etna.transforms.MeanEncoderTransform#

class MeanEncoderTransform(in_column: str, out_column: str, mode: EncoderMode | str = 'per-segment', handle_missing: str = MissingMode.category, smoothing: int = 1)[source]#

Bases: IrreversibleTransform

Makes encoding of categorical feature.

For timestamps that are before the last timestamp seen in fit transformations are made using the formula below:

\[\frac{TargetSum + RunningMean * Smoothing}{FeatureCount + Smoothing}\]

where

TargetSum is the sum of target up to the current timestamp for the current category, not including the current timestamp
RunningMean is target mean up to the current timestamp, not including the current timestamp
FeatureCount is the number of categories with the same value as in the current timestamp, not including the current timestamp

For future timestamps:

for known categories encoding are filled with global mean of target for these categories calculated during fit
for unknown categories encoding are filled with global mean of target in the whole dataset calculated during fit

All types of NaN values are considering as one category.

Init MeanEncoderTransform.

Parameters:

in_column (str) – categorical column to apply transform
out_column (str) – name of added column
mode (EncoderMode | str) –
mode to encode segments
- ’per-segment’ - statistics are calculated across each segment individually
- ’macro’ - statistics are calculated across all segments. In this mode transform can work with new segments that were not seen during fit
handle_missing (str) –
mode to handle missing values in in_column
- ’category’ - NaNs they are interpreted as a separate categorical feature
- ’global_mean’ - NaNs are filled with the running mean
smoothing (int) – smoothing parameter

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.

Attributes

This class stores its __init__ parameters as attributes.

idx

fit(ts: TSDataset) → Transform[source]#

Fit the transform.

Parameters:: ts (TSDataset) – Dataset to fit the transform on.
Returns:: The fitted transform instance.
Return type:: Transform

fit_transform(ts: TSDataset) → TSDataset[source]#

Fit and transform TSDataset.

May be reimplemented. But it is not recommended.

Parameters:: ts (TSDataset) – TSDataset to transform.
Returns:: Transformed TSDataset.
Return type:: TSDataset

get_regressors_info() → List[str][source]#

Return the list with regressors created by the transform.

Return type:: List[str]

inverse_transform(ts: TSDataset) → TSDataset[source]#

Inverse transform TSDataset.

Do nothing.

Parameters:: ts (TSDataset) – TSDataset to be inverse transformed.
Returns:: TSDataset after applying inverse transformation.
Return type:: TSDataset

classmethod load(path: Path) → Self[source]#

Load an object.

Warning

This method uses dill module which is not secure. It is possible to construct malicious data which will execute arbitrary code during loading. Never load data that could have come from an untrusted source, or that could have been tampered with.

Parameters:: path (Path) – Path to load object from.
Returns:: Loaded object.
Return type:: Self

params_to_tune() → Dict[str, BaseDistribution][source]#

Get default grid for tuning hyperparameters.

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

Returns:: Grid to tune.
Return type:: Dict[str, BaseDistribution]

save(path: Path)[source]#

Save the object.

Parameters:: path (Path) – Path to save object to.

set_params(**params: dict) → Self[source]#

Return new object instance with modified parameters.

Method also allows to change parameters of nested objects within the current object. For example, it is possible to change parameters of a model in a Pipeline.

Nested parameters are expected to be in a <component_1>.<...>.<parameter> form, where components are separated by a dot.

Parameters:: **params (dict) – Estimator parameters
Returns:: New instance with changed parameters
Return type:: Self

Examples

>>> from etna.pipeline import Pipeline
>>> from etna.models import NaiveModel
>>> from etna.transforms import AddConstTransform
>>> model = NaiveModel(lag=1)
>>> transforms = [AddConstTransform(in_column="target", value=1)]
>>> pipeline = Pipeline(model, transforms=transforms, horizon=3)
>>> pipeline.set_params(**{"model.lag": 3, "transforms.0.value": 2})
Pipeline(model = NaiveModel(lag = 3, ), transforms = [AddConstTransform(in_column = 'target', value = 2, inplace = True, out_column = None, )], horizon = 3, )

to_dict()[source]#: Collect all information about etna object in dict.

transform(ts: TSDataset) → TSDataset[source]#

Transform TSDataset inplace.

Parameters:: ts (TSDataset) – Dataset to transform.
Returns:: Transformed TSDataset.
Return type:: TSDataset