KernelPCA
Kernel Principal component analysis (KPCA) [1].
Non-linear dimensionality reduction through the use of kernels (see Pairwise metrics, Affinities and Kernels).
It uses the scipy.linalg.eigh (opens in a new tab) LAPACK implementation of the full SVD or the scipy.sparse.linalg.eigsh (opens in a new tab) ARPACK implementation of the truncated SVD, depending on the shape of the input data and the number of components to extract. It can also use a randomized truncated SVD by the method proposed in [3], see eigen\_solver.
Read more in the User Guide.
Python Reference (opens in a new tab)
Constructors
constructor()
Signature
new KernelPCA(opts?: object): KernelPCA;Parameters
| Name | Type | Description |
|---|---|---|
opts? | object | - |
opts.alpha? | number | Hyperparameter of the ridge regression that learns the inverse transform (when fit_inverse_transform=true). Default Value 1 |
opts.coef0? | number | Independent term in poly and sigmoid kernels. Ignored by other kernels. Default Value 1 |
opts.copy_X? | boolean | If true, input X is copied and stored by the model in the X\_fit\_ attribute. If no further changes will be done to X, setting copy\_X=False saves memory by storing a reference. Default Value true |
opts.degree? | number | Degree for poly kernels. Ignored by other kernels. Default Value 3 |
opts.eigen_solver? | "auto" | "randomized" | "arpack" | "dense" | Select eigensolver to use. If n\_components is much less than the number of training samples, randomized (or arpack to a smaller extent) may be more efficient than the dense eigensolver. Randomized SVD is performed according to the method of Halko et al [3]. Default Value 'auto' |
opts.fit_inverse_transform? | boolean | Learn the inverse transform for non-precomputed kernels (i.e. learn to find the pre-image of a point). This method is based on [2]. Default Value false |
opts.gamma? | number | Kernel coefficient for rbf, poly and sigmoid kernels. Ignored by other kernels. If gamma is undefined, then it is set to 1/n\_features. |
opts.iterated_power? | "auto" | Number of iterations for the power method computed by svd_solver == ‘randomized’. When ‘auto’, it is set to 7 when n\_components < 0.1 \* min(X.shape), other it is set to 4. Default Value 'auto' |
opts.kernel? | "sigmoid" | "precomputed" | "linear" | "poly" | "rbf" | "cosine" | Kernel used for PCA. Default Value 'linear' |
opts.kernel_params? | any | Parameters (keyword arguments) and values for kernel passed as callable object. Ignored by other kernels. |
opts.max_iter? | number | Maximum number of iterations for arpack. If undefined, optimal value will be chosen by arpack. |
opts.n_components? | number | Number of components. If undefined, all non-zero components are kept. |
opts.n_jobs? | number | The 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 when eigen\_solver == ‘arpack’ or ‘randomized’. Pass an int for reproducible results across multiple function calls. See Glossary. |
opts.remove_zero_eig? | boolean | If true, then all components with zero eigenvalues are removed, so that the number of components in the output may be < n_components (and sometimes even zero due to numerical instability). When n_components is undefined, this parameter is ignored and components with zero eigenvalues are removed regardless. Default Value false |
opts.tol? | number | Convergence tolerance for arpack. If 0, optimal value will be chosen by arpack. Default Value 0 |
Returns
Defined in: generated/decomposition/KernelPCA.ts:27 (opens in a new tab)
Properties
_isDisposed
boolean=false
Defined in: generated/decomposition/KernelPCA.ts:25 (opens in a new tab)
_isInitialized
boolean=false
Defined in: generated/decomposition/KernelPCA.ts:24 (opens in a new tab)
_py
PythonBridge
Defined in: generated/decomposition/KernelPCA.ts:23 (opens in a new tab)
id
string
Defined in: generated/decomposition/KernelPCA.ts:20 (opens in a new tab)
opts
any
Defined in: generated/decomposition/KernelPCA.ts:21 (opens in a new tab)
Accessors
X_fit_
The data used to fit the model. If copy\_X=False, then X\_fit\_ is a reference. This attribute is used for the calls to transform.
Signature
X_fit_(): Promise<ArrayLike[]>;Returns
Promise<ArrayLike[]>
Defined in: generated/decomposition/KernelPCA.ts:539 (opens in a new tab)
X_transformed_fit_
Projection of the fitted data on the kernel principal components. Only available when fit\_inverse\_transform is true.
Signature
X_transformed_fit_(): Promise<ArrayLike[]>;Returns
Promise<ArrayLike[]>
Defined in: generated/decomposition/KernelPCA.ts:514 (opens in a new tab)
dual_coef_
Inverse transform matrix. Only available when fit\_inverse\_transform is true.
Signature
dual_coef_(): Promise<ArrayLike[]>;Returns
Promise<ArrayLike[]>
Defined in: generated/decomposition/KernelPCA.ts:491 (opens in a new tab)
eigenvalues_
Eigenvalues of the centered kernel matrix in decreasing order. If n\_components and remove\_zero\_eig are not set, then all values are stored.
Signature
eigenvalues_(): Promise<ArrayLike>;Returns
Promise<ArrayLike>
Defined in: generated/decomposition/KernelPCA.ts:441 (opens in a new tab)
eigenvectors_
Eigenvectors of the centered kernel matrix. If n\_components and remove\_zero\_eig are not set, then all components are stored.
Signature
eigenvectors_(): Promise<ArrayLike[]>;Returns
Promise<ArrayLike[]>
Defined in: generated/decomposition/KernelPCA.ts:466 (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/KernelPCA.ts:587 (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/KernelPCA.ts:562 (opens in a new tab)
py
Signature
py(): PythonBridge;Returns
PythonBridge
Defined in: generated/decomposition/KernelPCA.ts:132 (opens in a new tab)
Signature
py(pythonBridge: PythonBridge): void;Parameters
| Name | Type |
|---|---|
pythonBridge | PythonBridge |
Returns
void
Defined in: generated/decomposition/KernelPCA.ts:136 (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/KernelPCA.ts:200 (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 for API consistency by convention. |
Returns
Promise<any>
Defined in: generated/decomposition/KernelPCA.ts:217 (opens in a new tab)
fit_transform()
Fit the model from data in X and transform X.
Signature
fit_transform(opts: object): Promise<ArrayLike[]>;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.params? | any | Parameters (keyword arguments) and values passed to the fit_transform instance. |
opts.y? | any | Not used, present for API consistency by convention. |
Returns
Promise<ArrayLike[]>
Defined in: generated/decomposition/KernelPCA.ts:255 (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/KernelPCA.ts:302 (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/KernelPCA.ts:145 (opens in a new tab)
inverse_transform()
Transform X back to original space.
inverse\_transform approximates the inverse transformation using a learned pre-image. The pre-image is learned by kernel ridge regression of the original data on their low-dimensional representation vectors.
Signature
inverse_transform(opts: object): Promise<ArrayLike[]>;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. |
Returns
Promise<ArrayLike[]>
Defined in: generated/decomposition/KernelPCA.ts:340 (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/KernelPCA.ts:375 (opens in a new tab)
transform()
Transform X.
Signature
transform(opts: object): Promise<ArrayLike[]>;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. |
Returns
Promise<ArrayLike[]>
Defined in: generated/decomposition/KernelPCA.ts:408 (opens in a new tab)