| Parameters: |
- dimensions : list, shape=(n_dims,)
List of search space dimensions.
Each search dimension can be defined either as
- a (upper_bound, lower_bound) tuple (for Real or Integer
dimensions),
- a (upper_bound, lower_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).
- base_estimator : string or Regressor, optional (default: ‘ET’)
The regressor to use as surrogate model. Can be either
- “RF” for random forest regressor
- “ET” for extra trees regressor
- “GP” for Gaussian process estimator with Matern kernel
- “GBRT” for gradient boosted trees
- instance of regressor with support for return_std in its predict
method
The predefined models are initilized 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.
- maximize : boolean, optional (default: True)
Maximize return or minimize cost?
- n_random_starts : int, optional (default: 10)
Number of evaluations of func with random initialization points
before approximating the func with base_estimator. While random
points are being suggested no model will be fit to the observations.
- acq_func : string, optional (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.
- acq_optimizer : string, ‘sampling’ or ‘lbfgs’, optional (default:’lbfgs’)
Method to minimize the acquistion 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 sampled randomly.
- If set to “lbfgs”, then acq_func is optimized by
- Sampling n_restarts_optimizer points randomly.
- “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.
- random_state : int, RandomState instance, or None, optional (default: None)
Set random state to something other than None for reproducible
results.
- n_points : int, optional (default: 500)
Number of points to sample to determine the next “best” point.
Useless if acq_optimizer is set to “lbfgs”.
- n_restarts_optimizer : int, optional (default: 5)
The number of restarts of the optimizer when acq_optimizer
is “lbfgs”.
- xi : float, optional (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, optional (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, optional (default: 1)
Number of cores to run in parallel while running the lbfgs
optimizations over the acquisition function. Valid only when
acq_optimizer is set to “lbfgs.”
Defaults to 1 core. If n_jobs=-1, then number of jobs is set
to number of cores.
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