remove score from dnns

aeon/clustering/base.py

@@ -160,9 +160,7 @@ def _fit_predict(self, X, y=None) -> np.ndarray:
             Index of the cluster each time series in X belongs to.
         """
         self.fit(X)
-        if hasattr(self, "labels_"):
-            return self.labels_
-        return self.predict(X)
+        return self.labels_
 
     def _predict_proba(self, X) -> np.ndarray:
         """Predicts labels probabilities for sequences in X.

aeon/clustering/deep_learning/_ae_abgru.py

@@ -298,12 +298,6 @@ def _fit(self, X):
 
         return self
 
-    def _score(self, X, y=None):
-        # Transpose to conform to Keras input style.
-        X = X.transpose(0, 2, 1)
-        latent_space = self.model_.layers[1].predict(X)
-        return self._estimator.score(latent_space)
-
     @classmethod
     def _get_test_params(cls, parameter_set="default"):
         """Return testing parameter settings for the estimator.

aeon/clustering/deep_learning/_ae_bgru.py

@@ -296,12 +296,6 @@ def _fit(self, X):
 
         return self
 
-    def _score(self, X, y=None):
-        # Transpose to conform to Keras input style.
-        X = X.transpose(0, 2, 1)
-        latent_space = self.model_.layers[1].predict(X)
-        return self._estimator.score(latent_space)
-
     @classmethod
     def _get_test_params(cls, parameter_set="default"):
         """Return testing parameter settings for the estimator.

aeon/clustering/deep_learning/_ae_dcnn.py

@@ -322,12 +322,6 @@ def _fit(self, X):
 
         return self
 
-    def _score(self, X, y=None):
-        # Transpose to conform to Keras input style.
-        X = X.transpose(0, 2, 1)
-        latent_space = self.model_.layers[1].predict(X)
-        return self._estimator.score(latent_space)
-
     @classmethod
     def _get_test_params(cls, parameter_set="default"):
         """Return testing parameter settings for the estimator.

aeon/clustering/deep_learning/_ae_drnn.py

@@ -328,12 +328,6 @@ def _fit(self, X):
 
         return self
 
-    def _score(self, X, y=None):
-        # Transpose to conform to Keras input style.
-        X = X.transpose(0, 2, 1)
-        latent_space = self.model_.layers[1].predict(X)
-        return self._estimator.score(latent_space)
-
     @classmethod
     def _get_test_params(cls, parameter_set="default"):
         """Return testing parameter settings for the estimator.

aeon/clustering/deep_learning/_ae_resnet.py

@@ -362,12 +362,6 @@ def _fit(self, X):
 
         return self
 
-    def _score(self, X, y=None):
-        # Transpose to conform to Keras input style.
-        X = X.transpose(0, 2, 1)
-        latent_space = self.model_.layers[1].predict(X)
-        return self._estimator.score(latent_space)
-
     def _fit_multi_rec_model(
         self,
         autoencoder,