculator(model)
calc.create(configs)
analyzer = EnergyForcesRMSE(calc)
analyzer.run(verbose=2, sort="energy")
def test_main():
# training set
tset = Dataset()
tset.read("./configs_extxyz/Si_4")
configs = tset.get_configs()
# model
modelname = "SW_StillingerWeber_1985_Si__MO_405512056662_005"
model = KIM(modelname)
# calculator
calc = Calculator(model)
compute_arguments = calc.create(configs)
for i, ca in enumerate(compute_arguments):
calc.compute(ca)
energy = calc.get_energy(ca)
forces = calc.get_forces(ca)[:3]
assert energy == pytest.approx(ref_energies[i], 1e-6)
assert np.allclose(forces, ref_forces[i])
# Cannot set them all by calling this function only once, because the assertion
# depends on order
model.set_fitting_params(sigma=[["default"]])
model.set_fitting_params(A=[["default", "fix"]])
model.set_fitting_params(B=[["default"]])
# update params
x0 = calc.get_opt_params()
x1 = [i + 0.1 for i in x0]
calc.update_opt_params(x1)
params = model.inquire_params()
assert np.allclose(params["sigma"].get_value(), [x1[0]])
assert np.allclose(params["B"].get_value(), [x1[1]])
# restore params
calc.update_opt_params(x0)