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"""Random search."""
from .base import base_minimize
def 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,
space_constraint=None,
):
"""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 :func:`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.
space_constraint : callable or None, default: None
Constraint function. Should take a single list of parameters
(i.e. a point in space) and return True if the point satisfies
the constraints.
If None, the space is not conditionally constrained.
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
.. seealso:: functions :class:`skopt.gp_minimize`,
:class:`skopt.forest_minimize`, :class:`skopt.gbrt_minimize`
"""
# all our calls want random suggestions, except if we need to evaluate
# some initial points
if x0 is not None and y0 is None:
n_initial_points = n_calls - len(x0)
else:
n_initial_points = n_calls
return base_minimize(
func,
dimensions,
base_estimator="dummy",
# explicitly set optimizer to sampling as "dummy"
# minimizer does not provide gradients.
acq_optimizer="sampling",
n_calls=n_calls,
n_initial_points=n_initial_points,
initial_point_generator=initial_point_generator,
x0=x0,
y0=y0,
random_state=random_state,
verbose=verbose,
space_constraint=space_constraint,
callback=callback,
model_queue_size=model_queue_size,
)
|
