sksurv.datasets.load_arff_files_standardized

sksurv.datasets.load_arff_files_standardized(path_training, attr_labels, pos_label=None, path_testing=None, survival=True, standardize_numeric=True, to_numeric=True)

Load dataset in ARFF format.

Parameters:
  • path_training (str) – Path to ARFF file containing data.
  • attr_labels (sequence of str) – Names of attributes denoting dependent variables. If survival is set, it must be a sequence with two items: the name of the event indicator and the name of the survival/censoring time.
  • pos_label (any type, optional) – Value corresponding to an event in survival analysis. Only considered if survival is True.
  • path_testing (str, optional) – Path to ARFF file containing hold-out data. Only columns that are available in both training and testing are considered (excluding dependent variables). If standardize_numeric is set, data is normalized by considering both training and testing data.
  • survival (bool, optional, default: True) – Whether the dependent variables denote event indicator and survival/censoring time.
  • standardize_numeric (bool, optional, default: True) – Whether to standardize data to zero mean and unit variance. See sksurv.column.standardize().
  • to_numeric (boo, optional, default: True) – Whether to convert categorical variables to numeric values. See sksurv.column.categorical_to_numeric().
Returns:

  • x_train (pandas.DataFrame, shape = (n_train, n_features)) – Training data.
  • y_train (pandas.DataFrame, shape = (n_train, n_labels)) – Dependent variables of training data.
  • x_test (None or pandas.DataFrame, shape = (n_train, n_features)) – Testing data if path_testing was provided.
  • y_test (None or pandas.DataFrame, shape = (n_train, n_labels)) – Dependent variables of testing data if path_testing was provided.