def __init__(self):
self.wnl = WordNetLemmatizer()
self.lancaster_stemmer = LancasterStemmer()
self.porter_stemmer = PorterStemmer()
self.snowball_stemmer = SnowballStemmer('english')
wn.ensure_loaded()
def main():
global bow_corpus
global word_to_idx
global users
wn.ensure_loaded()
if NEW_CORPUS:
bow_corpus = build_bow_corpus(get_users())
save_corpus(bow_corpus)
else:
bow_corpus = get_corpus()
bow_corpus_tmp = [w[0] for w in bow_corpus.items() if w[1] > 2]
word_to_idx = {k: idx for idx, k in enumerate(bow_corpus_tmp)}
idx_to_word = {idx: k for k, idx in word_to_idx.items()}
users = get_users()
results = []
N = 0
# for chik, svdk in exp:
# r= []
#for N in range(15):
results.append(truth_prediction_for_users(users, idx_to_word, 10000, 20,
N))
print(np.average(np.asarray(results), axis=1))
def __init__(self):
self.wnl = WordNetLemmatizer()
wn.ensure_loaded()
self.contractions = {
'isn\'t': ['is', 'not'],
'aren\'t': ['are', 'not'],
'wasn\'t': ['was', 'not'],
'weren\'t': ['were', 'not'],
'don\'t': ['do', 'not'],
'doestn\'t': ['does', 'not'],
'didn\'t': ['did', 'not'],
'can\'t': ['cannot'],
'we\'re': ['we', 'are'],
'i\'m': ['I', 'am'],
'it\'s': ['it', 'is'],
'haven\'t': ['have', 'not'],
'hasn\'t': ['has', 'not'],
'hadn\'t': ['had', 'not'],
'couldn\'t': ['could', 'not'],
'mightn\'t': ['might', 'not'],
'mustn\'t': ['must', 'not'],
'shan\'t': ['shall', 'not'],
'mayn\'t': ['may', 'not'],
'shouldn\'t': ['should', 'not'],
'won\'t': ['will', 'not'],
'wouldn\'t': ['would', 'not'],
'daren\'t': ['dare', 'not'],
'needn\'t': ['need', 'not'],
'usedn\'t': ['use', 'not'],
'let\'s': ['let', 'us'],
'you\'ve': ['you', 'have'],
'i\'ve': ['I', 'have'],
}
def __init__(self):
#Ensuring that the wordnet corpus is loaded, so we can support multithreading
wn.ensure_loaded()
self.lemmatizer = wn_stem.WordNetLemmatizer()
self.lemmas_dict = {}
self.synsets_dict = {}
self.similarity_dict = {}
def init():
nonlocal cached_stopwords
try:
wordnet.ensure_loaded()
cached_stopwords = set(stopwords.words("english"))
except LookupError:
nltk.download("punkt")
nltk.download("stopwords")
def build_set(self):
wn.ensure_loaded() # `LazyCorpusLoader` conversion into `WordNetCorpusReader` starts
print ("WordNet loaded")
swn.ensure_loaded() # `LazyCorpusLoader` conversion into `SentiWordNetCorpusReader` starts
print ("SentiWordNet loaded")
self.tweet_tokenizer = TweetTokenizer(preserve_case=True, reduce_len=False, strip_handles=False)
print ("Tweet tokenizer loaded")
self.it_tokenizer = MosesTokenizer(lang='it')
print ("Moses tokenizer loaded")
self.it_tagger = treetaggerwrapper.TreeTagger(TAGLANG="it", TAGDIR=flags.tagger_path)
# self.en_tagger = treetaggerwrapper.TreeTagger(TAGLANG="en", TAGDIR=flags.tagger_path)
print ("Tagger loaded")
self.stop_words = set(stopwords.words('italian'))
print ("Stopwords loaded")
self.lexicon = lm.LexiconSent('it')
print ("OpeNER lexicon loaded")
self.emoji = self.get_emoji_sentiment_lexicon(flags.emoji_sentiment_lexicon)
print ("Emoji sentiment lexicon loaded")
self.translator = Translator()
print ("Setting up support dictionaries")
self.translated_lemma_tokens = self.load_obj(flags.translated_lemma_tokens)
self.lexeme_sentiment_dict = self.load_obj(flags.lexeme_sentiment_dict)
print ("Translator loaded")
# Build test annotations
print ("Building test annotations..")
test_set = self.load_obj(flags.test_annotations)
if not test_set:
test_set = self.get_annotations(flags.test_set_path)
self.save_obj(test_set, flags.test_annotations)
print ("Test annotations built")
# Build training annotations
print ("Building training annotations..")
training_set = self.load_obj(flags.training_annotations)
if not training_set:
training_set = self.get_annotations(flags.training_set_path)
self.save_obj(training_set, flags.training_annotations)
print ("Training annotations built")
print ("Saving support dictionaries")
self.save_obj(self.translated_lemma_tokens, flags.translated_lemma_tokens)
self.save_obj(self.lexeme_sentiment_dict, flags.lexeme_sentiment_dict)
# Build distributional docvec from training and test sets
self.doc2vec = self.build_distributional_docvec([test_set, training_set])
print ("Doc2Vec built")
self.add_context_to_annotations(test_set)
print ("Distr. docvec added to test annotations")
self.add_context_to_annotations(training_set)
print ("Distr. docvec added to training annotations")
self.free_ram()
print ("Loading pre-trained model..")
self.model = ft.load_model(flags.word2vec_path)
print ("Pre-trained model loaded")
self.add_wordvecs_to_annotations(test_set)
print ("Wordvecs added to test annotations")
self.add_wordvecs_to_annotations(training_set)
print ("Wordvecs added to training annotations")
# Save to npy
self.free_ram()
self.save_obj({"test_set":test_set, "training_set":training_set}, flags.preprocessed_dict)
def feature_pred(features, chik, ldak):
global users
wn.ensure_loaded()
facts = gt.get_fact_topics(DIR)
if NEW_DATA:
users = gt.get_users(DIR)
transactions = gt.get_transactions(DIR)
print(transactions.describe())
tr_hsh = transactions['fact'].values
# if castillo: comment cond2 out
cond = facts['hash'].isin(tr_hsh)
cond2 = facts['true'] == 1 | facts['true'] == 0
facts = facts[cond & cond2]
facts = Parallel(n_jobs=num_jobs)(delayed(get_features)(
fact, transactions[transactions['fact'] == fact['hash']], [
u for u in users if int(u.user_id) in list(transactions[
transactions['fact'] == fact['hash']]['user_id'].values)
]) for idx, fact in facts.iterrows())
facts = pd.DataFrame(facts)
with open('model_data/feature_data', 'wb') as tmpfile:
pickle.dump(facts, tmpfile)
else:
with open('model_data/feature_data', 'rb') as tmpfile:
facts = pickle.load(tmpfile)
print(facts[list(features)].describe())
X = facts[list(features)].values
y = facts['y'].values
X_train, X_test, y_train, y_test = train_test_split(X, y, test_size=0.20)
std_clf = make_pipeline(StandardScaler(), PCA(n_components=ldak),
SVC(C=1, gamma=1))
std_clf.fit(X_train, y_train)
pred_test_std = std_clf.predict(X_test)
precision, recall, fscore, sup = precision_recall_fscore_support(
y_test, pred_test_std, average='macro')
score = metrics.accuracy_score(y_test, pred_test_std)
print("Accuracy: %0.3f, Precision: %0.3f, Recall: %0.3f, F1 score: %0.3f" %
(score, precision, recall, fscore))
acc_scores = cross_val_score(std_clf, X, y, cv=3)
pr_scores = cross_val_score(std_clf, X, y, scoring='precision', cv=3)
re_scores = cross_val_score(std_clf, X, y, scoring='recall', cv=3)
f1_scores = cross_val_score(std_clf, X, y, scoring='f1', cv=3)
print("\t Cross validated Accuracy: %0.3f (+/- %0.3f)" %
(acc_scores.mean(), acc_scores.std() * 2))
print("\t Cross validated Precision: %0.3f (+/- %0.3f)" %
(pr_scores.mean(), pr_scores.std() * 2))
print("\t Cross validated Recall: %0.3f (+/- %0.3f)" %
(re_scores.mean(), re_scores.std() * 2))
print("\t Cross validated F1: %0.3f (+/- %0.3f)" %
(f1_scores.mean(), f1_scores.std() * 2))
return acc_scores.mean()
def elaborateText(tweet_text):
#removing urls
text = re.sub('http[s]?://(?:[a-zA-Z]|[0-9]|[$-_@.&+#]|[!*\(\),]|'\
'(?:%[0-9a-fA-F][0-9a-fA-F]))+','', tweet_text)
#removing emoticon
text = remove_emoji(text)
text = re.sub("(@[A-Za-z0-9_]+)","", text)
text = text.lower()
for punct_sign in string.punctuation:
text = text.replace(punct_sign," ")
#others puntuations not within the set of string.punctuation->{!"#$%&'()*+,-./:;<=>?@[\]^_`{|}~}
text = text.replace("’"," ")
text = text.replace("”"," ")
text = text.replace("“"," ")
text = text.replace("\n"," ")
# removing numbers
text = re.sub(r'\d+', '', text)
#tokenaziton
tokens = nltk.word_tokenize(text)
#removing stopwords
stopWords = stopwords.words('english')
filteredTokens = []
filteredTokens = [word for word in tokens if word not in stopWords]
#lemmatization
wn.ensure_loaded()
lemmatizer = WordNetLemmatizer()
lemmatized_sentence = []
lemmatized_sentence_joined = ""
tokensTagging = pos_tag(tokens)
# pos tagging & lemmatization
for word, tag in tokensTagging:
if tag.startswith('NN'):
pos = 'n'
elif tag.startswith('VB'):
pos = 'v'
else:
pos = 'a'
lemmatized_sentence.append(lemmatizer.lemmatize(word, pos))
lemmatized_sentence_set = ' '.join(lemmatized_sentence)
return lemmatized_sentence_set
def get_clean_content(file: str):
# https://stackoverflow.com/questions/27433370/what-would-cause-wordnetcorpusreader-to-have-no-attribute-lazycorpusloader
# not the best fix but it works
nltk_load_lock.acquire()
wordnet.ensure_loaded()
nltk_load_lock.release()
meta, content = extract(file)
if content is not None:
lang = meta["language"]
content = clean(content, lang)
return meta, content
def get(self, request, format=None):
wordnet.ensure_loaded()
seminars = self.request.query_params.get('seminars')
guid = self.request.query_params.get('guid')
user = get_user(guid)
similarities = {}
for seminar_id in seminars.split(','):
try:
# Load seminar and its keywords
seminar = Seminar.objects.get(id=seminar_id)
keywords = json.loads(seminar.keywords)
# User has not set any interests or seminar has no keywords so set similarity to 0
if not user.interests or not keywords:
similarities[seminar.id] = 0
continue
# Get synsets of words for user interests and keywords
interest_syns = set(
synset for interest in user.interests
for synset in wordnet.synsets(interest)
)
keyword_syns = set(
synset for keyword in keywords[0:3]
for synset in wordnet.synsets(keyword['text'])
)
# If no synsets of words, set similiarity to 0
if not interest_syns or not keyword_syns:
similarities[seminar.id] = 0
continue
# Calculate best similarity between the sets of words
best = max(
wordnet.wup_similarity(i, j) or 0
for i, j in product(interest_syns, keyword_syns)
)
# Convert to percentage and round to 1 decimal place
similarities[seminar.id] = round(best * 100, 1)
except Seminar.DoesNotExist:
similarities[seminar.id] = 0
continue
return Response(similarities)
def preload(fill_cache: bool):
"""
Pre-loads any data so the user experience is better, i.e. there is less delay during.
:param fill_cache: if true, will run all parsing tests to fill the cache for the semantic distance function.
"""
# Preload the WordNet dictionary.
print('Loading WordNet...')
wn.ensure_loaded()
if fill_cache:
print('Filling Cache (Running Tests)...')
loader = TestLoader()
suite = loader.discover(start_dir='tests/parsing')
TextTestRunner(verbosity=1).run(suite)
def __init__(self):
#news = pd.read_csv("classifier/training_dataset.csv", names=['id', 'text', 'category'])
# load the label encoder to decode category numbers
#self.encoder = LabelEncoder()
#self.encoder.fit_transform(news['category'])
# load the text classifer
#self.text_clf = open("classifier/nb_classifier.pkl", "rb")
#self.text_clf = pickle.load(self.text_clf)
self.porter = PorterStemmer()
# prevents odd nltk error
# https://stackoverflow.com/questions/27433370/what-would-cause-wordnetcorpusreader-to-have-no-attribute-lazycorpusloader
wn.ensure_loaded()
def main():
wn.ensure_loaded()
users = get_users()
#users = [was_user_correct(user) for user in users]
#print("Linguistic features..")
#users = Parallel(n_jobs=num_jobs)(delayed(linguistic_f)(user) for user in users)
#print("Calculating tweet sentiment for each user")
users = Parallel(n_jobs=num_jobs)(
delayed(feature_user_tweet_sentiment)(user) for user in users)
print("Avg time to retweet")
users = Parallel(n_jobs=num_jobs)(delayed(time_til_retweet)(user)
for user in users)
print([u.sent_tweets_avg for u in users[:10]])
print([u.avg_time_to_retweet for u in users[:10]])
[store_result(user) for user in users]
def main():
global bow_corpus
global word_to_idx, idx_to_word
global bow_corpus_top_n
global users
wn.ensure_loaded()
bow_corpus = get_corpus()
users = get_users()
bow_corpus_tmp = [w[0] for w in bow_corpus.items() if w[1] > 2]
#print("Corpus size: {}".format(len(bow_corpus_tmp)))
word_to_idx = {k: idx for idx, k in enumerate(bow_corpus_tmp)}
idx_to_word = {idx: k for k, idx in word_to_idx.items()}
#for n in range(0,10,1):
lstm_pred(-1)
def preprocess_text(docs):
num_task = os.cpu_count()
len_slices = len(docs) // num_task
remainder_slices = len(docs) % num_task
texts = []
stoplist = set(stopwords.words('english'))
wn.ensure_loaded()
t_start = time.perf_counter()
with ProcessPoolExecutor(max_workers=num_task) as executor:
futures_tokenize = []
for n in range(0, num_task):
upper_bound = (n+1) * len_slices
if n == num_task - 1:
upper_bound = (n+1) * len_slices + remainder_slices
print(n, upper_bound)
futures_tokenize.append(executor.submit(preprocess_tokenize, docs[n * len_slices:upper_bound],
stoplist))
for future in concurrent.futures.as_completed(futures_tokenize):
texts += future.result()
t_stop = time.perf_counter()
print("removed stopwords and lemmatized in {} s".format(t_stop - t_start))
# Add bigrams and trigrams to docs (only ones that appear 20 times or more).
bigram = Phraser(Phrases(texts, min_count=20))
for idx in range(len(texts)):
for token in bigram[texts[idx]]:
if '_' in token:
# Token is a bigram, add to document.
texts[idx].append(token)
print("Done bigrams")
dictionary = Dictionary(texts)
dictionary.filter_extremes(no_below=30, no_above=0.5)
dictionary.filter_tokens(bad_ids=[dictionary.token2id["like"]])
special_tokens = {'_pad_': 0}
dictionary.patch_with_special_tokens(special_tokens)
return texts, dictionary
def IndexWebSite(self, url, urlSet, depth, urlAmount, flag=False):
print("indexleme basladi")
keywords = self.FindKeywords(url, 5)
print(keywords)
if (len(keywords) == 0): return []
threads = []
self.resultArr = []
wn.ensure_loaded()
for i in range(len(urlSet)):
t = Thread(target=self.IndexSiteWithThread,
args=(urlSet[i], depth, urlAmount, keywords))
t.start()
threads.append(t)
for thread in threads:
thread.join()
self.bubbleSort(self.resultArr)
return self.resultArr
def main():
wn.ensure_loaded()
# batch
i = int(sys.argv[1])
user_files = sorted(glob.glob(DIR + 'user_tweets/' + 'user_*.json'))
r = 1960
user_files = user_files[(i-1)*r:min(r*i,len(user_files))]
users = get_users(user_files)
facts, transactions = get_data()
users = Parallel(n_jobs=num_jobs)(delayed(was_user_correct)(user, facts, transactions) for user in users)
# print("Linguistic features..")
# users = Parallel(n_jobs=num_jobs)(delayed(linguistic_f)(user) for user in users)
# print("Calculating tweet sentiment for each user")
# users = Parallel(n_jobs=num_jobs)(delayed(feature_user_tweet_sentiment)(user) for user in users)
# print("Avg time to retweet")
# users = Parallel(n_jobs=num_jobs)(delayed(time_til_retweet)(user) for user in users)
# print([u.sent_tweets_avg for u in users[:10]])
# print([u.avg_time_to_retweet for u in users[:10]])
[store_result(user) for user in users]
def main():
global bow_corpus
global word_to_idx
wn.ensure_loaded()
if NEW_CORPUS:
bow_corpus = build_bow_corpus(get_users())
save_corpus(bow_corpus)
else:
bow_corpus = get_corpus()
bow_corpus_tmp = [w[0] for w in bow_corpus.items() if w[1] > 2]
word_to_idx = {k: idx for idx, k in enumerate(bow_corpus_tmp)}
idx_to_word = {idx: k for k, idx in word_to_idx.items()}
users = get_users()
facts = gt.get_fact_topics()
transactions = gt.get_transactions()
users_df = pd.DataFrame([vars(u) for u in users])
print(users_df.describe())
print(users_df[users_df['stance'] == 0].describe())
print(users_df[users_df['stance'] == 1].describe())
print(users_df[users_df['stance'] == 2].describe())
print(users_df[users_df['stance'] == 3].describe())
users_df['f_t'] = users_df['fact'].map(
lambda x: facts[facts['hash'] == x]['true'].values[0])
c_true = users_df['f_t'] == '1'
c_fal = users_df['f_t'] == '0'
c_fal1 = users_df['f_t'] == 0
c_den = users_df['stance'] == 0
c_sup = users_df['stance'] == 1
print(users_df[c_true & c_sup].describe())
print(users_df[c_fal | c_fal1][c_den].describe())
print(users_df[c_fal | c_fal1][c_sup].describe())
print(users_df[c_true & c_den].describe())
print(users_df[users_df['was_correct'] == 1].describe())
print(users_df[users_df['was_correct'] == 0].describe())
print(len([t for u in users for t in u.tweets if u.tweets is not None]))
corpus_analysis(bow_corpus, word_to_idx, idx_to_word)
# temporal_analysis(get_users())
cluster_users_on_tweets(users, word_to_idx, idx_to_word)
def main():
global bow_corpus
global word_to_idx
wn.ensure_loaded()
if NEW_CORPUS:
bow_corpus = build_bow_corpus(get_users())
save_corpus(bow_corpus)
else:
bow_corpus = get_corpus()
bow_corpus_tmp = [w[0] for w in bow_corpus.items() if w[1] > 2]
word_to_idx = {k: idx for idx, k in enumerate(bow_corpus_tmp)}
idx_to_word = {idx: k for k, idx in word_to_idx.items()}
users = get_users()
corpus_analysis(bow_corpus, word_to_idx, idx_to_word)
# temporal_analysis(get_users())
cluster_users_on_tweets(users, word_to_idx, idx_to_word)
def __init__(self):
print('loading the wordnet corpus...')
wordnet.ensure_loaded()
print('loading done')
self.nlp = spacy.load('en')
self.nlp.add_pipe(WordnetAnnotator(self.nlp.lang), after='tagger')
f = open('sorted_first_names.txt', 'r')
lines = f.readlines()
self.first_name_array = []
for line in lines:
line = line.rstrip()
self.first_name_array.append(line)
f = open('sorted_last_names.txt', 'r')
lines = f.readlines()
self.last_name_array = []
for line in lines:
line = line.rstrip()
self.last_name_array.append(line)
f = open('bad_words.txt', 'r')
lines = f.readlines()
self.profane_words_array = []
for line in lines:
line = line.rstrip()
self.profane_words_array.append(line)
def search(self, queryString,search_length=10,return_rank_list=False):
wn.ensure_loaded()
stop_words = set(stopwords.words('english'))
porter_stemmer = PorterStemmer()
wordnet_lemmatizer = WordNetLemmatizer()
query = word_tokenize(queryString)
query=[w.lower() for w in query if (w.isalpha() and w not in stop_words)]
query=[(wordnet_lemmatizer.lemmatize(w)) for w in query]
query=[porter_stemmer.stem(w) for w in query]
self.processQuery( self.vocab, query )
#Getting the page ranking for the above query
obj = CosineScore(self.queryVector, self.tfidfMatrix)
rankList = obj.getPages(search_length)
if return_rank_list==True:
return rankList
#Getting the id and url name
finalList = []
for docIndex in rankList:
finalList.append((self.titleList[docIndex], self.urlList[docIndex]))
return (finalList)
def process(file_input_name, file_output_name):
try:
wordnet.ensure_loaded()
data = pd.read_csv(file_input_name, encoding='ISO-8859-1')
input_text = data['text']
threads = []
result = []
for i in range(10):
t = lemmatizeThread(thread_name='thread' + str(i),
the_queue=workQueue)
t.start()
threads.append(t)
threadLock.acquire()
for i in range(len(input_text) - 1):
workQueue.put(str(i) + '-' + input_text[i])
threadLock.release()
while not workQueue.empty():
pass
global exitFlag
exitFlag = 1
for t in threads:
t.join()
result.append(t.data)
with open(file_output_name, 'w') as f:
while (not resultQueue.empty()):
result = resultQueue.get()
f.write(result + '\n')
except Exception as e:
print(e)
from nltk.corpus import wordnet as wn
#right = wn.synset('right_whale.n.01')
wn.ensure_loaded()
help(wn)
def main():
global bow_corpus
global word_to_idx, idx_to_word, fact_to_words
global bow_corpus_top_n
wn.ensure_loaded()
print('Grabbing Data')
bow_corpus = gt.get_corpus()
facts = gt.get_fact_topics()
facts = facts[facts['true'] != 'unknown']
bow_corpus_tmp = [w[0] for w in bow_corpus.items() if w[1] > 2]
word_to_idx = {k: idx for idx, k in enumerate(bow_corpus_tmp)}
idx_to_word = {idx: k for k, idx in word_to_idx.items()}
fact_to_words = {
r['hash']: [w for w in r['fact_terms']]
for index, r in facts[['hash', 'fact_terms']].iterrows()
}
if NEW_MODEL:
users = gt.get_users()
# Prepping lstm model
top_words = 50000
X, y, user_order = lstm_cred.get_prebuilt_data()
X, y, user_order = lstm_cred.balance_classes(X, y, user_order)
#X_train, X_test, y_train, y_test = train_test_split_every_user(X, y, user_order)
#X_train, X_test, y_train, y_test = train_test_split_on_facts(X, y, user_order, facts_train.values, users)
#X_train, X_test, y_train, y_test = lstm_cred.train_test_split_on_users(X, y, user_order, users, 100)
X_train, X_test, y_train, y_test = train_test_split(X,
y,
test_size=0.5)
X_train, X_test, word_to_idx = lstm_cred.keep_n_best_words(
X_train, y_train, X_test, y_test, idx_to_word, top_words)
max_tweet_length = 12
X_train = sequence.pad_sequences(X_train, maxlen=max_tweet_length)
X_test = sequence.pad_sequences(X_test, maxlen=max_tweet_length)
# Training lstm model
embedding_vecor_length = 32
model = Sequential()
model.add(
Embedding(top_words,
embedding_vecor_length,
input_length=max_tweet_length))
model.add(Dropout(0.2))
model.add(LSTM(100))
model.add(Dropout(0.2))
model.add(Dense(1, activation='sigmoid'))
model.compile(loss='binary_crossentropy',
optimizer='adam',
metrics=['accuracy'])
print(model.summary())
model.fit(X_train,
y_train,
validation_data=(X_test, y_test),
epochs=5,
batch_size=64)
model.save('model_data/cred_model.h5')
scores = model.evaluate(X_test, y_test, verbose=0)
print("Accuracy: %.2f%%" % (scores[1] * 100))
if NEW_REL_TWEETS:
print('Building new relevant tweets')
users = Parallel(n_jobs=num_jobs)(
delayed(get_relevant_tweets)(user) for user in users)
#users = Parallel(n_jobs=num_jobs)(delayed(get_relevant_tweets_test_set)(user, X_test) for user in users)
user_to_rel_tweet = {
user.user_id: user.features['relevant_tweets']
for user in users if 'relevant_tweets' in user.features
}
with open('model_data/relevant_tweets.pkl', 'wb') as tmpfile:
pickle.dump(user_to_rel_tweet, tmpfile)
else:
with open('model_data/relevant_tweets.pkl', 'rb') as tmpfile:
user_to_rel_tweet = pickle.load(tmpfile)
for user in users:
if 'relevant_tweets' in user.features:
user.features['relevant_tweets'] = user_to_rel_tweet[
user.user_id]
# Build credibility scores for all users on their topic
print('Computing credibility')
users = [prebuild_cred(model, u) for u in users]
users_df = pd.DataFrame([vars(u) for u in users])
[store_result(u) for u in users]
with open('model_data/cred_pred_data', 'wb') as tmpfile:
pickle.dump({'users': users_df, 'map': word_to_idx}, tmpfile)
else:
print('Loading users & model')
with open('model_data/cred_pred_data', 'rb') as tmpfile:
construct = pickle.load(tmpfile)
users_df = construct['users']
word_to_idx = construct['map']
print('Making cred*sent predictions')
X = []
y = []
for idx, hsh in enumerate(facts['hash'].values):
this_users = users_df[users_df['fact'] == hsh]
this_x = cred_stance_prediction(this_users)
this_y = facts['true'].iloc[idx]
X.append((np.average(this_x), np.std(this_x)))
y.append(int(this_y))
X_train, X_test, y_train, y_test = train_test_split(X, y, test_size=0.20)
std_clf = make_pipeline(StandardScaler(), LinearSVC)
std_clf.fit(X_train, y_train)
pred = std_clf.predict(X_test)
score = metrics.accuracy_score(y_test, pred)
precision, recall, fscore, sup = metrics.precision_recall_fscore_support(
y_test, pred, average='macro')
print(
"Rumors: Accuracy: %0.3f, Precision: %0.3f, Recall: %0.3f, F1 score: %0.3f"
% (score, precision, recall, fscore))
acc_scores = cross_val_score(std_clf, X, y, cv=3)
pr_scores = cross_val_score(std_clf, X, y, scoring='precision', cv=3)
re_scores = cross_val_score(std_clf, X, y, scoring='recall', cv=3)
f1_scores = cross_val_score(std_clf, X, y, scoring='f1', cv=3)
print("\t Cross validated Accuracy: %0.3f (+/- %0.3f)" %
(acc_scores.mean(), acc_scores.std() * 2))
print("\t Cross validated Precision: %0.3f (+/- %0.3f)" %
(pr_scores.mean(), pr_scores.std() * 2))
print("\t Cross validated Recall: %0.3f (+/- %0.3f)" %
(re_scores.mean(), re_scores.std() * 2))
print("\t Cross validated F1: %0.3f (+/- %0.3f)" %
(f1_scores.mean(), f1_scores.std() * 2))
print('Making cred*stance predictions')
X = []
y = []
all_evidence = []
for idx, hsh in enumerate(facts['hash'].values):
this_users = users_df[users_df['fact'] == hsh]
this_x, evidence = only_cred_support_deny_pred(this_users)
this_y = facts['true'].iloc[idx]
evidence = sorted(evidence, reverse=True, key=lambda x: x[0])
# print(facts[facts['hash']==hsh]['text'].values, int(this_y), this_x[-1])
# print(evidence if len(evidence) <3 else evidence[:3])
X.append((np.average(this_x), np.std(this_x)))
y.append(int(this_y))
print(X[:20])
print(y[:20])
X_train, X_test, y_train, y_test = train_test_split(X, y, test_size=0.20)
std_clf = make_pipeline(StandardScaler(), LinearSVC())
std_clf.fit(X_train, y_train)
pred = std_clf.predict(X_test)
score = metrics.accuracy_score(y_test, pred)
precision, recall, fscore, sup = metrics.precision_recall_fscore_support(
y_test, pred, average='macro')
print(
"Rumors: Accuracy: %0.3f, Precision: %0.3f, Recall: %0.3f, F1 score: %0.3f"
% (score, precision, recall, fscore))
acc_scores = cross_val_score(std_clf, X, y, cv=3)
pr_scores = cross_val_score(std_clf, X, y, scoring='precision', cv=3)
re_scores = cross_val_score(std_clf, X, y, scoring='recall', cv=3)
f1_scores = cross_val_score(std_clf, X, y, scoring='f1', cv=3)
print(acc_scores)
print(pr_scores)
print(re_scores)
print(f1_scores)
print("\t Cross validated Accuracy: %0.3f (+/- %0.3f)" %
(acc_scores.mean(), acc_scores.std() * 2))
print("\t Cross validated Precision: %0.3f (+/- %0.3f)" %
(pr_scores.mean(), pr_scores.std() * 2))
print("\t Cross validated Recall: %0.3f (+/- %0.3f)" %
(re_scores.mean(), re_scores.std() * 2))
print("\t Cross validated F1: %0.3f (+/- %0.3f)" %
(f1_scores.mean(), f1_scores.std() * 2))
def text_processing(ques1, ques2):
# from nltk.corpus.reader.wordnet import WordNetError
from nltk.stem.porter import PorterStemmer
from nltk.stem import WordNetLemmatizer
from nltk.tokenize import word_tokenize
from nltk.corpus import wordnet as wn
from nltk.corpus import stopwords
import num2words as nw
import string
"""Function to remove punctions in the strings"""
r_p1 = list(
map(
lambda ques: ''.join(
[word for word in ques1 if word not in string.punctuation]),
[ques1]))
r_p2 = list(
map(
lambda ques: ''.join(
[word for word in ques2 if word not in string.punctuation]),
[ques2]))
"""Function to create word token from the document"""
w_t1 = list(
map(
lambda r_p: ' '.join([
nw.num2words(word) if word.isdigit() else word
for word in word_tokenize(r_p[0].replace("°", "").replace(
"²", ""))
]), [r_p1]))
w_t2 = list(
map(
lambda r_p: ' '.join([
nw.num2words(word) if word.isdigit() else word
for word in word_tokenize(r_p[0].replace("°", "").replace(
"²", ""))
]), [r_p2]))
l_w_t1 = len(word_tokenize(r_p1[0]))
l_w_t2 = len(word_tokenize(r_p1[0]))
"""Function to remove stop words from the document"""
wn.ensure_loaded()
words = stopwords.words('english')
r_s_w1 = list(
map(
lambda w_t: " ".join(
[word for word in w_t[0].split() if word not in words]),
[w_t1]))
r_s_w2 = list(
map(
lambda w_t: " ".join(
[word for word in w_t[0].split() if word not in words]),
[w_t2]))
l_r_s_w1 = len(word_tokenize(r_s_w1[0]))
l_r_s_w2 = len(word_tokenize(r_s_w2[0]))
"""Function to stem tokens of string"""
stemmer = PorterStemmer()
stems1 = list(
map(
lambda r_s_w: " ".join(
[stemmer.stem(word) for word in r_s_w[0].split(" ")]),
[r_s_w1]))
stems2 = list(
map(
lambda r_s_w: " ".join(
[stemmer.stem(word) for word in r_s_w[0].split(" ")]),
[r_s_w2]))
"""Function to lemmatize tokens of string"""
lemmatizer = WordNetLemmatizer()
lamit1 = list(
map(
lambda stems: " ".join(
[lemmatizer.lemmatize(word) for word in stems[0].split()]),
[stems1]))
lamit2 = list(
map(
lambda stems: " ".join(
[lemmatizer.lemmatize(word) for word in stems[0].split()]),
[stems2]))
# print([lamit1[0], lamit2[0]], [int(l_w_t1 / l_w_t2), int(l_r_s_w1 / l_r_s_w2)])
return [lamit1[0],
lamit2[0]], [int(l_w_t1 / l_w_t2),
int(l_r_s_w1 / l_r_s_w2)]
def get_queryset(self):
wordnet.ensure_loaded()
# Wordnet incorporates a lazy corpus model, which starts loading
# only on first call to itself, which can cause some issues with
# multithreading
return
outputFile = open("result_trigram.txt", "w+")
corpusLines = inputFile.readlines()
totalLines = len(corpusLines)
print(" Total Process : ", totalProcess)
if (totalLines < totalProcess):
print(" Total needed process : ", totalLines)
totalProcess = totalLines
allocationForProcess = int(totalLines / totalProcess)
lastAllocation = totalLines - allocationForProcess * totalProcess
print(" Total Lines : ", totalLines)
print(" Allocation for a Process : ", allocationForProcess)
print(" Last Allocation for a Process : ", lastAllocation)
wordnet.ensure_loaded() # first access to wn transforms it
if (allocationForProcess > 0):
# linePrinter.initCompletedstate(totalProcess+1)
processes = [
Thread(target=readByLines,
args=(processId, corpusLines, processId * allocationForProcess,
(processId + 1) * allocationForProcess,
processId * allocationForProcess, True,
allocationForProcess))
for processId in range(totalProcess)
]
# Run processes
for p in processes:
p.start()
inputFile = open(basePath + inputFileName, "r")
corpusLines = inputFile.readlines()
totalLines = len(corpusLines)
print(" Started File - ", inputFileName, " | Total Lines : ", totalLines)
if not os.path.exists(outputPath + inputFileName):
os.makedirs(outputPath + inputFileName)
readByLines(corpusLines, totalLines, inputFileName)
inputFile.close()
return inputFileName
startTime = datetime.now()
print("Process started : ", startTime)
print("Total Files : ", TOTAL_FILES_FOR_READ)
FileNames = []
for i in range(0, TOTAL_FILES_FOR_READ):
listToken = []
if (i < 9):
FileNames.append("news.en-0000" + str(i + 1) + "-of-00100")
else:
FileNames.append("news.en-000" + str(i + 1) + "-of-00100")
wordnet.ensure_loaded()
with concurrent.futures.ProcessPoolExecutor() as executor:
for lemma, result in zip(FileNames, executor.map(doWorker, FileNames)):
print(f"Finished for input file {lemma} was saved inside the {result}")
endTime = datetime.now()
print("\n\n Process Stopped : ", endTime)
print("\n\n Duration : ", endTime - startTime)
import nltk
from nltk.corpus import wordnet
__all__ = (
'get_related',
'fix_determiners'
)
# NLTK seems to check for updates and tries to unzip the corpus even when it’s
# already installed, which slows down the import, so only invoke nltk.download()
# if wordnet isn’t already available.
try:
wordnet.ensure_loaded()
except LookupError:
try:
nltk.download('wordnet')
except OSError as exc:
raise ImportError("Could not download WordNet:", exc)
_VOWELS = "aeiou"
def get_related(query, pos='n'):
"""If query is for a noun, return a random hyponym for query. If query is
for an adjective, return a related adjective.
Args:
def main():
global bow_corpus
global word_to_idx, idx_to_word, fact_to_words
global bow_corpus_top_n
wn.ensure_loaded()
print('Grabbing Data')
bow_corpus = gt.get_corpus()
facts = gt.get_fact_topics()
facts = facts[facts['true'] != 'unknown']
bow_corpus_tmp = [w[0] for w in bow_corpus.items() if w[1] > 2]
word_to_idx = {k: idx for idx, k in enumerate(bow_corpus_tmp)}
idx_to_word = {idx: k for k, idx in word_to_idx.items()}
fact_to_words = {
r['hash']: [w for w in r['fact_terms']]
for index, r in facts[['hash', 'fact_terms']].iterrows()
}
# Credibility data
print('Loading users & model')
with open('model_data/cred_pred_data', 'rb') as tmpfile:
construct = pickle.load(tmpfile)
users_df = construct['users']
word_to_idx = construct['map']
# Feature data
with open('model_data/feature_data', 'rb') as tmpfile:
fact_features = pickle.load(tmpfile)
features = [
'avg_links', 'avg_sent_neg', 'avg_sentiment', 'fr_has_url', 'lvl_size',
'avg_len', 'avg_special_symbol', 'avg_time_retweet',
'avg_count_distinct_words', 'avg_sent_pos', 'cred_pred',
'cred_pred_std'
]
print('Making cred*stance +best features predictions')
facts['cred_pred'] = facts['hash'].map(
lambda x: only_cred_support_deny_pred(users_df[users_df['fact'] == x]))
facts['cred_pred_std'] = facts['cred_pred'].map(lambda x: np.std(x))
facts['cred_pred'] = facts['cred_pred'].map(lambda x: x[-1])
facts = facts.set_index('hash').join(fact_features.set_index('hash'),
rsuffix='_other')
X = facts[features].values
y = facts['y'].values
X_train, X_test, y_train, y_test = train_test_split(X, y, test_size=0.20)
std_clf = make_pipeline(StandardScaler(), SVC(C=1, gamma=1))
std_clf.fit(X_train, y_train)
pred = std_clf.predict(X_test)
score = metrics.accuracy_score(y_test, pred)
precision, recall, fscore, sup = metrics.precision_recall_fscore_support(
y_test, pred, average='macro')
print(
"Rumors: Accuracy: %0.3f, Precision: %0.3f, Recall: %0.3f, F1 score: %0.3f"
% (score, precision, recall, fscore))
acc_scores = cross_val_score(std_clf, X, y, cv=3)
pr_scores = cross_val_score(std_clf, X, y, scoring='precision', cv=3)
re_scores = cross_val_score(std_clf, X, y, scoring='recall', cv=3)
f1_scores = cross_val_score(std_clf, X, y, scoring='f1', cv=3)
print("\t Cross validated Accuracy: %0.3f (+/- %0.3f)" %
(acc_scores.mean(), acc_scores.std() * 2))
print("\t Cross validated Precision: %0.3f (+/- %0.3f)" %
(pr_scores.mean(), pr_scores.std() * 2))
print("\t Cross validated Recall: %0.3f (+/- %0.3f)" %
(re_scores.mean(), re_scores.std() * 2))
print("\t Cross validated F1: %0.3f (+/- %0.3f)" %
(f1_scores.mean(), f1_scores.std() * 2))
def run(self):
out_dir = flags.cgmh_output_dir
fout = open(os.path.join(out_dir, "log" + str(self.__idx) + ".log"),
"w")
res_path = os.path.join(out_dir, "res" + str(self.__idx) + ".res")
bb_atk_data = self.__bb_atk_data
bb_atk_data_size = len(self.__bb_atk_data['raw'])
bb_word2idx = self.__bb_word2idx
bb_idx2word = self.__bb_idx2word
vocab = self.__vocab
bb = self.__bb
m = self.__model
bb_max_seqlen = self.__bb_max_seqlen
sess = self.__sess
negations = self.__negs
op_prob = [
flags.swp_prob, flags.ins_prob, flags.del_prob, flags.pass_prob
]
op_prob = op_prob / numpy.sum(op_prob)
n_sample = flags.sample_max_n
n_candidate = flags.n_candidate
just_acc_rate = flags.just_acc_rate
swp_lm_threshold = flags.lm_swp_threshold
ins_lm_threshold = flags.lm_ins_threshold
del_lm_threshold = flags.lm_del_threshold
swp_prob_threshold = flags.swp_threshold
ins_prob_threshold = flags.ins_threshold
del_prob_threshold = flags.del_threshold
swn_obj_threshold = flags.senti_obj_threshold
swn_pos_threshold = flags.senti_pos_threshold
seq_min_len = flags.seq_min_len
mode = flags.index_mode
res_log = []
sents = []
idx = 0
op = 3
total_time = 0
n_succ = 0
lemmatzr = WordNetLemmatizer()
for i in range(bb_atk_data_size):
start_time = time.time()
print("===== DATA %d/%d =====" % (i + 1, bb_atk_data_size),
file=fout,
flush=True)
print("DATA %d/%d, id=%d" % (i + 1, bb_atk_data_size, self.__idx))
flush()
res_log.append([])
raw = copy.deepcopy(bb_atk_data["raw"][i])
raw = nltk.word_tokenize(raw.lower())
l = len(raw) + 1
if (l > flags.seq_max_len):
l = flags.seq_max_len
seq = [vocab.get_init_idx()]
for ii in range(1, l):
seq.append(vocab.get_vocab_idx(raw[ii - 1]))
while len(seq) < flags.seq_max_len:
seq.append(vocab.get_pad_idx())
mask = [True]
for ii in range(1, l):
mask.append(False)
bb_y = bb_atk_data["y"][i]
bb_l = len(raw)
if (bb_l > bb_max_seqlen):
bb_l = bb_max_seqlen
bb_seq = []
for ii in range(bb_l):
if raw[ii] in bb_word2idx.keys():
bb_seq.append(bb_word2idx[raw[ii]])
else:
bb_seq.append(bb_word2idx["<unk>"])
while len(bb_seq) < bb_max_seqlen:
bb_seq.append(bb_word2idx["<pad>"])
sents.append([])
sample_cnt = 0
sample_all = 0
idx = 0
sents[-1].append(copy.deepcopy(raw))
print("%d/%d\tOriginal\tFAIL with %.5f" %
(i + 1, bb_atk_data_size, 1 - bb_atk_data["prob"][i]),
end="\n\t",
file=fout,
flush=True)
for ii in range(len(raw)):
print(raw[ii], end=" ", file=fout, flush=True)
if bb_y == 1:
print("\t<POS>", file=fout, flush=True)
else:
print("\t<NEG>", file=fout, flush=True)
while sample_all < n_sample:
try:
wn.ensure_loaded()
sample_all += 1
op = random_pick_idx_with_unnormalized_prob(op_prob)
succ = False
if op == 3:
tmp_prob = sess.run(bb.prob,
feed_dict={
bb.X: [bb_seq],
bb.L: [bb_l]
})[0][1 - bb_y]
if tmp_prob >= 0.5:
res_log[i].append((sample_all, 1))
print(
"%d/%d\t%d acc / %d all\tPASS\t SUCC with %.5f"
% (i + 1, bb_atk_data_size, sample_cnt + 1,
sample_all + 1, tmp_prob),
file=fout,
flush=True)
succ = True
else:
res_log[i].append((sample_all, 0))
print(
"%d/%d\t%d acc / %d all\tPASS\t FAIL with %.5f"
% (i + 1, bb_atk_data_size, sample_cnt + 1,
sample_all + 1, tmp_prob),
file=fout,
flush=True)
sample_cnt += 1
sents[-1].append(copy.deepcopy(raw))
print("", end="\t", file=fout, flush=True)
for ii in range(len(raw)):
print(raw[ii], end=" ", file=fout, flush=True)
if bb_y == 1:
print("\t<POS>", file=fout, flush=True)
else:
print("\t<NEG>", file=fout, flush=True)
if succ:
print("\tSUCC!")
flush()
break
continue
if mode == "random":
idx = random.randint(0, l - 1)
elif mode == "traverse":
idx = (idx + 1) % l
elif mode == "grad":
if op == 1:
idx = random.randint(0, l - 1)
else:
grad_vecs = sess.run(bb.embed_grad,
feed_dict={
bb.X: [bb_seq],
bb.L: [bb_l],
bb.Y: [1 - bb_y]
})[0][0]
grads = numpy.linalg.norm(grad_vecs, axis=-1)
candidate_grads = []
candidate_idxs = []
position_tag = nltk.pos_tag(raw)
for pos in range(len(position_tag)):
tmp_tag = get_part_of_speech(
position_tag[pos][1])
if tmp_tag is None:
candidate_grads.append(grads[pos])
candidate_idxs.append(pos + 1)
continue
tmp_wn = wn.synsets(lemmatzr.lemmatize(
raw[pos]),
pos=tmp_tag)
if len(tmp_wn) <= 0:
candidate_grads.append(grads[pos])
candidate_idxs.append(pos + 1)
continue
tmp_swn = swn.senti_synset(tmp_wn[0].name())
if (tmp_swn.obj_score() > swn_obj_threshold \
or (tmp_swn.obj_score() <= swn_obj_threshold \
and abs(tmp_swn.pos_score()-tmp_swn.neg_score()) <= swn_pos_threshold)):
candidate_grads.append(grads[pos])
candidate_idxs.append(pos + 1)
continue
idx_idx = random_pick_idx_with_unnormalized_prob(
candidate_grads)
idx = candidate_idxs[idx_idx]
else:
assert False, "Invalid mode \"" + mode + "\""
old_wrong_prob = sess.run(bb.prob,
feed_dict={
bb.X: [bb_seq],
bb.L: [bb_l]
})[0][1 - bb_y]
if op == 0:
if mask[idx]:
continue
proposal = m.op_replace(sess, copy.deepcopy(seq), l,
copy.deepcopy(bb_seq), bb_l,
1 - bb_y, idx, n_candidate,
op_prob)
tmp_bb_seq = copy.deepcopy(bb_seq)
tmp_str = vocab.get_vocab(proposal['proposal'][idx])
if tmp_str in bb_word2idx.keys():
tmp_bb_seq[idx - 1] = bb_word2idx[tmp_str]
else:
tmp_bb_seq[idx - 1] = bb_word2idx["<unk>"]
new_wrong_prob = sess.run(bb.prob,
feed_dict={
bb.X: [tmp_bb_seq],
bb.L: [bb_l]
})[0][1 - bb_y]
tmp_raw = copy.deepcopy(raw)
tmp_raw[idx - 1] = vocab.get_vocab(
proposal["proposal"][idx])
new_tag = get_part_of_speech(
nltk.pos_tag(tmp_raw)[idx - 1][1])
if new_tag is None:
new_obj = 1
new_pos = 0
else:
new_wn = wn.synsets(lemmatzr.lemmatize(
tmp_raw[idx - 1]),
pos=new_tag)
if len(new_wn) <= 0:
new_obj = 1
new_pos = 0
else:
new_swn = swn.senti_synset(new_wn[0].name())
new_obj = new_swn.obj_score()
new_pos = new_swn.pos_score(
) - new_swn.neg_score()
if (just_acc(just_acc_rate)
or (numpy.random.uniform(0,1) <= \
proposal["alpha"] * new_wrong_prob / old_wrong_prob
and proposal["old_prob"] * swp_lm_threshold <= proposal["new_prob"]
and old_wrong_prob * swp_prob_threshold <= new_wrong_prob
and (new_obj > swn_obj_threshold # objective
or (new_obj <= swn_obj_threshold # neutral
and abs(new_pos) <= swn_pos_threshold))
and (tmp_str not in negations))):
if new_wrong_prob >= 0.5:
res_log[i].append((sample_all, 1))
print(
"%d/%d\t%d acc / %d all\tSWP\t SUCC with %.5f\t[%s](%d) => [%s](%d) (%d)"
% (i + 1, bb_atk_data_size, sample_cnt + 1,
sample_all, new_wrong_prob,
vocab.get_vocab(seq[idx]), seq[idx],
vocab.get_vocab(
proposal["proposal"][idx]),
proposal["proposal"][idx], idx),
file=fout,
flush=True)
succ = True
else:
res_log[i].append((sample_all, 0))
print(
"%d/%d\t%d acc / %d all\tSWP\t FAIL with %.5f\t[%s](%d) => [%s](%d) (%d)"
% (i + 1, bb_atk_data_size, sample_cnt + 1,
sample_all, new_wrong_prob,
vocab.get_vocab(seq[idx]), seq[idx],
vocab.get_vocab(
proposal["proposal"][idx]),
proposal["proposal"][idx], idx),
file=fout,
flush=True)
sample_cnt += 1
seq = proposal["proposal"]
bb_seq = tmp_bb_seq
raw = tmp_raw
sents[-1].append(copy.deepcopy(raw))
print("", end="\t", file=fout, flush=True)
for ii in range(len(raw)):
print(raw[ii], end=" ", file=fout, flush=True)
if bb_y == 1:
print("\t<POS>", file=fout, flush=True)
else:
print("\t<NEG>", file=fout, flush=True)
else:
print("%d/%d\t%d acc / %d all\tSWP\talpha %.2e" %
(i + 1, bb_atk_data_size, sample_cnt,
sample_all, proposal["alpha"]),
file=fout,
flush=True)
elif op == 1:
if idx == l - 1:
continue
proposal = m.op_insert(sess, copy.deepcopy(seq), l,
copy.deepcopy(bb_seq), bb_l,
1 - bb_y, idx, n_candidate,
op_prob)
tmp_bb_seq = numpy.asarray(
copy.deepcopy(bb_seq)).tolist()
tmp_str = vocab.get_vocab(proposal['proposal'][idx +
1])
if tmp_str in bb_word2idx.keys():
tmp_bb_seq = tmp_bb_seq[:idx] + [
bb_word2idx[tmp_str]
] + tmp_bb_seq[idx:]
else:
tmp_bb_seq = tmp_bb_seq[:idx] + [
bb_word2idx["<unk>"]
] + tmp_bb_seq[idx:]
tmp_bb_seq = tmp_bb_seq[:-1]
tmp_bb_l = bb_l + 1
if tmp_bb_l > bb_max_seqlen:
tmp_bb_l = bb_max_seqlen
new_wrong_prob = sess.run(bb.prob,
feed_dict={
bb.X: [tmp_bb_seq],
bb.L: [tmp_bb_l]
})[0][1 - bb_y]
tmp_raw = copy.deepcopy(raw)
tmp_raw = tmp_raw[:idx] + [tmp_str] + tmp_raw[idx:]
new_tag = get_part_of_speech(
nltk.pos_tag(tmp_raw)[idx][1])
if new_tag is None:
new_obj = 1
new_pos = 0
else:
new_wn = wn.synsets(lemmatzr.lemmatize(
tmp_raw[idx]),
pos=new_tag)
if len(new_wn) <= 0:
new_obj = 1
new_pos = 0
else:
new_swn = swn.senti_synset(new_wn[0].name())
new_obj = new_swn.obj_score()
new_pos = new_swn.pos_score(
) - new_swn.neg_score()
if (just_acc(just_acc_rate)
or (numpy.random.uniform(0,1) <= \
proposal["alpha"] * new_wrong_prob / old_wrong_prob
and proposal["old_prob"] * ins_lm_threshold <= proposal["new_prob"]
and old_wrong_prob * ins_prob_threshold <= new_wrong_prob
and (new_obj > swn_obj_threshold # objective
or (new_obj <= swn_obj_threshold # neutral
and new_pos <= swn_pos_threshold))
and (tmp_str not in negations))):
if new_wrong_prob >= 0.5:
res_log[i].append((sample_all, 1))
print(
"%d/%d\t%d acc / %d all\tINS\t SUCC with %.5f\t[] => [%s](%d,%.1f,%.1f) (%d)"
% (i + 1, bb_atk_data_size, sample_cnt + 1,
sample_all, new_wrong_prob,
vocab.get_vocab(
proposal["proposal"][idx + 1]),
proposal["proposal"][idx + 1], new_obj,
new_pos, idx),
file=fout,
flush=True)
succ = True
else:
res_log[i].append((sample_all, 0))
print(
"%d/%d\t%d acc / %d all\tINS\t FAIL with %.5f\t[] => [%s](%d,%.1f,%.1f) (%d)"
% (i + 1, bb_atk_data_size, sample_cnt + 1,
sample_all, new_wrong_prob,
vocab.get_vocab(
proposal["proposal"][idx + 1]),
proposal["proposal"][idx + 1], new_obj,
new_pos, idx),
file=fout,
flush=True)
sample_cnt += 1
seq = proposal["proposal"]
bb_seq = tmp_bb_seq
l += 1
mask = mask[:idx + 1] + [False] + mask[idx + 1:]
if l > flags.seq_max_len:
l = flags.seq_max_len
mask = mask[:l]
bb_l = tmp_bb_l
raw = raw[:idx] + [vocab.get_vocab(seq[idx + 1])
] + raw[idx:]
sents[-1].append(copy.deepcopy(raw))
print("", end="\t", file=fout, flush=True)
for ii in range(len(raw)):
print(raw[ii], end=" ", file=fout, flush=True)
if bb_y == 1:
print("\t<POS>", file=fout, flush=True)
else:
print("\t<NEG>", file=fout, flush=True)
else:
print("%d/%d\t%d acc / %d all\tINS\talpha %.2e" %
(i + 1, bb_atk_data_size, sample_cnt,
sample_all, proposal["alpha"]),
file=fout,
flush=True)
elif op == 2:
if mask[idx] or l - 1 < seq_min_len:
continue
proposal = m.op_delete(sess, copy.deepcopy(seq), l,
copy.deepcopy(bb_seq), bb_l,
1 - bb_y, idx, n_candidate,
op_prob)
tmp_bb_seq = numpy.asarray(
copy.deepcopy(bb_seq)).tolist()
tmp_str = vocab.get_vocab(seq[idx])
tmp_bb_seq = tmp_bb_seq[:idx -
1] + tmp_bb_seq[idx:] + [
bb_word2idx['<pad>']
]
tmp_bb_l = bb_l - 1
new_wrong_prob = sess.run(bb.prob,
feed_dict={
bb.X: [tmp_bb_seq],
bb.L: [tmp_bb_l]
})[0][1 - bb_y]
if (just_acc(just_acc_rate)
or (numpy.random.uniform(0,1) <= \
proposal["alpha"] * new_wrong_prob / old_wrong_prob
and proposal["old_prob"] * del_lm_threshold <= proposal["new_prob"]
and old_wrong_prob * del_prob_threshold <= new_wrong_prob)
and (tmp_str not in negations)):
if new_wrong_prob >= 0.5:
res_log[i].append((sample_all, 1))
print(
"%d/%d\t%d acc / %d all\tDEL\t SUCC with %.5f\t[%s](%d) => [] (%d)"
%
(i + 1, bb_atk_data_size, sample_cnt + 1,
sample_all, new_wrong_prob,
vocab.get_vocab(seq[idx]), seq[idx], idx),
file=fout,
flush=True)
succ = True
else:
res_log[i].append((sample_all, 0))
print(
"%d/%d\t%d acc / %d all\tDEL\t FAIL with %.5f\t[%s](%d) => [] (%d)"
%
(i + 1, bb_atk_data_size, sample_cnt + 1,
sample_all, new_wrong_prob,
vocab.get_vocab(seq[idx]), seq[idx], idx),
file=fout,
flush=True)
sample_cnt += 1
seq = proposal["proposal"]
bb_seq = tmp_bb_seq
l -= 1
mask = mask[:idx] + mask[idx + 1:]
bb_l = tmp_bb_l
raw = raw[:idx - 1] + raw[idx:]
sents[-1].append(copy.deepcopy(raw))
print("", end="\t", file=fout, flush=True)
for ii in range(len(raw)):
print(raw[ii], end=" ", file=fout, flush=True)
if bb_y == 1:
print("\t<POS>", file=fout, flush=True)
else:
print("\t<NEG>", file=fout, flush=True)
else:
print("%d/%d\t%d acc / %d all\tDEL\talpha %.2e" %
(i + 1, bb_atk_data_size, sample_cnt,
sample_all, proposal["alpha"]),
file=fout,
flush=True)
if succ:
end_time = time.time()
total_time += end_time - start_time
n_succ += 1
print("\tSUCC!")
print("\t\ttime =", total_time, n_succ)
flush()
assert len(mask) == l
except Exception as e:
print("Something went wrong... Abort!",
file=fout,
flush=True)
print("Something went wrong... Abort! -- Thread %d" %
self.__idx)
print("\t", e)
sys.stdout.flush()
sys.stderr.flush()
continue
with open(res_path, "wb") as f:
pkl.dump((res_log, sents), f)
Unit tests for nltk.corpus.wordnet
See also nltk/test/wordnet.doctest
"""
from __future__ import unicode_literals
from nose import SkipTest
import unittest
import os
from nltk.corpus.reader.wordnet import WordNetCorpusReader
from nltk.corpus import wordnet as wn
from nltk.corpus import wordnet_ic as wnic
from nltk.data import find as find_data
wn.ensure_loaded()
S = wn.synset
L = wn.lemma
class WordnNetDemo(unittest.TestCase):
def test_retrieve_synset(self):
move_synset = S('go.v.21')
self.assertEqual(move_synset.name(), "move.v.15")
self.assertEqual(move_synset.lemma_names(), ['move', 'go'])
self.assertEqual(move_synset.definition(), "have a turn; make one's move in a game")
self.assertEqual(move_synset.examples(), ['Can I go now?'])
def test_retrieve_synsets(self):
self.assertEqual(sorted(wn.synsets('zap', pos='n')),
