Class: HalvingGridSearchCV
Search over specified parameter values with successive halving.
The search strategy starts evaluating all the candidates with a small amount of resources and iteratively selects the best candidates, using more and more resources.
Read more in the User guide.
Constructors
new HalvingGridSearchCV()
new HalvingGridSearchCV(
opts?):HalvingGridSearchCV
Parameters
| Parameter | Type | Description |
|---|---|---|
opts? | object | - |
opts.aggressive_elimination? | boolean | This is only relevant in cases where there isn’t enough resources to reduce the remaining candidates to at most factor after the last iteration. If true, then the search process will ‘replay’ the first iteration for as long as needed until the number of candidates is small enough. This is false by default, which means that the last iteration may evaluate more than factor candidates. See Aggressive elimination of candidates for more details. |
opts.cv? | number | Determines the cross-validation splitting strategy. Possible inputs for cv are: |
opts.error_score? | "raise" | Value to assign to the score if an error occurs in estimator fitting. If set to ‘raise’, the error is raised. If a numeric value is given, FitFailedWarning is raised. This parameter does not affect the refit step, which will always raise the error. Default is np.nan. |
opts.estimator? | any | This is assumed to implement the scikit-learn estimator interface. Either estimator needs to provide a score function, or scoring must be passed. |
opts.factor? | number | The ‘halving’ parameter, which determines the proportion of candidates that are selected for each subsequent iteration. For example, factor=3 means that only one third of the candidates are selected. |
opts.max_resources? | number | The maximum amount of resource that any candidate is allowed to use for a given iteration. By default, this is set to n_samples when resource='n_samples' (default), else an error is raised. |
opts.min_resources? | number | "exhaust" | "smallest" | The minimum amount of resource that any candidate is allowed to use for a given iteration. Equivalently, this defines the amount of resources r0 that are allocated for each candidate at the first iteration. |
opts.n_jobs? | number | Number of jobs to run in parallel. undefined means 1 unless in a joblib.parallel_backend context. \-1 means using all processors. See Glossary for more details. |
opts.param_grid? | any | Dictionary with parameters names (string) as keys and lists of parameter settings to try as values, or a list of such dictionaries, in which case the grids spanned by each dictionary in the list are explored. This enables searching over any sequence of parameter settings. |
opts.random_state? | number | Pseudo random number generator state used for subsampling the dataset when resources != 'n_samples'. Ignored otherwise. Pass an int for reproducible output across multiple function calls. See Glossary. |
opts.refit? | boolean | If true, refit an estimator using the best found parameters on the whole dataset. The refitted estimator is made available at the best_estimator_ attribute and permits using predict directly on this HalvingGridSearchCV instance. |
opts.resource? | string | Defines the resource that increases with each iteration. By default, the resource is the number of samples. It can also be set to any parameter of the base estimator that accepts positive integer values, e.g. ‘n_iterations’ or ‘n_estimators’ for a gradient boosting estimator. In this case max_resources cannot be ‘auto’ and must be set explicitly. |
opts.return_train_score? | boolean | If false, the cv_results_ attribute will not include training scores. Computing training scores is used to get insights on how different parameter settings impact the overfitting/underfitting trade-off. However computing the scores on the training set can be computationally expensive and is not strictly required to select the parameters that yield the best generalization performance. |
opts.scoring? | string | A single string (see The scoring parameter: defining model evaluation rules) or a callable (see Defining your scoring strategy from metric functions) to evaluate the predictions on the test set. If undefined, the estimator’s score method is used. |
opts.verbose? | number | Controls the verbosity: the higher, the more messages. |
Returns HalvingGridSearchCV
Defined in generated/model_selection/HalvingGridSearchCV.ts:25
Properties
| Property | Type | Default value | Defined in |
|---|---|---|---|
_isDisposed | boolean | false | generated/model_selection/HalvingGridSearchCV.ts:23 |
_isInitialized | boolean | false | generated/model_selection/HalvingGridSearchCV.ts:22 |
_py | PythonBridge | undefined | generated/model_selection/HalvingGridSearchCV.ts:21 |
id | string | undefined | generated/model_selection/HalvingGridSearchCV.ts:18 |
opts | any | undefined | generated/model_selection/HalvingGridSearchCV.ts:19 |
Accessors
best_estimator_
Get Signature
get best_estimator_():
Promise<any>
Estimator that was chosen by the search, i.e. estimator which gave highest score (or smallest loss if specified) on the left out data. Not available if refit=False.
Returns Promise<any>
Defined in generated/model_selection/HalvingGridSearchCV.ts:834
best_index_
Get Signature
get best_index_():
Promise<number>
The index (of the cv_results_ arrays) which corresponds to the best candidate parameter setting.
The dict at search.cv_results_\['params'\]\[search.best_index_\] gives the parameter setting for the best model, that gives the highest mean score (search.best_score_).
Returns Promise<number>
Defined in generated/model_selection/HalvingGridSearchCV.ts:917
best_params_
Get Signature
get best_params_():
Promise<any>
Parameter setting that gave the best results on the hold out data.
Returns Promise<any>
Defined in generated/model_selection/HalvingGridSearchCV.ts:888
best_score_
Get Signature
get best_score_():
Promise<number>
Mean cross-validated score of the best_estimator.
Returns Promise<number>
Defined in generated/model_selection/HalvingGridSearchCV.ts:861
cv_results_
Get Signature
get cv_results_():
Promise<any>
A dict with keys as column headers and values as columns, that can be imported into a pandas DataFrame. It contains lots of information for analysing the results of a search. Please refer to the User guide for details.
Returns Promise<any>
Defined in generated/model_selection/HalvingGridSearchCV.ts:807
feature_names_in_
Get Signature
get feature_names_in_():
Promise<ArrayLike>
Names of features seen during fit. Only defined if best_estimator_ is defined (see the documentation for the refit parameter for more details) and that best_estimator_ exposes feature_names_in_ when fit.
Returns Promise<ArrayLike>
Defined in generated/model_selection/HalvingGridSearchCV.ts:1054
max_resources_
Get Signature
get max_resources_():
Promise<number>
The maximum number of resources that any candidate is allowed to use for a given iteration. Note that since the number of resources used at each iteration must be a multiple of min_resources_, the actual number of resources used at the last iteration may be smaller than max_resources_.
Returns Promise<number>
Defined in generated/model_selection/HalvingGridSearchCV.ts:672
min_resources_
Get Signature
get min_resources_():
Promise<number>
The amount of resources that are allocated for each candidate at the first iteration.
Returns Promise<number>
Defined in generated/model_selection/HalvingGridSearchCV.ts:699
multimetric_
Get Signature
get multimetric_():
Promise<boolean>
Whether or not the scorers compute several metrics.
Returns Promise<boolean>
Defined in generated/model_selection/HalvingGridSearchCV.ts:1027
n_candidates_
Get Signature
get n_candidates_():
Promise<any>
The number of candidate parameters that were evaluated at each iteration.
Returns Promise<any>
Defined in generated/model_selection/HalvingGridSearchCV.ts:618
n_iterations_
Get Signature
get n_iterations_():
Promise<number>
The actual number of iterations that were run. This is equal to n_required_iterations_ if aggressive_elimination is true. Else, this is equal to min(n_possible_iterations_, n_required_iterations_).
Returns Promise<number>
Defined in generated/model_selection/HalvingGridSearchCV.ts:726
n_possible_iterations_
Get Signature
get n_possible_iterations_():
Promise<number>
The number of iterations that are possible starting with min_resources_ resources and without exceeding max_resources_.
Returns Promise<number>
Defined in generated/model_selection/HalvingGridSearchCV.ts:753
n_remaining_candidates_
Get Signature
get n_remaining_candidates_():
Promise<number>
The number of candidate parameters that are left after the last iteration. It corresponds to ceil(n_candidates\[-1\] / factor)
Returns Promise<number>
Defined in generated/model_selection/HalvingGridSearchCV.ts:645
n_required_iterations_
Get Signature
get n_required_iterations_():
Promise<number>
The number of iterations that are required to end up with less than factor candidates at the last iteration, starting with min_resources_ resources. This will be smaller than n_possible_iterations_ when there isn’t enough resources.
Returns Promise<number>
Defined in generated/model_selection/HalvingGridSearchCV.ts:780
n_resources_
Get Signature
get n_resources_():
Promise<any>
The amount of resources used at each iteration.
Returns Promise<any>
Defined in generated/model_selection/HalvingGridSearchCV.ts:591
n_splits_
Get Signature
get n_splits_():
Promise<number>
The number of cross-validation splits (folds/iterations).
Returns Promise<number>
Defined in generated/model_selection/HalvingGridSearchCV.ts:971
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/HalvingGridSearchCV.ts:123
refit_time_
Get Signature
get refit_time_():
Promise<number>
Seconds used for refitting the best model on the whole dataset.
This is present only if refit is not false.
Returns Promise<number>
Defined in generated/model_selection/HalvingGridSearchCV.ts:1000
scorer_
Get Signature
get scorer_():
Promise<any>
Scorer function used on the held out data to choose the best parameters for the model.
Returns Promise<any>
Defined in generated/model_selection/HalvingGridSearchCV.ts:944
Methods
decision_function()
decision_function(
opts):Promise<ArrayLike>
Call decision_function on the estimator with the best found parameters.
Only available if refit=True and the underlying estimator supports decision_function.
Parameters
| Parameter | Type | Description |
|---|---|---|
opts | object | - |
opts.X? | any | Must fulfill the input assumptions of the underlying estimator. |
Returns Promise<ArrayLike>
Defined in generated/model_selection/HalvingGridSearchCV.ts:198
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/HalvingGridSearchCV.ts:179
fit()
fit(
opts):Promise<any>
Run fit with all sets of parameters.
Parameters
| Parameter | Type | Description |
|---|---|---|
opts | object | - |
opts.params? | any | Parameters passed to the fit method of the estimator. |
opts.X? | ArrayLike | Training vector, where n_samples is the number of samples and n_features is the number of features. |
opts.y? | ArrayLike | Target relative to X for classification or regression; undefined for unsupervised learning. |
Returns Promise<any>
Defined in generated/model_selection/HalvingGridSearchCV.ts:234
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 MetadataRouter encapsulating routing information. |
Returns Promise<any>
Defined in generated/model_selection/HalvingGridSearchCV.ts:280
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/HalvingGridSearchCV.ts:136
inverse_transform()
inverse_transform(
opts):Promise<ArrayLike>
Call inverse_transform on the estimator with the best found params.
Only available if the underlying estimator implements inverse_transform and refit=True.
Parameters
| Parameter | Type | Description |
|---|---|---|
opts | object | - |
opts.X? | any | Must fulfill the input assumptions of the underlying estimator. |
opts.Xt? | any | Must fulfill the input assumptions of the underlying estimator. |
Returns Promise<ArrayLike>
Defined in generated/model_selection/HalvingGridSearchCV.ts:318
predict()
predict(
opts):Promise<ArrayLike>
Call predict on the estimator with the best found parameters.
Only available if refit=True and the underlying estimator supports predict.
Parameters
| Parameter | Type | Description |
|---|---|---|
opts | object | - |
opts.X? | any | Must fulfill the input assumptions of the underlying estimator. |
Returns Promise<ArrayLike>
Defined in generated/model_selection/HalvingGridSearchCV.ts:361
predict_log_proba()
predict_log_proba(
opts):Promise<ArrayLike>
Call predict_log_proba on the estimator with the best found parameters.
Only available if refit=True and the underlying estimator supports predict_log_proba.
Parameters
| Parameter | Type | Description |
|---|---|---|
opts | object | - |
opts.X? | any | Must fulfill the input assumptions of the underlying estimator. |
Returns Promise<ArrayLike>
Defined in generated/model_selection/HalvingGridSearchCV.ts:397
predict_proba()
predict_proba(
opts):Promise<ArrayLike>
Call predict_proba on the estimator with the best found parameters.
Only available if refit=True and the underlying estimator supports predict_proba.
Parameters
| Parameter | Type | Description |
|---|---|---|
opts | object | - |
opts.X? | any | Must fulfill the input assumptions of the underlying estimator. |
Returns Promise<ArrayLike>
Defined in generated/model_selection/HalvingGridSearchCV.ts:435
score()
score(
opts):Promise<number>
Return the score on the given data, if the estimator has been refit.
This uses the score defined by scoring where provided, and the best_estimator_.score method otherwise.
Parameters
| Parameter | Type | Description |
|---|---|---|
opts | object | - |
opts.params? | any | Parameters to be passed to the underlying scorer(s). |
opts.X? | ArrayLike[] | Input data, where n_samples is the number of samples and n_features is the number of features. |
opts.y? | ArrayLike[] | Target relative to X for classification or regression; undefined for unsupervised learning. |
Returns Promise<number>
Defined in generated/model_selection/HalvingGridSearchCV.ts:473
score_samples()
score_samples(
opts):Promise<ArrayLike>
Call score_samples on the estimator with the best found parameters.
Only available if refit=True and the underlying estimator supports score_samples.
Parameters
| Parameter | Type | Description |
|---|---|---|
opts | object | - |
opts.X? | any | Data to predict on. Must fulfill input requirements of the underlying estimator. |
Returns Promise<ArrayLike>
Defined in generated/model_selection/HalvingGridSearchCV.ts:519
transform()
transform(
opts):Promise<ArrayLike>
Call transform on the estimator with the best found parameters.
Only available if the underlying estimator supports transform and refit=True.
Parameters
| Parameter | Type | Description |
|---|---|---|
opts | object | - |
opts.X? | any | Must fulfill the input assumptions of the underlying estimator. |
Returns Promise<ArrayLike>
Defined in generated/model_selection/HalvingGridSearchCV.ts:557