lale.lib.autogen.truncated_svd module

class lale.lib.autogen.truncated_svd.TruncatedSVD(*, n_components=2, algorithm='randomized', n_iter=5, random_state=None, tol=0.0)

Bases: PlannedIndividualOp

Combined schema for expected data and hyperparameters.

This documentation is auto-generated from JSON schemas.

Parameters

  • n_components (integer, >=2 for optimizer, <='X/items/maxItems', <=256 for optimizer, uniform distribution, default 2) – Desired dimensionality of output data

  • algorithm (‘arpack’ or ‘randomized’, default ‘randomized’) – SVD solver to use

  • n_iter (integer, >=5 for optimizer, <=1000 for optimizer, uniform distribution, default 5) – Number of iterations for randomized SVD solver

  • random_state (union type, not for optimizer, default None) –

    If int, random_state is the seed used by the random number generator; If RandomState instance, random_state is the random number generator; If None, the random number generator is the RandomState instance used by np.random.

    • integer

    • or numpy.random.RandomState

    • or None

  • tol (float, >=1e-08 for optimizer, <=0.01 for optimizer, default 0.0) – Tolerance for ARPACK

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 data.

  • y (any type) –

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) – New data.

Returns

result – Reduced version of X

Return type

array of items : array of items : float