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lstm_partial_grid_rri.v.16.py
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#CUDA_VISIBLE_DEVICES=1
import torch
import torch.autograd as autograd
import torch.nn as nn
import torch.nn.functional as F
import torch.optim as optim
from torch.nn import init
import numpy as np
import json
import os.path
import subprocess
import random
from operator import itemgetter
import sklearn.metrics as metrics
import sys
from benchmark_tools import *
np.set_printoptions(linewidth=1000000000)
torch.cuda.manual_seed(1)
verbose = False#True
file_name = sys.argv[1]
results_path = sys.argv[2]
if len(sys.argv)>3:
verbose = True
training = dict()
testing = dict()
training['features'], training['sequences'] = read_seq_pssm("pssm_dimers_450_list.tsv","dimers_450")
training['contacts'] = read_contacts("rri_dimers_450_list.tsv","dimers_450_rri",training['features'])
training['scop'] = read_scop("450_dimers_list.tsv")
testing['features'], testing['sequences'] = read_seq_pssm("pssm_dimers_bipspi_list.tsv","dimers_bipspi")
testing['contacts'] = read_contacts("rri_dimers_bipspi_list.tsv","dimers_bipspi_rri",testing['features'])
class BiLSTM(nn.Module):
def __init__( self, input_dim=22, lstm_hidden_dim=256, hidden_1_dim=1024, hidden_2_dim=512, hidden_3_dim=1024, rri_size=2 ):
super(BiLSTM, self).__init__()
self.input_dim = input_dim
self.lstm_hidden_dim = lstm_hidden_dim
self.hidden_1_dim = hidden_1_dim
self.hidden_2_dim = hidden_2_dim
self.hidden_3_dim = hidden_3_dim
self.rri_size = rri_size
self.lstm_h0 = None
self.lstm_c0 = None
self.update_lstm_hidden()
self.LSTM = nn.LSTM(input_dim, lstm_hidden_dim, num_layers=2, bidirectional=True, dropout=0.5)
self.drop_hidden_1 = nn.Dropout(p=0.5)
self.lstm2hidden_1 = nn.Linear(2*lstm_hidden_dim, hidden_1_dim)
self.drop_hidden_2 = nn.Dropout(p=0.5)
self.hidden2hidden_2 = nn.Linear(hidden_1_dim, hidden_2_dim)
self.drop_hidden_3 = nn.Dropout(p=0.5)
self.hidden2hidden_3 = nn.Linear(2*hidden_2_dim, hidden_3_dim)
self.hidden2out = nn.Linear(hidden_3_dim, rri_size)
def update_lstm_hidden(self):
self.lstm_h0 = autograd.Variable(torch.zeros(4, 1, self.lstm_hidden_dim)).cuda()
self.lstm_c0 = autograd.Variable(torch.zeros(4, 1, self.lstm_hidden_dim)).cuda()
def prepare_data(self, pdb, sequence, features):
list_pssm = []
for aa in sequence:
v = list(features[pdb][aa]["pssm"])
list_pssm.append(v)
return autograd.Variable( torch.unsqueeze(torch.FloatTensor(list_pssm),dim=1) ).cuda()
def forward( self, pdb, sequence_r, sequence_l, features, contacts, ch_r=None, ch_l=None, Flag=True ):
v_r = self.prepare_data( pdb, sequence_r, features )
v_l = self.prepare_data( pdb, sequence_l, features )
self.update_lstm_hidden()
out_LSTM_r, (hidden_LSTM_r, content_LSTM_r) = self.LSTM( v_r, (self.lstm_h0, self.lstm_c0))
self.update_lstm_hidden()
out_LSTM_l, (hidden_LSTM_l, content_LSTM_l) = self.LSTM( v_l, (self.lstm_h0, self.lstm_c0))
hidden_r_1 = self.lstm2hidden_1( out_LSTM_r.view(len(sequence_r), -1) )
hidden_r_1 = self.drop_hidden_1(hidden_r_1)
out_hidden_r_1 = F.relu(hidden_r_1)
hidden_r_2 = self.hidden2hidden_2( out_hidden_r_1 )
hidden_r_2 = self.drop_hidden_2(hidden_r_2)
out_hidden_r_2 = F.relu(hidden_r_2)
hidden_l_1 = self.lstm2hidden_1( out_LSTM_l.view(len(sequence_l), -1) )
hidden_l_1 = self.drop_hidden_1(hidden_l_1)
out_hidden_l_1 = F.relu(hidden_l_1)
hidden_l_2 = self.hidden2hidden_2( out_hidden_l_1 )
hidden_l_2 = self.drop_hidden_2(hidden_l_2)
out_hidden_l_2 = F.relu(hidden_l_2)
rl = 0
N_r = len(sequence_r)
N_l = len(sequence_l)
native_rri = []
if Flag:
res_rri = list(contacts['rri_ch_ch'][pdb][ch_r+":"+ch_l].keys())
N_rri = len(res_rri)
v_in = autograd.Variable( torch.FloatTensor(N_rri+len(res_rri),2*self.hidden_2_dim) ).cuda()
v_in_t = autograd.Variable( torch.FloatTensor(N_rri+len(res_rri),2*self.hidden_2_dim) ).cuda()
for rr in res_rri:
R = rr.split(":")
try:
i_r = sequence_r.index(R[0])
except ValueError as err:
raise err
try:
j_l = sequence_l.index(R[1])
except ValueError as err:
raise err
w_rc = out_hidden_r_2[i_r,:]
w_lc = out_hidden_l_2[j_l,:]
v_in[rl,:] = torch.cat( [w_rc, w_lc], dim=0 )
v_in_t[rl,:] = torch.cat( [w_lc, w_rc], dim=0 )
rl += 1
native_rri.append(1)
while N_rri>0:
i_r = random.randint(0,N_r-1)
j_l = random.randint(0,N_l-1)
if( not sequence_r[i_r]+":"+sequence_l[j_l] in contacts['rri'][pdb]):
w_rc = out_hidden_r_2[i_r,:]
w_lc = out_hidden_l_2[j_l,:]
v_in[rl,:] = torch.cat( [w_rc,w_lc], dim=0 )
v_in_t[rl,:] = torch.cat( [w_lc,w_rc], dim=0 )
rl += 1
N_rri -= 1
native_rri.append(0)
hidden_3 = self.hidden2hidden_3(v_in)
hidden_3 = self.drop_hidden_3(hidden_3)
hidden_3_t = self.hidden2hidden_3(v_in_t)
hidden_3_t = self.drop_hidden_3(hidden_3_t)
out_hidden_3 = F.relu(0.5*(hidden_3+hidden_3_t))
rri_out = self.hidden2out( out_hidden_3 )
rri_out = F.log_softmax( rri_out, dim=1 )
native_rri = autograd.Variable(torch.LongTensor(native_rri)).cuda()
return rri_out, native_rri
else:
v_in = autograd.Variable( torch.FloatTensor(N_r*N_l,2*self.hidden_2_dim) ).cuda()
v_in_t = autograd.Variable( torch.FloatTensor(N_r*N_l,2*self.hidden_2_dim) ).cuda()
for i_r in range( N_r ):
for j_l in range( N_l ):
if sequence_r[i_r]+":"+sequence_l[j_l] in contacts['rri'][pdb]:
native_rri.append(1)
else:
native_rri.append(0)
w_rc = out_hidden_r_2[i_r,:]
w_lc = out_hidden_l_2[j_l,:]
v_in[rl,:] = torch.cat( [w_rc,w_lc], dim=0 )
v_in_t[rl,:] = torch.cat( [w_lc,w_rc], dim=0 )
rl += 1
hidden_3 = self.hidden2hidden_3(v_in)
hidden_3 = self.drop_hidden_3(hidden_3)
hidden_3_t = self.hidden2hidden_3(v_in_t)
hidden_3_t = self.drop_hidden_3(hidden_3_t)
out_hidden_3 = F.relu(0.5*(hidden_3+hidden_3_t))
rri_out = self.hidden2out( out_hidden_3 )
rri_out = F.log_softmax( rri_out, dim=1 )
native_rri = autograd.Variable(torch.LongTensor(native_rri)).cuda()
return rri_out, native_rri
input_dim=22
lstm_hidden_dim=256
hidden_1_dim=512
hidden_2_dim=256
hidden_3_dim=512
#input_dim=22
#lstm_hidden_dim=16
#hidden_1_dim=32
#hidden_2_dim=16
#hidden_3_dim=64
model = BiLSTM( input_dim=input_dim, lstm_hidden_dim=lstm_hidden_dim, hidden_1_dim=hidden_1_dim, hidden_2_dim=hidden_2_dim, hidden_3_dim=hidden_3_dim, rri_size=2 )
model.cuda()
if(verbose): print(model)
loss_function = nn.NLLLoss()
if(verbose): print("Neural networking ...")
TRAGETS = list(map(str.strip, open(file_name,"r").readlines()))
for target_ in TRAGETS:
r = target_.split("\t")
target = r[0]
scop_r = r[3]
scop_l = r[4]
if os.path.isfile(results_path+"/"+target+".tsv"):
continue
lr = 0.1
model = BiLSTM( input_dim=input_dim, lstm_hidden_dim=lstm_hidden_dim, hidden_1_dim=hidden_1_dim, hidden_2_dim=hidden_2_dim, hidden_3_dim=hidden_3_dim, rri_size=2 )
model.cuda()
for epoch in range(50):
optimizer = optim.SGD(model.parameters(), lr=lr)
lr *= 0.99
N_current = training['contacts']['N_cci']-1
cci_ = list(training['contacts']['cci'].keys())
random.shuffle(cci_)
for pdb in cci_:
for ch_ch in training['contacts']['cci'][pdb]:
R = ch_ch.split(":")
ch_r = R[0]
ch_l = R[1]
if training['scop'][pdb][ch_ch] == scop_r+":::"+scop_l or training['scop'][pdb][ch_ch] == scop_l+":::"+scop_r:
N_current -= 1
continue
if(verbose): print("%d - %s %s:%s \r" %(N_current, pdb,ch_r,ch_l),end="")
N_current -= 1
local_sequence_r = training['sequences'][pdb][ch_r]
local_sequence_l = training['sequences'][pdb][ch_l]
model.zero_grad()
optimizer.zero_grad()
predicted_rri, native_rri = model( pdb, local_sequence_r, local_sequence_l, training['features'], training['contacts'], ch_r=ch_r, ch_l=ch_l, Flag=True )
loss = loss_function( predicted_rri, native_rri )
loss.backward()
optimizer.step()
model.train(mode=False)
##TRAINING AUC SCORE FOR EACH EPOCH
AUC = []
for pdb in cci_:
for ch_ch in training['contacts']['cci'][pdb]:
R = ch_ch.split(":")
ch_r = R[0]
ch_l = R[1]
local_sequence_r = training['sequences'][pdb][ch_r]
local_sequence_l = training['sequences'][pdb][ch_l]
predicted_rri, native_rri = model( pdb, local_sequence_r, local_sequence_l, training['features'], training['contacts'], ch_r=ch_r, ch_l=ch_l, Flag=True )
np_class = native_rri.data.cpu().numpy()
np_prediction = predicted_rri.data.cpu()[:,1].numpy()
fpr, tpr, thresholds = metrics.roc_curve(np_class, np_prediction, pos_label=1)
new_auc = metrics.auc(fpr, tpr)
AUC.append(new_auc)
training_auc = np.mean(AUC)
##TESTING FOR EACH EPOCH
for ch_ch in testing['contacts']['cci'][target]:
R = ch_ch.split(":")
ch_r = R[0]
ch_l = R[1]
local_sequence_r = testing['sequences'][target][ch_r]
local_sequence_l = testing['sequences'][target][ch_l]
N_r = len(local_sequence_r)
N_l = len(local_sequence_l)
model.zero_grad()
optimizer.zero_grad()
predicted_rri, native_rri = model( target, local_sequence_r, local_sequence_l, testing['features'], testing['contacts'], ch_r=ch_r, ch_l=ch_l, Flag=False )
np_class = native_rri.data.cpu().numpy()
np_prediction = predicted_rri.data.cpu()[:,1].numpy()
np_all = np.stack((np_prediction,np_class), axis=-1)
np_all = np.insert(np_all, 0, np.array((N_r,N_l)), 0)
np.savetxt(results_path+"/predictions/"+target+"."+ch_r+":"+ch_l+"."+str(epoch)+".tsv",np_all)
fpr, tpr, thresholds = metrics.roc_curve(np_class, np_prediction, pos_label=1)
testing_auc = metrics.auc(fpr, tpr)
if(verbose): print( "%d - %s %s:%s - AUC=%0.4f - TRAINING_AUC=%0.4f" % (epoch, target,ch_r,ch_l,testing_auc,training_auc) )
open(results_path+"/"+target+".tsv", "a").write("%d - %s %s:%s - AUC=%0.4f - TRAINING_AUC=%0.4f\n" % (epoch, target,ch_r,ch_l,testing_auc,training_auc) )
model.train(mode=True)