OneHotEncoderTransform

class OneHotEncoderTransform(in_column: str, out_column: Optional[str] = None)

Bases: etna.transforms.base.IrreversibleTransform

Encode categorical feature as a one-hot numeric features.

If unknown category is encountered during transform, the resulting one-hot encoded columns for this feature will be all zeros.

Init OneHotEncoderTransform.

Parameters

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

• out_column (Optional[str]) – Prefix of names of added columns. If not given, use self.__repr__()

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 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.OneHotEncoderTransform

Fit the transform.

Parameters

ts (etna.datasets.tsdataset.TSDataset) –

Return type

etna.transforms.encoders.categorical.OneHotEncoderTransform

get_regressors_info() → List[str]

Return the list with regressors created by the transform.

Return type

List[str]