[BUG] Fixed subsampling in highly imbalances datasets giving subsampl…

…es with only a single class (#2305)

* added argument 'max_subsamples' to subsample multiple times in case of unbalanced datasets giving subsamples with only one class.

* removed parameter max_subsamples and resample until valid subsample is found

---------

Co-authored-by: Ferdinand Rewicki <[email protected]>

aeon/classification/dictionary_based/_tde.py

@@ -320,9 +320,14 @@ def _fit(self, X, y, keep_train_preds=False):
                     rng.choice(np.flatnonzero(preds == preds.max()))
                 )
 
-            subsample = rng.choice(self.n_cases_, size=subsample_size, replace=False)
-            X_subsample = X[subsample]
-            y_subsample = y[subsample]
+            while True:
+                subsample = rng.choice(
+                    self.n_cases_, size=subsample_size, replace=False
+                )
+                X_subsample = X[subsample]
+                y_subsample = y[subsample]
+                if len(np.unique(y_subsample)) > 1:
+                    break
 
             tde = IndividualTDE(
                 *parameters,

aeon/classification/dictionary_based/tests/test_tde.py

@@ -103,3 +103,18 @@ def test_histogram_intersection():
 
     res = histogram_intersection(numba_first, numba_second)
     assert res == 2
+
+
+def test_subsampling_in_highly_imbalanced_datasets():
+    """Test the subsampling during fit for highly imbalanced datasets.
+
+    This test case tests the fix for bug #1726.
+    https://github.com/aeon-toolkit/aeon/issues/1726
+    """
+    X = np.random.rand(10, 1, 20)
+    y_sc = np.array([0, 0, 0, 0, 0, 1, 0, 0, 0, 0])
+
+    tde = TemporalDictionaryEnsemble(random_state=42)
+    tde.fit(X, y_sc)
+
+    assert tde.is_fitted