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run_GCN_train_mode.py
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run_GCN_train_mode.py
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import re
import os
import sys
import argparse
import numpy as np
import pandas as pd
import scipy.sparse as sp
from sklearn.model_selection import StratifiedKFold
import run_MLP_embedding_train_mode
from gcn_model_train_mode import encode_onehot,GCN,train,train_fs,test,normalize,sparse_mx_to_torch_sparse_tensor
from calculate_avg_acc_of_cross_validation_train_mode import cal_acc_cv
import torch
import torch.nn as nn
from sklearn.metrics.pairwise import cosine_similarity
from random import sample
import random
import uuid
from numpy import savetxt
#exit()
def setup_seed(seed):
torch.manual_seed(seed)
torch.cuda.manual_seed_all(seed)
np.random.seed(seed)
random.seed(seed)
torch.backends.cudnn.deterministic=True
def build_dir(inp):
if not os.path.exists(inp):
os.makedirs(inp)
def select_features(eg_fs,eg_fs_norm,train_idx,fdir,meta,disease,fn):
inmatrix=pd.read_table(eg_fs)
inmatrix=inmatrix.iloc[:,train_idx]
inmatrix.to_csv("tem_e.tsv",sep="\t")
f=open("tem_e.tsv",'r')
o=open('tem_e2.tsv','w+')
line=f.readline().strip()
o.write(line+'\n')
while True:
line=f.readline().strip()
if not line:break
o.write(line+'\n')
o.close()
os.system('rm tem_e.tsv')
fm=open(meta,'r')
om=open('tem_meta.tsv','w+')
line=fm.readline()
om.write(line)
c=0
while True:
line=fm.readline().strip()
if not line:break
if c in train_idx:
om.write(line+'\n')
c+=1
om.close()
print('Run the command: Rscript feature_select_model_nodirect.R tem_e2.tsv tem_meta.tsv eggNOG '+disease)
os.system('Rscript feature_select_model_nodirect.R tem_e2.tsv tem_meta.tsv eggNOG '+disease)
os.system('mv tem_meta.tsv '+fdir+'/meta_Fold'+str(fn)+'.tsv')
f2=open('eggNOG_feature_weight.csv','r')
line=f2.readline()
d={}
t=0
while True:
line=f2.readline().strip()
if not line:break
line=re.sub('\"','',line)
ele=re.split(',',line)
t+=1
if ele[-1]=='NA':continue
if float(ele[-1])==0:continue
d[ele[0]]=''
print(':: Log: There are '+str(len(d))+'/'+str(t)+' features selected!\n')
os.system('mv eggNOG_feature_weight.csv '+fdir+'/eggNOG_feature_weight_Fold'+str(fn)+'.csv')
os.system('mv eggNOG_evaluation.pdf '+fdir+'/eggNOG_evaluation_Fold'+str(fn)+'.pdf')
f3=open(eg_fs_norm,'r')
line=f3.readline()
o2=open('tem_e3.tsv','w+')
o2.write(line)
while True:
line=f3.readline().strip()
if not line:break
ele=line.split('\t')
if ele[0] not in d:continue
o2.write(line+'\n')
o2.close()
os.system('rm tem_e2.tsv')
os.system('mv tem_e3.tsv '+fdir+'/eggNOG_features_Fold'+str(fn)+'.tsv')
a=fdir+'/eggNOG_features_Fold'+str(fn)+'.tsv'
#a=fdir+'/eggNOG_features_Fold'+str(fn)+'.tsv'
return a
#def build_graph_mlp(eg_fs_sf,train_idx,val_idx,meta,disease,fn):
def hard_case_split(infeatures,inlabels):
splits=StratifiedKFold(n_splits=10,shuffle=True,random_state=1234)
dist=cosine_similarity(infeatures,infeatures)
dist_abs=np.maximum(dist,-dist)
did2d={} # ID -> Minimum distance
sid=0
for s in dist_abs:
res=np.argsort(s)[::-1]
for r in res:
if r==sid:continue
did2d[r]=s[r]
break
sid+=1
res=sorted(did2d.items(), key = lambda kv:(kv[1], kv[0]))
res_half=res[:int(len(res)/2)]
candidate_crc=[]
candidate_health=[]
#clabels=[]
for r in res_half:
if inlabels[r[0]]=='CRC':
candidate_crc.append(r[0])
else:
candidate_health.append(r[0])
#clabels.append(inlabels[r[0]])
#print(candidate,clabels)
train_val_idx=[]
for train_idx_sk,val_idx_sk in splits.split(infeatures,inlabels):
val_num=len(val_idx_sk)
#train_num=len(candidate)-val_num
crc_num=int(val_num/2)
health_num=val_num-crc_num
vi1=sample(candidate_crc,crc_num)
vi2=sample(candidate_health,health_num)
vid=vi1+vi2
tid=[]
for i in range(len(infeatures)):
if i in vid:continue
tid.append(i)
#print(tid,vid,len(tid),len(vid))
#print(len(train_idx_sk),len(val_idx_sk))
#exit()
train_val_idx.append((tid,vid))
#print(train_val_idx[:4])
#print(len(train_val_idx))
#exit()
return train_val_idx
def avg_score(avc,vnsa):
for s in avc:
if vnsa[s]==0:
avc[s]['Increase2Disease'] =0
avc[s]['Increase2Health'] = 0
avc[s]['Decrease2Disease'] = 0
avc[s]['Decrease2Health'] = 0
else:
avc[s]['Increase2Disease']=sum(avc[s]['Increase2Disease'])/vnsa[s]
avc[s]['Increase2Health']=sum(avc[s]['Increase2Health'])/vnsa[s]
avc[s]['Decrease2Disease'] = sum(avc[s]['Decrease2Disease']) / vnsa[s]
avc[s]['Decrease2Health'] = sum(avc[s]['Decrease2Health']) / vnsa[s]
return avc
def iter_run(features,train_id,test_id , adj, labels, ot2, rdir,classes_dict, tid2name):
model = GCN(nfeat=features.shape[1], nhid=32, nclass=labels.max().item() + 1, dropout=0.5)
optimizer = torch.optim.Adam(model.parameters(), lr=0.01, weight_decay=1e-5)
# val_idx=test_id
max_train_auc = 0
for epoch in range(150):
train_auc, train_prob = train_fs(epoch, np.array(train_id), np.array(test_id), model, optimizer, features, adj, labels, ot2, max_train_auc, rdir, 0, classes_dict, tid2name, 0)
train_auc = float(train_auc)
if train_auc > max_train_auc:
max_train_auc = train_auc
best_prob = train_prob
return best_prob
def detect_dsp(graph, eg_fs_norm,feature_id, labels_raw,labels,adj, train_id, test_id, rdir,ot2,classes_dict, tid2name,sid,sname,fn):
wwl=1
close_cv=0
setup_seed(10)
dn={}
idx_features_labels = np.genfromtxt("{}".format(eg_fs_norm), dtype=np.dtype(str))
features = idx_features_labels[:, 1:-1]
features = features.astype(float)
features = np.array(features)
features_raw=features.copy()
features = sp.csr_matrix(features, dtype=np.float32)
features = torch.FloatTensor(np.array(features.todense()))
#print(feature_id,graph)
feature_id = list(range(int(features.shape[1])))
#print(feature_id)
#exit()
f=open(graph,'r')
while True:
line=f.readline().strip()
if not line:break
ele=line.split('\t')
ele[0]=int(ele[0])
ele[1]=int(ele[1])
if ele[0] not in dn:
dn[ele[0]]={ele[1]:''}
else:
dn[ele[0]][ele[1]]=''
if ele[1] not in dn:
dn[ele[1]]={ele[0]:''}
else:
dn[ele[1]][ele[0]]=''
tg=[] # only consider training data for now
for s in dn:
p=0
n=0
if s not in train_id:continue
for s2 in dn[s]:
if s2 not in train_id:continue
if labels_raw[s2]=='Health':
n+=1
else:
p+=1
if p>=0 and n>=0:
tg.append(s)
print('There are '+str(len(tg))+' samples have both >=0 healthy and disease neighbors.')
#print(features)
#print(features.shape[1])
#print(labels)
#exit()
model = GCN(nfeat=features.shape[1], nhid=32, nclass=labels.max().item() + 1, dropout=0.5)
optimizer = torch.optim.Adam(model.parameters(), lr=0.01, weight_decay=1e-5)
# val_idx=test_id
# Only consider correctly identified samples
max_train_auc=0
for epoch in range(150):
print('DSD_Raw')
train_auc, train_prob = train_fs(epoch, np.array(train_id), np.array(test_id), model, optimizer, features,adj, labels, ot2, max_train_auc, rdir, 0, classes_dict, tid2name, 0)
train_auc = float(train_auc)
if train_auc > max_train_auc:
max_train_auc = train_auc
best_prob = train_prob
tgc=[]
for t in tg:
if best_prob[t][0]>best_prob[t][1]:
prl=0
else:
prl=1
if prl==labels[t]:
tgc.append(t)
print(len(tgc),' samples will be used to detect driver species...')
#exit()
# there are several types:
# Old rule:Raw: 0: raw_prob, Max: 1: Max2Median, 2: Max2Zero, Middle: 3: Middle2Max, 4: Middle2Zero, Zero: 5: Zero2Median, 6: Zero2Max
# New rule: Raw: 0: raw_prob, Max: 1: Max2Median, 2: Max2Min, Middle: 3: Middle2Max, 4: Middle2Min, Min: 5: Min2Median, 6: Min2Max
res={} # sample_id -> feature_id -> [0.55,-1,-1,0.52,0.33,-1,-1]
arr=[] # sample_id list
for t in tgc:
arr.append(t)
tem_train=train_id.copy()
# tem_train.remove(t)
raw_prob=best_prob[t][1]
c_feature=features_raw.copy()
t_feature=c_feature[t]
ab_max=np.max(t_feature)
ab_median=np.median(t_feature)
ab_min=np.min(t_feature)
res[t]={}
for s in feature_id:
if s not in sid:continue
res[t][s]=['-1','-1','-1','-1','-1','-1','-1']
res[t][s][0]=str(raw_prob)
raw_feature_value=t_feature[s]
if float(raw_feature_value)==0:continue
set_index=[]
if float(raw_feature_value)==ab_min:
features_one=features.clone().detach()
features_one[t][s]=ab_median
features_two=features.clone().detach()
features_two[t][s]=ab_max
set_index.append(5)
set_index.append(6)
elif float(raw_feature_value)==ab_max:
features_one =features.clone().detach()
features_one[t][s]=ab_median
features_two =features.clone().detach()
features_two[t][s]=ab_min
set_index.append(1)
set_index.append(2)
else:
features_one =features.clone().detach()
features_one[t][s] = ab_max
features_two =features.clone().detach()
features_two[t][s] = ab_min
set_index.append(3)
set_index.append(4)
bp1 = iter_run(features_one, train_id,test_id, adj, labels, ot2, rdir,classes_dict, tid2name)
bp2 = iter_run(features_two, train_id, test_id, adj, labels, ot2, rdir, classes_dict, tid2name)
res[t][s][set_index[0]]= str(bp1[t][1])
res[t][s][set_index[1]] = str(bp2[t][1])
disease_lab=0
health_lab=0
for c in classes_dict:
if c=='Health':
if classes_dict['Health'][0]==1:
health_lab=0
disease_lab=1
else:
health_lab = 1
disease_lab = 0
# Increase abundance (3, 5, 6) -> close to CRC or close to Health | Decrease abundance (1, 2, 4) -> close to CRC or close to Health
# Raw: 0: raw_prob, Max: 1: Max2Median, 2: Max2Zero, Middle: 3: Middle2Max, 4: Middle2Zero, Zero: 5: Zero2Median, 6: Zero2Max
# New rule: Raw: 0: raw_prob, Max: 1: Max2Median, 2: Max2Min, Middle: 3: Middle2Max, 4: Middle2Min, Min: 5: Min2Median, 6: Min2Max
o=open(rdir+'/driver_sp_stat_fold'+str(fn+1)+'.txt','w+')
iab=[3,5,6]
dab=[1,2,4]
avc={} # Calculate average change of each feature across disease and healthy samples
# feature_name-> Disease: change_value | Health: change_value
sp_name = dict(zip(sid, sname))
o.write('Sample_ID\tLabel\t'+'\t'.join(sname)+'\n')
# Calculate valid samples
vsa={} # sid -> valid sample
vnsa={} # sname -> valid sample
for t in res:
#valid=0
for s in feature_id:
valid=0
if s not in sid: continue
if s not in vsa:
vsa[s]=0
vnsa[sp_name[s]]=0
if not res[t][s][1] == '-1':
c1 = float(res[t][s][0]) - float(res[t][s][1])
c2 = float(res[t][s][0]) - float(res[t][s][2])
if c1 < 0 and c2 < 0 and abs(c1) < abs(c2):valid=1
if c1 > 0 and c2 > 0 and c1 < c2:valid=1
elif not res[t][s][3] == '-1':
c3 = float(res[t][s][0]) - float(res[t][s][3])
c4 = float(res[t][s][0]) - float(res[t][s][4])
if c3 > 0 and c4 < 0:valid=1
if c3 < 0 and c4 > 0:valid=1
elif not res[t][s][5] == '-1':
c5 = float(res[t][s][0]) - float(res[t][s][5])
c6 = float(res[t][s][0]) - float(res[t][s][6])
if c5 > 0 and c6 < 0 :valid=1
if c5 < 0 and c6 > 0 :valid=1
if valid==1:
vsa[s]+=1
vnsa[sp_name[s]]+=1
for t in res:
o.write(str(t)+'\t'+labels_raw[t]+'\t')
tem=[]
# tid=0
for s in feature_id:
if s not in sid: continue
tem.append(','.join(res[t][s]))
if sp_name[s] not in avc:
avc[sp_name[s]] = {'Increase2Disease': [], 'Increase2Health':[], 'Decrease2Disease': [],
'Decrease2Health': []}
if not res[t][s][1] == '-1':
c1 = float(res[t][s][0]) - float(res[t][s][1])
c2 = float(res[t][s][0]) - float(res[t][s][2])
if health_lab == 1:
if c1 < 0 and c2<0 and abs(c1)<abs(c2):
avc[sp_name[s]]['Decrease2Health'].append(abs(c1)+abs(c2))
if c1>0 and c2>0 and c1<c2:
avc[sp_name[s]]['Decrease2Disease'].append(abs(c1)+abs(c2))
else:
if c1 < 0 and c2<0 and abs(c1)<abs(c2):
avc[sp_name[s]]['Decrease2Disease'].append(abs(c1)+abs(c2))
if c1 > 0 and c2 > 0 and c1 < c2:
avc[sp_name[s]]['Decrease2Health'].append(abs(c1)+abs(c2))
elif not res[t][s][3] == '-1':
c3 = float(res[t][s][0]) - float(res[t][s][3])
c4 = float(res[t][s][0]) - float(res[t][s][4])
if vsa[s]<15:
if abs(c3) > 0.3 or abs(c4) > 0.3: continue
if health_lab == 1:
if c3 >0 and c4<0:
avc[sp_name[s]]['Increase2Disease'].append(abs(c3))
avc[sp_name[s]]['Decrease2Health'].append(abs(c4))
if c3<0 and c4>0:
avc[sp_name[s]]['Increase2Health'].append(abs(c3))
avc[sp_name[s]]['Decrease2Disease'].append(abs(c4))
else:
if c3 >0 and c4<0:
avc[sp_name[s]]['Increase2Health'].append(abs(c3))
avc[sp_name[s]]['Decrease2Disease'].append(abs(c4))
if c3 < 0 and c4 > 0:
avc[sp_name[s]]['Increase2Disease'].append(abs(c3))
avc[sp_name[s]]['Decrease2Health'].append(abs(c4))
elif not res[t][s][5] == '-1':
c5 = float(res[t][s][0]) - float(res[t][s][5])
c6 = float(res[t][s][0]) - float(res[t][s][6])
if vsa[s]<15:
if abs(c3) > 0.3 or abs(c4) > 0.3: continue
if health_lab == 1:
if c5 > 0 and c6<0:
avc[sp_name[s]]['Increase2Disease'].append(abs(c5))
avc[sp_name[s]]['Decrease2Health'].append(abs(c6))
if c5 <0 and c6 > 0:
avc[sp_name[s]]['Decrease2Health'].append(abs(c5))
avc[sp_name[s]]['Decrease2Disease'].append(abs(c6))
else:
if c5 > 0 and c6<0 and abs(c5)< abs(c6):
avc[sp_name[s]]['Increase2Health'].append(abs(c5))
avc[sp_name[s]]['Increase2Disease'].append(abs(c6))
if c5 < 0 and c6 > 0:
avc[sp_name[s]]['Increase2Disease'].append(abs(c5))
avc[sp_name[s]]['Increase2Health'].append(abs(c6))
o.write('\t'.join(tem)+'\n')
o.close()
#raw_avc=avc.copy()
avc=avg_score(avc,vnsa)
o2=open(rdir+'/driver_sp_change_fold'+str(fn+1)+'.txt','w+')
o2.write('Species_ID\tSpecies_name\tIncrease2Disease\tIncrease2Health\tDecrease2Disease\tDecrease2Health\tValid_s\n')
c=1
for s in sname:
o2.write(str(c)+'\t'+s+'\t'+str(avc[s]['Increase2Disease'])+'\t'+str(avc[s]['Increase2Health'])+'\t'+str(avc[s]['Decrease2Disease'])+'\t'+str(avc[s]['Decrease2Health'])+'\t'+str(vnsa[s])+'\n')
c+=1
def feature_importance_check(selected,selected_arr,feature_id,train_idx,val_idx,features,adj,labels,rdir,fn,classes_dict,tid2name,o3,ot,dcs,fnum,o4):
setup_seed(10)
cround=1
top100={}
while True:
res={}
prob_matrix = []
if cround==2:break
for i in feature_id:
max_train_auc=0
best_prob = []
i=int(i)
if i in selected:continue
features_tem=[[x[i]] for x in features]
features_tem=torch.Tensor(features_tem)
model=GCN(nfeat=features_tem.shape[1], nhid=32, nclass=labels.max().item() + 1, dropout=0.5)
optimizer = torch.optim.Adam(model.parameters(),lr=0.01, weight_decay=1e-5)
for epoch in range(50):
train_auc,sample_prob=train_fs(epoch,np.array(train_idx),np.array(val_idx),model,optimizer,features_tem,adj,labels,ot,max_train_auc,rdir,fn+1,classes_dict,tid2name,0)
train_auc=float(train_auc)
if train_auc>max_train_auc:
max_train_auc=train_auc
best_prob = sample_prob
res[i]=float(max_train_auc)
prob_matrix.append(best_prob[:, 1])
res2=sorted(res.items(), key = lambda kv:(kv[1], kv[0]), reverse = True)
sid=1
if cround==1:
for r in res2:
#if sid==fnum+1:break
o3.write(str(sid)+'\t'+str(dcs[r[0]])+'\t'+str(r[1])+'\n')
if sid<fnum+1:
top100[int(r[0])]=str(dcs[r[0]])
sid+=1
o3.close()
selected[res2[0][0]]=res2[0][1]
selected_arr.append(res2[0][0])
cround+=1
prob_matrix = np.array(prob_matrix).T
savetxt(o4, prob_matrix, delimiter=',')
sid=sorted(list(top100.keys()))
sname=[]
for s in sid:
sname.append(top100[s])
return sid,sname
def node_importance_check(selected,selected_arr,tem_train_id,val_idx,features,adj,labels,rdir,fn,classes_dict,tid2name,o5,o6,ot2,nnum):
cround=1
while True:
res={}
if cround==nnum+1:break
for i in tem_train_id:
max_val_auc=0
i=int(i)
if i in selected:continue
if i in val_idx:continue
train_idx=selected_arr+[i]
model=GCN(nfeat=features.shape[1], nhid=32, nclass=labels.max().item() + 1, dropout=0.5)
optimizer = torch.optim.Adam(model.parameters(),lr=0.01, weight_decay=1e-5)
for epoch in range(50):
val_auc=train(epoch,np.array(train_idx),np.array(val_idx),model,optimizer,features,adj,labels,ot2,max_val_auc,rdir,fn+1,classes_dict,tid2name,0)
val_auc=float(val_auc)
if val_auc>max_val_auc:
max_val_auc=val_auc
res[i]=float(max_val_auc)
res2=sorted(res.items(), key = lambda kv:(kv[1], kv[0]), reverse = True)
sid=1
if cround==1:
for r in res2:
if sid==nnum+1:break
o5.write(str(sid)+'\t'+str(tid2name[r[0]])+'\t'+str(r[1])+'\n')
sid+=1
o5.close()
selected[res2[0][0]]=res2[0][1]
selected_arr.append(res2[0][0])
cround+=1
sid=1
for r in selected_arr:
o6.write(str(sid)+'\t'+str(tid2name[r])+'\t'+str(selected[r])+'\n')
sid+=1
o6.close()
def trans_node(infile,meta,ofile):
f=open(meta,'r')
line=f.readline()
arr=[]
while True:
line=f.readline().strip()
if not line:break
ele=line.split('\t')
arr.append(ele[3])
a=pd.read_table(infile)
a=np.array(a).T
c=0
o=open(ofile,'w+')
for s in a:
o.write(str(c))
for e in s:
o.write('\t'+str(e))
o.write('\t'+arr[c]+'\n')
c+=1
o.close()
def load_dcs(infile,dcs):
f=open(infile,'r')
line=f.readline()
cs=0
while True:
line=f.readline().strip()
if not line:break
ele=line.split('\t')
dcs[cs]=ele[0]
cs+=1
def run(input_fs,eg_fs,eg_fs_norm,meta,disease,out,kneighbor,rseed,cvfold,insp,fnum,nnum,pre_features,anode,reverse,uf,rfi):
if not rseed==0:
setup_seed(rseed)
dcs={}
load_dcs(insp,dcs)
'''
f0=open(insp,'r')
line=f0.readline()
dcs={}
cs=0
while True:
line=f0.readline().strip()
if not line:break
ele=line.split('\t')
dcs[cs]=ele[0]
cs+=1
'''
idx_features_labels = np.genfromtxt("{}".format(input_fs),dtype=np.dtype(str))
features=idx_features_labels[:, 1:-1]
features=features.astype(float)
features=np.array(features)
labels_raw = idx_features_labels[:, -1]
labels_raw=np.array(labels_raw)
splits=StratifiedKFold(n_splits=cvfold,shuffle=True,random_state=1234)
fdir=out+'/Feature_File'
gdir=out+'/Graph_File'
rdir=out+'/Res_File'
build_dir(fdir)
build_dir(gdir)
build_dir(rdir)
ofile1=rdir+'/r1.txt'
ofile2=rdir+'/r2.txt'
tid2name={}
fm=open(meta,'r')
line=fm.readline()
tid2name={}
test_idx=[]
c=0
train_id=0
while True:
line=fm.readline().strip()
if not line:break
ele=line.split()
tid2name[c]=ele[2]
if ele[-1]=='test':
test_idx.append(c)
if ele[-1]=='train' or ele[-1]=='test':
train_id=c
c+=1
train_id=train_id+1
test_idx=np.array(test_idx)
o1=open(ofile1,'w+')
fn=0
#train_val_idx=hard_case_split(features[:train_id],labels_raw[:train_id])
#for train_idx,val_idx in splits.split(features[:train_id],labels_raw[:train_id]):
for train_idx,val_idx in splits.split(features[:train_id],labels_raw[:train_id]):
#print(labels_raw[train_idx],labels_raw[val_idx])
#exit()
#o3=open(rdir+'/sample_prob_fold'+str(fn+1)+'.txt','w+')
o1.write('Fold {}'.format(fn+1)+'\n')
print('Fold {}'.format(fn+1)+', Train:'+str(len(train_idx))+' Test:'+str(len(val_idx)))
# Select features using lasso
# Select features using lasso
if uf==0:
if len(pre_features)==0:
eg_fs_sf=select_features(eg_fs,eg_fs_norm,train_idx,fdir,meta,disease,fn+1)
else:
eg_fs_sf=pre_features[fn+1]
if reverse==1:
otem=uuid.uuid1().hex+'.csv'
eg_node=trans_node(eg_fs_sf,meta,otem)
idx_features_labels = np.genfromtxt("{}".format(otem),dtype=np.dtype(str))
features=idx_features_labels[:, 1:-1]
features=features.astype(float)
features=np.array(features)
os.system('rm '+otem)
dcs={}
load_dcs(eg_fs_sf,dcs)
# Usa all features
'''
eg_fs_sf=eg_fs_norm
'''
# Train MLP on selected features 10 times and selecte the best model to build the graph
#exit()
#graph=run_MLP_embedding.build_graph_mlp('../New_datasets/T2D_data_2012_Trans/T2D_eggNOG_norm.txt',train_idx,val_idx,meta,disease,fn+1,gdir)
if reverse==0 and uf==0:
graph=run_MLP_embedding_train_mode.build_graph_mlp(eg_fs_sf,train_idx,val_idx,meta,disease,fn+1,gdir,kneighbor,rseed,rdir)
else:
graph=run_MLP_embedding_train_mode.build_graph_mlp(insp,train_idx,val_idx,meta,disease,fn+1,gdir,kneighbor,rseed,rdir)
#exit()
# Train and testing
labels,classes_dict=encode_onehot(labels_raw)
features = sp.csr_matrix(features, dtype=np.float32)
features=torch.FloatTensor(np.array(features.todense()))
labels = torch.LongTensor(np.where(labels)[1])
idx = np.array(idx_features_labels[:, 0], dtype=np.int32)
idx_map = {j: i for i, j in enumerate(idx)}
edges_unordered = np.genfromtxt("{}".format(graph),dtype=np.int32)
edges = np.array(list(map(idx_map.get, edges_unordered.flatten())),dtype=np.int32).reshape(edges_unordered.shape)
adj = sp.coo_matrix((np.ones(edges.shape[0]), (edges[:, 0], edges[:, 1])),shape=(labels.shape[0], labels.shape[0]),dtype=np.float32)
adj = adj + adj.T.multiply(adj.T > adj) - adj.multiply(adj.T > adj)
adj = normalize(adj + sp.eye(adj.shape[0]))
adj = sparse_mx_to_torch_sparse_tensor(adj)
feature_id=list(range(int(features.shape[1])))
tem_train_id=list(range(train_id))
model=GCN(nfeat=features.shape[1], nhid=32, nclass=labels.max().item() + 1, dropout=0.5)
optimizer = torch.optim.Adam(model.parameters(),lr=0.01, weight_decay=1e-5)
max_val_auc=0
#max_test_auc=0
for epoch in range(150):
val_auc=train(epoch,train_idx,val_idx,model,optimizer,features,adj,labels,o1,max_val_auc,rdir,fn+1,classes_dict,tid2name,1)
if val_auc>max_val_auc:
max_val_auc=val_auc
#fn+=1
## Feature importance
if rfi==1:
selected={}
selected_arr=[]
o3=open(rdir+'/feature_importance_fold'+str(fn+1)+'.txt','w+')
o4 = open(rdir + '/feature_local_importance_fold' + str(fn + 1) + '.txt', 'w+')
#o4=open(rdir+'/feature_importance_iterative_fold'+str(fn+1)+'.txt','w+')
uid=uuid.uuid1().hex
ot=open(uid+'.log','w+')
sid,sname=feature_importance_check(selected,selected_arr,feature_id,train_idx,val_idx,features,adj,labels,rdir,fn,classes_dict,tid2name,o3,ot,dcs,fnum,o4)
ot.close()
os.system('rm '+uid+'.log')
uid=uuid.uuid1().hex
ot2=open(uid+'.log','w+')
detect_dsp(graph,eg_fs_norm,feature_id,labels_raw,labels,adj,train_idx,val_idx,rdir,ot2,classes_dict,tid2name,sid,sname,fn)
ot2.close()
os.system('rm '+uid+'.log')
## Node importance
if anode==1:
selected={}
selected_arr=[]
o5=open(rdir+'/node_importance_single_fold'+str(fn+1)+'.txt','w+')
o6=open(rdir+'/node_importance_combination_fold'+str(fn+1)+'.txt','w+')
uid=uuid.uuid1().hex
ot2=open(uid+'.log','w+')
node_importance_check(selected,selected_arr,tem_train_id,val_idx,features,adj,labels,rdir,fn,classes_dict,tid2name,o5,o6,ot2,nnum)
ot2.close()
os.system('rm '+uid+'.log')
fn+=1
o1.close()
cal_acc_cv(ofile1,ofile2)
# def main():
# usage="Herui's test scripts."
# parser=argparse.ArgumentParser(prog="run_GCN.py",description=usage)
# parser.add_argument('-i','--input_feature',dest='input_fs',type=str,help="The input species feature file. (Node format)")
# parser.add_argument('-e','--eggNOG_feature',dest='eg_fs',type=str,help="The input eggNOG feature file.")
# parser.add_argument('-n','--eggNOG_feature_norm',dest='eg_fs_norm',type=str,help="The input normalized eggNOG feature file.")
# parser.add_argument('-m','--metadata',dest='meta',type=str,help="The input metadata file.")
# parser.add_argument('-d','--disease',dest='disease',type=str,help="The name of the disease.")
# parser.add_argument('-o','--outdir',dest='outdir',type=str,help="Output directory of test results. (Default: GCN_res")
#
# args=parser.parse_args()
#
# input_fs=args.input_fs
# eg_fs=args.eg_fs
# eg_fs_norm=args.eg_fs_norm
# meta=args.meta
# disease=args.disease
# out=args.outdir
#
# run(input_fs,eg_fs,eg_fs_norm,meta,disease,out)
'''
if __name__=="__main__":
sys.exit(main())
'''