deephyper.ensemble.selector.GreedySelector#
- class deephyper.ensemble.selector.GreedySelector(loss_func: Callable, aggregator: Aggregator, k: int = 5, k_init: int = 5, max_it: int = -1, eps_tol: float = 0.001, with_replacement: bool = True, early_stopping: bool = True, bagging: bool = False, random_state=None, verbose: bool = False)[source]#
Bases:
Selector
Selection method implementing Greedy (a.k.a., Caruana) selection. This method iteratively and greedily selects the predictors that minimize the loss when aggregated together.
- Parameters:
loss_func (Callable or Loss) – a loss function that takes two arguments: the true target values and the predicted target values.
aggregator (Aggregator) – The aggregator to use to combine the predictions of the selected predictors.
k (int, optional) – The number of unique predictors to select for the ensemble. Defaults to
5
.k_init (int, optional) – Regularization parameter for greedy selection. It is the number of predictors to select in the initialization step. Defaults to
1
.max_it (int, optional) – Maximum number of iterations which also corresponds to the number of non-unique predictors added to the ensemble. Defaults to
-1
.eps_tol (float, optional) – Tolerance for the stopping criterion. Defaults to
1e-3
.with_replacement (bool, optional) – Performs greedy selection with replacement of models already selected. Defaults to
True
.early_stopping (bool, optional) – Stops the ensemble selection as soon as the loss stops improving. Defaults to
True
.bagging (bool, optional) – Performanced boostrap resampling of available predictors at each iteration. This can be particularly useful when the dataset used for selection is small. Defaults to
False
.verbose (bool, optional) – Turns on the verbose mode. Defaults to
False
.
Methods
The selection algorithms.
- select(y, y_predictors) Sequence[int] [source]#
The selection algorithms.
- Parameters:
y (np.ndarray) – the true target values.
y_predictors (_type_) – a sequence of predictions from available predictors. It should be a list of length
n_predictors
with each element being the prediction of a predictor.
- Returns:
the sequence of selected predictors. Sequence[float]: the sequence of weights associated to the selected predictors.
- Return type:
Sequence[int]