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test_pca.py
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test_pca.py
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from typing import Literal
import numpy as np
import pytest
import warnings
from anndata import AnnData
from anndata.tests.helpers import (
as_dense_dask_array,
as_sparse_dask_array,
assert_equal,
asarray,
)
from scipy import sparse
import scanpy as sc
from scanpy.testing._helpers.data import pbmc3k_normalized
from scanpy.testing._pytest.marks import needs
from scanpy.testing._pytest.params import ARRAY_TYPES_SUPPORTED, param_with
A_list = np.array(
[
[0, 0, 7, 0, 0],
[8, 5, 0, 2, 0],
[6, 0, 0, 2, 5],
[0, 0, 0, 1, 0],
[8, 8, 2, 1, 0],
[0, 0, 0, 4, 5],
]
)
A_pca = np.array(
[
[-4.4783009, 5.55508466, 1.73111572, -0.06029139, 0.17292555],
[5.4855141, -0.42651191, -0.74776055, -0.74532146, 0.74633582],
[0.01161428, -4.0156662, 2.37252748, -1.33122372, -0.29044446],
[-3.61934397, 0.48525412, -2.96861931, -1.16312545, -0.33230607],
[7.14050048, 1.86330409, -0.05786325, 1.25045782, -0.50213107],
[-4.53998399, -3.46146476, -0.32940009, 2.04950419, 0.20562023],
]
)
A_svd = np.array(
[
[-0.77034038, -2.00750922, 6.64603489, -0.39669256, -0.22212097],
[-9.47135856, -0.6326006, -1.33787112, -0.24894361, -1.02044665],
[-5.90007339, 4.99658727, 0.70712592, -2.15188849, 0.30430008],
[-0.19132409, 0.42172251, 0.11169531, 0.50977966, -0.71637566],
[-11.1286238, -2.73045559, 0.08040596, 1.06850585, 0.74173764],
[-1.50180389, 5.56886849, 1.64034442, 2.24476032, -0.05109001],
]
)
# If one uses dask for PCA it will always require dask-ml
@pytest.fixture(
params=[
param_with(at, marks=[needs("dask_ml")]) if "dask" in at.id else at
for at in ARRAY_TYPES_SUPPORTED
]
)
def array_type(request: pytest.FixtureRequest):
return request.param
@pytest.fixture(params=[None, "valid", "invalid"])
def svd_solver_type(request: pytest.FixtureRequest):
return request.param
@pytest.fixture(params=[True, False], ids=["zero_center", "no_zero_center"])
def zero_center(request: pytest.FixtureRequest):
return request.param
@pytest.fixture
def pca_params(
array_type, svd_solver_type: Literal[None, "valid", "invalid"], zero_center
):
all_svd_solvers = {"auto", "full", "arpack", "randomized", "tsqr", "lobpcg"}
expected_warning = None
svd_solver = None
if svd_solver_type is not None:
# TODO: are these right for sparse?
if array_type in {as_dense_dask_array, as_sparse_dask_array}:
svd_solver = (
{"auto", "full", "tsqr", "randomized"}
if zero_center
else {"tsqr", "randomized"}
)
elif array_type in {sparse.csr_matrix, sparse.csc_matrix}:
svd_solver = (
{"lobpcg", "arpack"} if zero_center else {"arpack", "randomized"}
)
elif array_type is asarray:
svd_solver = (
{"auto", "full", "arpack", "randomized"}
if zero_center
else {"arpack", "randomized"}
)
else:
assert False, f"Unknown array type {array_type}"
if svd_solver_type == "invalid":
svd_solver = all_svd_solvers - svd_solver
expected_warning = "Ignoring"
svd_solver = np.random.choice(list(svd_solver))
# explicit check for special case
if (
svd_solver == "randomized"
and zero_center
and array_type in [sparse.csr_matrix, sparse.csc_matrix]
):
expected_warning = "not work with sparse input"
return (svd_solver, expected_warning)
def test_pca_warnings(array_type, zero_center, pca_params):
svd_solver, expected_warning = pca_params
A = array_type(A_list).astype("float32")
adata = AnnData(A)
if expected_warning is not None:
with pytest.warns(UserWarning, match=expected_warning):
sc.pp.pca(adata, svd_solver=svd_solver, zero_center=zero_center)
return
try:
with warnings.catch_warnings():
warnings.simplefilter("error")
warnings.filterwarnings(
"ignore",
"pkg_resources is deprecated as an API",
DeprecationWarning,
)
sc.pp.pca(adata, svd_solver=svd_solver, zero_center=zero_center)
except UserWarning:
# TODO: Fix this case, maybe by increasing test data size.
# https://github.com/scverse/scanpy/issues/2744
if svd_solver == "lobpcg":
pytest.xfail(reason="lobpcg doesn’t work with this small test data")
raise
# This warning test is out of the fixture because it is a special case in the logic of the function
def test_pca_warnings_sparse():
for array_type in (sparse.csr_matrix, sparse.csc_matrix):
A = array_type(A_list).astype("float32")
adata = AnnData(A)
with pytest.warns(UserWarning, match="not work with sparse input"):
sc.pp.pca(adata, svd_solver="randomized", zero_center=True)
def test_pca_transform(array_type):
A = array_type(A_list).astype("float32")
A_pca_abs = np.abs(A_pca)
A_svd_abs = np.abs(A_svd)
adata = AnnData(A)
with warnings.catch_warnings(record=True) as record:
sc.pp.pca(adata, n_comps=4, zero_center=True, dtype="float64")
assert len(record) == 0
assert np.linalg.norm(A_pca_abs[:, :4] - np.abs(adata.obsm["X_pca"])) < 2e-05
with warnings.catch_warnings(record=True) as record:
sc.pp.pca(
adata,
n_comps=5,
zero_center=True,
svd_solver="randomized",
dtype="float64",
random_state=14,
)
if sparse.issparse(A):
assert any(
isinstance(r.message, UserWarning)
and "svd_solver 'randomized' does not work with sparse input"
in str(r.message)
for r in record
)
else:
assert len(record) == 0
assert np.linalg.norm(A_pca_abs - np.abs(adata.obsm["X_pca"])) < 2e-05
with warnings.catch_warnings(record=True) as record:
sc.pp.pca(adata, n_comps=4, zero_center=False, dtype="float64", random_state=14)
assert len(record) == 0
assert np.linalg.norm(A_svd_abs[:, :4] - np.abs(adata.obsm["X_pca"])) < 2e-05
def test_pca_shapes():
"""Tests that n_comps behaves correctly"""
# https://github.com/scverse/scanpy/issues/1051
adata = AnnData(np.random.randn(30, 20))
sc.pp.pca(adata)
assert adata.obsm["X_pca"].shape == (30, 19)
adata = AnnData(np.random.randn(20, 30))
sc.pp.pca(adata)
assert adata.obsm["X_pca"].shape == (20, 19)
with pytest.raises(ValueError):
sc.pp.pca(adata, n_comps=100)
def test_pca_sparse():
"""
Tests that implicitly centered pca on sparse arrays returns equivalent results to
explicit centering on dense arrays.
"""
pbmc = pbmc3k_normalized()
pbmc_dense = pbmc.copy()
pbmc_dense.X = pbmc_dense.X.toarray()
implicit = sc.pp.pca(pbmc, dtype=np.float64, copy=True)
explicit = sc.pp.pca(pbmc_dense, dtype=np.float64, copy=True)
assert np.allclose(implicit.uns["pca"]["variance"], explicit.uns["pca"]["variance"])
assert np.allclose(
implicit.uns["pca"]["variance_ratio"], explicit.uns["pca"]["variance_ratio"]
)
assert np.allclose(implicit.obsm["X_pca"], explicit.obsm["X_pca"])
assert np.allclose(implicit.varm["PCs"], explicit.varm["PCs"])
def test_pca_reproducible(array_type):
pbmc = pbmc3k_normalized()
pbmc.X = array_type(pbmc.X)
a = sc.pp.pca(pbmc, copy=True, dtype=np.float64, random_state=42)
b = sc.pp.pca(pbmc, copy=True, dtype=np.float64, random_state=42)
c = sc.pp.pca(pbmc, copy=True, dtype=np.float64, random_state=0)
assert_equal(a, b)
# Test that changing random seed changes result
# Does not show up reliably with 32 bit computation
assert not np.array_equal(a.obsm["X_pca"], c.obsm["X_pca"])
def test_pca_chunked():
# https://github.com/scverse/scanpy/issues/1590
# But also a more general test
# Subsetting for speed of test
pbmc_full = pbmc3k_normalized()
pbmc = pbmc_full[::6].copy()
pbmc.X = pbmc.X.astype(np.float64)
chunked = sc.pp.pca(pbmc_full, chunked=True, copy=True)
default = sc.pp.pca(pbmc_full, copy=True)
# Taking absolute value since sometimes dimensions are flipped
np.testing.assert_allclose(
np.abs(chunked.obsm["X_pca"]), np.abs(default.obsm["X_pca"])
)
np.testing.assert_allclose(np.abs(chunked.varm["PCs"]), np.abs(default.varm["PCs"]))
np.testing.assert_allclose(
np.abs(chunked.uns["pca"]["variance"]), np.abs(default.uns["pca"]["variance"])
)
np.testing.assert_allclose(
np.abs(chunked.uns["pca"]["variance_ratio"]),
np.abs(default.uns["pca"]["variance_ratio"]),
)
def test_pca_n_pcs():
"""
Tests that the n_pcs parameter also works for
representations not called "X_pca"
"""
pbmc = pbmc3k_normalized()
sc.pp.pca(pbmc, dtype=np.float64)
pbmc.obsm["X_pca_test"] = pbmc.obsm["X_pca"]
original = sc.pp.neighbors(pbmc, n_pcs=5, use_rep="X_pca", copy=True)
renamed = sc.pp.neighbors(pbmc, n_pcs=5, use_rep="X_pca_test", copy=True)
assert np.allclose(original.obsm["X_pca"], renamed.obsm["X_pca_test"])
assert np.allclose(
original.obsp["distances"].toarray(), renamed.obsp["distances"].toarray()
)
def test_pca_layer():
"""
Tests that layers works the same way as .X
"""
X_adata = pbmc3k_normalized()
layer_adata = X_adata.copy()
layer_adata.layers["counts"] = X_adata.X.copy()
del layer_adata.X
sc.pp.pca(X_adata, dtype=np.float64)
sc.pp.pca(layer_adata, layer="counts", dtype=np.float64)
assert layer_adata.uns["pca"]["params"]["layer"] == "counts"
assert "layer" not in X_adata.uns["pca"]["params"]
np.testing.assert_equal(
X_adata.uns["pca"]["variance"], layer_adata.uns["pca"]["variance"]
)
np.testing.assert_equal(
X_adata.uns["pca"]["variance_ratio"], layer_adata.uns["pca"]["variance_ratio"]
)
np.testing.assert_equal(X_adata.obsm["X_pca"], layer_adata.obsm["X_pca"])
np.testing.assert_equal(X_adata.varm["PCs"], layer_adata.varm["PCs"])