From 4c1e6d0c2f29aa2bdd23f806bba79ed525c8eeb8 Mon Sep 17 00:00:00 2001
From: chrisholder <chrisholder987@hotmail.com>
Date: Wed, 20 Nov 2024 11:53:44 +0100
Subject: [PATCH] remove score from dnns

---
 aeon/clustering/base.py                     | 4 +---
 aeon/clustering/deep_learning/_ae_abgru.py  | 6 ------
 aeon/clustering/deep_learning/_ae_bgru.py   | 6 ------
 aeon/clustering/deep_learning/_ae_dcnn.py   | 6 ------
 aeon/clustering/deep_learning/_ae_drnn.py   | 6 ------
 aeon/clustering/deep_learning/_ae_resnet.py | 6 ------
 6 files changed, 1 insertion(+), 33 deletions(-)

diff --git a/aeon/clustering/base.py b/aeon/clustering/base.py
index 92989a4c2c..39f216933a 100644
--- a/aeon/clustering/base.py
+++ b/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.
diff --git a/aeon/clustering/deep_learning/_ae_abgru.py b/aeon/clustering/deep_learning/_ae_abgru.py
index 88065ddffe..3b41dbfddc 100644
--- a/aeon/clustering/deep_learning/_ae_abgru.py
+++ b/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.
diff --git a/aeon/clustering/deep_learning/_ae_bgru.py b/aeon/clustering/deep_learning/_ae_bgru.py
index 6e12e23e03..609eaf5d65 100644
--- a/aeon/clustering/deep_learning/_ae_bgru.py
+++ b/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.
diff --git a/aeon/clustering/deep_learning/_ae_dcnn.py b/aeon/clustering/deep_learning/_ae_dcnn.py
index 4a83a10eb2..d6c6b8c3d5 100644
--- a/aeon/clustering/deep_learning/_ae_dcnn.py
+++ b/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.
diff --git a/aeon/clustering/deep_learning/_ae_drnn.py b/aeon/clustering/deep_learning/_ae_drnn.py
index a6551d411d..0efedfb730 100644
--- a/aeon/clustering/deep_learning/_ae_drnn.py
+++ b/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.
diff --git a/aeon/clustering/deep_learning/_ae_resnet.py b/aeon/clustering/deep_learning/_ae_resnet.py
index 26acce6b5b..ff34143281 100644
--- a/aeon/clustering/deep_learning/_ae_resnet.py
+++ b/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,