mo.pareto.mo_xy_contour

mo.pareto.mo_xy_contour(
    models,
    bounds,
    target_names=None,
    feature_names=None,
    resolution=50,
    feature_pairs=None,
    **kwargs,
)

Generates contour plots of every combination of two input variables x_i and x_j (where i < j) and for each of the multiple objectives f_k.

Parameters

Name	Type	Description	Default
models	list	List of trained models (one per objective).	required
bounds	list	List of tuples (min, max) for each input variable.	required
target_names	list	List of names for the objectives. Defaults to None.	None
feature_names	list	List of names for the input variables. Defaults to None.	None
resolution	int	Grid resolution for the contour plot. Defaults to 50.	50
feature_pairs	list	List of tuples (i, j) specifying which feature pairs to plot. If None, all combinations are plotted. Defaults to None.	None
**kwargs	Any	Additional keyword arguments passed to plt.subplots (e.g., figsize).	{}

Returns

Name	Type	Description
	None	None

Examples

>>> from sklearn.ensemble import RandomForestRegressor
>>> from spotoptim.mo.pareto import mo_xy_contour
>>> import numpy as np
>>> # Train dummy models
>>> X = np.random.rand(10, 2)
>>> y1 = X[:, 0] + X[:, 1]
>>> y2 = X[:, 0] * X[:, 1]
>>> m1 = RandomForestRegressor().fit(X, y1)
>>> m2 = RandomForestRegressor().fit(X, y2)
>>> # Plot
>>> mo_xy_contour([m1, m2], bounds=[(0, 1), (0, 1)], target_names=["Sum", "Prod"])