def get_alts_from_segmented_nested_logit(model_settings, segment_name,
trace_label):
"""Infer alts from logit spec
Parameters
----------
model_settings : dict
segment_column_name : str
trace_label : str
Returns
-------
list
"""
nest_spec = config.get_logit_model_settings(model_settings)
coefficients = simulate.get_segment_coefficients(model_settings,
segment_name)
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)
return tour_mode_alts
def compute_logsums(primary_purpose, trips, destination_sample, tours_merged,
model_settings, skim_hotel, chunk_size, trace_label):
"""
Calculate mode choice logsums using the same recipe as for trip_mode_choice, but do it twice
for each alternative since we need out-of-direction logsum
(i.e . origin to alt_dest, and alt_dest to half-tour destination)
Returns
-------
adds od_logsum and dp_logsum columns to trips (in place)
"""
trace_label = tracing.extend_trace_label(trace_label, 'compute_logsums')
logger.info("Running %s with %d samples", trace_label,
destination_sample.shape[0])
# chunk usage is uniform so better to combine
chunk_tag = 'trip_destination.compute_logsums'
# FIXME should pass this in?
network_los = inject.get_injectable('network_los')
# - trips_merged - merge trips and tours_merged
trips_merged = pd.merge(trips,
tours_merged,
left_on='tour_id',
right_index=True,
how="left")
assert trips_merged.index.equals(trips.index)
# - choosers - merge destination_sample and trips_merged
# re/set index because pandas merge does not preserve left index if it has duplicate values!
choosers = pd.merge(destination_sample,
trips_merged.reset_index(),
left_index=True,
right_on='trip_id',
how="left",
suffixes=('', '_r')).set_index('trip_id')
assert choosers.index.equals(destination_sample.index)
logsum_settings = config.read_model_settings(
model_settings['LOGSUM_SETTINGS'])
coefficients = simulate.get_segment_coefficients(logsum_settings,
primary_purpose)
nest_spec = config.get_logit_model_settings(logsum_settings)
nest_spec = simulate.eval_nest_coefficients(nest_spec, coefficients,
trace_label)
logsum_spec = simulate.read_model_spec(file_name=logsum_settings['SPEC'])
logsum_spec = simulate.eval_coefficients(logsum_spec,
coefficients,
estimator=None)
locals_dict = {}
locals_dict.update(config.get_model_constants(logsum_settings))
# coefficients can appear in expressions
locals_dict.update(coefficients)
skims = skim_hotel.logsum_skims()
if network_los.zone_system == los.THREE_ZONE:
# TVPB constants can appear in expressions
locals_dict.update(
network_los.setting('TVPB_SETTINGS.tour_mode_choice.CONSTANTS'))
# - od_logsums
od_skims = {
'ORIGIN': model_settings['TRIP_ORIGIN'],
'DESTINATION': model_settings['ALT_DEST_COL_NAME'],
"odt_skims": skims['odt_skims'],
"dot_skims": skims['dot_skims'],
"od_skims": skims['od_skims'],
}
if network_los.zone_system == los.THREE_ZONE:
od_skims.update({
'tvpb_logsum_odt': skims['tvpb_logsum_odt'],
'tvpb_logsum_dot': skims['tvpb_logsum_dot']
})
destination_sample['od_logsum'] = compute_ood_logsums(
choosers,
logsum_settings,
nest_spec,
logsum_spec,
od_skims,
locals_dict,
chunk_size,
trace_label=tracing.extend_trace_label(trace_label, 'od'),
chunk_tag=chunk_tag)
# - dp_logsums
dp_skims = {
'ORIGIN': model_settings['ALT_DEST_COL_NAME'],
'DESTINATION': model_settings['PRIMARY_DEST'],
"odt_skims": skims['dpt_skims'],
"dot_skims": skims['pdt_skims'],
"od_skims": skims['dp_skims'],
}
if network_los.zone_system == los.THREE_ZONE:
dp_skims.update({
'tvpb_logsum_odt': skims['tvpb_logsum_dpt'],
'tvpb_logsum_dot': skims['tvpb_logsum_pdt']
})
destination_sample['dp_logsum'] = compute_ood_logsums(
choosers,
logsum_settings,
nest_spec,
logsum_spec,
dp_skims,
locals_dict,
chunk_size,
trace_label=tracing.extend_trace_label(trace_label, 'dp'),
chunk_tag=chunk_tag)
return destination_sample
def run_tour_mode_choice_simulate(choosers,
tour_purpose,
model_settings,
mode_column_name,
logsum_column_name,
network_los,
skims,
constants,
estimator,
chunk_size,
trace_label=None,
trace_choice_name=None):
"""
This is a utility to run a mode choice model for each segment (usually
segments are tour/trip purposes). Pass in the tours/trip that need a mode,
the Skim object, the spec to evaluate with, and any additional expressions
you want to use in the evaluation of variables.
"""
spec = simulate.read_model_spec(file_name=model_settings['SPEC'])
coefficients = simulate.get_segment_coefficients(model_settings,
tour_purpose)
spec = simulate.eval_coefficients(spec, coefficients, estimator)
nest_spec = config.get_logit_model_settings(model_settings)
nest_spec = simulate.eval_nest_coefficients(nest_spec, coefficients,
trace_label)
locals_dict = {}
locals_dict.update(constants)
locals_dict.update(skims)
# coefficients can appear in expressions
locals_dict.update(coefficients)
assert ('in_period' not in choosers) and ('out_period' not in choosers)
in_time = skims['in_time_col_name']
out_time = skims['out_time_col_name']
choosers['in_period'] = network_los.skim_time_period_label(
choosers[in_time])
choosers['out_period'] = network_los.skim_time_period_label(
choosers[out_time])
expressions.annotate_preprocessors(choosers, locals_dict, skims,
model_settings, trace_label)
trace_column_names = choosers.index.name
assert trace_column_names == 'tour_id'
if trace_column_names not in choosers:
choosers[trace_column_names] = choosers.index
if estimator:
# write choosers after annotation
estimator.write_choosers(choosers)
choices = mode_choice_simulate(choosers=choosers,
spec=spec,
nest_spec=nest_spec,
skims=skims,
locals_d=locals_dict,
chunk_size=chunk_size,
mode_column_name=mode_column_name,
logsum_column_name=logsum_column_name,
trace_label=trace_label,
trace_choice_name=trace_choice_name,
trace_column_names=trace_column_names,
estimator=estimator)
return choices
def _compute_logsums(alt_tdd, tours_merged, tour_purpose, model_settings,
network_los, skims, trace_label):
"""
compute logsums for tours using skims for alt_tdd out_period and in_period
"""
trace_label = tracing.extend_trace_label(trace_label, 'logsums')
logsum_settings = config.read_model_settings(
model_settings['LOGSUM_SETTINGS'])
choosers = alt_tdd.join(tours_merged, how='left', rsuffix='_chooser')
logger.info(
f"{trace_label} compute_logsums for {choosers.shape[0]} choosers {alt_tdd.shape[0]} alts"
)
# - locals_dict
constants = config.get_model_constants(logsum_settings)
locals_dict = {}
locals_dict.update(constants)
if network_los.zone_system == los.THREE_ZONE:
# TVPB constants can appear in expressions
locals_dict.update(
network_los.setting('TVPB_SETTINGS.tour_mode_choice.CONSTANTS'))
locals_dict.update(skims)
# constrained coefficients can appear in expressions
coefficients = simulate.get_segment_coefficients(logsum_settings,
tour_purpose)
locals_dict.update(coefficients)
# - run preprocessor to annotate choosers
# allow specification of alternate preprocessor for nontour choosers
preprocessor = model_settings.get('LOGSUM_PREPROCESSOR', 'preprocessor')
preprocessor_settings = logsum_settings[preprocessor]
if preprocessor_settings:
simulate.set_skim_wrapper_targets(choosers, skims)
expressions.assign_columns(df=choosers,
model_settings=preprocessor_settings,
locals_dict=locals_dict,
trace_label=trace_label)
# - compute logsums
logsum_spec = simulate.read_model_spec(file_name=logsum_settings['SPEC'])
logsum_spec = simulate.eval_coefficients(logsum_spec,
coefficients,
estimator=None)
nest_spec = config.get_logit_model_settings(logsum_settings)
nest_spec = simulate.eval_nest_coefficients(nest_spec, coefficients,
trace_label)
logsums = simulate.simple_simulate_logsums(choosers,
logsum_spec,
nest_spec,
skims=skims,
locals_d=locals_dict,
chunk_size=0,
trace_label=trace_label)
return logsums
def _compute_logsums(alt_tdd, tours_merged, tour_purpose, model_settings,
trace_label):
"""
compute logsums for tours using skims for alt_tdd out_period and in_period
"""
trace_label = tracing.extend_trace_label(trace_label, 'logsums')
logsum_settings = config.read_model_settings(
model_settings['LOGSUM_SETTINGS'])
choosers = alt_tdd.join(tours_merged, how='left', rsuffix='_chooser')
logger.info("%s compute_logsums for %d choosers%s alts" %
(trace_label, choosers.shape[0], alt_tdd.shape[0]))
# - setup skims
skim_dict = inject.get_injectable('skim_dict')
skim_stack = inject.get_injectable('skim_stack')
orig_col_name = 'TAZ'
dest_col_name = model_settings.get('DESTINATION_FOR_TOUR_PURPOSE').get(
tour_purpose)
odt_skim_stack_wrapper = skim_stack.wrap(left_key=orig_col_name,
right_key=dest_col_name,
skim_key='out_period')
dot_skim_stack_wrapper = skim_stack.wrap(left_key=dest_col_name,
right_key=orig_col_name,
skim_key='in_period')
odr_skim_stack_wrapper = skim_stack.wrap(left_key=orig_col_name,
right_key=dest_col_name,
skim_key='in_period')
dor_skim_stack_wrapper = skim_stack.wrap(left_key=dest_col_name,
right_key=orig_col_name,
skim_key='out_period')
od_skim_stack_wrapper = skim_dict.wrap(orig_col_name, dest_col_name)
skims = {
"odt_skims": odt_skim_stack_wrapper,
"dot_skims": dot_skim_stack_wrapper,
"odr_skims": odr_skim_stack_wrapper,
"dor_skims": dor_skim_stack_wrapper,
"od_skims": od_skim_stack_wrapper,
'orig_col_name': orig_col_name,
'dest_col_name': dest_col_name,
}
# - locals_dict
constants = config.get_model_constants(logsum_settings)
locals_dict = {}
locals_dict.update(constants)
locals_dict.update(skims)
# - run preprocessor to annotate choosers
# allow specification of alternate preprocessor for nontour choosers
preprocessor = model_settings.get('LOGSUM_PREPROCESSOR', 'preprocessor')
preprocessor_settings = logsum_settings[preprocessor]
if preprocessor_settings:
simulate.set_skim_wrapper_targets(choosers, skims)
expressions.assign_columns(df=choosers,
model_settings=preprocessor_settings,
locals_dict=locals_dict,
trace_label=trace_label)
# - compute logsums
coefficients = simulate.get_segment_coefficients(logsum_settings,
tour_purpose)
logsum_spec = simulate.read_model_spec(file_name=logsum_settings['SPEC'])
logsum_spec = simulate.eval_coefficients(logsum_spec,
coefficients,
estimator=None)
nest_spec = config.get_logit_model_settings(logsum_settings)
nest_spec = simulate.eval_nest_coefficients(nest_spec, coefficients)
# constrained coefficients can appear in expressions
locals_dict.update(coefficients)
logsums = simulate.simple_simulate_logsums(choosers,
logsum_spec,
nest_spec,
skims=skims,
locals_d=locals_dict,
chunk_size=0,
trace_label=trace_label)
return logsums
def trip_mode_choice(trips, network_los, chunk_size, trace_hh_id):
"""
Trip mode choice - compute trip_mode (same values as for tour_mode) for each trip.
Modes for each primary tour putpose are calculated separately because they have different
coefficient values (stored in trip_mode_choice_coefficients.csv coefficient file.)
Adds trip_mode column to trip table
"""
trace_label = 'trip_mode_choice'
model_settings_file_name = 'trip_mode_choice.yaml'
model_settings = config.read_model_settings(model_settings_file_name)
logsum_column_name = model_settings.get('MODE_CHOICE_LOGSUM_COLUMN_NAME')
mode_column_name = 'trip_mode'
trips_df = trips.to_frame()
logger.info("Running %s with %d trips", trace_label, trips_df.shape[0])
# give trip mode choice the option to run without calling tours_merged. Useful for xborder
# model where tour_od_choice needs trip mode choice logsums before some of the join keys
# needed by tour_merged (e.g. home_zone_id) exist
tours_cols = [
col for col in model_settings['TOURS_MERGED_CHOOSER_COLUMNS']
if col not in trips_df.columns
]
if len(tours_cols) > 0:
tours_merged = inject.get_table('tours_merged').to_frame(
columns=tours_cols)
else:
tours_merged = pd.DataFrame()
# - trips_merged - merge trips and tours_merged
trips_merged = pd.merge(trips_df,
tours_merged,
left_on='tour_id',
right_index=True,
how="left")
assert trips_merged.index.equals(trips.index)
tracing.print_summary('primary_purpose',
trips_df.primary_purpose,
value_counts=True)
# setup skim keys
assert ('trip_period' not in trips_merged)
trips_merged['trip_period'] = network_los.skim_time_period_label(
trips_merged.depart)
orig_col = 'origin'
dest_col = 'destination'
min_per_period = network_los.skim_time_periods['period_minutes']
periods_per_hour = 60 / min_per_period
constants = {}
constants.update(config.get_model_constants(model_settings))
constants.update({
'ORIGIN': orig_col,
'DESTINATION': dest_col,
'MIN_PER_PERIOD': min_per_period,
'PERIODS_PER_HOUR': periods_per_hour
})
skim_dict = network_los.get_default_skim_dict()
odt_skim_stack_wrapper = skim_dict.wrap_3d(orig_key=orig_col,
dest_key=dest_col,
dim3_key='trip_period')
dot_skim_stack_wrapper = skim_dict.wrap_3d(orig_key=dest_col,
dest_key=orig_col,
dim3_key='trip_period')
od_skim_wrapper = skim_dict.wrap('origin', 'destination')
skims = {
"odt_skims": odt_skim_stack_wrapper,
"dot_skims": dot_skim_stack_wrapper,
"od_skims": od_skim_wrapper,
}
if network_los.zone_system == los.THREE_ZONE:
# fixme - is this a lightweight object?
tvpb = network_los.tvpb
tvpb_recipe = model_settings.get('TVPB_recipe', 'tour_mode_choice')
tvpb_logsum_odt = tvpb.wrap_logsum(orig_key=orig_col,
dest_key=dest_col,
tod_key='trip_period',
segment_key='demographic_segment',
recipe=tvpb_recipe,
cache_choices=True,
trace_label=trace_label,
tag='tvpb_logsum_odt')
skims.update({
'tvpb_logsum_odt': tvpb_logsum_odt,
})
# This if-clause gives the user the option of NOT inheriting constants
# from the tvpb settings. previously, these constants were inherited
# automatically, which had the undesirable effect of overwriting any
# trip mode choice model constants/coefficients that shared the same
# name. The default behavior is still the same (True), but the user
# can now avoid any chance of squashing these local variables by
# adding `use_TVPB_constants: False` to the trip_mode_choice.yaml file.
# the tvpb will still use the constants as defined in the recipe
# specified above in `tvpb.wrap_logsum()` but they will not be used
# in the trip mode choice expressions.
if model_settings.get('use_TVPB_constants', True):
constants.update(
network_los.setting(
'TVPB_SETTINGS.tour_mode_choice.CONSTANTS'))
# don't create estimation data bundle if trip mode choice is being called
# from another model step (e.g. tour mode choice logsum creation)
if pipeline._PIPELINE.rng().step_name != 'trip_mode_choice':
estimator = None
else:
estimator = estimation.manager.begin_estimation('trip_mode_choice')
if estimator:
estimator.write_coefficients(model_settings=model_settings)
estimator.write_coefficients_template(model_settings=model_settings)
estimator.write_spec(model_settings)
estimator.write_model_settings(model_settings,
model_settings_file_name)
model_spec = simulate.read_model_spec(file_name=model_settings['SPEC'])
nest_spec = config.get_logit_model_settings(model_settings)
choices_list = []
for primary_purpose, trips_segment in trips_merged.groupby(
'primary_purpose'):
segment_trace_label = tracing.extend_trace_label(
trace_label, primary_purpose)
logger.info("trip_mode_choice tour_type '%s' (%s trips)" % (
primary_purpose,
len(trips_segment.index),
))
# name index so tracing knows how to slice
assert trips_segment.index.name == 'trip_id'
if network_los.zone_system == los.THREE_ZONE:
tvpb_logsum_odt.extend_trace_label(primary_purpose)
# tvpb_logsum_dot.extend_trace_label(primary_purpose)
coefficients = simulate.get_segment_coefficients(
model_settings, primary_purpose)
locals_dict = {}
locals_dict.update(constants)
constants_keys = constants.keys()
if any([coeff in constants_keys for coeff in coefficients.keys()]):
logger.warning(
"coefficients are obscuring constants in locals_dict")
locals_dict.update(coefficients)
# have to initialize chunker for preprocessing in order to access
# tvpb logsum terms in preprocessor expressions.
with chunk.chunk_log(tracing.extend_trace_label(
trace_label, 'preprocessing'),
base=True):
expressions.annotate_preprocessors(trips_segment, locals_dict,
skims, model_settings,
segment_trace_label)
if estimator:
# write choosers after annotation
estimator.write_choosers(trips_segment)
locals_dict.update(skims)
choices = mode_choice_simulate(
choosers=trips_segment,
spec=simulate.eval_coefficients(model_spec, coefficients,
estimator),
nest_spec=simulate.eval_nest_coefficients(nest_spec, coefficients,
segment_trace_label),
skims=skims,
locals_d=locals_dict,
chunk_size=chunk_size,
mode_column_name=mode_column_name,
logsum_column_name=logsum_column_name,
trace_label=segment_trace_label,
trace_choice_name='trip_mode_choice',
estimator=estimator)
if trace_hh_id:
# trace the coefficients
tracing.trace_df(pd.Series(locals_dict),
label=tracing.extend_trace_label(
segment_trace_label, 'constants'),
transpose=False,
slicer='NONE')
# so we can trace with annotations
assign_in_place(trips_segment, choices)
tracing.trace_df(trips_segment,
label=tracing.extend_trace_label(
segment_trace_label, 'trip_mode'),
slicer='tour_id',
index_label='tour_id',
warn_if_empty=True)
choices_list.append(choices)
choices_df = pd.concat(choices_list)
# add cached tvpb_logsum tap choices for modes specified in tvpb_mode_path_types
if network_los.zone_system == los.THREE_ZONE:
tvpb_mode_path_types = model_settings.get('tvpb_mode_path_types')
for mode, path_type in tvpb_mode_path_types.items():
skim_cache = tvpb_logsum_odt.cache[path_type]
for c in skim_cache:
dest_col = c
if dest_col not in choices_df:
choices_df[
dest_col] = np.nan if pd.api.types.is_numeric_dtype(
skim_cache[c]) else ''
choices_df[dest_col].where(
choices_df[mode_column_name] != mode,
skim_cache[c],
inplace=True)
if estimator:
estimator.write_choices(choices_df.trip_mode)
choices_df.trip_mode = estimator.get_survey_values(
choices_df.trip_mode, 'trips', 'trip_mode')
estimator.write_override_choices(choices_df.trip_mode)
estimator.end_estimation()
trips_df = trips.to_frame()
assign_in_place(trips_df, choices_df)
tracing.print_summary('trip_modes',
trips_merged.tour_mode,
value_counts=True)
tracing.print_summary('trip_mode_choice choices',
trips_df[mode_column_name],
value_counts=True)
assert not trips_df[mode_column_name].isnull().any()
pipeline.replace_table("trips", trips_df)
if trace_hh_id:
tracing.trace_df(trips_df,
label=tracing.extend_trace_label(
trace_label, 'trip_mode'),
slicer='trip_id',
index_label='trip_id',
warn_if_empty=True)
def compute_logsums(choosers,
tour_purpose,
logsum_settings, model_settings,
network_los,
chunk_size,
chunk_tag,
trace_label):
"""
Parameters
----------
choosers
tour_purpose
logsum_settings
model_settings
network_los
chunk_size
trace_hh_id
trace_label
Returns
-------
logsums: pandas series
computed logsums with same index as choosers
"""
trace_label = tracing.extend_trace_label(trace_label, 'compute_logsums')
logger.debug("Running compute_logsums with %d choosers" % choosers.shape[0])
# compute_logsums needs to know name of dest column in interaction_sample
orig_col_name = model_settings['CHOOSER_ORIG_COL_NAME']
dest_col_name = model_settings['ALT_DEST_COL_NAME']
# FIXME - are we ok with altering choosers (so caller doesn't have to set these)?
assert ('in_period' not in choosers) and ('out_period' not in choosers)
choosers['in_period'] = network_los.skim_time_period_label(model_settings['IN_PERIOD'])
choosers['out_period'] = network_los.skim_time_period_label(model_settings['OUT_PERIOD'])
assert ('duration' not in choosers)
choosers['duration'] = model_settings['IN_PERIOD'] - model_settings['OUT_PERIOD']
logsum_spec = simulate.read_model_spec(file_name=logsum_settings['SPEC'])
coefficients = simulate.get_segment_coefficients(logsum_settings, tour_purpose)
logsum_spec = simulate.eval_coefficients(logsum_spec, coefficients, estimator=None)
nest_spec = config.get_logit_model_settings(logsum_settings)
nest_spec = simulate.eval_nest_coefficients(nest_spec, coefficients, trace_label)
locals_dict = {}
# model_constants can appear in expressions
locals_dict.update(config.get_model_constants(logsum_settings))
# constrained coefficients can appear in expressions
locals_dict.update(coefficients)
# setup skim keys
skim_dict = network_los.get_default_skim_dict()
odt_skim_stack_wrapper = skim_dict.wrap_3d(orig_key=orig_col_name, dest_key=dest_col_name,
dim3_key='out_period')
dot_skim_stack_wrapper = skim_dict.wrap_3d(orig_key=dest_col_name, dest_key=orig_col_name,
dim3_key='in_period')
odr_skim_stack_wrapper = skim_dict.wrap_3d(orig_key=orig_col_name, dest_key=dest_col_name,
dim3_key='in_period')
dor_skim_stack_wrapper = skim_dict.wrap_3d(orig_key=dest_col_name, dest_key=orig_col_name,
dim3_key='out_period')
od_skim_stack_wrapper = skim_dict.wrap(orig_col_name, dest_col_name)
skims = {
"odt_skims": odt_skim_stack_wrapper,
"dot_skims": dot_skim_stack_wrapper,
"odr_skims": odr_skim_stack_wrapper,
"dor_skims": dor_skim_stack_wrapper,
"od_skims": od_skim_stack_wrapper,
'orig_col_name': orig_col_name,
'dest_col_name': dest_col_name
}
if network_los.zone_system == los.THREE_ZONE:
# fixme - is this a lightweight object?
tvpb = network_los.tvpb
tvpb_logsum_odt = tvpb.wrap_logsum(orig_key=orig_col_name, dest_key=dest_col_name,
tod_key='out_period', segment_key='demographic_segment',
trace_label=trace_label, tag='tvpb_logsum_odt')
tvpb_logsum_dot = tvpb.wrap_logsum(orig_key=dest_col_name, dest_key=orig_col_name,
tod_key='in_period', segment_key='demographic_segment',
trace_label=trace_label, tag='tvpb_logsum_dot')
skims.update({
'tvpb_logsum_odt': tvpb_logsum_odt,
'tvpb_logsum_dot': tvpb_logsum_dot
})
# TVPB constants can appear in expressions
locals_dict.update(network_los.setting('TVPB_SETTINGS.tour_mode_choice.CONSTANTS'))
locals_dict.update(skims)
# - run preprocessor to annotate choosers
# allow specification of alternate preprocessor for nontour choosers
preprocessor = model_settings.get('LOGSUM_PREPROCESSOR', 'preprocessor')
preprocessor_settings = logsum_settings[preprocessor]
if preprocessor_settings:
simulate.set_skim_wrapper_targets(choosers, skims)
expressions.assign_columns(
df=choosers,
model_settings=preprocessor_settings,
locals_dict=locals_dict,
trace_label=trace_label)
logsums = simulate.simple_simulate_logsums(
choosers,
logsum_spec,
nest_spec,
skims=skims,
locals_d=locals_dict,
chunk_size=chunk_size,
chunk_tag=chunk_tag,
trace_label=trace_label)
return logsums
def trip_mode_choice(trips, tours_merged, network_los, chunk_size,
trace_hh_id):
"""
Trip mode choice - compute trip_mode (same values as for tour_mode) for each trip.
Modes for each primary tour putpose are calculated separately because they have different
coefficient values (stored in trip_mode_choice_coefficients.csv coefficient file.)
Adds trip_mode column to trip table
"""
trace_label = 'trip_mode_choice'
model_settings_file_name = 'trip_mode_choice.yaml'
model_settings = config.read_model_settings(model_settings_file_name)
logsum_column_name = model_settings.get('MODE_CHOICE_LOGSUM_COLUMN_NAME')
mode_column_name = 'trip_mode'
trips_df = trips.to_frame()
logger.info("Running %s with %d trips", trace_label, trips_df.shape[0])
tours_merged = tours_merged.to_frame()
tours_merged = tours_merged[model_settings['TOURS_MERGED_CHOOSER_COLUMNS']]
tracing.print_summary('primary_purpose',
trips_df.primary_purpose,
value_counts=True)
# - trips_merged - merge trips and tours_merged
trips_merged = pd.merge(trips_df,
tours_merged,
left_on='tour_id',
right_index=True,
how="left")
assert trips_merged.index.equals(trips.index)
# setup skim keys
assert ('trip_period' not in trips_merged)
trips_merged['trip_period'] = network_los.skim_time_period_label(
trips_merged.depart)
orig_col = 'origin'
dest_col = 'destination'
constants = {}
constants.update(config.get_model_constants(model_settings))
constants.update({'ORIGIN': orig_col, 'DESTINATION': dest_col})
skim_dict = network_los.get_default_skim_dict()
odt_skim_stack_wrapper = skim_dict.wrap_3d(orig_key=orig_col,
dest_key=dest_col,
dim3_key='trip_period')
dot_skim_stack_wrapper = skim_dict.wrap_3d(orig_key=dest_col,
dest_key=orig_col,
dim3_key='trip_period')
od_skim_wrapper = skim_dict.wrap('origin', 'destination')
skims = {
"odt_skims": odt_skim_stack_wrapper,
"dot_skims": dot_skim_stack_wrapper,
"od_skims": od_skim_wrapper,
}
if network_los.zone_system == los.THREE_ZONE:
# fixme - is this a lightweight object?
tvpb = network_los.tvpb
tvpb_logsum_odt = tvpb.wrap_logsum(orig_key=orig_col,
dest_key=dest_col,
tod_key='trip_period',
segment_key='demographic_segment',
cache_choices=True,
trace_label=trace_label,
tag='tvpb_logsum_odt')
skims.update({
'tvpb_logsum_odt': tvpb_logsum_odt,
# 'tvpb_logsum_dot': tvpb_logsum_dot
})
# TVPB constants can appear in expressions
constants.update(
network_los.setting('TVPB_SETTINGS.tour_mode_choice.CONSTANTS'))
estimator = estimation.manager.begin_estimation('trip_mode_choice')
if estimator:
estimator.write_coefficients(model_settings=model_settings)
estimator.write_coefficients_template(model_settings=model_settings)
estimator.write_spec(model_settings)
estimator.write_model_settings(model_settings,
model_settings_file_name)
model_spec = simulate.read_model_spec(file_name=model_settings['SPEC'])
nest_spec = config.get_logit_model_settings(model_settings)
choices_list = []
for primary_purpose, trips_segment in trips_merged.groupby(
'primary_purpose'):
segment_trace_label = tracing.extend_trace_label(
trace_label, primary_purpose)
logger.info("trip_mode_choice tour_type '%s' (%s trips)" % (
primary_purpose,
len(trips_segment.index),
))
# name index so tracing knows how to slice
assert trips_segment.index.name == 'trip_id'
if network_los.zone_system == los.THREE_ZONE:
tvpb_logsum_odt.extend_trace_label(primary_purpose)
# tvpb_logsum_dot.extend_trace_label(primary_purpose)
coefficients = simulate.get_segment_coefficients(
model_settings, primary_purpose)
locals_dict = {}
locals_dict.update(constants)
locals_dict.update(coefficients)
expressions.annotate_preprocessors(trips_segment, locals_dict, skims,
model_settings, segment_trace_label)
if estimator:
# write choosers after annotation
estimator.write_choosers(trips_segment)
locals_dict.update(skims)
choices = mode_choice_simulate(
choosers=trips_segment,
spec=simulate.eval_coefficients(model_spec, coefficients,
estimator),
nest_spec=simulate.eval_nest_coefficients(nest_spec, coefficients,
segment_trace_label),
skims=skims,
locals_d=locals_dict,
chunk_size=chunk_size,
mode_column_name=mode_column_name,
logsum_column_name=logsum_column_name,
trace_label=trace_label,
trace_choice_name='trip_mode_choice',
estimator=estimator)
if trace_hh_id:
# trace the coefficients
tracing.trace_df(pd.Series(locals_dict),
label=tracing.extend_trace_label(
segment_trace_label, 'constants'),
transpose=False,
slicer='NONE')
# so we can trace with annotations
assign_in_place(trips_segment, choices)
tracing.trace_df(trips_segment,
label=tracing.extend_trace_label(
segment_trace_label, 'trip_mode'),
slicer='tour_id',
index_label='tour_id',
warn_if_empty=True)
choices_list.append(choices)
# FIXME - force garbage collection
force_garbage_collect()
choices_df = pd.concat(choices_list)
# add cached tvpb_logsum tap choices for modes specified in tvpb_mode_path_types
if network_los.zone_system == los.THREE_ZONE:
tvpb_mode_path_types = model_settings.get('tvpb_mode_path_types')
for mode, path_type in tvpb_mode_path_types.items():
skim_cache = tvpb_logsum_odt.cache[path_type]
for c in skim_cache:
dest_col = c
if dest_col not in choices_df:
choices_df[
dest_col] = np.nan if pd.api.types.is_numeric_dtype(
skim_cache[c]) else ''
choices_df[dest_col].where(
choices_df[mode_column_name] != mode,
skim_cache[c],
inplace=True)
if estimator:
estimator.write_choices(choices_df.trip_mode)
choices_df.trip_mode = estimator.get_survey_values(
choices_df.trip_mode, 'trips', 'trip_mode')
estimator.write_override_choices(choices_df.trip_mode)
estimator.end_estimation()
# update trips table with choices (and potionally logssums)
trips_df = trips.to_frame()
assign_in_place(trips_df, choices_df)
tracing.print_summary('trip_modes',
trips_merged.tour_mode,
value_counts=True)
tracing.print_summary('trip_mode_choice choices',
trips_df[mode_column_name],
value_counts=True)
assert not trips_df[mode_column_name].isnull().any()
pipeline.replace_table("trips", trips_df)
if trace_hh_id:
tracing.trace_df(trips_df,
label=tracing.extend_trace_label(
trace_label, 'trip_mode'),
slicer='trip_id',
index_label='trip_id',
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 compute_logsums(choosers,
tour_purpose,
logsum_settings, model_settings,
skim_dict, skim_stack,
chunk_size, trace_label):
"""
Parameters
----------
choosers
tour_purpose
logsum_settings
model_settings
skim_dict
skim_stack
chunk_size
trace_hh_id
trace_label
Returns
-------
logsums: pandas series
computed logsums with same index as choosers
"""
trace_label = tracing.extend_trace_label(trace_label, 'compute_logsums')
# compute_logsums needs to know name of dest column in interaction_sample
orig_col_name = model_settings['CHOOSER_ORIG_COL_NAME']
dest_col_name = model_settings['ALT_DEST_COL_NAME']
# FIXME - are we ok with altering choosers (so caller doesn't have to set these)?
assert ('in_period' not in choosers) and ('out_period' not in choosers)
choosers['in_period'] = expressions.skim_time_period_label(model_settings['IN_PERIOD'])
choosers['out_period'] = expressions.skim_time_period_label(model_settings['OUT_PERIOD'])
assert ('duration' not in choosers)
choosers['duration'] = model_settings['IN_PERIOD'] - model_settings['OUT_PERIOD']
logsum_spec = simulate.read_model_spec(file_name=logsum_settings['SPEC'])
coefficients = simulate.get_segment_coefficients(logsum_settings, tour_purpose)
logsum_spec = simulate.eval_coefficients(logsum_spec, coefficients, estimator=None)
nest_spec = config.get_logit_model_settings(logsum_settings)
nest_spec = simulate.eval_nest_coefficients(nest_spec, coefficients)
constants = config.get_model_constants(logsum_settings)
logger.debug("Running compute_logsums with %d choosers" % choosers.shape[0])
# setup skim keys
odt_skim_stack_wrapper = skim_stack.wrap(left_key=orig_col_name, right_key=dest_col_name,
skim_key='out_period')
dot_skim_stack_wrapper = skim_stack.wrap(left_key=dest_col_name, right_key=orig_col_name,
skim_key='in_period')
odr_skim_stack_wrapper = skim_stack.wrap(left_key=orig_col_name, right_key=dest_col_name,
skim_key='in_period')
dor_skim_stack_wrapper = skim_stack.wrap(left_key=dest_col_name, right_key=orig_col_name,
skim_key='out_period')
od_skim_stack_wrapper = skim_dict.wrap(orig_col_name, dest_col_name)
skims = {
"odt_skims": odt_skim_stack_wrapper,
"dot_skims": dot_skim_stack_wrapper,
"odr_skims": odr_skim_stack_wrapper,
"dor_skims": dor_skim_stack_wrapper,
"od_skims": od_skim_stack_wrapper,
'orig_col_name': orig_col_name,
'dest_col_name': dest_col_name
}
locals_dict = {}
locals_dict.update(constants)
locals_dict.update(skims)
# constrained coefficients can appear in expressions
locals_dict.update(coefficients)
# - run preprocessor to annotate choosers
# allow specification of alternate preprocessor for nontour choosers
preprocessor = model_settings.get('LOGSUM_PREPROCESSOR', 'preprocessor')
preprocessor_settings = logsum_settings[preprocessor]
if preprocessor_settings:
simulate.set_skim_wrapper_targets(choosers, skims)
expressions.assign_columns(
df=choosers,
model_settings=preprocessor_settings,
locals_dict=locals_dict,
trace_label=trace_label)
logsums = simulate.simple_simulate_logsums(
choosers,
logsum_spec,
nest_spec,
skims=skims,
locals_d=locals_dict,
chunk_size=chunk_size,
trace_label=trace_label,
alt_col_name=dest_col_name)
return logsums