lale.lib.sklearn.nystroem module

class lale.lib.sklearn.nystroem.Nystroem(*, kernel='rbf', gamma=None, coef0=None, degree=None, kernel_params=None, n_components=100, random_state=None, n_jobs=None)

Bases: PlannedIndividualOp

Nystroem transformer from scikit-learn.

This documentation is auto-generated from JSON schemas.

Parameters

  • kernel (union type, default 'rbf') –

    Kernel map to be approximated.

    • ’additive_chi2’, ‘chi2’, ‘cosine’, ‘linear’, ‘poly’, ‘polynomial’, ‘rbf’, ‘laplacian’, or ‘sigmoid’

      keys of sklearn.metrics.pairwise.KERNEL_PARAMS

    • or callable, not for optimizer

  • gamma (union type, default None) –

    Gamma parameter.

    • None

    • or float, >=3.0517578125e-05 for optimizer, <=8 for optimizer, loguniform distribution

  • coef0 (union type, default None) –

    Zero coefficient.

    • None

    • or float, >=-1, <=1 for optimizer, uniform distribution

  • degree (union type, default None) –

    Degree of the polynomial kernel.

    • None

    • or integer, >=2 for optimizer, <=5 for optimizer

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

    Additional parameters (keyword arguments) for kernel function passed as callable object.

    • dict

    • or None

  • n_components (integer, >=1, >=10 for optimizer, <=256 for optimizer, uniform distribution, default 100) – Number of features to construct. How many data points will be used to construct the mapping.

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

    Seed of pseudo-random number generator.

    • integer

    • or numpy.random.RandomState

    • or None

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

    The number of jobs to use for the computation.

    • None

      1 unless in joblib.parallel_backend context.

    • or -1

      Use all processors.

    • or integer, >=1

      Number of CPU cores.

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) – Features; the outer array is over samples.

  • y (any type, optional) – Target class labels; the array is over samples.

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) – Features; the outer array is over samples.

Returns

result – Output data schema for predictions (projected data) using the Nystroem model from scikit-learn.

Return type

array of items : array of items : float