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run_MLP_embedding.py
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run_MLP_embedding.py
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import numpy as np
import pandas as pd
import torch
import torch.nn as nn
import torch.utils.data as Data
from torch.optim import SGD,Adam
from sklearn.metrics import accuracy_score
from sklearn import metrics
import matplotlib.pyplot as plt
#import hiddenlayer as hl
import os
import merge_embedding_vector
import build_graph_with_embedding
import random
from copy import deepcopy
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
class MLPclassifica(nn.Module):
def __init__(self,nfeat):
super(MLPclassifica,self).__init__()
self.hidden1=nn.Sequential(
nn.Linear(
in_features=nfeat,
#in_features=1261,
out_features=16,
bias=True,
),
nn.ReLU()
)
self.hidden2=nn.Sequential(
nn.Linear(16,10),
nn.ReLU()
)
self.classifica=nn.Sequential(
nn.Linear(10,2),
nn.Sigmoid()
)
self.dropout=0.5
def forward(self,x):
fc1=self.hidden1(x)
self.featuremap=fc1.detach()
fc2=self.hidden2(fc1)
output=self.classifica(fc2)
return fc1,fc2,output
def load_data(inmatrixf,inmetaf,train_idx,val_idx,test_idx,disease,wwl):
train_idx=np.array(train_idx)
val_idx=np.array(val_idx)
test_idx=np.array(test_idx)
inmatrix=pd.read_table(inmatrixf)
inmatrix_train=inmatrix.iloc[:,train_idx]
inmatrix_val=inmatrix.iloc[:,val_idx]
inmatrix_test=inmatrix.iloc[:,test_idx]
#print(inmatrix_train,inmatrix_train.shape)
#exit()
#inmatrix=inmatrix.T
#print(inmatrix)
#print(inmatrix.shape)
#exit()
inmeta=pd.read_table(inmetaf)
labels_train=inmeta.loc[train_idx,:]["disease"]
labels_val=inmeta.loc[val_idx,:]["disease"]
labels_test=inmeta.loc[test_idx,:]["disease"]
labels_train=labels_train.to_numpy()
labels_train[labels_train==[disease]]=1
labels_train[labels_train==['healthy']]=0
labels_val=labels_val.to_numpy()
labels_val[labels_val==[disease]]=1
labels_val[labels_val==['healthy']]=0
labels_test=labels_test.to_numpy()
if wwl==1:
labels_test[labels_test==[disease]]=1
labels_test[labels_test==['healthy']]=0
else:
labels_test[labels_test==["Unknown"]]=0
inmatrix_train=inmatrix_train.T
inmatrix_val=inmatrix_val.T
inmatrix_test=inmatrix_test.T
X_train=inmatrix_train.to_numpy()
X_val=inmatrix_val.to_numpy()
X_test=inmatrix_test.to_numpy()
return X_train,X_val,X_test,labels_train,labels_val,labels_test
def AUC(output,labels):
a=output.data.numpy()
preds=a[:,1]
fpr,tpr,thresholds=metrics.roc_curve(np.array(labels),np.array(preds))
auc=metrics.auc(fpr,tpr)
return auc
def build_graph_mlp(inmatrixf,train_idx,val_idx,inmetaf,disease,fn,odir,test_idx,kneighbor,rseed,wwl,rdir,close_cv,bsize,oin):
if not rseed==0:
setup_seed(rseed)
o=open(odir+'/train_res_stat_Fold'+str(fn)+'.txt','w+')
ofile1=odir+'/feature_out_train_Fold'+str(fn)+'_eggNOG.txt'
if close_cv==0:
ofile2=odir+'/feature_out_val_Fold'+str(fn)+'_eggNOG.txt'
ofile3=odir+'/feature_out_test_Fold'+str(fn)+'_eggNOG.txt'
#print(train_idx,val_idx)
#exit()
# Load datasets
X_train,X_val,X_test,y_train,y_val,y_test=load_data(inmatrixf,inmetaf,train_idx,val_idx,test_idx,disease,wwl)
#print(y_train,y_test)
#exit()
#trans vector to tensor
X_train_nots=torch.from_numpy(X_train.astype(np.float32))
y_train_t=torch.from_numpy(y_train.astype(np.int64))
if close_cv==0:
X_val_nots=torch.from_numpy(X_val.astype(np.float32))
y_val_t=torch.from_numpy(y_val.astype(np.int64))
X_test_nots=torch.from_numpy(X_test.astype(np.float32))
y_test_t=torch.from_numpy(y_test.astype(np.int64))
train_data_nots=Data.TensorDataset(X_train_nots,y_train_t)
train_nots_loader=Data.DataLoader(
dataset=train_data_nots,
batch_size=bsize,
shuffle=True,
num_workers=0,
)
max_val_acc=0
max_val_auc=0
max_train_acc=0
max_train_auc=0
max_test_acc=-1
max_test_auc=-1
go=0
for i in range(10):
best_auc=0
mlpc_raw=MLPclassifica(nfeat=X_train.shape[1])
optimizer=torch.optim.Adam(mlpc_raw.parameters(),lr=0.01,weight_decay=1e-5)
loss_func=nn.CrossEntropyLoss()
#history1=hl.History()
#canvas1=hl.Canvas()
print_step=25
for epoch in range(50):
for step, (b_x, b_y) in enumerate(train_nots_loader):
_,_,output=mlpc_raw(b_x)
train_loss=loss_func(output,b_y)
optimizer.zero_grad()
train_loss.backward()
optimizer.step()
niter=epoch*len(train_nots_loader)+step+1
#feature_output=mlpc.featuremap.cpu()
#feature_out=np.array(feature_output)
_,_,output=mlpc_raw(X_val_nots)
val_auc=AUC(output,y_val_t)
if float(val_auc)>float(best_auc):
best_auc=float(val_auc)
mlpc=deepcopy(mlpc_raw)
if niter%print_step==0 and close_cv==0:
#_,_,output=mlpc_raw(X_val_nots)
_,pre_lab=torch.max(output,1)
val_accuracy=accuracy_score(y_val_t,pre_lab)
val_auc=AUC(output,y_val_t)
'''
_,_,output=mlpc(X_test_nots)
_,pre_lab=torch.max(output,1)
test_accuracy=accuracy_score(y_test_t,pre_lab)
test_auc=AUC(output,y_test_t)
#print(niter,test_accuracy)
#history1.log(niter,train_loss=train_loss,test_accuracy=test_accuracy,test_AUC=test_auc)
with canvas1:
canvas1.draw_plot(history1["train_loss"])
canvas1.draw_plot(history1["test_accuracy"])
canvas1.draw_plot(history1["test_AUC"])
'''
#plt.savefig(odir+"/result.png",dpi=400)
_,_,output=mlpc(X_train_nots)
_,pre_lab=torch.max(output,1)
feature_output=mlpc.featuremap.cpu()
feature_out=np.array(feature_output)
train_acc=accuracy_score(y_train_t,pre_lab)
train_auc=AUC(output,y_train_t)
print("train_accuracy:",train_acc,"train_AUC:",train_auc)
if close_cv==0:
_,_,output=mlpc(X_val_nots)
_,pre_lab=torch.max(output,1)
feature_output_val=mlpc.featuremap.cpu()
feature_out_val=np.array(feature_output_val)
val_accuracy=accuracy_score(y_val_t,pre_lab)
val_auc=AUC(output,y_val_t)
print("val_accuracy:",val_accuracy,"val_AUC:",val_auc)
_,_,output=mlpc(X_test_nots)
_,pre_lab=torch.max(output,1)
feature_output_test=mlpc.featuremap.cpu()
feature_out_test=np.array(feature_output_test)
#go=0
if wwl==1:
test_accuracy=accuracy_score(y_test_t,pre_lab)
test_auc=AUC(output,y_test_t)
if len(y_test_t)<13:
test_auc=test_accuracy
if oin==0:
print("test_accuracy:",test_accuracy,"test_AUC:",test_auc)
if close_cv==0:
o.write("Train accuracy: "+str(train_acc)+" Train AUC: "+str(train_auc)+"\nVal accuracy: "+str(val_accuracy)+" Val AUC: "+str(val_auc)+"\nTest accuracy: "+str(test_accuracy)+" Test AUC: "+str(test_auc)+'\n')
else:
o.write("Train accuracy: "+str(train_acc)+" Train AUC: "+str(train_auc)+"\nTest accuracy: "+str(test_accuracy)+" Test AUC: "+str(test_auc)+'\n')
'''
if len(y_test_t)<13:
if go==0:
max_test_acc=test_accuracy
max_test_auc=test_auc
np.savetxt(ofile1,feature_out)
if close_cv==0:
np.savetxt(ofile2,feature_out_val)
np.savetxt(ofile3,feature_out_test)
go=1
if test_accuracy>max_test_acc:
max_test_acc=test_accuracy
max_test_auc=test_auc
np.savetxt(ofile1,feature_out)
if close_cv==0:
np.savetxt(ofile2,feature_out_val)
np.savetxt(ofile3,feature_out_test)
'''
#print('Herui_check :',test_auc,max_test_auc)
if test_auc>max_test_auc:
max_test_acc=test_accuracy
max_test_auc=test_auc
np.savetxt(ofile1,feature_out)
#print('Check_res ',ofile1)
#exit()
if close_cv==0:
np.savetxt(ofile2,feature_out_val)
np.savetxt(ofile3,feature_out_test)
if test_auc==max_test_auc and test_accuracy>max_test_acc:
max_test_acc=test_accuracy
max_test_auc=test_auc
np.savetxt(ofile1,feature_out)
if close_cv==0:
np.savetxt(ofile2,feature_out_val)
np.savetxt(ofile3,feature_out_test)
else:
if close_cv==0:
o.write("Train accuracy: "+str(train_acc)+" Train AUC: "+str(train_auc)+"\nVal accuracy: "+str(val_accuracy)+" Val AUC: "+str(val_auc)+'\n')
else:
o.write("Train accuracy: "+str(train_acc)+" Train AUC: "+str(train_auc)+"\n")
if close_cv==0:
if val_auc>max_val_auc:
max_val_acc=val_accuracy
max_val_auc=val_auc
np.savetxt(ofile1,feature_out)
np.savetxt(ofile2,feature_out_val)
np.savetxt(ofile3,feature_out_test)
if val_auc==max_val_auc and val_accuracy>max_val_acc:
max_val_acc=val_accuracy
max_val_auc=val_auc
np.savetxt(ofile1,feature_out)
np.savetxt(ofile2,feature_out_val)
np.savetxt(ofile3,feature_out_test)
else:
if train_auc>max_train_auc:
max_train_acc=train_acc
max_train_auc=train_auc
np.savetxt(ofile1,feature_out)
np.savetxt(ofile3,feature_out_test)
if train_auc==max_train_auc and train_acc>max_train_acc:
max_train_acc=train_acc
max_train_auc=train_auc
np.savetxt(ofile1,feature_out)
np.savetxt(ofile3,feature_out_test)
if close_cv==0:
merge_embedding_vector.merge(ofile1,ofile2,ofile3,train_idx,val_idx,test_idx,odir+'/merge_embedding_Fold'+str(fn)+'.txt')
else:
merge_embedding_vector.merge2(ofile1,ofile3,train_idx,test_idx,odir+'/merge_embedding_Fold'+str(fn)+'.txt')
build_graph_with_embedding.build(odir+'/merge_embedding_Fold'+str(fn)+'.txt',inmetaf,'eggNOG',odir+'/Fold'+str(fn),kneighbor,rdir+'/sample_kneighbors_all_fold'+str(fn)+'.txt')
graph=odir+'/Fold'+str(fn)+'/P3_build_graph/eggNOG_pca_knn_graph_final.txt'
return graph
#exit()
#graph=build_graph_mlp('../New_datasets/T2D_data_2012_Trans/T2D_eggNOG_norm.txt',list(range(340)),list(range(340,363)),'../New_datasets/T2D_data_2012_Trans/T2D_meta.tsv','T2D',1,'T2D_result/Graph_File')
#print(graph)