lale.lib.sklearn.quantile_transformer module

class lale.lib.sklearn.quantile_transformer.QuantileTransformer(*, n_quantiles=1000, output_distribution='uniform', ignore_implicit_zeros=False, subsample=100000, random_state=None, copy=True)

Bases: PlannedIndividualOp

Quantile transformer from scikit-learn.

This documentation is auto-generated from JSON schemas.

Parameters

  • n_quantiles (integer, >=10 for optimizer, <=2000 for optimizer, uniform distribution, default 1000) – Number of quantiles to be computed. It corresponds to the number

  • output_distribution (‘normal’ or ‘uniform’, default ‘uniform’) – Marginal distribution for the transformed data. The choices are

  • ignore_implicit_zeros (boolean, not for optimizer, default False) – Only applies to sparse matrices. If True, the sparse entries of the

  • subsample (integer, >=1 for optimizer, <=100000 for optimizer, uniform distribution, default 100000) – Maximum number of samples used to estimate the quantiles for

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

    If int, random_state is the seed used by the random number generator;

    • integer

    • or numpy.random.RandomState

    • or None

  • copy (boolean, not for optimizer, default True) – Set to False to perform inplace transformation and avoid a copy (if the

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) – The data used to scale along the features axis. If a sparse matrix is provided, it will be converted into a sparse csc_matrix. Additionally, the sparse matrix needs to be nonnegative if ignore_implicit_zeros is False.

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) – The data used to scale along the features axis. If a sparse matrix is provided, it will be converted into a sparse csc_matrix. Additionally, the sparse matrix needs to be nonnegative if ignore_implicit_zeros is False.

Returns

result – The projected data.

Return type

array of items : array of items : float