EDAspy.optimization package
Subpackages
- EDAspy.optimization.custom package
- Subpackages
- EDAspy.optimization.custom.initialization_models package
- Submodules
- EDAspy.optimization.custom.initialization_models.multi_gauss_geninit module
- EDAspy.optimization.custom.initialization_models.uni_bin_geninit module
- EDAspy.optimization.custom.initialization_models.uni_gauss_geninit module
- EDAspy.optimization.custom.initialization_models.uniform_geninit module
- Module contents
- EDAspy.optimization.custom.probabilistic_models package
- Submodules
- EDAspy.optimization.custom.probabilistic_models.semiparametric_bayesian_network module
- EDAspy.optimization.custom.probabilistic_models.gaussian_bayesian_network module
- EDAspy.optimization.custom.probabilistic_models.multivariate_gaussian module
- EDAspy.optimization.custom.probabilistic_models.univariate_binary module
- EDAspy.optimization.custom.probabilistic_models.univariate_gaussian module
- Module contents
- EDAspy.optimization.custom.initialization_models package
- Submodules
- EDAspy.optimization.custom.eda_custom module
- Module contents
- Subpackages
- EDAspy.optimization.multivariate package
- EDAspy.optimization.univariate package
Submodules
EDAspy.optimization.eda module
- class EDAspy.optimization.eda.EDA(size_gen: int, max_iter: int, dead_iter: int, n_variables: int, alpha: float = 0.5, elite_factor: float = 0.4, disp: bool = True, parallelize: bool = False, init_data: Optional[array] = None, *args, **kwargs)[source]
Bases:
ABC
Abstract class which defines the general performance of the algorithms. The baseline of the EDA approach is defined in this object. The specific configurations is defined in the class of each specific algorithm.
- export_settings() dict [source]
Export the configuration of the algorithm to an object to be loaded in other execution.
- Returns:
configuration dictionary.
- Return type:
dict
- minimize(cost_function: callable, output_runtime: bool = True, *args, **kwargs) EdaResult [source]
Minimize function to execute the EDA optimization. By default, the optimizer is designed to minimize a cost function; if maximization is desired, just add a minus sign to your cost function.
- Parameters:
cost_function – cost function to be optimized and accepts an array as argument.
output_runtime – true if information during runtime is desired.
- Returns:
EdaResult object with results and information.
- Return type:
- property pm: ProbabilisticModel
Returns the probabilistic model used in the EDA implementation.
- Returns:
probabilistic model.
- Return type:
ProbabilisticModel
- property init: GenInit
Returns the initializer used in the EDA implementation.
- Returns:
initializer.
- Return type:
GenInit
EDAspy.optimization.eda_result module
EDAspy.optimization.tools module
- EDAspy.optimization.tools.arcs2adj_mat(arcs: list, n_variables: int) array [source]
This function transforms the list of arcs in the BN structure to an adjacency matrix.
- Parameters:
arcs (list) – list of arcs in the BN structure.
n_variables (int) – number of variables.
- Returns:
adjacency matrix
- Return type:
np.array
- EDAspy.optimization.tools.plot_bn(arcs: list, var_names: list, pos: Optional[dict] = None, curved_arcs: bool = True, curvature: float = -0.3, node_size: int = 500, node_color: str = 'red', edge_color: str = 'black', arrow_size: int = 15, node_transparency: float = 0.9, edge_transparency: float = 0.9, node_line_widths: float = 2, title: Optional[str] = None, output_file: Optional[str] = None)[source]
This function Plots a BN structure as a directed acyclic graph.
- Parameters:
arcs (list(tuple)) – Arcs in the BN structure.
var_names (list) – List of variables.
pos (dict {name of variables: tuples with coordinates}) – Positions in the plot for each node.
curved_arcs (bool) – True if curved arcs are desired.
curvature (float) – Radians of curvature for edges. By default, -0.3.
node_size (int) – Size of the nodes in the graph. By default, 500.
node_color (str) – Color set to nodes. By default, ‘red’.
edge_color (str) – Color set to edges. By default, ‘black’.
arrow_size (int) – Size of arrows in edges. By default, 15.
node_transparency (float) – Alpha value [0, 1] that defines the transparency of the node. By default, 0.9.
edge_transparency (float) – Alpha value [0, 1] that defines the transparency of the edge. By default, 0.9.
node_line_widths (float) – Width of the nodes contour lines. By default, 2.0.
title (str) – Title for Figure. By default, None.
output_file (str) – Path to save the figure locally.
- Returns:
Figure.