def estimate(self, *args, **kargs):
agent_set = kargs["agent_set"]
data_objects = kargs.get("data_objects", {})
data_objects[agent_set.get_dataset_name()] = agent_set
estimate_config = kargs.get("estimate_config", {})
self.units_full_name = "%s.%s.%s" % (estimate_config.get("agent_variables_package", "urbansim"),
agent_set.get_dataset_name(), self.units)
agent_set.compute_variables(self.units_full_name, dataset_pool=self.dataset_pool, resources=Resources(data_objects))
#logger.log_status("Buildings size: %d" % (agent_set.get_attribute_by_index(self.units_full_name).sum()))
return AgentLocationChoiceModelMember.estimate(self, *args, **kargs)
def get_sampling_weights(self, config, agent_set=None, agents_index=None, **kwargs):
where_developable = AgentLocationChoiceModelMember.apply_filter(self, self.filter, None, agent_set,
agents_index)
if self.developable_maximum_unit_variable:
varlist = [self.developable_maximum_unit_variable]
else:
varlist = []
if self.developable_minimum_unit_variable is not None:
varlist.append(self.developable_minimum_unit_variable)
self.choice_set.compute_variables(varlist, dataset_pool = self.dataset_pool)
if self.developable_maximum_unit_variable is not None:
max_capacity = clip_to_zero_if_needed(self.choice_set.get_attribute_by_index(
self.developable_maximum_unit_variable, where_developable),
"get_weights_for_sampling_locations - computing developable_maximum_unit_variable")
else:
max_capacity = ones(where_developable.size) * agent_set.get_attribute(self.units).max()
if self.developable_minimum_unit_variable is not None:
min_capacity = clip_to_zero_if_needed(self.choice_set.get_attribute_by_index(
self.developable_minimum_unit_variable, where_developable),
"get_weights_for_sampling_locations - computing developable_minimum_unit_variable")
else:
min_capacity = zeros(max_capacity.size)
if max_capacity.sum(dtype="float32") == 0:
raise RuntimeError, "There are no choices with any capacity for %s buildings." % agent_set.what
#how many buildings fit in each developable location
max_building_size = sort(max_capacity)[-max(50,int(where_developable.size*0.001))]
proposed_building_sizes = agent_set.get_attribute_by_index(self.units_full_name, agents_index)
if proposed_building_sizes.max() > max_building_size: # change the size if there is not enough space
logger.log_status("Maximum building size: %s" % proposed_building_sizes.max())
self.modify_agents_size(agent_set, minimum(proposed_building_sizes,max_building_size), agents_index)
proposed_building_sizes = agent_set.get_attribute_by_index(self.units_full_name, agents_index)
if self.run_config.get("agent_units_string", None): # needs to be corrected
if VariableName(self.run_config["agent_units_string"]).get_alias() not in agent_set.get_known_attribute_names():
agent_set.compute_variables(self.run_config["agent_units_string"])
self.run_config["agent_units_all"] = agent_set.get_attribute_by_index(self.run_config["agent_units_string"], agents_index)
logger.log_warning("Not enough building capacity. Large building sizes reduced to %s" % max_building_size)
proposed_building_sizes = reshape(proposed_building_sizes, (proposed_building_sizes.size,1))
weight_array = logical_and(proposed_building_sizes >= reshape(min_capacity, (1, min_capacity.size)),
(reshape(max_capacity, (1, max_capacity.size)) >=
proposed_building_sizes))
# for memory reasons, discard columns that have only zeros
keep = where(weight_array.sum(axis=0, dtype=int32))[0]
where_developable = where_developable[keep]
weight_array = take(weight_array, keep, axis=1)
if where_developable.size <= 0:
logger.log_warning("No developable locations available.")
self.filter_index = where_developable
return weight_array
def run(self, *args, **kargs):
agent_set = kargs["agent_set"]
run_config = kargs.get("run_config", {})
self.units_full_name = "%s.%s.%s" % (run_config.get("agent_variables_package", "urbansim"),
agent_set.get_dataset_name(), self.units)
data_objects = kargs.get("data_objects",{})
if data_objects is not None:
self.dataset_pool.add_datasets_if_not_included(data_objects)
agent_set.compute_variables(self.units_full_name, dataset_pool=self.dataset_pool)
#logger.log_status("Buildings size: %d" % (agent_set.get_attribute_by_index(self.units_full_name).sum()))
return AgentLocationChoiceModelMember.run(self, *args, **kargs)
def estimate(self, *args, **kargs):
agent_set = kargs["agent_set"]
data_objects = kargs.get("data_objects", {})
data_objects[agent_set.get_dataset_name()] = agent_set
estimate_config = kargs.get("estimate_config", {})
self.units_full_name = "%s.%s.%s" % (estimate_config.get(
"agent_variables_package",
"urbansim"), agent_set.get_dataset_name(), self.units)
agent_set.compute_variables(self.units_full_name,
dataset_pool=self.dataset_pool,
resources=Resources(data_objects))
#logger.log_status("Buildings size: %d" % (agent_set.get_attribute_by_index(self.units_full_name).sum()))
return AgentLocationChoiceModelMember.estimate(self, *args, **kargs)
def run(self, *args, **kargs):
agent_set = kargs["agent_set"]
run_config = kargs.get("run_config", {})
self.units_full_name = "%s.%s.%s" % (run_config.get(
"agent_variables_package",
"urbansim"), agent_set.get_dataset_name(), self.units)
data_objects = kargs.get("data_objects", {})
if data_objects is not None:
self.dataset_pool.add_datasets_if_not_included(data_objects)
agent_set.compute_variables(self.units_full_name,
dataset_pool=self.dataset_pool)
#logger.log_status("Buildings size: %d" % (agent_set.get_attribute_by_index(self.units_full_name).sum()))
return AgentLocationChoiceModelMember.run(self, *args, **kargs)
def prepare_for_estimate_alt(self, agent_set, add_member_prefix=True, specification_dict=None, specification_storage=None,
specification_table=None, events_for_estimation_storage=None,
events_for_estimation_table=None, urbansim_constant=None, base_year=0,
building_categories=None,
location_id_variable=None, dataset_pool=None, **kwargs):
"""Remove new buildings of this type and add new projects from event history instead."""
type = self.group_member.get_member_name()
agent_set.resources["building_categories"] = building_categories
agent_set.compute_variables(["urbansim.%s.building_age" % agent_set.get_dataset_name(),
"urbansim.%s.is_building_type_%s" % (agent_set.get_dataset_name(),
type)], dataset_pool = dataset_pool)
idx_new_buildings = where(logical_and(where(ma.filled(agent_set.get_attribute("building_age"),
urbansim_constant['recent_years'] + 1) <= urbansim_constant['recent_years'], 1, 0),
agent_set.get_attribute("is_building_type_%s" % type)))[0]
# create agents for estimation
if events_for_estimation_storage is not None:
agent_set.remove_elements(idx_new_buildings)
event_set = DevelopmentEventDataset(urbansim_constant,
in_storage = events_for_estimation_storage,
in_table_name= events_for_estimation_table)
event_set.remove_non_recent_data(base_year, urbansim_constant['recent_years'])
BuildingCreator().add_events_from_history_to_existing_buildings(agent_set,
event_set, type,
self.group_member.get_member_code(),
self.units, building_categories, dataset_pool=dataset_pool)
if location_id_variable:
agent_set.compute_variables(location_id_variable, dataset_pool=dataset_pool)
if events_for_estimation_storage is None:
agent_set.compute_variables(["urbansim.%s.size_category_%s" % (agent_set.get_dataset_name(),
self.group_member.get_member_name()),
"urbansim.%s.building_age" % agent_set.get_dataset_name()],
dataset_pool = dataset_pool)
idx_new_buildings = where(ma.filled(agent_set.get_attribute("building_age"),
urbansim_constant['recent_years']+1) <= urbansim_constant['recent_years'])[0]
if (specification_dict is not None) or (specification_storage is not None):
specification, dummy = AgentLocationChoiceModelMember.prepare_for_estimate(self, add_member_prefix,
specification_dict, specification_storage,
specification_table,
location_id_variable=location_id_variable,
data_objects=dataset_pool.datasets_in_pool(), **kwargs)
else:
specification = None
return (specification, idx_new_buildings)
def prepare_for_estimate(self,
agent_set,
add_member_prefix=True,
specification_dict=None,
specification_storage=None,
specification_table=None,
urbansim_constant=None,
building_categories=None,
location_id_variable=None,
dataset_pool=None,
**kwargs):
"""Return index of buildings that are younger than 'recent_years'+2"""
type = self.group_member.get_member_name()
if location_id_variable:
agent_set.compute_variables(location_id_variable,
dataset_pool=dataset_pool)
agent_set.resources["building_categories"] = building_categories
agent_set.compute_variables("urbansim.%s.size_category_%s" %
(agent_set.get_dataset_name(),
self.group_member.get_member_name()),
dataset_pool=dataset_pool)
agent_set.compute_variables([
"urbansim.%s.building_age" % agent_set.get_dataset_name(),
"urbansim.%s.is_building_type_%s" %
(agent_set.get_dataset_name(), type)
],
dataset_pool=dataset_pool)
index = where(
logical_and(
ma.filled(agent_set.get_attribute("building_age"),
urbansim_constant['recent_years'] + 2) <=
urbansim_constant['recent_years'] + 1,
agent_set.get_attribute("is_building_type_%s" % type)))[0]
specification, dummy = AgentLocationChoiceModelMember.prepare_for_estimate(
self,
add_member_prefix,
specification_dict,
specification_storage,
specification_table,
location_id_variable=location_id_variable,
data_objects=dataset_pool.datasets_in_pool(),
**kwargs)
return (specification, index)
def prepare_for_estimate(self, agent_set, add_member_prefix=True, specification_dict=None, specification_storage=None,
specification_table=None, urbansim_constant=None, building_categories=None,
location_id_variable=None, dataset_pool=None, **kwargs):
"""Return index of buildings that are younger than 'recent_years'+2"""
type = self.group_member.get_member_name()
if location_id_variable:
agent_set.compute_variables(location_id_variable, dataset_pool=dataset_pool)
agent_set.resources["building_categories"] = building_categories
agent_set.compute_variables("urbansim.%s.size_category_%s" % (agent_set.get_dataset_name(), self.group_member.get_member_name()),
dataset_pool=dataset_pool)
agent_set.compute_variables(["urbansim.%s.building_age" % agent_set.get_dataset_name(),
"urbansim.%s.is_building_type_%s" % (agent_set.get_dataset_name(),
type)], dataset_pool=dataset_pool)
index = where(logical_and(ma.filled(agent_set.get_attribute("building_age"),
urbansim_constant['recent_years']+2) <= urbansim_constant['recent_years']+1,
agent_set.get_attribute("is_building_type_%s" % type)))[0]
specification, dummy = AgentLocationChoiceModelMember.prepare_for_estimate(self, add_member_prefix,
specification_dict, specification_storage,
specification_table,
location_id_variable=location_id_variable,
data_objects=dataset_pool.datasets_in_pool(),
**kwargs)
return (specification, index)
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 prepare_for_estimate_alt(self,
agent_set,
add_member_prefix=True,
specification_dict=None,
specification_storage=None,
specification_table=None,
events_for_estimation_storage=None,
events_for_estimation_table=None,
urbansim_constant=None,
base_year=0,
building_categories=None,
location_id_variable=None,
dataset_pool=None,
**kwargs):
"""Remove new buildings of this type and add new projects from event history instead."""
type = self.group_member.get_member_name()
agent_set.resources["building_categories"] = building_categories
agent_set.compute_variables([
"urbansim.%s.building_age" % agent_set.get_dataset_name(),
"urbansim.%s.is_building_type_%s" %
(agent_set.get_dataset_name(), type)
],
dataset_pool=dataset_pool)
idx_new_buildings = where(
logical_and(
where(
ma.filled(agent_set.get_attribute("building_age"),
urbansim_constant['recent_years'] + 1) <=
urbansim_constant['recent_years'], 1, 0),
agent_set.get_attribute("is_building_type_%s" % type)))[0]
# create agents for estimation
if events_for_estimation_storage is not None:
agent_set.remove_elements(idx_new_buildings)
event_set = DevelopmentEventDataset(
urbansim_constant,
in_storage=events_for_estimation_storage,
in_table_name=events_for_estimation_table)
event_set.remove_non_recent_data(base_year,
urbansim_constant['recent_years'])
BuildingCreator().add_events_from_history_to_existing_buildings(
agent_set,
event_set,
type,
self.group_member.get_member_code(),
self.units,
building_categories,
dataset_pool=dataset_pool)
if location_id_variable:
agent_set.compute_variables(location_id_variable,
dataset_pool=dataset_pool)
if events_for_estimation_storage is None:
agent_set.compute_variables([
"urbansim.%s.size_category_%s" %
(agent_set.get_dataset_name(),
self.group_member.get_member_name()),
"urbansim.%s.building_age" % agent_set.get_dataset_name()
],
dataset_pool=dataset_pool)
idx_new_buildings = where(
ma.filled(agent_set.get_attribute(
"building_age"), urbansim_constant['recent_years'] +
1) <= urbansim_constant['recent_years'])[0]
if (specification_dict is not None) or (specification_storage
is not None):
specification, dummy = AgentLocationChoiceModelMember.prepare_for_estimate(
self,
add_member_prefix,
specification_dict,
specification_storage,
specification_table,
location_id_variable=location_id_variable,
data_objects=dataset_pool.datasets_in_pool(),
**kwargs)
else:
specification = None
return (specification, idx_new_buildings)
def __init__(
self,
group_member,
location_set,
agents_grouping_attribute='building.building_type_id',
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",
capacity_string="is_developable_for_buildings_UNITS",
estimation_weight_string=None,
developable_maximum_unit_variable="developable_maximum_buildings_UNITS",
developable_minimum_unit_variable="developable_minimum_UNITS", # None means don't consider any minimum. For default, set it to empty string
number_of_agents_string="buildings_SSS_space",
number_of_units_string="total_maximum_development_SSS",
sample_proportion_locations=None,
sample_size_locations=30,
estimation_size_agents=1.0,
compute_capacity_flag=True,
filter="developable_maximum_buildings_UNITS", # None doesn't use any filter
submodel_string="size_category_SSS", # Put here None, if no submodels should be used.
nrecords_per_chunk_for_estimation_sampling=1000, # put here None, if everything in 1 chunk
location_id_string=None,
run_config=None,
estimate_config=None,
debuglevel=0,
dataset_pool=None,
variable_package="urbansim"):
""" 'group_member' is of type ModelGroupMember.
"""
group_member_name = group_member.get_member_name()
units = group_member.get_attribute_value("units")[0]
if capacity_string:
capacity_string = re.sub("UNITS", units, capacity_string)
capacity_string = re.sub("SSS", group_member_name, capacity_string)
if filter:
filter = re.sub("UNITS", units, filter)
filter = re.sub("SSS", group_member_name, filter)
if developable_maximum_unit_variable:
developable_maximum_unit_variable = re.sub(
"UNITS", units, developable_maximum_unit_variable)
developable_maximum_unit_variable = re.sub(
"SSS", group_member_name, developable_maximum_unit_variable)
if developable_minimum_unit_variable == "":
developable_minimum_unit_variable = "developable_minimum_%s" % units
elif developable_minimum_unit_variable:
developable_minimum_unit_variable = re.sub(
"UNITS", units, developable_minimum_unit_variable)
developable_minimum_unit_variable = re.sub(
"SSS", group_member_name, developable_minimum_unit_variable)
if submodel_string:
submodel_string = re.sub("UNITS", units, submodel_string)
submodel_string = re.sub("SSS", group_member_name, submodel_string)
if number_of_agents_string:
number_of_agents_string = re.sub('UNITS', units,
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('UNITS', units,
number_of_units_string)
number_of_units_string = re.sub('SSS', group_member_name,
number_of_units_string)
# create full names from (possibly) short names
tmpdict = Resources({
"filter": filter,
"max": developable_maximum_unit_variable,
"min": developable_minimum_unit_variable
})
self.add_prefix_to_variable_names(["filter", "max", "min"],
location_set,
variable_package,
resources=tmpdict)
filter = tmpdict["filter"]
if tmpdict["max"]:
self.developable_maximum_unit_variable = VariableName(
tmpdict["max"])
else:
self.developable_maximum_unit_variable = None
if tmpdict["min"]:
self.developable_minimum_unit_variable = VariableName(
tmpdict["min"])
else:
self.developable_minimum_unit_variable = None
self.project_type = group_member_name
self.units = units
run_config = merge_resources_with_defaults(run_config, [
("sample_proportion_locations", sample_proportion_locations),
("sample_size_locations", sample_size_locations),
("compute_capacity_flag", compute_capacity_flag),
("capacity_string", capacity_string),
("agent_units_string", "urbansim.building.building_size"),
("number_of_agents_string", number_of_agents_string),
("number_of_units_string", number_of_units_string),
])
estimate_config = merge_resources_with_defaults(
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),
("capacity_string", capacity_string),
("compute_capacity_flag", compute_capacity_flag)])
if nrecords_per_chunk_for_estimation_sampling:
estimate_config[
"chunk_specification_for_estimation"] = ChunkSpecification({
"records_per_chunk":
nrecords_per_chunk_for_estimation_sampling
})
AgentLocationChoiceModelMember.__init__(
self,
group_member,
location_set,
agents_grouping_attribute,
model_name="Building Location Choice Model",
short_name="BLCM",
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 get_sampling_weights(self,
config,
agent_set=None,
agents_index=None,
**kwargs):
where_developable = AgentLocationChoiceModelMember.apply_filter(
self, self.filter, None, agent_set, agents_index)
if self.developable_maximum_unit_variable:
varlist = [self.developable_maximum_unit_variable]
else:
varlist = []
if self.developable_minimum_unit_variable is not None:
varlist.append(self.developable_minimum_unit_variable)
self.choice_set.compute_variables(varlist,
dataset_pool=self.dataset_pool)
if self.developable_maximum_unit_variable is not None:
max_capacity = clip_to_zero_if_needed(
self.choice_set.get_attribute_by_index(
self.developable_maximum_unit_variable, where_developable),
"get_weights_for_sampling_locations - computing developable_maximum_unit_variable"
)
else:
max_capacity = ones(
where_developable.size) * agent_set.get_attribute(
self.units).max()
if self.developable_minimum_unit_variable is not None:
min_capacity = clip_to_zero_if_needed(
self.choice_set.get_attribute_by_index(
self.developable_minimum_unit_variable, where_developable),
"get_weights_for_sampling_locations - computing developable_minimum_unit_variable"
)
else:
min_capacity = zeros(max_capacity.size)
if max_capacity.sum(dtype="float32") == 0:
raise RuntimeError, "There are no choices with any capacity for %s buildings." % agent_set.what
#how many buildings fit in each developable location
max_building_size = sort(
max_capacity)[-max(50, int(where_developable.size * 0.001))]
proposed_building_sizes = agent_set.get_attribute_by_index(
self.units_full_name, agents_index)
if proposed_building_sizes.max(
) > max_building_size: # change the size if there is not enough space
logger.log_status("Maximum building size: %s" %
proposed_building_sizes.max())
self.modify_agents_size(
agent_set, minimum(proposed_building_sizes, max_building_size),
agents_index)
proposed_building_sizes = agent_set.get_attribute_by_index(
self.units_full_name, agents_index)
if self.run_config.get("agent_units_string",
None): # needs to be corrected
if VariableName(
self.run_config["agent_units_string"]).get_alias(
) not in agent_set.get_known_attribute_names():
agent_set.compute_variables(
self.run_config["agent_units_string"])
self.run_config[
"agent_units_all"] = agent_set.get_attribute_by_index(
self.run_config["agent_units_string"], agents_index)
logger.log_warning(
"Not enough building capacity. Large building sizes reduced to %s"
% max_building_size)
proposed_building_sizes = reshape(proposed_building_sizes,
(proposed_building_sizes.size, 1))
weight_array = logical_and(
proposed_building_sizes >= reshape(min_capacity,
(1, min_capacity.size)),
(reshape(max_capacity,
(1, max_capacity.size)) >= proposed_building_sizes))
# for memory reasons, discard columns that have only zeros
keep = where(weight_array.sum(axis=0, dtype=int32))[0]
where_developable = where_developable[keep]
weight_array = take(weight_array, keep, axis=1)
if where_developable.size <= 0:
logger.log_warning("No developable locations available.")
self.filter_index = where_developable
return weight_array
def __init__(self, group_member, location_set,
agents_grouping_attribute = 'building.building_type_id',
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",
capacity_string = "is_developable_for_buildings_UNITS",
estimation_weight_string = None,
developable_maximum_unit_variable = "developable_maximum_buildings_UNITS",
developable_minimum_unit_variable = "developable_minimum_UNITS", # None means don't consider any minimum. For default, set it to empty string
number_of_agents_string = "buildings_SSS_space",
number_of_units_string = "total_maximum_development_SSS",
sample_proportion_locations = None,
sample_size_locations = 30,
estimation_size_agents = 1.0,
compute_capacity_flag = True,
filter = "developable_maximum_buildings_UNITS", # None doesn't use any filter
submodel_string = "size_category_SSS", # Put here None, if no submodels should be used.
nrecords_per_chunk_for_estimation_sampling = 1000, # put here None, if everything in 1 chunk
location_id_string = None,
run_config = None, estimate_config=None, debuglevel=0, dataset_pool=None, variable_package = "urbansim"):
""" 'group_member' is of type ModelGroupMember.
"""
group_member_name = group_member.get_member_name()
units = group_member.get_attribute_value("units")[0]
if capacity_string:
capacity_string = re.sub("UNITS", units, capacity_string)
capacity_string = re.sub("SSS", group_member_name, capacity_string)
if filter:
filter = re.sub("UNITS", units, filter)
filter = re.sub("SSS", group_member_name, filter)
if developable_maximum_unit_variable:
developable_maximum_unit_variable = re.sub("UNITS", units, developable_maximum_unit_variable)
developable_maximum_unit_variable = re.sub("SSS", group_member_name, developable_maximum_unit_variable)
if developable_minimum_unit_variable == "":
developable_minimum_unit_variable = "developable_minimum_%s" % units
elif developable_minimum_unit_variable:
developable_minimum_unit_variable = re.sub("UNITS", units, developable_minimum_unit_variable)
developable_minimum_unit_variable = re.sub("SSS", group_member_name, developable_minimum_unit_variable)
if submodel_string:
submodel_string = re.sub("UNITS", units, submodel_string)
submodel_string = re.sub("SSS", group_member_name, submodel_string)
if number_of_agents_string:
number_of_agents_string = re.sub('UNITS', units, 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('UNITS', units, number_of_units_string)
number_of_units_string = re.sub('SSS', group_member_name, number_of_units_string)
# create full names from (possibly) short names
tmpdict = Resources({"filter": filter, "max": developable_maximum_unit_variable,
"min": developable_minimum_unit_variable})
self.add_prefix_to_variable_names(["filter", "max", "min"], location_set, variable_package, resources=tmpdict)
filter = tmpdict["filter"]
if tmpdict["max"]:
self.developable_maximum_unit_variable = VariableName(tmpdict["max"])
else:
self.developable_maximum_unit_variable = None
if tmpdict["min"]:
self.developable_minimum_unit_variable = VariableName(tmpdict["min"])
else:
self.developable_minimum_unit_variable = None
self.project_type = group_member_name
self.units = units
run_config = merge_resources_with_defaults(run_config, [
("sample_proportion_locations", sample_proportion_locations),
("sample_size_locations", sample_size_locations),
("compute_capacity_flag", compute_capacity_flag),
("capacity_string", capacity_string),
("agent_units_string", "urbansim.building.building_size"),
("number_of_agents_string", number_of_agents_string),
("number_of_units_string", number_of_units_string),
])
estimate_config = merge_resources_with_defaults(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),
("capacity_string", capacity_string),
("compute_capacity_flag", compute_capacity_flag)])
if nrecords_per_chunk_for_estimation_sampling:
estimate_config["chunk_specification_for_estimation"] = ChunkSpecification({
"records_per_chunk": nrecords_per_chunk_for_estimation_sampling})
AgentLocationChoiceModelMember.__init__(self, group_member, location_set,
agents_grouping_attribute,
model_name = "Building Location Choice Model",
short_name = "BLCM",
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)
