etna.models.LinearPerSegmentModel#
- class LinearPerSegmentModel(fit_intercept: bool = True, **kwargs)[source]#
Bases:
PerSegmentModelMixin
,NonPredictionIntervalContextIgnorantModelMixin
,NonPredictionIntervalContextIgnorantAbstractModel
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).
Methods
fit
(ts)Fit model.
forecast
(ts[, return_components])Make predictions.
Get internal models that are used inside etna class.
load
(path)Load an object.
Get default grid for tuning hyperparameters.
predict
(ts[, return_components])Make predictions with using true values as autoregression context if possible (teacher forcing).
save
(path)Save the object.
set_params
(**params)Return new object instance with modified parameters.
to_dict
()Collect all information about etna object in dict.
Attributes
This class stores its
__init__
parameters as attributes.Context size of the model.
- fit(ts: TSDataset) PerSegmentModelMixin [source]#
Fit model.
- Parameters:
ts (TSDataset) – Dataset with features
- Returns:
Model after fit
- Return type:
PerSegmentModelMixin
- get_model() Dict[str, Any] [source]#
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
orsklearn.linear_model.Ridge
.
- classmethod load(path: Path) Self [source]#
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 (Path) – Path to load object from.
- Returns:
Loaded object.
- Return type:
Self
- params_to_tune() Dict[str, BaseDistribution] [source]#
Get default grid for tuning hyperparameters.
- Returns:
Grid to tune.
- Return type:
- predict(ts: TSDataset, return_components: bool = False) TSDataset [source]#
Make predictions with using true values as autoregression context if possible (teacher forcing).
- set_params(**params: dict) Self [source]#
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 aPipeline
.Nested parameters are expected to be in a
<component_1>.<...>.<parameter>
form, where components are separated by a dot.- Parameters:
**params (dict) – Estimator parameters
- Returns:
New instance with changed parameters
- Return type:
Self
Examples
>>> from etna.pipeline import Pipeline >>> from etna.models import NaiveModel >>> from etna.transforms import AddConstTransform >>> 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, )