Clustering algorithms

Warning

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

Note

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

GaussianMixture

Module: gemseo.mlearning.cluster.gaussian_mixture

Required parameters

• data : Dataset

  The learning dataset.

Optional parameters

• n_components : int, optional

  The number of components of the Gaussian mixture.

  By default it is set to 5.

• transformer : Mapping[str, TransformerType] | None, optional

  The strategies to transform the variables. The values are instances of Transformer 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 Transformer will be applied to all the variables of this group. If None, do not transform the variables.

  By default it is set to None.

• var_names : Iterable[str] | None, optional

  The names of the variables. If None, consider all variables mentioned in the learning dataset.

  By default it is set to None.

• **parameters : int | float | str | bool | None

  The parameters of the machine learning algorithm.

KMeans

Module: gemseo.mlearning.cluster.kmeans

Required parameters

• data : Dataset

  The learning dataset.

Optional parameters

• n_clusters : int, optional

  The number of clusters of the K-means algorithm.

  By default it is set to 5.

• random_state : int | None, optional

  If None, use a random generation of the initial centroids. If not None, the integer is used to make the initialization deterministic.

  By default it is set to 0.

• transformer : Mapping[str, TransformerType] | None, optional

  The strategies to transform the variables. The values are instances of Transformer 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 Transformer will be applied to all the variables of this group. If None, do not transform the variables.

  By default it is set to None.

• var_names : Iterable[str] | None, optional

  The names of the variables. If None, consider all variables mentioned in the learning dataset.

  By default it is set to None.

• **parameters : int | float | bool | str | None

  The parameters of the machine learning algorithm.

MLPredictiveClusteringAlgo

Module: gemseo.mlearning.cluster.cluster

Required parameters

• data : Dataset

  The learning dataset.

Optional parameters

• transformer : Mapping[str, TransformerType] | None, optional

  The strategies to transform the variables. The values are instances of Transformer 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 Transformer will be applied to all the variables of this group. If None, do not transform the variables.

  By default it is set to None.

• var_names : Iterable[str] | None, optional

  The names of the variables. If None, consider all variables mentioned in the learning dataset.

  By default it is set to None.

• **parameters : MLAlgoParameterType

  The parameters of the machine learning algorithm.