UPD: parameter's name

mapie/conformity_scores/conformity_scores.py

@@ -211,7 +211,7 @@ def get_conformity_scores(
 
  @staticmethod
  def get_quantile(
- values: NDArray,
+ conformity_scores: NDArray,
  alpha_np: NDArray,
  axis: int,
  method: str
@@ -222,7 +222,7 @@ def get_quantile(
 
  Parameters
  ----------
- values: NDArray of shape (n_samples,) or
+ conformity_scores: NDArray of shape (n_samples,) or
  (n_samples, n_references)
  Values from which the quantile is computed, it can be the
  conformity scores or the conformity scores aggregated with
@@ -245,7 +245,7 @@ def get_quantile(
  """
  quantile = np.column_stack([
  np_nanquantile(
- values.astype(float),
+ conformity_scores.astype(float),
  _alpha,
  axis=axis,
  method=method
@@ -308,17 +308,17 @@ def get_bounds(
  alpha_low = alpha_np if self.sym else alpha_np / 2
  alpha_up = 1 - alpha_np if self.sym else 1 - alpha_np / 2
 
- values_low = self.get_estimation_distribution(
+ conformity_scores_low = self.get_estimation_distribution(
  X, y_pred_low, signed * conformity_scores
  )
- values_up = self.get_estimation_distribution(
+ conformity_scores_up = self.get_estimation_distribution(
  X, y_pred_up, conformity_scores
  )
  bound_low = self.get_quantile(
- values_low, alpha_low, axis=1, method="lower"
+ conformity_scores_low, alpha_low, axis=1, method="lower"
  )
  bound_up = self.get_quantile(
- values_up, alpha_up, axis=1, method="higher"
+ conformity_scores_up, alpha_up, axis=1, method="higher"
  )
  else:
  quantile_search = "higher" if self.sym else "lower"