Model Selection¶
GridSearchCV¶
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. :param estimator: This is assumed to implement the scikit-learn estimator interface.
Either estimator needs to provide ascore
function, orscoring
must be passed.
type estimator: | estimator object |
---|---|
param param_grid: | |
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. |
|
type param_grid: | |
dict or list of dictionaries |
|
param scoring: | 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 scoring_parameter); - a callable (see scoring) that returns a single value. If scoring represents multiple scores, one can use: - a list or tuple of unique strings; - a callable returning a dictionary where the keys are the metric
|
type scoring: | str, callable, list, tuple or dict, default=None |
param n_jobs: | Number of jobs to run in parallel.
|
type n_jobs: | int, default=None |
param refit: | 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, |
type refit: | bool, str, or callable, default=True |
param cv: | 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 |
type cv: | int, cross-validation generator or an iterable, default=None |
param verbose: | Controls the verbosity: the higher, the more messages. - >1 : the computation time for each fold and parameter candidate is
|
type verbose: | int |
param pre_dispatch: | |
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:
|
|
type pre_dispatch: | |
int, or str, default=’2*n_jobs’ |
|
param error_score: | |
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. |
|
type error_score: | |
‘raise’ or numeric, default=np.nan |
|
param return_train_score: | |
If |
|
type return_train_score: | |
bool, default=False |
-
cca_zoo.model_selection.GridSearchCV.
cv_results_
¶ A dict with keys as column headers and values as columns, that can be imported into a pandas
DataFrame
will be represented by acv_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. Themean_fit_time
,std_fit_time
,mean_score_time
andstd_score_time
are all in seconds. For multi-metric evaluation, the scores for all the scorers are available in thecv_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.)Type: dict of numpy (masked) ndarrays
-
cca_zoo.model_selection.GridSearchCV.
best_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
. Seerefit
parameter for more information on allowed values.Type: estimator
-
cca_zoo.model_selection.GridSearchCV.
best_score_
¶ 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 ifrefit
is a function.Type: float
-
cca_zoo.model_selection.GridSearchCV.
best_params_
¶ Parameter setting that gave the best results on the hold out data. For multi-metric evaluation, this is present only if
refit
is specified.Type: dict
-
cca_zoo.model_selection.GridSearchCV.
best_index_
¶ The index (of the
cv_results_
arrays) which corresponds to the best candidate parameter setting. The dict atsearch.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 ifrefit
is specified.Type: int
-
cca_zoo.model_selection.GridSearchCV.
scorer_
¶ 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.Type: function or a dict
-
cca_zoo.model_selection.GridSearchCV.
n_splits_
¶ The number of cross-validation splits (folds/iterations).
Type: int
-
cca_zoo.model_selection.GridSearchCV.
refit_time_
¶ Seconds used for refitting the best model on the whole dataset. This is present only if
refit
is not False.Type: float
-
cca_zoo.model_selection.GridSearchCV.
multimetric_
¶ Whether or not the scorers compute several metrics.
Type: bool
-
cca_zoo.model_selection.GridSearchCV.
classes_
¶ The classes labels. This is present only if
refit
is specified and the underlying estimator is a classifier.Type: ndarray of shape (n_classes,)
-
cca_zoo.model_selection.GridSearchCV.
n_features_in_
¶ Number of features seen during fit. Only defined if best_estimator_ is defined (see the documentation for the refit parameter for more details) and that best_estimator_ exposes n_features_in_ when fit.
Type: int
-
cca_zoo.model_selection.GridSearchCV.
feature_names_in_
¶ Names of features seen during fit. Only defined if best_estimator_ is defined (see the documentation for the refit parameter for more details) and that best_estimator_ exposes feature_names_in_ when fit.
Type: ndarray of shape (n_features_in_,)
Examples
>>> from cca_zoo.model_selection import GridSearchCV
>>> from cca_zoo.models import MCCA
>>> X1 = [[0, 0, 1], [1, 0, 0], [2, 2, 2], [3, 5, 4]]
>>> X2 = [[0.1, -0.2], [0.9, 1.1], [6.2, 5.9], [11.9, 12.3]]
>>> X3 = [[0, 1, 0], [1, 9, 0], [4, 3, 3,], [12, 8, 10]]
>>> model = MCCA()
>>> params = {'c': [[0.1, 0.2], [0.3, 0.4], 0.1]}
>>> GridSearchCV(model,param_grid=params, cv=3).fit([X1,X2,X3]).best_estimator_.c
[0.1, 0.3, 0.1]
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.
-
cca_zoo.model_selection.GridSearchCV.
classes_
Class labels.
Only available when refit=True and the estimator is a classifier.
-
cca_zoo.model_selection.GridSearchCV.
n_features_in_
Number of features seen during fit.
Only available when refit=True.
RandomizedSearchCV¶
Randomized search on hyper parameters. RandomizedSearchCV implements a “fit” and a “score” method. It also implements “score_samples”, “predict”, “predict_proba”, “decision_function”, “transform” and “inverse_transform” if they are implemented in the estimator used. The parameters of the estimator used to apply these methods are optimized by cross-validated search over parameter settings. In contrast to GridSearchCV, not all parameter values are tried out, but rather a fixed number of parameter settings is sampled from the specified distributions. The number of parameter settings that are tried is given by n_iter. If all parameters are presented as a list, sampling without replacement is performed. If at least one parameter is given as a distribution, sampling with replacement is used. It is highly recommended to use continuous distributions for continuous parameters.
param estimator: | |
---|---|
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 |
|
type estimator: | estimator object. |
param param_distributions: | |
Dictionary with parameters names (str) as keys and distributions
or lists of parameters to try. Distributions must provide a |
|
type param_distributions: | |
dict or list of dicts |
|
param n_iter: | Number of parameter settings that are sampled. n_iter trades off runtime vs quality of the solution. |
type n_iter: | int, default=10 |
param scoring: | 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 scoring_parameter); - a callable (see scoring) that returns a single value. If scoring represents multiple scores, one can use: - a list or tuple of unique strings; - a callable returning a dictionary where the keys are the metric
If None, the estimator’s score method is used. |
type scoring: | str, callable, list, tuple or dict, default=None |
param n_jobs: | Number of jobs to run in parallel.
|
type n_jobs: | int, default=None |
param refit: | 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, |
type refit: | bool, str, or callable, default=True |
param cv: | 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 |
type cv: | int, cross-validation generator or an iterable, default=None |
param verbose: | Controls the verbosity: the higher, the more messages. |
type verbose: | int |
param pre_dispatch: | |
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:
|
|
type pre_dispatch: | |
int, or str, default=’2*n_jobs’ |
|
param random_state: | |
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. |
|
type random_state: | |
int, RandomState instance or None, default=None |
|
param error_score: | |
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. |
|
type error_score: | |
‘raise’ or numeric, default=np.nan |
|
param return_train_score: | |
If |
|
type return_train_score: | |
bool, default=False |
-
cca_zoo.model_selection.RandomizedSearchCV.
cv_results_
¶ A dict with keys as column headers and values as columns, that can be imported into a pandas
DataFrame
. will be represented by acv_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. Themean_fit_time
,std_fit_time
,mean_score_time
andstd_score_time
are all in seconds. For multi-metric evaluation, the scores for all the scorers are available in thecv_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.)Type: dict of numpy (masked) ndarrays
-
cca_zoo.model_selection.RandomizedSearchCV.
best_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 ifrefit
is specified. Seerefit
parameter for more information on allowed values.Type: estimator
-
cca_zoo.model_selection.RandomizedSearchCV.
best_score_
¶ Mean cross-validated score of the best_estimator. For multi-metric evaluation, this is not available if
refit
isFalse
. Seerefit
parameter for more information. This attribute is not available ifrefit
is a function.Type: float
-
cca_zoo.model_selection.RandomizedSearchCV.
best_params_
¶ Parameter setting that gave the best results on the hold out data. For multi-metric evaluation, this is not available if
refit
isFalse
. Seerefit
parameter for more information.Type: dict
-
cca_zoo.model_selection.RandomizedSearchCV.
best_index_
¶ The index (of the
cv_results_
arrays) which corresponds to the best candidate parameter setting. The dict atsearch.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 ifrefit
isFalse
. Seerefit
parameter for more information.Type: int
-
cca_zoo.model_selection.RandomizedSearchCV.
scorer_
¶ 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.Type: function or a dict
-
cca_zoo.model_selection.RandomizedSearchCV.
n_splits_
¶ The number of cross-validation splits (folds/iterations).
Type: int
-
cca_zoo.model_selection.RandomizedSearchCV.
refit_time_
¶ Seconds used for refitting the best model on the whole dataset. This is present only if
refit
is not False.Type: float
-
cca_zoo.model_selection.RandomizedSearchCV.
multimetric_
¶ Whether or not the scorers compute several metrics.
Type: bool
-
cca_zoo.model_selection.RandomizedSearchCV.
classes_
¶ The classes labels. This is present only if
refit
is specified and the underlying estimator is a classifier.Type: ndarray of shape (n_classes,)
-
cca_zoo.model_selection.RandomizedSearchCV.
n_features_in_
¶ Number of features seen during fit. Only defined if best_estimator_ is defined and that best_estimator_ exposes n_features_in_ when fit.
Type: int
-
cca_zoo.model_selection.RandomizedSearchCV.
feature_names_in_
¶ Names of features seen during fit. Only defined if best_estimator_ is defined and that best_estimator_ exposes feature_names_in_ when fit.
Type: ndarray of shape (n_features_in_,)
Examples
>>> from cca_zoo.model_selection import RandomizedSearchCV
>>> from cca_zoo.models import MCCA
>>> from sklearn.utils.fixes import loguniform
>>> X1 = [[0, 0, 1], [1, 0, 0], [2, 2, 2], [3, 5, 4]]
>>> X2 = [[0.1, -0.2], [0.9, 1.1], [6.2, 5.9], [11.9, 12.3]]
>>> X3 = [[0, 1, 0], [1, 9, 0], [4, 3, 3,], [12, 8, 10]]
>>> model = MCCA()
>>> params = {'c': [loguniform(1e-4, 1e0), loguniform(1e-4, 1e0), [0.1]]}
>>> def scorer(estimator, X):
... scores = estimator.score(X)
... return np.mean(scores)
>>> RandomizedSearchCV(model,param_distributions=params, cv=3, scoring=scorer,n_iter=10).fit([X1,X2,X3]).n_iter
10
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.
-
cca_zoo.model_selection.RandomizedSearchCV.
classes_
Class labels.
Only available when refit=True and the estimator is a classifier.
-
cca_zoo.model_selection.RandomizedSearchCV.
n_features_in_
Number of features seen during fit.
Only available when refit=True.