QuantileTransformer

class lightautoml.transformers.numeric.QuantileTransformer(n_quantiles=None, subsample=1000000000.0, output_distribution='normal', noise=0.001, qnt_factor=30)[source]

Bases: LAMLTransformer

Transform features using quantiles information.

__init__(n_quantiles=None, subsample=1000000000.0, output_distribution='normal', noise=0.001, qnt_factor=30)[source]

QuantileTransformer.

Parameters:

n_quantiles (Optional[int]) – Number of quantiles to be computed.
subsample (int) – Maximum number of samples used to estimate the quantiles for computational efficiency.
output_distribution (str) – Marginal distribution for the transformed data. The choices are ‘uniform’ or ‘normal’.
noise (float) – Add noise with certain std to dataset before quantile transformation to make data more smooth.
qnt_factor (int) – If number of quantiles is none then it equals dataset size / factor

fit(dataset)[source]

Fit Sklearn QuantileTransformer.

Parameters:: dataset (Union[NumpyDataset, PandasDataset]) – Pandas or Numpy dataset of numeric features.
Returns:: self.

transform(dataset)[source]

Apply transformer.

Parameters:: dataset (Union[NumpyDataset, PandasDataset]) – Pandas or Numpy dataset of numeric features.
Return type:: NumpyDataset
Returns:: Numpy dataset with encoded labels.