LabelEncoderTransform

class LabelEncoderTransform(in_column: str, out_column: Optional[str] = None, strategy: str = ImputerMode.mean)

Bases: etna.transforms.base.IrreversibleTransform

Encode categorical feature with value between 0 and n_classes-1.

Init LabelEncoderTransform.

Parameters

• in_column (str) – Name of column to be transformed

• out_column (Optional[str]) – Name of added column. If not given, use self.__repr__()

• strategy (str) –

  Filling encoding in not fitted values:

  • If “new_value”, then replace missing values with ‘-1’

  • If “mean”, then replace missing values using the mean in encoded column

  • If “none”, then replace missing values with None

Inherited-members

Methods

fit(ts)	Fit the transform.
fit_transform(ts)	Fit and transform TSDataset.
get_regressors_info()	Return the list with regressors created by the transform.
inverse_transform(ts)	Inverse transform TSDataset.
load(path)	Load an object.
params_to_tune()	Get default grid for tuning hyperparameters.
save(path)	Save the object.
set_params(**params)	Return new object instance with modified parameters.
to_dict()	Collect all information about etna object in dict.
transform(ts)	Transform TSDataset inplace.

fit(ts: etna.datasets.tsdataset.TSDataset) → etna.transforms.encoders.categorical.LabelEncoderTransform

Fit the transform.

Parameters

ts (etna.datasets.tsdataset.TSDataset) –

Return type

etna.transforms.encoders.categorical.LabelEncoderTransform

get_regressors_info() → List[str]

Return the list with regressors created by the transform.

Return type

List[str]

params_to_tune() → Dict[str, etna.distributions.distributions.BaseDistribution]

Get default grid for tuning hyperparameters.

This grid tunes strategy parameter. Other parameters are expected to be set by the user.

Returns

Grid to tune.

Return type

Dict[str, etna.distributions.distributions.BaseDistribution]