The PRESC evaluations support configuration at a number of levels. Configuration settings are managed by presc.configuration.PrescConfig instances.

The persistent global configuration is available as global_config:

from presc import global_config

# Display the current config in YAML format

Report settings

Currently available config settings for the report are as follows:

# Look and structure of the report
  # Report title (passed to jupyter-book config)
  title: PRESC report
  # Report author (passed to jupyter-book config)
  author: ""
  # List of evaluations to show in the report. Each is added in a separate page.
  # Values must correspond to module names for available evaluations.
  # The report will include only those listed in `evaluations_include`,
  # after removing any listed in `evaluations_exclude`.
  # "*" means 'all available evaluations'.
  evaluations_include: "*"
  evaluations_exclude: null

These can set globally by passing a dict of option values to the global_config:

global_config.set({"report.title": "My Report"})

Overrides to the report settings can also be passed to the ReportRunner in a YAML file. For example, if the file myconf.yml contains

  title: Another report

this can be set using

report = ReportRunner("./my_output_dir", config_filepath="myconf.yml")

Overrides can also be passed in at runtime using:, test_dataset, settings={"report.title": "My new report"})

Instance- or method-level overrides like these do not change the global config. However, if global config options are changed, these changes will also be reflected in local configs (unless they are already overridden). For example:

global_config.set({"": "Me"})
# This will pull in the updated author string., test_dataset, settings={"report.title": "My new report"})

The goal of this flexibility is to make it easy to experiment with different settings while developing a report or exploring evaluations.


Include/exclude settings are available to control which evaluation methods are included in the report, and which parts of an evaluation are computed. They operate by restricting to listed include values, and then removing listed exclude values. The special values "*" and None are interpreted as “all” or “none”, respectively.

The evaluations included in a report can be specified by listing module names, eg.

  - conditional_metric
  - conditional_distribution

to include only these two, or

  - conditional_metric

to exclude this one. These can be passed either in a YAML file or a dict, as described above.

Evaluation settings

These are discussed in more detail in the Evaluations section. Evaluations provide similar local override functionality.