opensbt.algorithm.classification.decision_tree package

Submodules

opensbt.algorithm.classification.decision_tree.decision_tree module

class opensbt.algorithm.classification.decision_tree.decision_tree.Region(xl, xu, population)[source]

Bases: object

Represent a subspace of the design space and its criticality based on the tests in the region.

__init__(xl, xu, population)[source]
define_critical(threshold_min, threshold_max)[source]

Evaluate the criticality of the region based on the given population and the given thresholds.

Parameters:

  • threshold_min (float)

  • threshold_max (float)

opensbt.algorithm.classification.decision_tree.decision_tree.generate_critical_regions(population, problem, min_samples_split=0.07, min_samples_leaf=5, max_depth=100, min_impurity_decrease=0.05, criticality_threshold_min=0.5, criticality_threshold_max=1, save_folder=None)[source]

Derive critical regions from a population of individuals.

Parameters:

  • population (Population) – Population of individuals.

  • problem (Problem) – Problem instance.

  • min_samples_split (float, optional) – The minimum number of samples required to split an internal node.

  • min_samples_leaf (int, optional) – The minimum number of samples required to be at a leaf node.

  • max_depth (int, optional) – Maximum depth of the tree.

  • min_impurity_decrease (float, optional) – A node will be split if this split induces a decrease of the impurity greater than or equal to this value.

  • criticality_threshold_min (float, optional) – Minimum criticality threshold.

  • criticality_threshold_max (float, optional) – Maximum criticality threshold.

  • save_folder (str, optional) – Folder to save the tree and bounds of critical regions.

Module contents