Take second comments into account

Author: Vianney Taquet

mapie/estimators.py

@@ -301,7 +301,7 @@ def _check_cv(
 
     def _check_alpha(
         self,
-        alpha: Union[float, Iterable[float]]
+        alpha: Optional[Union[float, Iterable[float]]] = None
     ) -> np.ndarray:
         """
         Check alpha and prepare it as a np.ndarray
@@ -325,28 +325,31 @@ def _check_alpha(
         ValueError
             If alpha is not a float or an Iterable of floats between 0 and 1.
         """
-        if isinstance(alpha, float):
-            alpha_np = np.array([alpha])
-        elif isinstance(alpha, Iterable):
-            alpha_np = np.array(alpha)
+        if alpha is None:
+            return np.zeros(1)
         else:
-            raise ValueError(
-                "Invalid alpha. Allowed values are float or Iterable."
-            )
-        if len(alpha_np.shape) != 1:
-            raise ValueError(
-                "Invalid alpha. "
-                "Please provide a one-dimensional list of values."
-            )
-        if alpha_np.dtype.type not in [np.float64, np.float32]:
-            raise ValueError(
-                "Invalid alpha. Allowed values are Iterable of floats."
-            )
-        if np.any((alpha_np <= 0) | (alpha_np >= 1)):
-            raise ValueError(
-                "Invalid alpha. Allowed values are between 0 and 1."
-            )
-        return alpha_np
+            if isinstance(alpha, float):
+                alpha_np = np.array([alpha])
+            elif isinstance(alpha, Iterable):
+                alpha_np = np.array(alpha)
+            else:
+                raise ValueError(
+                    "Invalid alpha. Allowed values are float or Iterable."
+                )
+            if len(alpha_np.shape) != 1:
+                raise ValueError(
+                    "Invalid alpha. "
+                    "Please provide a one-dimensional list of values."
+                )
+            if alpha_np.dtype.type not in [np.float64, np.float32]:
+                raise ValueError(
+                    "Invalid alpha. Allowed values are Iterable of floats."
+                )
+            if np.any((alpha_np <= 0) | (alpha_np >= 1)):
+                raise ValueError(
+                    "Invalid alpha. Allowed values are between 0 and 1."
+                )
+            return alpha_np
 
     def _check_n_features_in(
         self,
@@ -578,6 +581,7 @@ def predict(
                 "residuals_"
             ]
         )
+        alpha_ = self._check_alpha(alpha)
         X = check_array(X, force_all_finite=False, dtype=["float64", "object"])
         y_pred = self.single_estimator_.predict(X)
 
@@ -589,7 +593,6 @@ def predict(
             # (n_alpha,) : alpha
             # (n_samples_test, n_alpha) : y_pred_low, y_pred_up
             # (n_samples_test, n_samples_train) : y_pred_multi, low/up_bounds
-            alpha_ = self._check_alpha(alpha)
             if self.method in ["naive", "base"] or self.cv == "prefit":
                 quantile = np.quantile(
                     self.residuals_, 1 - alpha_, interpolation="higher"