Sklearn, gridsearch: how to print out progress dur

Question

I am using GridSearch from sklearn to optimize parameters of the classifier. There is a lot of data, so the whole process of optimization takes a while: more than a day. I would like to watch the performance of the already-tried combinations of parameters during the execution. Is it possible?

Answer 1

Check out the GridSearchCVProgressBar

Just found it right now and I'm using it. Very into it:

In [1]: GridSearchCVProgressBar
Out[1]: pactools.grid_search.GridSearchCVProgressBar

In [2]:

In [2]: ??GridSearchCVProgressBar
Init signature: GridSearchCVProgressBar(estimator, param_grid, scoring=None, fit_params=None, n_jobs=1, iid=True, refit=True, cv=None, verbose=0, pre_dispatch='2*n_jobs', error_score='raise', return_train_score='warn')
Source:
class GridSearchCVProgressBar(model_selection.GridSearchCV):
    """Monkey patch Parallel to have a progress bar during grid search"""

    def _get_param_iterator(self):
        """Return ParameterGrid instance for the given param_grid"""

        iterator = super(GridSearchCVProgressBar, self)._get_param_iterator()
        iterator = list(iterator)
        n_candidates = len(iterator)

        cv = model_selection._split.check_cv(self.cv, None)
        n_splits = getattr(cv, 'n_splits', 3)
        max_value = n_candidates * n_splits

        class ParallelProgressBar(Parallel):
            def __call__(self, iterable):
                bar = ProgressBar(max_value=max_value, title='GridSearchCV')
                iterable = bar(iterable)
                return super(ParallelProgressBar, self).__call__(iterable)

        # Monkey patch
        model_selection._search.Parallel = ParallelProgressBar

        return iterator
File:           ~/anaconda/envs/python3/lib/python3.6/site-packages/pactools/grid_search.py
Type:           ABCMeta

In [3]: ?GridSearchCVProgressBar
Init signature: GridSearchCVProgressBar(estimator, param_grid, scoring=None, fit_params=None, n_jobs=1, iid=True, refit=True, cv=None, verbose=0, pre_dispatch='2*n_jobs', error_score='raise', return_train_score='warn')
Docstring:      Monkey patch Parallel to have a progress bar during grid search
File:           ~/anaconda/envs/python3/lib/python3.6/site-packages/pactools/grid_search.py
Type:           ABCMeta

Answer 2

I would just like to complement DavidS's answer

To give you an idea, for a very simple case, this is how it looks with verbose=1:

Fitting 10 folds for each of 1 candidates, totalling 10 fits
[Parallel(n_jobs=1)]: Using backend SequentialBackend with 1 concurrent workers.
[Parallel(n_jobs=1)]: Done  10 out of  10 | elapsed:  1.2min finished

And this is how it looks with verbose=10:

Fitting 10 folds for each of 1 candidates, totalling 10 fits
[CV] booster=gblinear, learning_rate=0.0001, max_depth=3, n_estimator=100, subsample=0.1 
[Parallel(n_jobs=1)]: Using backend SequentialBackend with 1 concurrent workers.
[CV]  booster=gblinear, learning_rate=0.0001, max_depth=3, n_estimator=100, subsample=0.1, score=0.637, total=   7.1s
[CV] booster=gblinear, learning_rate=0.0001, max_depth=3, n_estimator=100, subsample=0.1 
[Parallel(n_jobs=1)]: Done   1 out of   1 | elapsed:    7.0s remaining:    0.0s
[CV]  booster=gblinear, learning_rate=0.0001, max_depth=3, n_estimator=100, subsample=0.1, score=0.630, total=   6.5s
[CV] booster=gblinear, learning_rate=0.0001, max_depth=3, n_estimator=100, subsample=0.1 
[Parallel(n_jobs=1)]: Done   2 out of   2 | elapsed:   13.5s remaining:    0.0s
[CV]  booster=gblinear, learning_rate=0.0001, max_depth=3, n_estimator=100, subsample=0.1, score=0.637, total=   6.5s
[CV] booster=gblinear, learning_rate=0.0001, max_depth=3, n_estimator=100, subsample=0.1 
[Parallel(n_jobs=1)]: Done   3 out of   3 | elapsed:   20.0s remaining:    0.0s
[CV]  booster=gblinear, learning_rate=0.0001, max_depth=3, n_estimator=100, subsample=0.1, score=0.637, total=   6.7s
[CV] booster=gblinear, learning_rate=0.0001, max_depth=3, n_estimator=100, subsample=0.1 
[Parallel(n_jobs=1)]: Done   4 out of   4 | elapsed:   26.7s remaining:    0.0s
[CV]  booster=gblinear, learning_rate=0.0001, max_depth=3, n_estimator=100, subsample=0.1, score=0.632, total=   7.9s
[CV] booster=gblinear, learning_rate=0.0001, max_depth=3, n_estimator=100, subsample=0.1 
[Parallel(n_jobs=1)]: Done   5 out of   5 | elapsed:   34.7s remaining:    0.0s
[CV]  booster=gblinear, learning_rate=0.0001, max_depth=3, n_estimator=100, subsample=0.1, score=0.622, total=   6.9s
[CV] booster=gblinear, learning_rate=0.0001, max_depth=3, n_estimator=100, subsample=0.1 
[Parallel(n_jobs=1)]: Done   6 out of   6 | elapsed:   41.6s remaining:    0.0s
[CV]  booster=gblinear, learning_rate=0.0001, max_depth=3, n_estimator=100, subsample=0.1, score=0.627, total=   7.1s
[CV] booster=gblinear, learning_rate=0.0001, max_depth=3, n_estimator=100, subsample=0.1 
[Parallel(n_jobs=1)]: Done   7 out of   7 | elapsed:   48.7s remaining:    0.0s
[CV]  booster=gblinear, learning_rate=0.0001, max_depth=3, n_estimator=100, subsample=0.1, score=0.628, total=   7.2s
[CV] booster=gblinear, learning_rate=0.0001, max_depth=3, n_estimator=100, subsample=0.1 
[Parallel(n_jobs=1)]: Done   8 out of   8 | elapsed:   55.9s remaining:    0.0s
[CV]  booster=gblinear, learning_rate=0.0001, max_depth=3, n_estimator=100, subsample=0.1, score=0.640, total=   6.6s
[CV] booster=gblinear, learning_rate=0.0001, max_depth=3, n_estimator=100, subsample=0.1 
[Parallel(n_jobs=1)]: Done   9 out of   9 | elapsed:  1.0min remaining:    0.0s
[CV]  booster=gblinear, learning_rate=0.0001, max_depth=3, n_estimator=100, subsample=0.1, score=0.629, total=   6.6s
[Parallel(n_jobs=1)]: Done  10 out of  10 | elapsed:  1.2min finished

In my case, verbose=1 does the trick.

Answer 3

Set the verbose parameter in GridSearchCV to a positive number (the greater the number the more detail you will get). For instance:

GridSearchCV(clf, param_grid, cv=cv, scoring='accuracy', verbose=10)