GenSVMGridSearchCV¶

class gensvm.gridsearch.GenSVMGridSearchCV(param_grid='tiny', scoring=None, iid=True, cv=None, refit=True, verbose=0, return_train_score=True)

GenSVM cross validated grid search

This class implements efficient GenSVM grid search with cross validation. One of the strong features of GenSVM is that seeding the classifier properly can greatly reduce total training time. This class ensures that the grid search is done in the most efficient way possible.

The implementation of this class is based on the GridSearchCV class in scikit-learn. The documentation of the various parameters is therefore mostly the same. This is done to provide the user with a familiar and easy-to-use interface to doing a grid search with GenSVM. A separate class was needed to benefit from the fast low-level C implementation of grid search in the GenSVM library.

Parameters:

param_grid (string, dict, or list of dicts) –
If a string, it must be either ‘tiny’, ‘small’, or ‘full’ to load the predefined parameter grids (see the functions load_grid_tiny(), load_grid_small(), and load_grid_full()).

Otherwise, a dictionary of parameter names (strings) as keys and lists of parameter settings to evaluate as values, or a list of such dicts. The GenSVM model will be evaluated at all combinations of the parameters.
scoring (string, callable, list/tuple, dict or None) –
A single string (see The scoring parameter: defining model evaluation rules) or a callable (see Defining your scoring strategy from metric functions) to evaluate the predictions on the test set.

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

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

If None, the accuracy_score is used.
iid (boolean, default=True) – If True, the data is assumed to be identically distributed across the folds, and the loss minimized is the total loss per sample and not the mean loss across the folds.
cv (int, cross-validation generator or an iterable, optional) –
Determines the cross-validation splitting strategy. Possible inputs for cv are:
- None, to use the default 5-fold cross validation,
- integer, to specify the number of folds in a (Stratified)KFold,
- An object to be used as a cross-validation generator.
- An iterable yielding train, test splits.
For integer/None inputs, StratifiedKFold is used. In all other cases, KFold is used.

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

NOTE: At the moment, the ShuffleSplit and StratifiedShuffleSplit are not supported in this class. If you need these, you can use the GenSVM classifier directly with the GridSearchCV object from scikit-learn. (these methods require significant changes in the low-level library before they can be supported).
refit (boolean, or string, default=True) –
Refit the GenSVM estimator with the best found parameters on the whole dataset.

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

The refitted estimator is made available at the :attr:best_estimator_ <.GenSVMGridSearchCV.best_estimator_> attribute and allows the user to use the predict() method directly on this GenSVMGridSearchCV 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.
verbose (integer) – Controls the verbosity: the higher, the more messages.
return_train_score (boolean, default=True) – If False, the cv_results_ attribute will not include training scores.

Examples

>>> from gensvm import GenSVMGridSearchCV
>>> from sklearn.datasets import load_iris
>>> iris = load_iris()
>>> param_grid = {'p': [1.0, 2.0], 'kappa': [-0.9, 0.0, 1.0]}
>>> clf = GenSVMGridSearchCV(param_grid)
>>> clf.fit(iris.data, iris.target)
GenSVMGridSearchCV(cv=None, iid=True,
      param_grid={'p': [1.0, 2.0], 'kappa': [-0.9, 0.0, 1.0]},
      refit=True, return_train_score=True, scoring=None, verbose=0)

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	0.8	…	2
‘poly’	–	3	0.7	…	4
‘rbf’	0.1	–	0.8	…	3
‘rbf’	0.2	–	0.9	…	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'  : [0.8, 0.7, 0.8, 0.9],
'split1_test_score'  : [0.82, 0.5, 0.7, 0.78],
'mean_test_score'    : [0.81, 0.60, 0.75, 0.82],
'std_test_score'     : [0.02, 0.01, 0.03, 0.03],
'rank_test_score'    : [2, 4, 3, 1],
'split0_train_score' : [0.8, 0.9, 0.7],
'split1_train_score' : [0.82, 0.5, 0.7],
'mean_train_score'   : [0.81, 0.7, 0.7],
'std_train_score'    : [0.03, 0.03, 0.04],
'mean_fit_time'      : [0.73, 0.63, 0.43, 0.49],
'std_fit_time'       : [0.01, 0.02, 0.01, 0.01],
'mean_score_time'    : [0.007, 0.06, 0.04, 0.04],
'std_score_time'     : [0.001, 0.002, 0.003, 0.005],
'params'             : [{'kernel': '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 or dict – Estimator that was chosen by the search, i.e. estimator which gave highest score (or smallest loss if specified) on the left out data. Not available if refit=False.

See refit parameter for more information on allowed values.

best_score_¶

float – Mean cross-validated score of the best_estimator

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

best_params_¶

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

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

best_index_¶

int – The index (of the cv_results_ arrays) which corresponds to the best candidate parameter setting.

The dict at search.cv_results_['params'][search.best_index_] gives the parameter setting for the best model, that gives the highest mean score (search.best_score_).

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

scorer_¶

function or a dict – Scorer function used on the held out data to choose the best parameters for the model.

For multi-metric evaluation, this attribute holds the validated scoring dict which maps the scorer key to the scorer callable.

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

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.

Parameters:	X (array-like, shape = (n_samples, n_features)) – Training data, where n_samples is the number of observations and n_features is the number of features. y (array-like, shape = (n_samples, )) – Target vector for the training data. groups (array-like, with shape (n_samples, ), optional) – Group labels for the samples used while splitting the dataset into train/test sets.
Returns:	self – Return self.
Return type:	object

Parameters:	X (array-like, shape = (n_samples, n_features)) – Test data, where n_samples is the number of observations and n_features is the number of features. trainX (array, shape = [n_train_samples, n_features]) – Only for nonlinear prediction with kernels: the training data used to train the model.
Returns:	y_pred – Predicted class labels of the data in X.
Return type:	array-like, shape = (n_samples, )

Parameters:	X (array-like, shape = (n_samples, n_features)) – Test data, where n_samples is the number of observations and n_features is the number of features. y (array-like, shape = (n_samples, )) – True labels for the test data.
Returns:	score
Return type:	float