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 get_model(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",
sample_proportion_locations = None,
sample_size_locations = 20,
estimation_size_agents = 1.0,
compute_capacity_flag = False,
filter = None,
submodel_string = "sector_id",
run_config = None, estimate_config=None, debuglevel=0):
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)])
run_config = merge_resources_with_defaults(run_config,
[("capacity_string", self.capacity_string_default)])
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)])
estimate_config = merge_resources_with_defaults(estimate_config,
[("weights_for_estimation_string", self.estimation_weight_string_default)])
return AgentLocationChoiceModel(location_set, agent_name="job",
model_name="Employment Location Choice Model", \
short_name="ELCM", sampler=sampler, utilities=utilities,
probabilities=probabilities, choices=choices, filter=filter, submodel_string=submodel_string,
run_config=run_config, estimate_config=estimate_config,
debuglevel=debuglevel)
def __init__(self,
project_type,
location_set,
model_configuration,
sampler = "opus_core.samplers.weighted_sampler",
utilities = "opus_core.linear_utilities",
choices = "urbansim.first_agent_first_choices",
probabilities = "opus_core.mnl_probabilities",
estimation = "opus_core.bhhh_mnl_estimation",
sample_proportion_locations = None,
sample_size_locations = 30,
estimation_size_agents = 1.0,
compute_capacity_flag = True,
filter = "",
submodel_string = "size_category",
location_id_string = None,
run_config = None,
estimate_config=None,
debuglevel=0):
units = model_configuration['units']
developable_maximum_unit_variable_full_name = model_configuration['developable_maximum_unit_variable_full_name']
developable_minimum_unit_variable_full_name = model_configuration['developable_minimum_unit_variable_full_name']
default_capacity_attribute = "urbansim.gridcell.is_developable_for_%s" % units
default_filter = "urbansim.gridcell.developable_%s" % units
if filter == "":
filter = default_filter
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)])
run_config = merge_resources_with_defaults(run_config,
[("capacity_string", default_capacity_attribute)])
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)])
estimate_config = merge_resources_with_defaults(estimate_config,
[("weights_for_estimation_string", self.estimation_weight_string_default)])
DevelopmentProjectLocationChoiceModel.__init__(self, location_set,
project_type=project_type,
units=units,
developable_maximum_unit_variable_full_name=developable_maximum_unit_variable_full_name,
developable_minimum_unit_variable_full_name=developable_minimum_unit_variable_full_name,
model_name="Regional Development Project %s Location Choice Model" % project_type,
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)
def get_model(
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",
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):
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)])
run_config = merge_resources_with_defaults(
run_config,
[("capacity_string", self.capacity_string_default),
("number_of_agents_string", self.number_of_agents_string_default),
("number_of_units_string", self.number_of_units_string_default)])
if demand_string:
run_config["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)])
estimate_config = merge_resources_with_defaults(
estimate_config, [("weights_for_estimation_string",
self.estimation_weight_string_default)])
return AgentLocationChoiceModel(
location_set,
model_name="Household Location Choice Model",
short_name="HLCM",
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)
def get_model(self,
location_set,
sampler = "opus_core.samplers.weighted_sampler",
utilities = "opus_core.linear_utilities",
choices = "urbansim.first_agent_first_choices",
probabilities = "opus_core.mnl_probabilities",
estimation = "opus_core.bhhh_mnl_estimation",
sample_proportion_locations = None,
sample_size_locations = 30,
estimation_size_agents = 1.0,
compute_capacity_flag = True,
filter = "randstad.gridcell.is_developable",
submodel_string = "development_type_id",
run_config = None,
estimate_config=None,
debuglevel=0):
default_capacity_string = "randstad.gridcell.is_developable"
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)
("filter", filter)])
run_config = merge_resources_with_defaults(run_config,
[("capacity_string", default_capacity_string),
("simulation_sampling_include_current_choice", False)])
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)])
estimate_config = merge_resources_with_defaults(estimate_config,
[("weights_for_estimation_string", self.estimation_weight_string_default),
("simulation_sampling_include_current_choice", True)])
return LandUseDevelopmentLocationChoiceModel(location_set,
opus_package='randstad',
model_name="Landuse Development Location Choice Model",
sampler=sampler,
utilities=utilities,
probabilities=probabilities,
choices=choices,
filter=filter,
submodel_string=submodel_string,
run_config=run_config,
estimate_config=estimate_config,
debuglevel=debuglevel)
def get_model(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",
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):
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)])
run_config = merge_resources_with_defaults(run_config,
[("capacity_string", self.capacity_string_default),
("number_of_agents_string", self.number_of_agents_string_default),
("number_of_units_string", self.number_of_units_string_default)
])
if demand_string:
run_config["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)])
estimate_config = merge_resources_with_defaults(estimate_config,
[("weights_for_estimation_string", self.estimation_weight_string_default)])
return AgentLocationChoiceModel(location_set,
model_name="Household Location Choice Model",
short_name="HLCM",
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)
def get_model(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",
sample_proportion_locations=None,
sample_size_locations=20,
estimation_size_agents=1.0,
compute_capacity_flag=False,
filter=None,
submodel_string="sector_id",
run_config=None,
estimate_config=None,
debuglevel=0):
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)])
run_config = merge_resources_with_defaults(
run_config, [("capacity_string", self.capacity_string_default)])
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)])
estimate_config = merge_resources_with_defaults(
estimate_config, [("weights_for_estimation_string",
self.estimation_weight_string_default)])
return AgentLocationChoiceModel(location_set, agent_name="job",
model_name="Employment Location Choice Model", \
short_name="ELCM", sampler=sampler, utilities=utilities,
probabilities=probabilities, choices=choices, filter=filter, submodel_string=submodel_string,
run_config=run_config, estimate_config=estimate_config,
debuglevel=debuglevel)
def __init__(self,
probabilities = "opus_core.upc.rate_based_probabilities",
choices = "opus_core.random_choices",
model_name = None,
debuglevel=0,
resources=None
):
if model_name is not None:
self.model_name = model_name
self.debug = DebugPrinter(debuglevel)
self.upc_sequence = None
if probabilities is not None:
self.upc_sequence = UPCFactory().get_model(utilities=None,
probabilities=probabilities,
choices=choices,
debuglevel=debuglevel)
self.resources = merge_resources_if_not_None(resources)
def __init__(self,
probabilities = "urbansim.rate_based_probabilities",
choices = "opus_core.random_choices",
location_id_name="grid_id",
model_name = "Agent Relocation Model",
debuglevel=0,
resources=None
):
self.model_name = model_name
self.location_id_name = location_id_name
self.debug = DebugPrinter(debuglevel)
self.upc_sequence = None
if probabilities is not None:
self.upc_sequence = UPCFactory().get_model(utilities=None,
probabilities=probabilities,
choices=choices,
debuglevel=debuglevel)
self.resources = merge_resources_if_not_None(resources)
def __init__(self,
probabilities="urbansim.rate_based_probabilities",
choices="opus_core.random_choices",
location_id_name="grid_id",
model_name="Agent Relocation Model",
debuglevel=0,
resources=None):
self.model_name = model_name
self.location_id_name = location_id_name
self.debug = DebugPrinter(debuglevel)
self.upc_sequence = None
if probabilities is not None:
self.upc_sequence = UPCFactory().get_model(
utilities=None,
probabilities=probabilities,
choices=choices,
debuglevel=debuglevel)
self.resources = merge_resources_if_not_None(resources)
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,
group_member,
location_set,
agents_grouping_attribute='job.building_type',
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_SSS_job_space",
estimation_weight_string="total_number_of_possible_SSS_jobs",
simulation_weight_string=None, # if this is None, weights are proportional to the capacity
number_of_agents_string="number_of_SSS_jobs",
number_of_units_string="total_number_of_possible_SSS_jobs",
sample_proportion_locations=None,
sample_size_locations=30,
estimation_size_agents=1.0,
compute_capacity_flag=True,
filter=None,
submodel_string="sector_id",
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"):
""" 'group_member' is of type ModelGroupMember. All SSS in variable names are replaced by the group member name.
"""
group_member_name = group_member.get_member_name()
if capacity_string:
capacity_string = re.sub('SSS', group_member_name, capacity_string)
if estimation_weight_string:
estimation_weight_string = re.sub('SSS', group_member_name,
estimation_weight_string)
if simulation_weight_string:
simulation_weight_string = re.sub('SSS', group_member_name,
simulation_weight_string)
if number_of_agents_string:
number_of_agents_string = re.sub('SSS', group_member_name,
number_of_agents_string)
if number_of_units_string:
number_of_units_string = re.sub('SSS', group_member_name,
number_of_units_string)
if demand_string:
demand_string = re.sub('SSS', group_member_name, demand_string)
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)])
AgentLocationChoiceModelMember.__init__(
self,
group_member,
location_set,
agents_grouping_attribute,
model_name="Employment Location Choice Model",
short_name="ELCM",
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, group_member, location_set,
agents_grouping_attribute = 'job.building_type',
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_SSS_job_space",
estimation_weight_string = "total_number_of_possible_SSS_jobs",
simulation_weight_string = None, # if this is None, weights are proportional to the capacity
number_of_agents_string = "number_of_SSS_jobs",
number_of_units_string = "total_number_of_possible_SSS_jobs",
sample_proportion_locations = None,
sample_size_locations = 30,
estimation_size_agents = 1.0,
compute_capacity_flag = True,
filter = None,
submodel_string = "sector_id", 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"):
""" 'group_member' is of type ModelGroupMember. All SSS in variable names are replaced by the group member name.
"""
group_member_name = group_member.get_member_name()
if capacity_string:
capacity_string = re.sub('SSS', group_member_name, capacity_string)
if estimation_weight_string:
estimation_weight_string = re.sub('SSS', group_member_name, estimation_weight_string)
if simulation_weight_string:
simulation_weight_string = re.sub('SSS', group_member_name, simulation_weight_string)
if number_of_agents_string:
number_of_agents_string = re.sub('SSS', group_member_name, number_of_agents_string)
if number_of_units_string:
number_of_units_string = re.sub('SSS', group_member_name, number_of_units_string)
if demand_string:
demand_string = re.sub('SSS', group_member_name, demand_string)
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)])
AgentLocationChoiceModelMember.__init__(self, group_member, location_set,
agents_grouping_attribute,
model_name = "Employment Location Choice Model",
short_name = "ELCM",
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)
def get_model(self,
project_type,
location_set,
sampler = "opus_core.samplers.weighted_sampler",
utilities = "opus_core.linear_utilities",
choices = "urbansim.first_agent_first_choices",
probabilities = "opus_core.mnl_probabilities",
estimation = "opus_core.bhhh_mnl_estimation",
sample_proportion_locations = None,
sample_size_locations = 30,
estimation_size_agents = 1.0,
compute_capacity_flag = True,
filter = "",
submodel_string = "size_category",
model_configuration = None,
location_id_string = None,
run_config = None,
estimate_config = None,
debuglevel = 0,
units = '',
developable_maximum_unit_variable_full_name = '',
developable_minimum_unit_variable_full_name = '',
residential = False):
if model_configuration is not None:
units = model_configuration['units']
developable_maximum_unit_variable_full_name = model_configuration['developable_maximum_unit_variable_full_name']
developable_minimum_unit_variable_full_name = model_configuration['developable_minimum_unit_variable_full_name']
default_capacity_attribute = "urbansim.gridcell.is_developable_for_%s" % units
default_filter = "urbansim.gridcell.developable_%s" % units
if filter == "":
filter = default_filter
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)])
run_config = merge_resources_with_defaults(run_config,
[("capacity_string", default_capacity_attribute)])
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)])
estimate_config = merge_resources_with_defaults(estimate_config,
[("weights_for_estimation_string", self.estimation_weight_string_default)])
return DevelopmentProjectLocationChoiceModel(location_set,
project_type=project_type,
units=units,
developable_maximum_unit_variable_full_name=developable_maximum_unit_variable_full_name,
developable_minimum_unit_variable_full_name=developable_minimum_unit_variable_full_name,
model_name="Development Project %s Location Choice Model" % project_type,
# residential = residential,
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)
