def analyse(self, vi_ch, smart=False):
'''Tries every possible English letter for a Vietnamese mark.
Parameters
----------
vi_ch : char
Vietnamese mark
smart : bool
whether or not the method is smart enough to always choose the option with the higher probability
'''
for en_ch in vi_ch_alphabet[vi_ch]:
df = self.apply_ch(vi_ch, en_ch, smart=smart)
fer = (df['en_chosen'] * df['vi_prob']).sum()
fvr = (df['vi_chosen'] * df['en_prob']).sum()
score = fer + fvr
logger.info("{}: {}={}+{}".format(en_ch, score, fer, fvr))
def search_all(self, attempt_count=100, smart=False):
best_score = 100.0
for i in range(attempt_count):
input_method = ''
for vi_ch in vi_code:
s = vi_ch_alphabet[vi_ch]
input_method += s[randrange(len(s))]
input_method, df, fer, fvr, score = self.search(input_method,
smart=smart)
if score < best_score:
best_df = df
best_fer = fer
best_fvr = fvr
best_score = score
best_input_method = input_method
logger.info(
"------ Current best score {}={}+{} from {} -----".format(
best_score, best_fer, best_fvr, best_input_method))
return best_input_method, best_df, best_fer, best_fvr, best_score
def search(self, initial_input_method, smart=False):
'''Searches for the locally best input method. Not too optimal at the moment'''
best_input_method = initial_input_method
best_df, best_fer, best_fvr = self.apply(best_input_method,
smart=smart)
best_score = best_fer + best_fvr
logger.info("Initial score {}={}+{} from {}".format(
best_score, best_fer, best_fvr, best_input_method))
while True:
better = False
for i in range(len(vi_code)):
for ch in vi_alphabet:
input_method = best_input_method[:
i] + ch + best_input_method[
i + 1:]
if not valid(input_method):
continue
df, fer, fvr = self.apply(input_method, smart=smart)
score = fer + fvr
if score < best_score:
best_df = df
best_fer = fer
best_fvr = fvr
best_score = score
best_input_method = input_method
logger.info("Better score {}={}+{} from {}".format(
best_score, best_fer, best_fvr, best_input_method))
better = True
if not better:
break
logger.info("Best input method: {}".format(best_input_method))
return best_input_method, best_df, best_fer, best_fvr, best_score
def make_corpus(in_f, trie_f, out_f):
logger.info("Loading the enwiki trie '{}'...".format(trie_f))
trie = Trie.from_file(trie_f)
logger.info("Loading the viwiki split csv '{}'...".format(in_f))
df = pc.read_csv(in_f)
df.columns = ['word', 'count', 'vi_prob']
alphabet = 'bdfjklqrsvwxzadeou'
s = df['word'].str.slice(stop=-1)
for ch in alphabet:
logger.info(ch)
df[ch] = s.apply(lambda x: trie.prob(x + ch))
logger.info("Saving the expanded viwiki split csv '{}'...".format(out_f))
pc.to_csv(df, out_f, index=False)
def make_corpus(in_f, out_f):
"""Convert Wikipedia xml dump file to text corpus"""
logger.info("Opening the Wikipedia dump '{}'...".format(in_f))
wiki = WikiCorpus(in_f, token_min_len=1)
i = 0
w = 0
counter = Counter()
for text in wiki.get_texts():
if (i % 10000 == 0):
logger.info('Processed {} articles with {} words'.format(i, w))
w += len(text)
counter.update(text)
i = i + 1
logger.info('Processing {} articles with {} words complete!'.format(i, w))
df = pd.DataFrame.from_dict(counter, orient='index').reset_index()
df = df.rename(columns={'index': 'word', 0: 'count'})
df.sort_values('count', axis=0, ascending=False, inplace=True)
pc.to_csv(df, out_f, index=False)
def make_corpus(in_f, trie_f, out_f):
logger.info("Loading the enwiki trie '{}'...".format(trie_f))
trie = Trie.from_file(trie_f)
logger.info("Loading the viwiki split csv '{}'...".format(in_f))
df = pc.read_csv(in_f)
df.columns = ['word', 'count', 'vi_prob']
alphabet = 'bdfjklqrsvwxzadeou'
s = df['word'].str.slice(stop=-1)
for ch in alphabet:
logger.info(ch)
df[ch] = s.apply(lambda x: trie.prob(x + ch))
logger.info("Saving the expanded viwiki split csv '{}'...".format(out_f))
pc.to_csv(df, out_f, index=False)
if __name__ == '__main__':
if len(sys.argv) != 4:
logger.info(
'Usage: python expand_split_corpus.py <viwiki_prefix_splitX-....csv> <enwiki_restricted_....trie> <viwiki_prefix_splitX_expanded....csv>'
)
sys.exit(1)
in_f = sys.argv[1]
trie_f = sys.argv[2]
out_f = sys.argv[3]
make_corpus(in_f, trie_f, out_f)
s = vi_ch_alphabet[vi_ch]
input_method += s[randrange(len(s))]
input_method, df, fer, fvr, score = self.search(input_method,
smart=smart)
if score < best_score:
best_df = df
best_fer = fer
best_fvr = fvr
best_score = score
best_input_method = input_method
logger.info(
"------ Current best score {}={}+{} from {} -----".format(
best_score, best_fer, best_fvr, best_input_method))
return best_input_method, best_df, best_fer, best_fvr, best_score
if __name__ == '__main__':
if len(sys.argv) != 3:
logger.info(
'Usage: python process_wiki_dump.py <wikipedia_dump_file_that_ends_with_articles.xml.bz2> <processed_csv_file>'
)
sys.exit(1)
in_f = sys.argv[1]
out_f = sys.argv[2]
make_corpus(in_f, out_f)
# currently, the best input method is "qzxdzuzjfkaw" with fer = 0.0009225985366192045 and fvr = 0.002716891565571517
# itelex: "fesdowajrwwx" fer = 0.010456098466438603 and fvr = 0.014519511048841487
pass
