Class FeatureAgglomerationScikitsLearnNode

Agglomerate features.

This node has been automatically generated by wrapping the ``sklearn.cluster.hierarchical.FeatureAgglomeration`` class
from the ``sklearn`` library.  The wrapped instance can be accessed
through the ``scikits_alg`` attribute.

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

Read more in the :ref:`User Guide <hierarchical_clustering>`.

**Parameters**

n_clusters : int, default 2
    The number of clusters to find.

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.

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 : Instance of joblib.Memory or string, 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.

n_components : int (optional)
    Number of connected components. If None the number of connected
    components is estimated from the connectivity matrix.
    NOTE: This parameter is now directly determined from the connectivity
    matrix and will be removed in 0.18

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].

**Attributes**

``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`

Overrides: object.__init__

Transform a new matrix using the built clustering

This node has been automatically generated by wrapping the sklearn.cluster.hierarchical.FeatureAgglomeration class from the sklearn library. The wrapped instance can be accessed through the scikits_alg attribute.

Parameters

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.

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].

Returns

Y : array, shape = [n_samples, n_clusters] or [n_clusters]

The pooled values for each feature cluster.

Overrides: Node.execute

Overrides: Node.is_invertible

(inherited documentation)

Overrides: Node.is_trainable

Fit the hierarchical clustering on the data

This node has been automatically generated by wrapping the sklearn.cluster.hierarchical.FeatureAgglomeration class from the sklearn library. The wrapped instance can be accessed through the scikits_alg attribute.

Parameters

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

The data

Returns

self

Overrides: Node.stop_training