code: def load_aids(endpoint="aids"):
      
      labels_aids = ['censor', 'time']
      labels_death = ['censor_d', 'time_d']
      if endpoint == "aids":
          attr_labels = labels_aids
          drop_columns = labels_death
      elif endpoint == "death":
          attr_labels = labels_death
          drop_columns = labels_aids
      else:
          raise ValueError("endpoint must be 'aids' or 'death'")
  
      fn = resource_filename(__name__, 'data/actg320.arff')
      x, y = get_x_y(loadarff(fn), attr_labels=attr_labels, pos_label='1')
      x.drop(drop_columns, axis=1, inplace=True)
      return x, y
docs: Load and return the AIDS Clinical Trial dataset
  
      The dataset has 1,151 samples and 11 features.
      The dataset has 2 endpoints:
  
      1. AIDS defining event, which occurred for 96 patients (8.3%)
      2. Death, which occurred for 26 patients (2.3%)
  
      Parameters
      ----------
      endpoint : aids|death
          The endpoint
  
      Returns
      -------
      x : pandas.DataFrame
          The measurements for each patient.
  
      y : structured array with 2 fields
          *censor*: boolean indicating whether the endpoint has been reached
          or the event time is right censored.
  
          *time*: total length of follow-up
  
          If ``endpoint`` is death, the fields are named *censor_d* and *time_d*.
  
      References
      ----------
      .. [1] http://www.umass.edu/statdata/statdata/data/
  
      .. [2] Hosmer, D., Lemeshow, S., May, S.:
          "Applied Survival Analysis: Regression Modeling of Time to Event Data."
          John Wiley & Sons, Inc. (2008)