_OneSegmentSpecialDaysTransform¶

class _OneSegmentSpecialDaysTransform(find_special_weekday: bool = True, find_special_month_day: bool = True)[source]¶

Bases: etna.transforms.base.OneSegmentTransform

Search for anomalies in values, marked this days as 1 (and return new column with 1 in corresponding places).

Notes

You can read more about other anomalies detection methods in: Time Series of Price Anomaly Detection

Create instance of _OneSegmentSpecialDaysTransform.

Parameters

find_special_weekday (bool) – flag, if True, find special weekdays in transform
find_special_month_day (bool) – flag, if True, find special monthdays in transform

Raises

ValueError: – if all the modes are False

Inherited-members

Methods

`fit`(df)	Fit _OneSegmentSpecialDaysTransform with data from df.
`fit_transform`(df)	Fit and transform Dataframe.
`inverse_transform`(df)	Inverse transform Dataframe.
`set_params`(**params)	Return new object instance with modified parameters.
`to_dict`()	Collect all information about etna object in dict.
`transform`(df)	Transform data from df with _OneSegmentSpecialDaysTransform and generate a column of special day flags.

fit(df: pandas.core.frame.DataFrame) → etna.transforms.timestamp.special_days._OneSegmentSpecialDaysTransform[source]¶

Fit _OneSegmentSpecialDaysTransform with data from df.

Parameters: df (pd.DataFrame) – value series with index column in timestamp format
Return type: etna.transforms.timestamp.special_days._OneSegmentSpecialDaysTransform

fit_transform(df: pandas.core.frame.DataFrame) → pandas.core.frame.DataFrame¶

Fit and transform Dataframe.

May be reimplemented. But it is not recommended.

Parameters: df (pandas.core.frame.DataFrame) – Dataframe in etna long format to transform.
Returns: Transformed Dataframe.
Return type: pandas.core.frame.DataFrame

inverse_transform(df: pandas.core.frame.DataFrame) → pandas.core.frame.DataFrame[source]¶

Inverse transform Dataframe.

Parameters: df (pandas.core.frame.DataFrame) –
Return type: pandas.core.frame.DataFrame

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(df: pandas.core.frame.DataFrame) → pandas.core.frame.DataFrame[source]¶

Transform data from df with _OneSegmentSpecialDaysTransform and generate a column of special day flags.

Parameters: df (pd.DataFrame) – value series with index column in timestamp format
Returns: pd.DataFrame with ‘anomaly_weekday’, ‘anomaly_monthday’ or both of them columns no-timestamp indexed that contains 1 at i-th position if i-th day is a special day
Return type: pandas.core.frame.DataFrame