package sklearn

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sklearn
- Sklearn

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Class type

type tag = [

| `FeatureAgglomeration

]

type t =
  [ `BaseEstimator
  | `ClusterMixin
  | `FeatureAgglomeration
  | `Object
  | `TransformerMixin ]
    Obj.t

val of_pyobject : Py.Object.t -> t

val to_pyobject : [> tag ] Obj.t -> Py.Object.t

val as_estimator : t -> [ `BaseEstimator ] Obj.t

val as_transformer : t -> [ `TransformerMixin ] Obj.t

val as_cluster : t -> [ `ClusterMixin ] Obj.t

val create : 
  ?n_clusters:int ->
  ?affinity:[ `S of string | `Callable of Py.Object.t ] ->
  ?memory:
    [ `S of string | `Object_with_the_joblib_Memory_interface of Py.Object.t ] ->
  ?connectivity:[ `Callable of Py.Object.t | `Arr of [> `ArrayLike ] Np.Obj.t ] ->
  ?compute_full_tree:[ `Auto | `Bool of bool ] ->
  ?linkage:[ `Ward | `Complete | `Average | `Single ] ->
  ?pooling_func:Py.Object.t ->
  ?distance_threshold:float ->
  unit ->
  t

Agglomerate features.

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. It must be ``None`` if ``distance_threshold`` is not ``None``.

affinity : str 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 : str 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.

connectivity : array-like 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, 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. 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.
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.
single uses the minimum of the distances between all observations 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.

distance_threshold : float, default=None The linkage distance threshold above which, clusters will not be merged. If not ``None``, ``n_clusters`` must be ``None`` and ``compute_full_tree`` must be ``True``.

.. versionadded:: 0.21

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.

.. versionadded:: 0.21 ``n_connected_components_`` was added to replace ``n_components_``.

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, childreni0 and childreni1 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 not None.

Examples -------- >>> import numpy as np >>> from sklearn import 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)

val fit : 
  ?y:Py.Object.t ->
  ?params:(string * Py.Object.t) list ->
  x:[> `ArrayLike ] Np.Obj.t ->
  [> tag ] Obj.t ->
  t

Fit the hierarchical clustering on the data

Parameters ---------- X : array-like of shape (n_samples, n_features) The data

y : Ignored

Returns ------- self

val fit_transform : 
  ?y:[> `ArrayLike ] Np.Obj.t ->
  ?fit_params:(string * Py.Object.t) list ->
  x:[> `ArrayLike ] Np.Obj.t ->
  [> tag ] Obj.t ->
  [> `ArrayLike ] Np.Obj.t

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 ---------- X : array-like, sparse matrix, dataframe of shape (n_samples, n_features)

y : ndarray of shape (n_samples,), default=None Target values.

**fit_params : dict Additional fit parameters.

Returns ------- X_new : ndarray array of shape (n_samples, n_features_new) Transformed array.

val get_params : ?deep:bool -> [> tag ] Obj.t -> Dict.t

Get parameters for this estimator.

Parameters ---------- deep : bool, default=True If True, will return the parameters for this estimator and contained subobjects that are estimators.

Returns ------- params : mapping of string to any Parameter names mapped to their values.

val inverse_transform : 
  xred:[> `ArrayLike ] Np.Obj.t ->
  [> tag ] Obj.t ->
  [> `ArrayLike ] Np.Obj.t

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

Parameters ---------- Xred : array-like of shape (n_samples, n_clusters) or (n_clusters,) The values to be assigned to each cluster of samples

Returns ------- 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.

val set_params : ?params:(string * Py.Object.t) list -> [> tag ] Obj.t -> t

Set the parameters of this estimator.

The method works on simple estimators as well as on nested objects (such as pipelines). The latter have parameters of the form ``<component>__<parameter>`` so that it's possible to update each component of a nested object.

Parameters ---------- **params : dict Estimator parameters.

Returns ------- self : object Estimator instance.

val transform : 
  x:[> `ArrayLike ] Np.Obj.t ->
  [> tag ] Obj.t ->
  [> `ArrayLike ] Np.Obj.t

Transform a new matrix using the built clustering

Parameters ---------- X : array-like of shape (n_samples, n_features) or (n_samples,) A M by N array of M observations in N dimensions or a length M array of M one-dimensional observations.

Returns ------- Y : array, shape = n_samples, n_clusters or n_clusters The pooled values for each feature cluster.

val n_clusters_ : t -> int

Attribute n_clusters_: get value or raise Not_found if None.

val n_clusters_opt : t -> int option

Attribute n_clusters_: get value as an option.

val labels_ : t -> [> `ArrayLike ] Np.Obj.t

Attribute labels_: get value or raise Not_found if None.

val labels_opt : t -> [> `ArrayLike ] Np.Obj.t option

Attribute labels_: get value as an option.

val n_leaves_ : t -> int

Attribute n_leaves_: get value or raise Not_found if None.

val n_leaves_opt : t -> int option

Attribute n_leaves_: get value as an option.

val n_connected_components_ : t -> int

Attribute n_connected_components_: get value or raise Not_found if None.

val n_connected_components_opt : t -> int option

Attribute n_connected_components_: get value as an option.

val children_ : t -> [> `ArrayLike ] Np.Obj.t

Attribute children_: get value or raise Not_found if None.

val children_opt : t -> [> `ArrayLike ] Np.Obj.t option

Attribute children_: get value as an option.

val distances_ : t -> [> `ArrayLike ] Np.Obj.t

Attribute distances_: get value or raise Not_found if None.

val distances_opt : t -> [> `ArrayLike ] Np.Obj.t option

Attribute distances_: get value as an option.

val to_string : t -> string

Print the object to a human-readable representation.

val show : t -> string

Print the object to a human-readable representation.

val pp : Stdlib.Format.formatter -> t -> unit

Pretty-print the object to a formatter.