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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 | """Tests for GeneGraphEncoder, graph_utils, and GIDModel with gene graph."""
import sys
import os
sys.path.insert(0, os.path.join(os.path.dirname(__file__), "..", "src"))
import pytest
import torch
from gidflow.data import (
load_pdgrapher_edge_index,
extract_subgraph,
build_normalized_adjacency,
)
from gidflow.models import GeneGraphEncoder, PopulationGIDModel
PDG_EI_PATH = (
"/data/boom/Protein/PDGrapher/data/processed/"
"torch_data/real_lognorm/edge_index_A375.pt"
)
real_data_available = pytest.mark.skipif(
not os.path.exists(PDG_EI_PATH), reason="PDGrapher edge_index not found"
)
# ---------------------------------------------------------------------------
# graph_utils
# ---------------------------------------------------------------------------
class TestGraphUtils:
def _small_ei(self, G=20, E=60):
src = torch.randint(0, G, (E,))
dst = torch.randint(0, G, (E,))
return torch.stack([src, dst])
def test_build_normalized_adjacency_shape(self):
ei = self._small_ei(G=10, E=30)
A = build_normalized_adjacency(ei, num_nodes=10)
assert A.shape == (10, 10)
def test_adjacency_nonneg_and_diagonal_positive(self):
ei = self._small_ei(G=10, E=40)
A = build_normalized_adjacency(ei, num_nodes=10)
# All values ≥ 0
assert (A >= 0).all()
# Diagonal (self-loops always added) should be positive
assert (A.diag() > 0).all()
def test_adjacency_undirected_is_symmetric(self):
G = 10
src = torch.randint(0, G, (30,)); dst = torch.randint(0, G, (30,))
# Build undirected: include both directions
ei = torch.stack([torch.cat([src, dst]), torch.cat([dst, src])])
A = build_normalized_adjacency(ei, num_nodes=G)
assert torch.allclose(A, A.T, atol=1e-5)
def test_extract_subgraph_reduces_edges(self):
G = 10
full_genes = [f"G{i}" for i in range(G)]
target_genes = ["G0", "G1", "G2", "G5"]
# Create edges covering full graph
ei = torch.tensor([[0,1,2,5,7,8],[1,2,5,0,8,9]])
sub_ei, mask = extract_subgraph(ei, full_genes, target_genes)
# Only edges between target nodes should remain
assert sub_ei.shape[0] == 2
assert sub_ei.max() < len(target_genes)
def test_extract_subgraph_correct_remapping(self):
full_genes = ["A", "B", "C", "D"]
target_genes = ["B", "D"]
# Add a B-D edge (idx 1 → idx 3) alongside A-B and C-D
ei = torch.tensor([[0, 1, 2, 1, 3],
[1, 0, 3, 3, 1]])
sub_ei, _ = extract_subgraph(ei, full_genes, target_genes)
# B→D and D→B edges: B=0 in target, D=1 in target
assert sub_ei.shape[1] > 0
assert sub_ei.max().item() < len(target_genes)
@real_data_available
def test_load_pdgrapher_edge_index(self):
ei, _ = load_pdgrapher_edge_index(PDG_EI_PATH)
assert ei.shape[0] == 2
assert ei.shape[1] == 303678
assert ei.max().item() == 10715
# ---------------------------------------------------------------------------
# GeneGraphEncoder
# ---------------------------------------------------------------------------
class TestGeneGraphEncoder:
def _make_encoder(self, G=50, out_dim=16):
enc = GeneGraphEncoder(
num_genes=G, node_feature_dim=16, hidden_dim=32,
output_dim=out_dim, n_layers=2
)
ei = torch.randint(0, G, (2, 200))
enc.set_graph(ei)
return enc
def test_output_shape(self):
enc = self._make_encoder(G=50, out_dim=16)
emb = enc()
assert emb.shape == (50, 16)
def test_no_nan(self):
enc = self._make_encoder(G=40, out_dim=8)
emb = enc()
assert not emb.isnan().any()
assert not emb.isinf().any()
def test_gradient_through_embedding(self):
enc = self._make_encoder(G=30, out_dim=8)
emb = enc()
emb.sum().backward()
# Gene embedding weights should have gradients
assert enc.gene_embedding.weight.grad is not None
def test_with_protein_features(self):
G, F_p = 40, 4
enc = GeneGraphEncoder(
num_genes=G, node_feature_dim=16, hidden_dim=32,
output_dim=16, n_layers=2, protein_feature_dim=F_p
)
ei = torch.randint(0, G, (2, 150))
enc.set_graph(ei)
prot = torch.rand(G, F_p)
emb = enc(prot)
assert emb.shape == (G, 16)
def test_different_graphs_give_different_embeddings(self):
G = 30
enc = GeneGraphEncoder(G, 16, 32, 16, n_layers=1)
ei1 = torch.randint(0, G, (2, 100))
ei2 = torch.randint(0, G, (2, 100))
enc.set_graph(ei1); emb1 = enc().detach()
enc.set_graph(ei2); emb2 = enc().detach()
# Different graphs → different embeddings (very likely with random edges)
assert not torch.allclose(emb1, emb2)
def test_set_graph_raises_before_forward(self):
enc = GeneGraphEncoder(20, 8, 16, 8, n_layers=1)
with pytest.raises(RuntimeError):
enc() # no set_graph called
@real_data_available
def test_real_pdgrapher_graph(self):
ei, _ = load_pdgrapher_edge_index(PDG_EI_PATH)
enc = GeneGraphEncoder(10716, node_feature_dim=16, hidden_dim=32, output_dim=16, n_layers=1)
enc.set_graph(ei)
emb = enc()
assert emb.shape == (10716, 16)
assert not emb.isnan().any()
# ---------------------------------------------------------------------------
# PopulationGIDModel with gene graph
# ---------------------------------------------------------------------------
class TestGIDModelWithGeneGraph:
@pytest.fixture
def model_and_ei(self):
G = 60
ei = torch.randint(0, G, (2, 250))
model = PopulationGIDModel(
num_genes=G, encoder_hidden=32, encoder_output=16,
gap_hidden=32, gap_output=32, planner_hidden=32,
response_hidden=32, response_pert_dim=16, n_layers=1,
planner_topk=2, encoder_use_var=False,
use_gene_graph=True,
gene_graph_node_dim=16, gene_graph_hidden_dim=32,
gene_graph_output_dim=16, gene_graph_n_layers=2,
)
model.set_gene_graph(ei)
return model, ei
def test_forward_shapes(self, model_and_ei):
model, _ = model_and_ei
B, G = 2, 60
src = torch.randn(B, 1, G)
tgt = torch.randn(B, 1, G)
out = model(src, tgt)
assert out["target_scores"].shape == (B, G)
assert out["pred_cells"].shape == (B, 1, G)
def test_no_nan(self, model_and_ei):
model, _ = model_and_ei
src = torch.randn(2, 1, 60); tgt = torch.randn(2, 1, 60)
out = model(src, tgt)
assert not out["target_scores"].isnan().any()
assert not out["pred_cells"].isnan().any()
def test_gradient_flows_through_gcn(self, model_and_ei):
model, _ = model_and_ei
src = torch.randn(2, 1, 60); tgt = torch.randn(2, 1, 60)
out = model(src, tgt)
out["target_scores"].sum().backward()
# GCN gene embedding should get gradient
gcn_grad = model.gene_graph_encoder.gene_embedding.weight.grad
assert gcn_grad is not None
assert gcn_grad.abs().sum() > 0
def test_gcn_off_matches_no_graph(self):
"""Without graph, model should still forward correctly."""
G = 60
model_no_graph = PopulationGIDModel(
num_genes=G, encoder_hidden=32, encoder_output=16,
gap_hidden=32, gap_output=32, planner_hidden=32,
response_hidden=32, response_pert_dim=16, n_layers=1,
planner_topk=2, encoder_use_var=False, use_gene_graph=False,
)
src = torch.randn(2, 1, G); tgt = torch.randn(2, 1, G)
out = model_no_graph(src, tgt)
assert out["target_scores"].shape == (2, G)
def test_predict_targets_shape(self, model_and_ei):
model, _ = model_and_ei
model.eval()
src = torch.randn(2, 1, 60); tgt = torch.randn(2, 1, 60)
mask = model.predict_targets(src, tgt, topk=3)
assert mask.shape == (2, 60)
assert mask.sum(dim=-1).eq(3).all()
def test_with_protein_features_and_graph(self):
G, F_p = 50, 4
ei = torch.randint(0, G, (2, 200))
model = PopulationGIDModel(
num_genes=G, encoder_hidden=32, encoder_output=16,
gap_hidden=32, gap_output=32, planner_hidden=32,
response_hidden=32, response_pert_dim=16, n_layers=1,
planner_topk=1, encoder_use_var=False,
protein_input_dim=F_p, protein_hidden_dim=16, protein_output_dim=8,
use_gene_graph=True,
gene_graph_node_dim=16, gene_graph_hidden_dim=32,
gene_graph_output_dim=16, gene_graph_n_layers=1,
)
model.set_gene_graph(ei)
model.set_protein_features(torch.rand(G, F_p))
src = torch.randn(2, 1, G); tgt = torch.randn(2, 1, G)
out = model(src, tgt)
assert out["target_scores"].shape == (2, G)
assert not out["target_scores"].isnan().any()
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