etna.transforms.EmbeddingSegmentTransform#

class EmbeddingSegmentTransform(in_columns: List[str], embedding_model: BaseEmbeddingModel, encoding_params: Dict[str, Any] | None = None, training_params: Dict[str, Any] | None = None, out_column: str = 'embedding_segment')[source]#

Bases: IrreversibleTransform

Create the constant embedding features using embedding model.

Init EmbeddingSegmentTransform.

Parameters:
  • in_columns (List[str]) – Columns to use for creating embeddings

  • embedding_model (BaseEmbeddingModel) – Model to create the embeddings

  • encoding_params (Dict[str, Any] | None) – Parameters to use during encoding. Parameters for corresponding models can be found at embedding section.

  • training_params (Dict[str, Any] | None) – Parameters to use during training. Parameters for corresponding models can be found at embedding section.

  • out_column (str) – Prefix for output columns, the output columns format is ‘{out_column}_{i}’

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.

Attributes

This class stores its __init__ parameters as attributes.

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) EmbeddingSegmentTransform[source]#

Load an object.

Parameters:

path (Path) – Path to load object from.

Returns:

Loaded object.

Return type:

EmbeddingSegmentTransform

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

Get grid for tuning hyperparameters.

This is default implementation with empty grid.

Returns:

Empty grid.

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