# linear¶

Classes

 `ElasticMultiSegmentModel`([alpha, l1_ratio, ...]) Class holding `sklearn.linear_model.ElasticNet` for all segments. `ElasticPerSegmentModel`([alpha, l1_ratio, ...]) Class holding per segment `sklearn.linear_model.ElasticNet`. `LinearMultiSegmentModel`([fit_intercept]) Class holding `sklearn.linear_model.LinearRegression` for all segments. `LinearPerSegmentModel`([fit_intercept]) Class holding per segment `sklearn.linear_model.LinearRegression`. `_LinearAdapter`(regressor)
class ElasticMultiSegmentModel(alpha: float = 1.0, l1_ratio: float = 0.5, fit_intercept: bool = True, **kwargs)[source]

Class holding `sklearn.linear_model.ElasticNet` for all segments.

Notes

Target components are formed as the terms from linear regression formula.

Create instance of ElasticNet with given parameters.

Parameters
• alpha (float) – Constant that multiplies the penalty terms. Defaults to 1.0. `alpha = 0` is equivalent to an ordinary least square, solved by the LinearRegression object. For numerical reasons, using `alpha = 0` with the Lasso object is not advised. Given this, you should use the `LinearMultiSegmentModel` object.

• l1_ratio (float) –

The ElasticNet mixing parameter, with `0 <= l1_ratio <= 1`.

• For `l1_ratio = 0` the penalty is an L2 penalty.

• For `l1_ratio = 1` it is an L1 penalty.

• For `0 < l1_ratio < 1`, the penalty is a combination of L1 and L2.

• fit_intercept (bool) – Whether to calculate the intercept for this model. If set to False, no intercept will be used in calculations (i.e. data is expected to be centered).

fit(ts: etna.datasets.tsdataset.TSDataset)

Fit model.

Parameters

ts (etna.datasets.tsdataset.TSDataset) – Dataset with features

Returns

Model after fit

Return type

etna.models.mixins.MultiSegmentModelMixin

forecast(ts: etna.datasets.tsdataset.TSDataset, return_components: bool = False)

Make predictions.

Parameters
Returns

Dataset with predictions

Return type

etna.datasets.tsdataset.TSDataset

get_model() Any

Get internal model that is used inside etna class.

Internal model is a model that is used inside etna to forecast segments, e.g. `catboost.CatBoostRegressor` or `sklearn.linear_model.Ridge`.

Returns

Internal model

Return type

Any

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 (pathlib.Path) – Path to load object from.

Returns

Return type

typing_extensions.Self

params_to_tune() [source]

Get default grid for tuning hyperparameters.

Returns

Grid to tune.

Return type
predict(ts: etna.datasets.tsdataset.TSDataset, return_components: bool = False)

Make predictions with using true values as autoregression context if possible (teacher forcing).

Parameters
Returns

Dataset with predictions

Return type

etna.datasets.tsdataset.TSDataset

save(path: pathlib.Path)

Save the object.

Parameters

path (pathlib.Path) – Path to save object to.

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
>>> model = model=NaiveModel(lag=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.

property context_size: int

Context size of the model. Determines how many history points do we ask to pass to the model.

Zero for this model.

class ElasticPerSegmentModel(alpha: float = 1.0, l1_ratio: float = 0.5, fit_intercept: bool = True, **kwargs)[source]

Class holding per segment `sklearn.linear_model.ElasticNet`.

Notes

Target components are formed as the terms from linear regression formula.

Create instance of ElasticNet with given parameters.

Parameters
• alpha (float) – Constant that multiplies the penalty terms. Defaults to 1.0. `alpha = 0` is equivalent to an ordinary least square, solved by the LinearRegression object. For numerical reasons, using `alpha = 0` with the Lasso object is not advised. Given this, you should use the `LinearPerSegmentModel` object.

• l1_ratio (float) –

The ElasticNet mixing parameter, with `0 <= l1_ratio <= 1`.

• For `l1_ratio = 0` the penalty is an L2 penalty.

• For `l1_ratio = 1` it is an L1 penalty.

• For `0 < l1_ratio < 1`, the penalty is a combination of L1 and L2.

• fit_intercept (bool) – Whether to calculate the intercept for this model. If set to False, no intercept will be used in calculations (i.e. data is expected to be centered).

fit(ts: etna.datasets.tsdataset.TSDataset)

Fit model.

Parameters

ts (etna.datasets.tsdataset.TSDataset) – Dataset with features

Returns

Model after fit

Return type

etna.models.mixins.PerSegmentModelMixin

forecast(ts: etna.datasets.tsdataset.TSDataset, return_components: bool = False)

Make predictions.

Parameters
Returns

Dataset with predictions

Return type

etna.datasets.tsdataset.TSDataset

get_model() Dict[str, Any]

Get internal models that are used inside etna class.

Internal model is a model that is used inside etna to forecast segments, e.g. `catboost.CatBoostRegressor` or `sklearn.linear_model.Ridge`.

Returns

dictionary where key is segment and value is internal model

Return type

Dict[str, Any]

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 (pathlib.Path) – Path to load object from.

Returns

Return type

typing_extensions.Self

params_to_tune() [source]

Get default grid for tuning hyperparameters.

Returns

Grid to tune.

Return type
predict(ts: etna.datasets.tsdataset.TSDataset, return_components: bool = False)

Make predictions with using true values as autoregression context if possible (teacher forcing).

Parameters
Returns

Dataset with predictions

Return type

etna.datasets.tsdataset.TSDataset

save(path: pathlib.Path)

Save the object.

Parameters

path (pathlib.Path) – Path to save object to.

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
>>> model = model=NaiveModel(lag=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.

property context_size: int

Context size of the model. Determines how many history points do we ask to pass to the model.

Zero for this model.

class LinearMultiSegmentModel(fit_intercept: bool = True, **kwargs)[source]

Class holding `sklearn.linear_model.LinearRegression` for all segments.

Notes

Target components are formed as the terms from linear regression formula.

Create instance of LinearModel with given parameters.

Parameters

fit_intercept (bool) – Whether to calculate the intercept for this model. If set to False, no intercept will be used in calculations (i.e. data is expected to be centered).

fit(ts: etna.datasets.tsdataset.TSDataset)

Fit model.

Parameters

ts (etna.datasets.tsdataset.TSDataset) – Dataset with features

Returns

Model after fit

Return type

etna.models.mixins.MultiSegmentModelMixin

forecast(ts: etna.datasets.tsdataset.TSDataset, return_components: bool = False)

Make predictions.

Parameters
Returns

Dataset with predictions

Return type

etna.datasets.tsdataset.TSDataset

get_model() Any

Get internal model that is used inside etna class.

Internal model is a model that is used inside etna to forecast segments, e.g. `catboost.CatBoostRegressor` or `sklearn.linear_model.Ridge`.

Returns

Internal model

Return type

Any

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 (pathlib.Path) – Path to load object from.

Returns

Return type

typing_extensions.Self

params_to_tune() [source]

Get default grid for tuning hyperparameters.

Returns

Grid to tune.

Return type
predict(ts: etna.datasets.tsdataset.TSDataset, return_components: bool = False)

Make predictions with using true values as autoregression context if possible (teacher forcing).

Parameters
Returns

Dataset with predictions

Return type

etna.datasets.tsdataset.TSDataset

save(path: pathlib.Path)

Save the object.

Parameters

path (pathlib.Path) – Path to save object to.

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
>>> model = model=NaiveModel(lag=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.

property context_size: int

Context size of the model. Determines how many history points do we ask to pass to the model.

Zero for this model.

class LinearPerSegmentModel(fit_intercept: bool = True, **kwargs)[source]

Class holding per segment `sklearn.linear_model.LinearRegression`.

Notes

Target components are formed as the terms from linear regression formula.

Create instance of LinearModel with given parameters.

Parameters

fit_intercept (bool) – Whether to calculate the intercept for this model. If set to False, no intercept will be used in calculations (i.e. data is expected to be centered).

fit(ts: etna.datasets.tsdataset.TSDataset)

Fit model.

Parameters

ts (etna.datasets.tsdataset.TSDataset) – Dataset with features

Returns

Model after fit

Return type

etna.models.mixins.PerSegmentModelMixin

forecast(ts: etna.datasets.tsdataset.TSDataset, return_components: bool = False)

Make predictions.

Parameters
Returns

Dataset with predictions

Return type

etna.datasets.tsdataset.TSDataset

get_model() Dict[str, Any]

Get internal models that are used inside etna class.

Internal model is a model that is used inside etna to forecast segments, e.g. `catboost.CatBoostRegressor` or `sklearn.linear_model.Ridge`.

Returns

dictionary where key is segment and value is internal model

Return type

Dict[str, Any]

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 (pathlib.Path) – Path to load object from.

Returns

Return type

typing_extensions.Self

params_to_tune() [source]

Get default grid for tuning hyperparameters.

Returns

Grid to tune.

Return type
predict(ts: etna.datasets.tsdataset.TSDataset, return_components: bool = False)

Make predictions with using true values as autoregression context if possible (teacher forcing).

Parameters
Returns

Dataset with predictions

Return type

etna.datasets.tsdataset.TSDataset

save(path: pathlib.Path)

Save the object.

Parameters

path (pathlib.Path) – Path to save object to.

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
>>> model = model=NaiveModel(lag=1)