FPLSRegression#

class skfda.ml.regression.FPLSRegression(n_components=None, regularization_X=None, weight_basis_X=None, weight_basis_Y=None, _integration_weights_X=None, _integration_weights_Y=None)[source]#

Regression using Functional Partial Least Squares.

Parameters:

n_components (int | None) – Number of components to keep. By default all available components are utilized.
regularization_X (L2Regularization[Any] | None) – Regularization for the calculation of the X weights.
weight_basis_X (Basis | None) – Basis to use for the X block. Only applicable if X is a FDataBasis. Otherwise it must be None.
weight_basis_Y (Basis | None) – Basis to use for the Y block. Only applicable if Y is a FDataBasis. Otherwise it must be None.
_integration_weights_X (NDArrayFloat | None)
_integration_weights_Y (NDArrayFloat | None)

Attributes:

coef_ – Coefficients of the linear model.
fpls_ – FPLS object used to fit the model.

Examples

Fit a FPLS regression model with two components.

>>> from skfda.ml.regression import FPLSRegression
>>> from skfda.datasets import fetch_tecator
>>> from skfda.representation import FDataGrid
>>> from skfda.typing._numpy import NDArrayFloat

>>> X, y = fetch_tecator(return_X_y=True)
>>> fpls = FPLSRegression[FDataGrid, NDArrayFloat](n_components=2)
>>> fpls = fpls.fit(X, y)

Methods

`fit`(X, y)	Fit the model using the data for both blocks.
`get_metadata_routing`()	Get metadata routing of this object.
`get_params`([deep])	Get parameters for this estimator.
`predict`(X)	Predict using the model.
`score`(X, y[, sample_weight])	Return coefficient of determination on test data.
`set_params`(**params)	Set the parameters of this estimator.
`set_score_request`(*[, sample_weight])	Configure whether metadata should be requested to be passed to the `score` method.

fit(X, y)[source]#

Fit the model using the data for both blocks.

Parameters:

X (InputType) – Data of the X block
y (OutputType) – Data of the Y block

Returns:

self

Return type:

FPLSRegression[InputType, OutputType]

get_metadata_routing()#

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)#

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)[source]#

Predict using the model.

Parameters:: X (InputType) – Data to predict.
Returns:: Predicted values.
Return type:: OutputType

score(X, y, sample_weight=None)[source]#

Return coefficient of determination on test data.

The coefficient of determination, $R^2$, is defined as $(1 - \frac{u}{v})$, where $u$ is the residual sum of squares ((y_true - y_pred)** 2).sum() and $v$ is the total sum of squares ((y_true - y_true.mean()) ** 2).sum(). The best possible score is 1.0 and it can be negative (because the model can be arbitrarily worse). A constant model that always predicts the expected value of y, disregarding the input features, would get a $R^2$ score of 0.0.

Parameters:

X (array-like of shape (n_samples, n_features)) – Test samples. For some estimators this may be a precomputed kernel matrix or a list of generic objects instead with shape (n_samples, n_samples_fitted), where n_samples_fitted is the number of samples used in the fitting for the estimator.
y (array-like of shape (n_samples,) or (n_samples, n_outputs)) – True values for X.
sample_weight (array-like of shape (n_samples,), default=None) – Sample weights.

Returns:

score – $R^2$ of self.predict(X) w.r.t. y.

Return type:

float

Notes

The $R^2$ score used when calling score on a regressor uses multioutput='uniform_average' from version 0.23 to keep consistent with default value of r2_score(). This influences the score method of all the multioutput regressors (except for MultiOutputRegressor).

set_params(**params)#

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

set_score_request(*, sample_weight='$UNCHANGED$')#

Configure whether metadata should be requested to be passed to the score method.

Note that this method is only relevant when this estimator is used as a sub-estimator within a meta-estimator and metadata routing is enabled with enable_metadata_routing=True (see sklearn.set_config()). Please check the User Guide on how the routing mechanism works.

The options for each parameter are:

True: metadata is requested, and passed to score 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 score.
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.

Parameters:

sample_weight (str, True, False, or None, default=sklearn.utils.metadata_routing.UNCHANGED) – Metadata routing for sample_weight parameter in score.
self (FPLSRegression)

Returns:

self – The updated object.

Return type:

object

FPLSRegression#

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