Building of Ensemble Classifier from MapieClassifier

mapie/conformity_scores/checks.py

@@ -0,0 +1,38 @@
+from typing import Optional
+
+from .conformity_scores import ConformityScore
+from .residual_conformity_scores import AbsoluteConformityScore
+
+
+def check_conformity_score(
+    conformity_score: Optional[ConformityScore],
+    sym: bool = True,
+) -> ConformityScore:
+    """
+    Check parameter ``conformity_score``.
+
+    Raises
+    ------
+    ValueError
+        If parameter is not valid.
+
+    Examples
+    --------
+    >>> from mapie.conformity_scores.checks import check_conformity_score
+    >>> try:
+    ...     check_conformity_score(1)
+    ... except Exception as exception:
+    ...     print(exception)
+    ...
+    Invalid conformity_score argument.
+    Must be None or a ConformityScore instance.
+    """
+    if conformity_score is None:
+        return AbsoluteConformityScore(sym=sym)
+    elif isinstance(conformity_score, ConformityScore):
+        return conformity_score
+    else:
+        raise ValueError(
+            "Invalid conformity_score argument.\n"
+            "Must be None or a ConformityScore instance."
+        )

mapie/conformity_scores/conformity_scores.py

@@ -5,7 +5,7 @@
 
 from mapie._compatibility import np_nanquantile
 from mapie._typing import ArrayLike, NDArray
-from mapie.estimator.interface import EnsembleEstimator
+from mapie.estimator.regressor import EnsembleRegressor
 
 
 class ConformityScore(metaclass=ABCMeta):
@@ -338,7 +338,7 @@ def _beta_optimize(
     def get_bounds(
         self,
         X: ArrayLike,
-        estimator: EnsembleEstimator,
+        estimator: EnsembleRegressor,
         conformity_scores: NDArray,
         alpha_np: NDArray,
         ensemble: bool = False,
@@ -348,14 +348,14 @@ def get_bounds(
     ) -> Tuple[NDArray, NDArray, NDArray]:
         """
         Compute bounds of the prediction intervals from the observed values,
-        the estimator of type ``EnsembleEstimator`` and the conformity scores.
+        the estimator of type ``EnsembleRegressor`` and the conformity scores.
 
         Parameters
         ----------
         X: ArrayLike of shape (n_samples, n_features)
             Observed feature values.
 
-        estimator: EnsembleEstimator
+        estimator: EnsembleRegressor
             Estimator that is fitted to predict y from X.
 
         conformity_scores: ArrayLike of shape (n_samples,)

mapie/estimator/__init__.py

@@ -0,0 +1,9 @@
+from .interface import EnsembleEstimator
+from .regressor import EnsembleRegressor
+from .classifier import EnsembleClassifier
+
+__all__ = [
+    "EnsembleEstimator",
+    "EnsembleRegressor",
+    "EnsembleClassifier",
+]