Parameter Spaces¶

Dimension Types¶

Continuous¶

A real-valued range with inclusive bounds.

from gds_psuu import Continuous

dim = Continuous(min_val=0.0, max_val=1.0)

Field	Type	Description
`min_val`	`float`	Lower bound (inclusive)
`max_val`	`float`	Upper bound (inclusive)

Validation: min_val < max_val, both must be finite.

Grid behavior: n_steps evenly spaced points from min_val to max_val.

Integer¶

An integer range with inclusive bounds.

from gds_psuu import Integer

dim = Integer(min_val=1, max_val=10)

Field	Type	Description
`min_val`	`int`	Lower bound (inclusive)
`max_val`	`int`	Upper bound (inclusive)

Validation: min_val < max_val.

Grid behavior: all integers from min_val to max_val (ignores n_steps).

Discrete¶

An explicit set of allowed values (any hashable type).

from gds_psuu import Discrete

dim = Discrete(values=("adam", "sgd", "rmsprop"))

Field	Type	Description
`values`	`tuple[Any, ...]`	Allowed values

Validation: at least 1 value.

Grid behavior: all values (ignores n_steps).

ParameterSpace¶

Combines dimensions into a named parameter space:

from gds_psuu import Continuous, Integer, Discrete, ParameterSpace

space = ParameterSpace(params={
    "learning_rate": Continuous(min_val=0.001, max_val=0.1),
    "batch_size": Integer(min_val=16, max_val=128),
    "optimizer": Discrete(values=("adam", "sgd")),
})

Grid Generation¶

points = space.grid_points(n_steps=5)
# Returns list of dicts, e.g.:
# [
#     {"learning_rate": 0.001, "batch_size": 16, "optimizer": "adam"},
#     {"learning_rate": 0.001, "batch_size": 16, "optimizer": "sgd"},
#     ...
# ]

The total number of grid points is the cartesian product: n_steps * (max_int - min_int + 1) * len(discrete_values)

For the example above: 5 * 113 * 2 = 1130 points.

Properties¶

Property	Returns	Description
`dimension_names`	`list[str]`	Ordered list of parameter names