module¶
The four main modules for auto graph learning are listed here.
feature¶
-
class
autogl.module.feature.selectors.
BaseSelector
(data_t='np', multigraph=False, **kwargs)[source]¶
model¶
train¶
hyper parameter optimization¶
ensemble¶
-
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
-
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
-
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
-
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
-
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