original_docstring: Generates cutoff times for the prediction problem.
  
          Args:
              es (featuretools.EntitySet):
                  An EntitySet with the loaded data.
  
          Returns:
              featuretools.EntitySet, str, pandas.DataFrame:
                  * An updated EntitySet if a new column is generated.
                  * A string indicating the selected target entity.
                  * A dataframe of cutoff times and their target labels.
  
          Raises:
              ValueError: An error occurs if the cutoff variable does not exist.
          
docstring: Generates cutoff times for the prediction problem.
docstring_tokens: Generates cutoff times for the prediction problem .
code: def generate_cutoff_times(self, es):
          """Generates cutoff times for the prediction problem.
  
          Args:
              es (featuretools.EntitySet):
                  An EntitySet with the loaded data.
  
          Returns:
              featuretools.EntitySet, str, pandas.DataFrame:
                  * An updated EntitySet if a new column is generated.
                  * A string indicating the selected target entity.
                  * A dataframe of cutoff times and their target labels.
  
          Raises:
              ValueError: An error occurs if the cutoff variable does not exist.
          """
  
          self.generate_target_label(es)
  
          if DataLoader().check_column_existence(
                  es,
                  self.cutoff_entity,
                  self.cutoff_time_label):  # check the existance of the cutoff label
  
              generated_cts = self.unify_cutoff_time_discharge_time(
                  es, self.cutoff_entity, self.cutoff_time_label)
  
              es = es.entity_from_dataframe(entity_id=self.cutoff_entity,
                                            dataframe=generated_cts,
                                            index='object_id')
              cutoff_times = es[self.cutoff_entity].df['ct'].to_frame()
  
              label = es[self.target_entity].df[self.conn].values
              instance_id = list(es[self.target_entity].df.index)
              cutoff_times = cutoff_times.reindex(index=label)
  
              cutoff_times = cutoff_times[cutoff_times.index.isin(label)]
  
              cutoff_times['instance_id'] = instance_id
              cutoff_times.columns = ['time', 'instance_id']
              cutoff_times['label'] = list(es[self.target_entity].df[self.target_label_column_name])
              return(es, self.target_entity, cutoff_times)
          else:
              raise ValueError('Cutoff time label {} in table {} does not exist'.format(
                  self.cutoff_time_label, self.target_entity))
code_tokens: def generate_cutoff_times ( self , es ) : self . generate_target_label ( es ) if DataLoader ( ) . check_column_existence ( es , self . cutoff_entity , self . cutoff_time_label ) : generated_cts = self . unify_cutoff_time_discharge_time ( es , self . cutoff_entity , self . cutoff_time_label ) es = es . entity_from_dataframe ( entity_id = self . cutoff_entity , dataframe = generated_cts , index = 'object_id' ) cutoff_times = es [ self . cutoff_entity ] . df [ 'ct' ] . to_frame ( ) label = es [ self . target_entity ] . df [ self . conn ] . values instance_id = list ( es [ self . target_entity ] . df . index ) cutoff_times = cutoff_times . reindex ( index = label ) cutoff_times = cutoff_times [ cutoff_times . index . isin ( label ) ] cutoff_times [ 'instance_id' ] = instance_id cutoff_times . columns = [ 'time' , 'instance_id' ] cutoff_times [ 'label' ] = list ( es [ self . target_entity ] . df [ self . target_label_column_name ] ) return ( es , self . target_entity , cutoff_times ) else : raise ValueError ( 'Cutoff time label {} in table {} does not exist' . format ( self . cutoff_time_label , self . target_entity ) )
short_docstring: Generates cutoff times for the prediction problem.
short_docstring_tokens: Generates cutoff times for the prediction problem .
comment: """Generates cutoff times for the prediction problem.
  
          Args:
              es (featuretools.EntitySet):
                  An EntitySet with the loaded data.
  
          Returns:
              featuretools.EntitySet, str, pandas.DataFrame:
                  * An updated EntitySet if a new column is generated.
                  * A string indicating the selected target entity.
                  * A dataframe of cutoff times and their target labels.
  
          Raises:
              ValueError: An error occurs if the cutoff variable does not exist.
          """$SEP$# check the existance of the cutoff label