import nltk
def timeit(func):
@functools.wraps(func)
def newfunc(*args, **kwargs):
startTime = time.time()
func(*args, **kwargs)
elapsedTime = time.time() - startTime
print('function [{}] finished in {} ms'.format(func.__name__,
int(elapsedTime *
1000)))
return newfunc
@timeit
def get_textblob_tags(sentence):
blob = TextBlob(sentence, pos_tagger=PerceptronTagger())
return blob.tags
@timeit
def get_nltk_tags(sentence):
return nltk.pos_tag(sentence)
for sentence in all_sentences()[:10]:
get_textblob_tags(sentence)
get_nltk_tags(sentence)
from bs4 import BeautifulSoup, NavigableString
from random import randint
from json import dumps
from himymutil.soupselect import select
from himymutil.sentences import all_sentences
from himymutil.ml import pos_features
import json
import csv
import nltk
import pickle
import itertools
graph = Graph()
all_sentences = all_sentences()
def extract_speaker(sentence):
tokenized_sentence = nltk.word_tokenize(sentence)
for i, word in enumerate(tokenized_sentence):
classification = classifier.classify(pos_features(tokenized_sentence, i))
with open("classifiers/decision_tree.pickle") as f:
classifier = pickle.load(f)
@get('/css/<filename:re:.*\.css>')
def get_css(filename):
return static_file(filename, root="static", mimetype="text/css")
@get('/images/<filename:re:.*\.png>')
from bs4 import BeautifulSoup, NavigableString
from random import randint
from json import dumps
from himymutil.soupselect import select
from himymutil.sentences import all_sentences
from himymutil.ml import pos_features
import json
import csv
import nltk
import pickle
import itertools
graph = Graph()
all_sentences = all_sentences()
def extract_speaker(sentence):
tokenized_sentence = nltk.word_tokenize(sentence)
for i, word in enumerate(tokenized_sentence):
classification = classifier.classify(
pos_features(tokenized_sentence, i))
with open("classifiers/decision_tree.pickle") as f:
classifier = pickle.load(f)
@get('/css/<filename:re:.*\.css>')
def get_css(filename):
from textblob import TextBlob
from textblob_aptagger import PerceptronTagger
from himymutil.sentences import all_sentences
import functools
import time
import nltk
def timeit(func):
@functools.wraps(func)
def newfunc(*args, **kwargs):
startTime = time.time()
func(*args, **kwargs)
elapsedTime = time.time() - startTime
print('function [{}] finished in {} ms'.format(
func.__name__, int(elapsedTime * 1000)))
return newfunc
@timeit
def get_textblob_tags(sentence):
blob = TextBlob(sentence, pos_tagger=PerceptronTagger())
return blob.tags
@timeit
def get_nltk_tags(sentence):
return nltk.pos_tag(sentence)
for sentence in all_sentences()[:10]:
get_textblob_tags(sentence)
get_nltk_tags(sentence)
