def stem_metn(request):
soz_class = NameForm
cumle_class = TextForm
morf_class = SozForm
porter = PorterStemmer()
lancaster = LancasterStemmer()
k = request.POST.get('metn', '')
alqo = request.POST.get('alqo', '')
txt = k
if alqo == 'Bizim Alqoritm':
txt = metn_oxu(k)
elif alqo == 'Porter Alqoritmi':
txt = porter.stem(txt)
elif alqo == 'Lancaster Alqoritmi':
txt = lancaster.stem(txt)
elif alqo == 'WordNet Alqoritmi':
wordnet_lemmatizer = WordNetLemmatizer()
txt = metn_oxu(wordnet_lemmatizer.stem(k))
return render(request, 'metn.html', {
'form': soz_class,
'cumle': cumle_class,
'morf': morf_class,
'txt': txt
})
def data_prepare(language_dict, ru=False, en=False, es=False):
if ru:
stop_words = set(stopwords.words('russian'))
lemmatizer = pymorphy2.MorphAnalyzer()
elif en:
stop_words = set(stopwords.words('english'))
lemmatizer = WordNetLemmatizer()
elif es:
stop_words = set(stopwords.words('spanish'))
lemmatizer = nltk.stem.SnowballStemmer('spanish')
dict_prepared = []
for text in tqdm(language_dict):
text = re.sub(r'[^\w\s]', '', text.lower())
text = re.sub(r'[0-9]', '', text)
word_tokens = word_tokenize(text)
word_tokens = [w for w in word_tokens if not w in stop_words]
if ru:
word_tokens = [
lemmatizer.parse(w)[0].normal_form for w in word_tokens
]
elif en:
word_tokens = [lemmatizer.lemmatize(w) for w in word_tokens]
elif es:
word_tokens = [lemmatizer.stem(w) for w in word_tokens]
word_tokens = [w for w in word_tokens if not w in stop_words]
filtered_text = ' '.join(word_tokens)
dict_prepared.append(filtered_text)
return dict_prepared
def stemming_word_with_WordNet(word):
print("actual word " + word)
word_net = WordNetLemmatizer()
"""
WordNetLemmatizer
arg-1 : word
arg-2 : noun, verb, adjective etc.. eg: (pos ="v")
"""
print("stemmed word " + word_net.stem(word))
def stem_cluster(data, mode = 10, length_at_least = 3):
global stemmer
# load default stemmer (nltk lemmatizer)
if stemmer == None:
try: # import if corpus exists
from nltk.stem import WordNetLemmatizer
except: # download corpora if does not exist
import nltk
if not nltk.download('wordnet'):
raise Exception('Error in downloading wordnet. \
Please make sure you are connected to the network, \
or try downloading manually.')
from nltk.stem import WordNetLemmatizer
# cache the default stemmer
stemmer = WordNetLemmatizer()
# port the lemmatizer as the stemmer
stemmer.stem = stemmer.lemmatize
from algoutils import flatten, split
from collections import defaultdict
# split data into words
words = flatten(split(data, ' '))
# collect frequency of individual words
frequency = defaultdict(int)
for word in words:
if len(word) >= length_at_least:
frequency[word] += 1
# filter words by frequency
words = filter(lambda (word,freq): freq >= mode, frequency.items())
words = list(zip(*words)[0])
# trim stems
stem_map = defaultdict(list)
stem = stemmer.stem
for word in words:
stem_map[stem(word)].append(word)
# only return representative
# aka. the word with least length
return map(lambda rep: min(rep, key=len), stem_map.values())
def stem_cluster(data, mode=10, length_at_least=3):
global stemmer
# load default stemmer (nltk lemmatizer)
if stemmer == None:
try: # import if corpus exists
from nltk.stem import WordNetLemmatizer
except: # download corpora if does not exist
import nltk
if not nltk.download('wordnet'):
raise Exception('Error in downloading wordnet. \
Please make sure you are connected to the network, \
or try downloading manually.')
from nltk.stem import WordNetLemmatizer
# cache the default stemmer
stemmer = WordNetLemmatizer()
# port the lemmatizer as the stemmer
stemmer.stem = stemmer.lemmatize
from algoutils import flatten, split
from collections import defaultdict
# split data into words
words = flatten(split(data, ' '))
# collect frequency of individual words
frequency = defaultdict(int)
for word in words:
if len(word) >= length_at_least:
frequency[word] += 1
# filter words by frequency
words = filter(lambda (word, freq): freq >= mode, frequency.items())
words = list(zip(*words)[0])
# trim stems
stem_map = defaultdict(list)
stem = stemmer.stem
for word in words:
stem_map[stem(word)].append(word)
# only return representative
# aka. the word with least length
return map(lambda rep: min(rep, key=len), stem_map.values())
class Stemmer:
stemmer = None
mode = None
join = True
def __init__(self, mode='Porter', join=True):
if (mode == 'Porter'):
from nltk.stem import PorterStemmer
self.stemmer = PorterStemmer()
elif (mode == 'Lancaster'):
from nltk.stem import LancasterStemmer
self.stemmer = LancasterStemmer()
elif (mode == 'Lemmatize'):
from nltk.stem import WordNetLemmatizer
self.stemmer = WordNetLemmatizer()
elif (mode == 'Snowball'):
raise Exception("TODO")
elif (mode == 'Regexp'):
raise Exception("TODO")
self.mode = mode
self.join = join
def __str__(self):
return ('NLTK Stemmer using ' + self.mode + ' Stemming')
# data is a list of strings or a list of list of strings
# returns either a list of words or a joined list
def fitTransform(self, data):
return self.transform(data)
# data is a list of list of words
# returns either a list of words or a joined list
def transform(self, data):
# for each list of words in data list, lemmatize/stem each word
if (self.mode == "Lemmatize"):
result = [[self.stemmer.lemmatize(word) for word in doc]
for doc in data]
else:
result = [[self.stemmer.stem(word) for word in doc]
for doc in data]
# if necessary join words in each list of words in result list
if (self.join):
result = [' '.join(doc) for doc in result]
return result
def _tokenize(doc, filter_stopwords=True, normalize='lemma'):
import nltk.corpus
from nltk.stem import PorterStemmer, WordNetLemmatizer
from nltk.tokenize import sent_tokenize, wordpunct_tokenize
from string import punctuation
# use NLTK's default set of english stop words
stops_list = nltk.corpus.stopwords.words('english')
if normalize == 'lemma':
# lemmatize with WordNet
normalizer = WordNetLemmatizer()
elif normalize == 'stem':
# stem with Porter
normalizer = PorterStemmer()
# tokenize the document into sentences with NLTK default
sents = sent_tokenize(doc)
# tokenize each sentence into words with NLTK default
tokenized_sents = [wordpunct_tokenize(sent) for sent in sents]
# filter out "bad" words, normalize good ones
normalized_sents = []
for tokenized_sent in tokenized_sents:
good_words = [word for word in tokenized_sent
# filter out too-long words
if len(word) < 25
# filter out bare punctuation
if word not in list(punctuation)]
if filter_stopwords is True:
good_words = [word for word in good_words
# filter out stop words
if word not in stops_list]
if normalize == 'lemma':
normalized_sents.append(
[normalizer.lemmatize(word, 'v') for word in good_words])
elif normalize == 'stem':
normalized_sents.append([normalizer.stem(word)
for word in good_words])
else:
normalized_sents.append([word for word in good_words])
return normalized_sents
def text_normalize(text, method='lemmas'):
"""
Parameters
----------
text: str
List or pandas column of texts as string
method: str, {'lemmas','stems'}, default 'lemmas'
Normalization method used on text.
Returns
-------
normalized_text: list
List of lists with lemmas or stems as strings
Examples
--------
normalize text with lemmas
>>> dickens = ["It was the best of times.", "it was the worst of times!"]
>>> normalized_text = px.text_normalize(dickens, method = 'lemmas')
>>> normalized_text
[['it', 'be', 'the', 'best', 'of', 'time'],
['it', 'be', 'the', 'worst', 'of', 'time']]
"""
if method == 'lemmas':
normalizer = WordNetLemmatizer()
if method == 'stems':
normalizer = PorterStemmer()
temp = []
for i in tqdm(range(len(text))):
words = word_tokenize(text[i])
words = [word.lower() for word in words if word.isalpha()]
if method == 'lemmas':
temp.append([normalizer.lemmatize(w, pos='v') for w in words])
if method == 'stems':
temp.append([normalizer.stem(w) for w in words])
return temp
class CrazyTokenizer(object):
"""
Tokenizer with Reddit- and Twitter-specific options
Parameters
----------
lowercase : bool, optional
If True, lowercase all tokens. Defaults to True.
keepcaps: bool, optional
If True, keep ALL CAPS WORDS uppercased. Defaults to False.
normalize: int or bool, optional
If not False, perform normalization of repeated charachers
("awesoooooome" -> "awesooome"). The value of parameter
determines the number of occurences to keep. Defaults to 3.
ignore_quotes: bool, optional
If True, ignore tokens contained within double quotes.
Defaults to False.
ignore_reddit_quotes: bool, optional
If True, remove quotes from the Reddit comments. Defaults to False.
ignore_stopwords: str, list, or boolean, optional
Whether to ignore stopwords
- str: language to get a list of stopwords for from NLTK package
- list: list of stopwords to remove
- True: use built-in list of the english stop words
- False: keep all tokens
Defaults to False
stem: {False, 'stem', 'lemm'}, optional
Whether to perform word stemming
- False: do not perform word stemming
- 'stem': use PorterStemmer from NLTK package
- 'lemm': use WordNetLemmatizer from NLTK package
remove_punct: bool, optional
If True, remove punctuation tokens. Defaults to True.
remove_breaks: bool, optional
If True, remove linebreak tokens. Defaults to True.
decontract: bool, optional
If True, attempt to expand certain contractions. Defaults to False.
Example: "'ll" -> " will"
numbers, subreddits, reddit_usernames, emails:
False or str, optional
Replacement of the different types of tokens
- False: leaves these tokens intact
- str: replacement token
- '': removes all occurrences of these tokens
twitter_handles: False, 'realname' or str, optional
Processing of twitter handles
- False: do nothing
- str: replacement token
- 'realname': replace with the real screen name of Twitter account
- 'split': split handles using Viterbi algorithm
Example: "#vladimirputinisthebest" -> "vladimir putin is the best"
hashtags: False or str, optional
Processing of hashtags
- False: do nothing
- str: replacement token
- 'split': split hashtags according using Viterbi algorithm
urls: False or str, optional
Replacement of parsed URLs
- False: leave URL intact
- str: replacement token
- dict: replace all URLs stored in keys with the corresponding values
- '': removes all occurrences of these tokens
- 'domain': extract domain ("http://cnn.com" -> "cnn")
- 'domain_unwrap_fast': extract domain after unwraping links
for a list of URL shorteners (goo.gl, t.co, bit.ly, tinyurl.com)
- 'domain_unwrap': extract domain after unwraping all links
- 'title': extract and tokenize title of each link after unwraping it
Defaults to False.
extra_patterns: None or list of tuples, optional
Replacement of any user-supplied extra patterns.
Tuples must have the following form: (name, re_pattern, replacement_token):
- name (str): name of the pattern
- re_pattern (_sre.SRE_Pattern): compiled re pattern
- replacement_token (str): replacement token
Defaults to None
keep_untokenized: None or list, optional
List of expressions to keep untokenized
Example: ["New York", "Los Angeles", "San Francisco"]
whitespaces_to_underscores: boolean, optional
If True, replace all whitespace characters with
underscores in the final tokens. Defaults to True.
remove_nonunicode: boolean, optional
If True, remove all non-unicode characters. Defaults to False.
pos_emojis, neg_emojis, neutral_emojis: None, True, or list, optional
Replace positive, negative, and neutral emojis with the special tokens
- None: do not perform replacement
- True: perform replacement of the default lists of emojis
- list: list of emojis to replace
print_url_warnings: bool, optional
If True, print URL-related warnings. Defaults to False.
latin_chars_fix: bool, optional
Try applying this fix if you have a lot of \\xe2\\x80\\x99-like
or U+1F601-like strings in your data. Defaults to False.
ngrams: int, optional
Add ngrams of tokens after tokenizing
"""
def __init__(self,
lowercase=True,
keepcaps=False,
normalize=3,
ignore_quotes=False,
ignore_reddit_quotes=False,
ignore_stopwords=False,
stem=False,
remove_punct=True,
remove_breaks=True,
decontract=False,
twitter_handles=False,
urls=False,
hashtags=False,
numbers=False,
subreddits=False,
reddit_usernames=False,
emails=False,
extra_patterns=None,
keep_untokenized=None,
whitespaces_to_underscores=True,
remove_nonunicode=False,
pos_emojis=None,
neg_emojis=None,
neutral_emojis=None,
print_url_warnings=False,
latin_chars_fix=False,
ngrams=1):
self.params = locals()
self._nlp = English()
self._merging_matcher = Matcher(self._nlp.vocab)
self._matcher = Matcher(self._nlp.vocab)
self._replacements = {}
self._domains = {}
self._realnames = {}
self._stopwords = None
alpha_digits_flag = self._nlp.vocab.add_flag(alpha_digits_check)
hashtag_flag = self._nlp.vocab.add_flag(hashtag_check)
twitter_handle_flag = self._nlp.vocab.add_flag(twitter_handle_check)
self._merging_matcher.add('HASHTAG', None, [{
'ORTH': '#'
}, {
'IS_ASCII': True
}])
self._merging_matcher.add('SUBREDDIT', None, [{
'ORTH': '/r'
}, {
'ORTH': '/'
}, {
alpha_digits_flag: True
}], [{
'ORTH': 'r'
}, {
'ORTH': '/'
}, {
alpha_digits_flag: True
}])
self._merging_matcher.add('REDDIT_USERNAME', None,
[{
'ORTH': '/u'
}, {
'ORTH': '/'
}, {
alpha_digits_flag: True
}], [{
'ORTH': 'u'
}, {
'ORTH': '/'
}, {
alpha_digits_flag: True
}])
if isinstance(ignore_stopwords, str) and ('nltk' in sys.modules):
try:
self._stopwords = stopwords.words(ignore_stopwords)
except OSError:
raise ValueError('Language {} was not found by NLTK'.format(
ignore_stopwords))
elif ignore_stopwords is True:
self._matcher.add('STOPWORDS', self._remove_token, [{
'IS_STOP': True
}])
elif isinstance(ignore_stopwords, list):
self._stopwords = [word.lower() for word in ignore_stopwords]
elif ignore_stopwords is not False:
raise TypeError(
'Type {} is not supported by ignore_stopwords parameter or NLTK is not installed'
.format(type(ignore_stopwords)))
if lowercase and (not keepcaps):
self._matcher.add('LOWERCASE', self._lowercase, [{
'IS_LOWER': False
}])
elif lowercase and keepcaps:
self._matcher.add('LOWERCASE', self._lowercase, [{
'IS_LOWER': False,
'IS_UPPER': False
}])
if remove_punct:
self._matcher.add('PUNCTUATION', self._remove_token,
[{
'IS_PUNCT': True
}])
if remove_breaks:
def break_check(text):
return bool(BREAKS_RE.fullmatch(text))
break_flag = self._nlp.vocab.add_flag(break_check)
self._matcher.add('BREAK', self._remove_token, [{
break_flag: True
}])
if normalize:
def normalize_check(text):
return bool(NORMALIZE_RE.search(text))
normalize_flag = self._nlp.vocab.add_flag(normalize_check)
self._matcher.add('NORMALIZE', self._normalize,
[{
normalize_flag: True
}])
if numbers is not False:
self._matcher.add('NUMBER', self._replace_token, [{
'LIKE_NUM': True
}])
self._replacements['NUMBER'] = numbers
if urls is not False:
if urls in [
'domain', 'domain_unwrap_fast', 'domain_unwrap', 'title'
]:
self._urls = urls
self._matcher.add('URL', self._process_url, [{
'LIKE_URL': True
}])
elif isinstance(urls, dict):
self._domains = urls
self._urls = 'domain_unwrap_fast'
self._matcher.add('URL', self._process_url, [{
'LIKE_URL': True
}])
else:
self._matcher.add('URL', self._replace_token, [{
'LIKE_URL': True
}])
self._replacements['URL'] = urls
if emails is not False:
self._matcher.add('EMAIL', self._replace_token, [{
'LIKE_EMAIL': True
}])
self._replacements['EMAIL'] = emails
if reddit_usernames is not False:
def reddit_username_check(text):
return bool(REDDITORS_RE.fullmatch(text))
reddit_username_flag = self._nlp.vocab.add_flag(
reddit_username_check)
self._matcher.add('REDDIT_USERNAME', self._replace_token,
[{
reddit_username_flag: True
}])
self._replacements['REDDIT_USERNAME'] = reddit_usernames
if subreddits is not False:
def subreddit_check(text):
return bool(SUBREDDITS_RE.fullmatch(text))
subreddit_flag = self._nlp.vocab.add_flag(subreddit_check)
self._matcher.add('SUBREDDIT', self._replace_token,
[{
subreddit_flag: True
}])
self._replacements['SUBREDDIT'] = subreddits
if twitter_handles is not False:
self._matcher.add('TWITTER_HANDLE', self._handles_postprocess,
[{
twitter_handle_flag: True
}])
if hashtags is not False:
self._matcher.add('HASHTAG', self._hashtag_postprocess,
[{
hashtag_flag: True
}])
if hashtags == 'split' or twitter_handles == 'split':
file = os.path.join(DATA_PATH, 'wordsfreq_wiki2.txt')
with open(file) as f:
self._words = f.read().split()
self._wordcost = dict((k, log((i + 1) * log(len(self._words))))
for i, k in enumerate(self._words))
self._maxword = max(len(x) for x in self._words)
if twitter_handles == 'realname':
with open(os.path.join(DATA_PATH, 'realnames.json')) as f:
self._realnames = json.load(f)
if ignore_quotes:
self._merging_matcher.add('QUOTE', None, [{
'ORTH': '"'
}, {
'OP': '*',
'IS_ASCII': True
}, {
'ORTH': '"'
}])
def doublequote_check(text):
return bool(QUOTES_RE.fullmatch(text))
doublequote_flag = self._nlp.vocab.add_flag(doublequote_check)
self._matcher.add('DOUBLE_QUOTES', self._remove_token,
[{
doublequote_flag: True
}])
if self._stopwords:
def stopword_check(text):
return bool(text.lower() in self._stopwords)
stopword_flag = self._nlp.vocab.add_flag(stopword_check)
self._matcher.add('STOPWORD', self._remove_token,
[{
stopword_flag: True
}])
if keep_untokenized is not None:
if not isinstance(keep_untokenized, list):
raise ValueError(
"keep_untokenized has to be either None or a list")
for i, phrase in enumerate(keep_untokenized):
phrase_tokens = phrase.split(' ')
rule = []
for token in phrase_tokens:
rule.append({'LOWER': token.lower()})
self._merging_matcher.add('RULE_' + str(i), None, rule)
if pos_emojis:
if not isinstance(pos_emojis, list):
pos_emojis = POS_EMOJIS
pos_patterns = [[{'ORTH': emoji}] for emoji in pos_emojis]
self._matcher.add('HAPPY', self._replace_token, *pos_patterns)
self._replacements['HAPPY'] = 'POS_EMOJI'
if neg_emojis:
if not isinstance(neg_emojis, list):
neg_emojis = NEG_EMOJIS
neg_patterns = [[{'ORTH': emoji}] for emoji in neg_emojis]
self._matcher.add('SAD', self._replace_token, *neg_patterns)
self._replacements['SAD'] = 'NEG_EMOJI'
if neutral_emojis:
if not isinstance(neutral_emojis, list):
neutral_emojis = NEUTRAL_EMOJIS
neutral_patterns = [[{'ORTH': emoji}] for emoji in neutral_emojis]
self._matcher.add('NEUTRAL', self._replace_token,
*neutral_patterns)
self._replacements['NEUTRAL'] = 'NEUTRAL_EMOJI'
if isinstance(extra_patterns, list):
self._flags = {}
for name, re_pattern, replacement_token in extra_patterns:
def flag(text):
return bool(re_pattern.match(text))
self._flags[name] = self._nlp.vocab.add_flag(flag)
self._matcher.add(name, self._replace_token,
[{
self._flags[name]: True
}])
self._replacements[name] = replacement_token
if stem and ('nltk' in sys.modules):
if stem == 'stem':
self._stemmer = PorterStemmer()
elif stem == 'lemm':
self._stemmer = WordNetLemmatizer()
else:
raise ValueError(
'Stemming method {} is not supported'.format(stem))
self._matcher.add('WORD_TO_STEM', self._stem_word,
[{
'IS_ALPHA': True
}])
retokenize_flag = self._nlp.vocab.add_flag(retokenize_check)
self._matcher.add('RETOKENIZE', self._retokenize,
[{
retokenize_flag: True,
'IS_PUNCT': False,
'LIKE_URL': False,
'LIKE_EMAIL': False,
'LIKE_NUM': False,
hashtag_flag: False,
twitter_handle_flag: False
}])
self._nlp.add_pipe(self._merge_doc, name='merge_doc', last=True)
self._nlp.add_pipe(self._match_doc, name='match_doc', last=True)
self._nlp.add_pipe(self._postproc_doc, name='postproc_doc', last=True)
@staticmethod
def _lowercase(__, doc, i, matches):
# Lowercase tokens
__, start, end = matches[i]
span = doc[start:end]
for tok in span:
tok._.transformed_text = tok._.transformed_text.lower()
def _stem_word(self, __, doc, i, matches):
# Stem tokens
__, start, end = matches[i]
span = doc[start:end]
for tok in span:
if self.params['stem'] == 'stem':
tok._.transformed_text = self._stemmer.stem(
tok._.transformed_text)
elif self.params['stem'] == 'lemm':
tok._.transformed_text = self._stemmer.lemmatize(
tok._.transformed_text)
def _normalize(self, __, doc, i, matches):
# Normalize repeating symbols
__, start, end = matches[i]
span = doc[start:end]
for tok in span:
tok._.transformed_text = NORMALIZE_RE.sub(
r"\1" * self.params['normalize'], tok._.transformed_text)
def _process_url(self, __, doc, i, matches):
# Process found URLs
__, start, end = matches[i]
span = doc[start:end]
for tok in span:
found_urls = URLS_RE.findall(tok.text)
if found_urls:
if found_urls[0] in self._domains:
tok._.transformed_text = self._domains[found_urls[0]]
elif self._urls == 'domain':
tok._.transformed_text = tldextract.extract(
found_urls[0]).domain
elif self._urls != 'title':
if self._urls == 'domain_unwrap':
domain = unshorten_url(
found_urls[0], None,
self.params['print_url_warnings'])
else:
domain = unshorten_url(
found_urls[0], URL_SHORTENERS,
self.params['print_url_warnings'])
self._domains[found_urls[0]] = domain
tok._.transformed_text = domain
elif self._urls == 'title':
domain = unshorten_url(found_urls[0], URL_SHORTENERS)
if domain != 'twitter':
title = get_url_title(
found_urls[0], self.params['print_url_warnings'])
title = self.tokenize(URLS_RE.sub('', title))
else:
title = ''
tok._.transformed_text = title
self._domains[found_urls[0]] = title
def _replace_token(self, __, doc, i, matches):
# Replace tokens with something else
match_id, start, end = matches[i]
span = doc[start:end]
replacement_token = self._replacements[doc.vocab.strings[match_id]]
for tok in span:
tok._.transformed_text = replacement_token
@staticmethod
def _remove_token(__, doc, i, matches):
# Remove tokens
__, start, end = matches[i]
span = doc[start:end]
for tok in span:
tok._.transformed_text = ''
def _retokenize(self, __, doc, i, matches):
# Retokenize
__, start, end = matches[i]
span = doc[start:end]
for tok in span:
text = tok.text
text = re.sub(r'([#@])', r' \1', text)
text = re.sub(r'\s{2,}', ' ', text).strip()
tok._.transformed_text = self.tokenize(text)
def _infer_spaces(self, text):
# Infer location of spaces in hashtags
text = text.lower()
text = re.sub(r'[^\w\s]', '', text)
def best_match(i):
# Find the best match for the first i characters
# assuming costs has been built for the first (i-1) characters
candidates = enumerate(reversed(cost[max(0, i - self._maxword):i]))
return min(
(c + self._wordcost.get(text[i - k - 1:i], 9e999), k + 1)
for k, c in candidates)
cost = [0]
for i in range(1, len(text) + 1):
cur_cost, k = best_match(i)
cost.append(cur_cost)
out = []
i = len(text)
while i > 0:
cur_cost, k = best_match(i)
assert cur_cost == cost[i]
out.append(text[i - k:i])
i -= k
return list(reversed(out))
def _handles_postprocess(self, __, doc, i, matches):
# Process twitter handles
__, start, end = matches[i]
span = doc[start:end]
for tok in span:
if self.params['twitter_handles'] == 'realname':
if tok.text in self._realnames:
tok._.transformed_text = self._realnames[tok.text]
else:
handle = get_twitter_realname(tok.text)
realname = self.tokenize(TWITTER_HANDLES_RE.sub(
'', handle))
tok._.transformed_text = realname
self._realnames[tok.text] = realname
elif self.params['twitter_handles'] == 'split':
poss = self._infer_spaces(tok._.transformed_text[1:])
if poss:
tok._.transformed_text = poss
else:
tok._.transformed_text = self.params['twitter_handles']
def _hashtag_postprocess(self, __, doc, i, matches):
# Process hashtags
__, start, end = matches[i]
span = doc[start:end]
for tok in span:
if self.params['hashtags'] == 'split':
poss = self._infer_spaces(tok._.transformed_text[1:])
if poss:
tok._.transformed_text = poss
else:
tok._.transformed_text = self.params['hashtags']
@staticmethod
def _decontract(text):
# Expand contractions
for contraction, decontraction in DECONTRACTIONS.items():
text = re.sub(contraction, decontraction, text)
return text
def _preprocess_text(self, text):
# Do some preprocessing
text = re.sub("’", "'", text)
if self.params['remove_nonunicode']:
try:
text = text.encode('utf-8').decode('unicode-escape')
text = ''.join(filter(lambda x: x in string.printable,
text)).strip()
except UnicodeDecodeError:
warnings.warn(
'(UnicodeDecodeError while trying to remove non-unicode characters'
)
if self.params['decontract']:
text = self._decontract(text)
text = html.unescape(text)
if self.params['latin_chars_fix']:
if EMOJIS_UTF_RE.findall(text):
text = EMOJIS_UTF_NOSPACE_RE.sub(r' \1', text)
for utf_code, emoji in EMOJIS_UTF.items():
text = EMOJIS_UTF_PATS[utf_code].sub(emoji, text)
if EMOJIS_UNICODE_RE.findall(text):
text = EMOJIS_UNICODE_NOSPACE_RE.sub(r'\1 \2', text)
for utf_code, emoji in EMOJIS_UNICODE.items():
text = EMOJIS_UNICODE_PATS[utf_code].sub(emoji, text)
if LATIN_CHARS_RE.findall(text):
for _hex, _char in LATIN_CHARS.items():
text = LATIN_CHARS_PATS[_hex].sub(_char, text)
if self.params['ignore_reddit_quotes']:
text = REDDIT_QUOTES_RE.sub(text, ' ')
text = text.replace('.@', '. @')
text = re.sub(r'([*;,!?\(\)\[\]])', r' \1', text)
text = re.sub(r'\s{2,}', ' ', text)
return text.strip()
def _merge_doc(self, doc):
# Perform merging for certain types of tokens
matches = self._merging_matcher(doc)
spans = []
for __, start, end in matches:
spans.append(doc[start:end])
for span in spans:
span.merge()
for tok in doc:
tok._.transformed_text = tok.text
return doc
def _match_doc(self, doc):
# Perform all additional processing
self._matcher(doc)
return doc
def _postproc_doc(self, doc):
# Perform postprocessing
doc._.tokens = []
for tok in doc:
if isinstance(tok._.transformed_text, list):
doc._.tokens.extend(tok._.transformed_text)
elif tok._.transformed_text.strip() != '':
if self.params['whitespaces_to_underscores']:
tok._.transformed_text = "_".join(
tok._.transformed_text.split())
doc._.tokens.append(tok._.transformed_text.strip())
return doc
def tokenize(self, text):
"""
Tokenize document
Parameters
----------
text : str
Document to tokenize
Returns
-------
list
List of tokens
Examples
--------
>>> from redditscore.tokenizer import CrazyTokenizer
>>> tokenizer = CrazyTokenizer(splithashtags=True, hashtags=False)
>>> tokenizer.tokenize("#makeamericagreatagain")
["make", "america", "great", "again"]
"""
if not isinstance(text, str):
warnings.warn('Document {} is not a string'.format(text))
return []
text = self._preprocess_text(text)
doc = self._nlp(text)
tokens = doc._.tokens
if self.params['ngrams'] > 1:
if self.params['whitespaces_to_underscores']:
tokens = word_ngrams(tokens, (1, self.params['ngrams']),
separator='_')
else:
tokens = word_ngrams(tokens, (1, self.params['ngrams']))
return tokens
class CorpusIterator(six.Iterator):
"""Class to do tokenization, tagging, stemming, etc. and yield each
document 1 at a time. Only ever loads one doc into memory"""
def __init__(self,
indir,
n,
stem=None,
stop_words=True,
tag=None,
tag_pattern=None,
punctuation=True,
split_clauses=False,
outdir=None):
"""Constructor
Input:
indir: path to directory of txt files
n: order of n gram
stem: {'snowball','porter','lemma',None} stemmer to use
Defaults to None.
stop_words: Boolean. include stopwords. Defaults to True
tag: {'nltk',None}. POS tagger to use. Defaults
to None
tag_pattern: list of of tag patterns to allow in simplified form.
Defaults to None. if tag_pattern = "default",
use default tag pattern.
punctuation: Boolean. include punctuation. Defaults to True
split_clauses: Boolean. Split on clauses
outdir: directory to write to. Defaults to indir/ngram_results
"""
self.indir = indir
# check if directory is zip archive or directory and act accordingly
if not zipfile.is_zipfile(indir):
# list the files in the directory
self.files = sorted([
os.path.join(indir, f) for f in os.listdir(indir)
if os.path.splitext(f)[1] == ".txt"
])
# create directory for results
if outdir is None:
outdir = os.path.join(indir, "ngram_results")
# check if directory exists, if not create direcotry
if not os.path.exists(outdir):
os.mkdir(outdir)
# set zip_corpus to None
self.zip_corpus = None
else:
# files is the namelist of the zip archive
self.zip_corpus = zipfile.ZipFile(indir)
self.files = self.zip_corpus.namelist()
# create directory for results in the directory of the zip archive
if outdir is None:
# get the directory of the zip archive
tmp = os.path.split(indir)[0]
outdir = os.path.join(tmp, "ngram_results")
# check if the directory exists, if not create the directory
if not os.path.exists(outdir):
os.mkdir(outdir)
# assign option variables
self.n = n
self.stem = stem
self.stop_words = stop_words
self.tag = tag
self.tag_pattern = tag_pattern
self.punctuation = punctuation
self.outdir = outdir
# keep an index for the __next__() function
self.index = 0
# class variable holding default tag patterns and dict for conversion
# to universal tag set
self.default_tagpatterns = set([
'AN', 'NN', 'VN', 'VV', 'VP', 'NNN', 'AAN', 'ANN', 'NAN', 'NPN',
'VAN', 'VNN', 'VPN', 'ANV', 'NVV', 'VDN', 'VVV', 'VVP'
])
self.default_tagset = set(''.join(self.default_tagpatterns))
self.tagdict = keydefaultdict(
lambda x: x, {
'NN': 'N',
'NNS': 'N',
'NNP': 'N',
'NNPS': 'N',
'JJ': 'A',
'JJR': 'A',
'JJS': 'A',
'VBG': 'A',
'RB': 'A',
'DT': 'D',
'IN': 'P',
'TO': 'P',
'VB': 'V',
'VBD': 'V',
'VBN': 'V',
'VBP': 'V',
'VBZ': 'V',
'MD': 'V',
'RP': 'P'
})
# class variable which contains english stop words as a set
self.stop = set(stopwords.words('english'))
# set up tagger if tag is not None
if tag is not None:
if tag == 'nltk':
# create a named tuple which holds nltk.pos_tag_sents as
# tag_sents
NLTKTagger = namedtuple("NLTKTagger", ["tag_sents", "tag"])
self.tagger = NLTKTagger(nltk.pos_tag_sents, nltk.pos_tag)
else:
# raise a value error if an unsupproted tagger is included
raise ValueError('Not an available tagger')
# initialize stemmer if stem is not None
if stem is not None:
if stem == 'porter':
self.stemmer = PorterStemmer()
elif stem == 'snowball':
self.stemmer = SnowballStemmer("english")
elif stem == 'lemma':
self.stemmer = WordNetLemmatizer()
# add stem as another name for lemmatize
self.stemmer.stem = stemmer.lemmatize
else:
# raise a value error if a wrong stemmer is chosen
raise ValueError('Not an available stemmer')
# set splitting on clauses
self.split_clauses = split_clauses
# current clauses
self.curr_clauses = []
def __len__(self):
"""len function, number of documents"""
return (len(self.files))
def __next__(self):
"""Next function for iterator"""
if self.index >= len(self.files):
raise StopIteration
# if not splitting on clauses
if not self.split_clauses:
# get sentences from file
sents = doc_sents(self.files[self.index], zipped=self.zip_corpus)
#get ngrams of doc and yield
ngrams = self.ngrams_from_sents(sents, self.n, self.stem,
self.stop_words, self.tag,
self.tag_pattern, self.punctuation)
self.index += 1
return (ngrams)
#if splitting on clauses use the clauses
else:
if len(self.curr_clauses) == 0:
#get the sentences for the current clauses
self.curr_clauses = doc_sents(self.files[self.index],
zipped=self.zip_corpus,
clauses=True)
self.index += 1
#pop one clauses from self.curr_clauses
sents = self.curr_clauses.pop()
ngrams = self.ngrams_from_sents(sents, self.n, self.stem,
self.stop_words, self.tag,
self.tag_pattern, self.punctuation)
return (ngrams)
def __iter__(self):
"""Iterator, does tokenization,stemming,tagging,etc on a doc before
returning it"""
if not self.split_clauses:
for i, fName in enumerate(sorted(self.files)):
if i % 100 == 0:
logging.info("Computing N-grams for %ith file %s" %
(i, fName))
#get sentences from file
sents = doc_sents(fName, zipped=self.zip_corpus)
#get ngrams of doc and yield
ngrams = self.ngrams_from_sents(sents, self.n, self.stem,
self.stop_words, self.tag,
self.tag_pattern,
self.punctuation)
yield (ngrams)
else:
for i, fName in enumerate(self.files):
if i % 100 == 0:
logging.info("Computing N-grams for %ith file %s" %
(i, fName))
#get sentences for clauses
clauses = doc_sents(fName,
zipped=self.zip_corpus,
clauses=True)
for sents in clauses:
ngrams = self.ngrams_from_sents(sents, self.n, self.stem,
self.stop_words, self.tag,
self.tag_pattern,
self.punctuation)
yield (ngrams)
def custom_ngrams(self, words, n):
"""Faster n gram generation than nltk.ngrams
Input:
words: word tokenized sentence
n: order of ngram
Output:
ngrams: list of ngrams
"""
ngrams = zip(*[words[i:] for i in range(n)])
return (ngrams)
def word_tokenize(self, words):
"""Faster word tokenization than nltk.word_tokenize
Input:
words: a string to be tokenized
Output:
tokens: tokenized words
"""
tokens = re.findall(r"[a-z]+-?[a-z]+",
words.lower(),
flags=re.UNICODE | re.LOCALE)
return (tokens)
def ngrams_from_sents(self,
sents,
n,
stem=None,
stop_words=True,
tag=None,
tag_pattern=None,
punctuation=True):
"""Gets the ngrams from a list of sentences
Input:
sents: list of sentences as strings
n: order of n gram
stem: {'snowball','porter','lemma',None} stemmer to use
Defaults to None.
stop_words: Boolean. include stopwords. Defaults to True
tag: {'ap','nltk','stanford',None}. POS tagger to use. Defaults
to None
tag_pattern: list of of tag patterns to allow in simplified form.
Defaults to None. if tag_pattern = "default",
use default tag pattern.
punctuation: Boolean. include punctuation. Defaults to True
Output:
ngrams: list of ngrams as "word1-word2" strings
"""
#tag sentences first
if tag is not None:
#tokenize the sentences
tmp = []
for sent in sents:
tmp.append([word.lower() for word in self.word_tokenize(sent)])
sents = tmp
if tag == 'nltk':
# tag words
tags = self.tagger.tag_sents(sents)
# extract the tags without the words
tags = [[self.tagdict[tagWord[1]] for tagWord in tag[i]]
for i in range(len(sents))]
else:
#raise a value error if an unsupproted tagger is included
raise ValueError('Not an available tagger')
#iterate through sentences and get ngrams
ngrams = []
for i, words in enumerate(sents):
if tag is None:
#if tag is None then word tokenization hasn't happend
words = self.word_tokenize(words)
#stem words if stem is not None
if stem is not None:
words = [self.stemmer.stem(word) for word in words]
#join tags and words if tag is not None
if tag is not None:
words = ['::'.join(tagWord) for tagWord in zip(words, tags[i])]
#remove stop words if stop = False
if not stop_words:
words = [
word for word in words
if not word.split("::")[0] in self.stop
]
#remove punctuation if punctuation is false
if not punctuation:
pun = string.punctuation
words = [
word for word in words if not word.split("::")[0] in pun
]
#get n grams and add to ngrams list
sent_grams = [
"_".join(gram) for gram in self.custom_ngrams(words, n)
]
#if tag_pattern isn't None, go through sent_grams and only keep those
#ngrams with the proper tag pattern
if tag_pattern is not None:
#assign default tag pattern if tag_pattern == 'default'
if tag_pattern == 'default':
tag_pattern = self.default_tagpatterns
tmp = []
#maybe make this a list comprehension?
for gram in sent_grams:
#get tags separately
tags_squash = [t.split("::")[1] for t in gram.split("_")]
#check if the tag pattern is allowed
if ''.join(tags_squash) in tag_pattern:
tmp.append(gram)
sent_grams = tmp
ngrams.extend(sent_grams)
return (ngrams)
# Parts of speech tagging
print "Processing data to tag parts of speech..."
pos_word_data = nltk.pos_tag(word_data)
print pos_word_data
# Stemming and Lemmatization
## Stemming with Porter Stemmer
print "Stemming with Porter Stemmer..."
porter_stemmer = PorterStemmer()
for w in word_data[:20]:
print "Actual: %s Stem: %s" % (w, porter_stemmer.stem(w))
## Stemming with Lancaster Stemmer
print "Stemming with Porter Stemmer..."
lancaster_stemmer = LancasterStemmer()
for w in word_data[:20]:
print "Actual: %s Stem: %s" % (w, lancaster_stemmer.stem(w))
## Stemming with Snowball Algorithm
print "Stemming with Snowball Stemmer..."
snowball_stemmer = SnowballStemmer("english")
for w in word_data[:20]:
print "Actual: %s Stem: %s" % (w, snowball_stemmer.stem(w))
## Lemmatization with WordNet
wordnet_lemmatizer = WordNetLemmatizer()
for w in word_data[:20]:
print "Actual: %s Stem: %s" % (w, wordnet_lemmatizer.stem(w))
class ConceptBasedILPSummarizer(LoadFile):
"""Implementation of the concept-based ILP model for summarization.
The original algorithm was published and described in:
* Dan Gillick and Benoit Favre, A Scalable Global Model for Summarization,
*Proceedings of the NAACL HLT Workshop on Integer Linear Programming for
Natural Language Processing*, pages 10–18, 2009.
"""
def __init__(self, input_directory, language):
"""
Args:
input_directory (str): the directory from which text documents to
be summarized are loaded.
@type language: str
"""
self.input_directory = input_directory
self.sentences = []
self.weights = {}
self.c2s = defaultdict(set)
self.concept_sets = defaultdict(frozenset)
self.LANGUAGE = language
# type: str
self.stoplist = set(stopwords.words(self.LANGUAGE))
self.stemmer = WordNetLemmatizer()
self.word_frequencies = defaultdict(int)
self.w2s = defaultdict(set)
def extract_ngrams2(self, concept_type='ngrams', n=2):
"""Extract the ngrams of words from the input sentences.
Args:
n (int): the number of words for ngrams, defaults to 2
"""
for i, sentence in enumerate(self.sentences):
untokenized_concepts = []
if concept_type == 'ngrams':
ngrams = extract_ngrams2([sentence.untokenized_form],
self.stemmer, self.LANGUAGE, n)
pruned_list = prune_ngrams(ngrams, self.stoplist, n)
elif concept_type == 'phrase':
pruned_list = self.sentences[i].phrases
for concept in pruned_list:
wrds = unstem_ngram(concept, sentence)
untokenized_concepts.append(" ".join(wrds))
self.sentences[i].concepts = pruned_list
self.sentences[i].untokenized_concepts = untokenized_concepts
#print(untokenized_concepts)
if len(self.sentences[i].concepts) != len(
self.sentences[i].untokenized_concepts):
raise BaseException(
"unexpected length difference between concepts and untokenized_concepts"
)
def extract_ngrams(self, n=2):
"""Extract the ngrams of words from the input sentences.
Args:
n (int): the number of words for ngrams, defaults to 2
"""
for i, sentence in enumerate(self.sentences):
# for each ngram of words
for j in range(len(sentence.tokens) - (n - 1)):
# initialize ngram container
ngram = []
# for each token of the ngram
for k in range(j, j + n):
ngram.append(sentence.tokens[k].lower())
# do not consider ngrams containing punctuation marks
marks = [t for t in ngram if not re.search('[a-zA-Z0-9]', t)]
if len(marks) > 0:
continue
# do not consider ngrams composed of only stopwords
stops = [t for t in ngram if t in self.stoplist]
if len(stops) == len(ngram):
continue
# stem the ngram
ngram = [self.stemmer.stem(t) for t in ngram]
#ngram = [self.stemmer.lemmatize(t) for t in ngram]
# add the ngram to the concepts
self.sentences[i].concepts.append(' '.join(ngram))
def compute_document_frequency(self):
"""Compute the document frequency of each concept.
"""
for i in range(len(self.sentences)):
# for each concept
for concept in self.sentences[i].concepts:
# add the document id to the concept weight container
if concept not in self.weights:
self.weights[concept] = set([])
self.weights[concept].add(self.sentences[i].doc_id)
# loop over the concepts and compute the document frequency
for concept in self.weights:
self.weights[concept] = len(self.weights[concept])
def compute_word_frequency(self):
"""Compute the frequency of each word in the set of documents. """
for i, sentence in enumerate(self.sentences):
for token in sentence.tokens:
t = token.lower()
if not re.search('[a-zA-Z0-9]', t) or t in self.stoplist:
continue
#t = self.stemmer.stem(t)
t = self.stemmer.lemmatize(t)
self.w2s[t].add(i)
self.word_frequencies[t] += 1
def prune_sentences(self,
mininum_sentence_length=5,
remove_citations=True,
remove_redundancy=True,
imp_list=None):
"""Prune the sentences.
Remove the sentences that are shorter than a given length, redundant
sentences and citations from entering the summary.
Args:
mininum_sentence_length (int): the minimum number of words for a
sentence to enter the summary, defaults to 5
remove_citations (bool): indicates that citations are pruned,
defaults to True
remove_redundancy (bool): indicates that redundant sentences are
pruned, defaults to True
"""
if imp_list is None:
imp_list = []
retained_sentences = []
# loop over the sentences
for i, sentence in enumerate(self.sentences):
if imp_list:
if imp_list[i] == 0:
continue
# prune short sentences
if sentence.length < mininum_sentence_length:
continue
# prune citations
first_token, last_token = sentence.tokens[0], sentence.tokens[-1]
if remove_citations and \
(first_token == u"``" or first_token == u'"' \
or last_token == u"''" or first_token == u'"' \
or last_token== u"'" or first_token==u"'") \
or last_token == u'"':
continue
# prune ___ said citations
# if remove_citations and \
# (sentence.tokens[0]==u"``" or sentence.tokens[0]==u'"') and \
# re.search('(?i)(''|") \w{,30} (said|reported|told)\.$',
# sentence.untokenized_form):
# continue
# prune identical and almost identical sentences
if remove_redundancy:
is_redundant = False
for prev_sentence in retained_sentences:
if sentence.tokens == prev_sentence.tokens:
is_redundant = True
break
if is_redundant:
continue
# otherwise add the sentence to the pruned sentence container
retained_sentences.append(sentence)
# from all concepts that are going to be pruned, keep only those that also appear elsewhere
retained_concepts = [
concept for s in retained_sentences for concept in s.concepts
]
for sentence in set(self.sentences).difference(retained_sentences):
for concept in sentence.concepts:
if concept not in retained_concepts \
and self.weights.has_key(concept):
del self.weights[concept]
log.debug("keeping %s unique sentences of %s sentences" %
(len(retained_sentences), len(self.sentences)))
self.sentences = retained_sentences
def prune_concepts(self, method="threshold", value=3, rejected_list=None):
"""Prune the concepts for efficient summarization.
Args:
method (str): the method for pruning concepts that can be whether
by using a minimal value for concept scores (threshold) or using
the top-N highest scoring concepts (top-n), defaults to
threshold.
value (int): the value used for pruning concepts, defaults to 3.
"""
if rejected_list is None:
rejected_list = []
if method == 'stopwords':
concepts = self.weights.keys()
for concept in concepts:
pruned_list = prune_ngrams(concept, self.stoplist, 1)
if not pruned_list:
#print concept, self.weights[concept]
del self.weights[concept]
if method == "list":
concepts = self.weights.keys()
for concept in concepts:
if concept in rejected_list:
#print concept, self.weights[concept]
del self.weights[concept]
# 'threshold' pruning method
if method == "threshold":
# iterates over the concept weights
concepts = self.weights.keys()
for concept in concepts:
if self.weights[concept] < value:
del self.weights[concept]
# 'top-n' pruning method
elif method == "top-n":
# sort concepts by scores
sorted_concepts = sorted(self.weights,
key=lambda x: self.weights[x],
reverse=True)
# iterates over the concept weights
concepts = self.weights.keys()
for concept in concepts:
if concept not in sorted_concepts[:value]:
del self.weights[concept]
# iterates over the sentences
for i in range(len(self.sentences)):
# current sentence concepts
concepts = self.sentences[i].concepts
# prune concepts
self.sentences[i].concepts = [
c for c in concepts if c in self.weights
]
def compute_c2s(self):
"""Compute the inverted concept to sentences dictionary. """
for i, sentence in enumerate(self.sentences):
for concept in sentence.concepts:
self.c2s[concept].add(i)
def compute_concept_sets(self):
"""Compute the concept sets for each sentence."""
for i, sentence in enumerate(self.sentences):
for concept in sentence.concepts:
self.concept_sets[i] |= {concept}
def greedy_approximation(self, summary_size=100):
"""Greedy approximation of the ILP model.
Args:
summary_size (int): the maximum size in words of the summary,
defaults to 100.
Returns:
(value, set) tuple (int, list): the value of the approximated
objective function and the set of selected sentences as a tuple.
"""
# initialize the inverted c2s dictionary if not already created
if not self.c2s:
self.compute_c2s()
# initialize weights
weights = {}
# initialize the score of the best singleton
best_singleton_score = 0
# compute indices of our sentences
sentences = range(len(self.sentences))
# compute initial weights and fill the reverse index
# while keeping track of the best singleton solution
for i, sentence in enumerate(self.sentences):
weights[i] = sum(self.weights[c] for c in set(sentence.concepts))
if sentence.length <= summary_size\
and weights[i] > best_singleton_score:
best_singleton_score = weights[i]
best_singleton = i
# initialize the selected solution properties
sel_subset, sel_concepts, sel_length, sel_score = set(), set(), 0, 0
# greedily select a sentence
while True:
###################################################################
# RETRIEVE THE BEST SENTENCE
###################################################################
# sort the sentences by gain and reverse length
sort_sent = sorted(((weights[i] / float(self.sentences[i].length),
-self.sentences[i].length, i)
for i in sentences),
reverse=True)
# select the first sentence that fits in the length limit
for sentence_gain, rev_length, sentence_index in sort_sent:
if sel_length - rev_length <= summary_size:
break
# if we don't find a sentence, break out of the main while loop
else:
break
# if the gain is null, break out of the main while loop
if not weights[sentence_index]:
break
# update the selected subset properties
sel_subset.add(sentence_index)
sel_score += weights[sentence_index]
sel_length -= rev_length
# update sentence weights with the reverse index
for concept in set(self.sentences[sentence_index].concepts):
if concept not in sel_concepts:
for sentence in self.c2s[concept]:
weights[sentence] -= self.weights[concept]
# update the last selected subset property
sel_concepts.update(self.sentences[sentence_index].concepts)
# check if a singleton has a better score than our greedy solution
if best_singleton_score > sel_score:
return best_singleton_score, set([best_singleton])
# returns the (objective function value, solution) tuple
return sel_score, sel_subset
def tabu_search(self,
summary_size=100,
memory_size=10,
iterations=100,
mutation_size=2,
mutation_group=True):
"""Greedy approximation of the ILP model with a tabu search
meta-heuristic.
Args:
summary_size (int): the maximum size in words of the summary,
defaults to 100.
memory_size (int): the maximum size of the pool of sentences
to ban at a given time, defaults at 5.
iterations (int): the number of iterations to run, defaults at
30.
mutation_size (int): number of sentences to unselect and add to
the tabu list at each iteration.
mutation_group (boolean): flag to consider the mutations as a
group: we'll check sentence combinations in the tabu list, not
sentences alone.
Returns:
(value, set) tuple (int, list): the value of the approximated
objective function and the set of selected sentences as a tuple.
"""
# compute concept to sentences and concept sets for each sentence
if not self.c2s:
self.compute_c2s()
if not self.concept_sets:
self.compute_concept_sets()
# initialize weights
weights = {}
# initialize the score of the best singleton
best_singleton_score = 0
# compute initial weights and fill the reverse index
# while keeping track of the best singleton solution
for i, sentence in enumerate(self.sentences):
weights[i] = sum(self.weights[c] for c in set(sentence.concepts))
if sentence.length <= summary_size\
and weights[i] > best_singleton_score:
best_singleton_score = weights[i]
best_singleton = i
best_subset, best_score = None, 0
state = State()
for i in xrange(iterations):
queue = deque([], memory_size)
# greedily select sentences
state = self.select_sentences(summary_size, weights, state, queue,
mutation_group)
if state.score > best_score:
best_subset = state.subset.copy()
best_score = state.score
to_tabu = set(random.sample(state.subset, mutation_size))
state = self.unselect_sentences(weights, state, to_tabu)
queue.extend(to_tabu)
# check if a singleton has a better score than our greedy solution
if best_singleton_score > best_score:
return best_singleton_score, set([best_singleton])
# returns the (objective function value, solution) tuple
return best_score, best_subset
def select_sentences(self, summary_size, weights, state, tabu_set,
mutation_group):
"""Greedy sentence selector.
Args:
summary_size (int): the maximum size in words of the summary,
defaults to 100.
weights (dictionary): the sentence weights dictionary. This
dictionnary is updated during this method call (in-place).
state (State): the state of the tabu search from which to start
selecting sentences.
tabu_set (iterable): set of sentences that are tabu: this
selector will not consider them.
mutation_group (boolean): flag to consider the mutations as a
group: we'll check sentence combinations in the tabu list, not
sentences alone.
Returns:
state (State): the new state of the search. Also note that
weights is modified in-place.
"""
# greedily select a sentence while respecting the tabu
while True:
###################################################################
# RETRIEVE THE BEST SENTENCE
###################################################################
# sort the sentences by gain and reverse length
sort_sent = sorted(
((weights[i] / float(self.sentences[i].length),
-self.sentences[i].length, i)
for i in range(len(self.sentences))
if self.sentences[i].length + state.length <= summary_size),
reverse=True)
# select the first sentence that fits in the length limit
for sentence_gain, rev_length, sentence_index in sort_sent:
if mutation_group:
subset = state.subset | {sentence_index}
for tabu in tabu_set:
if tabu <= subset:
break
else:
break
else:
if sentence_index not in tabu_set:
break
# if we don't find a sentence, break out of the main while loop
else:
break
# if the gain is null, break out of the main while loop
if not weights[sentence_index]:
break
# update state
state.subset |= {sentence_index}
state.concepts.update(self.concept_sets[sentence_index])
state.length -= rev_length
state.score += weights[sentence_index]
# update sentence weights with the reverse index
for concept in set(self.concept_sets[sentence_index]):
if state.concepts[concept] == 1:
for sentence in self.c2s[concept]:
weights[sentence] -= self.weights[concept]
return state
def unselect_sentences(self, weights, state, to_remove):
"""Sentence ``un-selector'' (reverse operation of the
select_sentences method).
Args:
weights (dictionary): the sentence weights dictionary. This
dictionnary is updated during this method call (in-place).
state (State): the state of the tabu search from which to start
un-selecting sentences.
to_remove (iterable): set of sentences to unselect.
Returns:
state (State): the new state of the search. Also note that
weights is modified in-place.
"""
# remove the sentence indices from the solution subset
state.subset -= to_remove
for sentence_index in to_remove:
# update state
state.concepts.subtract(self.concept_sets[sentence_index])
state.length -= self.sentences[sentence_index].length
# update sentence weights with the reverse index
for concept in set(self.concept_sets[sentence_index]):
if not state.concepts[concept]:
for sentence in self.c2s[concept]:
weights[sentence] += self.weights[concept]
state.score -= weights[sentence_index]
return state
def solve_ilp_problem(self,
summary_size=100,
units="WORDS",
solver='glpk',
excluded_solutions=None,
unique=False):
"""Solve the ILP formulation of the concept-based model.
:param summary_size: the maximum size in words of the summary, defaults to 100.
:param units: defaults to "WORDS"
:param solver: the solver used, defaults to glpk
:param excluded_solutions: (list of list): a list of subsets of sentences that are to be excluded,
defaults to []
:param unique: (bool): modify the model so that it produces only one optimal solution, defaults to False
:return: (value, set) tuple (int, list): the value of the objective function
and the set of selected sentences as a tuple.
"""
if excluded_solutions is None:
excluded_solutions = []
# initialize container shortcuts
concepts = self.weights.keys()
w = self.weights
L = summary_size
C = len(concepts)
S = len(self.sentences)
if not self.word_frequencies:
self.compute_word_frequency()
tokens = self.word_frequencies.keys()
f = self.word_frequencies
T = len(tokens)
# HACK Sort keys
concepts = sorted(self.weights, key=self.weights.get, reverse=True)
# formulation of the ILP problem
prob = pulp.LpProblem(self.input_directory, pulp.LpMaximize)
# initialize the concepts binary variables
c = pulp.LpVariable.dicts(name='c',
indexs=range(C),
lowBound=0,
upBound=1,
cat='Integer')
# initialize the sentences binary variables
s = pulp.LpVariable.dicts(name='s',
indexs=range(S),
lowBound=0,
upBound=1,
cat='Integer')
# initialize the word binary variables
t = pulp.LpVariable.dicts(name='t',
indexs=range(T),
lowBound=0,
upBound=1,
cat='Integer')
# OBJECTIVE FUNCTION
prob += pulp.lpSum(w[concepts[i]] * c[i] for i in range(C))
if unique:
prob += pulp.lpSum(w[concepts[i]] * c[i] for i in range(C)) + \
10e-6 * pulp.lpSum(f[tokens[k]] * t[k] for k in range(T))
# CONSTRAINT FOR SUMMARY SIZE
if units == "WORDS":
prob += pulp.lpSum(s[j] * self.sentences[j].length
for j in range(S)) <= L
if units == "CHARACTERS":
prob += pulp.lpSum(s[j] * len(self.sentences[j].untokenized_form)
for j in range(S)) <= L
# INTEGRITY CONSTRAINTS
for i in range(C):
for j in range(S):
if concepts[i] in self.sentences[j].concepts:
prob += s[j] <= c[i]
for i in range(C):
prob += pulp.lpSum(
s[j] for j in range(S)
if concepts[i] in self.sentences[j].concepts) >= c[i]
# WORD INTEGRITY CONSTRAINTS
if unique:
for k in range(T):
for j in self.w2s[tokens[k]]:
prob += s[j] <= t[k]
for k in range(T):
prob += pulp.lpSum(s[j] for j in self.w2s[tokens[k]]) >= t[k]
# CONSTRAINTS FOR FINDING OPTIMAL SOLUTIONS
for sentence_set in excluded_solutions:
prob += pulp.lpSum([s[j] for j in sentence_set
]) <= len(sentence_set) - 1
# prob.writeLP('test.lp')
# solving the ilp problem
# solving the ilp problem
try:
print('BASEILP with CPLEX')
prob.solve(pulp.CPLEX(msg=0))
except:
#print('BASEILP fallback to %s' % (solver))
if solver == 'gurobi':
prob.solve(pulp.GUROBI(msg=0))
elif solver == 'glpk':
print('BASEILP with GLPK')
prob.solve(pulp.GLPK(msg=0))
else:
sys.exit('no solver specified')
# retreive the optimal subset of sentences
solution = set([j for j in range(S) if s[j].varValue == 1])
# returns the (objective function value, solution) tuple
return (pulp.value(prob.objective), solution)
class Preprocessor:
def __init__(self, normalizer):
self.label_encoder = LabelEncoder()
self.tf_idf_vectorizer = TfidfVectorizer(
sublinear_tf=True,
stop_words=stopwords.words('english').append([
"nt", "get", "like", "would", "peopl", "one", "think", "time",
"becaus"
]),
smooth_idf=True,
norm="l2",
lowercase=True,
max_features=30000,
use_idf=True,
encoding="utf-8",
decode_error='ignore',
strip_accents='unicode',
analyzer="word")
if normalizer == "stemmer":
self.normalizer = SnowballStemmer("english")
elif normalizer == "lemmatizer":
self.normalizer = WordNetLemmatizer()
else:
raise Exception(
"Normalizer must be \"stemmer\" or \"lemmatizer\".")
def preprocess_reddit_train(self):
""" Stores a cleaned up version of the dataset on the current directory """
# Read dataset
df = pd.read_csv("data/reddit_train.csv")
# Apply stemming function
df["cleaned"] = df["comments"].apply(self.clean_text)
# Transform each subreddit into an unique integer
df["label"] = self.label_encoder.fit_transform(df["subreddits"])
# Save cleaned dataset
df.to_csv("data/preprocessed_reddit_train_" +
type(self.normalizer).__name__ + ".csv",
index=False)
# TODO: Implement Regularization (i.e. PCA, SVD, L1, L2...?)
def clean_text(self, sentence):
# Put all words to lower case
sentence = sentence.lower()
# Tokenize words
word_tokens = word_tokenize(sentence)
# Remove punctuation
word_tokens = [_ for _ in word_tokens if _ not in string.punctuation]
# Remove non-alphabetical char
word_tokens = [
re.sub(pattern="[^a-zA-Z0-9\s]", repl="", string=_)
for _ in word_tokens
]
# Remove empty strings
word_tokens = [_ for _ in word_tokens if _]
# Stem words
processed_sentence = self.normalize(" ".join(word_tokens))
# TODO: Remove links?
return processed_sentence.strip()
def normalize(self, sentence):
normalized_str = []
word_tokens = word_tokenize(sentence)
if type(self.normalizer).__name__ == "SnowballStemmer":
for i in word_tokens:
normalized_str.append(self.normalizer.stem(i))
elif type(self.normalizer).__name__ == "WordNetLemmatizer":
for i in word_tokens:
normalized_str.append(self.normalizer.lemmatize(i))
else:
raise Exception(
"Normalizer must be \"stemmer\" or \"lemmatizer\".")
return " ".join(normalized_str)
def preprocess_reddit_test(self):
""" Returns a cleaned up version of the test dataset """
# Read dataset
df = pd.read_csv("data/reddit_test.csv")
# Apply stemming function
df["cleaned"] = df["comments"].apply(self.clean_text)
# Store cleaned test set in
df.to_csv("data/preprocessed_reddit_test_" +
type(self.normalizer).__name__ + ".csv",
index=False)
def stem_test():
stemmer = WordNetLemmatizer()
print stemmer.lemmatize("environment")
print stemmer.lemmatize("environmental")
stemmer = PorterStemmer()
print stemmer.stem("environmental")
