Class: 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).
Contrary to other cross-validation strategies, the random splits do not guarantee that test sets across all folds will be mutually exclusive, and might include overlapping samples. However, this is still very likely for sizeable datasets.
Note: The parameters test_size and train_size refer to groups, and not to samples as in ShuffleSplit.
Read more in the User Guide.
For visualisation of cross-validation behaviour and comparison between common scikit-learn split methods refer to Visualizing cross-validation behavior in scikit-learn
Constructors
new GroupShuffleSplit()
new GroupShuffleSplit(
opts?):GroupShuffleSplit
Parameters
| Parameter | Type | Description |
|---|---|---|
opts? | object | - |
opts.n_splits? | number | Number of re-shuffling & splitting iterations. |
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. If train_size is also undefined, it will be set to 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:37
Properties
| Property | Type | Default value | Defined in |
|---|---|---|---|
_isDisposed | boolean | false | generated/model_selection/GroupShuffleSplit.ts:35 |
_isInitialized | boolean | false | generated/model_selection/GroupShuffleSplit.ts:34 |
_py | PythonBridge | undefined | generated/model_selection/GroupShuffleSplit.ts:33 |
id | string | undefined | generated/model_selection/GroupShuffleSplit.ts:30 |
opts | any | undefined | generated/model_selection/GroupShuffleSplit.ts:31 |
Accessors
py
Get Signature
get py():
PythonBridge
Returns PythonBridge
Set Signature
set py(
pythonBridge):void
Parameters
| Parameter | Type |
|---|---|
pythonBridge | PythonBridge |
Returns void
Defined in generated/model_selection/GroupShuffleSplit.ts:64
Methods
dispose()
dispose():
Promise<void>
Disposes of the underlying Python resources.
Once dispose() is called, the instance is no longer usable.
Returns Promise<void>
Defined in generated/model_selection/GroupShuffleSplit.ts:118
get_metadata_routing()
get_metadata_routing(
opts):Promise<any>
Get metadata routing of this object.
Please check User Guide on how the routing mechanism works.
Parameters
| Parameter | Type | Description |
|---|---|---|
opts | object | - |
opts.routing? | any | A MetadataRequest encapsulating routing information. |
Returns Promise<any>
Defined in generated/model_selection/GroupShuffleSplit.ts:137
get_n_splits()
get_n_splits(
opts):Promise<number>
Returns the number of splitting iterations in the cross-validator.
Parameters
| Parameter | Type | Description |
|---|---|---|
opts | object | - |
opts.groups? | any | Always ignored, exists for compatibility. |
opts.X? | any | Always ignored, exists for compatibility. |
opts.y? | any | Always ignored, exists for compatibility. |
Returns Promise<number>
Defined in generated/model_selection/GroupShuffleSplit.ts:173
init()
init(
py):Promise<void>
Initializes the underlying Python resources.
This instance is not usable until the Promise returned by init() resolves.
Parameters
| Parameter | Type |
|---|---|
py | PythonBridge |
Returns Promise<void>
Defined in generated/model_selection/GroupShuffleSplit.ts:77
set_split_request()
set_split_request(
opts):Promise<any>
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:
Parameters
| Parameter | 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:223
split()
split(
opts):Promise<ArrayLike>
Generate indices to split data into training and test set.
Parameters
| Parameter | Type | Description |
|---|---|---|
opts | object | - |
opts.groups? | ArrayLike | Group labels for the samples used while splitting the dataset into train/test set. |
opts.X? | ArrayLike[] | Training data, where n_samples is the number of samples and n_features is the number of features. |
opts.y? | ArrayLike | The target variable for supervised learning problems. |
Returns Promise<ArrayLike>
Defined in generated/model_selection/GroupShuffleSplit.ts:259