skopt.optimizer
.forest_minimize¶

skopt.optimizer.
forest_minimize
(func, dimensions, base_estimator='ET', n_calls=100, n_random_starts=None, n_initial_points=10, acq_func='EI', initial_point_generator='random', 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 optimisation using decision trees.
A tree based regression model is used to model the expensive to evaluate function
func
. The model is improved by sequentially evaluating the expensive function at the next best point. Thereby finding the minimum offunc
with as few evaluations as possible.The total number of evaluations,
n_calls
, are performed like the following. Ifx0
is provided but noty0
, then the elements ofx0
are first evaluated, followed byn_initial_points
evaluations. Finally,n_calls  len(x0)  n_initial_points
evaluations are made guided by the surrogate model. Ifx0
andy0
are both provided thenn_initial_points
evaluations are first made thenn_calls  n_initial_points
subsequent evaluations are made guided by the surrogate model.The first
n_initial_points
are generated by theinitial_point_generator
. Parameters
 funccallable
Function to minimize. Should take a single list of parameters and return the objective value.
If you have a searchspace where all dimensions have names, then you can use
skopt.utils.use_named_args()
as a decorator on your objective function, in order to call it directly with the named arguments. Seeskopt.utils.use_named_args()
for an example. dimensionslist, shape (n_dims,)
List of search space dimensions. Each search dimension can be defined either as
a
(lower_bound, upper_bound)
tuple (forReal
orInteger
dimensions),a
(lower_bound, upper_bound, prior)
tuple (forReal
dimensions),as a list of categories (for
Categorical
dimensions), oran instance of a
Dimension
object (Real
,Integer
orCategorical
).
Note
The upper and lower bounds are inclusive for
Integer
dimensions. base_estimatorstring or
Regressor
, default:"ET"
The regressor to use as surrogate model. Can be either
"RF"
for random forest regressor"ET"
for extra trees regressorinstance of regressor with support for
return_std
in its predict method
The predefined models are initialized with good defaults. If you want to adjust the model parameters pass your own instance of a regressor which returns the mean and standard deviation when making predictions.
 n_callsint, default: 100
Number of calls to
func
. n_random_startsint, default: None
Number of evaluations of
func
with random points before approximating it withbase_estimator
.Deprecated since version 0.8: use
n_initial_points
instead. n_initial_pointsint, default: 10
Number of evaluations of
func
with initialization points before approximating it withbase_estimator
. Initial point generator can be changed by settinginitial_point_generator
. initial_point_generatorstr, InitialPointGenerator instance, default:
"random"
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:
"LCB"
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 in seconds."PIps"
for negated probability of improvement per second. The return type of the objective function is assumed to be similar to that of"EIps"
 x0list, list of lists or
None
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
None
Evaluation of initial input points.
If it is a list, then it corresponds to evaluations of the function at each element of
x0
: the ith element ofy0
corresponds to the function evaluated at the ith element ofx0
.If it is a scalar, then it corresponds to the evaluation of the function at
x0
.If it is None and
x0
is provided, then the function is evaluated at each element ofx0
.
 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
"EI"
or"PI"
. 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
"LCB"
. n_jobsint, default: 1
The number of jobs to run in parallel for
fit
andpredict
. 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.
 Returns
 res
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.
For more details related to the OptimizeResult object, refer http://docs.scipy.org/doc/scipy/reference/generated/scipy.optimize.OptimizeResult.html
See also
functions
skopt.gp_minimize
,skopt.dummy_minimize
,skopt.gbrt_minimize
 res