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utils_KEGG_MED.py
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import os
import numpy as np
import torch
from torch.autograd import Variable
import torch.nn.functional as F
from torch import nn
import random
import scipy.io
from sklearn.decomposition import non_negative_factorization
import pandas as pd
random.seed(3)
def pre_processed_kegg_kg():
dataset_dir = os.path.sep.join(['kegg'])
dti = pd.read_table(os.path.sep.join([dataset_dir, 'dt_kegg_med.txt']), header=None)
# print(dti)
drug_list = set(dti[0])
# print(len(drug_list)) # 4284
target_list = set(dti[2])
# print(len(target_list)) # 945
kg = pd.read_table(os.path.sep.join([dataset_dir, 'kegg_kg.txt']), header=None)
kg_drug = kg[kg[1].str.contains('PATHWAY_DRUG')]
# print(kg_drug)
drug_pathway = []
for row in kg_drug.itertuples():
if getattr(row, '_3') in drug_list:
drug_pathway.append([getattr(row, '_1'), getattr(row, '_3')])
# print("drug_pathway", drug_pathway)
kg_gene = kg[kg[1].str.contains('PATHWAY_GENE')]
gene_pathway = []
for row in kg_gene.itertuples():
if getattr(row, '_3') in target_list:
gene_pathway.append([getattr(row, '_1'), getattr(row, '_3')])
# print("gene_pathway", gene_pathway)
drug_dict = {}
for index, i in enumerate(drug_list):
drug_dict[i] = index
print("drug_dict", drug_dict)
target_dict = {}
for index, i in enumerate(target_list):
target_dict[i] = index
# print("target_dict", target_dict)
pathway1 = np.array(drug_pathway)[:, 0]
pathway2 = np.array(gene_pathway)[:, 0]
pathway = set(np.concatenate((pathway1, pathway2), axis=0))
# print(len(pathway)) # 105
pathway_dict = {}
for index, i in enumerate(pathway):
pathway_dict[i] = index
# print("pathway_dict", pathway_dict)
drug_pathway_processed = []
for i in drug_pathway:
if i[0] in pathway_dict:
drug_pathway_processed.append([pathway_dict[i[0]], drug_dict[i[1]]])
# print("drug_pathway_processed", drug_pathway_processed)
# print(len(drug_pathway_processed)) # 7087
target_pathway_processed = []
for i in gene_pathway:
if i[0] in pathway_dict:
target_pathway_processed.append([pathway_dict[i[0]], target_dict[i[1]]])
# print("target_pathway_processed", target_pathway_processed)
# print(len(target_pathway_processed)) # 3390
drug_target_processed = []
for row in dti.itertuples():
drug_target_processed.append([drug_dict[getattr(row, '_1')], target_dict[getattr(row, '_3')]])
# print("drug_target_processed", drug_target_processed)
# print(len(drug_target_processed)) # 12112
H_drug_pathway = np.zeros((len(drug_dict), len(pathway_dict)), dtype=np.int32)
for i in drug_pathway_processed:
H_drug_pathway[i[1], i[0]] = 1
H_target_pathway = np.zeros((len(target_dict), len(pathway_dict)), dtype=np.int32)
for i in target_pathway_processed:
H_target_pathway[i[1], i[0]] = 1
# print(len(H_target_pathway), len(H_target_pathway[0])) # 945*105
with open(os.path.sep.join([dataset_dir, "drug_target_interaction.txt"]), "w") as f0:
for i in range(len(drug_target_processed)):
s = str(drug_target_processed[i]).replace('[', ' ').replace(']', ' ')
s = s.replace("'", ' ').replace(',', '') + '\n'
f0.write(s)
np.savetxt(os.path.sep.join([dataset_dir, "H_drug_pathway.txt"]), H_drug_pathway)
np.savetxt(os.path.sep.join([dataset_dir, "H_target_pathway.txt"]), H_target_pathway)
disease_drug = kg[kg[1].str.contains('DRUG_EFFICACY_DISEASE')]
# print(disease_drug)
drug_disease = []
for row in disease_drug.itertuples():
if getattr(row, '_1') in drug_list:
drug_disease.append([getattr(row, '_1'), getattr(row, '_3')])
print("drug_disease", drug_disease)
disease_target = kg[kg[1].str.contains('GENE_DISEASE')]
# print(disease_target)
target_disease = []
for row in disease_target.itertuples():
if getattr(row, '_1') in target_list:
target_disease.append([getattr(row, '_1'), getattr(row, '_3')])
print("target_disease", target_disease)
disease1 = np.array(drug_disease)[:, -1]
disease2 = np.array(target_disease)[:, -1]
disease = set(np.concatenate((disease1, disease2), axis=0))
# print(len(disease)) # 360
disease_dict = {}
for index, i in enumerate(disease):
disease_dict[i] = index
print("disease_dict", disease_dict)
drug_disease_processed = []
for i in drug_disease:
if i[1] in disease_dict:
drug_disease_processed.append([disease_dict[i[1]], drug_dict[i[0]]])
# print("drug_disease_processed", drug_disease_processed)
# print(len(drug_disease_processed)) # 365
target_disease_processed = []
for i in target_disease:
if i[1] in disease_dict:
target_disease_processed.append([disease_dict[i[1]], target_dict[i[0]]])
# print("target_disease_processed", target_disease_processed)
# print(len(target_disease_processed)) # 433
H_drug_disease = np.zeros((len(drug_dict), len(disease_dict)), dtype=np.int32)
for i in drug_disease_processed:
H_drug_disease[i[1], i[0]] = 1
# print(H_drug_disease)
# print(len(H_drug_disease), len(H_drug_disease[0])) # 4284*360
H_target_disease = np.zeros((len(target_dict), len(disease_dict)), dtype=np.int32)
for i in target_disease_processed:
H_target_disease[i[1], i[0]] = 1
# print(len(H_target_disease), len(H_target_disease[0])) # 945*360
np.savetxt(os.path.sep.join([dataset_dir, "H_drug_disease.txt"]), H_drug_disease)
np.savetxt(os.path.sep.join([dataset_dir, "H_target_disease.txt"]), H_target_disease)
# pre_processed_kegg_kg()
def load_data_KEGG_MED(dataset_train="kegg_train_0.8_0", dataset_test="kegg_test_0.8_0"):
dataset_dir = os.path.sep.join(['KEGG_MED'])
# build incidence matrix
edge_train = np.genfromtxt(os.path.sep.join([dataset_dir, '{}.txt'.format(dataset_train)]), dtype=np.int32)
edge_all = np.genfromtxt(os.path.sep.join([dataset_dir, '{}.txt'.format("kegg_all")]), dtype=np.int32)
edge_test = np.genfromtxt(os.path.sep.join([dataset_dir, '{}.txt'.format(dataset_test)]), dtype=np.int32)
# print('edge_test', len(edge_test) / 2)
# edge_val = np.genfromtxt(os.path.sep.join([dataset_dir, '{}.txt'.format(dataset_val)]), dtype=np.int32)
# print(edge_train)
i_m = np.genfromtxt(os.path.sep.join([dataset_dir, 'drug_target_interaction.txt']), dtype=np.int32)
H_T = np.zeros((4284, 945), dtype=np.int32)
H_T_all = np.zeros((4284, 945), dtype=np.int32)
for i in edge_train:
H_T[i[0]][i[1]] = 1
for i in edge_all:
H_T_all[i[0]][i[1]] = 1
# np.savetxt(os.path.sep.join([dataset_dir, "drugProtein.txt"]), H_T_all)
# with open(os.path.sep.join([dataset_dir, "drugProtein.txt"]), "w") as f3:
# for i in range(len(H_T_all)):
# s = str(H_T_all[i]).replace('[', ' ').replace(']', ' ')
# s = s.replace("'", ' ').replace(',', '') + '\n'
# f3.write(s)
test = np.zeros(len(edge_test))
for i in range(len(test)):
if i <= len(edge_test) // 2:
test[i] = 1
H_T = torch.Tensor(H_T)
H = H_T.t()
H_T_all = torch.Tensor(H_T_all)
H_all = H_T_all.t()
print("KEGG_MED", H.size()) # 945, 4284
drug_feat1 = torch.eye(4284)
prot_feat1 = torch.eye(945)
drugpathway = torch.Tensor(np.genfromtxt(os.path.sep.join([dataset_dir, 'H_drug_pathway.txt']), dtype=np.int32))
proteinpathway = torch.Tensor(np.genfromtxt(os.path.sep.join([dataset_dir, 'H_target_pathway.txt']), dtype=np.int32))
drugDisease = torch.Tensor(np.genfromtxt(os.path.sep.join([dataset_dir, 'H_drug_disease.txt']), dtype=np.int32))
proteinDisease = torch.Tensor(np.genfromtxt(os.path.sep.join([dataset_dir, 'H_target_disease.txt']), dtype=np.int32))
# print(drugDisease)
# print(drugDisease.size()) # 4284 360
# print(proteinDisease.size()) # 945 360
pos2 = []
for i in range(len(H_T)):
if 1 in H_T[i]:
pos2.append(1)
else:
pos2.append(0)
print(pos2)
print(edge_test)
pos_island = []
pos_ = []
neg_island = []
neg_ = []
for i in range(len(edge_test)):
if i < len(edge_test) // 2:
if pos2[edge_test[i][0]] == 1:
pos_.append(i)
else:
pos_island.append(i)
else:
if pos2[edge_test[i][0]] == 1:
neg_.append(i)
else:
neg_island.append(i)
return drugDisease, proteinDisease, drug_feat1, prot_feat1, H, H_T, edge_test, test
def generate_data_2(dataset_str="drug_target_interaction"):
# 将数据集分为训练集，测试集
dataset_dir = os.path.sep.join(['KEGG_MED'])
edge = np.genfromtxt(os.path.sep.join([dataset_dir, '{}.txt'.format(dataset_str)]), dtype=np.int32) # dtype='U75'
# print(edge)
data = torch.utils.data.DataLoader(edge, shuffle=True)
# print(data)
edge_shuffled = []
for i in data:
edge_shuffled.append(i[0].tolist())
# print(edge_shuffled)
drugs = []
targets = []
for i in edge:
if i[0] not in drugs:
drugs.append(i[0])
if i[1] not in targets:
targets.append(i[1])
test_ration = [0.2]
for d in test_ration:
for a in (range(1)):
edge_test = edge_shuffled[a * int(len(edge_shuffled) * d): (a + 1) * int(len(edge_shuffled) * d)]
edge_train = edge_shuffled[: a * int(len(edge_shuffled) * d)] + edge_shuffled[(a + 1) * int(len(edge_shuffled) * d):]
test_zeros = []
while len(test_zeros) < len(edge_test):
x1 = random.sample(range(0, len(drugs)), 1)[0]
y1 = random.sample(range(0, len(targets)), 1)[0]
if [x1, y1] not in edge_shuffled and [x1, y1] not in test_zeros:
test_zeros.append([x1, y1])
# print(test_zeros)
edge_test = edge_test + test_zeros
with open(os.path.sep.join([dataset_dir, "kegg_train_{ratio}_{fold}.txt".format(ratio=d, fold=a)]), "w") as f0:
for i in range(len(edge_train)):
s = str(edge_train[i]).replace('[', ' ').replace(']', ' ')
s = s.replace("'", ' ').replace(',', '') + '\n'
f0.write(s)
with open(os.path.sep.join([dataset_dir, "kegg_test_{ratio}_{fold}.txt".format(ratio=d, fold=a)]), "w") as f1:
for i in range(len(edge_test)):
s = str(edge_test[i]).replace('[', ' ').replace(']', ' ')
s = s.replace("'", ' ').replace(',', '') + '\n'
f1.write(s)
# with open(os.path.sep.join([dataset_dir, "kegg_all.txt"]), "w") as f3:
# for i in range(len(edge)):
# s = str(edge[i]).replace('[', ' ').replace(']', ' ')
# s = s.replace("'", ' ').replace(',', '') + '\n'
# f3.write(s)
# generate_data_2()