FeatureAgglomeration#

classsklearn.cluster.FeatureAgglomeration(n_clusters=2, *, metric='euclidean', memory=None, connectivity=None, compute_full_tree='auto', linkage='ward', pooling_func=<function mean>, distance_threshold=None, compute_distances=False)[source] #

Agglomerate features.

Recursively merges pair of clusters of features.

Refer to Feature agglomeration vs. univariate selection for an example comparison of FeatureAgglomeration strategy with a univariate feature selection strategy (based on ANOVA).

Read more in the User Guide.

Parameters:
n_clustersint or None, default=2

The number of clusters to find. It must be None if distance_threshold is not None.

metricstr or callable, default="euclidean"

Metric used to compute the linkage. Can be "euclidean", "l1", "l2", "manhattan", "cosine", or "precomputed". If linkage is "ward", only "euclidean" is accepted. If "precomputed", a distance matrix is needed as input for the fit method.

Added in version 1.2.

memorystr or object with the joblib.Memory interface, default=None

Used to cache the output of the computation of the tree. By default, no caching is done. If a string is given, it is the path to the caching directory.

connectivityarray-like, sparse matrix, or callable, default=None

Connectivity matrix. Defines for each feature the neighboring features following a given structure of the data. This can be a connectivity matrix itself or a callable that transforms the data into a connectivity matrix, such as derived from kneighbors_graph. Default is None, i.e, the hierarchical clustering algorithm is unstructured.

compute_full_tree‘auto’ or bool, default=’auto’

Stop early the construction of the tree at n_clusters. This is useful to decrease computation time if the number of clusters is not small compared to the number of features. This option is useful only when specifying a connectivity matrix. Note also that when varying the number of clusters and using caching, it may be advantageous to compute the full tree. It must be True if distance_threshold is not None. By default compute_full_tree is "auto", which is equivalent to True when distance_threshold is not None or that n_clusters is inferior to the maximum between 100 or 0.02 * n_samples. Otherwise, "auto" is equivalent to False.

linkage{"ward", "complete", "average", "single"}, default="ward"

Which linkage criterion to use. The linkage criterion determines which distance to use between sets of features. The algorithm will merge the pairs of cluster that minimize this criterion.

  • "ward" minimizes the variance of the clusters being merged.

  • "complete" or maximum linkage uses the maximum distances between all features of the two sets.

  • "average" uses the average of the distances of each feature of the two sets.

  • "single" uses the minimum of the distances between all features of the two sets.

pooling_funccallable, default=np.mean

This combines the values of agglomerated features into a single value, and should accept an array of shape [M, N] and the keyword argument axis=1, and reduce it to an array of size [M].

distance_thresholdfloat, default=None

The linkage distance threshold at or above which clusters will not be merged. If not None, n_clusters must be None and compute_full_tree must be True.

Added in version 0.21.

compute_distancesbool, default=False

Computes distances between clusters even if distance_threshold is not used. This can be used to make dendrogram visualization, but introduces a computational and memory overhead.

Added in version 0.24.

Attributes:
n_clusters_int

The number of clusters found by the algorithm. If distance_threshold=None, it will be equal to the given n_clusters.

labels_array-like of (n_features,)

Cluster labels for each feature.

n_leaves_int

Number of leaves in the hierarchical tree.

n_connected_components_int

The estimated number of connected components in the graph.

Added in version 0.21: n_connected_components_ was added to replace n_components_.

n_features_in_int

Number of features seen during fit.

Added in version 0.24.

feature_names_in_ndarray of shape (n_features_in_,)

Names of features seen during fit. Defined only when X has feature names that are all strings.

Added in version 1.0.

children_array-like of shape (n_nodes-1, 2)

The children of each non-leaf node. Values less than n_features correspond to leaves of the tree which are the original samples. A node i greater than or equal to n_features is a non-leaf node and has children children_[i - n_features]. Alternatively at the i-th iteration, children[i][0] and children[i][1] are merged to form node n_features + i.

distances_array-like of shape (n_nodes-1,)

Distances between nodes in the corresponding place in children_. Only computed if distance_threshold is used or compute_distances is set to True.

See also

AgglomerativeClustering

Agglomerative clustering samples instead of features.

ward_tree

Hierarchical clustering with ward linkage.

Examples

>>> importnumpyasnp
>>> fromsklearnimport datasets, cluster
>>> digits = datasets.load_digits()
>>> images = digits.images
>>> X = np.reshape(images, (len(images), -1))
>>> agglo = cluster.FeatureAgglomeration(n_clusters=32)
>>> agglo.fit(X)
FeatureAgglomeration(n_clusters=32)
>>> X_reduced = agglo.transform(X)
>>> X_reduced.shape
(1797, 32)
fit(X, y=None)[source] #

Fit the hierarchical clustering on the data.

Parameters:
Xarray-like of shape (n_samples, n_features)

The data.

yIgnored

Not used, present here for API consistency by convention.

Returns:
selfobject

Returns the transformer.

propertyfit_predict#

Fit and return the result of each sample’s clustering assignment.

fit_transform(X, y=None, **fit_params)[source] #

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_feature_names_out(input_features=None)[source] #

Get output feature names for transformation.

The feature names out will prefixed by the lowercased class name. For example, if the transformer outputs 3 features, then the feature names out are: ["class_name0", "class_name1", "class_name2"].

Parameters:
input_featuresarray-like of str or None, default=None

Only used to validate feature names with the names seen in fit.

Returns:
feature_names_outndarray of str objects

Transformed feature names.

get_metadata_routing()[source] #

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)[source] #

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.

inverse_transform(X)[source] #

Inverse the transformation and return a vector of size n_features.

Parameters:
Xarray-like of shape (n_samples, n_clusters) or (n_clusters,)

The values to be assigned to each cluster of samples.

Returns:
X_originalndarray of shape (n_samples, n_features) or (n_features,)

A vector of size n_samples with the values of X assigned to each of the cluster of samples.

set_output(*, transform=None)[source] #

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)[source] #

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.

transform(X)[source] #

Transform a new matrix using the built clustering.

Parameters:
Xarray-like of shape (n_samples, n_features) or (n_samples, n_samples)

A M by N array of M observations in N dimensions or a length M array of M one-dimensional observations.

Returns:
Yndarray of shape (n_samples, n_clusters) or (n_clusters,)

The pooled values for each feature cluster.