Class: FeatureAgglomeration

Agglomerate features.

Recursively merges pair of clusters of features.

Constructors

new FeatureAgglomeration()

new FeatureAgglomeration(opts?): FeatureAgglomeration

Parameters

Parameter	Type	Description
`opts`?	`object`	-
`opts.compute_distances`?	`boolean`	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.
`opts.compute_full_tree`?	`boolean` \| `"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 `undefined`. By default `compute_full_tree` is “auto”, which is equivalent to `true` when `distance_threshold` is not `undefined` or that `n_clusters` is inferior to the maximum between 100 or `0.02 \* n_samples`. Otherwise, “auto” is equivalent to `false`.
`opts.connectivity`?	`ArrayLike`	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 `undefined`, i.e, the hierarchical clustering algorithm is unstructured.
`opts.distance_threshold`?	`number`	The linkage distance threshold at or above which clusters will not be merged. If not `undefined`, `n_clusters` must be `undefined` and `compute_full_tree` must be `true`.
`opts.linkage`?	`"ward"` \| `"complete"` \| `"average"` \| `"single"`	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.
`opts.memory`?	`string`	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.
`opts.metric`?	`string`	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.
`opts.n_clusters`?	`number`	The number of clusters to find. It must be `undefined` if `distance_threshold` is not `undefined`.
`opts.pooling_func`?	`any`	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 FeatureAgglomeration

Defined in generated/cluster/FeatureAgglomeration.ts:25

Properties

Property	Type	Default value	Defined in
`_isDisposed`	`boolean`	`false`	generated/cluster/FeatureAgglomeration.ts:23
`_isInitialized`	`boolean`	`false`	generated/cluster/FeatureAgglomeration.ts:22
`_py`	`PythonBridge`	`undefined`	generated/cluster/FeatureAgglomeration.ts:21
`id`	`string`	`undefined`	generated/cluster/FeatureAgglomeration.ts:18
`opts`	`any`	`undefined`	generated/cluster/FeatureAgglomeration.ts:19

Accessors

children_

Get Signature

get children_(): Promise<ArrayLike[]>

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.

Returns Promise<ArrayLike[]>

Defined in generated/cluster/FeatureAgglomeration.ts:598

distances_

Get Signature

get distances_(): Promise<ArrayLike>

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

Returns Promise<ArrayLike>

Defined in generated/cluster/FeatureAgglomeration.ts:625

feature_names_in_

Get Signature

get feature_names_in_(): Promise<ArrayLike>

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

Returns Promise<ArrayLike>

Defined in generated/cluster/FeatureAgglomeration.ts:571

labels_

Get Signature

get labels_(): Promise<any>

Cluster labels for each feature.

Returns Promise<any>

Defined in generated/cluster/FeatureAgglomeration.ts:463

n_clusters_

Get Signature

get n_clusters_(): Promise<number>

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

Returns Promise<number>

Defined in generated/cluster/FeatureAgglomeration.ts:436

n_connected_components_

Get Signature

get n_connected_components_(): Promise<number>

The estimated number of connected components in the graph.

Returns Promise<number>

Defined in generated/cluster/FeatureAgglomeration.ts:517

n_features_in_

Get Signature

get n_features_in_(): Promise<number>

Number of features seen during fit.

Returns Promise<number>

Defined in generated/cluster/FeatureAgglomeration.ts:544

n_leaves_

Get Signature

get n_leaves_(): Promise<number>

Number of leaves in the hierarchical tree.

Returns Promise<number>

Defined in generated/cluster/FeatureAgglomeration.ts:490

py

Get Signature

get py(): PythonBridge

Returns PythonBridge

Set Signature

set py(pythonBridge): void

Parameters

Parameter	Type
`pythonBridge`	`PythonBridge`

Returns void

Defined in generated/cluster/FeatureAgglomeration.ts:85

Methods

dispose()

dispose(): Promise<void>

Disposes of the underlying Python resources.

Once dispose() is called, the instance is no longer usable.

Returns Promise<void>

Defined in generated/cluster/FeatureAgglomeration.ts:141

fit()

fit(opts): Promise<any>

Fit the hierarchical clustering on the data.

Parameters

Parameter	Type	Description
`opts`	`object`	-
`opts.X`?	`ArrayLike`[]	The data.
`opts.y`?	`any`	Not used, present here for API consistency by convention.

Returns Promise<any>

Defined in generated/cluster/FeatureAgglomeration.ts:158

fit_transform()

fit_transform(opts): Promise<any[]>

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

Parameter	Type	Description
`opts`	`object`	-
`opts.fit_params`?	`any`	Additional fit parameters.
`opts.X`?	`ArrayLike`[]	Input samples.
`opts.y`?	`ArrayLike`	Target values (`undefined` for unsupervised transformations).

Returns Promise<any[]>

Defined in generated/cluster/FeatureAgglomeration.ts:199

get_feature_names_out()

get_feature_names_out(opts): Promise<any>

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

Parameter	Type	Description
`opts`	`object`	-
`opts.input_features`?	`any`	Only used to validate feature names with the names seen in `fit`.

Returns Promise<any>

Defined in generated/cluster/FeatureAgglomeration.ts:247

get_metadata_routing()

get_metadata_routing(opts): Promise<any>

Get metadata routing of this object.

Please check User Guide on how the routing mechanism works.

Parameters

Parameter	Type	Description
`opts`	`object`	-
`opts.routing`?	`any`	A `MetadataRequest` encapsulating routing information.

Returns Promise<any>

Defined in generated/cluster/FeatureAgglomeration.ts:285

init()

init(py): Promise<void>

Initializes the underlying Python resources.

This instance is not usable until the Promise returned by init() resolves.

Parameters

Parameter	Type
`py`	`PythonBridge`

Returns Promise<void>

Defined in generated/cluster/FeatureAgglomeration.ts:98

inverse_transform()

inverse_transform(opts): Promise<ArrayLike[]>

Inverse the transformation and return a vector of size n_features.

Parameters

Parameter	Type	Description
`opts`	`object`	-
`opts.X`?	`ArrayLike`[]	The values to be assigned to each cluster of samples.
`opts.Xt`?	`ArrayLike`[]	The values to be assigned to each cluster of samples.