Getting help

If you need help, submit your questions to the users' group:

groups.google.com/d/forum/biogeme

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• Why is the file headers.py not generated?
• What initial values should I select for the parameters?
• Can I save intermediate iterations during the estimation?
• Does Biogeme provide support for out-of-sample validation?
• The init loglikelihood is -1.797693e+308 and no iteration is performed. What should I do?

In order to comply better with good programming practice in Python, the syntax to import the variable names from the data file has been modified since version 3.2.5. The file headers.py is not generated anymore. The statement

from headers import *

must be replaced by

globals().update(database.variables)

where database is the object containing the database, created as follows:

import biogeme.database as db
df = pd.read_csv("swissmetro.dat",'\t')
database = db.Database("swissmetro",df)

Moreover, in order to avoid any ambiguity, the operators used by Biogeme must be explicitly imported. For instance:

from biogeme.expressions import Beta, DefineVariable, bioDraws, PanelLikelihoodTrajectory, MonteCarlo, log

Note that it is also possible to import all of them using the following syntax

from biogeme.expressions import *

although this is not a good Python programming practice.

If you have the results of a previous estimation, it may be a good idea to use the estimated values as a starting point for the estimation of a similar models. If not, it depends on the nature of the parameters:

• If the parameter is a coefficient (traditionally denoted by β), the value 0 is appropriate.
• If the parameter is a nest parameter of a nested or cross-nested logit model (traditionally denoted by μ), the value 1 is appropriate. Make sure to define the lower bound of the parameter to 1.
• If the parameter is the nest membership coefficient of a cross-nested logit model (traditionally denoted by α), the value 0.5 is appropriate. Make sure to define the lower bound to 0 and the upper bound to 1.
• If the parameter captures the membership to a class of a latent class model, the value 0.5 is appropriate. Make sure to define the lower bound to 0 and the upper bound to 1.
• If the parameter is the scale of an error component in a mixture of logit model (traditionally denoted by σ), the value must be sufficient large so that the likelihood of each observation is not too close to zero. It is suggested to try first with the value one. If there are numerical issues, try a larger value, such as 10. See Section 7 in the report "Estimating choice models with latent variables with PandasBiogeme" for a detailed discussion.

Yes. Actually, Biogeme does it automatically for you. If the name of your model is mymodel, Biogeme creates a file named __mymodel.iter, where the values of the parameters are saved after each successful iteration of the estimation algorithm. When the estimation starts, Biogeme checks the existence of this file. If it exists, the value stored are used as a starting point for the estimation. Set the attribute saveIterations of the BIOGEME object to False is you want to turn that feature off. [Click here for the documentation]

Yes.

• You first split the data into estimation and validation samples. [Click here for the documentation.]
• You can use the validate function [Click here for the documentation] to perform a full validation. For each slice generated by the split, the model is re-estimated using all the data except the slice, and the estimated model is applied on the validation set (i.e. the slice). The value of the log likelihood for each observation in the validation set is reported in a dataframe. See example 04validation.py from here.