fix CI

fidelity · Aug 6, 2024 · a341d20 · a341d20
1 parent ccb9a59
commit a341d20
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Showing 2 changed files with 5 additions and 5 deletions.
diff --git a/mabwiser/mab.py b/mabwiser/mab.py
@@ -1295,7 +1295,7 @@ def _validate_mab_args(arms, learning_policy, neighborhood_policy, seed, n_jobs,
         check_true(isinstance(arms, list), TypeError("The arms should be provided in a list."))
         check_false(None in arms, ValueError("The arm list cannot contain None."))
         check_false(np.nan in arms, ValueError("The arm list cannot contain NaN."))
-        check_false(np.Inf in arms, ValueError("The arm list cannot contain Infinity."))
+        check_false(np.inf in arms, ValueError("The arm list cannot contain Infinity."))
         check_true(len(arms) == len(set(arms)), ValueError("The list of arms cannot contain duplicate values."))
 
         # Learning Policy type
@@ -1405,7 +1405,7 @@ def _validate_arm(arm):
         """
         check_false(arm is None, ValueError("The arm cannot be None."))
         check_false(np.nan in [arm], ValueError("The arm cannot be NaN."))
-        check_false(np.Inf in [arm], ValueError("The arm cannot be Infinity."))
+        check_false(np.inf in [arm], ValueError("The arm cannot be Infinity."))
 
     @staticmethod
     def _convert_array(array_like) -> np.ndarray:

diff --git a/tests/test_invalid.py b/tests/test_invalid.py
@@ -476,14 +476,14 @@ def test_rewards_null_df(self):
 
     def test_rewards_inf_array(self):
         decisions = np.asarray([1, 1, 1, 2, 2, 2, 3, 3, 3])
-        rewards = np.asarray([0, 0, 0, 0, 0, 0, 1, 1, np.Inf])
+        rewards = np.asarray([0, 0, 0, 0, 0, 0, 1, 1, np.inf])
         mab = MAB([1, 2, 3], LearningPolicy.EpsilonGreedy(epsilon=0))
         with self.assertRaises(TypeError):
             mab.fit(decisions, rewards)
 
     def test_rewards_inf_df(self):
         history = pd.DataFrame({'decision': [1, 1, 1, 2, 2, 2, 3, 3, 3],
-                                'reward': [0, 0, 0, 0, 0, 0, 1, 1, np.Inf]})
+                                'reward': [0, 0, 0, 0, 0, 0, 1, 1, np.inf]})
         mab = MAB([1, 2, 3], LearningPolicy.EpsilonGreedy(epsilon=0))
         with self.assertRaises(TypeError):
             mab.fit(history['decision'], history['reward'])
@@ -562,7 +562,7 @@ def test_invalid_add_arm(self):
         with self.assertRaises(ValueError):
             mab.add_arm(np.nan)
         with self.assertRaises(ValueError):
-            mab.add_arm(np.Inf)
+            mab.add_arm(np.inf)
         with self.assertRaises(ValueError):
             mab.add_arm(3)