Clustering algorithms

Warning

Some capabilities may require the installation of GEMSEO with all its features and some others may depend on plugins.

Warning

All the features of the wrapped libraries may not be exposed through GEMSEO.

Note

The algorithm settings can be passed to a function of the form

function(..., settings_model: Base | None = None, **settings: Any)

either one by one:

function(..., setting_name_1=setting_name_1, setting_name_2=setting_name_2, ...)

or using the argument name "settings" and the Pydantic model associated with the algorithm:

settings = AlgorithmSettings(setting_name_1=setting_name_1, setting_name_2=setting_name_2, ...)
function(..., settings_model=settings)

GaussianMixture

Module: gemseo.mlearning.clustering.algos.gaussian_mixture

from gemseo.settings.mlearning import GaussianMixture_Settings

Optional settings

• n_clusters : <class 'int'>, optional

  The number of clusters of the clustering algorithm.

  By default it is set to 5.

• parameters : collections.abc.Mapping[str, typing.Any], optional

  Other parameters.

  By default it is set to {}.

• random_state : typing.Optional[typing.Annotated[int, Ge(ge=0)]], optional

  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.

  By default it is set to 0.

• transformer : collections.abc.Mapping[str, typing.Any], optional

  The strategies to transform the variables.

  The values are instances of BaseTransformer while the keys are the names of either the variables or the groups of variables, e.g. "inputs" or "outputs" in the case of the regression algorithms. If a group is specified, the BaseTransformer will be applied to all the variables of this group. If IDENTITY, do not transform the variables.

  By default it is set to {}.

• var_names : collections.abc.Sequence[str], optional

  The names of the variables.

  By default it is set to ().

KMeans

Module: gemseo.mlearning.clustering.algos.kmeans

from gemseo.settings.mlearning import KMeans_Settings

Optional settings

• n_clusters : <class 'int'>, optional

  The number of clusters of the clustering algorithm.

  By default it is set to 5.

• parameters : collections.abc.Mapping[str, typing.Any], optional

  Other parameters.

  By default it is set to {}.

• random_state : typing.Optional[typing.Annotated[int, Ge(ge=0)]], optional

  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.

  By default it is set to 0.

• transformer : collections.abc.Mapping[str, typing.Any], optional

  The strategies to transform the variables.

  The values are instances of BaseTransformer while the keys are the names of either the variables or the groups of variables, e.g. "inputs" or "outputs" in the case of the regression algorithms. If a group is specified, the BaseTransformer will be applied to all the variables of this group. If IDENTITY, do not transform the variables.

  By default it is set to {}.

• var_names : collections.abc.Sequence[str], optional

  The names of the variables.

  By default it is set to ().