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run.py
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run.py
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import time
import random
import pickle
import sys
import os
import argparse
from copy import deepcopy
import numpy as np
from collections import OrderedDict # for OrderedDict()
import tensorflow as tf
from tensorflow.contrib import rnn
from tensorflow.contrib.tensorboard.plugins import projector
from ops.ops import *
from ops.embeddingOps import *
from ops.inputData import *
from model.CollaboNet import *
from model.RunModel import *
if __name__ == '__main__':
#Hyper Params
parser = argparse.ArgumentParser()
parser.add_argument('--guidee_data', type=str, help='data name', default='name')
parser.add_argument('--pretrained', type=int, help='pretrained STM expName', default=0)
parser.add_argument('--ncbi', action='store_true', help='include ncbi data', default=False)
parser.add_argument('--jnlpba', action='store_true', help='include jnlpba data', default=False)
parser.add_argument('--bc2', action='store_true', help='include bc2gm data', default=False)
parser.add_argument('--bc4', action='store_true', help='include bc4chemd data', default=False)
parser.add_argument('--bc5_disease', action='store_true', help='include bc5-disease data', default=False)
parser.add_argument('--bc5_chem', action='store_true', help='include bc5-chem data', default=False)
parser.add_argument('--bc5', action='store_true', help='include bc5cdr data', default=False)
parser.add_argument('--tensorboard', action='store_true', help='single flag [default]False', default=False)
parser.add_argument('--epoch', type=int, help='max epoch', default=100)
parser.add_argument('--num_class', type=int, help='result class bio(3) [default]biolu(5)', default=5)
parser.add_argument('--ce_dim', type=int, help='char embedding dim', default=30)
parser.add_argument('--clwe_dim', type=int, help='char level word embedding dim', default=200)
parser.add_argument('--clwe_method', type=str, help='clwe method: CNN biLSTM', default='CNN')
parser.add_argument('--batch_size', type=int, help='batch size', default=10)
parser.add_argument('--hidden_size', type=int, help='lstm hidden layer size', default=300)
parser.add_argument('--lr', type=float, help='learning rate', default=0.01)
parser.add_argument('--lr_decay', type=float, help='learning rate dacay rate', default=0.05)
parser.add_argument('--lr_pump', action='store_true', help='do lr_pump', default=False)
parser.add_argument('--loss_weight', type=float, help='loss weight between CRF, LSTM', default=1)
parser.add_argument('--fc_method', type=str, help='fc method', default='normal')
parser.add_argument('--mlp_layer', type=int, help='num highway layer ', default=1)
parser.add_argument('--char_maxlen', type=int, help='char max length', default=49)
parser.add_argument('--embdropout', type=float, help='input embedding dropout_rate', default=0.5)
parser.add_argument('--lstmdropout', type=float, help='lstm output dropout_rate', default=0.3)
parser.add_argument('--seed', type=int, help='seed value', default=0)
args = parser.parse_args()
os.environ['TF_CPP_MIN_LOG_LEVEL'] = '2' #tf verbose off(info, warning)
#seed initialize
expName = setExpName()
if args.seed != 0:
seedV = int(args.seed%100000)
else:
try:
tempSeed = int(expName)
except:
tempSeed = int(expName[:12])
seedV = int(tempSeed%100000)
random.seed(seedV)
np.random.seed(seedV)
tf.set_random_seed(seedV)
#gpu setting
gpu_config = tf.ConfigProto(device_count={'GPU':1}) # only use GPU no.1
gpu_config.gpu_options.allow_growth = True # only use required resource(memory)
gpu_config.gpu_options.per_process_gpu_memory_fraction = 1 # restrict to 100%
ID2wordVecIdx, wordVec2LineNo, wordEmbedding = input_wordVec()
ID2char=pickle.load(open('data/ID2char.pickle','rb'))
m_train = 'train_dev'
m_dev = 'dev'
m_test = 'test'
modelDict=OrderedDict()
if args.ncbi:
ncbi_args = deepcopy(args)
ncbi_args.guidee_data = 'NCBI'
modelDict['NCBI']={'args':ncbi_args}
if args.jnlpba:
jnl_args=deepcopy(args)
jnl_args.guidee_data='JNLPBA'
modelDict['JNLPBA']={'args':jnl_args}
if args.bc2:
bc2_args=deepcopy(args)
bc2_args.guidee_data='BC2GM'
modelDict['BC2GM']={'args':bc2_args}
if args.bc4:
bc4_args=deepcopy(args)
bc4_args.guidee_data='BC4CHEMD'
modelDict['BC4CHEMD']={'args':bc4_args}
if args.bc5_chem:
bc5c_args=deepcopy(args)
bc5c_args.guidee_data='BC5CDR-chem'
modelDict['BC5CDR-chem']={'args':bc5c_args}
if args.bc5_disease:
bc5d_args=deepcopy(args)
bc5d_args.guidee_data='BC5CDR-disease'
modelDict['BC5CDR-disease']={'args':bc5d_args}
if args.bc5:
bc5_args=deepcopy(args)
bc5_args.guidee_data='BC5CDR'
modelDict['BC5CDR']={'args':bc5_args}
modelStart = time.time()
modelClass = Model(args, wordEmbedding, seedV)
for dataSet in modelDict:
modelDict[dataSet]['summery']=dict()
modelDict[dataSet]['CLWE']=modelClass.clwe(args=modelDict[dataSet]['args'],ID2char=ID2char)
modelDict[dataSet]['WE']=modelClass.we(args=modelDict[dataSet]['args'])
modelDict[dataSet]['model']=modelClass.model(args=modelDict[dataSet]['args'],
X_embedded_data=modelDict[dataSet]['WE'],
X_embedded_char=modelDict[dataSet]['CLWE'],
guideeInfo=None,
summery=modelDict[dataSet]['summery'],
scopename=dataSet) #guideeInfo=None cuz in function we define
dataNames = list()
for dataSet in modelDict:
modelDict[dataSet]['lossList'] = list()
modelDict[dataSet]['f1ValList'] = list()
modelDict[dataSet]['f1ValWOCRFList'] = list()
modelDict[dataSet]['maxF1'] = 0.0
modelDict[dataSet]['maxF1idx'] = 0
modelDict[dataSet]['prevF1'] = 0.0
modelDict[dataSet]['stop_counter'] = 0
modelDict[dataSet]['early_stop'] = False
modelDict[dataSet]['m_name'] = modelDict[dataSet]['args'].guidee_data
dataNames.append(dataSet)
try:
os.mkdir('./modelSave/'+expName+'/'+modelDict[dataSet]['m_name'])
except OSError as e:
if e.errno == errno.EEXIST: # if file exists! Python2.7 doesn't support file exist exception so need to use this
print('./modelSave/'+expName+'/'+modelDict[dataSet]['m_name']+' Directory exists! not created.')
suffix+=1
else:
raise
modelDict[dataSet]['runner']=RunModel(model=modelDict[dataSet]['model'], args=modelDict[dataSet]['args'],
ID2wordVecIdx=ID2wordVecIdx, ID2char=ID2char,
expName=expName, m_name=modelDict[dataSet]['m_name'], m_train=m_train, m_dev=m_dev, m_test=m_test)
with tf.Session(config=gpu_config) as sess:
phase = 0
random.seed(seedV)
np.random.seed(seedV)
tf.set_random_seed(seedV)
sess.run(tf.global_variables_initializer())
saver = tf.train.Saver(max_to_keep=10000)
loader = tf.train.Saver(max_to_keep=10000)
for epoch_idx in range(args.epoch*len(dataNames)):
dataSet = dataNames[epoch_idx%len(dataNames)]
if epoch_idx%len(dataNames)==0:
if args.pretrained != 0:
phase += 1
print("[%d phase]"%(phase))
m_name = modelDict[dataSet]['m_name']
if modelDict[dataSet]['early_stop']:
continue
if modelDict[dataSet]['args'].tensorboard:
tbWriter = tf.summary.FileWriter('./modelSave/'+expName+'/'+m_name+'/train', sess.graph)
else: tbWriter = None
print('===='+m_name.upper()+"_MODEL Training=====")
startTime = time.time()
batch_idx = random.sample(range(0,len(modelDict[dataSet]['runner'].m_batchgroup[m_train])),
len(modelDict[dataSet]['runner'].m_batchgroup[m_train]))
if args.pretrained == 0:
intOuts = None
early_stops = [24,30,30,30,25,25]
if args.pretrained != 0:
intOuts = None
early_stops = [5,16,23,30,10,16]
if ((epoch_idx / len(dataNames)) == 0) and (args.pretrained != 0) :
intOuts = dict()
intOuts[m_train]=list()
intOuts[m_dev]=list()
intOuts[m_test]=list()
for d_sub in modelDict:
if d_sub==dataSet:
continue
else:
loadpath = './modelSave/'+str(args.pretrained)+'/'+d_sub+'/'
loader.restore(sess, tf.train.latest_checkpoint(loadpath))
intOuts[m_train].append(modelDict[d_sub]['runner'].info1epoch(m_train, modelDict[dataSet]['runner'], sess))
intOuts[m_dev].append(modelDict[d_sub]['runner'].info1epoch(m_dev, modelDict[dataSet]['runner'], sess))
intOuts[m_test].append(modelDict[d_sub]['runner'].info1epoch(m_test, modelDict[dataSet]['runner'], sess))
loadpath = './modelSave/'+str(args.pretrained)+'/'+dataSet+'/'
loader.restore(sess, tf.train.latest_checkpoint(loadpath))
elif ((epoch_idx / len(dataNames)) != 0):
if args.pretrained != 0:
intOuts = dict()
intOuts[m_train]=list()
intOuts[m_dev]=list()
intOuts[m_test]=list()
for d_sub in modelDict:
if d_sub==dataSet:
continue
else:
loadpath = './modelSave/'+expName+'/'+d_sub+'/'
loader.restore(sess, tf.train.latest_checkpoint(loadpath))
intOuts[m_train].append(modelDict[d_sub]['runner'].info1epoch(m_train, modelDict[dataSet]['runner'], sess))
intOuts[m_dev].append(modelDict[d_sub]['runner'].info1epoch(m_dev, modelDict[dataSet]['runner'], sess))
intOuts[m_test].append(modelDict[d_sub]['runner'].info1epoch(m_test, modelDict[dataSet]['runner'], sess))
loadpath = './modelSave/'+expName+'/'+dataSet+'/'
loader.restore(sess, tf.train.latest_checkpoint(loadpath))
(l, sl, tra, trsPara) = modelDict[dataSet]['runner'].train1epoch(
sess, batch_idx, infoInput=intOuts, tbWriter=tbWriter)
print("== Epoch:%4d == | train time : %d Min | \n train loss: %.6f"%(epoch_idx, (time.time()-startTime)/60, l))
modelDict[dataSet]['lossList'].append(l)
(t_predictionResult, t_prfValResult, t_prfValWOCRFResult,
test_x, test_ans, test_len) = modelDict[dataSet]['runner'].dev1epoch(m_test, trsPara, sess, infoInput=intOuts, epoch=epoch_idx)
modelDict[dataSet]['f1ValList'].append(t_prfValResult[2])
saver.save(sess, './modelSave/'+expName+'/'+m_name+'/modelSaved')
pickle.dump(trsPara, open('./modelSave/'+expName+'/'+m_name+'/trs_param.pickle','wb'))
if ((epoch_idx/len(dataNames)) == early_stops[epoch_idx%len(dataNames)]):
modelDict[dataSet]['early_stop'] = True
modelDict[dataSet]['maxF1'] = t_prfValResult[2]
modelDict[dataSet]['stop_counter'] = 0
modelDict[dataSet]['maxF1idx'] = epoch_idx
modelDict[dataSet]['trs_param'] = trsPara
modelDict[dataSet]['maxF1_x'] = test_x[:]
modelDict[dataSet]['maxF1_ans'] = test_ans[:]
modelDict[dataSet]['maxF1_len'] = test_len[:]
pickle.dump(modelDict[dataSet]['maxF1idx'], open('./modelSave/'+expName+'/'+dataSet+'/maxF1idx.pickle','wb'))
if args.pretrained != 0:
pickle.dump(intOuts[m_test], open('./modelSave/'+expName+'/'+dataSet+'/bestInouts.pickle','wb'))
for didx, dname in enumerate(dataNames):
if not modelDict[dname]['early_stop']:
esFlag = False
break
if modelDict[dname]['early_stop'] and didx==len(dataNames)-1:
esFlag = True
if esFlag:
break
# Get test result for each model
for dataSet in modelDict:
m_name = modelDict[dataSet]['args'].guidee_data
print('===='+m_name.upper()+"_MODEL Test=====")
with tf.Session(config=gpu_config) as sess:
random.seed(seedV)
np.random.seed(seedV)
tf.set_random_seed(seedV)
sess.run(tf.global_variables_initializer())
loader = tf.train.Saver(max_to_keep=10000)
loadpath = './modelSave/'+expName+'/'+m_name+'/'
if args.pretrained != 0:
intOuts = dict()
intOuts[m_test] = pickle.load(open(loadpath+'bestInouts.pickle','rb'))
else:
intOuts = None
trsPara = pickle.load(open(loadpath+'trs_param.pickle','rb'))
loader.restore(sess, tf.train.latest_checkpoint(loadpath))
if modelDict[dataSet]['args'].tensorboard:
tbWriter = tf.summary.FileWriter('test')
else: tbWriter = None
(t_predictionResult, t_prfValResult, t_prfValWOCRFResult,
test_x, test_ans, test_len) = modelDict[dataSet]['runner'].dev1epoch(m_test, trsPara, sess, infoInput=intOuts, epoch=None, report=True)