ENH: change warning into infolog

mapie/tests/test_utils.py

@@ -1,5 +1,6 @@
 from __future__ import annotations
 
+import logging
 import re
 from typing import Any, Optional, Tuple
 
@@ -228,22 +229,24 @@ def test_valid_verbose(verbose: Any) -> None:
     check_verbose(verbose)
 
 
-def test_initial_low_high_pred() -> None:
+def test_initial_low_high_pred(caplog) -> None:
     """Test lower/upper predictions of the quantiles regression crossing"""
     y_preds = np.array([[4, 5, 2], [4, 4, 4], [2, 3, 4]])
-    with pytest.warns(UserWarning, match=r"WARNING: The predictions are*"):
+    with caplog.at_level(logging.INFO):
         check_lower_upper_bounds(y_preds[0], y_preds[1], y_preds[2])
+    assert "The predictions are ill-sorted" in caplog.text
 
 
-def test_final_low_high_pred() -> None:
+def test_final_low_high_pred(caplog) -> None:
     """Test lower/upper predictions crossing"""
     y_preds = np.array(
         [[4, 3, 2], [3, 3, 3], [2, 3, 4]]
     )
     y_pred_low = np.array([4, 7, 2])
     y_pred_up = np.array([3, 3, 3])
-    with pytest.warns(UserWarning, match=r"WARNING: The predictions are*"):
+    with caplog.at_level(logging.INFO):
         check_lower_upper_bounds(y_pred_low, y_pred_up, y_preds[2])
+    assert "The predictions are ill-sorted" in caplog.text
 
 
 def test_ensemble_in_predict() -> None:
@@ -331,19 +334,6 @@ def test_quantile_prefit_non_iterable(estimator: Any) -> None:
         mapie_reg.fit([1, 2, 3], [4, 5, 6])
 
 
-# def test_calib_set_no_Xy_but_sample_weight() -> None:
-#     """Test warning message if sample weight provided but no X y in calib."""
-#     X = np.array([4, 5, 6])
-#     y = np.array([4, 3, 2])
-#     sample_weight = np.array([4, 4, 4])
-#     sample_weight_calib = np.array([4, 3, 4])
-#     with pytest.warns(UserWarning, match=r"WARNING: sample weight*"):
-#         check_calib_set(
-#             X=X, y=y, sample_weight=sample_weight,
-#             sample_weight_calib=sample_weight_calib
-#         )
-
-
 @pytest.mark.parametrize("strategy", ["quantile", "uniform", "array split"])
 def test_binning_group_strategies(strategy: str) -> None:
     """Test that different strategies have the correct outputs."""

mapie/utils.py

@@ -1,3 +1,4 @@
+import logging
 import warnings
 from inspect import signature
 from typing import Any, Iterable, Optional, Tuple, Union, cast
@@ -573,39 +574,6 @@ def check_lower_upper_bounds(
     y_pred_up: NDArray,
     y_preds: NDArray
 ) -> None:
-    """
-    Check if lower or upper bounds and prediction are consistent.
-
-    Parameters
-    ----------
-    y_pred_low: NDArray of shape (n_samples,)
-        Lower bound prediction.
-
-    y_pred_up: NDArray of shape (n_samples,)
-        Upper bound prediction.
-
-    y_preds: NDArray of shape (n_samples,)
-        Prediction.
-
-    Raises
-    ------
-    Warning
-        If any of the predictions are ill-sorted.
-
-    Examples
-    --------
-    >>> import warnings
-    >>> warnings.filterwarnings("error")
-    >>> import numpy as np
-    >>> from mapie.utils import check_lower_upper_bounds
-    >>> y_preds = np.array([[4, 3, 2], [4, 4, 4], [2, 3, 4]])
-    >>> try:
-    ...     check_lower_upper_bounds(y_preds[0], y_preds[1], y_preds[2])
-    ... except Exception as exception:
-    ...     print(exception)
-    ...
-    WARNING: The predictions are ill-sorted.
-    """
     y_pred_low = column_or_1d(y_pred_low)
     y_pred_up = column_or_1d(y_pred_up)
     y_preds = column_or_1d(y_preds)
@@ -617,9 +585,12 @@ def check_lower_upper_bounds(
     )
 
     if any_inversion:
-        warnings.warn(
-            "WARNING: The predictions are ill-sorted."
+        initial_logger_level = logging.root.level
+        logging.basicConfig(level=logging.INFO)
+        logging.info(
+            "The predictions are ill-sorted."
         )
+        logging.basicConfig(level=initial_logger_level)
 
 
 def check_defined_variables_predict_cqr(