'': {
'handlers': ['default'],
'level': 'INFO',
'propagate': True
}
}
})
# word embedding
vector_model, vocabulary, inversed_vocabulary = vectorizer.prepare_embedding_vocab('/home/lhchan/disease_normalization/data/pubmed2018_w2v_400D/pubmed2018_w2v_400D.bin', binary = True, limit = 1000000)
pretrained = vectorizer.load_pretrained_word_embeddings(vocabulary, vector_model)
# MEDIC dictionary
dictionary = load.Terminology()
# dictionary of entries, key = canonical id, value = named tuple in the form of
# MEDIC_ENTRY(DiseaseID='MESH:D005671', DiseaseName='Fused Teeth',
# AllDiseaseIDs=('MESH:D005671',), AllNames=('Fused Teeth', 'Teeth, Fused')
dictionary.loaded = load.load(config['terminology']['dict_file'],'MEDIC')
def concept_obj(conf,dictionary,order=None):
concept_ids = [] # list of all concept ids
concept_all_ids = [] # list of (lists of all concept ids with alt IDs)
concept_names = [] # list of all names, same length as concept_ids
concept_map = {} # names as keys, ids as concepts
if order:
use = order
logger.info('Re-initialing concept object.')
else:
def emb_baseline(emb_path):
#vector_model, vocabulary, inversed_vocabulary = prepare_embedding_vocab('/home/lenz/disease-normalization/data/embeddings/wvec_200_win-30_chiu-et-al.bin')
vector_model, vocabulary, inversed_vocabulary = prepare_embedding_vocab(emb_path, binary = True)
pretrained = load_pretrained_word_embeddings(vocabulary, vector_model)
# MEDIC dictionary
dictionary = load.Terminology()
# dictionary of entries, key = canonical id, value = named tuple in the form of
# MEDIC_ENTRY(DiseaseID='MESH:D005671', DiseaseName='Fused Teeth',
# AllDiseaseIDs=('MESH:D005671',), AllNames=('Fused Teeth', 'Teeth, Fused')
dictionary.loaded = load.load(config['terminology']['dict_file'],'MEDIC')
import vectorizer
dictionary.no_cangen_tokenized = vectorizer.MEDIC_dict_tokenizer_no_cangen(dictionary.loaded,config['methods']['tokenizer'])
dictionary.no_cangen_vectorized = vectorizer.MEDIC_dict_vectorizer_no_cangen(dictionary.no_cangen_tokenized,vocabulary)
# concepts
concept_ids = [] # list of all concept ids
concept_all_ids = [] # list of (lists of all concept ids with alt IDs)
concept_names = [] # list of all names, same length as concept_ids
concept_map = {} # names as keys, ids as concepts
for k in dictionary.loaded.keys(): # keys should be in congruent order
c_id = dictionary.loaded[k].DiseaseID
a_ids = dictionary.loaded[k].AllDiseaseIDs
if int(config['settings']['all_names']):
for n in dictionary.loaded[k].AllNames:
concept_ids.append(c_id)
concept_all_ids.append(a_ids)
concept_names.append(n)
if n in concept_map: # one name corresponds to multiple concepts
concept_map[n].append(c_id)
# logger.warning('{0} already in the dictionary with id {1}'.format(n,concept_map[n]))
else:
concept_map[n] = [c_id]
else:
for n in dictionary.loaded[k].DiseaseName:
concept_ids.append(c_id)
concept_all_ids.append(a_ids)
concept_names.append(n)
if n in concept_map: # one name corresponds to multiple concepts
concept_map[n].append(c_id)
# logger.warning('{0} already in the dictionary with id {1}'.format(n,concept_map[n]))
else:
concept_map[n] = [c_id]
# save the stuff to object
concept = sample.NewDataSet('concepts')
concept.ids = concept_ids
concept.all_ids = concept_all_ids
concept.names = concept_names
concept.map = concept_map
#concept_vectorize = np.array([dictionary.no_cangen_vectorized[k] for k in concept.ids])
# corpus
#corpus_train = sample.NewDataSet('training corpus')
#corpus_train.objects = load.load(config['corpus']['training_file'],'NCBI')
corpus_dev = sample.NewDataSet('dev corpus')
corpus_dev.objects = load.load(config['corpus']['development_file'],'NCBI')
#corpus_test = sample.NewDataSet('test corpus')
#corpus_test.objects = load.load('/home/lhchan/disease_normalization/data/NCBItestset_corpus.txt','NCBI')
#corpus_dev=corpus_test
for corpus in [corpus_dev]:
mention_ids = [] # list of all ids (gold standard for each mention)
mention_names = [] # list of all names
mention_all = [] # list of tuples (mention_text,gold,context,(start,end,docid))
#sth wrong here that sometimes throw an error
#import pdb;pdb.set_trace()
for abstract in corpus.objects:
for section in abstract.sections: # title and abstract
for mention in section.mentions:
nor_ids = [sample._nor_id(one_id) for one_id in mention.id]
mention_ids.append(nor_ids) # append list of ids, usually len(list)=1
mention_names.append(mention.text)
mention_all.append((mention.text,nor_ids,section.text,(mention.start,mention.end,abstract.docid)))
# tokenization & vectorization of mentions
#mention_tokenize = [nltk.word_tokenize(name) for name in mention_names]
#mention_vectorize = np.array([[vocabulary.get(text,1) for text in mention] for mention in mention_tokenize])
# mention_elmo = elmo_default([mention_names])
corpus.ids = mention_ids
corpus.names = mention_names
corpus.all = mention_all
# corpus.tokenize = mention_tokenize
# corpus.vectorize = mention_vectorize
# corpus.elmo = mention_elmo
# vector representations
import nltk
mention_embeddings = []
for mention in corpus.names:
tokenized = nltk.word_tokenize(mention.lower())
index = [vocabulary.get(token,1) for token in tokenized]
#emb = np.mean(np.array([pretrained[i] for i in index]), axis=0)
emb = np.sum(np.array([pretrained[i] for i in index]), axis=0)
mention_embeddings.append(emb)
mention_embeddings = np.array(mention_embeddings)
concept_embeddings = []
for mention in concept.names:
tokenized = nltk.word_tokenize(mention.lower())
index = [vocabulary.get(token,1) for token in tokenized]
#emb = np.mean(np.array([pretrained[i] for i in index]), axis=0)
emb = np.sum(np.array([pretrained[i] for i in index]), axis=0)
concept_embeddings.append(emb)
concept_embeddings = np.array(concept_embeddings)
'''
from vectorizer_elmo import elmo_default
# chunk the concepts down since the list is too big
concept_chunk = [concept.names[i:i + 5000] for i in range(0, len(concept.names), 5000)]
concept.elmo = []
for chunk in concept_chunk:
[elmo_chunk] = [c for c in elmo_default([chunk])]
concept.elmo.append(elmo_chunk)
[concept.elmo] = [chunk for chunk in elmo_default([concept_chunk])]
#with open('gitig_concept_elmo.pickle','wb') as f:
# pickle.dump(concept.elmo,f,protocol=4)
#concept.elmo = pickle.load(open('gitig_concept_elmo.pickle','rb'))
concept.elmo = np.array([item for sublist in concept.elmo for item in sublist])
[corpus_dev.elmo] = [chunk for chunk in elmo_default([corpus_dev.names])]
'''
concept_emb = concept_embeddings #concept.elmo
mention_emb = mention_embeddings #corpus_dev.elmo
from sklearn.preprocessing import normalize
nor_concepts = normalize(concept_emb)
nor_corpus_dev = normalize(mention_emb)
dot_product_matrix = np.dot(nor_corpus_dev,np.transpose(nor_concepts))
prediction_indices = np.argmax(dot_product_matrix,axis=1)
predictions = np.array(concept.ids)[prediction_indices].tolist()
correct = 0
#incorrect = 0
#incorrect_indices = []
for prediction, mention_gold in zip(predictions,corpus_dev.ids):
if prediction == mention_gold[0] and len(mention_gold)==1:
correct += 1
print('Accuracy:{0}'.format(correct/len(corpus_dev.names)))
