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)[source]#
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
Compute distance between point a and b.
Inverse transform samples from the warped space back into the original space.
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).
Define rvs and transformer spaces.
Transform samples form the original space to a warped space.
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
- inverse_transform(Xt)[source]#
Inverse transform samples from the warped space back into the original space.
- rvs(n_samples=1, random_state=None)[source]#
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')[source]#
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.