Source code for ase.optimize.gpmin.gpmin

import numpy as np
import pickle
import warnings

from scipy.optimize import minimize
from ase.parallel import world
from ase.optimize.optimize import Optimizer
from import GaussianProcess
from ase.optimize.gpmin.kernel import SquaredExponential
from ase.optimize.gpmin.prior import ConstantPrior

[docs]class GPMin(Optimizer, GaussianProcess): def __init__(self, atoms, restart=None, logfile='-', trajectory=None, prior=None, kernel=None, master=None, noise=None, weight=None, scale=None, force_consistent=None, batch_size=None, bounds=None, update_prior_strategy="maximum", update_hyperparams=False): """Optimize atomic positions using GPMin algorithm, which uses both potential energies and forces information to build a PES via Gaussian Process (GP) regression and then minimizes it. Default behaviour: -------------------- The default values of the scale, noise, weight, batch_size and bounds parameters depend on the value of update_hyperparams. In order to get the default value of any of them, they should be set up to None. Default values are: update_hyperparams = True scale : 0.3 noise : 0.004 weight: 2. bounds: 0.1 batch_size: 1 update_hyperparams = False scale : 0.4 noise : 0.005 weight: 1. bounds: irrelevant batch_size: irrelevant Parameters: ------------------ atoms: Atoms object The Atoms object to relax. restart: string Pickle file used to store the training set. If set, file with such a name will be searched and the data in the file incorporated to the new training set, if the file exists. logfile: file object or str If *logfile* is a string, a file with that name will be opened. Use '-' for stdout trajectory: string Pickle file used to store trajectory of atomic movement. master: boolean Defaults to None, which causes only rank 0 to save files. If set to True, this rank will save files. force_consistent: boolean or None Use force-consistent energy calls (as opposed to the energy extrapolated to 0 K). By default (force_consistent=None) uses force-consistent energies if available in the calculator, but falls back to force_consistent=False if not. prior: Prior object or None Prior for the GP regression of the PES surface See ase.optimize.gpmin.prior If *prior* is None, then it is set as the ConstantPrior with the constant being updated using the update_prior_strategy specified as a parameter kernel: Kernel object or None Kernel for the GP regression of the PES surface See ase.optimize.gpmin.kernel If *kernel* is None the SquaredExponential kernel is used. Note: It needs to be a kernel with derivatives!!!!! noise: float Regularization parameter for the Gaussian Process Regression. weight: float Prefactor of the Squared Exponential kernel. If *update_hyperparams* is False, changing this parameter has no effect on the dynamics of the algorithm. update_prior_strategy: string Strategy to update the constant from the ConstantPrior when more data is collected. It does only work when Prior = None options: 'maximum': update the prior to the maximum sampled energy 'init' : fix the prior to the initial energy 'average': use the average of sampled energies as prior scale: float scale of the Squared Exponential Kernel update_hyperparams: boolean Update the scale of the Squared exponential kernel every batch_size-th iteration by maximizing the marginal likelihood. batch_size: int Number of new points in the sample before updating the hyperparameters. Only relevant if the optimizer is executed in update_hyperparams mode: (update_hyperparams = True) bounds: float, 0<bounds<1 Set bounds to the optimization of the hyperparameters. Let t be a hyperparameter. Then it is optimized under the constraint (1-bound)*t_0 <= t <= (1+bound)*t_0 where t_0 is the value of the hyperparameter in the previous step. If bounds is False, no constraints are set in the optimization of the hyperparameters. .. warning:: The memory of the optimizer scales as O(n²N²) where N is the number of atoms and n the number of steps. If the number of atoms is sufficiently high, this may cause a memory issue. This class prints a warning if the user tries to run GPMin with more than 100 atoms in the unit cell. """ # Warn the user if the number of atoms is very large if len(atoms) > 100: warning = ('Possible Memory Issue. There are more than ' '100 atoms in the unit cell. The memory ' 'of the process will increase with the number ' 'of steps, potentially causing a memory issue. ' 'Consider using a different optimizer.') warnings.warn(warning) # Give it default hyperparameters if update_hyperparams: # Updated GPMin if scale is None: scale = 0.3 if noise is None: noise = 0.004 if weight is None: weight = 2. if bounds is None: self.eps = 0.1 elif bounds is False: self.eps = None else: self.eps = bounds if batch_size is None: self.nbatch = 1 else: self.nbatch = batch_size else: # GPMin without updates if scale is None: scale = 0.4 if noise is None: noise = 0.001 if weight is None: weight = 1. if bounds is not None: warning = ('The parameter bounds is of no use ' 'if update_hyperparams is False. ' 'The value provided by the user ' 'is being ignored.') warnings.warn(warning, UserWarning) if batch_size is not None: warning = ('The parameter batch_size is of no use ' 'if update_hyperparams is False. ' 'The value provided by the user ' 'is being ignored.') warnings.warn(warning, UserWarning) # Set the variables to something anyways self.eps = False self.nbatch = None self.strategy = update_prior_strategy self.update_hp = update_hyperparams self.function_calls = 1 self.force_calls = 0 self.x_list = [] # Training set features self.y_list = [] # Training set targets Optimizer.__init__(self, atoms, restart, logfile, trajectory, master, force_consistent) if prior is None: self.update_prior = True prior = ConstantPrior(constant=None) else: self.update_prior = False if kernel is None: kernel = SquaredExponential() GaussianProcess.__init__(self, prior, kernel) self.set_hyperparams(np.array([weight, scale, noise])) def acquisition(self, r): e = self.predict(r) return e[0], e[1:] def update(self, r, e, f): """Update the PES Update the training set, the prior and the hyperparameters. Finally, train the model """ # update the training set self.x_list.append(r) f = f.reshape(-1) y = np.append(np.array(e).reshape(-1), -f) self.y_list.append(y) # Set/update the constant for the prior if self.update_prior: if self.strategy == 'average': av_e = np.mean(np.array(self.y_list)[:, 0]) self.prior.set_constant(av_e) elif self.strategy == 'maximum': max_e = np.max(np.array(self.y_list)[:, 0]) self.prior.set_constant(max_e) elif self.strategy == 'init': self.prior.set_constant(e) self.update_prior = False # update hyperparams if (self.update_hp and self.function_calls % self.nbatch == 0 and self.function_calls != 0): self.fit_to_batch() # build the model self.train(np.array(self.x_list), np.array(self.y_list)) def relax_model(self, r0): result = minimize(self.acquisition, r0, method='L-BFGS-B', jac=True) if result.success: return result.x else: self.dump() raise RuntimeError("The minimization of the acquisition function " "has not converged") def fit_to_batch(self): """Fit hyperparameters keeping the ratio noise/weight fixed""" ratio = self.noise/self.kernel.weight self.fit_hyperparameters(np.array(self.x_list), np.array(self.y_list), eps=self.eps) self.noise = ratio*self.kernel.weight def step(self, f=None): atoms = self.atoms if f is None: f = atoms.get_forces() fc = self.force_consistent r0 = atoms.get_positions().reshape(-1) e0 = atoms.get_potential_energy(force_consistent=fc) self.update(r0, e0, f) r1 = self.relax_model(r0) self.atoms.set_positions(r1.reshape(-1, 3)) e1 = self.atoms.get_potential_energy(force_consistent=fc) f1 = self.atoms.get_forces() self.function_calls += 1 self.force_calls += 1 count = 0 while e1 >= e0: self.update(r1, e1, f1) r1 = self.relax_model(r0) self.atoms.set_positions(r1.reshape(-1, 3)) e1 = self.atoms.get_potential_energy(force_consistent=fc) f1 = self.atoms.get_forces() self.function_calls += 1 self.force_calls += 1 if self.converged(f1): break count += 1 if count == 30: raise RuntimeError("A descent model could not be built") self.dump() def dump(self): """Save the training set""" if world.rank == 0 and self.restart is not None: with open(self.restart, 'wb') as fd: pickle.dump((self.x_list, self.y_list), fd, protocol=2) def read(self): self.x_list, self.y_list = self.load()