From daf5d705492126160b2a920e7b691595c784cfb8 Mon Sep 17 00:00:00 2001
From: "Edwin (Ed) Onuonga" <edwinonuonga@gmail.com>
Date: Sat, 13 Apr 2024 17:12:28 +0100
Subject: [PATCH] fix: call `KNNMixin._dtw1d` when `independent=True` (#251)

---
 sequentia/models/knn/base.py                  |  2 +-
 tests/unit/test_models/knn/test_classifier.py | 20 ++++++++++++++++---
 2 files changed, 18 insertions(+), 4 deletions(-)

diff --git a/sequentia/models/knn/base.py b/sequentia/models/knn/base.py
index f60687c..d2d91e9 100644
--- a/sequentia/models/knn/base.py
+++ b/sequentia/models/knn/base.py
@@ -206,7 +206,7 @@ def _dtwi(self: KNNMixin, A: FloatArray, B: FloatArray) -> float:
 
         def dtw(a: FloatArray, b: FloatArray) -> float:
             """Windowed DTW wrapper function."""
-            return self._dtw(a, b, window=window)
+            return self._dtw1d(a, b, window=window)
 
         return np.sum([dtw(A[:, i], B[:, i]) for i in range(A.shape[1])])
 
diff --git a/tests/unit/test_models/knn/test_classifier.py b/tests/unit/test_models/knn/test_classifier.py
index ce3b0e8..15f4544 100644
--- a/tests/unit/test_models/knn/test_classifier.py
+++ b/tests/unit/test_models/knn/test_classifier.py
@@ -46,18 +46,32 @@ def assert_fit(clf: KNNClassifier, /, *, data: SequentialDataset) -> None:
 
 @pytest.mark.parametrize("k", [1, 2, 5])
 @pytest.mark.parametrize("weighting", [None, lambda x: np.exp(-x)])
+@pytest.mark.parametrize("independent", [False, True])
 def test_classifier_e2e(
     helpers: t.Any,
     request: SubRequest,
-    k: int,
-    weighting: t.Callable | None,
     dataset: SequentialDataset,
     random_state: np.random.RandomState,
+    *,
+    k: int,
+    weighting: t.Callable | None,
+    independent: bool,
 ) -> None:
-    clf = KNNClassifier(k=k, weighting=weighting, random_state=random_state)
+    clf = KNNClassifier(
+        k=k,
+        weighting=weighting,
+        independent=independent,
+        random_state=random_state,
+    )
 
     assert clf.k == k
     assert clf.weighting == weighting
+    assert clf.independent == independent
+
+    if independent:
+        assert clf._dtw().__name__ == "_dtwi"
+    else:
+        assert clf._dtw().__name__ == "_dtwd"
 
     data = dataset.copy()
     data._X = data._X[:, :1]  # only use one feature