Some import fixes

Author: Tanguy Damart

bluepyopt/__init__.py

@@ -32,6 +32,7 @@
 import bluepyopt.deapext.algorithms
 import bluepyopt.stoppingCriteria
 import bluepyopt.deapext.optimisations
+import bluepyopt.deapext.optimisationsCMA
 
 # Add some backward compatibility for the time when DEAPoptimisation not in
 # deapext yet

bluepyopt/deapext/CMA_MO.py

@@ -31,8 +31,7 @@
 from deap import cma
 
 from .stoppingCriteria import MaxNGen
-
-from .utils import _bound
+from . import utils
 
 logger = logging.getLogger('__main__')
 
@@ -211,7 +210,7 @@ def get_parents(self, to_space):
     def generate_new_pop(self, lbounds, ubounds):
         """Generate a new population bounded in the normalized space"""
         self.population = self.toolbox.generate()
-        return _bound(self.population, lbounds, ubounds)
+        return utils.bound(self.population, lbounds, ubounds)
 
     def update_strategy(self):
         self.toolbox.update(self.population)

bluepyopt/deapext/CMA_SO.py

@@ -30,7 +30,7 @@
 from .stoppingCriteria import MaxNGen, Stagnation, TolHistFun, EqualFunVals, \
     NoEffectAxis, TolUpSigma, TolX, ConditionCov, NoEffectCoor
 
-from .utils import _bound
+from . import utils
 
 logger = logging.getLogger('__main__')
 
@@ -157,7 +157,7 @@ def get_population(self, to_space):
     def generate_new_pop(self, lbounds, ubounds):
         """Generate a new population bounded in the normalized space"""
         self.population = self.toolbox.generate()
-        return _bound(self.population, lbounds, ubounds)
+        return utils.bound(self.population, lbounds, ubounds)
 
     def update_strategy(self):
         self.toolbox.update(self.population)

bluepyopt/deapext/algorithms.py

@@ -32,7 +32,7 @@
 import pickle
 
 from .stoppingCriteria import MaxNGen
-from .utils import _update_history_and_hof, _record_stats
+from . import utils
 
 logger = logging.getLogger('__main__')
 
@@ -140,8 +140,8 @@ def eaAlphaMuPlusLambdaCheckpoint(
         history = deap.tools.History()
 
         invalid_count = _evaluate_invalid_fitness(toolbox, population)
-        _update_history_and_hof(halloffame, history, population)
-        _record_stats(stats, logbook, start_gen, population, invalid_count)
+        utils.update_history_and_hof(halloffame, history, population)
+        utils.record_stats(stats, logbook, start_gen, population, invalid_count)
 
     stopping_criteria = [MaxNGen(ngen)]
 
@@ -154,8 +154,8 @@ def eaAlphaMuPlusLambdaCheckpoint(
         population = parents + offspring
 
         invalid_count = _evaluate_invalid_fitness(toolbox, offspring)
-        _update_history_and_hof(halloffame, history, population)
-        _record_stats(stats, logbook, gen, population, invalid_count)
+        utils.history_and_hof(halloffame, history, population)
+        utils.record_stats(stats, logbook, gen, population, invalid_count)
 
         # Select the next generation parents
         parents = toolbox.select(population, mu)

bluepyopt/deapext/optimisations.py

@@ -33,7 +33,7 @@
 
 from . import algorithms
 from . import tools
-from .utils import _reduce_method, _uniform
+from . import utils
 
 import bluepyopt.optimisations
 
@@ -171,7 +171,7 @@ def setup_deap(self):
             UPPER.append(parameter.upper_bound)
 
         # Register the 'uniform' function
-        self.toolbox.register("uniformparams", _uniform, LOWER, UPPER, IND_SIZE)
+        self.toolbox.register("uniformparams", utils.uniform, LOWER, UPPER, IND_SIZE)
 
         # Register the individual format
         # An indiviual is create by WSListIndividual and parameters
@@ -225,7 +225,7 @@ def setup_deap(self):
 
         import copyreg
         import types
-        copyreg.pickle(types.MethodType, _reduce_method)
+        copyreg.pickle(types.MethodType, utils.reduce_method)
 
         if self.use_scoop:
             if self.map_function:

bluepyopt/deapext/optimisationsCMA.py

@@ -23,14 +23,15 @@
 import numpy
 import pickle
 import random
-from functools import partial
+import functools
 
-from deap import tools
+import deap.tools
 
 from .CMA_SO import CMA_SO
 from .CMA_MO import CMA_MO
-from .utils import _update_history_and_hof, _record_stats, _reduce_method, \
-    _uniform
+from . import utils
+
+import bluepyopt.optimisations
 
 logger = logging.getLogger('__main__')
 
@@ -166,9 +167,9 @@ def __init__(self,
         self.to_space = []
         for r, m in zip(bounds_radius, bounds_mean):
             self.to_norm.append(
-                partial(lambda param, bm, br: (param - bm) / br, bm=m, br=r))
+                functools.partial(lambda param, bm, br: (param - bm) / br, bm=m, br=r))
             self.to_space.append(
-                partial(lambda param, bm, br: (param * br) + bm, bm=m, br=r))
+                functools.partial(lambda param, bm, br: (param * br) + bm, bm=m, br=r))
 
         # Overwrite the bounds with -1. and 1.
         self.lbounds = numpy.full(self.problem_size, -1.)
@@ -191,7 +192,9 @@ def setup_deap(self):
         numpy.random.seed(self.seed)
 
         # Register the 'uniform' function
-        self.toolbox.register("uniformparams", _uniform, self.lbounds,
+        self.toolbox.register("uniformparams", 
+                              utils.uniform, 
+                              self.lbounds,
                               self.ubounds,
                               self.ind_size)
 
@@ -222,8 +225,10 @@ def setup_deap(self):
 
         # Register the evaluation function for the individuals
         self.toolbox.register("evaluate", self.evaluator.evaluate_with_lists)
-
-        copyreg.pickle(types.MethodType, _reduce_method)
+        
+        import copyreg
+        import types
+        copyreg.pickle(types.MethodType, utils.reduce_method)
 
         if self.use_scoop:
             if self.map_function:
@@ -266,8 +271,8 @@ def run(self,
             CMA_es.map_function = self.map_function
 
         else:
-            history = tools.History()
-            logbook = tools.Logbook()
+            history = deap.tools.History()
+            logbook = deap.tools.Logbook()
             logbook.header = ["gen", "nevals"] + stats.fields
 
             # Instantiate the CMA strategies centered on the centroids
@@ -309,8 +314,8 @@ def run(self,
 
             # Update the hall of fame, history and logbook
             pop = CMA_es.get_population(self.to_space)
-            _update_history_and_hof(self.hof, history, pop)
-            record = _record_stats(stats, logbook, gen, pop, nevals)
+            utils.update_history_and_hof(self.hof, history, pop)
+            record = utils.record_stats(stats, logbook, gen, pop, nevals)
             logger.info(logbook.stream)
 
             # Update the CMA strategy using the new fitness and check if
@@ -345,4 +350,4 @@ def get_stats(self):
         stats.register("std", numpy.std)
         stats.register("min", numpy.min)
         stats.register("max", numpy.max)
-        return stats
+        return stats

bluepyopt/deapext/stoppingCriteria.py

@@ -20,6 +20,8 @@
 import logging
 import numpy
 
+from collections import deque
+
 import bluepyopt.stoppingCriteria
 
 logger = logging.getLogger('__main__')
@@ -42,7 +44,7 @@ def check(self, kwargs):
             self.criteria_met = True
 
 
-class Stagnation(StoppingCriteria):
+class Stagnation(bluepyopt.stoppingCriteria.StoppingCriteria):
     """Stagnation stopping criteria class"""
     name = "Stagnation"
 
@@ -78,7 +80,7 @@ def check(self, kwargs):
             self.criteria_met = True
 
 
-class TolHistFun(StoppingCriteria):
+class TolHistFun(bluepyopt.stoppingCriteria.StoppingCriteria):
     """TolHistFun stopping criteria class"""
     name = "TolHistFun"
 
@@ -100,7 +102,7 @@ def check(self, kwargs):
             self.criteria_met = True
 
 
-class EqualFunVals(StoppingCriteria):
+class EqualFunVals(bluepyopt.stoppingCriteria.StoppingCriteria):
     """EqualFunVals stopping criteria class"""
     name = "EqualFunVals"
 
@@ -131,7 +133,7 @@ def check(self, kwargs):
             self.criteria_met = True
 
 
-class TolX(StoppingCriteria):
+class TolX(bluepyopt.stoppingCriteria.StoppingCriteria):
     """TolX stopping criteria class"""
     name = "TolX"
 
@@ -150,7 +152,7 @@ def check(self, kwargs):
             self.criteria_met = True
 
 
-class TolUpSigma(StoppingCriteria):
+class TolUpSigma(bluepyopt.stoppingCriteria.StoppingCriteria):
     """TolUpSigma stopping criteria class"""
     name = "TolUpSigma"
 
@@ -169,7 +171,7 @@ def check(self, kwargs):
             self.criteria_met = True
 
 
-class ConditionCov(StoppingCriteria):
+class ConditionCov(bluepyopt.stoppingCriteria.StoppingCriteria):
     """ConditionCov stopping criteria class"""
     name = "ConditionCov"
 
@@ -187,7 +189,7 @@ def check(self, kwargs):
             self.criteria_met = True
 
 
-class NoEffectAxis(StoppingCriteria):
+class NoEffectAxis(bluepyopt.stoppingCriteria.StoppingCriteria):
     """NoEffectAxis stopping criteria class"""
     name = "NoEffectAxis"
 
@@ -212,7 +214,7 @@ def check(self, kwargs):
             self.criteria_met = True
 
 
-class NoEffectCoor(StoppingCriteria):
+class NoEffectCoor(bluepyopt.stoppingCriteria.StoppingCriteria):
     """NoEffectCoor stopping criteria class"""
     name = "NoEffectCoor"
 
@@ -227,4 +229,4 @@ def check(self, kwargs):
         C = kwargs.get("C")
 
         if any(centroid == centroid + 0.2 * sigma * numpy.diag(C)):
-            self.criteria_met = True
+            self.criteria_met = True

bluepyopt/deapext/utils.py

@@ -16,12 +16,12 @@
 """
 
 import numpy
-
+import random
 
 # pylint: disable=R0914, R0912
 
 
-def _update_history_and_hof(halloffame, history, population):
+def update_history_and_hof(halloffame, history, population):
     """Update the hall of fame with the generated individuals
     Note: History and Hall-of-Fame behave like dictionaries
     """
@@ -31,13 +31,13 @@ def _update_history_and_hof(halloffame, history, population):
     history.update(population)
 
 
-def _record_stats(stats, logbook, gen, population, invalid_count):
+def record_stats(stats, logbook, gen, population, invalid_count):
     """Update the statistics with the new population"""
     record = stats.compile(population) if stats is not None else {}
     logbook.record(gen=gen, nevals=invalid_count, **record)
 
 
-def _closest_feasible(individual, lbounds, ubounds):
+def closest_feasible(individual, lbounds, ubounds):
     """Returns the closest individual in the parameter bounds"""
     # TO DO: Fix 1e-9 hack
     for i, (u, l, el) in enumerate(zip(ubounds, lbounds, individual)):
@@ -48,23 +48,29 @@ def _closest_feasible(individual, lbounds, ubounds):
     return individual
 
 
-def _bound(population, lbounds, ubounds):
+def bound(population, lbounds, ubounds):
     """Bounds the population based on lower and upper parameter bounds."""
     n_out = 0
     for i, ind in enumerate(population):
         if numpy.any(numpy.less(ind, lbounds)) or numpy.any(
                 numpy.greater(ind, ubounds)):
-            population[i] = _closest_feasible(ind, lbounds, ubounds)
+            population[i] = closest_feasible(ind, lbounds, ubounds)
             n_out += 1
     return n_out
 
 
-def _uniform(lower_list, upper_list, dimensions):
+def uniform(lower_list, upper_list, dimensions):
     """Uniformly pick an individual"""
 
     if hasattr(lower_list, '__iter__'):
         return [random.uniform(lower, upper) for lower, upper in
                 zip(lower_list, upper_list)]
     else:
         return [random.uniform(lower_list, upper_list)
-                for _ in range(dimensions)]
+                for _ in range(dimensions)]
+
+
+def reduce_method(meth):
+    """Overwrite reduce"""
+    return (getattr, (meth.__self__, meth.__func__.__name__))
+