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from Transition import Transition, getMissingLinks, applyTransition
import Features
from Dicts import Dicts
from Util import timeStamp
import Networks
import Decode
import Config
from conll18_ud_eval import load_conllu, evaluate
################################################################################
def trainMode(debug, filename, type, modelDir, nbIter, batchSize, devFile, silent=False) :
transitionSet = [Transition(elem) for elem in ["RIGHT", "LEFT", "SHIFT", "REDUCE"]]
strategy = {"RIGHT" : 1, "SHIFT" : 1, "LEFT" : 0, "REDUCE" : 0}
sentences = Config.readConllu(filename)
if type == "oracle" :
trainModelOracle(debug, modelDir, filename, nbIter, batchSize, devFile, transitionSet, strategy, sentences, silent)
return
print("ERROR : unknown type '%s'"%type, file=sys.stderr)
exit(1)
################################################################################
################################################################################
def extractExamples(ts, strat, config, dicts, debug=False) :
examples = []
EOS = Transition("EOS")
config.moveWordIndex(0)
moved = True
while moved :
missingLinks = getMissingLinks(config)
candidates = sorted([[trans.getOracleScore(config, missingLinks), trans.name] for trans in ts if trans.appliable(config)])
if len(candidates) == 0 :
break
candidate = candidates[0][1]
candidateIndex = [trans.name for trans in ts].index(candidate)
features = Features.extractFeatures(dicts, config)
example = torch.cat([torch.LongTensor([candidateIndex]), features])
examples.append(example)
if debug :
config.printForDebug(sys.stderr)
print(str(candidates)+"\n"+("-"*80)+"\n", file=sys.stderr)
moved = applyTransition(ts, strat, config, candidate)
EOS.apply(config)
return examples
################################################################################
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################################################################################
def trainModelOracle(debug, modelDir, filename, nbIter, batchSize, devFile, transitionSet, strategy, sentences, silent=False) :
examples = []
dicts = Dicts()
dicts.readConllu(filename, ["FORM", "UPOS"])
dicts.save(modelDir+"/dicts.json")
print("%s : Starting to extract examples..."%(timeStamp()), file=sys.stderr)
for config in sentences :
examples += extractExamples(transitionSet, strategy, config, dicts, debug)
print("%s : Extracted %d examples"%(timeStamp(), len(examples)), file=sys.stderr)
examples = torch.stack(examples)
network = Networks.BaseNet(dicts, examples[0].size(0)-1, len(transitionSet))
network.train()
optimizer = torch.optim.Adam(network.parameters(), lr=0.0001)
lossFct = torch.nn.CrossEntropyLoss()
for iter in range(1,nbIter+1) :
examples = examples.index_select(0, torch.randperm(examples.size(0)))
totalLoss = 0.0
nbEx = 0
printInterval = 2000
advancement = 0
for batchIndex in range(0,examples.size(0)-batchSize,batchSize) :
batch = examples[batchIndex:batchIndex+batchSize]
targets = batch[:,:1].view(-1)
inputs = batch[:,1:]
nbEx += targets.size(0)
advancement += targets.size(0)
if not silent and advancement >= printInterval :
advancement = 0
print("Curent epoch %6.2f%%"%(100.0*nbEx/examples.size(0)), end="\r", file=sys.stderr)
outputs = network(inputs)
loss = lossFct(outputs, targets)
network.zero_grad()
loss.backward()
optimizer.step()
totalLoss += float(loss)
devScore = ""
if devFile is not None :
outFilename = modelDir+"/predicted_dev.conllu"
Decode.decodeMode(debug, devFile, "model", network, dicts, open(outFilename, "w"))
res = evaluate(load_conllu(open(devFile, "r")), load_conllu(open(outFilename, "r")), [])
devScore = ", Dev : UAS=%.2f"%(res["UAS"][0].f1)
print("%s : Epoch %d, loss=%.2f%s"%(timeStamp(), iter, totalLoss, devScore), file=sys.stderr)
################################################################################