scikit-learn · thomasjpfan · Mar 13, 2023 · Mar 13, 2023
diff --git a/sklearn/calibration.py b/sklearn/calibration.py
@@ -908,7 +908,6 @@ def calibration_curve(
    y_prob,
    *,
    pos_label=None,
-    normalize="deprecated",
    n_bins=5,
    strategy="uniform",
 ):
@@ -934,17 +933,6 @@ def calibration_curve(

        .. versionadded:: 1.1

-    normalize : bool, default="deprecated"
-        Whether y_prob needs to be normalized into the [0, 1] interval, i.e.
-        is not a proper probability. If True, the smallest value in y_prob
-        is linearly mapped onto 0 and the largest one onto 1.
-
-        .. deprecated:: 1.1
-            The normalize argument is deprecated in v1.1 and will be removed in v1.3.
-            Explicitly normalizing `y_prob` will reproduce this behavior, but it is
-            recommended that a proper probability is used (i.e. a classifier's
-            `predict_proba` positive class).
-
    n_bins : int, default=5
        Number of bins to discretize the [0, 1] interval. A bigger number
        requires more data. Bins with no samples (i.e. without
@@ -992,19 +980,6 @@ def calibration_curve(
    check_consistent_length(y_true, y_prob)
    pos_label = _check_pos_label_consistency(pos_label, y_true)

-    # TODO(1.3): Remove normalize conditional block.
-    if normalize != "deprecated":
-        warnings.warn(
-            "The normalize argument is deprecated in v1.1 and will be removed in v1.3."
-            " Explicitly normalizing y_prob will reproduce this behavior, but it is"
-            " recommended that a proper probability is used (i.e. a classifier's"
-            " `predict_proba` positive class or `decision_function` output calibrated"
-            " with `CalibratedClassifierCV`).",
-            FutureWarning,
-        )
-        if normalize:  # Normalize predicted values into interval [0, 1]
-            y_prob = (y_prob - y_prob.min()) / (y_prob.max() - y_prob.min())
-
    if y_prob.min() < 0 or y_prob.max() > 1:
        raise ValueError("y_prob has values outside [0, 1].")


diff --git a/sklearn/tests/test_calibration.py b/sklearn/tests/test_calibration.py
@@ -401,26 +401,6 @@ def test_calibration_curve():
        calibration_curve(y_true2, y_pred2, strategy="percentile")


-# TODO(1.3): Remove this test.
-def test_calibration_curve_with_unnormalized_proba():
-    """Tests the `normalize` parameter of `calibration_curve`"""
-    y_true = np.array([0, 0, 0, 1, 1, 1])
-    y_pred = np.array([0.0, 0.1, 0.2, 0.8, 0.9, 1.0])
-
-    # Ensure `normalize` == False raises a FutureWarning.
-    with pytest.warns(FutureWarning):
-        calibration_curve(y_true, y_pred, n_bins=2, normalize=False)
-
-    # Ensure `normalize` == True raises a FutureWarning and behaves as expected.
-    with pytest.warns(FutureWarning):
-        prob_true_unnormalized, prob_pred_unnormalized = calibration_curve(
-            y_true, y_pred * 2, n_bins=2, normalize=True
-        )
-        prob_true, prob_pred = calibration_curve(y_true, y_pred, n_bins=2)
-        assert_almost_equal(prob_true, prob_true_unnormalized)
-        assert_almost_equal(prob_pred, prob_pred_unnormalized)
-
-
 @pytest.mark.parametrize("ensemble", [True, False])
 def test_calibration_nan_imputer(ensemble):
    """Test that calibration can accept nan"""