TargetEncoder

class lightautoml.transformers.categorical.TargetEncoder(alphas=(0.5, 1.0, 2.0, 5.0, 10.0, 50.0, 250.0, 1000.0))

Bases: lightautoml.transformers.base.LAMLTransformer

Out-of-fold target encoding.

Limitation:

• Required .folds attribute in dataset - array of int from 0 to n_folds-1.

• Working only after label encoding.

__init__(alphas=(0.5, 1.0, 2.0, 5.0, 10.0, 50.0, 250.0, 1000.0))

Parameters

alphas (Sequence[float]) – Smooth coefficients.

static binary_score_func(candidates, target)

Score candidates alpha with logloss metric.

Parameters

• candidates (ndarray) – Candidate oof encoders.

• target (ndarray) – Target array.

Return type

int

Returns

Index of best encoder.

static reg_score_func(candidates, target)

Score candidates alpha with mse metric.

Parameters

• candidates (ndarray) – Candidate oof encoders.

• target (ndarray) – Target array.

Return type

int

Returns

Index of best encoder.

fit_transform(dataset)

Calc oof encoding and save encoding stats for new data.

Parameters

dataset (Union[NumpyDataset, PandasDataset]) – Pandas or Numpy dataset of categorical label encoded features.

Return type

NumpyDataset

Returns

NumpyDataset - target encoded features.

transform(dataset)

Transform categorical dataset to target encoding.

Parameters

dataset (Union[NumpyDataset, PandasDataset]) – Pandas or Numpy dataset of categorical features.

Return type

NumpyDataset

Returns

Numpy dataset with encoded labels.