def test_generate_by_line():
pm = ParameterHelper(verbose="DEBUG")
pm.define_group('specie', 'O', ['O'])
pm.define_group('specie', 'C', ['C'])
pm.define_group('specie', 'H', ['H'])
pm.define_group('twobody', '**', ['C', 'H'])
pm.define_group('twobody', 'OO', ['O', 'O'], atomic_str=True)
pm.define_group('threebody', '***', ['O', 'O', 'C'])
pm.define_group('threebody', 'OOO', ['O', 'O', 'O'])
pm.define_group('manybody', '1.5', ['C', 'H'])
pm.define_group('manybody', '1.5', ['C', 'O'])
pm.define_group('manybody', '1.5', ['O', 'H'])
pm.define_group('manybody', '2', ['O', 'O'])
pm.define_group('manybody', '2', ['H', 'O'])
pm.define_group('manybody', '2.8', ['O', 'O'])
pm.set_parameters('**', [1, 0.5])
pm.set_parameters('OO', [1, 0.5])
pm.set_parameters('***', [1, 0.5])
pm.set_parameters('OOO', [1, 0.5])
pm.set_parameters('1.5', [1, 0.5, 1.5])
pm.set_parameters('2', [1, 0.5, 2])
pm.set_parameters('2.8', [1, 0.5, 2.8])
pm.set_constraints('2', [True, False])
pm.set_constraints('2.8', False)
pm.set_parameters('cutoff_twobody', 5)
pm.set_parameters('cutoff_threebody', 4)
pm.set_parameters('cutoff_manybody', 3)
hm = pm.as_dict()
Parameters.check_instantiation(
hm['hyps'], hm['cutoffs'], hm['kernels'], hm)
def generate_hm(ntwobody,
nthreebody,
nmanybody=1,
constraint=False,
multihyps=True):
cutoff = 0.8
if multihyps is False:
kernels = []
parameters = {}
if ntwobody > 0:
kernels += ["twobody"]
parameters["cutoff_twobody"] = cutoff
if nthreebody > 0:
kernels += ["threebody"]
parameters["cutoff_threebody"] = cutoff
if nmanybody > 0:
kernels += ["manybody"]
parameters["cutoff_manybody"] = cutoff
pm = ParameterHelper(kernels=kernels,
random=True,
parameters=parameters)
hm = pm.as_dict()
hyps = hm["hyps"]
cut = hm["cutoffs"]
return hyps, hm, cut
pm = ParameterHelper(species=["H", "He"], parameters={"noise": 0.05})
if ntwobody > 0:
pm.define_group("twobody", "b1", ["*", "*"], parameters=random(2))
pm.set_parameters("cutoff_twobody", cutoff)
if nthreebody > 0:
pm.define_group("threebody",
"t1", ["*", "*", "*"],
parameters=random(2))
pm.set_parameters("cutoff_threebody", cutoff)
if nmanybody > 0:
pm.define_group("manybody",
"manybody1", ["*", "*"],
parameters=random(2))
pm.set_parameters("cutoff_manybody", cutoff)
if ntwobody > 1:
pm.define_group("twobody", "b2", ["H", "H"], parameters=random(2))
if nthreebody > 1:
pm.define_group("threebody",
"t2", ["H", "H", "H"],
parameters=random(2))
if constraint is False:
hm = pm.as_dict()
hyps = hm["hyps"]
cut = hm["cutoffs"]
return hyps, hm, cut
pm.set_constraints("b1", opt=[True, False])
pm.set_constraints("t1", opt=[False, True])
hm = pm.as_dict()
hyps = hm["hyps"]
cut = hm["cutoffs"]
return hyps, hm, cut
def test_generate_by_line():
pm = ParameterHelper(verbose="DEBUG")
pm.define_group("specie", "O", ["O"])
pm.define_group("specie", "C", ["C"])
pm.define_group("specie", "H", ["H"])
pm.define_group("twobody", "**", ["C", "H"])
pm.define_group("twobody", "OO", ["O", "O"], atomic_str=True)
pm.define_group("threebody", "***", ["O", "O", "C"])
pm.define_group("threebody", "OOO", ["O", "O", "O"])
pm.define_group("manybody", "1.5", ["C", "H"])
pm.define_group("manybody", "1.5", ["C", "O"])
pm.define_group("manybody", "1.5", ["O", "H"])
pm.define_group("manybody", "2", ["O", "O"])
pm.define_group("manybody", "2", ["H", "O"])
pm.define_group("manybody", "2.8", ["O", "O"])
pm.set_parameters("**", [1, 0.5])
pm.set_parameters("OO", [1, 0.5])
pm.set_parameters("***", [1, 0.5])
pm.set_parameters("OOO", [1, 0.5])
pm.set_parameters("1.5", [1, 0.5, 1.5])
pm.set_parameters("2", [1, 0.5, 2])
pm.set_parameters("2.8", [1, 0.5, 2.8])
pm.set_constraints("2", [True, False])
pm.set_constraints("2.8", False)
pm.set_parameters("cutoff_twobody", 5)
pm.set_parameters("cutoff_threebody", 4)
pm.set_parameters("cutoff_manybody", 3)
hm = pm.as_dict()
Parameters.check_instantiation(hm["hyps"], hm["cutoffs"], hm["kernels"], hm)
def generate_hm(ntwobody, nthreebody, nmanybody=1, constraint=False, multihyps=True):
cutoff = 0.8
if (multihyps is False):
kernels = []
parameters = {}
if (ntwobody > 0):
kernels += ['twobody']
parameters['cutoff_twobody'] = cutoff
if (nthreebody > 0):
kernels += ['threebody']
parameters['cutoff_threebody'] = cutoff
if (nmanybody > 0):
kernels += ['manybody']
parameters['cutoff_manybody'] = cutoff
pm = ParameterHelper(kernels=kernels, random=True,
parameters=parameters)
hm = pm.as_dict()
hyps = hm['hyps']
cut = hm['cutoffs']
return hyps, hm, cut
pm = ParameterHelper(species=['H', 'He'], parameters={'noise':0.05})
if (ntwobody > 0):
pm.define_group('twobody', 'b1', ['*', '*'], parameters=random(2))
pm.set_parameters('cutoff_twobody', cutoff)
if (nthreebody > 0):
pm.define_group('threebody', 't1', ['*', '*', '*'], parameters=random(2))
pm.set_parameters('cutoff_threebody', cutoff)
if (nmanybody > 0):
pm.define_group('manybody', 'manybody1', ['*', '*'], parameters=random(2))
pm.set_parameters('cutoff_manybody', cutoff)
if (ntwobody > 1):
pm.define_group('twobody', 'b2', ['H', 'H'], parameters=random(2))
if (nthreebody > 1):
pm.define_group('threebody', 't2', ['H', 'H', 'H'], parameters=random(2))
if (constraint is False):
hm = pm.as_dict()
hyps = hm['hyps']
cut = hm['cutoffs']
return hyps, hm, cut
pm.set_constraints('b1', opt=[True, False])
pm.set_constraints('t1', opt=[False, True])
hm = pm.as_dict()
hyps = hm['hyps']
cut = hm['cutoffs']
return hyps, hm, cut
def get_random_training_set(nenv, nstruc):
"""Create a random training_set array with parameters
And generate four different kinds of hyperparameter sets:
* multi hypper parameters with two twobody type and two threebody type
* constrained optimization, with noise parameter optimized
* constrained optimization, without noise parameter optimized
* simple hyper parameters without multihyps set up
"""
np.random.seed(0)
cutoffs = {"twobody": 0.8, "threebody": 0.8}
parameters = {
"cutoff_twobody": 0.8,
"cutoff_threebody": 0.8,
"noise": 0.05
}
# 9 different hyper-parameters
pm = ParameterHelper(
species=["H", "He"],
kernels={
"twobody": [["*", "*"], ["H", "H"]],
"threebody": [["*", "*", "*"], ["H", "H", "H"]],
},
parameters=parameters,
ones=True,
random=False,
verbose="DEBUG",
)
hyps_mask1 = pm.as_dict()
# 9 different hyper-parameters, onlye train the 0, 2, 4, 6, 8
pm.set_constraints("twobody0", [True, True])
pm.set_constraints("twobody1", [False, False])
pm.set_constraints("threebody0", [True, True])
pm.set_constraints("threebody1", [False, False])
hyps_mask2 = pm.as_dict()
# 9 different hyper-parameters, only train the 0, 2, 4, 6
pm.set_constraints("noise", False)
hyps_mask3 = pm.as_dict()
# 5 different hyper-parameters, equivalent to no multihyps
pm = ParameterHelper(
species=["H", "He"],
kernels={
"twobody": [["*", "*"]],
"threebody": [["*", "*", "*"]]
},
parameters=parameters,
ones=True,
random=False,
verbose="DEBUG",
)
hyps_mask4 = pm.as_dict()
# 5 different hyper-parameters, no multihyps
pm = ParameterHelper(
kernels=["twobody", "threebody"],
parameters=parameters,
ones=True,
random=False,
verbose="DEBUG",
)
hyps_mask5 = pm.as_dict()
hyps_mask_list = [
hyps_mask1, hyps_mask2, hyps_mask3, hyps_mask4, hyps_mask5
]
# create training environments and forces
cell = np.eye(3)
unique_species = [0, 1]
noa = 5
training_data = []
training_labels = []
for _ in range(nenv):
positions = np.random.uniform(-1, 1, [noa, 3])
species = np.random.randint(0, len(unique_species), noa) + 1
struc = Structure(cell, species, positions)
training_data += [AtomicEnvironment(struc, 1, cutoffs)]
training_labels += [np.random.uniform(-1, 1, 3)]
training_labels = np.hstack(training_labels)
# create training structures and energies
training_structures = []
energy_labels = []
for _ in range(nstruc):
positions = np.random.uniform(-1, 1, [noa, 3])
species = np.random.randint(0, len(unique_species), noa) + 1
struc = Structure(cell, species, positions)
struc_envs = []
for n in range(noa):
struc_envs.append(AtomicEnvironment(struc, n, cutoffs))
training_structures.append(struc_envs)
energy_labels.append(np.random.uniform(-1, 1))
energy_labels = np.array(energy_labels)
# store it as global variables
name = "unit_test"
flare.gp_algebra._global_training_data[name] = training_data
flare.gp_algebra._global_training_labels[name] = training_labels
flare.gp_algebra._global_training_structures[name] = training_structures
flare.gp_algebra._global_energy_labels[name] = energy_labels
energy_noise = 0.01
return name, cutoffs, hyps_mask_list, energy_noise