Class: NMF
Non-Negative Matrix Factorization (NMF).
Find two non-negative matrices, i.e. matrices with all non-negative elements, (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:
Constructors
new NMF()
new NMF(
opts?):NMF
Parameters
| Parameter | Type | Description |
|---|---|---|
opts? | object | - |
opts.alpha_H? | number | "same" | Constant that multiplies the regularization terms of H. Set it to zero to have no regularization on H. If “same” (default), it takes the same value as alpha_W. |
opts.alpha_W? | number | Constant that multiplies the regularization terms of W. Set it to zero (default) to have no regularization on W. |
opts.beta_loss? | number | "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. |
opts.init? | "random" | "nndsvd" | "nndsvda" | "nndsvdar" | "custom" | Method used to initialize the procedure. Valid options: |
opts.l1_ratio? | number | 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. |
opts.max_iter? | number | Maximum number of iterations before timing out. |
opts.n_components? | number | "auto" | Number of components, if n_components is not set all features are kept. If n_components='auto', the number of components is automatically inferred from W or H shapes. |
opts.random_state? | number | Used for initialisation (when init == ‘nndsvdar’ or ‘random’), and in Coordinate Descent. Pass an int for reproducible results across multiple function calls. See Glossary. |
opts.shuffle? | boolean | If true, randomize the order of coordinates in the CD solver. |
opts.solver? | "cd" | "mu" | Numerical solver to use: |
opts.tol? | number | Tolerance of the stopping condition. |
opts.verbose? | number | Whether to be verbose. |
Returns NMF
Defined in generated/decomposition/NMF.ts:25
Properties
| Property | Type | Default value | Defined in |
|---|---|---|---|
_isDisposed | boolean | false | generated/decomposition/NMF.ts:23 |
_isInitialized | boolean | false | generated/decomposition/NMF.ts:22 |
_py | PythonBridge | undefined | generated/decomposition/NMF.ts:21 |
id | string | undefined | generated/decomposition/NMF.ts:18 |
opts | any | undefined | generated/decomposition/NMF.ts:19 |
Accessors
components_
Get Signature
get components_():
Promise<ArrayLike[]>
Factorization matrix, sometimes called ‘dictionary’.
Returns Promise<ArrayLike[]>
Defined in generated/decomposition/NMF.ts:437
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/decomposition/NMF.ts:553
n_components_
Get Signature
get n_components_():
Promise<number>
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.
Returns Promise<number>
Defined in generated/decomposition/NMF.ts:460
n_features_in_
Get Signature
get n_features_in_():
Promise<number>
Number of features seen during fit.
Returns Promise<number>
Defined in generated/decomposition/NMF.ts:530
n_iter_
Get Signature
get n_iter_():
Promise<number>
Actual number of iterations.
Returns Promise<number>
Defined in generated/decomposition/NMF.ts:508
py
Get Signature
get py():
PythonBridge
Returns PythonBridge
Set Signature
set py(
pythonBridge):void
Parameters
| Parameter | Type |
|---|---|
pythonBridge | PythonBridge |
Returns void
Defined in generated/decomposition/NMF.ts:108
reconstruction_err_
Get Signature
get reconstruction_err_():
Promise<number>
Frobenius norm of the matrix difference, or beta-divergence, between the training data X and the reconstructed data WH from the fitted model.
Returns Promise<number>
Defined in generated/decomposition/NMF.ts:483
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/decomposition/NMF.ts:159
fit()
fit(
opts):Promise<any>
Learn a NMF model for the data X.
Parameters
| Parameter | Type | Description |
|---|---|---|
opts | object | - |
opts.params? | any | Parameters (keyword arguments) and values passed to the fit_transform instance. |
opts.X? | ArrayLike | Training vector, where n_samples is the number of samples and n_features is the number of features. |
opts.y? | any | Not used, present for API consistency by convention. |
Returns Promise<any>
Defined in generated/decomposition/NMF.ts:176
fit_transform()
fit_transform(
opts):Promise<ArrayLike[]>
Learn a NMF model for the data X and returns the transformed data.
This is more efficient than calling fit followed by transform.
Parameters
| Parameter | Type | Description |
|---|---|---|
opts | object | - |
opts.H? | ArrayLike[] | If init='custom', it is used as initial guess for the solution. If undefined, uses the initialisation method specified in init. |
opts.W? | ArrayLike[] | If init='custom', it is used as initial guess for the solution. If undefined, uses the initialisation method specified in init. |
opts.X? | ArrayLike | Training vector, where n_samples is the number of samples and n_features is the number of features. |
opts.y? | any | Not used, present for API consistency by convention. |
Returns Promise<ArrayLike[]>
Defined in generated/decomposition/NMF.ts:219
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/decomposition/NMF.ts:268
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/decomposition/NMF.ts:302
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/decomposition/NMF.ts:121
inverse_transform()
inverse_transform(
opts):Promise<ArrayLike[]>
Transform data back to its original space.
Parameters
| Parameter | Type | Description |
|---|---|---|
opts | object | - |
opts.X? | ArrayLike | Transformed data matrix. |
opts.Xt? | ArrayLike | Transformed data matrix. |
Returns Promise<ArrayLike[]>
Defined in generated/decomposition/NMF.ts:334
set_output()
set_output(
opts):Promise<any>
Set output container.
See Introducing the set_output API for an example on how to use the API.
Parameters
| Parameter | Type | Description |
|---|---|---|
opts | object | - |
opts.transform? | "default" | "pandas" | "polars" | Configure output of transform and fit_transform. |
Returns Promise<any>
Defined in generated/decomposition/NMF.ts:373
transform()
transform(
opts):Promise<ArrayLike[]>
Transform the data X according to the fitted NMF model.
Parameters
| Parameter | Type | Description |
|---|---|---|
opts | object | - |
opts.X? | ArrayLike | Training vector, where n_samples is the number of samples and n_features is the number of features. |
Returns Promise<ArrayLike[]>
Defined in generated/decomposition/NMF.ts:405