# deephyper.skopt.space.space.Integer#

class deephyper.skopt.space.space.Integer(low, high, prior='uniform', base=10, transform=None, name=None, dtype=<class 'numpy.int64'>, loc=None, scale=None)[source]#

Bases: `Dimension`

Search space dimension that can take on integer values.

Parameters:
• low (int) – Lower bound (inclusive).

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

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

Distribution to use when sampling random integers for this dimension.

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

• If “log-uniform”, integers 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 dimension, e.g., “number of trees”.

• dtype (str or dtype, default=np.int64) – integer type which will be used in inverse_transform, can be int, np.int16, np.uint32, np.int32, np.int64 (default). When set to int, inverse_transform returns a list instead of a numpy array

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. `set_transformer` Define _rvs and transformer spaces. `transform` Transform samples form the original space to a warped space.

Attributes

 `bounds` `is_constant` `name` `prior` `size` `transformed_bounds` `transformed_size`
distance(a, b)[source]#

Compute distance between point a and b.

Parameters:
• a (int) – First point.

• b (int) – Second point.

inverse_transform(Xt)[source]#

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

rvs(n_samples=1, random_state=None)#

Draw random samples.

Parameters:
• n_samples (int or None) – The number of samples to be drawn.

• random_state (int, RandomState instance, or None (default)) – 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.