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6fa9282 | 1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 16 17 18 19 20 21 22 23 24 25 26 27 28 29 30 31 32 33 34 35 36 37 38 39 40 41 42 43 44 45 46 47 48 49 50 51 52 53 54 55 56 57 58 59 60 61 62 63 64 65 66 67 68 69 70 71 72 73 74 75 76 77 78 79 80 81 82 83 84 85 86 87 88 89 90 91 92 93 94 95 96 97 98 99 100 101 102 103 104 105 106 107 108 109 110 111 112 113 114 115 116 117 118 119 120 121 122 123 124 125 126 127 128 129 130 131 132 133 134 135 136 137 138 139 140 141 142 143 144 145 146 147 148 149 150 151 152 153 154 155 156 157 158 159 160 161 162 163 164 165 166 167 168 169 170 171 172 173 174 175 176 177 178 179 180 181 182 183 184 185 186 187 188 189 190 191 192 193 194 195 196 197 198 199 | """Regression tests for data isolation, flow-map times, gradients and population metrics."""
from pathlib import Path
import json
import numpy as np
import pandas as pd
import pytest
import torch
from pivot.data.preprocess import assign_splits, prepare
from pivot.data.perturb_data import PerturbData
from pivot.models.flow_map import FlowMap
from pivot.models.encoders import PerturbationEncoder
from pivot.training.losses import compute_losses
from pivot.evaluation.rewards import Reward, rbf_mmd2
from pivot.evaluation.metrics import mmd2, retrieval_metrics
def test_diagonal_identity():
m = FlowMap(3, 2, hidden=8, depth=1)
torch.nn.init.normal_(m.net[-1].weight)
t = torch.rand(5)
c = torch.randn(5, 3)
e = torch.randn(5, 2)
torch.testing.assert_close(m(t, t, c, e), c, rtol=0, atol=0)
def test_pooling_permutation():
m = PerturbationEncoder(5, 2, 10, emb_dim=3)
g = torch.tensor([[1, 3]])
o = torch.ones_like(g)
mask = torch.ones_like(g)
torch.testing.assert_close(m(g, o, mask), m(g.flip(1), o, mask))
def test_semigroup_uses_corresponding_source_time():
class RecordedFlow:
def __init__(self):
self.calls = []
def __call__(self, s, t, c, e):
self.calls.append((s.clone(), t.clone(), c.clone()))
return c + (t - s)[:, None] * 2
def velocity(self, s, t, c, e):
return torch.ones_like(c) * 2
f = RecordedFlow()
c0 = torch.zeros(12, 3)
c1 = c0 + 2
e = torch.zeros(12, 2)
loss, parts = compute_losses(f, e, c0, c1, {})
# Call 0 is map supervision; calls 1 and 2 start the composition comparison.
for s, t, c in [f.calls[1], f.calls[2]]:
torch.testing.assert_close(c, 2 * s[:, None].expand_as(c))
assert parts["semi"] < 1e-10
def test_reward_gradient_matches_finite_difference():
torch.manual_seed(1)
a = torch.randn(3, 2, dtype=torch.double)
e = torch.randn(2, dtype=torch.double, requires_grad=True)
target = torch.randn(3, dtype=torch.double)
r = lambda z: -((a @ z - target) ** 2).sum()
(g,) = torch.autograd.grad(r(e), e)
h = 1e-6
numeric = torch.stack(
[
(
r(e.detach() + h * torch.eye(2, dtype=torch.double)[i])
- r(e.detach() - h * torch.eye(2, dtype=torch.double)[i])
)
/ (2 * h)
for i in range(2)
]
)
torch.testing.assert_close(g, numeric, rtol=1e-6, atol=1e-6)
def test_numpy_torch_population_statistic():
rng = np.random.default_rng(2)
x = rng.normal(size=(7, 3))
y = rng.normal(size=(11, 3))
assert np.isclose(
mmd2(x, y, 0.2), rbf_mmd2(torch.tensor(x), torch.tensor(y), 0.2).item()
)
assert abs(mmd2(x, x, 0.2)) < 1e-12
def test_retrieval_censoring():
assert retrieval_metrics(["A", "B"], "C") == dict(
top1=0.0, top5=0.0, ndcg10=0.0, rank=None
)
def test_held_out_genes_do_not_occur_in_training():
labels = [
"control",
"A",
"B",
"C",
"D",
"E",
"F",
"G",
"H",
"A_B",
"C_D",
"E_F",
"G_H",
]
o = pd.DataFrame({"perturbation": np.repeat(labels, 20)})
o["is_control"] = o.perturbation.eq("control")
split = assign_splits(o, "gene", 5)
genes = lambda part: {
g
for p in o.loc[(split == part) & ~o.is_control, "perturbation"]
for g in p.split("_")
}
held_single = set(
o.loc[
(split == "test") & ~o.is_control & ~o.perturbation.str.contains("_"),
"perturbation",
]
)
assert held_single and not genes("train") & held_single
assert not genes("val") & held_single
@pytest.fixture(scope="module")
def prepared(tmp_path_factory):
raw = Path(__file__).parents[1] / "fixtures/norman_small.h5ad"
d = tmp_path_factory.mktemp("cache")
prepare(str(raw), str(d), "norman", n_hvg=100, n_pca=5, seed=0)
return raw, PerturbData(str(d))
def test_partition_ids_are_disjoint(prepared):
_, d = prepared
parts = [set(d.indices(p)) for p in ["train", "val", "test", "reference"]]
assert len(set.union(*parts)) == len(d.obs)
for i, a in enumerate(parts):
for b in parts[i + 1 :]:
assert not a & b
assert all(len(d.indices(p, True)) for p in ["train", "val", "test"])
assert all(
len(np.intersect1d(d.indices("reference", False), ids))
for ids in d.pert_to_idx.values()
)
def test_pca_fit_uses_only_training_cells(prepared):
_, d = prepared
train = d.indices("train")
mean = np.asarray(d.Xhvg[train].mean(0)).ravel()
np.testing.assert_allclose(d.pca_mean, mean, rtol=1e-5, atol=1e-5)
def test_test_expression_cannot_change_fitted_features(prepared, tmp_path):
import anndata as ad
raw, d = prepared
a = ad.read_h5ad(raw)
held = d.obs.loc[d.obs.split.ne("train"), "cell_id"]
ids = a.obs_names.get_indexer(held)
x = a.X.tocsr()
x[ids] = x[ids] * 7
a.X = x
changed = tmp_path / "changed.h5ad"
a.write_h5ad(changed)
out = tmp_path / "second"
prepare(str(changed), str(out), "norman", n_hvg=100, n_pca=5, seed=0)
e = PerturbData(str(out))
assert e.genes == d.genes
np.testing.assert_allclose(e.pca_mean, d.pca_mean, atol=1e-6)
np.testing.assert_allclose(e.pca_components, d.pca_components, atol=1e-5)
def test_reference_outcomes_never_change_model_ranking(prepared):
from pivot.evaluation.runner import evaluate
_, d = prepared
# The predictor accesses source populations and action labels only.
predict = lambda c, p: c + len(d.parse(p)) * 0.1
root = Path(d.dir)
a = evaluate(d, predict, root / "a.json", catalog="all", n_cells=8)
ref = d.indices("reference", False)
saved = d.emb[ref].copy()
d.emb[ref] += 5
try:
b = evaluate(d, predict, root / "b.json", catalog="all", n_cells=8)
finally:
d.emb[ref] = saved
assert [r["selected"] for r in a["inverse"]] == [
r["selected"] for r in b["inverse"]
]
assert [r["measured_reward"] for r in a["inverse"]] != [
r["measured_reward"] for r in b["inverse"]
]
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