GroupShuffleSplit

Shuffle-Group(s)-Out cross-validation iterator

Provides randomized train/test indices to split data according to a third-party provided group. This group information can be used to encode arbitrary domain specific stratifications of the samples as integers.

For instance the groups could be the year of collection of the samples and thus allow for cross-validation against time-based splits.

The difference between LeavePGroupsOut and GroupShuffleSplit is that the former generates splits using all subsets of size p unique groups, whereas GroupShuffleSplit generates a user-determined number of random test splits, each with a user-determined fraction of unique groups.

For example, a less computationally intensive alternative to LeavePGroupsOut(p=10) would be GroupShuffleSplit(test\_size=10, n\_splits=100).

Note: The parameters test\_size and train\_size refer to groups, and not to samples, as in ShuffleSplit.

Constructors

constructor()

Signature

new GroupShuffleSplit(opts?: object): GroupShuffleSplit;

Parameters

Name	Type	Description
`opts?`	`object`	-
`opts.n_splits?`	`number`	Number of re-shuffling & splitting iterations. `Default Value` `5`
`opts.random_state?`	`number`	Controls the randomness of the training and testing indices produced. Pass an int for reproducible output across multiple function calls. See Glossary.
`opts.test_size?`	`number`	If float, should be between 0.0 and 1.0 and represent the proportion of groups to include in the test split (rounded up). If int, represents the absolute number of test groups. If `undefined`, the value is set to the complement of the train size. The default will change in version 0.21. It will remain 0.2 only if `train\_size` is unspecified, otherwise it will complement the specified `train\_size`. `Default Value` `0.2`
`opts.train_size?`	`number`	If float, should be between 0.0 and 1.0 and represent the proportion of the groups to include in the train split. If int, represents the absolute number of train groups. If `undefined`, the value is automatically set to the complement of the test size.

Returns

GroupShuffleSplit

Defined in: generated/model_selection/GroupShuffleSplit.ts:35 (opens in a new tab)

Methods

dispose()

Disposes of the underlying Python resources.

Once dispose() is called, the instance is no longer usable.

Signature

dispose(): Promise<void>;

Returns

Promise<void>

Defined in: generated/model_selection/GroupShuffleSplit.ts:121 (opens in a new tab)

get_metadata_routing()

Get metadata routing of this object.

Please check User Guide on how the routing mechanism works.

Signature

get_metadata_routing(opts: object): Promise<any>;

Parameters

Name	Type	Description
`opts`	`object`	-
`opts.routing?`	`any`	A `MetadataRequest` encapsulating routing information.

Returns

Promise<any>

Defined in: generated/model_selection/GroupShuffleSplit.ts:140 (opens in a new tab)

get_n_splits()

Returns the number of splitting iterations in the cross-validator

Signature

get_n_splits(opts: object): Promise<number>;

Parameters

Name	Type	Description
`opts`	`object`	-
`opts.X?`	`any`	Always ignored, exists for compatibility.
`opts.groups?`	`any`	Always ignored, exists for compatibility.
`opts.y?`	`any`	Always ignored, exists for compatibility.

Returns

Promise<number>

Defined in: generated/model_selection/GroupShuffleSplit.ts:178 (opens in a new tab)

init()

Initializes the underlying Python resources.

This instance is not usable until the Promise returned by init() resolves.

Signature

init(py: PythonBridge): Promise<void>;

Parameters

Name	Type
`py`	`PythonBridge`

Returns

Promise<void>

Defined in: generated/model_selection/GroupShuffleSplit.ts:77 (opens in a new tab)

set_split_request()

Request metadata passed to the split method.

Note that this method is only relevant if enable\_metadata\_routing=True (see sklearn.set\_config). Please see User Guide on how the routing mechanism works.

The options for each parameter are:

Signature

set_split_request(opts: object): Promise<any>;

Parameters

Name	Type	Description
`opts`	`object`	-
`opts.groups?`	`string` \| `boolean`	Metadata routing for `groups` parameter in `split`.

Returns

Promise<any>

Defined in: generated/model_selection/GroupShuffleSplit.ts:229 (opens in a new tab)

split()

Generate indices to split data into training and test set.

Signature

split(opts: object): Promise<ArrayLike>;

Parameters

Name	Type	Description
`opts`	`object`	-
`opts.X?`	`ArrayLike`[]	Training data, where `n\_samples` is the number of samples and `n\_features` is the number of features.
`opts.groups?`	`ArrayLike`	Group labels for the samples used while splitting the dataset into train/test set.
`opts.y?`	`ArrayLike`	The target variable for supervised learning problems.