gofee.py

import numpy as np
import pickle
from os.path import isfile

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 ase.calculators.dftb import Dftb

from bfgslinesearch_zlim import BFGSLineSearch_zlim
from bfgslinesearch_constrained import BFGSLineSearch_constrained
from ase.ga.relax_attaches import VariansBreak
from parallel_utils import split, parallel_function_eval

from candidate_operations.candidate_generation import OperationSelector
from candidate_operations.basic_mutations import RattleMutation


from mpi4py import MPI
world = MPI.COMM_WORLD

import traceback
import sys


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

    # Catch if linesearch fails
    try:
        dyn = BFGSLineSearch_constrained(a,
                                         logfile=None,
                                         pos_init=pos_init,
                                         dmax_cov=dmax_cov)
        dyn.run(fmax = Fmax, steps = steps_max)
    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():
    """GOFEE global structure search method.
        
    structures: Atoms-object, list of Atoms-objects or None.
    In initial structures from which to start the sesarch.
    If None, the startgenerator must be supplied.
    If less than Ninit structures is supplied, the remaining
    ones are generated using the startgenerator or by rattling
    the supplied structures, depending on wether the
    startgenerator is supplied.

    calc: ASE calculator specifying the energy-expression
    with respect to which the atomic coordinates are
    globally optimized.

    gpr: The Gaussian Process Regression model used as the
    surrogate model for the Potential energy surface.
    
    startgenerator: Object used to generate initial random
    structures. Must be supplied if structures if structues=None.
    (This is the recommended way to initialize the search.)

    candidate_generator:

    trajectory: Name of trajectory to which all structures,
    evaluated during the search, is saved.

    kappa: How much to weigh predicted uncertainty in acquition
    function.

    max_steps: Number of search steps.

    Ninit: Number of initial structures. If len(structures) <
    Ninit, the remaining structures are generated using the
    startgenerator (if supplied) or by rattling the supplied
    'structures'.

    dmax_cov: Max distance that an atom is allowed to move during
    surrogate relaxation (in units of covalent distance).

    Ncandidates: Number of new cancidate structures generated and
    surrogate-relaxed in each search iteration.

    population_size: Size of population.

    dualpoint: boolean
    Whether to use dualpoint evaluation or not.

    min_certainty: float
    Max predicted uncertainty allowed for structures to be
    considdered for evaluation. (in units of the maximum possible
    uncertainty.)

    restart: str
    Filename for restart file.  Default value is *None*.
    """
    def __init__(self, structures=None,
                 calc=None,
                 gpr=None,
                 startgenerator=None,
                 candidate_generator=None,
                 trajectory='structures.traj',
                 kappa=2,
                 max_steps=200,
                 Ninit=10,
                 dmax_cov=3.5,
                 Ncandidates=30,
                 population_size=5,
                 dualpoint=True,
                 min_certainty=0.7,
                 restart=None):
        
        if structures is None:
            assert startgenerator is not None
            self.structures = None
        else:
            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=':')
        
        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 None:
            assert structures is not None
            self.startgenerator = None
        else:
            self.startgenerator = startgenerator

        if startgenerator is not None:
            self.n_to_optimize = len(self.startgenerator.stoichiometry)
        else:
            self.n_to_optimize = len(self.structures[0])
            for constraint in self.structures[0].constraints:
                if isinstance(constraint, FixAtoms):
                    indices_fixed = constraint.get_indices()
                    self.n_to_optimize -= len(indices_fixed)
                    break
        
        if candidate_generator is not None:
            self.candidate_generator = candidate_generator
        else:
            rattle = RattleMutation(self.n_to_optimize,
                                    Nrattle=3,
                                    rattle_range=4)
            self.candidate_generator = OperationSelector([1.0],[rattle])

        # 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.max_steps = max_steps
        self.Ninit = Ninit
        self.dmax_cov = dmax_cov
        self.Ncandidates = Ncandidates
        self.dualpoint = dualpoint
        self.min_certainty = min_certainty
        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

        if restart is None or not isfile(restart):
            self.initialize()
        else:
            self.read()
            self.comm.barrier()

    def initialize(self):
        self.steps = 0

    def get_initial_structures(self):
        """Method to prepare the initial structures for the search.
        
        The method makes sure that there are atleast self.Ninit
        initial structures.
        These structures are first of all the potentially supplied
        structures. If more structures are required, these are
        generated using self.startgenerator (if supplied), otherwise
        they are generated by heavily rattling the supplied structures.
        """
        
        # Collect potentially supplied structures and evaluate
        # energies and forces if not present.
        structures_init = []
        if self.structures is not None:
            for a in self.structures:
                a.info = {'origin': 'PreSupplied'}
                calc = a.get_calculator()
                if isinstance(calc, SinglePointCalculator):
                    if 'energy' in calc.results and 'forces' in calc.results:
                        # Add without evaluating.
                        structures_init.append(a)
                        continue
                a = self.evaluate(a)
                structures_init.append(a)
        
        Nremaining = self.Ninit - len(structures_init)
        
        if Nremaining > 0 and self.startgenerator is None:
            # Initialize rattle-mutation for all atoms.
            rattle = RattleMutation(self.n_to_optimize,
                                    Nrattle=self.n_to_optimize,
                                    rattle_range=2)

        # Generation of remaining initial-structures (up to self.Ninit).
        for i in range(Nremaining):
            if self.startgenerator is not None:
                a = self.startgenerator.get_new_candidate()
            else:
                # Perform two times rattle of all atoms.
                a0 = structures_init[i % len(structures_init)]
                a = rattle.get_new_candidate([a])
                a = rattle.get_new_candidate([a])
            a = self.evaluate(a)
            structures_init.append(a)

        # Potentially do a few relaxation steps.
        ### missing code ###
        
        for a in structures_init:
            self.write(a)
        self.gpr.memory.save_data(structures_init)
        self.population.add(structures_init)
                
    def run(self):
        """ Method to run the search.
        """
        self.get_initial_structures()

        while self.steps < self.max_steps:
            self.print_master('\n ### steps: {} ###\n'.format(self.steps))
            self.train_surrogate()
            self.update_population()
            relaxed_candidates = self.get_surrogate_relaxed_candidates()

            kappa = self.kappa
            a_add = []
            for _ in range(5):
                try:
                    self.print_master('d1 trial', _)
                    anew = self.select_with_acquisition(relaxed_candidates, kappa)
                    self.print_master('d2 trial', _)
                    anew = self.evaluate(anew)
                    self.print_master('d3 trial', _)
                    a_add.append(anew)
                    if self.dualpoint:
                        adp = self.get_dualpoint(anew)
                        adp = self.evaluate(adp)
                        a_add.append(adp)
                    self.print_master('d4 trial', _)
                    break
                except Exception as err:
                    kappa /=2
                    if self.master:
                        traceback.print_exc(file=sys.stderr)
            self.gpr.memory.save_data(a_add)

            # Add structure to population
            index_lowest = np.argmin([a.get_potential_energy() for a in a_add])
            self.population.add([a_add[index_lowest]])

            # Save search state
            self.dump((self.steps, self.population, np.random.get_state()))
            
            if self.master:
                print('anew pred:', anew.info['key_value_pairs']['Epred'], anew.info['key_value_pairs']['Epred_std'])
                print('E_true:', [a.get_potential_energy() for a in a_add])
                print('pop:', [a.get_potential_energy() for a in self.population.pop])

            self.steps += 1
            
    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 get_surrogate_relaxed_candidates(self):
        """ Method supplying a number of surrogate-relaxed new
        candidates. The method combines the generation of new
        candidates with subsequent surrogate relaxation.
        The tasks are parrlelized over all avaliable cores.
        """
        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:
        if self.steps % 3 == 0:
            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 generate_candidate(self):
        """ Method to generate new candidate.
        """
        parents = self.population.get_structure_pair()
        a_mutated = self.candidate_generator.get_new_candidate(parents)
        return a_mutated

    def surrogate_relaxation(self, a, Fmax=0.1, steps=200, kappa=None):
        """ Method to carry out relaxations of new candidates in the
        surrogate potential.
        """
        calc = self.gpr.get_calculator(kappa)
        a_relaxed = relax(a, calc, dmax_cov=self.dmax_cov, Fmax=Fmax, steps_max=steps)

        # Evaluate uncertainty
        E, Estd = self.gpr.predict_energy(a_relaxed, eval_std=True)

        # Save prediction in info-dict
        a_relaxed.info['key_value_pairs']['Epred'] = E
        a_relaxed.info['key_value_pairs']['Epred_std'] = Estd
        a_relaxed.info['key_value_pairs']['kappa'] = self.kappa
        
        return a_relaxed
        
    def certainty_filter(self, structures):
        """ Method to filter away the most uncertain surrogate-relaxed
        candidates, which might otherewise get picked for first-principles
        evaluation, based on the very high uncertainty alone.
        """
        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):
        """ Method to update the population with the new pirst-principles
        evaluated structures.
        """
        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):
        """ Method to train the surrogate model.
        The method only performs hyperparameter optimization every 
        ten training instance, as carrying out the hyperparameter
        optimization is significantly more expensive than the basic
        training.
        """
        # Train
        if self.steps < 50 or (self.steps % 10) == 0:
            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 select_with_acquisition(self, structures, kappa):
        """ Method to select single most "promizing" candidate 
        for first-principles evaluation according to the acquisition
        function min(E-kappa*std(E)).
        """
        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):
        """ Method to evaluate the energy and forces of the selacted
        candidate.
        """
        a = self.comm.bcast(a, root=0)
        a.wrap()

        if isinstance(self.calc, Dftb):
            if self.master:
                try:
                    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)
                    success = True
                except:
                    print('dftb failed on master', flush=True)
                    success = False
            else:
                success = None
            success = self.comm.bcast(success, root=0)
            if success == False:
                write('fail.traj', a)
                print('Raising error rank', self.comm.rank, flush=True)
                raise RuntimeError('DFTB evaluation failed')
            a = self.comm.bcast(a, root=0)
        else:
            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):
        """ Method for writing new evaluated structures to file.
        """
        if self.trajectory is not None:
            self.trajectory.write(a)

    def dump(self, data):
        """ Method to save restart-file used if the search is
        restarted from some point in the search. 
        """
        if self.comm.rank == 0 and self.restart is not None:
            pickle.dump(data, open(self.restart, "wb"), protocol=2)

    def read(self):
        """ Method to restart a search from the restart-file and the
        trajectory-file containing all structures evaluated so far.
        """
        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)