API reference¶
Datasets¶
get_x_y (data_frame, attr_labels[, …]) |
Split data frame into features and labels. |
load_aids ([endpoint]) |
Load and return the AIDS Clinical Trial dataset |
load_arff_files_standardized (path_training, …) |
Load dataset in ARFF format. |
load_breast_cancer () |
Load and return the breast cancer dataset |
load_flchain () |
Load and return assay of serum free light chain for 7874 subjects. |
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 data from the Veterans’ Administration Lung Cancer Trial |
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. |
RandomSurvivalForest ([n_estimators, …]) |
A random survival forest. |
Functions¶
StepFunction (x, y[, a, b]) |
Callable step function. |
Hypothesis testing¶
compare_survival (y, group_indicator[, …]) |
K-sample log-rank hypothesis test of identical survival functions. |
I/O Utilities¶
loadarff (filename) |
Load ARFF file |
writearff (data, filename[, relation_name, index]) |
Write ARFF file |
Kernels¶
ClinicalKernelTransform ([fit_once, …]) |
Transform data using a clinical Kernel |
clinical_kernel (x[, y]) |
Computes clinical kernel |
Linear Models¶
CoxnetSurvivalAnalysis ([n_alphas, alphas, …]) |
Cox’s proportional hazard’s model with elastic net penalty. |
CoxPHSurvivalAnalysis ([alpha, ties, n_iter, …]) |
Cox proportional hazards model. |
IPCRidge ([alpha, fit_intercept, normalize, …]) |
Accelerated failure time model with inverse probability of censoring weights. |
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. |
Metrics¶
brier_score (survival_train, survival_test, …) |
Estimate the time-dependent Brier score for right censored data. |
concordance_index_censored (event_indicator, …) |
Concordance index for right-censored data |
concordance_index_ipcw (survival_train, …) |
Concordance index for right-censored data based on inverse probability of censoring weights. |
cumulative_dynamic_auc (survival_train, …) |
Estimator of cumulative/dynamic AUC for right-censored time-to-event data. |
integrated_brier_score (survival_train, …) |
The Integrated Brier Score (IBS) provides an overall calculation of the model performance at all available times \(t_1 \leq t \leq t_\text{max}\). |
Non-parametric Estimators¶
CensoringDistributionEstimator () |
Kaplan–Meier estimator for the censoring distribution. |
SurvivalFunctionEstimator () |
Kaplan–Meier estimate of the survival function. |
ipc_weights (event, time) |
Compute inverse probability of censoring weights |
kaplan_meier_estimator (event, time_exit[, …]) |
Kaplan-Meier estimator of survival function. |
nelson_aalen_estimator (event, time) |
Nelson-Aalen estimator of cumulative hazard function. |
Pre-Processing¶
OneHotEncoder ([allow_drop]) |
Encode categorical columns with M categories into M-1 columns according to the one-hot scheme. |
categorical_to_numeric (table) |
Encode categorical columns to numeric by converting each category to an integer value. |
encode_categorical (table[, columns]) |
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. |
Survival Support Vector Machine¶
HingeLossSurvivalSVM ([solver, alpha, …]) |
Naive implementation of kernel 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. |
NaiveSurvivalSVM ([penalty, loss, dual, tol, …]) |
Naive version of linear Survival Support Vector Machine. |
Survival Trees¶
SurvivalTree ([splitter, max_depth, …]) |
A survival tree. |