def simple_simulate_model(
name,
edb_directory="output/estimation_data_bundle/{name}/",
return_data=False,
choices=None,
construct_avail=False,
values_index_col="household_id",
):
data = simple_simulate_data(
name=name, edb_directory=edb_directory, values_index_col=values_index_col,
)
coefficients = data.coefficients
# coef_template = data.coef_template # not used
spec = data.spec
chooser_data = data.chooser_data
settings = data.settings
alt_names = data.alt_names
alt_codes = data.alt_codes
from .general import clean_values
chooser_data = clean_values(
chooser_data,
alt_names_to_codes=choices or data.alt_names_to_codes,
choice_code="override_choice_code",
)
if settings.get('LOGIT_TYPE') == 'NL':
tree = construct_nesting_tree(data.alt_names, settings["NESTS"])
m = Model(graph=tree)
else:
m = Model(alts=data.alt_codes_to_names)
m.utility_co = dict_of_linear_utility_from_spec(
spec, "Label", dict(zip(alt_names, alt_codes)),
)
apply_coefficients(coefficients, m)
if construct_avail:
avail = construct_availability(m, chooser_data, data.alt_codes_to_names)
else:
avail = True
d = DataFrames(co=chooser_data, av=avail, alt_codes=alt_codes, alt_names=alt_names, )
m.dataservice = d
m.choice_co_code = "override_choice_code"
if return_data:
return (
m,
Dict(
edb_directory=data.edb_directory,
chooser_data=chooser_data,
coefficients=coefficients,
spec=spec,
alt_names=alt_names,
alt_codes=alt_codes,
settings=settings,
),
)
return m
def stop_frequency_model(
edb_directory="output/estimation_data_bundle/{name}/",
return_data=False,
):
data = stop_frequency_data(
edb_directory=edb_directory,
values_index_col="tour_id",
)
models = []
for n in range(len(data.spec)):
coefficients = data.coefficients
# coef_template = data.coef_template # not used
spec = data.spec[n]
chooser_data = data.chooser_data[n]
settings = data.settings
alt_names = data.alt_names[n]
alt_codes = data.alt_codes[n]
from .general import clean_values
chooser_data = clean_values(
chooser_data,
alt_names_to_codes=data.alt_names_to_codes[n],
choice_code="override_choice_code",
)
if settings.get('LOGIT_TYPE') == 'NL':
tree = construct_nesting_tree(data.alt_names[n], settings["NESTS"])
m = Model(graph=tree)
else:
m = Model()
m.utility_co = dict_of_linear_utility_from_spec(
spec,
"Label",
dict(zip(alt_names, alt_codes)),
)
apply_coefficients(coefficients, m)
avail = True
d = DataFrames(
co=chooser_data,
av=avail,
alt_codes=alt_codes,
alt_names=alt_names,
)
m.dataservice = d
m.choice_co_code = "override_choice_code"
models.append(m)
from larch.model.model_group import ModelGroup
models = ModelGroup(models)
if return_data:
return (
models,
data,
)
return models
def auto_ownership_model(
name="auto_ownership",
edb_directory="output/estimation_data_bundle/{name}/",
return_data=False,
):
data = simple_simulate_data(
name=name,
edb_directory=edb_directory,
values_index_col="household_id",
)
coefficients = data.coefficients
# coef_template = data.coef_template # not used
spec = data.spec
chooser_data = data.chooser_data
settings = data.settings
altnames = list(spec.columns[3:])
altcodes = range(len(altnames))
chooser_data = remove_apostrophes(chooser_data)
chooser_data.fillna(0, inplace=True)
# Remove choosers with invalid observed choice
chooser_data = chooser_data[chooser_data["override_choice"] >= 0]
m = Model()
# One of the alternatives is coded as 0, so
# we need to explicitly initialize the MNL nesting graph
# and set to root_id to a value other than zero.
m.initialize_graph(alternative_codes=altcodes, root_id=99)
m.utility_co = dict_of_linear_utility_from_spec(
spec,
"Label",
dict(zip(altnames, altcodes)),
)
apply_coefficients(coefficients, m)
d = DataFrames(
co=chooser_data,
av=True,
alt_codes=altcodes,
alt_names=altnames,
)
m.dataservice = d
m.choice_co_code = "override_choice"
if return_data:
return (
m,
Dict(
edb_directory=data.edb_directory,
chooser_data=chooser_data,
coefficients=coefficients,
spec=spec,
altnames=altnames,
altcodes=altcodes,
),
)
return m