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c87881a | 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 | """Strict input schemas and a leakage-free BGC embedding dataset."""
from __future__ import annotations
from pathlib import Path
from typing import Iterable, Mapping
import h5py
import numpy as np
import pandas as pd
import torch
from torch.utils.data import Dataset
FORBIDDEN_MODEL_INPUTS = frozenset(
{
"pident", "qcovs", "evalue", "avg_mibig_identity", "deepbgc_score",
"product_activity", "product_class", "antibacterial", "cytotoxic",
"inhibitor", "antifungal", "Alkaloid", "NRP", "Other", "Polyketide",
"RiPP", "Saccharide", "Terpene",
}
)
ALLOWED_MODEL_INPUTS = frozenset(
{"gene_embeddings", "relative_positions", "padding_mask", "pfam_tokens"}
)
def require_columns(frame: pd.DataFrame, required: Iterable[str], table_name: str) -> None:
missing = set(required).difference(frame.columns)
if missing:
raise ValueError(f"{table_name} is missing columns: {sorted(missing)}")
def validate_model_input_names(names: Iterable[str]) -> None:
supplied = set(names)
forbidden = supplied.intersection(FORBIDDEN_MODEL_INPUTS)
unknown = supplied.difference(ALLOWED_MODEL_INPUTS)
if forbidden:
raise ValueError(f"Target-leaking model inputs are forbidden: {sorted(forbidden)}")
if unknown:
raise ValueError(f"Unknown model inputs: {sorted(unknown)}")
def build_pfam_vocab(atlas_csv: str | Path, training_bgc_ids: Iterable[str]) -> dict[str, int]:
"""Build a Pfam vocabulary from training BGCs only."""
atlas = pd.read_csv(atlas_csv, usecols=["bgc_id", "pfam_ids"])
wanted = {str(value) for value in training_bgc_ids}
tokens: set[str] = set()
for row in atlas.itertuples(index=False):
if str(row.bgc_id) not in wanted or pd.isna(row.pfam_ids):
continue
tokens.update(value for value in str(row.pfam_ids).split(";") if value)
return {token: index for index, token in enumerate(sorted(tokens), start=2)}
def load_legacy_labels(atlas_csv: str | Path) -> pd.DataFrame:
atlas = pd.read_csv(atlas_csv)
require_columns(atlas, ["bgc_id", "compound_family"], "legacy atlas")
labels = atlas[["bgc_id", "compound_family"]].rename(
columns={"compound_family": "mibig_reference_id"}
)
labels = labels.dropna(subset=["mibig_reference_id"]).copy()
labels["mibig_reference_id"] = labels["mibig_reference_id"].astype(str)
labels["group_id"] = labels["mibig_reference_id"]
labels["label_tier"] = "silver"
return labels
def load_gold_mapping(mapping_csv: str | Path) -> pd.DataFrame:
mapping = pd.read_csv(mapping_csv)
required = ["bgc_id", "product_group_id", "product_id", "source"]
require_columns(mapping, required, "gold mapping")
result = mapping.copy()
result["group_id"] = result["product_group_id"].astype(str)
result["label_tier"] = "gold"
return result
class BGCEmbeddingDataset(Dataset):
"""Load ESM embeddings in canonical atlas protein order.
The alignment-expanded gene table is deliberately not accepted here: it can
contain several hit rows per biological gene and therefore corrupt gene rank.
"""
def __init__(
self,
embeddings_h5: str | Path,
atlas_csv: str | Path,
assignments: pd.DataFrame,
esm_dimension: int = 1280,
pfam_vocab: Mapping[str, int] | None = None,
) -> None:
require_columns(assignments, ["bgc_id", "group_id", "split", "label_tier"], "assignments")
atlas = pd.read_csv(atlas_csv, usecols=["bgc_id", "protein_ids", "pfam_ids"])
if atlas["bgc_id"].duplicated().any():
raise ValueError("Atlas contains duplicate BGC identifiers")
wanted = set(assignments["bgc_id"].astype(str))
atlas = atlas[atlas["bgc_id"].astype(str).isin(wanted)].copy()
self.h5_path = str(Path(embeddings_h5).resolve())
self.esm_dimension = int(esm_dimension)
self._h5: h5py.File | None = None
with h5py.File(self.h5_path, "r") as handle:
available = set(handle.keys())
missing_rows: list[dict[str, str]] = []
grouped: dict[str, list[tuple[str, float]]] = {}
pfam_by_bgc: dict[str, list[str]] = {}
for row in atlas.itertuples(index=False):
bgc_id = str(row.bgc_id)
protein_ids = (
[value for value in str(row.protein_ids).split(";") if value]
if pd.notna(row.protein_ids)
else []
)
if len(protein_ids) != len(set(protein_ids)):
raise ValueError(f"Atlas protein order contains duplicate IDs for {bgc_id}")
denominator = max(1, len(protein_ids) - 1)
present: list[tuple[str, float]] = []
for rank, gene_id in enumerate(protein_ids):
if gene_id in available:
present.append((gene_id, rank / denominator))
else:
missing_rows.append({"bgc_id": bgc_id, "gene_id": gene_id})
if present:
grouped[bgc_id] = present
if pd.isna(row.pfam_ids):
pfam_by_bgc[bgc_id] = []
else:
pfam_by_bgc[bgc_id] = sorted(
{value for value in str(row.pfam_ids).split(";") if value}
)
self.missing_gene_rows = pd.DataFrame(missing_rows, columns=["bgc_id", "gene_id"])
metadata = assignments.drop_duplicates("bgc_id").set_index("bgc_id")
self.bgc_ids = [str(bgc_id) for bgc_id in metadata.index if str(bgc_id) in grouped]
rejected = set(metadata.index.astype(str)).difference(self.bgc_ids)
if rejected:
raise ValueError(f"BGCs have no usable ESM embeddings: {sorted(rejected)[:10]}")
self.bgc_to_genes = grouped
self.pfam_vocab = dict(pfam_vocab or {})
self.pfam_tokens_by_bgc = {
bgc_id: [self.pfam_vocab.get(token, 1) for token in pfam_by_bgc.get(bgc_id, [])]
for bgc_id in self.bgc_ids
}
self.group_by_bgc = metadata["group_id"].astype(str).to_dict()
self.tier_by_bgc = metadata["label_tier"].astype(str).to_dict()
self.split_by_bgc = metadata["split"].astype(str).to_dict()
@property
def h5(self) -> h5py.File:
if self._h5 is None:
self._h5 = h5py.File(self.h5_path, "r")
return self._h5
def __len__(self) -> int:
return len(self.bgc_ids)
def __getitem__(self, index: int) -> dict[str, object]:
bgc_id = self.bgc_ids[index]
embeddings: list[np.ndarray] = []
positions: list[float] = []
gene_ids: list[str] = []
for gene_id, position in self.bgc_to_genes[bgc_id]:
embedding = np.asarray(self.h5[gene_id][()], dtype=np.float32)
if embedding.shape != (self.esm_dimension,):
raise ValueError(f"{gene_id} has shape {embedding.shape}; expected {(self.esm_dimension,)}")
embeddings.append(embedding)
positions.append(position)
gene_ids.append(gene_id)
return {
"gene_embeddings": torch.from_numpy(np.stack(embeddings)),
"relative_positions": torch.tensor(positions, dtype=torch.float32),
"bgc_id": bgc_id,
"gene_ids": gene_ids,
"pfam_tokens": torch.tensor(
self.pfam_tokens_by_bgc[bgc_id], dtype=torch.long
),
"group_id": self.group_by_bgc[bgc_id],
"label_tier": self.tier_by_bgc[bgc_id],
"split": self.split_by_bgc[bgc_id],
}
def close(self) -> None:
if self._h5 is not None:
self._h5.close()
self._h5 = None
def __del__(self) -> None:
self.close()
def collate_bgcs(batch: list[dict[str, object]]) -> dict[str, object]:
if not batch:
raise ValueError("Cannot collate an empty batch")
max_genes = max(item["gene_embeddings"].shape[0] for item in batch)
max_pfams = max(1, max(item["pfam_tokens"].shape[0] for item in batch))
dimension = batch[0]["gene_embeddings"].shape[1]
embeddings = torch.zeros(len(batch), max_genes, dimension, dtype=torch.float32)
positions = torch.zeros(len(batch), max_genes, dtype=torch.float32)
padding_mask = torch.ones(len(batch), max_genes, dtype=torch.bool)
pfam_tokens = torch.zeros(len(batch), max_pfams, dtype=torch.long)
for row, item in enumerate(batch):
count = item["gene_embeddings"].shape[0]
embeddings[row, :count] = item["gene_embeddings"]
positions[row, :count] = item["relative_positions"]
padding_mask[row, :count] = False
pfam_count = item["pfam_tokens"].shape[0]
if pfam_count:
pfam_tokens[row, :pfam_count] = item["pfam_tokens"]
result: dict[str, object] = {
"gene_embeddings": embeddings,
"relative_positions": positions,
"padding_mask": padding_mask,
"pfam_tokens": pfam_tokens,
}
result["bgc_ids"] = [item["bgc_id"] for item in batch]
result["gene_ids"] = [item["gene_ids"] for item in batch]
result["group_ids"] = [item["group_id"] for item in batch]
result["label_tiers"] = [item["label_tier"] for item in batch]
result["splits"] = [item["split"] for item in batch]
validate_model_input_names(
["gene_embeddings", "relative_positions", "padding_mask", "pfam_tokens"]
)
return result
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