From ead830ccfd9d8d7bfccd457af0d1961bbd11a3e6 Mon Sep 17 00:00:00 2001
From: Maxime Petit <maxime.petit.3@etu.univ-amu.fr>
Date: Thu, 17 Jun 2021 14:33:53 +0200
Subject: [PATCH] New parameters : Nb of unaplayable actions picked before
breaking the analysis
---
Train.py | 21 +++++++++++++--------
main.py | 8 +++++---
2 files changed, 18 insertions(+), 11 deletions(-)
diff --git a/Train.py b/Train.py
index 01c5aaf..8b948d2 100644
--- a/Train.py
+++ b/Train.py
@@ -16,7 +16,7 @@ import Config
from conll18_ud_eval import load_conllu, evaluate
################################################################################
-def trainMode(debug, filename, type, transitionSet, strategy, modelDir, nbIter, batchSize, devFile, bootstrapInterval, incremental, rewardFunc, lr, gamma, probas, predicted, silent=False) :
+def trainMode(debug, filename, type, transitionSet, strategy, modelDir, nbIter, batchSize, devFile, bootstrapInterval, incremental, rewardFunc, lr, gamma, probas, countBreak, predicted, silent=False) :
sentences = Config.readConllu(filename, predicted)
if type == "oracle" :
@@ -24,7 +24,7 @@ def trainMode(debug, filename, type, transitionSet, strategy, modelDir, nbIter,
return
if type == "rl":
- trainModelRl(debug, modelDir, filename, nbIter, batchSize, devFile, transitionSet, strategy, sentences, incremental, rewardFunc, lr, gamma, probas, predicted, silent)
+ trainModelRl(debug, modelDir, filename, nbIter, batchSize, devFile, transitionSet, strategy, sentences, incremental, rewardFunc, lr, gamma, probas, countBreak, predicted, silent)
return
print("ERROR : unknown type '%s'"%type, file=sys.stderr)
@@ -56,15 +56,15 @@ def extractExamples(debug, ts, strat, config, dicts, network, dynamic) :
candidate = [[cand[0],cand[2]] for cand in scores if cand[1]][0][1]
if debug :
print(str(candidate), file=sys.stderr)
-
+
goldIndex = [str(trans) for trans in ts].index(str(candidateOracle))
example = torch.cat([torch.LongTensor([goldIndex]), features])
examples.append(example)
moved = applyTransition(strat, config, candidate, None)
-
+
EOS.apply(config, strat)
-
+
return examples
################################################################################
@@ -149,7 +149,7 @@ def trainModelOracle(debug, modelDir, filename, nbEpochs, batchSize, devFile, tr
################################################################################
################################################################################
-def trainModelRl(debug, modelDir, filename, nbIter, batchSize, devFile, transitionSet, strategy, sentencesOriginal, incremental, rewardFunc, lr, gamma, probas, predicted, silent=False) :
+def trainModelRl(debug, modelDir, filename, nbIter, batchSize, devFile, transitionSet, strategy, sentencesOriginal, incremental, rewardFunc, lr, gamma, probas, countBreak, predicted, silent=False) :
memory = None
dicts = Dicts()
@@ -189,6 +189,8 @@ def trainModelRl(debug, modelDir, filename, nbIter, batchSize, devFile, transiti
sentence.moveWordIndex(0)
state = policy_net.extractFeatures(dicts, sentence).to(getDevice())
+ count = 0
+
while True :
missingLinks = getMissingLinks(sentence)
if debug :
@@ -206,11 +208,14 @@ def trainModelRl(debug, modelDir, filename, nbIter, batchSize, devFile, transiti
reward_ = rewarding(appliable, sentence, action, missingLinks, rewardFunc)
reward = torch.FloatTensor([reward_]).to(getDevice())
- newState = None
+ #newState = None
if appliable :
applyTransition(strategy, sentence, action, reward_)
newState = policy_net.extractFeatures(dicts, sentence).to(getDevice())
+ else:
+ count+=1
+
if memory is None :
memory = ReplayMemory(5000, state.numel())
memory.push(state, torch.LongTensor([transitionSet.index(action)]).to(getDevice()), newState, reward)
@@ -223,7 +228,7 @@ def trainModelRl(debug, modelDir, filename, nbIter, batchSize, devFile, transiti
policy_net.train()
i += 1
- if state is None :
+ if state is None or count == countBreak:
break
if i >= nbExByEpoch :
break
diff --git a/main.py b/main.py
index 874ce0f..c82affe 100755
--- a/main.py
+++ b/main.py
@@ -41,7 +41,7 @@ if __name__ == "__main__" :
parser.add_argument("--gamma", default=0.99,
help="Importance given to future rewards.")
parser.add_argument("--bootstrap", default=None,
- help="If not none, extract examples in bootstrap mode (oracle train only).")
+ help="If not none, extract examples in bootstrap mode every n epochs (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",
@@ -51,7 +51,7 @@ if __name__ == "__main__" :
parser.add_argument("--silent", "-s", default=False, action="store_true",
help="Don't print advancement infos.")
parser.add_argument("--transitions", default="eager",
- help="Transition set to use (eager | swift).")
+ help="Transition set to use (eager | swift | tagparser).")
parser.add_argument("--ts", default="",
help="Comma separated list of supplementary transitions. Example \"BACK 1,BACK 2\"")
parser.add_argument("--reward", default="A",
@@ -60,6 +60,8 @@ if __name__ == "__main__" :
help="Evolution of probability to chose action at random : (start value, decay speed, end value)")
parser.add_argument("--probaOracle", default="0.3,2,0.0",
help="Evolution of probability to chose action from oracle : (start value, decay speed, end value)")
+ parser.add_argument("--countBreak", default=1,
+ help="Number of unaplayable transition picked before breaking the analysis.")
args = parser.parse_args()
if args.debug :
@@ -99,7 +101,7 @@ if __name__ == "__main__" :
json.dump(strategy, open(args.model+"/strategy.json", "w"))
printTS(transitionSet, sys.stderr)
probas = [list(map(float, args.probaRandom.split(','))), list(map(float, args.probaOracle.split(',')))]
- 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.reward, float(args.lr), float(args.gamma), probas, args.predicted, args.silent)
+ 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.reward, float(args.lr), float(args.gamma), probas, int(args.countBreak), args.predicted, args.silent)
elif args.mode == "decode" :
transNames = json.load(open(args.model+"/transitions.json", "r"))
transitionSet = [Transition(elem) for elem in transNames]
--
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