biogeme.segmentation

Examples of use of several functions.

This is designed for programmers who need examples of use of the functions of the module. The examples are designed to illustrate the syntax. They do not correspond to any meaningful model.

author:

Michel Bierlaire

date:

Thu Dec 7 15:18:45 2023

from biogeme.version import get_text
import biogeme.segmentation as seg
from biogeme.expressions import Variable, Beta

Version of Biogeme.

print(get_text())

biogeme 3.2.14 [2024-08-05]
Home page: http://biogeme.epfl.ch
Submit questions to https://groups.google.com/d/forum/biogeme
Michel Bierlaire, Transport and Mobility Laboratory, Ecole Polytechnique Fédérale de Lausanne (EPFL)

socio_eco_1 = Variable('socio_eco_1')

segmentation_1 = seg.DiscreteSegmentationTuple(
    variable=socio_eco_1,
    mapping={
        1: 'high',
        2: 'medium',
        3: 'low',
    },
)

socio_eco_2 = Variable('socio_eco_2')

segmentation_2 = seg.DiscreteSegmentationTuple(
    variable=socio_eco_2,
    mapping={
        1: 'level_1',
        2: 'level_2',
        3: 'level_3',
        4: 'level_4',
    },
)

beta_x = Beta('beta_x', 0, None, None, 0)

segmented_parameter = seg.Segmentation(beta_x, (segmentation_1,))

The expressions for the segmented parameter is obtained as follows:

segmented_parameter.segmented_beta()

bioMultSum(Beta('beta_x', 0, None, None, 0), (Beta('beta_x_medium', 0, None, None, 0) * (socio_eco_1 == `2.0`)), (Beta('beta_x_low', 0, None, None, 0) * (socio_eco_1 == `3.0`)))

The corresponding Python code can be obtained as follows.

print(segmented_parameter.segmented_code())

beta_x_medium = Beta('beta_x_medium', 0, None, None, 0)
beta_x_low = Beta('beta_x_low', 0, None, None, 0)
segmented_beta_x = bioMultSum([Beta('beta_x', 0, None, None, 0), beta_x_medium * (Variable('socio_eco_1') == 2), beta_x_low * (Variable('socio_eco_1') == 3)])

The code of the original Beta is also available:

print(segmented_parameter.beta_code())

Beta('beta_x', 0, None, None, 0)

Same with the second segmentation

segmented_parameter = seg.Segmentation(beta_x, (segmentation_2,))

segmented_parameter.segmented_beta()

bioMultSum(Beta('beta_x', 0, None, None, 0), (Beta('beta_x_level_2', 0, None, None, 0) * (socio_eco_2 == `2.0`)), (Beta('beta_x_level_3', 0, None, None, 0) * (socio_eco_2 == `3.0`)), (Beta('beta_x_level_4', 0, None, None, 0) * (socio_eco_2 == `4.0`)))

print(segmented_parameter.segmented_code())

beta_x_level_2 = Beta('beta_x_level_2', 0, None, None, 0)
beta_x_level_3 = Beta('beta_x_level_3', 0, None, None, 0)
beta_x_level_4 = Beta('beta_x_level_4', 0, None, None, 0)
segmented_beta_x = bioMultSum([Beta('beta_x', 0, None, None, 0), beta_x_level_2 * (Variable('socio_eco_2') == 2), beta_x_level_3 * (Variable('socio_eco_2') == 3), beta_x_level_4 * (Variable('socio_eco_2') == 4)])

The two segmentations can be combined.

segmented_parameter = seg.Segmentation(
    beta_x,
    (
        segmentation_1,
        segmentation_2,
    ),
)

segmented_parameter.segmented_beta()

bioMultSum(Beta('beta_x', 0, None, None, 0), (Beta('beta_x_medium', 0, None, None, 0) * (socio_eco_1 == `2.0`)), (Beta('beta_x_low', 0, None, None, 0) * (socio_eco_1 == `3.0`)), (Beta('beta_x_level_2', 0, None, None, 0) * (socio_eco_2 == `2.0`)), (Beta('beta_x_level_3', 0, None, None, 0) * (socio_eco_2 == `3.0`)), (Beta('beta_x_level_4', 0, None, None, 0) * (socio_eco_2 == `4.0`)))

print(segmented_parameter.segmented_code())

beta_x_medium = Beta('beta_x_medium', 0, None, None, 0)
beta_x_low = Beta('beta_x_low', 0, None, None, 0)
beta_x_level_2 = Beta('beta_x_level_2', 0, None, None, 0)
beta_x_level_3 = Beta('beta_x_level_3', 0, None, None, 0)
beta_x_level_4 = Beta('beta_x_level_4', 0, None, None, 0)
segmented_beta_x = bioMultSum([Beta('beta_x', 0, None, None, 0), beta_x_medium * (Variable('socio_eco_1') == 2), beta_x_low * (Variable('socio_eco_1') == 3), beta_x_level_2 * (Variable('socio_eco_2') == 2), beta_x_level_3 * (Variable('socio_eco_2') == 3), beta_x_level_4 * (Variable('socio_eco_2') == 4)])