def non_mandatory_tours(persons, non_mandatory_tour_frequency_alts):
t0 = print_elapsed_time("")
df = process_non_mandatory_tours(
persons.non_mandatory_tour_frequency.dropna(),
non_mandatory_tour_frequency_alts.local)
t0 = print_elapsed_time("process_non_mandatory_tours", t0)
return df
def run_model(model_name):
t0 = print_elapsed_time()
orca.run([model_name])
t0 = print_elapsed_time(model_name, t0)
def set_random_seed():
np.random.seed(0)
def run_model(model_name):
t0 = print_elapsed_time()
orca.run([model_name])
t0 = print_elapsed_time(model_name, t0)
# uncomment the line below to set random seed so that run results are reproducible
# orca.add_injectable("set_random_seed", set_random_seed)
tracing.config_logger()
t0 = print_elapsed_time()
run_model("compute_accessibility")
run_model("school_location_simulate")
run_model("workplace_location_simulate")
print orca.get_table("persons").distance_to_work.describe()
run_model("auto_ownership_simulate")
run_model("cdap_simulate")
run_model('mandatory_tour_frequency')
orca.get_table("mandatory_tours").tour_type.value_counts()
run_model("mandatory_scheduling")
run_model('non_mandatory_tour_frequency')
orca.get_table("non_mandatory_tours").tour_type.value_counts()
run_model("destination_choice")
run_model("non_mandatory_scheduling")
def run_model(model_name):
t0 = print_elapsed_time()
orca.run([model_name])
t0 = print_elapsed_time(model_name, t0)
log_memory_info('after %s' % model_name)
def non_mandatory_tour_frequency(set_random_seed, persons_merged,
non_mandatory_tour_frequency_alts,
non_mandatory_tour_frequency_spec,
non_mandatory_tour_frequency_settings,
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.
"""
t0 = print_elapsed_time()
choosers = persons_merged.to_frame()
alts = non_mandatory_tour_frequency_alts.to_frame()
# 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 = get_model_constants(non_mandatory_tour_frequency_settings)
choices_list = []
# segment by person type and pick the right spec for each person type
for name, segment in choosers.groupby('ptype_cat'):
logger.info("Running segment '%s' of size %d" % (name, len(segment)))
choices = asim.interaction_simulate(
segment,
alts,
# notice that we pick the column for the segment for each segment we run
spec=non_mandatory_tour_frequency_spec[[name]],
locals_d=constants,
sample_size=50,
chunk_size=chunk_size,
trace_label=trace_hh_id
and 'non_mandatory_tour_frequency.%s' % name,
trace_choice_name='non_mandatory_tour_frequency')
choices_list.append(choices)
t0 = print_elapsed_time("non_mandatory_tour_frequency.%s" % name, t0)
# FIXME - force garbage collection
# mem = asim.memory_info()
# logger.info('memory_info ptype %s, %s' % (name, mem))
choices = pd.concat(choices_list)
# FIXME - no need to reindex?
orca.add_column("persons", "non_mandatory_tour_frequency", choices)
add_dependent_columns("persons", "persons_nmtf")
if trace_hh_id:
trace_columns = ['non_mandatory_tour_frequency']
tracing.trace_df(orca.get_table('persons_merged').to_frame(),
label="non_mandatory_tour_frequency",
columns=trace_columns,
warn_if_empty=True)
