skopt
.dummy_minimize¶

skopt.
dummy_minimize
(func, dimensions, n_calls=100, initial_point_generator='random', x0=None, y0=None, random_state=None, verbose=False, callback=None, model_queue_size=None, init_point_gen_kwargs=None)[source][source]¶ Random search by uniform sampling within the given bounds.
 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. Seeuse_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
).
 n_callsint, default: 100
Number of calls to
func
to find the minimum. 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
 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, list of callables, optional
If callable then
callback(res)
is called after each call tofunc
. If list of callables, then each callable in the list is called. 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.x_iters
[list of lists]: location of function evaluation for each iteration.func_vals
[array]: function value for each iteration.space
[Space]: the optimisation 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
See also
functions
skopt.gp_minimize
,skopt.forest_minimize
,skopt.gbrt_minimize
 res