autoprognosis.explorers.risk_estimation module

class RiskEstimatorSeeker

Bases: object

AutoML core logic for risk estimation tasks.

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.

• 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

• top_k – int Number of candidates to return.

• 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’

• estimators – list. Plugins to use in the pipeline for risk prediction.

• hooks – Hooks. Custom callbacks to be notified about the search progress.

• random_state – int: Random seed

search(X: DataFrame, T: Series, Y: Series, group_ids: Series | None = None) → List

search_best_args_for_estimator(estimator: Any, X: DataFrame, T: DataFrame, Y: DataFrame, time_horizon: int, group_ids: Series | None = None) → Tuple[List[float], List[float]]

search_estimator(X: DataFrame, T: Series, Y: Series, time_horizon: int, group_ids: Series | None = None) → List