# RandomizedSearchCV¶

class ibex.sklearn.model_selection.RandomizedSearchCV(estimator, param_distributions, n_iter=10, scoring=None, fit_params=None, n_jobs=1, iid=True, refit=True, cv=None, verbose=0, pre_dispatch='2*n_jobs', random_state=None, error_score='raise', return_train_score='warn')

Bases: sklearn.model_selection._search.RandomizedSearchCV, ibex._base.FrameMixin

Note

The documentation following is of the class wrapped by this class. There are some changes, in particular:

Randomized search on hyper parameters.

RandomizedSearchCV implements a “fit” and a “score” method. It also implements “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.

estimator : estimator object.
A 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.
param_distributions : dict
Dictionary with parameters names (string) 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.
n_iter : int, default=10
Number of parameter settings that are sampled. n_iter trades off runtime vs quality of the solution.
scoring : string, callable, list/tuple, dict or None, default: None

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.

For evaluating multiple metrics, either give a list of (unique) strings or a dict with names as keys and callables as values.

NOTE that when using custom scorers, each scorer should return a single value. Metric functions returning a list/array of values can be wrapped into multiple scorers that return one value each.

See Specifying multiple metrics for evaluation for an example.

If None, the estimator’s default scorer (if available) is used.

fit_params : dict, optional

Parameters to pass to the fit method.

Deprecated since version 0.19: fit_params as a constructor argument was deprecated in version 0.19 and will be removed in version 0.21. Pass fit parameters to the fit method instead.

n_jobs : int, default=1
Number of jobs to run in parallel.
pre_dispatch : int, or string, optional

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:

• None, in which case all the jobs are immediately created and spawned. Use this for lightweight and fast-running jobs, to avoid delays due to on-demand spawning of the jobs
• An int, giving the exact number of total jobs that are spawned
• A string, giving an expression as a function of n_jobs, as in ‘2*n_jobs’
iid : boolean, default=True
If True, the data is assumed to be identically distributed across the folds, and the loss minimized is the total loss per sample, and not the mean loss across the folds.
cv : int, cross-validation generator or an iterable, optional

Determines the cross-validation splitting strategy. Possible inputs for cv are:

• None, to use the default 3-fold cross validation,
• integer, to specify the number of folds in a (Stratified)KFold,
• An object to be used as a cross-validation generator.
• An iterable yielding train, test splits.

For integer/None inputs, if the estimator is a classifier and y is either binary or multiclass, StratifiedKFold is used. In all other cases, KFold is used.

Refer User Guide for the various cross-validation strategies that can be used here.

refit : boolean, or string default=True

Refit an estimator using the best found parameters on the whole dataset.

For multiple metric evaluation, this needs to be a string denoting the scorer that would be used to find the best parameters for refitting the estimator at the end.

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_parameters_ 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.

verbose : integer
Controls the verbosity: the higher, the more messages.
random_state : int, RandomState instance or None, optional, default=None
Pseudo random number generator state used for random uniform sampling from lists of possible values instead of scipy.stats distributions. If int, random_state is the seed used by the random number generator; If RandomState instance, random_state is the random number generator; If None, the random number generator is the RandomState instance used by np.random.
error_score : ‘raise’ (default) or numeric
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.
return_train_score : boolean, optional

If False, the cv_results_ attribute will not include training scores.

Current default is 'warn', which behaves as True in addition to raising a warning when a training score is looked up. That default will be changed to False in 0.21. 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.

cv_results_ : dict of numpy (masked) ndarrays

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

param_kernel param_gamma split0_test_score rank_test_score
‘rbf’ 0.1 0.8 2
‘rbf’ 0.2 0.9 1
‘rbf’ 0.3 0.7 1

will be represented by a cv_results_ dict of:

{
'param_kernel' : masked_array(data = ['rbf', 'rbf', 'rbf'],
'split0_test_score'  : [0.8, 0.9, 0.7],
'split1_test_score'  : [0.82, 0.5, 0.7],
'mean_test_score'    : [0.81, 0.7, 0.7],
'std_test_score'     : [0.02, 0.2, 0.],
'rank_test_score'    : [3, 1, 1],
'split0_train_score' : [0.8, 0.9, 0.7],
'split1_train_score' : [0.82, 0.5, 0.7],
'mean_train_score'   : [0.81, 0.7, 0.7],
'std_train_score'    : [0.03, 0.03, 0.04],
'mean_fit_time'      : [0.73, 0.63, 0.43, 0.49],
'std_fit_time'       : [0.01, 0.02, 0.01, 0.01],
'mean_score_time'    : [0.007, 0.06, 0.04, 0.04],
'std_score_time'     : [0.001, 0.002, 0.003, 0.005],
'params'             : [{'kernel' : 'rbf', 'gamma' : 0.1}, ...],
}


NOTE

The key 'params' is used to store a list of parameter settings dicts for all the parameter candidates.

The mean_fit_time, std_fit_time, mean_score_time and std_score_time are all in seconds.

For multi-metric evaluation, the scores for all the scorers are available in the cv_results_ dict at the keys ending with that scorer’s name ('_<scorer_name>') instead of '_score' shown above. (‘split0_test_precision’, ‘mean_train_precision’ etc.)

best_estimator_ : estimator or dict

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.

best_score_ : float

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.

best_params_ : dict

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.

best_index_ : int

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.

scorer_ : function or a dict

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.

n_splits_ : int
The number of cross-validation splits (folds/iterations).

The parameters selected are those that maximize the score of the held-out data, according to the scoring parameter.

If n_jobs was set to a value higher than one, the data is copied for each parameter setting(and not n_jobs times). This is done for efficiency reasons if individual jobs take very little time, but may raise errors if the dataset is large and not enough memory is available. A workaround in this case is to set pre_dispatch. Then, the memory is copied only pre_dispatch many times. A reasonable value for pre_dispatch is 2 * n_jobs.

GridSearchCV:
Does exhaustive search over a grid of parameters.
ParameterSampler:
A generator over parameter settins, constructed from param_distributions.
decision_function(X)

Note

The documentation following is of the class wrapped by this class. There are some changes, in particular:

Call decision_function on the estimator with the best found parameters.

Only available if refit=True and the underlying estimator supports decision_function.

X : indexable, length n_samples
Must fulfill the input assumptions of the underlying estimator.
fit(X, y=None, groups=None, **fit_params)

Note

The documentation following is of the class wrapped by this class. There are some changes, in particular:

Run fit with all sets of parameters.

X : array-like, shape = [n_samples, n_features]
Training vector, where n_samples is the number of samples and n_features is the number of features.
y : array-like, shape = [n_samples] or [n_samples, n_output], optional
Target relative to X for classification or regression; None for unsupervised learning.
groups : array-like, with shape (n_samples,), optional
Group labels for the samples used while splitting the dataset into train/test set.
**fit_params : dict of string -> object
Parameters passed to the fit method of the estimator
inverse_transform(Xt)

Note

The documentation following is of the class wrapped by this class. There are some changes, in particular:

Call inverse_transform on the estimator with the best found params.

Only available if the underlying estimator implements inverse_transform and refit=True.

Xt : indexable, length n_samples
Must fulfill the input assumptions of the underlying estimator.
predict(X)

Note

The documentation following is of the class wrapped by this class. There are some changes, in particular:

Call predict on the estimator with the best found parameters.

Only available if refit=True and the underlying estimator supports predict.

X : indexable, length n_samples
Must fulfill the input assumptions of the underlying estimator.
predict_log_proba(X)

Note

The documentation following is of the class wrapped by this class. There are some changes, in particular:

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.

X : indexable, length n_samples
Must fulfill the input assumptions of the underlying estimator.
predict_proba(X)

Note

The documentation following is of the class wrapped by this class. There are some changes, in particular:

Call predict_proba on the estimator with the best found parameters.

Only available if refit=True and the underlying estimator supports predict_proba.

X : indexable, length n_samples
Must fulfill the input assumptions of the underlying estimator.
score(X, y=None)

Note

The documentation following is of the class wrapped by this class. There are some changes, in particular:

Returns 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.

X : array-like, shape = [n_samples, n_features]
Input data, where n_samples is the number of samples and n_features is the number of features.
y : array-like, shape = [n_samples] or [n_samples, n_output], optional
Target relative to X for classification or regression; None for unsupervised learning.

score : float

transform(X)

Note

The documentation following is of the class wrapped by this class. There are some changes, in particular:

Call transform on the estimator with the best found parameters.

Only available if the underlying estimator supports transform and refit=True.

X : indexable, length n_samples
Must fulfill the input assumptions of the underlying estimator.