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Valentin-LaurentValentin-Laurent
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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
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‎doc/theoretical_description_conformity_scores.rst

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

‎doc/v1_migration_guide.rst

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

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

‎mapie/conformity_scores/conformity_scores.py

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‎mapie/conformity_scores/residual_conformity_scores.py

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‎mapie/conformity_scores/utils.py

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

‎mapie/conformity_scores/utils_classification_conformity_scores.py

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‎mapie/estimator/estimator.py

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‎mapie/estimator/regressor.py

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

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