def run_RF(X, test_x, labels_x, f, REPORT, out_dir, dic_dir):
np.random.seed(500)
if REPORT == 'yes':
n_folds = 1
if REPORT == 'no':
n_folds = 10
VALIDATION_SPLIT = 0.2 # Validation %
l_precision, l_recall, l_f1 = [], [], []
for n_ in range(n_folds):
print('Running fold ' + str(n_))
Encoder = LabelEncoder()
labels = Encoder.fit_transform(labels_x)
if f == 'TF-IDF':
vect = TfidfVectorizer(max_features=1000)
if f == 'BoW':
vect = CountVectorizer(max_features=1000)
vect.fit(X)
Train_X_Tfidf = vect.transform(X)
features_train = build_features(dic_dir, X)
train = hstack((Train_X_Tfidf, features_train))
if REPORT == 'yes':
Test_X_Tfidf = vect.transform(test_x)
features_test = build_features(dic_dir, test_x)
test = hstack((Test_X_Tfidf, features_test))
RF = RandomForestClassifier(random_state=0, n_estimators=100)
if not os.path.exists(out_dir + '/models'):
os.makedirs(out_dir + '/models')
if REPORT == 'yes':
RF.fit(train,labels)
y_pred = RF.predict(test)
write_list(y_pred, out_dir + '/models/re_predictions.txt', iterate=True, encoding=encoding)
if REPORT == 'no':
train, test, labels, test_labels = train_test_split(train, labels, test_size=VALIDATION_SPLIT, random_state=42)
RF.fit(train,labels)
y_pred = RF.predict(test)
l_precision.append(precision_score(y_true=test_labels, y_pred=y_pred, average='macro'))
l_recall.append(recall_score(y_true=test_labels, y_pred=y_pred, average='macro'))
l_f1.append(f1_score(y_true=test_labels, y_pred=y_pred, average='macro'))
if REPORT == 'no':
l_results = 're_RF' + '\t' + str(np.mean(l_precision)) + '\t' + str(np.mean(l_recall)) + '\t' + str(np.mean(l_f1))
print(l_results)
positive.append(j.split('.')[0])
if str(j.split('.')[0]) in positive_abstracts:
c.append(j)
if str(j.split('.')[0]) not in positive_abstracts:
d.append(j)
c, d = list(set(c)), list(set(d))
c_tp, c_fp = len(c), len(d)
precision = c_tp / (c_tp + c_fp)
recall = c_tp / (c_tp + (len(positive_abstracts) - c_tp))
f1 = 2 * recall * precision / (recall + precision)
print(predictions + ': PRECISION = ' + str(precision) + ', RECALL = ' +
str(recall) + ', F1-SCORE = ' + str(f1))
write_list(list(set(positive)), args.i2 + '/triage_pmids.output', True,
'latin-1')
if task == 're':
labels = read_as_list(predictions + '.txt', encoding='latin-1')
map_idx = {}
map_idx['0'], map_idx['1'], map_idx['2'], map_idx[
'3'] = 'activation', 'none', 'repression', 'undefined'
labels = [map_idx[x] for x in labels]
export, final = [], []
df = pd.read_csv(args.i2 + '/re_test.csv')
values = df.values.tolist()
for l, label in zip(values, labels):
def build_data(l_texts, l_ann, type_data, f2, option, out_file):
''' Exports data. '''
nlp = spacy.load('en')
original, all_sentences, tags, l_gene1, l_gene2, l_pmids = [], [], [], [], [], []
def find_s_e(e, tag):
word = e.split('\t')[2]
b = e.split('\t')[1].split(' ')[1]
e = e.split('\t')[1].split(' ')[2]
return int(b), int(e), str(word)
for i, a in enumerate(l_ann):
if len(a.split('.')[0].split(':')) == 2:
sentence_index = a.split('.')[0].split(':')[1]
else:
sentence_index = 'None'
already = []
try:
ann = read_as_list(type_data + '/' + a, encoding=encoding)
txt = read_as_list(type_data + '/' + a.split('.')[0] + '.txt',
encoding=encoding)
txt = ''.join(txt)
relations = [x for x in ann if x[0] == 'R']
if type_data == 'train':
entities = [
x for x in ann if x[0] == 'T'
and x.split('\t')[1][0:14] != 'AnnotatorNotes'
]
else:
entities = ann
n_dbtfs = [
x for x in entities if x.split('\t')[1].split(' ')[0] == 'DBTF'
]
# If there is at least one DBTF and at least two entities...
if len(n_dbtfs) > 0 and len(entities) > 1:
# Build positive sentences!
if relations:
for r in relations:
tag = r.split('\t')[1].split(' ')[0]
ent1, ent2 = r.split('\t')[1].split(' ')[1].split(':')[
1], r.split('\t')[1].split(' ')[2].split(':')[1]
for e in entities:
if e.split('\t')[0] == ent1 and e.split(
'\t')[1][0:14] != 'AnnotatorNotes':
b1, e1, word1 = find_s_e(e, ent1)
if e.split('\t')[0] == ent2 and e.split(
'\t')[1][0:14] != 'AnnotatorNotes':
b2, e2, word2 = find_s_e(e, ent2)
invert = False
if b1 > b2:
invert = True
if not invert:
out = txt[:b1] + 'gene1' + txt[
e1:b2] + 'gene2' + txt[e2:]
if invert:
out = txt[:b2] + 'gene2' + txt[
e2:b1] + 'gene1' + txt[e1:]
s_ = nltk.sent_tokenize(out)
sentence = []
for i, s in enumerate(s_):
if "gene1" in s and "gene2" in s:
sentence = s
idx = i
if sentence:
sentence = sentence.replace('_', ' ')
sentence = sentence.replace('-', ' ')
for e in entities:
if e.split('\t')[0][0] == 'T' and e.split(
'\t')[1][0:14] != 'AnnotatorNotes':
w = e.split('\t')[2].replace('_', ' ')
w = ' '.join(w.replace('-', ' ').split())
if w:
sentence = sentence.replace(w, 'genex')
all_entities = []
all_entities.append('DBTF')
sentence, all_entities = find_experimental_methods(
f2, sentence, all_entities)
out1 = preprocess_text(sentence, nlp, all_entities)
if 'gene1' in out1 and 'gene2' in out1 and out1 not in all_sentences:
all_sentences.append(out1)
tags.append(tag.lower())
if sentence_index == 'None':
export = ' '.join(
nltk.sent_tokenize(txt)[int(
idx)].replace('_', ' ').replace(
'-', ' ').split())
original_ = out1[0:4] + export
original.append(original_)
ID = a.split('.')[0] + ':' + str(idx)
else:
txt = ' '.join(
txt.replace('_',
' ').replace('-',
' ').split())
original_ = out1[0:4] + txt
original.append(original_)
ID = a.split('.')[0]
word1, word2 = word1.replace(
'_', ' '), word2.replace('_', ' ')
word1, word2 = ' '.join(
word1.replace('-', ' ').split()), ' '.join(
word2.replace('-', ' ').split())
l_gene1.append(word1)
l_gene2.append(word2)
l_pmids.append(ID)
already.append(word1)
already.append(word2)
# Build negative sentences!
non_relations = []
for e in entities:
if e[0] == 'T' and e.split(
'\t')[1][0:14] != 'AnnotatorNotes':
if ' '.join(
e.split('\t')[2].replace('_', ' ').replace(
'-', ' ').split()) not in already:
non_relations.append(e)
DBTF = [
x for x in non_relations
if x.split('\t')[1].split(' ')[0] == 'DBTF'
]
combinations = [(x, y) for x in DBTF for y in non_relations
if y.split('\t')[1].split(' ')[0] == 'DBTF'
or y.split('\t')[1].split(' ')[0] == 'NONDBTF']
if combinations:
for d in combinations:
# Combine all DBTF with every possible other entity different from it. No self-regulation!
b1, e1 = int(d[0].split('\t')[1].split(' ')[1]), int(
d[0].split('\t')[1].split(' ')[2])
word1 = ' '.join(d[0].split('\t')[2].replace(
'_', ' ').replace('-', ' ').split())
b2, e2 = int(d[1].split('\t')[1].split(' ')[1]), int(
d[1].split('\t')[1].split(' ')[2])
word2 = ' '.join(d[1].split('\t')[2].replace(
'_', ' ').replace('-', ' ').split())
now = time.time()
if b1 != b2 and word1 != word2:
invert = False
if b1 > b2:
invert = True
if not invert:
out = txt[:b1] + 'gene1' + txt[
e1:b2] + 'gene2' + txt[e2:]
if invert:
out = txt[:b2] + 'gene2' + txt[
e2:b1] + 'gene1' + txt[e1:]
s_ = nltk.sent_tokenize(out)
sentence = []
for i, s in enumerate(s_):
if "gene1" in s and "gene2" in s:
sentence = s
idx = i
if sentence:
sentence = sentence.replace('_', ' ')
sentence = sentence.replace('-', ' ')
for e in entities:
if e.split('\t')[0][0] == 'T' and e.split(
'\t')[1][0:14] != 'AnnotatorNotes':
w = e.split('\t')[2].replace('_', ' ')
w = ' '.join(
w.replace('-', ' ').split())
sentence = sentence.replace(w, 'genex')
all_entities = []
all_entities.append('DBTF')
sentence, all_entities = find_experimental_methods(
f2, sentence, all_entities)
out1 = preprocess_text(sentence, nlp,
all_entities)
if 'gene1' in out1 and 'gene2' in out1 and out1 not in all_sentences:
all_sentences.append(out1)
tags.append('none')
if sentence_index == 'None':
export = ' '.join(
nltk.sent_tokenize(txt)[int(
idx)].replace('_',
' ').replace(
'-',
' ').split())
original_ = out1[0:4] + export
original.append(original_)
ID = a.split('.')[0] + ':' + str(idx)
else:
txt = ' '.join(
txt.replace('_', ' ').replace(
'-', ' ').split())
original_ = out1[0:4] + txt
original.append(original_)
ID = a.split('.')[0]
l_gene1.append(word1)
l_gene2.append(word2)
l_pmids.append(ID)
except Exception as e:
continue
df = pd.DataFrame()
df['all_sentences'] = all_sentences
df['tags'] = tags
df['original'] = original
df['l_gene1'] = l_gene1
df['l_gene2'] = l_gene2
df['l_pmids'] = l_pmids
if option == 'test':
df.to_csv(out_file + '/re_test.csv', index=False)
write_list(tags,
out_file + '/re_' + option + '_labels.txt',
iterate=True,
encoding=encoding)
write_list(original,
out_file + '/re_' + option + '_original.txt',
iterate=True,
encoding=encoding)
write_list(all_sentences,
out_file + '/re_' + option + '_preprocessed.txt',
iterate=True,
encoding=encoding)
def merge(f, f2, f3, f_out):
l_ntnu = [f for f in listdir(f) if f.endswith('.minfner')]
l_gnormplus = [f for f in listdir(f2) if f.endswith('.minfner')]
l_text = [f for f in listdir(f3) if f.endswith('.txt')]
for text in l_text:
rl, write_out, already, final_merge = [], [], [], []
tx = read_as_list(f3 + '/' + text, encoding=encoding)
ann = text + '.out.minfner'
ntnu_boolean, gn_boolean = False, False
if ann in l_ntnu:
ntnu = read_as_list(f + '/' + ann, encoding=encoding)
ntnu = ['N_' + s for s in ntnu]
ntnu_boolean = True
ann = text.split('.')[0] + ':0.txt.out.minfner'
if ann in l_gnormplus:
gn = read_as_list(f2 + '/' + ann, encoding=encoding)
gn = ['G_' + s for s in gn]
gn_boolean = True
# Merge both tools and keep only entities
if ntnu_boolean and gn_boolean:
entities = ntnu + gn
elif ntnu_boolean and not gn_boolean:
entities = ntnu
elif not ntnu_boolean and gn_boolean:
entities = gn
else:
entities = False
# Keep all N_DBTF
final_merge += [
x for x in entities
if x[0] == 'N' and x.split('\t')[1].split(' ')[0] == 'DBTF'
]
already = [(x.split('\t')[1].split(' ')[1],
x.split('\t')[1].split(' ')[2]) for x in final_merge
if x[2] == 'T']
# Keep all G_NONDBTF
final_merge += [
x for x in entities
if x[0] == 'G' and x.split('\t')[1].split(' ')[0] == 'NONDBTF' and
(x.split('\t')[1].split(' ')[1],
x.split('\t')[1].split(' ')[2]) not in already
]
already = [(x.split('\t')[1].split(' ')[1],
x.split('\t')[1].split(' ')[2]) for x in final_merge
if x[2] == 'T']
# Keep all N_NONDBTF
final_merge += [
x for x in entities
if x[0] == 'N' and x.split('\t')[1].split(' ')[0] == 'NONDBTF' and
(x.split('\t')[1].split(' ')[1],
x.split('\t')[1].split(' ')[2]) not in already
]
already = [(x.split('\t')[1].split(' ')[1],
x.split('\t')[1].split(' ')[2]) for x in final_merge
if x[2] == 'T']
# Keep all G_DBTF
final_merge += [
x for x in entities
if x[0] == 'G' and x.split('\t')[1].split(' ')[0] == 'DBTF' and (
x.split('\t')[1].split(' ')[1],
x.split('\t')[1].split(' ')[2]) not in already
]
already = [(x.split('\t')[1].split(' ')[1],
x.split('\t')[1].split(' ')[2]) for x in final_merge
if x[2] == 'T']
elements = [x.split('\t')[0][3:] for x in final_merge]
final_merge += [
x for x in entities
if x[0:3] == 'N_#' and x.split('\t')[0][3:] in elements
]
entities = [x[2:] for x in final_merge]
ann_out = text.split('.')[0] + '.ann'
if entities:
for e in entities:
if e[0] == 'T':
e_ = e.split('\t')
entity, tag, start, end, word = e_[0], e_[1].split(' ')[
0], e_[1].split(' ')[1], e_[1].split(' ')[2], e_[2]
write_out.append(
str(entity) + '\t' + tag + ' ' + str(start) + ' ' +
str(end) + '\t' + word)
if e[0] == '#':
e_ = e.split('\t')
entity, ID = e_[0], ' '.join(
e_[1].split(' ')[1:]) + ' ' + e_[2]
write_out.append(
str(entity) + '\t' + 'AnnotatorNotes T' +
str(entity[1:]) + '\t' + str(ID))
if write_out:
write_list(write_out,
f_out + '/' + ann_out,
iterate=True,
encoding=encoding)
write_list(tx, f_out + '/' + text, iterate=True, encoding=encoding)
def run_RNN(X, test, labels, path_to_glove, REPORT, out_folder):
''' Recurrent Neural Networks with Attention enabled '''
# Hyper-parameters of the model
MAX_SEQUENCE_LENGTH = 100
MAX_NB_WORDS = 500
LSTM_DIM = 100
# Categorize target labels !
n_labels = len(set(labels))
values = array(labels)
label_encoder = LabelEncoder()
integer_encoded = label_encoder.fit_transform(values)
onehot_encoder = OneHotEncoder(sparse=False, categories='auto')
integer_encoded = integer_encoded.reshape(len(integer_encoded), 1)
labels = onehot_encoder.fit_transform(integer_encoded)
print(list(label_encoder.inverse_transform([0, 1, 2, 3])))
texts, texts_test = [], []
for idx in X:
text = BeautifulSoup(idx, 'html.parser')
texts.append(str(text.get_text().encode()))
for idx in test:
text = BeautifulSoup(idx, 'html.parser')
texts_test.append(str(text.get_text().encode()))
tokenizer = Tokenizer(num_words=MAX_NB_WORDS)
tokenizer.fit_on_texts(texts)
sequences = tokenizer.texts_to_sequences(texts)
tokenizer_test = Tokenizer(num_words=MAX_NB_WORDS)
tokenizer_test.fit_on_texts(texts)
sequences_test = tokenizer_test.texts_to_sequences(texts_test)
word_index = tokenizer.word_index
word_index_test = tokenizer_test.word_index
data = pad_sequences(sequences, maxlen=MAX_SEQUENCE_LENGTH)
data_test = pad_sequences(sequences_test, maxlen=MAX_SEQUENCE_LENGTH)
# Run Model with Cross-validation
if REPORT == 'yes':
n_folds = 1
if REPORT == 'no':
n_folds = 10
VALIDATION_SPLIT = 0.2
l_precision, l_recall, l_f1, l_val_accuracy = [], [], [], []
for n_ in range(n_folds):
print('Running fold ' + str(n_))
# Shuffle data
indices = np.arange(data.shape[0])
np.random.shuffle(indices)
data = data[indices]
labels = labels[indices]
nb_validation_samples = int(VALIDATION_SPLIT * data.shape[0])
# Split data
if REPORT == 'yes':
x_train = data
y_train = labels
x_val = data
y_val = labels
if REPORT == 'no':
x_train = data[:-nb_validation_samples]
y_train = labels[:-nb_validation_samples]
x_val = data[-nb_validation_samples:]
y_val = labels[-nb_validation_samples:]
# Oversampling of minority label in training set
ada = RandomOverSampler(random_state=42, sampling_strategy='minority')
X_train_resampled, y_train_resampled = ada.fit_sample(x_train, y_train)
# Build GloVe dictionaries
embeddings_index = {}
f = open(path_to_glove, encoding='utf8')
for line in f:
values = line.split()
word = values[0]
coefs = np.asarray(values[1:], dtype='float32')
embeddings_index[word] = coefs
f.close()
embedding_matrix = np.random.random((len(word_index) + 1, MAX_SEQUENCE_LENGTH))
for word, i in word_index.items():
embedding_vector = embeddings_index.get(word)
if embedding_vector is not None:
# words not found in embedding index will be all-zeros.
embedding_matrix[i] = embedding_vector
embedding_layer = Embedding(len(word_index) + 1,
MAX_SEQUENCE_LENGTH,
weights=[embedding_matrix],
input_length=MAX_SEQUENCE_LENGTH,
trainable=True)
sequence_input = Input(shape=(MAX_SEQUENCE_LENGTH,), dtype='int32')
embedded_sequences = embedding_layer(sequence_input)
model_type = out_folder + "/Bidirectional LSTM with Attention"
out = Bidirectional(LSTM(LSTM_DIM, return_sequences=True, dropout=0.30, recurrent_dropout=0.30))(embedded_sequences)
out = Attention(MAX_SEQUENCE_LENGTH)(out)
out = Dense(LSTM_DIM, activation="relu")(out)
out = Dropout(0.30)(out)
out = Dense(n_labels, activation="softmax")(out)
model = Model(sequence_input, out)
# Save model architecture
if not os.path.isfile(model_type + '.png'):
plot_model(model, to_file=model_type+'.png', show_shapes=True, show_layer_names=True)
# Model optimizer and metrics
opt = Adam(lr=0.001, beta_1=0.9, beta_2=0.999, epsilon=None, decay=0.0, amsgrad=False)
model.compile(loss='categorical_crossentropy', optimizer=opt, metrics=['accuracy'])
# Model parameters
if not os.path.exists(out_folder + '/models'):
os.makedirs(out_folder + '/models')
early_stopping = EarlyStopping(monitor = 'val_loss', patience = 5, verbose = 0, mode= 'min')
model_filepath_weights = out_folder + '/models/triage_' + path_to_glove.split('/')[2].split('.')[0] + '_' + str(n_) + '.h5'
model_filepath_json = out_folder + '/models/triage_' + path_to_glove.split('/')[2].split('.')[0] + '_' + str(n_) + '.json'
checkpoint = ModelCheckpoint(model_filepath_weights, monitor='val_acc', verbose = 0, save_best_only=True, mode='max')
callbacks_list = [early_stopping, checkpoint]
history = model.fit(X_train_resampled, y_train_resampled, validation_data=(x_val, y_val), epochs=100, callbacks=callbacks_list, verbose = 0)
# Predictions
if REPORT == 'yes':
y_pred = model.predict(data_test)
y_pred = y_pred.argmax(axis=-1)
y_pred = [str(x) for x in y_pred]
write_list(y_pred, out_folder + '/models/re_predictions.txt', iterate=True, encoding=encoding)
# serialize model to JSON
model_json = model.to_json()
with open(model_filepath_json, "w") as json_file:
json_file.write(model_json)
# serialize weights to HDF5
keras.models.save_model(model, model_filepath_weights)
print("Saved model to disk")
if REPORT == 'no':
y_pred = model.predict(x_val)
y_pred = y_pred.argmax(axis=-1)
y_val = y_val.argmax(axis=-1)
y_pred = [str(x) for x in y_pred]
y_val = [str(x) for x in y_val]
# Results
l_precision.append(precision_score(y_true=y_val, y_pred=y_pred, average='macro'))
l_recall.append(recall_score(y_true=y_val, y_pred=y_pred, average='macro'))
l_f1.append(f1_score(y_true=y_val, y_pred=y_pred, average='macro'))
l_val_accuracy.append(history.history['val_acc'][-1])
if REPORT == 'no':
l_results = 're_RNN' + '\t' + str(np.mean(l_precision)) + '\t' + str(np.mean(l_recall)) + '\t' + str(np.mean(l_f1))
print(l_results)
return out, out_original, labels, list_pmids
if '__main__' == __name__:
''' Exports triage data. '''
encoding = "latin-1"
nltk.download('punkt')
nltk.download('stopwords')
parser = argparse.ArgumentParser(description='Options')
parser.add_argument('--i1', type=str, help="""Folder with abstracts and annotations.""")
parser.add_argument('--i2', type=str, help="""Data folder.""")
parser.add_argument('--i3', type=str, help="""Data folder.""")
parser.add_argument('--option', type=str, help="""Train or test.""")
parser.add_argument('--o', type=str, help="""Output folder.""")
args = parser.parse_args()
pubtator_articles = [f for f in listdir(args.i1) if f.endswith('.txt')]
# Replace words by entities!
data, data_original, labels, list_pmids = build_data(args.i1, args.i2, args.i3, pubtator_articles, args.option)
if args.option == 'test':
write_list(list_pmids, args.o + '/triage_test_pmids.txt', iterate=True, encoding=encoding)
write_list(data, args.o + '/triage_' + args.option + '_preprocessed.txt', iterate=True, encoding=encoding)
write_list(data_original, args.o + '/triage_' + args.option + '_original.txt', iterate=True, encoding=encoding)
write_list(labels, args.o + '/triage_' + args.option + '_labels.txt', iterate=True, encoding=encoding)
import argparse
from os import listdir
from export_abstracts import read_as_list, write_list
if '__main__' == __name__:
''' Removes wrong genes. '''
parser = argparse.ArgumentParser(description='')
parser.add_argument('--i1', type=str, help="""Data folder.""")
parser.add_argument('--i2', type=str, help="""Data folder.""")
args = parser.parse_args()
to_avoid = read_as_list(args.i1 + '/to_avoid.txt', encoding='latin-1')
l_ann = [f for f in listdir(args.i2) if f.endswith('.ann')]
for a in l_ann:
ann = read_as_list(args.i2 + '/' + a, encoding='latin-1')
final = []
for p in ann:
if p[0] == 'T':
if p.split('\t')[2].replace(' ', '') not in to_avoid:
final.append(p)
entities = [x.split('\t')[0][1:] for x in final]
final += [
x for x in ann if x[0] == '#' and x.split('\t')[0][1:] in entities
]
write_list(list(set(final)), args.i2 + '/' + a, True, 'latin-1')
encoding = "latin-1"
parser = argparse.ArgumentParser(description='Options')
parser.add_argument('--i', type=str, help="""Input directory.""")
parser.add_argument('--o', type=str, help="""Output directory.""")
args = parser.parse_args()
ann1 = pd.read_csv(args.i + '/abstracts.all.labeled.csv', sep='\n|\t', encoding=encoding, engine='python')
ann2 = read_as_list(args.i + '/hackaton_1.tsv', encoding=encoding)
ann2 = [x.split('\t') for x in ann2]
ann3 = read_as_list(args.i + '/hackaton_2.tsv', encoding=encoding)
ann3 = [x.split('\t') for x in ann3]
di = {True: 1, False: 0}
# The values 10 and 20 are replaced by 'A' and 'B'
ann1['label'].replace(di, inplace=True)
pmid_1, l_1, pmid_2, l_2, pmid_3, l_3 = list(ann1['pmid']), list(ann1['label']), [x[0] for x in ann2], [x[2] for x in ann2], [x[0] for x in ann3], [x[2] for x in ann3]
df = pd.DataFrame()
df['pmid'] = pmid_1 + pmid_2 + pmid_3
df['label'] = l_1 + l_2 + l_3
df = df.astype({'pmid': int, 'label': int})
df = remove_duplicates(df, 'pmid')
list_PMID = list(df['pmid'])
list_labels = list(df['label'])
write_list(list_PMID, args.o + '/triage_train_pmids.txt', iterate=True, encoding=encoding)
write_list(list_labels, args.o + '/triage_train_labels.txt', iterate=True, encoding=encoding)
