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07fcdfe | 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 99 100 101 102 103 104 105 106 107 108 109 110 111 112 113 114 115 116 117 118 119 120 121 122 123 124 125 126 127 128 129 130 131 132 133 134 135 136 137 138 139 140 141 142 143 144 145 146 147 148 149 150 151 152 153 154 155 156 157 158 159 160 161 162 163 164 165 166 167 168 169 170 171 172 173 174 175 176 177 178 179 180 181 182 183 184 185 186 187 188 189 190 191 192 193 194 195 196 197 198 199 200 201 202 203 204 205 206 207 208 209 210 211 212 213 214 215 216 217 218 219 220 221 222 223 224 225 226 227 228 229 230 231 232 233 234 235 236 237 238 239 240 241 242 243 244 245 246 247 248 249 250 251 252 253 254 255 256 257 258 259 260 261 262 263 264 265 266 267 268 269 270 271 272 273 274 275 276 277 278 279 280 281 282 283 284 285 286 287 288 289 290 291 292 293 294 295 296 297 298 299 300 301 302 303 304 | """Tests for P3: SparsePlanner, PopulationEncoder, GapEncoder, PopulationGIDModel,
target metrics, and perturbation eval metrics."""
import sys
import os
sys.path.insert(0, os.path.join(os.path.dirname(__file__), "..", "src"))
import pytest
import torch
from gidflow.models import (
PopulationEncoder,
GapEncoder,
SparsePlanner,
PopulationGIDModel,
)
from gidflow.metrics import (
recall_at_k, precision_at_k, ndcg_at_k, mrr, jaccard_topk,
compute_all_target_metrics,
pearson_r_mean, pearson_r_topk_de, de_direction_agreement,
compute_all_perturbation_metrics,
mmd_rbf_projected,
)
from gidflow.data import SyntheticPopulationPerturbationDataset, population_collate_fn
from torch.utils.data import DataLoader
# ---------------------------------------------------------------------------
# PopulationEncoder
# ---------------------------------------------------------------------------
class TestPopulationEncoder:
def test_output_shape_with_var(self):
enc = PopulationEncoder(num_genes=32, hidden_dim=64, output_dim=16, use_var=True)
cells = torch.randn(3, 10, 32)
z = enc(cells)
assert z.shape == (3, 16)
def test_output_shape_no_var(self):
enc = PopulationEncoder(num_genes=32, hidden_dim=64, output_dim=16, use_var=False)
z = enc(torch.randn(2, 8, 32))
assert z.shape == (2, 16)
def test_mask_accepted(self):
enc = PopulationEncoder(32, 64, 16, use_var=True)
cells = torch.randn(2, 8, 32)
mask = torch.ones(2, 8, dtype=torch.bool)
mask[0, 6:] = False
z = enc(cells, mask)
assert z.shape == (2, 16)
assert not z.isnan().any()
def test_gradient_flows(self):
enc = PopulationEncoder(16, 32, 8)
x = torch.randn(2, 5, 16, requires_grad=True)
z = enc(x)
z.mean().backward()
assert x.grad is not None
# ---------------------------------------------------------------------------
# GapEncoder
# ---------------------------------------------------------------------------
class TestGapEncoder:
def test_output_shape(self):
gap = GapEncoder(input_dim=16, hidden_dim=32, output_dim=24)
z_s = torch.randn(3, 16)
z_t = torch.randn(3, 16)
z_g = gap(z_s, z_t)
assert z_g.shape == (3, 24)
def test_gradient_flows(self):
gap = GapEncoder(16, 32, 24)
z_s = torch.randn(2, 16, requires_grad=True)
z_t = torch.randn(2, 16, requires_grad=True)
gap(z_s, z_t).mean().backward()
assert z_s.grad is not None
# ---------------------------------------------------------------------------
# SparsePlanner
# ---------------------------------------------------------------------------
class TestSparsePlanner:
@pytest.fixture
def planner(self):
return SparsePlanner(gap_dim=32, num_genes=64, hidden_dim=64)
def test_score_shape(self, planner):
z_gap = torch.randn(4, 32)
scores = planner(z_gap)
assert scores.shape == (4, 64)
def test_soft_mask_range(self, planner):
z_gap = torch.randn(2, 32)
mask = planner.get_soft_mask(z_gap)
assert (mask >= 0).all() and (mask <= 1).all()
def test_topk_mask_binary(self, planner):
z_gap = torch.randn(2, 32)
mask = planner.get_topk_mask(z_gap, k=5)
assert ((mask == 0) | (mask == 1)).all()
assert mask.sum(dim=-1).eq(5).all()
def test_ste_topk_gradient(self, planner):
z_gap = torch.randn(2, 32, requires_grad=True)
mask = planner.get_ste_topk_mask(z_gap, k=5)
mask.sum().backward()
assert z_gap.grad is not None
# Hard mask but gradient is not zero
assert z_gap.grad.abs().sum() > 0
def test_ste_mask_is_binary(self, planner):
z_gap = torch.randn(2, 32)
mask = planner.get_ste_topk_mask(z_gap, k=3)
assert ((mask == 0) | (mask == 1)).all()
# ---------------------------------------------------------------------------
# PopulationGIDModel (integration)
# ---------------------------------------------------------------------------
class TestPopulationGIDModel:
@pytest.fixture
def model(self):
return PopulationGIDModel(
num_genes=32,
encoder_hidden=64, encoder_output=16,
gap_hidden=32, gap_output=32,
planner_hidden=32,
response_hidden=64, response_pert_dim=32,
n_layers=1,
planner_topk=3,
)
def test_forward_keys(self, model):
B, N, G = 2, 6, 32
src = torch.randn(B, N, G)
tgt = torch.randn(B, N, G)
out = model(src, tgt)
assert "target_scores" in out
assert "target_mask" in out
assert "pred_cells" in out
def test_shapes(self, model):
B, Ns, Nt, G = 2, 8, 6, 32
src = torch.randn(B, Ns, G)
tgt = torch.randn(B, Nt, G)
out = model(src, tgt)
assert out["target_scores"].shape == (B, G)
assert out["pred_cells"].shape == (B, Ns, G)
def test_teacher_forcing(self, model):
B, N, G = 2, 5, 32
src = torch.randn(B, N, G)
tgt = torch.randn(B, N, G)
pert = torch.zeros(B, G); pert[:, :3] = 1.0
out = model(src, tgt, true_perturbation=pert)
assert out["pred_cells"].shape == (B, N, G)
def test_no_nan(self, model):
src = torch.randn(2, 6, 32)
tgt = torch.randn(2, 6, 32)
out = model(src, tgt)
assert not out["target_scores"].isnan().any()
assert not out["pred_cells"].isnan().any()
def test_end_to_end_gradient(self, model):
src = torch.randn(2, 5, 32, requires_grad=False)
tgt = torch.randn(2, 5, 32)
out = model(src, tgt)
out["pred_cells"].mean().backward()
# Check at least one param has gradient
grads = [p.grad for p in model.parameters() if p.grad is not None]
assert len(grads) > 0
def test_predict_targets_shape(self, model):
model.eval()
src = torch.randn(2, 5, 32)
tgt = torch.randn(2, 5, 32)
mask = model.predict_targets(src, tgt, topk=3)
assert mask.shape == (2, 32)
assert mask.sum(dim=-1).eq(3).all()
# ---------------------------------------------------------------------------
# Target metrics
# ---------------------------------------------------------------------------
class TestTargetMetrics:
def _make_scores_targets(self, B=4, G=64, k_true=3):
targets = torch.zeros(B, G)
for b in range(B):
idx = torch.randperm(G)[:k_true]
targets[b, idx] = 1.0
# Perfect scores: true targets get high score
scores = torch.rand(B, G)
scores[targets.bool()] += 10.0
return scores, targets
def test_recall_at_k_perfect(self):
scores, targets = self._make_scores_targets(k_true=3)
r = recall_at_k(scores, targets, k=3)
assert r.item() > 0.9
def test_recall_at_k_random(self):
torch.manual_seed(7)
targets = torch.zeros(100, 1000); targets[:, :3] = 1.0
scores = torch.rand(100, 1000)
r = recall_at_k(scores, targets, k=3)
# Expected ~3/1000 * 3 ≈ 0.009 → very low
assert r.item() < 0.1
def test_ndcg_perfect_vs_random(self):
scores, targets = self._make_scores_targets()
ndcg_good = ndcg_at_k(scores, targets, k=5).item()
scores_rand = torch.rand_like(scores)
ndcg_rand = ndcg_at_k(scores_rand, targets, k=5).item()
assert ndcg_good > ndcg_rand
def test_mrr_perfect(self):
B, G = 3, 32
targets = torch.zeros(B, G); targets[:, 0] = 1.0
scores = torch.zeros(B, G); scores[:, 0] = 10.0
r = mrr(scores, targets)
assert abs(r.item() - 1.0) < 0.01
def test_compute_all_returns_keys(self):
scores, targets = self._make_scores_targets()
out = compute_all_target_metrics(scores, targets, ks=(1, 5))
for k in ["recall@1", "recall@5", "ndcg@1", "ndcg@5", "mrr"]:
assert k in out
# ---------------------------------------------------------------------------
# Perturbation eval metrics
# ---------------------------------------------------------------------------
class TestPerturbationEval:
def _make_populations(self, B=2, N=20, G=16):
src = torch.randn(B, N, G)
tgt = src + torch.randn(B, 1, G) * 0.5 # shifted
pred = tgt + torch.randn(B, N, G) * 0.1 # close to true
rand = torch.randn(B, N, G) + 5.0 # far from true
return src, tgt, pred, rand
def test_pearson_r_perfect(self):
B, N, G = 2, 10, 16
cells = torch.randn(B, N, G)
r = pearson_r_mean(cells, cells)
assert r.item() > 0.99
def test_pearson_r_good_better_than_random(self):
src, tgt, pred, rand = self._make_populations()
r_good = pearson_r_mean(pred, tgt).item()
r_rand = pearson_r_mean(rand, tgt).item()
assert r_good > r_rand
def test_pearson_r_topk_de(self):
src, tgt, pred, _ = self._make_populations(G=32)
r = pearson_r_topk_de(pred, tgt, src, topk=8)
assert -1.0 <= r.item() <= 1.0
def test_de_direction_agreement_perfect(self):
src = torch.randn(2, 10, 16)
tgt = src + 1.0 # all positive shift
pred = src + 1.0 # identical shift
agr = de_direction_agreement(pred, tgt, src)
assert agr.item() > 0.95
def test_compute_all_returns_keys(self):
src, tgt, pred, _ = self._make_populations()
out = compute_all_perturbation_metrics(pred, tgt, src)
assert "pearson_r_mean" in out
assert "de_direction_agreement" in out
assert "nmse" in out
# ---------------------------------------------------------------------------
# mmd_rbf_projected
# ---------------------------------------------------------------------------
class TestMMDProjected:
def test_same_dist_low_mmd(self):
torch.manual_seed(0)
x = torch.randn(2, 20, 128)
mmd = mmd_rbf_projected(x, x, n_components=16)
assert mmd.item() < 0.5
def test_different_dist_higher_mmd(self):
torch.manual_seed(1)
x = torch.randn(2, 20, 128)
y = torch.randn(2, 20, 128) + 5.0
mmd_proj = mmd_rbf_projected(x, y, n_components=16)
mmd_zero = mmd_rbf_projected(x, x, n_components=16)
assert mmd_proj.item() > mmd_zero.item()
def test_shape(self):
x = torch.randn(3, 15, 64)
y = torch.randn(3, 12, 64)
mmd = mmd_rbf_projected(x, y, n_components=8)
assert mmd.shape == torch.Size([])
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