ENH: move get_true_label_position to utils in classification

mapie/classification.py

@@ -23,6 +23,11 @@
                     compute_quantiles, fit_estimator, fix_number_of_classes)
 
 
+from mapie.conformity_scores.utils_classification_conformity_scores import (
+    get_true_label_position,
+)
+
+
 class MapieClassifier(BaseEstimator, ClassifierMixin):
     """
     Prediction sets for classification.
@@ -737,39 +742,6 @@ def _regularize_conformity_score(
         )
         return conf_score
 
-    def _get_true_label_position(
-        self,
-        y_pred_proba: NDArray,
-        y: NDArray
-    ) -> NDArray:
-        """
-        Return the sorted position of the true label in the
-        prediction
-
-        Parameters
-        ----------
-        y_pred_proba: NDArray of shape (n_samples, n_calsses)
-            Model prediction.
-
-        y: NDArray of shape (n_samples)
-            Labels.
-
-        Returns
-        -------
-        NDArray of shape (n_samples, 1)
-            Position of the true label in the prediction.
-        """
-        index = np.argsort(
-                np.fliplr(np.argsort(y_pred_proba, axis=1))
-            )
-        position = np.take_along_axis(
-            index,
-            y.reshape(-1, 1),
-            axis=1
-        )
-
-        return position
-
     def _get_last_included_proba(
         self,
         y_pred_proba: NDArray,
@@ -1217,7 +1189,7 @@ def fit(
             self.y_pred_proba_raps = self.single_estimator_.predict_proba(
                 self.X_raps
             )
-            self.position_raps = self._get_true_label_position(
+            self.position_raps = get_true_label_position(
                 self.y_pred_proba_raps,
                 self.y_raps
             )
@@ -1249,7 +1221,7 @@ def fit(
             # Here we reorder the labels by decreasing probability
             # and get the position of each label from decreasing
             # probability
-            self.conformity_scores_ = self._get_true_label_position(
+            self.conformity_scores_ = get_true_label_position(
                 y_pred_proba,
                 y_enc
             )

mapie/conformity_scores/utils_classification_conformity_scores.py

@@ -0,0 +1,26 @@
+import numpy as np
+from mapie._typing import NDArray
+
+
+def get_true_label_position(y_pred_proba: NDArray, y: NDArray) -> NDArray:
+    """
+    Return the sorted position of the true label in the
+    prediction
+
+    Parameters
+    ----------
+    y_pred_proba: NDArray of shape (n_samples, n_classes)
+        Model prediction.
+
+    y: NDArray of shape (n_samples)
+        Labels.
+
+    Returns
+    -------
+    NDArray of shape (n_samples, 1)
+        Position of the true label in the prediction.
+    """
+    index = np.argsort(np.fliplr(np.argsort(y_pred_proba, axis=1)))
+    position = np.take_along_axis(index, y.reshape(-1, 1), axis=1)
+
+    return position

mapie/tests/test_classification.py

@@ -26,6 +26,10 @@
 from mapie.metrics import classification_coverage_score
 from mapie.utils import check_alpha
 
+from mapie.conformity_scores.utils_classification_conformity_scores import (
+    get_true_label_position
+)
+
 random_state = 42
 
 METHODS = ["lac", "aps", "raps"]
@@ -1865,8 +1869,7 @@ def test_get_true_label_position(
     y_pred_proba = y_true_proba_place[1]
     place = y_true_proba_place[2]
 
-    mapie = MapieClassifier(random_state=random_state)
-    found_place = mapie._get_true_label_position(y_pred_proba, y_true)
+    found_place = get_true_label_position(y_pred_proba, y_true)
 
     assert (found_place == place).all()
 

mapie/tests/test_utils_classification_conformity_scores.py

@@ -0,0 +1,21 @@
+import numpy as np
+from mapie.conformity_scores.utils_classification_conformity_scores import (
+    get_true_label_position,
+)
+
+
+def test_get_true_label_position() -> None:
+    y_pred_proba = np.array(
+        [[0.1, 0.5, 0.4], [0.3, 0.2, 0.5], [0.2, 0.8, 0.0], [0.4, 0.35, 0.25]]
+    )
+    y = np.array([1, 2, 0, 1])
+    y = np.reshape(
+        y, (-1, 1)
+    )  # add in order to have shape of form (4,1) instead of (4,)
+
+    position = get_true_label_position(y_pred_proba, y)
+
+    expected_position = np.array([[0], [0], [1], [1]])
+
+    assert np.array_equal(position, expected_position)
+    assert position.shape == y.shape