File size: 4,782 Bytes
6dd9839 | 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 | """带 label 的 RNA–RBP 数据集，复用 ProRiboGen generation 模块的 RnaRbpDataset 与 collate。"""
from __future__ import annotations
import random
from functools import partial
from typing import Iterator
import torch
from torch.utils.data import Dataset, Sampler
from transformers import AutoTokenizer
import sys
from pathlib import Path
# 网站部署包：generation 模块代码在 ../generator
_GENERATOR_ROOT = Path(__file__).resolve().parent.parent / "generator"
if str(_GENERATOR_ROOT) not in sys.path:
sys.path.insert(0, str(_GENERATOR_ROOT))
from src.utils import RnaRbpDataset, rna_rbp_collate_fn # noqa: E402
class LabeledRnaRbpDataset(RnaRbpDataset):
"""在父类基础上读取 ``label`` 列（0/1）。"""
def __getitem__(self, idx: int) -> dict:
sample = super().__getitem__(idx)
sample["label"] = int(self.data.iloc[idx]["label"])
return sample
def build_pair_indices(dataset: LabeledRnaRbpDataset) -> list[tuple[int, int]]:
"""从 labeled 表构造 (pos_idx, neg_idx) 列表，优先用 pair_id。"""
df = dataset.data.reset_index(drop=True)
if "pair_id" in df.columns:
pos_by: dict[str, list[int]] = {}
neg_by: dict[str, list[int]] = {}
for i in range(len(df)):
pid = str(df.iloc[i]["pair_id"])
if int(df.iloc[i]["label"]) == 1:
pos_by.setdefault(pid, []).append(i)
else:
neg_by.setdefault(pid, []).append(i)
pairs: list[tuple[int, int]] = []
for k in sorted(pos_by):
if k not in neg_by:
continue
ps, ns = pos_by[k], neg_by[k]
n = min(len(ps), len(ns))
pairs.extend(zip(ps[:n], ns[:n]))
if pairs:
return pairs
pos_idx = [i for i in range(len(df)) if int(df.iloc[i]["label"]) == 1]
neg_idx = [i for i in range(len(df)) if int(df.iloc[i]["label"]) == 0]
if len(pos_idx) != len(neg_idx):
raise ValueError(f"pos={len(pos_idx)} neg={len(neg_idx)}，无法配对")
return list(zip(pos_idx, neg_idx))
def split_pair_indices(
pairs: list[tuple[int, int]],
val_fraction: float,
seed: int,
) -> tuple[list[tuple[int, int]], list[tuple[int, int]]]:
n_val = max(1, int(len(pairs) * val_fraction))
rng = random.Random(seed)
order = list(range(len(pairs)))
rng.shuffle(order)
val_set = set(order[:n_val])
train_pairs = [pairs[i] for i in range(len(pairs)) if i not in val_set]
val_pairs = [pairs[i] for i in val_set]
return train_pairs, val_pairs
class PairedBatchSampler(Sampler[list[int]]):
"""每个 batch 含若干 (pos, neg) 对，保证 ranking loss 可配对。"""
def __init__(
self,
pairs: list[tuple[int, int]],
*,
pairs_per_batch: int,
num_replicas: int = 1,
rank: int = 0,
shuffle: bool = True,
seed: int = 42,
) -> None:
if pairs_per_batch < 1:
raise ValueError("pairs_per_batch 须 >= 1")
self.pairs = pairs
self.pairs_per_batch = pairs_per_batch
self.num_replicas = num_replicas
self.rank = rank
self.shuffle = shuffle
self.seed = seed
self.epoch = 0
def set_epoch(self, epoch: int) -> None:
self.epoch = epoch
def __iter__(self) -> Iterator[list[int]]:
order = list(range(len(self.pairs)))
if self.shuffle:
rng = random.Random(self.seed + self.epoch)
rng.shuffle(order)
order = order[self.rank :: self.num_replicas]
batch: list[int] = []
for pi in order:
pos_i, neg_i = self.pairs[pi]
batch.extend([pos_i, neg_i])
if len(batch) == 2 * self.pairs_per_batch:
yield batch
batch = []
if batch:
yield batch
def __len__(self) -> int:
n = len(self.pairs)
if n == 0:
return 0
n_rank = len(range(self.rank, n, self.num_replicas))
return (n_rank + self.pairs_per_batch - 1) // self.pairs_per_batch
def labeled_collate_fn(batch, tokenizer, *, append_eos: bool = False, max_rna_nt: int | None = None):
labels = torch.tensor([float(s["label"]) for s in batch], dtype=torch.float32)
base = rna_rbp_collate_fn(
batch,
tokenizer,
append_eos=append_eos,
max_rna_nt=max_rna_nt,
)
base["labels"] = labels
return base
def make_collate(tokenizer_path: str, append_eos: bool, max_rna_nt: int | None):
tok = AutoTokenizer.from_pretrained(tokenizer_path, trust_remote_code=True)
return partial(
labeled_collate_fn,
tokenizer=tok,
append_eos=append_eos,
max_rna_nt=max_rna_nt,
)
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