18530bab8f49b7a40a4d8793d4b6b5090941c3e7,methods/shogun/qda.py,SHOGUN_QDA,__init__,#SHOGUN_QDA#,25

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])

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

After Change

    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"])

    if "tolerance" in method_param:
      self.tolerance = float(method_param["tolerance"])

  """
  Return information about the benchmarking instance.

Instances