lale.lib.autogen.pls_canonical module

class lale.lib.autogen.pls_canonical.PLSCanonical(*, n_components=2, scale=True, algorithm='nipals', max_iter=500, tol=1e-06, 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) – Option to scale data

  • algorithm (‘nipals’ or ‘svd’, default ‘nipals’) – The algorithm used to estimate the weights

  • max_iter (integer, >=10 for optimizer, <=1000 for optimizer, uniform distribution, default 500) – the maximum number of iterations of the NIPALS inner loop (used only if algorithm=”nipals”)

  • tol (float, >=1e-08 for optimizer, <=0.01 for optimizer, default 1e-06) – the tolerance used in the iterative algorithm

  • copy (boolean, default True) – Whether the deflation should be done on a copy

Notes

constraint-1 : any type

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.

predict(X, **predict_params)

Make predictions.

Note: The predict 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.

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

Returns

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

Return type

Any

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.

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

Returns

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

Return type

Any