Datasets:
Formats:
parquet
Languages:
English
Size:
10M - 100M
Tags:
biology
chemistry
drug-discovery
clinical-trials
protein-protein-interaction
gene-essentiality
License:
File size: 6,513 Bytes
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 99 100 101 102 103 104 105 106 107 108 109 110 111 112 113 114 115 116 117 118 119 120 121 122 123 124 125 126 127 128 129 130 131 132 133 134 135 136 137 138 139 140 141 142 143 144 145 146 147 148 149 150 151 152 153 154 155 156 157 158 159 160 161 162 163 164 165 166 167 168 169 170 171 172 173 174 175 176 177 178 179 180 181 182 183 184 185 186 187 188 189 190 191 192 193 194 195 196 197 198 199 200 201 | """Tests for negbiodb_ppi.models — PPI ML baseline models."""
import sys
from pathlib import Path
import torch
import pytest
sys.path.insert(0, str(Path(__file__).resolve().parent.parent / "src"))
from negbiodb_ppi.models.siamese_cnn import SiameseCNN, seq_to_tensor, MAX_SEQ_LEN
from negbiodb_ppi.models.pipr import PIPR
from negbiodb_ppi.models.pipr import seq_to_tensor as pipr_seq_to_tensor
from negbiodb_ppi.models.mlp_features import (
MLPFeatures,
extract_features,
compute_aa_composition,
encode_subcellular,
FEATURE_DIM,
)
# ------------------------------------------------------------------
# Tokenization tests
# ------------------------------------------------------------------
class TestSeqToTensor:
def test_basic(self):
t = seq_to_tensor(["ACDEF", "GHI"])
assert t.shape == (2, MAX_SEQ_LEN)
assert t.dtype == torch.int64
# First 5 positions should be nonzero for "ACDEF"
assert t[0, :5].sum() > 0
# Position 5+ should be padding (0)
assert t[0, 5:].sum() == 0
def test_empty(self):
t = seq_to_tensor([""])
assert t.shape == (1, MAX_SEQ_LEN)
assert t.sum() == 0
def test_truncation(self):
long_seq = "A" * 2000
t = seq_to_tensor([long_seq])
assert t.shape == (1, MAX_SEQ_LEN)
# All MAX_SEQ_LEN positions should be the same nonzero token
assert (t[0, :MAX_SEQ_LEN] > 0).all()
# ------------------------------------------------------------------
# SiameseCNN tests
# ------------------------------------------------------------------
class TestSiameseCNN:
def test_forward_shape(self):
model = SiameseCNN()
B = 4
s1 = seq_to_tensor(["ACDEF" * 10] * B)
s2 = seq_to_tensor(["GHIKL" * 10] * B)
out = model(s1, s2)
assert out.shape == (B,)
def test_symmetry(self):
"""f(A, B) == f(B, A) due to shared encoder + |diff|."""
model = SiameseCNN()
model.eval()
s1 = seq_to_tensor(["ACDEFGHIKL"])
s2 = seq_to_tensor(["MNPQRSTVWY"])
with torch.no_grad():
out_12 = model(s1, s2)
out_21 = model(s2, s1)
assert torch.allclose(out_12, out_21, atol=1e-5)
def test_gradient_flow(self):
model = SiameseCNN()
s1 = seq_to_tensor(["ACDEF"])
s2 = seq_to_tensor(["GHIKL"])
out = model(s1, s2)
loss = out.sum()
loss.backward()
# Check all parameters have gradients
for name, p in model.named_parameters():
if p.requires_grad:
assert p.grad is not None, f"No gradient for {name}"
# ------------------------------------------------------------------
# PIPR tests
# ------------------------------------------------------------------
class TestPIPR:
def test_forward_shape(self):
model = PIPR()
B = 4
s1 = pipr_seq_to_tensor(["ACDEF" * 10] * B)
s2 = pipr_seq_to_tensor(["GHIKL" * 10] * B)
out = model(s1, s2)
assert out.shape == (B,)
def test_symmetry(self):
"""Shared encoder + symmetric attention pooling → f(A,B) ≈ f(B,A)."""
model = PIPR()
model.eval()
s1 = pipr_seq_to_tensor(["ACDEFGHIKL"])
s2 = pipr_seq_to_tensor(["MNPQRSTVWY"])
with torch.no_grad():
out_12 = model(s1, s2)
out_21 = model(s2, s1)
assert torch.allclose(out_12, out_21, atol=1e-5)
def test_gradient_flow(self):
model = PIPR()
s1 = pipr_seq_to_tensor(["ACDEF"])
s2 = pipr_seq_to_tensor(["GHIKL"])
out = model(s1, s2)
loss = out.sum()
loss.backward()
for name, p in model.named_parameters():
if p.requires_grad:
assert p.grad is not None, f"No gradient for {name}"
def test_handles_padding(self):
"""Model should handle sequences of different lengths gracefully."""
model = PIPR()
model.eval()
s1 = pipr_seq_to_tensor(["AC"]) # very short
s2 = pipr_seq_to_tensor(["GHIKL" * 50]) # medium
with torch.no_grad():
out = model(s1, s2)
assert out.shape == (1,)
assert torch.isfinite(out).all()
# ------------------------------------------------------------------
# MLPFeatures tests
# ------------------------------------------------------------------
class TestMLPFeatures:
def test_forward_shape(self):
model = MLPFeatures()
B = 4
features = torch.randn(B, FEATURE_DIM)
out = model(features)
assert out.shape == (B,)
def test_gradient_flow(self):
model = MLPFeatures()
features = torch.randn(2, FEATURE_DIM)
out = model(features)
loss = out.sum()
loss.backward()
for name, p in model.named_parameters():
if p.requires_grad:
assert p.grad is not None, f"No gradient for {name}"
class TestFeatureExtraction:
def test_aa_composition(self):
comp = compute_aa_composition("AAACCC")
assert len(comp) == 20
assert abs(sum(comp) - 1.0) < 1e-6
# A and C should dominate
assert comp[0] > 0.3 # A
assert comp[1] > 0.3 # C
def test_aa_composition_empty(self):
comp = compute_aa_composition("")
assert comp == [0.0] * 20
def test_subcellular_encoding(self):
vec = encode_subcellular("Nucleus")
assert len(vec) == 11
assert vec[0] == 1.0
assert sum(vec) == 1.0
def test_subcellular_none(self):
vec = encode_subcellular(None)
assert sum(vec) == 0.0
def test_subcellular_unknown(self):
vec = encode_subcellular("Unknown location XYZ")
assert vec[-1] == 1.0 # "other"
def test_extract_features_dim(self):
f = extract_features(
"ACDEF", "GHIKL",
degree1=10.0, degree2=20.0,
loc1="Nucleus", loc2="Cytoplasm",
)
assert len(f) == FEATURE_DIM
def test_extract_features_symmetry_invariant(self):
"""Feature vector should NOT be order-invariant (p1 != p2 features)."""
f12 = extract_features("ACDEF", "GHIKL", 10, 20, "Nucleus", "Cytoplasm")
f21 = extract_features("GHIKL", "ACDEF", 20, 10, "Cytoplasm", "Nucleus")
# f12 != f21 because AA compositions are in fixed order (p1 then p2)
# But they should have same values rearranged
assert len(f12) == len(f21)
|