API reference

Linear Models

CoxPHSurvivalAnalysis([alpha, n_iter, tol, ...]) Cox proportional hazards model.
IPCRidge([alpha, fit_intercept, normalize, ...]) Accelerated failure time model with inverse probability of censoring weights.

Ensemble Models

ComponentwiseGradientBoostingSurvivalAnalysis([...]) Gradient boosting with component-wise least squares as base learner.
GradientBoostingSurvivalAnalysis([loss, ...]) Gradient-boosted Cox proportional hazard loss with regression trees as base learner.

Survival Support Vector Machine

FastKernelSurvivalSVM([alpha, rank_ratio, ...]) Efficient Training of kernel Survival Support Vector Machine.
FastSurvivalSVM([alpha, rank_ratio, ...]) Efficient Training of linear Survival Support Vector Machine
MinlipSurvivalAnalysis([solver, alpha, ...]) Survival model related to survival SVM, using a minimal Lipschitz smoothness strategy instead of a maximal margin strategy.
HingeLossSurvivalSVM([solver, alpha, ...]) Naive implementation of kernel survival support vector machine.
NaiveSurvivalSVM([penalty, loss, dual, tol, ...]) Naive version of linear Survival Support Vector Machine.


clinical_kernel(x[, y]) Computes clinical kernel
ClinicalKernelTransform([fit_once, ...]) Transform data using a clinical Kernel

Meta Models

EnsembleSelection(base_estimators[, scorer, ...]) Ensemble selection for survival analysis that accounts for a score and correlations between predictions.
EnsembleSelectionRegressor(base_estimators) Ensemble selection for regression that accounts for the accuracy and correlation of errors.
Stacking(meta_estimator, base_estimators[, ...]) Meta estimator that combines multiple base learners.


concordance_index_censored(event_indicator, ...) Concordance index for right-censored data


categorical_to_numeric(table) Encode categorical columns to numeric by converting each category to an integer value.
encode_categorical(table, **kwargs) Encode categorical columns with M categories into M-1 columns according to the one-hot scheme.
standardize(table[, with_std]) Perform Z-Normalization on each numeric column of the given table.

I/O Utilities

loadarff(filename) Load ARFF file
writearff(data, filename[, relation_name, index]) Write ARFF file


get_x_y(data_frame, attr_labels[, ...]) Split data frame into features and labels.
load_arff_files_standardized(path_training, ...) Load dataset in ARFF format.
load_aids([endpoint]) Load and return the AIDS Clinical Trial dataset
load_breast_cancer() Load and return the breast cancer dataset
load_gbsg2() Load and return the German Breast Cancer Study Group 2 dataset
load_whas500() Load and return the Worcester Heart Attack Study dataset
load_veterans_lung_cancer() Load and return the Worcester Heart Attack Study dataset