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6d1bbc7 | 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 | """Tests for CT model definitions (ct_models.py).
3 test classes:
TestCTMLP: 4 tests — output shapes, gradient flow, dropout
TestCTGNNTab: 4 tests — output shapes, placeholder graph, gradient flow
TestModelFactory: 2 tests — correct model type, unknown raises ValueError
"""
import numpy as np
import pytest
import torch
from negbiodb_ct.ct_features import (
CONDITION_DIM,
DRUG_TAB_DIM,
M2_TRIAL_DIM,
TOTAL_M1_DIM,
TOTAL_M2_DIM,
)
from negbiodb_ct.ct_models import (
CT_GNN_Tab,
CT_MLP,
GNN_TAB_DIM_M1,
GNN_TAB_DIM_M2,
build_ct_model,
)
# Skip GNN tests if torch_geometric not installed
torch_geometric = pytest.importorskip("torch_geometric")
from torch_geometric.data import Batch, Data
from negbiodb.models.graphdta import NODE_FEATURE_DIM
def _make_dummy_graph(n_atoms: int = 5) -> Data:
"""Create a dummy molecular graph for testing."""
x = torch.randn(n_atoms, NODE_FEATURE_DIM)
# Simple chain graph: 0-1-2-...-n
edges = []
for i in range(n_atoms - 1):
edges.extend([[i, i + 1], [i + 1, i]])
edge_index = torch.tensor(edges, dtype=torch.long).t().contiguous()
return Data(x=x, edge_index=edge_index)
def _make_single_node_graph() -> Data:
"""Placeholder graph: single node, no edges."""
x = torch.zeros(1, NODE_FEATURE_DIM)
edge_index = torch.zeros((2, 0), dtype=torch.long)
return Data(x=x, edge_index=edge_index)
# ============================================================================
# TestCTMLP
# ============================================================================
class TestCTMLP:
"""Test CT_MLP forward pass and properties."""
def test_m1_output_shape(self):
"""M1 binary: output shape (B,)."""
model = CT_MLP(input_dim=TOTAL_M1_DIM, num_classes=1)
x = torch.randn(4, TOTAL_M1_DIM)
out = model(x)
assert out.shape == (4,)
def test_m2_output_shape(self):
"""M2 multiclass: output shape (B, 8)."""
model = CT_MLP(input_dim=TOTAL_M2_DIM, num_classes=8)
x = torch.randn(4, TOTAL_M2_DIM)
out = model(x)
assert out.shape == (4, 8)
def test_gradient_flows(self):
"""Gradient should flow through all parameters."""
model = CT_MLP(input_dim=TOTAL_M1_DIM, num_classes=1)
x = torch.randn(2, TOTAL_M1_DIM)
out = model(x)
loss = out.sum()
loss.backward()
for name, param in model.named_parameters():
assert param.grad is not None, f"No gradient for {name}"
assert not torch.all(param.grad == 0), f"Zero gradient for {name}"
def test_dropout_effect(self):
"""Dropout should cause different outputs in train vs eval mode."""
model = CT_MLP(input_dim=TOTAL_M1_DIM, num_classes=1, dropout=0.5)
x = torch.randn(8, TOTAL_M1_DIM)
model.train()
# Run multiple times to avoid the unlikely case of identical outputs
train_outputs = [model(x).detach() for _ in range(5)]
# At least some should differ due to dropout
differs = any(
not torch.allclose(train_outputs[0], train_outputs[i])
for i in range(1, 5)
)
assert differs, "Dropout should cause variation in train mode"
model.eval()
eval_out1 = model(x).detach()
eval_out2 = model(x).detach()
torch.testing.assert_close(eval_out1, eval_out2)
# ============================================================================
# TestCTGNNTab
# ============================================================================
class TestCTGNNTab:
"""Test CT_GNN_Tab forward pass."""
def test_m1_output_shape(self):
"""M1 binary: output (B,)."""
model = CT_GNN_Tab(tab_dim=GNN_TAB_DIM_M1, num_classes=1)
graphs = [_make_dummy_graph(5), _make_dummy_graph(3)]
batch = Batch.from_data_list(graphs)
tab = torch.randn(2, GNN_TAB_DIM_M1)
out = model(batch, tab)
assert out.shape == (2,)
def test_m2_output_shape(self):
"""M2 multiclass: output (B, 8)."""
model = CT_GNN_Tab(tab_dim=GNN_TAB_DIM_M2, num_classes=8)
graphs = [_make_dummy_graph(4), _make_dummy_graph(6)]
batch = Batch.from_data_list(graphs)
tab = torch.randn(2, GNN_TAB_DIM_M2)
out = model(batch, tab)
assert out.shape == (2, 8)
def test_single_node_placeholder(self):
"""Single-node placeholder graph should not crash."""
model = CT_GNN_Tab(tab_dim=GNN_TAB_DIM_M1, num_classes=1)
graphs = [_make_single_node_graph(), _make_dummy_graph(3)]
batch = Batch.from_data_list(graphs)
tab = torch.randn(2, GNN_TAB_DIM_M1)
out = model(batch, tab)
assert out.shape == (2,)
assert not torch.any(torch.isnan(out))
def test_gradient_flows(self):
"""Gradient should flow through GNN and tabular encoder."""
model = CT_GNN_Tab(tab_dim=GNN_TAB_DIM_M1, num_classes=1)
graphs = [_make_dummy_graph(5)]
batch = Batch.from_data_list(graphs)
tab = torch.randn(1, GNN_TAB_DIM_M1)
out = model(batch, tab)
loss = out.sum()
loss.backward()
for name, param in model.named_parameters():
assert param.grad is not None, f"No gradient for {name}"
# ============================================================================
# TestModelFactory
# ============================================================================
class TestModelFactory:
"""Test build_ct_model factory function."""
def test_correct_model_types(self):
"""Factory should return correct model types."""
mlp = build_ct_model("mlp", task="m1")
assert isinstance(mlp, CT_MLP)
gnn = build_ct_model("gnn", task="m2")
assert isinstance(gnn, CT_GNN_Tab)
def test_unknown_model_raises(self):
"""Unknown model name should raise ValueError."""
with pytest.raises(ValueError, match="Unknown model"):
build_ct_model("transformer", task="m1")
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