Source code for deephyper.skopt.optimizer.forest

"""Forest based minimization algorithms."""

from .base import base_minimize

[docs]def forest_minimize( func, dimensions, base_estimator="ET", n_calls=100, n_random_starts=None, n_initial_points=10, acq_func="EI", initial_point_generator="random", x0=None, y0=None, random_state=None, verbose=False, callback=None, n_points=10000, xi=0.01, kappa=1.96, n_jobs=1, model_queue_size=None, ): """Sequential optimisation using decision trees. A tree based regression model is used to model the expensive to evaluate function `func`. The model is improved by sequentially evaluating the expensive function at the next best point. Thereby finding the minimum of `func` with as few evaluations as possible. The total number of evaluations, `n_calls`, are performed like the following. If `x0` is provided but not `y0`, then the elements of `x0` are first evaluated, followed by `n_initial_points` evaluations. Finally, `n_calls - len(x0) - n_initial_points` evaluations are made guided by the surrogate model. If `x0` and `y0` are both provided then `n_initial_points` evaluations are first made then `n_calls - n_initial_points` subsequent evaluations are made guided by the surrogate model. The first `n_initial_points` are generated by the `initial_point_generator`. 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:`deephyper.skopt.utils.use_named_args` as a decorator on your objective function, in order to call it directly with the named arguments. See :func:`deephyper.skopt.utils.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 : string or `Regressor`, default: `"ET"` The regressor to use as surrogate model. Can be either - `"RF"` for random forest regressor - `"ET"` for extra trees regressor - instance of regressor with support for `return_std` in its predict method The predefined models are initialized with good defaults. If you want to adjust the model parameters pass your own instance of a regressor which returns the mean and standard deviation when making predictions. n_calls : int, default: 100 Number of calls to `func`. n_random_starts : int, default: None Number of evaluations of `func` with random points before approximating it with `base_estimator`. .. deprecated:: 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: `"LCB"` Function to minimize over the forest posterior. 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"` 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, optional If provided, then `callback(res)` is called after call to func. n_points : int, default: 10000 Number of points to sample when minimizing the acquisition function. 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 The number of jobs to run in parallel for `fit` and `predict`. If -1, then the number of jobs is set to the 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. 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. For more details related to the OptimizeResult object, refer .. seealso:: functions :class:`deephyper.skopt.gp_minimize`, :class:`deephyper.skopt.dummy_minimize`, :class:`deephyper.skopt.gbrt_minimize` """ return base_minimize( func, dimensions, base_estimator, n_calls=n_calls, n_points=n_points, n_random_starts=n_random_starts, n_initial_points=n_initial_points, initial_point_generator=initial_point_generator, x0=x0, y0=y0, random_state=random_state, n_jobs=n_jobs, acq_func=acq_func, xi=xi, kappa=kappa, verbose=verbose, callback=callback, acq_optimizer="sampling", model_queue_size=model_queue_size, )