def __init__(
self,
location_set,
nested_structure=None,
stratum=None,
model_name=None,
short_name=None,
sampler="opus_core.samplers.stratified_sampler",
utilities="opus_core.hierarchical_linear_utilities",
probabilities="opus_core.nl_probabilities",
**kwargs
):
AgentLocationChoiceModel.__init__(
self,
location_set=location_set,
model_name=model_name,
short_name=short_name,
sampler=sampler,
utilities=utilities,
probabilities=probabilities,
**kwargs
)
HierarchicalChoiceModel.create_nested_and_tree_structure(self, nested_structure, stratum, **kwargs)
self.model_interaction = ModelInteractionHierLCM(
self, kwargs.get("interaction_pkg", "urbansim"), self.choice_set
)
def __init__(self, location_set,
sampler = "opus_core.samplers.weighted_sampler",
utilities = "opus_core.linear_utilities",
choices = "opus_core.random_choices",
probabilities = "opus_core.mnl_probabilities",
estimation = "opus_core.bhhh_mnl_estimation",
capacity_string = "vacant_residential_units",
estimation_weight_string = "residential_units",
simulation_weight_string = None, # if this is None, weights are proportional to the capacity
number_of_agents_string = "number_of_households",
number_of_units_string = "residential_units",
sample_proportion_locations = None,
sample_size_locations = 30,
estimation_size_agents = 1.0,
compute_capacity_flag = True,
filter=None,
submodel_string = None, location_id_string = None,
demand_string = None, # if not None, the aggregate demand for locations will be stored in this attribute
run_config = None, estimate_config=None, debuglevel=0, dataset_pool=None,
variable_package="urbansim",
model_name=None,
model_short_name=None,
**kwargs):
run_config = merge_resources_if_not_None(run_config, [
("sample_proportion_locations", sample_proportion_locations),
("sample_size_locations", sample_size_locations),
("compute_capacity_flag", compute_capacity_flag),
("capacity_string", capacity_string),
("number_of_agents_string", number_of_agents_string),
("number_of_units_string", number_of_units_string),
("weights_for_simulation_string", simulation_weight_string),
("demand_string", demand_string)
])
estimate_config = merge_resources_if_not_None(estimate_config, [
("estimation", estimation),
("sample_proportion_locations", sample_proportion_locations),
("sample_size_locations", sample_size_locations),
("estimation_size_agents", estimation_size_agents),
("weights_for_estimation_string", estimation_weight_string)])
if model_name is not None:
self.model_name = model_name
if model_short_name is not None:
self.model_short_name = model_short_name
AgentLocationChoiceModel.__init__(self, location_set,
model_name=self.model_name,
short_name=self.model_short_name,
sampler=sampler,
utilities=utilities,
probabilities=probabilities,
choices=choices,
filter=filter,
submodel_string=submodel_string,
location_id_string=location_id_string,
run_config=run_config,
estimate_config=estimate_config,
debuglevel=debuglevel, dataset_pool=dataset_pool,
variable_package=variable_package,
**kwargs)
def __init__(self, location_set, nested_structure=None, stratum=None, model_name=None, short_name=None,
sampler="opus_core.samplers.stratified_sampler",
utilities="opus_core.hierarchical_linear_utilities",
probabilities="opus_core.nl_probabilities", **kwargs):
AgentLocationChoiceModel.__init__(self, location_set=location_set, model_name=model_name,
short_name=short_name, sampler=sampler, utilities=utilities,
probabilities=probabilities, **kwargs)
HierarchicalChoiceModel.create_nested_and_tree_structure(self, nested_structure, stratum, **kwargs)
self.model_interaction = ModelInteractionHierLCM(self, kwargs.get('interaction_pkg',"urbansim"), self.choice_set)
def __init__(self, group_member, location_set, agents_grouping_attribute, model_name, short_name, **kwargs):
""" 'group_member' is of type ModelGroupMember. 'agents_grouping_attribute' is attribute of the agent_set
(passed to the 'run' and 'estimate' method) that is used for grouping.
"""
self.group_member = group_member
group_member_name = group_member.get_member_name()
self.group_member.set_agents_grouping_attribute(agents_grouping_attribute)
AgentLocationChoiceModel.__init__(
self,
location_set,
model_name="%s %s" % (group_member_name.capitalize(), model_name),
short_name="%s %s" % (group_member_name.capitalize(), short_name),
**kwargs
)
def __init__(self, group_member, location_set, agents_grouping_attribute,
model_name, short_name, **kwargs):
""" 'group_member' is of type ModelGroupMember. 'agents_grouping_attribute' is attribute of the agent_set
(passed to the 'run' and 'estimate' method) that is used for grouping.
"""
self.group_member = group_member
group_member_name = group_member.get_member_name()
self.group_member.set_agents_grouping_attribute(
agents_grouping_attribute)
AgentLocationChoiceModel.__init__(
self,
location_set,
model_name="%s %s" % (group_member_name.capitalize(), model_name),
short_name="%s %s" % (group_member_name.capitalize(), short_name),
**kwargs)
def __init__(
self,
location_set,
sampler="opus_core.samplers.weighted_sampler",
utilities="opus_core.linear_utilities",
choices="opus_core.random_choices",
probabilities="opus_core.mnl_probabilities",
estimation="opus_core.bhhh_mnl_estimation",
capacity_string="vacant_residential_units",
estimation_weight_string="residential_units",
simulation_weight_string=None, # if this is None, weights are proportional to the capacity
number_of_agents_string="number_of_households",
number_of_units_string="residential_units",
sample_proportion_locations=None,
sample_size_locations=30,
estimation_size_agents=1.0,
compute_capacity_flag=True,
filter=None,
submodel_string=None,
location_id_string=None,
demand_string=None, # if not None, the aggregate demand for locations will be stored in this attribute
run_config=None,
estimate_config=None,
debuglevel=0,
dataset_pool=None,
variable_package="urbansim"):
run_config = merge_resources_if_not_None(
run_config,
[("sample_proportion_locations", sample_proportion_locations),
("sample_size_locations", sample_size_locations),
("compute_capacity_flag", compute_capacity_flag),
("capacity_string", capacity_string),
("number_of_agents_string", number_of_agents_string),
("number_of_units_string", number_of_units_string),
("weights_for_simulation_string", simulation_weight_string),
("demand_string", demand_string)])
estimate_config = merge_resources_if_not_None(
estimate_config,
[("estimation", estimation),
("sample_proportion_locations", sample_proportion_locations),
("sample_size_locations", sample_size_locations),
("estimation_size_agents", estimation_size_agents),
("weights_for_estimation_string", estimation_weight_string)])
AgentLocationChoiceModel.__init__(
self,
location_set,
model_name=self.model_name,
short_name=self.model_short_name,
sampler=sampler,
utilities=utilities,
probabilities=probabilities,
choices=choices,
filter=filter,
submodel_string=submodel_string,
location_id_string=location_id_string,
run_config=run_config,
estimate_config=estimate_config,
debuglevel=debuglevel,
dataset_pool=dataset_pool,
variable_package=variable_package)
def __init__(self, location_set,
sampler = "opus_core.samplers.weighted_sampler",
utilities = "opus_core.linear_utilities",
choices = "opus_core.random_choices",
probabilities = "opus_core.mnl_probabilities",
estimation = "opus_core.bhhh_mnl_estimation",
capacity_string = "clip_to_zero((parcel.parcel_acres*parcel.max_du_acre*(1-parcel.pct_undevelopable)) - (parcel.aggregate(building.non_residential_sqft)/1500.0) - parcel.aggregate(building.residential_units))",
estimation_weight_string = "parcel_acres",
simulation_weight_string = None, # if this is None, weights are proportional to the capacity
###number_of_agents_string = "building.residential_units",
#number_of_units_string = "clip_to_zero((parcel.parcel_acres*parcel.max_du_acre*(1-parcel.pct_undevelopable)) - (parcel.aggregate(building.non_residential_sqft)/1500.0) - parcel.aggregate(building.residential_units))",
agent_units_string = "building.residential_units",
sample_proportion_locations = None,
sample_size_locations = 250,
estimation_size_agents = 1.0,
compute_capacity_flag = True,
filter=None,
submodel_string = None, location_id_string = None,
demand_string = None, # if not None, the aggregate demand for locations will be stored in this attribute
run_config = None, estimate_config=None, debuglevel=0, dataset_pool=None,
variable_package="urbansim",
model_name=None,
model_short_name=None,
**kwargs):
run_config = merge_resources_if_not_None(run_config, [
("sample_proportion_locations", sample_proportion_locations),
("sample_size_locations", sample_size_locations),
("compute_capacity_flag", compute_capacity_flag),
("capacity_string", capacity_string),
###("number_of_agents_string", number_of_agents_string),
###("number_of_units_string", number_of_units_string),
("agent_units_string", agent_units_string),
("weights_for_simulation_string", simulation_weight_string),
("demand_string", demand_string),
("lottery_max_iterations", 20)
])
estimate_config = merge_resources_if_not_None(estimate_config, [
("estimation", estimation),
("sample_proportion_locations", sample_proportion_locations),
("sample_size_locations", sample_size_locations),
("estimation_size_agents", estimation_size_agents),
("weights_for_estimation_string", estimation_weight_string)])
if model_name is not None:
self.model_name = model_name
if model_short_name is not None:
self.model_short_name = model_short_name
AgentLocationChoiceModel.__init__(self, location_set,
model_name=self.model_name,
short_name=self.model_short_name,
sampler=sampler,
utilities=utilities,
probabilities=probabilities,
choices=choices,
filter=filter,
submodel_string=submodel_string,
location_id_string=location_id_string,
run_config=run_config,
estimate_config=estimate_config,
debuglevel=debuglevel, dataset_pool=dataset_pool,
variable_package=variable_package,
**kwargs)
