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.
Read more in the User Guide.
Python Reference (opens in a new tab)
Constructors
constructor()
Signature
new MiniBatchSparsePCA(opts?: object): MiniBatchSparsePCA;Parameters
| Name | Type | Description |
|---|---|---|
opts? | object | - |
opts.alpha? | number | Sparsity controlling parameter. Higher values lead to sparser components. Default Value 1 |
opts.batch_size? | number | The number of features to take in each mini batch. Default Value 3 |
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. If max\_iter is not undefined, n\_iter is ignored. |
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. Used only if max\_iter is not undefined. To disable convergence detection based on cost function, set max\_no\_improvement to undefined. Default Value 10 |
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. Default Value 'lars' |
opts.n_components? | number | Number of sparse atoms to extract. If undefined, then n\_components is set to n\_features. |
opts.n_iter? | number | Number of iterations to perform for each mini batch. Default Value 100 |
opts.n_jobs? | number | Number of parallel jobs to run. undefined means 1 unless in a joblib.parallel\_backend (opens in a new tab) 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. Default Value 0.01 |
opts.shuffle? | boolean | Whether to shuffle the data before splitting it in batches. Default Value true |
opts.tol? | number | Control early stopping based on the norm of the differences in the dictionary between 2 steps. Used only if max\_iter is not undefined. To disable early stopping based on changes in the dictionary, set tol to 0.0. Default Value 0.001 |
opts.verbose? | number | boolean | Controls the verbosity; the higher, the more messages. Defaults to 0. Default Value false |
Returns
Defined in: generated/decomposition/MiniBatchSparsePCA.ts:25 (opens in a new tab)
Properties
_isDisposed
boolean=false
Defined in: generated/decomposition/MiniBatchSparsePCA.ts:23 (opens in a new tab)
_isInitialized
boolean=false
Defined in: generated/decomposition/MiniBatchSparsePCA.ts:22 (opens in a new tab)
_py
PythonBridge
Defined in: generated/decomposition/MiniBatchSparsePCA.ts:21 (opens in a new tab)
id
string
Defined in: generated/decomposition/MiniBatchSparsePCA.ts:18 (opens in a new tab)
opts
any
Defined in: generated/decomposition/MiniBatchSparsePCA.ts:19 (opens in a new tab)
Accessors
components_
Sparse components extracted from the data.
Signature
components_(): Promise<ArrayLike[]>;Returns
Promise<ArrayLike[]>
Defined in: generated/decomposition/MiniBatchSparsePCA.ts:457 (opens in a new tab)
feature_names_in_
Names of features seen during fit. Defined only when X has feature names that are all strings.
Signature
feature_names_in_(): Promise<ArrayLike>;Returns
Promise<ArrayLike>
Defined in: generated/decomposition/MiniBatchSparsePCA.ts:592 (opens in a new tab)
mean_
Per-feature empirical mean, estimated from the training set. Equal to X.mean(axis=0).
Signature
mean_(): Promise<ArrayLike>;Returns
Promise<ArrayLike>
Defined in: generated/decomposition/MiniBatchSparsePCA.ts:538 (opens in a new tab)
n_components_
Estimated number of components.
Signature
n_components_(): Promise<number>;Returns
Promise<number>
Defined in: generated/decomposition/MiniBatchSparsePCA.ts:484 (opens in a new tab)
n_features_in_
Number of features seen during fit.
Signature
n_features_in_(): Promise<number>;Returns
Promise<number>
Defined in: generated/decomposition/MiniBatchSparsePCA.ts:565 (opens in a new tab)
n_iter_
Number of iterations run.
Signature
n_iter_(): Promise<number>;Returns
Promise<number>
Defined in: generated/decomposition/MiniBatchSparsePCA.ts:511 (opens in a new tab)
py
Signature
py(): PythonBridge;Returns
PythonBridge
Defined in: generated/decomposition/MiniBatchSparsePCA.ts:122 (opens in a new tab)
Signature
py(pythonBridge: PythonBridge): void;Parameters
| Name | Type |
|---|---|
pythonBridge | PythonBridge |
Returns
void
Defined in: generated/decomposition/MiniBatchSparsePCA.ts:126 (opens in a new tab)
Methods
dispose()
Disposes of the underlying Python resources.
Once dispose() is called, the instance is no longer usable.
Signature
dispose(): Promise<void>;Returns
Promise<void>
Defined in: generated/decomposition/MiniBatchSparsePCA.ts:191 (opens in a new tab)
fit()
Fit the model from data in X.
Signature
fit(opts: object): Promise<any>;Parameters
| Name | 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:208 (opens in a new tab)
fit_transform()
Fit to data, then transform it.
Fits transformer to X and y with optional parameters fit\_params and returns a transformed version of X.
Signature
fit_transform(opts: object): Promise<any[]>;Parameters
| Name | Type | Description |
|---|---|---|
opts | object | - |
opts.X? | ArrayLike[] | Input samples. |
opts.fit_params? | any | Additional fit parameters. |
opts.y? | ArrayLike | Target values (undefined for unsupervised transformations). |
Returns
Promise<any[]>
Defined in: generated/decomposition/MiniBatchSparsePCA.ts:250 (opens in a new tab)
get_feature_names_out()
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: \["class\_name0", "class\_name1", "class\_name2"\].
Signature
get_feature_names_out(opts: object): Promise<any>;Parameters
| Name | 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:303 (opens in a new tab)
init()
Initializes the underlying Python resources.
This instance is not usable until the Promise returned by init() resolves.
Signature
init(py: PythonBridge): Promise<void>;Parameters
| Name | Type |
|---|---|
py | PythonBridge |
Returns
Promise<void>
Defined in: generated/decomposition/MiniBatchSparsePCA.ts:135 (opens in a new tab)
inverse_transform()
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.
Signature
inverse_transform(opts: object): Promise<ArrayLike[]>;Parameters
| Name | Type | Description |
|---|---|---|
opts | object | - |
opts.X? | ArrayLike[] | Data in the latent space. |
Returns
Promise<ArrayLike[]>
Defined in: generated/decomposition/MiniBatchSparsePCA.ts:343 (opens in a new tab)
set_output()
Set output container.
See Introducing the set_output API for an example on how to use the API.
Signature
set_output(opts: object): Promise<any>;Parameters
| Name | Type | Description |
|---|---|---|
opts | object | - |
opts.transform? | "default" | "pandas" | Configure output of transform and fit\_transform. |
Returns
Promise<any>
Defined in: generated/decomposition/MiniBatchSparsePCA.ts:383 (opens in a new tab)
transform()
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.
Signature
transform(opts: object): Promise<ArrayLike[]>;Parameters
| Name | 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:422 (opens in a new tab)