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name: results-and-visuals description: "Summarize, plot, export, and debug completed PyGAD runs using summary, logger, plot_* methods, and PDF reports." metadata: disco-role: operating disable-model-invocation: true license: BSD 3-Clause

results-and-visuals

Use this sub-skill when the task is to inspect, visualize, or export a completed pygad.GA run.

Route elsewhere

• Tuning the optimizer, fitness, selection, crossover, mutation, callbacks, or save/load state: use genetic-algorithm.
• Built-in benchmarks and quality indicators: use benchmarks.
• GA-based neural-network adapters: use neural-networks.

Operating flow

1. Confirm the run completed and that the data you need was saved:
   • ga.generations_completed >= 1
   • save_solutions=True for history-based plots
   • save_best_solutions=True for plot_genes(solutions="best")
2. Switch plotting to a headless backend before creating figures in scripts or CI.
3. Use ga.summary() to inspect the lifecycle table and the configured logger. Pass a custom logging.Logger through pygad.GA(..., logger=...) when you want file output or structured logs.
4. Call the needed plot_*() method and save it via save_dir when you want a file artifact.
5. Call ga.generate_report() when you want one PDF that bundles the run summary and applicable plots.

Reference map

• API signatures, defaults, return values, and preconditions: references/api-reference.md
• Reusable workflows for summaries, headless plots, and PDF exports: references/workflows.md
• Common failure modes and fixes: references/troubleshooting.md

Bundled safe scripts

• scripts/plot_report_smoke.py: deterministic headless smoke that runs a small single-objective and multi-objective GA, prints a summary through a custom logger, writes temporary PNGs, and exports a temporary PDF report.

Safety notes

• Plot methods need matplotlib; generate_report() needs both matplotlib and reportlab.
• plot_new_solution_rate(), plot_fitness_band(), plot_population_diversity(), plot_non_dominated_hypervolume(), and plot_pareto_front_evolution() require save_solutions=True.
• plot_genes(solutions="best") requires save_best_solutions=True; plot_genes(solutions="all") requires save_solutions=True.
• Pareto-front curve/evolution plots require a completed multi-objective run; the 2D/3D curve only supports 2 or 3 objectives.