class OTF(object):
def __init__(self,
dft_input: str,
dt: float,
number_of_steps: int,
gp: gp.GaussianProcess,
dft_loc: str,
std_tolerance_factor: float = 1,
prev_pos_init: np.ndarray = None,
par: bool = False,
skip: int = 0,
init_atoms: List[int] = None,
calculate_energy=False,
output_name='otf_run',
max_atoms_added=1,
freeze_hyps=10,
rescale_steps=[],
rescale_temps=[],
dft_softwarename="qe",
no_cpus=1,
npool=None,
mpi="srun"):
self.dft_input = dft_input
self.dt = dt
self.number_of_steps = number_of_steps
self.gp = gp
self.dft_loc = dft_loc
self.std_tolerance = std_tolerance_factor
self.skip = skip
self.dft_step = True
self.freeze_hyps = freeze_hyps
self.dft_module = dft_software[dft_softwarename]
# parse input file
positions, species, cell, masses = \
self.dft_module.parse_dft_input(self.dft_input)
_, coded_species = struc.get_unique_species(species)
self.structure = struc.Structure(cell=cell,
species=coded_species,
positions=positions,
mass_dict=masses,
prev_positions=prev_pos_init,
species_labels=species)
self.noa = self.structure.positions.shape[0]
self.atom_list = list(range(self.noa))
self.curr_step = 0
self.max_atoms_added = max_atoms_added
# initialize local energies
if calculate_energy:
self.local_energies = np.zeros(self.noa)
else:
self.local_energies = None
# set atom list for initial dft run
if init_atoms is None:
self.init_atoms = [int(n) for n in range(self.noa)]
else:
self.init_atoms = init_atoms
self.dft_count = 0
# set pred function
if not par and not calculate_energy:
self.pred_func = predict.predict_on_structure
elif par and not calculate_energy:
self.pred_func = predict.predict_on_structure_par
elif not par and calculate_energy:
self.pred_func = predict.predict_on_structure_en
elif par and calculate_energy:
self.pred_func = predict.predict_on_structure_par_en
self.par = par
# set rescale attributes
self.rescale_steps = rescale_steps
self.rescale_temps = rescale_temps
self.output = Output(output_name, always_flush=True)
# set number of cpus and npool for qe runs
self.no_cpus = no_cpus
self.npool = npool
self.mpi = mpi
def run(self):
self.output.write_header(self.gp.cutoffs, self.gp.kernel_name,
self.gp.hyps, self.gp.algo, self.dt,
self.number_of_steps, self.structure,
self.std_tolerance)
counter = 0
self.start_time = time.time()
while self.curr_step < self.number_of_steps:
print('curr_step:', self.curr_step)
# run DFT and train initial model if first step and DFT is on
if self.curr_step == 0 and self.std_tolerance != 0:
# call dft and update positions
self.run_dft()
dft_frcs = copy.deepcopy(self.structure.forces)
new_pos = md.update_positions(self.dt, self.noa,
self.structure)
self.update_temperature(new_pos)
self.record_state()
# make initial gp model and predict forces
self.update_gp(self.init_atoms, dft_frcs)
if (self.dft_count - 1) < self.freeze_hyps:
self.train_gp()
# after step 1, try predicting with GP model
else:
self.gp.check_L_alpha()
self.pred_func(self.structure, self.gp, self.no_cpus)
self.dft_step = False
new_pos = md.update_positions(self.dt, self.noa,
self.structure)
# get max uncertainty atoms
std_in_bound, target_atoms = is_std_in_bound(
self.std_tolerance, self.gp.hyps[-1], self.structure,
self.max_atoms_added)
if not std_in_bound:
# record GP forces
self.update_temperature(new_pos)
self.record_state()
gp_frcs = copy.deepcopy(self.structure.forces)
# run DFT and record forces
self.dft_step = True
self.run_dft()
dft_frcs = copy.deepcopy(self.structure.forces)
new_pos = md.update_positions(self.dt, self.noa,
self.structure)
self.update_temperature(new_pos)
self.record_state()
# compute mae and write to output
mae = np.mean(np.abs(gp_frcs - dft_frcs))
mac = np.mean(np.abs(dft_frcs))
self.output.write_to_log('\nmean absolute error:'
' %.4f eV/A \n' % mae)
self.output.write_to_log('mean absolute dft component:'
' %.4f eV/A \n' % mac)
# add max uncertainty atoms to training set
self.update_gp(target_atoms, dft_frcs)
if (self.dft_count - 1) < self.freeze_hyps:
self.train_gp()
# write gp forces
if counter >= self.skip and not self.dft_step:
self.update_temperature(new_pos)
self.record_state()
counter = 0
counter += 1
self.update_positions(new_pos)
self.curr_step += 1
self.output.conclude_run()
def run_dft(self):
self.output.write_to_log('\nCalling DFT...\n')
# calculate DFT forces
forces = self.dft_module.run_dft_par(self.dft_input,
self.structure,
self.dft_loc,
no_cpus=self.no_cpus,
npool=self.npool,
mpi=self.mpi)
self.structure.forces = forces
# write wall time of DFT calculation
self.dft_count += 1
self.output.write_to_log('QE run complete.\n')
time_curr = time.time() - self.start_time
self.output.write_to_log('number of DFT calls: %i \n' % self.dft_count)
self.output.write_to_log('wall time from start: %.2f s \n' % time_curr)
def update_gp(self, train_atoms, dft_frcs):
self.output.write_to_log(
'\nAdding atom {} to the training set.\n'.format(train_atoms))
self.output.write_to_log('Uncertainty: {}.\n'.format(
self.structure.stds[train_atoms[0]]))
# update gp model
self.gp.update_db(self.structure, dft_frcs, custom_range=train_atoms)
self.gp.set_L_alpha()
def train_gp(self):
self.gp.train(self.output)
self.output.write_hyps(self.gp.hyp_labels, self.gp.hyps,
self.start_time, self.gp.likelihood,
self.gp.likelihood_gradient)
def update_positions(self, new_pos):
if self.curr_step in self.rescale_steps:
rescale_ind = self.rescale_steps.index(self.curr_step)
temp_fac = self.rescale_temps[rescale_ind] / self.temperature
vel_fac = np.sqrt(temp_fac)
self.structure.prev_positions = \
new_pos - self.velocities * self.dt * vel_fac
else:
self.structure.prev_positions = self.structure.positions
self.structure.positions = new_pos
self.structure.wrap_positions()
def update_temperature(self, new_pos):
KE, temperature, velocities = \
md.calculate_temperature(new_pos, self.structure, self.dt,
self.noa)
self.KE = KE
self.temperature = temperature
self.velocities = velocities
def record_state(self):
self.output.write_md_config(self.dt, self.curr_step, self.structure,
self.temperature, self.KE,
self.local_energies, self.start_time,
self.dft_step, self.velocities)
self.output.write_xyz_config(self.curr_step, self.structure,
self.dft_step)
class OTF(object):
def __init__(self,
qe_input: str,
dt: float,
number_of_steps: int,
gp: gp.GaussianProcess,
pw_loc: str,
std_tolerance_factor: float = 1,
prev_pos_init: np.ndarray = None,
par: bool = False,
skip: int = 0,
init_atoms: List[int] = None,
calculate_energy=False,
output_name='otf_run',
max_atoms_added=1,
freeze_hyps=10,
rescale_steps=[],
rescale_temps=[],
no_cpus=1):
self.qe_input = qe_input
self.dt = dt
self.number_of_steps = number_of_steps
self.gp = gp
self.pw_loc = pw_loc
self.std_tolerance = std_tolerance_factor
self.skip = skip
self.dft_step = True
self.freeze_hyps = freeze_hyps
# parse input file
positions, species, cell, masses = \
qe_util.parse_qe_input(self.qe_input)
_, coded_species = struc.get_unique_species(species)
self.structure = struc.Structure(cell=cell,
species=coded_species,
positions=positions,
mass_dict=masses,
prev_positions=prev_pos_init,
species_labels=species)
self.noa = self.structure.positions.shape[0]
self.atom_list = list(range(self.noa))
self.curr_step = 0
self.max_atoms_added = max_atoms_added
# initialize local energies
if calculate_energy:
self.local_energies = np.zeros(self.noa)
else:
self.local_energies = None
# set atom list for initial dft run
if init_atoms is None:
self.init_atoms = [int(n) for n in range(self.noa)]
else:
self.init_atoms = init_atoms
self.dft_count = 0
# set pred function
if not par and not calculate_energy:
self.pred_func = self.predict_on_structure
elif par and not calculate_energy:
self.pred_func = self.predict_on_structure_par
elif not par and calculate_energy:
self.pred_func = self.predict_on_structure_en
elif par and calculate_energy:
self.pred_func = self.predict_on_structure_par_en
self.par = par
# set rescale attributes
self.rescale_steps = rescale_steps
self.rescale_temps = rescale_temps
self.output = Output(output_name)
# set number of cpus for qe runs
self.no_cpus = no_cpus
def run(self):
self.output.write_header(self.gp.cutoffs, self.gp.kernel_name,
self.gp.hyps, self.gp.algo, self.dt,
self.number_of_steps, self.structure,
self.std_tolerance)
counter = 0
self.start_time = time.time()
while self.curr_step < self.number_of_steps:
print('curr_step:', self.curr_step)
# run DFT and train initial model if first step and DFT is on
if self.curr_step == 0 and self.std_tolerance != 0:
# call dft and update positions
self.run_dft()
dft_frcs = copy.deepcopy(self.structure.forces)
new_pos = md.update_positions(self.dt, self.noa,
self.structure)
self.update_temperature(new_pos)
self.record_state()
# make initial gp model and predict forces
self.update_gp(self.init_atoms, dft_frcs)
if (self.dft_count - 1) < self.freeze_hyps:
self.train_gp()
# after step 1, try predicting with GP model
else:
self.pred_func()
self.dft_step = False
new_pos = md.update_positions(self.dt, self.noa,
self.structure)
# get max uncertainty atoms
std_in_bound, target_atoms = self.is_std_in_bound()
if not std_in_bound:
# record GP forces
self.update_temperature(new_pos)
self.record_state()
gp_frcs = copy.deepcopy(self.structure.forces)
# run DFT and record forces
self.dft_step = True
self.run_dft()
dft_frcs = copy.deepcopy(self.structure.forces)
new_pos = md.update_positions(self.dt, self.noa,
self.structure)
self.update_temperature(new_pos)
self.record_state()
# compute mae and write to output
mae = np.mean(np.abs(gp_frcs - dft_frcs))
mac = np.mean(np.abs(dft_frcs))
self.output.write_to_log('\nmean absolute error:'
' %.4f eV/A \n' % mae)
self.output.write_to_log('mean absolute dft component:'
' %.4f eV/A \n' % mac)
# add max uncertainty atoms to training set
self.update_gp(target_atoms, dft_frcs)
if (self.dft_count - 1) < self.freeze_hyps:
self.train_gp()
# write gp forces
if counter >= self.skip and not self.dft_step:
self.update_temperature(new_pos)
self.record_state()
counter = 0
counter += 1
self.update_positions(new_pos)
self.curr_step += 1
self.output.conclude_run()
def predict_on_atom(self, atom):
chemenv = env.AtomicEnvironment(self.structure, atom, self.gp.cutoffs)
comps = []
stds = []
# predict force components and standard deviations
for i in range(3):
force, var = self.gp.predict(chemenv, i + 1)
comps.append(float(force))
stds.append(np.sqrt(np.abs(var)))
return comps, stds
def predict_on_atom_en(self, atom):
chemenv = env.AtomicEnvironment(self.structure, atom, self.gp.cutoffs)
comps = []
stds = []
# predict force components and standard deviations
for i in range(3):
force, var = self.gp.predict(chemenv, i + 1)
comps.append(float(force))
stds.append(np.sqrt(np.abs(var)))
# predict local energy
local_energy = self.gp.predict_local_energy(chemenv)
return comps, stds, local_energy
def predict_on_structure_par(self):
n = 0
with concurrent.futures.ProcessPoolExecutor() as executor:
for res in executor.map(self.predict_on_atom, self.atom_list):
for i in range(3):
self.structure.forces[n][i] = res[0][i]
self.structure.stds[n][i] = res[1][i]
n += 1
def predict_on_structure_par_en(self):
n = 0
with concurrent.futures.ProcessPoolExecutor() as executor:
for res in executor.map(self.predict_on_atom_en, self.atom_list):
for i in range(3):
self.structure.forces[n][i] = res[0][i]
self.structure.stds[n][i] = res[1][i]
self.local_energies[n] = res[2]
n += 1
def predict_on_structure(self):
for n in range(self.structure.nat):
chemenv = env.AtomicEnvironment(self.structure, n, self.gp.cutoffs)
for i in range(3):
force, var = self.gp.predict(chemenv, i + 1)
self.structure.forces[n][i] = float(force)
self.structure.stds[n][i] = np.sqrt(np.abs(var))
def predict_on_structure_en(self):
for n in range(self.structure.nat):
chemenv = env.AtomicEnvironment(self.structure, n, self.gp.cutoffs)
for i in range(3):
force, var = self.gp.predict(chemenv, i + 1)
self.structure.forces[n][i] = float(force)
self.structure.stds[n][i] = np.sqrt(np.abs(var))
self.local_energies[n] = self.gp.predict_local_energy(chemenv)
def run_dft(self):
self.output.write_to_log('\nCalling Quantum Espresso...\n')
# calculate DFT forces
forces = qe_util.run_espresso_par(self.qe_input, self.structure,
self.pw_loc, self.no_cpus)
self.structure.forces = forces
# write wall time of DFT calculation
self.dft_count += 1
self.output.write_to_log('QE run complete.\n')
time_curr = time.time() - self.start_time
self.output.write_to_log('number of DFT calls: %i \n' % self.dft_count)
self.output.write_to_log('wall time from start: %.2f s \n' % time_curr)
def update_gp(self, train_atoms, dft_frcs):
self.output.write_to_log(
'\nAdding atom {} to the training set.\n'.format(train_atoms))
self.output.write_to_log('Uncertainty: {}.\n'.format(
self.structure.stds[train_atoms[0]]))
# update gp model
self.gp.update_db(self.structure, dft_frcs, custom_range=train_atoms)
self.gp.set_L_alpha()
# if self.curr_step == 0:
# self.gp.set_L_alpha()
# else:
# self.gp.update_L_alpha()
def train_gp(self):
self.gp.train(self.output)
self.output.write_hyps(self.gp.hyp_labels, self.gp.hyps,
self.start_time, self.gp.like,
self.gp.like_grad)
def is_std_in_bound(self):
# set uncertainty threshold
if self.std_tolerance == 0:
return True, -1
elif self.std_tolerance > 0:
threshold = self.std_tolerance * np.abs(self.gp.hyps[-1])
else:
threshold = np.abs(self.std_tolerance)
# sort max stds
max_stds = np.zeros((self.noa))
for atom, std in enumerate(self.structure.stds):
max_stds[atom] = np.max(std)
stds_sorted = np.argsort(max_stds)
target_atoms = list(stds_sorted[-self.max_atoms_added:])
# if above threshold, return atom
if max_stds[stds_sorted[-1]] > threshold:
return False, target_atoms
else:
return True, [-1]
def update_positions(self, new_pos):
if self.curr_step in self.rescale_steps:
rescale_ind = self.rescale_steps.index(self.curr_step)
temp_fac = self.rescale_temps[rescale_ind] / self.temperature
vel_fac = np.sqrt(temp_fac)
self.structure.prev_positions = \
new_pos - self.velocities * self.dt * vel_fac
else:
self.structure.prev_positions = self.structure.positions
self.structure.positions = new_pos
self.structure.wrap_positions()
def update_temperature(self, new_pos):
KE, temperature, velocities = \
md.calculate_temperature(new_pos, self.structure, self.dt,
self.noa)
self.KE = KE
self.temperature = temperature
self.velocities = velocities
def record_state(self):
self.output.write_md_config(self.dt, self.curr_step, self.structure,
self.temperature, self.KE,
self.local_energies, self.start_time,
self.dft_step, self.velocities)
self.output.write_xyz_config(self.curr_step, self.structure,
self.dft_step)
class TrajectoryTrainer(object):
def __init__(self, frames: List[Structure],
gp: GaussianProcess,
rel_std_tolerance: float = 1,
abs_std_tolerance: float = 1,
parallel: bool = False,
skip: int = 0,
calculate_energy: bool = False,
output_name: str = 'gp_from_aimd',
max_atoms_from_frame: int = np.inf, max_trains: int = np.inf,
min_atoms_added: int = 1,
n_cpus: int = 1, shuffle_frames: bool = False,
verbose: int = 0, model_write: str = '',
pre_train_on_skips: bool = False,
pre_train_seed_frames: List[Structure] = None,
pre_train_seed_envs: List[Tuple[AtomicEnvironment,
np.array]] = None,
pre_train_atoms_per_element: dict = None):
"""
Class which trains a GP off of an AIMD trajectory, and generates
error statistics between the DFT and GP calls.
:param frames: List of structures to evaluate / train GP on
:param gp: Gaussian Process object
:param rel_std_tolerance: Train if uncertainty is above this *
noise variance hyperparameter
:param abs_std_tolerance: Train if uncertainty is above this
:param parallel: Use parallel functions or not
:param skip: Skip through frames
:param calculate_energy: Use local energy kernel or not
:param output_name: Write output of training to this file
:param max_atoms_from_frame: Largest # of atoms added from one frame
:param min_atoms_added: Only train when this many atoms have been added
:param max_trains: Stop training GP after this many calls to train
:param n_cpus: Number of CPUs to parallelize over
:param shuffle_frames: Randomize order of frames for better training
:param verbose: 0: Silent, 1: Minimal, 2: Lots of information
:param model_write: Write output model here
:param pre_train_on_skips: Train model on every n frames before running
:param pre_train_seed_frames: Frames to train on before running
:param pre_train_seed_envs: Environments to train on before running
:param pre_train_atoms_per_element: Max # of environments to add from
each species in the seed pre-training steps
"""
self.frames = frames
if shuffle_frames:
np.random.shuffle(frames)
self.gp = gp
self.rel_std_tolerance = rel_std_tolerance
self.abs_std_tolerance = abs_std_tolerance
self.skip = skip
self.max_trains = max_trains
self.curr_step = 0
self.max_atoms_from_frame = max_atoms_from_frame
self.min_atoms_added = min_atoms_added
self.verbose = verbose
self.train_count = 0
self.parallel = parallel
# set pred function
if parallel:
if calculate_energy:
self.pred_func = predict_on_structure_par_en
else:
self.pred_func = predict_on_structure_par
else:
if calculate_energy:
self.pred_func = predict_on_structure_en
else:
self.pred_func = predict_on_structure
self.output = Output(output_name)
# set number of cpus for parallelization
self.n_cpus = n_cpus
# To later be filled in using the time library
self.start_time = None
self.pickle_name = model_write
self.pre_train_on_skips = pre_train_on_skips
self.seed_envs = [] if pre_train_seed_envs is None else \
pre_train_seed_envs
self.seed_frames = [] if pre_train_seed_frames is None \
else pre_train_seed_frames
self.pre_train_env_per_species = {} if pre_train_atoms_per_element \
is None else pre_train_atoms_per_element
def pre_run(self):
"""
Various tasks to set up the AIMD training before commencing
the run through the AIMD trajectory.
1. Print the output.
2. Pre-train the GP with the seed frames and
environments. If no seed frames or environments and the GP has no
training set, then seed with at least one atom from each
"""
self.output.write_header(self.gp.cutoffs,
self.gp.kernel_name,
self.gp.hyps,
self.gp.algo,
dt=0,
Nsteps=len(self.frames),
structure=self.frames[0],
std_tolerance=(self.rel_std_tolerance,
self.abs_std_tolerance))
self.start_time = time.time()
# If seed environments were passed in, add them to the GP.
for point in self.seed_envs:
self.gp.add_one_env(point[0], point[1], train=False)
# No training set ("blank slate" run) and no seeds specified:
# Take one of each atom species in the first frame
# so all atomic species are represented in the first step.
# Otherwise use the seed frames passed in by user.
if len(self.gp.training_data) == 0 and self.seed_frames is None:
self.seed_frames = [self.frames[0]]
for frame in self.seed_frames:
train_atoms = []
for species_i in set(frame.coded_species):
# Get a randomized set of atoms of species i from the frame
# So that it is not always the lowest-indexed atoms chosen
atoms_of_specie = frame.indices_of_specie(species_i)
np.random.shuffle(atoms_of_specie)
n_at = len(atoms_of_specie)
# Determine how many to add based on user defined cutoffs
n_to_add = min(n_at, self.pre_train_env_per_species.get(
species_i, np.inf), self.max_atoms_from_frame)
for atom in atoms_of_specie[:n_to_add]:
train_atoms.append(atom)
self.update_gp_and_print(frame, train_atoms, train=False)
# These conditions correspond to if either the GP was never trained
# or if data was added to it during the pre-run.
if (self.gp.l_mat is None) \
or (self.seed_frames is not None
or self.seed_envs is not None):
self.gp.train(output=self.output if self.verbose > 0 else None)
def run(self):
"""
Loop through frames and record the error between
the GP predictions and the ground-truth forces. Train the GP and update
the training set upon the triggering of the uncertainty threshold.
:return:
"""
self.pre_run()
# Loop through trajectory
for i, cur_frame in enumerate(self.frames):
if self.verbose >= 2:
print("=====NOW ON FRAME {}=====".format(i))
dft_forces = deepcopy(cur_frame.forces)
self.pred_func(cur_frame, self.gp)
# Convert to meV/A
mae = np.mean(np.abs(cur_frame.forces - dft_forces)) * 1000
mac = np.mean(np.abs(dft_forces)) * 1000
self.output.write_gp_dft_comparison(
curr_step=i, frame=cur_frame,
start_time=time.time(),
dft_forces=dft_forces,
mae=mae, mac=mac, local_energies=None)
# get max uncertainty atoms
std_in_bound, train_atoms = self.is_std_in_bound(cur_frame)
if not std_in_bound:
# compute mae and write to output
# add max uncertainty atoms to training set
self.update_gp_and_print(cur_frame, train_atoms, train=False)
if self.train_count < self.max_trains:
self.train_gp()
self.output.conclude_run()
if self.pickle_name:
with open(self.pickle_name, 'wb') as f:
pickle.dump(self.gp, f)
def update_gp_and_print(self, frame: Structure, train_atoms: List[int],
train: bool=True):
"""
Update the internal GP model training set with a list of training
atoms indexing atoms within the frame. If train is True, re-train
the GP by optimizing hyperparameters.
:param frame: Structure to train on
:param train_atoms: Index atoms to train on
:param train: Train or not
:return:
"""
self.output.write_to_log('\nAdding atom(s) {} to the '
'training set.\n'
.format(train_atoms, ))
self.output.write_to_log('Uncertainties: {}.\n'
.format(frame.stds[train_atoms]))
# update gp model
self.gp.update_db(frame, frame.forces, custom_range=train_atoms)
self.gp.set_L_alpha()
if train:
self.train_gp()
def train_gp(self):
"""
Train the Gaussian process and write the results to the output file.
"""
self.gp.train(output=self.output if self.verbose >= 2 else None)
self.output.write_hyps(self.gp.hyp_labels, self.gp.hyps,
self.start_time,
self.gp.like, self.gp.like_grad)
self.train_count += 1
def is_std_in_bound(self, frame: Structure)->(bool, List[int]):
"""
If the predicted variance is too high, returns a list of atoms
with the highest uncertainty
:param frame: Structure
:return:
"""
# This indicates test mode, as the GP is not being modified in any way
if self.rel_std_tolerance == 0 and self.abs_std_tolerance == 0:
return True, [-1]
# set uncertainty threshold
if self.rel_std_tolerance == 0:
threshold = self.abs_std_tolerance
elif self.abs_std_tolerance == 0:
threshold = self.rel_std_tolerance * np.abs(self.gp.hyps[-1])
else:
threshold = min(self.rel_std_tolerance * np.abs(self.gp.hyps[-1]),
self.abs_std_tolerance)
# sort max stds
max_stds = np.zeros(frame.nat)
for atom_idx, std in enumerate(frame.stds):
max_stds[atom_idx] = np.max(std)
stds_sorted = np.argsort(max_stds)
# Handle case where unlimited atoms are added
# or if max # of atoms exceeds size of frame
if self.max_atoms_from_frame == np.inf or \
self.max_atoms_from_frame > len(frame):
target_atoms = list(stds_sorted)
else:
target_atoms = list(stds_sorted[-self.max_atoms_from_frame:])
# if above threshold, return atom
if max_stds[stds_sorted[-1]] > threshold:
return False, target_atoms
else:
return True, [-1]
class TrajectoryTrainer:
def __init__(self,
frames: List[Structure],
gp: Union[GaussianProcess, MappedGaussianProcess],
rel_std_tolerance: float = 4,
abs_std_tolerance: float = 1,
abs_force_tolerance: float = 0,
max_force_error: float = inf,
parallel: bool = False,
n_cpus: int = 1,
skip: int = 1,
validate_ratio: float = 0.0,
calculate_energy: bool = False,
output_name: str = 'gp_from_aimd',
pre_train_max_iter: int = 50,
max_atoms_from_frame: int = np.inf,
max_trains: int = np.inf,
min_atoms_per_train: int = 1,
shuffle_frames: bool = False,
verbose: int = 1,
pre_train_on_skips: int = -1,
pre_train_seed_frames: List[Structure] = None,
pre_train_seed_envs: List[Tuple[AtomicEnvironment,
'np.array']] = None,
pre_train_atoms_per_element: dict = None,
train_atoms_per_element: dict = None,
predict_atoms_per_element: dict = None,
train_checkpoint_interval: int = 1,
checkpoint_interval: int = 1,
atom_checkpoint_interval: int = 100,
model_format: str = 'json'):
"""
Class which trains a GP off of an AIMD trajectory, and generates
error statistics between the DFT and GP calls.
There are a variety of options which can give you a finer control
over the training process.
:param frames: List of structures to evaluate / train GP on
:param gp: Gaussian Process object
:param rel_std_tolerance: Train if uncertainty is above this *
noise variance hyperparameter
:param abs_std_tolerance: Train if uncertainty is above this
:param abs_force_tolerance: Add atom force error exceeds this
:param max_force_error: Don't add atom if force error exceeds this
:param parallel: Use parallel functions or not
:param validate_ratio: Fraction of frames used for validation
:param skip: Skip through frames
:param calculate_energy: Use local energy kernel or not
:param output_name: Write output of training to this file
:param max_atoms_from_frame: Largest # of atoms added from one frame
:param min_atoms_per_train: Only train when this many atoms have been
added
:param max_trains: Stop training GP after this many calls to train
:param n_cpus: Number of CPUs to parallelize over for parallelization
over atoms
:param shuffle_frames: Randomize order of frames for better training
:param verbose: 0: Silent, NO output written or printed at all.
1: Minimal,
2: Lots of information
:param pre_train_on_skips: Train model on every n frames before running
:param pre_train_seed_frames: Frames to train on before running
:param pre_train_seed_envs: Environments to train on before running
:param pre_train_atoms_per_element: Max # of environments to add from
each species in the seed pre-training steps
:param train_atoms_per_element: Max # of environments to add from
each species in the training steps
:param predict_atoms_per_element: Choose a random subset of N random
atoms from each specified element to predict on. For instance,
{"H":5} will only predict the forces and uncertainties
associated with 5 Hydrogen atoms per frame. Elements not
specified will be predicted as normal. This is useful for
systems where you are most interested in a subset of elements.
This will result in a faster but less exhaustive learning process.
:param checkpoint_interval: Will be deprecated. Same as
train_checkpoint_interval
:param train_checkpoint_interval: How often to write model after
trainings
:param atom_checkpoint_interval: How often to write model after atoms are
added (since atoms may be added without training)
:param model_format: Format to write GP model to
"""
# Set up parameters
self.frames = frames
if shuffle_frames:
np.random.shuffle(frames)
# GP Training and Execution parameters
self.gp = gp
# Check to see if GP is MGP for later flagging
self.mgp = isinstance(gp, MappedGaussianProcess)
self.rel_std_tolerance = rel_std_tolerance
self.abs_std_tolerance = abs_std_tolerance
self.abs_force_tolerance = abs_force_tolerance
self.max_force_error = max_force_error
self.max_trains = max_trains
self.max_atoms_from_frame = max_atoms_from_frame
self.min_atoms_per_train = min_atoms_per_train
self.predict_atoms_per_element = predict_atoms_per_element
self.verbose = verbose
self.train_count = 0
self.calculate_energy = calculate_energy
self.n_cpus = n_cpus
if parallel is True:
warnings.warn(
"Parallel flag will be deprecated;"
"we will instead use n_cpu alone.", DeprecationWarning)
# Set prediction function based on if forces or energies are
# desired, and parallelization accordingly
if not self.mgp:
if calculate_energy:
self.pred_func = predict_on_structure_par_en
else:
self.pred_func = predict_on_structure_par
elif self.mgp:
self.pred_func = predict_on_structure_mgp
# Parameters for negotiating with the training frames
# To later be filled in using the time library
self.start_time = None
self.skip = skip
assert (isinstance(skip, int) and skip >= 1), "Skip needs to be a " \
"positive integer."
self.validate_ratio = validate_ratio
assert (0 <= validate_ratio <= 1), \
"validate_ratio needs to be [0,1]"
# Set up for pretraining
self.pre_train_max_iter = pre_train_max_iter
self.pre_train_on_skips = pre_train_on_skips
self.seed_envs = [] if pre_train_seed_envs is None else \
pre_train_seed_envs
self.seed_frames = [] if pre_train_seed_frames is None \
else pre_train_seed_frames
self.pre_train_env_per_species = {} if pre_train_atoms_per_element \
is None else pre_train_atoms_per_element
self.train_env_per_species = {} if train_atoms_per_element \
is None else train_atoms_per_element
# Convert to Coded Species
if self.pre_train_env_per_species:
pre_train_species = list(self.pre_train_env_per_species.keys())
for key in pre_train_species:
self.pre_train_env_per_species[element_to_Z(key)] = \
self.pre_train_env_per_species[key]
# Output parameters
self.verbose = verbose
if self.verbose:
self.output = Output(output_name, always_flush=True)
else:
self.output = None
self.train_checkpoint_interval = train_checkpoint_interval or \
checkpoint_interval
self.atom_checkpoint_interval = atom_checkpoint_interval
self.model_format = model_format
self.output_name = output_name
# Defining variables to be used later
self.curr_step = 0
self.train_count = 0
self.start_time = None
def pre_run(self):
"""
Various tasks to set up the AIMD training before commencing
the run through the AIMD trajectory.
1. Print the output.
2. Pre-train the GP with the seed frames and
environments. If no seed frames or environments and the GP has no
training set, then seed with at least one atom from each
"""
if self.mgp:
raise NotImplementedError("Pre-running not"
"yet configured for MGP")
if self.verbose:
self.output.write_header(
self.gp.cutoffs,
self.gp.kernel_name,
self.gp.hyps,
self.gp.opt_algorithm,
dt=0,
Nsteps=len(self.frames),
structure=None,
std_tolerance=(self.rel_std_tolerance, self.abs_std_tolerance),
optional={
'GP Statistics':
json.dumps(self.gp.training_statistics),
'GP Name':
self.gp.name,
'GP Write Name':
self.output_name + "_model." + self.model_format
})
self.start_time = time.time()
if self.verbose >= 3:
print("Now beginning pre-run activity.")
# If seed environments were passed in, add them to the GP.
for point in self.seed_envs:
self.gp.add_one_env(point[0], point[1], train=False)
# No training set ("blank slate" run) and no seeds specified:
# Take one of each atom species in the first frame
# so all atomic species are represented in the first step.
# Otherwise use the seed frames passed in by user.
# Remove frames used as seed from later part of training
if self.pre_train_on_skips > 0:
self.seed_frames = []
newframes = []
for i in range(len(self.frames)):
if (i % self.pre_train_on_skips) == 0:
self.seed_frames += [self.frames[i]]
else:
newframes += [self.frames[i]]
self.frames = newframes
# If the GP is empty, use the first frame as a seed frame.
elif len(self.gp.training_data) == 0 and len(self.seed_frames) == 0:
self.seed_frames = [self.frames[0]]
self.frames = self.frames[1:]
atom_count = 0
for frame in self.seed_frames:
train_atoms = []
for species_i in set(frame.coded_species):
# Get a randomized set of atoms of species i from the frame
# So that it is not always the lowest-indexed atoms chosen
atoms_of_specie = frame.indices_of_specie(species_i)
np.random.shuffle(atoms_of_specie)
n_at = len(atoms_of_specie)
# Determine how many to add based on user defined cutoffs
n_to_add = min(
n_at, self.pre_train_env_per_species.get(species_i, inf),
self.max_atoms_from_frame)
for atom in atoms_of_specie[:n_to_add]:
train_atoms.append(atom)
atom_count += 1
self.update_gp_and_print(frame=frame,
train_atoms=train_atoms,
uncertainties=[],
train=False)
if self.verbose and atom_count > 0:
self.output.write_to_log(
f"Added {atom_count} atoms to "
f"pretrain.\n"
f"Pre-run GP Statistics: "
f"{json.dumps(self.gp.training_statistics)} \n",
flush=True)
if (self.seed_envs or atom_count or self.seed_frames) and \
(self.pre_train_max_iter or self.max_trains):
if self.verbose >= 3:
print("Now commencing pre-run training of GP (which has "
"non-empty training set)")
self.train_gp(max_iter=self.pre_train_max_iter)
else:
if self.verbose >= 3:
print("Now commencing pre-run set up of GP (which has "
"non-empty training set)")
self.gp.set_L_alpha()
if self.model_format and not self.mgp:
self.gp.write_model(f'{self.output_name}_prerun',
self.model_format)
def run(self):
"""
Loop through frames and record the error between
the GP predictions and the ground-truth forces. Train the GP and update
the training set upon the triggering of the uncertainty or force error
threshold.
:return: None
"""
# Perform pre-run, in which seed trames are used.
if self.verbose >= 3:
print("Commencing run with pre-run...")
if not self.mgp:
self.pre_run()
# Past this frame, stop adding atoms to the training set
# (used for validation of model)
train_frame = int(
len(self.frames[::self.skip]) * (1 - self.validate_ratio))
# Loop through trajectory.
cur_atoms_added_train = 0 # Track atoms added for training
cur_atoms_added_write = 0 # Track atoms added for writing
cur_trains_done_write = 0 # Track training done for writing
for i, cur_frame in enumerate(self.frames[::self.skip]):
if self.verbose >= 2:
print(f"=====NOW ON FRAME {i}=====")
# If no predict_atoms_per_element was specified, predict_atoms
# will be equal to every atom in the frame.
predict_atoms = subset_of_frame_by_element(
cur_frame, self.predict_atoms_per_element)
# Atoms which are skipped will have NaN as their force / std values
local_energies = None
# Three different predictions: Either MGP, GP with energy,
# or GP without
if self.mgp:
pred_forces, pred_stds = self.pred_func(
structure=cur_frame,
mgp=self.gp,
write_to_structure=False,
selective_atoms=predict_atoms,
skipped_atom_value=np.nan)
elif self.calculate_energy:
pred_forces, pred_stds, local_energies = self.pred_func(
structure=cur_frame,
gp=self.gp,
n_cpus=self.n_cpus,
write_to_structure=False,
selective_atoms=predict_atoms,
skipped_atom_value=np.nan)
else:
pred_forces, pred_stds = self.pred_func(
structure=cur_frame,
gp=self.gp,
n_cpus=self.n_cpus,
write_to_structure=False,
selective_atoms=predict_atoms,
skipped_atom_value=np.nan)
# Get Error
dft_forces = cur_frame.forces
error = np.abs(pred_forces - dft_forces)
# Create dummy frame with the predicted forces written
dummy_frame = deepcopy(cur_frame)
dummy_frame.forces = pred_forces
dummy_frame.stds = pred_stds
if self.verbose:
self.output.write_gp_dft_comparison(
curr_step=i,
frame=dummy_frame,
start_time=time.time(),
dft_forces=dft_forces,
error=error,
local_energies=local_energies)
if i < train_frame:
# Noise hyperparameter & relative std tolerance is not for mgp.
if self.mgp:
noise = 0
else:
noise = self.gp.hyps[-1]
std_in_bound, std_train_atoms = is_std_in_bound_per_species(
rel_std_tolerance=self.rel_std_tolerance,
abs_std_tolerance=self.abs_std_tolerance,
noise=noise,
structure=dummy_frame,
max_atoms_added=self.max_atoms_from_frame,
max_by_species=self.train_env_per_species)
# Get max force error atoms
force_in_bound, force_train_atoms = \
is_force_in_bound_per_species(
abs_force_tolerance=self.abs_force_tolerance,
predicted_forces=pred_forces,
label_forces=dft_forces,
structure=dummy_frame,
max_atoms_added=self.max_atoms_from_frame,
max_by_species=self.train_env_per_species,
max_force_error=self.max_force_error)
if not std_in_bound or not force_in_bound:
# -1 is returned from the is_in_bound methods,
# so filter that out and the use sets to remove repeats
train_atoms = list(
set(std_train_atoms).union(force_train_atoms) - {-1})
# Compute mae and write to output;
# Add max uncertainty atoms to training set
self.update_gp_and_print(
cur_frame,
train_atoms=train_atoms,
uncertainties=pred_stds[train_atoms],
train=False)
cur_atoms_added_train += len(train_atoms)
cur_atoms_added_write += len(train_atoms)
# Re-train if number of sampled atoms is high enough
if cur_atoms_added_train >= self.min_atoms_per_train or (
i + 1) == train_frame:
if self.train_count < self.max_trains:
self.train_gp()
cur_trains_done_write += 1
else:
self.gp.update_L_alpha()
cur_atoms_added_train = 0
else:
self.gp.update_L_alpha()
# Loop to decide of a model should be written this
# iteration
will_write = False
if self.train_checkpoint_interval and \
cur_trains_done_write and \
self.train_checkpoint_interval \
% cur_trains_done_write == 0:
will_write = True
cur_trains_done_write = 0
if self.atom_checkpoint_interval \
and cur_atoms_added_write \
and self.atom_checkpoint_interval \
% cur_atoms_added_write == 0:
will_write = True
cur_atoms_added_write = 0
if self.model_format and will_write:
self.gp.write_model(f'{self.output_name}_checkpt',
self.model_format)
if (i + 1) == train_frame and not self.mgp:
self.gp.check_L_alpha()
if self.verbose:
self.output.conclude_run()
if self.model_format and not self.mgp:
self.gp.write_model(f'{self.output_name}_model', self.model_format)
def update_gp_and_print(self,
frame: Structure,
train_atoms: List[int],
uncertainties: List[int] = None,
train: bool = True):
"""
Update the internal GP model training set with a list of training
atoms indexing atoms within the frame. If train is True, re-train
the GP by optimizing hyperparameters.
:param frame: Structure to train on
:param train_atoms: Index atoms to train on
:param uncertainties: Uncertainties to print, pass in [] to silence
:param train: Train or not
:return: None
"""
# Group added atoms by species for easier output
added_species = [
Z_to_element(frame.coded_species[at]) for at in train_atoms
]
added_atoms = {spec: [] for spec in set(added_species)}
for atom, spec in zip(train_atoms, added_species):
added_atoms[spec].append(atom)
if self.verbose:
self.output.write_to_log(
'\nAdding atom(s) '
f'{json.dumps(added_atoms,cls=NumpyEncoder)}'
' to the training set.\n')
if uncertainties is None or len(uncertainties) != 0:
uncertainties = frame.stds[train_atoms]
if self.verbose and len(uncertainties) != 0:
self.output.write_to_log(f'Uncertainties: '
f'{uncertainties}.\n',
flush=True)
# update gp model; handling differently if it's an MGP
if not self.mgp:
self.gp.update_db(frame, frame.forces, custom_range=train_atoms)
if train:
self.train_gp()
else:
raise NotImplementedError
def train_gp(self, max_iter: int = None):
"""
Train the Gaussian process and write the results to the output file.
:param max_iter: Maximum iterations associated with this training run,
overriding the Gaussian Process's internally set maxiter.
:type max_iter: int
"""
if self.verbose >= 1:
self.output.write_to_log('Train GP\n')
# TODO: Improve flexibility in GP training to make this next step
# unnecessary, so maxiter can be passed as an argument
# Don't train if maxiter == 0
if max_iter == 0:
self.gp.check_L_alpha()
elif max_iter is not None:
temp_maxiter = self.gp.maxiter
self.gp.maxiter = max_iter
self.gp.train(output=self.output if self.verbose >= 2 else None)
self.gp.maxiter = temp_maxiter
else:
self.gp.train(output=self.output if self.verbose >= 2 else None)
if self.verbose:
self.output.write_hyps(self.gp.hyp_labels,
self.gp.hyps,
self.start_time,
self.gp.likelihood,
self.gp.likelihood_gradient,
hyps_mask=self.gp.hyps_mask)
self.train_count += 1
class TrajectoryTrainer:
def __init__(self, frames: List[Structure],
gp: GaussianProcess,
rel_std_tolerance: float = 4,
abs_std_tolerance: float = 1,
abs_force_tolerance: float = 0,
max_force_error: float = inf,
parallel: bool = False,
n_cpus: int = None,
skip: int = 1,
validate_ratio: float = 0.1,
calculate_energy: bool = False,
output_name: str = 'gp_from_aimd',
pre_train_max_iter: int = 50,
max_atoms_from_frame: int = np.inf,
max_trains: int = np.inf,
min_atoms_per_train: int = 1,
shuffle_frames: bool = False,
verbose: int = 0,
pre_train_on_skips: int = -1,
pre_train_seed_frames: List[Structure] = None,
pre_train_seed_envs: List[Tuple[AtomicEnvironment,
'np.array']] = None,
pre_train_atoms_per_element: dict = None,
train_atoms_per_element: dict = None,
checkpoint_interval: int = None,
model_format: str = 'json'):
"""
Class which trains a GP off of an AIMD trajectory, and generates
error statistics between the DFT and GP calls.
There are a variety of options which can give you a finer control
over the training process.
:param frames: List of structures to evaluate / train GP on
:param gp: Gaussian Process object
:param rel_std_tolerance: Train if uncertainty is above this *
noise variance hyperparameter
:param abs_std_tolerance: Train if uncertainty is above this
:param abs_force_tolerance: Add atom force error exceeds this
:param max_force_error: Don't add atom if force error exceeds this
:param parallel: Use parallel functions or not
:param validate_ratio: Fraction of frames used for validation
:param n_cpus: number of cpus to run with multithreading
:param skip: Skip through frames
:param calculate_energy: Use local energy kernel or not
:param output_name: Write output of training to this file
:param max_atoms_from_frame: Largest # of atoms added from one frame
:param min_atoms_added: Only train when this many atoms have been
added
:param max_trains: Stop training GP after this many calls to train
:param n_cpus: Number of CPUs to parallelize over
:param shuffle_frames: Randomize order of frames for better training
:param verbose: 0: Silent, 1: Minimal, 2: Lots of information
:param pre_train_on_skips: Train model on every n frames before running
:param pre_train_seed_frames: Frames to train on before running
:param pre_train_seed_envs: Environments to train on before running
:param pre_train_atoms_per_element: Max # of environments to add from
each species in the seed pre-training steps
:param train_atoms_per_element: Max # of environments to add from
each species in the training steps
:param checkpoint_interval: How often to write model after trainings
:param model_format: Format to write GP model to
"""
# Set up parameters
self.frames = frames
if shuffle_frames:
np.random.shuffle(frames)
# GP Training and Execution parameters
self.gp = gp
self.rel_std_tolerance = rel_std_tolerance
self.abs_std_tolerance = abs_std_tolerance
self.abs_force_tolerance = abs_force_tolerance
self.max_force_error = max_force_error
self.max_trains = max_trains
self.max_atoms_from_frame = max_atoms_from_frame
self.min_atoms_per_train = min_atoms_per_train
self.verbose = verbose
self.train_count = 0
self.parallel = parallel
self.n_cpus = n_cpus
# Set prediction function based on if forces or energies are
# desired, and parallelization accordingly
if (parallel and gp.par and gp.per_atom_par):
if calculate_energy:
self.pred_func = predict_on_structure_par_en
else:
self.pred_func = predict_on_structure_par
else:
if calculate_energy:
self.pred_func = predict_on_structure_en
else:
self.pred_func = predict_on_structure
# Parameters for negotiating with the training frames
self.output = Output(output_name, always_flush=True)
# To later be filled in using the time library
self.start_time = None
self.skip = skip
assert (isinstance(skip, int) and skip >= 1), "Skip needs to be a " \
"positive integer."
self.validate_ratio = validate_ratio
assert (validate_ratio>=0 and validate_ratio<=1), \
"validate_ratio needs to be [0,1]"
# Set up for pretraining
self.pre_train_max_iter = pre_train_max_iter
self.pre_train_on_skips = pre_train_on_skips
self.seed_envs = [] if pre_train_seed_envs is None else \
pre_train_seed_envs
self.seed_frames = [] if pre_train_seed_frames is None \
else pre_train_seed_frames
self.pre_train_env_per_species = {} if pre_train_atoms_per_element \
is None else pre_train_atoms_per_element
self.train_env_per_species = {} if train_atoms_per_element \
is None else train_atoms_per_element
# Convert to Coded Species
if self.pre_train_env_per_species:
pre_train_species = list(self.pre_train_env_per_species.keys())
for key in pre_train_species:
self.pre_train_env_per_species[element_to_Z(key)] = \
self.pre_train_env_per_species[key]
# Output parameters
self.output = Output(output_name, always_flush=True)
self.verbose = verbose
self.checkpoint_interval = checkpoint_interval
self.model_format = model_format
self.output_name = output_name
# Defining variables to be used later
self.curr_step = 0
self.train_count = 0
self.start_time = None
def pre_run(self):
"""
Various tasks to set up the AIMD training before commencing
the run through the AIMD trajectory.
1. Print the output.
2. Pre-train the GP with the seed frames and
environments. If no seed frames or environments and the GP has no
training set, then seed with at least one atom from each
"""
self.output.write_header(self.gp.cutoffs,
self.gp.kernel_name,
self.gp.hyps,
self.gp.algo,
dt=0,
Nsteps=len(self.frames),
structure=self.frames[0],
std_tolerance=(self.rel_std_tolerance,
self.abs_std_tolerance))
self.start_time = time.time()
if self.verbose >= 3:
print("Now beginning pre-run activity.")
# If seed environments were passed in, add them to the GP.
for point in self.seed_envs:
self.gp.add_one_env(point[0], point[1], train=False)
# No training set ("blank slate" run) and no seeds specified:
# Take one of each atom species in the first frame
# so all atomic species are represented in the first step.
# Otherwise use the seed frames passed in by user.
# Remove frames used as seed from later part of training
if self.pre_train_on_skips > 0:
self.seed_frames = []
newframes = []
for i in range(len(self.frames)):
if (i % self.pre_train_on_skips) == 0:
self.seed_frames += [self.frames[i]]
else:
newframes += [self.frames[i]]
self.frames = newframes
elif len(self.gp.training_data) == 0 and len(self.seed_frames) == 0:
self.seed_frames = [self.frames[0]]
self.frames = self.frames[1:]
atom_count = 0
for frame in self.seed_frames:
train_atoms = []
for species_i in set(frame.coded_species):
# Get a randomized set of atoms of species i from the frame
# So that it is not always the lowest-indexed atoms chosen
atoms_of_specie = frame.indices_of_specie(species_i)
np.random.shuffle(atoms_of_specie)
n_at = len(atoms_of_specie)
# Determine how many to add based on user defined cutoffs
n_to_add = min(n_at, self.pre_train_env_per_species.get(
species_i, inf), self.max_atoms_from_frame)
for atom in atoms_of_specie[:n_to_add]:
train_atoms.append(atom)
atom_count += 1
self.update_gp_and_print(frame, train_atoms, train=False)
if self.verbose >= 3 and atom_count > 0:
print(f"Added {atom_count} atoms to pretrain")
if (self.seed_envs or atom_count or self.seed_frames) and self.max_trains>0:
if self.verbose >= 3:
print("Now commencing pre-run training of GP (which has "
"non-empty training set)")
self.train_gp(max_iter=self.pre_train_max_iter)
else:
if self.verbose >= 3:
print("Now commencing pre-run set up of GP (which has "
"non-empty training set)")
self.gp.set_L_alpha()
if self.model_format:
self.gp.write_model(f'{self.output_name}_prerun',
self.model_format)
def run(self):
"""
Loop through frames and record the error between
the GP predictions and the ground-truth forces. Train the GP and update
the training set upon the triggering of the uncertainty or force error
threshold.
:return: None
"""
if self.verbose >= 3:
print("Commencing run with pre-run...")
self.pre_run()
train_frame = int(len(self.frames) * (1 - self.validate_ratio))
# Loop through trajectory
nsample = 0
for i, cur_frame in enumerate(self.frames[::self.skip]):
if self.verbose >= 2:
print("=====NOW ON FRAME {}=====".format(i))
dft_forces = deepcopy(cur_frame.forces)
self.pred_func(cur_frame, self.gp, self.n_cpus)
# Convert to meV/A
error = np.abs(cur_frame.forces - dft_forces)
self.output.write_gp_dft_comparison(
curr_step=i, frame=cur_frame,
start_time=time.time(),
dft_forces=dft_forces,
error=error,
local_energies=None)
if i < train_frame:
# Get max uncertainty atoms
std_in_bound, std_train_atoms = is_std_in_bound_per_species(
rel_std_tolerance=self.rel_std_tolerance,
abs_std_tolerance=self.abs_std_tolerance,
noise=self.gp.hyps[-1], structure=cur_frame,
max_atoms_added=self.max_atoms_from_frame,
max_by_species=self.train_env_per_species)
# Get max force error atoms
force_in_bound, force_train_atoms = \
is_force_in_bound_per_species(
abs_force_tolerance=self.abs_force_tolerance,
predicted_forces=cur_frame.forces,
label_forces=dft_forces,
structure=cur_frame,
max_atoms_added=self.max_atoms_from_frame,
max_by_species=self.train_env_per_species,
max_force_error=self.max_force_error)
if (not std_in_bound) or (not force_in_bound):
train_atoms = list(set(std_train_atoms).union(
force_train_atoms) - {-1})
# Compute mae and write to output;
# Add max uncertainty atoms to training set
self.update_gp_and_print(
cur_frame, train_atoms, train=False)
nsample += len(train_atoms)
# Re-train if number of sampled atoms is high enough
if nsample >= self.min_atoms_per_train or (
i + 1) == train_frame:
if self.train_count < self.max_trains:
self.train_gp()
else:
self.gp.update_L_alpha()
nsample = 0
else:
self.gp.update_L_alpha()
if self.checkpoint_interval \
and self.train_count % self.checkpoint_interval == 0 \
and self.model_format:
self.gp.write_model(f'{self.output_name}_ckpt',
self.model_format)
if (i + 1) == train_frame:
self.gp.check_L_alpha()
self.output.conclude_run()
if self.model_format:
self.gp.write_model(f'{self.output_name}_model',
self.model_format)
def update_gp_and_print(self, frame: Structure, train_atoms: List[int],
train: bool = True):
"""
Update the internal GP model training set with a list of training
atoms indexing atoms within the frame. If train is True, re-train
the GP by optimizing hyperparameters.
:param frame: Structure to train on
:param train_atoms: Index atoms to train on
:param train: Train or not
:return: None
"""
self.output.write_to_log('\nAdding atom(s) {} to the '
'training set.\n'
.format(train_atoms, ))
self.output.write_to_log('Uncertainties: {}.\n'
.format(frame.stds[train_atoms]),
flush=True)
# update gp model
self.gp.update_db(frame, frame.forces, custom_range=train_atoms)
if train:
self.train_gp()
def train_gp(self, max_iter: int = None):
"""
Train the Gaussian process and write the results to the output file.
:param max_iter: Maximum iterations associated with this training run,
overriding the Gaussian Process's internally set maxiter.
:type max_iter: int
"""
if self.verbose >= 1:
self.output.write_to_log('Train GP\n')
# TODO: Improve flexibility in GP training to make this next step
# unnecessary, so maxiter can be passed as an argument
# Don't train if maxiter == 0
if max_iter == 0:
self.gp.check_L_alpha()
elif max_iter is not None:
temp_maxiter = self.gp.maxiter
self.gp.maxiter = max_iter
self.gp.train(output=self.output if self.verbose >= 2 else None)
self.gp.maxiter = temp_maxiter
else:
self.gp.train(output=self.output if self.verbose >= 2 else None)
self.output.write_hyps(self.gp.hyp_labels, self.gp.hyps,
self.start_time,
self.gp.likelihood, self.gp.likelihood_gradient)
self.train_count += 1
class OTF:
"""Trains a Gaussian process force field on the fly during
molecular dynamics.
Args:
dft_input (str): Input file.
dt (float): MD timestep.
number_of_steps (int): Number of timesteps in the training
simulation.
gp (gp.GaussianProcess): Initial GP model.
dft_loc (str): Location of DFT executable.
std_tolerance_factor (float, optional): Threshold that determines
when DFT is called. Specifies a multiple of the current noise
hyperparameter. If the epistemic uncertainty on a force
component exceeds this value, DFT is called. Defaults to 1.
prev_pos_init ([type], optional): Previous positions. Defaults
to None.
par (bool, optional): If True, force predictions are made in
parallel. Defaults to False.
skip (int, optional): Number of frames that are skipped when
dumping to the output file. Defaults to 0.
init_atoms (List[int], optional): List of atoms from the input
structure whose local environments and force components are
used to train the initial GP model. If None is specified, all
atoms are used to train the initial GP. Defaults to None.
calculate_energy (bool, optional): If True, the energy of each
frame is calculated with the GP. Defaults to False.
output_name (str, optional): Name of the output file. Defaults to
'otf_run'.
max_atoms_added (int, optional): Number of atoms added each time
DFT is called. Defaults to 1.
freeze_hyps (int, optional): Specifies the number of times the
hyperparameters of the GP are optimized. After this many
updates to the GP, the hyperparameters are frozen.
Defaults to 10.
rescale_steps (List[int], optional): List of frames for which the
velocities of the atoms are rescaled. Defaults to [].
rescale_temps (List[int], optional): List of rescaled temperatures.
Defaults to [].
dft_softwarename (str, optional): DFT code used to calculate
ab initio forces during training. Defaults to "qe".
no_cpus (int, optional): Number of cpus used during training.
Defaults to 1.
npool (int, optional): Number of k-point pools for DFT
calculations. Defaults to None.
mpi (str, optional): Determines how mpi is called. Defaults to
"srun".
dft_kwargs ([type], optional): Additional arguments which are
passed when DFT is called; keyword arguments vary based on the
program (e.g. ESPRESSO vs. VASP). Defaults to None.
store_dft_output (Tuple[Union[str,List[str]],str], optional):
After DFT calculations are called, copy the file or files
specified in the first element of the tuple to a directory
specified as the second element of the tuple.
Useful when DFT calculations are expensive and want to be kept
for later use. The first element of the tuple can either be a
single file name, or a list of several. Copied files will be
prepended with the date and time with the format
'Year.Month.Day:Hour:Minute:Second:'.
"""
def __init__(self,
dft_input: str,
dt: float,
number_of_steps: int,
gp: gp.GaussianProcess,
dft_loc: str,
std_tolerance_factor: float = 1,
prev_pos_init: 'ndarray' = None,
par: bool = False,
skip: int = 0,
init_atoms: List[int] = None,
calculate_energy: bool = False,
output_name: str = 'otf_run',
max_atoms_added: int = 1,
freeze_hyps: int = 10,
rescale_steps: List[int] = [],
rescale_temps: List[int] = [],
dft_softwarename: str = "qe",
no_cpus: int = 1,
npool: int = None,
mpi: str = "srun",
dft_kwargs=None,
store_dft_output: Tuple[Union[str, List[str]], str] = None):
self.dft_input = dft_input
self.dt = dt
self.number_of_steps = number_of_steps
self.gp = gp
self.dft_loc = dft_loc
self.std_tolerance = std_tolerance_factor
self.skip = skip
self.dft_step = True
self.freeze_hyps = freeze_hyps
self.dft_module = dft_software[dft_softwarename]
# parse input file
positions, species, cell, masses = \
self.dft_module.parse_dft_input(self.dft_input)
_, coded_species = struc.get_unique_species(species)
self.structure = struc.Structure(cell=cell,
species=coded_species,
positions=positions,
mass_dict=masses,
prev_positions=prev_pos_init,
species_labels=species)
self.noa = self.structure.positions.shape[0]
self.atom_list = list(range(self.noa))
self.curr_step = 0
self.max_atoms_added = max_atoms_added
# initialize local energies
if calculate_energy:
self.local_energies = np.zeros(self.noa)
else:
self.local_energies = None
# set atom list for initial dft run
if init_atoms is None:
self.init_atoms = [int(n) for n in range(self.noa)]
else:
self.init_atoms = init_atoms
self.dft_count = 0
# set pred function
if not par and not calculate_energy:
self.pred_func = predict.predict_on_structure
elif par and not calculate_energy:
self.pred_func = predict.predict_on_structure_par
elif not par and calculate_energy:
self.pred_func = predict.predict_on_structure_en
elif par and calculate_energy:
self.pred_func = predict.predict_on_structure_par_en
self.par = par
# set rescale attributes
self.rescale_steps = rescale_steps
self.rescale_temps = rescale_temps
self.output = Output(output_name, always_flush=True)
# set number of cpus and npool for DFT runs
self.no_cpus = no_cpus
self.npool = npool
self.mpi = mpi
self.dft_kwargs = dft_kwargs
self.store_dft_output = store_dft_output
def run(self):
"""
Performs an on-the-fly training run.
"""
self.output.write_header(self.gp.cutoffs, self.gp.kernel_name,
self.gp.hyps, self.gp.algo, self.dt,
self.number_of_steps, self.structure,
self.std_tolerance)
counter = 0
self.start_time = time.time()
while self.curr_step < self.number_of_steps:
print('curr_step:', self.curr_step)
# run DFT and train initial model if first step and DFT is on
if self.curr_step == 0 and self.std_tolerance != 0:
# call dft and update positions
self.run_dft()
dft_frcs = copy.deepcopy(self.structure.forces)
new_pos = md.update_positions(self.dt, self.noa,
self.structure)
self.update_temperature(new_pos)
self.record_state()
# make initial gp model and predict forces
self.update_gp(self.init_atoms, dft_frcs)
if (self.dft_count - 1) < self.freeze_hyps:
self.train_gp()
# after step 1, try predicting with GP model
else:
self.gp.check_L_alpha()
self.pred_func(self.structure, self.gp, self.no_cpus)
self.dft_step = False
new_pos = md.update_positions(self.dt, self.noa,
self.structure)
# get max uncertainty atoms
std_in_bound, target_atoms = \
is_std_in_bound(self.std_tolerance,
self.gp.hyps[-1], self.structure,
self.max_atoms_added)
if not std_in_bound:
# record GP forces
self.update_temperature(new_pos)
self.record_state()
gp_frcs = copy.deepcopy(self.structure.forces)
# run DFT and record forces
self.dft_step = True
self.run_dft()
dft_frcs = copy.deepcopy(self.structure.forces)
new_pos = md.update_positions(self.dt, self.noa,
self.structure)
self.update_temperature(new_pos)
self.record_state()
# compute mae and write to output
mae = np.mean(np.abs(gp_frcs - dft_frcs))
mac = np.mean(np.abs(dft_frcs))
self.output.write_to_log('\nmean absolute error:'
' %.4f eV/A \n' % mae)
self.output.write_to_log('mean absolute dft component:'
' %.4f eV/A \n' % mac)
# add max uncertainty atoms to training set
self.update_gp(target_atoms, dft_frcs)
if (self.dft_count - 1) < self.freeze_hyps:
self.train_gp()
# write gp forces
if counter >= self.skip and not self.dft_step:
self.update_temperature(new_pos)
self.record_state()
counter = 0
counter += 1
self.update_positions(new_pos)
self.curr_step += 1
self.output.conclude_run()
def run_dft(self):
"""Calculates DFT forces on atoms in the current structure.
If OTF has store_dft_output set, then the specified DFT files will
be copied with the current date and time prepended in the format
'Year.Month.Day:Hour:Minute:Second:'.
"""
self.output.write_to_log('\nCalling DFT...\n')
# calculate DFT forces
forces = self.dft_module.run_dft_par(self.dft_input,
self.structure,
self.dft_loc,
ncpus=self.no_cpus,
npool=self.npool,
mpi=self.mpi,
dft_kwargs=self.dft_kwargs)
self.structure.forces = forces
# write wall time of DFT calculation
self.dft_count += 1
self.output.write_to_log('DFT run complete.\n')
time_curr = time.time() - self.start_time
self.output.write_to_log('number of DFT calls: %i \n' % self.dft_count)
self.output.write_to_log('wall time from start: %.2f s \n' % time_curr)
# Store DFT outputs in another folder if desired
# specified in self.store_dft_output
if self.store_dft_output is not None:
dest = self.store_dft_output[1]
target_files = self.store_dft_output[0]
now = datetime.now()
dt_string = now.strftime("%Y.%m.%d:%H:%M:%S:")
if isinstance(target_files, str):
to_copy = [target_files]
else:
to_copy = target_files
for file in to_copy:
copyfile(file, dest + '/' + dt_string + file)
def update_gp(self, train_atoms: List[int], dft_frcs: 'ndarray'):
"""Updates the current GP model.
Args:
train_atoms (List[int]): List of atoms whose local environments
will be added to the training set.
dft_frcs (np.ndarray): DFT forces on all atoms in the structure.
"""
self.output.write_to_log(
'\nAdding atom {} to the training set.\n'.format(train_atoms))
self.output.write_to_log('Uncertainty: {}.\n'.format(
self.structure.stds[train_atoms[0]]))
# update gp model
self.gp.update_db(self.structure, dft_frcs, custom_range=train_atoms)
self.gp.set_L_alpha()
def train_gp(self):
"""Optimizes the hyperparameters of the current GP model."""
self.gp.train(self.output)
self.output.write_hyps(self.gp.hyp_labels, self.gp.hyps,
self.start_time, self.gp.likelihood,
self.gp.likelihood_gradient)
def update_positions(self, new_pos: 'ndarray'):
"""Performs a Verlet update of the atomic positions.
Args:
new_pos (np.ndarray): Positions of atoms in the next MD frame.
"""
if self.curr_step in self.rescale_steps:
rescale_ind = self.rescale_steps.index(self.curr_step)
temp_fac = self.rescale_temps[rescale_ind] / self.temperature
vel_fac = np.sqrt(temp_fac)
self.structure.prev_positions = \
new_pos - self.velocities * self.dt * vel_fac
else:
self.structure.prev_positions = self.structure.positions
self.structure.positions = new_pos
self.structure.wrap_positions()
def update_temperature(self, new_pos: 'ndarray'):
"""Updates the instantaneous temperatures of the system.
Args:
new_pos (np.ndarray): Positions of atoms in the next MD frame.
"""
KE, temperature, velocities = \
md.calculate_temperature(new_pos, self.structure, self.dt,
self.noa)
self.KE = KE
self.temperature = temperature
self.velocities = velocities
def record_state(self):
self.output.write_md_config(self.dt, self.curr_step, self.structure,
self.temperature, self.KE,
self.local_energies, self.start_time,
self.dft_step, self.velocities)
self.output.write_xyz_config(self.curr_step, self.structure,
self.dft_step)
