gale_shapley¶

Classes

`BaseGaleShapley`(name, ranked_candidates)	Base class for a member of Gale-Shapley matching.
`FeatureGaleShapley`(name, ranked_candidates)	Class for feature member of Gale-Shapley matching.
`GaleShapleyFeatureSelectionTransform`(...[, ...])	Transform that provides feature filtering by Gale-Shapley matching algorithm according to the relevance table.
`GaleShapleyMatcher`(segments, features)	Class for handling Gale-Shapley matching algo.
`SegmentGaleShapley`(name, ranked_candidates)	Class for segment member of Gale-Shapley matching.

class BaseGaleShapley(name: str, ranked_candidates: List[str])[source]¶

Base class for a member of Gale-Shapley matching.

Init BaseGaleShapley.

Parameters

name (str) – name of object
ranked_candidates (List[str]) – list of preferences for the object ranked descending by importance

reset_tmp_match()[source]¶: Break tmp current.

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.

update_tmp_match(name: str)[source]¶

Create match with object name.

Parameters: name (str) – name of candidate to match

class FeatureGaleShapley(name: str, ranked_candidates: List[str])[source]¶

Class for feature member of Gale-Shapley matching.

Init BaseGaleShapley.

Parameters

name (str) – name of object
ranked_candidates (List[str]) – list of preferences for the object ranked descending by importance

check_segment(segment: str) → bool[source]¶

Check if given segment is better than current match according to preference list.

Parameters: segment (str) – segment to check
Returns: returns True if given segment is a better candidate than current match.
Return type: is_better

reset_tmp_match()¶: Break tmp current.

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.

update_tmp_match(name: str)¶

Create match with object name.

Parameters: name (str) – name of candidate to match

class GaleShapleyFeatureSelectionTransform(relevance_table: etna.analysis.feature_relevance.relevance.RelevanceTable, top_k: int, features_to_use: Union[List[str], Literal['all']] = 'all', use_rank: bool = False, return_features: bool = False, **relevance_params)[source]¶

Transform that provides feature filtering by Gale-Shapley matching algorithm according to the relevance table.

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.

As input, we have a table of relevances with size \(N\_{f} imes N\_{s}\) where \(N\_{f}\) – number of features, \(N\_{s}\) – number of segments. Procedure of filtering features consist of :math:`lceil

rac{k}{N_{s}} ceil` iterations.

Algorithm of each iteration:

build a matching between segments and features by Gale–Shapley algorithm

according to the relevance table, during the matching segments send proposals to features; - select features to add by taking matched feature for each segment; - add selected features to accumulated list of selected features taking into account that this list shouldn’t exceed the size of top_k; - remove added features from future consideration.

Init GaleShapleyFeatureSelectionTransform.

Parameters

relevance_table (etna.analysis.feature_relevance.relevance.RelevanceTable) – class to build relevance table
top_k (int) – number of features that should be selected from all the given ones
features_to_use (Union[List[str], Literal['all']]) – columns of the dataset to select from if “all” value is given, all columns are used
use_rank (bool) – if True, use rank in relevance table computation
return_features (bool) – indicates whether to return features or not.

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 parameters: top_k, use_rank. 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

class GaleShapleyMatcher(segments: List[etna.transforms.feature_selection.gale_shapley.SegmentGaleShapley], features: List[etna.transforms.feature_selection.gale_shapley.FeatureGaleShapley])[source]¶

Class for handling Gale-Shapley matching algo.

Init GaleShapleyMatcher.

Parameters

segments (List[etna.transforms.feature_selection.gale_shapley.SegmentGaleShapley]) – list of segments to build matches
features (List[etna.transforms.feature_selection.gale_shapley.FeatureGaleShapley]) – list of features to build matches

static break_match(segment: etna.transforms.feature_selection.gale_shapley.SegmentGaleShapley, feature: etna.transforms.feature_selection.gale_shapley.FeatureGaleShapley)[source]¶

Break match between segment and feature.

Parameters

segment (etna.transforms.feature_selection.gale_shapley.SegmentGaleShapley) – segment to break match
feature (etna.transforms.feature_selection.gale_shapley.FeatureGaleShapley) – feature to break match

static match(segment: etna.transforms.feature_selection.gale_shapley.SegmentGaleShapley, feature: etna.transforms.feature_selection.gale_shapley.FeatureGaleShapley)[source]¶

Build match between segment and feature.

Parameters

segment (etna.transforms.feature_selection.gale_shapley.SegmentGaleShapley) – segment to match
feature (etna.transforms.feature_selection.gale_shapley.FeatureGaleShapley) – feature to match

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.

class SegmentGaleShapley(name: str, ranked_candidates: List[str])[source]¶

Class for segment member of Gale-Shapley matching.

Init SegmentGaleShapley.

Parameters

name (str) – name of segment
ranked_candidates (List[str]) – list of features sorted descending by importance

get_next_candidate() → Optional[str][source]¶

Get name of the next feature to try.

Returns: name of feature
Return type: name

reset_tmp_match()¶: Break tmp current.

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.

update_tmp_match(name: str)[source]¶

Create match with given feature.

Parameters: name (str) – name of feature to match