55bef1eea344a530ebda48d1a5d0c2456f0c3fc1,methods/shogun/nbc.py,SHOGUN_NBC,__init__,#SHOGUN_NBC#,23

Before Change

    self.data = load_dataset(method_param["datasets"], ["csv"])
    self.data_split = split_dataset(self.data[0])

    self.build_opts = {}
    if "algorithm" in method_param:
      self.build_opts["solver"] = str(method_param["algorithm"])
    if "epsilon" in method_param:
      self.build_opts["epsilon"] = float(method_param["epsilon"])
    if "max_iterations" in method_param:
      self.build_opts["max_iter"] = int(method_param["max_iterations"])

    self.train_feat = RealFeatures(self.data_split[0].T)
    self.train_labels = MulticlassLabels(self.data_split[1])
    self.test_feat = RealFeatures(self.data[1].T)

  def __str__(self):
    return self.info

After Change

  @param run_param - Path option for executing the benckmark. Not used for 
  Shogun.
  """
  def __init__(self, method_param, run_param):
    self.info = "SHOGUN_NBC ("  + str(method_param) +  ")"

    // Assemble run model parameter.
    self.data = load_dataset(method_param["datasets"], ["csv"])
    self.data_split = split_dataset(self.data[0])

    self.train_feat = RealFeatures(self.data_split[0].T)

	// Encode the labels into {0,1,2,3,......,num_classes-1}
    self.train_labels, self.label_map = label_encoder(self.data_split[1])
    self.train_labels = MulticlassLabels(self.train_labels)

    if len(self.data) >= 2:
      self.test_feat = RealFeatures(self.data[1].T)

    self.solver = "auto"
    if "solver" in method_param:
      self.solver = str(method_param["solver"])

  """
  Return information about the benchmarking instance.

Instances