_OneSegmentLinearTrendBaseTransform¶

class _OneSegmentLinearTrendBaseTransform(in_column: str, regressor: sklearn.base.RegressorMixin, poly_degree: int = 1)[source]¶

Bases: etna.transforms.base.OneSegmentTransform

Transform for one segment that implements trend subtraction and reconstruction feature.

Create instance of _OneSegmentLinearTrendBaseTransform.

Parameters

in_column (str) – name of processed column
regressor (sklearn.base.RegressorMixin) – instance of sklearn :py:class`sklearn.base.RegressorMixin` to predict trend
poly_degree (int) – degree of polynomial to fit trend on

Inherited-members

Methods

`fit`(df)	Fit regression detrend_model with data from df.
`fit_transform`(df)	Fit regression detrend_model with data from df and subtract the trend from df.
`inverse_transform`(df)	Inverse transformation for trend subtraction: add trend to prediction.
`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: subtract linear trend found by regressor.

fit(df: pandas.core.frame.DataFrame) → etna.transforms.decomposition.detrend._OneSegmentLinearTrendBaseTransform[source]¶

Fit regression detrend_model with data from df.

Parameters: df (pandas.core.frame.DataFrame) – data that regressor should be trained with
Returns: instance with trained regressor
Return type: _OneSegmentLinearTrendBaseTransform

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

Fit regression detrend_model with data from df and subtract the trend from df.

Parameters: df (pandas.core.frame.DataFrame) – data to train regressor and transform
Returns: residue after trend subtraction
Return type: pd.DataFrame

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

Inverse transformation for trend subtraction: add trend to prediction.

Parameters: df (pandas.core.frame.DataFrame) – data to transform
Returns: data with reconstructed trend
Return type: pd.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: subtract linear trend found by regressor.

Parameters: df (pandas.core.frame.DataFrame) – data to subtract trend from
Returns: residue after trend subtraction
Return type: pd.DataFrame