Merge pull request #252 from eonu/release/2.0.2

release: v2.0.2
eonu · Apr 13, 2024 · b5a4b0f · b5a4b0f
2 parents 5062288 + fe7f488
commit b5a4b0f
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diff --git a/CHANGELOG.md b/CHANGELOG.md
@@ -388,6 +388,12 @@ Nothing, initial release!
 
 </details>
 
+## [v2.0.2](https://github.com/eonu/sequentia/releases/tag/v2.0.2) - 2024-04-13
+
+### Bug Fixes
+
+- call `KNNMixin._dtw1d` when `independent=True` ([#251](https://github.com/eonu/sequentia/issues/251))
+
 ## [v2.0.1](https://github.com/eonu/sequentia/releases/tag/v2.0.1) - 2024-04-02
 
 ### Bug Fixes

diff --git a/docs/source/conf.py b/docs/source/conf.py
@@ -23,7 +23,7 @@
 project = "sequentia"
 copyright = "2019-2025, Sequentia Developers"  # noqa: A001
 author = "Edwin Onuonga (eonu)"
-release = "2.0.1"
+release = "2.0.2"
 
 # -- General configuration ---------------------------------------------------
 

diff --git a/pyproject.toml b/pyproject.toml
@@ -1,6 +1,6 @@
 [tool.poetry]
 name = "sequentia"
-version = "2.0.1"
+version = "2.0.2"
 license = "MIT"
 authors = ["Edwin Onuonga <ed@eonu.net>"]
 maintainers = ["Edwin Onuonga <ed@eonu.net>"]

diff --git 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/sequentia/version.py b/sequentia/version.py
@@ -33,7 +33,7 @@
 
 __all__ = ["VERSION", "version_info"]
 
-VERSION = "2.0.1"
+VERSION = "2.0.2"
 
 
 def version_info() -> str:

diff --git 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