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import shutil
from typing import Literal
import numpy as np
import pytest
from cell_eval import MetricsEvaluator
from cell_eval.data import (
CONTROL_VAR,
PERT_COL,
build_random_anndata,
downsample_cells,
)
OUTDIR = "TEST_OUTPUT_DIRECTORY"
KNOWN_PROFILES: list[Literal["full", "vcc", "minimal", "de", "anndata"]] = [
"full",
"vcc",
"minimal",
"de",
"anndata",
]
def test_broken_adata_mismatched_var_size():
adata_real = build_random_anndata(normlog=False)
adata_pred = adata_real.copy()
# Randomly subset genes on pred
var_mask = np.random.random(adata_real.shape[1]) < 0.8
adata_pred = adata_pred[:, var_mask]
with pytest.raises(Exception):
MetricsEvaluator(
adata_pred=adata_pred,
adata_real=adata_real,
control_pert=CONTROL_VAR,
pert_col=PERT_COL,
outdir=OUTDIR,
)
def test_broken_adata_mismatched_var_ordering():
adata_real = build_random_anndata(normlog=False)
adata_pred = adata_real.copy()
# Randomly subset genes on pred
indices = np.arange(adata_real.shape[1])
np.random.shuffle(indices)
adata_pred = adata_pred[:, indices]
with pytest.raises(Exception):
MetricsEvaluator(
adata_pred=adata_pred,
adata_real=adata_real,
control_pert=CONTROL_VAR,
pert_col=PERT_COL,
outdir=OUTDIR,
)
def test_broken_adata_not_normlog():
adata_real = build_random_anndata(normlog=False)
adata_pred = adata_real.copy()
evaluator = MetricsEvaluator(
adata_pred=adata_pred,
adata_real=adata_real,
control_pert=CONTROL_VAR,
pert_col=PERT_COL,
outdir=OUTDIR,
)
evaluator.compute(
break_on_error=True,
)
def test_broken_adata_not_normlog_skip_check():
adata_real = build_random_anndata(normlog=False)
adata_pred = adata_real.copy()
evaluator = MetricsEvaluator(
adata_pred=adata_pred,
adata_real=adata_real,
control_pert=CONTROL_VAR,
pert_col=PERT_COL,
outdir=OUTDIR,
allow_discrete=True,
)
evaluator.compute(
break_on_error=True,
)
def test_broken_adata_invalid_pred_scale():
"""Test that predicted data with invalid scale is rejected."""
adata_real = build_random_anndata(normlog=True)
adata_pred = adata_real.copy()
# Create invalid predicted data: mix of raw counts and log1p
adata_pred.X = np.random.uniform(
0,
5000,
size=adata_pred.X.shape, # type: ignore
)
with pytest.raises(ValueError, match="Invalid scale.*exceeds log1p threshold"):
MetricsEvaluator(
adata_pred=adata_pred,
adata_real=adata_real,
control_pert=CONTROL_VAR,
pert_col=PERT_COL,
outdir=OUTDIR,
)
def test_broken_adata_missing_pertcol_in_real():
adata_real = build_random_anndata()
adata_pred = adata_real.copy()
# Remove pert_col from adata_real
adata_real.obs.drop(columns=[PERT_COL], inplace=True)
with pytest.raises(Exception):
MetricsEvaluator(
adata_pred=adata_pred,
adata_real=adata_real,
control_pert=CONTROL_VAR,
pert_col=PERT_COL,
outdir=OUTDIR,
)
def test_broken_adata_missing_pertcol_in_pred():
adata_real = build_random_anndata()
adata_pred = adata_real.copy()
# Remove pert_col from adata_pred
adata_pred.obs.drop(columns=[PERT_COL], inplace=True)
with pytest.raises(Exception):
MetricsEvaluator(
adata_pred=adata_pred,
adata_real=adata_real,
control_pert=CONTROL_VAR,
pert_col=PERT_COL,
outdir=OUTDIR,
)
def test_broken_adata_missing_control_in_real():
adata_real = build_random_anndata()
adata_pred = adata_real.copy()
# Remove control_pert from adata_real
adata_real = adata_real[adata_real.obs[PERT_COL] != CONTROL_VAR].copy()
with pytest.raises(Exception):
MetricsEvaluator(
adata_pred=adata_pred,
adata_real=adata_real,
control_pert=CONTROL_VAR,
pert_col=PERT_COL,
outdir=OUTDIR,
)
def test_broken_adata_missing_control_in_pred():
adata_real = build_random_anndata()
adata_pred = adata_real.copy()
# Remove control_pert from adata_pred
adata_pred = adata_pred[adata_pred.obs[PERT_COL] != CONTROL_VAR].copy()
with pytest.raises(Exception):
MetricsEvaluator(
adata_pred=adata_pred,
adata_real=adata_real,
control_pert=CONTROL_VAR,
pert_col=PERT_COL,
outdir=OUTDIR,
)
def test_unknown_alternative_de_metric():
adata_real = build_random_anndata()
adata_pred = adata_real.copy()
# Remove control_pert from adata_pred
adata_pred = adata_pred[adata_pred.obs[PERT_COL] != CONTROL_VAR].copy()
with pytest.raises(Exception):
MetricsEvaluator(
adata_pred=adata_pred,
adata_real=adata_real,
control_pert=CONTROL_VAR,
pert_col=PERT_COL,
outdir=OUTDIR,
de_method="unknown",
).compute()
def test_eval_simple():
adata_real = build_random_anndata()
adata_pred = downsample_cells(adata_real, fraction=0.5)
evaluator = MetricsEvaluator(
adata_pred=adata_pred,
adata_real=adata_real,
control_pert="control",
pert_col="perturbation",
)
evaluator.compute(
break_on_error=True,
)
def test_eval_simple_profiles():
adata_real = build_random_anndata()
adata_pred = downsample_cells(adata_real, fraction=0.5)
evaluator = MetricsEvaluator(
adata_pred=adata_pred,
adata_real=adata_real,
control_pert="control",
pert_col="perturbation",
)
for profile in KNOWN_PROFILES:
evaluator.compute(
profile=profile,
break_on_error=True,
)
with pytest.raises(ValueError):
evaluator.compute(
profile="unknown", # type: ignore
break_on_error=True,
)
def test_eval_missing_celltype_col():
adata_real = build_random_anndata()
adata_pred = downsample_cells(adata_real, fraction=0.5)
adata_real.obs.drop(columns="celltype", inplace=True)
adata_pred.obs.drop(columns="celltype", inplace=True)
assert "celltype" not in adata_real.obs.columns
assert "celltype" not in adata_pred.obs.columns
evaluator = MetricsEvaluator(
adata_pred=adata_pred,
adata_real=adata_real,
control_pert="control",
pert_col="perturbation",
)
evaluator.compute(
break_on_error=True,
)
def test_eval_pdex_kwargs():
adata_real = build_random_anndata()
adata_pred = downsample_cells(adata_real, fraction=0.5)
evaluator = MetricsEvaluator(
adata_pred=adata_pred,
adata_real=adata_real,
control_pert="control",
pert_col="perturbation",
pdex_kwargs={
"exp_post_agg": True,
},
)
evaluator.compute(
break_on_error=True,
)
def test_eval_pdex_kwargs_duplicated():
adata_real = build_random_anndata()
adata_pred = downsample_cells(adata_real, fraction=0.5)
evaluator = MetricsEvaluator(
adata_pred=adata_pred,
adata_real=adata_real,
control_pert="control",
pert_col="perturbation",
pdex_kwargs={
"exp_post_agg": True,
"num_workers": 4,
},
)
evaluator.compute(
break_on_error=True,
)
def validate_expected_files(
outdir: str, prefix: str | None = None, remove: bool = True
):
base_real_de = "real_de.csv" if not prefix else f"{prefix}_real_de.csv"
base_pred_de = "pred_de.csv" if not prefix else f"{prefix}_pred_de.csv"
base_results = "results.csv" if not prefix else f"{prefix}_results.csv"
assert os.path.exists(f"{outdir}/{base_real_de}"), (
"Expected file for real DE results missing"
)
assert os.path.exists(f"{outdir}/{base_pred_de}"), (
"Expected file for predicted DE results missing"
)
assert os.path.exists(f"{outdir}/{base_results}"), (
"Expected file for results missing"
)
if remove:
shutil.rmtree(outdir)
def test_eval():
adata_real = build_random_anndata()
adata_pred = adata_real.copy()
evaluator = MetricsEvaluator(
adata_pred=adata_pred,
adata_real=adata_real,
control_pert=CONTROL_VAR,
pert_col=PERT_COL,
outdir=OUTDIR,
)
evaluator.compute(
break_on_error=True,
)
validate_expected_files(OUTDIR)
def test_eval_prefix():
adata_real = build_random_anndata()
adata_pred = adata_real.copy()
evaluator = MetricsEvaluator(
adata_pred=adata_pred,
adata_real=adata_real,
control_pert=CONTROL_VAR,
pert_col=PERT_COL,
outdir=OUTDIR,
prefix="arbitrary",
)
evaluator.compute(
break_on_error=True,
)
validate_expected_files(OUTDIR, prefix="arbitrary")
def test_minimal_eval():
adata_real = build_random_anndata()
adata_pred = adata_real.copy()
evaluator = MetricsEvaluator(
adata_pred=adata_pred,
adata_real=adata_real,
control_pert=CONTROL_VAR,
pert_col=PERT_COL,
outdir=OUTDIR,
)
evaluator.compute(
profile="minimal",
break_on_error=True,
)
validate_expected_files(OUTDIR)
def test_eval_sparse():
adata_real = build_random_anndata(as_sparse=True)
adata_pred = adata_real.copy()
evaluator = MetricsEvaluator(
adata_pred=adata_pred,
adata_real=adata_real,
control_pert=CONTROL_VAR,
pert_col=PERT_COL,
outdir=OUTDIR,
)
evaluator.compute(
break_on_error=True,
)
validate_expected_files(OUTDIR)
def test_eval_downsampled_cells():
adata_real = build_random_anndata()
adata_pred = downsample_cells(adata_real, fraction=0.5)
evaluator = MetricsEvaluator(
adata_pred=adata_pred,
adata_real=adata_real,
control_pert=CONTROL_VAR,
pert_col=PERT_COL,
outdir=OUTDIR,
)
evaluator.compute(
break_on_error=True,
)
validate_expected_files(OUTDIR)
def test_eval_alt_metric():
adata_real = build_random_anndata()
adata_pred = downsample_cells(adata_real, fraction=0.5)
evaluator = MetricsEvaluator(
adata_pred=adata_pred,
adata_real=adata_real,
control_pert=CONTROL_VAR,
pert_col=PERT_COL,
outdir=OUTDIR,
de_method="anderson",
)
evaluator.compute(
break_on_error=True,
)
validate_expected_files(OUTDIR)
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