def interaction_simulate_data(
name="non_mandatory_tour_frequency",
edb_directory="output/estimation_data_bundle/{name}/",
settings_file="{name}_model_settings.yaml",
spec_file="{name}_SPEC.csv",
alt_def_file="{name}_alternatives.csv",
coefficients_files="{segment_name}/{name}_coefficients_{segment_name}.csv",
chooser_data_files="{segment_name}/{name}_choosers_combined.csv",
alt_values_files="{segment_name}/{name}_interaction_expression_values.csv",
):
edb_directory = edb_directory.format(name=name)
def _read_csv(filename, **kwargs):
filename = filename.format(name=name)
return pd.read_csv(os.path.join(edb_directory, filename), **kwargs)
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,
)
coefficients = {}
chooser_data = {}
alt_values = {}
segment_names = [s['NAME'] for s in settings['SPEC_SEGMENTS']]
for segment_name in segment_names:
coefficients[segment_name] = _read_csv(
coefficients_files.format(name=name, segment_name=segment_name),
index_col='coefficient_name',
)
chooser_data[segment_name] = _read_csv(
chooser_data_files.format(name=name, segment_name=segment_name), )
alt_values[segment_name] = _read_csv(
alt_values_files.format(name=name, segment_name=segment_name), )
spec = _read_csv(spec_file, )
spec = remove_apostrophes(spec, ['Label'])
# alt_names = list(spec.columns[3:])
# alt_codes = np.arange(1, len(alt_names) + 1)
# alt_names_to_codes = dict(zip(alt_names, alt_codes))
# alt_codes_to_names = dict(zip(alt_codes, alt_names))
alt_def = _read_csv(alt_def_file.format(name=name), index_col=0)
return Dict(
edb_directory=Path(edb_directory),
settings=settings,
chooser_data=chooser_data,
coefficients=coefficients,
alt_values=alt_values,
spec=spec,
alt_def=alt_def,
)
def generate_alternatives(n_persons):
"""
Generate a dictionary of CDAP alternatives.
The keys are the names of the patterns, and
the values are the alternative code numbers.
Parameters
----------
n_persons : int
Returns
-------
Dict
"""
basic_patterns = ["M", "N", "H"]
alt_names = list(
"".join(i)
for i in itertools.product(basic_patterns, repeat=n_persons))
alt_codes = np.arange(1, len(alt_names) + 1)
return Dict(zip(alt_names, alt_codes))
def stop_frequency_data(
edb_directory="output/estimation_data_bundle/{name}/",
settings_file="{name}_model_settings.yaml",
chooser_data_file="{name}_values_combined.csv",
values_index_col="tour_id",
):
name = 'stop_frequency'
edb_directory = edb_directory.format(name=name)
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,
)
segments = [i['primary_purpose'] for i in settings['SPEC_SEGMENTS']]
master_coef = {}
prior_segs = []
coef_map = {seg: {} for seg in segments}
segment_coef = {}
for seg_ in settings["SPEC_SEGMENTS"]:
seg_purpose = seg_['primary_purpose']
seg_subdir = Path(os.path.join(edb_directory, seg_purpose))
segment_coef[seg_['primary_purpose']] = pd.read_csv(
seg_subdir / seg_['COEFFICIENTS'],
index_col="coefficient_name",
)
for seg in segments:
for cname, value in segment_coef[seg].value.items():
if cname in master_coef:
if master_coef[cname] == value:
coef_map[seg][cname] = cname
else:
for pseg in prior_segs:
if master_coef.get(f"{cname}_{pseg}", None) == value:
coef_map[seg][cname] = f"{cname}_{pseg}"
break
else: # no break
master_coef[f"{cname}_{seg}"] = value
coef_map[seg][cname] = f"{cname}_{seg}"
else:
master_coef[cname] = value
coef_map[seg][cname] = cname
prior_segs.append(seg)
# rewrite revised spec files with common segment_coef names
for seg in segments:
seg_subdir = Path(os.path.join(edb_directory, seg))
with open(seg_subdir / f"stop_frequency_SPEC.csv", 'rt') as f:
spec = f.read()
for kcoef, v in coef_map[seg].items():
spec = spec.replace(kcoef, v)
with open(seg_subdir / f"stop_frequency_SPEC_.csv", 'wt') as f:
f.write(spec)
master_coef_df = pd.DataFrame(data=master_coef, index=['value']).T
master_coef_df.index.name = "coefficient_name"
seg_coefficients = []
seg_spec = []
seg_alt_names = []
seg_alt_codes = []
seg_alt_names_to_codes = []
seg_alt_codes_to_names = []
seg_chooser_data = []
for seg in settings["SPEC_SEGMENTS"]:
seg_purpose = seg['primary_purpose']
seg_subdir = Path(os.path.join(edb_directory, seg_purpose))
coeffs_ = pd.read_csv(seg_subdir / seg['COEFFICIENTS'],
index_col="coefficient_name")
coeffs_.index = pd.Index([f"{i}_{seg_purpose}" for i in coeffs_.index],
name="coefficient_name")
seg_coefficients.append(coeffs_)
spec = pd.read_csv(seg_subdir / "stop_frequency_SPEC_.csv")
spec = remove_apostrophes(spec, ["Label"])
# spec.iloc[:, 3:] = spec.iloc[:, 3:].applymap(lambda x: f"{x}_{seg_purpose}" if not pd.isna(x) else x)
seg_spec.append(spec)
alt_names = list(spec.columns[3:])
alt_codes = np.arange(1, len(alt_names) + 1)
alt_names_to_codes = dict(zip(alt_names, alt_codes))
alt_codes_to_names = dict(zip(alt_codes, alt_names))
seg_alt_names.append(alt_names)
seg_alt_codes.append(alt_codes)
seg_alt_names_to_codes.append(alt_names_to_codes)
seg_alt_codes_to_names.append(alt_codes_to_names)
chooser_data = pd.read_csv(
seg_subdir / chooser_data_file.format(name=name),
index_col=values_index_col,
)
seg_chooser_data.append(chooser_data)
return Dict(
edb_directory=Path(edb_directory),
settings=settings,
chooser_data=seg_chooser_data,
coefficients=master_coef_df,
spec=seg_spec,
alt_names=seg_alt_names,
alt_codes=seg_alt_codes,
alt_names_to_codes=seg_alt_names_to_codes,
alt_codes_to_names=seg_alt_codes_to_names,
segments=segments,
coefficient_map=coef_map,
segment_coefficients=segment_coef,
)
def location_choice_model(
name="workplace_location",
edb_directory="output/estimation_data_bundle/{name}/",
coefficients_file="{name}_coefficients.csv",
spec_file="{name}_SPEC.csv",
size_spec_file="{name}_size_terms.csv",
alt_values_file="{name}_alternatives_combined.csv",
chooser_file="{name}_choosers_combined.csv",
settings_file="{name}_model_settings.yaml",
landuse_file="{name}_landuse.csv",
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", "")
if model_selector == 'joint':
model_selector = 'non_mandatory'
edb_directory = edb_directory.format(name=name)
def _read_csv(filename, **kwargs):
filename = filename.format(name=name)
return pd.read_csv(os.path.join(edb_directory, filename), **kwargs)
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)
landuse = _read_csv(landuse_file, index_col="zone_id")
master_size_spec = _read_csv(size_spec_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.isna()]
spec = spec.loc[~spec.Expression.str.startswith("_")].copy()
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,
)
include_settings = settings.get("include_settings")
if include_settings:
include_settings = os.path.join(edb_directory, include_settings)
if include_settings and os.path.exists(include_settings):
with open(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}
SIZE_TERM_SELECTOR = settings.get('SIZE_TERM_SELECTOR', model_selector)
# filter size spec for this location choice only
size_spec = (master_size_spec.query(
f"model_selector == '{SIZE_TERM_SELECTOR}'").drop(
columns="model_selector").set_index("segment"))
size_spec = size_spec.loc[:, size_spec.max() > 0]
size_coef = size_coefficients_from_spec(size_spec)
indexes_to_drop = [
"util_size_variable", # pre-computed size (will be re-estimated)
"util_size_variable_atwork", # pre-computed size (will be re-estimated)
"util_utility_adjustment", # shadow pricing (ignored in estimation)
"@df['size_term'].apply(np.log1p)", # pre-computed size (will be re-estimated)
]
if 'Label' in spec.columns:
indexes_to_drop = [
i for i in indexes_to_drop if i in spec.Label.to_numpy()
]
label_column_name = 'Label'
elif 'Expression' in spec.columns:
indexes_to_drop = [
i for i in indexes_to_drop if i in spec.Expression.to_numpy()
]
label_column_name = 'Expression'
else:
raise ValueError("cannot find Label or Expression in spec file")
expression_labels = None
if label_column_name == 'Expression':
expression_labels = {
expr: f"variable_label{n:04d}"
for n, expr in enumerate(spec.Expression.to_numpy())
}
# Remove shadow pricing and pre-existing size expression for re-estimation
spec = (spec.set_index(label_column_name).drop(
index=indexes_to_drop).reset_index())
if label_column_name == 'Expression':
spec.insert(0, "Label", spec['Expression'].map(expression_labels))
alt_values['variable'] = alt_values['variable'].map(expression_labels)
label_column_name = "Label"
if name == 'trip_destination':
CHOOSER_SEGMENT_COLUMN_NAME = 'primary_purpose'
primary_purposes = spec.columns[3:]
SEGMENT_IDS = {pp: pp for pp in primary_purposes}
chooser_index_name = chooser_data.columns[0]
x_co = chooser_data.set_index(chooser_index_name)
x_ca = cv_to_ca(
alt_values.set_index([chooser_index_name, alt_values.columns[1]]))
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]
# compute total size values by segment
for segment in size_spec.index:
total_size_segment = pd.Series(0, index=landuse.index)
x_co["total_size_" + segment] = 0
for land_use_field in size_spec.loc[segment].index:
total_size_segment += (landuse[land_use_field] *
size_spec.loc[segment, land_use_field])
x_co["total_size_" + segment] = total_size_segment.loc[
x_co["override_choice"]].to_numpy()
# for each chooser, collate the appropriate total size value
x_co["total_size_segment"] = 0
for segment in size_spec.index:
labels = "total_size_" + segment
rows = x_co["_segment_label"] == segment
x_co.loc[rows, "total_size_segment"] = x_co[labels][rows]
# Remove choosers with invalid observed choice (appropriate total size value = 0)
valid_observed_zone = x_co["total_size_segment"] > 0
x_co = x_co[valid_observed_zone]
x_ca = x_ca[x_ca.index.get_level_values(chooser_index_name).isin(
x_co.index)]
# Merge land use characteristics into CA data
try:
x_ca_1 = pd.merge(x_ca, landuse, on="zone_id", how="left")
except KeyError:
# Missing the zone_id variable?
# Use the alternative id's instead, which assumes no sampling of alternatives
x_ca_1 = pd.merge(x_ca,
landuse,
left_on=x_ca.index.get_level_values(1),
right_index=True,
how="left")
x_ca_1.index = x_ca.index
# Availability of choice zones
if "util_no_attractions" in x_ca_1:
av = x_ca_1["util_no_attractions"].apply(
lambda x: False if x == 1 else True).astype(np.int8)
elif "@df['size_term']==0" in x_ca_1:
av = x_ca_1["@df['size_term']==0"].apply(
lambda x: False if x == 1 else True).astype(np.int8)
else:
av = 1
d = DataFrames(co=x_co, ca=x_ca_1, av=av)
m = Model(dataservice=d)
if len(spec.columns) == 4 and all(
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,
)
if CHOOSER_SEGMENT_COLUMN_NAME is None:
assert len(size_spec) == 1
m.quantity_ca = sum(
P(f"{i}_{q}") * X(q) for i in size_spec.index
for q in size_spec.columns if size_spec.loc[i, q] != 0)
else:
m.quantity_ca = sum(
P(f"{i}_{q}") * X(q) *
X(f"{CHOOSER_SEGMENT_COLUMN_NAME}=={str_repr(SEGMENT_IDS[i])}")
for i in size_spec.index for q in size_spec.columns
if size_spec.loc[i, q] != 0)
apply_coefficients(coefficients, m)
apply_coefficients(size_coef, m, minimum=-6, maximum=6)
m.choice_co_code = "override_choice"
if return_data:
return (
m,
Dict(
edb_directory=Path(edb_directory),
alt_values=alt_values,
chooser_data=chooser_data,
coefficients=coefficients,
landuse=landuse,
spec=spec,
size_spec=size_spec,
master_size_spec=master_size_spec,
model_selector=model_selector,
settings=settings,
),
)
return m
def cdap_data(
name="cdap",
edb_directory="output/estimation_data_bundle/{name}/",
coefficients_file="{name}_coefficients.csv",
interaction_coeffs_file="{name}_interaction_coefficients.csv",
households_file="../../final_households.csv",
persons_file="../../final_persons.csv",
spec1_file="{name}_INDIV_AND_HHSIZE1_SPEC.csv",
settings_file="{name}_model_settings.yaml",
chooser_data_file="{name}_values_combined.csv",
):
edb_directory = edb_directory.format(name=name)
if not os.path.exists(edb_directory):
raise FileNotFoundError(edb_directory)
def read_csv(filename, **kwargs):
filename = filename.format(name=name)
return pd.read_csv(os.path.join(edb_directory, filename), **kwargs)
def read_yaml(filename, **kwargs):
filename = filename.format(name=name)
with open(os.path.join(edb_directory, filename), 'rt') as f:
return yaml.load(f, Loader=yaml.SafeLoader, **kwargs)
settings = read_yaml(settings_file)
try:
hhs = read_csv(households_file)
except FileNotFoundError:
hhs = pd.read_csv(households_file)
try:
persons = read_csv(persons_file)
except FileNotFoundError:
persons = pd.read_csv(persons_file)
person_rank = asim_cdap.assign_cdap_rank(persons)
coefficients = read_csv(
coefficients_file,
index_col='coefficient_name',
comment="#",
)
interaction_coef = read_csv(
interaction_coeffs_file,
dtype={
'interaction_ptypes': str,
},
keep_default_na=False,
comment="#",
)
spec1 = read_csv(spec1_file, comment='#')
values = read_csv(chooser_data_file, comment='#')
values['cdap_rank'] = person_rank
return Dict(
edb_directory=Path(edb_directory),
person_data=values,
spec1=spec1,
interaction_coef=interaction_coef,
coefficients=coefficients,
households=hhs,
settings=settings,
)
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 mode_choice_model(
name,
edb_directory="output/estimation_data_bundle/{name}/",
return_data=False,
override_filenames=None,
):
if override_filenames is None:
override_filenames = {}
data = simple_simulate_data(
name=name,
edb_directory=edb_directory,
**override_filenames,
)
coefficients = data.coefficients
coef_template = data.coef_template
spec = data.spec
chooser_data = data.chooser_data
settings = data.settings
chooser_data = clean_values(
chooser_data,
alt_names_to_codes=data.alt_names_to_codes,
choice_code="override_choice_code",
)
tree = construct_nesting_tree(data.alt_names, settings["NESTS"])
purposes = list(coef_template.columns)
if "atwork" in name:
purposes = ['atwork']
elif 'atwork' in purposes:
purposes.remove('atwork')
# Setup purpose specific models
m = {purpose: Model(graph=tree, title=purpose) for purpose in purposes}
for alt_code, alt_name in tree.elemental_names().items():
# Read in base utility function for this alt_name
u = linear_utility_from_spec(
spec,
x_col="Label",
p_col=alt_name,
ignore_x=("#", ),
)
for purpose in purposes:
# Modify utility function based on template for purpose
u_purp = sum((P(coef_template[purpose].get(i.param, i.param)) *
i.data * i.scale) for i in u)
m[purpose].utility_co[alt_code] = u_purp
for model in m.values():
explicit_value_parameters(model)
apply_coefficients(coefficients, m)
avail = construct_availability(m[purposes[0]], chooser_data,
data.alt_codes_to_names)
d = DataFrames(
co=chooser_data,
av=avail,
alt_codes=data.alt_codes,
alt_names=data.alt_names,
)
if 'atwork' not in name:
for purpose, model in m.items():
model.dataservice = d.selector_co(f"tour_type=='{purpose}'")
model.choice_co_code = "override_choice_code"
else:
for purpose, model in m.items():
model.dataservice = d
model.choice_co_code = "override_choice_code"
from larch.model.model_group import ModelGroup
mg = ModelGroup(m.values())
if return_data:
return (
mg,
Dict(
edb_directory=Path(edb_directory),
chooser_data=chooser_data,
avail=avail,
coefficients=coefficients,
coef_template=coef_template,
spec=spec,
settings=settings,
),
)
return mg
def simple_simulate_data(
name="tour_mode_choice",
edb_directory="output/estimation_data_bundle/{name}/",
coefficients_file="{name}_coefficients.csv",
coefficients_template="{name}_coefficients_template.csv",
spec_file="{name}_SPEC.csv",
settings_file="{name}_model_settings.yaml",
chooser_data_file="{name}_values_combined.csv",
values_index_col="tour_id",
):
edb_directory = edb_directory.format(name=name)
def _read_csv(filename, **kwargs):
filename = filename.format(name=name)
return pd.read_csv(os.path.join(edb_directory, filename), **kwargs)
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",)
try:
coef_template = _read_csv(coefficients_template, index_col="coefficient_name",)
except FileNotFoundError:
coef_template = None
spec = _read_csv(spec_file, comment="#")
spec = remove_apostrophes(spec, ["Label"])
# 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.isna()]
spec = spec.loc[~spec.Expression.str.startswith("_")].copy()
alt_names = list(spec.columns[3:])
alt_codes = np.arange(1, len(alt_names) + 1)
alt_names_to_codes = dict(zip(alt_names, alt_codes))
alt_codes_to_names = dict(zip(alt_codes, alt_names))
chooser_data = _read_csv(chooser_data_file, index_col=values_index_col,)
except Exception:
# when an error happens in reading anything other than settings, print settings
from pprint import pprint
pprint(settings)
raise
return Dict(
edb_directory=Path(edb_directory),
settings=settings,
chooser_data=chooser_data,
coefficients=coefficients,
coef_template=coef_template,
spec=spec,
alt_names=alt_names,
alt_codes=alt_codes,
alt_names_to_codes=alt_names_to_codes,
alt_codes_to_names=alt_codes_to_names,
)
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 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
