Catalog

Code to manage catalogs of expressions.

biogeme.catalog module

Defines a catalog of expressions that may be considered in a specification

author:: Michel Bierlaire
date:: Fri Mar 31 08:54:07 2023

class biogeme.catalog.Catalog(catalog_name, named_expressions, controlled_by=None)[source]

Bases: MultipleExpression

Catalog of expressions that are interchangeable. Only one of them defines the specification. They are designed to be modified algorithmically by a controller.

__init__(catalog_name, named_expressions, controlled_by=None)[source]

Ctor

Parameters:

name (str) – name of the catalog of expressions
list_of_named_expressions (list(NamedExpression)) – list of NamedExpression, each containing a name and an expression.
controlled_by (Controller) – Object controlling the selection of the specifications.

Raises:

BiogemeError – if list_of_named_expressions is empty
BiogemeError – if incompatible Controller

catalog_size()[source]

Return the size of the catalog.

Return type:: int

classmethod from_dict(catalog_name, dict_of_expressions, controlled_by=None)[source]

Ctor using a dict instead of a list.

Python does not guarantee the order of elements of a dict, although, in practice, it is always preserved. If the order is critical, it is better to use the main constructor. If not, this constructor provides a more readable code.

Parameters:

catalog_name (str) – name of the catalog
dict_of_expressions (dict(str:biogeme.expressions.Expression)) – dict associating the name of an expression and the expression itself.
controlled_by (Controller) – Object controlling the selection of the specifications.

get_all_controllers()[source]

Provides all controllers controlling the specifications of: a multiple expression

Returns:: a set of controllers
Return type:: set(Controller)

get_iterator()[source]: Obtain an iterator on the named expressions

selected()[source]

Return the selected expression and its name

Returns:: the name and the selected expression
Return type:: NamedExpression

selected_name()[source]

Return the name of the selected expression

Returns:: the name of the selected expression
Return type:: str

class biogeme.catalog.SegmentedParameters(beta_parameters, alternatives)[source]

Bases: object

Class managing the names of segmented and alternative specific parameters

__init__(beta_parameters, alternatives)[source]: Constructor

get_beta(beta_index, alternative)[source]

Return the beta parameters for the given index and given alternative

Parameters:

beta_index (int) – index of the beta in the generic list
alternative (str or None) – name of the alternative, or None for the generic parameter

get_index(beta_index, alternative)[source]

Returns the index in the list of the beta parameter with: the given index specific to the given alternative

Parameters:

beta_index (int) – index of the beta in the generic list
alternative (str or None) – name of the alternative, or None for the generic parameter

biogeme.catalog.generic_alt_specific_catalogs(generic_name, beta_parameters, alternatives, potential_segmentations=None, maximum_number=5)[source]

Generate catalogs selecting generic or alternative specific coefficients

Parameters:

generic_name (str) – name associated with all the parameters in the catalog
beta_parameters (list(biogeme.expressions.Beta)) – coefficients of interest
alternatives (tuple(str)) – names of the alternatives
potential_segmentations (tuple(DiscreteSegmentationTuple)) – tuple of potential segmentations, or None
maximum_number (int) – maximum number of segmentations to consider

Returns:

a list of catalogs for each alternative

Return type:

list(dict(str: biogeme.catalog.Catalog))

biogeme.catalog.segmentation_catalogs(generic_name, beta_parameters, potential_segmentations, maximum_number)[source]

Generate catalogs for potential segmentations of a parameter

Parameters:

generic_name (str) – name used for the definition of the group of catalogs
beta_parameters (list(biogeme.expressions.Beta)) – list of parameters to be segmented
potential_segmentations (tuple(DiscreteSegmentationTuple)) – tuple of potential segmentations
maximum_number (int) – maximum number of segmentations to consider