lale.lib.autogen.plssvd module

class lale.lib.autogen.plssvd.PLSSVD(*, n_components=2, scale=True, copy=True)

Bases: PlannedIndividualOp

Combined schema for expected data and hyperparameters.

This documentation is auto-generated from JSON schemas.

Parameters

• n_components (integer, >=2 for optimizer, <=256 for optimizer, uniform distribution, default 2) – Number of components to keep.

• scale (boolean, default True) – Whether to scale X and Y.

• copy (boolean, default True) – Whether to copy X and Y, or perform in-place computations.

fit(X, y=None, **fit_params)

Train the operator.

Note: The fit method is not available until this operator is trainable.

Once this method is available, it will have the following signature:

Parameters

• X (array of items : array of items : float) – Training vectors, where n_samples is the number of samples and n_features is the number of predictors.

• Y (array, optional of items : array of items : float) – Target vectors, where n_samples is the number of samples and n_targets is the number of response variables.

transform(X, y=None)

Transform the data.

Note: The transform method is not available until this operator is trained.

Once this method is available, it will have the following signature:

Parameters

• X (array of items : array of items : float) – Training vectors, where n_samples is the number of samples and n_features is the number of predictors.

• Y (array, optional of items : array of items : float) – Target vectors, where n_samples is the number of samples and n_targets is the number of response variables.

Returns

result – Apply the dimension reduction learned on the train data.

Return type

Any