bayeso.bo.bo_w_trees

It defines a class of Bayesian optimization with tree-based surrogate models.

class bayeso.bo.bo_w_trees.BOwTrees(range_X: numpy.ndarray, str_surrogate: str = 'rf', str_acq: str = 'ei', normalize_Y: bool = True, str_optimizer_method_bo: str = 'random_search', debug: bool = False)

Bases: bayeso.bo.base_bo.BaseBO

It is a Bayesian optimization class with tree-based surrogate models.

Parameters:
  • range_X (numpy.ndarray) – a search space. Shape: (d, 2).
  • str_surrogate (str., optional) – the name of surrogate model.
  • str_acq (str., optional) – the name of acquisition function.
  • normalize_Y (bool., optional) – flag for normalizing outputs.
  • str_optimizer_method_bo (str., optional) – the name of optimization method for Bayesian optimization.
  • debug (bool., optional) – flag for printing log messages.
_abc_impl = <_abc_data object>
compute_acquisitions(X: numpy.ndarray, X_train: numpy.ndarray, Y_train: numpy.ndarray, trees: List[T]) → numpy.ndarray

It computes acquisition function values over ‘X’, where X_train and Y_train are given.

Parameters:
  • X (numpy.ndarray) – inputs. Shape: (l, d).
  • X_train (numpy.ndarray) – inputs. Shape: (n, d).
  • Y_train (numpy.ndarray) – outputs. Shape: (n, 1).
  • trees (list) – list of trees.
Returns:

acquisition function values over X. Shape: (l, ).
Return type:

numpy.ndarray
Raises:

AssertionError
compute_posteriors(X: numpy.ndarray, trees: List[T]) → numpy.ndarray

It returns posterior mean and standard deviation functions over X.

Parameters:
  • X (numpy.ndarray) – inputs to test. Shape: (l, d).
  • trees (list) – list of trees.
Returns:

posterior mean and standard deviation functions over X. Shape: ((l, ), (l, )).
Return type:

(numpy.ndarray, numpy.ndarray)
Raises:

AssertionError
get_trees(X_train, Y_train, num_trees=100, depth_max=5, size_min_leaf=1)

It returns a list of trees.

Parameters:
  • X_train (numpy.ndarray) – inputs. Shape: (n, d).
  • Y_train (numpy.ndarray) – outputs. Shape: (n, 1).
  • num_trees (int., optional) – the number of trees.
  • depth_max (int., optional) – maximum depth.
  • size_min_leaf (int., optional) – minimum size of leaves.
Returns:

list of trees.
Return type:

list
Raises:

AssertionError
optimize(X_train: numpy.ndarray, Y_train: numpy.ndarray, str_sampling_method: str = 'uniform', num_samples: int = 5000) → Tuple[numpy.ndarray, dict]

It computes acquired example, candidates of acquired examples, acquisition function values for the candidates, covariance matrix, inverse matrix of the covariance matrix, hyperparameters optimized, and execution times.

Parameters:
  • X_train (numpy.ndarray) – inputs. Shape: (n, d).
  • Y_train (numpy.ndarray) – outputs. Shape: (n, 1).
  • str_sampling_method (str., optional) – the name of sampling method for acquisition function optimization.
  • num_samples (int., optional) – the number of samples.
Returns:

acquired example and dictionary of information. Shape: ((d, ), dict.).
Return type:

(numpy.ndarray, dict.)
Raises:

AssertionError