Declare different parameter types

ParameterSpace accepts five kinds of parameter. You can mix all of them in one space, and the value you give each key decides its type.

from mergen import ParameterSpace, Sampler

space = ParameterSpace({
    'flow_rate':   ('continuous', 0.1, 10.0, {'resolution': 25, 'round': 2}),
    'temperature': range(20, 101, 5),
    'n_stages':    ('integer', 1, 20),
    'catalyst':    ('nominal', ['A', 'B', 'C']),
    'grade':       ('ordinal', ['low', 'med', 'high']),
})

Continuous parameters take a ('continuous', low, high) tuple. The optional fourth element controls how the range is discretised onto the candidate grid: resolution sets the number of levels and round the decimal places. A finer grid gives the optimiser more freedom at the cost of a larger candidate set.

Discrete numeric parameters are given as any explicit sequence of values, such as a list or a range. Only these exact values appear in the design, which is the right choice for settings your equipment supports at fixed steps.

Integer parameters use an ('integer', low, high) tuple and take whole-number values across the interval.

Nominal parameters, ('nominal', [...]), are unordered categories: buffer types, algorithm names, material grades with no natural ranking. Ordinal parameters, ('ordinal', [...]), are categories with a meaningful order, and Mergen preserves that order when scoring distances.

Criterion compatibility

Nominal and ordinal factors change how the design must be scored, because distance between unordered labels is not Euclidean. If your space contains any nominal factor, use a criterion that supports qualitative factors, \(\mathrm{MaxPro_{QQ}}\) ('maxproqq') or QQD ('qqd'); the other five criteria are for purely numeric spaces. mergen.nominal_supporting_criteria() lists the compatible ones, and Sampler.run raises an informative error if the criterion and the space are incompatible.

For a logarithmically spaced parameter, such as a learning rate or a concentration spanning orders of magnitude, append the 'log' flag to a continuous or integer specification and Mergen builds the grid on a logarithmic ladder:

'learning_rate': ('continuous', 1e-4, 1e-1, 'log'),
'batch_size':    ('integer', 8, 256, 'log'),

Alternatively, list explicit levels when you want exact control over the sampled decades:

'learning_rate': [1e-4, 3e-4, 1e-3, 3e-3, 1e-2],