Visualization Guide

Overview

ORruns provides powerful visualization tools to help you analyze and present your optimization results effectively.

Basic Visualizations

Performance Curves

from orruns.visualization import plot_performance

# Plot convergence curve
plot_performance(
    experiment_name="optimization_experiment",
    metric="hypervolume",
    title="Convergence Analysis"
)

Parameter Distribution

from orruns.visualization import plot_parameter_distribution

# Visualize parameter distributions
plot_parameter_distribution(
    experiment_name="optimization_experiment",
    parameter="mutation_rate"
)

Pareto Front Visualization

from orruns.visualization import plot_pareto_front

# Plot Pareto front
plot_pareto_front(
    experiment_name="optimization_experiment",
    objectives=["cost", "performance"]
)

Advanced Visualization Features

Custom Plotting

from orruns.visualization import create_custom_plot

def custom_plot_function(data, ax):
    # Your custom plotting logic
    pass

create_custom_plot(
    experiment_name="optimization_experiment",
    plot_function=custom_plot_function
)

Interactive Dashboard

from orruns.visualization import launch_dashboard

# Launch interactive dashboard
launch_dashboard(
    experiment_name="optimization_experiment",
    port=8050
)

Best Practices

  1. Plot Customization
  2. Use consistent color schemes
  3. Add proper labels and titles

  4. Include error bars when applicable

  5. Performance Optimization

  6. Cache visualization data
  7. Use appropriate figure sizes

  8. Handle large datasets efficiently

  9. Export Options

  10. Save plots in various formats
  11. Generate publication-ready figures
  12. Create interactive HTML reports