deephyper.skopt.space.Integer#
- class deephyper.skopt.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
Compute distance between point a and b.
Inverse transform samples from the warped space back into the original space.
Draw random samples.
Define _rvs and transformer spaces.
Transform samples form the original space to a warped space.
Attributes
Bounds before transform/preprocessing.
Test if the dimension is a constant.
Name of the dimension.
prior
Dimensionality of sampel from the dimension before the transform/preprocessing.
Bounds after the transform/preprocessing.
Dimensionality of samples from the dimension after the transform/preprocessing.
- property bounds#
Bounds before transform/preprocessing.
- distance(a, b)[source]#
Compute distance between point a and b.
Args: a : int
First point.
- bint
Second point.
- inverse_transform(Xt)[source]#
Inverse transform samples from the warped space back into the original space.
- property is_constant#
Test if the dimension is a constant.
- property name#
Name of the dimension.
- rvs(n_samples=1, random_state=None)#
Draw random samples.
Args: n_samples : int or None
The number of samples to be drawn.
- random_stateint, 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.
Args: transform : str
Can be ‘normalize’ or ‘identity’
- property size#
Dimensionality of sampel from the dimension before the transform/preprocessing.
- transform(X)#
Transform samples form the original space to a warped space.
- property transformed_bounds#
Bounds after the transform/preprocessing.
- property transformed_size#
Dimensionality of samples from the dimension after the transform/preprocessing.