def process_documents(corpus_path): corpus = Corpus(corpus_path) final_text = [] corenlp_client = StanfordCoreNLP('http://localhost:9000') lcount = 0 starts = set() with codecs.open(corpus_path, 'r', 'utf-8') as docfile: for l in docfile: print lcount if l[:10] in starts: print "repeated abstract:", l[:10] continue lcount += 1 starts.add(l[:10]) values = l.strip().split("\t") pmid = values[0] abs_text = " ".join(values[1:]) newdoc = Document(abs_text, did="PMID" + pmid) newdoc.process_document(corenlp_client) #for sentence in newdoc.sentences: # print [t.text for t in sentence.tokens] newtext = "" newdoc.did = "PMID" + pmid corpus.documents["PMID" + pmid] = newdoc """for s in newdoc.sentences: for t in s.tokens: newtext += t.text + " " final_text.append(newtext)""" # if lcount > 10: # break if lcount % 1000 == 0: corpus.save("{}_{}.pickle".format(corpus_path, str(lcount/1000))) corpus = Corpus(corpus_path) corpus.save("{}_{}.pickle".format(corpus_path, str(lcount / 1000)))
def process_documents(): corpus = Corpus("corpora/Thaliana/pubmed") final_text = [] corenlp_client = StanfordCoreNLP('http://localhost:9000') lcount = 0 starts = set() with codecs.open("corpora/Thaliana/documents.txt", 'r', 'utf-8') as docfile: for l in docfile: print lcount if l[:20] in starts: continue lcount += 1 starts.add(l[:20]) newdoc = Document(l.strip()) newdoc.process_document(corenlp_client) for sentence in newdoc.sentences: print[t.text for t in sentence.tokens] newtext = "" corpus.documents["d" + str(lcount)] = newdoc """for s in newdoc.sentences: for t in s.tokens: newtext += t.text + " " final_text.append(newtext)""" # if lcount > 10: # break if lcount % 1000 == 0: corpus.save( "corpora/Thaliana/thaliana-documents_{}.pickle".format( str(lcount / 1000)))
def process_documents(corpus_path): corpus = Corpus(corpus_path) final_text = [] corenlp_client = StanfordCoreNLP('http://localhost:9000') lcount = 0 starts = set() with codecs.open(corpus_path, 'r', 'utf-8') as docfile: for l in docfile: print lcount if l[:10] in starts: print "repeated abstract:", l[:10] continue lcount += 1 starts.add(l[:10]) values = l.strip().split("\t") pmid = values[0] abs_text = " ".join(values[1:]) newdoc = Document(abs_text, did="PMID" + pmid) newdoc.process_document(corenlp_client) #for sentence in newdoc.sentences: # print [t.text for t in sentence.tokens] newtext = "" newdoc.did = "PMID" + pmid corpus.documents["PMID" + pmid] = newdoc """for s in newdoc.sentences: for t in s.tokens: newtext += t.text + " " final_text.append(newtext)""" # if lcount > 10: # break if lcount % 1000 == 0: corpus.save("{}_{}.pickle".format(corpus_path, str(lcount / 1000))) corpus = Corpus(corpus_path) corpus.save("{}_{}.pickle".format(corpus_path, str(lcount / 1000)))
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", "tuples", "generate_data"]) 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("--entitysubtype", dest="subtype", help="subtype 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", "ensemble"]) 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) #corenlp_process.kill() #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 == "tuples": 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.info("converting to tuples...") corpus.to_tuple() 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')) corpus.name = options.goldstd 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')) #logging.info("adding {} documents".format(len(documents))) # docs = this_corpus.documents docs = dict((k, this_corpus.documents[k]) for k in this_corpus.documents.keys()[:13000]) corpus.documents.update(docs) 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 == "generate_data": corpus_path = paths[options.goldstd[0]]["corpus"] print "writing to " + options.goldstd[0] + "_event_time_contains.txt" with open(options.goldstd[0] + "_event_time_contains.txt", 'w') as datafile: for sentence in corpus.get_sentences("goldstandard"): sentence_entities = [entity for entity in sentence.entities.elist["goldstandard"]] for pair in itertools.combinations(sentence_entities, 2): if pair[0].type == "event" and pair[1].type == "time": pair_label = (pair[1].eid, "temporal") in pair[0].targets between_text = sentence.text[pair[0].start:pair[1].end] datafile.write("{0}\t{1.original_id}\t{1.text}\t{2.original_id}\t{2.text}\t{3}\n".format(pair_label, pair[0], pair[1], between_text)) elif options.actions == "train": if options.crf == "stanford": model = StanfordNERModel(options.models, options.etype) elif options.crf == "crfsuite": model = CrfSuiteModel(options.models, options.etype, subtype=options.subtype) elif options.crf == "ensemble": model = EnsembleModel(options.models, options.etype, goldstd=options.goldstd[0]) features = feature_extractors.keys() if options.etype.startswith("time"): features = time_features elif options.etype.startswith("event"): features = event_features model.load_data(corpus, features, options.etype, subtype=options.subtype) model.train() elif options.actions == "train_matcher": # Train a simple classifier based on string matching model = MatcherModel(options.models, options.etype) model.train_list("temporal_list.txt") # 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 + "_stanford", options.etype) elif options.crf == "crfsuite": model = CrfSuiteModel(options.models + "_crfsuite", options.etype, subtype=options.subtype) elif options.crf == "banner": model = BANNERModel(options.models, options.etype) elif options.crf == "ensemble": model = EnsembleModel(options.models, options.etype, goldstd=options.goldstd[0]) model.load_tagger() features = feature_extractors.keys() if options.etype.startswith("time"): features = time_features elif options.etype.startswith("event"): features = event_features model.load_data(corpus, features, options.etype, mode="test", subtype=options.subtype) 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, options.etype) 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 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)