From b16c7b5c089669de0ef3a55db006d01dab8560bb Mon Sep 17 00:00:00 2001 From: itzzdev09 Date: Thu, 17 Sep 2026 00:55:57 +0530 Subject: [PATCH] Remove a debug print and fix an invalid escape in a docstring --- RELEASES.md | 1 + ot/datasets.py | 2 +- ot/utils.py | 1 - 3 files changed, 2 insertions(+), 2 deletions(-) diff --git a/RELEASES.md b/RELEASES.md index d2b3acdc1..43ac6de4b 100644 --- a/RELEASES.md +++ b/RELEASES.md @@ -12,6 +12,7 @@ #### Closed issues +- Remove a leftover debug `print` from `ot.utils.projection_sparse_simplex` with `axis=1`, and make the `ot.datasets.make_gauss_hd` docstring a raw string so importing `ot` no longer emits a `SyntaxWarning` (PR #860) - Fix swapped arguments to `div_to_product` in `ot.gromov.fused_unbalanced_across_spaces_cost`: with `reg_type="independent"` (UCOOT) the entropic terms used the plan marginals as the reference measures and vice versa (PR #855, Issue #854) - Fix device placement in `ot.batch.bregman_projection_batch` so `ot.solve_batch(..., method="sinkhorn")` no longer crashes on GPU when the torch default device is CPU (PR #851) - Preserve input dtype and device for expected sliced plans, avoid materializing dense distance matrices for sparse plans, and fix weighted sparse-distance ordering (PR #846, Issue #845) diff --git a/ot/datasets.py b/ot/datasets.py index f3d074bc0..8aa65a398 100644 --- a/ot/datasets.py +++ b/ot/datasets.py @@ -186,7 +186,7 @@ def get_data_classif(dataset, n, nz=0.5, theta=0, random_state=None, **kwargs): def make_gauss_hd( ns, nt, p=100, dim=5, m_diff=3.0, a=(10.0, 15.0), b=(3.0, 3.0), sub_the_same=False ): - """Generation of source and target domains from Gaussian HD distributions + r"""Generation of source and target domains from Gaussian HD distributions Parameters ---------- diff --git a/ot/utils.py b/ot/utils.py index bb7dc1884..a8695322f 100644 --- a/ot/utils.py +++ b/ot/utils.py @@ -201,7 +201,6 @@ def projection_sparse_simplex(V, max_nz, z=1, axis=None, nx=None): row_indices = nx.arange(V.shape[0]) row_indices = row_indices.reshape(-1, 1) - print(row_indices.shape) # Extract the top max_nz values for each row # and then project to simplex. U = V[row_indices, max_nz_indices]