def split_merge_training(grammar,
term_labelling,
corpus,
cycles,
em_epochs,
init="rfe",
tie_breaking=False,
sigma=0.005,
seed=0,
merge_threshold=0.5,
debug=False,
rule_pruning=exp(-100)):
print("creating trace", file=stderr)
trace = PySDCPTraceManager(grammar, term_labelling, debug=debug)
print("computing reducts", file=stderr)
trace.compute_reducts(corpus)
print("pre em-training", file=stderr)
emTrainer = PyEMTrainer(trace)
emTrainer.em_training(grammar, em_epochs, init, tie_breaking, sigma, seed)
print("starting actual split/merge training", file=stderr)
grammarInfo = PyGrammarInfo(grammar, trace.get_nonterminal_map())
storageManager = PyStorageManager()
las = [
build_PyLatentAnnotation_initial(grammar, grammarInfo, storageManager)
]
trainer = PySplitMergeTrainerBuilder(trace, grammarInfo).set_em_epochs(
em_epochs).set_threshold_merger(merge_threshold).build()
for i in range(cycles):
las.append(trainer.split_merge_cycle(las[i]))
smGrammar = build_sm_grammar(las[-1], grammar, grammarInfo,
rule_pruning)
yield smGrammar
def create_initial_la(self):
# randomize initial weights and do em training
la_no_splits = build_PyLatentAnnotation_initial(
self.base_grammar, self.organizer.grammarInfo,
self.organizer.storageManager)
la_no_splits.add_random_noise(seed=self.organizer.seed)
return la_no_splits
def test_la_viterbi_parsing_3(self):
grammar = LCFRS("S")
# rule 0
lhs = LCFRS_lhs("B")
lhs.add_arg(["a"])
grammar.add_rule(lhs, [], 0.25)
# rule 1
lhs = LCFRS_lhs("A")
lhs.add_arg(["a"])
grammar.add_rule(lhs, [], 0.5)
# rule 2
lhs = LCFRS_lhs("S")
lhs.add_arg([LCFRS_var(0, 0)])
grammar.add_rule(lhs, ["B"], 1.0)
# rule 3
lhs = LCFRS_lhs("A")
lhs.add_arg([LCFRS_var(0, 0), LCFRS_var(1, 0)])
grammar.add_rule(lhs, ["A", "B"], 0.5)
# rule 4
lhs = LCFRS_lhs("B")
lhs.add_arg([LCFRS_var(0, 0), LCFRS_var(1, 0)])
grammar.add_rule(lhs, ["A", "B"], 0.75)
grammar.make_proper()
inp = ["a"] * 3
nontMap = Enumerator()
gi = PyGrammarInfo(grammar, nontMap)
sm = PyStorageManager()
print(nontMap.object_index("S"))
print(nontMap.object_index("B"))
la = build_PyLatentAnnotation_initial(grammar, gi, sm)
parser = DiscodopKbestParser(grammar,
la=la,
nontMap=nontMap,
grammarInfo=gi,
latent_viterbi_mode=True)
parser.set_input(inp)
parser.parse()
self.assertTrue(parser.recognized())
der = parser.latent_viterbi_derivation(True)
print(der)
der2 = None
for w, der_ in parser.k_best_derivation_trees():
if der2 is None:
der2 = der_
print(w, der_)
print(der2)
def test_projection_based_parser_k_best_hack(self):
grammar = LCFRS("S")
# rule 0
lhs = LCFRS_lhs("B")
lhs.add_arg(["a"])
grammar.add_rule(lhs, [], 0.25)
# rule 1
lhs = LCFRS_lhs("A")
lhs.add_arg(["a"])
grammar.add_rule(lhs, [], 0.5)
# rule 2
lhs = LCFRS_lhs("S")
lhs.add_arg([LCFRS_var(0, 0)])
grammar.add_rule(lhs, ["B"], 1.0)
# rule 3
lhs = LCFRS_lhs("A")
lhs.add_arg([LCFRS_var(0, 0), LCFRS_var(1, 0)])
grammar.add_rule(lhs, ["A", "B"], 0.5)
# rule 4
lhs = LCFRS_lhs("B")
lhs.add_arg([LCFRS_var(0, 0), LCFRS_var(1, 0)])
grammar.add_rule(lhs, ["A", "B"], 0.75)
grammar.make_proper()
inp = ["a"] * 3
nontMap = Enumerator()
gi = PyGrammarInfo(grammar, nontMap)
sm = PyStorageManager()
la = build_PyLatentAnnotation_initial(grammar, gi, sm)
parser = Coarse_to_fine_parser(grammar,
la,
gi,
nontMap,
base_parser_type=GFParser_k_best)
parser.set_input(inp)
parser.parse()
self.assertTrue(parser.recognized())
der = parser.max_rule_product_derivation()
print(der)
der = parser.best_derivation_tree()
print(der)
for node in der.ids():
print(der.getRule(node), der.spanned_ranges(node))
def test_la_viterbi_parsing(self):
grammar = self.build_grammar()
inp = ["a"] * 3
nontMap = Enumerator()
gi = PyGrammarInfo(grammar, nontMap)
sm = PyStorageManager()
la = build_PyLatentAnnotation_initial(grammar, gi, sm)
parser = DiscodopKbestParser(grammar, la=la, nontMap=nontMap, grammarInfo=gi, latent_viterbi_mode=True)
parser.set_input(inp)
parser.parse()
self.assertTrue(parser.recognized())
der = parser.best_derivation_tree()
print(der)
for node in der.ids():
print(node, der.getRule(node), der.spanned_ranges(node))
def test_projection_based_parser_k_best_hack(self):
grammar = self.build_grammar()
inp = ["a"] * 3
nontMap = Enumerator()
gi = PyGrammarInfo(grammar, nontMap)
sm = PyStorageManager()
la = build_PyLatentAnnotation_initial(grammar, gi, sm)
parser = Coarse_to_fine_parser(grammar,
la,
gi,
nontMap,
base_parser_type=GFParser_k_best)
parser.set_input(inp)
parser.parse()
self.assertTrue(parser.recognized())
der = parser.max_rule_product_derivation()
print(der)
der = parser.best_derivation_tree()
print(der)
for node in der.ids():
print(der.getRule(node), der.spanned_ranges(node))
def main():
# induce grammar from a corpus
trees = parse_conll_corpus(train, False, limit_train)
nonterminal_labelling = the_labeling_factory(
).create_simple_labeling_strategy("childtop", "deprel")
term_labelling = the_terminal_labeling_factory().get_strategy('pos')
start = 'START'
recursive_partitioning = [cfg]
_, grammar = induce_grammar(trees, nonterminal_labelling,
term_labelling.token_label,
recursive_partitioning, start)
# compute some derivations
derivations = obtain_derivations(grammar, term_labelling)
# create derivation manager and add derivations
manager = PyDerivationManager(grammar)
manager.convert_derivations_to_hypergraphs(derivations)
manager.serialize(b"/tmp/derivations.txt")
# build and configure split/merge trainer and supplementary objects
rule_to_nonterminals = []
for i in range(0, len(grammar.rule_index())):
rule = grammar.rule_index(i)
nonts = [
manager.get_nonterminal_map().object_index(rule.lhs().nont())
] + [
manager.get_nonterminal_map().object_index(nont)
for nont in rule.rhs()
]
rule_to_nonterminals.append(nonts)
grammarInfo = PyGrammarInfo(grammar, manager.get_nonterminal_map())
storageManager = PyStorageManager()
builder = PySplitMergeTrainerBuilder(manager, grammarInfo)
builder.set_em_epochs(20)
builder.set_percent_merger(60.0)
splitMergeTrainer = builder.build()
latentAnnotation = [
build_PyLatentAnnotation_initial(grammar, grammarInfo, storageManager)
]
for i in range(max_cycles + 1):
latentAnnotation.append(
splitMergeTrainer.split_merge_cycle(latentAnnotation[-1]))
# pickle.dump(map(lambda la: la.serialize(), latentAnnotation), open(sm_info_path, 'wb'))
smGrammar = build_sm_grammar(latentAnnotation[i],
grammar,
grammarInfo,
rule_pruning=0.0001,
rule_smoothing=0.01)
print("Cycle: ", i, "Rules: ", len(smGrammar.rules()))
if parsing:
parser = GFParser(smGrammar)
trees = parse_conll_corpus(test, False, limit_test)
for tree in trees:
parser.set_input(
term_labelling.prepare_parser_input(tree.token_yield()))
parser.parse()
if parser.recognized():
print(
derivation_to_hybrid_tree(
parser.best_derivation_tree(),
[token.pos() for token in tree.token_yield()],
[token.form() for token in tree.token_yield()],
construct_constituent_token))
def test_individual_parsing_stages(self):
grammar = self.build_grammar()
for r in transform_grammar(grammar):
pprint(r)
rule_list = list(transform_grammar(grammar))
pprint(rule_list)
disco_grammar = Grammar(rule_list, start=grammar.start())
print(disco_grammar)
inp = ["a"] * 3
estimates = 'SXlrgaps', getestimates(disco_grammar, 40, grammar.start())
print(type(estimates))
chart, msg = parse(inp, disco_grammar, estimates=estimates)
print(chart)
print(msg)
chart.filter()
print("filtered chart")
print(disco_grammar.nonterminals)
print(type(disco_grammar.nonterminals))
print(chart)
# print(help(chart))
root = chart.root()
print("root", root, type(root))
print(chart.indices(root))
print(chart.itemstr(root))
print(chart.stats())
print("root label", chart.label(root))
print(root, chart.itemid1(chart.label(root), chart.indices(root)))
for i in range(1, chart.numitems() + 1):
print(i, chart.label(i), chart.indices(i), chart.numedges(i))
if True or len(chart.indices(i)) > 1:
for edge_num in range(chart.numedges(i)):
edge = chart.getEdgeForItem(i, edge_num)
if isinstance(edge, tuple):
print("\t", disco_grammar.nonterminalstr(chart.label(i)) + "[" + str(i) + "]", "->", ' '.join([disco_grammar.nonterminalstr(chart.label(j)) + "[" + str(j) + "]" for j in [edge[1], edge[2]] if j != 0]))
else:
print("\t", disco_grammar.nonterminalstr(chart.label(i)) + "[" + str(i) + "]", "->", inp[edge])
print(chart.getEdgeForItem(root, 0))
# print(lazykbest(chart, 5))
manager = PyDerivationManager(grammar)
manager.convert_chart_to_hypergraph(chart, disco_grammar, debug=True)
file = tempfile.mktemp()
print(file)
manager.serialize(bytes(file, encoding="utf-8"))
gi = PyGrammarInfo(grammar, manager.get_nonterminal_map())
sm = PyStorageManager()
la = build_PyLatentAnnotation_initial(grammar, gi, sm)
vec = py_edge_weight_projection(la, manager, variational=True, debug=True, log_mode=False)
print(vec)
self.assertEqual([1.0, 1.0, 1.0, 0.5, 0.5, 0.5, 0.5, 0.25, 0.25, 0.25, 0.25, 1.0], vec)
vec = py_edge_weight_projection(la, manager, variational=False, debug=True, log_mode=False)
print(vec)
self.assertEqual([1.0, 1.0, 1.0, 0.25, 0.25, 0.25, 0.25, 0.25, 0.25, 0.25, 0.25, 1.0], vec)
der = manager.viterbi_derivation(0, vec, grammar)
print(der)
# print(disco_grammar.rulenos)
# print(disco_grammar.numrules)
# print(disco_grammar.lexicalbylhs)
# print(disco_grammar.lexicalbyword)
# print(disco_grammar.lexicalbynum)
# print(disco_grammar.origrules, type(disco_grammar.origrules))
# print(disco_grammar.numbinary)
# print(disco_grammar.numunary)
# print(disco_grammar.toid)
# print(disco_grammar.tolabel)
# print(disco_grammar.bitpar)
# striplabelre = re.compile(r'-\d+$')
# msg = disco_grammar.getmapping(None, None)
# disco_grammar.getrulemapping(disco_grammar, striplabelre)
# mapping = disco_grammar.rulemapping
# print(mapping)
# for idx, group in enumerate(mapping):
# print("Index", idx)
# for elem in group:
# print(grammar.rule_index(elem))
# for _, item in zip(range(20), chart.parseforest):
# edge = chart.parseforest[item]
# print(item, item.binrepr(), item.__repr__(), item.lexidx())
# print(type(edge))
for _ in range(5):
vec2 = py_edge_weight_projection(la, manager, debug=True, log_mode=True)
print(vec2)
def main():
# # induce or load grammar
# if not os.path.isfile(grammar_path):
# grammar = LCFRS('START')
# for tree in train_corpus:
# if not tree.complete() or tree.empty_fringe():
# continue
# part = recursive_partitioning(tree)
# tree_grammar = fringe_extract_lcfrs(tree, part, naming='child', term_labeling=terminal_labeling)
# grammar.add_gram(tree_grammar)
# grammar.make_proper()
# pickle.dump(grammar, open(grammar_path, 'wb'))
# else:
# grammar = pickle.load(open(grammar_path, 'rb'))
grammar = LCFRS('START')
for tree in train_corpus:
if not tree.complete() or tree.empty_fringe():
continue
part = recursive_partitioning(tree)
tree_grammar = fringe_extract_lcfrs(tree,
part,
naming='child',
term_labeling=terminal_labeling)
grammar.add_gram(tree_grammar)
grammar.make_proper()
# # compute or load reducts
# if not os.path.isfile(reduct_path):
# traceTrain = compute_reducts(grammar, train_corpus, terminal_labeling)
# traceTrain.serialize(reduct_path)
# else:
# traceTrain = PySDCPTraceManager(grammar, terminal_labeling)
# traceTrain.load_traces_from_file(reduct_path)
traceTrain = compute_reducts(grammar, train_corpus, terminal_labeling)
traceValidationGenetic = compute_reducts(grammar,
validation_genetic_corpus,
terminal_labeling)
traceValidation = compute_reducts(grammar, validation_corpus,
terminal_labeling)
# prepare EM training
grammarInfo = PyGrammarInfo(grammar, traceTrain.get_nonterminal_map())
if not grammarInfo.check_for_consistency():
print("[Genetic] GrammarInfo is not consistent!")
storageManager = PyStorageManager()
em_builder = PySplitMergeTrainerBuilder(traceTrain, grammarInfo)
em_builder.set_em_epochs(em_epochs)
em_builder.set_simple_expector(threads=threads)
emTrainer = em_builder.build()
# randomize initial weights and do em training
la_no_splits = build_PyLatentAnnotation_initial(grammar, grammarInfo,
storageManager)
la_no_splits.add_random_noise(seed=seed)
emTrainer.em_train(la_no_splits)
la_no_splits.project_weights(grammar, grammarInfo)
# emTrainerOld = PyEMTrainer(traceTrain)
# emTrainerOld.em_training(grammar, 30, "rfe", tie_breaking=True)
# compute parses for validation set
baseline_parser = GFParser_k_best(grammar, k=k_best)
validator = build_score_validator(grammar, grammarInfo,
traceTrain.get_nonterminal_map(),
storageManager, terminal_labeling,
baseline_parser, validation_corpus,
validationMethod)
del baseline_parser
# prepare SM training
builder = PySplitMergeTrainerBuilder(traceTrain, grammarInfo)
builder.set_em_epochs(em_epochs)
builder.set_split_randomization(1.0, seed + 1)
builder.set_simple_expector(threads=threads)
builder.set_score_validator(validator, validationDropIterations)
builder.set_smoothing_factor(smoothingFactor=smoothing_factor)
builder.set_split_randomization(percent=split_randomization)
splitMergeTrainer = builder.set_scc_merger(threshold=scc_merger_threshold,
threads=threads).build()
splitMergeTrainer.setMaxDrops(validationDropIterations, mode="smoothing")
splitMergeTrainer.setEMepochs(em_epochs, mode="smoothing")
# set initial latent annotation
latentAnnotations = []
for i in range(0, genetic_initial):
splitMergeTrainer.reset_random_seed(seed + i + 1)
la = splitMergeTrainer.split_merge_cycle(la_no_splits)
if not la.check_for_validity():
print('[Genetic] Initial LA', i,
'is not consistent! (See details before)')
if not la.is_proper():
print('[Genetic] Initial LA', i, 'is not proper!')
heapq.heappush(
latentAnnotations,
(evaluate_la(grammar, grammarInfo, la, traceValidationGenetic,
validation_genetic_corpus), i, la))
print('[Genetic] added initial LA', i)
(fBest, idBest, laBest) = min(latentAnnotations)
validation_score = evaluate_la(grammar, grammarInfo, laBest,
traceValidation, test_corpus)
print("[Genetic] Started with best F-Score (Test) of", validation_score,
"from Annotation ", idBest)
geneticCount = genetic_initial
random.seed(seed)
for round in range(1, genetic_cycles + 1):
print("[Genetic] Starting Recombination Round ", round)
# newpopulation = list(latentAnnotations)
newpopulation = []
# Cross all candidates!
for leftIndex in range(0, len(latentAnnotations)):
(fLeft, idLeft, left) = latentAnnotations[leftIndex]
# TODO: How to determine NTs to keep?
# do SM-Training
print("[Genetic] do SM-training on", idLeft, "and create LA",
geneticCount)
la = splitMergeTrainer.split_merge_cycle(la)
if not la.check_for_validity():
print(
'[Genetic] Split/Merge introduced invalid weights into LA',
geneticCount)
if not la.is_proper():
print(
'[Genetic] Split/Merge introduced problems with properness of LA',
geneticCount)
fscore = evaluate_la(grammar, grammarInfo, la,
traceValidationGenetic,
validation_genetic_corpus)
print("[Genetic] LA", geneticCount, "has F-score: ", fscore)
heapq.heappush(newpopulation, (fscore, geneticCount, la))
geneticCount += 1
heapq.heapify(newpopulation)
latentAnnotations = heapq.nsmallest(
genetic_population, heapq.merge(latentAnnotations, newpopulation))
heapq.heapify(latentAnnotations)
(fBest, idBest, laBest) = min(latentAnnotations)
validation_score = evaluate_la(grammar, grammarInfo, laBest,
traceValidation, test_corpus)
print("[Genetic] Best LA", idBest, "has F-Score (Test) of ",
validation_score)
def main(limit=3000,
test_limit=sys.maxint,
max_length=sys.maxint,
dir=dir,
train='../res/negra-dep/negra-lower-punct-train.conll',
test='../res/negra-dep/negra-lower-punct-test.conll',
recursive_partitioning='cfg',
nonterminal_labeling='childtop-deprel',
terminal_labeling='form-unk-30/pos',
emEpochs=20,
emTieBreaking=True,
emInit="rfe",
splitRandomization=1.0,
mergePercentage=85.0,
smCycles=6,
rule_pruning=0.0001,
rule_smoothing=0.01,
validation=True,
validationMethod='likelihood',
validationCorpus=None,
validationSplit=20,
validationDropIterations=6,
seed=1337,
discr=False,
maxScaleDiscr=10,
recompileGrammar="True",
retrain=False,
parsing=True,
reparse=False,
parser="CFG",
k_best=50,
minimum_risk=False,
oracle_parse=False):
# set various parameters
recompileGrammar = True if recompileGrammar == "True" else False
# print(recompileGrammar)
def result(gram, add=None):
if add is not None:
return os.path.join(
dir, gram + '_experiment_parse_results_' + add + '.conll')
else:
return os.path.join(dir, gram + '_experiment_parse_results.conll')
recursive_partitioning = grammar.induction.recursive_partitioning.the_recursive_partitioning_factory(
).get_partitioning(recursive_partitioning)
top_level, low_level = tuple(nonterminal_labeling.split('-'))
nonterminal_labeling = d_l.the_labeling_factory(
).create_simple_labeling_strategy(top_level, low_level)
if parser == "CFG":
assert all([
rp.__name__
in ["left_branching", "right_branching", "cfg", "fanout_1"]
for rp in recursive_partitioning
])
parser = CFGParser
elif parser == "GF":
parser = GFParser
elif parser == "GF-k-best":
parser = GFParser_k_best
elif parser == "CoarseToFine":
parser = Coarse_to_fine_parser
elif parser == "FST":
if recursive_partitioning == "left_branching":
parser = LeftBranchingFSTParser
elif recursive_partitioning == "right_branching":
parser = RightBranchingFSTParser
else:
assert False and "expect left/right branching recursive partitioning for FST parsing"
if validation:
if validationCorpus is not None:
corpus_validation = Corpus(validationCorpus)
train_limit = limit
else:
train_limit = int(limit * (100.0 - validationSplit) / 100.0)
corpus_validation = Corpus(train, start=train_limit, end=limit)
else:
train_limit = limit
corpus_induce = Corpus(train, end=limit)
corpus_train = Corpus(train, end=train_limit)
corpus_test = Corpus(test, end=test_limit)
match = re.match(r'^form-unk-(\d+)-morph.*$', terminal_labeling)
if match:
unk_threshold = int(match.group(1))
term_labelling = grammar.induction.terminal_labeling.FormPosTerminalsUnkMorph(
corpus_induce.get_trees(),
unk_threshold,
pos_filter=["NE", "CARD"],
add_morph={
'NN': ['case', 'number', 'gender']
# , 'NE': ['case', 'number', 'gender']
# , 'VMFIN': ['number', 'person']
# , 'VVFIN': ['number', 'person']
# , 'VAFIN': ['number', 'person']
})
else:
match = re.match(r'^form-unk-(\d+).*$', terminal_labeling)
if match:
unk_threshold = int(match.group(1))
term_labelling = grammar.induction.terminal_labeling.FormPosTerminalsUnk(
corpus_induce.get_trees(),
unk_threshold,
pos_filter=["NE", "CARD"])
else:
term_labelling = grammar.induction.terminal_labeling.the_terminal_labeling_factory(
).get_strategy(terminal_labeling)
if not os.path.isdir(dir):
os.makedirs(dir)
# start actual training
# we use the training corpus until limit for grammar induction (i.e., also the validation section)
print("Computing baseline id: ")
baseline_id = grammar_id(corpus_induce, nonterminal_labeling,
term_labelling, recursive_partitioning)
print(baseline_id)
baseline_path = compute_grammar_name(dir, baseline_id, "baseline")
if recompileGrammar or not os.path.isfile(baseline_path):
print("Inducing grammar from corpus")
(n_trees, baseline_grammar) = d_i.induce_grammar(
corpus_induce.get_trees(), nonterminal_labeling,
term_labelling.token_label, recursive_partitioning, start)
print("Induced grammar using", n_trees, ".")
pickle.dump(baseline_grammar, open(baseline_path, 'wb'))
else:
print("Loading grammar from file")
baseline_grammar = pickle.load(open(baseline_path))
print("Rules: ", len(baseline_grammar.rules()))
if parsing:
parser_ = GFParser_k_best if parser == Coarse_to_fine_parser else parser
baseline_parser = do_parsing(baseline_grammar,
corpus_test,
term_labelling,
result,
baseline_id,
parser_,
k_best=k_best,
minimum_risk=minimum_risk,
oracle_parse=oracle_parse,
recompile=recompileGrammar,
dir=dir,
reparse=reparse)
if True:
em_trained = pickle.load(open(baseline_path))
reduct_path = compute_reduct_name(dir, baseline_id, corpus_train)
if recompileGrammar or not os.path.isfile(reduct_path):
trace = compute_reducts(em_trained, corpus_train.get_trees(),
term_labelling)
trace.serialize(reduct_path)
else:
print("loading trace")
trace = PySDCPTraceManager(em_trained, term_labelling)
trace.load_traces_from_file(reduct_path)
if discr:
reduct_path_discr = compute_reduct_name(dir, baseline_id,
corpus_train, '_discr')
if recompileGrammar or not os.path.isfile(reduct_path_discr):
trace_discr = compute_LCFRS_reducts(
em_trained,
corpus_train.get_trees(),
terminal_labelling=term_labelling,
nonterminal_map=trace.get_nonterminal_map())
trace_discr.serialize(reduct_path_discr)
else:
print("loading trace discriminative")
trace_discr = PyLCFRSTraceManager(em_trained,
trace.get_nonterminal_map())
trace_discr.load_traces_from_file(reduct_path_discr)
# todo refactor EM training, to use the LA version (but without any splits)
"""
em_trained_path_ = em_trained_path(dir, grammar_id, n_epochs=emEpochs, init=emInit, tie_breaking=emTieBreaking, seed=seed)
if recompileGrammar or retrain or not os.path.isfile(em_trained_path_):
emTrainer = PyEMTrainer(trace)
emTrainer.em_training(em_trained, n_epochs=emEpochs, init=emInit, tie_breaking=emTieBreaking, seed=seed)
pickle.dump(em_trained, open(em_trained_path_, 'wb'))
else:
em_trained = pickle.load(open(em_trained_path_, 'rb'))
if parsing:
do_parsing(em_trained, test_limit, ignore_punctuation, term_labelling, recompileGrammar or retrain, [dir, "em_trained_gf_grammar"])
"""
grammarInfo = PyGrammarInfo(baseline_grammar,
trace.get_nonterminal_map())
storageManager = PyStorageManager()
builder = PySplitMergeTrainerBuilder(trace, grammarInfo)
builder.set_em_epochs(emEpochs)
builder.set_smoothing_factor(rule_smoothing)
builder.set_split_randomization(splitRandomization, seed + 1)
if discr:
builder.set_discriminative_expector(trace_discr,
maxScale=maxScaleDiscr,
threads=1)
else:
builder.set_simple_expector(threads=1)
if validation:
if validationMethod is "likelihood":
reduct_path_validation = compute_reduct_name(
dir, baseline_id, corpus_validation)
if recompileGrammar or not os.path.isfile(
reduct_path_validation):
validation_trace = compute_reducts(
em_trained, corpus_validation.get_trees(),
term_labelling)
validation_trace.serialize(reduct_path_validation)
else:
print("loading trace validation")
validation_trace = PySDCPTraceManager(
em_trained, term_labelling)
validation_trace.load_traces_from_file(
reduct_path_validation)
builder.set_simple_validator(validation_trace,
maxDrops=validationDropIterations,
threads=1)
else:
validator = build_score_validator(
baseline_grammar, grammarInfo, trace.get_nonterminal_map(),
storageManager, term_labelling, baseline_parser,
corpus_validation, validationMethod)
builder.set_score_validator(validator,
validationDropIterations)
splitMergeTrainer = builder.set_percent_merger(mergePercentage).build()
if validation:
splitMergeTrainer.setMaxDrops(1, mode="smoothing")
splitMergeTrainer.setEMepochs(1, mode="smoothing")
sm_info_path = compute_sm_info_path(dir, baseline_id, emEpochs,
rule_smoothing, splitRandomization,
seed, discr, validation,
corpus_validation, emInit)
if (not recompileGrammar) and (
not retrain) and os.path.isfile(sm_info_path):
print("Loading splits and weights of LA rules")
latentAnnotation = map(
lambda t: build_PyLatentAnnotation(t[0], t[1], t[
2], grammarInfo, storageManager),
pickle.load(open(sm_info_path, 'rb')))
else:
# latentAnnotation = [build_PyLatentAnnotation_initial(em_trained, grammarInfo, storageManager)]
latentAnnotation = [
build_PyLatentAnnotation_initial(baseline_grammar, grammarInfo,
storageManager)
]
for cycle in range(smCycles + 1):
if cycle < len(latentAnnotation):
smGrammar = latentAnnotation[cycle].build_sm_grammar(
baseline_grammar,
grammarInfo,
rule_pruning=rule_pruning
# , rule_smoothing=rule_smoothing
)
else:
# setting the seed to achieve reproducibility in case of continued training
splitMergeTrainer.reset_random_seed(seed + cycle + 1)
latentAnnotation.append(
splitMergeTrainer.split_merge_cycle(latentAnnotation[-1]))
pickle.dump(map(lambda la: la.serialize(), latentAnnotation),
open(sm_info_path, 'wb'))
smGrammar = latentAnnotation[cycle].build_sm_grammar(
baseline_grammar,
grammarInfo,
rule_pruning=rule_pruning
# , rule_smoothing=rule_smoothing
)
print("Cycle: ", cycle, "Rules: ", len(smGrammar.rules()))
if parsing:
grammar_identifier = compute_sm_grammar_id(
baseline_id, emEpochs, rule_smoothing, splitRandomization,
seed, discr, validation, corpus_validation, emInit, cycle)
if parser == Coarse_to_fine_parser:
opt = {
'latentAnnotation':
latentAnnotation[:cycle + 1] #[cycle]
,
'grammarInfo': grammarInfo,
'nontMap': trace.get_nonterminal_map()
}
do_parsing(baseline_grammar,
corpus_test,
term_labelling,
result,
grammar_identifier,
parser,
k_best=k_best,
minimum_risk=minimum_risk,
oracle_parse=oracle_parse,
recompile=recompileGrammar,
dir=dir,
reparse=reparse,
opt=opt)
else:
do_parsing(smGrammar,
corpus_test,
term_labelling,
result,
grammar_identifier,
parser,
k_best=k_best,
minimum_risk=minimum_risk,
oracle_parse=oracle_parse,
recompile=recompileGrammar,
dir=dir,
reparse=reparse)
def main(limit=300,
ignore_punctuation=False,
baseline_path=baseline_path,
recompileGrammar=True,
retrain=True,
parsing=True,
seed=1337):
max_length = 20
trees = length_limit(parse_conll_corpus(train, False, limit), max_length)
if recompileGrammar or not os.path.isfile(baseline_path):
(n_trees,
baseline_grammar) = d_i.induce_grammar(trees, empty_labelling,
term_labelling.token_label,
recursive_partitioning, start)
pickle.dump(baseline_grammar, open(baseline_path, 'wb'))
else:
baseline_grammar = pickle.load(open(baseline_path))
test_limit = 10000
print("Rules: ", len(baseline_grammar.rules()))
if parsing:
do_parsing(baseline_grammar, test_limit, ignore_punctuation,
recompileGrammar, [dir, "baseline_gf_grammar"])
em_trained = pickle.load(open(baseline_path))
if recompileGrammar or not os.path.isfile(reduct_path):
trees = length_limit(parse_conll_corpus(train, False, limit),
max_length)
trace = compute_reducts(em_trained, trees, term_labelling)
trace.serialize(reduct_path)
else:
print("loading trace")
trace = PySDCPTraceManager(em_trained, term_labelling)
trace.load_traces_from_file(reduct_path)
discr = False
if discr:
if recompileGrammar or not os.path.isfile(reduct_path_discr):
trees = length_limit(parse_conll_corpus(train, False, limit),
max_length)
trace_discr = compute_LCFRS_reducts(
em_trained,
trees,
term_labelling,
nonterminal_map=trace.get_nonterminal_map())
trace_discr.serialize(reduct_path_discr)
else:
print("loading trace discriminative")
trace_discr = PyLCFRSTraceManager(em_trained,
trace.get_nonterminal_map())
trace_discr.load_traces_from_file(reduct_path_discr)
n_epochs = 20
init = "rfe"
tie_breaking = True
em_trained_path_ = em_trained_path(n_epochs, init, tie_breaking)
if recompileGrammar or retrain or not os.path.isfile(em_trained_path_):
emTrainer = PyEMTrainer(trace)
emTrainer.em_training(em_trained,
n_epochs=n_epochs,
init=init,
tie_breaking=tie_breaking,
seed=seed)
pickle.dump(em_trained, open(em_trained_path_, 'wb'))
else:
em_trained = pickle.load(open(em_trained_path_, 'rb'))
if parsing:
do_parsing(em_trained, test_limit, ignore_punctuation, recompileGrammar
or retrain, [dir, "em_trained_gf_grammar"])
grammarInfo = PyGrammarInfo(baseline_grammar, trace.get_nonterminal_map())
storageManager = PyStorageManager()
builder = PySplitMergeTrainerBuilder(trace, grammarInfo)
builder.set_em_epochs(n_epochs)
builder.set_split_randomization(1.0, seed + 1)
if discr:
builder.set_discriminative_expector(trace_discr,
maxScale=10,
threads=1)
else:
builder.set_simple_expector(threads=1)
splitMergeTrainer = builder.set_percent_merger(65.0).build()
if (not recompileGrammar) and (
not retrain) and os.path.isfile(sm_info_path):
print("Loading splits and weights of LA rules")
latentAnnotation = map(
lambda t: build_PyLatentAnnotation(t[0], t[1], t[2], grammarInfo,
storageManager),
pickle.load(open(sm_info_path, 'rb')))
else:
latentAnnotation = [
build_PyLatentAnnotation_initial(em_trained, grammarInfo,
storageManager)
]
max_cycles = 4
reparse = False
# parsing = False
for i in range(max_cycles + 1):
if i < len(latentAnnotation):
if reparse:
smGrammar = latentAnnotation[i].build_sm_grammar(
baseline_grammar,
grammarInfo,
rule_pruning=0.0001,
rule_smoothing=0.01)
print("Cycle: ", i, "Rules: ", len(smGrammar.rules()))
do_parsing(smGrammar, test_limit, ignore_punctuation,
recompileGrammar or retrain,
[dir, "sm_cycles" + str(i) + "_gf_grammar"])
else:
# setting the seed to achieve reproducibility in case of continued training
splitMergeTrainer.reset_random_seed(seed + i + 1)
latentAnnotation.append(
splitMergeTrainer.split_merge_cycle(latentAnnotation[-1]))
pickle.dump(map(lambda la: la.serialize(), latentAnnotation),
open(sm_info_path, 'wb'))
smGrammar = latentAnnotation[i].build_sm_grammar(
baseline_grammar,
grammarInfo,
rule_pruning=0.0001,
rule_smoothing=0.1)
print("Cycle: ", i, "Rules: ", len(smGrammar.rules()))
if parsing:
do_parsing(smGrammar, test_limit, ignore_punctuation,
recompileGrammar or retrain,
[dir, "sm_cycles" + str(i) + "_gf_grammar"])
def run_experiment(rec_part_strategy,
nonterminal_labeling,
exp,
reorder_children,
binarize=True):
start = 1
stop = 7000
test_start = 7001
test_stop = 7200
# path = "res/tiger/tiger_release_aug07.corrected.16012013.utf8.xml"
corpus_path = "res/tiger/tiger_8000.xml"
exclude = []
train_dsgs = sentence_names_to_deep_syntax_graphs(
['s' + str(i) for i in range(start, stop + 1) if i not in exclude],
corpus_path,
hold=False,
reorder_children=reorder_children)
test_dsgs = sentence_names_to_deep_syntax_graphs(
[
's' + str(i)
for i in range(test_start, test_stop + 1) if i not in exclude
],
corpus_path,
hold=False,
reorder_children=reorder_children)
# Grammar induction
term_labeling_token = PosTerminals()
def term_labeling(token):
if isinstance(token, ConstituentTerminal):
return term_labeling_token.token_label(token)
else:
return token
if binarize:
def modify_token(token):
if isinstance(token, ConstituentCategory):
token_new = deepcopy(token)
token_new.set_category(token.category() + '-BAR')
return token_new
elif isinstance(token, str):
return token + '-BAR'
else:
assert False
train_dsgs = [
dsg.binarize(bin_modifier=modify_token) for dsg in train_dsgs
]
def is_bin(token):
if isinstance(token, ConstituentCategory):
if token.category().endswith('-BAR'):
return True
elif isinstance(token, str):
if token.endswith('-BAR'):
return True
return False
def debinarize(dsg):
return dsg.debinarize(is_bin=is_bin)
else:
debinarize = id
grammar = induction_on_a_corpus(train_dsgs, rec_part_strategy,
nonterminal_labeling, term_labeling)
grammar.make_proper()
print("Nonterminals", len(grammar.nonts()), "Rules", len(grammar.rules()))
parser = GFParser_k_best(grammar, k=500)
return do_parsing(parser,
test_dsgs,
term_labeling_token,
oracle=True,
debinarize=debinarize)
# Compute reducts, i.e., intersect grammar with each training dsg
basedir = path.join('/tmp/dog_experiments', 'exp' + str(exp))
reduct_dir = path.join(basedir, 'reduct_grammars')
terminal_map = Enumerator()
if not os.path.isdir(basedir):
os.makedirs(basedir)
data = export_dog_grammar_to_json(grammar, terminal_map)
grammar_path = path.join(basedir, 'grammar.json')
with open(grammar_path, 'w') as file:
json.dump(data, file)
corpus_path = path.join(basedir, 'corpus.json')
with open(corpus_path, 'w') as file:
json.dump(
export_corpus_to_json(train_dsgs,
terminal_map,
terminal_labeling=term_labeling), file)
with open(path.join(basedir, 'enumerator.enum'), 'w') as file:
terminal_map.print_index(file)
if os.path.isdir(reduct_dir):
shutil.rmtree(reduct_dir)
os.makedirs(reduct_dir)
p = subprocess.Popen([
' '.join([
"java", "-jar",
os.path.join("util", SCHICK_PARSER_JAR), 'dog-reduct', '-g',
grammar_path, '-t', corpus_path, "-o", reduct_dir
])
],
shell=True,
stdout=subprocess.PIPE,
stderr=subprocess.STDOUT)
while True:
nextline = p.stdout.readline()
if nextline == '' and p.poll() is not None:
break
sys.stdout.write(nextline)
sys.stdout.flush()
p.wait()
p.stdout.close()
rtgs = []
for i in range(1, len(train_dsgs) + 1):
rtgs.append(read_rtg(path.join(reduct_dir, str(i) + '.gra')))
derivation_manager = PyDerivationManager(grammar)
derivation_manager.convert_rtgs_to_hypergraphs(rtgs)
derivation_manager.serialize(path.join(basedir, 'reduct_manager.trace'))
# Training
## prepare EM training
em_epochs = 20
seed = 0
smoothing_factor = 0.01
split_randomization = 0.01
sm_cycles = 2
merge_percentage = 50.0
grammarInfo = PyGrammarInfo(grammar,
derivation_manager.get_nonterminal_map())
storageManager = PyStorageManager()
em_builder = PySplitMergeTrainerBuilder(derivation_manager, grammarInfo)
em_builder.set_em_epochs(em_epochs)
em_builder.set_simple_expector(threads=THREADS)
emTrainer = em_builder.build()
# randomize initial weights and do em training
la_no_splits = build_PyLatentAnnotation_initial(grammar, grammarInfo,
storageManager)
la_no_splits.add_random_noise(seed=seed)
emTrainer.em_train(la_no_splits)
la_no_splits.project_weights(grammar, grammarInfo)
do_parsing(CFGParser(grammar), test_dsgs, term_labeling_token)
return
## prepare SM training
builder = PySplitMergeTrainerBuilder(derivation_manager, grammarInfo)
builder.set_em_epochs(em_epochs)
builder.set_split_randomization(1.0, seed + 1)
builder.set_simple_expector(threads=THREADS)
builder.set_smoothing_factor(smoothingFactor=smoothing_factor)
builder.set_split_randomization(percent=split_randomization)
# builder.set_scc_merger(-0.2)
builder.set_percent_merger(merge_percentage)
splitMergeTrainer = builder.build()
# splitMergeTrainer.setMaxDrops(validationDropIterations, mode="smoothing")
splitMergeTrainer.setEMepochs(em_epochs, mode="smoothing")
# set initial latent annotation
latentAnnotation = [la_no_splits]
# carry out split/merge training and do parsing
parsing_method = "filter-ctf"
# parsing_method = "single-best-annotation"
k_best = 50
for i in range(1, sm_cycles + 1):
splitMergeTrainer.reset_random_seed(seed + i + 1)
latentAnnotation.append(
splitMergeTrainer.split_merge_cycle(latentAnnotation[-1]))
print("Cycle: ", i)
if parsing_method == "single-best-annotation":
smGrammar = latentAnnotation[i].build_sm_grammar(
grammar, grammarInfo, rule_pruning=0.0001, rule_smoothing=0.1)
print("Rules in smoothed grammar: ", len(smGrammar.rules()))
parser = GFParser(smGrammar)
elif parsing_method == "filter-ctf":
latentAnnotation[-1].project_weights(grammar, grammarInfo)
parser = Coarse_to_fine_parser(
grammar,
latentAnnotation[-1],
grammarInfo,
derivation_manager.get_nonterminal_map(),
base_parser_type=GFParser_k_best,
k=k_best)
else:
raise (Exception())
do_parsing(parser, test_dsgs, term_labeling_token)
del parser