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 estimate(self, specification, agent_set, agents_index=None, **kwargs):
if agents_index is None:
agents_index = arange(agent_set.size())
# filter out agents for this group
new_agents_index = self.group_member.get_index_of_my_agents(agent_set, agents_index)
return AgentLocationChoiceModel.estimate(
self, specification, agent_set, agents_index=agents_index[new_agents_index], **kwargs
)
def run(self, *args, **kwargs):
if 'agent_set' in args and args['agent_set'] is None:
logger.log_status("Development project dataset is empty. Skip DPLCM")
return
elif kwargs.has_key('agent_set') and kwargs['agent_set'] is None:
logger.log_status("Development project dataset is empty. Skip DPLCM")
return
else:
return AgentLocationChoiceModel.run(self, *args, **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=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 run(self, *args, **kwargs):
if 'agent_set' in args and args['agent_set'] is None:
logger.log_status(
"Development project dataset is empty. Skip DPLCM")
return
elif kwargs.has_key('agent_set') and kwargs['agent_set'] is None:
logger.log_status(
"Development project dataset is empty. Skip DPLCM")
return
else:
return AgentLocationChoiceModel.run(self, *args, **kwargs)
def estimate(self, specification, agent_set, agents_index=None, **kwargs):
if agents_index is None:
agents_index = arange(agent_set.size())
# filter out agents for this group
new_agents_index = self.group_member.get_index_of_my_agents(
agent_set, agents_index)
return AgentLocationChoiceModel.estimate(
self,
specification,
agent_set,
agents_index=agents_index[new_agents_index],
**kwargs)
def prepare_for_estimate(self, estimation_storage, agents_for_estimation_table, agent_set,
agents_filter=None, households_for_estimation_table=None, **kwargs):
estimation_set = Dataset(in_storage = estimation_storage,
in_table_name=agents_for_estimation_table,
id_name=agent_set.get_id_name(), dataset_name=agent_set.get_dataset_name())
self.dataset_pool.replace_dataset('person', estimation_set)
if households_for_estimation_table is not None:
hhs = HouseholdDataset(in_storage=estimation_storage, in_table_name='households_for_estimation')
self.dataset_pool.replace_dataset('household', hhs)
spec, index = AgentLocationChoiceModel.prepare_for_estimate(self, estimation_set, **kwargs)
if agents_filter is not None:
filter_condition = estimation_set.compute_variables(agents_filter,
dataset_pool= self.dataset_pool)
index = intersect1d(where(filter_condition)[0], index)
return (spec, index, estimation_set)
def prepare_for_estimate(
self,
add_member_prefix=True,
specification_dict=None,
specification_storage=None,
specification_table=None,
**kwargs
):
if add_member_prefix:
specification_table = self.group_member.add_member_prefix_to_table_name(specification_table)
if "movers_variable" in kwargs.keys():
kwargs["movers_variable"] = re.sub("SSS", self.group_member.get_member_name(), kwargs["movers_variable"])
return AgentLocationChoiceModel.prepare_for_estimate(
self, specification_dict, specification_storage, specification_table, **kwargs
)
def prepare_for_estimate(self,
add_member_prefix=True,
specification_dict=None,
specification_storage=None,
specification_table=None,
**kwargs):
if add_member_prefix:
specification_table = self.group_member.add_member_prefix_to_table_name(
specification_table)
if 'movers_variable' in kwargs.keys():
kwargs['movers_variable'] = re.sub(
'SSS', self.group_member.get_member_name(),
kwargs['movers_variable'])
return AgentLocationChoiceModel.prepare_for_estimate(
self, specification_dict, specification_storage,
specification_table, **kwargs)
def run(self, specification, coefficients, agent_set, agents_index=None, **kwargs):
if agents_index is None:
agents_index = arange(agent_set.size())
data_objects = kwargs.get("data_objects", {})
if data_objects is not None:
self.dataset_pool.add_datasets_if_not_included(data_objects)
# filter out agents for this group
new_agents_index = self.group_member.get_index_of_my_agents(
agent_set, agents_index, dataset_pool=self.dataset_pool
)
choices = AgentLocationChoiceModel.run(
self, specification, coefficients, agent_set, agents_index=agents_index[new_agents_index], **kwargs
)
result = resize(array([-1], dtype=choices.dtype), agents_index.size)
result[new_agents_index] = choices
return result
def prepare_for_run(self,
add_member_prefix=True,
specification_storage=None,
specification_table=None,
coefficients_storage=None,
coefficients_table=None,
**kwargs):
if add_member_prefix:
specification_table, coefficients_table = \
self.group_member.add_member_prefix_to_table_names([specification_table, coefficients_table])
return AgentLocationChoiceModel.prepare_for_run(
self,
specification_storage=specification_storage,
specification_table=specification_table,
coefficients_storage=coefficients_storage,
coefficients_table=coefficients_table,
**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 prepare_for_run(
self,
add_member_prefix=True,
specification_storage=None,
specification_table=None,
coefficients_storage=None,
coefficients_table=None,
**kwargs
):
if add_member_prefix:
specification_table, coefficients_table = self.group_member.add_member_prefix_to_table_names(
[specification_table, coefficients_table]
)
return AgentLocationChoiceModel.prepare_for_run(
self,
specification_storage=specification_storage,
specification_table=specification_table,
coefficients_storage=coefficients_storage,
coefficients_table=coefficients_table,
**kwargs
)
def run(self,
specification,
coefficients,
agent_set,
agents_index=None,
**kwargs):
if agents_index is None:
agents_index = arange(agent_set.size())
data_objects = kwargs.get("data_objects", {})
if data_objects is not None:
self.dataset_pool.add_datasets_if_not_included(data_objects)
# filter out agents for this group
new_agents_index = self.group_member.get_index_of_my_agents(
agent_set, agents_index, dataset_pool=self.dataset_pool)
choices = AgentLocationChoiceModel.run(
self,
specification,
coefficients,
agent_set,
agents_index=agents_index[new_agents_index],
**kwargs)
result = resize(array([-1], dtype=choices.dtype), agents_index.size)
result[new_agents_index] = choices
return result
def create_interaction_datasets(self, agent_set, agents_index, config, submodels=[-2], **kwargs):
"""Like the parent method but it also deals with filtering by groups.
"""
def mywhere(x, index):
i = where(index == x)[0]
if i.size == 0:
i=-1
return i
nchoices = self.get_choice_set_size()
if nchoices <> self.choice_set.size() or self.filter is None:
return AgentLocationChoiceModel.create_interaction_datasets(self, agent_set, agents_index, config, submodels, **kwargs)
# apply filter without doing sampling
groups = [-2]
group_values = None
if config.get("filter_by_groups", False):
group_var = config.get("group_definition_for_filtering_alternatives", None)
if group_var is None:
raise 'No group variable defined for filtering alternatives. Set "group_definition_for_filtering_alternatives" in run_config/estimate_config.'
else:
group_values = agent_set.compute_variables([group_var], dataset_pool=self.dataset_pool)[agents_index]
groups = unique(group_values)
where_group = {}
choice_index = {}
chosen_choice = {}
maxsize = 0
for submodel in submodels:
where_submodel = self.observations_mapping[submodel]
is_submodel = zeros(agents_index.size, dtype='bool8')
is_submodel[where_submodel] = True
for group in groups:
if group_values is None:
where_group[(submodel, group)] = where_submodel
else:
where_group[(submodel, group)] = where(logical_and(group_values == group, is_submodel))[0]
if where_group[(submodel, group)].size==0:
continue
agents_index_in_group = agents_index[where_group[(submodel, group)]]
choice_index[(submodel, group)] = self.apply_filter(self.filter, agent_set=agent_set,
agents_index=agents_index_in_group,
submodel=submodel,
replace_dict={'SUBMODEL': submodel, 'GROUP': group})
if choice_index[(submodel, group)] is not None and choice_index[(submodel, group)].size == 0:
logger.log_error("There is no alternative that passes filter %s for SUBMODEL=%s and GROUP=%s; %s agents with id %s will remain unplaced." % \
(self.filter, submodel, group, agents_index_in_group.size, agent_set.get_id_attribute()[agents_index_in_group]))
continue
if config.get('include_chosen_choice', False):
chosen = self.choice_set.get_id_index(id=agent_set.get_attribute_by_index(self.choice_set.get_id_name()[0],
agents_index_in_group))
chosen_choice[(submodel, group)] = apply_along_axis(mywhere, 1, chosen[:,newaxis], choice_index[(submodel, group)])
maxsize = maximum(maxsize, choice_index[(submodel, group)].size)
filter_index = -1 + zeros((agents_index.size, maxsize), dtype="int32")
for submodel, group in choice_index.keys():
filter_index[where_group[(submodel, group)],0:choice_index[(submodel, group)].size] = choice_index[(submodel, group)][newaxis, :].repeat(where_group[(submodel, group)].size, axis=0)
self.model_interaction.create_interaction_datasets(agents_index, filter_index)
if config.get('include_chosen_choice', False):
chosen_choice_attr = zeros((agents_index.size, maxsize), dtype="bool8")
for submodel, group in chosen_choice.keys():
chosen_choice_attr[where_group[(submodel, group)], chosen_choice[(submodel, group)].flat] = True
self.model_interaction.interaction_dataset.add_attribute(data=chosen_choice_attr, name="chosen_choice")
self.update_choice_set_size(maxsize)
return
def estimate(self, specification, *args, **kwargs):
HierarchicalChoiceModel.init_membership_in_nests(self)
HierarchicalChoiceModel.delete_logsum_from_specification(self, specification)
return AgentLocationChoiceModel.estimate(self, specification, *args, **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 estimate_step(self):
self.set_correct_for_sampling()
result = AgentLocationChoiceModel.estimate_step(self)
HierarchicalChoiceModel.add_logsum_to_coefficients(self, result)
return result
def estimate(self, specification, *args, **kwargs):
HierarchicalChoiceModel.init_membership_in_nests(self)
HierarchicalChoiceModel.delete_logsum_from_specification(self, specification)
return AgentLocationChoiceModel.estimate(self, specification, *args, **kwargs)
def run_chunk(self, agents_index, agent_set, specification, coefficients):
HierarchicalChoiceModel.add_logsum_to_specification(self, specification, coefficients)
HierarchicalChoiceModel.init_membership_in_nests(self)
return AgentLocationChoiceModel.run_chunk(self, agents_index, agent_set, specification, coefficients)
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 run_chunk(self, agents_index, agent_set, specification, coefficients):
HierarchicalChoiceModel.add_logsum_to_specification(self, specification, coefficients)
HierarchicalChoiceModel.init_membership_in_nests(self)
return AgentLocationChoiceModel.run_chunk(self, agents_index, agent_set, specification, coefficients)
def estimate_step(self):
self.set_correct_for_sampling()
result = AgentLocationChoiceModel.estimate_step(self)
HierarchicalChoiceModel.add_logsum_to_coefficients(self, result)
return result
