Class: MiniBatchNMF

Mini-Batch Non-Negative Matrix Factorization (NMF).

Constructors

new MiniBatchNMF()

new MiniBatchNMF(opts?): MiniBatchNMF

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.batch_size`?	`number`	Number of samples in each mini-batch. Large batch sizes give better long-term convergence at the cost of a slower start.
`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.
`opts.forget_factor`?	`number`	Amount of rescaling of past information. Its value could be 1 with finite datasets. Choosing values < 1 is recommended with online learning as more recent batches will weight more than past batches.
`opts.fresh_restarts`?	`boolean`	Whether to completely solve for W at each step. Doing fresh restarts will likely lead to a better solution for a same number of iterations but it is much slower.
`opts.fresh_restarts_max_iter`?	`number`	Maximum number of iterations when solving for W at each step. Only used when doing fresh restarts. These iterations may be stopped early based on a small change of W controlled by `tol`.
`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 over the complete dataset before timing out.
`opts.max_no_improvement`?	`number`	Control early stopping based on the consecutive number of mini batches that does not yield an improvement on the smoothed cost function. To disable convergence detection based on cost function, set `max_no_improvement` to `undefined`.
`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.tol`?	`number`	Control early stopping based on the norm of the differences in `H` between 2 steps. To disable early stopping based on changes in `H`, set `tol` to 0.0.
`opts.transform_max_iter`?	`number`	Maximum number of iterations when solving for W at transform time. If `undefined`, it defaults to `max_iter`.
`opts.verbose`?	`boolean`	Whether to be verbose.

Returns MiniBatchNMF

Defined in generated/decomposition/MiniBatchNMF.ts:21

Properties

Property	Type	Default value	Defined in
`_isDisposed`	`boolean`	`false`	generated/decomposition/MiniBatchNMF.ts:19
`_isInitialized`	`boolean`	`false`	generated/decomposition/MiniBatchNMF.ts:18
`_py`	`PythonBridge`	`undefined`	generated/decomposition/MiniBatchNMF.ts:17
`id`	`string`	`undefined`	generated/decomposition/MiniBatchNMF.ts:14
`opts`	`any`	`undefined`	generated/decomposition/MiniBatchNMF.ts:15

Accessors

components_

Get Signature

get components_(): Promise<ArrayLike[]>

Factorization matrix, sometimes called ‘dictionary’.

Returns Promise<ArrayLike[]>

Defined in generated/decomposition/MiniBatchNMF.ts:518

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/MiniBatchNMF.ts:664

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/MiniBatchNMF.ts:543

n_features_in_

Get Signature

get n_features_in_(): Promise<number>

Number of features seen during fit.

Returns Promise<number>

Defined in generated/decomposition/MiniBatchNMF.ts:639

n_iter_

Get Signature

get n_iter_(): Promise<number>

Actual number of started iterations over the whole dataset.

Returns Promise<number>

Defined in generated/decomposition/MiniBatchNMF.ts:593

n_steps_

Get Signature

get n_steps_(): Promise<number>

Number of mini-batches processed.

Returns Promise<number>

Defined in generated/decomposition/MiniBatchNMF.ts:616

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/MiniBatchNMF.ts:130

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/MiniBatchNMF.ts:568

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/MiniBatchNMF.ts:182

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/MiniBatchNMF.ts:199

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`	Data matrix to be decomposed.
`opts.y`?	`any`	Not used, present here for API consistency by convention.

Returns Promise<ArrayLike[]>

Defined in generated/decomposition/MiniBatchNMF.ts:243

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/MiniBatchNMF.ts:292

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/MiniBatchNMF.ts:328

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/MiniBatchNMF.ts:143

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/MiniBatchNMF.ts:362

partial_fit()

partial_fit(opts): Promise<any>

Update the model using the data in X as a mini-batch.

This method is expected to be called several times consecutively on different chunks of a dataset so as to implement out-of-core or online learning.

This is especially useful when the whole dataset is too big to fit in memory at once (see Strategies to scale computationally: bigger data).

Parameters

Parameter	Type	Description
`opts`	`object`	-
`opts.H`?	`ArrayLike`[]	If `init='custom'`, it is used as initial guess for the solution. Only used for the first call to `partial_fit`.
`opts.W`?	`ArrayLike`[]	If `init='custom'`, it is used as initial guess for the solution. Only used for the first call to `partial_fit`.
`opts.X`?	`ArrayLike`	Data matrix to be decomposed.
`opts.y`?	`any`	Not used, present here for API consistency by convention.