Plots

from sklearn_genetic.plots import (
    SearchPlotter,
    plot_candidate_rankings,
    plot_candidate_scores,
    plot_convergence,
    plot_cv_scores,
    plot_diversity,
    plot_feature_selection,
    plot_fitness_evolution,
    plot_history,
    plot_optimizer_events,
    plot_parameter_evolution,
    plot_search_decisions,
    plot_search_overview,
    plot_search_space,
    plot_score_landscape,
)

Requires seaborn: pip install sklearn-genetic-opt[all].

Quick diagnostic overview

Use plot_search_overview after .fit(...) when you want one compact answer to:

• how did the search converge?
• did diversity collapse?
• did optimizer controls intervene?
• which candidates or features won?

from sklearn_genetic.plots import plot_search_overview

plot_search_overview(search)

Returns a 2x2 array of matplotlib.axes.Axes.

Object-oriented API

Use SearchPlotter when you want a compact, sklearn-style object around a fitted search.

plotter = SearchPlotter(search)

plotter.overview()
plotter.convergence()
plotter.diversity()
plotter.optimizer_events()
plotter.parameter_evolution(["max_depth", "ccp_alpha"])
plotter.score_landscape("max_depth", "min_samples_split")
plotter.candidate_rankings(top_k=15)
plotter.cv_scores(top_k=5)

plot_fitness_evolution

Plot the fitness score over generations.

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:

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.

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:

from sklearn_genetic.plots import plot_search_space

plot_search_space(search, features=["learning_rate", "max_depth", "n_estimators"])
plt.show()

Search diagnostics

plot_convergence

Plot fitness convergence without mixing diversity or event signals.

plot_convergence(search)

plot_diversity

Plot diversity ratios on the main axis and stagnation on a secondary axis.

plot_diversity(search)

plot_parameter_evolution

Plot the explored values for each parameter across evaluation order, colored by score.

plot_parameter_evolution(search, parameters=["learning_rate", "max_depth"])

plot_search_decisions

Plot optimizer-control telemetry such as mutation probability, selection pressure, diversity-control triggers, duplicate replacements, random immigrants, local refinements, and fitness sharing.

plot_search_decisions(search)

plot_optimizer_events

Show optimizer interventions as a timeline instead of overlapping many step lines.

plot_optimizer_events(search)

plot_score_landscape

Inspect promising regions in a two-parameter slice of the search space.

plot_score_landscape(search, x="max_depth", y="min_samples_split")
plot_score_landscape(search, x="max_depth", y="min_samples_split", kind="hexbin")

plot_candidate_scores

Show the top evaluated candidates from cv_results_.

plot_candidate_scores(search, top_k=10)

plot_candidate_rankings

Compare top candidates with mean CV score and standard deviation.

plot_candidate_rankings(search, top_k=15)
plot_candidate_rankings(search, top_k=15, label_params=["learning_rate", "max_depth"])

plot_cv_scores

Inspect fold-level robustness for top candidates.

plot_cv_scores(search, top_k=5, kind="box")
plot_cv_scores(search, top_k=5, kind="violin")
plot_cv_scores(search, top_k=5, label_params=["learning_rate", "max_depth"])

Candidate labels are compact by default: numeric parameters are prioritized, float values are rounded to a few significant digits, and hidden parameters are summarized with +N more.

plot_feature_selection

For GAFeatureSelectionCV, show the selected feature mask.

plot_feature_selection(selector, feature_names=X.columns)

Which plot answers what?

Question	Function
What did I train?	plot_candidate_rankings, plot_candidate_scores, plot_feature_selection
What space did the algorithm explore?	plot_search_space, plot_parameter_evolution, plot_score_landscape
What decisions did it make?	plot_optimizer_events, plot_search_decisions
How did it converge?	plot_convergence, plot_fitness_evolution, plot_search_overview
Did diversity collapse?	plot_diversity, plot_search_overview
Are top solutions robust?	plot_cv_scores, plot_candidate_rankings

See Also

• Basic Usage — tutorial with both plot types