Class: SparsePCA
Sparse Principal Components Analysis (SparsePCA).
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.
Read more in the User Guide.
Constructors
new SparsePCA()
new SparsePCA(
opts?):SparsePCA
Parameters
| Parameter | Type | Description |
|---|---|---|
opts? | object | - |
opts.alpha? | number | Sparsity controlling parameter. Higher values lead to sparser components. |
opts.max_iter? | number | Maximum number of iterations to perform. |
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 during 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.tol? | number | Tolerance for the stopping condition. |
opts.U_init? | ArrayLike[] | Initial values for the loadings for warm restart scenarios. Only used if U_init and V_init are not undefined. |
opts.V_init? | ArrayLike[] | Initial values for the components for warm restart scenarios. Only used if U_init and V_init are not undefined. |
opts.verbose? | number | boolean | Controls the verbosity; the higher, the more messages. Defaults to 0. |
Returns SparsePCA
Defined in generated/decomposition/SparsePCA.ts:25
Properties
| Property | Type | Default value | Defined in |
|---|---|---|---|
_isDisposed | boolean | false | generated/decomposition/SparsePCA.ts:23 |
_isInitialized | boolean | false | generated/decomposition/SparsePCA.ts:22 |
_py | PythonBridge | undefined | generated/decomposition/SparsePCA.ts:21 |
id | string | undefined | generated/decomposition/SparsePCA.ts:18 |
opts | any | undefined | generated/decomposition/SparsePCA.ts:19 |
Accessors
components_
Get Signature
get components_():
Promise<ArrayLike[]>
Sparse components extracted from the data.
Returns Promise<ArrayLike[]>
Defined in generated/decomposition/SparsePCA.ts:422
error_
Get Signature
get error_():
Promise<ArrayLike>
Vector of errors at each iteration.
Returns Promise<ArrayLike>
Defined in generated/decomposition/SparsePCA.ts:445
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/SparsePCA.ts:564
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/SparsePCA.ts:516
n_components_
Get Signature
get n_components_():
Promise<number>
Estimated number of components.
Returns Promise<number>
Defined in generated/decomposition/SparsePCA.ts:468
n_features_in_
Get Signature
get n_features_in_():
Promise<number>
Number of features seen during fit.
Returns Promise<number>
Defined in generated/decomposition/SparsePCA.ts:539
n_iter_
Get Signature
get n_iter_():
Promise<number>
Number of iterations run.
Returns Promise<number>
Defined in generated/decomposition/SparsePCA.ts:493
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/SparsePCA.ts:97
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/SparsePCA.ts:148
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/SparsePCA.ts:165
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/SparsePCA.ts:204
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/SparsePCA.ts:248
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/SparsePCA.ts:284
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/SparsePCA.ts:110
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/SparsePCA.ts:320
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/SparsePCA.ts:354
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/SparsePCA.ts:390