Utility functions to load standard datasets to train and evaluate the Deep Survival Machines models.
def increase_censoring(e, t, p)
def load_dataset(dataset='SUPPORT', **kwargs)
Helper function to load datasets to test Survival Analysis models.
Currently implemented datasets include:
SUPPORT: This dataset comes from the Vanderbilt University study to estimate survival for seriously ill hospitalized adults . (Refer to http://biostat.mc.vanderbilt.edu/wiki/Main/SupportDesc. for the original datasource.)
PBC: The Primary biliary cirrhosis dataset  is well known dataset for evaluating survival analysis models with time dependent covariates.
FRAMINGHAM: This dataset is a subset of 4,434 participants of the well known, ongoing Framingham Heart study  for studying epidemiology for hypertensive and arteriosclerotic cardiovascular disease. It is a popular dataset for longitudinal survival analysis with time dependent covariates.
: Knaus WA, Harrell FE, Lynn J et al. (1995): The SUPPORT prognostic model: Objective estimates of survival for seriously ill hospitalized adults. Annals of Internal Medicine 122:191-203.
 Fleming, Thomas R., and David P. Harrington. Counting processes and survival analysis. Vol. 169. John Wiley & Sons, 2011.
 Dawber, Thomas R., Gilcin F. Meadors, and Felix E. Moore Jr. "Epidemiological approaches to heart disease: the Framingham Study." American Journal of Public Health and the Nations Health 41.3 (1951).
- The choice of dataset to load. Currently implemented is 'SUPPORT', 'PBC' and 'FRAMINGHAM'.
- Dataset specific keyword arguments.
(np.ndarray, np.ndarray, np.ndarray)
- A tuple of the form of (x, t, e) where x, t, e are the input covariates, event times and the censoring indicators respectively.