如何解决使用Optuna进行超参数调整中的CoNLL文件
我一直在尝试找出如何在Bi-LSTM模型中为PoS和依赖项解析（https://github.com/datquocnguyen/jPTDP）优化超参数。 该模型将CoNLL-U文件作为输入，对于如何在Optuna中使用它们，我一无所知。
模型中的超参数定义如下：
from optparse import OptionParser
import pickle,utils,learner,os,os.path,time
if __name__ == '__main__':
parser = OptionParser()
parser.add_option("--train",dest="conll_train",help="Path to annotated CONLL train file",metavar="FILE",default="N/A")
parser.add_option("--dev",dest="conll_dev",help="Path to annotated CONLL dev file",default="N/A")
parser.add_option("--test",dest="conll_test",help="Path to CONLL test file",default="N/A")
parser.add_option("--output",dest="conll_test_output",help="File name for predicted output",default="N/A")
parser.add_option("--prevectors",dest="external_embedding",help="Pre-trained vector embeddings",metavar="FILE")
parser.add_option("--params",dest="params",help="Parameters file",default="model.params")
parser.add_option("--model",dest="model",help="Load/Save model file",default="model")
parser.add_option("--wembedding",type="int",dest="wembedding_dims",default=100)
parser.add_option("--cembedding",dest="cembedding_dims",default=50)
parser.add_option("--pembedding",dest="pembedding_dims",default=100)
parser.add_option("--epochs",dest="epochs",default=30)
parser.add_option("--hidden",dest="hidden_units",default=100)
#parser.add_option("--lr",type="float",dest="learning_rate",default=None)
parser.add_option("--outdir",type="string",dest="output",default="results")
parser.add_option("--activation",dest="activation",default="tanh")
parser.add_option("--lstmlayers",dest="lstm_layers",default=2)
parser.add_option("--lstmdims",dest="lstm_dims",default=128)
parser.add_option("--disableblstm",action="store_false",dest="blstmFlag",default=True)
parser.add_option("--disablelabels",dest="labelsFlag",default=True)
parser.add_option("--predict",action="store_true",dest="predictFlag",default=False)
parser.add_option("--bibi-lstm",dest="bibiFlag",default=True)
parser.add_option("--disablecostaug",dest="costaugFlag",default=True)
parser.add_option("--dynet-seed",dest="seed",default=0)
parser.add_option("--dynet-mem",dest="mem",default=0)
(options,args) = parser.parse_args()
#print 'Using external embedding:',options.external_embedding
if options.predictFlag:
with open(options.params,'r') as paramsfp:
words,w2i,c2i,pos,rels,stored_opt = pickle.load(paramsfp)
stored_opt.external_embedding = None
print 'Loading pre-trained model'
parser = learner.jPosDepLearner(words,stored_opt)
parser.Load(options.model)
testoutpath = os.path.join(options.output,options.conll_test_output)
print 'Predicting POS tags and parsing dependencies'
#ts = time.time()
#test_pred = list(parser.Predict(options.conll_test))
#te = time.time()
#print 'Finished in',te-ts,'seconds.'
#utils.write_conll(testoutpath,test_pred)
with open(testoutpath,'w') as fh:
for sentence in parser.Predict(options.conll_test):
for entry in sentence[1:]:
fh.write(str(entry) + '\n')
fh.write('\n')
else:
print("Training file: " + options.conll_train)
if options.conll_dev != "N/A":
print("Development file: " + options.conll_dev)
highestScore = 0.0
eId = 0
if os.path.isfile(os.path.join(options.output,options.params)) and \
os.path.isfile(os.path.join(options.output,os.path.basename(options.model))) :
print 'Found a previous saved model => Loading this model'
with open(os.path.join(options.output,options.params),'r') as paramsfp:
words,stored_opt = pickle.load(paramsfp)
stored_opt.external_embedding = None
parser = learner.jPosDepLearner(words,stored_opt)
parser.Load(os.path.join(options.output,os.path.basename(options.model)))
parser.trainer.restart()
if options.conll_dev != "N/A":
devPredSents = parser.Predict(options.conll_dev)
count = 0
lasCount = 0
uasCount = 0
posCount = 0
poslasCount = 0
for idSent,devSent in enumerate(devPredSents):
conll_devSent = [entry for entry in devSent if isinstance(entry,utils.ConllEntry)]
for entry in conll_devSent:
if entry.id <= 0:
continue
if entry.pos == entry.pred_pos and entry.parent_id == entry.pred_parent_id and entry.pred_relation == entry.relation:
poslasCount += 1
if entry.pos == entry.pred_pos:
posCount += 1
if entry.parent_id == entry.pred_parent_id and entry.pred_relation == entry.relation:
lasCount += 1
if entry.parent_id == entry.pred_parent_id:
uasCount += 1
count += 1
print "---\nLAS accuracy:\t%.2f" % (float(lasCount) * 100 / count)
print "UAS accuracy:\t%.2f" % (float(uasCount) * 100 / count)
print "POS accuracy:\t%.2f" % (float(posCount) * 100 / count)
print "POS&LAS:\t%.2f" % (float(poslasCount) * 100 / count)
score = float(poslasCount) * 100 / count
if score >= highestScore:
parser.Save(os.path.join(options.output,os.path.basename(options.model)))
highestScore = score
print "POS&LAS of the previous saved model: %.2f" % (highestScore)
else:
print 'Extracting vocabulary'
words,rels = utils.vocab(options.conll_train)
with open(os.path.join(options.output,'w') as paramsfp:
pickle.dump((words,options),paramsfp)
#print 'Initializing joint model'
parser = learner.jPosDepLearner(words,options)
for epoch in xrange(options.epochs):
print '\n-----------------\nStarting epoch',epoch + 1
if epoch % 10 == 0:
if epoch == 0:
parser.trainer.restart(learning_rate=0.001)
elif epoch == 10:
parser.trainer.restart(learning_rate=0.0005)
else:
parser.trainer.restart(learning_rate=0.00025)
parser.Train(options.conll_train)
if options.conll_dev == "N/A":
parser.Save(os.path.join(options.output,os.path.basename(options.model)))
else:
devPredSents = parser.Predict(options.conll_dev)
count = 0
lasCount = 0
uasCount = 0
posCount = 0
poslasCount = 0
for idSent,utils.ConllEntry)]
for entry in conll_devSent:
if entry.id <= 0:
continue
if entry.pos == entry.pred_pos and entry.parent_id == entry.pred_parent_id and entry.pred_relation == entry.relation:
poslasCount += 1
if entry.pos == entry.pred_pos:
posCount += 1
if entry.parent_id == entry.pred_parent_id and entry.pred_relation == entry.relation:
lasCount += 1
if entry.parent_id == entry.pred_parent_id:
uasCount += 1
count += 1
print "---\nLAS accuracy:\t%.2f" % (float(lasCount) * 100 / count)
print "UAS accuracy:\t%.2f" % (float(uasCount) * 100 / count)
print "POS accuracy:\t%.2f" % (float(posCount) * 100 / count)
print "POS&LAS:\t%.2f" % (float(poslasCount) * 100 / count)
score = float(poslasCount) * 100 / count
if score >= highestScore:
parser.Save(os.path.join(options.output,os.path.basename(options.model)))
highestScore = score
eId = epoch + 1
print "Highest POS&LAS: %.2f at epoch %d" % (highestScore,eId)
现在，我尝试编写一个单独的脚本进行优化，但是我真的不知道它如何读取CoNLL-U文件！这是我的暂定（无效）代码（其中jPTDP是模型的名称）：
import pickle,time,jPTDP
import optuna
df = open("Desktop/experimentoptuna/train.conllu")
def objective(trial):
lstmlayers = trial.suggest_int("lstmlayers",1,2)
lstmdims = trial.suggest_int("lstmdims",128,256)
hidden = trial.suggest_int("hidden",100,200,300)
epochs = trial.suggest_int("epochs",25,30,35)
model = jPTDP(
lstmlayers=lstmlayers,lstmdims=lstmdims,hidden=hidden,epochs=epochs
)
study = optuna.create_study(direction="maximize")
study.optimize(objective,n_trials=15)
任何帮助，不胜感激！
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