sksurv.meta.Stacking#

class sksurv.meta.Stacking(meta_estimator, base_estimators, *, probabilities=True)[source]#

Meta estimator that combines multiple base learners.

By default, base estimators’ output corresponds to the array returned by predict_proba. If predict_proba is not available or probabilities = False, the output of predict is used.

Parameters:

meta_estimator (instance of estimator) – The estimator that is used to combine the output of different base estimators.
base_estimators (list) – List of (name, estimator) tuples (implementing fit/predict) that are part of the ensemble.
probabilities (bool, optional, default: True) – Whether to allow using predict_proba method of base learners, if available.

estimators_#

The elements of the estimators parameter, having been fitted on the training data.

Type:: list of estimators

named_estimators_#

Attribute to access any fitted sub-estimators by name.

Type:: dict

final_estimator_#

The estimator which combines the output of estimators_.

Type:: estimator

n_features_in_#

Number of features seen during fit.

Type:: int

feature_names_in_#

Names of features seen during fit. Defined only when X has feature names that are all strings.

Type:: ndarray of shape (n_features_in_,)

unique_times_[source]#

Unique time points.

Type:: array of shape = (n_unique_times,)

__init__(meta_estimator, base_estimators, *, probabilities=True)[source]#

Methods

`__init__`(meta_estimator, base_estimators, *)
`fit`(X[, y])	Fit base estimators.
`get_metadata_routing`()	Get metadata routing of this object.
`get_params`([deep])	Get the parameters of an estimator from the ensemble.
`predict`(X)	Perform prediction.
`predict_cumulative_hazard_function`(X[, ...])	Perform prediction.
`predict_log_proba`(X)	Perform prediction.
`predict_proba`(X)	Perform prediction.
`predict_survival_function`(X[, return_array])	Perform prediction.
`score`(X, y)	Returns the concordance index of the prediction.
`set_params`(**params)	Set the parameters of an estimator from the ensemble.

Attributes

`steps`
`unique_times_`

fit(X, y=None, **fit_params)[source]#

Fit base estimators.

Parameters:

X (array-like, shape = (n_samples, n_features)) – Training data.
y (array-like, optional) – Target data if base estimators are supervised.

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)[source]#

Get the parameters of an estimator from the ensemble.

Returns the parameters given in the constructor as well as the estimators contained within the estimators parameter.

Parameters:: deep (bool, default=True) – Setting it to True gets the various estimators and the parameters of the estimators as well.
Returns:: params – Parameter and estimator names mapped to their values or parameter names mapped to their values.
Return type:: dict

predict(X)#

Perform prediction.

Only available of the meta estimator has a predict method.

Parameters:: X (array-like, shape = (n_samples, n_features)) – Data with samples to predict.
Returns:: prediction – Prediction of meta estimator that combines predictions of base estimators. n_dim depends on the return value of meta estimator’s predict method.
Return type:: array, shape = (n_samples, n_dim)

predict_cumulative_hazard_function(X, return_array=False)#

Perform prediction.

Only available if the meta estimator has a predict_cumulative_hazard_function method.

Parameters:

X (array-like, shape = (n_samples, n_features)) – Data with samples to predict.
return_array (boolean, default: False) – If set, return an array with the cumulative hazard rate for each self.unique_times_, otherwise an array of sksurv.functions.StepFunction.

Returns:

cum_hazard – If return_array is set, an array with the cumulative hazard rate for each self.unique_times_, otherwise an array of length n_samples of sksurv.functions.StepFunction instances will be returned.

Return type:

ndarray

predict_log_proba(X)#

Perform prediction.

Only available if the meta estimator has a predict_log_proba method.

Parameters:: X (array-like, shape = (n_samples, n_features)) – Data with samples to predict.
Returns:: prediction – Prediction of meta estimator that combines predictions of base estimators. n_dim depends on the return value of meta estimator’s predict method.
Return type:: ndarray, shape = (n_samples, n_dim)

predict_proba(X)#

Perform prediction.

Only available if the meta estimator has a predict_proba method.

Parameters:: X (array-like, shape = (n_samples, n_features)) – Data with samples to predict.
Returns:: prediction – Prediction of meta estimator that combines predictions of base estimators. n_dim depends on the return value of meta estimator’s predict method.
Return type:: ndarray, shape = (n_samples, n_dim)

predict_survival_function(X, return_array=False)#

Perform prediction.

Only available if the meta estimator has a predict_survival_function method.

Parameters:

X (array-like, shape = (n_samples, n_features)) – Data with samples to predict.

Returns:

survival (ndarray) – If return_array is set, an array with the probability of survival for each self.unique_times_, otherwise an array of length n_samples of sksurv.functions.StepFunction instances will be returned.
return_array (boolean, default: False) – If set, return an array with the probability of survival for each self.unique_times_, otherwise an array of sksurv.functions.StepFunction.

score(X, y)[source]#

Returns the concordance index of the prediction.

Parameters:

X (array-like, shape = (n_samples, n_features)) – Test samples.
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:

cindex – Estimated concordance index.

Return type:

float

set_params(**params)[source]#

Set the parameters of an estimator from the ensemble.

Valid parameter keys can be listed with get_params(). Note that you can directly set the parameters of the estimators contained in estimators.

Parameters:: **params (keyword arguments) – Specific parameters using e.g. set_params(parameter_name=new_value). In addition, to setting the parameters of the estimator, the individual estimator of the estimators can also be set, or can be removed by setting them to ‘drop’.
Returns:: self – Estimator instance.
Return type:: object