Spaces:
Running on Zero
Running on Zero
File size: 7,127 Bytes
31376a7 2e6a2a4 31376a7 2e6a2a4 31376a7 | 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 | from __future__ import annotations
import pickle
import numpy as np
import pandas as pd
import torch
from biolmnet.artifacts import load_bundle, save_bundle
from biolmnet.data import (
BranchPriors,
PreparedWorkspace,
attach_embeddings_and_pathways,
build_biological_mask,
deterministic_gene_embeddings,
)
from biolmnet.model import BioMaskedLinear, BioLMNet
from biolmnet.training import Hyperparameters, predict, train
def test_biological_mask_uses_pdi_and_undirected_ppi() -> None:
pdi = pd.DataFrame(
{"TF": ["A", "C", "outside"], "Target": ["B", "D", "A"]}
)
ppi = pd.DataFrame(
{
"protein1": ["A", "X", "B"],
"protein2": ["X", "C", "Y"],
"combined_score": [950, 950, 710],
}
)
branch = build_biological_mask(["A", "B", "C", "D"], pdi, ppi)
assert {"B", "D", "X"}.issubset(branch.hidden_genes)
x_index = branch.hidden_genes.index("X")
assert branch.biological_mask[0, x_index] > 0
assert branch.biological_mask[2, x_index] > 0
assert branch.pdi_edges == 2
def test_masked_linear_disconnects_unlisted_weights() -> None:
mask = torch.tensor([[1.0, 0.0], [0.0, 1.0]])
layer = BioMaskedLinear(mask, bias=False)
values = torch.tensor([[2.0, 3.0]])
baseline = layer(values).detach().clone()
with torch.no_grad():
layer.weight[0, 1] = 10_000
layer.weight[1, 0] = -10_000
changed = layer(values).detach()
assert torch.allclose(baseline, changed)
def _tiny_workspace() -> PreparedWorkspace:
rng = np.random.default_rng(7)
sample_count = 48
labels = np.repeat(np.array([0, 1]), sample_count // 2)
gene_values = rng.normal(size=(sample_count, 4)).astype(np.float32)
dna_values = rng.normal(size=(sample_count, 4)).astype(np.float32)
gene_values[:, 0] += labels * 1.5
dna_values[:, 1] -= labels * 1.2
input_genes = ["A", "B", "C", "D"]
hidden_genes = ["A", "B", "C"]
biological = np.array(
[
[1.0, 1.0, 0.0],
[0.0, 1.0, 1.0],
[1.0, 0.0, 1.0],
[0.0, 1.0, 0.0],
],
dtype=np.float32,
)
pathway_mapping = pd.DataFrame(
{
"SYMBOL": ["A", "B", "B", "C"],
"PathwayID": ["hsa1", "hsa1", "hsa2", "hsa2"],
}
)
embeddings = deterministic_gene_embeddings(hidden_genes, dimensions=8)
def branch() -> BranchPriors:
value = BranchPriors(
input_genes=input_genes.copy(),
hidden_genes=hidden_genes.copy(),
biological_mask=biological.copy(),
pdi_edges=3,
ppi_edges=4,
)
attach_embeddings_and_pathways(
value,
embeddings,
pathway_mapping,
precomputed_significant=True,
)
return value
return PreparedWorkspace(
gene_expression=gene_values,
dna_methylation=dna_values,
labels=labels,
label_names=["control", "case"],
gene_branch=branch(),
dna_branch=branch(),
source_name="unit test",
)
def test_model_forward_probabilistic_shape() -> None:
workspace = _tiny_workspace()
gene = workspace.gene_branch
dna = workspace.dna_branch
model = BioLMNet(
torch.from_numpy(gene.biological_mask),
torch.from_numpy(dna.biological_mask),
torch.from_numpy(gene.embeddings),
torch.from_numpy(dna.embeddings),
torch.from_numpy(gene.pathway_mask),
torch.from_numpy(dna.pathway_mask),
n_classes=2,
projection_dim=4,
fusion_dim=3,
dropout=0.0,
)
logits = model(torch.randn(5, 4), torch.randn(5, 4))
assert logits.shape == (5, 2)
assert torch.allclose(
model.gene_branch.pathway_attention.attention_weights().sum(dim=0),
torch.ones(2),
)
def test_duplicate_embedding_rows_do_not_break_pathway_attachment() -> None:
branch = BranchPriors(
input_genes=["A", "B"],
hidden_genes=["A", "B", "C"],
biological_mask=np.ones((2, 3), dtype=np.float32),
)
embeddings = pd.DataFrame(
np.arange(16, dtype=np.float32).reshape(4, 4),
index=["A", "B", "B", "C"],
)
pathway_mapping = pd.DataFrame(
{
"SYMBOL": ["A", "B", "C"],
"PathwayID": ["hsa1", "hsa1", "hsa2"],
}
)
attach_embeddings_and_pathways(
branch,
embeddings,
pathway_mapping,
precomputed_significant=True,
)
assert branch.embeddings.shape[0] == len(branch.hidden_genes)
assert branch.biological_mask.shape[1] == len(branch.hidden_genes)
assert branch.pathway_mask.shape[0] == len(branch.hidden_genes)
def test_training_artifact_roundtrip(tmp_path) -> None:
workspace = _tiny_workspace()
result = train(
workspace,
Hyperparameters(
epochs=3,
batch_size=8,
projection_dim=4,
fusion_dim=3,
dropout=0.0,
early_stopping_patience=3,
),
)
restored_from_process_boundary = pickle.loads(pickle.dumps(result.bundle))
assert restored_from_process_boundary.label_names == ["control", "case"]
artifact = save_bundle(result.bundle, tmp_path / "model.zip")
restored = load_bundle(artifact)
gene_frame = pd.DataFrame(
workspace.gene_expression[:5], columns=restored.gene_features
)
dna_frame = pd.DataFrame(
workspace.dna_methylation[:5], columns=restored.dna_features
)
before = predict(gene_frame, dna_frame, result.bundle)
after = predict(gene_frame, dna_frame, restored)
probability_columns = [column for column in before if column.startswith("P(")]
np.testing.assert_allclose(
before[probability_columns].to_numpy(),
after[probability_columns].to_numpy(),
atol=1e-6,
)
def test_prediction_can_score_prepared_workspace_without_uploads() -> None:
from app import run_prediction
workspace = _tiny_workspace()
result = train(
workspace,
Hyperparameters(
epochs=3,
batch_size=8,
projection_dim=4,
fusion_dim=3,
dropout=0.0,
early_stopping_patience=3,
),
)
_, status, output, _, download_path, predict_meta = run_prediction(
result.bundle,
workspace,
None,
False,
True,
None,
None,
{},
)
assert "Predicted 48 samples" in status
assert len(output) == len(workspace.labels)
assert download_path is not None
assert predict_meta["n_samples"] == len(workspace.labels)
def test_zerogpu_duration_estimator_is_bounded_and_scales() -> None:
from app import estimate_training_duration
workspace = _tiny_workspace()
common = (workspace, 16, 0.001, 0.01, 0.3, 64, 12, 0.2, "Adam", True)
short = estimate_training_duration(common[0], 10, *common[1:])
long = estimate_training_duration(common[0], 200, *common[1:])
assert 30 <= short <= 300
assert short <= long <= 300
|