def run(self, specification, coefficients, agent_set,
agents_index=None, *args, **kargs):
if agent_set is None:
logger.log_status('No developments need to be allocated')
return
if agents_index is None:
agents_index = arange(agent_set.size())
if not isinstance(agents_index, ndarray):
try:
agents_index = array(agents_index)
except:
raise TypeError, "Argument agents_index is of wrong type (numpy array or list allowed.)"
id_name = self.choice_set.get_id_name()[0]
unplaced = arange(agents_index.size)
for run in range(self.max_runs):
choices = LocationChoiceModel.run(self, specification, coefficients, agent_set,
agents_index[unplaced], *args, **kargs)
if run == 0:
all_choices=choices
else:
all_choices[unplaced]=choices
from opus_core.sampling_toolbox import find_duplicates
unplaced = where(find_duplicates(all_choices))[0]
if unplaced.size <= 0:
break
agent_set.set_values_of_one_attribute(id_name, -1, agents_index[unplaced])
return all_choices
LocationChoiceModel.run(self, *args, **kargs)
def run(self, *args, **kargs):
"""disable filter for simulation, since it's been handled by get_weights_for_sampling_locations method"""
self.filter = None
agent_set = kargs["agent_set"]
if agent_set is None:
logger.log_status("No development projects for this model")
return None
logger.log_status("project size: %d" % (agent_set.get_attribute(agent_set.get_attribute_name()).sum()))
LocationChoiceModel.run(self, *args, **kargs)
def run(self, *args, **kargs):
"""disable filter for simulation, since it's been handled by get_weights_for_sampling_locations method"""
self.filter = None
agent_set = kargs["agent_set"]
if agent_set is None:
logger.log_status("No development projects for this model")
return None
logger.log_status(
"project size: %d" %
(agent_set.get_attribute(agent_set.get_attribute_name()).sum()))
LocationChoiceModel.run(self, *args, **kargs)
def run(self,
specification,
coefficients,
agent_set,
agents_index=None,
*args,
**kargs):
if agent_set is None:
logger.log_status('No developments need to be allocated')
return
if agents_index is None:
agents_index = arange(agent_set.size())
if not isinstance(agents_index, ndarray):
try:
agents_index = array(agents_index)
except:
raise TypeError, "Argument agents_index is of wrong type (numpy array or list allowed.)"
id_name = self.choice_set.get_id_name()[0]
unplaced = arange(agents_index.size)
for run in range(self.max_runs):
choices = LocationChoiceModel.run(self, specification,
coefficients, agent_set,
agents_index[unplaced], *args,
**kargs)
if run == 0:
all_choices = choices
else:
all_choices[unplaced] = choices
from opus_core.sampling_toolbox import find_duplicates
unplaced = where(find_duplicates(all_choices))[0]
if unplaced.size <= 0:
break
agent_set.set_values_of_one_attribute(id_name, -1,
agents_index[unplaced])
return all_choices
LocationChoiceModel.run(self, *args, **kargs)
def run(self, specification, coefficients, agent_set,
agents_index=None, agents_filter=None,
chunk_specification=None, data_objects=None,
run_config=None, debuglevel=0, maximum_runs=10):
if data_objects is not None:
self.dataset_pool.add_datasets_if_not_included(data_objects)
if agents_index is None:
if agents_filter is not None:
agent_set.compute_variables(agents_filter, dataset_pool=self.dataset_pool)
agents_index = where(agent_set.get_attribute(VariableName(agents_filter).get_alias()))[0]
else:
agents_index = arange(agent_set.size())
if not isinstance(agents_index, ndarray):
try:
agents_index = array(agents_index)
except:
raise TypeError, "Argument agents_index is of wrong type (numpy array or list allowed.)"
if agents_index.size <= 0:
logger.log_status("Nothing to be done.")
return array([], dtype='int32')
if run_config == None:
run_config = Resources()
self.run_config = run_config.merge_with_defaults(self.run_config)
self.number_of_units_string = self.run_config.get("number_of_units_string", None)
self.number_of_agents_string = self.run_config.get(
"number_of_agents_string",
"%s.number_of_agents(%s)" % (self.choice_set.get_dataset_name(), agent_set.get_dataset_name()))
if self.number_of_units_string is None:
maximum_runs = 1
unplaced = arange(agents_index.size)
id_name = self.choice_set.get_id_name()[0]
for run in range(maximum_runs):
unplaced_size_before_model = unplaced.size
choices = LocationChoiceModel.run(self, specification, coefficients, agent_set,
agents_index[unplaced], chunk_specification, debuglevel=debuglevel)
if run == 0:
all_choices=choices
else:
all_choices[unplaced]=choices
unplaced = self.get_movers_from_overfilled_locations(agent_set, agents_index, config=run_config)
if (unplaced.size <= 0) or (unplaced_size_before_model == unplaced.size) or (unplaced.size == (unplaced_size_before_model - self.observations_mapping['mapped_index'].size)):
break
agent_set.set_values_of_one_attribute(id_name, -1, agents_index[unplaced])
return all_choices
def run(self, specification, coefficients, agent_set,
agents_index=None, agents_filter=None,
chunk_specification=None, data_objects=None,
run_config=None, debuglevel=0, maximum_runs=10):
if data_objects is not None:
self.dataset_pool.add_datasets_if_not_included(data_objects)
bindex = zeros(agent_set.size(), dtype='b')
if agents_filter is not None:
bfilter = agent_set.compute_variables(agents_filter,
dataset_pool=self.dataset_pool)
if agents_index is not None:
bindex[agents_index] = True
agents_index = where(bindex * bfilter)[0]
else:
agents_index = where(bfilter)[0]
else:
if agents_index is not None:
agents_index = agents_index
else:
agents_index = arange(agent_set.size())
if not isinstance(agents_index, ndarray):
try:
agents_index = array(agents_index)
except:
raise TypeError, "Argument agents_index is of wrong type (numpy array or list allowed.)"
if agents_index.size == 0:
logger.log_status("Nothing to be done.")
return array([], dtype='int32')
if run_config == None:
run_config = Resources()
self.run_config = run_config.merge_with_defaults(self.run_config)
#this is handled by choices module (in UPC sequence)
self.number_of_units_string = self.run_config.get("number_of_units_string", None)
#self.number_of_agents_string = self.run_config.get(
# "number_of_agents_string",
# "%s.number_of_agents(%s)" % (self.choice_set.get_dataset_name(), agent_set.get_dataset_name()))
if self.number_of_units_string is None:
maximum_runs = 1
unplaced = ones_like(agents_index).astype('bool')
#boolean of the same shape as agents_index
end_choices = -1 * ones_like(agents_index)
id_name = self.choice_set.get_id_name()[0]
demand_string = self.run_config.get("demand_string")
supply_string = self.run_config.get("supply_string")
for run in range(maximum_runs):
unplaced_size_before = unplaced.sum()
choices = LocationChoiceModel.run(self,
specification=specification,
coefficients=coefficients,
agent_set=agent_set,
agents_index=agents_index[unplaced],
chunk_specification=chunk_specification,
debuglevel=debuglevel)
end_choices[unplaced] = choices
if run > 0:
## delete demand_string and supply string for later iterations to
## avoid these variables being distorted by assigning overfilled agents
if demand_string: del self.run_config["demand_string"]
if supply_string: del self.run_config["supply_string"]
unplaced = agent_set[id_name][agents_index] <= 0
## these two lines are inside the loop because self.observations_mapping is
## not initialized before calling LocationChoiceModel.run
agents_size_mapped = self.observations_mapping['mapped_index'].size
agents_size_unmapped = agents_index.size - agents_size_mapped
logger.log_status("Agent Location Choice Model iteration %s/%s: %s unplaced agents" % \
(run+1, maximum_runs, unplaced.sum()))
if unplaced.sum() in (0, unplaced_size_before, agents_size_unmapped):
logger.log_status("All agents placed or number of unplaced agents doesn't change; exit ALCM.")
break
agent_set.set_values_of_one_attribute(id_name, -1, agents_index[unplaced])
if demand_string: self.run_config["demand_string"] = demand_string
if supply_string: self.run_config["supply_string"] = supply_string
return end_choices
def run(self,
specification,
coefficients,
agent_set,
agents_index=None,
agents_filter=None,
chunk_specification=None,
data_objects=None,
run_config=None,
debuglevel=0,
maximum_runs=10):
if data_objects is not None:
self.dataset_pool.add_datasets_if_not_included(data_objects)
if agents_index is None:
if agents_filter is not None:
agent_set.compute_variables(agents_filter,
dataset_pool=self.dataset_pool)
agents_index = where(
agent_set.get_attribute(
VariableName(agents_filter).get_alias()))[0]
else:
agents_index = arange(agent_set.size())
if not isinstance(agents_index, ndarray):
try:
agents_index = array(agents_index)
except:
raise TypeError, "Argument agents_index is of wrong type (numpy array or list allowed.)"
if agents_index.size <= 0:
logger.log_status("Nothing to be done.")
return array([], dtype='int32')
if run_config == None:
run_config = Resources()
self.run_config = run_config.merge_with_defaults(self.run_config)
self.number_of_units_string = self.run_config.get(
"number_of_units_string", None)
self.number_of_agents_string = self.run_config.get(
"number_of_agents_string", "%s.number_of_agents(%s)" %
(self.choice_set.get_dataset_name(), agent_set.get_dataset_name()))
if self.number_of_units_string is None:
maximum_runs = 1
unplaced = arange(agents_index.size)
id_name = self.choice_set.get_id_name()[0]
for run in range(maximum_runs):
unplaced_size_before_model = unplaced.size
choices = LocationChoiceModel.run(self,
specification,
coefficients,
agent_set,
agents_index[unplaced],
chunk_specification,
debuglevel=debuglevel)
if run == 0:
all_choices = choices
else:
all_choices[unplaced] = choices
unplaced = self.get_movers_from_overfilled_locations(
agent_set, agents_index, config=run_config)
if (unplaced.size <=
0) or (unplaced_size_before_model == unplaced.size) or (
unplaced.size
== (unplaced_size_before_model -
self.observations_mapping['mapped_index'].size)):
break
agent_set.set_values_of_one_attribute(id_name, -1,
agents_index[unplaced])
return all_choices
