def test_slice_start_from_space(self):
offff = 20
txt = ' ' * offff + '''основании Устава, с одной стороны, и Фонд «Благо»'''
tm = TextMap(txt)
print(tm.map[0])
print(tm.tokens[11])
print(tm.map[11])
# print(f'[{doc.tokens_map.text}]')
print(f'[{tm.text}]')
print(len(tm))
tm_sliced = tm.slice(slice(0, len(tm)))
print('span-0')
print(tm.map[0])
print(tm_sliced.map[0])
self.assertEqual(len(tm), len(tm_sliced))
self.assertEqual(tm.map[0], tm_sliced.map[0])
for c in range(len(tm.tokens[0])):
print(c)
self.assertEqual(0, tm.token_index_by_char(c))
self.assertEqual(0, tm_sliced.token_index_by_char(c))
self.assertEqual(tm.text, tm_sliced.text)
self.assertEqual(0, tm.token_index_by_char(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_map_tokens_in_range(self):
text = '1.2. мама ಶ್ರೀರಾಮ'
tm = TextMap(text)
tokens = tm.tokens_by_range([0, 2])
self.assertEqual(len(tokens), 2)
self.assertEqual(tokens[0], '1.2.')
self.assertEqual(tokens[1], 'мама')
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_concat_TextMap3(self):
tm1 = TextMap('text prefix \n')
tm2 = TextMap('more words')
N = 10
expected_tokens = len(tm1.tokens) + N * len(tm2.tokens)
for _ in range(N):
tm1 += tm2
self.assertEqual(expected_tokens, len(tm1))
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_char_range(self):
text = 'этилен мама ಶ್ರೀರಾಮ'
tm = TextMap(text)
cr = tm.char_range([0, 1])
self.assertEqual('этилен', text[cr[0]:cr[1]])
cr = tm.char_range([2, None])
self.assertEqual('ಶ್ರೀರಾಮ', text[cr[0]:cr[1]])
cr = tm.char_range([None, 1])
self.assertEqual('этилен', text[cr[0]:cr[1]])
def embedd_contextualized_patterns(self, patterns, trim_padding=True):
tokenized_sentences_list: [Tokens] = []
regions = []
i = 0
maxlen = 0
lens = []
for (ctx_prefix, pattern, ctx_postfix) in patterns:
# sentence = ' '.join((ctx_prefix, pattern, ctx_postfix))
prefix_tokens = TextMap(
ctx_prefix).tokens # tokenize_text(ctx_prefix)
pattern_tokens = TextMap(pattern).tokens
suffix_tokens = TextMap(ctx_postfix).tokens
start = len(prefix_tokens)
end = start + len(pattern_tokens)
sentence_tokens = prefix_tokens + pattern_tokens + suffix_tokens
regions.append((start, end))
tokenized_sentences_list.append(sentence_tokens)
lens.append(len(sentence_tokens))
if len(sentence_tokens) > maxlen:
maxlen = len(sentence_tokens)
i = i + 1
_strings = []
for s in tokenized_sentences_list:
s.extend([' '] * (maxlen - len(s)))
_strings.append(s)
_strings = np.array(_strings)
# ======== call TENSORFLOW -----==================
sentences_emb = self.embedd_tokenized_text(_strings, lens)
# ================================================
patterns_emb = []
if trim_padding:
for i, (start, end) in enumerate(regions):
sentence_emb = sentences_emb[i]
pattern_emb = sentence_emb[start:end]
patterns_emb.append(pattern_emb)
patterns_emb = np.array(patterns_emb)
else:
patterns_emb = sentences_emb
return patterns_emb, regions
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 test_concat_TextMap(self):
tm1 = TextMap('a')
tm2 = TextMap('b')
tm1 += tm2
self.assertEqual('ab', tm1.text)
self.assertEqual('a', tm1.tokens[0])
self.assertEqual('b', tm1.tokens[1])
self.assertEqual(2, len(tm1))
self.assertEqual(1, len(tm2))
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_concat_TextMap2(self):
tm1 = TextMap('alpha \n')
tm2 = TextMap('bet')
tm1 += tm2
self.assertEqual(3, len(tm1))
self.assertEqual(1, len(tm2))
self.assertEqual('alpha \nbet', tm1.text)
self.assertEqual('alpha', tm1.tokens[0])
self.assertEqual('bet', tm1.tokens[2])
def find_most_relevant_paragraphs(section: TextMap,
attention_vector: FixedVector,
min_len: int = 20,
return_delimiters=True,
threshold=0.45):
_blur = int(HyperParameters.subject_paragraph_attention_blur)
_padding = int(_blur * 2 + 1)
paragraph_attention_vector = smooth_safe(
np.pad(attention_vector, _padding, mode='constant'),
_blur)[_padding:-_padding]
paragraph_attention_vector = relu(paragraph_attention_vector, threshold)
top_indices = [
i for i, v in enumerate(paragraph_attention_vector) if v > 0.00001
]
spans = []
for i in top_indices:
span = section.sentence_at_index(i, return_delimiters)
if min_len is not None and span[1] - span[0] < min_len:
if not span in spans:
spans.append(span)
return spans, paragraph_attention_vector
def test_tokens_in_range_start_from_space(self):
text = ' мама'
tm = TextMap(text)
self.assertEqual(1, tm.map[0][0])
self.assertEqual(0, tm.token_index_by_char(0))
txt = ' ' * 20 + '''основании Устава, с одной стороны, и Фонд «Благо»'''
# tm = TextMap(txt)
doc = LegalDocument(txt).parse()
tm = doc.tokens_map
print(tm.map[0])
print(tm.tokens[11])
print(tm.map[11])
print(f'[{doc.tokens_map.text}]')
print(f'[{doc.text}]')
def test_token_indices_by_char_range_sliced(self):
text = 'm йe qwert'
__tm = TextMap(text) # tokenization
tm = __tm.slice(slice(1, 2))
self.assertEqual('йe', tm.tokens[0])
self.assertEqual('йe', tm.text)
char_range = tm.char_range([0, 1])
ti = tm.token_indices_by_char_range(char_range)
self.assertEqual(0, ti[0])
self.assertEqual(1, ti[1])
ti = tm.token_indices_by_char_range([1, 2])
self.assertEqual(0, ti[0])
def test_slice(self):
text = 'этилен мама ಶ್ರೀರಾಮ'
tm = TextMap(text)
tm2: TextMap = tm.slice(slice(1, 2))
self.assertEqual(tm2[0], 'мама')
self.assertEqual(tm2.text, 'мама')
tm3 = tm2.slice(slice(0, 1))
self.assertEqual(tm3[0], 'мама')
self.assertEqual(0, tm.token_index_by_char(1))
self.assertEqual(0, tm2.token_index_by_char(1))
self.assertEqual(0, tm3.token_index_by_char(1))
self.assertEqual('мама', tm3.text)
self.assertEqual('мама', tm3.text_range([0, 1]))
self.assertEqual('мама', tm3.text_range([0, 2]))
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_normalize_basics(self):
cn = CaseNormalizer()
tm = TextMap('стороны Заключили (ХОРОШИЙ)договор, (уррраа!!) ПРЕДМЕТ ДОГОВОРА')
tm2 = cn.normalize_tokens_map_case(tm)
self.assertEqual(tm.map, tm2.map)
self.assertEqual(tm2[1], 'заключили')
self.assertEqual(tm2[12], 'Предмет')
for i in range(len(tm)):
self.assertEqual(tm2[i].lower(), tm[i].lower())
def test_tokens_in_range(self):
text = 'мама'
tm = TextMap(text)
self.assertEqual(0, tm.token_index_by_char(0))
self.assertEqual(0, tm.token_index_by_char(1))
self.assertEqual(0, tm.token_index_by_char(2))
self.assertEqual(0, tm.token_index_by_char(3))
text = 'мама выла папу'
tm = TextMap(text)
self.assertEqual(1, tm.token_index_by_char(5))
self.assertEqual(1, tm.token_index_by_char(6))
self.assertEqual(1, tm.token_index_by_char(7))
self.assertEqual(1, tm.token_index_by_char(8))
self.assertEqual(2, tm.token_index_by_char(9))
self.assertEqual(1, tm.token_index_by_char(4))
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_get_by_index(self):
ಶ್ರೀರಾಮ = self
ಮ = 'ቋንቋ የድምጽ፣ የምልክት ወይም የምስል ቅንብር ሆኖ ለማሰብ'
ቅ = TextMap(ಮ)
ಶ್ರೀರಾಮ.assertEqual(ቅ[0], 'ቋንቋ')
ಶ್ರೀರಾಮ.assertEqual(ቅ[1], 'የድምጽ፣')
ಶ್ರೀರಾಮ.assertEqual(ቅ[2], 'የምልክት')
# test iteration
for x in ቅ:
print(x)
# test slicing
print(ቅ[0:2])
def doc_features(tokens_map: TextMap):
body_lines_ranges = tokens_map.split_spans(PARAGRAPH_DELIMITER,
add_delimiter=True)
_doc_features = []
_line_spans = []
ln = 0
_prev_features = None
for line_span in body_lines_ranges:
_line_spans.append(line_span)
_features = line_features(tokens_map, line_span, ln, _prev_features)
_doc_features.append(_features)
_prev_features = _features
ln += 1
doc_featuresX_data = pd.DataFrame.from_records(_doc_features)
doc_features_data = np.array(doc_featuresX_data)
return doc_features_data, _line_spans
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 get_sentence_map(self) -> TextMap:
if 'sentences' in self.analysis['tokenization_maps']:
_map = self.analysis['tokenization_maps']['sentences']
tokens_map = TextMap(self.analysis['normal_text'], _map)
return tokens_map
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 test_PARAGRAPH_DELIMITER(self):
tm = TextMap('a' + PARAGRAPH_DELIMITER + 'b')
print(tm.tokens)
self.assertEqual(3, len(tm))
def test_get_len(self):
text = 'а б с'
tm = TextMap(text)
self.assertEqual(3, len(tm))
def test_get_tokens(self): text = 'ಉಂದು ಅರ್ತೊಪೂರ್ಣೊ ವಾಕ್ಯೊಲೆನ್ ಕೊರ್ಪುನ ಸಾಮರ್ತ್ಯೊನು ಹೊಂದೊಂತ್ ' tm = TextMap(text) print(tm.tokens) self.assertEqual(6, len(tm.tokens))
def test_tokenize_numbered(self): text = '1. этилен мама, этилен!' tm = TextMap(text) self.assertEqual(tm.tokens[0], '1.') self.assertEqual(tm.tokens[1], 'этилен')
def test_split(self):
text = '1 2 3\nмама\nಶ್ರೀರಾಮ'
tm = TextMap(text)
for k in tm.split('\n'):
print(k)