gbrt_minimize(func, dimensions, base_estimator=None, n_calls=100, n_random_starts=None, n_initial_points=10, initial_point_generator='random', acq_func='EI', acq_optimizer='auto', x0=None, y0=None, random_state=None, verbose=False, callback=None, n_points=10000, xi=0.01, kappa=1.96, n_jobs=1, model_queue_size=None)[source][source]¶
Sequential optimization using gradient boosted trees.
Gradient boosted regression trees are used to model the (very) expensive to evaluate function
func. The model is improved by sequentially evaluating the expensive function at the next best point. Thereby finding the minimum of
funcwith as few evaluations as possible.
The total number of evaluations,
n_calls, are performed like the following. If
x0is provided but not
y0, then the elements of
x0are first evaluated, followed by
n_calls - len(x0) - n_initial_pointsevaluations are made guided by the surrogate model. If
y0are both provided then
n_initial_pointsevaluations are first made then
n_calls - n_initial_pointssubsequent evaluations are made guided by the surrogate model.
n_initial_pointsare generated by the
Function to minimize. Should take a single list of parameters and return the objective value.
If you have a search-space where all dimensions have names, then you can use
skopt.utils.use_named_argsas a decorator on your objective function, in order to call it directly with the named arguments. See
use_named_argsfor an example.
- dimensionslist, shape (n_dims,)
List of search space dimensions. Each search dimension can be defined either as
(lower_bound, upper_bound)tuple (for
(lower_bound, upper_bound, "prior")tuple (for
as a list of categories (for
an instance of a
The regressor to use as surrogate model
- n_callsint, default: 100
Number of calls to
- n_random_startsint, default: None
Number of evaluations of
funcwith random points before approximating it with
Deprecated since version 0.8: use
- n_initial_pointsint, default: 10
Number of evaluations of
funcwith initialization points before approximating it with
base_estimator. Initial point generator can be changed by setting
- initial_point_generatorstr, InitialPointGenerator instance, default:
Sets a initial points generator. Can be either
"random"for uniform random numbers,
"sobol"for a Sobol sequence,
"halton"for a Halton sequence,
"hammersly"for a Hammersly sequence,
"lhs"for a latin hypercube sequence,
"grid"for a uniform grid sequence
- acq_funcstring, default:
Function to minimize over the forest posterior. Can be either
"LCB"for lower confidence bound.
"EI"for negative expected improvement.
"PI"for negative probability of improvement.
"EIps"for negated expected improvement per second to take into account the function compute time. Then, the objective function is assumed to return two values, the first being the objective value and the second being the time taken.
"PIps"for negated probability of improvement per second.
- x0list, list of lists or
Initial input points.
If it is a list of lists, use it as a list of input points.
If it is a list, use it as a single initial input point.
If it is
None, no initial input points are used.
- y0list, scalar or
Evaluation of initial input points.
If it is a list, then it corresponds to evaluations of the function at each element of
x0: the i-th element of
y0corresponds to the function evaluated at the i-th element of
If it is a scalar, then it corresponds to the evaluation of the function at
If it is None and
x0is provided, then the function is evaluated at each element of
- random_stateint, RandomState instance, or None (default)
Set random state to something other than None for reproducible results.
- verboseboolean, default: False
Control the verbosity. It is advised to set the verbosity to True for long optimization runs.
- callbackcallable, optional
If provided, then
callback(res)is called after call to func.
- n_pointsint, default: 10000
Number of points to sample when minimizing the acquisition function.
- xifloat, default: 0.01
Controls how much improvement one wants over the previous best values. Used when the acquisition is either
- kappafloat, default: 1.96
Controls how much of the variance in the predicted values should be taken into account. If set to be very high, then we are favouring exploration over exploitation and vice versa. Used when the acquisition is
- n_jobsint, default: 1
The number of jobs to run in parallel for
predict. If -1, then the number of jobs is set to the number of cores.
- model_queue_sizeint or None, default: None
Keeps list of models only as long as the argument given. In the case of None, the list has no capped length.
OptimizeResult, scipy object
The optimization result returned as a OptimizeResult object. Important attributes are:
x[list]: location of the minimum.
fun[float]: function value at the minimum.
models: surrogate models used for each iteration.
x_iters[list of lists]: location of function evaluation for each iteration.
func_vals[array]: function value for each iteration.
space[Space]: the optimization space.
specs[dict]`: the call specifications.
rng[RandomState instance]: State of the random state at the end of minimization.
For more details related to the OptimizeResult object, refer http://docs.scipy.org/doc/scipy/reference/generated/scipy.optimize.OptimizeResult.html