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 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 schedule_choice_model(
name,
edb_directory="output/estimation_data_bundle/{name}/",
coefficients_file="{name}_coefficients.csv",
spec_file="{name}_SPEC.csv",
alt_values_file="{name}_alternatives_combined.csv",
chooser_file="{name}_choosers_combined.csv",
settings_file="{name}_model_settings.yaml",
return_data=False,
):
model_selector = name.replace("_location", "")
model_selector = model_selector.replace("_destination", "")
model_selector = model_selector.replace("_subtour", "")
model_selector = model_selector.replace("_tour", "")
edb_directory = edb_directory.format(name=name)
def _read_csv(filename, optional=False, **kwargs):
filename = filename.format(name=name)
try:
return pd.read_csv(os.path.join(edb_directory, filename), **kwargs)
except FileNotFoundError:
if optional:
return None
else:
raise
settings_file = settings_file.format(name=name)
with open(os.path.join(edb_directory, settings_file), "r") as yf:
settings = yaml.load(
yf,
Loader=yaml.SafeLoader,
)
try:
coefficients = _read_csv(
coefficients_file,
index_col="coefficient_name",
)
except FileNotFoundError:
# possibly mis-named file is shown in settings
coefficients_file = settings.get('COEFFICIENTS', coefficients_file)
coefficients = _read_csv(
coefficients_file,
index_col="coefficient_name",
)
spec = _read_csv(spec_file, comment='#')
alt_values = _read_csv(alt_values_file)
chooser_data = _read_csv(chooser_file)
# remove temp rows from spec, ASim uses them to calculate the other values written
# to the EDB, but they are not actually part of the utility function themselves.
spec = spec.loc[~spec.Expression.str.startswith("_")].copy()
include_settings = settings.get("include_settings")
if include_settings:
with open(os.path.join(edb_directory, include_settings), "r") as yf:
more_settings = yaml.load(
yf,
Loader=yaml.SafeLoader,
)
settings.update(more_settings)
CHOOSER_SEGMENT_COLUMN_NAME = settings.get("CHOOSER_SEGMENT_COLUMN_NAME")
SEGMENT_IDS = settings.get("SEGMENT_IDS")
if SEGMENT_IDS is None:
SEGMENTS = settings.get("SEGMENTS")
if SEGMENTS is not None:
SEGMENT_IDS = {i: i for i in SEGMENTS}
if 'Label' in spec.columns:
label_column_name = 'Label'
elif 'Expression' in spec.columns:
label_column_name = 'Expression'
else:
raise ValueError("cannot find Label or Expression in spec file")
m = Model()
if len(spec.columns) == 4 and ([c.lower() for c in spec.columns] == [
'label', 'description', 'expression', 'coefficient'
]):
m.utility_ca = linear_utility_from_spec(
spec,
x_col="Label",
p_col=spec.columns[-1],
ignore_x=("local_dist", ),
)
elif len(spec.columns) == 4 \
and all(spec.columns[:3] == ['Label', 'Description', 'Expression']) \
and len(SEGMENT_IDS) == 1 \
and spec.columns[3] == list(SEGMENT_IDS.values())[0]:
m.utility_ca = linear_utility_from_spec(
spec,
x_col="Label",
p_col=spec.columns[-1],
ignore_x=("local_dist", ),
)
else:
m.utility_ca = linear_utility_from_spec(
spec,
x_col=label_column_name,
p_col=SEGMENT_IDS,
ignore_x=("local_dist", ),
segment_id=CHOOSER_SEGMENT_COLUMN_NAME,
)
apply_coefficients(coefficients, m, minimum=-25, maximum=25)
chooser_index_name = chooser_data.columns[0]
x_co = chooser_data.set_index(chooser_index_name)
alt_values.fillna(0, inplace=True)
x_ca = cv_to_ca(
alt_values.set_index([chooser_index_name, alt_values.columns[1]]),
required_labels=spec[label_column_name],
)
# if CHOOSER_SEGMENT_COLUMN_NAME is not None:
# # label segments with names
# SEGMENT_IDS_REVERSE = {v: k for k, v in SEGMENT_IDS.items()}
# x_co["_segment_label"] = x_co[CHOOSER_SEGMENT_COLUMN_NAME].apply(
# lambda x: SEGMENT_IDS_REVERSE[x]
# )
# else:
# x_co["_segment_label"] = size_spec.index[0]
alt_codes = np.arange(len(x_ca.index.levels[1])) + 1
x_ca.index = x_ca.index.set_levels(alt_codes, 1)
x_co["override_choice_plus1"] = x_co["override_choice"] + 1
x_co["model_choice_plus1"] = x_co["model_choice"] + 1
unavail_coefs = coefficients.query(
"(constrain == 'T') & (value < -900)").index
unavail_data = [i.data for i in m.utility_ca if i.param in unavail_coefs]
if len(unavail_data):
joint_unavail = "|".join(f"({i}>0)" for i in unavail_data)
joint_avail = f"~({joint_unavail})"
else:
joint_avail = 1
d = DataFrames(co=x_co, ca=x_ca, av=joint_avail)
m.dataservice = d
m.choice_co_code = "override_choice_plus1"
# m.choice_co_code = "model_choice_plus1"
if return_data:
return (
m,
Dict(
edb_directory=Path(edb_directory),
alt_values=alt_values,
chooser_data=chooser_data,
coefficients=coefficients,
spec=spec,
model_selector=model_selector,
joint_avail=joint_avail,
),
)
return m
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