skopt.plots
.plot_evaluations¶

skopt.plots.
plot_evaluations
(result, bins=20, dimensions=None, plot_dims=None)[source][source]¶ Visualize the order in which points were sampled during optimization.
This creates a 2d matrix plot where the diagonal plots are histograms that show the distribution of samples for each searchspace dimension.
The plots below the diagonal are scatterplots of the samples for all combinations of searchspace dimensions.
The order in which samples were evaluated is encoded in each point’s color.
A red star shows the best found parameters.
 Parameters
 result
OptimizeResult
The optimization results from calling e.g.
gp_minimize()
. binsint, bins=20
Number of bins to use for histograms on the diagonal.
 dimensionslist of str, default=None
Labels of the dimension variables.
None
defaults tospace.dimensions[i].name
, or if alsoNone
to['X_0', 'X_1', ..]
. plot_dimslist of str and int, default=None
List of dimension names or dimension indices from the searchspace dimensions to be included in the plot. If
None
then use all dimensions except constant ones from the searchspace.
 result
 Returns
 ax
Matplotlib.Axes
A 2d matrix of Axesobjects with the subplots.
 ax