_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