def test_rng_access():
configs_dir = os.path.join(os.path.dirname(__file__), 'configs')
orca.add_injectable("configs_dir", configs_dir)
output_dir = os.path.join(os.path.dirname(__file__), 'output')
orca.add_injectable("output_dir", output_dir)
data_dir = os.path.join(os.path.dirname(__file__), 'data')
orca.add_injectable("data_dir", data_dir)
inject_settings(configs_dir, households_sample_size=HOUSEHOLDS_SAMPLE_SIZE)
orca.clear_cache()
pipeline.set_rn_generator_base_seed(0)
pipeline.open_pipeline()
with pytest.raises(RuntimeError) as excinfo:
pipeline.set_rn_generator_base_seed(0)
assert "call set_rn_generator_base_seed before the first step" in str(
excinfo.value)
rng = pipeline.get_rn_generator()
pipeline.close_pipeline()
orca.clear_cache()
def test_mp_run():
mp_configs_dir = os.path.join(os.path.dirname(__file__), 'configs_mp')
configs_dir = os.path.join(os.path.dirname(__file__), 'configs')
inject.add_injectable('configs_dir', [mp_configs_dir, configs_dir])
output_dir = os.path.join(os.path.dirname(__file__), 'output')
inject.add_injectable("output_dir", output_dir)
data_dir = os.path.join(os.path.dirname(__file__), 'data')
inject.add_injectable("data_dir", data_dir)
tracing.config_logger()
run_list = mp_tasks.get_run_list()
mp_tasks.print_run_list(run_list)
# do this after config.handle_standard_args, as command line args may override injectables
injectables = ['data_dir', 'configs_dir', 'output_dir']
injectables = {k: inject.get_injectable(k) for k in injectables}
# pipeline.run(models=run_list['models'], resume_after=run_list['resume_after'])
mp_tasks.run_multiprocess(run_list, injectables)
pipeline.open_pipeline('_')
regress_mini_auto()
pipeline.close_pipeline()
def test_mp_run():
mp_configs_dir = os.path.join(os.path.dirname(__file__), 'configs_mp')
configs_dir = os.path.join(os.path.dirname(__file__), 'configs')
inject.add_injectable('configs_dir', [mp_configs_dir, configs_dir])
output_dir = os.path.join(os.path.dirname(__file__), 'output')
inject.add_injectable("output_dir", output_dir)
data_dir = os.path.join(os.path.dirname(__file__), 'data')
inject.add_injectable("data_dir", data_dir)
tracing.config_logger()
run_list = mp_tasks.get_run_list()
mp_tasks.print_run_list(run_list)
# do this after config.handle_standard_args, as command line args may override injectables
injectables = ['data_dir', 'configs_dir', 'output_dir']
injectables = {k: inject.get_injectable(k) for k in injectables}
# pipeline.run(models=run_list['models'], resume_after=run_list['resume_after'])
mp_tasks.run_multiprocess(run_list, injectables)
pipeline.open_pipeline('_')
regress_mini_auto()
pipeline.close_pipeline()
def test_mini_pipeline_run2():
# the important thing here is that we should get
# exactly the same results as for test_mini_pipeline_run
# when we restart pipeline
configs_dir = os.path.join(os.path.dirname(__file__), 'configs')
setup_dirs(configs_dir)
inject_settings(configs_dir, households_sample_size=HOUSEHOLDS_SAMPLE_SIZE)
# should be able to get this BEFORE pipeline is opened
checkpoints_df = pipeline.get_checkpoints()
prev_checkpoint_count = len(checkpoints_df.index)
# print "checkpoints_df\n", checkpoints_df[['checkpoint_name']]
assert prev_checkpoint_count == 8
pipeline.open_pipeline('auto_ownership_simulate')
regress_mini_auto()
# try to run a model already in pipeline
with pytest.raises(RuntimeError) as excinfo:
pipeline.run_model('auto_ownership_simulate')
assert "run model 'auto_ownership_simulate' more than once" in str(
excinfo.value)
# and these new ones
pipeline.run_model('cdap_simulate')
pipeline.run_model('mandatory_tour_frequency')
regress_mini_mtf()
# should be able to get this before pipeline is closed (from existing open store)
checkpoints_df = pipeline.get_checkpoints()
assert len(checkpoints_df.index) == prev_checkpoint_count
# - write list of override_hh_ids to override_hh_ids.csv in data for use in next test
num_hh_ids = 10
hh_ids = pipeline.get_table("households").head(num_hh_ids).index.values
hh_ids = pd.DataFrame({'household_id': hh_ids})
data_dir = inject.get_injectable('data_dir')
hh_ids.to_csv(os.path.join(data_dir, 'override_hh_ids.csv'),
index=False,
header=True)
pipeline.close_pipeline()
inject.clear_cache()
close_handlers()
def test_rng_access():
setup_dirs()
inject.add_injectable('rng_base_seed', 0)
pipeline.open_pipeline()
rng = pipeline.get_rn_generator()
assert isinstance(rng, random.Random)
pipeline.close_pipeline()
inject.clear_cache()
def test_mini_pipeline_run2():
# the important thing here is that we should get
# exactly the same results as for test_mini_pipeline_run
# when we restart pipeline
configs_dir = os.path.join(os.path.dirname(__file__), 'configs')
setup_dirs(configs_dir)
inject_settings(configs_dir, households_sample_size=HOUSEHOLDS_SAMPLE_SIZE)
# should be able to get this BEFORE pipeline is opened
checkpoints_df = pipeline.get_checkpoints()
prev_checkpoint_count = len(checkpoints_df.index)
# print "checkpoints_df\n", checkpoints_df[['checkpoint_name']]
assert prev_checkpoint_count == 8
pipeline.open_pipeline('auto_ownership_simulate')
regress_mini_auto()
# try to run a model already in pipeline
with pytest.raises(RuntimeError) as excinfo:
pipeline.run_model('auto_ownership_simulate')
assert "run model 'auto_ownership_simulate' more than once" in str(excinfo.value)
# and these new ones
pipeline.run_model('cdap_simulate')
pipeline.run_model('mandatory_tour_frequency')
regress_mini_mtf()
# should be able to get this before pipeline is closed (from existing open store)
checkpoints_df = pipeline.get_checkpoints()
assert len(checkpoints_df.index) == prev_checkpoint_count
# - write list of override_hh_ids to override_hh_ids.csv in data for use in next test
num_hh_ids = 10
hh_ids = pipeline.get_table("households").head(num_hh_ids).index.values
hh_ids = pd.DataFrame({'household_id': hh_ids})
data_dir = inject.get_injectable('data_dir')
hh_ids.to_csv(os.path.join(data_dir, 'override_hh_ids.csv'), index=False, header=True)
pipeline.close_pipeline()
inject.clear_cache()
close_handlers()
def test_rng_access():
configs_dir = os.path.join(os.path.dirname(__file__), 'configs')
setup_dirs(configs_dir)
inject.add_injectable('rng_base_seed', 0)
pipeline.open_pipeline()
rng = pipeline.get_rn_generator()
assert isinstance(rng, random.Random)
pipeline.close_pipeline()
inject.clear_cache()
def test_mp_run():
mp_configs_dir = os.path.join(os.path.dirname(__file__), 'configs_mp')
setup_dirs(ancillary_configs_dir=mp_configs_dir)
run_list = mp_tasks.get_run_list()
mp_tasks.print_run_list(run_list)
# do this after config.handle_standard_args, as command line args may override injectables
injectables = ['data_dir', 'configs_dir', 'output_dir']
injectables = {k: inject.get_injectable(k) for k in injectables}
# pipeline.run(models=run_list['models'], resume_after=run_list['resume_after'])
mp_tasks.run_multiprocess(run_list, injectables)
pipeline.open_pipeline('_')
regress_mini_auto()
pipeline.close_pipeline()
def test_mp_run():
setup_dirs()
# Debugging ----------------------
run_list = mp_tasks.get_run_list()
mp_tasks.print_run_list(run_list)
# --------------------------------
# do this after config.handle_standard_args, as command line args
# may override injectables
injectables = ["data_dir", "configs_dir", "output_dir"]
injectables = {k: inject.get_injectable(k) for k in injectables}
mp_tasks.run_multiprocess(injectables)
pipeline.open_pipeline("_")
regress()
pipeline.close_pipeline()
def test_mp_run():
configs_dir = [example_path('configs_3_zone'), example_path('configs')]
data_dir = example_path('data_3')
setup_dirs(configs_dir, data_dir)
inject.add_injectable('settings_file_name', 'settings_mp.yaml')
run_list = mp_tasks.get_run_list()
mp_tasks.print_run_list(run_list)
# do this after config.handle_standard_args, as command line args may override injectables
injectables = [
'data_dir', 'configs_dir', 'output_dir', 'settings_file_name'
]
injectables = {k: inject.get_injectable(k) for k in injectables}
mp_tasks.run_multiprocess(run_list, injectables)
pipeline.open_pipeline('_')
regress_3_zone()
pipeline.close_pipeline()
os.getcwd()
os.chdir('rFirm')
from activitysim.core import inject_defaults
from activitysim.core import tracing
from activitysim.core import pipeline
from activitysim.core import inject
from activitysim.core.config import setting
import rFirm
tracing.config_logger()
pipeline.open_pipeline('_')
# pipeline.preload_injectables()
df = pipeline.get_table("table1").to_frame()
import pandas as pd
file_path = "/Users/jeff.doyle/work/rFirm/example/regression_data/results/firm_sim_types/outputs/Firms.csv"
rfirms = pd.read_csv(file_path, comment='#'). \
rename(columns={'BusID': 'bus_id', 'TAZ1': 'TAZ', 'Model_EmpCat': 'model_emp_cat'}). \
set_index('bus_id')
rfirms.groupby(['TAZ', 'SCTG']).bus_id.count()
def test_mini_pipeline_run2():
# the important thing here is that we should get
# exactly the same results as for test_mini_pipeline_run
# when we restart pipeline
configs_dir = os.path.join(os.path.dirname(__file__), 'configs')
orca.add_injectable("configs_dir", configs_dir)
output_dir = os.path.join(os.path.dirname(__file__), 'output')
orca.add_injectable("output_dir", output_dir)
data_dir = os.path.join(os.path.dirname(__file__), 'data')
orca.add_injectable("data_dir", data_dir)
inject_settings(configs_dir, households_sample_size=HOUSEHOLDS_SAMPLE_SIZE)
orca.clear_cache()
# should be able to get this BEFORE pipeline is opened
checkpoints_df = pipeline.get_checkpoints()
prev_checkpoint_count = len(checkpoints_df.index)
# print "checkpoints_df\n", checkpoints_df[['checkpoint_name']]
assert prev_checkpoint_count == 11
pipeline.open_pipeline('auto_ownership_simulate')
auto_choice = pipeline.get_table("households").auto_ownership
# regression test: these are the same as in test_mini_pipeline_run1
hh_ids = [464138, 1918238, 2201602]
choices = [0, 1, 2]
expected_choice = pd.Series(choices,
index=pd.Index(hh_ids, name="HHID"),
name='auto_ownership')
print "auto_choice\n", auto_choice.head(4)
pdt.assert_series_equal(auto_choice[hh_ids], expected_choice)
# try to run a model already in pipeline
with pytest.raises(RuntimeError) as excinfo:
pipeline.run_model('auto_ownership_simulate')
assert "run model 'auto_ownership_simulate' more than once" in str(
excinfo.value)
# and these new ones
pipeline.run_model('cdap_simulate')
pipeline.run_model('mandatory_tour_frequency')
mtf_choice = pipeline.get_table("persons").mandatory_tour_frequency
per_ids = [24375, 92744, 172491]
choices = ['school2', 'work_and_school', 'work1']
expected_choice = pd.Series(choices,
index=pd.Index(per_ids, name='PERID'),
name='mandatory_tour_frequency')
print "mtf_choice\n", mtf_choice.head(20)
pdt.assert_series_equal(mtf_choice[per_ids], expected_choice)
# should be able to get this before pipeline is closed (from existing open store)
checkpoints_df = pipeline.get_checkpoints()
assert len(checkpoints_df.index) == prev_checkpoint_count
pipeline.close_pipeline()
orca.clear_cache()