- deephyper.skopt.optimizer.dummy.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)#
Random search by uniform sampling within the given bounds.
func (callable) –
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
deephyper.skopt.utils.use_named_args()as a decorator on your objective function, in order to call it directly with the named arguments. See use_named_args for an example.
dimensions (list, shape (n_dims,)) –
List of search space dimensions. Each search dimension can be defined either as
a (lower_bound, upper_bound) tuple (for Real or Integer dimensions),
a (lower_bound, upper_bound, prior) tuple (for Real dimensions),
as a list of categories (for Categorical dimensions), or
an instance of a Dimension object (Real, Integer or Categorical).
n_calls (int, default: 100) – Number of calls to func to find the minimum.
initial_point_generator (str, 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
x0 (list, 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.
y0 (list, 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 i-th element of y0 corresponds to the function evaluated at the i-th element of x0.
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 of x0.
random_state (int, RandomState instance, or None (default)) – Set random state to something other than None for reproducible results.
verbose (boolean, default: False) – Control the verbosity. It is advised to set the verbosity to True for long optimization runs.
callback (callable, list of callables, optional) – If callable then callback(res) is called after each call to func. If list of callables, then each callable in the list is called.
model_queue_size (int 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.
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