smac.epm.random_epm module

class smac.epm.random_epm.RandomEPM(configspace: ConfigSpace.configuration_space.ConfigurationSpace, types: List[int], bounds: List[Tuple[float, float]], seed: int, instance_features: Optional[numpy.ndarray] = None, pca_components: Optional[int] = None)

Bases: smac.epm.base_epm.AbstractEPM

EPM which returns random values on a call to fit.

_predict(X: numpy.ndarray, cov_return_type: Optional[str] = 'diagonal_cov') Tuple[numpy.ndarray, numpy.ndarray]

Predict means and variances for given X.

  • X (np.ndarray of shape = [n_samples, n_features (config + instance features)]) –

  • cov_return_type (typing.Optional[str]) – Specifies what to return along with the mean. Refer predict() for more information.


  • means (np.ndarray of shape = [n_samples, n_objectives]) – Predictive mean

  • vars (np.ndarray of shape = [n_samples, n_objectives]) – Predictive variance

_train(X: numpy.ndarray, Y: numpy.ndarray) smac.epm.random_epm.RandomEPM

Pseudo training on X and Y.

  • X (np.ndarray (N, D)) – Input data points. The dimensionality of X is (N, D), with N as the number of points and D is the number of features.

  • Y (np.ndarray (N, 1)) – The corresponding target values.