SplineTransformer

Generate univariate B-spline bases for features.

Generate a new feature matrix consisting of n\_splines=n\_knots + degree \- 1 (n\_knots \- 1 for extrapolation="periodic") spline basis functions (B-splines) of polynomial order=degree for each feature.

Constructors

constructor()

Signature

new SplineTransformer(opts?: object): SplineTransformer;

Parameters

Name	Type	Description
`opts?`	`object`	-
`opts.degree?`	`number`	The polynomial degree of the spline basis. Must be a non-negative integer. `Default Value` `3`
`opts.extrapolation?`	`"linear"` \| `"constant"` \| `"error"` \| `"continue"` \| `"periodic"`	If ‘error’, values outside the min and max values of the training features raises a `ValueError`. If ‘constant’, the value of the splines at minimum and maximum value of the features is used as constant extrapolation. If ‘linear’, a linear extrapolation is used. If ‘continue’, the splines are extrapolated as is, i.e. option `extrapolate=True` in `scipy.interpolate.BSpline` (opens in a new tab). If ‘periodic’, periodic splines with a periodicity equal to the distance between the first and last knot are used. Periodic splines enforce equal function values and derivatives at the first and last knot. For example, this makes it possible to avoid introducing an arbitrary jump between Dec 31st and Jan 1st in spline features derived from a naturally periodic “day-of-year” input feature. In this case it is recommended to manually set the knot values to control the period. `Default Value` `'constant'`
`opts.include_bias?`	`boolean`	If `false`, then the last spline element inside the data range of a feature is dropped. As B-splines sum to one over the spline basis functions for each data point, they implicitly include a bias term, i.e. a column of ones. It acts as an intercept term in a linear models. `Default Value` `true`
`opts.knots?`	`ArrayLike`[] \| `"uniform"` \| `"quantile"`	Set knot positions such that first knot <= features <= last knot. `Default Value` `'uniform'`
`opts.n_knots?`	`number`	Number of knots of the splines if `knots` equals one of {‘uniform’, ‘quantile’}. Must be larger or equal 2. Ignored if `knots` is array-like. `Default Value` `5`
`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'`
`opts.sparse_output?`	`boolean`	Will return sparse CSR matrix if set `true` else will return an array. This option is only available with `scipy>=1.8`. `Default Value` `false`

Returns

SplineTransformer

Defined in: generated/preprocessing/SplineTransformer.ts:25 (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/SplineTransformer.ts:140 (opens in a new tab)

fit()

Compute knot positions of splines.

Signature

fit(opts: object): Promise<any>;

Parameters

Name	Type	Description
`opts`	`object`	-
`opts.X?`	`ArrayLike`[]	The data.
`opts.sample_weight?`	`ArrayLike`	Individual weights for each sample. Used to calculate quantiles if `knots="quantile"`. For `knots="uniform"`, zero weighted observations are ignored for finding the min and max of `X`.
`opts.y?`	`any`	Ignored.

Returns

Promise<any>

Defined in: generated/preprocessing/SplineTransformer.ts:157 (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/SplineTransformer.ts:208 (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/SplineTransformer.ts:259 (opens in a new tab)

get_metadata_routing()

Get metadata routing of this object.

Please check User Guide on how the routing mechanism works.

Signature

get_metadata_routing(opts: object): Promise<any>;

Parameters

Name	Type	Description
`opts`	`object`	-
`opts.routing?`	`any`	A `MetadataRequest` encapsulating routing information.

Returns

Promise<any>

Defined in: generated/preprocessing/SplineTransformer.ts:299 (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/SplineTransformer.ts:92 (opens in a new tab)

set_fit_request()

Request metadata passed to the fit method.

Note that this method is only relevant if enable\_metadata\_routing=True (see sklearn.set\_config). Please see User Guide on how the routing mechanism works.

The options for each parameter are:

Signature

set_fit_request(opts: object): Promise<any>;

Parameters

Name	Type	Description
`opts`	`object`	-
`opts.sample_weight?`	`string` \| `boolean`	Metadata routing for `sample\_weight` parameter in `fit`.

Returns

Promise<any>

Defined in: generated/preprocessing/SplineTransformer.ts:341 (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/SplineTransformer.ts:381 (opens in a new tab)

transform()

Transform each feature data to B-splines.

Signature

transform(opts: object): Promise<ArrayLike>;

Parameters

Name	Type	Description
`opts`	`object`	-
`opts.X?`	`ArrayLike`[]	The data to transform.

Returns

Promise<ArrayLike>

Defined in: generated/preprocessing/SplineTransformer.ts:416 (opens in a new tab)

Properties

_isDisposed

boolean = false

Defined in: generated/preprocessing/SplineTransformer.ts:23 (opens in a new tab)

_isInitialized

boolean = false

Defined in: generated/preprocessing/SplineTransformer.ts:22 (opens in a new tab)

_py

PythonBridge

Defined in: generated/preprocessing/SplineTransformer.ts:21 (opens in a new tab)

id

string

Defined in: generated/preprocessing/SplineTransformer.ts:18 (opens in a new tab)

opts

any

Defined in: generated/preprocessing/SplineTransformer.ts:19 (opens in a new tab)

Accessors

bsplines_

List of BSplines objects, one for each feature.

Signature

bsplines_(): Promise<any[]>;

Returns

Promise<any[]>

Defined in: generated/preprocessing/SplineTransformer.ts:451 (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/preprocessing/SplineTransformer.ts:505 (opens in a new tab)

n_features_in_

The total number of input features.

Signature

n_features_in_(): Promise<number>;

Returns

Promise<number>

Defined in: generated/preprocessing/SplineTransformer.ts:478 (opens in a new tab)

n_features_out_

The total number of output features, which is computed as n\_features \* n\_splines, where n\_splines is the number of bases elements of the B-splines, n\_knots + degree \- 1 for non-periodic splines and n\_knots \- 1 for periodic ones. If include\_bias=False, then it is only n\_features \* (n\_splines \- 1).