Some tests are now faster.

GAA-UAM · Oct 13, 2023 · 9f74e24 · 9f74e24
1 parent 4f0bd4c
commit 9f74e24
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Showing 2 changed files with 8 additions and 8 deletions.
diff --git a/skfda/ml/classification/_logistic_regression.py b/skfda/ml/classification/_logistic_regression.py
@@ -62,8 +62,8 @@ class LogisticRegression(
         >>> from skfda.datasets import make_gaussian_process
         >>> from skfda.ml.classification import LogisticRegression
         >>>
-        >>> n_samples = 10000
-        >>> n_features = 200
+        >>> n_samples = 2000
+        >>> n_features = 101
         >>>
         >>> def mean_1(t):
         ...     return (np.abs(t - 0.25)
@@ -90,7 +90,7 @@ class LogisticRegression(
         >>> np.allclose(sorted(lr.points_), [0.25, 0.5, 0.75], rtol=1e-2)
         True
         >>> lr.score(X[1::2],y[1::2])
-        0.7498
+        0.768
 
         References:
             .. footbibliography::

diff --git a/skfda/tests/test_recursive_maxima_hunting.py b/skfda/tests/test_recursive_maxima_hunting.py
@@ -25,8 +25,8 @@ def test_gaussian_homoscedastic(self) -> None:
         join.
 
         """
-        n_samples = 10000
-        n_features = 100
+        n_samples = 1000
+        n_features = 101
 
         def mean_1(  # noqa: WPS430
             t: np.typing.NDArray[np.float_],
@@ -66,7 +66,7 @@ def mean_1(  # noqa: WPS430
         rmh.fit(X, y)
         point_mask = rmh.get_support()
         points = X.grid_points[0][point_mask]
-        np.testing.assert_allclose(points, [0.25, 0.5, 0.75], rtol=1e-1)
+        np.testing.assert_allclose(points, [0.25, 0.5, 0.75], rtol=1e-2)
 
     @pytest.mark.filterwarnings(
         'ignore::sklearn.exceptions.ConvergenceWarning'
@@ -83,7 +83,7 @@ def test_fit_exponential(self) -> None:
 
         """
         n_samples = 1000
-        n_features = 100
+        n_features = 101
 
         def mean_1(  # noqa: WPS430
             t: np.typing.NDArray[np.float_],
@@ -129,7 +129,7 @@ def mean_1(  # noqa: WPS430
         rmh.fit(X, y)
         point_mask = rmh.get_support()
         points = X.grid_points[0][point_mask]
-        np.testing.assert_allclose(points, [0.25, 0.5, 0.75], rtol=1e-1)
+        np.testing.assert_allclose(points, [0.25, 0.5, 0.75], rtol=1e-2)
 
 
 if __name__ == '__main__':