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])

Configure whether metadata should be requested to be 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#

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.

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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