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# `skopt.optimizer`.base_minimize

skopt.optimizer.base_minimize(func, dimensions, base_estimator, n_calls=100, n_random_starts=None, n_initial_points=10, initial_point_generator='random', acq_func='EI', acq_optimizer='lbfgs', x0=None, y0=None, random_state=None, verbose=False, callback=None, n_points=10000, n_restarts_optimizer=5, xi=0.01, kappa=1.96, n_jobs=1, model_queue_size=None, space_constraint=None)

  Base optimizer class.

  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 `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`).

      > Note

      > The upper and lower bounds are inclusive for `Integer` dimensions.

    base_estimator sklearn regressor

      Should inherit from `sklearn.base.RegressorMixin`. In addition, should have an optional `return_std` argument, which returns `std(Y | x)` along with `E[Y | x]`.

    n_calls int, default: 100

      Maximum number of calls to `func`. An objective function will always be evaluated this number of times; Various options to supply initialization points do not affect this value.

    n_random_starts int, default: None

      Number of evaluations of `func` with random points before approximating it with `base_estimator`.

      Deprecated since version 0.8: use `n_initial_points` instead.

    n_initial_points int, default: 10

      Number of evaluations of `func` with initialization points before approximating it with `base_estimator`. Initial point generator can be changed by setting `initial_point_generator`.

    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

    acq_func string, default: `"EI"`

      Function to minimize over the posterior distribution. 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"`

    acq_optimizer string, `"sampling"` or `"lbfgs"`, default: `"lbfgs"`

      Method to minimize the acquisition function. The fit model is updated with the optimal value obtained by optimizing `acq_func` with `acq_optimizer`.

      - If set to `"sampling"`, then `acq_func` is optimized by computing `acq_func` at `n_points` randomly sampled points and the smallest value found is used.

      - If set to `"lbfgs"`, then

        - The `n_restarts_optimizer` no. of points which the acquisition function is least are taken as start points.

        - `"lbfgs"` is run for 20 iterations with these points as initial points to find local minima.

        - The optimal of these local minima is used to update the prior.

    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 no corresponding outputs `y0` are supplied, then len(x0) of total calls to the objective function will be spent evaluating the points in `x0`. If the corresponding outputs are provided, then they will be used together with evaluated points during a run of the algorithm to construct a surrogate.

      - If it is a list, use it as a single initial input point. The algorithm will spend 1 call to evaluate the initial point, if the outputs are not provided.

      - If it is `None`, no initial input points are used.

    y0 list, scalar or `None`

      Objective values at 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.

    n_points int, default: 10000

      If `acq_optimizer` is set to `"sampling"`, then `acq_func` is optimized by computing `acq_func` at `n_points` randomly sampled points.

    n_restarts_optimizer int, default: 5

      The number of restarts of the optimizer when `acq_optimizer` is `"lbfgs"`.

    xi float, default: 0.01

      Controls how much improvement one wants over the previous best values. Used when the acquisition is either `"EI"` or `"PI"`.

    kappa float, 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_jobs int, default: 1

      Number of cores to run in parallel while running the lbfgs optimizations over the acquisition function and given to the base_estimator. Valid only when `acq_optimizer` is set to “lbfgs”. or when the base_estimator supports n_jobs as parameter and was given as string. Defaults to 1 core. If `n_jobs=-1`, then number of jobs is set to number of cores.

    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.

      - `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.

      - `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