def test_split_span_add_delimiters(self):
text = '1 2 3\nмама\nಶ್ರೀರಾಮ'
tm = TextMap(text)
spans = [s for s in tm.split_spans('\n', add_delimiter=True)]
for k in spans:
print(tm.text_range(k))
self.assertEqual('1 2 3\n', tm.text_range(spans[0]))
def test_map_text_range(self):
text = """1.2. мама молилась ಶ್ರೀರಾಮ\n\nРама -- Вишну, А Вишну
ел... черешню? (черешня по 10 руб. 20 коп.) '' """
tm = TextMap(text)
t = tm.text_range([0, 3])
self.assertEqual(t, '1.2. мама молилась')
def test_sentence_at_index_return_delimiters(self):
tm = TextMap('стороны Заключили\n договор ПРЕДМЕТ \nДОГОВОРА')
for i in range(len(tm)):
print(i, tm[i])
bounds = tm.sentence_at_index(0)
print(bounds)
print(tm.text_range(bounds))
for i in range(0, 3):
bounds = tm.sentence_at_index(i)
self.assertEqual('стороны Заключили\n', tm.text_range(bounds), str(i))
for i in range(3, 5):
bounds = tm.sentence_at_index(i)
self.assertEqual('договор ПРЕДМЕТ \n', tm.text_range(bounds))
for i in range(6, 7):
bounds = tm.sentence_at_index(i)
self.assertEqual('ДОГОВОРА', tm.text_range(bounds))
def test_find_value_sign_c(self):
for (sign_expected, price, currency, _, text) in data:
tm = TextMap(text)
sign, span = find_value_sign(tm)
if sign_expected:
self.assertEqual(sign_expected, sign, text)
quote = ''
if span:
quote = tm.text_range(span)
print(f'{sign},\t {span},\t {quote}')
def test_token_indices_by_char_range(self):
text = 'мама'
span = [0, 4]
expected = text[span[0]:span[1]]
print(expected)
tm = TextMap(text) # tokenization
ti = tm.token_indices_by_char_range(span)
self.assertEqual(0, ti[0])
self.assertEqual(1, ti[1])
self.assertEqual(expected, tm.text_range(ti))
def test_finditer(self):
from analyser.transaction_values import _re_greather_then
text = """стоимость, равную или превышающую 2000000 ( два миллиона ) долларов сша, но менее"""
tm = TextMap(text)
iter = tm.finditer(_re_greather_then)
results = [t for t in iter]
results = results[0]
self.assertEqual('превышающую', tm.text_range(results))
self.assertEqual(4, results[0])
self.assertEqual(5, results[1])
def nn_get_tag_value(tagname: str,
textmap: TextMap,
semantic_map: DataFrame,
threshold=0.3) -> SemanticTag or None:
att = semantic_map[tagname].values
slices = find_top_spans(att, threshold=threshold,
limit=1) # TODO: estimate per-tag thresholds
if len(slices) > 0:
span = slices[0].start, slices[0].stop
value = textmap.text_range(span)
tag = SemanticTag(tagname, value, span)
tag.confidence = float(att[slices[0]].mean())
return tag
return None
def test_concat_then_slice(self):
text1 = 'этилен мама'
text2 = 'этилен папа'
tm0 = TextMap('')
tm1 = TextMap(text1)
tm2 = TextMap(text2)
tm0 += tm1
tm0 += tm2
print(tm1.tokens)
self.assertEqual(text1 + text2, tm0.text)
self.assertEqual('мамаэтилен', tm0.text_range([1, 3]))
tm3 = tm0.slice(slice(1, 3))
self.assertEqual('мамаэтилен', tm3.text)
def test_find_value_sign_b(self):
text = """стоимость, равную или превышающую 2000000 ( два миллиона ) долларов сша, но менее"""
tm = TextMap(text)
sign, span = find_value_sign(tm)
quote = tm.text_range(span)
self.assertEqual('менее', quote)
def line_features(tokens_map: TextMap, line_span: (int, int), line_number: int,
prev_features):
tokens: Tokens = tokens_map.tokens_by_range(line_span)
# TODO: add previous and next lines features
txt: str = tokens_map.text_range(line_span)
numbers, span, k, s = get_tokenized_line_number(tokens, 0)
if not numbers:
numbers = []
number_minor = -2
number_major = -2
else:
number_minor = numbers[-1]
number_major = numbers[0]
header_id = ' '.join(tokens[span[1]:])
header_id = header_id.lower()
all_upper = header_id.upper() == header_id
features = {
'line_number': line_number,
# 'popular': _onehot(header_id in popular_headers),
# 'cr_count': txt.count('\r'),
'has_contract': _onehot(txt.lower().find('договор')),
'has_article': _onehot(txt.lower().find('статья')),
'all_uppercase': _onehot(all_upper),
'len_tokens': len(tokens),
'len_chars': len(txt),
'number_level': len(numbers),
'number_minor': number_minor,
'number_major': number_major,
'number_roman': _onehot(s),
'endswith_dot': _onehot(txt.rstrip().endswith('.')),
'endswith_comma': _onehot(txt.rstrip().endswith(',')),
'endswith_underscore': _onehot(txt.rstrip().endswith('_')),
# counts
'dots': header_id.count('.'),
'tabs': txt.count('\t'),
'spaces_inside': txt.strip().count(' '),
'spaces_all': txt.count(' '),
'commas': header_id.count(','),
'brackets': _count_strange_symbols(txt, '(){}[]'),
'dashes': header_id.count('-'),
'colons': header_id.count(':'),
'semicolons': header_id.count(';'),
'strange_symbols': _count_strange_symbols(header_id, '[$@+]?^&'),
'capitals': _count_capitals(txt),
'digits': _count_digits(header_id),
'quotes': _count_strange_symbols(txt, '«»"\'"'),
'underscores': _count_strange_symbols(txt, '_')
}
# if prev_features is None:
# # features['prev-number_level'] = 0
# features['prev-len_chars']=-1
# else:
# # features['prev-number_level'] = prev_features['number_level']
# features['prev-len_chars'] = prev_features['len_chars']
return features
