class IterativeMetaModel(Model):
""" This meta model iterates over a set of given models and stops when a given condition is fulfilled.
"""
model_name = "Iterative Meta Model"
def __init__(self, models, configuration, datasets_to_preload=None):
""" 'models' is a list of strings determining the models to be run.
'configuration' is a dictionary based configuration used for ModelSystem.
Its entry 'models_configuration' must contain the given 'models'.
'datasets_to_preload' is a list of dataset names that should
be pre-loaded for the use of the 'models'. If it is None, all datasets
in configuration['datasets_to_preload'] are loaded prior to each run.
Setting this entry can speed the run-time, since all pre-loaded datasets
are also cached after each iteration.
"""
self.config = Resources(configuration)
self.config['models'] = models
if datasets_to_preload is not None:
new_datasets_to_preload = {}
for dataset in datasets_to_preload:
new_datasets_to_preload[dataset] = self.config['datasets_to_preload'].get(dataset, {})
self.config['datasets_to_preload'] = new_datasets_to_preload
self.model_system = ModelSystem()
def run(self, year, condition=None, max_iter=10):
"""
'year' is the current year of the simulation.
'condition' should be a boolean expression defined on any dataset.
The method iterates over the given models until all values of the expression are True.
'max_iter' gives the maximum number of iterations to run, if 'condition' is not fulfilled.
If it is None, there is no limit and thus, the condition must be fulfilled in order to terminate.
If 'condition' is None, the set of models is run only once.
"""
self.config['years'] = (year, year)
if condition is None:
return self.model_system.run_in_same_process(self.config)
dataset_pool = SessionConfiguration().get_dataset_pool()
variable_name = VariableName(condition)
dataset = dataset_pool.get_dataset(variable_name.get_dataset_name())
condition_value = dataset.compute_variables(variable_name, dataset_pool=dataset_pool)
result = None
iter = 1
while not alltrue(condition_value):
result = self.model_system.run_in_same_process(self.config)
if max_iter is None or iter > max_iter:
break
iter = iter + 1
# force to recompute the condition
dataset = SessionConfiguration().get_dataset_pool().get_dataset(variable_name.get_dataset_name())
dataset.delete_computed_attributes()
condition_value = dataset.compute_variables(variable_name,
dataset_pool=SessionConfiguration().get_dataset_pool())
if not alltrue(condition_value):
logger.log_status('%s did not converge. Maximum number of iterations (%s) reached.' % (self.model_name, max_iter))
else:
logger.log_status('%s converged in %s iterations.' % (self.model_name, iter-1))
return result
class IterativeMetaModel(Model):
""" This meta model iterates over a set of given models and stops when a given condition is fulfilled.
"""
model_name = "Iterative Meta Model"
def __init__(self, models, configuration, datasets_to_preload=None):
""" 'models' is a list of strings determining the models to be run.
'configuration' is a dictionary based configuration used for ModelSystem.
Its entry 'models_configuration' must contain the given 'models'.
'datasets_to_preload' is a list of dataset names that should
be pre-loaded for the use of the 'models'. If it is None, all datasets
in configuration['datasets_to_preload'] are loaded prior to each run.
Setting this entry can speed the run-time, since all pre-loaded datasets
are also cached after each iteration.
"""
self.config = Resources(configuration)
self.config['models'] = models
if datasets_to_preload is not None:
new_datasets_to_preload = {}
for dataset in datasets_to_preload:
new_datasets_to_preload[dataset] = self.config[
'datasets_to_preload'].get(dataset, {})
self.config['datasets_to_preload'] = new_datasets_to_preload
self.model_system = ModelSystem()
def run(self, year, condition=None, max_iter=10):
"""
'year' is the current year of the simulation.
'condition' should be a boolean expression defined on any dataset.
The method iterates over the given models until all values of the expression are True.
'max_iter' gives the maximum number of iterations to run, if 'condition' is not fulfilled.
If it is None, there is no limit and thus, the condition must be fulfilled in order to terminate.
If 'condition' is None, the set of models is run only once.
"""
self.config['years'] = (year, year)
if condition is None:
return self.model_system.run_in_same_process(self.config)
dataset_pool = SessionConfiguration().get_dataset_pool()
variable_name = VariableName(condition)
dataset = dataset_pool.get_dataset(variable_name.get_dataset_name())
condition_value = dataset.compute_variables(variable_name,
dataset_pool=dataset_pool)
result = None
iter = 1
while not alltrue(condition_value):
result = self.model_system.run_in_same_process(self.config)
if max_iter is None or iter > max_iter:
break
iter = iter + 1
# force to recompute the condition
dataset = SessionConfiguration().get_dataset_pool().get_dataset(
variable_name.get_dataset_name())
dataset.delete_computed_attributes()
condition_value = dataset.compute_variables(
variable_name,
dataset_pool=SessionConfiguration().get_dataset_pool())
if not alltrue(condition_value):
logger.log_status(
'%s did not converge. Maximum number of iterations (%s) reached.'
% (self.model_name, max_iter))
else:
logger.log_status('%s converged in %s iterations.' %
(self.model_name, iter - 1))
return result
