`mlresearch.model_selection`.HalvingModelSearchCV¶

class mlresearch.model_selection.HalvingModelSearchCV(estimators, param_grids, factor=3, resource='n_samples', max_resources='auto', min_resources='exhaust', aggressive_elimination=False, cv=5, scoring=None, refit=True, error_score='raise', return_train_score=False, random_state=None, n_jobs=None, verbose=0)[source]¶

Search over specified parameter values for a collection of estimators 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.

Parameters:

estimatorslist of (string, estimator) tuples

Each estimator is assumed to implement the scikit-learn estimator interface. Either estimator needs to provide a score function, or scoring must be passed.

param_gridsdict or list of dictionaries

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.

factorint or float, default=3

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.

resource'n_samples' or str, default=’n_samples’

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.

max_resourcesint, default=’auto’

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.

min_resources{‘exhaust’, ‘smallest’} or int, default=’exhaust’

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.

‘smallest’ is a heuristic that sets r0 to a small value:
- n_splits * 2 when resource='n_samples' for a regression problem
- n_classes * n_splits * 2 when resource='n_samples' for a classification problem
- 1 when resource != 'n_samples'
‘exhaust’ will set r0 such that the last iteration uses as much resources as possible. Namely, the last iteration will use the highest value smaller than max_resources that is a multiple of both min_resources and factor. In general, using ‘exhaust’ leads to a more accurate estimator, but is slightly more time consuming.

Note that the amount of resources used at each iteration is always a multiple of min_resources.

aggressive_eliminationbool, default=False

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 for more details.

scoringstring, callable, list/tuple, dict or None, default=None

A single string or a callable 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.

If None, the estimator’s score method is used.

cvint, cross-validation generator or an iterable, default=5

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 as arrays of indices.

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. These splitters are instantiated with shuffle=False so the splits will be the same across calls.

refitboolean, string, or callable, 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.

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 cv_results_. In that case, the best_estimator_ and best_parameters_ will be set according to the returned best_index_ while the best_score_ attribute will not be availble.

The refitted estimator is made available at the best_estimator_ attribute and permits using predict directly on this ModelSearchCV 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.

error_score‘raise’ or numeric, default=np.nan

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.

return_train_scoreboolean, default=False

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.

n_jobsint, default=None

Number of jobs to run in parallel. None means 1 unless in a joblib.parallel_backend context. -1 means using all processors. See Glossary for more details.

random_stateint, RandomState instance or None, default=None

Pseudo random number generator state used for subsampling the dataset when resources != ‘n_samples’. Also 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.

verboseinteger, default=0

Controls the verbosity: the higher, the more messages.

Attributes:

n_resources_list of int

The amount of resources used at each iteration.

n_candidates_list of int

The number of candidate parameters that were evaluated at each iteration.

n_remaining_candidates_int

The number of candidate parameters that are left after the last iteration. It corresponds to ceil(n_candidates[-1] / factor)

max_resources_int

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_.

min_resources_int

The amount of resources that are allocated for each candidate at the first iteration.

n_iterations_int

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_).

n_possible_iterations_int

The number of iterations that are possible starting with min_resources_ resources and without exceeding max_resources_.

n_required_iterations_int

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.

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. It contains lots of information for analysing the results of a search. Please refer to the User guide for details.

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.

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 present only if refit is specified.

best_params_dict

Parameter setting that gave the best results on the hold out data.

For multi-metric evaluation, this is present only if refit is specified.

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 present only if refit is specified.

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).

refit_time_float

Seconds used for refitting the best model on the whole dataset.

This is present only if refit is not False.

Notes

The parameters selected are those that maximize the score of the held out data, unless an explicit score is passed in which case it is used instead.

All parameter combinations scored with a NaN will share the lowest rank.

Examples

>>> from sklearn.datasets import load_breast_cancer
>>> from sklearn.tree import DecisionTreeClassifier
>>> from sklearn.neighbors import KNeighborsClassifier
>>> from mlresearch.model_selection import HalvingModelSearchCV
>>> X, y, *_ = load_breast_cancer().values()
>>> param_grids = [{'dt__max_depth': [3, 6]}, {'kn__n_neighbors': [3, 5]}]
>>> estimators = [('dt', DecisionTreeClassifier()), ('kn', KNeighborsClassifier())]
>>> model_search_cv = HalvingModelSearchCV(estimators, param_grids)
>>> model_search_cv.fit(X, y)
HalvingModelSearchCV(...)
>>> sorted(model_search_cv.cv_results_.keys())
['mean_fit_time', 'mean_score_time', 'mean_test_score',...]

property classes_¶

Class labels.

Only available when refit=True and the estimator is a classifier.

decision_function(X)¶

Call decision_function on the estimator with the best found parameters.

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

Parameters:

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

Returns:

y_scorendarray of shape (n_samples,) or (n_samples, n_classes) or (n_samples, n_classes * (n_classes-1) / 2): Result of the decision function for X based on the estimator with the best found parameters.

fit(X, y=None, groups=None, **fit_params)[source]¶

Run fit with all sets of parameters.

Parameters:

Xarray-like, shape (n_samples, n_features): Training vector, where n_samples is the number of samples and n_features is the number of features.
yarray-like, shape (n_samples,) or (n_samples, n_output), optional: Target relative to X for classification or regression; None for unsupervised learning.
**paramsdict of string -> object: Parameters passed to the fit method of the estimator.

Returns:

selfobject: Instance of fitted estimator.

get_metadata_routing()¶

Get metadata routing of this object.

Please check User Guide on how the routing mechanism works.

Added in version 1.4.

Returns:

routingMetadataRouter: A MetadataRouter encapsulating routing information.

get_params(deep=True)¶

Get parameters for this estimator.

Parameters:

deepbool, default=True: If True, will return the parameters for this estimator and contained subobjects that are estimators.

Returns:

paramsdict: Parameter names mapped to their values.

inverse_transform(X=None, Xt=None)¶

Call inverse_transform on the estimator with the best found params.

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

Parameters:

Xindexable, length n_samples: Must fulfill the input assumptions of the underlying estimator.
Xtindexable, length n_samples: Must fulfill the input assumptions of the underlying estimator.

Deprecated since version 1.5: Xt was deprecated in 1.5 and will be removed in 1.7. Use X instead.

Returns:

X{ndarray, sparse matrix} of shape (n_samples, n_features): Result of the inverse_transform function for Xt based on the estimator with the best found parameters.

property n_features_in_¶

Number of features seen during fit.

Only available when refit=True.

predict(X)¶

Call predict on the estimator with the best found parameters.

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

Parameters:

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

Returns:

y_predndarray of shape (n_samples,): The predicted labels or values for X based on the estimator with the best found parameters.

predict_log_proba(X)¶

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:

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

Returns:

y_predndarray of shape (n_samples,) or (n_samples, n_classes): Predicted class log-probabilities for X based on the estimator with the best found parameters. The order of the classes corresponds to that in the fitted attribute classes_.

predict_proba(X)¶

Call predict_proba on the estimator with the best found parameters.

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

Parameters:

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

Returns:

y_predndarray of shape (n_samples,) or (n_samples, n_classes): Predicted class probabilities for X based on the estimator with the best found parameters. The order of the classes corresponds to that in the fitted attribute classes_.

score(X, y=None, **params)¶

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:

Xarray-like of shape (n_samples, n_features)

Input data, where n_samples is the number of samples and n_features is the number of features.

yarray-like of shape (n_samples, n_output) or (n_samples,), default=None

Target relative to X for classification or regression; None for unsupervised learning.

**paramsdict

Parameters to be passed to the underlying scorer(s).

..versionadded:: 1.4: Only available if enable_metadata_routing=True. See Metadata Routing User Guide for more details.

Returns:

scorefloat: The score defined by scoring if provided, and the best_estimator_.score method otherwise.

score_samples(X)¶

Call score_samples on the estimator with the best found parameters.

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

Added in version 0.24.

Parameters:

Xiterable: Data to predict on. Must fulfill input requirements of the underlying estimator.

Returns:

y_scorendarray of shape (n_samples,): The best_estimator_.score_samples method.

set_fit_request(*, groups: bool | None | str = '$UNCHANGED$') → HalvingModelSearchCV¶

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:

True: metadata is requested, and passed to fit if provided. The request is ignored if metadata is not provided.
False: metadata is not requested and the meta-estimator will not pass it to fit.
None: metadata is not requested, and the meta-estimator will raise an error if the user provides it.
str: metadata should be passed to the meta-estimator with this given alias instead of the original name.

The default (sklearn.utils.metadata_routing.UNCHANGED) retains the existing request. This allows you to change the request for some parameters and not others.

Added in version 1.3.

Note

This method is only relevant if this estimator is used as a sub-estimator of a meta-estimator, e.g. used inside a Pipeline. Otherwise it has no effect.

Parameters:

groupsstr, True, False, or None, default=sklearn.utils.metadata_routing.UNCHANGED: Metadata routing for groups parameter in fit.

Returns:

selfobject: The updated object.

set_params(**params)¶

Set the parameters of this estimator.

The method works on simple estimators as well as on nested objects (such as Pipeline). The latter have parameters of the form <component>__<parameter> so that it’s possible to update each component of a nested object.

Parameters:

**paramsdict: Estimator parameters.

Returns:

selfestimator instance: Estimator instance.

transform(X)¶

Call transform on the estimator with the best found parameters.

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

Parameters:

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

Returns:

Xt{ndarray, sparse matrix} of shape (n_samples, n_features): X transformed in the new space based on the estimator with the best found parameters.

mlresearch.model_selection.HalvingModelSearchCV¶

`mlresearch.model_selection`.HalvingModelSearchCV¶