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conftest.py
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from typing import Tuple, Optional
import pytest
import warnings
from sys import version_info
from pathlib import Path
from _helpers import create_model
from filelock import FileLock
import scanpy as sc
import cellrank as cr
from anndata import AnnData
from cellrank.models import GAM, GAMR, SKLearnModel
from cellrank.kernels import VelocityKernel, ConnectivityKernel
from cellrank.estimators import GPCCA, CFLARE
import numpy as np
from numba.core.errors import NumbaPerformanceWarning
import matplotlib
_adata_small = sc.read("tests/_ground_truth_adatas/adata_50.h5ad")
_adata_medium = sc.read("tests/_ground_truth_adatas/adata_100.h5ad")
_adata_large = sc.read("tests/_ground_truth_adatas/adata_200.h5ad")
def pytest_sessionstart(session: pytest.Session) -> None:
matplotlib.use("Agg")
matplotlib.rcParams["figure.max_open_warning"] = 0
np.random.seed(42)
# https://github.com/theislab/cellrank/issues/683
warnings.simplefilter("ignore", NumbaPerformanceWarning)
# removes overly verbose and useless logging errors for rpy2
# see: https://github.com/pytest-dev/pytest/issues/5502#issuecomment-647157873
def pytest_sessionfinish(session: pytest.Session, exitstatus) -> None:
import logging
loggers = [logging.getLogger()] + list(logging.Logger.manager.loggerDict.values())
for logger in loggers:
handlers = getattr(logger, "handlers", [])
for handler in handlers:
logger.removeHandler(handler)
def _create_cflare(*, backward: bool = False) -> Tuple[AnnData, CFLARE]:
adata = _adata_medium.copy()
sc.tl.paga(adata, groups="clusters")
vk = VelocityKernel(adata, backward=backward).compute_transition_matrix(
softmax_scale=4
)
ck = ConnectivityKernel(adata).compute_transition_matrix()
final_kernel = 0.8 * vk + 0.2 * ck
mc = CFLARE(final_kernel)
final_kernel.write_to_adata()
mc.compute_eigendecomposition()
mc.predict(use=2, method="kmeans")
mc.compute_fate_probabilities(use_petsc=False)
mc.compute_lineage_drivers(cluster_key="clusters", use_raw=False)
assert adata is mc.adata
np.testing.assert_allclose(mc.fate_probabilities.X.sum(1), 1.0, rtol=1e-6)
return adata, mc
def _create_gpcca(*, backward: bool = False) -> Tuple[AnnData, GPCCA]:
adata = _adata_medium.copy()
sc.tl.paga(adata, groups="clusters")
vk = VelocityKernel(adata, backward=backward).compute_transition_matrix(
softmax_scale=4
)
ck = ConnectivityKernel(adata).compute_transition_matrix()
final_kernel = 0.8 * vk + 0.2 * ck
mc = GPCCA(final_kernel)
final_kernel.write_to_adata()
mc.compute_eigendecomposition()
mc.compute_schur(method="krylov")
mc.compute_macrostates(n_states=2)
mc.set_terminal_states()
mc.compute_fate_probabilities(use_petsc=False)
mc.compute_lineage_drivers(cluster_key="clusters", use_raw=False)
assert adata is mc.adata
np.testing.assert_allclose(mc.fate_probabilities.X.sum(1), 1.0, rtol=1e-6)
return adata, mc
def _create_gamr_model(_adata: AnnData) -> Optional[GAMR]:
try:
m = GAMR(_adata)
m.prepare(_adata.var_names[0], "0", "latent_time").fit()
m.predict(level=0.95)
return m
except Exception:
return None
@pytest.fixture
def adata() -> AnnData:
return _adata_small.copy()
@pytest.fixture
def adata_large() -> AnnData:
return _adata_large.copy()
@pytest.fixture
def adata_cflare_fwd(
adata_cflare=_create_cflare(backward=False),
) -> Tuple[AnnData, CFLARE]:
adata, cflare = adata_cflare
return adata.copy(), cflare
@pytest.fixture
def adata_gpcca_fwd(adata_gpcca=_create_gpcca(backward=False)) -> Tuple[AnnData, GPCCA]:
adata, gpcca = adata_gpcca
return adata.copy(), gpcca
@pytest.fixture
def adata_gpcca_bwd(adata_gpcca=_create_gpcca(backward=True)) -> Tuple[AnnData, GPCCA]:
adata, gpcca = adata_gpcca
return adata.copy(), gpcca
@pytest.fixture
def adata_cflare(adata_cflare=_create_cflare(backward=False)) -> AnnData:
return adata_cflare[0].copy()
@pytest.fixture
def g(adata_gpcca=_create_gpcca(backward=False)) -> Tuple[AnnData, GPCCA]:
return adata_gpcca[1].copy()
@pytest.fixture(scope="session")
def adata_gamr(adata_cflare=_create_cflare(backward=False)) -> AnnData:
return adata_cflare[0].copy()
@pytest.fixture(scope="session")
def gamr_model(
adata_gamr: AnnData, tmp_path_factory: Path, worker_id: str
) -> Optional[GAMR]:
model = None
if version_info[:2] <= (3, 6):
pytest.skip("Pickling of Enums doesn't work in Python3.6.")
elif worker_id == "master":
model = _create_gamr_model(adata_gamr)
else:
root_tmp_dir = tmp_path_factory.getbasetemp().parent
fn = root_tmp_dir / "model.pickle"
with FileLock(f"{fn}.lock"):
if fn.is_file():
model = GAMR.read(fn)
else:
model = _create_gamr_model(adata_gamr)
if model is not None:
model.write(fn)
if model is None:
pytest.skip("Unable to create `cellrank.models.GAMR`.")
return model
@pytest.fixture
def pygam_model(adata_cflare: AnnData) -> GAM:
m = GAM(adata_cflare)
m.prepare(adata_cflare.var_names[0], "0", "latent_time").fit()
m.predict()
m.confidence_interval()
return m
@pytest.fixture
def sklearn_model(adata_cflare: AnnData) -> SKLearnModel:
m = create_model(adata_cflare)
assert isinstance(m, SKLearnModel), m
m.prepare(adata_cflare.var_names[0], "0", "latent_time").fit()
m.predict()
m.confidence_interval()
return m
@pytest.fixture
def lineage():
x = cr._utils.Lineage(
np.array(
[
[1.23459664e-01, 1.29965675e-01, 1.92828002e-01, 9.39402664e-01],
[1.05635239e00, 4.45833459e-01, 2.29080759e00, 1.90132652e00],
[6.77880737e-02, 4.97556864e-02, 1.18428661e00, 2.02318999e-01],
[4.87500398e-01, 1.00657498e00, 2.20834882e-02, 5.03008905e-01],
[6.27190917e00, 7.27864781e00, 1.03978903e00, 1.55903460e01],
[3.85149269e-01, 3.54765380e-01, 1.77871487e-01, 8.22138648e-02],
[7.06618729e00, 1.33133671e01, 1.44904591e00, 5.79813391e00],
[8.18005744e-02, 5.36844933e-01, 1.86646162e00, 2.41141727e00],
[1.44892035e-01, 2.34036215e-01, 6.32392890e-01, 1.13211403e-02],
[2.44926466e-01, 2.50293183e-01, 1.77540208e-01, 3.27240144e-01],
]
),
names=["foo", "bar", "baz", "quux"],
)
return x / x.sum(1)
@pytest.fixture
def kernel(adata_large: AnnData):
vk = VelocityKernel(adata_large).compute_transition_matrix(softmax_scale=4)
ck = ConnectivityKernel(adata_large).compute_transition_matrix()
return (0.8 * vk + 0.2 * ck).compute_transition_matrix()
@pytest.fixture(scope="session")
def test_matrix_1() -> np.ndarray:
"""
Parameters
----------
Returns
-------
:class:`numpy.ndarray`
Row-normalized transition matrix. This matrix is
- connected
- irreducible
- not reversible
"""
# fmt: off
p = np.array([
# 0. 1. 2. 3. 4. 5. 6. 7. 8. 9. 10. 11.
[0.0, 0.8, 0.2, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0], # 0
[0.2, 0.0, 0.6, 0.2, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0], # 1
[0.6, 0.2, 0.0, 0.2, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0], # 2
[0.0, 0.05, 0.05, 0.0, 0.6, 0.3, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0], # 3
[0.0, 0.0, 0.0, 0.25, 0.0, 0.25, 0.4, 0.0, 0.0, 0.1, 0.0, 0.0], # 4
[0.0, 0.0, 0.0, 0.25, 0.25, 0.0, 0.1, 0.0, 0.0, 0.4, 0.0, 0.0], # 5
[0.0, 0.0, 0.0, 0.0, 0.05, 0.05, 0.0, 0.7, 0.2, 0.0, 0.0, 0.0], # 6
[0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.2, 0.0, 0.8, 0.0, 0.0, 0.0], # 7
[0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.8, 0.2, 0.0, 0.0, 0.0, 0.0], # 8
[0.0, 0.0, 0.0, 0.0, 0.05, 0.05, 0.0, 0.0, 0.0, 0.0, 0.7, 0.2], # 9
[0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.2, 0.0, 0.8], # 10
[0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.8, 0.2, 0.0], # 11
])
# fmt: on
return p
@pytest.fixture(scope="session")
def test_matrix_2() -> np.ndarray:
"""
Parameters
----------
Returns
-------
:class:`numpy.ndarray`
Row-normalized transition matrix. This matrix is
- connected
- not irreducible (1 recurrent, 1 transient class)
- not reversible
"""
# fmt: off
p = np.array([
# 0. 1. 2. 3. 4. 5. 6. 7. 8. 9. 10. 11. 12. 13.
[0.0, 0.8, 0.2, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0], # 0
[0.2, 0.0, 0.6, 0.2, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0], # 1
[0.6, 0.2, 0.0, 0.2, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0], # 2
[0.0, 0.05, 0.05, 0.0, 0.6, 0.3, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0], # 3
[0.0, 0.0, 0.0, 0.25, 0.0, 0.25, 0.4, 0.0, 0.0, 0.1, 0.0, 0.0, 0.0, 0.0], # 4
[0.0, 0.0, 0.0, 0.25, 0.25, 0.0, 0.1, 0.0, 0.0, 0.3, 0.0, 0.0, 0.1, 0.0], # 5
[0.0, 0.0, 0.0, 0.0, 0.05, 0.05, 0.0, 0.7, 0.2, 0.0, 0.0, 0.0, 0.0, 0.0], # 6
[0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.2, 0.0, 0.8, 0.0, 0.0, 0.0, 0.0, 0.0], # 7
[0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.8, 0.2, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0], # 8
[0.0, 0.0, 0.0, 0.0, 0.05, 0.05, 0.0, 0.0, 0.0, 0.0, 0.7, 0.2, 0.0, 0.0], # 9
[0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.2, 0.0, 0.8, 0.0, 0.0], # 10
[0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.8, 0.2, 0.0, 0.0, 0.0], # 11
[0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.6, 0.4], # 12
[0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.3, 0.7], # 13
])
# fmt: on
return p
@pytest.fixture(scope="session")
def test_matrix_3() -> np.ndarray:
"""
Parameters
----------
Returns
-------
:class:`numpy.ndarray`
Row-normalized transition matrix. This matrix is
- not connected
- not irreducible (2 recurrent classes)
- not reversible
"""
# fmt: off
p = np.array([
# 0. 1. 2. 3. 4. 5. 6. 7. 8. 9. 10. 11. 12. 13.
[0.0, 0.8, 0.2, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0], # 0
[0.2, 0.0, 0.6, 0.2, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0], # 1
[0.6, 0.2, 0.0, 0.2, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0], # 2
[0.0, 0.05, 0.05, 0.0, 0.6, 0.3, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0], # 3
[0.0, 0.0, 0.0, 0.25, 0.0, 0.25, 0.4, 0.0, 0.0, 0.1, 0.0, 0.0, 0.0, 0.0], # 4
[0.0, 0.0, 0.0, 0.25, 0.25, 0.0, 0.1, 0.0, 0.0, 0.4, 0.0, 0.0, 0.0, 0.0], # 5
[0.0, 0.0, 0.0, 0.0, 0.05, 0.05, 0.0, 0.7, 0.2, 0.0, 0.0, 0.0, 0.0, 0.0], # 6
[0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.2, 0.0, 0.8, 0.0, 0.0, 0.0, 0.0, 0.0], # 7
[0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.8, 0.2, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0], # 8
[0.0, 0.0, 0.0, 0.0, 0.05, 0.05, 0.0, 0.0, 0.0, 0.0, 0.7, 0.2, 0.0, 0.0], # 9
[0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.2, 0.0, 0.8, 0.0, 0.0], # 10
[0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.8, 0.2, 0.0, 0.0, 0.0], # 11
[0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.6, 0.4], # 12
[0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.3, 0.7], # 13
])
# fmt: on
return p
@pytest.fixture(scope="session")
def test_matrix_4() -> np.ndarray:
"""
Parameters
----------
Returns
-------
:class:`numpy.ndarray`
Symmetric matrix. Not a transition matrix.
"""
# fmt: off
p = np.array(
[
# 0. 1. 2. 3.
[0.0, 0.8, 0.2, 0.0], # 0
[0.8, 0.0, 0.6, 0.2], # 1
[0.2, 0.6, 0.0, 0.2], # 2
[0.0, 0.2, 0.2, 0.0], # 3
]
)
# fmt: on
return p