def show_matches(rule, *lines):
parser = Parser(rule)
for line in lines:
matches = parser.findall(line)
spans = [_.span for _ in matches]
show_markup(line, spans)
def test_samples(rules: Union[NamedRule, List[NamedRule]],
texts: List[str],
num: int = 20,
seed: int = None,
markup=None,
fact=False):
from random import seed as sed, sample
sed(seed)
texts, num = (texts,
len(texts)) if len(texts) < num else (sample(texts, num),
num)
results: Dict[int, Dict[int, List]] = defaultdict(dict)
if not (isinstance(rules, list) or isinstance(rules, tuple)):
rules = [rules]
for rule_idx, rule in enumerate(rules):
parser = Parser(rule)
for text_idx in range(num):
matches = parser.findall(texts[text_idx])
results[text_idx][rule_idx] = list(matches)
for text_idx, rule_matches in results.items():
spans = [(m.span[0], m.span[1], str(rules[rule_idx].name))
for rule_idx, matches in rule_matches.items()
for m in matches]
show_markup(texts[text_idx], spans, markup or BoxLabelMarkup)
if fact:
for rule_idx, matches in rule_matches.items():
for m in matches:
display(m.fact)
def test(rule, *lines, tree=False, facts=False):
is_at = lambda span, set: any((span == s) for s in set)
parser = Parser(rule)
for line in lines:
if isinstance(line, str):
text, expected = line, []
else:
text = line[0]
expected = [find(text, substr) for substr in line[1:]]
matches = list(sorted(parser.findall(text), key=lambda _: _.span))
# display(matches)
matched_spans = [_.span for _ in matches]
spans = [(s[0], s[1], '#aec7e8' if is_at(s, expected) else '#ff9896') for s in matched_spans] \
+ [(s[0], s[1], '#ccc') for s in expected if not is_at((s[0], s[1]), matched_spans)]
show_markup(text, [s for s in spans if s[0] < s[1]], LineMarkup)
if matches:
for _ in matches:
if tree:
display(matches[0].tree.as_dot)
if facts:
display(_.fact)
def test_on_k_random_records(K):
records = load_lenta(lenta_path)
records_num = [i for i in range(N)]
chosen_records_num = random.choices(records_num, k=K)
my_records = []
for i in chosen_records_num:
my_records.append(get_k_record(records, i))
print(f'This is ' + tp.BOLD + tp.RED + f'{chosen_records_num}' + tp.END +
' records\n')
for i in range(K):
print(tp.BOLD + tp.RED + f'{chosen_records_num[i]}' + tp.END + '\t')
markup = ner(my_records[i].text)
show_markup(markup.text, markup.spans)
print('\n--------------------------\n\n')
def save_syntax_analysis_by_text(self,
text,
file,
is_many_sentences=False):
f = open(file, 'a')
sys.stdout = f
print('-' * 100)
if text != 'None':
if not is_many_sentences:
chunk = list()
for sent in sentenize(text):
tokens = [_.text for _ in tokenize(sent.text)]
chunk.append(tokens)
markup = next(self.syntax.map(chunk))
words, deps = list(), list()
for token in markup.tokens:
words.append(token.text)
source = int(token.head_id) - 1
target = int(token.id) - 1
if source > 0 and source != target:
deps.append([source, target, token.rel])
show_markup(words, deps)
else:
for sentence in text.split('.'):
if len(sentence.split()) > 5:
chunk = list()
for sent in sentenize(sentence):
tokens = [_.text for _ in tokenize(sent.text)]
chunk.append(tokens)
markup = next(self.syntax.map(chunk))
words, deps = list(), list()
for token in markup.tokens:
words.append(token.text)
source = int(token.head_id) - 1
target = int(token.id) - 1
if source > 0 and source != target:
deps.append([source, target, token.rel])
show_markup(words, deps)
else:
print('None')
print('-' * 100)
# print(df)
from razdel import tokenize, sentenize
from navec import Navec
from slovnet import Morph, Syntax, NER
from ipymarkup import show_span_ascii_markup as show_markup
df = pd.read_csv('categories.csv')
categories = df.to_records(index=False)
navec = Navec.load('navec_news_v1_1B_250K_300d_100q.tar')
# morph = Morph.load('slovnet_morph_news_v1.tar')
# syntax = Syntax.load('slovnet_syntax_news_v1.tar')
ner = NER.load('slovnet_ner_news_v1.tar')
# morph.navec(navec)
# syntax.navec(navec)
ner.navec(navec)
with open('sport_texts_clear.txt', 'r', encoding='UTF-8') as f:
i = 1
for line in f:
sents = [sent.text for sent in sentenize(line)]
for sent in sents:
i += 1
# tokens = tokenize(sent)
# print(list(tokens))
markup = ner(sent)
show_markup(markup.text, markup.spans)
if i > 20:
break
'packages')
from yargy import Parser
from ipymarkup import show_markup
from random import sample
from IPython.display import display
from dataloader import DataLoader
from fact import CONTENT_TYPE_PARSER, ContentTypeFact
lines = DataLoader().exemple_offers()
parser = Parser(CONTENT_TYPE_PARSER)
for line in lines[:10]:
result_list = []
matches = list(parser.findall(line))
print(
'----------------------------------------------------------------------'
)
print("Content: ")
for match in matches:
if match is not None:
try:
result_list.append(match.fact.contenttype)
print("\t", match.fact.contenttype)
except KeyError:
print("Content: KeyError")
result_list = list(dict.fromkeys(result_list))
print("Result:")
print(result_list)
show_markup(line, [_.span for _ in matches])
def test_on_random_record():
records = load_lenta(lenta_path)
record, k = get_random_record(records)
markup = ner(record.text)
print('This is ' + tp.BOLD + tp.RED + f'{k}' + tp.END + ' record\n')
show_markup(markup.text, markup.spans)