scikit-survival

scikit-survival

Scope

Use this skill for scikit-survival 0.28.0 workflows involving:

• right-censored structured outcomes;
• Cox PH, Coxnet, IPC ridge, survival trees, forests, boosting, and SVMs;
• discrimination, prediction error, calibration-oriented checks, and time-dependent prediction;
• nonparametric cumulative incidence with competing risks;
• scikit-learn pipelines, nested model selection, and reproducible reports.

scikit-survival primarily models right-censored outcomes. Its built-in competing-risk support is nonparametric cumulative incidence; it does not provide Fine-Gray regression. Do not present model output as clinical advice, causal evidence, or proof of clinical utility.

Current release and installation

Verified 2026-07-23:

• Latest stable: scikit-survival 0.28.0, released 2026-07-05.
• Python: 3.11 or later; PyPI wheels cover CPython 3.11-3.14 on Linux x86-64, macOS x86-64/ARM64, and Windows x86-64.
• Runtime bounds: NumPy >=2.0.0, pandas >=2.2.0, SciPy >=1.13.0, scikit-learn >=1.9.0,<1.10, OSQP >=1.0.2, narwhals >=2.0.1.
• 0.28 adds pandas/Polars estimator support through narwhals and removes criterion from GradientBoostingSurvivalAnalysis.

Create an isolated environment and install the tested snapshot:

uv venv --python 3.11
source .venv/bin/activate
uv pip install \
  "scikit-survival==0.28.0" \
  "scikit-learn==1.9.0" \
  "numpy==2.4.6" \
  "pandas==3.0.5" \
  "scipy==1.17.1" \
  "ecos==2.0.14" \
  "osqp==1.1.3" \
  "joblib==1.5.3" \
  "numexpr==2.14.2" \
  "narwhals==2.24.0"

Binary wheels are preferred. A source build requires a C/C++ compiler; OSQP may also require CMake. This skill is MIT-licensed; the upstream scikit-survival package is GPL-3.0-or-later, so review upstream licensing before redistribution.

Non-negotiable workflow

1. Define the estimand and event coding. Decide whether the target is all-event survival, cause-specific hazard, or cause-specific cumulative incidence.
2. Validate outcomes. Standard estimators need a two-field structured array: boolean event first, observed time second. Competing-risk CIF instead needs a separate integer event vector: 0=censored, 1..K=causes.
3. Split before learned preprocessing. Never fit imputers, encoders, scalers, feature selectors, or alpha choices on all rows before splitting.
4. Fit preprocessing inside a pipeline. Unknown categories and missingness must be handled using training-fold state only.
5. Tune without reusing evaluation data. Use nested CV when reporting cross-validated tuned performance, or reserve a truly untouched final holdout.
6. Fit censoring distributions on training data. IPCW concordance, dynamic AUC, and Brier metrics receive survival_train, never a pooled train+test outcome.
7. Restrict evaluation times. Use a strictly increasing grid inside test follow-up and below the end of training support where the estimated censoring survival remains positive.
8. Match predictions to metrics. Concordance/dynamic AUC consume higher-is-riskier scores. Brier metrics consume survival probabilities with shape (n_test, n_times), not risk scores or unevaluated step functions.
9. Handle competing causes explicitly. Standard survival probabilities and CIFs answer different questions. Never estimate event-specific probability with 1 - Kaplan-Meier while censoring competing events.
10. Report limits. Separate discrimination, calibration, prediction error, and cumulative incidence. None alone establishes decision or clinical utility.

Outcome construction

from sksurv.util import Surv

y = Surv.from_arrays(event=event_bool, time=observed_time)
# Equivalent for pandas or Polars:
y = Surv.from_dataframe("event", "time", frame)

The first field is boolean (True=event, False=right-censored); the second is floating-point time. Field names may vary, but field order and meaning may not. Use references/data-handling.md before loading custom or competing-risk data.

Leakage-safe pipeline

from sklearn.compose import ColumnTransformer
from sklearn.impute import SimpleImputer
from sklearn.model_selection import train_test_split
from sklearn.pipeline import make_pipeline
from sklearn.preprocessing import OneHotEncoder, StandardScaler
from sksurv.linear_model import CoxPHSurvivalAnalysis

X_train, X_test, y_train, y_test = train_test_split(
    X, y, test_size=0.25, stratify=y["event"], random_state=20260723
)

preprocess = ColumnTransformer(
    [
        ("num", make_pipeline(SimpleImputer(strategy="median"), StandardScaler()), numeric),
        (
            "cat",
            make_pipeline(
                SimpleImputer(strategy="most_frequent"),
                OneHotEncoder(handle_unknown="ignore", drop="first", sparse_output=False),
            ),
            categorical,
        ),
    ],
    sparse_threshold=0.0,
)
model = make_pipeline(preprocess, CoxPHSurvivalAnalysis(alpha=0.1, ties="efron"))
model.fit(X_train, y_train)
risk = model.predict(X_test)

The split precedes every learned transformation. For repeated or grouped records, use a group-aware split; for temporal deployment, use a time-respecting split.

Model choice

• CoxPHSurvivalAnalysis: interpretable log-hazard coefficients under proportional hazards; alpha is ridge shrinkage and ties is "breslow" or "efron".
• CoxnetSurvivalAnalysis: LASSO/elastic-net path for high-dimensional data. l1_ratio is in (0, 1]; use fit_baseline_model=True before requesting survival or cumulative-hazard functions.
• IPCRidge: IPC-weighted ridge AFT model; prediction is on a time/log-time scale, not a Cox risk score.
• RandomSurvivalForest / ExtraSurvivalTrees: nonlinear survival and cumulative hazard predictions; use permutation importance, not impurity importance.
• GradientBoostingSurvivalAnalysis: tree boosting with "coxph", "squared", or "ipcwls" loss. criterion was removed in 0.28.
• ComponentwiseGradientBoostingSurvivalAnalysis: sparse linear componentwise boosting.
• FastSurvivalSVM / FastKernelSurvivalSVM: ranking or regression objectives. Only rank_ratio=1 directly returns higher-is-riskier scores; SVMs do not yield survival probabilities for Brier metrics.

Read the model-specific reference before interpreting coefficients or predictions: references/cox-models.md, references/ensemble-models.md, or references/svm-models.md.

Prediction and metric contracts

import numpy as np
from sksurv.metrics import (
    brier_score,
    concordance_index_ipcw,
    cumulative_dynamic_auc,
    integrated_brier_score,
)

risk = model.predict(X_test)  # (n_test,), higher means higher event risk
uno_c = concordance_index_ipcw(y_train, y_test, risk, tau=times[-1])[0]
auc_t, mean_auc = cumulative_dynamic_auc(y_train, y_test, risk, times)

surv_fns = model.predict_survival_function(X_test)
surv_prob = np.vstack([fn(times) for fn in surv_fns])  # (n_test, n_times)
_, brier_t = brier_score(y_train, y_test, surv_prob, times)
ibs = integrated_brier_score(y_train, y_test, surv_prob, times)

• Harrell C and Uno C measure rank discrimination, not calibration.
• Cumulative/dynamic AUC measures discrimination at selected horizons and accepts 1D or time-dependent 2D risk scores; it rejects survival probabilities.
• Brier score is censoring-weighted probability error and reflects both discrimination and calibration. It is not a standalone calibration curve.
• Calibration requires horizon-specific predicted-versus-observed checks on independent data. scikit-survival 0.28 has no dedicated calibration-curve API.

See references/evaluation-metrics.md for assumptions, primary literature, safe time-grid construction, and scorer wrappers.

Pipelines, metadata routing, and tuning

Ordinary Pipeline.fit(X, y) needs no metadata-routing setup. Metric wrappers such as as_concordance_index_ipcw_scorer are estimator wrappers, not scoring= callables:

from sklearn.model_selection import GridSearchCV
from sksurv.metrics import as_concordance_index_ipcw_scorer

wrapped = as_concordance_index_ipcw_scorer(model, tau=tau)
search = GridSearchCV(
    wrapped,
    {"estimator__coxphsurvivalanalysis__alpha": [0.01, 0.1, 1.0]},
    cv=inner_splits,
)

The wrapper learns the censoring distribution from each fit fold. Prefix wrapped parameters with estimator__. Enable scikit-learn metadata routing only when passing extra metadata through a meta-estimator. For example, Coxnet's set_predict_request(alpha=True) matters only when routing the alpha prediction argument with sklearn.set_config(enable_metadata_routing=True).

Use an outer CV loop for an unbiased CV performance estimate after inner tuning. Do not select parameters and report performance from the same folds as if external.

Competing risks

from sksurv.nonparametric import cumulative_incidence_competing_risks

# status: integer array, 0=censored, 1..K=mutually exclusive causes
time, cif = cumulative_incidence_competing_risks(status, observed_time)
total_cif = cif[0]
cause_1_cif = cif[1]

cif has shape (K + 1, n_times); row 0 is total risk and rows 1..K are cause-specific cumulative incidence. Cause-specific Cox models treat other causes as censored to estimate cause-specific hazards, but one such model's 1 - survival is not the cause-specific CIF. See references/competing-risks.md.

Bundled local CLIs

All helpers use deterministic synthetic data when no input is given. They make no network calls, reject URLs and symlinks, bound files/rows/features, avoid unsafe pickle loading, and lazily import scientific packages.

python skills/scikit-survival/scripts/validate_survival_csv.py --help
python skills/scikit-survival/scripts/train_survival_model.py --help
python skills/scikit-survival/scripts/evaluate_survival_metrics.py --help
python skills/scikit-survi

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