def compute_ood_logsums(choosers, logsum_settings, nest_spec, logsum_spec,
od_skims, locals_dict, chunk_size, trace_label,
chunk_tag):
"""
Compute one (of two) out-of-direction logsums for destination alternatives
Will either be trip_origin -> alt_dest or alt_dest -> primary_dest
"""
locals_dict.update(od_skims)
expressions.annotate_preprocessors(choosers, locals_dict, od_skims,
logsum_settings, trace_label)
logsums = simulate.simple_simulate_logsums(choosers,
logsum_spec,
nest_spec,
skims=od_skims,
locals_d=locals_dict,
chunk_size=chunk_size,
trace_label=trace_label,
chunk_tag=chunk_tag)
assert logsums.index.equals(choosers.index)
# FIXME not strictly necessary, but would make trace files more legible?
# logsums = logsums.replace(-np.inf, -999)
return logsums
def compute_ood_logsums(
choosers,
logsum_settings,
od_skims,
locals_dict,
chunk_size,
trace_label):
"""
Compute one (of two) out-of-direction logsums for destination alternatives
Will either be trip_origin -> alt_dest or alt_dest -> primary_dest
"""
locals_dict.update(od_skims)
expressions.annotate_preprocessors(
choosers, locals_dict, od_skims,
logsum_settings,
trace_label)
nest_spec = config.get_logit_model_settings(logsum_settings)
logsum_spec = simulate.read_model_spec(file_name=logsum_settings['SPEC'])
logsums = simulate.simple_simulate_logsums(
choosers,
logsum_spec,
nest_spec,
skims=od_skims,
locals_d=locals_dict,
chunk_size=chunk_size,
trace_label=trace_label)
assert logsums.index.equals(choosers.index)
# FIXME not strictly necessary, but would make trace files more legible?
# logsums = logsums.replace(-np.inf, -999)
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')
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,
"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)
omnibus_coefficient_spec = get_coeffecients_spec(logsum_settings)
coefficient_spec = omnibus_coefficient_spec[tour_purpose]
coefficients = assign.evaluate_constants(coefficient_spec, constants=constants)
locals_dict = {}
locals_dict.update(coefficients)
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
logsum_spec = get_logsum_spec(logsum_settings)
nest_spec = config.get_logit_model_settings(logsum_settings)
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,
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 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 compute_logsums(choosers, tour_purpose, logsum_settings, model_settings,
skim_dict, skim_stack, chunk_size, trace_hh_id,
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')
logsum_spec = get_logsum_spec(logsum_settings)
omnibus_coefficient_spec = get_coeffecients_spec(logsum_settings)
coefficient_spec = omnibus_coefficient_spec[tour_purpose]
# 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']
nest_spec = config.get_logit_model_settings(logsum_settings)
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')
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,
"od_skims": od_skim_stack_wrapper,
'orig_col_name': orig_col_name,
'dest_col_name': dest_col_name
}
locals_dict = evaluate_constants(coefficient_spec, constants=constants)
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)
logsums = simulate.simple_simulate_logsums(choosers,
logsum_spec,
nest_spec,
skims=skims,
locals_d=locals_dict,
chunk_size=chunk_size,
trace_label=trace_label)
return logsums
def compute_logsums(choosers, logsum_spec, logsum_settings, skim_dict,
skim_stack, chooser_col_name, alt_col_name, chunk_size,
trace_hh_id, trace_label):
"""
Parameters
----------
choosers
logsum_spec
logsum_settings
skim_dict
skim_stack
alt_col_name
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')
nest_spec = config.get_logit_model_settings(logsum_settings)
constants = config.get_model_constants(logsum_settings)
logger.info("Running compute_logsums with %d choosers" %
len(choosers.index))
if trace_hh_id:
tracing.trace_df(logsum_spec,
tracing.extend_trace_label(trace_label, 'spec'),
slicer='NONE',
transpose=False)
# setup skim keys
odt_skim_stack_wrapper = skim_stack.wrap(left_key=chooser_col_name,
right_key=alt_col_name,
skim_key="out_period")
dot_skim_stack_wrapper = skim_stack.wrap(left_key=alt_col_name,
right_key=chooser_col_name,
skim_key="in_period")
od_skim_stack_wrapper = skim_dict.wrap(chooser_col_name, alt_col_name)
skims = [
odt_skim_stack_wrapper, dot_skim_stack_wrapper, od_skim_stack_wrapper
]
locals_d = {
"odt_skims": odt_skim_stack_wrapper,
"dot_skims": dot_skim_stack_wrapper,
"od_skims": od_skim_stack_wrapper
}
if constants is not None:
locals_d.update(constants)
logsums = simulate.simple_simulate_logsums(choosers,
logsum_spec,
nest_spec,
skims=skims,
locals_d=locals_d,
chunk_size=chunk_size,
trace_label=trace_label)
return logsums