doc_content stringlengths 1 386k | doc_id stringlengths 5 188 |
|---|---|
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. | sklearn.modules.generated.sklearn.model_selection.gridsearchcv#sklearn.model_selection.GridSearchCV |
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 | sklearn.modules.generated.sklearn.model_selection.gridsearchcv |
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. | sklearn.modules.generated.sklearn.model_selection.gridsearchcv#sklearn.model_selection.GridSearchCV.decision_function |
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 | sklearn.modules.generated.sklearn.model_selection.gridsearchcv#sklearn.model_selection.GridSearchCV.fit |
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. | sklearn.modules.generated.sklearn.model_selection.gridsearchcv#sklearn.model_selection.GridSearchCV.get_params |
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. | sklearn.modules.generated.sklearn.model_selection.gridsearchcv#sklearn.model_selection.GridSearchCV.inverse_transform |
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. | sklearn.modules.generated.sklearn.model_selection.gridsearchcv#sklearn.model_selection.GridSearchCV.predict |
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. | sklearn.modules.generated.sklearn.model_selection.gridsearchcv#sklearn.model_selection.GridSearchCV.predict_log_proba |
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. | sklearn.modules.generated.sklearn.model_selection.gridsearchcv#sklearn.model_selection.GridSearchCV.predict_proba |
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 | sklearn.modules.generated.sklearn.model_selection.gridsearchcv#sklearn.model_selection.GridSearchCV.score |
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,) | sklearn.modules.generated.sklearn.model_selection.gridsearchcv#sklearn.model_selection.GridSearchCV.score_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. | sklearn.modules.generated.sklearn.model_selection.gridsearchcv#sklearn.model_selection.GridSearchCV.set_params |
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.modules.generated.sklearn.model_selection.gridsearchcv#sklearn.model_selection.GridSearchCV.transform |
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.modules.generated.sklearn.model_selection.groupkfold#sklearn.model_selection.GroupKFold |
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 | sklearn.modules.generated.sklearn.model_selection.groupkfold |
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. | sklearn.modules.generated.sklearn.model_selection.groupkfold#sklearn.model_selection.GroupKFold.get_n_splits |
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.modules.generated.sklearn.model_selection.groupkfold#sklearn.model_selection.GroupKFold.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.modules.generated.sklearn.model_selection.groupshufflesplit#sklearn.model_selection.GroupShuffleSplit |
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 | sklearn.modules.generated.sklearn.model_selection.groupshufflesplit |
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. | sklearn.modules.generated.sklearn.model_selection.groupshufflesplit#sklearn.model_selection.GroupShuffleSplit.get_n_splits |
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.modules.generated.sklearn.model_selection.groupshufflesplit#sklearn.model_selection.GroupShuffleSplit.split |
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.modules.generated.sklearn.model_selection.halvinggridsearchcv#sklearn.model_selection.HalvingGridSearchCV |
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 | sklearn.modules.generated.sklearn.model_selection.halvinggridsearchcv |
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. | sklearn.modules.generated.sklearn.model_selection.halvinggridsearchcv#sklearn.model_selection.HalvingGridSearchCV.decision_function |
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 | sklearn.modules.generated.sklearn.model_selection.halvinggridsearchcv#sklearn.model_selection.HalvingGridSearchCV.fit |
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. | sklearn.modules.generated.sklearn.model_selection.halvinggridsearchcv#sklearn.model_selection.HalvingGridSearchCV.get_params |
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. | sklearn.modules.generated.sklearn.model_selection.halvinggridsearchcv#sklearn.model_selection.HalvingGridSearchCV.inverse_transform |
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. | sklearn.modules.generated.sklearn.model_selection.halvinggridsearchcv#sklearn.model_selection.HalvingGridSearchCV.predict |
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. | sklearn.modules.generated.sklearn.model_selection.halvinggridsearchcv#sklearn.model_selection.HalvingGridSearchCV.predict_log_proba |
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. | sklearn.modules.generated.sklearn.model_selection.halvinggridsearchcv#sklearn.model_selection.HalvingGridSearchCV.predict_proba |
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 | sklearn.modules.generated.sklearn.model_selection.halvinggridsearchcv#sklearn.model_selection.HalvingGridSearchCV.score |
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,) | sklearn.modules.generated.sklearn.model_selection.halvinggridsearchcv#sklearn.model_selection.HalvingGridSearchCV.score_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. | sklearn.modules.generated.sklearn.model_selection.halvinggridsearchcv#sklearn.model_selection.HalvingGridSearchCV.set_params |
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.modules.generated.sklearn.model_selection.halvinggridsearchcv#sklearn.model_selection.HalvingGridSearchCV.transform |
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. | sklearn.modules.generated.sklearn.model_selection.halvingrandomsearchcv#sklearn.model_selection.HalvingRandomSearchCV |
sklearn.model_selection.HalvingRandomSearchCV
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.
Examples using sklearn.model_selection.HalvingRandomSearchCV
Release Highlights for scikit-learn 0.24
Successive Halving Iterations | sklearn.modules.generated.sklearn.model_selection.halvingrandomsearchcv |
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. | sklearn.modules.generated.sklearn.model_selection.halvingrandomsearchcv#sklearn.model_selection.HalvingRandomSearchCV.decision_function |
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 | sklearn.modules.generated.sklearn.model_selection.halvingrandomsearchcv#sklearn.model_selection.HalvingRandomSearchCV.fit |
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. | sklearn.modules.generated.sklearn.model_selection.halvingrandomsearchcv#sklearn.model_selection.HalvingRandomSearchCV.get_params |
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. | sklearn.modules.generated.sklearn.model_selection.halvingrandomsearchcv#sklearn.model_selection.HalvingRandomSearchCV.inverse_transform |
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. | sklearn.modules.generated.sklearn.model_selection.halvingrandomsearchcv#sklearn.model_selection.HalvingRandomSearchCV.predict |
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. | sklearn.modules.generated.sklearn.model_selection.halvingrandomsearchcv#sklearn.model_selection.HalvingRandomSearchCV.predict_log_proba |
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. | sklearn.modules.generated.sklearn.model_selection.halvingrandomsearchcv#sklearn.model_selection.HalvingRandomSearchCV.predict_proba |
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 | sklearn.modules.generated.sklearn.model_selection.halvingrandomsearchcv#sklearn.model_selection.HalvingRandomSearchCV.score |
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,) | sklearn.modules.generated.sklearn.model_selection.halvingrandomsearchcv#sklearn.model_selection.HalvingRandomSearchCV.score_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. | sklearn.modules.generated.sklearn.model_selection.halvingrandomsearchcv#sklearn.model_selection.HalvingRandomSearchCV.set_params |
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.modules.generated.sklearn.model_selection.halvingrandomsearchcv#sklearn.model_selection.HalvingRandomSearchCV.transform |
class sklearn.model_selection.KFold(n_splits=5, *, shuffle=False, random_state=None) [source]
K-Folds cross-validator Provides train/test indices to split data in train/test sets. Split dataset into k consecutive folds (without shuffling by default). Each fold is then used once as a validation while the k - 1 remaining folds form the training set. 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.
shufflebool, default=False
Whether to shuffle the data before splitting into batches. Note that the samples within each split will not be shuffled.
random_stateint, RandomState instance or None, default=None
When shuffle is True, random_state affects the ordering of the indices, which controls the randomness of each fold. Otherwise, this parameter has no effect. Pass an int for reproducible output across multiple function calls. See Glossary. See also
StratifiedKFold
Takes group information into account to avoid building folds with imbalanced class distributions (for binary or multiclass classification tasks).
GroupKFold
K-fold iterator variant with non-overlapping groups.
RepeatedKFold
Repeats K-Fold n times. Notes The first n_samples % n_splits folds have size n_samples // n_splits + 1, other folds have size n_samples // n_splits, where n_samples is the number of samples. 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 >>> import numpy as np
>>> from sklearn.model_selection import KFold
>>> X = np.array([[1, 2], [3, 4], [1, 2], [3, 4]])
>>> y = np.array([1, 2, 3, 4])
>>> kf = KFold(n_splits=2)
>>> kf.get_n_splits(X)
2
>>> print(kf)
KFold(n_splits=2, random_state=None, shuffle=False)
>>> for train_index, test_index in kf.split(X):
... 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]
TRAIN: [2 3] TEST: [0 1]
TRAIN: [0 1] TEST: [2 3]
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,), default=None
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.modules.generated.sklearn.model_selection.kfold#sklearn.model_selection.KFold |
sklearn.model_selection.KFold
class sklearn.model_selection.KFold(n_splits=5, *, shuffle=False, random_state=None) [source]
K-Folds cross-validator Provides train/test indices to split data in train/test sets. Split dataset into k consecutive folds (without shuffling by default). Each fold is then used once as a validation while the k - 1 remaining folds form the training set. 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.
shufflebool, default=False
Whether to shuffle the data before splitting into batches. Note that the samples within each split will not be shuffled.
random_stateint, RandomState instance or None, default=None
When shuffle is True, random_state affects the ordering of the indices, which controls the randomness of each fold. Otherwise, this parameter has no effect. Pass an int for reproducible output across multiple function calls. See Glossary. See also
StratifiedKFold
Takes group information into account to avoid building folds with imbalanced class distributions (for binary or multiclass classification tasks).
GroupKFold
K-fold iterator variant with non-overlapping groups.
RepeatedKFold
Repeats K-Fold n times. Notes The first n_samples % n_splits folds have size n_samples // n_splits + 1, other folds have size n_samples // n_splits, where n_samples is the number of samples. 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 >>> import numpy as np
>>> from sklearn.model_selection import KFold
>>> X = np.array([[1, 2], [3, 4], [1, 2], [3, 4]])
>>> y = np.array([1, 2, 3, 4])
>>> kf = KFold(n_splits=2)
>>> kf.get_n_splits(X)
2
>>> print(kf)
KFold(n_splits=2, random_state=None, shuffle=False)
>>> for train_index, test_index in kf.split(X):
... 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]
TRAIN: [2 3] TEST: [0 1]
TRAIN: [0 1] TEST: [2 3]
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,), default=None
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.KFold
Feature agglomeration vs. univariate selection
Gradient Boosting Out-of-Bag estimates
Nested versus non-nested cross-validation
Visualizing cross-validation behavior in scikit-learn
Cross-validation on diabetes Dataset Exercise | sklearn.modules.generated.sklearn.model_selection.kfold |
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. | sklearn.modules.generated.sklearn.model_selection.kfold#sklearn.model_selection.KFold.get_n_splits |
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,), default=None
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.modules.generated.sklearn.model_selection.kfold#sklearn.model_selection.KFold.split |
sklearn.model_selection.learning_curve(estimator, X, y, *, groups=None, train_sizes=array([0.1, 0.33, 0.55, 0.78, 1.0]), cv=None, scoring=None, exploit_incremental_learning=False, n_jobs=None, pre_dispatch='all', verbose=0, shuffle=False, random_state=None, error_score=nan, return_times=False, fit_params=None) [source]
Learning curve. Determines cross-validated training and test scores for different training set sizes. A cross-validation generator splits the whole dataset k times in training and test data. Subsets of the training set with varying sizes will be used to train the estimator and a score for each training subset size and the test set will be computed. Afterwards, the scores will be averaged over all k runs for each training subset size. Read more in the User Guide. Parameters
estimatorobject type that implements the “fit” and “predict” methods
An object of that type which is cloned for each validation.
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,) or (n_samples, n_outputs)
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).
train_sizesarray-like of shape (n_ticks,), default=np.linspace(0.1, 1.0, 5)
Relative or absolute numbers of training examples that will be used to generate the learning curve. If the dtype is float, it is regarded as a fraction of the maximum size of the training set (that is determined by the selected validation method), i.e. it has to be within (0, 1]. Otherwise it is interpreted as absolute sizes of the training sets. Note that for classification the number of samples usually have to be big enough to contain at least one sample from each class.
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, int, to specify the number of folds in a (Stratified)KFold,
CV splitter, An iterable yielding (train, test) splits as arrays of indices. For int/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.
scoringstr or callable, default=None
A str (see model evaluation documentation) or a scorer callable object / function with signature scorer(estimator, X, y).
exploit_incremental_learningbool, default=False
If the estimator supports incremental learning, this will be used to speed up fitting for different training set sizes.
n_jobsint, default=None
Number of jobs to run in parallel. Training the estimator and computing the score are parallelized over the different training and test sets. None means 1 unless in a joblib.parallel_backend context. -1 means using all processors. See Glossary for more details.
pre_dispatchint or str, default=’all’
Number of predispatched jobs for parallel execution (default is all). The option can reduce the allocated memory. The str can be an expression like ‘2*n_jobs’.
verboseint, default=0
Controls the verbosity: the higher, the more messages.
shufflebool, default=False
Whether to shuffle training data before taking prefixes of it based on``train_sizes``.
random_stateint, RandomState instance or None, default=None
Used when shuffle is True. Pass an int for reproducible output across multiple function calls. See Glossary.
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. New in version 0.20.
return_timesbool, default=False
Whether to return the fit and score times.
fit_paramsdict, default=None
Parameters to pass to the fit method of the estimator. New in version 0.24. Returns
train_sizes_absarray of shape (n_unique_ticks,)
Numbers of training examples that has been used to generate the learning curve. Note that the number of ticks might be less than n_ticks because duplicate entries will be removed.
train_scoresarray of shape (n_ticks, n_cv_folds)
Scores on training sets.
test_scoresarray of shape (n_ticks, n_cv_folds)
Scores on test set.
fit_timesarray of shape (n_ticks, n_cv_folds)
Times spent for fitting in seconds. Only present if return_times is True.
score_timesarray of shape (n_ticks, n_cv_folds)
Times spent for scoring in seconds. Only present if return_times is True. Notes See examples/model_selection/plot_learning_curve.py | sklearn.modules.generated.sklearn.model_selection.learning_curve#sklearn.model_selection.learning_curve |
sklearn.model_selection.LeaveOneGroupOut
class sklearn.model_selection.LeaveOneGroupOut [source]
Leave One Group Out cross-validator Provides 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. Read more in the User Guide. Examples >>> import numpy as np
>>> from sklearn.model_selection import LeaveOneGroupOut
>>> X = np.array([[1, 2], [3, 4], [5, 6], [7, 8]])
>>> y = np.array([1, 2, 1, 2])
>>> groups = np.array([1, 1, 2, 2])
>>> logo = LeaveOneGroupOut()
>>> logo.get_n_splits(X, y, groups)
2
>>> logo.get_n_splits(groups=groups) # 'groups' is always required
2
>>> print(logo)
LeaveOneGroupOut()
>>> for train_index, test_index in logo.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: [2 3] TEST: [0 1]
[[5 6]
[7 8]] [[1 2]
[3 4]] [1 2] [1 2]
TRAIN: [0 1] TEST: [2 3]
[[1 2]
[3 4]] [[5 6]
[7 8]] [1 2] [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.
groupsarray-like of shape (n_samples,)
Group labels for the samples used while splitting the dataset into train/test set. This ‘groups’ parameter must always be specified to calculate the number of splits, though the other parameters can be omitted. 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.modules.generated.sklearn.model_selection.leaveonegroupout |
class sklearn.model_selection.LeaveOneGroupOut [source]
Leave One Group Out cross-validator Provides 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. Read more in the User Guide. Examples >>> import numpy as np
>>> from sklearn.model_selection import LeaveOneGroupOut
>>> X = np.array([[1, 2], [3, 4], [5, 6], [7, 8]])
>>> y = np.array([1, 2, 1, 2])
>>> groups = np.array([1, 1, 2, 2])
>>> logo = LeaveOneGroupOut()
>>> logo.get_n_splits(X, y, groups)
2
>>> logo.get_n_splits(groups=groups) # 'groups' is always required
2
>>> print(logo)
LeaveOneGroupOut()
>>> for train_index, test_index in logo.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: [2 3] TEST: [0 1]
[[5 6]
[7 8]] [[1 2]
[3 4]] [1 2] [1 2]
TRAIN: [0 1] TEST: [2 3]
[[1 2]
[3 4]] [[5 6]
[7 8]] [1 2] [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.
groupsarray-like of shape (n_samples,)
Group labels for the samples used while splitting the dataset into train/test set. This ‘groups’ parameter must always be specified to calculate the number of splits, though the other parameters can be omitted. 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.modules.generated.sklearn.model_selection.leaveonegroupout#sklearn.model_selection.LeaveOneGroupOut |
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.
groupsarray-like of shape (n_samples,)
Group labels for the samples used while splitting the dataset into train/test set. This ‘groups’ parameter must always be specified to calculate the number of splits, though the other parameters can be omitted. Returns
n_splitsint
Returns the number of splitting iterations in the cross-validator. | sklearn.modules.generated.sklearn.model_selection.leaveonegroupout#sklearn.model_selection.LeaveOneGroupOut.get_n_splits |
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.modules.generated.sklearn.model_selection.leaveonegroupout#sklearn.model_selection.LeaveOneGroupOut.split |
class sklearn.model_selection.LeaveOneOut [source]
Leave-One-Out cross-validator Provides train/test indices to split data in train/test sets. Each sample is used once as a test set (singleton) while the remaining samples form the training set. Note: LeaveOneOut() is equivalent to KFold(n_splits=n) and LeavePOut(p=1) where n is the number of samples. Due to the high number of test sets (which is the same as the number of samples) this cross-validation method can be very costly. For large datasets one should favor KFold, ShuffleSplit or StratifiedKFold. Read more in the User Guide. See also
LeaveOneGroupOut
For splitting the data according to explicit, domain-specific stratification of the dataset.
GroupKFold
K-fold iterator variant with non-overlapping groups. Examples >>> import numpy as np
>>> from sklearn.model_selection import LeaveOneOut
>>> X = np.array([[1, 2], [3, 4]])
>>> y = np.array([1, 2])
>>> loo = LeaveOneOut()
>>> loo.get_n_splits(X)
2
>>> print(loo)
LeaveOneOut()
>>> for train_index, test_index in loo.split(X):
... 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: [1] TEST: [0]
[[3 4]] [[1 2]] [2] [1]
TRAIN: [0] TEST: [1]
[[1 2]] [[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, y=None, groups=None) [source]
Returns the number of splitting iterations in the cross-validator 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.
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,)
The target variable for supervised learning problems.
groupsarray-like of shape (n_samples,), default=None
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.modules.generated.sklearn.model_selection.leaveoneout#sklearn.model_selection.LeaveOneOut |
sklearn.model_selection.LeaveOneOut
class sklearn.model_selection.LeaveOneOut [source]
Leave-One-Out cross-validator Provides train/test indices to split data in train/test sets. Each sample is used once as a test set (singleton) while the remaining samples form the training set. Note: LeaveOneOut() is equivalent to KFold(n_splits=n) and LeavePOut(p=1) where n is the number of samples. Due to the high number of test sets (which is the same as the number of samples) this cross-validation method can be very costly. For large datasets one should favor KFold, ShuffleSplit or StratifiedKFold. Read more in the User Guide. See also
LeaveOneGroupOut
For splitting the data according to explicit, domain-specific stratification of the dataset.
GroupKFold
K-fold iterator variant with non-overlapping groups. Examples >>> import numpy as np
>>> from sklearn.model_selection import LeaveOneOut
>>> X = np.array([[1, 2], [3, 4]])
>>> y = np.array([1, 2])
>>> loo = LeaveOneOut()
>>> loo.get_n_splits(X)
2
>>> print(loo)
LeaveOneOut()
>>> for train_index, test_index in loo.split(X):
... 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: [1] TEST: [0]
[[3 4]] [[1 2]] [2] [1]
TRAIN: [0] TEST: [1]
[[1 2]] [[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, y=None, groups=None) [source]
Returns the number of splitting iterations in the cross-validator 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.
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,)
The target variable for supervised learning problems.
groupsarray-like of shape (n_samples,), default=None
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.modules.generated.sklearn.model_selection.leaveoneout |
get_n_splits(X, y=None, groups=None) [source]
Returns the number of splitting iterations in the cross-validator 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.
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. | sklearn.modules.generated.sklearn.model_selection.leaveoneout#sklearn.model_selection.LeaveOneOut.get_n_splits |
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,)
The target variable for supervised learning problems.
groupsarray-like of shape (n_samples,), default=None
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.modules.generated.sklearn.model_selection.leaveoneout#sklearn.model_selection.LeaveOneOut.split |
class sklearn.model_selection.LeavePGroupsOut(n_groups) [source]
Leave P Group(s) Out cross-validator Provides 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 LeaveOneGroupOut is that the former builds the test sets with all the samples assigned to p different values of the groups while the latter uses samples all assigned the same groups. Read more in the User Guide. Parameters
n_groupsint
Number of groups (p) to leave out in the test split. See also
GroupKFold
K-fold iterator variant with non-overlapping groups. Examples >>> import numpy as np
>>> from sklearn.model_selection import LeavePGroupsOut
>>> X = np.array([[1, 2], [3, 4], [5, 6]])
>>> y = np.array([1, 2, 1])
>>> groups = np.array([1, 2, 3])
>>> lpgo = LeavePGroupsOut(n_groups=2)
>>> lpgo.get_n_splits(X, y, groups)
3
>>> lpgo.get_n_splits(groups=groups) # 'groups' is always required
3
>>> print(lpgo)
LeavePGroupsOut(n_groups=2)
>>> for train_index, test_index in lpgo.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: [2] TEST: [0 1]
[[5 6]] [[1 2]
[3 4]] [1] [1 2]
TRAIN: [1] TEST: [0 2]
[[3 4]] [[1 2]
[5 6]] [2] [1 1]
TRAIN: [0] TEST: [1 2]
[[1 2]] [[3 4]
[5 6]] [1] [2 1]
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.
groupsarray-like of shape (n_samples,)
Group labels for the samples used while splitting the dataset into train/test set. This ‘groups’ parameter must always be specified to calculate the number of splits, though the other parameters can be omitted. 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.modules.generated.sklearn.model_selection.leavepgroupsout#sklearn.model_selection.LeavePGroupsOut |
sklearn.model_selection.LeavePGroupsOut
class sklearn.model_selection.LeavePGroupsOut(n_groups) [source]
Leave P Group(s) Out cross-validator Provides 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 LeaveOneGroupOut is that the former builds the test sets with all the samples assigned to p different values of the groups while the latter uses samples all assigned the same groups. Read more in the User Guide. Parameters
n_groupsint
Number of groups (p) to leave out in the test split. See also
GroupKFold
K-fold iterator variant with non-overlapping groups. Examples >>> import numpy as np
>>> from sklearn.model_selection import LeavePGroupsOut
>>> X = np.array([[1, 2], [3, 4], [5, 6]])
>>> y = np.array([1, 2, 1])
>>> groups = np.array([1, 2, 3])
>>> lpgo = LeavePGroupsOut(n_groups=2)
>>> lpgo.get_n_splits(X, y, groups)
3
>>> lpgo.get_n_splits(groups=groups) # 'groups' is always required
3
>>> print(lpgo)
LeavePGroupsOut(n_groups=2)
>>> for train_index, test_index in lpgo.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: [2] TEST: [0 1]
[[5 6]] [[1 2]
[3 4]] [1] [1 2]
TRAIN: [1] TEST: [0 2]
[[3 4]] [[1 2]
[5 6]] [2] [1 1]
TRAIN: [0] TEST: [1 2]
[[1 2]] [[3 4]
[5 6]] [1] [2 1]
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.
groupsarray-like of shape (n_samples,)
Group labels for the samples used while splitting the dataset into train/test set. This ‘groups’ parameter must always be specified to calculate the number of splits, though the other parameters can be omitted. 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.modules.generated.sklearn.model_selection.leavepgroupsout |
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.
groupsarray-like of shape (n_samples,)
Group labels for the samples used while splitting the dataset into train/test set. This ‘groups’ parameter must always be specified to calculate the number of splits, though the other parameters can be omitted. Returns
n_splitsint
Returns the number of splitting iterations in the cross-validator. | sklearn.modules.generated.sklearn.model_selection.leavepgroupsout#sklearn.model_selection.LeavePGroupsOut.get_n_splits |
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.modules.generated.sklearn.model_selection.leavepgroupsout#sklearn.model_selection.LeavePGroupsOut.split |
class sklearn.model_selection.LeavePOut(p) [source]
Leave-P-Out cross-validator Provides train/test indices to split data in train/test sets. This results in testing on all distinct samples of size p, while the remaining n - p samples form the training set in each iteration. Note: LeavePOut(p) is NOT equivalent to KFold(n_splits=n_samples // p) which creates non-overlapping test sets. Due to the high number of iterations which grows combinatorically with the number of samples this cross-validation method can be very costly. For large datasets one should favor KFold, StratifiedKFold or ShuffleSplit. Read more in the User Guide. Parameters
pint
Size of the test sets. Must be strictly less than the number of samples. Examples >>> import numpy as np
>>> from sklearn.model_selection import LeavePOut
>>> X = np.array([[1, 2], [3, 4], [5, 6], [7, 8]])
>>> y = np.array([1, 2, 3, 4])
>>> lpo = LeavePOut(2)
>>> lpo.get_n_splits(X)
6
>>> print(lpo)
LeavePOut(p=2)
>>> for train_index, test_index in lpo.split(X):
... 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]
TRAIN: [2 3] TEST: [0 1]
TRAIN: [1 3] TEST: [0 2]
TRAIN: [1 2] TEST: [0 3]
TRAIN: [0 3] TEST: [1 2]
TRAIN: [0 2] TEST: [1 3]
TRAIN: [0 1] TEST: [2 3]
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, y=None, groups=None) [source]
Returns the number of splitting iterations in the cross-validator 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.
yobject
Always ignored, exists for compatibility.
groupsobject
Always ignored, exists for compatibility.
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,)
The target variable for supervised learning problems.
groupsarray-like of shape (n_samples,), default=None
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.modules.generated.sklearn.model_selection.leavepout#sklearn.model_selection.LeavePOut |
sklearn.model_selection.LeavePOut
class sklearn.model_selection.LeavePOut(p) [source]
Leave-P-Out cross-validator Provides train/test indices to split data in train/test sets. This results in testing on all distinct samples of size p, while the remaining n - p samples form the training set in each iteration. Note: LeavePOut(p) is NOT equivalent to KFold(n_splits=n_samples // p) which creates non-overlapping test sets. Due to the high number of iterations which grows combinatorically with the number of samples this cross-validation method can be very costly. For large datasets one should favor KFold, StratifiedKFold or ShuffleSplit. Read more in the User Guide. Parameters
pint
Size of the test sets. Must be strictly less than the number of samples. Examples >>> import numpy as np
>>> from sklearn.model_selection import LeavePOut
>>> X = np.array([[1, 2], [3, 4], [5, 6], [7, 8]])
>>> y = np.array([1, 2, 3, 4])
>>> lpo = LeavePOut(2)
>>> lpo.get_n_splits(X)
6
>>> print(lpo)
LeavePOut(p=2)
>>> for train_index, test_index in lpo.split(X):
... 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]
TRAIN: [2 3] TEST: [0 1]
TRAIN: [1 3] TEST: [0 2]
TRAIN: [1 2] TEST: [0 3]
TRAIN: [0 3] TEST: [1 2]
TRAIN: [0 2] TEST: [1 3]
TRAIN: [0 1] TEST: [2 3]
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, y=None, groups=None) [source]
Returns the number of splitting iterations in the cross-validator 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.
yobject
Always ignored, exists for compatibility.
groupsobject
Always ignored, exists for compatibility.
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,)
The target variable for supervised learning problems.
groupsarray-like of shape (n_samples,), default=None
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.modules.generated.sklearn.model_selection.leavepout |
get_n_splits(X, y=None, groups=None) [source]
Returns the number of splitting iterations in the cross-validator 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.
yobject
Always ignored, exists for compatibility.
groupsobject
Always ignored, exists for compatibility. | sklearn.modules.generated.sklearn.model_selection.leavepout#sklearn.model_selection.LeavePOut.get_n_splits |
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,)
The target variable for supervised learning problems.
groupsarray-like of shape (n_samples,), default=None
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.modules.generated.sklearn.model_selection.leavepout#sklearn.model_selection.LeavePOut.split |
class sklearn.model_selection.ParameterGrid(param_grid) [source]
Grid of parameters with a discrete number of values for each. Can be used to iterate over parameter value combinations with the Python built-in function iter. The order of the generated parameter combinations is deterministic. Read more in the User Guide. Parameters
param_griddict of str to sequence, or sequence of such
The parameter grid to explore, as a dictionary mapping estimator parameters to sequences of allowed values. An empty dict signifies default parameters. A sequence of dicts signifies a sequence of grids to search, and is useful to avoid exploring parameter combinations that make no sense or have no effect. See the examples below. See also
GridSearchCV
Uses ParameterGrid to perform a full parallelized parameter search. Examples >>> from sklearn.model_selection import ParameterGrid
>>> param_grid = {'a': [1, 2], 'b': [True, False]}
>>> list(ParameterGrid(param_grid)) == (
... [{'a': 1, 'b': True}, {'a': 1, 'b': False},
... {'a': 2, 'b': True}, {'a': 2, 'b': False}])
True
>>> grid = [{'kernel': ['linear']}, {'kernel': ['rbf'], 'gamma': [1, 10]}]
>>> list(ParameterGrid(grid)) == [{'kernel': 'linear'},
... {'kernel': 'rbf', 'gamma': 1},
... {'kernel': 'rbf', 'gamma': 10}]
True
>>> ParameterGrid(grid)[1] == {'kernel': 'rbf', 'gamma': 1}
True | sklearn.modules.generated.sklearn.model_selection.parametergrid#sklearn.model_selection.ParameterGrid |
sklearn.model_selection.ParameterGrid
class sklearn.model_selection.ParameterGrid(param_grid) [source]
Grid of parameters with a discrete number of values for each. Can be used to iterate over parameter value combinations with the Python built-in function iter. The order of the generated parameter combinations is deterministic. Read more in the User Guide. Parameters
param_griddict of str to sequence, or sequence of such
The parameter grid to explore, as a dictionary mapping estimator parameters to sequences of allowed values. An empty dict signifies default parameters. A sequence of dicts signifies a sequence of grids to search, and is useful to avoid exploring parameter combinations that make no sense or have no effect. See the examples below. See also
GridSearchCV
Uses ParameterGrid to perform a full parallelized parameter search. Examples >>> from sklearn.model_selection import ParameterGrid
>>> param_grid = {'a': [1, 2], 'b': [True, False]}
>>> list(ParameterGrid(param_grid)) == (
... [{'a': 1, 'b': True}, {'a': 1, 'b': False},
... {'a': 2, 'b': True}, {'a': 2, 'b': False}])
True
>>> grid = [{'kernel': ['linear']}, {'kernel': ['rbf'], 'gamma': [1, 10]}]
>>> list(ParameterGrid(grid)) == [{'kernel': 'linear'},
... {'kernel': 'rbf', 'gamma': 1},
... {'kernel': 'rbf', 'gamma': 10}]
True
>>> ParameterGrid(grid)[1] == {'kernel': 'rbf', 'gamma': 1}
True | sklearn.modules.generated.sklearn.model_selection.parametergrid |
class sklearn.model_selection.ParameterSampler(param_distributions, n_iter, *, random_state=None) [source]
Generator on parameters sampled from given distributions. Non-deterministic iterable over random candidate combinations for hyper- parameter search. If all parameters are presented as a list, sampling without replacement is performed. If at least one parameter is given as a distribution, sampling with replacement is used. It is highly recommended to use continuous distributions for continuous parameters. Read more in the User Guide. Parameters
param_distributionsdict
Dictionary with parameters names (str) as keys and distributions or lists of parameters to try. Distributions must provide a rvs method for sampling (such as those from scipy.stats.distributions). If a list is given, it is sampled uniformly. If a list of dicts is given, first a dict is sampled uniformly, and then a parameter is sampled using that dict as above.
n_iterint
Number of parameter settings that are produced.
random_stateint, RandomState instance or None, default=None
Pseudo random number generator state used for random uniform sampling from lists of possible values instead of scipy.stats distributions. Pass an int for reproducible output across multiple function calls. See Glossary. Returns
paramsdict of str to any
Yields dictionaries mapping each estimator parameter to as sampled value. Examples >>> from sklearn.model_selection import ParameterSampler
>>> from scipy.stats.distributions import expon
>>> import numpy as np
>>> rng = np.random.RandomState(0)
>>> param_grid = {'a':[1, 2], 'b': expon()}
>>> param_list = list(ParameterSampler(param_grid, n_iter=4,
... random_state=rng))
>>> rounded_list = [dict((k, round(v, 6)) for (k, v) in d.items())
... for d in param_list]
>>> rounded_list == [{'b': 0.89856, 'a': 1},
... {'b': 0.923223, 'a': 1},
... {'b': 1.878964, 'a': 2},
... {'b': 1.038159, 'a': 2}]
True | sklearn.modules.generated.sklearn.model_selection.parametersampler#sklearn.model_selection.ParameterSampler |
sklearn.model_selection.ParameterSampler
class sklearn.model_selection.ParameterSampler(param_distributions, n_iter, *, random_state=None) [source]
Generator on parameters sampled from given distributions. Non-deterministic iterable over random candidate combinations for hyper- parameter search. If all parameters are presented as a list, sampling without replacement is performed. If at least one parameter is given as a distribution, sampling with replacement is used. It is highly recommended to use continuous distributions for continuous parameters. Read more in the User Guide. Parameters
param_distributionsdict
Dictionary with parameters names (str) as keys and distributions or lists of parameters to try. Distributions must provide a rvs method for sampling (such as those from scipy.stats.distributions). If a list is given, it is sampled uniformly. If a list of dicts is given, first a dict is sampled uniformly, and then a parameter is sampled using that dict as above.
n_iterint
Number of parameter settings that are produced.
random_stateint, RandomState instance or None, default=None
Pseudo random number generator state used for random uniform sampling from lists of possible values instead of scipy.stats distributions. Pass an int for reproducible output across multiple function calls. See Glossary. Returns
paramsdict of str to any
Yields dictionaries mapping each estimator parameter to as sampled value. Examples >>> from sklearn.model_selection import ParameterSampler
>>> from scipy.stats.distributions import expon
>>> import numpy as np
>>> rng = np.random.RandomState(0)
>>> param_grid = {'a':[1, 2], 'b': expon()}
>>> param_list = list(ParameterSampler(param_grid, n_iter=4,
... random_state=rng))
>>> rounded_list = [dict((k, round(v, 6)) for (k, v) in d.items())
... for d in param_list]
>>> rounded_list == [{'b': 0.89856, 'a': 1},
... {'b': 0.923223, 'a': 1},
... {'b': 1.878964, 'a': 2},
... {'b': 1.038159, 'a': 2}]
True | sklearn.modules.generated.sklearn.model_selection.parametersampler |
sklearn.model_selection.permutation_test_score(estimator, X, y, *, groups=None, cv=None, n_permutations=100, n_jobs=None, random_state=0, verbose=0, scoring=None, fit_params=None) [source]
Evaluate the significance of a cross-validated score with permutations Permutes targets to generate ‘randomized data’ and compute the empirical p-value against the null hypothesis that features and targets are independent. The p-value represents the fraction of randomized data sets where the estimator performed as well or better than in the original data. A small p-value suggests that there is a real dependency between features and targets which has been used by the estimator to give good predictions. A large p-value may be due to lack of real dependency between features and targets or the estimator was not able to use the dependency to give good predictions. Read more in the User Guide. Parameters
estimatorestimator object implementing ‘fit’
The object to use to fit the data.
Xarray-like of shape at least 2D
The data to fit.
yarray-like of shape (n_samples,) or (n_samples, n_outputs) or None
The target variable to try to predict in the case of supervised learning.
groupsarray-like of shape (n_samples,), default=None
Labels to constrain permutation within groups, i.e. y values are permuted among samples with the same group identifier. When not specified, y values are permuted among all samples. When a grouped cross-validator is used, the group labels are also passed on to the split method of the cross-validator. The cross-validator uses them for grouping the samples while splitting the dataset into train/test set.
scoringstr or callable, default=None
A single str (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.
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, int, to specify the number of folds in a (Stratified)KFold,
CV splitter, An iterable yielding (train, test) splits as arrays of indices. For int/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.
n_permutationsint, default=100
Number of times to permute y.
n_jobsint, default=None
Number of jobs to run in parallel. Training the estimator and computing the cross-validated score are parallelized over the permutations. 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=0
Pass an int for reproducible output for permutation of y values among samples. See Glossary.
verboseint, default=0
The verbosity level.
fit_paramsdict, default=None
Parameters to pass to the fit method of the estimator. New in version 0.24. Returns
scorefloat
The true score without permuting targets.
permutation_scoresarray of shape (n_permutations,)
The scores obtained for each permutations.
pvaluefloat
The p-value, which approximates the probability that the score would be obtained by chance. This is calculated as: (C + 1) / (n_permutations + 1) Where C is the number of permutations whose score >= the true score. The best possible p-value is 1/(n_permutations + 1), the worst is 1.0. Notes This function implements Test 1 in: Ojala and Garriga. Permutation Tests for Studying Classifier Performance. The Journal of Machine Learning Research (2010) vol. 11 | sklearn.modules.generated.sklearn.model_selection.permutation_test_score#sklearn.model_selection.permutation_test_score |
class sklearn.model_selection.PredefinedSplit(test_fold) [source]
Predefined split cross-validator Provides train/test indices to split data into train/test sets using a predefined scheme specified by the user with the test_fold parameter. Read more in the User Guide. New in version 0.16. Parameters
test_foldarray-like of shape (n_samples,)
The entry test_fold[i] represents the index of the test set that sample i belongs to. It is possible to exclude sample i from any test set (i.e. include sample i in every training set) by setting test_fold[i] equal to -1. Examples >>> import numpy as np
>>> from sklearn.model_selection import PredefinedSplit
>>> X = np.array([[1, 2], [3, 4], [1, 2], [3, 4]])
>>> y = np.array([0, 0, 1, 1])
>>> test_fold = [0, 1, -1, 1]
>>> ps = PredefinedSplit(test_fold)
>>> ps.get_n_splits()
2
>>> print(ps)
PredefinedSplit(test_fold=array([ 0, 1, -1, 1]))
>>> for train_index, test_index in ps.split():
... 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]
TRAIN: [1 2 3] TEST: [0]
TRAIN: [0 2] TEST: [1 3]
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=None, y=None, groups=None) [source]
Generate indices to split data into training and test set. Parameters
Xobject
Always ignored, exists for compatibility.
yobject
Always ignored, exists for compatibility.
groupsobject
Always ignored, exists for compatibility. Yields
trainndarray
The training set indices for that split.
testndarray
The testing set indices for that split. | sklearn.modules.generated.sklearn.model_selection.predefinedsplit#sklearn.model_selection.PredefinedSplit |
sklearn.model_selection.PredefinedSplit
class sklearn.model_selection.PredefinedSplit(test_fold) [source]
Predefined split cross-validator Provides train/test indices to split data into train/test sets using a predefined scheme specified by the user with the test_fold parameter. Read more in the User Guide. New in version 0.16. Parameters
test_foldarray-like of shape (n_samples,)
The entry test_fold[i] represents the index of the test set that sample i belongs to. It is possible to exclude sample i from any test set (i.e. include sample i in every training set) by setting test_fold[i] equal to -1. Examples >>> import numpy as np
>>> from sklearn.model_selection import PredefinedSplit
>>> X = np.array([[1, 2], [3, 4], [1, 2], [3, 4]])
>>> y = np.array([0, 0, 1, 1])
>>> test_fold = [0, 1, -1, 1]
>>> ps = PredefinedSplit(test_fold)
>>> ps.get_n_splits()
2
>>> print(ps)
PredefinedSplit(test_fold=array([ 0, 1, -1, 1]))
>>> for train_index, test_index in ps.split():
... 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]
TRAIN: [1 2 3] TEST: [0]
TRAIN: [0 2] TEST: [1 3]
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=None, y=None, groups=None) [source]
Generate indices to split data into training and test set. Parameters
Xobject
Always ignored, exists for compatibility.
yobject
Always ignored, exists for compatibility.
groupsobject
Always ignored, exists for compatibility. Yields
trainndarray
The training set indices for that split.
testndarray
The testing set indices for that split. | sklearn.modules.generated.sklearn.model_selection.predefinedsplit |
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. | sklearn.modules.generated.sklearn.model_selection.predefinedsplit#sklearn.model_selection.PredefinedSplit.get_n_splits |
split(X=None, y=None, groups=None) [source]
Generate indices to split data into training and test set. Parameters
Xobject
Always ignored, exists for compatibility.
yobject
Always ignored, exists for compatibility.
groupsobject
Always ignored, exists for compatibility. Yields
trainndarray
The training set indices for that split.
testndarray
The testing set indices for that split. | sklearn.modules.generated.sklearn.model_selection.predefinedsplit#sklearn.model_selection.PredefinedSplit.split |
class sklearn.model_selection.RandomizedSearchCV(estimator, param_distributions, *, n_iter=10, scoring=None, n_jobs=None, refit=True, cv=None, verbose=0, pre_dispatch='2*n_jobs', random_state=None, error_score=nan, return_train_score=False) [source]
Randomized search on hyper parameters. RandomizedSearchCV implements a “fit” and a “score” method. It also implements “score_samples”, “predict”, “predict_proba”, “decision_function”, “transform” and “inverse_transform” if they are implemented in the estimator used. The parameters of the estimator used to apply these methods are optimized by cross-validated search over parameter settings. In contrast to GridSearchCV, not all parameter values are tried out, but rather a fixed number of parameter settings is sampled from the specified distributions. The number of parameter settings that are tried is given by n_iter. If all parameters are presented as a list, sampling without replacement is performed. If at least one parameter is given as a distribution, sampling with replacement is used. It is highly recommended to use continuous distributions for continuous parameters. Read more in the User Guide. New in version 0.14. Parameters
estimatorestimator object.
A object of that type is instantiated for each grid point. This is assumed to implement the scikit-learn estimator interface. Either estimator needs to provide a score function, or scoring must be passed.
param_distributionsdict or list of dicts
Dictionary with parameters names (str) as keys and distributions or lists of parameters to try. Distributions must provide a rvs method for sampling (such as those from scipy.stats.distributions). If a list is given, it is sampled uniformly. If a list of dicts is given, first a dict is sampled uniformly, and then a parameter is sampled using that dict as above.
n_iterint, default=10
Number of parameter settings that are sampled. n_iter trades off runtime vs quality of the solution.
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. If None, the estimator’s score method is used.
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=None
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 the cv_results. In that case, the best_estimator_ and best_params_ will be set according to the returned best_index_ while the best_score_ attribute will not be available. The refitted estimator is made available at the best_estimator_ attribute and permits using predict directly on this RandomizedSearchCV instance. Also for multiple metric evaluation, the attributes best_index_, best_score_ and best_params_ will only be available if refit is set and all of them will be determined w.r.t this specific scorer. See scoring parameter to know more about multiple metric evaluation. Changed in version 0.20: Support for callable added.
verboseint
Controls the verbosity: the higher, the more messages.
random_stateint, RandomState instance or None, default=None
Pseudo random number generator state used for random uniform sampling from lists of possible values instead of scipy.stats distributions. Pass an int for reproducible output across multiple function calls. See Glossary.
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 split0_test_score … rank_test_score
‘rbf’ 0.1 0.80 … 1
‘rbf’ 0.2 0.84 … 3
‘rbf’ 0.3 0.70 … 2 will be represented by a cv_results_ dict of: {
'param_kernel' : masked_array(data = ['rbf', 'rbf', 'rbf'],
mask = False),
'param_gamma' : masked_array(data = [0.1 0.2 0.3], mask = False),
'split0_test_score' : [0.80, 0.84, 0.70],
'split1_test_score' : [0.82, 0.50, 0.70],
'mean_test_score' : [0.81, 0.67, 0.70],
'std_test_score' : [0.01, 0.24, 0.00],
'rank_test_score' : [1, 3, 2],
'split0_train_score' : [0.80, 0.92, 0.70],
'split1_train_score' : [0.82, 0.55, 0.70],
'mean_train_score' : [0.81, 0.74, 0.70],
'std_train_score' : [0.01, 0.19, 0.00],
'mean_fit_time' : [0.73, 0.63, 0.43],
'std_fit_time' : [0.01, 0.02, 0.01],
'mean_score_time' : [0.01, 0.06, 0.04],
'std_score_time' : [0.00, 0.00, 0.00],
'params' : [{'kernel' : 'rbf', 'gamma' : 0.1}, ...],
}
NOTE The key 'params' is used to store a list of parameter settings dicts for all the parameter candidates. The mean_fit_time, std_fit_time, mean_score_time and std_score_time are all in seconds. For multi-metric evaluation, the scores for all the scorers are available in the cv_results_ dict at the keys ending with that scorer’s name ('_<scorer_name>') instead of '_score' shown above. (‘split0_test_precision’, ‘mean_train_precision’ etc.)
best_estimator_estimator
Estimator that was chosen by the search, i.e. estimator which gave highest score (or smallest loss if specified) on the left out data. Not available if refit=False. For multi-metric evaluation, this attribute is present only if refit is specified. See refit parameter for more information on allowed values.
best_score_float
Mean cross-validated score of the best_estimator. For multi-metric evaluation, this is not available if refit is False. See refit parameter for more information. 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 not available if refit is False. See refit parameter for more information.
best_index_int
The index (of the cv_results_ arrays) which corresponds to the best candidate parameter setting. The dict at search.cv_results_['params'][search.best_index_] gives the parameter setting for the best model, that gives the highest mean score (search.best_score_). For multi-metric evaluation, this is not available if refit is False. See refit parameter for more information.
scorer_function or a dict
Scorer function used on the held out data to choose the best parameters for the model. For multi-metric evaluation, this attribute holds the validated scoring dict which maps the scorer key to the scorer callable.
n_splits_int
The number of cross-validation splits (folds/iterations).
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
GridSearchCV
Does exhaustive search over a grid of parameters.
ParameterSampler
A generator over parameter settings, constructed from param_distributions. Notes The parameters selected are those that maximize the score of the held-out data, according to the scoring parameter. If n_jobs was set to a value higher than one, the data is copied for each parameter setting(and not n_jobs times). This is done for efficiency reasons if individual jobs take very little time, but may raise errors if the dataset is large and not enough memory is available. A workaround in this case is to set pre_dispatch. Then, the memory is copied only pre_dispatch many times. A reasonable value for pre_dispatch is 2 *
n_jobs. Examples >>> from sklearn.datasets import load_iris
>>> from sklearn.linear_model import LogisticRegression
>>> from sklearn.model_selection import RandomizedSearchCV
>>> from scipy.stats import uniform
>>> iris = load_iris()
>>> logistic = LogisticRegression(solver='saga', tol=1e-2, max_iter=200,
... random_state=0)
>>> distributions = dict(C=uniform(loc=0, scale=4),
... penalty=['l2', 'l1'])
>>> clf = RandomizedSearchCV(logistic, distributions, random_state=0)
>>> search = clf.fit(iris.data, iris.target)
>>> search.best_params_
{'C': 2..., 'penalty': 'l1'}
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. | sklearn.modules.generated.sklearn.model_selection.randomizedsearchcv#sklearn.model_selection.RandomizedSearchCV |
sklearn.model_selection.RandomizedSearchCV
class sklearn.model_selection.RandomizedSearchCV(estimator, param_distributions, *, n_iter=10, scoring=None, n_jobs=None, refit=True, cv=None, verbose=0, pre_dispatch='2*n_jobs', random_state=None, error_score=nan, return_train_score=False) [source]
Randomized search on hyper parameters. RandomizedSearchCV implements a “fit” and a “score” method. It also implements “score_samples”, “predict”, “predict_proba”, “decision_function”, “transform” and “inverse_transform” if they are implemented in the estimator used. The parameters of the estimator used to apply these methods are optimized by cross-validated search over parameter settings. In contrast to GridSearchCV, not all parameter values are tried out, but rather a fixed number of parameter settings is sampled from the specified distributions. The number of parameter settings that are tried is given by n_iter. If all parameters are presented as a list, sampling without replacement is performed. If at least one parameter is given as a distribution, sampling with replacement is used. It is highly recommended to use continuous distributions for continuous parameters. Read more in the User Guide. New in version 0.14. Parameters
estimatorestimator object.
A object of that type is instantiated for each grid point. This is assumed to implement the scikit-learn estimator interface. Either estimator needs to provide a score function, or scoring must be passed.
param_distributionsdict or list of dicts
Dictionary with parameters names (str) as keys and distributions or lists of parameters to try. Distributions must provide a rvs method for sampling (such as those from scipy.stats.distributions). If a list is given, it is sampled uniformly. If a list of dicts is given, first a dict is sampled uniformly, and then a parameter is sampled using that dict as above.
n_iterint, default=10
Number of parameter settings that are sampled. n_iter trades off runtime vs quality of the solution.
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. If None, the estimator’s score method is used.
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=None
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 the cv_results. In that case, the best_estimator_ and best_params_ will be set according to the returned best_index_ while the best_score_ attribute will not be available. The refitted estimator is made available at the best_estimator_ attribute and permits using predict directly on this RandomizedSearchCV instance. Also for multiple metric evaluation, the attributes best_index_, best_score_ and best_params_ will only be available if refit is set and all of them will be determined w.r.t this specific scorer. See scoring parameter to know more about multiple metric evaluation. Changed in version 0.20: Support for callable added.
verboseint
Controls the verbosity: the higher, the more messages.
random_stateint, RandomState instance or None, default=None
Pseudo random number generator state used for random uniform sampling from lists of possible values instead of scipy.stats distributions. Pass an int for reproducible output across multiple function calls. See Glossary.
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 split0_test_score … rank_test_score
‘rbf’ 0.1 0.80 … 1
‘rbf’ 0.2 0.84 … 3
‘rbf’ 0.3 0.70 … 2 will be represented by a cv_results_ dict of: {
'param_kernel' : masked_array(data = ['rbf', 'rbf', 'rbf'],
mask = False),
'param_gamma' : masked_array(data = [0.1 0.2 0.3], mask = False),
'split0_test_score' : [0.80, 0.84, 0.70],
'split1_test_score' : [0.82, 0.50, 0.70],
'mean_test_score' : [0.81, 0.67, 0.70],
'std_test_score' : [0.01, 0.24, 0.00],
'rank_test_score' : [1, 3, 2],
'split0_train_score' : [0.80, 0.92, 0.70],
'split1_train_score' : [0.82, 0.55, 0.70],
'mean_train_score' : [0.81, 0.74, 0.70],
'std_train_score' : [0.01, 0.19, 0.00],
'mean_fit_time' : [0.73, 0.63, 0.43],
'std_fit_time' : [0.01, 0.02, 0.01],
'mean_score_time' : [0.01, 0.06, 0.04],
'std_score_time' : [0.00, 0.00, 0.00],
'params' : [{'kernel' : 'rbf', 'gamma' : 0.1}, ...],
}
NOTE The key 'params' is used to store a list of parameter settings dicts for all the parameter candidates. The mean_fit_time, std_fit_time, mean_score_time and std_score_time are all in seconds. For multi-metric evaluation, the scores for all the scorers are available in the cv_results_ dict at the keys ending with that scorer’s name ('_<scorer_name>') instead of '_score' shown above. (‘split0_test_precision’, ‘mean_train_precision’ etc.)
best_estimator_estimator
Estimator that was chosen by the search, i.e. estimator which gave highest score (or smallest loss if specified) on the left out data. Not available if refit=False. For multi-metric evaluation, this attribute is present only if refit is specified. See refit parameter for more information on allowed values.
best_score_float
Mean cross-validated score of the best_estimator. For multi-metric evaluation, this is not available if refit is False. See refit parameter for more information. 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 not available if refit is False. See refit parameter for more information.
best_index_int
The index (of the cv_results_ arrays) which corresponds to the best candidate parameter setting. The dict at search.cv_results_['params'][search.best_index_] gives the parameter setting for the best model, that gives the highest mean score (search.best_score_). For multi-metric evaluation, this is not available if refit is False. See refit parameter for more information.
scorer_function or a dict
Scorer function used on the held out data to choose the best parameters for the model. For multi-metric evaluation, this attribute holds the validated scoring dict which maps the scorer key to the scorer callable.
n_splits_int
The number of cross-validation splits (folds/iterations).
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
GridSearchCV
Does exhaustive search over a grid of parameters.
ParameterSampler
A generator over parameter settings, constructed from param_distributions. Notes The parameters selected are those that maximize the score of the held-out data, according to the scoring parameter. If n_jobs was set to a value higher than one, the data is copied for each parameter setting(and not n_jobs times). This is done for efficiency reasons if individual jobs take very little time, but may raise errors if the dataset is large and not enough memory is available. A workaround in this case is to set pre_dispatch. Then, the memory is copied only pre_dispatch many times. A reasonable value for pre_dispatch is 2 *
n_jobs. Examples >>> from sklearn.datasets import load_iris
>>> from sklearn.linear_model import LogisticRegression
>>> from sklearn.model_selection import RandomizedSearchCV
>>> from scipy.stats import uniform
>>> iris = load_iris()
>>> logistic = LogisticRegression(solver='saga', tol=1e-2, max_iter=200,
... random_state=0)
>>> distributions = dict(C=uniform(loc=0, scale=4),
... penalty=['l2', 'l1'])
>>> clf = RandomizedSearchCV(logistic, distributions, random_state=0)
>>> search = clf.fit(iris.data, iris.target)
>>> search.best_params_
{'C': 2..., 'penalty': 'l1'}
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.RandomizedSearchCV
Release Highlights for scikit-learn 0.24
Comparing randomized search and grid search for hyperparameter estimation | sklearn.modules.generated.sklearn.model_selection.randomizedsearchcv |
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. | sklearn.modules.generated.sklearn.model_selection.randomizedsearchcv#sklearn.model_selection.RandomizedSearchCV.decision_function |
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 | sklearn.modules.generated.sklearn.model_selection.randomizedsearchcv#sklearn.model_selection.RandomizedSearchCV.fit |
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. | sklearn.modules.generated.sklearn.model_selection.randomizedsearchcv#sklearn.model_selection.RandomizedSearchCV.get_params |
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. | sklearn.modules.generated.sklearn.model_selection.randomizedsearchcv#sklearn.model_selection.RandomizedSearchCV.inverse_transform |
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. | sklearn.modules.generated.sklearn.model_selection.randomizedsearchcv#sklearn.model_selection.RandomizedSearchCV.predict |
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. | sklearn.modules.generated.sklearn.model_selection.randomizedsearchcv#sklearn.model_selection.RandomizedSearchCV.predict_log_proba |
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. | sklearn.modules.generated.sklearn.model_selection.randomizedsearchcv#sklearn.model_selection.RandomizedSearchCV.predict_proba |
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 | sklearn.modules.generated.sklearn.model_selection.randomizedsearchcv#sklearn.model_selection.RandomizedSearchCV.score |
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,) | sklearn.modules.generated.sklearn.model_selection.randomizedsearchcv#sklearn.model_selection.RandomizedSearchCV.score_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. | sklearn.modules.generated.sklearn.model_selection.randomizedsearchcv#sklearn.model_selection.RandomizedSearchCV.set_params |
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.modules.generated.sklearn.model_selection.randomizedsearchcv#sklearn.model_selection.RandomizedSearchCV.transform |
class sklearn.model_selection.RepeatedKFold(*, n_splits=5, n_repeats=10, random_state=None) [source]
Repeated K-Fold cross validator. Repeats K-Fold n times with different randomization in each repetition. Read more in the User Guide. Parameters
n_splitsint, default=5
Number of folds. Must be at least 2.
n_repeatsint, default=10
Number of times cross-validator needs to be repeated.
random_stateint, RandomState instance or None, default=None
Controls the randomness of each repeated cross-validation instance. Pass an int for reproducible output across multiple function calls. See Glossary. See also
RepeatedStratifiedKFold
Repeats Stratified K-Fold n times. 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 >>> import numpy as np
>>> from sklearn.model_selection import RepeatedKFold
>>> X = np.array([[1, 2], [3, 4], [1, 2], [3, 4]])
>>> y = np.array([0, 0, 1, 1])
>>> rkf = RepeatedKFold(n_splits=2, n_repeats=2, random_state=2652124)
>>> for train_index, test_index in rkf.split(X):
... 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]
...
TRAIN: [0 1] TEST: [2 3]
TRAIN: [2 3] TEST: [0 1]
TRAIN: [1 2] TEST: [0 3]
TRAIN: [0 3] TEST: [1 2]
Methods
get_n_splits([X, y, groups]) Returns the number of splitting iterations in the cross-validator
split(X[, y, groups]) Generates 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. np.zeros(n_samples) may be used as a placeholder.
yobject
Always ignored, exists for compatibility. np.zeros(n_samples) may be used as a placeholder.
groupsarray-like of shape (n_samples,), default=None
Group labels for the samples used while splitting the dataset into train/test set. Returns
n_splitsint
Returns the number of splitting iterations in the cross-validator.
split(X, y=None, groups=None) [source]
Generates 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,)
The target variable for supervised learning problems.
groupsarray-like of shape (n_samples,), default=None
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.modules.generated.sklearn.model_selection.repeatedkfold#sklearn.model_selection.RepeatedKFold |
sklearn.model_selection.RepeatedKFold
class sklearn.model_selection.RepeatedKFold(*, n_splits=5, n_repeats=10, random_state=None) [source]
Repeated K-Fold cross validator. Repeats K-Fold n times with different randomization in each repetition. Read more in the User Guide. Parameters
n_splitsint, default=5
Number of folds. Must be at least 2.
n_repeatsint, default=10
Number of times cross-validator needs to be repeated.
random_stateint, RandomState instance or None, default=None
Controls the randomness of each repeated cross-validation instance. Pass an int for reproducible output across multiple function calls. See Glossary. See also
RepeatedStratifiedKFold
Repeats Stratified K-Fold n times. 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 >>> import numpy as np
>>> from sklearn.model_selection import RepeatedKFold
>>> X = np.array([[1, 2], [3, 4], [1, 2], [3, 4]])
>>> y = np.array([0, 0, 1, 1])
>>> rkf = RepeatedKFold(n_splits=2, n_repeats=2, random_state=2652124)
>>> for train_index, test_index in rkf.split(X):
... 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]
...
TRAIN: [0 1] TEST: [2 3]
TRAIN: [2 3] TEST: [0 1]
TRAIN: [1 2] TEST: [0 3]
TRAIN: [0 3] TEST: [1 2]
Methods
get_n_splits([X, y, groups]) Returns the number of splitting iterations in the cross-validator
split(X[, y, groups]) Generates 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. np.zeros(n_samples) may be used as a placeholder.
yobject
Always ignored, exists for compatibility. np.zeros(n_samples) may be used as a placeholder.
groupsarray-like of shape (n_samples,), default=None
Group labels for the samples used while splitting the dataset into train/test set. Returns
n_splitsint
Returns the number of splitting iterations in the cross-validator.
split(X, y=None, groups=None) [source]
Generates 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,)
The target variable for supervised learning problems.
groupsarray-like of shape (n_samples,), default=None
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.RepeatedKFold
Common pitfalls in interpretation of coefficients of linear models | sklearn.modules.generated.sklearn.model_selection.repeatedkfold |
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. np.zeros(n_samples) may be used as a placeholder.
yobject
Always ignored, exists for compatibility. np.zeros(n_samples) may be used as a placeholder.
groupsarray-like of shape (n_samples,), default=None
Group labels for the samples used while splitting the dataset into train/test set. Returns
n_splitsint
Returns the number of splitting iterations in the cross-validator. | sklearn.modules.generated.sklearn.model_selection.repeatedkfold#sklearn.model_selection.RepeatedKFold.get_n_splits |
split(X, y=None, groups=None) [source]
Generates 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,)
The target variable for supervised learning problems.
groupsarray-like of shape (n_samples,), default=None
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.modules.generated.sklearn.model_selection.repeatedkfold#sklearn.model_selection.RepeatedKFold.split |
class sklearn.model_selection.RepeatedStratifiedKFold(*, n_splits=5, n_repeats=10, random_state=None) [source]
Repeated Stratified K-Fold cross validator. Repeats Stratified K-Fold n times with different randomization in each repetition. Read more in the User Guide. Parameters
n_splitsint, default=5
Number of folds. Must be at least 2.
n_repeatsint, default=10
Number of times cross-validator needs to be repeated.
random_stateint, RandomState instance or None, default=None
Controls the generation of the random states for each repetition. Pass an int for reproducible output across multiple function calls. See Glossary. See also
RepeatedKFold
Repeats K-Fold n times. 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 >>> import numpy as np
>>> from sklearn.model_selection import RepeatedStratifiedKFold
>>> X = np.array([[1, 2], [3, 4], [1, 2], [3, 4]])
>>> y = np.array([0, 0, 1, 1])
>>> rskf = RepeatedStratifiedKFold(n_splits=2, n_repeats=2,
... random_state=36851234)
>>> for train_index, test_index in rskf.split(X, y):
... 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]
...
TRAIN: [1 2] TEST: [0 3]
TRAIN: [0 3] TEST: [1 2]
TRAIN: [1 3] TEST: [0 2]
TRAIN: [0 2] TEST: [1 3]
Methods
get_n_splits([X, y, groups]) Returns the number of splitting iterations in the cross-validator
split(X[, y, groups]) Generates 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. np.zeros(n_samples) may be used as a placeholder.
yobject
Always ignored, exists for compatibility. np.zeros(n_samples) may be used as a placeholder.
groupsarray-like of shape (n_samples,), default=None
Group labels for the samples used while splitting the dataset into train/test set. Returns
n_splitsint
Returns the number of splitting iterations in the cross-validator.
split(X, y=None, groups=None) [source]
Generates 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,)
The target variable for supervised learning problems.
groupsarray-like of shape (n_samples,), default=None
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.modules.generated.sklearn.model_selection.repeatedstratifiedkfold#sklearn.model_selection.RepeatedStratifiedKFold |
sklearn.model_selection.RepeatedStratifiedKFold
class sklearn.model_selection.RepeatedStratifiedKFold(*, n_splits=5, n_repeats=10, random_state=None) [source]
Repeated Stratified K-Fold cross validator. Repeats Stratified K-Fold n times with different randomization in each repetition. Read more in the User Guide. Parameters
n_splitsint, default=5
Number of folds. Must be at least 2.
n_repeatsint, default=10
Number of times cross-validator needs to be repeated.
random_stateint, RandomState instance or None, default=None
Controls the generation of the random states for each repetition. Pass an int for reproducible output across multiple function calls. See Glossary. See also
RepeatedKFold
Repeats K-Fold n times. 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 >>> import numpy as np
>>> from sklearn.model_selection import RepeatedStratifiedKFold
>>> X = np.array([[1, 2], [3, 4], [1, 2], [3, 4]])
>>> y = np.array([0, 0, 1, 1])
>>> rskf = RepeatedStratifiedKFold(n_splits=2, n_repeats=2,
... random_state=36851234)
>>> for train_index, test_index in rskf.split(X, y):
... 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]
...
TRAIN: [1 2] TEST: [0 3]
TRAIN: [0 3] TEST: [1 2]
TRAIN: [1 3] TEST: [0 2]
TRAIN: [0 2] TEST: [1 3]
Methods
get_n_splits([X, y, groups]) Returns the number of splitting iterations in the cross-validator
split(X[, y, groups]) Generates 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. np.zeros(n_samples) may be used as a placeholder.
yobject
Always ignored, exists for compatibility. np.zeros(n_samples) may be used as a placeholder.
groupsarray-like of shape (n_samples,), default=None
Group labels for the samples used while splitting the dataset into train/test set. Returns
n_splitsint
Returns the number of splitting iterations in the cross-validator.
split(X, y=None, groups=None) [source]
Generates 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,)
The target variable for supervised learning problems.
groupsarray-like of shape (n_samples,), default=None
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.RepeatedStratifiedKFold
Statistical comparison of models using grid search | sklearn.modules.generated.sklearn.model_selection.repeatedstratifiedkfold |
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. np.zeros(n_samples) may be used as a placeholder.
yobject
Always ignored, exists for compatibility. np.zeros(n_samples) may be used as a placeholder.
groupsarray-like of shape (n_samples,), default=None
Group labels for the samples used while splitting the dataset into train/test set. Returns
n_splitsint
Returns the number of splitting iterations in the cross-validator. | sklearn.modules.generated.sklearn.model_selection.repeatedstratifiedkfold#sklearn.model_selection.RepeatedStratifiedKFold.get_n_splits |
split(X, y=None, groups=None) [source]
Generates 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,)
The target variable for supervised learning problems.
groupsarray-like of shape (n_samples,), default=None
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.modules.generated.sklearn.model_selection.repeatedstratifiedkfold#sklearn.model_selection.RepeatedStratifiedKFold.split |
class sklearn.model_selection.ShuffleSplit(n_splits=10, *, test_size=None, train_size=None, random_state=None) [source]
Random permutation cross-validator Yields indices to split data into training and test sets. Note: contrary to other cross-validation strategies, random splits do not guarantee that all folds will be different, although this is still very likely for sizeable datasets. Read more in the User Guide. Parameters
n_splitsint, default=10
Number of re-shuffling & splitting iterations.
test_sizefloat or int, default=None
If float, should be between 0.0 and 1.0 and represent the proportion of the dataset to include in the test split. If int, represents the absolute number of test samples. If None, the value is set to the complement of the train size. If train_size is also None, it will be set to 0.1.
train_sizefloat or int, default=None
If float, should be between 0.0 and 1.0 and represent the proportion of the dataset to include in the train split. If int, represents the absolute number of train samples. 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 ShuffleSplit
>>> X = np.array([[1, 2], [3, 4], [5, 6], [7, 8], [3, 4], [5, 6]])
>>> y = np.array([1, 2, 1, 2, 1, 2])
>>> rs = ShuffleSplit(n_splits=5, test_size=.25, random_state=0)
>>> rs.get_n_splits(X)
5
>>> print(rs)
ShuffleSplit(n_splits=5, random_state=0, test_size=0.25, train_size=None)
>>> for train_index, test_index in rs.split(X):
... print("TRAIN:", train_index, "TEST:", test_index)
TRAIN: [1 3 0 4] TEST: [5 2]
TRAIN: [4 0 2 5] TEST: [1 3]
TRAIN: [1 2 4 0] TEST: [3 5]
TRAIN: [3 4 1 0] TEST: [5 2]
TRAIN: [3 5 1 0] TEST: [2 4]
>>> rs = ShuffleSplit(n_splits=5, train_size=0.5, test_size=.25,
... random_state=0)
>>> for train_index, test_index in rs.split(X):
... print("TRAIN:", train_index, "TEST:", test_index)
TRAIN: [1 3 0] TEST: [5 2]
TRAIN: [4 0 2] TEST: [1 3]
TRAIN: [1 2 4] TEST: [3 5]
TRAIN: [3 4 1] TEST: [5 2]
TRAIN: [3 5 1] TEST: [2 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,)
The target variable for supervised learning problems.
groupsarray-like of shape (n_samples,), default=None
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.modules.generated.sklearn.model_selection.shufflesplit#sklearn.model_selection.ShuffleSplit |
sklearn.model_selection.ShuffleSplit
class sklearn.model_selection.ShuffleSplit(n_splits=10, *, test_size=None, train_size=None, random_state=None) [source]
Random permutation cross-validator Yields indices to split data into training and test sets. Note: contrary to other cross-validation strategies, random splits do not guarantee that all folds will be different, although this is still very likely for sizeable datasets. Read more in the User Guide. Parameters
n_splitsint, default=10
Number of re-shuffling & splitting iterations.
test_sizefloat or int, default=None
If float, should be between 0.0 and 1.0 and represent the proportion of the dataset to include in the test split. If int, represents the absolute number of test samples. If None, the value is set to the complement of the train size. If train_size is also None, it will be set to 0.1.
train_sizefloat or int, default=None
If float, should be between 0.0 and 1.0 and represent the proportion of the dataset to include in the train split. If int, represents the absolute number of train samples. 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 ShuffleSplit
>>> X = np.array([[1, 2], [3, 4], [5, 6], [7, 8], [3, 4], [5, 6]])
>>> y = np.array([1, 2, 1, 2, 1, 2])
>>> rs = ShuffleSplit(n_splits=5, test_size=.25, random_state=0)
>>> rs.get_n_splits(X)
5
>>> print(rs)
ShuffleSplit(n_splits=5, random_state=0, test_size=0.25, train_size=None)
>>> for train_index, test_index in rs.split(X):
... print("TRAIN:", train_index, "TEST:", test_index)
TRAIN: [1 3 0 4] TEST: [5 2]
TRAIN: [4 0 2 5] TEST: [1 3]
TRAIN: [1 2 4 0] TEST: [3 5]
TRAIN: [3 4 1 0] TEST: [5 2]
TRAIN: [3 5 1 0] TEST: [2 4]
>>> rs = ShuffleSplit(n_splits=5, train_size=0.5, test_size=.25,
... random_state=0)
>>> for train_index, test_index in rs.split(X):
... print("TRAIN:", train_index, "TEST:", test_index)
TRAIN: [1 3 0] TEST: [5 2]
TRAIN: [4 0 2] TEST: [1 3]
TRAIN: [1 2 4] TEST: [3 5]
TRAIN: [3 4 1] TEST: [5 2]
TRAIN: [3 5 1] TEST: [2 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,)
The target variable for supervised learning problems.
groupsarray-like of shape (n_samples,), default=None
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.ShuffleSplit
Visualizing cross-validation behavior in scikit-learn
Plotting Learning Curves
Scaling the regularization parameter for SVCs | sklearn.modules.generated.sklearn.model_selection.shufflesplit |
Subsets and Splits
No community queries yet
The top public SQL queries from the community will appear here once available.