本文整理匯總了Python中extractor.Extractor.user_extract方法的典型用法代碼示例。如果您正苦於以下問題：Python Extractor.user_extract方法的具體用法？Python Extractor.user_extract怎麽用？Python Extractor.user_extract使用的例子？那麽, 這裏精選的方法代碼示例或許可以為您提供幫助。您也可以進一步了解該方法所在類extractor.Extractor的用法示例。
在下文中一共展示了Extractor.user_extract方法的1個代碼示例，這些例子默認根據受歡迎程度排序。您可以為喜歡或者感覺有用的代碼點讚，您的評價將有助於係統推薦出更棒的Python代碼示例。
示例1: predict
# 需要導入模塊: from extractor import Extractor [as 別名]
# 或者: from extractor.Extractor import user_extract [as 別名]
def predict(article_link, image_link):
"""
output: predicted emotion as: [ 0. 1. 0. 0. 0.]
"""
e = Extractor()
user_input = {
"article_link": article_link,
"image_link": image_link
}
friendly_json = e.user_extract(user_input)
tax_list = friendly_json['alchemy']['taxonomy']
tax_primary = []
for t in tax_list:
tax_primary.append(t['label'].split('/')[1])
tax_primary = list(set(tax_primary))[0]
extracted_articles = dict()
extracted_articles['articles'] = [friendly_json]
textEmotions = text_emotions_x(extracted_articles)
picEmotions = picture_emotions_x(extracted_articles)
with open('emotionClassification/trained_models/bbac_1150_all_clf.pkl','r') as f:
clf = cPickle.load(f)
test_article = makeDataMatrix(textEmotions, picEmotions)
reaction = predictReactions(clf, test_article)
return reaction[0], tax_primary