sklearn¶

Classes

`SklearnMultiSegmentModel`(regressor)	Class for holding Sklearn model for all segments.
`SklearnPerSegmentModel`(regressor)	Class for holding per segment Sklearn model.
`_SklearnAdapter`(regressor)

class SklearnMultiSegmentModel(regressor: sklearn.base.RegressorMixin)[source]¶

Class for holding Sklearn model for all segments.

Create instance of SklearnMultiSegmentModel with given parameters.

Parameters: regressor (sklearn.base.RegressorMixin) – Sklearn model for regression

fit(ts: etna.datasets.tsdataset.TSDataset) → etna.models.mixins.MultiSegmentModelMixin¶

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) → etna.datasets.tsdataset.TSDataset¶

Make predictions.

Parameters

ts (etna.datasets.tsdataset.TSDataset) – Dataset with features
return_components (bool) – If True additionally returns forecast components

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

classmethod load(path: pathlib.Path) → typing_extensions.Self¶

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 (pathlib.Path) – Path to load object from.
Returns: Loaded object.
Return type: typing_extensions.Self

params_to_tune() → Dict[str, etna.distributions.distributions.BaseDistribution]¶

Get grid for tuning hyperparameters.

This is default implementation with empty grid.

Returns: Empty grid.
Return type: Dict[str, etna.distributions.distributions.BaseDistribution]

predict(ts: etna.datasets.tsdataset.TSDataset, return_components: bool = False) → etna.datasets.tsdataset.TSDataset¶

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

Parameters

ts (etna.datasets.tsdataset.TSDataset) – Dataset with features
return_components (bool) – If True additionally returns prediction components

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
>>> 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.

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 SklearnPerSegmentModel(regressor: sklearn.base.RegressorMixin)[source]¶

Class for holding per segment Sklearn model.

Create instance of SklearnPerSegmentModel with given parameters.

Parameters: regressor (sklearn.base.RegressorMixin) – sklearn model for regression

fit(ts: etna.datasets.tsdataset.TSDataset) → etna.models.mixins.PerSegmentModelMixin¶

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) → etna.datasets.tsdataset.TSDataset¶

Make predictions.

Parameters

ts (etna.datasets.tsdataset.TSDataset) – Dataset with features
return_components (bool) – If True additionally returns forecast components

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]

classmethod load(path: pathlib.Path) → typing_extensions.Self¶

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 (pathlib.Path) – Path to load object from.
Returns: Loaded object.
Return type: typing_extensions.Self

params_to_tune() → Dict[str, etna.distributions.distributions.BaseDistribution]¶

Get grid for tuning hyperparameters.

This is default implementation with empty grid.

Returns: Empty grid.
Return type: Dict[str, etna.distributions.distributions.BaseDistribution]

predict(ts: etna.datasets.tsdataset.TSDataset, return_components: bool = False) → etna.datasets.tsdataset.TSDataset¶

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

Parameters

ts (etna.datasets.tsdataset.TSDataset) – Dataset with features
return_components (bool) – If True additionally returns prediction components

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
>>> 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.

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.