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)[source]¶
Hierarchical KMeans.
First clusterize the samples into big clusters. Then clusterize the samples inside these big clusters into smaller ones.
- Parameters:
- n_clusters: int
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.
- scaling: bool, 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.
- verbose: int, 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)[source]¶
- fit(X, y=None)[source]¶
Compute clustering of the data.
- Parameters:
- X: ndarray, shape = [n_features, n_samples]
Training data.
- y: Ignored
- Returns:
- self
- transform(X, y=None)[source]¶
Apply clustering, reduce the dimensionality of the data.
- Parameters:
- X: ndarray, shape = [n_features, n_samples]
Data to transform with the fitted clustering.
- Returns:
- X_red: ndarray, shape = [n_clusters, n_samples]
Data reduced with agglomerated signal for each cluster
- inverse_transform(X_red)[source]¶
Send the reduced 2D data matrix back to the original feature space (voxels).
- Parameters:
- X_red: ndarray , shape = [n_clusters, n_samples]
Data reduced with agglomerated signal for each cluster
- Returns:
- X_inv: ndarray, 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
(seesklearn.set_config
). Please see User Guide on how the routing mechanism works.The options for each parameter are:
True
: metadata is requested, and passed toinverse_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 toinverse_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 ininverse_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”, “polars”}, 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.