Class: MiniBatchSparsePCA
Mini-batch Sparse Principal Components Analysis.
Finds the set of sparse components that can optimally reconstruct the data. The amount of sparseness is controllable by the coefficient of the L1 penalty, given by the parameter alpha.
For an example comparing sparse PCA to PCA, see Faces dataset decompositions
Read more in the User Guide.
Constructors
new MiniBatchSparsePCA()
new MiniBatchSparsePCA(
opts?):MiniBatchSparsePCA
Parameters
| Parameter | Type | Description |
|---|---|---|
opts? | object | - |
opts.alpha? | number | Sparsity controlling parameter. Higher values lead to sparser components. |
opts.batch_size? | number | The number of features to take in each mini batch. |
opts.callback? | any | Callable that gets invoked every five iterations. |
opts.max_iter? | number | Maximum number of iterations over the complete dataset before stopping independently of any early stopping criterion heuristics. |
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.method? | "cd" | "lars" | Method to be used for optimization. lars: uses the least angle regression method to solve the lasso problem (linear_model.lars_path) cd: uses the coordinate descent method to compute the Lasso solution (linear_model.Lasso). Lars will be faster if the estimated components are sparse. |
opts.n_components? | number | Number of sparse atoms to extract. If undefined, then n_components is set to n_features. |
opts.n_jobs? | number | Number of parallel jobs to run. undefined means 1 unless in a joblib.parallel_backend context. \-1 means using all processors. See Glossary for more details. |
opts.random_state? | number | Used for random shuffling when shuffle is set to true, during online dictionary learning. Pass an int for reproducible results across multiple function calls. See Glossary. |
opts.ridge_alpha? | number | Amount of ridge shrinkage to apply in order to improve conditioning when calling the transform method. |
opts.shuffle? | boolean | Whether to shuffle the data before splitting it in batches. |
opts.tol? | number | Control early stopping based on the norm of the differences in the dictionary between 2 steps. To disable early stopping based on changes in the dictionary, set tol to 0.0. |
opts.verbose? | number | boolean | Controls the verbosity; the higher, the more messages. Defaults to 0. |
Returns MiniBatchSparsePCA
Defined in generated/decomposition/MiniBatchSparsePCA.ts:27
Properties
| Property | Type | Default value | Defined in |
|---|---|---|---|
_isDisposed | boolean | false | generated/decomposition/MiniBatchSparsePCA.ts:25 |
_isInitialized | boolean | false | generated/decomposition/MiniBatchSparsePCA.ts:24 |
_py | PythonBridge | undefined | generated/decomposition/MiniBatchSparsePCA.ts:23 |
id | string | undefined | generated/decomposition/MiniBatchSparsePCA.ts:20 |
opts | any | undefined | generated/decomposition/MiniBatchSparsePCA.ts:21 |
Accessors
components_
Get Signature
get components_():
Promise<ArrayLike[]>
Sparse components extracted from the data.
Returns Promise<ArrayLike[]>
Defined in generated/decomposition/MiniBatchSparsePCA.ts:467
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/MiniBatchSparsePCA.ts:602
mean_
Get Signature
get mean_():
Promise<ArrayLike>
Per-feature empirical mean, estimated from the training set. Equal to X.mean(axis=0).
Returns Promise<ArrayLike>
Defined in generated/decomposition/MiniBatchSparsePCA.ts:548
n_components_
Get Signature
get n_components_():
Promise<number>
Estimated number of components.
Returns Promise<number>
Defined in generated/decomposition/MiniBatchSparsePCA.ts:494
n_features_in_
Get Signature
get n_features_in_():
Promise<number>
Number of features seen during fit.
Returns Promise<number>
Defined in generated/decomposition/MiniBatchSparsePCA.ts:575
n_iter_
Get Signature
get n_iter_():
Promise<number>
Number of iterations run.
Returns Promise<number>
Defined in generated/decomposition/MiniBatchSparsePCA.ts:521
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/MiniBatchSparsePCA.ts:119
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/MiniBatchSparsePCA.ts:175
fit()
fit(
opts):Promise<any>
Fit the model from data in X.
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. |
opts.y? | any | Not used, present here for API consistency by convention. |
Returns Promise<any>
Defined in generated/decomposition/MiniBatchSparsePCA.ts:192
fit_transform()
fit_transform(
opts):Promise<any[]>
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
| Parameter | Type | Description |
|---|---|---|
opts | object | - |
opts.fit_params? | any | Additional fit parameters. |
opts.X? | ArrayLike[] | Input samples. |
opts.y? | ArrayLike | Target values (undefined for unsupervised transformations). |
Returns Promise<any[]>
Defined in generated/decomposition/MiniBatchSparsePCA.ts:233
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/MiniBatchSparsePCA.ts:281
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/MiniBatchSparsePCA.ts:319
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/MiniBatchSparsePCA.ts:132
inverse_transform()
inverse_transform(
opts):Promise<ArrayLike[]>
Transform data from the latent space to the original space.
This inversion is an approximation due to the loss of information induced by the forward decomposition.
Parameters
| Parameter | Type | Description |
|---|---|---|
opts | object | - |
opts.X? | ArrayLike[] | Data in the latent space. |
Returns Promise<ArrayLike[]>
Defined in generated/decomposition/MiniBatchSparsePCA.ts:357
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/MiniBatchSparsePCA.ts:395
transform()
transform(
opts):Promise<ArrayLike[]>
Least Squares projection of the data onto the sparse components.
To avoid instability issues in case the system is under-determined, regularization can be applied (Ridge regression) via the ridge_alpha parameter.
Note that Sparse PCA components orthogonality is not enforced as in PCA hence one cannot use a simple linear projection.
Parameters
| Parameter | Type | Description |
|---|---|---|
opts | object | - |
opts.X? | ArrayLike[] | Test data to be transformed, must have the same number of features as the data used to train the model. |
Returns Promise<ArrayLike[]>
Defined in generated/decomposition/MiniBatchSparsePCA.ts:433