lale.lib.autogen.incremental_pca module

class lale.lib.autogen.incremental_pca.IncrementalPCA(*, n_components=None, whiten=False, copy=True, batch_size=None)

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 to keep

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

    • or None

  • whiten (boolean, default False) – When True (False by default) the components_ vectors are divided by n_samples times components_ to ensure uncorrelated outputs with unit component-wise variances

  • copy (boolean, default True) – If False, X will be overwritten

  • batch_size (union type, default None) –

    The number of samples to use for each batch

    • integer, >=3 for optimizer, <=128 for optimizer, uniform distribution

    • or None

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

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

Returns

result – Apply dimensionality reduction to X.

Return type

array of items : array of items : float