gemseo.mlearning.classification.algos.svm_settings module#

Settings of the SVM classification algorithm.

Settings SVMClassifier_Settings(*, transformer=<factory>, parameters=<factory>, input_names=(), output_names=(), C=1.0, kernel='rbf', probability=False, random_state=0)[source]#

Bases: BaseClassifierSettings

The settings of the SV classification algorithm.

Create a new model by parsing and validating input data from keyword arguments.

Raises [ValidationError][pydantic_core.ValidationError] if the input data cannot be validated to form a valid model.

self is explicitly positional-only to allow self as a field name.

Parameters:

  • transformer (Mapping[str, Any]) --

    By default it is set to <factory>.

  • parameters (Mapping[str, Any]) --

    By default it is set to <factory>.

  • input_names (Sequence[str]) --

    By default it is set to ().

  • output_names (Sequence[str]) --

    By default it is set to ().

  • C (Annotated[float, Gt(gt=0)]) --

    By default it is set to 1.0.

  • kernel (str | Annotated[Callable, WithJsonSchema(json_schema={}, mode=None)]) --

    By default it is set to "rbf".

  • probability (bool) --

    By default it is set to False.

  • random_state (Annotated[int, Ge(ge=0)] | None) --

    By default it is set to 0.

Return type:

None

C: PositiveFloat = 1.0#

The inverse L2 regularization parameter.

Constraints:

  • gt = 0

kernel: str | Annotated[Callable, WithJsonSchema({})] = 'rbf'#

The name of the kernel or a callable for the SVM.

Examples of names: "linear", "poly", "rbf", "sigmoid", "precomputed".

probability: bool = False#

Whether to enable the probability estimates.

random_state: NonNegativeInt | None = 0#

The random state parameter.

If None, use the global random state instance from numpy.random. Creating the model multiple times will produce different results. If int, use a new random number generator seeded by this integer. This will produce the same results.

model_post_init(context, /)#

This function is meant to behave like a BaseModel method to initialise private attributes.

It takes context as an argument since that's what pydantic-core passes when calling it.

Parameters:

  • self (BaseModel) -- The BaseModel instance.

  • context (Any) -- The context.

Return type:

None