deephyper.skopt.space.space.Real

class deephyper.skopt.space.space.Real(low, high, prior='uniform', base=10, transform=None, name=None, dtype=<class 'float'>, loc=None, scale=None)

Bases: Dimension

Search space dimension that can take on any real value.

Parameters:

• low (float) – Lower bound (inclusive).

• high (float) – Upper bound (inclusive).

• prior ("uniform" or "log-uniform", default="uniform") –

  Distribution to use when sampling random points for this dimension.

  • If “uniform”, points are sampled uniformly between the lower and upper bounds.

  • If “log-uniform”, points are sampled uniformly between log(lower, base) and log(upper, base) where log has base base.

• base (int) – The logarithmic base to use for a log-uniform prior. - Default 10, otherwise commonly 2.

• transform ("identity", "normalize", optional) –

  The following transformations are supported.

  • ”identity”, (default) the transformed space is the same as the original space.

  • ”normalize”, the transformed space is scaled to be between 0 and 1.

• name (str or None) – Name associated with the dimension, e.g., “learning rate”.

• dtype (str or dtype, default=float) – float type which will be used in inverse_transform, can be float.

Methods

distance	Compute distance between point a and b.
inverse_transform	Inverse transform samples from the warped space back into the original space.
rvs	Draw random samples. :param n_samples: The number of samples to be drawn. :type n_samples: int or None :param random_state: Set random state to something other than None for reproducible results. :type random_state: int, RandomState instance, or None (default).
set_transformer	Define rvs and transformer spaces.
transform	Transform samples form the original space to a warped space.
update_prior	Fit a Kernel Density Estimator to the data to increase density of samples around regions of interest instead of uniform random-sampling.

Attributes

bounds
is_constant
name
prior
size
transformed_bounds
transformed_size

distance(a, b)

Compute distance between point a and b.

Parameters:

• a (float) – First point.

• b (float) – Second point.

inverse_transform(Xt)

Inverse transform samples from the warped space back into the original space.

rvs(n_samples=1, random_state=None)

Draw random samples. :param n_samples: The number of samples to be drawn. :type n_samples: int or None :param random_state: Set random state to something other than None for reproducible

results.

set_transformer(transform='identity')

Define rvs and transformer spaces.

Parameters:

transform (str) – Can be ‘normalize’ or ‘identity’

transform(X)

Transform samples form the original space to a warped space.

update_prior(X, y, q=0.9)

Fit a Kernel Density Estimator to the data to increase density of samples around regions of interest instead of uniform random-sampling.