cluster module

class mvcluster.cluster.LMGEC(n_clusters: int = 3, embedding_dim: int = 10, temperature: float = 0.5, max_iter: int = 30, tolerance: float = 1e-06)[source]

Bases: BaseEstimator, ClusterMixin

Localized Multi-Graph Embedding Clustering (LMGEC) model.

Parameters:

  • n_clusters (int) – Number of clusters to form.

  • embedding_dim (int) – Dimension of embedding space.

  • temperature (float) – Temperature for view weighting.

  • max_iter (int) – Max training iterations.

  • tolerance (float) – Convergence threshold.

fit(X_views, y=None)[source]

Fit the LMGEC model to multiple data views.

Parameters:

  • X_views (list) – List of 2D arrays (one per view), shape (n_samples, n_features) for each.

  • y – Ignored, for API compatibility.

Returns:

The fitted estimator.

Return type:

self

predict(X_views)[source]

Predict cluster labels for input views after fitting.

Parameters:

X_views (list) – List of feature matrices (ignored).

Returns:

Cluster labels from fit.

Return type:

array-like

set_fit_request(*, X_views: bool | None | str = '$UNCHANGED$') → LMGEC

Configure whether metadata should be requested to be passed to the fit method.

Note that this method is only relevant when this estimator is used as a sub-estimator within a meta-estimator and metadata routing is enabled with enable_metadata_routing=True (see sklearn.set_config()). Please check the User Guide on how the routing mechanism works.

The options for each parameter are:

  • True: metadata is requested, and passed to fit if provided. The request is ignored if metadata is not provided.

  • False: metadata is not requested and the meta-estimator will not pass it to fit.

  • None: metadata is not requested, and the meta-estimator will raise an error if the user provides it.

  • str: metadata should be passed to the meta-estimator with this given alias instead of the original name.

The default (sklearn.utils.metadata_routing.UNCHANGED) retains the existing request. This allows you to change the request for some parameters and not others.

Added in version 1.3.

X_viewsstr, True, False, or None, default=sklearn.utils.metadata_routing.UNCHANGED

Metadata routing for X_views parameter in fit.

selfobject

The updated object.

set_predict_request(*, X_views: bool | None | str = '$UNCHANGED$') → LMGEC

Configure whether metadata should be requested to be passed to the predict method.

Note that this method is only relevant when this estimator is used as a sub-estimator within a meta-estimator and metadata routing is enabled with enable_metadata_routing=True (see sklearn.set_config()). Please check the User Guide on how the routing mechanism works.

The options for each parameter are:

  • True: metadata is requested, and passed to predict if provided. The request is ignored if metadata is not provided.

  • False: metadata is not requested and the meta-estimator will not pass it to predict.

  • None: metadata is not requested, and the meta-estimator will raise an error if the user provides it.

  • str: metadata should be passed to the meta-estimator with this given alias instead of the original name.

The default (sklearn.utils.metadata_routing.UNCHANGED) retains the existing request. This allows you to change the request for some parameters and not others.

Added in version 1.3.

X_viewsstr, True, False, or None, default=sklearn.utils.metadata_routing.UNCHANGED

Metadata routing for X_views parameter in predict.

selfobject

The updated object.

set_transform_request(*, X_views: bool | None | str = '$UNCHANGED$') → LMGEC

Configure whether metadata should be requested to be passed to the transform method.

Note that this method is only relevant when this estimator is used as a sub-estimator within a meta-estimator and metadata routing is enabled with enable_metadata_routing=True (see sklearn.set_config()). Please check the User Guide on how the routing mechanism works.

The options for each parameter are:

  • True: metadata is requested, and passed to transform if provided. The request is ignored if metadata is not provided.

  • False: metadata is not requested and the meta-estimator will not pass it to transform.

  • None: metadata is not requested, and the meta-estimator will raise an error if the user provides it.

  • str: metadata should be passed to the meta-estimator with this given alias instead of the original name.

The default (sklearn.utils.metadata_routing.UNCHANGED) retains the existing request. This allows you to change the request for some parameters and not others.

Added in version 1.3.

X_viewsstr, True, False, or None, default=sklearn.utils.metadata_routing.UNCHANGED

Metadata routing for X_views parameter in transform.

selfobject

The updated object.

transform(X_views)[source]

Transform input views into the final embedding space.

Parameters:

X_views (list) – List of feature matrices (ignored).

Returns:

Consensus embedding from fit.

Return type:

array-like