Note
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Nested logit model
Example of a nested logit model, using the original syntax for nests. Since biogeme 3.13, a new syntax, more explicit, has been adopted.
- author:
Michel Bierlaire, EPFL
- date:
Tue Oct 24 13:37:27 2023
import biogeme.biogeme_logging as blog
import biogeme.biogeme as bio
from biogeme import models
from biogeme.expressions import Beta
See the data processing script: Data preparation for Swissmetro.
from swissmetro_data import (
database,
CHOICE,
SM_AV,
CAR_AV_SP,
TRAIN_AV_SP,
TRAIN_TT_SCALED,
TRAIN_COST_SCALED,
SM_TT_SCALED,
SM_COST_SCALED,
CAR_TT_SCALED,
CAR_CO_SCALED,
)
logger = blog.get_screen_logger(level=blog.INFO)
logger.info('Example b09nested')
Example b09nested
Parameters to be estimated.
ASC_CAR = Beta('ASC_CAR', 0, None, None, 0)
ASC_TRAIN = Beta('ASC_TRAIN', 0, None, None, 0)
ASC_SM = Beta('ASC_SM', 0, None, None, 1)
B_TIME = Beta('B_TIME', 0, None, None, 0)
B_COST = Beta('B_COST', 0, None, None, 0)
MU = Beta('MU', 1, 1, 10, 0)
Definition of the utility functions.
V1 = ASC_TRAIN + B_TIME * TRAIN_TT_SCALED + B_COST * TRAIN_COST_SCALED
V2 = ASC_SM + B_TIME * SM_TT_SCALED + B_COST * SM_COST_SCALED
V3 = ASC_CAR + B_TIME * CAR_TT_SCALED + B_COST * CAR_CO_SCALED
Associate utility functions with the numbering of alternatives.
V = {1: V1, 2: V2, 3: V3}
Associate the availability conditions with the alternatives.
av = {1: TRAIN_AV_SP, 2: SM_AV, 3: CAR_AV_SP}
Definition of nests. In this example, we create a nest for the existing modes, that is train (1) and car (3). Each nest is associated with a tuple containing (i) the nest parameter and (ii) the list of alternatives.
existing = MU, [1, 3]
future = 1.0, [2]
nests = existing, future
Definition of the model. This is the contribution of each observation to the log likelihood function. The choice model is a nested logit, with availability conditions.
logprob = models.lognested(V, av, nests, CHOICE)
It is recommended to define the nests of the nested logit model using the objects OneNestForNestedLogit and NestsForNestedLogit defined in biogeme.nests.
Create the Biogeme object.
the_biogeme = bio.BIOGEME(database, logprob)
the_biogeme.modelName = "b09nested_old"
Biogeme parameters read from biogeme.toml.
Calculate the null log likelihood for reporting.
the_biogeme.calculate_null_loglikelihood(av)
np.float64(-6964.662979191462)
Estimate the parameters.
results = the_biogeme.estimate()
As the model is not too complex, we activate the calculation of second derivatives. If you want to change it, change the name of the algorithm in the TOML file from "automatic" to "simple_bounds"
*** Initial values of the parameters are obtained from the file __b09nested_old.iter
Cannot read file __b09nested_old.iter. Statement is ignored.
As the model is not too complex, we activate the calculation of second derivatives. If you want to change it, change the name of the algorithm in the TOML file from "automatic" to "simple_bounds"
Optimization algorithm: hybrid Newton/BFGS with simple bounds [simple_bounds]
** Optimization: Newton with trust region for simple bounds
Iter. ASC_CAR ASC_TRAIN B_COST B_TIME MU Function Relgrad Radius Rho
0 0.1 -0.75 -1 -0.8 1.5 5.4e+03 0.082 10 0.92 ++
1 -0.22 -0.28 -0.82 -0.86 2.2 5.3e+03 0.076 10 0.44 +
2 -0.17 -0.52 -0.7 -0.7 2.7 5.3e+03 0.023 10 0.72 +
3 -0.17 -0.52 -0.7 -0.7 2.7 5.3e+03 0.023 0.84 -4.6 -
4 -0.14 -0.52 -0.87 -0.92 1.8 5.2e+03 0.006 0.84 0.63 +
5 -0.16 -0.51 -0.87 -0.91 2 5.2e+03 0.0014 8.4 1.1 ++
6 -0.16 -0.51 -0.87 -0.91 2 5.2e+03 8.1e-05 8.4 1 ++
Results saved in file b09nested_old.html
Results saved in file b09nested_old.pickle
print(results.short_summary())
Results for model b09nested_old
Nbr of parameters: 5
Sample size: 6768
Excluded data: 3960
Null log likelihood: -6964.663
Final log likelihood: -5236.9
Likelihood ratio test (null): 3455.525
Rho square (null): 0.248
Rho bar square (null): 0.247
Akaike Information Criterion: 10483.8
Bayesian Information Criterion: 10517.9
pandas_results = results.get_estimated_parameters()
pandas_results
Total running time of the script: (0 minutes 0.402 seconds)