PolynomialCountSketch
Polynomial kernel approximation via Tensor Sketch.
Implements Tensor Sketch, which approximates the feature map of the polynomial kernel:
Python Reference (opens in a new tab)
Constructors
constructor()
Signature
new PolynomialCountSketch(opts?: object): PolynomialCountSketch;Parameters
| Name | Type | Description |
|---|---|---|
opts? | object | - |
opts.coef0? | number | Constant term of the polynomial kernel whose feature map will be approximated. Default Value 0 |
opts.degree? | number | Degree of the polynomial kernel whose feature map will be approximated. Default Value 2 |
opts.gamma? | number | Parameter of the polynomial kernel whose feature map will be approximated. Default Value 1 |
opts.n_components? | number | Dimensionality of the output feature space. Usually, n\_components should be greater than the number of features in input samples in order to achieve good performance. The optimal score / run time balance is typically achieved around n\_components = 10 * n\_features, but this depends on the specific dataset being used. Default Value 100 |
opts.random_state? | number | Determines random number generation for indexHash and bitHash initialization. Pass an int for reproducible results across multiple function calls. See Glossary. |
Returns
Defined in: generated/kernel_approximation/PolynomialCountSketch.ts:23 (opens in a new tab)
Properties
_isDisposed
boolean=false
Defined in: generated/kernel_approximation/PolynomialCountSketch.ts:21 (opens in a new tab)
_isInitialized
boolean=false
Defined in: generated/kernel_approximation/PolynomialCountSketch.ts:20 (opens in a new tab)
_py
PythonBridge
Defined in: generated/kernel_approximation/PolynomialCountSketch.ts:19 (opens in a new tab)
id
string
Defined in: generated/kernel_approximation/PolynomialCountSketch.ts:16 (opens in a new tab)
opts
any
Defined in: generated/kernel_approximation/PolynomialCountSketch.ts:17 (opens in a new tab)
Accessors
bitHash_
Array with random entries in {+1, -1}, used to represent the 2-wise independent hash functions for Count Sketch computation.
Signature
bitHash_(): Promise<ArrayLike[]>;Returns
Promise<ArrayLike[]>
Defined in: generated/kernel_approximation/PolynomialCountSketch.ts:380 (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/kernel_approximation/PolynomialCountSketch.ts:434 (opens in a new tab)
indexHash_
Array of indexes in range [0, n_components) used to represent the 2-wise independent hash functions for Count Sketch computation.
Signature
indexHash_(): Promise<ArrayLike[]>;Returns
Promise<ArrayLike[]>
Defined in: generated/kernel_approximation/PolynomialCountSketch.ts:353 (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/kernel_approximation/PolynomialCountSketch.ts:407 (opens in a new tab)
py
Signature
py(): PythonBridge;Returns
PythonBridge
Defined in: generated/kernel_approximation/PolynomialCountSketch.ts:61 (opens in a new tab)
Signature
py(pythonBridge: PythonBridge): void;Parameters
| Name | Type |
|---|---|
pythonBridge | PythonBridge |
Returns
void
Defined in: generated/kernel_approximation/PolynomialCountSketch.ts:65 (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/kernel_approximation/PolynomialCountSketch.ts:122 (opens in a new tab)
fit()
Fit the model with X.
Initializes the internal variables. The method needs no information about the distribution of data, so we only care about n_features in X.
Signature
fit(opts: object): Promise<any>;Parameters
| Name | Type | Description |
|---|---|---|
opts | object | - |
opts.X? | ArrayLike | Training data, where n\_samples is the number of samples and n\_features is the number of features. |
opts.y? | ArrayLike | Target values (undefined for unsupervised transformations). |
Returns
Promise<any>
Defined in: generated/kernel_approximation/PolynomialCountSketch.ts:141 (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/kernel_approximation/PolynomialCountSketch.ts:185 (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/kernel_approximation/PolynomialCountSketch.ts:239 (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/kernel_approximation/PolynomialCountSketch.ts:74 (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/kernel_approximation/PolynomialCountSketch.ts:279 (opens in a new tab)
transform()
Generate the feature map approximation for X.
Signature
transform(opts: object): Promise<ArrayLike>;Parameters
| Name | Type | Description |
|---|---|---|
opts | object | - |
opts.X? | ArrayLike | New data, where n\_samples is the number of samples and n\_features is the number of features. |
Returns
Promise<ArrayLike>
Defined in: generated/kernel_approximation/PolynomialCountSketch.ts:316 (opens in a new tab)