Plots
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- sklearn_genetic.plots.noise(score)[source]
- Parameters
- score: Series
The score column from the logbook data of
GASearchCV
- Returns
- Noise to be added to each element of the score to avoid non-unique bin edges
- sklearn_genetic.plots.plot_fitness_evolution(estimator, metric='fitness')[source]
- Parameters
- estimator: estimator object
A fitted estimator from
GASearchCV
- metric: {“fitness”, “fitness_std”, “fitness_max”, “fitness_min”}, default=”fitness”
Logged metric into the estimator history to plot
- Returns
- Lines plot with the fitness value in each generation
- sklearn_genetic.plots.plot_parallel_coordinates(estimator, features: Optional[list] = None)[source]
- Parameters
- estimator: estimator object
A fitted estimator from
GASearchCV
- features: list, default=None
Subset of features to plot, if
None
it plots all the features by default
- Returns
- Parallel Coordinates plot of the non-categorical values
- sklearn_genetic.plots.plot_search_space(estimator, height=2, s=25, features: Optional[list] = None)[source]
- Parameters
- estimator: estimator object
A fitted estimator from
GASearchCV
- height: float, default=2
Height of each facet
- s: float, default=5
Size of the markers in scatter plot
- features: list, default=None
Subset of features to plot, if
None
it plots all the features by default
- Returns
- Pair plot of the used hyperparameters during the search