MultiDistInput#

class caf.distribute.gravity_model.multi_area.MultiDistInput(*, tld_file, tld_lookup_file, cat_col, min_col, max_col, ave_col, trips_col, lookup_cat_col, lookup_zone_col, init_params, log_path, furness_tolerance=1e-06, furness_jac=False)#

Bases: BaseConfig

Input to multi cost distribution calibrator.

Parameters:

  • tld_file (Path) – Path to a file containing distributions. This should contain 5 columns, the names of which must be specified below.

  • tld_lookup_file (Path) – Path to a lookup from distribution areas to zones. Should contain 2 columns which are explained below.

  • cat_col (str) – The name of the column containing distribution area/categories in TLDFile. E.g. ‘City’, ‘Village’, ‘Town’, if there are different distributions for these different are types

  • min_col (str) – The name of the column containing lower bounds of cost bands.

  • max_col (str) – The name of the column containing upper bounds of cost bands.

  • ave_col (str) – The name of the column containing average values of cost bands.

  • trips_col (str) – The name of the column containing numbers of trips for a given cost band.

  • lookup_cat_col (str) – The name of the column in the lookup containing the categories. The names of the values (but not the column name) must match the names in the cat_col of the TLD file. There must not be any distributions defined in the TLDFile which do not appear in the lookup.

  • lookup_zone_col (str) – The column in the lookup containing zone identifiers. The lookup must contain all zones in the zone system.

  • init_params (dict[str, float]) – A dict containing init_params for the cost function when calibrating. If left blank the default value from the cost_function will be used.

  • log_path (Path) – Path to where the log file should be saved. Saved as a csv but this can also be a path to a txt file.

  • furness_tolerance (float) – The tolerance for the furness in the gravity function. In general lower tolerance will take longer but may yield better results.

  • furness_jac (bool) – Whether to furness within the jacobian function. Not furnessing within the jacobian does not represent knock on effects to other areas of altering parameters for a given area. If you expect these effects to be significant this should be set to True, but otherwise the process runs quicker with it set to False.

Attributes Summary

model_config

Configuration for the model, should be a dictionary conforming to [ConfigDict][pydantic.config.ConfigDict].

Attributes Documentation

model_config: ClassVar[ConfigDict] = {}#

Configuration for the model, should be a dictionary conforming to [ConfigDict][pydantic.config.ConfigDict].