def _location_sample(segment_name, choosers, alternatives, skims, estimator,
model_settings, alt_dest_col_name, chunk_size, chunk_tag,
trace_label):
"""
select a sample of alternative locations.
Logsum calculations are expensive, so we build a table of persons * all zones
and then select a sample subset of potential locations
The sample subset is generated by making multiple choices (<sample_size> number of choices)
which results in sample containing up to <sample_size> choices for each choose (e.g. person)
and a pick_count indicating how many times that choice was selected for that chooser.)
person_id, dest_zone_id, rand, pick_count
23750, 14, 0.565502716034, 4
23750, 16, 0.711135838871, 6
...
23751, 12, 0.408038878552, 1
23751, 14, 0.972732479292, 2
"""
assert not choosers.empty
logger.info("Running %s with %d persons" %
(trace_label, len(choosers.index)))
sample_size = model_settings["SAMPLE_SIZE"]
if config.setting('disable_destination_sampling',
False) or (estimator
and estimator.want_unsampled_alternatives):
# FIXME interaction_sample will return unsampled complete alternatives with probs and pick_count
logger.info(
"Estimation mode for %s using unsampled alternatives short_circuit_choices"
% (trace_label, ))
sample_size = 0
locals_d = {'skims': skims, 'segment_size': segment_name}
constants = config.get_model_constants(model_settings)
locals_d.update(constants)
spec = simulate.spec_for_segment(model_settings,
spec_id='SAMPLE_SPEC',
segment_name=segment_name,
estimator=estimator)
# here since presumably we want this when called for either sample or presample
log_alt_losers = config.setting('log_alt_losers', False)
choices = interaction_sample(choosers,
alternatives,
spec=spec,
sample_size=sample_size,
alt_col_name=alt_dest_col_name,
log_alt_losers=log_alt_losers,
skims=skims,
locals_d=locals_d,
chunk_size=chunk_size,
chunk_tag=chunk_tag,
trace_label=trace_label)
return choices
def _destination_sample(primary_purpose, trips, alternatives, model_settings,
size_term_matrix, skims, alt_dest_col_name, estimator,
chunk_size, chunk_tag, trace_label):
"""
Note: trips with no viable destination receive no sample rows
(because we call interaction_sample with allow_zero_probs=True)
All other trips will have one or more rows with pick_count summing to sample_size
returns
choices: pandas.DataFrame
alt_dest prob pick_count
trip_id
102829169 2898 0.002333 1
102829169 2901 0.004976 1
102829169 3193 0.002628 1
"""
spec = simulate.spec_for_segment(model_settings,
spec_id='DESTINATION_SAMPLE_SPEC',
segment_name=primary_purpose,
estimator=estimator)
sample_size = model_settings['SAMPLE_SIZE']
if config.setting('disable_destination_sampling',
False) or (estimator
and estimator.want_unsampled_alternatives):
# FIXME interaction_sample will return unsampled complete alternatives with probs and pick_count
logger.info(
"Estimation mode for %s using unsampled alternatives short_circuit_choices"
% (trace_label, ))
sample_size = 0
locals_dict = config.get_model_constants(model_settings).copy()
# size_terms of destination zones are purpose-specific, and trips have various purposes
# so the relevant size_term for each interaction_sample row
# cannot be determined until after choosers are joined with alternatives
# (unless we iterate over trip.purpose - which we could, though we are already iterating over trip_num)
# so, instead, expressions determine row-specific size_term by a call to: size_terms.get(df.alt_dest, df.purpose)
locals_dict.update({'size_terms': size_term_matrix})
locals_dict.update(skims)
log_alt_losers = config.setting('log_alt_losers', False)
choices = interaction_sample(choosers=trips,
alternatives=alternatives,
sample_size=sample_size,
alt_col_name=alt_dest_col_name,
log_alt_losers=log_alt_losers,
allow_zero_probs=True,
spec=spec,
skims=skims,
locals_d=locals_dict,
chunk_size=chunk_size,
chunk_tag=chunk_tag,
trace_label=trace_label)
return choices
def _destination_sample(spec_segment_name, choosers, destination_size_terms,
skims, estimator, model_settings, alt_dest_col_name,
chunk_size, chunk_tag, trace_label):
model_spec = simulate.spec_for_segment(model_settings,
spec_id='SAMPLE_SPEC',
segment_name=spec_segment_name,
estimator=estimator)
logger.info("running %s with %d tours", trace_label, len(choosers))
sample_size = model_settings['SAMPLE_SIZE']
if config.setting('disable_destination_sampling',
False) or (estimator
and estimator.want_unsampled_alternatives):
# FIXME interaction_sample will return unsampled complete alternatives with probs and pick_count
logger.info(
"Estimation mode for %s using unsampled alternatives short_circuit_choices"
% (trace_label, ))
sample_size = 0
locals_d = {'skims': skims}
constants = config.get_model_constants(model_settings)
if constants is not None:
locals_d.update(constants)
log_alt_losers = config.setting('log_alt_losers', False)
choices = interaction_sample(choosers,
alternatives=destination_size_terms,
sample_size=sample_size,
alt_col_name=alt_dest_col_name,
log_alt_losers=log_alt_losers,
spec=model_spec,
skims=skims,
locals_d=locals_d,
chunk_size=chunk_size,
chunk_tag=chunk_tag,
trace_label=trace_label)
# remember person_id in chosen alts so we can merge with persons in subsequent steps
# (broadcasts person_id onto all alternatives sharing the same tour_id index value)
choices['person_id'] = choosers.person_id
return choices
def run_od_sample(spec_segment_name, tours, model_settings, network_los,
destination_size_terms, estimator, chunk_size, trace_label):
model_spec = simulate.spec_for_segment(model_settings,
spec_id='SAMPLE_SPEC',
segment_name=spec_segment_name,
estimator=estimator)
choosers = tours
# FIXME - MEMORY HACK - only include columns actually used in spec
chooser_columns = model_settings['SIMULATE_CHOOSER_COLUMNS']
choosers = choosers[chooser_columns]
# interaction_sample requires that choosers.index.is_monotonic_increasing
if not choosers.index.is_monotonic_increasing:
logger.debug(
f"run_destination_sample {trace_label} sorting choosers because not monotonic_increasing"
)
choosers = choosers.sort_index()
# by default, enable presampling for multizone systems, unless they disable it in settings file
pre_sample_taz = not (network_los.zone_system == los.ONE_ZONE)
if pre_sample_taz and not config.setting('want_dest_choice_presampling',
True):
pre_sample_taz = False
logger.info(f"Disabled destination zone presampling for {trace_label} "
f"because 'want_dest_choice_presampling' setting is False")
if pre_sample_taz:
logger.info("Running %s destination_presample with %d tours" %
(trace_label, len(tours)))
choices = od_presample(spec_segment_name, choosers, model_settings,
network_los, destination_size_terms, estimator,
chunk_size, trace_label)
else:
choices = od_sample(spec_segment_name, choosers, model_settings,
network_los, destination_size_terms, estimator,
chunk_size, trace_label)
return choices
def trip_destination_simulate(primary_purpose, trips, destination_sample,
model_settings, want_logsums, size_term_matrix,
skim_hotel, estimator, chunk_size, trace_hh_id,
trace_label):
"""
Chose destination from destination_sample (with od_logsum and dp_logsum columns added)
Returns
-------
choices - pandas.Series
destination alt chosen
"""
trace_label = tracing.extend_trace_label(trace_label, 'trip_dest_simulate')
chunk_tag = 'trip_destination.simulate'
spec = simulate.spec_for_segment(model_settings,
spec_id='DESTINATION_SPEC',
segment_name=primary_purpose,
estimator=estimator)
if estimator:
estimator.write_choosers(trips)
alt_dest_col_name = model_settings['ALT_DEST_COL_NAME']
logger.info("Running trip_destination_simulate with %d trips", len(trips))
skims = skim_hotel.sample_skims(presample=False)
locals_dict = config.get_model_constants(model_settings).copy()
locals_dict.update({'size_terms': size_term_matrix})
locals_dict.update(skims)
log_alt_losers = config.setting('log_alt_losers', False)
destinations = interaction_sample_simulate(
choosers=trips,
alternatives=destination_sample,
spec=spec,
choice_column=alt_dest_col_name,
log_alt_losers=log_alt_losers,
want_logsums=want_logsums,
allow_zero_probs=True,
zero_prob_choice_val=NO_DESTINATION,
skims=skims,
locals_d=locals_dict,
chunk_size=chunk_size,
chunk_tag=chunk_tag,
trace_label=trace_label,
trace_choice_name='trip_dest',
estimator=estimator)
if not want_logsums:
# for consistency, always return a dataframe with canonical column name
assert isinstance(destinations, pd.Series)
destinations = destinations.to_frame('choice')
if estimator:
# need to overwrite choices here before any failed choices are suppressed
estimator.write_choices(destinations.choice)
destinations.choice = estimator.get_survey_values(
destinations.choice, 'trips', 'destination')
estimator.write_override_choices(destinations.choice)
# drop any failed zero_prob destinations
if (destinations.choice == NO_DESTINATION).any():
# logger.debug("dropping %s failed destinations", (destinations == NO_DESTINATION).sum())
destinations = destinations[destinations.choice != NO_DESTINATION]
return destinations
def run_destination_simulate(spec_segment_name, tours, persons_merged,
destination_sample, want_logsums, model_settings,
network_los, destination_size_terms, estimator,
chunk_size, trace_label):
"""
run destination_simulate on tour_destination_sample
annotated with mode_choice logsum to select a destination from sample alternatives
"""
model_spec = simulate.spec_for_segment(model_settings,
spec_id='SPEC',
segment_name=spec_segment_name,
estimator=estimator)
# FIXME - MEMORY HACK - only include columns actually used in spec (omit them pre-merge)
chooser_columns = model_settings['SIMULATE_CHOOSER_COLUMNS']
persons_merged = persons_merged[[
c for c in persons_merged.columns if c in chooser_columns
]]
tours = tours[[
c for c in tours.columns if c in chooser_columns or c == 'person_id'
]]
choosers = pd.merge(tours,
persons_merged,
left_on='person_id',
right_index=True,
how='left')
# interaction_sample requires that choosers.index.is_monotonic_increasing
if not choosers.index.is_monotonic_increasing:
logger.debug(
f"run_destination_simulate {trace_label} sorting choosers because not monotonic_increasing"
)
choosers = choosers.sort_index()
if estimator:
estimator.write_choosers(choosers)
alt_dest_col_name = model_settings['ALT_DEST_COL_NAME']
origin_col_name = model_settings['CHOOSER_ORIG_COL_NAME']
# alternatives are pre-sampled and annotated with logsums and pick_count
# but we have to merge size_terms column into alt sample list
destination_sample['size_term'] = \
reindex(destination_size_terms.size_term, destination_sample[alt_dest_col_name])
tracing.dump_df(DUMP, destination_sample, trace_label, 'alternatives')
constants = config.get_model_constants(model_settings)
logger.info("Running tour_destination_simulate with %d persons",
len(choosers))
# create wrapper with keys for this lookup - in this case there is a home_zone_id in the choosers
# and a zone_id in the alternatives which get merged during interaction
# the skims will be available under the name "skims" for any @ expressions
skim_dict = network_los.get_default_skim_dict()
skims = skim_dict.wrap(origin_col_name, alt_dest_col_name)
locals_d = {
'skims': skims,
}
if constants is not None:
locals_d.update(constants)
tracing.dump_df(DUMP, choosers, trace_label, 'choosers')
choices = interaction_sample_simulate(choosers,
destination_sample,
spec=model_spec,
choice_column=alt_dest_col_name,
want_logsums=want_logsums,
skims=skims,
locals_d=locals_d,
chunk_size=chunk_size,
trace_label=trace_label,
trace_choice_name='destination',
estimator=estimator)
if not want_logsums:
# for consistency, always return a dataframe with canonical column name
assert isinstance(choices, pd.Series)
choices = choices.to_frame('choice')
return choices
def run_destination_sample(spec_segment_name, tours, households_merged,
model_settings, network_los, destination_size_terms,
estimator, chunk_size, trace_label):
spec = simulate.spec_for_segment(model_settings,
spec_id='SAMPLE_SPEC',
segment_name=spec_segment_name,
estimator=estimator)
# choosers are tours - in a sense tours are choosing their destination
choosers = pd.merge(tours,
households_merged,
left_on='household_id',
right_index=True,
how='left')
# FIXME - MEMORY HACK - only include columns actually used in spec
chooser_columns = model_settings['SIMULATE_CHOOSER_COLUMNS']
choosers = choosers[chooser_columns]
alt_dest_col_name = model_settings["ALT_DEST_COL_NAME"]
logger.info("running %s with %d tours", trace_label, len(choosers))
sample_size = model_settings["SAMPLE_SIZE"]
if estimator:
# FIXME interaction_sample will return unsampled complete alternatives with probs and pick_count
logger.info(
"Estimation mode for %s using unsampled alternatives short_circuit_choices"
% (trace_label, ))
sample_size = 0
# create wrapper with keys for this lookup - in this case there is a workplace_zone_id
# in the choosers and a zone_id in the alternatives which ge t merged during interaction
# the skims will be available under the name "skims" for any @ expressions
origin_col_name = model_settings['CHOOSER_ORIG_COL_NAME']
dest_column_name = destination_size_terms.index.name
# (logit.interaction_dataset suffixes duplicate chooser column with '_chooser')
if (origin_col_name == dest_column_name):
origin_col_name = f'{origin_col_name}_chooser'
skim_dict = network_los.get_default_skim_dict()
skims = skim_dict.wrap(origin_col_name, dest_column_name)
locals_d = {'skims': skims}
constants = config.get_model_constants(model_settings)
if constants is not None:
locals_d.update(constants)
choices = interaction_sample(choosers,
alternatives=destination_size_terms,
sample_size=sample_size,
alt_col_name=alt_dest_col_name,
spec=spec,
skims=skims,
locals_d=locals_d,
chunk_size=chunk_size,
trace_label=trace_label)
# remember person_id in chosen alts so we can merge with persons in subsequent steps
# (broadcasts person_id onto all alternatives sharing the same tour_id index value)
choices['person_id'] = choosers.person_id
return choices
def run_destination_simulate(spec_segment_name, tours, persons_merged,
destination_sample, want_logsums, model_settings,
skim_dict, destination_size_terms, estimator,
chunk_size, trace_label):
"""
run destination_simulate on tour_destination_sample
annotated with mode_choice logsum to select a destination from sample alternatives
"""
spec = simulate.spec_for_segment(model_settings,
spec_id='SPEC',
segment_name=spec_segment_name,
estimator=estimator)
# merge persons into tours
choosers = pd.merge(tours,
persons_merged,
left_on='person_id',
right_index=True,
how='left')
# FIXME - MEMORY HACK - only include columns actually used in spec
chooser_columns = model_settings['SIMULATE_CHOOSER_COLUMNS']
choosers = choosers[chooser_columns]
if estimator:
estimator.write_choosers(choosers)
alt_dest_col_name = model_settings["ALT_DEST_COL_NAME"]
origin_col_name = model_settings['CHOOSER_ORIG_COL_NAME']
# alternatives are pre-sampled and annotated with logsums and pick_count
# but we have to merge size_terms column into alt sample list
destination_sample['size_term'] = \
reindex(destination_size_terms.size_term, destination_sample[alt_dest_col_name])
constants = config.get_model_constants(model_settings)
logger.info("Running tour_destination_simulate with %d persons",
len(choosers))
# create wrapper with keys for this lookup - in this case there is a TAZ in the choosers
# and a TAZ in the alternatives which get merged during interaction
# the skims will be available under the name "skims" for any @ expressions
skims = skim_dict.wrap(origin_col_name, alt_dest_col_name)
locals_d = {
'skims': skims,
}
if constants is not None:
locals_d.update(constants)
choices = interaction_sample_simulate(choosers,
destination_sample,
spec=spec,
choice_column=alt_dest_col_name,
want_logsums=want_logsums,
skims=skims,
locals_d=locals_d,
chunk_size=chunk_size,
trace_label=trace_label,
trace_choice_name='destination',
estimator=estimator)
return choices
def run_location_simulate(segment_name, persons_merged, location_sample_df,
network_los, dest_size_terms, want_logsums,
estimator, model_settings, chunk_size, chunk_tag,
trace_label):
"""
run location model on location_sample annotated with mode_choice logsum
to select a dest zone from sample alternatives
Returns
-------
choices : pandas.DataFrame indexed by persons_merged_df.index
choice : location choices (zone ids)
logsum : float logsum of choice utilities across alternatives
logsums optional & only returned if DEST_CHOICE_LOGSUM_COLUMN_NAME specified in model_settings
"""
assert not persons_merged.empty
# FIXME - MEMORY HACK - only include columns actually used in spec
chooser_columns = model_settings['SIMULATE_CHOOSER_COLUMNS']
choosers = persons_merged[chooser_columns]
alt_dest_col_name = model_settings['ALT_DEST_COL_NAME']
# alternatives are pre-sampled and annotated with logsums and pick_count
# but we have to merge additional alt columns into alt sample list
alternatives = \
pd.merge(location_sample_df, dest_size_terms,
left_on=alt_dest_col_name, right_index=True, how="left")
logger.info("Running %s with %d persons" % (trace_label, len(choosers)))
# create wrapper with keys for this lookup - in this case there is a home_zone_id in the choosers
# and a zone_id in the alternatives which get merged during interaction
# the skims will be available under the name "skims" for any @ expressions
skim_dict = network_los.get_default_skim_dict()
skims = skim_dict.wrap('home_zone_id', alt_dest_col_name)
locals_d = {'skims': skims, 'segment_size': segment_name}
constants = config.get_model_constants(model_settings)
if constants is not None:
locals_d.update(constants)
if estimator:
# write choosers after annotation
estimator.write_choosers(choosers)
estimator.set_alt_id(alt_dest_col_name)
estimator.write_interaction_sample_alternatives(alternatives)
spec = simulate.spec_for_segment(model_settings,
spec_id='SPEC',
segment_name=segment_name,
estimator=estimator)
log_alt_losers = config.setting('log_alt_losers', False)
choices = interaction_sample_simulate(
choosers,
alternatives,
spec=spec,
choice_column=alt_dest_col_name,
log_alt_losers=log_alt_losers,
want_logsums=want_logsums,
skims=skims,
locals_d=locals_d,
chunk_size=chunk_size,
chunk_tag=chunk_tag,
trace_label=trace_label,
trace_choice_name=model_settings['DEST_CHOICE_COLUMN_NAME'],
estimator=estimator)
if not want_logsums:
# for consistency, always return a dataframe with canonical column name
assert isinstance(choices, pd.Series)
choices = choices.to_frame('choice')
assert isinstance(choices, pd.DataFrame)
return choices
def run_location_sample(
segment_name,
persons_merged,
network_los,
dest_size_terms,
estimator,
model_settings,
chunk_size, trace_label):
"""
select a sample of alternative locations.
Logsum calculations are expensive, so we build a table of persons * all zones
and then select a sample subset of potential locations
The sample subset is generated by making multiple choices (<sample_size> number of choices)
which results in sample containing up to <sample_size> choices for each choose (e.g. person)
and a pick_count indicating how many times that choice was selected for that chooser.)
person_id, dest_zone_id, rand, pick_count
23750, 14, 0.565502716034, 4
23750, 16, 0.711135838871, 6
...
23751, 12, 0.408038878552, 1
23751, 14, 0.972732479292, 2
"""
assert not persons_merged.empty
# FIXME - MEMORY HACK - only include columns actually used in spec
chooser_columns = model_settings['SIMULATE_CHOOSER_COLUMNS']
choosers = persons_merged[chooser_columns]
alternatives = dest_size_terms
alt_dest_col_name = model_settings['ALT_DEST_COL_NAME']
logger.info("Running %s with %d persons" % (trace_label, len(choosers.index)))
sample_size = model_settings["SAMPLE_SIZE"]
if estimator:
# FIXME interaction_sample will return unsampled complete alternatives with probs and pick_count
logger.info("Estimation mode for %s using unsampled alternatives short_circuit_choices" % (trace_label,))
sample_size = 0
# create wrapper with keys for this lookup - in this case there is a home_zone_id in the choosers
# and a zone_id in the alternatives which get merged during interaction
# (logit.interaction_dataset suffixes duplicate chooser column with '_chooser')
# the skims will be available under the name "skims" for any @ expressions
skim_dict = network_los.get_default_skim_dict()
skims = skim_dict.wrap('home_zone_id', 'zone_id')
locals_d = {
'skims': skims,
'segment_size': segment_name
}
constants = config.get_model_constants(model_settings)
locals_d.update(constants)
spec = simulate.spec_for_segment(model_settings, spec_id='SAMPLE_SPEC',
segment_name=segment_name, estimator=estimator)
choices = interaction_sample(
choosers,
alternatives,
sample_size=sample_size,
alt_col_name=alt_dest_col_name,
spec=spec,
skims=skims,
locals_d=locals_d,
chunk_size=chunk_size,
trace_label=trace_label)
return choices
def run_od_simulate(spec_segment_name, tours, od_sample, want_logsums,
model_settings, network_los, destination_size_terms,
estimator, chunk_size, trace_label):
"""
run simulate OD choices on tour_od_sample annotated with mode_choice
logsum to select a tour OD from sample alternatives
"""
model_spec = simulate.spec_for_segment(model_settings,
spec_id='SPEC',
segment_name=spec_segment_name,
estimator=estimator)
# merge persons into tours
choosers = tours
# FIXME - MEMORY HACK - only include columns actually used in spec
chooser_columns = model_settings['SIMULATE_CHOOSER_COLUMNS']
choosers = choosers[chooser_columns]
# interaction_sample requires that choosers.index.is_monotonic_increasing
if not choosers.index.is_monotonic_increasing:
logger.debug(
f"run_destination_simulate {trace_label} sorting choosers because not monotonic_increasing"
)
choosers = choosers.sort_index()
if estimator:
estimator.write_choosers(choosers)
origin_col_name = model_settings['ORIG_COL_NAME']
dest_col_name = model_settings['DEST_COL_NAME']
alt_dest_col_name = model_settings['ALT_DEST_COL_NAME']
origin_attr_cols = model_settings['ORIGIN_ATTR_COLS_TO_USE']
alt_od_col_name = get_od_id_col(origin_col_name, dest_col_name)
od_sample[alt_od_col_name] = create_od_id_col(od_sample, origin_col_name,
dest_col_name)
# alternatives are pre-sampled and annotated with logsums and pick_count
# but we have to merge size_terms column into alt sample list
od_sample['size_term'] = \
reindex(destination_size_terms.size_term, od_sample[alt_dest_col_name])
# also have to add origin attribute columns
lu = inject.get_table('land_use').to_frame(columns=origin_attr_cols)
od_sample = pd.merge(od_sample,
lu,
left_on=origin_col_name,
right_index=True,
how='left')
tracing.dump_df(DUMP, od_sample, trace_label, 'alternatives')
constants = config.get_model_constants(model_settings)
logger.info("Running tour_destination_simulate with %d persons",
len(choosers))
# create wrapper with keys for this lookup - in this case there is an origin ID
# column and a destination ID columns in the alternatives table.
# the skims will be available under the name "skims" for any @ expressions
skim_dict = network_los.get_default_skim_dict()
skims = skim_dict.wrap(origin_col_name, dest_col_name)
locals_d = {
'skims': skims,
}
if constants is not None:
locals_d.update(constants)
tracing.dump_df(DUMP, choosers, trace_label, 'choosers')
choices = interaction_sample_simulate(
choosers,
od_sample,
spec=model_spec,
choice_column=alt_od_col_name,
want_logsums=want_logsums,
skims=skims,
locals_d=locals_d,
chunk_size=chunk_size,
trace_label=trace_label,
trace_choice_name='origin_destination',
estimator=estimator)
if not want_logsums:
choices = choices.to_frame('choice')
choices = _get_od_cols_from_od_id(choices, origin_col_name, dest_col_name)
return choices
def _od_sample(spec_segment_name, choosers, network_los,
destination_size_terms, origin_id_col, dest_id_col, skims,
estimator, model_settings, alt_od_col_name, chunk_size,
chunk_tag, trace_label):
model_spec = simulate.spec_for_segment(model_settings,
spec_id='SAMPLE_SPEC',
segment_name=spec_segment_name,
estimator=estimator)
if alt_od_col_name is None:
alt_col_name = get_od_id_col(origin_id_col, dest_id_col)
else:
alt_col_name = alt_od_col_name
logger.info("running %s with %d tours", trace_label, len(choosers))
sample_size = model_settings['SAMPLE_SIZE']
if config.setting('disable_destination_sampling',
False) or (estimator
and estimator.want_unsampled_alternatives):
# FIXME interaction_sample will return unsampled complete alternatives
# with probs and pick_count
logger.info(("Estimation mode for %s using unsampled alternatives "
"short_circuit_choices") % trace_label)
sample_size = 0
locals_d = {'skims': skims}
constants = config.get_model_constants(model_settings)
if constants is not None:
locals_d.update(constants)
origin_filter = model_settings.get('ORIG_FILTER', None)
origin_attr_cols = model_settings['ORIGIN_ATTR_COLS_TO_USE']
od_alts_df = _create_od_alts_from_dest_size_terms(
destination_size_terms,
spec_segment_name,
od_id_col=alt_col_name,
origin_id_col=origin_id_col,
dest_id_col=dest_id_col,
origin_filter=origin_filter,
origin_attr_cols=origin_attr_cols)
if skims.orig_key == ORIG_TAZ:
od_alts_df[ORIG_TAZ] = map_maz_to_taz(od_alts_df[origin_id_col],
network_los)
elif skims.orig_key not in od_alts_df:
logger.error("Alts df is missing origin skim key column.")
choices = interaction_sample(choosers,
alternatives=od_alts_df,
sample_size=sample_size,
alt_col_name=alt_col_name,
spec=model_spec,
skims=skims,
locals_d=locals_d,
chunk_size=chunk_size,
chunk_tag=chunk_tag,
trace_label=trace_label)
return choices
