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Running on Zero
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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 | 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_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_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
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