etna.transforms.TimeSeriesImputerTransform#

class TimeSeriesImputerTransform(in_column: str = 'target', strategy: str = ImputerMode.constant, window: int = -1, seasonality: int = 1, default_value: float | None = None, constant_value: float = 0)[source]#

Bases: ReversibleTransform

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 in autoregressive manner,
point are imputed one by one in time order, already imputed points are used to impute the next points
- If “seasonal_nonautoreg” then replace missing dates using seasonal moving average of existing values,
all points are imputed using only existing points
- 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 (float | None) – 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

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.

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.

Apply the _inverse_transform method.

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 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, 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