def step_forget_tab():
table_name = inject.get_step_arg('table_name')
assert table_name is not None
table = pipeline.get_table(table_name)
pipeline.drop_table(table_name)
def atwork_subtour_destination_simulate(tours, persons_merged,
atwork_subtour_destination_sample,
atwork_subtour_destination_spec,
skim_dict, destination_size_terms,
configs_dir, chunk_size, trace_hh_id):
"""
atwork_subtour_destination model on atwork_subtour_destination_sample
annotated with mode_choice logsum to select a destination from sample alternatives
"""
trace_label = 'atwork_subtour_destination_simulate'
model_settings = inject.get_injectable(
'atwork_subtour_destination_settings')
tours = tours.to_frame()
subtours = tours[tours.tour_category == 'subtour']
# merge persons into tours
choosers = pd.merge(subtours,
persons_merged.to_frame(),
left_on='person_id',
right_index=True)
alt_col_name = model_settings["ALT_COL_NAME"]
chooser_col_name = 'workplace_taz'
# alternatives are pre-sampled and annotated with logsums and pick_count
# but we have to merge additional alt columns into alt sample list
atwork_subtour_destination_sample = atwork_subtour_destination_sample.to_frame(
)
destination_size_terms = destination_size_terms.to_frame()
alternatives = \
pd.merge(atwork_subtour_destination_sample, destination_size_terms,
left_on=alt_col_name, right_index=True, how="left")
tracing.dump_df(DUMP, alternatives, trace_label, 'alternatives')
constants = config.get_model_constants(model_settings)
sample_pool_size = len(destination_size_terms.index)
logger.info("Running atwork_subtour_destination_simulate with %d persons" %
len(choosers))
# create wrapper with keys for this lookup - in this case there is a TAZ in the choosers
# and a TAZ in the alternatives which get merged during interaction
# the skims will be available under the name "skims" for any @ expressions
skims = skim_dict.wrap(chooser_col_name, alt_col_name)
locals_d = {'skims': skims, 'sample_pool_size': float(sample_pool_size)}
if constants is not None:
locals_d.update(constants)
# FIXME - MEMORY HACK - only include columns actually used in spec
chooser_columns = model_settings['SIMULATE_CHOOSER_COLUMNS']
choosers = choosers[chooser_columns]
tracing.dump_df(DUMP, choosers, trace_label, 'choosers')
choices = interaction_sample_simulate(
choosers,
alternatives,
spec=atwork_subtour_destination_spec,
choice_column=alt_col_name,
skims=skims,
locals_d=locals_d,
chunk_size=chunk_size,
trace_label=trace_label,
trace_choice_name='workplace_location')
tracing.print_summary('subtour destination', choices, describe=True)
subtours['destination'] = choices
results = expressions.compute_columns(
df=subtours,
model_settings='annotate_tours_with_dest',
configs_dir=configs_dir,
trace_label=trace_label)
assign_in_place(tours, subtours[['destination']])
assign_in_place(tours, results)
pipeline.replace_table("tours", tours)
pipeline.drop_table('atwork_subtour_destination_sample')
if trace_hh_id:
tracing.trace_df(tours,
label=trace_label,
columns=['destination'],
warn_if_empty=True)
def school_location_simulate(persons_merged, school_location_sample,
school_location_spec, school_location_settings,
skim_dict, destination_size_terms, chunk_size,
trace_hh_id):
"""
School location model on school_location_sample annotated with mode_choice logsum
to select a school_taz from sample alternatives
"""
choosers = persons_merged.to_frame()
school_location_sample = school_location_sample.to_frame()
destination_size_terms = destination_size_terms.to_frame()
trace_label = 'school_location_simulate'
alt_col_name = school_location_settings["ALT_COL_NAME"]
constants = config.get_model_constants(school_location_settings)
# create wrapper with keys for this lookup - in this case there is a TAZ in the choosers
# and a TAZ in the alternatives which get merged during interaction
# the skims will be available under the name "skims" for any @ expressions
skims = skim_dict.wrap("TAZ", alt_col_name)
locals_d = {
'skims': skims,
}
if constants is not None:
locals_d.update(constants)
# FIXME - MEMORY HACK - only include columns actually used in spec
chooser_columns = school_location_settings['SIMULATE_CHOOSER_COLUMNS']
choosers = choosers[chooser_columns]
tracing.dump_df(DUMP, choosers, 'school_location_simulate', 'choosers')
choices_list = []
for school_type, school_type_id in SCHOOL_TYPE_ID.iteritems():
locals_d['segment'] = school_type
choosers_segment = choosers[choosers["is_" + school_type]]
alts_segment = \
school_location_sample[school_location_sample['school_type'] == school_type_id]
# alternatives are pre-sampled and annotated with logsums and pick_count
# but we have to merge additional alt columns into alt sample list
alts_segment = \
pd.merge(alts_segment, destination_size_terms,
left_on=alt_col_name, right_index=True, how="left")
tracing.dump_df(DUMP, alts_segment, trace_label,
'%s_alternatives' % school_type)
choices = interaction_sample_simulate(
choosers_segment,
alts_segment,
spec=school_location_spec[[school_type]],
choice_column=alt_col_name,
skims=skims,
locals_d=locals_d,
chunk_size=chunk_size,
trace_label=tracing.extend_trace_label(trace_label, school_type),
trace_choice_name='school_location')
choices_list.append(choices)
choices = pd.concat(choices_list)
# We only chose school locations for the subset of persons who go to school
# so we backfill the empty choices with -1 to code as no school location
choices = choices.reindex(persons_merged.index).fillna(-1).astype(int)
tracing.dump_df(DUMP, choices, trace_label, 'choices')
tracing.print_summary('school_taz', choices, describe=True)
inject.add_column("persons", "school_taz", choices)
pipeline.add_dependent_columns("persons", "persons_school")
pipeline.drop_table('school_location_sample')
if trace_hh_id:
trace_columns = ['school_taz'
] + inject.get_table('persons_school').columns
tracing.trace_df(inject.get_table('persons_merged').to_frame(),
label="school_location",
columns=trace_columns,
warn_if_empty=True)
def workplace_location_simulate(persons_merged, workplace_location_sample,
workplace_location_spec,
workplace_location_settings, skim_dict,
destination_size_terms, chunk_size,
trace_hh_id):
"""
Workplace location model on workplace_location_sample annotated with mode_choice logsum
to select a work_taz from sample alternatives
"""
trace_label = 'workplace_location_simulate'
# for now I'm going to generate a workplace location for everyone -
# presumably it will not get used in downstream models for everyone -
# it should depend on CDAP and mandatory tour generation as to whether
# it gets used
choosers = persons_merged.to_frame()
alt_col_name = workplace_location_settings["ALT_COL_NAME"]
# alternatives are pre-sampled and annotated with logsums and pick_count
# but we have to merge additional alt columns into alt sample list
workplace_location_sample = workplace_location_sample.to_frame()
destination_size_terms = destination_size_terms.to_frame()
alternatives = \
pd.merge(workplace_location_sample, destination_size_terms,
left_on=alt_col_name, right_index=True, how="left")
tracing.dump_df(DUMP, alternatives, 'workplace_location_simulate',
'alternatives')
constants = config.get_model_constants(workplace_location_settings)
sample_pool_size = len(destination_size_terms.index)
logger.info("Running workplace_location_simulate with %d persons" %
len(choosers))
# create wrapper with keys for this lookup - in this case there is a TAZ in the choosers
# and a TAZ in the alternatives which get merged during interaction
# the skims will be available under the name "skims" for any @ expressions
skims = skim_dict.wrap("TAZ", alt_col_name)
locals_d = {'skims': skims, 'sample_pool_size': float(sample_pool_size)}
if constants is not None:
locals_d.update(constants)
# FIXME - MEMORY HACK - only include columns actually used in spec
chooser_columns = workplace_location_settings['SIMULATE_CHOOSER_COLUMNS']
choosers = choosers[chooser_columns]
tracing.dump_df(DUMP, choosers, 'workplace_location_simulate', 'choosers')
choices = interaction_sample_simulate(
choosers,
alternatives,
spec=workplace_location_spec,
choice_column=alt_col_name,
skims=skims,
locals_d=locals_d,
chunk_size=chunk_size,
trace_label=trace_label,
trace_choice_name='workplace_location')
# FIXME - no need to reindex since we didn't slice choosers
# choices = choices.reindex(persons_merged.index)
tracing.print_summary('workplace_taz', choices, describe=True)
inject.add_column("persons", "workplace_taz", choices)
pipeline.add_dependent_columns("persons", "persons_workplace")
pipeline.drop_table('workplace_location_sample')
if trace_hh_id:
trace_columns = ['workplace_taz'
] + inject.get_table('persons_workplace').columns
tracing.trace_df(inject.get_table('persons_merged').to_frame(),
label="workplace_location",
columns=trace_columns,
warn_if_empty=True)