def test_weighted_bhhh(mtc2):
j1, j2 = mtc2
m5 = NumbaModel()
from larch.roles import P, X, PX
m5.utility_co[2] = P("ASC_SR2") + P("hhinc#2") * X("hhinc")
m5.utility_co[3] = P("ASC_SR3P") + P("hhinc#3") * X("hhinc")
m5.utility_co[4] = P("ASC_TRAN") + P("hhinc#4") * X("hhinc")
m5.utility_co[5] = P("ASC_BIKE") + P("hhinc#5") * X("hhinc")
m5.utility_co[6] = P("ASC_WALK") + P("hhinc#6") * X("hhinc")
m5.utility_ca = PX("tottime") + PX("totcost")
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.dataframes = j1
m5.pf_sort()
ll1 = m5.loglike(beta_in1)
dll1 = m5.d_loglike(beta_in1, return_series=True)
bhhh1 = m5.bhhh(beta_in1, return_dataframe=True)
m5.dataframes = j2
m5.pf_sort()
ll2 = m5.loglike(beta_in1)
dll2 = m5.d_loglike(beta_in1, return_series=True)
bhhh2 = m5.bhhh(beta_in1, return_dataframe=True)
q1_dll = {
'ASC_BIKE': -518.3145850719714,
'ASC_SR2': 6659.9870966633935,
'ASC_SR3P': -702.5461471592637,
'ASC_TRAN': -2069.2556854096474,
'ASC_WALK': -680.4136747673049,
'hhinc#2': 390300.04704708763,
'hhinc#3': -44451.89987844542,
'hhinc#4': -117769.88300441334,
'hhinc#5': -29774.93396444093,
'hhinc#6': -36754.12651709895,
'totcost': -280658.27799924824,
'tottime': -66172.15328009706,
}
assert (j1.weight_normalization, j2.weight_normalization) == (3.0, 1.5)
assert (ll1, ll2) == approx((-18829.858031378415, -18829.858031378433))
dict_ll1_dll = dict(dll1)
dict_ll2_dll = dict(dll2)
for k in q1_dll:
assert q1_dll[k] == approx(
dict_ll1_dll[k], rel=1e-5), f"{k} {q1_dll[k]} != {dict_ll1_dll[k]}"
assert q1_dll[k] == approx(
dict_ll2_dll[k], rel=1e-5), f"{k} {q1_dll[k]} != {dict_ll2_dll[k]}"
bhhh_correct = {
('ASC_BIKE', 'ASC_BIKE'): 102.45820678523617,
('ASC_BIKE', 'ASC_SR2'): -285.118579955482,
('ASC_BIKE', 'ASC_SR3P'): 19.760852749005203,
('ASC_BIKE', 'ASC_TRAN'): 76.57398723818505,
('ASC_BIKE', 'ASC_WALK'): 32.34845311016433,
('ASC_BIKE', 'hhinc#2'): -16063.872652571783,
('ASC_BIKE', 'hhinc#3'): 1231.2989480753972,
('ASC_BIKE', 'hhinc#4'): 4436.148875125188,
('ASC_BIKE', 'hhinc#5'): 5322.601183863066,
('ASC_BIKE', 'hhinc#6'): 1870.2419763938155,
('ASC_BIKE', 'totcost'): 651.7010091466211,
('ASC_BIKE', 'tottime'): 3410.0934222585456,
('ASC_SR2', 'ASC_BIKE'): -285.118579955482,
('ASC_SR2', 'ASC_SR2'): 6156.860848428149,
('ASC_SR2', 'ASC_SR3P'): -412.00155512537174,
('ASC_SR2', 'ASC_TRAN'): -1312.7312255613908,
('ASC_SR2', 'ASC_WALK'): -461.36148965146094,
('ASC_SR2', 'hhinc#2'): 360960.5064929568,
('ASC_SR2', 'hhinc#3'): -25836.407171358598,
('ASC_SR2', 'hhinc#4'): -73800.95798232155,
('ASC_SR2', 'hhinc#5'): -16063.872652571783,
('ASC_SR2', 'hhinc#6'): -23821.20824758219,
('ASC_SR2', 'totcost'): -255314.72401330443,
('ASC_SR2', 'tottime'): -26043.72175056138,
('ASC_SR3P', 'ASC_BIKE'): 19.760852749005203,
('ASC_SR3P', 'ASC_SR2'): -412.00155512537174,
('ASC_SR3P', 'ASC_SR3P'): 194.01812799791645,
('ASC_SR3P', 'ASC_TRAN'): 75.72048947864153,
('ASC_SR3P', 'ASC_WALK'): 21.268891825845834,
('ASC_SR3P', 'hhinc#2'): -25836.407171358598,
('ASC_SR3P', 'hhinc#3'): 11951.689201021733,
('ASC_SR3P', 'hhinc#4'): 4654.093182472213,
('ASC_SR3P', 'hhinc#5'): 1231.2989480753972,
('ASC_SR3P', 'hhinc#6'): 1291.5421535124271,
('ASC_SR3P', 'totcost'): -873.8405986324893,
('ASC_SR3P', 'tottime'): 2847.414853230761,
('ASC_TRAN', 'ASC_BIKE'): 76.57398723818505,
('ASC_TRAN', 'ASC_SR2'): -1312.7312255613908,
('ASC_TRAN', 'ASC_SR3P'): 75.72048947864153,
('ASC_TRAN', 'ASC_TRAN'): 795.8802149505715,
('ASC_TRAN', 'ASC_WALK'): 106.98212315599287,
('ASC_TRAN', 'hhinc#2'): -73800.95798232155,
('ASC_TRAN', 'hhinc#3'): 4654.093182472214,
('ASC_TRAN', 'hhinc#4'): 43744.97724907039,
('ASC_TRAN', 'hhinc#5'): 4436.148875125188,
('ASC_TRAN', 'hhinc#6'): 5766.003794801968,
('ASC_TRAN', 'totcost'): -289.2663620264443,
('ASC_TRAN', 'tottime'): 17843.995404571946,
('ASC_WALK', 'ASC_BIKE'): 32.34845311016433,
('ASC_WALK', 'ASC_SR2'): -461.36148965146094,
('ASC_WALK', 'ASC_SR3P'): 21.268891825845834,
('ASC_WALK', 'ASC_TRAN'): 106.98212315599287,
('ASC_WALK', 'ASC_WALK'): 257.42682167907014,
('ASC_WALK', 'hhinc#2'): -23821.20824758219,
('ASC_WALK', 'hhinc#3'): 1291.5421535124271,
('ASC_WALK', 'hhinc#4'): 5766.003794801967,
('ASC_WALK', 'hhinc#5'): 1870.2419763938155,
('ASC_WALK', 'hhinc#6'): 12476.241347103956,
('ASC_WALK', 'totcost'): -4472.049603002317,
('ASC_WALK', 'tottime'): 7147.124392574634,
('hhinc#2', 'ASC_BIKE'): -16063.872652571783,
('hhinc#2', 'ASC_SR2'): 360960.5064929568,
('hhinc#2', 'ASC_SR3P'): -25836.407171358598,
('hhinc#2', 'ASC_TRAN'): -73800.95798232155,
('hhinc#2', 'ASC_WALK'): -23821.20824758219,
('hhinc#2', 'hhinc#2'): 27739015.863625936,
('hhinc#2', 'hhinc#3'): -2107887.1245679897,
('hhinc#2', 'hhinc#4'): -5551797.986970257,
('hhinc#2', 'hhinc#5'): -1180261.954185782,
('hhinc#2', 'hhinc#6'): -1731206.5786703676,
('hhinc#2', 'totcost'): -15915701.570008647,
('hhinc#2', 'tottime'): -1404099.9397647786,
('hhinc#3', 'ASC_BIKE'): 1231.2989480753972,
('hhinc#3', 'ASC_SR2'): -25836.407171358598,
('hhinc#3', 'ASC_SR3P'): 11951.689201021733,
('hhinc#3', 'ASC_TRAN'): 4654.093182472214,
('hhinc#3', 'ASC_WALK'): 1291.5421535124271,
('hhinc#3', 'hhinc#2'): -2107887.1245679897,
('hhinc#3', 'hhinc#3'): 985365.3310746389,
('hhinc#3', 'hhinc#4'): 365082.18492341647,
('hhinc#3', 'hhinc#5'): 96863.5554530797,
('hhinc#3', 'hhinc#6'): 105646.64587551638,
('hhinc#3', 'totcost'): -18197.967760858926,
('hhinc#3', 'tottime'): 173393.88742844874,
('hhinc#4', 'ASC_BIKE'): 4436.148875125188,
('hhinc#4', 'ASC_SR2'): -73800.95798232155,
('hhinc#4', 'ASC_SR3P'): 4654.093182472213,
('hhinc#4', 'ASC_TRAN'): 43744.97724907039,
('hhinc#4', 'ASC_WALK'): 5766.003794801967,
('hhinc#4', 'hhinc#2'): -5551797.986970257,
('hhinc#4', 'hhinc#3'): 365082.18492341647,
('hhinc#4', 'hhinc#4'): 3238425.3752979557,
('hhinc#4', 'hhinc#5'): 328415.2849030458,
('hhinc#4', 'hhinc#6'): 431510.210405761,
('hhinc#4', 'totcost'): -203942.90089356547,
('hhinc#4', 'tottime'): 996131.6491729214,
('hhinc#5', 'ASC_BIKE'): 5322.601183863066,
('hhinc#5', 'ASC_SR2'): -16063.872652571783,
('hhinc#5', 'ASC_SR3P'): 1231.2989480753972,
('hhinc#5', 'ASC_TRAN'): 4436.148875125188,
('hhinc#5', 'ASC_WALK'): 1870.2419763938155,
('hhinc#5', 'hhinc#2'): -1180261.954185782,
('hhinc#5', 'hhinc#3'): 96863.5554530797,
('hhinc#5', 'hhinc#4'): 328415.2849030458,
('hhinc#5', 'hhinc#5'): 376816.6222240454,
('hhinc#5', 'hhinc#6'): 139543.81527069444,
('hhinc#5', 'totcost'): 64871.45953365492,
('hhinc#5', 'tottime'): 191184.20323081187,
('hhinc#6', 'ASC_BIKE'): 1870.2419763938155,
('hhinc#6', 'ASC_SR2'): -23821.20824758219,
('hhinc#6', 'ASC_SR3P'): 1291.5421535124271,
('hhinc#6', 'ASC_TRAN'): 5766.003794801968,
('hhinc#6', 'ASC_WALK'): 12476.241347103956,
('hhinc#6', 'hhinc#2'): -1731206.5786703676,
('hhinc#6', 'hhinc#3'): 105646.64587551638,
('hhinc#6', 'hhinc#4'): 431510.210405761,
('hhinc#6', 'hhinc#5'): 139543.81527069444,
('hhinc#6', 'hhinc#6'): 872604.6192807176,
('hhinc#6', 'totcost'): -95081.80795513398,
('hhinc#6', 'tottime'): 364196.75026433205,
('totcost', 'ASC_BIKE'): 651.7010091466211,
('totcost', 'ASC_SR2'): -255314.72401330443,
('totcost', 'ASC_SR3P'): -873.8405986324893,
('totcost', 'ASC_TRAN'): -289.2663620264443,
('totcost', 'ASC_WALK'): -4472.049603002317,
('totcost', 'hhinc#2'): -15915701.570008647,
('totcost', 'hhinc#3'): -18197.967760858926,
('totcost', 'hhinc#4'): -203942.90089356547,
('totcost', 'hhinc#5'): 64871.45953365492,
('totcost', 'hhinc#6'): -95081.80795513398,
('totcost', 'totcost'): 67516567.00440338,
('totcost', 'tottime'): -775245.3645765022,
('tottime', 'ASC_BIKE'): 3410.0934222585456,
('tottime', 'ASC_SR2'): -26043.72175056138,
('tottime', 'ASC_SR3P'): 2847.414853230761,
('tottime', 'ASC_TRAN'): 17843.995404571946,
('tottime', 'ASC_WALK'): 7147.124392574634,
('tottime', 'hhinc#2'): -1404099.9397647786,
('tottime', 'hhinc#3'): 173393.88742844874,
('tottime', 'hhinc#4'): 996131.6491729214,
('tottime', 'hhinc#5'): 191184.20323081187,
('tottime', 'hhinc#6'): 364196.75026433205,
('tottime', 'totcost'): -775245.3645765022,
('tottime', 'tottime'): 910724.9012464394,
}
assert dict(bhhh1.unstack()) == approx(bhhh_correct)
assert dict(bhhh2.unstack()) == approx(bhhh_correct)
assert m5.check_d_loglike().data.similarity.min() > 4
def test_dataframes_nl5(mtc):
m5 = NumbaModel()
m5.utility_co[2] = P("ASC_SR2") + P("hhinc#2") * X("hhinc")
m5.utility_co[3] = P("ASC_SR3P") + P("hhinc#3") * X("hhinc")
m5.utility_co[4] = P("ASC_TRAN") + P("hhinc#4") * X("hhinc")
m5.utility_co[5] = P("ASC_BIKE") + P("hhinc#5") * X("hhinc")
m5.utility_co[6] = P("ASC_WALK") + P("hhinc#6") * X("hhinc")
m5.utility_ca = PX("tottime") + PX("totcost")
m5.dataframes = mtc
m5.graph.add_node(9, children=(5, 6), parameter='MU_NonMotorized')
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,
}
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 approx(ll2.ll) == -4930.3212890625
dict_ll2_dll = dict(ll2.dll)
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]}"
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,
'MU_NonMotorized': 0.5,
}
ll2b = m5.loglike2(beta_in1, return_series=True)
q1_dllb = {
'ASC_BIKE': -94.071343,
'ASC_SR2': -800.092341,
'ASC_SR3P': -129.354567,
'ASC_TRAN': -369.808551,
'ASC_WALK': -114.786728,
'MU_NonMotorized': -34.816070,
'hhinc#2': -47089.611079,
'hhinc#3': -8505.116916,
'hhinc#4': -22071.859018,
'hhinc#5': -5844.969336,
'hhinc#6': -7168.859044,
'totcost': 37322.528282,
'tottime': -26479.290942,
}
assert -4897.764630665653 == approx(ll2b.ll)
dict_ll2b_dll = dict(ll2b.dll)
for k in q1_dllb:
assert q1_dllb[k] == approx(
dict_ll2b_dll[k],
rel=1e-5), f"{k} {q1_dllb[k]} != {dict_ll2b_dll[k]}"
print(m5.check_d_loglike().data['similarity'].min())
chk = m5.check_d_loglike()
assert chk.data['similarity'].min() > 4