lale.lib.autogen.kernel_pca module

class lale.lib.autogen.kernel_pca.KernelPCA(*, n_components=None, kernel='linear', gamma=None, degree=3, coef0=1, kernel_params=None, alpha=1.0, fit_inverse_transform=False, eigen_solver='auto', tol=0, max_iter=None, remove_zero_eig=False, random_state=None, copy_X=True, n_jobs=1)

Bases: PlannedIndividualOp

Combined schema for expected data and hyperparameters.

This documentation is auto-generated from JSON schemas.

Parameters

  • n_components (union type, default None) –

    Number of components

    • integer, >=2 for optimizer, <=256 for optimizer, uniform distribution

    • or None

  • kernel (‘linear’, ‘poly’, ‘rbf’, ‘sigmoid’, ‘cosine’, or ‘precomputed’, default ‘linear’) –

    Kernel

    See also constraint-1.

  • gamma (None, not for optimizer, default None) – Kernel coefficient for rbf, poly and sigmoid kernels

  • degree (union type, default 3) –

    Degree for poly kernels

    • integer, >=2 for optimizer, <=3 for optimizer, uniform distribution

    • or float, not for optimizer

  • coef0 (float, >=0.0 for optimizer, <=1.0 for optimizer, uniform distribution, default 1) – Independent term in poly and sigmoid kernels

  • kernel_params (None, not for optimizer, default None) – Parameters (keyword arguments) and values for kernel passed as callable object

  • alpha (union type, default 1.0) –

    Hyperparameter of the ridge regression that learns the inverse transform (when fit_inverse_transform=True).

    • integer, not for optimizer

    • or float, >=1e-10 for optimizer, <=1.0 for optimizer, loguniform distribution

  • fit_inverse_transform (boolean, not for optimizer, default False) –

    Learn the inverse transform for non-precomputed kernels

    See also constraint-1.

  • eigen_solver (‘auto’, ‘dense’, or ‘arpack’, default ‘auto’) – Select eigensolver to use

  • tol (float, >=1e-08 for optimizer, <=0.01 for optimizer, default 0) – Convergence tolerance for arpack

  • max_iter (union type, default None) –

    Maximum number of iterations for arpack

    • integer, >=10 for optimizer, <=1000 for optimizer, uniform distribution

    • or None

  • remove_zero_eig (boolean, default False) – If True, then all components with zero eigenvalues are removed, so that the number of components in the output may be < n_components (and sometimes even zero due to numerical instability)

  • 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

  • copy_X (boolean, default True) – If True, input X is copied and stored by the model in the X_fit_ attribute

  • n_jobs (union type, not for optimizer, default 1) –

    The number of parallel jobs to run

    • integer

    • or None

Notes

constraint-1 : union type

Cannot fit_inverse_transform with a precomputed kernel.

  • fit_inverse_transform : False

  • or kernel : negated type of ‘precomputed’

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 vector, where n_samples in the number of samples and n_features is the number of features.

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) –

Returns

result – Transform X.

Return type

array of items : array of items : float