skopt.plots
.partial_dependence_2D¶

skopt.plots.
partial_dependence_2D
(space, model, i, j, samples, n_points=40)[source][source]¶ Calculate the partial dependence for two dimensions in the searchspace.
This uses the given model to calculate the average objective value for all the samples, where the given dimensions are fixed at regular intervals between their bounds.
This shows how the given dimensions affect the objective value when the influence of all other dimensions are averaged out.
 Parameters
 space
Space
The parameter space over which the minimization was performed.
 model
Surrogate model for the objective function.
 iint
The first dimension for which to calculate the partial dependence.
 jint
The second dimension for which to calculate the partial dependence.
 samplesnp.array, shape=(n_points, n_dims)
Randomly sampled and transformed points to use when averaging the model function at each of the
n_points
when using partial dependence. n_pointsint, default=40
Number of points at which to evaluate the partial dependence along each dimension
i
andj
.
 space
 Returns
 xinp.array, shape=n_points
The points at which the partial dependence was evaluated.
 yinp.array, shape=n_points
The points at which the partial dependence was evaluated.
 zinp.array, shape=(n_points, n_points)
The average value of the objective function at each point
(xi, yi)
.