no rng warning

Author: flying-sheep

src/scanpy/preprocessing/_pca/__init__.py

@@ -11,11 +11,7 @@
 from ..._docs import doc_rng
 from ..._settings import settings
 from ..._utils import _doc_params, _empty, get_literal_vals, is_backed_type
-from ..._utils.random import (
-    _accepts_legacy_random_state,
-    _legacy_random_state,
-    _LegacyRng,
-)
+from ..._utils.random import _accepts_legacy_random_state, _legacy_random_state
 from ...get import _check_mask, _get_obs_rep
 from .._docs import doc_mask_var_hvg
 from ._compat import _pca_compat_sparse
@@ -207,7 +203,6 @@ def pca(  # noqa: PLR0912, PLR0913, PLR0915
     """
     logg_start = logg.info("computing PCA")
     rng = np.random.default_rng(rng)
-    rng_is_default = isinstance(rng, _LegacyRng) and rng.arg == 0
     if (layer is not None or obsm is not None) and chunked:
         # Current chunking implementation relies on pca being called on X
         msg = "Cannot use `layer`/`obsm` and `chunked` at the same time."
@@ -247,7 +242,7 @@ def pca(  # noqa: PLR0912, PLR0913, PLR0915
         raise NotImplementedError(msg)
 
     if chunked:
-        if not zero_center or not rng_is_default or svd_solver not in {None, "arpack"}:
+        if not zero_center or svd_solver not in {None, "arpack"}:
             logg.debug("Ignoring zero_center, rng, svd_solver")
 
         incremental_pca_kwargs = dict()
@@ -296,14 +291,6 @@ def pca(  # noqa: PLR0912, PLR0913, PLR0915
             elif isinstance(x._meta, CSBase) or svd_solver == "covariance_eigh":
                 from ._dask import PCAEighDask
 
-                if not rng_is_default:
-                    dbg = (
-                        f"random_state={_legacy_random_state(rng)!r}"
-                        if isinstance(rng, _LegacyRng)
-                        else f"rng={rng!r}"
-                    )
-                    msg = f"Ignoring {dbg} when using a sparse dask array"
-                    warn(msg, UserWarning)
                 if svd_solver not in {None, "covariance_eigh"}:
                     msg = f"Ignoring {svd_solver=} when using a sparse dask array"
                     warn(msg, UserWarning)

tests/test_pca.py

@@ -1,7 +1,7 @@
 from __future__ import annotations
 
 import warnings
-from contextlib import ExitStack, nullcontext
+from contextlib import nullcontext
 from typing import TYPE_CHECKING, Literal
 
 import numpy as np
@@ -242,19 +242,17 @@ def test_pca_transform_randomized(array_type):
     a_pca_abs = np.abs(A_pca)
 
     if isinstance(adata.X, DaskArray) and isinstance(adata.X._meta, CSBase):
-        patterns = (
-            r"Ignoring random_state=14 when using a sparse dask array",
-            r"Ignoring svd_solver='randomized' when using a sparse dask array",
+        ctx = pytest.warns(
+            UserWarning,
+            match=r"Ignoring svd_solver='randomized' when using a sparse dask array",
         )
     elif isinstance(adata.X, CSBase):
-        patterns = [r"Ignoring.*'randomized"]
+        ctx = pytest.warns(UserWarning, match=r"Ignoring.*'randomized")
     else:
-        patterns = []
+        ctx = nullcontext()
 
     warnings.filterwarnings("error")
-    with ExitStack() as stack:
-        for pat in patterns:
-            stack.enter_context(pytest.warns(UserWarning, match=pat))
+    with ctx:
         sc.pp.pca(
             adata,
             n_comps=4,
@@ -339,15 +337,10 @@ def test_pca_reproducible(
     pbmc = pbmc3k_normalized()
     pbmc.X = array_type(pbmc.X)
 
-    with (
-        pytest.warns(UserWarning, match=rf"Ignoring {rng_arg}=.*sparse dask array")
-        if isinstance(pbmc.X, DaskArray) and isinstance(pbmc.X._meta, CSBase)
-        else nullcontext()
-    ):
-        a, b, c = (
-            sc.pp.pca(pbmc, copy=True, dtype=np.float64, **{rng_arg: seed})
-            for seed in (42, 42, 0)
-        )
+    a, b, c = (
+        sc.pp.pca(pbmc, copy=True, dtype=np.float64, **{rng_arg: seed})
+        for seed in (42, 42, 0)
+    )
 
     with subtests.test("reproducible"):
         assert_equal(a, b)