def estimate(self, specification, agent_set, agents_index=None, procedure=None, data_objects=None,
estimate_config=None, debuglevel=0):
""" Computes capacity if required and calls the estimate method of ChoiceModel.
See ChoiceModel.estimate for details on arguments.
"""
if agents_index==None:
agents_index=arange(agent_set.size())
if agents_index.size <= 0:
logger.log_status("Nothing to be done.")
return (None, None)
if estimate_config == None:
estimate_config = Resources()
self.estimate_config = estimate_config.merge_with_defaults(self.estimate_config)
if data_objects is not None:
self.dataset_pool.add_datasets_if_not_included(data_objects)
if self.location_id_string is not None:
agent_set.compute_variables(self.location_id_string, dataset_pool=self.dataset_pool)
capacity_for_estimation = None
if self.estimate_config.get("compute_capacity_flag", False):
capacity_string_for_estimation = self.estimate_config.get("capacity_string", None)
capacity_for_estimation = self.determine_capacity(capacity_string=capacity_string_for_estimation,
agent_set=agent_set,
agents_index=agents_index)
self.estimate_config.merge({"capacity":capacity_for_estimation})
return ChoiceModel.estimate(self,specification, agent_set,
agents_index, procedure, estimate_config=self.estimate_config,
debuglevel=debuglevel)
def estimate(self, specification, agent_set_year1, agent_set_year2, agents_index=None,
procedure="opus_core.bhhh_mnl_estimation", calibrate_constants=False,
data_objects=None, estimate_config=None, debuglevel=0):
"""Set calibrate_constants to True only if agent_set_year1 is the full dataset. Otherwise,
the calibration can be done separately by calling the method "calibrate" with the full dataset.
"""
if self.choice_attribute_name.get_alias() not in agent_set_year2.get_known_attribute_names():
agent_set_year2.compute_variables([self.choice_attribute_name])
if self.submodel_string not in agent_set_year1.get_known_attribute_names():
agent_set_year1.compute_variables([self.submodel_string])
lct_y2 = agent_set_year2.get_attribute(self.choice_attribute_name)
attributes_switched = False
if self.submodel_string == self.choice_attribute_name.get_alias():
new_submodel_string = self.choice_attribute_name.get_alias() + "_start"
original_submodel_string = self.submodel_string
agent_set_year1.add_attribute(name=new_submodel_string,
data=agent_set_year1.get_attribute(original_submodel_string).astype(int16))
agent_set_year1.add_attribute(name=original_submodel_string, data=lct_y2.astype(int16))
self.submodel_string = new_submodel_string
attributes_switched = True
self.specification = specification
results = ChoiceModel.estimate(self,specification, agent_set_year1,
agents_index, procedure, data_objects, estimate_config, debuglevel=debuglevel)
if calibrate_constants:
self.calibrate(agent_set_year1, agent_set_year2, agents_index)
if attributes_switched:
agent_set_year1.add_attribute(name=self.choice_attribute_name.get_alias() + "_end",
data=agent_set_year1.get_attribute(self.choice_attribute_name))
agent_set_year1.add_attribute(name=original_submodel_string,
data=agent_set_year1.get_attribute(new_submodel_string))
agent_set_year1.delete_one_attribute(new_submodel_string)
return self.coefficients, results[1]
def estimate(self,
specification,
agent_set,
agents_index=None,
procedure=None,
data_objects=None,
estimate_config=None,
debuglevel=0):
""" Computes capacity if required and calls the estimate method of ChoiceModel.
See ChoiceModel.estimate for details on arguments.
"""
if agents_index == None:
agents_index = arange(agent_set.size())
if agents_index.size <= 0:
logger.log_status("Nothing to be done.")
return (None, None)
if estimate_config == None:
estimate_config = Resources()
self.estimate_config = estimate_config.merge_with_defaults(
self.estimate_config)
if data_objects is not None:
self.dataset_pool.add_datasets_if_not_included(data_objects)
if self.location_id_string is not None:
agent_set.compute_variables(self.location_id_string,
dataset_pool=self.dataset_pool)
capacity_for_estimation = None
if self.estimate_config.get("compute_capacity_flag", False):
capacity_string_for_estimation = self.estimate_config.get(
"capacity_string", None)
capacity_for_estimation = self.determine_capacity(
capacity_string=capacity_string_for_estimation,
agent_set=agent_set,
agents_index=agents_index)
self.estimate_config.merge({"capacity": capacity_for_estimation})
return ChoiceModel.estimate(self,
specification,
agent_set,
agents_index,
procedure,
estimate_config=self.estimate_config,
debuglevel=debuglevel)
from opus_core.equation_specification import EquationSpecification
specification = EquationSpecification(
coefficients = array([
"beta01", "beta12", "beta03", "beta13"
]),
variables = array([
"constant","household.persons", "constant", "household.persons"
]),
equations = array([
1, 2, 3, 3
])
)
households.add_primary_attribute(data=[1,2,2,2,1,3,3,1,2,1], name="choice_id")
coefficients, other_results = choicemodel.estimate(specification,
households, procedure="opus_core.bhhh_mnl_estimation")
#
# Uncomment to output mycoef.tab
#
#coefficients.write(out_storage=storage, out_table_name="mycoef")
from numpy.random import seed
seed(1)
choices = choicemodel.run(
specification, coefficients, households, debuglevel=1)
households.modify_attribute(name="choice_id", data=choices)
from opus_core.coefficients import Coefficients
coefficients = Coefficients(
names=array(["beta01", "beta12", "beta03", "beta13"]),
def estimate(self, specification, *args, **kwargs):
self.init_membership_in_nests()
# This is because there will be __logsum_ variables in the specification when configured from the GUI,
# in order to define starting values. They are not supposed to be included there.
self.delete_logsum_from_specification(specification)
return ChoiceModel.estimate(self, specification, *args, **kwargs)
def estimate(self, specification, *args, **kwargs):
self.init_membership_in_nests()
# This is because there will be __logsum_ variables in the specification when configured from the GUI,
# in order to define starting values. They are not supposed to be included there.
self.delete_logsum_from_specification(specification)
return ChoiceModel.estimate(self, specification, *args, **kwargs)
from opus_core.equation_specification import EquationSpecification
specification = EquationSpecification(
coefficients = array([
"beta01", "beta12", "beta03", "beta13"
]),
variables = array([
"constant","household.persons", "constant", "household.persons"
]),
equations = array([
1, 2, 3, 3
])
)
households.add_primary_attribute(data=[1,2,2,2,1,3,3,1,2,1], name="choice_id")
coefficients, other_results = choicemodel.estimate(specification,
households, procedure="opus_core.bhhh_mnl_estimation")
#
# Uncomment to output mycoef.tab
#
#coefficients.write(out_storage=storage, out_table_name="mycoef")
from numpy.random import seed
seed(1)
choices = choicemodel.run(
specification, coefficients, households, debuglevel=1)
households.modify_attribute(name="choice_id", data=choices)
from opus_core.coefficients import Coefficients
coefficients = Coefficients(
names=array(["beta01", "beta12", "beta03", "beta13"]),
