Class: StratifiedGroupKFold
Stratified K-Fold iterator variant with non-overlapping groups.
This cross-validation object is a variation of StratifiedKFold attempts to return stratified folds with non-overlapping groups. The folds are made by preserving the percentage of samples for each class.
Each group will appear exactly once in the test set across all folds (the number of distinct groups has to be at least equal to the number of folds).
The difference between GroupKFold and StratifiedGroupKFold is that the former attempts to create balanced folds such that the number of distinct groups is approximately the same in each fold, whereas StratifiedGroupKFold attempts to create folds which preserve the percentage of samples for each class as much as possible given the constraint of non-overlapping groups between splits.
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 StratifiedGroupKFold()
new StratifiedGroupKFold(
opts?):StratifiedGroupKFold
Parameters
| Parameter | Type | Description |
|---|---|---|
opts? | object | - |
opts.n_splits? | number | Number of folds. Must be at least 2. |
opts.random_state? | number | When shuffle is true, random_state affects the ordering of the indices, which controls the randomness of each fold for each class. Otherwise, leave random_state as undefined. Pass an int for reproducible output across multiple function calls. See Glossary. |
opts.shuffle? | boolean | Whether to shuffle each class’s samples before splitting into batches. Note that the samples within each split will not be shuffled. This implementation can only shuffle groups that have approximately the same y distribution, no global shuffle will be performed. |
Returns StratifiedGroupKFold
Defined in generated/model_selection/StratifiedGroupKFold.ts:31
Properties
| Property | Type | Default value | Defined in |
|---|---|---|---|
_isDisposed | boolean | false | generated/model_selection/StratifiedGroupKFold.ts:29 |
_isInitialized | boolean | false | generated/model_selection/StratifiedGroupKFold.ts:28 |
_py | PythonBridge | undefined | generated/model_selection/StratifiedGroupKFold.ts:27 |
id | string | undefined | generated/model_selection/StratifiedGroupKFold.ts:24 |
opts | any | undefined | generated/model_selection/StratifiedGroupKFold.ts:25 |
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/StratifiedGroupKFold.ts:55
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/StratifiedGroupKFold.ts:111
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/StratifiedGroupKFold.ts:130
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/StratifiedGroupKFold.ts:166
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/StratifiedGroupKFold.ts:68
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/StratifiedGroupKFold.ts:216
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/StratifiedGroupKFold.ts:252