sksurv.meta.EnsembleSelection#
- class sksurv.meta.EnsembleSelection(base_estimators, *, scorer=None, n_estimators=0.2, min_score=0.2, correlation='pearson', min_correlation=0.6, cv=None, n_jobs=1, verbose=0)[source]#
Ensemble selection for survival analysis that accounts for a score and correlations between predictions.
The ensemble is pruned during training only according to the specified score (accuracy) and additionally for prediction according to the correlation between predictions (diversity).
The hillclimbing is based on cross-validation to avoid having to create a separate validation set.
See [1], [2], [3] for further description.
- Parameters:
base_estimators (list) – List of (name, estimator) tuples (implementing fit/predict) that are part of the ensemble.
scorer (callable) – Function with signature
func(estimator, X_test, y_test, **test_predict_params)
that evaluates the error of the prediction on the test data. The function should return a scalar value. Larger values of the score are assumed to be better.n_estimators (float or int, optional, default: 0.2) – If a float, the percentage of estimators in the ensemble to retain, if an int the absolute number of estimators to retain.
min_score (float, optional, default: 0.66) – Threshold for pruning estimators based on scoring metric. After fit, only estimators with a score above min_score are retained.
min_correlation (float, optional, default: 0.6) – Threshold for Pearson’s correlation coefficient that determines when predictions of two estimators are significantly correlated.
cv (int, a cv generator instance, or None, optional) – The input specifying which cv generator to use. It can be an integer, in which case it is the number of folds in a KFold, None, in which case 3 fold is used, or another object, that will then be used as a cv generator. The generator has to ensure that each sample is only used once for testing.
n_jobs (int, optional, default: 1) – Number of jobs to run in parallel.
verbose (integer) – Controls the verbosity: the higher, the more messages.
- scores_#
Array of scores (relative to best performing estimator)
- Type:
ndarray, shape = (n_base_estimators,)
- fitted_models_#
Selected models during training based on scorer.
- Type:
ndarray
- 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_,)
References
- __init__(base_estimators, *, scorer=None, n_estimators=0.2, min_score=0.2, correlation='pearson', min_correlation=0.6, cv=None, n_jobs=1, verbose=0)[source]#
Methods
__init__
(base_estimators, *[, scorer, ...])fit
(X[, y])Fit ensemble of models
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.
Perform prediction.
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 ensemble of models
- 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