def get_data():
output_folder = join(dirname(dirname(dirname(__file__))), "data",
"corpus_2", "test", "output")
model_output_folder = join(dirname(dirname(dirname(__file__))), "data",
"corpus_2", "test", "output_%s" % model_name)
expected_corpus = PlainTextCorpus()
expected_corpus.load(output_folder)
actual_corpus = PlainTextCorpus()
actual_corpus.load(model_output_folder)
return expected_corpus, actual_corpus
def compare_dictionary(model_output_folder):
# f = open(join(dirname(__file__), "logs", "crf", "new_word.txt"), "w")
# f1 = open(join(dirname(__file__), "logs", "crf", "word_in_dictionary.txt"), "w")
corpus = PlainTextCorpus()
corpus.load(model_output_folder)
new_words = []
words = []
for document in corpus.documents:
for sentences in document.sentences:
for word in sentences.split(" "):
if '_' in word:
new_words.append(word)
dictionary = viet_dict_11K.words
for word in new_words:
words.append(word.replace('_', ' '))
new_word = [x for x in words if x not in dictionary]
new_word = set(new_word)
new_word = sorted(new_word)
new_word_per_dict = float(len(new_word)) / float(len(dictionary)) * 100
# f.write("Scale word not in dictionary %0.2f: \n" % new_word_per_dict)
# for word in new_word:
# f.write(word.encode('utf-8') + "\n")
word_in_dictionary = [x for x in words if x in dictionary]
word_in_dictionary = set(word_in_dictionary)
word_in_dictionary = sorted(word_in_dictionary)
word_in_dictionary_per_total = float(len(word_in_dictionary)) / float(
len(viet_dict_11K.words))
# f1.write("scale word in dictionary: %0.2f \n" % word_in_dictionary_per_total)
# for word in word_in_dictionary:
# f1.write(word.encode('utf-8') + "\n")
return new_word, word_in_dictionary
def find_punctuation(folder, file_name):
f_write = open(file_name, "w")
punctuations = []
corpus = PlainTextCorpus()
corpus.load(folder)
punctuations = sorted(punctuations)
for punctuation in punctuations:
f_write.write(punctuation.encode("utf-8") + "\n")
return punctuations
def test_save(self):
corpus = PlainTextCorpus()
corpus.load(self.plaintext_folder)
corpus.save(self.saved_plaintext_folder)
files = listdir(self.saved_plaintext_folder)
self.assertEqual(4, len(files))
try:
shutil.rmtree(self.saved_plaintext_folder)
except Exception:
pass
def segment_words(corpus_dir, target_dir):
try:
mkdir(target_dir)
except:
pass
corpus = PlainTextCorpus()
corpus.load(corpus_dir)
existed_documents = listdir(target_dir)
n_ignore = 0
for document in corpus.documents:
document_id = document.id
if document_id not in existed_documents:
print("Process %s" % document_id)
sentences = document.sentences
sentences = _.flatten(sentences)
sentences = [tokenize(s).split(" . ") for s in sentences]
sentences = _.flatten(sentences)
segmented_sentences = [word_sent(s) for s in sentences if s not in [u""]]
content = convert_to_text(segmented_sentences)
filepath = join(target_dir, document.id)
io.open(filepath, "w", encoding="utf8").write(content)
else:
n_ignore += 1
print("Ignore %s documents" % n_ignore)
"vi_ud_no_comment.conllu")
dev_file = join(dirname(dirname(dirname(__file__))), "data", "ud",
"dev.conllu")
test_file = join(dirname(dirname(dirname(__file__))), "data", "ud",
"test.conllu")
# sample_ud_file = join("sample", "sample_vi_ud.conllu")
# ud_file = sample_ud_file
def extract_tokens(text):
matched = re.match("(.*)/(.*)", text, re.UNICODE)
return [matched.group(1), matched.group(2)]
if __name__ == '__main__':
corpus = PlainTextCorpus()
corpus.load(raw_folder)
tagged_corpus = TaggedCorpus()
tagged_documents = []
documents = corpus.documents
# documents = random.sample(documents, 10)
for document in documents:
sentences = []
for sentence in document.sentences:
tagged_tokens = sentence.split()
tagged_words = [extract_tokens(token) for token in tagged_tokens]
tagged_words = [
TaggedWord(tagged_word[0].replace("_", " "), tagged_word[1])
for tagged_word in tagged_words
]
sentence = TaggedSentence(tagged_words)
# -*- coding: utf-8 -*-
from os.path import dirname
from os.path import join
import time
from model import CRFModel
from underthesea.corpus import PlainTextCorpus
start = time.time()
input_folder = join(dirname(dirname(dirname(__file__))), "data", "corpus",
"test", "input")
output_crf_folder = join(dirname(dirname(dirname(__file__))), "data", "corpus",
"test", "output_crf")
# input_folder = join(dirname(dirname(dirname(__file__))), "data", "test", "input")
# output_crf_folder = join(dirname(dirname(dirname(__file__))), "data", "test", "output")
corpus = PlainTextCorpus()
corpus.load(input_folder)
output = PlainTextCorpus()
model = CRFModel()
for document in corpus.documents:
print document.id
sentences = document.sentences
output = []
for sentence in sentences:
sentence = model.predict(sentence)
output.append(sentence)
document.sentences = output
count = 0
for document in corpus.documents:
def test_unicode(self):
corpus = PlainTextCorpus()
corpus.load(self.plaintext_folder)
self.assertEqual(unicode, type(corpus.documents[0].sentences[0]))
def test_sentences(self):
corpus = PlainTextCorpus()
corpus.load(self.plaintext_folder)
self.assertEqual(list, type(corpus.documents[0].sentences))
def test_load(self):
corpus = PlainTextCorpus()
corpus.load(self.plaintext_folder)
self.assertEqual(4, len(corpus.documents))
def test___init__(self):
corpus = PlainTextCorpus()
self.assertIsNone(corpus.documents)