本文整理汇总了Python中classifier.Classifier.predict_late_fusion_testing_data方法的典型用法代码示例。如果您正苦于以下问题：Python Classifier.predict_late_fusion_testing_data方法的具体用法？Python Classifier.predict_late_fusion_testing_data怎么用？Python Classifier.predict_late_fusion_testing_data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classifier.Classifier的用法示例。
在下文中一共展示了Classifier.predict_late_fusion_testing_data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: __init__
# 需要导入模块: from classifier import Classifier [as 别名]
# 或者: from classifier.Classifier import predict_late_fusion_testing_data [as 别名]
class InterestAnalyzer:
def __init__(self):
self.feature_builder = FeatureBuilder()
def rebuild_features(self):
print 'Parsing Facebook...'
fbparser.parse({'in': TRAINING_DIRECTORIES['fb'], 'out': TRAINING_FILES['fb']})
print 'Parsing Twitter...'
tweetparser.parse({'in': TRAINING_DIRECTORIES['tweets'], 'out': TRAINING_FILES['tweets']})
print 'Parsing LinkedIn...'
linkedinparser.parse({'in': TRAINING_DIRECTORIES['linkedin'], 'out': TRAINING_FILES['linkedin']})
print 'Building features...'
# build features for training data
self.feature_builder.create_feature_vectors(TRAINING_FILES['linkedin'], TRAINING_FEATURE_FILES['linkedin'], 'linkedin')
self.feature_builder.create_feature_vectors(TRAINING_FILES['tweets'], TRAINING_FEATURE_FILES['tweets'], 'tweets')
self.feature_builder.create_feature_vectors(TRAINING_FILES['fb'], TRAINING_FEATURE_FILES['fb'], 'fb')
def retrain_classifier(self):
print 'Training classifier...'
self.classifier = Classifier()
def save_classifier(self):
pickle.dump(self.classifier, open(CLASSIFIER_FILE, 'wb'))
def load_classifier(self):
self.classifier = pickle.load(open(CLASSIFIER_FILE, 'rb'))
def classifier_predict(self):
print 'Parsing Facebook...'
fbparser.parse({'in': TESTING_DIRECTORIES['fb'], 'out': TESTING_FILES['fb']})
print 'Parsing Twitter...'
tweetparser.parse({'in': TESTING_DIRECTORIES['tweets'], 'out': TESTING_FILES['tweets']})
print 'Parsing LinkedIn...'
linkedinparser.parse({'in': TESTING_DIRECTORIES['linkedin'], 'out': TESTING_FILES['linkedin']})
print 'Building features...'
self.feature_builder.create_feature_vectors(TESTING_FILES['linkedin'], TESTING_FEATURE_FILES['linkedin'], 'linkedin')
self.feature_builder.create_feature_vectors(TESTING_FILES['tweets'], TESTING_FEATURE_FILES['tweets'], 'tweets')
self.feature_builder.create_feature_vectors(TESTING_FILES['fb'], TESTING_FEATURE_FILES['fb'], 'fb')
linkedin_testing_features = np.loadtxt(TESTING_FEATURE_FILES['linkedin'], delimiter=',')
tweets_testing_features = np.loadtxt(TESTING_FEATURE_FILES['tweets'], delimiter=',')
fb_testing_features = np.loadtxt(TESTING_FEATURE_FILES['fb'], delimiter=',')
print 'Predicting labels...'
print 'LinkedIn classifier:'
print self.classifier.predict_testing_data('linkedin', linkedin_testing_features, TESTING_LABELS_FILE, 'results_l.txt')
print 'Twitter classifier:'
print self.classifier.predict_testing_data('tweets', tweets_testing_features, TESTING_LABELS_FILE, 'results_t.txt')
tweets_result_labels = np.loadtxt('results_t.txt', delimiter=',')
linkedin_result_labels = np.loadtxt('results_l.txt', delimiter=',')
print 'Late fusion classifier:'
print self.classifier.predict_late_fusion_testing_data([tweets_result_labels, linkedin_result_labels], TESTING_LABELS_FILE, 'result.txt')