etna.transforms.BinaryOperationTransform#

class BinaryOperationTransform(left_column: str, right_column: str, operator: str, out_column: str | None = None)[source]#

Bases: ReversibleTransform

Perform binary operation on the columns of dataset.

Inverse_transform functionality is only supported for operations +, -, * , /.
If during the operation a division by zero of a positive number occurs, writes +inf to this cell of the column, if negative - -inf, if 0/0 - nan.
In the case of raising a negative number to a non-integer power, writes nan to this cell of the column.

Examples

>>> import numpy as np
>>> from etna.datasets import generate_ar_df
>>> df = generate_ar_df(start_time="2020-01-01", periods=30, freq="D", n_segments=1)
>>> df["feature"] = np.full(30, 10)
>>> df["target"] = np.full(30, 1)
>>> ts = TSDataset(df, "D")
>>> ts["2020-01-01":"2020-01-06", "segment_0", ["feature", "target"]]
segment    segment_0
feature      feature target
timestamp
2020-01-01        10      1
2020-01-02        10      1
2020-01-03        10      1
2020-01-04        10      1
2020-01-05        10      1
2020-01-06        10      1
>>> transformer = BinaryOperationTransform(left_column="feature", right_column="target", operator="+", out_column="target")
>>> new_ts = transformer.fit_transform(ts=ts)
>>> new_ts["2020-01-01":"2020-01-06", "segment_0", ["feature", "target"]]
segment    segment_0
feature      feature target
timestamp
2020-01-01        10      11
2020-01-02        10      11
2020-01-03        10      11
2020-01-04        10      11
2020-01-05        10      11
2020-01-06        10      11

Create instance of BinaryOperationTransform.

Parameters:

left_column (str) – Name of the left column
right_column (str) – Name of the right column
operator (str) – Operation to perform on the columns, see BinaryOperator
out_column (str | None) –
- Resulting column name, if don’t set, name will be left_column operator right_column.
- If out_column is left_column or right_column, apply changes to the existing column out_column, else create new column.

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) → BinaryOperationTransform[source]#

Fit the transform.

Parameters:: ts (TSDataset) –
Return type:: BinaryOperationTransform

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 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