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problem.py
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problem.py
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import asyncio
import logging
from .db import write_evaluations_batch
from .io.lammps import LammpsLocalDFTFITCalculator
from .io.lammps_cython import LammpsCythonDFTFITCalculator
from .predict import Predict
from . import objective
logger = logging.getLogger(__name__)
class DFTFITProblemBase:
def __init__(self, potential, training, features, weights, calculator='lammps_cython', dbm=None, db_write_interval=10, run_id=None, loop=None, **kwargs):
self.loop = loop or asyncio.get_event_loop()
# Training Initialization
self.training = training
# DFTFIT Calculator Initialization
dftfit_calculator_mapper = {
'lammps': LammpsLocalDFTFITCalculator,
'lammps_cython': LammpsCythonDFTFITCalculator,
}
structures = [c.structure for c in self.training.calculations]
self.dftfit_calculator = dftfit_calculator_mapper[calculator](structures=structures, potential=potential, **kwargs)
self.loop.run_until_complete(self.dftfit_calculator.create())
logger.info('(problem) initialized dftfit calculator %s' % calculator)
# MD Calculator Initialization
self.md_calculator = None
if training.material_properties:
self.md_calculator = Predict(calculator, loop=self.loop)
logger.info('(problem) initialized md calculator %s' % calculator)
# Potential Initialization
self.potential = potential
logger.info('(problem) potential has %d parameters' % len(potential.optimization_parameters))
# Objective Initialization
self.features = features
self.weights = weights
self.objective_functions = []
self.md_calculations = set()
feature_function_mapping = {
'forces': objective.force_objective_function,
'stress': objective.stress_objective_function,
'energy': objective.energy_objective_function,
'lattice_constants': objective.lattice_constant_objective_function,
'elastic_constants': objective.elastic_constants_objective_function,
'bulk_modulus': objective.bulk_modulus_objective_function,
'shear_modulus': objective.shear_modulus_objective_function
}
for feature in self.features:
self.objective_functions.append(feature_function_mapping[feature])
if feature in {'lattice_constants'}:
self.md_calculations.add('lattice_constants')
elif feature in {'elastic_constants', 'bulk_modulus', 'shear_modulus'}:
self.md_calculations = self.md_calculations | {'lattice_constants', 'elastic_constants'}
logger.info('(problem) optimizing features with weights: ' + ', '.join('{}={:.2f}'.format(f, w) for f, w in zip(self.features, self.weights)))
# Database Logging Initialization
self.dbm = dbm
self._run_id = run_id
self.db_write_interval = db_write_interval
self._evaluation_buffer = []
if self.dbm and not isinstance(self._run_id, int):
raise ValueError('cannot write evaluation to database without integer run_id')
def store_evaluation(self, potential, errors, value):
if self.dbm:
self._evaluation_buffer.append([potential, errors, value])
if len(self._evaluation_buffer) >= self.db_write_interval:
write_evaluations_batch(self.dbm, self._run_id, self._evaluation_buffer)
self._evaluation_buffer = []
def _fitness(self, parameters):
potential = self.potential.copy()
potential.optimization_parameters = parameters
# dftfit calculations
md_calculations = self.loop.run_until_complete(self.dftfit_calculator.submit(potential))
# material property calculations
predict_calculations = {}
if self.md_calculations:
if 'lattice_constants' in self.md_calculations:
old_lattice, new_lattice = self.md_calculator.lattice_constant(self.training.reference_ground_state, potential)
predict_calculations['lattice_constants'] = new_lattice
if 'elastic_constants' in self.md_calculations:
structure = self.training.reference_ground_state.copy()
structure.modify_lattice(predict_calculations['lattice_constants'])
predict_calculations['elastic_constants'] = self.md_calculator.elastic_constant(structure, potential)
value = 0.0
errors = []
for feature, weight, func in zip(self.features, self.weights, self.objective_functions):
if feature in {'forces', 'stress', 'energy'}:
v = func(md_calculations, self.training.calculations)
elif feature in {'lattice_constants'}:
v = func(predict_calculations['lattice_constants'], self.training.material_properties[feature])
elif feature in {'elastic_constants', 'bulk_modulus', 'shear_modulus'}:
v = func(predict_calculations['elastic_constants'], self.training.material_properties[feature])
if weight:
value += v * weight
errors.append(v)
self.store_evaluation(potential, errors, value)
formatted_errors = ', '.join('{:10.4g}'.format(_) for _ in errors)
logger.info(f'evaluation = {value:10.4g} errors = [ {formatted_errors} ]')
return errors, value
def __deepcopy__(self, memo):
return self # override copy method
def finalize(self):
if self._evaluation_buffer: # ensure that all evaluations have been written
write_evaluations_batch(self.dbm, self._run_id, self._evaluation_buffer)
self._evaluation_buffer = []
def __del__(self):
self.dftfit_calculator.shutdown()
def get_bounds(self):
return tuple(zip(*self.potential.optimization_bounds.tolist()))
class DFTFITSingleProblem(DFTFITProblemBase):
def __init__(self, **kwargs):
super().__init__(**kwargs)
def get_nobj(self):
return 1
def fitness(self, parameters):
errors, value = self._fitness(parameters)
return (value,)
class DFTFITMultiProblem(DFTFITProblemBase):
def __init__(self, **kwargs):
super().__init__(**kwargs)
def get_nobj(self):
return len(self.features)
def fitness(self, parameters):
errors, value = self._fitness(parameters)
return tuple(errors)