[MNT] Distance module changed to method rather than metric

aeon/classification/distance_based/_proximity_tree.py

@@ -234,7 +234,7 @@ def _get_best_splitter(self, X, y):
                     dist = distance(
                         X[j],
                         splitter[0][labels[k]],
-                        measure=measure,
+                        method=measure,
                         **splitter[1][measure],
                     )
                     if dist < min_dist:
@@ -320,7 +320,7 @@ def _build_tree(self, X, y, depth, node_id, parent_target_value=None):
                 dist = distance(
                     X[i],
                     splitter[0][labels[j]],
-                    measure=measure,
+                    method=measure,
                     **splitter[1][measure],
                 )
                 if dist < min_dist:
@@ -404,7 +404,7 @@ def _classify(self, treenode, x):
                 dist = distance(
                     x,
                     treenode.splitter[0][branches[i]],
-                    measure=measure,
+                    method=measure,
                     **treenode.splitter[1][measure],
                 )
                 if dist < min_dist:

aeon/classification/distance_based/_time_series_neighbors.py

@@ -111,7 +111,7 @@ def _fit(self, X, y):
         y : array-like, shape = (n_cases)
             The class labels.
         """
-        self.metric_ = get_distance_function(measure=self.distance)
+        self.metric_ = get_distance_function(method=self.distance)
         self.X_ = X
         self.classes_, self.y_ = np.unique(y, return_inverse=True)
         return self

aeon/clustering/_clara.py

@@ -42,7 +42,7 @@ class TimeSeriesCLARA(BaseClusterer):
         If a np.ndarray provided it must be of shape (n_clusters,) and contain
         the indexes of the time series to use as centroids.
     distance : str or Callable, default='msm'
-        Distance measure to compute similarity between time series. A list of valid
+        Distance method to compute similarity between time series. A list of valid
         strings for metrics can be found in the documentation for
         :func:`aeon.distances.get_distance_function`. If a callable is passed it must be
         a function that takes two 2d numpy arrays as input and returns a float.
@@ -73,7 +73,7 @@ class TimeSeriesCLARA(BaseClusterer):
         If `None`, the random number generator is the `RandomState` instance used
         by `np.random`.
     distance_params : dict, default=None
-        Dictionary containing kwargs for the distance measure being used.
+        Dictionary containing kwargs for the distance method being used.
 
     Attributes
     ----------
@@ -189,7 +189,7 @@ def _fit(self, X: np.ndarray, y=None):
             curr_centers = pam.cluster_centers_
             if isinstance(pam.distance, str):
                 pairwise_matrix = pairwise_distance(
-                    X, curr_centers, measure=self.distance, **pam._distance_params
+                    X, curr_centers, method=self.distance, **pam._distance_params
                 )
             else:
                 pairwise_matrix = pairwise_distance(

aeon/clustering/_clarans.py

@@ -43,7 +43,7 @@ class TimeSeriesCLARANS(TimeSeriesKMedoids):
         If a np.ndarray provided it must be of shape (n_clusters,) and contain
         the indexes of the time series to use as centroids.
     distance : str or Callable, default='msm'
-        Distance measure to compute similarity between time series. A list of valid
+        Distance method to compute similarity between time series. A list of valid
         strings for measures can be found in the documentation for
         :func:`aeon.distances.get_distance_function`. If a callable is passed it must be
         a function that takes two 2d numpy arrays as input and returns a float.
@@ -62,7 +62,7 @@ class TimeSeriesCLARANS(TimeSeriesKMedoids):
     random_state : int or np.random.RandomState instance or None, default=None
         Determines random number generation for centroid initialization.
     distance_params : dict, default=None
-        Dictionary containing kwargs for the distance measure being used.
+        Dictionary containing kwargs for the distance method being used.
 
     Attributes
     ----------

aeon/clustering/_elastic_som.py

@@ -44,7 +44,7 @@ class ElasticSOM(BaseClusterer):
     n_clusters : int, default=8
         The number of clusters to form as well as the number of centroids to generate.
     distance : str or Callable, default='dtw'
-        Distance measure to compute similarity between time series. A list of valid
+        Distance method to compute similarity between time series. A list of valid
         strings for measures can be found in the documentation for
         :func:`aeon.distances.get_distance_function`. If a callable is passed it must be
         a function that takes two 2d numpy arrays as input and returns a float.
@@ -105,7 +105,7 @@ class ElasticSOM(BaseClusterer):
         by `np.random`.
     custom_alignment_path : Callable, default=None
         Custom alignment path function to use for the distance. If None, the default
-        alignment path function for the distance will be used. If the distance measure
+        alignment path function for the distance will be used. If the distance method
         does not have an elastic alignment path then the default SOM einsum update will
         be used. See aeon.clustering.elastic_som.VALID_ELASTIC_SOM_METRICS for a list of
         distances that have an elastic alignment path.
@@ -224,7 +224,7 @@ def _find_bmu(self, x, weights):
         pairwise_matrix = pairwise_distance(
             x,
             weights,
-            measure=self.distance,
+            method=self.distance,
             **self._distance_params,
         )
         return pairwise_matrix.argmin(axis=1)
@@ -366,7 +366,7 @@ def _kmeans_plus_plus_center_initializer(self, X: np.ndarray):
 
         for _ in range(1, self.n_clusters):
             pw_dist = pairwise_distance(
-                X, X[indexes], measure=self.distance, **self._distance_params
+                X, X[indexes], method=self.distance, **self._distance_params
             )
             min_distances = pw_dist.min(axis=1)
             probabilities = min_distances / min_distances.sum()

aeon/clustering/_k_means.py

@@ -54,7 +54,7 @@ class TimeSeriesKMeans(BaseClusterer):
         n_timepoints)
         and contains the time series to use as centroids.
     distance : str or Callable, default='msm'
-        Distance measure to compute similarity between time series. A list of valid
+        Distance method to compute similarity between time series. A list of valid
         strings for measures can be found in the documentation for
         :func:`aeon.distances.get_distance_function`. If a callable is passed it must be
         a function that takes two 2d numpy arrays as input and returns a float.
@@ -82,9 +82,9 @@ class TimeSeriesKMeans(BaseClusterer):
         Averaging method to compute the average of a cluster. Any of the following
         strings are valid: ['mean', 'ba']. If a Callable is provided must take the form
         Callable[[np.ndarray], np.ndarray].
-        If you specify 'ba' then by default the distance measure used will be the same
-        as the distance measure used for clustering. If you wish to use a different
-        distance measure you can specify it by passing {"distance": "dtw"} as
+        If you specify 'ba' then by default the distance method used will be the same
+        as the distance method used for clustering. If you wish to use a different
+        distance method you can specify it by passing {"distance": "dtw"} as
         averaging_params. BA yields 'better' clustering results but is very
         computationally expensive so you may want to consider setting a bounding window
         or using a different averaging method if time complexity is a concern.
@@ -236,7 +236,7 @@ def _fit_one_init(self, X: np.ndarray) -> tuple:
         prev_labels = None
         for i in range(self.max_iter):
             curr_pw = pairwise_distance(
-                X, cluster_centres, measure=self.distance, **self._distance_params
+                X, cluster_centres, method=self.distance, **self._distance_params
             )
             curr_labels = curr_pw.argmin(axis=1)
             curr_inertia = curr_pw.min(axis=1).sum()
@@ -273,13 +273,13 @@ def _fit_one_init(self, X: np.ndarray) -> tuple:
     def _predict(self, X: np.ndarray, y=None) -> np.ndarray:
         if isinstance(self.distance, str):
             pairwise_matrix = pairwise_distance(
-                X, self.cluster_centers_, measure=self.distance, **self._distance_params
+                X, self.cluster_centers_, method=self.distance, **self._distance_params
             )
         else:
             pairwise_matrix = pairwise_distance(
                 X,
                 self.cluster_centers_,
-                measure=self.distance,
+                method=self.distance,
                 **self._distance_params,
             )
         return pairwise_matrix.argmin(axis=1)
@@ -346,7 +346,7 @@ def _kmeans_plus_plus_center_initializer(self, X: np.ndarray):
 
         for _ in range(1, self.n_clusters):
             pw_dist = pairwise_distance(
-                X, X[indexes], measure=self.distance, **self._distance_params
+                X, X[indexes], method=self.distance, **self._distance_params
             )
             min_distances = pw_dist.min(axis=1)
             probabilities = min_distances / min_distances.sum()
@@ -381,7 +381,7 @@ def _handle_empty_cluster(
             index_furthest_from_centre = curr_pw.min(axis=1).argmax()
             cluster_centres[current_empty_cluster_index] = X[index_furthest_from_centre]
             curr_pw = pairwise_distance(
-                X, cluster_centres, measure=self.distance, **self._distance_params
+                X, cluster_centres, method=self.distance, **self._distance_params
             )
             curr_labels = curr_pw.argmin(axis=1)
             curr_inertia = curr_pw.min(axis=1).sum()

aeon/clustering/_k_medoids.py

@@ -36,7 +36,7 @@ class TimeSeriesKMedoids(BaseClusterer):
     accurate than PAM. For a full review of varations of k-medoids for time series
     see [5]_.
 
-    K-medoids for time series uses a dissimilarity measure to compute the distance
+    K-medoids for time series uses a dissimilarity method to compute the distance
     between time series. The default is 'msm' (move split merge) as
     it was found to significantly outperform the other measures in [2]_.
 
@@ -56,7 +56,7 @@ class TimeSeriesKMedoids(BaseClusterer):
         If a np.ndarray provided it must be of shape (n_clusters,) and contain
         the indexes of the time series to use as centroids.
     distance : str or Callable, default='msm'
-        Distance measure to compute similarity between time series. A list of valid
+        Distance method to compute similarity between time series. A list of valid
         strings for measures can be found in the documentation for
         :func:`aeon.distances.get_distance_function`. If a callable is passed it must be
         a function that takes two 2d numpy arrays as input and returns a float.
@@ -88,7 +88,7 @@ class TimeSeriesKMedoids(BaseClusterer):
         If `None`, the random number generator is the `RandomState` instance used
         by `np.random`.
     distance_params: dict, default=None
-        Dictionary containing kwargs for the distance measure being used.
+        Dictionary containing kwargs for the distance method being used.
 
     Attributes
     ----------
@@ -211,7 +211,7 @@ def _fit(self, X: np.ndarray, y=None):
     def _predict(self, X: np.ndarray, y=None) -> np.ndarray:
         if isinstance(self.distance, str):
             pairwise_matrix = pairwise_distance(
-                X, self.cluster_centers_, measure=self.distance, **self._distance_params
+                X, self.cluster_centers_, method=self.distance, **self._distance_params
             )
         else:
             pairwise_matrix = pairwise_distance(
@@ -456,7 +456,7 @@ def _check_params(self, X: np.ndarray) -> None:
                 f"n_clusters ({self.n_clusters}) cannot be larger than "
                 f"n_cases ({X.shape[0]})"
             )
-        self._distance_callable = get_distance_function(measure=self.distance)
+        self._distance_callable = get_distance_function(method=self.distance)
         self._distance_cache = np.full((X.shape[0], X.shape[0]), np.inf)
 
         if self.method == "alternate":
@@ -486,7 +486,7 @@ def _kmedoids_plus_plus_center_initializer(self, X: np.ndarray):
 
         for _ in range(1, self.n_clusters):
             pw_dist = pairwise_distance(
-                X, X[indexes], measure=self.distance, **self._distance_params
+                X, X[indexes], method=self.distance, **self._distance_params
             )
             min_distances = pw_dist.min(axis=1)
             probabilities = min_distances / min_distances.sum()

aeon/clustering/averaging/_ba_petitjean.py

@@ -55,7 +55,7 @@ def petitjean_barycenter_average(
     random_state: int or None, default=None
         Random state to use for the barycenter averaging.
     **kwargs
-        Keyword arguments to pass to the distance measure.
+        Keyword arguments to pass to the distance method.
 
     Returns
     -------

aeon/clustering/averaging/_ba_subgradient.py

@@ -70,7 +70,7 @@ def subgradient_barycenter_average(
     random_state: int or None, default=None
         Random state to use for the barycenter averaging.
     **kwargs
-        Keyword arguments to pass to the distance measure.
+        Keyword arguments to pass to the distance method.
 
     Returns
     -------

aeon/clustering/averaging/_ba_utils.py

@@ -31,7 +31,7 @@ def _medoids(
         return X
 
     if precomputed_pairwise_distance is None:
-        precomputed_pairwise_distance = pairwise_distance(X, measure=distance, **kwargs)
+        precomputed_pairwise_distance = pairwise_distance(X, method=distance, **kwargs)
 
     x_size = X.shape[0]
     distance_matrix = np.zeros((x_size, x_size))
@@ -155,6 +155,6 @@ def _get_alignment_path(
     elif distance == "adtw":
         return adtw_alignment_path(ts, center, window=window, warp_penalty=warp_penalty)
     else:
-        # When numba version > 0.57 add more informative error with what measure
+        # When numba version > 0.57 add more informative error with what method
         # was passed.
         raise ValueError("Distance parameter invalid")

aeon/clustering/averaging/_barycenter_averaging.py

@@ -84,7 +84,7 @@ def elastic_barycenter_average(
     random_state: int or None, default=None
         Random state to use for the barycenter averaging.
     **kwargs
-        Keyword arguments to pass to the distance measure.
+        Keyword arguments to pass to the distance method.
 
     Returns
     -------