old_contents: from libact.base.interfaces import ContinuousModel
  import sklearn.linear_model
  import numpy as np
  
  
  class LogisticRegression(ContinuousModel):
  
      def __init__(self, *args, **kwargs):
          self.model = sklearn.linear_model.LogisticRegression(*args, **kwargs)
  
      def train(self, dataset, *args, **kwargs):
          return self.model.fit(*(dataset.format_sklearn() + args), **kwargs)
  
      def predict(self, feature, *args, **kwargs):
          return self.model.predict(feature, *args, **kwargs)
  
      def score(self, testing_dataset, *args, **kwargs):
          return self.model.score(*(testing_dataset.format_sklearn() + args), **kwargs)
  
      def predict_real(self, feature, *args, **kwargs):
          return self.model.predict_proba(feature, *args, **kwargs)
  
new_contents: from libact.base.interfaces import ContinuousModel
  import sklearn.linear_model
  import numpy as np
  
  
  class LogisticRegression(ContinuousModel):
  
      def __init__(self, *args, **kwargs):
          self.model = sklearn.linear_model.LogisticRegression(*args, **kwargs)
  
      def train(self, dataset, *args, **kwargs):
          return self.model.fit(*(dataset.format_sklearn() + args), **kwargs)
  
      def predict(self, feature, *args, **kwargs):
          return self.model.predict(feature, *args, **kwargs)
  
      def score(self, testing_dataset, *args, **kwargs):
          return self.model.score(*(testing_dataset.format_sklearn() + args), **kwargs)
  
      def predict_real(self, feature, *args, **kwargs):
          return self.model.decision_function(feature, *args, **kwargs)
  
subject: change model.logistic_regression's predict_real from using predict_proba to decision_function so the interface output can be unified