smac.model.abstract_model¶
Classes¶
|
Abstract implementation of the surrogate model. |
Interfaces¶
- class smac.model.abstract_model.AbstractModel(configspace, instance_features=None, pca_components=7, seed=0)[source]¶
Bases:
object
Abstract implementation of the surrogate model.
Note
The input dimensionality of Y for training and the output dimensions of all predictions depend on the concrete implementation of this abstract class.
- Parameters:
configspace (ConfigurationSpace)
instance_features (dict[str, list[int | float]] | None, defaults to None) – Features (list of int or floats) of the instances (str). The features are incorporated into the X data, on which the model is trained on.
pca_components (float, defaults to 7) – Number of components to keep when using PCA to reduce dimensionality of instance features.
seed (int)
- property meta: dict[str, Any]¶
Returns the meta data of the created object.
- predict(X, covariance_type='diagonal')[source]¶
Predicts mean and variance for a given X. Internally, calls the method _predict.
- Parameters:
X (np.ndarray [#samples, #hyperparameters + #features]) – Input data points.
covariance_type (str | None, defaults to "diagonal") – Specifies what to return along with the mean. Applied only to Gaussian Processes. Takes four valid inputs: * None: Only the mean is returned. * “std”: Standard deviation at test points is returned. * “diagonal”: Diagonal of the covariance matrix is returned. * “full”: Whole covariance matrix between the test points is returned.
- Return type:
tuple
[ndarray
,ndarray
|None
]- Returns:
means (np.ndarray [#samples, #objectives]) – The predictive mean.
vars (np.ndarray [#samples, #objectives] or [#samples, #samples] | None) – Predictive variance or standard deviation.
- predict_marginalized(X)[source]¶
Predicts mean and variance marginalized over all instances.
Warning
The input data must not include any features.
- Parameters:
X (np.ndarray [#samples, #hyperparameters]) – Input data points.
- Return type:
tuple
[ndarray
,ndarray
]- Returns:
means (np.ndarray [#samples, 1]) – The predictive mean.
vars (np.ndarray [#samples, 1]) – The predictive variance.