def __preprocess_search_query(self, query):
# create tokens
consumed = AnalyzeContent(text_weights=[(query, 1)])
search_tokens = consumed.content_token_list()
for i, token in enumerate(search_tokens):
# attempt to spellcheck invalid tokens
if len(token) >= 3:
if self._spellcheck.unknown([token]):
search_tokens[i] = self._spellcheck.correction(token)
# print('corrected ' + token + ' to ' + search_tokens[i])
token = search_tokens[i]
if i > 0 and autocomplete.predict_currword(search_tokens[i - 1]):
prev = search_tokens[i - 1]
pred = autocomplete.predict(prev, token)
else:
pred = autocomplete.predict_currword(token)
if len(pred) > 0:
best_pred = pred[0][0]
else:
best_pred = token
search_tokens[i] = self._lemmatizer.lemmatize(best_pred)
# remove stopwords if we have non-stopword terms. otherwise search purely by stopwords as a last case.
non_stopwords = [x for x in search_tokens if x not in self._stopwords]
if non_stopwords:
search_tokens = non_stopwords
return search_tokens
def predict(statement=[]):
sentence = "".join(statement).split(" ")
choices = []
one = None
two = None
if len(sentence) > 1:
length = len(
autocomplete.predict_currword_given_lastword(
sentence[-2], sentence[-1]))
if length >= 1:
one = autocomplete.predict_currword_given_lastword(
sentence[-2], sentence[-1])[0]
if length >= 2:
two = autocomplete.predict_currword_given_lastword(
sentence[-2], sentence[-1])[1]
else:
length = len(autocomplete.predict_currword(sentence[-1]))
if length >= 1: one = autocomplete.predict_currword(sentence[-1])[0]
if length >= 2: two = autocomplete.predict_currword(sentence[-1])[1]
if one is not None: one = extract(one)
if two is not None: two = extract(two)
if one is not None: choices.append(one)
else: choices.append("N/A")
choices.append(sentence[-1])
if two is not None: choices.append(two)
else: choices.append("N/A")
print " - ".join(choices)
return choices
def __init__(self, word, prev_word, num_choices): if prev_word is None: word_list = autocomplete.predict_currword(word,num_choice) else: tmp_wordlist1 = autocomplete.predict_currword(word,num_choice) tmp_wordlist2 = autocomplete.predict_currword_given_lastword(prev_word, word, num_choice) if len(tmp_wordlist1) > len(tmp_wordlist2): word_list = tmp_wordlist1 else: word_list = tmp_wordlist2 self.w_size = len(word_list) for i in range(0, len(word_list)-1): (word_list[i])[1] = (len(word_list)/2 - 1)+((-1)^(i+1))*i self.make_wordTree(word_list)
def get_ac_list(str, count):
ac_list = []
words = str.split()
# Note that the auto-complete library only supports up to two full words
if len(words) > 2:
return ac_list
if len(words) > 1:
try:
l = autocomplete.predict(words[0], words[1], count)
for name,num in l:
ac_list.append(words[0]+' '+name)
except:
# Due to a bug in the auto-complete library,
# words that contain number (e.g. abcde10) doesn't get
# handled properly
return []
elif len(words) == 1:
l = autocomplete.predict_currword(words[0], count)
for name,num in l:
ac_list.append(name)
return ac_list
def display():
global curr_word
#print(curr_word)
result = autocomplete.predict_currword(curr_word, top_n=1000)
#print(result)
total = calculate_frequency(result)
#bn print(str(total) + "\n")
ignite(total)
def autocomplete_word(text):
try:
autocomplete.load()
autoword = autocomplete.predict_currword(text)
autoword = re.sub(r'[^\w]', ' ', str(autoword[0]))
autoword = re.sub(r'\d+', ' ', autoword)
return True,autoword.strip()
except:
return False,"Can not predict word"
async def completing(letters):
"""
predict_currword()
"""
text = ' '
words = await Complete.fetch_all_saved_user_events()
for i in range(0, len(words)):
pos = words[i]
text = text + ' ' + str(pos['event'])
models.train_models(text)
return autocomplete.predict_currword(letters)
def predict_word(text, max_suggestions=10):
if " " not in text:
results = [
x[0] for x in autocomplete.predict_currword(text.lower(),
top_n=max_suggestions)
]
else:
words = text.lower().split(" ")
results = [
x[0] for x in autocomplete.predict_currword_given_lastword(
words[-2], words[-1], top_n=max_suggestions)
]
return results
def predictCity(self, string, threshold, newLocationDir, export=True):
if not string: return None
words = self.getFuzzTags(string)
match = None
score = 0
autoCompleteCache = {}
for word in words:
searchWord = word[:4].strip().lower()
if autoCompleteCache.get(searchWord, False):
predictedWords = autoCompleteCache.get(searchWord, 0)
else:
predictedWords = autocomplete.predict_currword(searchWord,
top_n=10)
autoCompleteCache[searchWord] = predictedWords
for predictedWord in predictedWords:
predictedCityNames = self.cityNames[predictedWord[0]]
if not predictedCityNames:
continue
for candidate in predictedCityNames:
newScore = fuzz.token_sort_ratio(string, candidate)
if newScore > score:
score = newScore
match = candidate
if export is True:
f = open(os.path.join(newLocationDir, 'no_match.txt'), 'a+')
if match is None:
if export is True:
f.write('{0},{1},"{2}"\n'.format(str(score), str(match),
string))
f.close()
return False #No match
if score >= threshold:
print 'match:' + str(score) + '---' + str(match) + '---' + string
return match
else:
if export is True:
f.write('{0},"{1}","{2}"\n'.format(str(score), str(match),
string))
f.close()
return True #Not close enough match
def predictCity(self,string,threshold,newLocationDir,export=True):
if not string: return None
words = self.getFuzzTags(string)
match = None
score = 0
autoCompleteCache = {}
for word in words:
searchWord = word[:4].strip().lower()
if autoCompleteCache.get(searchWord,False):
predictedWords = autoCompleteCache.get(searchWord,0)
else:
predictedWords = autocomplete.predict_currword(searchWord,top_n=10)
autoCompleteCache[searchWord] = predictedWords
for predictedWord in predictedWords:
predictedCityNames = self.cityNames[predictedWord[0]]
if not predictedCityNames:
continue
for candidate in predictedCityNames:
newScore = fuzz.token_sort_ratio(string,candidate)
if newScore > score:
score = newScore
match = candidate
if export is True: f = open(os.path.join(newLocationDir,'no_match.txt'),'a+')
if match is None:
if export is True:
f.write('{0},{1},"{2}"\n'.format(str(score),str(match),string))
f.close()
return False #No match
if score >= threshold:
print 'match:'+str(score)+'---'+str(match)+'---'+string
return match
else:
if export is True:
f.write('{0},"{1}","{2}"\n'.format(str(score),str(match),string))
f.close()
return True #Not close enough match
def predict_character(text, max_suggestions=27):
if " " not in text:
results = autocomplete.predict_currword(text.lower(),
top_n=max_suggestions)
characters = [
x[0][len(text)] if not x[0][len(text):] == "" else " "
for x in results
]
else:
words = text.lower().split(" ")
results = autocomplete.predict_currword_given_lastword(
words[-2], words[-1], top_n=max_suggestions)
characters = [
x[0][len(words[-1])] if not x[0][len(words[-1]):] == "" else " "
for x in results
]
probabilities = [x[1] for x in results]
character_dict = {}
for character in range(len(characters)):
character_dict[characters[character]] = sum([
probabilities[x] for x in range(len(characters))
if characters[character] == characters[x]
])
return sorted(character_dict, key=character_dict.__getitem__, reverse=True)