package sklearn

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package sklearn

sklearn
- Sklearn

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type tag = [

| `NMF

]

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

val of_pyobject : Py.Object.t -> t

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

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

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

val create : 
  ?n_components:int ->
  ?init:[ `Random | `Nndsvda | `Custom | `Nndsvdar | `Nndsvd ] ->
  ?solver:[ `Cd | `Mu ] ->
  ?beta_loss:[ `S of string | `F of float ] ->
  ?tol:float ->
  ?max_iter:int ->
  ?random_state:int ->
  ?alpha:float ->
  ?l1_ratio:float ->
  ?verbose:int ->
  ?shuffle:bool ->
  unit ->
  t

Non-Negative Matrix Factorization (NMF)

Find two non-negative matrices (W, H) whose product approximates the non- negative matrix X. This factorization can be used for example for dimensionality reduction, source separation or topic extraction.

The objective function is::

0.5 * ||X - WH||_Fro^2

alpha * l1_ratio * ||vec(W)||_1
alpha * l1_ratio * ||vec(H)||_1
0.5 * alpha * (1 - l1_ratio) * ||W||_Fro^2
0.5 * alpha * (1 - l1_ratio) * ||H||_Fro^2

Where::

||A||_Fro^2 = \sum_,j A_j^2 (Frobenius norm) ||vec(A)||_1 = \sum_,j abs(A_j) (Elementwise L1 norm)

For multiplicative-update ('mu') solver, the Frobenius norm (0.5 * ||X - WH||_Fro^2) can be changed into another beta-divergence loss, by changing the beta_loss parameter.

The objective function is minimized with an alternating minimization of W and H.

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

Parameters ---------- n_components : int or None Number of components, if n_components is not set all features are kept.

None: 'nndsvd' if n_components <= min(n_samples, n_features), otherwise random.

'random': non-negative random matrices, scaled with: sqrt(X.mean() / n_components)

'nndsvd': Nonnegative Double Singular Value Decomposition (NNDSVD) initialization (better for sparseness)

'nndsvda': NNDSVD with zeros filled with the average of X (better when sparsity is not desired)

'nndsvdar': NNDSVD with zeros filled with small random values (generally faster, less accurate alternative to NNDSVDa for when sparsity is not desired)

'custom': use custom matrices W and H

solver : 'cd' | 'mu' Numerical solver to use: 'cd' is a Coordinate Descent solver. 'mu' is a Multiplicative Update solver.

.. versionadded:: 0.17 Coordinate Descent solver.

.. versionadded:: 0.19 Multiplicative Update solver.

beta_loss : float or string, default 'frobenius' String must be in 'frobenius', 'kullback-leibler', 'itakura-saito'. Beta divergence to be minimized, measuring the distance between X and the dot product WH. Note that values different from 'frobenius' (or 2) and 'kullback-leibler' (or 1) lead to significantly slower fits. Note that for beta_loss <= 0 (or 'itakura-saito'), the input matrix X cannot contain zeros. Used only in 'mu' solver.

.. versionadded:: 0.19

tol : float, default: 1e-4 Tolerance of the stopping condition.

max_iter : integer, default: 200 Maximum number of iterations before timing out.

random_state : int, RandomState instance, default=None Used for initialisation (when ``init`` == 'nndsvdar' or 'random'), and in Coordinate Descent. Pass an int for reproducible results across multiple function calls. See :term:`Glossary <random_state>`.

alpha : double, default: 0. Constant that multiplies the regularization terms. Set it to zero to have no regularization.

.. versionadded:: 0.17 *alpha* used in the Coordinate Descent solver.

l1_ratio : double, default: 0. The regularization mixing parameter, with 0 <= l1_ratio <= 1. For l1_ratio = 0 the penalty is an elementwise L2 penalty (aka Frobenius Norm). For l1_ratio = 1 it is an elementwise L1 penalty. For 0 < l1_ratio < 1, the penalty is a combination of L1 and L2.

.. versionadded:: 0.17 Regularization parameter *l1_ratio* used in the Coordinate Descent solver.

verbose : bool, default=False Whether to be verbose.

shuffle : boolean, default: False If true, randomize the order of coordinates in the CD solver.

.. versionadded:: 0.17 *shuffle* parameter used in the Coordinate Descent solver.

Attributes ---------- components_ : array, n_components, n_features Factorization matrix, sometimes called 'dictionary'.

n_components_ : integer The number of components. It is same as the `n_components` parameter if it was given. Otherwise, it will be same as the number of features.

reconstruction_err_ : number Frobenius norm of the matrix difference, or beta-divergence, between the training data ``X`` and the reconstructed data ``WH`` from the fitted model.

n_iter_ : int Actual number of iterations.

Examples -------- >>> import numpy as np >>> X = np.array([1, 1], [2, 1], [3, 1.2], [4, 1], [5, 0.8], [6, 1]) >>> from sklearn.decomposition import NMF >>> model = NMF(n_components=2, init='random', random_state=0) >>> W = model.fit_transform(X) >>> H = model.components_

References ---------- Cichocki, Andrzej, and P. H. A. N. Anh-Huy. 'Fast local algorithms for large scale nonnegative matrix and tensor factorizations.' IEICE transactions on fundamentals of electronics, communications and computer sciences 92.3: 708-721, 2009.

Fevotte, C., & Idier, J. (2011). Algorithms for nonnegative matrix factorization with the beta-divergence. Neural Computation, 23(9).

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

Learn a NMF model for the data X.

Parameters ---------- X : array-like, sparse matrix, shape (n_samples, n_features) Data matrix to be decomposed

y : Ignored

Returns ------- self

val fit_transform : 
  ?y:Py.Object.t ->
  ?w:[> `ArrayLike ] Np.Obj.t ->
  ?h:[> `ArrayLike ] Np.Obj.t ->
  x:[> `ArrayLike ] Np.Obj.t ->
  [> tag ] Obj.t ->
  [> `ArrayLike ] Np.Obj.t

Learn a NMF model for the data X and returns the transformed data.

This is more efficient than calling fit followed by transform.

Parameters ---------- X : array-like, sparse matrix, shape (n_samples, n_features) Data matrix to be decomposed

y : Ignored

W : array-like, shape (n_samples, n_components) If init='custom', it is used as initial guess for the solution.

H : array-like, shape (n_components, n_features) If init='custom', it is used as initial guess for the solution.

Returns ------- W : array, shape (n_samples, n_components) Transformed data.

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 : 
  w:[> `ArrayLike ] Np.Obj.t ->
  [> tag ] Obj.t ->
  [> `ArrayLike ] Np.Obj.t

Transform data back to its original space.

Parameters ---------- W : array-like, sparse matrix, shape (n_samples, n_components) Transformed data matrix

Returns ------- X : array-like, sparse matrix, shape (n_samples, n_features) Data matrix of original shape

.. versionadded:: 0.18

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 the data X according to the fitted NMF model

Parameters ---------- X : array-like, sparse matrix, shape (n_samples, n_features) Data matrix to be transformed by the model

Returns ------- W : array, shape (n_samples, n_components) Transformed data

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

Attribute components_: get value or raise Not_found if None.

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

Attribute components_: get value as an option.

val n_components_ : t -> int

Attribute n_components_: get value or raise Not_found if None.

val n_components_opt : t -> int option

Attribute n_components_: get value as an option.

val reconstruction_err_ : t -> [ `I of int | `F of float ]

Attribute reconstruction_err_: get value or raise Not_found if None.

val reconstruction_err_opt : t -> [ `I of int | `F of float ] option

Attribute reconstruction_err_: get value as an option.

val n_iter_ : t -> int

Attribute n_iter_: get value or raise Not_found if None.

val n_iter_opt : t -> int option

Attribute n_iter_: 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 : Format.formatter -> t -> unit

Pretty-print the object to a formatter.