Improved testing

Author: lucasimi

src/tdamapper/core.py

@@ -377,7 +377,8 @@ def transform(self, x_arr: ArrayLike) -> Generator[List[int], None, None]:
         :param x_arr: A dataset of n points.
         :yield: A generator yielding a single list of indices.
         """
-        yield list(range(len(x_arr)))
+        if len(x_arr) > 0:
+            yield list(range(len(x_arr)))
 
     def fit_transform(self, x_arr: ArrayLike) -> Generator[List[int], None, None]:
         """

tests/test_unit_cover.py

@@ -4,18 +4,31 @@
 from tdamapper.cover import BallCover, CubicalCover, KNNCover
 
 
-def dataset(dim=1, num=10000):
-    return [np.random.rand(dim) for _ in range(num)]
+def test_trivial_cover_empty():
+    data = []
+    cover = TrivialCover()
+    cover.fit(data)
+    charts = list(cover.transform(data))
+    assert 0 == len(charts)
 
 
-def test_trivial_cover():
-    data = dataset()
+def test_trivial_cover_ok():
+    data = np.array([[0.0, 1.0], [1.0, 0.0], [0.0, 0.0], [1.0, 1.0]])
     cover = TrivialCover()
+    cover.fit(data)
     charts = list(cover.transform(data))
     assert 1 == len(charts)
 
 
-def test_ball_cover_simple():
+def test_ball_cover_empty():
+    data = []
+    cover = BallCover(radius=1.0, metric="euclidean")
+    cover.fit(data)
+    charts = list(cover.transform(data))
+    assert 0 == len(charts)
+
+
+def test_ball_cover_ok():
     data = [
         np.array([0.0, 1.0]),
         np.array([1.0, 0.0]),
@@ -28,7 +41,22 @@ def test_ball_cover_simple():
     assert 2 == len(charts)
 
 
-def test_knn_cover_simple():
+def test_ball_cover_params():
+    cover = BallCover(radius=1.0, metric="euclidean")
+    params = cover.get_params(deep=True)
+    assert 1.0 == params["radius"]
+    assert "euclidean" == params["metric"]
+
+
+def test_knn_cover_empty():
+    data = []
+    cover = KNNCover(neighbors=2, metric="euclidean")
+    cover.fit(data)
+    charts = list(cover.transform(data))
+    assert 0 == len(charts)
+
+
+def test_knn_cover_ok():
     data = [
         np.array([0.0, 1.0]),
         np.array([1.1, 0.0]),
@@ -41,7 +69,22 @@ def test_knn_cover_simple():
     assert 2 == len(charts)
 
 
-def test_cubical_cover_simple():
+def test_knn_cover_params():
+    cover = KNNCover(neighbors=2, metric="euclidean")
+    params = cover.get_params(deep=True)
+    assert 2 == params["neighbors"]
+    assert "euclidean" == params["metric"]
+
+
+def test_cubical_cover_empty():
+    data = []
+    cover = CubicalCover(n_intervals=2, overlap_frac=0.5)
+    cover.fit(data)
+    charts = list(cover.transform(data))
+    assert 0 == len(charts)
+
+
+def test_cubical_cover_ok():
     data = [
         np.array([0.0, 1.0]),
         np.array([1.1, 0.0]),
@@ -54,14 +97,25 @@ def test_cubical_cover_simple():
     assert 4 == len(charts)
 
 
-def test_params():
+def test_cubical_cover_params():
     cover = CubicalCover(n_intervals=2, overlap_frac=0.5)
     params = cover.get_params(deep=True)
     assert 2 == params["n_intervals"]
     assert 0.5 == params["overlap_frac"]
 
 
-def test_standard_cover_simple():
+def test_standard_cover_empty():
+    data = []
+    cover = CubicalCover(
+        n_intervals=2,
+        overlap_frac=0.5,
+    )
+    cover.fit(data)
+    charts = list(cover.transform(data))
+    assert 0 == len(charts)
+
+
+def test_standard_cover_ok():
     data = [
         np.array([0.0, 1.0]),
         np.array([1.1, 0.0]),
@@ -76,3 +130,15 @@ def test_standard_cover_simple():
     cover.fit(data)
     charts = list(cover.transform(data))
     assert 4 == len(charts)
+
+
+def test_standard_cover_params():
+    cover = CubicalCover(
+        n_intervals=2,
+        overlap_frac=0.5,
+        algorithm="standard",
+    )
+    params = cover.get_params(deep=True)
+    assert 2 == params["n_intervals"]
+    assert 0.5 == params["overlap_frac"]
+    assert "standard" == params["algorithm"]