def __make_pool(self, model_kwargs):
'''
helper method for generating the pool in case of running in parallel.
'''
self._pool = CalculatorPool(self._msis,
processes=self.processes,
kwargs=model_kwargs)
class ModelEnsemble(object):
'''
One of the two main classes for performing EMA. The ensemble class is
responsible for running experiments on one or more model structures across
one or more policies, and returning the results.
The sampling is delegated to a sampler instance.
The storing or results is delegated to a callback instance
the class has an attribute 'parallel' that specifies whether the
experiments are to be run in parallel or not. By default, 'parallel' is
False.
.. rubric:: an illustration of use
>>> model = UserSpecifiedModelInterface(r'working directory', 'name')
>>> ensemble = SimpleModelEnsemble()
>>> ensemble.set_model_structure(model)
>>> ensemble.parallel = True #parallel processing is turned on
>>> results = ensemble.perform_experiments(1000) #perform 1000 experiments
In this example, a 1000 experiments will be carried out in parallel on
the user specified model interface. The uncertainties are retrieved from
model.uncertainties and the outcomes are assumed to be specified in
model.outcomes.
'''
#: In case of parallel computing, the number of
#: processes to be spawned. Default is None, meaning
#: that the number of processes will be equal to the
#: number of available cores.
processes=None
#: boolean for turning parallel on (default is False)
parallel = False
_pool = None
_policies = []
def __init__(self, sampler=LHSSampler()):
"""
Class responsible for running experiments on diverse model
structures and storing the results.
:param sampler: the sampler to be used for generating experiments.
By default, the sampling technique is
:class:`~samplers.LHSSampler`.
"""
super(ModelEnsemble, self).__init__()
self.output = {}
self._policies = []
self._msis = []
self.sampler = sampler
def add_policy(self, policy):
"""
Add a policy.
:param policy: policy to be added, policy should be a dict with at
least a name.
"""
self._policies.append(policy)
def add_policies(self, policies):
"""
Add policies, policies should be a collection of policies.
:param policies: policies to be added, every policy should be a
dict with at least a name.
"""
[self._policies.append(policy) for policy in policies]
def set_model_structure(self, modelStructure):
'''
Set the model structure. This function wraps the model structure
in a tuple, limiting the number of model structures to 1.
:param modelStructure: a :class:`~model.ModelStructureInterface`
instance.
'''
self._msis = tuple([modelStructure])
def add_model_structure(self, ms):
'''
Add a model structure to the list of model structures.
:param ms: a :class:`~model.ModelStructureInterface` instance.
'''
self._msis.append(ms)
def add_model_structures(self, mss):
'''
add a collection of model structures to the list of model structures.
:param mss: a collection of :class:`~model.ModelStructureInterface`
instances
'''
[self._msis.append(ms) for ms in mss]
def determine_uncertainties(self):
'''
Helper method for determining the unique uncertainties and how
the uncertainties are shared across multiple model structure
interfaces.
:returns: An overview dictionary which shows which uncertainties are
used by which model structure interface, or interfaces, and
a dictionary with the unique uncertainties across all the
model structure interfaces, with the name as key.
'''
return self.__determine_unique_attributes('uncertainties')
def __determine_unique_attributes(self, attribute):
'''
Helper method for determining the unique values on attributes of model
interfaces, and how these values are shared across multiple model
structure interfaces. The working assumption is that this function
:param attribute: the attribute to check on the msi
:returns: An overview dictionary which shows which uncertainties are
used by which model structure interface, or interfaces, and
a dictionary with the unique uncertainties across all the
model structure interfaces, with the name as key.
'''
# check whether uncertainties exist with the same name
# but different other attributes
element_dict = {}
overview_dict = {}
for msi in self._msis:
elements = getattr(msi, attribute)
for element in elements:
if element_dict.has_key(element.name):
if element==element_dict[element.name]:
overview_dict[element.name].append(msi)
else:
raise EMAError("%s `%s` is shared but has different state"
% (element.__class__.__name__,
element.name))
else:
element_dict[element.name]= element
overview_dict[element.name] = [msi]
temp_overview = defaultdict(list)
for key, value in overview_dict.iteritems():
temp_overview[tuple([msi.name for msi in value])].append(element_dict[key])
overview_dict = temp_overview
return overview_dict, element_dict
def _determine_outcomes(self):
'''
Helper method for determining the unique outcomes and how
the outcomes are shared across multiple model structure
interfaces.
:returns: An overview dictionary which shows which uncertainties are
used by which model structure interface, or interfaces, and
a dictionary with the unique uncertainties across all the
model structure interfaces, with the name as key.
'''
return self.__determine_unique_attributes('outcomes')
def _generate_samples(self, nr_of_samples, which_uncertainties):
'''
number of cases specifies the number of cases to generate in case
of Monte Carlo and Latin Hypercube sampling.
In case of full factorial sampling nr_of_cases specifies the resolution
on non categorical uncertainties.
In case of multiple model structures, the uncertainties over
which to explore is the intersection of the sets of uncertainties of
the model interface instances.
:param nr_of_samples: The number of samples to generate for the
uncertainties. In case of mc and lhs sampling,
the number of samples is generated. In case of
ff sampling, the number of samples is interpreted
as the upper limit for the resolution to use.
:returns: a dict with the samples uncertainties and a dict
with the unique uncertainties. The first dict containsall the
unique uncertainties. The dict has the uncertainty name as
key and the values are the generated samples. The second dict
has the name as key and an instance of the associated
uncertainty as value.
'''
overview_dict, unc_dict = self.determine_uncertainties()
if which_uncertainties==UNION:
if isinstance(self.sampler, FullFactorialSampler):
raise EMAError("full factorial sampling cannot be combined with exploring the union of uncertainties")
sampled_unc = self.sampler.generate_samples(unc_dict.values(),
nr_of_samples)
elif which_uncertainties==INTERSECTION:
uncertainties = overview_dict[tuple([msi.name for msi in self._msis])]
unc_dict = {key.name:unc_dict[key.name] for key in uncertainties}
uncertainties = [unc_dict[unc.name] for unc in uncertainties]
sampled_unc = self.sampler.generate_samples(unc_dict.values(),
nr_of_samples)
else:
raise ValueError("incompatible value for which_uncertainties")
return sampled_unc, unc_dict
def __make_pool(self, model_kwargs):
'''
helper method for generating the pool in case of running in parallel.
'''
self._pool = CalculatorPool(self._msis,
processes=self.processes,
kwargs=model_kwargs)
def __generate_experiments(self, sampled_unc):
'''
Helper method for turning the sampled uncertainties into actual
complete experiments, including the model structure interface and the
policy. The actual generation is delegated to the experiments_generator
function, which returns a generator. In this way, not all the
experiments are kept in memory, but they are generated only when
needed.
:param sampled_unc: the sampled uncertainty dictionary
:returns: a generator object that yields the experiments
'''
if isinstance(sampled_unc, list):
return experiment_generator_predef_cases(copy.deepcopy(sampled_unc),\
self._msis,\
self._policies,)
return experiment_generator(sampled_unc, self._msis,\
self._policies, self.sampler)
def perform_experiments(self,
cases,
callback=DefaultCallback,
reporting_interval=100,
model_kwargs = {},
which_uncertainties=INTERSECTION,
which_outcomes=INTERSECTION,
**kwargs):
"""
Method responsible for running the experiments on a structure. In case
of multiple model structures, the outcomes are set to the intersection
of the sets of outcomes of the various models.
:param cases: In case of Latin Hypercube sampling and Monte Carlo
sampling, cases specifies the number of cases to
generate. In case of Full Factorial sampling,
cases specifies the resolution to use for sampling
continuous uncertainties. Alternatively, one can supply
a list of dicts, where each dicts contains a case.
That is, an uncertainty name as key, and its value.
:param callback: Class that will be called after finishing a
single experiment,
:param reporting_interval: parameter for specifying the frequency with
which the callback reports the progress.
(Default is 100)
:param model_kwargs: dictionary of keyword arguments to be passed to
model_init
:param which_uncertainties: keyword argument for controlling whether,
in case of multiple model structure
interfaces, the intersection or the union
of uncertainties should be used.
(Default is intersection).
:param which_uncertainties: keyword argument for controlling whether,
in case of multiple model structure
interfaces, the intersection or the union
of outcomes should be used.
(Default is intersection).
:param kwargs: generic keyword arguments to pass on to callback
:returns: a `structured numpy array <http://docs.scipy.org/doc/numpy/user/basics.rec.html>`_
containing the experiments, and a dict with the names of the
outcomes as keys and an numpy array as value.
.. rubric:: suggested use
In general, analysis scripts require both the structured array of the
experiments and the dictionary of arrays containing the results. The
recommended use is the following::
>>> results = ensemble.perform_experiments(10000) #recommended use
>>> experiments, output = ensemble.perform_experiments(10000) #will work fine
The latter option will work fine, but most analysis scripts require
to wrap it up into a tuple again::
>>> data = (experiments, output)
Another reason for the recommended use is that you can save this tuple
directly::
>>> import expWorkbench.util as util
>>> util.save_results(results, file)
.. note:: The current implementation has a hard coded limit to the
number of designs possible. This is set to 50.000 designs.
If one want to go beyond this, set `self.max_designs` to
a higher value.
"""
if not self._policies:
self._policies.append({"name": "None"})
# identify the uncertainties and sample over them
if type(cases) == types.IntType:
sampled_unc, unc_dict = self._generate_samples(cases, which_uncertainties)
nr_of_exp =self.sampler.deterimine_nr_of_designs(sampled_unc)\
*len(self._policies)*len(self._msis)
experiments = self.__generate_experiments(sampled_unc)
elif type(cases) == types.ListType:
# TODO, don't know what this should look like until the\
# default case of generating experiments the normal way is working
# again
# what still needs to be done is the unc_dict, which should
# become a dict {name: unc for name in unc_names}
# from where can we get the unc?
unc_dict = self.determine_uncertainties()[1]
unc_names = cases[0].keys()
sampled_unc = {name:[] for name in unc_names}
nr_of_exp = len(cases)*len(self._policies)*len(self._msis)
experiments = self.__generate_experiments(cases)
else:
raise EMAError("unknown type for cases")
uncertainties = [unc_dict[unc] for unc in sorted(sampled_unc)]
# identify the outcomes that are to be included
overview_dict, element_dict = self.__determine_unique_attributes("outcomes")
if which_outcomes==UNION:
outcomes = element_dict.keys()
elif which_outcomes==INTERSECTION:
outcomes = overview_dict[tuple([msi.name for msi in self._msis])]
outcomes = [outcome.name for outcome in outcomes]
else:
raise ValueError("incomplete value for which_outcomes")
info(str(nr_of_exp) + " experiment will be executed")
#initialize the callback object
callback = callback(uncertainties,
outcomes,
nr_of_exp,
reporting_interval=reporting_interval,
**kwargs)
if self.parallel:
info("preparing to perform experiment in parallel")
if not self._pool:
self.__make_pool(model_kwargs)
info("starting to perform experiments in parallel")
self._pool.run_experiments(experiments, callback)
else:
info("starting to perform experiments sequentially")
def cleanup(modelInterfaces):
for msi in modelInterfaces:
msi.cleanup()
del msi
msi_initialization_dict = {}
msis = {msi.name: msi for msi in self._msis}
for experiment in experiments:
policy = experiment.pop('policy')
msi = experiment.pop('model')
# check whether we already initialized the model for this
# policy
if not msi_initialization_dict.has_key((policy['name'], msi)):
try:
debug("invoking model init")
msis[msi].model_init(copy.deepcopy(policy),\
copy.deepcopy(model_kwargs))
except (EMAError, NotImplementedError) as inst:
exception(inst)
cleanup(self._msis)
raise
except Exception:
exception("some exception occurred when invoking the init")
cleanup(self._msis)
raise
debug("initialized model %s with policy %s" % (msi, policy['name']))
#always, only a single initialized msi instance
msi_initialization_dict = {(policy['name'], msi):msis[msi]}
msi = msis[msi]
case = copy.deepcopy(experiment)
try:
debug("trying to run model")
msi.run_model(case)
except CaseError as e:
warning(str(e))
debug("trying to retrieve output")
result = msi.retrieve_output()
msi.reset_model()
debug("trying to reset model")
callback(case, policy, msi.name, result)
cleanup(self._msis)
results = callback.get_results()
info("experiments finished")
return results
def perform_outcome_optimization(self,
reporting_interval=100,
obj_function=None,
weights = (),
nr_of_generations=100,
pop_size=100,
crossover_rate=0.5,
mutation_rate=0.02,
**kwargs
):
"""
Method responsible for performing outcome optimization. The
optimization will be performed over the intersection of the
uncertainties in case of multiple model structures.
:param reporting_interval: parameter for specifying the frequency with
which the callback reports the progress.
(Default is 100)
:param obj_function: the objective function used by the optimization
:param weights: tuple of weights on the various outcomes of the
objective function. Use the constants MINIMIZE and
MAXIMIZE.
:param nr_of_generations: the number of generations for which the
GA will be run
:param pop_size: the population size for the GA
:param crossover_rate: crossover rate for the GA
:param mutation_rate: mutation_rate for the GA
"""
#create a class for the individual
creator.create("Fitness", base.Fitness, weights=weights)
creator.create("Individual", dict,
fitness=creator.Fitness) #@UndefinedVariable
toolbox = base.Toolbox()
# Attribute generator
od = self.__determine_unique_attributes('uncertainties')[0]
shared_uncertainties = od[tuple([msi.name for msi in self._msis])]
#make a dictionary with the shared uncertainties and their range
uncertainty_dict = {}
for uncertainty in shared_uncertainties:
uncertainty_dict[uncertainty.name] = uncertainty
keys = sorted(uncertainty_dict.keys())
attr_list = []
levers = {}
low = []
high = []
for key in keys:
specification = {}
uncertainty = uncertainty_dict[key]
value = uncertainty.values
if isinstance(uncertainty, CategoricalUncertainty):
value = uncertainty.categories
toolbox.register(key, random.choice, value)
attr_list.append(getattr(toolbox, key))
low.append(0)
high.append(len(value)-1)
specification["type"]='list'
specification['values']=value
elif isinstance(uncertainty, ParameterUncertainty):
if uncertainty.dist=='integer':
toolbox.register(key, random.randint, value[0], value[1])
specification["type"]='range int'
else:
toolbox.register(key, random.uniform, value[0], value[1])
specification["type"]='range float'
attr_list.append(getattr(toolbox, key))
low.append(value[0])
high.append(value[1])
specification['values']=value
else:
raise EMAError("unknown allele type: possible types are range and list")
levers[key] = specification
return self.__run_optimization(toolbox, generate_individual_outcome,
evaluate_population_outcome, attr_list,
keys, obj_function, pop_size,
reporting_interval, weights,
nr_of_generations, crossover_rate,
mutation_rate, levers, **kwargs)
def __run_optimization(self, toolbox, generate_individual,
evaluate_population, attr_list, keys, obj_function,
pop_size, reporting_interval, weights,
nr_of_generations, crossover_rate, mutation_rate,
levers, **kwargs):
'''
Helper function that runs the actual optimization
:param toolbox:
:param generate_individual: helper function for generating an
individual
:param evaluate_population: helper function for evaluating the
population
:param attr_list: list of attributes (alleles)
:param keys: the names of the attributes in the same order as attr_list
:param obj_function: the objective function
:param pop_size: the size of the population
:param reporting_interval: the interval for reporting progress, passed
on to perform_experiments
:param weights: the weights on the outcomes
:param nr_of_generations: number of generations for which the GA will
be run
:param crossover_rate: the crossover rate of the GA
:param mutation_rate: the muation rate of the GA
:param levers: a dictionary with param keys as keys, and as values
info used in mutation.
'''
# figure out whether we are doing single or multi-objective
# optimization
#TODO raise error if not specified
single_obj = True
if len(weights) >1:
single_obj=False
# Structure initializers
toolbox.register("individual",
generate_individual,
creator.Individual, #@UndefinedVariable
attr_list, keys=keys)
toolbox.register("population", tools.initRepeat, list,
toolbox.individual)
# Operator registering
toolbox.register("evaluate", obj_function)
toolbox.register("crossover", tools.cxOnePoint)
toolbox.register("mutate", mut_polynomial_bounded)
if single_obj:
toolbox.register("select", tools.selTournament)
else:
toolbox.register("select", tools.selNSGA2)
# generate population
# for some stupid reason, DEAP demands a multiple of four for
# population size in case of NSGA-2
pop_size = closest_multiple_of_four(pop_size)
info("population size restricted to %s " % (pop_size))
pop = toolbox.population(pop_size)
debug("Start of evolution")
# Evaluate the entire population
evaluate_population(pop, reporting_interval, toolbox, self)
if not single_obj:
# This is just to assign the crowding distance to the individuals
tools.assignCrowdingDist(pop)
#some statistics logging
stats_callback = NSGA2StatisticsCallback(weights=weights,
nr_of_generations=nr_of_generations,
crossover_rate=crossover_rate,
mutation_rate=mutation_rate,
pop_size=pop_size)
stats_callback(pop)
stats_callback.log_stats(0)
# Begin the generational process
for gen in range(nr_of_generations):
pop = self.__run_geneneration(pop, crossover_rate, mutation_rate,
toolbox, reporting_interval, levers,
evaluate_population, keys,
single_obj, stats_callback, **kwargs)
stats_callback(pop)
stats_callback.log_stats(gen)
info("-- End of (successful) evolution --")
return stats_callback, pop
def __run_geneneration(self,
pop,
crossover_rate,
mutation_rate,
toolbox,
reporting_interval,
allele_dict,
evaluate_population,
keys,
single_obj,
stats_callback,
**kwargs):
'''
Helper function for runing a single generation.
:param pop:
:param crossover_rate:
:param mutation_rate:
:param toolbox:
:param reporting_interval:
:param allele_dict:
:param evaluate_population:
:param keys:
:param single_obj:
'''
# Variate the population
pop_size = len(pop)
a = pop[0:closest_multiple_of_four(len(pop))]
if single_obj:
offspring = toolbox.select(pop, pop_size, min(pop_size, 10))
else:
offspring = tools.selTournamentDCD(a, len(pop))
offspring = [toolbox.clone(ind) for ind in offspring]
no_name=False
for child1, child2 in zip(offspring[::2], offspring[1::2]):
# Apply crossover
if random.random() < crossover_rate:
keys = sorted(child1.keys())
try:
keys.pop(keys.index("name"))
except ValueError:
no_name = True
child1_temp = [child1[key] for key in keys]
child2_temp = [child2[key] for key in keys]
toolbox.crossover(child1_temp, child2_temp)
if not no_name:
for child, child_temp in zip((child1, child2),
(child1_temp,child2_temp)):
name = ""
for key, value in zip(keys, child_temp):
child[key] = value
name += " "+str(child[key])
child['name'] = name
else:
for child, child_temp in zip((child1, child2),
(child1_temp,child2_temp)):
for key, value in zip(keys, child_temp):
child[key] = value
#apply mutation
toolbox.mutate(child1, mutation_rate, allele_dict, keys, 0.05)
toolbox.mutate(child2, mutation_rate, allele_dict, keys, 0.05)
for entry in (child1, child2):
try:
ind = stats_callback.tried_solutions[entry]
except KeyError:
del entry.fitness.values
continue
entry.fitness = ind.fitness
# Evaluate the individuals with an invalid fitness
invalid_ind = [ind for ind in offspring if not ind.fitness.valid]
evaluate_population(invalid_ind, reporting_interval, toolbox, self)
# Select the next generation population
if single_obj:
pop = offspring
else:
pop = toolbox.select(pop + offspring, pop_size)
return pop
def perform_robust_optimization(self,
cases,
reporting_interval=100,
obj_function=None,
policy_levers={},
weights = (),
nr_of_generations=100,
pop_size=100,
crossover_rate=0.5,
mutation_rate=0.02,
**kwargs):
"""
Method responsible for performing robust optimization.
:param cases: In case of Latin Hypercube sampling and Monte Carlo
sampling, cases specifies the number of cases to
generate. In case of Full Factorial sampling,
cases specifies the resolution to use for sampling
continuous uncertainties. Alternatively, one can supply
a list of dicts, where each dicts contains a case.
That is, an uncertainty name as key, and its value.
:param reporting_interval: parameter for specifying the frequency with
which the callback reports the progress.
(Default is 100)
:param obj_function: the objective function used by the optimization
:param policy_levers: A dictionary with model parameter names as key
and a dict as value. The dict should have two
fields: 'type' and 'values. Type is either
list or range, and determines the appropriate
allele type. Values are the parameters to
be used for the specific allele.
:param weights: tuple of weights on the various outcomes of the
objective function. Use the constants MINIMIZE and
MAXIMIZE.
:param nr_of_generations: the number of generations for which the
GA will be run
:param pop_size: the population size for the GA
:param crossover_rate: crossover rate for the GA
:param mutation_rate: mutation_rate for the GA
"""
evaluate_population = functools.partial(evaluate_population_robust,
cases=cases)
#create a class for the individual
creator.create("Fitness", base.Fitness, weights=weights)
creator.create("Individual", dict,
fitness=creator.Fitness) #@UndefinedVariable
toolbox = base.Toolbox()
# Attribute generator
keys = sorted(policy_levers.keys())
attr_list = []
low = []
high = []
for key in keys:
value = policy_levers[key]
type_allele = value['type']
value = value['values']
if type_allele=='range':
toolbox.register(key, random.uniform, value[0], value[1])
attr_list.append(getattr(toolbox, key))
low.append(value[0])
high.append(value[1])
elif type_allele=='list':
toolbox.register(key, random.choice, value)
attr_list.append(getattr(toolbox, key))
low.append(0)
high.append(len(value)-1)
else:
raise EMAError("unknown allele type: possible types are range and list")
return self.__run_optimization(toolbox, generate_individual_robust,
evaluate_population, attr_list, keys,
obj_function, pop_size,
reporting_interval, weights,
nr_of_generations, crossover_rate,
mutation_rate, policy_levers, **kwargs)
def continue_robust_optimization(self,
cases=None,
nr_of_generations=10,
pop=None,
stats_callback=None,
policy_levers=None,
obj_function=None,
crossover_rate=0.5,
mutation_rate=0.02,
reporting_interval=100,
**kwargs):
'''
Continue the robust optimization from a previously saved state. To
make this work, one should save the return from
perform_robust_optimization. The typical use case for this method is
to manually track convergence of the optimization after a number of
specified generations.
:param cases: In case of Latin Hypercube sampling and Monte Carlo
sampling, cases specifies the number of cases to
generate. In case of Full Factorial sampling,
cases specifies the resolution to use for sampling
continuous uncertainties. Alternatively, one can supply
a list of dicts, where each dicts contains a case.
That is, an uncertainty name as key, and its value.
:param nr_of_generations: the number of generations for which the
GA will be run
:param pop: the last ran population, returned
by perform_robust_optimization
:param stats_callback: the NSGA2StatisticsCallback instance returned
by perform_robust_optimization
:param reporting_interval: parameter for specifying the frequency with
which the callback reports the progress.
(Default is 100)
:param policy_levers: A dictionary with model parameter names as key
and a dict as value. The dict should have two
fields: 'type' and 'values. Type is either
list or range, and determines the appropriate
allele type. Values are the parameters to
be used for the specific allele.
:param obj_function: the objective function used by the optimization
:param crossover_rate: crossover rate for the GA
:param mutation_rate: mutation_rate for the GA
.. note:: There is some tricky stuff involved in loading
the stats_callback via cPickle. cPickle requires that the
classes in the pickle file exist. The individual class used
by deap is generated dynamicly. Loading the cPickle should
thus be preceded by reinstantiating the correct individual.
'''
# figure out whether we are doing single or multi-objective
# optimization
single_obj = True
if len(creator.Fitness.weights) >1: #@UndefinedVariable
single_obj=False
evaluate_population = functools.partial(evaluate_population_robust,
cases=cases)
toolbox = base.Toolbox()
# Attribute generator
keys = sorted(policy_levers.keys())
attr_list = []
low = []
high = []
for key in keys:
value = policy_levers[key]
type_allele = value['type']
value = value['values']
if type_allele=='range':
toolbox.register(key, random.uniform, value[0], value[1])
attr_list.append(getattr(toolbox, key))
low.append(value[0])
high.append(value[1])
elif type_allele=='list':
toolbox.register(key, random.choice, value)
attr_list.append(getattr(toolbox, key))
low.append(0)
high.append(len(value)-1)
else:
raise EMAError("unknown allele type: possible types are range and list")
# Operator registering
toolbox.register("evaluate", obj_function)
toolbox.register("crossover", tools.cxOnePoint)
if single_obj:
toolbox.register("select", tools.selTournament)
else:
toolbox.register("select", tools.selNSGA2)
toolbox.register("mutate", mut_polynomial_bounded)
# generate population
# for some stupid reason, DEAP demands a multiple of four for
# population size in case of NSGA-2
debug("Start of evolution")
# Begin the generational process
for gen in range(nr_of_generations):
pop = self.__run_geneneration(pop, crossover_rate, mutation_rate,
toolbox, reporting_interval,
policy_levers, evaluate_population,
keys, single_obj, **kwargs)
stats_callback(pop)
stats_callback.log_stats(gen)
info("-- End of (successful) evolution --")
return stats_callback, pop
