deephyper.skopt.dummy_minimize#

deephyper.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]#

Random search by uniform sampling within the given bounds.

Parameters:

  • 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.

Returns: