Release 0.21.0 (#4894)

Release PR for 0.21.0

* version number bump
* changelog
* minor typo fixes

README.md

@@ -4,7 +4,7 @@
 
 > A unified interface for machine learning with time series
 
-:rocket: **Version 0.20.1 out now!** [Check out the release notes here](https://www.sktime.net/en/latest/changelog.html).
+:rocket: **Version 0.21.0 out now!** [Check out the release notes here](https://www.sktime.net/en/latest/changelog.html).
 
 sktime is a library for time series analysis in Python. It provides a unified interface for multiple time series learning tasks. Currently, this includes time series classification, regression, clustering, annotation, and forecasting. It comes with [time series algorithms](https://www.sktime.net/en/stable/estimator_overview.html) and [scikit-learn] compatible tools to build, tune and validate time series models.
 

docs/source/changelog.rst

@@ -13,6 +13,98 @@ All notable changes to this project will be documented in this file. We keep tra
 For upcoming changes and next releases, see our `milestones <https://github.com/sktime/sktime/milestones?direction=asc&sort=due_date&state=open>`_.
 For our long-term plan, see our :ref:`roadmap`.
 
+Version 0.21.0 - 2023-07-19
+---------------------------
+
+Maintenance release - dependency updates, scheduled deprecations.
+
+For last non-maintenance content updates, see 0.20.1.
+
+Contents
+~~~~~~~~
+
+* ``sktime`` is now compatible with ``sklearn 1.3.X``
+* start of change in column naming convention for univariate probabilistic forecasts,
+  in preparation for 0.23.0 - see below for details for users and developers
+* scheduled 0.21.0 deprecation actions
+
+Dependency changes
+~~~~~~~~~~~~~~~~~~
+
+* ``scikit-learn`` version bounds now allow versions ``1.3.X``
+* the ``deprecated`` package is deprecated as a core dependency of ``sktime``, and
+  will cease to be a dependency from 0.22.0 onwards. No action is required of users
+  or developers, as the package was used only for internal deprecation actions.
+* ``pycatch22`` has been added back as a soft dependency, after python 3.7 EOL
+
+Deprecations and removals
+~~~~~~~~~~~~~~~~~~~~~~~~~
+
+Forecasting - change of column naming for univariate probabilistic forecasts
+^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^
+
+From 0.23.0, returns of forecasters' ``predict_quantiles`` and ``predict_intervals``
+in the univariate case will be made consistent with the multivariate case:
+the name of the uppermost (0-indexed) column level will always be the variable name.
+Previously, in the univariate case, it was always ``Coverage`` or ``Quantiles``,
+irrespective of the variable name present in ``y``, whereas in the multivariate case,
+it was always the variable names present in ``y``.
+
+The change will take place over two MINOR cycles, 0.21.X (early phase) and 0.22.X (late phase),
+the union of which makes up the change period.
+We explain the schedule below, for users, and then for maintainers of third party forecasters ("extenders").
+
+Users should use a new, temporary ``legacy_interface`` argument to handle the change:
+
+* Users - change period. The two forecaster methods ``predict_quantiles`` and ``predict_intervals``
+  will have a new boolean argument, ``legacy_interface``. If ``True``, the methods
+  produce returns with the current naming convention. If ``False``, the methods produce
+  returns with the future, post-change naming covention.
+* Users - early and late phase. In the early phase (0.21.X), the default value of ``legacy_interface``
+  will be ``True``. In the late phase (0.22.X), the default value of ``legacy_interface`` will be ``False``.
+  This change of default will occur in 0.22.0, and may be breaking for users who do not specify the argument.
+* Users - post-deprecation. In 0.23.0, the ``legacy_interface`` argument will be removed.
+  The methods will always produce returns with the future, post-change naming convention.
+  This change may be breaking for users who do not remove the argument by 0.23.0.
+* Appropriate deprecation warnings will be raised from 0.21.0 onwards, until 0.22.last.
+* Users - recommended change actions. Users should aim to upgrade dependent code to ``legacy_interface=False`` behaviour by 0.21.last,
+  and to remove ``legacy_interface`` arguments after 0.22.0 and before 0.23.0.
+  Users who need more time to upgrade dependent code can set ``legacy_interface=True`` until 0.22.last.
+
+Extenders should use the new ``"pred_int:legacy_interface:testcfg"`` config field to upgrade their third party extensions:
+
+* Extenders - change period. The config field ``"pred_int:legacy_interface:testcfg"`` has been added
+  to all descendants of the ``BaseForecaster`` class. This config controls the contract
+  that the ``check_estimator`` and ``pytest`` tests check against, and can be set by ``set_config``.
+* The default value of the tag is ``"auto"`` - this means that the tests will check against the current
+  naming convention in the early phase (0.21.X), and against the future naming convention in the late phase (0.22.X),
+  for ``_predict_quantiles`` or ``_predict_intervals`` having the standard signature, without ``legacy_interface``.
+  From 0.23.0 on, the tag will have no effect.
+* In the change period: if the tag is set to ``"new"``, the tests will always check against the new interface;
+  if the tag is set to ``"old"``, the tests will check against the old interface, irrespective of the phase.
+  From 0.23.0, the setting will have no effect and the tests will always check against the new interface.
+* Extenders - recommended change actions: Extenders should aim to upgrade their third party extensions
+  to ``"pred_int:legacy_interface:testcfg=new"`` behaviour by 0.21.last. Tests against late stage
+  and post-deprecation behaviour can be enforced by setting ``forecaster.set_config({"pred_int:legacy_interface:testcfg": "new"})``,
+  before passing it to ``check_estimator``.
+  The ``set_config`` call can be removed after 0.22.0, and should be removed before 0.23.0, but will not be breaking if not removed.
+* Extenders with a substantial user base of their own can, alternatively, implement and release ``_predict_quantiles`` and ``_predict_intervals``
+  with a ``legacy_interface`` argument before 0.22.0, the default of which should be ``False`` from the beginning on (even in early phase).
+  In this case, the ``"pred_int:legacy_interface:testcfg"`` tag should be set to ``"auto"``,
+  and the tests will check both new and old behaviour. The ``legacy_interface`` argument can be removed after 0.23.0.
+  This will result in the same transition experience for users of the extenders' forecasters as for users of ``sktime`` proper.
+
+
+List of PR
+~~~~~~~~~~
+
+* [ENH] replace ``"Coverage"`` and ``"Quantiles"`` default variable name in univariate case with variable name (:pr:`4880`) :user:`fkiraly`, :user:`benheid`
+* [BUG] 0.21.0 release bugfix - fix interaction of ``sklearn 1.3.0`` with dynamic error metic based on ``partial`` in ``test_make_scorer`` (:pr:`4915`) :user:`fkiraly`
+* [MNT] xfail ``mlflow`` failure #4904 until debugged, gitignore for ``py-spy`` (:pr:`4913`) :user:`fkiraly`
+* [DOC] 0.21.0 release action - update deprecation guide to reflect deprecation of use of `deprecated` (:pr:`4914`) :user:`fkiraly`
+* [MNT] 0.21.0 release action - update ``sklearn`` bound to ``<1.4.0`` (:pr:`4778`) :user:`fkiraly`
+* [MNT] 0.21.0 release action - add back ``pycatch22`` as a soft dependency post python 3.7 EOL (:pr:`4790`) :user:`fkiraly`
+
 Version 0.20.1 - 2023-07-14
 ---------------------------
 

pyproject.toml

@@ -1,6 +1,6 @@
 [project]
 name = "sktime"
-version = "0.20.1"
+version = "0.21.0"
 description = "A unified framework for machine learning with time series"
 authors = [
     {name = "sktime developers", email = "sktime.toolbox@gmail.com"},

sktime/__init__.py

@@ -1,6 +1,6 @@
 """sktime."""
 
-__version__ = "0.20.1"
+__version__ = "0.21.0"
 
 __all__ = ["show_versions"]
 

sktime/classification/early_classification/_probability_threshold.py

@@ -20,7 +20,7 @@
 from sktime.utils.validation.panel import check_X
 
 
-# TODO: fix this in 0.21.0
+# TODO: fix this in 0.22.0
 # base class should have been changed to BaseEarlyClassifier
 class ProbabilityThresholdEarlyClassifier(BaseClassifier):
     """Probability Threshold Early Classifier.

sktime/forecasting/base/_fh.py

@@ -722,7 +722,7 @@ def _to_relative(fh: ForecastingHorizon, cutoff=None) -> ForecastingHorizon:
             absolute = _coerce_to_period(absolute, freq=fh.freq)
             cutoff = _coerce_to_period(cutoff, freq=fh.freq)
 
-        # TODO: 0.21.0:
+        # TODO: 0.22.0:
         # Check at every minor release whether lower pandas bound >=0.15.0
         # if yes, can remove the workaround in the "else" condition and the check
         #

sktime/forecasting/compose/_bagging.py

@@ -286,7 +286,7 @@ def _update(self, y, X=None, update_params=True):
         self : reference to self
         """
         # Need to construct a completely new y out of ol self._y and y and then
-        # fit_treansform the transformer and re-fit the foreaster.
+        # fit_treansform the transformer and re-fit the forecaster.
         _y = update_data(self._y, y)
 
         self.bootstrap_transformer_.fit(X=_y)

sktime/forecasting/model_evaluation/_functions.py

@@ -349,7 +349,7 @@ def evaluate(
 
         Optionally, users may select other metrics that can be supplied
         by `scoring` argument. These can be forecast metrics of any kind,
-        i.e., point forecast metrics, interval metrics, quantile foreast metrics.
+        i.e., point forecast metrics, interval metrics, quantile forecast metrics.
         https://www.sktime.net/en/stable/api_reference/performance_metrics.html?highlight=metrics
         To evaluate estimators using a specific metric, provide them to the scoring arg.
     >>> from sktime.performance_metrics.forecasting import MeanAbsoluteError