def grammar_dependencies_count(headlines, bodies):
parser = spacy.load('en')
grammar_counts = {}
print("starting parser")
# tagsDict = {k: v for v, k in enumerate(parser.pipe_labels['parser'])}
tagsDict = parser.pipe_labels['parser']
for i, doc in enumerate(parser.pipe(bodies, batch_size=1000, n_threads=4)):
counts = collections.Counter()
for w in doc:
counts[w.dep_] += 1
ssum = sum(counts.values())
for k, v in counts.items():
counts[k] = (counts[k] / ssum)
grammar_counts[i] = counts
rv = list(range(len(bodies)))
print("starting lists")
for i, b in tqdm(enumerate(bodies)):
try:
rv[i] = []
for k in tagsDict:
if grammar_counts[i].keys().__contains__(k):
rv[i].append(grammar_counts[i][k])
else:
rv[i].append(0)
except Exception as e:
# Ocassionally the way Spacey processes unusual characters (bullet points, em dashes) will cause the lookup based on the original characters to fail.
# In that case, just set to None.
print("Error in GrammarTransformer, setting to None")
# print(text)
rv[i] = {}
continue
return rv
def wiki_test(request, word1, word2):
page = requests.get("https://en.wikipedia.org/wiki/" + word1)
soup = BeautifulSoup(page.content, features="html.parser")
word_tokenized1 = []
size = len(soup.find_all('p'))
content = soup.find_all('p')
for i in range(size):
word_tokenized1.append(word_tokenize(content[i].get_text()))
filtered_words1 = []
for list in word_tokenized1:
for word in list:
if word not in stopwords.words('english'):
if word.isalpha():
filtered_words1.append(word)
most_common_words1 = (collections.Counter(filtered_words1).most_common(100))
#---------------------------------------------------------
page = requests.get("https://en.wikipedia.org/wiki/" + word2)
soup = BeautifulSoup(page.content, features="html.parser")
word_tokenized2 = []
size = len(soup.find_all('p'))
content = soup.find_all('p')
for i in range(size):
word_tokenized2.append(word_tokenize(content[i].get_text()))
filtered_words2 = []
for list in word_tokenized2:
for word in list:
if word not in stopwords.words('english'):
if word.isalpha():
filtered_words2.append(word)
most_common_words2 = (collections.Counter(filtered_words2).most_common(100))
len2 = 0
i = 0
x = 0
for w, c in most_common_words2:
len2 += c
if w == word1:
x = i
i += 1
return JsonResponse({'s': (most_common_words2[x][1] / len2) * 100})
def contexts(input, mode, keyword):
sys.stdout = open(
'./01_out/' + input.split('_')[0] + '_cont_' + keyword + '_' + mode +
'.txt', 'w')
f = open('./00_data/' + input, encoding=mode)
token = sent_tokenize(f.read())
f.close()
data = [i.split(' ') for i in token]
list = []
for i in data:
for j in range(1, len(i) - 1):
if i[j].lower() == keyword.lower():
list.append(i[j - 1] + '_' + i[j + 1])
print(collections.Counter(list))
def get_word_counts(config, from_timestamp, to_timestamp, count):
posts = get_posts_from_range(config, from_timestamp, to_timestamp)
wordcount = {}
for post in posts:
words = get_cleaned_text(post['text']).split()
for word in words:
if word not in wordcount:
wordcount[word] = 1
else:
wordcount[word] += 1
word_counter = collections.Counter(wordcount)
words = []
for word in word_counter.most_common(count):
words.append({'word': word[0], 'count': word[1]})
return words
def build_dictionary(sentences, vocabulary_size):
# 把句子转换成单词列表
split_sentences = [s.split() for s in sentences]
words = [x for sublist in split_sentences for x in sublist]
# count里面存放的内容 [word, word_count]，存放的第一个[单词，单词数]对是[未知单词，-1]
count = [('RARE', -1)]
# 使用nltk.collections提供的工具统计单词出现的频率
count.extend(collections.Counter(words).most_common(vocabulary_size - 1))
# 将单词与索引对应
# ToKnown：原作者使用单词序列长度累加的方法获得索引，速度慢而且不容易理解，我修改为直接枚举
word_dict = {x: i for i, (x, _) in enumerate(count)}
# word_dict = {}
# for word, word_count in count:
# word_dict[word] = len(word_dict)
return word_dict
def calculate_and_store_types(self):
for tweet in self.tweets:
text = nltk.word_tokenize(tweet)
liste = nltk.pos_tag(text)
for word_and_type in liste:
self.types.append(word_and_type[1])
self.types_and_numbers_list = list(collections.Counter(self.types).items())
feature_list = ['VBZ', ',', 'CD', 'JJS', 'WDT', 'VBP', '#', 'PRP$', 'JJR']
#feature_list = [',', 'CD', 'VBP', '#', 'PRP$']
for j in feature_list:
self.func(j)
self.valid_types_and_numbers.sort(key=lambda x: x[0])
for i in self.valid_types_and_numbers:
self.features_array.append(i[1])
############################################# KENDİ ÇIKARDIĞIM KELİMELERİN FEATURE OLARAK EKLENMESİ
# avg = int(sum(self.features_array) / len(self.features_array))
for word in commonly_used_words_by_males:
if word in self.words:
self.features_array.append(Counter(self.words).get(word))
else:
self.features_array.append(0)
for word in words_of_technology:
if word in self.words:
self.features_array.append(Counter(self.words).get(word)) #
else:
self.features_array.append(0) #
for word in commonly_used_words_by_females:
if word in self.words:
self.features_array.append(Counter(self.words).get(word)) # okay
else:
self.features_array.append(0)
def sentence_vector(self, sentence: str, sample=None) -> ndarray:
tokens = [
token for token in tokenizer.tokenize(sentence)
if token.isprintable() and token not in _stop_words
]
if sample:
tokens = list(everygrams(tokens, 1,
1)) # .sort(key=lambda x: x[1])[:10]
tokens = [
word[0] for word, count in collections.Counter(
tokens).most_common(sample)
]
key = hashlib.md5(sentence.encode('utf-8')).hexdigest()
if self.redis is not None and self.redis.exists(key):
vector = Embeddings.load_vector_from_cache(key, self.redis)
else:
vector = self.arithmetic_mean_bow_embedding(tokens)
if self.redis is not None:
Embeddings.cache_vector(key, vector, self.redis)
return vector
total = vocabsize['aggregations']['counts']['value']
print("%s\t%s\t%s\t%s" % (genre['key'], unq / total, total, unq))
for genre in res['aggregations']['genres']['buckets']:
curr_gnere = genre["key"]
genre_songs_list = []
songs_by_genre = {
"query": {
"match": {
"album.genre": curr_gnere
}
},
"size": NUM_OF_SONGS
}
res = es.search(index=ES_INDEX, doc_type=ES_TYPE, body=songs_by_genre)
for song in res["hits"]["hits"]:
lyrics = song["_source"]["lyrics"]
tokens = nltk.word_tokenize(lyrics)
filtered_tokens = [
token for token in tokens
if token not in stopwords.words('english')
]
tokens_counter = collections.Counter(filtered_tokens)
tuple_list = list(tokens_counter.items())
genre_songs_list.append(tuple_list)
with open(
'/home/omri/Dev/Python/IntroToDS/Data/pickle_list/' + curr_gnere +
'.pickle', 'wb') as handle:
pickle.dump(genre_songs_list, handle, protocol=pickle.HIGHEST_PROTOCOL)
def generator(self, spell, sentence, call):
if (call == 5):
return sentence
if (sentence == "" and self.n > 1):
index = random.randrange(len(self.smootedTable) - 1)
heceler = list(list(self.smootedTable.items())[index])[0]
sentence = ''.join([str(elem) for elem in heceler])
newSpell = []
if self.n > 4:
newSpell.append(heceler[len(heceler) - 4])
if self.n > 3:
newSpell.append(heceler[len(heceler) - 3])
if self.n > 2:
newSpell.append(heceler[len(heceler) - 2])
newSpell.append(heceler[len(heceler) - 1])
call += 1
return self.generator(newSpell, sentence, call)
elif self.n == 1:
founded = collections.Counter(self.smootedTable).most_common(5)
for i in founded:
sentence += i[0]
return sentence
else:
newSpell = []
founded = dict()
if self.n == 2:
for key in self.smootedTable:
if key[0] == spell[0]:
founded[key] = self.smootedTable[key]
elif self.n == 3:
for key in self.smootedTable:
if key[0] == spell[0] and key[1] == spell[1]:
founded[key] = self.smootedTable[key]
elif self.n == 4:
for key in self.smootedTable:
if key[0] == spell[0] and key[1] == spell[1] and key[
2] == spell[2]:
founded[key] = self.smootedTable[key]
else:
for key in self.smootedTable:
if key[0] == spell[0] and key[1] == spell[1] and key[
2] == spell[2] and key[3] == spell[3]:
founded[key] = self.smootedTable[key]
founded = collections.Counter(founded).most_common(1)
if len(founded) == 0:
return sentence
found = founded[0]
start = self.n - 1
sentence += str(found[0][len(found[0]) - 1])
if self.n > 4:
newSpell.append(found[0][len(found[0]) - 4])
if self.n > 3:
newSpell.append(found[0][len(found[0]) - 3])
if self.n > 2:
newSpell.append(found[0][len(found[0]) - 2])
newSpell.append(found[0][len(found[0]) - 1])
call += 1
return self.generator(newSpell, sentence, call)
def setNGramTable(self):
self.nGramTable = collections.Counter(self.nGram)
def topKFrequent2(self, nums: List[int], k: int) -> List[int]:
from nltk import collections
counts = collections.Counter(nums)
print(counts.keys())
return heapq.nlargest(k, counts.keys(), key=counts.get)
df['token'] = df['token'].apply(
lambda x: [item for item in x if item not in sw])
df['tokenstring'] = [' '.join(map(str, l)) for l in df['token']]
lists = df['token']
row_list = []
no_of_lists_per_name = Counter(chain.from_iterable(map(set, lists)))
for name, no_of_lists in no_of_lists_per_name.most_common():
if no_of_lists == 1:
break # since it is ordered by count, once we get this low we are done
row_list.append([name, no_of_lists])
df_cat_1 = pd.DataFrame(row_list, columns=['cat', 'cat_count'])
#print(Counter(list(ngrams(df['token'], 2))))
counts = collections.Counter() # or nltk.FreqDist()
#for sent in df['token']:
# counts.update(nltk.ngrams(sent, 2))
#print(counts)
for sent in df['token']:
counts.update(" ".join(n) for n in nltk.ngrams(sent, 2))
df_cat_2 = pd.DataFrame.from_records(counts.most_common(),
columns=['cat', 'cat_count'])
df_cat = df_cat_1.append(df_cat_2)
df = pd.merge(df, df_cat, left_on=["tokenstring"], right_on="cat")
df.to_csv('temp.csv', encoding='utf-8-sig', index=False)
df = df.groupby(['category']).agg({
def find_tf_string(token_list):
weight_dict = collections.Counter(token_list)
tf_dict = {}
for word, weight in zip(weight_dict.keys(), weight_dict.values()):
tf_dict[word] = (weight / len(token_list))
return tf_dict
print("Topic Stats")
print(get_data_frame_stats(df))
train, test = train_test_split(df, random_state=42, test_size=0.33, shuffle=True)
X_train = train.text.apply(lambda text: get_cleaned_text(text))
X_test = test.text.apply(lambda text: get_cleaned_text(text))
wordcount = {}
for text in X_train.append(X_test):
for word in text.split():
if word not in wordcount:
wordcount[word] = 1
else:
wordcount[word] += 1
word_counter = collections.Counter(wordcount)
n_print = int(input("How many most common words to print: "))
for word, count in word_counter.most_common(n_print):
print(count, word)
# MultiLabelBinarizer().fit_transform(train)
start = time.time()
train, test = train_test_split(df, random_state=42, test_size=0.33, shuffle=True)
X_train = train.text.apply(lambda text: get_cleaned_text(text))
X_test = test.text.apply(lambda text: get_cleaned_text(text))
NB_pipeline = Pipeline([
('tfidf', TfidfVectorizer()),
# ('clf', OneVsRestClassifier(LinearSVC(), n_jobs=1)),
# ('clf', LinearSVC()),
