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train_data.py
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train_data.py
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import os
import numpy as np
import pandas as pd
import torch
import torch_geometric.datasets as datasets
import torch_geometric.data as data
import torch_geometric.transforms as transforms
import networkx as nx
from torch_geometric.utils.convert import to_networkx
import CONSTANTS
from Classes.Diamond import Diamond
from Classes.Fasta import Fasta
from Classes.Interpro import Interpro, create_indicies
from Classes.STRING import STRING
from Utils import count_proteins, get_proteins_from_fasta, pickle_load, pickle_save
import urllib.request
onts = ['cc', 'bp', 'mf']
sess = ['validation', 'train']
key_val = pickle_load("cc_string_comps").key_val
data = torch.load("cc_string_comp.pt")
data = data.cpu()
# res = dict((v,k) for k,v in _data.items())
# you = {}
# for i, j in enumerate(data):
# you[res[i]] = j
# print(len(you))
for ont in onts[0:1]:
print(ont)
for s in sess:
print(s)
dt = list(pickle_load(CONSTANTS.ROOT_DIR + "{}/{}_proteins".format(ont, s)))
indicies = torch.tensor([key_val[i] for i in dt])
bn = torch.index_select(data, 0, indicies)
bn = bn.tolist()
labels = pickle_load(CONSTANTS.ROOT_DIR + "{}/labels".format(ont))
store = {'labels': [],
'string': bn
}
for i in dt:
label = torch.tensor(labels[i], dtype=torch.float32).view(1, -1)
store['labels'].append(label)
pickle_save(store, CONSTANTS.ROOT_DIR + "{}/{}_data_2".format(ont, s))
exit()
onts = ['cc', 'bp', 'mf']
sess = ['train', 'validation']
for ont in onts:
print(ont)
for s in sess:
dt = list(pickle_load(CONSTANTS.ROOT_DIR + "{}/{}_proteins".format(ont, s)))
labels = pickle_load(CONSTANTS.ROOT_DIR + "{}/labels".format(ont))
store = {'labels': [],
'esm2_t48': [],
'msa_1b': [],
'interpro': [],
'diamond': [],
'string': [],
'protein': []
}
for i in dt:
print("{}, {}, {}".format(ont, s, i))
tmp = torch.load(CONSTANTS.ROOT_DIR + "data/processed/{}.pt".format(i))
esm = tmp['esm2_t48'].x.squeeze(0)
msa = torch.mean(tmp['esm_msa1b'].x, dim=0).detach()#.unsqueeze(0)
diamond = tmp['diamond_{}'.format(ont)].x
diamond = torch.mean(diamond, dim=0)#.unsqueeze(0)
interpro = tmp['interpro_{}'.format(ont)].x.squeeze(0)
string_data = tmp['string_{}'.format(ont)].x
string_data = torch.mean(string_data, dim=0)#.unsqueeze(0)
label = torch.tensor(labels[i], dtype=torch.float32).view(1, -1)
store['labels'].append(label)
store['esm2_t48'].append(esm)
store['msa_1b'].append(msa)
store['diamond'].append(diamond)
store['interpro'].append(interpro)
store['string'].append(string_data)
store['protein'].append(i)
pickle_save(store, CONSTANTS.ROOT_DIR + "{}/{}_data".format(ont, s))
exit()
# device = 'cuda:1'
train_data = pickle_load("com_data/{}.data_{}".format('cc', 'train'))
print(train_data['esm2_t48'].shape)
print(train_data['msa_1b'].shape)
print(train_data['diamond'].shape)
print(train_data['interpro'].shape)
print(train_data['string'].shape)
print(train_data['labels'].shape)
# # labels = torch.cat(labels, dim=0).to(device)
# # labels = torch.index_select(labels, 1, term_indicies)
# msa_features = train_data['msa_1b']
# for i in msa_features:
# print(i)
# exit()
# print(type(msa_features))
# exit()
# msa_features = torch.cat(msa_features, dim=0).to(device)
# esm_features = train_data['esm2_t48']
# esm_features = torch.cat(esm_features, dim=0).to(device)
# data = pickle_load("com_data/{}.data_{}".format(self.ont, self.session))
# labels = data['labels']
# labels = torch.cat(labels, dim=0).to(device)
# labels = torch.index_select(labels, 1, term_indicies)
# esm_features = train_data[args.submodel]
# esm_features = torch.cat(esm_features, dim=0).to(device)
# exit()
# print(esm_features.shape, msa_features.shape)
# print(esm_features.dtype, msa_features.dtype)
# print(esm_features.device, msa_features.device)
# print(esm_features[0].dtype, msa_features[0].dtype)