def main(args):
if args.out_dir:
os.makedirs(args.out_dir, exist_ok=True)
if not args.tikz:
import matplotlib
matplotlib.use('Agg')
to_stdout = (args.tikz or args.standoff) and not args.out_dir
t = args.passages
t = get_passages(t) if to_stdout else get_passages_with_progress_bar(
t, desc="Visualizing")
if args.sentences:
t = (sentence for passage in t
for sentence in split2sentences(passage))
for passage in t:
if args.tikz:
print_text(args, visualization.tikz(passage),
passage.ID + ".tikz.txt")
elif args.standoff:
print_text(args, visualization.standoff(passage),
passage.ID + ".ann")
else:
import matplotlib.pyplot as plt
width = len(passage.layer(layer0.LAYER_ID).all) * 19 / 27
plt.figure(passage.ID, figsize=(width, width * 10 / 19))
visualization.draw(passage, node_ids=args.node_ids)
if args.out_dir:
plt.savefig(
os.path.join(args.out_dir, passage.ID + "." + args.format))
plt.close()
else:
plt.show()
def test_split(self):
"""Test that splitting a single-sentence SDP graph converted to UCCA returns the same SDP graph"""
for passage, ref, _ in read_test_sdp():
sentences = split2sentences(passage)
self.assertEqual(len(sentences), 1, "Should be one sentence: %s" % passage)
sentence = sentences[0]
self.convert_and_evaluate(sentence, ref)
def filter_nodes(categories=(),
tokens=(),
tokens_mode=CONSECUTIVE,
case_insensitive=False,
comment=False,
sentence_level=False,
**kwargs):
for passage, task_id, user_id in TaskDownloader(**kwargs).download_tasks(
**kwargs):
for node in [p.layer(layer1.LAYER_ID).heads[0] for p in convert.split2sentences(passage)] if sentence_level \
else passage.layer(layer1.LAYER_ID).all:
if comment and node.extra.get("remarks"):
yield "comment", node, task_id, user_id
if tokens and not node.attrib.get("implicit"):
unit_tokens = [t.text for t in node.get_terminals(punct=True)]
if case_insensitive:
unit_tokens = [x.lower() for x in unit_tokens]
tokens = [x.lower() for x in tokens]
if tokens_match(unit_tokens, tokens, tokens_mode):
yield 'TOKENS', node, task_id, user_id
elif categories:
intersection = set(categories).intersection(
c.tag for e in node for c in e.categories)
if intersection:
yield str(intersection), node, task_id, user_id
def main(args):
splitter = Splitter.read_file(args.sentences,
enum=args.enumerate,
suffix_format=args.suffix_format,
suffix_start=args.suffix_start)
os.makedirs(args.outdir, exist_ok=True)
i = 0
for passage in get_passages_with_progress_bar(args.filenames, "Splitting"):
for sentence in splitter.split(
passage) if splitter else split2sentences(
passage,
remarks=args.remarks,
lang=args.lang,
ids=map(str, count(i)) if args.enumerate else None):
i += 1
outfile = os.path.join(
args.outdir, args.prefix + sentence.ID +
(".pickle" if args.binary else ".xml"))
if args.verbose:
with external_write_mode():
print("Writing passage file for sentence '%s'..." %
outfile,
file=sys.stderr)
if args.normalize:
normalize(sentence)
passage2file(sentence, outfile, binary=args.binary)
if splitter and len(splitter.matched_indices) < len(splitter.sentences):
print("Unmatched sentences:",
*[
s for i, s in enumerate(splitter.sentences)
if i not in splitter.matched_indices
],
sep="\n")
def main(output = None, comment = False, sentence_level = False, categories = (), tokens = (), tokens_mode = CONSECUTIVE,
case_insensitive = False, write = False, **kwargs):
filtered_nodes = []
for passage, task_id, user_id in TaskDownloader(**kwargs).download_tasks(write=False, **kwargs):
if sentence_level:
cur_passages = convert.split2sentences(passage)
all_nodes = [P.layer('1').heads[0] for P in cur_passages]
else:
all_nodes = list(passage.layer(layer1.LAYER_ID).all)
for node in all_nodes:
if comment and node.extra.get("remarks"):
filtered_nodes.append(("comment",node,task_id,user_id))
if tokens and not node.attrib.get("implicit"):
unit_tokens = [t.text for t in node.get_terminals(punct=True)]
if case_insensitive:
unit_tokens = [x.lower() for x in unit_tokens]
tokens = [x.lower() for x in tokens]
if tokens_match(unit_tokens, tokens, tokens_mode):
filtered_nodes.append(('TOKENS', node, task_id, user_id))
else:
all_tags = []
for edge in node:
all_tags.extend([c.tag for c in edge.categories])
if all_tags:
intersection = set(categories) & set(all_tags)
if intersection:
filtered_nodes.append((str(list(intersection)), node, task_id, user_id))
if output:
with open(output, 'w') as f:
for filter_type,node,task_id,user_id in filtered_nodes:
ancestor = get_top_level_ancestor(node)
print(filter_type, task_id, user_id, node.extra.get("tree_id"), node.to_text(),
ancestor, str(node.extra.get("remarks")).replace("\n","|"), file=f, sep="\t")
def test_split(self):
"""Test that splitting a single-sentence Universal Dependencies tree converted to UCCA returns the same tree"""
for passage, ref, _ in read_test_conllu():
sentences = split2sentences(passage)
self.assertEqual(len(sentences), 1,
"Should be one sentence: %s" % passage)
sentence = sentences[0]
self.convert_and_evaluate(sentence, ref)
def test_split(self):
"""Test that splitting a single-sentence AMR converted to UCCA returns the same AMR"""
for passage, ref, amr_id in read_test_amr():
sentences = split2sentences(passage)
self.assertEqual(len(sentences), 1,
"Should be one sentence: %s" % passage)
sentence = sentences[0]
converted = "\n".join(to_amr(sentence, metadata=False))
scores = evaluate(converted, ref, amr_id)
self.assertAlmostEqual(scores.f1, 1, msg=converted)
def convert_passage(filename, converter, args):
"""Opens a passage file and returns a string after conversion
:param filename: input passage file
:param converter: function to use for conversion
:param args: ArgumentParser object
"""
passage = file2passage(filename)
passages = convert.split2sentences(passage) if args.sentences else [passage]
output = "\n".join(line for p in passages for line in
converter(p, args.test, args.tree, args.markaux))
return output, passage.ID
def convert_passage(filename, converter, args):
"""Opens a passage file and returns a string after conversion
:param filename: input passage file
:param converter: function to use for conversion
:param args: ArgumentParser object
"""
passage = file2passage(filename)
passages = convert.split2sentences(passage) if args.sentences else [
passage
]
output = "\n".join(
line for p in passages
for line in converter(p, args.test, args.tree, args.markaux))
return output, passage.ID
def main(args):
splitter = Splitter.read_file(args.sentences, enum=args.enumerate)
os.makedirs(args.outdir, exist_ok=True)
i = 0
for passage in get_passages_with_progress_bar(args.filenames, "Splitting"):
for sentence in splitter.split(passage) if splitter else split2sentences(
passage, remarks=args.remarks, lang=args.lang, ids=map(str, count(i)) if args.enumerate else None):
i += 1
outfile = os.path.join(args.outdir, args.prefix + sentence.ID + (".pickle" if args.binary else ".xml"))
with external_write_mode():
print("Writing passage file for sentence '%s'..." % outfile, file=sys.stderr)
if args.normalize:
normalize(sentence)
passage2file(sentence, outfile, binary=args.binary)
def test_split2sentences():
"""Tests splitting a passage by sentence ends.
"""
p = multi_sent()
split = convert.split2sentences(p)
assert len(split) == 3
terms = [[t.text for t in s.layer(layer0.LAYER_ID).all] for s in split]
assert terms[0] == ["1", "2", "3", "."]
assert terms[1] == ["5", "6", "."]
assert terms[2] == ["8", ".", "10", "."]
assert all(t.paragraph == 1 for s in split for t in s.layer(layer0.LAYER_ID).all)
top_scenes = [s.layer(layer1.LAYER_ID).top_scenes for s in split]
for t in top_scenes:
assert len(t) == 1
assert t[0].incoming[0].tag == layer1.EdgeTags.ParallelScene
def main(args):
order = None
if args.sentences:
with open(args.sentences, encoding="utf-8") as f:
order = dict(map(reversed, enumerate(map(str.strip, f))))
for passage in get_passages_with_progress_bar(args.filenames, "Splitting"):
for sentence in split(passage, order) if order else split2sentences(
passage, remarks=args.remarks, lang=args.lang):
outfile = os.path.join(
args.outdir, args.prefix + sentence.ID +
(".pickle" if args.binary else ".xml"))
with tqdm.external_write_mode():
print("Writing passage file for sentence '%s'..." % outfile,
file=sys.stderr)
passage2file(sentence, outfile, args.binary)
def write_passage(passage, args):
ext = {
None: UCCA_EXT[args.binary],
"amr": ".txt"
}.get(args.output_format) or "." + args.output_format
outfile = args.outdir + os.path.sep + args.prefix + passage.ID + ext
sys.stderr.write("Writing '%s'...\n" % outfile)
if args.output_format is None: # UCCA output
ioutil.passage2file(passage, outfile, args.binary)
else:
converter = CONVERTERS[args.output_format][1]
output = "\n".join(converter(passage)) if args.output_format == "amr" else \
"\n".join(line for p in (convert.split2sentences(passage) if args.split else [passage]) for line in
converter(p, test=args.test, tree=args.tree, mark_aux=args.mark_aux))
with open(outfile, "w", encoding="utf-8") as f:
print(output, file=f)
def main(args):
for passage in get_passages_with_progress_bar(args.passages):
t = split2sentences(passage)
i = 0
for sen in t:
#print('sentence %d\n\n%s\n%s' %(i,convert.to_text(sen), convert.to_sequence(sen)))
print('sentence %d\n\n%s\n' % (i, convert.to_text(sen)))
i += 1
compunds = []
for node in sen.nodes:
if (sen.nodes[node].layer.ID == '0'):
find_id = ''
l = sen.nodes[node]
if (l.parents[0].ftag == 'C'):
if (l.parents[0].ID not in compunds):
compunds.append(l.parents[0].ID)
tmp_c = []
for n in l.parents[0].children:
tmp_c.append(n.text)
#print('Word: %s\nWord ID: %s' %(tmp_c,l.parents[0].ID))
find_id = l.parents[0].ID
path = []
path.append(' '.join(tmp_c))
path = find_path(sen.nodes[find_id], path)
print(' '.join(path))
'''
for j in path:
print(j)
'''
print('-------')
else:
#print('Word: %s\nWord ID: %s' % (l.text, l.ID))
find_id = l.ID
path = []
path = find_path(sen.nodes[find_id], path)
print(' '.join(path))
'''
for j in path:
print(j)
'''
print('-------')
print(
'------------------------------------------------------------------'
)
def filter_nodes(categories=(), tokens=(), tokens_mode=CONSECUTIVE, case_insensitive=False, comment=False,
sentence_level=False, **kwargs):
for passage, task_id, user_id in TaskDownloader(**kwargs).download_tasks(**kwargs):
for node in [p.layer(layer1.LAYER_ID).heads[0] for p in convert.split2sentences(passage)] if sentence_level \
else passage.layer(layer1.LAYER_ID).all:
if comment and node.extra.get("remarks"):
yield "comment", node, task_id, user_id
if tokens and not node.attrib.get("implicit"):
unit_tokens = [t.text for t in node.get_terminals(punct=True)]
if case_insensitive:
unit_tokens = [x.lower() for x in unit_tokens]
tokens = [x.lower() for x in tokens]
if tokens_match(unit_tokens, tokens, tokens_mode):
yield 'TOKENS', node, task_id, user_id
elif categories:
intersection = set(categories).intersection(c.tag for e in node for c in e.categories)
if intersection:
yield str(intersection), node, task_id, user_id
def test_split2sentences(self):
"""Tests splitting a passage by sentence ends.
"""
p = TestUtil.create_multi_passage()
split = convert.split2sentences(p)
self.assertEqual(len(split), 3)
terms = [[t.text for t in s.layer(layer0.LAYER_ID).all] for s in split]
self.assertSequenceEqual(terms[0], ["1", "2", "3", "."])
self.assertSequenceEqual(terms[1], ["5", "6", "."])
self.assertSequenceEqual(terms[2], ["8", ".", "10", "."])
self.assertTrue(all(t.paragraph == 1 for s in split[0:2]
for t in s.layer(layer0.LAYER_ID).all))
self.assertTrue(all(t.paragraph == 2
for t in split[2].layer(layer0.LAYER_ID).all))
top_scenes = [s.layer(layer1.LAYER_ID).top_scenes for s in split]
for t in top_scenes:
self.assertEqual(len(t), 1)
self.assertEqual(t[0].incoming[0].tag, layer1.EdgeTags.ParallelScene)
def test_split2sentences(self):
"""Tests splitting a passage by sentence ends.
"""
p = TestUtil.create_multi_passage()
split = convert.split2sentences(p)
self.assertEqual(len(split), 3)
terms = [[t.text for t in s.layer(layer0.LAYER_ID).all] for s in split]
self.assertSequenceEqual(terms[0], ["1", "2", "3", "."])
self.assertSequenceEqual(terms[1], ["5", "6", "."])
self.assertSequenceEqual(terms[2], ["8", ".", "10", "."])
self.assertTrue(
all(t.paragraph == 1 for s in split
for t in s.layer(layer0.LAYER_ID).all))
top_scenes = [s.layer(layer1.LAYER_ID).top_scenes for s in split]
for t in top_scenes:
self.assertEqual(len(t), 1)
self.assertEqual(t[0].incoming[0].tag,
layer1.EdgeTags.ParallelScene)
def main(args):
splitter = Splitter.read_file(args.sentences, enum=args.enumerate,
suffix_format=args.suffix_format, suffix_start=args.suffix_start)
os.makedirs(args.outdir, exist_ok=True)
i = 0
for passage in get_passages_with_progress_bar(args.filenames, "Splitting"):
for sentence in splitter.split(passage) if splitter else split2sentences(
passage, remarks=args.remarks, lang=args.lang, ids=map(str, count(i)) if args.enumerate else None):
i += 1
outfile = os.path.join(args.outdir, args.prefix + sentence.ID + (".pickle" if args.binary else ".xml"))
if args.verbose:
with external_write_mode():
print(sentence, file=sys.stderr)
print("Writing passage file for sentence '%s'..." % outfile, file=sys.stderr)
if args.normalize:
normalize(sentence)
passage2file(sentence, outfile, binary=args.binary)
if splitter and len(splitter.matched_indices) < len(splitter.sentences):
print("", "Unmatched sentences:", *[s for i, s in enumerate(splitter.sentences)
if i not in splitter.matched_indices], sep="\n")
def test_split_join_sentences(create):
p = create()
split = convert.split2sentences(p, remarks=True)
copy = convert.join_passages(split)
diffutil.diff_passages(p, copy)
assert p.equals(copy)
def test_split_join_sentences(self):
p = TestUtil.create_multi_passage()
split = convert.split2sentences(p, remarks=True)
copy = convert.join_passages(split)
diffutil.diff_passages(p, copy)
self.assertTrue(p.equals(copy))
def test_split_join_sentences(self):
p = TestUtil.create_multi_passage()
split = convert.split2sentences(p, remarks=True)
copy = convert.join_passages(split)
diffutil.diff_passages(p, copy)
self.assertTrue(p.equals(copy))
def main(args):
for passage in get_passages_with_progress_bar(args.passages):
#passage = convert.from_standard(elem)
#print("Linearised\n------\n")
# print(convert.to_sequence(passage))
words = {}
xmltoconll(passage)
t = split2sentences(passage)
i = 0
for sen in t:
print('sentence %d\n\n%s\n' % (i, convert.to_text(sen)))
i += 1
while (1):
word = input('\nType the word below\n\n')
for node in passage.nodes:
t = passage.nodes[node]
if (re.match(rf'\b{word}\b', t.text, re.IGNORECASE)):
#print('Word: %s\nWord ID: %s' %(t.text,t.ID))
#ans = input('\nDo you want to continue with wordi Id : %s', t.ID)
path = []
path = find_path(passage.nodes[t.ID], path)
break
print(' '.join(path))
def main(args):
for passage in get_passages_with_progress_bar(args.passages):
t = split2sentences(passage)
sen_no = 0
for sen in t:
#print('sentence %d\n\n%s\n%s' %(i,convert.to_text(sen), convert.to_sequence(sen)))
print('sentence %d\n\n%s\n' % (sen_no, convert.to_text(sen)))
root = sen.nodes['1.1']
first = 1
tab_len = {}
tab_len[0] = len('1.1')
for i in root.children:
print('\n')
path = []
level = 1
path.append((i.ftag, i.ID, level, False))
path = find_children(i, path, level)
end = 0
if (first):
pstr = root.ID
first = 0
else:
for k in range(0, tab_len[0]):
pstr = pstr + ' '
for j in path:
if (j == 'End'):
print(pstr)
pstr = ''
end = 1
continue
rel = j[0]
nd = j[1]
tab = int(j[2])
remote = j[3]
if (end):
q_mark = 0
for k in range(0, tab_len[tab - 1]):
if (k == tab_len[q_mark]):
pstr = pstr + '.'
q_mark += 1
else:
pstr = pstr + ' '
end = 0
if (rel in descr):
rel_desc = rel + ':' + descr[rel]
else:
rel_desc = rel
if (remote):
pstr = pstr + '|-->Remote(' + rel_desc + ')-->' + nd
else:
pstr = pstr + '|-->(' + rel_desc + ')-->' + nd
tab_len[tab] = len(pstr)
print('-----------------------------------\n')
sen_no += 1
