sksurv.datasets.
load_arff_files_standardized
Load dataset in ARFF format.
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.
survival
pos_label (any type, optional) – Value corresponding to an event in survival analysis. Only considered if survival is True.
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.
standardize_numeric
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().
sksurv.column.standardize()
to_numeric (boo, optional, default: True) – Whether to convert categorical variables to numeric values. See sksurv.column.categorical_to_numeric().
sksurv.column.categorical_to_numeric()
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.