cv_coxnet()

Select the CoxNet penalizer by k-fold cross-validation.

Usage

cv_coxnet(
    surv,
    covariates,
    *,
    data=None,
    l1_ratio=1.0,
    penalizers=None,
    n_penalizers=50,
    eps=0.001,
    k=5,
    metric="concordance",
    times=None,
    standardize=True,
    max_iter=1000,
    tol=1e-07,
    stratified=True,
    seed=None
)

Evaluates a log-spaced grid of penalizer values (or a user-supplied sequence) with stratified k-fold cross-validation and returns the value that maximises concordance (or minimises the integrated Brier score).

Parameters

surv: Surv

A right-censored or counting-process Surv response.

covariates: Any

Covariate design (dataframe, 2-D array, or formula string with data).

data: Any = None

DataFrame to evaluate a formula covariates string against.

l1_ratio: float = 1.0

Elastic-net mixing parameter (default 1.0 = lasso). Fixed across the entire path (only the penalizer is varied).

penalizers: Any = None

An explicit sequence of penalizer values to evaluate. When None (the default) a log-spaced path of n_penalizers= values is generated automatically from lambda_max down to eps * lambda_max.

n_penalizers: int = 50

Number of log-spaced values in the auto-generated path (the default is 50). Ignored when penalizers= is supplied.

eps: float = 0.001

Ratio of smallest to largest auto-generated penalizer (default of 1e-3). Smaller values extend the path toward weaker regularisation. Ignored when penalizers= is supplied.

k: int = 5

Number of cross-validation folds (the default is 5).

metric: str = "concordance"

Scoring metric:

"concordance" (the default): Harrell’s C-statistic, where higher is better.
"brier": integrated IPCW Brier score, which requires times= (here lower is better).

times: Any = None

Evaluation times for metric="brier" (1-D array-like, \ge 2 values).

standardize: bool = True

Standardize covariates before penalising (default is True). Must match the standardize= argument used for the final CoxNet fit.

max_iter: int = 1000

FISTA iteration limit and convergence tolerance for each inner fit.

tol: int = 1000

FISTA iteration limit and convergence tolerance for each inner fit.

stratified: bool = True

Use stratified k-fold to preserve the event rate in every fold (the default is True).

seed: int | None = None

Random seed for reproducible fold assignments.

Returns

CoxNetCVResult: Object exposing best_penalizer_, penalizer_1se_, mean_scores_, std_scores_, n_nonzero_, and a to_frame() method for the path.

Details

Lambda path. When penalizers= is not supplied the path is

\lambda_{\max} = \frac{\max_j \bigl|\nabla_j\,\ell(\beta=0)\bigr|}{n \cdot \max(\alpha,\, 0.01)}, \quad \lambda_i = \lambda_{\max} \cdot \varepsilon^{i/(m-1)}, \quad i = 0,\ldots,m-1

where \nabla\ell(\beta=0) is the score of the partial log-likelihood at the null model, \alpha = l1_ratio, \varepsilon = eps, and m = n_penalizers.

1-SE rule. penalizer_1se_ is the largest penalizer (most regularized) whose mean score lies within one standard error of best_score_. Prefer it when parsimony matters as it yields a sparser model at no significant loss of predictive accuracy.

Fitting the final model. After selecting a penalizer, refit on all data:

result = gw.cv_coxnet(y, x, seed=23)
final = gw.CoxNet(penalizer=result.best_penalizer_).fit(y, x)

Examples

Run cross-validated lasso selection on the lung dataset:

import greenwood as gw

# Load data and build a right-censored response
lung = gw.load_dataset("lung", backend="polars")
y = gw.Surv.right(lung["time"], event=(lung["status"] == 2))
cols = ["age", "sex", "ph.ecog", "ph.karno", "wt.loss"]

# Run cross-validated lasso selection with 3 folds
result = gw.cv_coxnet(y, lung[cols], l1_ratio=1.0, n_penalizers=10, k=3, seed=23)
result

CoxNetCV (concordance, ↑ higher is better, l1_ratio=1.0, 3-fold)

  best penalizer : 0.046941  (mean concordance: 0.6377)
  1-SE  penalizer : 0.10113  (mean concordance: 0.6358)

  10 penalizers tested, range [0.000218, 0.218]

Inspect the full penalizer path:

# Inspect the full penalizer path
result.to_frame(format="polars")

shape: (10, 4)

penalizer	mean_score	std_score	n_nonzero
f64	f64	f64	f64
0.217882	0.51631	0.02825	0.333333
0.101132	0.635798	0.046519	2.333333
0.046941	0.637712	0.032918	4.0
0.021788	0.636273	0.032888	4.333333
0.010113	0.632944	0.032819	5.0
0.004694	0.634751	0.032518	5.0
0.002179	0.6333	0.033855	5.0
0.001011	0.632527	0.034677	5.0
0.000469	0.632501	0.034725	5.0
0.000218	0.633018	0.035003	5.0

Fit the final model at the best penalizer:

# Refit at the best penalizer on all data
final = gw.CoxNet(penalizer=result.best_penalizer_, l1_ratio=1.0).fit(y, lung[cols])
final

CoxNet (elastic-net Cox, penalizer=0.046941245091417096, l1_ratio=1.0)

               coef
age        0.006585
sex         -0.4198
ph.ecog      0.3761
ph.karno          0
wt.loss   -0.001172

n = 213, events = 151, nonzero coefficients = 4

Use the 1-SE rule for a sparser model:

# Refit at the 1-SE penalizer for a sparser model
sparse = gw.CoxNet(penalizer=result.penalizer_1se_, l1_ratio=1.0).fit(y, lung[cols])
sparse

CoxNet (elastic-net Cox, penalizer=0.10113184681823788, l1_ratio=1.0)

             coef
age             0
sex       -0.2499
ph.ecog    0.2646
ph.karno       -0
wt.loss        -0

n = 213, events = 151, nonzero coefficients = 2