gemseo.mlearning.clustering.algos.base_clusterer_settings module#

Settings for the clustering algorithms.

Settings BaseClustererSettings(*, transformer=<factory>, parameters=<factory>, var_names=(), n_clusters=5, random_state=0)[source]#

Bases: BaseMLUnsupervisedAlgoSettings

The settings common to all the clustering algorithms.

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>.

  • var_names (Sequence[str]) --

    By default it is set to ().

  • n_clusters (Annotated[int, Gt(gt=0)]) --

    By default it is set to 5.

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

    By default it is set to 0.

Return type:

None

n_clusters: PositiveInt = 5#

The number of clusters of the clustering algorithm.

Constraints:

  • gt = 0

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.