skopt.plots.partial_dependence¶

skopt.plots.partial_dependence(space, model, i, j=None, sample_points=None, n_samples=250, n_points=40, x_eval=None)[source][source]¶

Calculate the partial dependence for dimensions i and j with respect to the objective value, as approximated by model.

The partial dependence plot shows how the value of the dimensions i and j influence the model predictions after “averaging out” the influence of all other dimensions.

When x_eval is not None, the given values are used instead of random samples. In this case, n_samples will be ignored.

Parameters

spaceSpace

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, default=None

The second dimension for which to calculate the partial dependence. To calculate the 1D partial dependence on i alone set j=None.

sample_pointsnp.array, shape=(n_points, n_dims), default=None

Only used when x_eval=None, i.e in case partial dependence should be calculated. Randomly sampled and transformed points to use when averaging the model function at each of the n_points when using partial dependence.

n_samplesint, default=100

Number of random samples to use for averaging the model function at each of the n_points when using partial dependence. Only used when sample_points=None and x_eval=None.

n_pointsint, default=40

Number of points at which to evaluate the partial dependence along each dimension i and j.

x_evallist, default=None

x_eval is a list of parameter values or None. In case x_eval is not None, the parsed dependence will be calculated using these values. Otherwise, random selected samples will be used.

Returns

For 1D partial dependence:

xinp.array

The points at which the partial dependence was evaluated.

yinp.array

The value of the model at each point xi.

For 2D partial dependence:

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 value of the model at each point (xi, yi).

For Categorical variables, the xi (and yi for 2D) returned are

the indices of the variable in Dimension.categories.