sklearn.model_selection.GridSearchCV
class sklearn.model_selection.GridSearchCV(estimator, param_grid, *, scoring=None, n_jobs=None, refit=True, cv=None, verbose=0, pre_dispatch='2*n_jobs', error_score=nan, return_train_score=False) [source]

Exhaustive search over specified parameter values for an estimator. Important members are fit, predict. GridSearchCV 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 grid-search over a parameter grid. Read more in the User Guide.  Parameters

estimatorestimator object.

This is assumed to implement the scikit-learn estimator interface. Either estimator needs to provide a score function, or scoring must be passed.
param_griddict or list of dictionaries

Dictionary with parameters names (str) 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.
scoringstr, callable, list, tuple or dict, default=None

Strategy to evaluate the performance of the cross-validated model on the test set. If scoring represents a single score, one can use:  a single string (see The scoring parameter: defining model evaluation rules); a callable (see Defining your scoring strategy from metric functions) that returns a single value.  If scoring reprents multiple scores, one can use:  a list or tuple of unique strings; a callable returning a dictionary where the keys are the metric names and the values are the metric scores; a dictionary with metric names as keys and callables a values.  See Specifying multiple metrics for evaluation for an example.
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.  Changed in version v0.20: n_jobs default changed from 1 to None
pre_dispatchint, or str, default=n_jobs

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 str, giving an expression as a function of n_jobs, as in ‘2*n_jobs’
cvint, cross-validation generator or an iterable, default=None

Determines the cross-validation splitting strategy. Possible inputs for cv are:  None, to use the default 5-fold cross validation, integer, to specify the number of folds in a (Stratified)KFold,
CV splitter, 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. Refer User Guide for the various cross-validation strategies that can be used here.  Changed in version 0.22: cv default value if None changed from 3-fold to 5-fold.
refitbool, str, 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 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 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 GridSearchCV 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.  Changed in version 0.20: Support for callable added.
verboseint

Controls the verbosity: the higher, the more messages.  >1 : the computation time for each fold and parameter candidate is displayed; >2 : the score is also displayed; >3 : the fold and candidate parameter indexes are also displayed together with the starting time of the computation.
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.
return_train_scorebool, 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.  New in version 0.19.   Changed in version 0.21: Default value was changed from True to False     Attributes

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 param_degree split0_test_score … rank_t…
‘poly’ – 2 0.80 … 2
‘poly’ – 3 0.70 … 4
‘rbf’ 0.1 – 0.80 … 3
‘rbf’ 0.2 – 0.93 … 1   will be represented by a cv_results_ dict of: {
'param_kernel': masked_array(data = ['poly', 'poly', 'rbf', 'rbf'],
                             mask = [False False False False]...)
'param_gamma': masked_array(data = [-- -- 0.1 0.2],
                            mask = [ True  True False False]...),
'param_degree': masked_array(data = [2.0 3.0 -- --],
                             mask = [False False  True  True]...),
'split0_test_score'  : [0.80, 0.70, 0.80, 0.93],
'split1_test_score'  : [0.82, 0.50, 0.70, 0.78],
'mean_test_score'    : [0.81, 0.60, 0.75, 0.85],
'std_test_score'     : [0.01, 0.10, 0.05, 0.08],
'rank_test_score'    : [2, 4, 3, 1],
'split0_train_score' : [0.80, 0.92, 0.70, 0.93],
'split1_train_score' : [0.82, 0.55, 0.70, 0.87],
'mean_train_score'   : [0.81, 0.74, 0.70, 0.90],
'std_train_score'    : [0.01, 0.19, 0.00, 0.03],
'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'    : [0.01, 0.06, 0.04, 0.04],
'std_score_time'     : [0.00, 0.00, 0.00, 0.01],
'params'             : [{'kernel': 'poly', 'degree': 2}, ...],
}
 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

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. This attribute is not available if refit is a function.
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.  New in version 0.20.
multimetric_bool

Whether or not the scorers compute several metrics.      See also
ParameterGrid

Generates all the combinations of a hyperparameter grid.
train_test_split

Utility function to split the data into a development set usable for fitting a GridSearchCV instance and an evaluation set for its final evaluation.
sklearn.metrics.make_scorer

Make a scorer from a performance metric or loss function.    Notes The parameters selected are those that maximize the score of the left out data, unless an explicit score is passed in which case it is used instead. If n_jobs was set to a value higher than one, the data is copied for each point in the grid (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. Examples >>> from sklearn import svm, datasets
>>> from sklearn.model_selection import GridSearchCV
>>> iris = datasets.load_iris()
>>> parameters = {'kernel':('linear', 'rbf'), 'C':[1, 10]}
>>> svc = svm.SVC()
>>> clf = GridSearchCV(svc, parameters)
>>> clf.fit(iris.data, iris.target)
GridSearchCV(estimator=SVC(),
             param_grid={'C': [1, 10], 'kernel': ('linear', 'rbf')})
>>> sorted(clf.cv_results_.keys())
['mean_fit_time', 'mean_score_time', 'mean_test_score',...
 'param_C', 'param_kernel', 'params',...
 'rank_test_score', 'split0_test_score',...
 'split2_test_score', ...
 'std_fit_time', 'std_score_time', 'std_test_score']
 Methods
decision_function(X) Call decision_function on the estimator with the best found parameters.
fit(X[, y, groups]) Run fit with all sets of parameters.
get_params([deep]) Get parameters for this estimator.
inverse_transform(Xt) Call inverse_transform on the estimator with the best found params.
predict(X) Call predict on the estimator with the best found parameters.
predict_log_proba(X) Call predict_log_proba on the estimator with the best found parameters.
predict_proba(X) Call predict_proba on the estimator with the best found parameters.
score(X[, y]) Returns the score on the given data, if the estimator has been refit.
score_samples(X) Call score_samples on the estimator with the best found parameters.
set_params(**params) Set the parameters of this estimator.
transform(X) Call transform on the estimator with the best found parameters.
decision_function(X) [source]

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.

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

Run fit with all sets of parameters.  Parameters

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

Target relative to X for classification or regression; None for unsupervised learning.
groupsarray-like of shape (n_samples,), default=None

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).
**fit_paramsdict of str -> object

Parameters passed to the fit method of the estimator

get_params(deep=True) [source]

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(Xt) [source]

Call inverse_transform on the estimator with the best found params. Only available if the underlying estimator implements inverse_transform and refit=True.  Parameters

Xtindexable, length n_samples

Must fulfill the input assumptions of the underlying estimator.

predict(X) [source]

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.

predict_log_proba(X) [source]

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.

predict_proba(X) [source]

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.

score(X, y=None) [source]

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.  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.    Returns

scorefloat

score_samples(X) [source]

Call score_samples on the estimator with the best found parameters. Only available if refit=True and the underlying estimator supports score_samples.  New 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,)

set_params(**params) [source]

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) [source]

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.

 Examples using sklearn.model_selection.GridSearchCV

  Release Highlights for scikit-learn 0.24

  Feature agglomeration vs. univariate selection

  Shrinkage covariance estimation: LedoitWolf vs OAS and max-likelihood

  Model selection with Probabilistic PCA and Factor Analysis (FA)

  Faces recognition example using eigenfaces and SVMs

  Comparison of kernel ridge and Gaussian process regression

  Comparison of kernel ridge regression and SVR

  Parameter estimation using grid search with cross-validation

  Comparing randomized search and grid search for hyperparameter estimation

  Nested versus non-nested cross-validation

  Demonstration of multi-metric evaluation on cross_val_score and GridSearchCV

  Sample pipeline for text feature extraction and evaluation

  Balance model complexity and cross-validated score

  Comparison between grid search and successive halving

  Statistical comparison of models using grid search

  Kernel Density Estimation

  Caching nearest neighbors

  Concatenating multiple feature extraction methods

  Pipelining: chaining a PCA and a logistic regression

  Selecting dimensionality reduction with Pipeline and GridSearchCV

  Column Transformer with Mixed Types

  Feature discretization

  Scaling the regularization parameter for SVCs

  RBF SVM parameters

  Cross-validation on diabetes Dataset Exercise


decision_function(X) [source]

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.


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

Run fit with all sets of parameters.  Parameters

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

Target relative to X for classification or regression; None for unsupervised learning.
groupsarray-like of shape (n_samples,), default=None

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).
**fit_paramsdict of str -> object

Parameters passed to the fit method of the estimator


get_params(deep=True) [source]

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(Xt) [source]

Call inverse_transform on the estimator with the best found params. Only available if the underlying estimator implements inverse_transform and refit=True.  Parameters

Xtindexable, length n_samples

Must fulfill the input assumptions of the underlying estimator.


predict(X) [source]

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.


predict_log_proba(X) [source]

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.


predict_proba(X) [source]

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.


score(X, y=None) [source]

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.  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.    Returns

scorefloat


score_samples(X) [source]

Call score_samples on the estimator with the best found parameters. Only available if refit=True and the underlying estimator supports score_samples.  New 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,)


set_params(**params) [source]

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) [source]

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.


class sklearn.model_selection.GroupKFold(n_splits=5) [source]

K-fold iterator variant with non-overlapping groups. The same group will not appear in two different folds (the number of distinct groups has to be at least equal to the number of folds). The folds are approximately balanced in the sense that the number of distinct groups is approximately the same in each fold. Read more in the User Guide.  Parameters

n_splitsint, default=5

Number of folds. Must be at least 2.  Changed in version 0.22: n_splits default value changed from 3 to 5.       See also
LeaveOneGroupOut

For splitting the data according to explicit domain-specific stratification of the dataset.    Examples >>> import numpy as np
>>> from sklearn.model_selection import GroupKFold
>>> X = np.array([[1, 2], [3, 4], [5, 6], [7, 8]])
>>> y = np.array([1, 2, 3, 4])
>>> groups = np.array([0, 0, 2, 2])
>>> group_kfold = GroupKFold(n_splits=2)
>>> group_kfold.get_n_splits(X, y, groups)
2
>>> print(group_kfold)
GroupKFold(n_splits=2)
>>> for train_index, test_index in group_kfold.split(X, y, groups):
...     print("TRAIN:", train_index, "TEST:", test_index)
...     X_train, X_test = X[train_index], X[test_index]
...     y_train, y_test = y[train_index], y[test_index]
...     print(X_train, X_test, y_train, y_test)
...
TRAIN: [0 1] TEST: [2 3]
[[1 2]
 [3 4]] [[5 6]
 [7 8]] [1 2] [3 4]
TRAIN: [2 3] TEST: [0 1]
[[5 6]
 [7 8]] [[1 2]
 [3 4]] [3 4] [1 2]
 Methods
get_n_splits([X, y, groups]) Returns the number of splitting iterations in the cross-validator
split(X[, y, groups]) Generate indices to split data into training and test set.
get_n_splits(X=None, y=None, groups=None) [source]

Returns the number of splitting iterations in the cross-validator  Parameters

Xobject

Always ignored, exists for compatibility.
yobject

Always ignored, exists for compatibility.
groupsobject

Always ignored, exists for compatibility.    Returns

n_splitsint

Returns the number of splitting iterations in the cross-validator.

split(X, y=None, groups=None) [source]

Generate indices to split data into training and test set.  Parameters

Xarray-like of shape (n_samples, n_features)

Training data, where n_samples is the number of samples and n_features is the number of features.
yarray-like of shape (n_samples,), default=None

The target variable for supervised learning problems.
groupsarray-like of shape (n_samples,)

Group labels for the samples used while splitting the dataset into train/test set.    Yields

trainndarray

The training set indices for that split.
testndarray

The testing set indices for that split.


sklearn.model_selection.GroupKFold
class sklearn.model_selection.GroupKFold(n_splits=5) [source]

K-fold iterator variant with non-overlapping groups. The same group will not appear in two different folds (the number of distinct groups has to be at least equal to the number of folds). The folds are approximately balanced in the sense that the number of distinct groups is approximately the same in each fold. Read more in the User Guide.  Parameters

n_splitsint, default=5

Number of folds. Must be at least 2.  Changed in version 0.22: n_splits default value changed from 3 to 5.       See also
LeaveOneGroupOut

For splitting the data according to explicit domain-specific stratification of the dataset.    Examples >>> import numpy as np
>>> from sklearn.model_selection import GroupKFold
>>> X = np.array([[1, 2], [3, 4], [5, 6], [7, 8]])
>>> y = np.array([1, 2, 3, 4])
>>> groups = np.array([0, 0, 2, 2])
>>> group_kfold = GroupKFold(n_splits=2)
>>> group_kfold.get_n_splits(X, y, groups)
2
>>> print(group_kfold)
GroupKFold(n_splits=2)
>>> for train_index, test_index in group_kfold.split(X, y, groups):
...     print("TRAIN:", train_index, "TEST:", test_index)
...     X_train, X_test = X[train_index], X[test_index]
...     y_train, y_test = y[train_index], y[test_index]
...     print(X_train, X_test, y_train, y_test)
...
TRAIN: [0 1] TEST: [2 3]
[[1 2]
 [3 4]] [[5 6]
 [7 8]] [1 2] [3 4]
TRAIN: [2 3] TEST: [0 1]
[[5 6]
 [7 8]] [[1 2]
 [3 4]] [3 4] [1 2]
 Methods
get_n_splits([X, y, groups]) Returns the number of splitting iterations in the cross-validator
split(X[, y, groups]) Generate indices to split data into training and test set.
get_n_splits(X=None, y=None, groups=None) [source]

Returns the number of splitting iterations in the cross-validator  Parameters

Xobject

Always ignored, exists for compatibility.
yobject

Always ignored, exists for compatibility.
groupsobject

Always ignored, exists for compatibility.    Returns

n_splitsint

Returns the number of splitting iterations in the cross-validator.

split(X, y=None, groups=None) [source]

Generate indices to split data into training and test set.  Parameters

Xarray-like of shape (n_samples, n_features)

Training data, where n_samples is the number of samples and n_features is the number of features.
yarray-like of shape (n_samples,), default=None

The target variable for supervised learning problems.
groupsarray-like of shape (n_samples,)

Group labels for the samples used while splitting the dataset into train/test set.    Yields

trainndarray

The training set indices for that split.
testndarray

The testing set indices for that split.

 Examples using sklearn.model_selection.GroupKFold

  Visualizing cross-validation behavior in scikit-learn


get_n_splits(X=None, y=None, groups=None) [source]

Returns the number of splitting iterations in the cross-validator  Parameters

Xobject

Always ignored, exists for compatibility.
yobject

Always ignored, exists for compatibility.
groupsobject

Always ignored, exists for compatibility.    Returns

n_splitsint

Returns the number of splitting iterations in the cross-validator.


split(X, y=None, groups=None) [source]

Generate indices to split data into training and test set.  Parameters

Xarray-like of shape (n_samples, n_features)

Training data, where n_samples is the number of samples and n_features is the number of features.
yarray-like of shape (n_samples,), default=None

The target variable for supervised learning problems.
groupsarray-like of shape (n_samples,)

Group labels for the samples used while splitting the dataset into train/test set.    Yields

trainndarray

The training set indices for that split.
testndarray

The testing set indices for that split.


class sklearn.model_selection.GroupShuffleSplit(n_splits=5, *, test_size=None, train_size=None, random_state=None) [source]

Shuffle-Group(s)-Out cross-validation iterator Provides randomized train/test indices to split data according to a third-party provided group. This group information can be used to encode arbitrary domain specific stratifications of the samples as integers. For instance the groups could be the year of collection of the samples and thus allow for cross-validation against time-based splits. The difference between LeavePGroupsOut and GroupShuffleSplit is that the former generates splits using all subsets of size p unique groups, whereas GroupShuffleSplit generates a user-determined number of random test splits, each with a user-determined fraction of unique groups. For example, a less computationally intensive alternative to LeavePGroupsOut(p=10) would be GroupShuffleSplit(test_size=10, n_splits=100). Note: The parameters test_size and train_size refer to groups, and not to samples, as in ShuffleSplit. Read more in the User Guide.  Parameters

n_splitsint, default=5

Number of re-shuffling & splitting iterations.
test_sizefloat, int, default=0.2

If float, should be between 0.0 and 1.0 and represent the proportion of groups to include in the test split (rounded up). If int, represents the absolute number of test groups. If None, the value is set to the complement of the train size. The default will change in version 0.21. It will remain 0.2 only if train_size is unspecified, otherwise it will complement the specified train_size.
train_sizefloat or int, default=None

If float, should be between 0.0 and 1.0 and represent the proportion of the groups to include in the train split. If int, represents the absolute number of train groups. If None, the value is automatically set to the complement of the test size.
random_stateint, RandomState instance or None, default=None

Controls the randomness of the training and testing indices produced. Pass an int for reproducible output across multiple function calls. See Glossary.     Examples >>> import numpy as np
>>> from sklearn.model_selection import GroupShuffleSplit
>>> X = np.ones(shape=(8, 2))
>>> y = np.ones(shape=(8, 1))
>>> groups = np.array([1, 1, 2, 2, 2, 3, 3, 3])
>>> print(groups.shape)
(8,)
>>> gss = GroupShuffleSplit(n_splits=2, train_size=.7, random_state=42)
>>> gss.get_n_splits()
2
>>> for train_idx, test_idx in gss.split(X, y, groups):
...     print("TRAIN:", train_idx, "TEST:", test_idx)
TRAIN: [2 3 4 5 6 7] TEST: [0 1]
TRAIN: [0 1 5 6 7] TEST: [2 3 4]
 Methods
get_n_splits([X, y, groups]) Returns the number of splitting iterations in the cross-validator
split(X[, y, groups]) Generate indices to split data into training and test set.
get_n_splits(X=None, y=None, groups=None) [source]

Returns the number of splitting iterations in the cross-validator  Parameters

Xobject

Always ignored, exists for compatibility.
yobject

Always ignored, exists for compatibility.
groupsobject

Always ignored, exists for compatibility.    Returns

n_splitsint

Returns the number of splitting iterations in the cross-validator.

split(X, y=None, groups=None) [source]

Generate indices to split data into training and test set.  Parameters

Xarray-like of shape (n_samples, n_features)

Training data, where n_samples is the number of samples and n_features is the number of features.
yarray-like of shape (n_samples,), default=None

The target variable for supervised learning problems.
groupsarray-like of shape (n_samples,)

Group labels for the samples used while splitting the dataset into train/test set.    Yields

trainndarray

The training set indices for that split.
testndarray

The testing set indices for that split.     Notes Randomized CV splitters may return different results for each call of split. You can make the results identical by setting random_state to an integer.


sklearn.model_selection.GroupShuffleSplit
class sklearn.model_selection.GroupShuffleSplit(n_splits=5, *, test_size=None, train_size=None, random_state=None) [source]

Shuffle-Group(s)-Out cross-validation iterator Provides randomized train/test indices to split data according to a third-party provided group. This group information can be used to encode arbitrary domain specific stratifications of the samples as integers. For instance the groups could be the year of collection of the samples and thus allow for cross-validation against time-based splits. The difference between LeavePGroupsOut and GroupShuffleSplit is that the former generates splits using all subsets of size p unique groups, whereas GroupShuffleSplit generates a user-determined number of random test splits, each with a user-determined fraction of unique groups. For example, a less computationally intensive alternative to LeavePGroupsOut(p=10) would be GroupShuffleSplit(test_size=10, n_splits=100). Note: The parameters test_size and train_size refer to groups, and not to samples, as in ShuffleSplit. Read more in the User Guide.  Parameters

n_splitsint, default=5

Number of re-shuffling & splitting iterations.
test_sizefloat, int, default=0.2

If float, should be between 0.0 and 1.0 and represent the proportion of groups to include in the test split (rounded up). If int, represents the absolute number of test groups. If None, the value is set to the complement of the train size. The default will change in version 0.21. It will remain 0.2 only if train_size is unspecified, otherwise it will complement the specified train_size.
train_sizefloat or int, default=None

If float, should be between 0.0 and 1.0 and represent the proportion of the groups to include in the train split. If int, represents the absolute number of train groups. If None, the value is automatically set to the complement of the test size.
random_stateint, RandomState instance or None, default=None

Controls the randomness of the training and testing indices produced. Pass an int for reproducible output across multiple function calls. See Glossary.     Examples >>> import numpy as np
>>> from sklearn.model_selection import GroupShuffleSplit
>>> X = np.ones(shape=(8, 2))
>>> y = np.ones(shape=(8, 1))
>>> groups = np.array([1, 1, 2, 2, 2, 3, 3, 3])
>>> print(groups.shape)
(8,)
>>> gss = GroupShuffleSplit(n_splits=2, train_size=.7, random_state=42)
>>> gss.get_n_splits()
2
>>> for train_idx, test_idx in gss.split(X, y, groups):
...     print("TRAIN:", train_idx, "TEST:", test_idx)
TRAIN: [2 3 4 5 6 7] TEST: [0 1]
TRAIN: [0 1 5 6 7] TEST: [2 3 4]
 Methods
get_n_splits([X, y, groups]) Returns the number of splitting iterations in the cross-validator
split(X[, y, groups]) Generate indices to split data into training and test set.
get_n_splits(X=None, y=None, groups=None) [source]

Returns the number of splitting iterations in the cross-validator  Parameters

Xobject

Always ignored, exists for compatibility.
yobject

Always ignored, exists for compatibility.
groupsobject

Always ignored, exists for compatibility.    Returns

n_splitsint

Returns the number of splitting iterations in the cross-validator.

split(X, y=None, groups=None) [source]

Generate indices to split data into training and test set.  Parameters

Xarray-like of shape (n_samples, n_features)

Training data, where n_samples is the number of samples and n_features is the number of features.
yarray-like of shape (n_samples,), default=None

The target variable for supervised learning problems.
groupsarray-like of shape (n_samples,)

Group labels for the samples used while splitting the dataset into train/test set.    Yields

trainndarray

The training set indices for that split.
testndarray

The testing set indices for that split.     Notes Randomized CV splitters may return different results for each call of split. You can make the results identical by setting random_state to an integer.

 Examples using sklearn.model_selection.GroupShuffleSplit

  Visualizing cross-validation behavior in scikit-learn


split(X, y=None, groups=None) [source]

Generate indices to split data into training and test set.  Parameters

Xarray-like of shape (n_samples, n_features)

Training data, where n_samples is the number of samples and n_features is the number of features.
yarray-like of shape (n_samples,), default=None

The target variable for supervised learning problems.
groupsarray-like of shape (n_samples,)

Group labels for the samples used while splitting the dataset into train/test set.    Yields

trainndarray

The training set indices for that split.
testndarray

The testing set indices for that split.     Notes Randomized CV splitters may return different results for each call of split. You can make the results identical by setting random_state to an integer.


class sklearn.model_selection.HalvingGridSearchCV(estimator, param_grid, *, factor=3, resource='n_samples', max_resources='auto', min_resources='exhaust', aggressive_elimination=False, cv=5, scoring=None, refit=True, error_score=nan, return_train_score=True, random_state=None, n_jobs=None, verbose=0) [source]

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.  Note This estimator is still experimental for now: the predictions and the API might change without any deprecation cycle. To use it, you need to explicitly import enable_halving_search_cv: >>> # explicitly require this experimental feature
>>> from sklearn.experimental import enable_halving_search_cv # noqa
>>> # now you can import normally from model_selection
>>> from sklearn.model_selection import HalvingGridSearchCV
   Parameters

estimatorestimator object.

This is assumed to implement the scikit-learn estimator interface. Either estimator needs to provide a score function, or scoring must be passed.
param_griddict 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 of candidates for more details.
cvint, cross-validation generator or iterable, default=5

Determines the cross-validation splitting strategy. Possible inputs for cv are:  integer, to specify the number of folds in a (Stratified)KFold,
CV splitter, 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. Refer User Guide for the various cross-validation strategies that can be used here.  Note Due to implementation details, the folds produced by cv must be the same across multiple calls to cv.split(). For built-in scikit-learn iterators, this can be achieved by deactivating shuffling (shuffle=False), or by setting the cv’s random_state parameter to an integer.
scoringstring, callable, 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. If None, the estimator’s score method is used.
refitbool, default=True

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.
error_score‘raise’ 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. Default is np.nan
return_train_scorebool, 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.
random_stateint, RandomState instance or None, default=None

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.
n_jobsint or None, 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.
verboseint

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 many informations 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.
best_score_float

Mean cross-validated score of the best_estimator.
best_params_dict

Parameter setting that gave the best results on the hold out data.
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_).
scorer_function or a dict

Scorer function used on the held out data to choose the best parameters for the model.
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.      See also
HalvingRandomSearchCV

Random search over a set of parameters using successive halving.    Notes The parameters selected are those that maximize the score of the held-out data, according to the scoring parameter. Examples >>> from sklearn.datasets import load_iris
>>> from sklearn.ensemble import RandomForestClassifier
>>> from sklearn.experimental import enable_halving_search_cv  # noqa
>>> from sklearn.model_selection import HalvingGridSearchCV
...
>>> X, y = load_iris(return_X_y=True)
>>> clf = RandomForestClassifier(random_state=0)
...
>>> param_grid = {"max_depth": [3, None],
...               "min_samples_split": [5, 10]}
>>> search = HalvingGridSearchCV(clf, param_grid, resource='n_estimators',
...                              max_resources=10,
...                              random_state=0).fit(X, y)
>>> search.best_params_
{'max_depth': None, 'min_samples_split': 10, 'n_estimators': 9}
 Methods
decision_function(X) Call decision_function on the estimator with the best found parameters.
fit(X[, y, groups]) Run fit with all sets of parameters.
get_params([deep]) Get parameters for this estimator.
inverse_transform(Xt) Call inverse_transform on the estimator with the best found params.
predict(X) Call predict on the estimator with the best found parameters.
predict_log_proba(X) Call predict_log_proba on the estimator with the best found parameters.
predict_proba(X) Call predict_proba on the estimator with the best found parameters.
score(X[, y]) Returns the score on the given data, if the estimator has been refit.
score_samples(X) Call score_samples on the estimator with the best found parameters.
set_params(**params) Set the parameters of this estimator.
transform(X) Call transform on the estimator with the best found parameters.
decision_function(X) [source]

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.

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.
groupsarray-like of shape (n_samples,), default=None

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).
**fit_paramsdict of string -> object

Parameters passed to the fit method of the estimator

get_params(deep=True) [source]

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(Xt) [source]

Call inverse_transform on the estimator with the best found params. Only available if the underlying estimator implements inverse_transform and refit=True.  Parameters

Xtindexable, length n_samples

Must fulfill the input assumptions of the underlying estimator.

predict(X) [source]

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.

predict_log_proba(X) [source]

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.

predict_proba(X) [source]

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.

score(X, y=None) [source]

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.  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.    Returns

scorefloat

score_samples(X) [source]

Call score_samples on the estimator with the best found parameters. Only available if refit=True and the underlying estimator supports score_samples.  New 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,)

set_params(**params) [source]

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) [source]

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.


sklearn.model_selection.HalvingGridSearchCV
class sklearn.model_selection.HalvingGridSearchCV(estimator, param_grid, *, factor=3, resource='n_samples', max_resources='auto', min_resources='exhaust', aggressive_elimination=False, cv=5, scoring=None, refit=True, error_score=nan, return_train_score=True, random_state=None, n_jobs=None, verbose=0) [source]

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.  Note This estimator is still experimental for now: the predictions and the API might change without any deprecation cycle. To use it, you need to explicitly import enable_halving_search_cv: >>> # explicitly require this experimental feature
>>> from sklearn.experimental import enable_halving_search_cv # noqa
>>> # now you can import normally from model_selection
>>> from sklearn.model_selection import HalvingGridSearchCV
   Parameters

estimatorestimator object.

This is assumed to implement the scikit-learn estimator interface. Either estimator needs to provide a score function, or scoring must be passed.
param_griddict 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 of candidates for more details.
cvint, cross-validation generator or iterable, default=5

Determines the cross-validation splitting strategy. Possible inputs for cv are:  integer, to specify the number of folds in a (Stratified)KFold,
CV splitter, 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. Refer User Guide for the various cross-validation strategies that can be used here.  Note Due to implementation details, the folds produced by cv must be the same across multiple calls to cv.split(). For built-in scikit-learn iterators, this can be achieved by deactivating shuffling (shuffle=False), or by setting the cv’s random_state parameter to an integer.
scoringstring, callable, 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. If None, the estimator’s score method is used.
refitbool, default=True

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.
error_score‘raise’ 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. Default is np.nan
return_train_scorebool, 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.
random_stateint, RandomState instance or None, default=None

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.
n_jobsint or None, 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.
verboseint

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 many informations 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.
best_score_float

Mean cross-validated score of the best_estimator.
best_params_dict

Parameter setting that gave the best results on the hold out data.
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_).
scorer_function or a dict

Scorer function used on the held out data to choose the best parameters for the model.
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.      See also
HalvingRandomSearchCV

Random search over a set of parameters using successive halving.    Notes The parameters selected are those that maximize the score of the held-out data, according to the scoring parameter. Examples >>> from sklearn.datasets import load_iris
>>> from sklearn.ensemble import RandomForestClassifier
>>> from sklearn.experimental import enable_halving_search_cv  # noqa
>>> from sklearn.model_selection import HalvingGridSearchCV
...
>>> X, y = load_iris(return_X_y=True)
>>> clf = RandomForestClassifier(random_state=0)
...
>>> param_grid = {"max_depth": [3, None],
...               "min_samples_split": [5, 10]}
>>> search = HalvingGridSearchCV(clf, param_grid, resource='n_estimators',
...                              max_resources=10,
...                              random_state=0).fit(X, y)
>>> search.best_params_
{'max_depth': None, 'min_samples_split': 10, 'n_estimators': 9}
 Methods
decision_function(X) Call decision_function on the estimator with the best found parameters.
fit(X[, y, groups]) Run fit with all sets of parameters.
get_params([deep]) Get parameters for this estimator.
inverse_transform(Xt) Call inverse_transform on the estimator with the best found params.
predict(X) Call predict on the estimator with the best found parameters.
predict_log_proba(X) Call predict_log_proba on the estimator with the best found parameters.
predict_proba(X) Call predict_proba on the estimator with the best found parameters.
score(X[, y]) Returns the score on the given data, if the estimator has been refit.
score_samples(X) Call score_samples on the estimator with the best found parameters.
set_params(**params) Set the parameters of this estimator.
transform(X) Call transform on the estimator with the best found parameters.
decision_function(X) [source]

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.

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.
groupsarray-like of shape (n_samples,), default=None

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).
**fit_paramsdict of string -> object

Parameters passed to the fit method of the estimator

get_params(deep=True) [source]

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(Xt) [source]

Call inverse_transform on the estimator with the best found params. Only available if the underlying estimator implements inverse_transform and refit=True.  Parameters

Xtindexable, length n_samples

Must fulfill the input assumptions of the underlying estimator.

predict(X) [source]

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.

predict_log_proba(X) [source]

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.

predict_proba(X) [source]

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.

score(X, y=None) [source]

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.  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.    Returns

scorefloat

score_samples(X) [source]

Call score_samples on the estimator with the best found parameters. Only available if refit=True and the underlying estimator supports score_samples.  New 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,)

set_params(**params) [source]

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) [source]

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.

 Examples using sklearn.model_selection.HalvingGridSearchCV

  Release Highlights for scikit-learn 0.24

  Successive Halving Iterations

  Comparison between grid search and successive halving


decision_function(X) [source]

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.


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.
groupsarray-like of shape (n_samples,), default=None

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).
**fit_paramsdict of string -> object

Parameters passed to the fit method of the estimator


get_params(deep=True) [source]

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(Xt) [source]

Call inverse_transform on the estimator with the best found params. Only available if the underlying estimator implements inverse_transform and refit=True.  Parameters

Xtindexable, length n_samples

Must fulfill the input assumptions of the underlying estimator.


predict(X) [source]

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.


predict_log_proba(X) [source]

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.


predict_proba(X) [source]

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.


score(X, y=None) [source]

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.  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.    Returns

scorefloat


score_samples(X) [source]

Call score_samples on the estimator with the best found parameters. Only available if refit=True and the underlying estimator supports score_samples.  New 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,)


set_params(**params) [source]

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) [source]

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.


class sklearn.model_selection.HalvingRandomSearchCV(estimator, param_distributions, *, n_candidates='exhaust', factor=3, resource='n_samples', max_resources='auto', min_resources='smallest', aggressive_elimination=False, cv=5, scoring=None, refit=True, error_score=nan, return_train_score=True, random_state=None, n_jobs=None, verbose=0) [source]

Randomized search on hyper parameters. 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. The candidates are sampled at random from the parameter space and the number of sampled candidates is determined by n_candidates. Read more in the User guide.  Note This estimator is still experimental for now: the predictions and the API might change without any deprecation cycle. To use it, you need to explicitly import enable_halving_search_cv: >>> # explicitly require this experimental feature
>>> from sklearn.experimental import enable_halving_search_cv # noqa
>>> # now you can import normally from model_selection
>>> from sklearn.model_selection import HalvingRandomSearchCV
   Parameters

estimatorestimator object.

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

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_candidatesint, default=’exhaust’

The number of candidate parameters to sample, at the first iteration. Using ‘exhaust’ will sample enough candidates so that the last iteration uses as many resources as possible, based on min_resources, max_resources and factor. In this case, min_resources cannot be ‘exhaust’.
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 number of resources that any candidate is allowed to use for a given iteration. By default, this is set n_samples when resource='n_samples' (default), else an error is raised.
min_resources{‘exhaust’, ‘smallest’} or int, default=’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.

‘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. ‘exhaust’ isn’t available when n_candidates='exhaust'.  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 of candidates for more details.
cvint, cross-validation generator or an iterable, default=5

Determines the cross-validation splitting strategy. Possible inputs for cv are:  integer, to specify the number of folds in a (Stratified)KFold,
CV splitter, 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. Refer User Guide for the various cross-validation strategies that can be used here.  Note Due to implementation details, the folds produced by cv must be the same across multiple calls to cv.split(). For built-in scikit-learn iterators, this can be achieved by deactivating shuffling (shuffle=False), or by setting the cv’s random_state parameter to an integer.
scoringstring, callable, 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. If None, the estimator’s score method is used.
refitbool, default=True

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 HalvingRandomSearchCV instance.
error_score‘raise’ 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. Default is np.nan
return_train_scorebool, 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.
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.
n_jobsint or None, 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.
verboseint

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 many informations 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.
best_score_float

Mean cross-validated score of the best_estimator.
best_params_dict

Parameter setting that gave the best results on the hold out data.
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_).
scorer_function or a dict

Scorer function used on the held out data to choose the best parameters for the model.
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.      See also
HalvingGridSearchCV

Search over a grid of parameters using successive halving.    Notes The parameters selected are those that maximize the score of the held-out data, according to the scoring parameter. Examples >>> from sklearn.datasets import load_iris
>>> from sklearn.ensemble import RandomForestClassifier
>>> from sklearn.experimental import enable_halving_search_cv  # noqa
>>> from sklearn.model_selection import HalvingRandomSearchCV
>>> from scipy.stats import randint
...
>>> X, y = load_iris(return_X_y=True)
>>> clf = RandomForestClassifier(random_state=0)
>>> np.random.seed(0)
...
>>> param_distributions = {"max_depth": [3, None],
...                        "min_samples_split": randint(2, 11)}
>>> search = HalvingRandomSearchCV(clf, param_distributions,
...                                resource='n_estimators',
...                                max_resources=10,
...                                random_state=0).fit(X, y)
>>> search.best_params_
{'max_depth': None, 'min_samples_split': 10, 'n_estimators': 9}
 Methods
decision_function(X) Call decision_function on the estimator with the best found parameters.
fit(X[, y, groups]) Run fit with all sets of parameters.
get_params([deep]) Get parameters for this estimator.
inverse_transform(Xt) Call inverse_transform on the estimator with the best found params.
predict(X) Call predict on the estimator with the best found parameters.
predict_log_proba(X) Call predict_log_proba on the estimator with the best found parameters.
predict_proba(X) Call predict_proba on the estimator with the best found parameters.
score(X[, y]) Returns the score on the given data, if the estimator has been refit.
score_samples(X) Call score_samples on the estimator with the best found parameters.
set_params(**params) Set the parameters of this estimator.
transform(X) Call transform on the estimator with the best found parameters.
decision_function(X) [source]

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.

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.
groupsarray-like of shape (n_samples,), default=None

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).
**fit_paramsdict of string -> object

Parameters passed to the fit method of the estimator

get_params(deep=True) [source]

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(Xt) [source]

Call inverse_transform on the estimator with the best found params. Only available if the underlying estimator implements inverse_transform and refit=True.  Parameters

Xtindexable, length n_samples

Must fulfill the input assumptions of the underlying estimator.

predict(X) [source]

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.

predict_log_proba(X) [source]

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.

predict_proba(X) [source]

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.

score(X, y=None) [source]

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.  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.    Returns

scorefloat

score_samples(X) [source]

Call score_samples on the estimator with the best found parameters. Only available if refit=True and the underlying estimator supports score_samples.  New 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,)

set_params(**params) [source]

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) [source]

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.