from malaya.text.normalization import _is_number_regex
from malaya.text.function import (
check_ratio_numbers,
check_ratio_punct,
is_emoji,
is_laugh,
is_mengeluh,
PUNCTUATION,
)
from malaya.dictionary import is_malay, is_english
from typing import List
import logging
logger = logging.getLogger(__name__)
[docs]class LanguageDict:
def __init__(self, model, **kwargs):
enchant_available = True
try:
import enchant
except BaseException:
logger.warning(
'pyenchant not installed. Please install it by `pip3 install pyenchant` and try again. For now, pyenchant will be disabled.')
enchant_available = False
try:
self.d = enchant.Dict('en_US')
self.d.check('Hello')
except BaseException:
logger.warning(
'cannot load `en_US` enchant dictionary. Please install it from https://pyenchant.github.io/pyenchant/install.html and try again. For now, pyenchant will be disabled.')
enchant_available = False
self._enchant_available = enchant_available
self._model = model
[docs] def predict(
self,
words: List[str],
acceptable_ms_label: List[str] = ['malay', 'ind'],
acceptable_en_label: List[str] = ['eng', 'manglish'],
use_is_malay: bool = True,
):
"""
Predict [EN, MS, OTHERS, CAPITAL, NOT_LANG] on word level.
This method assumed the string already tokenized.
Parameters
----------
words: List[str]
acceptable_ms_label: List[str], optional (default = ['malay', 'ind'])
accept labels from language detection model to assume a word is `MS`.
acceptable_en_label: List[str], optional (default = ['eng', 'manglish'])
accept labels from language detection model to assume a word is `EN`.
use_is_malay: bool, optional (default=True)
if True`, will predict MS word using `malaya.dictionary.is_malay`,
else use language detection model.
Returns
-------
result: List[str]
"""
results, others, indices = [], [], []
for no, word in enumerate(words):
if is_emoji(word):
results.append('NOT_LANG')
elif word.isupper():
results.append('CAPITAL')
elif _is_number_regex(word.replace(',', '').replace('.', '')):
results.append('NOT_LANG')
elif word in PUNCTUATION:
results.append('NOT_LANG')
elif is_laugh(word):
results.append('NOT_LANG')
elif is_mengeluh(word):
results.append('NOT_LANG')
elif check_ratio_numbers(word) > 0.6666:
results.append('NOT_LANG')
elif check_ratio_punct(word) > 0.66666:
results.append('NOT_LANG')
elif self._enchant_available and self.d.check(word):
results.append('EN')
elif use_is_malay and is_malay(word.lower()):
results.append('MS')
else:
results.append('REPLACE_ME')
others.append(word)
indices.append(no)
labels = self._model.predict(others)
for no in range(len(labels)):
if labels[no] in acceptable_ms_label:
results[indices[no]] = 'MS'
elif labels[no] in acceptable_en_label:
results[indices[no]] = 'EN'
else:
results[indices[no]] = 'OTHERS'
return results
