Moved to module-level logging, fixed Python 2.7 syntax error in ctrsbox, removed default logging from constrained example

Author: lindonroberts

dfols/controller.py

@@ -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_TR_INCREASE_WARNING = 5  # warning, TR increase in proj constrained case - likely due to multiple active constraints
 EXIT_AUTO_DETECT_RESTART_WARNING = 4  # warning, auto-detected restart criteria
 EXIT_FALSE_SUCCESS_WARNING = 3  # warning, maximum fake successful steps reached
@@ -134,7 +136,7 @@ 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"
@@ -311,7 +313,7 @@ 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
         assert 1 <= num_directions < self.model.num_pts, "Initialisation: must have 1 <= ndirs_initial < npt"
 
@@ -442,7 +444,7 @@ def trust_region_step(self, params):
 
     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 = self.model.lagrange_gradient(knew)
             # c = 1.0 if knew == self.model.kopt else 0.0  # based at xopt, just like d
@@ -643,12 +645,12 @@ 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, rvec_to_save=None,
@@ -677,7 +679,7 @@ 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
@@ -734,7 +736,7 @@ def soft_restart(self, number_of_samples, nruns_so_far, params, x_in_abs_coords_
                     self.model.add_new_sample(self.model.npt() - 1, rvec_extra=rvec_list[i, :])
 
             if self.do_logging:
-                logging.info("Soft restart: added %g new directions, npt is now %g" % (num_pts_to_add, self.model.npt()))
+                module_logger.info("Soft restart: added %g new directions, npt is now %g" % (num_pts_to_add, self.model.npt()))
 
         # Otherwise, we are doing a restart
         self.last_successful_iter = 0

dfols/model.py

@@ -40,6 +40,9 @@
 
 __all__ = ['Model']
 
+module_logger = logging.getLogger(__name__) 
+
+
 class Model(object):
     def __init__(self, npt, x0, r0, xl, xu, projections, r0_nsamples, n=None, m=None, abs_tol=1e-12, rel_tol=1e-20, precondition=True,
                  do_logging=True):
@@ -306,7 +309,7 @@ def solve_geom_system(self, rhs):
                 return col_scale(LA.solve_triangular(self.R, Qb), self.right_scaling)
         else:
             if self.do_logging:
-                logging.warning("model.solve_geom_system not using factorisation")
+                module_logger.warning("model.solve_geom_system not using factorisation")
             W, left_scaling, right_scaling = self.interpolation_matrix()
             return col_scale(LA.lstsq(W, col_scale(rhs * left_scaling))[0], right_scaling)
 
@@ -334,7 +337,7 @@ def interpolate_mini_models_svd(self, verbose=False, make_full_rank=False, min_s
         rhs = self.fval_v[fval_row_idx, :]  # size npt * m
         if np.any(np.isnan(rhs)) and throw_error_on_nans:
             if self.do_logging:
-                logging.warning("model.interpolate_mini_models_svd: NaNs encountered in objective evaluations, raising error")
+                module_logger.warning("model.interpolate_mini_models_svd: NaNs encountered in objective evaluations, raising error")
             raise np.linalg.LinAlgError("NaN encountered in objective evaluations")
         try:
             dg = self.solve_geom_system(rhs)  # size (n+1)*m