Types of dependencies#

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


  • 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 depencies. 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 import dependencies. Exceptions will automatically raised when constructing the estimator in an environment without the required packages.

  • 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".

  • If including docstring examples that use soft dependencies, ensure to skip doctest. To do this add a # doctest: +SKIP to the end of each line in the doctest to skip. Check out the arima estimator as as an example. If concerned that skipping the test will reduce test coverage, consider exposing the doctest example as a pytest test function instead, see below how to handle soft dependencies in pytest functions.”.

  • Decorate all pytest tests that import soft dependencies with a @pytest.mark.skipif(...) conditional on a check to _check_soft_dependencies for your new soft depenency. Be sure that all soft dependencies which are imported for testing are imported within the test funciton itself, rather than for the whole module! This decorator will then skip your test unless the system has the required packages installed. Doing this is helpful for any users running check_estimator on all estimators, or a full local pytest run without the required soft dependency. Again, see the tests for pydarima (in forecasting) for a concrete example.

Adding a new soft dependency#

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.

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.