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)
def test_dataframes_mnl5_co(mtc):
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.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,
}
ll2 = m5.loglike2(beta_in1, return_series=True)
q1_dll = {
'ASC_BIKE': -139.2947,
'ASC_SR2': -598.531,
'ASC_SR3P': -77.68647,
'ASC_TRAN': -715.4206,
'ASC_WALK': -235.8408,
'hhinc#2': -35611.855,
'hhinc#3': -5276.0254,
'hhinc#4': -42263.88,
'hhinc#5': -8355.174,
'hhinc#6': -13866.567,
}
assert -5594.70654296875 == approx(ll2.ll, rel=1e-5)
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]}"
def test_constrained_optimization():
from larch.numba import example
m = example(1)
from larch.model.constraints import RatioBound, OrderingBound
m.set_value("totcost", -0.001, maximum=0)
m.set_value("tottime", maximum=0)
m.constraints = [
RatioBound(P("totcost"),
P("tottime"),
min_ratio=0.1,
max_ratio=1.0,
scale=1),
OrderingBound(P("ASC_WALK"), P("ASC_BIKE")),
]
m.load_data()
r = m.maximize_loglike(method='slsqp')
assert r.loglike == approx(-3647.76149525901)
x = {
'ASC_BIKE': -0.8087472748965431,
'ASC_SR2': -2.193449976582375,
'ASC_SR3P': -3.744188833076006,
'ASC_TRAN': -0.7603092451373663,
'ASC_WALK': -0.8087472751682576,
'hhinc#2': -0.0021699330391421407,
'hhinc#3': 0.0003696763687090173,
'hhinc#4': -0.00509836274463602,
'hhinc#5': -0.0431749907425252,
'hhinc#6': -0.002373556571769923,
'totcost': -0.004910169034222911,
'tottime': -0.04790588175791953,
}
assert dict(r.x) == approx(x)
assert r.iteration_number == 48
m.set_values("null")
m.set_value("totcost", -0.001, maximum=0)
r2 = m.maximize_loglike(method='slsqp', bhhh_start=3)
assert r2.iteration_number == 24
assert r2.loglike == approx(-3647.76149525901)
assert dict(r2.x) == approx(x, rel=1e-2)
def test_latent_class_mixed_data(swissmetro_raw_df):
dfs = larch.DataFrames(swissmetro_raw_df, alt_codes=[1, 2, 3])
m1 = larch.Model(dataservice=dfs)
m1.availability_co_vars = {
1: "TRAIN_AV_SP",
2: "SM_AV",
3: "CAR_AV_SP",
}
m1.choice_co_code = 'CHOICE'
m1.utility_co[1] = P("ASC_TRAIN") + X("TRAIN_CO*(GA==0)") * P("B_COST")
m1.utility_co[2] = X("SM_CO*(GA==0)") * P("B_COST")
m1.utility_co[3] = P("ASC_CAR") + X("CAR_CO") * P("B_COST")
m2 = larch.Model(dataservice=dfs)
m2.availability_co_vars = {
1: "TRAIN_AV_SP",
2: "SM_AV",
3: "CAR_AV_SP",
}
m2.choice_co_code = 'CHOICE'
m2.utility_co[1] = P("ASC_TRAIN") + X("TRAIN_TT") * P("B_TIME") + X(
"TRAIN_CO*(GA==0)") * P("B_COST")
m2.utility_co[
2] = X("SM_TT") * P("B_TIME") + X("SM_CO*(GA==0)") * P("B_COST")
m2.utility_co[3] = P(
"ASC_CAR") + X("CAR_TT") * P("B_TIME") + X("CAR_CO") * P("B_COST")
dfs2 = larch.DataFrames(swissmetro_raw_df, alt_codes=[1, 2])
km = larch.Model(dataservice=dfs2)
km.utility_co[2] = P.W_OTHER
from larch.model.latentclass import LatentClassModel
with raises(ValueError):
m = LatentClassModel(km, {1: m1, 2: m2})
def test_latent_class(swissmetro_raw_df):
dfs = larch.DataFrames(swissmetro_raw_df, alt_codes=[1, 2, 3])
m1 = larch.Model(dataservice=dfs)
m1.availability_co_vars = {
1: "TRAIN_AV_SP",
2: "SM_AV",
3: "CAR_AV_SP",
}
m1.choice_co_code = 'CHOICE'
m1.utility_co[1] = P("ASC_TRAIN") + X("TRAIN_CO*(GA==0)") * P("B_COST")
m1.utility_co[2] = X("SM_CO*(GA==0)") * P("B_COST")
m1.utility_co[3] = P("ASC_CAR") + X("CAR_CO") * P("B_COST")
m2 = larch.Model(dataservice=dfs)
m2.availability_co_vars = {
1: "TRAIN_AV_SP",
2: "SM_AV",
3: "CAR_AV_SP",
}
m2.choice_co_code = 'CHOICE'
m2.utility_co[1] = P("ASC_TRAIN") + X("TRAIN_TT") * P("B_TIME") + X(
"TRAIN_CO*(GA==0)") * P("B_COST")
m2.utility_co[
2] = X("SM_TT") * P("B_TIME") + X("SM_CO*(GA==0)") * P("B_COST")
m2.utility_co[3] = P(
"ASC_CAR") + X("CAR_TT") * P("B_TIME") + X("CAR_CO") * P("B_COST")
km = larch.Model()
km.utility_co[2] = P.W_OTHER
from larch.model.latentclass import LatentClassModel
m = LatentClassModel(km, {1: m1, 2: m2})
m.load_data()
m.set_value(P.ASC_CAR, 0.125 / 2)
m.set_value(P.ASC_TRAIN, -0.398 / 2)
m.set_value(P.B_COST, -.0126 / 2)
m.set_value(P.B_TIME, -0.028 / 2)
m.set_value(P.W_OTHER, 1.095 / 2)
check1 = m.check_d_loglike()
assert dict(check1.data.analytic) == approx({
'ASC_CAR': -81.69736186616234,
'ASC_TRAIN': -613.131371089499,
'B_COST': -6697.31706964777,
'B_TIME': -40104.940072046316,
'W_OTHER': 245.43145056623683,
})
assert check1.data.similarity.min() > 4
m.set_value(P.ASC_CAR, 0.125)
m.set_value(P.ASC_TRAIN, -0.398)
m.set_value(P.B_COST, -.0126)
m.set_value(P.B_TIME, -0.028)
m.set_value(P.W_OTHER, 1.095)
assert m.loglike() == approx(-5208.502259337974)
check2 = m.check_d_loglike()
assert dict(check2.data.analytic) == approx({
'ASC_CAR': 0.6243716033364302,
'ASC_TRAIN': 0.9297965389102578,
'B_COST': -154.03997923797007,
'B_TIME': 76.19297915128493,
'W_OTHER': -0.7936963902343083,
})
assert check2.data.similarity.min(
) > 2 # similarity is a bit lower very close to the optimum
def test_latent_class_full_data(swissmetro_raw_df):
dfs = larch.DataFrames(swissmetro_raw_df, alt_codes=[1, 2, 3])
m1 = larch.Model(dataservice=dfs)
m1.availability_co_vars = {
1: "TRAIN_AV_SP",
2: "SM_AV",
3: "CAR_AV_SP",
}
m1.choice_co_code = 'CHOICE'
m1.utility_co[1] = P("ASC_TRAIN") + X("TRAIN_CO*(GA==0)") * P("B_COST")
m1.utility_co[2] = X("SM_CO*(GA==0)") * P("B_COST")
m1.utility_co[3] = P("ASC_CAR") + X("CAR_CO") * P("B_COST")
m2 = larch.Model(dataservice=dfs)
m2.availability_co_vars = {
1: "TRAIN_AV_SP",
2: "SM_AV",
3: "CAR_AV_SP",
}
m2.choice_co_code = 'CHOICE'
m2.utility_co[1] = P("ASC_TRAIN") + X("TRAIN_TT") * P("B_TIME") + X(
"TRAIN_CO*(GA==0)") * P("B_COST")
m2.utility_co[
2] = X("SM_TT") * P("B_TIME") + X("SM_CO*(GA==0)") * P("B_COST")
m2.utility_co[3] = P(
"ASC_CAR") + X("CAR_TT") * P("B_TIME") + X("CAR_CO") * P("B_COST")
km = larch.Model(dataservice=dfs, alts=[1, 2])
km.utility_co[2] = P.W_OTHER
from larch.model.latentclass import LatentClassModel
m = LatentClassModel(km, {1: m1, 2: m2})
m.load_data()
m.set_value(P.ASC_CAR, 0.125 / 2)
m.set_value(P.ASC_TRAIN, -0.398 / 2)
m.set_value(P.B_COST, -.0126 / 2)
m.set_value(P.B_TIME, -0.028 / 2)
m.set_value(P.W_OTHER, 1.095 / 2)
check1 = m.check_d_loglike()
assert dict(check1.data.analytic) == approx({
'ASC_CAR': -81.69736186616234,
'ASC_TRAIN': -613.131371089499,
'B_COST': -6697.31706964777,
'B_TIME': -40104.940072046316,
'W_OTHER': 245.43145056623683,
})
def test_dataframes_mnl5qt(mtcq):
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")
m5.quantity_ca = (+P("FakeSizeAlt") * X('altnum+1') +
P("FakeSizeIvtt") * X('ivtt+1'))
m5.quantity_scale = P.Theta
m5.dataframes = mtcq
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,
'FakeSizeAlt': 0.123,
'Theta': 1.0,
}
ll2 = m5.loglike2(beta_in1, return_series=True)
q1_dll = {
'ASC_BIKE': -272.10342,
'ASC_SR2': -884.91547,
'ASC_SR3P': -181.50142,
'ASC_TRAN': -519.74567,
'ASC_WALK': -37.595825,
'FakeSizeAlt': -104.971085,
'FakeSizeIvtt': 104.971085,
'hhinc#2': -51884.465,
'hhinc#3': -11712.436,
'hhinc#4': -30848.334,
'hhinc#5': -15970.957,
'hhinc#6': -3269.796,
'totcost': 59049.66,
'tottime': -34646.656,
'Theta': -838.5296020507812,
}
assert ll2.ll == approx(-5598.75244140625, rel=1e-5), f"ll2.ll={ll2.ll}"
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]}"
correct_null_dloglike = {
'ASC_SR2': -676.598075,
'ASC_SR3P': -1166.26503,
'ASC_TRAN': -491.818432,
'ASC_BIKE': -443.123432,
'ASC_WALK': 3.970966339111328,
'FakeSizeAlt': -86.9414603,
'FakeSizeIvtt': 86.94156646728516,
'hhinc#2': -40249.0548,
'hhinc#3': -67312.464,
'hhinc#4': -30693.2152,
'hhinc#5': -27236.7637,
'hhinc#6': -1389.66274,
'totcost': 145788.421875,
'tottime': -48732.99609375,
'Theta': -1362.409129,
}
ll0 = m5.loglike2('null', return_series=True)
assert (ll0.ll == approx(-8486.55377320886))
dict_ll0_dll = dict(ll0.dll)
for k in dict_ll0_dll:
assert dict_ll0_dll[k] == approx(
correct_null_dloglike[k], rel=1e-5
), f'{k} {dict_ll0_dll[k]} == {(correct_null_dloglike[k])}'
def test_weighted_nl_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")
m5.initialize_graph(alternative_codes=[1, 2, 3, 4, 5, 6])
m5.graph.add_node(10, children=(5, 6), parameter='MU_nonmotor')
m5.graph.add_node(11, children=(1, 2, 3), parameter='MU_car')
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.pf_sort()
m5.dataframes = j1
ll1 = m5.loglike2(beta_in1)
dll1 = m5.d_loglike(beta_in1, return_series=True)
bhhh1 = m5.bhhh(beta_in1, return_dataframe=True)
from larch.model import PERSIST_ALL
ll1 = m5.loglike2_bhhh(beta_in1, return_series=True, persist=PERSIST_ALL)
m5.dataframes = j2
m5.mangle()
ll2 = m5.loglike2_bhhh(beta_in1, return_series=True, persist=PERSIST_ALL)
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.ll, ll2.ll) == approx(
(-18829.858031378415, -18829.858031378433))
dict_ll1_dll = dict(ll1.dll)
dict_ll2_dll = dict(ll2.dll)
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'): 102.45820678523614,
('ASC_BIKE', 'ASC_SR2'): -285.118579955482,
('ASC_BIKE', 'ASC_SR3P'): 19.760852749005203,
('ASC_BIKE', 'ASC_TRAN'): 76.573987238185,
('ASC_BIKE', 'ASC_WALK'): 32.348453110164314,
('ASC_BIKE', 'MU_car'): -219.27982884201612,
('ASC_BIKE', 'MU_nonmotor'): 69.38016083689357,
('ASC_BIKE', 'hhinc#2'): -16063.872652571805,
('ASC_BIKE', 'hhinc#3'): 1231.2989480753972,
('ASC_BIKE', 'hhinc#4'): 4436.148875125189,
('ASC_BIKE', 'hhinc#5'): 5322.601183863066,
('ASC_BIKE', 'hhinc#6'): 1870.2419763938155,
('ASC_BIKE', 'totcost'): 651.7010091466225,
('ASC_BIKE', 'tottime'): 3410.093422258546,
('ASC_SR2', 'ASC_BIKE'): -285.118579955482,
('ASC_SR2', 'ASC_SR2'): 6156.860848428152,
('ASC_SR2', 'ASC_SR3P'): -412.00155512537197,
('ASC_SR2', 'ASC_TRAN'): -1312.7312255613883,
('ASC_SR2', 'ASC_WALK'): -461.36148965146094,
('ASC_SR2', 'MU_car'): 3491.370489213462,
('ASC_SR2', 'MU_nonmotor'): -244.85789490150424,
('ASC_SR2', 'hhinc#2'): 360960.5064929566,
('ASC_SR2', 'hhinc#3'): -25836.407171358624,
('ASC_SR2', 'hhinc#4'): -73800.95798232155,
('ASC_SR2', 'hhinc#5'): -16063.872652571805,
('ASC_SR2', 'hhinc#6'): -23821.208247582188,
('ASC_SR2', 'totcost'): -255314.7240133045,
('ASC_SR2', 'tottime'): -26043.721750561366,
('ASC_SR3P', 'ASC_BIKE'): 19.760852749005203,
('ASC_SR3P', 'ASC_SR2'): -412.00155512537197,
('ASC_SR3P', 'ASC_SR3P'): 194.01812799791654,
('ASC_SR3P', 'ASC_TRAN'): 75.72048947864153,
('ASC_SR3P', 'ASC_WALK'): 21.26889182584585,
('ASC_SR3P', 'MU_car'): 108.36680636408329,
('ASC_SR3P', 'MU_nonmotor'): 11.214695666269147,
('ASC_SR3P', 'hhinc#2'): -25836.407171358624,
('ASC_SR3P', 'hhinc#3'): 11951.689201021742,
('ASC_SR3P', 'hhinc#4'): 4654.093182472214,
('ASC_SR3P', 'hhinc#5'): 1231.298948075397,
('ASC_SR3P', 'hhinc#6'): 1291.5421535124285,
('ASC_SR3P', 'totcost'): -873.8405986324798,
('ASC_SR3P', 'tottime'): 2847.414853230762,
('ASC_TRAN', 'ASC_BIKE'): 76.573987238185,
('ASC_TRAN', 'ASC_SR2'): -1312.7312255613883,
('ASC_TRAN', 'ASC_SR3P'): 75.72048947864153,
('ASC_TRAN', 'ASC_TRAN'): 795.8802149505714,
('ASC_TRAN', 'ASC_WALK'): 106.98212315599287,
('ASC_TRAN', 'MU_car'): -930.0194408568389,
('ASC_TRAN', 'MU_nonmotor'): 58.620406514681704,
('ASC_TRAN', 'hhinc#2'): -73800.95798232155,
('ASC_TRAN', 'hhinc#3'): 4654.093182472214,
('ASC_TRAN', 'hhinc#4'): 43744.97724907036,
('ASC_TRAN', 'hhinc#5'): 4436.148875125189,
('ASC_TRAN', 'hhinc#6'): 5766.003794801971,
('ASC_TRAN', 'totcost'): -289.26636202646205,
('ASC_TRAN', 'tottime'): 17843.995404571957,
('ASC_WALK', 'ASC_BIKE'): 32.348453110164314,
('ASC_WALK', 'ASC_SR2'): -461.36148965146094,
('ASC_WALK', 'ASC_SR3P'): 21.26889182584585,
('ASC_WALK', 'ASC_TRAN'): 106.98212315599287,
('ASC_WALK', 'ASC_WALK'): 257.4268216790701,
('ASC_WALK', 'MU_car'): -372.1302408319178,
('ASC_WALK', 'MU_nonmotor'): 84.61754029339109,
('ASC_WALK', 'hhinc#2'): -23821.208247582188,
('ASC_WALK', 'hhinc#3'): 1291.5421535124283,
('ASC_WALK', 'hhinc#4'): 5766.003794801973,
('ASC_WALK', 'hhinc#5'): 1870.2419763938155,
('ASC_WALK', 'hhinc#6'): 12476.241347103962,
('ASC_WALK', 'totcost'): -4472.049603002317,
('ASC_WALK', 'tottime'): 7147.124392574635,
('MU_car', 'ASC_BIKE'): -219.27982884201612,
('MU_car', 'ASC_SR2'): 3491.370489213462,
('MU_car', 'ASC_SR3P'): 108.36680636408329,
('MU_car', 'ASC_TRAN'): -930.0194408568389,
('MU_car', 'ASC_WALK'): -372.1302408319178,
('MU_car', 'MU_car'): 3048.745424759559,
('MU_car', 'MU_nonmotor'): -215.09078801898488,
('MU_car', 'hhinc#2'): 209343.58472860648,
('MU_car', 'hhinc#3'): 4904.0435782757395,
('MU_car', 'hhinc#4'): -54645.9131974324,
('MU_car', 'hhinc#5'): -12553.188578813708,
('MU_car', 'hhinc#6'): -20397.013767114706,
('MU_car', 'totcost'): -130926.84443581512,
('MU_car', 'tottime'): -19903.25282015786,
('MU_nonmotor', 'ASC_BIKE'): 69.38016083689357,
('MU_nonmotor', 'ASC_SR2'): -244.85789490150424,
('MU_nonmotor', 'ASC_SR3P'): 11.214695666269147,
('MU_nonmotor', 'ASC_TRAN'): 58.620406514681704,
('MU_nonmotor', 'ASC_WALK'): 84.61754029339109,
('MU_nonmotor', 'MU_car'): -215.09078801898488,
('MU_nonmotor', 'MU_nonmotor'): 106.11047953795583,
('MU_nonmotor', 'hhinc#2'): -13788.1097937347,
('MU_nonmotor', 'hhinc#3'): 711.5550773867994,
('MU_nonmotor', 'hhinc#4'): 3435.2281478513137,
('MU_nonmotor', 'hhinc#5'): 3589.230542100178,
('MU_nonmotor', 'hhinc#6'): 4773.341396211997,
('MU_nonmotor', 'totcost'): -571.9687206358277,
('MU_nonmotor', 'tottime'): 3082.561197668304,
('hhinc#2', 'ASC_BIKE'): -16063.872652571805,
('hhinc#2', 'ASC_SR2'): 360960.5064929566,
('hhinc#2', 'ASC_SR3P'): -25836.407171358624,
('hhinc#2', 'ASC_TRAN'): -73800.95798232155,
('hhinc#2', 'ASC_WALK'): -23821.208247582188,
('hhinc#2', 'MU_car'): 209343.58472860648,
('hhinc#2', 'MU_nonmotor'): -13788.1097937347,
('hhinc#2', 'hhinc#2'): 27739015.863625906,
('hhinc#2', 'hhinc#3'): -2107887.124567991,
('hhinc#2', 'hhinc#4'): -5551797.986970259,
('hhinc#2', 'hhinc#5'): -1180261.9541857818,
('hhinc#2', 'hhinc#6'): -1731206.5786703678,
('hhinc#2', 'totcost'): -15915701.570008647,
('hhinc#2', 'tottime'): -1404099.9397647784,
('hhinc#3', 'ASC_BIKE'): 1231.2989480753972,
('hhinc#3', 'ASC_SR2'): -25836.407171358624,
('hhinc#3', 'ASC_SR3P'): 11951.689201021742,
('hhinc#3', 'ASC_TRAN'): 4654.093182472214,
('hhinc#3', 'ASC_WALK'): 1291.5421535124283,
('hhinc#3', 'MU_car'): 4904.0435782757395,
('hhinc#3', 'MU_nonmotor'): 711.5550773867994,
('hhinc#3', 'hhinc#2'): -2107887.124567991,
('hhinc#3', 'hhinc#3'): 985365.3310746389,
('hhinc#3', 'hhinc#4'): 365082.18492341647,
('hhinc#3', 'hhinc#5'): 96863.55545307972,
('hhinc#3', 'hhinc#6'): 105646.64587551646,
('hhinc#3', 'totcost'): -18197.96776085889,
('hhinc#3', 'tottime'): 173393.88742844868,
('hhinc#4', 'ASC_BIKE'): 4436.148875125189,
('hhinc#4', 'ASC_SR2'): -73800.95798232155,
('hhinc#4', 'ASC_SR3P'): 4654.093182472214,
('hhinc#4', 'ASC_TRAN'): 43744.97724907036,
('hhinc#4', 'ASC_WALK'): 5766.003794801973,
('hhinc#4', 'MU_car'): -54645.9131974324,
('hhinc#4', 'MU_nonmotor'): 3435.2281478513137,
('hhinc#4', 'hhinc#2'): -5551797.986970259,
('hhinc#4', 'hhinc#3'): 365082.18492341647,
('hhinc#4', 'hhinc#4'): 3238425.3752979534,
('hhinc#4', 'hhinc#5'): 328415.284903046,
('hhinc#4', 'hhinc#6'): 431510.21040576114,
('hhinc#4', 'totcost'): -203942.9008935652,
('hhinc#4', 'tottime'): 996131.649172921,
('hhinc#5', 'ASC_BIKE'): 5322.601183863066,
('hhinc#5', 'ASC_SR2'): -16063.872652571805,
('hhinc#5', 'ASC_SR3P'): 1231.298948075397,
('hhinc#5', 'ASC_TRAN'): 4436.148875125189,
('hhinc#5', 'ASC_WALK'): 1870.2419763938155,
('hhinc#5', 'MU_car'): -12553.188578813708,
('hhinc#5', 'MU_nonmotor'): 3589.230542100178,
('hhinc#5', 'hhinc#2'): -1180261.9541857818,
('hhinc#5', 'hhinc#3'): 96863.55545307972,
('hhinc#5', 'hhinc#4'): 328415.284903046,
('hhinc#5', 'hhinc#5'): 376816.62222404545,
('hhinc#5', 'hhinc#6'): 139543.8152706944,
('hhinc#5', 'totcost'): 64871.45953365474,
('hhinc#5', 'tottime'): 191184.20323081187,
('hhinc#6', 'ASC_BIKE'): 1870.2419763938155,
('hhinc#6', 'ASC_SR2'): -23821.208247582188,
('hhinc#6', 'ASC_SR3P'): 1291.5421535124285,
('hhinc#6', 'ASC_TRAN'): 5766.003794801971,
('hhinc#6', 'ASC_WALK'): 12476.241347103962,
('hhinc#6', 'MU_car'): -20397.013767114706,
('hhinc#6', 'MU_nonmotor'): 4773.341396211997,
('hhinc#6', 'hhinc#2'): -1731206.5786703678,
('hhinc#6', 'hhinc#3'): 105646.64587551646,
('hhinc#6', 'hhinc#4'): 431510.21040576114,
('hhinc#6', 'hhinc#5'): 139543.8152706944,
('hhinc#6', 'hhinc#6'): 872604.6192807175,
('hhinc#6', 'totcost'): -95081.8079551341,
('hhinc#6', 'tottime'): 364196.75026433234,
('totcost', 'ASC_BIKE'): 651.7010091466225,
('totcost', 'ASC_SR2'): -255314.7240133045,
('totcost', 'ASC_SR3P'): -873.8405986324798,
('totcost', 'ASC_TRAN'): -289.26636202646205,
('totcost', 'ASC_WALK'): -4472.049603002317,
('totcost', 'MU_car'): -130926.84443581512,
('totcost', 'MU_nonmotor'): -571.9687206358277,
('totcost', 'hhinc#2'): -15915701.570008647,
('totcost', 'hhinc#3'): -18197.96776085889,
('totcost', 'hhinc#4'): -203942.9008935652,
('totcost', 'hhinc#5'): 64871.45953365474,
('totcost', 'hhinc#6'): -95081.8079551341,
('totcost', 'totcost'): 67516567.00440338,
('totcost', 'tottime'): -775245.3645765011,
('tottime', 'ASC_BIKE'): 3410.093422258546,
('tottime', 'ASC_SR2'): -26043.721750561366,
('tottime', 'ASC_SR3P'): 2847.414853230762,
('tottime', 'ASC_TRAN'): 17843.995404571957,
('tottime', 'ASC_WALK'): 7147.124392574635,
('tottime', 'MU_car'): -19903.25282015786,
('tottime', 'MU_nonmotor'): 3082.561197668304,
('tottime', 'hhinc#2'): -1404099.9397647784,
('tottime', 'hhinc#3'): 173393.88742844868,
('tottime', 'hhinc#4'): 996131.649172921,
('tottime', 'hhinc#5'): 191184.20323081187,
('tottime', 'hhinc#6'): 364196.75026433234,
('tottime', 'totcost'): -775245.3645765011,
('tottime', 'tottime'): 910724.9012464393,
}
assert dict(ll1.bhhh.unstack()) == approx(bhhh_correct)
assert dict(ll2.bhhh.unstack()) == approx(bhhh_correct)
dll_casewise_A = ll2.dll_casewise / j2.weight_normalization
dll_casewise_B = np.asarray(dll_casewise_A) / j2.data_wt.values
corrected_bhhh = pd.DataFrame(
np.dot(dll_casewise_A.T, dll_casewise_B),
index=ll2.dll.index,
columns=ll2.dll.index,
) * j2.weight_normalization
assert dict(ll1.bhhh.unstack()) == approx(dict(corrected_bhhh.unstack()))
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
def qmnl_straw_man_model_1():
from larch.roles import P, X
altcodes = (1, 2, 3, 4, 5, 6)
from larch.data_services.examples import MTC
dt = MTC()
p = Model(parameters=[], alts=altcodes, dataservice=dt, graph=None)
from larch.roles import P, X
p.utility_ca = (+P('tottime') * X('tottime') + P('totcost') * X('totcost'))
p.utility_co = {
2: (P('ASC#2') * X('1') + P('hhinc#2') * X('hhinc')),
3: (P('ASC#3') * X('1') + P('hhinc#3') * X('hhinc')),
4: (P('ASC#4') * X('1') + P('hhinc#4') * X('hhinc')),
5: (P('ASC#5') * X('1') + P('hhinc#5') * X('hhinc')),
6: (P('ASC#6') * X('1') + P('hhinc#6') * X('hhinc')),
}
p.quantity_ca = (+P("FakeSizeAlt") * X('altnum+1') +
P("FakeSizeIvtt") * X('ivtt+1'))
p.availability_var = '_avail_'
p.choice_ca_var = '_choice_'
p.load_data()
return p
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