fix test (#2528)

aeon/clustering/deep_learning/_ae_fcn.py

@@ -317,6 +317,7 @@ def _fit(self, X):
                 outputs=X,
                 batch_size=mini_batch_size,
                 epochs=self.n_epochs,
+                verbose=self.verbose,
             )
 
         try:
@@ -345,6 +346,7 @@ def _fit_multi_rec_model(
         outputs,
         batch_size,
         epochs,
+        verbose,
     ):
         import tensorflow as tf
 
@@ -451,9 +453,10 @@ def loss(y_true, y_pred):
             epoch_loss /= num_batches
             history["loss"].append(epoch_loss)
 
-            sys.stdout.write(
-                "Training loss at epoch %d: %.4f\n" % (epoch, float(epoch_loss))
-            )
+            if verbose:
+                sys.stdout.write(
+                    "Training loss at epoch %d: %.4f\n" % (epoch, float(epoch_loss))
+                )
 
             for callback in self.callbacks_:
                 callback.on_epoch_end(epoch, {"loss": float(epoch_loss)})

aeon/clustering/deep_learning/_ae_resnet.py

@@ -329,6 +329,7 @@ def _fit(self, X):
                 outputs=X,
                 batch_size=mini_batch_size,
                 epochs=self.n_epochs,
+                verbose=self.verbose,
             )
 
         try:
@@ -359,6 +360,7 @@ def _fit_multi_rec_model(
         outputs,
         batch_size,
         epochs,
+        verbose,
     ):
         import tensorflow as tf
 
@@ -463,9 +465,10 @@ def loss(y_true, y_pred):
             epoch_loss /= num_batches
             history["loss"].append(epoch_loss)
 
-            sys.stdout.write(
-                "Training loss at epoch %d: %.4f\n" % (epoch, float(epoch_loss))
-            )
+            if verbose:
+                sys.stdout.write(
+                    "Training loss at epoch %d: %.4f\n" % (epoch, float(epoch_loss))
+                )
 
             for callback in self.callbacks_:
                 callback.on_epoch_end(epoch, {"loss": float(epoch_loss)})

aeon/testing/estimator_checking/_yield_clustering_checks.py

@@ -77,18 +77,33 @@ def check_clustering_random_state_deep_learning(estimator, datatype):
     deep_clr1 = _clone_estimator(estimator, random_state=random_state)
     deep_clr1.fit(FULL_TEST_DATA_DICT[datatype]["train"][0])
 
-    layers1 = deep_clr1.training_model_.layers[1:]
+    encoder_layers1 = deep_clr1.training_model_.layers[1].layers[1:]
+    decoder_layers1 = deep_clr1.training_model_.layers[2].layers[1:]
 
     deep_clr2 = _clone_estimator(estimator, random_state=random_state)
     deep_clr2.fit(FULL_TEST_DATA_DICT[datatype]["train"][0])
 
-    layers2 = deep_clr2.training_model_.layers[1:]
+    encoder_layers2 = deep_clr2.training_model_.layers[1].layers[1:]
+    decoder_layers2 = deep_clr2.training_model_.layers[2].layers[1:]
 
-    assert len(layers1) == len(layers2)
+    assert len(encoder_layers1) == len(encoder_layers2)
+    assert len(decoder_layers1) == len(decoder_layers2)
 
-    for i in range(len(layers1)):
-        weights1 = layers1[i].get_weights()
-        weights2 = layers2[i].get_weights()
+    for i in range(len(encoder_layers1)):
+        weights1 = encoder_layers1[i].get_weights()
+        weights2 = encoder_layers2[i].get_weights()
+
+        assert len(weights1) == len(weights2)
+
+        for j in range(len(weights1)):
+            _weight1 = np.asarray(weights1[j])
+            _weight2 = np.asarray(weights2[j])
+
+            np.testing.assert_almost_equal(_weight1, _weight2, 4)
+
+    for i in range(len(decoder_layers1)):
+        weights1 = decoder_layers1[i].get_weights()
+        weights2 = decoder_layers2[i].get_weights()
 
         assert len(weights1) == len(weights2)