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. |