scale function(_get_mean_var) updated for dense array, speedup upto ~4.65x #3280
Azure Pipelines / scverse.scanpy
failed
Jan 8, 2025 in 28m 16s
Build #20250108.1 had test failures
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- Failed: 152 (1.39%)
- Passed: 9,868 (90.32%)
- Other: 905 (8.28%)
- Total: 10,925
- 9924 of 12990 line covered (76.40%)
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azure-pipelines / scverse.scanpy
Build log #L4523
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azure-pipelines / scverse.scanpy
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azure-pipelines / scverse.scanpy
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Check failure on line 1 in tests/test_rank_genes_groups.py::test_results[numpy_ndarray]
azure-pipelines / scverse.scanpy
tests/test_rank_genes_groups.py::test_results[numpy_ndarray]
array_type = <function asarray at 0x7fa771984f40>
@pytest.mark.parametrize("array_type", ARRAY_TYPES_MEM)
def test_results(array_type):
seed(1234)
adata = get_example_data(array_type)
assert adata.raw is None # Assumption for later checks
(
true_names_t_test,
true_names_wilcoxon,
true_scores_t_test,
true_scores_wilcoxon,
) = get_true_scores()
rank_genes_groups(adata, "true_groups", n_genes=20, method="t-test")
adata.uns["rank_genes_groups"]["names"] = adata.uns["rank_genes_groups"][
"names"
].astype(true_names_t_test.dtype)
for name in true_scores_t_test.dtype.names:
> assert np.allclose(
true_scores_t_test[name], adata.uns["rank_genes_groups"]["scores"][name]
)
E assert False
E + where False = <function allclose at 0x7fa77be9a030>(array([ 0.88779753 , 0.7680498 , 0.57457656 , 0.34870306 ,\n 0.26995564 , 0.25511038 , 0.11647039 , 0.0773671 ,\n 0.030090872, 0. , -0.020448448, -0.236924 ,\n -0.9331775 , -1.5263917 , -1.5304459 , -2.0760367 ,\n -2.3138392 , -2.3522618 , -2.7801418 , -2.8450732 ],\n dtype=float32), array([ 0.845086 , 0.729268 , 0.54912335 , 0.33181465 ,\n 0.2582014 , 0.24294989 , 0.110755555, 0.07385662 ,\n 0.028677907, 0. , -0.01953408 , -0.22624032 ,\n -0.9099657 , -1.494625 , -1.5004543 , -2.0644708 ,\n -2.2684083 , -2.3391573 , -2.7646534 , -2.8130772 ],\n dtype=float32))
E + where <function allclose at 0x7fa77be9a030> = np.allclose
tests/test_rank_genes_groups.py:105: AssertionErrorRaw output
/home/vsts/work/1/s/tests/test_rank_genes_groups.py:105: assert False
Check failure on line 1 in tests/test_rank_genes_groups.py::test_results_layers[numpy_ndarray]
azure-pipelines / scverse.scanpy
tests/test_rank_genes_groups.py::test_results_layers[numpy_ndarray]
array_type = <function asarray at 0x7fa771984f40>
@pytest.mark.parametrize("array_type", ARRAY_TYPES_MEM)
def test_results_layers(array_type):
seed(1234)
adata = get_example_data(array_type)
adata.layers["to_test"] = adata.X.copy()
adata.X = elem_mul(adata.X, np.random.randint(0, 2, adata.shape, dtype=bool))
_, _, true_scores_t_test, true_scores_wilcoxon = get_true_scores()
# Wilcoxon
rank_genes_groups(
adata,
"true_groups",
method="wilcoxon",
layer="to_test",
n_genes=20,
)
assert adata.uns["rank_genes_groups"]["params"]["use_raw"] is False
for name in true_scores_t_test.dtype.names:
assert np.allclose(
true_scores_wilcoxon[name][:7],
adata.uns["rank_genes_groups"]["scores"][name][:7],
)
rank_genes_groups(adata, "true_groups", method="wilcoxon", n_genes=20)
for name in true_scores_t_test.dtype.names:
assert not np.allclose(
true_scores_wilcoxon[name][:7],
adata.uns["rank_genes_groups"]["scores"][name][:7],
)
# t-test
rank_genes_groups(
adata,
"true_groups",
method="t-test",
layer="to_test",
use_raw=False,
n_genes=20,
)
for name in true_scores_t_test.dtype.names:
> assert np.allclose(
true_scores_t_test[name][:7],
adata.uns["rank_genes_groups"]["scores"][name][:7],
)
E assert False
E + where False = <function allclose at 0x7fa77be9a030>(array([0.88779753, 0.7680498 , 0.57457656, 0.34870306, 0.26995564,\n 0.25511038, 0.11647039], dtype=float32), array([0.845086 , 0.729268 , 0.54912335 , 0.33181465 , 0.2582014 ,\n 0.24294989 , 0.110755555], dtype=float32))
E + where <function allclose at 0x7fa77be9a030> = np.allclose
tests/test_rank_genes_groups.py:170: AssertionErrorRaw output
/home/vsts/work/1/s/tests/test_rank_genes_groups.py:170: assert False
Check failure on line 1 in tests/test_preprocessing.py::test_mean_var[numpy_ndarray]
azure-pipelines / scverse.scanpy
tests/test_preprocessing.py::test_mean_var[numpy_ndarray]
array_type = <function asarray at 0x7fa771984f40>
@pytest.mark.parametrize("array_type", ARRAY_TYPES)
def test_mean_var(array_type):
pbmc = pbmc3k()
pbmc.X = array_type(pbmc.X)
true_mean = np.mean(asarray(pbmc.X), axis=0)
true_var = np.var(asarray(pbmc.X), axis=0, dtype=np.float64, ddof=1)
means, variances = sc.pp._utils._get_mean_var(pbmc.X)
np.testing.assert_allclose(true_mean, means)
> np.testing.assert_allclose(true_var, variances)
tests/test_preprocessing.py:91:
_ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _
args = (<function assert_allclose.<locals>.compare at 0x7fa7345b8ae0>, array([0., 0., 0., ..., 0., 0., 0.]), array([0., 0., 0., ..., 0., 0., 0.]))
kwds = {'equal_nan': True, 'err_msg': '', 'header': 'Not equal to tolerance rtol=1e-07, atol=0', 'strict': False, ...}
@wraps(func)
def inner(*args, **kwds):
with self._recreate_cm():
> return func(*args, **kwds)
E AssertionError:
E Not equal to tolerance rtol=1e-07, atol=0
E
E Mismatched elements: 16634 / 32738 (50.8%)
E Max absolute difference among violations: 0.744234493
E Max relative difference among violations: 0.0003703704
E ACTUAL: array([0., 0., 0., ..., 0., 0., 0.])
E DESIRED: array([0., 0., 0., ..., 0., 0., 0.])
/opt/hostedtoolcache/Python/3.12.7/x64/lib/python3.12/contextlib.py:81: AssertionErrorRaw output
/opt/hostedtoolcache/Python/3.12.7/x64/lib/python3.12/contextlib.py:81: AssertionError:
azure-pipelines / scverse.scanpy
tests/test_preprocessing.py::test_scale_matrix_types[None-False-numpy_ndarray]
array_type = <function asarray at 0x7fa771984f40>, zero_center = False
max_value = None
@pytest.mark.parametrize("array_type", ARRAY_TYPES)
@pytest.mark.parametrize("zero_center", [True, False])
@pytest.mark.parametrize("max_value", [None, 1.0])
def test_scale_matrix_types(array_type, zero_center, max_value):
adata = pbmc68k_reduced()
adata.X = adata.raw.X
adata_casted = adata.copy()
adata_casted.X = array_type(adata_casted.raw.X)
sc.pp.scale(adata, zero_center=zero_center, max_value=max_value)
with maybe_dask_process_context():
sc.pp.scale(adata_casted, zero_center=zero_center, max_value=max_value)
X = adata_casted.X
if "dask" in array_type.__name__:
X = X.compute()
if issparse(X):
X = X.todense()
if issparse(adata.X):
adata.X = adata.X.todense()
> assert_allclose(X, adata.X, rtol=1e-5, atol=1e-5)
tests/test_preprocessing.py:322:
_ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _
args = (<function assert_allclose.<locals>.compare at 0x7fa7335e44a0>, array([[0. , 0. , 0. , ..., 0. ... ],
[0. , 0. , 1.4205601126, ..., 0. ,
0. , 0. ]]))
kwds = {'equal_nan': True, 'err_msg': '', 'header': 'Not equal to tolerance rtol=1e-05, atol=1e-05', 'strict': False, ...}
@wraps(func)
def inner(*args, **kwds):
with self._recreate_cm():
> return func(*args, **kwds)
E AssertionError:
E Not equal to tolerance rtol=1e-05, atol=1e-05
E
E Mismatched elements: 174400 / 535500 (32.6%)
E Max absolute difference among violations: 0.012948173
E Max relative difference among violations: 0.0007146004
E ACTUAL: array([[0. , 0. , 0. , ..., 0. , 0. , 3.337128],
E [2.378524, 0. , 1.926703, ..., 0. , 2.769665, 0. ],
E [0. , 0. , 1.707929, ..., 0. , 1.78579 , 0. ],...
E DESIRED: array([[0. , 0. , 0. , ..., 0. , 0. , 3.339514],
E [2.380225, 0. , 1.928081, ..., 0. , 2.771645, 0. ],
E [0. , 0. , 1.70915 , ..., 0. , 1.787067, 0. ],...
/opt/hostedtoolcache/Python/3.12.7/x64/lib/python3.12/contextlib.py:81: AssertionErrorRaw output
/opt/hostedtoolcache/Python/3.12.7/x64/lib/python3.12/contextlib.py:81: AssertionError:
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