RandomizedSearchCV
Randomized search on hyper parameters.
RandomizedSearchCV implements a “fit” and a “score” method. It also implements “score_samples”, “predict”, “predict_proba”, “decision_function”, “transform” and “inverse_transform” if they are implemented in the estimator used.
The parameters of the estimator used to apply these methods are optimized by cross-validated search over parameter settings.
In contrast to GridSearchCV, not all parameter values are tried out, but rather a fixed number of parameter settings is sampled from the specified distributions. The number of parameter settings that are tried is given by n_iter.
If all parameters are presented as a list, sampling without replacement is performed. If at least one parameter is given as a distribution, sampling with replacement is used. It is highly recommended to use continuous distributions for continuous parameters.
Read more in the User Guide.
Python Reference (opens in a new tab)
Constructors
constructor()
Signature
new RandomizedSearchCV(opts?: object): RandomizedSearchCV;Parameters
| Name | Type | Description |
|---|---|---|
opts? | object | - |
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. |
opts.estimator? | any | An object of that type is instantiated for each grid point. This is assumed to implement the scikit-learn estimator interface. Either estimator needs to provide a score function, or scoring must be passed. |
opts.n_iter? | number | Number of parameter settings that are sampled. n_iter trades off runtime vs quality of the solution. Default Value 10 |
opts.n_jobs? | number | Number of jobs to run in parallel. undefined means 1 unless in a joblib.parallel\_backend (opens in a new tab) context. \-1 means using all processors. See Glossary for more details. |
opts.param_distributions? | any | Dictionary with parameters names (str) as keys and distributions or lists of parameters to try. Distributions must provide a rvs method for sampling (such as those from scipy.stats.distributions). If a list is given, it is sampled uniformly. If a list of dicts is given, first a dict is sampled uniformly, and then a parameter is sampled using that dict as above. |
opts.pre_dispatch? | string | Controls the number of jobs that get dispatched during parallel execution. Reducing this number can be useful to avoid an explosion of memory consumption when more jobs get dispatched than CPUs can process. This parameter can be: Default Value '2*n_jobs' |
opts.random_state? | number | Pseudo random number generator state used for random uniform sampling from lists of possible values instead of scipy.stats distributions. Pass an int for reproducible output across multiple function calls. See Glossary. |
opts.refit? | boolean | Refit an estimator using the best found parameters on the whole dataset. For multiple metric evaluation, this needs to be a str denoting the scorer that would be used to find the best parameters for refitting the estimator at the end. Where there are considerations other than maximum score in choosing a best estimator, refit can be set to a function which returns the selected best\_index\_ given the cv\_results. In that case, the best\_estimator\_ and best\_params\_ will be set according to the returned best\_index\_ while the best\_score\_ attribute will not be available. The refitted estimator is made available at the best\_estimator\_ attribute and permits using predict directly on this RandomizedSearchCV instance. Also for multiple metric evaluation, the attributes best\_index\_, best\_score\_ and best\_params\_ will only be available if refit is set and all of them will be determined w.r.t this specific scorer. See scoring parameter to know more about multiple metric evaluation. Default Value true |
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. Default Value false |
opts.scoring? | any | Strategy to evaluate the performance of the cross-validated model on the test set. If scoring represents a single score, one can use: |
opts.verbose? | number | Controls the verbosity: the higher, the more messages. |
Returns
Defined in: generated/model_selection/RandomizedSearchCV.ts:31 (opens in a new tab)
Methods
decision_function()
Call decision_function on the estimator with the best found parameters.
Only available if refit=True and the underlying estimator supports decision\_function.
Signature
decision_function(opts: object): Promise<ArrayLike>;Parameters
| Name | Type | Description |
|---|---|---|
opts | object | - |
opts.X? | any | Must fulfill the input assumptions of the underlying estimator. |
Returns
Promise<ArrayLike>
Defined in: generated/model_selection/RandomizedSearchCV.ts:206 (opens in a new tab)
dispose()
Disposes of the underlying Python resources.
Once dispose() is called, the instance is no longer usable.
Signature
dispose(): Promise<void>;Returns
Promise<void>
Defined in: generated/model_selection/RandomizedSearchCV.ts:187 (opens in a new tab)
fit()
Run fit with all sets of parameters.
Signature
fit(opts: object): Promise<any>;Parameters
| Name | Type | Description |
|---|---|---|
opts | object | - |
opts.X? | ArrayLike[] | Training vector, where n\_samples is the number of samples and n\_features is the number of features. |
opts.fit_params? | any | Parameters passed to the fit method of the estimator. If a fit parameter is an array-like whose length is equal to num\_samples then it will be split across CV groups along with X and y. For example, the sample_weight parameter is split because len(sample\_weights) \= len(X). |
opts.groups? | ArrayLike | Group labels for the samples used while splitting the dataset into train/test set. Only used in conjunction with a “Group” cv instance (e.g., GroupKFold). |
opts.y? | ArrayLike[] | Target relative to X for classification or regression; undefined for unsupervised learning. |
Returns
Promise<any>
Defined in: generated/model_selection/RandomizedSearchCV.ts:243 (opens in a new tab)
get_metadata_routing()
Get metadata routing of this object.
Please check User Guide on how the routing mechanism works.
Signature
get_metadata_routing(opts: object): Promise<any>;Parameters
| Name | Type | Description |
|---|---|---|
opts | object | - |
opts.routing? | any | A MetadataRequest encapsulating routing information. |
Returns
Promise<any>
Defined in: generated/model_selection/RandomizedSearchCV.ts:303 (opens in a new tab)
init()
Initializes the underlying Python resources.
This instance is not usable until the Promise returned by init() resolves.
Signature
init(py: PythonBridge): Promise<void>;Parameters
| Name | Type |
|---|---|
py | PythonBridge |
Returns
Promise<void>
Defined in: generated/model_selection/RandomizedSearchCV.ts:129 (opens in a new tab)
inverse_transform()
Call inverse_transform on the estimator with the best found params.
Only available if the underlying estimator implements inverse\_transform and refit=True.
Signature
inverse_transform(opts: object): Promise<ArrayLike>;Parameters
| Name | Type | Description |
|---|---|---|
opts | object | - |
opts.Xt? | any | Must fulfill the input assumptions of the underlying estimator. |
Returns
Promise<ArrayLike>
Defined in: generated/model_selection/RandomizedSearchCV.ts:343 (opens in a new tab)
predict()
Call predict on the estimator with the best found parameters.
Only available if refit=True and the underlying estimator supports predict.
Signature
predict(opts: object): Promise<ArrayLike>;Parameters
| Name | Type | Description |
|---|---|---|
opts | object | - |
opts.X? | any | Must fulfill the input assumptions of the underlying estimator. |
Returns
Promise<ArrayLike>
Defined in: generated/model_selection/RandomizedSearchCV.ts:382 (opens in a new tab)
predict_log_proba()
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.
Signature
predict_log_proba(opts: object): Promise<ArrayLike>;Parameters
| Name | Type | Description |
|---|---|---|
opts | object | - |
opts.X? | any | Must fulfill the input assumptions of the underlying estimator. |
Returns
Promise<ArrayLike>
Defined in: generated/model_selection/RandomizedSearchCV.ts:419 (opens in a new tab)
predict_proba()
Call predict_proba on the estimator with the best found parameters.
Only available if refit=True and the underlying estimator supports predict\_proba.
Signature
predict_proba(opts: object): Promise<ArrayLike>;Parameters
| Name | Type | Description |
|---|---|---|
opts | object | - |
opts.X? | any | Must fulfill the input assumptions of the underlying estimator. |
Returns
Promise<ArrayLike>
Defined in: generated/model_selection/RandomizedSearchCV.ts:458 (opens in a new tab)
score()
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.
Signature
score(opts: object): Promise<number>;Parameters
| Name | Type | Description |
|---|---|---|
opts | object | - |
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/RandomizedSearchCV.ts:497 (opens in a new tab)
score_samples()
Call score_samples on the estimator with the best found parameters.
Only available if refit=True and the underlying estimator supports score\_samples.
Signature
score_samples(opts: object): Promise<ArrayLike>;Parameters
| Name | 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/RandomizedSearchCV.ts:541 (opens in a new tab)
set_fit_request()
Request metadata passed to the fit 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:
Signature
set_fit_request(opts: object): Promise<any>;Parameters
| Name | Type | Description |
|---|---|---|
opts | object | - |
opts.groups? | string | boolean | Metadata routing for groups parameter in fit. |
Returns
Promise<any>
Defined in: generated/model_selection/RandomizedSearchCV.ts:582 (opens in a new tab)
transform()
Call transform on the estimator with the best found parameters.
Only available if the underlying estimator supports transform and refit=True.
Signature
transform(opts: object): Promise<ArrayLike>;Parameters
| Name | Type | Description |
|---|---|---|
opts | object | - |
opts.X? | any | Must fulfill the input assumptions of the underlying estimator. |
Returns
Promise<ArrayLike>
Defined in: generated/model_selection/RandomizedSearchCV.ts:621 (opens in a new tab)
Properties
_isDisposed
boolean=false
Defined in: generated/model_selection/RandomizedSearchCV.ts:29 (opens in a new tab)
_isInitialized
boolean=false
Defined in: generated/model_selection/RandomizedSearchCV.ts:28 (opens in a new tab)
_py
PythonBridge
Defined in: generated/model_selection/RandomizedSearchCV.ts:27 (opens in a new tab)
id
string
Defined in: generated/model_selection/RandomizedSearchCV.ts:24 (opens in a new tab)
opts
any
Defined in: generated/model_selection/RandomizedSearchCV.ts:25 (opens in a new tab)
Accessors
best_estimator_
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.
For multi-metric evaluation, this attribute is present only if refit is specified.
See refit parameter for more information on allowed values.
Signature
best_estimator_(): Promise<any>;Returns
Promise<any>
Defined in: generated/model_selection/RandomizedSearchCV.ts:689 (opens in a new tab)
best_index_
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\_).
For multi-metric evaluation, this is not available if refit is false. See refit parameter for more information.
Signature
best_index_(): Promise<number>;Returns
Promise<number>
Defined in: generated/model_selection/RandomizedSearchCV.ts:780 (opens in a new tab)
best_params_
Parameter setting that gave the best results on the hold out data.
For multi-metric evaluation, this is not available if refit is false. See refit parameter for more information.
Signature
best_params_(): Promise<any>;Returns
Promise<any>
Defined in: generated/model_selection/RandomizedSearchCV.ts:749 (opens in a new tab)
best_score_
Mean cross-validated score of the best_estimator.
For multi-metric evaluation, this is not available if refit is false. See refit parameter for more information.
This attribute is not available if refit is a function.
Signature
best_score_(): Promise<number>;Returns
Promise<number>
Defined in: generated/model_selection/RandomizedSearchCV.ts:720 (opens in a new tab)
cv_results_
A dict with keys as column headers and values as columns, that can be imported into a pandas DataFrame.
For instance the below given table
Signature
cv_results_(): Promise<any>;Returns
Promise<any>
Defined in: generated/model_selection/RandomizedSearchCV.ts:658 (opens in a new tab)
feature_names_in_
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.
Signature
feature_names_in_(): Promise<ArrayLike>;Returns
Promise<ArrayLike>
Defined in: generated/model_selection/RandomizedSearchCV.ts:919 (opens in a new tab)
multimetric_
Whether or not the scorers compute several metrics.
Signature
multimetric_(): Promise<boolean>;Returns
Promise<boolean>
Defined in: generated/model_selection/RandomizedSearchCV.ts:892 (opens in a new tab)
n_splits_
The number of cross-validation splits (folds/iterations).
Signature
n_splits_(): Promise<number>;Returns
Promise<number>
Defined in: generated/model_selection/RandomizedSearchCV.ts:836 (opens in a new tab)
py
Signature
py(): PythonBridge;Returns
PythonBridge
Defined in: generated/model_selection/RandomizedSearchCV.ts:116 (opens in a new tab)
Signature
py(pythonBridge: PythonBridge): void;Parameters
| Name | Type |
|---|---|
pythonBridge | PythonBridge |
Returns
void
Defined in: generated/model_selection/RandomizedSearchCV.ts:120 (opens in a new tab)
refit_time_
Seconds used for refitting the best model on the whole dataset.
This is present only if refit is not false.
Signature
refit_time_(): Promise<number>;Returns
Promise<number>
Defined in: generated/model_selection/RandomizedSearchCV.ts:865 (opens in a new tab)
scorer_
Scorer function used on the held out data to choose the best parameters for the model.
For multi-metric evaluation, this attribute holds the validated scoring dict which maps the scorer key to the scorer callable.
Signature
scorer_(): Promise<any>;Returns
Promise<any>
Defined in: generated/model_selection/RandomizedSearchCV.ts:809 (opens in a new tab)