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gnn_data.py
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gnn_data.py
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import os
import json
import numpy as np
import copy
import torch
import random
from tqdm import tqdm
from utils import UnionFindSet, get_bfs_sub_graph, get_dfs_sub_graph
from torch_geometric.data import Data, Dataset, InMemoryDataset, DataLoader
class GNN_DATA:
def __init__(self, ppi_path, exclude_protein_path=None, max_len=2000, skip_head=True, p1_index=0, p2_index=1,
label_index=2, graph_undirection=True, bigger_ppi_path=None):
self.ppi_list = []
self.ppi_dict = {}
self.ppi_label_list = []
self.protein_dict = {}
self.protein_name = {}
self.ppi_path = ppi_path
self.bigger_ppi_path = bigger_ppi_path
self.max_len = max_len
name = 0
ppi_name = 0
# maxlen = 0
self.node_num = 0
self.edge_num = 0
if exclude_protein_path != None:
with open(exclude_protein_path, 'r') as f:
ex_protein = json.load(f)
f.close()
ex_protein = {p: i for i, p in enumerate(ex_protein)}
else:
ex_protein = {}
class_map = {'reaction': 0, 'binding': 1, 'ptmod': 2, 'activation': 3, 'inhibition': 4, 'catalysis': 5,
'expression': 6}
for line in tqdm(open(ppi_path)):
if skip_head:
skip_head = False
continue
line = line.strip().split('\t')
if line[p1_index] in ex_protein.keys() or line[p2_index] in ex_protein.keys():
continue
# get node and node name
if line[p1_index] not in self.protein_name.keys():
self.protein_name[line[p1_index]] = name
name += 1
if line[p2_index] not in self.protein_name.keys():
self.protein_name[line[p2_index]] = name
name += 1
if p1_index == 11:
aaaaaa = 1
# get edge and its label
temp_data = ""
zj1 = line[p1_index]
zj2 = line[p2_index]
if line[p1_index] < line[p2_index]:
temp_data = line[p1_index] + "__" + line[p2_index]
else:
temp_data = line[p2_index] + "__" + line[p1_index]
if temp_data not in self.ppi_dict.keys():
self.ppi_dict[temp_data] = ppi_name
temp_label = [0, 0, 0, 0, 0, 0, 0]
temp_label[class_map[line[label_index]]] = 1
self.ppi_label_list.append(temp_label)
ppi_name += 1
else:
index = self.ppi_dict[temp_data]
temp_label = self.ppi_label_list[index]
temp_label[class_map[line[label_index]]] = 1
self.ppi_label_list[index] = temp_label
if bigger_ppi_path != None:
skip_head = True
for line in tqdm(open(bigger_ppi_path)):
if skip_head:
skip_head = False
continue
line = line.strip().split('\t')
if line[p1_index] not in self.protein_name.keys():
self.protein_name[line[p1_index]] = name
name += 1
if line[p2_index] not in self.protein_name.keys():
self.protein_name[line[p2_index]] = name
name += 1
temp_data = ""
if line[p1_index] < line[p2_index]:
temp_data = line[p1_index] + "__" + line[p2_index]
else:
temp_data = line[p2_index] + "__" + line[p1_index]
if temp_data not in self.ppi_dict.keys():
self.ppi_dict[temp_data] = ppi_name
temp_label = [0, 0, 0, 0, 0, 0, 0]
temp_label[class_map[line[label_index]]] = 1
self.ppi_label_list.append(temp_label)
ppi_name += 1
else:
index = self.ppi_dict[temp_data]
temp_label = self.ppi_label_list[index]
temp_label[class_map[line[label_index]]] = 1
self.ppi_label_list[index] = temp_label
i = 0
for ppi in tqdm(self.ppi_dict.keys()):
name = self.ppi_dict[ppi]
assert name == i
i += 1
temp = ppi.strip().split('__')
self.ppi_list.append(temp)
ppi_num = len(self.ppi_list)
self.origin_ppi_list = copy.deepcopy(self.ppi_list)
assert len(self.ppi_list) == len(self.ppi_label_list)
for i in tqdm(range(ppi_num)):
seq1_name = self.ppi_list[i][0]
seq2_name = self.ppi_list[i][1]
# print(len(self.protein_name))
self.ppi_list[i][0] = self.protein_name[seq1_name]
self.ppi_list[i][1] = self.protein_name[seq2_name]
if graph_undirection:
for i in tqdm(range(ppi_num)):
temp_ppi = self.ppi_list[i][::-1]
temp_ppi_label = self.ppi_label_list[i]
# if temp_ppi not in self.ppi_list:
self.ppi_list.append(temp_ppi)
self.ppi_label_list.append(temp_ppi_label)
self.node_num = len(self.protein_name)
self.edge_num = len(self.ppi_list)
def get_protein_aac(self, pseq_path):
# aac: amino acid sequences
self.pseq_path = pseq_path
self.pseq_dict = {}
self.protein_len = []
for line in tqdm(open(self.pseq_path)):
line = line.strip().split('\t')
ls1 = line[0]
ls2 = line[1]
if line[0] not in self.pseq_dict.keys():
self.pseq_dict[line[0]] = line[1]
self.protein_len.append(len(line[1]))
print("protein num: {}".format(len(self.pseq_dict)))
print("protein average length: {}".format(np.average(self.protein_len)))
print("protein max & min length: {}, {}".format(np.max(self.protein_len), np.min(self.protein_len)))
def embed_normal(self, seq, dim):
if len(seq) > self.max_len:
return seq[:self.max_len]
elif len(seq) < self.max_len:
less_len = self.max_len - len(seq)
return np.concatenate((seq, np.zeros((less_len, dim))))
return seq
def vectorize(self, vec_path):
self.acid2vec = {}
self.dim = None
for line in open(vec_path):
line = line.strip().split('\t')
temp = np.array([float(x) for x in line[1].split()])
self.acid2vec[line[0]] = temp
if self.dim is None:
self.dim = len(temp)
print("acid vector dimension: {}".format(self.dim))
self.pvec_dict = {}
for p_name in tqdm(self.pseq_dict.keys()):
temp_seq = self.pseq_dict[p_name]
temp_vec = []
for acid in temp_seq:
temp_vec.append(self.acid2vec[acid])
temp_vec = np.array(temp_vec)
temp_vec = self.embed_normal(temp_vec, self.dim)
self.pvec_dict[p_name] = temp_vec
def get_feature_origin(self, pseq_path, vec_path):
self.get_protein_aac(pseq_path)
self.vectorize(vec_path)
self.protein_dict = {}
for name in tqdm(self.protein_name.keys()):
self.protein_dict[name] = self.pvec_dict[name]
def get_connected_num(self):
self.ufs = UnionFindSet(self.node_num)
ppi_ndary = np.array(self.ppi_list)
for edge in ppi_ndary:
start, end = edge[0], edge[1]
self.ufs.union(start, end)
def generate_data(self):
self.get_connected_num()
print("Connected domain num: {}".format(self.ufs.count))
ppi_list = np.array(self.ppi_list)
ppi_label_list = np.array(self.ppi_label_list)
self.edge_index = torch.tensor(ppi_list, dtype=torch.long)
self.edge_attr = torch.tensor(ppi_label_list, dtype=torch.long)
self.x = []
i = 0
for name in self.protein_name:
assert self.protein_name[name] == i
i += 1
self.x.append(self.protein_dict[name])
self.x = np.array(self.x)
self.x = torch.tensor(self.x, dtype=torch.float)
self.data = Data(x=self.x, edge_index=self.edge_index.T, edge_attr_1=self.edge_attr)
def split_dataset(self, train_valid_index_path, test_size=0.2, random_new=False, mode='random'):
if random_new:
if mode == 'random':
ppi_num = int(self.edge_num // 2)
random_list = [i for i in range(ppi_num)]
random.shuffle(random_list)
self.ppi_split_dict = {}
self.ppi_split_dict['train_index'] = random_list[: int(ppi_num * (1 - test_size))]
self.ppi_split_dict['valid_index'] = random_list[int(ppi_num * (1 - test_size)):]
jsobj = json.dumps(self.ppi_split_dict)
with open(train_valid_index_path, 'w') as f:
f.write(jsobj)
f.close()
elif mode == 'bfs' or mode == 'dfs':
print("use {} methed split train and valid dataset".format(mode))
node_to_edge_index = {}
edge_num = int(self.edge_num // 2)
for i in range(edge_num):
edge = self.ppi_list[i]
if edge[0] not in node_to_edge_index.keys():
node_to_edge_index[edge[0]] = []
node_to_edge_index[edge[0]].append(i)
if edge[1] not in node_to_edge_index.keys():
node_to_edge_index[edge[1]] = []
node_to_edge_index[edge[1]].append(i)
node_num = len(node_to_edge_index)
sub_graph_size = int(edge_num * test_size)
if mode == 'bfs':
selected_edge_index = get_bfs_sub_graph(self.ppi_list, node_num, node_to_edge_index, sub_graph_size)
elif mode == 'dfs':
selected_edge_index = get_dfs_sub_graph(self.ppi_list, node_num, node_to_edge_index, sub_graph_size)
all_edge_index = [i for i in range(edge_num)]
unselected_edge_index = list(set(all_edge_index).difference(set(selected_edge_index)))
self.ppi_split_dict = {}
self.ppi_split_dict['train_index'] = unselected_edge_index
self.ppi_split_dict['valid_index'] = selected_edge_index
assert len(unselected_edge_index) + len(selected_edge_index) == edge_num
jsobj = json.dumps(self.ppi_split_dict)
with open(train_valid_index_path, 'w') as f:
f.write(jsobj)
f.close()
else:
print("your mode is {}, you should use bfs, dfs or random".format(mode))
return
else:
with open(train_valid_index_path, encoding='utf-8-sig',errors='ignore') as f:
str = f.read()
self.ppi_split_dict = json.loads(str, strict=False)
f.close()
# ppi_data = GNN_DATA(ppi_path = './data/protein.actions.SHS27k.STRING.txt')
# ppi_data.get_feature_origin(pseq_path = './data/protein.SHS27k.sequences.dictionary.tsv', vec_path = './data/vec5_CTC.txt')
# ppi_data = GNN_DATA(ppi_path = './data/protein.actions.SHS27k.STRING.pro2.txt')
# ppi_data.get_feature_origin(pseq_path = './data/protein.SHS27k.sequences.dictionary.pro3.tsv', vec_path = './data/vec5_CTC.txt')
#
# ppi_data.generate_data()
# ppi_data.split_dataset(train_valid_index_path='./train_val_split_data/train_val_split.json', random_new=True, mode='random')
# graph = ppi_data.data
# for liness in tqdm(open('./data/protein.SHS27k.sequences.dictionary.pro2.tsv')):
# aa = ppi_data.protein_name
# id1 = liness[:20]
# resu = id1 in aa.keys()
# if resu == False:
# print(id1)
# qs = aa.keys()
# count_k = 0
# aad = 1
# ff = open('./data/protein.SHS27k.sequences.dictionary.pro3.tsv', 'w', encoding = "utf-8")
# for liness in tqdm(open('./data/protein.SHS27k.sequences.dictionary.pro2.tsv')):
# liness_id = liness.split("\t")
# liness_id1 = liness_id[0]
# count_k = count_k + 1
# qs1 = liness_id1 in qs
# if qs1 == True:
# ff.writelines(liness)