gofee.py 12.1 KB
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import numpy as np
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import pickle
from os.path import isfile
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from surrogate.gpr import GPR
from population import population

from ase import Atoms
from ase.io import read, write, Trajectory
from ase.calculators.singlepoint import SinglePointCalculator

from bfgslinesearch_zlim import BFGSLineSearch_zlim
from ase.ga.relax_attaches import VariansBreak
from parallel_utils import split, parallel_function_eval
from mpi4py import MPI
world = MPI.COMM_WORLD

import traceback
import sys


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def relax(structure, calc, Fmax=0.05, steps_max=200, dmax_cov=None):
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    a = structure.copy()
    # Set calculator 
    a.set_calculator(calc)
    pos_init = a.get_positions()

    # Catch if linesearch fails
    try:
        dyn = BFGSLineSearch_zlim(a,
                                  logfile=None,
                                  pos_init=pos_init,
                                  dmax_cov=dmax_cov)
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        dyn.run(fmax = Fmax, steps = steps_max)
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    except Exception as err:
        print('Error in surrogate-relaxation:', err, flush=True)
        traceback.print_exc()
        traceback.print_exc(file=sys.stderr)
    return a


class GOFEE():
    def __init__(self, structures=None,
                 calc=None,
                 gpr=None,
                 startgenerator=None,
                 candidate_generator=None,
                 trajectory=None,
                 kappa=2,
                 Neval=200,
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                 Ninit=10,
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                 dmax_cov=3.5,
                 Ncandidates=30,
                 population_size=5,
                 dualpoint=True,
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                 min_certainty=0.7,
                 restart=None):
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        """GOFEE global structure search method.
        
        structures: 
        """
        if structures is not None:
            if isinstance(structures, Atoms):
                self.structures = [structures]
            elif isinstance(structures, list):
                assert isinstance(structures[0], Atoms)
                self.structures = structures
            elif isinstance(structures, str):
                self.structures = read(structures, index=':')
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        else:
            assert startgenerator is not None
            self.structures = None
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        if calc is not None:
            self.calc = calc
        else:
            assert structures is not None
            calc = structures[0].get_calculator()
            assert calc is not None and not isinstance(calc, SinglePointCalculator)
            print('Using calculator from supplied structure(s)')
            self.calc = calc

        if gpr is not None:
            self.gpr = gpr
        else:
            self.gpr = GPR()

        if startgenerator is not None:
            self.startgenerator = startgenerator
        else:
            assert structures is not None
            self.startgenerator = None

        if candidate_generator is not None:
            self.candidate_generator = candidate_generator
        else:
            assert False

        # Initialize population
        self.population = population(population_size=population_size, gpr=self.gpr, similarity2equal=0.9999)

        # Define parallel communication
        self.comm = world.Dup()  # Important to avoid mpi-problems from call to ase.parallel in BFGS
        self.master = self.comm.rank == 0

        self.kappa = kappa
        self.Neval = Neval
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        self.Ninit = Ninit
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        self.dmax_cov = dmax_cov
        self.Ncandidates = Ncandidates
        self.dualpoint = dualpoint
        self.min_certainty = min_certainty
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        self.restart = restart
        
        if isinstance(trajectory, str):
            self.trajectory = Trajectory(filename=trajectory, mode='a', master=self.master)
            if self.restart:
                self.traj_name = trajectory
        elif isinstance(trajectory, Trajectory):
            self.trajectory = trajectory
        else:
            assert trajectory is None
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        if restart is None or not isfile(restart):
            self.initialize()
        else:
            self.read()
            self.comm.barrier()

    def initialize(self):
        self.steps = 0
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    def get_initial_structures(self):
        # Initial structures
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        if self.structures is not None:
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            for a in self.structures:
                Ei = a.get_potential_energy()
                Fi = a.get_forces()
                self.gpr.memory.save_data([a])
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                self.trajectory.write(a)
                self.population.add(a)
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        elif self.startgenerator is not None:
            for i in range(int(np.ceil(self.Ninit/2))):
                a, _ = self.startgenerator.get_new_individual()
                a = self.evaluate(a)
                a2 = self.get_dualpoint(a) 
                a2 = self.evaluate(a2)
                self.gpr.memory.save_data([a,a2])
                self.population.add(a2)
        else:
            assert False
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    def run(self):
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        self.get_initial_structures()
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        while self.steps < self.Neval:
            self.print_master('steps:', self.steps)
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            self.train_surrogate()
            self.update_population()
            relaxed_candidates = self.get_surrogate_relaxed_candidates()

            kappa = self.kappa
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            a_add = []
            for _ in range(5):
                try:
                    anew = self.select_with_acquisition(relaxed_candidates, kappa)
                    self.print_master('aq done')
                    anew = self.evaluate(anew)
                    a_add.append(anew)
                    self.print_master('sp done')
                    if self.dualpoint:
                        adp = self.get_dualpoint(anew)
                        adp = self.evaluate(adp)
                        a_add.append(adp)
                    self.print_master('dp done')
                    break
                except Exception as err:
                    kappa /=2
                    if self.master:
                        traceback.print_exc(file=sys.stderr)
            self.gpr.memory.save_data(a_add)
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            self.steps += 1
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            # Add structure to population
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            self.population.add(a_add)
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            # Save search state
            self.dump((self.steps, self.population, np.random.get_state()))
            
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            if self.master:
                print('anew pred:', anew.info['key_value_pairs']['Epred'], anew.info['key_value_pairs']['Epred_std'])
                print('E_true:', anew.get_potential_energy(), adp.get_potential_energy())
                print('pop:', [a.get_potential_energy() for a in self.population.pop])
            
    def get_dualpoint(self, a, lmax=0.10, Fmax_flat=5):
        """Returns dual-point structure, i.e. the original structure
        perturbed slightly along the forces.
        
        lmax: The atom with the largest force will be displaced by
        this distance
        
        Fmax_flat: maximum atomic displacement. is increased linearely
        with force until Fmax = Fmax_flat, after which it remains
        constant as lmax.
        """
        F = a.get_forces()
        a_dp = a.copy()

        # Calculate and set new positions
        Fmax = np.sqrt((F**2).sum(axis=1).max())
        pos_displace = lmax * F*min(1/Fmax_flat, 1/Fmax)
        pos_dp = a.positions + pos_displace
        a_dp.set_positions(pos_dp)
        return a_dp

    def print_master(self, *args):
        self.comm.barrier()
        if self.master:
            print(*args, flush=True)

    def generate_candidate(self):
        Ntrials = 5
        for i_trial in range(Ntrials):
            parents = self.population.get_structure_pair()
            a_mutated, _ = self.candidate_generator.get_new_individual(parents)
            # break trial loop if successful
            if a_mutated is not None:
                break
            # If no success in max number of trials
        if a_mutated is None:
            a_mutated = parents[0].copy()
        return a_mutated

    def get_surrogate_relaxed_candidates(self):
        Njobs = self.Ncandidates
        task_split = split(Njobs, self.comm.size)
        def func1():
            return [self.generate_candidate() for i in task_split[self.comm.rank]]
        candidates = parallel_function_eval(self.comm, func1)
        
        def func2():
            return [self.surrogate_relaxation(candidates[i], Fmax=0.1, steps=200, kappa=self.kappa)
                    for i in task_split[self.comm.rank]]
        relaxed_candidates = parallel_function_eval(self.comm, func2)
        relaxed_candidates = self.certainty_filter(relaxed_candidates)

        #if (self.NsearchIter % self.Nuse_pop_as_candidates) == 0:
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        if self.steps % 3 == 0:
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            relaxed_candidates = self.population.pop_MLrelaxed + relaxed_candidates
        
        if self.master:
            Epred = np.array([a.info['key_value_pairs']['Epred'] for a in relaxed_candidates])
            Epred_std = np.array([a.info['key_value_pairs']['Epred_std'] for a in relaxed_candidates])
            fitness = Epred - self.kappa*Epred_std
            print(np.c_[Epred, Epred_std, fitness])
        return relaxed_candidates
        
    def certainty_filter(self, structures):
        certainty = np.array([a.info['key_value_pairs']['Epred_std']
                              for a in structures]) / np.sqrt(self.gpr.K0)
        min_certainty = self.min_certainty
        for _ in range(5):
            filt = certainty < min_certainty
            if np.sum(filt.astype(int)) > 0:
                structures = [structures[i] for i in range(len(filt)) if filt[i]]
                break
            else:
                min_certainty = min_certainty + (1-min_certainty)/2
        return structures

    def update_population(self):
        Njobs = len(self.population.pop)
        task_split = split(Njobs, self.comm.size)
        func = lambda: [self.surrogate_relaxation(self.population.pop[i],
                                                  Fmax=0.001, steps=200, kappa=None)
                        for i in task_split[self.comm.rank]]
        self.population.pop_MLrelaxed = parallel_function_eval(self.comm, func)
        if self.master:
            print('ML-relaxed pop forces:\n',
                  [(a.get_forces()**2).sum(axis=1).max()**0.5 for a in self.population.pop_MLrelaxed])

    def train_surrogate(self):
        # Train
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        if self.steps < 50 or (self.steps % 10) == 0:
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            self.gpr.optimize_hyperparameters(comm=self.comm)
        else:
            self.gpr.train()
        if self.master:
            print('kernel:', list(np.exp(self.gpr.kernel.theta)))
            print('lml:', self.gpr.lml)

    def surrogate_relaxation(self, a, Fmax=0.1, steps=200, kappa=None):
        calc = self.gpr.get_calculator(kappa)
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        a_relaxed = relax(a, calc, dmax_cov=self.dmax_cov, Fmax=Fmax, steps_max=steps)
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        # Evaluate uncertainty
        E, Estd = self.gpr.predict_energy(a_relaxed, eval_std=True)
        a_relaxed.info['key_value_pairs']['Epred'] = E
        a_relaxed.info['key_value_pairs']['Epred_std'] = Estd

        return a_relaxed

    def select_with_acquisition(self, structures, kappa):
        Epred = np.array([a.info['key_value_pairs']['Epred']
                          for a in structures])
        Epred_std = np.array([a.info['key_value_pairs']['Epred_std']
                              for a in structures])
        acquisition = Epred - kappa*Epred_std
        index_select = np.argmin(acquisition)
        return structures[index_select]

    def evaluate(self, a):
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        a = self.comm.bcast(a, root=0)
        
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        a.set_calculator(self.calc)
        E = a.get_potential_energy()
        F = a.get_forces()
        
        results = {'energy': E, 'forces': F}
        calc_sp = SinglePointCalculator(a, **results)
        a.set_calculator(calc_sp)

        self.write(a)

        return a

    def write(self, a):
        if self.trajectory is not None:
            self.trajectory.write(a)

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    def dump(self, data):
        if self.comm.rank == 0 and self.restart is not None:
            pickle.dump(data, open(self.restart, "wb"), protocol=2)
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    def read(self):
        self.steps, self.population, random_state = pickle.load(open(self.restart, "rb"))
        np.random.set_state(random_state)
        training_structures = read(self.traj_name, index=':')
        self.gpr.memory.save_data(training_structures)