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)