def load_models(self):
# Run load_tagger method of all models
for i, a in enumerate(self.entity_annotators.keys()):
self.create_annotationset(a[0])
if a[1] == "stanfordner":
model = StanfordNERModel("annotators/{}/{}".format(a[2], a[0]), a[2])
model.load_tagger(self.baseport + i)
self.entity_annotators[a] = model
elif a[1] == "crfsuite":
model = CrfSuiteModel("annotators/{}/{}".format(a[2], a[0]), a[2])
model.load_tagger(self.baseport + i)
self.entity_annotators[a] = model
elif a[1] == "banner":
model = BANNERModel("annotators/{}/{}".format(a[2], a[0]), a[2])
# model.load_tagger(self.baseport + i)
self.entity_annotators[a] = model
for i, a in enumerate(self.relation_annotators.keys()):
self.create_annotationset(a[0])
if a[1] == "jsre":
model = JSREKernel(None, a[2], train=False, modelname="annotators/{}/{}.model".format(a[2], a[0]), ner="all")
model.load_classifier()
self.relation_annotators[a] = model
elif a[1] == "smil":
model = MILClassifier(None, a[2], relations=[], modelname="{}.model".format(a[0]),
ner="all", generate=False, test=True)
model.basedir = "annotators/{}".format(a[2])
model.load_kb("corpora/transmir/transmir_relations.txt")
model.load_classifier()
self.relation_annotators[a] = model
def load_models(self):
for i, t in enumerate(self.types):
model = StanfordNERModel(self.basepath + "_" + t,
t,
subtypes=self.basemodel.subtypes)
model.load_tagger(self.baseport + i)
self.models[t] = model
def run_crossvalidation(goldstd_list,
corpus,
model,
cv,
crf="stanford",
entity_type="all",
cvlog="cv.log"):
logfile = open(cvlog, 'w')
doclist = corpus.documents.keys()
random.shuffle(doclist)
size = int(len(doclist) / cv)
sublists = chunks(doclist, size)
logging.debug("Chunks:")
logging.debug(sublists)
p, r = [], []
all_results = ResultsNER(model)
all_results.path = model + "_results"
for nlist in range(cv):
testids, trainids = None, None
testids = sublists[nlist]
trainids = list(itertools.chain.from_iterable(sublists[:nlist]))
trainids += list(itertools.chain.from_iterable(sublists[nlist + 1:]))
train_corpus, test_corpus = None, None
print 'CV{} - test set: {}; train set: {}'.format(
nlist, len(testids), len(trainids))
train_corpus = Corpus(
corpus.path + "_train",
documents={did: corpus.documents[did]
for did in trainids})
test_corpus = Corpus(
corpus.path + "_test",
documents={did: corpus.documents[did]
for did in testids})
# logging.debug("train corpus docs: {}".format("\n".join(train_corpus.documents.keys())))
#test_entities = len(test_corpus.get_all_entities("goldstandard"))
#train_entities = len(train_corpus.get_all_entities("goldstandard"))
#logging.info("test set entities: {}; train set entities: {}".format(test_entities, train_entities))
basemodel = model + "_cv{}".format(nlist)
logging.debug('CV{} - test set: {}; train set: {}'.format(
nlist, len(test_corpus.documents), len(train_corpus.documents)))
'''for d in train_corpus.documents:
for s in train_corpus.documents[d].sentences:
print len([t.tags.get("goldstandard") for t in s.tokens if t.tags.get("goldstandard") != "other"])
sys.exit()'''
# train
logging.info('CV{} - TRAIN'.format(nlist))
# train_model = StanfordNERModel(basemodel)
train_model = None
if crf == "stanford":
train_model = StanfordNERModel(basemodel, entity_type)
elif crf == "crfsuite":
train_model = CrfSuiteModel(basemodel, entity_type)
train_model.load_data(train_corpus, feature_extractors.keys())
train_model.train()
# test
logging.info('CV{} - TEST'.format(nlist))
test_model = None
if crf == "stanford":
test_model = StanfordNERModel(basemodel, entity_type)
elif crf == "crfsuite":
test_model = CrfSuiteModel(basemodel, entity_type)
test_model.load_tagger(port=9191 + nlist)
test_model.load_data(test_corpus,
feature_extractors.keys(),
mode="test")
final_results = None
final_results = test_model.test(test_corpus, port=9191 + nlist)
if crf == "stanford":
test_model.kill_process()
final_results.basepath = basemodel + "_results"
final_results.path = basemodel
all_results.entities.update(final_results.entities)
all_results.corpus.documents.update(final_results.corpus.documents)
# validate
"""if config.use_chebi:
logging.info('CV{} - VALIDATE'.format(nlist))
final_results = add_chebi_mappings(final_results, basemodel)
final_results = add_ssm_score(final_results, basemodel)
final_results.combine_results(basemodel, basemodel)"""
# evaluate
logging.info('CV{} - EVALUATE'.format(nlist))
test_goldset = set()
for gs in goldstd_list:
goldset = get_gold_ann_set(config.paths[gs]["format"],
config.paths[gs]["annotations"],
entity_type, "pairtype",
config.paths[gs]["text"])
for g in goldset[0]:
if g[0] in testids:
test_goldset.add(g)
precision, recall = get_results(final_results, basemodel, test_goldset,
{}, [])
# evaluation = run_chemdner_evaluation(config.paths[goldstd]["cem"], basemodel + "_results.txt", "-t")
# values = evaluation.split("\n")[1].split('\t')
p.append(precision)
r.append(recall)
# logging.info("precision: {} recall:{}".format(str(values[13]), str(values[14])))
pavg = sum(p) / cv
ravg = sum(r) / cv
print "precision: average={} all={}".format(
str(pavg), '|'.join([str(pp) for pp in p]))
print "recall: average={} all={}".format(str(ravg),
'|'.join([str(rr) for rr in r]))
all_goldset = set()
for gs in goldstd_list:
goldset = get_gold_ann_set(config.paths[gs]["format"],
config.paths[gs]["annotations"],
entity_type, config.paths[gs]["text"])
for g in goldset:
all_goldset.add(g)
get_results(all_results, model, all_goldset, {}, [])
def run_crossvalidation(goldstd_list, corpus, model, cv, crf="stanford", entity_type="all", cvlog="cv.log"):
logfile = open(cvlog, 'w')
doclist = corpus.documents.keys()
random.shuffle(doclist)
size = int(len(doclist)/cv)
sublists = chunks(doclist, size)
logging.debug("Chunks:")
logging.debug(sublists)
p, r = [], []
all_results = ResultsNER(model)
all_results.path = model + "_results"
for nlist in range(cv):
testids, trainids = None, None
testids = sublists[nlist]
trainids = list(itertools.chain.from_iterable(sublists[:nlist]))
trainids += list(itertools.chain.from_iterable(sublists[nlist+1:]))
train_corpus, test_corpus = None, None
print 'CV{} - test set: {}; train set: {}'.format(nlist, len(testids), len(trainids))
train_corpus = Corpus(corpus.path + "_train", documents={did: corpus.documents[did] for did in trainids})
test_corpus = Corpus(corpus.path + "_test", documents={did: corpus.documents[did] for did in testids})
# logging.debug("train corpus docs: {}".format("\n".join(train_corpus.documents.keys())))
#test_entities = len(test_corpus.get_all_entities("goldstandard"))
#train_entities = len(train_corpus.get_all_entities("goldstandard"))
#logging.info("test set entities: {}; train set entities: {}".format(test_entities, train_entities))
basemodel = model + "_cv{}".format(nlist)
logging.debug('CV{} - test set: {}; train set: {}'.format(nlist, len(test_corpus.documents), len(train_corpus.documents)))
'''for d in train_corpus.documents:
for s in train_corpus.documents[d].sentences:
print len([t.tags.get("goldstandard") for t in s.tokens if t.tags.get("goldstandard") != "other"])
sys.exit()'''
# train
logging.info('CV{} - TRAIN'.format(nlist))
# train_model = StanfordNERModel(basemodel)
train_model = None
if crf == "stanford":
train_model = StanfordNERModel(basemodel, entity_type)
elif crf == "crfsuite":
train_model = CrfSuiteModel(basemodel, entity_type)
train_model.load_data(train_corpus, feature_extractors.keys())
train_model.train()
# test
logging.info('CV{} - TEST'.format(nlist))
test_model = None
if crf == "stanford":
test_model = StanfordNERModel(basemodel, entity_type)
elif crf == "crfsuite":
test_model = CrfSuiteModel(basemodel, entity_type)
test_model.load_tagger(port=9191+nlist)
test_model.load_data(test_corpus, feature_extractors.keys(), mode="test")
final_results = None
final_results = test_model.test(test_corpus, port=9191+nlist)
if crf == "stanford":
test_model.kill_process()
final_results.basepath = basemodel + "_results"
final_results.path = basemodel
all_results.entities.update(final_results.entities)
all_results.corpus.documents.update(final_results.corpus.documents)
# validate
"""if config.use_chebi:
logging.info('CV{} - VALIDATE'.format(nlist))
final_results = add_chebi_mappings(final_results, basemodel)
final_results = add_ssm_score(final_results, basemodel)
final_results.combine_results(basemodel, basemodel)"""
# evaluate
logging.info('CV{} - EVALUATE'.format(nlist))
test_goldset = set()
for gs in goldstd_list:
goldset = get_gold_ann_set(config.corpus_paths.paths[gs]["format"], config.corpus_paths.paths[gs]["annotations"], entity_type,
"pairtype", config.corpus_paths.paths[gs]["text"])
for g in goldset[0]:
if g[0] in testids:
test_goldset.add(g)
precision, recall = get_results(final_results, basemodel, test_goldset, {}, [])
# evaluation = run_chemdner_evaluation(config.paths[goldstd]["cem"], basemodel + "_results.txt", "-t")
# values = evaluation.split("\n")[1].split('\t')
p.append(precision)
r.append(recall)
# logging.info("precision: {} recall:{}".format(str(values[13]), str(values[14])))
pavg = sum(p)/cv
ravg = sum(r)/cv
print "precision: average={} all={}".format(str(pavg), '|'.join([str(pp) for pp in p]))
print "recall: average={} all={}".format(str(ravg), '|'.join([str(rr) for rr in r]))
all_goldset = set()
for gs in goldstd_list:
goldset = get_gold_ann_set(config.corpus_paths.paths[gs]["format"], config.corpus_paths.paths[gs]["annotations"], entity_type, "",
config.corpus_paths.paths[gs]["text"])
for g in goldset[0]:
all_goldset.add(g)
get_results(all_results, model, all_goldset, {}, [])
