imputation

Classes

ImputerMode(value)

Enum for different imputation strategy.

TimeSeriesImputerTransform([in_column, ...])

Transform to fill NaNs in series of a given dataframe.
class TimeSeriesImputerTransform(in_column: str = 'target', strategy: str = ImputerMode.constant, window: int = - 1, seasonality: int = 1, default_value: Optional[float] = None, constant_value: float = 0)[source]

Transform to fill NaNs in series of a given dataframe.

  • It is assumed that given series begins with first non NaN value.

  • This transform can’t fill NaNs in the future, only on train data.

  • This transform can’t fill NaNs if all values are NaNs. In this case exception is raised.

Warning

This transform can suffer from look-ahead bias in ‘mean’ mode. For transforming data at some timestamp it uses information from the whole train part.

Create instance of TimeSeriesImputerTransform.

Parameters

  • in_column (str) – name of processed column

  • strategy (str) –

    filling value in missing timestamps:

    • If “mean”, then replace missing dates using the mean in fit stage.

    • If “running_mean” then replace missing dates using mean of subset of data

    • If “forward_fill” then replace missing dates using last existing value

    • If “seasonal” then replace missing dates using seasonal moving average

    • If “constant” then replace missing dates using constant value.

  • window (int) –

    In case of moving average and seasonality.

    • If window=-1 all previous dates are taken in account

    • Otherwise only window previous dates

  • seasonality (int) – the length of the seasonality

  • default_value (Optional[float]) – value which will be used to impute the NaNs left after applying the imputer with the chosen strategy

  • constant_value (float) – value to fill gaps in “constant” strategy

Raises

ValueError: – if incorrect strategy given

fit(ts: etna.datasets.tsdataset.TSDataset) → etna.transforms.base.Transform

Fit the transform.

Parameters

ts (etna.datasets.tsdataset.TSDataset) – Dataset to fit the transform on.

Returns

The fitted transform instance.

Return type

etna.transforms.base.Transform

fit_transform(ts: etna.datasets.tsdataset.TSDataset) → etna.datasets.tsdataset.TSDataset

Fit and transform TSDataset.

May be reimplemented. But it is not recommended.

Parameters

ts (etna.datasets.tsdataset.TSDataset) – TSDataset to transform.

Returns

Transformed TSDataset.

Return type

etna.datasets.tsdataset.TSDataset

get_regressors_info() → List[str][source]

Return the list with regressors created by the transform.

Return type

List[str]

inverse_transform(ts: etna.datasets.tsdataset.TSDataset) → etna.datasets.tsdataset.TSDataset

Inverse transform TSDataset.

Apply the _inverse_transform method.

Parameters

ts (etna.datasets.tsdataset.TSDataset) – TSDataset to be inverse transformed.

Returns

TSDataset after applying inverse transformation.

Return type

etna.datasets.tsdataset.TSDataset

classmethod load(path: pathlib.Path) → typing_extensions.Self

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 (pathlib.Path) – Path to load object from.

Returns

Loaded object.

Return type

typing_extensions.Self

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

Get default grid for tuning hyperparameters.

This grid tunes parameters: strategy, window. Other parameters are expected to be set by the user.

Strategy “seasonal” is suggested only if self.seasonality is set higher than 1.

Returns

Grid to tune.

Return type

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

save(path: pathlib.Path)

Save the object.

Parameters

path (pathlib.Path) – Path to save object to.

set_params(**params: dict) → etna.core.mixins.TMixin

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 – Estimator parameters

  • self (etna.core.mixins.TMixin) –

  • params (dict) –

Returns

New instance with changed parameters

Return type

etna.core.mixins.TMixin

Examples

>>> from etna.pipeline import Pipeline
>>> from etna.models import NaiveModel
>>> from etna.transforms import AddConstTransform
>>> model = 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()

Collect all information about etna object in dict.

transform(ts: etna.datasets.tsdataset.TSDataset) → etna.datasets.tsdataset.TSDataset

Transform TSDataset inplace.

Parameters

ts (etna.datasets.tsdataset.TSDataset) – Dataset to transform.

Returns

Transformed TSDataset.

Return type

etna.datasets.tsdataset.TSDataset