Parameters

siapy.optimizers.parameters

ParametersDictType module-attribute

ParametersDictType = dict[str, list[dict[str, Any]]]

FloatParameter

Bases: BaseModel

name instance-attribute

name: str

low instance-attribute

low: float

high instance-attribute

high: float

step class-attribute instance-attribute

step: float | None = None

log class-attribute instance-attribute

log: bool = False

IntParameter

Bases: BaseModel

name instance-attribute

name: str

low instance-attribute

low: int

high instance-attribute

high: int

step class-attribute instance-attribute

step: int = 1

log class-attribute instance-attribute

log: bool = False

CategoricalParameter

Bases: BaseModel

name instance-attribute

name: str

choices instance-attribute

choices: Sequence[None | bool | int | float | str]

TrialParameters dataclass

TrialParameters(float_parameters: list[FloatParameter] | None = None, int_parameters: list[IntParameter] | None = None, categorical_parameters: list[CategoricalParameter] | None = None)

PARAMETER	DESCRIPTION
float_parameters	TYPE: list[FloatParameter] | None DEFAULT: None
int_parameters	TYPE: list[IntParameter] | None DEFAULT: None
categorical_parameters	TYPE: list[CategoricalParameter] | None DEFAULT: None

Source code in siapy/optimizers/parameters.py

def __init__(
    self,
    float_parameters: list[FloatParameter] | None = None,
    int_parameters: list[IntParameter] | None = None,
    categorical_parameters: list[CategoricalParameter] | None = None,
):
    self._float_parameters = float_parameters or []
    self._int_parameters = int_parameters or []
    self._categorical_parameters = categorical_parameters or []

float_parameters property

float_parameters: list[FloatParameter]

int_parameters property

int_parameters: list[IntParameter]

categorical_parameters property

categorical_parameters: list[CategoricalParameter]

from_dict classmethod

from_dict(parameters: ParametersDictType) -> TrialParameters

PARAMETER	DESCRIPTION
parameters	TYPE: ParametersDictType

Source code in siapy/optimizers/parameters.py

@classmethod
def from_dict(cls, parameters: ParametersDictType) -> "TrialParameters":
    float_params = [
        FloatParameter(**fp) for fp in parameters.get("float_parameters", [])
    ]
    int_params = [IntParameter(**ip) for ip in parameters.get("int_parameters", [])]
    cat_params = [
        CategoricalParameter(**cp)
        for cp in parameters.get("categorical_parameters", [])
    ]
    return cls(
        float_parameters=float_params,
        int_parameters=int_params,
        categorical_parameters=cat_params,
    )