def main(args):
argp = ARGPARSER.parse_args(args[1:])
if not argp.no_cache:
# We can't do it iteratively listening to stdin, read it all
doc = Document('<classify>', [], [], '<classify>')
for _string in (l.rstrip('\n') for l in argp.input):
doc.abstract.append(_string_to_ann_sent(_string))
docs = (doc, )
else:
docs = (Document('Line: %s' % i, [], [_string_to_ann_sent(_string)],
'<stdin>') for i, _string in enumerate(
(l.rstrip('\n') for l in argp.input), start=1))
# Cache the strings for speed
if not argp.no_cache:
cache_simstring((docs, ), verbose=argp.verbose)
with open(argp.model_path, 'r') as model_file:
classifier = pickle_load(model_file)
# TODO: Faster to do it in a batch instead
for doc in docs:
for sent in doc:
for ann in sent:
print '%s\t%s' % (sent.annotation_text(ann),
str(classifier.classify(doc, sent, ann, ranked=True)))
def __handleDocument(self, xmlDocument):
d = Document()
root = xmlDocument.firstChild
maxNumParag = 2
counter = 0
if root.hasChildNodes():
for xmlNode in root.childNodes:
#print 'xmlNode.nodeName '+str(xmlNode.nodeName)
if xmlNode.nodeName == 'DESC' or xmlNode.nodeName == 'TIME':
pass
if xmlNode.nodeName == 'P':
counter += 1
p = Paragraph()
print 'before hangle_paragraph'
self.__handleParagraph( xmlNode, p )
print 'after handle_paragraph'
d.addParagraph(p)
p.document = d
print p.idx
if counter >= maxNumParag:
break
print d, d.paragraphs
"""
# After all the paragraphs, therefore sentences, therefore words
# and markables have been parsed, the markables
# will have its slices relative to the word indexes in the sentence,
# being the first word index 0 and the last index numWords-1
# Transform the indexes to absolute indexes, relative to the
# word objects' ID attributes
for paragraph in d._paragraphs:
for sentence in paragraph._sentences:
for markable in sentence._markables:
sliceIndex = markable._slice
fromIndex, toIndex = sliceIndex.split(":")[0], sliceIndex.split(":")[1]
try:
fromId = sentence._words[int(fromIndex)]._id
toId = sentence._words[int(toIndex)]._id
#print fromId + " " + toId
markable._slice = fromId + ":" + toId
except ValueError:
# Value error here means that the key couldn't be converted to an int
# value, in that case it has already been converted by another
# markable and can be used "as is"
markable._slice = fromIndex + ":" + toIndex
"""
return d
def add_document(self):
schema = DocumentSchema()
form = Form(schema, buttons=('submit',))
if 'submit' in self.request.POST:
# Make a new Document
title = self.request.POST['title']
content = self.request.POST['content']
name = str(randint(0,999999))
new_document = Document(title, content)
new_document.__name__ = name
new_document.__parent__ = self.context
self.context[name] = new_document
# Redirect to the new document
url = self.request.resource_url(new_document)
return HTTPFound(location=url)
return {"form": form.render()}
def add_document(self):
schema = DocumentSchema()
form = Form(schema, buttons=('submit', ))
if 'submit' in self.request.POST:
# Make a new Document
title = self.request.POST['title']
content = self.request.POST['content']
name = str(randint(0, 999999))
new_document = Document(title, content)
new_document.__name__ = name
new_document.__parent__ = self.context
self.context[name] = new_document
# Redirect to the new document
url = self.request.resource_url(new_document)
return HTTPFound(location=url)
return {"form": form.render()}
def _tab_separated_input_to_doc(input):
# Create a dataset out of the input
doc = Document(input.name, [], [], '<%s>' % input.name)
for _string, _type in (l.rstrip('\n').split('\t') for l in input):
doc.abstract.append(
Sentence(_string, [
Annotation(0, len(_string), _type),
]))
return doc
def add_document_view(self):
# Make a new Document
title = self.request.POST['document_title']
name = str(randint(0, 999999))
new_document = Document(name, self.context, title)
self.context[name] = new_document
# Redirect to the new document
url = self.request.resource_url(new_document)
return HTTPFound(location=url)
def add_document(self):
schema = DocumentSchema()
form = Form(schema, buttons=('submit', ))
if 'submit' in self.request.POST:
# Make a new Document
title = self.request.POST['title']
content = self.request.POST['content']
doc_id = self.document_map.new_docid()
name = "document%s" % doc_id
new_document = Document(title, content)
new_document.__name__ = name
new_document.__parent__ = self.context
self.context[name] = new_document
# map object path to catalog id
path = resource_path(new_document)
self.document_map.add(path, doc_id)
# index new folder
self.catalog.index_doc(doc_id, new_document)
# Redirect to the new document
url = self.request.resource_url(new_document)
return HTTPFound(location=url)
return {"form": form.render()}
def add_document(self):
schema = DocumentSchema()
form = Form(schema, buttons=('submit',))
if 'submit' in self.request.POST:
# Make a new Document
title = self.request.POST['title']
content = self.request.POST['content']
doc_id = self.document_map.new_docid()
name = "document%s" % doc_id
new_document = Document(title, content)
new_document.__name__ = name
new_document.__parent__ = self.context
self.context[name] = new_document
# map object path to catalog id
path = resource_path(new_document)
self.document_map.add(path, doc_id)
# index new folder
self.catalog.index_doc(doc_id, new_document)
# Redirect to the new document
url = self.request.resource_url(new_document)
return HTTPFound(location=url)
return {"form": form.render()}
def _get_documents(dir):
for id, txt_path, ss_path, a1_path, a2_path in _get_aligned_resources(dir):
#print id
# First we align the text and the sentences since we need to map the
# offsets of the stand-off to map to the sentences in the sentence
# split file
#with open(txt_path, 'r') as txt_file, open(ss_path, 'r') as ss_file:
with open(txt_path, 'r') as txt_file:
if ENCODE_WRAP:
txt_file = _encode_wrap(txt_file)
with open(ss_path, 'r') as ss_file:
if ENCODE_WRAP:
ss_file = _encode_wrap(ss_file)
#sentences, s_offset_by_sentence = (
s_starts_and_sentences = (_get_sentences_and_offsets(
txt_file, ss_file))
#XXX: HACK!
if a2_path is None:
a2_path = '/dev/null'
#with open(a1_path, 'r') as a1_file, open(a2_path, 'r') as a2_file:
with open(a1_path, 'r') as a1_file:
if ENCODE_WRAP:
a1_file = _encode_wrap(a1_file)
with open(a2_path, 'r') as a2_file:
if ENCODE_WRAP:
a2_file = _encode_wrap(a2_file)
for line in (l.rstrip('\n') for l in chain(a1_file, a2_file)):
# We ignore everything apart from the text-bound annotations
match = TB_SO_REGEX.match(line)
if match is not None:
g_dict = match.groupdict()
ann_start = int(g_dict['start'])
ann_end = int(g_dict['end'])
# Find the sentence and its index containing the annotation
s_idx, sentence = _find_containing_idx(
ann_start, s_starts_and_sentences)
# XXX: There are cases where an annotation is cut-off
# by a sentence break. If this is the case, merge
# the sentences.
if ann_end > s_idx + len(sentence.text):
next_s_idx, next_sentence = _find_containing_idx(
ann_end, s_starts_and_sentences)
# Merge the next sentence into this one
# XXX: Just assumes a space! May be wrong!
sentence = Sentence(
sentence.text + ' ' + next_sentence.text,
sentence.annotations +
next_sentence.annotations)
# Remove the old one
s_starts_and_sentences.remove(
(next_s_idx, next_sentence))
# Create an annotation object but adjust the indices to
# be relative to the sentence and not to the file
new_ann_start = ann_start - s_idx
assert 0 <= new_ann_start < len(
sentence.text
), '0 <= {} < {} ({}, {}) {} "{}" {}'.format(
new_ann_start, len(sentence.text), s_idx,
g_dict['start'], id, g_dict['text'], s_idx)
new_ann_end = ann_end - s_idx
assert 0 < new_ann_end <= len(
sentence.text
), '0 < {} <= {} ({}, {}) {} {}'.format(
new_ann_end, len(sentence.text), s_idx,
g_dict['end'], id, g_dict['text'])
assert new_ann_start < new_ann_end
annotation = Annotation(ann_start - s_idx,
ann_end - s_idx,
g_dict['type'])
# If we have a text span in the stand-off we sanity check
# it against what is in the sentence
#XXX: Run this again!
if g_dict['text'] is not None:
g_dict['text'] = unicode(
g_dict['text'].strip('\r\n'),
encoding='utf-8') #XXX: Regex is not perfect
# it leaves spaces around
target_ann_text = sentence.annotation_text(
annotation)
assert target_ann_text == g_dict['text'], (
'text span mismatch in {} '
'target: "{}" != source: "{}" {} "{}" {} {} {}'
).format(id, target_ann_text, g_dict['text'],
annotation, sentence.text, g_dict,
type(target_ann_text),
type(g_dict['text']))
sentence.add_annotation(annotation)
#else:
# assert False, line.replace(' ', '\s').replace('\t', '\\t')
yield Document(id, [],
[sentence for _, sentence in s_starts_and_sentences],
txt_path)
