def run_bumps(problem, handler, curr_thread):
def abort_test():
if curr_thread is None: return False
try:
curr_thread.isquit()
except KeyboardInterrupt:
if handler is not None:
handler.stop("Fitting: Terminated!!!")
return True
return False
fitclass, options = get_fitter()
steps = options.get('steps', 0)
if steps == 0:
pop = options.get('pop', 0) * len(problem._parameters)
samples = options.get('samples', 0)
steps = (samples + pop - 1) / pop if pop != 0 else samples
max_step = steps + options.get('burn', 0)
pars = [p.name for p in problem._parameters]
#x0 = np.asarray([p.value for p in problem._parameters])
options['monitors'] = [
BumpsMonitor(handler, max_step, pars, problem.dof),
ConvergenceMonitor(),
]
fitdriver = fitters.FitDriver(fitclass,
problem=problem,
abort_test=abort_test,
**options)
omp_threads = int(os.environ.get('OMP_NUM_THREADS', '0'))
mapper = MPMapper if omp_threads == 1 else SerialMapper
fitdriver.mapper = mapper.start_mapper(problem, None)
#import time; T0 = time.time()
try:
best, fbest = fitdriver.fit()
errors = []
except Exception as exc:
best, fbest = None, np.NaN
errors = [str(exc), traceback.format_exc()]
finally:
mapper.stop_mapper(fitdriver.mapper)
convergence_list = options['monitors'][-1].convergence
convergence = (2 * np.asarray(convergence_list) /
problem.dof if convergence_list else np.empty((0, 1), 'd'))
success = best is not None
try:
stderr = fitdriver.stderr() if success else None
except Exception as exc:
errors.append(str(exc))
errors.append(traceback.format_exc())
stderr = None
return {
'value': best if success else None,
'stderr': stderr,
'success': success,
'convergence': convergence,
'uncertainty': getattr(fitdriver.fitter, 'state', None),
'errors': '\n'.join(errors),
}
def run_bumps(problem, handler, curr_thread):
def abort_test():
if curr_thread is None: return False
try:
curr_thread.isquit()
except KeyboardInterrupt:
if handler is not None:
handler.stop("Fitting: Terminated!!!")
return True
return False
fitopts = fitters.FIT_OPTIONS[fitters.FIT_DEFAULT]
fitclass = fitopts.fitclass
options = fitopts.options.copy()
max_step = fitopts.options.get('steps', 0) + fitopts.options.get('burn', 0)
pars = [p.name for p in problem._parameters]
options['monitors'] = [
BumpsMonitor(handler, max_step, pars, problem.dof),
ConvergenceMonitor(),
]
fitdriver = fitters.FitDriver(fitclass,
problem=problem,
abort_test=abort_test,
**options)
omp_threads = int(os.environ.get('OMP_NUM_THREADS', '0'))
mapper = MPMapper if omp_threads == 1 else SerialMapper
fitdriver.mapper = mapper.start_mapper(problem, None)
#import time; T0 = time.time()
try:
best, fbest = fitdriver.fit()
except:
import traceback
traceback.print_exc()
raise
finally:
mapper.stop_mapper(fitdriver.mapper)
convergence_list = options['monitors'][-1].convergence
convergence = (2 * numpy.asarray(convergence_list) /
problem.dof if convergence_list else numpy.empty(
(0, 1), 'd'))
return {
'value': best,
'stderr': fitdriver.stderr(),
'success': True, # better success reporting in bumps
'convergence': convergence,
'uncertainty': getattr(fitdriver.fitter, 'state', None),
}