Module auton_survival.estimators

Utilities to train survival regression models and estimate survival.

Classes

class SurvivalModel (model, random_seed=0, **hyperparams)

Universal interface to train multiple different survival models.

Parameters

model : str

A string that determines the choice of the surival analysis model. Survival model choices include:

• dsm : Deep Survival Machines [3] model
• dcph : Deep Cox Proportional Hazards [2] model
• dcm : Deep Cox Mixtures [4] model
• rsf : Random Survival Forests [1] model
• cph : Cox Proportional Hazards [2] model

random_seed : int

Controls the reproducibility of called functions.

References

[1] Hemant Ishwaran et al. Random survival forests. The annals of applied statistics, 2(3):841–860, 2008.

[2] Cox, D. R. (1972). Regression models and life-tables. Journal of the Royal Statistical Society: Series B (Methodological).

[3] Chirag Nagpal, Xinyu Li, and Artur Dubrawski. Deep survival machines: Fully parametric survival regression and representation learning for censored data with competing risks. 2020.

[4] Nagpal, C., Yadlowsky, S., Rostamzadeh, N., and Heller, K. (2021c). Deep cox mixtures for survival regression. In Machine Learning for Healthcare Conference, pages 674–708. PMLR

Browse git

Methods

def fit(self, features, outcomes, vsize=0.15, val_data=None, weights=None, weights_val=None, resample_size=1.0)

This method is used to train an instance of the survival model.

Parameters

features : pd.DataFrame

a pandas dataframe with rows corresponding to individual samples and columns as covariates.

outcomes : pd.DataFrame

a pandas dataframe with columns 'time' and 'event'.

vsize : float, default=0.15

Amount of data to set aside as the validation set. Not applicable to 'rsf' and 'cph' models.

val_data : tuple

A tuple of the validation dataset features and outcomes of 'time' and 'event'. If passed, vsize is ignored. Not applicable to 'rsf' and 'cph' models.

weights_train : list or np.array

a list or numpy array of importance weights for each sample.

weights_val :  list or np.array

a list or numpy array of importance weights for each validation set sample. Ignored if val_data is None.

resample_size : float

a float between 0 and 1 that controls the size of the resampled dataset.

Returns

self

Trained instance of a survival model.

Browse git

def predict_survival(self, features, times)

Predict survival probabilities at specified time(s).

Parameters

features : pd.DataFrame

a pandas dataframe with rows corresponding to individual samples and columns as covariates.

times : float or list

a float or list of the times at which to compute the survival probability.

Returns

np.array : An array of the survival probabilites at each

 

time point in times.

Browse git

def predict_risk(self, features, times)

Predict risk of an outcome occurring within the specified time(s).

Parameters

features : pd.DataFrame

a pandas dataframe with rows corresponding to individual samples and columns as covariates.

times : float or list

a float or list of the times at which to compute the risk.

Returns

np.array

numpy array of the outcome risks at each time point in times.

Browse git

class CounterfactualSurvivalModel (treated_model, control_model)

Universal interface to train multiple different counterfactual survival models.

Browse git

Methods

def predict_counterfactual_survival(self, features, times)

Browse git

def predict_counterfactual_risk(self, features, times)

Browse git