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Backport PR #3393 on branch 1.3.x ([pre-commit.ci] pre-commit autoupdate) (#3398)
Backport PR #3393: [pre-commit.ci] pre-commit autoupdate Co-authored-by: pre-commit-ci[bot] <66853113+pre-commit-ci[bot]@users.noreply.github.com>
1 parent 9eea1cc commit 912a832

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+121
-97
lines changed

‎.pre-commit-config.yaml

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)$
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- repo: https://github.com/astral-sh/ruff-pre-commit
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rev: v0.11.13
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rev: v0.12.0
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hooks:
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- id: ruff
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args: [--fix, --exit-non-zero-on-fix]

‎CHANGELOG.md

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@@ -24,8 +24,13 @@ to [Semantic Versioning]. Full commit history is available in the
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#### Changed
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- Update Read the docs tutorials with one main preprocessing tutorial {pr}`3363`.
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#### Removed
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- Removed default arguments from test function parameters due to ruff pre-commit v0.12.0 with
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PT028 rule {pr}`3393`.
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### 1.3.1 (2025-05-15)
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#### Added

‎src/scvi/nn/__init__.py

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@@ -10,7 +10,6 @@
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MultiEncoder,
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)
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from ._embedding import Embedding
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from ._utils import one_hot
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__all__ = [
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"FCLayers",
@@ -22,6 +21,5 @@
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"LinearDecoderSCVI",
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"MultiEncoder",
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"MultiDecoder",
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"one_hot",
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"Embedding",
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]

‎src/scvi/nn/_utils.py

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@@ -1,24 +1,6 @@
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import warnings
2-
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import torch
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from torch import nn
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6-
from scvi import settings
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8-
9-
def one_hot(index: torch.Tensor, n_cat: int) -> torch.Tensor:
10-
"""One hot a tensor of categories."""
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# TODO: remove in 1.3.0
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warnings.warn(
13-
"The `one_hot` function is deprecated in v1.2 and will be removed in v1.3. "
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"Please use the `one_hot` function in PyTorch instead.",
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DeprecationWarning,
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stacklevel=settings.warnings_stacklevel,
17-
)
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onehot = torch.zeros(index.size(0), n_cat, device=index.device)
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onehot.scatter_(1, index.type(torch.long), 1)
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return onehot.type(torch.float32)
21-
224

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class ExpActivation(nn.Module):
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def __init__(self):

‎tests/autotune/test_tune.py

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@@ -43,7 +43,8 @@ def test_run_autotune_scvi_basic(save_path: str):
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@pytest.mark.autotune
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def test_run_autotune_scvi_no_anndata(save_path: str, n_batches: int = 3):
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@pytest.mark.parametrize("n_batches", [3])
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def test_run_autotune_scvi_no_anndata(save_path: str, n_batches: int):
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from ray import tune
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from ray.tune import ResultGrid
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@@ -150,7 +151,8 @@ def test_run_autotune_scvi_with_scib(model_cls, metric: str, save_path: str):
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152153
@pytest.mark.autotune
153-
def test_run_autotune_scvi_with_scib_ext_indices(save_path: str, metric: str = "iLISI"):
154+
@pytest.mark.parametrize("metric", ["iLISI"])
155+
def test_run_autotune_scvi_with_scib_ext_indices(save_path: str, metric: str):
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from ray import tune
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from ray.tune import ResultGrid
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‎tests/criticism/test_criticism.py

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@@ -4,6 +4,7 @@
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import numpy as np
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import pandas as pd
7+
import pytest
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from sparse import GCXS
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from xarray import Dataset
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@@ -101,7 +102,8 @@ def test_ppc_zero_fraction():
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assert ppc.metrics["zero_fraction"].columns.tolist() == ["model1", "model2", "Raw"]
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104-
def test_ppc_de(n_genes: int = 200):
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@pytest.mark.parametrize("n_genes", [200])
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def test_ppc_de(n_genes: int):
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adata = synthetic_iid(n_genes=n_genes)
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ppc, _ = get_ppc_with_samples(adata, n_samples=4)
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‎tests/data/test_anntorchdataset.py

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@@ -73,7 +73,8 @@ def test_getitem_tensors():
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manager.create_torch_dataset(data_and_attributes=1)
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def test_getitem(n_genes: int = 50):
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@pytest.mark.parametrize("n_genes", [50])
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def test_getitem(n_genes: int):
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adata = scvi.data.synthetic_iid(n_genes=n_genes)
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manager = generic_setup_adata_manager(adata, batch_key="batch")
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@pytest.mark.parametrize("sparse_format", ["csr_matrix", "csc_matrix"])
108-
def test_load_sparse_tensor(sparse_format: str | None, n_genes: int = 50):
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@pytest.mark.parametrize("n_genes", [50])
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def test_load_sparse_tensor(sparse_format: str | None, n_genes: int):
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adata = scvi.data.synthetic_iid(sparse_format=sparse_format, n_genes=n_genes)
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manager = generic_setup_adata_manager(adata, batch_key="batch")
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assert data[REGISTRY_KEYS.BATCH_KEY].shape == (1, 1)
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139-
def test_load_sparse_tensor_backed(save_path: str, n_genes: int = 50):
141+
@pytest.mark.parametrize("n_genes", [50])
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def test_load_sparse_tensor_backed(save_path: str, n_genes: int):
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adata = scvi.data.synthetic_iid(sparse_format="csr_matrix", n_genes=n_genes)
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adata_path = os.path.join(save_path, "adata.h5ad")
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adata.write(adata_path)

‎tests/dataloaders/test_dataloaders.py

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@@ -51,19 +51,14 @@ def training_step(self, batch, batch_idx):
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return super().training_step(batch, batch_idx)
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54-
def test_semisuperviseddataloader_subsampling(
55-
batch_size: int = 128,
56-
n_batches: int = 2,
57-
n_labels: int = 3,
58-
n_samples_per_label: int = 10,
59-
):
60-
adata = scvi.data.synthetic_iid(batch_size=batch_size, n_batches=n_batches, n_labels=n_labels)
54+
def test_semisuperviseddataloader_subsampling():
55+
adata = scvi.data.synthetic_iid(batch_size=128, n_batches=2, n_labels=3)
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adata.obs["indices"] = np.arange(adata.n_obs)
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original_training_plan_cls = scvi.model.SCANVI._training_plan_cls
6459
scvi.model.SCANVI._training_plan_cls = TestSemiSupervisedTrainingPlan
6560
plan_kwargs = {
66-
"n_samples_per_label": n_samples_per_label,
61+
"n_samples_per_label": 10,
6762
}
6863
scvi.model.SCANVI.setup_anndata(
6964
adata,
@@ -74,8 +69,8 @@ def test_semisuperviseddataloader_subsampling(
7469
model = scvi.model.SCANVI(adata)
7570
model.train(
7671
max_epochs=10,
77-
batch_size=batch_size // 2,
78-
n_samples_per_label=n_samples_per_label,
72+
batch_size=128 // 2,
73+
n_samples_per_label=10,
7974
plan_kwargs=plan_kwargs,
8075
)
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‎tests/dataloaders/test_samplers.py

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from scvi.dataloaders import BatchDistributedSampler
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@pytest.mark.parametrize("batch_size", [128])
12+
@pytest.mark.parametrize("n_batches", [2])
1113
def test_batchdistributedsampler_init(
12-
batch_size: int = 128,
13-
n_batches: int = 2,
14+
batch_size: int,
15+
n_batches: int,
1416
):
1517
adata = scvi.data.synthetic_iid(batch_size=batch_size, n_batches=n_batches)
1618
manager = generic_setup_adata_manager(adata)
@@ -35,12 +37,15 @@ def test_batchdistributedsampler_init(
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3638
@pytest.mark.parametrize("drop_last", [True, False])
3739
@pytest.mark.parametrize("drop_dataset_tail", [True, False])
40+
@pytest.mark.parametrize("batch_size", [128])
41+
@pytest.mark.parametrize("n_batches", [3])
42+
@pytest.mark.parametrize("num_replicas", [2])
3843
def test_batchdistributedsampler_drop_last(
3944
drop_last: bool,
4045
drop_dataset_tail: bool,
41-
batch_size: int = 128,
42-
n_batches: int = 3,
43-
num_replicas: int = 2,
46+
batch_size: int,
47+
n_batches: int,
48+
num_replicas: int,
4449
):
4550
"""Expected behavior:
4651
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116121
check_samplers(samplers, sampler_batch_size)
117122

118123

124+
@pytest.mark.parametrize("batch_size", [128])
125+
@pytest.mark.parametrize("n_batches", [3])
126+
@pytest.mark.parametrize("num_replicas", [2])
119127
def test_batchdistributedsampler_indices(
120-
batch_size: int = 128,
121-
n_batches: int = 3,
122-
num_replicas: int = 2,
128+
batch_size: int,
129+
n_batches: int,
130+
num_replicas: int,
123131
):
124132
adata = scvi.data.synthetic_iid(batch_size=batch_size, n_batches=n_batches)
125133
manager = generic_setup_adata_manager(adata)

‎tests/distributions/test_negative_binomial.py

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4949
ZeroInflatedNegativeBinomial(
5050
mu=-mu, theta=theta, zi_logits=pi, scale=scales, validate_args=True
5151
)
52-
with pytest.warns(UserWarning):
52+
with pytest.warns(UserWarning, match="The value argument must be within the support"):
5353
dist1.log_prob(-x) # ensures neg values raise warning
54-
with pytest.warns(UserWarning):
54+
with pytest.warns(UserWarning, match="The value argument must be within the support"):
5555
dist2.log_prob(0.5 * x) # ensures float values raise warning
5656

5757
# test with no scale
@@ -62,7 +62,7 @@ def test_zinb_distribution():
6262
assert dist1.log_prob(x).shape == size
6363
assert dist2.log_prob(x).shape == size
6464

65-
with pytest.warns(UserWarning):
65+
with pytest.warns(UserWarning, match="The value argument must be within the support"):
6666
dist1.log_prob(-x)
67-
with pytest.warns(UserWarning):
67+
with pytest.warns(UserWarning, match="The value argument must be within the support"):
6868
dist2.log_prob(0.5 * x)

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