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 of this object.
get_params
([deep])Get parameters for this estimator.
predict
(X)Predict using the model.
score
(X, y[, sample_weight])Return the coefficient of determination of the prediction.
set_params
(**params)Set the parameters of this estimator.
set_score_request
(*[, sample_weight])Request metadata 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.
- 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 the coefficient of determination of the prediction.
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)
, wheren_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:
Notes
The \(R^2\) score used when calling
score
on a regressor usesmultioutput='uniform_average'
from version 0.23 to keep consistent with default value ofr2_score()
. This influences thescore
method of all the multioutput regressors (except forMultiOutputRegressor
).
- 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='$UNCHANGED$')#
Request metadata passed to the
score
method.Note that this method is only relevant if
enable_metadata_routing=True
(seesklearn.set_config()
). Please see User Guide on how the routing mechanism works.The options for each parameter are:
True
: metadata is requested, and passed toscore
if provided. The request is ignored if metadata is not provided.False
: metadata is not requested and the meta-estimator will not pass it toscore
.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.New 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 inscore
.self (FPLSRegression) –
- Returns:
self – The updated object.
- Return type: