def load_corpus(self, corenlpserver, process=True):
total_lines = sum(1 for line in open(self.path))
widgets = [pb.Percentage(), " ", pb.Bar(), " ", pb.AdaptiveETA(), " ", pb.Timer()]
pbar = pb.ProgressBar(widgets=widgets, maxval=total_lines, redirect_stdout=True).start()
time_per_abs = []
with codecs.open(self.path, "r", "utf-8") as trainfile:
current = 0
for line in trainfile:
# logging.debug('%s:%s/%s', f, current + 1, total)
x = line.strip().split(" ")
did = x[0]
doctext = " ".join(x[1:])
newdoc = Document(doctext, process=False, did=did)
# newdoc.sentence_tokenize("biomedical")
sid = did + ".s0"
newdoc.sentences.append(Sentence(doctext, offset=0, sid=sid, did=did))
if process:
newdoc.process_document(corenlpserver, "biomedical")
self.documents[newdoc.did] = newdoc
# abs_time = time.time() - t
# time_per_abs.append(abs_time)
# logging.info("%s sentences, %ss processing time" % (len(newdoc.sentences), abs_time))
pbar.update(current + 1)
current += 1
pbar.finish()
def load_corpus(self, corenlpserver, process=True):
"""Load the CHEMDNER corpus file on the dir element"""
# open filename and parse lines
total_lines = sum(1 for line in open(self.path))
widgets = [pb.Percentage(), ' ', pb.Bar(), ' ', pb.ETA(), ' ', pb.Timer()]
pbar = pb.ProgressBar(widgets=widgets, maxval=total_lines).start()
n_lines = 1
time_per_abs = []
with io.open(self.path, 'r', encoding="utf-8") as inputfile:
for line in inputfile:
t = time.time()
# each line is PMID title abs
tsv = line.split('\t')
doctext = tsv[1].strip().replace("<", "(").replace(">", ")") + " "
doctext += tsv[2].strip().replace("<", "(").replace(">", ")")
newdoc = Document(doctext, process=False,
did=tsv[0], title=tsv[1].strip() + ".")
newdoc.sentence_tokenize("biomedical")
if process:
newdoc.process_document(corenlpserver, "biomedical")
self.documents[newdoc.did] = newdoc
abs_time = time.time() - t
time_per_abs.append(abs_time)
pbar.update(n_lines)
n_lines += 1
pbar.finish()
abs_avg = sum(time_per_abs)*1.0/len(time_per_abs)
logging.info("average time per abstract: %ss" % abs_avg)
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 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 load_corpus(self, corenlpserver, process=True):
widgets = [
pb.Percentage(), ' ',
pb.Bar(), ' ',
pb.ETA(), ' ',
pb.Timer()
]
nlines = 0
with open(self.path) as f:
for nlines, l in enumerate(f):
pass
print nlines
pbar = pb.ProgressBar(widgets=widgets, maxval=nlines).start()
with codecs.open(self.path, 'r', "utf-8") as corpusfile:
doc_text = ""
sentences = []
for i, l in enumerate(corpusfile):
if l.startswith("###"): # new doc
if doc_text != "":
logging.debug("creating document: {}".format(doc_text))
newdoc = Document(doc_text, process=False, did=did)
newdoc.sentences = sentences[:]
newdoc.process_document(corenlpserver, "biomedical")
# logging.info(len(newdoc.sentences))
self.documents[newdoc.did] = newdoc
doc_text = ""
did = "JNLPBA" + l.strip().split(":")[-1]
logging.debug("starting new document:" + did)
sentence_text = ""
doc_offset = 0
sentences = []
elif l.strip() == "" and sentence_text != "": # new sentence
#logging.debug("creating mew sentence: {}".format(sentence_text))
sid = did + ".s" + str(len(sentences))
this_sentence = Sentence(sentence_text,
offset=doc_offset,
sid=sid,
did=did)
doc_offset += len(sentence_text) + 1
doc_text += sentence_text + " "
sentences.append(this_sentence)
if i == nlines:
logging.debug("creating document: {}".format(doc_text))
newdoc = Document(doc_text, process=False, did=did)
newdoc.sentences = sentences[:]
newdoc.process_document(corenlpserver, "biomedical")
# logging.info(len(newdoc.sentences))
self.documents[newdoc.did] = newdoc
doc_text = ""
# start new sentence
sentence_text = ""
else:
#logging.debug(str(i) + "/" + str(l))
t = l.strip().split("\t")
if sentence_text != "":
sentence_text += " "
#if t[1] == "B-protein"
sentence_text += t[0]
pbar.update(i)
pbar.finish()
def load_corpus(self, corenlpserver, process=True):
# self.path is the base directory of the files of this corpus
trainfiles = [self.path + '/' + f for f in os.listdir(self.path) if f.endswith('.txt')]
total = len(trainfiles)
widgets = [pb.Percentage(), ' ', pb.Bar(), ' ', pb.AdaptiveETA(), ' ', pb.Timer()]
pbar = pb.ProgressBar(widgets=widgets, maxval=total, redirect_stdout=True).start()
time_per_abs = []
for current, f in enumerate(trainfiles):
#logging.debug('%s:%s/%s', f, current + 1, total)
print '{}:{}/{}'.format(f, current + 1, total)
did = f.split(".")[0]
t = time.time()
with open(f, 'r') as txt:
doctext = txt.read()
newdoc = Document(doctext, process=False, did=did)
newdoc.sentence_tokenize("biomedical")
if process:
newdoc.process_document(corenlpserver, "biomedical")
self.documents[newdoc.did] = newdoc
abs_time = time.time() - t
time_per_abs.append(abs_time)
#logging.info("%s sentences, %ss processing time" % (len(newdoc.sentences), abs_time))
pbar.update(current+1)
pbar.finish()
abs_avg = sum(time_per_abs)*1.0/len(time_per_abs)
logging.info("average time per abstract: %ss" % abs_avg)
def load_corpus(self, corenlpserver, process=True):
trainfiles = [self.path + '/' + f for f in os.listdir(self.path)]
total = len(trainfiles)
widgets = [pb.Percentage(), ' ', pb.Bar(), ' ', pb.AdaptiveETA(), ' ', pb.Timer()]
pbar = pb.ProgressBar(widgets=widgets, maxval=total, redirect_stdout=True).start()
time_per_abs = []
for current, f in enumerate(trainfiles):
#logging.debug('%s:%s/%s', f, current + 1, total)
print '{}:{}/{}'.format(f, current + 1, total)
did = f
t = time.time()
with open(f, 'r') as f:
article = "<Article>" + f.read() + "</Article>"
soup = BeautifulSoup(article, 'xml')
#doc = soup.find_all("article")
title = soup.ArticleTitle.get_text()
abstract = soup.AbstractText.get_text()
doc_text = title + " " + abstract
newdoc = Document(doc_text, process=False, did=did)
newdoc.sentence_tokenize("biomedical")
newdoc.process_document(corenlpserver, "biomedical")
#logging.info(len(newdoc.sentences))
self.documents[newdoc.did] = newdoc
abs_time = time.time() - t
time_per_abs.append(abs_time)
logging.debug("%s sentences, %ss processing time" % (len(newdoc.sentences), abs_time))
pbar.update(current)
pbar.finish()
abs_avg = sum(time_per_abs)*1.0/len(time_per_abs)
logging.info("average time per abstract: %ss" % abs_avg)
def load_corpus(self, corenlpserver, process=True):
total_lines = sum(1 for line in open(self.path))
widgets = [
pb.Percentage(), ' ',
pb.Bar(), ' ',
pb.ETA(), ' ',
pb.Timer()
]
pbar = pb.ProgressBar(widgets=widgets,
maxval=total_lines,
redirect_stdout=True).start()
time_per_abs = []
with codecs.open(self.path, 'r', "utf-8") as trainfile:
current = 0
for line in trainfile:
#logging.debug('%s:%s/%s', f, current + 1, total)
x = line.strip().split(" ")
did = x[0]
doctext = " ".join(x[1:])
newdoc = Document(doctext, process=False, did=did)
#newdoc.sentence_tokenize("biomedical")
sid = did + ".s0"
newdoc.sentences.append(
Sentence(doctext, offset=0, sid=sid, did=did))
if process:
newdoc.process_document(corenlpserver, "biomedical")
self.documents[newdoc.did] = newdoc
# abs_time = time.time() - t
# time_per_abs.append(abs_time)
#logging.info("%s sentences, %ss processing time" % (len(newdoc.sentences), abs_time))
pbar.update(current + 1)
current += 1
pbar.finish()
def load_corpus(self, corenlpserver):
# self.path is the base directory of the files of this corpus
# if more than one file:
trainfiles = [self.path + f for f in os.listdir(self.path) if not f.endswith('~')] # opens all files in folder (see config file)
widgets = [pb.Percentage(), ' ', pb.Bar(), ' ', ' ', pb.Timer()]
pbar = pb.ProgressBar(widgets=widgets, maxval=len(trainfiles)).start()
for i, openfile in enumerate(trainfiles):
# print("file: "+openfile)
with open(openfile, 'r') as inputfile:
newdoc = Document(inputfile.read(), process=False, did=os.path.basename(openfile), title = "titulo_"+os.path.basename(openfile))
newdoc.process_document(corenlpserver, "biomedical") #process_document chama o tokenizer
valid = True
invalid_sids = []
for s in newdoc.sentences:
if s.text in ['[start section id="{}"]'.format(section) for section in self.invalid_sections]:
valid = False
if not valid:
invalid_sids.append(s.sid)
if s.text in ['[end section id="{}"]'.format(section) for section in self.invalid_sections]:
valid = True
if (s.text.startswith("[") and s.text.endswith("]")) or s.text.istitle():
newdoc.title_sids.append(s.sid)
newdoc.invalid_sids = invalid_sids
logging.debug("invalid sentences: {}".format(invalid_sids))
logging.debug("title sentences: {}".format(newdoc.title_sids))
self.documents[newdoc.did] = newdoc
pbar.update(i+1)
def load_corpus(self, corenlpserver, process=True):
# self.path is just one file with every document
time_per_abs = []
with open(self.path, 'r') as xml:
t = time.time()
root = ET.fromstring(xml.read())
all_docs = root.findall("document")
widgets = [pb.Percentage(), ' ', pb.Bar(), ' ', pb.AdaptiveETA(), ' ', pb.Timer()]
pbar = pb.ProgressBar(widgets=widgets, maxval=len(all_docs)).start()
for i, doc in enumerate(all_docs):
doctext = ""
did = doc.get('id')
doc_sentences = [] # get the sentences of this document
doc_offset = 0 # offset of the current sentence relative to the document
for sentence in doc.findall('sentence'):
sid = sentence.get('id')
#logging.info(sid)
text = sentence.get('text')
#text = text.replace('\r\n', ' ')
doctext += " " + text # generate the full text of this document
this_sentence = Sentence(text, offset=doc_offset, sid=sid, did=did)
doc_offset = len(doctext)
doc_sentences.append(this_sentence)
newdoc = Document(doctext, process=False, did=did)
newdoc.sentences = doc_sentences[:]
newdoc.process_document(corenlpserver, "biomedical")
self.documents[newdoc.did] = newdoc
abs_time = time.time() - t
time_per_abs.append(abs_time)
pbar.update(i+1)
pbar.finish()
abs_avg = sum(time_per_abs)*1.0/len(time_per_abs)
logging.info("average time per abstract: %ss" % abs_avg)
def load_corpus(self, corenlpserver):
docs = self.get_docs(self.path)
total = len(docs)
current = 0
time_per_abs = []
ts = set()
for f in docs:
logging.debug('%s:%s/%s', f[0], current + 1, total)
current += 1
#parse DDI corpus file
t = time.time()
#print root.tag
docid = f[
0] # TODO: actually each paragraph should be it's own documents, that should help offset issues
doctext = ""
doc_sentences = [] # get the sentences of this document
doc_offset = 0 # offset of the current sentence relative to the document
sents = self.get_paragraphs(f)
for p in sents:
logging.debug("processing {}".format(p[0]))
senttext = p[1].text.replace("\n", " ")
for ne in p[1].findall("ne"):
#doctext += ne.text
senttext += ne.text
if ne.tail:
#doctext += ne.tail
senttext += ne.tail.replace("\n", " ")
#logging.debug(senttext)
#this_sentence = Sentence(senttext, offset=doc_offset, sid=p[0], did=docid)
doctext += senttext + "\n"
doc_offset = len(doctext)
#doc_sentences.append(this_sentence)
#logging.info(len(doc_sentences))
newdoc = Document(doctext, process=False, did=docid, ssplit=True)
#newdoc.sentences = doc_sentences[:]
newdoc.process_document(corenlpserver, "biomedical")
#logging.info(len(newdoc.sentences))
self.documents[newdoc.did] = newdoc
#for s in self.documents[newdoc.did].sentences:
# logging.debug("sentence {} has {} tokens".format(s.sid, len(s.tokens)))
# logging.debug([(t.start, t.end) for t in s.tokens])
abs_time = time.time() - t
time_per_abs.append(abs_time)
logging.info("%s sentences, %ss processing time" %
(len(newdoc.sentences), abs_time))
abs_avg = sum(time_per_abs) * 1.0 / len(time_per_abs)
logging.info("average time per abstract: %ss" % abs_avg)
def load_corpus(self, corenlpserver, process=True):
soup = BeautifulSoup(codecs.open(self.path, 'r', "utf-8"),
'html.parser')
docs = soup.find_all("article")
widgets = [
pb.Percentage(), ' ',
pb.Bar(), ' ',
pb.ETA(), ' ',
pb.Timer()
]
pbar = pb.ProgressBar(widgets=widgets, maxval=len(docs)).start()
n_lines = 1
time_per_abs = []
for doc in docs:
did = "GENIA" + doc.articleinfo.bibliomisc.text.split(":")[1]
title = doc.title.sentence.get_text()
sentences = doc.abstract.find_all("sentence")
doc_sentences = []
doc_text = title + " "
doc_offset = 0
for si, s in enumerate(sentences):
t = time.time()
stext = s.get_text()
sid = did + ".s" + str(si)
doc_text += stext + " "
this_sentence = Sentence(stext,
offset=doc_offset,
sid=sid,
did=did)
doc_offset = len(doc_text)
doc_sentences.append(this_sentence)
newdoc = Document(doc_text, process=False, did=did)
newdoc.sentences = doc_sentences[:]
newdoc.process_document(corenlpserver, "biomedical")
#logging.info(len(newdoc.sentences))
self.documents[newdoc.did] = newdoc
abs_time = time.time() - t
time_per_abs.append(abs_time)
logging.debug("%s sentences, %ss processing time" %
(len(newdoc.sentences), abs_time))
pbar.update(n_lines)
n_lines += 1
pbar.finish()
abs_avg = sum(time_per_abs) * 1.0 / len(time_per_abs)
logging.info("average time per abstract: %ss" % abs_avg)
def load_corpus(self, corenlpserver):
docs = self.get_docs(self.path)
total = len(docs)
current = 0
time_per_abs = []
ts = set()
for f in docs:
logging.debug("%s:%s/%s", f[0], current + 1, total)
current += 1
# parse DDI corpus file
t = time.time()
# print root.tag
docid = f[0] # TODO: actually each paragraph should be it's own documents, that should help offset issues
doctext = ""
doc_sentences = [] # get the sentences of this document
doc_offset = 0 # offset of the current sentence relative to the document
sents = self.get_paragraphs(f)
for p in sents:
logging.debug("processing {}".format(p[0]))
senttext = p[1].text.replace("\n", " ")
for ne in p[1].findall("ne"):
# doctext += ne.text
senttext += ne.text
if ne.tail:
# doctext += ne.tail
senttext += ne.tail.replace("\n", " ")
# logging.debug(senttext)
# this_sentence = Sentence(senttext, offset=doc_offset, sid=p[0], did=docid)
doctext += senttext + "\n"
doc_offset = len(doctext)
# doc_sentences.append(this_sentence)
# logging.info(len(doc_sentences))
newdoc = Document(doctext, process=False, did=docid, ssplit=True)
# newdoc.sentences = doc_sentences[:]
newdoc.process_document(corenlpserver, "biomedical")
# logging.info(len(newdoc.sentences))
self.documents[newdoc.did] = newdoc
# for s in self.documents[newdoc.did].sentences:
# logging.debug("sentence {} has {} tokens".format(s.sid, len(s.tokens)))
# logging.debug([(t.start, t.end) for t in s.tokens])
abs_time = time.time() - t
time_per_abs.append(abs_time)
logging.info("%s sentences, %ss processing time" % (len(newdoc.sentences), abs_time))
abs_avg = sum(time_per_abs) * 1.0 / len(time_per_abs)
logging.info("average time per abstract: %ss" % abs_avg)
def create_sentences(self, doctag, text):
# Create sentence entries based on text from document doctag
cur = self.db_conn.cursor()
newdoc = Document(text, process=False,
did=doctag)
newdoc.sentence_tokenize("biomedical")
for i, sentence in enumerate(newdoc.sentences):
corenlpres = sentence.process_sentence(self.corenlp)
query = """INSERT INTO sentence(senttag, doctag, senttext, sentoffset, corenlp) VALUES (%s, %s, %s, %s, %s);"""
try:
cur.execute(query, (sentence.sid, doctag, sentence.text.encode("utf8"), sentence.offset,
str(corenlpres).encode("utf8")))
self.db_conn.commit()
#inserted_id = cur.lastrowid
#return str(inserted_id)
except MySQLdb.MySQLError as e:
self.db_conn.rollback()
logging.debug(e)
def load_corpus(self, corenlpserver):
# self.path is the base directory of the files of this corpus
trainfiles = [
self.path + '/' + f for f in os.listdir(self.path)
if f.endswith('.xml')
]
total = len(trainfiles)
current = 0
time_per_abs = []
for f in trainfiles:
logging.debug('%s:%s/%s', f, current + 1, total)
current += 1
with open(f, 'r') as xml:
#parse DDI corpus file
t = time.time()
root = ET.fromstring(xml.read())
doctext = ""
did = root.get('id')
doc_sentences = [] # get the sentences of this document
doc_offset = 0 # offset of the current sentence relative to the document
for sentence in root.findall('sentence'):
sid = sentence.get('id')
#logging.info(sid)
text = sentence.get('text')
text = text.replace('\r\n', ' ')
doctext += " " + text # generate the full text of this document
this_sentence = Sentence(text,
offset=doc_offset,
sid=sid,
did=did)
doc_offset = len(doctext)
doc_sentences.append(this_sentence)
#logging.info(len(doc_sentences))
newdoc = Document(doctext, process=False, did=did)
newdoc.sentences = doc_sentences[:]
newdoc.process_document(corenlpserver, "biomedical")
#logging.info(len(newdoc.sentences))
self.documents[newdoc.did] = newdoc
abs_time = time.time() - t
time_per_abs.append(abs_time)
logging.info("%s sentences, %ss processing time" %
(len(newdoc.sentences), abs_time))
abs_avg = sum(time_per_abs) * 1.0 / len(time_per_abs)
logging.info("average time per abstract: %ss" % abs_avg)
def load_corpus(self, corenlpserver, process=True):
total_lines = sum(1 for line in open(self.path))
time_per_abs = []
with codecs.open(self.path, 'r', "utf-8") as trainfile:
current = 0
ddi = ""
for line in trainfile:
#logging.debug('%s:%s/%s', f, current + 1, total)
if line.startswith("ID"):
did = line.strip().split("\t")[1]
print did
elif line.startswith("sentence"):
doctext = line.strip().split("\t")[1]
newdoc = Document(doctext, process=False, did=did)
sid = did + ".s0"
newdoc.sentences.append(Sentence(doctext, offset=0, sid=sid, did=did))
if process:
newdoc.process_document(corenlpserver)
self.documents[newdoc.did] = newdoc
def load_corpus(self, corenlpserver, process=True):
# self.path is just one file with every document
time_per_abs = []
with open(self.path, 'r') as xml:
t = time.time()
root = ET.fromstring(xml.read())
all_docs = root.findall("document")
widgets = [
pb.Percentage(), ' ',
pb.Bar(), ' ',
pb.AdaptiveETA(), ' ',
pb.Timer()
]
pbar = pb.ProgressBar(widgets=widgets,
maxval=len(all_docs)).start()
for i, doc in enumerate(all_docs):
doctext = ""
did = doc.get('id')
doc_sentences = [] # get the sentences of this document
doc_offset = 0 # offset of the current sentence relative to the document
for sentence in doc.findall('sentence'):
sid = sentence.get('id')
#logging.info(sid)
text = sentence.get('text')
#text = text.replace('\r\n', ' ')
doctext += " " + text # generate the full text of this document
this_sentence = Sentence(text,
offset=doc_offset,
sid=sid,
did=did)
doc_offset = len(doctext)
doc_sentences.append(this_sentence)
newdoc = Document(doctext, process=False, did=did)
newdoc.sentences = doc_sentences[:]
newdoc.process_document(corenlpserver, "biomedical")
self.documents[newdoc.did] = newdoc
abs_time = time.time() - t
time_per_abs.append(abs_time)
pbar.update(i + 1)
pbar.finish()
abs_avg = sum(time_per_abs) * 1.0 / len(time_per_abs)
logging.info("average time per abstract: %ss" % abs_avg)
def load_corpus(self, corenlpserver, process=True):
widgets = [pb.Percentage(), " ", pb.Bar(), " ", pb.ETA(), " ", pb.Timer()]
nlines = 0
with open(self.path) as f:
for nlines, l in enumerate(f):
pass
print nlines
pbar = pb.ProgressBar(widgets=widgets, maxval=nlines).start()
with codecs.open(self.path, "r", "utf-8") as corpusfile:
doc_text = ""
sentences = []
for i, l in enumerate(corpusfile):
if l.startswith("###"): # new doc
if doc_text != "":
logging.debug("creating document: {}".format(doc_text))
newdoc = Document(doc_text, process=False, did=did)
newdoc.sentences = sentences[:]
newdoc.process_document(corenlpserver, "biomedical")
# logging.info(len(newdoc.sentences))
self.documents[newdoc.did] = newdoc
doc_text = ""
did = "JNLPBA" + l.strip().split(":")[-1]
logging.debug("starting new document:" + did)
sentence_text = ""
doc_offset = 0
sentences = []
elif l.strip() == "" and sentence_text != "": # new sentence
# logging.debug("creating mew sentence: {}".format(sentence_text))
sid = did + ".s" + str(len(sentences))
this_sentence = Sentence(sentence_text, offset=doc_offset, sid=sid, did=did)
doc_offset += len(sentence_text) + 1
doc_text += sentence_text + " "
sentences.append(this_sentence)
if i == nlines:
logging.debug("creating document: {}".format(doc_text))
newdoc = Document(doc_text, process=False, did=did)
newdoc.sentences = sentences[:]
newdoc.process_document(corenlpserver, "biomedical")
# logging.info(len(newdoc.sentences))
self.documents[newdoc.did] = newdoc
doc_text = ""
# start new sentence
sentence_text = ""
else:
# logging.debug(str(i) + "/" + str(l))
t = l.strip().split("\t")
if sentence_text != "":
sentence_text += " "
# if t[1] == "B-protein"
sentence_text += t[0]
pbar.update(i)
pbar.finish()
def load_corpus(self, corenlpserver, process=True):
total_lines = sum(1 for line in open(self.path))
time_per_abs = []
with codecs.open(self.path, 'r', "utf-8") as trainfile:
current = 0
ddi = ""
for line in trainfile:
#logging.debug('%s:%s/%s', f, current + 1, total)
if line.startswith("ID"):
did = line.strip().split("\t")[1]
print did
elif line.startswith("sentence"):
doctext = line.strip().split("\t")[1]
newdoc = Document(doctext, process=False, did=did)
sid = did + ".s0"
newdoc.sentences.append(
Sentence(doctext, offset=0, sid=sid, did=did))
if process:
newdoc.process_document(corenlpserver)
self.documents[newdoc.did] = newdoc
def load_corpus(self, corenlpserver, process=True):
"""Load the CHEMDNER corpus file on the dir element"""
# open filename and parse lines
total_lines = sum(1 for line in open(self.path))
widgets = [pb.Percentage(), ' ', pb.Bar(), ' ', pb.ETA(), ' ', pb.Timer()]
pbar = pb.ProgressBar(widgets=widgets, maxval=total_lines).start()
n_lines = 1
time_per_abs = []
with codecs.open(self.path, 'r', "utf-8") as inputfile:
for line in inputfile:
t = time.time()
# each line is PMID title abs
tsv = line.split('\t')
doctext = tsv[2].strip().replace("<", "(").replace(">", ")")
newdoc = Document(doctext, process=False,
did=tsv[0], title=tsv[1].strip())
newdoc.sentence_tokenize("biomedical")
if process:
newdoc.process_document(corenlpserver, "biomedical")
self.documents[newdoc.did] = newdoc
n_lines += 1
abs_time = time.time() - t
time_per_abs.append(abs_time)
pbar.update(n_lines+1)
pbar.finish()
abs_avg = sum(time_per_abs)*1.0/len(time_per_abs)
logging.info("average time per abstract: %ss" % abs_avg)
def load_corpus(self, corenlpserver, process=True):
# self.path is the base directory of the files of this corpus
trainfiles = [self.path + '/' + f for f in os.listdir(self.path) if f.endswith('.txt')]
total = len(trainfiles)
widgets = [pb.Percentage(), ' ', pb.Bar(), ' ', pb.ETA(), ' ', pb.Timer()]
pbar = pb.ProgressBar(widgets=widgets, maxval=total, redirect_stdout=True).start()
time_per_abs = []
for current, f in enumerate(trainfiles):
#logging.debug('%s:%s/%s', f, current + 1, total)
print '{}:{}/{}'.format(f, current + 1, total)
did = f.split(".")[0].split("/")[-1]
t = time.time()
with io.open(f, 'r', encoding='utf8') as txt:
doctext = txt.read()
newdoc = Document(doctext, process=False, did=did)
newdoc.sentence_tokenize("biomedical")
if process:
newdoc.process_document(corenlpserver, "biomedical")
self.documents[newdoc.did] = newdoc
abs_time = time.time() - t
time_per_abs.append(abs_time)
#logging.info("%s sentences, %ss processing time" % (len(newdoc.sentences), abs_time))
pbar.update(current+1)
pbar.finish()
abs_avg = sum(time_per_abs)*1.0/len(time_per_abs)
logging.info("average time per abstract: %ss" % abs_avg)
def load_corpus(self, corenlpserver, process=True):
trainfiles = [self.path + '/' + f for f in os.listdir(self.path)]
total = len(trainfiles)
widgets = [pb.Percentage(), ' ', pb.Bar(), ' ', pb.AdaptiveETA(), ' ', pb.Timer()]
pbar = pb.ProgressBar(widgets=widgets, maxval=total, redirect_stdout=True).start()
time_per_abs = []
for current, f in enumerate(trainfiles):
#logging.debug('%s:%s/%s', f, current + 1, total)
print '{}:{}/{}'.format(f, current + 1, total)
did = f
t = time.time()
with open(f, 'r') as f:
article = "<Article>" + f.read() + "</Article>"
soup = BeautifulSoup(article, 'xml')
#doc = soup.find_all("article")
title = soup.ArticleTitle.get_text()
abstract = soup.AbstractText.get_text()
doc_text = title + " " + abstract
newdoc = Document(doc_text, process=False, did=did)
newdoc.sentence_tokenize("biomedical")
newdoc.process_document(corenlpserver, "biomedical")
#logging.info(len(newdoc.sentences))
self.documents[newdoc.did] = newdoc
abs_time = time.time() - t
time_per_abs.append(abs_time)
logging.debug("%s sentences, %ss processing time" % (len(newdoc.sentences), abs_time))
pbar.update(current)
pbar.finish()
abs_avg = sum(time_per_abs)*1.0/len(time_per_abs)
logging.info("average time per abstract: %ss" % abs_avg)
def add_more_sentences(self, corpuspath):
"""
Load sentences with relations from another corpus
:param corpuspath: corpus path
:return:
"""
corpus2 = pickle.load(open(corpuspath, 'rb'))
for did in corpus2.documents:
for sentence in corpus2.documents[did].sentences:
if any([len(e.targets)> 1 for e in sentence.entities.elist["goldstandard"]]):
print ("found sentence with relations:", sentence.sid)
self.documents[sentence.sid] = Document(sentence.text, sentences=[sentence])
self.save("corpora/Thaliana/seedev-extended.pickle")
def load_corpus(self, corenlpserver, process=True):
soup = BeautifulSoup(codecs.open(self.path, 'r', "utf-8"), 'html.parser')
docs = soup.find_all("article")
widgets = [pb.Percentage(), ' ', pb.Bar(), ' ', pb.ETA(), ' ', pb.Timer()]
pbar = pb.ProgressBar(widgets=widgets, maxval=len(docs)).start()
n_lines = 1
time_per_abs = []
for doc in docs:
did = "GENIA" + doc.articleinfo.bibliomisc.text.split(":")[1]
title = doc.title.sentence.get_text()
sentences = doc.abstract.find_all("sentence")
doc_sentences = []
doc_text = title + " "
doc_offset = 0
for si, s in enumerate(sentences):
t = time.time()
stext = s.get_text()
sid = did + ".s" + str(si)
doc_text += stext + " "
this_sentence = Sentence(stext, offset=doc_offset, sid=sid, did=did)
doc_offset = len(doc_text)
doc_sentences.append(this_sentence)
newdoc = Document(doc_text, process=False, did=did)
newdoc.sentences = doc_sentences[:]
newdoc.process_document(corenlpserver, "biomedical")
#logging.info(len(newdoc.sentences))
self.documents[newdoc.did] = newdoc
abs_time = time.time() - t
time_per_abs.append(abs_time)
logging.debug("%s sentences, %ss processing time" % (len(newdoc.sentences), abs_time))
pbar.update(n_lines)
n_lines += 1
pbar.finish()
abs_avg = sum(time_per_abs)*1.0/len(time_per_abs)
logging.info("average time per abstract: %ss" % abs_avg)
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