Visualisation of Optimization Results¶
If you would like to plot the optimization process you can use one of the following built-in functions from pacakge sklearn_nature_inspired_algorithms.helpers.
score_by_generation_lineplot¶
sklearn_nature_inspired_algorithms.helpers.score_by_generation_lineplot Plots the score (min, max, mean or median) of each algorithm run by generation of the optimization process using the lineplot. The score (or “fitness” in the field of nature-inspired algorithms) is set by the provided estimator. Estimators have a score method providing a default evaluation criterion for the problem they are designed to solve (reference). You can set your own score by providing the scoring paramter to the NatureInspiredSearchCV.
score_by_generation_lineplot(nia_search, metric='max', ax=None, ylim=None)
Parameters¶
- nia_search: NatureInspiresSearchCV object
- metric: str, default=’max’. The metric to be plotted, can be min, max, mean or median.
- ax: matplotlib Axes, default=None. The axes object where to be plotted. If not defined, it will be created.
- ylim: tuple, default=None. Only applicable if the ax parameter is None, it sets the boundaries to Y axis.
score_by_generation_violinplot¶
sklearn_nature_inspired_algorithms.helpers.score_by_generation_lineplot Plots the score of some run by generation of the optimization process using the violinplots.
score_by_generation_violinplot(nia_search, run=0, ax=None, ylim=None)
Parameters¶
- nia_search: NatureInspiresSearchCV object
- run: int, default=0. The run to be plotted.
- ax: matplotlib Axes, default=None. The axes object where to be plotted. If not defined, it will be created.
- ylim: tuple, default=None. Only applicable if the ax parameter is None, it sets the boundaries to Y axis.