Replace np.object type with object which is equivalent.

PiperOrigin-RevId: 479580624

Author: zoyahav

tensorflow_transform/beam/bucketize_integration_test.py

@@ -699,7 +699,7 @@ def analyzer_fn(inputs):
     })
     # The expected data has 2 boundaries that divides the data into 3 buckets.
     expected_outputs = {
-        'key_vocab': np.array([b'a', b'b'], np.object),
+        'key_vocab': np.array([b'a', b'b'], object),
         'q_b': np.array([0., 1., 2.], np.float32),
         'scale_factor_per_key': np.array([0.0625, 0.05882353], np.float32),
         'shift_per_key': np.array([-1.0625, -2.88235283], np.float32),

tensorflow_transform/beam/impl_test.py

@@ -2107,7 +2107,7 @@ def analyzer_fn(inputs):
         'key': tf.io.FixedLenFeature([], tf.string)
     })
     expected_outputs = {
-        'key_vocab': np.array([b'a', b'b'], np.object),
+        'key_vocab': np.array([b'a', b'b'], object),
         'mean': np.array([25, 75], np.float32),
         'var': np.array([200, 216.67], np.float32)
     }
@@ -2153,7 +2153,7 @@ def analyzer_fn(inputs):
         'key': tf.io.FixedLenFeature([], tf.string)
     })
     expected_outputs = {
-        'key_vocab': np.array([b'a', b'b'], np.object),
+        'key_vocab': np.array([b'a', b'b'], object),
         'mean': np.array([[3.0, -0.5], [5.0, 5.0]], np.float32),
         'var': np.array([[25.0, 0.25], [25.0, 25.0]], np.float32)
     }
@@ -2191,7 +2191,7 @@ def analyzer_fn(inputs):
           }),
           reduce_instance_dims=True,
           expected_outputs={
-              'key_vocab': np.array([b'a', b'b'], np.object),
+              'key_vocab': np.array([b'a', b'b'], object),
               'min_x_value': np.array([1, -2], np.float32),
               'max_x_value': np.array([9, 4], np.float32),
           }),
@@ -2225,7 +2225,7 @@ def analyzer_fn(inputs):
           }),
           reduce_instance_dims=False,
           expected_outputs={
-              'key_vocab': np.array([b'a', b'b'], np.object),
+              'key_vocab': np.array([b'a', b'b'], object),
               'min_x_value': np.array([[1, 5], [-2, 0]], np.float32),
               'max_x_value': np.array([[5, 9], [2, 4]], np.float32),
           }),
@@ -2255,7 +2255,7 @@ def analyzer_fn(inputs):
           }),
           reduce_instance_dims=True,
           expected_outputs={
-              'key_vocab': np.array([b'a', b'b'], np.object),
+              'key_vocab': np.array([b'a', b'b'], object),
               'min_x_value': np.array([1, -3], np.float32),
               'max_x_value': np.array([5, 3], np.float32),
           }),
@@ -2286,7 +2286,7 @@ def analyzer_fn(inputs):
           reduce_instance_dims=False,
           expected_outputs={
               'key_vocab':
-                  np.array([b'a', b'b'], np.object),
+                  np.array([b'a', b'b'], object),
               'min_x_value':
                   np.array([[[1, 1], [1, 1]], [[3, -3], [3, 3]]], np.float32),
               'max_x_value':
@@ -2390,7 +2390,7 @@ def preprocessing_fn(inputs):
           input_metadata=tft.DatasetMetadata.from_feature_spec(
               {'key': tf.io.FixedLenFeature([], tf.string)}),
           expected_outputs={
-              'elements': np.array([b'a', b'b'], np.object),
+              'elements': np.array([b'a', b'b'], object),
               'counts': np.array([25, 75], np.int64)
           }),
       dict(
@@ -2481,7 +2481,7 @@ def analyzer_fn(inputs):
                           tf.compat.as_bytes(str(x % 10) + '_')
                           for x in range(10)
                       ]),
-                      dtype=np.object)
+                      dtype=object)
           },
       ),
       dict(

tensorflow_transform/impl_helper_test.py

@@ -239,7 +239,7 @@
             'varlen':
                 tf.compat.v1.SparseTensorValue(
                     indices=np.array([(0, 0), (2, 0)]),
-                    values=np.array([b'a', b'b'], np.object),
+                    values=np.array([b'a', b'b'], object),
                     dense_shape=(4, 1)),
         }),
     dict(
@@ -259,7 +259,7 @@
             'sparse':
                 tf.compat.v1.SparseTensorValue(
                     indices=np.empty([0, 2]),
-                    values=np.array([], np.object),
+                    values=np.array([], object),
                     dense_shape=[1, 10])
         }),
     dict(

tensorflow_transform/test_case.py

@@ -303,7 +303,7 @@ def assertDataCloseOrEqual(self, a_data, b_data):
   def _assertValuesCloseOrEqual(self, a_value, b_value, msg=None):
     if (isinstance(a_value, (bytes, str)) or isinstance(a_value, list) and
         a_value and isinstance(a_value[0], (bytes, str)) or
-        isinstance(a_value, np.ndarray) and a_value.dtype == np.object):
+        isinstance(a_value, np.ndarray) and a_value.dtype == object):
       self.assertAllEqual(a_value, b_value)
     else:
       # TODO(varshaan): Change atol only for tests for which 1e-6 is too strict.