[MNT] Distance module refactor part 2

aeon/classification/distance_based/_proximity_tree.py

@@ -235,7 +235,7 @@ def _get_best_splitter(self, X, y):
                         X[j],
                         splitter[0][labels[k]],
                         metric=measure,
-                        kwargs=splitter[1][measure],
+                        **splitter[1][measure],
                     )
                     if dist < min_dist:
                         min_dist = dist
@@ -321,7 +321,7 @@ def _build_tree(self, X, y, depth, node_id, parent_target_value=None):
                     X[i],
                     splitter[0][labels[j]],
                     metric=measure,
-                    kwargs=splitter[1][measure],
+                    **splitter[1][measure],
                 )
                 if dist < min_dist:
                     min_dist = dist
@@ -405,7 +405,7 @@ def _classify(self, treenode, x):
                     x,
                     treenode.splitter[0][branches[i]],
                     metric=measure,
-                    kwargs=treenode.splitter[1][measure],
+                    **treenode.splitter[1][measure],
                 )
                 if dist < min_dist:
                     min_dist = dist

aeon/clustering/tests/test_elastic_som.py

@@ -5,7 +5,7 @@
 
 from aeon.clustering import ElasticSOM
 from aeon.distances import dtw_distance, msm_alignment_path
-from aeon.distances._distance import DISTANCES
+from aeon.distances._distance import ELASTIC_DISTANCES
 from aeon.testing.data_generation import make_example_3d_numpy
 
 
@@ -245,7 +245,7 @@ def custom_neighborhood_function(neuron_position, c, sigma):
         clst.fit(X)
 
 
-@pytest.mark.parametrize("dist", DISTANCES)
+@pytest.mark.parametrize("dist", ELASTIC_DISTANCES)
 def test_elastic_som_distances(dist):
     """Test ElasticSOM distances."""
     if "distance" not in dist:

aeon/distances/__init__.py

@@ -65,12 +65,12 @@
     "shape_dtw_pairwise_distance",
     "sbd_distance",
     "sbd_pairwise_distance",
-    "mpdist",
-    "mpdist_pairwise_distance",
-    "paa_sax_mindist",
-    "sax_mindist",
-    "sfa_mindist",
-    "dft_sfa_mindist",
+    "mp_distance",
+    "mp_pairwise_distance",
+    "mindist_paa_sax_distance",
+    "mindist_sax_distance",
+    "mindist_sfa_distance",
+    "mindist_dft_sfa_distance",
     "shift_scale_invariant_distance",
     "shift_scale_invariant_pairwise_distance",
     "shift_scale_invariant_best_shift",
@@ -80,7 +80,6 @@
     "soft_dtw_cost_matrix",
 ]
 
-from aeon.distances._dft_sfa_mindist import dft_sfa_mindist
 from aeon.distances._distance import (
     alignment_path,
     cost_matrix,
@@ -92,20 +91,13 @@
     get_pairwise_distance_function,
     pairwise_distance,
 )
-from aeon.distances._euclidean import euclidean_distance, euclidean_pairwise_distance
-from aeon.distances._manhattan import manhattan_distance, manhattan_pairwise_distance
-from aeon.distances._minkowski import minkowski_distance, minkowski_pairwise_distance
-from aeon.distances._mpdist import mpdist, mpdist_pairwise_distance
-from aeon.distances._paa_sax_mindist import paa_sax_mindist
-from aeon.distances._sax_mindist import sax_mindist
+from aeon.distances._mpdist import mp_distance, mp_pairwise_distance
 from aeon.distances._sbd import sbd_distance, sbd_pairwise_distance
-from aeon.distances._sfa_mindist import sfa_mindist
 from aeon.distances._shift_scale_invariant import (
     shift_scale_invariant_best_shift,
     shift_scale_invariant_distance,
     shift_scale_invariant_pairwise_distance,
 )
-from aeon.distances._squared import squared_distance, squared_pairwise_distance
 from aeon.distances.elastic import (
     adtw_alignment_path,
     adtw_cost_matrix,
@@ -157,3 +149,23 @@
     wdtw_distance,
     wdtw_pairwise_distance,
 )
+from aeon.distances.mindist._dft_sfa import mindist_dft_sfa_distance
+from aeon.distances.mindist._paa_sax import mindist_paa_sax_distance
+from aeon.distances.mindist._sax import mindist_sax_distance
+from aeon.distances.mindist._sfa import mindist_sfa_distance
+from aeon.distances.pointwise._euclidean import (
+    euclidean_distance,
+    euclidean_pairwise_distance,
+)
+from aeon.distances.pointwise._manhattan import (
+    manhattan_distance,
+    manhattan_pairwise_distance,
+)
+from aeon.distances.pointwise._minkowski import (
+    minkowski_distance,
+    minkowski_pairwise_distance,
+)
+from aeon.distances.pointwise._squared import (
+    squared_distance,
+    squared_pairwise_distance,
+)