def process_vocab((vocab, word_dicts, max_weight)):
from greek_tools import is_capitalized
debug = True
(dict_words, words_clean, words_freq) = word_dicts
output_string = ''
euro_sign = unicode(u"\N{EURO SIGN}")
for wordIn in vocab:
wordIn_original = wordIn
wordIn = preprocess_word(wordIn)
output_words = getCloseWords(wordIn,
word_dicts,
teubner_serif_weights,
max_weight,
threshold=3)
# If the word doesn't have an exact match, and it is capitalized, then redo with
# a uncapitalized version
isCapitalized = False
hasBeenLowered = False
if debug:
print
print wordIn.encode('utf-8')
if is_capitalized(wordIn):
if debug:
print wordIn.encode('utf-8'), "is capitalized"
isCapitalized = True
min_weight = max_weight + 1
for output_word in output_words:
if output_word[3] < min_weight:
min_weight = output_word[3]
if debug:
print "minweight is ", min_weight
if isCapitalized and (len(output_words) == 0
or min_weight > max_weight):
if debug:
for word, lev_distance, n, w, junk1, junk2 in output_words[:8]:
print word, words_clean[word].encode(
'utf-8'), w, lev_distance, words_freq[word]
print "not found directly, so using", wordIn.lower().encode(
'utf-8')
output_words = getCloseWords(wordIn.lower(),
word_dicts,
teubner_serif_weights,
max_weight,
threshold=3)
hasBeenLowered = True
# print
# print wordIn, ":"
# If the input word is in the dictionary
if len(output_words) > 0 and output_words[0][1] == 0:
if debug:
print "*"
else:
if len(output_words) > 0 and output_words[0][3] < max_weight:
best_result_word = words_clean[output_words[0][0]]
if (hasBeenLowered):
best_result_word = best_result_word.capitalize()
if not (best_result_word == wordIn_original
or best_result_word == wordIn_original.lower()):
output_string += wordIn_original + euro_sign + best_result_word + '\n'
if debug:
dump(wordIn_original)
print
dump(wordIn)
print
dump(best_result_word)
for word, lev_distance, n, w, junk1, junk2 in output_words[:8]:
if (hasBeenLowered):
word_to_print = word.capitalize()
else:
word_to_print = word
if debug:
print word_to_print, words_clean[word].encode(
'utf-8'), w, lev_distance, words_freq[word]
# dump(word_to_print)
# print
# dump(words_clean[word])
# dump(word)
if (lev_distance == 0):
break
return output_string
def process_vocab((vocab,word_dicts, max_weight)):
from greek_tools import is_capitalized
debug = True
(dict_words, words_clean, words_freq) = word_dicts
output_string = ''
euro_sign = unicode(u"\N{EURO SIGN}")
for wordIn in vocab:
wordIn_original = wordIn
wordIn = preprocess_word(wordIn)
output_words = getCloseWords(
wordIn, word_dicts, teubner_serif_weights, max_weight, threshold=3)
# If the word doesn't have an exact match, and it is capitalized, then redo with
# a uncapitalized version
isCapitalized = False
hasBeenLowered = False
if debug:
print
print wordIn.encode('utf-8')
if is_capitalized(wordIn):
if debug:
print wordIn.encode('utf-8'), "is capitalized"
isCapitalized = True
min_weight = max_weight + 1
for output_word in output_words:
if output_word[3] < min_weight:
min_weight = output_word[3]
if debug:
print "minweight is ", min_weight
if isCapitalized and (len(output_words) == 0 or min_weight > max_weight):
if debug:
for word, lev_distance, n, w, junk1, junk2 in output_words[:8]:
print word, words_clean[word].encode('utf-8'), w, lev_distance, words_freq[word]
print "not found directly, so using", wordIn.lower().encode('utf-8')
output_words = getCloseWords(wordIn.lower(
), word_dicts, teubner_serif_weights, max_weight, threshold=3)
hasBeenLowered = True
# print
# print wordIn, ":"
# If the input word is in the dictionary
if len(output_words) > 0 and output_words[0][1] == 0:
if debug:
print "*"
else:
if len(output_words) > 0 and output_words[0][3] < max_weight:
best_result_word = words_clean[output_words[0][0]]
if (hasBeenLowered):
best_result_word = best_result_word.capitalize()
if not (best_result_word == wordIn_original or best_result_word == wordIn_original.lower()):
output_string += wordIn_original + euro_sign + best_result_word + '\n'
if debug:
dump(wordIn_original)
print
dump(wordIn)
print
dump(best_result_word)
for word, lev_distance, n, w, junk1, junk2 in output_words[:8]:
if (hasBeenLowered):
word_to_print = word.capitalize()
else:
word_to_print = word
if debug:
print word_to_print, words_clean[word].encode('utf-8'), w, lev_distance, words_freq[word]
# dump(word_to_print)
# print
# dump(words_clean[word])
# dump(word)
if (lev_distance == 0):
break
return output_string
def spellcheck_urls(dict_file,
urls,
output_file_name,
max_weight=9,
debug=False):
from urllib import urlopen
import nltk
from nltk.tokenize import RegexpTokenizer
from itertools import repeat
from multiprocessing import Pool
import codecs
all_tokens = []
output_file = codecs.open(output_file_name, 'w', 'utf-8')
#print "numbre of urls: ", len(urls)
for url in urls:
raw = urlopen(url).read().decode('utf-8')
n = 0
lines = raw.split("\n")
#if debug:
#print 'page:', url
#for line in lines:
# print line
tokens = Dehyphenate(lines)
#if tokens[-1][-1] = '-':
# tokens = tokens[:-1]
#if debug:
# for token in tokens:
# print token
all_tokens = all_tokens + delete_non_greek_tokens(tokens)
if debug:
for token in all_tokens:
print token
vocab = sorted(set(all_tokens))
print "vocab of ", len(vocab), " words"
#for word in vocab:
# print word
vocab = [word for word in vocab if not is_uc_word(word)]
vocab = [word.rstrip() for word in vocab]
vocab = [word for word in vocab if not word[-1] == '-']
vocab = [word for word in vocab if not len(word) == 1]
print "non-capital words: ", len(vocab)
if debug:
print "Are they capitalized?"
from greek_tools import is_capitalized
for wordIn in vocab:
#wordIn = preprocess_word(wordIn)
print wordIn, is_capitalized(wordIn)
print "making dicts"
import time
start_time = time.time()
word_dicts = makeDict(dict_file)
dict_time = time.time() - start_time
minutes = dict_time / 60.0
print "dict building took", minutes, " minutes."
vocab_chunks = list(chunks(vocab, len(vocab) / 8))
print "vocab is ", len(vocab)
processed_vocab_chunks = zip(vocab_chunks, repeat(word_dicts),
repeat(max_weight))
print "there are ", len(processed_vocab_chunks), "chunks"
start_time = time.time()
# print "dictionary of ", len(dict_words), "words"
# vocab = [preprocess_word(a_word) for a_word in vocab]
# why doesn't this trimm all the ones that pass spellcheck?
# vocab = sorted(set(vocab).difference(set(dict_words)))
# print "vocab trimmed of dictionary words to ", len(vocab)
p = Pool(processes=10)
output = p.map(process_vocab, processed_vocab_chunks)
for output_chunk in output:
output_file.write(output_chunk)
pool_time = time.time() - start_time
minutes = pool_time / 60.0
print "processing took", minutes, " minutes"
def spellcheck_urls(dict_file, urls, output_file_name, max_weight=9, debug=False):
from urllib import urlopen
import nltk
from nltk.tokenize import RegexpTokenizer
from itertools import repeat
from multiprocessing import Pool
import codecs
all_tokens = []
output_file= codecs.open(output_file_name, 'w', 'utf-8')
#print "numbre of urls: ", len(urls)
for url in urls:
raw = urlopen(url).read().decode('utf-8')
n = 0
lines = raw.split("\n")
#if debug:
#print 'page:', url
#for line in lines:
# print line
tokens = Dehyphenate(lines)
#if tokens[-1][-1] = '-':
# tokens = tokens[:-1]
#if debug:
# for token in tokens:
# print token
all_tokens = all_tokens + delete_non_greek_tokens(tokens)
if debug:
for token in all_tokens:
print token
vocab = sorted(set(all_tokens))
print "vocab of ", len(vocab), " words"
#for word in vocab:
# print word
vocab = [word for word in vocab if not is_uc_word(word)]
vocab = [word.rstrip() for word in vocab]
vocab = [word for word in vocab if not word[-1] == '-']
vocab = [word for word in vocab if not len(word) == 1 ]
print "non-capital words: ", len(vocab)
if debug:
print "Are they capitalized?"
from greek_tools import is_capitalized
for wordIn in vocab:
#wordIn = preprocess_word(wordIn)
print wordIn, is_capitalized(wordIn)
print "making dicts"
import time
start_time = time.time()
word_dicts = makeDict(dict_file)
dict_time = time.time() - start_time
minutes = dict_time / 60.0
print "dict building took", minutes, " minutes."
vocab_chunks = list(chunks(vocab, len(vocab) / 8))
print "vocab is ", len(vocab)
processed_vocab_chunks = zip(vocab_chunks, repeat(word_dicts), repeat(max_weight))
print "there are ", len(processed_vocab_chunks), "chunks"
start_time = time.time()
# print "dictionary of ", len(dict_words), "words"
# vocab = [preprocess_word(a_word) for a_word in vocab]
# why doesn't this trimm all the ones that pass spellcheck?
# vocab = sorted(set(vocab).difference(set(dict_words)))
# print "vocab trimmed of dictionary words to ", len(vocab)
p = Pool(processes=10)
output = p.map(process_vocab,processed_vocab_chunks)
for output_chunk in output:
output_file.write(output_chunk)
pool_time = time.time() - start_time
minutes = pool_time / 60.0
print "processing took", minutes, " minutes"
