if not os.path.exists(train_env.path):
os.makedirs(train_env.path)
if np.size(os.listdir(train_env.path)) > 0:
resume = FLAGS.resume
else:
resume = False
""" Main training step """
with tf.Session() as sess:
if FLAGS.use_GPU:
sess = tf.Session(config=tf.ConfigProto(device_count={'GPU': 0}))
else:
sess = tf.Session(config=tf.ConfigProto(device_count={'CPU': 0}))
sess.run(init)
agent = Agent(FLAGS, True, train_env, train_model)
if resume:
print('Loading Model...')
ckpt = tf.train.get_checkpoint_state(train_env.path)
saver.restore(sess, ckpt.model_checkpoint_path)
startdate = time.localtime()
for i in range(train_model.param.num_episodes):
rT1, rT2, processingtime, j = agent.Global(sess)
print("Train scenario :", i, agent.total_steps, rT1, rT2,
str(float("{0:.2f}".format(processingtime))) + "s")
# Periodically test the model.
if i % train_model.param.test_freq == 0 and agent.total_steps > agent.param.pre_train_steps:
filename = "result/" + FLAGS.model_name + "/training%04d%02d%02d%02d%02d%02d_%d_%d.txt" % (
print('Loading Model...')
ckpt = tf.train.get_checkpoint_state(train_env.path)
saver.restore(sess, ckpt.model_checkpoint_path)
start = time.time()
startdate = time.localtime()
#SNP case
SNPprob = [0.05, 0.1, 0.2]
for _ in range(3):
print(_)
for __ in range(10):
train_env.maxI = 2
train_env.p = [SNPprob[_], 0]
agent = Agent(FLAGS, False, train_env, train_model)
seq1 = agent.env.seq1
seq2 = agent.env.seq2
start1, start2, lcslen = lcs.longestSubstring(seq1, seq2)
if FLAGS.show_align:
dot_plot = 255 * np.ones((len(seq1), len(seq2)))
for i in range(lcslen):
dot_plot[start1 + i, start2 + i] = 0
if FLAGS.print_align:
record = recordalign.record_align()
print("test", _, __)
print("raw seq len", len(seq1), len(seq2))
print("lcs len 1", start1, lcslen, len(seq1) - start1 - lcslen)
print("lcs len 2", start2, lcslen, len(seq2) - start2 - lcslen)
sess.run(init)
if resume:
print('Loading Model...')
ckpt = tf.train.get_checkpoint_state(game_env().path)
saver.restore(sess, ckpt.model_checkpoint_path)
startdate = time.localtime()
copyGraph(train_model.copyOps, sess)
startdate = time.localtime()
SeedNum = REMiner2.REMiner2(1, seq1, seq2)
uX1, uX2, uY1, uY2 = REMiner2.GetSEED(SeedNum,True)
uX1, uX2, uY1, uY2 = function.sortalign(uX1, uX2, uY1, uY2)
X = 200
endlen = 20000
print(len(uX1))
for i in range(param.num_finetune):
index = random.randrange(len(uX1))
agent = Agent(FLAGS, False, game_env(), train_model, seq1[uX2[index]:uX2[index]+endlen]+"A", seq2[uY2[index]:uY2[index]+endlen]+"A", ismeta=True)
rT11, rT21, j1, _ = agent.metatrain(sess, X=X)
agent = Agent(FLAGS, False, game_env(), train_model, seq1[uX1[index]-endlen:uX1[index]][::-1]+"A", seq2[uY1[index]-endlen:uY1[index]][::-1]+"A", ismeta=True)
rT12, rT22, j2, _ = agent.metatrain(sess, X=X)
print(i,index,j1+np.abs(uX2[index]-uX1[index])+j2,rT21+np.abs(uX2[index]-uX1[index])+rT22,rT11+np.abs(uX2[index]-uX1[index])+rT12)
copyGraph(train_model.copyOps2, sess)
saver.save(sess, game_env().path + '/tuned-' + str(i) + '.ckpt')
print(str(i)+"th Model Saved")
os.makedirs(train_env.path)
if np.size(os.listdir(train_env.path)) > 0:
resume = FLAGS.resume
else:
resume = False
""" Main test step """
with tf.Session() as sess:
if FLAGS.use_GPU:
sess = tf.Session(config=tf.ConfigProto(device_count={'GPU': 0}))
else:
sess = tf.Session(config=tf.ConfigProto(device_count={'CPU': 0}))
sess.run(init)
agent = Agent(FLAGS, False, train_env, train_model)
print('Loading Model...')
ckpt = tf.train.get_checkpoint_state(train_env.path)
saver.restore(sess, ckpt.model_checkpoint_path)
start = time.time()
startdate = time.localtime()
for _ in range(47):
#print(_)
for __ in range(_+1,47):
seq1 = seq[_]
seq2 = seq[__]
rT2o = 0
jo = 0
if not os.path.exists(train_env.path):
os.makedirs(train_env.path)
if np.size(os.listdir(train_env.path)) > 0:
resume = FLAGS.resume
else:
resume = False
""" Main test step """
with tf.Session() as sess:
if FLAGS.use_GPU:
sess = tf.Session(config=tf.ConfigProto(device_count={'GPU': 0}))
else:
sess = tf.Session(config=tf.ConfigProto(device_count={'CPU': 0}))
sess.run(init)
agent = Agent(FLAGS, False, train_env, train_model)
print('Loading Model...')
ckpt = tf.train.get_checkpoint_state(train_env.path)
saver.restore(sess, ckpt.model_checkpoint_path)
start = time.time()
startdate = time.localtime()
X = 100 # Greedy-X algorithm parameter
agent.set(seq1, seq2)
path = []
score = []
ptime = []
os.makedirs(train_env.path)
if np.size(os.listdir(train_env.path)) > 0:
resume = FLAGS.resume
else:
resume = False
""" Main test step """
with tf.Session() as sess:
if FLAGS.use_GPU:
sess = tf.Session(config=tf.ConfigProto(device_count={'GPU': 0}))
else:
sess = tf.Session(config=tf.ConfigProto(device_count={'CPU': 0}))
sess.run(init)
agent = Agent(FLAGS, False, train_env, train_model)
print('Loading Model...')
ckpt = tf.train.get_checkpoint_state(train_env.path)
saver.restore(sess, ckpt.model_checkpoint_path)
start = time.time()
startdate = time.localtime()
rT2o = 0
jo = 0
processingtimeo = 0
#start1,start2,lcslen = lcs.longestSubstring(seq1,seq2)
if FLAGS.show_align:
dot_plot = 255*np.ones((len(seq1),len(seq2)))
if FLAGS.print_align:
ckpt = tf.train.get_checkpoint_state(game_env().path)
saver.restore(sess, ckpt.model_checkpoint_path)
startdate = time.localtime()
copyGraph(train_model.copyOps, sess)
startdate = time.localtime()
for i in range(param.num_finetune):
pair = random.sample(range_seq, 2)
seq1 = seq[pair[0]]
seq2 = seq[pair[1]]
start1, start2, lcslen = lcs.longestSubstring(seq1, seq2)
agent = Agent(FLAGS,
False,
game_env(),
train_model,
seq1[start1 + lcslen:] + "A",
seq2[start2 + lcslen:] + "A",
ismeta=True)
rT11, rT21, j1, _ = agent.metatrain(sess)
agent = Agent(FLAGS,
False,
game_env(),
train_model,
seq1[start1 - 1::-1] + "A",
seq2[start2 - 1::-1] + "A",
ismeta=True)
rT12, rT22, j2, _ = agent.metatrain(sess)
print(i, pair[0], pair[1], j1 + lcslen + j2, rT21 + lcslen + rT22,
rT11 + lcslen + rT12)
if not os.path.exists(train_env.path):
os.makedirs(train_env.path)
if np.size(os.listdir(train_env.path)) > 0:
resume = FLAGS.resume
else:
resume = False
""" Main test step """
with tf.Session() as sess:
if FLAGS.use_GPU:
sess = tf.Session(config=tf.ConfigProto(device_count={'GPU': 0}))
else:
sess = tf.Session(config=tf.ConfigProto(device_count={'CPU': 0}))
sess.run(init)
agent = Agent(FLAGS, False, train_env, train_model)
print('Loading Model...')
ckpt = tf.train.get_checkpoint_state(train_env.path)
saver.restore(sess, ckpt.model_checkpoint_path)
start = time.time()
startdate = time.localtime()
for _ in range(47):
#print(_)
for __ in range(_ + 1, 47):
seq1 = seq[_]
seq2 = seq[__]
print("test", _, __)
if not os.path.exists(train_env.path):
os.makedirs(train_env.path)
if np.size(os.listdir(train_env.path)) > 0:
resume = FLAGS.resume
else:
resume = False
""" Main test step """
with tf.Session() as sess:
if FLAGS.use_GPU:
sess = tf.Session(config=tf.ConfigProto(device_count={'GPU': 0}))
else:
sess = tf.Session(config=tf.ConfigProto(device_count={'CPU': 0}))
sess.run(init)
agent = Agent(FLAGS, False, train_env, train_model)
print('Loading Model...')
ckpt = tf.train.get_checkpoint_state(train_env.path)
saver.restore(sess, ckpt.model_checkpoint_path)
start = time.time()
startdate = time.localtime()
for pairs in [["Homo_sapiens_BRCA", "Mus_musculus_BRCA"],
["Mus_musculus_BRCA", "Rattus_norvegicus_BRCA"],
["Rattus_norvegicus_BRCA", "Homo_sapiens_BRCA"],
["Homo_sapiens_ELK1", "Mus_musculus_ELK1"],
["Mus_musculus_ELK1", "Rattus_norvegicus_ELK1"],
["Rattus_norvegicus_ELK1", "Homo_sapiens_ELK1"],
["Homo_sapiens_CCDC91", "Mus_musculus_CCDC91"],
sess = tf.Session(config=tf.ConfigProto(device_count={'CPU': 0}))
sess.run(init)
if resume:
print('Loading Model...')
ckpt = tf.train.get_checkpoint_state(game_env().path)
saver.restore(sess, ckpt.model_checkpoint_path)
startdate = time.localtime()
for i in range(param.num_episodes):
mainBuffer = experience_buffer()
p = [random.choice(range_SNP), random.choice(range_indel)]
maxI = random.choice(range_maxI)
print(i, p, maxI)
agent = Agent(FLAGS, True, game_env(p, maxI), train_model, ismeta=True)
for k in range(param.K):
copyGraph(agent.copyOps, sess)
rT1, rT2, j, mainBuffer = agent.metatrain2(sess, mainBuffer)
print(i,k,j,rT1)
for t in range(param.meta_train_step):
# update the main network
trainBatch = mainBuffer.sample(param.meta_batch_size) # Select the batch from the experience buffer
# The estimated Q value from main network is Q1, from target network is Q2
Q1 = sess.run(agent.mainQN.predict, feed_dict={agent.mainQN.scalarInput: np.vstack(trainBatch[:, 3])})
Q2 = sess.run(agent.mainQN.Qout, feed_dict={agent.mainQN.scalarInput: np.vstack(trainBatch[:, 3])})
# trainBatch[:,4] means that the action was the last step of the episode
os.makedirs(train_env.path)
if np.size(os.listdir(train_env.path)) > 0:
resume = FLAGS.resume
else:
resume = False
""" Main test step """
with tf.Session() as sess:
if FLAGS.use_GPU:
sess = tf.Session(config=tf.ConfigProto(device_count={'GPU': 0}))
else:
sess = tf.Session(config=tf.ConfigProto(device_count={'CPU': 0}))
sess.run(init)
agent = Agent(FLAGS, False, train_env, train_model)
print('Loading Model...')
ckpt = tf.train.get_checkpoint_state(train_env.path)
saver.restore(sess, ckpt.model_checkpoint_path)
start = time.time()
startdate = time.localtime()
#start1,start2,lcslen = lcs.longestSubstring(seq1,seq2)
if FLAGS.print_align:
record = recordalign.record_align()
print("Ecoli test")
print("raw seq len",len(seq1),len(seq2))
#print("lcs len 1",start1,lcslen,len(seq1)-start1-lcslen)
os.makedirs(train_env.path)
if np.size(os.listdir(train_env.path)) > 0:
resume = FLAGS.resume
else:
resume = False
""" Main test step """
with tf.Session() as sess:
if FLAGS.use_GPU:
sess = tf.Session(config=tf.ConfigProto(device_count={'GPU': 0}))
else:
sess = tf.Session(config=tf.ConfigProto(device_count={'CPU': 0}))
sess.run(init)
agent = Agent(FLAGS, False, train_env, train_model)
print('Loading Model...')
ckpt = tf.train.get_checkpoint_state(train_env.path)
saver.restore(sess, ckpt.model_checkpoint_path)
start = time.time()
startdate = time.localtime()
rT2o = 0
jo = 0
processingtimeo = 0
#start1,start2,lcslen = lcs.longestSubstring(seq1,seq2)
if FLAGS.show_align:
dot_plot = 255*np.ones((len(seq1),len(seq2)))
if FLAGS.print_align: