# FeatureAgglomeration¶

class ibex.sklearn.cluster.FeatureAgglomeration(n_clusters=2, affinity='euclidean', memory=None, connectivity=None, compute_full_tree='auto', linkage='ward', pooling_func=<function mean>)

Bases: sklearn.cluster.hierarchical.FeatureAgglomeration, ibex._base.FrameMixin

Note

The documentation following is of the class wrapped by this class. There are some changes, in particular:

Agglomerate features.

Similar to AgglomerativeClustering, but recursively merges features instead of samples.

Read more in the User Guide.

n_clusters : int, default 2
The number of clusters to find.
affinity : string 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.
memory : None, str or object with the joblib.Memory interface, optional
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.
connectivity : array-like or callable, optional
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 : bool or ‘auto’, optional, 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.
linkage : {“ward”, “complete”, “average”}, optional, 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.
• average uses the average of the distances of each feature of the two sets.
• complete or maximum linkage uses the maximum distances between all features of the two sets.
pooling_func : callable, 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].
labels_ : array-like, (n_features,)
cluster labels for each feature.
n_leaves_ : int
Number of leaves in the hierarchical tree.
n_components_ : int
The estimated number of connected components in the graph.
children_ : array-like, 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
fit(X, y=None, **params)[source]

Note

The documentation following is of the class wrapped by this class. There are some changes, in particular:

Fit the hierarchical clustering on the data

X : array-like, shape = [n_samples, n_features]
The data

y : Ignored

self

fit_predict()

Note

The documentation following is of the class wrapped by this class. There are some changes, in particular:

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

Note

The documentation following is of the class wrapped by this class. There are some changes, in particular:

Fit to data, then transform it.

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

X : numpy array of shape [n_samples, n_features]
Training set.
y : numpy array of shape [n_samples]
Target values.
X_new : numpy array of shape [n_samples, n_features_new]
Transformed array.
inverse_transform(Xred)

Note

The documentation following is of the class wrapped by this class. There are some changes, in particular:

Inverse the transformation. Return a vector of size nb_features with the values of Xred assigned to each group of features

Xred : array-like, shape=[n_samples, n_clusters] or [n_clusters,]
The values to be assigned to each cluster of samples
X : array, shape=[n_samples, n_features] or [n_features]
A vector of size n_samples with the values of Xred assigned to each of the cluster of samples.
transform(X)

Note

The documentation following is of the class wrapped by this class. There are some changes, in particular:

Transform a new matrix using the built clustering

X : array-like, shape = [n_samples, n_features] or [n_features]
A M by N array of M observations in N dimensions or a length M array of M one-dimensional observations.
Y : array, shape = [n_samples, n_clusters] or [n_clusters]
The pooled values for each feature cluster.