tensorly.regression.CP_PLSR

class CP_PLSR(n_components, tol=1e-09, n_iter_max=100, random_state=None, verbose=False)[source]

CP tensor regression

Learns a low rank CP tensor weight, This performs a partial least square regression to a tensor X (>= 2 modes) against a matrix Y. The first modes of X and Y will be considered coupled, and the decomposition will maximize the covariance between them.

Parameters:

n_componentsint

rank of the CP decomposition of the regression weights

tolfloat

convergence value

n_iter_maxint, optional, default is 100

maximum number of iteration

random_stateNone, int or RandomState, optional, default is None

verbosebool, default is False

whether to be verbose during fitting

References

[1]

Rasmus Bro, “Multiway calibration. Multilinear PLS”, Chemometrics, 1996

fit(X, Y)[source]

Fits the model to the data (X, Y)

Parameters:

Xndarray

tensor data of shape (n_samples, N1, …, NS)

Y2D-array of shape (n_samples, n_predictions)

labels associated with each sample

Returns:

self

fit_transform(X, Y)[source]

Learn and apply the dimension reduction on the train data.

Parameters:

Xarray-like of shape (n_samples, n_features)

Training vectors, where n_samples is the number of samples and n_features is the number of predictors.

yarray-like of shape (n_samples, n_targets), default=None

Target vectors, where n_samples is the number of samples and n_targets is the number of response variables.

Returns:

selfndarray of shape (n_samples, n_components)

Return x_scores if Y is not given, (x_scores, y_scores) otherwise.

get_params(**kwargs)[source]

Returns a dictionary of parameters

predict(X)[source]

Returns the predicted labels for a new data tensor

Parameters:

Xndarray

tensor data of shape (n_samples, N1, …, NS)

set_params(**parameters)[source]

Sets the value of the provided parameters

transform(X, Y=None)[source]

Apply the dimension reduction from fitting to a new tensor.

Parameters:

Xarray-like of shape (n_samples, n_features)

Samples to transform.

Yarray-like of shape (n_samples, n_targets), default=None

Target vectors.

Returns:

X_scores, Y_scoresarray-like or tuple of array-like

Return X_scores if Y is not given, (X_scores, Y_scores) otherwise.