sklearn.decomposition
.NMF¶

class
sklearn.decomposition.
NMF
(n_components=None, *, init='warn', solver='cd', beta_loss='frobenius', tol=0.0001, max_iter=200, random_state=None, alpha=0.0, l1_ratio=0.0, verbose=0, shuffle=False, regularization='both')[source]¶ NonNegative Matrix Factorization (NMF).
Find two nonnegative 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:
\[ \begin{align}\begin{aligned}0.5 * X  WH_{loss}^2 + alpha * l1_{ratio} * vec(W)_1\\+ alpha * l1_{ratio} * vec(H)_1\\+ 0.5 * alpha * (1  l1_{ratio}) * W_{Fro}^2\\+ 0.5 * alpha * (1  l1_{ratio}) * H_{Fro}^2\end{aligned}\end{align} \]Where:
\(A_{Fro}^2 = \sum_{i,j} A_{ij}^2\) (Frobenius norm)
\(vec(A)_1 = \sum_{i,j} abs(A_{ij})\) (Elementwise L1 norm)
The generic norm \(X  WH_{loss}\) may represent the Frobenius norm or another supported betadivergence loss. The choice between options is controlled by the
beta_loss
parameter.The objective function is minimized with an alternating minimization of W and H.
Read more in the User Guide.
 Parameters
 n_componentsint, default=None
Number of components, if n_components is not set all features are kept.
 init{‘random’, ‘nndsvd’, ‘nndsvda’, ‘nndsvdar’, ‘custom’}, default=None
Method used to initialize the procedure. Default: None. Valid options:
None
: ‘nndsvd’ if n_components <= min(n_samples, n_features), otherwise random.'random'
: nonnegative 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’}, default=’cd’
Numerical solver to use: ‘cd’ is a Coordinate Descent solver. ‘mu’ is a Multiplicative Update solver.
New in version 0.17: Coordinate Descent solver.
New in version 0.19: Multiplicative Update solver.
 beta_lossfloat or {‘frobenius’, ‘kullbackleibler’, ‘itakurasaito’}, default=’frobenius’
Beta divergence to be minimized, measuring the distance between X and the dot product WH. Note that values different from ‘frobenius’ (or 2) and ‘kullbackleibler’ (or 1) lead to significantly slower fits. Note that for beta_loss <= 0 (or ‘itakurasaito’), the input matrix X cannot contain zeros. Used only in ‘mu’ solver.
New in version 0.19.
 tolfloat, default=1e4
Tolerance of the stopping condition.
 max_iterint, default=200
Maximum number of iterations before timing out.
 random_stateint, RandomState instance or None, 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 Glossary. alphafloat, default=0.
Constant that multiplies the regularization terms. Set it to zero to have no regularization.
New in version 0.17: alpha used in the Coordinate Descent solver.
 l1_ratiofloat, 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.
New in version 0.17: Regularization parameter l1_ratio used in the Coordinate Descent solver.
 verboseint, default=0
Whether to be verbose.
 shufflebool, default=False
If true, randomize the order of coordinates in the CD solver.
New in version 0.17: shuffle parameter used in the Coordinate Descent solver.
 regularization{‘both’, ‘components’, ‘transformation’, None}, default=’both’
Select whether the regularization affects the components (H), the transformation (W), both or none of them.
New in version 0.24.
 Attributes
 components_ndarray of shape (n_components, n_features)
Factorization matrix, sometimes called ‘dictionary’.
 n_components_int
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_float
Frobenius norm of the matrix difference, or betadivergence, between the training data
X
and the reconstructed dataWH
from the fitted model. n_iter_int
Actual number of iterations.
References
Cichocki, Andrzej, and P. H. A. N. AnhHuy. “Fast local algorithms for large scale nonnegative matrix and tensor factorizations.” IEICE transactions on fundamentals of electronics, communications and computer sciences 92.3: 708721, 2009.
Fevotte, C., & Idier, J. (2011). Algorithms for nonnegative matrix factorization with the betadivergence. Neural Computation, 23(9).
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_
Methods
fit
(X[, y])Learn a NMF model for the data X.
fit_transform
(X[, y, W, H])Learn a NMF model for the data X and returns the transformed data.
get_params
([deep])Get parameters for this estimator.
Transform data back to its original space.
set_params
(**params)Set the parameters of this estimator.
transform
(X)Transform the data X according to the fitted NMF model.

fit
(X, y=None, **params)[source]¶ Learn a NMF model for the data X.
 Parameters
 X{arraylike, sparse matrix} of shape (n_samples, n_features)
Data matrix to be decomposed
 yIgnored
 Returns
 self

fit_transform
(X, y=None, W=None, H=None)[source]¶ 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{arraylike, sparse matrix} of shape (n_samples, n_features)
Data matrix to be decomposed
 yIgnored
 Warraylike of shape (n_samples, n_components)
If init=’custom’, it is used as initial guess for the solution.
 Harraylike of shape (n_components, n_features)
If init=’custom’, it is used as initial guess for the solution.
 Returns
 Wndarray of shape (n_samples, n_components)
Transformed data.

get_params
(deep=True)[source]¶ Get parameters for this estimator.
 Parameters
 deepbool, default=True
If True, will return the parameters for this estimator and contained subobjects that are estimators.
 Returns
 paramsdict
Parameter names mapped to their values.

inverse_transform
(W)[source]¶ Transform data back to its original space.
 Parameters
 W{ndarray, sparse matrix} of shape (n_samples, n_components)
Transformed data matrix.
 Returns
 X{ndarray, sparse matrix} of shape (n_samples, n_features)
Data matrix of original shape.
New in version 0.18: ..

set_params
(**params)[source]¶ Set the parameters of this estimator.
The method works on simple estimators as well as on nested objects (such as
Pipeline
). The latter have parameters of the form<component>__<parameter>
so that it’s possible to update each component of a nested object. Parameters
 **paramsdict
Estimator parameters.
 Returns
 selfestimator instance
Estimator instance.