autoprognosis.explorers.risk_estimation_combos module
- class RiskEnsembleSeeker
Bases:
objectAutoML core logic for risk estimation ensemble search.
- Parameters:
study_name – str. Study ID, used for caching.
time_horizons – list. list of time horizons.
num_iter – int. Maximum Number of optimization trials. This is the limit of trials for each base estimator in the “risk_estimators” list, used in combination with the “timeout” parameter. For each estimator, the search will end after “num_iter” trials or “timeout” seconds.
num_ensemble_iter – int. Number of optimization trials for the ensemble weights.
timeout – int. Maximum wait time(seconds) for each estimator hyperparameter search. This timeout will apply to each estimator in the “risk_estimators” list.
n_folds_cv – int. Number of folds to use for evaluation
feature_scaling –
list. Plugin search pool to use in the pipeline for scaling. Defaults to : [‘maxabs_scaler’, ‘scaler’, ‘feature_normalizer’, ‘normal_transform’, ‘uniform_transform’, ‘nop’, ‘minmax_scaler’] Available plugins, retrieved using Preprocessors(category=”feature_scaling”).list_available():
’maxabs_scaler’
’scaler’
’feature_normalizer’
’normal_transform’
’uniform_transform’
’nop’ # empty operation
’minmax_scaler’
feature_selection –
list. Plugin search pool to use in the pipeline for feature selection. Defaults [“nop”, “variance_threshold”, “pca”, “fast_ica”] Available plugins, retrieved using Preprocessors(category=”dimensionality_reduction”).list_available():
’feature_agglomeration’
’fast_ica’
’variance_threshold’
’gauss_projection’
’pca’
’nop’ # no operation
imputers –
list. Plugin search pool to use in the pipeline for imputation. Defaults to [“mean”, “ice”, “missforest”, “hyperimpute”]. Available plugins, retrieved using Imputers().list_available():
’sinkhorn’
’EM’
’mice’
’ice’
’hyperimpute’
’most_frequent’
’median’
’missforest’
’softimpute’
’nop’
’mean’
’gain’
estimators –
list. Plugin search pool to use in the pipeline for risk estimation. Defaults to [“survival_xgboost”, “loglogistic_aft”, “deephit”, “cox_ph”, “weibull_aft”, “lognormal_aft”, “coxnet”] Available plugins:
’survival_xgboost’
’loglogistic_aft’
’deephit’
’cox_ph’
’weibull_aft’
’lognormal_aft’
’coxnet’
hooks – Hooks. Custom callbacks to be notified about the search progress.
random_state – int: Random seed
- pretrain_for_cv(ensemble: List, X: DataFrame, T: DataFrame, Y: DataFrame, time_horizon: int, seed: int = 0, group_ids: str | None = None) List
- search(X: DataFrame, T: Series, Y: Series, skip_recap: bool = False, group_ids: Series | None = None) RiskEnsemble
- search_weights(ensemble: List, X: DataFrame, T: DataFrame, Y: DataFrame, time_horizon: int, skip_recap: bool = False, group_ids: Series | None = None) List[float]