def test_dataframes_mnl5(mtc):
m5 = NumbaModel()
from larch.roles import P, X, PX
m5.utility_co[2] = P("ASC_SR2") * X("1") + P("hhinc#2") * X("hhinc")
m5.utility_co[3] = P("ASC_SR3P") * X("1") + P("hhinc#3") * X("hhinc")
m5.utility_co[4] = P("ASC_TRAN") * X("1") + P("hhinc#4") * X("hhinc")
m5.utility_co[5] = P("ASC_BIKE") * X("1") + P("hhinc#5") * X("hhinc")
m5.utility_co[6] = P("ASC_WALK") * X("1") + P("hhinc#6") * X("hhinc")
m5.utility_ca = PX("tottime") + PX("totcost")
m5.dataframes = mtc
beta_in1 = {
'ASC_BIKE': -0.8523646111088327,
'ASC_SR2': -0.5233769323949348,
'ASC_SR3P': -2.3202089848081027,
'ASC_TRAN': -0.05615933557609158,
'ASC_WALK': 0.050082767550586924,
'hhinc#2': -0.001040241396513087,
'hhinc#3': 0.0031822969445656542,
'hhinc#4': -0.0017162484345735326,
'hhinc#5': -0.004071521055900851,
'hhinc#6': -0.0021316332241034445,
'totcost': -0.001336661560553717,
'tottime': -0.01862990704919887,
}
m5.choice_ca_var = 'chose'
m5.availability_var = '_avail_'
ll2 = m5.loglike2(beta_in1, return_series=True)
q1_dll = {
'ASC_BIKE': -139.43832,
'ASC_SR2': -788.00574,
'ASC_SR3P': -126.84879,
'ASC_TRAN': -357.75186,
'ASC_WALK': -116.137886,
'hhinc#2': -46416.28,
'hhinc#3': -8353.63,
'hhinc#4': -21409.012,
'hhinc#5': -8299.654,
'hhinc#6': -7395.375,
'totcost': 39520.043,
'tottime': -26556.303,
}
assert ll2.ll == approx(-4930.3212890625)
for k in q1_dll:
assert q1_dll[k] == approx(dict(
ll2.dll)[k], rel=1e-5), f"{k} {q1_dll[k]} != {dict(ll2.dll)[k]}"
# Test calculate_parameter_covariance doesn't choke if all holdfasts are on:
m5.lock_values(*beta_in1.keys())
m5.calculate_parameter_covariance()
assert np.all(m5.pf['std_err'] == 0)
assert np.all(m5.pf['robust_std_err'] == 0)
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)