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utils.py
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utils.py
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import numpy as np
import csv
import torch as t
import random
from numpy import *
import torch
def gaussian_normalization(x: t.Tensor, dim: None or 0 or 1):
"""
Gaussian normalization.
:param x: matrix
:param dim: dimension, global normalization if None, column normalization if 0, else row normalization
:return: After normalized matrix
"""
if dim is None:
mean = t.mean(x)
std = t.std(x)
x = t.div(t.sub(x, mean), std)
else:
mean = t.mean(x, dim=dim)
std = t.std(x, dim=dim)
if dim:
x = t.div(t.sub(x, mean.view([-1, 1])), std.view([-1, 1]))
else:
x = t.div(t.sub(x, mean.view([1, -1])), std.view([1, -1]))
return x
def create_resultlist(result,testset,Index_PositiveRow,Index_PositiveCol,Index_zeroRow,Index_zeroCol,test_length_p,zero_length,test_f):
#result_list = zeros((test_length+zero_length, 1))
result_list = zeros((test_length_p+len(test_f), 1))
for i in range(test_length_p):
result_list[i,0] = result[Index_PositiveRow[testset[i]], Index_PositiveCol[testset[i]]]
for i in range(len(test_f)):
result_list[i+test_length_p, 0] = result[Index_zeroRow[test_f[i]], Index_zeroCol[test_f[i]]]
# for i in range(zero_length):
# result_list[i+test_length, 0] = result[Index_zeroRow[i],Index_zeroCol[i]]
return result_list
def create_resultmatrix(result,testset,prolist):
leave_col = prolist[testset]
result = result[:,leave_col]
return result
def computer_GIP(intMat,gamadd = 1,gamall = 1):
num_miRNAs, num_diseasesss = intMat.shape
sd = []
sl = []
for i in range(num_miRNAs):
sd.append((np.linalg.norm(intMat[i, :])) ** 2)
gamad = num_miRNAs / sum(sd) * gamadd
for j in range(num_diseasesss):
sl.append((np.linalg.norm(intMat[:, j])) ** 2)
gamal = num_diseasesss / sum(sl) * gamall
kd = np.ones([num_miRNAs, num_miRNAs],dtype=np.float64)
for i in range(num_miRNAs):
for j in range(i+1,num_miRNAs):
kd[i, j] = np.e ** (-gamad * pow(np.linalg.norm(intMat[i, :] - intMat[j, :]),2))
kd[j, i] = kd[i, j]
kt = np.ones([num_diseasesss, num_diseasesss],dtype=np.float64)
for i in range(num_diseasesss):
for j in range(i+1,num_diseasesss):
kt[i, j] = np.e ** (-gamal * pow(np.linalg.norm(intMat[:, i] - intMat[:, j]),2))
kt[j, i] = kt[i, j]
return kd,kt
def read_csv(path):
with open(path, 'r', newline='') as csv_file:
reader = csv.reader(csv_file)
md_data = []
md_data += [[float(i) for i in row] for row in reader]
return t.FloatTensor(md_data)
def read_txt(path):
with open(path, 'r', newline='') as txt_file:
reader = txt_file.readlines()
md_data = []
md_data += [[float(i) for i in row.split()] for row in reader]
return t.FloatTensor(md_data)
# dis m g
def get_edge_index(matrix,i_offset,j_offset):
edge_index = [[], []]
for i in range(matrix.size(0)):
for j in range(matrix.size(1)):
if matrix[i][j] != 0:
edge_index[0].append(i+i_offset)
edge_index[1].append(j+j_offset)
return t.LongTensor(edge_index)
def f1_score_binary(true_data: torch.Tensor, predict_data: torch.Tensor):
"""
:param true_data: true data,torch tensor 1D
:param predict_data: predict data, torch tensor 1D
:return: max F1 score and threshold
"""
assert torch.all(true_data.ge(0)) and torch.all(true_data.le(1)), "Out of range!"
with torch.no_grad():
thresholds = torch.unique(predict_data)
size = torch.tensor([thresholds.size()[0], true_data.size()[0]], dtype=torch.int32, device=true_data.device)
ones = torch.ones([size[0].item(), size[1].item()], dtype=torch.float32, device=true_data.device)
zeros = torch.zeros([size[0].item(), size[1].item()], dtype=torch.float32, device=true_data.device)
predict_value = torch.where(predict_data.view([1, -1]).ge(thresholds.view([-1, 1])), ones, zeros)
tpn = torch.sum(torch.where(predict_value.eq(true_data.view([1, -1])), ones, zeros), dim=1)
tp = torch.sum(torch.mul(predict_value, true_data.view([1, -1])), dim=1)
two = torch.tensor(2, dtype=torch.float32, device=true_data.device)
n = torch.tensor(size[1].item(), dtype=torch.float32, device=true_data.device)
scores = torch.div(torch.mul(two, tp), torch.add(n, torch.sub(torch.mul(two, tp), tpn)))
max_f1_score = torch.max(scores)
threshold = thresholds[torch.argmax(scores)]
return max_f1_score, threshold
def accuracy_binary(true_data: torch.Tensor, predict_data: torch.Tensor, threshold: float or torch.Tensor):
"""
:param true_data: true data, 1D torch Tensor
:param predict_data: predict data , 1D torch Tensor
:param threshold: threshold, float or torch Tensor
:return: acc
"""
assert torch.all(true_data.ge(0)) and torch.all(true_data.le(1)), "Out of range!"
n = true_data.size()[0]
ones = torch.ones(n, dtype=torch.float32, device=true_data.device)
zeros = torch.zeros(n, dtype=torch.float32, device=true_data.device)
predict_value = torch.where(predict_data.ge(threshold), ones, zeros)
tpn = torch.sum(torch.where(predict_value.eq(true_data), ones, zeros))
score = torch.div(tpn, n)
return score
def precision_binary(true_data: torch.Tensor, predict_data: torch.Tensor, threshold: float or torch.Tensor):
"""
:param true_data: true data, 1D torch Tensor
:param predict_data: predict data , 1D torch Tensor
:param threshold: threshold, float or torch Tensor
:return: precision
"""
assert torch.all(true_data.ge(0)) and torch.all(true_data.le(1)), "Out of range!"
ones = torch.ones(true_data.size()[0], dtype=torch.float32, device=true_data.device)
zeros = torch.zeros(true_data.size()[0], dtype=torch.float32, device=true_data.device)
predict_value = torch.where(predict_data.ge(threshold), ones, zeros)
tp = torch.sum(torch.mul(true_data, predict_value))
true_neg = torch.sub(ones, true_data)
tf = torch.sum(torch.mul(true_neg, predict_value))
score = torch.div(tp, torch.add(tp, tf))
return score
def recall_binary(true_data: torch.Tensor, predict_data: torch.Tensor, threshold: float or torch.Tensor):
"""
:param true_data: true data, 1D torch Tensor
:param predict_data: predict data , 1D torch Tensor
:param threshold: threshold, float or torch Tensor
:return: precision
"""
assert torch.all(true_data.ge(0)) and torch.all(true_data.le(1)), "Out of range!"
ones = torch.ones(true_data.size()[0], dtype=torch.float32, device=true_data.device)
zeros = torch.zeros(true_data.size()[0], dtype=torch.float32, device=true_data.device)
predict_value = torch.where(predict_data.ge(threshold), ones, zeros)
tp = torch.sum(torch.mul(true_data, predict_value))
predict_neg = torch.sub(ones, predict_value)
fn = torch.sum(torch.mul(predict_neg, true_data))
score = torch.div(tp, torch.add(tp, fn))
return score
def mcc_binary(true_data: torch.Tensor, predict_data: torch.Tensor, threshold: float or torch.Tensor):
"""
:param true_data: true data, 1D torch Tensor
:param predict_data: predict data , 1D torch Tensor
:param threshold: threshold, float or torch Tensor
:return: precision
"""
assert torch.all(true_data.ge(0)) and torch.all(true_data.le(1)), "Out of range!"
ones = torch.ones(true_data.size()[0], dtype=torch.float32, device=true_data.device)
zeros = torch.zeros(true_data.size()[0], dtype=torch.float32, device=true_data.device)
predict_value = torch.where(predict_data.ge(threshold), ones, zeros)
predict_neg = torch.sub(ones, predict_value)
true_neg = torch.sub(ones, true_data)
tp = torch.sum(torch.mul(true_data, predict_value))
tn = torch.sum(torch.mul(true_neg, predict_neg))
fp = torch.sum(torch.mul(true_neg, predict_value))
fn = torch.sum(torch.mul(true_data, predict_neg))
delta = torch.tensor(0.00001, dtype=torch.float32, device=true_data.device)
score = torch.div((tp*tn-fp*fn), torch.add(delta, torch.sqrt((tp+fp)*(tp+fn)*(tn+fp)*(tn+fn))))
return score
#这里D1就是1/5清零之后的md关联数据,R11是miRNA相似性数据,R22是疾病相似性数据
def prepare_data(opt,D2, D1,R11,R22,gg,dg,mg, zero_index ):
[row_m, col_d] = np.shape(D1)
[row_g, col_d] = np.shape(dg.T)
dataset = dict()
#dat = dict()
#D1(1207,894)
dataset['md_p'] = t.FloatTensor(D1)
dataset['md_true'] = t.FloatTensor(D1)
#dat['md'] = t.FloatTensor(D2)
# zero_index = []
one_index = []
#读取正样本的坐标位置
for i in range(dataset['md_p'].size(0)):
for j in range(dataset['md_p'].size(1)):
# if dataset['md_p'][i][j] < 1:
# #读取负样本的坐标
# zero_index.append([i, j])
if dataset['md_p'][i][j] >= 1:
#读取正样本坐标
one_index.append([i, j])
# ###读取负样本坐标
# for i in range(dat['md'].size(0)):
# for j in range(dat['md'].size(1)):
# if dat['md'][i][j] < 1:
# #读取负样本的坐标
# zero_index.append([i, j])
#打乱位置
random.shuffle(one_index)
random.shuffle(zero_index)
zero_tensor = t.LongTensor(zero_index)
one_tensor = t.LongTensor(one_index)
dataset['md'] = dict()
#md,train中放正样本和负样本
dataset['md']['train'] = [one_tensor, zero_tensor]
# 疾病相似性数据
dd_matrix = t.FloatTensor(R22)
# dataset['dd']=dd_matrix
# 这里是返回疾病-疾病关联矩阵中数值不是0的坐标位置,
dd_edge_index = get_edge_index(dd_matrix, 0, 0)
# dd中的数据就是所有的相似性值和值大于0的坐标位置
dataset['dd'] = {'data': dd_matrix, 'edge_index': dd_edge_index}
# miRNA相似性数据
mm_matrix = t.FloatTensor(R11)
# dataset['mm']=t.FloatTensor(mm_matrix)
mm_edge_index = get_edge_index(mm_matrix, 0, 0)
dataset['mm'] = {'data': mm_matrix, 'edge_index': mm_edge_index}
# gene相似性数据
gg_matrix = t.FloatTensor(gg)
# dataset['gg']=gg_matrix
gg_edge_index = get_edge_index(gg_matrix, 0, 0)
dataset['gg'] = {'data': gg_matrix, 'edge_index': gg_edge_index}
# gd边
gd_matrix = t.FloatTensor(dg.T)
gd_edge_index = get_edge_index(gd_matrix, col_d + row_m, 0)
dg_edge1 = get_edge_index(t.FloatTensor(dg), 0, col_d)
dataset['gd'] = {'data': gd_matrix, 'edge_index': gd_edge_index, 'dg_edge_gcn': dg_edge1}
# gm边
gm_matrix = t.FloatTensor(mg.T)
gm_edge_index = get_edge_index(gm_matrix, col_d + row_m, col_d)
mg_edge1 = get_edge_index(t.FloatTensor(mg), 0, row_m)
dataset['gm'] = {'data': gm_matrix, 'edge_index': gm_edge_index, 'mg_edge_gcn': mg_edge1}
# dm边
dm_matrix = t.FloatTensor(D1.T)
dm_edge_index = get_edge_index(dm_matrix, 0, col_d)
dataset['dm'] = {'data': dm_matrix, 'edge_index': dm_edge_index}
# d1d3
d1d3_matrix = t.FloatTensor(R22)
d1d3_edge_index_rgcn = get_edge_index(d1d3_matrix, 0, col_d+col_d)
#d1m1
d1m1_matrix = t.FloatTensor(D1.T)
d1m1_edge_index_rgcn = get_edge_index(d1m1_matrix, 0, col_d+col_d+col_d)
# d1m2
d1m2_matrix = t.FloatTensor(D1.T)
d1m2_edge_index_rgcn = get_edge_index(d1m2_matrix, 0, col_d+col_d+col_d + row_m)
# g2d1
g2d1_matrix = t.FloatTensor(dg.T)
g2d1_edge_index_rgcn = get_edge_index(g2d1_matrix, col_d+col_d+col_d + row_m + row_m + row_m + row_g, 0)
#d2m1
d2m1_matrix = t.FloatTensor(D1.T)
d2m1_edge_index_rgcn = get_edge_index(d2m1_matrix, col_d, col_d + col_d + col_d)
# m1m3
m1m3_matrix = t.FloatTensor(R11)
m1m3_edge_index_rgcn = get_edge_index(m1m3_matrix, col_d+col_d + col_d, col_d+col_d + col_d + row_m + row_m)
# g1m1
g1m1_matrix = t.FloatTensor(mg.T)
g1m1_edge_index_rgcn = get_edge_index(g1m1_matrix, col_d+col_d+col_d + row_m + row_m + row_m, col_d+col_d+col_d)
dataset['RGCN_edge'] = {'d1d3_edge_index': d1d3_edge_index_rgcn, 'd1m1_edge_index': d1m1_edge_index_rgcn,
'd1m2_edge_index': d1m2_edge_index_rgcn, 'g2d1_edge_index': g2d1_edge_index_rgcn,
'd2m1_edge_index': d2m1_edge_index_rgcn, 'm1m3_edge_index': m1m3_edge_index_rgcn,
'g1m1_edge_index': g1m1_edge_index_rgcn}
return dataset