deephyper.skopt.sampler.lhs.Lhs#
- class deephyper.skopt.sampler.lhs.Lhs(lhs_type='classic', criterion='maximin', iterations=1000)[source]#
Bases:
InitialPointGenerator
Latin hypercube sampling
- Parameters:
lhs_type (str, default='classic') –
‘classic’ - a small random number is added
’centered’ - points are set uniformly in each interval
criterion (str or None, default='maximin') –
When set to None, the LHS is not optimized
’correlation’ : optimized LHS by minimizing the correlation
’maximin’ : optimized LHS by maximizing the minimal pdist
’ratio’ : optimized LHS by minimizing the ratio max(pdist) / min(pdist)
iterations (int) – Defines the number of iterations for optimizing LHS
Methods
Creates latin hypercube samples.
Set the parameters of this initial point generator.
- generate(dimensions, n_samples, random_state=None)[source]#
Creates latin hypercube samples.
Args: dimensions : list, shape (n_dims,)
List of search space dimensions. Each search dimension can be defined either as
a (lower_bound, upper_bound) tuple (for Real or Integer dimensions),
a (lower_bound, upper_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).
- n_samplesint
The order of the LHS sequence. Defines the number of samples.
- random_stateint, RandomState instance, or None (default)
Set random state to something other than None for reproducible results.
Returns: np.array, shape=(n_dim, n_samples)
LHS set