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Copy pathN03_LM_Embeddings.py
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N03_LM_Embeddings.py
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#!/usr/bin/env python
# coding: utf-8
###################################################################################################################
###################################################################################################################
# The following code ensures the code work properly in
# MS VS, MS VS CODE and jupyter notebook on both Linux and Windows.
#--------------------------------------------------#
import os
import sys
import os.path
from sys import platform
from pathlib import Path
#--------------------------------------------------#
if __name__ == "__main__":
print("="*80)
if os.name == 'nt' or platform == 'win32':
print("Running on Windows")
if 'ptvsd' in sys.modules:
print("Running in Visual Studio")
#--------------------------------------------------#
if os.name != 'nt' and platform != 'win32':
print("Not Running on Windows")
#--------------------------------------------------#
if "__file__" in globals().keys():
print('CurrentDir: ', os.getcwd())
try:
os.chdir(os.path.dirname(__file__))
except:
print("Problems with navigating to the file dir.")
print('CurrentDir: ', os.getcwd())
else:
print("Running in python jupyter notebook.")
try:
if not 'workbookDir' in globals():
workbookDir = os.getcwd()
print('workbookDir: ' + workbookDir)
os.chdir(workbookDir)
except:
print("Problems with navigating to the workbook dir.")
#--------------------------------------------------#
###################################################################################################################
###################################################################################################################
'''
import simpleaudio as sa
#--------------------------------------------------#
def alert_sound(frequency, seconds):
frequency # Our played note will be 440 Hz
seconds # Note duration of 3 seconds
fs = 44100 # 44100 samples per second
t = np.linspace(0, seconds, seconds * fs, False) # Generate array with seconds*sample_rate steps, ranging between 0 and seconds
note = np.sin(frequency * t * 2 * np.pi) # Generate a 440 Hz sine wave
audio = note * (2**15 - 1) / np.max(np.abs(note)) # Ensure that highest value is in 16-bit range
audio = audio.astype(np.int16) # Convert to 16-bit data
play_obj = sa.play_buffer(audio, 1, 2, fs) # Start playback
play_obj.wait_done() # Wait for playback to finish before exiting
'''
###################################################################################################################
###################################################################################################################
import sys
import time
import torch
import numpy as np
import pandas as pd
import pickle
import argparse
import requests
import subprocess
#--------------------------------------------------#
from torch import nn
from torch.utils import data as data
#--------------------------------------------------#
from tape import datasets
from tape import TAPETokenizer
from tape import ProteinBertForMaskedLM
from tape import UniRepForLM
#--------------------------------------------------#
from Z01_ModifiedModels import *
from pathlib import Path
#--------------------------------------------------#
from Bio import SeqIO
from tqdm.auto import tqdm
#====================================================================================================#
# Imports for LM
from transformers import BertModel, BertTokenizer
from transformers import AlbertModel, AlbertTokenizer
from transformers import ElectraTokenizer, ElectraForPreTraining, ElectraForMaskedLM, ElectraModel
from transformers import T5EncoderModel, T5Tokenizer
from transformers import XLNetModel, XLNetTokenizer
#--------------------------------------------------#
import esm
#====================================================================================================#
# Imports for MSA
from glob import glob
from Bio.Align.Applications import MafftCommandline
#$$$$$$$$$$$$$$$$$$$$$$$$$$$$$$$$$$$$$$$$$$$$$$$$$$$$$$$$$$$$$$$$$$$$$$$$$$$$$$$$$$$$$$$$$$$$$$$$$$$$$$$$$$$$$$$$$$$$$$$$$$$$$$$$$$$$$$$$$$$$$$$$$$$$$$#
#$$$$$$$$$$$$$$$$$$$$$$$$$$$$$$$$$$$$$$$$$$$$$$$$$$$$$$$$$$$$$$$$$$$$$$$$$$$$$$$$$$$$$$$$$$$$$$$$$$$$$$$$$$$$$$$$$$$$$$$$$$$$$$$$$$$$$$$$$$$$$$$$$$$$$$#
#$$$$$$$$$$$$$$$$$$$$$$$$$$$$$$$$$$$$$$$$$$$$$$$$$$$$$$$$$$$$$$$$$$$$$$$$$$$$$$$$$$$$$$$$$$$$$$$$$$$$$$$$$$$$$$$$$$$$$$$$$$$$$$$$$$$$$$$$$$$$$$$$$$$$$$#
class LoaderClass(data.Dataset):
def __init__(self, input_ids, attention_mask):
super().__init__()
self.input_ids = input_ids
self.attention_mask = attention_mask
def __len__(self):
return self.input_ids.shape[0]
def __getitem__(self, idx):
return self.input_ids[idx], self.attention_mask[idx]
#====================================================================================================#
class Identity(nn.Module):
def __init__(self):
super(Identity, self).__init__()
def forward(self, x,target = None):
return (x,)
#====================================================================================================#
#$$$$$$$$$$$$$$$$$$$$$$$$$$$$$$$$$$$$$$$$$$$$$$$$$$$$$$$$$$$$$$$$$$$$$$$$$$$$$$$$$$$$$$$$$$$$$$$$$$$$$$$$$$$$$$$$$$$$$$$$$$$$$$$$$$$$$$$$$$$$$$$$$$$$$$#
# `7MM"""YMM `7MMM. ,MMF'`7MM"""Yp, `7MM"""YMM `7MMF' `7MF'`7MN. `7MF' .g8"""bgd #
# MM `7 MMMb dPMM MM Yb MM `7 MM M MMN. M .dP' `M #
# MM d M YM ,M MM MM dP MM d MM M M YMb M dM' ` #
# MMmmMM M Mb M' MM MM"""bg. MM""MM MM M M `MN. M MM #
# MM Y , M YM.P' MM MM `Y MM Y MM M M `MM.M MM. #
# MM ,M M `YM' MM MM ,9 MM YM. ,M M YMM `Mb. ,' #
# .JMMmmmmMMM .JML. `' .JMML..JMMmmmd9 .JMML. `bmmmmd"' .JML. YM `"bmmmd' #
#$$$$$$$$$$$$$$$$$$$$$$$$$$$$$$$$$$$$$$$$$$$$$$$$$$$$$$$$$$$$$$$$$$$$$$$$$$$$$$$$$$$$$$$$$$$$$$$$$$$$$$$$$$$$$$$$$$$$$$$$$$$$$$$$$$$$$$$$$$$$$$$$$$$$$$#
def N03_embedding_LM(dataset_nme,
model_select,
data_folder ,
input_seqs_fasta_file,
output_file_name_header,
pretraining_name=None,
batch_size=100,
xlnet_mem_len=512):
#====================================================================================================#
assert model_select in models_list, "query model is not found, currently support ESM-1b, TAPE, BERT, AlBERT, Electra, T5 and Xlnet !!"
#====================================================================================================#
input_file = data_folder / input_seqs_fasta_file #data path (fasta)
output_file = data_folder / (output_file_name_header + model_select + ".p") #output path (pickle)
###################################################################################################################
# `7MMF' `7MF'`7MN. `7MF'`7MMF'`7MM"""Mq. `7MM"""YMM `7MM"""Mq. #
# MM M MMN. M MM MM `MM. MM `7 MM `MM. #
# MM M M YMb M MM MM ,M9 MM d MM ,M9 #
# MM M M `MN. M MM MMmmdM9 MMmmMM MMmmdM9 #
# MM M M `MM.M MM MM YM. MM Y , MM #
# YM. ,M M YMM MM MM `Mb. MM ,M MM #
# `bmmmmd"' .JML. YM .JMML..JMML. .JMM..JMMmmmmMMM .JMML. #
###################################################################################################################
if model_select == "Unirep" or model_select == "Unirep_FT":
#--------------------------------------------------#
# Load model.
model = UniRepForLM.from_pretrained('babbler-1900')
#--------------------------------------------------#
if model_select == "Unirep":
print("No pretraining model is used.")
pretraining_name = None
#--------------------------------------------------#
if pretraining_name is not None:
checkpoint = torch.load( data_folder / pretraining_name )
model.load_state_dict(checkpoint['model_state_dict'])
#--------------------------------------------------#
model.mlm = Identity()
model.eval()
embed = datasets.EmbedDataset(data_file = input_file, tokenizer = "unirep")
loader = data.DataLoader(embed, batch_size, False, collate_fn = embed.collate_fn)
#--------------------------------------------------#
count_x = 0
model.cuda()
#--------------------------------------------------#
seq_encodings = []
seq_all_hiddens = []
seq_ids = []
for seq_batch in loader:
count_x += 1
ids, input_ids, input_mask = seq_batch["ids"],seq_batch["input_ids"],seq_batch["input_mask"]
input_ids, input_mask = input_ids.cuda(), input_mask.cuda()
with torch.no_grad():
output = model(input_ids,input_mask)
output = output[0].cpu().detach().numpy()
features = []
for seq_num in range(len(output)):
seq_len = (input_mask[seq_num] == 1).sum()
seq_emd = output[seq_num][1:seq_len-1]
#print(seq_emd)
seq_all_hiddens.append(seq_emd)
features.append(np.mean(seq_emd,axis=0))
features = np.stack(features)
print("features.shape: ", features.shape)
seq_encodings.append(features)
seq_ids += ids
seq_embeddings = np.concatenate(seq_encodings)
print("seq_embeddings.shape: ", seq_embeddings.shape)
seq_embedding_output = {"seq_embeddings" : seq_embeddings, "seq_ids" : seq_ids, "seq_all_hiddens" : seq_all_hiddens}
pickle.dump( seq_embedding_output, open( output_file, "wb" ) )
print("done")
###################################################################################################################
# MMP""MM""YMM db `7MM"""Mq. `7MM"""YMM #
# P' MM `7 ;MM: MM `MM. MM `7 #
# MM ,V^MM. MM ,M9 MM d #
# MM ,M `MM MMmmdM9 MMmmMM #
# MM AbmmmqMA MM MM Y , #
# MM A' VML MM MM ,M #
# .JMML. .AMA. .AMMA..JMML. .JMMmmmmMMM #
###################################################################################################################
if model_select == "TAPE_FT" or model_select == "TAPE":
model = ProteinBertForMaskedLM.from_pretrained('bert-base')
#--------------------------------------------------#
if model_select == "TAPE":
pretraining_name == None
#--------------------------------------------------#
if pretraining_name is not None:
checkpoint = torch.load( data_folder / pretraining_name )
model.load_state_dict(checkpoint['model_state_dict'])
#--------------------------------------------------#
model.mlm = Identity()
model.eval()
embed = datasets.EmbedDataset(data_file=input_file,tokenizer="iupac")
loader = data.DataLoader(embed,batch_size,False,collate_fn = embed.collate_fn)
#--------------------------------------------------#
count_x = 0
model.cuda()
#--------------------------------------------------#
seq_encodings = []
seq_all_hiddens = []
seq_ids = []
for seq_batch in loader:
count_x+=1
ids, input_ids, input_mask = seq_batch["ids"],seq_batch["input_ids"],seq_batch["input_mask"]
input_ids, input_mask = input_ids.cuda(), input_mask.cuda()
with torch.no_grad():
output = model(input_ids,input_mask)
output = output[0].cpu().detach().numpy()
features = []
for seq_num in range(len(output)):
seq_len = (input_mask[seq_num] == 1).sum()
seq_emd = output[seq_num][1:seq_len-1]
#print(seq_emd)
seq_all_hiddens.append(seq_emd)
features.append(np.mean(seq_emd,axis=0))
features = np.stack(features)
print("features.shape: ", features.shape)
seq_encodings.append(features)
seq_ids += ids
seq_embeddings = np.concatenate(seq_encodings)
print("seq_embeddings.shape: ", seq_embeddings.shape)
seq_embedding_output = {"seq_embeddings":seq_embeddings, "seq_ids":seq_ids, "seq_all_hiddens":seq_all_hiddens}
pickle.dump( seq_embedding_output, open( output_file, "wb" ) )
print("done")
###################################################################################################################
# `7MM"""Yp, `7MM"""YMM `7MM"""Mq. MMP""MM""YMM #
# MM Yb MM `7 MM `MM. P' MM `7 #
# MM dP MM d MM ,M9 MM #
# MM"""bg. MMmmMM MMmmdM9 MM #
# MM `Y MM Y , MM YM. MM #
# MM ,9 MM ,M MM `Mb. MM #
# .JMMmmmd9 .JMMmmmmMMM .JMML. .JMM. .JMML. #
###################################################################################################################
if model_select == "BERT":
tokenizer = BertTokenizer.from_pretrained("Rostlab/prot_bert", do_lower_case=False)
model = BertModel.from_pretrained("Rostlab/prot_bert")
training_set = []
seq_ids = []
for seq_record in SeqIO.parse(input_file, "fasta"):
seq = str(seq_record.seq)
new_string = ""
for i in range(len(seq)-1):
new_string += seq[i]
new_string += " "
new_string += seq[-1]
seq_ids.append(str(seq_record.id))
training_set.append(new_string)
ids = tokenizer.batch_encode_plus(training_set, add_special_tokens=True, padding=True)
#--------------------------------------------------#
loader = data.DataLoader(LoaderClass(np.array(ids["input_ids"]), np.array(ids["attention_mask"])), batch_size, False)
model.cuda()
model.eval()
seq_encodings = []
seq_all_hiddens = []
count = 0
#--------------------------------------------------#
for seq_batch in loader:
count+=1
input_ids, input_mask = seq_batch[0],seq_batch[1]
input_ids, input_mask = input_ids.cuda(), input_mask.cuda()
with torch.no_grad():
output = model(input_ids,input_mask)
output = output[0].cpu().detach().numpy()
features = []
for seq_num in range(len(output)):
seq_len = (input_mask[seq_num] == 1).sum()
seq_emd = output[seq_num][1:seq_len-1]
seq_all_hiddens.append(seq_emd)
features.append(np.mean(seq_emd,axis=0))
features = np.stack(features)
print(features.shape)
seq_encodings.append(features)
seq_embeddings = np.concatenate(seq_encodings)
print("seq_embeddings.shape: ", seq_embeddings.shape)
seq_embedding_output = {"seq_embeddings":seq_embeddings, "seq_ids":seq_ids, "seq_all_hiddens":seq_all_hiddens}
pickle.dump( seq_embedding_output, open( output_file, "wb" ) )
print("done")
###################################################################################################################
# db `7MMF' `7MM"""Yp, `7MM"""YMM `7MM"""Mq. MMP""MM""YMM #
# ;MM: MM MM Yb MM `7 MM `MM. P' MM `7 #
# ,V^MM. MM MM dP MM d MM ,M9 MM #
# ,M `MM MM MM"""bg. MMmmMM MMmmdM9 MM #
# AbmmmqMA MM , MM `Y MM Y , MM YM. MM #
# A' VML MM ,M MM ,9 MM ,M MM `Mb. MM #
# .AMA. .AMMA..JMMmmmmMMM .JMMmmmd9 .JMMmmmmMMM .JMML. .JMM. .JMML. #
###################################################################################################################
if model_select == "ALBERT":
tokenizer = AlbertTokenizer.from_pretrained("Rostlab/prot_albert", do_lower_case=False)
model = AlbertModel.from_pretrained("Rostlab/prot_albert")
training_set = []
seq_ids = []
for seq_record in SeqIO.parse(input_file, "fasta"):
seq = str(seq_record.seq)
new_string = ""
for i in range(len(seq)-1):
new_string += seq[i]
new_string += " "
new_string += seq[-1]
seq_ids.append(str(seq_record.id))
training_set.append(new_string)
ids = tokenizer.batch_encode_plus(training_set, add_special_tokens=True, padding=True)
#--------------------------------------------------#
loader = data.DataLoader(LoaderClass(np.array(ids["input_ids"]), np.array(ids["attention_mask"])), batch_size, False)
model.cuda()
model.eval()
seq_encodings = []
seq_all_hiddens = []
count = 0
#--------------------------------------------------#
for seq_batch in loader:
count+=1
input_ids, input_mask = seq_batch[0],seq_batch[1]
input_ids, input_mask = input_ids.cuda(), input_mask.cuda()
with torch.no_grad():
output = model(input_ids,input_mask)
output = output[0].cpu().detach().numpy()
features = []
for seq_num in range(len(output)):
seq_len = (input_mask[seq_num] == 1).sum()
seq_emd = output[seq_num][1:seq_len-1]
seq_all_hiddens.append(seq_emd)
features.append(np.mean(seq_emd,axis=0))
features = np.stack(features)
print(features.shape)
seq_encodings.append(features)
seq_embeddings = np.concatenate(seq_encodings)
print("seq_embeddings.shape: ", seq_embeddings.shape)
seq_embedding_output = {"seq_embeddings":seq_embeddings, "seq_ids":seq_ids, "seq_all_hiddens":seq_all_hiddens}
pickle.dump( seq_embedding_output, open( output_file, "wb" ) )
print("done")
###################################################################################################################
# `7MM"""YMM `7MM `7MM"""YMM .g8"""bgd MMP""MM""YMM `7MM"""Mq. db .g8""8q. #
# MM `7 MM MM `7 .dP' `M P' MM `7 MM `MM. ;MM: .dP YM. #
# MM d MM MM d dM' ` MM MM ,M9 ,V^MM. dM' ,V`MM #
# MMmmMM MM MMmmMM MM MM MMmmdM9 ,M `MM MM ,M' MM #
# MM Y , MM MM Y , MM. MM MM YM. AbmmmqMA MM.,V' ,MP #
# MM ,M MM MM ,M `Mb. ,' MM MM `Mb. A' VML `Mb ,dP' #
# .JMMmmmmMMM .JMML..JMMmmmmMMM `"bmmmd' .JMML. .JMML. .JMM..AMA. .AMMA. `"bmmd"' #
###################################################################################################################
# A now deprecated model.
if model_select == "Electra": ##### !!!!! Deprecated!
generatorModelUrl = 'https://www.dropbox.com/s/5x5et5q84y3r01m/pytorch_model.bin?dl=1'
discriminatorModelUrl = 'https://www.dropbox.com/s/9ptrgtc8ranf0pa/pytorch_model.bin?dl=1'
generatorConfigUrl = 'https://www.dropbox.com/s/9059fvix18i6why/config.json?dl=1'
discriminatorConfigUrl = 'https://www.dropbox.com/s/jq568evzexyla0p/config.json?dl=1'
vocabUrl = 'https://www.dropbox.com/s/wck3w1q15bc53s0/vocab.txt?dl=1'
downloadFolderPath = 'models/electra/'
discriminatorFolderPath = os.path.join(downloadFolderPath, 'discriminator')
generatorFolderPath = os.path.join(downloadFolderPath, 'generator')
discriminatorModelFilePath = os.path.join(discriminatorFolderPath, 'pytorch_model.bin')
generatorModelFilePath = os.path.join(generatorFolderPath, 'pytorch_model.bin')
discriminatorConfigFilePath = os.path.join(discriminatorFolderPath, 'config.json')
generatorConfigFilePath = os.path.join(generatorFolderPath, 'config.json')
vocabFilePath = os.path.join(downloadFolderPath, 'vocab.txt')
if not os.path.exists(discriminatorFolderPath):
os.makedirs(discriminatorFolderPath)
if not os.path.exists(generatorFolderPath):
os.makedirs(generatorFolderPath)
def download_file(url, filename):
response = requests.get(url, stream=True)
with tqdm.wrapattr(open(filename, "wb"), "write", miniters=1,
total=int(response.headers.get('content-length', 0)),
desc=filename) as fout:
for chunk in response.iter_content(chunk_size=4096):
fout.write(chunk)
if not os.path.exists(generatorModelFilePath):
download_file(generatorModelUrl, generatorModelFilePath)
if not os.path.exists(discriminatorModelFilePath):
download_file(discriminatorModelUrl, discriminatorModelFilePath)
if not os.path.exists(generatorConfigFilePath):
download_file(generatorConfigUrl, generatorConfigFilePath)
if not os.path.exists(discriminatorConfigFilePath):
download_file(discriminatorConfigUrl, discriminatorConfigFilePath)
if not os.path.exists(vocabFilePath):
download_file(vocabUrl, vocabFilePath)
tokenizer = ElectraTokenizer(vocabFilePath, do_lower_case=False )
model = ElectraModel.from_pretrained(discriminatorFolderPath)
model.cuda()
model.eval()
training_set = []
seq_ids = []
for seq_record in SeqIO.parse(input_file, "fasta"):
seq = str(seq_record.seq)
new_string = ""
for i in range(len(seq)-1):
new_string += seq[i]
new_string += " "
new_string += seq[-1]
seq_ids.append(str(seq_record.id))
training_set.append(new_string)
ids = tokenizer.batch_encode_plus(training_set, add_special_tokens=True, padding=True)
loader = data.DataLoader(LoaderClass(np.array(ids["input_ids"]), np.array(ids["attention_mask"])), batch_size, False)
seq_encodings = []
seq_all_hiddens = []
count = 0
for seq_batch in loader:
count+=1
input_ids, input_mask = seq_batch[0],seq_batch[1]
input_ids, input_mask = input_ids.cuda(), input_mask.cuda()
with torch.no_grad():
output = model(input_ids,input_mask)
output = output[0].cpu().detach().numpy()
features = []
for seq_num in range(len(output)):
seq_len = (input_mask[seq_num] == 1).sum()
seq_emd = output[seq_num][1:seq_len-1]
seq_all_hiddens.append(seq_emd)
features.append(np.mean(seq_emd,axis=0))
features = np.stack(features)
print(features.shape)
seq_encodings.append(features)
seq_embeddings = np.concatenate(seq_encodings)
print("seq_embeddings.shape: ", seq_embeddings.shape)
seq_embedding_output = {"seq_embeddings":seq_embeddings, "seq_ids":seq_ids, "seq_all_hiddens":seq_all_hiddens}
pickle.dump( seq_embedding_output, open( output_file, "wb" ) )
print("done")
###################################################################################################################
# MMP""MM""YMM M****** #
# P' MM `7 .M #
# MM |bMMAg. #
# MM `Mb #
# MM jM #
# MM (O) ,M9 #
# .JMML. 6mmm9 #
###################################################################################################################
if model_select == "T5":
tokenizer = T5Tokenizer.from_pretrained("Rostlab/prot_t5_xl_uniref50", do_lower_case = False )
model = T5EncoderModel.from_pretrained("Rostlab/prot_t5_xl_uniref50")
model.cuda()
model.eval()
training_set = []
seq_ids = []
for seq_record in SeqIO.parse(input_file, "fasta"):
seq = str(seq_record.seq)
new_string = ""
for i in range(len(seq)-1):
new_string += seq[i]
new_string += " "
new_string += seq[-1]
seq_ids.append(str(seq_record.id))
training_set.append(new_string)
ids = tokenizer.batch_encode_plus(training_set, add_special_tokens = True, padding = True)
loader = data.DataLoader(LoaderClass(np.array(ids["input_ids"]), np.array(ids["attention_mask"])), batch_size, False)
seq_encodings = []
seq_all_hiddens = []
count = 0
for seq_batch in loader:
count += 1
input_ids, input_mask = seq_batch[0], seq_batch[1]
input_ids, input_mask = input_ids.cuda(), input_mask.cuda()
with torch.no_grad():
output = model(input_ids,input_mask)
output = output[0].cpu().detach().numpy()
features = []
for seq_num in range(len(output)):
seq_len = (input_mask[seq_num] == 1).sum()
seq_emd = output[seq_num][:seq_len-1]
seq_all_hiddens.append(seq_emd)
features.append(np.mean(seq_emd, axis=0))
features = np.stack(features)
print(features.shape)
seq_encodings.append(features)
seq_embeddings = np.concatenate(seq_encodings)
print("seq_embeddings.shape: ", seq_embeddings.shape)
seq_embedding_output = {"seq_embeddings": seq_embeddings, "seq_ids": seq_ids, "seq_all_hiddens": seq_all_hiddens}
pickle.dump( seq_embedding_output, open( output_file, "wb" ) )
print("done")
###################################################################################################################
# `YMM' `MP' `7MMF' `7MN. `7MF'`7MM"""YMM MMP""MM""YMM #
# VMb. ,P MM MMN. M MM `7 P' MM `7 #
# `MM.M' MM M YMb M MM d MM #
# MMb MM M `MN. M MMmmMM MM #
# ,M'`Mb. MM , M `MM.M MM Y , MM #
# ,P `MM. MM ,M M YMM MM ,M MM #
# .MM:. .:MMa..JMMmmmmMMM .JML. YM .JMMmmmmMMM .JMML. #
###################################################################################################################
if model_select == "Xlnet":
tokenizer = XLNetTokenizer.from_pretrained("Rostlab/prot_xlnet", do_lower_case = False)
model = XLNetModel.from_pretrained("Rostlab/prot_xlnet", mem_len = xlnet_mem_len)
model.cuda()
model.eval()
training_set = []
seq_ids = []
for seq_record in SeqIO.parse(input_file, "fasta"):
seq = str(seq_record.seq)
new_string = ""
for i in range(len(seq)-1):
new_string += seq[i]
new_string += " "
new_string += seq[-1]
seq_ids.append(str(seq_record.id))
training_set.append(new_string)
ids = tokenizer.batch_encode_plus(training_set, add_special_tokens = True, padding = True)
loader = data.DataLoader(LoaderClass(np.array(ids["input_ids"]), np.array(ids["attention_mask"])), batch_size, False)
seq_encodings = []
seq_all_hiddens = []
count = 0
for seq_batch in loader:
count += 1
input_ids, input_mask = seq_batch[0],seq_batch[1]
input_ids, input_mask = input_ids.cuda(), input_mask.cuda()
with torch.no_grad():
output = model(input_ids,input_mask)
output = output[0].cpu().detach().numpy()
features = []
for seq_num in range(len(output)):
seq_len = (input_mask[seq_num] == 1).sum()
padded_seq_len = len(input_mask[seq_num])
seq_emd = output[seq_num][padded_seq_len-seq_len:padded_seq_len-2]
seq_all_hiddens.append(seq_emd)
features.append(np.mean(seq_emd, axis=0))
features = np.stack(features)
print(features.shape)
seq_encodings.append(features)
seq_embeddings = np.concatenate(seq_encodings)
print("seq_embeddings.shape: ", seq_embeddings.shape)
seq_embedding_output = {"seq_embeddings": seq_embeddings, "seq_ids": seq_ids, "seq_all_hiddens": seq_all_hiddens}
pickle.dump( seq_embedding_output, open( output_file, "wb" ) )
print("done")
###################################################################################################################
# `7MM"""YMM .M"""bgd `7MMM. ,MMF' *MM #
# MM `7 ,MI "Y MMMb dPMM __, MM #
# MM d `MMb. M YM ,M MM `7MM MM,dMMb. #
# MMmmMM `YMMNq. M Mb M' MM MM MM `Mb #
# MM Y , . `MM M YM.P' MM mmmmm MM MM M8 #
# MM ,M Mb dM M `YM' MM MM MM. ,M9 #
# .JMMmmmmMMM P"Ybmmd" .JML. `' .JMML. .JMML. P^YbmdP' #
###################################################################################################################
if model_select == "ESM_1B":
#model, alphabet = torch.hub.load("facebookresearch/esm", "esm1b_t33_650M_UR50S")
model, alphabet = esm.pretrained.esm1b_t33_650M_UR50S()
batch_converter = alphabet.get_batch_converter()
data_set = []
for seq_record in SeqIO.parse(input_file, "fasta"):
data_set.append((str(seq_record.id), str(seq_record.seq)))
model.eval()
model.cuda()
seq_encodings = []
seq_all_hiddens = []
seq_ids = []
for i in range(0, len(data_set), batch_size):
print(i, "out of", len(data_set))
if i+batch_size<=len(data_set):
batch = data_set[i:i+batch_size]
else:
batch = data_set[i:]
#print(batch)
batch_labels, batch_strs, batch_tokens = batch_converter(batch)
seq_ids += batch_labels
print(batch_tokens.size())
batch_tokens = batch_tokens.cuda()
with torch.no_grad():
results = model(batch_tokens, repr_layers=[33])
results = results["representations"][33].cpu().detach()
print(results.size())
sequence_representations = []
for i, ( _ , seq ) in enumerate(batch):
seq_all_hiddens.append(results[i, 1 : len(seq) + 1].numpy())
sequence_representations.append(results[i, 1 : len(seq) + 1].mean(0))
sequence_representations = np.stack(sequence_representations)
seq_encodings.append(sequence_representations)
seq_embeddings = np.concatenate(seq_encodings)
print("seq_embeddings.shape: ", seq_embeddings.shape)
seq_embedding_output = {"seq_embeddings": seq_embeddings, "seq_ids": seq_ids, "seq_all_hiddens": seq_all_hiddens}
pickle.dump( seq_embedding_output, open( output_file, "wb" ) )
print("done")
###################################################################################################################
# `7MM"""YMM .M"""bgd `7MMM. ,MMF' #
# MM `7 ,MI "Y MMMb dPMM __, #
# MM d `MMb. M YM ,M MM `7MM `7M' `MF' #
# MMmmMM `YMMNq. M Mb M' MM MM VA ,V #
# MM Y , . `MM M YM.P' MM mmmmm MM VA ,V #
# MM ,M Mb dM M `YM' MM MM VVV #
# .JMMmmmmMMM P"Ybmmd" .JML. `' .JMML. .JMML. W #
###################################################################################################################
if model_select == "ESM_1V":
#model, alphabet = torch.hub.load("facebookresearch/esm", "esm1v_t33_650M_UR90S_1")
model, alphabet = esm.pretrained.esm1v_t33_650M_UR90S_1()
batch_converter = alphabet.get_batch_converter()
data_set = []
for seq_record in SeqIO.parse(input_file, "fasta"):
data_set.append((str(seq_record.id), str(seq_record.seq)))
model.eval()
model.cuda()
seq_encodings = []
seq_all_hiddens = []
seq_ids = []
for i in range(0, len(data_set), batch_size):
print(i, "out of", len(data_set))
if i+batch_size<=len(data_set):
batch = data_set[i:i+batch_size]
else:
batch = data_set[i:]
#print(batch)
batch_labels, batch_strs, batch_tokens = batch_converter(batch)
seq_ids += batch_labels
print(batch_tokens.size())
batch_tokens = batch_tokens.cuda()
with torch.no_grad():
results = model(batch_tokens, repr_layers=[33])
results = results["representations"][33].cpu().detach()
print(results.size())
sequence_representations = []
for i, ( _ , seq ) in enumerate(batch):
seq_all_hiddens.append(results[i, 1 : len(seq) + 1].numpy())
sequence_representations.append(results[i, 1 : len(seq) + 1].mean(0))
sequence_representations = np.stack(sequence_representations)
seq_encodings.append(sequence_representations)
seq_embeddings = np.concatenate(seq_encodings)
print("seq_embeddings.shape: ", seq_embeddings.shape)
seq_embedding_output = {"seq_embeddings": seq_embeddings, "seq_ids": seq_ids, "seq_all_hiddens": seq_all_hiddens}
pickle.dump( seq_embedding_output, open( output_file, "wb" ) )
print("done")
###################################################################################################################
# `7MM"""YMM .M"""bgd `7MMM. ,MMF' .6*" M****** #
# MM `7 ,MI "Y MMMb dPMM ,M' .M #
# MM d `MMb. M YM ,M MM pd*"*b. ,Mbmmm. |bMMAg. ,pP""Yq. #
# MMmmMM `YMMNq. M Mb M' MM (O) j8 6M' `Mb. `Mb 6W' `Wb #
# MM Y , . `MM M YM.P' MM mmmmm ,;j9 mmmmm MI M8 jM 8M M8 #
# MM ,M Mb dM M `YM' MM ,-=' WM. ,M9 (O) ,M9 YA. ,A9 #
# .JMMmmmmMMM P"Ybmmd" .JML. `' .JMML. Ammmmmmm WMbmmd9 6mmm9 `Ybmmd9' #
###################################################################################################################
if model_select == "ESM_2_650":
# model, alphabet = torch.hub.load("facebookresearch/esm:main", "esm2_t33_650M_UR50D")
model, alphabet = esm.pretrained.esm2_t33_650M_UR50D()
batch_converter = alphabet.get_batch_converter()
data_set = []
for seq_record in SeqIO.parse(input_file, "fasta"):
data_set.append((str(seq_record.id), str(seq_record.seq)))
chunk_size = 8000
data_set_list = [data_set[i:i + chunk_size] for i in range(0, len(data_set), chunk_size)]
for data_set_id, one_data_set in enumerate(data_set_list):
model.eval()
model.cuda()
seq_encodings = []
seq_all_hiddens = []
seq_ids = []
for i in range(0, len(one_data_set), batch_size):
print(i, "out of", len(one_data_set), "; ", data_set_id, "out of", len(data_set_list))
if i+batch_size<=len(one_data_set):
batch = one_data_set[i:i+batch_size]
else:
batch = one_data_set[i:]
#print(batch)
batch_labels, batch_strs, batch_tokens = batch_converter(batch)
seq_ids += batch_labels
print(batch_tokens.size())
batch_tokens = batch_tokens.cuda()
with torch.no_grad():
results = model(batch_tokens, repr_layers=[33])
results = results["representations"][33].cpu().detach()
print(results.size())
sequence_representations = []
for i, ( _ , seq ) in enumerate(batch):
seq_all_hiddens.append(results[i, 1 : len(seq) + 1].numpy())
sequence_representations.append(results[i, 1 : len(seq) + 1].mean(0))
sequence_representations = np.stack(sequence_representations)
seq_encodings.append(sequence_representations)
seq_embeddings = np.concatenate(seq_encodings)
print("seq_embeddings.shape: ", seq_embeddings.shape)
seq_embedding_output = {"seq_embeddings": seq_embeddings, "seq_ids": seq_ids, "seq_all_hiddens": seq_all_hiddens}
with open( Path(str(output_file).replace(".p", "_"+ str(data_set_id) +".p").replace("`", "")), "wb" ) as h:
pickle.dump( seq_embedding_output, h )
print("done")
###################################################################################################################
# `7MM"""YMM .M"""bgd `7MMM. ,MMF' pd""b. `7MM"""Yp, ,' #
# MM `7 ,MI "Y MMMb dPMM (O) `8b MM Yb ;' #
# MM d `MMb. M YM ,M MM pd*"*b. ,89 MM dP bg #
# MMmmMM `YMMNq. M Mb M' MM (O) j8 ""Yb. MM"""bg. "" #
# MM Y , . `MM M YM.P' MM mmmmm ,;j9 mmmmm 88 MM `Y #
# MM ,M Mb dM M `YM' MM ,-=' (O) .M' MM ,9 #
# .JMMmmmmMMM P"Ybmmd" .JML. `' .JMML. Ammmmmmm bmmmd' .JMMmmmd9 #
###################################################################################################################
if model_select == "ESM_2_3B`":
data_set = []
for seq_record in SeqIO.parse(input_file, "fasta"):
data_set.append((str(seq_record.id), str(seq_record.seq)))
#model, alphabet = torch.hub.load("facebookresearch/esm", "esm2_t36_3B_UR50D")
model, alphabet = esm.pretrained.esm2_t36_3B_UR50D()
batch_converter = alphabet.get_batch_converter()
model.eval()
model.cuda()
seq_encodings = []
seq_all_hiddens = []
seq_ids = []
for i in range(0, len(data_set), batch_size):
print(i, "out of", len(data_set))
if i+batch_size<=len(data_set):
batch = data_set[i:i+batch_size]
else:
batch = data_set[i:]
#print(batch)
batch_labels, batch_strs, batch_tokens = batch_converter(batch)
seq_ids += batch_labels
print(batch_tokens.size())
batch_tokens = batch_tokens.cuda()
with torch.no_grad():
results = model(batch_tokens, repr_layers=[36])
results = results["representations"][36].cpu().detach()
print(results.size())
sequence_representations = []
for i, ( _ , seq ) in enumerate(batch):
seq_all_hiddens.append(results[i, 1 : len(seq) + 1].numpy())
#sequence_representations.append(results[i, 1 : len(seq) + 1].mean(0))
#sequence_representations = np.stack(sequence_representations)
#seq_encodings.append(sequence_representations)
#seq_embeddings = np.concatenate(seq_encodings)
#print("seq_embeddings.shape: ", seq_embeddings.shape)
#seq_embedding_output = {"seq_embeddings": seq_embeddings, "seq_ids": seq_ids, "seq_all_hiddens": seq_all_hiddens}
seq_embedding_output = {"seq_ids": seq_ids, "seq_all_hiddens": seq_all_hiddens}
pickle.dump( seq_embedding_output, open( output_file, "wb" ) )
print("done")
###################################################################################################################
# `7MM"""YMM .M"""bgd `7MMM. ,MMF' pd""b. `7MM"""Yp, #
# MM `7 ,MI "Y MMMb dPMM (O) `8b MM Yb #
# MM d `MMb. M YM ,M MM pd*"*b. ,89 MM dP #
# MMmmMM `YMMNq. M Mb M' MM (O) j8 ""Yb. MM"""bg. #
# MM Y , . `MM M YM.P' MM mmmmm ,;j9 mmmmm 88 MM `Y #
# MM ,M Mb dM M `YM' MM ,-=' (O) .M' MM ,9 #
# .JMMmmmmMMM P"Ybmmd" .JML. `' .JMML. Ammmmmmm bmmmd' .JMMmmmd9 #
###################################################################################################################
if model_select == "ESM_2_3B":
# model, alphabet = torch.hub.load("facebookresearch/esm:main", "esm2_t36_3B_UR50D")
model, alphabet = esm.pretrained.esm2_t36_3B_UR50D()
batch_converter = alphabet.get_batch_converter()
data_set = []
for seq_record in SeqIO.parse(input_file, "fasta"):
data_set.append((str(seq_record.id), str(seq_record.seq)))
chunk_size = 500
data_set_list = [data_set[i:i + chunk_size] for i in range(0, len(data_set), chunk_size)]
for data_set_id, one_data_set in enumerate(data_set_list):
model.eval()
model.cuda()
seq_encodings = []
seq_all_hiddens = []
seq_ids = []
for i in range(0, len(one_data_set), batch_size):
print(i, "out of", len(one_data_set), "; ", data_set_id + 1, "out of", len(data_set_list))
if i+batch_size<=len(one_data_set):
batch = one_data_set[i:i+batch_size]
else:
batch = one_data_set[i:]
#print(batch)
batch_labels, batch_strs, batch_tokens = batch_converter(batch)
seq_ids += batch_labels
print(batch_tokens.size())
batch_tokens = batch_tokens.cuda()
with torch.no_grad():
results = model(batch_tokens, repr_layers=[36])
results = results["representations"][36].cpu().detach()
print(results.size())
sequence_representations = []
for i, ( _ , seq ) in enumerate(batch):
seq_all_hiddens.append(results[i, 1 : len(seq) + 1].numpy())
sequence_representations.append(results[i, 1 : len(seq) + 1].mean(0))
sequence_representations = np.stack(sequence_representations)
seq_encodings.append(sequence_representations)
seq_embeddings = np.concatenate(seq_encodings)
print("seq_embeddings.shape: ", seq_embeddings.shape)
seq_embedding_output = {"seq_embeddings": seq_embeddings, "seq_ids": seq_ids, "seq_all_hiddens": seq_all_hiddens}
with open( Path(str(output_file).replace(".p", "_"+ str(data_set_id) +".p").replace("`", "")), "wb" ) as h:
pickle.dump( seq_embedding_output, h )
print("done")
###################################################################################################################
# `7MM"""YMM .M"""bgd `7MMM. ,MMF' M****** `7MM"""Yp, #
# MM `7 ,MI "Y MMMb dPMM __, .M MM Yb #
# MM d `MMb. M YM ,M MM pd*"*b. `7MM |bMMAg. MM dP #
# MMmmMM `YMMNq. M Mb M' MM (O) j8 MM `Mb MM"""bg. #
# MM Y , . `MM M YM.P' MM mmmmm ,;j9 mmmmm MM jM MM `Y #
# MM ,M Mb dM M `YM' MM ,-=' MM (O) ,M9 MM ,9 #
# .JMMmmmmMMM P"Ybmmd" .JML. `' .JMML. Ammmmmmm .JMML. 6mmm9 .JMMmmmd9 #
###################################################################################################################
if model_select == "ESM_2_15B":
# model, alphabet = torch.hub.load("facebookresearch/esm:main", "esm2_t48_15B_UR50D")
model, alphabet = esm.pretrained.esm2_t48_15B_UR50D()
batch_converter = alphabet.get_batch_converter()
data_set = []
for seq_record in SeqIO.parse(input_file, "fasta"):
data_set.append((str(seq_record.id), str(seq_record.seq)))
chunk_size = 2000
data_set_list = [data_set[i:i + chunk_size] for i in range(0, len(data_set), chunk_size)]
for data_set_id, one_data_set in enumerate(data_set_list):
model.eval()
model.cuda()
seq_encodings = []
seq_all_hiddens = []
seq_ids = []
for i in range(0, len(one_data_set), batch_size):
print(i, "out of", len(one_data_set), "; ", data_set_id, "out of", len(data_set_list))
if i+batch_size<=len(one_data_set):
batch = one_data_set[i:i+batch_size]
else:
batch = one_data_set[i:]
#print(batch)
batch_labels, batch_strs, batch_tokens = batch_converter(batch)
seq_ids += batch_labels
print(batch_tokens.size())
batch_tokens = batch_tokens.cuda()
with torch.no_grad():
results = model(batch_tokens, repr_layers=[48])
results = results["representations"][48].cpu().detach()
print(results.size())
sequence_representations = []
for i, ( _ , seq ) in enumerate(batch):
seq_all_hiddens.append(results[i, 1 : len(seq) + 1].numpy())
sequence_representations.append(results[i, 1 : len(seq) + 1].mean(0))
sequence_representations = np.stack(sequence_representations)
seq_encodings.append(sequence_representations)
seq_embeddings = np.concatenate(seq_encodings)
print("seq_embeddings.shape: ", seq_embeddings.shape)
seq_embedding_output = {"seq_embeddings": seq_embeddings, "seq_ids": seq_ids, "seq_all_hiddens": seq_all_hiddens}
with open( Path(str(output_file).replace(".p", "_"+ str(data_set_id) +".p")), "wb" ) as h:
pickle.dump( seq_embedding_output, h )
print("done")
###################################################################################################################
###################################################################################################################
return seq_embedding_output
#$$$$$$$$$$$$$$$$$$$$$$$$$$$$$$$$$$$$$$$$$$$$$$$$$$$$$$$$$$$$$$$$$$$$$$$$$$$$$$$$$$$$$$$$$$$$$$$$$$$$$$$$$$$$$$$$$$$$$$$$$$$$$$$$$$$$$$$$$$$$$$$$$$$$$$#
# `7MMM. ,MMF' db `7MMF'`7MN. `7MF' M M M #
# MMMb dPMM ;MM: MM MMN. M M M M #
# M YM ,M MM ,V^MM. MM M YMb M M M M #
# M Mb M' MM ,M `MM MM M `MN. M `7M'M`MF' `7M'M`MF' `7M'M`MF' #
# M YM.P' MM AbmmmqMA MM M `MM.M VAMAV VAMAV VAMAV #
# M `YM' MM A' VML MM M YMM VVV VVV VVV #
# .JML. `' .JMML..AMA. .AMMA..JMML..JML. YM V V V #
#$$$$$$$$$$$$$$$$$$$$$$$$$$$$$$$$$$$$$$$$$$$$$$$$$$$$$$$$$$$$$$$$$$$$$$$$$$$$$$$$$$$$$$$$$$$$$$$$$$$$$$$$$$$$$$$$$$$$$$$$$$$$$$$$$$$$$$$$$$$$$$$$$$$$$$#
if __name__ == "__main__":
#====================================================================================================#
# Args
Step_code = "N03_"
#--------------------------------------------------#
dataset_nme_list = ["NovoEnzyme", # 0
"PafAVariants", # 1
"GFP", # 2
"Rubisco", # 3
]
dataset_nme = dataset_nme_list[1]
data_folder = Path("N_DataProcessing/")
input_seqs_fasta_file = "N00_" + dataset_nme + ".fasta"
#====================================================================================================#
#====================================================================================================#
# List Index: [0] [1] [2] [3] [4] [5] [6] [7] [8]
models_list = ["TAPE", "TAPE_FT", "BERT", "ALBERT", "Electra", "T5", "Xlnet", "ESM_1B", "ESM_1V",
# [9] [10] [11] [12] [13]
"ESM_2_650", "ESM_2_3B`", "ESM_2_3B", "ESM_2_15B", "Unirep"]
# Select model using index. ( ##### !!!!! models_list[3] Electra deprecated ! )
model_select = models_list[13]
pretraining_name = "X01_" + dataset_nme + "_FT_inter_epoch5_trial_training.pt"
#====================================================================================================#
output_file_name_header = Step_code + dataset_nme + "_embedding_"
#====================================================================================================#
batch_size = 20
xlnet_mem_len = 512
#====================================================================================================#
# ArgParser
parser = argparse.ArgumentParser(
description="Preprocesses the sequence datafile.",
formatter_class=argparse.ArgumentDefaultsHelpFormatter
)
parser.add_argument("--dataset_nme" , type = str , default = dataset_nme , help = "dataset_nme." )
parser.add_argument("--model_select" , type = str , default = model_select , help = "model_select." )
parser.add_argument("--data_folder" , type = Path , default = data_folder , help = "Path to the dir contains the datasets." )
parser.add_argument("--input_seqs_fasta_file" , type = str , default = input_seqs_fasta_file , help = "input_seqs_fasta_file." )
parser.add_argument("--output_file_name_header" , type = str , default = output_file_name_header , help = "output_file_name_header." )
parser.add_argument("--pretraining_name" , type = str , default = pretraining_name , help = "pretraining_name." )
parser.add_argument("--batch_size" , type = int , default = batch_size , help = "Batch size." )
parser.add_argument("--xlnet_mem_len" , type = int , default = 512 , help = "xlnet_mem_len=512." )
args = parser.parse_args()
#====================================================================================================#
# If dataset_nme is specified, use default pretrained model name and output name.
vars_dict = vars(args)
vars_dict["input_seqs_fasta_file"] = "N00_" + vars_dict["dataset_nme"] + ".fasta"
vars_dict["pretraining_name"] = "N01_" + vars_dict["dataset_nme"] + "_FT_inter_epoch5_trial_training.pt"
vars_dict["output_file_name_header"] = Step_code + vars_dict["dataset_nme"] + "_embedding_"
print(vars_dict)
#====================================================================================================#
# Main
N03_embedding_LM(**vars_dict)
print("*"*50)
print(Step_code + " Done!")
# M M M M M M M M M M #
# M M M M M M M M M M #
# M M M M M M M M M M #
# `7M'M`MF' `7M'M`MF' `7M'M`MF' `7M'M`MF' `7M'M`MF' `7M'M`MF' `7M'M`MF' `7M'M`MF' `7M'M`MF' `7M'M`MF' #
# VAMAV VAMAV VAMAV VAMAV VAMAV VAMAV VAMAV VAMAV VAMAV VAMAV #
# VVV VVV VVV VVV VVV VVV VVV VVV VVV VVV #
# V V V V V V V V V V #
#$$$$$$$$$$$$$$$$$$$$$$$$$$$$$$$$$$$$$$$$$$$$$$$$$$$$$$$$$$$$$$$$$$$$$$$$$$$$$$$$$$$$$$$$$$$$$$$$$$$$$$$$$$$$$$$$$$$$$$$$$$$$$$$$$$$$$$$$$$$$$$$$$$$$$$#
#$$$$$$$$$$$$$$$$$$$$$$$$$$$$$$$$$$$$$$$$$$$$$$$$$$$$$$$$$$$$$$$$$$$$$$$$$$$$$$$$$$$$$$$$$$$$$$$$$$$$$$$$$$$$$$$$$$$$$$$$$$$$$$$$$$$$$$$$$$$$$$$$$$$$$$#
#$$$$$$$$$$$$$$$$$$$$$$$$$$$$$$$$$$$$$$$$$$$$$$$$$$$$$$$$$$$$$$$$$$$$$$$$$$$$$$$$$$$$$$$$$$$$$$$$$$$$$$$$$$$$$$$$$$$$$$$$$$$$$$$$$$$$$$$$$$$$$$$$$$$$$$#
###################################################################################################################
###################################################################################################################
#====================================================================================================#
#++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++#
#--------------------------------------------------#
#------------------------------