Merge branch 'master' into Rl

.gitignore

@@ -3,4 +3,4 @@ bin/*
 .idea
 total_test_gold.conllu
 total_test_predicted.conllu
-venv/*
+venv
\ No newline at end of file

Config.py

@@ -11,6 +11,7 @@ class Config :
     self.index2col = index2col
     self.predicted = set({"HEAD", "DEPREL"})
     self.wordIndex = 0
+    self.maxWordIndex = 0 #To keep a track of the max value, in case of backtrack
     self.stack = []
     self.comments = []
     self.history = []
@@ -64,10 +65,12 @@ class Config :
       if self.wordIndex+relMov in range(0, len((self.lines))) :
         self.wordIndex += relMov
       else :
+        self.maxWordIndex = max(self.maxWordIndex, self.wordIndex)
         return False
       if self.isMultiword(self.wordIndex) :
         self.wordIndex += relMov
       done += 1
+    self.maxWordIndex = max(self.maxWordIndex, self.wordIndex)
     return True
 
   def isMultiword(self, index) :

Dicts.py

@@ -12,6 +12,7 @@ class Dicts :
     self.noDepLeft = "__nodepleft__"
     self.noDepRight = "__nodepright__"
     self.noGov = "__nogov__"
+    self.notSeen = "__notseen__"
 
   def addDict(self, name, d) :
     if name in self.dicts :
@@ -38,7 +39,7 @@ class Dicts :
           targetColumns = list(col2index.keys())
         else :
           targetColumns = list(colsSet)
-        self.dicts = {col : {self.unkToken : (0,minCount), self.nullToken : (1,minCount), self.noStackToken : (2,minCount), self.oobToken : (3,minCount), self.noDepLeft : (4,minCount), self.noDepRight : (5,minCount), self.noGov : (6,minCount)} for col in targetColumns}
+        self.dicts = {col : {self.unkToken : (0,minCount), self.nullToken : (1,minCount), self.noStackToken : (2,minCount), self.oobToken : (3,minCount), self.noDepLeft : (4,minCount), self.noDepRight : (5,minCount), self.noGov : (6,minCount), self.notSeen : (7,minCount)} for col in targetColumns}
 
       splited = line.split('\t')
       for col in targetColumns :

Features.py

@@ -11,7 +11,10 @@ from Util import isEmpty
 # -3 : No dependent left
 # -4 : No dependent right
 # -5 : No gov
-def extractIndexes(config, featureFunction) :
+# -6 : Not seen
+# If incremental is true, only words that have been 'seen' (at wordIndex) can be used
+#   others will be marked as not seen.
+def extractIndexes(config, featureFunction, incremental) :
   features = featureFunction.split()
   res = []
   for feature in features :
@@ -27,6 +30,8 @@ def extractIndexes(config, featureFunction) :
         index = -2
       else :
         index = config.stack[-1-index]
+    if incremental and index > config.maxWordIndex :
+      index = -6
     for depIndex in map(int,splited[2:]) :
       if index < 0 :
         break
@@ -56,10 +61,10 @@ def extractIndexes(config, featureFunction) :
 
 ################################################################################
 # For each element of the feature function and for each column, concatenante the dict index
-def extractColsFeatures(dicts, config, featureFunction, cols) :
-  specialValues = {-1 : dicts.oobToken, -2 : dicts.noStackToken, -3 : dicts.noDepLeft, -4 : dicts.noDepRight, -5 : dicts.noGov}
+def extractColsFeatures(dicts, config, featureFunction, cols, incremental) :
+  specialValues = {-1 : dicts.oobToken, -2 : dicts.noStackToken, -3 : dicts.noDepLeft, -4 : dicts.noDepRight, -5 : dicts.noGov, -6 : dicts.notSeen}
 
-  indexes = extractIndexes(config, featureFunction)
+  indexes = extractIndexes(config, featureFunction, incremental)
   totalSize = len(cols)*len(indexes)
   result = torch.zeros(totalSize, dtype=torch.int)
 

Networks.py

@@ -5,10 +5,11 @@ import Features
 
 ################################################################################
 class BaseNet(nn.Module):
-  def __init__(self, dicts, outputSize) :
+  def __init__(self, dicts, outputSize, incremental) :
     super().__init__()
     self.dummyParam = nn.Parameter(torch.empty(0), requires_grad=False)
 
+    self.incremental = incremental
     self.featureFunction = "b.-2 b.-1 b.0 b.1 b.2 s.0 s.1 s.2 s.0.0 s.0.-1 s.0.1 s.1.0 s.1.-1 s.1.1 s.2.0 s.2.-1 s.2.1"
     self.historyNb = 5
     self.columns = ["UPOS", "FORM"]
@@ -47,7 +48,7 @@ class BaseNet(nn.Module):
       m.bias.data.fill_(0.01)
 
   def extractFeatures(self, dicts, config) :
-    colsValues = Features.extractColsFeatures(dicts, config, self.featureFunction, self.columns)
+    colsValues = Features.extractColsFeatures(dicts, config, self.featureFunction, self.columns, self.incremental)
     historyValues = Features.extractHistoryFeatures(dicts, config, self.historyNb)
     return torch.cat([colsValues, historyValues])
 

Train.py

@@ -16,15 +16,15 @@ import Config
 from conll18_ud_eval import load_conllu, evaluate
 
 ################################################################################
-def trainMode(debug, filename, type, transitionSet, strategy, modelDir, nbIter, batchSize, devFile, bootstrapInterval, silent=False) :
+def trainMode(debug, filename, type, transitionSet, strategy, modelDir, nbIter, batchSize, devFile, bootstrapInterval, incremental, silent=False) :
   sentences = Config.readConllu(filename)
 
   if type == "oracle" :
-    trainModelOracle(debug, modelDir, filename, nbIter, batchSize, devFile, transitionSet, strategy, sentences, bootstrapInterval, silent)
+    trainModelOracle(debug, modelDir, filename, nbIter, batchSize, devFile, transitionSet, strategy, sentences, bootstrapInterval, incremental, silent)
     return
 
   if type == "rl":
-    trainModelRl(debug, modelDir, filename, nbIter, batchSize, devFile, transitionSet, strategy, sentences, silent)
+    trainModelRl(debug, modelDir, filename, nbIter, batchSize, devFile, transitionSet, strategy, sentences, incremental, silent)
     return
 
   print("ERROR : unknown type '%s'"%type, file=sys.stderr)
@@ -70,7 +70,7 @@ def extractExamples(debug, ts, strat, config, dicts, network, dynamic) :
 ################################################################################
 
 ################################################################################
-def evalModelAndSave(debug, model, ts, strat, dicts, modelDir, devFile, bestLoss, totalLoss, bestScore, epoch, nbIter) :
+def evalModelAndSave(debug, model, ts, strat, dicts, modelDir, devFile, bestLoss, totalLoss, bestScore, epoch, nbIter, incremental) :
   devScore = ""
   saved = True if bestLoss is None else totalLoss < bestLoss
   bestLoss = totalLoss if bestLoss is None else min(bestLoss, totalLoss)
@@ -92,12 +92,12 @@ def evalModelAndSave(debug, model, ts, strat, dicts, modelDir, devFile, bestLoss
 ################################################################################
 
 ################################################################################
-def trainModelOracle(debug, modelDir, filename, nbEpochs, batchSize, devFile, transitionSet, strategy, sentencesOriginal, bootstrapInterval, silent=False) :
+def trainModelOracle(debug, modelDir, filename, nbEpochs, batchSize, devFile, transitionSet, strategy, sentencesOriginal, bootstrapInterval, incremental, silent=False) :
   dicts = Dicts()
   dicts.readConllu(filename, ["FORM","UPOS"], 2)
   dicts.addDict("HISTORY", {**{t.name : (transitionSet.index(t),0) for t in transitionSet}, **{dicts.nullToken : (len(transitionSet),0)}})
   dicts.save(modelDir+"/dicts.json")
-  network = Networks.BaseNet(dicts, len(transitionSet)).to(getDevice())
+  network = Networks.BaseNet(dicts, len(transitionSet), incremental).to(getDevice())
   examples = []
   sentences = copy.deepcopy(sentencesOriginal)
   print("%s : Starting to extract examples..."%(timeStamp()), file=sys.stderr)
@@ -143,11 +143,11 @@ def trainModelOracle(debug, modelDir, filename, nbEpochs, batchSize, devFile, tr
       optimizer.step()
       totalLoss += float(loss)
 
-    bestLoss, bestScore = evalModelAndSave(debug, network, transitionSet, strategy, dicts, modelDir, devFile, bestLoss, totalLoss, bestScore, epoch, nbEpochs)
+    bestLoss, bestScore = evalModelAndSave(debug, network, transitionSet, strategy, dicts, modelDir, devFile, bestLoss, totalLoss, bestScore, epoch, nbEpochs, incremental)
 ################################################################################
 
 ################################################################################
-def trainModelRl(debug, modelDir, filename, nbIter, batchSize, devFile, transitionSet, strategy, sentencesOriginal, silent=False) :
+def trainModelRl(debug, modelDir, filename, nbIter, batchSize, devFile, transitionSet, strategy, sentencesOriginal, incremental, silent=False) :
 
   memory = None
   dicts = Dicts()
@@ -155,8 +155,8 @@ def trainModelRl(debug, modelDir, filename, nbIter, batchSize, devFile, transiti
   dicts.addDict("HISTORY", {**{t.name : (transitionSet.index(t),0) for t in transitionSet}, **{dicts.nullToken : (len(transitionSet),0)}})
   dicts.save(modelDir + "/dicts.json")
 
-  policy_net = Networks.BaseNet(dicts, len(transitionSet)).to(getDevice())
-  target_net = Networks.BaseNet(dicts, len(transitionSet)).to(getDevice())
+  policy_net = Networks.BaseNet(dicts, len(transitionSet), incremental).to(getDevice())
+  target_net = Networks.BaseNet(dicts, len(transitionSet), incremental).to(getDevice())
   target_net.load_state_dict(policy_net.state_dict())
   target_net.eval()
   policy_net.train()
@@ -226,6 +226,6 @@ def trainModelRl(debug, modelDir, filename, nbIter, batchSize, devFile, transiti
       if i >= nbExByEpoch :
         break
       sentIndex += 1
-    bestLoss, bestScore = evalModelAndSave(debug, policy_net, transitionSet, strategy, dicts, modelDir, devFile, bestLoss, totalLoss, bestScore, epoch, nbIter)
+    bestLoss, bestScore = evalModelAndSave(debug, policy_net, transitionSet, strategy, dicts, modelDir, devFile, bestLoss, totalLoss, bestScore, epoch, nbIter, incremental)
 ################################################################################
 

Transition.py

@@ -4,7 +4,6 @@ from Util import isEmpty
 
 ################################################################################
 class Transition :
-  available = lambda self,x: x in {"RIGHT", "LEFT", "SHIFT", "REDUCE", "EOS"} or ("BACK" in x and len(x.split()) == 2)
 
   def __init__(self, name) :
     if not self.available(name) :
@@ -14,11 +13,12 @@ class Transition :
   def __lt__(self, other) :
     return self.name < other.name
 
+  def available(self, x) :
+    return x in {"RIGHT", "LEFT", "SHIFT", "REDUCE", "EOS"} or ("BACK" in x and len(x.split()) == 2)
+
   def apply(self, config, strategy) :
     data = None
 
-    config.historyHistory.add(str([t[0].name for t in config.historyPop]))
-
     if self.name == "RIGHT" :
       applyRight(config)
     elif self.name == "LEFT" :
@@ -30,6 +30,7 @@ class Transition :
     elif self.name == "EOS" :
       applyEOS(config)
     elif "BACK" in self.name :
+      config.historyHistory.add(str([t[0].name for t in config.historyPop]))
       size = int(self.name.split()[-1])
       applyBack(config, strategy, size)
     else :

main.py

@@ -5,6 +5,7 @@ import os
 import argparse
 import random
 import torch
+import json
 
 import Util
 import Train
@@ -32,6 +33,8 @@ if __name__ == "__main__" :
     help="If not none, extract examples in bootstrap mode (oracle train only).")
   parser.add_argument("--dev", default=None,
     help="Name of the CoNLL-U file of the dev corpus.")
+  parser.add_argument("--incr", "-i", default=False, action="store_true",
+    help="If true, the neural network will be 'incremenal' i.e. will not see right context words if they have never been the word index.")
   parser.add_argument("--debug", "-d", default=False, action="store_true",
     help="Print debug infos on stderr.")
   parser.add_argument("--silent", "-s", default=False, action="store_true",
@@ -59,8 +62,15 @@ if __name__ == "__main__" :
   print("Transition Set :", [trans.name for trans in transitionSet])
 
   if args.mode == "train" :
-    Train.trainMode(args.debug, args.corpus, args.type, transitionSet, strategy, args.model, int(args.iter), int(args.batchSize), args.dev, args.bootstrap, args.silent)
+    json.dump([t.name for t in transitionSet], open(args.model+"/transitions.json", "w"))
+    json.dump(strategy, open(args.model+"/strategy.json", "w"))
+    print("Transition Set :", [trans.name for trans in transitionSet], file=sys.stderr)
+    Train.trainMode(args.debug, args.corpus, args.type, transitionSet, strategy, args.model, int(args.iter), int(args.batchSize), args.dev, args.bootstrap, args.incr, args.silent)
   elif args.mode == "decode" :
+    transNames = json.load(open(args.model+"/transitions.json", "r"))
+    transitionSet = [Transition(elem) for elem in transNames]
+    strategy = json.load(open(args.model+"/strategy.json", "r"))
+    print("Transition Set :", [trans.name for trans in transitionSet], file=sys.stderr)
     Decode.decodeMode(args.debug, args.corpus, args.type, transitionSet, strategy, args.model)
   else :
     print("ERROR : unknown mode '%s'"%args.mode, file=sys.stderr)