sksurv.nonparametric.CensoringDistributionEstimator#

class sksurv.nonparametric.CensoringDistributionEstimator(conf_level=0.95, conf_type=None)[source]#

Kaplan–Meier estimator for the censoring distribution.

__init__(conf_level=0.95, conf_type=None)[source]#

Methods

__init__([conf_level, conf_type])

fit(y)

Estimate censoring distribution from training data.

get_metadata_routing()

Get metadata routing of this object.

get_params([deep])

Get parameters for this estimator.

predict_ipcw(y)

Return inverse probability of censoring weights at given time points.

predict_proba(time[, return_conf_int])

Return probability of an event after given time point.

set_params(**params)

Set the parameters of this estimator.

set_predict_proba_request(*[, ...])

Request metadata passed to the predict_proba method.

fit(y)[source]#

Estimate censoring distribution from training data.

Parameters:

y (structured array, shape = (n_samples,)) – A structured array containing the binary event indicator as first field, and time of event or time of censoring as second field.

Return type:

self

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_ipcw(y)[source]#

Return inverse probability of censoring weights at given time points.

\(\omega_i = \delta_i / \hat{G}(y_i)\)

Parameters:

y (structured array, shape = (n_samples,)) – A structured array containing the binary event indicator as first field, and time of event or time of censoring as second field.

Returns:

ipcw – Inverse probability of censoring weights.

Return type:

array, shape = (n_samples,)

predict_proba(time, return_conf_int=False)[source]#

Return probability of an event after given time point.

\(\hat{S}(t) = P(T > t)\)

Parameters:
  • time (array, shape = (n_samples,)) – Time to estimate probability at.

  • return_conf_int (bool, optional, default: False) – Whether to return the pointwise confidence interval of the survival function. Only available if fit() has been called with the conf_type parameter set.

Returns:

  • prob (array, shape = (n_samples,)) – Probability of an event at the passed time points.

  • conf_int (array, shape = (2, n_samples)) – Pointwise confidence interval at the passed time points. Only provided if return_conf_int is True.

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

set_predict_proba_request(*, return_conf_int: bool | None | str = '$UNCHANGED$', time: bool | None | str = '$UNCHANGED$') CensoringDistributionEstimator#

Request metadata passed to the predict_proba method.

Note that this method is only relevant if enable_metadata_routing=True (see sklearn.set_config()). Please see User Guide on how the routing mechanism works.

The options for each parameter are:

  • True: metadata is requested, and passed to predict_proba if provided. The request is ignored if metadata is not provided.

  • False: metadata is not requested and the meta-estimator will not pass it to predict_proba.

  • None: metadata is not requested, and the meta-estimator will raise an error if the user provides it.

  • str: metadata should be passed to the meta-estimator with this given alias instead of the original name.

The default (sklearn.utils.metadata_routing.UNCHANGED) retains the existing request. This allows you to change the request for some parameters and not others.

New in version 1.3.

Note

This method is only relevant if this estimator is used as a sub-estimator of a meta-estimator, e.g. used inside a Pipeline. Otherwise it has no effect.

Parameters:
  • return_conf_int (str, True, False, or None, default=sklearn.utils.metadata_routing.UNCHANGED) – Metadata routing for return_conf_int parameter in predict_proba.

  • time (str, True, False, or None, default=sklearn.utils.metadata_routing.UNCHANGED) – Metadata routing for time parameter in predict_proba.

Returns:

self – The updated object.

Return type:

object