[NumPy] Remove references to deprecated NumPy type aliases.

This change replaces references to a number of deprecated NumPy type aliases (np.bool, np.int, np.float, np.complex, np.object, np.str) with their recommended replacement (bool, int, float, complex, object, str). NumPy 1.24 drops the deprecated aliases, so we must remove uses before updating NumPy. PiperOrigin-RevId: 496908874 Change-Id: I7ee2af3bc548c4bbe6069defc3f3d143ba7eb88a
tensorflow · Dec 21, 2022 · 0f33452 · 0f33452
1 parent d42b3cc
commit 0f33452
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Showing 5 changed files with 17 additions and 17 deletions.
diff --git a/tf_agents/agents/ppo/ppo_policy_test.py b/tf_agents/agents/ppo/ppo_policy_test.py
@@ -326,7 +326,7 @@ def splitter_fn(observation_and_mask):
         clip=False)
 
     # Take a step.
-    mask = np.array([True, False, True, False, True], dtype=np.bool)
+    mask = np.array([True, False, True, False, True], dtype=bool)
     self.assertLen(mask, num_categories)
     time_step = ts.TimeStep(
         step_type=tf.constant([1], dtype=tf.int32),

diff --git a/tf_agents/bandits/environments/dataset_utilities.py b/tf_agents/bandits/environments/dataset_utilities.py
@@ -141,7 +141,7 @@ def mushroom_reward_distribution(r_noeat, r_eat_safe, r_eat_poison_bad,
 
 def convert_covertype_dataset(file_path, buffer_size=40000):
   with tf.io.gfile.GFile(file_path, 'r') as infile:
-    data_array = np.genfromtxt(infile, dtype=np.int, delimiter=',')
+    data_array = np.genfromtxt(infile, dtype=int, delimiter=',')
   contexts = data_array[:, :-1]
   context_tensor = tf.cast(contexts, tf.float32)
   labels = data_array[:, -1] - 1  # Classes are from [1, 7].

diff --git a/tf_agents/bandits/environments/ranking_environment_test.py b/tf_agents/bandits/environments/ranking_environment_test.py
@@ -76,7 +76,7 @@ def _item_sampling_fn():
       return np.random.randint(-2, 3, [item_dim])
 
     scores_weight_matrix = (np.reshape(
-        np.arange(global_dim * item_dim, dtype=np.float),
+        np.arange(global_dim * item_dim, dtype=float),
         newshape=[item_dim, global_dim]) - 10) / 5
 
     env = ranking_environment.RankingPyEnvironment(
@@ -127,7 +127,7 @@ def _global_sampling_fn():
     def _item_sampling_fn():
       return np.random.randint(-2, 3, [item_dim])
     scores_weight_matrix = (np.reshape(
-        np.arange(global_dim * item_dim, dtype=np.float),
+        np.arange(global_dim * item_dim, dtype=float),
         newshape=[item_dim, global_dim]) - 10) / 5
     env = ranking_environment.RankingPyEnvironment(
         _global_sampling_fn,

diff --git a/tf_agents/environments/gym_wrapper_test.py b/tf_agents/environments/gym_wrapper_test.py
@@ -46,27 +46,27 @@ def test_spec_from_gym_space_multi_discrete(self):
 
     self.assertEqual((4,), spec.shape)
     self.assertEqual(np.int32, spec.dtype)
-    np.testing.assert_array_equal(np.array([0], dtype=np.int), spec.minimum)
+    np.testing.assert_array_equal(np.array([0], dtype=int), spec.minimum)
     np.testing.assert_array_equal(
-        np.array([0, 1, 2, 3], dtype=np.int), spec.maximum)
+        np.array([0, 1, 2, 3], dtype=int), spec.maximum)
 
   def test_spec_from_gym_space_multi_binary(self):
     multi_binary_space = gym.spaces.MultiBinary(4)
     spec = gym_wrapper.spec_from_gym_space(multi_binary_space)
 
     self.assertEqual((4,), spec.shape)
     self.assertEqual(np.int32, spec.dtype)
-    np.testing.assert_array_equal(np.array([0], dtype=np.int), spec.minimum)
-    np.testing.assert_array_equal(np.array([1], dtype=np.int), spec.maximum)
+    np.testing.assert_array_equal(np.array([0], dtype=int), spec.minimum)
+    np.testing.assert_array_equal(np.array([1], dtype=int), spec.maximum)
 
   def test_spec_from_gym_space_multi_binary_2d(self):
     multi_binary_space = gym.spaces.MultiBinary((8, 8))
     spec = gym_wrapper.spec_from_gym_space(multi_binary_space)
 
     self.assertEqual((8, 8), spec.shape)
     self.assertEqual(np.int32, spec.dtype)
-    np.testing.assert_array_equal(np.array([0], dtype=np.int), spec.minimum)
-    np.testing.assert_array_equal(np.array([1], dtype=np.int), spec.maximum)
+    np.testing.assert_array_equal(np.array([0], dtype=int), spec.minimum)
+    np.testing.assert_array_equal(np.array([1], dtype=int), spec.maximum)
 
   def test_spec_from_gym_space_box_scalars(self):
     for dtype in (np.float32, np.float64):
@@ -84,8 +84,8 @@ def test_spec_from_gym_space_box_scalars_simplify_bounds(self):
 
     self.assertEqual((3, 4), spec.shape)
     self.assertEqual(np.float32, spec.dtype)
-    np.testing.assert_array_equal(np.array([-1], dtype=np.int), spec.minimum)
-    np.testing.assert_array_equal(np.array([1], dtype=np.int), spec.maximum)
+    np.testing.assert_array_equal(np.array([-1], dtype=int), spec.minimum)
+    np.testing.assert_array_equal(np.array([1], dtype=int), spec.maximum)
 
   def test_spec_from_gym_space_when_simplify_box_bounds_false(self):
     # testing on gym.spaces.Dict which makes recursive calls to
@@ -101,13 +101,13 @@ def test_spec_from_gym_space_when_simplify_box_bounds_false(self):
     self.assertEqual(np.float32, spec['box2'].dtype)
     self.assertEqual('box1', spec['box1'].name)
     self.assertEqual('box2', spec['box2'].name)
-    np.testing.assert_array_equal(np.array([-1, -1], dtype=np.int),
+    np.testing.assert_array_equal(np.array([-1, -1], dtype=int),
                                   spec['box1'].minimum)
-    np.testing.assert_array_equal(np.array([1, 1], dtype=np.int),
+    np.testing.assert_array_equal(np.array([1, 1], dtype=int),
                                   spec['box1'].maximum)
-    np.testing.assert_array_equal(np.array([-1, -1], dtype=np.int),
+    np.testing.assert_array_equal(np.array([-1, -1], dtype=int),
                                   spec['box2'].minimum)
-    np.testing.assert_array_equal(np.array([1, 1], dtype=np.int),
+    np.testing.assert_array_equal(np.array([1, 1], dtype=int),
                                   spec['box2'].maximum)
 
   def test_spec_from_gym_space_box_array(self):

diff --git a/tf_agents/typing/types.py b/tf_agents/typing/types.py
@@ -78,7 +78,7 @@
 NestedSpecTensorOrArray = Union[NestedSpec, NestedTensor, NestedArray]
 
 Int = Union[int, np.int16, np.int32, np.int64, Tensor, Array]
-Bool = Union[bool, np.bool, Tensor, Array]
+Bool = Union[bool, bool, Tensor, Array]
 
 Float = Union[float, np.float16, np.float32, np.float64, Tensor, Array]
 FloatOrReturningFloat = Union[Float, Callable[[], Float]]