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Move logging to module level for easier user control of amount of inf…
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…ormation given (#10)
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@lindonroberts
lindonroberts committed Nov 3, 2021
1 parent 3b65e18 commit ff95fd3
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Showing 4 changed files with 49 additions and 40 deletions.
14 changes: 8 additions & 6 deletions pybobyqa/controller.py
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Original file line number Diff line number Diff line change
Expand Up @@ -43,6 +43,8 @@
'EXIT_INPUT_ERROR', 'EXIT_TR_INCREASE_ERROR', 'EXIT_LINALG_ERROR', 'EXIT_FALSE_SUCCESS_WARNING',
'EXIT_AUTO_DETECT_RESTART_WARNING']

module_logger = logging.getLogger(__name__)

EXIT_AUTO_DETECT_RESTART_WARNING = 4 # warning, auto-detected restart criteria
EXIT_FALSE_SUCCESS_WARNING = 3 # warning, maximum fake successful steps reached
EXIT_SLOW_WARNING = 2 # warning, maximum number of slow (successful) iterations reached
Expand Down Expand Up @@ -131,7 +133,7 @@ def npt(self):

def initialise_coordinate_directions(self, number_of_samples, num_directions, params):
if self.do_logging:
logging.debug("Initialising with coordinate directions")
module_logger.debug("Initialising with coordinate directions")
# self.model already has x0 evaluated, so only need to initialise the other points
# num_directions = params("growing.ndirs_initial")
assert self.model.num_pts <= (self.n() + 1) * (self.n() + 2) // 2, "prelim: must have npt <= (n+1)(n+2)/2"
Expand Down Expand Up @@ -205,7 +207,7 @@ def initialise_coordinate_directions(self, number_of_samples, num_directions, pa

def initialise_random_directions(self, number_of_samples, num_directions, params):
if self.do_logging:
logging.debug("Initialising with random orthogonal directions")
module_logger.debug("Initialising with random orthogonal directions")
# self.model already has x0 evaluated, so only need to initialise the other points
# num_directions = params("growing.ndirs_initial")
assert 1 <= num_directions < self.model.num_pts, "Initialisation: must have 1 <= ndirs_initial < npt"
Expand Down Expand Up @@ -282,7 +284,7 @@ def trust_region_step(self):

def geometry_step(self, knew, adelt, number_of_samples, params):
if self.do_logging:
logging.debug("Running geometry-fixing step")
module_logger.debug("Running geometry-fixing step")
try:
c, g, H = self.model.lagrange_polynomial(knew) # based at xopt
# Solve problem: bounds are sl <= xnew <= su, and ||xnew-xopt|| <= adelt
Expand Down Expand Up @@ -467,12 +469,12 @@ def terminate_from_slow_iterations(self, current_iter, params):
if this_iter_slow:
self.num_slow_iters += 1
if self.do_logging:
logging.info("Slow iteration (%g consecutive so far, max allowed %g)"
module_logger.info("Slow iteration (%g consecutive so far, max allowed %g)"
% (self.num_slow_iters, params("slow.max_slow_iters")))
else:
self.num_slow_iters = 0
if self.do_logging:
logging.debug("Non-slow iteration")
module_logger.debug("Non-slow iteration")
return this_iter_slow, self.num_slow_iters >= params("slow.max_slow_iters")

def soft_restart(self, number_of_samples, nruns_so_far, params, x_in_abs_coords_to_save=None, f_to_save=None,
Expand Down Expand Up @@ -507,7 +509,7 @@ def soft_restart(self, number_of_samples, nruns_so_far, params, x_in_abs_coords_
self.model.nsamples[self.model.kopt], x_in_abs_coords=True)

if self.do_logging:
logging.info("Soft restart [currently, f = %g after %g function evals]" % (self.model.fopt(), self.nf))
module_logger.info("Soft restart [currently, f = %g after %g function evals]" % (self.model.fopt(), self.nf))
# Resetting method: reset delta and rho, then move the closest 'num_steps' points to xk to improve geometry
# Note: closest points because we are suddenly increasing delta & rho, so we want to encourage spreading out points
self.delta = self.rhobeg
Expand Down
5 changes: 4 additions & 1 deletion pybobyqa/model.py
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Original file line number Diff line number Diff line change
Expand Up @@ -41,6 +41,9 @@
__all__ = ['Model']


module_logger = logging.getLogger(__name__)


class Model(object):
def __init__(self, npt, x0, f0, xl, xu, f0_nsamples, n=None, abs_tol=-1e20, precondition=True, do_logging=True):
if n is None:
Expand Down Expand Up @@ -260,7 +263,7 @@ def solve_system(self, rhs):
return col_scale(LA.lu_solve((self.lu, self.piv), col_scale(rhs, self.left_scaling)), self.right_scaling)
else:
if self.do_logging:
logging.warning("model.solve_system not using factorisation")
module_logger.warning("model.solve_system not using factorisation")
A, left_scaling, right_scaling = self.interpolation_matrix()
return col_scale(LA.solve(A, col_scale(rhs, left_scaling)), right_scaling)

Expand Down
64 changes: 33 additions & 31 deletions pybobyqa/solver.py
View file
Original file line number Diff line number Diff line change
Expand Up @@ -42,6 +42,8 @@

__all__ = ['solve']

module_logger = logging.getLogger(__name__)


# A container for the results of the optimization routine
class OptimResults(object):
Expand Down Expand Up @@ -149,11 +151,11 @@ def solve_main(objfun, x0, args, xl, xu, npt, rhobeg, rhoend, maxfun, nruns_so_f
num_directions = npt - 1
if params("init.random_initial_directions"):
if do_logging:
logging.info("Initialising (random directions)")
module_logger.info("Initialising (random directions)")
exit_info = control.initialise_random_directions(number_of_samples, num_directions, params)
else:
if do_logging:
logging.info("Initialising (coordinate directions)")
module_logger.info("Initialising (coordinate directions)")
exit_info = control.initialise_coordinate_directions(number_of_samples, num_directions, params)
if exit_info is not None:
x, f, gradmin, hessmin, nsamples = control.model.get_final_results()
Expand All @@ -174,7 +176,7 @@ def solve_main(objfun, x0, args, xl, xu, npt, rhobeg, rhoend, maxfun, nruns_so_f
# ------------------------------------------
current_iter = -1
if do_logging:
logging.info("Beginning main loop")
module_logger.info("Beginning main loop")
if print_progress:
print("{:^5}{:^7}{:^10}{:^10}{:^10}{:^10}{:^7}".format("Run", "Iter", "Obj", "Grad", "Delta", "rho", "Evals"))
while True:
Expand Down Expand Up @@ -231,7 +233,7 @@ def solve_main(objfun, x0, args, xl, xu, npt, rhobeg, rhoend, maxfun, nruns_so_f
# Trust region step
d, gopt, H, gnew, crvmin = control.trust_region_step()
if do_logging:
logging.debug("Trust region step is d = " + str(d))
module_logger.debug("Trust region step is d = " + str(d))
xnew = control.model.xopt() + d
dnorm = min(LA.norm(d), control.delta)

Expand All @@ -250,7 +252,7 @@ def solve_main(objfun, x0, args, xl, xu, npt, rhobeg, rhoend, maxfun, nruns_so_f
if dnorm < params("general.safety_step_thresh") * control.rho:
# (start safety step)
if do_logging:
logging.debug("Safety step (main phase)")
module_logger.debug("Safety step (main phase)")

if params("logging.save_diagnostic_info"):
diagnostic_info.update_ratio(np.nan)
Expand Down Expand Up @@ -295,12 +297,12 @@ def solve_main(objfun, x0, args, xl, xu, npt, rhobeg, rhoend, maxfun, nruns_so_f
# Reduce rho
control.reduce_rho(current_iter, params)
if do_logging:
logging.info("New rho = %g after %i function evaluations" % (control.rho, control.nf))
module_logger.info("New rho = %g after %i function evaluations" % (control.rho, control.nf))
if control.n() < params("logging.n_to_print_whole_x_vector"):
logging.debug("Best so far: f = %.15g at x = " % (control.model.fopt())
module_logger.debug("Best so far: f = %.15g at x = " % (control.model.fopt())
+ str(control.model.xopt(abs_coordinates=True)))
else:
logging.debug("Best so far: f = %.15g at x = [...]" % (control.model.fopt()))
module_logger.debug("Best so far: f = %.15g at x = [...]" % (control.model.fopt()))
continue # next iteration
else:
# Quit on rho=rhoend
Expand Down Expand Up @@ -340,7 +342,7 @@ def solve_main(objfun, x0, args, xl, xu, npt, rhobeg, rhoend, maxfun, nruns_so_f
else:
# (start trust region step)
if do_logging:
logging.debug("Standard trust region step")
module_logger.debug("Standard trust region step")

# Add chgJ and delta to restart auto-detect set
if params("restarts.use_restarts") and params("restarts.auto_detect"):
Expand Down Expand Up @@ -420,26 +422,26 @@ def solve_main(objfun, x0, args, xl, xu, npt, rhobeg, rhoend, maxfun, nruns_so_f

# Update delta
if do_logging:
logging.debug("Ratio = %g" % ratio)
module_logger.debug("Ratio = %g" % ratio)
if params("logging.save_diagnostic_info"):
diagnostic_info.update_ratio(ratio)
diagnostic_info.update_slow_iter(-1) # n/a, unless otherwise update
if ratio < params("tr_radius.eta1"): # ratio < 0.1
control.delta = min(params("tr_radius.gamma_dec") * control.delta, dnorm)
if params("logging.save_diagnostic_info"):
# logging.info("Last eval was for unsuccessful step (ratio = %g)" % ratio)
# module_logger.info("Last eval was for unsuccessful step (ratio = %g)" % ratio)
diagnostic_info.update_iter_type(ITER_ACCEPTABLE_NO_GEOM if ratio > 0.0
else ITER_UNSUCCESSFUL_NO_GEOM) # we flag geom update below
elif ratio <= params("tr_radius.eta2"): # 0.1 <= ratio <= 0.7
control.delta = max(params("tr_radius.gamma_dec") * control.delta, dnorm)
if params("logging.save_diagnostic_info"):
# logging.info("Last eval was for acceptable step (ratio = %g)" % ratio)
# module_logger.info("Last eval was for acceptable step (ratio = %g)" % ratio)
diagnostic_info.update_iter_type(ITER_SUCCESSFUL)
else: # (ratio > eta2 = 0.7)
control.delta = min(max(params("tr_radius.gamma_inc") * control.delta,
params("tr_radius.gamma_inc_overline") * dnorm), 1.0e10)
if params("logging.save_diagnostic_info"):
# logging.info("Last eval was for successful step (ratio = %g)" % ratio)
# module_logger.info("Last eval was for successful step (ratio = %g)" % ratio)
diagnostic_info.update_iter_type(ITER_VERY_SUCCESSFUL)
if control.delta <= 1.5 * control.rho: # cap trust region radius at rho
control.delta = control.rho
Expand Down Expand Up @@ -472,7 +474,7 @@ def solve_main(objfun, x0, args, xl, xu, npt, rhobeg, rhoend, maxfun, nruns_so_f

# Update point
if do_logging:
logging.debug("Updating with knew = %i" % knew)
module_logger.debug("Updating with knew = %i" % knew)
control.model.change_point(knew, xnew, f_list[0]) # expect step, not absolute x
for i in range(1, num_samples_run):
control.model.add_new_sample(knew, f_extra=f_list[i])
Expand All @@ -484,7 +486,7 @@ def solve_main(objfun, x0, args, xl, xu, npt, rhobeg, rhoend, maxfun, nruns_so_f
diagnostic_info.update_slow_iter(1 if this_iter_slow else 0)
if should_terminate:
if do_logging:
logging.info("Slow iteration - terminating/restarting")
module_logger.info("Slow iteration - terminating/restarting")
if params("restarts.use_restarts") and params("restarts.use_soft_restarts"):
number_of_samples = max(nsamples(control.delta, control.rho, current_iter, nruns_so_far), 1)
exit_info = control.soft_restart(number_of_samples, nruns_so_far, params,
Expand Down Expand Up @@ -539,7 +541,7 @@ def solve_main(objfun, x0, args, xl, xu, npt, rhobeg, rhoend, maxfun, nruns_so_f
slope2, intercept2, r_value2, p_value2, std_err2 = slope, intercept, r_value, p_value, std_err

if do_logging:
logging.debug("Iter %g: (slope, intercept, r_value) = (%g, %g, %g)" % (current_iter, slope, intercept, r_value))
module_logger.debug("Iter %g: (slope, intercept, r_value) = (%g, %g, %g)" % (current_iter, slope, intercept, r_value))
if min(slope, slope2) > params("restarts.auto_detect.min_chg_model_slope") \
and min(r_value, r_value2) > params("restarts.auto_detect.min_correl"):
# increasing trend, with at least some positive correlation
Expand All @@ -549,10 +551,10 @@ def solve_main(objfun, x0, args, xl, xu, npt, rhobeg, rhoend, maxfun, nruns_so_f

if do_restart and params("restarts.use_soft_restarts"):
if do_logging:
logging.info("Auto detection: need to do a restart")
logging.debug("delta history = %s" % str(restart_auto_detect_delta))
logging.debug("chg_grad history = %s" % str(restart_auto_detect_chg_grad))
logging.debug("chg_hess history = %s" % str(restart_auto_detect_chg_hess))
module_logger.info("Auto detection: need to do a restart")
module_logger.debug("delta history = %s" % str(restart_auto_detect_delta))
module_logger.debug("chg_grad history = %s" % str(restart_auto_detect_chg_grad))
module_logger.debug("chg_hess history = %s" % str(restart_auto_detect_chg_hess))
number_of_samples = max(nsamples(control.delta, control.rho, current_iter, nruns_so_far), 1)
exit_info = control.soft_restart(number_of_samples, nruns_so_far, params,
x_in_abs_coords_to_save=None, f_to_save=None,
Expand All @@ -570,15 +572,15 @@ def solve_main(objfun, x0, args, xl, xu, npt, rhobeg, rhoend, maxfun, nruns_so_f
continue # next iteration
elif do_restart:
if do_logging:
logging.info("Auto detection: need to do a restart")
module_logger.info("Auto detection: need to do a restart")
exit_info = ExitInformation(EXIT_AUTO_DETECT_RESTART_WARNING, "Auto-detected restart")
nruns_so_far += 1
break # quit
# If not doing restart, just continue as below (geom steps, etc.)

# Otherwise (ratio < eta1 = 0.1), check & fix geometry
if do_logging:
logging.debug("Checking and possibly improving geometry (unsuccessful step)")
module_logger.debug("Checking and possibly improving geometry (unsuccessful step)")
distsq = max((2.0 * control.delta) ** 2, (10.0 * control.rho) ** 2)
update_delta = False
number_of_samples = max(nsamples(control.delta, control.rho, current_iter, nruns_so_far), 1)
Expand Down Expand Up @@ -624,12 +626,12 @@ def solve_main(objfun, x0, args, xl, xu, npt, rhobeg, rhoend, maxfun, nruns_so_f
# Reduce rho
control.reduce_rho(current_iter, params)
if do_logging:
logging.info("New rho = %g after %i function evaluations" % (control.rho, control.nf))
module_logger.info("New rho = %g after %i function evaluations" % (control.rho, control.nf))
if control.n() < params("logging.n_to_print_whole_x_vector"):
logging.debug("Best so far: f = %.15g at x = " % (control.model.fopt())
module_logger.debug("Best so far: f = %.15g at x = " % (control.model.fopt())
+ str(control.model.xopt(abs_coordinates=True)))
else:
logging.debug("Best so far: f = %.15g at x = [...]" % (control.model.fopt()))
module_logger.debug("Best so far: f = %.15g at x = [...]" % (control.model.fopt()))
continue # next iteration
else:
# Quit on rho=rhoend
Expand Down Expand Up @@ -658,8 +660,8 @@ def solve_main(objfun, x0, args, xl, xu, npt, rhobeg, rhoend, maxfun, nruns_so_f
# Quit & return the important information
x, f, gradmin, hessmin, nsamples = control.model.get_final_results()
if do_logging:
logging.debug("At return from solver, number of function evals = %i" % nf)
logging.debug("Smallest objective value = %.15g at x = " % f + str(x))
module_logger.debug("At return from solver, number of function evals = %i" % nf)
module_logger.debug("Smallest objective value = %.15g at x = " % f + str(x))
return x, f, gradmin, hessmin, nsamples, control.nf, control.nx, nruns_so_far, exit_info, diagnostic_info


Expand Down Expand Up @@ -823,7 +825,7 @@ def solve(objfun, x0, args=(), bounds=None, npt=None, rhobeg=None, rhoend=1e-8,
if not reduction_last_run:
_rhobeg = _rhobeg * params("restarts.rhobeg_scale_after_unsuccessful_restart")

logging.info("Restarting from finish point (f = %g) after %g function evals; using rhobeg = %g and rhoend = %g"
module_logger.info("Restarting from finish point (f = %g) after %g function evals; using rhobeg = %g and rhoend = %g"
% (fmin, nf, _rhobeg, _rhoend))
if params("restarts.hard.use_old_fk"):
xmin2, fmin2, gradmin2, hessmin2, nsamples2, nf, nx, nruns, exit_info, diagnostic_info = \
Expand All @@ -835,7 +837,7 @@ def solve(objfun, x0, args=(), bounds=None, npt=None, rhobeg=None, rhoend=1e-8,
diagnostic_info, scaling_changes, do_logging=do_logging, print_progress=print_progress)

if fmin2 < fmin or np.isnan(fmin):
logging.info("Successful run with new f = %s compared to old f = %s" % (fmin2, fmin))
module_logger.info("Successful run with new f = %s compared to old f = %s" % (fmin2, fmin))
last_successful_run = nruns
(xmin, fmin, nsamples_min) = (xmin2, fmin2, nsamples2)
if gradmin2 is not None: # may be None if finished during setup phase, in which case just use old gradient
Expand All @@ -844,7 +846,7 @@ def solve(objfun, x0, args=(), bounds=None, npt=None, rhobeg=None, rhoend=1e-8,
hessmin = hessmin2
reduction_last_run = True
else:
logging.info("Unsuccessful run with new f = %s compared to old f = %s" % (fmin2, fmin))
module_logger.info("Unsuccessful run with new f = %s compared to old f = %s" % (fmin2, fmin))
reduction_last_run = False
total_unsuccessful_restarts += 1

Expand All @@ -868,7 +870,7 @@ def solve(objfun, x0, args=(), bounds=None, npt=None, rhobeg=None, rhoend=1e-8,
results.diagnostic_info = df

if do_logging:
logging.info("Did a total of %g run(s)" % nruns)
module_logger.info("Did a total of %g run(s)" % nruns)

return results

6 changes: 4 additions & 2 deletions pybobyqa/util.py
View file
Original file line number Diff line number Diff line change
Expand Up @@ -33,6 +33,8 @@
__all__ = ['sumsq', 'eval_objective', 'model_value', 'random_orthog_directions_within_bounds',
'random_directions_within_bounds', 'apply_scaling', 'remove_scaling']

module_logger = logging.getLogger(__name__)


def sumsq(x):
# There are several ways to calculate sum of squares of a vector:
Expand All @@ -56,9 +58,9 @@ def eval_objective(objfun, x, args=(), verbose=True, eval_num=0, pt_num=0, full_

if verbose:
if len(x) < full_x_thresh:
logging.info("Function eval %i at point %i has f = %.15g at x = " % (eval_num, pt_num, f) + str(x))
module_logger.info("Function eval %i at point %i has f = %.15g at x = " % (eval_num, pt_num, f) + str(x))
else:
logging.info("Function eval %i at point %i has f = %.15g at x = [...]" % (eval_num, pt_num, f))
module_logger.info("Function eval %i at point %i has f = %.15g at x = [...]" % (eval_num, pt_num, f))

return f

Expand Down

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