Class: StratifiedShuffleSplit
Stratified ShuffleSplit cross-validator.
Provides train/test indices to split data in train/test sets.
This cross-validation object is a merge of StratifiedKFold and ShuffleSplit, which returns stratified randomized folds. The folds are made by preserving the percentage of samples for each class.
Note: like the ShuffleSplit strategy, stratified 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.
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 StratifiedShuffleSplit()
new StratifiedShuffleSplit(
opts?):StratifiedShuffleSplit
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 the dataset to include in the test split. If int, represents the absolute number of test samples. If undefined, the value is set to the complement of the train size. If train_size is also undefined, it will be set to 0.1. |
opts.train_size? | number | If float, should be between 0.0 and 1.0 and represent the proportion of the dataset to include in the train split. If int, represents the absolute number of train samples. If undefined, the value is automatically set to the complement of the test size. |
Returns StratifiedShuffleSplit
Defined in generated/model_selection/StratifiedShuffleSplit.ts:31
Properties
| Property | Type | Default value | Defined in |
|---|---|---|---|
_isDisposed | boolean | false | generated/model_selection/StratifiedShuffleSplit.ts:29 |
_isInitialized | boolean | false | generated/model_selection/StratifiedShuffleSplit.ts:28 |
_py | PythonBridge | undefined | generated/model_selection/StratifiedShuffleSplit.ts:27 |
id | string | undefined | generated/model_selection/StratifiedShuffleSplit.ts:24 |
opts | any | undefined | generated/model_selection/StratifiedShuffleSplit.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/StratifiedShuffleSplit.ts:58
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/StratifiedShuffleSplit.ts:114
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/StratifiedShuffleSplit.ts:133
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/StratifiedShuffleSplit.ts:169
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/StratifiedShuffleSplit.ts:71
split()
split(
opts):Promise<ArrayLike>
Generate indices to split data into training and test set.
Parameters
| Parameter | Type | Description |
|---|---|---|
opts | object | - |
opts.groups? | any | Always ignored, exists for compatibility. |
opts.X? | ArrayLike[] | Training data, where n_samples is the number of samples and n_features is the number of features. Note that providing y is sufficient to generate the splits and hence np.zeros(n_samples) may be used as a placeholder for X instead of actual training data. |
opts.y? | ArrayLike | The target variable for supervised learning problems. Stratification is done based on the y labels. |
Returns Promise<ArrayLike>
Defined in generated/model_selection/StratifiedShuffleSplit.ts:215