PolynomialFeatures
Generate polynomial and interaction features.
Generate a new feature matrix consisting of all polynomial combinations of the features with degree less than or equal to the specified degree. For example, if an input sample is two dimensional and of the form [a, b], the degree-2 polynomial features are [1, a, b, a^2, ab, b^2].
Read more in the User Guide.
Python Reference (opens in a new tab)
Constructors
constructor()
Signature
new PolynomialFeatures(opts?: object): PolynomialFeatures;Parameters
| Name | Type | Description |
|---|---|---|
opts? | object | - |
opts.degree? | number | If a single int is given, it specifies the maximal degree of the polynomial features. If a tuple (min\_degree, max\_degree) is passed, then min\_degree is the minimum and max\_degree is the maximum polynomial degree of the generated features. Note that min\_degree=0 and min\_degree=1 are equivalent as outputting the degree zero term is determined by include\_bias. Default Value 2 |
opts.include_bias? | boolean | If true (default), then include a bias column, the feature in which all polynomial powers are zero (i.e. a column of ones - acts as an intercept term in a linear model). Default Value true |
opts.interaction_only? | boolean | If true, only interaction features are produced: features that are products of at most degree distinct input features, i.e. terms with power of 2 or higher of the same input feature are excluded: Default Value false |
opts.order? | "C" | "F" | Order of output array in the dense case. 'F' order is faster to compute, but may slow down subsequent estimators. Default Value 'C' |
Returns
Defined in: generated/preprocessing/PolynomialFeatures.ts:25 (opens in a new tab)
Properties
_isDisposed
boolean=false
Defined in: generated/preprocessing/PolynomialFeatures.ts:23 (opens in a new tab)
_isInitialized
boolean=false
Defined in: generated/preprocessing/PolynomialFeatures.ts:22 (opens in a new tab)
_py
PythonBridge
Defined in: generated/preprocessing/PolynomialFeatures.ts:21 (opens in a new tab)
id
string
Defined in: generated/preprocessing/PolynomialFeatures.ts:18 (opens in a new tab)
opts
any
Defined in: generated/preprocessing/PolynomialFeatures.ts:19 (opens in a new tab)
Accessors
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/preprocessing/PolynomialFeatures.ts:370 (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/preprocessing/PolynomialFeatures.ts:343 (opens in a new tab)
n_output_features_
The total number of polynomial output features. The number of output features is computed by iterating over all suitably sized combinations of input features.
Signature
n_output_features_(): Promise<number>;Returns
Promise<number>
Defined in: generated/preprocessing/PolynomialFeatures.ts:397 (opens in a new tab)
py
Signature
py(): PythonBridge;Returns
PythonBridge
Defined in: generated/preprocessing/PolynomialFeatures.ts:58 (opens in a new tab)
Signature
py(pythonBridge: PythonBridge): void;Parameters
| Name | Type |
|---|---|
pythonBridge | PythonBridge |
Returns
void
Defined in: generated/preprocessing/PolynomialFeatures.ts:62 (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/preprocessing/PolynomialFeatures.ts:119 (opens in a new tab)
fit()
Compute number of output features.
Signature
fit(opts: object): Promise<any>;Parameters
| Name | Type | Description |
|---|---|---|
opts | object | - |
opts.X? | ArrayLike | The data. |
opts.y? | any | Not used, present here for API consistency by convention. |
Returns
Promise<any>
Defined in: generated/preprocessing/PolynomialFeatures.ts:136 (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/preprocessing/PolynomialFeatures.ts:178 (opens in a new tab)
get_feature_names_out()
Get output feature names for transformation.
Signature
get_feature_names_out(opts: object): Promise<any>;Parameters
| Name | Type | Description |
|---|---|---|
opts | object | - |
opts.input_features? | any | Input features. |
Returns
Promise<any>
Defined in: generated/preprocessing/PolynomialFeatures.ts:229 (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/preprocessing/PolynomialFeatures.ts:71 (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/preprocessing/PolynomialFeatures.ts:269 (opens in a new tab)
transform()
Transform data to polynomial features.
Signature
transform(opts: object): Promise<ArrayLike>;Parameters
| Name | Type | Description |
|---|---|---|
opts | object | - |
opts.X? | ArrayLike | The data to transform, row by row. Prefer CSR over CSC for sparse input (for speed), but CSC is required if the degree is 4 or higher. If the degree is less than 4 and the input format is CSC, it will be converted to CSR, have its polynomial features generated, then converted back to CSC. If the degree is 2 or 3, the method described in “Leveraging Sparsity to Speed Up Polynomial Feature Expansions of CSR Matrices Using K-Simplex Numbers” by Andrew Nystrom and John Hughes is used, which is much faster than the method used on CSC input. For this reason, a CSC input will be converted to CSR, and the output will be converted back to CSC prior to being returned, hence the preference of CSR. |
Returns
Promise<ArrayLike>
Defined in: generated/preprocessing/PolynomialFeatures.ts:304 (opens in a new tab)