def create_mandatory_tours():
# FIXME - move this to body?
persons = inject.get_table('persons')
configs_dir = inject.get_injectable('configs_dir')
persons = persons.to_frame(columns=[
"mandatory_tour_frequency", "is_worker", "school_taz", "workplace_taz"
])
persons = persons[~persons.mandatory_tour_frequency.isnull()]
tour_frequency_alternatives = inject.get_injectable(
'mandatory_tour_frequency_alternatives')
tours = process_mandatory_tours(persons, tour_frequency_alternatives)
expressions.assign_columns(df=tours,
model_settings='annotate_tours_with_dest',
configs_dir=configs_dir,
trace_label='create_mandatory_tours')
pipeline.extend_table("tours", tours)
tracing.register_traceable_table('tours', tours)
pipeline.get_rn_generator().add_channel(tours, 'tours')
def create_simple_trips(tours, households, persons, trace_hh_id):
"""
Create a simple trip table
"""
logger.info("Running simple trips table creation with %d tours" %
len(tours.index))
tours_df = tours.to_frame()
# we now have a tour_id column
tours_df.reset_index(inplace=True)
tours_df['household_id'] = reindex(persons.household_id,
tours_df.person_id)
tours_df['TAZ'] = reindex(households.TAZ, tours_df.household_id)
# create inbound and outbound records
trips = pd.concat([tours_df, tours_df], ignore_index=True)
# first half are outbound, second half are inbound
trips['INBOUND'] = np.repeat([False, True], len(trips.index) / 2)
# TRIPID for outbound trips = 1, inbound_trips = 2
trips['trip_num'] = np.repeat([1, 2], len(trips.index) / 2)
# set key fields from tour fields: 'TAZ','destination','start','end'
trips['OTAZ'] = trips.TAZ
trips['OTAZ'][trips.INBOUND] = trips.destination[trips.INBOUND]
trips['DTAZ'] = trips.destination
trips['DTAZ'][trips.INBOUND] = trips.TAZ[trips.INBOUND]
trips['start_trip'] = trips.start
trips['start_trip'][trips.INBOUND] = trips.end[trips.INBOUND]
trips['end_trip'] = trips.end
trips['end_trip'][trips.INBOUND] = trips.start[trips.INBOUND]
# create a stable (predictable) index based on tour_id and trip_num
possible_trips_count = 2
trips['trip_id'] = (trips.tour_id *
possible_trips_count) + (trips.trip_num - 1)
trips.set_index('trip_id', inplace=True, verify_integrity=True)
trip_columns = [
'tour_id', 'INBOUND', 'trip_num', 'OTAZ', 'DTAZ', 'start_trip',
'end_trip'
]
trips = trips[trip_columns]
orca.add_table("trips", trips)
tracing.register_traceable_table('trips', trips)
pipeline.get_rn_generator().add_channel(trips, 'trips')
if trace_hh_id:
tracing.trace_df(trips, label="trips", warn_if_empty=True)
def create_households(trace_hh_id):
df = pd.DataFrame({'HHID': [1, 2, 3], 'TAZ': {100, 100, 101}})
inject.add_table('households', df)
pipeline.get_rn_generator().add_channel(df, 'households')
if trace_hh_id:
tracing.register_traceable_table('households', df)
def create_households(trace_hh_id):
df = pd.DataFrame({
'household_id': [1, 2, 3],
'home_zone_id': {100, 100, 101}
})
inject.add_table('households', df)
pipeline.get_rn_generator().add_channel('households', df)
tracing.register_traceable_table('households', df)
def create_mandatory_tours_table():
persons = orca.get_table('persons')
persons = persons.to_frame(columns=["mandatory_tour_frequency",
"is_worker", "school_taz", "workplace_taz"])
persons = persons[~persons.mandatory_tour_frequency.isnull()]
df = process_mandatory_tours(persons)
orca.add_table("mandatory_tours", df)
tracing.register_traceable_table('mandatory_tours', df)
pipeline.get_rn_generator().add_channel(df, 'tours')
def create_non_mandatory_tours_table():
persons = orca.get_table('persons')
non_mandatory_tour_frequency_alts = orca.get_table('non_mandatory_tour_frequency_alts')
df = process_non_mandatory_tours(
persons.non_mandatory_tour_frequency.dropna(),
non_mandatory_tour_frequency_alts.local
)
orca.add_table("non_mandatory_tours", df)
tracing.register_traceable_table('non_mandatory_tours', df)
pipeline.get_rn_generator().add_channel(df, 'tours')
def initialize_tours(network_los, households, persons, trace_hh_id):
trace_label = 'initialize_tours'
tours = read_input_table("tours")
# FIXME can't use households_sliced injectable as flag like persons table does in case of resume_after.
# FIXME could just always slice...
slice_happened = \
inject.get_injectable('households_sample_size', 0) > 0 \
or inject.get_injectable('households_sample_size', 0) > 0
if slice_happened:
logger.info("slicing tours %s" % (tours.shape,))
# keep all persons in the sampled households
tours = tours[tours.person_id.isin(persons.index)]
# annotate before patching tour_id to allow addition of REQUIRED_TOUR_COLUMNS defined above
model_settings = config.read_model_settings('initialize_tours.yaml', mandatory=True)
expressions.assign_columns(
df=tours,
model_settings=model_settings.get('annotate_tours'),
trace_label=tracing.extend_trace_label(trace_label, 'annotate_tours'))
skip_patch_tour_ids = model_settings.get('skip_patch_tour_ids', False)
if skip_patch_tour_ids:
pass
else:
tours = patch_tour_ids(tours)
assert tours.index.name == 'tour_id'
# replace table function with dataframe
inject.add_table('tours', tours)
pipeline.get_rn_generator().add_channel('tours', tours)
tracing.register_traceable_table('tours', tours)
logger.debug(f"{len(tours.household_id.unique())} unique household_ids in tours")
logger.debug(f"{len(households.index.unique())} unique household_ids in households")
assert not tours.index.duplicated().any()
tours_without_persons = ~tours.person_id.isin(persons.index)
if tours_without_persons.any():
logger.error(f"{tours_without_persons.sum()} tours out of {len(persons)} without persons\n"
f"{pd.Series({'person_id': tours_without_persons.index.values})}")
raise RuntimeError(f"{tours_without_persons.sum()} tours with bad person_id")
if trace_hh_id:
tracing.trace_df(tours,
label='initialize_tours',
warn_if_empty=True)
def persons(households, trace_hh_id):
df = read_raw_persons(households)
logger.info("loaded persons %s" % (df.shape, ))
# replace table function with dataframe
inject.add_table('persons', df)
pipeline.get_rn_generator().add_channel('persons', df)
if trace_hh_id:
tracing.register_traceable_table('persons', df)
tracing.trace_df(df, "raw.persons", warn_if_empty=True)
return df
def create_non_mandatory_tours():
"""
We have now generated non-mandatory tours, but they are attributes of the person table
Now we create a "tours" table which has one row per tour that has been generated
(and the person id it is associated with)
"""
persons = inject.get_table('persons')
alts = inject.get_injectable('non_mandatory_tour_frequency_alts')
df = process_non_mandatory_tours(
persons.non_mandatory_tour_frequency.dropna(),
alts
)
pipeline.extend_table("tours", df)
tracing.register_traceable_table('tours', df)
pipeline.get_rn_generator().add_channel(df, 'tours')
def persons(households, trace_hh_id):
df = read_raw_persons(households)
logger.info("loaded persons %s" % (df.shape,))
df.index.name = 'person_id'
# replace table function with dataframe
inject.add_table('persons', df)
pipeline.get_rn_generator().add_channel('persons', df)
if trace_hh_id:
tracing.register_traceable_table('persons', df)
tracing.trace_df(df, "raw.persons", warn_if_empty=True)
return df
def persons(households, trace_hh_id):
df = read_raw_persons(households)
logger.info("loaded persons %s" % (df.shape, ))
# replace table function with dataframe
inject.add_table('persons', df)
pipeline.get_rn_generator().add_channel('persons', df)
tracing.register_traceable_table('persons', df)
if trace_hh_id:
tracing.trace_df(df, "raw.persons", warn_if_empty=True)
logger.debug(
f"{len(df.household_id.unique())} unique household_ids in persons")
logger.debug(
f"{len(households.index.unique())} unique household_ids in households")
assert not households.index.duplicated().any()
assert not df.index.duplicated().any()
persons_without_households = ~df.household_id.isin(households.index)
if persons_without_households.any():
logger.error(
f"{persons_without_households.sum()} persons out of {len(persons)} without households\n"
f"{pd.Series({'person_id': persons_without_households.index.values})}"
)
raise RuntimeError(
f"{persons_without_households.sum()} persons with bad household_id"
)
households_without_persons = df.groupby('household_id').size().reindex(
households.index).isnull()
if households_without_persons.any():
logger.error(
f"{households_without_persons.sum()} households out of {len(households.index)} without persons\n"
f"{pd.Series({'household_id': households_without_persons.index.values})}"
)
raise RuntimeError(
f"{households_without_persons.sum()} households with no persons")
return df
def persons(store, households_sample_size, households, trace_hh_id):
df = store["persons"]
if households_sample_size > 0:
# keep all persons in the sampled households
df = df[df.household_id.isin(households.index)]
logger.info("loaded persons %s" % (df.shape, ))
# replace table function with dataframe
orca.add_table('persons', df)
pipeline.get_rn_generator().add_channel(df, 'persons')
if trace_hh_id:
tracing.register_traceable_table('persons', df)
tracing.trace_df(df, "persons", warn_if_empty=True)
return df
def households(store, households_sample_size, trace_hh_id):
df_full = store["households"]
# if we are tracing hh exclusively
if trace_hh_id and households_sample_size == 1:
# df contains only trace_hh (or empty if not in full store)
df = tracing.slice_ids(df_full, trace_hh_id)
# if we need sample a subset of full store
elif households_sample_size > 0 and df_full.shape[0] > households_sample_size:
logger.info("sampling %s of %s households" % (households_sample_size, df_full.shape[0]))
# take the requested random sample
df = asim.random_rows(df_full, households_sample_size)
# if tracing and we missed trace_hh in sample, but it is in full store
if trace_hh_id and trace_hh_id not in df.index and trace_hh_id in df_full.index:
# replace first hh in sample with trace_hh
logger.debug("replacing household %s with %s in household sample" %
(df.index[0], trace_hh_id))
df_hh = tracing.slice_ids(df_full, trace_hh_id)
df = pd.concat([df_hh, df[1:]])
else:
df = df_full
logger.info("loaded households %s" % (df.shape,))
# replace table function with dataframe
inject.add_table('households', df)
pipeline.get_rn_generator().add_channel(df, 'households')
if trace_hh_id:
tracing.register_traceable_table('households', df)
tracing.trace_df(df, "households", warn_if_empty=True)
return df
def get_trip_mc_logsums_for_all_modes(tours, segment_column_name,
model_settings, trace_label):
"""Creates pseudo-trips from tours and runs trip mode choice to get logsums
Parameters
----------
tours : pandas.DataFrame
segment_column_name : str
column in tours table used for segmenting model spec
model_settings : dict
trace_label : str
Returns
-------
tours : pd.DataFrame
Adds two * n_modes logsum columns to each tour row, e.g. "logsum_DRIVE_outbound"
"""
# create pseudo-trips from tours for all tour modes
logsum_trips = create_logsum_trips(tours, segment_column_name,
model_settings, trace_label)
# temporarily register trips in the pipeline
pipeline.replace_table('trips', logsum_trips)
tracing.register_traceable_table('trips', logsum_trips)
pipeline.get_rn_generator().add_channel('trips', logsum_trips)
# run trip mode choice on pseudo-trips. use orca instead of pipeline to
# execute the step because pipeline can only handle one open step at a time
orca.run(['trip_mode_choice'])
# add trip mode choice logsums as new cols in tours
tours = append_tour_leg_trip_mode_choice_logsums(tours)
# de-register logsum trips table
pipeline.get_rn_generator().drop_channel('trips')
tracing.deregister_traceable_table('trips')
return tours
def create_non_mandatory_tours(trace_hh_id):
"""
We have now generated non-mandatory tours, but they are attributes of the person table
Now we create a "tours" table which has one row per tour that has been generated
(and the person id it is associated with)
"""
persons = inject.get_table('persons')
alts = inject.get_injectable('non_mandatory_tour_frequency_alts')
non_mandatory_tours = process_non_mandatory_tours(
persons.non_mandatory_tour_frequency.dropna(),
alts
)
tours = pipeline.extend_table("tours", non_mandatory_tours)
tracing.register_traceable_table('tours', tours)
pipeline.get_rn_generator().add_channel(non_mandatory_tours, 'tours')
if trace_hh_id:
tracing.trace_df(non_mandatory_tours,
label="non_mandatory_tour_frequency.non_mandatory_tours",
warn_if_empty=True)
def stop_frequency(
tours, tours_merged,
stop_frequency_alts,
skim_dict,
chunk_size,
trace_hh_id):
"""
stop frequency model
For each tour, shoose a number of intermediate inbound stops and outbound stops.
Create a trip table with inbound and outbound trips.
Thus, a tour with stop_frequency '2out_0in' will have two outbound and zero inbound stops,
and four corresponding trips: three outbound, and one inbound.
Adds stop_frequency str column to trips, with fields
creates trips table with columns:
::
- person_id
- household_id
- tour_id
- primary_purpose
- atwork
- trip_num
- outbound
- trip_count
"""
trace_label = 'stop_frequency'
model_settings = config.read_model_settings('stop_frequency.yaml')
tours = tours.to_frame()
tours_merged = tours_merged.to_frame()
assert not tours_merged.household_id.isnull().any()
assert not (tours_merged.origin == -1).any()
assert not (tours_merged.destination == -1).any()
nest_spec = config.get_logit_model_settings(model_settings)
constants = config.get_model_constants(model_settings)
# - run preprocessor to annotate tours_merged
preprocessor_settings = model_settings.get('preprocessor', None)
if preprocessor_settings:
# hack: preprocessor adds origin column in place if it does not exist already
od_skim_stack_wrapper = skim_dict.wrap('origin', 'destination')
skims = [od_skim_stack_wrapper]
locals_dict = {
"od_skims": od_skim_stack_wrapper
}
if constants is not None:
locals_dict.update(constants)
simulate.set_skim_wrapper_targets(tours_merged, skims)
# this should be pre-slice as some expressions may count tours by type
annotations = expressions.compute_columns(
df=tours_merged,
model_settings=preprocessor_settings,
locals_dict=locals_dict,
trace_label=trace_label)
assign_in_place(tours_merged, annotations)
tracing.print_summary('stop_frequency segments',
tours_merged.primary_purpose, value_counts=True)
choices_list = []
for segment_type, choosers in tours_merged.groupby('primary_purpose'):
logging.info("%s running segment %s with %s chooser rows" %
(trace_label, segment_type, choosers.shape[0]))
spec = simulate.read_model_spec(file_name='stop_frequency_%s.csv' % segment_type)
assert spec is not None, "spec for segment_type %s not found" % segment_type
choices = simulate.simple_simulate(
choosers=choosers,
spec=spec,
nest_spec=nest_spec,
locals_d=constants,
chunk_size=chunk_size,
trace_label=tracing.extend_trace_label(trace_label, segment_type),
trace_choice_name='stops')
# convert indexes to alternative names
choices = pd.Series(spec.columns[choices.values], index=choices.index)
choices_list.append(choices)
choices = pd.concat(choices_list)
tracing.print_summary('stop_frequency', choices, value_counts=True)
# add stop_frequency choices to tours table
assign_in_place(tours, choices.to_frame('stop_frequency'))
if 'primary_purpose' not in tours.columns:
assign_in_place(tours, tours_merged[['primary_purpose']])
pipeline.replace_table("tours", tours)
# create trips table
trips = process_trips(tours, stop_frequency_alts)
trips = pipeline.extend_table("trips", trips)
tracing.register_traceable_table('trips', trips)
pipeline.get_rn_generator().add_channel('trips', trips)
if trace_hh_id:
tracing.trace_df(tours,
label="stop_frequency.tours",
slicer='person_id',
columns=None)
tracing.trace_df(trips,
label="stop_frequency.trips",
slicer='person_id',
columns=None)
tracing.trace_df(annotations,
label="stop_frequency.annotations",
columns=None)
tracing.trace_df(tours_merged,
label="stop_frequency.tours_merged",
slicer='person_id',
columns=None)
def joint_tour_participation(
tours, persons_merged,
chunk_size,
trace_hh_id):
"""
Predicts for each eligible person to participate or not participate in each joint tour.
"""
trace_label = 'joint_tour_participation'
model_settings_file_name = 'joint_tour_participation.yaml'
model_settings = config.read_model_settings(model_settings_file_name)
tours = tours.to_frame()
joint_tours = tours[tours.tour_category == 'joint']
# - if no joint tours
if joint_tours.shape[0] == 0:
add_null_results(model_settings, trace_label)
return
persons_merged = persons_merged.to_frame()
# - create joint_tour_participation_candidates table
candidates = joint_tour_participation_candidates(joint_tours, persons_merged)
tracing.register_traceable_table('joint_tour_participants', candidates)
pipeline.get_rn_generator().add_channel('joint_tour_participants', candidates)
logger.info("Running joint_tours_participation with %d potential participants (candidates)" %
candidates.shape[0])
# - preprocessor
preprocessor_settings = model_settings.get('preprocessor', None)
if preprocessor_settings:
locals_dict = {
'person_time_window_overlap': person_time_window_overlap,
'persons': persons_merged
}
expressions.assign_columns(
df=candidates,
model_settings=preprocessor_settings,
locals_dict=locals_dict,
trace_label=trace_label)
# - simple_simulate
estimator = estimation.manager.begin_estimation('joint_tour_participation')
model_spec = simulate.read_model_spec(file_name=model_settings['SPEC'])
coefficients_df = simulate.read_model_coefficients(model_settings)
model_spec = simulate.eval_coefficients(model_spec, coefficients_df, estimator)
nest_spec = config.get_logit_model_settings(model_settings)
constants = config.get_model_constants(model_settings)
if estimator:
estimator.write_model_settings(model_settings, model_settings_file_name)
estimator.write_spec(model_settings)
estimator.write_coefficients(coefficients_df, model_settings)
estimator.write_choosers(candidates)
# add tour-based chunk_id so we can chunk all trips in tour together
assert 'chunk_id' not in candidates.columns
unique_household_ids = candidates.household_id.unique()
household_chunk_ids = pd.Series(range(len(unique_household_ids)), index=unique_household_ids)
candidates['chunk_id'] = reindex(household_chunk_ids, candidates.household_id)
choices = simulate.simple_simulate_by_chunk_id(
choosers=candidates,
spec=model_spec,
nest_spec=nest_spec,
locals_d=constants,
chunk_size=chunk_size,
trace_label=trace_label,
trace_choice_name='participation',
custom_chooser=participants_chooser,
estimator=estimator)
# choice is boolean (participate or not)
choice_col = model_settings.get('participation_choice', 'participate')
assert choice_col in model_spec.columns, \
"couldn't find participation choice column '%s' in spec"
PARTICIPATE_CHOICE = model_spec.columns.get_loc(choice_col)
participate = (choices == PARTICIPATE_CHOICE)
if estimator:
estimator.write_choices(choices)
# we override the 'participate' boolean series, instead of raw alternative index in 'choices' series
# its value depends on whether the candidate's 'participant_id' is in the joint_tour_participant index
survey_participants_df = estimator.get_survey_table('joint_tour_participants')
participate = pd.Series(choices.index.isin(survey_participants_df.index.values), index=choices.index)
# but estimation software wants to know the choices value (alternative index)
choices = participate.replace({True: PARTICIPATE_CHOICE, False: 1-PARTICIPATE_CHOICE})
# estimator.write_override_choices(participate) # write choices as boolean participate
estimator.write_override_choices(choices) # write choices as int alt indexes
estimator.end_estimation()
# satisfaction indexed by tour_id
tour_satisfaction = get_tour_satisfaction(candidates, participate)
assert tour_satisfaction.all()
candidates['satisfied'] = reindex(tour_satisfaction, candidates.tour_id)
PARTICIPANT_COLS = ['tour_id', 'household_id', 'person_id']
participants = candidates[participate][PARTICIPANT_COLS].copy()
# assign participant_num
# FIXME do we want something smarter than the participant with the lowest person_id?
participants['participant_num'] = \
participants.sort_values(by=['tour_id', 'person_id']).\
groupby('tour_id').cumcount() + 1
pipeline.replace_table("joint_tour_participants", participants)
# drop channel as we aren't using any more (and it has candidates that weren't chosen)
pipeline.get_rn_generator().drop_channel('joint_tour_participants')
# - assign joint tour 'point person' (participant_num == 1)
point_persons = participants[participants.participant_num == 1]
joint_tours['person_id'] = point_persons.set_index('tour_id').person_id
# update number_of_participants which was initialized to 1
joint_tours['number_of_participants'] = participants.groupby('tour_id').size()
assign_in_place(tours, joint_tours[['person_id', 'number_of_participants']])
pipeline.replace_table("tours", tours)
# - run annotations
annotate_jtp(model_settings, trace_label)
if trace_hh_id:
tracing.trace_df(participants,
label="joint_tour_participation.participants")
tracing.trace_df(joint_tours,
label="joint_tour_participation.joint_tours")
def joint_tour_participation(
tours, persons_merged,
chunk_size,
trace_hh_id):
"""
Predicts for each eligible person to participate or not participate in each joint tour.
"""
trace_label = 'joint_tour_participation'
model_settings = config.read_model_settings('joint_tour_participation.yaml')
model_spec = simulate.read_model_spec(file_name='joint_tour_participation.csv')
tours = tours.to_frame()
joint_tours = tours[tours.tour_category == 'joint']
# - if no joint tours
if joint_tours.shape[0] == 0:
add_null_results(model_settings, trace_label)
return
persons_merged = persons_merged.to_frame()
# - create joint_tour_participation_candidates table
candidates = joint_tour_participation_candidates(joint_tours, persons_merged)
tracing.register_traceable_table('joint_tour_participants', candidates)
pipeline.get_rn_generator().add_channel('joint_tour_participants', candidates)
logger.info("Running joint_tours_participation with %d potential participants (candidates)" %
candidates.shape[0])
# - preprocessor
preprocessor_settings = model_settings.get('preprocessor', None)
if preprocessor_settings:
locals_dict = {
'person_time_window_overlap': person_time_window_overlap,
'persons': persons_merged
}
expressions.assign_columns(
df=candidates,
model_settings=preprocessor_settings,
locals_dict=locals_dict,
trace_label=trace_label)
# - simple_simulate
nest_spec = config.get_logit_model_settings(model_settings)
constants = config.get_model_constants(model_settings)
choices = simulate.simple_simulate(
choosers=candidates,
spec=model_spec,
nest_spec=nest_spec,
locals_d=constants,
chunk_size=chunk_size,
trace_label=trace_label,
trace_choice_name='participation',
custom_chooser=participants_chooser)
# choice is boolean (participate or not)
choice_col = model_settings.get('participation_choice', 'participate')
assert choice_col in model_spec.columns, \
"couldn't find participation choice column '%s' in spec"
PARTICIPATE_CHOICE = model_spec.columns.get_loc(choice_col)
participate = (choices == PARTICIPATE_CHOICE)
# satisfaction indexed by tour_id
tour_satisfaction = get_tour_satisfaction(candidates, participate)
assert tour_satisfaction.all()
candidates['satisfied'] = reindex(tour_satisfaction, candidates.tour_id)
PARTICIPANT_COLS = ['tour_id', 'household_id', 'person_id']
participants = candidates[participate][PARTICIPANT_COLS].copy()
# assign participant_num
# FIXME do we want something smarter than the participant with the lowest person_id?
participants['participant_num'] = \
participants.sort_values(by=['tour_id', 'person_id']).\
groupby('tour_id').cumcount() + 1
pipeline.replace_table("joint_tour_participants", participants)
# drop channel as we aren't using any more (and it has candidates that weren't chosen)
pipeline.get_rn_generator().drop_channel('joint_tour_participants')
# - assign joint tour 'point person' (participant_num == 1)
point_persons = participants[participants.participant_num == 1]
joint_tours['person_id'] = point_persons.set_index('tour_id').person_id
# update number_of_participants which was initialized to 1
joint_tours['number_of_participants'] = participants.groupby('tour_id').size()
assign_in_place(tours, joint_tours[['person_id', 'number_of_participants']])
pipeline.replace_table("tours", tours)
# - run annotations
annotate_jtp(model_settings, trace_label)
if trace_hh_id:
tracing.trace_df(participants,
label="joint_tour_participation.participants")
tracing.trace_df(joint_tours,
label="joint_tour_participation.joint_tours")
def mandatory_tour_frequency(persons_merged,
chunk_size,
trace_hh_id):
"""
This model predicts the frequency of making mandatory trips (see the
alternatives above) - these trips include work and school in some combination.
"""
trace_label = 'mandatory_tour_frequency'
model_settings = config.read_model_settings('mandatory_tour_frequency.yaml')
model_spec = simulate.read_model_spec(file_name='mandatory_tour_frequency.csv')
alternatives = simulate.read_model_alts(
config.config_file_path('mandatory_tour_frequency_alternatives.csv'), set_index='alt')
choosers = persons_merged.to_frame()
# filter based on results of CDAP
choosers = choosers[choosers.cdap_activity == 'M']
logger.info("Running mandatory_tour_frequency with %d persons", len(choosers))
# - if no mandatory tours
if choosers.shape[0] == 0:
add_null_results(trace_label, model_settings)
return
# - preprocessor
preprocessor_settings = model_settings.get('preprocessor', None)
if preprocessor_settings:
locals_dict = {}
expressions.assign_columns(
df=choosers,
model_settings=preprocessor_settings,
locals_dict=locals_dict,
trace_label=trace_label)
nest_spec = config.get_logit_model_settings(model_settings)
constants = config.get_model_constants(model_settings)
choices = simulate.simple_simulate(
choosers=choosers,
spec=model_spec,
nest_spec=nest_spec,
locals_d=constants,
chunk_size=chunk_size,
trace_label=trace_label,
trace_choice_name='mandatory_tour_frequency')
# convert indexes to alternative names
choices = pd.Series(
model_spec.columns[choices.values],
index=choices.index).reindex(persons_merged.local.index)
# - create mandatory tours
"""
This reprocesses the choice of index of the mandatory tour frequency
alternatives into an actual dataframe of tours. Ending format is
the same as got non_mandatory_tours except trip types are "work" and "school"
"""
choosers['mandatory_tour_frequency'] = choices
mandatory_tours = process_mandatory_tours(
persons=choosers,
mandatory_tour_frequency_alts=alternatives
)
tours = pipeline.extend_table("tours", mandatory_tours)
tracing.register_traceable_table('tours', mandatory_tours)
pipeline.get_rn_generator().add_channel('tours', mandatory_tours)
# - annotate persons
persons = inject.get_table('persons').to_frame()
# need to reindex as we only handled persons with cdap_activity == 'M'
persons['mandatory_tour_frequency'] = choices.reindex(persons.index).fillna('').astype(str)
expressions.assign_columns(
df=persons,
model_settings=model_settings.get('annotate_persons'),
trace_label=tracing.extend_trace_label(trace_label, 'annotate_persons'))
pipeline.replace_table("persons", persons)
tracing.print_summary('mandatory_tour_frequency', persons.mandatory_tour_frequency,
value_counts=True)
if trace_hh_id:
tracing.trace_df(mandatory_tours,
label="mandatory_tour_frequency.mandatory_tours",
warn_if_empty=True)
tracing.trace_df(persons,
label="mandatory_tour_frequency.persons",
warn_if_empty=True)
def stop_frequency(tours, tours_merged, stop_frequency_alts, skim_dict,
chunk_size, trace_hh_id):
"""
stop frequency model
For each tour, shoose a number of intermediate inbound stops and outbound stops.
Create a trip table with inbound and outbound trips.
Thus, a tour with stop_frequency '2out_0in' will have two outbound and zero inbound stops,
and four corresponding trips: three outbound, and one inbound.
Adds stop_frequency str column to trips, with fields
creates trips table with columns:
::
- person_id
- household_id
- tour_id
- primary_purpose
- atwork
- trip_num
- outbound
- trip_count
"""
trace_label = 'stop_frequency'
model_settings = config.read_model_settings('stop_frequency.yaml')
tours = tours.to_frame()
tours_merged = tours_merged.to_frame()
assert not tours_merged.household_id.isnull().any()
assert not (tours_merged.origin == -1).any()
assert not (tours_merged.destination == -1).any()
nest_spec = config.get_logit_model_settings(model_settings)
constants = config.get_model_constants(model_settings)
# - run preprocessor to annotate tours_merged
preprocessor_settings = model_settings.get('preprocessor', None)
if preprocessor_settings:
# hack: preprocessor adds origin column in place if it does not exist already
od_skim_stack_wrapper = skim_dict.wrap('origin', 'destination')
skims = [od_skim_stack_wrapper]
locals_dict = {"od_skims": od_skim_stack_wrapper}
if constants is not None:
locals_dict.update(constants)
simulate.set_skim_wrapper_targets(tours_merged, skims)
# this should be pre-slice as some expressions may count tours by type
annotations = expressions.compute_columns(
df=tours_merged,
model_settings=preprocessor_settings,
locals_dict=locals_dict,
trace_label=trace_label)
assign_in_place(tours_merged, annotations)
tracing.print_summary('stop_frequency segments',
tours_merged.primary_purpose,
value_counts=True)
choices_list = []
for segment_type, choosers in tours_merged.groupby('primary_purpose'):
logging.info("%s running segment %s with %s chooser rows" %
(trace_label, segment_type, choosers.shape[0]))
spec = simulate.read_model_spec(file_name='stop_frequency_%s.csv' %
segment_type)
assert spec is not None, "spec for segment_type %s not found" % segment_type
choices = simulate.simple_simulate(
choosers=choosers,
spec=spec,
nest_spec=nest_spec,
locals_d=constants,
chunk_size=chunk_size,
trace_label=tracing.extend_trace_label(trace_label, segment_type),
trace_choice_name='stops')
# convert indexes to alternative names
choices = pd.Series(spec.columns[choices.values], index=choices.index)
choices_list.append(choices)
choices = pd.concat(choices_list)
tracing.print_summary('stop_frequency', choices, value_counts=True)
# add stop_frequency choices to tours table
assign_in_place(tours, choices.to_frame('stop_frequency'))
if 'primary_purpose' not in tours.columns:
assign_in_place(tours, tours_merged[['primary_purpose']])
pipeline.replace_table("tours", tours)
# create trips table
trips = process_trips(tours, stop_frequency_alts)
trips = pipeline.extend_table("trips", trips)
tracing.register_traceable_table('trips', trips)
pipeline.get_rn_generator().add_channel('trips', trips)
if trace_hh_id:
tracing.trace_df(tours,
label="stop_frequency.tours",
slicer='person_id',
columns=None)
tracing.trace_df(trips,
label="stop_frequency.trips",
slicer='person_id',
columns=None)
tracing.trace_df(annotations,
label="stop_frequency.annotations",
columns=None)
tracing.trace_df(tours_merged,
label="stop_frequency.tours_merged",
slicer='person_id',
columns=None)
def atwork_subtour_frequency(tours, persons_merged,
atwork_subtour_frequency_spec,
atwork_subtour_frequency_settings,
atwork_subtour_frequency_alternatives, chunk_size,
trace_hh_id):
"""
This model predicts the frequency of making at-work subtour tours
(alternatives for this model come from a separate csv file which is
configured by the user).
"""
trace_label = 'atwork_subtour_frequency'
tours = tours.to_frame()
persons_merged = persons_merged.to_frame()
work_tours = tours[tours.tour_type == 'work']
# merge persons into work_tours
work_tours = pd.merge(work_tours,
persons_merged,
left_on='person_id',
right_index=True)
logger.info("Running atwork_subtour_frequency with %d work tours" %
len(work_tours))
nest_spec = config.get_logit_model_settings(
atwork_subtour_frequency_settings)
constants = config.get_model_constants(atwork_subtour_frequency_settings)
choices = simulate.simple_simulate(
choosers=work_tours,
spec=atwork_subtour_frequency_spec,
nest_spec=nest_spec,
locals_d=constants,
trace_label=trace_label,
trace_choice_name='atwork_subtour_frequency')
# convert indexes to alternative names
choices = pd.Series(atwork_subtour_frequency_spec.columns[choices.values],
index=choices.index)
tracing.print_summary('atwork_subtour_frequency',
choices,
value_counts=True)
# reindex since we are working with a subset of tours
choices = choices.reindex(tours.index)
# add atwork_subtour_frequency column to tours
tours['atwork_subtour_frequency'] = choices
pipeline.replace_table("tours", tours)
# - create atwork_subtours based on atwork_subtour_frequency choice names
work_tours = tours[tours.tour_type == 'work']
assert not work_tours.atwork_subtour_frequency.isnull().any()
subtours = process_atwork_subtours(work_tours,
atwork_subtour_frequency_alternatives)
pipeline.extend_table("tours", subtours)
tracing.register_traceable_table('tours', subtours)
pipeline.get_rn_generator().add_channel(subtours, 'tours')
if trace_hh_id:
trace_columns = ['atwork_subtour_frequency']
tracing.trace_df(inject.get_table('tours').to_frame(),
label=trace_label,
columns=trace_columns,
warn_if_empty=True)
def non_mandatory_tour_frequency(persons, persons_merged, chunk_size,
trace_hh_id):
"""
This model predicts the frequency of making non-mandatory trips
(alternatives for this model come from a separate csv file which is
configured by the user) - these trips include escort, shopping, othmaint,
othdiscr, eatout, and social trips in various combination.
"""
trace_label = 'non_mandatory_tour_frequency'
model_settings_file_name = 'non_mandatory_tour_frequency.yaml'
model_settings = config.read_model_settings(model_settings_file_name)
# FIXME kind of tacky both that we know to add this here and del it below
# 'tot_tours' is used in model_spec expressions
alternatives = simulate.read_model_alts(
'non_mandatory_tour_frequency_alternatives.csv', set_index=None)
alternatives['tot_tours'] = alternatives.sum(axis=1)
# filter based on results of CDAP
choosers = persons_merged.to_frame()
choosers = choosers[choosers.cdap_activity.isin(['M', 'N'])]
# - preprocessor
preprocessor_settings = model_settings.get('preprocessor', None)
if preprocessor_settings:
locals_dict = {'person_max_window': person_max_window}
expressions.assign_columns(df=choosers,
model_settings=preprocessor_settings,
locals_dict=locals_dict,
trace_label=trace_label)
logger.info("Running non_mandatory_tour_frequency with %d persons",
len(choosers))
constants = config.get_model_constants(model_settings)
model_spec = simulate.read_model_spec(file_name=model_settings['SPEC'])
spec_segments = model_settings.get('SPEC_SEGMENTS', {})
# segment by person type and pick the right spec for each person type
choices_list = []
for segment_settings in spec_segments:
segment_name = segment_settings['NAME']
ptype = segment_settings['PTYPE']
# pick the spec column for the segment
segment_spec = model_spec[[segment_name]]
chooser_segment = choosers[choosers.ptype == ptype]
logger.info("Running segment '%s' of size %d", segment_name,
len(chooser_segment))
if len(chooser_segment) == 0:
# skip empty segments
continue
estimator = \
estimation.manager.begin_estimation(model_name=segment_name, bundle_name='non_mandatory_tour_frequency')
coefficients_df = simulate.read_model_coefficients(segment_settings)
segment_spec = simulate.eval_coefficients(segment_spec,
coefficients_df, estimator)
if estimator:
estimator.write_spec(model_settings, bundle_directory=True)
estimator.write_model_settings(model_settings,
model_settings_file_name,
bundle_directory=True)
# preserving coefficients file name makes bringing back updated coefficients more straightforward
estimator.write_coefficients(coefficients_df, segment_settings)
estimator.write_choosers(chooser_segment)
estimator.write_alternatives(alternatives, bundle_directory=True)
# FIXME #interaction_simulate_estimation_requires_chooser_id_in_df_column
# shuold we do it here or have interaction_simulate do it?
# chooser index must be duplicated in column or it will be omitted from interaction_dataset
# estimation requires that chooser_id is either in index or a column of interaction_dataset
# so it can be reformatted (melted) and indexed by chooser_id and alt_id
assert chooser_segment.index.name == 'person_id'
assert 'person_id' not in chooser_segment.columns
chooser_segment['person_id'] = chooser_segment.index
# FIXME set_alt_id - do we need this for interaction_simulate estimation bundle tables?
estimator.set_alt_id('alt_id')
estimator.set_chooser_id(chooser_segment.index.name)
choices = interaction_simulate(
chooser_segment,
alternatives,
spec=segment_spec,
locals_d=constants,
chunk_size=chunk_size,
trace_label='non_mandatory_tour_frequency.%s' % segment_name,
trace_choice_name='non_mandatory_tour_frequency',
estimator=estimator)
if estimator:
estimator.write_choices(choices)
choices = estimator.get_survey_values(
choices, 'persons', 'non_mandatory_tour_frequency')
estimator.write_override_choices(choices)
estimator.end_estimation()
choices_list.append(choices)
# FIXME - force garbage collection?
force_garbage_collect()
del alternatives['tot_tours'] # del tot_tours column we added above
# The choice value 'non_mandatory_tour_frequency' assigned by interaction_simulate
# is the index value of the chosen alternative in the alternatives table.
choices = pd.concat(choices_list).sort_index()
# add non_mandatory_tour_frequency column to persons
persons = persons.to_frame()
# we expect there to be an alt with no tours - which we can use to backfill non-travelers
no_tours_alt = (alternatives.sum(axis=1) == 0).index[0]
# need to reindex as we only handled persons with cdap_activity in ['M', 'N']
persons['non_mandatory_tour_frequency'] = \
choices.reindex(persons.index).fillna(no_tours_alt).astype(np.int8)
"""
We have now generated non-mandatory tour frequencies, but they are attributes of the person table
Now we create a "tours" table which has one row per tour that has been generated
(and the person id it is associated with)
But before we do that, we run an additional probablilistic step to extend/increase tour counts
beyond the strict limits of the tour_frequency alternatives chosen above (which are currently limited
to at most 2 escort tours and 1 each of shopping, othmaint, othdiscr, eatout, and social tours)
The choice value 'non_mandatory_tour_frequency' assigned by interaction_simulate is simply the
index value of the chosen alternative in the alternatives table.
get counts of each of the tour type alternatives (so we can extend)
escort shopping othmaint othdiscr eatout social
parent_id
2588676 2 0 0 1 1 0
2588677 0 1 0 1 0 0
"""
# counts of each of the tour type alternatives (so we can extend)
modeled_tour_counts = alternatives.loc[choices]
modeled_tour_counts.index = choices.index # assign person ids to the index
# - extend_tour_counts - probabalistic
extended_tour_counts = \
extend_tour_counts(choosers, modeled_tour_counts.copy(), alternatives,
trace_hh_id, tracing.extend_trace_label(trace_label, 'extend_tour_counts'))
num_modeled_tours = modeled_tour_counts.sum().sum()
num_extended_tours = extended_tour_counts.sum().sum()
logger.info("extend_tour_counts increased tour count by %s from %s to %s" %
(num_extended_tours - num_modeled_tours, num_modeled_tours,
num_extended_tours))
"""
create the non_mandatory tours based on extended_tour_counts
"""
if estimator:
override_tour_counts = \
estimation.manager.get_survey_values(extended_tour_counts,
table_name='persons',
column_names=['_%s' % c for c in extended_tour_counts.columns])
override_tour_counts = \
override_tour_counts.rename(columns={('_%s' % c): c for c in extended_tour_counts.columns})
logger.info(
"estimation get_survey_values override_tour_counts %s changed cells"
% (override_tour_counts != extended_tour_counts).sum().sum())
extended_tour_counts = override_tour_counts
"""
create the non_mandatory tours based on extended_tour_counts
"""
non_mandatory_tours = process_non_mandatory_tours(persons,
extended_tour_counts)
assert len(non_mandatory_tours) == extended_tour_counts.sum().sum()
if estimator:
# make sure they created the right tours
survey_tours = estimation.manager.get_survey_table(
'tours').sort_index()
non_mandatory_survey_tours = survey_tours[survey_tours.tour_category ==
'non_mandatory']
assert len(non_mandatory_survey_tours) == len(non_mandatory_tours)
assert non_mandatory_survey_tours.index.equals(
non_mandatory_tours.sort_index().index)
# make sure they created tours with the expected tour_ids
columns = ['person_id', 'household_id', 'tour_type', 'tour_category']
survey_tours = \
estimation.manager.get_survey_values(non_mandatory_tours,
table_name='tours',
column_names=columns)
tours_differ = (non_mandatory_tours[columns] !=
survey_tours[columns]).any(axis=1)
if tours_differ.any():
print("tours_differ\n%s" % tours_differ)
print("%s of %s tours differ" %
(tours_differ.sum(), len(tours_differ)))
print("differing survey_tours\n%s" % survey_tours[tours_differ])
print("differing modeled_tours\n%s" %
non_mandatory_tours[columns][tours_differ])
assert (not tours_differ.any())
pipeline.extend_table("tours", non_mandatory_tours)
tracing.register_traceable_table('tours', non_mandatory_tours)
pipeline.get_rn_generator().add_channel('tours', non_mandatory_tours)
expressions.assign_columns(
df=persons,
model_settings=model_settings.get('annotate_persons'),
trace_label=trace_label)
pipeline.replace_table("persons", persons)
tracing.print_summary('non_mandatory_tour_frequency',
persons.non_mandatory_tour_frequency,
value_counts=True)
if trace_hh_id:
tracing.trace_df(
non_mandatory_tours,
label="non_mandatory_tour_frequency.non_mandatory_tours",
warn_if_empty=True)
tracing.trace_df(choosers,
label="non_mandatory_tour_frequency.choosers",
warn_if_empty=True)
tracing.trace_df(
persons,
label="non_mandatory_tour_frequency.annotated_persons",
warn_if_empty=True)
def atwork_subtour_frequency(tours, persons_merged, chunk_size, trace_hh_id):
"""
This model predicts the frequency of making at-work subtour tours
(alternatives for this model come from a separate csv file which is
configured by the user).
"""
trace_label = 'atwork_subtour_frequency'
model_settings = config.read_model_settings(
'atwork_subtour_frequency.yaml')
model_spec = simulate.read_model_spec(
file_name='atwork_subtour_frequency.csv')
alternatives = simulate.read_model_alts(
config.config_file_path('atwork_subtour_frequency_alternatives.csv'),
set_index='alt')
tours = tours.to_frame()
persons_merged = persons_merged.to_frame()
work_tours = tours[tours.tour_type == 'work']
# - if no work_tours
if len(work_tours) == 0:
add_null_results(trace_label, tours)
return
# merge persons into work_tours
work_tours = pd.merge(work_tours,
persons_merged,
left_on='person_id',
right_index=True)
logger.info("Running atwork_subtour_frequency with %d work tours",
len(work_tours))
nest_spec = config.get_logit_model_settings(model_settings)
constants = config.get_model_constants(model_settings)
# - preprocessor
preprocessor_settings = model_settings.get('preprocessor', None)
if preprocessor_settings:
assign_columns(df=work_tours,
model_settings=preprocessor_settings,
trace_label=trace_label)
choices = simulate.simple_simulate(
choosers=work_tours,
spec=model_spec,
nest_spec=nest_spec,
locals_d=constants,
chunk_size=chunk_size,
trace_label=trace_label,
trace_choice_name='atwork_subtour_frequency')
# convert indexes to alternative names
choices = pd.Series(model_spec.columns[choices.values],
index=choices.index)
tracing.print_summary('atwork_subtour_frequency',
choices,
value_counts=True)
# add atwork_subtour_frequency column to tours
# reindex since we are working with a subset of tours
tours['atwork_subtour_frequency'] = choices.reindex(tours.index)
pipeline.replace_table("tours", tours)
# - create atwork_subtours based on atwork_subtour_frequency choice names
work_tours = tours[tours.tour_type == 'work']
assert not work_tours.atwork_subtour_frequency.isnull().any()
subtours = process_atwork_subtours(work_tours, alternatives)
tours = pipeline.extend_table("tours", subtours)
tracing.register_traceable_table('tours', subtours)
pipeline.get_rn_generator().add_channel('tours', subtours)
if trace_hh_id:
tracing.trace_df(tours, label='atwork_subtour_frequency.tours')
def joint_tour_frequency(households, persons, chunk_size, trace_hh_id):
"""
This model predicts the frequency of making fully joint trips (see the
alternatives above).
"""
trace_label = 'joint_tour_frequency'
model_settings = config.read_model_settings('joint_tour_frequency.yaml')
model_spec = simulate.read_model_spec(file_name='joint_tour_frequency.csv')
alternatives = simulate.read_model_alts(
config.config_file_path('joint_tour_frequency_alternatives.csv'),
set_index='alt')
# - only interested in households with more than one cdap travel_active person
households = households.to_frame()
multi_person_households = households[
households.num_travel_active > 1].copy()
# - only interested in persons in multi_person_households
# FIXME - gratuitous pathological efficiency move, just let yaml specify persons?
persons = persons.to_frame()
persons = persons[persons.household_id.isin(multi_person_households.index)]
logger.info(
"Running joint_tour_frequency with %d multi-person households" %
multi_person_households.shape[0])
# - preprocessor
preprocessor_settings = model_settings.get('preprocessor', None)
if preprocessor_settings:
locals_dict = {
'persons': persons,
'hh_time_window_overlap': hh_time_window_overlap
}
expressions.assign_columns(df=multi_person_households,
model_settings=preprocessor_settings,
locals_dict=locals_dict,
trace_label=trace_label)
# - simple_simulate
nest_spec = config.get_logit_model_settings(model_settings)
constants = config.get_model_constants(model_settings)
choices = simulate.simple_simulate(
choosers=multi_person_households,
spec=model_spec,
nest_spec=nest_spec,
locals_d=constants,
chunk_size=chunk_size,
trace_label=trace_label,
trace_choice_name='joint_tour_frequency')
# convert indexes to alternative names
choices = pd.Series(model_spec.columns[choices.values],
index=choices.index)
# - create joint_tours based on joint_tour_frequency choices
# - we need a person_id in order to generate the tour index (and for register_traceable_table)
# - but we don't know the tour participants yet
# - so we arbitrarily choose the first person in the household
# - to be point person for the purpose of generating an index and setting origin
temp_point_persons = persons.loc[persons.PNUM == 1]
temp_point_persons['person_id'] = temp_point_persons.index
temp_point_persons = temp_point_persons.set_index('household_id')
temp_point_persons = temp_point_persons[['person_id', 'home_taz']]
joint_tours = \
process_joint_tours(choices, alternatives, temp_point_persons)
tours = pipeline.extend_table("tours", joint_tours)
tracing.register_traceable_table('tours', joint_tours)
pipeline.get_rn_generator().add_channel('tours', joint_tours)
# - annotate households
# add joint_tour_frequency and num_hh_joint_tours columns to households
# reindex since we ran model on a subset of households
households['joint_tour_frequency'] = choices.reindex(
households.index).fillna('').astype(str)
households['num_hh_joint_tours'] = joint_tours.groupby('household_id').size().\
reindex(households.index).fillna(0).astype(np.int8)
pipeline.replace_table("households", households)
tracing.print_summary('joint_tour_frequency',
households.joint_tour_frequency,
value_counts=True)
if trace_hh_id:
tracing.trace_df(households, label="joint_tour_frequency.households")
tracing.trace_df(joint_tours,
label="joint_tour_frequency.joint_tours",
slicer='household_id')
def non_mandatory_tour_frequency(persons, persons_merged,
chunk_size,
trace_hh_id):
"""
This model predicts the frequency of making non-mandatory trips
(alternatives for this model come from a separate csv file which is
configured by the user) - these trips include escort, shopping, othmaint,
othdiscr, eatout, and social trips in various combination.
"""
trace_label = 'non_mandatory_tour_frequency'
model_settings = config.read_model_settings('non_mandatory_tour_frequency.yaml')
model_spec = simulate.read_model_spec(file_name='non_mandatory_tour_frequency.csv')
alternatives = simulate.read_model_alts(
config.config_file_path('non_mandatory_tour_frequency_alternatives.csv'),
set_index=None)
choosers = persons_merged.to_frame()
# FIXME kind of tacky both that we know to add this here and del it below
# 'tot_tours' is used in model_spec expressions
alternatives['tot_tours'] = alternatives.sum(axis=1)
# - preprocessor
preprocessor_settings = model_settings.get('preprocessor', None)
if preprocessor_settings:
locals_dict = {
'person_max_window': person_max_window
}
expressions.assign_columns(
df=choosers,
model_settings=preprocessor_settings,
locals_dict=locals_dict,
trace_label=trace_label)
# filter based on results of CDAP
choosers = choosers[choosers.cdap_activity.isin(['M', 'N'])]
logger.info("Running non_mandatory_tour_frequency with %d persons", len(choosers))
constants = config.get_model_constants(model_settings)
choices_list = []
# segment by person type and pick the right spec for each person type
for ptype, segment in choosers.groupby('ptype'):
name = PTYPE_NAME[ptype]
# pick the spec column for the segment
spec = model_spec[[name]]
# drop any zero-valued rows
spec = spec[spec[name] != 0]
logger.info("Running segment '%s' of size %d", name, len(segment))
choices = interaction_simulate(
segment,
alternatives,
spec=spec,
locals_d=constants,
chunk_size=chunk_size,
trace_label='non_mandatory_tour_frequency.%s' % name,
trace_choice_name='non_mandatory_tour_frequency')
choices_list.append(choices)
# FIXME - force garbage collection?
# force_garbage_collect()
choices = pd.concat(choices_list)
del alternatives['tot_tours'] # del tot_tours column we added above
# - add non_mandatory_tour_frequency column to persons
persons = persons.to_frame()
# need to reindex as we only handled persons with cdap_activity in ['M', 'N']
# (we expect there to be an alt with no tours - which we can use to backfill non-travelers)
no_tours_alt = (alternatives.sum(axis=1) == 0).index[0]
persons['non_mandatory_tour_frequency'] = \
choices.reindex(persons.index).fillna(no_tours_alt).astype(np.int8)
"""
We have now generated non-mandatory tours, but they are attributes of the person table
Now we create a "tours" table which has one row per tour that has been generated
(and the person id it is associated with)
"""
# - get counts of each of the alternatives (so we can extend)
# (choices is just the index values for the chosen alts)
"""
escort shopping othmaint othdiscr eatout social
parent_id
2588676 2 0 0 1 1 0
2588677 0 1 0 1 0 0
"""
tour_counts = alternatives.loc[choices]
tour_counts.index = choices.index # assign person ids to the index
prev_tour_count = tour_counts.sum().sum()
# - extend_tour_counts
tour_counts = extend_tour_counts(choosers, tour_counts, alternatives,
trace_hh_id,
tracing.extend_trace_label(trace_label, 'extend_tour_counts'))
extended_tour_count = tour_counts.sum().sum()
logging.info("extend_tour_counts increased nmtf tour count by %s from %s to %s" %
(extended_tour_count - prev_tour_count, prev_tour_count, extended_tour_count))
# - create the non_mandatory tours
non_mandatory_tours = process_non_mandatory_tours(persons, tour_counts)
assert len(non_mandatory_tours) == extended_tour_count
pipeline.extend_table("tours", non_mandatory_tours)
tracing.register_traceable_table('tours', non_mandatory_tours)
pipeline.get_rn_generator().add_channel('tours', non_mandatory_tours)
expressions.assign_columns(
df=persons,
model_settings=model_settings.get('annotate_persons'),
trace_label=trace_label)
pipeline.replace_table("persons", persons)
tracing.print_summary('non_mandatory_tour_frequency',
persons.non_mandatory_tour_frequency, value_counts=True)
if trace_hh_id:
tracing.trace_df(non_mandatory_tours,
label="non_mandatory_tour_frequency.non_mandatory_tours",
warn_if_empty=True)
tracing.trace_df(choosers,
label="non_mandatory_tour_frequency.choosers",
warn_if_empty=True)
tracing.trace_df(persons,
label="non_mandatory_tour_frequency.annotated_persons",
warn_if_empty=True)
def stop_frequency(tours, tours_merged, stop_frequency_alts, network_los,
chunk_size, trace_hh_id):
"""
stop frequency model
For each tour, shoose a number of intermediate inbound stops and outbound stops.
Create a trip table with inbound and outbound trips.
Thus, a tour with stop_frequency '2out_0in' will have two outbound and zero inbound stops,
and four corresponding trips: three outbound, and one inbound.
Adds stop_frequency str column to trips, with fields
creates trips table with columns:
::
- person_id
- household_id
- tour_id
- primary_purpose
- atwork
- trip_num
- outbound
- trip_count
"""
trace_label = 'stop_frequency'
model_settings_file_name = 'stop_frequency.yaml'
model_settings = config.read_model_settings(model_settings_file_name)
tours = tours.to_frame()
tours_merged = tours_merged.to_frame()
assert not tours_merged.household_id.isnull().any()
assert not (tours_merged.origin == -1).any()
assert not (tours_merged.destination == -1).any()
nest_spec = config.get_logit_model_settings(model_settings)
constants = config.get_model_constants(model_settings)
# - run preprocessor to annotate tours_merged
preprocessor_settings = model_settings.get('preprocessor', None)
if preprocessor_settings:
# hack: preprocessor adds origin column in place if it does not exist already
assert 'origin' in tours_merged
assert 'destination' in tours_merged
od_skim_stack_wrapper = network_los.get_default_skim_dict().wrap(
'origin', 'destination')
skims = [od_skim_stack_wrapper]
locals_dict = {
"od_skims": od_skim_stack_wrapper,
'network_los': network_los
}
locals_dict.update(constants)
simulate.set_skim_wrapper_targets(tours_merged, skims)
# this should be pre-slice as some expressions may count tours by type
annotations = expressions.compute_columns(
df=tours_merged,
model_settings=preprocessor_settings,
locals_dict=locals_dict,
trace_label=trace_label)
assign_in_place(tours_merged, annotations)
tracing.print_summary('stop_frequency segments',
tours_merged.primary_purpose,
value_counts=True)
spec_segments = model_settings.get('SPEC_SEGMENTS')
assert spec_segments is not None, f"SPEC_SEGMENTS setting not found in model settings: {model_settings_file_name}"
segment_col = model_settings.get('SEGMENT_COL')
assert segment_col is not None, f"SEGMENT_COL setting not found in model settings: {model_settings_file_name}"
nest_spec = config.get_logit_model_settings(model_settings)
choices_list = []
for segment_settings in spec_segments:
segment_name = segment_settings[segment_col]
segment_value = segment_settings[segment_col]
chooser_segment = tours_merged[tours_merged[segment_col] ==
segment_value]
if len(chooser_segment) == 0:
logging.info(
f"{trace_label} skipping empty segment {segment_name}")
continue
logging.info(
f"{trace_label} running segment {segment_name} with {chooser_segment.shape[0]} chooser rows"
)
estimator = estimation.manager.begin_estimation(
model_name=segment_name, bundle_name='stop_frequency')
segment_spec = simulate.read_model_spec(
file_name=segment_settings['SPEC'])
assert segment_spec is not None, "spec for segment_type %s not found" % segment_name
coefficients_file_name = segment_settings['COEFFICIENTS']
coefficients_df = simulate.read_model_coefficients(
file_name=coefficients_file_name)
segment_spec = simulate.eval_coefficients(segment_spec,
coefficients_df, estimator)
if estimator:
estimator.write_spec(segment_settings, bundle_directory=False)
estimator.write_model_settings(model_settings,
model_settings_file_name,
bundle_directory=True)
estimator.write_coefficients(coefficients_df, segment_settings)
estimator.write_choosers(chooser_segment)
estimator.set_chooser_id(chooser_segment.index.name)
choices = simulate.simple_simulate(
choosers=chooser_segment,
spec=segment_spec,
nest_spec=nest_spec,
locals_d=constants,
chunk_size=chunk_size,
trace_label=tracing.extend_trace_label(trace_label, segment_name),
trace_choice_name='stops',
estimator=estimator)
# convert indexes to alternative names
choices = pd.Series(segment_spec.columns[choices.values],
index=choices.index)
if estimator:
estimator.write_choices(choices)
choices = estimator.get_survey_values(
choices, 'tours', 'stop_frequency') # override choices
estimator.write_override_choices(choices)
estimator.end_estimation()
choices_list.append(choices)
choices = pd.concat(choices_list)
tracing.print_summary('stop_frequency', choices, value_counts=True)
# add stop_frequency choices to tours table
assign_in_place(tours, choices.to_frame('stop_frequency'))
# FIXME should have added this when tours created?
assert 'primary_purpose' not in tours
if 'primary_purpose' not in tours.columns:
# if not already there, then it will have been added by annotate tours preprocessor
assign_in_place(tours, tours_merged[['primary_purpose']])
pipeline.replace_table("tours", tours)
# create trips table
trips = process_trips(tours, stop_frequency_alts)
trips = pipeline.extend_table("trips", trips)
tracing.register_traceable_table('trips', trips)
pipeline.get_rn_generator().add_channel('trips', trips)
if estimator:
# make sure they created trips with the expected tour_ids
columns = ['person_id', 'household_id', 'tour_id', 'outbound']
survey_trips = estimation.manager.get_survey_table(table_name='trips')
different = False
survey_trips_not_in_trips = survey_trips[~survey_trips.index.
isin(trips.index)]
if len(survey_trips_not_in_trips) > 0:
print(f"survey_trips_not_in_trips\n{survey_trips_not_in_trips}")
different = True
trips_not_in_survey_trips = trips[~trips.index.isin(survey_trips.index
)]
if len(survey_trips_not_in_trips) > 0:
print(f"trips_not_in_survey_trips\n{trips_not_in_survey_trips}")
different = True
assert not different
survey_trips = \
estimation.manager.get_survey_values(trips,
table_name='trips',
column_names=columns)
trips_differ = (trips[columns] != survey_trips[columns]).any(axis=1)
if trips_differ.any():
print("trips_differ\n%s" % trips_differ)
print("%s of %s tours differ" %
(trips_differ.sum(), len(trips_differ)))
print("differing survey_trips\n%s" % survey_trips[trips_differ])
print("differing modeled_trips\n%s" % trips[columns][trips_differ])
assert (not trips_differ.any())
if trace_hh_id:
tracing.trace_df(tours,
label="stop_frequency.tours",
slicer='person_id',
columns=None)
tracing.trace_df(trips,
label="stop_frequency.trips",
slicer='person_id',
columns=None)
tracing.trace_df(annotations,
label="stop_frequency.annotations",
columns=None)
tracing.trace_df(tours_merged,
label="stop_frequency.tours_merged",
slicer='person_id',
columns=None)
def mandatory_tour_frequency(persons_merged, chunk_size, trace_hh_id):
"""
This model predicts the frequency of making mandatory trips (see the
alternatives above) - these trips include work and school in some combination.
"""
trace_label = 'mandatory_tour_frequency'
model_settings = config.read_model_settings(
'mandatory_tour_frequency.yaml')
model_spec = simulate.read_model_spec(
file_name='mandatory_tour_frequency.csv')
alternatives = simulate.read_model_alts(
config.config_file_path('mandatory_tour_frequency_alternatives.csv'),
set_index='alt')
choosers = persons_merged.to_frame()
# filter based on results of CDAP
choosers = choosers[choosers.cdap_activity == 'M']
logger.info("Running mandatory_tour_frequency with %d persons",
len(choosers))
# - if no mandatory tours
if choosers.shape[0] == 0:
add_null_results(trace_label, model_settings)
return
# - preprocessor
preprocessor_settings = model_settings.get('preprocessor', None)
if preprocessor_settings:
locals_dict = {}
expressions.assign_columns(df=choosers,
model_settings=preprocessor_settings,
locals_dict=locals_dict,
trace_label=trace_label)
nest_spec = config.get_logit_model_settings(model_settings)
constants = config.get_model_constants(model_settings)
choices = simulate.simple_simulate(
choosers=choosers,
spec=model_spec,
nest_spec=nest_spec,
locals_d=constants,
chunk_size=chunk_size,
trace_label=trace_label,
trace_choice_name='mandatory_tour_frequency')
# convert indexes to alternative names
choices = pd.Series(model_spec.columns[choices.values],
index=choices.index).reindex(
persons_merged.local.index)
# - create mandatory tours
"""
This reprocesses the choice of index of the mandatory tour frequency
alternatives into an actual dataframe of tours. Ending format is
the same as got non_mandatory_tours except trip types are "work" and "school"
"""
choosers['mandatory_tour_frequency'] = choices
mandatory_tours = process_mandatory_tours(
persons=choosers, mandatory_tour_frequency_alts=alternatives)
tours = pipeline.extend_table("tours", mandatory_tours)
tracing.register_traceable_table('tours', mandatory_tours)
pipeline.get_rn_generator().add_channel('tours', mandatory_tours)
# - annotate persons
persons = inject.get_table('persons').to_frame()
# need to reindex as we only handled persons with cdap_activity == 'M'
persons['mandatory_tour_frequency'] = choices.reindex(
persons.index).fillna('').astype(str)
expressions.assign_columns(
df=persons,
model_settings=model_settings.get('annotate_persons'),
trace_label=tracing.extend_trace_label(trace_label,
'annotate_persons'))
pipeline.replace_table("persons", persons)
tracing.print_summary('mandatory_tour_frequency',
persons.mandatory_tour_frequency,
value_counts=True)
if trace_hh_id:
tracing.trace_df(mandatory_tours,
label="mandatory_tour_frequency.mandatory_tours",
warn_if_empty=True)
tracing.trace_df(persons,
label="mandatory_tour_frequency.persons",
warn_if_empty=True)
def run_od_logsums(spec_segment_name, tours_merged_df, od_sample,
model_settings, network_los, estimator, chunk_size,
trace_hh_id, trace_label):
"""
add logsum column to existing tour_destination_sample table
logsum is calculated by running the mode_choice model for each sample
(person, OD_id) pair in od_sample, and computing the logsum of all the utilities
"""
chunk_tag = 'tour_od.logsums'
logsum_settings = config.read_model_settings(
model_settings['LOGSUM_SETTINGS'])
origin_id_col = model_settings['ORIG_COL_NAME']
dest_id_col = model_settings['DEST_COL_NAME']
tour_od_id_col = get_od_id_col(origin_id_col, dest_id_col)
# FIXME - MEMORY HACK - only include columns actually used in spec
tours_merged_df = \
logsum.filter_chooser_columns(tours_merged_df, logsum_settings, model_settings)
# merge ods into choosers table
choosers = od_sample.join(tours_merged_df, how='left')
choosers[tour_od_id_col] = choosers[origin_id_col].astype(
str) + '_' + choosers[dest_id_col].astype(str)
logger.info("Running %s with %s rows", trace_label, len(choosers))
tracing.dump_df(DUMP, choosers, trace_label, 'choosers')
# run trip mode choice to compute tour mode choice logsums
if logsum_settings.get('COMPUTE_TRIP_MODE_CHOICE_LOGSUMS', False):
pseudo_tours = choosers.copy()
trip_mode_choice_settings = config.read_model_settings(
'trip_mode_choice')
# tours_merged table doesn't yet have all the cols it needs to be called (e.g.
# home_zone_id), so in order to compute tour mode choice/trip mode choice logsums
# in this step we have to pass all tour-level attributes in with the main trips
# table. see trip_mode_choice.py L56-61 for more details.
tour_cols_needed = trip_mode_choice_settings.get(
'TOURS_MERGED_CHOOSER_COLUMNS', [])
tour_cols_needed.append(tour_od_id_col)
# from tour_mode_choice.py
not_university = (pseudo_tours.tour_type !=
'school') | ~pseudo_tours.is_university
pseudo_tours['tour_purpose'] = \
pseudo_tours.tour_type.where(not_university, 'univ')
pseudo_tours['stop_frequency'] = '0out_0in'
pseudo_tours['primary_purpose'] = pseudo_tours['tour_purpose']
choosers_og_index = choosers.index.name
pseudo_tours.reset_index(inplace=True)
pseudo_tours.index.name = 'unique_id'
# need dest_id_col to create dest col in trips, but need to preserve
# tour dest as separate column in the trips table bc the trip mode choice
# preprocessor isn't able to get the tour dest from the tours table bc the
# tours don't yet have ODs.
stop_frequency_alts = inject.get_injectable('stop_frequency_alts')
pseudo_tours['tour_destination'] = pseudo_tours[dest_id_col]
trips = trip.initialize_from_tours(
pseudo_tours, stop_frequency_alts,
[origin_id_col, dest_id_col, 'tour_destination', 'unique_id'])
outbound = trips['outbound']
trips['depart'] = reindex(pseudo_tours.start, trips.unique_id)
trips.loc[~outbound,
'depart'] = reindex(pseudo_tours.end, trips.loc[~outbound,
'unique_id'])
logsum_trips = pd.DataFrame()
nest_spec = config.get_logit_model_settings(logsum_settings)
# actual coeffs dont matter here, just need them to load the nest structure
coefficients = simulate.get_segment_coefficients(
logsum_settings, pseudo_tours.iloc[0]['tour_purpose'])
nest_spec = simulate.eval_nest_coefficients(nest_spec, coefficients,
trace_label)
tour_mode_alts = []
for nest in logit.each_nest(nest_spec):
if nest.is_leaf:
tour_mode_alts.append(nest.name)
# repeat rows from the trips table iterating over tour mode
for tour_mode in tour_mode_alts:
trips['tour_mode'] = tour_mode
logsum_trips = pd.concat((logsum_trips, trips), ignore_index=True)
assert len(logsum_trips) == len(trips) * len(tour_mode_alts)
logsum_trips.index.name = 'trip_id'
for col in tour_cols_needed:
if col not in trips:
logsum_trips[col] = reindex(pseudo_tours[col],
logsum_trips.unique_id)
pipeline.replace_table('trips', logsum_trips)
tracing.register_traceable_table('trips', logsum_trips)
pipeline.get_rn_generator().add_channel('trips', logsum_trips)
# run trip mode choice on pseudo-trips. use orca instead of pipeline to
# execute the step because pipeline can only handle one open step at a time
orca.run(['trip_mode_choice'])
# grab trip mode choice logsums and pivot by tour mode and direction, index
# on tour_id to enable merge back to choosers table
trips = inject.get_table('trips').to_frame()
trip_dir_mode_logsums = trips.pivot(index=['tour_id', tour_od_id_col],
columns=['tour_mode', 'outbound'],
values='trip_mode_choice_logsum')
new_cols = [
'_'.join(['logsum', mode, 'outbound' if outbound else 'inbound'])
for mode, outbound in trip_dir_mode_logsums.columns
]
trip_dir_mode_logsums.columns = new_cols
choosers.reset_index(inplace=True)
choosers.set_index(['tour_id', tour_od_id_col], inplace=True)
choosers = pd.merge(choosers,
trip_dir_mode_logsums,
left_index=True,
right_index=True)
choosers.reset_index(inplace=True)
choosers.set_index(choosers_og_index, inplace=True)
pipeline.get_rn_generator().drop_channel('trips')
tracing.deregister_traceable_table('trips')
assert (od_sample.index == choosers.index).all()
for col in new_cols:
od_sample[col] = choosers[col]
logsums = logsum.compute_logsums(choosers, spec_segment_name,
logsum_settings, model_settings,
network_los, chunk_size, chunk_tag,
trace_label, 'end', 'start', 'duration')
assert (od_sample.index == logsums.index).all()
od_sample['tour_mode_choice_logsum'] = logsums
return od_sample
def households(households_sample_size, override_hh_ids, trace_hh_id):
df_full = read_input_table("households")
households_sliced = False
logger.info("full household list contains %s households" % df_full.shape[0])
# only using households listed in override_hh_ids
if override_hh_ids is not None:
# trace_hh_id will not used if it is not in list of override_hh_ids
logger.info("override household list containing %s households" % len(override_hh_ids))
df = df_full[df_full.index.isin(override_hh_ids)]
households_sliced = True
if df.shape[0] < len(override_hh_ids):
logger.info("found %s of %s households in override household list" %
(df.shape[0], len(override_hh_ids)))
if df.shape[0] == 0:
raise RuntimeError('No override households found in store')
# if we are tracing hh exclusively
elif trace_hh_id and households_sample_size == 1:
# df contains only trace_hh (or empty if not in full store)
df = tracing.slice_ids(df_full, trace_hh_id)
households_sliced = True
# if we need a subset of full store
elif households_sample_size > 0 and df_full.shape[0] > households_sample_size:
logger.info("sampling %s of %s households" % (households_sample_size, df_full.shape[0]))
"""
Because random seed is set differently for each step, sampling of households using
Random.global_rng would sample differently depending upon which step it was called from.
We use a one-off rng seeded with the pseudo step name 'sample_households' to provide
repeatable sampling no matter when the table is loaded.
Note that the external_rng is also seeded with base_seed so the sample will (rightly) change
if the pipeline rng's base_seed is changed
"""
prng = pipeline.get_rn_generator().get_external_rng('sample_households')
df = df_full.take(prng.choice(len(df_full), size=households_sample_size, replace=False))
households_sliced = True
# if tracing and we missed trace_hh in sample, but it is in full store
if trace_hh_id and trace_hh_id not in df.index and trace_hh_id in df_full.index:
# replace first hh in sample with trace_hh
logger.debug("replacing household %s with %s in household sample" %
(df.index[0], trace_hh_id))
df_hh = df_full.loc[[trace_hh_id]]
df = pd.concat([df_hh, df[1:]])
else:
df = df_full
# persons table
inject.add_injectable('households_sliced', households_sliced)
logger.info("loaded households %s" % (df.shape,))
df.index.name = 'household_id'
# FIXME - pathological knowledge of name of chunk_id column used by chunked_choosers_by_chunk_id
assert 'chunk_id' not in df.columns
df['chunk_id'] = pd.Series(list(range(len(df))), df.index)
# replace table function with dataframe
inject.add_table('households', df)
pipeline.get_rn_generator().add_channel('households', df)
if trace_hh_id:
tracing.register_traceable_table('households', df)
tracing.trace_df(df, "raw.households", warn_if_empty=True)
return df
def households(households_sample_size, override_hh_ids, trace_hh_id):
df_full = read_input_table("households")
households_sliced = False
logger.info("full household list contains %s households" % df_full.shape[0])
# only using households listed in override_hh_ids
if override_hh_ids is not None:
# trace_hh_id will not used if it is not in list of override_hh_ids
logger.info("override household list containing %s households" % len(override_hh_ids))
df = df_full[df_full.index.isin(override_hh_ids)]
households_sliced = True
if df.shape[0] < len(override_hh_ids):
logger.info("found %s of %s households in override household list" %
(df.shape[0], len(override_hh_ids)))
if df.shape[0] == 0:
raise RuntimeError('No override households found in store')
# if we are tracing hh exclusively
elif trace_hh_id and households_sample_size == 1:
# df contains only trace_hh (or empty if not in full store)
df = tracing.slice_ids(df_full, trace_hh_id)
households_sliced = True
# if we need a subset of full store
elif households_sample_size > 0 and df_full.shape[0] > households_sample_size:
logger.info("sampling %s of %s households" % (households_sample_size, df_full.shape[0]))
"""
Because random seed is set differently for each step, sampling of households using
Random.global_rng would sample differently depending upon which step it was called from.
We use a one-off rng seeded with the pseudo step name 'sample_households' to provide
repeatable sampling no matter when the table is loaded.
Note that the external_rng is also seeded with base_seed so the sample will (rightly) change
if the pipeline rng's base_seed is changed
"""
prng = pipeline.get_rn_generator().get_external_rng('sample_households')
df = df_full.take(prng.choice(len(df_full), size=households_sample_size, replace=False))
households_sliced = True
# if tracing and we missed trace_hh in sample, but it is in full store
if trace_hh_id and trace_hh_id not in df.index and trace_hh_id in df_full.index:
# replace first hh in sample with trace_hh
logger.debug("replacing household %s with %s in household sample" %
(df.index[0], trace_hh_id))
df_hh = df_full.loc[[trace_hh_id]]
df = pd.concat([df_hh, df[1:]])
else:
df = df_full
# persons table
inject.add_injectable('households_sliced', households_sliced)
logger.info("loaded households %s" % (df.shape,))
# FIXME - pathological knowledge of name of chunk_id column used by chunked_choosers_by_chunk_id
assert 'chunk_id' not in df.columns
df['chunk_id'] = pd.Series(list(range(len(df))), df.index)
# replace table function with dataframe
inject.add_table('households', df)
pipeline.get_rn_generator().add_channel('households', df)
if trace_hh_id:
tracing.register_traceable_table('households', df)
tracing.trace_df(df, "raw.households", warn_if_empty=True)
return df
def joint_tour_frequency(households, persons, chunk_size, trace_hh_id):
"""
This model predicts the frequency of making fully joint trips (see the
alternatives above).
"""
trace_label = 'joint_tour_frequency'
model_settings_file_name = 'joint_tour_frequency.yaml'
estimator = estimation.manager.begin_estimation('joint_tour_frequency')
model_settings = config.read_model_settings(model_settings_file_name)
alternatives = simulate.read_model_alts(
'joint_tour_frequency_alternatives.csv', set_index='alt')
# - only interested in households with more than one cdap travel_active person and
# - at least one non-preschooler
households = households.to_frame()
multi_person_households = households[
households.participates_in_jtf_model].copy()
# - only interested in persons in multi_person_households
# FIXME - gratuitous pathological efficiency move, just let yaml specify persons?
persons = persons.to_frame()
persons = persons[persons.household_id.isin(multi_person_households.index)]
logger.info(
"Running joint_tour_frequency with %d multi-person households" %
multi_person_households.shape[0])
# - preprocessor
preprocessor_settings = model_settings.get('preprocessor', None)
if preprocessor_settings:
locals_dict = {
'persons': persons,
'hh_time_window_overlap': hh_time_window_overlap
}
expressions.assign_columns(df=multi_person_households,
model_settings=preprocessor_settings,
locals_dict=locals_dict,
trace_label=trace_label)
model_spec = simulate.read_model_spec(file_name=model_settings['SPEC'])
coefficients_df = simulate.read_model_coefficients(model_settings)
model_spec = simulate.eval_coefficients(model_spec, coefficients_df,
estimator)
nest_spec = config.get_logit_model_settings(model_settings)
constants = config.get_model_constants(model_settings)
if estimator:
estimator.write_spec(model_settings)
estimator.write_model_settings(model_settings,
model_settings_file_name)
estimator.write_coefficients(coefficients_df, model_settings)
estimator.write_choosers(multi_person_households)
choices = simulate.simple_simulate(
choosers=multi_person_households,
spec=model_spec,
nest_spec=nest_spec,
locals_d=constants,
chunk_size=chunk_size,
trace_label=trace_label,
trace_choice_name='joint_tour_frequency',
estimator=estimator)
# convert indexes to alternative names
choices = pd.Series(model_spec.columns[choices.values],
index=choices.index)
if estimator:
estimator.write_choices(choices)
choices = estimator.get_survey_values(choices, 'households',
'joint_tour_frequency')
estimator.write_override_choices(choices)
estimator.end_estimation()
# - create joint_tours based on joint_tour_frequency choices
# - we need a person_id in order to generate the tour index (and for register_traceable_table)
# - but we don't know the tour participants yet
# - so we arbitrarily choose the first person in the household
# - to be point person for the purpose of generating an index and setting origin
temp_point_persons = persons.loc[persons.PNUM == 1]
temp_point_persons['person_id'] = temp_point_persons.index
temp_point_persons = temp_point_persons.set_index('household_id')
temp_point_persons = temp_point_persons[['person_id', 'home_zone_id']]
joint_tours = \
process_joint_tours(choices, alternatives, temp_point_persons)
tours = pipeline.extend_table("tours", joint_tours)
tracing.register_traceable_table('tours', joint_tours)
pipeline.get_rn_generator().add_channel('tours', joint_tours)
# - annotate households
# we expect there to be an alt with no tours - which we can use to backfill non-travelers
no_tours_alt = (alternatives.sum(axis=1) == 0).index[0]
households['joint_tour_frequency'] = choices.reindex(
households.index).fillna(no_tours_alt).astype(str)
households['num_hh_joint_tours'] = joint_tours.groupby('household_id').size().\
reindex(households.index).fillna(0).astype(np.int8)
pipeline.replace_table("households", households)
tracing.print_summary('joint_tour_frequency',
households.joint_tour_frequency,
value_counts=True)
if trace_hh_id:
tracing.trace_df(households, label="joint_tour_frequency.households")
tracing.trace_df(joint_tours,
label="joint_tour_frequency.joint_tours",
slicer='household_id')
if estimator:
survey_tours = estimation.manager.get_survey_table('tours')
survey_tours = survey_tours[survey_tours.tour_category == 'joint']
print(f"len(survey_tours) {len(survey_tours)}")
print(f"len(joint_tours) {len(joint_tours)}")
different = False
survey_tours_not_in_tours = survey_tours[~survey_tours.index.
isin(joint_tours.index)]
if len(survey_tours_not_in_tours) > 0:
print(f"survey_tours_not_in_tours\n{survey_tours_not_in_tours}")
different = True
tours_not_in_survey_tours = joint_tours[~joint_tours.index.
isin(survey_tours.index)]
if len(survey_tours_not_in_tours) > 0:
print(f"tours_not_in_survey_tours\n{tours_not_in_survey_tours}")
different = True
assert not different
