def sentipol(post_seg):
'''emotion polarity (情緒極性) determiner'''
pos_con, neg_con = [], []
pos_cnt, neg_cnt = 0, 0
for word in post_seg:
if word in pos_lst:
pos_cnt += 1
pos_con.append(word)
elif word in neg_lst:
neg_cnt += 1
neg_con.append(word)
diff = pos_cnt - neg_cnt
pos_fin = count(pos_con).items()
neg_fin = count(neg_con).items()
pos_fin = sorted(pos_fin, key=lambda x: x[-1], reverse=True)
neg_fin = sorted(neg_fin, key=lambda x: x[-1], reverse=True)
postmod = ' '.join(post_seg)
for w, n in pos_fin:
postmod = postmod.replace(w, '<span class="positive">%s</span>' % w)
for w, n in neg_fin:
postmod = postmod.replace(w, '<span class="negative">%s</span>' % w)
return {'content': postmod, 'diff': diff, 'pos': pos_fin, 'neg': neg_fin}
def roots_and_lemmas():
print(stem('cars', PORTER)) #Root
print(stem('cars', LEMMA))
print(stem('studies', PORTER)) # Root
print(stem('studies', LEMMA))
text = "People who teach find teaching very rewarding."
tokens = words(text)
print(count(tokens, stopwords=True, stemmer=PORTER))
print(count(tokens, stopwords=True, stemmer=LEMMA))
def instance(review): # "Great book!"
# [("Great", "JJ"), ("book", "NN"), ("!", "!")]
v = tag(review)
v = [word for (word, pos) in v if pos in ("JJ", "RB") or word in ("!")]
v = [predicative(word) for word in v] # ["great", "!", "!"]
v = count(v) # {"great": 1, "!": 1}
return v
def get_keywords(self, comment_history):
comments = [str(x) for x in comment_history]
keywords = count(words(comments.__str__()))
sorted_keywords = sorted(keywords.iteritems(),
key=operator.itemgetter(1),
reverse=True)
return sorted_keywords
def instance(review): # "Great book!"
# [("Great", "JJ"), ("book", "NN"), ("!", "!")]
v = tag(review)
v = [word for (word, pos) in v if pos in ("JJ", "RB") or word in ("!")]
v = [predicative(word) for word in v] # ["great", "!", "!"]
v = count(v) # {"great": 1, "!": 1}
return v
def run(self,minePackage):
clouds=minePackage['clouds']
urlContent=UrlToPlainText()
for cloud in clouds:
for n in cloud.graph.nodes():#Itera una lista de enlaces de la nube
# print cloud.graph.node[n]['link']
pageContent=urlContent.plainTextConverter(cloud.graph.node[n]['link'])
cloud.graph.node[n]['methodData']=MethodData(count(words(Sentence(parse(pageContent))), stemmer=PORTER))
def __init__(self , data, url=""):
if url != "":
urlContent = UrlToPlainText()
self.contenidoConEtiquetas = urlContent.plainTextConverter(url,"mantenerEtiquetas")
self.contenido = plaintext(self.contenidoConEtiquetas,keep={})
else:
self.contenido = ""
self.data = count(words(Sentence(parse(self.contenido))), stemmer=PORTER)
def trigram_text_feature(text):
"""
return probability distribution {term: count} of each word in <text>
"""
wl = nltk.word_tokenize(text.lower())
trigrams = nltk.util.trigrams(wl)
features = patvec.count(trigrams)
return features
def test_count(self):
# Assert wordcount with stemming, stopwords and pruning.
w = ["The", "cats", "sat", "on", "the", "mat", "."]
v1 = vector.count(w)
v2 = vector.count(w, stemmer=vector.LEMMA)
v3 = vector.count(w, exclude=["."])
v4 = vector.count(w, stopwords=True)
v5 = vector.count(w, stopwords=True, top=3)
v6 = vector.count(w, stopwords=True, top=3, threshold=1)
v7 = vector.count(w, dict=vector.readonlydict, cached=False)
self.assertEqual(v1, {"cats": 1, "sat": 1, "mat": 1, ".": 1})
self.assertEqual(v2, {"cat": 1, "sat": 1, "mat": 1, ".": 1})
self.assertEqual(v3, {"cats": 1, "sat": 1, "mat": 1})
self.assertEqual(v4, {
"the": 2,
"cats": 1,
"sat": 1,
"on": 1,
"mat": 1,
".": 1
})
self.assertEqual(v5, {"the": 2, "cats": 1, ".": 1})
self.assertEqual(v6, {"the": 2})
# Assert custom dict class.
self.assertTrue(isinstance(v7, vector.readonlydict))
print "pattern.vector.count()"
def pos_feature_builder(
text, target_pos=('JJ', 'NN', 'VB', '!', 'NP', 'RB', 'CD')):
"""
builds features from target part of speech tags specified by <target_pos>
"""
if not text:
return patvec.count([])
try:
parsed_text = paten.parsetree(text, lemmata=True)[0]
selected = [
word.lemma for word in parsed_text if word.tag.startswith(
target_pos)]
except IndexError as e:
print text, e
selected = []
result = patvec.count(selected)
return result
def vector(self, name):
""" Returns a dictionary with character bigrams and suffix.
For example, "Felix" => {"Fe":1, "el":1, "li":1, "ix":1, "ix$":1, 5:1}
"""
v = chngrams(name, n=2)
v = count(v)
v[name[-2:] + "$"] = 1
v[len(name)] = 1
return v
def vector(self, name):
""" Returns a dictionary with character bigrams and suffix.
For example, "Felix" => {"Fe":1, "el":1, "li":1, "ix":1, "ix$":1, 5:1}
"""
v = chngrams(name, n=2)
v = count(v)
v[name[-2:]+"$"] = 1
v[len(name)] = 1
return v
def start(self):
cloudSize = dameCloudSize(self.id_request)
cloudSize = cloudSize[0][0]
searchKey = dameSerchKey(self.id_request)
searchKey = searchKey[0][0]
step = 0
while step <= 5: #Mas adelante setear get_stop; esto indica la cantidad de niveles
for id_cloud in dameIdCloud(
self.id_request
): #Obtiene IDS de los clouds que pertenecen al proyecto
print "Id Cloud: " + str(id_cloud[0])
cloud = self.generar_cloud(dameNodo(id_cloud[0]))
true_nodes = self.trueNodesSelection(cloud)
for n in true_nodes:
try:
cloud.graph.node[n]['select'] = False
crawler = SimpleCrawler1(n, delay=0.1)
crawler.newStructure(cloud.graph)
time = 0
except:
continue
while len(crawler.visited) < cloudSize:
print "Cloudsize = " + str(
cloudSize) + " Crawler Visited = " + str(
len(crawler.visited)) + " Nivel = " + str(
step)
print 'Explorando ...'
crawler.crawl(method=None)
time += 1
if time > cloudSize * 10:
break
actualizarSelect(cloud.graph.node[n]['ID'],
cloud.graph.node[n]['select'])
print
print '#####Generando documentos#####'
#Creacion de minePackage
clouds = list()
clouds.append(cloud)
minePackage = dict()
minePackage['clouds'] = clouds
minePackage['searchKey'] = searchKey
minePackage['searchKeyStemmer'] = count(words(
Sentence(parse(searchKey))),
stemmer=PORTER)
self.IRController.start(minePackage) #Recupera Informacion
#FALTA SCRAPPER CONTROLLER
#Se pone none para que no ocupen espacio innecesario, todo ya fue guardado en BD
minePackage = None
cloud = None
gc.collect
step += 1
print "Explorando nivel nro: " + str(step)
#Controla los niveles a expandir, en este caso 10
print "Proceso Finalizado"
def selectWords(review):
'''
a function that gets a review and selects the nouns, adjectives, verbs and exclamation mark
'''
review = parsetree(review, lemmata=True)[0] #lemmatize the review
#select adjectives (JJ), nouns (NN), verbs (VB) and exclamation marks
review = [
w.lemma for w in review if w.tag.startswith(('JJ', 'NN', 'VB', '!'))
]
review = count(review) #a dictionary of (word, count)
return review
def run(self, minePackage):
clouds = minePackage['clouds']
urlContent = UrlToPlainText()
for cloud in clouds:
for n in cloud.graph.nodes(
): #Itera una lista de enlaces de la nube
print cloud.graph.node[n]['link']
pageContent = urlContent.plainTextConverter(
cloud.graph.node[n]['link'])
cloud.graph.node[n]['methodData'] = MethodData(
count(words(Sentence(parse(pageContent))), stemmer=PORTER))
def train(cls, train_file, model_file):
sents_dic = (json.loads(jsonl)
for jsonl in SoftSkills.load(train_file))
model = KNN()
for sent in sents_dic:
text = sent['text']
v = count([word for word, pos in tag(text)]) # {'sweet': 1}
if v:
model.train(v, type=sent['soft skill'])
model.save(model_file)
return model
def __init__(self, data, url="", contenidoBd=""):
if url != "":
urlContent = UrlToPlainText()
self.contenidoConEtiquetas = urlContent.plainTextConverter(
url, "mantenerEtiquetas")
self.contenido = plaintext(self.contenidoConEtiquetas, keep={})
else:
if (contenidoBd != ""):
self.contenidoConEtiquetas = contenidoBd
self.contenido = plaintext(self.contenidoConEtiquetas, keep={})
else:
self.contenido = ""
self.data = count(words(Sentence(parse(self.contenido))),
stemmer=PORTER)
def tokenizer(self,url):
#text = 'The black cat was spying on the white cat.'
#stemmer=None, stemmer=LEMMA, stemmer=PORTER
#print count(words(pageContent), stemmer=PORTER)
#print count(words(pageContent), stemmer=LEMMA)
#url_content = UrlToplainTextConverter()
#page_content = url_content.plainTextConverter(url)
page_content = url
s = Sentence(parse(page_content))
tokenized_file = count(words(s), stemmer=PORTER)
print
print tokenized_file
print
def count_one_artist(name, bad_words):
# ok, this is a bad way to get number of songs for that artist, so we can average out
# the words per song
default_dir = basedir + name
num_songs = len(os.listdir(default_dir))
# we need the number of songs, this is so annoying
dict = {}
docs = vec.count(vec.words(get_artist_docs(name)))
for w in bad_words:
if w in docs:
dict[w] = docs[w]
dict['num_songs'] = num_songs # this is cheap
return dict
def count_one_artist(name, bad_words):
# ok, this is a bad way to get number of songs for that artist, so we can average out
# the words per song
default_dir = basedir + name
num_songs = len(os.listdir(default_dir))
# we need the number of songs, this is so annoying
dict = {}
docs = vec.count(vec.words(get_artist_docs(name)))
for w in bad_words:
if w in docs:
dict[w] = docs[w]
dict['num_songs'] = num_songs # this is cheap
return dict
def postag_feature_builder(
text, target_pos=('JJ', 'NN', 'VB', 'NP', 'RB', 'CD')):
"""
faster version of the tag feature builder
uses paten.tag instead of paten.parsetree
"""
if not text:
return {}
# tag each word
try:
result = patvec.count(
(word for word,
tag in paten.tag(text,
tokenize=True,
encoding='utf-8') if tag in target_pos))
except IndexError as e:
print text, e
result = {}
return result
def test_count(self):
# Assert wordcount with stemming, stopwords and pruning.
w = ["The", "cats", "sat", "on", "the", "mat", "."]
v1 = vector.count(w)
v2 = vector.count(w, stemmer=vector.LEMMA)
v3 = vector.count(w, exclude=["."])
v4 = vector.count(w, stopwords=True)
v5 = vector.count(w, stopwords=True, top=3)
v6 = vector.count(w, stopwords=True, top=3, threshold=1)
v7 = vector.count(w, dict=vector.readonlydict, cached=False)
self.assertEqual(v1, {"cats":1, "sat":1, "mat":1, ".":1})
self.assertEqual(v2, {"cat":1, "sat":1, "mat":1, ".":1})
self.assertEqual(v3, {"cats":1, "sat":1, "mat":1})
self.assertEqual(v4, {"the":2, "cats":1, "sat":1, "on":1, "mat":1, ".":1})
self.assertEqual(v5, {"the":2, "cats":1, ".":1})
self.assertEqual(v6, {"the":2})
# Assert custom dict class.
self.assertTrue(isinstance(v7, vector.readonlydict))
print("pattern.vector.count()")
#!/usr/bin/env python3
# -*- coding: utf-8 -*-
"""
Created on Mon Mar 25 19:37:34 2019
@author: alternatif
"""
from pattern.web import Twitter
from pattern.en import tag
from pattern.vector import KNN, count
twitter, knn = Twitter(), KNN()
for i in range(1, 3):
for tweet in twitter.search('#win OR #fail', start=i, count=100):
s = tweet.text.lower()
p = '#win' in s and 'WIN' or 'FAIL'
v = tag(s)
v = [word for word, pos in v if pos == 'JJ'] # JJ = adjective
v = count(v) # {'sweet': 1}
if v:
knn.train(v, type=p)
print(knn.classify('sweet potato burger'))
print(knn.classify('stupid autocorrect'))
def v(review1):
v3 = parsetree(review1, lemmata=True)[0]
v4 = [w.lemma for w in v3 if w.tag.startswith(('JJ', 'NN', 'VB', '!'))]
v5 = count(v4)
return v5
def tokenization():
text = "My new car is better than my new bed"
tokens = words(text)
print(tokens)
print(count(tokens))
def makeSameTense(self, w1, w2):
tense = count([i[0] for i in tenses(w2)], stopwords=True)
tense = sorted(tense, key=operator.itemgetter(2))
return verbs.conjugate(w1, tense[0])
from pattern.en import parse, Sentence, parsetree, lexicon
from pattern.db import csv
from random import choice
# word count
freq_dic = {}
with open('data/input/corpus.txt', 'r') as fp:
words_list = words(fp.read(),
filter=lambda w: w.strip("'").isalnum(),
punctuation='.,;:!?()[]{}`'
'\"@#$^&*+-|=~_')
# returns a list of words by splitting the string on spaces.
freq_dic = count( # takes a list of words and returns a dictionary of (word, count)-items.
words=words_list,
top=None, # Filter words not in the top most frequent (int).
threshold=0, # Filter words whose count <= threshold.
stemmer=None, # PORTER | LEMMA | function | None
exclude=[], # Filter words in the exclude list.
stopwords=False, # Include stop words?
language='en') # en, es, de, fr, it, nl
for k, v in freq_dic.iteritems():
print k, v
# stop words and stemming
print stem('spies', stemmer=PORTER)
print stem('spies', stemmer=LEMMA)
s = 'The black cat was spying on the white cat.'
print count(words(s), stemmer=PORTER)
print count(words(s), stemmer=LEMMA)
s = 'The black cat was spying on the white cat.'
s = Sentence(parse(s))
print count(s, stemmer=LEMMA)
# character n-grams
def countWords(self):
wordDict = count(
words(plaintext(self.content),
filter=lambda w: w.strip("'").isalpha()))
return Counter(wordDict)
# -*- coding: utf-8 -*-
"""
Created on Wed Mar 19 19:45:49 2014
@author: scut1
"""
from pattern.web import Twitter
from pattern.en import tag
from pattern.vector import KNN, count
twitter, knn = Twitter(), KNN()
for i in range(1, 3):
for tweet in twitter.search('#win OR #fail', start=i, count=100):
s = tweet.text.lower()
p = '#win' in s and 'WIN' or 'FAIL'
v = tag(s)
v = [word for word, pos in v if pos == 'JJ'] # JJ = adjective
v = count(v) # {'sweet': 1}
if v:
knn.train(v, type=p)
print knn.classify('sweet potato burger')
print knn.classify('stupid autocorrect')
# language processing (part-of-speech taggers, n-gram search, sentiment analysis, WordNet), machine learning
# (vector space model, clustering, SVM), network analysis and <canvas> visualization.
# pattern.vector
# The pattern.vector module is a toolkit for machine learning, based on a vector space model of bag-of-words
# documents with weighted features (e.g., tf-idf) and distance metrics (e.g., cosine similarity, infogain).
# Models can be used for clustering (k-means, hierarchical), classification (Naive Bayes, Perceptron, k-NN, SVM)
# and latent semantic analysis (LSA).
from pattern.web import Twitter
from pattern.en import tag
from pattern.vector import KNN, count
twitter, knn = Twitter(), KNN()
for i in range(1, 10):
for tweet in twitter.search('#win OR #fail', start=i, count=100):
s = tweet.text.lower()
p = '#win' in s and 'WIN' or 'FAIL'
v = tag(s)
v = [word for word, pos in v if pos == 'JJ'] # JJ = adjective
v = count(v)
if v:
knn.train(v, type=p)
print knn.classify('sweet potato burger')
print knn.classify('stupid autocorrect')
# Displays
# 'WIN'
# 'FAIL'
def makeSameTense(self, w1, w2):
tense = count([i[0] for i in tenses(w2)], stopwords=True)
tense = sorted(tense, key=operator.itemgetter(2))
return verbs.conjugate(w1, tense[0])
def processor(self, minePackage):
print '####SEARCH_KEY:', minePackage['searchKey']
var = minePackage['searchKey']
s = Sentence(parse(var))
return count(words(s),
stemmer=PORTER) #Retorna diccionario {palabra: cantidad}
def unigram_text_feature(text):
"""
return probability distribution {term: count} of each word in <text>
"""
features = patvec.count(nltk.word_tokenize(text.lower()))
return features
def processor(self,minePackage):
# print '####SEARCH_KEY:',minePackage['searchKey']
s = Sentence(parse(minePackage['searchKey']))
minePackage['searchKey']=count(words(s), stemmer=PORTER)
return minePackage['searchKey']
def get_keywords(self, comment_history):
comments = [str(x) for x in comment_history]
keywords = count(words(comments.__str__()))
sorted_keywords = sorted(keywords.iteritems(), key=operator.itemgetter(1), reverse=True)
return sorted_keywords
def v(s):
""" Returns a bag-of-words vector for the given string.
"""
v = {}
v.update(count(words(s)))
return v
def get_results(query, quantity, force=False, news=False, analysis=True):
query = query.lower()
start = datetime.now()
query = query.replace('_', '%20')
breakdown = 50
if breakdown > quantity:
breakdown = quantity
data_to_be_written = []
knowledgeKeywords = []
duplicates = []
results, created = webSearch.objects.get_or_create(queryText=query.strip())
if created or force or len(results.results.all()) < quantity:
all_results = getGoogleResults(query, quantity, news, force)
else:
all_results = []
if len(all_results) == 0 and not created:
all_results = [r.url for r in results.results.all()]
all_results = all_results[:quantity]
print "TOTAL RESULTS ", str(len(all_results))
# Done with getting search results
for index, i in enumerate(all_results):
try:
wr, created = WebResource.objects.get_or_create(url=i)
if created:
wr = parseURL(i, True)
data = {'url': i}
keywords = [
w for w in count(wr.text, top=10, stemmer=LEMMA)
if w not in stop
]
if 'books.google' in i:
text = ''
else:
text = wr.text
data.update({
'keywords': keywords,
'text': plaintext(text),
'title': wr.title,
'urls': wr.urls,
'type': 'result',
'index': index + 1,
'similar': [],
'duplicates': [],
'category': 0,
})
if wr not in results.results.all():
results.results.add(wr)
data['plaintext'] = data['text'].split('\n')
# while '' in data['plaintext']:
# data['plaintext'].remove('')
# knowledgeKeywords.extend(data['keywords'])
data_to_be_written.append(data)
except Exception as e:
print e
print "Response Result model Prepared"
if not analysis:
return data_to_be_written
list_of_sim_docs, model, m = find_similarity(data_to_be_written)
for i in list_of_sim_docs:
similar = {
'type': 'similar',
's': i.get('source'),
'd': i.get('dest'),
'source': i.get('source'),
'dest': i.get('dest'),
'score': i.get('score'),
}
data_to_be_written.append(similar)
if similar['score'] > 0.9:
for res in data_to_be_written:
if res['type'] in [
'result', 'duplicate'
] and res['url'] == i.get('dest') and len(res['text']) > 0:
print "Duplicate [{0}].[{1}]".format(
i['source'][:20], i['dest'][:20])
res['type'] = 'duplicate'
items = [
Document(i.get('text'),
name=i.get('url'),
description=i.get('index'),
stemmer=LEMMA) for i in data_to_be_written
]
m = Model(items, weight=TFIDF)
# k = 10
####### BEGIN Experimental Setup ##########
# v,d = m.features, m.documents
# y,x = len(m.documents),len(m.features)
def build_matrix(w=None, d=None):
y, x = len(d), len(w)
model = np.zeros((y, x))
for i in range(y):
model[i] = [1 if w[j] in d[i].words else 0 for j in range(x)]
return model
# def find_word_matches(model, words = None, d = None):
# y,x = model.shape
# for i in range(y):
# for j in range(i+1,y):
# a = np.copy(model[i])
# b = np.copy(model[j])
# a_ones = np.count_nonzero(a)
# b_ones = np.count_nonzero(b)
# comparison = (a==b)
# cross_product = a*b
# intersection = np.count_nonzero(cross_product)
# union = a_ones+b_ones-intersection
# if a_ones+b_ones>0 and intersection > 0:
# score = intersection/union
# else:
# score = 0
# if model[i].any() and model[j].any() and comparison.any() and score > 0.4:
# print "Match [{0}] {1}:[{2} words] - [{3}] {4}:[{5} words] : {6} words".format(d[i].description,d[i].name[:30], np.count_nonzero(a), d[j].description,d[j].name[:30], np.count_nonzero(b), score, math.fabs(d[i].description - d[j].description))
# similar = {
# 'type' : 'similar',
# 'source' : d[i].name,
# 'dest' : d[j].name,
# 'score' : score,
# }
# data_to_be_written.append(similar)
# if score >= 0.9:
# for res in data_to_be_written:
# if res['type'] in ['result','duplicate'] and res['url'] == d[j].name and len(res['text'])>0:
# print "Duplicate [{0}].[{1}]".format(i+1,j+1)
# res['type'] = 'duplicate'
# return model
def word_frequency(model,
words=None,
documents=None,
threshold1=0,
threshold2=1,
transpose=False):
"Returns frequent word amoung documents in range of threshold"
y, x = model.shape
data = {}
for i in range(x):
count = np.count_nonzero(model[:, i]) / y
if count >= threshold1 and count <= threshold2:
if words:
data[words[i]] = count
else:
data[i] = count
return data
model = build_matrix(m.features, m.documents)
# model = find_word_matches(model, m.features, m.documents)
knowledgeKeywords = [
w for w in word_frequency(model, m.features, m.documents, 0.2, 0.8)
][:20]
####### END Experimental Setup ##########
# c = m.cluster(method=HIERARCHICAL, k=k)
# for i in c:
# cluster = []
# k = []
# contains_text = False
# for item in i:
# for data in data_to_be_written:
# if data.get('type') == 'result' and data.get('url')==item.name:
# cluster.append({
# 'url' : data.get('url'),
# 'index' : item.description,
# })
# if data.get('text'):
# k.extend([w for w in count(words(data.get('text')), top=50, stemmer = PORTER, exclude=[], stopwords=False, language='en')])
# contains_text=True
# cluster = {
# 'type' : 'cluster',
# 'data' : cluster,
# 'index' : min([c.get('index') for c in cluster] + [0]),
# 'keywords' : [w for w in count(k, top=10, stemmer = PORTER, exclude=[], stopwords=False, language='en')]
# }
# cluster['contains_text'] = contains_text
# data_to_be_written.append(cluster)
# print "{0} results".format(len(data_to_be_written))
data_to_be_written.append({
'type': 'meta',
'keywords': knowledgeKeywords,
})
result = {}
for i in data_to_be_written:
if i.get('type') in ['result', 'duplicate']:
url = i.get('url')
index = int(i.get('index'))
result[index] = [
1 for r in data_to_be_written
if r.get('type') == 'similar' and r['source'] == url
]
result2 = [i for i, j in result.iteritems()]
result3 = [len(j) for i, j in result.iteritems()]
Process(target=plot_graph, args=(result2, result3)).start()
return data_to_be_written
def v(s):
""" Returns a bag-of-words vector for the given string.
"""
v = {}
v.update(count(words(s)))
return v
#!/usr/bin/env python
from pattern.en import referenced
# Imports
import cgi, cgitb
import sys, json
from pattern.vector import count, LEMMA
from pattern.en import parse, Sentence
# Logic
data = cgi.FieldStorage()
output = data.getvalue("documentText")
s = Sentence(parse(output))
res = count(s, exclude=[".", ",", "-", "!", '"', "'", ":", ";", "?"], stemmer=LEMMA)
print "Content-Type: application/json\n\n"
print json.dumps(res)
for row in all_q:
row = filter(None, row) #remove nulls
def fluency(questions):
return len(questions)
def elaboration(questions):
return sum(min(len(parsetree(a)[0].pnp), 2) for a in questions)
def variance(cluster):
return avg([distance(centroid(cluster), v) for v in cluster])
vectors = []
for q in all_q:
v = count(words(q), stemmer='lemma')
v = Vector(v)
vectors.append(v)
clusters = hierarchical(vectors, k=250, distance='cosine')
clusters = [isinstance(v, Vector) and [v] or v.flatten() for v in clusters]
clusters = sorted(clusters, key=variance)
categories = {}
for i, cluster in enumerate(clusters):
for v in cluster:
categories[row[vectors.index(v)]] = i
def flex(questions):
ml_categories = []