flowsom.models.ConsensusCluster

flowsom.models.ConsensusCluster#

class flowsom.models.ConsensusCluster(n_clusters, K=None, H=100, resample_proportion=0.9, linkage='average', z_score=False, z_cap=3, cluster=<class 'sklearn.cluster._agglomerative.AgglomerativeClustering'>)#

Implementation of Consensus clustering.

This follows the paper https://link.springer.com/content/pdf/10.1023%2FA%3A1023949509487.pdf ZigaSajovic/Consensus_Clustering

  • cluster -> clustering class

  • NOTE: the class is to be instantiated with parameter n_clusters, and possess a fit_predict method, which is invoked on data.

  • L -> smallest number of clusters to try

  • K -> biggest number of clusters to try

  • H -> number of resamplings for each cluster number

  • resample_proportion -> percentage to sample.

Methods table#

fit(data)

Fits a consensus matrix for each number of clusters.

fit_predict(data)

Predicts on the consensus matrix, for best found cluster number.

get_metadata_routing()

Get metadata routing of this object.

get_params([deep])

Get parameters for this estimator.

set_fit_request(*[, data])

Request metadata passed to the fit method.

set_params(**params)

Set the parameters of this estimator.

Methods#

ConsensusCluster.fit(data)#

Fits a consensus matrix for each number of clusters.

Args:

  • data -> (examples,attributes) format

ConsensusCluster.fit_predict(data)#

Predicts on the consensus matrix, for best found cluster number.

ConsensusCluster.get_metadata_routing()#

Get metadata routing of this object.

Please check User Guide on how the routing mechanism works.

Returns:

-routing (MetadataRequest) A MetadataRequest encapsulating routing information.

ConsensusCluster.get_params(deep=True)#

Get parameters for this estimator.

Parameters:

deep (bool, default=True) – If True, will return the parameters for this estimator and contained subobjects that are estimators.

Returns:

-params (dict) Parameter names mapped to their values.

ConsensusCluster.set_fit_request(*, data: bool | None | str = '$UNCHANGED$') → ConsensusCluster#

Request metadata passed to the fit method.

Note that this method is only relevant if enable_metadata_routing=True (see sklearn.set_config()). Please see 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.

Note

This method is only relevant if this estimator is used as a sub-estimator of a meta-estimator, e.g. used inside a Pipeline. Otherwise it has no effect.

Parameters:

data (str, True, False, or None, default=sklearn.utils.metadata_routing.UNCHANGED) – Metadata routing for data parameter in fit.

Returns:

-self (object) The updated object.

ConsensusCluster.set_params(**params)#

Set the parameters of this estimator.

The method works on simple estimators as well as on nested objects (such as Pipeline). The latter have parameters of the form <component>__<parameter> so that it’s possible to update each component of a nested object.

Parameters:

**params (dict) – Estimator parameters.

Returns:

self : estimator instance Estimator instance.

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