Dependencies#

Types of dependencies#

There are three types of dependencies in sktime: core, soft, or developer.

Note

  • Core dependencies are required to install and run sktime and are automatically installed with sktime, e.g. pandas;

  • Soft dependencies are only required to import certain modules, but not necessary to use most functionalities. A soft dependency is not installed automatically with the package. Instead, users need to install it manually if they want to use a module that requires a soft dependency, e.g. pmdarima;

  • Developer dependencies are required for sktime developers, but not for typical users of sktime, e.g. pytest.

We try to keep the number of core dependencies to a minimum and rely on other packages as soft dependencies when feasible.

Handling soft dependencies#

This section explains how to handle existing soft dependencies. For adding a new soft dependency, see the section “adding a new soft dependency”.

Soft dependencies in sktime should usually be isolated to estimators.

Informative warnings or error messages for missing soft dependencies should be raised, in a situation where a user would need them. This is handled through our _check_soft_dependencies utility here. There are specific conventions to add such warnings in estimators, as below.

Estimators with a soft dependency need to ensure the following:

  • imports of the soft dependency only happen inside the estimator, e.g., in _fit or __init__ methods of the estimator. In __init__, imports should happen only after calls to super(cls).__init__.

  • the python_dependencies tag of the estimator is populated with a str, or a list of str, of dependency requirements, where str are PEP 440 compliant version specification str such as pandas>=2.0.1. Exceptions will automatically be raised when constructing the estimator in an environment where the requirements are not met.

  • In a case where the package import differs from the package name, i.e., import package_string is different from pip install different-package-string (usually the case for packages containing a dash in the name), the python_dependencies_alias tag should be populated to pass the information on package and import strings as dict such as {"scikit-learn": "sklearn"}.

  • If the soft dependencies require specific python versions, the python_version tag should also be populated, with a PEP 440 compliant version specification str such as "<3.10" or ">3.6,~=3.8".

  • Decorate all pytest tests that import soft dependencies with a @pytest.mark.skipif(...) conditional on a soft dependency check. If the test is specific to a single estimator or object, use run_test_for_class from sktime.tests.test_switch to mediate the condition through the class tags. Otherwise, use _check_soft_dependencies for your new soft dependency, with severity="none". Be sure that all soft dependencies imported for testing are imported within the test function itself, rather than at root level (at the top) of the module. This decorator will then skip your test, including imports, unless the system has the required packages installed. This prevents crashes for any users running check_estimator on all estimators, or a full local pytest run without the required soft dependency. See the tests in forecasting.tests.test_pmdarima for a concrete example of run_test_for_class usage to decorate a test. See utils.tests.test_plotting for an example of _check_soft_dependencies usage.

Adding and maintaining soft dependencies#

When adding a new soft dependency or changing the version of an existing one, the following need to be updated:

  • in pyproject.toml, add the dependency or update version bounds in the all_extras dependency set. Following the PEP 621 convention, all dependencies including build time dependencies and optional dependencies are specified in pyproject.toml.

  • Soft dependencies compatible with pandas 2 should also be added/updated in the all_extras_pandas2 dependency set in pyproject.toml. This dependency set is used only in testing.

It should be checked that new soft dependencies do not imply upper bounds on sktime core dependencies, or severe limitations to the user installation workflow. In such a case, it is strongly suggested not to add the soft dependency.

For maintenance purposes, it has been decided that all soft-dependencies will have lower and upper bounds specified mandatorily. The soft-dependencies will be specified in separate extras per each component of sktime, for example forecasting, classification, regression, etc. It is possible to have different upper and lower bounds for a single package when present in different extras, and can be modified in one without affecting the others.

Upper bounds will be preferred to be set up as the next minor release of the packages, as patch updates should never contain breaking changes by convention of semantic versioning. For stable packages, next major version can be used as well.

Upper bounds will be automatically updated using dependabot, which has been set up to run daily based on releases on PyPI. The CI introducing newer upper bound will be merged into main branch only if all unit tests for the affected component(s) pass.

Lower bounds maintenance planning is in progress and will be updated here soon.

Adding a core or developer dependency#

Core or developer dependencies can be added only by core developers after discussion in the core developer meeting.

When adding a new core dependency or changing the version of an existing one, the following files need to be updated:

  • pyproject.toml, adding the dependency or version bounds in the dependencies dependency set.

When adding a new developer dependency or changing the version of an existing one, the following files need to be updated:

  • pyproject.toml, adding the dependency or version bounds in the dev dependency set.