def list_features(city_name): cities = data.cities city = None for i in range(0, len(cities)): if cities[i].name == city_name: feature_list = feature_selection.get_hardcoded_features(cities[i]) city = cities[i] if city: latitude = city.lat longitude = city.lon city_name = city.name city_tweet_count = data.create_region_tweet_count(city) city_word_count = data.find_leng_city_corpus(city) return render_template("city_words.html", features= feature_list, city_name=city_name, latitude=latitude, longitude=longitude, city_tweet_count=city_tweet_count, city_word_count=city_word_count)
def classify_text(): tweet = request.form['tweet'] start = datetime.datetime.now() rankings = data.create_ranking(tweet) end = datetime.datetime.now() print 'getting city rankings takes: %s' % (end - start) start = datetime.datetime.now() feature_strings_dict = {} city_corpus_leng_dict = {} city_tweet_count_dict = {} for city in cities: corpus_leng = data.find_leng_city_corpus(city) city_corpus_leng_dict[city.name] = corpus_leng city_tweet_count = data.create_region_tweet_count(city) city_tweet_count_dict[city.name] = city_tweet_count feature_strings = feature_selection.included_feature_strings( city, tweet) feature_strings_dict[city.name] = feature_strings end = datetime.datetime.now() print 'getting top 5 words takes: %s' % (end - start) start = datetime.datetime.now() cty_corpus_dict = data.city_corpus_dict() word_count_dict = cty_corpus_dict[rankings[0][0].name] end = datetime.datetime.now() print 'getting bogus word count dict takes: %s' % (end - start) start = datetime.datetime.now() names = [] for i in range(0, len(rankings)): city_name = rankings[i][0].name names.append(city_name) end = datetime.datetime.now() print 'generating lists takes: %s' % (end - start) return render_template("map.html", tweet=tweet, city_tweet_count_dict=city_tweet_count_dict, names=names, city_corpus_leng_dict=city_corpus_leng_dict, feature_strings_dict=feature_strings_dict, rankings=rankings)
def list_features(city_name): cities = data.cities city = None for i in range(0, len(cities)): if cities[i].name == city_name: feature_list = feature_selection.get_hardcoded_features(cities[i]) city = cities[i] if city: latitude = city.lat longitude = city.lon city_name = city.name city_tweet_count = data.create_region_tweet_count(city) city_word_count = data.find_leng_city_corpus(city) return render_template("city_words.html", features=feature_list, city_name=city_name, latitude=latitude, longitude=longitude, city_tweet_count=city_tweet_count, city_word_count=city_word_count)
def classify_text(): tweet = request.form['tweet'] start = datetime.datetime.now() rankings = data.create_ranking(tweet) end = datetime.datetime.now() print 'getting city rankings takes: %s' % (end - start) start = datetime.datetime.now() feature_strings_dict = {} city_corpus_leng_dict = {} city_tweet_count_dict = {} for city in cities: corpus_leng = data.find_leng_city_corpus(city) city_corpus_leng_dict[city.name] = corpus_leng city_tweet_count = data.create_region_tweet_count(city) city_tweet_count_dict[city.name] = city_tweet_count feature_strings = feature_selection.included_feature_strings(city, tweet) feature_strings_dict[city.name] = feature_strings end = datetime.datetime.now() print 'getting top 5 words takes: %s' % (end - start) start = datetime.datetime.now() cty_corpus_dict = data.city_corpus_dict() word_count_dict = cty_corpus_dict[rankings[0][0].name] end = datetime.datetime.now() print 'getting bogus word count dict takes: %s' % (end - start) start = datetime.datetime.now() names = [] for i in range(0, len(rankings)): city_name = rankings[i][0].name names.append(city_name) end = datetime.datetime.now() print 'generating lists takes: %s' % (end - start) return render_template("map.html", tweet=tweet, city_tweet_count_dict=city_tweet_count_dict, names=names, city_corpus_leng_dict=city_corpus_leng_dict, feature_strings_dict=feature_strings_dict, rankings=rankings)