Class: TimeSeriesSplit
Time Series cross-validator.
Provides train/test indices to split time series data samples that are observed at fixed time intervals, in train/test sets. In each split, test indices must be higher than before, and thus shuffling in cross validator is inappropriate.
This cross-validation object is a variation of KFold. In the kth split, it returns first k folds as train set and the (k+1)th fold as test set.
Note that unlike standard cross-validation methods, successive training sets are supersets of those that come before them.
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 TimeSeriesSplit()
new TimeSeriesSplit(
opts?):TimeSeriesSplit
Parameters
| Parameter | Type | Description |
|---|---|---|
opts? | object | - |
opts.gap? | number | Number of samples to exclude from the end of each train set before the test set. |
opts.max_train_size? | number | Maximum size for a single training set. |
opts.n_splits? | number | Number of splits. Must be at least 2. |
opts.test_size? | number | Used to limit the size of the test set. Defaults to n_samples // (n_splits + 1), which is the maximum allowed value with gap=0. |
Returns TimeSeriesSplit
Defined in generated/model_selection/TimeSeriesSplit.ts:31
Properties
| Property | Type | Default value | Defined in |
|---|---|---|---|
_isDisposed | boolean | false | generated/model_selection/TimeSeriesSplit.ts:29 |
_isInitialized | boolean | false | generated/model_selection/TimeSeriesSplit.ts:28 |
_py | PythonBridge | undefined | generated/model_selection/TimeSeriesSplit.ts:27 |
id | string | undefined | generated/model_selection/TimeSeriesSplit.ts:24 |
opts | any | undefined | generated/model_selection/TimeSeriesSplit.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/TimeSeriesSplit.ts:60
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/TimeSeriesSplit.ts:112
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/TimeSeriesSplit.ts:131
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/TimeSeriesSplit.ts:165
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/TimeSeriesSplit.ts:73
split()
split(
opts):Promise<ArrayLike>
Generate indices to split data into training and test set.
Parameters
| Parameter | Type | Description |
|---|---|---|
opts | object | - |
opts.groups? | ArrayLike | 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. |
opts.y? | ArrayLike | Always ignored, exists for compatibility. |
Returns Promise<ArrayLike>