Note

This page is a reference documentation. It only explains the class signature, and not how to use it. Please refer to the user guide for the big picture.

nilearn.regions.HierarchicalKMeans

class nilearn.regions.HierarchicalKMeans(n_clusters, init='k-means++', batch_size=1000, n_init=10, max_no_improvement=10, verbose=0, random_state=0, scaling=False)

Hierarchical KMeans.

First clusterize the samples into big clusters. Then clusterize the samples inside these big clusters into smaller ones.

Parameters:

n_clustersint

The number of clusters to find.

init{‘k-means++’, ‘random’ or an ndarray}, default=’k-means++’

Method for initialization.

• ‘k-means++’ : selects initial cluster centers for k-means clustering in a smart way to speed up convergence. See section Notes in k_init for more details.

• ‘random’: choose k observations (rows) at random from data for the initial centroids.

• If an ndarray is passed, it should be of shape (n_clusters, n_features) and gives the initial centers.

batch_sizeint, optional, default: 1000

Size of the mini batches. (Kmeans performed through MiniBatchKMeans)

n_initint, default=10

Number of random initializations that are tried. In contrast to KMeans, the algorithm is only run once, using the best of the n_init initializations as measured by inertia.

max_no_improvementint, default: 10

Control early stopping based on the consecutive number of mini batches that does not yield an improvement on the smoothed inertia. To disable convergence detection based on inertia, set max_no_improvement to None.

random_stateint, RandomState instance or None (default)

Determines random number generation for centroid initialization and random reassignment. Use an int to make the randomness deterministic.

scalingbool, optional (default False)

If scaling is True, each cluster is scaled by the square root of its size during transform(), preserving the l2-norm of the image. inverse_transform() will apply inversed scaling to yield an image with same l2-norm as input.

verboseint, optional (default 0)

Verbosity level.

Attributes:

labels_ndarray, shape = [n_features]

cluster labels for each feature.

sizes_ndarray, shape = [n_features]

It contains the size of each cluster.

__init__(n_clusters, init='k-means++', batch_size=1000, n_init=10, max_no_improvement=10, verbose=0, random_state=0, scaling=False)

fit(X, y=None)

Compute clustering of the data.

Parameters:

Xndarray, shape = [n_features, n_samples]

Training data.

yIgnored

Returns:

self

transform(X, y=None)

Apply clustering, reduce the dimensionality of the data.

Parameters:

Xndarray, shape = [n_features, n_samples]

Data to transform with the fitted clustering.

Returns:

X_redndarray, shape = [n_clusters, n_samples]

Data reduced with agglomerated signal for each cluster

inverse_transform(X_red)

Send the reduced 2D data matrix back to the original feature space (voxels).

Parameters:

X_redndarray , shape = [n_clusters, n_samples]

Data reduced with agglomerated signal for each cluster

Returns:

X_invndarray, shape = [n_features, n_samples]

Data reduced expanded to the original feature space

fit_predict(X, y=None, **kwargs)

Perform clustering on X and returns cluster labels.

Parameters:

Xarray-like of shape (n_samples, n_features)

Input data.

yIgnored

Not used, present for API consistency by convention.

**kwargsdict

Arguments to be passed to fit.

Added in version 1.4.

Returns:

labelsndarray of shape (n_samples,), dtype=np.int64

Cluster labels.

fit_transform(X, y=None, **fit_params)

Fit to data, then transform it.

Fits transformer to X and y with optional parameters fit_params and returns a transformed version of X.

Parameters:

Xarray-like of shape (n_samples, n_features)

Input samples.

yarray-like of shape (n_samples,) or (n_samples, n_outputs), default=None

Target values (None for unsupervised transformations).

**fit_paramsdict

Additional fit parameters.

Returns:

X_newndarray array of shape (n_samples, n_features_new)

Transformed array.

get_metadata_routing()

Get metadata routing of this object.

Please check User Guide on how the routing mechanism works.

Returns:

routingMetadataRequest

A MetadataRequest encapsulating routing information.

get_params(deep=True)

Get parameters for this estimator.

Parameters:

deepbool, default=True

If True, will return the parameters for this estimator and contained subobjects that are estimators.

Returns:

paramsdict

Parameter names mapped to their values.

set_inverse_transform_request(*, X_red='$UNCHANGED$')

Request metadata passed to the inverse_transform 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 inverse_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 inverse_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.

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:

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

Metadata routing for X_red parameter in inverse_transform.

Returns:

selfobject

The updated object.

set_output(*, transform=None)

Set output container.

See Introducing the set_output API for an example on how to use the API.

Parameters:

transform{“default”, “pandas”}, default=None

Configure output of transform and fit_transform.

• “default”: Default output format of a transformer

• “pandas”: DataFrame output

• “polars”: Polars output

• None: Transform configuration is unchanged

Added in version 1.4: “polars” option was added.

Returns:

selfestimator instance

Estimator instance.

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:

**paramsdict

Estimator parameters.

Returns:

selfestimator instance

Estimator instance.