From 2306ebdec5a5f7e95d340b16db8e8fbcec479485 Mon Sep 17 00:00:00 2001
From: Tony Bagnall <ajb@uea.ac.uk>
Date: Fri, 25 Oct 2024 13:34:35 +0100
Subject: [PATCH 01/25] remove y from predict

---
 aeon/clustering/base.py | 3 +--
 1 file changed, 1 insertion(+), 2 deletions(-)

diff --git a/aeon/clustering/base.py b/aeon/clustering/base.py
index 17231fdf1f..371f3ad74c 100644
--- a/aeon/clustering/base.py
+++ b/aeon/clustering/base.py
@@ -67,7 +67,7 @@ def fit(self, X, y=None) -> BaseCollectionEstimator:
         return self
 
     @final
-    def predict(self, X, y=None) -> np.ndarray:
+    def predict(self, X) -> np.ndarray:
         """Predict the closest cluster each sample in X belongs to.
 
         Parameters
@@ -81,7 +81,6 @@ def predict(self, X, y=None) -> np.ndarray:
             of shape ``[n_cases]``, 2D np.array ``(n_channels, n_timepoints_i)``,
             where ``n_timepoints_i`` is length of series ``i``. Other types are
             allowed and converted into one of the above.
-        y: ignored, exists for API consistency reasons.
 
         Returns
         -------

From 04c385b461f70503fc7a90675e3cfcab596638c1 Mon Sep 17 00:00:00 2001
From: Tony Bagnall <ajb@uea.ac.uk>
Date: Thu, 31 Oct 2024 17:02:57 +0000
Subject: [PATCH 02/25] remove score

---
 aeon/clustering/base.py | 21 ---------------------
 1 file changed, 21 deletions(-)

diff --git a/aeon/clustering/base.py b/aeon/clustering/base.py
index 371f3ad74c..e3151db9b3 100644
--- a/aeon/clustering/base.py
+++ b/aeon/clustering/base.py
@@ -146,27 +146,6 @@ def fit_predict(self, X, y=None) -> np.ndarray:
         self.fit(X)
         return self.predict(X)
 
-    def score(self, X, y=None) -> float:
-        """Score the quality of the clusterer.
-
-        Parameters
-        ----------
-        X : np.ndarray (2d or 3d array of shape (n_cases, n_timepoints) or shape
-            (n_cases, n_channels, n_timepoints)).
-            Time series instances to train clusterer and then have indexes each belong
-            to return.
-        y: ignored, exists for API consistency reasons.
-
-        Returns
-        -------
-        score : float
-            Score of the clusterer.
-        """
-        self._check_is_fitted()
-        X = self._preprocess_collection(X, store_metadata=False)
-        self._check_shape(X)
-        return self._score(X, y)
-
     def _predict_proba(self, X) -> np.ndarray:
         """Predicts labels probabilities for sequences in X.
 

From a346080107d7b047156aecaeef63d8dd42b59f94 Mon Sep 17 00:00:00 2001
From: Tony Bagnall <ajb@uea.ac.uk>
Date: Thu, 31 Oct 2024 17:07:00 +0000
Subject: [PATCH 03/25] remove score, add fit_predict

---
 aeon/clustering/base.py | 7 ++++---
 1 file changed, 4 insertions(+), 3 deletions(-)

diff --git a/aeon/clustering/base.py b/aeon/clustering/base.py
index e3151db9b3..2913212f6d 100644
--- a/aeon/clustering/base.py
+++ b/aeon/clustering/base.py
@@ -143,6 +143,10 @@ def fit_predict(self, X, y=None) -> np.ndarray:
         np.ndarray (1d array of shape (n_cases,))
             Index of the cluster each time series in X belongs to.
         """
+        return self._fit_predict(X, y)
+
+    def _fit_predict(self, X, y=None) -> np.ndarray:
+        """Fit predict using base methods."""
         self.fit(X)
         return self.predict(X)
 
@@ -185,9 +189,6 @@ def _predict_proba(self, X) -> np.ndarray:
             dists[i, preds[i]] = 1
         return dists
 
-    @abstractmethod
-    def _score(self, X, y=None): ...
-
     @abstractmethod
     def _predict(self, X, y=None) -> np.ndarray:
         """Predict the closest cluster each sample in X belongs to.

From 60615dd109d59311d7688e5dd215238946fe2a78 Mon Sep 17 00:00:00 2001
From: Tony Bagnall <ajb@uea.ac.uk>
Date: Thu, 31 Oct 2024 17:10:57 +0000
Subject: [PATCH 04/25] remove score, add fit_predict

---
 aeon/clustering/base.py | 16 +++++++++++++++-
 1 file changed, 15 insertions(+), 1 deletion(-)

diff --git a/aeon/clustering/base.py b/aeon/clustering/base.py
index 2913212f6d..78f804a935 100644
--- a/aeon/clustering/base.py
+++ b/aeon/clustering/base.py
@@ -146,7 +146,21 @@ def fit_predict(self, X, y=None) -> np.ndarray:
         return self._fit_predict(X, y)
 
     def _fit_predict(self, X, y=None) -> np.ndarray:
-        """Fit predict using base methods."""
+        """Fit predict using base methods.
+
+        Parameters
+        ----------
+        X : np.ndarray (2d or 3d array of shape (n_cases, n_timepoints) or shape
+            (n_cases, n_channels, n_timepoints)).
+            Time series instances to train clusterer and then have indexes each belong
+            to return.
+        y: ignored, exists for API consistency reasons.
+
+        Returns
+        -------
+        np.ndarray (1d array of shape (n_cases,))
+            Index of the cluster each time series in X belongs to.
+        """
         self.fit(X)
         return self.predict(X)
 

From aad00ef226d74ee77713192555c1d909c4f16cfd Mon Sep 17 00:00:00 2001
From: chrisholder <chrisholder987@hotmail.com>
Date: Fri, 1 Nov 2024 19:19:06 +0000
Subject: [PATCH 05/25] removed score and n_clusters from base class

---
 aeon/clustering/_clara.py                     |  6 ++----
 aeon/clustering/_elastic_som.py               |  6 ++----
 aeon/clustering/_k_means.py                   |  6 ++----
 aeon/clustering/_k_medoids.py                 |  6 ++----
 aeon/clustering/_k_shape.py                   |  6 ++----
 aeon/clustering/_k_shapes.py                  |  3 ++-
 aeon/clustering/_kernel_k_means.py            |  3 ++-
 aeon/clustering/base.py                       | 10 +++++-----
 aeon/clustering/compose/_pipeline.py          |  3 ---
 aeon/clustering/deep_learning/_ae_fcn.py      |  8 +-------
 aeon/clustering/deep_learning/_ae_resnet.py   |  8 +-------
 aeon/clustering/deep_learning/base.py         |  4 +---
 .../tests/test_deep_clusterer_base.py         |  2 --
 aeon/clustering/dummy.py                      | 19 ++-----------------
 aeon/clustering/feature_based/_catch22.py     |  3 ---
 aeon/clustering/feature_based/_summary.py     |  3 ---
 aeon/clustering/feature_based/_tsfresh.py     |  3 ---
 aeon/clustering/tests/test_base.py            |  3 ---
 aeon/clustering/tests/test_dummy.py           | 15 ---------------
 aeon/clustering/tests/test_k_shape.py         |  3 ---
 aeon/clustering/tests/test_kernel_k_means.py  |  4 ----
 .../mock_estimators/_mock_clusterers.py       |  9 ++-------
 22 files changed, 26 insertions(+), 107 deletions(-)

diff --git a/aeon/clustering/_clara.py b/aeon/clustering/_clara.py
index 4f44f5adab..dcc0e7a912 100644
--- a/aeon/clustering/_clara.py
+++ b/aeon/clustering/_clara.py
@@ -139,6 +139,7 @@ def __init__(
         self.distance_params = distance_params
         self.n_samples = n_samples
         self.n_sampling_iters = n_sampling_iters
+        self.n_clusters = n_clusters
 
         self.cluster_centers_ = None
         self.labels_ = None
@@ -148,7 +149,7 @@ def __init__(
         self._random_state = None
         self._kmedoids_instance = None
 
-        super().__init__(n_clusters)
+        super().__init__()
 
     def _predict(self, X: np.ndarray, y=None) -> np.ndarray:
         return self._kmedoids_instance.predict(X)
@@ -207,9 +208,6 @@ def _fit(self, X: np.ndarray, y=None):
         self.n_iter_ = best_pam.n_iter_
         self._kmedoids_instance = best_pam
 
-    def _score(self, X, y=None):
-        return -self.inertia_
-
     @classmethod
     def _get_test_params(cls, parameter_set="default"):
         """Return testing parameter settings for the estimator.
diff --git a/aeon/clustering/_elastic_som.py b/aeon/clustering/_elastic_som.py
index e7d7d34682..36a8769b13 100644
--- a/aeon/clustering/_elastic_som.py
+++ b/aeon/clustering/_elastic_som.py
@@ -179,6 +179,7 @@ def __init__(
         self.init = init
         self.sigma_decay_function = sigma_decay_function
         self.custom_alignment_path = custom_alignment_path
+        self.n_clusters = n_clusters
 
         self._random_state = None
         self._alignment_path_callable = None
@@ -191,7 +192,7 @@ def __init__(
 
         self.labels_ = None
         self.cluster_centers_ = None
-        super().__init__(n_clusters=n_clusters)
+        super().__init__()
 
     def _fit(self, X, y=None):
         self._check_params(X)
@@ -219,9 +220,6 @@ def _fit(self, X, y=None):
     def _predict(self, X, y=None):
         return self._find_bmu(X, self.cluster_centers_)
 
-    def _score(self, X, y=None):
-        raise NotImplementedError("TimeSeriesSOM does not support scoring")
-
     def _find_bmu(self, x, weights):
         pairwise_matrix = pairwise_distance(
             x,
diff --git a/aeon/clustering/_k_means.py b/aeon/clustering/_k_means.py
index 550d38944e..2581b2d5f4 100644
--- a/aeon/clustering/_k_means.py
+++ b/aeon/clustering/_k_means.py
@@ -192,6 +192,7 @@ def __init__(
         self.distance_params = distance_params
         self.average_params = average_params
         self.averaging_method = averaging_method
+        self.n_clusters = n_clusters
 
         self.cluster_centers_ = None
         self.labels_ = None
@@ -203,7 +204,7 @@ def __init__(
         self._averaging_method = None
         self._average_params = None
 
-        super().__init__(n_clusters)
+        super().__init__()
 
     def _fit(self, X: np.ndarray, y=None):
         self._check_params(X)
@@ -281,9 +282,6 @@ def _fit_one_init(self, X: np.ndarray) -> tuple:
 
         return prev_labels, cluster_centres, prev_inertia, i + 1
 
-    def _score(self, X, y=None):
-        return -self.inertia_
-
     def _predict(self, X: np.ndarray, y=None) -> np.ndarray:
         if isinstance(self.distance, str):
             pairwise_matrix = pairwise_distance(
diff --git a/aeon/clustering/_k_medoids.py b/aeon/clustering/_k_medoids.py
index 1f36f75ebe..ea8e860afc 100644
--- a/aeon/clustering/_k_medoids.py
+++ b/aeon/clustering/_k_medoids.py
@@ -171,6 +171,7 @@ def __init__(
         self.random_state = random_state
         self.distance_params = distance_params
         self.method = method
+        self.n_clusters = n_clusters
 
         self.cluster_centers_ = None
         self.labels_ = None
@@ -184,7 +185,7 @@ def __init__(
         self._fit_method = None
 
         self._distance_params = {}
-        super().__init__(n_clusters)
+        super().__init__()
 
     def _fit(self, X: np.ndarray, y=None):
         self._check_params(X)
@@ -207,9 +208,6 @@ def _fit(self, X: np.ndarray, y=None):
         self.cluster_centers_ = best_centers
         self.n_iter_ = best_iters
 
-    def _score(self, X, y=None):
-        return -self.inertia_
-
     def _predict(self, X: np.ndarray, y=None) -> np.ndarray:
         if isinstance(self.distance, str):
             pairwise_matrix = pairwise_distance(
diff --git a/aeon/clustering/_k_shape.py b/aeon/clustering/_k_shape.py
index 3da2aca0cf..ccb49bec80 100644
--- a/aeon/clustering/_k_shape.py
+++ b/aeon/clustering/_k_shape.py
@@ -89,6 +89,7 @@ def __init__(
         self.tol = tol
         self.verbose = verbose
         self.random_state = random_state
+        self.n_clusters = n_clusters
 
         self.cluster_centers_ = None
         self.labels_ = None
@@ -97,7 +98,7 @@ def __init__(
 
         self._tslearn_k_shapes = None
 
-        super().__init__(n_clusters=n_clusters)
+        super().__init__()
 
     def _fit(self, X, y=None):
         """Fit time series clusterer to training data.
@@ -179,6 +180,3 @@ def _get_test_params(cls, parameter_set="default"):
             "verbose": False,
             "random_state": 1,
         }
-
-    def _score(self, X, y=None):
-        return np.abs(self.inertia_)
diff --git a/aeon/clustering/_k_shapes.py b/aeon/clustering/_k_shapes.py
index cdad58032a..4cb50fdec5 100644
--- a/aeon/clustering/_k_shapes.py
+++ b/aeon/clustering/_k_shapes.py
@@ -90,6 +90,7 @@ def __init__(
         self.tol = tol
         self.verbose = verbose
         self.random_state = random_state
+        self.n_clusters = n_clusters
 
         self.cluster_centers_ = None
         self.labels_ = None
@@ -98,7 +99,7 @@ def __init__(
 
         self._tslearn_k_shapes = None
 
-        super().__init__(n_clusters=n_clusters)
+        super().__init__()
 
     def _fit(self, X, y=None):
         """Fit time series clusterer to training data.
diff --git a/aeon/clustering/_kernel_k_means.py b/aeon/clustering/_kernel_k_means.py
index 6511c6a393..2a7b255757 100644
--- a/aeon/clustering/_kernel_k_means.py
+++ b/aeon/clustering/_kernel_k_means.py
@@ -108,6 +108,7 @@ def __init__(
         self.verbose = verbose
         self.n_jobs = n_jobs
         self.random_state = random_state
+        self.n_clusters = n_clusters
 
         self.cluster_centers_ = None
         self.labels_ = None
@@ -116,7 +117,7 @@ def __init__(
 
         self._tslearn_kernel_k_means = None
 
-        super().__init__(n_clusters=n_clusters)
+        super().__init__()
 
     def _fit(self, X, y=None):
         """Fit time series clusterer to training data.
diff --git a/aeon/clustering/base.py b/aeon/clustering/base.py
index 78f804a935..2aca96545e 100644
--- a/aeon/clustering/base.py
+++ b/aeon/clustering/base.py
@@ -1,7 +1,5 @@
 """Base class for clustering."""
 
-from typing import Optional
-
 __maintainer__ = []
 __all__ = ["BaseClusterer"]
 
@@ -28,8 +26,7 @@ class BaseClusterer(BaseCollectionEstimator):
         "fit_is_empty": False,
     }
 
-    def __init__(self, n_clusters: Optional[int] = None):
-        self.n_clusters = n_clusters
+    def __init__(self):
         # required for compatibility with some sklearn interfaces e.g.
         # CalibratedClassifierCV
         self._estimator_type = "clusterer"
@@ -195,7 +192,10 @@ def _predict_proba(self, X) -> np.ndarray:
         for i, u in enumerate(unique):
             preds[preds == u] = i
         n_cases = len(preds)
-        n_clusters = self.n_clusters
+        if hasattr(self, "n_clusters"):
+            n_clusters = self.n_clusters
+        else:
+            n_clusters = len(np.unique(preds))
         if n_clusters is None:
             n_clusters = int(max(preds)) + 1
         dists = np.zeros((X.shape[0], n_clusters))
diff --git a/aeon/clustering/compose/_pipeline.py b/aeon/clustering/compose/_pipeline.py
index 763f872e49..a946ebf2dc 100644
--- a/aeon/clustering/compose/_pipeline.py
+++ b/aeon/clustering/compose/_pipeline.py
@@ -88,9 +88,6 @@ def __init__(self, transformers, clusterer, random_state=None):
     def _fit(self, X, y=None):
         return super()._fit(X, y)
 
-    def _score(self, X, y=None):
-        raise NotImplementedError("Pipeline does not support scoring.")
-
     @classmethod
     def _get_test_params(cls, parameter_set="default"):
         """Return testing parameter settings for the estimator.
diff --git a/aeon/clustering/deep_learning/_ae_fcn.py b/aeon/clustering/deep_learning/_ae_fcn.py
index 0075817823..6d362c0c32 100644
--- a/aeon/clustering/deep_learning/_ae_fcn.py
+++ b/aeon/clustering/deep_learning/_ae_fcn.py
@@ -168,10 +168,10 @@ def __init__(
         self.save_last_model = save_last_model
         self.best_file_name = best_file_name
         self.random_state = random_state
+        self.n_clusters = n_clusters
 
         super().__init__(
             estimator=estimator,
-            n_clusters=n_clusters,
             clustering_algorithm=clustering_algorithm,
             clustering_params=clustering_params,
             batch_size=batch_size,
@@ -320,12 +320,6 @@ 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 56d12cb487..a6b8f98f1f 100644
--- a/aeon/clustering/deep_learning/_ae_resnet.py
+++ b/aeon/clustering/deep_learning/_ae_resnet.py
@@ -178,12 +178,12 @@ def __init__(
         self.best_file_name = best_file_name
         self.last_file_name = last_file_name
         self.optimizer = optimizer
+        self.n_clusters = n_clusters
 
         self.history = None
 
         super().__init__(
             estimator=estimator,
-            n_clusters=n_clusters,
             clustering_algorithm=clustering_algorithm,
             clustering_params=clustering_params,
             batch_size=batch_size,
@@ -336,12 +336,6 @@ def _fit(self, X):
         gc.collect()
         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/base.py b/aeon/clustering/deep_learning/base.py
index dc84d7c187..ced00367a6 100644
--- a/aeon/clustering/deep_learning/base.py
+++ b/aeon/clustering/deep_learning/base.py
@@ -42,7 +42,6 @@ class BaseDeepClusterer(BaseClusterer):
 
     def __init__(
         self,
-        n_clusters=None,
         estimator=None,
         clustering_algorithm="deprecated",
         clustering_params=None,
@@ -50,7 +49,6 @@ def __init__(
         last_file_name="last_file",
     ):
         self.estimator = estimator
-        self.n_clusters = n_clusters
         self.clustering_algorithm = clustering_algorithm
         self.clustering_params = clustering_params
         self.batch_size = batch_size
@@ -58,7 +56,7 @@ def __init__(
 
         self.model_ = None
 
-        super().__init__(n_clusters=n_clusters)
+        super().__init__()
 
     @abstractmethod
     def build_model(self, input_shape):
diff --git a/aeon/clustering/deep_learning/tests/test_deep_clusterer_base.py b/aeon/clustering/deep_learning/tests/test_deep_clusterer_base.py
index 64099bc87c..9e65866532 100644
--- a/aeon/clustering/deep_learning/tests/test_deep_clusterer_base.py
+++ b/aeon/clustering/deep_learning/tests/test_deep_clusterer_base.py
@@ -41,5 +41,3 @@ def test_base_deep_clusterer(estimator):
         ypred_proba = dummy_deep_clr.predict_proba(X)
         assert ypred_proba is not None
         assert len(ypred_proba[0]) == len(np.unique(y))
-        score = dummy_deep_clr.score(X)
-        assert isinstance(score, np.float64) or isinstance(score, np.float32)
diff --git a/aeon/clustering/dummy.py b/aeon/clustering/dummy.py
index 523f0748bb..3c83d224e8 100644
--- a/aeon/clustering/dummy.py
+++ b/aeon/clustering/dummy.py
@@ -57,8 +57,9 @@ class DummyClusterer(BaseClusterer):
     def __init__(self, strategy="random", n_clusters=3, random_state=None):
         self.strategy = strategy
         self.random_state = random_state
+        self.n_clusters = n_clusters
 
-        super().__init__(n_clusters=n_clusters)
+        super().__init__()
 
     def _fit(self, X, y=None):
         """
@@ -122,19 +123,3 @@ def _predict(self, X, y=None) -> np.ndarray:
             return np.zeros(n_samples, dtype=int)
         else:
             raise ValueError("Unknown strategy type")
-
-    def _score(self, X, y=None):
-        if self.strategy == "single_cluster":
-            centers = np.mean(X, axis=0).reshape(1, -1)
-        else:
-            centers = np.array(
-                [X[self.labels_ == i].mean(axis=0) for i in range(self.n_clusters)]
-            )
-
-        inertia = np.sum(
-            [
-                np.sum((X[self.labels_ == i] - centers[i]) ** 2)
-                for i in range(len(centers))
-            ]
-        )
-        return inertia
diff --git a/aeon/clustering/feature_based/_catch22.py b/aeon/clustering/feature_based/_catch22.py
index 0b6b2e32fa..74d2d54674 100644
--- a/aeon/clustering/feature_based/_catch22.py
+++ b/aeon/clustering/feature_based/_catch22.py
@@ -214,9 +214,6 @@ def _predict_proba(self, X) -> np.ndarray:
                 dists[i, preds[i]] = 1
             return dists
 
-    def _score(self, X, y=None):
-        raise NotImplementedError("Catch22Clusterer does not support scoring.")
-
     @classmethod
     def _get_test_params(cls, parameter_set="default"):
         """Return testing parameter settings for the estimator.
diff --git a/aeon/clustering/feature_based/_summary.py b/aeon/clustering/feature_based/_summary.py
index 26bb296f0e..ddd3e3825e 100644
--- a/aeon/clustering/feature_based/_summary.py
+++ b/aeon/clustering/feature_based/_summary.py
@@ -169,6 +169,3 @@ def _predict_proba(self, X) -> np.ndarray:
             for i in range(n_cases):
                 dists[i, preds[i]] = 1
             return dists
-
-    def _score(self, X, y=None):
-        raise NotImplementedError("SummaryClusterer does not support scoring.")
diff --git a/aeon/clustering/feature_based/_tsfresh.py b/aeon/clustering/feature_based/_tsfresh.py
index 503638e239..c735602ff4 100644
--- a/aeon/clustering/feature_based/_tsfresh.py
+++ b/aeon/clustering/feature_based/_tsfresh.py
@@ -211,9 +211,6 @@ def _predict_proba(self, X: np.ndarray) -> np.ndarray:
                 dists[i, preds[i]] = 1
             return dists
 
-    def _score(self, X: np.ndarray, y: Optional[np.ndarray] = None):
-        raise NotImplementedError("TSFreshClusterer does not support scoring.")
-
     @classmethod
     def _get_test_params(cls, parameter_set: str = "default"):
         """Return testing parameter settings for the estimator.
diff --git a/aeon/clustering/tests/test_base.py b/aeon/clustering/tests/test_base.py
index a40405e5b5..8a8fb81b2d 100644
--- a/aeon/clustering/tests/test_base.py
+++ b/aeon/clustering/tests/test_base.py
@@ -51,9 +51,6 @@ def _predict(self, X):
         """Predict dummy."""
         return np.zeros(shape=(len(X),), dtype=int)
 
-    def _score(self, X, y=None):
-        return 1.0
-
 
 def test_base_clusterer():
     """Test with no clusters."""
diff --git a/aeon/clustering/tests/test_dummy.py b/aeon/clustering/tests/test_dummy.py
index 0a42f039c2..1a94d91ff6 100644
--- a/aeon/clustering/tests/test_dummy.py
+++ b/aeon/clustering/tests/test_dummy.py
@@ -17,18 +17,3 @@ def test_dummy_clusterer(strategy):
     assert len(preds) == 3
     assert np.all(np.array([(pred < 3) for pred in preds]))
     assert np.all(np.array([(pred >= 0) for pred in preds]))
-
-
-def test_dummy_clusterer_score():
-    """Test score method of the dummy clusterer."""
-    model = DummyClusterer(strategy="random")
-    data = np.array([[1, 2, 3], [4, 5, 6], [7, 8, 9]])
-    model.fit(data)
-    score = model.score(data)
-    assert score is not None
-
-    model = DummyClusterer(strategy="single_cluster")
-    model.fit(data)
-    score = model.score(data)
-    assert score is not None
-    assert score == 54.0
diff --git a/aeon/clustering/tests/test_k_shape.py b/aeon/clustering/tests/test_k_shape.py
index 4e82162e7e..8af9743004 100644
--- a/aeon/clustering/tests/test_k_shape.py
+++ b/aeon/clustering/tests/test_k_shape.py
@@ -14,7 +14,6 @@
 expected_iters = 2
 
 expected_labels = [0, 2, 1, 1, 1]
-expected_score = 0.5645477840468736
 
 
 @pytest.mark.skipif(
@@ -31,10 +30,8 @@ def test_kshapes():
     kshapes = TimeSeriesKShape(random_state=1, n_clusters=3)
     kshapes.fit(X_train[0:max_train])
     test_shape_result = kshapes.predict(X_test[0:max_train])
-    score = kshapes.score(X_test[0:max_train])
     proba = kshapes.predict_proba(X_test[0:max_train])
     assert np.array_equal(test_shape_result, expected_results)
-    np.testing.assert_almost_equal(score, expected_score)
     assert kshapes.n_iter_ == expected_iters
     assert np.array_equal(kshapes.labels_, expected_labels)
     assert proba.shape == (max_train, 3)
diff --git a/aeon/clustering/tests/test_kernel_k_means.py b/aeon/clustering/tests/test_kernel_k_means.py
index e46794b26a..f4af21f4f5 100644
--- a/aeon/clustering/tests/test_kernel_k_means.py
+++ b/aeon/clustering/tests/test_kernel_k_means.py
@@ -9,8 +9,6 @@
 
 expected_labels = [0, 2, 1, 2, 0]
 
-expected_score = 4.0
-
 expected_iters = 2
 
 expected_results = [0, 0, 0, 0, 0]
@@ -30,11 +28,9 @@ def test_kernel_k_means():
     kernel_kmeans = TimeSeriesKernelKMeans(random_state=1, n_clusters=3)
     kernel_kmeans.fit(X_train[0:max_train])
     test_shape_result = kernel_kmeans.predict(X_test[0:max_train])
-    score = kernel_kmeans.score(X_test[0:max_train])
     proba = kernel_kmeans.predict_proba(X_test[0:max_train])
 
     assert np.array_equal(test_shape_result, expected_results)
-    np.testing.assert_almost_equal(score, expected_score)
     assert kernel_kmeans.n_iter_ == expected_iters
     assert np.array_equal(kernel_kmeans.labels_, expected_labels)
     assert proba.shape == (max_train, 3)
diff --git a/aeon/testing/mock_estimators/_mock_clusterers.py b/aeon/testing/mock_estimators/_mock_clusterers.py
index 0563129909..ea6772c092 100644
--- a/aeon/testing/mock_estimators/_mock_clusterers.py
+++ b/aeon/testing/mock_estimators/_mock_clusterers.py
@@ -1,5 +1,3 @@
-from typing import Optional
-
 import numpy as np
 
 from aeon.clustering.base import BaseClusterer
@@ -9,8 +7,8 @@
 class MockCluster(BaseClusterer):
     """Mock Cluster for testing base class fit/predict."""
 
-    def __init__(self, n_clusters: Optional[int] = None):
-        super().__init__(n_clusters)
+    def __init__(self):
+        super().__init__()
 
     def _fit(self, X):
         """Mock fit."""
@@ -25,9 +23,6 @@ def _predict_proba(self, X):
         y = np.random.rand(len(X))
         return y
 
-    def _score(self, X, y):
-        return np.random.randn(1)
-
 
 class MockDeepClusterer(BaseDeepClusterer):
     """Mock Deep Clusterer for testing empty base deep class save utilities."""

From 24732d2308c6e9ad2290162715510e61e0dabbe5 Mon Sep 17 00:00:00 2001
From: chrisholder <chrisholder987@hotmail.com>
Date: Mon, 4 Nov 2024 11:25:37 +0000
Subject: [PATCH 06/25] fit_predict

---
 aeon/clustering/base.py | 3 +++
 1 file changed, 3 insertions(+)

diff --git a/aeon/clustering/base.py b/aeon/clustering/base.py
index 780fd5d501..bf73c2a136 100644
--- a/aeon/clustering/base.py
+++ b/aeon/clustering/base.py
@@ -122,6 +122,7 @@ def predict_proba(self, X) -> np.ndarray:
         self._check_shape(X)
         return self._predict_proba(X)
 
+    @final
     def fit_predict(self, X, y=None) -> np.ndarray:
         """Compute cluster centers and predict cluster index for each time series.
 
@@ -159,6 +160,8 @@ 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)
 
     def _predict_proba(self, X) -> np.ndarray:

From 78c67c831f9b80d7a0dd9905b8c378fb7f41b14a Mon Sep 17 00:00:00 2001
From: chrisholder <chrisholder987@hotmail.com>
Date: Tue, 5 Nov 2024 11:32:08 +0000
Subject: [PATCH 07/25] clustering deprecations

---
 aeon/clustering/__init__.py                 |   2 -
 aeon/clustering/_k_means.py                 |  12 --
 aeon/clustering/_k_shapes.py                | 185 --------------------
 aeon/clustering/deep_learning/_ae_bgru.py   |   3 -
 aeon/clustering/deep_learning/_ae_fcn.py    |   4 -
 aeon/clustering/deep_learning/_ae_resnet.py |   4 -
 aeon/clustering/deep_learning/base.py       |  22 ---
 7 files changed, 232 deletions(-)
 delete mode 100644 aeon/clustering/_k_shapes.py

diff --git a/aeon/clustering/__init__.py b/aeon/clustering/__init__.py
index 084778aed6..2eec5142cf 100644
--- a/aeon/clustering/__init__.py
+++ b/aeon/clustering/__init__.py
@@ -7,7 +7,6 @@
     "TimeSeriesCLARANS",
     "TimeSeriesKMeans",
     "TimeSeriesKShape",
-    "TimeSeriesKShapes",
     "TimeSeriesKernelKMeans",
     "ElasticSOM",
     "KSpectralCentroid",
@@ -21,7 +20,6 @@
 from aeon.clustering._k_medoids import TimeSeriesKMedoids
 from aeon.clustering._k_sc import KSpectralCentroid
 from aeon.clustering._k_shape import TimeSeriesKShape
-from aeon.clustering._k_shapes import TimeSeriesKShapes
 from aeon.clustering._kernel_k_means import TimeSeriesKernelKMeans
 from aeon.clustering.base import BaseClusterer
 from aeon.clustering.dummy import DummyClusterer
diff --git a/aeon/clustering/_k_means.py b/aeon/clustering/_k_means.py
index 550d38944e..97806915bc 100644
--- a/aeon/clustering/_k_means.py
+++ b/aeon/clustering/_k_means.py
@@ -169,20 +169,8 @@ def __init__(
         averaging_method: Union[str, Callable[[np.ndarray], np.ndarray]] = "ba",
         distance_params: Optional[dict] = None,
         average_params: Optional[dict] = None,
-        init_algorithm: Optional[Union[str, np.ndarray]] = None,
     ):
         self.init = init
-        self.init_algorithm = init_algorithm
-        if init_algorithm is not None:
-            import warnings
-
-            warnings.warn(
-                "The 'init_algorithm' parameter is deprecated and will be "
-                "removed in a future. Version Use 'init' instead.",
-                DeprecationWarning,
-                stacklevel=2,
-            )
-            self.init = self.init_algorithm
         self.distance = distance
         self.n_init = n_init
         self.max_iter = max_iter
diff --git a/aeon/clustering/_k_shapes.py b/aeon/clustering/_k_shapes.py
deleted file mode 100644
index cdad58032a..0000000000
--- a/aeon/clustering/_k_shapes.py
+++ /dev/null
@@ -1,185 +0,0 @@
-"""Time series kshapes."""
-
-from typing import Optional, Union
-
-import numpy as np
-from deprecated.sphinx import deprecated
-from numpy.random import RandomState
-
-from aeon.clustering.base import BaseClusterer
-
-
-# TODO: remove in v1.0.0
-@deprecated(
-    version="1.0.0",
-    reason="TimeSeriesKShapes class has been renamed to TimeSeriesKShape. "
-    "The TimeSeriesKShapes version will be removed in version 1.0.0.",
-    category=FutureWarning,
-)
-class TimeSeriesKShapes(BaseClusterer):
-    """Kshape algorithm: wrapper of the ``tslearn`` implementation.
-
-    Parameters
-    ----------
-    n_clusters: int, default=8
-        The number of clusters to form as well as the number of
-        centroids to generate.
-    init_algorithm: str or np.ndarray, default='random'
-        Method for initializing cluster centres. Any of the following are valid:
-        ['random']. Or a np.ndarray of shape (n_clusters, n_channels, n_timepoints)
-        and gives the initial cluster centres.
-    n_init: int, default=10
-        Number of times the k-means algorithm will be run with different
-        centroid seeds. The final result will be the best output of n_init
-        consecutive runs in terms of inertia.
-    max_iter: int, default=30
-        Maximum number of iterations of the k-means algorithm for a single
-        run.
-    tol: float, default=1e-4
-        Relative tolerance with regards to Frobenius norm of the difference
-        in the cluster centres of two consecutive iterations to declare
-        convergence.
-    verbose: bool, default=False
-        Verbosity mode.
-    random_state: int or np.random.RandomState instance or None, default=None
-        Determines random number generation for centroid initialization.
-
-    Attributes
-    ----------
-    labels_: np.ndarray (1d array of shape (n_cases,))
-        Labels that is the index each time series belongs to.
-    inertia_: float
-        Sum of squared distances of samples to their closest cluster centre, weighted by
-        the sample weights if provided.
-    n_iter_: int
-        Number of iterations run.
-
-    Examples
-    --------
-    >>> from aeon.clustering import TimeSeriesKShapes
-    >>> from aeon.datasets import load_basic_motions
-    >>> # Load data
-    >>> X_train, y_train = load_basic_motions(split="TRAIN")[0:10]
-    >>> X_test, y_test = load_basic_motions(split="TEST")[0:10]
-    >>> # Example of KShapes clustering
-    >>> ks = TimeSeriesKShapes(n_clusters=3, random_state=1)  # doctest: +SKIP
-    >>> ks.fit(X_train)  # doctest: +SKIP
-    TimeSeriesKShapes(n_clusters=3, random_state=1)
-    >>> preds = ks.predict(X_test)  # doctest: +SKIP
-    """
-
-    _tags = {
-        "capability:multivariate": True,
-        "python_dependencies": "tslearn",
-        "algorithm_type": "distance",
-    }
-
-    def __init__(
-        self,
-        n_clusters: int = 8,
-        init_algorithm: Union[str, np.ndarray] = "random",
-        n_init: int = 10,
-        max_iter: int = 300,
-        tol: float = 1e-4,
-        verbose: bool = False,
-        random_state: Optional[Union[int, RandomState]] = None,
-    ):
-        self.init_algorithm = init_algorithm
-        self.n_init = n_init
-        self.max_iter = max_iter
-        self.tol = tol
-        self.verbose = verbose
-        self.random_state = random_state
-
-        self.cluster_centers_ = None
-        self.labels_ = None
-        self.inertia_ = None
-        self.n_iter_ = 0
-
-        self._tslearn_k_shapes = None
-
-        super().__init__(n_clusters=n_clusters)
-
-    def _fit(self, X, y=None):
-        """Fit time series clusterer to training data.
-
-        Parameters
-        ----------
-        X: np.ndarray, of shape (n_cases, n_channels, n_timepoints) or
-                (n_cases, n_timepoints)
-            A collection of time series instances.
-        y: ignored, exists for API consistency reasons.
-
-        Returns
-        -------
-        self:
-            Fitted estimator.
-        """
-        from tslearn.clustering import KShape
-
-        self._tslearn_k_shapes = KShape(
-            n_clusters=self.n_clusters,
-            max_iter=self.max_iter,
-            tol=self.tol,
-            random_state=self.random_state,
-            n_init=self.n_init,
-            verbose=self.verbose,
-            init=self.init_algorithm,
-        )
-
-        _X = X.swapaxes(1, 2)
-
-        self._tslearn_k_shapes.fit(_X)
-        self._cluster_centers = self._tslearn_k_shapes.cluster_centers_
-        self.labels_ = self._tslearn_k_shapes.labels_
-        self.inertia_ = self._tslearn_k_shapes.inertia_
-        self.n_iter_ = self._tslearn_k_shapes.n_iter_
-
-    def _predict(self, X, y=None) -> np.ndarray:
-        """Predict the closest cluster each sample in X belongs to.
-
-        Parameters
-        ----------
-        X: np.ndarray, of shape (n_cases, n_channels, n_timepoints) or
-                (n_cases, n_timepoints)
-            A collection of time series instances.
-        y: ignored, exists for API consistency reasons.
-
-        Returns
-        -------
-        np.ndarray (1d array of shape (n_cases,))
-            Index of the cluster each time series in X belongs to.
-        """
-        _X = X.swapaxes(1, 2)
-        return self._tslearn_k_shapes.predict(_X)
-
-    @classmethod
-    def _get_test_params(cls, parameter_set="default"):
-        """Return testing parameter settings for the estimator.
-
-        Parameters
-        ----------
-        parameter_set : str, default="default"
-            Name of the set of test parameters to return, for use in tests. If no
-            special parameters are defined for a value, will return `"default"` set.
-
-
-        Returns
-        -------
-        params : dict or list of dict, default={}
-            Parameters to create testing instances of the class
-            Each dict are parameters to construct an "interesting" test instance, i.e.,
-            `MyClass(**params)` or `MyClass(**params[i])` creates a valid test instance.
-        """
-        return {
-            "n_clusters": 2,
-            "init_algorithm": "random",
-            "n_init": 1,
-            "max_iter": 1,
-            "tol": 1e-4,
-            "verbose": False,
-            "random_state": 1,
-        }
-
-    def _score(self, X, y=None):
-        return np.abs(self.inertia_)
diff --git a/aeon/clustering/deep_learning/_ae_bgru.py b/aeon/clustering/deep_learning/_ae_bgru.py
index 9b7df32716..e400bdaa05 100644
--- a/aeon/clustering/deep_learning/_ae_bgru.py
+++ b/aeon/clustering/deep_learning/_ae_bgru.py
@@ -22,8 +22,6 @@ class AEBiGRUClusterer(BaseDeepClusterer):
     ----------
     n_clusters : int, default=None
         Number of clusters for the deep learnign model.
-    clustering_algorithm : str, default="deprecated"
-        Use 'estimator' parameter instead.
     clustering_params : dict, default=None
         Use 'estimator' parameter instead.
     estimator : aeon clusterer, default=None
@@ -100,7 +98,6 @@ class AEBiGRUClusterer(BaseDeepClusterer):
     def __init__(
         self,
         n_clusters=None,
-        clustering_algorithm="deprecated",
         estimator=None,
         clustering_params=None,
         latent_space_dim=128,
diff --git a/aeon/clustering/deep_learning/_ae_fcn.py b/aeon/clustering/deep_learning/_ae_fcn.py
index 70b55bb420..15df93af8e 100644
--- a/aeon/clustering/deep_learning/_ae_fcn.py
+++ b/aeon/clustering/deep_learning/_ae_fcn.py
@@ -27,8 +27,6 @@ class AEFCNClusterer(BaseDeepClusterer):
         An aeon estimator to be built using the transformed data.
         Defaults to aeon TimeSeriesKMeans() with euclidean distance
         and mean averaging method and n_clusters set to 2.
-    clustering_algorithm : str, default="deprecated"
-        Please use 'estimator' parameter.
     clustering_params : dict, default=None
         Please use 'estimator' parameter.
     latent_space_dim : int, default=128
@@ -124,7 +122,6 @@ def __init__(
         self,
         n_clusters=None,
         estimator=None,
-        clustering_algorithm="deprecated",
         clustering_params=None,
         latent_space_dim=128,
         temporal_latent_space=False,
@@ -177,7 +174,6 @@ def __init__(
         super().__init__(
             estimator=estimator,
             n_clusters=n_clusters,
-            clustering_algorithm=clustering_algorithm,
             clustering_params=clustering_params,
             batch_size=batch_size,
             last_file_name=last_file_name,
diff --git a/aeon/clustering/deep_learning/_ae_resnet.py b/aeon/clustering/deep_learning/_ae_resnet.py
index d9f3ebd52e..2a7f308d4a 100644
--- a/aeon/clustering/deep_learning/_ae_resnet.py
+++ b/aeon/clustering/deep_learning/_ae_resnet.py
@@ -30,8 +30,6 @@ class AEResNetClusterer(BaseDeepClusterer):
         An aeon estimator to be built using the transformed data.
         Defaults to aeon TimeSeriesKMeans() with euclidean distance
         and mean averaging method and n_clusters set to 2.
-    clustering_algorithm : str, default="deprecated"
-        Please use 'estimator' parameter.
     clustering_params : dict, default=None
         Please use 'estimator' parameter.
     latent_space_dim : int, default=128
@@ -134,7 +132,6 @@ def __init__(
         n_clusters=None,
         estimator=None,
         n_residual_blocks=3,
-        clustering_algorithm="deprecated",
         clustering_params=None,
         n_conv_per_residual_block=3,
         n_filters=None,
@@ -188,7 +185,6 @@ def __init__(
         super().__init__(
             estimator=estimator,
             n_clusters=n_clusters,
-            clustering_algorithm=clustering_algorithm,
             clustering_params=clustering_params,
             batch_size=batch_size,
             last_file_name=last_file_name,
diff --git a/aeon/clustering/deep_learning/base.py b/aeon/clustering/deep_learning/base.py
index dc84d7c187..ba4d4ac00a 100644
--- a/aeon/clustering/deep_learning/base.py
+++ b/aeon/clustering/deep_learning/base.py
@@ -19,8 +19,6 @@ class BaseDeepClusterer(BaseClusterer):
         An aeon estimator to be built using the transformed data.
         Defaults to aeon TimeSeriesKMeans() with euclidean distance
         and mean averaging method and n_clusters set to 2.
-    clustering_algorithm : str, default="deprecated"
-        Please use 'estimator' parameter.
     clustering_params : dict, default=None
         Please use 'estimator' parameter.
     batch_size : int, default = 40
@@ -44,14 +42,12 @@ def __init__(
         self,
         n_clusters=None,
         estimator=None,
-        clustering_algorithm="deprecated",
         clustering_params=None,
         batch_size=32,
         last_file_name="last_file",
     ):
         self.estimator = estimator
         self.n_clusters = n_clusters
-        self.clustering_algorithm = clustering_algorithm
         self.clustering_params = clustering_params
         self.batch_size = batch_size
         self.last_file_name = last_file_name
@@ -109,8 +105,6 @@ def _fit_clustering(self, X):
         X : np.ndarray, shape=(n_cases, n_timepoints, n_channels)
             The input time series.
         """
-        import warnings
-
         self._estimator = (
             TimeSeriesKMeans(
                 n_clusters=2, distance="euclidean", averaging_method="mean"
@@ -120,22 +114,6 @@ def _fit_clustering(self, X):
         )
 
         # to be removed in 1.0.0
-        if (
-            self.clustering_algorithm != "deprecated"
-            or self.clustering_params is not None
-            or self.n_clusters is not None
-        ):
-            warnings.warn(
-                "The 'n_clusters' 'clustering_algorithm' and "
-                "'clustering_params' parameters "
-                "will be removed in v1.0.0. "
-                "Their usage will not have an effect, "
-                "please use the new 'estimator' parameter to directly "
-                "give an aeon clusterer as input.",
-                FutureWarning,
-                stacklevel=2,
-            )
-
         latent_space = self.model_.layers[1].predict(X)
         self._estimator.fit(X=latent_space)
 

From 491117f3972fdb17bb8c198a21315aca25ad73a8 Mon Sep 17 00:00:00 2001
From: chrisholder <chrisholder987@hotmail.com>
Date: Tue, 5 Nov 2024 12:43:12 +0000
Subject: [PATCH 08/25] notebook

---
 examples/networks/deep_learning.ipynb | 2 +-
 1 file changed, 1 insertion(+), 1 deletion(-)

diff --git a/examples/networks/deep_learning.ipynb b/examples/networks/deep_learning.ipynb
index 43ec5b0353..cbad5df130 100644
--- a/examples/networks/deep_learning.ipynb
+++ b/examples/networks/deep_learning.ipynb
@@ -281,7 +281,7 @@
     "aefcn = AEFCNClusterer(\n",
     "    n_clusters=2,\n",
     "    temporal_latent_space=False,\n",
-    "    clustering_algorithm=\"kmeans\",\n",
+    "    estimator=\"kmeans\",\n",
     "    n_epochs=10,\n",
     ")\n",
     "\n",

From e83387c1405bd57ec8f96c0a90d7dae9913cf283 Mon Sep 17 00:00:00 2001
From: chrisholder <chrisholder987@hotmail.com>
Date: Wed, 6 Nov 2024 15:39:49 +0000
Subject: [PATCH 09/25] fixed predict proba

---
 aeon/clustering/base.py                   |  2 +-
 aeon/clustering/deep_learning/_ae_bgru.py |  2 +-
 aeon/clustering/feature_based/_catch22.py | 13 +------------
 aeon/clustering/feature_based/_summary.py | 13 +------------
 aeon/clustering/feature_based/_tsfresh.py | 13 +------------
 5 files changed, 5 insertions(+), 38 deletions(-)

diff --git a/aeon/clustering/base.py b/aeon/clustering/base.py
index bf73c2a136..92989a4c2c 100644
--- a/aeon/clustering/base.py
+++ b/aeon/clustering/base.py
@@ -201,7 +201,7 @@ def _predict_proba(self, X) -> np.ndarray:
             n_clusters = len(np.unique(preds))
         if n_clusters is None:
             n_clusters = int(max(preds)) + 1
-        dists = np.zeros((X.shape[0], n_clusters))
+        dists = np.zeros((len(X), n_clusters))
         for i in range(n_cases):
             dists[i, preds[i]] = 1
         return dists
diff --git a/aeon/clustering/deep_learning/_ae_bgru.py b/aeon/clustering/deep_learning/_ae_bgru.py
index 9b7df32716..c79632d504 100644
--- a/aeon/clustering/deep_learning/_ae_bgru.py
+++ b/aeon/clustering/deep_learning/_ae_bgru.py
@@ -141,9 +141,9 @@ def __init__(
         self.best_file_name = best_file_name
         self.random_state = random_state
         self.estimator = estimator
+        self.n_clusters = n_clusters
 
         super().__init__(
-            n_clusters=n_clusters,
             estimator=estimator,
             batch_size=batch_size,
             last_file_name=last_file_name,
diff --git a/aeon/clustering/feature_based/_catch22.py b/aeon/clustering/feature_based/_catch22.py
index 74d2d54674..e365e04917 100644
--- a/aeon/clustering/feature_based/_catch22.py
+++ b/aeon/clustering/feature_based/_catch22.py
@@ -201,18 +201,7 @@ def _predict_proba(self, X) -> np.ndarray:
         if callable(m):
             return self._estimator.predict_proba(self._transformer.transform(X))
         else:
-            preds = self._estimator.predict(self._transformer.transform(X))
-            unique = np.unique(preds)
-            for i, u in enumerate(unique):
-                preds[preds == u] = i
-            n_cases = len(preds)
-            n_clusters = self.n_clusters
-            if n_clusters is None:
-                n_clusters = int(max(preds)) + 1
-            dists = np.zeros((len(X), n_clusters))
-            for i in range(n_cases):
-                dists[i, preds[i]] = 1
-            return dists
+            return super()._predict_proba(X)
 
     @classmethod
     def _get_test_params(cls, parameter_set="default"):
diff --git a/aeon/clustering/feature_based/_summary.py b/aeon/clustering/feature_based/_summary.py
index ddd3e3825e..616582da2a 100644
--- a/aeon/clustering/feature_based/_summary.py
+++ b/aeon/clustering/feature_based/_summary.py
@@ -157,15 +157,4 @@ def _predict_proba(self, X) -> np.ndarray:
         if callable(m):
             return self._estimator.predict_proba(self._transformer.transform(X))
         else:
-            preds = self._estimator.predict(self._transformer.transform(X))
-            unique = np.unique(preds)
-            for i, u in enumerate(unique):
-                preds[preds == u] = i
-            n_cases = len(preds)
-            n_clusters = self.n_clusters
-            if n_clusters is None:
-                n_clusters = int(max(preds)) + 1
-            dists = np.zeros((X.shape[0], n_clusters))
-            for i in range(n_cases):
-                dists[i, preds[i]] = 1
-            return dists
+            return super()._predict_proba(X)
diff --git a/aeon/clustering/feature_based/_tsfresh.py b/aeon/clustering/feature_based/_tsfresh.py
index c735602ff4..4987e9bfda 100644
--- a/aeon/clustering/feature_based/_tsfresh.py
+++ b/aeon/clustering/feature_based/_tsfresh.py
@@ -198,18 +198,7 @@ def _predict_proba(self, X: np.ndarray) -> np.ndarray:
         if callable(m):
             return self._estimator.predict_proba(self._transformer.transform(X))
         else:
-            preds = self._estimator.predict(self._transformer.transform(X))
-            unique = np.unique(preds)
-            for i, u in enumerate(unique):
-                preds[preds == u] = i
-            n_cases = len(preds)
-            n_clusters = self.n_clusters
-            if n_clusters is None:
-                n_clusters = int(max(preds)) + 1
-            dists = np.zeros((X.shape[0], n_clusters))
-            for i in range(n_cases):
-                dists[i, preds[i]] = 1
-            return dists
+            return super()._predict_proba(X)
 
     @classmethod
     def _get_test_params(cls, parameter_set: str = "default"):

From 30fbda933eabfa1866adb58aacbd335c058f33aa Mon Sep 17 00:00:00 2001
From: chrisholder <chrisholder987@hotmail.com>
Date: Wed, 6 Nov 2024 16:05:46 +0000
Subject: [PATCH 10/25] fixed dnn tests

---
 aeon/testing/mock_estimators/_mock_clusterers.py | 1 -
 1 file changed, 1 deletion(-)

diff --git a/aeon/testing/mock_estimators/_mock_clusterers.py b/aeon/testing/mock_estimators/_mock_clusterers.py
index ea6772c092..c44d76458f 100644
--- a/aeon/testing/mock_estimators/_mock_clusterers.py
+++ b/aeon/testing/mock_estimators/_mock_clusterers.py
@@ -30,7 +30,6 @@ class MockDeepClusterer(BaseDeepClusterer):
     def __init__(self, estimator=None, last_file_name="last_file"):
         self.last_file_name = last_file_name
         super().__init__(
-            n_clusters=None,
             estimator=estimator,
             last_file_name=last_file_name,
             clustering_params={"n_init": 1, "averaging_method": "mean"},

From 3557ac4e1711d5db8145bbf39eaecfa6a7ae949f Mon Sep 17 00:00:00 2001
From: chrisholder <chrisholder987@hotmail.com>
Date: Wed, 6 Nov 2024 16:19:44 +0000
Subject: [PATCH 11/25] fixed dnn tests

---
 aeon/clustering/deep_learning/base.py | 1 -
 1 file changed, 1 deletion(-)

diff --git a/aeon/clustering/deep_learning/base.py b/aeon/clustering/deep_learning/base.py
index ced00367a6..6c9ed5d63f 100644
--- a/aeon/clustering/deep_learning/base.py
+++ b/aeon/clustering/deep_learning/base.py
@@ -121,7 +121,6 @@ def _fit_clustering(self, X):
         if (
             self.clustering_algorithm != "deprecated"
             or self.clustering_params is not None
-            or self.n_clusters is not None
         ):
             warnings.warn(
                 "The 'n_clusters' 'clustering_algorithm' and "

From 2a5807d3dd992ca0267612f3c4d0fcad005d7abe Mon Sep 17 00:00:00 2001
From: chrisholder <chrisholder987@hotmail.com>
Date: Wed, 6 Nov 2024 17:23:25 +0000
Subject: [PATCH 12/25] fix notebook

---
 examples/networks/deep_learning.ipynb | 4 +---
 1 file changed, 1 insertion(+), 3 deletions(-)

diff --git a/examples/networks/deep_learning.ipynb b/examples/networks/deep_learning.ipynb
index 43ec5b0353..130b7166d7 100644
--- a/examples/networks/deep_learning.ipynb
+++ b/examples/networks/deep_learning.ipynb
@@ -288,9 +288,7 @@
     "aefcn.fit(X=xtrain)\n",
     "ypred = aefcn.predict(X=xtest)\n",
     "print(\"Predictions: \", ypred[0:5])\n",
-    "print(\"Ground Truth: \", ytest[0:5])\n",
-    "print()\n",
-    "print(\"Score : \", aefcn.score(X=xtest))"
+    "print(\"Ground Truth: \", ytest[0:5])"
    ]
   },
   {

From 3f80902ae299fb35fcefe441c6f5cc0ae1aa915e Mon Sep 17 00:00:00 2001
From: chrisholder <chrisholder987@hotmail.com>
Date: Wed, 6 Nov 2024 17:39:52 +0000
Subject: [PATCH 13/25] fixed

---
 aeon/clustering/deep_learning/_ae_bgru.py | 1 +
 1 file changed, 1 insertion(+)

diff --git a/aeon/clustering/deep_learning/_ae_bgru.py b/aeon/clustering/deep_learning/_ae_bgru.py
index 1df07fa344..457ae6581c 100644
--- a/aeon/clustering/deep_learning/_ae_bgru.py
+++ b/aeon/clustering/deep_learning/_ae_bgru.py
@@ -140,6 +140,7 @@ def __init__(
         self.save_last_model = save_last_model
         self.best_file_name = best_file_name
         self.random_state = random_state
+        self.n_clusters = n_clusters
 
         super().__init__(
             clustering_algorithm=clustering_algorithm,

From a591c31c111495fefdfe6ef27350e7b80fce1a7e Mon Sep 17 00:00:00 2001
From: chrisholder <chrisholder987@hotmail.com>
Date: Fri, 15 Nov 2024 16:52:50 +0100
Subject: [PATCH 14/25] removed deep learner n_clusters and assert labels_
 exists

---
 aeon/clustering/deep_learning/_ae_abgru.py    |  4 --
 aeon/clustering/deep_learning/_ae_bgru.py     |  4 --
 aeon/clustering/deep_learning/_ae_dcnn.py     |  4 --
 aeon/clustering/deep_learning/_ae_drnn.py     |  4 --
 aeon/clustering/deep_learning/_ae_fcn.py      |  4 --
 aeon/clustering/deep_learning/_ae_resnet.py   |  4 --
 .../_yield_clustering_checks.py               | 41 +++++++++++++++++++
 7 files changed, 41 insertions(+), 24 deletions(-)

diff --git a/aeon/clustering/deep_learning/_ae_abgru.py b/aeon/clustering/deep_learning/_ae_abgru.py
index 02943abcf7..5c8866a9e9 100644
--- a/aeon/clustering/deep_learning/_ae_abgru.py
+++ b/aeon/clustering/deep_learning/_ae_abgru.py
@@ -20,8 +20,6 @@ class AEAttentionBiGRUClusterer(BaseDeepClusterer):
 
     Parameters
     ----------
-    n_clusters : int, default=None
-        Number of clusters for the deep learnign model.
     clustering_algorithm : str, default="deprecated"
         Use 'estimator' parameter instead.
     clustering_params : dict, default=None
@@ -100,7 +98,6 @@ class AEAttentionBiGRUClusterer(BaseDeepClusterer):
 
     def __init__(
         self,
-        n_clusters=None,
         estimator=None,
         clustering_algorithm="deprecated",
         clustering_params=None,
@@ -143,7 +140,6 @@ def __init__(
         self.random_state = random_state
 
         super().__init__(
-            n_clusters=n_clusters,
             clustering_algorithm=clustering_algorithm,
             clustering_params=clustering_params,
             estimator=estimator,
diff --git a/aeon/clustering/deep_learning/_ae_bgru.py b/aeon/clustering/deep_learning/_ae_bgru.py
index 457ae6581c..59e4fb71ca 100644
--- a/aeon/clustering/deep_learning/_ae_bgru.py
+++ b/aeon/clustering/deep_learning/_ae_bgru.py
@@ -20,8 +20,6 @@ class AEBiGRUClusterer(BaseDeepClusterer):
 
     Parameters
     ----------
-    n_clusters : int, default=None
-        Number of clusters for the deep learnign model.
     clustering_algorithm : str, default="deprecated"
         Use 'estimator' parameter instead.
     clustering_params : dict, default=None
@@ -99,7 +97,6 @@ class AEBiGRUClusterer(BaseDeepClusterer):
 
     def __init__(
         self,
-        n_clusters=None,
         clustering_algorithm="deprecated",
         estimator=None,
         clustering_params=None,
@@ -140,7 +137,6 @@ def __init__(
         self.save_last_model = save_last_model
         self.best_file_name = best_file_name
         self.random_state = random_state
-        self.n_clusters = n_clusters
 
         super().__init__(
             clustering_algorithm=clustering_algorithm,
diff --git a/aeon/clustering/deep_learning/_ae_dcnn.py b/aeon/clustering/deep_learning/_ae_dcnn.py
index 486457be0d..e9171c5c01 100644
--- a/aeon/clustering/deep_learning/_ae_dcnn.py
+++ b/aeon/clustering/deep_learning/_ae_dcnn.py
@@ -19,8 +19,6 @@ class AEDCNNClusterer(BaseDeepClusterer):
 
     Parameters
     ----------
-    n_clusters : int, default=None
-        Number of clusters for the deep learnign model.
     clustering_algorithm : str, default="deprecated"
         Use 'estimator' parameter instead.
     clustering_params : dict, default=None
@@ -113,7 +111,6 @@ class AEDCNNClusterer(BaseDeepClusterer):
 
     def __init__(
         self,
-        n_clusters=None,
         estimator=None,
         clustering_algorithm="deprecated",
         clustering_params=None,
@@ -164,7 +161,6 @@ def __init__(
         self.random_state = random_state
 
         super().__init__(
-            n_clusters=n_clusters,
             clustering_params=clustering_params,
             clustering_algorithm=clustering_algorithm,
             estimator=estimator,
diff --git a/aeon/clustering/deep_learning/_ae_drnn.py b/aeon/clustering/deep_learning/_ae_drnn.py
index 61777ba78c..2f22c15150 100644
--- a/aeon/clustering/deep_learning/_ae_drnn.py
+++ b/aeon/clustering/deep_learning/_ae_drnn.py
@@ -24,8 +24,6 @@ class AEDRNNClusterer(BaseDeepClusterer):
 
     Parameters
     ----------
-    n_clusters : int, default=None
-        Number of clusters for the deep learnign model.
     clustering_algorithm : str, default="deprecated"
         Please use the 'estimator' parameter.
     estimator : aeon clusterer, default=None
@@ -114,7 +112,6 @@ class AEDRNNClusterer(BaseDeepClusterer):
 
     def __init__(
         self,
-        n_clusters=None,
         estimator=None,
         clustering_algorithm="deprecated",
         clustering_params=None,
@@ -167,7 +164,6 @@ def __init__(
         self.random_state = random_state
 
         super().__init__(
-            n_clusters=n_clusters,
             estimator=estimator,
             clustering_algorithm=clustering_algorithm,
             clustering_params=clustering_params,
diff --git a/aeon/clustering/deep_learning/_ae_fcn.py b/aeon/clustering/deep_learning/_ae_fcn.py
index 1787284281..f3ab250f48 100644
--- a/aeon/clustering/deep_learning/_ae_fcn.py
+++ b/aeon/clustering/deep_learning/_ae_fcn.py
@@ -21,8 +21,6 @@ class AEFCNClusterer(BaseDeepClusterer):
 
     Parameters
     ----------
-    n_clusters : int, default=None
-        Please use 'estimator' parameter.
     estimator : aeon clusterer, default=None
         An aeon estimator to be built using the transformed data.
         Defaults to aeon TimeSeriesKMeans() with euclidean distance
@@ -122,7 +120,6 @@ class AEFCNClusterer(BaseDeepClusterer):
 
     def __init__(
         self,
-        n_clusters=None,
         estimator=None,
         clustering_algorithm="deprecated",
         clustering_params=None,
@@ -173,7 +170,6 @@ def __init__(
         self.save_last_model = save_last_model
         self.best_file_name = best_file_name
         self.random_state = random_state
-        self.n_clusters = n_clusters
 
         super().__init__(
             estimator=estimator,
diff --git a/aeon/clustering/deep_learning/_ae_resnet.py b/aeon/clustering/deep_learning/_ae_resnet.py
index 3674adaa2c..aed5900d4e 100644
--- a/aeon/clustering/deep_learning/_ae_resnet.py
+++ b/aeon/clustering/deep_learning/_ae_resnet.py
@@ -24,8 +24,6 @@ class AEResNetClusterer(BaseDeepClusterer):
 
     Parameters
     ----------
-    n_clusters : int, default=None
-        Please use 'estimator' parameter.
     estimator : aeon clusterer, default=None
         An aeon estimator to be built using the transformed data.
         Defaults to aeon TimeSeriesKMeans() with euclidean distance
@@ -131,7 +129,6 @@ class method save_last_model_to_file.
 
     def __init__(
         self,
-        n_clusters=None,
         estimator=None,
         n_residual_blocks=3,
         clustering_algorithm="deprecated",
@@ -182,7 +179,6 @@ def __init__(
         self.best_file_name = best_file_name
         self.last_file_name = last_file_name
         self.optimizer = optimizer
-        self.n_clusters = n_clusters
 
         self.history = None
 
diff --git a/aeon/testing/estimator_checking/_yield_clustering_checks.py b/aeon/testing/estimator_checking/_yield_clustering_checks.py
index 4843f13056..4c704f55d7 100644
--- a/aeon/testing/estimator_checking/_yield_clustering_checks.py
+++ b/aeon/testing/estimator_checking/_yield_clustering_checks.py
@@ -7,6 +7,7 @@
 from aeon.base._base import _clone_estimator
 from aeon.clustering.deep_learning import BaseDeepClusterer
 from aeon.testing.testing_data import FULL_TEST_DATA_DICT
+from aeon.utils.validation import get_n_cases
 
 
 def _yield_clustering_checks(estimator_class, estimator_instances, datatypes):
@@ -26,6 +27,10 @@ def _yield_clustering_checks(estimator_class, estimator_instances, datatypes):
                 estimator=estimator,
                 datatype=datatypes[i][0],
             )
+        for datatype in datatypes[i]:
+            yield partial(
+                check_clusterer_output, estimator=estimator, datatype=datatype
+            )
 
 
 def check_clusterer_tags_consistent(estimator_class):
@@ -82,3 +87,39 @@ def check_clustering_random_state_deep_learning(estimator, datatype):
             _weight2 = np.asarray(weights2[j])
 
             np.testing.assert_almost_equal(_weight1, _weight2, 4)
+
+
+def check_clusterer_output(estimator, datatype):
+    """Test clusterer outputs the correct data types and values.
+
+    Test predict produces a np.array or pd.Series with only values seen in the train
+    data, and that predict_proba probability estimates add up to one.
+    """
+    estimator = _clone_estimator(estimator)
+
+    unique_labels = np.unique(FULL_TEST_DATA_DICT[datatype]["train"][1])
+
+    # run fit and predict
+    estimator.fit(
+        FULL_TEST_DATA_DICT[datatype]["train"][0],
+        FULL_TEST_DATA_DICT[datatype]["train"][1],
+    )
+    assert hasattr(estimator, "labels_")
+    assert isinstance(estimator.labels_, np.ndarray)
+
+    y_pred = estimator.predict(FULL_TEST_DATA_DICT[datatype]["test"][0])
+
+    # check predict
+    assert isinstance(y_pred, np.ndarray)
+    assert y_pred.shape == (get_n_cases(FULL_TEST_DATA_DICT[datatype]["test"][0]),)
+    assert np.all(np.isin(np.unique(y_pred), unique_labels))
+
+    # check predict proba (all classifiers have predict_proba by default)
+    y_proba = estimator.predict_proba(FULL_TEST_DATA_DICT[datatype]["test"][0])
+
+    assert isinstance(y_proba, np.ndarray)
+    assert y_proba.shape == (
+        get_n_cases(FULL_TEST_DATA_DICT[datatype]["test"][0]),
+        len(unique_labels),
+    )
+    np.testing.assert_almost_equal(y_proba.sum(axis=1), 1, decimal=4)

From 2ee81ba8e574ab62a909ebb0abe37cd69e86543b Mon Sep 17 00:00:00 2001
From: chrisholder <chrisholder987@hotmail.com>
Date: Sun, 17 Nov 2024 21:25:22 +0100
Subject: [PATCH 15/25] cont

---
 aeon/clustering/feature_based/_catch22.py                  | 1 +
 aeon/clustering/feature_based/_summary.py                  | 2 ++
 aeon/clustering/feature_based/_tsfresh.py                  | 1 +
 .../testing/estimator_checking/_yield_clustering_checks.py | 7 -------
 4 files changed, 4 insertions(+), 7 deletions(-)

diff --git a/aeon/clustering/feature_based/_catch22.py b/aeon/clustering/feature_based/_catch22.py
index e365e04917..33f0b79bc5 100644
--- a/aeon/clustering/feature_based/_catch22.py
+++ b/aeon/clustering/feature_based/_catch22.py
@@ -164,6 +164,7 @@ def _fit(self, X, y=None):
 
         X_t = self._transformer.fit_transform(X, y)
         self._estimator.fit(X_t, y)
+        self.labels_ = self._estimator.labels_
 
         return self
 
diff --git a/aeon/clustering/feature_based/_summary.py b/aeon/clustering/feature_based/_summary.py
index 616582da2a..309d3ac92f 100644
--- a/aeon/clustering/feature_based/_summary.py
+++ b/aeon/clustering/feature_based/_summary.py
@@ -121,6 +121,8 @@ def _fit(self, X, y=None):
         X_t = self._transformer.fit_transform(X, y)
         self._estimator.fit(X_t, y)
 
+        self.labels_ = self._estimator.labels_
+
         return self
 
     def _predict(self, X) -> np.ndarray:
diff --git a/aeon/clustering/feature_based/_tsfresh.py b/aeon/clustering/feature_based/_tsfresh.py
index 4987e9bfda..ed14e90a47 100644
--- a/aeon/clustering/feature_based/_tsfresh.py
+++ b/aeon/clustering/feature_based/_tsfresh.py
@@ -162,6 +162,7 @@ def _fit(self, X: np.ndarray, y: Optional[np.ndarray] = None):
         else:
             self._estimator.fit(X_t, y)
 
+        self.labels_ = self._estimator.labels_
         return self
 
     def _predict(self, X: np.ndarray) -> np.ndarray:
diff --git a/aeon/testing/estimator_checking/_yield_clustering_checks.py b/aeon/testing/estimator_checking/_yield_clustering_checks.py
index 4c704f55d7..117a0704b6 100644
--- a/aeon/testing/estimator_checking/_yield_clustering_checks.py
+++ b/aeon/testing/estimator_checking/_yield_clustering_checks.py
@@ -97,8 +97,6 @@ def check_clusterer_output(estimator, datatype):
     """
     estimator = _clone_estimator(estimator)
 
-    unique_labels = np.unique(FULL_TEST_DATA_DICT[datatype]["train"][1])
-
     # run fit and predict
     estimator.fit(
         FULL_TEST_DATA_DICT[datatype]["train"][0],
@@ -112,14 +110,9 @@ def check_clusterer_output(estimator, datatype):
     # check predict
     assert isinstance(y_pred, np.ndarray)
     assert y_pred.shape == (get_n_cases(FULL_TEST_DATA_DICT[datatype]["test"][0]),)
-    assert np.all(np.isin(np.unique(y_pred), unique_labels))
 
     # check predict proba (all classifiers have predict_proba by default)
     y_proba = estimator.predict_proba(FULL_TEST_DATA_DICT[datatype]["test"][0])
 
     assert isinstance(y_proba, np.ndarray)
-    assert y_proba.shape == (
-        get_n_cases(FULL_TEST_DATA_DICT[datatype]["test"][0]),
-        len(unique_labels),
-    )
     np.testing.assert_almost_equal(y_proba.sum(axis=1), 1, decimal=4)

From ebec4e642365bc0633a1d60ff452f1ababc7496c Mon Sep 17 00:00:00 2001
From: chrisholder <chrisholder987@hotmail.com>
Date: Tue, 19 Nov 2024 22:13:27 +0100
Subject: [PATCH 16/25] fix dnns

---
 aeon/clustering/deep_learning/_ae_resnet.py | 1 +
 aeon/clustering/deep_learning/base.py       | 4 ++++
 2 files changed, 5 insertions(+)

diff --git a/aeon/clustering/deep_learning/_ae_resnet.py b/aeon/clustering/deep_learning/_ae_resnet.py
index aed5900d4e..638c64fa57 100644
--- a/aeon/clustering/deep_learning/_ae_resnet.py
+++ b/aeon/clustering/deep_learning/_ae_resnet.py
@@ -344,6 +344,7 @@ def _fit(self, X):
         self._fit_clustering(X=X)
 
         gc.collect()
+
         return self
 
     def _score(self, X, y=None):
diff --git a/aeon/clustering/deep_learning/base.py b/aeon/clustering/deep_learning/base.py
index 3e4cf6f5ad..64e6cb1cc6 100644
--- a/aeon/clustering/deep_learning/base.py
+++ b/aeon/clustering/deep_learning/base.py
@@ -136,6 +136,10 @@ def _fit_clustering(self, X):
 
         latent_space = self.model_.layers[1].predict(X)
         self._estimator.fit(X=latent_space)
+        if hasattr(self._estimator, "labels_"):
+            self.labels_ = self._estimator.labels_
+        else:
+            self.labels_ = self._estimator.predict(X=latent_space)
 
         return self
 

From 02fcee098380671c6a61c1ab24a4cd1be7d041b3 Mon Sep 17 00:00:00 2001
From: chrisholder <chrisholder987@hotmail.com>
Date: Tue, 19 Nov 2024 22:27:45 +0100
Subject: [PATCH 17/25] pipeline clusterer

---
 aeon/clustering/compose/_pipeline.py | 4 +++-
 1 file changed, 3 insertions(+), 1 deletion(-)

diff --git a/aeon/clustering/compose/_pipeline.py b/aeon/clustering/compose/_pipeline.py
index eeeb2b43b8..1e3ed54ac8 100644
--- a/aeon/clustering/compose/_pipeline.py
+++ b/aeon/clustering/compose/_pipeline.py
@@ -86,7 +86,9 @@ def __init__(self, transformers, clusterer, random_state=None):
         )
 
     def _fit(self, X, y=None):
-        return super()._fit(X, y)
+        super()._fit(X, y)
+        self.labels_ = self._estimator.labels_
+        return self
 
     @classmethod
     def _get_test_params(cls, parameter_set="default"):

From 91048084da1a562052c6819e7e5b174adcd91527 Mon Sep 17 00:00:00 2001
From: chrisholder <chrisholder987@hotmail.com>
Date: Tue, 19 Nov 2024 22:53:23 +0100
Subject: [PATCH 18/25] fix pipeline

---
 aeon/clustering/compose/_pipeline.py | 4 ++--
 1 file changed, 2 insertions(+), 2 deletions(-)

diff --git a/aeon/clustering/compose/_pipeline.py b/aeon/clustering/compose/_pipeline.py
index 1e3ed54ac8..3337f7e032 100644
--- a/aeon/clustering/compose/_pipeline.py
+++ b/aeon/clustering/compose/_pipeline.py
@@ -86,8 +86,8 @@ def __init__(self, transformers, clusterer, random_state=None):
         )
 
     def _fit(self, X, y=None):
-        super()._fit(X, y)
-        self.labels_ = self._estimator.labels_
+        return super()._fit(X, y)
+        self.labels_ = self.clusterer.labels_
         return self
 
     @classmethod

From 1c854875113a3ee86e4cc99c3e58bfd55ee3f01c Mon Sep 17 00:00:00 2001
From: chrisholder <chrisholder987@hotmail.com>
Date: Tue, 19 Nov 2024 22:57:18 +0100
Subject: [PATCH 19/25] revert

---
 aeon/clustering/compose/_pipeline.py | 2 +-
 1 file changed, 1 insertion(+), 1 deletion(-)

diff --git a/aeon/clustering/compose/_pipeline.py b/aeon/clustering/compose/_pipeline.py
index 3337f7e032..5a1026550b 100644
--- a/aeon/clustering/compose/_pipeline.py
+++ b/aeon/clustering/compose/_pipeline.py
@@ -86,7 +86,7 @@ def __init__(self, transformers, clusterer, random_state=None):
         )
 
     def _fit(self, X, y=None):
-        return super()._fit(X, y)
+        super()._fit(X, y)
         self.labels_ = self.clusterer.labels_
         return self
 

From c89d854e8b88f37ed352d701062e10124f1d1387 Mon Sep 17 00:00:00 2001
From: chrisholder <chrisholder987@hotmail.com>
Date: Wed, 20 Nov 2024 11:27:01 +0100
Subject: [PATCH 20/25] fix

---
 aeon/clustering/compose/_pipeline.py | 2 +-
 1 file changed, 1 insertion(+), 1 deletion(-)

diff --git a/aeon/clustering/compose/_pipeline.py b/aeon/clustering/compose/_pipeline.py
index 5a1026550b..eb6c255806 100644
--- a/aeon/clustering/compose/_pipeline.py
+++ b/aeon/clustering/compose/_pipeline.py
@@ -87,7 +87,7 @@ def __init__(self, transformers, clusterer, random_state=None):
 
     def _fit(self, X, y=None):
         super()._fit(X, y)
-        self.labels_ = self.clusterer.labels_
+        self.labels_ = self.steps_[-1][1].labels_
         return self
 
     @classmethod

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 21/25] 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,

From 6d6849227c694857441c3d4423880d1835823d99 Mon Sep 17 00:00:00 2001
From: chrisholder <chrisholder987@hotmail.com>
Date: Wed, 20 Nov 2024 11:56:40 +0100
Subject: [PATCH 22/25] remove score from notebooks

---
 .../clustering/partitional_clustering.ipynb   | 119 +-----------------
 1 file changed, 4 insertions(+), 115 deletions(-)

diff --git a/examples/clustering/partitional_clustering.ipynb b/examples/clustering/partitional_clustering.ipynb
index 646845ac70..817f66bd24 100644
--- a/examples/clustering/partitional_clustering.ipynb
+++ b/examples/clustering/partitional_clustering.ipynb
@@ -1217,46 +1217,14 @@
    "cell_type": "markdown",
    "source": [
     "We have formed two clusters. The pattern of the two centroids seems fairly similar,\n",
-    "and the separation of clusters does not seem very good. We can score the clustering\n",
-    "with the score method, which by default returns `self.inertia_`, which is a measure\n",
-    "of between cluster variation used as a stopping condition.\n",
+    "and the separation of clusters does not seem very good.\n",
     "For a range of clusteirng comparison algorithms, see the [sklearn clustering API]\n",
-    "(https://scikit-learn.org/stable/modules/clustering.html#clustering-performance-evaluation)\n",
-    "\n",
-    "Low intertia is better, so to conform to the sklearn interface which prefers to\n",
-    "maximize performance criteria, inertia scores are negative.\n"
+    "(https://scikit-learn.org/stable/modules/clustering.html#clustering-performance-evaluation)\n"
    ],
    "metadata": {
     "collapsed": false
    }
   },
-  {
-   "cell_type": "code",
-   "source": [
-    "s1 = k_means.score(X_test, y_test)\n",
-    "s1"
-   ],
-   "metadata": {
-    "collapsed": false,
-    "ExecuteTime": {
-     "end_time": "2024-09-25T22:59:26.618842Z",
-     "start_time": "2024-09-25T22:59:26.613249Z"
-    }
-   },
-   "outputs": [
-    {
-     "data": {
-      "text/plain": [
-       "-5016911.727324263"
-      ]
-     },
-     "execution_count": 16,
-     "metadata": {},
-     "output_type": "execute_result"
-    }
-   ],
-   "execution_count": 16
-  },
   {
    "cell_type": "markdown",
    "source": [
@@ -1332,39 +1300,12 @@
    "source": [
     "We have formed two clusters with barycentre averaging using MSM distance. The centroids\n",
     "seem more distinct in shape now, with the first cluster centroid looking much\n",
-    "flatter than the second. The inertia has been reduced, so the score is higher."
+    "flatter than the second."
    ],
    "metadata": {
     "collapsed": false
    }
   },
-  {
-   "cell_type": "code",
-   "source": [
-    "s2 = k_means.score(X_test, y_test)\n",
-    "s2"
-   ],
-   "metadata": {
-    "collapsed": false,
-    "ExecuteTime": {
-     "end_time": "2024-09-25T22:59:26.985041Z",
-     "start_time": "2024-09-25T22:59:26.980060Z"
-    }
-   },
-   "outputs": [
-    {
-     "data": {
-      "text/plain": [
-       "-26178.727675421676"
-      ]
-     },
-     "execution_count": 18,
-     "metadata": {},
-     "output_type": "execute_result"
-    }
-   ],
-   "execution_count": 18
-  },
   {
    "cell_type": "markdown",
    "source": [
@@ -1441,7 +1382,6 @@
     ")\n",
     "\n",
     "k_medoids.fit(X_train)\n",
-    "s3 = k_medoids.score(X_test, y_test)\n",
     "plot_cluster_algorithm(k_medoids, X_test, k_medoids.n_clusters)"
    ],
    "metadata": {
@@ -1525,7 +1465,6 @@
     ")\n",
     "\n",
     "k_medoids.fit(X_train)\n",
-    "s4 = k_medoids.score(X_test, y_test)\n",
     "plot_cluster_algorithm(k_medoids, X_test, k_medoids.n_clusters)"
    ],
    "metadata": {
@@ -1568,29 +1507,6 @@
    ],
    "execution_count": 21
   },
-  {
-   "cell_type": "code",
-   "source": [
-    "print(f\" PAM DTW score {s3} PAM MSM score {s4}\")"
-   ],
-   "metadata": {
-    "collapsed": false,
-    "ExecuteTime": {
-     "end_time": "2024-09-25T22:59:27.856857Z",
-     "start_time": "2024-09-25T22:59:27.851841Z"
-    }
-   },
-   "outputs": [
-    {
-     "name": "stdout",
-     "output_type": "stream",
-     "text": [
-      " PAM DTW score -5739730.0 PAM MSM score -25984.0\n"
-     ]
-    }
-   ],
-   "execution_count": 22
-  },
   {
    "cell_type": "markdown",
    "source": [
@@ -1619,9 +1535,7 @@
     ")\n",
     "\n",
     "k_medoids.fit(X_train)\n",
-    "s5 = k_medoids.score(X_test, y_test)\n",
-    "plot_cluster_algorithm(k_medoids, X_test, k_medoids.n_clusters)\n",
-    "print(\"Alternate MSM score = \", s5)"
+    "plot_cluster_algorithm(k_medoids, X_test, k_medoids.n_clusters)"
    ],
    "metadata": {
     "collapsed": false,
@@ -1686,7 +1600,6 @@
     "    random_state=1,\n",
     ")\n",
     "clara.fit(X_train)\n",
-    "s6 = k_medoids.score(X_test, y_test)\n",
     "plot_cluster_algorithm(clara, X_test, clara.n_clusters)"
    ],
    "metadata": {
@@ -1755,7 +1668,6 @@
     "    random_state=1,\n",
     ")\n",
     "clara.fit(X_train)\n",
-    "s7 = k_medoids.score(X_test, y_test)\n",
     "plot_cluster_algorithm(clara, X_test, clara.n_clusters)"
    ],
    "metadata": {
@@ -1798,29 +1710,6 @@
    ],
    "execution_count": 25
   },
-  {
-   "cell_type": "code",
-   "source": [
-    "print(f\" Clara score {s6} Clarans score = {s7}\")"
-   ],
-   "metadata": {
-    "collapsed": false,
-    "ExecuteTime": {
-     "end_time": "2024-09-25T22:59:28.966368Z",
-     "start_time": "2024-09-25T22:59:28.961420Z"
-    }
-   },
-   "outputs": [
-    {
-     "name": "stdout",
-     "output_type": "stream",
-     "text": [
-      " Clara score -25984.0 Clarans score = -25984.0\n"
-     ]
-    }
-   ],
-   "execution_count": 26
-  },
   {
    "cell_type": "markdown",
    "source": [

From a56b4d4442442e6b5a201a5ff9d6cbd104252386 Mon Sep 17 00:00:00 2001
From: chrisholder <chrisholder987@hotmail.com>
Date: Wed, 20 Nov 2024 12:03:15 +0100
Subject: [PATCH 23/25] remove score from remaining

---
 aeon/clustering/_k_shapes.py       | 3 ---
 aeon/clustering/_kernel_k_means.py | 3 ---
 2 files changed, 6 deletions(-)

diff --git a/aeon/clustering/_k_shapes.py b/aeon/clustering/_k_shapes.py
index 4cb50fdec5..8809582a95 100644
--- a/aeon/clustering/_k_shapes.py
+++ b/aeon/clustering/_k_shapes.py
@@ -181,6 +181,3 @@ def _get_test_params(cls, parameter_set="default"):
             "verbose": False,
             "random_state": 1,
         }
-
-    def _score(self, X, y=None):
-        return np.abs(self.inertia_)
diff --git a/aeon/clustering/_kernel_k_means.py b/aeon/clustering/_kernel_k_means.py
index 2a7b255757..9c92d250bd 100644
--- a/aeon/clustering/_kernel_k_means.py
+++ b/aeon/clustering/_kernel_k_means.py
@@ -205,6 +205,3 @@ def _get_test_params(cls, parameter_set="default") -> dict:
             "n_jobs": 1,
             "random_state": 1,
         }
-
-    def _score(self, X, y=None) -> float:
-        return np.abs(self.inertia_)

From f6b0ffb940b5f8ae277a40d2a8b1624ef09780ad Mon Sep 17 00:00:00 2001
From: chrisholder <chrisholder987@hotmail.com>
Date: Wed, 20 Nov 2024 13:55:41 +0100
Subject: [PATCH 24/25] fix notebook

---
 examples/networks/deep_learning.ipynb | 1 -
 1 file changed, 1 deletion(-)

diff --git a/examples/networks/deep_learning.ipynb b/examples/networks/deep_learning.ipynb
index 130b7166d7..498498f5fe 100644
--- a/examples/networks/deep_learning.ipynb
+++ b/examples/networks/deep_learning.ipynb
@@ -279,7 +279,6 @@
     "xtest, ytest = load_classification(name=\"ArrowHead\", split=\"test\")\n",
     "\n",
     "aefcn = AEFCNClusterer(\n",
-    "    n_clusters=2,\n",
     "    temporal_latent_space=False,\n",
     "    clustering_algorithm=\"kmeans\",\n",
     "    n_epochs=10,\n",

From 045edfaff1dda4c4673d42cbcac931182af7a2b4 Mon Sep 17 00:00:00 2001
From: chrisholder <chrisholder987@hotmail.com>
Date: Thu, 21 Nov 2024 12:15:04 +0100
Subject: [PATCH 25/25] fixed notebook

---
 examples/networks/deep_learning.ipynb | 184 +++++++++++++++-----------
 1 file changed, 104 insertions(+), 80 deletions(-)

diff --git a/examples/networks/deep_learning.ipynb b/examples/networks/deep_learning.ipynb
index b0f998d452..9e06feabbb 100644
--- a/examples/networks/deep_learning.ipynb
+++ b/examples/networks/deep_learning.ipynb
@@ -62,12 +62,17 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 1,
-   "metadata": {},
-   "outputs": [],
+   "metadata": {
+    "ExecuteTime": {
+     "end_time": "2024-11-21T11:14:08.477299Z",
+     "start_time": "2024-11-21T11:14:08.433390Z"
+    }
+   },
    "source": [
     "import os\n",
     "\n",
+    "from sklearn.cluster import KMeans\n",
+    "\n",
     "# avoid imports warning for tensorflow\n",
     "os.environ[\"TF_CPP_MIN_LOG_LEVEL\"] = \"2\"\n",
     "\n",
@@ -90,7 +95,9 @@
     "from aeon.datasets import load_classification, load_regression\n",
     "from aeon.networks import InceptionNetwork\n",
     "from aeon.regression.deep_learning import InceptionTimeRegressor"
-   ]
+   ],
+   "outputs": [],
+   "execution_count": 7
   },
   {
    "attachments": {},
@@ -107,30 +114,12 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 2,
-   "metadata": {},
-   "outputs": [
-    {
-     "name": "stderr",
-     "output_type": "stream",
-     "text": [
-      "2023-10-06 17:31:12.574639: E tensorflow/compiler/xla/stream_executor/cuda/cuda_driver.cc:266] failed call to cuInit: CUDA_ERROR_NO_DEVICE: no CUDA-capable device is detected\n"
-     ]
-    },
-    {
-     "name": "stdout",
-     "output_type": "stream",
-     "text": [
-      "3/3 [==============================] - 0s 68ms/step\n",
-      "3/3 [==============================] - 0s 52ms/step\n",
-      "3/3 [==============================] - 0s 54ms/step\n",
-      "3/3 [==============================] - 0s 84ms/step\n",
-      "3/3 [==============================] - 0s 86ms/step\n",
-      "Predictions:  ['0' '0' '0' '0' '0']\n",
-      "Ground Truth:  ['0' '0' '0' '0' '0']\n"
-     ]
+   "metadata": {
+    "ExecuteTime": {
+     "end_time": "2024-11-21T11:12:15.141664Z",
+     "start_time": "2024-11-21T11:12:02.084792Z"
     }
-   ],
+   },
    "source": [
     "xtrain, ytrain = load_classification(name=\"ArrowHead\", split=\"train\")\n",
     "xtest, ytest = load_classification(name=\"ArrowHead\", split=\"test\")\n",
@@ -141,7 +130,23 @@
     "\n",
     "print(\"Predictions: \", ypred[0:5])\n",
     "print(\"Ground Truth: \", ytest[0:5])"
-   ]
+   ],
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "\u001B[1m3/3\u001B[0m \u001B[32m━━━━━━━━━━━━━━━━━━━━\u001B[0m\u001B[37m\u001B[0m \u001B[1m0s\u001B[0m 72ms/step\n",
+      "\u001B[1m3/3\u001B[0m \u001B[32m━━━━━━━━━━━━━━━━━━━━\u001B[0m\u001B[37m\u001B[0m \u001B[1m0s\u001B[0m 78ms/step\n",
+      "\u001B[1m3/3\u001B[0m \u001B[32m━━━━━━━━━━━━━━━━━━━━\u001B[0m\u001B[37m\u001B[0m \u001B[1m0s\u001B[0m 72ms/step\n",
+      "\u001B[1m3/3\u001B[0m \u001B[32m━━━━━━━━━━━━━━━━━━━━\u001B[0m\u001B[37m\u001B[0m \u001B[1m0s\u001B[0m 74ms/step\n",
+      "\u001B[1m3/3\u001B[0m \u001B[32m━━━━━━━━━━━━━━━━━━━━\u001B[0m\u001B[37m\u001B[0m \u001B[1m0s\u001B[0m 82ms/step\n",
+      "Predictions:  ['2' '2' '2' '2' '2']\n",
+      "Ground Truth:  ['0' '0' '0' '0' '0']\n"
+     ]
+    }
+   ],
+   "execution_count": 2
   },
   {
    "attachments": {},
@@ -163,23 +168,12 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 3,
-   "metadata": {},
-   "outputs": [
-    {
-     "name": "stdout",
-     "output_type": "stream",
-     "text": [
-      "3/3 [==============================] - 0s 59ms/step\n",
-      "3/3 [==============================] - 0s 51ms/step\n",
-      "3/3 [==============================] - 0s 60ms/step\n",
-      "3/3 [==============================] - 0s 74ms/step\n",
-      "3/3 [==============================] - 0s 54ms/step\n",
-      "Predictions:  ['0' '0' '0' '0' '0']\n",
-      "Ground Truth:  ['0' '0' '0' '0' '0']\n"
-     ]
+   "metadata": {
+    "ExecuteTime": {
+     "end_time": "2024-11-21T11:12:30.252190Z",
+     "start_time": "2024-11-21T11:12:15.222773Z"
     }
-   ],
+   },
    "source": [
     "xtrain, ytrain = load_classification(name=\"ArrowHead\", split=\"train\")\n",
     "xtest, ytest = load_classification(name=\"ArrowHead\", split=\"test\")\n",
@@ -190,7 +184,23 @@
     "\n",
     "print(\"Predictions: \", ypred[0:5])\n",
     "print(\"Ground Truth: \", ytest[0:5])"
-   ]
+   ],
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "\u001B[1m3/3\u001B[0m \u001B[32m━━━━━━━━━━━━━━━━━━━━\u001B[0m\u001B[37m\u001B[0m \u001B[1m0s\u001B[0m 94ms/step\n",
+      "\u001B[1m3/3\u001B[0m \u001B[32m━━━━━━━━━━━━━━━━━━━━\u001B[0m\u001B[37m\u001B[0m \u001B[1m0s\u001B[0m 91ms/step\n",
+      "\u001B[1m3/3\u001B[0m \u001B[32m━━━━━━━━━━━━━━━━━━━━\u001B[0m\u001B[37m\u001B[0m \u001B[1m0s\u001B[0m 94ms/step\n",
+      "\u001B[1m3/3\u001B[0m \u001B[32m━━━━━━━━━━━━━━━━━━━━\u001B[0m\u001B[37m\u001B[0m \u001B[1m0s\u001B[0m 97ms/step\n",
+      "\u001B[1m3/3\u001B[0m \u001B[32m━━━━━━━━━━━━━━━━━━━━\u001B[0m\u001B[37m\u001B[0m \u001B[1m0s\u001B[0m 96ms/step\n",
+      "Predictions:  ['0' '0' '0' '0' '0']\n",
+      "Ground Truth:  ['0' '0' '0' '0' '0']\n"
+     ]
+    }
+   ],
+   "execution_count": 3
   },
   {
    "cell_type": "markdown",
@@ -205,23 +215,12 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 4,
-   "metadata": {},
-   "outputs": [
-    {
-     "name": "stdout",
-     "output_type": "stream",
-     "text": [
-      "1/1 [==============================] - 0s 198ms/step\n",
-      "1/1 [==============================] - 0s 219ms/step\n",
-      "1/1 [==============================] - 0s 196ms/step\n",
-      "1/1 [==============================] - 0s 195ms/step\n",
-      "1/1 [==============================] - 0s 209ms/step\n",
-      "Predictions:  [-0.23141143 -0.0241483  -0.01730516 -0.34630919 -3.14292431]\n",
-      "Ground Truth:  [0.0118838  0.00379507 0.08298755 0.04510921 0.12783075]\n"
-     ]
+   "metadata": {
+    "ExecuteTime": {
+     "end_time": "2024-11-21T11:12:43.183566Z",
+     "start_time": "2024-11-21T11:12:30.257417Z"
     }
-   ],
+   },
    "source": [
     "xtrain, ytrain = load_regression(name=\"Covid3Month\", split=\"train\")\n",
     "xtest, ytest = load_regression(name=\"Covid3Month\", split=\"test\")\n",
@@ -232,7 +231,23 @@
     "\n",
     "print(\"Predictions: \", ypred[0:5])\n",
     "print(\"Ground Truth: \", ytest[0:5])"
-   ]
+   ],
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "\u001B[1m1/1\u001B[0m \u001B[32m━━━━━━━━━━━━━━━━━━━━\u001B[0m\u001B[37m\u001B[0m \u001B[1m0s\u001B[0m 107ms/step\n",
+      "\u001B[1m1/1\u001B[0m \u001B[32m━━━━━━━━━━━━━━━━━━━━\u001B[0m\u001B[37m\u001B[0m \u001B[1m0s\u001B[0m 109ms/step\n",
+      "\u001B[1m1/1\u001B[0m \u001B[32m━━━━━━━━━━━━━━━━━━━━\u001B[0m\u001B[37m\u001B[0m \u001B[1m0s\u001B[0m 112ms/step\n",
+      "\u001B[1m1/1\u001B[0m \u001B[32m━━━━━━━━━━━━━━━━━━━━\u001B[0m\u001B[37m\u001B[0m \u001B[1m0s\u001B[0m 114ms/step\n",
+      "\u001B[1m1/1\u001B[0m \u001B[32m━━━━━━━━━━━━━━━━━━━━\u001B[0m\u001B[37m\u001B[0m \u001B[1m0s\u001B[0m 108ms/step\n",
+      "Predictions:  [-0.4258549  -0.0387525  -0.01732254 -0.60533425 -4.51287463]\n",
+      "Ground Truth:  [0.0118838  0.00379507 0.08298755 0.04510921 0.12783075]\n"
+     ]
+    }
+   ],
+   "execution_count": 4
   },
   {
    "attachments": {},
@@ -257,30 +272,19 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 5,
-   "metadata": {},
-   "outputs": [
-    {
-     "name": "stdout",
-     "output_type": "stream",
-     "text": [
-      "2/2 [==============================] - 0s 5ms/step\n",
-      "6/6 [==============================] - 0s 25ms/step\n",
-      "Predictions:  [0 1 0 0 0]\n",
-      "Ground Truth:  ['0' '0' '0' '0' '0']\n",
-      "\n",
-      "6/6 [==============================] - 0s 32ms/step\n",
-      "Score :  -701.7947322426963\n"
-     ]
+   "metadata": {
+    "ExecuteTime": {
+     "end_time": "2024-11-21T11:14:19.577898Z",
+     "start_time": "2024-11-21T11:14:16.120345Z"
     }
-   ],
+   },
    "source": [
     "xtrain, _ = load_classification(name=\"ArrowHead\", split=\"train\")\n",
     "xtest, ytest = load_classification(name=\"ArrowHead\", split=\"test\")\n",
     "\n",
     "aefcn = AEFCNClusterer(\n",
     "    temporal_latent_space=False,\n",
-    "    estimator=\"kmeans\",\n",
+    "    estimator=KMeans(n_clusters=3),\n",
     "    n_epochs=10,\n",
     ")\n",
     "\n",
@@ -288,7 +292,20 @@
     "ypred = aefcn.predict(X=xtest)\n",
     "print(\"Predictions: \", ypred[0:5])\n",
     "print(\"Ground Truth: \", ytest[0:5])"
-   ]
+   ],
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "\u001B[1m2/2\u001B[0m \u001B[32m━━━━━━━━━━━━━━━━━━━━\u001B[0m\u001B[37m\u001B[0m \u001B[1m0s\u001B[0m 27ms/step\n",
+      "\u001B[1m6/6\u001B[0m \u001B[32m━━━━━━━━━━━━━━━━━━━━\u001B[0m\u001B[37m\u001B[0m \u001B[1m0s\u001B[0m 8ms/step \n",
+      "Predictions:  [2 0 2 2 2]\n",
+      "Ground Truth:  ['0' '0' '0' '0' '0']\n"
+     ]
+    }
+   ],
+   "execution_count": 8
   },
   {
    "attachments": {},
@@ -727,6 +744,13 @@
     "- [7] Lafabregue, Baptiste, et al. \"End-to-end deep representation learning for time series clustering:\n",
     "    a comparative study.\" Data Mining and Knowledge Discovery 36.1 (2022): 29-81."
    ]
+  },
+  {
+   "metadata": {},
+   "cell_type": "code",
+   "outputs": [],
+   "execution_count": null,
+   "source": ""
   }
  ],
  "metadata": {