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4d12519 | 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 | import json
from pytorch_lightning import LightningDataModule
from torch.utils.data import DataLoader, ConcatDataset, Dataset
from data_provider.stage1_dm import SwissProtDataset, OntoProteinDataset
import pandas as pd
class TAPE_Stability(Dataset):
def __init__(self, data_path, prompt='', return_prompt=False):
super(TAPE_Stability, self).__init__()
self.data_path = data_path
self.user_prompt = prompt
self.return_prompt = return_prompt
self.data_list = self._load_and_preprocess(self.data_path)
self.text2id = self._build_text_vocab()
def _load_and_preprocess(self, data_path):
data_list = []
df = pd.read_csv(data_path)
for _, row in df.iterrows():
try:
prot_seq = str(row['aa_seq']).strip()
result = str(row['label']).strip()
text_seq = f"<answer>{result}</answer>\n"
prompt = """
【Task】Predict the thermostability score of the given protein sequence, which reflects its ability to maintain proper folding above a concentration threshold.
【Background】Protein stability is an important biophysical property indicating a protein’s resistance to denaturation or unfolding under thermal or chemical stress. In this task, each protein is evaluated by a numerical stability score, where higher values indicate greater ability to remain folded under extreme conditions. This score serves as a proxy for the protein’s intrinsic stability.
【Question】What is the predicted stability score for this sequence?
【Output Format】You must return only the score number, wrapped in <answer></answer> tags.
"""
if self.user_prompt:
prompt += self.user_prompt
# extra可以返回原始feather字符串，也可以返回feather_vals
# 或 feather_raw
data_list.append((prot_seq, text_seq, prompt))
except Exception as e:
print(f"警告: 跳过有问题的行: {row}，原因: {e}")
return data_list
def _build_text_vocab(self):
text2id = {}
for _, text_seq, _ in self.data_list:
if text_seq not in text2id:
text2id[text_seq] = len(text2id)
return text2id
def shuffle(self):
random.shuffle(self.data_list)
return self
def __len__(self):
return len(self.data_list)
def __getitem__(self, index):
prot_seq, text_seq, prompt = self.data_list[index]
if self.return_prompt:
return prot_seq, prompt, text_seq,index
return prot_seq, text_seq, index
class TAPE_Fluorescence(Dataset):
def __init__(self, data_path, prompt='', return_prompt=False):
super(TAPE_Fluorescence, self).__init__()
self.data_path = data_path
self.user_prompt = prompt
self.return_prompt = return_prompt
self.data_list = self._load_and_preprocess(self.data_path)
self.text2id = self._build_text_vocab()
def _load_and_preprocess(self, data_path):
data_list = []
df = pd.read_csv(data_path)
for _, row in df.iterrows():
try:
prot_seq = str(row['aa_seq']).strip()
result = str(row['label']).strip()
text_seq = f"<answer>{result}</answer>\n"
prompt = """
【Task】Predict the log fluorescence intensity of the given protein sequence.
【Output Format】You must return only the numerical value, wrapped in <answer></answer> tags.
"""
# 【Background】Fluorescence intensity reflects how strongly a protein emits light when excited by a specific wavelength. It is commonly measured in protein variants such as GFP (Green Fluorescent Protein) mutants. The log-transformed fluorescence value quantifies the brightness on a logarithmic scale. Mutations in the sequence can increase or decrease fluorescence intensity.
# 【Question】What is the predicted log fluorescence intensity for this sequence?
if self.user_prompt:
prompt += self.user_prompt
# extra可以返回原始feather字符串，也可以返回feather_vals
# 或 feather_raw
data_list.append((prot_seq, text_seq, prompt))
except Exception as e:
print(f"警告: 跳过有问题的行: {row}，原因: {e}")
return data_list
def _build_text_vocab(self):
text2id = {}
for _, text_seq, _ in self.data_list:
if text_seq not in text2id:
text2id[text_seq] = len(text2id)
return text2id
def shuffle(self):
random.shuffle(self.data_list)
return self
def __len__(self):
return len(self.data_list)
def __getitem__(self, index):
prot_seq, text_seq, prompt = self.data_list[index]
if self.return_prompt:
return prot_seq, prompt, text_seq,index
return prot_seq, text_seq, index |