Class: 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.
Constructors
new RandomizedSearchCV()
new RandomizedSearchCV(
opts?):RandomizedSearchCV
Parameters
| Parameter | 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. |
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_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: |
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. |
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? | 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 RandomizedSearchCV
Defined in generated/model_selection/RandomizedSearchCV.ts:31
Properties
| Property | Type | Default value | Defined in |
|---|---|---|---|
_isDisposed | boolean | false | generated/model_selection/RandomizedSearchCV.ts:29 |
_isInitialized | boolean | false | generated/model_selection/RandomizedSearchCV.ts:28 |
_py | PythonBridge | undefined | generated/model_selection/RandomizedSearchCV.ts:27 |
id | string | undefined | generated/model_selection/RandomizedSearchCV.ts:24 |
opts | any | undefined | generated/model_selection/RandomizedSearchCV.ts:25 |
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.
For multi-metric evaluation, this attribute is present only if refit is specified.
See refit parameter for more information on allowed values.
Returns Promise<any>
Defined in generated/model_selection/RandomizedSearchCV.ts:619
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_).
For multi-metric evaluation, this is not available if refit is false. See refit parameter for more information.
Returns Promise<number>
Defined in generated/model_selection/RandomizedSearchCV.ts:710
best_params_
Get Signature
get best_params_():
Promise<any>
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.
Returns Promise<any>
Defined in generated/model_selection/RandomizedSearchCV.ts:679
best_score_
Get Signature
get best_score_():
Promise<number>
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.
Returns Promise<number>
Defined in generated/model_selection/RandomizedSearchCV.ts:650
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.
For instance the below given table
Returns Promise<any>
Defined in generated/model_selection/RandomizedSearchCV.ts:588
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/RandomizedSearchCV.ts:849
multimetric_
Get Signature
get multimetric_():
Promise<boolean>
Whether or not the scorers compute several metrics.
Returns Promise<boolean>
Defined in generated/model_selection/RandomizedSearchCV.ts:822
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/RandomizedSearchCV.ts:766
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/RandomizedSearchCV.ts:116
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/RandomizedSearchCV.ts:795
scorer_
Get Signature
get scorer_():
Promise<any>
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.
Returns Promise<any>
Defined in generated/model_selection/RandomizedSearchCV.ts:739
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/RandomizedSearchCV.ts:191
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/RandomizedSearchCV.ts:172
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, the scorer, and the CV splitter. 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.X? | ArrayLike[] | Training vectors, where n_samples is the number of samples and n_features is the number of features. For precomputed kernel or distance matrix, the expected shape of X is (n_samples, n_samples). |
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:227
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/RandomizedSearchCV.ts:275
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/RandomizedSearchCV.ts:129
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/RandomizedSearchCV.ts:313
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/RandomizedSearchCV.ts:356
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/RandomizedSearchCV.ts:392
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/RandomizedSearchCV.ts:430
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/RandomizedSearchCV.ts:468
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/RandomizedSearchCV.ts:514
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/RandomizedSearchCV.ts:552