MRMRFeatureSelectionTransform#

class MRMRFeatureSelectionTransform(relevance_table: RelevanceTable, top_k: int, features_to_use: List[str] | Literal['all'] = 'all', fast_redundancy: bool = False, relevance_aggregation_mode: str = AggregationMode.mean, redundancy_aggregation_mode: str = AggregationMode.mean, atol: float = 1e-10, return_features: bool = False, **relevance_params)[source]#

Bases: BaseFeatureSelectionTransform

Transform that selects features according to MRMR variable selection method adapted to the timeseries case.

Notes

Transform works with any type of features, however most of the models works only with regressors. Therefore, it is recommended to pass the regressors into the feature selection transforms.

Init MRMRFeatureSelectionTransform.

Parameters:

relevance_table (RelevanceTable) – method to calculate relevance table
top_k (int) – num of features to select; if there are not enough features, then all will be selected
features_to_use (List[str] | Literal['all']) – columns of the dataset to select from if “all” value is given, all columns are used
fast_redundancy (bool) –
- True: compute redundancy only inside the the segments, time complexity :math:`O(top_k * n_segments * n_features * history_len)
- False: compute redundancy for all the pairs of segments, time complexity \(O(top\_k * n\_segments^2 * n\_features * history\_len)\)
relevance_aggregation_mode (str) – the method for relevance values per-segment aggregation
redundancy_aggregation_mode (str) – the method for redundancy values per-segment aggregation
atol (float) – the absolute tolerance to compare the float values
return_features (bool) – indicates whether to return features or not.

Methods

`fit`(ts)	Fit the transform.
`fit_transform`(ts)	Fit and transform TSDataset.
`get_regressors_info`()	Return the list with regressors created by the transform.
`inverse_transform`(ts)	Inverse transform TSDataset.
`load`(path)	Load an object.
`params_to_tune`()	Get default grid for tuning hyperparameters.
`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.
`transform`(ts)	Transform TSDataset inplace.

Attributes

This class stores its __init__ parameters as attributes.

fit(ts: TSDataset) → Transform[source]#

Fit the transform.

Parameters:: ts (TSDataset) – Dataset to fit the transform on.
Returns:: The fitted transform instance.
Return type:: Transform

fit_transform(ts: TSDataset) → TSDataset[source]#

Fit and transform TSDataset.

May be reimplemented. But it is not recommended.

Parameters:: ts (TSDataset) – TSDataset to transform.
Returns:: Transformed TSDataset.
Return type:: TSDataset

get_regressors_info() → List[str][source]#

Return the list with regressors created by the transform.

Return type:: List[str]

inverse_transform(ts: TSDataset) → TSDataset[source]#

Inverse transform TSDataset.

Apply the _inverse_transform method.

Parameters:: ts (TSDataset) – TSDataset to be inverse transformed.
Returns:: TSDataset after applying inverse transformation.
Return type:: TSDataset

classmethod load(path: Path) → Self[source]#

Load an object.

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.

This grid tunes top_k parameter. Other parameters are expected to be set by the user.

For top_k parameter the maximum suggested value is not greater than self.top_k.

Returns:: Grid to tune.
Return type:: Dict[str, BaseDistribution]

save(path: Path)[source]#

Save the object.

Parameters:: path (Path) – Path to save object to.

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 a Pipeline.

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 = 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()[source]#: Collect all information about etna object in dict.

transform(ts: TSDataset) → TSDataset[source]#

Transform TSDataset inplace.

Parameters:: ts (TSDataset) – Dataset to transform.
Returns:: Transformed TSDataset.
Return type:: TSDataset