MTN - End support of some already deprecated functionalities (#693)

* MTN: remove deprecated ConformityScore (used for regression)

* MTN: remove deprecated path conformity_scores.residual_conformity_scores

* MTN: remove deprecated size_raps parameter from _MapieClassifier

* MTN: remove import deprecation for get_true_label_position, that shouldn't be public anyway

* DOC: update migration guide

* MTN: remove deprecated path to import EnsembleRegressor, that is not public anyway

* MTN: remove incorrect deprecation and clarify the issue in docstring

Author: Valentin-Laurent

doc/theoretical_description_conformity_scores.rst

@@ -13,7 +13,7 @@ Note: in theoretical parts of the documentation, we use the following terms empl
 
 —
 
-The :class:`mapie.conformity_scores.ConformityScore` class implements various
+The :class:`mapie.conformity_scores.BaseRegressionScore` class implements various
 methods to compute conformity scores for regression.
 We give here a brief theoretical description of the scores included in the module.
 Note that it is possible for the user to create any conformal scores that are not 

doc/v1_migration_guide.rst

@@ -170,7 +170,7 @@ Parameter specific to APS or RAPS conformity scores in classification.
 """"""""""""""""""""""""""""""""""""""""""""""""""""""""""""""""""""""""""""""""""""""""""""""""""""""""""""""""""""""""""
 Parameter specific to the RAPS conformity score in classification.
 
-- **v0.x**: This parameter is passed to the ``fit`` method of ``MapieClassifier``.
+- **v0.x**: Passing this parameter to the ``fit`` method of ``MapieClassifier`` is deprecated.
 - **v1**: This parameter must now be passed to the ``conformity_score`` argument at initialization. Ex: ``SplitConformalClassifier(conformity_score=RAPSConformityScore(size_raps=0.3))``
 
 None defaults
@@ -466,6 +466,17 @@ Now only one version exists (``regression_coverage_score``), that corresponds to
 - **v0.x**: Took ``alpha`` as input.
 - **v1**: Now takes ``confidence_level`` as input (``confidence_level`` is equivalent to ``1 - alpha``).
 
+Conformity scores
+~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~
+
+The import of ``AbsoluteConformityScore``, ``GammaConformityScore`` and ``ResidualNormalisedScore`` from ``mapie.conformity_scores.residual_conformity_scores`` was deprecated and is now unsupported.
+
+You can now import those scores from ``mapie.conformity_scores.bounds`` or simply ``mapie.conformity_scores``.
+
+The usage of ``ConformityScore`` was deprecated and is now unsupported. The new class to use is ``BaseRegressionScore``, that can be found in ``mapie.conformity_scores.regression``.
+
+We may clarify the ``conformity_scores`` package structure in the future.
+
 Python, scikit-learn and NumPy versions support
 --------------------------------------------------------------------------
 

mapie/classification.py

@@ -21,7 +21,7 @@
 from mapie.conformity_scores import BaseClassificationScore
 from mapie.conformity_scores.sets.raps import RAPSConformityScore
 from mapie.conformity_scores.utils import (
-    check_depreciated_size_raps, check_classification_conformity_score,
+    check_classification_conformity_score,
     check_target, check_and_select_conformity_score,
 )
 from mapie.estimator.classifier import EnsembleClassifier
@@ -816,7 +816,6 @@ def _check_fit_parameter(
         y: ArrayLike,
         sample_weight: Optional[ArrayLike] = None,
         groups: Optional[ArrayLike] = None,
-        size_raps: Optional[float] = None,
     ):
         """
         Perform several checks on class parameters.
@@ -842,11 +841,9 @@ def _check_fit_parameter(
         y_enc = self.label_encoder_.transform(y)
 
         cs_estimator = check_classification_conformity_score(self.conformity_score)
-        check_depreciated_size_raps(size_raps)
         cs_estimator.set_external_attributes(
             classes=self.classes_,
             label_encoder=self.label_encoder_,
-            size_raps=size_raps,
             random_state=self.random_state
         )
         if (
@@ -881,7 +878,6 @@ def fit(
         X: ArrayLike,
         y: ArrayLike,
         sample_weight: Optional[ArrayLike] = None,
-        size_raps: Optional[float] = None,
         groups: Optional[ArrayLike] = None,
         **kwargs: Any
     ) -> _MapieClassifier:
@@ -905,12 +901,6 @@ def fit(
 
             By default ``None``.
 
-        size_raps: Optional[float]
-            Percentage of the data to be used for choosing lambda_star and
-            k_star for the RAPS method.
-
-            By default ``None``.
-
         groups: Optional[ArrayLike] of shape (n_samples,)
             Group labels for the samples used while splitting the dataset into
             train/test set.
@@ -942,7 +932,7 @@ def fit(
          y_enc,
          sample_weight,
          groups) = self._check_fit_parameter(
-            X, y, sample_weight, groups, size_raps
+            X, y, sample_weight, groups
         )
 
         # Cast

mapie/conformity_scores/conformity_scores.py

@@ -1,5 +1,4 @@
 from typing import Optional, no_type_check
-import warnings
 
 from sklearn.utils.multiclass import (
     check_classification_targets,
@@ -97,29 +96,6 @@ def check_regression_conformity_score(
         )
 
 
-def check_depreciated_size_raps(
-    size_raps: Optional[float]
-) -> None:
-    """
-    Check if the parameter ``size_raps`` is used. If so, raise a warning.
-
-    Raises
-    ------
-    Warning
-        If ``size_raps`` is not ``None``.
-    """
-    if not (size_raps is None):
-        warnings.warn(
-            "WARNING: Deprecated parameter. "
-            "The parameter `size_raps` is deprecated. "
-            "In the next release, `RAPSConformityScore` takes precedence over "
-            "`_MapieClassifier` for setting the size used. "
-            "Prefer to define `size_raps` in `RAPSConformityScore` rather "
-            "than in the `fit` method of `_MapieClassifier`.",
-            DeprecationWarning
-        )
-
-
 def check_target(
     conformity_score: BaseClassificationScore,
     y: ArrayLike

mapie/conformity_scores/residual_conformity_scores.py

@@ -6,7 +6,7 @@
 from joblib import Parallel, delayed
 from sklearn.base import RegressorMixin, clone
 from sklearn.model_selection import BaseCrossValidator
-from sklearn.utils import _safe_indexing, deprecated
+from sklearn.utils import _safe_indexing
 from sklearn.utils.validation import _num_samples, check_is_fitted
 
 from numpy.typing import ArrayLike, NDArray
@@ -405,11 +405,6 @@ def predict_calib(
 
         return y_pred
 
-    @deprecated(
-        "WARNING: EnsembleRegressor.fit is deprecated."
-        "Instead use EnsembleRegressor.fit_single_estimator"
-        "then EnsembleRegressor.fit_multi_estimators"
-    )
     def fit(
         self,
         X: ArrayLike,
@@ -419,6 +414,11 @@ def fit(
         **fit_params
     ) -> EnsembleRegressor:
         """
+        Note to developer: this fit method has been broken down into
+        fit_single_estimator and fit_multi_estimators,
+        but we kept it so that EnsembleRegressor passes sklearn.check_is_fitted.
+        Prefer using fit_single_estimator and fit_multi_estimators.
+
         Fit the base estimator under the ``single_estimator_`` attribute.
         Fit all cross-validated estimator clones
         and rearrange them into a list, the ``estimators_`` attribute.