def test_load_one_frame_and_run():
the_gp = GaussianProcess(kernel=two_plus_three_body_mc,
kernel_grad=two_plus_three_body_mc_grad,
hyps=np.array([
3.75996759e-06, 1.53990678e-02,
2.50624782e-05, 5.07884426e-01, 1.70172923e-03
]),
cutoffs=np.array([7, 7]),
hyp_labels=['l2', 's2', 'l3', 's3', 'n0'],
maxiter=1,
opt_algorithm='L-BFGS-B')
with open('./test_files/methanol_frames.json', 'r') as f:
frames = [Structure.from_dict(loads(s)) for s in f.readlines()]
tt = TrajectoryTrainer(frames,
gp=the_gp,
shuffle_frames=True,
rel_std_tolerance=0,
abs_std_tolerance=0,
skip=15)
tt.run()
os.system('rm ./gp_from_aimd.gp')
os.system('rm ./gp_from_aimd.out')
os.system('rm ./gp_from_aimd.xyz')
os.system('rm ./gp_from_aimd-f.xyz')
def test_load_one_frame_and_run():
the_gp = GaussianProcess(
kernel_name="2+3_mc",
hyps=np.array([
3.75996759e-06,
1.53990678e-02,
2.50624782e-05,
5.07884426e-01,
1.70172923e-03,
]),
cutoffs=np.array([5, 3]),
hyp_labels=["l2", "s2", "l3", "s3", "n0"],
maxiter=1,
opt_algorithm="L-BFGS-B",
)
with open(path.join(TEST_FILE_DIR, "methanol_frames.json"), "r") as f:
frames = [Structure.from_dict(loads(s)) for s in f.readlines()]
tt = TrajectoryTrainer(
frames,
gp=the_gp,
shuffle_frames=True,
print_as_xyz=True,
rel_std_tolerance=0,
abs_std_tolerance=0,
skip=15,
)
tt.run()
for f in glob(f"gp_from_aimd*"):
remove(f)
def test_pred_on_elements():
the_gp = GaussianProcess(kernel_name="2+3_mc",
hyps=np.array([
3.75996759e-06, 1.53990678e-02,
2.50624782e-05, 5.07884426e-01, 1.70172923e-03
]),
cutoffs=np.array([7, 3]),
hyp_labels=['l2', 's2', 'l3', 's3', 'n0'],
maxiter=1,
opt_algorithm='L-BFGS-B')
with open('./test_files/methanol_frames.json', 'r') as f:
frames = [Structure.from_dict(loads(s)) for s in f.readlines()]
with open('./test_files/methanol_envs.json', 'r') as f:
data_dicts = [loads(s) for s in f.readlines()[:6]]
envs = [AtomicEnvironment.from_dict(d) for d in data_dicts]
forces = [np.array(d['forces']) for d in data_dicts]
seeds = list(zip(envs, forces))
all_frames = deepcopy(frames)
tt = TrajectoryTrainer(frames,
gp=the_gp,
shuffle_frames=False,
rel_std_tolerance=0,
abs_std_tolerance=0,
abs_force_tolerance=.001,
skip=5,
min_atoms_per_train=100,
pre_train_seed_envs=seeds,
pre_train_seed_frames=[frames[-1]],
max_atoms_from_frame=4,
output_name='meth_test',
model_format='json',
atom_checkpoint_interval=50,
pre_train_atoms_per_element={'H': 1},
predict_atoms_per_element={
'H': 0,
'C': 1,
'O': 0
})
# Set to predict only on Carbon after training on H to ensure errors are
# high and that they get added to the gp
tt.run()
# Ensure forces weren't written directly to structure
for i in range(len(all_frames)):
assert np.array_equal(all_frames[i].forces, frames[i].forces)
# Assert that Carbon atoms were correctly added
assert the_gp.training_statistics['envs_by_species']['C'] > 2
for f in glob(f"meth_test*"):
remove(f)
for f in glob(f"gp_from_aimd*"):
remove(f)
def test_mgp_gpfa(all_mgp, all_gp):
"""
Ensure that passing in an MGP also works for the trajectory trainer
:param all_mgp:
:param all_gp:
:return:
"""
np.random.seed(10)
gp_model = get_gp("3", "mc", False)
gp_model.set_L_alpha()
grid_num_3 = 3
lower_cut = 0.01
grid_params_3b = {
"lower_bound": [lower_cut] * 3,
"grid_num": [grid_num_3] * 3,
"svd_rank": "auto",
}
grid_params = {"load_grid": None, "update": False}
grid_params["threebody"] = grid_params_3b
unique_species = gp_model.training_statistics["species"]
mgp_model = MappedGaussianProcess(grid_params=grid_params,
unique_species=unique_species,
n_cpus=1)
mgp_model.build_map(gp_model)
nenv = 10
cell = np.eye(3)
struc, f = get_random_structure(cell, unique_species, nenv)
struc.forces = np.array(f)
frames = [struc]
tt = TrajectoryTrainer(
frames,
mgp_model,
rel_std_tolerance=0,
abs_std_tolerance=0,
abs_force_tolerance=1e-8,
print_training_plan=True,
)
assert tt.gp_is_mapped is True
tt.run()
# Test that training plan is properly written
with open("gp_from_aimd_training_plan.json", "r") as f:
plan = json.loads(f.readline())
assert isinstance(plan["0"], list)
assert len(plan["0"]) == len(struc)
assert [p[0] for p in plan["0"]] == list(range(len(struc)))
for f in glob(f"gp_from_aimd*"):
remove(f)
def test_instantiation_of_trajectory_trainer(fake_gp):
a = TrajectoryTrainer(frames=[], gp=fake_gp)
assert isinstance(a, TrajectoryTrainer)
_ = TrajectoryTrainer([], fake_gp, parallel=True, calculate_energy=True)
_ = TrajectoryTrainer([], fake_gp, parallel=True, calculate_energy=False)
_ = TrajectoryTrainer([], fake_gp, parallel=False, calculate_energy=True)
_ = TrajectoryTrainer([], fake_gp, parallel=False, calculate_energy=False)
def test_seed_and_run():
the_gp = GaussianProcess(
kernel_name="2+3_mc",
hyps=np.array([
3.75996759e-06,
1.53990678e-02,
2.50624782e-05,
5.07884426e-01,
1.70172923e-03,
]),
cutoffs=np.array([5, 3]),
hyp_labels=["l2", "s2", "l3", "s3", "n0"],
maxiter=1,
opt_algorithm="L-BFGS-B",
)
with open(path.join(TEST_FILE_DIR, "methanol_frames.json"), "r") as f:
frames = [Structure.from_dict(loads(s)) for s in f.readlines()]
with open(path.join(TEST_FILE_DIR, "methanol_envs.json"), "r") as f:
data_dicts = [loads(s) for s in f.readlines()[:6]]
envs = [AtomicEnvironment.from_dict(d) for d in data_dicts]
forces = [np.array(d["forces"]) for d in data_dicts]
seeds = list(zip(envs, forces))
tt = TrajectoryTrainer(
frames,
gp=the_gp,
shuffle_frames=True,
rel_std_tolerance=0,
abs_std_tolerance=0,
skip=10,
pre_train_seed_envs=seeds,
pre_train_seed_frames=[frames[-1]],
max_atoms_from_frame=4,
output_name="meth_test",
model_format="pickle",
train_checkpoint_interval=1,
pre_train_atoms_per_element={"H": 1},
)
tt.run()
with open("meth_test_model.pickle", "rb") as f:
new_gp = pickle.load(f)
test_env = envs[0]
for d in [1, 2, 3]:
assert np.all(
the_gp.predict(x_t=test_env, d=d) == new_gp.predict(x_t=test_env,
d=d))
for f in glob(f"meth_test*"):
remove(f)
def test_load_trained_gp_and_run(methanol_gp):
with open('./test_files/methanol_frames.json', 'r') as f:
frames = [Structure.from_dict(loads(s)) for s in f.readlines()]
tt = TrajectoryTrainer(frames,
gp=methanol_gp,
rel_std_tolerance=0,
abs_std_tolerance=0,
skip=15)
tt.run()
os.system('rm ./gp_from_aimd*')
def test_seed_and_run():
the_gp = GaussianProcess(kernel=two_plus_three_body_mc,
kernel_grad=two_plus_three_body_mc_grad,
hyps=np.array([
3.75996759e-06, 1.53990678e-02,
2.50624782e-05, 5.07884426e-01, 1.70172923e-03
]),
cutoffs=np.array([7, 7]),
hyp_labels=['l2', 's2', 'l3', 's3', 'n0'],
maxiter=1,
opt_algorithm='L-BFGS-B')
with open('./test_files/methanol_frames.json', 'r') as f:
frames = [Structure.from_dict(loads(s)) for s in f.readlines()]
with open('./test_files/methanol_envs.json', 'r') as f:
data_dicts = [loads(s) for s in f.readlines()[:6]]
envs = [AtomicEnvironment.from_dict(d) for d in data_dicts]
forces = [np.array(d['forces']) for d in data_dicts]
seeds = list(zip(envs, forces))
tt = TrajectoryTrainer(frames,
gp=the_gp,
shuffle_frames=True,
rel_std_tolerance=0,
abs_std_tolerance=0,
skip=15,
pre_train_seed_envs=seeds,
pre_train_seed_frames=[frames[-1]],
max_atoms_from_frame=4,
model_write='meth_test.pickle',
model_format='pickle',
checkpoint_interval=1,
pre_train_atoms_per_element={'H': 1})
tt.run()
with open('meth_test.pickle', 'rb') as f:
new_gp = pickle.load(f)
test_env = envs[0]
for d in [0, 1, 2]:
assert np.all(
the_gp.predict(x_t=test_env, d=d) == new_gp.predict(x_t=test_env,
d=d))
os.system('rm ./gp_from_aimd.out')
os.system('rm ./gp_from_aimd.xyz')
os.system('rm ./gp_from_aimd-f.xyz')
os.system('rm ./meth_test.pickle')
def test_load_trained_gp_and_run(methanol_gp):
with open('./test_files/methanol_frames.json', 'r') as f:
frames = [Structure.from_dict(loads(s)) for s in f.readlines()]
tt = TrajectoryTrainer(frames,
gp=methanol_gp,
rel_std_tolerance=0,
abs_std_tolerance=0,
skip=15,
train_checkpoint_interval=10)
tt.run()
for f in glob(f"gp_from_aimd*"):
remove(f)
def test_load_trained_gp_and_run(methanol_gp):
with open(path.join(TEST_FILE_DIR, "methanol_frames.json"), "r") as f:
frames = [Structure.from_dict(loads(s)) for s in f.readlines()]
tt = TrajectoryTrainer(
frames,
gp=methanol_gp,
rel_std_tolerance=0,
abs_std_tolerance=0,
skip=15,
train_checkpoint_interval=10,
)
tt.run()
for f in glob(f"gp_from_aimd*"):
remove(f)
def test_load_one_frame_and_run():
the_gp = GaussianProcess(kernel_name="2+3_mc",
hyps=np.array([
3.75996759e-06, 1.53990678e-02,
2.50624782e-05, 5.07884426e-01, 1.70172923e-03
]),
cutoffs=np.array([7, 7]),
hyp_labels=['l2', 's2', 'l3', 's3', 'n0'],
maxiter=1,
opt_algorithm='L-BFGS-B')
with open('./test_files/methanol_frames.json', 'r') as f:
frames = [Structure.from_dict(loads(s)) for s in f.readlines()]
tt = TrajectoryTrainer(frames,
gp=the_gp,
shuffle_frames=True,
rel_std_tolerance=0,
abs_std_tolerance=0,
skip=15)
tt.run()
for f in glob(f"gp_from_aimd*"):
remove(f)
def test_mgp_gpfa(all_mgp, all_gp):
'''
Ensure that passing in an MGP also works for the trajectory trainer
:param all_mgp:
:param all_gp:
:return:
'''
gp_model = get_gp('3', 'mc', False)
gp_model.set_L_alpha()
grid_num_2 = 5
grid_num_3 = 3
lower_cut = 0.01
two_cut = gp_model.cutoffs[0]
three_cut = gp_model.cutoffs[1]
# set struc params. cell and masses arbitrary?
mapped_cell = np.eye(3) * 2
struc_params = {
'species': [1, 2],
'cube_lat': mapped_cell,
'mass_dict': {
'0': 27,
'1': 16
}
}
# grid parameters
train_size = len(gp_model.training_data)
grid_params = {
'bodies': [2],
'cutoffs':
gp_model.cutoffs,
'bounds_2': [[lower_cut], [two_cut]],
'bounds_3': [[lower_cut, lower_cut, lower_cut],
[three_cut, three_cut, three_cut]],
'grid_num_2':
grid_num_2,
'grid_num_3': [grid_num_3, grid_num_3, grid_num_3],
'svd_rank_2':
np.min((grid_num_2, 3 * train_size)),
'svd_rank_3':
np.min((grid_num_3**3, 3 * train_size)),
'load_grid':
None,
'update':
False
}
struc_params = {
'species': [1, 2],
'cube_lat': np.eye(3) * 2,
'mass_dict': {
'0': 27,
'1': 16
}
}
mgp_model = MappedGaussianProcess(grid_params, struc_params)
mgp_model.build_map(gp_model)
nenv = 10
cell = np.eye(3)
unique_species = gp_model.training_data[0].species
struc, f = get_random_structure(cell, unique_species, nenv)
struc.forces = np.array(f)
frames = [struc]
tt = TrajectoryTrainer(frames,
mgp_model,
rel_std_tolerance=0,
abs_std_tolerance=0,
abs_force_tolerance=0)
assert tt.mgp is True
tt.run()
def test_active_learning_simple_run():
"""
Test simple mechanics of active learning method.
:return:
"""
the_gp = GaussianProcess(
kernel_name="2+3_mc",
hyps=np.array([
3.75996759e-06,
1.53990678e-02,
2.50624782e-05,
5.07884426e-01,
1.70172923e-03,
]),
cutoffs=np.array([5, 3]),
hyp_labels=["l2", "s2", "l3", "s3", "n0"],
maxiter=1,
opt_algorithm="L-BFGS-B",
)
frames = Structure.from_file(
path.join(TEST_FILE_DIR, "methanol_frames.json"))
# Assign fake energies to structures
for frame in frames:
frame.energy = np.random.random()
tt = TrajectoryTrainer(gp=the_gp, include_energies=True)
tt.run_passive_learning(
frames=frames[:1],
max_elts_per_frame={
"C": 1,
"O": 1,
"H": 1
},
post_training_iterations=0,
post_build_matrices=True,
)
assert len(the_gp.training_structures) == 1
prev_gp_len = len(the_gp)
prev_gp_stats = the_gp.training_statistics
tt.run_active_learning(frames[:2],
rel_std_tolerance=0,
abs_std_tolerance=0,
abs_force_tolerance=0)
assert len(the_gp) == prev_gp_len
# Try on a frame where the Carbon atom is guaranteed to trip the
# abs. force tolerance condition.
# Turn off include energies so that the number of training structures
# does not change.
tt.include_energies = False
tt.run_active_learning(
frames[1:2],
rel_std_tolerance=0,
abs_std_tolerance=0,
abs_force_tolerance=0.1,
max_elts_per_frame={
"H": 0,
"O": 0
},
max_model_elts={"C": 2},
)
assert len(the_gp) == prev_gp_len + 1
assert len(the_gp.training_structures) == 1
prev_carbon_atoms = prev_gp_stats["envs_by_species"]["C"]
assert the_gp.training_statistics["envs_by_species"][
"C"] == prev_carbon_atoms + 1
prev_gp_len = len(the_gp)
tt.run_active_learning(
frames[3:4],
rel_std_tolerance=0,
abs_std_tolerance=0,
abs_force_tolerance=0.1,
max_model_size=prev_gp_len,
)
assert len(the_gp) == prev_gp_len
# Test that model doesn't add atoms
prev_gp_len = len(the_gp)
tt.run_active_learning(
frames[5:6],
rel_std_tolerance=0,
abs_std_tolerance=0,
abs_force_tolerance=0.1,
max_model_elts={
"C": 2,
"H": 1,
"O": 1
},
)
assert len(the_gp) == prev_gp_len
for f in glob(f"gp_from_aimd*"):
remove(f)
def test_passive_learning():
the_gp = GaussianProcess(
kernel_name="2+3_mc",
hyps=np.array([
3.75996759e-06,
1.53990678e-02,
2.50624782e-05,
5.07884426e-01,
1.70172923e-03,
]),
cutoffs=np.array([5, 3]),
hyp_labels=["l2", "s2", "l3", "s3", "n0"],
maxiter=1,
opt_algorithm="L-BFGS-B",
)
frames = Structure.from_file(
path.join(TEST_FILE_DIR, "methanol_frames.json"))
envs = AtomicEnvironment.from_file(
path.join(TEST_FILE_DIR, "methanol_envs.json"))
cur_gp = deepcopy(the_gp)
tt = TrajectoryTrainer(frames=None, gp=cur_gp)
# TEST ENVIRONMENT ADDITION
envs_species = set(Z_to_element(env.ctype) for env in envs)
tt.run_passive_learning(environments=envs, post_build_matrices=False)
assert cur_gp.training_statistics["N"] == len(envs)
assert set(cur_gp.training_statistics["species"]) == envs_species
# TEST FRAME ADDITION: ALL ARE ADDED
cur_gp = deepcopy(the_gp)
tt.gp = cur_gp
tt.run_passive_learning(frames=frames, post_build_matrices=False)
assert len(cur_gp.training_data) == sum([len(fr) for fr in frames])
# TEST FRAME ADDITION: MAX OUT MODEL SIZE AT 1
cur_gp = deepcopy(the_gp)
tt.gp = cur_gp
tt.run_passive_learning(frames=frames,
max_model_size=1,
post_training_iterations=1)
assert len(cur_gp.training_data) == 1
# TEST FRAME ADDITION: EXCLUDE OXYGEN, LIMIT CARBON TO 1, 1 H PER FRAME
cur_gp = deepcopy(the_gp)
tt.gp = cur_gp
tt.run_passive_learning(
frames=frames,
max_model_elts={
"O": 0,
"C": 1,
"H": 5
},
max_elts_per_frame={"H": 1},
post_build_matrices=False,
)
assert "O" not in cur_gp.training_statistics["species"]
assert cur_gp.training_statistics["envs_by_species"]["C"] == 1
assert cur_gp.training_statistics["envs_by_species"]["H"] == 5
def test_pred_on_elements():
the_gp = GaussianProcess(
kernel_name="2+3_mc",
hyps=np.array([
3.75996759e-06,
1.53990678e-02,
2.50624782e-05,
5.07884426e-01,
1.70172923e-03,
]),
cutoffs=np.array([5, 3]),
hyp_labels=["l2", "s2", "l3", "s3", "n0"],
maxiter=1,
opt_algorithm="L-BFGS-B",
)
with open(path.join(TEST_FILE_DIR, "methanol_frames.json"), "r") as f:
frames = [Structure.from_dict(loads(s)) for s in f.readlines()]
with open(path.join(TEST_FILE_DIR, "methanol_envs.json"), "r") as f:
data_dicts = [loads(s) for s in f.readlines()[:6]]
envs = [AtomicEnvironment.from_dict(d) for d in data_dicts]
forces = [np.array(d["forces"]) for d in data_dicts]
seeds = list(zip(envs, forces))
all_frames = deepcopy(frames)
tt = TrajectoryTrainer(
frames,
gp=the_gp,
shuffle_frames=False,
rel_std_tolerance=0,
abs_std_tolerance=0,
abs_force_tolerance=0.001,
skip=5,
min_atoms_per_train=100,
pre_train_seed_envs=seeds,
pre_train_seed_frames=[frames[-1]],
max_atoms_from_frame=4,
output_name="meth_test",
print_as_xyz=True,
model_format="json",
atom_checkpoint_interval=50,
pre_train_atoms_per_element={"H": 1},
predict_atoms_per_element={
"H": 0,
"C": 1,
"O": 0
},
)
# Set to predict only on Carbon after training on H to ensure errors are
# high and that they get added to the gp
tt.run()
# Ensure forces weren't written directly to structure
for i in range(len(all_frames)):
assert np.array_equal(all_frames[i].forces, frames[i].forces)
# Assert that Carbon atoms were correctly added
assert the_gp.training_statistics["envs_by_species"]["C"] > 2
for f in glob(f"meth_test*"):
remove(f)
for f in glob(f"gp_from_aimd*"):
remove(f)
def test_uncertainty_threshold(fake_gp):
tt = TrajectoryTrainer([], fake_gp, rel_std_tolerance=.5,
abs_std_tolerance=.01)
fake_structure = Structure(cell=np.eye(3), species=["H"],
positions=np.array([[0, 0, 0]]))
# Test a structure with no variance passes
fake_structure.stds = np.array([[0, 0, 0]])
res1, res2 = tt.is_std_in_bound(fake_structure)
assert res1 is True
assert res2 == [-1]
# Test that the absolute criteria trips the threshold
fake_structure.stds = np.array([[.02, 0, 0]])
res1, res2 = tt.is_std_in_bound(fake_structure)
assert res1 is False
assert res2 == [0]
tt.abs_std_tolerance = 100
# Test that the relative criteria trips the threshold
fake_structure.stds = np.array([[.6, 0, 0]])
res1, res2 = tt.is_std_in_bound(fake_structure)
assert res1 is False
assert res2 == [0]
# Test that 'test mode' works, where no GP modification occurs
tt.abs_std_tolerance = 0
tt.rel_std_tolerance = 0
res1, res2 = tt.is_std_in_bound(fake_structure)
assert res1 is True
assert res2 == [-1]
# Test permutations of one / another being off
tt.abs_std_tolerance = 1
tt.rel_std_tolerance = 0
res1, res2 = tt.is_std_in_bound(fake_structure)
assert res1 is True
assert res2 == [-1]
tt.abs_std_tolerance = 0
tt.rel_std_tolerance = 1
res1, res2 = tt.is_std_in_bound(fake_structure)
assert res1 is True
assert res2 == [-1]
