def __init__(self,
proposal_set,
sampler="opus_core.samplers.weighted_sampler",
weight_string = None,
run_config=None,
estimate_config=None,
debuglevel=0,
dataset_pool=None,
filter="development_project_proposal.status_id==%s" % DevelopmentProjectProposalDataset.id_tentative,
choice_attribute_name='is_chosen',
**kwargs):
self.id_selected = 9
self.proposal_set = proposal_set
self.filter = filter
self.choice_attribute_name = copy.copy(choice_attribute_name)
ChoiceModel.__init__(self, [1, 2],
choice_attribute_name=choice_attribute_name,
**kwargs)
DevelopmentProjectProposalSamplingModel.__init__(self,
proposal_set,
sampler="opus_core.samplers.weighted_sampler",
weight_string = "development_project_proposal.status_id==%s" % self.id_selected,
#weight_string = "development_project_proposal.status_id==%s" % self.id_selected,
run_config=run_config,
estimate_config=estimate_config,
debuglevel=debuglevel,
dataset_pool=dataset_pool)
def __init__(self,
choice_set=[0, 1],
location_id_name="grid_id",
movers_ratio=None,
**kwargs):
self.location_id_name = location_id_name
self.movers_ratio = movers_ratio
ChoiceModel.__init__(self, choice_set, **kwargs)
def __init__(self,
location_set,
sampler="opus_core.samplers.weighted_sampler",
utilities="opus_core.linear_utilities",
probabilities="opus_core.mnl_probabilities",
choices="opus_core.random_choices",
interaction_pkg="urbansim.datasets",
filter=None,
submodel_string=None,
location_id_string=None,
run_config=None,
estimate_config=None,
debuglevel=0,
dataset_pool=None,
**kwargs):
"""
Arguments:
location_set - Dataset of locations to be chosen from.
sampler - name of sampling module to be used for sampling locations. If it is None, no sampling is performed
and all locations are considered for choice.
utilities - name of utilities module
probabilities - name of probabilities module
choices - name of module for computing agent choices
filter - filter is applied on location weights for sampling (by multiplication). It is either a string specifying
an attribute name of the filter, or a 1D/2D array giving the filter directly,
or a dictionary specifying filter for each submodel. If it is None, no filter is applied.
submodel_string - character string specifying what agent attribute determines submodels.
location_id_string - character string giving the fully qualified name of the agent attribute
that specifies the location. Only needed when the attribute is a variable.
Use it without the "as" clausel, since the code adds an alias which is the id name of the location set.
run_config - collection of additional arguments that control a simulation run. It is of class Resources.
estimate_config - collection of additional arguments that control an estimation run. It is of class Resources.
debuglevel - debuglevel for the constructor. The level is overwritten by the argument in the run and estimate method.
An instance of upc_sequence class with components utilities, probabilities and choices is created. Also an instance
of Sampler class for given sampler procedure is created.
"""
self.dataset_pool = self.create_dataset_pool(dataset_pool,
["urbansim", "opus_core"])
ChoiceModel.__init__(self,
choice_set=location_set,
utilities=utilities,
probabilities=probabilities,
choices=choices,
sampler=sampler,
submodel_string=submodel_string,
interaction_pkg=interaction_pkg,
run_config=run_config,
estimate_config=estimate_config,
debuglevel=debuglevel,
dataset_pool=dataset_pool,
**kwargs)
self.filter = filter
self.location_id_string = location_id_string
if self.location_id_string is not None:
self.location_id_string = VariableName(self.location_id_string)
def __init__(self, choice_set, filter=None, choice_attribute_name='work_at_home',
location_id_name='urbansim_parcel.person.building_id', match_number_of_jobs=False, **kwargs):
"""If match_number_of_jobs is True, the choices are drawn from the probability distribution in a way
that the final number matches the number of jobs.
"""
self.job_set = choice_set
self.filter = filter
self.choice_attribute_name = choice_attribute_name
self.location_id_name = location_id_name
self.match_number_of_jobs=match_number_of_jobs
ChoiceModel.__init__(self, [0, 1], choice_attribute_name=choice_attribute_name, **kwargs)
def __init__(self, choice_set, nested_structure=None, stratum=None, **kwargs):
"""'nested_structure' is a dictionary with keys being the nest identifiers and each value being
a list of identifiers of the elemental alternatives belonging to that nest.
'stratum' is either a string giving the name of variable/expression determining
the membership of choice's elements to nests. Or, it is an array of the size as choice set
giving directly the membership of choice's elements to nests.
Either 'nested_structure' or 'stratum' must be given.
All arguments of the Choice Model can be used.
"""
ChoiceModel.__init__(self, choice_set, **kwargs)
self.create_nested_and_tree_structure(nested_structure, stratum, **kwargs)
self.set_model_interaction(**kwargs)
def __init__(self,
choice_set,
filter=None,
choice_attribute_name='work_at_home',
location_id_name='urbansim_parcel.person.building_id',
**kwargs):
self.job_set = choice_set
self.filter = filter
self.choice_attribute_name = choice_attribute_name
self.location_id_name = location_id_name
ChoiceModel.__init__(self, [0, 1],
choice_attribute_name=choice_attribute_name,
**kwargs)
def __init__(self, choice_set, utilities="opus_core.linear_utilities",
probabilities="opus_core.mnl_probabilities",
choices="opus_core.random_choices",
interaction_pkg="biocomplexity.datasets",
submodel_string="lct",
choice_attribute_name="lct",
run_config=None, estimate_config=None,
debuglevel=0):
self.choice_attribute_name = VariableName(choice_attribute_name)
ChoiceModel.__init__(self, choice_set=choice_set, utilities=utilities,
probabilities=probabilities, choices=choices,
submodel_string=submodel_string,
interaction_pkg=interaction_pkg,
choice_attribute_name=self.choice_attribute_name.get_alias(),
run_config=run_config, estimate_config=estimate_config,
debuglevel=debuglevel)
def __init__(self,
choice_set,
nested_structure=None,
stratum=None,
**kwargs):
"""'nested_structure' is a dictionary with keys being the nest identifiers and each value being
a list of identifiers of the elemental alternatives belonging to that nest.
'stratum' is either a string giving the name of variable/expression determining
the membership of choice's elements to nests. Or, it is an array of the size as choice set
giving directly the membership of choice's elements to nests.
Either 'nested_structure' or 'stratum' must be given.
All arguments of the Choice Model can be used.
"""
ChoiceModel.__init__(self, choice_set, **kwargs)
self.create_nested_and_tree_structure(nested_structure, stratum,
**kwargs)
self.set_model_interaction(**kwargs)
def __init__(self, location_set, sampler="opus_core.samplers.weighted_sampler",
utilities="opus_core.linear_utilities",
probabilities="opus_core.mnl_probabilities",
choices="opus_core.random_choices",
interaction_pkg="urbansim.datasets",
filter=None, submodel_string=None,
location_id_string = None,
run_config=None, estimate_config=None,
debuglevel=0, dataset_pool=None, **kwargs):
"""
Arguments:
location_set - Dataset of locations to be chosen from.
sampler - name of sampling module to be used for sampling locations. If it is None, no sampling is performed
and all locations are considered for choice.
utilities - name of utilities module
probabilities - name of probabilities module
choices - name of module for computing agent choices
filter - filter is applied on location weights for sampling (by multiplication). It is either a string specifying
an attribute name of the filter, or a 1D/2D array giving the filter directly,
or a dictionary specifying filter for each submodel. If it is None, no filter is applied.
submodel_string - character string specifying what agent attribute determines submodels.
location_id_string - character string giving the fully qualified name of the agent attribute
that specifies the location. Only needed when the attribute is a variable.
Use it without the "as" clausel, since the code adds an alias which is the id name of the location set.
run_config - collection of additional arguments that control a simulation run. It is of class Resources.
estimate_config - collection of additional arguments that control an estimation run. It is of class Resources.
debuglevel - debuglevel for the constructor. The level is overwritten by the argument in the run and estimate method.
An instance of upc_sequence class with components utilities, probabilities and choices is created. Also an instance
of Sampler class for given sampler procedure is created.
"""
self.dataset_pool = self.create_dataset_pool(dataset_pool, ["urbansim", "opus_core"])
ChoiceModel.__init__(self, choice_set=location_set, utilities=utilities,
probabilities=probabilities, choices=choices, sampler=sampler,
submodel_string=submodel_string,
interaction_pkg=interaction_pkg,
run_config=run_config, estimate_config=estimate_config,
debuglevel=debuglevel, dataset_pool=dataset_pool, **kwargs)
self.filter = filter
self.location_id_string = location_id_string
if self.location_id_string is not None:
self.location_id_string = VariableName(self.location_id_string)
def __init__(self, choice_set=[0,1], location_id_name="grid_id", movers_ratio=None, **kwargs):
self.location_id_name = location_id_name
self.movers_ratio = movers_ratio
ChoiceModel.__init__(self, choice_set, **kwargs)
def __init__(self, choice_set, filter=None, choice_attribute_name='work_at_home', location_id_name='urbansim_parcel.person.building_id', **kwargs):
self.job_set = choice_set
self.filter = filter
self.choice_attribute_name = choice_attribute_name
self.location_id_name = location_id_name
ChoiceModel.__init__(self, [0, 1], choice_attribute_name=choice_attribute_name, **kwargs)
