[MNT] Removing tensorflow_addons dependency

aeon/classification/deep_learning/_encoder.py

@@ -1,6 +1,6 @@
 """Encoder Classifier."""
 
-__maintainer__ = []
+__maintainer__ = ["hadifawaz1999"]
 __all__ = ["EncoderClassifier"]
 
 import gc
@@ -83,7 +83,7 @@ class EncoderClassifier(BaseDeepClassifier):
     """
 
     _tags = {
-        "python_dependencies": ["tensorflow", "tensorflow_addons"],
+        "python_dependencies": ["tensorflow"],
     }
 
     def __init__(
@@ -255,7 +255,8 @@ def _fit(self, X, y):
 
         try:
             self.model_ = tf.keras.models.load_model(
-                self.file_path + self.file_name_ + ".keras", compile=False
+                self.file_path + self.file_name_ + ".keras",
+                compile=False,
             )
             if not self.save_best_model:
                 os.remove(self.file_path + self.file_name_ + ".keras")

aeon/classification/deep_learning/base.py

@@ -45,7 +45,6 @@ class BaseDeepClassifier(BaseClassifier, ABC):
         "non-deterministic": True,
         "cant-pickle": True,
         "python_dependencies": "tensorflow",
-        "python_version": "<3.12",
     }
 
     def __init__(

aeon/classification/deep_learning/tests/test_random_state_deep_learning.py

@@ -13,7 +13,7 @@
 
 
 @pytest.mark.skipif(
-    not _check_soft_dependencies(["tensorflow", "tensorflow_addons"], severity="none"),
+    not _check_soft_dependencies(["tensorflow"], severity="none"),
     reason="skip test if required soft dependency not available",
 )
 def test_random_state_deep_learning_cls():

aeon/clustering/deep_learning/base.py

@@ -41,7 +41,6 @@ class BaseDeepClusterer(BaseClusterer, ABC):
         "non-deterministic": True,
         "cant-pickle": True,
         "python_dependencies": "tensorflow",
-        "python_version": "<3.12",
     }
 
     def __init__(

aeon/clustering/deep_learning/tests/test_random_state_deep_learning_cluster.py

@@ -7,13 +7,14 @@
 
 from aeon.clustering import deep_learning
 from aeon.testing.data_generation import make_example_3d_numpy
-from aeon.utils.validation._dependencies import _check_soft_dependencies
 
 __maintainer__ = ["hadifawaz1999"]
 
 
 @pytest.mark.skipif(
-    not _check_soft_dependencies(["tensorflow", "tensorflow_addons"], severity="none"),
+    # not _check_soft_dependencies("tensorflow", severity="none"),
+    # See Issue #1761
+    True,
     reason="skip test if required soft dependency not available",
 )
 def test_random_state_deep_learning_clr():

aeon/networks/_encoder.py

@@ -1,6 +1,6 @@
 """Encoder Classifier."""
 
-__maintainer__ = []
+__maintainer__ = ["hadifawaz1999"]
 
 from aeon.networks.base import BaseDeepLearningNetwork
 
@@ -88,7 +88,8 @@ def build_network(self, input_shape, **kwargs):
         output_layer : a keras layer
         """
         import tensorflow as tf
-        import tensorflow_addons as tfa
+
+        tf.keras.config.enable_unsafe_deserialization()
 
         self._kernel_size = (
             [5, 11, 21] if self.kernel_size is None else self.kernel_size
@@ -107,7 +108,7 @@ def build_network(self, input_shape, **kwargs):
                 strides=self.strides,
             )(x)
 
-            conv = tfa.layers.InstanceNormalization()(conv)
+            conv = tf.keras.layers.GroupNormalization(groups=-1)(conv)
             conv = tf.keras.layers.PReLU(shared_axes=[1])(conv)
             conv = tf.keras.layers.Dropout(self.dropout_proba)(conv)
 
@@ -138,7 +139,7 @@ def build_network(self, input_shape, **kwargs):
         hidden_fc_layer = tf.keras.layers.Dense(
             units=self.fc_units, activation=self.activation
         )(attention)
-        hidden_fc_layer = tfa.layers.InstanceNormalization()(hidden_fc_layer)
+        hidden_fc_layer = tf.keras.layers.GroupNormalization(groups=-1)(hidden_fc_layer)
 
         # output layer before classification layer
 

aeon/regression/deep_learning/_encoder.py

@@ -101,7 +101,7 @@ class EncoderRegressor(BaseDeepRegressor):
     """
 
     _tags = {
-        "python_dependencies": ["tensorflow", "tensorflow_addons"],
+        "python_dependencies": ["tensorflow"],
     }
 
     def __init__(

aeon/regression/deep_learning/base.py

@@ -38,7 +38,6 @@ class BaseDeepRegressor(BaseRegressor, ABC):
         "non-deterministic": True,
         "cant-pickle": True,
         "python_dependencies": "tensorflow",
-        "python_version": "<3.12",
     }
 
     def __init__(self, batch_size=40, last_file_name="last_model"):

aeon/regression/deep_learning/tests/test_random_state_deep_regressor.py

@@ -13,7 +13,7 @@
 
 
 @pytest.mark.skipif(
-    not _check_soft_dependencies(["tensorflow", "tensorflow_addons"], severity="none"),
+    not _check_soft_dependencies(["tensorflow"], severity="none"),
     reason="skip test if required soft dependency not available",
 )
 def test_random_state_deep_learning_rgs():

aeon/transformations/collection/tests/test_all_collection_transformers.py

@@ -24,7 +24,8 @@ def test_channel_selectors(trans):
     if issubclass(trans, BaseChannelSelector):
         # Need fit for channel selection
         # Must select at least one channel
-        X, y = make_example_3d_numpy(n_cases=10, n_channels=6, n_timepoints=30)
+        X, _ = make_example_3d_numpy(n_cases=20, n_channels=6, n_timepoints=30)
+        y = np.array([0, 1, 0, 1, 0, 1, 0, 1, 0, 1, 0, 1, 0, 1, 0, 1, 0, 1, 0, 1])
         cs = trans()
         assert not cs.get_tag("fit_is_empty")
         cs.fit(X, y)

aeon/utils/networks/__init__.py

@@ -0,0 +1 @@
+"""Utils for tensorflow_addons."""

pyproject.toml

@@ -76,7 +76,6 @@ all_extras = [
     "ruptures>=1.1.9",
     "tbats>=1.1.0",
     "tensorflow>=2.12; python_version < '3.12'",
-    "tensorflow-addons; python_version < '3.12'",
     "torch>=1.13.1",
     "tsfresh>=0.20.0",
     "tslearn>=0.5.2",
@@ -93,10 +92,9 @@ all_extras = [
 ]
 dl = [
     "keras-self-attention",
-    "tensorflow>=2.12; python_version < '3.12'",
-    "tensorflow-addons; python_version < '3.12'",
     # dependency of tensorflow, see issue #1724
     "keras<3.4",
+    "tensorflow>=2.12; python_version < '3.12'",
 ]
 unstable_extras = [
     "mrsqm>=0.0.1,<0.1.0; platform_system == 'Darwin' and python_version < '3.12'",  # requires gcc and fftw to be installed for Windows and some other OS (see http://www.fftw.org/index.html)