etna.transforms.EventTransform#

class EventTransform(in_column: str, out_column: str, n_pre: int, n_post: int, mode: str = ImputerMode.binary)[source]#

Bases: IrreversibleTransform

EventTransform marks days before and after event depending on mode.

It creates two columns for future and past.

In ‘binary’ mode shows whether there will be or were events regarding current date.
In ‘distance’ mode shows distance to the previous and future events regarding current date. Computed as \(1 / x\), where x is a distance to the nearest event.

Examples

>>> from copy import deepcopy
>>> import numpy as np
>>> import pandas as pd
>>> from etna.datasets import generate_const_df
>>> from etna.datasets import TSDataset
>>> from etna.transforms import EventTransform
>>>
>>> df = generate_const_df(start_time="2020-01-01", periods=5, freq="D", scale=1, n_segments=1)
>>> df_exog = generate_const_df(start_time="2020-01-01", periods=10, freq="D", scale=1, n_segments=1)
>>> df_exog.rename(columns={"target": "holiday"}, inplace=True)
>>> df_exog["holiday"] = np.array([0, 0, 1, 0, 0, 0, 0, 1, 1, 0])
>>> df = TSDataset.to_dataset(df)
>>> df_exog = TSDataset.to_dataset(df_exog)
>>> ts = TSDataset(df, freq="D", df_exog=df_exog, known_future="all")
>>> transform = EventTransform(in_column='holiday', out_column='holiday', n_pre=1, n_post=1)
>>> transform.fit_transform(deepcopy(ts))
segment    segment_0
feature      holiday holiday_post holiday_pre target
timestamp
2020-01-01         0          0.0          0.0    1.0
2020-01-02         0          0.0          1.0    1.0
2020-01-03         1          0.0          0.0    1.0
2020-01-04         0          1.0          0.0    1.0
2020-01-05         0          0.0          0.0    1.0

>>> transform = EventTransform(in_column='holiday', out_column='holiday', n_pre=2, n_post=2, mode='distance')
>>> transform.fit_transform(deepcopy(ts))
segment    segment_0
feature      holiday holiday_post holiday_pre target
timestamp
2020-01-01         0          0.0          0.5    1.0
2020-01-02         0          0.0          1.0    1.0
2020-01-03         1          0.0          0.0    1.0
2020-01-04         0          1.0          0.0    1.0
2020-01-05         0          0.5          0.0    1.0

Init EventTransform.

Parameters:

in_column (str) – binary column with event indicator.
out_column (str) – base for creating out columns names for future and past - ‘{out_column}_pre’ and ‘{out_column}_post’
n_pre (int) – number of days before the event to react.
n_post (int) – number of days after the event to react.
mode (str) –
mode of marking events:
- ’binary’: whether there will be or were events regarding current date in binary type;
- ’distance’: distance to the previous and future events regarding current date;

Raises:

ValueError: – Some in_column features are not binary.
ValueError: – n_pre or n_post values are less than one.
NotImplementedError: – Given mode value is not supported.

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) → EventTransform[source]#

Fit the transform.

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

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 parameters: n_pre, n_post. 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