sksurv.metrics.as_cumulative_dynamic_auc_scorer#
- class sksurv.metrics.as_cumulative_dynamic_auc_scorer(estimator, times, tied_tol=1e-08)[source]#
Wraps an estimator to use
cumulative_dynamic_auc()
asscore
function.See the User Guide for using it for hyper-parameter optimization.
- Parameters:
estimator (object) – Instance of an estimator.
times (array-like, shape = (n_times,)) – The time points for which the area under the time-dependent ROC curve is computed. Values must be within the range of follow-up times of the test data survival_test.
tied_tol (float, optional, default: 1e-8) – The tolerance value for considering ties. If the absolute difference between risk scores is smaller or equal than tied_tol, risk scores are considered tied.
- estimator_#
Estimator that was fit.
- Type:
estimator
See also
Methods
__init__
(estimator, times[, tied_tol])fit
(X, y, **fit_params)Get metadata routing of this object.
get_params
([deep])Get parameters for this estimator.
predict
(X)Call predict on the estimator.
Call predict_cumulative_hazard_function on the estimator.
Call predict_survival_function on the estimator.
score
(X, y)Returns the score on the given data.
set_params
(**params)Set the parameters of this estimator.
- get_metadata_routing()#
Get metadata routing of this object.
Please check User Guide on how the routing mechanism works.
- Returns:
routing – A
MetadataRequest
encapsulating routing information.- Return type:
MetadataRequest
- get_params(deep=True)#
Get parameters for this estimator.
- Parameters:
deep (bool, default=True) – If True, will return the parameters for this estimator and contained subobjects that are estimators.
- Returns:
params – Parameter names mapped to their values.
- Return type:
dict
- predict(X)#
Call predict on the estimator.
Only available if estimator supports
predict
.- Parameters:
X (indexable, length n_samples) – Must fulfill the input assumptions of the underlying estimator.
- predict_cumulative_hazard_function(X)#
Call predict_cumulative_hazard_function on the estimator.
Only available if estimator supports
predict_cumulative_hazard_function
.- Parameters:
X (indexable, length n_samples) – Must fulfill the input assumptions of the underlying estimator.
- predict_survival_function(X)#
Call predict_survival_function on the estimator.
Only available if estimator supports
predict_survival_function
.- Parameters:
X (indexable, length n_samples) – Must fulfill the input assumptions of the underlying estimator.
- score(X, y)[source]#
Returns the score on the given data.
- Parameters:
X (array-like of shape (n_samples, n_features)) – Input data, where n_samples is the number of samples and n_features is the number of features.
y (array-like of shape (n_samples,)) – Target relative to X for classification or regression; None for unsupervised learning.
- Returns:
score
- Return type:
float
- set_params(**params)#
Set the parameters of this estimator.
The method works on simple estimators as well as on nested objects (such as
Pipeline
). The latter have parameters of the form<component>__<parameter>
so that it’s possible to update each component of a nested object.- Parameters:
**params (dict) – Estimator parameters.
- Returns:
self – Estimator instance.
- Return type:
estimator instance