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.

Python snippets using study-defined variables such as X_train, y_train, frame, or times are illustrative integration templates. The native regression tests and local CLI smoke runs use small synthetic fixtures; they do not validate an unprovided study, clinical dataset, or every optional dataframe backend.

Current release and installation

Verified 2026-10-01:

• 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.

The native refresh tests used Python 3.13 and the following snapshot (NumPy 2.5.1 was cached; this is a tested snapshot, not a claim that every pin is newest). Create an isolated environment:

uv venv .venv-survival --python 3.13
source .venv-survival/bin/activate
uv pip install \
  "scikit-survival==0.28.0" \
  "scikit-learn==1.9.1" \
  "numpy==2.5.1" \
  "pandas==3.0.6" \
  "scipy==1.18.1" \
  "ecos==2.0.14" \
  "osqp==1.1.3" \
  "joblib==1.6.0" \
  "numexpr==2.14.2" \
  "narwhals==2.26.0"

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; predict() returns original-time values (the fitted objective uses log time), 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. The ipcwls implementation has log-time validation and missing gradient-weight defects; read the ensemble reference before relying on it.
• 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. Do not rely on a runtime rejection to catch survival probabilities: they are numeric arrays too, but their ranking runs in the opposite direction. Verify that higher input values mean greater event risk before interpreting AUC.
• 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.

The evaluator applies a conservative common-support rule for the combined metrics; AUC also needs observed cases and controls at every horizon. Prediction export checks this contract and fails clearly on an unsupported holdout.

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. In 0.28.0 the raw total-risk confidence interval is reversed and ignores conf_level; the bundled helper repairs it using Kaplan–Meier. Conditional time_min that removes times is guarded because of an upstream shape defect. See the competing-risk reference before requesting intervals. 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; the report-only example contains illustrative metric values, not results from a model fit. 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-survival/scripts/competing_risk_cif.py --help
python skills/scikit-survival/scripts/model_report.py --help

Typical local flow:

python skills/scikit-survival/scripts/validate_survival_csv.py \
  --input data.csv --event-column event --time-column time \
  --feature-columns age,group,measurement --structured-output outcome.npy

python skills/scikit-survival/scripts/train_survival_model.py \
  --input data.csv --event-column event --time-column time \
  --numeric-columns age,measurement --categorical-columns group \
  --model coxph --tune --prediction-output predictions.npz \
  --output training-summary.json

python skills/scikit-survival/scripts/evaluate_survival_metrics.py \
  --input predictions.npz --output metrics-summary.json

python skills/scikit-survival/scripts/model_report.py \
  --training-summary training-summary.json \
  --metrics-summary metrics-summary.json --output model-report.md

The report helper summarizes the two supplied JSON files; it cannot establish that they describe the same fitted model and held-out cohort. Verify that provenance before combining summaries. It emits Markdown text, not plots or CIF results.

Use only de-identified, authorized local data. The bundled tests contain synthetic records only and no patient data or PHI.

Security triage

SECURITY.md previously claimed this skill bundled package-shadowing files named sklearn.py and sksurv.py. The earlier 2026-07-23 inventory confirmed those files did not exist; the claim was a phantom analyzer finding. This refresh adds only descriptively named helpers and no shadow modules, environment reads, or network calls.

Never name a project script after an imported package (including sklearn.py, sksurv.py, numpy.py, or pandas.py), because Python may import the local file instead of the installed library. Inspect the working directory before executing examples copied from untrusted sources.

Reference files

• references/data-handling.md — structured arrays, datasets, schema validation, pandas/Polars preprocessing, and leakage-safe splitting.
• references/cox-models.md — Cox PH, Coxnet, IPCRidge, assumptions, and tuning.
• references/ensemble-models.md — forests, trees, boosting, predictions, and permutation importance.
• references/svm-models.md — SVM objectives, prediction direction, scaling, kernels, and limitations.
• references/evaluation-metrics.md — metric inputs, censoring assumptions, time grids, calibration, nested CV, and primary literature.
• references/competing-risks.md — integer event coding, CIF API, built-in datasets, cause-specific hazards, and unsupported Fine-Gray regression.

Dated sources

Official API and compatibility sources, checked 2026-10-01. Released v0.28.0 source and native execution take precedence over stale cached API pages:

• PyPI 0.28.0 — released 2026-07-05.
• GitHub v0.28.0 release — published 2026-07-05.
• 0.28 release notes.
• Installation guide.
• Stable user guide.
• Released implementation.
• Stable API reference.