git import in doctests of sliced

Author: lchapel

examples/sliced-wasserstein/plot_sliced_plans.py

@@ -5,8 +5,8 @@
 ===============
 
 Compares different Sliced OT plans between two 2D point clouds. The min-Pivot
-Sliced plan was introduced in [82], and the Expected Sliced plan in [84], both
-were further studied theoretically in [83].
+Sliced plan was introduced in [83], and the Expected Sliced plan in [85], both
+were further studied theoretically in [84].
 
 .. [83] Mahey, G., Chapel, L., Gasso, G., Bonet, C., & Courty, N. (2023). Fast Optimal Transport through Sliced Generalized Wasserstein Geodesics. Advances in Neural Information Processing Systems, 36, 35350–35385.
 

ot/sliced/_sliced_distances.py

@@ -68,9 +68,10 @@ def sliced_wasserstein_distance(
     Examples
     --------
 
+    >>> import ot
     >>> n_samples_a = 20
     >>> X = np.random.normal(0., 1., (n_samples_a, 5))
-    >>> sliced_wasserstein_distance(X, X, seed=0)  # doctest: +NORMALIZE_WHITESPACE
+    >>> ot.sliced_wasserstein_distance(X, X, seed=0)  # doctest: +NORMALIZE_WHITESPACE
     0.0
 
     References
@@ -174,9 +175,10 @@ def max_sliced_wasserstein_distance(
     Examples
     --------
 
+    >>> import ot
     >>> n_samples_a = 20
     >>> X = np.random.normal(0., 1., (n_samples_a, 5))
-    >>> sliced_wasserstein_distance(X, X, seed=0)  # doctest: +NORMALIZE_WHITESPACE
+    >>> ot.sliced_wasserstein_distance(X, X, seed=0)  # doctest: +NORMALIZE_WHITESPACE
     0.0
 
     References

ot/sliced/_sliced_plans.py

@@ -249,10 +249,11 @@ def min_pivot_sliced(
 
     Examples
     --------
+    >>> import ot
     >>> x=np.array([[3.,3.], [1.,1.]])
     >>> y=np.array([[2.,2.5], [3.,2.]])
     >>> thetas=np.array([[1, 0], [0, 1]])
-    >>> plan, cost = min_pivot_sliced(x, y, thetas=thetas)
+    >>> plan, cost = ot.min_pivot_sliced(x, y, thetas=thetas)
     >>> plan
     array([[0. , 0.5],
            [0.5, 0. ]])
@@ -409,10 +410,11 @@ def expected_sliced(
 
     Examples
     --------
+    >>> import ot
     >>> x=np.array([[3.,3.], [1.,1.]])
     >>> y=np.array([[2.,2.5], [3.,2.]])
     >>> thetas=np.array([[1, 0], [0, 1]])
-    >>> plan, cost = expected_sliced(x, y, thetas=thetas)
+    >>> plan, cost = ot.expected_sliced(x, y, thetas=thetas)
     >>> plan
     array([[0.25, 0.25],
            [0.25, 0.25]])

ot/sliced/_spherical_sliced.py

@@ -70,10 +70,11 @@ def sliced_wasserstein_sphere(
 
     Examples
     --------
+    >>> import ot
     >>> n_samples_a = 20
     >>> X = np.random.normal(0., 1., (n_samples_a, 5))
     >>> X = X / np.sqrt(np.sum(X**2, -1, keepdims=True))
-    >>> sliced_wasserstein_sphere(X, X, seed=0)  # doctest: +NORMALIZE_WHITESPACE
+    >>> ot.sliced_wasserstein_sphere(X, X, seed=0)  # doctest: +NORMALIZE_WHITESPACE
     0.0
 
     References
@@ -155,11 +156,12 @@ def sliced_wasserstein_sphere_unif(
 
     Examples
     ---------
+    >>> import ot
     >>> np.random.seed(42)
     >>> x0 = np.random.randn(500,3)
     >>> x0 = x0 / np.sqrt(np.sum(x0**2, -1, keepdims=True))
-    >>> ssw = sliced_wasserstein_sphere_unif(x0, seed=42)
-    >>> np.allclose(sliced_wasserstein_sphere_unif(x0, seed=42), 0.01734, atol=1e-3)
+    >>> ssw = ot.sliced_wasserstein_sphere_unif(x0, seed=42)
+    >>> np.allclose(ot.sliced_wasserstein_sphere_unif(x0, seed=42), 0.01734, atol=1e-3)
     True
 
     References:
@@ -240,10 +242,11 @@ def linear_sliced_wasserstein_sphere(
 
     Examples
     ---------
+    >>> import ot
     >>> n_samples_a = 20
     >>> X = np.random.normal(0., 1., (n_samples_a, 5))
     >>> X = X / np.sqrt(np.sum(X**2, -1, keepdims=True))
-    >>> linear_sliced_wasserstein_sphere(X, X, seed=0)  # doctest: +NORMALIZE_WHITESPACE
+    >>> ot.linear_sliced_wasserstein_sphere(X, X, seed=0)  # doctest: +NORMALIZE_WHITESPACE
     0.0