class TaggerCollection(object):
"""
Collection of tagger classifiers used to train and test specific subtype models
"""
CHEMDNER_TYPES = [
"IDENTIFIER", "MULTIPLE", "FAMILY", "FORMULA", "SYSTEMATIC",
"ABBREVIATION", "TRIVIAL"
]
GPRO_TYPES = ["NESTED", "IDENTIFIER", "FULL_NAME", "ABBREVIATION"]
DDI_TYPES = ["drug", "group", "brand", "drug_n"]
def __init__(self, basepath, baseport=9191, **kwargs):
self.models = {}
self.basepath = basepath
self.corpus = kwargs.get("corpus")
submodels = []
self.baseport = baseport
self.types = []
if basepath.split("/")[-1].startswith("chemdner+ddi"):
self.types = self.DDI_TYPES + self.CHEMDNER_TYPES + [
"chemdner", "ddi"
]
elif basepath.split("/")[-1].startswith("ddi"):
self.types = self.DDI_TYPES + ["all"]
elif basepath.split("/")[-1].startswith("chemdner") or basepath.split(
"/")[-1].startswith("cemp"):
self.types = ["all"] + self.CHEMDNER_TYPES
elif basepath.split("/")[-1].startswith("gpro"):
self.types = self.GPRO_TYPES + ["all"]
self.basemodel = StanfordNERModel(self.basepath, "all")
def train_types(self):
"""
Train models for each subtype of entity, and a general model.
:param types: subtypes of entities to train individual models, as well as a general model
"""
self.basemodel.load_data(self.corpus,
feature_extractors.keys(),
subtype="all")
for t in self.types:
typepath = self.basepath + "_" + t
model = StanfordNERModel(typepath,
subtypes=self.basemodel.subtypes)
model.copy_data(self.basemodel, t)
logging.info("training subtype %s" % t)
model.train()
self.models[t] = 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 process_type(self, modelst, t, corpus, basemodel, basepath, port):
# load data only for one model since this takes at least 5 minutes each time
logging.debug("{}: copying data...".format(t))
modelst.copy_data(basemodel)
#logging.debug("pre test %s" % model)
logging.debug("{}: testing...".format(t))
res = modelst.test(corpus, port)
logging.info("{}:done...".format(t))
return res
def test_types(self, corpus):
"""
Classify the corpus with multiple classifiers from different subtypes
:return ResultSetNER object with the results obtained for the models
"""
# TODO: parallelize
results = ResultSetNER(corpus, self.basepath)
self.basemodel.load_data(corpus, feature_extractors.keys())
all_results = []
tasks = [(self.models[t], t, corpus, self.basemodel, self.basepath,
self.baseport + i) for i, t in enumerate(self.types)]
all_results = []
for t in tasks:
r = self.process_type(*t)
all_results.append(r)
logging.info("adding results...")
for res, i in enumerate(all_results):
#logging.debug("adding these results: {}".format(self.types[i]))
results.add_results(res)
return results
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, {}, [])
