Development version
You are reading the latest (dev) docs. For the stable version, see 0.13.
Plots
python
from sklearn_genetic.plots import plot_fitness_evolution, plot_search_spaceRequires seaborn: pip install sklearn-genetic-opt[all].
plot_fitness_evolution
Plot the fitness score over generations.
python
plot_fitness_evolution(estimator, metric="fitness")| Parameter | Type | Default | Description |
|---|---|---|---|
estimator | fitted GASearchCV or GAFeatureSelectionCV | — | The fitted search estimator |
metric | str | "fitness" | Which fitness column to plot: "fitness", "fitness_best", "fitness_std" |
Returns a matplotlib.axes.Axes.
Example:
python
import matplotlib.pyplot as plt
from sklearn_genetic.plots import plot_fitness_evolution
plot_fitness_evolution(search, metric="fitness_best")
plt.title("Best fitness over generations")
plt.show()plot_search_space
Plot the distribution of sampled hyperparameter values as a pair plot.
python
plot_search_space(estimator, features=None, figsize=(12, 12))| Parameter | Type | Default | Description |
|---|---|---|---|
estimator | fitted GASearchCV | — | The fitted search estimator |
features | list of str | None | Parameter names to include. None includes all |
figsize | tuple | (12, 12) | Figure size |
Returns a seaborn.PairGrid.
Example:
python
from sklearn_genetic.plots import plot_search_space
plot_search_space(search, features=["learning_rate", "max_depth", "n_estimators"])
plt.show()See Also
- Basic Usage — tutorial with both plot types