@vincentblot28 It think this problem is related to the fact that we can pass to LightGBM a DataFrame with categorical columns to get a prediction. However, if we pass the same information in a numpy format, LightGBM throws an exception.

@valeman Below a simple fix that works with LightGBM.

def get_signed_conformity_scores(
        self,
        X: ArrayLike,
        y: ArrayLike,
        y_pred: ArrayLike
    ) -> NDArray:
         # .....
         (X_array, y_array, y_pred,
         self.residual_estimator_,
         random_state) = self._check_parameters(X, y, y_pred)

        full_indexes = np.argwhere(
            np.logical_not(np.isnan(y_pred))
        ).reshape((-1,))

        if not self.prefit:
            cal_indexes, res_indexes = train_test_split(
                full_indexes,
                test_size=self.split_size,
                random_state=random_state,
            )
            
            # ToDo: Check how workaround that
            X_array = pd.DataFrame(X_array, columns=X.columns)
            X_array = X_array.astype(X.dtypes.to_dict())
            
            self.residual_estimator_ = self._fit_residual_estimator(
                clone(self.residual_estimator_),
                X_array.iloc[res_indexes],
                y_array[res_indexes], 
                y_pred[res_indexes]
            )
            residuals_pred = np.maximum(
                np.exp(self._predict_residual_estimator(X_array.iloc[cal_indexes])),
                self.eps
            )
        else:
            X_array = pd.DataFrame(X_array, columns=X.columns)
            X_array = X_array.astype(X.dtypes.to_dict())
            
            cal_indexes = full_indexes
            residuals_pred = np.maximum(
                self._predict_residual_estimator(X_array.iloc[cal_indexes]),
                self.eps
            )
    #.....
```

Does MAPIE Regressor support categorical variables? #406

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