randomml.base.RVFLBase#

class randomml.base.RVFLBase(in_dim: int, n_nodes: int = 100, alpha: float | ndarray | None = None, ridge: bool = True, direct_link: bool = True, activation: Literal['sigmoid', 'relu', 'tanh'] = 'sigmoid', random_state: int = 23)[source]#

Base class for Random Vector Functional Link (RVFL) networks.

__init__(in_dim: int, n_nodes: int = 100, alpha: float | ndarray | None = None, ridge: bool = True, direct_link: bool = True, activation: Literal['sigmoid', 'relu', 'tanh'] = 'sigmoid', random_state: int = 23)[source]#

Initializes the RVFL base model.

Args:

in_dim: Input feature dimension. n_nodes: Number of enhancement nodes. Defaults to 100. alpha: Regularization strength for Ridge regression.

If None, alpha will be selected using LOOCV over a log scale. Can be a float for fixed Ridge regularization or an array-like of alphas for RidgeCV. In the case of Moore-Penrose pseudoinverse (ridge=False), alpha is still used as a regularization parameter to improve stability. Defaults to None.

ridge: Use Ridge regression if True; else use Moore-Penrose Pseudoinverse.: Defaults to True.
direct_link: Whether to include direct input-output connections.: Defaults to True.
activation: Activation function for FFNN. Options are “sigmoid”, “relu”, “tanh”.: Defaults to “sigmoid”.

random_state: Seed for reproducibility. Defaults to 23.

Methods

`__init__`(in_dim[, n_nodes, alpha, ridge, ...])	Initializes the RVFL base model.
`fit`(X, y[, sample_weight])	Fits the RVFL model.
`fit_transform`(X[, y])	Fit to data, then transform it.
`get_metadata_routing`()	Get metadata routing of this object.
`get_params`([deep])	Get parameters for this estimator.
`predict`(X)	Predicts the target values.
`set_fit_request`(*[, sample_weight])	Request metadata passed to the `fit` method.
`set_output`(*[, transform])	Set output container.
`set_params`(**params)	Set the parameters of this estimator.

fit(X: ndarray, y: ndarray, sample_weight: ndarray | None = None) → RVFLBase[source]#

Fits the RVFL model.

Args:: X: Input features. y: Target values. sample_weight: Sample weights (for AdaBoost compatibility). Defaults to None.
Returns:: self: Returns the fitted estimator.

fit_transform(X, y=None, **fit_params)[source]#

Fit to data, then transform it.

Fits transformer to X and y with optional parameters fit_params and returns a transformed version of X.

Parameters:

X (array-like of shape (n_samples, n_features)) – Input samples.
y (array-like of shape (n_samples,) or (n_samples, n_outputs), default=None) – Target values (None for unsupervised transformations).
**fit_params (dict) – Additional fit parameters.

Returns:

X_new – Transformed array.

Return type:

ndarray array of shape (n_samples, n_features_new)

get_metadata_routing()[source]#

Get metadata routing of this object.

Please check User Guide on how the routing mechanism works.

Returns:: routing – A MetadataRequest encapsulating routing information.
Return type:: MetadataRequest

get_params(deep=True)[source]#

Get parameters for this estimator.

Parameters:: deep (bool, default=True) – If True, will return the parameters for this estimator and contained subobjects that are estimators.
Returns:: params – Parameter names mapped to their values.
Return type:: dict

predict(X: ndarray) → ndarray[source]#

Predicts the target values.

Args:: X: Input features.
Returns:: Predicted values.

set_fit_request(*, sample_weight: bool | None | str = '$UNCHANGED$') → RVFLBase[source]#

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:

True: metadata is requested, and passed to fit if provided. The request is ignored if metadata is not provided.
False: metadata is not requested and the meta-estimator will not pass it to fit.
None: metadata is not requested, and the meta-estimator will raise an error if the user provides it.
str: metadata should be passed to the meta-estimator with this given alias instead of the original name.

The default (sklearn.utils.metadata_routing.UNCHANGED) retains the existing request. This allows you to change the request for some parameters and not others.

Added in version 1.3.

Note

This method is only relevant if this estimator is used as a sub-estimator of a meta-estimator, e.g. used inside a Pipeline. Otherwise it has no effect.

Parameters:: sample_weight (str, True, False, or None, default=sklearn.utils.metadata_routing.UNCHANGED) – Metadata routing for sample_weight parameter in fit.
Returns:: self – The updated object.
Return type:: object

set_output(*, transform=None)[source]#

Set output container.

See sphx_glr_auto_examples_miscellaneous_plot_set_output.py for an example on how to use the API.

Parameters:

transform ({"default", "pandas", "polars"}, default=None) –

Configure output of transform and fit_transform.

”default”: Default output format of a transformer
”pandas”: DataFrame output
”polars”: Polars output
None: Transform configuration is unchanged

Added in version 1.4: “polars” option was added.

Returns:

self – Estimator instance.

Return type:

estimator instance

set_params(**params)[source]#

Set the parameters of this estimator.

The method works on simple estimators as well as on nested objects (such as Pipeline). The latter have parameters of the form <component>__<parameter> so that it’s possible to update each component of a nested object.

Parameters:: **params (dict) – Estimator parameters.
Returns:: self – Estimator instance.
Return type:: estimator instance