本文整理汇总了Python中model.Document.add_document方法的典型用法代码示例。如果您正苦于以下问题：Python Document.add_document方法的具体用法？Python Document.add_document怎么用？Python Document.add_document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model.Document的用法示例。
在下文中一共展示了Document.add_document方法的2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: load_data
# 需要导入模块: from model import Document [as 别名]
# 或者: from model.Document import add_document [as 别名]
def load_data(self):
X, Y = [], []
for file in os.listdir(self.path):
if file == 'truth.txt' or file == '.DS_Store':
continue
print "loading file -->" + file
tree = ET.parse(os.path.join(self.path, file))
root = tree.getroot()
document = Document(language=root.get('lang'), name=root.get('id'))
for d in root.findall('document'):
document.add_document(d.text)
user, gender, age_group, extroverted, stable, agreeable, conscientious, open = self.truth[
root.get('id')].split(":::")
traits = PersonalityTraits(extroverted=float(extroverted), stable=float(stable), agreeable=float(agreeable),
conscientious=float(conscientious), open=float(open))
usr = Author(gender=gender, age_group=age_group, traits=traits)
document.author = usr
X.append(document)
Y.append(self.truth[root.get('id')])
print "done loading files"
self.X = X
self.Y = Y
return self示例2: run
# 需要导入模块: from model import Document [as 别名]
# 或者: from model.Document import add_document [as 别名]
def run(self):
result = {}
x, y, y_actual = [], [], []
for file in os.listdir(self.path):
if file == 'truth.txt' or file == '.DS_Store':
continue
tree = ET.parse(os.path.join(self.path, file))
root = tree.getroot()
document = Document(language=root.get('lang'), name=root.get('id'))
for d in root.findall('document'):
document.add_document(d.text)
x_test = [document] # vector
temp_result = {}
for predictor in self.model:
# print predictor
if predictor.name == 'age_gender':
prediction = predictor.clf.predict(x_test) # predict
temp_result.update(
predictor.label_extractor(list(predictor.label_encoder.inverse_transform(prediction))[0]))
document.author.gender = temp_result['gender']
document.author.age_group = temp_result['age_group']
if predictor.name == 'personality':
target = predictor.label_encoder.classes_
prediction = list(predictor.clf.predict_proba(x_test))[0]
prediction = [change_range(p, 1.0, 0.0, 0.5, -0.5) for p in prediction]
temp_result.update(predictor.label_extractor(target, prediction))
document.author.personality_traits.extroverted = temp_result['extroverted']
document.author.personality_traits.agreeable = temp_result['agreeable']
document.author.personality_traits.conscientious = temp_result['conscientious']
document.author.personality_traits.stable = temp_result['stable']
document.author.personality_traits.open = temp_result['open']
result[os.path.splitext(file)[0]] = document
# y.extend(prediction)
# print y
x.append(os.path.splitext(file)[0])
# y_actual.append(predictor.label_extractor(self.truth[root.get('id')]))
self.x_test = x_test
# self.y_prediction = y
# self.y_actual = self.label_encoder.transform(y_actual)
self.result = result