def process_results(self, corpus): results = ResultsNER(self.path) results.corpus = corpus for isent, sentence in enumerate(self.predicted): results = self.process_sentence(sentence, isent, results) logging.info("found {} entities".format(len(results.entities))) return results
def process_results(self, sentences, corpus): results = ResultsNER(self.path) results.corpus = corpus for isent, sentence_output in enumerate(sentences): sid = self.sids[isent] did = '.'.join(sid.split('.')[:-1]) sentence = results.corpus.documents[did].get_sentence(sid) if sentence is None: print sid print "not found!" print results.corpus.documents['.'.join(sid.split('.')[:-1])] print [s.sid for s in results.corpus.documents['.'.join(sid.split('.')[:-1])].sentences] sys.exit() sentence_results = self.process_sentence(sentence_output, sentence) for eid in sentence_results: results.entities[eid] = sentence_results[eid] logging.info("found {} entities".format(len(results.entities))) return results
def process_results(self, outputfile, corpus): """ Process BANNER results associated with multiple sentences :param outputfile: Path to BANNER output :param corpus: Corpus object containing the sentences :return: Results object """ results = ResultsNER(self.path) results.corpus = corpus with codecs.open(outputfile, 'r', 'utf-8') as ofile: for line in ofile: elements = line.strip().split("\t") sentence = corpus.get_sentence(elements[0]) sentence, new_entity = self.process_entity(elements, sentence) if new_entity: results.entities[new_entity.eid] = new_entity new_entity = None logging.info("found {} entities".format(len(results.entities))) return results
def test(self, corpus): #train_data, labels, offsets = self.generate_data(self.etype, mode="test") pred = self.pipeline.predict(self.train_data) #print pred #results = self.process_results(corpus) results = ResultsNER(self.path) results.corpus = corpus for i, p in enumerate(pred): if p: sentence = corpus.get_sentence(self.offsets[i][0]) eid = sentence.tag_entity(self.offsets[i][1], self.offsets[i][2], self.etype, source=self.path, score=1) results.entities[eid] = sentence.entities.get_entity( eid, self.path) #else: # print self.offsets[i] return results
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 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 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)
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, {}, [])