def __init__(self, basemodel, ensemble_model, submodels):
self.corenlp = StanfordCoreNLP(config.corenlp_dir)
self.basemodel = basemodel
self.ensemble_model = ensemble_model
self.subtypes = submodels
self.models = TaggerCollection(basepath=self.basemodel)
self.models.load_models()
self.ensemble = EnsembleNER(self.ensemble_model, None, self.basemodel + "_combined", types=self.subtypes,
features=[])
self.ensemble.load()
def main():
start_time = time.time()
parser = argparse.ArgumentParser(description='')
parser.add_argument("actions",
default="classify",
help="Actions to be performed.",
choices=[
"load_corpus", "annotate", "classify",
"write_results", "write_goldstandard", "train",
"test", "train_multiple", "test_multiple",
"train_matcher", "test_matcher", "crossvalidation",
"train_relations", "test_relations"
])
parser.add_argument(
"--goldstd",
default="",
dest="goldstd",
nargs="+",
help="Gold standard to be used. Will override corpus, annotations",
choices=config.paths.keys())
parser.add_argument("--submodels",
default="",
nargs='+',
help="sub types of classifiers"),
parser.add_argument(
"-i",
"--input",
dest="input",
action="store",
default='''Administration of a higher dose of indinavir should be \\
considered when coadministering with megestrol acetate.''',
help="Text to classify.")
parser.add_argument(
"--corpus",
dest="corpus",
nargs=2,
default=[
"chemdner",
"CHEMDNER/CHEMDNER_SAMPLE_JUNE25/chemdner_sample_abstracts.txt"
],
help="format path")
parser.add_argument("--annotations", dest="annotations")
parser.add_argument("--tag",
dest="tag",
default="0",
help="Tag to identify the text.")
parser.add_argument("--models",
dest="models",
help="model destination path, without extension")
parser.add_argument("--entitytype",
dest="etype",
help="type of entities to be considered",
default="all")
parser.add_argument("--pairtype",
dest="ptype",
help="type of pairs to be considered",
default="all")
parser.add_argument("--doctype",
dest="doctype",
help="type of document to be considered",
default="all")
parser.add_argument("--annotated",
action="store_true",
default=False,
dest="annotated",
help="True if the input has <entity> tags.")
parser.add_argument(
"-o",
"--output",
"--format",
dest="output",
nargs=2,
help="format path; output formats: xml, html, tsv, text, chemdner.")
parser.add_argument("--crf",
dest="crf",
help="CRF implementation",
default="stanford",
choices=["stanford", "crfsuite"])
parser.add_argument("--log",
action="store",
dest="loglevel",
default="WARNING",
help="Log level")
parser.add_argument("--kernel",
action="store",
dest="kernel",
default="svmtk",
help="Kernel for relation extraction")
options = parser.parse_args()
# set logger
numeric_level = getattr(logging, options.loglevel.upper(), None)
if not isinstance(numeric_level, int):
raise ValueError('Invalid log level: %s' % options.loglevel)
while len(logging.root.handlers) > 0:
logging.root.removeHandler(logging.root.handlers[-1])
logging_format = '%(asctime)s %(levelname)s %(filename)s:%(lineno)s:%(funcName)s %(message)s'
logging.basicConfig(level=numeric_level,
format=logging_format,
filename="debug.log")
logging.getLogger().setLevel(numeric_level)
logging.getLogger("requests.packages").setLevel(30)
logging.info("Processing action {0} on {1}".format(options.actions,
options.goldstd))
# set configuration variables based on the goldstd option if the corpus has a gold standard,
# or on corpus and annotation options
# pre-processing options
if options.actions == "load_corpus":
if len(options.goldstd) > 1:
print("load only one corpus each time")
sys.exit()
options.goldstd = options.goldstd[0]
corpus_format = config.paths[options.goldstd]["format"]
corpus_path = config.paths[options.goldstd]["text"]
corpus_ann = config.paths[options.goldstd]["annotations"]
corenlp_client = StanfordCoreNLP('http://localhost:9000')
corpus = load_corpus(options.goldstd, corpus_path, corpus_format,
corenlp_client)
corpus.save(config.paths[options.goldstd]["corpus"])
if corpus_ann: #add annotation if it is not a test set
corpus.load_annotations(corpus_ann, options.etype, options.ptype)
corpus.save(config.paths[options.goldstd]["corpus"])
elif options.actions == "annotate": # rext-add annotation to corpus
if len(options.goldstd) > 1:
print("load only one corpus each time")
sys.exit()
options.goldstd = options.goldstd[0]
corpus_path = config.paths[options.goldstd]["corpus"]
corpus_ann = config.paths[options.goldstd]["annotations"]
logging.info("loading corpus %s" % corpus_path)
corpus = pickle.load(open(corpus_path, 'rb'))
logging.debug("loading annotations...")
corpus.clear_annotations(options.etype)
corpus.load_annotations(corpus_ann, options.etype, options.ptype)
# corpus.get_invalid_sentences()
corpus.save(config.paths[options.goldstd]["corpus"])
else:
corpus = Corpus("corpus/" + "&".join(options.goldstd))
for g in options.goldstd:
corpus_path = config.paths[g]["corpus"]
logging.info("loading corpus %s" % corpus_path)
this_corpus = pickle.load(open(corpus_path, 'rb'))
corpus.documents.update(this_corpus.documents)
if options.actions == "write_goldstandard":
model = BiasModel(options.output[1])
model.load_data(corpus, [])
results = model.test()
#results = ResultsNER(options.output[1])
#results.get_ner_results(corpus, model)
results.save(options.output[1] + ".pickle")
#logging.info("saved gold standard results to " + options.output[1] + ".txt")
# training
elif options.actions == "train":
if options.crf == "stanford":
model = StanfordNERModel(options.models, options.etype)
elif options.crf == "crfsuite":
model = CrfSuiteModel(options.models, options.etype)
model.load_data(corpus, feature_extractors.keys(), options.etype)
model.train()
elif options.actions == "train_matcher": # Train a simple classifier based on string matching
model = MatcherModel(options.models)
model.train(corpus)
# TODO: term list option
#model.train("TermList.txt")
elif options.actions == "train_multiple": # Train one classifier for each type of entity in this corpus
# logging.info(corpus.subtypes)
models = TaggerCollection(basepath=options.models,
corpus=corpus,
subtypes=corpus.subtypes)
models.train_types()
elif options.actions == "train_relations":
if options.kernel == "jsre":
model = JSREKernel(corpus, options.ptype)
elif options.kernel == "svmtk":
model = SVMTKernel(corpus, options.ptype)
elif options.kernel == "stanfordre":
model = StanfordRE(corpus, options.ptype)
elif options.kernel == "multir":
model = MultiR(corpus, options.ptype)
elif options.kernel == "scikit":
model = ScikitRE(corpus, options.ptype)
elif options.kernel == "crf":
model = CrfSuiteRE(corpus, options.ptype)
model.train()
# testing
elif options.actions == "test":
base_port = 9191
if len(options.submodels) > 1:
allresults = ResultSetNER(corpus, options.output[1])
for i, submodel in enumerate(options.submodels):
model = StanfordNERModel(options.models + "_" + submodel)
model.load_tagger(base_port + i)
# load data into the model format
model.load_data(corpus,
feature_extractors.keys(),
mode="test")
# run the classifier on the data
results = model.test(corpus, port=base_port + i)
allresults.add_results(results)
model.kill_process()
# save the results to an object that can be read again, and log files to debug
final_results = allresults.combine_results()
else:
if options.crf == "stanford":
model = StanfordNERModel(options.models, options.etype)
elif options.crf == "crfsuite":
model = CrfSuiteModel(options.models, options.etype)
model.load_tagger()
model.load_data(corpus, feature_extractors.keys(), mode="test")
final_results = model.test(corpus)
#with codecs.open(options.output[1] + ".txt", 'w', 'utf-8') as outfile:
# lines = final_results.corpus.write_chemdner_results(options.models, outfile)
#final_results.lines = lines
final_results.save(options.output[1] + ".pickle")
elif options.actions == "test_matcher":
if "mirna" in options.models:
model = MirnaMatcher(options.models)
else:
model = MatcherModel(options.models)
results = ResultsNER(options.models)
results.corpus, results.entities = model.test(corpus)
allentities = set()
for e in results.entities:
allentities.add(results.entities[e].text)
with codecs.open(options.output[1] + ".txt", 'w',
'utf-8') as outfile:
outfile.write('\n'.join(allentities))
results.save(options.output[1] + ".pickle")
elif options.actions == "test_multiple":
logging.info("testing with multiple classifiers... {}".format(
' '.join(options.submodels)))
allresults = ResultSetNER(corpus, options.output[1])
if len(options.submodels) < 2:
models = TaggerCollection(basepath=options.models)
models.load_models()
results = models.test_types(corpus)
final_results = results.combine_results()
else:
base_port = 9191
for submodel in options.submodels:
models = TaggerCollection(basepath=options.models + "_" +
submodel,
baseport=base_port)
models.load_models()
results = models.test_types(corpus)
logging.info("combining results...")
submodel_results = results.combine_results()
allresults.add_results(submodel_results)
base_port += len(models.models)
final_results = allresults.combine_results()
logging.info("saving results...")
final_results.save(options.output[1] + ".pickle")
elif options.actions == "test_relations":
if options.kernel == "jsre":
model = JSREKernel(corpus, options.ptype, train=False)
elif options.kernel == "svmtk":
model = SVMTKernel(corpus, options.ptype)
elif options.kernel == "rules":
model = RuleClassifier(corpus, options.ptype)
elif options.kernel == "stanfordre":
model = StanfordRE(corpus, options.ptype)
elif options.kernel == "scikit":
model = ScikitRE(corpus, options.ptype)
elif options.kernel == "crf":
model = CrfSuiteRE(corpus, options.ptype, test=True)
model.load_classifier()
model.test()
results = model.get_predictions(corpus)
results.save(options.output[1] + ".pickle")
total_time = time.time() - start_time
logging.info("Total time: %ss" % total_time)
def main():
start_time = time.time()
parser = argparse.ArgumentParser(description='')
parser.add_argument("actions",
default="classify",
help="Actions to be performed.")
parser.add_argument(
"--goldstd",
default="",
dest="goldstd",
nargs="+",
help="Gold standard to be used. Will override corpus, annotations",
choices=paths.keys())
parser.add_argument("--submodels",
default="",
nargs='+',
help="sub types of classifiers"),
parser.add_argument("--models",
dest="models",
help="model destination path, without extension")
parser.add_argument("--pairtype",
dest="ptype",
help="type of pairs to be considered",
default="all")
parser.add_argument("--doctype",
dest="doctype",
help="type of document to be considered",
default="all")
parser.add_argument(
"-o",
"--output",
"--format",
dest="output",
nargs=2,
help="format path; output formats: xml, html, tsv, text, chemdner.")
parser.add_argument("--log",
action="store",
dest="loglevel",
default="WARNING",
help="Log level")
parser.add_argument("--kernel",
action="store",
dest="kernel",
default="svmtk",
help="Kernel for relation extraction")
options = parser.parse_args()
# set logger
numeric_level = getattr(logging, options.loglevel.upper(), None)
if not isinstance(numeric_level, int):
raise ValueError('Invalid log level: %s' % options.loglevel)
while len(logging.root.handlers) > 0:
logging.root.removeHandler(logging.root.handlers[-1])
logging_format = '%(asctime)s %(levelname)s %(filename)s:%(lineno)s:%(funcName)s %(message)s'
logging.basicConfig(level=numeric_level, format=logging_format)
logging.getLogger().setLevel(numeric_level)
logging.getLogger("requests.packages").setLevel(30)
logging.info("Processing action {0} on {1}".format(options.actions,
options.goldstd))
# set configuration variables based on the goldstd option if the corpus has a gold standard,
# or on corpus and annotation options
# pre-processing options
if options.actions == "load_corpus":
if len(options.goldstd) > 1:
print "load only one corpus each time"
sys.exit()
options.goldstd = options.goldstd[0]
corpus_format = paths[options.goldstd]["format"]
corpus_path = paths[options.goldstd]["text"]
corpus_ann = paths[options.goldstd]["annotations"]
corenlp_client = StanfordCoreNLP('http://localhost:9000')
# corpus = load_corpus(options.goldstd, corpus_path, corpus_format, corenlp_client)
corpus = SeeDevCorpus(corpus_path)
corpus.load_corpus(corenlp_client)
corpus.save(paths[options.goldstd]["corpus"])
if corpus_ann: #add annotation if it is not a test set
corpus.load_annotations(corpus_ann, "all")
corpus.save(paths[options.goldstd]["corpus"])
elif options.actions == "annotate": # rext-add annotation to corpus
if len(options.goldstd) > 1:
print "load only one corpus each time"
sys.exit()
options.goldstd = options.goldstd[0]
corpus_path = paths[options.goldstd]["corpus"]
corpus_ann = paths[options.goldstd]["annotations"]
logging.info("loading corpus %s" % corpus_path)
corpus = pickle.load(open(corpus_path, 'rb'))
logging.debug("loading annotations...")
# corpus.clear_annotations("all")
corpus.load_annotations(corpus_ann, "all", options.ptype)
# corpus.get_invalid_sentences()
corpus.save(paths[options.goldstd]["corpus"])
else:
#corpus = SeeDevCorpus("corpus/" + "&".join(options.goldstd))
corpus_path = paths[options.goldstd[0]]["corpus"]
logging.info("loading corpus %s" % corpus_path)
basecorpus = pickle.load(open(corpus_path, 'rb'))
corpus = SeeDevCorpus(corpus_path)
corpus.documents = basecorpus.documents
if options.actions == "add_sentences":
corpus.add_more_sentences(options.models)
elif options.actions == "add_goldstandard":
corpus.convert_entities_to_goldstandard()
corpus.find_ds_relations()
#corpus.save(config.paths[options.goldstd[0]]["corpus"])
elif options.actions == "train_multiple": # Train one classifier for each type of entity in this corpus
# logging.info(corpus.subtypes)
models = TaggerCollection(basepath=options.models,
corpus=corpus,
subtypes=all_entity_types)
models.train_types()
elif options.actions == "train_relations":
if options.ptype == "all":
ptypes = pair_types.keys()
# ptypes = config.event_types.keys()
else:
ptypes = [options.ptype]
for p in ptypes:
print p
if options.kernel == "jsre":
model = JSREKernel(corpus, p, train=True)
elif options.kernel == "svmtk":
model = SVMTKernel(corpus, p)
elif options.kernel == "stanfordre":
model = StanfordRE(corpus, p)
elif options.kernel == "multir":
model = MultiR(corpus, p)
elif options.kernel == "scikit":
model = ScikitRE(corpus, p)
elif options.kernel == "crf":
model = CrfSuiteRE(corpus, p)
# model.train()
# testing
elif options.actions == "test_multiple":
logging.info("testing with multiple classifiers... {}".format(
' '.join(options.submodels)))
models = TaggerCollection(basepath=options.models,
subtypes=all_entity_types)
models.load_models()
results = models.test_types(corpus)
final_results = results.combine_results()
logging.info("saving results...")
final_results.save(options.output[1] + ".pickle")
elif options.actions == "test_relations":
if options.ptype == "all":
ptypes = pair_types.keys()
# ptypes = config.event_types.keys()
all_results = ResultsRE(options.output[1])
all_results.corpus = corpus
all_results.path = options.output[1]
else:
ptypes = [options.ptype]
for p in ptypes:
print p
if options.kernel == "jsre":
model = JSREKernel(corpus, p, train=False)
elif options.kernel == "svmtk":
model = SVMTKernel(corpus, p)
elif options.kernel == "rules":
model = RuleClassifier(corpus, p)
elif options.kernel == "stanfordre":
model = StanfordRE(corpus, p)
elif options.kernel == "scikit":
model = ScikitRE(corpus, p)
elif options.kernel == "crf":
model = CrfSuiteRE(corpus, p, test=True)
model.load_classifier()
model.test()
results = model.get_predictions(corpus)
# results.save(options.output[1] + "_" + p.lower() + ".pickle")
# results.load_corpus(options.goldstd[0])
results.path = options.output[1] + "_" + p.lower()
goldset = get_gold_ann_set(
paths[options.goldstd[0]]["format"],
paths[options.goldstd[0]]["annotations"], "all", p,
paths[options.goldstd[0]]["text"])
get_relations_results(results, options.models, goldset[1], [],
[])
if options.ptype == "all":
for did in results.document_pairs:
if did not in all_results.document_pairs:
all_results.document_pairs[did] = Pairs(did=did)
all_results.document_pairs[
did].pairs += results.document_pairs[did].pairs
if options.ptype == "all":
goldset = get_gold_ann_set(
paths[options.goldstd[0]]["format"],
paths[options.goldstd[0]]["annotations"], "all", "all",
paths[options.goldstd[0]]["text"])
get_relations_results(all_results, options.models, goldset[1],
[], [])
write_seedev_results(all_results, options.output[1])
elif options.actions == "train_sentences": #and evaluate
if options.ptype == "all":
avg = [0, 0, 0]
for p in pair_types:
print p
tps, fps, fns = corpus.train_sentence_classifier(p)
if tps == 0 and fns == 0:
precision, recall, fscore = 0, 1, 1
else:
precision = 1.0 * tps / (fps + tps)
recall = 1.0 * fns / (fns + tps)
fscore = 2.0 * precision * recall / (recall +
precision)
print precision, recall, fscore
avg[0] += tps
avg[1] += fps
avg[2] += fns
#print [a/len(config.pair_types) for a in avg]
precision = 1.0 * avg[1] / (avg[0] + avg[1])
recall = 1.0 * avg[2] / (avg[0] + avg[2])
fscore = 2.0 * precision * recall / (recall + precision)
print precision, recall, fscore
else:
res = corpus.train_sentence_classifier(options.ptype)
print res
corpus.save(paths[options.goldstd[0]]["corpus"])
elif options.actions == "test_sentences": #and evaluate
if options.ptype == "all":
avg = [0, 0, 0]
for p in pair_types:
print p
tps, fps, fns = corpus.test_sentence_classifier(p)
if tps == 0 and fns == 0:
precision, recall, fscore = 0, 1, 1
else:
precision = 1.0 * tps / (fps + tps)
recall = 1.0 * fns / (fns + tps)
fscore = 2.0 * precision * recall / (recall + precision)
print precision, recall, fscore
avg[0] += tps
avg[1] += fps
avg[2] += fns
#print [a/len(config.pair_types) for a in avg]
precision = 1.0 * avg[1] / (avg[0] + avg[1])
recall = 1.0 * avg[2] / (avg[0] + avg[2])
fscore = 2.0 * precision * recall / (recall + precision)
print precision, recall, fscore
#else:
# res = corpus.test_sentence_classifier(options.ptype)
# print res
elif options.actions == "evaluate_ner":
if os.path.exists(options.output[1] + ".pickle"):
results = pickle.load(open(options.output[1] + ".pickle",
'rb'))
results.load_corpus(options.goldstd[0])
results.path = options.output[1]
logging.info("loading gold standard %s" %
paths[options.goldstd[0]]["annotations"])
for t in all_entity_types:
print t
results.path = options.output[1] + "_" + t
goldset = get_gold_ann_set(
paths[options.goldstd[0]]["format"],
paths[options.goldstd[0]]["annotations"], t, options.ptype,
paths[options.goldstd[0]]["text"])
get_results(results, options.models + "_" + t, goldset[0], {},
{})
corpus.save(paths[options.goldstd[0]]["corpus"])
total_time = time.time() - start_time
logging.info("Total time: %ss" % total_time)
def main():
start_time = time.time()
parser = argparse.ArgumentParser(description='')
parser.add_argument("actions", default="classify", help="Actions to be performed.")
parser.add_argument("--goldstd", default="", dest="goldstd", nargs="+",
help="Gold standard to be used. Will override corpus, annotations",
choices=paths.keys())
parser.add_argument("--submodels", default="", nargs='+', help="sub types of classifiers"),
parser.add_argument("--models", dest="models", help="model destination path, without extension")
parser.add_argument("--pairtype", dest="ptype", help="type of pairs to be considered", default="all")
parser.add_argument("--doctype", dest="doctype", help="type of document to be considered", default="all")
parser.add_argument("-o", "--output", "--format", dest="output",
nargs=2, help="format path; output formats: xml, html, tsv, text, chemdner.")
parser.add_argument("--log", action="store", dest="loglevel", default="WARNING", help="Log level")
parser.add_argument("--kernel", action="store", dest="kernel", default="svmtk", help="Kernel for relation extraction")
options = parser.parse_args()
# set logger
numeric_level = getattr(logging, options.loglevel.upper(), None)
if not isinstance(numeric_level, int):
raise ValueError('Invalid log level: %s' % options.loglevel)
while len(logging.root.handlers) > 0:
logging.root.removeHandler(logging.root.handlers[-1])
logging_format = '%(asctime)s %(levelname)s %(filename)s:%(lineno)s:%(funcName)s %(message)s'
logging.basicConfig(level=numeric_level, format=logging_format)
logging.getLogger().setLevel(numeric_level)
logging.getLogger("requests.packages").setLevel(30)
logging.info("Processing action {0} on {1}".format(options.actions, options.goldstd))
# set configuration variables based on the goldstd option if the corpus has a gold standard,
# or on corpus and annotation options
# pre-processing options
if options.actions == "load_corpus":
if len(options.goldstd) > 1:
print "load only one corpus each time"
sys.exit()
options.goldstd = options.goldstd[0]
corpus_format = paths[options.goldstd]["format"]
corpus_path = paths[options.goldstd]["text"]
corpus_ann = paths[options.goldstd]["annotations"]
corenlp_client = StanfordCoreNLP('http://localhost:9000')
# corpus = load_corpus(options.goldstd, corpus_path, corpus_format, corenlp_client)
corpus = SeeDevCorpus(corpus_path)
corpus.load_corpus(corenlp_client)
corpus.save(paths[options.goldstd]["corpus"])
if corpus_ann: #add annotation if it is not a test set
corpus.load_annotations(corpus_ann, "all")
corpus.save(paths[options.goldstd]["corpus"])
elif options.actions == "annotate": # rext-add annotation to corpus
if len(options.goldstd) > 1:
print "load only one corpus each time"
sys.exit()
options.goldstd = options.goldstd[0]
corpus_path = paths[options.goldstd]["corpus"]
corpus_ann = paths[options.goldstd]["annotations"]
logging.info("loading corpus %s" % corpus_path)
corpus = pickle.load(open(corpus_path, 'rb'))
logging.debug("loading annotations...")
# corpus.clear_annotations("all")
corpus.load_annotations(corpus_ann, "all", options.ptype)
# corpus.get_invalid_sentences()
corpus.save(paths[options.goldstd]["corpus"])
else:
#corpus = SeeDevCorpus("corpus/" + "&".join(options.goldstd))
corpus_path = paths[options.goldstd[0]]["corpus"]
logging.info("loading corpus %s" % corpus_path)
basecorpus = pickle.load(open(corpus_path, 'rb'))
corpus = SeeDevCorpus(corpus_path)
corpus.documents = basecorpus.documents
if options.actions == "add_sentences":
corpus.add_more_sentences(options.models)
elif options.actions == "add_goldstandard":
corpus.convert_entities_to_goldstandard()
corpus.find_ds_relations()
#corpus.save(config.paths[options.goldstd[0]]["corpus"])
elif options.actions == "train_multiple": # Train one classifier for each type of entity in this corpus
# logging.info(corpus.subtypes)
models = TaggerCollection(basepath=options.models, corpus=corpus, subtypes=all_entity_types)
models.train_types()
elif options.actions == "train_relations":
if options.ptype == "all":
ptypes = pair_types.keys()
# ptypes = config.event_types.keys()
else:
ptypes = [options.ptype]
for p in ptypes:
print p
if options.kernel == "jsre":
model = JSREKernel(corpus, p, train=True)
elif options.kernel == "svmtk":
model = SVMTKernel(corpus, p)
elif options.kernel == "stanfordre":
model = StanfordRE(corpus, p)
elif options.kernel == "multir":
model = MultiR(corpus, p)
elif options.kernel == "scikit":
model = ScikitRE(corpus, p)
elif options.kernel == "crf":
model = CrfSuiteRE(corpus, p)
# model.train()
# testing
elif options.actions == "test_multiple":
logging.info("testing with multiple classifiers... {}".format(' '.join(options.submodels)))
models = TaggerCollection(basepath=options.models, subtypes=all_entity_types)
models.load_models()
results = models.test_types(corpus)
final_results = results.combine_results()
logging.info("saving results...")
final_results.save(options.output[1] + ".pickle")
elif options.actions == "test_relations":
if options.ptype == "all":
ptypes = pair_types.keys()
# ptypes = config.event_types.keys()
all_results = ResultsRE(options.output[1])
all_results.corpus = corpus
all_results.path = options.output[1]
else:
ptypes = [options.ptype]
for p in ptypes:
print p
if options.kernel == "jsre":
model = JSREKernel(corpus, p, train=False)
elif options.kernel == "svmtk":
model = SVMTKernel(corpus, p)
elif options.kernel == "rules":
model = RuleClassifier(corpus, p)
elif options.kernel == "stanfordre":
model = StanfordRE(corpus, p)
elif options.kernel == "scikit":
model = ScikitRE(corpus, p)
elif options.kernel == "crf":
model = CrfSuiteRE(corpus, p, test=True)
model.load_classifier()
model.test()
results = model.get_predictions(corpus)
# results.save(options.output[1] + "_" + p.lower() + ".pickle")
# results.load_corpus(options.goldstd[0])
results.path = options.output[1] + "_" + p.lower()
goldset = get_gold_ann_set(paths[options.goldstd[0]]["format"], paths[options.goldstd[0]]["annotations"],
"all", p, paths[options.goldstd[0]]["text"])
get_relations_results(results, options.models, goldset[1],[], [])
if options.ptype == "all":
for did in results.document_pairs:
if did not in all_results.document_pairs:
all_results.document_pairs[did] = Pairs(did=did)
all_results.document_pairs[did].pairs += results.document_pairs[did].pairs
if options.ptype == "all":
goldset = get_gold_ann_set(paths[options.goldstd[0]]["format"], paths[options.goldstd[0]]["annotations"],
"all", "all", paths[options.goldstd[0]]["text"])
get_relations_results(all_results, options.models, goldset[1],[], [])
write_seedev_results(all_results, options.output[1])
elif options.actions == "train_sentences": #and evaluate
if options.ptype == "all":
avg = [0,0,0]
for p in pair_types:
print p
tps, fps, fns = corpus.train_sentence_classifier(p)
if tps == 0 and fns == 0:
precision, recall, fscore = 0, 1, 1
else:
precision = 1.0 * tps / (fps + tps)
recall = 1.0 * fns / (fns + tps)
fscore = 2.0 * precision * recall / (recall + precision)
print precision, recall, fscore
avg[0] += tps
avg[1] += fps
avg[2] += fns
#print [a/len(config.pair_types) for a in avg]
precision = 1.0 * avg[1] / (avg[0] + avg[1])
recall = 1.0 * avg[2] / (avg[0] + avg[2])
fscore = 2.0 * precision * recall / (recall + precision)
print precision, recall, fscore
else:
res = corpus.train_sentence_classifier(options.ptype)
print res
corpus.save(paths[options.goldstd[0]]["corpus"])
elif options.actions == "test_sentences": #and evaluate
if options.ptype == "all":
avg = [0,0,0]
for p in pair_types:
print p
tps, fps, fns = corpus.test_sentence_classifier(p)
if tps == 0 and fns == 0:
precision, recall, fscore = 0, 1, 1
else:
precision = 1.0 * tps / (fps + tps)
recall = 1.0 * fns / (fns + tps)
fscore = 2.0 * precision * recall / (recall + precision)
print precision, recall, fscore
avg[0] += tps
avg[1] += fps
avg[2] += fns
#print [a/len(config.pair_types) for a in avg]
precision = 1.0 * avg[1] / (avg[0] + avg[1])
recall = 1.0 * avg[2] / (avg[0] + avg[2])
fscore = 2.0 * precision * recall / (recall + precision)
print precision, recall, fscore
#else:
# res = corpus.test_sentence_classifier(options.ptype)
# print res
elif options.actions == "evaluate_ner":
if os.path.exists(options.output[1] + ".pickle"):
results = pickle.load(open(options.output[1] + ".pickle", 'rb'))
results.load_corpus(options.goldstd[0])
results.path = options.output[1]
logging.info("loading gold standard %s" % paths[options.goldstd[0]]["annotations"])
for t in all_entity_types:
print t
results.path = options.output[1] + "_" + t
goldset = get_gold_ann_set(paths[options.goldstd[0]]["format"],
paths[options.goldstd[0]]["annotations"],
t, options.ptype, paths[options.goldstd[0]]["text"])
get_results(results, options.models + "_" + t, goldset[0], {}, {})
corpus.save(paths[options.goldstd[0]]["corpus"])
total_time = time.time() - start_time
logging.info("Total time: %ss" % total_time)
class IICEServer(object):
def __init__(self, basemodel, ensemble_model, submodels):
self.corenlp = StanfordCoreNLP(config.corenlp_dir)
self.basemodel = basemodel
self.ensemble_model = ensemble_model
self.subtypes = submodels
self.models = TaggerCollection(basepath=self.basemodel)
self.models.load_models()
self.ensemble = EnsembleNER(self.ensemble_model, None, self.basemodel + "_combined", types=self.subtypes,
features=[])
self.ensemble.load()
def hello(self):
return "Hello World!"
def process_pubmed(self, pmid):
title, text = pubmed.get_pubmed_abs(pmid)
def id_generator(self, size=6, chars=string.ascii_uppercase + string.digits):
return ''.join(random.choice(chars) for _ in range(size))
def process_multiple(self):
bottle.response.content_type = "application/json"
data = bottle.request.json
text = data["text"]
format = data["format"]
test_corpus = self.generate_corpus(text)
multiple_results = self.models.test_types(test_corpus)
final_results = multiple_results.combine_results()
final_results = add_chebi_mappings(final_results, self.basemodel)
final_results = add_ssm_score(final_results, self.basemodel)
final_results.combine_results(self.basemodel, self.basemodel + "_combined")
# self.ensemble.generate_data(final_results, supervisioned=False)
#self.ensemble.test()
# ensemble_results = ResultsNER(self.basemodel + "_combined_ensemble")
# ensemble_results.get_ensemble_results(self.ensemble, final_results.corpus, self.basemodel + "_combined")
#output = get_output(final_results, basemodel + "_combined")
results_id = self.id_generator()
#output = self.get_output(ensemble_results, self.basemodel + "_combined_ensemble", format, id=results_id)
output = self.get_output(final_results, self.basemodel + "_combined", format=format, results_id=results_id)
#self.models.load_models()
self.clean_up()
# save corpus to pickel and add ID to the output as corpusfile
pickle.dump(final_results.corpus, open("temp/{}.pickle".format(results_id), 'w'))
return output
def clean_up(self):
for m in self.models.models:
self.models.models[m].reset()
self.models.basemodel.reset()
def process(self, text="", modeltype="all"):
test_corpus = self.generate_corpus(text)
model = SimpleTaggerModel("models/chemdner_train_f13_lbfgs_" + modeltype)
model.load_tagger()
# load data into the model format
model.load_data(test_corpus, feature_extractors.keys())
# run the classifier on the data
results = model.test(stats=False)
#results = ResultsNER("models/chemdner_train_f13_lbfgs_" + modeltype)
# process the results
#results.get_ner_results(test_corpus, model)
output = self.get_output(results, "models/chemdner_train_f13_lbfgs_" + modeltype)
return output
def get_relations(self):
"""
Process the results dictionary, identify relations
:return: results dictionary with relations
"""
data = bottle.request.json
# logging.debug(str(data))
if "corpusfile" in data:
corpus = pickle.load(open("temp/{}.pickle".format(data["corpusfile"])))
logging.info("loaded corpus {}".format(data["corpusfile"]))
else:
# create corpus
corpus = None
pass
did = "d0"
#for sentence in data["abstract"]["sentences"]:
for sentence in corpus.documents["d0"].sentences[1:]:
sentence_pairs = []
sentence_entities = sentence.entities.elist[self.basemodel + "_combined"]
# logging.info("sentence entities:" + str(sentence_entities))
sid = sentence.sid
for i1, e1 in enumerate(sentence_entities):
logging.info("sentence entities:" + str(e1))
if i1 < len(sentence_entities)-1:
for i2, e2 in enumerate(sentence_entities[i1+1:]):
logging.info("sentence entities:" + str(e2))
pid = sentence.sid + ".p{}".format(len(sentence_pairs))
newpair = Pair(entities=[e1, e2], sid=sid, pid=pid, did=did)
sentence_pairs.append(newpair)
sentence.pairs.pairs[pid] = newpair
logging.info(str(sentence_pairs))
if len(sentence_pairs) > 0:
corpus.documents[did].get_sentence(sid).test_relations(sentence_pairs, self.basemodel + "_combined")
return data
def generate_corpus(self, text):
"""
Create a corpus object from the input text.
:param text:
:return:
"""
test_corpus = Corpus("")
newdoc = Document(text, process=False, did="d0", title="Test document")
newdoc.sentence_tokenize("biomedical")
newdoc.process_document(self.corenlp, "biomedical")
test_corpus.documents["d0"] = newdoc
return test_corpus
def get_output(self, results, model_name, format="bioc", results_id=None):
if format == "bioc":
a = ET.Element('collection')
bioc = results.corpus.documents["d0"].write_bioc_results(a, model_name)
rough_string = ET.tostring(a, 'utf-8')
reparsed = minidom.parseString(rough_string)
output = reparsed.toprettyxml(indent="\t")
elif format == "chemdner":
with codecs.open("/dev/null", 'w', 'utf-8') as outfile:
lines = results.corpus.write_chemdner_results(model_name, outfile)
output = ""
for l in lines:
output += ' '.join(l) + " "
else: # default should be json
results_dic = results.corpus.documents["d0"].get_dic(model_name)
results_dic["corpusfile"] = results_id
output = json.dumps(results_dic)
return output
def main():
start_time = time.time()
parser = argparse.ArgumentParser(description='')
parser.add_argument("actions", default="classify", help="Actions to be performed.",
choices=["load_corpus", "annotate", "classify", "write_results", "write_goldstandard",
"train", "test", "train_multiple", "test_multiple", "train_matcher", "test_matcher",
"crossvalidation", "train_relations", "test_relations", "load_genia", "load_biomodel",
"merge_corpus"])
parser.add_argument("--goldstd", default="", dest="goldstd", nargs="+",
help="Gold standard to be used. Will override corpus, annotations",
choices=paths.keys())
parser.add_argument("--submodels", default="", nargs='+', help="sub types of classifiers"),
parser.add_argument("-i", "--input", dest="input", action="store",
default='''Administration of a higher dose of indinavir should be \\
considered when coadministering with megestrol acetate.''',
help="Text to classify.")
parser.add_argument("--corpus", dest="corpus", nargs=2,
default=["chemdner", "CHEMDNER/CHEMDNER_SAMPLE_JUNE25/chemdner_sample_abstracts.txt"],
help="format path")
parser.add_argument("--annotations", dest="annotations")
parser.add_argument("--tag", dest="tag", default="0", help="Tag to identify the experiment")
parser.add_argument("--models", dest="models", help="model destination path, without extension")
parser.add_argument("--entitytype", dest="etype", help="type of entities to be considered", default="all")
parser.add_argument("--pairtype", dest="ptype", help="type of pairs to be considered", default="all")
parser.add_argument("--doctype", dest="doctype", help="type of document to be considered", default="all")
parser.add_argument("--annotated", action="store_true", default=False, dest="annotated",
help="True if the input has <entity> tags.")
parser.add_argument("-o", "--output", "--format", dest="output",
nargs=2, help="format path; output formats: xml, html, tsv, text, chemdner.")
parser.add_argument("--crf", dest="crf", help="CRF implementation", default="stanford",
choices=["stanford", "crfsuite", "banner"])
parser.add_argument("--log", action="store", dest="loglevel", default="WARNING", help="Log level")
parser.add_argument("--kernel", action="store", dest="kernel", default="svmtk", help="Kernel for relation extraction")
options = parser.parse_args()
# set logger
numeric_level = getattr(logging, options.loglevel.upper(), None)
if not isinstance(numeric_level, int):
raise ValueError('Invalid log level: %s' % options.loglevel)
while len(logging.root.handlers) > 0:
logging.root.removeHandler(logging.root.handlers[-1])
logging_format = '%(asctime)s %(levelname)s %(filename)s:%(lineno)s:%(funcName)s %(message)s'
logging.basicConfig(level=numeric_level, format=logging_format)
logging.getLogger().setLevel(numeric_level)
logging.getLogger("requests.packages").setLevel(30)
logging.info("Processing action {0} on {1}".format(options.actions, options.goldstd))
# set configuration variables based on the goldstd option if the corpus has a gold standard,
# or on corpus and annotation options
# pre-processing options
if options.actions == "load_corpus":
if len(options.goldstd) > 1:
print "load only one corpus each time"
sys.exit()
options.goldstd = options.goldstd[0]
corpus_format = paths[options.goldstd]["format"]
corpus_path = paths[options.goldstd]["text"]
corpus_ann = paths[options.goldstd]["annotations"]
corenlp_client = StanfordCoreNLP('http://localhost:9000')
corpus = load_corpus(options.goldstd, corpus_path, corpus_format, corenlp_client)
#corpus.load_genia() #TODO optional genia
corpus.save(paths[options.goldstd]["corpus"])
if corpus_ann: #add annotation if it is not a test set
corpus.load_annotations(corpus_ann, options.etype, options.ptype)
corpus.save(paths[options.goldstd]["corpus"])
elif options.actions == "load_genia":
options.goldstd = options.goldstd[0]
corpus_path = paths[options.goldstd]["corpus"]
corpus_ann = paths[options.goldstd]["annotations"]
logging.info("loading corpus %s" % corpus_path)
corpus = pickle.load(open(corpus_path, 'rb'))
corpus.load_genia()
corpus.save(paths[options.goldstd]["corpus"])
elif options.actions == "load_biomodel":
options.goldstd = options.goldstd[0]
corpus_path = paths[options.goldstd]["corpus"]
corpus_ann = paths[options.goldstd]["annotations"]
logging.info("loading corpus %s" % corpus_path)
corpus = pickle.load(open(corpus_path, 'rb'))
corpus.load_biomodel()
corpus.save(paths[options.goldstd]["corpus"])
elif options.actions == "annotate": # rext-add annotation to corpus
if len(options.goldstd) > 1:
print "load only one corpus each time"
sys.exit()
options.goldstd = options.goldstd[0]
corpus_path = paths[options.goldstd]["corpus"]
corpus_ann = paths[options.goldstd]["annotations"]
logging.info("loading corpus %s" % corpus_path)
corpus = pickle.load(open(corpus_path, 'rb'))
logging.debug("loading annotations...")
corpus.clear_annotations(options.etype)
corpus.load_annotations(corpus_ann, options.etype, options.ptype)
# corpus.get_invalid_sentences()
corpus.save(paths[options.goldstd]["corpus"])
else:
corpus = Corpus("corpus/" + "&".join(options.goldstd))
for g in options.goldstd:
corpus_path = paths[g]["corpus"]
logging.info("loading corpus %s" % corpus_path)
this_corpus = pickle.load(open(corpus_path, 'rb'))
corpus.documents.update(this_corpus.documents)
if options.actions == "write_goldstandard":
model = BiasModel(options.output[1])
model.load_data(corpus, [])
results = model.test()
#results = ResultsNER(options.output[1])
#results.get_ner_results(corpus, model)
results.save(options.output[1] + ".pickle")
#logging.info("saved gold standard results to " + options.output[1] + ".txt")
elif options.actions == "merge_corpus":
corpus.save(paths[options.output[1]]["corpus"])
# training
elif options.actions == "train":
if options.crf == "stanford":
model = StanfordNERModel(options.models, options.etype)
elif options.crf == "crfsuite":
model = CrfSuiteModel(options.models, options.etype)
model.load_data(corpus, feature_extractors.keys(), options.etype)
model.train()
elif options.actions == "train_matcher": # Train a simple classifier based on string matching
model = MatcherModel(options.models)
model.train(corpus)
# TODO: term list option
#model.train("TermList.txt")
elif options.actions == "train_multiple": # Train one classifier for each type of entity in this corpus
# logging.info(corpus.subtypes)
models = TaggerCollection(basepath=options.models, corpus=corpus, subtypes=corpus.subtypes)
models.train_types()
elif options.actions == "train_relations":
if options.kernel == "jsre":
model = JSREKernel(corpus, options.ptype, modelname=options.tag)
elif options.kernel == "svmtk":
model = SVMTKernel(corpus, options.ptype, modelname=options.tag)
#elif options.kernel == "stanfordre":
# model = StanfordRE(corpus, options.ptype)
#elif options.kernel == "multir":
# model = MultiR(corpus, options.ptype)
#elif options.kernel == "scikit":
# model = ScikitRE(corpus, options.ptype)
#elif options.kernel == "crf":
# model = CrfSuiteRE(corpus, options.ptype)
elif options.kernel == "mil":
relations = set()
with open("corpora/transmir/transmir_relations.txt") as rfile:
for l in rfile:
relations.add(tuple(l.strip().split('\t')))
model = MILClassifier(corpus, options.ptype, relations, ner=options.models)
model.train()
# testing
elif options.actions == "test":
base_port = 9191
if len(options.submodels) > 1:
allresults = ResultSetNER(corpus, options.output[1])
for i, submodel in enumerate(options.submodels):
model = StanfordNERModel(options.models + "_" + submodel)
model.load_tagger(base_port + i)
# load data into the model format
model.load_data(corpus, feature_extractors.keys(), mode="test")
# run the classifier on the data
results = model.test(corpus, port=base_port + i)
allresults.add_results(results)
model.kill_process()
# save the results to an object that can be read again, and log files to debug
final_results = allresults.combine_results()
else:
if options.crf == "stanford":
model = StanfordNERModel(options.models, options.etype)
elif options.crf == "crfsuite":
model = CrfSuiteModel(options.models, options.etype)
elif options.crf == "banner":
model = BANNERModel(options.models, options.etype)
model.load_tagger()
model.load_data(corpus, feature_extractors.keys(), mode="test")
final_results = model.test(corpus)
#with codecs.open(options.output[1] + ".txt", 'w', 'utf-8') as outfile:
# lines = final_results.corpus.write_chemdner_results(options.models, outfile)
#final_results.lines = lines
final_results.save(options.output[1] + ".pickle")
elif options.actions == "test_matcher":
if "mirna" in options.models:
model = MirnaMatcher(options.models)
else:
model = MatcherModel(options.models)
results = ResultsNER(options.models)
results.corpus, results.entities = model.test(corpus)
allentities = set()
for e in results.entities:
allentities.add(results.entities[e].text)
with codecs.open(options.output[1] + ".txt", 'w', 'utf-8') as outfile:
outfile.write('\n'.join(allentities))
results.save(options.output[1] + ".pickle")
elif options.actions == "test_multiple":
logging.info("testing with multiple classifiers... {}".format(' '.join(options.submodels)))
allresults = ResultSetNER(corpus, options.output[1])
if len(options.submodels) < 2:
models = TaggerCollection(basepath=options.models)
models.load_models()
results = models.test_types(corpus)
final_results = results.combine_results()
else:
base_port = 9191
for submodel in options.submodels:
models = TaggerCollection(basepath=options.models + "_" + submodel, baseport = base_port)
models.load_models()
results = models.test_types(corpus)
logging.info("combining results...")
submodel_results = results.combine_results()
allresults.add_results(submodel_results)
base_port += len(models.models)
final_results = allresults.combine_results()
logging.info("saving results...")
final_results.save(options.output[1] + ".pickle")
elif options.actions == "test_relations":
if options.kernel == "jsre":
model = JSREKernel(corpus, options.ptype, train=False, modelname=options.tag, ner=options.models)
elif options.kernel == "svmtk":
model = SVMTKernel(corpus, options.ptype, modelname=options.tag, ner=options.models)
elif options.kernel == "rules":
model = RuleClassifier(corpus, options.ptype, ner=options.models)
elif options.kernel == "mirtex_rules":
model = MirtexClassifier(corpus, options.ptype)
elif options.kernel == "stanfordre":
model = StanfordRE(corpus, options.ptype)
elif options.kernel == "scikit":
model = ScikitRE(corpus, options.ptype)
elif options.kernel == "crf":
model = CrfSuiteRE(corpus, options.ptype, test=True)
elif options.kernel == "mil":
relations = set()
with open("corpora/transmir/transmir_relations.txt") as rfile:
for l in rfile:
relations.add(tuple(l.strip().split('\t')))
model = MILClassifier(corpus, options.ptype, relations, test=True, ner=options.models)
model.load_classifier()
model.test()
results = model.get_predictions(corpus)
results.save(options.output[1] + ".pickle")
total_time = time.time() - start_time
logging.info("Total time: %ss" % total_time)
