本文整理汇总了Python中extractor.Extractor.user_extract方法的典型用法代码示例。如果您正苦于以下问题：Python Extractor.user_extract方法的具体用法？Python Extractor.user_extract怎么用？Python Extractor.user_extract使用的例子？那么恭喜您, 这里精选的方法代码示例或许可以为您提供帮助。您也可以进一步了解该方法所在类extractor.Extractor的用法示例。
在下文中一共展示了Extractor.user_extract方法的1个代码示例，这些例子默认根据受欢迎程度排序。您可以为喜欢或者感觉有用的代码点赞，您的评价将有助于系统推荐出更棒的Python代码示例。
示例1: predict
# 需要导入模块: from extractor import Extractor [as 别名]
# 或者: from extractor.Extractor import user_extract [as 别名]
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