plot_simple_pareto_front

site.SiteSolutionSet.plot_simple_pareto_front(
    x_axis='weighted_average',
    y_axis='max',
    height=4,
    show_points=True,
    theme='whitegrid',
    maxx=None,
    maxy=None,
    **kwargs,
)

Plot a Pareto front for two selected solution metrics.

This method generates a Pareto front visualisation comparing two performance metrics across all evaluated solutions. It highlights the trade-offs between objectives and optionally displays all points alongside the Pareto-optimal frontier.

Parameters

Name Type Description Default
x_axis (weighted_average, unweighted_average, '90th_percentile', max, proportion_within_coverage_threshold) Column name representing the metric to plot on the x-axis. "weighted_average"
y_axis (weighted_average, unweighted_average, '90th_percentile', max, proportion_within_coverage_threshold) Column name representing the metric to plot on the y-axis. "weighted_average"
height float Height of the plot in inches. 4
show_points bool If True, all solutions are plotted as points in addition to the Pareto front. True
theme str Visual theme passed to the underlying plotting function. "whitegrid"
maxx bool If True, the Pareto front is computed assuming the x-axis metric is to be maximised. If False, it is minimised. If None, the function automatically infers the value based on the metric. None
maxy bool If True, the Pareto front is computed assuming the y-axis metric is to be maximised. If False, it is minimised. If None, the function automatically infers the value based on the metric. None
**kwargs Additional keyword arguments passed to spv.pareto_plot. {}

Returns

Name Type Description
object A Pareto plot object returned by spv.pareto_plot. This is typically a wrapper that can be rendered or further customised.

Notes

The method relies on the external spv.pareto_plot function for computation and visualisation of the Pareto front.

The interpretation of “optimal” depends on the maxx and maxy flags, which determine whether each axis is treated as a maximisation or minimisation objective.

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