diff --git a/.gitignore b/.gitignore index d75ca3432..38b2187d5 100644 --- a/.gitignore +++ b/.gitignore @@ -11,7 +11,9 @@ tags pwscf.save/ /docs/source/_build/html /docs/build/ +/docs/doctrees/ /datasets/ +/tests/otf_data/ /tests/test_ase/out/ /tests/test_ase/Al/ /tests/test_ase/__pycache__/ @@ -30,6 +32,17 @@ test_files/qe_output_*.out /tests/pwscf.xml /tests/*-RESTART.kp* /tests/*.wfn* +/tests/*.pwi* +/tests/*.pwo* /tests/*-bak /tests/cp2k.in* /tests/*.in_run +/tests/tmp* +/tests/grid3*npy +/tests/AgI_Molten_15.txt +/tests/log.lammps +/tests/*.txt +/tests/*.mgp +/tests/*.log +/tests/POSCAR* +.idea/ diff --git a/.travis.yml b/.travis.yml index e35569ad5..53e87185a 100644 --- a/.travis.yml +++ b/.travis.yml @@ -9,10 +9,9 @@ cache: before_install: - sudo apt update - - sudo apt install -y quantum-espresso tree - - pip install codecov pytest pytest-cov + - sudo apt install -y quantum-espresso + - pip install codecov pytest pytest-cov pytest_mock - pwd - - tree - wget http://folk.uio.no/anjohan/lmp - "wget https://github.com/cp2k/cp2k/releases/download/\ v6.1.0/cp2k-6.1-Linux-x86_64.sopt" @@ -22,10 +21,10 @@ before_install: script: - pwd - - tree - cd tests - - PWSCF_COMMAND=pw.x CP2K_COMMAND=../cp2k-6.1-Linux-x86_64.sopt lmp=../lmp - pytest --cov=../flare/ + - PWSCF_COMMAND=pw.x CP2K_COMMAND=../cp2k-6.1-Linux-x86_64.sopt + lmp=$(pwd)/../lmp + pytest --show-capture=all -vv --durations=0 --cov=../flare/ - coverage xml after_success: diff --git a/docs/source/FAQs/all.rst b/docs/source/FAQs/all.rst index 1a9fb96d0..2999a68d8 100644 --- a/docs/source/FAQs/all.rst +++ b/docs/source/FAQs/all.rst @@ -35,7 +35,7 @@ Gaussian Processes and OTF * `maxiter`: maximal number of iterations, usually set to ~10 to prevent training for too long. - * `par`: if `True`, then parallelization is used in optimization. + * `parallel`: if `True`, then parallelization is used in optimization. The serial version could be very slow. * `output` (in `train` function): set up an output file for monitoring optimization iterations. diff --git a/docs/source/flare/gp.rst b/docs/source/flare/gp.rst index 9cef3541a..d7588fc8f 100644 --- a/docs/source/flare/gp.rst +++ b/docs/source/flare/gp.rst @@ -6,7 +6,7 @@ Gaussian Process Force Fields struc env - kernels/kernels + kernels/sc kernels/mc_simple kernels/mc_sephyps cutoffs diff --git a/docs/source/flare/kernels/kernels.rst b/docs/source/flare/kernels/sc.rst similarity index 61% rename from docs/source/flare/kernels/kernels.rst rename to docs/source/flare/kernels/sc.rst index 2a3bc9211..8356f40a7 100644 --- a/docs/source/flare/kernels/kernels.rst +++ b/docs/source/flare/kernels/sc.rst @@ -1,5 +1,5 @@ Single-element Kernels ====================== -.. automodule:: flare.kernels.kernels +.. automodule:: flare.kernels.sc :members: diff --git a/docs/source/flare/kernels/util.rst b/docs/source/flare/kernels/util.rst new file mode 100644 index 000000000..cbce13d45 --- /dev/null +++ b/docs/source/flare/kernels/util.rst @@ -0,0 +1,5 @@ +Kernel interface (for developer) +====================== + +.. automodule:: flare.kernels.utils + :members: diff --git a/docs/source/install.rst b/docs/source/install.rst index 014ff2fde..4e57486ed 100644 --- a/docs/source/install.rst +++ b/docs/source/install.rst @@ -85,8 +85,8 @@ Second, in the initialization of the GP class and OTF class, turn on the GP para .. code-block:: python - gp_model = GaussianProcess(..., n_cpus=2) - otf_instance = OTF(..., par=False, n_cpus=2) + gp_model = GaussianProcess(..., parallel=True, per_atom_par=False, n_cpus=2) + otf_instance = OTF(..., par, n_cpus=2) Third, set the number of threads for MKL before running your python script. @@ -100,7 +100,7 @@ Third, set the number of threads for MKL before running your python script. If these numbers are larger than the actual CPUs number, it can lead to an overload of the machine. .. note:: - If OTF.par=True and GaussianProcess.n_cpus>1, it is equivalent to run with n_cpu**2 threads + If gp_model.per_atom_par=True and NUM_OMB_THREAD>1, it is equivalent to run with NUM_OMB_THREAD*otf.n_cpus threads because the MKL calls are nested in the multiprocessing code. The current version of FLARE can only support parallel calculations within one compute node. diff --git a/docs/source/tutorials/after_training.py b/docs/source/tutorials/after_training.py index c5b5ff0d0..eb45325db 100644 --- a/docs/source/tutorials/after_training.py +++ b/docs/source/tutorials/after_training.py @@ -8,13 +8,13 @@ # ------------------------------------------------------------------------- # reconstruct gp model from otf snippet # ------------------------------------------------------------------------- -import flare.kernels.mc_simple as mc_simple -kernel = mc_simple.two_plus_three_body_mc -kernel_grad = mc_simple.two_plus_three_body_mc_grad -gp_model = otf_object.make_gp(kernel=kernel, kernel_grad=kernel_grad, + + + +gp_model = otf_object.make_gp(kernel_name="2+3_mc", hyp_no=hyp_no) -gp_model.par = True +gp_model.parallel = True gp_model.hyp_labels = ['sig2', 'ls2', 'sig3', 'ls3', 'noise'] # write model to a binary file diff --git a/docs/source/tutorials/gpfa.rst b/docs/source/tutorials/gpfa.rst index 25800cb68..19c3834e2 100644 --- a/docs/source/tutorials/gpfa.rst +++ b/docs/source/tutorials/gpfa.rst @@ -18,7 +18,7 @@ Step 1: Setting up a Gaussian Process Object Our goal is to train a GP, which first must be instantiated with a set of parameters. For the sake of this example, which is a molecule, we will use a two-plus-three body kernel. -We must provide the kernel and the kernel gradient as ``callable`` s to the GP. +We must provide the kernel name to the GP, "two-plus-three-mc" or "2+3mc'. Our initial guesses for the hyperparameters are not important. The hyperparameter labels are included below for later output. The system contains a small number of atoms, so we choose a relatively @@ -30,7 +30,6 @@ At the header of a file, include the following imports: .. code-block:: python from flare.gp import GaussianProcess - from flare.kernels.mc_simple import two_plus_three_body_mc, two_plus_three_body_mc_grad We will then set up the ``GaussianProcess`` object. @@ -47,17 +46,15 @@ We will then set up the ``GaussianProcess`` object. * | The two cutoff values correspond to the functions which set up | the two- and three-body Atomic Environments. Since Methanol is a small | molecule, 7 Angstrom each will be sufficent. -* | The kernels which facilitate these comparisons must be imported as Python ``callable`` s. +* | The kernel name must contain the terms you want to use. * | Here, we will use the ``two_plus_three_body_mc`` kernel, which | uses two-body and three-body comparisons. ``mc`` means multi-component, | indicating that it can handle multiple atomic species being present. -* | We must also import the gradient of the kernel, which is - | ``two_plus_three_body_mc_grad``. .. code-block:: python - gp = GaussianProcess(kernel=two_plus_three_body_mc, kernel_grad=two_plus_three_body_mc_grad, + gp = GaussianProcess(kernel_name="2+3mc", hyps=[0.01, 0.01, 0.01, 0.01, 0.01], cutoffs = (7,7), hyp_labels=['Two-Body Signal Variance','Two-Body Length Scale','Three-Body Signal Variance', diff --git a/docs/source/tutorials/otf_al.rst b/docs/source/tutorials/otf_al.rst index 0b432e89a..296a9bc99 100644 --- a/docs/source/tutorials/otf_al.rst +++ b/docs/source/tutorials/otf_al.rst @@ -8,42 +8,35 @@ Step 1: Set up a GP Model ------------------------- Let's start up with the GP model with three-body kernel function. -(See :doc:`kernels.py <../flare/kernels/kernels>` (single component) +(See :doc:`kernels.py <../flare/kernels/sc>` (single component) or :doc:`mc_simple.py <../flare/kernels/mc_simple>` (multi-component) for more options.) .. code-block:: python :linenos: # make gp model - kernel = en.three_body - kernel_grad = en.three_body_grad hyps = np.array([0.1, 1, 0.01]) hyp_labels = ['Signal Std', 'Length Scale', 'Noise Std'] cutoffs = np.array([3.9, 3.9]) - energy_force_kernel = en.three_body_force_en gp = \ - GaussianProcess(kernel=kernel, - kernel_grad=kernel_grad, + GaussianProcess(kernel_name='3b', hyps=hyps, cutoffs=cutoffs, hyp_labels=hyp_labels, - energy_force_kernel=energy_force_kernel, maxiter=50) **Some Explanation about the parameters:** -* ``kernel``: set to be the kernel function +* ``kernel_name``: set to be the name of kernel functions - * import from :doc:`kernels.py <../flare/kernels/kernels>` (single-component system) + * import from :doc:`sc.py <../flare/kernels/sc>` (single-component system) or :doc:`mc_simple.py <../flare/kernels/mc_simple>` (multi-component system). * Currently we have the choices of two-body, three-body and two-plus-three-body kernel functions. * Two-plus-three-body kernel function is simply the summation of two-body and three-body kernels, and is tested to have best performance. -* ``kernel_grad``: set to be the gradient of kernel function used for hyperparameter training. - * ``hyps``: the array of hyperparameters, whose names are shown in ``hyp_labels``. * For two-body kernel function, an array of length 3 is needed, ``hyps=[sigma_2, ls_2, sigma_n]``; @@ -53,8 +46,6 @@ or :doc:`mc_simple.py <../flare/kernels/mc_simple>` (multi-component) for more o * ``cutoffs``: consists of two values. The 1st is the cutoff of two-body and the 2nd is for three-body kernel. Usually we will set a larger one for two-body. -* ``energy_force_kernel``: set to calculate local energy for each atom based on the integral of forces. - * ``maxiter``: set to constrain the number of steps in training hyperparameters. @@ -62,12 +53,6 @@ or :doc:`mc_simple.py <../flare/kernels/mc_simple>` (multi-component) for more o 1. See :doc:`GaussianProcess <../flare/gp>` for complete description of arguments of ``GaussianProcess`` class. -2. In any case, you need to set up ``kernel``. - - a. If you want to train the hyperparameters, you need to set up ``kernel_grad``; - b. If you want the output of local energy, you need to set up ``energy_force_kernel`` and ``energy_kernel``, - otherwise you don't need them. - Step 2: Set up DFT Calculator ----------------------------- diff --git a/flare/ase/calculator.py b/flare/ase/calculator.py index e406c4ba6..21e165dbf 100644 --- a/flare/ase/calculator.py +++ b/flare/ase/calculator.py @@ -37,8 +37,6 @@ def get_property(self, name, atoms=None, allow_calculation=True): def get_potential_energy(self, atoms=None, force_consistent=False): - if self.use_mapping: - print('MGP energy mapping not implemented, temporarily set to 0') return self.get_property('energy', atoms) @@ -99,32 +97,30 @@ def calculate_mgp_serial(self, atoms): atoms.get_atomic_numbers(), atoms.positions) - forces = np.zeros((nat, 3)) - stress = np.zeros((nat, 6)) - stds = np.zeros((nat, 3)) + self.results['forces'] = np.zeros((nat, 3)) + self.results['stresses'] = np.zeros((nat, 6)) + self.results['stds'] = np.zeros((nat, 3)) + self.results['local_energies'] = np.zeros(nat) for n in range(nat): chemenv = AtomicEnvironment(struc_curr, n, self.mgp_model.cutoffs) - f, v, vir = self.mgp_model.predict(chemenv, mean_only=False) - forces[n] = f - stress[n] = vir - stds[n] = np.sqrt(np.absolute(v)) + f, v, vir, e = self.mgp_model.predict(chemenv, mean_only=False) + self.results['forces'][n] = f + self.results['stresses'][n] = vir + self.results['stds'][n] = np.sqrt(np.absolute(v)) + self.results['local_energies'][n] = e - self.results['forces'] = forces - self.results['stds'] = stds - self.results['stresses'] = stress - self.results['stress'] = np.sum(stress, axis=0) - - # TODO: implement energy mapping - self.results['local_energies'] = np.zeros(forces.shape) - self.results['energy'] = 0 + volume = atoms.get_volume() + total_stress = np.sum(self.results['stresses'], axis=0) + self.results['stress'] = total_stress / volume + self.results['energy'] = np.sum(self.results['local_energies']) atoms.get_uncertainties = self.get_uncertainties - return forces def calculate_mgp_par(self, atoms): - return self.calculate_mgp_serial(atoms) + # TODO: to be done + self.calculate_mgp_serial(atoms) def calculation_required(self, atoms, quantities): diff --git a/flare/ase/logger.py b/flare/ase/logger.py index 333b1080c..84b2cdc75 100644 --- a/flare/ase/logger.py +++ b/flare/ase/logger.py @@ -12,7 +12,7 @@ class OTFLogger(MDLogger): def __init__(self, dyn, atoms, logfile, header=False, stress=False, - peratom=False, mode="w", data_folder='otf_data', + peratom=False, mode="w", data_folder='otf_data', data_in_logfile=False): super().__init__(dyn, atoms, logfile, header, stress, @@ -22,14 +22,14 @@ def __init__(self, dyn, atoms, logfile, header=False, stress=False, self.write_header_info() self.start_time = time.time() if data_folder not in os.listdir(): - os.system('mkdir '+data_folder) + os.mkdir(data_folder) self.positions_xyz = open(data_folder+'/positions.xyz', mode=mode) self.velocities_dat = open(data_folder+'/velocities.dat', mode=mode) self.forces_dat = open(data_folder+'/forces.dat', mode=mode) - self.uncertainties_dat = open(data_folder+'/uncertainties.dat', + self.uncertainties_dat = open(data_folder+'/uncertainties.dat', mode=mode) - self.dft_positions_xyz = open(data_folder+'/dft_positions.xyz', + self.dft_positions_xyz = open(data_folder+'/dft_positions.xyz', mode=mode) self.dft_forces_dat = open(data_folder+'/dft_forces.dat', mode=mode) self.added_atoms_dat = open(data_folder+'/added_atoms.dat', mode=mode) @@ -44,11 +44,11 @@ def write_header_info(self): self.logfile.write(str(datetime.datetime.now())) self.logfile.write('\nnumber of cpu cores: ') # TODO self.logfile.write('\ncutoffs: '+str(gp_model.cutoffs)) - self.logfile.write('\nkernel: '+gp_model.kernel.__name__) + self.logfile.write('\nkernel_name: '+gp_model.kernel.__name__) self.logfile.write('\nnumber of hyperparameters: '+str(len(gp_model.hyps))) self.logfile.write('\nhyperparameters: '+str(gp_model.hyps)) self.logfile.write('\nhyperparameter optimization algorithm: ' + - gp_model.algo) + gp_model.opt_algorithm) self.logfile.write('\nuncertainty tolerance: {} times noise'.format( str(self.dyn.std_tolerance_factor))) self.logfile.write('\ntimestep (ps): {}'.format(self.dyn.dt/1000)) @@ -84,7 +84,7 @@ def write_prev_positions(self): self.logfile.write('previous positions (A):\n') template = '{} {:9f} {:9f} {:9f}\n' for n in range(nat): - self.logfile.write(template.format(species[n], + self.logfile.write(template.format(species[n], prev_pos[n,0], prev_pos[n,1], prev_pos[n,2])) def __call__(self): @@ -99,7 +99,7 @@ def write_datafiles(self): species = self.atoms.get_chemical_symbols() positions = self.atoms.get_positions() forces = self.atoms.get_forces() - if type(self.atoms.calc) == FLARE_Calculator: + if type(self.atoms.calc) == FLARE_Calculator: velocities = self.atoms.get_velocities() stds = self.atoms.get_uncertainties(self.atoms) data_files = self.traj_files @@ -108,14 +108,14 @@ def write_datafiles(self): data_files = self.dft_data_files data = [positions, forces] self.added_atoms_dat.write('Frame '+str(steps)+'\n') - + for ind, f in enumerate(data_files): f.write(str(self.natoms)) f.write('\nFrame '+str(steps)+'\n') for atom in range(self.natoms): dat = template.format(species[atom], - data[ind][atom][0], - data[ind][atom][1], + data[ind][atom][0], + data[ind][atom][1], data[ind][atom][2]) f.write(dat+'\n') f.flush() @@ -126,7 +126,7 @@ def write_logfile(self): if self.dyn is not None: steps = self.dyn.nsteps t = steps / 1000 - if type(self.atoms.calc) != FLARE_Calculator: + if type(self.atoms.calc) != FLARE_Calculator: self.logfile.write('\n*-Frame: '+str(steps)) else: self.logfile.write('\n-Frame: '+str(steps)) @@ -149,14 +149,14 @@ def write_logfile(self): self.write_wall_time() self.logfile.flush() - + def write_data_to_logfile(self): # add positions, forces and stds to be written species = self.atoms.get_chemical_symbols() positions = self.atoms.get_positions() forces = self.atoms.get_forces() velocities = self.atoms.get_velocities() - if type(self.atoms.calc) == FLARE_Calculator: + if type(self.atoms.calc) == FLARE_Calculator: stds = self.atoms.get_uncertainties(self.atoms) force_str = 'GP Forces' else: @@ -170,9 +170,9 @@ def write_data_to_logfile(self): '{:9f} {:9f} {:9f} {:9f} {:9f} {:9f}' for atom in range(len(positions)): dat = \ - template.format(species[atom], positions[atom][0], - positions[atom][1], positions[atom][2], - forces[atom][0], forces[atom][1], + template.format(species[atom], positions[atom][0], + positions[atom][1], positions[atom][2], + forces[atom][0], forces[atom][1], forces[atom][2], stds[atom][0], stds[atom][1], stds[atom][2], velocities[atom][0], velocities[atom][1], @@ -200,3 +200,4 @@ def run_complete(self): f.close() for f in self.dft_data_files: f.close() + self.added_atoms_dat.close() diff --git a/flare/ase/otf.py b/flare/ase/otf.py index a0f3b61d6..607b832cc 100644 --- a/flare/ase/otf.py +++ b/flare/ase/otf.py @@ -10,12 +10,13 @@ from copy import deepcopy from flare.struc import Structure +from flare.gp import GaussianProcess from flare.util import is_std_in_bound from flare.mgp.utils import get_l_bound import numpy as np -from ase.md.md import MolecularDynamics from ase import units +from ase.calculators.espresso import Espresso class OTF: @@ -104,13 +105,16 @@ def otf_run(self, steps, rescale_temp=[], rescale_steps=[]): self.logger_ind = i break + # initialize gp by a dft calculation + calc = self.atoms.calc + calc.mgp_updated = False + # restart from previous OTF training if self.restart_from is not None: self.restart() f = self.atoms.calc.results['forces'] - # initialize gp by a dft calculation - if not self.atoms.calc.gp_model.training_data: + if not calc.gp_model.training_data: self.dft_count = 0 self.stds = np.zeros((self.noa, 3)) dft_forces = self.call_DFT() @@ -121,7 +125,7 @@ def otf_run(self, steps, rescale_temp=[], rescale_steps=[]): self.l_bound = get_l_bound(100, curr_struc, self.two_d) print('l_bound:', self.l_bound) - self.atoms.calc.gp_model.update_db(curr_struc, dft_forces, + calc.gp_model.update_db(curr_struc, dft_forces, custom_range=self.init_atoms) # train calculator @@ -130,6 +134,10 @@ def otf_run(self, steps, rescale_temp=[], rescale_steps=[]): self.observers[self.logger_ind][0].add_atom_info(atom, self.stds[atom]) self.train() + + if self.use_mapping: + self.build_mgp() + self.observers[self.logger_ind][0].write_wall_time() if self.md_engine == 'NPT': @@ -143,7 +151,8 @@ def otf_run(self, steps, rescale_temp=[], rescale_steps=[]): step_0 = self.nsteps for i in range(step_0, steps): print('step:', i) - self.atoms.calc.results = {} # clear the calculation from last step + + calc.results = {} # clear the calculation from last step self.stds = np.zeros((self.noa, 3)) # temperature rescaling @@ -167,12 +176,11 @@ def otf_run(self, steps, rescale_temp=[], rescale_steps=[]): self.l_bound = get_l_bound(self.l_bound, curr_struc, self.two_d) print('l_bound:', self.l_bound) curr_struc.stds = np.copy(self.stds) - noise = self.atoms.calc.gp_model.hyps[-1] + noise = calc.gp_model.hyps[-1] self.std_in_bound, self.target_atoms = is_std_in_bound(\ noise, self.std_tolerance_factor, curr_struc, self.max_atoms_added) print('std in bound:', self.std_in_bound, self.target_atoms) - #self.is_std_in_bound([]) if not self.std_in_bound: # call dft/eam @@ -182,11 +190,34 @@ def otf_run(self, steps, rescale_temp=[], rescale_steps=[]): # update gp print('updating gp') self.update_GP(dft_forces) + calc.mgp_updated = False + + if self.use_mapping: + self.build_mgp() self.observers[self.logger_ind][0].run_complete() + def build_mgp(self): + # build mgp + calc = self.atoms.calc + if self.nsteps in self.non_mapping_steps: + calc.use_mapping = False + skip = True + else: + calc.use_mapping = True + + if calc.mgp_updated: + skip = True + else: + skip = False + calc.mgp_updated = True + + calc.build_mgp(skip) + + def call_DFT(self): + self.dft_calc.nsteps = self.nsteps prev_calc = self.atoms.calc calc = deepcopy(self.dft_calc) self.atoms.set_calculator(calc) @@ -247,15 +278,8 @@ def train(self, output=None, skip=False): #TODO: change to update_L_alpha() calc.gp_model.set_L_alpha() - # build mgp - if self.use_mapping: - if self.get_time() in self.non_mapping_steps: - skip = True - - calc.build_mgp(skip) - - np.save('ky_mat_inv', calc.gp_model.ky_mat_inv) - np.save('alpha', calc.gp_model.alpha) + # save gp_model everytime after training + calc.gp_model.write_model('otf_data/gp_model', format='pickle') def restart(self): # Recover atomic configuration: positions, velocities, forces @@ -265,27 +289,29 @@ def restart(self): self.atoms.calc.results['forces'] = self.read_frame('forces.dat', -1)[0] print('Last frame recovered') - # Recover training data set - gp_model = self.atoms.calc.gp_model - atoms = deepcopy(self.atoms) - nat = len(self.atoms.positions) - dft_positions = self.read_all_frames('dft_positions.xyz', nat) - dft_forces = self.read_all_frames('dft_forces.dat', nat) - added_atoms = self.read_all_frames('added_atoms.dat', 1, 1, 'int') - for i, frame in enumerate(dft_positions): - atoms.set_positions(frame) - curr_struc = Structure.from_ase_atoms(atoms) - gp_model.update_db(curr_struc, dft_forces[i], added_atoms[i]) - gp_model.set_L_alpha() - print('GP training set ready') +# # Recover training data set +# gp_model = self.atoms.calc.gp_model +# atoms = deepcopy(self.atoms) +# nat = len(self.atoms.positions) +# dft_positions = self.read_all_frames('dft_positions.xyz', nat) +# dft_forces = self.read_all_frames('dft_forces.dat', nat) +# added_atoms = self.read_all_frames('added_atoms.dat', 1, 1, 'int') +# for i, frame in enumerate(dft_positions): +# atoms.set_positions(frame) +# curr_struc = Structure.from_ase_atoms(atoms) +# gp_model.update_db(curr_struc, dft_forces[i], added_atoms[i].tolist()) +# gp_model.set_L_alpha() +# print('GP training set ready') # Recover FLARE calculator - gp_model.ky_mat_inv = np.load(self.restart_from+'/ky_mat_inv.npy') - gp_model.alpha = np.load(self.restart_from+'/alpha.npy') + self.atoms.calc.gp_model = GaussianProcess.from_file(self.restart_from+'/gp_model.pickle') +# gp_model.ky_mat_inv = np.load(self.restart_from+'/ky_mat_inv.npy') +# gp_model.alpha = np.load(self.restart_from+'/alpha.npy') if self.atoms.calc.use_mapping: for map_3 in self.atoms.calc.mgp_model.maps_3: map_3.load_grid = self.restart_from + '/' self.atoms.calc.build_mgp(skip=False) + self.atoms.calc.mgp_updated = True print('GP and MGP ready') self.l_bound = 10 @@ -329,3 +355,4 @@ def read_frame(self, filename, frame_num): return np.array(properties), len(lines)//(nat+2) + diff --git a/flare/ase/otf_md.py b/flare/ase/otf_md.py index 9782c1943..201d9dae3 100644 --- a/flare/ase/otf_md.py +++ b/flare/ase/otf_md.py @@ -1,5 +1,5 @@ ''' -This module provides OTF training with ASE MD engines: VerlocityVerlet, NVTBerendsen, NPTBerendsen, NPT and Langevin. +This module provides OTF training with ASE MD engines: VerlocityVerlet, NVTBerendsen, NPTBerendsen, NPT and Langevin. Please see the function `otf_md` below for usage ''' import os @@ -23,18 +23,19 @@ class OTF_VelocityVerlet(VelocityVerlet, OTF): On-the-fly training with ASE's VelocityVerlet molecular dynamics engine. Inherit from ASE `VelocityVerlet `_ class and our ASE-coupled on-the-fly training engine `flare.ase.OTF` - Args: + Args: atoms, timestep, trajectory, dt: see `VelocityVerlet `_ kwargs: same parameters as :class:`flare.ase.OTF` """ - def __init__(self, atoms, timestep=None, trajectory=None, dt=None, + def __init__(self, atoms, timestep, trajectory=None, dt=None, **kwargs): - VelocityVerlet.__init__(self, atoms, timestep, trajectory, dt=dt) + VelocityVerlet.__init__(self, atoms=atoms, timestep=timestep, + trajectory=trajectory, dt=dt) + OTF.__init__(self, **kwargs) - self.md_engine = 'VelocityVerlet' class OTF_NVTBerendsen(NVTBerendsen, OTF): @@ -52,9 +53,9 @@ class OTF_NVTBerendsen(NVTBerendsen, OTF): def __init__(self, atoms, timestep, temperature, taut, fixcm=True, trajectory=None, **kwargs): - NVTBerendsen.__init__(self, atoms, timestep, temperature, taut, + NVTBerendsen.__init__(self, atoms, timestep, temperature, taut, fixcm, trajectory) - + OTF.__init__(self, **kwargs) self.md_engine = 'NVTBerendsen' @@ -96,7 +97,7 @@ class OTF_NPT(NPT, OTF): """ - def __init__(self, atoms, timestep, temperature, externalstress, + def __init__(self, atoms, timestep, temperature, externalstress, ttime, pfactor, mask=None, trajectory=None, **kwargs): NPT.__init__(self, atoms, timestep, temperature, @@ -118,29 +119,29 @@ class OTF_Langevin(Langevin, OTF): kwargs: same parameters as :class:`flare.ase.OTF` """ - def __init__(self, atoms, timestep=None, temperature=None, friction=None, + def __init__(self, atoms, timestep=None, temperature=None, friction=None, fixcm=True, trajectory=None, **kwargs): - Langevin.__init__(self, atoms, timestep, temperature, friction, + Langevin.__init__(self, atoms, timestep, temperature, friction, fixcm, trajectory) OTF.__init__(self, **kwargs) - + self.md_engine = 'Langevin' def otf_md(md_engine: str, atoms, md_params: dict, otf_params: dict): ''' - Create an OTF MD engine - + Create an OTF MD engine + Args: md_engine (str): the name of md engine, including `VelocityVerlet`, `NVTBerendsen`, `NPTBerendsen`, `NPT`, `Langevin` atoms (Atoms): ASE Atoms to apply this md engine - md_params (dict): parameters used in MD engines, + md_params (dict): parameters used in MD engines, must include: `timestep`, `trajectory` (usually set to None). - Also include those parameters required for ASE MD engine, + Also include those parameters required for ASE MD engine, please look at ASE website to find out parameters for different engines otf_params (dict): parameters used in OTF module @@ -150,18 +151,18 @@ def otf_md(md_engine: str, atoms, md_params: dict, otf_params: dict): Example: >>> from ase import units >>> from ase.spacegroup import crystal - >>> super_cell = crystal(['Ag', 'I'], + >>> super_cell = crystal(['Ag', 'I'], basis=[(0, 0, 0), (0.5, 0.5, 0.5)], size=(2, 1, 1), cellpar=[3.85, 3.85, 3.85, 90, 90, 90]) >>> md_engine = 'VelocityVerlet' - >>> md_params = {'timestep': 1 * units.fs, 'trajectory': None, - 'dt': None} - >>> otf_params = {'dft_calc': dft_calc, + >>> md_params = {'timestep': 1 * units.fs, 'trajectory': None, + 'dt': None} + >>> otf_params = {'dft_calc': dft_calc, 'init_atoms': [0], - 'std_tolerance_factor': 1, + 'std_tolerance_factor': 1, 'max_atoms_added' : len(super_cell.positions), - 'freeze_hyps': 10, + 'freeze_hyps': 10, 'use_mapping': False} >>> test_otf = otf_md(md_engine, super_cell, md_params, otf_params) ''' @@ -171,21 +172,21 @@ def otf_md(md_engine: str, atoms, md_params: dict, otf_params: dict): trajectory = md['trajectory'] if md_engine == 'VelocityVerlet': - return OTF_VelocityVerlet(atoms, timestep, trajectory, dt=md['dt'], - **otf_params) - + return OTF_VelocityVerlet(atoms, timestep, trajectory=trajectory, + dt=md['dt'], **otf_params) + elif md_engine == 'NVTBerendsen': - return OTF_NVTBerendsen(atoms, timestep, md['temperature'], + return OTF_NVTBerendsen(atoms, timestep, md['temperature'], md['taut'], md['fixcm'], trajectory, **otf_params) - + elif md_engine == 'NPTBerendsen': - return OTF_NPTBerendsen(atoms, timestep, md['temperature'], - md['taut'], md['pressure'], md['taup'], + return OTF_NPTBerendsen(atoms, timestep, md['temperature'], + md['taut'], md['pressure'], md['taup'], md['compressibility'], md['fixcm'], trajectory, **otf_params) elif md_engine == 'NPT': return OTF_NPT(atoms, timestep, md['temperature'], - md['externalstress'], md['ttime'], md['pfactor'], + md['externalstress'], md['ttime'], md['pfactor'], md['mask'], trajectory, **otf_params) elif md_engine == 'Langevin': diff --git a/flare/dft_interface/cp2k_util.py b/flare/dft_interface/cp2k_util.py index 578bb89e6..f5b9beb16 100644 --- a/flare/dft_interface/cp2k_util.py +++ b/flare/dft_interface/cp2k_util.py @@ -37,14 +37,17 @@ def run_dft_par(dft_input, structure, dft_loc, ncpus=1, dft_out="dft.out", newfilename = edit_dft_input_positions(dft_input, structure) dft_command = \ - f'{dft_loc} -i {newfilename} > {dft_out}' + f'{dft_loc} -i {newfilename}' if (ncpus > 1): if (mpi == "mpi"): dft_command = f'mpirun -np {ncpus} {dft_command}' else: dft_command = f'srun -n {ncpus} {dft_command}' + # output.write_to_output(dft_command+'\n') - call(dft_command, shell=True) + with open(dft_out, "w+") as fout: + call(dft_command.split(), stdout=fout) + os.remove(newfilename) return parse_dft_forces(dft_out) diff --git a/flare/dft_interface/qe_util.py b/flare/dft_interface/qe_util.py index 95cbc7817..87017c791 100644 --- a/flare/dft_interface/qe_util.py +++ b/flare/dft_interface/qe_util.py @@ -38,10 +38,10 @@ def run_dft_par(dft_input, structure, dft_loc, n_cpus=1, dft_out='pwscf.out', if npool is None: dft_command = \ - f'{dft_loc} -i {newfilename} > {dft_out}' + f'{dft_loc} -i {newfilename}' else: dft_command = \ - f'{dft_loc} -nk {npool} -i {newfilename} > {dft_out}' + f'{dft_loc} -nk {npool} -i {newfilename}' if (n_cpus > 1): if (mpi == "mpi"): @@ -49,7 +49,9 @@ def run_dft_par(dft_input, structure, dft_loc, n_cpus=1, dft_out='pwscf.out', else: dft_command = f'srun -n {n_cpus} --mpi=pmi2 {dft_command}' - call(dft_command, shell=True) + with open(dft_out, "w+") as fout: + call(dft_command.split(), stdout=fout) + os.remove(newfilename) return parse_dft_forces(dft_out) @@ -75,8 +77,10 @@ def run_dft_en_par(dft_input, structure, dft_loc, n_cpus): run_qe_path = dft_input edit_dft_input_positions(run_qe_path, structure) qe_command = \ - 'mpirun -np {n_cpus} {dft_loc} < {run_qe_path} > pwscf.out' - call(qe_command, shell=True) + 'mpirun -np {n_cpus} {dft_loc} -i {run_qe_path}' + + with open('pwscf.out', "w+") as fout: + call(qe_command.split(), stdout=fout) forces, energy = parse_dft_forces_and_energy('pwscf.out') diff --git a/flare/dft_interface/vasp_util.py b/flare/dft_interface/vasp_util.py index b93643a0e..c42f5afa1 100644 --- a/flare/dft_interface/vasp_util.py +++ b/flare/dft_interface/vasp_util.py @@ -187,8 +187,8 @@ def md_trajectory_from_vasprun(vasprun: Union[str, Vasprun], structure = Structure.from_pmg_structure(step["structure"]) structure.energy = step["electronic_steps"][-1]["e_0_energy"] # TODO should choose e_wo_entrp or e_fr_energy? - structure.forces = np.array(step["forces"]) - structure.stress = np.array(step["stress"]) + structure.forces = np.array(step.get("forces")) + structure.stress = np.array(step.get("stress")) struc_lst.append(structure) return struc_lst diff --git a/flare/env.py b/flare/env.py index 61333f320..4ccdefe06 100644 --- a/flare/env.py +++ b/flare/env.py @@ -21,6 +21,7 @@ class AtomicEnvironment: given, 2+3-body if two are passed. :type cutoffs: np.ndarray """ + def __init__(self, structure: Structure, atom: int, cutoffs): self.structure = structure self.positions = structure.wrapped_positions @@ -30,25 +31,40 @@ def __init__(self, structure: Structure, atom: int, cutoffs): self.atom = atom self.ctype = structure.coded_species[atom] - self.cutoff_2 = cutoffs[0] + self.cutoffs = np.copy(cutoffs) + + self.compute_env() + + def compute_env(self): # get 2-body arrays bond_array_2, bond_positions_2, etypes = \ get_2_body_arrays(self.positions, self.atom, self.cell, - self.cutoff_2, self.species) + self.cutoffs[0], self.species) self.bond_array_2 = bond_array_2 self.etypes = etypes - # if multiple cutoffs are given, create 3-body arrays - if len(cutoffs) > 1: - self.cutoff_3 = cutoffs[1] + # if 2 cutoffs are given, create 3-body arrays + if len(self.cutoffs) > 1: bond_array_3, cross_bond_inds, cross_bond_dists, triplet_counts = \ - get_3_body_arrays(bond_array_2, bond_positions_2, cutoffs[1]) + get_3_body_arrays(bond_array_2, bond_positions_2, self.cutoffs[1]) self.bond_array_3 = bond_array_3 self.cross_bond_inds = cross_bond_inds self.cross_bond_dists = cross_bond_dists self.triplet_counts = triplet_counts + # if 3 cutoffs are given, create many-body arrays + if len(self.cutoffs) > 2: + self.bond_array_mb, self.neigh_dists_mb, self.num_neighs_mb, self.etype_mb, \ + self.bond_array_mb_etypes = get_m_body_arrays( + self.positions, self.atom, self.cell, self.cutoffs[2], self.species) + else: + self.bond_array_mb = None + self.neigh_dists_mb = None + self.num_neighs_mb = None + self.etype_mb = None + + def as_dict(self): """ Returns Atomic Environment object as a dictionary for serialization @@ -64,6 +80,7 @@ def as_dict(self): dictionary['forces'] = self.structure.forces dictionary['energy'] = self.structure.energy dictionary['stress'] = self.structure.stress + del dictionary['structure'] return dictionary @@ -84,15 +101,15 @@ def from_dict(dictionary): species=dictionary['species']) index = dictionary['atom'] - cutoffs = [] - if dictionary.get('cutoff_2', False): - cutoffs.append(dictionary.get('cutoff_2')) + if dictionary.get('cutoffs') is not None: + cutoffs = dictionary['cutoffs'] + else: + cutoffs = [] + for cutoff in ['cutoff_2','cutoff_3','cutoff_mb']: + if dictionary.get(cutoff): + cutoffs.append(dictionary[cutoff]) - if dictionary.get('cutoff_3', False): - cutoffs.append(dictionary.get('cutoff_3')) - cutoffs = np.array(cutoffs) - - return AtomicEnvironment(struc, index, cutoffs) + return AtomicEnvironment(struc, index, np.array(cutoffs)) def __str__(self): atom_type = self.ctype @@ -124,7 +141,7 @@ class to allow for njit acceleration with Numba. :type species: np.ndarray :return: Tuple of arrays describing pairs of atoms in the 2-body local environment. - + bond_array_2: Array containing the distances and relative coordinates of atoms in the 2-body local environment. First column contains distances, remaining columns contain Cartesian coordinates @@ -139,10 +156,10 @@ class to allow for njit acceleration with Numba. """ noa = len(positions) pos_atom = positions[atom] - coords = np.zeros((noa, 3, 27)) - dists = np.zeros((noa, 27)) + coords = np.zeros((noa, 3, 27), dtype=np.float64) + dists = np.zeros((noa, 27), dtype=np.float64) cutoff_count = 0 - super_sweep = np.array([-1, 0, 1]) + super_sweep = np.array([-1, 0, 1], dtype=np.float64) vec1 = cell[0] vec2 = cell[1] @@ -164,8 +181,8 @@ class to allow for njit acceleration with Numba. im_count += 1 # create 2-body bond array - bond_array_2 = np.zeros((cutoff_count, 4)) - bond_positions_2 = np.zeros((cutoff_count, 3)) + bond_array_2 = np.zeros((cutoff_count, 4), dtype=np.float64) + bond_positions_2 = np.zeros((cutoff_count, 3), dtype=np.float64) etypes = np.zeros(cutoff_count, dtype=np.int8) bond_count = 0 @@ -190,6 +207,97 @@ class to allow for njit acceleration with Numba. return bond_array_2, bond_positions_2, etypes +@njit +def get_2_body_arrays_ind(positions, atom: int, cell, cutoff_2: float, species): + """Returns distances, coordinates, species of atoms, and indexes of neighbors + in the 2-body local environment. This method is implemented outside + the AtomicEnvironment class to allow for njit acceleration with Numba. + + :param positions: Positions of atoms in the structure. + :type positions: np.ndarray + :param atom: Index of the central atom of the local environment. + :type atom: int + :param cell: 3x3 array whose rows are the Bravais lattice vectors of the + cell. + :type cell: np.ndarray + :param cutoff_2: 2-body cutoff radius. + :type cutoff_2: float + :param species: Numpy array of species represented by their atomic numbers. + :type species: np.ndarray + :return: Tuple of arrays describing pairs of atoms in the 2-body local + environment. + + bond_array_2: Array containing the distances and relative + coordinates of atoms in the 2-body local environment. First column + contains distances, remaining columns contain Cartesian coordinates + divided by the distance (with the origin defined as the position of the + central atom). The rows are sorted by distance from the central atom. + + bond_positions_2: Coordinates of atoms in the 2-body local environment. + + etypes: Species of atoms in the 2-body local environment represented by + their atomic number. + + bond_indexes: Structure indexes of atoms in the local environment. + + :rtype: np.ndarray, np.ndarray, np.ndarray, np.ndarray + """ + noa = len(positions) + pos_atom = positions[atom] + coords = np.zeros((noa, 3, 27), dtype=np.float64) + dists = np.zeros((noa, 27), dtype=np.float64) + cutoff_count = 0 + super_sweep = np.array([-1, 0, 1], dtype=np.float64) + + vec1 = cell[0] + vec2 = cell[1] + vec3 = cell[2] + + # record distances and positions of images + for n in range(noa): + diff_curr = positions[n] - pos_atom + im_count = 0 + for s1 in super_sweep: + for s2 in super_sweep: + for s3 in super_sweep: + im = diff_curr + s1 * vec1 + s2 * vec2 + s3 * vec3 + dist = sqrt(im[0] * im[0] + im[1] * im[1] + im[2] * im[2]) + if (dist < cutoff_2) and (dist != 0): + dists[n, im_count] = dist + coords[n, :, im_count] = im + cutoff_count += 1 + im_count += 1 + + # create 2-body bond array + bond_indexes = np.zeros(cutoff_count, dtype=np.int8) + bond_array_2 = np.zeros((cutoff_count, 4), dtype=np.float64) + bond_positions_2 = np.zeros((cutoff_count, 3), dtype=np.float64) + etypes = np.zeros(cutoff_count, dtype=np.int8) + bond_count = 0 + + for m in range(noa): + spec_curr = species[m] + for n in range(27): + dist_curr = dists[m, n] + if (dist_curr < cutoff_2) and (dist_curr != 0): + coord = coords[m, :, n] + bond_array_2[bond_count, 0] = dist_curr + bond_array_2[bond_count, 1:4] = coord / dist_curr + bond_positions_2[bond_count, :] = coord + etypes[bond_count] = spec_curr + bond_indexes[bond_count] = m + bond_count += 1 + + # sort by distance + sort_inds = bond_array_2[:, 0].argsort() + bond_array_2 = bond_array_2[sort_inds] + bond_positions_2 = bond_positions_2[sort_inds] + bond_indexes = bond_indexes[sort_inds] + etypes = etypes[sort_inds] + + return bond_array_2, bond_positions_2, etypes, bond_indexes + + @njit def get_3_body_arrays(bond_array_2, bond_positions_2, cutoff_3: float): """Returns distances and coordinates of triplets of atoms in the @@ -240,7 +348,7 @@ def get_3_body_arrays(bond_array_2, bond_positions_2, cutoff_3: float): # get cross bond array cross_bond_inds = np.zeros((ind_3, ind_3), dtype=np.int8) - 1 - cross_bond_dists = np.zeros((ind_3, ind_3)) + cross_bond_dists = np.zeros((ind_3, ind_3), dtype=np.float64) triplet_counts = np.zeros(ind_3, dtype=np.int8) for m in range(ind_3): pos1 = bond_positions_3[m] @@ -262,5 +370,76 @@ def get_3_body_arrays(bond_array_2, bond_positions_2, cutoff_3: float): return bond_array_3, cross_bond_inds, cross_bond_dists, triplet_counts +@njit +def get_m_body_arrays(positions, atom: int, cell, cutoff_mb: float, species): + """Returns distances, and species of atoms in the many-body + local environment, and returns distances and numbers of neighbours for atoms in the one + many-body local environment. This method is implemented outside the AtomicEnvironment + class to allow for njit acceleration with Numba. + + :param positions: Positions of atoms in the structure. + :type positions: np.ndarray + :param atom: Index of the central atom of the local environment. + :type atom: int + :param cell: 3x3 array whose rows are the Bravais lattice vectors of the + cell. + :type cell: np.ndarray + :param cutoff_mb: 2-body cutoff radius. + :type cutoff_mb: float + :param species: Numpy array of species represented by their atomic numbers. + :type species: np.ndarray + :param indexes: Boolean indicating whether indexes of neighbours are returned + :type indexes: boolean + :return: Tuple of arrays describing pairs of atoms in the 2-body local + environment. + + bond_array_mb: Array containing the distances and relative + coordinates of atoms in the 2-body local environment. First column + contains distances, remaining columns contain Cartesian coordinates + divided by the distance (with the origin defined as the position of the + central atom). The rows are sorted by distance from the central atom. + + etypes: Species of atoms in the 2-body local environment represented by + their atomic number. + + neigh_dists_mb: Matrix padded with zero values of distances + of neighbours for the atoms in the local environment. + + num_neighs_mb: number of neighbours of each atom in the local environment + + etypes_mb_array: species of neighbours of each atom in the local environment + + :rtype: np.ndarray, np.ndarray, np.ndarray, np.ndarray + """ + # TODO: this can be probably improved using stored arrays, redundant calls to get_2_body_arrays + # Get distances, positions, species and indexes of neighbouring atoms + bond_array_mb, __, etypes, bond_inds = get_2_body_arrays_ind( + positions, atom, cell, cutoff_mb, species) + + # For each neighbouring atom, get distances in its neighbourhood + neighbouring_dists = [] + neighbouring_etypes = [] + max_neighbours = 0 + for m in bond_inds: + neighbour_bond_array_2, ___, etypes_mb = get_2_body_arrays(positions, m, cell, + cutoff_mb, species) + neighbouring_dists.append(neighbour_bond_array_2[:, 0]) + neighbouring_etypes.append(etypes_mb) + if len(neighbour_bond_array_2[:, 0]) > max_neighbours: + max_neighbours = len(neighbour_bond_array_2[:, 0]) + + # Transform list of distances into Numpy array + neigh_dists_mb = np.zeros((len(bond_inds), max_neighbours), dtype=np.float64) + num_neighs_mb = np.zeros(len(bond_inds), dtype=np.int8) + etypes_mb_array = np.zeros((len(bond_inds), max_neighbours), dtype=np.int8) + for i in range(len(bond_inds)): + num_neighs_mb[i] = len(neighbouring_dists[i]) + neigh_dists_mb[i, :num_neighs_mb[i]] = neighbouring_dists[i] + etypes_mb_array[i, :num_neighs_mb[i]] = neighbouring_etypes[i] + + + return bond_array_mb, neigh_dists_mb, num_neighs_mb, etypes_mb_array, etypes + + if __name__ == '__main__': pass diff --git a/flare/gp.py b/flare/gp.py index b1bfb5134..c35bf72f4 100644 --- a/flare/gp.py +++ b/flare/gp.py @@ -1,28 +1,31 @@ import time import math import pickle +import inspect import json import numpy as np import multiprocessing as mp +from collections import Counter from copy import deepcopy -from typing import List, Callable +from numpy.random import random +from typing import List, Callable, Union from scipy.linalg import solve_triangular from scipy.optimize import minimize +from flare.util import Z_to_element from flare.env import AtomicEnvironment from flare.struc import Structure from flare.gp_algebra import get_neg_likelihood, \ - get_like_from_ky_mat -from flare.gp_algebra import get_kernel_vector_par -from flare.gp_algebra import get_ky_mat_par -from flare.gp_algebra import get_ky_mat_update_par -from flare.gp_algebra import get_neg_like_grad -from flare.kernels.utils import str_to_kernels + get_like_from_mats, get_neg_like_grad, \ + get_kernel_vector, en_kern_vec, \ + get_ky_mat, get_ky_mat_update, \ + _global_training_data, _global_training_labels + +from flare.kernels.utils import str_to_kernel_set, from_mask_to_args from flare.util import NumpyEncoder from flare.output import Output -import flare.cutoffs as cf class GaussianProcess: @@ -31,12 +34,11 @@ class GaussianProcess: Williams. Args: - kernel (Callable): Force/force kernel of the GP used to make force - predictions. - kernel_grad (Callable): Function that returns the gradient of the GP + kernel (Callable, optional): Name of the kernel to use, or the kernel itself. + kernel_grad (Callable, optional): Function that returns the gradient of the GP kernel with respect to the hyperparameters. - hyps (np.ndarray): Hyperparameters of the GP. - cutoffs (np.ndarray): Cutoffs of the GP kernel. + hyps (np.ndarray, optional): Hyperparameters of the GP. + cutoffs (np.ndarray, optional): Cutoffs of the GP kernel. hyp_labels (List, optional): List of hyperparameter labels. Defaults to None. energy_force_kernel (Callable, optional): Energy/force kernel of the @@ -47,48 +49,101 @@ class GaussianProcess: Defaults to 'L-BFGS-B'. maxiter (int, optional): Maximum number of iterations of the hyperparameter optimization algorithm. Defaults to 10. - par (bool, optional): If True, the covariance matrix K of the GP is + parallel (bool, optional): If True, the covariance matrix K of the GP is computed in parallel. Defaults to False. n_cpus (int, optional): Number of cpus used for parallel - calculations. Defaults to 1. + calculations. Defaults to 1 (serial) + n_samples (int, optional): Size of submatrix to use when parallelizing + predictions. output (Output, optional): Output object used to dump hyperparameters during optimization. Defaults to None. + multihyps (bool, optional): If True, turn on multiple-group of hyper- + parameters. + hyps_mask (dict, optional): If multihyps is True, hyps_mask can set up + which hyper parameter is used for what interaction. Details see + kernels/mc_sephyps.py + kernel_name (str, optional): Determine the type of kernels. Example: + 2+3_mc, 2+3+mb_mc, 2_mc, 2_sc, 3_sc, ... + name (str, optional): Name for the GP instance. """ - def __init__(self, kernel: Callable, - kernel_grad: Callable, hyps: 'ndarray', - cutoffs: 'ndarray', + def __init__(self, kernel: Callable = None, + kernel_grad: Callable = None, + hyps: 'ndarray' = None, + cutoffs: 'ndarray' = None, hyp_labels: List = None, - energy_force_kernel: Callable = None, - energy_kernel: Callable = None, opt_algorithm: str = 'L-BFGS-B', - maxiter: int = 10, par: bool = False, + maxiter: int = 10, parallel: bool = False, per_atom_par: bool = True, - n_cpus: int = 1, nsample: int = 100, + n_cpus: int = 1, n_sample: int = 100, output: Output = None, - multihyps: bool = False, hyps_mask: dict = None): + multihyps: bool = False, hyps_mask: dict = None, + kernel_name="2+3_mc", name="default_gp", **kwargs): """Initialize GP parameters and training data.""" - self.kernel = kernel - self.kernel_grad = kernel_grad - self.energy_kernel = energy_kernel - self.energy_force_kernel = energy_force_kernel - self.kernel_name = kernel.__name__ - self.hyps = hyps + # load arguments into attributes + self.hyp_labels = hyp_labels - self.cutoffs = cutoffs - self.algo = opt_algorithm - self.bounds = None + self.cutoffs = np.array(cutoffs, dtype=np.float64) + self.opt_algorithm = opt_algorithm - self.training_data = [] - self.training_labels = [] - self.training_labels_np = np.empty(0, ) - self.maxiter = maxiter - self.par = par + if hyps is None: + # If no hyperparameters are passed in, assume 2 hyps for each + # cutoff, plus one noise hyperparameter, and use a guess value + self.hyps = np.array([0.1]*(1+2*len(cutoffs))) + else: + self.hyps = np.array(hyps, dtype=np.float64) + + + self.output = output self.per_atom_par = per_atom_par + self.maxiter = maxiter self.n_cpus = n_cpus - self.nsample = nsample - self.output = output + self.n_sample = n_sample + self.parallel = parallel + + if 'nsample' in kwargs: + DeprecationWarning("nsample is being replaced with n_sample") + self.n_sample =kwargs.get('nsample') + if 'par' in kwargs: + DeprecationWarning("par is being replaced with parallel") + self.parallel = kwargs.get('par') + if 'no_cpus' in kwargs: + DeprecationWarning("no_cpus is being replaced with n_cpu") + self.n_cpus = kwargs.get('no_cpus') + + # TO DO, clean up all the other kernel arguments + if kernel is None: + kernel, grad, ek, efk = str_to_kernel_set(kernel_name, multihyps) + self.kernel = kernel + self.kernel_grad = grad + self.energy_force_kernel = efk + self.energy_kernel = ek + self.kernel_name = kernel.__name__ + else: + DeprecationWarning("kernel, kernel_grad, energy_force_kernel " + "and energy_kernel will be replaced by kernel_name") + self.kernel_name = kernel.__name__ + self.kernel = kernel + self.kernel_grad = kernel_grad + self.energy_force_kernel = kwargs.get('energy_force_kernel') + self.energy_kernel = kwargs.get('energy_kernel') + + self.name = name + + # parallelization + if self.parallel: + if n_cpus is None: + self.n_cpus = mp.cpu_count() + else: + self.n_cpus = n_cpus + else: + self.n_cpus = 1 + + + self.training_data = [] # Atomic environments + self.training_labels = [] # Forces acting on central atoms of at. envs. + self.training_labels_np = np.empty(0, ) # Parameters set during training self.ky_mat = None @@ -97,76 +152,169 @@ def __init__(self, kernel: Callable, self.ky_mat_inv = None self.likelihood = None self.likelihood_gradient = None + self.bounds = None + + self.hyps_mask = hyps_mask + self.multihyps = multihyps + self.check_instantiation() + + def check_instantiation(self): + """ + Runs a series of checks to ensure that the user has not supplied + contradictory arguments which will result in undefined behavior + with multiple hyperparameters. + :return: + """ - if multihyps is True and hyps_mask is None: + if (self.name in _global_training_labels): + base = f'{self.name}' + count = 2 + while (self.name in _global_training_labels and count<100): + time.sleep(random()) + self.name = f'{base}_{count}' + print("Specified GP name is present in global memory; " + "Attempting to rename the " + f"GP instance to {self.name}") + count += 1 + if (self.name in _global_training_labels): + milliseconds = int(round(time.time() * 1000)%10000000) + self.name = f"{base}_{milliseconds}" + print("Specified GP name still present in global memory: " + f"renaming the gp instance to {self.name}") + print(f"Final name of the gp instance is {self.name}") + + assert (self.name not in _global_training_labels), \ + f"the gp instance name, {self.name} is used" + assert (self.name not in _global_training_data), \ + f"the gp instance name, {self.name} is used" + + _global_training_data[self.name] = self.training_data + _global_training_labels[self.name] = self.training_labels_np + + assert (len(self.cutoffs)<=3) + + if (len(self.cutoffs)>1): + assert self.cutoffs[0]>=self.cutoffs[1], \ + "2b cutoff has to be larger than 3b cutoffs" + + if ('three' in self.kernel_name): + assert len(self.cutoffs)>=2, \ + "3b kernel needs two cutoffs, one for building"\ + " neighbor list and one for the 3b" + if ('many' in self.kernel_name): + assert len(self.cutoffs)>=3, \ + "many-body kernel needs three cutoffs, one for building"\ + " neighbor list and one for the 3b" + + if self.multihyps is True and self.hyps_mask is None: raise ValueError("Warning! Multihyperparameter mode enabled," "but no configuration hyperparameter mask was " "passed. Did you mean to set multihyps to False?") - elif multihyps is False and hyps_mask is not None: + elif self.multihyps is False and self.hyps_mask is not None: raise ValueError("Warning! Multihyperparameter mode disabled," "but a configuration hyperparameter mask was " "passed. Did you mean to set multihyps to True?") - self.hyps_mask = None - if (isinstance(hyps_mask, dict) and multihyps is True): + if isinstance(self.hyps_mask, dict) and self.multihyps is True: self.multihyps = True - assert 'nspec' in hyps_mask.keys(), "nspec key missing in " \ - "hyps_mask dictionary" - assert 'spec_mask' in hyps_mask.keys(), "spec_mask key missing " \ - "in hyps_mask dicticnary" + assert 'nspec' in self.hyps_mask, "nspec key missing in " \ + "hyps_mask dictionary" + assert 'spec_mask' in self.hyps_mask, "spec_mask key " \ + "missing " \ + "in hyps_mask dicticnary" - self.hyps_mask = deepcopy(hyps_mask) + hyps_mask = deepcopy(self.hyps_mask) nspec = hyps_mask['nspec'] + self.hyps_mask['spec_mask'] = np.array(hyps_mask['spec_mask'], dtype=int) - if ('nbond' in hyps_mask.keys()): + if 'nbond' in hyps_mask: n2b = self.hyps_mask['nbond'] - if (n2b>0): - assert (np.max(hyps_mask['bond_mask']) < n2b) - assert len(hyps_mask['bond_mask']) == nspec**2, \ - f"wrong dimension of bond_mask: " \ - f" {len(hyps_mask['bond_mask']) != {nspec**2}}" + self.hyps_mask['bond_mask'] = np.array(hyps_mask['bond_mask'], dtype=int) + if n2b > 0: + bmask = hyps_mask['bond_mask'] + assert (np.max(bmask) < n2b) + assert len(bmask) == nspec ** 2, \ + f"wrong dimension of bond_mask: " \ + f" {len(bmask)} != nspec^2 {nspec**2}" + for t2b in range(nspec): + for t2b_2 in range(t2b, nspec): + assert bmask[t2b*nspec+t2b_2] == bmask[t2b_2*nspec+t2b], \ + 'bond_mask has to be symmetric' else: n2b = 0 - if ('ntriplet' in hyps_mask.keys()): + if 'ntriplet' in hyps_mask: n3b = self.hyps_mask['ntriplet'] - if (n3b>0): - assert (np.max(hyps_mask['triplet_mask']) < n3b) - assert len(hyps_mask['triplet_mask']) == nspec**3, \ - f"wrong dimension of triplet_mask" \ - f"{len(hyps_mask['triplet_mask']) != {nspec**3}}" + self.hyps_mask['triplet_mask'] = np.array(hyps_mask['triplet_mask'], dtype=int) + if n3b > 0: + tmask = hyps_mask['triplet_mask'] + assert (np.max(tmask) < n3b) + assert len(tmask) == nspec ** 3, \ + f"wrong dimension of bond_mask: " \ + f" {len(tmask)} != nspec^3 {nspec**3}" + + for t3b in range(nspec): + for t3b_2 in range(t3b, nspec): + for t3b_3 in range(t3b_2, nspec): + assert tmask[t3b*nspec*nspec+t3b_2*nspec+t3b_3] \ + == tmask[t3b*nspec*nspec+t3b_3*nspec+t3b_2], \ + 'bond_mask has to be symmetric' + assert tmask[t3b*nspec*nspec+t3b_2*nspec+t3b_3] \ + == tmask[t3b_2*nspec*nspec+t3b*nspec+t3b_3], \ + 'bond_mask has to be symmetric' + assert tmask[t3b*nspec*nspec+t3b_2*nspec+t3b_3] \ + == tmask[t3b_2*nspec*nspec+t3b_3*nspec+t3b], \ + 'bond_mask has to be symmetric' + assert tmask[t3b*nspec*nspec+t3b_2*nspec+t3b_3] \ + == tmask[t3b_3*nspec*nspec+t3b*nspec+t3b_2], \ + 'bond_mask has to be symmetric' + assert tmask[t3b*nspec*nspec+t3b_2*nspec+t3b_3] \ + == tmask[t3b_3*nspec*nspec+t3b_2*nspec+t3b], \ + 'bond_mask has to be symmetric' else: n3b = 0 - assert ((n2b+n3b)>0) + if (len(self.cutoffs)<=2): + assert ((n2b + n3b) > 0) + else: + assert ((n2b + n3b + 1) > 0) - if ('map' in hyps_mask.keys()): - assert ('original' in hyps_mask.keys()), \ - "original hyper parameters have to be defined" + if 'map' in hyps_mask: + assert ('original' in hyps_mask), \ + "original hyper parameters have to be defined" # Ensure typed correctly as numpy array self.hyps_mask['original'] = np.array(hyps_mask['original']) - assert (n2b*2+n3b*2+1) == len(hyps_mask['original']) , \ + if (len(self.cutoffs)<=2): + assert (n2b * 2 + n3b * 2 + 1) == len(hyps_mask['original']), \ "the hyperparmeter length is inconsistent with the mask" - assert len(hyps_mask['map']) == len(hyps), \ + else: + assert (n2b * 2 + n3b * 2 + 1 * 2 + 1) == len(hyps_mask['original']), \ "the hyperparmeter length is inconsistent with the mask" - if ((len(hyps_mask['original'])-1) not in hyps_mask['map']): + assert len(hyps_mask['map']) == len(self.hyps), \ + "the hyperparmeter length is inconsistent with the mask" + if (len(hyps_mask['original']) - 1) not in hyps_mask['map']: assert hyps_mask['train_noise'] is False, \ - "train_noise should be False when noise is not in hyps" + "train_noise should be False when noise is not in hyps" else: assert hyps_mask['train_noise'] is True, \ - "train_noise should be True when map is not used" - assert (n2b*2+n3b*2+1) == len(hyps), \ + "train_noise should be True when map is not used" + if (len(self.cutoffs)<=2): + assert (n2b * 2 + n3b * 2 + 1) == len(self.hyps), \ + "the hyperparmeter length is inconsistent with the mask" + else: + assert (n2b * 2 + n3b * 2 + 1*2 + 1) == len(self.hyps), \ "the hyperparmeter length is inconsistent with the mask" - if ('bounds' in hyps_mask.keys()): + if 'bounds' in hyps_mask: self.bounds = deepcopy(hyps_mask['bounds']) + + else: self.multihyps = False self.hyps_mask = None - # TODO unit test custom range def update_db(self, struc: Structure, forces: List, custom_range: List[int] = ()): """Given a structure and forces, add local environments from the @@ -195,6 +343,8 @@ def update_db(self, struc: Structure, forces: List, # create numpy array of training labels self.training_labels_np = np.hstack(self.training_labels) + _global_training_data[self.name] = self.training_data + _global_training_labels[self.name] = self.training_labels_np def add_one_env(self, env: AtomicEnvironment, force, train: bool = False, **kwargs): @@ -212,6 +362,8 @@ def add_one_env(self, env: AtomicEnvironment, self.training_data.append(env) self.training_labels.append(force) self.training_labels_np = np.hstack(self.training_labels) + _global_training_data[self.name] = self.training_data + _global_training_labels[self.name] = self.training_labels_np if train: self.train(**kwargs) @@ -237,20 +389,25 @@ def train(self, output=None, custom_bounds=None, hyperparameter optimization. """ + if len(self.training_data)==0 or len(self.training_labels) ==0: + raise Warning ("You are attempting to train a GP with no " + "training data. Add environments and forces " + "to the GP and try again.") + return None + x_0 = self.hyps - args = (self.training_data, self.training_labels_np, - self.kernel_grad, output, + args = (self.name, self.kernel_grad, output, self.cutoffs, self.hyps_mask, - self.n_cpus, self.nsample, - print_progress) + self.n_cpus, self.n_sample, print_progress) + objective_func = get_neg_like_grad res = None - if self.algo == 'L-BFGS-B': + if self.opt_algorithm == 'L-BFGS-B': # bound signal noise below to avoid overfitting - if (self.bounds is None): + if self.bounds is None: bounds = np.array([(1e-6, np.inf)] * len(x_0)) bounds[-1, 0] = 1e-3 else: @@ -263,10 +420,10 @@ def train(self, output=None, custom_bounds=None, options={'disp': False, 'gtol': grad_tol, 'maxls': line_steps, 'maxiter': self.maxiter}) - except: + except np.linalg.LinAlgError: print("Warning! Algorithm for L-BFGS-B failed. Changing to " "BFGS for remainder of run.") - self.algo = 'BFGS' + self.opt_algorithm = 'BFGS' if custom_bounds is not None: res = minimize(get_neg_like_grad, x_0, args, @@ -275,13 +432,13 @@ def train(self, output=None, custom_bounds=None, 'maxls': line_steps, 'maxiter': self.maxiter}) - elif self.algo == 'BFGS': + elif self.opt_algorithm == 'BFGS': res = minimize(get_neg_like_grad, x_0, args, method='BFGS', jac=True, options={'disp': False, 'gtol': grad_tol, 'maxiter': self.maxiter}) - elif self.algo == 'nelder-mead': + elif self.opt_algorithm == 'nelder-mead': res = minimize(get_neg_likelihood, x_0, args, method='nelder-mead', options={'disp': False, @@ -301,10 +458,20 @@ def check_L_alpha(self): not, update_L_alpha is called. """ - # check that alpha is up to date with training set - if self.alpha is None or 3 * len(self.training_data) != len( - self.alpha): + # Check that alpha is up to date with training set + size3 = len(self.training_data)*3 + + # If model is empty, then just return + if size3 == 0: + return + + if (self.alpha is None): + self.update_L_alpha() + elif (size3 > self.alpha.shape[0]): self.update_L_alpha() + elif (size3 != self.alpha.shape[0]): + self.set_L_alpha() + def predict(self, x_t: AtomicEnvironment, d: int) -> [float, float]: """ @@ -320,38 +487,36 @@ def predict(self, x_t: AtomicEnvironment, d: int) -> [float, float]: """ # Kernel vector allows for evaluation of atomic environments. - if (self.par and not self.per_atom_par): + if self.parallel and not self.per_atom_par: n_cpus = self.n_cpus else: n_cpus = 1 - k_v = get_kernel_vector_par(self.training_data, self.kernel, - x_t, d, - self.hyps, - cutoffs=self.cutoffs, - hyps_mask=self.hyps_mask, - n_cpus=n_cpus, - nsample=self.nsample) + _global_training_data[self.name] = self.training_data + _global_training_labels[self.name] = self.training_labels_np + + k_v = get_kernel_vector(self.name, self.kernel, + x_t, d, + self.hyps, + cutoffs=self.cutoffs, + hyps_mask=self.hyps_mask, + n_cpus=n_cpus, + n_sample=self.n_sample) # Guarantee that alpha is up to date with training set - if self.alpha is None or\ - (3 * len(self.training_data) != len(self.alpha)): - self.update_L_alpha() + self.check_L_alpha() # get predictive mean pred_mean = np.matmul(k_v, self.alpha) # get predictive variance without cholesky (possibly faster) # pass args to kernel based on if mult. hyperparameters in use - if (self.multihyps): - self_kern = self.kernel(x_t, x_t, d, d, self.hyps, - self.cutoffs, hyps_mask=self.hyps_mask) - else: - self_kern = self.kernel(x_t, x_t, d, d, self.hyps, - self.cutoffs) + args = from_mask_to_args(self.hyps, self.hyps_mask, self.cutoffs) + + self_kern = self.kernel(x_t, x_t, d, d, *args) pred_var = self_kern - \ - np.matmul(np.matmul(k_v, self.ky_mat_inv), k_v) + np.matmul(np.matmul(k_v, self.ky_mat_inv), k_v) return pred_mean, pred_var @@ -365,7 +530,22 @@ def predict_local_energy(self, x_t: AtomicEnvironment) -> float: float: Local energy predicted by the GP. """ - k_v = self.en_kern_vec(x_t) + if self.parallel and not self.per_atom_par: + n_cpus = self.n_cpus + else: + n_cpus = 1 + + _global_training_data[self.name] = self.training_data + _global_training_labels[self.name] = self.training_labels_np + + k_v = en_kern_vec(self.name, + self.energy_force_kernel, + x_t, self.hyps, + cutoffs=self.cutoffs, + hyps_mask=self.hyps_mask, + n_cpus=n_cpus, + n_sample=self.n_sample) + pred_mean = np.matmul(k_v, self.alpha) return pred_mean @@ -381,93 +561,35 @@ def predict_local_energy_and_var(self, x_t: AtomicEnvironment): (float, float): Mean and predictive variance predicted by the GP. """ + if self.parallel and not self.per_atom_par: + n_cpus = self.n_cpus + else: + n_cpus = 1 + + _global_training_data[self.name] = self.training_data + _global_training_labels[self.name] = self.training_labels_np + # get kernel vector - k_v = self.en_kern_vec(x_t) + k_v = en_kern_vec(self.name, + self.energy_force_kernel, + x_t, self.hyps, + cutoffs=self.cutoffs, + hyps_mask=self.hyps_mask, + n_cpus=n_cpus, + n_sample=self.n_sample) # get predictive mean pred_mean = np.matmul(k_v, self.alpha) # get predictive variance v_vec = solve_triangular(self.l_mat, k_v, lower=True) - if (self.multihyps): - self_kern = self.energy_kernel(x_t, x_t, self.hyps, - self.cutoffs, - hyps_mask=self.hyps_mask) - else: - self_kern = self.energy_kernel(x_t, x_t, self.hyps, - self.cutoffs) - pred_var = self_kern - np.matmul(v_vec, v_vec) - - return pred_mean, pred_var - - def get_kernel_vector(self, x: AtomicEnvironment, - d_1: int): - """ - Compute kernel vector, comparing input environment to all environments - in the GP's training set. - - Args: - x (AtomicEnvironment): Local environment to compare against - the training environments. - d_1 (int): Cartesian component of the kernel (1=x, 2=y, 3=z). + args = from_mask_to_args(self.hyps, self.hyps_mask, self.cutoffs) - Return: - np.ndarray: Kernel vector. - """ + self_kern = self.energy_kernel(x_t, x_t, *args) - ds = [1, 2, 3] - size = len(self.training_data) * 3 - k_v = np.zeros(size, ) - - if (self.multihyps): - for m_index in range(size): - x_2 = self.training_data[int(math.floor(m_index / 3))] - d_2 = ds[m_index % 3] - k_v[m_index] = self.kernel(x, x_2, d_1, d_2, - self.hyps, self.cutoffs, - hyps_mask=self.hyps_mask) - else: - for m_index in range(size): - x_2 = self.training_data[int(math.floor(m_index / 3))] - d_2 = ds[m_index % 3] - k_v[m_index] = self.kernel(x, x_2, d_1, d_2, - self.hyps, self.cutoffs) - - return k_v - - def en_kern_vec(self, x: AtomicEnvironment): - """Compute the vector of energy/force kernels between an atomic - environment and the environments in the training set. - - Args: - x (AtomicEnvironment): Local environment to compare against - the training environments. - - Return: - np.ndarray: Kernel vector. - """ - - ds = [1, 2, 3] - size = len(self.training_data) * 3 - k_v = np.zeros(size, ) - - if (self.multihyps): - for m_index in range(size): - x_2 = self.training_data[int(math.floor(m_index / 3))] - d_2 = ds[m_index % 3] - k_v[m_index] = \ - self.energy_force_kernel(x_2, x, d_2, self.hyps, - self.cutoffs, - hyps_mask=self.hyps_mask) - else: - for m_index in range(size): - x_2 = self.training_data[int(math.floor(m_index / 3))] - d_2 = ds[m_index % 3] - k_v[m_index] = self.energy_force_kernel(x_2, x, d_2, - self.hyps, - self.cutoffs) + pred_var = self_kern - np.matmul(v_vec, v_vec) - return k_v + return pred_mean, pred_var def set_L_alpha(self): """ @@ -477,13 +599,16 @@ def set_L_alpha(self): The forces and variances are later obtained using alpha. """ - ky_mat = get_ky_mat_par(self.hyps, - self.training_data, - self.kernel, - cutoffs=self.cutoffs, - hyps_mask=self.hyps_mask, - n_cpus=self.n_cpus, - nsample=self.nsample) + _global_training_data[self.name] = self.training_data + _global_training_labels[self.name] = self.training_labels_np + + ky_mat = get_ky_mat(self.hyps, + self.name, + self.kernel, + cutoffs=self.cutoffs, + hyps_mask=self.hyps_mask, + n_cpus=self.n_cpus, + n_sample=self.n_sample) l_mat = np.linalg.cholesky(ky_mat) l_mat_inv = np.linalg.inv(l_mat) @@ -495,8 +620,8 @@ def set_L_alpha(self): self.alpha = alpha self.ky_mat_inv = ky_mat_inv - self.likelihood = \ - get_like_from_ky_mat(self.ky_mat, self.training_labels_np) + self.likelihood = get_like_from_mats(ky_mat, l_mat, + alpha, self.name) def update_L_alpha(self): """ @@ -505,22 +630,20 @@ def update_L_alpha(self): """ # Set L matrix and alpha if set_L_alpha has not been called yet - if self.l_mat is None: + if self.l_mat is None or np.array(self.ky_mat) is np.array(None): self.set_L_alpha() return - if (self.par and not self.per_atom_par): - n_cpus = self.n_cpus - else: - n_cpus = 1 + _global_training_data[self.name] = self.training_data + _global_training_labels[self.name] = self.training_labels_np - ky_mat = get_ky_mat_update_par(self.ky_mat, self.hyps, - self.training_data, - self.kernel, - cutoffs=self.cutoffs, - hyps_mask=self.hyps_mask, - n_cpus=n_cpus, - nsample=self.nsample) + ky_mat = get_ky_mat_update(self.ky_mat, self.hyps, + self.name, + self.kernel, + cutoffs=self.cutoffs, + hyps_mask=self.hyps_mask, + n_cpus=self.n_cpus, + n_sample=self.n_sample) l_mat = np.linalg.cholesky(ky_mat) l_mat_inv = np.linalg.inv(l_mat) @@ -551,7 +674,7 @@ def __str__(self): for hyp, label in zip(self.hyps, self.hyp_labels): thestr += f"{label}: {hyp}\n" - if (self.multihyps): + if self.multihyps: nspec = self.hyps_mask['nspec'] thestr += f'nspec: {nspec}\n' thestr += f'spec_mask: \n' @@ -559,13 +682,14 @@ def __str__(self): nbond = self.hyps_mask['nbond'] thestr += f'nbond: {nbond}\n' - if (nbond > 0): - thestr += f'bond_mask: \n' + + if nbond > 0: + thestr += f'bond_mask: \n' thestr += str(self.hyps_mask['bond_mask']) + '\n' ntriplet = self.hyps_mask['ntriplet'] thestr += f'ntriplet: {ntriplet}\n' - if (ntriplet > 0): + if ntriplet > 0: thestr += f'triplet_mask: \n' thestr += str(self.hyps_mask['triplet_mask']) + '\n' @@ -574,13 +698,17 @@ def __str__(self): def as_dict(self): """Dictionary representation of the GP model.""" + self.check_L_alpha() + out_dict = deepcopy(dict(vars(self))) out_dict['training_data'] = [env.as_dict() for env in self.training_data] # Remove the callables - del out_dict['kernel'] - del out_dict['kernel_grad'] + for key in ['kernel', 'kernel_grad', 'energy_kernel', + 'energy_force_kernel']: + if out_dict.get(key) is not None: + del out_dict[key] return out_dict @@ -590,39 +718,26 @@ def from_dict(dictionary): multihyps = dictionary.get('multihyps', False) - force_kernel, grad = str_to_kernels(dictionary['kernel_name'], - multihyps, - include_grad=True) - - if dictionary['energy_kernel'] is not None: - energy_kernel = str_to_kernel(dictionary['energy_kernel'], - multihyps) - else: - energy_kernel = None - - if dictionary['energy_force_kernel'] is not None: - energy_force_kernel = \ - str_to_kernel(dictionary['energy_force_kernel'], - multihyps) - else: - energy_force_kernel = None - - new_gp = GaussianProcess(kernel=force_kernel, - kernel_grad=grad, - energy_kernel=energy_kernel, - energy_force_kernel=energy_force_kernel, + new_gp = GaussianProcess(kernel_name=dictionary['kernel_name'], cutoffs=np.array(dictionary['cutoffs']), hyps=np.array(dictionary['hyps']), hyp_labels=dictionary['hyp_labels'], - par=dictionary['par'], - per_atom_par=dictionary.get('per_atom_par',True), - n_cpus=dictionary.get('n_cpus') or dictionary.get('no_cpus'), + parallel=dictionary.get('parallel',False) or + dictionary.get('par',False), + per_atom_par=dictionary.get('per_atom_par', + True), + n_cpus=dictionary.get( + 'n_cpus') or dictionary.get('no_cpus'), maxiter=dictionary['maxiter'], - opt_algorithm=dictionary['algo'], + opt_algorithm=dictionary.get( + 'opt_algorithm','L-BFGS-B'), multihyps=multihyps, - hyps_mask=dictionary.get('hyps_mask',None) + hyps_mask=dictionary.get('hyps_mask', None), + name=dictionary.get('name','default_gp') ) + + # Save time by attempting to load in computed attributes new_gp.training_data = [AtomicEnvironment.from_dict(env) for env in dictionary['training_data']] new_gp.training_labels = deepcopy(dictionary['training_labels']) @@ -632,10 +747,22 @@ def from_dict(dictionary): new_gp.likelihood_gradient = dictionary['likelihood_gradient'] new_gp.training_labels_np = np.hstack(new_gp.training_labels) + _global_training_data[new_gp.name] = new_gp.training_data + _global_training_labels[new_gp.name] = new_gp.training_labels_np + # Save time by attempting to load in computed attributes - if (len(new_gp.training_data)>5000): - new_gp.ky_mat = np.load(dictionary['ky_mat_file']) - new_gp.compute_matrices() + if len(new_gp.training_data) > 5000: + try: + new_gp.ky_mat = np.load(dictionary['ky_mat_file']) + new_gp.compute_matrices() + except: + new_gp.ky_mat = None + new_gp.l_mat = None + new_gp.alpha = None + new_gp.ky_mat_inv = None + filename = dictionary['ky_mat_file'] + Warning("the covariance matrices are not loaded"\ + f"because {filename} cannot be found") else: new_gp.ky_mat_inv = np.array(dictionary['ky_mat_inv']) \ if dictionary.get('ky_mat_inv') is not None else None @@ -644,7 +771,8 @@ def from_dict(dictionary): new_gp.l_mat = np.array(dictionary['l_mat']) \ if dictionary.get('l_mat') is not None else None new_gp.alpha = np.array(dictionary['alpha']) \ - if dictionary.get('alpha') is not None else None + if dictionary.get('alpha') is not None else None + return new_gp def compute_matrices(self): @@ -659,6 +787,44 @@ def compute_matrices(self): self.alpha = alpha self.ky_mat_inv = ky_mat_inv + def adjust_cutoffs(self, new_cutoffs: Union[list, tuple, 'np.ndarray'], + reset_L_alpha = True, + train = True): + """ + Loop through atomic environment objects stored in the training data, + and re-compute cutoffs for each. Useful if you want to gauge the + impact of cutoffs given a certain training set! Unless you know + *exactly* what you are doing for some development or test purpose, + it is **highly** suggested that you call set_L_alpha and + re-optimize your hyperparameters afterwards as is default here. + + :param new_cutoffs: + :return: + """ + + old_structures = [env.structure for env in self.training_data] + old_atoms = [env.atom for env in self.training_data] + new_environments = [AtomicEnvironment(struc, atom, new_cutoffs) for + struc, atom in zip(old_structures, old_atoms)] + + self.training_data = new_environments + # Ensure that training data and labels are still consistent + _global_training_data[self.name] = self.training_data + _global_training_labels[self.name] = self.training_labels_np + + + self.cutoffs = np.array(new_cutoffs) + + if reset_L_alpha: + del self.l_mat + del self.ky_mat + self.set_L_alpha() + + if train: + self.train() + + + def write_model(self, name: str, format: str = 'json'): """ Write model in a variety of formats to a file for later re-use. @@ -667,7 +833,7 @@ def write_model(self, name: str, format: str = 'json'): format (str): Output format. """ - if (len(self.training_data)>5000): + if len(self.training_data) > 5000: np.save(f"{name}_ky_mat.npy", self.ky_mat) self.ky_mat_file = f"{name}_ky_mat.npy" del self.ky_mat @@ -689,13 +855,12 @@ def write_model(self, name: str, format: str = 'json'): raise ValueError("Output format not supported: try from " "{}".format(supported_formats)) - if (len(self.training_data)>5000): + if len(self.training_data) > 5000: self.ky_mat = np.load(f"{name}_ky_mat.npy") self.compute_matrices() - @staticmethod - def from_file(filename: str, format: str=''): + def from_file(filename: str, format: str = ''): """ One-line convenience method to load a GP from a file stored using write_file @@ -708,12 +873,76 @@ def from_file(filename: str, format: str=''): if '.json' in filename or 'json' in format: with open(filename, 'r') as f: - return GaussianProcess.from_dict(json.loads(f.readline())) + gp_model = GaussianProcess.from_dict(json.loads(f.readline())) + gp_model.check_instantiation() + _global_training_data[gp_model.name] \ + = gp_model.training_data + _global_training_labels[gp_model.name] \ + = gp_model.training_labels_np + elif '.pickle' in filename or 'pickle' in format: with open(filename, 'rb') as f: - return pickle.load(f) + gp_model = pickle.load(f) + gp_model.check_instantiation() + + _global_training_data[gp_model.name] \ + = gp_model.training_data + _global_training_labels[gp_model.name] \ + = gp_model.training_labels_np + + if len(gp_model.training_data) > 5000: + try: + gp_model.ky_mat = np.load(gp_model.ky_mat_file) + gp_model.compute_matrices() + except: + gp_model.ky_mat = None + gp_model.l_mat = None + gp_model.alpha = None + gp_model.ky_mat_inv = None + Warning("the covariance matrices are not loaded"\ + f"it can take extra long time to recompute") else: raise ValueError("Warning: Format unspecified or file is not " ".json or .pickle format.") + + return gp_model + + + @property + def training_statistics(self) -> dict: + """ + Return a dictionary with statistics about the current training data. + Useful for quickly summarizing info about the GP. + :return: + """ + + data = {} + + data['N'] = len(self.training_data) + + # Count all of the present species in the atomic env. data + present_species = [] + for env,force in zip(self.training_data,self.training_labels): + present_species.append(Z_to_element(env.structure.coded_species[ + env.atom])) + + # Summarize the relevant information + data['species'] = set(present_species) + data['envs_by_species'] = dict(Counter(present_species)) + + return data + + + @property + def par(self): + """ + Backwards compability attribute + :return: + """ + return self.parallel + + def __del__(self): + _global_training_labels.pop(self.name, None) + _global_training_data.pop(self.name, None) diff --git a/flare/gp_algebra.py b/flare/gp_algebra.py index c06ffea2b..4fe054db0 100644 --- a/flare/gp_algebra.py +++ b/flare/gp_algebra.py @@ -4,50 +4,135 @@ import multiprocessing as mp from typing import List, Callable +from flare.kernels.utils import from_mask_to_args, from_grad_to_mask -####################################### -##### KY MATRIX FUNCTIONS -####################################### +_global_training_data = {} +_global_training_labels = {} -def get_ky_mat(hyps: np.ndarray, training_data: list, - kernel, cutoffs=None, hyps_mask=None): +def queue_wrapper(result_queue, wid, + func, args): + """ + wrapper function for multiprocessing queue + """ + result_queue.put((wid, func(*args))) - """ Compute covariance matrix K by comparing training data with itself - :param hyps: list of hyper-parameters - :param training_data: list of atomic envirionments - :param kernel: function object of the kernel - :param cutoffs: The cutoff values used for the atomic environments - :type cutoffs: list of 2 float numbers - :param hyps_mask: dictionary used for multi-group hyperparmeters +def partition_cr(n_sample, size, n_cpus): + """ + partition the training data for matrix calculation + the number of blocks are close to n_cpus + since mp.Process does not allow to change the thread number + """ - :return: covariance matrix + # divid the block by n_cpu partitions, with size n_sample0 + # if the divided chunk is smaller than the requested chunk n_sample + # use the requested chunk size + n_sample0 = int(math.ceil(np.sqrt(size*size/n_cpus/2.))) + if (n_sample0 > n_sample): + n_sample = n_sample0 + + block_id=[] + nbatch = 0 + nbatch1 = 0 + nbatch2 = 0 + e1 = 0 + while (e1 < size): + s1 = int(n_sample*nbatch1) + e1 = int(np.min([s1 + n_sample, size])) + nbatch2 = nbatch1 + nbatch1 += 1 + e2 = 0 + while (e2 n_sample): + n_sample = n_sample0 + + block_id = [] + nbatch = 0 + e = 0 + while (e < size): + s = n_sample*nbatch + e = np.min([s + n_sample, size]) + block_id += [(s, e)] + nbatch += 1 + return block_id, nbatch + +def partition_update(n_sample, size, old_size, n_cpus): + + ns = int(math.ceil(size/n_sample)) + nproc = (size*3-old_size*3)*(ns+old_size*3)//2 + if (nproc < n_cpus): + n_sample = int(math.ceil(size/np.sqrt(n_cpus*2))) + ns = int(math.ceil(size/n_sample)) + + ns_new = int(math.ceil((size-old_size)/n_sample)) + old_ns = int(math.ceil(old_size/n_sample)) + + nbatch = 0 + block_id = [] + for ibatch1 in range(old_ns): + s1 = int(n_sample*ibatch1) + e1 = int(np.min([s1 + n_sample, old_size])) + for ibatch2 in range(ns_new): + s2 = int(n_sample*ibatch2)+old_size + e2 = int(np.min([s2 + n_sample, size])) + block_id += [(s1, e1, s2, e2)] + nbatch += 1 + + for ibatch1 in range(ns_new): + s1 = int(n_sample*ibatch1)+old_size + e1 = int(np.min([s1 + n_sample, size])) + for ibatch2 in range(ns_new): + s2 = int(n_sample*ibatch2)+old_size + e2 = int(np.min([s2 + n_sample, size])) + block_id += [(s1, e1, s2, e2)] + nbatch += 1 + + return block_id, nbatch + + +def obtain_noise_len(hyps, hyps_mask): + """ + obtain the noise parameter from hyps and mask """ # assume sigma_n is the final hyperparameter - number_of_hyps = len(hyps) - sigma_n = hyps[number_of_hyps - 1] + sigma_n = hyps[-1] + # correct it if map is defined + non_noise_hyps = len(hyps)-1 + train_noise = True if (hyps_mask is not None): - if ('train_noise' in hyps_mask): - if (hyps_mask['train_noise'] is False): - sigma_n = hyps_mask['original'][-1] + train_noise = hyps_mask.get('train_noise', True) + if (train_noise is False): + sigma_n = hyps_mask['original'][-1] + non_noise_hyps = len(hyps) - # matrix manipulation - k_mat = get_ky_mat_pack(hyps, training_data, - training_data, True, kernel, - cutoffs, hyps_mask) - size3 = len(training_data)*3 - ky_mat = k_mat + sigma_n ** 2 * np.eye(size3) + return sigma_n, non_noise_hyps, train_noise - return ky_mat +####################################### +##### KY MATRIX FUNCTIONS +####################################### -def get_ky_mat_pack(hyps: np.ndarray, training_data1: list, - training_data2:list, same: bool, - kernel, cutoffs=None, hyps_mask=None): +def get_ky_mat_pack(hyps: np.ndarray, name: str, + s1: int, e1: int, s2: int, e2: int, + same: bool, kernel, cutoffs, hyps_mask): """ Compute covariance matrix element between set1 and set2 :param hyps: list of hyper-parameters - :param training_data1: row elements - :param training_data2: column elements + :param name: name of the gp instance. :param same: whether the row and column are the same :param kernel: function object of the kernel :param cutoffs: The cutoff values used for the atomic environments @@ -59,32 +144,27 @@ def get_ky_mat_pack(hyps: np.ndarray, training_data1: list, # initialize matrices - size1 = len(training_data1) * 3 - size2 = len(training_data2) * 3 + training_data = _global_training_data[name] + size1 = (e1-s1)*3 + size2 = (e2-s2)*3 k_mat = np.zeros([size1, size2]) ds = [1, 2, 3] # calculate elements + args = from_mask_to_args(hyps, hyps_mask, cutoffs) + for m_index in range(size1): - x_1 = training_data1[int(math.floor(m_index / 3))] + x_1 = training_data[int(math.floor(m_index / 3))+s1] d_1 = ds[m_index % 3] if (same): lowbound = m_index else: lowbound = 0 for n_index in range(lowbound, size2): - x_2 = training_data2[int(math.floor(n_index / 3))] + x_2 = training_data[int(math.floor(n_index / 3))+s2] d_2 = ds[n_index % 3] - - # calculate kernel and gradient - if (hyps_mask is not None): - kern_curr = kernel(x_1, x_2, d_1, d_2, hyps, - cutoffs, hyps_mask=hyps_mask) - else: - kern_curr = kernel(x_1, x_2, d_1, d_2, hyps, - cutoffs) - + kern_curr = kernel(x_1, x_2, d_1, d_2, *args) # store kernel value k_mat[m_index, n_index] = kern_curr if (same): @@ -92,13 +172,12 @@ def get_ky_mat_pack(hyps: np.ndarray, training_data1: list, return k_mat - -def get_ky_mat_par(hyps: np.ndarray, training_data: list, - kernel, cutoffs=None, hyps_mask=None, - n_cpus=None, nsample=100): +def get_ky_mat(hyps: np.ndarray, name: str, + kernel, cutoffs=None, hyps_mask=None, + n_cpus=1, n_sample=100): """ parallel version of get_ky_mat :param hyps: list of hyper-parameters - :param training_data: list of atomic envirionments + :param name: name of the gp instance. :param kernel: function object of the kernel :param cutoffs: The cutoff values used for the atomic environments :type cutoffs: list of 2 float numbers @@ -107,101 +186,70 @@ def get_ky_mat_par(hyps: np.ndarray, training_data: list, :return: covariance matrix """ + training_data = _global_training_data[name] + size = len(training_data) + size3 = 3*size + if (n_cpus is None): - n_cpus =mp.cpu_count() + n_cpus = mp.cpu_count() if (n_cpus == 1): - return get_ky_mat(hyps, training_data, - kernel, cutoffs, hyps_mask) - - # assume sigma_n is the final hyperparameter - number_of_hyps = len(hyps) - sigma_n = hyps[-1] - if (hyps_mask is not None): - if ('train_noise' in hyps_mask.keys()): - if (hyps_mask['train_noise'] is False): - sigma_n = hyps_mask['original'][-1] - - # initialize matrices + k_mat = get_ky_mat_pack( + hyps, name, 0, size, 0, size, True, + kernel, cutoffs, hyps_mask) + else: - size = len(training_data) - size3 = 3*len(training_data) - k_mat_slice = [] - k_mat = np.zeros([size3, size3]) - with mp.Pool(processes=n_cpus) as pool: - - ns = int(math.ceil(size/nsample)) - nproc = ns*(ns+1)//2 - if (nproc < n_cpus): - nsample = int(math.ceil(size/int(np.sqrt(n_cpus*2)))) - ns = int(math.ceil(size/nsample)) - - block_id = [] - nbatch = 0 - for ibatch1 in range(ns): - s1 = int(nsample*ibatch1) - e1 = int(np.min([s1 + nsample, size])) - for ibatch2 in range(ibatch1, ns): - s2 = int(nsample*ibatch2) - e2 = int(np.min([s2 + nsample, size])) - block_id += [(s1, e1, s2, e2)] - nbatch += 1 - - k_mat_slice = [] - count = 0 - base = 0 - time0 = time.time() - for ibatch in range(nbatch): - s1, e1, s2, e2 = block_id[ibatch] - t1 = training_data[s1:e1] - t2 = training_data[s2:e2] - k_mat_slice.append(pool.apply_async( - get_ky_mat_pack, - args=(hyps, - t1, t2, bool(s1==s2), - kernel, cutoffs, - hyps_mask))) - count += 1 - if (count >= n_cpus*3): - for iget in range(base, count+base): - s1, e1, s2, e2 = block_id[iget] - k_mat_block = k_mat_slice[iget-base].get() - k_mat[s1*3:e1*3, s2*3:e2*3] = k_mat_block - if (s1 != s2): - k_mat[s2*3:e2*3, s1*3:e1*3] = k_mat_block.T - if (size>5000): - print("computed block", base, base+count, nbatch, time.time()-time0) - time0 = time.time() - k_mat_slice = [] - base = ibatch+1 - count = 0 - if (count>0): - if (size>5000): - print("computed block", base, base+count, nbatch, time.time()-time0) - time0 = time.time() - for iget in range(base, nbatch): - s1, e1, s2, e2 = block_id[iget] - k_mat_block = k_mat_slice[iget-base].get() - k_mat[s1*3:e1*3, s2*3:e2*3] = k_mat_block - if (s1 != s2): - k_mat[s2*3:e2*3, s1*3:e1*3] = k_mat_block.T - del k_mat_slice - pool.close() - pool.join() + # initialize matrices + block_id, nbatch = partition_cr(n_sample, size, n_cpus) + + result_queue = mp.Queue() + children = [] + for wid in range(nbatch): + s1, e1, s2, e2 = block_id[wid] + children.append( + mp.Process( + target=queue_wrapper, + args=(result_queue, wid, + get_ky_mat_pack, + (hyps, name, s1, e1, s2, e2, + s1==s2, kernel, cutoffs, hyps_mask + ) + ) + ) + ) + + # Run child processes. + for c in children: + c.start() + + # Wait for all results to arrive. + k_mat = np.zeros([size3, size3]) + for _ in range(nbatch): + wid, result_chunk = result_queue.get(block=True) + s1, e1, s2, e2 = block_id[wid] + k_mat[s1*3:e1*3, s2*3:e2*3] = result_chunk + if (s1 != s2): + k_mat[s2*3:e2*3, s1*3:e1*3] = result_chunk.T + + # Join child processes (clean up zombies). + for c in children: + c.join() + + # matrix manipulation + del result_queue + del children + + sigma_n, _, __ = obtain_noise_len(hyps, hyps_mask) - # matrix manipulation ky_mat = k_mat - del k_mat_block ky_mat += sigma_n ** 2 * np.eye(size3) return ky_mat -def get_like_from_ky_mat(ky_mat, training_labels_np): +def get_like_from_ky_mat(ky_mat, name): """ compute the likelihood from the covariance matrix :param ky_mat: the covariance matrix - :param training_labels_np: the numpy array of forces - :type training_labels_np: np.array :return: float, likelihood """ @@ -209,83 +257,42 @@ def get_like_from_ky_mat(ky_mat, training_labels_np): try: ky_mat_inv = np.linalg.inv(ky_mat) l_mat = np.linalg.cholesky(ky_mat) + alpha = np.matmul(ky_mat_inv, labels) except: return -1e8 - alpha = np.matmul(ky_mat_inv, training_labels_np) + return get_like_from_mats(ky_mat, l_mat, alpha, name) + +def get_like_from_mats(ky_mat, l_mat, alpha, name): + """ compute the likelihood from the covariance matrix + + :param ky_mat: the covariance matrix + + :return: float, likelihood + """ + # catch linear algebra errors + labels = _global_training_labels[name] # calculate likelihood - like = (-0.5 * np.matmul(training_labels_np, alpha) - + like = (-0.5 * np.matmul(labels, alpha) - np.sum(np.log(np.diagonal(l_mat))) - math.log(2 * np.pi) * ky_mat.shape[1] / 2) return like + ####################################### ##### KY MATRIX FUNCTIONS and gradients ####################################### - -def get_ky_and_hyp(hyps: np.ndarray, training_data: list, - kernel_grad, cutoffs=None, hyps_mask=None): - - """ - computes ky matrix and its derivative to hyper-parameter - If the cpu set up is None, it uses as much as posible cpus - - :param hyps: list of hyper-parameters - :param training_data: list of atomic envirionments - :param kernel_grad: function object of the kernel gradient - :param cutoffs: The cutoff values used for the atomic environments - :type cutoffs: list of 2 float numbers - :param hyps_mask: dictionary used for multi-group hyperparmeters - - :return: hyp_mat, ky_mat - """ - - hyp_mat0, k_mat = get_ky_and_hyp_pack(hyps, training_data, - training_data, True, - kernel_grad, - cutoffs=cutoffs, - hyps_mask=hyps_mask) - - - # obtain noise parameter - train_noise = True - sigma_n = hyps[-1] - if (hyps_mask is not None): - if ('train_noise' in hyps_mask): - if (hyps_mask['train_noise'] is False): - train_noise = False - sigma_n = hyps_mask['original'][-1] - # add gradient of noise variance - size3 = 3*len(training_data) - if (train_noise): - sigma_mat = np.eye(size3) * 2 * sigma_n - hyp_mat = np.zeros([hyp_mat0.shape[0]+1, - hyp_mat0.shape[1], - hyp_mat0.shape[2]]) - hyp_mat[-1, :, :] = sigma_mat - hyp_mat[:-1, :, :] = hyp_mat0 - else: - hyp_mat = hyp_mat0 - - # matrix manipulation - ky_mat = k_mat + sigma_n ** 2 * np.eye(size3) - - return hyp_mat, ky_mat - - -def get_ky_and_hyp_pack(hyps: np.ndarray, training_data1: list, - training_data2: list, same: bool, - kernel_grad, cutoffs=None, hyps_mask=None): +def get_ky_and_hyp_pack(name, s1, e1, s2, e2, same: bool, + hyps: np.ndarray, kernel_grad, cutoffs=None, hyps_mask=None): """ computes a block of ky matrix and its derivative to hyper-parameter If the cpu set up is None, it uses as much as posible cpus :param hyps: list of hyper-parameters - :param training_data1: row elements - :param training_data2: column elements + :param name: name of the gp instance. :param kernel_grad: function object of the kernel gradient :param cutoffs: The cutoff values used for the atomic environments :type cutoffs: list of 2 float numbers @@ -295,26 +302,22 @@ def get_ky_and_hyp_pack(hyps: np.ndarray, training_data1: list, """ # assume sigma_n is the final hyperparameter - number_of_hyps = len(hyps) - non_noise_hyps = len(hyps)-1 - train_noise = True - if (hyps_mask is not None): - if ('train_noise' in hyps_mask.keys()): - if (hyps_mask['train_noise'] is False): - train_noise = False - non_noise_hyps = len(hyps) + sigma_n, non_noise_hyps, _ = obtain_noise_len(hyps, hyps_mask) # initialize matrices - size1 = len(training_data1) * 3 - size2 = len(training_data2) * 3 + size1 = (e1-s1) * 3 + size2 = (e2-s2) * 3 k_mat = np.zeros([size1, size2]) hyp_mat = np.zeros([non_noise_hyps, size1, size2]) + args = from_mask_to_args(hyps, hyps_mask, cutoffs) + ds = [1, 2, 3] + training_data = _global_training_data[name] # calculate elements for m_index in range(size1): - x_1 = training_data1[int(math.floor(m_index / 3))] + x_1 = training_data[int(math.floor(m_index / 3))+s1] d_1 = ds[m_index % 3] if (same): @@ -322,137 +325,90 @@ def get_ky_and_hyp_pack(hyps: np.ndarray, training_data1: list, else: lowbound = 0 for n_index in range(lowbound, size2): - x_2 = training_data2[int(math.floor(n_index / 3))] + x_2 = training_data[int(math.floor(n_index / 3))+s2] d_2 = ds[n_index % 3] # calculate kernel and gradient - if (hyps_mask is not None): - cov = kernel_grad(x_1, x_2, d_1, d_2, hyps, - cutoffs=cutoffs, hyps_mask=hyps_mask) - else: - cov = kernel_grad(x_1, x_2, d_1, d_2, hyps, cutoffs) + cov = kernel_grad(x_1, x_2, d_1, d_2, *args) # store kernel value k_mat[m_index, n_index] = cov[0] - hyp_mat[:, m_index, n_index] = cov[1] - + grad = from_grad_to_mask(cov[1], hyps_mask) + hyp_mat[:, m_index, n_index] = grad if (same): k_mat[n_index, m_index] = cov[0] - hyp_mat[:, n_index, m_index] = cov[1] + hyp_mat[:, n_index, m_index] = grad return hyp_mat, k_mat -def get_ky_and_hyp_par(hyps: np.ndarray, training_data: list, - kernel_grad, cutoffs=None, - hyps_mask=None, - n_cpus=None, nsample=100): +def get_ky_and_hyp(hyps: np.ndarray, name, + kernel_grad, cutoffs=None, + hyps_mask=None, + n_cpus=1, n_sample=100): """ parallel version of get_ky_and_hyp :param hyps: list of hyper-parameters - :param training_data: list of atomic envirionments + :param name: name of the gp instance. :param kernel_grad: function object of the kernel gradient :param cutoffs: The cutoff values used for the atomic environments :type cutoffs: list of 2 float numbers :param hyps_mask: dictionary used for multi-group hyperparmeters :param n_cpus: number of cpus to use. - :param nsample: the size of block for matrix to compute + :param n_sample: the size of block for matrix to compute :return: hyp_mat, ky_mat """ + training_data = _global_training_data[name] + size = len(training_data) + size3 = size*3 + + sigma_n, non_noise_hyps, train_noise = obtain_noise_len(hyps, hyps_mask) if (n_cpus is None): n_cpus = mp.cpu_count() if (n_cpus == 1): - return get_ky_and_hyp(hyps, training_data, - kernel_grad, - cutoffs, hyps_mask) - - # assume sigma_n is the final hyperparameter - number_of_hyps = len(hyps) - non_noise_hyps = len(hyps)-1 - sigma_n = hyps[-1] - train_noise = True - if (hyps_mask is not None): - if ('train_noise' in hyps_mask.keys()): - if (hyps_mask['train_noise'] is False): - sigma_n = hyps_mask['original'][-1] - train_noise = False - non_noise_hyps = len(hyps) + hyp_mat0, k_mat = get_ky_and_hyp_pack( + name, 0, size, 0, size, True, + hyps, kernel_grad, cutoffs, hyps_mask) + else: - # initialize matrices - size = len(training_data) - size3 = size*3 - k_mat = np.zeros([size3, size3]) - hyp_mat0 = np.zeros([non_noise_hyps, size3, size3]) - - with mp.Pool(processes=n_cpus) as pool: - - ns = int(math.ceil(size/nsample)) - nproc = ns*(ns+1)//2 - if (nproc < n_cpus): - nsample = int(math.ceil(size/np.sqrt(n_cpus*2))) - ns = int(math.ceil(size/nsample)) - - block_id = [] - nbatch = 0 - for ibatch1 in range(ns): - s1 = int(nsample*ibatch1) - e1 = int(np.min([s1 + nsample, size])) - for ibatch2 in range(ibatch1, ns): - s2 = int(nsample*ibatch2) - e2 = int(np.min([s2 + nsample, size])) - block_id += [(s1, e1, s2, e2)] - nbatch += 1 - - count = 0 - base = 0 - mat_slice = [] - for ibatch in range(nbatch): - s1, e1, s2, e2 = block_id[ibatch] - if (size>5000): - print("sending", s1, e1, s2, e2) - t1 = training_data[s1:e1] - t2 = training_data[s2:e2] - mat_slice.append(pool.apply_async( - get_ky_and_hyp_pack, - args=( - hyps, t1, t2, - bool(s1==s2), - kernel_grad, - cutoffs, hyps_mask))) - - count += 1 - if (count > n_cpus*3): - for iget in range(base, count+base): - s1, e1, s2, e2 = block_id[iget] - h_mat_block, k_mat_block = mat_slice[iget-base].get() - k_mat[s1*3:e1*3, s2*3:e2*3] = k_mat_block - hyp_mat0[:, s1*3:e1*3, s2*3:e2*3] = h_mat_block - if (s1 != s2): - k_mat[s2*3:e2*3, s1*3:e1*3] = k_mat_block.T - for idx in range(hyp_mat0.shape[0]): - hyp_mat0[idx, s2*3:e2*3, s1*3:e1*3] = h_mat_block[idx].T - if (size>5000): - print("computed block", base, base+count) - mat_slice = [] - base = ibatch+1 - count = 0 - if (count>0): - for iget in range(base, nbatch): - s1, e1, s2, e2 = block_id[iget] - h_mat_block, k_mat_block = mat_slice[iget-base].get() - k_mat[s1*3:e1*3, s2*3:e2*3] = k_mat_block - hyp_mat0[:, s1*3:e1*3, s2*3:e2*3] = h_mat_block - if (s1 != s2): - k_mat[s2*3:e2*3, s1*3:e1*3] = k_mat_block.T - for idx in range(hyp_mat0.shape[0]): - hyp_mat0[idx, s2*3:e2*3, s1*3:e1*3] = h_mat_block[idx].T - del mat_slice - pool.close() - pool.join() + block_id, nbatch = partition_cr(n_sample, size, n_cpus) + + result_queue = mp.Queue() + children = [] + for wid in range(nbatch): + s1, e1, s2, e2 = block_id[wid] + children.append( + mp.Process( + target=queue_wrapper, + args=(result_queue, wid, + get_ky_and_hyp_pack, + (name, s1, e1, s2, e2, s1==s2, + hyps, kernel_grad, cutoffs, hyps_mask)))) + + # Run child processes. + for c in children: + c.start() + + k_mat = np.zeros([size3, size3]) + hyp_mat0 = np.zeros([non_noise_hyps, size3, size3]) + for _ in range(nbatch): + wid, result_chunk = result_queue.get(block=True) + s1, e1, s2, e2 = block_id[wid] + h_mat_block, k_mat_block = result_chunk + k_mat[s1*3:e1*3, s2*3:e2*3] = k_mat_block + hyp_mat0[:, s1*3:e1*3, s2*3:e2*3] = h_mat_block + if (s1 != s2): + k_mat[s2*3:e2*3, s1*3:e1*3] = k_mat_block.T + for idx in range(hyp_mat0.shape[0]): + hyp_mat0[idx, s2*3:e2*3, s1*3:e1*3] = h_mat_block[idx].T + + # Join child processes (clean up zombies). + for c in children: + c.join() # add gradient of noise variance if (train_noise): @@ -470,19 +426,15 @@ def get_ky_and_hyp_par(hyps: np.ndarray, training_data: list, return hyp_mat, ky_mat -def get_neg_likelihood(hyps: np.ndarray, training_data: list, - training_labels_np: np.ndarray, +def get_neg_likelihood(hyps: np.ndarray, name, kernel: Callable, output = None, cutoffs=None, hyps_mask=None, - n_cpus=None, nsample=100): + n_cpus=1, n_sample=100): """compute the negative log likelihood :param hyps: list of hyper-parameters :type hyps: np.ndarray - :param training_data: Set of atomic environments to compare against - :type training_data: list of AtomicEnvironment objects - :param training_labels_np: forces - :type training_labels_np: np.array + :param name: name of the gp instance. :param kernel: function object of the kernel :param output: Output object for dumping every hyper-parameter sets computed @@ -491,7 +443,7 @@ def get_neg_likelihood(hyps: np.ndarray, training_data: list, :type cutoffs: list of 2 float numbers :param hyps_mask: dictionary used for multi-group hyperparmeters :param n_cpus: number of cpus to use. - :param nsample: the size of block for matrix to compute + :param n_sample: the size of block for matrix to compute :return: float """ @@ -506,15 +458,15 @@ def get_neg_likelihood(hyps: np.ndarray, training_data: list, time0 = time.time() ky_mat = \ - get_ky_mat_par(hyps, training_data, kernel, - cutoffs=cutoffs, hyps_mask=hyps_mask, - n_cpus=n_cpus, nsample=nsample) + get_ky_mat(hyps, name, kernel, + cutoffs=cutoffs, hyps_mask=hyps_mask, + n_cpus=n_cpus, n_sample=n_sample) output.write_to_log(f"get_key_mat {time.time()-time0}\n", name="hyps") time0 = time.time() - like = get_like_from_ky_mat(ky_mat, training_labels_np) + like = get_like_from_ky_mat(ky_mat, name) output.write_to_log(f"get_like_from_ky_mat {time.time()-time0}\n", name="hyps") @@ -525,20 +477,15 @@ def get_neg_likelihood(hyps: np.ndarray, training_data: list, return -like -def get_neg_like_grad(hyps: np.ndarray, training_data: list, - training_labels_np: np.ndarray, +def get_neg_like_grad(hyps: np.ndarray, name: str, kernel_grad, output = None, cutoffs=None, hyps_mask=None, - n_cpus=None, nsample=100, - print_progress=False): + n_cpus=1, n_sample=100, print_progress=False): """compute the log likelihood and its gradients :param hyps: list of hyper-parameters :type hyps: np.ndarray - :param training_data: Set of atomic environments to compare against - :type training_data: list of AtomicEnvironment objects - :param training_labels_np: forces - :type training_labels_np: np.array + :param name: name of the gp instance. :param kernel_grad: function object of the kernel gradient :param output: Output object for dumping every hyper-parameter sets computed @@ -547,7 +494,7 @@ def get_neg_like_grad(hyps: np.ndarray, training_data: list, :type cutoffs: list of 2 float numbers :param hyps_mask: dictionary used for multi-group hyperparmeters :param n_cpus: number of cpus to use. - :param nsample: the size of block for matrix to compute + :param n_sample: the size of block for matrix to compute :return: float, np.array """ @@ -561,12 +508,12 @@ def get_neg_like_grad(hyps: np.ndarray, training_data: list, output.write_to_log(ostring, name="hyps") hyp_mat, ky_mat = \ - get_ky_and_hyp_par(hyps, - training_data, - kernel_grad, - cutoffs=cutoffs, - hyps_mask=hyps_mask, - n_cpus=n_cpus, nsample=nsample) + get_ky_and_hyp(hyps, + name, + kernel_grad, + cutoffs=cutoffs, + hyps_mask=hyps_mask, + n_cpus=n_cpus, n_sample=n_sample) if output is not None: output.write_to_log(f"get_ky_and_hyp {time.time()-time0}\n", @@ -575,7 +522,7 @@ def get_neg_like_grad(hyps: np.ndarray, training_data: list, time0 = time.time() like, like_grad = \ - get_like_grad_from_mats(ky_mat, hyp_mat, training_labels_np) + get_like_grad_from_mats(ky_mat, hyp_mat, name) if output is not None: output.write_to_log(f"get_like_grad_from_mats {time.time()-time0}\n", @@ -590,14 +537,15 @@ def get_neg_like_grad(hyps: np.ndarray, training_data: list, output.write_to_log('like: ' + str(like)+'\n', name="hyps") if print_progress: - print('\nhyperparameters: '+str(hyps)) - print('likelihood: ' + str(like)) - print('likelihood gradient: ' + str(like_grad)) + print('\nHyperparameters: ', list(hyps)) + print('Likelihood: ' + str(like)) + print('Likelihood Gradient: ',list(like_grad)) return -like, -like_grad -def get_like_grad_from_mats(ky_mat, hyp_mat, training_labels_np): + +def get_like_grad_from_mats(ky_mat, hyp_mat, name): """compute the gradient of likelihood to hyper-parameters from covariance matrix and its gradient @@ -605,8 +553,7 @@ def get_like_grad_from_mats(ky_mat, hyp_mat, training_labels_np): :type ky_mat: np.array :param hyp_mat: dky/d(hyper parameter) matrix :type hyp_mat: np.array - :param training_labels_np: forces - :type training_labels_np: np.array + :param name: name of the gp instance. :return: float, list. the likelihood and its gradients """ @@ -620,13 +567,15 @@ def get_like_grad_from_mats(ky_mat, hyp_mat, training_labels_np): except: return -1e8, np.zeros(number_of_hyps) - alpha = np.matmul(ky_mat_inv, training_labels_np) - alpha_mat = np.matmul(alpha.reshape(alpha.shape[0], 1), - alpha.reshape(1, alpha.shape[0])) + labels = _global_training_labels[name] + + alpha = np.matmul(ky_mat_inv, labels) + alpha_mat = np.matmul(alpha.reshape(-1, 1), + alpha.reshape(1, -1)) like_mat = alpha_mat - ky_mat_inv # calculate likelihood - like = (-0.5 * np.matmul(training_labels_np, alpha) - + like = (-0.5 * np.matmul(labels, alpha) - np.sum(np.log(np.diagonal(l_mat))) - math.log(2 * np.pi) * ky_mat.shape[1] / 2) @@ -639,9 +588,9 @@ def get_like_grad_from_mats(ky_mat, hyp_mat, training_labels_np): return like, like_grad -def get_ky_mat_update_par(ky_mat_old, hyps: np.ndarray, training_data: list, +def get_ky_mat_update(ky_mat_old, hyps: np.ndarray, name: str, kernel, cutoffs=None, hyps_mask=None, - n_cpus=None, nsample=100): + n_cpus=1, n_sample=100): ''' used for update_L_alpha, especially for parallelization parallelized for added atoms, for example, if add 10 atoms to the training @@ -656,7 +605,7 @@ def get_ky_mat_update_par(ky_mat_old, hyps: np.ndarray, training_data: list, :type cutoffs: list of 2 float numbers :param hyps_mask: dictionary used for multi-group hyperparmeters :param n_cpus: number of cpus to use. - :param nsample: the size of block for matrix to compute + :param n_sample: the size of block for matrix to compute :return: updated covariance matrix @@ -665,108 +614,67 @@ def get_ky_mat_update_par(ky_mat_old, hyps: np.ndarray, training_data: list, if (n_cpus is None): n_cpus = mp.cpu_count() if (n_cpus == 1): - return get_ky_mat_update(ky_mat_old, hyps, training_data, - kernel, cutoffs, hyps_mask) + return get_ky_mat_update_serial( + ky_mat_old, hyps, name, kernel, cutoffs, hyps_mask) - # assume sigma_n is the final hyperparameter - sigma_n = hyps[-1] - if (hyps_mask is not None): - if ('train_noise' in hyps_mask.keys()): - if (hyps_mask['train_noise'] is False): - sigma_n = hyps_mask['original'][-1] + sigma_n, non_noise_hyps, _ = obtain_noise_len(hyps, hyps_mask) # initialize matrices old_size3 = ky_mat_old.shape[0] old_size = old_size3//3 - size = len(training_data) - size3 = 3*len(training_data) + size = len(_global_training_data[name]) + size3 = 3*size + ds = [1, 2, 3] + + block_id, nbatch = partition_update(n_sample, size, old_size, n_cpus) + + # Send and Run child processes. + result_queue = mp.Queue() + children = [] + for wid in range(nbatch): + s1, e1, s2, e2 = block_id[wid] + children.append( + mp.Process( + target=queue_wrapper, + args=(result_queue, wid, + get_ky_mat_pack, + (hyps, name, s1, e1, s2, e2, + s1==s2, kernel, cutoffs, hyps_mask + ) + ) + ) + ) + for c in children: + c.start() + + # Wait for all results to arrive. + size3 = 3*size ky_mat = np.zeros([size3, size3]) ky_mat[:old_size3, :old_size3] = ky_mat_old - ds = [1, 2, 3] - with mp.Pool(processes=n_cpus) as pool: - ns = int(math.ceil(size/nsample)) - nproc = (size3-old_size3)*(ns+old_size3)//2 - if (nproc < n_cpus): - nsample = int(math.ceil(size/np.sqrt(n_cpus*2))) - ns = int(math.ceil(size/nsample)) - - ns_new = int(math.ceil((size-old_size)/nsample)) - old_ns = int(math.ceil(old_size/nsample)) - - nbatch = 0 - block_id = [] - for ibatch1 in range(old_ns): - s1 = int(nsample*ibatch1) - e1 = int(np.min([s1 + nsample, old_size])) - for ibatch2 in range(ns_new): - s2 = int(nsample*ibatch2)+old_size - e2 = int(np.min([s2 + nsample, size])) - block_id += [(s1, e1, s2, e2)] - nbatch += 1 - - for ibatch1 in range(ns_new): - s1 = int(nsample*ibatch1)+old_size - e1 = int(np.min([s1 + nsample, size])) - for ibatch2 in range(ns_new): - s2 = int(nsample*ibatch2)+old_size - e2 = int(np.min([s2 + nsample, size])) - block_id += [(s1, e1, s2, e2)] - nbatch += 1 - - k_mat_slice = [] - count = 0 - base = 0 - time0 = time.time() - for ibatch in range(nbatch): - s1, e1, s2, e2 = block_id[ibatch] - t1 = training_data[s1:e1] - t2 = training_data[s2:e2] - k_mat_slice += [pool.apply_async( - get_ky_mat_pack, - args=(hyps, - t1, t2, - bool(s1==s2), - kernel, cutoffs, hyps_mask))] - count += 1 - if (count >= n_cpus*3): - for iget in range(base, count+base): - s1, e1, s2, e2 = block_id[iget] - k_mat_block = k_mat_slice[iget-base].get() - ky_mat[s1*3:e1*3, s2*3:e2*3] = k_mat_block - if (s1 != s2): - ky_mat[s2*3:e2*3, s1*3:e1*3] = k_mat_block.T - if (size>5000): - print("computed block", base, base+count, nbatch, time.time()-time0) - time0 = time.time() - k_mat_slice = [] - base = ibatch+1 - count = 0 - if (count>0): - if (size>5000): - print("computed block", base, base+count, nbatch, time.time()-time0) - time0 = time.time() - for iget in range(base, nbatch): - s1, e1, s2, e2 = block_id[iget] - k_mat_block = k_mat_slice[iget-base].get() - ky_mat[s1*3:e1*3, s2*3:e2*3] = k_mat_block - if (s1 != s2): - ky_mat[s2*3:e2*3, s1*3:e1*3] = k_mat_block.T - del k_mat_slice - pool.close() - pool.join() + del ky_mat_old + for _ in range(nbatch): + wid, result_chunk = result_queue.get(block=True) + s1, e1, s2, e2 = block_id[wid] + ky_mat[s1*3:e1*3, s2*3:e2*3] = result_chunk + if (s1 != s2): + ky_mat[s2*3:e2*3, s1*3:e1*3] = result_chunk.T + + # Join child processes (clean up zombies). + for c in children: + c.join() # matrix manipulation ky_mat[old_size3:, old_size3:] += sigma_n ** 2 * np.eye(size3-old_size3) return ky_mat -def get_ky_mat_update(ky_mat_old, hyps: np.ndarray, training_data: list, - kernel, cutoffs=None, hyps_mask=None): +def get_ky_mat_update_serial(\ + ky_mat_old, hyps: np.ndarray, name, + kernel, cutoffs=None, hyps_mask=None): ''' used for update_L_alpha. if add 10 atoms to the training set, the K matrix will add 10x3 columns and 10x3 rows - :param ky_mat_old: old covariance matrix :param hyps: list of hyper-parameters :param training_data: Set of atomic environments to compare against @@ -776,6 +684,9 @@ def get_ky_mat_update(ky_mat_old, hyps: np.ndarray, training_data: list, :param hyps_mask: dictionary used for multi-group hyperparmeters ''' + + training_data = _global_training_data[name] + n = ky_mat_old.shape[0] size = len(training_data) size3 = size*3 @@ -785,6 +696,8 @@ def get_ky_mat_update(ky_mat_old, hyps: np.ndarray, training_data: list, ds = [1, 2, 3] + args = from_mask_to_args(hyps, hyps_mask, cutoffs) + # calculate elements for m_index in range(size3): x_1 = training_data[int(math.floor(m_index / 3))] @@ -793,31 +706,19 @@ def get_ky_mat_update(ky_mat_old, hyps: np.ndarray, training_data: list, for n_index in range(low, size3): x_2 = training_data[int(math.floor(n_index / 3))] d_2 = ds[n_index % 3] - - # calculate kernel and gradient - if (hyps_mask is not None): - kern_curr = kernel(x_1, x_2, d_1, d_2, hyps, - cutoffs, hyps_mask=hyps_mask) - else: - kern_curr = kernel(x_1, x_2, d_1, d_2, hyps, - cutoffs) - # store kernel value + # calculate kernel + kern_curr = kernel(x_1, x_2, d_1, d_2, *args) ky_mat[m_index, n_index] = kern_curr ky_mat[n_index, m_index] = kern_curr # matrix manipulation - sigma_n = hyps[-1] - if (hyps_mask is not None): - if ('train_noise' in hyps_mask.keys()): - if (hyps_mask['train_noise'] is False): - sigma_n = hyps_mask['original'][-1] + sigma_n, _, __ = obtain_noise_len(hyps, hyps_mask) ky_mat[n:, n:] += sigma_n ** 2 * np.eye(size3-n) return ky_mat -def get_kernel_vector_unit(training_data, kernel, x, - d_1, hyps, - cutoffs=None, hyps_mask=None): +def get_kernel_vector_unit(name, s, e, x, d_1, kernel, hyps, + cutoffs, hyps_mask): """ Compute kernel vector, comparing input environment to all environments in the GP's training set. @@ -836,26 +737,23 @@ def get_kernel_vector_unit(training_data, kernel, x, :rtype: np.ndarray """ - + size = (e-s) ds = [1, 2, 3] - size = len(training_data) * 3 - k_v = np.zeros(size, ) + + args = from_mask_to_args(hyps, hyps_mask, cutoffs) + + k_v = np.zeros(size*3, ) for m_index in range(size): - x_2 = training_data[int(math.floor(m_index / 3))] - d_2 = ds[m_index % 3] - if (hyps_mask is not None): - k_v[m_index] = kernel(x, x_2, d_1, d_2, - hyps, cutoffs, hyps_mask=hyps_mask) - else: - k_v[m_index] = kernel(x, x_2, d_1, d_2, - hyps, cutoffs) + x_2 = _global_training_data[name][m_index+s] + for d_2 in ds: + k_v[m_index*3+d_2-1] = kernel(x, x_2, d_1, d_2, *args) + return k_v -def get_kernel_vector_par(training_data, kernel, - x, d_1, hyps, - cutoffs=None, hyps_mask=None, - n_cpus=None, nsample=100): +def get_kernel_vector(name, kernel, x, d_1, hyps, + cutoffs=None, hyps_mask=None, + n_cpus=1, n_sample=100): """ Compute kernel vector, comparing input environment to all environments in the GP's training set. @@ -869,79 +767,147 @@ def get_kernel_vector_par(training_data, kernel, :type cutoffs: list of 2 float numbers :param hyps_mask: dictionary used for multi-group hyperparmeters :param n_cpus: number of cpus to use. - :param nsample: the size of block for matrix to compute + :param n_sample: the size of block for matrix to compute :return: kernel vector :rtype: np.ndarray """ + size = len(_global_training_data[name]) + if (n_cpus is None): n_cpus = mp.cpu_count() if (n_cpus == 1): - return get_kernel_vector(training_data, kernel, - x, d_1, hyps, - cutoffs, hyps_mask) - - with mp.Pool(processes=n_cpus) as pool: - - # sort of partition - size = len(training_data) - ns = int(math.ceil(size/nsample/n_cpus)*n_cpus) - nsample = int(math.ceil(size/ns)) - ns = int(math.ceil(size/nsample)) - - k12_slice = [] - for ibatch in range(ns): - s = nsample*ibatch - e = np.min([s + nsample, size]) - k12_slice.append(pool.apply_async(get_kernel_vector_unit, - args=(training_data[s: e], - kernel, x, d_1, hyps, - cutoffs, hyps_mask))) - - size3 = size*3 - nsample3 = nsample*3 - k12_v = np.zeros(size3) - for ibatch in range(ns): - s = nsample3*ibatch - e = np.min([s + nsample3, size3]) - k12_v[s:e] = k12_slice[ibatch].get() - pool.close() - pool.join() + return get_kernel_vector_unit( + name, 0, size, x, d_1, kernel, hyps, + cutoffs, hyps_mask) + + block_id, nbatch = partition_c(n_sample, size, n_cpus) + + result_queue = mp.Queue() + children = [] + for wid in range(nbatch): + s, e = block_id[wid] + children.append( + mp.Process( + target=queue_wrapper, + args=(result_queue, wid, + get_kernel_vector_unit, + (name, s, e, x, d_1, kernel, hyps, + cutoffs, hyps_mask + ) + ) + ) + ) + + # Run child processes. + for c in children: + c.start() + + # Wait for all results to arrive. + k12_v = np.zeros(size*3) + for _ in range(nbatch): + wid, result_chunk = result_queue.get(block=True) + s, e = block_id[wid] + k12_v[s*3:e*3] = result_chunk + + # Join child processes (clean up zombies). + for c in children: + c.join() return k12_v -def get_kernel_vector(training_data, kernel, - x, d_1: int, - hyps, cutoffs=None, hyps_mask=None): + +def en_kern_vec_unit(name, s, e, x, kernel, + hyps, cutoffs=None, hyps_mask=None): """ - Compute kernel vector, comparing input environment to all environments + Compute energy kernel vector, comparing input environment to all environments in the GP's training set. - + :param training_data: Set of atomic environments to compare against + :param kernel: :param x: data point to compare against kernel matrix :type x: AtomicEnvironment - :param d_1: Cartesian component of force vector to get (1=x,2=y,3=z) - :type d_1: int :param hyps: list of hyper-parameters :param cutoffs: The cutoff values used for the atomic environments :type cutoffs: list of 2 float numbers :param hyps_mask: dictionary used for multi-group hyperparmeters + :return: kernel vector + :rtype: np.ndarray """ + training_data = _global_training_data[name] + ds = [1, 2, 3] - size = len(training_data) * 3 + size = (e-s) * 3 k_v = np.zeros(size, ) + args = from_mask_to_args(hyps, hyps_mask, cutoffs) + for m_index in range(size): - x_2 = training_data[int(math.floor(m_index / 3))] + x_2 = training_data[int(math.floor(m_index / 3))+s] d_2 = ds[m_index % 3] - if (hyps_mask is not None): - k_v[m_index] = kernel(x, x_2, d_1, d_2, - hyps, cutoffs, - hyps_mask=hyps_mask) - else: - k_v[m_index] = kernel(x, x_2, d_1, d_2, - hyps, cutoffs) + k_v[m_index] = kernel(x_2, x, d_2, *args) return k_v + + +def en_kern_vec(name, kernel, + x, hyps, + cutoffs=None, hyps_mask=None, + n_cpus=1, n_sample=100): + """ + Compute kernel vector, comparing input environment to all environments + in the GP's training set. + + :param x: data point to compare against kernel matrix + :type x: AtomicEnvironment + :param hyps: list of hyper-parameters + :param cutoffs: The cutoff values used for the atomic environments + :type cutoffs: list of 2 float numbers + :param hyps_mask: dictionary used for multi-group hyperparmeters + :param n_cpus: number of cpus to use. + :param n_sample: the size of block for matrix to compute + + :return: kernel vector + :rtype: np.ndarray + """ + + training_data = _global_training_data[name] + size = len(training_data) + + if (n_cpus is None): + n_cpus = mp.cpu_count() + if (n_cpus == 1): + return en_kern_vec_unit( + name, 0, size, x, kernel, hyps, + cutoffs, hyps_mask) + + block_id, nbatch = partition_c(n_sample, size, n_cpus) + result_queue = mp.Queue() + children = [] + for wid in range(nbatch): + children.append( + mp.Process( + target=queue_wrapper, + args=(result_queue, wid, + en_kern_vec_unit, + (name, block_id[wid][0], block_id[wid][1], x, + kernel, hyps, cutoffs, hyps_mask)))) + + # Run child processes. + for c in children: + c.start() + + # Wait for all results to arrive. + k12_v = np.zeros(size*3) + for _ in range(nbatch): + wid, result_chunk = result_queue.get(block=True) + s, e = block_id[wid] + k12_v[s*3:e*3] = result_chunk + + # Join child processes (clean up zombies). + for c in children: + c.join() + + return k12_v diff --git a/flare/gp_from_aimd.py b/flare/gp_from_aimd.py index ee8512677..c34766a22 100644 --- a/flare/gp_from_aimd.py +++ b/flare/gp_from_aimd.py @@ -34,34 +34,37 @@ """ import time from copy import deepcopy -from typing import List, Tuple +from typing import List, Tuple, Union import numpy as np from math import inf +import warnings from flare.env import AtomicEnvironment from flare.gp import GaussianProcess from flare.output import Output -from flare.predict import predict_on_structure, \ - predict_on_structure_par, predict_on_structure_en, \ - predict_on_structure_par_en +from flare.predict import predict_on_atom, predict_on_atom_en, \ + predict_on_structure_par, predict_on_structure_par_en from flare.struc import Structure from flare.util import element_to_Z, \ - is_std_in_bound_per_species, is_force_in_bound_per_species + is_std_in_bound_per_species, is_force_in_bound_per_species, \ + Z_to_element, subset_of_frame_by_element +from flare.mgp.otf import predict_on_structure_mgp +from flare.mgp.mgp_en import MappedGaussianProcess class TrajectoryTrainer: def __init__(self, frames: List[Structure], - gp: GaussianProcess, + 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 = None, + n_cpus: int = 1, skip: int = 1, - validate_ratio: float = 0.1, + validate_ratio: float = 0.0, calculate_energy: bool = False, output_name: str = 'gp_from_aimd', pre_train_max_iter: int = 50, @@ -69,14 +72,17 @@ def __init__(self, frames: List[Structure], max_trains: int = np.inf, min_atoms_per_train: int = 1, shuffle_frames: bool = False, - verbose: int = 0, + 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, - checkpoint_interval: int = 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 @@ -94,17 +100,18 @@ def __init__(self, frames: List[Structure], :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 + :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 + :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, 1: Minimal, 2: Lots of information + :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 @@ -112,7 +119,19 @@ def __init__(self, frames: List[Structure], 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 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 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 """ @@ -123,6 +142,8 @@ def __init__(self, frames: List[Structure], # 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 @@ -130,38 +151,41 @@ def __init__(self, frames: List[Structure], 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.parallel = parallel + 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 (parallel and gp.par and gp.per_atom_par): + if not self.mgp: 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 + + elif self.mgp: + self.pred_func = predict_on_structure_mgp # 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." + "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 + 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 \ @@ -170,9 +194,9 @@ def __init__(self, frames: List[Structure], 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 + 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 + is None else train_atoms_per_element # Convert to Coded Species if self.pre_train_env_per_species: @@ -182,9 +206,15 @@ def __init__(self, frames: List[Structure], 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 + 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 @@ -203,15 +233,21 @@ def pre_run(self): 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)) + 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=self.frames[0], + std_tolerance=(self.rel_std_tolerance, + self.abs_std_tolerance), + optional={ + 'GP Statistics': self.gp.training_statistics}) self.start_time = time.time() if self.verbose >= 3: @@ -237,6 +273,7 @@ def pre_run(self): 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:] @@ -258,12 +295,17 @@ def pre_run(self): train_atoms.append(atom) atom_count += 1 - self.update_gp_and_print(frame, train_atoms, train=False) + self.update_gp_and_print(frame=frame, + train_atoms=train_atoms, + uncertainties=[], train=False) if self.verbose >= 3 and atom_count > 0: - print(f"Added {atom_count} atoms to pretrain") + self.output.write_to_log(f"Added {atom_count} atoms to pretrain\n" \ + f"In total {len(self.gp.training_data)} atoms", + flush=True) - if (self.seed_envs or atom_count or self.seed_frames) and self.max_trains>0: + 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)") @@ -274,9 +316,9 @@ def pre_run(self): "non-empty training set)") self.gp.set_L_alpha() - if self.model_format: + if self.model_format and not self.mgp: self.gp.write_model(f'{self.output_name}_prerun', - self.model_format) + self.model_format) def run(self): """ @@ -287,38 +329,82 @@ def run(self): :return: None """ + + # Perform pre-run, in which seed trames are used. if self.verbose >= 3: print("Commencing run with pre-run...") - self.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)) - train_frame = int(len(self.frames) * (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 - # 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) + 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: - # Get max uncertainty atoms + # 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=self.gp.hyps[-1], structure=cur_frame, + noise=noise, structure=dummy_frame, max_atoms_added=self.max_atoms_from_frame, max_by_species=self.train_env_per_species) @@ -326,50 +412,75 @@ def run(self): force_in_bound, force_train_atoms = \ is_force_in_bound_per_species( abs_force_tolerance=self.abs_force_tolerance, - predicted_forces=cur_frame.forces, + predicted_forces=pred_forces, label_forces=dft_forces, - structure=cur_frame, + 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): + 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=False) - nsample += len(train_atoms) + 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 nsample >= self.min_atoms_per_train or ( + + 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() - nsample = 0 + cur_atoms_added_train = 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: + # 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() - self.output.conclude_run() + if self.verbose: + self.output.conclude_run() - if self.model_format: + if self.model_format and not self.mgp: self.gp.write_model(f'{self.output_name}_model', - self.model_format) + 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 @@ -377,22 +488,43 @@ def update_gp_and_print(self, frame: Structure, train_atoms: List[int], 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 """ - 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() + # 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(f'\nAdding atom(s) {added_atoms}' + ' 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): """ @@ -420,7 +552,10 @@ def train_gp(self, max_iter: int = None): 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) + 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 diff --git a/flare/kernels/kernels.py b/flare/kernels/kernels.py index dc11d1c0f..fe65ed663 100644 --- a/flare/kernels/kernels.py +++ b/flare/kernels/kernels.py @@ -1,1013 +1,9 @@ -"""Single element 2-, 3-, and 2+3-body kernels. -The kernel functions to choose: - -* Two body: - - * two_body: force kernel - * two_body_en: energy kernel - * two_body_grad: gradient of kernel function - * two_body_force_en: energy force kernel - -* Three body: - - * three_body, - * three_body_grad, - * three_body_en, - * three_body_force_en, - -* Two plus three body: - - * two_plus_three_body, - * two_plus_three_body_grad, - * two_plus_three_en, - * two_plus_three_force_en - -**Example:** - ->>> gp_model = GaussianProcess(kernel=two_body, - kernel_grad=two_body_grad, - energy_force_kernel=two_body_force_en, - energy_kernel=two_body_en, - ) -""" - - import numpy as np from math import exp from flare.env import AtomicEnvironment from numba import njit import flare.cutoffs as cf - -# ----------------------------------------------------------------------------- -# two plus three body kernels -# ----------------------------------------------------------------------------- - - -def two_plus_three_body(env1: AtomicEnvironment, env2: AtomicEnvironment, - d1: int, d2: int, hyps, cutoffs, - cutoff_func=cf.quadratic_cutoff): - """2+3-body single-element kernel between two force components. - - Args: - env1 (AtomicEnvironment): First local environment. - env2 (AtomicEnvironment): Second local environment. - d1 (int): Force component of the first environment. - d2 (int): Force component of the second environment. - hyps (np.ndarray): Hyperparameters of the kernel function (sig1, ls1, - sig2, ls2, sig_n). - cutoffs (np.ndarray): Two-element array containing the 2- and 3-body - cutoffs. - cutoff_func (Callable): Cutoff function of the kernel. - - Return: - float: Value of the 2+3-body kernel. - """ - - two_term = two_body_jit(env1.bond_array_2, env2.bond_array_2, - d1, d2, hyps[0], hyps[1], cutoffs[0], cutoff_func) - - three_term = \ - three_body_jit(env1.bond_array_3, env2.bond_array_3, - env1.cross_bond_inds, env2.cross_bond_inds, - env1.cross_bond_dists, env2.cross_bond_dists, - env1.triplet_counts, env2.triplet_counts, - d1, d2, hyps[2], hyps[3], cutoffs[1], cutoff_func) - return two_term + three_term - - -def two_plus_three_body_grad(env1, env2, d1, d2, hyps, cutoffs, - cutoff_func=cf.quadratic_cutoff): - """2+3-body single-element kernel between two force components and its - gradient with respect to the hyperparameters. - - Args: - env1 (AtomicEnvironment): First local environment. - env2 (AtomicEnvironment): Second local environment. - d1 (int): Force component of the first environment. - d2 (int): Force component of the second environment. - hyps (np.ndarray): Hyperparameters of the kernel function (sig1, ls1, - sig2, ls2, sig_n). - cutoffs (np.ndarray): Two-element array containing the 2- and 3-body - cutoffs. - cutoff_func (Callable): Cutoff function of the kernel. - - Return: - (float, np.ndarray): Value of the 2+3-body kernel and its gradient - with respect to the hyperparameters. - """ - - kern2, ls2, sig2 = \ - two_body_grad_jit(env1.bond_array_2, env2.bond_array_2, - d1, d2, hyps[0], hyps[1], cutoffs[0], cutoff_func) - - kern3, sig3, ls3 = \ - three_body_grad_jit(env1.bond_array_3, env2.bond_array_3, - env1.cross_bond_inds, env2.cross_bond_inds, - env1.cross_bond_dists, env2.cross_bond_dists, - env1.triplet_counts, env2.triplet_counts, - d1, d2, hyps[2], hyps[3], cutoffs[1], cutoff_func) - - return kern2 + kern3, np.array([sig2, ls2, sig3, ls3]) - - -def two_plus_three_force_en(env1, env2, d1, hyps, cutoffs, - cutoff_func=cf.quadratic_cutoff): - """2+3-body single-element kernel between a force component and a local - energy. - - Args: - env1 (AtomicEnvironment): Local environment associated with the - force component. - env2 (AtomicEnvironment): Local environment associated with the - local energy. - d1 (int): Force component of the first environment. - hyps (np.ndarray): Hyperparameters of the kernel function (sig1, ls1, - sig2, ls2). - cutoffs (np.ndarray): Two-element array containing the 2- and 3-body - cutoffs. - cutoff_func (Callable): Cutoff function of the kernel. - - Return: - float: Value of the 2+3-body force/energy kernel. - """ - - two_term = two_body_force_en_jit(env1.bond_array_2, env2.bond_array_2, - d1, hyps[0], hyps[1], cutoffs[0], - cutoff_func)/2 - - three_term = \ - three_body_force_en_jit(env1.bond_array_3, env2.bond_array_3, - env1.cross_bond_inds, env2.cross_bond_inds, - env1.cross_bond_dists, - env2.cross_bond_dists, - env1.triplet_counts, env2.triplet_counts, - d1, hyps[2], hyps[3], cutoffs[1], - cutoff_func)/3 - - return two_term + three_term - - -def two_plus_three_en(env1, env2, hyps, cutoffs, - cutoff_func=cf.quadratic_cutoff): - """2+3-body single-element kernel between two local energies. - - Args: - env1 (AtomicEnvironment): First local environment. - env2 (AtomicEnvironment): Second local environment. - hyps (np.ndarray): Hyperparameters of the kernel function (sig1, ls1, - sig2, ls2). - cutoffs (np.ndarray): Two-element array containing the 2- and 3-body - cutoffs. - cutoff_func (Callable): Cutoff function of the kernel. - - Return: - float: Value of the 2+3-body force/energy kernel. - """ - - two_term = two_body_en_jit(env1.bond_array_2, env2.bond_array_2, - hyps[0], hyps[1], cutoffs[0], cutoff_func) - - three_term = \ - three_body_en_jit(env1.bond_array_3, env2.bond_array_3, - env1.cross_bond_inds, env2.cross_bond_inds, - env1.cross_bond_dists, env2.cross_bond_dists, - env1.triplet_counts, env2.triplet_counts, - hyps[2], hyps[3], cutoffs[1], cutoff_func) - - return two_term + three_term - - -# ----------------------------------------------------------------------------- -# two body kernels -# ----------------------------------------------------------------------------- - - -def two_body(env1, env2, d1, d2, hyps, cutoffs, - cutoff_func=cf.quadratic_cutoff): - """2-body single-element kernel between two force components. - - Args: - env1 (AtomicEnvironment): First local environment. - env2 (AtomicEnvironment): Second local environment. - d1 (int): Force component of the first environment. - d2 (int): Force component of the second environment. - hyps (np.ndarray): Hyperparameters of the kernel function (sig, ls). - cutoffs (np.ndarray): One-element array containing the 2-body - cutoff. - cutoff_func (Callable): Cutoff function of the kernel. - - Return: - float: Value of the 2-body kernel. - """ - - sig = hyps[0] - ls = hyps[1] - r_cut = cutoffs[0] - - return two_body_jit(env1.bond_array_2, env2.bond_array_2, - d1, d2, sig, ls, r_cut, cutoff_func) - - -def two_body_grad(env1, env2, d1, d2, hyps, cutoffs, - cutoff_func=cf.quadratic_cutoff): - """2-body single-element kernel between two force components and its - gradient with respect to the hyperparameters. - - Args: - env1 (AtomicEnvironment): First local environment. - env2 (AtomicEnvironment): Second local environment. - d1 (int): Force component of the first environment. - d2 (int): Force component of the second environment. - hyps (np.ndarray): Hyperparameters of the kernel function (sig, ls). - cutoffs (np.ndarray): One-element array containing the 2-body - cutoff. - cutoff_func (Callable): Cutoff function of the kernel. - - Return: - (float, np.ndarray): Value of the 2-body kernel and its gradient - with respect to the hyperparameters. - """ - - sig = hyps[0] - ls = hyps[1] - r_cut = cutoffs[0] - - kernel, ls_derv, sig_derv = \ - two_body_grad_jit(env1.bond_array_2, env2.bond_array_2, - d1, d2, sig, ls, r_cut, cutoff_func) - kernel_grad = np.array([sig_derv, ls_derv]) - return kernel, kernel_grad - - -def two_body_force_en(env1, env2, d1, hyps, cutoffs, - cutoff_func=cf.quadratic_cutoff): - """2-body single-element kernel between a force component and a local - energy. - - Args: - env1 (AtomicEnvironment): Local environment associated with the - force component. - env2 (AtomicEnvironment): Local environment associated with the - local energy. - d1 (int): Force component of the first environment. - hyps (np.ndarray): Hyperparameters of the kernel function (sig, ls). - cutoffs (np.ndarray): One-element array containing the 2-body - cutoff. - cutoff_func (Callable): Cutoff function of the kernel. - - Return: - float: Value of the 2-body force/energy kernel. - """ - - sig = hyps[0] - ls = hyps[1] - r_cut = cutoffs[0] - - # divide by two to account for double counting - return two_body_force_en_jit(env1.bond_array_2, env2.bond_array_2, - d1, sig, ls, r_cut, cutoff_func)/2 - - -def two_body_en(env1, env2, hyps, cutoffs, - cutoff_func=cf.quadratic_cutoff): - """2-body single-element kernel between two local energies. - - Args: - env1 (AtomicEnvironment): First local environment. - env2 (AtomicEnvironment): Second local environment. - hyps (np.ndarray): Hyperparameters of the kernel function (sig, ls). - cutoffs (np.ndarray): One-element array containing the 2-body - cutoff. - cutoff_func (Callable): Cutoff function of the kernel. - - Return: - float: Value of the 2-body force/energy kernel. - """ - sig = hyps[0] - ls = hyps[1] - r_cut = cutoffs[0] - - return two_body_en_jit(env1.bond_array_2, env2.bond_array_2, - sig, ls, r_cut, cutoff_func) - - -# ----------------------------------------------------------------------------- -# three body kernels -# ----------------------------------------------------------------------------- - - -def three_body(env1, env2, d1, d2, hyps, cutoffs, - cutoff_func=cf.quadratic_cutoff): - """3-body single-element kernel between two force components. - - Args: - env1 (AtomicEnvironment): First local environment. - env2 (AtomicEnvironment): Second local environment. - d1 (int): Force component of the first environment. - d2 (int): Force component of the second environment. - hyps (np.ndarray): Hyperparameters of the kernel function (sig, ls). - cutoffs (np.ndarray): Two-element array containing the 2- and 3-body - cutoffs. - cutoff_func (Callable): Cutoff function of the kernel. - - Return: - float: Value of the 3-body kernel. - """ - - sig = hyps[0] - ls = hyps[1] - r_cut = cutoffs[1] - - return three_body_jit(env1.bond_array_3, env2.bond_array_3, - env1.cross_bond_inds, env2.cross_bond_inds, - env1.cross_bond_dists, env2.cross_bond_dists, - env1.triplet_counts, env2.triplet_counts, - d1, d2, sig, ls, r_cut, cutoff_func) - - -def three_body_grad(env1, env2, d1, d2, hyps, cutoffs, - cutoff_func=cf.quadratic_cutoff): - """3-body single-element kernel between two force components and its - gradient with respect to the hyperparameters. - - Args: - env1 (AtomicEnvironment): First local environment. - env2 (AtomicEnvironment): Second local environment. - d1 (int): Force component of the first environment. - d2 (int): Force component of the second environment. - hyps (np.ndarray): Hyperparameters of the kernel function (sig, ls). - cutoffs (np.ndarray): Two-element array containing the 2- and 3-body - cutoffs. - cutoff_func (Callable): Cutoff function of the kernel. - - Return: - (float, np.ndarray): Value of the 3-body kernel and its gradient - with respect to the hyperparameters. - """ - sig = hyps[0] - ls = hyps[1] - r_cut = cutoffs[1] - - kernel, sig_derv, ls_derv = \ - three_body_grad_jit(env1.bond_array_3, env2.bond_array_3, - env1.cross_bond_inds, env2.cross_bond_inds, - env1.cross_bond_dists, env2.cross_bond_dists, - env1.triplet_counts, env2.triplet_counts, - d1, d2, sig, ls, r_cut, cutoff_func) - - kernel_grad = np.array([sig_derv, ls_derv]) - - return kernel, kernel_grad - - -def three_body_force_en(env1, env2, d1, hyps, cutoffs, - cutoff_func=cf.quadratic_cutoff): - """3-body single-element kernel between a force component and a local - energy. - - Args: - env1 (AtomicEnvironment): Local environment associated with the - force component. - env2 (AtomicEnvironment): Local environment associated with the - local energy. - d1 (int): Force component of the first environment. - hyps (np.ndarray): Hyperparameters of the kernel function (sig, ls). - cutoffs (np.ndarray): Two-element array containing the 2- and 3-body - cutoffs. - cutoff_func (Callable): Cutoff function of the kernel. - - Return: - float: Value of the 3-body force/energy kernel. - """ - sig = hyps[0] - ls = hyps[1] - r_cut = cutoffs[1] - - # divide by three to account for triple counting - return three_body_force_en_jit(env1.bond_array_3, env2.bond_array_3, - env1.cross_bond_inds, env2.cross_bond_inds, - env1.cross_bond_dists, - env2.cross_bond_dists, - env1.triplet_counts, env2.triplet_counts, - d1, sig, ls, r_cut, cutoff_func)/3 - - -def three_body_en(env1, env2, hyps, cutoffs, - cutoff_func=cf.quadratic_cutoff): - """3-body single-element kernel between two local energies. - - Args: - env1 (AtomicEnvironment): First local environment. - env2 (AtomicEnvironment): Second local environment. - hyps (np.ndarray): Hyperparameters of the kernel function (sig, ls). - cutoffs (np.ndarray): Two-element array containing the 2- and 3-body - cutoffs. - cutoff_func (Callable): Cutoff function of the kernel. - - Return: - float: Value of the 3-body force/energy kernel. - """ - sig = hyps[0] - ls = hyps[1] - r_cut = cutoffs[1] - - return three_body_en_jit(env1.bond_array_3, env2.bond_array_3, - env1.cross_bond_inds, env2.cross_bond_inds, - env1.cross_bond_dists, env2.cross_bond_dists, - env1.triplet_counts, env2.triplet_counts, - sig, ls, r_cut, cutoff_func) - - -# ----------------------------------------------------------------------------- -# two body numba functions -# ----------------------------------------------------------------------------- - - -@njit -def two_body_jit(bond_array_1, bond_array_2, d1, d2, sig, ls, - r_cut, cutoff_func): - """2-body single-element kernel between two force components accelerated - with Numba. - - Args: - bond_array_1 (np.ndarray): 2-body bond array of the first local - environment. - bond_array_2 (np.ndarray): 2-body bond array of the second local - environment. - d1 (int): Force component of the first environment (1=x, 2=y, 3=z). - d2 (int): Force component of the second environment (1=x, 2=y, 3=z). - sig (float): 2-body signal variance hyperparameter. - ls (float): 2-body length scale hyperparameter. - r_cut (float): 2-body cutoff radius. - cutoff_func (Callable): Cutoff function. - - Return: - float: Value of the 2-body kernel. - """ - kern = 0 - - ls1 = 1 / (2 * ls * ls) - ls2 = 1 / (ls * ls) - ls3 = ls2 * ls2 - sig2 = sig*sig - - for m in range(bond_array_1.shape[0]): - ri = bond_array_1[m, 0] - ci = bond_array_1[m, d1] - fi, fdi = cutoff_func(r_cut, ri, ci) - - for n in range(bond_array_2.shape[0]): - rj = bond_array_2[n, 0] - cj = bond_array_2[n, d2] - fj, fdj = cutoff_func(r_cut, rj, cj) - r11 = ri - rj - - A = ci * cj - B = r11 * ci - C = r11 * cj - D = r11 * r11 - - kern += force_helper(A, B, C, D, fi, fj, fdi, fdj, ls1, ls2, - ls3, sig2) - - return kern - - -@njit -def two_body_grad_jit(bond_array_1, bond_array_2, d1, d2, sig, ls, - r_cut, cutoff_func): - """2-body single-element kernel between two force components and its - gradient with respect to the hyperparameters. - - Args: - bond_array_1 (np.ndarray): 2-body bond array of the first local - environment. - bond_array_2 (np.ndarray): 2-body bond array of the second local - environment. - d1 (int): Force component of the first environment (1=x, 2=y, 3=z). - d2 (int): Force component of the second environment (1=x, 2=y, 3=z). - sig (float): 2-body signal variance hyperparameter. - ls (float): 2-body length scale hyperparameter. - r_cut (float): 2-body cutoff radius. - cutoff_func (Callable): Cutoff function. - - Returns: - (float, float): - Value of the 2-body kernel and its gradient with respect to the - hyperparameters. - """ - - kern = 0 - sig_derv = 0 - ls_derv = 0 - - sig2, sig3, ls1, ls2, ls3, ls4, ls5, ls6 = grad_constants(sig, ls) - - for m in range(bond_array_1.shape[0]): - ri = bond_array_1[m, 0] - ci = bond_array_1[m, d1] - fi, fdi = cutoff_func(r_cut, ri, ci) - - for n in range(bond_array_2.shape[0]): - rj = bond_array_2[n, 0] - cj = bond_array_2[n, d2] - fj, fdj = cutoff_func(r_cut, rj, cj) - - r11 = ri - rj - - A = ci * cj - B = r11 * ci - C = r11 * cj - D = r11 * r11 - - kern_term, sig_term, ls_term = \ - grad_helper(A, B, C, D, fi, fj, fdi, fdj, ls1, ls2, ls3, ls4, - ls5, ls6, sig2, sig3) - - kern += kern_term - sig_derv += sig_term - ls_derv += ls_term - - return kern, ls_derv, sig_derv - - -@njit -def two_body_force_en_jit(bond_array_1, bond_array_2, d1, sig, ls, r_cut, - cutoff_func): - """2-body single-element kernel between a force component and a local - energy accelerated with Numba. - - Args: - bond_array_1 (np.ndarray): 2-body bond array of the first local - environment. - bond_array_2 (np.ndarray): 2-body bond array of the second local - environment. - d1 (int): Force component of the first environment (1=x, 2=y, 3=z). - sig (float): 2-body signal variance hyperparameter. - ls (float): 2-body length scale hyperparameter. - r_cut (float): 2-body cutoff radius. - cutoff_func (Callable): Cutoff function. - - Returns: - float: - Value of the 2-body force/energy kernel. - """ - kern = 0 - - ls1 = 1 / (2 * ls * ls) - ls2 = 1 / (ls * ls) - sig2 = sig*sig - - for m in range(bond_array_1.shape[0]): - ri = bond_array_1[m, 0] - ci = bond_array_1[m, d1] - fi, fdi = cutoff_func(r_cut, ri, ci) - - for n in range(bond_array_2.shape[0]): - rj = bond_array_2[n, 0] - fj, _ = cutoff_func(r_cut, rj, 0) - - r11 = ri - rj - B = r11 * ci - D = r11 * r11 - kern += force_energy_helper(B, D, fi, fj, fdi, ls1, ls2, sig2) - - return kern - - -@njit -def two_body_en_jit(bond_array_1, bond_array_2, sig, ls, r_cut, cutoff_func): - """2-body single-element kernel between two local energies accelerated - with Numba. - - Args: - bond_array_1 (np.ndarray): 2-body bond array of the first local - environment. - bond_array_2 (np.ndarray): 2-body bond array of the second local - environment. - sig (float): 2-body signal variance hyperparameter. - ls (float): 2-body length scale hyperparameter. - r_cut (float): 2-body cutoff radius. - cutoff_func (Callable): Cutoff function. - - Returns: - float: - Value of the 2-body local energy kernel. - """ - kern = 0 - - ls1 = 1 / (2 * ls * ls) - sig2 = sig * sig - - for m in range(bond_array_1.shape[0]): - ri = bond_array_1[m, 0] - fi, _ = cutoff_func(r_cut, ri, 0) - - for n in range(bond_array_2.shape[0]): - rj = bond_array_2[n, 0] - fj, _ = cutoff_func(r_cut, rj, 0) - r11 = ri - rj - kern += fi * fj * sig2 * exp(-r11 * r11 * ls1) - - return kern - - -# ----------------------------------------------------------------------------- -# three body numba functions -# ----------------------------------------------------------------------------- - - -@njit -def three_body_jit(bond_array_1, bond_array_2, - cross_bond_inds_1, cross_bond_inds_2, - cross_bond_dists_1, cross_bond_dists_2, - triplets_1, triplets_2, - d1, d2, sig, ls, r_cut, cutoff_func): - """3-body single-element kernel between two force components accelerated - with Numba. - - Args: - bond_array_1 (np.ndarray): 3-body bond array of the first local - environment. - bond_array_2 (np.ndarray): 3-body bond array of the second local - environment. - cross_bond_inds_1 (np.ndarray): Two dimensional array whose row m - contains the indices of atoms n > m in the first local - environment that are within a distance r_cut of both atom n and - the central atom. - cross_bond_inds_2 (np.ndarray): Two dimensional array whose row m - contains the indices of atoms n > m in the second local - environment that are within a distance r_cut of both atom n and - the central atom. - cross_bond_dists_1 (np.ndarray): Two dimensional array whose row m - contains the distances from atom m of atoms n > m in the first - local environment that are within a distance r_cut of both atom - n and the central atom. - cross_bond_dists_2 (np.ndarray): Two dimensional array whose row m - contains the distances from atom m of atoms n > m in the second - local environment that are within a distance r_cut of both atom - n and the central atom. - triplets_1 (np.ndarray): One dimensional array of integers whose entry - m is the number of atoms in the first local environment that are - within a distance r_cut of atom m. - triplets_2 (np.ndarray): One dimensional array of integers whose entry - m is the number of atoms in the second local environment that are - within a distance r_cut of atom m. - d1 (int): Force component of the first environment. - d2 (int): Force component of the second environment. - sig (float): 3-body signal variance hyperparameter. - ls (float): 3-body length scale hyperparameter. - r_cut (float): 3-body cutoff radius. - cutoff_func (Callable): Cutoff function. - - Return: - float: Value of the 3-body kernel. - """ - kern = 0 - - # pre-compute constants that appear in the inner loop - sig2 = sig*sig - ls1 = 1 / (2*ls*ls) - ls2 = 1 / (ls*ls) - ls3 = ls2*ls2 - - for m in range(bond_array_1.shape[0]): - ri1 = bond_array_1[m, 0] - ci1 = bond_array_1[m, d1] - fi1, fdi1 = cutoff_func(r_cut, ri1, ci1) - - for n in range(triplets_1[m]): - ind1 = cross_bond_inds_1[m, m+n+1] - ri2 = bond_array_1[ind1, 0] - ci2 = bond_array_1[ind1, d1] - fi2, fdi2 = cutoff_func(r_cut, ri2, ci2) - - ri3 = cross_bond_dists_1[m, m+n+1] - fi3, _ = cutoff_func(r_cut, ri3, 0) - - fi = fi1*fi2*fi3 - fdi = fdi1*fi2*fi3+fi1*fdi2*fi3 - - for p in range(bond_array_2.shape[0]): - rj1 = bond_array_2[p, 0] - cj1 = bond_array_2[p, d2] - fj1, fdj1 = cutoff_func(r_cut, rj1, cj1) - - for q in range(triplets_2[p]): - ind2 = cross_bond_inds_2[p, p+1+q] - rj2 = bond_array_2[ind2, 0] - cj2 = bond_array_2[ind2, d2] - fj2, fdj2 = cutoff_func(r_cut, rj2, cj2) - - rj3 = cross_bond_dists_2[p, p+1+q] - fj3, _ = cutoff_func(r_cut, rj3, 0) - - fj = fj1*fj2*fj3 - fdj = fdj1*fj2*fj3+fj1*fdj2*fj3 - - kern += triplet_kernel(ci1, ci2, cj1, cj2, ri1, ri2, ri3, - rj1, rj2, rj3, fi, fj, fdi, fdj, - ls1, ls2, ls3, sig2) - return kern - - -@njit -def three_body_grad_jit(bond_array_1, bond_array_2, - cross_bond_inds_1, cross_bond_inds_2, - cross_bond_dists_1, cross_bond_dists_2, - triplets_1, triplets_2, - d1, d2, sig, ls, r_cut, cutoff_func): - """3-body single-element kernel between two force components and its - gradient with respect to the hyperparameters. - - Args: - bond_array_1 (np.ndarray): 3-body bond array of the first local - environment. - bond_array_2 (np.ndarray): 3-body bond array of the second local - environment. - cross_bond_inds_1 (np.ndarray): Two dimensional array whose row m - contains the indices of atoms n > m in the first local - environment that are within a distance r_cut of both atom n and - the central atom. - cross_bond_inds_2 (np.ndarray): Two dimensional array whose row m - contains the indices of atoms n > m in the second local - environment that are within a distance r_cut of both atom n and - the central atom. - cross_bond_dists_1 (np.ndarray): Two dimensional array whose row m - contains the distances from atom m of atoms n > m in the first - local environment that are within a distance r_cut of both atom - n and the central atom. - cross_bond_dists_2 (np.ndarray): Two dimensional array whose row m - contains the distances from atom m of atoms n > m in the second - local environment that are within a distance r_cut of both atom - n and the central atom. - triplets_1 (np.ndarray): One dimensional array of integers whose entry - m is the number of atoms in the first local environment that are - within a distance r_cut of atom m. - triplets_2 (np.ndarray): One dimensional array of integers whose entry - m is the number of atoms in the second local environment that are - within a distance r_cut of atom m. - d1 (int): Force component of the first environment. - d2 (int): Force component of the second environment. - sig (float): 3-body signal variance hyperparameter. - ls (float): 3-body length scale hyperparameter. - r_cut (float): 3-body cutoff radius. - cutoff_func (Callable): Cutoff function. - - Returns: - (float, float): - Value of the 3-body kernel and its gradient with respect to the - hyperparameters. - """ - - kern = 0 - sig_derv = 0 - ls_derv = 0 - - # pre-compute constants that appear in the inner loop - sig2, sig3, ls1, ls2, ls3, ls4, ls5, ls6 = grad_constants(sig, ls) - - for m in range(bond_array_1.shape[0]): - ri1 = bond_array_1[m, 0] - ci1 = bond_array_1[m, d1] - fi1, fdi1 = cutoff_func(r_cut, ri1, ci1) - - for n in range(triplets_1[m]): - ind1 = cross_bond_inds_1[m, m+n+1] - ri3 = cross_bond_dists_1[m, m+n+1] - ri2 = bond_array_1[ind1, 0] - ci2 = bond_array_1[ind1, d1] - - fi2, fdi2 = cutoff_func(r_cut, ri2, ci2) - fi3, _ = cutoff_func(r_cut, ri3, 0) - - fi = fi1*fi2*fi3 - fdi = fdi1*fi2*fi3+fi1*fdi2*fi3 - - for p in range(bond_array_2.shape[0]): - rj1 = bond_array_2[p, 0] - cj1 = bond_array_2[p, d2] - fj1, fdj1 = cutoff_func(r_cut, rj1, cj1) - - for q in range(triplets_2[p]): - ind2 = cross_bond_inds_2[p, p+q+1] - rj3 = cross_bond_dists_2[p, p+q+1] - rj2 = bond_array_2[ind2, 0] - cj2 = bond_array_2[ind2, d2] - - fj2, fdj2 = cutoff_func(r_cut, rj2, cj2) - fj3, _ = cutoff_func(r_cut, rj3, 0) - - fj = fj1*fj2*fj3 - fdj = fdj1*fj2*fj3+fj1*fdj2*fj3 - - N, O, X = \ - triplet_kernel_grad(ci1, ci2, cj1, cj2, ri1, ri2, ri3, - rj1, rj2, rj3, fi, fj, fdi, fdj, - ls1, ls2, ls3, ls4, ls5, ls6, sig2, - sig3) - - kern += N - sig_derv += O - ls_derv += X - - return kern, sig_derv, ls_derv - - -@njit -def three_body_force_en_jit(bond_array_1, bond_array_2, - cross_bond_inds_1, - cross_bond_inds_2, - cross_bond_dists_1, - cross_bond_dists_2, - triplets_1, triplets_2, - d1, sig, ls, r_cut, cutoff_func): - """3-body single-element kernel between a force component and a local - energy accelerated with Numba. - - Args: - bond_array_1 (np.ndarray): 3-body bond array of the first local - environment. - bond_array_2 (np.ndarray): 3-body bond array of the second local - environment. - cross_bond_inds_1 (np.ndarray): Two dimensional array whose row m - contains the indices of atoms n > m in the first local - environment that are within a distance r_cut of both atom n and - the central atom. - cross_bond_inds_2 (np.ndarray): Two dimensional array whose row m - contains the indices of atoms n > m in the second local - environment that are within a distance r_cut of both atom n and - the central atom. - cross_bond_dists_1 (np.ndarray): Two dimensional array whose row m - contains the distances from atom m of atoms n > m in the first - local environment that are within a distance r_cut of both atom - n and the central atom. - cross_bond_dists_2 (np.ndarray): Two dimensional array whose row m - contains the distances from atom m of atoms n > m in the second - local environment that are within a distance r_cut of both atom - n and the central atom. - triplets_1 (np.ndarray): One dimensional array of integers whose entry - m is the number of atoms in the first local environment that are - within a distance r_cut of atom m. - triplets_2 (np.ndarray): One dimensional array of integers whose entry - m is the number of atoms in the second local environment that are - within a distance r_cut of atom m. - d1 (int): Force component of the first environment (1=x, 2=y, 3=z). - sig (float): 3-body signal variance hyperparameter. - ls (float): 3-body length scale hyperparameter. - r_cut (float): 3-body cutoff radius. - cutoff_func (Callable): Cutoff function. - - Returns: - float: - Value of the 3-body force/energy kernel. - """ - - kern = 0 - - # pre-compute constants that appear in the inner loop - sig2 = sig*sig - ls1 = 1 / (2*ls*ls) - ls2 = 1 / (ls*ls) - - for m in range(bond_array_1.shape[0]): - ri1 = bond_array_1[m, 0] - ci1 = bond_array_1[m, d1] - fi1, fdi1 = cutoff_func(r_cut, ri1, ci1) - - for n in range(triplets_1[m]): - ind1 = cross_bond_inds_1[m, m+n+1] - ri2 = bond_array_1[ind1, 0] - ci2 = bond_array_1[ind1, d1] - fi2, fdi2 = cutoff_func(r_cut, ri2, ci2) - ri3 = cross_bond_dists_1[m, m+n+1] - fi3, _ = cutoff_func(r_cut, ri3, 0) - fi = fi1*fi2*fi3 - fdi = fdi1*fi2*fi3+fi1*fdi2*fi3 - - for p in range(bond_array_2.shape[0]): - rj1 = bond_array_2[p, 0] - fj1, _ = cutoff_func(r_cut, rj1, 0) - - for q in range(triplets_2[p]): - ind2 = cross_bond_inds_2[p, p+q+1] - rj2 = bond_array_2[ind2, 0] - fj2, _ = cutoff_func(r_cut, rj2, 0) - rj3 = cross_bond_dists_2[p, p+q+1] - fj3, _ = cutoff_func(r_cut, rj3, 0) - fj = fj1*fj2*fj3 - - kern += triplet_force_en_kernel(ci1, ci2, ri1, ri2, ri3, - rj1, rj2, rj3, fi, fj, fdi, - ls1, ls2, sig2) - - return kern - - -@njit -def three_body_en_jit(bond_array_1, bond_array_2, - cross_bond_inds_1, - cross_bond_inds_2, - cross_bond_dists_1, - cross_bond_dists_2, - triplets_1, triplets_2, - sig, ls, r_cut, cutoff_func): - """3-body single-element kernel between two local energies accelerated - with Numba. - - Args: - bond_array_1 (np.ndarray): 3-body bond array of the first local - environment. - bond_array_2 (np.ndarray): 3-body bond array of the second local - environment. - cross_bond_inds_1 (np.ndarray): Two dimensional array whose row m - contains the indices of atoms n > m in the first local - environment that are within a distance r_cut of both atom n and - the central atom. - cross_bond_inds_2 (np.ndarray): Two dimensional array whose row m - contains the indices of atoms n > m in the second local - environment that are within a distance r_cut of both atom n and - the central atom. - cross_bond_dists_1 (np.ndarray): Two dimensional array whose row m - contains the distances from atom m of atoms n > m in the first - local environment that are within a distance r_cut of both atom - n and the central atom. - cross_bond_dists_2 (np.ndarray): Two dimensional array whose row m - contains the distances from atom m of atoms n > m in the second - local environment that are within a distance r_cut of both atom - n and the central atom. - triplets_1 (np.ndarray): One dimensional array of integers whose entry - m is the number of atoms in the first local environment that are - within a distance r_cut of atom m. - triplets_2 (np.ndarray): One dimensional array of integers whose entry - m is the number of atoms in the second local environment that are - within a distance r_cut of atom m. - sig (float): 3-body signal variance hyperparameter. - ls (float): 3-body length scale hyperparameter. - r_cut (float): 3-body cutoff radius. - cutoff_func (Callable): Cutoff function. - - Returns: - float: - Value of the 3-body local energy kernel. - """ - kern = 0 - - sig2 = sig*sig - ls2 = 1 / (2*ls*ls) - - for m in range(bond_array_1.shape[0]): - ri1 = bond_array_1[m, 0] - fi1, _ = cutoff_func(r_cut, ri1, 0) - - for n in range(triplets_1[m]): - ind1 = cross_bond_inds_1[m, m + n + 1] - ri2 = bond_array_1[ind1, 0] - fi2, _ = cutoff_func(r_cut, ri2, 0) - - ri3 = cross_bond_dists_1[m, m + n + 1] - fi3, _ = cutoff_func(r_cut, ri3, 0) - fi = fi1*fi2*fi3 - - for p in range(bond_array_2.shape[0]): - rj1 = bond_array_2[p, 0] - fj1, _ = cutoff_func(r_cut, rj1, 0) - - for q in range(triplets_2[p]): - ind2 = cross_bond_inds_2[p, p + q + 1] - rj2 = bond_array_2[ind2, 0] - fj2, _ = cutoff_func(r_cut, rj2, 0) - - rj3 = cross_bond_dists_2[p, p + q + 1] - fj3, _ = cutoff_func(r_cut, rj3, 0) - fj = fj1*fj2*fj3 - - r11 = ri1-rj1 - r12 = ri1-rj2 - r13 = ri1-rj3 - r21 = ri2-rj1 - r22 = ri2-rj2 - r23 = ri2-rj3 - r31 = ri3-rj1 - r32 = ri3-rj2 - r33 = ri3-rj3 - - C1 = r11*r11+r22*r22+r33*r33 - C2 = r11*r11+r23*r23+r32*r32 - C3 = r12*r12+r21*r21+r33*r33 - C4 = r12*r12+r23*r23+r31*r31 - C5 = r13*r13+r21*r21+r32*r32 - C6 = r13*r13+r22*r22+r31*r31 - - k = exp(-C1*ls2)+exp(-C2*ls2)+exp(-C3*ls2)+exp(-C4*ls2) + \ - exp(-C5*ls2)+exp(-C6*ls2) - - kern += sig2*k*fi*fj - - return kern - - # ----------------------------------------------------------------------------- # general helper functions # ----------------------------------------------------------------------------- @@ -1091,93 +87,14 @@ def force_energy_helper(B, D, fi, fj, fdi, ls1, ls2, sig2): return I -# ----------------------------------------------------------------------------- -# three body helper functions -# ----------------------------------------------------------------------------- - - -@njit -def triplet_kernel(ci1, ci2, cj1, cj2, ri1, ri2, ri3, rj1, rj2, rj3, fi, fj, - fdi, fdj, ls1, ls2, ls3, sig2): - r11 = ri1-rj1 - r12 = ri1-rj2 - r13 = ri1-rj3 - r21 = ri2-rj1 - r22 = ri2-rj2 - r23 = ri2-rj3 - r31 = ri3-rj1 - r32 = ri3-rj2 - r33 = ri3-rj3 - - # sum over all six permutations - M1 = three_body_helper_1(ci1, ci2, cj1, cj2, r11, r22, r33, fi, fj, fdi, - fdj, ls1, ls2, ls3, sig2) - M2 = three_body_helper_2(ci2, ci1, cj2, cj1, r21, r13, r32, fi, fj, fdi, - fdj, ls1, ls2, ls3, sig2) - M3 = three_body_helper_2(ci1, ci2, cj1, cj2, r12, r23, r31, fi, fj, fdi, - fdj, ls1, ls2, ls3, sig2) - M4 = three_body_helper_1(ci1, ci2, cj2, cj1, r12, r21, r33, fi, fj, fdi, - fdj, ls1, ls2, ls3, sig2) - M5 = three_body_helper_2(ci2, ci1, cj1, cj2, r22, r13, r31, fi, fj, fdi, - fdj, ls1, ls2, ls3, sig2) - M6 = three_body_helper_2(ci1, ci2, cj2, cj1, r11, r23, r32, fi, fj, fdi, - fdj, ls1, ls2, ls3, sig2) - - return M1 + M2 + M3 + M4 + M5 + M6 - - -@njit -def triplet_kernel_grad(ci1, ci2, cj1, cj2, ri1, ri2, ri3, rj1, rj2, rj3, fi, - fj, fdi, fdj, ls1, ls2, ls3, ls4, ls5, ls6, sig2, - sig3): - r11 = ri1-rj1 - r12 = ri1-rj2 - r13 = ri1-rj3 - r21 = ri2-rj1 - r22 = ri2-rj2 - r23 = ri2-rj3 - r31 = ri3-rj1 - r32 = ri3-rj2 - r33 = ri3-rj3 - - N1, O1, X1 = \ - three_body_grad_helper_1(ci1, ci2, cj1, cj2, r11, r22, r33, fi, fj, - fdi, fdj, ls1, ls2, ls3, ls4, ls5, ls6, sig2, - sig3) - N2, O2, X2 = \ - three_body_grad_helper_2(ci2, ci1, cj2, cj1, r21, r13, r32, fi, fj, - fdi, fdj, ls1, ls2, ls3, ls4, ls5, ls6, sig2, - sig3) - N3, O3, X3 = \ - three_body_grad_helper_2(ci1, ci2, cj1, cj2, r12, r23, r31, fi, fj, - fdi, fdj, ls1, ls2, ls3, ls4, ls5, ls6, sig2, - sig3) - N4, O4, X4 = \ - three_body_grad_helper_1(ci1, ci2, cj2, cj1, r12, r21, r33, fi, fj, - fdi, fdj, ls1, ls2, ls3, ls4, ls5, ls6, sig2, - sig3) - N5, O5, X5 = \ - three_body_grad_helper_2(ci2, ci1, cj1, cj2, r22, r13, r31, fi, fj, - fdi, fdj, ls1, ls2, ls3, ls4, ls5, ls6, sig2, - sig3) - N6, O6, X6 = \ - three_body_grad_helper_2(ci1, ci2, cj2, cj1, r11, r23, r32, fi, fj, - fdi, fdj, ls1, ls2, ls3, ls4, ls5, ls6, sig2, - sig3) - N = N1 + N2 + N3 + N4 + N5 + N6 - O = O1 + O2 + O3 + O4 + O5 + O6 - X = X1 + X2 + X3 + X4 + X5 + X6 - return N, O, X - - @njit def three_body_helper_1(ci1, ci2, cj1, cj2, r11, r22, r33, fi, fj, fdi, fdj, ls1, ls2, ls3, sig2): - A = ci1*cj1+ci2*cj2 - B = r11*ci1+r22*ci2 - C = r11*cj1+r22*cj2 - D = r11*r11+r22*r22+r33*r33 + A = ci1 * cj1 + ci2 * cj2 + B = r11 * ci1 + r22 * ci2 + C = r11 * cj1 + r22 * cj2 + D = r11 * r11 + r22 * r22 + r33 * r33 M = force_helper(A, B, C, D, fi, fj, fdi, fdj, ls1, ls2, ls3, sig2) @@ -1188,10 +105,10 @@ def three_body_helper_1(ci1, ci2, cj1, cj2, r11, r22, r33, def three_body_helper_2(ci1, ci2, cj1, cj2, r12, r23, r31, fi, fj, fdi, fdj, ls1, ls2, ls3, sig2): - A = ci1*cj2 - B = r12*ci1+r23*ci2 - C = r12*cj2+r31*cj1 - D = r12*r12+r23*r23+r31*r31 + A = ci1 * cj2 + B = r12 * ci1 + r23 * ci2 + C = r12 * cj2 + r31 * cj1 + D = r12 * r12 + r23 * r23 + r31 * r31 M = force_helper(A, B, C, D, fi, fj, fdi, fdj, ls1, ls2, ls3, sig2) @@ -1201,10 +118,10 @@ def three_body_helper_2(ci1, ci2, cj1, cj2, r12, r23, r31, @njit def three_body_grad_helper_1(ci1, ci2, cj1, cj2, r11, r22, r33, fi, fj, fdi, fdj, ls1, ls2, ls3, ls4, ls5, ls6, sig2, sig3): - A = ci1*cj1+ci2*cj2 - B = r11*ci1+r22*ci2 - C = r11*cj1+r22*cj2 - D = r11*r11+r22*r22+r33*r33 + A = ci1 * cj1 + ci2 * cj2 + B = r11 * ci1 + r22 * ci2 + C = r11 * cj1 + r22 * cj2 + D = r11 * r11 + r22 * r22 + r33 * r33 N, O, X = grad_helper(A, B, C, D, fi, fj, fdi, fdj, ls1, ls2, ls3, ls4, ls5, ls6, sig2, sig3) @@ -1215,10 +132,10 @@ def three_body_grad_helper_1(ci1, ci2, cj1, cj2, r11, r22, r33, fi, fj, fdi, @njit def three_body_grad_helper_2(ci1, ci2, cj1, cj2, r12, r23, r31, fi, fj, fdi, fdj, ls1, ls2, ls3, ls4, ls5, ls6, sig2, sig3): - A = ci1*cj2 - B = r12*ci1+r23*ci2 - C = r12*cj2+r31*cj1 - D = r12*r12+r23*r23+r31*r31 + A = ci1 * cj2 + B = r12 * ci1 + r23 * ci2 + C = r12 * cj2 + r31 * cj1 + D = r12 * r12 + r23 * r23 + r31 * r31 N, O, X = grad_helper(A, B, C, D, fi, fj, fdi, fdj, ls1, ls2, ls3, ls4, ls5, ls6, sig2, sig3) @@ -1233,63 +150,164 @@ def three_body_en_helper(ci1, ci2, r11, r22, r33, fi, fj, fdi, ls1, ls2, sig2): return force_energy_helper(B, D, fi, fj, fdi, ls1, ls2, sig2) +# ----------------------------------------------------------------------------- +# many body helper functions +# ----------------------------------------------------------------------------- + +@njit + + +def k_sq_exp_double_dev(q1, q2, sig, ls): + """Second Gradient of generic squared exponential kernel on two many body functions + + Args: + q1 (float): the many body descriptor of the first local environment + q2 (float): the many body descriptor of the second local environment + sig (float): amplitude hyperparameter + ls2 (float): squared lenghtscale hyperparameter + Return: + float: the value of the double derivative of the squared exponential kernel + """ + + qdiffsq = (q1 - q2) * (q1 - q2) + + ls2 = ls * ls + + ker = exp(-qdiffsq / (2 * ls2)) + + ret = sig * sig * ker / ls2 * (1 - qdiffsq / ls2) + + return ret @njit -def triplet_force_en_kernel(ci1, ci2, ri1, ri2, ri3, rj1, rj2, rj3, - fi, fj, fdi, ls1, ls2, sig2): - r11 = ri1-rj1 - r12 = ri1-rj2 - r13 = ri1-rj3 - r21 = ri2-rj1 - r22 = ri2-rj2 - r23 = ri2-rj3 - r31 = ri3-rj1 - r32 = ri3-rj2 - r33 = ri3-rj3 - - I1 = three_body_en_helper(ci1, ci2, r11, r22, r33, fi, fj, - fdi, ls1, ls2, sig2) - I2 = three_body_en_helper(ci1, ci2, r13, r21, r32, fi, fj, - fdi, ls1, ls2, sig2) - I3 = three_body_en_helper(ci1, ci2, r12, r23, r31, fi, fj, - fdi, ls1, ls2, sig2) - I4 = three_body_en_helper(ci1, ci2, r12, r21, r33, fi, fj, - fdi, ls1, ls2, sig2) - I5 = three_body_en_helper(ci1, ci2, r13, r22, r31, fi, fj, - fdi, ls1, ls2, sig2) - I6 = three_body_en_helper(ci1, ci2, r11, r23, r32, fi, fj, - fdi, ls1, ls2, sig2) - - return I1 + I2 + I3 + I4 + I5 + I6 - - -_str_to_kernel = {'two_body': two_body, - 'two_body_en': two_body_en, - 'two_body_force_en': two_body_force_en, - 'three_body': three_body, - 'three_body_en': three_body_en, - 'three_body_force_en': three_body_force_en, - 'two_plus_three_body': two_plus_three_body, - 'two_plus_three_en': two_plus_three_en, - 'two_plus_three_force_en': two_plus_three_force_en - } - - -def str_to_kernel(string: str, include_grad: bool = False): - - if string not in _str_to_kernel.keys(): - raise ValueError("Kernel {} not found in list of available " - "kernels{}:".format(string, _str_to_kernel.keys())) - - if not include_grad: - return _str_to_kernel[string] - else: - if 'two' in string and 'three' in string: - return _str_to_kernel[string], two_plus_three_body_grad - elif 'two' in string and 'three' not in string: - return _str_to_kernel[string], two_body_grad - elif 'two' not in string and 'three' in string: - return _str_to_kernel[string], three_body_grad - else: - raise ValueError("Gradient callable for {} not found".format( - string)) + + +def k_sq_exp_dev(q1, q2, sig, ls): + """First Gradient of generic squared exponential kernel on two many body functions + + Args: + q1 (float): the many body descriptor of the first local environment + q2 (float): the many body descriptor of the second local environment + sig (float): amplitude hyperparameter + ls2 (float): squared lenghtscale hyperparameter + Return: + float: the value of the derivative of the squared exponential kernel + """ + + qdiff = (q1 - q2) + + ls2 = ls * ls + + ker = exp(-qdiff * qdiff / (2 * ls2)) + + ret = - sig * sig * ker / ls2 * qdiff + + return ret + + +@njit +def coordination_number(rij, cij, r_cut, cutoff_func): + """Pairwise contribution to many-body descriptor based on number of + atoms in the environment + + Args: + rij (float): distance between atoms i and j + cij (float): Component of versor of rij along given direction + r_cut (float): cutoff hyperparameter + cutoff_func (callable): cutoff function + Return: + float: the value of the pairwise many-body contribution + float: the value of the derivative of the pairwise many-body + contribution w.r.t. the central atom displacement + """ + + fij, fdij = cutoff_func(r_cut, rij, cij) + + return fij, fdij + + +@njit +def q_value(distances, r_cut, cutoff_func, q_func=coordination_number): + """Compute value of many-body descriptor based on distances of atoms + in the local many-body environment. + + Args: + distances (np.ndarray): distances between atoms i and j + r_cut (float): cutoff hyperparameter + cutoff_func (callable): cutoff function + q_func (callable): many-body pairwise descrptor function + + Return: + float: the value of the many-body descriptor + """ + q = 0 + + for d in distances: + q_, _ = q_func(d, 0, r_cut, cutoff_func) + q += q_ + + return q + +@njit + + +def q_value_mc(distances, r_cut, ref_species, species, cutoff_func, q_func=coordination_number): + """Compute value of many-body many components descriptor based + on distances of atoms in the local many-body environment. + + Args: + distances (np.ndarray): distances between atoms i and j + r_cut (float): cutoff hyperparameter + ref_species (int): species to consider to compute the contribution + species (np.ndarray): atomic species of neighbours + cutoff_func (callable): cutoff function + q_func (callable): many-body pairwise descrptor function + + Return: + float: the value of the many-body descriptor + """ + + q = 0 + + for i in range(len(distances)): + if species[i] == ref_species: + q_, _ = q_func(distances[i], 0, r_cut, cutoff_func) + q += q_ + + return q + + +@njit +def mb_grad_helper_ls_(qdiffsq, sig, ls): + """ + Derivative of a many body force-force kernel w.r.t. ls + """ + + ls2 = ls * ls + + prefact = exp(-(qdiffsq / (2 * ls2))) * (sig * sig) / ls ** 5 + + ret = - prefact * (qdiffsq ** 2 / ls2 - 5 * qdiffsq + 2 * ls2) + + return ret + +@njit + + +def mb_grad_helper_ls(q1, q2, qi, qj, sig, ls): + """ + Helper function for many body gradient collecting all the derivatives + of the force-force many body kernel w.r.t. ls + """ + + q12diffsq = ((q1 - q2) * (q1 - q2)) + qijdiffsq = ((qi - qj) * (qi - qj)) + qi2diffsq = ((qi - q2) * (qi - q2)) + q1jdiffsq = ((q1 - qj) * (q1 - qj)) + + dk12 = mb_grad_helper_ls_(q12diffsq, sig, ls) + dkij = mb_grad_helper_ls_(qijdiffsq, sig, ls) + dki2 = mb_grad_helper_ls_(qi2diffsq, sig, ls) + dk1j = mb_grad_helper_ls_(q1jdiffsq, sig, ls) + + return dk12 + dkij + dki2 + dk1j diff --git a/flare/kernels/mc_sephyps.py b/flare/kernels/mc_sephyps.py index 9543cde19..2ea3cb1bc 100644 --- a/flare/kernels/mc_sephyps.py +++ b/flare/kernels/mc_sephyps.py @@ -5,6 +5,7 @@ from flare.kernels.kernels import force_helper, grad_constants, grad_helper, \ force_energy_helper, three_body_en_helper, three_body_helper_1, \ three_body_helper_2, three_body_grad_helper_1, three_body_grad_helper_2 +from flare.kernels.mc_simple import many_body_mc_en_jit, many_body_mc_force_en_jit, many_body_mc_grad_jit, many_body_mc_jit """ Multicomponent kernels that restrict all signal variance hyperparameters to @@ -14,8 +15,8 @@ hyps_mask is a dictionary with the following keys and values: spec_mask: 118-long integer array descirbing which elements belong to like groups for determining which bond hyperparameters to use. For - instance, [0,1,1,0 ...] assigns H to group 0, He and Li to group 1, - and Be to group 0. + instance, [0,0,1,1,0 ...] assigns H to group 0, He and Li to group 1, + and Be to group 0 (the 0th register is ignored). nspec: Integer, number of different species groups (equal to number of unique values in spec_mask). nbond: Integer, number of different hyperparameter sets to associate with @@ -64,78 +65,175 @@ """ - # ----------------------------------------------------------------------------- -# two plus three body kernels +# two plus three plus many body kernels # ----------------------------------------------------------------------------- -def from_mask_to_hyps(hyps, hyps_mask: dict = {}): - """ - :param hyps: - :param hyps_mask: - :return: - """ - n2b = hyps_mask.get('nbond', 0) - n3b = hyps_mask.get('ntriplet', 0) - if ('map' in hyps_mask.keys()): - orig_hyps = hyps_mask['original'] - hm = hyps_mask['map'] - for i, h in enumerate(hyps): - orig_hyps[hm[i]] = h - else: - orig_hyps = hyps +def two_three_many_body_mc(env1, env2, d1, d2, cutoffs, + nspec, spec_mask, + nbond, bond_mask, ntriplet, triplet_mask, + sig2, ls2, sig3, ls3, sigm, lsm, + cutoff_func=cf.quadratic_cutoff): - if (n2b != 0) and (n3b != 0): - sig2 = orig_hyps[:n2b] - ls2 = orig_hyps[n2b:n2b * 2] - sig3 = orig_hyps[n2b * 2:n2b * 2 + n3b] - ls3 = orig_hyps[n2b * 2 + n3b:n2b * 2 + n3b * 2] - return n2b, n3b, sig2, ls2, sig3, ls3 + r_cut_2 = cutoffs[0] + r_cut_3 = cutoffs[1] + r_cut_m = cutoffs[2] + + two_term = two_body_mc_jit(env1.bond_array_2, env1.ctype, env1.etypes, + env2.bond_array_2, env2.ctype, env2.etypes, + d1, d2, sig2, ls2, r_cut_2, cutoff_func, + nspec, spec_mask, bond_mask) - elif (n2b == 0) and (n3b != 0): - sig = orig_hyps[:n3b] - ls = orig_hyps[n3b:n3b * 2] - return 0, n3b, None, None, sig, ls + three_term = \ + three_body_mc_jit(env1.bond_array_3, env1.ctype, env1.etypes, + env2.bond_array_3, env2.ctype, env2.etypes, + env1.cross_bond_inds, env2.cross_bond_inds, + env1.cross_bond_dists, env2.cross_bond_dists, + env1.triplet_counts, env2.triplet_counts, + d1, d2, sig3, ls3, r_cut_3, cutoff_func, + nspec, spec_mask, triplet_mask) - elif (n2b != 0) and (n3b == 0): - sig = orig_hyps[:n2b] - ls = orig_hyps[n2b:n2b * 2] - return n2b, 0, sig, ls, None, None + many_term = many_body_mc_jit(env1.bond_array_mb, env2.bond_array_mb, + env1.neigh_dists_mb, env2.neigh_dists_mb, + env1.num_neighs_mb, env2.num_neighs_mb, + env1.ctype, env2.ctype, + env1.bond_array_mb_etypes, env2.bond_array_mb_etypes, + env1.etype_mb, env2.etype_mb, + env1.species, env2.species, + d1, d2, sigm, lsm, r_cut_m, cutoff_func) - elif (n2b == 0) and (n3b == 0): - raise NameError("Hyperparameter mask missing nbond and/or" - "ntriplet key") + return two_term + three_term + many_term -def from_grad_to_mask(grad, hyps_mask): - """ - Return gradient which only includes hyperparameters - which are meant to vary - :param grad: - :param hyps_mask: - :return: - """ - if 'map' not in hyps_mask.keys(): - return grad +def two_three_many_body_mc_grad(env1, env2, d1, d2, cutoffs, + nspec, spec_mask, + nbond, bond_mask, ntriplet, triplet_mask, + sig2, ls2, sig3, ls3, sigm, lsm, + cutoff_func=cf.quadratic_cutoff): - # if the last element is not sigma_noise - if (hyps_mask['map'][-1] == len(grad)): - hm = hyps_mask['map'][:-1] - else: - hm = hyps_mask['map'] + r_cut_2 = cutoffs[0] + r_cut_3 = cutoffs[1] + r_cut_m = cutoffs[2] + + kern2, grad2 = \ + two_body_mc_grad_jit(env1.bond_array_2, env1.ctype, env1.etypes, + env2.bond_array_2, env2.ctype, env2.etypes, + d1, d2, sig2, ls2, r_cut_2, cutoff_func, + nspec, spec_mask, + nbond, bond_mask) + + kern3, grad3 = \ + three_body_mc_grad_jit(env1.bond_array_3, env1.ctype, env1.etypes, + env2.bond_array_3, env2.ctype, env2.etypes, + env1.cross_bond_inds, env2.cross_bond_inds, + env1.cross_bond_dists, env2.cross_bond_dists, + env1.triplet_counts, env2.triplet_counts, + d1, d2, sig3, ls3, r_cut_3, + cutoff_func, + nspec, spec_mask, + ntriplet, triplet_mask) + + kern_many, gradm = many_body_mc_grad_jit(env1.bond_array_mb, env2.bond_array_mb, + env1.neigh_dists_mb, env2.neigh_dists_mb, + env1.num_neighs_mb, env2.num_neighs_mb, + env1.ctype, env2.ctype, + env1.bond_array_mb_etypes, env2.bond_array_mb_etypes, + env1.etype_mb, env2.etype_mb, + env1.species, env2.species, + d1, d2, sigm, lsm, r_cut_m, cutoff_func) - newgrad = np.zeros(len(hm)) - for i, mapid in enumerate(hm): - newgrad[i] = grad[mapid] - return newgrad + g = np.hstack([grad2, grad3, gradm]) + return kern2 + kern3 + kern_many, g -def two_plus_three_body_mc(env1, env2, d1, d2, hyps, cutoffs, - cutoff_func=cf.quadratic_cutoff, - hyps_mask=None): - n2b, n3b, sig2, ls2, sig3, ls3 = \ - from_mask_to_hyps(hyps, hyps_mask) + +def two_three_many_mc_force_en(env1, env2, d1, cutoffs, + nspec, spec_mask, + nbond, bond_mask, ntriplet, triplet_mask, + sig2, ls2, sig3, ls3, sigm, lsm, + cutoff_func=cf.quadratic_cutoff): + r_cut_2 = cutoffs[0] + r_cut_3 = cutoffs[1] + r_cut_m = cutoffs[2] + + two_term = \ + two_body_mc_force_en_jit(env1.bond_array_2, env1.ctype, env1.etypes, + env2.bond_array_2, env2.ctype, env2.etypes, + d1, sig2, ls2, r_cut_2, cutoff_func, + nspec, spec_mask, + bond_mask) / 2 + + three_term = \ + three_body_mc_force_en_jit(env1.bond_array_3, env1.ctype, env1.etypes, + env2.bond_array_3, env2.ctype, env2.etypes, + env1.cross_bond_inds, env2.cross_bond_inds, + env1.cross_bond_dists, + env2.cross_bond_dists, + env1.triplet_counts, env2.triplet_counts, + d1, sig3, ls3, r_cut_3, cutoff_func, + nspec, + spec_mask, + triplet_mask) / 3 + + many_term = many_body_mc_force_en_jit(env1.bond_array_mb, env2.bond_array_mb, + env1.neigh_dists_mb, + env1.num_neighs_mb, + env1.ctype, env2.ctype, + env1.bond_array_mb_etypes, env2.bond_array_mb_etypes, + env1.etype_mb, + env1.species, env2.species, + d1, sigm, lsm, r_cut_m, cutoff_func) + + return two_term + three_term + many_term + + +def two_three_many_mc_en(env1, env2, cutoffs, + nspec, spec_mask, + nbond, bond_mask, ntriplet, triplet_mask, + sig2, ls2, sig3, ls3, sigm, lsm, + cutoff_func=cf.quadratic_cutoff): + r_cut_2 = cutoffs[0] + r_cut_3 = cutoffs[1] + r_cut_m = cutoffs[2] + + two_term = two_body_mc_en_jit(env1.bond_array_2, env1.ctype, env1.etypes, + env2.bond_array_2, env2.ctype, env2.etypes, + sig2, ls2, r_cut_2, cutoff_func, + nspec, + spec_mask, + bond_mask) + + three_term = \ + three_body_mc_en_jit(env1.bond_array_3, env1.ctype, env1.etypes, + env2.bond_array_3, env2.ctype, env2.etypes, + env1.cross_bond_inds, env2.cross_bond_inds, + env1.cross_bond_dists, env2.cross_bond_dists, + env1.triplet_counts, env2.triplet_counts, + sig3, ls3, r_cut_3, cutoff_func, + nspec, spec_mask, + triplet_mask) + + many_term = many_body_mc_en_jit(env1.bond_array_2, env2.bond_array_2, + env1.ctype, env2.ctype, + env1.bond_array_mb_etypes, env2.bond_array_mb_etypes, + env1.species, env2.species, + sigm, lsm, r_cut_m, cutoff_func) + + + return two_term + three_term + many_term + + +# ----------------------------------------------------------------------------- +# two plus three body kernels +# ----------------------------------------------------------------------------- + + +def two_plus_three_body_mc(env1, env2, d1, d2, cutoffs, + nspec, spec_mask, + nbond, bond_mask, ntriplet, triplet_mask, + sig2, ls2, sig3, ls3, + cutoff_func=cf.quadratic_cutoff): r_cut_2 = cutoffs[0] r_cut_3 = cutoffs[1] @@ -143,8 +241,7 @@ def two_plus_three_body_mc(env1, env2, d1, d2, hyps, cutoffs, two_term = two_body_mc_jit(env1.bond_array_2, env1.ctype, env1.etypes, env2.bond_array_2, env2.ctype, env2.etypes, d1, d2, sig2, ls2, r_cut_2, cutoff_func, - hyps_mask['nspec'], hyps_mask['spec_mask'], - hyps_mask['bond_mask']) + nspec, spec_mask, bond_mask) three_term = \ three_body_mc_jit(env1.bond_array_3, env1.ctype, env1.etypes, @@ -153,17 +250,16 @@ def two_plus_three_body_mc(env1, env2, d1, d2, hyps, cutoffs, env1.cross_bond_dists, env2.cross_bond_dists, env1.triplet_counts, env2.triplet_counts, d1, d2, sig3, ls3, r_cut_3, cutoff_func, - hyps_mask['nspec'], hyps_mask['spec_mask'], - hyps_mask['triplet_mask']) + nspec, spec_mask, triplet_mask) return two_term + three_term -def two_plus_three_body_mc_grad(env1, env2, d1, d2, hyps, cutoffs, - cutoff_func=cf.quadratic_cutoff, - hyps_mask=None): - n2b, n3b, sig2, ls2, sig3, ls3 = \ - from_mask_to_hyps(hyps, hyps_mask) +def two_plus_three_body_mc_grad(env1, env2, d1, d2, cutoffs, + nspec, spec_mask, + nbond, bond_mask, ntriplet, triplet_mask, + sig2, ls2, sig3, ls3, + cutoff_func=cf.quadratic_cutoff): r_cut_2 = cutoffs[0] r_cut_3 = cutoffs[1] @@ -172,8 +268,8 @@ def two_plus_three_body_mc_grad(env1, env2, d1, d2, hyps, cutoffs, two_body_mc_grad_jit(env1.bond_array_2, env1.ctype, env1.etypes, env2.bond_array_2, env2.ctype, env2.etypes, d1, d2, sig2, ls2, r_cut_2, cutoff_func, - hyps_mask['nspec'], hyps_mask['spec_mask'], - hyps_mask['nbond'], hyps_mask['bond_mask']) + nspec, spec_mask, + nbond, bond_mask) kern3, grad3 = \ three_body_mc_grad_jit(env1.bond_array_3, env1.ctype, env1.etypes, @@ -183,20 +279,19 @@ def two_plus_three_body_mc_grad(env1, env2, d1, d2, hyps, cutoffs, env1.triplet_counts, env2.triplet_counts, d1, d2, sig3, ls3, r_cut_3, cutoff_func, - hyps_mask['nspec'], hyps_mask['spec_mask'], - hyps_mask['ntriplet'], - hyps_mask['triplet_mask']) + nspec, spec_mask, + ntriplet, triplet_mask) - g = from_grad_to_mask(np.hstack([grad2, grad3]), hyps_mask) + g = np.hstack([grad2, grad3]) return kern2 + kern3, g -def two_plus_three_mc_force_en(env1, env2, d1, hyps, cutoffs, - cutoff_func=cf.quadratic_cutoff, - hyps_mask=None): - n2b, n3b, sig2, ls2, sig3, ls3 = \ - from_mask_to_hyps(hyps, hyps_mask) +def two_plus_three_mc_force_en(env1, env2, d1, cutoffs, + nspec, spec_mask, + nbond, bond_mask, ntriplet, triplet_mask, + sig2, ls2, sig3, ls3, + cutoff_func=cf.quadratic_cutoff): r_cut_2 = cutoffs[0] r_cut_3 = cutoffs[1] @@ -204,8 +299,8 @@ def two_plus_three_mc_force_en(env1, env2, d1, hyps, cutoffs, two_body_mc_force_en_jit(env1.bond_array_2, env1.ctype, env1.etypes, env2.bond_array_2, env2.ctype, env2.etypes, d1, sig2, ls2, r_cut_2, cutoff_func, - hyps_mask['nspec'], hyps_mask['spec_mask'], - hyps_mask['bond_mask']) / 2 + nspec, spec_mask, + bond_mask) / 2 three_term = \ three_body_mc_force_en_jit(env1.bond_array_3, env1.ctype, env1.etypes, @@ -215,26 +310,27 @@ def two_plus_three_mc_force_en(env1, env2, d1, hyps, cutoffs, env2.cross_bond_dists, env1.triplet_counts, env2.triplet_counts, d1, sig3, ls3, r_cut_3, cutoff_func, - hyps_mask['nspec'], - hyps_mask['spec_mask'], - hyps_mask['triplet_mask']) / 3 + nspec, + spec_mask, + triplet_mask) / 3 return two_term + three_term -def two_plus_three_mc_en(env1, env2, hyps, cutoffs, - cutoff_func=cf.quadratic_cutoff, hyps_mask=None): - n2b, n3b, sig2, ls2, sig3, ls3 = \ - from_mask_to_hyps(hyps, hyps_mask) +def two_plus_three_mc_en(env1, env2, cutoffs, + nspec, spec_mask, + nbond, bond_mask, ntriplet, triplet_mask, + sig2, ls2, sig3, ls3, + cutoff_func=cf.quadratic_cutoff): r_cut_2 = cutoffs[0] r_cut_3 = cutoffs[1] two_term = two_body_mc_en_jit(env1.bond_array_2, env1.ctype, env1.etypes, env2.bond_array_2, env2.ctype, env2.etypes, sig2, ls2, r_cut_2, cutoff_func, - hyps_mask['nspec'], - hyps_mask['spec_mask'], - hyps_mask['bond_mask']) + nspec, + spec_mask, + bond_mask) three_term = \ three_body_mc_en_jit(env1.bond_array_3, env1.ctype, env1.etypes, @@ -243,8 +339,8 @@ def two_plus_three_mc_en(env1, env2, hyps, cutoffs, env1.cross_bond_dists, env2.cross_bond_dists, env1.triplet_counts, env2.triplet_counts, sig3, ls3, r_cut_3, cutoff_func, - hyps_mask['nspec'], hyps_mask['spec_mask'], - hyps_mask['triplet_mask']) + nspec, spec_mask, + triplet_mask) return two_term + three_term @@ -254,11 +350,11 @@ def two_plus_three_mc_en(env1, env2, hyps, cutoffs, # ----------------------------------------------------------------------------- -def three_body_mc(env1, env2, d1, d2, hyps, cutoffs, - cutoff_func=cf.quadratic_cutoff, - hyps_mask=None): - n2b, n3b, sig2, ls2, sig, ls = \ - from_mask_to_hyps(hyps, hyps_mask) +def three_body_mc(env1, env2, d1, d2, cutoffs, + nspec, spec_mask, + nbond, bond_mask, ntriplet, triplet_mask, + sig2, ls2, sig3, ls3, + cutoff_func=cf.quadratic_cutoff): r_cut = cutoffs[1] @@ -267,39 +363,33 @@ def three_body_mc(env1, env2, d1, d2, hyps, cutoffs, env1.cross_bond_inds, env2.cross_bond_inds, env1.cross_bond_dists, env2.cross_bond_dists, env1.triplet_counts, env2.triplet_counts, - d1, d2, sig, ls, r_cut, cutoff_func, - hyps_mask['nspec'], hyps_mask['spec_mask'], - hyps_mask['triplet_mask']) + d1, d2, sig3, ls3, r_cut, cutoff_func, + nspec, spec_mask, + triplet_mask) -def three_body_mc_grad(env1, env2, d1, d2, hyps, cutoffs, - cutoff_func=cf.quadratic_cutoff, hyps_mask=None): - n2b, n3b, sig2, ls2, sig, ls = \ - from_mask_to_hyps(hyps, hyps_mask) +def three_body_mc_grad(env1, env2, d1, d2, cutoffs, + nspec, spec_mask, + nbond, bond_mask, ntriplet, triplet_mask, + sig2, ls2, sig3, ls3, + cutoff_func=cf.quadratic_cutoff): r_cut = cutoffs[0] - k, grad = three_body_mc_grad_jit(env1.bond_array_3, env1.ctype, - env1.etypes, - env2.bond_array_3, env2.ctype, - env2.etypes, - env1.cross_bond_inds, - env2.cross_bond_inds, - env1.cross_bond_dists, - env2.cross_bond_dists, - env1.triplet_counts, env2.triplet_counts, - d1, d2, sig, ls, r_cut, cutoff_func, - hyps_mask['nspec'], - hyps_mask['spec_mask'], - hyps_mask['ntriplet'], - hyps_mask['triplet_mask']) - return k, from_grad_to_mask(grad, hyps_mask) - - -def three_body_mc_force_en(env1, env2, d1, hyps, cutoffs, - cutoff_func=cf.quadratic_cutoff, - hyps_mask=None): - n2b, n3b, sig2, ls2, sig, ls = \ - from_mask_to_hyps(hyps, hyps_mask) + return three_body_mc_grad_jit( + env1.bond_array_3, env1.ctype, env1.etypes, + env2.bond_array_3, env2.ctype, env2.etypes, + env1.cross_bond_inds, env2.cross_bond_inds, + env1.cross_bond_dists, env2.cross_bond_dists, + env1.triplet_counts, env2.triplet_counts, + d1, d2, sig3, ls3, r_cut, cutoff_func, + nspec, spec_mask, ntriplet, triplet_mask) + + +def three_body_mc_force_en( + env1, env2, d1, cutoffs, nspec, spec_mask, + nbond, bond_mask, ntriplet, triplet_mask, sig2, ls2, sig3, ls3, + cutoff_func=cf.quadratic_cutoff): + r_cut = cutoffs[1] return three_body_mc_force_en_jit(env1.bond_array_3, env1.ctype, @@ -312,18 +402,17 @@ def three_body_mc_force_en(env1, env2, d1, hyps, cutoffs, env2.cross_bond_dists, env1.triplet_counts, env2.triplet_counts, - d1, sig, ls, r_cut, + d1, sig3, ls3, r_cut, cutoff_func, - hyps_mask['nspec'], - hyps_mask['spec_mask'], - hyps_mask['triplet_mask']) / 3 + nspec, + spec_mask, + triplet_mask) / 3 -def three_body_mc_en(env1, env2, hyps, cutoffs, - cutoff_func=cf.quadratic_cutoff, - hyps_mask=None): - n2b, n3b, sig2, ls2, sig, ls = \ - from_mask_to_hyps(hyps, hyps_mask) +def three_body_mc_en(env1, env2, cutoffs, nspec, spec_mask, + nbond, bond_mask, ntriplet, triplet_mask, sig2, ls2, sig3, ls3, + cutoff_func=cf.quadratic_cutoff): + r_cut = cutoffs[1] return three_body_mc_en_jit(env1.bond_array_3, env1.ctype, env1.etypes, @@ -331,9 +420,9 @@ def three_body_mc_en(env1, env2, hyps, cutoffs, env1.cross_bond_inds, env2.cross_bond_inds, env1.cross_bond_dists, env2.cross_bond_dists, env1.triplet_counts, env2.triplet_counts, - sig, ls, r_cut, cutoff_func, - hyps_mask['nspec'], hyps_mask['spec_mask'], - hyps_mask['triplet_mask']) + sig3, ls3, r_cut, cutoff_func, + nspec, spec_mask, + triplet_mask) # ----------------------------------------------------------------------------- @@ -341,62 +430,54 @@ def three_body_mc_en(env1, env2, hyps, cutoffs, # ----------------------------------------------------------------------------- -def two_body_mc(env1, env2, d1, d2, hyps, cutoffs, - cutoff_func=cf.quadratic_cutoff, hyps_mask=None): - n2b, n3b, sig, ls, sig3, ls3 = \ - from_mask_to_hyps(hyps, hyps_mask) +def two_body_mc( + env1, env2, d1, d2, cutoffs, nspec, spec_mask, + nbond, bond_mask, ntriplet, triplet_mask, sig2, ls2, sig3, ls3, + cutoff_func=cf.quadratic_cutoff): + r_cut = cutoffs[0] return two_body_mc_jit(env1.bond_array_2, env1.ctype, env1.etypes, env2.bond_array_2, env2.ctype, env2.etypes, - d1, d2, sig, ls, r_cut, cutoff_func, - hyps_mask['nspec'], hyps_mask['spec_mask'], - hyps_mask['bond_mask']) + d1, d2, sig2, ls2, r_cut, cutoff_func, + nspec, spec_mask, bond_mask) -def two_body_mc_grad(env1, env2, d1, d2, hyps, cutoffs, - cutoff_func=cf.quadratic_cutoff, - hyps_mask=None): - n2b, n3b, sig, ls, sig3, ls3 = \ - from_mask_to_hyps(hyps, hyps_mask) +def two_body_mc_grad( + env1, env2, d1, d2, cutoffs, nspec, spec_mask, + nbond, bond_mask, ntriplet, triplet_mask, sig2, ls2, sig3, ls3, + cutoff_func=cf.quadratic_cutoff): r_cut = cutoffs[0] - k, grad = two_body_mc_grad_jit(env1.bond_array_2, env1.ctype, env1.etypes, - env2.bond_array_2, env2.ctype, env2.etypes, - d1, d2, sig, ls, r_cut, cutoff_func, - hyps_mask['nspec'], hyps_mask['spec_mask'], - hyps_mask['nbond'], hyps_mask['bond_mask']) - return k, from_grad_to_mask(grad, hyps_mask) + return two_body_mc_grad_jit( + env1.bond_array_2, env1.ctype, env1.etypes, + env2.bond_array_2, env2.ctype, env2.etypes, + d1, d2, sig2, ls2, r_cut, cutoff_func, + nspec, spec_mask, nbond, bond_mask) -def two_body_mc_force_en(env1, env2, d1, hyps, cutoffs, - cutoff_func=cf.quadratic_cutoff, - hyps_mask=None): - n2b, n3b, sig, ls, sig3, ls3 = \ - from_mask_to_hyps(hyps, hyps_mask) +def two_body_mc_force_en(env1, env2, d1, cutoffs, + nspec, spec_mask, nbond, bond_mask, ntriplet, triplet_mask, + sig2, ls2, sig3, ls3, cutoff_func=cf.quadratic_cutoff): r_cut = cutoffs[0] - return two_body_mc_force_en_jit(env1.bond_array_2, env1.ctype, env1.etypes, - env2.bond_array_2, env2.ctype, env2.etypes, - d1, sig, ls, r_cut, cutoff_func, - hyps_mask['nspec'], - hyps_mask['spec_mask'], - hyps_mask['bond_mask']) / 2 + return two_body_mc_force_en_jit( + env1.bond_array_2, env1.ctype, env1.etypes, + env2.bond_array_2, env2.ctype, env2.etypes, + d1, sig2, ls2, r_cut, cutoff_func, + nspec, spec_mask, bond_mask) / 2 -def two_body_mc_en(env1, env2, hyps, cutoffs, - cutoff_func=cf.quadratic_cutoff, - hyps_mask=None): - n2b, n3b, sig, ls, sig3, ls3 = \ - from_mask_to_hyps(hyps, hyps_mask) +def two_body_mc_en(env1, env2, cutoffs, nspec, spec_mask, + nbond, bond_mask, ntriplet, triplet_mask, sig2, ls2, sig3, ls3, + cutoff_func=cf.quadratic_cutoff): r_cut = cutoffs[0] return two_body_mc_en_jit(env1.bond_array_2, env1.ctype, env1.etypes, env2.bond_array_2, env2.ctype, env2.etypes, - sig, ls, r_cut, cutoff_func, - hyps_mask['nspec'], hyps_mask['spec_mask'], - hyps_mask['bond_mask']) + sig2, ls2, r_cut, cutoff_func, + nspec, spec_mask, bond_mask) # ----------------------------------------------------------------------------- @@ -540,9 +621,9 @@ def three_body_mc_grad_jit(bond_array_1, c1, etypes1, """Kernel gradient for 3-body force comparisons.""" kern = 0 - sig_derv = np.zeros(ntriplet) - ls_derv = np.zeros(ntriplet) - kern_grad = np.zeros(2) + sig_derv = np.zeros(ntriplet, dtype=np.float64) + ls_derv = np.zeros(ntriplet, dtype=np.float64) + kern_grad = np.zeros(2, dtype=np.float64) # pre-compute constants that appear in the inner loop sig2, sig3, ls1, ls2, ls3, ls4, ls5, ls6 = grad_constants(sig, ls) @@ -690,7 +771,7 @@ def three_body_mc_grad_jit(bond_array_1, c1, etypes1, sig_derv[ttypei] += sig_term ls_derv[ttypei] += ls_term - kern_grad = np.zeros(2 * ntriplet) + kern_grad = np.zeros(2 * ntriplet, dtype=np.float64) for i in range(ntriplet): kern_grad[i] = sig_derv[i] for i in range(ntriplet): @@ -907,8 +988,7 @@ def three_body_mc_en_jit(bond_array_1, c1, etypes1, @njit -def two_body_mc_jit(bond_array_1, c1, etypes1, - bond_array_2, c2, etypes2, +def two_body_mc_jit(bond_array_1, c1, etypes1, bond_array_2, c2, etypes2, d1, d2, sig, ls, r_cut, cutoff_func, nspec, spec_mask, bond_mask): """Multicomponent two-body force/force kernel accelerated with Numba's @@ -971,8 +1051,8 @@ def two_body_mc_grad_jit(bond_array_1, c1, etypes1, Numba's njit decorator.""" kern = 0 - sig_derv = np.zeros(nbond) - ls_derv = np.zeros(nbond) + sig_derv = np.zeros(nbond, dtype=np.float64) + ls_derv = np.zeros(nbond, dtype=np.float64) ls1 = 1 / (2 * ls * ls) ls2 = 1 / (ls * ls) @@ -1031,11 +1111,7 @@ def two_body_mc_grad_jit(bond_array_1, c1, etypes1, sig_derv[btype] += sig_term ls_derv[btype] += ls_term - kern_grad = np.zeros(2 * nbond) - for i in range(nbond): - kern_grad[i] = sig_derv[i] - for i in range(nbond): - kern_grad[i + nbond] = ls_derv[i] + kern_grad = np.hstack((sig_derv, ls_derv)) return kern, kern_grad @@ -1158,7 +1234,23 @@ def two_body_mc_en_jit(bond_array_1, c1, etypes1, 'two_plus_three_mc_sh': two_plus_three_body_mc, 'two_plus_three_mc_sh_grad': two_plus_three_body_mc_grad, 'two_plus_three_mc_sh_en': two_plus_three_mc_en, - 'two_plus_three_mc_sh_force_en': two_plus_three_mc_force_en + 'two_plus_three_mc_sh_force_en': two_plus_three_mc_force_en, + '2': two_body_mc, + '2_en': two_body_mc_en, + '2_grad': two_body_mc_grad, + '2_force_en': two_body_mc_force_en, + '3': three_body_mc, + '3_grad': three_body_mc_grad, + '3_en': three_body_mc_en, + '3_force_en': three_body_mc_force_en, + '2+3': two_plus_three_body_mc, + '2+3_grad': two_plus_three_body_mc_grad, + '2+3_en': two_plus_three_mc_en, + '2+3_force_en': two_plus_three_mc_force_en, + '2+3+many': two_three_many_body_mc, + '2+3+many_grad': two_three_many_body_mc_grad, + '2+3+many_en': two_three_many_mc_en, + '2+3+many_force_en': two_three_many_mc_force_en } @@ -1179,3 +1271,4 @@ def str_to_mc_kernel(string: str, include_grad: bool = False): else: raise ValueError("Gradient callable for {} not found".format( string)) + diff --git a/flare/kernels/mc_simple.py b/flare/kernels/mc_simple.py index 2a844e7a9..c3cb70ba5 100644 --- a/flare/kernels/mc_simple.py +++ b/flare/kernels/mc_simple.py @@ -9,9 +9,12 @@ import flare.cutoffs as cf from flare.kernels.kernels import force_helper, grad_constants, grad_helper, \ force_energy_helper, three_body_en_helper, three_body_helper_1, \ - three_body_helper_2, three_body_grad_helper_1, three_body_grad_helper_2 + three_body_helper_2, three_body_grad_helper_1, three_body_grad_helper_2, \ + k_sq_exp_double_dev, k_sq_exp_dev, coordination_number, q_value, q_value_mc, \ + mb_grad_helper_ls_, mb_grad_helper_ls_ from typing import Callable + # ----------------------------------------------------------------------------- # two plus three body kernels # ----------------------------------------------------------------------------- @@ -21,7 +24,7 @@ def two_plus_three_body_mc(env1: AtomicEnvironment, env2: AtomicEnvironment, d1: int, d2: int, hyps: 'ndarray', cutoffs: 'ndarray', cutoff_func: Callable = cf.quadratic_cutoff) \ - -> float: + -> float: """2+3-body multi-element kernel between two force components. Args: @@ -66,7 +69,7 @@ def two_plus_three_body_mc_grad(env1: AtomicEnvironment, d1: int, d2: int, hyps: 'ndarray', cutoffs: 'ndarray', cutoff_func: Callable = cf.quadratic_cutoff) \ - -> ('float', 'ndarray'): + -> ('float', 'ndarray'): """2+3-body multi-element kernel between two force components and its gradient with respect to the hyperparameters. @@ -115,7 +118,7 @@ def two_plus_three_mc_force_en(env1: AtomicEnvironment, env2: AtomicEnvironment, d1: int, hyps: 'ndarray', cutoffs: 'ndarray', cutoff_func: Callable = cf.quadratic_cutoff) \ - -> float: + -> float: """2+3-body multi-element kernel between a force component and a local energy. @@ -145,7 +148,7 @@ def two_plus_three_mc_force_en(env1: AtomicEnvironment, two_term = \ two_body_mc_force_en_jit(env1.bond_array_2, env1.ctype, env1.etypes, env2.bond_array_2, env2.ctype, env2.etypes, - d1, sig2, ls2, r_cut_2, cutoff_func)/2 + d1, sig2, ls2, r_cut_2, cutoff_func) / 2 three_term = \ three_body_mc_force_en_jit(env1.bond_array_3, env1.ctype, env1.etypes, @@ -154,7 +157,7 @@ def two_plus_three_mc_force_en(env1: AtomicEnvironment, env1.cross_bond_dists, env2.cross_bond_dists, env1.triplet_counts, env2.triplet_counts, - d1, sig3, ls3, r_cut_3, cutoff_func)/3 + d1, sig3, ls3, r_cut_3, cutoff_func) / 3 return two_term + three_term @@ -162,7 +165,7 @@ def two_plus_three_mc_force_en(env1: AtomicEnvironment, def two_plus_three_mc_en(env1: AtomicEnvironment, env2: AtomicEnvironment, hyps: 'ndarray', cutoffs: 'ndarray', cutoff_func: Callable = cf.quadratic_cutoff) \ - -> float: + -> float: """2+3-body multi-element kernel between two local energies. Args: @@ -200,6 +203,222 @@ def two_plus_three_mc_en(env1: AtomicEnvironment, env2: AtomicEnvironment, return two_term + three_term +# ----------------------------------------------------------------------------- +# two plus three plus many body kernels +# ----------------------------------------------------------------------------- + +def two_plus_three_plus_many_body_mc(env1: AtomicEnvironment, env2: AtomicEnvironment, + d1: int, d2: int, hyps, cutoffs, + cutoff_func=cf.quadratic_cutoff): + """2+3-body single-element kernel between two force components. + + Args: + env1 (AtomicEnvironment): First local environment. + env2 (AtomicEnvironment): Second local environment. + d1 (int): Force component of the first environment. + d2 (int): Force component of the second environment. + hyps (np.ndarray): Hyperparameters of the kernel function (sig1, ls1, + sig2, ls2, sig3, ls3, sig_n). + cutoffs (np.ndarray): Two-element array containing the 2- and 3-body + cutoffs. + cutoff_func (Callable): Cutoff function of the kernel. + + Return: + float: Value of the 2+3+many-body kernel. + """ + + sig2 = hyps[0] + ls2 = hyps[1] + sig3 = hyps[2] + ls3 = hyps[3] + sigm = hyps[4] + lsm = hyps[5] + + r_cut_2 = cutoffs[0] + r_cut_3 = cutoffs[1] + r_cut_m = cutoffs[2] + + two_term = two_body_mc_jit(env1.bond_array_2, env1.ctype, env1.etypes, + env2.bond_array_2, env2.ctype, env2.etypes, + d1, d2, sig2, ls2, r_cut_2, cutoff_func) + + three_term = \ + three_body_mc_jit(env1.bond_array_3, env1.ctype, env1.etypes, + env2.bond_array_3, env2.ctype, env2.etypes, + env1.cross_bond_inds, env2.cross_bond_inds, + env1.cross_bond_dists, env2.cross_bond_dists, + env1.triplet_counts, env2.triplet_counts, + d1, d2, sig3, ls3, r_cut_3, cutoff_func) + + many_term = many_body_mc_jit(env1.bond_array_mb, env2.bond_array_mb, env1.neigh_dists_mb, + env2.neigh_dists_mb, env1.num_neighs_mb, env2.num_neighs_mb, + env1.ctype, env2.ctype, + env1.bond_array_mb_etypes, env2.bond_array_mb_etypes, + env1.etype_mb, env2.etype_mb, env1.species, env2.species, + d1, d2, sigm, lsm, r_cut_m, cutoff_func) + + return two_term + three_term + many_term + + +def two_plus_three_plus_many_body_mc_grad(env1: AtomicEnvironment, env2: AtomicEnvironment, + d1: int, d2: int, hyps, cutoffs, + cutoff_func=cf.quadratic_cutoff): + """2+3+many-body single-element kernel between two force components. + + Args: + env1 (AtomicEnvironment): First local environment. + env2 (AtomicEnvironment): Second local environment. + d1 (int): Force component of the first environment. + d2 (int): Force component of the second environment. + hyps (np.ndarray): Hyperparameters of the kernel function (sig1, ls1, + sig2, ls2, sig3, ls3, sig_n). + cutoffs (np.ndarray): Two-element array containing the 2- and 3-body + cutoffs. + cutoff_func (Callable): Cutoff function of the kernel. + + Return: + float: Value of the 2+3+many-body kernel. + """ + + sig2 = hyps[0] + ls2 = hyps[1] + sig3 = hyps[2] + ls3 = hyps[3] + sigm = hyps[4] + lsm = hyps[5] + + r_cut_2 = cutoffs[0] + r_cut_3 = cutoffs[1] + r_cut_m = cutoffs[2] + + kern2, grad2 = two_body_mc_grad_jit(env1.bond_array_2, env1.ctype, env1.etypes, + env2.bond_array_2, env2.ctype, env2.etypes, + d1, d2, sig2, ls2, r_cut_2, cutoff_func) + + kern3, grad3 = \ + three_body_mc_grad_jit(env1.bond_array_3, env1.ctype, env1.etypes, + env2.bond_array_3, env2.ctype, env2.etypes, + env1.cross_bond_inds, env2.cross_bond_inds, + env1.cross_bond_dists, env2.cross_bond_dists, + env1.triplet_counts, env2.triplet_counts, + d1, d2, sig3, ls3, r_cut_3, cutoff_func) + + kern_many, gradm = many_body_mc_grad_jit(env1.bond_array_mb, env2.bond_array_mb, + env1.neigh_dists_mb, env2.neigh_dists_mb, + env1.num_neighs_mb, env2.num_neighs_mb, env1.ctype, + env2.ctype, env1.bond_array_mb_etypes, + env2.bond_array_mb_etypes, + env1.etype_mb, env2.etype_mb, + env1.species, env2.species, d1, d2, sigm, + lsm, r_cut_m, cutoff_func) + + return kern2 + kern3 + kern_many, np.hstack([grad2, grad3, gradm]) + + +def two_plus_three_plus_many_body_mc_force_en(env1: AtomicEnvironment, env2: AtomicEnvironment, + d1: int, hyps, cutoffs, + cutoff_func=cf.quadratic_cutoff): + """2+3+many-body single-element kernel between two force and energy components. + + Args: + env1 (AtomicEnvironment): First local environment. + env2 (AtomicEnvironment): Second local environment. + d1 (int): Force component of the first environment. + hyps (np.ndarray): Hyperparameters of the kernel function (sig1, ls1, + sig2, ls2, sig3, ls3, sig_n). + cutoffs (np.ndarray): Two-element array containing the 2- and 3-body + cutoffs. + cutoff_func (Callable): Cutoff function of the kernel. + + Return: + float: Value of the 2+3+many-body kernel. + """ + + sig2 = hyps[0] + ls2 = hyps[1] + sig3 = hyps[2] + ls3 = hyps[3] + sigm = hyps[4] + lsm = hyps[5] + + r_cut_2 = cutoffs[0] + r_cut_3 = cutoffs[1] + r_cut_m = cutoffs[2] + + two_term = \ + two_body_mc_force_en_jit(env1.bond_array_2, env1.ctype, env1.etypes, + env2.bond_array_2, env2.ctype, env2.etypes, + d1, sig2, ls2, r_cut_2, cutoff_func) / 2 + + three_term = \ + three_body_mc_force_en_jit(env1.bond_array_3, env1.ctype, env1.etypes, + env2.bond_array_3, env2.ctype, env2.etypes, + env1.cross_bond_inds, env2.cross_bond_inds, + env1.cross_bond_dists, + env2.cross_bond_dists, + env1.triplet_counts, env2.triplet_counts, + d1, sig3, ls3, r_cut_3, cutoff_func) / 3 + + many_term = many_body_mc_force_en_jit(env1.bond_array_mb, env2.bond_array_mb, + env1.neigh_dists_mb, env1.num_neighs_mb, + env1.ctype, env2.ctype, env1.bond_array_mb_etypes, + env2.bond_array_mb_etypes, + env1.etype_mb, + env1.species, env2.species, d1, sigm, lsm, r_cut_m, + cutoff_func) + + return two_term + three_term + many_term + + +def two_plus_three_plus_many_body_mc_en(env1: AtomicEnvironment, env2: AtomicEnvironment, + hyps, cutoffs, cutoff_func=cf.quadratic_cutoff): + """2+3+many-body single-element energy kernel. + + Args: + env1 (AtomicEnvironment): First local environment. + env2 (AtomicEnvironment): Second local environment. + hyps (np.ndarray): Hyperparameters of the kernel function (sig1, ls1, + sig2, ls2, sig3, ls3, sig_n). + cutoffs (np.ndarray): Two-element array containing the 2- and 3-body + cutoffs. + cutoff_func (Callable): Cutoff function of the kernel. + + Return: + float: Value of the 2+3+many-body kernel. + """ + + sig2 = hyps[0] + ls2 = hyps[1] + sig3 = hyps[2] + ls3 = hyps[3] + sigm = hyps[4] + lsm = hyps[5] + + r_cut_2 = cutoffs[0] + r_cut_3 = cutoffs[1] + r_cut_m = cutoffs[2] + + two_term = two_body_mc_en_jit(env1.bond_array_2, env1.ctype, env1.etypes, + env2.bond_array_2, env2.ctype, env2.etypes, + sig2, ls2, r_cut_2, cutoff_func) + + three_term = \ + three_body_mc_en_jit(env1.bond_array_3, env1.ctype, env1.etypes, + env2.bond_array_3, env2.ctype, env2.etypes, + env1.cross_bond_inds, env2.cross_bond_inds, + env1.cross_bond_dists, env2.cross_bond_dists, + env1.triplet_counts, env2.triplet_counts, + sig3, ls3, r_cut_3, cutoff_func) + + many_term = many_body_mc_en_jit(env1.bond_array_mb, env2.bond_array_mb, env1.ctype, + env2.ctype, env1.bond_array_mb_etypes, + env2.bond_array_mb_etypes, env1.species, + env2.species, + sigm, lsm, r_cut_m, cutoff_func) + + return two_term + three_term + many_term + + # ----------------------------------------------------------------------------- # three body multicomponent kernel # ----------------------------------------------------------------------------- @@ -238,7 +457,7 @@ def three_body_mc(env1: AtomicEnvironment, env2: AtomicEnvironment, def three_body_mc_grad(env1: AtomicEnvironment, env2: AtomicEnvironment, d1: int, d2: int, hyps: 'ndarray', cutoffs: 'ndarray', cutoff_func: Callable = cf.quadratic_cutoff) \ - -> ('float', 'ndarray'): + -> ('float', 'ndarray'): """3-body multi-element kernel between two force components and its gradient with respect to the hyperparameters. @@ -272,7 +491,7 @@ def three_body_mc_grad(env1: AtomicEnvironment, env2: AtomicEnvironment, def three_body_mc_force_en(env1: AtomicEnvironment, env2: AtomicEnvironment, d1: int, hyps: 'ndarray', cutoffs: 'ndarray', cutoff_func: Callable = cf.quadratic_cutoff) \ - -> float: + -> float: """3-body multi-element kernel between a force component and a local energy. @@ -303,13 +522,13 @@ def three_body_mc_force_en(env1: AtomicEnvironment, env2: AtomicEnvironment, env1.cross_bond_dists, env2.cross_bond_dists, env1.triplet_counts, env2.triplet_counts, - d1, sig, ls, r_cut, cutoff_func)/3 + d1, sig, ls, r_cut, cutoff_func) / 3 def three_body_mc_en(env1: AtomicEnvironment, env2: AtomicEnvironment, hyps: 'ndarray', cutoffs: 'ndarray', cutoff_func: Callable = cf.quadratic_cutoff) \ - -> float: + -> float: """3-body multi-element kernel between two local energies. Args: @@ -334,6 +553,7 @@ def three_body_mc_en(env1: AtomicEnvironment, env2: AtomicEnvironment, env1.triplet_counts, env2.triplet_counts, sig, ls, r_cut, cutoff_func) + # ----------------------------------------------------------------------------- # two body multicomponent kernel # ----------------------------------------------------------------------------- @@ -369,7 +589,7 @@ def two_body_mc(env1: AtomicEnvironment, env2: AtomicEnvironment, def two_body_mc_grad(env1: AtomicEnvironment, env2: AtomicEnvironment, d1: int, d2: int, hyps: 'ndarray', cutoffs: 'ndarray', cutoff_func: Callable = cf.quadratic_cutoff) \ - -> (float, 'ndarray'): + -> (float, 'ndarray'): """2-body multi-element kernel between two force components and its gradient with respect to the hyperparameters. @@ -400,7 +620,7 @@ def two_body_mc_grad(env1: AtomicEnvironment, env2: AtomicEnvironment, def two_body_mc_force_en(env1: AtomicEnvironment, env2: AtomicEnvironment, d1: int, hyps: 'ndarray', cutoffs: 'ndarray', cutoff_func: Callable = cf.quadratic_cutoff) \ - -> float: + -> float: """2-body multi-element kernel between a force component and a local energy. @@ -424,13 +644,13 @@ def two_body_mc_force_en(env1: AtomicEnvironment, env2: AtomicEnvironment, return two_body_mc_force_en_jit(env1.bond_array_2, env1.ctype, env1.etypes, env2.bond_array_2, env2.ctype, env2.etypes, - d1, sig, ls, r_cut, cutoff_func)/2 + d1, sig, ls, r_cut, cutoff_func) / 2 def two_body_mc_en(env1: AtomicEnvironment, env2: AtomicEnvironment, hyps: 'ndarray', cutoffs: 'ndarray', cutoff_func: Callable = cf.quadratic_cutoff) \ - -> float: + -> float: """2-body multi-element kernel between two local energies. Args: @@ -453,6 +673,145 @@ def two_body_mc_en(env1: AtomicEnvironment, env2: AtomicEnvironment, sig, ls, r_cut, cutoff_func) +# ----------------------------------------------------------------------------- +# many body multicomponent kernel +# ----------------------------------------------------------------------------- + + +def many_body_mc(env1: AtomicEnvironment, env2: AtomicEnvironment, + d1: int, d2: int, hyps: 'ndarray', cutoffs: 'ndarray', + cutoff_func: Callable = cf.quadratic_cutoff) -> float: + """many-body multi-element kernel between two force components. + + Args: + env1 (AtomicEnvironment): First local environment. + env2 (AtomicEnvironment): Second local environment. + d1 (int): Force component of the first environment. + d2 (int): Force component of the second environment. + hyps (np.ndarray): Hyperparameters of the kernel function (sig, ls). + cutoffs (np.ndarray): Two-element array containing the 2- and 3-body + cutoffs. + cutoff_func (Callable): Cutoff function of the kernel. + + Return: + float: Value of the 3-body kernel. + """ + sig = hyps[0] + ls = hyps[1] + r_cut = cutoffs[2] + + bond_array_1 = env1.bond_array_mb + bond_array_2 = env2.bond_array_mb + + neigh_dists_1 = env1.neigh_dists_mb + num_neigh_1 = env1.num_neighs_mb + + neigh_dists_2 = env2.neigh_dists_mb + num_neigh_2 = env2.num_neighs_mb + + # Get atomic species of central atom, their neighbours, and their neighbours' neighbours + c1, c2 = env1.ctype, env2.ctype + etypes1, etypes2 = env1.bond_array_mb_etypes, env2.bond_array_mb_etypes + etypes_neigh_1, etypes_neigh_2 = env1.etype_mb, env2.etype_mb + + return many_body_mc_jit(bond_array_1, bond_array_2, neigh_dists_1, neigh_dists_2, num_neigh_1, + num_neigh_2, c1, c2, etypes1, etypes2, etypes_neigh_1, etypes_neigh_2, + env1.species, env2.species, d1, d2, sig, ls, r_cut, cutoff_func) + + +def many_body_mc_grad(env1: AtomicEnvironment, env2: AtomicEnvironment, + d1: int, d2: int, hyps: 'ndarray', cutoffs: 'ndarray', + cutoff_func: Callable = cf.quadratic_cutoff) -> float: + """gradient manybody-body multi-element kernel between two force components. + + """ + sig = hyps[0] + ls = hyps[1] + r_cut = cutoffs[2] + + bond_array_1 = env1.bond_array_mb + bond_array_2 = env2.bond_array_mb + + neigh_dists_1 = env1.neigh_dists_mb + num_neigh_1 = env1.num_neighs_mb + + neigh_dists_2 = env2.neigh_dists_mb + num_neigh_2 = env2.num_neighs_mb + + c1, c2 = env1.ctype, env2.ctype + etypes1, etypes2 = env1.bond_array_mb_etypes, env2.bond_array_mb_etypes + etypes_neigh_1, etypes_neigh_2 = env1.etype_mb, env2.etype_mb + + kernel, kernel_grad = many_body_mc_grad_jit(bond_array_1, bond_array_2, neigh_dists_1, + neigh_dists_2, num_neigh_1, num_neigh_2, c1, + c2, etypes1, etypes2, etypes_neigh_1, + etypes_neigh_2, env1.species, env2.species, + d1, d2, sig, ls, r_cut, cutoff_func) + + return kernel, kernel_grad + + +def many_body_mc_force_en(env1, env2, d1, hyps, cutoffs, + cutoff_func=cf.quadratic_cutoff): + """many-body single-element kernel between two local energies. + + Args: + env1 (AtomicEnvironment): First local environment. + env2 (AtomicEnvironment): Second local environment. + hyps (np.ndarray): Hyperparameters of the kernel function (sig, ls). + cutoffs (np.ndarray): Two-element array containing the 2-, 3-, and + many-body cutoffs. + cutoff_func (Callable): Cutoff function of the kernel. + + Return: + float: Value of the many-body force/energy kernel. + """ + sig = hyps[0] + ls = hyps[1] + r_cut = cutoffs[2] + + bond_array_1 = env1.bond_array_mb + bond_array_2 = env2.bond_array_mb + + neigh_dists_1 = env1.neigh_dists_mb + num_neigh_1 = env1.num_neighs_mb + + c1, c2 = env1.ctype, env2.ctype + etypes1, etypes2 = env1.bond_array_mb_etypes, env2.bond_array_mb_etypes + etypes_neigh_1 = env1.etype_mb + + # divide by three to account for triple counting + return many_body_mc_force_en_jit(bond_array_1, bond_array_2, neigh_dists_1, num_neigh_1, + c1, c2, etypes1, etypes2, etypes_neigh_1, + env1.species, env2.species, d1, sig, ls, r_cut, cutoff_func) + + +def many_body_mc_en(env1: AtomicEnvironment, env2: AtomicEnvironment, + hyps: 'ndarray', cutoffs: 'ndarray', + cutoff_func: Callable = cf.quadratic_cutoff) -> float: + """many-body multi-element kernel between two local energies. + + Args: + env1 (AtomicEnvironment): First local environment. + env2 (AtomicEnvironment): Second local environment. + hyps (np.ndarray): Hyperparameters of the kernel function (sig, ls). + cutoffs (np.ndarray): One-element array containing the 2-body + cutoff. + cutoff_func (Callable): Cutoff function of the kernel. + + Return: + float: Value of the 2-body force/energy kernel. + """ + sig = hyps[0] + ls = hyps[1] + r_cut = cutoffs[2] + + return many_body_mc_en_jit(env1.bond_array_mb, env2.bond_array_mb, env1.ctype, + env2.ctype, env1.bond_array_mb_etypes, env2.bond_array_mb_etypes, + env1.species, env2.species, + sig, ls, r_cut, cutoff_func) + + # ----------------------------------------------------------------------------- # three body multicomponent kernel (numba) # ----------------------------------------------------------------------------- @@ -511,13 +870,13 @@ def three_body_mc_jit(bond_array_1, c1, etypes1, Return: float: Value of the 3-body kernel. """ - kern = 0 + kern = 0.0 # pre-compute constants that appear in the inner loop - sig2 = sig*sig - ls1 = 1 / (2*ls*ls) - ls2 = 1 / (ls*ls) - ls3 = ls2*ls2 + sig2 = sig * sig + ls1 = 1 / (2 * ls * ls) + ls2 = 1 / (ls * ls) + ls3 = ls2 * ls2 # first loop over the first 3-body environment for m in range(bond_array_1.shape[0]): @@ -528,17 +887,17 @@ def three_body_mc_jit(bond_array_1, c1, etypes1, # second loop over the first 3-body environment for n in range(triplets_1[m]): - ind1 = cross_bond_inds_1[m, m+n+1] + ind1 = cross_bond_inds_1[m, m + n + 1] ri2 = bond_array_1[ind1, 0] ci2 = bond_array_1[ind1, d1] fi2, fdi2 = cutoff_func(r_cut, ri2, ci2) ei2 = etypes1[ind1] - ri3 = cross_bond_dists_1[m, m+n+1] + ri3 = cross_bond_dists_1[m, m + n + 1] fi3, _ = cutoff_func(r_cut, ri3, 0) - fi = fi1*fi2*fi3 - fdi = fdi1*fi2*fi3+fi1*fdi2*fi3 + fi = fi1 * fi2 * fi3 + fdi = fdi1 * fi2 * fi3 + fi1 * fdi2 * fi3 # first loop over the second 3-body environment for p in range(bond_array_2.shape[0]): @@ -549,27 +908,27 @@ def three_body_mc_jit(bond_array_1, c1, etypes1, # second loop over the second 3-body environment for q in range(triplets_2[p]): - ind2 = cross_bond_inds_2[p, p+1+q] + ind2 = cross_bond_inds_2[p, p + 1 + q] rj2 = bond_array_2[ind2, 0] cj2 = bond_array_2[ind2, d2] fj2, fdj2 = cutoff_func(r_cut, rj2, cj2) ej2 = etypes2[ind2] - rj3 = cross_bond_dists_2[p, p+1+q] + rj3 = cross_bond_dists_2[p, p + 1 + q] fj3, _ = cutoff_func(r_cut, rj3, 0) - fj = fj1*fj2*fj3 - fdj = fdj1*fj2*fj3+fj1*fdj2*fj3 + fj = fj1 * fj2 * fj3 + fdj = fdj1 * fj2 * fj3 + fj1 * fdj2 * fj3 - r11 = ri1-rj1 - r12 = ri1-rj2 - r13 = ri1-rj3 - r21 = ri2-rj1 - r22 = ri2-rj2 - r23 = ri2-rj3 - r31 = ri3-rj1 - r32 = ri3-rj2 - r33 = ri3-rj3 + r11 = ri1 - rj1 + r12 = ri1 - rj2 + r13 = ri1 - rj3 + r21 = ri2 - rj1 + r22 = ri2 - rj2 + r23 = ri2 - rj3 + r31 = ri3 - rj1 + r32 = ri3 - rj2 + r33 = ri3 - rj3 # consider six permutations if (c1 == c2): @@ -664,10 +1023,10 @@ def three_body_mc_grad_jit(bond_array_1, c1, etypes1, Value of the 3-body kernel and its gradient with respect to the hyperparameters. """ - kern = 0 - sig_derv = 0 - ls_derv = 0 - kern_grad = np.zeros(2) + kern = 0.0 + sig_derv = 0.0 + ls_derv = 0.0 + kern_grad = np.zeros(2, dtype=np.float64) # pre-compute constants that appear in the inner loop sig2, sig3, ls1, ls2, ls3, ls4, ls5, ls6 = grad_constants(sig, ls) @@ -679,8 +1038,8 @@ def three_body_mc_grad_jit(bond_array_1, c1, etypes1, ei1 = etypes1[m] for n in range(triplets_1[m]): - ind1 = cross_bond_inds_1[m, m+n+1] - ri3 = cross_bond_dists_1[m, m+n+1] + ind1 = cross_bond_inds_1[m, m + n + 1] + ri3 = cross_bond_dists_1[m, m + n + 1] ri2 = bond_array_1[ind1, 0] ci2 = bond_array_1[ind1, d1] ei2 = etypes1[ind1] @@ -688,8 +1047,8 @@ def three_body_mc_grad_jit(bond_array_1, c1, etypes1, fi2, fdi2 = cutoff_func(r_cut, ri2, ci2) fi3, _ = cutoff_func(r_cut, ri3, 0) - fi = fi1*fi2*fi3 - fdi = fdi1*fi2*fi3+fi1*fdi2*fi3 + fi = fi1 * fi2 * fi3 + fdi = fdi1 * fi2 * fi3 + fi1 * fdi2 * fi3 for p in range(bond_array_2.shape[0]): rj1 = bond_array_2[p, 0] @@ -698,8 +1057,8 @@ def three_body_mc_grad_jit(bond_array_1, c1, etypes1, ej1 = etypes2[p] for q in range(triplets_2[p]): - ind2 = cross_bond_inds_2[p, p+q+1] - rj3 = cross_bond_dists_2[p, p+q+1] + ind2 = cross_bond_inds_2[p, p + q + 1] + rj3 = cross_bond_dists_2[p, p + q + 1] rj2 = bond_array_2[ind2, 0] cj2 = bond_array_2[ind2, d2] ej2 = etypes2[ind2] @@ -707,18 +1066,18 @@ def three_body_mc_grad_jit(bond_array_1, c1, etypes1, fj2, fdj2 = cutoff_func(r_cut, rj2, cj2) fj3, _ = cutoff_func(r_cut, rj3, 0) - fj = fj1*fj2*fj3 - fdj = fdj1*fj2*fj3+fj1*fdj2*fj3 + fj = fj1 * fj2 * fj3 + fdj = fdj1 * fj2 * fj3 + fj1 * fdj2 * fj3 - r11 = ri1-rj1 - r12 = ri1-rj2 - r13 = ri1-rj3 - r21 = ri2-rj1 - r22 = ri2-rj2 - r23 = ri2-rj3 - r31 = ri3-rj1 - r32 = ri3-rj2 - r33 = ri3-rj3 + r11 = ri1 - rj1 + r12 = ri1 - rj2 + r13 = ri1 - rj3 + r21 = ri2 - rj1 + r22 = ri2 - rj2 + r23 = ri2 - rj3 + r31 = ri3 - rj1 + r32 = ri3 - rj2 + r33 = ri3 - rj3 if (c1 == c2): if (ei1 == ej1) and (ei2 == ej2): @@ -852,9 +1211,9 @@ def three_body_mc_force_en_jit(bond_array_1, c1, etypes1, kern = 0 # pre-compute constants that appear in the inner loop - sig2 = sig*sig - ls1 = 1 / (2*ls*ls) - ls2 = 1 / (ls*ls) + sig2 = sig * sig + ls1 = 1 / (2 * ls * ls) + ls2 = 1 / (ls * ls) for m in range(bond_array_1.shape[0]): ri1 = bond_array_1[m, 0] @@ -863,17 +1222,17 @@ def three_body_mc_force_en_jit(bond_array_1, c1, etypes1, ei1 = etypes1[m] for n in range(triplets_1[m]): - ind1 = cross_bond_inds_1[m, m+n+1] + ind1 = cross_bond_inds_1[m, m + n + 1] ri2 = bond_array_1[ind1, 0] ci2 = bond_array_1[ind1, d1] fi2, fdi2 = cutoff_func(r_cut, ri2, ci2) ei2 = etypes1[ind1] - ri3 = cross_bond_dists_1[m, m+n+1] + ri3 = cross_bond_dists_1[m, m + n + 1] fi3, _ = cutoff_func(r_cut, ri3, 0) - fi = fi1*fi2*fi3 - fdi = fdi1*fi2*fi3+fi1*fdi2*fi3 + fi = fi1 * fi2 * fi3 + fdi = fdi1 * fi2 * fi3 + fi1 * fdi2 * fi3 for p in range(bond_array_2.shape[0]): rj1 = bond_array_2[p, 0] @@ -881,23 +1240,23 @@ def three_body_mc_force_en_jit(bond_array_1, c1, etypes1, ej1 = etypes2[p] for q in range(triplets_2[p]): - ind2 = cross_bond_inds_2[p, p+q+1] + ind2 = cross_bond_inds_2[p, p + q + 1] rj2 = bond_array_2[ind2, 0] fj2, _ = cutoff_func(r_cut, rj2, 0) ej2 = etypes2[ind2] - rj3 = cross_bond_dists_2[p, p+q+1] + rj3 = cross_bond_dists_2[p, p + q + 1] fj3, _ = cutoff_func(r_cut, rj3, 0) - fj = fj1*fj2*fj3 - - r11 = ri1-rj1 - r12 = ri1-rj2 - r13 = ri1-rj3 - r21 = ri2-rj1 - r22 = ri2-rj2 - r23 = ri2-rj3 - r31 = ri3-rj1 - r32 = ri3-rj2 - r33 = ri3-rj3 + fj = fj1 * fj2 * fj3 + + r11 = ri1 - rj1 + r12 = ri1 - rj2 + r13 = ri1 - rj3 + r21 = ri2 - rj1 + r22 = ri2 - rj2 + r23 = ri2 - rj3 + r31 = ri3 - rj1 + r32 = ri3 - rj2 + r33 = ri3 - rj3 if (c1 == c2): if (ei1 == ej1) and (ei2 == ej2): @@ -985,8 +1344,8 @@ def three_body_mc_en_jit(bond_array_1, c1, etypes1, kern = 0 - sig2 = sig*sig - ls2 = 1 / (2*ls*ls) + sig2 = sig * sig + ls2 = 1 / (2 * ls * ls) for m in range(bond_array_1.shape[0]): ri1 = bond_array_1[m, 0] @@ -1001,7 +1360,7 @@ def three_body_mc_en_jit(bond_array_1, c1, etypes1, ri3 = cross_bond_dists_1[m, m + n + 1] fi3, _ = cutoff_func(r_cut, ri3, 0) - fi = fi1*fi2*fi3 + fi = fi1 * fi2 * fi3 for p in range(bond_array_2.shape[0]): rj1 = bond_array_2[p, 0] @@ -1016,38 +1375,38 @@ def three_body_mc_en_jit(bond_array_1, c1, etypes1, rj3 = cross_bond_dists_2[p, p + q + 1] fj3, _ = cutoff_func(r_cut, rj3, 0) - fj = fj1*fj2*fj3 - - r11 = ri1-rj1 - r12 = ri1-rj2 - r13 = ri1-rj3 - r21 = ri2-rj1 - r22 = ri2-rj2 - r23 = ri2-rj3 - r31 = ri3-rj1 - r32 = ri3-rj2 - r33 = ri3-rj3 + fj = fj1 * fj2 * fj3 + + r11 = ri1 - rj1 + r12 = ri1 - rj2 + r13 = ri1 - rj3 + r21 = ri2 - rj1 + r22 = ri2 - rj2 + r23 = ri2 - rj3 + r31 = ri3 - rj1 + r32 = ri3 - rj2 + r33 = ri3 - rj3 if (c1 == c2): if (ei1 == ej1) and (ei2 == ej2): - C1 = r11*r11+r22*r22+r33*r33 + C1 = r11 * r11 + r22 * r22 + r33 * r33 kern += sig2 * exp(-C1 * ls2) * fi * fj if (ei1 == ej2) and (ei2 == ej1): - C3 = r12*r12+r21*r21+r33*r33 + C3 = r12 * r12 + r21 * r21 + r33 * r33 kern += sig2 * exp(-C3 * ls2) * fi * fj if (c1 == ej1): if (ei1 == ej2) and (ei2 == c2): - C5 = r13*r13+r21*r21+r32*r32 + C5 = r13 * r13 + r21 * r21 + r32 * r32 kern += sig2 * exp(-C5 * ls2) * fi * fj if (ei1 == c2) and (ei2 == ej2): - C2 = r11*r11+r23*r23+r32*r32 + C2 = r11 * r11 + r23 * r23 + r32 * r32 kern += sig2 * exp(-C2 * ls2) * fi * fj if (c1 == ej2): if (ei1 == ej1) and (ei2 == c2): - C6 = r13*r13+r22*r22+r31*r31 + C6 = r13 * r13 + r22 * r22 + r31 * r31 kern += sig2 * exp(-C6 * ls2) * fi * fj if (ei1 == c2) and (ei2 == ej1): - C4 = r12*r12+r23*r23+r31*r31 + C4 = r12 * r12 + r23 * r23 + r31 * r31 kern += sig2 * exp(-C4 * ls2) * fi * fj return kern @@ -1151,10 +1510,10 @@ def two_body_mc_grad_jit(bond_array_1, c1, etypes1, hyperparameters. """ - kern = 0 - sig_derv = 0 - ls_derv = 0 - kern_grad = np.zeros(2) + kern = 0.0 + sig_derv = 0.0 + ls_derv = 0.0 + kern_grad = np.zeros(2, dtype=np.float64) ls1 = 1 / (2 * ls * ls) ls2 = 1 / (ls * ls) @@ -1163,7 +1522,7 @@ def two_body_mc_grad_jit(bond_array_1, c1, etypes1, ls5 = ls * ls ls6 = ls2 * ls4 - sig2 = sig*sig + sig2 = sig * sig sig3 = 2 * sig for m in range(bond_array_1.shape[0]): @@ -1308,6 +1667,536 @@ def two_body_mc_en_jit(bond_array_1, c1, etypes1, return kern +# ----------------------------------------------------------------------------- +# many body multicomponent kernel (numba) +# ----------------------------------------------------------------------------- + +@njit +def many_body_mc_jit_(bond_array_1, bond_array_2, neigh_dists_1, neigh_dists_2, num_neigh_1, + num_neigh_2, c1, c2, etypes1, etypes2, etypes_neigh_1, etypes_neigh_2, + species1, species2, d1, d2, sig, ls, r_cut, cutoff_func): + """many-body multi-element kernel between two force components accelerated + with Numba. + + Args: + bond_array_1 (np.ndarray): many-body bond array of the first local + environment. + bond_array_2 (np.ndarray): many-body bond array of the second local + environment. + neigh_dists_1 (np.ndarray): matrix padded with zero values of distances + of neighbours for the atoms in the first local environment. + neigh_dists_2 (np.ndarray): matrix padded with zero values of distances + of neighbours for the atoms in the second local environment. + num_neigh_1 (np.ndarray): number of neighbours of each atom in the first + local environment + num_neigh_2 (np.ndarray): number of neighbours of each atom in the second + local environment + c1 (int): atomic species of the central atom in env 1 + c2 (int): atomic species of the central atom in env 2 + etypes1 (np.ndarray): atomic species of atoms in env 1 + etypes2 (np.ndarray): atomic species of atoms in env 2 + etypes_neigh_1 (np.ndarray): atomic species of atoms in the neighbourhoods + of atoms in env 1 + etypes_neigh_2 (np.ndarray): atomic species of atoms in the neighbourhoods + of atoms in env 2 + species1 (np.ndarray): all the atomic species present in trajectory 1 + species2 (np.ndarray): all the atomic species present in trajectory 2 + d1 (int): Force component of the first environment. + d2 (int): Force component of the second environment. + sig (float): many-body signal variance hyperparameter. + ls (float): many-body length scale hyperparameter. + r_cut (float): many-body cutoff radius. + cutoff_func (Callable): Cutoff function. + + Return: + float: Value of the many-body kernel. + """ + + kern = 0 + + useful_species = np.array( + list(set(species1).union(set(species2))), dtype=np.int8) + + # loop over all possible species + for s in useful_species: + # Calculate many-body descriptor values for central atoms 1 and 2 + q1 = q_value_mc(bond_array_1[:, 0], r_cut, s, etypes1, cutoff_func) + q2 = q_value_mc(bond_array_2[:, 0], r_cut, s, etypes2, cutoff_func) + + # kernel is nonzero only if central atoms are of the same species + if c1 == c2: + k12 = k_sq_exp_double_dev(q1, q2, sig, ls) + else: + k12 = 0 + + qis = np.zeros(bond_array_1.shape[0], dtype=np.float64) + q1i_grads = np.zeros(bond_array_1.shape[0], dtype=np.float64) + qi1_grads = np.zeros(bond_array_1.shape[0], dtype=np.float64) + ki2s = np.zeros(bond_array_1.shape[0], dtype=np.float64) + + qjs = np.zeros(bond_array_2.shape[0], dtype=np.float64) + qj2_grads = np.zeros(bond_array_2.shape[0], dtype=np.float64) + q2j_grads = np.zeros(bond_array_2.shape[0], dtype=np.float64) + k1js = np.zeros(bond_array_2.shape[0], dtype=np.float64) + + # Loop over neighbours i of 1 + for i in range(bond_array_1.shape[0]): + ri1 = bond_array_1[i, 0] + ci1 = bond_array_1[i, d1] + + if etypes1[i] == s: + qi1, qi1_grads[i] = coordination_number( + ri1, ci1, r_cut, cutoff_func) + + if c1 == s: + qi1, q1i_grads[i] = coordination_number( + ri1, ci1, r_cut, cutoff_func) + + # kernel is nonzero only if central atoms are of the same species + if c2 == etypes1[i]: + ki2s[i] = k_sq_exp_double_dev(qis[i], q2, sig, ls) + + # Loop over neighbours j of 2 + for j in range(bond_array_2.shape[0]): + rj2 = bond_array_2[j, 0] + cj2 = bond_array_2[j, d2] + + if etypes2[j] == s: + qj2, qj2_grads[j] = coordination_number( + rj2, cj2, r_cut, cutoff_func) + + if c2 == s: + qj2, q2j_grads[j] = coordination_number( + rj2, cj2, r_cut, cutoff_func) + + # Calculate many-body descriptor value for j + qjs[j] = q_value_mc(neigh_dists_2[j, :num_neigh_2[j]], r_cut, + s, etypes_neigh_2[j, :num_neigh_2[j]], cutoff_func) + + # kernel is nonzero only if central atoms are of the same species + if c1 == etypes2[j]: + k1js[j] = k_sq_exp_double_dev(q1, qjs[j], sig, ls) + + for i in range(bond_array_1.shape[0]): + for j in range(bond_array_2.shape[0]): + # kernel is nonzero only if central atoms are of the same species + if etypes1[i] == etypes2[j]: + kij = k_sq_exp_double_dev(qis[i], qjs[j], sig, ls) + else: + kij = 0 + + kern += qi1_grads[i] * qj2_grads[j] * \ + (k12 + ki2s[i] + k1js[j] + kij) + + return kern + + +def many_body_mc_jit(bond_array_1, bond_array_2, neigh_dists_1, neigh_dists_2, num_neigh_1, + num_neigh_2, c1, c2, etypes1, etypes2, etypes_neigh_1, etypes_neigh_2, + species1, species2, d1, d2, sig, ls, r_cut, cutoff_func): + """many-body multi-element kernel between two force components accelerated + with Numba. + + Args: + bond_array_1 (np.ndarray): many-body bond array of the first local + environment. + bond_array_2 (np.ndarray): many-body bond array of the second local + environment. + neigh_dists_1 (np.ndarray): matrix padded with zero values of distances + of neighbours for the atoms in the first local environment. + neigh_dists_2 (np.ndarray): matrix padded with zero values of distances + of neighbours for the atoms in the second local environment. + num_neigh_1 (np.ndarray): number of neighbours of each atom in the first + local environment + num_neigh_2 (np.ndarray): number of neighbours of each atom in the second + local environment + c1 (int): atomic species of the central atom in env 1 + c2 (int): atomic species of the central atom in env 2 + etypes1 (np.ndarray): atomic species of atoms in env 1 + etypes2 (np.ndarray): atomic species of atoms in env 2 + etypes_neigh_1 (np.ndarray): atomic species of atoms in the neighbourhoods + of atoms in env 1 + etypes_neigh_2 (np.ndarray): atomic species of atoms in the neighbourhoods + of atoms in env 2 + species1 (np.ndarray): all the atomic species present in trajectory 1 + species2 (np.ndarray): all the atomic species present in trajectory 2 + d1 (int): Force component of the first environment. + d2 (int): Force component of the second environment. + sig (float): many-body signal variance hyperparameter. + ls (float): many-body length scale hyperparameter. + r_cut (float): many-body cutoff radius. + cutoff_func (Callable): Cutoff function. + + Return: + float: Value of the many-body kernel. + """ + + kern = 0 + + useful_species = np.array( + list(set(species1).union(set(species2))), dtype=np.int8) + + # loop over all possible species + for s in useful_species: + + # Calculate many-body descriptor values for central atoms 1 and 2 + q1 = q_value_mc(bond_array_1[:, 0], r_cut, s, etypes1, cutoff_func) + q2 = q_value_mc(bond_array_2[:, 0], r_cut, s, etypes2, cutoff_func) + + # compute kernel between central atoms only if central atoms are of the same species + if c1 == c2: + k12 = k_sq_exp_double_dev(q1, q2, sig, ls) + else: + k12 = 0 + + # initialise arrays of many body descriptors and gradients for the neighbour atoms in + # the two configurations + qis = np.zeros(bond_array_1.shape[0], dtype=np.float64) + q1i_grads = np.zeros(bond_array_1.shape[0], dtype=np.float64) + qi1_grads = np.zeros(bond_array_1.shape[0], dtype=np.float64) + ki2s = np.zeros(bond_array_1.shape[0], dtype=np.float64) + + qjs = np.zeros(bond_array_2.shape[0], dtype=np.float64) + qj2_grads = np.zeros(bond_array_2.shape[0], dtype=np.float64) + q2j_grads = np.zeros(bond_array_2.shape[0], dtype=np.float64) + k1js = np.zeros(bond_array_2.shape[0], dtype=np.float64) + + # Loop over neighbours i of 1st configuration + for i in range(bond_array_1.shape[0]): + ri1 = bond_array_1[i, 0] + ci1 = bond_array_1[i, d1] + + if etypes1[i] == s: + # derivative of pairwise component of many body descriptor q1i + _, q1i_grads[i] = coordination_number( + ri1, ci1, r_cut, cutoff_func) + + if c1 == s: + # derivative of pairwise component of many body descriptor qi1 + _, qi1_grads[i] = coordination_number( + ri1, ci1, r_cut, cutoff_func) + + # Calculate many-body descriptor value for i + qis[i] = q_value_mc(neigh_dists_1[i, :num_neigh_1[i]], r_cut, + s, etypes_neigh_1[i, :num_neigh_1[i]], cutoff_func) + + # kernel is nonzero only if central atoms are of the same species + if c2 == etypes1[i]: + ki2s[i] = k_sq_exp_double_dev(qis[i], q2, sig, ls) + + # Loop over neighbours j of 2 + for j in range(bond_array_2.shape[0]): + rj2 = bond_array_2[j, 0] + cj2 = bond_array_2[j, d2] + + if etypes2[j] == s: + _, q2j_grads[j] = coordination_number( + rj2, cj2, r_cut, cutoff_func) + + if c2 == s: + _, qj2_grads[j] = coordination_number( + rj2, cj2, r_cut, cutoff_func) + + # Calculate many-body descriptor value for j + qjs[j] = q_value_mc(neigh_dists_2[j, :num_neigh_2[j]], r_cut, + s, etypes_neigh_2[j, :num_neigh_2[j]], cutoff_func) + + # kernel is nonzero only if central atoms are of the same species + if c1 == etypes2[j]: + k1js[j] = k_sq_exp_double_dev(q1, qjs[j], sig, ls) + + for i in range(bond_array_1.shape[0]): + for j in range(bond_array_2.shape[0]): + # kernel is nonzero only if central atoms are of the same species + if etypes1[i] == etypes2[j]: + kij = k_sq_exp_double_dev(qis[i], qjs[j], sig, ls) + else: + kij = 0 + + kern += q1i_grads[i] * q2j_grads[j] * k12 + kern += qi1_grads[i] * q2j_grads[j] * ki2s[i] + kern += q1i_grads[i] * qj2_grads[j] * k1js[j] + kern += qi1_grads[i] * qj2_grads[j] * kij + return kern + + +@njit +def many_body_mc_grad_jit(bond_array_1, bond_array_2, neigh_dists_1, neigh_dists_2, num_neigh_1, + num_neigh_2, c1, c2, etypes1, etypes2, etypes_neigh_1, etypes_neigh_2, + species1, species2, d1, d2, sig, ls, r_cut, cutoff_func): + """gradient of many-body multi-element kernel between two force components + w.r.t. the hyperparameters, accelerated with Numba. + + Args: + bond_array_1 (np.ndarray): many-body bond array of the first local + environment. + bond_array_2 (np.ndarray): many-body bond array of the second local + environment. + neigh_dists_1 (np.ndarray): matrix padded with zero values of distances + of neighbours for the atoms in the first local environment. + neigh_dists_2 (np.ndarray): matrix padded with zero values of distances + of neighbours for the atoms in the second local environment. + num_neigh_1 (np.ndarray): number of neighbours of each atom in the first + local environment + num_neigh_2 (np.ndarray): number of neighbours of each atom in the second + local environment + c1 (int): atomic species of the central atom in env 1 + c2 (int): atomic species of the central atom in env 2 + etypes1 (np.ndarray): atomic species of atoms in env 1 + etypes2 (np.ndarray): atomic species of atoms in env 2 + etypes_neigh_1 (np.ndarray): atomic species of atoms in the neighbourhoods + of atoms in env 1 + etypes_neigh_2 (np.ndarray): atomic species of atoms in the neighbourhoods + of atoms in env 2 + species1 (np.ndarray): all the atomic species present in trajectory 1 + species2 (np.ndarray): all the atomic species present in trajectory 2 + d1 (int): Force component of the first environment. + d2 (int): Force component of the second environment. + sig (float): many-body signal variance hyperparameter. + ls (float): many-body length scale hyperparameter. + r_cut (float): many-body cutoff radius. + cutoff_func (Callable): Cutoff function. + + Return: + array: Value of the many-body kernel and its gradient w.r.t. sig and ls + """ + + kern = 0.0 + sig_derv = 0.0 + ls_derv = 0.0 + + useful_species = np.array( + list(set(species1).union(set(species2))), dtype=np.int8) + + for s in useful_species: + + # Calculate many-body descriptor values for 1 and 2 + q1 = q_value_mc(bond_array_1[:, 0], r_cut, s, etypes1, cutoff_func) + q2 = q_value_mc(bond_array_2[:, 0], r_cut, s, etypes2, cutoff_func) + + if c1 == c2: + k12 = k_sq_exp_double_dev(q1, q2, sig, ls) + q12diffsq = (q1 - q2) ** 2 # * (q1 - q2) + dk12 = mb_grad_helper_ls_(q12diffsq, sig, ls) + else: + k12 = 0 + dk12 = 0 + + qis = np.zeros(bond_array_1.shape[0], dtype=np.float64) + q1i_grads = np.zeros(bond_array_1.shape[0], dtype=np.float64) + qi1_grads = np.zeros(bond_array_1.shape[0], dtype=np.float64) + ki2s = np.zeros(bond_array_1.shape[0], dtype=np.float64) + dki2s = np.zeros(bond_array_1.shape[0], dtype=np.float64) + + qjs = np.zeros(bond_array_2.shape[0], dtype=np.float64) + qj2_grads = np.zeros(bond_array_2.shape[0], dtype=np.float64) + q2j_grads = np.zeros(bond_array_2.shape[0], dtype=np.float64) + k1js = np.zeros(bond_array_2.shape[0], dtype=np.float64) + dk1js = np.zeros(bond_array_2.shape[0], dtype=np.float64) + + # Compute ki2s, qi1_grads, and qis + for i in range(bond_array_1.shape[0]): + ri1 = bond_array_1[i, 0] + ci1 = bond_array_1[i, d1] + + if etypes1[i] == s: + _, q1i_grads[i] = coordination_number( + ri1, ci1, r_cut, cutoff_func) + + if c1 == s: + # derivative of pairwise component of many body descriptor qi1 + __, qi1_grads[i] = coordination_number( + ri1, ci1, r_cut, cutoff_func) + + # Calculate many-body descriptor value for i + qis[i] = q_value_mc(neigh_dists_1[i, :num_neigh_1[i]], r_cut, + s, etypes_neigh_1[i, :num_neigh_1[i]], cutoff_func) + + # ki2s[i] = k_sq_exp_double_dev(qis[i], q2, sig, ls) + if c2 == etypes1[i]: + ki2s[i] = k_sq_exp_double_dev(qis[i], q2, sig, ls) + qi2diffsq = (qis[i] - q2) * (qis[i] - q2) + dki2s[i] = mb_grad_helper_ls_(qi2diffsq, sig, ls) + + # Compute k1js, qj2_grads and qjs + for j in range(bond_array_2.shape[0]): + rj2 = bond_array_2[j, 0] + cj2 = bond_array_2[j, d2] + + if etypes2[j] == s: + _, q2j_grads[j] = coordination_number( + rj2, cj2, r_cut, cutoff_func) + + if c2 == s: + _, qj2_grads[j] = coordination_number( + rj2, cj2, r_cut, cutoff_func) + + # Calculate many-body descriptor value for j + qjs[j] = q_value_mc(neigh_dists_2[j, :num_neigh_2[j]], r_cut, + s, etypes_neigh_2[j, :num_neigh_2[j]], cutoff_func) + + # k1js[j] = k_sq_exp_double_dev(q1, qjs[j], sig, ls) + + if c1 == etypes2[j]: + k1js[j] = k_sq_exp_double_dev(q1, qjs[j], sig, ls) + q1jdiffsq = (q1 - qjs[j]) * (q1 - qjs[j]) + dk1js[j] = mb_grad_helper_ls_(q1jdiffsq, sig, ls) + + for i in range(bond_array_1.shape[0]): + for j in range(bond_array_2.shape[0]): + + # kij = k_sq_exp_double_dev(qis[i], qjs[j], sig, ls) + + if etypes1[i] == etypes2[j]: + kij = k_sq_exp_double_dev(qis[i], qjs[j], sig, ls) + qijdiffsq = (qis[i] - qjs[j]) * (qis[i] - qjs[j]) + dkij = mb_grad_helper_ls_(qijdiffsq, sig, ls) + else: + kij = 0 + dkij = 0 + + kern_term = q1i_grads[i] * q2j_grads[j] * k12 + kern_term += qi1_grads[i] * q2j_grads[j] * ki2s[i] + kern_term += q1i_grads[i] * qj2_grads[j] * k1js[j] + kern_term += qi1_grads[i] * qj2_grads[j] * kij + + sig_term = 2. / sig * kern_term + + ls_term = q1i_grads[i] * q2j_grads[j] * dk12 + ls_term += qi1_grads[i] * q2j_grads[j] * dki2s[i] + ls_term += q1i_grads[i] * qj2_grads[j] * dk1js[j] + ls_term += qi1_grads[i] * qj2_grads[j] * dkij + + kern += kern_term + + sig_derv += sig_term + + ls_derv += ls_term + + grad = np.array([sig_derv, ls_derv]) + + return kern, grad + + +@njit +def many_body_mc_force_en_jit(bond_array_1, bond_array_2, neigh_dists_1, num_neigh_1, + c1, c2, etypes1, etypes2, etypes_neigh_1, + species1, species2, d1, sig, ls, r_cut, cutoff_func): + """many-body many-element kernel between force and energy components accelerated + with Numba. + + Args: + bond_array_1 (np.ndarray): many-body bond array of the first local + environment. + bond_array_2 (np.ndarray): many-body bond array of the second local + environment. + neigh_dists_1 (np.ndarray): matrix padded with zero values of distances + of neighbours for the atoms in the first local environment. + num_neigh_1 (np.ndarray): number of neighbours of each atom in the first + local environment + c1 (int): atomic species of the central atom in env 1 + c2 (int): atomic species of the central atom in env 2 + etypes1 (np.ndarray): atomic species of atoms in env 1 + etypes2 (np.ndarray): atomic species of atoms in env 2 + etypes_neigh_1 (np.ndarray): atomic species of atoms in the neighbourhoods + of atoms in env 1 + species1 (np.ndarray): all the atomic species present in trajectory 1 + species2 (np.ndarray): all the atomic species present in trajectory 2 + d1 (int): Force component of the first environment. + sig (float): many-body signal variance hyperparameter. + ls (float): many-body length scale hyperparameter. + r_cut (float): many-body cutoff radius. + cutoff_func (Callable): Cutoff function. + + Return: + float: Value of the many-body kernel. + """ + + kern = 0 + + useful_species = np.array( + list(set(species1).union(set(species2))), dtype=np.int8) + + for s in useful_species: + + q1 = q_value_mc(bond_array_1[:, 0], r_cut, s, etypes1, cutoff_func) + q2 = q_value_mc(bond_array_2[:, 0], r_cut, s, etypes2, cutoff_func) + + if c1 == c2: + k12 = k_sq_exp_dev(q1, q2, sig, ls) + else: + k12 = 0 + + qis = np.zeros(bond_array_1.shape[0], dtype=np.float64) + qi1_grads = np.zeros(bond_array_1.shape[0], dtype=np.float64) + q1i_grads = np.zeros(bond_array_1.shape[0], dtype=np.float64) + ki2s = np.zeros(bond_array_1.shape[0], dtype=np.float64) + + # Loop over neighbours i of 1 + for i in range(bond_array_1.shape[0]): + ri1 = bond_array_1[i, 0] + ci1 = bond_array_1[i, d1] + + if etypes1[i] == s: + _, q1i_grads[i] = coordination_number( + ri1, ci1, r_cut, cutoff_func) + + if c1 == s: + _, qi1_grads[i] = coordination_number( + ri1, ci1, r_cut, cutoff_func) + + # Calculate many-body descriptor value for i + qis[i] = q_value_mc(neigh_dists_1[i, :num_neigh_1[i]], r_cut, + s, etypes_neigh_1[i, :num_neigh_1[i]], cutoff_func) + + if c2 == etypes1[i]: + ki2s[i] = k_sq_exp_dev(qis[i], q2, sig, ls) + + kern += - (q1i_grads[i] * k12 + qi1_grads[i] * ki2s[i]) + + return kern + + +@njit +def many_body_mc_en_jit(bond_array_1, bond_array_2, c1, c2, etypes1, etypes2, + species1, species2, sig, ls, r_cut, cutoff_func): + """many-body many-element kernel between energy components accelerated + with Numba. + + Args: + bond_array_1 (np.ndarray): many-body bond array of the first local + environment. + bond_array_2 (np.ndarray): many-body bond array of the second local + environment. + c1 (int): atomic species of the central atom in env 1 + c2 (int): atomic species of the central atom in env 2 + etypes1 (np.ndarray): atomic species of atoms in env 1 + etypes2 (np.ndarray): atomic species of atoms in env 2 + species1 (np.ndarray): all the atomic species present in trajectory 1 + species2 (np.ndarray): all the atomic species present in trajectory 2 + sig (float): many-body signal variance hyperparameter. + ls (float): many-body length scale hyperparameter. + r_cut (float): many-body cutoff radius. + cutoff_func (Callable): Cutoff function. + + Return: + float: Value of the many-body kernel. + """ + useful_species = np.array( + list(set(species1).union(set(species2))), dtype=np.int8) + kern = 0 + + if c1 == c2: + for s in useful_species: + q1 = q_value_mc(bond_array_1[:, 0], r_cut, s, etypes1, cutoff_func) + q2 = q_value_mc(bond_array_2[:, 0], r_cut, s, etypes2, cutoff_func) + q1q2diff = q1 - q2 + + kern += sig * sig * exp(-q1q2diff * q1q2diff / (2 * ls * ls)) + + return kern + + _str_to_kernel = {'two_body_mc': two_body_mc, 'two_body_mc_en': two_body_mc_en, 'two_body_mc_grad': two_body_mc_grad, @@ -1319,35 +2208,33 @@ def two_body_mc_en_jit(bond_array_1, c1, etypes1, 'two_plus_three_body_mc': two_plus_three_body_mc, 'two_plus_three_body_mc_grad': two_plus_three_body_mc_grad, 'two_plus_three_mc_en': two_plus_three_mc_en, - 'two_plus_three_mc_force_en': two_plus_three_mc_force_en + 'two_plus_three_mc_force_en': two_plus_three_mc_force_en, + '2': two_body_mc, + '2_en': two_body_mc_en, + '2_grad': two_body_mc_grad, + '2_force_en': two_body_mc_force_en, + '3': three_body_mc, + '3_grad': three_body_mc_grad, + '3_en': three_body_mc_en, + '3_force_en': three_body_mc_force_en, + '2+3': two_plus_three_body_mc, + '2+3_grad': two_plus_three_body_mc_grad, + '2+3_en': two_plus_three_mc_en, + '2+3_force_en': two_plus_three_mc_force_en, + 'many_body_mc': many_body_mc, + 'many_body_mc_en': many_body_mc_en, + 'many_body_mc_grad': many_body_mc_grad, + 'many_body_mc_force_en': many_body_mc_force_en, + 'many': many_body_mc, + 'many_en': many_body_mc_en, + 'many_grad': many_body_mc_grad, + 'many_force_en': many_body_mc_force_en, + 'two_plus_three_plus_many_body_mc': two_plus_three_plus_many_body_mc, + 'two_plus_three_plus_many_body_mc_grad': two_plus_three_plus_many_body_mc_grad, + 'two_plus_three_plus_many_body_mc_en': two_plus_three_plus_many_body_mc_en, + 'two_plus_three_plus_many_body_mc_force_en': two_plus_three_plus_many_body_mc_force_en, + '2+3+many': two_plus_three_plus_many_body_mc, + '2+3+many_grad': two_plus_three_plus_many_body_mc_grad, + '2+3+many_en': two_plus_three_plus_many_body_mc_en, + '2+3+many_force_en': two_plus_three_plus_many_body_mc_force_en } - - -def str_to_mc_kernel(string: str, include_grad: bool = False): - """Converts a kernel label to the corresponding kernel function. - - Args: - string (str): Name of the kernel function, e.g. "two_body_mc". - include_grad (bool, optional): If True, returns the kernel gradient - in addition to the kernel function. Defaults to False. - - Returns: - Callable: Kernel function (and its gradient if include_grad = True). - """ - - if string not in _str_to_kernel.keys(): - raise ValueError("Kernel {} not found in list of available " - "kernels{}:".format(string, _str_to_kernel.keys())) - - if not include_grad: - return _str_to_kernel[string] - else: - if 'two' in string and 'three' in string: - return _str_to_kernel[string], two_plus_three_body_mc_grad - elif 'two' in string and 'three' not in string: - return _str_to_kernel[string], two_body_mc_grad - elif 'two' not in string and 'three' in string: - return _str_to_kernel[string], three_body_mc_grad - else: - raise ValueError("Gradient callable for {} not found".format( - string)) diff --git a/flare/kernels/sc.py b/flare/kernels/sc.py new file mode 100644 index 000000000..66c3502c1 --- /dev/null +++ b/flare/kernels/sc.py @@ -0,0 +1,1882 @@ +"""Single element 2-, 3-, and 2+3-body kernels. +The kernel functions to choose: + +* Two body: + + * two_body: force kernel + * two_body_en: energy kernel + * two_body_grad: gradient of kernel function + * two_body_force_en: energy force kernel + +* Three body: + + * three_body, + * three_body_grad, + * three_body_en, + * three_body_force_en, + +* Two plus three body: + + * two_plus_three_body, + * two_plus_three_body_grad, + * two_plus_three_en, + * two_plus_three_force_en + +* Two plus three plus many body: + + * two_plus_three_plus_many_body, + * two_plus_three_plus_many_body_grad, + * two_plus_three_plus_many_body_en, + * two_plus_three_plus_many_body_force_en + +**Example:** + +>>> gp_model = GaussianProcess(kernel_name='2b', + ) +""" + +import numpy as np +from math import exp +from flare.env import AtomicEnvironment + +import flare.cutoffs as cf + +from numba import njit + +from flare.kernels.kernels import force_helper, grad_constants, grad_helper, \ + force_energy_helper, three_body_en_helper, three_body_helper_1, \ + three_body_helper_2, three_body_grad_helper_1, three_body_grad_helper_2 + + +# ----------------------------------------------------------------------------- +# two plus three body kernels +# ----------------------------------------------------------------------------- + + +def two_plus_three_body(env1: AtomicEnvironment, env2: AtomicEnvironment, + d1: int, d2: int, hyps, cutoffs, + cutoff_func=cf.quadratic_cutoff): + """2+3-body single-element kernel between two force components. + + Args: + env1 (AtomicEnvironment): First local environment. + env2 (AtomicEnvironment): Second local environment. + d1 (int): Force component of the first environment. + d2 (int): Force component of the second environment. + hyps (np.ndarray): Hyperparameters of the kernel function (sig1, ls1, + sig2, ls2, sig_n). + cutoffs (np.ndarray): Two-element array containing the 2- and 3-body + cutoffs. + cutoff_func (Callable): Cutoff function of the kernel. + + Return: + float: Value of the 2+3-body kernel. + """ + + two_term = two_body_jit(env1.bond_array_2, env2.bond_array_2, + d1, d2, hyps[0], hyps[1], cutoffs[0], cutoff_func) + + three_term = \ + three_body_jit(env1.bond_array_3, env2.bond_array_3, + env1.cross_bond_inds, env2.cross_bond_inds, + env1.cross_bond_dists, env2.cross_bond_dists, + env1.triplet_counts, env2.triplet_counts, + d1, d2, hyps[2], hyps[3], cutoffs[1], cutoff_func) + return two_term + three_term + + +def two_plus_three_body_grad(env1, env2, d1, d2, hyps, cutoffs, + cutoff_func=cf.quadratic_cutoff): + """2+3-body single-element kernel between two force components and its + gradient with respect to the hyperparameters. + + Args: + env1 (AtomicEnvironment): First local environment. + env2 (AtomicEnvironment): Second local environment. + d1 (int): Force component of the first environment. + d2 (int): Force component of the second environment. + hyps (np.ndarray): Hyperparameters of the kernel function (sig1, ls1, + sig2, ls2, sig_n). + cutoffs (np.ndarray): Two-element array containing the 2- and 3-body + cutoffs. + cutoff_func (Callable): Cutoff function of the kernel. + + Return: + (float, np.ndarray): Value of the 2+3-body kernel and its gradient + with respect to the hyperparameters. + """ + + kern2, ls2, sig2 = \ + two_body_grad_jit(env1.bond_array_2, env2.bond_array_2, + d1, d2, hyps[0], hyps[1], cutoffs[0], cutoff_func) + + kern3, sig3, ls3 = \ + three_body_grad_jit(env1.bond_array_3, env2.bond_array_3, + env1.cross_bond_inds, env2.cross_bond_inds, + env1.cross_bond_dists, env2.cross_bond_dists, + env1.triplet_counts, env2.triplet_counts, + d1, d2, hyps[2], hyps[3], cutoffs[1], cutoff_func) + + return kern2 + kern3, np.array([sig2, ls2, sig3, ls3]) + + +def two_plus_three_force_en(env1, env2, d1, hyps, cutoffs, + cutoff_func=cf.quadratic_cutoff): + """2+3-body single-element kernel between a force component and a local + energy. + + Args: + env1 (AtomicEnvironment): Local environment associated with the + force component. + env2 (AtomicEnvironment): Local environment associated with the + local energy. + d1 (int): Force component of the first environment. + hyps (np.ndarray): Hyperparameters of the kernel function (sig1, ls1, + sig2, ls2). + cutoffs (np.ndarray): Two-element array containing the 2- and 3-body + cutoffs. + cutoff_func (Callable): Cutoff function of the kernel. + + Return: + float: Value of the 2+3-body force/energy kernel. + """ + + two_term = two_body_force_en_jit(env1.bond_array_2, env2.bond_array_2, + d1, hyps[0], hyps[1], cutoffs[0], + cutoff_func) / 2 + + three_term = \ + three_body_force_en_jit(env1.bond_array_3, env2.bond_array_3, + env1.cross_bond_inds, env2.cross_bond_inds, + env1.cross_bond_dists, + env2.cross_bond_dists, + env1.triplet_counts, env2.triplet_counts, + d1, hyps[2], hyps[3], cutoffs[1], + cutoff_func) / 3 + + return two_term + three_term + + +def two_plus_three_en(env1, env2, hyps, cutoffs, + cutoff_func=cf.quadratic_cutoff): + """2+3-body single-element kernel between two local energies. + + Args: + env1 (AtomicEnvironment): First local environment. + env2 (AtomicEnvironment): Second local environment. + hyps (np.ndarray): Hyperparameters of the kernel function (sig1, ls1, + sig2, ls2). + cutoffs (np.ndarray): Two-element array containing the 2- and 3-body + cutoffs. + cutoff_func (Callable): Cutoff function of the kernel. + + Return: + float: Value of the 2+3-body force/energy kernel. + """ + + two_term = two_body_en_jit(env1.bond_array_2, env2.bond_array_2, + hyps[0], hyps[1], cutoffs[0], cutoff_func) + + three_term = \ + three_body_en_jit(env1.bond_array_3, env2.bond_array_3, + env1.cross_bond_inds, env2.cross_bond_inds, + env1.cross_bond_dists, env2.cross_bond_dists, + env1.triplet_counts, env2.triplet_counts, + hyps[2], hyps[3], cutoffs[1], cutoff_func) + + return two_term + three_term + + +# ----------------------------------------------------------------------------- +# two plus three plus many body kernels +# ----------------------------------------------------------------------------- + + +def two_plus_three_plus_many_body(env1: AtomicEnvironment, env2: AtomicEnvironment, + d1: int, d2: int, hyps, cutoffs, + cutoff_func=cf.quadratic_cutoff): + """2+3-body single-element kernel between two force components. + + Args: + env1 (AtomicEnvironment): First local environment. + env2 (AtomicEnvironment): Second local environment. + d1 (int): Force component of the first environment. + d2 (int): Force component of the second environment. + hyps (np.ndarray): Hyperparameters of the kernel function (sig2, ls2, + sig3, ls3, sigm, lsm, sig_n). + cutoffs (np.ndarray): Two-element array containing the 2- and 3-body + cutoffs. + cutoff_func (Callable): Cutoff function of the kernel. + + Return: + float: Value of the 2+3+many-body kernel. + """ + + two_term = two_body_jit(env1.bond_array_2, env2.bond_array_2, + d1, d2, hyps[0], hyps[1], cutoffs[0], cutoff_func) + + three_term = \ + three_body_jit(env1.bond_array_3, env2.bond_array_3, + env1.cross_bond_inds, env2.cross_bond_inds, + env1.cross_bond_dists, env2.cross_bond_dists, + env1.triplet_counts, env2.triplet_counts, + d1, d2, hyps[2], hyps[3], cutoffs[1], cutoff_func) + + many_term = many_body_jit(env1.bond_array_mb, env2.bond_array_mb, env1.neigh_dists_mb, + env2.neigh_dists_mb, env1.num_neighs_mb, env2.num_neighs_mb, + d1, d2, hyps[4], hyps[5], cutoffs[2], cutoff_func) + + return two_term + three_term + many_term + + +def two_plus_three_plus_many_body_grad(env1: AtomicEnvironment, env2: AtomicEnvironment, + d1: int, d2: int, hyps, cutoffs, + cutoff_func=cf.quadratic_cutoff): + """2+3+many-body single-element kernel between two force components. + + Args: + env1 (AtomicEnvironment): First local environment. + env2 (AtomicEnvironment): Second local environment. + d1 (int): Force component of the first environment. + d2 (int): Force component of the second environment. + hyps (np.ndarray): Hyperparameters of the kernel function (sig2, ls2, + sig3, ls3, sigm, lsm, sig_n). + cutoffs (np.ndarray): Two-element array containing the 2- and 3-body + cutoffs. + cutoff_func (Callable): Cutoff function of the kernel. + + Return: + float: Value of the 2+3+many-body kernel. + """ + + kern2, ls2, sig2 = \ + two_body_grad_jit(env1.bond_array_2, env2.bond_array_2, + d1, d2, hyps[0], hyps[1], cutoffs[0], cutoff_func) + + kern3, sig3, ls3 = \ + three_body_grad_jit(env1.bond_array_3, env2.bond_array_3, + env1.cross_bond_inds, env2.cross_bond_inds, + env1.cross_bond_dists, env2.cross_bond_dists, + env1.triplet_counts, env2.triplet_counts, + d1, d2, hyps[2], hyps[3], cutoffs[1], cutoff_func) + + kern_many, sigm, lsm = many_body_grad_jit(env1.bond_array_mb, env2.bond_array_mb, + env1.neigh_dists_mb, + env2.neigh_dists_mb, env1.num_neighs_mb, + env2.num_neighs_mb, + d1, d2, hyps[4], hyps[5], cutoffs[2], + cutoff_func) + + return kern2 + kern3 + kern_many, np.array([sig2, ls2, sig3, ls3, sigm, lsm]) + + +def two_plus_three_plus_many_body_force_en(env1: AtomicEnvironment, env2: AtomicEnvironment, + d1: int, hyps, cutoffs, + cutoff_func=cf.quadratic_cutoff): + """2+3+many-body single-element kernel between two force and energy components. + + Args: + env1 (AtomicEnvironment): First local environment. + env2 (AtomicEnvironment): Second local environment. + d1 (int): Force component of the first environment. + hyps (np.ndarray): Hyperparameters of the kernel function (sig2, ls2, + sig3, ls3, sigm, lsm, sig_n). + cutoffs (np.ndarray): Two-element array containing the 2- and 3-body + cutoffs. + cutoff_func (Callable): Cutoff function of the kernel. + + Return: + float: Value of the 2+3+many-body kernel. + """ + + two_term = two_body_force_en_jit(env1.bond_array_2, env2.bond_array_2, + d1, hyps[0], hyps[1], cutoffs[0], + cutoff_func) / 2 + + three_term = \ + three_body_force_en_jit(env1.bond_array_3, env2.bond_array_3, + env1.cross_bond_inds, env2.cross_bond_inds, + env1.cross_bond_dists, + env2.cross_bond_dists, + env1.triplet_counts, env2.triplet_counts, + d1, hyps[2], hyps[3], cutoffs[1], + cutoff_func) / 3 + + many_term = many_body_force_en_jit(env1.bond_array_mb, env2.bond_array_mb, + env1.neigh_dists_mb, + env1.num_neighs_mb, + d1, hyps[4], hyps[5], cutoffs[2], + cutoff_func) + + return two_term + three_term + many_term + + +def two_plus_three_plus_many_body_en(env1: AtomicEnvironment, env2: AtomicEnvironment, + hyps, cutoffs, cutoff_func=cf.quadratic_cutoff): + """2+3+many-body single-element energy kernel. + + Args: + env1 (AtomicEnvironment): First local environment. + env2 (AtomicEnvironment): Second local environment. + hyps (np.ndarray): Hyperparameters of the kernel function (sig2, ls2, + sig3, ls3, sigm, lsm, sig_n). + cutoffs (np.ndarray): Two-element array containing the 2- and 3-body + cutoffs. + cutoff_func (Callable): Cutoff function of the kernel. + + Return: + float: Value of the 2+3+many-body kernel. + """ + + two_term = two_body_en_jit(env1.bond_array_2, env2.bond_array_2, + hyps[0], hyps[1], cutoffs[0], cutoff_func) + + three_term = \ + three_body_en_jit(env1.bond_array_3, env2.bond_array_3, + env1.cross_bond_inds, env2.cross_bond_inds, + env1.cross_bond_dists, env2.cross_bond_dists, + env1.triplet_counts, env2.triplet_counts, + hyps[2], hyps[3], cutoffs[1], cutoff_func) + + many_term = many_body_en_jit(env1.bond_array_mb, env2.bond_array_mb, hyps[4], hyps[5], cutoffs[2], + cutoff_func) + + return two_term + three_term + many_term + + +# ----------------------------------------------------------------------------- +# two body kernels +# ----------------------------------------------------------------------------- + + +def two_body(env1, env2, d1, d2, hyps, cutoffs, + cutoff_func=cf.quadratic_cutoff): + """2-body single-element kernel between two force components. + + Args: + env1 (AtomicEnvironment): First local environment. + env2 (AtomicEnvironment): Second local environment. + d1 (int): Force component of the first environment. + d2 (int): Force component of the second environment. + hyps (np.ndarray): Hyperparameters of the kernel function (sig, ls). + cutoffs (np.ndarray): One-element array containing the 2-body + cutoff. + cutoff_func (Callable): Cutoff function of the kernel. + + Return: + float: Value of the 2-body kernel. + """ + + sig = hyps[0] + ls = hyps[1] + r_cut = cutoffs[0] + + return two_body_jit(env1.bond_array_2, env2.bond_array_2, + d1, d2, sig, ls, r_cut, cutoff_func) + + +def two_body_grad(env1, env2, d1, d2, hyps, cutoffs, + cutoff_func=cf.quadratic_cutoff): + """2-body single-element kernel between two force components and its + gradient with respect to the hyperparameters. + + Args: + env1 (AtomicEnvironment): First local environment. + env2 (AtomicEnvironment): Second local environment. + d1 (int): Force component of the first environment. + d2 (int): Force component of the second environment. + hyps (np.ndarray): Hyperparameters of the kernel function (sig, ls). + cutoffs (np.ndarray): One-element array containing the 2-body + cutoff. + cutoff_func (Callable): Cutoff function of the kernel. + + Return: + (float, np.ndarray): Value of the 2-body kernel and its gradient + with respect to the hyperparameters. + """ + + sig = hyps[0] + ls = hyps[1] + r_cut = cutoffs[0] + + kernel, ls_derv, sig_derv = \ + two_body_grad_jit(env1.bond_array_2, env2.bond_array_2, + d1, d2, sig, ls, r_cut, cutoff_func) + kernel_grad = np.array([sig_derv, ls_derv]) + return kernel, kernel_grad + + +def two_body_force_en(env1, env2, d1, hyps, cutoffs, + cutoff_func=cf.quadratic_cutoff): + """2-body single-element kernel between a force component and a local + energy. + + Args: + env1 (AtomicEnvironment): Local environment associated with the + force component. + env2 (AtomicEnvironment): Local environment associated with the + local energy. + d1 (int): Force component of the first environment. + hyps (np.ndarray): Hyperparameters of the kernel function (sig, ls). + cutoffs (np.ndarray): One-element array containing the 2-body + cutoff. + cutoff_func (Callable): Cutoff function of the kernel. + + Return: + float: Value of the 2-body force/energy kernel. + """ + + sig = hyps[0] + ls = hyps[1] + r_cut = cutoffs[0] + + # divide by two to account for double counting + return two_body_force_en_jit(env1.bond_array_2, env2.bond_array_2, + d1, sig, ls, r_cut, cutoff_func) / 2 + + +def two_body_en(env1, env2, hyps, cutoffs, + cutoff_func=cf.quadratic_cutoff): + """2-body single-element kernel between two local energies. + + Args: + env1 (AtomicEnvironment): First local environment. + env2 (AtomicEnvironment): Second local environment. + hyps (np.ndarray): Hyperparameters of the kernel function (sig, ls). + cutoffs (np.ndarray): One-element array containing the 2-body + cutoff. + cutoff_func (Callable): Cutoff function of the kernel. + + Return: + float: Value of the 2-body force/energy kernel. + """ + sig = hyps[0] + ls = hyps[1] + r_cut = cutoffs[0] + + return two_body_en_jit(env1.bond_array_2, env2.bond_array_2, + sig, ls, r_cut, cutoff_func) + + +# ----------------------------------------------------------------------------- +# three body kernels +# ----------------------------------------------------------------------------- + + +def three_body(env1, env2, d1, d2, hyps, cutoffs, + cutoff_func=cf.quadratic_cutoff): + """3-body single-element kernel between two force components. + + Args: + env1 (AtomicEnvironment): First local environment. + env2 (AtomicEnvironment): Second local environment. + d1 (int): Force component of the first environment. + d2 (int): Force component of the second environment. + hyps (np.ndarray): Hyperparameters of the kernel function (sig, ls). + cutoffs (np.ndarray): Two-element array containing the 2- and 3-body + cutoffs. + cutoff_func (Callable): Cutoff function of the kernel. + + Return: + float: Value of the 3-body kernel. + """ + + sig = hyps[0] + ls = hyps[1] + r_cut = cutoffs[1] + + return three_body_jit(env1.bond_array_3, env2.bond_array_3, + env1.cross_bond_inds, env2.cross_bond_inds, + env1.cross_bond_dists, env2.cross_bond_dists, + env1.triplet_counts, env2.triplet_counts, + d1, d2, sig, ls, r_cut, cutoff_func) + + +def three_body_grad(env1, env2, d1, d2, hyps, cutoffs, + cutoff_func=cf.quadratic_cutoff): + """3-body single-element kernel between two force components and its + gradient with respect to the hyperparameters. + + Args: + env1 (AtomicEnvironment): First local environment. + env2 (AtomicEnvironment): Second local environment. + d1 (int): Force component of the first environment. + d2 (int): Force component of the second environment. + hyps (np.ndarray): Hyperparameters of the kernel function (sig, ls). + cutoffs (np.ndarray): Two-element array containing the 2- and 3-body + cutoffs. + cutoff_func (Callable): Cutoff function of the kernel. + + Return: + (float, np.ndarray): Value of the 3-body kernel and its gradient + with respect to the hyperparameters. + """ + sig = hyps[0] + ls = hyps[1] + r_cut = cutoffs[1] + + kernel, sig_derv, ls_derv = three_body_grad_jit(env1.bond_array_3, env2.bond_array_3, + env1.cross_bond_inds, env2.cross_bond_inds, + env1.cross_bond_dists, env2.cross_bond_dists, + env1.triplet_counts, env2.triplet_counts, + d1, d2, sig, ls, r_cut, cutoff_func) + + kernel_grad = np.array([sig_derv, ls_derv]) + + return kernel, kernel_grad + + +def three_body_force_en(env1, env2, d1, hyps, cutoffs, + cutoff_func=cf.quadratic_cutoff): + """3-body single-element kernel between a force component and a local + energy. + + Args: + env1 (AtomicEnvironment): Local environment associated with the + force component. + env2 (AtomicEnvironment): Local environment associated with the + local energy. + d1 (int): Force component of the first environment. + hyps (np.ndarray): Hyperparameters of the kernel function (sig, ls). + cutoffs (np.ndarray): Two-element array containing the 2- and 3-body + cutoffs. + cutoff_func (Callable): Cutoff function of the kernel. + + Return: + float: Value of the 3-body force/energy kernel. + """ + sig = hyps[0] + ls = hyps[1] + r_cut = cutoffs[1] + + # divide by three to account for triple counting + return three_body_force_en_jit(env1.bond_array_3, env2.bond_array_3, + env1.cross_bond_inds, env2.cross_bond_inds, + env1.cross_bond_dists, + env2.cross_bond_dists, + env1.triplet_counts, env2.triplet_counts, + d1, sig, ls, r_cut, cutoff_func) / 3 + + +def three_body_en(env1, env2, hyps, cutoffs, + cutoff_func=cf.quadratic_cutoff): + """3-body single-element kernel between two local energies. + + Args: + env1 (AtomicEnvironment): First local environment. + env2 (AtomicEnvironment): Second local environment. + hyps (np.ndarray): Hyperparameters of the kernel function (sig, ls). + cutoffs (np.ndarray): Two-element array containing the 2- and 3-body + cutoffs. + cutoff_func (Callable): Cutoff function of the kernel. + + Return: + float: Value of the 3-body force/energy kernel. + """ + sig = hyps[0] + ls = hyps[1] + r_cut = cutoffs[1] + + return three_body_en_jit(env1.bond_array_3, env2.bond_array_3, + env1.cross_bond_inds, env2.cross_bond_inds, + env1.cross_bond_dists, env2.cross_bond_dists, + env1.triplet_counts, env2.triplet_counts, + sig, ls, r_cut, cutoff_func) + + +# ----------------------------------------------------------------------------- +# many body kernels +# ----------------------------------------------------------------------------- + + +def many_body(env1, env2, d1, d2, hyps, cutoffs, + cutoff_func=cf.quadratic_cutoff): + """many-body single-element kernel between two forces. + + Args: + env1 (AtomicEnvironment): First local environment. + env2 (AtomicEnvironment): Second local environment. + d1 (int): Force component of the first environment. + d2 (int): Force component of the second environment. + hyps (np.ndarray): Hyperparameters of the kernel function (sig, ls). + cutoffs (np.ndarray): Two-element array containing the 2-, 3-, and + many-body cutoffs. + cutoff_func (Callable): Cutoff function of the kernel. + + Return: + float: Value of the many-body force/force kernel. + """ + + sig = hyps[0] + ls = hyps[1] + r_cut = cutoffs[2] + + bond_array_1 = env1.bond_array_mb + bond_array_2 = env2.bond_array_mb + + neigh_dists_1 = env1.neigh_dists_mb + num_neigh_1 = env1.num_neighs_mb + + neigh_dists_2 = env2.neigh_dists_mb + num_neigh_2 = env2.num_neighs_mb + + return many_body_jit(bond_array_1, bond_array_2, neigh_dists_1, neigh_dists_2, num_neigh_1, + num_neigh_2, d1, d2, sig, ls, r_cut, cutoff_func) + + +def many_body_grad(env1, env2, d1, d2, hyps, cutoffs, + cutoff_func=cf.quadratic_cutoff): + """many-body single-element kernel between two force components and its + gradient with respect to the hyperparameters. + + Args: + env1 (AtomicEnvironment): First local environment. + env2 (AtomicEnvironment): Second local environment. + d1 (int): Force component of the first environment. + d2 (int): Force component of the second environment. + hyps (np.ndarray): Hyperparameters of the kernel function (sig, ls). + cutoffs (np.ndarray): Two-element array containing the 2-, 3-, and + many-body cutoffs. + cutoff_func (Callable): Cutoff function of the kernel. + + Return: + (float, np.ndarray): Value of the many-body kernel and its gradient + with respect to the hyperparameters. + """ + + sig = hyps[0] + ls = hyps[1] + r_cut = cutoffs[2] + + bond_array_1 = env1.bond_array_mb + bond_array_2 = env2.bond_array_mb + + neigh_dists_1 = env1.neigh_dists_mb + num_neigh_1 = env1.num_neighs_mb + + neigh_dists_2 = env2.neigh_dists_mb + num_neigh_2 = env2.num_neighs_mb + + kernel, sig_derv, ls_derv = many_body_grad_jit(bond_array_1, bond_array_2, + neigh_dists_1, neigh_dists_2, + num_neigh_1, num_neigh_2, d1, d2, sig, + ls, r_cut, cutoff_func) + + kernel_grad = np.array([sig_derv, ls_derv]) + + return kernel, kernel_grad + + +def many_body_force_en(env1, env2, d1, hyps, cutoffs, + cutoff_func=cf.quadratic_cutoff): + """many-body single-element kernel between two local energies. + + Args: + env1 (AtomicEnvironment): First local environment. + env2 (AtomicEnvironment): Second local environment. + hyps (np.ndarray): Hyperparameters of the kernel function (sig, ls). + cutoffs (np.ndarray): Two-element array containing the 2-, 3-, and + many-body cutoffs. + cutoff_func (Callable): Cutoff function of the kernel. + + Return: + float: Value of the many-body force/energy kernel. + """ + sig = hyps[0] + ls = hyps[1] + r_cut = cutoffs[2] + + # divide by three to account for triple counting + return many_body_force_en_jit(env1.bond_array_mb, env2.bond_array_mb, env1.neigh_dists_mb, + env2.num_neighs_mb, d1, sig, ls, r_cut, cutoff_func) + + +def many_body_en(env1, env2, hyps, cutoffs, + cutoff_func=cf.quadratic_cutoff): + """many-body single-element kernel between two local energies. + + Args: + env1 (AtomicEnvironment): First local environment. + env2 (AtomicEnvironment): Second local environment. + hyps (np.ndarray): Hyperparameters of the kernel function (sig, ls). + cutoffs (np.ndarray): Two-element array containing the 2-, 3-, and + many-body cutoffs. + cutoff_func (Callable): Cutoff function of the kernel. + + Return: + float: Value of the many-body energy/energy kernel. + """ + + sig = hyps[0] + ls = hyps[1] + r_cut = cutoffs[2] + + bond_array_1 = env1.bond_array_mb + bond_array_2 = env2.bond_array_mb + + return many_body_en_jit(bond_array_1, bond_array_2, + sig, ls, r_cut, cutoff_func) + + +# ----------------------------------------------------------------------------- +# two body numba functions +# ----------------------------------------------------------------------------- + + +@njit +def two_body_jit(bond_array_1, bond_array_2, d1, d2, sig, ls, + r_cut, cutoff_func): + """2-body single-element kernel between two force components accelerated + with Numba. + + Args: + bond_array_1 (np.ndarray): 2-body bond array of the first local + environment. + bond_array_2 (np.ndarray): 2-body bond array of the second local + environment. + d1 (int): Force component of the first environment (1=x, 2=y, 3=z). + d2 (int): Force component of the second environment (1=x, 2=y, 3=z). + sig (float): 2-body signal variance hyperparameter. + ls (float): 2-body length scale hyperparameter. + r_cut (float): 2-body cutoff radius. + cutoff_func (Callable): Cutoff function. + + Return: + float: Value of the 2-body kernel. + """ + kern = 0 + + ls1 = 1 / (2 * ls * ls) + ls2 = 1 / (ls * ls) + ls3 = ls2 * ls2 + sig2 = sig * sig + + for m in range(bond_array_1.shape[0]): + ri = bond_array_1[m, 0] + ci = bond_array_1[m, d1] + fi, fdi = cutoff_func(r_cut, ri, ci) + + for n in range(bond_array_2.shape[0]): + rj = bond_array_2[n, 0] + cj = bond_array_2[n, d2] + fj, fdj = cutoff_func(r_cut, rj, cj) + r11 = ri - rj + + A = ci * cj + B = r11 * ci + C = r11 * cj + D = r11 * r11 + + kern += force_helper(A, B, C, D, fi, fj, fdi, fdj, ls1, ls2, + ls3, sig2) + + return kern + + +@njit +def two_body_grad_jit(bond_array_1, bond_array_2, d1, d2, sig, ls, + r_cut, cutoff_func): + """2-body single-element kernel between two force components and its + gradient with respect to the hyperparameters. + + Args: + bond_array_1 (np.ndarray): 2-body bond array of the first local + environment. + bond_array_2 (np.ndarray): 2-body bond array of the second local + environment. + d1 (int): Force component of the first environment (1=x, 2=y, 3=z). + d2 (int): Force component of the second environment (1=x, 2=y, 3=z). + sig (float): 2-body signal variance hyperparameter. + ls (float): 2-body length scale hyperparameter. + r_cut (float): 2-body cutoff radius. + cutoff_func (Callable): Cutoff function. + + Returns: + (float, float): + Value of the 2-body kernel and its gradient with respect to the + hyperparameters. + """ + + kern = 0 + sig_derv = 0 + ls_derv = 0 + + sig2, sig3, ls1, ls2, ls3, ls4, ls5, ls6 = grad_constants(sig, ls) + + for m in range(bond_array_1.shape[0]): + ri = bond_array_1[m, 0] + ci = bond_array_1[m, d1] + fi, fdi = cutoff_func(r_cut, ri, ci) + + for n in range(bond_array_2.shape[0]): + rj = bond_array_2[n, 0] + cj = bond_array_2[n, d2] + fj, fdj = cutoff_func(r_cut, rj, cj) + + r11 = ri - rj + + A = ci * cj + B = r11 * ci + C = r11 * cj + D = r11 * r11 + + kern_term, sig_term, ls_term = \ + grad_helper(A, B, C, D, fi, fj, fdi, fdj, ls1, ls2, ls3, ls4, + ls5, ls6, sig2, sig3) + + kern += kern_term + sig_derv += sig_term + ls_derv += ls_term + + return kern, ls_derv, sig_derv + + +@njit +def two_body_force_en_jit(bond_array_1, bond_array_2, d1, sig, ls, r_cut, + cutoff_func): + """2-body single-element kernel between a force component and a local + energy accelerated with Numba. + + Args: + bond_array_1 (np.ndarray): 2-body bond array of the first local + environment. + bond_array_2 (np.ndarray): 2-body bond array of the second local + environment. + d1 (int): Force component of the first environment (1=x, 2=y, 3=z). + sig (float): 2-body signal variance hyperparameter. + ls (float): 2-body length scale hyperparameter. + r_cut (float): 2-body cutoff radius. + cutoff_func (Callable): Cutoff function. + + Returns: + float: + Value of the 2-body force/energy kernel. + """ + kern = 0 + + ls1 = 1 / (2 * ls * ls) + ls2 = 1 / (ls * ls) + sig2 = sig * sig + + for m in range(bond_array_1.shape[0]): + ri = bond_array_1[m, 0] + ci = bond_array_1[m, d1] + fi, fdi = cutoff_func(r_cut, ri, ci) + + for n in range(bond_array_2.shape[0]): + rj = bond_array_2[n, 0] + fj, _ = cutoff_func(r_cut, rj, 0) + + r11 = ri - rj + B = r11 * ci + D = r11 * r11 + kern += force_energy_helper(B, D, fi, fj, fdi, ls1, ls2, sig2) + + return kern + + +@njit +def two_body_en_jit(bond_array_1, bond_array_2, sig, ls, r_cut, cutoff_func): + """2-body single-element kernel between two local energies accelerated + with Numba. + + Args: + bond_array_1 (np.ndarray): 2-body bond array of the first local + environment. + bond_array_2 (np.ndarray): 2-body bond array of the second local + environment. + sig (float): 2-body signal variance hyperparameter. + ls (float): 2-body length scale hyperparameter. + r_cut (float): 2-body cutoff radius. + cutoff_func (Callable): Cutoff function. + + Returns: + float: + Value of the 2-body local energy kernel. + """ + kern = 0 + + ls1 = 1 / (2 * ls * ls) + sig2 = sig * sig + + for m in range(bond_array_1.shape[0]): + ri = bond_array_1[m, 0] + fi, _ = cutoff_func(r_cut, ri, 0) + + for n in range(bond_array_2.shape[0]): + rj = bond_array_2[n, 0] + fj, _ = cutoff_func(r_cut, rj, 0) + r11 = ri - rj + kern += fi * fj * sig2 * exp(-r11 * r11 * ls1) + + return kern + + +# ----------------------------------------------------------------------------- +# three body numba functions +# ----------------------------------------------------------------------------- + + +@njit +def three_body_jit(bond_array_1, bond_array_2, + cross_bond_inds_1, cross_bond_inds_2, + cross_bond_dists_1, cross_bond_dists_2, + triplets_1, triplets_2, + d1, d2, sig, ls, r_cut, cutoff_func): + """3-body single-element kernel between two force components accelerated + with Numba. + + Args: + bond_array_1 (np.ndarray): 3-body bond array of the first local + environment. + bond_array_2 (np.ndarray): 3-body bond array of the second local + environment. + cross_bond_inds_1 (np.ndarray): Two dimensional array whose row m + contains the indices of atoms n > m in the first local + environment that are within a distance r_cut of both atom n and + the central atom. + cross_bond_inds_2 (np.ndarray): Two dimensional array whose row m + contains the indices of atoms n > m in the second local + environment that are within a distance r_cut of both atom n and + the central atom. + cross_bond_dists_1 (np.ndarray): Two dimensional array whose row m + contains the distances from atom m of atoms n > m in the first + local environment that are within a distance r_cut of both atom + n and the central atom. + cross_bond_dists_2 (np.ndarray): Two dimensional array whose row m + contains the distances from atom m of atoms n > m in the second + local environment that are within a distance r_cut of both atom + n and the central atom. + triplets_1 (np.ndarray): One dimensional array of integers whose entry + m is the number of atoms in the first local environment that are + within a distance r_cut of atom m. + triplets_2 (np.ndarray): One dimensional array of integers whose entry + m is the number of atoms in the second local environment that are + within a distance r_cut of atom m. + d1 (int): Force component of the first environment. + d2 (int): Force component of the second environment. + sig (float): 3-body signal variance hyperparameter. + ls (float): 3-body length scale hyperparameter. + r_cut (float): 3-body cutoff radius. + cutoff_func (Callable): Cutoff function. + + Return: + float: Value of the 3-body kernel. + """ + kern = 0 + + # pre-compute constants that appear in the inner loop + sig2 = sig * sig + ls1 = 1 / (2 * ls * ls) + ls2 = 1 / (ls * ls) + ls3 = ls2 * ls2 + + for m in range(bond_array_1.shape[0]): + ri1 = bond_array_1[m, 0] + ci1 = bond_array_1[m, d1] + fi1, fdi1 = cutoff_func(r_cut, ri1, ci1) + + for n in range(triplets_1[m]): + ind1 = cross_bond_inds_1[m, m + n + 1] + ri2 = bond_array_1[ind1, 0] + ci2 = bond_array_1[ind1, d1] + fi2, fdi2 = cutoff_func(r_cut, ri2, ci2) + + ri3 = cross_bond_dists_1[m, m + n + 1] + fi3, _ = cutoff_func(r_cut, ri3, 0) + + fi = fi1 * fi2 * fi3 + fdi = fdi1 * fi2 * fi3 + fi1 * fdi2 * fi3 + + for p in range(bond_array_2.shape[0]): + rj1 = bond_array_2[p, 0] + cj1 = bond_array_2[p, d2] + fj1, fdj1 = cutoff_func(r_cut, rj1, cj1) + + for q in range(triplets_2[p]): + ind2 = cross_bond_inds_2[p, p + 1 + q] + rj2 = bond_array_2[ind2, 0] + cj2 = bond_array_2[ind2, d2] + fj2, fdj2 = cutoff_func(r_cut, rj2, cj2) + + rj3 = cross_bond_dists_2[p, p + 1 + q] + fj3, _ = cutoff_func(r_cut, rj3, 0) + + fj = fj1 * fj2 * fj3 + fdj = fdj1 * fj2 * fj3 + fj1 * fdj2 * fj3 + + kern += triplet_kernel(ci1, ci2, cj1, cj2, ri1, ri2, ri3, + rj1, rj2, rj3, fi, fj, fdi, fdj, + ls1, ls2, ls3, sig2) + return kern + + +@njit +def three_body_grad_jit(bond_array_1, bond_array_2, + cross_bond_inds_1, cross_bond_inds_2, + cross_bond_dists_1, cross_bond_dists_2, + triplets_1, triplets_2, + d1, d2, sig, ls, r_cut, cutoff_func): + """3-body single-element kernel between two force components and its + gradient with respect to the hyperparameters. + + Args: + bond_array_1 (np.ndarray): 3-body bond array of the first local + environment. + bond_array_2 (np.ndarray): 3-body bond array of the second local + environment. + cross_bond_inds_1 (np.ndarray): Two dimensional array whose row m + contains the indices of atoms n > m in the first local + environment that are within a distance r_cut of both atom n and + the central atom. + cross_bond_inds_2 (np.ndarray): Two dimensional array whose row m + contains the indices of atoms n > m in the second local + environment that are within a distance r_cut of both atom n and + the central atom. + cross_bond_dists_1 (np.ndarray): Two dimensional array whose row m + contains the distances from atom m of atoms n > m in the first + local environment that are within a distance r_cut of both atom + n and the central atom. + cross_bond_dists_2 (np.ndarray): Two dimensional array whose row m + contains the distances from atom m of atoms n > m in the second + local environment that are within a distance r_cut of both atom + n and the central atom. + triplets_1 (np.ndarray): One dimensional array of integers whose entry + m is the number of atoms in the first local environment that are + within a distance r_cut of atom m. + triplets_2 (np.ndarray): One dimensional array of integers whose entry + m is the number of atoms in the second local environment that are + within a distance r_cut of atom m. + d1 (int): Force component of the first environment. + d2 (int): Force component of the second environment. + sig (float): 3-body signal variance hyperparameter. + ls (float): 3-body length scale hyperparameter. + r_cut (float): 3-body cutoff radius. + cutoff_func (Callable): Cutoff function. + + Returns: + (float, float): + Value of the 3-body kernel and its gradient with respect to the + hyperparameters. + """ + + kern = 0 + sig_derv = 0 + ls_derv = 0 + + # pre-compute constants that appear in the inner loop + sig2, sig3, ls1, ls2, ls3, ls4, ls5, ls6 = grad_constants(sig, ls) + + for m in range(bond_array_1.shape[0]): + ri1 = bond_array_1[m, 0] + ci1 = bond_array_1[m, d1] + fi1, fdi1 = cutoff_func(r_cut, ri1, ci1) + + for n in range(triplets_1[m]): + ind1 = cross_bond_inds_1[m, m + n + 1] + ri3 = cross_bond_dists_1[m, m + n + 1] + ri2 = bond_array_1[ind1, 0] + ci2 = bond_array_1[ind1, d1] + + fi2, fdi2 = cutoff_func(r_cut, ri2, ci2) + fi3, _ = cutoff_func(r_cut, ri3, 0) + + fi = fi1 * fi2 * fi3 + fdi = fdi1 * fi2 * fi3 + fi1 * fdi2 * fi3 + + for p in range(bond_array_2.shape[0]): + rj1 = bond_array_2[p, 0] + cj1 = bond_array_2[p, d2] + fj1, fdj1 = cutoff_func(r_cut, rj1, cj1) + + for q in range(triplets_2[p]): + ind2 = cross_bond_inds_2[p, p + q + 1] + rj3 = cross_bond_dists_2[p, p + q + 1] + rj2 = bond_array_2[ind2, 0] + cj2 = bond_array_2[ind2, d2] + + fj2, fdj2 = cutoff_func(r_cut, rj2, cj2) + fj3, _ = cutoff_func(r_cut, rj3, 0) + + fj = fj1 * fj2 * fj3 + fdj = fdj1 * fj2 * fj3 + fj1 * fdj2 * fj3 + + N, O, X = \ + triplet_kernel_grad(ci1, ci2, cj1, cj2, ri1, ri2, ri3, + rj1, rj2, rj3, fi, fj, fdi, fdj, + ls1, ls2, ls3, ls4, ls5, ls6, sig2, + sig3) + + kern += N + sig_derv += O + ls_derv += X + + return kern, sig_derv, ls_derv + + +@njit +def three_body_force_en_jit(bond_array_1, bond_array_2, + cross_bond_inds_1, + cross_bond_inds_2, + cross_bond_dists_1, + cross_bond_dists_2, + triplets_1, triplets_2, + d1, sig, ls, r_cut, cutoff_func): + """3-body single-element kernel between a force component and a local + energy accelerated with Numba. + + Args: + bond_array_1 (np.ndarray): 3-body bond array of the first local + environment. + bond_array_2 (np.ndarray): 3-body bond array of the second local + environment. + cross_bond_inds_1 (np.ndarray): Two dimensional array whose row m + contains the indices of atoms n > m in the first local + environment that are within a distance r_cut of both atom n and + the central atom. + cross_bond_inds_2 (np.ndarray): Two dimensional array whose row m + contains the indices of atoms n > m in the second local + environment that are within a distance r_cut of both atom n and + the central atom. + cross_bond_dists_1 (np.ndarray): Two dimensional array whose row m + contains the distances from atom m of atoms n > m in the first + local environment that are within a distance r_cut of both atom + n and the central atom. + cross_bond_dists_2 (np.ndarray): Two dimensional array whose row m + contains the distances from atom m of atoms n > m in the second + local environment that are within a distance r_cut of both atom + n and the central atom. + triplets_1 (np.ndarray): One dimensional array of integers whose entry + m is the number of atoms in the first local environment that are + within a distance r_cut of atom m. + triplets_2 (np.ndarray): One dimensional array of integers whose entry + m is the number of atoms in the second local environment that are + within a distance r_cut of atom m. + d1 (int): Force component of the first environment (1=x, 2=y, 3=z). + sig (float): 3-body signal variance hyperparameter. + ls (float): 3-body length scale hyperparameter. + r_cut (float): 3-body cutoff radius. + cutoff_func (Callable): Cutoff function. + + Returns: + float: + Value of the 3-body force/energy kernel. + """ + + kern = 0 + + # pre-compute constants that appear in the inner loop + sig2 = sig * sig + ls1 = 1 / (2 * ls * ls) + ls2 = 1 / (ls * ls) + + for m in range(bond_array_1.shape[0]): + ri1 = bond_array_1[m, 0] + ci1 = bond_array_1[m, d1] + fi1, fdi1 = cutoff_func(r_cut, ri1, ci1) + + for n in range(triplets_1[m]): + ind1 = cross_bond_inds_1[m, m + n + 1] + ri2 = bond_array_1[ind1, 0] + ci2 = bond_array_1[ind1, d1] + fi2, fdi2 = cutoff_func(r_cut, ri2, ci2) + ri3 = cross_bond_dists_1[m, m + n + 1] + fi3, _ = cutoff_func(r_cut, ri3, 0) + fi = fi1 * fi2 * fi3 + fdi = fdi1 * fi2 * fi3 + fi1 * fdi2 * fi3 + + for p in range(bond_array_2.shape[0]): + rj1 = bond_array_2[p, 0] + fj1, _ = cutoff_func(r_cut, rj1, 0) + + for q in range(triplets_2[p]): + ind2 = cross_bond_inds_2[p, p + q + 1] + rj2 = bond_array_2[ind2, 0] + fj2, _ = cutoff_func(r_cut, rj2, 0) + rj3 = cross_bond_dists_2[p, p + q + 1] + fj3, _ = cutoff_func(r_cut, rj3, 0) + fj = fj1 * fj2 * fj3 + + kern += triplet_force_en_kernel(ci1, ci2, ri1, ri2, ri3, + rj1, rj2, rj3, fi, fj, fdi, + ls1, ls2, sig2) + + return kern + + +@njit +def three_body_en_jit(bond_array_1, bond_array_2, + cross_bond_inds_1, + cross_bond_inds_2, + cross_bond_dists_1, + cross_bond_dists_2, + triplets_1, triplets_2, + sig, ls, r_cut, cutoff_func): + """3-body single-element kernel between two local energies accelerated + with Numba. + + Args: + bond_array_1 (np.ndarray): 3-body bond array of the first local + environment. + bond_array_2 (np.ndarray): 3-body bond array of the second local + environment. + cross_bond_inds_1 (np.ndarray): Two dimensional array whose row m + contains the indices of atoms n > m in the first local + environment that are within a distance r_cut of both atom n and + the central atom. + cross_bond_inds_2 (np.ndarray): Two dimensional array whose row m + contains the indices of atoms n > m in the second local + environment that are within a distance r_cut of both atom n and + the central atom. + cross_bond_dists_1 (np.ndarray): Two dimensional array whose row m + contains the distances from atom m of atoms n > m in the first + local environment that are within a distance r_cut of both atom + n and the central atom. + cross_bond_dists_2 (np.ndarray): Two dimensional array whose row m + contains the distances from atom m of atoms n > m in the second + local environment that are within a distance r_cut of both atom + n and the central atom. + triplets_1 (np.ndarray): One dimensional array of integers whose entry + m is the number of atoms in the first local environment that are + within a distance r_cut of atom m. + triplets_2 (np.ndarray): One dimensional array of integers whose entry + m is the number of atoms in the second local environment that are + within a distance r_cut of atom m. + sig (float): 3-body signal variance hyperparameter. + ls (float): 3-body length scale hyperparameter. + r_cut (float): 3-body cutoff radius. + cutoff_func (Callable): Cutoff function. + + Returns: + float: + Value of the 3-body local energy kernel. + """ + kern = 0 + + sig2 = sig * sig + ls2 = 1 / (2 * ls * ls) + + for m in range(bond_array_1.shape[0]): + ri1 = bond_array_1[m, 0] + fi1, _ = cutoff_func(r_cut, ri1, 0) + + for n in range(triplets_1[m]): + ind1 = cross_bond_inds_1[m, m + n + 1] + ri2 = bond_array_1[ind1, 0] + fi2, _ = cutoff_func(r_cut, ri2, 0) + + ri3 = cross_bond_dists_1[m, m + n + 1] + fi3, _ = cutoff_func(r_cut, ri3, 0) + fi = fi1 * fi2 * fi3 + + for p in range(bond_array_2.shape[0]): + rj1 = bond_array_2[p, 0] + fj1, _ = cutoff_func(r_cut, rj1, 0) + + for q in range(triplets_2[p]): + ind2 = cross_bond_inds_2[p, p + q + 1] + rj2 = bond_array_2[ind2, 0] + fj2, _ = cutoff_func(r_cut, rj2, 0) + + rj3 = cross_bond_dists_2[p, p + q + 1] + fj3, _ = cutoff_func(r_cut, rj3, 0) + fj = fj1 * fj2 * fj3 + + r11 = ri1 - rj1 + r12 = ri1 - rj2 + r13 = ri1 - rj3 + r21 = ri2 - rj1 + r22 = ri2 - rj2 + r23 = ri2 - rj3 + r31 = ri3 - rj1 + r32 = ri3 - rj2 + r33 = ri3 - rj3 + + C1 = r11 * r11 + r22 * r22 + r33 * r33 + C2 = r11 * r11 + r23 * r23 + r32 * r32 + C3 = r12 * r12 + r21 * r21 + r33 * r33 + C4 = r12 * r12 + r23 * r23 + r31 * r31 + C5 = r13 * r13 + r21 * r21 + r32 * r32 + C6 = r13 * r13 + r22 * r22 + r31 * r31 + + k = exp(-C1 * ls2) + exp(-C2 * ls2) + exp(-C3 * ls2) + exp(-C4 * ls2) + \ + exp(-C5 * ls2) + exp(-C6 * ls2) + + kern += sig2 * k * fi * fj + + return kern + + +# ----------------------------------------------------------------------------- +# many body numba functions +# ----------------------------------------------------------------------------- + +@njit +def many_body_jit(bond_array_1, bond_array_2, + neighbouring_dists_array_1, neighbouring_dists_array_2, + num_neighbours_1, num_neighbours_2, + d1, d2, sig, ls, r_cut, cutoff_func): + """many-body single-element kernel between two force components accelerated + with Numba. + + Args: + bond_array_1 (np.ndarray): many-body bond array of the first local + environment. + bond_array_2 (np.ndarray): many-body bond array of the second local + environment. + neighbouring_dists_array_1 (np.ndarray): matrix padded with zero values of distances + of neighbours for the atoms in the first local environment. + neighbouring_dists_array_2 (np.ndarray): matrix padded with zero values of distances + of neighbours for the atoms in the second local environment. + num_neighbours_1 (np.nsdarray): number of neighbours of each atom in the first + local environment + num_neighbours_2 (np.ndarray): number of neighbours of each atom in the second + local environment + d1 (int): Force component of the first environment. + d2 (int): Force component of the second environment. + sig (float): many-body signal variance hyperparameter. + ls (float): many-body length scale hyperparameter. + r_cut (float): many-body cutoff radius. + cutoff_func (Callable): Cutoff function. + + Return: + float: Value of the many-body kernel. + """ + kern = 0 + + # Calculate many-body descriptor values for 1 and 2 + q1 = q_value(bond_array_1[:, 0], r_cut, cutoff_func) + q2 = q_value(bond_array_2[:, 0], r_cut, cutoff_func) + + k12 = k_sq_exp_double_dev(q1, q2, sig, ls) + + qis = np.zeros(bond_array_1.shape[0], dtype=np.float64) + qi1_grads = np.zeros(bond_array_1.shape[0], dtype=np.float64) + ki2s = np.zeros(bond_array_1.shape[0], dtype=np.float64) + + qjs = np.zeros(bond_array_2.shape[0], dtype=np.float64) + qj2_grads = np.zeros(bond_array_2.shape[0], dtype=np.float64) + k1js = np.zeros(bond_array_2.shape[0], dtype=np.float64) + + # Loop over neighbours i of 1 + for i in range(bond_array_1.shape[0]): + ri1 = bond_array_1[i, 0] + ci1 = bond_array_1[i, d1] + qi1, qi1_grads[i] = coordination_number(ri1, ci1, r_cut, cutoff_func) + # Calculate many-body descriptor value for i + qis[i] = q_value(neighbouring_dists_array_1[i, :num_neighbours_1[i]], r_cut, + cutoff_func) + + ki2s[i] = k_sq_exp_double_dev(qis[i], q2, sig, ls) + + # Loop over neighbours j of 2 + for j in range(bond_array_2.shape[0]): + rj2 = bond_array_2[j, 0] + cj2 = bond_array_2[j, d2] + qj2, qj2_grads[j] = coordination_number(rj2, cj2, r_cut, cutoff_func) + + # Calculate many-body descriptor value for j + qjs[j] = q_value(neighbouring_dists_array_2[j, :num_neighbours_2[j]], r_cut, + cutoff_func) + + k1js[j] = k_sq_exp_double_dev(q1, qjs[j], sig, ls) + + for i in range(bond_array_1.shape[0]): + for j in range(bond_array_2.shape[0]): + kij = k_sq_exp_double_dev(qis[i], qjs[j], sig, ls) + kern += qi1_grads[i] * qj2_grads[j] * \ + (k12 + ki2s[i] + k1js[j] + kij) + + return kern + +@njit + + +def many_body_grad_jit(bond_array_1, bond_array_2, + neighbouring_dists_array_1, neighbouring_dists_array_2, + num_neighbours_1, num_neighbours_2, + d1, d2, sig, ls, r_cut, cutoff_func): + """gradient of many-body single-element kernel between two force components + w.r.t. the hyperparameters, accelerated with Numba. + + Args: + bond_array_1 (np.ndarray): many-body bond array of the first local + environment. + bond_array_2 (np.ndarray): many-body bond array of the second local + environment. + neighbouring_dists_array_1 (np.ndarray): matrix padded with zero values of distances + of neighbours for the atoms in the first local environment. + neighbouring_dists_array_2 (np.ndarray): matrix padded with zero values of distances + of neighbours for the atoms in the second local environment. + num_neighbours_1 (np.nsdarray): number of neighbours of each atom in the first + local environment + num_neighbours_2 (np.ndarray): number of neighbours of each atom in the second + local environment + d1 (int): Force component of the first environment. + d2 (int): Force component of the second environment. + sig (float): many-body signal variance hyperparameter. + ls (float): many-body length scale hyperparameter. + r_cut (float): many-body cutoff radius. + cutoff_func (Callable): Cutoff function. + + Return: + array: Value of the many-body kernel and its gradient w.r.t. sig and ls + """ + + kern = 0 + sig_derv = 0 + ls_derv = 0 + + # Calculate many-body descriptor values for 1 and 2 + q1 = q_value(bond_array_1[:, 0], r_cut, cutoff_func) + q2 = q_value(bond_array_2[:, 0], r_cut, cutoff_func) + + k12 = k_sq_exp_double_dev(q1, q2, sig, ls) + + qis = np.zeros(bond_array_1.shape[0], dtype=np.float64) + + qi1_grads = np.zeros(bond_array_1.shape[0], dtype=np.float64) + + ki2s = np.zeros(bond_array_1.shape[0], dtype=np.float64) + + qjs = np.zeros(bond_array_2.shape[0], dtype=np.float64) + + qj2_grads = np.zeros(bond_array_2.shape[0], dtype=np.float64) + + k1js = np.zeros(bond_array_2.shape[0], dtype=np.float64) + + # Compute ki2s, qi1_grads, and qis + for i in range(bond_array_1.shape[0]): + ri1 = bond_array_1[i, 0] + ci1 = bond_array_1[i, d1] + qi1, qi1_grads[i] = coordination_number(ri1, ci1, r_cut, cutoff_func) + + # Calculate many-body descriptor value for i + qis[i] = q_value(neighbouring_dists_array_1[i, :num_neighbours_1[i]], r_cut, + cutoff_func) + + ki2s[i] = k_sq_exp_double_dev(qis[i], q2, sig, ls) + + # Compute k1js, qj2_grads and qjs + for j in range(bond_array_2.shape[0]): + rj2 = bond_array_2[j, 0] + cj2 = bond_array_2[j, d2] + qj2, qj2_grads[j] = coordination_number(rj2, cj2, r_cut, cutoff_func) + + # Calculate many-body descriptor value for j + qjs[j] = q_value(neighbouring_dists_array_2[j, :num_neighbours_2[j]], r_cut, + cutoff_func) + + k1js[j] = k_sq_exp_double_dev(q1, qjs[j], sig, ls) + + for i in range(bond_array_1.shape[0]): + for j in range(bond_array_2.shape[0]): + kij = k_sq_exp_double_dev(qis[i], qjs[j], sig, ls) + + kern_term = qi1_grads[i] * qj2_grads[j] * \ + (k12 + ki2s[i] + k1js[j] + kij) + + sig_term = 2. / sig * kern_term + + ls_term = qi1_grads[i] * qj2_grads[j] * mb_grad_helper_ls(q1, q2, qis[i], qjs[j], sig, + ls) + + kern += kern_term + + sig_derv += sig_term + + ls_derv += ls_term + + # sig_derv = 2./sig * kern + + return kern, sig_derv, ls_derv + + +@njit +def many_body_force_en_jit(bond_array_1, bond_array_2, + neighbouring_dists_array_1, + num_neighbours_1, + d1, sig, ls, r_cut, cutoff_func): + """many-body single-element kernel between force and energy components accelerated + with Numba. + + Args: + bond_array_1 (np.ndarray): many-body bond array of the first local + environment. + bond_array_2 (np.ndarray): many-body bond array of the second local + environment. + neighbouring_dists_array_1 (np.ndarray): matrix padded with zero values of distances + of neighbours for the atoms in the first local environment. + num_neighbours_1 (np.nsdarray): number of neighbours of each atom in the first + local environment + d1 (int): Force component of the first environment. + sig (float): many-body signal variance hyperparameter. + ls (float): many-body length scale hyperparameter. + r_cut (float): many-body cutoff radius. + cutoff_func (Callable): Cutoff function. + + Return: + float: Value of the many-body kernel. + """ + + kern = 0 + + q1 = q_value(bond_array_1[:, 0], r_cut, cutoff_func) + q2 = q_value(bond_array_2[:, 0], r_cut, cutoff_func) + + k12 = k_sq_exp_dev(q1, q2, sig, ls) + + qis = np.zeros(bond_array_1.shape[0], dtype=np.float64) + qi1_grads = np.zeros(bond_array_1.shape[0], dtype=np.float64) + ki2s = np.zeros(bond_array_1.shape[0], dtype=np.float64) + + # Loop over neighbours i of 1 + for i in range(bond_array_1.shape[0]): + ri1 = bond_array_1[i, 0] + ci1 = bond_array_1[i, d1] + _, qi1_grads[i] = coordination_number(ri1, ci1, r_cut, cutoff_func) + # Calculate many-body descriptor value for i + qis[i] = q_value(neighbouring_dists_array_1[i, :num_neighbours_1[i]], r_cut, + cutoff_func) + + ki2s[i] = k_sq_exp_dev(qis[i], q2, sig, ls) + + kern += - qi1_grads[i] * (k12 + ki2s[i]) + + return kern + +@njit + + +def many_body_en_jit(bond_array_1, bond_array_2, + sig, ls, r_cut, cutoff_func): + """many-body single-element energy kernel between accelerated + with Numba. + + Args: + bond_array_1 (np.ndarray): many-body bond array of the first local + environment. + bond_array_2 (np.ndarray): many-body bond array of the second local + environment. + neighbouring_dists_array_1 (np.ndarray): matrix padded with zero values of distances + of neighbours for the atoms in the first local environment. + neighbouring_dists_array_2 (np.ndarray): matrix padded with zero values of distances + of neighbours for the atoms in the second local environment. + num_neighbours_1 (np.nsdarray): number of neighbours of each atom in the first + local environment + num_neighbours_2 (np.ndarray): number of neighbours of each atom in the second + local environment + d1 (int): Force component of the first environment. + d2 (int): Force component of the second environment. + sig (float): many-body signal variance hyperparameter. + ls (float): many-body length scale hyperparameter. + r_cut (float): many-body cutoff radius. + cutoff_func (Callable): Cutoff function. + + Return: + float: Value of the many-body kernel. + """ + + q1 = q_value(bond_array_1[:, 0], r_cut, cutoff_func) + q2 = q_value(bond_array_2[:, 0], r_cut, cutoff_func) + q1q2diff = q1 - q2 + + kern = sig * sig * exp(-q1q2diff * q1q2diff / (2 * ls * ls)) + + return kern + +# ----------------------------------------------------------------------------- +# three body helper functions +# ----------------------------------------------------------------------------- + + +@njit +def triplet_kernel(ci1, ci2, cj1, cj2, ri1, ri2, ri3, rj1, rj2, rj3, fi, fj, + fdi, fdj, ls1, ls2, ls3, sig2): + r11 = ri1 - rj1 + r12 = ri1 - rj2 + r13 = ri1 - rj3 + r21 = ri2 - rj1 + r22 = ri2 - rj2 + r23 = ri2 - rj3 + r31 = ri3 - rj1 + r32 = ri3 - rj2 + r33 = ri3 - rj3 + + # sum over all six permutations + M1 = three_body_helper_1(ci1, ci2, cj1, cj2, r11, r22, r33, fi, fj, fdi, + fdj, ls1, ls2, ls3, sig2) + M2 = three_body_helper_2(ci2, ci1, cj2, cj1, r21, r13, r32, fi, fj, fdi, + fdj, ls1, ls2, ls3, sig2) + M3 = three_body_helper_2(ci1, ci2, cj1, cj2, r12, r23, r31, fi, fj, fdi, + fdj, ls1, ls2, ls3, sig2) + M4 = three_body_helper_1(ci1, ci2, cj2, cj1, r12, r21, r33, fi, fj, fdi, + fdj, ls1, ls2, ls3, sig2) + M5 = three_body_helper_2(ci2, ci1, cj1, cj2, r22, r13, r31, fi, fj, fdi, + fdj, ls1, ls2, ls3, sig2) + M6 = three_body_helper_2(ci1, ci2, cj2, cj1, r11, r23, r32, fi, fj, fdi, + fdj, ls1, ls2, ls3, sig2) + + return M1 + M2 + M3 + M4 + M5 + M6 + + +@njit +def triplet_kernel_grad(ci1, ci2, cj1, cj2, ri1, ri2, ri3, rj1, rj2, rj3, fi, + fj, fdi, fdj, ls1, ls2, ls3, ls4, ls5, ls6, sig2, + sig3): + r11 = ri1 - rj1 + r12 = ri1 - rj2 + r13 = ri1 - rj3 + r21 = ri2 - rj1 + r22 = ri2 - rj2 + r23 = ri2 - rj3 + r31 = ri3 - rj1 + r32 = ri3 - rj2 + r33 = ri3 - rj3 + + N1, O1, X1 = \ + three_body_grad_helper_1(ci1, ci2, cj1, cj2, r11, r22, r33, fi, fj, + fdi, fdj, ls1, ls2, ls3, ls4, ls5, ls6, sig2, + sig3) + N2, O2, X2 = \ + three_body_grad_helper_2(ci2, ci1, cj2, cj1, r21, r13, r32, fi, fj, + fdi, fdj, ls1, ls2, ls3, ls4, ls5, ls6, sig2, + sig3) + N3, O3, X3 = \ + three_body_grad_helper_2(ci1, ci2, cj1, cj2, r12, r23, r31, fi, fj, + fdi, fdj, ls1, ls2, ls3, ls4, ls5, ls6, sig2, + sig3) + N4, O4, X4 = \ + three_body_grad_helper_1(ci1, ci2, cj2, cj1, r12, r21, r33, fi, fj, + fdi, fdj, ls1, ls2, ls3, ls4, ls5, ls6, sig2, + sig3) + N5, O5, X5 = \ + three_body_grad_helper_2(ci2, ci1, cj1, cj2, r22, r13, r31, fi, fj, + fdi, fdj, ls1, ls2, ls3, ls4, ls5, ls6, sig2, + sig3) + N6, O6, X6 = \ + three_body_grad_helper_2(ci1, ci2, cj2, cj1, r11, r23, r32, fi, fj, + fdi, fdj, ls1, ls2, ls3, ls4, ls5, ls6, sig2, + sig3) + N = N1 + N2 + N3 + N4 + N5 + N6 + O = O1 + O2 + O3 + O4 + O5 + O6 + X = X1 + X2 + X3 + X4 + X5 + X6 + return N, O, X + +@njit + + +def triplet_force_en_kernel(ci1, ci2, ri1, ri2, ri3, rj1, rj2, rj3, + fi, fj, fdi, ls1, ls2, sig2): + r11 = ri1 - rj1 + r12 = ri1 - rj2 + r13 = ri1 - rj3 + r21 = ri2 - rj1 + r22 = ri2 - rj2 + r23 = ri2 - rj3 + r31 = ri3 - rj1 + r32 = ri3 - rj2 + r33 = ri3 - rj3 + + I1 = three_body_en_helper(ci1, ci2, r11, r22, r33, fi, fj, + fdi, ls1, ls2, sig2) + I2 = three_body_en_helper(ci1, ci2, r13, r21, r32, fi, fj, + fdi, ls1, ls2, sig2) + I3 = three_body_en_helper(ci1, ci2, r12, r23, r31, fi, fj, + fdi, ls1, ls2, sig2) + I4 = three_body_en_helper(ci1, ci2, r12, r21, r33, fi, fj, + fdi, ls1, ls2, sig2) + I5 = three_body_en_helper(ci1, ci2, r13, r22, r31, fi, fj, + fdi, ls1, ls2, sig2) + I6 = three_body_en_helper(ci1, ci2, r11, r23, r32, fi, fj, + fdi, ls1, ls2, sig2) + + return I1 + I2 + I3 + I4 + I5 + I6 + + +# ----------------------------------------------------------------------------- +# many body helper functions +# ----------------------------------------------------------------------------- + +@njit +def k_sq_exp_double_dev(q1, q2, sig, ls): + """Second Gradient of generic squared exponential kernel on two many body functions + + Args: + q1 (float): the many body descriptor of the first local environment + q2 (float): the many body descriptor of the second local environment + sig (float): amplitude hyperparameter + ls2 (float): squared lenghtscale hyperparameter + Return: + float: the value of the double derivative of the squared exponential kernel + """ + + qdiffsq = (q1 - q2) * (q1 - q2) + + ls2 = ls * ls + + ker = exp(-qdiffsq / (2 * ls2)) + + ret = sig * sig * ker / ls2 * (1 - qdiffsq / ls2) + + return ret + +@njit + + +def k_sq_exp_dev(q1, q2, sig, ls): + """Second Gradient of generic squared exponential kernel on two many body functions + + Args: + q1 (float): the many body descriptor of the first local environment + q2 (float): the many body descriptor of the second local environment + sig (float): amplitude hyperparameter + ls2 (float): squared lenghtscale hyperparameter + Return: + float: the value of the derivative of the squared exponential kernel + """ + + qdiff = (q1 - q2) + + ls2 = ls * ls + + ker = exp(-qdiff * qdiff / (2 * ls2)) + + ret = - sig * sig * ker / ls2 * qdiff + + return ret + + +@njit +def coordination_number(rij, cij, r_cut, cutoff_func): + """Pairwise contribution to many-body descriptor based on number of + atoms in the environment + + Args: + rij (float): distance between atoms i and j + cij (float): Component of versor of rij along given direction + r_cut (float): cutoff hyperparameter + cutoff_func (callable): cutoff function + Return: + float: the value of the pairwise many-body contribution + float: the value of the derivative of the pairwise many-body + contribution w.r.t. the central atom displacement + """ + + fij, fdij = cutoff_func(r_cut, rij, cij) + + return fij, fdij + + +@njit +def q_value(distances, r_cut, cutoff_func, q_func=coordination_number): + """Compute value of many-body descriptor based on distances of atoms + in the local amny-body environment. + + Args: + distances (np.ndarray): distances between atoms i and j + r_cut (float): cutoff hyperparameter + cutoff_func (callable): cutoff function + q_func (callable): many-body pairwise descrptor function + + Return: + float: the value of the many-body descriptor + """ + q = 0 + + for d in distances: + q_, _ = q_func(d, 0, r_cut, cutoff_func) + q += q_ + + return q + + +@njit +def mb_grad_helper_ls_(qdiffsq, sig, ls): + """Derivative of a many body force-force kernel wrt ls + + """ + + ls2 = ls * ls + + prefact = exp(-(qdiffsq / (2 * ls2))) * (sig * sig) / ls ** 5 + + ret = - prefact * (qdiffsq ** 2 / ls2 - 5 * qdiffsq + 2 * ls2) + + return ret + +@njit + + +def mb_grad_helper_ls(q1, q2, qi, qj, sig, ls): + """Helper function fr many body gradient collecting all the derivatives + of the force-foce many body kernel wrt ls + + """ + + q12diffsq = ((q1 - q2) * (q1 - q2)) + qijdiffsq = ((qi - qj) * (qi - qj)) + qi2diffsq = ((qi - q2) * (qi - q2)) + q1jdiffsq = ((q1 - qj) * (q1 - qj)) + + dk12 = mb_grad_helper_ls_(q12diffsq, sig, ls) + dkij = mb_grad_helper_ls_(qijdiffsq, sig, ls) + dki2 = mb_grad_helper_ls_(qi2diffsq, sig, ls) + dk1j = mb_grad_helper_ls_(q1jdiffsq, sig, ls) + + return dk12 + dkij + dki2 + dk1j + + +_str_to_kernel = {'two_body': two_body, + 'two_body_en': two_body_en, + 'two_body_force_en': two_body_force_en, + 'three_body': three_body, + 'three_body_en': three_body_en, + 'three_body_force_en': three_body_force_en, + 'two_plus_three_body': two_plus_three_body, + 'two_plus_three_en': two_plus_three_en, + 'two_plus_three_force_en': two_plus_three_force_en, + '2': two_body, + '2_en': two_body_en, + '2_grad': two_body_grad, + '2_force_en': two_body_force_en, + '3': three_body, + '3_grad': three_body_grad, + '3_en': three_body_en, + '3_force_en': three_body_force_en, + '2+3': two_plus_three_body, + '2+3_grad': two_plus_three_body_grad, + '2+3_en': two_plus_three_en, + '2+3_force_en': two_plus_three_force_en, + 'many': many_body, + 'many_en': many_body_en, + 'many_grad': many_body_grad, + 'many_force_en': many_body_force_en, + 'two_plus_three_plus_many_body': two_plus_three_plus_many_body, + 'two_plus_three_plus_many_body_grad': two_plus_three_plus_many_body_grad, + 'two_plus_three_plus_many_body_en': two_plus_three_plus_many_body_en, + 'two_plus_three_plus_many_body_force_en': two_plus_three_plus_many_body_force_en, + '2+3+many': two_plus_three_plus_many_body, + '2+3+many_grad': two_plus_three_plus_many_body_grad, + '2+3+many_en': two_plus_three_plus_many_body_en, + '2+3+many_force_en': two_plus_three_plus_many_body_force_en + } + + +def str_to_kernel(string: str, include_grad: bool = False): + if string not in _str_to_kernel.keys(): + raise ValueError("Kernel {} not found in list of available " + "kernels{}:".format(string, _str_to_kernel.keys())) + + if not include_grad: + return _str_to_kernel[string] + else: + if 'two' in string and 'three' in string: + return _str_to_kernel[string], two_plus_three_body_grad + elif 'two' in string and 'three' not in string: + return _str_to_kernel[string], two_body_grad + elif 'two' not in string and 'three' in string: + return _str_to_kernel[string], three_body_grad + else: + raise ValueError("Gradient callable for {} not found".format( + string)) diff --git a/flare/kernels/utils.py b/flare/kernels/utils.py index 080f9febb..4641628a1 100644 --- a/flare/kernels/utils.py +++ b/flare/kernels/utils.py @@ -1,40 +1,190 @@ -from flare.kernels.kernels import str_to_kernel -from flare.kernels.mc_simple import str_to_mc_kernel -from flare.kernels.mc_sephyps import str_to_mc_kernel as str_to_mc_sephyps_kernel +import numpy as np +from flare.kernels import sc, mc_simple, mc_sephyps -def str_to_kernels(name: str, multihyps: bool =False, - include_grad: bool = False): +""" +This module includes interface functions between kernels and gp/gp_algebra + +str_to_kernel_set is used in GaussianProcess class to search for a kernel function +based on a string name. + +from_mask_to_args converts the hyperparameter vector and the dictionary of hyps_mask +to the list of arguments needed by the kernel function. + +from_grad_to_mask(grad, hyps_mask) converts the gradient matrix to the actual gradient +matrix by removing the fixed dimensions. +""" + + +def str_to_kernel_set(name: str, multihyps: bool = False): """ - return kernels and kernel gradient base on a string + return kernels and kernel gradient function base on a string. + If it contains 'sc', it will use the kernel in sc module; + otherwise, it uses the kernel in mc_simple; + if sc is not included and multihyps is True, + it will use the kernel in mc_sephyps module + otherwise, it will use the kernel in the sc module Args: - name (str): - multihyps (bool): - include_grad (bool) : whether gradient should be include + name (str): name for kernels. example: "2+3mc" + multihyps (bool, optional): True for using multiple hyperparameter groups + + :return: kernel function, kernel gradient, energy kernel, + energy_and_force kernel """ - if (include_grad): - if 'mc' in name: - if (multihyps is False): - force_kernel, grad = \ - str_to_mc_kernel(name, include_grad=True) - else: - force_kernel, grad = \ - str_to_mc_sephyps_kernel(name, - include_grad=True) - else: - force_kernel, grad = str_to_kernel(name, - include_grad=True) - return force_kernel, grad + if 'sc' in name: + stk = sc._str_to_kernel else: - if 'mc' in name: - if (multihyps is False): - kernel = str_to_mc_kernel(name) - else: - kernel = str_to_mc_sephyps_kernel(name) + if (multihyps is False): + stk = mc_simple._str_to_kernel else: - kernel = str_to_kernel(name) + stk = mc_sephyps._str_to_kernel + + # b2 = Two body in use, b3 = Three body in use + b2 = False + b3 = False + many = False + + for s in ['2', 'two', 'Two', 'TWO']: + if (s in name): + b2 = True + for s in ['3', 'three', 'Three', 'THREE']: + if (s in name): + b3 = True + for s in ['mb', 'manybody', 'many', 'Many', 'ManyBody']: + if (s in name): + many = True + + prefix = '' + str_term = {'2': b2, '3': b3, 'many': many} + for term in str_term: + if str_term[term]: + if (len(prefix) > 0): + prefix += '+' + prefix += term + if len(prefix) == 0: + raise RuntimeError( + f"the name has to include at least one number {name}") + + for suffix in ['', '_grad', '_en', '_force_en']: + if prefix+suffix not in stk: + raise RuntimeError( + f"cannot find kernel function of {prefix}{suffix}") + + return stk[prefix], stk[prefix+'_grad'], stk[prefix+'_en'], \ + stk[prefix+'_force_en'] + + +def from_mask_to_args(hyps, hyps_mask: dict, cutoffs): + """ return the tuple of arguments needed for kernel function + the order of the tuple has to be exactly the same as the one + taken by the kernel function + + :param hyps: list of hyperparmeter values + :type hyps: nd.array + :param hyps_mask: all the remaining parameters needed + :type hyps_mask: dictionary + :param cutoffs: cutoffs used + + :return: args + """ + + # no special setting + if (hyps_mask is None): + return (hyps, cutoffs) + + # setting for mc_sephyps + n2b = hyps_mask.get('nbond', 0) + n3b = hyps_mask.get('ntriplet', 0) + + bond_mask = hyps_mask.get('bond_mask', None) + triplet_mask = hyps_mask.get('triplet_mask', None) + + sig2 = None + ls2 = None + sig3 = None + ls3 = None + sigm = None + lsm = None + + if ('map' in hyps_mask): + orig_hyps = hyps_mask['original'] + hm = hyps_mask['map'] + for i, h in enumerate(hyps): + orig_hyps[hm[i]] = h + else: + orig_hyps = hyps + + ncutoff = len(cutoffs) + + if (ncutoff <= 2): + if (n2b != 0): + sig2 = np.array(orig_hyps[:n2b], dtype=np.float64) + ls2 = np.array(orig_hyps[n2b:n2b * 2], dtype=np.float64) + if (n3b != 0): + sig3 = np.array(orig_hyps[n2b * 2:n2b * 2 + n3b], dtype=np.float64) + ls3 = np.array(orig_hyps[n2b * 2 + n3b:n2b * 2 + n3b * 2], dtype=np.float64) + if (n2b == 0) and (n3b == 0): + raise NameError("Hyperparameter mask missing nbond and/or" + "ntriplet key") + return (cutoffs, + hyps_mask['nspec'], hyps_mask['spec_mask'], + n2b, bond_mask, n3b, triplet_mask, + sig2, ls2, sig3, ls3) + + elif (ncutoff == 3): + + if (n2b != 0): + sig2 = np.array(orig_hyps[:n2b], dtype=np.float64) + ls2 = np.array(orig_hyps[n2b:n2b * 2], dtype=np.float64) + if (n3b != 0): + start = n2b*2 + sig3 = np.array(orig_hyps[start:start + n3b], dtype=np.float64) + ls3 = np.array(orig_hyps[start + n3b:start + n3b * 2], dtype=np.float64) + sigm = orig_hyps[n2b*2+n3b*2] + lsm = orig_hyps[n2b*2+n3b*2+1] + + return (cutoffs, + hyps_mask['nspec'], + np.array(hyps_mask['spec_mask'], dtype=np.int8), + n2b, bond_mask, + n3b, triplet_mask, + sig2, ls2, sig3, ls3, sigm, lsm) + else: + raise RuntimeError("only support up to 3 cutoffs") + + +def from_grad_to_mask(grad, hyps_mask): + """ + Return gradient which only includes hyperparameters + which are meant to vary + + :param grad: original gradient vector + :param hyps_mask: dictionary for hyper-parameters + + :return: newgrad + """ + + # no special setting + if (hyps_mask is None): return grad + + # setting for mc_sephyps + # no constrained optimization + if 'map' not in hyps_mask: + return grad + + # setting for mc_sephyps + # if the last element is not sigma_noise + if (hyps_mask['map'][-1] == len(grad)): + hm = hyps_mask['map'][:-1] + else: + hm = hyps_mask['map'] + + newgrad = np.zeros(len(hm), dtype=np.float64) + for i, mapid in enumerate(hm): + newgrad[i] = grad[mapid] + return newgrad diff --git a/flare/lammps/lammps_calculator.py b/flare/lammps/lammps_calculator.py index 28f992202..ec65b5c1f 100644 --- a/flare/lammps/lammps_calculator.py +++ b/flare/lammps/lammps_calculator.py @@ -1,11 +1,10 @@ """LAMMPS calculator for preparing and parsing single-point LAMMPS \ calculations.""" -import os +import subprocess import numpy as np # TODO: split LAMMPS input and data files into separate classes - def run_lammps(lammps_executable, input_file, output_file): """Runs a single point LAMMPS calculation. @@ -17,10 +16,10 @@ def run_lammps(lammps_executable, input_file, output_file): :type output_file: str """ # run lammps - lammps_command = '%s < %s > %s' % (lammps_executable, - input_file, - output_file) - os.system(lammps_command) + lammps_command = f'{lammps_executable} -in {input_file} ' + print("run command:", lammps_command) + with open("tmp2False.out", "w+") as fout: + subprocess.call(lammps_command.split(), stdout=fout) def lammps_parser(dump_file): @@ -67,11 +66,11 @@ def lammps_dat(structure, atom_types, atom_masses, species): :type species: List[int] """ - dat_text = """Header of the LAMMPS data file + dat_text = f"""Header of the LAMMPS data file -%i atoms -%i atom types -""" % (structure.nat, len(atom_types)) +{structure.nat} atoms +{len(atom_types)} atom types +""" dat_text += lammps_cell_text(structure) dat_text += """ @@ -80,7 +79,7 @@ def lammps_dat(structure, atom_types, atom_masses, species): """ mass_text = '' for atom_type, atom_mass in zip(atom_types, atom_masses): - mass_text += '%i %i\n' % (atom_type, atom_mass) + mass_text += f'{atom_type} {atom_mass}\n' dat_text += mass_text dat_text += """ Atoms @@ -106,11 +105,11 @@ def lammps_dat_charged(structure, atom_types, atom_charges, atom_masses, :type species: List[int] """ - dat_text = """Header of the LAMMPS data file + dat_text = f"""Header of the LAMMPS data file -%i atoms -%i atom types -""" % (structure.nat, len(atom_types)) +{structure.nat} atoms +{len(atom_types)} atom types +""" dat_text += lammps_cell_text(structure) dat_text += """ @@ -120,7 +119,7 @@ def lammps_dat_charged(structure, atom_types, atom_charges, atom_masses, mass_text = '' for atom_type, atom_mass in zip(atom_types, atom_masses): - mass_text += '%i %i\n' % (atom_type, atom_mass) + mass_text += f'{atom_type} {atom_mass}\n' dat_text += mass_text dat_text += """ Atoms @@ -133,17 +132,12 @@ def lammps_dat_charged(structure, atom_types, atom_charges, atom_masses, def lammps_cell_text(structure): """ Write cell from structure object.""" - cell_text = """ -0.0 %f xlo xhi -0.0 %f ylo yhi -0.0 %f zlo zhi -%f %f %f xy xz yz -""" % (structure.cell[0, 0], - structure.cell[1, 1], - structure.cell[2, 2], - structure.cell[1, 0], - structure.cell[2, 0], - structure.cell[2, 1]) + cell_text = f""" +0.0 {structure.cell[0, 0]} xlo xhi +0.0 {structure.cell[1, 1]} ylo yhi +0.0 {structure.cell[2, 2]} zlo zhi +{structure.cell[1, 0]} {structure.cell[2, 0]} {structure.cell[2, 1]} xy xz yz +""" return cell_text @@ -153,8 +147,7 @@ def lammps_pos_text(structure, species): pos_text = '\n' for count, (pos, spec) in enumerate(zip(structure.positions, species)): - pos_text += '%i %i %f %f %f \n' % \ - (count+1, spec, pos[0], pos[1], pos[2]) + pos_text += f'{count+1} {spec} {pos[0]} {pos[1]} {pos[2]}\n' return pos_text @@ -164,8 +157,7 @@ def lammps_pos_text_charged(structure, charges, species): pos_text = '\n' for count, (pos, chrg, spec) in enumerate(zip(structure.positions, charges, species)): - pos_text += '%i %i %f %f %f %f \n' % \ - (count+1, spec, chrg, pos[0], pos[1], pos[2]) + pos_text += f'{count+1} {spec} {chrg} {pos[0]} {pos[1]} {pos[2]}\n' return pos_text @@ -180,47 +172,57 @@ def write_text(file, text): # ----------------------------------------------------------------------------- -def generic_lammps_input(dat_file, style_string, coeff_string, dump_file): +def generic_lammps_input(dat_file, style_string, coeff_string, dump_file, newton=True): """Create text for generic LAMMPS input file.""" - input_text = """# generic lammps input file + if newton is True: + ntn = 'on' + else: + ntn = 'off' + + input_text = f"""# generic lammps input file units metal atom_style atomic dimension 3 boundary p p p -newton off -read_data %s +newton {ntn} +read_data {dat_file} -pair_style %s -pair_coeff %s +pair_style {style_string} +pair_coeff {coeff_string} thermo_style one -dump 1 all custom 1 %s id type x y z fx fy fz +dump 1 all custom 1 {dump_file} id type x y z fx fy fz dump_modify 1 sort id run 0 -""" % (dat_file, style_string, coeff_string, dump_file) +""" return input_text -def ewald_input(dat_file, short_cut, kspace_accuracy, dump_file): +def ewald_input(dat_file, short_cut, kspace_accuracy, dump_file, newton=True): """Create text for Ewald input file.""" + if newton is True: + ntn = 'on' + else: + ntn = 'off' - input_text = """# Ewald input file + input_text = f"""# Ewald input file +newton {ntn} units metal atom_style charge dimension 3 boundary p p p -read_data %s +read_data {dat_file} -pair_style coul/long %s +pair_style coul/long {short_cut} pair_coeff * * -kspace_style ewald %s +kspace_style ewald {kspace_accuracy} thermo_style one -dump 1 all custom 1 %s id type x y z fx fy fz +dump 1 all custom 1 {dump_file} id type x y z fx fy fz dump_modify 1 sort id run 0 -""" % (dat_file, short_cut, kspace_accuracy, dump_file) +""" return input_text diff --git a/flare/mc_simple.py b/flare/mc_simple.py deleted file mode 100644 index 89bbb726d..000000000 --- a/flare/mc_simple.py +++ /dev/null @@ -1,21 +0,0 @@ -""" -a dummy to reproduce the kernels.mc_simple module -such that the flare.mc_simple import can work as it was -""" - -import flare.kernels.mc_simple as origin - -two_plus_three_body_mc = origin.two_plus_three_body_mc -two_plus_three_body_mc_grad = origin.two_plus_three_body_mc_grad -two_plus_three_mc_force_en = origin.two_plus_three_mc_force_en -two_plus_three_mc_en = origin.two_plus_three_mc_en -three_body_mc = origin.three_body_mc -three_body_mc_grad = origin.three_body_mc_grad -three_body_mc_force_en = origin.three_body_mc_force_en -three_body_mc_en = origin.three_body_mc_en -two_body_mc = origin.two_body_mc -two_body_mc_grad = origin.two_body_mc_grad -two_body_mc_force_en = origin.two_body_mc_force_en -two_body_mc_en = origin.two_body_mc_en -str_to_mc_kernel = origin.str_to_mc_kernel -_str_to_kernel = origin._str_to_kernel diff --git a/flare/md_run.py b/flare/md_run.py deleted file mode 100644 index bcc94f79a..000000000 --- a/flare/md_run.py +++ /dev/null @@ -1,83 +0,0 @@ -from flare.gp import GaussianProcess -from flare.struc import Structure -from flare.env import AtomicEnvironment -import numpy as np -import time -import datetime - -from flare import md -from flare.output import Output -import flare.predict as predict - -class MD: - """Generates NVE dynamics from a GP model.""" - - def __init__(self, dt: float, number_of_steps: int, gp: GaussianProcess, - pos_init: np.ndarray, species, cell, masses, - prev_pos_init: np.ndarray=None, par: bool=False, skip: int=0, - output_name='otf_run.out'): - - self.dt = dt - self.Nsteps = number_of_steps - self.gp = gp - - self.structure = Structure(cell=cell, species=species, - positions=pos_init, - mass_dict=masses, - prev_positions=prev_pos_init) - - self.noa = self.structure.positions.shape[0] - self.atom_list = list(range(self.noa)) - self.curr_step = 0 - - # choose prediction function - if par is True: - self.pred_func = predict.predict_on_structure_par_en - else: - self.pred_func = predict.predict_on_structure_en - - # initialize local energies - self.local_energies = np.zeros(self.noa) - - self.pes = [] - self.kes = [] - - self.output = Output(output_name) - - def run(self): - self.output.write_header(self.gp.cutoffs, self.gp.kernel_name, self.gp.hyps, - self.gp.algo, self.dt, self.Nsteps, self.structure) - self.start_time = time.time() - - while self.curr_step < self.Nsteps: - # verlet algorithm follows Frenkel p. 70 - self.gp.check_L_alpha() - self.pred_func() - new_pos = md.update_positions(self.dt, self.noa, self.structure) - self.update_temperature(new_pos) - self.record_state() - self.update_positions(new_pos) - self.curr_step += 1 - - self.output.conclude_run() - - def update_positions(self, new_pos): - self.structure.prev_positions = self.structure.positions - self.structure.positions = new_pos - self.structure.wrap_positions() - - def update_temperature(self, new_pos): - KE, temperature = \ - md.calculate_temperature(new_pos, self.structure, self.dt, - self.noa) - self.KE = KE - self.temperature = temperature - - def record_state(self): - self.pes.append(np.sum(self.local_energies)) - self.kes.append(self.KE) - self.output.write_md_config(self.dt, self.curr_step, self.structure, - self.temperature, self.KE, self.local_energies, - self.start_time) - self.output.write_xyz_config(self.curr_step, self.structure, - self.dft_step) diff --git a/flare/mgp/cubic_splines_numba.py b/flare/mgp/cubic_splines_numba.py index 0d01f0d5f..cae483bcf 100644 --- a/flare/mgp/cubic_splines_numba.py +++ b/flare/mgp/cubic_splines_numba.py @@ -423,6 +423,255 @@ def vec_eval_cubic_spline_3(a, b, orders, coefs, points, out): out[n] = Phi0_0*(Phi1_0*(Phi2_0*(coefs[i0+0,i1+0,i2+0]) + Phi2_1*(coefs[i0+0,i1+0,i2+1]) + Phi2_2*(coefs[i0+0,i1+0,i2+2]) + Phi2_3*(coefs[i0+0,i1+0,i2+3])) + Phi1_1*(Phi2_0*(coefs[i0+0,i1+1,i2+0]) + Phi2_1*(coefs[i0+0,i1+1,i2+1]) + Phi2_2*(coefs[i0+0,i1+1,i2+2]) + Phi2_3*(coefs[i0+0,i1+1,i2+3])) + Phi1_2*(Phi2_0*(coefs[i0+0,i1+2,i2+0]) + Phi2_1*(coefs[i0+0,i1+2,i2+1]) + Phi2_2*(coefs[i0+0,i1+2,i2+2]) + Phi2_3*(coefs[i0+0,i1+2,i2+3])) + Phi1_3*(Phi2_0*(coefs[i0+0,i1+3,i2+0]) + Phi2_1*(coefs[i0+0,i1+3,i2+1]) + Phi2_2*(coefs[i0+0,i1+3,i2+2]) + Phi2_3*(coefs[i0+0,i1+3,i2+3]))) + Phi0_1*(Phi1_0*(Phi2_0*(coefs[i0+1,i1+0,i2+0]) + Phi2_1*(coefs[i0+1,i1+0,i2+1]) + Phi2_2*(coefs[i0+1,i1+0,i2+2]) + Phi2_3*(coefs[i0+1,i1+0,i2+3])) + Phi1_1*(Phi2_0*(coefs[i0+1,i1+1,i2+0]) + Phi2_1*(coefs[i0+1,i1+1,i2+1]) + Phi2_2*(coefs[i0+1,i1+1,i2+2]) + Phi2_3*(coefs[i0+1,i1+1,i2+3])) + Phi1_2*(Phi2_0*(coefs[i0+1,i1+2,i2+0]) + Phi2_1*(coefs[i0+1,i1+2,i2+1]) + Phi2_2*(coefs[i0+1,i1+2,i2+2]) + Phi2_3*(coefs[i0+1,i1+2,i2+3])) + Phi1_3*(Phi2_0*(coefs[i0+1,i1+3,i2+0]) + Phi2_1*(coefs[i0+1,i1+3,i2+1]) + Phi2_2*(coefs[i0+1,i1+3,i2+2]) + Phi2_3*(coefs[i0+1,i1+3,i2+3]))) + Phi0_2*(Phi1_0*(Phi2_0*(coefs[i0+2,i1+0,i2+0]) + Phi2_1*(coefs[i0+2,i1+0,i2+1]) + Phi2_2*(coefs[i0+2,i1+0,i2+2]) + Phi2_3*(coefs[i0+2,i1+0,i2+3])) + Phi1_1*(Phi2_0*(coefs[i0+2,i1+1,i2+0]) + Phi2_1*(coefs[i0+2,i1+1,i2+1]) + Phi2_2*(coefs[i0+2,i1+1,i2+2]) + Phi2_3*(coefs[i0+2,i1+1,i2+3])) + Phi1_2*(Phi2_0*(coefs[i0+2,i1+2,i2+0]) + Phi2_1*(coefs[i0+2,i1+2,i2+1]) + Phi2_2*(coefs[i0+2,i1+2,i2+2]) + Phi2_3*(coefs[i0+2,i1+2,i2+3])) + Phi1_3*(Phi2_0*(coefs[i0+2,i1+3,i2+0]) + Phi2_1*(coefs[i0+2,i1+3,i2+1]) + Phi2_2*(coefs[i0+2,i1+3,i2+2]) + Phi2_3*(coefs[i0+2,i1+3,i2+3]))) + Phi0_3*(Phi1_0*(Phi2_0*(coefs[i0+3,i1+0,i2+0]) + Phi2_1*(coefs[i0+3,i1+0,i2+1]) + Phi2_2*(coefs[i0+3,i1+0,i2+2]) + Phi2_3*(coefs[i0+3,i1+0,i2+3])) + Phi1_1*(Phi2_0*(coefs[i0+3,i1+1,i2+0]) + Phi2_1*(coefs[i0+3,i1+1,i2+1]) + Phi2_2*(coefs[i0+3,i1+1,i2+2]) + Phi2_3*(coefs[i0+3,i1+1,i2+3])) + Phi1_2*(Phi2_0*(coefs[i0+3,i1+2,i2+0]) + Phi2_1*(coefs[i0+3,i1+2,i2+1]) + Phi2_2*(coefs[i0+3,i1+2,i2+2]) + Phi2_3*(coefs[i0+3,i1+2,i2+3])) + Phi1_3*(Phi2_0*(coefs[i0+3,i1+3,i2+0]) + Phi2_1*(coefs[i0+3,i1+3,i2+1]) + Phi2_2*(coefs[i0+3,i1+3,i2+2]) + Phi2_3*(coefs[i0+3,i1+3,i2+3]))) +Ad = array([ +# t^3 t^2 t 1 + [-1.0/6.0, 3.0/6.0, -3.0/6.0, 1.0/6.0], + [ 3.0/6.0, -6.0/6.0, 0.0/6.0, 4.0/6.0], + [-3.0/6.0, 3.0/6.0, 3.0/6.0, 1.0/6.0], + [ 1.0/6.0, 0.0/6.0, 0.0/6.0, 0.0/6.0] +]) + +dAd = zeros((4,4)) +for i in range(1,4): + Ad_i = Ad[:, i-1] + dAd[:,i] = (4-i) * Ad_i + +d2Ad = zeros((4,4)) +for i in range(1,4): + dAd_i = dAd[:, i-1] + d2Ad[:,i] = (4-i) * dAd_i + + + + +@njit(cache=True) +def vec_eval_cubic_splines_G_1(a, b, orders, coefs, points, vals, dvals): + + #n_vals = coefs.shape[1] + N = points.shape[0] + + M0 = orders[0] + start0 = a[0] + dinv0 = (orders[0]-1.0)/(b[0]-a[0]) + + for n in range(N): + + x0 = points[n,0] + u0 = (x0 - start0)*dinv0 + i0 = int( floor( u0 ) ) + i0 = max( min(i0,M0-2), 0 ) + t0 = u0-i0 + tp0_0 = t0*t0*t0; tp0_1 = t0*t0; tp0_2 = t0; tp0_3 = 1.0; + Phi0_0 = 0 + Phi0_1 = 0 + Phi0_2 = 0 + Phi0_3 = 0 + if t0 < 0: + Phi0_0 = dAd[0,3]*t0 + Ad[0,3] + Phi0_1 = dAd[1,3]*t0 + Ad[1,3] + Phi0_2 = dAd[2,3]*t0 + Ad[2,3] + Phi0_3 = dAd[3,3]*t0 + Ad[3,3] + elif t0 > 1: + Phi0_0 = (3*Ad[0,0] + 2*Ad[0,1] + Ad[0,2])*(t0-1) + (Ad[0,0]+Ad[0,1]+Ad[0,2]+Ad[0,3]) + Phi0_1 = (3*Ad[1,0] + 2*Ad[1,1] + Ad[1,2])*(t0-1) + (Ad[1,0]+Ad[1,1]+Ad[1,2]+Ad[1,3]) + Phi0_2 = (3*Ad[2,0] + 2*Ad[2,1] + Ad[2,2])*(t0-1) + (Ad[2,0]+Ad[2,1]+Ad[2,2]+Ad[2,3]) + Phi0_3 = (3*Ad[3,0] + 2*Ad[3,1] + Ad[3,2])*(t0-1) + (Ad[3,0]+Ad[3,1]+Ad[3,2]+Ad[3,3]) + else: + Phi0_0 = (Ad[0,0]*tp0_0 + Ad[0,1]*tp0_1 + Ad[0,2]*tp0_2 + Ad[0,3]*tp0_3) + Phi0_1 = (Ad[1,0]*tp0_0 + Ad[1,1]*tp0_1 + Ad[1,2]*tp0_2 + Ad[1,3]*tp0_3) + Phi0_2 = (Ad[2,0]*tp0_0 + Ad[2,1]*tp0_1 + Ad[2,2]*tp0_2 + Ad[2,3]*tp0_3) + Phi0_3 = (Ad[3,0]*tp0_0 + Ad[3,1]*tp0_1 + Ad[3,2]*tp0_2 + Ad[3,3]*tp0_3) + dPhi0_0 = (dAd[0,0]*tp0_0 + dAd[0,1]*tp0_1 + dAd[0,2]*tp0_2 + dAd[0,3]*tp0_3)*dinv0 + dPhi0_1 = (dAd[1,0]*tp0_0 + dAd[1,1]*tp0_1 + dAd[1,2]*tp0_2 + dAd[1,3]*tp0_3)*dinv0 + dPhi0_2 = (dAd[2,0]*tp0_0 + dAd[2,1]*tp0_1 + dAd[2,2]*tp0_2 + dAd[2,3]*tp0_3)*dinv0 + dPhi0_3 = (dAd[3,0]*tp0_0 + dAd[3,1]*tp0_1 + dAd[3,2]*tp0_2 + dAd[3,3]*tp0_3)*dinv0 + + vals[n, 0] = Phi0_0*(coefs[i0+0]) + Phi0_1*(coefs[i0+1]) + Phi0_2*(coefs[i0+2]) + Phi0_3*(coefs[i0+3]) + dvals[n, 0, 0] = dPhi0_0*(coefs[i0+0]) + dPhi0_1*(coefs[i0+1]) + dPhi0_2*(coefs[i0+2]) + dPhi0_3*(coefs[i0+3]) + + vals = vals[:, 0] + dvals = dvals[:, :, 0] + +@njit(cache=True) +def vec_eval_cubic_splines_G_3(a, b, orders, coefs, points, vals, dvals): + + coefs = np.expand_dims(coefs, 3) + n_vals = coefs.shape[-1] + N = points.shape[0] + + M0 = orders[0] + start0 = a[0] + dinv0 = (orders[0]-1.0)/(b[0]-a[0]) + M1 = orders[1] + start1 = a[1] + dinv1 = (orders[1]-1.0)/(b[1]-a[1]) + M2 = orders[2] + start2 = a[2] + dinv2 = (orders[2]-1.0)/(b[2]-a[2]) + + for n in range(N): + + x0 = points[n,0] + x1 = points[n,1] + x2 = points[n,2] + u0 = (x0 - start0)*dinv0 + i0 = int( floor( u0 ) ) + i0 = max( min(i0,M0-2), 0 ) + t0 = u0-i0 + u1 = (x1 - start1)*dinv1 + i1 = int( floor( u1 ) ) + i1 = max( min(i1,M1-2), 0 ) + t1 = u1-i1 + u2 = (x2 - start2)*dinv2 + i2 = int( floor( u2 ) ) + i2 = max( min(i2,M2-2), 0 ) + t2 = u2-i2 + tp0_0 = t0*t0*t0; tp0_1 = t0*t0; tp0_2 = t0; tp0_3 = 1.0; + tp1_0 = t1*t1*t1; tp1_1 = t1*t1; tp1_2 = t1; tp1_3 = 1.0; + tp2_0 = t2*t2*t2; tp2_1 = t2*t2; tp2_2 = t2; tp2_3 = 1.0; + Phi0_0 = 0 + Phi0_1 = 0 + Phi0_2 = 0 + Phi0_3 = 0 + if t0 < 0: + Phi0_0 = dAd[0,3]*t0 + Ad[0,3] + Phi0_1 = dAd[1,3]*t0 + Ad[1,3] + Phi0_2 = dAd[2,3]*t0 + Ad[2,3] + Phi0_3 = dAd[3,3]*t0 + Ad[3,3] + elif t0 > 1: + Phi0_0 = (3*Ad[0,0] + 2*Ad[0,1] + Ad[0,2])*(t0-1) + (Ad[0,0]+Ad[0,1]+Ad[0,2]+Ad[0,3]) + Phi0_1 = (3*Ad[1,0] + 2*Ad[1,1] + Ad[1,2])*(t0-1) + (Ad[1,0]+Ad[1,1]+Ad[1,2]+Ad[1,3]) + Phi0_2 = (3*Ad[2,0] + 2*Ad[2,1] + Ad[2,2])*(t0-1) + (Ad[2,0]+Ad[2,1]+Ad[2,2]+Ad[2,3]) + Phi0_3 = (3*Ad[3,0] + 2*Ad[3,1] + Ad[3,2])*(t0-1) + (Ad[3,0]+Ad[3,1]+Ad[3,2]+Ad[3,3]) + else: + Phi0_0 = (Ad[0,0]*tp0_0 + Ad[0,1]*tp0_1 + Ad[0,2]*tp0_2 + Ad[0,3]*tp0_3) + Phi0_1 = (Ad[1,0]*tp0_0 + Ad[1,1]*tp0_1 + Ad[1,2]*tp0_2 + Ad[1,3]*tp0_3) + Phi0_2 = (Ad[2,0]*tp0_0 + Ad[2,1]*tp0_1 + Ad[2,2]*tp0_2 + Ad[2,3]*tp0_3) + Phi0_3 = (Ad[3,0]*tp0_0 + Ad[3,1]*tp0_1 + Ad[3,2]*tp0_2 + Ad[3,3]*tp0_3) + Phi1_0 = 0 + Phi1_1 = 0 + Phi1_2 = 0 + Phi1_3 = 0 + if t1 < 0: + Phi1_0 = dAd[0,3]*t1 + Ad[0,3] + Phi1_1 = dAd[1,3]*t1 + Ad[1,3] + Phi1_2 = dAd[2,3]*t1 + Ad[2,3] + Phi1_3 = dAd[3,3]*t1 + Ad[3,3] + elif t1 > 1: + Phi1_0 = (3*Ad[0,0] + 2*Ad[0,1] + Ad[0,2])*(t1-1) + (Ad[0,0]+Ad[0,1]+Ad[0,2]+Ad[0,3]) + Phi1_1 = (3*Ad[1,0] + 2*Ad[1,1] + Ad[1,2])*(t1-1) + (Ad[1,0]+Ad[1,1]+Ad[1,2]+Ad[1,3]) + Phi1_2 = (3*Ad[2,0] + 2*Ad[2,1] + Ad[2,2])*(t1-1) + (Ad[2,0]+Ad[2,1]+Ad[2,2]+Ad[2,3]) + Phi1_3 = (3*Ad[3,0] + 2*Ad[3,1] + Ad[3,2])*(t1-1) + (Ad[3,0]+Ad[3,1]+Ad[3,2]+Ad[3,3]) + else: + Phi1_0 = (Ad[0,0]*tp1_0 + Ad[0,1]*tp1_1 + Ad[0,2]*tp1_2 + Ad[0,3]*tp1_3) + Phi1_1 = (Ad[1,0]*tp1_0 + Ad[1,1]*tp1_1 + Ad[1,2]*tp1_2 + Ad[1,3]*tp1_3) + Phi1_2 = (Ad[2,0]*tp1_0 + Ad[2,1]*tp1_1 + Ad[2,2]*tp1_2 + Ad[2,3]*tp1_3) + Phi1_3 = (Ad[3,0]*tp1_0 + Ad[3,1]*tp1_1 + Ad[3,2]*tp1_2 + Ad[3,3]*tp1_3) + Phi2_0 = 0 + Phi2_1 = 0 + Phi2_2 = 0 + Phi2_3 = 0 + if t2 < 0: + Phi2_0 = dAd[0,3]*t2 + Ad[0,3] + Phi2_1 = dAd[1,3]*t2 + Ad[1,3] + Phi2_2 = dAd[2,3]*t2 + Ad[2,3] + Phi2_3 = dAd[3,3]*t2 + Ad[3,3] + elif t2 > 1: + Phi2_0 = (3*Ad[0,0] + 2*Ad[0,1] + Ad[0,2])*(t2-1) + (Ad[0,0]+Ad[0,1]+Ad[0,2]+Ad[0,3]) + Phi2_1 = (3*Ad[1,0] + 2*Ad[1,1] + Ad[1,2])*(t2-1) + (Ad[1,0]+Ad[1,1]+Ad[1,2]+Ad[1,3]) + Phi2_2 = (3*Ad[2,0] + 2*Ad[2,1] + Ad[2,2])*(t2-1) + (Ad[2,0]+Ad[2,1]+Ad[2,2]+Ad[2,3]) + Phi2_3 = (3*Ad[3,0] + 2*Ad[3,1] + Ad[3,2])*(t2-1) + (Ad[3,0]+Ad[3,1]+Ad[3,2]+Ad[3,3]) + else: + Phi2_0 = (Ad[0,0]*tp2_0 + Ad[0,1]*tp2_1 + Ad[0,2]*tp2_2 + Ad[0,3]*tp2_3) + Phi2_1 = (Ad[1,0]*tp2_0 + Ad[1,1]*tp2_1 + Ad[1,2]*tp2_2 + Ad[1,3]*tp2_3) + Phi2_2 = (Ad[2,0]*tp2_0 + Ad[2,1]*tp2_1 + Ad[2,2]*tp2_2 + Ad[2,3]*tp2_3) + Phi2_3 = (Ad[3,0]*tp2_0 + Ad[3,1]*tp2_1 + Ad[3,2]*tp2_2 + Ad[3,3]*tp2_3) + dPhi0_0 = (dAd[0,0]*tp0_0 + dAd[0,1]*tp0_1 + dAd[0,2]*tp0_2 + dAd[0,3]*tp0_3)*dinv0 + dPhi0_1 = (dAd[1,0]*tp0_0 + dAd[1,1]*tp0_1 + dAd[1,2]*tp0_2 + dAd[1,3]*tp0_3)*dinv0 + dPhi0_2 = (dAd[2,0]*tp0_0 + dAd[2,1]*tp0_1 + dAd[2,2]*tp0_2 + dAd[2,3]*tp0_3)*dinv0 + dPhi0_3 = (dAd[3,0]*tp0_0 + dAd[3,1]*tp0_1 + dAd[3,2]*tp0_2 + dAd[3,3]*tp0_3)*dinv0 + dPhi1_0 = (dAd[0,0]*tp1_0 + dAd[0,1]*tp1_1 + dAd[0,2]*tp1_2 + dAd[0,3]*tp1_3)*dinv1 + dPhi1_1 = (dAd[1,0]*tp1_0 + dAd[1,1]*tp1_1 + dAd[1,2]*tp1_2 + dAd[1,3]*tp1_3)*dinv1 + dPhi1_2 = (dAd[2,0]*tp1_0 + dAd[2,1]*tp1_1 + dAd[2,2]*tp1_2 + dAd[2,3]*tp1_3)*dinv1 + dPhi1_3 = (dAd[3,0]*tp1_0 + dAd[3,1]*tp1_1 + dAd[3,2]*tp1_2 + dAd[3,3]*tp1_3)*dinv1 + dPhi2_0 = (dAd[0,0]*tp2_0 + dAd[0,1]*tp2_1 + dAd[0,2]*tp2_2 + dAd[0,3]*tp2_3)*dinv2 + dPhi2_1 = (dAd[1,0]*tp2_0 + dAd[1,1]*tp2_1 + dAd[1,2]*tp2_2 + dAd[1,3]*tp2_3)*dinv2 + dPhi2_2 = (dAd[2,0]*tp2_0 + dAd[2,1]*tp2_1 + dAd[2,2]*tp2_2 + dAd[2,3]*tp2_3)*dinv2 + dPhi2_3 = (dAd[3,0]*tp2_0 + dAd[3,1]*tp2_1 + dAd[3,2]*tp2_2 + dAd[3,3]*tp2_3)*dinv2 + + for k in range(n_vals): + vals[n,k] = Phi0_0*(Phi1_0*(Phi2_0*(coefs[i0+0,i1+0,i2+0,k]) + Phi2_1*(coefs[i0+0,i1+0,i2+1,k]) + Phi2_2*(coefs[i0+0,i1+0,i2+2,k]) + Phi2_3*(coefs[i0+0,i1+0,i2+3,k])) + \ + Phi1_1*(Phi2_0*(coefs[i0+0,i1+1,i2+0,k]) + Phi2_1*(coefs[i0+0,i1+1,i2+1,k]) + Phi2_2*(coefs[i0+0,i1+1,i2+2,k]) + Phi2_3*(coefs[i0+0,i1+1,i2+3,k])) + \ + Phi1_2*(Phi2_0*(coefs[i0+0,i1+2,i2+0,k]) + Phi2_1*(coefs[i0+0,i1+2,i2+1,k]) + Phi2_2*(coefs[i0+0,i1+2,i2+2,k]) + Phi2_3*(coefs[i0+0,i1+2,i2+3,k])) + \ + Phi1_3*(Phi2_0*(coefs[i0+0,i1+3,i2+0,k]) + Phi2_1*(coefs[i0+0,i1+3,i2+1,k]) + Phi2_2*(coefs[i0+0,i1+3,i2+2,k]) + Phi2_3*(coefs[i0+0,i1+3,i2+3,k]))) + \ + Phi0_1*(Phi1_0*(Phi2_0*(coefs[i0+1,i1+0,i2+0,k]) + Phi2_1*(coefs[i0+1,i1+0,i2+1,k]) + Phi2_2*(coefs[i0+1,i1+0,i2+2,k]) + Phi2_3*(coefs[i0+1,i1+0,i2+3,k])) + \ + Phi1_1*(Phi2_0*(coefs[i0+1,i1+1,i2+0,k]) + Phi2_1*(coefs[i0+1,i1+1,i2+1,k]) + Phi2_2*(coefs[i0+1,i1+1,i2+2,k]) + Phi2_3*(coefs[i0+1,i1+1,i2+3,k])) + \ + Phi1_2*(Phi2_0*(coefs[i0+1,i1+2,i2+0,k]) + Phi2_1*(coefs[i0+1,i1+2,i2+1,k]) + Phi2_2*(coefs[i0+1,i1+2,i2+2,k]) + Phi2_3*(coefs[i0+1,i1+2,i2+3,k])) + \ + Phi1_3*(Phi2_0*(coefs[i0+1,i1+3,i2+0,k]) + Phi2_1*(coefs[i0+1,i1+3,i2+1,k]) + Phi2_2*(coefs[i0+1,i1+3,i2+2,k]) + Phi2_3*(coefs[i0+1,i1+3,i2+3,k]))) + \ + Phi0_2*(Phi1_0*(Phi2_0*(coefs[i0+2,i1+0,i2+0,k]) + Phi2_1*(coefs[i0+2,i1+0,i2+1,k]) + Phi2_2*(coefs[i0+2,i1+0,i2+2,k]) + Phi2_3*(coefs[i0+2,i1+0,i2+3,k])) + \ + Phi1_1*(Phi2_0*(coefs[i0+2,i1+1,i2+0,k]) + Phi2_1*(coefs[i0+2,i1+1,i2+1,k]) + Phi2_2*(coefs[i0+2,i1+1,i2+2,k]) + Phi2_3*(coefs[i0+2,i1+1,i2+3,k])) + \ + Phi1_2*(Phi2_0*(coefs[i0+2,i1+2,i2+0,k]) + Phi2_1*(coefs[i0+2,i1+2,i2+1,k]) + Phi2_2*(coefs[i0+2,i1+2,i2+2,k]) + Phi2_3*(coefs[i0+2,i1+2,i2+3,k])) + \ + Phi1_3*(Phi2_0*(coefs[i0+2,i1+3,i2+0,k]) + Phi2_1*(coefs[i0+2,i1+3,i2+1,k]) + Phi2_2*(coefs[i0+2,i1+3,i2+2,k]) + Phi2_3*(coefs[i0+2,i1+3,i2+3,k]))) + \ + Phi0_3*(Phi1_0*(Phi2_0*(coefs[i0+3,i1+0,i2+0,k]) + Phi2_1*(coefs[i0+3,i1+0,i2+1,k]) + Phi2_2*(coefs[i0+3,i1+0,i2+2,k]) + Phi2_3*(coefs[i0+3,i1+0,i2+3,k])) + \ + Phi1_1*(Phi2_0*(coefs[i0+3,i1+1,i2+0,k]) + Phi2_1*(coefs[i0+3,i1+1,i2+1,k]) + Phi2_2*(coefs[i0+3,i1+1,i2+2,k]) + Phi2_3*(coefs[i0+3,i1+1,i2+3,k])) + \ + Phi1_2*(Phi2_0*(coefs[i0+3,i1+2,i2+0,k]) + Phi2_1*(coefs[i0+3,i1+2,i2+1,k]) + Phi2_2*(coefs[i0+3,i1+2,i2+2,k]) + Phi2_3*(coefs[i0+3,i1+2,i2+3,k])) + \ + Phi1_3*(Phi2_0*(coefs[i0+3,i1+3,i2+0,k]) + Phi2_1*(coefs[i0+3,i1+3,i2+1,k]) + Phi2_2*(coefs[i0+3,i1+3,i2+2,k]) + Phi2_3*(coefs[i0+3,i1+3,i2+3,k]))) + + dvals[n,0,k] = dPhi0_0*(Phi1_0*(Phi2_0*(coefs[i0+0,i1+0,i2+0,k]) + Phi2_1*(coefs[i0+0,i1+0,i2+1,k]) + Phi2_2*(coefs[i0+0,i1+0,i2+2,k]) + Phi2_3*(coefs[i0+0,i1+0,i2+3,k])) + \ + Phi1_1*(Phi2_0*(coefs[i0+0,i1+1,i2+0,k]) + Phi2_1*(coefs[i0+0,i1+1,i2+1,k]) + Phi2_2*(coefs[i0+0,i1+1,i2+2,k]) + Phi2_3*(coefs[i0+0,i1+1,i2+3,k])) + \ + Phi1_2*(Phi2_0*(coefs[i0+0,i1+2,i2+0,k]) + Phi2_1*(coefs[i0+0,i1+2,i2+1,k]) + Phi2_2*(coefs[i0+0,i1+2,i2+2,k]) + Phi2_3*(coefs[i0+0,i1+2,i2+3,k])) + \ + Phi1_3*(Phi2_0*(coefs[i0+0,i1+3,i2+0,k]) + Phi2_1*(coefs[i0+0,i1+3,i2+1,k]) + Phi2_2*(coefs[i0+0,i1+3,i2+2,k]) + Phi2_3*(coefs[i0+0,i1+3,i2+3,k]))) + \ + dPhi0_1*(Phi1_0*(Phi2_0*(coefs[i0+1,i1+0,i2+0,k]) + Phi2_1*(coefs[i0+1,i1+0,i2+1,k]) + Phi2_2*(coefs[i0+1,i1+0,i2+2,k]) + Phi2_3*(coefs[i0+1,i1+0,i2+3,k])) + \ + Phi1_1*(Phi2_0*(coefs[i0+1,i1+1,i2+0,k]) + Phi2_1*(coefs[i0+1,i1+1,i2+1,k]) + Phi2_2*(coefs[i0+1,i1+1,i2+2,k]) + Phi2_3*(coefs[i0+1,i1+1,i2+3,k])) + \ + Phi1_2*(Phi2_0*(coefs[i0+1,i1+2,i2+0,k]) + Phi2_1*(coefs[i0+1,i1+2,i2+1,k]) + Phi2_2*(coefs[i0+1,i1+2,i2+2,k]) + Phi2_3*(coefs[i0+1,i1+2,i2+3,k])) + \ + Phi1_3*(Phi2_0*(coefs[i0+1,i1+3,i2+0,k]) + Phi2_1*(coefs[i0+1,i1+3,i2+1,k]) + Phi2_2*(coefs[i0+1,i1+3,i2+2,k]) + Phi2_3*(coefs[i0+1,i1+3,i2+3,k]))) + \ + dPhi0_2*(Phi1_0*(Phi2_0*(coefs[i0+2,i1+0,i2+0,k]) + Phi2_1*(coefs[i0+2,i1+0,i2+1,k]) + Phi2_2*(coefs[i0+2,i1+0,i2+2,k]) + Phi2_3*(coefs[i0+2,i1+0,i2+3,k])) + \ + Phi1_1*(Phi2_0*(coefs[i0+2,i1+1,i2+0,k]) + Phi2_1*(coefs[i0+2,i1+1,i2+1,k]) + Phi2_2*(coefs[i0+2,i1+1,i2+2,k]) + Phi2_3*(coefs[i0+2,i1+1,i2+3,k])) + \ + Phi1_2*(Phi2_0*(coefs[i0+2,i1+2,i2+0,k]) + Phi2_1*(coefs[i0+2,i1+2,i2+1,k]) + Phi2_2*(coefs[i0+2,i1+2,i2+2,k]) + Phi2_3*(coefs[i0+2,i1+2,i2+3,k])) + \ + Phi1_3*(Phi2_0*(coefs[i0+2,i1+3,i2+0,k]) + Phi2_1*(coefs[i0+2,i1+3,i2+1,k]) + Phi2_2*(coefs[i0+2,i1+3,i2+2,k]) + Phi2_3*(coefs[i0+2,i1+3,i2+3,k]))) + \ + dPhi0_3*(Phi1_0*(Phi2_0*(coefs[i0+3,i1+0,i2+0,k]) + Phi2_1*(coefs[i0+3,i1+0,i2+1,k]) + Phi2_2*(coefs[i0+3,i1+0,i2+2,k]) + Phi2_3*(coefs[i0+3,i1+0,i2+3,k])) + \ + Phi1_1*(Phi2_0*(coefs[i0+3,i1+1,i2+0,k]) + Phi2_1*(coefs[i0+3,i1+1,i2+1,k]) + Phi2_2*(coefs[i0+3,i1+1,i2+2,k]) + Phi2_3*(coefs[i0+3,i1+1,i2+3,k])) + \ + Phi1_2*(Phi2_0*(coefs[i0+3,i1+2,i2+0,k]) + Phi2_1*(coefs[i0+3,i1+2,i2+1,k]) + Phi2_2*(coefs[i0+3,i1+2,i2+2,k]) + Phi2_3*(coefs[i0+3,i1+2,i2+3,k])) + \ + Phi1_3*(Phi2_0*(coefs[i0+3,i1+3,i2+0,k]) + Phi2_1*(coefs[i0+3,i1+3,i2+1,k]) + Phi2_2*(coefs[i0+3,i1+3,i2+2,k]) + Phi2_3*(coefs[i0+3,i1+3,i2+3,k]))) + + dvals[n,1,k] = Phi0_0*(dPhi1_0*(Phi2_0*(coefs[i0+0,i1+0,i2+0,k]) + Phi2_1*(coefs[i0+0,i1+0,i2+1,k]) + Phi2_2*(coefs[i0+0,i1+0,i2+2,k]) + Phi2_3*(coefs[i0+0,i1+0,i2+3,k])) + \ + dPhi1_1*(Phi2_0*(coefs[i0+0,i1+1,i2+0,k]) + Phi2_1*(coefs[i0+0,i1+1,i2+1,k]) + Phi2_2*(coefs[i0+0,i1+1,i2+2,k]) + Phi2_3*(coefs[i0+0,i1+1,i2+3,k])) + \ + dPhi1_2*(Phi2_0*(coefs[i0+0,i1+2,i2+0,k]) + Phi2_1*(coefs[i0+0,i1+2,i2+1,k]) + Phi2_2*(coefs[i0+0,i1+2,i2+2,k]) + Phi2_3*(coefs[i0+0,i1+2,i2+3,k])) + \ + dPhi1_3*(Phi2_0*(coefs[i0+0,i1+3,i2+0,k]) + Phi2_1*(coefs[i0+0,i1+3,i2+1,k]) + Phi2_2*(coefs[i0+0,i1+3,i2+2,k]) + Phi2_3*(coefs[i0+0,i1+3,i2+3,k]))) + \ + Phi0_1*(dPhi1_0*(Phi2_0*(coefs[i0+1,i1+0,i2+0,k]) + Phi2_1*(coefs[i0+1,i1+0,i2+1,k]) + Phi2_2*(coefs[i0+1,i1+0,i2+2,k]) + Phi2_3*(coefs[i0+1,i1+0,i2+3,k])) + \ + dPhi1_1*(Phi2_0*(coefs[i0+1,i1+1,i2+0,k]) + Phi2_1*(coefs[i0+1,i1+1,i2+1,k]) + Phi2_2*(coefs[i0+1,i1+1,i2+2,k]) + Phi2_3*(coefs[i0+1,i1+1,i2+3,k])) + \ + dPhi1_2*(Phi2_0*(coefs[i0+1,i1+2,i2+0,k]) + Phi2_1*(coefs[i0+1,i1+2,i2+1,k]) + Phi2_2*(coefs[i0+1,i1+2,i2+2,k]) + Phi2_3*(coefs[i0+1,i1+2,i2+3,k])) + \ + dPhi1_3*(Phi2_0*(coefs[i0+1,i1+3,i2+0,k]) + Phi2_1*(coefs[i0+1,i1+3,i2+1,k]) + Phi2_2*(coefs[i0+1,i1+3,i2+2,k]) + Phi2_3*(coefs[i0+1,i1+3,i2+3,k]))) + \ + Phi0_2*(dPhi1_0*(Phi2_0*(coefs[i0+2,i1+0,i2+0,k]) + Phi2_1*(coefs[i0+2,i1+0,i2+1,k]) + Phi2_2*(coefs[i0+2,i1+0,i2+2,k]) + Phi2_3*(coefs[i0+2,i1+0,i2+3,k])) + \ + dPhi1_1*(Phi2_0*(coefs[i0+2,i1+1,i2+0,k]) + Phi2_1*(coefs[i0+2,i1+1,i2+1,k]) + Phi2_2*(coefs[i0+2,i1+1,i2+2,k]) + Phi2_3*(coefs[i0+2,i1+1,i2+3,k])) + \ + dPhi1_2*(Phi2_0*(coefs[i0+2,i1+2,i2+0,k]) + Phi2_1*(coefs[i0+2,i1+2,i2+1,k]) + Phi2_2*(coefs[i0+2,i1+2,i2+2,k]) + Phi2_3*(coefs[i0+2,i1+2,i2+3,k])) + \ + dPhi1_3*(Phi2_0*(coefs[i0+2,i1+3,i2+0,k]) + Phi2_1*(coefs[i0+2,i1+3,i2+1,k]) + Phi2_2*(coefs[i0+2,i1+3,i2+2,k]) + Phi2_3*(coefs[i0+2,i1+3,i2+3,k]))) + \ + Phi0_3*(dPhi1_0*(Phi2_0*(coefs[i0+3,i1+0,i2+0,k]) + Phi2_1*(coefs[i0+3,i1+0,i2+1,k]) + Phi2_2*(coefs[i0+3,i1+0,i2+2,k]) + Phi2_3*(coefs[i0+3,i1+0,i2+3,k])) + \ + dPhi1_1*(Phi2_0*(coefs[i0+3,i1+1,i2+0,k]) + Phi2_1*(coefs[i0+3,i1+1,i2+1,k]) + Phi2_2*(coefs[i0+3,i1+1,i2+2,k]) + Phi2_3*(coefs[i0+3,i1+1,i2+3,k])) + \ + dPhi1_2*(Phi2_0*(coefs[i0+3,i1+2,i2+0,k]) + Phi2_1*(coefs[i0+3,i1+2,i2+1,k]) + Phi2_2*(coefs[i0+3,i1+2,i2+2,k]) + Phi2_3*(coefs[i0+3,i1+2,i2+3,k])) + \ + dPhi1_3*(Phi2_0*(coefs[i0+3,i1+3,i2+0,k]) + Phi2_1*(coefs[i0+3,i1+3,i2+1,k]) + Phi2_2*(coefs[i0+3,i1+3,i2+2,k]) + Phi2_3*(coefs[i0+3,i1+3,i2+3,k]))) + + dvals[n,2,k] = Phi0_0*(Phi1_0*(dPhi2_0*(coefs[i0+0,i1+0,i2+0,k]) + dPhi2_1*(coefs[i0+0,i1+0,i2+1,k]) + dPhi2_2*(coefs[i0+0,i1+0,i2+2,k]) + dPhi2_3*(coefs[i0+0,i1+0,i2+3,k])) + \ + Phi1_1*(dPhi2_0*(coefs[i0+0,i1+1,i2+0,k]) + dPhi2_1*(coefs[i0+0,i1+1,i2+1,k]) + dPhi2_2*(coefs[i0+0,i1+1,i2+2,k]) + dPhi2_3*(coefs[i0+0,i1+1,i2+3,k])) + \ + Phi1_2*(dPhi2_0*(coefs[i0+0,i1+2,i2+0,k]) + dPhi2_1*(coefs[i0+0,i1+2,i2+1,k]) + dPhi2_2*(coefs[i0+0,i1+2,i2+2,k]) + dPhi2_3*(coefs[i0+0,i1+2,i2+3,k])) + \ + Phi1_3*(dPhi2_0*(coefs[i0+0,i1+3,i2+0,k]) + dPhi2_1*(coefs[i0+0,i1+3,i2+1,k]) + dPhi2_2*(coefs[i0+0,i1+3,i2+2,k]) + dPhi2_3*(coefs[i0+0,i1+3,i2+3,k]))) + \ + Phi0_1*(Phi1_0*(dPhi2_0*(coefs[i0+1,i1+0,i2+0,k]) + dPhi2_1*(coefs[i0+1,i1+0,i2+1,k]) + dPhi2_2*(coefs[i0+1,i1+0,i2+2,k]) + dPhi2_3*(coefs[i0+1,i1+0,i2+3,k])) + \ + Phi1_1*(dPhi2_0*(coefs[i0+1,i1+1,i2+0,k]) + dPhi2_1*(coefs[i0+1,i1+1,i2+1,k]) + dPhi2_2*(coefs[i0+1,i1+1,i2+2,k]) + dPhi2_3*(coefs[i0+1,i1+1,i2+3,k])) + \ + Phi1_2*(dPhi2_0*(coefs[i0+1,i1+2,i2+0,k]) + dPhi2_1*(coefs[i0+1,i1+2,i2+1,k]) + dPhi2_2*(coefs[i0+1,i1+2,i2+2,k]) + dPhi2_3*(coefs[i0+1,i1+2,i2+3,k])) + \ + Phi1_3*(dPhi2_0*(coefs[i0+1,i1+3,i2+0,k]) + dPhi2_1*(coefs[i0+1,i1+3,i2+1,k]) + dPhi2_2*(coefs[i0+1,i1+3,i2+2,k]) + dPhi2_3*(coefs[i0+1,i1+3,i2+3,k]))) + \ + Phi0_2*(Phi1_0*(dPhi2_0*(coefs[i0+2,i1+0,i2+0,k]) + dPhi2_1*(coefs[i0+2,i1+0,i2+1,k]) + dPhi2_2*(coefs[i0+2,i1+0,i2+2,k]) + dPhi2_3*(coefs[i0+2,i1+0,i2+3,k])) + \ + Phi1_1*(dPhi2_0*(coefs[i0+2,i1+1,i2+0,k]) + dPhi2_1*(coefs[i0+2,i1+1,i2+1,k]) + dPhi2_2*(coefs[i0+2,i1+1,i2+2,k]) + dPhi2_3*(coefs[i0+2,i1+1,i2+3,k])) + \ + Phi1_2*(dPhi2_0*(coefs[i0+2,i1+2,i2+0,k]) + dPhi2_1*(coefs[i0+2,i1+2,i2+1,k]) + dPhi2_2*(coefs[i0+2,i1+2,i2+2,k]) + dPhi2_3*(coefs[i0+2,i1+2,i2+3,k])) + \ + Phi1_3*(dPhi2_0*(coefs[i0+2,i1+3,i2+0,k]) + dPhi2_1*(coefs[i0+2,i1+3,i2+1,k]) + dPhi2_2*(coefs[i0+2,i1+3,i2+2,k]) + dPhi2_3*(coefs[i0+2,i1+3,i2+3,k]))) + \ + Phi0_3*(Phi1_0*(dPhi2_0*(coefs[i0+3,i1+0,i2+0,k]) + dPhi2_1*(coefs[i0+3,i1+0,i2+1,k]) + dPhi2_2*(coefs[i0+3,i1+0,i2+2,k]) + dPhi2_3*(coefs[i0+3,i1+0,i2+3,k])) + \ + Phi1_1*(dPhi2_0*(coefs[i0+3,i1+1,i2+0,k]) + dPhi2_1*(coefs[i0+3,i1+1,i2+1,k]) + dPhi2_2*(coefs[i0+3,i1+1,i2+2,k]) + dPhi2_3*(coefs[i0+3,i1+1,i2+3,k])) + \ + Phi1_2*(dPhi2_0*(coefs[i0+3,i1+2,i2+0,k]) + dPhi2_1*(coefs[i0+3,i1+2,i2+1,k]) + dPhi2_2*(coefs[i0+3,i1+2,i2+2,k]) + dPhi2_3*(coefs[i0+3,i1+2,i2+3,k])) + \ + Phi1_3*(dPhi2_0*(coefs[i0+3,i1+3,i2+0,k]) + dPhi2_1*(coefs[i0+3,i1+3,i2+1,k]) + dPhi2_2*(coefs[i0+3,i1+3,i2+2,k]) + dPhi2_3*(coefs[i0+3,i1+3,i2+3,k]))) + + vals = vals[:, 0] + dvals = dvals[:, :, 0] + + # used by njitted routines (frozen) basis = np.array([1.0 / 6.0, 2.0 / 3.0, 1.0 / 6.0, 0.0]) @@ -571,7 +820,49 @@ def filter_coeffs_3d(dinv, data): return coefs -def filter_coeffs(smin, smax, orders, data): +@njit(cache=True) +def filter_coeffs_4d(dinv, data): + + Mx = data.shape[0] + My = data.shape[1] + Mz = data.shape[2] + Mz4 = data.shape[3] + + Nx = Mx + 2 + Ny = My + 2 + Nz = Mz + 2 + Nz4 = Mz4 +2 + + coefs = np.zeros((Nx, Ny, Nz, Nz4)) + + # First, solve in the X-direction + for iy in range(My): + for iz in range(Mz): + for iz4 in range(Mz4): + find_coefs_1d(dinv[0], Mx, data[:, iy, iz, iz4], coefs[:, iy, iz, iz4]) + + # Now, solve in the Y-direction + for ix in range(Nx): + for iz in range(Mz): + for iz4 in range(Mz4): + find_coefs_1d(dinv[1], My, coefs[ix, :, iz, iz4], coefs[ix, :, iz, iz4]) + + # Now, solve in the Z-direction + for ix in range(Nx): + for iy in range(Ny): + for iz4 in range(Mz4): + find_coefs_1d(dinv[2], Mz, coefs[ix, iy, :, iz4], coefs[ix, iy, :, iz4]) + + # Now, solve in the Z4-direction + for ix in range(Nx): + for iy in range(Ny): + for iz in range(Nz): + find_coefs_1d(dinv[3], Mz4, coefs[ix, iy, iz, :], coefs[ix, iy, iz, :]) + + return coefs + + +def compute_const(): global Ad Ad = array([ # t^3 t^2 t 1 @@ -593,6 +884,9 @@ def filter_coeffs(smin, smax, orders, data): dAd_i = dAd[:, i-1] d2Ad[:,i] = (4-i) * dAd_i + +def filter_coeffs(smin, smax, orders, data): + compute_const() smin = np.array(smin, dtype=float) smax = np.array(smax, dtype=float) dinv = (smax - smin) / orders diff --git a/flare/mgp/mgp.py b/flare/mgp/mgp.py index 3bab2a8b3..23a8bde08 100644 --- a/flare/mgp/mgp.py +++ b/flare/mgp/mgp.py @@ -13,18 +13,18 @@ import subprocess import os -from flare import gp, struc +from flare import gp, struc, gp_algebra from flare.env import AtomicEnvironment from flare.gp import GaussianProcess -from flare.kernels.kernels import two_body, three_body, two_plus_three_body,\ - two_body_jit +from flare.gp_algebra import get_kernel_vector_unit, partition_c from flare.cutoffs import quadratic_cutoff -from flare.kernels.mc_simple import two_body_mc, three_body_mc, two_plus_three_body_mc +from flare.kernels.mc_simple import two_body_mc, three_body_mc from flare.util import Z_to_element import flare.mgp.utils as utils from flare.mgp.utils import get_bonds, get_triplets, self_two_body_mc_jit, \ - self_three_body_mc_jit + self_three_body_mc_jit, \ + get_2bkernel, get_3bkernel from flare.mgp.splines_methods import PCASplines, CubicSpline @@ -65,7 +65,7 @@ class MappedGaussianProcess: def __init__(self, hyps, cutoffs, grid_params: dict, struc_params: dict, mean_only=False, container_only=True, GP=None, - lmp_file_name='lmp.mgp'): + lmp_file_name='lmp.mgp', n_cpus=None, n_sample=100): self.hyps = hyps self.cutoffs = cutoffs @@ -82,6 +82,8 @@ def __init__(self, hyps, cutoffs, grid_params: dict, struc_params: dict, self.update = grid_params['update'] self.mean_only = mean_only self.lmp_file_name = lmp_file_name + self.n_cpus = n_cpus + self.n_sample = n_sample self.build_bond_struc(struc_params) self.maps_2 = [] @@ -99,7 +101,7 @@ def build_map_container(self): for b_struc in self.bond_struc[0]: map_2 = Map2body(self.grid_num_2, self.bounds_2, self.cutoffs, b_struc, self.bodies, self.svd_rank_2, - self.mean_only) + self.mean_only, self.n_cpus, self.n_sample) self.maps_2.append(map_2) if 3 in self.bodies: for b_struc in self.bond_struc[1]: @@ -107,7 +109,8 @@ def build_map_container(self): b_struc, self.bodies, self.svd_rank_3, self.mean_only, self.grid_params['load_grid'], - self.update) + self.update, + self.n_cpus, self.n_sample) self.maps_3.append(map_3) def build_map(self, GP): @@ -225,10 +228,11 @@ def predict(self, atom_env: AtomicEnvironment, mean_only: bool=False): self.get_3body_comp, self.spcs[1], self.maps_3, mean_only) - f = f2 + f3 - vir = vir2 + vir3 - v = kern2 + kern3 - np.sum((v2 + v3)**2, axis=0) - return f, v, vir + force = f2 + f3 + variance = kern2 + kern3 - np.sum((v2 + v3)**2, axis=0) + virial_stress = vir2 + vir3 + + return force, variance, virial_stress, 0 def get_2body_comp(self, atom_env, sig, ls, r_cut): ''' @@ -328,59 +332,6 @@ def predict_component(self, lengths, xyzs, mapping, mean_only): v = mapping.var.V @ v_d return f, vir, v - def write_two_body(self, f): - a = self.bounds_2[0][0] - b = self.bounds_2[1][0] - order = self.grid_num_2 - - for ind, spc in enumerate(self.spcs[0]): - coefs_2 = self.maps_2[ind].mean.__coeffs__ - - elem1 = Z_to_element(spc[0]) - elem2 = Z_to_element(spc[1]) - header_2 = '{elem1} {elem2} {a} {b} {order}\n'\ - .format(elem1=elem1, elem2=elem2, a=a, b=b, order=order) - f.write(header_2) - - for c, coef in enumerate(coefs_2): - f.write('{:.10e} '.format(coef)) - if c % 5 == 4 and c != len(coefs_2)-1: - f.write('\n') - - f.write('\n') - - def write_three_body(self, f): - a = self.bounds_3[0] - b = self.bounds_3[1] - order = self.grid_num_3 - - for ind, spc in enumerate(self.spcs[1]): - coefs_3 = self.maps_3[ind].mean.__coeffs__ - - elem1 = Z_to_element(spc[0]) - elem2 = Z_to_element(spc[1]) - elem3 = Z_to_element(spc[2]) - - header_3 = '{elem1} {elem2} {elem3} {a1} {a2} {a3} {b1}'\ - ' {b2} {b3:.10e} {order1} {order2} {order3}\n'\ - .format(elem1=elem1, elem2=elem2, elem3=elem3, - a1=a[0], a2=a[1], a3=a[2], - b1=b[0], b2=b[1], b3=b[2], - order1=order[0], order2=order[1], order3=order[2]) - f.write(header_3) - - n = 0 - for i in range(coefs_3.shape[0]): - for j in range(coefs_3.shape[1]): - for k in range(coefs_3.shape[2]): - coef = coefs_3[i, j, k] - f.write('{:.10e} '.format(coef)) - if n % 5 == 4: - f.write('\n') - n += 1 - - f.write('\n') - def write_lmp_file(self, lammps_name): ''' @@ -401,18 +352,20 @@ def write_lmp_file(self, lammps_name): # write two body if twobodyarray > 0: - self.write_two_body(f) + for ind, spc in enumerate(self.spcs[0]): + self.maps_2[ind].write(f, spc) # write three body if threebodyarray > 0: - self.write_three_body(f) + for ind, spc in enumerate(self.spcs[1]): + self.maps_3[ind].write(f, spc) f.close() class Map2body: def __init__(self, grid_num, bounds, cutoffs, bond_struc, bodies='2', - svd_rank=0, mean_only=False): + svd_rank=0, mean_only=False, n_cpus=1, n_sample=100): ''' Build 2-body MGP ''' @@ -425,68 +378,98 @@ def __init__(self, grid_num, bounds, cutoffs, bond_struc, bodies='2', self.bodies = bodies self.svd_rank = svd_rank self.mean_only = mean_only + self.n_cpus = n_cpus + self.n_sample = n_sample self.build_map_container() - def GenGrid(self, GP, processes=mp.cpu_count()): + def GenGrid(self, GP, processes=1): ''' generate grid data of mean prediction and L^{-1}k* for each triplet implemented in a parallelized style ''' + kernel_info = get_2bkernel(GP) - # ------ change GP kernel to 2 body ------ - original_kernel = GP.kernel - GP.kernel = two_body_mc - original_cutoffs = np.copy(GP.cutoffs) - GP.cutoffs = [GP.cutoffs[0]] - original_hyps = np.copy(GP.hyps) - GP.hyps = [GP.hyps[0], GP.hyps[1], GP.hyps[-1]] + if (self.n_cpus is None): + processes = mp.cpu_count() + else: + processes = self.n_cpus # ------ construct grids ------ nop = self.grid_num bond_lengths = np.linspace(self.l_bounds[0], self.u_bounds[0], nop) bond_means = np.zeros([nop]) - bond_vars = np.zeros([nop, len(GP.alpha)]) + if not self.mean_only: + bond_vars = np.zeros([nop, len(GP.alpha)]) + else: + bond_vars = None env12 = AtomicEnvironment(self.bond_struc, 0, self.cutoffs) - pool_list = [(i, bond_lengths, GP, env12) - for i in range(nop)] - pool = mp.Pool(processes=processes) - A_list = pool.map(self._GenGrid_inner, pool_list) - for p in range(nop): - bond_means[p] = A_list[p][0] - bond_vars[p, :] = A_list[p][1] - pool.close() - pool.join() - - # ------ change back original GP ------ - GP.cutoffs = original_cutoffs - GP.hyps = original_hyps - GP.kernel = original_kernel + if processes == 1 : + k12_v_all = self._GenGrid_inner(GP.name, 0, len(GP.training_data), + bond_lengths, env12, kernel_info) + else: + with mp.Pool(processes=processes) as pool: + size = len(GP.training_data) + block_id, nbatch = partition_c(self.n_sample, size, processes) + + k12_slice = [] + k12_v_all = np.zeros([len(bond_lengths), size*3]) + count = 0 + base = 0 + for ibatch in range(nbatch): + s, e = block_id[ibatch] + k12_slice.append(\ + pool.apply_async(self._GenGrid_inner, + args=(GP.name, s, e, + bond_lengths, + env12, kernel_info))) + count += 1 + # when there are too many threads, collect some of + # the result to reduce memory footprint + if (count > processes*2): + for ibase in range(count): + s, e = block_id[ibase+base] + k12_v_all[:, s*3:e*3] = k12_slice[ibase].get() + del k12_slice + k12_slice = [] + count = 0 + base = ibatch+1 + if (count > 0): + for ibase in range(count): + s, e = block_id[ibase+base] + vec = k12_slice[ibase].get() + k12_v_all[:, s*3:e*3] = k12_slice[ibase].get() + del k12_slice + pool.close() + pool.join() + + for b, r in enumerate(bond_lengths): + k12_v = k12_v_all[b, :] + bond_means[b] = np.matmul(k12_v, GP.alpha) + if not self.mean_only: + bond_vars[b, :] = solve_triangular(GP.l_mat, k12_v, lower=True) return bond_means, bond_vars - def _GenGrid_inner(self, params): + + def _GenGrid_inner(self, name, s, e, bond_lengths, + env12, kernel_info): ''' - generate grid for each angle, used to parallelize grid generation + generate grid for each cos angle, used to parallelize grid generation ''' - b, bond_lengths, GP, env12 = params - # nop = self.grid_num - r = bond_lengths[b] - env12.bond_array_2 = np.array([[r, 1, 0, 0]]) - k12_v = GP.get_kernel_vector(env12, 1) - mean_diff = np.matmul(k12_v, GP.alpha) - bond_means = mean_diff - bond_vars = np.zeros(k12_v.shape) - - if not self.mean_only: - v12_vec = solve_triangular(GP.l_mat, k12_v, lower=True) - bond_vars = v12_vec - - return bond_means, bond_vars + kernel, efk, cutoffs, hyps, hyps_mask = kernel_info + size = e - s + k12_v = np.zeros([len(bond_lengths), size*3]) + for b, r in enumerate(bond_lengths): + env12.bond_array_2 = np.array([[r, 1, 0, 0]]) + k12_v[b, :] = get_kernel_vector_unit( + name, s, e, env12, 1, + kernel, hyps, cutoffs, hyps_mask) + return k12_v def build_map_container(self): self.mean = CubicSpline(self.l_bounds, self.u_bounds, @@ -507,12 +490,36 @@ def build_map(self, GP): if not self.mean_only: self.var.set_values(y_var) + def write(self, f, spc): + ''' + Write LAMMPS coefficient file + ''' + a = self.l_bounds[0] + b = self.u_bounds[0] + order = self.grid_num + + coefs_2 = self.mean.__coeffs__ + + elem1 = Z_to_element(spc[0]) + elem2 = Z_to_element(spc[1]) + header_2 = '{elem1} {elem2} {a} {b} {order}\n'\ + .format(elem1=elem1, elem2=elem2, a=a, b=b, order=order) + f.write(header_2) + + for c, coef in enumerate(coefs_2): + f.write('{:.10e} '.format(coef)) + if c % 5 == 4 and c != len(coefs_2)-1: + f.write('\n') + + f.write('\n') + class Map3body: def __init__(self, grid_num, bounds, cutoffs, bond_struc, bodies='3', - svd_rank=0, mean_only=False, load_grid=None, update=True): + svd_rank=0, mean_only=False, load_grid=None, update=True, + n_cpus=1, n_sample=100): ''' Build 3-body MGP ''' @@ -527,21 +534,109 @@ def __init__(self, grid_num, bounds, cutoffs, bond_struc, bodies='3', self.mean_only = mean_only self.load_grid = load_grid self.update = update + self.n_cpus = n_cpus + self.n_sample = n_sample self.build_map_container() - def GenGrid(self, GP, processes=mp.cpu_count()): + def GenGrid(self, GP): + ''' + generate grid data of mean prediction and L^{-1}k* for each triplet + implemented in a parallelized style + ''' + + if (self.n_cpus is None): + processes = mp.cpu_count() + else: + processes = self.n_cpus + + if processes == 1: + return self.GenGrid_serial(GP) + + # ------ get 3body kernel info ------ + kernel_info = get_3bkernel(GP) + + # ------ construct grids ------ + nop = self.grid_num[0] + noa = self.grid_num[2] + bond_lengths = np.linspace(self.l_bounds[0], self.u_bounds[0], nop) + cos_angles = np.linspace(self.l_bounds[2], self.u_bounds[2], noa) + + bond_means = np.zeros([nop, nop, noa]) + if not self.mean_only: + bond_vars = np.zeros([nop, nop, noa, len(GP.alpha)]) + else: + bond_vars = None + env12 = AtomicEnvironment(self.bond_struc, 0, self.cutoffs) + + with mp.Pool(processes=processes) as pool: + if self.update: + if 'kv3' in os.listdir(): + os.rmdir('kv3') + os.mkdir('kv3') + + size = len(GP.training_data) + block_id, nbatch = partition_c(self.n_sample, size, processes) + + k12_slice = [] + if (size>5000): + print('parallel set up:', size, ns, n_sample, time.time()) + count = 0 + base = 0 + k12_v_all = np.zeros([len(bond_lengths), len(bond_lengths), len(cos_angles), size*3]) + for ibatch in range(nbatch): + s, e = block_id[ibatch] + k12_slice.append(\ + pool.apply_async(self._GenGrid_inner_most, + args=(GP.name, s, e, + cos_angles, bond_lengths, + env12, kernel_info))) + if (size>5000): + print('send', ibatch, ns, s, e, time.time()) + count += 1 + if (count > processes*2): + for ibase in range(count): + s, e = block_id[ibase+base] + k12_v_all[:, :, :, s*3:e*3] = k12_slice[ibase].get() + if (size>5000): + print('get', ibase+base) + del k12_slice + k12_slice = [] + count = 0 + base = ibatch+1 + if (count > 0): + for ibase in range(count): + s, e = block_id[ibase+base] + k12_v_all[:, :, :, s*3:e*3] = k12_slice[ibase].get() + del k12_slice + + pool.close() + pool.join() + + for a12, cos_angle in enumerate(cos_angles): + for b1, r1 in enumerate(bond_lengths): + for b2, r2 in enumerate(bond_lengths): + k12_v = k12_v_all[b1, b2, a12, :] + bond_means[b1, b2, a12] = np.matmul(k12_v, GP.alpha) + if not self.mean_only: + bond_vars[b1, b2, a12, :] = solve_triangular(GP.l_mat, k12_v, lower=True) + + + # # ------ save mean and var to file ------- + np.save('grid3_mean', bond_means) + np.save('grid3_var', bond_vars) + + return bond_means, bond_vars + def GenGrid_serial(self, GP): ''' generate grid data of mean prediction and L^{-1}k* for each triplet implemented in a parallelized style ''' - # ------ change GP kernel to 3 body ------ - original_kernel = GP.kernel - original_hyps = np.copy(GP.hyps) - GP.kernel = three_body_mc - GP.hyps = GP.hyps[-3:] + + # ------ get 3body kernel info ------ + kernel, efk, cutoffs, hyps, hyps_mask = get_3bkernel(GP) # ------ construct grids ------ nop = self.grid_num[0] @@ -552,104 +647,87 @@ def GenGrid(self, GP, processes=mp.cpu_count()): bond_vars = np.zeros([nop, nop, noa, len(GP.alpha)]) env12 = AtomicEnvironment(self.bond_struc, 0, self.cutoffs) - pool_list = [(i, cos_angles[i], bond_lengths, GP, env12, self.update)\ - for i in range(noa)] - pool = mp.Pool(processes=processes) - if self.update: if 'kv3' in os.listdir(): - subprocess.run(['rm', '-r', 'kv3']) - subprocess.run(['mkdir', 'kv3']) + os.rmdir('kv3') + os.mkdir('kv3') + + size = len(GP.training_data) + ds = [1, 2, 3] + k_v = np.zeros(3) + k12_v_all = np.zeros([len(bond_lengths), len(bond_lengths), + len(cos_angles), size*3]) + for b1, r1 in enumerate(bond_lengths): + for b2, r2 in enumerate(bond_lengths): + for a12, cos_angle12 in enumerate(cos_angles): + + x2 = r2 * cos_angle12 + y2 = r2 * np.sqrt(1-cos_angle12**2) + r12 = np.linalg.norm(np.array([x2-r1, y2, 0])) - A_list = pool.map(self._GenGrid_inner, pool_list) - for a12 in range(noa): - bond_means[:, :, a12] = A_list[a12][0] - bond_vars[:, :, a12, :] = A_list[a12][1] - pool.close() - pool.join() + env12.bond_array_3 = np.array([[r1, 1, 0, 0], + [r2, 0, 0, 0]]) + env12.cross_bond_dists = np.array([[0, r12], [r12, 0]]) - # ------ change back to original GP ------ - GP.hyps = original_hyps - GP.kernel = original_kernel + for isample, sample in enumerate(GP.training_data): + for d in ds: + k_v[d-1] = kernel(env12, sample, 1, d, + hyps, cutoffs) - # ------ save mean and var to file ------- + k12_v_all[b1, b2, a12, isample*3:isample*3+3] = k_v + + for b1, r1 in enumerate(bond_lengths): + for b2, r2 in enumerate(bond_lengths): + for a12, cos_angle in enumerate(cos_angles): + k12_v = k12_v_all[b1, b2, a12, :] + bond_means[b1, b2, a12] = np.matmul(k12_v, GP.alpha) + if not self.mean_only: + bond_vars[b1, b2, a12, :] = solve_triangular(GP.l_mat, k12_v, lower=True) + + # # ------ save mean and var to file ------- np.save('grid3_mean', bond_means) np.save('grid3_var', bond_vars) return bond_means, bond_vars - def _GenGrid_inner(self, params): + def _GenGrid_inner_most(self, name, s, e, cos_angles, bond_lengths, env12, kernel_info): ''' - generate grid for each angle, used to parallelize grid generation + generate grid for each cos_angle, used to parallelize grid generation ''' - a12, cos_angle12, bond_lengths, GP, env12, update = params - nop = self.grid_num[0] - bond_means = np.zeros([nop, nop]) - bond_vars = np.zeros([nop, nop, len(GP.alpha)]) + kernel, efk, cutoffs, hyps, hyps_mask = kernel_info + training_data = gp_algebra._global_training_data[name] # open saved k vector file, and write to new file - if update: - kv_filename = 'kv3/'+str(a12) - size = len(GP.training_data) * 3 - new_kv_file = np.zeros((nop**2+1, size)) - new_kv_file[0,0] = size - if str(a12)+'.npy' in os.listdir('kv3'): - old_kv_file = np.load(kv_filename+'.npy') - last_size = int(old_kv_file[0,0]) - new_kv_file[:, :last_size] = old_kv_file - else: - last_size = 0 - ds = [1, 2, 3] + size = (e-s)*3 + k12_v = np.zeros([len(bond_lengths), len(bond_lengths), + len(cos_angles), size]) + for a12, cos_angle12 in enumerate(cos_angles): + for b1, r1 in enumerate(bond_lengths): + for b2, r2 in enumerate(bond_lengths): - for b1, r1 in enumerate(bond_lengths): - r1 = bond_lengths[b1] - for b2, r2 in enumerate(bond_lengths): - x2 = r2 * cos_angle12 - y2 = np.sqrt(r2**2 - x2**2) - r12 = np.linalg.norm(np.array([x2-r1, y2, 0])) - - env12.bond_array_3 = np.array([[r1, 1, 0, 0], [r2, 0, 0, 0]]) - env12.cross_bond_dists = np.array([[0, r12], [r12, 0]]) - - # calculate kernel functions of those newly added training data - if update: - k12_v = new_kv_file[1+b1*nop+b2, :] - for m_index in range(last_size, size): - x_2 = GP.training_data[int(math.floor(m_index / 3))] - d_2 = ds[m_index % 3] - k12_v[m_index] = GP.kernel(env12, x_2, 1, d_2, - GP.hyps, GP.cutoffs) - else: - k12_v = GP.get_kernel_vector(env12, 1) - - if update: - new_kv_file[1+b1*nop+b2, :] = k12_v - - # calculate mean and var value for the mapping - mean_diff = np.matmul(k12_v, GP.alpha) - bond_means[b1, b2] = mean_diff - - if not self.mean_only: - v12_vec = solve_triangular(GP.l_mat, k12_v, lower=True) - bond_vars[b1, b2, :] = v12_vec - - # replace the old file with the new file - if update: - np.save(kv_filename, new_kv_file) + x2 = r2 * cos_angle12 + y2 = r2 * np.sqrt(1-cos_angle12**2) + r12 = np.linalg.norm(np.array([x2-r1, y2, 0])) - return bond_means, bond_vars + env12.bond_array_3 = np.array([[r1, 1, 0, 0], + [r2, 0, 0, 0]]) + env12.cross_bond_dists = np.array([[0, r12], [r12, 0]]) + + k12_v[b1, b2, a12, :] = \ + get_kernel_vector_unit(name, s, e, env12, 1, + kernel, hyps, cutoffs, hyps_mask) + + return k12_v def build_map_container(self): # create spline interpolation class object - nop = self.grid_num[0] - noa = self.grid_num[2] self.mean = CubicSpline(self.l_bounds, self.u_bounds, - orders=[nop, nop, noa]) + orders=self.grid_num) if not self.mean_only: self.var = PCASplines(self.l_bounds, self.u_bounds, - orders=[nop, nop, noa], + orders=self.grid_num, svd_rank=self.svd_rank) def build_map(self, GP): @@ -672,3 +750,35 @@ def build_map(self, GP): if not self.mean_only: self.var.set_values(y_var) + def write(self, f, spc): + a = self.l_bounds + b = self.u_bounds + order = self.grid_num + + coefs_3 = self.mean.__coeffs__ + + elem1 = Z_to_element(spc[0]) + elem2 = Z_to_element(spc[1]) + elem3 = Z_to_element(spc[2]) + + header_3 = '{elem1} {elem2} {elem3} {a1} {a2} {a3} {b1}'\ + ' {b2} {b3:.10e} {order1} {order2} {order3}\n'\ + .format(elem1=elem1, elem2=elem2, elem3=elem3, + a1=a[0], a2=a[1], a3=a[2], + b1=b[0], b2=b[1], b3=b[2], + order1=order[0], order2=order[1], order3=order[2]) + f.write(header_3) + + n = 0 + for i in range(coefs_3.shape[0]): + for j in range(coefs_3.shape[1]): + for k in range(coefs_3.shape[2]): + coef = coefs_3[i, j, k] + f.write('{:.10e} '.format(coef)) + if n % 5 == 4: + f.write('\n') + n += 1 + + f.write('\n') + + diff --git a/flare/mgp/mgp_en.py b/flare/mgp/mgp_en.py new file mode 100644 index 000000000..9389fb0e6 --- /dev/null +++ b/flare/mgp/mgp_en.py @@ -0,0 +1,1001 @@ +import time, os, math, inspect, subprocess +import numpy as np +import multiprocessing as mp +import json +import warnings + +from copy import deepcopy +import pickle as pickle +from scipy.linalg import solve_triangular +from typing import List + +from flare.struc import Structure +from flare.env import AtomicEnvironment +from flare.gp import GaussianProcess +from flare.gp_algebra import partition_c +from flare.gp_algebra import en_kern_vec_unit as en_kern_vec +from flare.kernels.utils import from_mask_to_args, str_to_kernel_set +from flare.cutoffs import quadratic_cutoff +from flare.util import Z_to_element +from flare.mgp.utils import get_bonds, get_triplets, get_triplets_en, \ + get_2bkernel, get_3bkernel +from flare.mgp.splines_methods import PCASplines, CubicSpline +from flare.util import Z_to_element, NumpyEncoder + +class MappedGaussianProcess: + ''' + Build Mapped Gaussian Process (MGP) + and automatically save coefficients for LAMMPS pair style. + + :param: struc_params : Parameters for a dummy structure which will be + internally used to probe/store forces associated with different atomic + configurations + :param: grid_params : Parameters for the mapping itself, such as + grid size of spline fit, etc. + :param: mean_only : if True: only build mapping for mean (force) + :param: container_only : if True: only build splines container + (with no coefficients) + :param: GP: None or a GaussianProcess object. If a GP is input, + and autorun is true, automatically build a mapping corresponding + to the GaussianProcess. + :param: lmp_file_name : LAMMPS coefficient file name + :param: autorun: Attempt to build map immediately + Examples: + + >>> struc_params = {'species': [0, 1], + 'cube_lat': cell, # should input the cell matrix + 'mass_dict': {'0': 27 * unit, '1': 16 * unit}} + >>> grid_params = {'bounds_2': [[1.2], [3.5]], + # [[lower_bound], [upper_bound]] + # These describe the lower and upper + # bounds used to specify the 2-body spline + # fits. + 'bounds_3': [[1.2, 1.2, 1.2], [3.5, 3.5, 3.5]], + # [[lower,lower,lower],[upper,upper,upper]] + # Values describe lower and upper bounds + # for the bondlength-bondlength-bondlength + # grid used to construct and fit 3-body + # kernels; note that for force MGPs + # bondlength-bondlength-costheta + # are the bounds used instead. + 'bodies': [2, 3] # use 2+3 body + 'grid_num_2': 64,# Fidelity of the grid + 'grid_num_3': [16, 16, 16],# Fidelity of the grid + 'svd_rank_2': 64, #Fidelity of uncertainty estimation + 'svd_rank_3': 16**3, + 'update': True, # if True: accelerating grids + # generating by saving intermediate + # coeff when generating grids + 'load_grid': None # Used to load from file + } + ''' + + def __init__(self, + grid_params: dict, + struc_params: dict, + GP: GaussianProcess=None, + mean_only: bool=False, + container_only: bool=True, + lmp_file_name: str='lmp.mgp', + n_cpus: int =None, + n_sample:int =100, + autorun: bool = True): + + # load all arguments as attributes + self.mean_only = mean_only + self.lmp_file_name = lmp_file_name + self.n_cpus = n_cpus + self.n_sample = n_sample + self.grid_params = grid_params + self.struc_params = struc_params + + # arg_dict = inspect.getargvalues(inspect.currentframe())[3] + # del arg_dict['self'], arg_dict['GP'] + # self.__dict__.update(arg_dict) + self.__dict__.update(grid_params) + + # if GP exists, the GP setup overrides the grid_params setup + if GP is not None: + + self.cutoffs = GP.cutoffs + + self.bodies = [] + if "two" in GP.kernel_name: + self.bodies.append(2) + self.kernel2b_info = get_2bkernel(GP) + if "three" in GP.kernel_name: + self.bodies.append(3) + self.kernel3b_info = get_3bkernel(GP) + + self.build_bond_struc(struc_params) + self.maps_2 = [] + self.maps_3 = [] + self.build_map_container() + self.mean_only = mean_only + + if not container_only and (GP is not None) and \ + (len(GP.training_data) > 0) and autorun: + self.build_map(GP) + + def build_map_container(self): + ''' + construct an empty spline container without coefficients. + ''' + if 2 in self.bodies: + for b_struc in self.bond_struc[0]: + map_2 = Map2body(self.grid_num_2, self.bounds_2, + b_struc, self.svd_rank_2, + self.mean_only, self.n_cpus, self.n_sample) + self.maps_2.append(map_2) + if 3 in self.bodies: + for b_struc in self.bond_struc[1]: + map_3 = Map3body(self.grid_num_3, self.bounds_3, + b_struc, self.svd_rank_3, + self.mean_only, + self.grid_params['load_grid'], + self.update, self.n_cpus, self.n_sample) + self.maps_3.append(map_3) + + def build_map(self, GP): + ''' + generate/load grids and get spline coefficients + ''' + + if 2 in self.bodies: + self.kernel2b_info = get_2bkernel(GP) + if 3 in self.bodies: + self.kernel3b_info = get_3bkernel(GP) + + for map_2 in self.maps_2: + map_2.build_map(GP) + for map_3 in self.maps_3: + map_3.build_map(GP) + + # write to lammps pair style coefficient file + self.write_lmp_file(self.lmp_file_name) + + def build_bond_struc(self, struc_params): + + ''' + build a bond structure, used in grid generating + ''' + + cutoff = np.min(self.cutoffs) + cell = struc_params['cube_lat'] + mass_dict = struc_params['mass_dict'] + species_list = struc_params['species'] + N_spc = len(species_list) + + # 2 body (2 atoms (1 bond) config) + bond_struc_2 = [] + spc_2 = [] + spc_2_set = [] + if 2 in self.bodies: + bodies = 2 + for spc1_ind, spc1 in enumerate(species_list): + for spc2 in species_list[spc1_ind:]: + species = [spc1, spc2] + spc_2.append(species) + spc_2_set.append(set(species)) + positions = [[(i+1)/(bodies+1)*cutoff, 0, 0] + for i in range(bodies)] + spc_struc = \ + Structure(cell, species, positions, mass_dict) + spc_struc.coded_species = np.array(species) + bond_struc_2.append(spc_struc) + + # 3 body (3 atoms (1 triplet) config) + bond_struc_3 = [] + spc_3 = [] + if 3 in self.bodies: + bodies = 3 + for spc1_ind in range(N_spc): + spc1 = species_list[spc1_ind] + for spc2_ind in range(N_spc): # (spc1_ind, N_spc): + spc2 = species_list[spc2_ind] + for spc3_ind in range(N_spc): # (spc2_ind, N_spc): + spc3 = species_list[spc3_ind] + species = [spc1, spc2, spc3] + spc_3.append(species) + positions = [[(i+1)/(bodies+1)*cutoff, 0, 0] + for i in range(bodies)] + spc_struc = Structure(cell, species, positions, + mass_dict) + spc_struc.coded_species = np.array(species) + bond_struc_3.append(spc_struc) +# if spc1 != spc2: +# species = [spc2, spc3, spc1] +# spc_3.append(species) +# positions = [[(i+1)/(bodies+1)*cutoff, 0, 0] \ +# for i in range(bodies)] +# spc_struc = Structure(cell, species, positions, +# mass_dict) +# spc_struc.coded_species = np.array(species) +# bond_struc_3.append(spc_struc) +# if spc2 != spc3: +# species = [spc3, spc1, spc2] +# spc_3.append(species) +# positions = [[(i+1)/(bodies+1)*cutoff, 0, 0] \ +# for i in range(bodies)] +# spc_struc = Structure(cell, species, positions, +# mass_dict) +# spc_struc.coded_species = np.array(species) +# bond_struc_3.append(spc_struc) + + self.bond_struc = [bond_struc_2, bond_struc_3] + self.spcs = [spc_2, spc_3] + self.spcs_set = [spc_2_set, spc_3] + + def predict(self, atom_env: AtomicEnvironment, mean_only: bool=False)-> \ + (float, 'ndarray','ndarray', float): + ''' + predict force, variance, stress and local energy for given atomic environment + Args: + atom_env: atomic environment (with a center atom and its neighbors) + mean_only: if True: only predict force (variance is always 0) + Return: + force: 3d array of atomic force + variance: 3d array of the predictive variance + stress: 6d array of the virial stress + energy: the local energy (atomic energy) + ''' + if self.mean_only: # if not build mapping for var + mean_only = True + + # ---------------- predict for two body ------------------- + f2 = vir2 = kern2 = v2 = e2 = 0 + if 2 in self.bodies: + + f2, vir2, kern2, v2, e2 = \ + self.predict_multicomponent(2, atom_env, self.kernel2b_info, + self.spcs_set[0], + self.maps_2, mean_only) + + # ---------------- predict for three body ------------------- + f3 = vir3 = kern3 = v3 = e3 = 0 + if 3 in self.bodies: + + f3, vir3, kern3, v3, e3 = \ + self.predict_multicomponent(3, atom_env, self.kernel3b_info, + self.spcs[1], self.maps_3, + mean_only) + + force = f2 + f3 + variance = kern2 + kern3 - np.sum((v2 + v3)**2, axis=0) + virial = vir2 + vir3 + energy = e2 + e3 + + return force, variance, virial, energy + + + def predict_multicomponent(self, body, atom_env, kernel_info, + spcs_list, mappings, mean_only): + ''' + Add up results from `predict_component` to get the total contribution + of all species + ''' + + kernel, en_force_kernel, cutoffs, hyps, hyps_mask = kernel_info + + args = from_mask_to_args(hyps, hyps_mask, cutoffs) + + kern = np.zeros(3) + for d in range(3): + kern[d] = \ + kernel(atom_env, atom_env, d+1, d+1, *args) + + if (body == 2): + spcs, comp_r, comp_xyz = get_bonds(atom_env.ctype, + atom_env.etypes, atom_env.bond_array_2) + set_spcs = [] + for spc in spcs: + set_spcs += [set(spc)] + spcs = set_spcs + elif (body == 3): + spcs, comp_r, comp_xyz = \ + get_triplets_en(atom_env.ctype, atom_env.etypes, + atom_env.bond_array_3, atom_env.cross_bond_inds, + atom_env.cross_bond_dists, atom_env.triplet_counts) + + # predict for each species + f_spcs = 0 + vir_spcs = 0 + v_spcs = 0 + e_spcs = 0 + for i, spc in enumerate(spcs): + lengths = np.array(comp_r[i]) + xyzs = np.array(comp_xyz[i]) + map_ind = spcs_list.index(spc) + f, vir, v, e = self.predict_component(lengths, xyzs, + mappings[map_ind], mean_only) + f_spcs += f + vir_spcs += vir + v_spcs += v + e_spcs += e + + return f_spcs, vir_spcs, kern, v_spcs, e_spcs + + def predict_component(self, lengths, xyzs, mapping, mean_only): + ''' + predict force and variance contribution of one component + ''' + lengths = np.array(lengths) + xyzs = np.array(xyzs) + + # predict mean + e_0, f_0 = mapping.mean(lengths, with_derivatives=True) + e = np.sum(e_0) # energy + + # predict forces and stress + vir = np.zeros(6) + vir_order = ((0,0), (1,1), (2,2), (1,2), (0,2), (0,1)) # match the ASE order + + # two-body + if lengths.shape[-1] == 1: + f_d = np.diag(f_0[:,0,0]) @ xyzs + f = 2 * np.sum(f_d, axis=0) # force: need to check prefactor 2 + + for i in range(6): + vir_i = f_d[:,vir_order[i][0]]\ + * xyzs[:,vir_order[i][1]] * lengths[:,0] + vir[i] = np.sum(vir_i) + + # three-body + if lengths.shape[-1] == 3: +# factor1 = 1/lengths[:,1] - 1/lengths[:,0] * lengths[:,2] +# factor2 = 1/lengths[:,0] - 1/lengths[:,1] * lengths[:,2] +# f_d1 = np.diag(f_0[:,0,0]+f_0[:,2,0]*factor1) @ xyzs[:,0,:] +# f_d2 = np.diag(f_0[:,1,0]+f_0[:,2,0]*factor2) @ xyzs[:,1,:] + f_d1 = np.diag(f_0[:,0,0]) @ xyzs[:,0,:] + f_d2 = np.diag(f_0[:,1,0]) @ xyzs[:,1,:] + f_d = f_d1 + f_d2 + f = 3 * np.sum(f_d, axis=0) # force: need to check prefactor 3 + + for i in range(6): + vir_i1 = f_d1[:,vir_order[i][0]]\ + * xyzs[:,0,vir_order[i][1]] * lengths[:,0] + vir_i2 = f_d2[:,vir_order[i][0]]\ + * xyzs[:,1,vir_order[i][1]] * lengths[:,1] + vir[i] = np.sum(vir_i1 + vir_i2) + vir *= 1.5 + + + # predict var + v = np.zeros(3) + if not mean_only: + v_0 = mapping.var(lengths) + v_d = v_0 @ xyzs + v = mapping.var.V @ v_d + return f, vir, v, e + + def write_lmp_file(self, lammps_name): + ''' + write the coefficients to a file that can be used by lammps pair style + ''' + + # write header + f = open(lammps_name, 'w') + + header_comment = '''# #2bodyarray #3bodyarray\n# elem1 elem2 a b order + ''' + f.write(header_comment) + + twobodyarray = len(self.spcs[0]) + threebodyarray = len(self.spcs[1]) + header = '\n{} {}\n'.format(twobodyarray, threebodyarray) + f.write(header) + + # write two body + if twobodyarray > 0: + for ind, spc in enumerate(self.spcs[0]): + self.maps_2[ind].write(f, spc) + + # write three body + if threebodyarray > 0: + for ind, spc in enumerate(self.spcs[1]): + self.maps_3[ind].write(f, spc) + + f.close() + + + def as_dict(self)->dict: + """ + Dictionary representation of the MGP model. + """ + + out_dict = deepcopy(dict(vars(self))) + + # Uncertainty mappings currently not serializable; + if not self.mean_only: + warnings.warn("Uncertainty mappings cannot be serialized, " + "and so the MGP dict outputted will not have " + "them.", Warning) + out_dict['mean_only'] = True + + + # Iterate through the mappings for various bodies + for i in self.bodies: + kern_info = f'kernel{i}b_info' + kernel, efk, cutoffs, hyps, hyps_mask = out_dict[kern_info] + out_dict[kern_info] = (kernel.__name__, efk.__name__, + cutoffs, hyps, hyps_mask) + + # only save the coefficients + out_dict['maps_2'] = [map_2.mean.__coeffs__ for map_2 in self.maps_2] + out_dict['maps_3'] = [map_3.mean.__coeffs__ for map_3 in self.maps_3] + + + # don't need these since they are built in the __init__ function + key_list = ['bond_struc', 'spcs_set', ] + for key in key_list: + if out_dict.get(key) is not None: + del out_dict[key] + + return out_dict + + + @staticmethod + def from_dict(dictionary:dict): + """ + Create MGP object from dictionary representation. + """ + new_mgp = MappedGaussianProcess(grid_params=dictionary['grid_params'], + struc_params=dictionary['struc_params'], + GP=None, + mean_only=dictionary['mean_only'], + container_only=True, + lmp_file_name=dictionary['lmp_file_name'], + n_cpus=dictionary['n_cpus'], + n_sample=dictionary['n_sample'], + autorun=False) + + # Restore kernel_info + for i in dictionary['bodies']: + kern_info = f'kernel{i}b_info' + hyps_mask = dictionary[kern_info][-1] + if (hyps_mask is None): + multihyps = False + else: + multihyps = True + + kernel_info = dictionary[kern_info] + kernel_name = kernel_info[0] + kernel, _, _, efk = str_to_kernel_set(kernel_name, multihyps) + kernel_info[0] = kernel + kernel_info[1] = efk + setattr(new_mgp, kern_info, kernel_info) + + + # Fill up the model with the saved coeffs + for m, map_2 in enumerate(new_mgp.maps_2): + map_2.mean.__coeffs__ = np.array(dictionary['maps_2'][m]) + for m, map_3 in enumerate(new_mgp.maps_3): + map_3.mean.__coeffs__ = np.array(dictionary['maps_3'][m]) + + # Set GP + if dictionary.get('GP'): + new_mgp.GP = GaussianProcess.from_dict(dictionary.get("GP")) + + return new_mgp + + def write_model(self, name: str, format='json'): + """ + Write everything necessary to re-load and re-use the model + :param model_name: + :return: + """ + if 'json' in format.lower(): + with open(f'{name}.json', 'w') as f: + json.dump(self.as_dict(), f, cls=NumpyEncoder) + + elif 'pickle' in format.lower() or 'binary' in format.lower(): + with open(f'{name}.pickle', 'wb') as f: + pickle.dump(self, f) + + else: + raise ValueError("Requested format not found.") + + +# @staticmethod +# def load_model(elements: List[int], directory:str = './', +# kernel: str = '2+3', +# load_var: bool = False): +# """ +# Loads the relevant files in a directory to an MGP model +# :param elements: +# :param directory: +# :param kernel: +# :return: +# """ +# +# # Check to see if two body or three body kernels will be loaded +# # so "2+3" makes b2 and b3 true +# b2 = '2' in kernel or 'two' in kernel +# b3 = '3' in kernel or 'three' in kernel +# +# +# raise NotImplementedError + + + @staticmethod + def from_file(filename: str): + if '.json' in filename: + with open(filename, 'r') as f: + model = MappedGaussianProcess.from_dict(json.loads(f.readline())) + return model + + elif 'pickle' in filename: + with open(filename, 'rb') as f: + return pickle.load(f) + else: + raise NotImplementedError + + +class Map2body: + def __init__(self, grid_num: int, bounds, bond_struc: Structure, + svd_rank=0, mean_only: bool=False, n_cpus: int=None, + n_sample: int=100): + ''' + Build 2-body MGP + + bond_struc: Mock structure used to sample 2-body forces on 2 atoms + ''' + + self.grid_num = grid_num + self.bounds = bounds + self.bond_struc = bond_struc + self.svd_rank = svd_rank + self.mean_only = mean_only + self.n_cpus = n_cpus + self.n_sample = n_sample + + spc = bond_struc.coded_species + self.species_code = Z_to_element(spc[0]) + '_' + Z_to_element(spc[1]) + +# arg_dict = inspect.getargvalues(inspect.currentframe())[3] +# del arg_dict['self'] +# self.__dict__.update(arg_dict) + + self.build_map_container() + + + def GenGrid(self, GP): + + ''' + To use GP to predict value on each grid point, we need to generate the + kernel vector kv whose length is the same as the training set size. + + 1. We divide the training set into several batches, corresponding to + different segments of kv + 2. Distribute each batch to a processor, i.e. each processor calculate + the kv segment of one batch for all grids + 3. Collect kv segments and form a complete kv vector for each grid, + and calculate the grid value by multiplying the complete kv vector + with GP.alpha + ''' + + kernel_info = get_2bkernel(GP) + + if (self.n_cpus is None): + processes = mp.cpu_count() + else: + processes = self.n_cpus + + # ------ construct grids ------ + nop = self.grid_num + bond_lengths = np.linspace(self.bounds[0][0], self.bounds[1][0], nop) + bond_means = np.zeros([nop]) + if not self.mean_only: + bond_vars = np.zeros([nop, len(GP.alpha)]) + else: + bond_vars = None + env12 = AtomicEnvironment(self.bond_struc, 0, GP.cutoffs) + + with mp.Pool(processes=processes) as pool: + # A_list = pool.map(self._GenGrid_inner_most, pool_list) + # break it into pieces + size = len(GP.training_data) + block_id, nbatch = partition_c(self.n_sample, size, processes) + + k12_slice = [] + k12_v_all = np.zeros([len(bond_lengths), size*3]) + count = 0 + base = 0 + for ibatch in range(nbatch): + s, e = block_id[ibatch] + k12_slice.append(pool.apply_async(self._GenGrid_inner, + args=(GP.name, s, e, + bond_lengths, + env12, kernel_info))) + count += 1 + if (count > processes*2): + for ibase in range(count): + s, e = block_id[ibase+base] + k12_v_all[:, s*3:e*3] = k12_slice[ibase].get() + del k12_slice + k12_slice = [] + count = 0 + base = ibatch+1 + if (count > 0): + for ibase in range(count): + s, e = block_id[ibase+base] + k12_v_all[:, s*3:e*3] = k12_slice[ibase].get() + del k12_slice + pool.close() + pool.join() + + for b, r in enumerate(bond_lengths): + k12_v = k12_v_all[b, :] + bond_means[b] = np.matmul(k12_v, GP.alpha) + if not self.mean_only: + bond_vars[b, :] = solve_triangular(GP.l_mat, k12_v, lower=True) + + write_species_name = '' + for x in self.bond_struc.coded_species: + write_species_name += "_" + Z_to_element(x) + # ------ save mean and var to file ------- + np.save('grid2_mean' + write_species_name, bond_means) + np.save('grid2_var' + write_species_name, bond_vars) + + return bond_means, bond_vars + + + def _GenGrid_inner(self, name, s, e, bond_lengths, + env12, kernel_info): + + ''' + Calculate kv segments of the given batch of training data for all grids + ''' + + kernel, en_force_kernel, cutoffs, hyps, hyps_mask = kernel_info + size = e - s + k12_v = np.zeros([len(bond_lengths), size*3]) + for b, r in enumerate(bond_lengths): + env12.bond_array_2 = np.array([[r, 1, 0, 0]]) + k12_v[b, :] = en_kern_vec(name, s, e, + env12, en_force_kernel, + hyps, cutoffs, hyps_mask) + return k12_v + + + def build_map_container(self): + + ''' + build 1-d spline function for mean, 2-d for var + ''' + self.mean = CubicSpline(self.bounds[0], self.bounds[1], + orders=[self.grid_num]) + + if not self.mean_only: + self.var = PCASplines(self.bounds[0], self.bounds[1], + orders=[self.grid_num], + svd_rank=self.svd_rank) + + def build_map(self, GP): + y_mean, y_var = self.GenGrid(GP) + self.mean.set_values(y_mean) + if not self.mean_only: + self.var.set_values(y_var) + + def write(self, f, spc): + ''' + Write LAMMPS coefficient file + ''' + a = self.bounds[0][0] + b = self.bounds[1][0] + order = self.grid_num + + coefs_2 = self.mean.__coeffs__ + + elem1 = Z_to_element(spc[0]) + elem2 = Z_to_element(spc[1]) + header_2 = '{elem1} {elem2} {a} {b} {order}\n'\ + .format(elem1=elem1, elem2=elem2, a=a, b=b, order=order) + f.write(header_2) + + for c, coef in enumerate(coefs_2): + f.write('{:.10e} '.format(coef)) + if c % 5 == 4 and c != len(coefs_2)-1: + f.write('\n') + + f.write('\n') + + + +class Map3body: + + def __init__(self, grid_num, bounds, bond_struc: Structure, + svd_rank: int=0, mean_only: bool=False, load_grid: str='', + update: bool=True, n_cpus=None, n_sample=100): + ''' + Build 3-body MGP + + bond_struc: Mock Structure object which contains 3 atoms to get map + from + ''' + self.grid_num = grid_num + self.bounds = bounds + self.bond_struc = bond_struc + self.svd_rank = svd_rank + self.mean_only = mean_only + self.load_grid = load_grid + self.update = update + self.n_sample = n_sample + + spc = bond_struc.coded_species + self.species_code = Z_to_element(spc[0]) + '_' + \ + Z_to_element(spc[1]) + '_' + Z_to_element(spc[2]) + self.kv3name = f'kv3_{self.species_code}' + + self.build_map_container() + self.n_cpus = n_cpus + self.bounds = bounds + self.mean_only = mean_only + + + def GenGrid(self, GP): + + ''' + To use GP to predict value on each grid point, we need to generate the + kernel vector kv whose length is the same as the training set size. + + 1. We divide the training set into several batches, corresponding to + different segments of kv + 2. Distribute each batch to a processor, i.e. each processor calculate + the kv segment of one batch for all grids + 3. Collect kv segments and form a complete kv vector for each grid, + and calculate the grid value by multiplying the complete kv vector + with GP.alpha + ''' + + if self.n_cpus is None: + processes = mp.cpu_count() + else: + processes = self.n_cpus + + # ------ get 3body kernel info ------ + kernel_info = get_3bkernel(GP) + + # ------ construct grids ------ + n1, n2, n12 = self.grid_num + bonds1 = np.linspace(self.bounds[0][0], self.bounds[1][0], n1) + bonds2 = np.linspace(self.bounds[0][0], self.bounds[1][0], n2) + bonds12 = np.linspace(self.bounds[0][2], self.bounds[1][2], n12) + grid_means = np.zeros([n1, n2, n12]) + + if not self.mean_only: + grid_vars = np.zeros([n1, n2, n12, len(GP.alpha)]) + else: + grid_vars = None + + env12 = AtomicEnvironment(self.bond_struc, 0, GP.cutoffs) + size = len(GP.training_data) + + if processes == 1: + if self.update: + raise NotImplementedError("the update function is " + "not yet implemented") + else: + k12_v_all = self._GenGrid_inner(GP.name, 0, size, bonds1, + bonds2, bonds12, env12, + kernel_info) + else: + with mp.Pool(processes=processes) as pool: + + if self.update: + + raise NotImplementedError("the update function is " + "not yet implemented") + + if self.kv3name in os.listdir(): + subprocess.run(['rm', '-rf', self.kv3name]) + + os.mkdir(self.kv3name) + + # get the size of saved kv vector + kv_filename = f'{self.kv3name}/{0}' + if kv_filename in os.listdir(self.kv3name): + old_kv_file = np.load(kv_filename+'.npy') + last_size = int(old_kv_file[0,0]) + new_kv_file[i, :, :last_size] = old_kv_file + + k12_v_all = np.zeros([len(bonds1), len(bonds2), len(bonds12), + size * 3]) + + for i in range(n12): + if f'{self.kv3name}/{i}.npy' in os.listdir(self.kv3name): + old_kv_file = np.load(f'{self.kv3name}/{i}.npy') + last_size = int(old_kv_file[0,0]) + #TODO k12_v_all[] + else: + last_size = 0 + + # parallelize based on grids, since usually the number of + # the added training points are small + ngrids = int(math.ceil(n12 / processes)) + nbatch = int(math.ceil(n12 / ngrids)) + + block_id = [] + for ibatch in range(nbatch): + s = int(ibatch * processes) + e = int(np.min(((ibatch+1)*processes, n12))) + block_id += [(s, e)] + + k12_slice = [] + for ibatch in range(nbatch): + k12_slice.append(pool.apply_async(self._GenGrid_inner, + args=(GP.name, last_size, size, + bonds1, bonds2, bonds12[s:e], + env12, kernel_info))) + + for ibatch in range(nbatch): + s, e = block_id[ibatch] + k12_v_all[:, :, s:e, :] = k12_slice[ibatch].get() + + else: + block_id, nbatch = partition_c(self.n_sample, size, processes) + + k12_slice = [] + #print('before for', ns, nsample, time.time()) + count = 0 + base = 0 + k12_v_all = np.zeros([len(bonds1), len(bonds2), len(bonds12), + size * 3]) + for ibatch in range(nbatch): + s, e = block_id[ibatch] + k12_slice.append(pool.apply_async(self._GenGrid_inner, + args=(GP.name, s, e, + bonds1, bonds2, bonds12, + env12, kernel_info))) + #print('send', ibatch, ns, s, e, time.time()) + count += 1 + if (count > processes*2): + for ibase in range(count): + s, e = block_id[ibase+base] + k12_v_all[:, :, :, s*3:e*3] = k12_slice[ibase].get() + del k12_slice + k12_slice = [] + count = 0 + base = ibatch+1 + if (count > 0): + for ibase in range(count): + s, e = block_id[ibase+base] + k12_v_all[:, :, :, s*3:e*3] = k12_slice[ibase].get() + del k12_slice + + pool.close() + pool.join() + + for b12 in range(len(bonds12)): + for b1 in range(len(bonds1)): + for b2 in range(len(bonds2)): + k12_v = k12_v_all[b1, b2, b12, :] + grid_means[b1, b2, b12] = np.matmul(k12_v, GP.alpha) + if not self.mean_only: + grid_vars[b1, b2, b12, :] = solve_triangular(GP.l_mat, + k12_v, lower=True) + + + # Construct file names according to current mapping + + # ------ save mean and var to file ------- + np.save('grid3_mean_'+self.species_code, grid_means) + np.save('grid3_var_'+self.species_code, grid_vars) + + return grid_means, grid_vars + + def _GenGrid_inner(self, name, s, e, bonds1, bonds2, bonds12, env12, kernel_info): + + ''' + Calculate kv segments of the given batch of training data for all grids + ''' + + kernel, en_force_kernel, cutoffs, hyps, hyps_mask = kernel_info + # open saved k vector file, and write to new file + size = (e - s) * 3 + k12_v = np.zeros([len(bonds1), len(bonds2), len(bonds12), size]) + for b12, r12 in enumerate(bonds12): + for b1, r1 in enumerate(bonds1): + for b2, r2 in enumerate(bonds2): + + env12.bond_array_3 = np.array([[r1, 1, 0, 0], + [r2, 0, 0, 0]]) + env12.cross_bond_dists = np.array([[0, r12], [r12, 0]]) + k12_v[b1, b2, b12, :] = en_kern_vec(name, s, e, + env12, en_force_kernel, + hyps, cutoffs, hyps_mask) + + # open saved k vector file, and write to new file + if self.update: + + raise NotImplementedError("the update function is not yet"\ + "implemented") + + s, e = block + chunk = e - s + new_kv_file = np.zeros((chunk, + self.grid_num[0]*self.grid_num[1]+1, + total_size)) + new_kv_file[:,0,0] = np.ones(chunk) * total_size + for i in range(s, e): + kv_filename = f'{self.kv3name}/{i}' + if kv_filename in os.listdir(self.kv3name): + old_kv_file = np.load(kv_filename+'.npy') + last_size = int(old_kv_file[0,0]) + new_kv_file[i, :, :last_size] = old_kv_file + else: + last_size = 0 + ds = [1, 2, 3] + nop = self.grid_num[0] + + k12_v = new_kv_file[:,1:,:] + for i in range(s, e): + np.save(f'{self.kv3name}/{i}', new_kv_file[i,:,:]) + + return k12_v + + + def build_map_container(self): + + ''' + build 3-d spline function for mean, + 3-d for the low rank approximation of L^{-1}k* + ''' + + # create spline interpolation class object + self.mean = CubicSpline(self.bounds[0], self.bounds[1], + orders=self.grid_num) + + if not self.mean_only: + self.var = PCASplines(self.bounds[0], self.bounds[1], + orders=self.grid_num, + svd_rank=self.svd_rank) + + def build_map(self, GP): + # Load grid or generate grid values + # If load grid was not specified, will be none + if not self.load_grid: + y_mean, y_var = self.GenGrid(GP) + # If load grid is blank string '' or pre-fix, load in + else: + y_mean = np.load(self.load_grid+'grid3_mean_'+\ + self.species_code+'.npy') + y_var = np.load(self.load_grid+'grid3_var_'+\ + self.species_code+'.npy') + + self.mean.set_values(y_mean) + if not self.mean_only: + self.var.set_values(y_var) + + def write(self, f, spc): + a = self.bounds[0] + b = self.bounds[1] + order = self.grid_num + + coefs_3 = self.mean.__coeffs__ + + elem1 = Z_to_element(spc[0]) + elem2 = Z_to_element(spc[1]) + elem3 = Z_to_element(spc[2]) + + header_3 = '{elem1} {elem2} {elem3} {a1} {a2} {a3} {b1}'\ + ' {b2} {b3:.10e} {order1} {order2} {order3}\n'\ + .format(elem1=elem1, elem2=elem2, elem3=elem3, + a1=a[0], a2=a[1], a3=a[2], + b1=b[0], b2=b[1], b3=b[2], + order1=order[0], order2=order[1], order3=order[2]) + f.write(header_3) + + n = 0 + for i in range(coefs_3.shape[0]): + for j in range(coefs_3.shape[1]): + for k in range(coefs_3.shape[2]): + coef = coefs_3[i, j, k] + f.write('{:.10e} '.format(coef)) + if n % 5 == 4: + f.write('\n') + n += 1 + + f.write('\n') + + diff --git a/flare/mgp/otf.py b/flare/mgp/otf.py index 48deb201f..e016c540b 100644 --- a/flare/mgp/otf.py +++ b/flare/mgp/otf.py @@ -1,7 +1,3 @@ -from copy import deepcopy -import os -import sys -import datetime import time import multiprocessing as mp from typing import List @@ -9,57 +5,56 @@ from flare.mgp.mgp import MappedGaussianProcess from flare.otf import OTF -from flare.struc import Structure import flare.gp as gp -from flare.gp import GaussianProcess from flare.env import AtomicEnvironment -from flare.qe_util import run_espresso, parse_qe_input, \ - qe_input_to_structure, parse_qe_forces + from flare import output + class MGPOTF(OTF): def __init__(self, qe_input: str, dt: float, number_of_steps: int, gp_model: 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, + prev_pos_init: np.ndarray = None, par: bool = False, + skip: int = 0, init_atoms: List[int] = None, calculate_energy=False, output_name='otf_run.out', backup_name='otf_run_backup.out', max_atoms_added=None, freeze_hyps=False, rescale_steps=[], rescale_temps=[], add_all=False, no_cpus=1, use_mapping=True, non_mapping_steps=[], l_bound=None, two_d=False, - grid_params: dict={}, struc_params: dict={}): - - ''' + grid_params: dict = {}, struc_params: dict = {}): + + """ On-the-fly training with MGP inserted, added parameters: :param non_mapping_steps: steps that not to use MGP :param l_bound: initial minimal interatomic distance :param two_d: if the system is a 2-D system :param grid_params: see class `MappedGaussianProcess` :param struc_params: see class `MappedGaussianProcess` - ''' + """ super().__init__(qe_input, dt, number_of_steps, - gp_model, pw_loc, - std_tolerance_factor, + gp_model, pw_loc, + std_tolerance_factor, prev_pos_init, par, - skip, init_atoms, - calculate_energy, output_name, + skip, init_atoms, + calculate_energy, output_name, backup_name, max_atoms_added, freeze_hyps, rescale_steps, rescale_temps, add_all, no_cpus, use_mapping, non_mapping_steps, l_bound, two_d) - + self.grid_params = grid_params self.struc_params = struc_params if par: self.pool = mp.Pool(processes=no_cpus) def predict_on_structure_par_mgp(self): - args_list = [(atom, self.structure, self.gp.cutoffs, self.mgp) for atom in self.atom_list] + args_list = [(atom, self.structure, self.gp.cutoffs, self.mgp) for atom + in self.atom_list] results = self.pool.starmap(predict_on_atom_mgp, args_list) for atom in self.atom_list: res = results[atom] @@ -68,8 +63,7 @@ def predict_on_structure_par_mgp(self): self.local_energies[atom] = res[2] self.structure.dft_forces = False - - def predict_on_structure_mgp(self): # changed + def predict_on_structure_mgp(self): # changed """ Assign forces to self.structure based on self.gp """ @@ -85,42 +79,86 @@ def predict_on_structure_mgp(self): # changed def train_mgp(self, skip=True): t0 = time.time() - if self.l_bound < self.grid_params['bounds_2'][0,0]: - self.grid_params['bounds_2'][0,0] = self.l_bound - 0.01 - self.grid_params['bounds_3'][0,:2] = np.ones(2)*self.l_bound - 0.01 + if self.l_bound < self.grid_params['bounds_2'][0, 0]: + self.grid_params['bounds_2'][0, 0] = self.l_bound - 0.01 + self.grid_params['bounds_3'][0, :2] = np.ones( + 2) * self.l_bound - 0.01 if skip and (self.curr_step in self.non_mapping_steps): return 1 # set svd rank based on the training set, grid number and threshold 1000 train_size = len(self.gp.training_data) - rank_2 = np.min([1000, self.grid_params['grid_num_2'], train_size*3]) - rank_3 = np.min([1000, self.grid_params['grid_num_3'][0]**3, train_size*3]) + rank_2 = np.min([1000, self.grid_params['grid_num_2'], train_size * 3]) + rank_3 = np.min( + [1000, self.grid_params['grid_num_3'][0] ** 3, train_size * 3]) self.grid_params['svd_rank_2'] = rank_2 self.grid_params['svd_rank_3'] = rank_3 - + output.write_to_output('\ntraining set size: {}\n'.format(train_size), - self.output_name) + self.output_name) output.write_to_output('lower bound: {}\n'.format(self.l_bound)) - output.write_to_output('mgp l_bound: {}\n'.format(self.grid_params['bounds_2'][0,0])) - output.write_to_output('Constructing mapped force field...\n', - self.output_name) - self.mgp = MappedGaussianProcess(self.gp, self.grid_params, self.struc_params) - output.write_to_output('building mapping time: {}'.format(time.time()-t0), - self.output_name) + output.write_to_output( + 'mgp l_bound: {}\n'.format(self.grid_params['bounds_2'][0, 0])) + output.write_to_output('Constructing mapped force field...\n', + self.output_name) + self.mgp = MappedGaussianProcess(self.gp, self.grid_params, + self.struc_params) + output.write_to_output( + 'building mapping time: {}'.format(time.time() - t0), + self.output_name) self.is_mgp_built = True -def predict_on_atom_mgp(atom, structure, cutoffs, mgp): + +def predict_on_atom_mgp(atom: int, structure, cutoffs, mgp, + write_to_structure=False): chemenv = AtomicEnvironment(structure, atom, cutoffs) # predict force components and standard deviations - force, var = mgp.predict(chemenv) + force, var, virial, local_energy = mgp.predict(chemenv) comps = force stds = np.sqrt(np.absolute(var)) - # predict local energy -# local_energy = self.gp.predict_local_energy(chemenv) - local_energy = 0 + if write_to_structure: + structure.forces[atom][:] = force + structure.stds[atom][:] = stds + return comps, stds, local_energy +def predict_on_structure_mgp(structure, mgp, output=None, + output_name=None, n_cpus=None, + write_to_structure=True, + selective_atoms: List[int] = None, + skipped_atom_value=0): # changed + """ + Assign forces to structure based on an mgp + """ + if output and output_name: + output.write_to_output('\npredict with mapping:\n', output_name) + + forces = np.zeros(shape=(structure.nat, 3)) + vars = np.zeros(shape=(structure.nat, 3)) + + if selective_atoms: + forces.fill(skipped_atom_value) + vars.fill(skipped_atom_value) + else: + selective_atoms = [] + + + for n in range(structure.nat): + + if n not in selective_atoms and selective_atoms: + continue + + chemenv = AtomicEnvironment(structure, n, mgp.cutoffs) + force, var, _, _ = mgp.predict(chemenv) + if write_to_structure: + structure.forces[n][:] = force + structure.stds[n][:] = np.sqrt(np.absolute(var)) + forces[n, :] = force + vars[n, :] = var + + + return forces, vars diff --git a/flare/mgp/splines_methods.py b/flare/mgp/splines_methods.py index dbe4f9b7e..ca1a3891a 100644 --- a/flare/mgp/splines_methods.py +++ b/flare/mgp/splines_methods.py @@ -2,10 +2,6 @@ ''' import numpy as np import numpy -import time -from memory_profiler import profile - -import flare.mgp.utils as utils from flare.mgp.cubic_splines_numba import * class PCASplines: @@ -53,8 +49,8 @@ def set_values(self, y): dim_1 = y.shape[-1] var_matr = np.reshape(y, (dim_0, dim_1)) - U, S, Vh = utils.svd_grid(var_matr, rank=self.svd_rank) - self.V = Vh.T + U, S, Vh = np.linalg.svd(var_matr, full_matrices=False) + self.V = Vh[:self.svd_rank,:].T for r in range(self.svd_rank): self.models[r].set_values(S[r]*U[:, r]) @@ -141,17 +137,24 @@ def interpolate(self, points, values=None, with_derivatives=False): if not with_derivatives: if points.ndim == 1: # evaluate only on one point - return eval_cubic_spline(self.a, self.b, self.orders, self.__coeffs__, points) + return eval_cubic_spline(self.a, self.b, self.orders, + self.__coeffs__, points) else: N, d = points.shape assert(d==self.d) if values is None: values = np.empty(N, dtype=self.dtype) - vec_eval_cubic_spline(self.a, self.b, self.orders, self.__coeffs__, points, values) + vec_eval_cubic_spline(self.a, self.b, self.orders, + self.__coeffs__, points, values) return values else: - raise Exception("Not implemented.") + N, d = points.shape + assert(d==self.d) + values, dvalues = vec_eval_cubic_splines_G(self.a, self.b, + self.orders, self.__coeffs__, points, values, dvalues=None) + + return values, dvalues @property @@ -162,14 +165,14 @@ def grid(self): self.__grid__ = mlinspace(self.a, self.b, self.orders) return self.__grid__ - def __call__(self, s): + def __call__(self, s, with_derivatives=False): """Interpolate the spline at one or many points""" if s.ndim == 1: res = self.__call__( numpy.atleast_2d(s) ) return res[0] - return self.interpolate(s) + return self.interpolate(s, with_derivatives=with_derivatives) def cartesian(arrays, out=None): @@ -302,4 +305,38 @@ def vec_eval_cubic_spline(a, b, orders, coefs, points, values=None): return values +def vec_eval_cubic_splines_G(a, b, orders, mcoefs, points, + values=None, dvalues=None): + + a = numpy.array(a, dtype=float) + b = numpy.array(b, dtype=float) + orders = numpy.array(orders, dtype=int) + + d = a.shape[0] + N = points.shape[0] + #n_sp = mcoefs.shape[-1] + n_sp = 1 + + if values is None: + values = numpy.empty((N, n_sp)) + + if dvalues is None: + dvalues = numpy.empty((N, d, n_sp)) + + if d == 1: + vec_eval_cubic_splines_G_1(a, b, orders, mcoefs, points, values, + dvalues) + + elif d == 2: + vec_eval_cubic_splines_G_2(a, b, orders, mcoefs, points, values, + dvalues) + + elif d == 3: + vec_eval_cubic_splines_G_3(a, b, orders, mcoefs, points, values, + dvalues) + + elif d == 4: + vec_eval_cubic_splines_G_4(a, b, orders, mcoefs, points, values, + dvalues) + return [values, dvalues] diff --git a/flare/mgp/utils.py b/flare/mgp/utils.py index 3cd789764..45d7ff735 100644 --- a/flare/mgp/utils.py +++ b/flare/mgp/utils.py @@ -1,59 +1,90 @@ import numpy as np from numpy import array from numba import njit -import io, os, sys, time, random, logging +import io, os, sys, time, random, math import multiprocessing as mp -import cProfile +import flare.gp_algebra import flare.gp as gp import flare.env as env import flare.struc as struc +import flare.kernels.mc_simple as mc_simple +import flare.kernels.mc_sephyps as mc_sephyps +import flare.kernels.sc as sc from flare.env import AtomicEnvironment from flare.kernels.kernels import three_body_helper_1, \ three_body_helper_2, force_helper from flare.cutoffs import quadratic_cutoff +from flare.kernels.utils import str_to_kernel_set as stks -def save_GP(GP, prefix): - np.save(prefix+'alpha', GP.alpha) - np.save(prefix+'hyps', GP.hyps) - np.save(prefix+'l_mat', GP.l_mat) - -def load_GP(GP, prefix): - GP.alpha = np.load(prefix+'alpha.npy') - GP.hyps = np.load(prefix+'hyps.npy') - GP.l_mat = np.load(prefix+'l_mat.npy') - l_mat_inv = np.linalg.inv(GP.l_mat) - GP.ky_mat_inv = l_mat_inv.T @ l_mat_inv - - -def save_grid(bond_lens, bond_ens_diff, bond_vars_diff, prefix): - np.save(prefix+'-bond_lens', bond_lens) - np.save(prefix+'-bond_ens_diff', bond_ens_diff) - np.save(prefix+'-bond_vars_diff', bond_vars_diff) - - -def load_grid(prefix): - bond_lens = np.load(prefix+'bond_lens.npy') - bond_ens_diff = np.load(prefix+'bond_ens_diff.npy') - bond_vars_diff = np.load(prefix+'bond_vars_diff.npy') - return bond_lens, bond_ens_diff, bond_vars_diff +def get_2bkernel(GP): + if 'mc' in GP.kernel_name: + kernel, _, _, efk = stks('2mc', GP.multihyps) + else: + kernel, _, _, efk = stks('2', GP.multihyps) + + cutoffs = [GP.cutoffs[0]] + + original_hyps = np.copy(GP.hyps) + if (GP.multihyps is True): + o_hyps_mask = GP.hyps_mask + if ('map' in o_hyps_mask.keys()): + ori_hyps = o_hyps_mask['original'] + hm = o_hyps_mask['map'] + for i, h in enumerate(original_hyps): + ori_hyps[hm[i]]=h + else: + ori_hyps = original_hyps + n2b = o_hyps_mask['nbond'] + hyps = np.hstack([ori_hyps[:n2b*2], ori_hyps[-1]]) + hyps_mask = {'nbond':n2b, 'ntriplet':0, + 'nspec':o_hyps_mask['nspec'], + 'spec_mask':o_hyps_mask['spec_mask'], + 'bond_mask': o_hyps_mask['bond_mask']} + else: + hyps = [GP.hyps[0], GP.hyps[1], GP.hyps[-1]] + hyps_mask = None + return (kernel, efk, cutoffs, hyps, hyps_mask) -def merge(prefix, a_num, g_num): - grid_means = np.zeros((g_num, g_num, a_num)) - grid_vars = np.zeros((g_num, g_num, a_num, g_num, g_num, a_num)) - for a12 in range(a_num): - grid_means[:,:,a12] = np.load(prefix+str((a12, 0))+'-bond_means.npy') - for a34 in range(a_num): - grid_vars[:,:,a12,:,:,a34] = np.load(prefix+str((a12, a34))+'-bond_vars.npy') - return grid_means, grid_vars +def get_3bkernel(GP): + if 'mc' in GP.kernel_name: + kernel, _, _, efk = stks('3mc', GP.multihyps) + else: + kernel, _, _, efk = stks('3', GP.multihyps) + + base = 0 + for t in ['two', '2']: + if t in GP.kernel_name: + base = 2 + + cutoffs = np.copy(GP.cutoffs) + + original_hyps = np.copy(GP.hyps) + if (GP.multihyps is True): + o_hyps_mask = GP.hyps_mask + if ('map' in o_hyps_mask.keys()): + ori_hyps = o_hyps_mask['original'] + hm = o_hyps_mask['map'] + for i, h in enumerate(original_hyps): + ori_hyps[hm[i]]=h + else: + ori_hyps = original_hyps + n2b = o_hyps_mask['nbond'] + n3b = o_hyps_mask['ntriplet'] + hyps = ori_hyps[n2b*2:] + hyps_mask = {'ntriplet':n3b,'nbond':0, + 'nspec':o_hyps_mask['nspec'], + 'spec_mask':o_hyps_mask['spec_mask'], + 'triplet_mask': o_hyps_mask['triplet_mask']} + else: + hyps = [GP.hyps[0+base], GP.hyps[1+base], GP.hyps[-1]] + hyps_mask = None -def svd_grid(matr, rank): - u, s, vh = np.linalg.svd(matr, full_matrices=False) - return u[:,:rank], s[:rank], vh[:rank, :] + return (kernel, efk, cutoffs, hyps, hyps_mask) def get_l_bound(curr_l_bound, structure, two_d=False): @@ -90,15 +121,21 @@ def get_bonds(ctype, etypes, bond_array): bond_dirs = [] for i in range(len(bond_array)): bond = bond_array[i] - spc = sorted([ctype, etypes[i]]) + if ctype <= etypes[i]: + spc = [ctype, etypes[i]] + b_dir = bond[1:] + else: + spc = [etypes[i], ctype] + b_dir = bond[1:] + if spc in exist_species: ind = exist_species.index(spc) bond_lengths[ind].append([bond[0]]) - bond_dirs[ind].append(bond[1:]) + bond_dirs[ind].append(b_dir) else: exist_species.append(spc) bond_lengths.append([[bond[0]]]) - bond_dirs.append([bond[1:]]) + bond_dirs.append([b_dir]) return exist_species, bond_lengths, bond_dirs @@ -168,6 +205,56 @@ def get_triplets(ctype, etypes, bond_array, cross_bond_inds, return exist_species, tris, tri_dir +@njit +def get_triplets_en(ctype, etypes, bond_array, cross_bond_inds, + cross_bond_dists, triplets): + exist_species = [] + tris = [] + tri_dir = [] + + for m in range(bond_array.shape[0]): + r1 = bond_array[m, 0] + c1 = bond_array[m, 1:] + spc1 = etypes[m] + + for n in range(triplets[m]): + ind1 = cross_bond_inds[m, m+n+1] + r2 = bond_array[ind1, 0] + c2 = bond_array[ind1, 1:] + c12 = np.sum(c1*c2) + r12 = np.sqrt(r1**2 + r2**2 - 2*r1*r2*c12) + + spc2 = etypes[ind1] + +# if spc1 == spc2: +# spcs_list = [[ctype, spc1, spc2], [ctype, spc1, spc2]] +# elif ctype == spc1: # spc1 != spc2 +# spcs_list = [[ctype, spc1, spc2], [spc2, ctype, spc1]] +# elif ctype == spc2: # spc1 != spc2 +# spcs_list = [[spc1, spc2, ctype], [spc2, ctype, spc1]] +# else: # all different +# spcs_list = [[ctype, spc1, spc2], [ctype, spc2, spc1]] + + if spc1 <= spc2: + spcs = [ctype, spc1, spc2] + triplet = array([r1, r2, r12]) + coord = [c1, c2] + else: + spcs = [ctype, spc2, spc1] + triplet = array([r2, r1, r12]) + coord = [c2, c1] + + if spcs not in exist_species: + exist_species.append(spcs) + tris.append([triplet]) + tri_dir.append([coord]) + else: + k = exist_species.index(spcs) + tris[k].append(triplet) + tri_dir[k].append(coord) + + return exist_species, tris, tri_dir + @njit def self_two_body_mc_jit(bond_array, c, etypes, diff --git a/flare/otf.py b/flare/otf.py index 38e2f12fb..683da075f 100644 --- a/flare/otf.py +++ b/flare/otf.py @@ -80,7 +80,11 @@ class OTF: 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:'. + write_model (int, optional): If 0, write never. If 1, write at + end of run. If 2, write after each training and end of run. + If 3, write after each time atoms are added and end of run. """ + def __init__(self, dft_input: str, dt: float, number_of_steps: int, gp: gp.GaussianProcess, dft_loc: str, std_tolerance_factor: float = 1, @@ -89,11 +93,14 @@ def __init__(self, dft_input: str, dt: float, number_of_steps: int, 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] = [], - force_source: Union[str, object] = "qe", n_cpus: int = 1, - npool: int = None, mpi: str = "srun", dft_kwargs=None, - store_dft_output: Tuple[Union[str, List[str]], str] = None): + force_source: str = "qe", + n_cpus: int = 1, npool: int = None, mpi: str = "srun", + dft_kwargs=None, dft_output='dft.out', + store_dft_output: Tuple[Union[str, List[str]],str] = None, + write_model: int = 0): self.dft_input = dft_input + self.dft_output = dft_output self.dt = dt self.number_of_steps = number_of_steps self.gp = gp @@ -154,7 +161,7 @@ def __init__(self, dft_input: str, dt: float, number_of_steps: int, self.rescale_temps = rescale_temps self.output = Output(output_name, always_flush=True) - + self.output_name = output_name # set number of cpus and npool for DFT runs self.n_cpus = n_cpus self.npool = npool @@ -162,6 +169,7 @@ def __init__(self, dft_input: str, dt: float, number_of_steps: int, self.dft_kwargs = dft_kwargs self.store_dft_output = store_dft_output + self.write_model = write_model def run(self): """ @@ -173,7 +181,7 @@ def run(self): """ self.output.write_header(self.gp.cutoffs, self.gp.kernel_name, - self.gp.hyps, self.gp.algo, + self.gp.hyps, self.gp.opt_algorithm, self.dt, self.number_of_steps, self.structure, self.std_tolerance) @@ -195,6 +203,8 @@ def run(self): self.update_gp(self.init_atoms, dft_frcs) if (self.dft_count-1) < self.freeze_hyps: self.train_gp() + if self.write_model >= 2: + self.gp.write_model(self.output_name+"_model") # after step 1, try predicting with GP model else: @@ -236,8 +246,13 @@ def run(self): # 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() + if self.write_model == 2: + self.gp.write_model(self.output_name+"_model") + if self.write_model == 3: + self.gp.write_model(self.output_name+'_model') # Store DFT outputs in another folder if desired # specified in self.store_dft_output @@ -265,6 +280,9 @@ def run(self): self.output.conclude_run() + if self.write_model >= 1: + self.gp.write_model(self.output_name+"_model") + def run_dft(self): """Calculates DFT forces on atoms in the current structure. @@ -280,6 +298,7 @@ def run_dft(self): forces = self.dft_module.run_dft_par(self.dft_input, self.structure, self.dft_loc, n_cpus=self.n_cpus, + dft_out=self.dft_output, npool=self.npool, mpi=self.mpi, dft_kwargs=self.dft_kwargs) @@ -303,8 +322,8 @@ def run_dft(self): to_copy = [target_files] else: to_copy = target_files - for file in to_copy: - copyfile(file, dest+'/'+dt_string+file) + for ofile in to_copy: + copyfile(ofile, dest+'/'+dt_string+ofile) def update_gp(self, train_atoms: List[int], dft_frcs: 'ndarray'): """ @@ -333,7 +352,8 @@ 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) + self.gp.likelihood, self.gp.likelihood_gradient, + hyps_mask=self.gp.hyps_mask) def update_positions(self, new_pos: 'ndarray'): """Performs a Verlet update of the atomic positions. diff --git a/flare/otf_parser.py b/flare/otf_parser.py index 44c9d816c..529ded91e 100644 --- a/flare/otf_parser.py +++ b/flare/otf_parser.py @@ -1,7 +1,7 @@ import sys import numpy as np from typing import List, Tuple -from flare import gp, env, struc, kernels, otf +from flare import gp, env, struc, otf class OtfAnalysis: @@ -42,19 +42,17 @@ def __init__(self, filename, calculate_energy=False): self.gp_species_list = gp_species_list self.gp_atom_count = gp_atom_count - def make_gp(self, cell=None, kernel=None, kernel_grad=None, algo=None, + def make_gp(self, cell=None, kernel_name=None, algo=None, call_no=None, cutoffs=None, hyps=None, init_gp=None, - energy_force_kernel=None, hyp_no=None, par=True): + hyp_no=None, par=True, kernel=None): if init_gp is None: # Use run's values as extracted from header # TODO Allow for kernel gradient in header if cell is None: cell = self.header['cell'] - if kernel is None: - kernel = self.header['kernel'] - if kernel_grad is None: - raise Exception('Kernel gradient not supplied') + if kernel_name is None: + kernel_name = self.header['kernel_name'] if algo is None: algo = self.header['algo'] if cutoffs is None: @@ -68,10 +66,14 @@ def make_gp(self, cell=None, kernel=None, kernel_grad=None, algo=None, else: gp_hyps = hyps + if (kernel is not None) and (kernel_name is None): + DeprecationWarning("kernel replaced with kernel_name") + kernel_name = kernel.__name__ + gp_model = \ - gp.GaussianProcess(kernel, kernel_grad, gp_hyps, - cutoffs, opt_algorithm=algo, - energy_force_kernel=energy_force_kernel, + gp.GaussianProcess(kernel_name=kernel_name, + hyps=gp_hyps, + cutoffs=cutoffs, opt_algorithm=algo, par=par) else: gp_model = init_gp @@ -359,8 +361,10 @@ def parse_header_information(outfile: str = 'otf_run.out') -> dict: header_info['cutoffs'] = cutoffs if 'frames' in line: header_info['frames'] = int(line.split(':')[1]) + if 'kernel_name' in line: + header_info['kernel_name'] = line.split(':')[1].strip() if 'kernel' in line: - header_info['kernel'] = line.split(':')[1].strip() + header_info['kernel_name'] = line.split(':')[1].strip() if 'number of hyperparameters:' in line: header_info['n_hyps'] = int(line.split(':')[1]) if 'optimization algorithm' in line: diff --git a/flare/output.py b/flare/output.py index c97635754..0866a2567 100644 --- a/flare/output.py +++ b/flare/output.py @@ -130,9 +130,9 @@ def write_header(self, cutoffs, kernel_name: str, headerstring += \ f'number of cpu cores: {multiprocessing.cpu_count()}\n' headerstring += f'cutoffs: {cutoffs}\n' - headerstring += f'kernel: {kernel_name}\n' + headerstring += f'kernel_name: {kernel_name}\n' headerstring += f'number of hyperparameters: {len(hyps)}\n' - headerstring += f'hyperparameters: {hyps}\n' + headerstring += f'hyperparameters: {str(hyps)}\n' headerstring += f'hyperparameter optimization algorithm: {algo}\n' headerstring += std_string headerstring += f'timestep (ps): {dt}\n' @@ -140,7 +140,7 @@ def write_header(self, cutoffs, kernel_name: str, headerstring += f'number of atoms: {structure.nat}\n' headerstring += f'system species: {set(structure.species_labels)}\n' headerstring += 'periodic cell: \n' - headerstring += str(structure.cell) + headerstring += str(structure.cell)+'\n' if optional: for key, value in optional.items(): @@ -314,7 +314,7 @@ def write_xyz_config(self, curr_step, structure, dft_step, forces=forces, stds=stds, forces_2=forces_2) def write_hyps(self, hyp_labels, hyps, start_time, like, like_grad, - name='log'): + name='log', hyps_mask=None): """ write hyperparameters to logfile :param name: @@ -329,6 +329,12 @@ def write_hyps(self, hyp_labels, hyps, start_time, like, like_grad, f = self.outfiles[name] f.write('\nGP hyperparameters: \n') + if hyps_mask is not None: + if 'map' in hyps_mask: + hyps = hyps_mask['original'] + if len(hyp_labels)!=len(hyps): + hyp_labels = None + if hyp_labels is not None: for i, label in enumerate(hyp_labels): f.write(f'Hyp{i} : {label} = {hyps[i]:.4f}\n') @@ -347,7 +353,8 @@ def write_hyps(self, hyp_labels, hyps, start_time, like, like_grad, def write_gp_dft_comparison(self, curr_step, frame, start_time, dft_forces, - error, local_energies=None, KE=None): + error, local_energies=None, KE=None, + mgp= False): """ write the comparison to logfile :param curr_step: current timestep @@ -368,6 +375,8 @@ def write_gp_dft_comparison(self, curr_step, frame, # Construct Header line string += '\nEl Position (A) \t\t\t\t ' + if mgp: + string += 'M' string += 'GP Force (ev/A) \t\t\t\t' string += 'Std. Dev (ev/A) \t\t\t\t' string += 'DFT Force (ev/A) \t\t\t\t \n' @@ -394,31 +403,34 @@ def write_gp_dft_comparison(self, curr_step, frame, # stds=frame.stds, forces_2=dft_forces, # dft_step=True) - mae = np.mean(error) * 1000 + mae = np.nanmean(error) * 1000 mac = np.mean(np.abs(dft_forces)) * 1000 string += f'mean absolute error: {mae:.2f} meV/A\n' string += f'mean absolute dft component: {mac:.2f} meV/A\n' stat = f'{curr_step} {mae:.2} {mac:.2}' - mae_ps = {} - count_ps = {} + mae_per_species = {} + count_per_species = {} species = [Z_to_element(Z) for Z in set(frame.coded_species)] for ele in species: - mae_ps[ele] = 0 - count_ps[ele] = 0 + mae_per_species[ele] = 0 + count_per_species[ele] = 0 + for atom in range(frame.nat): Z = frame.coded_species[atom] ele = Z_to_element(Z) - mae_ps[ele] += np.sum(error[atom, :]) - count_ps[ele] += 1 + if np.isnan(np.sum(error[atom, :])): + continue + mae_per_species[ele] += np.sum(error[atom, :]) + count_per_species[ele] += 1 string += "mae per species\n" for ele in species: - if count_ps[ele] > 0: - mae_ps[ele] /= (count_ps[ele] * 3) - mae_ps[ele] *= 1000 # Put in meV/A - string += f"type {ele} mae: {mae_ps[ele]:.2f} meV/A\n" - stat += f' {mae_ps[ele]:.2f}' + if count_per_species[ele] > 0: + mae_per_species[ele] /= (count_per_species[ele] * 3) + mae_per_species[ele] *= 1000 # Put in meV/A + string += f"type {ele} mae: {mae_per_species[ele]:.2f} meV/A\n" + stat += f' {mae_per_species[ele]:.2f}' # calculate potential and total energy if local_energies is not None: diff --git a/flare/predict.py b/flare/predict.py index 341ce5e3b..1d7048583 100644 --- a/flare/predict.py +++ b/flare/predict.py @@ -6,7 +6,7 @@ import numpy as np import multiprocessing as mp -from typing import Tuple +from typing import Tuple, List from flare.env import AtomicEnvironment from flare.gp import GaussianProcess from flare.struc import Structure @@ -73,8 +73,10 @@ def predict_on_atom_en(param: Tuple[Structure, int, GaussianProcess]) -> ( def predict_on_structure(structure: Structure, gp: GaussianProcess, - n_cpus: int=None) -> ( - 'np.ndarray', 'np.ndarray'): + n_cpus: int=None, write_to_structure: bool = True, + selective_atoms: List[int] = None, + skipped_atom_value = 0) \ + -> ('np.ndarray', 'np.ndarray'): """ Return the forces/std. dev. uncertainty associated with each individual atom in a structure. Forces are stored directly to the @@ -82,28 +84,56 @@ def predict_on_structure(structure: Structure, gp: GaussianProcess, :param structure: FLARE structure to obtain forces for, with N atoms :param gp: Gaussian Process model + :param write_to_structure: Write results to structure's forces, + std attributes + :param selective_atoms: Only predict on these atoms; e.g. [0,1,2] will + only predict and return for those atoms + :param skipped_atom_value: What value to use for atoms that are skipped. + Defaults to 0 but other options could be e.g. NaN. Will NOT + write this to the structure if write_to_structure is True. :return: N x 3 numpy array of foces, Nx3 numpy array of uncertainties :rtype: (np.ndarray, np.ndarray) """ # Loop through individual atoms, cast to atomic environments, # make predictions + forces = np.zeros(shape=(structure.nat, 3)) + stds = np.zeros(shape=(structure.nat, 3)) + + if selective_atoms: + forces.fill(skipped_atom_value) + stds.fill(skipped_atom_value) + else: + selective_atoms = [] + for n in range(structure.nat): + + # Skip the atoms which we aren't predicting on if + # selective atoms is on + if n not in selective_atoms and selective_atoms: + continue + chemenv = AtomicEnvironment(structure, n, gp.cutoffs) + for i in range(3): force, var = gp.predict(chemenv, i + 1) - structure.forces[n][i] = float(force) - structure.stds[n][i] = np.sqrt(np.abs(var)) + forces[n][i] = float(force) + stds[n][i] = float(np.sqrt(np.absolute(var))) - forces = np.array(structure.forces) - stds = np.array(structure.stds) + if write_to_structure: + structure.forces[n][i] = force + structure.stds[n][i] = np.sqrt(np.abs(var)) return forces, stds def predict_on_structure_par(structure: Structure, gp: GaussianProcess, - n_cpus: int = None) -> ( + n_cpus: int = None, + write_to_structure: bool = True, + selective_atoms: List[int] = None, + skipped_atom_value = 0 + ) -> ( 'np.ndarray', 'np.ndarray'): """ Return the forces/std. dev. uncertainty associated with each @@ -113,15 +143,36 @@ def predict_on_structure_par(structure: Structure, :param structure: FLARE structure to obtain forces for, with N atoms :param gp: Gaussian Process model :param n_cpus: Number of cores to parallelize over + :param write_to_structure: Write results to structure's forces, + std attributes + :param selective_atoms: Only predict on these atoms; e.g. [0,1,2] will + only predict and return for those atoms + :param skipped_atom_value: What value to use for atoms that are skipped. + Defaults to 0 but other options could be e.g. NaN. Will NOT + write this to the structure if write_to_structure is True. :return: N x 3 array of forces, N x 3 array of uncertainties :rtype: (np.ndarray, np.ndarray) """ # Just work in serial in the number of cpus is 1 if n_cpus is 1: - return predict_on_structure(structure, gp) + return predict_on_structure(structure=structure, + gp = gp, + n_cpus=n_cpus, + write_to_structure = write_to_structure, + selective_atoms =selective_atoms, + skipped_atom_value = skipped_atom_value) + + forces = np.zeros(shape=(structure.nat, 3)) + stds = np.zeros(shape=(structure.nat, 3)) + + if selective_atoms: + forces.fill(skipped_atom_value) + stds.fill(skipped_atom_value) + else: + selective_atoms = [] # Automatically detect number of cpus available - if (n_cpus is None): + if n_cpus is None: pool = mp.Pool(processes=mp.cpu_count()) else: pool = mp.Pool(processes=n_cpus) @@ -129,23 +180,35 @@ def predict_on_structure_par(structure: Structure, # Parallelize over atoms in structure results = [] for atom in range(structure.nat): + # If selective atoms is on, skip ones that was skipped + if atom not in selective_atoms and selective_atoms: + # Keep length of results equal to nat + results.append(None) + continue results.append(pool.apply_async(predict_on_atom, args=[(structure, atom, gp)])) pool.close() pool.join() + for i in range(structure.nat): + if i not in selective_atoms: + continue r = results[i].get() - structure.forces[i] = r[0] - structure.stds[i] = r[1] + forces[i] = r[0] + stds[i] = r[1] + if write_to_structure: + structure.forces[i] = r[0] + structure.stds[i] = r[1] - forces = np.array(structure.forces) - stds = np.array(structure.stds) return forces, stds def predict_on_structure_en(structure: Structure, gp: GaussianProcess, - n_cpus: int = None) -> ( + n_cpus: int = None, + write_to_structure: bool=True, + selective_atoms: List[int] = None, + skipped_atom_value = 0 ) -> ( 'np.ndarray', 'np.ndarray', 'np.ndarray'): """ Return the forces/std. dev. uncertainty / local energy associated with each @@ -162,24 +225,43 @@ def predict_on_structure_en(structure: Structure, gp: GaussianProcess, """ # Set up local energy array local_energies = np.zeros(structure.nat) + forces = np.zeros(shape=(structure.nat, 3)) + stds = np.zeros(shape=(structure.nat, 3)) + + if selective_atoms: + forces.fill(skipped_atom_value) + stds.fill(skipped_atom_value) + local_energies.fill(skipped_atom_value) + else: + selective_atoms = [] # Loop through atoms in structure and predict forces, uncertainties, # and energies for n in range(structure.nat): + + if selective_atoms and n not in selective_atoms: + continue + chemenv = AtomicEnvironment(structure, n, gp.cutoffs) for i in range(3): force, var = gp.predict(chemenv, i + 1) + forces[n][i] = float(force) + stds[n][i] = np.sqrt(np.abs(var)) + + if write_to_structure: structure.forces[n][i] = float(force) structure.stds[n][i] = np.sqrt(np.abs(var)) + local_energies[n] = gp.predict_local_energy(chemenv) - forces = np.array(structure.forces) - stds = np.array(structure.stds) return forces, stds, local_energies def predict_on_structure_par_en(structure: Structure, gp: GaussianProcess, - n_cpus: int = None) -> ( + n_cpus: int = None, + write_to_structure: bool = True, + selective_atoms: List[int] = None, + skipped_atom_value = 0) -> ( 'np.ndarray', 'np.ndarray', 'np.ndarray'): """ Return the forces/std. dev. uncertainty / local energy associated with each @@ -193,11 +275,24 @@ def predict_on_structure_par_en(structure: Structure, gp: GaussianProcess, N-length array of energies :rtype: (np.ndarray, np.ndarray, np.ndarray) """ - # Work in serial if the number of cpus is 1 - if n_cpus is 1: - predict_on_structure_en(structure, gp) local_energies = np.zeros(structure.nat) + forces = np.zeros(shape=(structure.nat, 3)) + stds = np.zeros(shape=(structure.nat, 3)) + + if selective_atoms: + forces.fill(skipped_atom_value) + stds.fill(skipped_atom_value) + local_energies.fill(skipped_atom_value) + else: + selective_atoms = [] + + # Work in serial if the number of cpus is 1 + if n_cpus is 1: + return predict_on_structure_en(structure, gp, + write_to_structure=write_to_structure, + selective_atoms=selective_atoms, + skipped_atom_value=skipped_atom_value) if n_cpus is None: pool = mp.Pool(processes=mp.cpu_count()) @@ -207,6 +302,11 @@ def predict_on_structure_par_en(structure: Structure, gp: GaussianProcess, results = [] # Parallelize over atoms in structure for atom_i in range(structure.nat): + + if atom_i not in selective_atoms and selective_atoms: + results.append(None) + continue + results.append(pool.apply_async(predict_on_atom_en, args=[(structure, atom_i, gp)])) pool.close() @@ -214,11 +314,17 @@ def predict_on_structure_par_en(structure: Structure, gp: GaussianProcess, # Compile results for i in range(structure.nat): + + if i not in selective_atoms and selective_atoms: + continue + r = results[i].get() - structure.forces[i] = r[0] - structure.stds[i] = r[1] + forces[i][:] =r[0] + stds[i][:] = r[1] local_energies[i] = r[2] - forces = np.array(structure.forces) - stds = np.array(structure.stds) + if write_to_structure: + structure.forces[i] = forces[i] + structure.stds[i] = stds[i] + return forces, stds, local_energies diff --git a/flare/struc.py b/flare/struc.py index b60183430..a57ceb943 100644 --- a/flare/struc.py +++ b/flare/struc.py @@ -8,15 +8,15 @@ used to train ML models. """ import numpy as np -from flare.util import element_to_Z, NumpyEncoder -from json import dumps +from flare.util import element_to_Z, Z_to_element, NumpyEncoder +from json import dumps, loads from typing import List, Union, Any try: # Used for to_pmg_structure method import pymatgen.core.structure as pmgstruc - + import pymatgen.io.vasp.inputs as pmgvaspio _pmg_present = True except ImportError: _pmg_present = False @@ -107,6 +107,15 @@ def __init__(self, cell: 'ndarray', species: Union[List[str], self.mass_dict = mass_dict + # Convert from elements to atomic numbers in mass dict + if mass_dict is not None: + keys = list(mass_dict.keys()) + for elt in keys: + if isinstance(elt, str): + mass_dict[element_to_Z(elt)] = mass_dict[elt] + if elt.isnumeric(): + mass_dict[int(elt)] = mass_dict[elt] + def get_cell_dot(self): """ Compute 3x3 array of dot products of cell vectors used to @@ -280,7 +289,8 @@ def to_ase_atoms(self)-> 'ase.Atoms': from ase import Atoms return Atoms(self.species_labels, positions=self.positions, - cell=self.cell) + cell=self.cell, + pbc=True) def to_pmg_structure(self): @@ -333,6 +343,114 @@ def from_pmg_structure(structure: 'pymatgen Structure')-> \ return new_struc + def to_xyz(self, extended_xyz: bool = True, + print_stds: bool = False, + print_forces : bool = False, + print_max_stds: bool = False, + write_file: str = '')->str: + """ + Convenience function which turns a structure into an extended .xyz + file; useful for further input into visualization programs like VESTA + or Ovito. Can be saved to an output file via write_file. + + :param print_stds: Print the stds associated with the structure. + :param print_forces: + :param extended_xyz: + :param print_max_stds: + :param write_file: + :return: + """ + species_list = [Z_to_element(x) for x in self.coded_species] + xyz_str = '' + xyz_str += f'{len(self.coded_species)} \n' + + # Add header line with info about lattice and properties if extended + # xyz option is called. + if extended_xyz: + cell = self.cell + + xyz_str += f'Lattice="{cell[0,0]} {cell[0,1]} {cell[0,2]}' + xyz_str += f' {cell[1,0]} {cell[1,1]} {cell[1,2]}' + xyz_str += f' {cell[2,0]} {cell[2,1]} {cell[2,2]}"' + xyz_str += f' Proprties="species:S:1:pos:R:3' + + if print_stds: + xyz_str += ':stds:R:3' + stds = self.stds + if print_forces: + xyz_str += ':forces:R:3' + forces = self.forces + if print_max_stds: + xyz_str += ':max_std:R:1' + xyz_str += '\n' + else: + xyz_str += '\n' + + for i, pos in enumerate(self.positions): + # Write positions + xyz_str += f"{species_list[i]} {pos[0]} {pos[1]} {pos[2]}" + + # If extended XYZ: Add in extra information + if print_stds and extended_xyz: + xyz_str += f" {stds[i,0]} {stds[i,1]} {stds[i,2]}" + if print_forces and extended_xyz: + xyz_str += f" {forces[i,0]} {forces[i,1]} {forces[i,2]}" + if print_max_stds and extended_xyz: + xyz_str += f" {np.max(stds[i,:])} " + xyz_str += '\n' + + # Write to file, optionally + if write_file: + with open(write_file, 'w') as f: + f.write(xyz_str) + + return xyz_str + + @staticmethod + def from_file(file_name, format='') -> Union['flare.struc.Structure', + List['flare.struc.Structure']]: + """ + Load a FLARE structure from a file or a series of FLARE structures + :param file_name: + :param format: + :return: + """ + + try: + with open(file_name, 'r') as _: + pass + except: + raise FileNotFoundError + + if 'xyz' in file_name or 'xyz' in format.lower(): + raise NotImplementedError + + if 'json' in format.lower() or '.json' in file_name: + # Assumed format is one FLARE structure per line, + # or one line with many FLARE structures + with open(file_name, 'r') as f: + thelines = f.readlines() + + non_empty_lines = [loads(line) for line in thelines if + len(line) > 2] + + structures = [Structure.from_dict(struc_dict) for struc_dict in + non_empty_lines] + + if len(structures) == 1: + return structures[0] + else: + return structures + + is_poscar = 'POSCAR' in file_name or 'CONTCAR' in file_name or 'vasp' in format.lower() + if is_poscar and _pmg_present: + pmg_structure = pmgvaspio.Poscar.from_file(file_name).structure + return Structure.from_pmg_structure(pmg_structure) + elif is_poscar and not _pmg_present: + raise ImportError("Pymatgen not imported; " \ + "functionality requires pymatgen.") + + def get_unique_species(species: List[Any])-> (List, List[int]): """ @@ -353,3 +471,5 @@ def get_unique_species(species: List[Any])-> (List, List[int]): coded_species = np.array(coded_species) return unique_species, coded_species + + diff --git a/flare/util.py b/flare/util.py index 96aaec224..f43b277e7 100644 --- a/flare/util.py +++ b/flare/util.py @@ -430,8 +430,8 @@ def is_std_in_bound_per_species(rel_std_tolerance: float, # Determine if any std component will trigger the threshold # before looking through individual species. - max_std_components = [np.max(std) for std in structure.stds] - if max(max_std_components) < threshold: + max_std_components = [np.nanmax(std) for std in structure.stds] + if np.nanmax(max_std_components) < threshold: return True, [-1] target_atoms = [] @@ -447,9 +447,13 @@ def is_std_in_bound_per_species(rel_std_tolerance: float, # If max atoms added reached or stds of atoms considered are now below # threshold, conclude if len(target_atoms) == max_atoms_added or \ - max_std_components[i] < threshold: + (max_std_components[i] < threshold and max_std_components[i] + != np.nan): break + if np.isnan(max_std_components[i]): + continue + # Only add up to species allowance, if it exists cur_spec = structure.species_labels[i] if present_species[cur_spec] < \ @@ -511,7 +515,7 @@ def is_force_in_bound_per_species(abs_force_tolerance: float, # Determine if any force component will trigger the threshold max_error_components = np.amax(errors, axis=1) - if np.max(max_error_components) < abs_force_tolerance: + if np.nanmax(max_error_components) < abs_force_tolerance: return True, [-1] target_atoms = [] @@ -527,7 +531,8 @@ def is_force_in_bound_per_species(abs_force_tolerance: float, # If max atoms added reached or force errors are now below threshold, # conclude if len(target_atoms) == max_atoms_added or \ - max_error_components[i] < abs_force_tolerance: + (max_error_components[i] < abs_force_tolerance and + max_error_components[i] != np.nan): break cur_spec = structure.species_labels[i] @@ -545,3 +550,46 @@ def is_force_in_bound_per_species(abs_force_tolerance: float, return False, target_atoms else: return True, [-1] + + +def subset_of_frame_by_element(frame: 'flare.Structure', + predict_atoms_per_element: dict)->List[int]: + """ + Given a structure and a dictionary formatted as {"Symbol":int, + ..} describing a number of atoms per element, return a sorted list of + indices corresponding to a random subset of atoms by species + :param frame: + :param predict_atoms_by_species: + :return: + """ + + # Null case: No dictionary or empty dict passed in; just return all indices + if not predict_atoms_per_element: + return list(range(len(frame))) + + # Keep track of atoms which were considered (dictionary may only cover a + # subset of species of the whole frame) + all_atoms = set(range(len(frame))) + return_atoms = [] + considered_atoms = set([]) + + species = frame.species_labels + + # Main loop: Obtain the number of relevant atoms for each element + for elt, n in predict_atoms_per_element.items(): + + matching_atoms = [i for i in all_atoms if species[i] == elt] + considered_atoms.update(matching_atoms) + + if len(matching_atoms) == 0: + continue + # Choose the atoms to add + to_add_atoms = np.random.choice(matching_atoms,replace=False, + size=min(n, len(matching_atoms))) + return_atoms += list(to_add_atoms) + + return_atoms += list(all_atoms - considered_atoms) + + return_atoms.sort() + + return return_atoms \ No newline at end of file diff --git a/tests/fake_gp.py b/tests/fake_gp.py new file mode 100644 index 000000000..2816b296a --- /dev/null +++ b/tests/fake_gp.py @@ -0,0 +1,377 @@ +from copy import deepcopy +import pytest +import numpy as np +from numpy.random import random, randint, permutation + +from flare import env, struc, gp +from flare.gp import GaussianProcess +from flare.env import AtomicEnvironment +from flare.struc import Structure +from flare.otf_parser import OtfAnalysis + + +def get_random_structure(cell, unique_species, noa): + """Create a random test structure """ + np.random.seed(0) + + positions = [] + forces = [] + species = [] + + for n in range(noa): + positions.append(np.random.uniform(-1, 1, 3)) + forces.append(np.random.uniform(-1, 1, 3)) + species.append(unique_species[np.random.randint(0, + len(unique_species))]) + + test_structure = Structure(cell, species, positions) + + return test_structure, forces + + +def generate_hm(nbond, ntriplet, cutoffs=[1, 1], constraint=False, multihyps=True): + + if (multihyps is False): + hyps_label = [] + if (nbond > 0): + nbond = 1 + hyps_label += ['Length', 'Signal Var.'] + if (ntriplet > 0): + ntriplet = 1 + hyps_label += ['Length', 'Signal Var.'] + hyps_label += ['Noise Var.'] + return random((nbond+ntriplet)*2+1), {'hyps_label': hyps_label}, deepcopy(cutoffs) + + specs_mask = np.zeros(118, dtype=int) + specs_mask[1] = 0 + specs_mask[2] = 1 + nspecs = 2 + + specs_mask = np.zeros(118, dtype=int) + specs_mask[1] = 0 + specs_mask[2] = 1 + nspecs = 2 + + cut = [] + cut += [cutoffs[0]] + cut += [cutoffs[1]] + + if (nbond > 1): + sig1 = random(nbond) + ls1 = random(nbond) + bond_mask = np.ones(nspecs**2, dtype=int) + bond_mask[0] = 0 + bond_name = ["sig2"]*nbond+["ls2"]*nbond + else: + sig1 = [random()] + ls1 = [random()] + bond_mask = np.zeros(nspecs**2, dtype=int) + bond_name = ["sig2"]+["ls2"] + + if (ntriplet > 1): + sig2 = random(ntriplet) + ls2 = random(ntriplet) + triplet_mask = np.ones(nspecs**3, dtype=int) + triplet_mask[0] = 0 + triplet_name = ["sig3"]*ntriplet+["ls3"]*ntriplet + else: + sig2 = [random()] + ls2 = [random()] + triplet_mask = np.zeros(nspecs**3, dtype=int) + triplet_name = ["sig3"]+["ls3"] + + sigman = [0.05] + + if (nbond > 0 and ntriplet > 0): + hyps = np.hstack([sig1, ls1, sig2, ls2, sigman]) + hyps_label = np.hstack([bond_name, triplet_name, ['noise']]) + elif (nbond > 0): + hyps = np.hstack([sig1, ls1, sigman]) + hyps_label = np.hstack([bond_name, ['noise']]) + else: + hyps = np.hstack([sig2, ls2, sigman]) + hyps_label = np.hstack([triplet_name, ['noise']]) + + hyps_mask = {'nspec': nspecs, + 'spec_mask': specs_mask, + 'nbond': nbond, + 'bond_mask': bond_mask, + 'ntriplet': ntriplet, + 'triplet_mask': triplet_mask, + 'hyps_label': hyps_label, + 'train_noise': True} + if (constraint is False): + return hyps, hyps_mask, cut + + hyps_mask['map'] = [] + hyps_mask['original'] = hyps + hm = hyps_mask['map'] + count = 0 + newhyps = [] + newlabel = [] + if (nbond > 1): + # fix type 0, and only compute type 1 of bonds + hm += [1] + newhyps += [hyps[1]] + newlabel += [hyps_label[1]] + hm += [3] + newhyps += [hyps[3]] + newlabel += [hyps_label[3]] + count += 4 + elif (nbond > 0): + # fix sigma, and only vary ls + hm += [1] + newhyps += [hyps[1]] + newlabel += [hyps_label[1]] + count += 2 + if (ntriplet > 1): + # fix type 0, and only compute type 1 of triplets + hm += [1+count] + newhyps += [hyps[1+count]] + newlabel += [hyps_label[1+count]] + hm += [3+count] + newhyps += [hyps[3+count]] + newlabel += [hyps_label[3+count]] + elif (ntriplet > 0): + # fix sigma, and only vary ls + hm += [1+count] + newhyps += [hyps[1+count]] + newlabel += [hyps_label[1+count]] + hm += [len(hyps)-1] + newhyps += [hyps[-1]] + newlabel += ['noise'] + hyps = np.hstack(newhyps) + hyps_mask['hyps_label'] = np.hstack(hyps_label) + + return hyps, hyps_mask, cut + + +def get_gp(bodies, kernel_type='mc', multihyps=True) -> GaussianProcess: + """Returns a GP instance with a two-body numba-based kernel""" + print("\nSetting up...\n") + + # params + cell = np.diag(np.array([1, 1, 1.5])) + unique_species = [2, 1] + cutoffs = np.array([0.8, 0.8]) + noa = 5 + + nbond = 0 + ntriplet = 0 + prefix = bodies + if ('2' in bodies or 'two' in bodies): + nbond = 1 + if ('3' in bodies or 'three' in bodies): + ntriplet = 1 + + hyps, hm, _ = generate_hm(nbond, ntriplet, multihyps=multihyps) + + # create test structure + test_structure, forces = get_random_structure(cell, unique_species, + noa) + + hl = hm['hyps_label'] + if (multihyps is False): + hm = None + + # test update_db + gaussian = \ + GaussianProcess(kernel_name=f'{prefix}{kernel_type}', + hyps=hyps, + hyp_labels=hl, + cutoffs=cutoffs, multihyps=multihyps, hyps_mask=hm, + parallel=False, n_cpus=1) + gaussian.update_db(test_structure, forces) + gaussian.check_L_alpha() + + return gaussian + + +def get_params(): + parameters = {'unique_species': [2, 1], + 'cutoff': 0.8, + 'noa': 5, + 'cell': np.eye(3), + 'db_pts': 30} + return parameters + + +def get_tstp() -> AtomicEnvironment: + """Create test point for kernel to compare against""" + # params + cell = np.eye(3) + unique_species = [2, 1] + cutoffs = np.ones(3)*0.8 + noa = 10 + + test_structure_2, _ = get_random_structure(cell, unique_species, + noa) + + test_pt = AtomicEnvironment(test_structure_2, 0, + cutoffs) + return test_pt + + +def generate_mb_envs(cutoffs, cell, delt, d1, mask=None): + positions0 = np.array([[0., 0., 0.], + [0., 0.3, 0.], + [0.3, 0., 0.], + [0.0, 0., 0.3], + [1., 1., 0.]]) + positions0[1:] += 0.1*np.random.random([4, 3]) + triplet = [1, 1, 2, 1] + np.random.shuffle(triplet) + species_1 = np.hstack([triplet, randint(1, 2)]) + return generate_mb_envs_pos(positions0, species_1, cutoffs, cell, delt, d1, mask) + + +def generate_mb_twin_envs(cutoffs, cell, delt, d1, mask=None): + + positions0 = np.array([[0., 0., 0.], + [0., 0.3, 0.], + [0.3, 0., 0.], + [0.0, 0., 0.3], + [1., 1., 0.]]) + positions0[1:] += 0.1*np.random.random([4, 3]) + + species_1 = np.array([1, 2, 1, 1, 1], dtype=np.int) + env1 = generate_mb_envs_pos( + positions0, species_1, cutoffs, cell, delt, d1, mask) + + positions1 = positions0[[0, 2, 3, 4]] + species_2 = species_1[[0, 2, 3, 4]] + env2 = generate_mb_envs_pos( + positions1, species_2, cutoffs, cell, delt, d1, mask) + + return env1, env2 + + +def generate_mb_envs_pos(positions0, species_1, cutoffs, cell, delt, d1, mask=None): + + positions = [positions0] + + noa = len(positions0) + + positions_2 = deepcopy(positions0) + positions_2[0][d1-1] = delt + positions += [positions_2] + + positions_3 = deepcopy(positions[0]) + positions_3[0][d1-1] = -delt + positions += [positions_3] + + test_struc = [] + for i in range(3): + test_struc += [struc.Structure(cell, species_1, positions[i])] + + env_0 = [] + env_p = [] + env_m = [] + for i in range(noa): + env_0 += [env.AtomicEnvironment(test_struc[0], i, cutoffs)] + env_p += [env.AtomicEnvironment(test_struc[1], i, cutoffs)] + env_m += [env.AtomicEnvironment(test_struc[2], i, cutoffs)] + + return [env_0, env_p, env_m] +def generate_envs(cutoffs, delta): + """ + create environment with perturbation on + direction i + """ + # create env 1 + # perturb the x direction of atom 0 for +- delta + cell = np.eye(3)*np.max(cutoffs+0.1) + atom_1 = 0 + pos_1 = np.vstack([[0, 0, 0], random([3, 3])]) + pos_2 = deepcopy(pos_1) + pos_2[0][0] = delta + pos_3 = deepcopy(pos_1) + pos_3[0][0] = -delta + + species_1 = [1, 2, 1, 1] # , 1, 1, 2, 1, 2] + test_structure_1 = struc.Structure(cell, species_1, pos_1) + test_structure_2 = struc.Structure(cell, species_1, pos_2) + test_structure_3 = struc.Structure(cell, species_1, pos_3) + + env1_1 = env.AtomicEnvironment(test_structure_1, atom_1, cutoffs) + env1_2 = env.AtomicEnvironment(test_structure_2, atom_1, cutoffs) + env1_3 = env.AtomicEnvironment(test_structure_3, atom_1, cutoffs) + + + # create env 2 + # perturb the y direction + pos_1 = np.vstack([[0, 0, 0], random([3, 3])]) + pos_2 = deepcopy(pos_1) + pos_2[0][1] = delta + pos_3 = deepcopy(pos_1) + pos_3[0][1] = -delta + + atom_2 = 0 + species_2 = [1, 1, 2, 1] #, 2, 1, 2, 2, 2] + + test_structure_1 = struc.Structure(cell, species_2, pos_1) + test_structure_2 = struc.Structure(cell, species_2, pos_2) + test_structure_3 = struc.Structure(cell, species_2, pos_3) + + env2_1 = env.AtomicEnvironment(test_structure_1, atom_2, cutoffs) + env2_2 = env.AtomicEnvironment(test_structure_2, atom_2, cutoffs) + env2_3 = env.AtomicEnvironment(test_structure_3, atom_2, cutoffs) + + return env1_1, env1_2, env1_3, env2_1, env2_2, env2_3 + +def another_env(cutoffs, delt): + + cell = 10.0 * np.eye(3) + + # atomic structure 1 + pos_1 = np.vstack([[0, 0, 0], 0.1*random([3, 3])]) + pos_1[1, 1] += 1 + pos_1[2, 0] += 1 + pos_1[3, :2] += 1 + pos_2 = deepcopy(pos_1) + pos_2[0][0] = delt + pos_3 = deepcopy(pos_1) + pos_3[0][0] = -delt + + + species_1 = [1, 1, 1, 1] + + test_structure_1 = struc.Structure(cell, species_1, pos_1) + test_structure_2 = struc.Structure(cell, species_1, pos_2) + test_structure_3 = struc.Structure(cell, species_1, pos_3) + + # atom 0, original position + env1_1_0 = env.AtomicEnvironment(test_structure_1, 0, cutoffs) + # atom 0, 0 perturbe along x + env1_2_0 = env.AtomicEnvironment(test_structure_2, 0, cutoffs) + # atom 1, 0 perturbe along x + env1_2_1 = env.AtomicEnvironment(test_structure_2, 1, cutoffs) + # atom 2, 0 perturbe along x + env1_2_2 = env.AtomicEnvironment(test_structure_2, 2, cutoffs) + + # atom 0, 0 perturbe along -x + env1_3_0 = env.AtomicEnvironment(test_structure_3, 0, cutoffs) + # atom 1, 0 perturbe along -x + env1_3_1 = env.AtomicEnvironment(test_structure_3, 1, cutoffs) + # atom 2, 0 perturbe along -x + env1_3_2 = env.AtomicEnvironment(test_structure_3, 2, cutoffs) + + # create env 2 + pos_1 = np.vstack([[0, 0, 0], 0.1*random([3, 3])]) + pos_1[1, 1] += 1 + pos_1[2, 0] += 1 + pos_1[3, :2] += 1 + pos_2 = deepcopy(pos_1) + pos_2[0][0] = delt + pos_3 = deepcopy(pos_1) + pos_3[0][0] = -delt + + species_2 = [1, 2, 2, 1] + + test_structure_1 = struc.Structure(cell, species_2, pos_1) + + env2_1_0 = env.AtomicEnvironment(test_structure_1, 0, cutoffs) + + return env1_1_0, env1_2_0, env1_3_0, \ + env1_2_1, env1_3_1, env1_2_2, env1_3_2, env2_1_0 + diff --git a/tests/test_OTF_cp2k.py b/tests/test_OTF_cp2k.py deleted file mode 100644 index 4fcbbe2c6..000000000 --- a/tests/test_OTF_cp2k.py +++ /dev/null @@ -1,121 +0,0 @@ -import pytest -import os -import sys -from subprocess import call -import numpy as np -from flare.otf import OTF -from flare.gp import GaussianProcess -from flare.struc import Structure -import flare.kernels.kernels as en - -def cleanup(casename: str = None): - try: - os.remove('cp2k.in') - os.remove('cp2k-RESTART.wfn') - if (casename is not None): - for suffix in ["-f.xyz", "-hyps.dat", ".out", - "_par-f.xyz", "_par-hyps.dat", "_par.out", - "_par-stat.dat", "_par-std.xyz", "_par.xyz", - "-stat.dat", "-std.xyz", ".xyz"]: - os.remove(casename+suffix) - except: - pass - -# ------------------------------------------------------ -# test otf runs -# ------------------------------------------------------ -@pytest.mark.skipif(not os.environ.get('CP2K_COMMAND', - False), reason='CP2K_COMMAND not found ' - 'in environment: Please install CP2K ' - 'and set the CP2K_COMMAND env. ' - 'variable to point to cp2k.popt') -def test_otf_h2(): - """ - Test that an otf run can survive going for more steps - :return: - """ - call('cp ./test_files/cp2k_input_1.in ./cp2k.in'.split()) - - cp2k_input = './cp2k.in' - dt = 0.0001 - number_of_steps = 5 - cutoffs = np.array([5]) - dft_loc = os.environ.get('CP2K_COMMAND') - std_tolerance_factor = -0.1 - - # make gp model - kernel = en.two_body - kernel_grad = en.two_body_grad - hyps = np.array([1, 1, 1]) - hyp_labels = ['Signal Std', 'Length Scale', 'Noise Std'] - energy_force_kernel = en.two_body_force_en - - gp = \ - GaussianProcess(kernel=kernel, - kernel_grad=kernel_grad, - hyps=hyps, - cutoffs=cutoffs, - hyp_labels=hyp_labels, - energy_force_kernel=energy_force_kernel, - maxiter=50) - - otf = OTF(cp2k_input, dt, number_of_steps, gp, dft_loc, - std_tolerance_factor, init_atoms=[0], - calculate_energy=True, max_atoms_added=1, - force_source="cp2k", - output_name='h2_otf_cp2k') - - otf.run() - call('mkdir test_outputs'.split()) - call('mv h2_otf_cp2k*.* test_outputs'.split()) - cleanup("h2_otf_cp2k") - -@pytest.mark.skipif(not os.environ.get('CP2K_COMMAND', - False), reason='CP2K_COMMAND not found ' - 'in environment: Please install CP2K ' - ' and set the CP2K_COMMAND env. ' - 'variable to point to pw.x.') -def test_otf_al(): - """ - Test that an otf run can survive going for more steps - :return: - """ - call('cp ./test_files/cp2k_input_2.in ./cp2k.in'.split()) - - cp2k_input = './cp2k.in' - dt = 0.001 - number_of_steps = 5 - cutoffs = np.array([3.9, 3.9]) - dft_loc = os.environ.get('CP2K_COMMAND') - std_tolerance_factor = 1 - max_atoms_added = 2 - freeze_hyps = 3 - - # make gp model - kernel = en.three_body - kernel_grad = en.three_body_grad - hyps = np.array([0.1, 1, 0.01]) - hyp_labels = ['Signal Std', 'Length Scale', 'Noise Std'] - energy_force_kernel = en.three_body_force_en - - gp = \ - GaussianProcess(kernel=kernel, - kernel_grad=kernel_grad, - hyps=hyps, - cutoffs=cutoffs, - hyp_labels=hyp_labels, - energy_force_kernel=energy_force_kernel, - maxiter=50) - - otf = OTF(cp2k_input, dt, number_of_steps, gp, dft_loc, - std_tolerance_factor, init_atoms=[0], - calculate_energy=True, output_name='al_otf_cp2k', - freeze_hyps=freeze_hyps, skip=5, - force_source="cp2k", - max_atoms_added=max_atoms_added) - - otf.run() - call('mkdir test_outputs'.split()) - call('mv al_otf_cp2k*.* test_outputs'.split()) - - cleanup("al_otf_cp2k") diff --git a/tests/test_OTF_cp2k_par.py b/tests/test_OTF_cp2k_par.py deleted file mode 100644 index c27153a53..000000000 --- a/tests/test_OTF_cp2k_par.py +++ /dev/null @@ -1,78 +0,0 @@ -import pytest -from subprocess import call -import os -import sys -import numpy as np -from flare.otf import OTF -from flare.gp import GaussianProcess -from flare.struc import Structure -import flare.kernels.kernels as en - -def cleanup(target: list = None): - os.remove('cp2k.in') - os.remove('cp2k-RESTART.wfn') - if (target is not None): - for i in target: - os.remove(i) - -# ------------------------------------------------------ -# test otf runs -# ------------------------------------------------------ -try: - cp2k = os.environ.get('CP2K_COMMAND', False) - if ("popt" in cp2k): - par = True - else: - par = False -except: - par = False - -@pytest.mark.skipif(not os.environ.get('CP2K_COMMAND', False) or not par, - reason='parallel CP2K_COMMAND not found ' - 'in environment: Please install CP2K ' - 'and set the CP2K_COMMAND env. ' - 'variable to point to cp2k.popt') -def test_otf_h2_par(): - """ - Test that an otf run can survive going for more steps - :return: - """ - call('cp ./test_files/cp2k_input_1.in ./cp2k.in'.split()) - - cp2k_input = './cp2k.in' - dt = 0.0001 - number_of_steps = 5 - cutoffs = np.array([5]) - dft_loc = os.environ.get('CP2K_COMMAND') - std_tolerance_factor = -0.1 - - # make gp model - kernel = en.two_body - kernel_grad = en.two_body_grad - hyps = np.array([1, 1, 1]) - hyp_labels = ['Signal Std', 'Length Scale', 'Noise Std'] - energy_force_kernel = en.two_body_force_en - - gp = \ - GaussianProcess(kernel=kernel, - kernel_grad=kernel_grad, - hyps=hyps, - cutoffs=cutoffs, - hyp_labels=hyp_labels, - energy_force_kernel=energy_force_kernel, - par=True, - per_atom_par=False, - maxiter=50) - - otf = OTF(cp2k_input, dt, number_of_steps, gp, dft_loc, - std_tolerance_factor, init_atoms=[0], - calculate_energy=True, max_atoms_added=1, - force_source="cp2k", - n_cpus=2, - par=True, mpi="mpi", - output_name='h2_otf_cp2k_par') - - otf.run() - call('mkdir test_outputs'.split()) - call('mv h2_otf_cp2k_par* test_outputs'.split()) - cleanup() diff --git a/tests/test_OTF_qe.py b/tests/test_OTF_qe.py deleted file mode 100644 index 0177b2798..000000000 --- a/tests/test_OTF_qe.py +++ /dev/null @@ -1,123 +0,0 @@ -import pytest -import os -import sys -import numpy as np -from flare.otf import OTF -from flare.gp import GaussianProcess -from flare.struc import Structure -import flare.kernels.kernels as en - - -def cleanup_espresso_run(target: str = None): - os.system('rm pwscf.out') - os.system('rm pwscf.wfc') - os.system('rm -r pwscf.save') - os.system('rm pwscf.in') - os.system('rm pwscf.wfc1') - os.system('rm pwscf.wfc2') - if target: - os.system('rm ' + target) - - -# ------------------------------------------------------ -# test otf runs -# ------------------------------------------------------ -@pytest.mark.skipif(not os.environ.get('PWSCF_COMMAND', - False), reason='PWSCF_COMMAND not found ' - 'in environment: Please install Quantum ' - 'ESPRESSO and set the PWSCF_COMMAND env. ' - 'variable to point to pw.x.') -def test_otf_h2(): - """ - Test that an otf run can survive going for more steps - :return: - """ - os.system('cp ./test_files/qe_input_1.in ./pwscf.in') - - qe_input = './pwscf.in' - dt = 0.0001 - number_of_steps = 20 - cutoffs = np.array([5]) - dft_loc = os.environ.get('PWSCF_COMMAND') - std_tolerance_factor = -0.1 - - # make gp model - kernel = en.two_body - kernel_grad = en.two_body_grad - hyps = np.array([1, 1, 1]) - hyp_labels = ['Signal Std', 'Length Scale', 'Noise Std'] - energy_force_kernel = en.two_body_force_en - - gp = \ - GaussianProcess(kernel=kernel, - kernel_grad=kernel_grad, - hyps=hyps, - cutoffs=cutoffs, - hyp_labels=hyp_labels, - energy_force_kernel=energy_force_kernel, - maxiter=50,par=False, per_atom_par = True, n_cpus=1) - - otf = OTF(qe_input, dt, number_of_steps, gp, dft_loc, - std_tolerance_factor, init_atoms=[0], - calculate_energy=True, max_atoms_added=1, - output_name='h2_otf_qe', - store_dft_output=(['pwscf.out', 'pwscf.in'], '.')) - - otf.run() - os.system('mkdir test_outputs') - os.system('mv h2_otf_qe* test_outputs') - cleanup_espresso_run("{*pwscf.out,*pwscf.in}") - -@pytest.mark.skipif(not os.environ.get('PWSCF_COMMAND', - False), reason='PWSCF_COMMAND not found ' - 'in environment: Please install Quantum ' - 'ESPRESSO and set the PWSCF_COMMAND env. ' - 'variable to point to pw.x.') -def test_otf_al(): - """ - Test that an otf run can survive going for more steps - :return: - """ - os.system('cp ./test_files/qe_input_2.in ./pwscf.in') - - # make gp model - kernel = en.three_body - kernel_grad = en.three_body_grad - hyps = np.array([0.1, 1, 0.01]) - hyp_labels = ['Signal Std', 'Length Scale', 'Noise Std'] - cutoffs = np.array([3.9, 3.9]) - energy_force_kernel = en.three_body_force_en - - gp = \ - GaussianProcess(kernel=kernel, - kernel_grad=kernel_grad, - hyps=hyps, - cutoffs=cutoffs, - hyp_labels=hyp_labels, - energy_force_kernel=energy_force_kernel, - maxiter=50,n_cpus=1) - - # set up DFT calculator - qe_input = './pwscf.in' # quantum espresso input file - dft_loc = os.environ.get('PWSCF_COMMAND') - - # set up OTF parameters - dt = 0.001 # timestep (ps) - number_of_steps = 100 # number of steps - std_tolerance_factor = 1 - max_atoms_added = 2 - freeze_hyps = 3 - - otf = OTF(qe_input, dt, number_of_steps, gp, dft_loc, - std_tolerance_factor, init_atoms=[0], - calculate_energy=True, output_name='al_otf_qe', - freeze_hyps=freeze_hyps, skip=5, - max_atoms_added=max_atoms_added) - - # run OTF MD - otf.run() - - os.system('mkdir test_outputs') - os.system('mv al_otf_qe* test_outputs') - - cleanup_espresso_run() diff --git a/tests/test_OTF_qe_par.py b/tests/test_OTF_qe_par.py deleted file mode 100644 index 22aa738d1..000000000 --- a/tests/test_OTF_qe_par.py +++ /dev/null @@ -1,122 +0,0 @@ -import pytest -import os -import sys -import numpy as np -from flare.otf import OTF -from flare.gp import GaussianProcess -from flare.struc import Structure -import flare.kernels.kernels as en - - -def cleanup_espresso_run(target: str = None): - os.system('rm pwscf.out') - os.system('rm pwscf.wfc') - os.system('rm -r pwscf.save') - os.system('rm pwscf.in') - os.system('rm pwscf.wfc1') - os.system('rm pwscf.wfc2') - if target: - os.system('rm ' + target) - - -# ------------------------------------------------------ -# test otf runs -# ------------------------------------------------------ -@pytest.mark.skipif(not os.environ.get('PWSCF_COMMAND', - False), reason='PWSCF_COMMAND not found ' - 'in environment: Please install Quantum ' - 'ESPRESSO and set the PWSCF_COMMAND env. ' - 'variable to point to pw.x.') -def test_otf_h2(): - """ - Test that an otf run can survive going for more steps - :return: - """ - os.system('cp ./test_files/qe_input_1.in ./pwscf.in') - - qe_input = './pwscf.in' - dt = 0.0001 - number_of_steps = 4 - cutoffs = np.array([5]) - dft_loc = os.environ.get('PWSCF_COMMAND') - std_tolerance_factor = -0.1 - - # make gp model - kernel = en.two_body - kernel_grad = en.two_body_grad - hyps = np.array([1, 1, 1]) - hyp_labels = ['Signal Std', 'Length Scale', 'Noise Std'] - energy_force_kernel = en.two_body_force_en - - gp = \ - GaussianProcess(kernel=kernel, - kernel_grad=kernel_grad, - hyps=hyps, - cutoffs=cutoffs, - hyp_labels=hyp_labels, - par=True, - per_atom_par=False, - energy_force_kernel=energy_force_kernel, - maxiter=50) - - otf = OTF(qe_input, dt, number_of_steps, gp, dft_loc, - std_tolerance_factor, init_atoms=[0], - calculate_energy=True, max_atoms_added=1, - n_cpus=2, par=True, - mpi="mpi", - output_name='h2_otf_qe_par', - store_dft_output=('pwscf.out', '.')) - - otf.run() - os.system('mkdir test_outputs') - os.system('mv h2_otf_qe_par* test_outputs') - cleanup_espresso_run("*pwscf.out") - -@pytest.mark.skipif(not os.environ.get('PWSCF_COMMAND', - False), reason='PWSCF_COMMAND not found ' - 'in environment: Please install Quantum ' - 'ESPRESSO and set the PWSCF_COMMAND env. ' - 'variable to point to pw.x.') -def test_otf_Al_npool(): - """ - Test that an otf run can survive going for more steps - :return: - """ - os.system('cp ./test_files/qe_input_2.in ./pwscf.in') - - qe_input = './pwscf.in' - dt = 0.0001 - number_of_steps = 4 - cutoffs = np.array([5]) - dft_loc = os.environ.get('PWSCF_COMMAND') - std_tolerance_factor = -0.1 - - # make gp model - kernel = en.two_body - kernel_grad = en.two_body_grad - hyps = np.array([1, 1, 1]) - hyp_labels = ['Signal Std', 'Length Scale', 'Noise Std'] - energy_force_kernel = en.two_body_force_en - - gp = \ - GaussianProcess(kernel=kernel, - kernel_grad=kernel_grad, - hyps=hyps, - cutoffs=cutoffs, - hyp_labels=hyp_labels, - par=True, - per_atom_par=False, - energy_force_kernel=energy_force_kernel, - maxiter=50) - - otf = OTF(qe_input, dt, number_of_steps, gp, dft_loc, - std_tolerance_factor, init_atoms=[0], - calculate_energy=True, max_atoms_added=1, - n_cpus=2, par=True, npool=2, - mpi="mpi", - output_name='h2_otf_qe_par') - - otf.run() - os.system('mkdir test_outputs') - os.system('mv h2_otf_qe_par* test_outputs') - cleanup_espresso_run() diff --git a/tests/test_OTF_vasp.py b/tests/test_OTF_vasp.py index 5f40abe77..633efec78 100644 --- a/tests/test_OTF_vasp.py +++ b/tests/test_OTF_vasp.py @@ -5,7 +5,6 @@ from flare.otf import OTF from flare.gp import GaussianProcess from flare.struc import Structure -import flare.kernels.kernels as en from flare.dft_interface.vasp_util import * @@ -28,24 +27,20 @@ def test_otf_h2(): std_tolerance_factor = -0.1 # make gp model - kernel = en.two_body - kernel_grad = en.two_body_grad hyps = np.array([1, 1, 1]) hyp_labels = ['Signal Std', 'Length Scale', 'Noise Std'] - energy_force_kernel = en.two_body_force_en - gp = GaussianProcess(kernel=kernel, - kernel_grad=kernel_grad, + gp = GaussianProcess(kernel_name='2', hyps=hyps, cutoffs=cutoffs, hyp_labels=hyp_labels, - energy_force_kernel=energy_force_kernel, maxiter=50) otf = OTF(vasp_input, dt, number_of_steps, gp, dft_loc, std_tolerance_factor, init_atoms=[0], calculate_energy=True, max_atoms_added=1, n_cpus=1, force_source='vasp', + dft_output="vasprun.xml", output_name='h2_otf_vasp') otf.run() diff --git a/tests/test_ase_otf.py b/tests/test_ase_otf.py new file mode 100644 index 000000000..3ad6c5551 --- /dev/null +++ b/tests/test_ase_otf.py @@ -0,0 +1,190 @@ +import os, shutil, glob +from copy import deepcopy +import pytest +import numpy as np + +from flare import otf, kernels +from flare.gp import GaussianProcess +from flare.mgp.mgp import MappedGaussianProcess +from flare.ase.calculator import FLARE_Calculator +from flare.ase.otf_md import otf_md +from flare.ase.logger import OTFLogger + +from ase import units +from ase.md.velocitydistribution import (MaxwellBoltzmannDistribution, + Stationary, ZeroRotation) +from ase.spacegroup import crystal +from ase.calculators.espresso import Espresso + + +md_list = ['VelocityVerlet', 'NVTBerendsen', 'NPTBerendsen', 'NPT', 'Langevin'] + +@pytest.fixture(scope='module') +def super_cell(): + + # create primitive cell based on materials project + # url: https://materialsproject.org/materials/mp-22915/ + a = 3.855 + alpha = 90 + atoms = crystal(['H', 'He'], # Ag, I + basis=[(0, 0, 0), (0.5, 0.5, 0.5)], + size=(2, 1, 1), + cellpar=[a, a, a, alpha, alpha, alpha]) + + # jitter positions to give nonzero force on first frame + for atom_pos in atoms.positions: + for coord in range(3): + atom_pos[coord] += (2*np.random.random()-1) * 0.5 + + yield atoms + del atoms + + +@pytest.fixture(scope='module') +def flare_calc(): + flare_calc_dict = {} + for md_engine in md_list: + + # ---------- create gaussian process model ------------------- + gp_model = GaussianProcess(kernel_name='2+3_mc', + hyps=[0.1, 1., 0.001, 1, 0.06], + cutoffs=(5.0, 5.0), + hyp_labels=['sig2', 'ls2', 'sig3', + 'ls3', 'noise'], + opt_algorithm='BFGS', + par=False) + + # ----------- create mapped gaussian process ------------------ + struc_params = {'species': [1, 2], + 'cube_lat': np.eye(3) * 100, + 'mass_dict': {'0': 2, '1': 4}} + + # grid parameters + lower_cut = 2.5 + two_cut, three_cut = gp_model.cutoffs + grid_num_2 = 8 + grid_num_3 = 8 + grid_params = {'bounds_2': [[lower_cut], [two_cut]], + 'bounds_3': [[lower_cut, lower_cut, -1], + [three_cut, three_cut, 1]], + 'grid_num_2': grid_num_2, + 'grid_num_3': [grid_num_3, grid_num_3, grid_num_3], + 'svd_rank_2': 0, + 'svd_rank_3': 0, + 'bodies': [2, 3], + 'load_grid': None, + 'update': True} + + mgp_model = MappedGaussianProcess(gp_model.hyps, gp_model.cutoffs, + grid_params, struc_params, mean_only=False, container_only=False, + GP=gp_model, lmp_file_name='lmp.mgp', n_cpus=1) + + # ------------ create ASE's flare calculator ----------------------- + flare_calculator = FLARE_Calculator(gp_model, mgp_model, + par=True, use_mapping=True) + + + flare_calc_dict[md_engine] = flare_calculator + print(md_engine) + yield flare_calc_dict + del flare_calc_dict + + +@pytest.mark.skipif(not os.environ.get('PWSCF_COMMAND', + False), reason='PWSCF_COMMAND not found ' + 'in environment: Please install Quantum ' + 'ESPRESSO and set the PWSCF_COMMAND env. ' + 'variable to point to pw.x.') +@pytest.fixture(scope='module') +def qe_calc(): + # set up executable + label = 'scf' + input_file = label+'.pwi' + output_file = label+'.pwo' + no_cpus = 1 + pw = os.environ.get('PWSCF_COMMAND') + os.environ['ASE_ESPRESSO_COMMAND'] = f'{pw} < {input_file} > {output_file}' + + # set up input parameters + input_data = {'control': {'prefix': label, + 'pseudo_dir': 'test_files/pseudos/', + 'outdir': './out', + 'calculation': 'scf'}, + 'system': {'ibrav': 0, + 'ecutwfc': 20, + 'ecutrho': 40, + 'smearing': 'gauss', + 'degauss': 0.02, + 'occupations': 'smearing'}, + 'electrons': {'conv_thr': 1.0e-02, + 'electron_maxstep': 100, + 'mixing_beta': 0.7}} + + # pseudo-potentials + ion_pseudo = {'H': 'H.pbe-kjpaw.UPF', + 'He': 'He.pbe-kjpaw_psl.1.0.0.UPF'} + + # create ASE calculator + dft_calculator = Espresso(pseudopotentials=ion_pseudo, label=label, + tstress=True, tprnfor=True, nosym=True, + input_data=input_data, kpts=(1,1,1)) + + yield dft_calculator + del dft_calculator + + +@pytest.mark.skipif(not os.environ.get('PWSCF_COMMAND', + False), reason='PWSCF_COMMAND not found ' + 'in environment: Please install Quantum ' + 'ESPRESSO and set the PWSCF_COMMAND env. ' + 'variable to point to pw.x.') +@pytest.mark.parametrize('md_engine', md_list) +def test_otf_md(md_engine, super_cell, flare_calc, qe_calc): + np.random.seed(12345) + + flare_calculator = flare_calc[md_engine] + # set up OTF MD engine + md_params = {'timestep': 1 * units.fs, 'trajectory': None, 'dt': 1* + units.fs, + 'externalstress': 0, 'ttime': 25, 'pfactor': 3375, + 'mask': None, 'temperature': 500, 'taut': 1, 'taup': 1, + 'pressure': 0, 'compressibility': 0, 'fixcm': 1, + 'friction': 0.02} + + otf_params = {'dft_calc': qe_calc, + 'init_atoms': [0, 1, 2, 3], + 'std_tolerance_factor': 2, + 'max_atoms_added' : len(super_cell.positions), + 'freeze_hyps': 10, + 'use_mapping': flare_calculator.use_mapping} + + # intialize velocity + temperature = md_params['temperature'] + MaxwellBoltzmannDistribution(super_cell, temperature * units.kB) + Stationary(super_cell) # zero linear momentum + ZeroRotation(super_cell) # zero angular momentum + + super_cell.set_calculator(flare_calculator) + test_otf = otf_md(md_engine, super_cell, md_params, otf_params) + + # set up logger + otf_logger = OTFLogger(test_otf, super_cell, + logfile=md_engine+'.log', mode="w", data_in_logfile=True) + test_otf.attach(otf_logger, interval=1) + + # run otf + number_of_steps = 3 + test_otf.otf_run(number_of_steps) + + for f in glob.glob("scf.pw*"): + os.remove(f) + for f in glob.glob("*.npy"): + os.remove(f) + for f in glob.glob("kv3*"): + shutil.rmtree(f) + + for f in os.listdir("./"): + if f in [f'{md_engine}.log', 'lmp.mgp']: + os.remove(f) + if f in ['out', 'otf_data']: + shutil.rmtree(f) diff --git a/tests/test_ase_setup/Al/al.log b/tests/test_ase_setup/Al/al.log deleted file mode 100644 index f3930d1ba..000000000 --- a/tests/test_ase_setup/Al/al.log +++ /dev/null @@ -1,177 +0,0 @@ -2019-07-30 17:44:50.170038 -number of cpu cores: -cutoffs: [4.5 4.5] -kernel: two_plus_three_body -number of parameters: 5 -hyperparameters: [2.e-01 1.e+00 1.e-04 1.e+00 5.e-03] -hyperparameter optimization algorithm: L-BFGS-B -uncertainty tolerance: 1 -periodic cell: -[[4.05 0. 0. ] - [0. 4.05 0. ] - [0. 0. 4.05]] - --------------------------------------------------- --*Frame: 0.0 -Simulation time: 0.0 - Positions | DFT Forces | Velocities | Uncertainties - 0.000000 0.000000 0.000000 | -0.000000 -0.000000 -0.000000 | -0.006997 0.000947 -0.027547 | 0.000000 0.000000 0.000000 - 0.000000 2.025000 2.025000 | 0.000000 -0.000000 0.000000 | 0.012754 -0.015224 0.022073 | 0.000000 0.000000 0.000000 - 2.025000 0.000000 2.025000 | 0.000000 0.000000 0.000000 | -0.007280 0.005639 0.037132 | 0.000000 0.000000 0.000000 - 2.025000 2.025000 0.000000 | 0.000000 0.000000 0.000000 | 0.001523 0.008638 -0.031658 | 0.000000 0.000000 0.000000 - -total energy: -13.382893278944781 -kinetic energy: 0.05710667359507701 -temperature: 110.44927558836517 -wall time from start: 0.08571887016296387 - --------------------------------------------------- --*Frame: 1.0 -Simulation time: 0.010180505671156723 - Positions | DFT Forces | Velocities | Uncertainties --0.006997 0.000947 -0.027547 | 0.000000 0.000000 -0.000000 | -0.007003 0.000948 -0.027571 | 0.000000 0.000000 0.000000 - 0.012754 2.009776 2.047073 | 0.000000 -0.000000 0.000000 | 0.012765 -0.015237 0.022093 | 0.000000 0.000000 0.000000 - 2.017720 0.005639 2.062132 | -0.000000 0.000000 0.000000 | -0.007287 0.005644 0.037165 | 0.000000 0.000000 0.000000 - 2.026523 2.033638 -0.031658 | -0.000000 -0.000000 -0.000000 | 0.001524 0.008646 -0.031686 | 0.000000 0.000000 0.000000 - -total energy: 1.0572091716693608 -kinetic energy: 0.05720917166936083 -temperature: 110.64751578239435 -wall time from start: 6.15107798576355 - --------------------------------------------------- --*Frame: 1.0 -Simulation time: 0.010180505671156723 - Positions | DFT Forces | Velocities | Uncertainties --0.006997 0.000947 -0.027547 | 0.011425 -0.022349 0.275019 | -0.007003 0.000948 -0.027571 | 0.000000 0.000000 0.000000 - 0.012754 2.009776 2.047073 | -0.066659 0.078745 -0.253485 | 0.012765 -0.015237 0.022093 | 0.000000 0.000000 0.000000 - 2.017720 0.005639 2.062132 | 0.044965 -0.040797 -0.312683 | -0.007287 0.005644 0.037165 | 0.000000 0.000000 0.000000 - 2.026523 2.033638 -0.031658 | 0.010269 -0.015598 0.291150 | 0.001524 0.008646 -0.031686 | 0.000000 0.000000 0.000000 - -total energy: -13.36436764702953 -kinetic energy: 0.05720917166936083 -temperature: 110.64751578239435 -wall time from start: 6.253512144088745 - -Adding atom 2 to the training set with uncertainty: [0.00393406 0.00165358 0.00496471] -Adding atom 1 to the training set with uncertainty: [0.0025289 0.00305361 0.00418258] --------------------------------------------------- --*Frame: 2.0 -Simulation time: 0.020361011342313447 - Positions | DFT Forces | Velocities | Uncertainties --0.013997 0.001895 -0.055110 | 0.009981 -0.000129 0.025805 | -0.006832 0.000947 -0.027150 | 0.000000 0.000000 0.000000 - 0.025516 1.993344 2.067959 | -0.015506 0.018555 -0.018733 | 0.012504 -0.014925 0.021791 | 0.000000 0.000000 0.000000 - 2.009236 0.011280 2.098086 | 0.008446 -0.006192 -0.038188 | -0.007145 0.005540 0.036534 | 0.000000 0.000000 0.000000 - 2.026846 2.041081 -0.063335 | -0.002921 -0.012234 0.031117 | 0.001473 0.008437 -0.031175 | 0.000000 0.000000 0.000000 - -total energy: 1.055317705639362 -kinetic energy: 0.05531770563936181 -temperature: 106.98925590379056 -wall time from start: 7.91826868057251 - --------------------------------------------------- --*Frame: 2.0 -Simulation time: 0.020361011342313447 - Positions | DFT Forces | Velocities | Uncertainties --0.013997 0.001895 -0.055110 | 0.022831 -0.047923 0.556884 | -0.006832 0.000947 -0.027150 | 0.000000 0.000000 0.000000 - 0.025516 1.993344 2.067959 | -0.136808 0.161391 -0.512299 | 0.012504 -0.014925 0.021791 | 0.000000 0.000000 0.000000 - 2.009236 0.011280 2.098086 | 0.090561 -0.080329 -0.634880 | -0.007145 0.005540 0.036534 | 0.000000 0.000000 0.000000 - 2.026846 2.041081 -0.063335 | 0.023415 -0.033139 0.590295 | 0.001473 0.008437 -0.031175 | 0.000000 0.000000 0.000000 - -total energy: -13.31052182452157 -kinetic energy: 0.05531770563936181 -temperature: 106.98925590379056 -wall time from start: 8.011371374130249 - -Adding atom 2 to the training set with uncertainty: [0.00177767 0.00101113 0.00424937] -Adding atom 1 to the training set with uncertainty: [0.00233314 0.00280334 0.00378939] --------------------------------------------------- --*Frame: 3.0 -Simulation time: 0.030541517013470174 - Positions | DFT Forces | Velocities | Uncertainties --0.020645 0.002840 -0.081783 | 0.011643 -0.001674 0.046946 | -0.006457 0.000918 -0.025905 | 0.000000 0.000000 0.000000 - 0.037733 1.977564 2.088205 | -0.021442 0.025589 -0.037777 | 0.011867 -0.014163 0.020826 | 0.000000 0.000000 0.000000 - 2.001048 0.016706 2.132714 | 0.012273 -0.009533 -0.063005 | -0.006788 0.005270 0.034797 | 0.000000 0.000000 0.000000 - 2.027066 2.048093 -0.093933 | -0.002474 -0.014383 0.053835 | 0.001378 0.007975 -0.029718 | 0.000000 0.000000 0.000000 - -total energy: 1.050213695020581 -kinetic energy: 0.050213695020580956 -temperature: 97.11765526676334 -wall time from start: 9.268652439117432 - --------------------------------------------------- --*Frame: 3.0 -Simulation time: 0.030541517013470174 - Positions | DFT Forces | Velocities | Uncertainties --0.020645 0.002840 -0.081783 | 0.032682 -0.085014 0.856430 | -0.006457 0.000918 -0.025905 | 0.000000 0.000000 0.000000 - 0.037733 1.977564 2.088205 | -0.215888 0.254621 -0.783986 | 0.011867 -0.014163 0.020826 | 0.000000 0.000000 0.000000 - 2.001048 0.016706 2.132714 | 0.136650 -0.117701 -0.981017 | -0.006788 0.005270 0.034797 | 0.000000 0.000000 0.000000 - 2.027066 2.048093 -0.093933 | 0.046557 -0.051907 0.908572 | 0.001378 0.007975 -0.029718 | 0.000000 0.000000 0.000000 - -total energy: -13.22355089848021 -kinetic energy: 0.050213695020580956 -temperature: 97.11765526676334 -wall time from start: 9.362038612365723 - -Adding atom 2 to the training set with uncertainty: [0.00138347 0.0012388 0.00441751] -Adding atom 1 to the training set with uncertainty: [0.00235743 0.00276705 0.00368224] --------------------------------------------------- --*Frame: 4.0 -Simulation time: 0.04072202268462689 - Positions | DFT Forces | Velocities | Uncertainties --0.026872 0.003724 -0.106760 | -0.001204 -0.030081 0.323989 | -0.006296 0.000332 -0.019139 | 0.000000 0.000000 0.000000 - 0.049176 1.961514 2.105892 | -0.063913 0.075121 -0.307286 | 0.010340 -0.012363 0.014516 | 0.000000 0.000000 0.000000 - 1.992110 0.021788 2.163874 | 0.046205 -0.044209 -0.353131 | -0.005735 0.004298 0.027236 | 0.000000 0.000000 0.000000 - 2.026003 2.053391 -0.122589 | 0.018915 -0.000838 0.336418 | 0.001691 0.007733 -0.022613 | 0.000000 0.000000 0.000000 - -total energy: 1.030269930101223 -kinetic energy: 0.030269930101223037 -temperature: 58.54467860440655 -wall time from start: 10.825520753860474 - --------------------------------------------------- --*Frame: 4.0 -Simulation time: 0.04072202268462689 - Positions | DFT Forces | Velocities | Uncertainties --0.026872 0.003724 -0.106760 | 0.041220 -0.131444 1.167619 | -0.006296 0.000332 -0.019139 | 0.000000 0.000000 0.000000 - 0.049176 1.961514 2.105892 | -0.303667 0.358675 -1.065436 | 0.010340 -0.012363 0.014516 | 0.000000 0.000000 0.000000 - 1.992110 0.021788 2.163874 | 0.185394 -0.155629 -1.341607 | -0.005735 0.004298 0.027236 | 0.000000 0.000000 0.000000 - 2.026003 2.053391 -0.122589 | 0.077052 -0.071601 1.239425 | 0.001691 0.007733 -0.022613 | 0.000000 0.000000 0.000000 - -total energy: -13.117563990675048 -kinetic energy: 0.030269930101223037 -temperature: 58.54467860440655 -wall time from start: 10.920106887817383 - -Adding atom 2 to the training set with uncertainty: [0.0013695 0.00138419 0.00453307] -Adding atom 1 to the training set with uncertainty: [0.00248927 0.00285666 0.00383064] --------------------------------------------------- --*Frame: 5.0 -Simulation time: 0.05090252835578362 - Positions | DFT Forces | Velocities | Uncertainties --0.033173 0.003496 -0.119823 | 0.004424 -0.052331 0.576349 | -0.006280 -0.001199 -0.002519 | 0.000000 0.000000 0.000000 - 0.058300 1.948194 2.112267 | -0.120745 0.142142 -0.543239 | 0.006976 -0.008405 -0.001209 | 0.000000 0.000000 0.000000 - 1.984862 0.025252 2.182083 | 0.085278 -0.080267 -0.634055 | -0.003329 0.002011 0.009056 | 0.000000 0.000000 0.000000 - 2.025654 2.058701 -0.138885 | 0.031049 -0.009559 0.600924 | 0.002633 0.007593 -0.005329 | 0.000000 0.000000 0.000000 - -total energy: 1.0048312651776636 -kinetic energy: 0.004831265177663668 -temperature: 9.34408722230754 -wall time from start: 12.750687837600708 - --------------------------------------------------- --*Frame: 5.0 -Simulation time: 0.05090252835578362 - Positions | DFT Forces | Velocities | Uncertainties --0.033173 0.003496 -0.119823 | 0.059882 -0.160302 1.354328 | -0.006280 -0.001199 -0.002519 | 0.000000 0.000000 0.000000 - 0.058300 1.948194 2.112267 | -0.374024 0.441539 -1.225242 | 0.006976 -0.008405 -0.001209 | 0.000000 0.000000 0.000000 - 1.984862 0.025252 2.182083 | 0.220264 -0.178791 -1.572517 | -0.003329 0.002011 0.009056 | 0.000000 0.000000 0.000000 - 2.025654 2.058701 -0.138885 | 0.093877 -0.102446 1.443431 | 0.002633 0.007593 -0.005329 | 0.000000 0.000000 0.000000 - -total energy: -13.05008123205844 -kinetic energy: 0.004831265177663668 -temperature: 9.34408722230754 -wall time from start: 12.847416400909424 - -Adding atom 2 to the training set with uncertainty: [0.00134721 0.00158882 0.00401919] -Adding atom 1 to the training set with uncertainty: [0.00235302 0.00267548 0.00369375] \ No newline at end of file diff --git a/tests/test_ase_setup/Al/al.py b/tests/test_ase_setup/Al/al.py deleted file mode 100644 index 86694badc..000000000 --- a/tests/test_ase_setup/Al/al.py +++ /dev/null @@ -1,102 +0,0 @@ -import numpy as np -import sys -sys.path.append('../..') - -from flare import kernels -from flare.gp import GaussianProcess -from flare.mff.mff_new import MappedForceField -from flare.modules.ase_calculator import FLARE_Calculator -from flare.modules.ase_otf_md import OTF_NPT -from flare.modules.ase_otf_logger import OTFLogger - -from ase import Atoms -from ase import units -from ase.spacegroup import crystal -from ase.build import bulk, make_supercell -from ase.calculators.espresso import Espresso -from ase.calculators.eam import EAM -from ase.md.velocitydistribution import (MaxwellBoltzmannDistribution, - Stationary, ZeroRotation) - -# -------------- build atomic structure ------------------- -symbol = 'Al' -a = 4.05 # Angstrom lattice spacing -unit_cell = crystal('Al', [(0,0,0)], spacegroup=225, - cellpar=[a, a, a, 90, 90, 90]) -#unit_cell = bulk('Al', 'fcc', a=a) -multiplier = np.array([[1,0,0],[0,1,0],[0,0,1]]) -super_cell = make_supercell(unit_cell, multiplier) -nat = len(super_cell.positions) -print('number of atoms:', nat) - -# -------------- set up gp calculator --------------------- -kernel = kernels.two_plus_three_body -kernel_grad = kernels.two_plus_three_body_grad -hyps = np.array([0.2, 1., 0.0001, 1, 0.005]) -cutoffs = np.array([4.5, 4.5]) -hyp_labels = ['sig2', 'ls2', 'sig3', 'ls3', 'noise'] -opt_algorithm = 'BFGS' -gp_model = GaussianProcess(kernel, kernel_grad, hyps, cutoffs, - hyp_labels, opt_algorithm, par=True) - -# -------------- set up mff calculator --------------------- -grid_params = {'bounds_2': np.array([[3.5], [4.5]]), - 'bounds_3': np.array([[3.5, 3.5, 0.0], [4.5, 4.5, np.pi]]), - 'grid_num_2': 8, - 'grid_num_3': [8, 8, 8], - 'svd_rank_2': 0, - 'svd_rank_3': 0, - 'bodies': '2+3', - 'load_grid': None, - 'load_svd': None, - 'update': False} - -struc_params = {'species': 'Al', - 'cube_lat': 100*np.eye(3), #super_cell.cell, - 'mass_dict': {'Al': 0.000103642695727*27}} - -mff_model = MappedForceField(gp_model, grid_params, struc_params) -calc = FLARE_Calculator(gp_model, mff_model=mff_model, use_mapping=True) -super_cell.set_calculator(calc) - -# -------------- set up dft calculator ---------------- -dft_input = {'label': 'al', - 'pseudopotentials': 'Al99.eam.alloy'} -dft_calc = EAM(potential=dft_input['pseudopotentials']) -#pw_loc = "/n/home08/xiey/q-e/bin/pw.x" -#no_cpus = 1 -#npool = 1 -#pwi_file = dft_input['label'] + '.pwi' -#pwo_file = dft_input['label'] + '.pwo' -#os.environ['ASE_ESPRESSO_COMMAND'] = 'srun -n {0} --mpi=pmi2 {1} -npool {2} < {3} > {4}'.format(no_cpus, pw_loc, npool, pwi_file, pwo_file) -#input_data = dft_input['input_data'] -#dft_calc = Espresso(pseudopotentials=dft_input['pseudopotentials'], label=dft_input['label'], -# tstress=True, tprnfor=True, nosym=True, -# input_data=input_data, kpts=dft_input['kpts']) - -# -------------- set up otf npt md -------------------- -timestep = 1 # fs -temperature = 100 -externalstress = 0 -ttime = 25 -pfactor = 3375 -logfile = 'al.log' - -# intialize velocity -MaxwellBoltzmannDistribution(super_cell, 200 * units.kB) -Stationary(super_cell) # zero linear momentum -ZeroRotation(super_cell) # zero angular momentum - -test_otf_npt = OTF_NPT(super_cell, timestep, temperature, - externalstress, ttime, pfactor, mask=None, - # on-the-fly parameters - dft_calc=dft_calc, - std_tolerance_factor=1, max_atoms_added=nat, - freeze_hyps=0, - # mff parameters - use_mapping=super_cell.calc.use_mapping) - -test_otf_npt.attach(OTFLogger(test_otf_npt, super_cell, logfile, mode="w"), interval=1) - -test_otf_npt.otf_run(5) - diff --git a/tests/test_ase_setup/Al/al_mgp.log b/tests/test_ase_setup/Al/al_mgp.log deleted file mode 100644 index aa5cfd675..000000000 --- a/tests/test_ase_setup/Al/al_mgp.log +++ /dev/null @@ -1,172 +0,0 @@ -2019-07-30 17:18:59.075022 -number of cpu cores: -cutoffs: [4.5 4.5] -kernel: two_plus_three_body -number of parameters: 5 -hyperparameters: [2.e-01 1.e+00 1.e-04 1.e+00 5.e-03] -hyperparameter optimization algorithm: L-BFGS-B -uncertainty tolerance: 1 -periodic cell: -[[4.05 0. 0. ] - [0. 4.05 0. ] - [0. 0. 4.05]] - --------------------------------------------------- --*Frame: 0.0 -Simulation time: 0.0 - Positions | DFT Forces | Velocities | Uncertainties - 0.000000 0.000000 0.000000 | -0.000000 -0.000000 -0.000000 | 0.014131 0.018721 -0.013347 | 0.000000 0.000000 0.000000 - 0.000000 2.025000 2.025000 | 0.000000 -0.000000 0.000000 | -0.007252 -0.001701 0.017710 | 0.000000 0.000000 0.000000 - 2.025000 0.000000 2.025000 | 0.000000 0.000000 0.000000 | 0.002888 0.038481 -0.023433 | 0.000000 0.000000 0.000000 - 2.025000 2.025000 0.000000 | 0.000000 0.000000 0.000000 | -0.009768 -0.055500 0.019070 | 0.000000 0.000000 0.000000 - -total energy: -13.34994895262821 -kinetic energy: 0.09005099991164922 -temperature: 174.16646917265044 -wall time from start: 0.17605924606323242 - --------------------------------------------------- --*Frame: 1.0 -Simulation time: 0.010180505671156723 - Positions | DFT Forces | Velocities | Uncertainties - 0.014131 0.018721 -0.013347 | 0.000000 0.000000 -0.000000 | 0.014144 0.018738 -0.013358 | 0.000000 0.000000 0.000000 --0.007252 2.023299 2.042710 | 0.000000 -0.000000 0.000000 | -0.007258 -0.001703 0.017726 | 0.000000 0.000000 0.000000 - 2.027888 0.038481 2.001567 | -0.000000 0.000000 -0.000000 | 0.002891 0.038515 -0.023454 | 0.000000 0.000000 0.000000 - 2.015232 1.969500 0.019070 | -0.000000 -0.000000 0.000000 | -0.009777 -0.055550 0.019087 | 0.000000 0.000000 0.000000 - -total energy: 1.0902126202705797 -kinetic energy: 0.0902126202705798 -temperature: 174.47905700942042 -wall time from start: 6.275839328765869 - --------------------------------------------------- --*Frame: 1.0 -Simulation time: 0.010180505671156723 - Positions | DFT Forces | Velocities | Uncertainties - 0.014131 0.018721 -0.013347 | -0.075554 -0.234597 0.036442 | 0.014144 0.018738 -0.013358 | 0.000000 0.000000 0.000000 --0.007252 2.023299 2.042710 | 0.009330 0.167449 -0.053880 | -0.007258 -0.001703 0.017726 | 0.000000 0.000000 0.000000 - 2.027888 0.038481 2.001567 | 0.008091 -0.315184 0.112313 | 0.002891 0.038515 -0.023454 | 0.000000 0.000000 0.000000 - 2.015232 1.969500 0.019070 | 0.058134 0.382333 -0.094875 | -0.009777 -0.055550 0.019087 | 0.000000 0.000000 0.000000 - -total energy: -13.327139034787761 -kinetic energy: 0.0902126202705798 -temperature: 174.47905700942042 -wall time from start: 6.392784357070923 - -Adding atom 3 to the training set with uncertainty: [0.00118572 0.00496825 0.00476187] --------------------------------------------------- --*Frame: 2.0 -Simulation time: 0.020361011342313447 - Positions | DFT Forces | Velocities | Uncertainties - 0.028271 0.037453 -0.026701 | -0.014302 0.017308 0.022866 | 0.013908 0.019098 -0.012962 | 0.000000 0.000000 0.000000 --0.014508 2.020397 2.059231 | 0.015261 -0.040920 -0.028877 | -0.006990 -0.002466 0.017227 | 0.000000 0.000000 0.000000 - 2.029579 0.076985 1.976920 | -0.009236 -0.010142 0.027916 | 0.002726 0.038408 -0.022985 | 0.000000 0.000000 0.000000 - 2.004258 1.912765 0.038151 | 0.008277 0.033756 -0.021905 | -0.009643 -0.055040 0.018720 | 0.000000 0.000000 0.000000 - -total energy: 1.0885221476123994 -kinetic energy: 0.08852214761239945 -temperature: 171.20953580036064 -wall time from start: 7.81196928024292 - --------------------------------------------------- --*Frame: 2.0 -Simulation time: 0.020361011342313447 - Positions | DFT Forces | Velocities | Uncertainties - 0.028271 0.037453 -0.026701 | -0.169058 -0.484057 0.072223 | 0.013908 0.019098 -0.012962 | 0.000000 0.000000 0.000000 --0.014508 2.020397 2.059231 | 0.019233 0.335283 -0.108995 | -0.006990 -0.002466 0.017227 | 0.000000 0.000000 0.000000 - 2.029579 0.076985 1.976920 | 0.017368 -0.668308 0.271360 | 0.002726 0.038408 -0.022985 | 0.000000 0.000000 0.000000 - 2.004258 1.912765 0.038151 | 0.132457 0.817081 -0.234587 | -0.009643 -0.055040 0.018720 | 0.000000 0.000000 0.000000 - -total energy: -13.257330726435 -kinetic energy: 0.08852214761239945 -temperature: 171.20953580036064 -wall time from start: 7.930527210235596 - -Adding atom 3 to the training set with uncertainty: [0.0011261 0.00462697 0.00452984] --------------------------------------------------- --*Frame: 3.0 -Simulation time: 0.030541517013470174 - Positions | DFT Forces | Velocities | Uncertainties - 0.041914 0.056873 -0.039239 | -0.006563 -0.036178 -0.001045 | 0.013575 0.018822 -0.012607 | 0.000000 0.000000 0.000000 --0.021215 2.015973 2.074724 | -0.002700 0.033246 0.000227 | -0.006784 -0.002618 0.016762 | 0.000000 0.000000 0.000000 - 2.030935 0.115206 1.953253 | 0.003495 -0.039552 0.007792 | 0.002630 0.037634 -0.022412 | 0.000000 0.000000 0.000000 - 1.993570 1.857151 0.056465 | 0.005773 0.042526 -0.006984 | -0.009420 -0.053838 0.018257 | 0.000000 0.000000 0.000000 - -total energy: 1.084686857845178 -kinetic energy: 0.08468685784517807 -temperature: 163.79175168173552 -wall time from start: 8.734238862991333 - --------------------------------------------------- --*Frame: 3.0 -Simulation time: 0.030541517013470174 - Positions | DFT Forces | Velocities | Uncertainties - 0.041914 0.056873 -0.039239 | -0.286944 -0.767043 0.106602 | 0.013575 0.018822 -0.012607 | 0.000000 0.000000 0.000000 --0.021215 2.015973 2.074724 | 0.031912 0.519933 -0.166036 | -0.006784 -0.002618 0.016762 | 0.000000 0.000000 0.000000 - 2.030935 0.115206 1.953253 | 0.026667 -1.068732 0.483416 | 0.002630 0.037634 -0.022412 | 0.000000 0.000000 0.000000 - 1.993570 1.857151 0.056465 | 0.228364 1.315842 -0.423982 | -0.009420 -0.053838 0.018257 | 0.000000 0.000000 0.000000 - -total energy: -13.133228880488305 -kinetic energy: 0.08468685784517807 -temperature: 163.79175168173552 -wall time from start: 8.852224588394165 - -Adding atom 3 to the training set with uncertainty: [0.00128935 0.0046337 0.00440192] --------------------------------------------------- --*Frame: 4.0 -Simulation time: 0.04072202268462689 - Positions | DFT Forces | Velocities | Uncertainties - 0.055338 0.074986 -0.051839 | -0.072901 -0.345496 0.001701 | 0.012166 0.011820 -0.012659 | 0.000000 0.000000 0.000000 --0.028035 2.011582 2.089061 | -0.018416 0.304359 -0.013347 | -0.007212 0.003647 0.016604 | 0.000000 0.000000 0.000000 - 2.031230 0.152028 1.928641 | 0.029709 -0.392372 0.093742 | 0.003261 0.029794 -0.020638 | 0.000000 0.000000 0.000000 - 1.981884 1.801823 0.074553 | 0.061618 0.433614 -0.082127 | -0.008215 -0.045258 0.016693 | 0.000000 0.000000 0.000000 - -total energy: 1.0608118156473025 -kinetic energy: 0.060811815647302564 -temperature: 117.61534270202698 -wall time from start: 9.743515491485596 - --------------------------------------------------- --*Frame: 4.0 -Simulation time: 0.04072202268462689 - Positions | DFT Forces | Velocities | Uncertainties - 0.055338 0.074986 -0.051839 | -0.429155 -1.069299 0.144072 | 0.012166 0.011820 -0.012659 | 0.000000 0.000000 0.000000 --0.028035 2.011582 2.089061 | 0.052049 0.706599 -0.232704 | -0.007212 0.003647 0.016604 | 0.000000 0.000000 0.000000 - 2.031230 0.152028 1.928641 | 0.034291 -1.508936 0.756474 | 0.003261 0.029794 -0.020638 | 0.000000 0.000000 0.000000 - 1.981884 1.801823 0.074553 | 0.342815 1.871637 -0.667842 | -0.008215 -0.045258 0.016693 | 0.000000 0.000000 0.000000 - -total energy: -12.967407731564581 -kinetic energy: 0.060811815647302564 -temperature: 117.61534270202698 -wall time from start: 9.86207103729248 - -Adding atom 3 to the training set with uncertainty: [0.00179995 0.00460767 0.0043491 ] --------------------------------------------------- --*Frame: 5.0 -Simulation time: 0.05090252835578362 - Positions | DFT Forces | Velocities | Uncertainties - 0.066119 0.080351 -0.064436 | -0.183652 -0.574774 0.082551 | 0.007476 -0.005221 -0.011179 | 0.000000 0.000000 0.000000 --0.035571 2.018494 2.102988 | 0.019167 0.416960 -0.125133 | -0.007247 0.017092 0.014141 | 0.000000 0.000000 0.000000 - 2.032648 0.174452 1.907410 | 0.022669 -0.756052 0.270214 | 0.004257 0.008631 -0.014007 | 0.000000 0.000000 0.000000 - 1.972448 1.762353 0.089678 | 0.141850 0.913956 -0.227671 | -0.004486 -0.020496 0.011043 | 0.000000 0.000000 0.000000 - -total energy: 1.0216353377544887 -kinetic energy: 0.0216353377544886 -temperature: 41.84462570936497 -wall time from start: 10.92754316329956 - --------------------------------------------------- --*Frame: 5.0 -Simulation time: 0.05090252835578362 - Positions | DFT Forces | Velocities | Uncertainties - 0.066119 0.080351 -0.064436 | -0.545514 -1.231923 0.183652 | 0.007476 -0.005221 -0.011179 | 0.000000 0.000000 0.000000 --0.035571 2.018494 2.102988 | 0.082847 0.763839 -0.303201 | -0.007247 0.017092 0.014141 | 0.000000 0.000000 0.000000 - 2.032648 0.174452 1.907410 | 0.032542 -1.813511 1.015944 | 0.004257 0.008631 -0.014007 | 0.000000 0.000000 0.000000 - 1.972448 1.762353 0.089678 | 0.430125 2.281595 -0.896395 | -0.004486 -0.020496 0.011043 | 0.000000 0.000000 0.000000 - -total energy: -12.842588386652807 -kinetic energy: 0.0216353377544886 -temperature: 41.84462570936497 -wall time from start: 11.047457218170166 - -Adding atom 3 to the training set with uncertainty: [0.00202196 0.00429078 0.0041464 ] \ No newline at end of file diff --git a/tests/test_ase_setup/Al/ase_otf.ipynb b/tests/test_ase_setup/Al/ase_otf.ipynb deleted file mode 100644 index f2f135385..000000000 --- a/tests/test_ase_setup/Al/ase_otf.ipynb +++ /dev/null @@ -1,441 +0,0 @@ -{ - "cells": [ - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "# On-the-fly training with ASE\n", - "In this notebook, we will see how to run on-the-fly (OTF) training with ASE module" - ] - }, - { - "cell_type": "code", - "execution_count": 32, - "metadata": {}, - "outputs": [ - { - "name": "stdout", - "output_type": "stream", - "text": [ - "The autoreload extension is already loaded. To reload it, use:\n", - " %reload_ext autoreload\n" - ] - } - ], - "source": [ - "flare_path = \"../..\" # the path to flare source code folder\n", - "\n", - "import numpy as np\n", - "import sys\n", - "sys.path.append(flare_path)\n", - "\n", - "%load_ext autoreload\n", - "%autoreload 2" - ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "## Build atomic structure\n", - "Let's use bulk Al as a simple example. First of all, we can use ASE to create a cubic unit cell with lattice constant $a=4.05$. Then we build a 1x1x1 super cell for simplicity (it's basically the same as the unit cell, but we can build super cell of other sizes as well). \n", - "\n", - "(For more examples, see [ASE-Atoms object](https://wiki.fysik.dtu.dk/ase/ase/atoms.html?highlight=atoms#module-ase.atoms), [Using the spacegroup subpackage](https://wiki.fysik.dtu.dk/ase/ase/spacegroup/spacegroup.html))" - ] - }, - { - "cell_type": "code", - "execution_count": 33, - "metadata": {}, - "outputs": [ - { - "name": "stdout", - "output_type": "stream", - "text": [ - "number of atoms: 4\n" - ] - } - ], - "source": [ - "from ase import Atoms\n", - "from ase import units\n", - "from ase.spacegroup import crystal\n", - "from ase.build import bulk, make_supercell\n", - "\n", - "symbol = 'Al'\n", - "a = 4.05 # Angstrom lattice spacing\n", - "unit_cell = crystal(symbol, [(0,0,0)], spacegroup=225,\n", - " cellpar=[a, a, a, 90, 90, 90])\n", - "multiplier = np.array([[1,0,0],[0,1,0],[0,0,1]])\n", - "super_cell = make_supercell(unit_cell, multiplier)\n", - "nat = len(super_cell.positions)\n", - "print('number of atoms:', nat)" - ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "## Set up gp calculator\n", - "Let's import the FLARE module to build a GP force field predictor. \n", - "- We choose `two_plus_three_body` kernel function, meaning our kernel consists of 2-body and 3-body interactions. `two_plus_three_body_grad` is the gradient function of the kernel, which is used in training hyperparameters.\n", - "- Hyperparameters are randomly chosen to be: $\\sigma_2=0.2$, $l_2=1$, $\\sigma_3=0,0001$, $l_3=1$, $noise=0.005$\n", - "- The cutoffs of 2-body and 3-body are all set to be $4.5$\n", - "- We use 'BFGS' as the algorithm for optimizing hyperparameters\n", - "- Then we can create a GP model of class `GaussianProcess`" - ] - }, - { - "cell_type": "code", - "execution_count": 34, - "metadata": {}, - "outputs": [], - "source": [ - "from flare import kernels\n", - "from flare.gp import GaussianProcess\n", - "\n", - "kernel = kernels.two_plus_three_body\n", - "kernel_grad = kernels.two_plus_three_body_grad\n", - "hyps = np.array([0.2, 1., 0.0001, 1, 0.005])\n", - "cutoffs = np.array([4.5, 4.5])\n", - "hyp_labels = ['sig2', 'ls2', 'sig3', 'ls3', 'noise']\n", - "opt_algorithm = 'BFGS'\n", - "gp_model = GaussianProcess(kernel, kernel_grad, hyps, cutoffs,\n", - " hyp_labels, opt_algorithm, par=True)" - ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "## Set up mff calculator\n", - "If we don't want to use the mapped force field method for GP, we can skip this step. If we want, we need to set up parameters for the mapped force field model.\n", - "- `grid_params` is a dict, specifying how the grids are set for the construction of the mapping. `bounds_2` and `bounds_3` are lower & upper bound of the interpolation domain\n", - " - `bounds_2`: lower & upper bound of bond length, the upper bound is usually set to be the 2-body cutoff of GP, and the lower bound is usually set to be 0.05A lower than the minimal interatomic distance\n", - " - `bounds_3`: the three dimensions represent *bond_length_1*, *bond_length_2*, *angle_12*. So the lower bound of the two *bond_length*s are set to be the same as 2-body. And the upper bound is set to be the same as the 3-body cutoff of GP. The angle range is always set to be $[0,\\pi]$\n", - "- `struc_params` is a dict, specifying parameters of the atomic system\n", - " - `cube_lat` can be set as any cell matrix that is much larger than the super cell we are running with" - ] - }, - { - "cell_type": "code", - "execution_count": 35, - "metadata": {}, - "outputs": [], - "source": [ - "from flare.mff.mff_new import MappedForceField\n", - "\n", - "grid_params = {'bounds_2': np.array([[3.5], [4.5]]),\n", - " 'bounds_3': np.array([[3.5, 3.5, 0.0], [4.5, 4.5, np.pi]]),\n", - " 'grid_num_2': 8,\n", - " 'grid_num_3': [8, 8, 8],\n", - " 'svd_rank_2': 0,\n", - " 'svd_rank_3': 0,\n", - " 'bodies': '2+3',\n", - " 'load_grid': None,\n", - " 'load_svd': None,\n", - " 'update': False}\n", - "\n", - "struc_params = {'species': 'Al',\n", - " 'cube_lat': 100*np.eye(3), #super_cell.cell, \n", - " 'mass_dict': {'Al': 0.000103642695727*27}}\n", - "\n", - "mff_model = MappedForceField(gp_model, grid_params, struc_params)" - ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "## Set up FLARE calculator\n", - "With GP and MFF ready, we can set up our ASE calculator using `FLARE_Calculator` module. If we want to use MFF method, we need to specify `use_mapping=True`, otherwise its default value is `False`. Then we set the calculator of the super cell as our FLARE calculator." - ] - }, - { - "cell_type": "code", - "execution_count": 36, - "metadata": {}, - "outputs": [], - "source": [ - "from flare.modules.ase_calculator import FLARE_Calculator\n", - "calc = FLARE_Calculator(gp_model=gp_model, mff_model=mff_model, use_mapping=True)\n", - "super_cell.set_calculator(calc)" - ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "## Set up DFT calculator\n", - "Since the OTF training requires calling DFT for high uncertainty frames, we need to set up the DFT calculator. Note that we can use any calculator that we like, as long as ASE supports it. For a quick presentation, we use [ASE - EAM calculator](https://wiki.fysik.dtu.dk/ase/ase/calculators/eam.html), and we will give examples below on using others.\n", - "\n", - "- Download an EAM potential for Al, here we choose `Al99.eam.alloy`\n", - "- create an EAM calculator use ASE module" - ] - }, - { - "cell_type": "code", - "execution_count": 37, - "metadata": {}, - "outputs": [], - "source": [ - "from ase.calculators.espresso import Espresso\n", - "from ase.calculators.eam import EAM\n", - "\n", - "dft_calc = EAM(potential='Al99.eam.alloy')" - ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "\\* **Below is an example of Quantum Espresso**" - ] - }, - { - "cell_type": "code", - "execution_count": 38, - "metadata": {}, - "outputs": [], - "source": [ - "# set up input parameters\n", - "input_data = {'control': {'prefix': 'al',\n", - " 'pseudo_dir': './',\n", - " 'outdir': './out',\n", - " 'verbosity': 'high',\n", - " 'calculation': 'scf'},\n", - " 'system': {'ibrav': 0,\n", - " 'ecutwfc': 50,\n", - " 'ecutrho': 300,\n", - " 'smearing': 'mv',\n", - " 'degauss': 0.02,\n", - " 'occupations': 'smearing'},\n", - " 'electrons':{'conv_thr': 1.0e-08,\n", - " #'startingwfc': 'file',\n", - " 'electron_maxstep': 200,\n", - " 'mixing_beta': 0.4}}\n", - "\n", - "dft_input = {'label': 'al',\n", - " 'pseudopotential': {'Al', 'Al.pbe-n-kjpaw_psl.1.0.0.UPF'},\n", - " 'kpts': (4, 4, 1),\n", - " 'input_data': input_data}\n", - "\n", - "# set up scf executable command\n", - "pw_loc = \"/n/home08/xiey/q-e/bin/pw.x\"\n", - "no_cpus = 1\n", - "npool = 1\n", - "pwi_file = dft_input['label'] + '.pwi'\n", - "pwo_file = dft_input['label'] + '.pwo'\n", - "os.environ['ASE_ESPRESSO_COMMAND'] = 'srun -n {0} --mpi=pmi2 {1} -npool {2} < {3} > {4}'.format(no_cpus, \n", - " pw_loc, npool, pwi_file, pwo_file)\n", - "\n", - "# create ASE calculator\n", - "dft_calc = Espresso(pseudopotentials=dft_input['pseudopotentials'], label=dft_input['label'], \n", - " tstress=True, tprnfor=True, nosym=True, \n", - " input_data=dft_input['input_data'], kpts=dft_input['kpts']) " - ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "\\* **Below is an example of LAMMPS**" - ] - }, - { - "cell_type": "code", - "execution_count": null, - "metadata": {}, - "outputs": [], - "source": [ - "# set up input parameters\n", - "label = 'al'\n", - "pot_path = './'\n", - "parameters = {'pair_style': 'eam/alloy', \n", - " 'pair_coeff': ['* * Al99.eam.alloy Al'], \n", - " 'mass': ['* 27']}\n", - "files = [pot_path+'Al99.eam.alloy']\n", - "\n", - "# set up executable command\n", - "os.environ['LAMMPS_COMMAND'] = './lmp_intel_cpu_intelmpi'\n", - "\n", - "# create ASE calculator\n", - "lmp_calc = LAMMPS(label=label, keep_tmp_files=True, tmp_dir='./', parameters=parameters, files=files)" - ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "## Set up on-the-fly NPT molecular dynamics" - ] - }, - { - "cell_type": "code", - "execution_count": 39, - "metadata": {}, - "outputs": [], - "source": [ - "from ase.md.velocitydistribution import MaxwellBoltzmannDistribution, Stationary, ZeroRotation\n", - "\n", - "# intialize velocity\n", - "MaxwellBoltzmannDistribution(super_cell, 200 * units.kB)\n", - "Stationary(super_cell) # zero linear momentum\n", - "ZeroRotation(super_cell) # zero angular momentum" - ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "Other OTF thermo-stat modules:" - ] - }, - { - "cell_type": "code", - "execution_count": 40, - "metadata": {}, - "outputs": [], - "source": [ - "from flare.modules.ase_otf_md import OTF_NPT\n", - "\n", - "timestep = 1 # fs\n", - "temperature = 100\n", - "externalstress = 0\n", - "ttime = 25\n", - "pfactor = 3375\n", - "\n", - "test_otf_npt = OTF_NPT(super_cell, timestep, temperature,\n", - " externalstress, ttime, pfactor, mask=None,\n", - " # on-the-fly parameters\n", - " dft_calc=dft_calc,\n", - " std_tolerance_factor=1, max_atoms_added=nat,\n", - " freeze_hyps=0,\n", - " # mff parameters\n", - " use_mapping=super_cell.calc.use_mapping)" - ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "Set up the `OTFLogger` so that we will get a log file, which records all the information of the on-the-fly training process, including the positions, velocities, forces, uncertainties of each step, and the parameters of GP and MFF. The dump interval is set to be $1$, meaning the trajectory is dumped every step." - ] - }, - { - "cell_type": "code", - "execution_count": 41, - "metadata": {}, - "outputs": [], - "source": [ - "from flare.modules.ase_otf_logger import OTFLogger\n", - "logfile = 'al.log'\n", - "test_otf_npt.attach(OTFLogger(test_otf_npt, super_cell, logfile, mode=\"w\"), interval=1)" - ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "Now we can run for 5 time steps" - ] - }, - { - "cell_type": "code", - "execution_count": 42, - "metadata": {}, - "outputs": [ - { - "name": "stdout", - "output_type": "stream", - "text": [ - "step: 0\n", - "calling dft\n", - "updating gp\n", - "step: 1\n", - "calling dft\n", - "updating gp\n", - "step: 2\n", - "calling dft\n", - "updating gp\n", - "step: 3\n", - "calling dft\n", - "updating gp\n", - "step: 4\n", - "calling dft\n", - "updating gp\n" - ] - } - ], - "source": [ - "test_otf_npt.otf_run(5)" - ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "It looks like we are done. Let's look at the first several lines of our log file and see what's in there." - ] - }, - { - "cell_type": "code", - "execution_count": 43, - "metadata": {}, - "outputs": [ - { - "name": "stdout", - "output_type": "stream", - "text": [ - "2019-07-31 15:29:05.374015\n", - "number of cpu cores: \n", - "cutoffs: [4.5 4.5]\n", - "kernel: two_plus_three_body\n", - "number of parameters: 5\n", - "hyperparameters: [2.e-01 1.e+00 1.e-04 1.e+00 5.e-03]\n", - "hyperparameter optimization algorithm: L-BFGS-B\n", - "uncertainty tolerance: 1\n", - "periodic cell:\n", - "[[4.05 0. 0. ]\n", - " [0. 4.05 0. ]\n", - " [0. 0. 4.05]]\n", - "\n", - "--------------------------------------------------\n", - "-*Frame: 0.0\n", - "Simulation time: 0.0\n", - " Positions | DFT Forces | Velocities | Uncertainties\n", - " 0.000000 0.000000 0.000000 | -0.000000 -0.000000 -0.000000 | 0.026679 -0.032448 0.010608 | 0.000000 0.000000 0.000000\n", - " 0.000000 2.025000 2.025000 | 0.000000 -0.000000 0.000000 | -0.000962 0.027003 0.022478 | 0.000000 0.000000 0.000000\n", - " 2.025000 0.000000 2.025000 | 0.000000 0.000000 0.000000 | -0.032124 -0.019127 -0.018484 | 0.000000 0.000000 0.000000\n" - ] - } - ], - "source": [ - "f = open(logfile)\n", - "for l in range(20):\n", - " print(f.readline(), end='')" - ] - } - ], - "metadata": { - "kernelspec": { - "display_name": "Python 3", - "language": "python", - "name": "python3" - }, - "language_info": { - "codemirror_mode": { - "name": "ipython", - "version": 3 - }, - "file_extension": ".py", - "mimetype": "text/x-python", - "name": "python", - "nbconvert_exporter": "python", - "pygments_lexer": "ipython3", - "version": "3.7.1" - } - }, - "nbformat": 4, - "nbformat_minor": 2 -} diff --git a/tests/test_ase_setup/atom_setup.py b/tests/test_ase_setup/atom_setup.py deleted file mode 100644 index 80d08c722..000000000 --- a/tests/test_ase_setup/atom_setup.py +++ /dev/null @@ -1,18 +0,0 @@ -import numpy as np -from ase import units -from ase.spacegroup import crystal - -# create primitive cell based on materials project -# url: https://materialsproject.org/materials/mp-22915/ -a = 3.855 -alpha = 90 -super_cell = crystal(['Ag', 'I'], # Ag, I - basis=[(0, 0, 0), (0.5, 0.5, 0.5)], - size=(2, 1, 1), - cellpar=[a, a, a, alpha, alpha, alpha]) - -# jitter positions to give nonzero force on first frame -for atom_pos in super_cell.positions: - for coord in range(3): - atom_pos[coord] += (2*np.random.random()-1) * 0.5 - diff --git a/tests/test_ase_setup/flare_setup.py b/tests/test_ase_setup/flare_setup.py deleted file mode 100644 index 12ca237d5..000000000 --- a/tests/test_ase_setup/flare_setup.py +++ /dev/null @@ -1,53 +0,0 @@ -import numpy as np - -from flare import otf, kernels -from flare.gp import GaussianProcess -from flare.mgp.mgp import MappedGaussianProcess -from flare.ase.calculator import FLARE_Calculator -import flare.kernels.mc_simple as mc_simple - -# ---------- create gaussian process model ------------------- -kernel = mc_simple.two_plus_three_body_mc -kernel_grad = mc_simple.two_plus_three_body_mc_grad -energy_force_kernel = mc_simple.two_plus_three_mc_force_en -energy_kernel = mc_simple.two_plus_three_mc_en - -hyps = np.array([0.1, 1., 0.001, 1, 0.06]) -two_cut = 5.0 -three_cut = 5.0 -cutoffs = np.array([two_cut, three_cut]) -hyp_labels = ['sig2', 'ls2', 'sig3', 'ls3', 'noise'] -opt_algorithm = 'BFGS' - -gp_model = GaussianProcess(kernel, kernel_grad, hyps, cutoffs, - energy_kernel=energy_kernel, - energy_force_kernel=energy_force_kernel, - hyp_labels=hyp_labels, - opt_algorithm=opt_algorithm, par=False) - -# ----------- create mapped gaussian process ------------------ -struc_params = {'species': [47, 53], - 'cube_lat': np.eye(3) * 100, - 'mass_dict': {'0': 27, '1': 16}} - -# grid parameters -lower_cut = 2.5 -grid_num_2 = 8 -grid_num_3 = 8 -grid_params = {'bounds_2': [[lower_cut], [two_cut]], - 'bounds_3': [[lower_cut, lower_cut, -1], - [three_cut, three_cut, 1]], - 'grid_num_2': grid_num_2, - 'grid_num_3': [grid_num_3, grid_num_3, grid_num_3], - 'svd_rank_2': 0, - 'svd_rank_3': 0, - 'bodies': [2, 3], - 'load_grid': None, - 'update': True} - -mgp_model = MappedGaussianProcess(gp_model.hyps, gp_model.cutoffs, - grid_params, struc_params, mean_only=False, container_only=False, - GP=gp_model, lmp_file_name='lmp.mgp') - -# ------------ create ASE's flare calculator ----------------------- -flare_calc = FLARE_Calculator(gp_model, mgp_model, par=True, use_mapping=True) diff --git a/tests/test_ase_setup/otf_setup.py b/tests/test_ase_setup/otf_setup.py deleted file mode 100644 index 6781ca86a..000000000 --- a/tests/test_ase_setup/otf_setup.py +++ /dev/null @@ -1,59 +0,0 @@ -import numpy as np -from copy import deepcopy - -from ase import units -from ase.md.velocitydistribution import (MaxwellBoltzmannDistribution, - Stationary, ZeroRotation) - -from flare.ase.otf_md import otf_md -from flare.ase.logger import OTFLogger - -import atom_setup, flare_setup, qe_setup - -np.random.seed(12345) - -md_engines = ['VelocityVerlet'] #, 'NVTBerendsen', 'NPTBerendsen', 'NPT'] -for md_engine in md_engines: - print(md_engine) - - # ----------- set up atoms ----------------- - super_cell = atom_setup.super_cell - - # ----------- setup flare calculator --------------- - flare_calc = deepcopy(flare_setup.flare_calc) - super_cell.set_calculator(flare_calc) - - # ----------- setup qe calculator -------------- - dft_calc = qe_setup.dft_calc - - # ----------- create otf object ----------- - # set up OTF MD engine - md_params = {'timestep': 1, 'trajectory': None, 'dt': None, - 'externalstress': 0, 'ttime': 25, 'pfactor': 3375, - 'mask': None, 'temperature': 500, 'taut': 1, 'taup': 1, - 'pressure': 0, 'compressibility': 0, 'fixcm': 1} - otf_params = {'dft_calc': dft_calc, - 'init_atoms': [0, 1, 2, 3], - 'std_tolerance_factor': 1, - 'max_atoms_added' : len(super_cell.positions), - 'freeze_hyps': 10, - 'use_mapping': super_cell.calc.use_mapping} - - # intialize velocity - temperature = md_params['temperature'] - MaxwellBoltzmannDistribution(super_cell, temperature * units.kB) - Stationary(super_cell) # zero linear momentum - ZeroRotation(super_cell) # zero angular momentum - - test_otf = otf_md(md_engine, super_cell, md_params, otf_params) - - # set up logger - test_otf.attach(OTFLogger(test_otf, super_cell, - logfile=md_engine+'.log', mode="w"), interval=1) - - # run otf - number_of_steps = 3 - test_otf.otf_run(number_of_steps) - - - diff --git a/tests/test_ase_setup/qe_setup.py b/tests/test_ase_setup/qe_setup.py deleted file mode 100644 index 00d992f9a..000000000 --- a/tests/test_ase_setup/qe_setup.py +++ /dev/null @@ -1,40 +0,0 @@ -import os -from ase.calculators.espresso import Espresso - -# set up executable -label = 'AgI' -input_file_name = label+'.pwi' -output_file_name = label+'.pwo' -no_cpus = 1 -npool = 1 -pw_loc = os.environ.get('PWSCF_COMMAND') -#pw_loc = '/n/home08/xiey/q-e/bin/pw.x' -#os.environ['ASE_ESPRESSO_COMMAND'] = 'srun -n {0} --mpi=pmi2 {1} -npool {2} < {3} > {4}'.format(no_cpus, -# pw_loc, npool, input_file_name, output_file_name) -os.environ['ASE_ESPRESSO_COMMAND'] = '{0} < {1} > {2}'.format(pw_loc, input_file_name, output_file_name) - -# set up input parameters -input_data = {'control': {'prefix': label, - 'pseudo_dir': 'test_files/pseudos/', - 'outdir': './out', - #'verbosity': 'high', - 'calculation': 'scf'}, - 'system': {'ibrav': 0, - 'ecutwfc': 20, # 45, - 'ecutrho': 40, # 181, - 'smearing': 'gauss', - 'degauss': 0.02, - 'occupations': 'smearing'}, - 'electrons': {'conv_thr': 1.0e-02, - #'startingwfc': 'file', - 'electron_maxstep': 100, - 'mixing_beta': 0.7}} - -# pseudo-potentials -ion_pseudo = {'Ag': 'Ag.pbe-n-kjpaw_psl.1.0.0.UPF', - 'I': 'I.pbe-n-kjpaw_psl.1.0.0.UPF'} - -# create ASE calculator -dft_calc = Espresso(pseudopotentials=ion_pseudo, label=label, - tstress=True, tprnfor=True, nosym=True, #noinv=True, - input_data=input_data, kpts=(1,1,1)) diff --git a/tests/test_ase_setup/test_ase_otf.sh b/tests/test_ase_setup/test_ase_otf.sh deleted file mode 100644 index 3019474e4..000000000 --- a/tests/test_ase_setup/test_ase_otf.sh +++ /dev/null @@ -1,19 +0,0 @@ -#!/bin/sh -#SBATCH -n 32 -#SBATCH -N 1 -#SBATCH -t 1-00:00 -#SBATCH -e test.err -#SBATCH -p kozinsky -#SBATCH -o test.out -#SBATCH --job-name="test" -#SBATCH --mem-per-cpu=5000 -#SBATCH --mail-type=ALL -#SBATCH --mail-user=xiey@g.harvard.edu - -module load intel intel-mkl impi -module load Anaconda3/5.0.1-fasrc01 -source activate venv - -#srun -n 160 --mpi=pmi2 /n/home08/xiey/q-e/bin/pw.x -npool 10 -in scf.pwi > pwscf.out -#python otf_setup.py -pytest -q test_ase_otf.py diff --git a/tests/test_ase_setup/test_otf.py b/tests/test_ase_setup/test_otf.py deleted file mode 100644 index ef20ba76a..000000000 --- a/tests/test_ase_setup/test_otf.py +++ /dev/null @@ -1,93 +0,0 @@ -import sys -import os -from copy import deepcopy -import pytest -import numpy as np - -from flare.ase.otf_md import otf_md -from flare.ase.logger import OTFLogger - -from ase import units -from ase.md.velocitydistribution import (MaxwellBoltzmannDistribution, - Stationary, ZeroRotation) - - -def otf_md_test(md_engine): - import atom_setup, flare_setup, qe_setup - np.random.seed(12345) - - print(md_engine) - - # ----------- set up atoms ----------------- - super_cell = atom_setup.super_cell - - # ----------- setup flare calculator --------------- - flare_calc = deepcopy(flare_setup.flare_calc) - super_cell.set_calculator(flare_calc) - - # ----------- setup qe calculator -------------- - dft_calc = qe_setup.dft_calc - - # ----------- create otf object ----------- - # set up OTF MD engine - md_params = {'timestep': 1 * units.fs, 'trajectory': 'otf_md.traj', 'dt': None, - 'externalstress': 0, 'ttime': 25, 'pfactor': 3375, - 'mask': None, 'temperature': 500, 'taut': 1, 'taup': 1, - 'pressure': 0, 'compressibility': 0, 'fixcm': 1, - 'friction': 0.02} - otf_params = {'dft_calc': dft_calc, - 'init_atoms': [0, 1, 2, 3], - 'std_tolerance_factor': 2, - 'max_atoms_added' : len(super_cell.positions), - 'freeze_hyps': 10, - 'use_mapping': super_cell.calc.use_mapping} - - # intialize velocity - temperature = md_params['temperature'] - MaxwellBoltzmannDistribution(super_cell, temperature * units.kB) - Stationary(super_cell) # zero linear momentum - ZeroRotation(super_cell) # zero angular momentum - - test_otf = otf_md(md_engine, super_cell, md_params, otf_params) - - print(test_otf.observers) - - # set up logger - otf_logger = OTFLogger(test_otf, super_cell, - logfile=md_engine+'.log', mode="w", data_in_logfile=True) - test_otf.attach(otf_logger, interval=1) - - # run otf - number_of_steps = 3 - test_otf.otf_run(number_of_steps) - - os.system('rm {}.log'.format(md_engine)) - os.system('rm AgI.pw*') - os.system('rm -r out') - os.system('rm -r __pycache__') - os.system('rm -r kv3') - os.system('rm lmp.mgp') - os.system('rm -r otf_data') - os.system('rm *.npy') - - -@pytest.mark.skipif(not os.environ.get('PWSCF_COMMAND', - False), reason='PWSCF_COMMAND not found ' - 'in environment: Please install Quantum ' - 'ESPRESSO and set the PWSCF_COMMAND env. ' - 'variable to point to pw.x.') -def test_VelocityVerlet(): - otf_md_test('VelocityVerlet') - -def test_NVTBerendsen(): - otf_md_test('NVTBerendsen') - -def test_NPTBerendsen(): - otf_md_test('NPTBerendsen') - -def test_NPT(): - otf_md_test('NPT') - -def test_Langevin(): - otf_md_test('Langevin') - diff --git a/tests/test_ase_setup/test_stress.py b/tests/test_ase_setup/test_stress.py deleted file mode 100644 index af93f94dd..000000000 --- a/tests/test_ase_setup/test_stress.py +++ /dev/null @@ -1,86 +0,0 @@ -import os -import sys -import numpy as np -from flare.otf_parser import OtfAnalysis -from flare.kernels.kernels import two_plus_three_body, two_plus_three_body_grad -from flare.mgp.mgp import MappedGaussianProcess -from flare.ase.calculator import FLARE_Calculator -from flare.kernels.mc_simple import two_plus_three_body_mc, two_plus_three_body_mc_grad -from flare.env import AtomicEnvironment -from flare.predict import predict_on_structure - -from ase.spacegroup import crystal - -def test_stress_with_lammps(): - """ - Based on gp_test_al.out, ensures that given hyperparameters and DFT calls - a GP model can be reproduced and correctly re-predict forces and - uncertainties - :return: - """ - - # build up GP from a previous trajectory - parsed = OtfAnalysis('test_files/VelocityVerlet.log') - - positions = parsed.position_list - forces = parsed.force_list - - gp_model = parsed.make_gp(kernel=two_plus_three_body_mc, - kernel_grad=two_plus_three_body_mc_grad) - - # build up MGP from GP - struc_params = {'species': [47, 53], - 'cube_lat': np.eye(3) * 100, - 'mass_dict': {'0': 27, '1': 16}} - - # grid parameters - lower_cut = 2.5 - grid_num_2 = 64 - grid_num_3 = 32 - two_cut = 5.0 - three_cut = 5.0 - grid_params = {'bounds_2': [[lower_cut], [two_cut]], - 'bounds_3': [[lower_cut, lower_cut, -1], - [three_cut, three_cut, 1]], - 'grid_num_2': grid_num_2, - 'grid_num_3': [grid_num_3, grid_num_3, grid_num_3], - 'svd_rank_2': 0, - 'svd_rank_3': 0, - 'bodies': [2, 3], - 'load_grid': None, - 'update': True} - - mgp_model = MappedGaussianProcess(gp_model.hyps, gp_model.cutoffs, - grid_params, struc_params, mean_only=True, container_only=False, - GP=gp_model, lmp_file_name='lmp.mgp') - - # ------------ create ASE's flare calculator ----------------------- - flare_calc = FLARE_Calculator(gp_model, mgp_model, par=True, use_mapping=True) - - a = 3.855 - alpha = 90 - super_cell = crystal(['Ag', 'I'], # Ag, I - basis=[(0, 0, 0), (0.5, 0.5, 0.5)], - size=(2, 1, 1), - cellpar=[a, a, a, alpha, alpha, alpha]) - super_cell.positions = positions[-1] - super_cell.set_calculator(flare_calc) - super_cell.get_forces() - stresses = super_cell.calc.results['stresses'] - - # parse lammps stress - lmp_file = open('test_files/stress.lammps') - lines = lmp_file.readlines()[9:] - for ind, line in enumerate(lines): - line = line.split() - strs = np.array([float(l) for l in line[1:]]) / 1.60217662e6 - assert np.isclose(stresses[ind], strs, rtol=1e-3).all() - - os.system('rm -r __pycache__') - os.system('rm grid3*') - os.system('rm -r kv3') - os.system('rm lmp.mgp') - - - - diff --git a/tests/test_cp2k_util.py b/tests/test_cp2k_util.py index d44a1ad0c..af0206d51 100644 --- a/tests/test_cp2k_util.py +++ b/tests/test_cp2k_util.py @@ -1,19 +1,22 @@ import pytest -import os +from os import remove, environ +from shutil import copyfile import sys import numpy as np from flare.struc import Structure, get_unique_species from flare.dft_interface.cp2k_util import parse_dft_input, parse_dft_forces, run_dft_par, \ edit_dft_input_positions, dft_input_to_structure + def cleanup(target: str = None): for i in ['cp2k-RESTART.wfn', 'dft.out', 'cp2k.in']: try: - os.remove(i) + remove(i) except: pass if (target is not None): - os.remove(target) + remove(target) + @pytest.mark.parametrize("cp2k_input,exp_spec", [ @@ -21,13 +24,13 @@ def cleanup(target: str = None): ['H', 'H']) ] ) - def test_species_parsing(cp2k_input, exp_spec): positions, species, cell, masses = parse_dft_input(cp2k_input) assert len(species) == len(exp_spec) for i, spec in enumerate(species): assert spec == exp_spec[i] + @pytest.mark.parametrize("cp2k_input,exp_cell", [ ('./test_files/cp2k_input_1.in', @@ -68,16 +71,15 @@ def test_input_to_structure(cp2k_input): './test_files/cp2k_output_1.out') ] ) -@pytest.mark.skipif(not os.environ.get('CP2K_COMMAND', - False), reason='CP2K_COMMAND not found ' - 'in environment: Please install CP2K ' - ' and set the CP2K_COMMAND env. ' - 'variable to point to cp2k.popt') - +@pytest.mark.skipif(not environ.get('CP2K_COMMAND', + False), reason='CP2K_COMMAND not found ' + 'in environment: Please install CP2K ' + ' and set the CP2K_COMMAND env. ' + 'variable to point to cp2k.popt') def test_cp2k_calling(cp2k_input, cp2k_output): - dft_loc = os.environ.get('CP2K_COMMAND') - os.system(' '.join(['cp', cp2k_input, 'cp2k.in'])) + dft_loc = environ.get('CP2K_COMMAND') + copyfile(cp2k_input, 'cp2k.in') positions, species, cell, masses = parse_dft_input(cp2k_input) structure = Structure(cell=cell, species=species, @@ -85,7 +87,7 @@ def test_cp2k_calling(cp2k_input, cp2k_output): mass_dict=masses, species_labels=species) forces = run_dft_par('cp2k.in', - structure, dft_loc) + structure, dft_loc) ref_forces = parse_dft_forces(cp2k_output) @@ -96,13 +98,15 @@ def test_cp2k_calling(cp2k_input, cp2k_output): cleanup() + def test_cp2k_input_edit(): """ Load a structure in from cp2k_input_1, change the position and cell, then edit and re-parse :return: """ - positions, species, cell, masses = parse_dft_input('./test_files/cp2k_input_1.in') + positions, species, cell, masses = parse_dft_input( + './test_files/cp2k_input_1.in') _, coded_species = get_unique_species(species) structure = Structure(cell, coded_species, positions, masses, species_labels=species) @@ -110,12 +114,13 @@ def test_cp2k_input_edit(): structure.vec1 += np.random.randn(3) structure.positions[0] += np.random.randn(3) - newfilename = edit_dft_input_positions('./test_files/cp2k_input_1.in', structure=structure) + newfilename = edit_dft_input_positions( + './test_files/cp2k_input_1.in', structure=structure) positions, species, cell, masses = parse_dft_input(newfilename) assert np.equal(positions[0], structure.positions[0]).all() assert np.equal(structure.vec1, cell[0, :]).all() - os.remove(newfilename) + remove(newfilename) cleanup() diff --git a/tests/test_env.py b/tests/test_env.py index 3a7dac909..70c76b7c9 100644 --- a/tests/test_env.py +++ b/tests/test_env.py @@ -3,8 +3,10 @@ from flare.struc import Structure from flare.env import AtomicEnvironment +cutoff_list=[np.ones(2), np.ones(3)*0.8] -def test_species_count(): +@pytest.mark.parametrize('cutoff', cutoff_list) +def test_species_count(cutoff): cell = np.eye(3) species = [1, 2, 3] positions = np.array([[0, 0, 0], [0.5, 0.5, 0.5], [0.1, 0.1, 0.1]]) @@ -16,8 +18,8 @@ def test_species_count(): assert (len(env_test.bond_array_2) == len(env_test.etypes)) assert (isinstance(env_test.etypes[0], np.int8)) - -def test_env_methods(): +@pytest.mark.parametrize('cutoff', cutoff_list) +def test_env_methods(cutoff): cell = np.eye(3) species = [1, 2, 3] positions = np.array([[0, 0, 0], [0.5, 0.5, 0.5], [0.1, 0.1, 0.1]]) @@ -28,8 +30,7 @@ def test_env_methods(): the_dict = env_test.as_dict() assert isinstance(the_dict, dict) - for key in ['positions', 'cell', 'atom', 'cutoff_2', 'cutoff_3', - 'species']: + for key in ['positions', 'cell', 'atom', 'cutoffs', 'species']: assert key in the_dict.keys() remade_env = AtomicEnvironment.from_dict(the_dict) @@ -37,3 +38,4 @@ def test_env_methods(): assert np.array_equal(remade_env.bond_array_2, env_test.bond_array_2) assert np.array_equal(remade_env.bond_array_3, env_test.bond_array_3) + assert np.array_equal(remade_env.bond_array_mb, env_test.bond_array_mb) diff --git a/tests/test_files/cp2k_input_1.in b/tests/test_files/cp2k_input_1.in index b2dc7fbf6..e5eb318a8 100644 --- a/tests/test_files/cp2k_input_1.in +++ b/tests/test_files/cp2k_input_1.in @@ -36,8 +36,8 @@ H 2.8 2.5 2.5 &END QS &MGRID NGRIDS 4 - CUTOFF 360 - REL_CUTOFF 60 + CUTOFF 200 + REL_CUTOFF 50 &END MGRID &XC &XC_FUNCTIONAL PBE diff --git a/tests/test_files/cp2k_input_2.in b/tests/test_files/cp2k_input_2.in index 36cf642de..06e258daf 100644 --- a/tests/test_files/cp2k_input_2.in +++ b/tests/test_files/cp2k_input_2.in @@ -38,15 +38,15 @@ &END QS &MGRID NGRIDS 4 - CUTOFF 360 - REL_CUTOFF 60 + CUTOFF 200 + REL_CUTOFF 50 &END MGRID &XC &XC_FUNCTIONAL PBE &END XC_FUNCTIONAL &END XC &KPOINTS - SCHEME MONKHORST-PACK 4 4 4 + SCHEME MONKHORST-PACK 2 2 2 SYMMETRY OFF FULL_GRID .TRUE. &END KPOINTS diff --git a/tests/test_files/cp2k_output_1.out b/tests/test_files/cp2k_output_1.out index c7f7c11a1..1c24bcf8c 100644 --- a/tests/test_files/cp2k_output_1.out +++ b/tests/test_files/cp2k_output_1.out @@ -11,12 +11,12 @@ DBCSR| Communication thread load 87 - **** **** ****** ** PROGRAM STARTED AT 2019-09-20 20:18:51.918 - ***** ** *** *** ** PROGRAM STARTED ON holy2b17301.rc.fas.harvard.edu + **** **** ****** ** PROGRAM STARTED AT 2020-02-14 00:11:51.543 + ***** ** *** *** ** PROGRAM STARTED ON holy2c02301.rc.fas.harvard.edu ** **** ****** PROGRAM STARTED BY leleslx - ***** ** ** ** ** PROGRAM PROCESS ID 155402 - **** ** ******* ** PROGRAM STARTED IN /n/home06/leleslx/software/flare-inte - rnal-dev/tests + ***** ** ** ** ** PROGRAM PROCESS ID 171619 + **** ** ******* ** PROGRAM STARTED IN /n/home06/leleslx/software/new_flare/ + flare/tests CP2K| version string: CP2K version 6.1 CP2K| source code revision number: svn:18464 @@ -50,12 +50,12 @@ MEMORY| system memory details [Kb] MEMORY| rank 0 min max average MEMORY| MemTotal 196503704 196503704 196503704 196503704 - MEMORY| MemFree 185503988 185503988 185503988 185503988 - MEMORY| Buffers 3392 3392 3392 3392 - MEMORY| Cached 5687232 5687232 5687232 5687232 - MEMORY| Slab 1480960 1480960 1480960 1480960 - MEMORY| SReclaimable 575188 575188 575188 575188 - MEMORY| MemLikelyFree 191769800 191769800 191769800 191769800 + MEMORY| MemFree 160346800 160346800 160346800 160346800 + MEMORY| Buffers 1256 1256 1256 1256 + MEMORY| Cached 23555008 23555008 23555008 23555008 + MEMORY| Slab 8113080 8113080 8113080 8113080 + MEMORY| SReclaimable 1472820 1472820 1472820 1472820 + MEMORY| MemLikelyFree 185375884 185375884 185375884 185375884 GENERATE| Preliminary Number of Bonds generated: 0 @@ -137,46 +137,46 @@ Step Update method Time Convergence Total energy Change ------------------------------------------------------------------------------ - 1 NoMix/Diag. 0.40E+00 0.4 0.52273065 -0.8584428329 -8.58E-01 - 2 Broy./Diag. 0.40E+00 0.7 0.01800548 -0.5755263671 2.83E-01 - 3 Broy./Diag. 0.40E+00 0.7 0.02878929 -0.9969489471 -4.21E-01 - 4 Broy./Diag. 0.40E+00 0.7 0.00198352 -1.0832975096 -8.63E-02 - 5 Broy./Diag. 0.40E+00 0.7 0.00216899 -1.0861327988 -2.84E-03 - 6 Broy./Diag. 0.40E+00 0.7 0.00021878 -1.0915151459 -5.38E-03 - 7 Broy./Diag. 0.40E+00 0.7 0.00013034 -1.0917618903 -2.47E-04 - 8 Broy./Diag. 0.40E+00 0.7 0.00001340 -1.0920124528 -2.51E-04 - 9 Broy./Diag. 0.40E+00 0.7 0.00000708 -1.0919588997 5.36E-05 - 10 Broy./Diag. 0.40E+00 0.7 0.00000058 -1.0919740231 -1.51E-05 - 11 Broy./Diag. 0.40E+00 0.7 0.00000023 -1.0919725678 1.46E-06 - 12 Broy./Diag. 0.40E+00 0.7 8.5025E-09 -1.0919731152 -5.47E-07 + 1 NoMix/Diag. 0.40E+00 0.1 0.52272070 -0.8585591896 -8.59E-01 + 2 Broy./Diag. 0.40E+00 0.2 0.01800774 -0.5756437590 2.83E-01 + 3 Broy./Diag. 0.40E+00 0.2 0.02879822 -0.9970955257 -4.21E-01 + 4 Broy./Diag. 0.40E+00 0.2 0.00199554 -1.0833733819 -8.63E-02 + 5 Broy./Diag. 0.40E+00 0.2 0.00217236 -1.0862310176 -2.86E-03 + 6 Broy./Diag. 0.40E+00 0.2 0.00022075 -1.0916219305 -5.39E-03 + 7 Broy./Diag. 0.40E+00 0.2 0.00013076 -1.0918681597 -2.46E-04 + 8 Broy./Diag. 0.40E+00 0.2 0.00001327 -1.0921204691 -2.52E-04 + 9 Broy./Diag. 0.40E+00 0.2 0.00000712 -1.0920670308 5.34E-05 + 10 Broy./Diag. 0.40E+00 0.2 0.00000058 -1.0920823956 -1.54E-05 + 11 Broy./Diag. 0.40E+00 0.2 0.00000023 -1.0920810214 1.37E-06 + 12 Broy./Diag. 0.40E+00 0.2 9.0579E-09 -1.0920815646 -5.43E-07 *** SCF run converged in 12 steps *** - Electronic density on regular grids: -2.0000000000 0.0000000000 - Core density on regular grids: 1.9999999999 -0.0000000001 - Total charge density on r-space grids: -0.0000000001 - Total charge density g-space grids: -0.0000000001 + Electronic density on regular grids: -1.9999998522 0.0000001478 + Core density on regular grids: 1.9999998665 -0.0000001335 + Total charge density on r-space grids: 0.0000000143 + Total charge density g-space grids: 0.0000000143 Overlap energy of the core charge distribution: 0.00088477319713 Self energy of the core charge distribution: -2.82094791773878 - Core Hamiltonian energy: 1.20081565211180 - Hartree energy: 1.30824132174120 - Exchange-correlation energy: -0.78096694454962 + Core Hamiltonian energy: 1.20079604563393 + Hartree energy: 1.30814074355464 + Exchange-correlation energy: -0.78095520922623 Electronic entropic energy: -0.00000000000000 - Fermi energy: 0.14407163925527 + Fermi energy: 0.14408041695107 - Total energy: -1.09197311523827 + Total energy: -1.09208156457930 - ENERGY| Total FORCE_EVAL ( QS ) energy (a.u.): -1.091973044533034 + ENERGY| Total FORCE_EVAL ( QS ) energy (a.u.): -1.092081493628632 ATOMIC FORCES in [a.u.] # Atom Kind Element X Y Z - 1 1 H -0.43643419 -0.00000000 -0.00000000 - 2 1 H 0.43643401 0.00000000 0.00000000 - SUM OF ATOMIC FORCES -0.00000019 -0.00000000 -0.00000000 0.00000019 + 1 1 H -0.43720823 0.00000000 0.00000000 + 2 1 H 0.43722416 0.00000000 0.00000000 + SUM OF ATOMIC FORCES 0.00001593 0.00000000 0.00000000 0.00001593 ------------------------------------------------------------------------------- - - @@ -195,7 +195,7 @@ marketing flops 2.600000E+03 ------------------------------------------------------------------------------- # multiplications 13 - max memory usage/rank 313.237504E+06 + max memory usage/rank 141.066240E+06 # max total images/rank 1 # max 3D layers 1 # MPI messages exchanged 0 @@ -214,7 +214,7 @@ 16777216 < size 0 0 ------------------------------------------------------------------------------- - MEMORY| Estimated peak process memory [MiB] 299 + MEMORY| Estimated peak process memory [MiB] 135 ------------------------------------------------------------------------------- - - @@ -321,42 +321,41 @@ ------------------------------------------------------------------------------- SUBROUTINE CALLS ASD SELF TIME TOTAL TIME MAXIMUM AVERAGE MAXIMUM AVERAGE MAXIMUM - CP2K 1 1.0 0.005 0.005 9.702 9.702 - qs_forces 1 2.0 0.000 0.000 9.584 9.584 - qs_energies 1 3.0 0.000 0.000 9.106 9.106 - scf_env_do_scf 1 4.0 0.000 0.000 8.714 8.714 - scf_env_do_scf_inner_loop 12 5.0 0.001 0.001 8.714 8.714 - rebuild_ks_matrix 13 6.8 0.000 0.000 5.349 5.349 - qs_ks_build_kohn_sham_matrix 13 7.8 0.001 0.001 5.349 5.349 - qs_ks_update_qs_env 12 6.0 0.000 0.000 4.900 4.900 - qs_rho_update_rho 13 6.0 0.000 0.000 3.614 3.614 - calculate_rho_elec 13 7.0 3.288 3.288 3.614 3.614 - sum_up_and_integrate 13 8.8 0.006 0.006 2.590 2.590 - integrate_v_rspace 13 9.8 2.422 2.422 2.584 2.584 - qs_vxc_create 13 8.8 0.000 0.000 2.524 2.524 - xc_vxc_pw_create 13 9.8 0.029 0.029 2.524 2.524 - xc_rho_set_and_dset_create 13 10.8 0.013 0.013 1.920 1.920 - pw_transfer 259 10.3 0.008 0.008 1.565 1.565 - fft_wrap_pw1pw2 233 11.4 0.001 0.001 1.534 1.534 - fft_wrap_pw1pw2_260 155 12.3 0.091 0.091 1.482 1.482 - xc_functional_eval 13 11.8 0.000 0.000 1.459 1.459 - pbe_lda_eval 13 12.8 1.459 1.459 1.459 1.459 - fft3d_s 234 13.3 1.045 1.045 1.051 1.051 - gspace_mixing 11 6.0 0.005 0.005 0.465 0.465 - qs_ks_update_qs_env_forces 1 3.0 0.000 0.000 0.449 0.449 - init_scf_run 1 4.0 0.000 0.000 0.352 0.352 - scf_env_initial_rho_setup 1 5.0 0.000 0.000 0.351 0.351 - broyden_mixing 11 7.0 0.350 0.350 0.351 0.351 - density_rs2pw 13 8.0 0.000 0.000 0.320 0.320 - pw_scatter_s 128 13.7 0.219 0.219 0.219 0.219 + CP2K 1 1.0 0.004 0.004 3.043 3.043 + qs_forces 1 2.0 0.000 0.000 2.998 2.998 + qs_energies 1 3.0 0.000 0.000 2.831 2.831 + scf_env_do_scf 1 4.0 0.000 0.000 2.689 2.689 + scf_env_do_scf_inner_loop 12 5.0 0.001 0.001 2.689 2.689 + rebuild_ks_matrix 13 6.8 0.000 0.000 1.612 1.612 + qs_ks_build_kohn_sham_matrix 13 7.8 0.001 0.001 1.612 1.612 + qs_ks_update_qs_env 12 6.0 0.000 0.000 1.474 1.474 + qs_rho_update_rho 13 6.0 0.000 0.000 1.191 1.191 + calculate_rho_elec 13 7.0 1.124 1.124 1.191 1.191 + sum_up_and_integrate 13 8.8 0.001 0.001 0.981 0.981 + integrate_v_rspace 13 9.8 0.944 0.944 0.980 0.980 + qs_vxc_create 13 8.8 0.000 0.000 0.581 0.581 + xc_vxc_pw_create 13 9.8 0.006 0.006 0.581 0.581 + xc_rho_set_and_dset_create 13 10.8 0.003 0.003 0.454 0.454 + xc_functional_eval 13 11.8 0.000 0.000 0.356 0.356 + pbe_lda_eval 13 12.8 0.356 0.356 0.356 0.356 + pw_transfer 259 10.3 0.005 0.005 0.335 0.335 + fft_wrap_pw1pw2 233 11.4 0.001 0.001 0.325 0.325 + fft_wrap_pw1pw2_100 155 12.3 0.018 0.018 0.316 0.316 + fft3d_s 234 13.3 0.231 0.231 0.233 0.233 + qs_ks_update_qs_env_forces 1 3.0 0.000 0.000 0.138 0.138 + init_scf_run 1 4.0 0.000 0.000 0.113 0.113 + scf_env_initial_rho_setup 1 5.0 0.000 0.000 0.112 0.112 + gspace_mixing 11 6.0 0.001 0.001 0.094 0.094 + broyden_mixing 11 7.0 0.071 0.071 0.071 0.071 + density_rs2pw 13 8.0 0.000 0.000 0.066 0.066 ------------------------------------------------------------------------------- The number of warnings for this run is : 1 ------------------------------------------------------------------------------- - **** **** ****** ** PROGRAM ENDED AT 2019-09-20 20:19:01.878 - ***** ** *** *** ** PROGRAM RAN ON holy2b17301.rc.fas.harvard.edu + **** **** ****** ** PROGRAM ENDED AT 2020-02-14 00:11:54.849 + ***** ** *** *** ** PROGRAM RAN ON holy2c02301.rc.fas.harvard.edu ** **** ****** PROGRAM RAN BY leleslx - ***** ** ** ** ** PROGRAM PROCESS ID 155402 - **** ** ******* ** PROGRAM STOPPED IN /n/home06/leleslx/software/flare-inte - rnal-dev/tests + ***** ** ** ** ** PROGRAM PROCESS ID 171619 + **** ** ******* ** PROGRAM STOPPED IN /n/home06/leleslx/software/new_flare/ + flare/tests diff --git a/tests/test_files/methanol_envs.json b/tests/test_files/methanol_envs.json index 631f90298..9d51479aa 100644 --- a/tests/test_files/methanol_envs.json +++ b/tests/test_files/methanol_envs.json @@ -1,30 +1,30 @@ -{"positions": [[5.03563633866, 5.36271556620095, 11.2933952], [5.38915186548, 4.054992895620321, 10.8335726], [6.100133659559999, 4.143064139723637, 10.176066800000001], [4.6459741644, 6.0015730673637, 10.4806232], [5.88253198662, 5.878838012303517, 11.7798308], [4.240205739, 5.227405596018015, 12.0381068]], "cell": [[10.638, 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-0.30820008, 0.28712989], "object": "AtomicEnvironment", "energy": null, "stress": null} diff --git a/tests/test_files/pseudos/Ag.pbe-n-kjpaw_psl.1.0.0.UPF b/tests/test_files/pseudos/Ag.pbe-n-kjpaw_psl.1.0.0.UPF deleted file mode 100644 index 938057b80..000000000 --- a/tests/test_files/pseudos/Ag.pbe-n-kjpaw_psl.1.0.0.UPF +++ /dev/null @@ -1,21995 +0,0 @@ - - -Generated using "atomic" code by A. Dal Corso v.6.2.2 -Author: ADC -Generation date: 2May2018 -Pseudopotential type: PAW -Element: Ag -Functional: SLA PW PBX PBC - Suggested minimum cutoff for wavefunctions: 45. Ry - Suggested minimum cutoff for charge density: 181. Ry - The Pseudo was generated with a Scalar-Relativistic Calculation - Local Potential by smoothing AE potential with Bessel fncs, cutoff radius: 2.4000 - Pseudopotential contains additional information for GIPAW reconstruction. - Valence configuration: - nl pn l occ Rcut Rcut US E pseu - 5S 1 0 1.50 1.600 2.200 -0.365815 - 5P 2 1 0.00 1.600 2.200 -0.072012 - 4D 3 2 9.50 1.600 1.700 -0.664556 - Generation configuration: - 5S 1 0 1.50 1.600 2.200 -0.365813 - 5S 1 0 0.00 1.600 2.200 6.500000 - 5P 2 1 0.00 1.600 2.200 -0.072012 - 5P 2 1 0.00 1.600 2.200 6.000000 - 4D 3 2 9.50 1.600 1.700 -0.664555 - 4D 3 2 0.00 1.600 1.700 0.030000 - Pseudization used: troullier-martins - - - &input - title='Ag', - zed=47., - rel=1, - config='[Kr] 5s1.5 5p0 4d9.5', - iswitch=3, - dft='PBE' - / - &inputp - lpaw=.true., - use_xsd=.FALSE., - pseudotype=3, - file_pseudopw='Ag.pbe-n-kjpaw_psl.1.0.0.UPF', - author='ADC', - lloc=-1, - rcloc=2.4, - which_augfun='PSQ', - rmatch_augfun_nc=.true., - nlcc=.true., - new_core_ps=.true., - 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Dal Corso v.6.3MaX +Author: ADC +Generation date: 3Sep2018 +Pseudopotential type: PAW +Element: He +Functional: SLA PW PBX PBC + Suggested minimum cutoff for wavefunctions: 46. Ry + Suggested minimum cutoff for charge density: 203. Ry + The Pseudo was generated with a Scalar-Relativistic Calculation + Local Potential by smoothing AE potential with Bessel fncs, cutoff radius: 0.6000 + Pseudopotential contains additional information for GIPAW reconstruction. + Valence configuration: + nl pn l occ Rcut Rcut US E pseu + 1S 1 0 2.00 0.900 1.100 -1.158627 + Generation configuration: + 1S 1 0 2.00 0.900 1.100 -1.158625 + 1S 1 0 0.00 0.900 1.100 0.050000 + Pseudization used: troullier-martins + + + &input + title='He', + zed=2., + rel=1, + config='1s2 2p-2', + iswitch=3, + dft='PBE' + / + &inputp + lpaw=.true., + use_xsd=.FALSE., + pseudotype=3, + file_pseudopw='He.pbe-kjpaw_psl.1.0.0.UPF', + author='ADC', + lloc=-1, + rcloc=0.6 + which_augfun='PSQ', + rmatch_augfun_nc=.true., + tm=.true. + / +2 +1S 1 0 2.00 0.00 0.90 1.10 0.0 +1S 1 0 0.00 0.05 0.90 1.10 0.0 + + + + + + + 4.559409827772581e-4 4.616760143345111e-4 4.674831837082911e-4 4.733633982806269e-4 + 4.793175768470100e-4 4.853466497599545e-4 4.914515590743693e-4 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0.000000000000000e0 + + + + diff --git a/tests/test_files/pseudos/I.pbe-n-kjpaw_psl.1.0.0.UPF b/tests/test_files/pseudos/I.pbe-n-kjpaw_psl.1.0.0.UPF deleted file mode 100644 index 4d60d9571..000000000 --- a/tests/test_files/pseudos/I.pbe-n-kjpaw_psl.1.0.0.UPF +++ /dev/null @@ -1,22134 +0,0 @@ - - -Generated using "atomic" code by A. Dal Corso v.6.3 -Author: ADC -Generation date: 6Sep2018 -Pseudopotential type: PAW -Element: I -Functional: SLA PW PBX PBC - Suggested minimum cutoff for wavefunctions: 36. Ry - Suggested minimum cutoff for charge density: 154. Ry - The Pseudo was generated with a Scalar-Relativistic Calculation - Local Potential by smoothing AE potential with Bessel fncs, cutoff radius: 2.1000 - Pseudopotential contains additional information for GIPAW reconstruction. - Valence configuration: - nl pn l occ Rcut Rcut US E pseu - 5S 1 0 2.00 1.500 1.800 -1.279409 - 5P 2 1 5.00 1.800 2.000 -0.520797 - Generation configuration: - 5S 1 0 2.00 1.500 1.800 -1.279404 - 5S 1 0 0.00 1.500 1.800 6.300000 - 5P 2 1 5.00 1.800 2.000 -0.520794 - 5P 2 1 0.00 1.800 2.000 6.300000 - 5D 3 2 0.00 1.600 2.100 0.500000 - 5D 3 2 0.00 1.600 2.100 4.300000 - Pseudization used: troullier-martins - - - &input - title='I', - zed=53., - rel=1, - config='[Kr] 5s2 5p5 4d10 5d-1', - iswitch=3, - dft='PBE' - / - &inputp - lpaw = .true., - use_xsd=.FALSE., - pseudotype=3, - file_pseudopw='I.pbe-n-kjpaw_psl.1.0.0.UPF', - author='ADC', - lloc=-1, - rcloc=2.1, - which_augfun='PSQ', - rmatch_augfun_nc=.true., - nlcc=.true., - new_core_ps=.true., - rcore=1.8, - tm=.true. - 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0.000000000000000e0 0.000000000000000e0 0.000000000000000e0 - - - - diff --git a/tests/test_files/sample_al_otf.out b/tests/test_files/sample_al_otf.out index 80a858b95..000c4ba48 100644 --- a/tests/test_files/sample_al_otf.out +++ b/tests/test_files/sample_al_otf.out @@ -1,7 +1,7 @@ 2019-03-28 01:01:12.085624 number of cpu cores: 4 cutoffs: [3.9 3.9] -kernel: three_body +kernel_name: three_body number of hyperparameters: 3 hyperparameters: [0.1 1. 0.01] hyperparameter optimization algorithm: L-BFGS-B diff --git a/tests/test_files/sample_h2_otf.out b/tests/test_files/sample_h2_otf.out index be1d88228..d3c6ab3a1 100644 --- a/tests/test_files/sample_h2_otf.out +++ b/tests/test_files/sample_h2_otf.out @@ -1,7 +1,7 @@ 2019-03-26 12:26:08.749589 number of cpu cores: 4 cutoffs: [5] -kernel: two_body +kernel_name: two_body number of hyperparameters: 3 hyperparameters: [1 1 1] hyperparameter optimization algorithm: L-BFGS-B diff --git a/tests/test_files/sample_slab_otf.out b/tests/test_files/sample_slab_otf.out index 5fb1b20dc..eb257d1bb 100644 --- a/tests/test_files/sample_slab_otf.out +++ b/tests/test_files/sample_slab_otf.out @@ -1,7 +1,7 @@ 2019-04-15 18:34:47.476879 number of cpu cores: 16 cutoffs: [7. 4.5] -kernel: two_plus_three_body +kernel_name: two_plus_three_body number of hyperparameters: 5 hyperparameters: [0.1 1. 0.01 1. 0.05] hyperparameter optimization algorithm: BFGS diff --git a/tests/test_files/stress.lammps b/tests/test_files/stress.lammps index 9b750dc68..031ba630c 100644 --- a/tests/test_files/stress.lammps +++ b/tests/test_files/stress.lammps @@ -7,7 +7,7 @@ ITEM: BOX BOUNDS pp pp pp 0.0000000000000000e+00 3.8550000000000000e+00 0.0000000000000000e+00 3.8550000000000000e+00 ITEM: ATOMS id c_myPress[1] c_myPress[2] c_myPress[3] c_myPress[4] c_myPress[5] c_myPress[6] -1 2.51506e+06 3.81827e+06 4.84041e+06 96527.1 133966 395000 -2 -1.71386e+06 -2.14715e+06 -4.75512e+06 -966030 498826 157066 -3 3.63328e+06 5.08673e+06 3.97462e+06 77313.7 -83640.4 -256131 -4 -1.73674e+06 -3.98693e+06 -2.41198e+06 980022 -104617 -10352.2 +1 2.51198e+06 3.81503e+06 4.83892e+06 95393.9 133085 393271 +2 -1.69974e+06 -2.13962e+06 -4.74858e+06 -966574 502294 158801 +3 3.63502e+06 5.08776e+06 3.97311e+06 76821.5 -82439.8 -257357 +4 -1.73095e+06 -3.98967e+06 -2.41151e+06 979102 -103187 -11958.8 diff --git a/tests/test_gp.py b/tests/test_gp.py index 81aa84f46..f0c91ad56 100644 --- a/tests/test_gp.py +++ b/tests/test_gp.py @@ -6,78 +6,63 @@ from typing import List from pytest import raises +from scipy.optimize import OptimizeResult +import flare from flare.gp import GaussianProcess from flare.env import AtomicEnvironment from flare.struc import Structure -import flare.kernels.kernels as en +import flare.kernels.sc as en import flare.kernels.mc_simple as mc_simple from flare.otf_parser import OtfAnalysis +from .fake_gp import generate_hm, get_tstp, get_random_structure +from copy import deepcopy -def get_random_structure(cell, unique_species, noa): - """Create a random test structure """ - np.random.seed(0) +multihyps_list = [True, False] - positions = [] - forces = [] - species = [] - for n in range(noa): - positions.append(np.random.uniform(-1, 1, 3)) - forces.append(np.random.uniform(-1, 1, 3)) - species.append(unique_species[np.random.randint(0, - len(unique_species))]) - - test_structure = Structure(cell, species, positions) - - return test_structure, forces - - -# ------------------------------------------------------ -# fixtures -# ------------------------------------------------------ - - -# set the scope to module so it will only be setup once -@pytest.fixture(scope='module') -def two_body_gp() -> GaussianProcess: +@pytest.fixture(scope='class') +def all_gps() -> GaussianProcess: """Returns a GP instance with a two-body numba-based kernel""" - print("\nSetting up...\n") - # params - cell = np.eye(3) - unique_species = [2, 1] - cutoffs = np.array([0.8, 0.8]) - noa = 5 + gp_dict = {True: None, False: None} + for multihyps in multihyps_list: + cutoffs = np.ones(2)*0.8 + hyps, hm, _ = generate_hm(1, 1, multihyps=multihyps) + hl = hm['hyps_label'] + if (multihyps is False): + hm = None - # create test structure - test_structure, forces = get_random_structure(cell, unique_species, - noa) + # test update_db + gpname = '2+3+mb_mc' + hyps = np.hstack([hyps, [1, 1]]) + hl = np.hstack([hl[:-1], ['sigm', 'lsm'], hl[-1]]) + cutoffs = np.ones(3)*0.8 - # test update_db - gaussian = \ - GaussianProcess(kernel=en.three_body, - kernel_grad=en.three_body_grad, - hyps=np.array([1, 1, 1]), - hyp_labels=['Length', 'Signal Var.', 'Noise Var.'], - cutoffs=cutoffs) - gaussian.update_db(test_structure, forces) + gp_dict[multihyps] = \ + GaussianProcess(kernel_name=gpname, + hyps=hyps, + hyp_labels=hl, + cutoffs=cutoffs, + multihyps=multihyps, hyps_mask=hm, + parallel=False, n_cpus=1) - # return gaussian - yield gaussian + test_structure, forces = get_random_structure(np.eye(3), + [1, 2], + 3) - # code after yield will be executed once all tests are run - # this will not be run if an exception is raised in the setup - print("\n\nTearing down\n") - del gaussian + gp_dict[multihyps].update_db(test_structure, forces) + + yield gp_dict + del gp_dict @pytest.fixture(scope='module') def params(): parameters = {'unique_species': [2, 1], 'cutoff': 0.8, - 'noa': 5, + 'noa': 3, 'cell': np.eye(3), 'db_pts': 30} yield parameters @@ -85,210 +70,323 @@ def params(): @pytest.fixture(scope='module') -def test_point() -> AtomicEnvironment: - """Create test point for kernel to compare against""" - # params - cell = np.eye(3) - unique_species = [2, 1] - cutoff = 0.8 - noa = 10 - - test_structure_2, _ = get_random_structure(cell, unique_species, - noa) - - test_pt = AtomicEnvironment(test_structure_2, 0, - np.array([cutoff, cutoff])) - +def validation_env() -> AtomicEnvironment: + test_pt = get_tstp() yield test_pt del test_pt - # ------------------------------------------------------ # test GP methods # ------------------------------------------------------ -def test_update_db(two_body_gp, params): - # params - test_structure, forces = get_random_structure(params['cell'], - params['unique_species'], - params['noa']) +class TestDataUpdating(): - # add structure and forces to db - two_body_gp.update_db(test_structure, forces) + @pytest.mark.parametrize('multihyps', multihyps_list) + def test_update_db(self, all_gps, multihyps, params): - assert (len(two_body_gp.training_data) == params['noa'] * 2) - assert (len(two_body_gp.training_labels_np) == params['noa'] * 2 * 3) + test_gp = all_gps[multihyps] + oldsize = len(test_gp.training_data) + # add structure and forces to db + test_structure, forces = get_random_structure(params['cell'], + params['unique_species'], + params['noa']) -def test_get_kernel_vector(two_body_gp, test_point, params): - assert (two_body_gp.get_kernel_vector(test_point, 1).shape == - (params['db_pts'],)) + test_gp.update_db(test_structure, forces) + assert (len(test_gp.training_data) == params['noa']+oldsize) + assert (len(test_gp.training_labels_np) == (params['noa']+oldsize)*3) -def test_train(two_body_gp, params): - hyp = list(two_body_gp.hyps) - - # add struc and forces to db - test_structure, forces = get_random_structure(params['cell'], - params['unique_species'], - params['noa']) - two_body_gp.update_db(test_structure, forces) +# - # train gp - two_body_gp.train() - hyp_post = list(two_body_gp.hyps) +class TestTraining(): + @pytest.mark.parametrize('par, n_cpus', [(False, 1), + (True, 2)]) + @pytest.mark.parametrize('multihyps', multihyps_list) + def test_train(self, all_gps, params, par, n_cpus, multihyps): - # check if hyperparams have been updated - assert (hyp != hyp_post) + test_gp = all_gps[multihyps] + test_gp.parallel = par + test_gp.n_cpus = n_cpus + + test_gp.maxiter = 1 + + # train gp + test_gp.hyps = np.ones(len(test_gp.hyps)) + hyp = list(test_gp.hyps) + test_gp.train() + + hyp_post = list(test_gp.hyps) + + # check if hyperparams have been updated + assert (hyp != hyp_post) + + def test_train_failure(self, all_gps, params, mocker): + """ + Tests the case when 'L-BFGS-B' fails due to a linear algebra error and + training falls back to BFGS + """ + # Sets up mocker for scipy minimize. Note that we are mocking + # 'flare.gp.minimize' because of how the imports are done in gp + x_result = np.random.rand() + fun_result = np.random.rand() + jac_result = np.random.rand() + train_result = OptimizeResult(x=x_result, fun=fun_result, + jac=jac_result) + + side_effects = [np.linalg.LinAlgError(), train_result] + mocker.patch('flare.gp.minimize', side_effect=side_effects) + two_body_gp = all_gps[True] + two_body_gp.set_L_alpha = mocker.Mock() + + # Executes training + two_body_gp.algo = 'L-BFGS-B' + two_body_gp.train() + + # Assert that everything happened as expected + assert(flare.gp.minimize.call_count == 2) + + calls = flare.gp.minimize.call_args_list + args, kwargs = calls[0] + assert(kwargs['method'] == 'L-BFGS-B') + + args, kwargs = calls[1] + assert(kwargs['method'] == 'BFGS') + + two_body_gp.set_L_alpha.assert_called_once() + assert(two_body_gp.hyps == x_result) + assert(two_body_gp.likelihood == -1 * fun_result) + assert(two_body_gp.likelihood_gradient == -1 * jac_result) + + +class TestConstraint(): + + def test_constrained_optimization_simple(self, all_gps): + """ + Test constrained optimization with a standard + number of hyperparameters (3 for a 3-body) + :return: + """ + + test_gp = all_gps[True] + orig_hyp_labels = test_gp.hyp_labels + orig_hyps = np.copy(test_gp.hyps) + + test_gp.hyps_mask['map'] = np.array([1, 3, 5], dtype=int) + test_gp.hyps_mask['train_noise'] = False + test_gp.hyps_mask['original'] = orig_hyps + + test_gp.hyp_labels = orig_hyp_labels[test_gp.hyps_mask['map']] + test_gp.hyps = orig_hyps[test_gp.hyps_mask['map']] + + # Check that the hyperparameters were updated + test_gp.maxiter = 1 + init_hyps = np.copy(test_gp.hyps) + results = test_gp.train() + assert not np.equal(results.x, init_hyps).all() + + +class TestAlgebra(): + + @pytest.mark.parametrize('par, per_atom_par, n_cpus', + [(False, False, 1), + (True, True, 2), + (True, False, 2)]) + @pytest.mark.parametrize('multihyps', multihyps_list) + def test_predict(self, all_gps, validation_env, + par, per_atom_par, n_cpus, multihyps): + test_gp = all_gps[multihyps] + test_gp.parallel = par + test_gp.per_atom_par = per_atom_par + pred = test_gp.predict(x_t=validation_env, d=1) + assert (len(pred) == 2) + assert (isinstance(pred[0], float)) + assert (isinstance(pred[1], float)) + + @pytest.mark.parametrize('par, n_cpus', [(True, 2), + (False, 1)]) + @pytest.mark.parametrize('multihyps', multihyps_list) + def test_set_L_alpha(self, all_gps, params, par, n_cpus, multihyps): + test_gp = all_gps[multihyps] + test_gp.parallel = par + test_gp.n_cpus = n_cpus + test_gp.set_L_alpha() + + @pytest.mark.parametrize('par, n_cpus', [(True, 2), + (False, 1)]) + @pytest.mark.parametrize('multihyps', multihyps_list) + def test_update_L_alpha(self, all_gps, params, par, n_cpus, multihyps): + # set up gp model + test_gp = all_gps[multihyps] + test_gp.parallel = par + test_gp.n_cpus = n_cpus + + test_structure, forces = \ + get_random_structure(params['cell'], + params['unique_species'], + 2) + test_gp.check_L_alpha() + test_gp.update_db(test_structure, forces) + test_gp.update_L_alpha() + + # compare results with set_L_alpha + ky_mat_from_update = np.copy(test_gp.ky_mat) + test_gp.set_L_alpha() + ky_mat_from_set = np.copy(test_gp.ky_mat) + + assert (np.all(np.absolute(ky_mat_from_update - ky_mat_from_set)) < 1e-6) + + +class TestIO(): + @pytest.mark.parametrize('multihyps', multihyps_list) + def test_representation_method(self, all_gps, multihyps): + test_gp = all_gps[multihyps] + the_str = str(test_gp) + assert 'GaussianProcess Object' in the_str + if (multihyps): + assert 'Kernel: two_three_many_body_mc' in the_str + else: + assert 'Kernel: two_plus_three_plus_many_body_mc' in the_str + assert 'Cutoffs: [0.8 0.8 0.8]' in the_str + assert 'Model Likelihood: ' in the_str + if not multihyps: + assert 'Length: ' in the_str + assert 'Signal Var.: ' in the_str + assert "Noise Var.: " in the_str + @pytest.mark.parametrize('multihyps', multihyps_list) + def test_serialization_method(self, all_gps, validation_env, multihyps): + """ + Serialize and then un-serialize a GP and ensure that no info was lost. + Compare one calculation to ensure predictions work correctly. + :param test_gp: + :return: + """ + test_gp = all_gps[multihyps] + old_gp_dict = test_gp.as_dict() + new_gp = GaussianProcess.from_dict(old_gp_dict) + new_gp_dict = new_gp.as_dict() -def test_predict(two_body_gp, test_point): - pred = two_body_gp.predict(x_t=test_point, d=1) - assert (len(pred) == 2) - assert (isinstance(pred[0], float)) - assert (isinstance(pred[1], float)) + assert len(new_gp_dict) == len(old_gp_dict) + dumpcompare(new_gp_dict, old_gp_dict) -def test_set_L_alpha(two_body_gp, params): - # params - cell = np.eye(3) - unique_species = [2, 1] - noa = 2 + for d in [0, 1, 2]: + assert np.all(test_gp.predict(x_t=validation_env, d=d) == + new_gp.predict(x_t=validation_env, d=d)) - # create test structure - test_structure, forces = get_random_structure(cell, unique_species, - noa) + @pytest.mark.parametrize('multihyps', multihyps_list) + def test_load_and_reload(self, all_gps, validation_env, multihyps): - # set gp model - kernel = en.two_plus_three_body - kernel_grad = en.two_plus_three_body_grad - hyps = np.array([2.23751151e-01, 8.19990316e-01, 1.28421842e-04, - 1.07467158e+00, 5.50677932e-02]) - cutoffs = np.array([5.4, 5.4]) - hyp_labels = ['sig2', 'ls2', 'sig3', 'ls3', 'noise'] - energy_force_kernel = en.two_plus_three_force_en - energy_kernel = en.two_plus_three_en - opt_algorithm = 'BFGS' + test_gp = all_gps[multihyps] - # test update_db - gaussian = \ - GaussianProcess(kernel, kernel_grad, hyps, cutoffs, hyp_labels, - energy_force_kernel, energy_kernel, - opt_algorithm) - gaussian.update_db(test_structure, forces) + test_gp.write_model('test_gp_write', 'pickle') - gaussian.set_L_alpha() + new_gp = GaussianProcess.from_file('test_gp_write.pickle') + for d in [0, 1, 2]: + assert np.all(test_gp.predict(x_t=validation_env, d=d) == + new_gp.predict(x_t=validation_env, d=d)) + os.remove('test_gp_write.pickle') -def test_update_L_alpha(): - # set up gp model - kernel = mc_simple.two_plus_three_body_mc - kernel_grad = mc_simple.two_plus_three_body_mc_grad - cutoffs = [6.0, 5.0] - hyps = np.array([0.001770, 0.183868, -0.001415, 0.372588, 0.026315]) + test_gp.write_model('test_gp_write', 'json') - # get an otf traj from file for training data - old_otf = OtfAnalysis('test_files/AgI_snippet.out') - call_no = 1 - cell = old_otf.header['cell'] - gp_model = old_otf.make_gp(kernel=kernel, - kernel_grad=kernel_grad, - call_no=call_no, - cutoffs=cutoffs, - hyps=hyps) + with open('test_gp_write.json', 'r') as f: + new_gp = GaussianProcess.from_dict(json.loads(f.readline())) + for d in [0, 1, 2]: + assert np.all(test_gp.predict(x_t=validation_env, d=d) == + new_gp.predict(x_t=validation_env, d=d)) + os.remove('test_gp_write.json') - gp_model.par = True - # update database & use update_L_alpha to get ky_mat - for n in range(call_no, call_no + 1): - positions = old_otf.gp_position_list[n] - species = old_otf.gp_species_list[n] - atoms = old_otf.gp_atom_list[n] - forces = old_otf.gp_force_list[n] + with raises(ValueError): + test_gp.write_model('test_gp_write', 'cucumber') - struc_curr = Structure(cell, species, positions) - gp_model.update_db(struc_curr, forces, custom_range=atoms) - gp_model.update_L_alpha() - ky_mat_from_update = np.copy(gp_model.ky_mat) +def dumpcompare(obj1, obj2): + '''this source code comes from + http://stackoverflow.com/questions/15785719/how-to-print-a-dictionary-line-by-line-in-python''' - # use set_L_alpha to get ky_mat - gp_model.set_L_alpha() - ky_mat_from_set = np.copy(gp_model.ky_mat) + assert isinstance(obj1, type( + obj2)), "the two objects are of different types" - assert (np.all(np.absolute(ky_mat_from_update - ky_mat_from_set)) < 1e-6) + if isinstance(obj1, dict): + assert len(obj1.keys()) == len( + obj2.keys()), f"key1 {list(obj1.keys())}, \n key2 {list(obj2.keys())}" -def test_representation_method(two_body_gp): - the_str = str(two_body_gp) - assert 'GaussianProcess Object' in the_str - assert 'Kernel: three_body' in the_str - assert 'Cutoffs: [0.8 0.8]' in the_str - assert 'Model Likelihood: ' in the_str - assert 'Length: ' in the_str - assert 'Signal Var.: ' in the_str - assert "Noise Var.: " in the_str - - -def test_serialization_method(two_body_gp, test_point): - """ - Serialize and then un-serialize a GP and ensure that no info was lost. - Compare one calculation to ensure predictions work correctly. - :param two_body_gp: - :return: - """ - old_gp_dict = two_body_gp.as_dict() - new_gp = GaussianProcess.from_dict(old_gp_dict) - new_gp_dict = new_gp.as_dict() + for k1, k2 in zip(sorted(obj1.keys()), sorted(obj2.keys())): - assert len(new_gp_dict) == len(old_gp_dict) + assert k1 == k2, f"key {k1} is not the same as {k2}" + if (k1 != "name"): + assert dumpcompare(obj1[k1], obj2[k2] + ), f"value {k1} is not the same as {k2}" - for k1, k2 in zip(sorted(new_gp_dict.keys()), sorted(old_gp_dict.keys())): + elif isinstance(obj1, (list, tuple)): - x = new_gp_dict[k1] - y = new_gp_dict[k2] + assert len(obj1) == len(obj2) + for k1, k2 in zip(obj1, obj2): + assert dumpcompare(k1, k2), f"list elements are different" - if isinstance(x, np.ndarray): - assert np.equal(x, y).all() - elif hasattr(x, '__len__'): + elif isinstance(obj1, np.ndarray): - if isinstance(x[0], np.ndarray): - assert np.equal(x, y).all() + assert obj1.shape == obj2.shape - else: - for xx, yy in zip(x, y): - assert xx == yy + if (not isinstance(obj1[0], np.str_)): + assert np.equal(obj1, obj2).all(), "ndarray is not all the same" else: - assert x == y + for xx, yy in zip(obj1, obj2): + assert dumpcompare(xx, yy) + else: + assert obj1 == obj2 + + return True + + +def test_training_statistics(): + """ + Ensure training statistics are being recorded correctly + :return: + """ + + test_structure, forces = get_random_structure(np.eye(3), + ['H', 'Be'], + 10) + + gp = GaussianProcess(kernel_name='2', cutoffs=[10]) + + data = gp.training_statistics + + assert data['N'] == 0 + assert len(data['species']) == 0 + assert len(data['envs_by_species']) == 0 - for d in [0, 1, 2]: - assert np.all(two_body_gp.predict(x_t=test_point, d=d) == - new_gp.predict(x_t=test_point, d=d)) + gp.update_db(test_structure, forces) + data = gp.training_statistics -def test_load_and_reload(two_body_gp, test_point): + assert data['N'] == 10 + assert len(data['species']) == len(set(test_structure.coded_species)) + assert len(data['envs_by_species']) == len(set( + test_structure.coded_species)) - two_body_gp.write_model('two_body', 'pickle') - with open('two_body.pickle', 'rb') as f: - new_gp = pickle.load(f) +class TestHelper(): - for d in [0, 1, 2]: - assert np.all(two_body_gp.predict(x_t=test_point, d=d) == - new_gp.predict(x_t=test_point, d=d)) - os.remove('two_body.pickle') + def test_adjust_cutoffs(self, all_gps): - two_body_gp.write_model('two_body', 'json') + test_gp = all_gps[False] + # global training data + # No need to ajust the other global values since we're not + # testing on the predictions made, just that the cutoffs in the + # atomic environments are correctly re-created - with open('two_body.json', 'r') as f: - new_gp = GaussianProcess.from_dict(json.loads(f.readline())) - for d in [0, 1, 2]: - assert np.all(two_body_gp.predict(x_t=test_point, d=d) == - new_gp.predict(x_t=test_point, d=d)) - os.remove('two_body.json') + old_cutoffs = np.copy(test_gp.cutoffs) - with raises(ValueError): - two_body_gp.write_model('two_body', 'cucumber') + test_gp.adjust_cutoffs(np.array(test_gp.cutoffs) + .5, train=False) + assert np.array_equal(test_gp.cutoffs, old_cutoffs + .5) + for env in test_gp.training_data: + assert np.array_equal(env.cutoffs, test_gp.cutoffs) diff --git a/tests/test_gp_algebra.py b/tests/test_gp_algebra.py index a38081dfc..4155cf4ac 100644 --- a/tests/test_gp_algebra.py +++ b/tests/test_gp_algebra.py @@ -2,7 +2,8 @@ import pytest import numpy as np -from flare import gp, gp_algebra +import flare.gp_algebra +from flare import gp from flare.env import AtomicEnvironment from flare.struc import Structure from flare.kernels.mc_simple import two_plus_three_body_mc, \ @@ -12,11 +13,29 @@ from flare.kernels.mc_sephyps import two_plus_three_body_mc_grad \ as two_plus_three_body_mc_grad_multi -from flare.gp_algebra import get_like_grad_from_mats +from flare.gp_algebra import get_like_grad_from_mats, \ + get_kernel_vector, get_ky_mat, \ + get_ky_mat_update, get_ky_and_hyp + +from .fake_gp import get_tstp + +@pytest.fixture(scope='module') +def params(): + + parameters = get_random_training_set(10) + + yield parameters + del parameters def get_random_training_set(nenv): - """Create a random training_set array with parameters """ + """Create a random training_set array with parameters + And generate four different kinds of hyperparameter sets: + * multi hypper parameters with two bond type and two triplet type + * constrained optimization, with noise parameter optimized + * constrained optimization, without noise parameter optimized + * simple hyper parameters without multihyps set up + """ np.random.seed(0) @@ -86,50 +105,54 @@ def get_random_training_set(nenv): training_labels += [np.random.uniform(-1, 1, 3)] training_labels = np.hstack(training_labels) - return hyps, training_data, training_labels, kernel, cutoffs, \ + # store it as global variables + name = "unit_test" + flare.gp_algebra._global_training_data[name] = training_data + flare.gp_algebra._global_training_labels[name] = training_labels + + return hyps, name, kernel, cutoffs, \ kernel_m, hyps_list, hyps_mask_list -def test_ky_mat(): +def test_ky_mat(params): """ test function get_ky_mat in gp_algebra, gp_algebra_multi using gp_algebra_origin as reference TO DO: store the reference... and call it explicitely """ - hyps, training_data, training_labels, kernel, cutoffs, \ - kernel_m, hyps_list, hyps_mask_list = \ - get_random_training_set(10) + hyps, name, kernel, cutoffs, \ + kernel_m, hyps_list, hyps_mask_list = params - func = [gp_algebra.get_ky_mat, - gp_algebra.get_ky_mat_par] # get the reference - # timer0 = time.time() - ky_mat0 = func[0](hyps, training_data, - kernel[0], cutoffs) - - # print("linear", time.time()-timer0) + # without multi hyps + time0 = time.time() + ky_mat0 = get_ky_mat(hyps, name, kernel[0], cutoffs) + print("compute ky_mat serial", time.time()-time0) # parallel version - ky_mat = func[1](hyps, training_data, + time0 = time.time() + ky_mat = get_ky_mat(hyps, name, kernel[0], cutoffs, - n_cpus=2, nsample=20) + n_cpus=2, n_sample=20) + print("compute ky_mat parallel", time.time()-time0) + diff = (np.max(np.abs(ky_mat-ky_mat0))) assert (diff==0), "parallel implementation is wrong" # this part of the code can be use for timing the parallel performance # for n in [10, 50, 100]: # timer0 = time.time() - # ky_mat = func[1](hyps, training_data, + # ky_mat = get_ky_mat(hyps, name, # kernel[0], cutoffs, - # ncpus=8, nsample=n) + # ncpus=8, n_sample=n) # diff = (np.max(np.abs(ky_mat-ky_mat0))) # print("parallel", n, time.time()-timer0, diff) # assert (diff==0), "parallel implementation is wrong" # check multi hyps implementation - + # compute the ky_mat with different parameters for i in range(len(hyps_list)): hyps = hyps_list[i] @@ -141,52 +164,59 @@ def test_ky_mat(): ker = kernel_m[0] # serial implementation - ky_mat = func[0](hyps, training_data, + time0 = time.time() + ky_mat = get_ky_mat(hyps, name, ker, cutoffs, hyps_mask) + print(f"compute ky_mat with multihyps, test {i}, n_cpus=1", time.time()-time0) diff = (np.max(np.abs(ky_mat-ky_mat0))) assert (diff==0), "multi hyps implementation is wrong"\ f"with case {i}" # parallel implementation - ky_mat = func[1](hyps, training_data, + time0 = time.time() + ky_mat = get_ky_mat(hyps, name, ker, - cutoffs, hyps_mask, n_cpus=2, nsample=20) + cutoffs, hyps_mask, n_cpus=2, n_sample=20) + print(f"compute ky_mat with multihyps, test {i}, n_cpus=2", time.time()-time0) diff = (np.max(np.abs(ky_mat-ky_mat0))) assert (diff==0), "multi hyps parallel "\ "implementation is wrong"\ f"with case {i}" -def test_ky_mat_update(): +def test_ky_mat_update(params): """ check ky_mat_update function """ - hyps, training_data, training_labels, kernel, cutoffs, \ - kernel_m, hyps_list, hyps_mask_list = \ - get_random_training_set(10) + hyps, name, kernel, cutoffs, \ + kernel_m, hyps_list, hyps_mask_list = params + # prepare old data set as the starting point + n = 5 + training_data = flare.gp_algebra._global_training_data[name] + flare.gp_algebra._global_training_data['old'] = training_data[:n] + training_labels = flare.gp_algebra._global_training_labels[name] + flare.gp_algebra._global_training_labels['old'] = training_labels[:n] - func = [gp_algebra.get_ky_mat, - gp_algebra.get_ky_mat_update, - gp_algebra.get_ky_mat_update_par] + func = [get_ky_mat, + get_ky_mat_update] # get the reference - ky_mat0 = func[0](hyps, training_data, + ky_mat0 = func[0](hyps, name, kernel[0], cutoffs) - n = 5 - ky_mat_old = func[0](hyps, training_data[:n], + ky_mat_old = func[0](hyps, 'old', kernel[0], cutoffs) # update - ky_mat = func[1](ky_mat_old, hyps, training_data, + ky_mat = func[1](ky_mat_old, hyps, name, kernel[0], cutoffs) diff = (np.max(np.abs(ky_mat-ky_mat0))) assert (diff==0), "update function is wrong" # parallel version - ky_mat = func[2](ky_mat_old, hyps, training_data, + ky_mat = func[1](ky_mat_old, hyps, name, kernel[0], cutoffs, - n_cpus=2, nsample=20) + n_cpus=2, n_sample=20) diff = (np.max(np.abs(ky_mat-ky_mat0))) assert (diff==0), "parallel implementation is wrong" @@ -202,33 +232,52 @@ def test_ky_mat_update(): ker = kernel_m[0] # serial implementation - ky_mat = func[1](ky_mat_old, hyps, training_data, + ky_mat = func[1](ky_mat_old, hyps, name, ker, cutoffs, hyps_mask) diff = (np.max(np.abs(ky_mat-ky_mat0))) assert (diff<1e-12), "multi hyps parameter implementation is wrong" # parallel implementation - ky_mat = func[2](ky_mat_old, hyps, training_data, + ky_mat = func[1](ky_mat_old, hyps, name, ker, cutoffs, - hyps_mask, n_cpus=2, nsample=20) + hyps_mask, n_cpus=2, n_sample=20) diff = (np.max(np.abs(ky_mat-ky_mat0))) assert (diff<1e-12), "multi hyps parameter parallel "\ "implementation is wrong" -def test_ky_and_hyp(): +def test_get_kernel_vector(params): + + hyps, name, kernel, cutoffs, \ + kernel_m, hyps_list, hyps_mask_list = params + + test_point = get_tstp() + + size = len(flare.gp_algebra._global_training_data[name]) + + # test the parallel implementation for multihyps + vec = get_kernel_vector(name, kernel_m[0], + test_point, 1, hyps, + cutoffs, hyps_mask_list[0]) - hyps, training_data, training_labels, kernel, cutoffs, \ - kernel_m, hyps_list, hyps_mask_list = \ - get_random_training_set(10) + vec_par = get_kernel_vector(name, kernel_m[0], + test_point, 1, hyps, + cutoffs, hyps_mask_list[0], + n_cpus=2, n_sample=100) - func = [gp_algebra.get_ky_and_hyp, - gp_algebra.get_ky_and_hyp_par] + assert (all(np.equal(vec, vec_par))), "parallel implementation is wrong" + assert (vec.shape[0] == size*3), \ + f"{vec} {size}" - hypmat_0, ky_mat0 = func[0](hyps, training_data, +def test_ky_and_hyp(params): + + hyps, name, kernel, cutoffs, \ + kernel_m, hyps_list, hyps_mask_list = params + + hypmat_0, ky_mat0 = get_ky_and_hyp(hyps, name, kernel[1], cutoffs) # parallel version - hypmat, ky_mat = func[1](hyps, training_data, + hypmat, ky_mat = get_ky_and_hyp(hyps, name, kernel[1], cutoffs, n_cpus=2) diff = (np.max(np.abs(ky_mat-ky_mat0))) assert (diff==0), "parallel implementation is wrong" @@ -244,8 +293,9 @@ def test_ky_and_hyp(): ker = kernel_m[1] # serial implementation - hypmat, ky_mat = func[0](hyps, training_data, - ker, cutoffs, hyps_mask) + hypmat, ky_mat = get_ky_and_hyp( + hyps, name, ker, cutoffs, hyps_mask) + if (i == 0): hypmat9 = hypmat diff = (np.max(np.abs(ky_mat-ky_mat0))) @@ -253,8 +303,6 @@ def test_ky_and_hyp(): # compare to no hyps_mask version diff = 0 - print(i, hypmat.shape) - print(i, hypmat_0.shape) if (i == 1): diff = (np.max(np.abs(hypmat-hypmat9[[0,2,4,6,8], :, :]))) elif (i==2): @@ -267,9 +315,9 @@ def test_ky_and_hyp(): f"in case {i}" # parallel implementation - hypmat_par, ky_mat_par = func[1](hyps, training_data, + hypmat_par, ky_mat_par = get_ky_and_hyp(hyps, name, ker, cutoffs, hyps_mask, - n_cpus=2, nsample=2) + n_cpus=2, n_sample=2) # compare to serial implementation diff = (np.max(np.abs(ky_mat-ky_mat_par))) @@ -280,31 +328,30 @@ def test_ky_and_hyp(): assert (diff==0), f"multi hyps parallel implementation is wrong"\ f" in case{i}" -def test_grad(): +def test_grad(params): - hyps, training_data, training_labels, kernel, cutoffs, \ - kernel_m, hyps_list, hyps_mask_list = \ - get_random_training_set(10) + hyps, name, kernel, cutoffs, \ + kernel_m, hyps_list, hyps_mask_list = params # obtain reference - func = gp_algebra.get_ky_and_hyp - hyp_mat, ky_mat = func(hyps, training_data, + func = get_ky_and_hyp + hyp_mat, ky_mat = func(hyps, name, kernel[1], cutoffs) like0, like_grad0 = \ - get_like_grad_from_mats(ky_mat, hyp_mat, training_labels) + get_like_grad_from_mats(ky_mat, hyp_mat, name) # serial implementation - func = gp_algebra.get_ky_and_hyp - hyp_mat, ky_mat = func(hyps, training_data, + func = get_ky_and_hyp + hyp_mat, ky_mat = func(hyps, name, kernel[1], cutoffs) like, like_grad = \ - get_like_grad_from_mats(ky_mat, hyp_mat, training_labels) + get_like_grad_from_mats(ky_mat, hyp_mat, name) assert (like==like0), "wrong likelihood" assert np.max(np.abs(like_grad-like_grad0))==0, "wrong likelihood" - func = gp_algebra.get_ky_and_hyp + func = get_ky_and_hyp for i in range(len(hyps_list)): hyps = hyps_list[i] hyps_mask = hyps_mask_list[i] @@ -314,18 +361,15 @@ def test_grad(): else: ker = kernel_m[1] - hyp_mat, ky_mat = func(hyps, training_data, + hyp_mat, ky_mat = func(hyps, name, ker, cutoffs, hyps_mask) like, like_grad = \ - get_like_grad_from_mats(ky_mat, hyp_mat, training_labels) - print(like, like0) + get_like_grad_from_mats(ky_mat, hyp_mat, name) assert (like==like0), "wrong likelihood" if (i==0): like_grad9 = like_grad - print(like_grad, like_grad0) - diff = 0 if (i==1): diff = (np.max(np.abs(like_grad-like_grad9[[0,2,4,6,8]]))) @@ -338,34 +382,33 @@ def test_grad(): assert (diff==0), "multi hyps implementation is wrong"\ f"in case {i}" -def test_ky_hyp_grad(): +def test_ky_hyp_grad(params): - hyps, training_data, training_labels, kernel, cutoffs, \ - kernel_m, hyps_list, hyps_mask_list = \ - get_random_training_set(10) + hyps, name, kernel, cutoffs, \ + kernel_m, hyps_list, hyps_mask_list = params - func = gp_algebra.get_ky_and_hyp + func = get_ky_and_hyp - hyp_mat, ky_mat = func(hyps, training_data, + hyp_mat, ky_mat = func(hyps, name, kernel[1], cutoffs) - print(hyp_mat.shape, ky_mat.shape, len(training_labels), training_labels[0]) + size = len(flare.gp_algebra._global_training_data[name]) like, like_grad = \ - get_like_grad_from_mats(ky_mat, hyp_mat, training_labels) + get_like_grad_from_mats(ky_mat, hyp_mat, name) delta = 0.001 for i in range(len(hyps)): newhyps = np.copy(hyps) newhyps[i] += delta - hyp_mat_p, ky_mat_p = func(newhyps, training_data, + hyp_mat_p, ky_mat_p = func(newhyps, name, kernel[1], cutoffs) like_p, like_grad_p = \ - get_like_grad_from_mats(ky_mat_p, hyp_mat_p, training_labels) + get_like_grad_from_mats(ky_mat_p, hyp_mat_p, name) newhyps[i] -= 2*delta - hyp_mat_m, ky_mat_m = func(newhyps, training_data, + hyp_mat_m, ky_mat_m = func(newhyps, name, kernel[1], cutoffs) like_m, like_grad_m = \ - get_like_grad_from_mats(ky_mat_m, hyp_mat_m, training_labels) + get_like_grad_from_mats(ky_mat_m, hyp_mat_m, name) diff = np.abs(like_grad[i]-(like_p-like_m)/2./delta) assert (diff < 1e-3), "wrong calculation of hyp_mat" diff --git a/tests/test_gp_from_aimd.py b/tests/test_gp_from_aimd.py index 20012894d..ce77f50b2 100644 --- a/tests/test_gp_from_aimd.py +++ b/tests/test_gp_from_aimd.py @@ -1,21 +1,23 @@ import pytest import numpy as np import pickle +from glob import glob +from os import remove +from copy import deepcopy from flare.env import AtomicEnvironment from flare.struc import Structure -from flare.kernels.kernels import two_plus_three_body, two_plus_three_body_grad from flare.gp import GaussianProcess -from flare.gp_from_aimd import TrajectoryTrainer -from flare.kernels.mc_simple import two_plus_three_body_mc, two_plus_three_body_mc_grad +from flare.mgp.mgp_en import MappedGaussianProcess +from flare.gp_from_aimd import TrajectoryTrainer, subset_of_frame_by_element from json import loads -import os - +from flare.env import AtomicEnvironment +from .test_mgp_unit import all_mgp, all_gp, get_random_structure +from .fake_gp import get_gp @pytest.fixture def methanol_gp(): - the_gp = GaussianProcess(kernel=two_plus_three_body_mc, - kernel_grad=two_plus_three_body_mc_grad, + 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]), @@ -38,10 +40,9 @@ def methanol_gp(): @pytest.fixture def fake_gp(): - return GaussianProcess(kernel=two_plus_three_body, - kernel_grad=two_plus_three_body_grad, + return GaussianProcess(kernel_name="2+3", hyps=np.array([1]), - cutoffs=np.array([])) + cutoffs=np.array([4, 3])) def test_instantiation_of_trajectory_trainer(fake_gp): @@ -49,13 +50,13 @@ def test_instantiation_of_trajectory_trainer(fake_gp): assert isinstance(a, TrajectoryTrainer) - fake_gp.par = True - _ = TrajectoryTrainer([], fake_gp, parallel=True, calculate_energy=True) - _ = TrajectoryTrainer([], fake_gp, parallel=True, calculate_energy=False) + fake_gp.parallel = True + _ = TrajectoryTrainer([], fake_gp, n_cpus=2, calculate_energy=True) + _ = TrajectoryTrainer([], fake_gp, n_cpus=2, calculate_energy=False) - fake_gp.par = False - _ = TrajectoryTrainer([], fake_gp, parallel=False, calculate_energy=True) - _ = TrajectoryTrainer([], fake_gp, parallel=False, calculate_energy=False) + fake_gp.parallel = False + _ = TrajectoryTrainer([], fake_gp, n_cpus=2, calculate_energy=True) + _ = TrajectoryTrainer([], fake_gp, n_cpus=2, calculate_energy=False) def test_load_trained_gp_and_run(methanol_gp): @@ -66,15 +67,15 @@ def test_load_trained_gp_and_run(methanol_gp): gp=methanol_gp, rel_std_tolerance=0, abs_std_tolerance=0, - skip=15) + skip=15, train_checkpoint_interval=10) tt.run() - os.system('rm ./gp_from_aimd*') + for f in glob(f"gp_from_aimd*"): + remove(f) def test_load_one_frame_and_run(): - the_gp = GaussianProcess(kernel=two_plus_three_body_mc, - kernel_grad=two_plus_three_body_mc_grad, + 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]), @@ -93,12 +94,12 @@ def test_load_one_frame_and_run(): skip=15) tt.run() - os.system('rm ./gp_from_aimd*') + for f in glob(f"gp_from_aimd*"): + remove(f) def test_seed_and_run(): - the_gp = GaussianProcess(kernel=two_plus_three_body_mc, - kernel_grad=two_plus_three_body_mc_grad, + 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]), @@ -126,7 +127,7 @@ def test_seed_and_run(): max_atoms_from_frame=4, output_name='meth_test', model_format='pickle', - checkpoint_interval=1, + train_checkpoint_interval=1, pre_train_atoms_per_element={'H': 1}) tt.run() @@ -140,4 +141,117 @@ def test_seed_and_run(): assert np.all(the_gp.predict(x_t=test_env, d=d) == new_gp.predict(x_t=test_env, d=d)) - os.system('rm ./meth_test*') + for f in glob(f"meth_test*"): + 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: + ''' + + 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() diff --git a/tests/test_gp_multi.py b/tests/test_gp_multi.py deleted file mode 100644 index 273f8f3da..000000000 --- a/tests/test_gp_multi.py +++ /dev/null @@ -1,454 +0,0 @@ -import pytest -import numpy as np -from numpy.random import random, randint, permutation - -import flare.kernels.mc_sephyps as en - -from flare import env, struc, gp -from flare.gp import GaussianProcess -from flare.env import AtomicEnvironment -from flare.struc import Structure -from flare import mc_simple -from flare.otf_parser import OtfAnalysis -from flare.kernels.mc_sephyps import _str_to_kernel as stk - -def get_random_structure(cell, unique_species, noa): - """Create a random test structure """ - np.random.seed(0) - - positions = [] - forces = [] - species = [] - - for n in range(noa): - positions.append(np.random.uniform(-1, 1, 3)) - forces.append(np.random.uniform(-1, 1, 3)) - species.append(unique_species[np.random.randint(0, - len(unique_species))]) - - test_structure = Structure(cell, species, positions) - - return test_structure, forces - - -def generate_hm(nbond, ntriplet): - - specs_mask = np.zeros(118, dtype=int) - specs_mask[1] = 0 - specs_mask[2] = 1 - nspecs = 2 - - specs_mask = np.zeros(118, dtype=int) - specs_mask[1] = 0 - specs_mask[2] = 1 - nspecs = 2 - - if (nbond==2): - sig1 = [1, 1] - ls1 = [1, 1] - bond_mask = np.ones(nspecs**2, dtype=int) - bond_mask[0] = 0 - bond_name = ["sig2"]*2+["ls2"]*2 - elif (nbond==1): - sig1 = [1] - ls1 = [1] - bond_mask = np.zeros(nspecs**2, dtype=int) - bond_name = ["sig2"]+["ls2"] - else: - bond_mask = None - bond_name = [] - - if (ntriplet==2): - sig2 = [1, 1] - ls2 = [1, 1] - triplet_mask = np.ones(nspecs**3, dtype=int) - triplet_mask[0] = 0 - triplet_name = ["sig3"]*2+["ls3"]*2 - elif (ntriplet==1): - sig2 = [1] - ls2 = [1] - triplet_mask = np.zeros(nspecs**3, dtype=int) - triplet_name = ["sig3"]+["ls3"] - else: - triplet_mask = None - triplet_name = [] - - if (nbond>0 and ntriplet>0): - hyps = np.hstack([sig1, ls1, sig2, ls2, [1]]) - hyps_label = np.hstack([bond_name, triplet_name, ['noise']]) - elif (nbond>0): - hyps = np.hstack([sig1, ls1, [1]]) - hyps_label = np.hstack([bond_name, ['noise']]) - else: - hyps = np.hstack([sig2, ls2, [1]]) - hyps_label = np.hstack([triplet_name, ['noise']]) - - hyps_mask = {'nspec': nspecs, - 'spec_mask': specs_mask, - 'nbond': nbond, - 'bond_mask': bond_mask, - 'ntriplet': ntriplet, - 'triplet_mask': triplet_mask, - 'hyps_label': hyps_label, - 'train_noise': True} - - return hyps, hyps_mask - - -# ------------------------------------------------------ -# fixtures -# ------------------------------------------------------ - - -# set the scope to module so it will only be setup once -@pytest.fixture(scope='module') -def two_body_gp() -> GaussianProcess: - """Returns a GP instance with a two-body numba-based kernel""" - print("\nSetting up...\n") - - # params - cell = np.eye(3) - unique_species = [2, 1] - cutoffs = np.array([0.8, 0.8]) - noa = 5 - - nbond = 1 - ntriplet = 0 - hyps, hm = generate_hm(nbond, ntriplet) - - # create test structure - test_structure, forces = get_random_structure(cell, unique_species, - noa) - - # test update_db - gaussian = \ - GaussianProcess(kernel=en.two_body_mc, - kernel_grad=en.two_body_mc_grad, - hyps=hyps, - hyp_labels=hm['hyps_label'], - cutoffs=cutoffs, multihyps=True, hyps_mask=hm, - par=False, n_cpus=1) - gaussian.update_db(test_structure, forces) - - # return gaussian - yield gaussian - - # code after yield will be executed once all tests are run - # this will not be run if an exception is raised in the setup - print("\n\nTearing down\n") - del gaussian - -@pytest.fixture(scope='module') -def three_body_gp() -> GaussianProcess: - """Returns a GP instance with a two-body numba-based kernel""" - print("\nSetting up...\n") - - # params - cell = np.eye(3) - unique_species = [2, 1] - cutoffs = np.array([0.8, 0.8]) - noa = 5 - - nbond = 0 - ntriplet = 1 - hyps, hm = generate_hm(nbond, ntriplet) - - # create test structure - test_structure, forces = get_random_structure(cell, unique_species, - noa) - - # test update_db - gaussian = \ - GaussianProcess(kernel=en.three_body_mc, - kernel_grad=en.three_body_mc_grad, - hyps=hyps, - hyp_labels=hm['hyps_label'], - cutoffs=cutoffs, multihyps=True, hyps_mask=hm) - gaussian.update_db(test_structure, forces) - - # return gaussian - yield gaussian - - # code after yield will be executed once all tests are run - # this will not be run if an exception is raised in the setup - print("\n\nTearing down\n") - del gaussian - -# set the scope to module so it will only be setup once -@pytest.fixture(scope='module') -def two_plus_three_gp() -> GaussianProcess: - """Returns a GP instance with a two-body numba-based kernel""" - print("\nSetting up...\n") - - # params - cell = np.eye(3) - unique_species = [2, 1] - cutoffs = np.array([0.8, 0.8]) - noa = 5 - - nbond = 1 - ntriplet = 1 - hyps, hm = generate_hm(nbond, ntriplet) - - # create test structure - test_structure, forces = get_random_structure(cell, unique_species, - noa) - efk = en.two_plus_three_mc_force_en - ek = en.two_plus_three_mc_en - - # test update_db - gaussian = \ - GaussianProcess(kernel=en.two_plus_three_body_mc, - kernel_grad=en.two_plus_three_body_mc_grad, - hyps=hyps, - energy_force_kernel = efk, - energy_kernel=ek, - opt_algorithm = 'BFGS', - hyp_labels=hm['hyps_label'], - cutoffs=cutoffs, multihyps=True, hyps_mask=hm) - gaussian.update_db(test_structure, forces) - - # return gaussian - yield gaussian - - # code after yield will be executed once all tests are run - # this will not be run if an exception is raised in the setup - print("\n\nTearing down\n") - del gaussian - - -@pytest.fixture(scope='module') -def params(): - parameters = {'unique_species': [2, 1], - 'cutoff': 0.8, - 'noa': 5, - 'cell': np.eye(3), - 'db_pts': 30} - yield parameters - del parameters - - -@pytest.fixture(scope='module') -def test_point() -> AtomicEnvironment: - """Create test point for kernel to compare against""" - # params - cell = np.eye(3) - unique_species = [2, 1] - cutoff = 0.8 - noa = 10 - - test_structure_2, _ = get_random_structure(cell, unique_species, - noa) - - test_pt = AtomicEnvironment(test_structure_2, 0, - np.array([cutoff, cutoff])) - - yield test_pt - del test_pt - - -# ------------------------------------------------------ -# test GP methods -# ------------------------------------------------------ - -def test_update_db(two_body_gp, params): - # params - test_structure, forces = get_random_structure(params['cell'], - params['unique_species'], - params['noa']) - - # add structure and forces to db - two_body_gp.update_db(test_structure, forces) - - assert (len(two_body_gp.training_data) == params['noa'] * 2) - assert (len(two_body_gp.training_labels_np) == params['noa'] * 2 * 3) - - -def test_get_kernel_vector(two_body_gp, test_point, params): - - print("hello", two_body_gp.get_kernel_vector(test_point, 1).shape) - print("hello", two_body_gp.get_kernel_vector(test_point, 1).shape) - assert (two_body_gp.get_kernel_vector(test_point, 1).shape == - (params['db_pts'],)), \ - f"{two_body_gp.get_kernel_vector(test_point, 1)} {params['db_pts']}" - - -def test_train(two_body_gp, params): - hyp = list(two_body_gp.hyps) - - # add struc and forces to db - test_structure, forces = get_random_structure(params['cell'], - params['unique_species'], - params['noa']) - two_body_gp.update_db(test_structure, forces) - - # train gp - res = two_body_gp.train() - - hyp_post = list(two_body_gp.hyps) - print(res) - - # check if hyperparams have been updated - assert (hyp != hyp_post) - - -def test_predict(two_body_gp, test_point): - pred = two_body_gp.predict(x_t=test_point, d=1) - assert (len(pred) == 2) - assert (isinstance(pred[0], float)) - assert (isinstance(pred[1], float)) - - -def test_set_L_alpha(two_plus_three_gp): - two_plus_three_gp.set_L_alpha() - - -def test_update_L_alpha(two_plus_three_gp): - two_plus_three_gp.set_L_alpha() - # update database & use update_L_alpha to get ky_mat - cell = np.eye(3) - unique_species = [2, 1] - cutoffs = np.array([0.8, 0.8]) - noa = 5 - for n in range(3): - positions = random([noa, 3]) - forces = random([noa, 3]) - species = randint(2, size=noa) - atoms = list(permutation(noa)[:2]) - - struc_curr = Structure(cell, species, positions) - two_plus_three_gp.update_db(struc_curr, forces, custom_range=atoms) - two_plus_three_gp.update_L_alpha() - - ky_mat_from_update = np.copy(two_plus_three_gp.ky_mat) - - # use set_L_alpha to get ky_mat - two_plus_three_gp.set_L_alpha() - ky_mat_from_set = np.copy(two_plus_three_gp.ky_mat) - - assert (np.all(np.absolute(ky_mat_from_update - ky_mat_from_set)) < 1e-6) - - -def test_representation_method(two_body_gp): - the_str = str(two_body_gp) - assert 'GaussianProcess Object' in the_str - assert 'Kernel: two_body_mc' in the_str - assert 'Cutoffs: [0.8 0.8]' in the_str - assert 'Model Likelihood: ' in the_str - assert 'ls2: ' in the_str - assert 'sig2: ' in the_str - assert "noise: " in the_str - - -def test_serialization_method(two_body_gp, test_point): - """ - Serialize and then un-serialize a GP and ensure that no info was lost. - Compare one calculation to ensure predictions work correctly. - :param two_body_gp: - :return: - """ - old_gp_dict = two_body_gp.as_dict() - new_gp = GaussianProcess.from_dict(old_gp_dict) - new_gp_dict = new_gp.as_dict() - - dumpcompare(new_gp_dict, old_gp_dict) - - for d in [0, 1, 2]: - assert np.all(two_body_gp.predict(x_t=test_point, d=d) == - new_gp.predict(x_t=test_point, d=d)) - -def dumpcompare(obj1, obj2): - '''this source code comes from - http://stackoverflow.com/questions/15785719/how-to-print-a-dictionary-line-by-line-in-python''' - - assert isinstance(obj1, type(obj2)), "the two objects are of different types" - - if isinstance(obj1, dict): - - assert len(obj1.keys()) == len(obj2.keys()) - - for k1, k2 in zip(sorted(obj1.keys()), sorted(obj2.keys())): - - assert k1==k2, f"key {k1} is not the same as {k2}" - assert dumpcompare(obj1[k1], obj2[k2]), f"value {k1} is not the same as {k2}" - - elif isinstance(obj1, (list, tuple)): - - assert len(obj1) == len(obj2) - for k1, k2 in zip(obj1, obj2): - assert dumpcompare(k1, k2), f"list elements are different" - - elif isinstance(obj1, np.ndarray): - - assert obj1.shape == obj2.shape - - if (not isinstance(obj1[0], np.str_)): - assert np.equal(obj1, obj2).all(), "ndarray is not all the same" - else: - for xx, yy in zip(obj1, obj2): - assert dumpcompare(xx, yy) - else: - assert obj1==obj2 - - return True - -def test_constrained_optimization_simple(): - """ - Test constrained optimization with a standard - number of hyperparameters (3 for a 3-body) - :return: - """ - - # params - cell = np.eye(3) - species = [1,1,2,2,2] - positions = np.random.uniform(0,1,(5,3)) - forces = np.random.uniform(0,1,(5,3)) - - two_species_structure = Structure(cell=cell,species=species, - positions=positions, - forces=forces) - - - hyp_labels=['2-Body_sig2,', - '2-Body_l2', - '3-Body_sig2', - '3-Body_l2', - 'noise'] - hyps = np.array([1.2, 2.2, 3.2, 4.2, 12.]) - cutoffs = np.array((.8, .8)) - - # Define hyp masks - - spec_mask = np.zeros(118, dtype=int) - spec_mask[1] = 1 - - hyps_mask = {'nspec': 2, - 'spec_mask': spec_mask, - 'nbond': 2, - 'bond_mask': [0, 1, 1, 1], - 'ntriplet': 2, - 'triplet_mask': [0, 1, 1, 1, 1, 1, 1, 1], - 'original': np.array([1.1, 1.2, 2.1, 2.2, 3.1, 3.2, 4.1, 4.2, - 12.]), - 'train_noise': True, - 'map': [1, 3, 5, 7, 8]} - - gp = GaussianProcess(kernel=en.two_plus_three_body_mc, - kernel_grad=en.two_plus_three_body_mc_grad, - hyps=hyps, - hyp_labels=hyp_labels, - cutoffs=cutoffs, par=False, n_cpus=1, - hyps_mask=hyps_mask, - maxiter=1,multihyps=True) - - - gp.update_db(two_species_structure, - two_species_structure.forces) - - # Check that the hyperparameters were updated - results = gp.train() - assert not np.equal(results.x, hyps).all() - - #TODO check that the predictions match up with a 2+3 body - # kernel without hyperparameter masking diff --git a/tests/test_gp_par.py b/tests/test_gp_par.py deleted file mode 100644 index 4466eff77..000000000 --- a/tests/test_gp_par.py +++ /dev/null @@ -1,260 +0,0 @@ -import pytest -import numpy as np -from flare.gp import GaussianProcess -from flare.env import AtomicEnvironment -from flare.struc import Structure -import flare.kernels.kernels as en -import flare.kernels.mc_simple as mc_simple -from flare.otf_parser import OtfAnalysis - - -def get_random_structure(cell, unique_species, noa): - """Create a random test structure """ - np.random.seed(0) - - positions = [] - forces = [] - species = [] - - for n in range(noa): - positions.append(np.random.uniform(-1, 1, 3)) - forces.append(np.random.uniform(-1, 1, 3)) - species.append(unique_species[np.random.randint(0, - len(unique_species))]) - - test_structure = Structure(cell, species, positions) - - return test_structure, forces - - -# ------------------------------------------------------ -# fixtures -# ------------------------------------------------------ - - -# set the scope to module so it will only be setup once -@pytest.fixture(scope='module') -def two_body_gp() -> GaussianProcess: - """Returns a GP instance with a two-body numba-based kernel""" - print("\nSetting up...\n") - - # params - cell = np.eye(3) - unique_species = [2, 1] - cutoffs = np.array([0.8, 0.8]) - noa = 5 - - # create test structure - test_structure, forces = get_random_structure(cell, unique_species, - noa) - - # test update_db - gaussian = \ - GaussianProcess(kernel=en.three_body, - kernel_grad=en.three_body_grad, - hyps=np.array([1, 1, 1]), - hyp_labels=['Length', 'Signal Var.', 'Noise Var.'], - par=True, n_cpus=2, - cutoffs=cutoffs) - gaussian.update_db(test_structure, forces) - - # return gaussian - yield gaussian - - # code after yield will be executed once all tests are run - # this will not be run if an exception is raised in the setup - print("\n\nTearing down\n") - del gaussian - - -@pytest.fixture(scope='module') -def params(): - parameters = {'unique_species': [2, 1], - 'cutoff': 0.8, - 'noa': 5, - 'cell': np.eye(3), - 'db_pts': 30} - yield parameters - del parameters - - -@pytest.fixture(scope='module') -def test_point() -> AtomicEnvironment: - """Create test point for kernel to compare against""" - # params - cell = np.eye(3) - unique_species = [2, 1] - cutoff = 0.8 - noa = 10 - - test_structure_2, _ = get_random_structure(cell, unique_species, - noa) - - test_pt = AtomicEnvironment(test_structure_2, 0, - np.array([cutoff, cutoff])) - - yield test_pt - del test_pt - - -# ------------------------------------------------------ -# test GP methods -# ------------------------------------------------------ - -def test_update_db(two_body_gp, params): - # params - test_structure, forces = get_random_structure(params['cell'], - params['unique_species'], - params['noa']) - - # add structure and forces to db - two_body_gp.update_db(test_structure, forces) - - assert (len(two_body_gp.training_data) == params['noa'] * 2) - assert (len(two_body_gp.training_labels_np) == params['noa'] * 2 * 3) - - -def test_get_kernel_vector(two_body_gp, test_point, params): - assert (two_body_gp.get_kernel_vector(test_point, 1).shape == - (params['db_pts'],)) - - -def test_train(two_body_gp, params): - hyp = list(two_body_gp.hyps) - - # add struc and forces to db - test_structure, forces = get_random_structure(params['cell'], - params['unique_species'], - params['noa']) - two_body_gp.update_db(test_structure, forces) - - # train gp - two_body_gp.train() - - hyp_post = list(two_body_gp.hyps) - - # check if hyperparams have been updated - assert (hyp != hyp_post) - - -def test_predict(two_body_gp, test_point): - pred = two_body_gp.predict(x_t=test_point, d=1) - assert (len(pred) == 2) - assert (isinstance(pred[0], float)) - assert (isinstance(pred[1], float)) - - -def test_set_L_alpha(two_body_gp, params): - # params - cell = np.eye(3) - unique_species = [2, 1] - noa = 2 - - # create test structure - test_structure, forces = get_random_structure(cell, unique_species, - noa) - - # set gp model - kernel = en.two_plus_three_body - kernel_grad = en.two_plus_three_body_grad - hyps = np.array([2.23751151e-01, 8.19990316e-01, 1.28421842e-04, - 1.07467158e+00, 5.50677932e-02]) - cutoffs = np.array([5.4, 5.4]) - hyp_labels = ['sig2', 'ls2', 'sig3', 'ls3', 'noise'] - energy_force_kernel = en.two_plus_three_force_en - energy_kernel = en.two_plus_three_en - opt_algorithm = 'BFGS' - - # test update_db - gaussian = \ - GaussianProcess(kernel, kernel_grad, hyps, cutoffs, hyp_labels, - energy_force_kernel, energy_kernel, - opt_algorithm, - par=True, n_cpus=2) - gaussian.update_db(test_structure, forces) - - gaussian.set_L_alpha() - - -def test_update_L_alpha(): - # set up gp model - kernel = mc_simple.two_plus_three_body_mc - kernel_grad = mc_simple.two_plus_three_body_mc_grad - cutoffs = [6.0, 5.0] - hyps = np.array([0.001770, 0.183868, -0.001415, 0.372588, 0.026315]) - - # get an otf traj from file for training data - old_otf = OtfAnalysis('test_files/AgI_snippet.out') - call_no = 1 - cell = old_otf.header['cell'] - gp_model = old_otf.make_gp(kernel=kernel, - kernel_grad=kernel_grad, - call_no=call_no, - cutoffs=cutoffs, - hyps=hyps) - - # update database & use update_L_alpha to get ky_mat - for n in range(call_no, call_no + 1): - positions = old_otf.gp_position_list[n] - species = old_otf.gp_species_list[n] - atoms = old_otf.gp_atom_list[n] - forces = old_otf.gp_force_list[n] - - struc_curr = Structure(cell, species, positions) - gp_model.update_db(struc_curr, forces, custom_range=atoms) - gp_model.update_L_alpha() - - ky_mat_from_update = np.copy(gp_model.ky_mat) - - # use set_L_alpha to get ky_mat - gp_model.set_L_alpha() - ky_mat_from_set = np.copy(gp_model.ky_mat) - - assert (np.all(np.absolute(ky_mat_from_update - ky_mat_from_set)) < 1e-6) - - -def test_representation_method(two_body_gp): - the_str = str(two_body_gp) - assert 'GaussianProcess Object' in the_str - assert 'Kernel: three_body' in the_str - assert 'Cutoffs: [0.8 0.8]' in the_str - assert 'Model Likelihood: ' in the_str - assert 'Length: ' in the_str - assert 'Signal Var.: ' in the_str - assert "Noise Var.: " in the_str - - -def test_serialization_method(two_body_gp, test_point): - """ - Serialize and then un-serialize a GP and ensure that no info was lost. - Compare one calculation to ensure predictions work correctly. - :param two_body_gp: - :return: - """ - old_gp_dict = two_body_gp.as_dict() - new_gp = GaussianProcess.from_dict(old_gp_dict) - new_gp_dict = new_gp.as_dict() - - assert len(new_gp_dict) == len(old_gp_dict) - - for k1, k2 in zip(sorted(new_gp_dict.keys()), sorted(old_gp_dict.keys())): - - x = new_gp_dict[k1] - y = new_gp_dict[k2] - - if isinstance(x, np.ndarray): - assert np.equal(x, y).all() - elif hasattr(x, '__len__'): - - if isinstance(x[0], np.ndarray): - assert np.equal(x, y).all() - - else: - for xx, yy in zip(x, y): - assert xx == yy - else: - assert x == y - - for d in [0, 1, 2]: - assert np.all(two_body_gp.predict(x_t=test_point, d=d) == - new_gp.predict(x_t=test_point, d=d)) diff --git a/tests/test_kernel.py b/tests/test_kernel.py new file mode 100644 index 000000000..1f16fc51f --- /dev/null +++ b/tests/test_kernel.py @@ -0,0 +1,167 @@ +import sys +from copy import deepcopy +import pytest +import numpy as np +from numpy import isclose +from numpy.random import random, randint + +from flare import env, struc, gp +from flare.kernels.utils import str_to_kernel_set + +from .fake_gp import generate_mb_envs + +list_to_test = ['2', '3', '2+3', 'mb', '2+3+mb'] +list_type = ['sc', 'mc'] + +def generate_hm(kernel_name): + hyps = [] + for term in ['2', '3', 'mb']: + if (term in kernel_name): + hyps += [random(2)] + hyps += [random()] + return np.hstack(hyps) + + +@pytest.mark.parametrize('kernel_name', list_to_test) +@pytest.mark.parametrize('kernel_type', list_type) +def test_force_en(kernel_name, kernel_type): + """Check that the analytical force/en kernel matches finite difference of + energy kernel.""" + + cutoffs = np.ones(3)*1.2 + delta = 1e-5 + tol = 1e-4 + cell = 1e7 * np.eye(3) + + # set hyperparameters + d1 = 1 + + np.random.seed(10) + env1 = generate_mb_envs(cutoffs, cell, delta, d1) + env2 = generate_mb_envs(cutoffs, cell, delta, d1) + + hyps = generate_hm(kernel_name) + + _, __, en_kernel, force_en_kernel = str_to_kernel_set(kernel_name+kernel_type) + print(force_en_kernel.__name__) + + nterm = 0 + for term in ['2', '3', 'mb']: + if (term in kernel_name): + nterm += 1 + + kern_finite_diff = 0 + if ('mb' in kernel_name): + _, __, enm_kernel, ___ = str_to_kernel_set('mb'+kernel_type) + mhyps = hyps[(nterm-1)*2:] + calc = 0 + for i in range(len(env1[0])): + calc += enm_kernel(env1[2][i], env2[0][0], mhyps, cutoffs) + calc -= enm_kernel(env1[1][i], env2[0][0], mhyps, cutoffs) + mb_diff = calc / (2 * delta) + kern_finite_diff += mb_diff + + if ('2' in kernel_name): + nbond = 1 + _, __, en2_kernel, ___ = str_to_kernel_set('2'+kernel_type) + calc1 = en2_kernel(env1[2][0], env2[0][0], hyps[0:nbond * 2], cutoffs) + calc2 = en2_kernel(env1[1][0], env2[0][0], hyps[0:nbond * 2], cutoffs) + diff2b = (calc1 - calc2) / 2.0 / 2.0 / delta + + kern_finite_diff += diff2b + else: + nbond = 0 + + if ('3' in kernel_name): + _, __, en3_kernel, ___ = str_to_kernel_set('3'+kernel_type) + calc1 = en3_kernel(env1[2][0], env2[0][0], hyps[nbond * 2:], cutoffs) + calc2 = en3_kernel(env1[1][0], env2[0][0], hyps[nbond * 2:], cutoffs) + diff3b = (calc1 - calc2) / 2.0 / 3.0 / delta + + kern_finite_diff += diff3b + + kern_analytical = force_en_kernel(env1[0][0], env2[0][0], d1, hyps, cutoffs) + + print("\nforce_en", kernel_name, kern_finite_diff, kern_analytical) + + assert (isclose(kern_finite_diff, kern_analytical, rtol=tol)) + + +@pytest.mark.parametrize('kernel_name', list_to_test) +@pytest.mark.parametrize('kernel_type', list_type) +def test_force(kernel_name, kernel_type): + """Check that the analytical force kernel matches finite difference of + energy kernel.""" + + d1 = 1 + d2 = 2 + tol = 1e-3 + cell = 1e7 * np.eye(3) + delta = 1e-4 + cutoffs = np.ones(3)*1.2 + + np.random.seed(10) + + hyps = generate_hm(kernel_name) + kernel, kg, en_kernel, fek = str_to_kernel_set(kernel_name+kernel_type, False) + args = (hyps, cutoffs) + + env1 = generate_mb_envs(cutoffs, cell, delta, d1) + env2 = generate_mb_envs(cutoffs, cell, delta, d2) + + # check force kernel + if ('mb' == kernel_name): + cal = 0 + for i in range(3): + for j in range(len(env1[0])): + cal += en_kernel(env1[1][i], env2[1][j], *args) + cal += en_kernel(env1[2][i], env2[2][j], *args) + cal -= en_kernel(env1[1][i], env2[2][j], *args) + cal -= en_kernel(env1[2][i], env2[1][j], *args) + kern_finite_diff = cal / (4 * delta ** 2) + elif ('mb' not in kernel_name): + calc1 = en_kernel(env1[1][0], env2[1][0], *args) + calc2 = en_kernel(env1[2][0], env2[2][0], *args) + calc3 = en_kernel(env1[1][0], env2[2][0], *args) + calc4 = en_kernel(env1[2][0], env2[1][0], *args) + kern_finite_diff = (calc1 + calc2 - calc3 - calc4) / (4*delta**2) + else: + return + + kern_analytical = kernel(env1[0][0], env2[0][0], + d1, d2, *args) + assert(isclose(kern_finite_diff, kern_analytical, rtol=tol)) + + +@pytest.mark.parametrize('kernel_name', list_to_test) +@pytest.mark.parametrize('kernel_type', list_type) +def test_hyps_grad(kernel_name, kernel_type): + + d1 = randint(1, 3) + d2 = randint(1, 3) + tol = 1e-4 + cell = 1e7 * np.eye(3) + delta = 1e-8 + cutoffs = np.ones(3)*1.2 + + np.random.seed(10) + hyps = generate_hm(kernel_name) + env1 = generate_mb_envs(cutoffs, cell, 0, d1)[0][0] + env2 = generate_mb_envs(cutoffs, cell, 0, d2)[0][0] + + kernel, kernel_grad, _, _ = str_to_kernel_set(kernel_name+kernel_type, False) + + grad_test = kernel_grad(env1, env2, + d1, d2, hyps, cutoffs) + + original = kernel(env1, env2, d1, d2, + hyps, cutoffs) + for i in range(len(hyps)-1): + newhyps = np.copy(hyps) + newhyps[i] += delta + hgrad = (kernel(env1, env2, d1, d2, newhyps, + cutoffs) - + original)/delta + print("numerical gradients", hgrad) + print("analytical gradients", grad_test[1][i]) + assert(isclose(grad_test[1][i], hgrad, rtol=tol)) diff --git a/tests/test_kernels.py b/tests/test_kernels.py deleted file mode 100644 index cc9dd7521..000000000 --- a/tests/test_kernels.py +++ /dev/null @@ -1,646 +0,0 @@ -import pytest -import numpy as np -import sys -from random import random, randint -from copy import deepcopy -from flare import env, gp, struc -import flare.kernels.kernels as en - -from flare.kernels.mc_sephyps import from_mask_to_hyps, from_grad_to_mask - -# ----------------------------------------------------------------------------- -# test two plus three body kernels -# ----------------------------------------------------------------------------- - -# TODO: fix this test to properly account for factors of 2 and 3 -def test_two_plus_three_body_force_en(): - """Check that the analytical force/en kernel matches finite difference of - energy kernel.""" - - # create env 1 - delt = 1e-8 - cell = np.eye(3) - cutoffs = np.array([1, 1]) - - positions_1 = [np.array([0., 0., 0.]), - np.array([random(), random(), random()]), - np.array([random(), random(), random()]), - np.array([random(), random(), random()]), - np.array([random(), random(), random()]), - np.array([random(), random(), random()]), - np.array([random(), random(), random()]), - np.array([random(), random(), random()]), - np.array([random(), random(), random()])] - positions_2 = deepcopy(positions_1) - positions_2[0][0] = delt - - species_1 = [1, 2, 1] - atom_1 = 0 - test_structure_1 = struc.Structure(cell, species_1, positions_1) - test_structure_2 = struc.Structure(cell, species_1, positions_2) - - env1_1 = env.AtomicEnvironment(test_structure_1, atom_1, cutoffs) - env1_2 = env.AtomicEnvironment(test_structure_2, atom_1, cutoffs) - - # create env 2 - positions_1 = [np.array([0., 0., 0.]), - np.array([random(), random(), random()]), - np.array([random(), random(), random()]), - np.array([random(), random(), random()]), - np.array([random(), random(), random()]), - np.array([random(), random(), random()]), - np.array([random(), random(), random()]), - np.array([random(), random(), random()]), - np.array([random(), random(), random()])] - - species_2 = [1, 1, 2] - atom_2 = 0 - test_structure_1 = struc.Structure(cell, species_2, positions_1) - env2 = env.AtomicEnvironment(test_structure_1, atom_2, cutoffs) - - # set hyperparameters - sig1 = random() - ls1 = random() - sig2 = random() - ls2 = random() - d1 = 1 - - hyps = np.array([sig1, ls1, sig2, ls2]) - - # check force kernel - calc1 = en.two_body_en(env1_2, env2, hyps[0:2], cutoffs) - calc2 = en.two_body_en(env1_1, env2, hyps[0:2], cutoffs) - calc3 = en.three_body_en(env1_2, env2, hyps[2:4], cutoffs) - calc4 = en.three_body_en(env1_1, env2, hyps[2:4], cutoffs) - - kern_finite_diff = (calc1 - calc2) / (2 * delt) + \ - (calc3 - calc4) / (3 * delt) - kern_analytical = \ - en.two_plus_three_force_en(env1_1, env2, d1, hyps, cutoffs) - - tol = 1e-4 - assert(np.isclose(-kern_finite_diff, kern_analytical, atol=tol)) - - -def test_two_plus_three_body_force(): - """Check that the analytical force kernel matches finite difference of - energy kernel.""" - - # create env 1 - delt = 1e-5 - cell = np.eye(3) - cutoffs = np.array([1, 0.9]) - - positions_1 = [np.array([0., 0., 0.]), - np.array([random(), random(), random()]), - np.array([random(), random(), random()])] - positions_2 = deepcopy(positions_1) - positions_2[0][0] = delt - - positions_3 = deepcopy(positions_1) - positions_3[0][0] = -delt - - species_1 = [1, 2, 1] - atom_1 = 0 - test_structure_1 = struc.Structure(cell, species_1, positions_1) - test_structure_2 = struc.Structure(cell, species_1, positions_2) - test_structure_3 = struc.Structure(cell, species_1, positions_3) - - env1_1 = env.AtomicEnvironment(test_structure_1, atom_1, cutoffs) - env1_2 = env.AtomicEnvironment(test_structure_2, atom_1, cutoffs) - env1_3 = env.AtomicEnvironment(test_structure_3, atom_1, cutoffs) - - # create env 2 - positions_1 = [np.array([0., 0., 0.]), - np.array([random(), random(), random()]), - np.array([random(), random(), random()])] - positions_2 = deepcopy(positions_1) - positions_2[0][1] = delt - positions_3 = deepcopy(positions_1) - positions_3[0][1] = -delt - - species_2 = [1, 1, 2] - atom_2 = 0 - test_structure_1 = struc.Structure(cell, species_2, positions_1) - test_structure_2 = struc.Structure(cell, species_2, positions_2) - test_structure_3 = struc.Structure(cell, species_2, positions_3) - - env2_1 = env.AtomicEnvironment(test_structure_1, atom_2, cutoffs) - env2_2 = env.AtomicEnvironment(test_structure_2, atom_2, cutoffs) - env2_3 = env.AtomicEnvironment(test_structure_3, atom_2, cutoffs) - - # set hyperparameters - sig1 = random() - ls1 = random() - sig2 = random() - ls2 = random() - d1 = 1 - d2 = 2 - - hyps = np.array([sig1, ls1, sig2, ls2]) - - # check force kernel - calc1 = en.two_plus_three_en(env1_2, env2_2, hyps, cutoffs) - calc2 = en.two_plus_three_en(env1_3, env2_3, hyps, cutoffs) - calc3 = en.two_plus_three_en(env1_2, env2_3, hyps, cutoffs) - calc4 = en.two_plus_three_en(env1_3, env2_2, hyps, cutoffs) - - kern_finite_diff = (calc1 + calc2 - calc3 - calc4) / (4*delt**2) - kern_analytical = en.two_plus_three_body(env1_1, env2_1, - d1, d2, hyps, cutoffs) - - tol = 1e-4 - assert(np.isclose(kern_finite_diff, kern_analytical, atol=tol)) - - -def test_two_plus_three_body_grad(): - # create env 1 - cell = np.eye(3) - cutoffs = np.array([1, 1]) - - positions_1 = [np.array([0., 0., 0.]), - np.array([random(), random(), random()]), - np.array([random(), random(), random()])] - - species_1 = [1, 2, 1] - atom_1 = 0 - test_structure_1 = struc.Structure(cell, species_1, positions_1) - env1 = env.AtomicEnvironment(test_structure_1, atom_1, cutoffs) - - # create env 2 - positions_1 = [np.array([0., 0., 0.]), - np.array([random(), random(), random()]), - np.array([random(), random(), random()])] - - species_2 = [1, 1, 2] - atom_2 = 0 - test_structure_1 = struc.Structure(cell, species_2, positions_1) - env2 = env.AtomicEnvironment(test_structure_1, atom_2, cutoffs) - - # set hyperparameters - sig1 = random() - ls1 = random() - sig2 = random() - ls2 = random() - - d1 = randint(1, 3) - d2 = randint(1, 3) - - delta = 1e-8 - - hyps = np.array([sig1, ls1, sig2, ls2]) - hyps1 = np.array([sig1+delta, ls1, sig2, ls2]) - hyps2 = np.array([sig1, ls1+delta, sig2, ls2]) - hyps3 = np.array([sig1, ls1, sig2+delta, ls2]) - hyps4 = np.array([sig1, ls1, sig2, ls2+delta]) - - grad_test = en.two_plus_three_body_grad(env1, env2, d1, d2, hyps, cutoffs) - - sig1_derv_brute = (en.two_plus_three_body(env1, env2, d1, d2, - hyps1, cutoffs) - - en.two_plus_three_body(env1, env2, d1, d2, - hyps, cutoffs)) / delta - - l1_derv_brute = \ - (en.two_plus_three_body(env1, env2, d1, d2, hyps2, cutoffs) - - en.two_plus_three_body(env1, env2, d1, d2, hyps, cutoffs)) / delta - - sig2_derv_brute = \ - (en.two_plus_three_body(env1, env2, d1, d2, - hyps3, cutoffs) - - en.two_plus_three_body(env1, env2, d1, d2, - hyps, cutoffs)) / delta - - l2_derv_brute = \ - (en.two_plus_three_body(env1, env2, d1, d2, hyps4, cutoffs) - - en.two_plus_three_body(env1, env2, d1, d2, hyps, cutoffs)) / delta - - tol = 1e-4 - assert(np.isclose(grad_test[1][0], sig1_derv_brute, atol=tol)) - assert(np.isclose(grad_test[1][1], l1_derv_brute, atol=tol)) - assert(np.isclose(grad_test[1][2], sig2_derv_brute, atol=tol)) - assert(np.isclose(grad_test[1][3], l2_derv_brute, atol=tol)) - - -# ----------------------------------------------------------------------------- -# test two body kernels -# ----------------------------------------------------------------------------- - -def test_two_body_force_en(): - """Check that the analytical force/en kernel matches finite difference of - energy kernel.""" - - # create env 1 - delt = 1e-8 - cell = np.eye(3) - cutoffs = np.array([1]) - - positions_1 = [np.array([0., 0., 0.]), - np.array([random(), random(), random()]), - np.array([random(), random(), random()])] - positions_2 = deepcopy(positions_1) - positions_2[0][0] = delt - - species_1 = [1, 2, 1] - atom_1 = 0 - test_structure_1 = struc.Structure(cell, species_1, positions_1) - test_structure_2 = struc.Structure(cell, species_1, positions_2) - - env1_1 = env.AtomicEnvironment(test_structure_1, atom_1, cutoffs) - env1_2 = env.AtomicEnvironment(test_structure_2, atom_1, cutoffs) - - # create env 2 - positions_1 = [np.array([0., 0., 0.]), - np.array([random(), random(), random()]), - np.array([random(), random(), random()])] - - species_2 = [1, 1, 2] - atom_2 = 0 - test_structure_1 = struc.Structure(cell, species_2, positions_1) - env2 = env.AtomicEnvironment(test_structure_1, atom_2, cutoffs) - - sig = random() - ls = random() - d1 = 1 - - hyps = np.array([sig, ls]) - - # check force kernel - calc1 = en.two_body_en(env1_2, env2, hyps, cutoffs) - calc2 = en.two_body_en(env1_1, env2, hyps, cutoffs) - - kern_finite_diff = (calc1 - calc2) / delt - kern_analytical = en.two_body_force_en(env1_1, env2, d1, hyps, cutoffs) - - tol = 1e-4 - assert(np.isclose(-kern_finite_diff/2, kern_analytical, atol=tol)) - - -def test_two_body_force(): - """Check that the analytical force kernel matches finite difference of - energy kernel.""" - - # create env 1 - delt = 1e-5 - cell = np.eye(3) - cutoffs = np.array([1, 1]) - - positions_1 = [np.array([0., 0., 0.]), - np.array([random(), random(), random()]), - np.array([random(), random(), random()])] - positions_2 = deepcopy(positions_1) - positions_2[0][0] = delt - - positions_3 = deepcopy(positions_1) - positions_3[0][0] = -delt - - species_1 = [1, 2, 1] - atom_1 = 0 - test_structure_1 = struc.Structure(cell, species_1, positions_1) - test_structure_2 = struc.Structure(cell, species_1, positions_2) - test_structure_3 = struc.Structure(cell, species_1, positions_3) - - env1_1 = env.AtomicEnvironment(test_structure_1, atom_1, cutoffs) - env1_2 = env.AtomicEnvironment(test_structure_2, atom_1, cutoffs) - env1_3 = env.AtomicEnvironment(test_structure_3, atom_1, cutoffs) - - # create env 2 - positions_1 = [np.array([0., 0., 0.]), - np.array([random(), random(), random()]), - np.array([random(), random(), random()])] - positions_2 = deepcopy(positions_1) - positions_2[0][1] = delt - positions_3 = deepcopy(positions_1) - positions_3[0][1] = -delt - - species_2 = [1, 1, 2] - atom_2 = 0 - test_structure_1 = struc.Structure(cell, species_2, positions_1) - test_structure_2 = struc.Structure(cell, species_2, positions_2) - test_structure_3 = struc.Structure(cell, species_2, positions_3) - - env2_1 = env.AtomicEnvironment(test_structure_1, atom_2, cutoffs) - env2_2 = env.AtomicEnvironment(test_structure_2, atom_2, cutoffs) - env2_3 = env.AtomicEnvironment(test_structure_3, atom_2, cutoffs) - - sig = 1 - ls = 0.1 - d1 = 1 - d2 = 2 - - hyps = np.array([sig, ls]) - - # check force kernel - calc1 = en.two_body_en(env1_2, env2_2, hyps, cutoffs) - calc2 = en.two_body_en(env1_3, env2_3, hyps, cutoffs) - calc3 = en.two_body_en(env1_2, env2_3, hyps, cutoffs) - calc4 = en.two_body_en(env1_3, env2_2, hyps, cutoffs) - - kern_finite_diff = (calc1 + calc2 - calc3 - calc4) / (4*delt**2) - kern_analytical = en.two_body(env1_1, env2_1, - d1, d2, hyps, cutoffs) - - tol = 1e-4 - assert(np.isclose(kern_finite_diff, kern_analytical, atol=tol)) - - -def test_two_body_grad(): - # create env 1 - cell = np.eye(3) - cutoffs = np.array([1, 1]) - - positions_1 = [np.array([0., 0., 0.]), - np.array([random(), random(), random()]), - np.array([random(), random(), random()])] - - species_1 = [1, 2, 1] - atom_1 = 0 - test_structure_1 = struc.Structure(cell, species_1, positions_1) - env1 = env.AtomicEnvironment(test_structure_1, atom_1, cutoffs) - - # create env 2 - positions_1 = [np.array([0., 0., 0.]), - np.array([random(), random(), random()]), - np.array([random(), random(), random()])] - - species_2 = [1, 1, 2] - atom_2 = 0 - test_structure_1 = struc.Structure(cell, species_2, positions_1) - env2 = env.AtomicEnvironment(test_structure_1, atom_2, cutoffs) - - sig = random() - ls = random() - d1 = randint(1, 3) - d2 = randint(1, 3) - - hyps = np.array([sig, ls]) - - grad_test = en.two_body_grad(env1, env2, d1, d2, hyps, cutoffs) - - delta = 1e-8 - new_sig = sig + delta - new_ls = ls + delta - - sig_derv_brute = (en.two_body(env1, env2, d1, d2, - np.array([new_sig, ls]), - cutoffs) - - en.two_body(env1, env2, d1, d2, - hyps, cutoffs)) / delta - - l_derv_brute = (en.two_body(env1, env2, d1, d2, - np.array([sig, new_ls]), - cutoffs) - - en.two_body(env1, env2, d1, d2, - hyps, cutoffs)) / delta - - tol = 1e-4 - assert(np.isclose(grad_test[1][0], sig_derv_brute, atol=tol)) - assert(np.isclose(grad_test[1][1], l_derv_brute, atol=tol)) - - -# ----------------------------------------------------------------------------- -# test three body kernels -# ----------------------------------------------------------------------------- - - -def test_three_body_force_en(): - """Check that the analytical force/en kernel matches finite difference of - energy kernel.""" - - # create env 1 - delt = 1e-8 - cell = np.eye(3) - cutoffs = np.array([1, 1]) - - positions_1 = [np.array([0., 0., 0.]), - np.array([random(), random(), random()]), - np.array([random(), random(), random()])] - positions_2 = deepcopy(positions_1) - positions_2[0][0] = delt - - species_1 = [1, 2, 1] - atom_1 = 0 - test_structure_1 = struc.Structure(cell, species_1, positions_1) - test_structure_2 = struc.Structure(cell, species_1, positions_2) - - env1_1 = env.AtomicEnvironment(test_structure_1, atom_1, cutoffs) - env1_2 = env.AtomicEnvironment(test_structure_2, atom_1, cutoffs) - - # create env 2 - positions_1 = [np.array([0., 0., 0.]), - np.array([random(), random(), random()]), - np.array([random(), random(), random()])] - - species_2 = [1, 1, 2] - atom_2 = 0 - test_structure_1 = struc.Structure(cell, species_2, positions_1) - env2 = env.AtomicEnvironment(test_structure_1, atom_2, cutoffs) - - sig = random() - ls = random() - d1 = 1 - - hyps = np.array([sig, ls]) - - # check force kernel - calc1 = en.three_body_en(env1_2, env2, hyps, cutoffs) - calc2 = en.three_body_en(env1_1, env2, hyps, cutoffs) - - kern_finite_diff = (calc1 - calc2) / delt - kern_analytical = en.three_body_force_en(env1_1, env2, d1, hyps, cutoffs) - - tol = 1e-4 - assert(np.isclose(-kern_finite_diff/3, kern_analytical, atol=tol)) - - -def test_three_body_force(): - """Check that the analytical force kernel matches finite difference of - energy kernel.""" - - # create env 1 - delt = 1e-5 - cell = np.eye(3) - cutoffs = np.array([1, 1]) - - positions_1 = [np.array([0., 0., 0.]), - np.array([random(), random(), random()]), - np.array([random(), random(), random()])] - positions_2 = deepcopy(positions_1) - positions_2[0][0] = delt - - positions_3 = deepcopy(positions_1) - positions_3[0][0] = -delt - - species_1 = [1, 2, 1] - atom_1 = 0 - test_structure_1 = struc.Structure(cell, species_1, positions_1) - test_structure_2 = struc.Structure(cell, species_1, positions_2) - test_structure_3 = struc.Structure(cell, species_1, positions_3) - - env1_1 = env.AtomicEnvironment(test_structure_1, atom_1, cutoffs) - env1_2 = env.AtomicEnvironment(test_structure_2, atom_1, cutoffs) - env1_3 = env.AtomicEnvironment(test_structure_3, atom_1, cutoffs) - - # create env 2 - positions_1 = [np.array([0., 0., 0.]), - np.array([random(), random(), random()]), - np.array([random(), random(), random()])] - positions_2 = deepcopy(positions_1) - positions_2[0][1] = delt - positions_3 = deepcopy(positions_1) - positions_3[0][1] = -delt - - species_2 = [1, 1, 2] - atom_2 = 0 - test_structure_1 = struc.Structure(cell, species_2, positions_1) - test_structure_2 = struc.Structure(cell, species_2, positions_2) - test_structure_3 = struc.Structure(cell, species_2, positions_3) - - env2_1 = env.AtomicEnvironment(test_structure_1, atom_2, cutoffs) - env2_2 = env.AtomicEnvironment(test_structure_2, atom_2, cutoffs) - env2_3 = env.AtomicEnvironment(test_structure_3, atom_2, cutoffs) - - sig = 1 - ls = 0.1 - d1 = 1 - d2 = 2 - - hyps = np.array([sig, ls]) - - # check force kernel - calc1 = en.three_body_en(env1_2, env2_2, hyps, cutoffs) - calc2 = en.three_body_en(env1_3, env2_3, hyps, cutoffs) - calc3 = en.three_body_en(env1_2, env2_3, hyps, cutoffs) - calc4 = en.three_body_en(env1_3, env2_2, hyps, cutoffs) - - kern_finite_diff = (calc1 + calc2 - calc3 - calc4) / (4*delt**2) - kern_analytical = en.three_body(env1_1, env2_1, - d1, d2, hyps, cutoffs) - - tol = 1e-4 - assert(np.isclose(kern_finite_diff, kern_analytical, atol=tol)) - - -def test_three_body_grad(): - # create env 1 - cell = np.eye(3) - cutoffs = np.array([1, 1]) - - positions_1 = [np.array([0., 0., 0.]), - np.array([random(), random(), random()]), - np.array([random(), random(), random()])] - - species_1 = [1, 2, 1] - atom_1 = 0 - test_structure_1 = struc.Structure(cell, species_1, positions_1) - env1 = env.AtomicEnvironment(test_structure_1, atom_1, cutoffs) - - # create env 2 - positions_1 = [np.array([0., 0., 0.]), - np.array([random(), random(), random()]), - np.array([random(), random(), random()])] - - species_2 = [1, 1, 2] - atom_2 = 0 - test_structure_1 = struc.Structure(cell, species_2, positions_1) - env2 = env.AtomicEnvironment(test_structure_1, atom_2, cutoffs) - - sig = random() - ls = random() - d1 = randint(1, 3) - d2 = randint(1, 3) - - hyps = np.array([sig, ls]) - - grad_test = en.three_body_grad(env1, env2, d1, d2, hyps, cutoffs) - - delta = 1e-8 - new_sig = sig + delta - new_ls = ls + delta - - sig_derv_brute = (en.three_body(env1, env2, d1, d2, - np.array([new_sig, ls]), - cutoffs) - - en.three_body(env1, env2, d1, d2, - hyps, cutoffs)) / delta - - l_derv_brute = (en.three_body(env1, env2, d1, d2, - np.array([sig, new_ls]), - cutoffs) - - en.three_body(env1, env2, d1, d2, - hyps, cutoffs)) / delta - - tol = 1e-4 - assert(np.isclose(grad_test[1][0], sig_derv_brute, atol=tol)) - assert(np.isclose(grad_test[1][1], l_derv_brute, atol=tol)) - - -def test_masked_hyperparameter_function(): - """ - Test simple input permutations for the from_mask_to_hyps function - :return: - """ - - # Standard sig2, ls2, sig3, ls3, noise hyp array - with pytest.raises(NameError): - from_mask_to_hyps(hyps=[], hyps_mask={}) - # ----------------------- - # Test simple input cases - # ----------------------- - hyps_mask = {'nbond': 1} - hyps = [1,2,5] - assert (from_mask_to_hyps(hyps, hyps_mask) == (1, 0, [1], [2], None, - None)) - - hyps = [3,4,5] - hyps_mask = {'ntriplet': 1} - assert (from_mask_to_hyps(hyps, hyps_mask) == (0, 1, None, None, [3], - [4])) - hyps = [1, 2, 3, 4, 5] - hyps_mask = {'nbond': 1, 'ntriplet':1 } - assert (from_mask_to_hyps(hyps, hyps_mask) == (1, 1, [1], [2], [3], - [4])) - - hyps = [1, 2, 3, 4, 5] - hyps_mask['map']=[0, 1, 2, 3, 4] - hyps_mask['original'] = [1, 2, 3, 4, 5, 6] - assert (from_mask_to_hyps(hyps, hyps_mask) == (1, 1, [1], [2], [3], - [4])) - # ----------------------- - # Test simple 2+3 body input case - # ----------------------- - - # Hyps : sig21, sig22, ls21, ls22, sig31, sig32, ls31, ls32, noise - hyps = [1.1,1.2, 2.1, 2.2, 3.1, 3.2, 4.1, 4.2, 5] - hyps_mask = {'nbond': 2, 'ntriplet': 2} - - assert (from_mask_to_hyps(hyps,hyps_mask) == (2, 2, [1.1, 1.2], [2.1,2.2], - [3.1, 3.2], [4.1, 4.2])) - - - -def test_grad_mask_function(): - """ - Test simple permutations for the from_grad_to_mask function - :return: - """ - - grad = 'A' - - assert from_grad_to_mask(grad,hyps_mask={}) == 'A' - - # Test map for a standard slate of hyperparameters - grad = [1, 2, 3, 4, 5] - hyps_mask = {'map': [0, 3]} - assert (from_grad_to_mask(grad, hyps_mask) == [1, 4]).all() - - - # Test map when noise variance is included - grad = [1, 2, 3, 4, 5] - hyps_mask = {'map': [0, 3, 4]} - assert (from_grad_to_mask(grad, hyps_mask) == [1, 4, 5]).all() - - # Test map if the last parameter is not sigma_noise - grad = [1, 2, 3, 4, 5] - hyps_mask = {'map': [0, 3, 5]} - assert (from_grad_to_mask(grad, hyps_mask) == [1, 4]).all() diff --git a/tests/test_lmp.py b/tests/test_lmp.py new file mode 100644 index 000000000..0831c9d6f --- /dev/null +++ b/tests/test_lmp.py @@ -0,0 +1,245 @@ +import numpy as np +import time +import pytest +import os, pickle, re, shutil + +from flare import struc, env, gp +from flare import otf_parser +from flare.mgp.mgp_en import MappedGaussianProcess +from flare.lammps import lammps_calculator +from flare.ase.calculator import FLARE_Calculator +from ase.calculators.lammpsrun import LAMMPS + +from tests.fake_gp import get_gp, get_random_structure + +body_list = ['2', '3'] +multi_list = [False, True] +curr_path = os.getcwd() + +def clean(): + for f in os.listdir("./"): + if re.search(r"grid.*npy", f): + os.remove(f) + if re.search("kv3", f): + os.rmdir(f) + + +# ASSUMPTION: You have a Lammps executable with the mgp pair style with $lmp +# as the corresponding environment variable. +@pytest.mark.skipif(not os.environ.get('lmp', + False), reason='lmp not found ' + 'in environment: Please install LAMMPS ' + 'and set the $lmp env. ' + 'variable to point to the executatble.') + +@pytest.fixture(scope='module') +def all_gp(): + + allgp_dict = {} + np.random.seed(0) + for bodies in ['2', '3', '2+3']: + for multihyps in [False, True]: + gp_model = get_gp(bodies, 'mc', multihyps) + gp_model.parallel = True + gp_model.n_cpus = 2 + allgp_dict[f'{bodies}{multihyps}'] = gp_model + + yield allgp_dict + del allgp_dict + +@pytest.fixture(scope='module') +def all_mgp(): + + allmgp_dict = {} + for bodies in ['2', '3', '2+3']: + for multihyps in [False, True]: + allmgp_dict[f'{bodies}{multihyps}'] = None + + yield allmgp_dict + del allmgp_dict + +@pytest.fixture(scope='module') +def all_ase_calc(): + + all_ase_calc_dict = {} + for bodies in ['2', '3', '2+3']: + for multihyps in [False, True]: + all_ase_calc_dict[f'{bodies}{multihyps}'] = None + + yield all_ase_calc_dict + del all_ase_calc_dict + +@pytest.fixture(scope='module') +def all_lmp_calc(): + + if 'tmp' not in os.listdir("./"): + os.mkdir('tmp') + + all_lmp_calc_dict = {} + for bodies in ['2', '3', '2+3']: + for multihyps in [False, True]: + all_lmp_calc_dict[f'{bodies}{multihyps}'] = None + + yield all_lmp_calc_dict + del all_lmp_calc_dict + + +@pytest.mark.parametrize('bodies', body_list) +@pytest.mark.parametrize('multihyps', multi_list) +def test_init(bodies, multihyps, all_mgp, all_gp): + """ + test the init function + """ + + gp_model = all_gp[f'{bodies}{multihyps}'] + + grid_num_2 = 64 + grid_num_3 = 16 + lower_cut = 0.01 + two_cut = gp_model.cutoffs[0] + three_cut = gp_model.cutoffs[1] + lammps_location = f'{bodies}{multihyps}.mgp' + + # set struc params. cell and masses arbitrary? + mapped_cell = np.eye(3) * 20 + struc_params = {'species': [1, 2], + 'cube_lat': mapped_cell, + 'mass_dict': {'0': 2, '1': 4}} + + # grid parameters + blist = [] + if ('2' in bodies): + blist+= [2] + if ('3' in bodies): + blist+= [3] + train_size = len(gp_model.training_data) + grid_params = {'bodies': blist, + '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': 14, + 'svd_rank_3': 14, + '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, n_cpus=4, + mean_only=True, lmp_file_name=lammps_location) + all_mgp[f'{bodies}{multihyps}'] = mgp_model + + +@pytest.mark.parametrize('bodies', body_list) +@pytest.mark.parametrize('multihyps', multi_list) +def test_build_map(all_gp, all_mgp, all_ase_calc, bodies, multihyps): + """ + test the mapping for mc_simple kernel + """ + + # multihyps = False + gp_model = all_gp[f'{bodies}{multihyps}'] + mgp_model = all_mgp[f'{bodies}{multihyps}'] + + mgp_model.build_map(gp_model) + + all_ase_calc[f'{bodies}{multihyps}'] = FLARE_Calculator(gp_model, + mgp_model, par=False, use_mapping=True) + + clean() + +@pytest.mark.parametrize('bodies', body_list) +@pytest.mark.parametrize('multihyps', multi_list) +def test_lmp_calc(bodies, multihyps, all_lmp_calc): + + label = f'{bodies}{multihyps}' + # set up input params + + by = 'no' + ty = 'no' + if '2' in bodies: + by = 'yes' + if '3' in bodies: + ty = 'yes' + + parameters = {'command': os.environ.get('lmp'), # set up executable for ASE + 'newton': 'off', + 'pair_style': 'mgp', + 'pair_coeff': [f'* * {label}.mgp H He {by} {ty}'], + 'mass': ['1 2', '2 4']} + files = [f'{label}.mgp'] + + + # create ASE calc + lmp_calc = LAMMPS(label=f'tmp{label}', keep_tmp_files=True, tmp_dir='./tmp/', + parameters=parameters, files=files) + + all_lmp_calc[label] = lmp_calc + + +@pytest.mark.skipif(not os.environ.get('lmp', + False), reason='lmp not found ' + 'in environment: Please install LAMMPS ' + 'and set the $lmp env. ' + 'variable to point to the executatble.') +@pytest.mark.parametrize('bodies', body_list) +@pytest.mark.parametrize('multihyps', multi_list) +def test_lmp_predict(all_ase_calc, all_lmp_calc, bodies, multihyps): + """ + test the lammps implementation + """ + + label = f'{bodies}{multihyps}' + + for f in os.listdir("./"): + if label in f: + os.remove(f) + if f in ['log.lammps']: + os.remove(f) + clean() + + flare_calc = all_ase_calc[label] + lmp_calc = all_lmp_calc[label] + + gp_model = flare_calc.gp_model + mgp_model = flare_calc.mgp_model + lammps_location = mgp_model.lmp_file_name + + # lmp file is automatically written now every time MGP is constructed + mgp_model.write_lmp_file(lammps_location) + + # create test structure + cell = np.diag(np.array([1, 1, 1.5])) * 4 + nenv = 10 + unique_species = gp_model.training_data[0].species + cutoffs = gp_model.cutoffs + struc_test, f = get_random_structure(cell, unique_species, nenv) + struc_test.positions *= 4 + + # build ase atom from struc + ase_atoms_flare = struc_test.to_ase_atoms() + ase_atoms_flare.set_calculator(flare_calc) + ase_atoms_lmp = struc_test.to_ase_atoms() + ase_atoms_lmp.set_calculator(lmp_calc) + + lmp_en = ase_atoms_lmp.get_potential_energy() + flare_en = ase_atoms_flare.get_potential_energy() + + lmp_stress = ase_atoms_lmp.get_stress() + flare_stress = ase_atoms_flare.get_stress() + + lmp_forces = ase_atoms_lmp.get_forces() + flare_forces = ase_atoms_flare.get_forces() + + # check that lammps agrees with gp to within 1 meV/A + assert np.all(np.abs(lmp_en - flare_en) < 1e-4) + assert np.all(np.abs(lmp_forces - flare_forces) < 1e-4) + assert np.all(np.abs(lmp_stress - flare_stress) < 1e-3) + + for f in os.listdir('./'): + if (label in f) or (f in ['log.lammps']): + os.remove(f) + diff --git a/tests/test_mc_kernel.py b/tests/test_mc_kernel.py deleted file mode 100644 index 94b42c174..000000000 --- a/tests/test_mc_kernel.py +++ /dev/null @@ -1,174 +0,0 @@ -import sys -from copy import deepcopy -import pytest -import numpy as np -from numpy.random import random, randint - -from flare import env, struc, gp -from flare.kernels.mc_simple import _str_to_kernel as stk - - -def generate_envs(cutoffs, delta): - # create env 1 - cell = np.eye(3) - - positions_1 = np.vstack([[0, 0, 0], random([3, 3])]) - positions_2 = np.copy(positions_1) - positions_2[0][0] = delta - positions_3 = np.copy(positions_1) - positions_3[0][0] = -delta - - species_1 = [1, 2, 1, 1, 1, 1, 2, 1, 2] - atom_1 = 0 - test_structure_1 = struc.Structure(cell, species_1, positions_1) - test_structure_2 = struc.Structure(cell, species_1, positions_2) - test_structure_3 = struc.Structure(cell, species_1, positions_3) - - env1_1 = env.AtomicEnvironment(test_structure_1, atom_1, cutoffs) - env1_2 = env.AtomicEnvironment(test_structure_2, atom_1, cutoffs) - env1_3 = env.AtomicEnvironment(test_structure_3, atom_1, cutoffs) - - # create env 2 - positions_1 = np.vstack([[0, 0, 0], random([3, 3])]) - positions_2 = np.copy(positions_1) - positions_2[0][1] = delta - positions_3 = np.copy(positions_1) - positions_3[0][1] = -delta - - atom_2 = 0 - species_2 = [1, 1, 2, 1, 2, 1, 2, 2, 2] - - test_structure_1 = struc.Structure(cell, species_2, positions_1) - test_structure_2 = struc.Structure(cell, species_2, positions_2) - test_structure_3 = struc.Structure(cell, species_2, positions_3) - - env2_1 = env.AtomicEnvironment(test_structure_1, atom_2, cutoffs) - env2_2 = env.AtomicEnvironment(test_structure_2, atom_2, cutoffs) - env2_3 = env.AtomicEnvironment(test_structure_3, atom_2, cutoffs) - - return env1_1, env1_2, env1_3, env2_1, env2_2, env2_3 - - -def generate_hm(nbond, ntriplet): - - if bool(nbond>0)!=bool(ntriplet>0): - return np.array([random(), random()]) - else: - return np.array([random(), random(), random(), random()]) - -@pytest.mark.parametrize('kernel_name, nbond, ntriplet', - [ ('two_body_mc', 1, 0), - ('three_body_mc', 0, 1), - ('two_plus_three_mc', 1, 1) ] - ) - -def test_force_en(kernel_name, nbond, ntriplet): - """Check that the analytical force/en kernel matches finite difference of - energy kernel.""" - - cutoffs = np.array([1, 1]) - delta = 1e-8 - env1_1, env1_2, env1_3, env2_1, env2_2, env2_3 = generate_envs(cutoffs, delta) - - # set hyperparameters - d1 = 1 - hyps = generate_hm(nbond, ntriplet) - - force_en_kernel = stk[kernel_name+"_force_en"] - en_kernel = stk[kernel_name+"_en"] - if bool('two' in kernel_name) != bool('three' in kernel_name): - - # check force kernel - calc1 = en_kernel(env1_2, env2_1, hyps, cutoffs) - calc2 = en_kernel(env1_1, env2_1, hyps, cutoffs) - - kern_finite_diff = (calc1 - calc2) / delta - if ('two' in kernel_name): - kern_finite_diff /= 2 - else: - kern_finite_diff /= 3 - else: - en2_kernel = stk['two_body_mc_en'] - en3_kernel = stk['three_body_mc_en'] - # check force kernel - calc1 = en2_kernel(env1_2, env2_1, hyps[0:nbond*2], cutoffs) - calc2 = en2_kernel(env1_1, env2_1, hyps[0:nbond*2], cutoffs) - kern_finite_diff = (calc1 - calc2) / 2.0 / delta - calc1 = en3_kernel(env1_2, env2_1, hyps[nbond*2:], cutoffs) - calc2 = en3_kernel(env1_1, env2_1, hyps[nbond*2:], cutoffs) - kern_finite_diff += (calc1 - calc2) / 3.0 / delta - - kern_analytical = force_en_kernel(env1_1, env2_1, d1, hyps, cutoffs) - - tol = 1e-4 - assert(np.isclose(-kern_finite_diff, kern_analytical, atol=tol)) - -@pytest.mark.parametrize('kernel_name, nbond, ntriplet', - [ ('two_body_mc', 1, 0), - ('three_body_mc', 0, 1), - ('two_plus_three_body_mc', 1, 1) ] - ) -def test_force(kernel_name, nbond, ntriplet): - """Check that the analytical force kernel matches finite difference of - energy kernel.""" - - # create env 1 - delta = 1e-5 - cutoffs = np.array([1, 1]) - env1_1, env1_2, env1_3, env2_1, env2_2, env2_3 = generate_envs(cutoffs, delta) - - # set hyperparameters - hyps = generate_hm(nbond, ntriplet) - d1 = 1 - d2 = 2 - - kernel = stk[kernel_name] - if bool('two' in kernel_name) != bool('three' in kernel_name): - en_kernel = stk[kernel_name+"_en"] - else: - en_kernel = stk['two_plus_three_mc_en'] - - # check force kernel - calc1 = en_kernel(env1_2, env2_2, hyps, cutoffs) - calc2 = en_kernel(env1_3, env2_3, hyps, cutoffs) - calc3 = en_kernel(env1_2, env2_3, hyps, cutoffs) - calc4 = en_kernel(env1_3, env2_2, hyps, cutoffs) - - kern_finite_diff = (calc1 + calc2 - calc3 - calc4) / (4*delta**2) - kern_analytical = kernel(env1_1, env2_1, - d1, d2, hyps, cutoffs) - tol = 1e-4 - assert(np.isclose(kern_finite_diff, kern_analytical, atol=tol)) - - -@pytest.mark.parametrize('kernel_name, nbond, ntriplet', - [ ('two_body_mc', 1, 0), - ('three_body_mc', 0, 1), - ('two_plus_three_body_mc', 1, 1) ] - ) -def test_hyps_grad(kernel_name, nbond, ntriplet): - - delta = 1e-8 - cutoffs = np.array([1, 1]) - env1_1, env1_2, env1_3, env2_1, env2_2, env2_3 = generate_envs(cutoffs, delta) - - hyps = generate_hm(nbond, ntriplet) - d1 = randint(1, 3) - d2 = randint(1, 3) - - kernel = stk[kernel_name] - kernel_grad = stk[kernel_name+"_grad"] - - grad_test = kernel_grad(env1_1, env2_1, - d1, d2, hyps, cutoffs) - - tol = 1e-4 - original = kernel(env1_1, env2_1, d1, d2, - hyps, cutoffs) - for i in range(len(hyps)): - newhyps = np.copy(hyps) - newhyps[i] += delta - hgrad = (kernel(env1_1, env2_1, d1, d2, newhyps, - cutoffs)- - original)/delta - assert(np.isclose(grad_test[1][i], hgrad, atol=tol)) diff --git a/tests/test_mc_sephyps.py b/tests/test_mc_sephyps.py index 4b14317ad..a8c7249aa 100644 --- a/tests/test_mc_sephyps.py +++ b/tests/test_mc_sephyps.py @@ -7,128 +7,59 @@ from flare import env, struc, gp from flare.kernels.mc_sephyps import _str_to_kernel as stk +from flare.kernels.utils import from_mask_to_args, str_to_kernel_set +from .fake_gp import generate_hm, generate_envs -def generate_envs(cutoffs, delta): - # create env 1 - cell = np.eye(3) - - positions_1 = np.vstack([[0, 0, 0], random([3, 3])]) - positions_2 = deepcopy(positions_1) - positions_2[0][0] = delta - positions_3 = deepcopy(positions_1) - positions_3[0][0] = -delta - - species_1 = [1, 2, 1, 1, 1, 1, 2, 1, 2] - atom_1 = 0 - test_structure_1 = struc.Structure(cell, species_1, positions_1) - test_structure_2 = struc.Structure(cell, species_1, positions_2) - test_structure_3 = struc.Structure(cell, species_1, positions_3) - - env1_1 = env.AtomicEnvironment(test_structure_1, atom_1, cutoffs) - env1_2 = env.AtomicEnvironment(test_structure_2, atom_1, cutoffs) - env1_3 = env.AtomicEnvironment(test_structure_3, atom_1, cutoffs) +def test_force_en_multi_vs_simple(): + """Check that the analytical kernel matches the one implemented + in mc_simple.py""" - # create env 2 - positions_1 = np.vstack([[0, 0, 0], random([3, 3])]) - positions_2 = deepcopy(positions_1) - positions_2[0][1] = delta - positions_3 = deepcopy(positions_1) - positions_3[0][1] = -delta - - atom_2 = 0 - species_2 = [1, 1, 2, 1, 2, 1, 2, 2, 2] + cutoffs = np.ones(3, dtype=np.float64) + delta = 1e-8 + env1_1, env1_2, env1_3, env2_1, env2_2, env2_3 = generate_envs(cutoffs, delta) - test_structure_1 = struc.Structure(cell, species_2, positions_1) - test_structure_2 = struc.Structure(cell, species_2, positions_2) - test_structure_3 = struc.Structure(cell, species_2, positions_3) + # set hyperparameters + d1 = 1 + d2 = 2 + tol = 1e-4 - env2_1 = env.AtomicEnvironment(test_structure_1, atom_2, cutoffs) - env2_2 = env.AtomicEnvironment(test_structure_2, atom_2, cutoffs) - env2_3 = env.AtomicEnvironment(test_structure_3, atom_2, cutoffs) + hyps, hm, cut = generate_hm(1, 1, cutoffs, False) - return env1_1, env1_2, env1_3, env2_1, env2_2, env2_3 + # mc_simple + kernel0, kg0, en_kernel0, force_en_kernel0 = str_to_kernel_set("2+3+mb+mc", False) + hyps = np.ones(7, dtype=np.float64) + args0 = (hyps, cutoffs) + # mc_sephyps + kernel, kg, en_kernel, force_en_kernel = str_to_kernel_set("2+3+mb+mc", True) + args1 = from_mask_to_args(hyps, hm, cutoffs) -def generate_hm(nbond, ntriplet, cutoffs, constraint=False): + funcs = [[kernel0, kg0, en_kernel0, force_en_kernel0], + [kernel, kg, en_kernel, force_en_kernel]] - specs_mask = np.zeros(118, dtype=int) - specs_mask[1] = 0 - specs_mask[2] = 1 - nspecs = 2 + i = 0 + reference = funcs[0][i](env1_1, env2_1, d1, d2, *args0) + result = funcs[1][i](env1_1, env2_1, d1, d2, *args1) + assert(np.isclose(reference, result, atol=tol)) - specs_mask = np.zeros(118, dtype=int) - specs_mask[1] = 0 - specs_mask[2] = 1 - nspecs = 2 + i = 1 + reference = funcs[0][i](env1_1, env2_1, d1, d2, *args0) + result = funcs[1][i](env1_1, env2_1, d1, d2, *args1) + assert(np.isclose(reference[0], result[0], atol=tol)) + assert(np.isclose(reference[1], result[1], atol=tol).all()) - cut = [] - cut += [cutoffs[0]] - cut += [cutoffs[1]] + i = 2 + reference = funcs[0][i](env1_1, env2_1, *args0) + result = funcs[1][i](env1_1, env2_1, *args1) + assert(np.isclose(reference, result, atol=tol)) - if (nbond==2): - sig1 = random(nbond) - ls1 = random(nbond) - bond_mask = np.ones(nspecs**2, dtype=int) - bond_mask[0] = 0 - else: - sig1 = [random()] - ls1 = [random()] - bond_mask = np.zeros(nspecs**2, dtype=int) - - if (ntriplet==2): - sig2 = random(ntriplet) - ls2 = random(ntriplet) - triplet_mask = np.ones(nspecs**3, dtype=int) - triplet_mask[0] = 0 - else: - sig2 = [random()] - ls2 = [random()] - triplet_mask = np.zeros(nspecs**3, dtype=int) + i = 3 + reference = funcs[0][i](env1_1, env2_1, d1, *args0) + result = funcs[1][i](env1_1, env2_1, d1, *args1) + assert(np.isclose(reference, result, atol=tol)) - sigman = [0.05] - - if (nbond>0 and ntriplet>0): - hyps = np.hstack([sig1, ls1, sig2, ls2, sigman]) - elif (nbond>0): - hyps = np.hstack([sig1, ls1, sigman]) - else: - hyps = np.hstack([sig2, ls2, sigman]) - - hyps_mask = {'nspec': nspecs, - 'spec_mask': specs_mask, - 'nbond': nbond, - 'bond_mask': bond_mask, - 'ntriplet': ntriplet, - 'triplet_mask': triplet_mask} - if (constraint is False): - return hyps, hyps_mask, cut - - hyps_mask['map'] = [] - hyps_mask['original'] = hyps - hm = hyps_mask['map'] - count = 0 - newhyps = [] - if (nbond>0): - # fix type 0, and only compute type 1 of bonds - hm += [1] - newhyps += [hyps[1]] - hm += [3] - newhyps += [hyps[3]] - count += 4 - if (ntriplet>0): - # fix type 0, and only compute type 1 of triplets - hm += [1+count] - newhyps += [hyps[1+count]] - hm += [3+count] - newhyps += [hyps[3+count]] - hm += [len(hyps)-1] - newhyps += [hyps[-1]] - hyps = np.hstack(newhyps) - print(hm, hyps, newhyps) - - return hyps, hyps_mask, cut @pytest.mark.parametrize('kernel_name, nbond, ntriplet, constraint', [ ('two_body_mc', 2, 0, True), @@ -148,17 +79,18 @@ def test_force_en(kernel_name, nbond, ntriplet, constraint): # set hyperparameters d1 = 1 + hyps, hm, cut = generate_hm(nbond, ntriplet, cutoffs, constraint) + args0 = from_mask_to_args(hyps, hm, cutoffs) + force_en_kernel = stk[kernel_name+"_force_en"] en_kernel = stk[kernel_name+"_en"] if bool('two' in kernel_name) != bool('three' in kernel_name): # check force kernel - calc1 = en_kernel(env1_2, env2_1, hyps, cut, - hyps_mask=hm) - calc2 = en_kernel(env1_1, env2_1, hyps, cut, - hyps_mask=hm) + calc1 = en_kernel(env1_2, env2_1, *args0) + calc2 = en_kernel(env1_1, env2_1, *args0) kern_finite_diff = (calc1 - calc2) / delta if ('two' in kernel_name): @@ -181,19 +113,19 @@ def test_force_en(kernel_name, nbond, ntriplet, constraint): hm2['ntriplet']=0 hm3['nbond']=0 - calc1 = en2_kernel(env1_2, env2_1, hyps[0:nbond*2], cutoffs, - hyps_mask=hm2) - calc2 = en2_kernel(env1_1, env2_1, hyps[0:nbond*2], cutoffs, - hyps_mask=hm2) + args2 = from_mask_to_args(hyps[0:nbond*2], hm2, cutoffs) + + calc1 = en2_kernel(env1_2, env2_1, *args2) + calc2 = en2_kernel(env1_1, env2_1, *args2) kern_finite_diff = (calc1 - calc2) / 2.0 / delta - calc1 = en3_kernel(env1_2, env2_1, hyps[nbond*2:-1], cutoffs, - hyps_mask=hm3) - calc2 = en3_kernel(env1_1, env2_1, hyps[nbond*2:-1], cutoffs, - hyps_mask=hm3) + + args3 = from_mask_to_args(hyps[nbond*2:-1], hm3, cutoffs) + + calc1 = en3_kernel(env1_2, env2_1, *args3) + calc2 = en3_kernel(env1_1, env2_1, *args3) kern_finite_diff += (calc1 - calc2) / 3.0 / delta - kern_analytical = force_en_kernel(env1_1, env2_1, d1, hyps, cutoffs, - hyps_mask=hm) + kern_analytical = force_en_kernel(env1_1, env2_1, d1, *args0) tol = 1e-4 assert(np.isclose(-kern_finite_diff, kern_analytical, atol=tol)) @@ -217,6 +149,7 @@ def test_force(kernel_name, nbond, ntriplet, constraint): # set hyperparameters hyps, hm, cut = generate_hm(nbond, ntriplet, cutoffs, constraint) + args0 = from_mask_to_args(hyps, hm, cutoffs) d1 = 1 d2 = 2 @@ -227,14 +160,14 @@ def test_force(kernel_name, nbond, ntriplet, constraint): en_kernel = stk['two_plus_three_mc_en'] # check force kernel - calc1 = en_kernel(env1_2, env2_2, hyps, cut, hyps_mask=hm) - calc2 = en_kernel(env1_3, env2_3, hyps, cut, hyps_mask=hm) - calc3 = en_kernel(env1_2, env2_3, hyps, cut, hyps_mask=hm) - calc4 = en_kernel(env1_3, env2_2, hyps, cut, hyps_mask=hm) + calc1 = en_kernel(env1_2, env2_2, *args0) + calc2 = en_kernel(env1_3, env2_3, *args0) + calc3 = en_kernel(env1_2, env2_3, *args0) + calc4 = en_kernel(env1_3, env2_2, *args0) kern_finite_diff = (calc1 + calc2 - calc3 - calc4) / (4*delta**2) kern_analytical = kernel(env1_1, env2_1, - d1, d2, hyps, cut, hyps_mask=hm) + d1, d2, *args0) tol = 1e-4 assert(np.isclose(kern_finite_diff, kern_analytical, atol=tol)) @@ -249,26 +182,29 @@ def test_force(kernel_name, nbond, ntriplet, constraint): ) def test_hyps_grad(kernel_name, nbond, ntriplet, constraint): + np.random.seed(0) + delta = 1e-8 cutoffs = np.array([1, 1]) env1_1, env1_2, env1_3, env2_1, env2_2, env2_3 = generate_envs(cutoffs, delta) hyps, hm, cut = generate_hm(nbond, ntriplet, cutoffs, constraint) - d1 = randint(1, 3) - d2 = randint(1, 3) + args = from_mask_to_args(hyps, hm, cutoffs) + d1 = 1 + d2 = 2 kernel = stk[kernel_name] kernel_grad = stk[kernel_name+"_grad"] - k, grad = kernel_grad(env1_1, env2_1, - d1, d2, hyps, cut, hyps_mask=hm) + # compute analytical values + k, grad = kernel_grad(env1_1, env2_1, d1, d2, *args) + print(kernel_name) print("grad", grad) print("hyps", hyps) tol = 1e-4 - original = kernel(env1_1, env2_1, d1, d2, - hyps, cut, hyps_mask=hm) + original = kernel(env1_1, env2_1, d1, d2, *args) nhyps = len(hyps)-1 if ('map' in hm.keys()): @@ -284,8 +220,12 @@ def test_hyps_grad(kernel_name, nbond, ntriplet, constraint): newid = hm['map'][i] hm['original'] = np.copy(original_hyps) hm['original'][newid] += delta - hgrad = (kernel(env1_1, env2_1, d1, d2, newhyps, - cut, hyps_mask=hm) - - original)/delta - print(i, "hgrad", hgrad) - assert(np.isclose(grad[i], hgrad, atol=tol)) + newargs = from_mask_to_args(newhyps, hm, cutoffs) + + hgrad = (kernel(env1_1, env2_1, d1, d2, *newargs) - original)/delta + if ('map' in hm.keys()): + print(i, "hgrad", hgrad, grad[hm['map'][i]]) + assert(np.isclose(grad[hm['map'][i]], hgrad, atol=tol)) + else: + print(i, "hgrad", hgrad, grad[i]) + assert(np.isclose(grad[i], hgrad, atol=tol)) diff --git a/tests/test_mgp.py b/tests/test_mgp.py deleted file mode 100644 index 6b9342a73..000000000 --- a/tests/test_mgp.py +++ /dev/null @@ -1,209 +0,0 @@ -import numpy as np -import time -import pytest -from flare import struc, env -from flare import otf_parser -from flare.mgp.mgp import MappedGaussianProcess -from flare import mc_simple -from flare.lammps import lammps_calculator -import pickle -import os - -@pytest.fixture(scope='module') -def otf_object(): - file_name = 'test_files/AgI_snippet.out' - - # parse otf output - otf_traj = otf_parser.OtfAnalysis(file_name) - - yield otf_traj - del otf_traj - - -@pytest.fixture(scope='module') -def structure(otf_object): - # create test structure - otf_cell = otf_object.header['cell'] - species = np.array([47, 53] * 27) - positions = otf_object.position_list[-1] - test_struc = struc.Structure(otf_cell, species, positions) - - yield test_struc - del test_struc - - -@pytest.fixture(scope='module') -def params(): - grid_num_2 = 64 - grid_num_3 = 25 - lower_cut = 2.5 - two_cut = 7. - three_cut = 5. - - # set struc params. cell and masses arbitrary? - mapped_cell = np.eye(3) * 100 - struc_params = {'species': [47, 53], - 'cube_lat': mapped_cell} - - # grid parameters - grid_params = {'bounds_2': [[lower_cut], [two_cut]], - 'bounds_3': [[lower_cut, lower_cut, -1], - [three_cut, three_cut, 1]], - 'grid_num_2': grid_num_2, - 'grid_num_3': [grid_num_3, grid_num_3, grid_num_3], - 'svd_rank_2': 64, - 'svd_rank_3': 90, - 'bodies': [2, 3], - 'load_grid': None, - 'update': False} - - map_params = {'grid': grid_params, - 'struc': struc_params} - - yield map_params - del map_params - -def test_2_body(otf_object, structure, params): - # reconstruct gp model - hyp_no = 2 - otf_cell = otf_object.header['cell'] - sig2 = otf_object.gp_hyp_list[hyp_no-1][-1][0] - ls2 = otf_object.gp_hyp_list[hyp_no-1][-1][1] - noise = otf_object.gp_hyp_list[hyp_no-1][-1][-1] - hyps = np.array([sig2, ls2, noise]) - - kernel = mc_simple.two_body_mc - kernel_grad = mc_simple.two_body_mc_grad - gp_model = otf_object.make_gp(kernel=kernel, kernel_grad=kernel_grad, - hyps=hyps, hyp_no=hyp_no) - gp_model.par = True - gp_model.hyp_labels = ['sig2', 'ls2', 'noise'] - - # create MGP - grid_params = params['grid'] - grid_params['bodies'] = [2] - struc_params = params['struc'] - mgp_model = MappedGaussianProcess(gp_model.hyps, gp_model.cutoffs, - grid_params, struc_params, mean_only=True, container_only=False, - GP=gp_model, lmp_file_name='AgI_Molten_15.txt') - - # test if MGP prediction matches GP - atom = 0 - environ = env.AtomicEnvironment(structure, atom, gp_model.cutoffs) - - gp_pred_x = gp_model.predict(environ, 1) - mgp_pred = mgp_model.predict(environ, mean_only=True) - - # check mgp is within 1 meV/A of the gp - assert(np.abs(mgp_pred[0][0] - gp_pred_x[0]) < 1e-3) - - -def test_3_body(otf_object, structure, params): - # reconstruct gp model - hyp_no = 2 - otf_cell = otf_object.header['cell'] - sig = otf_object.gp_hyp_list[hyp_no-1][-1][2] - ls = otf_object.gp_hyp_list[hyp_no-1][-1][3] - noise = otf_object.gp_hyp_list[hyp_no-1][-1][-1] - hyps = np.array([sig, ls, noise]) - - kernel = mc_simple.three_body_mc - kernel_grad = mc_simple.three_body_mc_grad - gp_model = otf_object.make_gp(kernel=kernel, kernel_grad=kernel_grad, - hyps=hyps, hyp_no=hyp_no) - gp_model.par = True - gp_model.hyp_labels = ['sig3', 'ls3', 'noise'] - - # create MGP - grid_params = params['grid'] - grid_params['bodies'] = [3] - struc_params = params['struc'] - mgp_model = MappedGaussianProcess(gp_model.hyps, gp_model.cutoffs, - grid_params, struc_params, mean_only=True, container_only=False, - GP=gp_model, lmp_file_name='AgI_Molten_15.txt') - - # test if MGP prediction matches GP - atom = 0 - environ = env.AtomicEnvironment(structure, atom, gp_model.cutoffs) - - gp_pred_x = gp_model.predict(environ, 1) - mgp_pred = mgp_model.predict(environ, mean_only=True) - - # check mgp is within 1 meV/A of the gp - assert(np.abs(mgp_pred[0][0] - gp_pred_x[0]) < 1e-3) - - -def test_2_plus_3_body(otf_object, structure, params): - # reconstruct gp model - kernel = mc_simple.two_plus_three_body_mc - kernel_grad = mc_simple.two_plus_three_body_mc_grad - gp_model = otf_object.make_gp(kernel=kernel, kernel_grad=kernel_grad, - hyp_no=2) - gp_model.par = True - gp_model.hyp_labels = ['sig2', 'ls2', 'sig3', 'ls3', 'noise'] - - # create MGP - grid_params = params['grid'] - grid_params['bodies'] = [2, 3] - struc_params = params['struc'] - mgp_model = MappedGaussianProcess(gp_model.hyps, gp_model.cutoffs, - grid_params, struc_params, mean_only=True, container_only=False, - GP=gp_model, lmp_file_name='AgI_Molten_15.txt') - - # test if MGP prediction matches GP - atom = 0 - environ = env.AtomicEnvironment(structure, atom, gp_model.cutoffs) - - gp_pred_x = gp_model.predict(environ, 1) - mgp_pred = mgp_model.predict(environ, mean_only=True) - - # check mgp is within 1 meV/A of the gp - assert(np.abs(mgp_pred[0][0] - gp_pred_x[0]) < 1e-3) - - -# ASSUMPTION: You have a Lammps executable with the mgp pair style with $lmp -# as the corresponding environment variable. -@pytest.mark.skipif(not os.environ.get('lmp', - False), reason='lmp not found ' - 'in environment: Please install LAMMPS ' - 'and set the $lmp env. ' - 'variable to point to the executatble.') -def test_lammps(otf_object, structure): - atom_types = [1, 2] - atom_masses = [108, 127] - atom_species = [1, 2] * 27 - - # create data file - lammps_location = 'AgI_Molten_15.txt' - data_file_name = 'tmp.data' - data_text = lammps_calculator.lammps_dat(structure, atom_types, - atom_masses, atom_species) - lammps_calculator.write_text(data_file_name, data_text) - - # create lammps input - style_string = 'mgpf' # use mgpf for the new lammps executable pair_style, - # because mgp is the energy version - coeff_string = '* * {} Ag I yes yes'.format(lammps_location) - lammps_executable = '$lmp' - dump_file_name = 'tmp.dump' - input_file_name = 'tmp.in' - output_file_name = 'tmp.out' - input_text = \ - lammps_calculator.generic_lammps_input(data_file_name, style_string, - coeff_string, dump_file_name) - lammps_calculator.write_text(input_file_name, input_text) - - lammps_calculator.run_lammps(lammps_executable, input_file_name, - output_file_name) - - lammps_forces = lammps_calculator.lammps_parser(dump_file_name) - - # check that lammps agrees with gp to within 1 meV/A - forces = otf_object.force_list[-1] - assert(np.abs(lammps_forces[0, 1] - forces[0, 1]) < 1e-3) - - os.system('rm tmp.in tmp.out tmp.dump tmp.data' - ' log.lammps') - os.system('rm '+lammps_location) - os.system('rm grid3*.npy') - os.system('rm -r kv3') diff --git a/tests/test_mgp_unit.py b/tests/test_mgp_unit.py new file mode 100644 index 000000000..958ffc353 --- /dev/null +++ b/tests/test_mgp_unit.py @@ -0,0 +1,265 @@ +import numpy as np +import time +import pytest +import os, pickle, re + +from flare import struc, env, gp +from flare import otf_parser +from flare.mgp.mgp_en import MappedGaussianProcess +from flare.lammps import lammps_calculator + +from .fake_gp import get_gp, get_random_structure + +body_list = ['2', '3'] +multi_list = [False, True] + +def clean(): + for f in os.listdir("./"): + if re.search(r"grid.*npy", f): + os.remove(f) + if re.search("kv3", f): + os.rmdir(f) + + +# ASSUMPTION: You have a Lammps executable with the mgp pair style with $lmp +# as the corresponding environment variable. +@pytest.mark.skipif(not os.environ.get('lmp', + False), reason='lmp not found ' + 'in environment: Please install LAMMPS ' + 'and set the $lmp env. ' + 'variable to point to the executatble.') + +@pytest.fixture(scope='module') +def all_gp(): + + allgp_dict = {} + np.random.seed(0) + for bodies in ['2', '3', '2+3']: + for multihyps in [False, True]: + gp_model = get_gp(bodies, 'mc', multihyps) + gp_model.parallel = True + gp_model.n_cpus = 2 + allgp_dict[f'{bodies}{multihyps}'] = gp_model + + yield allgp_dict + del allgp_dict + +@pytest.fixture(scope='module') +def all_mgp(): + + allmgp_dict = {} + for bodies in ['2', '3', '2+3']: + for multihyps in [False, True]: + allmgp_dict[f'{bodies}{multihyps}'] = None + + yield allmgp_dict + del allmgp_dict + +@pytest.mark.parametrize('bodies', body_list) +@pytest.mark.parametrize('multihyps', multi_list) +def test_init(bodies, multihyps, all_mgp, all_gp): + """ + test the init function + """ + + gp_model = all_gp[f'{bodies}{multihyps}'] + + grid_num_2 = 64 + grid_num_3 = 20 + lower_cut = 0.01 + two_cut = gp_model.cutoffs[0] + three_cut = gp_model.cutoffs[1] + lammps_location = f'{bodies}{multihyps}.mgp' + + # 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 + blist = [] + if ('2' in bodies): + blist+= [2] + if ('3' in bodies): + blist+= [3] + train_size = len(gp_model.training_data) + grid_params = {'bodies': blist, + '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': 14, + 'svd_rank_3': 14, + '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, n_cpus=4, + lmp_file_name=lammps_location) + all_mgp[f'{bodies}{multihyps}'] = mgp_model + + +@pytest.mark.parametrize('bodies', body_list) +@pytest.mark.parametrize('multihyps', multi_list) +def test_build_map(all_gp, all_mgp, bodies, multihyps): + """ + test the mapping for mc_simple kernel + """ + + # multihyps = False + gp_model = all_gp[f'{bodies}{multihyps}'] + mgp_model = all_mgp[f'{bodies}{multihyps}'] + + mgp_model.build_map(gp_model) + +@pytest.mark.parametrize('bodies', body_list) +@pytest.mark.parametrize('multihyps', multi_list) +def test_write_model(all_mgp, bodies, multihyps): + """ + test the mapping for mc_simple kernel + """ + + # multihyps = False + mgp_model = all_mgp[f'{bodies}{multihyps}'] + mgp_model.mean_only = True + mgp_model.write_model(f'my_mgp_{bodies}_{multihyps}') + + mgp_model.write_model(f'my_mgp_{bodies}_{multihyps}', format='pickle') + + # Ensure that user is warned when a non-mean_only + # model is serialized into a Dictionary + with pytest.warns(Warning): + mgp_model.mean_only = False + mgp_model.as_dict() + mgp_model.mean_only = True + + +@pytest.mark.parametrize('bodies', body_list) +@pytest.mark.parametrize('multihyps', multi_list) +def test_load_model(all_mgp, bodies, multihyps): + """ + test the mapping for mc_simple kernel + """ + + # multihyps = False + name = f'my_mgp_{bodies}_{multihyps}.json' + all_mgp[f'{bodies}{multihyps}'] = MappedGaussianProcess.from_file(name) + os.remove(name) + + name = f'my_mgp_{bodies}_{multihyps}.pickle' + all_mgp[f'{bodies}{multihyps}'] = MappedGaussianProcess.from_file(name) + os.remove(name) + + +@pytest.mark.parametrize('bodies', body_list) +@pytest.mark.parametrize('multihyps', multi_list) +def test_predict(all_gp, all_mgp, bodies, multihyps): + """ + test the predict for mc_simple kernel + """ + + # multihyps = False + gp_model = all_gp[f'{bodies}{multihyps}'] + mgp_model = all_mgp[f'{bodies}{multihyps}'] + + nenv=10 + cell = np.eye(3) + cutoffs = gp_model.cutoffs + unique_species = gp_model.training_data[0].species + struc_test, f = get_random_structure(cell, unique_species, nenv) + test_envi = env.AtomicEnvironment(struc_test, 1, cutoffs) + + gp_pred_en = gp_model.predict_local_energy(test_envi) + gp_pred_x = gp_model.predict(test_envi, 1) + mgp_pred = mgp_model.predict(test_envi, mean_only=True) + + # check mgp is within 1 meV/A of the gp + assert(np.abs(mgp_pred[3] - gp_pred_en) < 1e-3), \ + f"{bodies} body energy mapping is wrong" + assert(np.abs(mgp_pred[0][0] - gp_pred_x[0]) < 1e-3), \ + f"{bodies} body mapping is wrong" + + clean() + +@pytest.mark.skipif(not os.environ.get('lmp', + False), reason='lmp not found ' + 'in environment: Please install LAMMPS ' + 'and set the $lmp env. ' + 'variable to point to the executatble.') +@pytest.mark.parametrize('bodies', body_list) +@pytest.mark.parametrize('multihyps', multi_list) +def test_lmp_predict(all_gp, all_mgp, bodies, multihyps): + """ + test the lammps implementation + """ + + for f in os.listdir("./"): + if f in [f'tmp{bodies}{multihyps}in', f'tmp{bodies}{multihyps}out', f'tmp{bodies}{multihyps}dump', + f'tmp{bodies}{multihyps}data', 'log.lammps']: + os.remove(f) + clean() + + mgp_model = all_mgp[f'{bodies}{multihyps}'] + gp_model = all_gp[f'{bodies}{multihyps}'] + lammps_location = mgp_model.lmp_file_name + + # lmp file is automatically written now every time MGP is constructed + mgp_model.write_lmp_file(lammps_location) + + # create test structure + cell = np.eye(3) + nenv = 10 + unique_species = gp_model.training_data[0].species + cutoffs = gp_model.cutoffs + struc_test, f = get_random_structure(cell, unique_species, nenv) + atom_num = 1 + test_envi = env.AtomicEnvironment(struc_test, atom_num, cutoffs) + atom_types = [1, 2] + atom_masses = [108, 127] + atom_species = struc_test.coded_species + + # create data file + data_file_name = f'tmp{bodies}{multihyps}.data' + data_text = lammps_calculator.lammps_dat(struc_test, atom_types, + atom_masses, atom_species) + lammps_calculator.write_text(data_file_name, data_text) + + # create lammps input + by = 'no' + ty = 'no' + if '2' in bodies: + by = 'yes' + if '3' in bodies: + ty = 'yes' + style_string = 'mgp' + coeff_string = f'* * {lammps_location} H He {by} {ty}' + lammps_executable = os.environ.get('lmp') + dump_file_name = f'tmp{bodies}{multihyps}.dump' + input_file_name = f'tmp{bodies}{multihyps}.in' + output_file_name = f'tmp{bodies}{multihyps}.out' + input_text = \ + lammps_calculator.generic_lammps_input(data_file_name, style_string, + coeff_string, dump_file_name, + newton=True) + lammps_calculator.write_text(input_file_name, input_text) + + lammps_calculator.run_lammps(lammps_executable, input_file_name, + output_file_name) + + lammps_forces = lammps_calculator.lammps_parser(dump_file_name) + mgp_forces = mgp_model.predict(test_envi, mean_only=True) + + # check that lammps agrees with gp to within 1 meV/A + for i in range(3): + assert (np.abs(lammps_forces[atom_num, i] - mgp_forces[0][i]) < 1e-3) + + for f in os.listdir("./"): + if f in [f'tmp{bodies}{multihyps}in', f'tmp{bodies}{multihyps}out', f'tmp{bodies}{multihyps}dump', + f'tmp{bodies}{multihyps}data', 'log.lammps', lammps_location]: + os.remove(f) + clean() diff --git a/tests/test_mgp_unit_ff.py b/tests/test_mgp_unit_ff.py new file mode 100644 index 000000000..0418d06a4 --- /dev/null +++ b/tests/test_mgp_unit_ff.py @@ -0,0 +1,221 @@ +import numpy as np +import time +import pytest +import os, pickle, re + +from flare import struc, env, gp +from flare import otf_parser +from flare.mgp.mgp import MappedGaussianProcess +from flare.kernels.utils import str_to_kernel_set +from flare.lammps import lammps_calculator + +from .fake_gp import get_gp, get_random_structure + +body_list = ['2', '3'] + +def clean(): + for f in os.listdir("./"): + if re.search(r"grid.*npy", f): + os.remove(f) + if re.search("kv3", f): + os.rmdir(f) + + +# ASSUMPTION: You have a Lammps executable with the mgp pair style with $lmp +# as the corresponding environment variable. +@pytest.mark.skipif(not os.environ.get('lmp', + False), reason='lmp not found ' + 'in environment: Please install LAMMPS ' + 'and set the $lmp env. ' + 'variable to point to the executatble.') + +@pytest.fixture(scope='module') +def all_gp(): + + allgp_dict = {} + np.random.seed(0) + for bodies in ['2', '3', '2+3']: + gp_model = get_gp(bodies, 'mc', False) + gp_model.parallel = True + gp_model.n_cpus = 2 + gp_model.set_L_alpha() + allgp_dict[f'{bodies}'] = gp_model + + yield allgp_dict + del allgp_dict + +@pytest.fixture(scope='module') +def all_mgp(): + + allmgp_dict = {} + for bodies in ['2', '3', '2+3']: + allmgp_dict[f'{bodies}'] = None + + yield allmgp_dict + del allmgp_dict + +@pytest.mark.parametrize('bodies', body_list) +def test_init(bodies, all_gp, all_mgp): + """ + test the init function + """ + + gp_model = all_gp[f'{bodies}'] + + grid_num_2 = 64 + grid_num_3 = 20 + lower_cut = 0.01 + two_cut = gp_model.cutoffs[0] + three_cut = gp_model.cutoffs[1] + lammps_location = f'{bodies}_mgp_ff.txt' + + # 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 + # grid parameters + blist = [] + if ('2' in bodies): + blist+= [2] + if ('3' in bodies): + blist+= [3] + grid_params = {'bounds_2': [[lower_cut], [two_cut]], + 'bounds_3': [[lower_cut, lower_cut, -1], + [three_cut, three_cut, 1]], + 'grid_num_2': grid_num_2, + 'grid_num_3': [grid_num_3, grid_num_3, grid_num_3], + 'svd_rank_2': 64, + 'svd_rank_3': 90, + 'bodies': blist, + 'cutoffs': [two_cut, three_cut], + 'load_grid': None, + 'update': False} + + struc_params = {'species': [1, 2], + 'cube_lat': mapped_cell, + 'mass_dict': {'0': 27, '1': 16}} + + mgp_model = MappedGaussianProcess(gp_model.hyps, gp_model.cutoffs, + grid_params, struc_params, mean_only=True, container_only=True, + GP=gp_model, lmp_file_name=lammps_location) + all_mgp[f'{bodies}'] = mgp_model + + +@pytest.mark.parametrize('bodies', body_list) +def test_build_map(all_gp, all_mgp, bodies): + """ + test the mapping for mc_simple kernel + """ + + gp_model = all_gp[f'{bodies}'] + mgp_model = all_mgp[f'{bodies}'] + + mgp_model.build_map(gp_model) + + clean() + + +@pytest.mark.parametrize('bodies', body_list) +def test_predict(all_gp, all_mgp, bodies): + """ + test the predict for mc_simple kernel + """ + + gp_model = all_gp[f'{bodies}'] + mgp_model = all_mgp[f'{bodies}'] + atom_id = 1 + + nenv=10 + cell = np.eye(3) + cutoffs = gp_model.cutoffs + unique_species = gp_model.training_data[0].species + struc_test, f = get_random_structure(cell, unique_species, nenv) + test_envi = env.AtomicEnvironment(struc_test, atom_id, cutoffs) + + mgp_pred = mgp_model.predict(test_envi, mean_only=True) + + # check mgp is within 1 meV/A of the gp + for s in range(3): + gp_pred_x = gp_model.predict(test_envi, s+1) + print(mgp_pred, gp_pred_x) + assert(np.abs(mgp_pred[0][s] - gp_pred_x[0]) < 1e-3), \ + f"{bodies} body mapping is wrong" + + clean() + + +@pytest.mark.skipif(not os.environ.get('lmp', + False), reason='lmp not found ' + 'in environment: Please install LAMMPS ' + 'and set the $lmp env. ' + 'variable to point to the executatble.') +@pytest.mark.parametrize('bodies', body_list) +def test_lmp_predict(all_gp, all_mgp, bodies): + """ + test the lammps implementation + """ + + mgp_model = all_mgp[f'{bodies}'] + gp_model = all_gp[f'{bodies}'] + + atom_id = 1 + + # lmp file is automatically written now every time MGP is constructed + lammps_location = mgp_model.lmp_file_name + mgp_model.write_lmp_file(lammps_location) + + # create test structure + cell = np.eye(3) + unique_species = gp_model.training_data[0].species + nenv = 10 + cutoffs = gp_model.cutoffs + struc_test, f = get_random_structure(cell, unique_species, nenv) + test_envi = env.AtomicEnvironment(struc_test, atom_id, cutoffs) + atom_types = [1, 2] + atom_masses = [108, 127] + atom_species = struc_test.coded_species + + # create data file + data_file_name = f'tmp{bodies}.data' + data_text = lammps_calculator.lammps_dat(struc_test, atom_types, + atom_masses, atom_species) + lammps_calculator.write_text(data_file_name, data_text) + + # create lammps input + by = 'no' + ty = 'no' + if '2' in bodies: + by = 'yes' + if '3' in bodies: + ty = 'yes' + style_string = 'mgpf' + coeff_string = f'* * {lammps_location} H He {by} {ty}' + lammps_executable = os.environ.get('lmp') + dump_file_name = f'tmp{bodies}.dump' + input_file_name = f'tmp{bodies}.in' + output_file_name = f'tmp{bodies}.out' + input_text = \ + lammps_calculator.generic_lammps_input(data_file_name, style_string, + coeff_string, dump_file_name, + newton=False) + lammps_calculator.write_text(input_file_name, input_text) + + lammps_calculator.run_lammps(lammps_executable, input_file_name, + output_file_name) + + lammps_forces = lammps_calculator.lammps_parser(dump_file_name) + mgp_forces = mgp_model.predict(test_envi, mean_only=True) + + # check that lammps agrees with gp to within 1 meV/A + for s in range(3): + assert (np.abs(lammps_forces[atom_id, s] - mgp_forces[0][s]) < 1e-3) + + for f in os.listdir("./"): + if f in [f'tmp{bodies}.in', f'tmp{bodies}.out', f'tmp{bodies}.dump', + f'tmp{bodies}.data', 'log.lammps', lammps_location]: + os.remove(f) + + clean() diff --git a/tests/test_otf.py b/tests/test_otf.py new file mode 100644 index 000000000..98e6d822e --- /dev/null +++ b/tests/test_otf.py @@ -0,0 +1,153 @@ +import pytest + +import glob, os, re, shutil +import numpy as np + +from flare.otf import OTF +from flare.gp import GaussianProcess +from flare.struc import Structure + + +cmd = {'cp2k':'CP2K_COMMAND', 'qe':'PWSCF_COMMAND'} +software_list = ['cp2k', 'qe'] +example_list = [1, 2] +name_list = {1:'h2', 2:'al'} + +def get_gp(par=False, per_atom_par=False, n_cpus=1): + hyps = np.array([1, 1, 1, 1, 1]) + hyp_labels = ['Signal Std 2b', 'Length Scale 2b', + 'Signal Std 3b', 'Length Scale 3b', + 'Noise Std'] + cutoffs = np.array([4, 4]) + return GaussianProcess(\ + kernel_name='23mc', hyps=hyps, cutoffs=cutoffs, + hyp_labels=hyp_labels, maxiter=50, par=par, + per_atom_par=per_atom_par, n_cpus=n_cpus) + +def cleanup(software="qe", casename="h2_otf_cp2k"): + + for f in glob.glob(f"{casename}*"): + os.remove(f) + for f in glob.glob(f"*{software}.in"): + os.remove(f) + for f in glob.glob(f"*{software}.out"): + os.remove(f) + + if (software is 'qe'): + for f in glob.glob(f"pwscf.wfc*"): + os.remove(f) + for f in glob.glob(f"*pwscf.out"): + os.remove(f) + for f in os.listdir("./"): + if f in ['pwscf.save']: + shutil.rmtree(f) + else: + for f in os.listdir("./"): + for f in glob.glob(f"{software}*"): + os.remove(f) + + +@pytest.mark.parametrize('software', software_list) +@pytest.mark.parametrize('example', example_list) +def test_otf(software, example): + """ + Test that an otf run can survive going for more steps + :return: + """ + + outdir = f'test_outputs_{software}' + if os.path.isdir(outdir): + shutil.rmtree(outdir) + + if (not os.environ.get(cmd[software], False)): + pytest.skip(f'{cmd[software]} not found in environment:' + f' Please install the code ' + f' and set the {cmd[software]} env. ' + 'variable to point to the executable.') + + dft_input = f'{software}.in' + dft_output = f'{software}.out' + shutil.copy(f'./test_files/{software}_input_{example}.in', dft_input) + + dt = 0.0001 + number_of_steps = 5 + dft_loc = os.environ.get(cmd[software]) + std_tolerance_factor = -0.1 + + casename=name_list[example] + + gp = get_gp() + + otf = OTF(dft_input, dt, number_of_steps, gp, dft_loc, + std_tolerance_factor, init_atoms=[0], + calculate_energy=True, max_atoms_added=1, + freeze_hyps=1, skip=1, + force_source=software, + dft_output=dft_output, + output_name=f'{casename}_otf_{software}', + store_dft_output=([dft_output, dft_input], '.'), + write_model=3) + + otf.run() + + if not os.path.isdir(outdir): + os.mkdir(outdir) + for f in os.listdir("./"): + if f'{casename}_otf_{software}' in f: + shutil.move(f, outdir) + cleanup(software, f'{casename}_otf_{software}') + +@pytest.mark.parametrize('software', software_list) +@pytest.mark.parametrize('per_atom_par', [True, False]) +@pytest.mark.parametrize('n_cpus', [2]) +def test_otf_par(software, per_atom_par, n_cpus): + """ + Test that an otf run can survive going for more steps + :return: + """ + example = 1 + outdir = f'test_outputs_{software}' + if os.path.isdir(outdir): + shutil.rmtree(outdir) + + if (not os.environ.get(cmd[software], False)): + pytest.skip(f'{cmd[software]} not found in environment:' + f' Please install the code ' + f' and set the {cmd[software]} env. ' + 'variable to point to the executable.') + if (software is 'cp2k'): + if (not "popt" in os.environ.get(cmd[software])): + pytest.skip(f'cp2k is serial version' + f' skipping the parallel test') + + dft_input = f'{software}.in' + dft_output = f'{software}.out' + shutil.copy(f'./test_files/{software}_input_{example}.in', dft_input) + + dt = 0.0001 + number_of_steps = 5 + dft_loc = os.environ.get(cmd[software]) + std_tolerance_factor = -0.1 + + casename = name_list[example] + + gp = get_gp(par=True, n_cpus=n_cpus, per_atom_par=per_atom_par) + + otf = OTF(dft_input, dt, number_of_steps, gp, dft_loc, + std_tolerance_factor, init_atoms=[0], + calculate_energy=True, max_atoms_added=1, + par=True, n_cpus=n_cpus, + freeze_hyps=1, skip=1, + mpi="mpi", force_source=software, + dft_output=dft_output, + output_name=f'{casename}_otf_{software}', + store_dft_output=([dft_output, dft_input], '.')) + + otf.run() + + if not os.path.isdir(outdir): + os.mkdir(outdir) + for f in os.listdir("./"): + if f'{casename}_otf_{software}' in f: + shutil.move(f, outdir) + cleanup(software, f'{casename}_otf_{software}') diff --git a/tests/test_ase_setup/test_parse_otf.py b/tests/test_parse_ase_otf.py similarity index 88% rename from tests/test_ase_setup/test_parse_otf.py rename to tests/test_parse_ase_otf.py index 5adeeca47..e4bff0bcd 100644 --- a/tests/test_ase_setup/test_parse_otf.py +++ b/tests/test_parse_ase_otf.py @@ -1,9 +1,5 @@ -import os -import sys import numpy as np from flare.otf_parser import OtfAnalysis -from flare.kernels.kernels import two_plus_three_body, two_plus_three_body_grad -from flare.kernels.mc_simple import two_plus_three_body_mc, two_plus_three_body_mc_grad from flare.env import AtomicEnvironment from flare.predict import predict_on_structure @@ -16,7 +12,7 @@ def test_parse_header(): assert header_dict['atoms'] == 4 assert header_dict['species_set'] == {'Ag', 'I'} assert header_dict['dt'] == .001 - assert header_dict['kernel'] == 'two_plus_three_body_mc' + assert header_dict['kernel_name'] == 'two_plus_three_body_mc' assert header_dict['n_hyps'] == 5 assert header_dict['algo'] == 'BFGS' assert np.equal(header_dict['cell'], @@ -86,8 +82,7 @@ def test_replicate_gp(): positions = parsed.position_list forces = parsed.force_list - gp_model = parsed.make_gp(kernel=two_plus_three_body_mc, - kernel_grad=two_plus_three_body_mc_grad) + gp_model = parsed.make_gp(kernel_name="two_plus_three_body_mc") structures = parsed.output_md_structures() diff --git a/tests/test_parse_otf.py b/tests/test_parse_otf.py index 0cbd9abfd..9d0b28122 100644 --- a/tests/test_parse_otf.py +++ b/tests/test_parse_otf.py @@ -2,7 +2,6 @@ import sys import numpy as np from flare.otf_parser import OtfAnalysis -from flare.kernels.kernels import two_plus_three_body, two_plus_three_body_grad from flare.env import AtomicEnvironment from flare.predict import predict_on_structure @@ -17,7 +16,7 @@ def test_parse_header(): assert header_dict['atoms'] == 28 assert header_dict['species_set'] == {'Al'} assert header_dict['dt'] == .001 - assert header_dict['kernel'] == 'two_plus_three_body' + assert header_dict['kernel_name'] == 'two_plus_three_body' assert header_dict['n_hyps'] == 5 assert header_dict['algo'] == 'BFGS' assert np.equal(header_dict['cell'], @@ -96,8 +95,8 @@ def test_replicate_gp(): positions = parsed.position_list forces = parsed.force_list - gp_model = parsed.make_gp(kernel=two_plus_three_body, - kernel_grad=two_plus_three_body_grad) + gp_model = parsed.make_gp(kernel_name='2+3') + structures = parsed.output_md_structures() diff --git a/tests/test_predict.py b/tests/test_predict.py new file mode 100644 index 000000000..11b723a9f --- /dev/null +++ b/tests/test_predict.py @@ -0,0 +1,125 @@ +""" +Helper functions which obtain forces and energies +corresponding to atoms in structures. These functions automatically +cast atoms into their respective atomic environments. +""" +import numpy as np +from flare.gp import GaussianProcess +from flare.struc import Structure + +from flare.predict import predict_on_structure, predict_on_structure_par +import pytest + +def fake_predict(x,d): + + return np.random.uniform(-1, 1), np.random.uniform(-1, 1) + + +_fake_gp = GaussianProcess(kernel_name='2_sc', cutoffs=[5], hyps=[1, 1, 1]) + +_fake_structure = Structure(cell=np.eye(3), species=[1, 1, 1], + positions=np.random.uniform(0, 1, size=(3, 3))) + +_fake_gp.predict = fake_predict + #lambda _, __: ( + #np.random.uniform(-1, 1), np.random.uniform(-1, 1)) + +print(_fake_gp.predict(1, 2)) + + +@pytest.mark.parametrize('n_cpu', [1, 2]) +def test_predict_on_structure_par(n_cpu): + + # Predict only on the first atom, and make rest NAN + selective_atoms=[0] + + skipped_atom_value = np.nan + + forces, stds = predict_on_structure_par(_fake_structure, + _fake_gp, + n_cpus=n_cpu, + write_to_structure=False, + selective_atoms=selective_atoms, + skipped_atom_value=skipped_atom_value) + + + for x in forces[0][:]: + assert isinstance(x,float) + for x in forces[1:]: + assert np.isnan(x).all() + + + # Predict only on the second and third, and make rest 0 + + selective_atoms = [1,2] + skipped_atom_value =0 + + forces, stds = predict_on_structure_par(_fake_structure, + _fake_gp, + write_to_structure=False, + n_cpus=n_cpu, + selective_atoms=selective_atoms, + skipped_atom_value=skipped_atom_value) + + for x in forces[1]: + assert isinstance(x, float) + for x in forces[2]: + assert isinstance(x, float) + + assert np.equal(forces[0], 0).all() + + + + # Make selective atoms be all and ensure results are normal + + selective_atoms = [0, 1, 2] + + forces, stds = predict_on_structure_par(_fake_structure, + _fake_gp, + write_to_structure=True, + n_cpus=n_cpu, + selective_atoms=selective_atoms, + skipped_atom_value=skipped_atom_value) + + + for x in forces.flatten(): + assert isinstance(x, float) + for x in stds.flatten(): + assert isinstance(x, float) + + assert np.array_equal(_fake_structure.forces, forces) + assert np.array_equal(_fake_structure.stds, stds) + + + # Get new examples to also test the results not being written + + selective_atoms = [0,1] + + forces, stds = predict_on_structure_par(_fake_structure, + _fake_gp, + write_to_structure=True, + n_cpus=n_cpu, + selective_atoms=selective_atoms, + skipped_atom_value=skipped_atom_value) + + + for x in forces.flatten(): + assert isinstance(x, float) + + for x in stds.flatten(): + assert isinstance(x, float) + + assert np.array_equal(_fake_structure.forces[:2][:], forces[:2][:]) + assert not np.array_equal(_fake_structure.forces[2][:], forces[2][:]) + + + assert np.array_equal(_fake_structure.stds[:2][:], stds[:2][:]) + assert not np.array_equal(_fake_structure.stds[2][:], stds[2][:]) + + + + + + + + diff --git a/tests/test_qe_util.py b/tests/test_qe_util.py index 2ef51ec86..3e72ed226 100644 --- a/tests/test_qe_util.py +++ b/tests/test_qe_util.py @@ -1,21 +1,16 @@ import pytest -import os -import sys +import os, shutil, glob import numpy as np from flare.struc import Structure, get_unique_species from flare.dft_interface.qe_util import parse_dft_input, parse_dft_forces, run_dft_par, \ edit_dft_input_positions, dft_input_to_structure -def cleanup_espresso_run(basename: str, target: str = None): - os.system(f'rm {basename}.out') - os.system(f'rm {basename}.wfc') - os.system(f'rm -r {basename}.save') - os.system(f'rm {basename}.in') - os.system(f'rm {basename}.wfc1') - os.system(f'rm {basename}.wfc2') - os.system(f'rm {basename}.xml') - if target: - os.system(f'rm {target}') +def cleanup_espresso_run(basename: str): + for f in glob.glob(f"{basename}*"): + if os.path.isfile(f): + os.remove(f) + else: + shutil.rmtree(f) # ------------------------------------------------------ @@ -84,7 +79,7 @@ def test_input_to_structure(qe_input): def test_espresso_calling(qe_input, qe_output): dft_loc = os.environ.get('PWSCF_COMMAND') - os.system(' '.join(['cp', qe_input, 'pwscf.in'])) + shutil.copyfile(qe_input, 'pwscf.in') positions, species, cell, masses = parse_dft_input(qe_input) structure = Structure(cell=cell, species=species, @@ -125,4 +120,4 @@ def test_espresso_input_edit(): assert np.equal(positions[0], structure.positions[0]).all() assert np.equal(structure.vec1, cell[0, :]).all() - os.system('rm ./qe_input_1.in') + os.remove('qe_input_1.in') diff --git a/tests/test_str_to_kernel.py b/tests/test_str_to_kernel.py new file mode 100644 index 000000000..ca80adc45 --- /dev/null +++ b/tests/test_str_to_kernel.py @@ -0,0 +1,20 @@ +import sys +from copy import deepcopy +import pytest +import numpy as np +from numpy.random import random, randint + +from flare import env, struc, gp +from flare.kernels.utils import str_to_kernel_set as stks + +@pytest.mark.parametrize('kernel_name', ['2mc', '3mc', '2+3mc', + '2', '3', '2+3', + '2+3+many', '2+3mb']) +def test_stk(kernel_name): + """Check whether the str_to_kernel_set can return kernel functions + properly""" + + try: + k, kg, ek, efk = stks(kernel_name) + except: + raise RuntimeError(f"fail to return kernel {kernel_name}") diff --git a/tests/test_struc.py b/tests/test_struc.py index 33f2121e4..786c5731d 100644 --- a/tests/test_struc.py +++ b/tests/test_struc.py @@ -1,16 +1,19 @@ import pytest import numpy as np -import sys +import os + from tests.test_gp import get_random_structure from flare.struc import Structure -from json import loads - +from json import loads, dumps +from flare.util import Z_to_element, NumpyEncoder try: import pymatgen.core.structure as pmgstruc _test_pmg = True except ImportError: _test_pmg = False +from .test_gp import dumpcompare + def test_random_structure_setup(): struct, forces = get_random_structure(cell=np.eye(3), @@ -191,3 +194,81 @@ def test_to_pmg_structure(varied_test_struc): assert len(varied_test_struc) == len(varied_test_struc) assert np.equal(new_struc.cart_coords, varied_test_struc.positions).all() assert (new_struc.atomic_numbers == varied_test_struc.coded_species).all() + +def test_to_xyz(varied_test_struc): + + simple_str = varied_test_struc.to_xyz(extended_xyz=False, + print_stds=False, print_forces=False, print_max_stds=False) + + simple_str_by_line = simple_str.split('\n') + + assert len(simple_str_by_line)-3 == len(varied_test_struc) + + for i, atom_line in enumerate(simple_str_by_line[2:-1]): + split_line = atom_line.split() + assert split_line[0] == \ + Z_to_element(int(varied_test_struc.species_labels[i])) + for j in range(3): + assert float(split_line[1+j]) == varied_test_struc.positions[i][j] + + + complex_str = varied_test_struc.to_xyz(True,True,True,True) + complex_str_by_line = complex_str.split('\n') + + assert len(complex_str_by_line)-3 == len(varied_test_struc) + + for i, atom_line in enumerate(complex_str_by_line[2:-1]): + split_line = atom_line.split() + assert split_line[0] == \ + Z_to_element(int(varied_test_struc.species_labels[i])) + for j in range(1,4): + assert float(split_line[j]) == varied_test_struc.positions[i][j-1] + for j in range(4,7): + assert float(split_line[j]) == varied_test_struc.stds[i][j-4] + for j in range(7,10): + assert float(split_line[j]) == varied_test_struc.forces[i][j-7] + assert float(split_line[10]) == np.max(varied_test_struc.stds[i]) + + +def test_file_load(): + + struct1, forces = get_random_structure(cell=np.eye(3), + unique_species=[1, 2], + noa=2) + + struct2, forces = get_random_structure(cell=np.eye(3), + unique_species=[1, 2], + noa=2) + + with open("test_write.json",'w') as f: + f.write(dumps(struct1.as_dict(),cls=NumpyEncoder)) + + with pytest.raises(NotImplementedError): + Structure.from_file(file_name='test_write.json', format='xyz') + + struct1a = Structure.from_file('test_write.json') + assert dumpcompare(struct1.as_dict() , struct1a.as_dict()) + os.remove('test_write.json') + + with open("test_write_set.json", 'w') as f: + f.write(dumps(struct1.as_dict(), cls=NumpyEncoder)+'\n') + f.write(dumps(struct2.as_dict(), cls=NumpyEncoder)+'\n') + + structs = Structure.from_file('test_write_set.json') + for struc in structs: + assert isinstance(struc, Structure) + assert len(structs) == 2 + assert dumpcompare(structs[0].as_dict(), struct1.as_dict()) + assert dumpcompare(structs[1].as_dict(), struct2.as_dict()) + + os.remove('test_write_set.json') + + with pytest.raises(FileNotFoundError): + Structure.from_file('fhqwhgads') + + vasp_struct = Structure.from_file('./test_files/test_POSCAR') + assert isinstance(vasp_struct, Structure) + assert len(vasp_struct) == 6 + + + diff --git a/tests/test_util.py b/tests/test_util.py index 114ec1f11..af9bd3d9b 100644 --- a/tests/test_util.py +++ b/tests/test_util.py @@ -1,5 +1,8 @@ from flare.util import element_to_Z, Z_to_element, \ - is_std_in_bound_per_species, is_force_in_bound_per_species + is_std_in_bound_per_species, is_force_in_bound_per_species ,\ + subset_of_frame_by_element +from flare.struc import Structure + import pytest import numpy as np @@ -110,6 +113,25 @@ def test_std_in_bound_per_species(): 'O': 0}) assert result is False and target_atoms == [1, 0] + # Ensure NANS can be handled + test_structure.stds = np.array([[np.nan, np.nan, np.nan], + [2, 0, 0], [3, 0, 0]]) + result, target_atoms = \ + is_std_in_bound_per_species(rel_std_tolerance=1, abs_std_tolerance=.1, + noise=2.9, structure=test_structure, + max_atoms_added=3, max_by_species={'H': 2, + 'O': 0}) + assert result is False and target_atoms == [1] + + test_structure.stds = np.array([[np.nan, np.nan, np.nan], + [np.nan, np.nan, np.nan], [3, 0, 0]]) + result, target_atoms = \ + is_std_in_bound_per_species(rel_std_tolerance=1, abs_std_tolerance=.1, + noise=2.9, structure=test_structure, + max_atoms_added=3, max_by_species={'H': 2, + 'O': 0}) + assert result is True and target_atoms == [-1] + def test_force_in_bound_per_species(): test_structure, _ = get_random_structure(np.eye(3), ['H', 'O'], 3) @@ -193,4 +215,54 @@ def test_force_in_bound_per_species(): predicted_forces=test_structure.forces, label_forces=true_forces, structure=test_structure,) - assert result is False and target_atoms == [1] \ No newline at end of file + assert result is False and target_atoms == [1] + + + # Ensure that nans can be handled + test_structure.forces = np.array([[np.nan, np.nan, np.nan], [2, 0, 0], [3, + 0, + 0]]) + result, target_atoms = is_force_in_bound_per_species( + abs_force_tolerance=1, max_force_error=1.5, + predicted_forces=test_structure.forces, + label_forces=true_forces, + structure=test_structure) + assert result is False and target_atoms == [1] + + result, target_atoms = is_force_in_bound_per_species( + abs_force_tolerance=.9, max_force_error=500, + predicted_forces=test_structure.forces, + label_forces=true_forces, + structure=test_structure) + assert result is False and set(target_atoms) == set([1, 2]) + + + + +def test_subset_of_frame_by_element(): + spec_list = ['H', 'H', 'O', 'O','O', 'C'] + test_struc_1 = Structure(cell=np.eye(3), + species=spec_list, + positions=np.zeros(shape=(len(spec_list), 3))) + + assert np.array_equal(subset_of_frame_by_element(test_struc_1, {}), + list(range(len(test_struc_1)))) + + assert np.array_equal(subset_of_frame_by_element(test_struc_1, + {'H':2,'O':3}), + list(range(len(test_struc_1)))) + assert np.array_equal(subset_of_frame_by_element(test_struc_1, + {'H': 2, 'O': 15}), + list(range(len(test_struc_1)))) + + assert set(subset_of_frame_by_element(test_struc_1,{'H': 1, + 'O': 1})).issubset(range(len(spec_list))) + assert len(subset_of_frame_by_element(test_struc_1,{'H':1,'O':1, + 'C':1}))==3 + + assert subset_of_frame_by_element(test_struc_1, {'H': 0, 'O': 0, + 'C': 0}) == [] + + + assert subset_of_frame_by_element(test_struc_1, {'H': 0, 'O': 0, + 'C': 1}) == [5]