def test_mtc_with_dataset(mtc_dataset):
pytest.importorskip("sharrow")
m = NumbaModel(alts=mtc_dataset['_altid_'].values)
from larch.roles import P, X, PX
m.utility_co[2] = P("ASC_SR2") + P("hhinc#2") * X("hhinc")
m.utility_co[3] = P("ASC_SR3P") + P("hhinc#3") * X("hhinc")
m.utility_co[4] = P("ASC_TRAN") + P("hhinc#4") * X("hhinc")
m.utility_co[5] = P("ASC_BIKE") + P("hhinc#5") * X("hhinc")
m.utility_co[6] = P("ASC_WALK") + P("hhinc#6") * X("hhinc")
m.utility_ca = PX("tottime") + PX("totcost")
m.availability_var = 'avail'
m.choice_ca_var = 'chose'
m.datatree = mtc_dataset
assert m.loglike() == approx(-7309.600971749634)
m.set_cap(20)
result = m.maximize_loglike(method='slsqp')
assert result.loglike == approx(-3626.1862595453385)
def test_eville_mode_with_dataset():
pytest.importorskip("sharrow")
from larch.examples import EXAMPVILLE
tree = EXAMPVILLE('datatree')
DA = 1
SR = 2
Walk = 3
Bike = 4
Transit = 5
m = NumbaModel(
alts={
DA: 'DA',
SR: 'SR',
Walk: 'Walk',
Bike: 'Bike',
Transit: 'Transit',
},
datatree=tree,
)
m.title = "Exampville Work Tour Mode Choice v1"
m.utility_co[DA] = (
+P.InVehTime * X("od.AUTO_TIME + do.AUTO_TIME") +
P.Cost * X("od.AUTO_COST + do.AUTO_COST") # dollars per mile
)
m.utility_co[SR] = (
+P.ASC_SR + P.InVehTime * X("od.AUTO_TIME + do.AUTO_TIME") + P.Cost *
X("od.AUTO_COST + do.AUTO_COST") * 0.5 # dollars per mile, half share
+ P("LogIncome:SR") * X("log(INCOME)"))
m.utility_co[Walk] = (+P.ASC_Walk +
P.NonMotorTime * X("od.WALK_TIME + do.WALK_TIME") +
P("LogIncome:Walk") * X("log(INCOME)"))
m.utility_co[Bike] = (+P.ASC_Bike +
P.NonMotorTime * X("od.BIKE_TIME + do.BIKE_TIME") +
P("LogIncome:Bike") * X("log(INCOME)"))
m.utility_co[Transit] = (
+P.ASC_Transit + P.InVehTime * X("od.TRANSIT_IVTT + do.TRANSIT_IVTT") +
P.OutVehTime * X("od.TRANSIT_OVTT + do.TRANSIT_OVTT") +
P.Cost * X("od.TRANSIT_FARE + do.TRANSIT_FARE") +
P("LogIncome:Transit") * X('log(INCOME)'))
Car = m.graph.new_node(parameter='Mu:Car', children=[DA, SR], name='Car')
NonMotor = m.graph.new_node(parameter='Mu:NonMotor',
children=[Walk, Bike],
name='NonMotor')
Motor = m.graph.new_node(parameter='Mu:Motor',
children=[Car, Transit],
name='Motor')
m.choice_co_code = 'TOURMODE'
m.availability_co_vars = {
DA: 'AGE >= 16',
SR: '1',
Walk: 'WALK_TIME < 60',
Bike: 'BIKE_TIME < 60',
Transit: 'TRANSIT_FARE>0',
}
assert m.loglike() == approx(-28846.81581153095)
assert m.dataflows.keys() == {'co', 'avail_co'}
assert m.d_loglike() == approx(
np.array([
-5.02935000e+03, -2.69235000e+03, -1.72110000e+03, -2.21118333e+03,
1.67050996e+04, 1.26952101e+05, -5.50687172e+04, -3.03043210e+04,
-1.93304978e+04, -2.41203790e+04, 5.82518580e+03, 2.92870835e+02,
-3.31973600e+03, -3.47182502e+05, -2.26234434e+05
]))
m.set_cap(30)
r = m.maximize_loglike(method='slsqp')
assert dict(r.x) == approx(
{
'ASC_Bike': 1.0207805052947376,
'ASC_SR': 2.9895850624870013,
'ASC_Transit': 8.508746668124973,
'ASC_Walk': 7.473491883981414,
'Cost': -0.17750345102327197,
'InVehTime': -0.06760741744874971,
'LogIncome:Bike': -0.3648653520247778,
'LogIncome:SR': -0.4222047281799176,
'LogIncome:Transit': -0.6960684260831979,
'LogIncome:Walk': -0.4548368480289394,
'Mu:Car': 0.5466601043649147,
'Mu:Motor': 0.9540629350784797,
'Mu:NonMotor': 0.8636508452469879,
'NonMotorTime': -0.1268142189962758,
'OutVehTime': -0.14752266135040631,
},
rel=1e-5)
assert r.loglike == approx(-8047.006193851376)
worktours = tree.query_cases('TOURPURP==1')
m.datatree = worktours
assert m.loglike() == approx(-3527.6797690247113)
m.float_dtype = np.float32
assert m.loglike() == approx(-3527.68115234375)
assert m.n_cases == 7564
m.datatree = tree
assert m.loglike() == approx(-8047.006193851376)
assert m.n_cases == 20739