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train.py
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import pickle
import numpy as np
import sys
import tensorflow as tf
from model import create_model
def create_branch_length_padding(bl):
maxlen=0
for x in bl:
if len(x)>maxlen:
maxlen=len(x)
for x in range(len(bl)):
temp=bl[x]
for i in range(len(temp),maxlen):
temp=np.append(temp,[0])
bl[x]=temp
return bl
def train(train_synteny_matrices_global,train_synteny_matrices_local,train_pfam_matrices,train_branch_length_species,train_branch_length_homology_species,train_dist_p_s,train_dist_p_hs,train_distance,train_labels,v,num_epochs,learning_rate,decay,size_train,batch_size,model_name):
print("Going to train model {} for:\n Batch Size:{} \n Learning Rate:{} \n Decay:{}\n On {} Samples".format(v,batch_size,learning_rate,decay,len(train_synteny_matrices_global)))
graph,saver=create_model()
synmg,synml,pfam,bls,blhs,dps,dphs,dis,lr,y=graph.get_collection("input_nodes")
loss,t_op,accuracy,init,summary=graph.get_collection("output_nodes")
with tf.Session(graph=graph) as sess:
writer = tf.summary.FileWriter('./'+model_name+'_v'+str(v), sess.graph)
sess.run(init)
learn=learning_rate
for j in range(num_epochs):
for i in range(size_train//batch_size):
feed_dict={
synmg:train_synteny_matrices_global[i*batch_size:(i+1)*batch_size],
synml:train_synteny_matrices_local[i*batch_size:(i+1)*batch_size],
pfam:train_pfam_matrices[i*batch_size:(i+1)*batch_size].reshape((batch_size,7,7,1)),
bls:train_branch_length_species[i*batch_size:(i+1)*batch_size],
blhs:train_branch_length_homology_species[i*batch_size:(i+1)*batch_size],
dps:train_dist_p_s[i*batch_size:(i+1)*batch_size].reshape((batch_size,1)),
dphs:train_dist_p_hs[i*batch_size:(i+1)*batch_size].reshape((batch_size,1)),
dis:train_distance[i*batch_size:(i+1)*batch_size].reshape((batch_size,1)),
lr:learn,
y:train_labels[i*batch_size:(i+1)*batch_size]
}
sess.run(t_op,feed_dict=feed_dict)
test_dict={
synmg:train_synteny_matrices_global[size_train:],
synml:train_synteny_matrices_local[size_train:],
pfam:train_pfam_matrices[size_train:].reshape((len(train_synteny_matrices_global)-size_train,7,7,1)),
bls:train_branch_length_species[size_train:],
blhs:train_branch_length_homology_species[size_train:],
dps:train_dist_p_s[size_train:].reshape((len(train_synteny_matrices_global)-size_train,1)),
dphs:train_dist_p_hs[size_train:].reshape((len(train_synteny_matrices_global)-size_train,1)),
dis:train_distance[size_train:].reshape((len(train_synteny_matrices_global)-size_train,1)),
y:train_labels[size_train:],
lr:learn
}
accuracy_test,loss_test,summary_write=sess.run([accuracy,loss,summary],feed_dict=test_dict)
writer.add_summary(summary_write,i+1)
print("Epoch:{} Test Accuracy:{} Test Loss:{}".format(j+1,accuracy_test*100,loss_test))
learn*=decay
saver.save(sess,model_name+"_v"+str(v)+"/model.ckpt")
writer.close()
def read_positive(len_p,bls,blhs,dis,dps,dphs,sml,smg,pfam,label):
rowsh=[]
with open("dataset","rb") as file:
rowsh=pickle.load(file)
shi=np.random.permutation(len(rowsh))
rows_shuffled=[]
for i in range(len(shi)):
rows_shuffled.append(rowsh[shi[i]])
rowsh=rows_shuffled
spco={}
spcp={}
for row in rowsh:
if row["species"] not in spco:
spco[row["species"]]=0
spcp[row["species"]]=0
maxcount_o=int((len_p)*0.3/14)
maxcount_p=int((len_p)*0.7/14)
for row in rowsh:
if row["label"]==2:
continue
if row["label"]==1 and spco[row["species"]]>maxcount_o:
continue
if row["label"]==0 and spcp[row["species"]]>maxcount_p:
continue
bls.append(np.array(row["bls"]))
blhs.append(np.array(row["blhs"]))
dis.append(row["dis"])
dps.append(row["dps"])
dphs.append(row["dphs"])
sml.append(row["local_alignment_matrix"])
smg.append(row["global_alignment_matrix"])
pfam.append(row["pfam_matrix"])
label.append(row["label"])
if row["label"]==1:
spco[row["species"]]+=1
if row["label"]==0:
spcp[row["species"]]+=1
def read_negative(len_n,bls,blhs,dis,dps,dphs,sml,smg,pfam,label):
rows=[]
with open("dataset","rb") as file:
rows=pickle.load(file)
rows=[row for row in rows if row["label"]==2]
shi=np.random.permutation(len(rows))
rows_shuffled=[]
for i in range(len(shi)):
rows_shuffled.append(rows[shi[i]])
rows=rows_shuffled
rows=rows[:len_n]
for row in rows:
bls.append(np.array(row["bls"]))
blhs.append(np.array(row["blhs"]))
dis.append(row["dis"])
dps.append(row["dps"])
dphs.append(row["dphs"])
sml.append(row["local_alignment_matrix"])
smg.append(row["global_alignment_matrix"])
label.append(row["label"])
pfam.append(row["pfam_matrix"])
def train_models(model_name,start,end,num_epochs,learn_rate,decay,size_train,batch_size):
k=1
for i in range(start//10,end//10+1):
bls=[]
blhs=[]
dis=[]
dps=[]
dphs=[]
sml=[]
smg=[]
pfam=[]
label=[]
portion=float(i/10)
len_n=int(size_train*portion)
len_p=int(size_train*(1-portion))
read_positive(len_p,bls,blhs,dis,dps,dphs,sml,smg,pfam,label)
read_negative(len_n,bls,blhs,dis,dps,dphs,sml,smg,pfam,label)
bls=create_branch_length_padding(bls)
blhs=create_branch_length_padding(blhs)
bls=np.array(bls)
print(bls.shape)
blhs=np.array(blhs)
print(blhs.shape)
dis=np.array(dis)
print(dis.shape)
dps=np.array(dps)
print(dps.shape)
dphs=np.array(dphs)
print(dphs.shape)
sml=np.array(sml)
print(sml.shape)
smg=np.array(smg)
print(smg.shape)
pfam=np.array(pfam)
print(pfam.shape)
label=np.array(label)
print(label.shape)
shi=np.random.permutation(len(label))
labels=label[shi]
bls=bls[shi]
blhs=blhs[shi]
dis=dis[shi]
dps=dps[shi]
dphs=dphs[shi]
sml=sml[shi]
smg=smg[shi]
pfam=pfam[shi]
train(smg,sml,pfam,bls,blhs,dps,dphs,dis,labels,k,num_epochs,learn_rate,decay,int(0.9*size_train),batch_size,model_name)
k+=1
def main():
arg=sys.argv
model_name=arg[-8]
start_p=int(arg[-7])
end_p=int(arg[-6])
num_epochs=int(arg[-5])
learn_rate=float(arg[-4])
decay=float(arg[-3])
size_train=float(arg[-2])
batch_size=int(arg[-1])
train_models(model_name,start_p,end_p,num_epochs,learn_rate,decay,size_train,batch_size)
if __name__=="__main__":
main()