MRMRFeatureSelectionTransform

class MRMRFeatureSelectionTransform(relevance_table: etna.analysis.feature_relevance.relevance.RelevanceTable, top_k: int, features_to_use: Union[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: etna.transforms.feature_selection.base.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 (etna.analysis.feature_relevance.relevance.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 (Union[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.

Inherited-members

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.
fit(ts: etna.datasets.tsdataset.TSDataset) → etna.transforms.base.Transform

Fit the transform.

Parameters

ts (etna.datasets.tsdataset.TSDataset) – Dataset to fit the transform on.

Returns

The fitted transform instance.

Return type

etna.transforms.base.Transform

fit_transform(ts: etna.datasets.tsdataset.TSDataset) → etna.datasets.tsdataset.TSDataset

Fit and transform TSDataset.

May be reimplemented. But it is not recommended.

Parameters

ts (etna.datasets.tsdataset.TSDataset) – TSDataset to transform.

Returns

Transformed TSDataset.

Return type

etna.datasets.tsdataset.TSDataset

get_regressors_info() → List[str]

Return the list with regressors created by the transform.

Return type

List[str]

inverse_transform(ts: etna.datasets.tsdataset.TSDataset) → etna.datasets.tsdataset.TSDataset

Inverse transform TSDataset.

Apply the _inverse_transform method.

Parameters

ts (etna.datasets.tsdataset.TSDataset) – TSDataset to be inverse transformed.

Returns

TSDataset after applying inverse transformation.

Return type

etna.datasets.tsdataset.TSDataset

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][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, etna.distributions.distributions.BaseDistribution]

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.

transform(ts: etna.datasets.tsdataset.TSDataset) → etna.datasets.tsdataset.TSDataset

Transform TSDataset inplace.

Parameters

ts (etna.datasets.tsdataset.TSDataset) – Dataset to transform.

Returns

Transformed TSDataset.

Return type

etna.datasets.tsdataset.TSDataset