diff --git a/mapie/control_risk/ltt.py b/mapie/control_risk/ltt.py
index 6fc4a0a6..216c24f6 100644
--- a/mapie/control_risk/ltt.py
+++ b/mapie/control_risk/ltt.py
@@ -18,7 +18,7 @@ def _ltt_procedure(
     Apply the Learn-Then-Test procedure for risk control.
     This procedure is called in ``MapieMultiLabelClassifier``
     if ``metric=precision``.
-    This will apply learn then test procedure for 
+    This will apply learn then test procedure for
     precision control.
     Note that we will do a multiple test for ``r_hat`` that are
     less than level ``alpha_np``.
diff --git a/mapie/multi_label_classification.py b/mapie/multi_label_classification.py
index 9908cf14..f9d53966 100644
--- a/mapie/multi_label_classification.py
+++ b/mapie/multi_label_classification.py
@@ -3,8 +3,6 @@
 import warnings
 from typing import Iterable, Optional, Sequence, Tuple, Union, cast
 
-import itertools
-
 import numpy as np
 from sklearn.base import BaseEstimator, ClassifierMixin
 from sklearn.linear_model import LogisticRegression
@@ -99,7 +97,7 @@ class MapieMultiLabelClassifier(BaseEstimator, ClassifierMixin):
 
     lambdas_star: ArrayLike of shape (n_lambdas)
         Optimal threshold for a given alpha.
-    
+
     valid_index: List[List[Any]]
         List of list of all index that satisfy fwer controlling
         for learn then test procedure. This attribute is compute
diff --git a/mapie/tests/test_control_risk.py b/mapie/tests/test_control_risk.py
index 79c9174d..481035ea 100644
--- a/mapie/tests/test_control_risk.py
+++ b/mapie/tests/test_control_risk.py
@@ -66,6 +66,12 @@
 
 wrong_delta = None
 
+random_state = 42
+prng = np.random.RandomState(random_state)
+y_1 = prng.random(51)
+y_2 = prng.random((51, 5))
+y_3 = prng.randint(0, 2, 51)
+
 
 def test_compute_recall_equal() -> None:
     """Test that compute_recall give good result"""
@@ -122,7 +128,9 @@ def test_compute_precision_with_wrong_shape() -> None:
     with pytest.raises(ValueError, match=r".*y_pred_proba should be a 3d*"):
         _compute_risk_precision(lambdas, y_preds_proba.squeeze(), y_toy)
     with pytest.raises(ValueError, match=r".*y should be a 2d*"):
-        _compute_risk_precision(lambdas, y_preds_proba, np.expand_dims(y_toy, 2))
+        _compute_risk_precision(
+            lambdas, y_preds_proba, np.expand_dims(y_toy, 2)
+        )
     with pytest.raises(ValueError, match=r".*could not be broadcast*"):
         _compute_risk_precision(lambdas, y_preds_proba, y_toy[:-1])
 
@@ -188,4 +196,4 @@ def test_invalid_shape_alpha_hb() -> None:
 def test_delta_none_ltt(delta: Optional[float]) -> None:
     """Test error message when invalid delta"""
     with pytest.raises(ValueError, match=r".*Invalid delta"):
-        _ltt_procedure(r_hat, alpha, delta, n)
\ No newline at end of file
+        _ltt_procedure(r_hat, alpha, delta, n)
diff --git a/mapie/tests/test_multi_label_classification.py b/mapie/tests/test_multi_label_classification.py
index 1d40c675..39f96623 100644
--- a/mapie/tests/test_multi_label_classification.py
+++ b/mapie/tests/test_multi_label_classification.py
@@ -324,7 +324,7 @@ def test_results_for_partial_fit(strategy: str) -> None:
 
 @pytest.mark.parametrize("strategy", [*STRATEGIES])
 @pytest.mark.parametrize(
-    "alpha", [np.array([0.3, 0.4]), [0.3, 0.4], (0.3, 0.4)]
+    "alpha", [np.array([0.05, 0.1]), [0.05, 0.1], (0.05, 0.1)]
 )
 def test_results_for_alpha_as_float_and_arraylike(
     strategy: str, alpha: Any