autogl.module.ensemble
-
class
autogl.module.ensemble.
Voting
(ensemble_size=10, *args, **kwargs)[source] An ensembler using the voting method.
Parameters: ensemble_size (int) – The number of base models selected by the voter. These selected models can be redundant. Default as 10. -
ensemble
(predictions, identifiers, *args, **kwargs)[source] Ensemble the predictions of base models.
Parameters: - predictions (a list of np.ndarray) – Predictions of base learners (corresponding to the elements in identifiers).
- identifiers (a list of str) – The names of base models.
Returns: The ensembled predictions.
Return type: np.ndarray
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fit
(predictions, label, identifiers, feval, *args, **kwargs)[source] Fit the ensembler to the given data using Rich Caruana’s ensemble selection method.
Parameters: - predictions (a list of np.ndarray) – Predictions of base learners (corresponding to the elements in identifiers).
- labels (a list of int) – Class labels of instances.
- identifiers (a list of str) – The names of base models.
- feval ((a list of) instances in autogl.module.train.evaluate) – Performance evaluation metrices.
Returns: The validation performance of the final voter.
Return type: (a list of)
float
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-
class
autogl.module.ensemble.
Stacking
(meta_model='gbm', meta_params={}, *args, **kwargs)[source] A stacking ensembler. Currently we support gradient boosting as the meta-algorithm.
Parameters: - meta_model ('gbm' or 'glm' (Optional)) –
- Type of the stacker:
- ’gbm’ : Gradient boosting model. This is the default. ‘glm’ : Generalized linear model.
- meta_params (a
dict
(Optional)) – Whenmeta_model
is specified, you can customize the parameters of the stacker. If this argument is not provided, the stacker will be configurated with default parameters. Default{}
.
-
ensemble
(predictions, identifiers, *args, **kwargs)[source] Ensemble the predictions of base models.
Parameters: - predictions (a list of
np.ndarray
) – Predictions of base learners (corresponding to the elements in identifiers). - identifiers (a list of
str
) – The names of base models.
Returns: The ensembled predictions.
Return type: np.ndarray
- predictions (a list of
-
fit
(predictions, label, identifiers, feval, n_classes='auto', *args, **kwargs)[source] Fit the ensembler to the given data using Stacking method.
Parameters: - predictions (a list of np.ndarray) – Predictions of base learners (corresponding to the elements in identifiers).
- label (a list of int) – Class labels of instances.
- identifiers (a list of str) – The names of base models.
- feval ((a list of) autogl.module.train.evaluate) – Performance evaluation metrices.
- n_classes (int or str (Optional)) – The number of classes. Default as
'auto'
, which will use maximum label.
Returns: The validation performance of the final stacker.
Return type: (a list of) float
- meta_model ('gbm' or 'glm' (Optional)) –
-
autogl.module.ensemble.
build_ensembler_from_name
(name: str) → autogl.module.ensemble.base.BaseEnsembler[source] Parameters: name ( str
) – the name of ensemble module.Returns: the ensembler built using default parameters Return type: BaseEnsembler Raises: AssertionError
– If an invalid name is passed in