def test_ase_api(self):
"""Test the ase api."""
gadb = DataConnection('{}/data/gadb.db'.format(wkdir))
all_cand = gadb.get_all_relaxed_candidates()
cf = all_cand[0].get_chemical_formula()
extend_atoms_class(all_cand[0])
self.assertTrue(isinstance(all_cand[0], type(all_cand[1])))
f = FeatureGenerator()
fp = f.composition_vec(all_cand[0])
all_cand[0].set_features(fp)
self.assertTrue(np.allclose(all_cand[0].get_features(), fp))
self.assertTrue(all_cand[0].get_chemical_formula() == cf)
extend_atoms_class(all_cand[1])
self.assertTrue(all_cand[1].get_features() is None)
g = ase_to_networkx(all_cand[2])
all_cand[2].set_graph(g)
self.assertTrue(all_cand[2].get_graph() == g)
self.assertTrue(all_cand[1].get_graph() is None)
def run_ga(n_to_test):
"""
This method specifies how to run the GA once the
initial random structures have been stored in godb.db.
"""
# Various initializations:
population_size = 10 # maximal size of the population
da = DataConnection('godb.db')
atom_numbers_to_optimize = da.get_atom_numbers_to_optimize() # = [14] * 7
n_to_optimize = len(atom_numbers_to_optimize) # = 7
# This defines how close the Si atoms are allowed to get
# in candidate structures generated by the genetic operators:
blmin = closest_distances_generator(atom_numbers_to_optimize,
ratio_of_covalent_radii=0.4)
# This is our OFPComparator instance which will be
# used to judge whether or not two structures are identical:
comparator = OFPComparator(n_top=None, dE=1.0, cos_dist_max=1e-3,
rcut=10., binwidth=0.05, pbc=[False]*3,
sigma=0.1, nsigma=4, recalculate=False)
# Defining a typical combination of genetic operators:
pairing = CutAndSplicePairing(da.get_slab(), n_to_optimize, blmin)
rattlemut = RattleMutation(blmin, n_to_optimize, rattle_prop=0.8,
rattle_strength=1.5)
operators = OperationSelector([2., 1.], [pairing, rattlemut])
# Relax the randomly generated initial candidates:
while da.get_number_of_unrelaxed_candidates() > 0:
a = da.get_an_unrelaxed_candidate()
a = relax_one(a)
da.add_relaxed_step(a)
# Create the population
population = Population(data_connection=da,
population_size=population_size,
comparator=comparator,
logfile='log.txt')
current_pop = population.get_current_population()
# Test n_to_test new candidates
for step in range(n_to_test):
print('Starting configuration number %d' % step, flush=True)
a3 = None
while a3 is None:
a1, a2 = population.get_two_candidates()
a3, description = operators.get_new_individual([a1, a2])
da.add_unrelaxed_candidate(a3, description=description)
a3 = relax_one(a3)
da.add_relaxed_step(a3)
population.update()
best = population.get_current_population()[0]
print('Highest raw score at this point: %.3f' % get_raw_score(best))
print('GA finished after step %d' % step)
write('all_candidates.traj', da.get_all_relaxed_candidates())
write('current_population.traj', population.get_current_population())
def test_networkx_api(self):
"""Test the ase api."""
gadb = DataConnection('{}/data/gadb.db'.format(wkdir))
all_cand = gadb.get_all_relaxed_candidates()
g = ase_to_networkx(all_cand[1])
self.assertEqual(len(g), len(all_cand[1]))
matrix = networkx_to_adjacency(g)
self.assertEqual(np.shape(matrix),
(len(all_cand[1]), len(all_cand[1])))
def test_feature_base(self):
"""Test the base feature generator."""
gadb = DataConnection('{}/data/gadb.db'.format(wkdir))
all_cand = gadb.get_all_relaxed_candidates()
f = BaseGenerator()
nl = ase_neighborlist(all_cand[0])
assert f.get_neighborlist(all_cand[0]) == nl
pos = all_cand[0].get_positions()
assert np.allclose(f.get_positions(all_cand[0]), pos)
def test_ase_nl(self):
"""Function to test the ase wrapper."""
# Connect database generated by a GA search.
gadb = DataConnection('{}/data/gadb.db'.format(wkdir))
# Get all relaxed candidates from the db file.
all_cand = gadb.get_all_relaxed_candidates(use_extinct=False)
nl = ase_neighborlist(all_cand[0])
self.assertEqual(len(all_cand[0]), len(nl))
def test_pdf(self):
gadb = DataConnection('{}/data/gadb.db'.format(wkdir))
all_cand = gadb.get_all_relaxed_candidates(use_extinct=False)
cutoff_dictionary = {}
for z in range(1, 92):
cutoff_dictionary[z] = covalent_radii[z]
pdf, x1 = pair_distribution(all_cand)
# Get bond length deviations from touching spheres.
dev, x2 = pair_deviation(all_cand, cutoffs=cutoff_dictionary)
def test_catlearn_nl(self):
"""Function to test the ase wrapper."""
# Connect database generated by a GA search.
gadb = DataConnection('{}/data/gadb.db'.format(wkdir))
# Get all relaxed candidates from the db file.
all_cand = gadb.get_all_relaxed_candidates(use_extinct=False)
nl1 = catlearn_neighborlist(all_cand[0], max_neighbor=1)
self.assertEqual((len(all_cand[0]), len(all_cand[0])), np.shape(nl1))
nl4 = catlearn_neighborlist(all_cand[0], max_neighbor=4)
self.assertFalse(np.allclose(nl1, nl4))
nl5 = catlearn_neighborlist(all_cand[0], max_neighbor=5)
nlfull = catlearn_neighborlist(all_cand[0], max_neighbor='full')
self.assertFalse(np.allclose(nl4, nl5))
self.assertTrue(np.allclose(nl5, nlfull))
def get_data(self):
"""Generate features from atoms objects."""
# Connect database generated by a GA search.
gadb = DataConnection('{}/data/gadb.db'.format(wkdir))
# Get all relaxed candidates from the db file.
print('Getting candidates from the database')
all_cand = gadb.get_all_relaxed_candidates(use_extinct=False)
# Setup the test and training datasets.
testset = get_unique(atoms=all_cand, size=test_size, key='raw_score')
trainset = get_train(atoms=all_cand,
size=train_size,
taken=testset['taken'],
key='raw_score')
# Clear out some old saved data.
for i in trainset['atoms']:
del i.info['data']['nnmat']
# Initiate the fingerprint generators with relevant input variables.
print('Getting the fingerprints')
f = FeatureGenerator()
train_features = f.return_vec(trainset['atoms'],
[f.nearestneighbour_vec])
test_features = f.return_vec(testset['atoms'],
[f.nearestneighbour_vec])
train_targets = []
for a in trainset['atoms']:
train_targets.append(a.info['key_value_pairs']['raw_score'])
test_targets = []
for a in testset['atoms']:
test_targets.append(a.info['key_value_pairs']['raw_score'])
return train_features, train_targets, trainset['atoms'], \
test_features, test_targets, testset['atoms']
da.add_relaxed_step(a)
# create the population
population = Population(data_connection=da, population_size=population_size, comparator=comp)
# test n_to_test new candidates
for i in xrange(n_to_test):
print("Now starting configuration number {0}".format(i))
a1, a2 = population.get_two_candidates()
a3, desc = pairing.get_new_individual([a1, a2])
if a3 == None:
continue
da.add_unrelaxed_candidate(a3, description=desc)
# Check if we want to do a mutation
if random() < mutation_probability:
a3_mut, desc = mutations.get_new_individual([a3])
if a3_mut != None:
da.add_unrelaxed_step(a3_mut, desc)
a3 = a3_mut
# Relax the new candidate
a3.set_calculator(EMT())
dyn = BFGS(a3, trajectory=None, logfile=None)
dyn.run(fmax=0.05, steps=100)
a3.set_raw_score(-a3.get_potential_energy())
da.add_relaxed_step(a3)
population.update()
write("all_candidates.traj", da.get_all_relaxed_candidates())
# create the population
population = Population(data_connection=da,
population_size=population_size,
comparator=comp)
# test n_to_test new candidates
for i in xrange(n_to_test):
print('Now starting configuration number {0}'.format(i))
a1, a2 = population.get_two_candidates()
a3, desc = pairing.get_new_individual([a1, a2])
if a3 is None:
continue
da.add_unrelaxed_candidate(a3, description=desc)
# Check if we want to do a mutation
if random() < mutation_probability:
a3_mut, desc = mutations.get_new_individual([a3])
if a3_mut is not None:
da.add_unrelaxed_step(a3_mut, desc)
a3 = a3_mut
# Relax the new candidate
parallel_local_run.relax(a3)
population.update()
# Wait until the last candidates are relaxed
while parallel_local_run.get_number_of_jobs_running() > 0:
time.sleep(5.)
write('all_candidates.traj', da.get_all_relaxed_candidates())
dc = DataConnection(db_file) slab_get = dc.get_slab() an_get = dc.get_atom_numbers_to_optimize() assert dc.get_number_of_unrelaxed_candidates() == 20 a1 = dc.get_an_unrelaxed_candidate() dc.mark_as_queued(a1) assert dc.get_number_of_unrelaxed_candidates() == 19 assert len(dc.get_all_candidates_in_queue()) == 1 a1.set_raw_score(0.0) dc.add_relaxed_step(a1) assert dc.get_number_of_unrelaxed_candidates() == 19 assert len(dc.get_all_candidates_in_queue()) == 0 assert len(dc.get_all_relaxed_candidates()) == 1 a2 = dc.get_an_unrelaxed_candidate() dc.mark_as_queued(a2) confid = a2.info['confid'] assert dc.get_all_candidates_in_queue()[0] == confid dc.remove_from_queue(confid) assert len(dc.get_all_candidates_in_queue()) == 0 os.remove(db_file)
.format(time_to_wait))
sleep(time_to_wait)
pbs_run.relax(a3)
redirect_print('-(sub-motion)-> New job submitted.')
if pbs_run.number_of_jobs_running() != 0:
redirect_print('MUTATION| Wait! There are still jobs unfinished.')
while pbs_run.number_of_jobs_running() != 0:
redirect_print('MUTATION| Wait for {} seconds and check again...'.format(
time_to_wait))
sleep(time_to_wait)
coords_to_write = da.get_all_relaxed_candidates()
write('all_candidates.traj', coords_to_write)
if len(coords_to_write) == 0:
redirect_print(
'ERROR| Something still goes wrong, no structures are generated, exit.'
)
redirect_print('ERROR| execuating command scancel -p amd_256...')
os.system('scancel -p amd_256')
exit()
redirect_print('=' * 50)
redirect_print(
'POST-PROCESSING| Now all individuals are saved in the same database {}'.
format(database_name))
idx_coord = 0
for icoord in coords_to_write:
# create the population
population = Population(data_connection=da,
population_size=population_size,
comparator=comp)
# test n_to_test new candidates
for i in range(n_to_test):
print('Now starting configuration number {0}'.format(i))
a1, a2 = population.get_two_candidates()
a3, desc = pairing.get_new_individual([a1, a2])
if a3 is None:
continue
da.add_unrelaxed_candidate(a3, description=desc)
# Check if we want to do a mutation
if random() < mutation_probability:
a3_mut, desc = mutations.get_new_individual([a3])
if a3_mut is not None:
da.add_unrelaxed_step(a3_mut, desc)
a3 = a3_mut
# Relax the new candidate
a3.set_calculator(EMT())
dyn = BFGS(a3, trajectory=None, logfile=None)
dyn.run(fmax=0.05, steps=100)
a3.info['key_value_pairs']['raw_score'] = -a3.get_potential_energy()
da.add_relaxed_step(a3)
population.update()
write('all_candidates.traj', da.get_all_relaxed_candidates())
def run_ga(n_to_test, kptdensity=3.5):
population_size = 20
da = DataConnection('godb.db')
atom_numbers_to_optimize = da.get_atom_numbers_to_optimize()
n_to_optimize = len(atom_numbers_to_optimize)
slab = da.get_slab()
all_atom_types = get_all_atom_types(slab, atom_numbers_to_optimize)
blmin = closest_distances_generator(all_atom_types, 0.05) # 0.5
# defining genetic operators:
mutation_probability = 0.75
pairing = CutAndSplicePairing(blmin,
p1=1.,
p2=0.,
minfrac=0.15,
use_tags=False)
cellbounds = CellBounds(
bounds={
'phi': [0.2 * 180., 0.8 * 180.],
'chi': [0.2 * 180., 0.8 * 180.],
'psi': [0.2 * 180., 0.8 * 180.]
})
strainmut = StrainMutation(blmin,
stddev=0.7,
cellbounds=cellbounds,
use_tags=False)
blmin_soft = closest_distances_generator(all_atom_types, 0.1)
softmut = SoftMutation(blmin_soft, bounds=[2., 5.], use_tags=False)
rattlemut = RattleMutation(blmin,
n_to_optimize,
rattle_prop=0.8,
rattle_strength=2.5,
use_tags=False)
mutations = OperationSelector([4., 4., 2], [softmut, strainmut, rattlemut])
if True:
# recalculate raw scores
structures = da.get_all_relaxed_candidates()
for atoms in structures:
atoms = singlepoint(atoms, kptdensity=kptdensity)
da.c.delete([atoms.info['relax_id']])
if 'data' not in atoms.info:
atoms.info['data'] = {}
da.add_relaxed_step(atoms)
print('Finished recalculating raw scores')
# relaxing the initial candidates:
while da.get_number_of_unrelaxed_candidates() > 0:
a = da.get_an_unrelaxed_candidate()
a.wrap()
a = relax_one(a, kptdensity=kptdensity)
da.add_relaxed_step(a)
# create the population
population = Population(data_connection=da,
population_size=population_size,
comparator=comparator,
logfile='log.txt')
current_pop = population.get_current_population()
strainmut.update_scaling_volume(current_pop, w_adapt=0.5, n_adapt=4)
pairing.update_scaling_volume(current_pop, w_adapt=0.5, n_adapt=4)
# Test n_to_test new candidates
ga_raw_scores = []
step = 0
for step in range(n_to_test):
print('Starting configuration number %d' % step, flush=True)
clock = time()
a3 = None
r = random()
if r > mutation_probability:
while a3 is None:
a1, a2 = population.get_two_candidates()
a3, desc = pairing.get_new_individual([a1, a2])
else:
while a3 is None:
a1 = population.get_one_candidate()
a3, desc = mutations.get_new_individual([a1])
dt = time() - clock
op = 'pairing' if r > mutation_probability else 'mutating'
print('Time for %s candidate(s): %.3f' % (op, dt), flush=True)
a3.wrap()
da.add_unrelaxed_candidate(a3, description=desc)
a3 = relax_one(a3, kptdensity=kptdensity)
da.add_relaxed_step(a3)
# Various updates:
population.update()
current_pop = population.get_current_population()
if step % 10 == 0:
strainmut.update_scaling_volume(current_pop,
w_adapt=0.5,
n_adapt=4)
pairing.update_scaling_volume(current_pop, w_adapt=0.5, n_adapt=4)
write('current_population.traj', current_pop)
# Print out information for easy analysis/plotting afterwards:
if r > mutation_probability:
print('Step %d %s %.3f %.3f %.3f' % (step, desc,\
get_raw_score(a1), get_raw_score(a2), get_raw_score(a3)))
else:
print('Step %d %s %.3f %.3f' % (step, desc,\
get_raw_score(a1), get_raw_score(a3)))
print('Step %d highest raw score in pop: %.3f' % \
(step, get_raw_score(current_pop[0])))
ga_raw_scores.append(get_raw_score(a3))
print('Step %d highest raw score generated by GA: %.3f' % \
(step, max(ga_raw_scores)))
emin = population.pop[0].get_potential_energy()
print('GA finished after step %d' % step)
print('Lowest energy = %8.3f eV' % emin, flush=True)
write('all_candidates.traj', da.get_all_relaxed_candidates())
write('current_population.traj', population.get_current_population())
def run_ga(n_to_test, kptdensity=None):
''' This method specifies how to run the GA once the
initial random structures have been stored in godb.db.
'''
# Various initializations:
population_size = 10
da = DataConnection('godb.db')
atom_numbers_to_optimize = da.get_atom_numbers_to_optimize()
n_to_optimize = len(atom_numbers_to_optimize)
slab = da.get_slab()
all_atom_types = get_all_atom_types(slab, atom_numbers_to_optimize)
blmin = closest_distances_generator(all_atom_types,
ratio_of_covalent_radii=0.05)
# Defining the mix of genetic operators:
mutation_probability = 0.3333
pairing = CutAndSplicePairing(slab, n_to_optimize, blmin)
rattlemut = RattleMutation(blmin, n_to_optimize,
rattle_prop=0.8, rattle_strength=1.5)
mirrormut = MirrorMutation(blmin, n_to_optimize)
mutations = OperationSelector([1., 1.], [rattlemut, mirrormut])
if True:
# Recalculate raw scores of any relaxed candidates
# present in the godb.db database (only applies to
# iter007).
structures = da.get_all_relaxed_candidates()
for atoms in structures:
atoms = singlepoint(atoms)
da.c.delete([atoms.info['relax_id']])
if 'data' not in atoms.info:
atoms.info['data'] = {}
da.add_relaxed_step(atoms)
print('Finished recalculating raw scores')
# Relax the randomly generated initial candidates:
while da.get_number_of_unrelaxed_candidates() > 0:
a = da.get_an_unrelaxed_candidate()
a.wrap()
a = relax_one(a)
da.add_relaxed_step(a)
# Create the population
population = Population(data_connection=da,
population_size=population_size,
comparator=comparator,
logfile='log.txt')
current_pop = population.get_current_population()
# Test n_to_test new candidates
ga_raw_scores = []
step = 0
for step in range(n_to_test):
print('Starting configuration number %d' % step, flush=True)
clock = time()
a3 = None
r = random()
if r > mutation_probability:
while a3 is None:
a1, a2 = population.get_two_candidates()
a3, desc = pairing.get_new_individual([a1, a2])
else:
while a3 is None:
a1 = population.get_one_candidate()
a3, desc = mutations.get_new_individual([a1])
dt = time() - clock
op = 'pairing' if r > mutation_probability else 'mutating'
print('Time for %s candidate(s): %.3f' % (op, dt), flush=True)
a3.wrap()
da.add_unrelaxed_candidate(a3, description=desc)
a3 = relax_one(a3)
da.add_relaxed_step(a3)
# Various updates:
population.update()
current_pop = population.get_current_population()
write('current_population.traj', current_pop)
# Print out information for easy analysis/plotting afterwards:
if r > mutation_probability:
print('Step %d %s %.3f %.3f %.3f' % (step, desc,\
get_raw_score(a1), get_raw_score(a2), get_raw_score(a3)))
else:
print('Step %d %s %.3f %.3f' % (step, desc,\
get_raw_score(a1), get_raw_score(a3)))
print('Step %d highest raw score in pop: %.3f' % \
(step, get_raw_score(current_pop[0])))
ga_raw_scores.append(get_raw_score(a3))
print('Step %d highest raw score generated by GA: %.3f' % \
(step, max(ga_raw_scores)))
emin = population.pop[0].get_potential_energy()
print('GA finished after step %d' % step)
print('Lowest energy = %8.3f eV' % emin, flush=True)
write('all_candidates.traj', da.get_all_relaxed_candidates())
write('current_population.traj', population.get_current_population())
def test_basic_example_main_run(seed, testdir):
# set up the random number generator
rng = np.random.RandomState(seed)
# create the surface
slab = fcc111('Au', size=(4, 4, 1), vacuum=10.0, orthogonal=True)
slab.set_constraint(FixAtoms(mask=len(slab) * [True]))
# define the volume in which the adsorbed cluster is optimized
# the volume is defined by a corner position (p0)
# and three spanning vectors (v1, v2, v3)
pos = slab.get_positions()
cell = slab.get_cell()
p0 = np.array([0., 0., max(pos[:, 2]) + 2.])
v1 = cell[0, :] * 0.8
v2 = cell[1, :] * 0.8
v3 = cell[2, :]
v3[2] = 3.
# Define the composition of the atoms to optimize
atom_numbers = 2 * [47] + 2 * [79]
# define the closest distance two atoms of a given species can be to each other
unique_atom_types = get_all_atom_types(slab, atom_numbers)
blmin = closest_distances_generator(atom_numbers=unique_atom_types,
ratio_of_covalent_radii=0.7)
# create the starting population
sg = StartGenerator(slab=slab,
blocks=atom_numbers,
blmin=blmin,
box_to_place_in=[p0, [v1, v2, v3]],
rng=rng)
# generate the starting population
population_size = 5
starting_population = [sg.get_new_candidate() for i in range(population_size)]
# from ase.visualize import view # uncomment these lines
# view(starting_population) # to see the starting population
# create the database to store information in
d = PrepareDB(db_file_name=db_file,
simulation_cell=slab,
stoichiometry=atom_numbers)
for a in starting_population:
d.add_unrelaxed_candidate(a)
# XXXXXXXXXX This should be the beginning of a new test,
# but we are using some resources from the precious part.
# Maybe refactor those things as (module-level?) fixtures.
# Change the following three parameters to suit your needs
population_size = 5
mutation_probability = 0.3
n_to_test = 5
# Initialize the different components of the GA
da = DataConnection('gadb.db')
atom_numbers_to_optimize = da.get_atom_numbers_to_optimize()
n_to_optimize = len(atom_numbers_to_optimize)
slab = da.get_slab()
all_atom_types = get_all_atom_types(slab, atom_numbers_to_optimize)
blmin = closest_distances_generator(all_atom_types,
ratio_of_covalent_radii=0.7)
comp = InteratomicDistanceComparator(n_top=n_to_optimize,
pair_cor_cum_diff=0.015,
pair_cor_max=0.7,
dE=0.02,
mic=False)
pairing = CutAndSplicePairing(slab, n_to_optimize, blmin, rng=rng)
mutations = OperationSelector([1., 1., 1.],
[MirrorMutation(blmin, n_to_optimize, rng=rng),
RattleMutation(blmin, n_to_optimize, rng=rng),
PermutationMutation(n_to_optimize, rng=rng)],
rng=rng)
# Relax all unrelaxed structures (e.g. the starting population)
while da.get_number_of_unrelaxed_candidates() > 0:
a = da.get_an_unrelaxed_candidate()
a.calc = EMT()
print('Relaxing starting candidate {0}'.format(a.info['confid']))
dyn = BFGS(a, trajectory=None, logfile=None)
dyn.run(fmax=0.05, steps=100)
set_raw_score(a, -a.get_potential_energy())
da.add_relaxed_step(a)
# create the population
population = Population(data_connection=da,
population_size=population_size,
comparator=comp,
rng=rng)
# test n_to_test new candidates
for i in range(n_to_test):
print('Now starting configuration number {0}'.format(i))
a1, a2 = population.get_two_candidates()
a3, desc = pairing.get_new_individual([a1, a2])
if a3 is None:
continue
da.add_unrelaxed_candidate(a3, description=desc)
# Check if we want to do a mutation
if rng.rand() < mutation_probability:
a3_mut, desc = mutations.get_new_individual([a3])
if a3_mut is not None:
da.add_unrelaxed_step(a3_mut, desc)
a3 = a3_mut
# Relax the new candidate
a3.calc = EMT()
dyn = BFGS(a3, trajectory=None, logfile=None)
dyn.run(fmax=0.05, steps=100)
set_raw_score(a3, -a3.get_potential_energy())
da.add_relaxed_step(a3)
population.update()
write('all_candidates.traj', da.get_all_relaxed_candidates())
def test_generators(self):
"""Generate features from atoms objects."""
# Test generic features for Pt then both Pt and Au.
get_mendeleev_params(atomic_number=78)
get_mendeleev_params(atomic_number=[78, 79],
params=default_params + ['en_ghosh'])
# Connect database generated by a GA search.
gadb = DataConnection('{}/data/gadb.db'.format(wkdir))
# Get all relaxed candidates from the db file.
print('Getting candidates from the database')
all_cand = gadb.get_all_relaxed_candidates(use_extinct=False)
# Setup the test and training datasets.
testset = get_unique(atoms=all_cand, size=test_size, key='raw_score')
self.assertTrue(len(testset['atoms']) == test_size)
self.assertTrue(len(testset['taken']) == test_size)
trainset = get_train(atoms=all_cand,
size=train_size,
taken=testset['taken'],
key='raw_score')
self.assertTrue(len(trainset['atoms']) == train_size)
self.assertTrue(len(trainset['target']) == train_size)
# Initiate the fingerprint generators with relevant input variables.
print('Getting the fingerprints')
f = FeatureGenerator(element_parameters='atomic_radius', nprocs=1)
f.normalize_features(trainset['atoms'], testset['atoms'])
data = f.return_vec(trainset['atoms'], [f.nearestneighbour_vec])
n, d = np.shape(data)
self.assertTrue(n == train_size and d == 4)
self.assertTrue(len(f.return_names([f.nearestneighbour_vec])) == d)
print('passed nearestneighbour_vec')
train_fp = f.return_vec(trainset['atoms'], [f.bond_count_vec])
n, d = np.shape(train_fp)
data = np.concatenate((data, train_fp), axis=1)
self.assertTrue(n == train_size and d == 52)
print('passed bond_count_vec')
train_fp = f.return_vec(trainset['atoms'], [f.distribution_vec])
n, d = np.shape(train_fp)
data = np.concatenate((data, train_fp), axis=1)
self.assertTrue(n == train_size and d == 10)
print('passed distribution_vec')
# EXPENSIVE to calculate. Not included in training data.
train_fp = f.return_vec(testset['atoms'], [f.connections_vec])
n, d = np.shape(train_fp)
self.assertTrue(n == test_size and d == 26)
print('passed connections_vec')
train_fp = f.return_vec(trainset['atoms'], [f.rdf_vec])
n, d = np.shape(train_fp)
data = np.concatenate((data, train_fp), axis=1)
self.assertTrue(n == train_size and d == 20)
print('passed rdf_vec')
# Start testing the standard fingerprint vector generators.
train_fp = f.return_vec(trainset['atoms'], [f.element_mass_vec])
n, d = np.shape(train_fp)
data = np.concatenate((data, train_fp), axis=1)
self.assertTrue(n == train_size and d == 1)
self.assertTrue(len(f.return_names([f.element_mass_vec])) == d)
print('passed element_mass_vec')
train_fp = f.return_vec(trainset['atoms'], [f.element_parameter_vec])
n, d = np.shape(train_fp)
data = np.concatenate((data, train_fp), axis=1)
# print(f.return_names([f.element_parameter_vec]))
self.assertTrue(n == train_size and d == 4)
self.assertTrue(len(f.return_names([f.element_parameter_vec])) == d)
print('passed element_parameter_vec')
train_fp = f.return_vec(trainset['atoms'], [f.composition_vec])
n, d = np.shape(train_fp)
data = np.concatenate((data, train_fp), axis=1)
self.assertTrue(n == train_size and d == 2)
self.assertTrue(len(f.return_names([f.composition_vec])) == d)
print('passed composition_vec')
train_fp = f.return_vec(trainset['atoms'], [f.eigenspectrum_vec])
n, d = np.shape(train_fp)
data = np.concatenate((data, train_fp), axis=1)
self.assertTrue(n == train_size and d == 147)
self.assertTrue(len(f.return_names([f.eigenspectrum_vec])) == d)
print('passed eigenspectrum_vec')
train_fp = f.return_vec(trainset['atoms'], [f.distance_vec])
n, d = np.shape(train_fp)
data = np.concatenate((data, train_fp), axis=1)
self.assertTrue(n == train_size and d == 2)
self.assertTrue(len(f.return_names([f.distance_vec])) == d)
print('passed distance_vec')
train_fp = f.return_vec(
trainset['atoms'],
[f.eigenspectrum_vec, f.element_mass_vec, f.composition_vec])
n, d = np.shape(train_fp)
self.assertTrue(n == train_size and d == 150)
self.assertTrue(
len(
f.return_names([
f.eigenspectrum_vec, f.element_mass_vec, f.composition_vec
])) == d)
print('passed combined generation')
train_fp = f.return_vec(trainset['atoms'], [f.neighbor_sum_vec])
n, d = np.shape(train_fp)
self.assertTrue(n == train_size and d == len(trainset['atoms'][0]))
# self.assertTrue(len(f.return_names([f.distance_vec])) == d)
print('passed neighbor_sum_vec')
train_fp = f.return_vec(trainset['atoms'], [f.neighbor_mean_vec])
n, d = np.shape(train_fp)
self.assertTrue(n == train_size and d == len(trainset['atoms'][0]))
# self.assertTrue(len(f.return_names([f.distance_vec])) == d)
print('passed neighbor_mean_vec')
f = FeatureGenerator(element_parameters='atomic_radius',
max_neighbors='full',
nprocs=1)
f.normalize_features(trainset['atoms'], testset['atoms'])
train_fp = f.return_vec(trainset['atoms'], [f.neighbor_sum_vec])
n, d = np.shape(train_fp)
self.assertTrue(n == train_size and d == len(trainset['atoms'][0]))
print('passed neighbor_sum_vec all neighbors')
train_fp = f.return_vec(trainset['atoms'], [f.neighbor_mean_vec])
n, d = np.shape(train_fp)
self.assertTrue(n == train_size and d == len(trainset['atoms'][0]))
print('passed neighbor_mean_vec all neighbors')
# Do basic check for atomic porperties.
no_prop = []
an_prop = []
# EXPENSIVE to calculate. Not included in training data.
for atoms in testset['atoms']:
no_prop.append(neighbor_features(atoms=atoms))
an_prop.append(
neighbor_features(atoms=atoms, property=['atomic_number']))
self.assertTrue(np.shape(no_prop) == (test_size, 15))
self.assertTrue(np.shape(an_prop) == (test_size, 30))
print('passed graph_vec')
self.__class__.all_cand = all_cand
self.__class__.data = data
from catlearn.featurize.setup import FeatureGenerator
from catlearn.regression import GaussianProcess
from catlearn.preprocess.feature_engineering import single_transform
from catlearn.ga import GeneticAlgorithm
# ## Data Generation
#
# To start with we import some data. For this tutorial, the data for alloyed nanoparticles are used.
# In[2]:
# Connect ase atoms database.
gadb = DataConnection('../../data/gadb.db')
# Get all relaxed candidates from the db file.
all_cand = gadb.get_all_relaxed_candidates(use_extinct=False)
# We then split this data into some training data and a holdout test set.
# In[3]:
testset = get_unique(atoms=all_cand, size=100, key='raw_score')
trainset = get_train(atoms=all_cand,
size=500,
taken=testset['taken'],
key='raw_score')
trainval = trainset['target']
testval = testset['target']
def test_database_logic(seed, testdir):
from ase.ga.data import PrepareDB
from ase.ga.data import DataConnection
from ase.ga.startgenerator import StartGenerator
from ase.ga.utilities import closest_distances_generator
from ase.ga import set_raw_score
import numpy as np
from ase.build import fcc111
from ase.constraints import FixAtoms
# set up the random number generator
rng = np.random.RandomState(seed)
slab = fcc111('Au', size=(4, 4, 2), vacuum=10.0, orthogonal=True)
slab.set_constraint(FixAtoms(mask=slab.positions[:, 2] <= 10.))
# define the volume in which the adsorbed cluster is optimized
# the volume is defined by a corner position (p0)
# and three spanning vectors (v1, v2, v3)
pos = slab.get_positions()
cell = slab.get_cell()
p0 = np.array([0., 0., max(pos[:, 2]) + 2.])
v1 = cell[0, :] * 0.8
v2 = cell[1, :] * 0.8
v3 = cell[2, :]
v3[2] = 3.
# define the closest distance between two atoms of a given species
blmin = closest_distances_generator(atom_numbers=[47, 79],
ratio_of_covalent_radii=0.7)
# Define the composition of the atoms to optimize
atom_numbers = 2 * [47] + 2 * [79]
# create the starting population
sg = StartGenerator(slab=slab,
blocks=atom_numbers,
blmin=blmin,
box_to_place_in=[p0, [v1, v2, v3]],
rng=rng)
# generate the starting population
starting_population = [sg.get_new_candidate() for i in range(20)]
d = PrepareDB(db_file_name=db_file,
simulation_cell=slab,
stoichiometry=atom_numbers)
for a in starting_population:
d.add_unrelaxed_candidate(a)
# and now for the actual test
dc = DataConnection(db_file)
dc.get_slab()
dc.get_atom_numbers_to_optimize()
assert dc.get_number_of_unrelaxed_candidates() == 20
a1 = dc.get_an_unrelaxed_candidate()
dc.mark_as_queued(a1)
assert dc.get_number_of_unrelaxed_candidates() == 19
assert len(dc.get_all_candidates_in_queue()) == 1
set_raw_score(a1, 0.0)
dc.add_relaxed_step(a1)
assert dc.get_number_of_unrelaxed_candidates() == 19
assert len(dc.get_all_candidates_in_queue()) == 0
assert len(dc.get_all_relaxed_candidates()) == 1
a2 = dc.get_an_unrelaxed_candidate()
dc.mark_as_queued(a2)
confid = a2.info['confid']
assert dc.get_all_candidates_in_queue()[0] == confid
dc.remove_from_queue(confid)
assert len(dc.get_all_candidates_in_queue()) == 0
relax(a3, cellbounds=cellbounds)
da.add_relaxed_step(a3)
# If the relaxation has changed the cell parameters
# beyond the bounds we disregard it in the population
cell = a3.get_cell()
if not cellbounds.is_within_bounds(cell):
da.kill_candidate(a3.info['confid'])
# Update the population
population.update()
if step % 10 == 0:
# Update the scaling volumes of the strain mutation
# and the pairing operator based on the current
# best structures contained in the population
current_pop = population.get_current_population()
strainmut.update_scaling_volume(current_pop, w_adapt=0.5, n_adapt=4)
pairing.update_scaling_volume(current_pop, w_adapt=0.5, n_adapt=4)
write('current_population.traj', current_pop)
print('GA finished after step %d' % step)
hiscore = get_raw_score(current_pop[0])
print('Highest raw score = %8.4f eV' % hiscore)
all_candidates = da.get_all_relaxed_candidates()
write('all_candidates.traj', all_candidates)
current_pop = population.get_current_population()
write('current_population.traj', current_pop)
