SKlearn, 그리드 검색: 실행 중 진행 상황을 출력하는 방법은 무엇입니까?

SKlearn, 그리드 검색: 실행 중 진행 상황을 출력하는 방법은 무엇입니까?

사용 중GridSearch부터sklearn분류기의 매개 변수를 최적화합니다.데이터가 많기 때문에 최적화의 전체 프로세스에는 하루 이상의 시간이 소요됩니다.실행 중에 이미 시도된 매개 변수 조합의 성능을 보고 싶습니다.가능합니까?

설정verbose의 매개 변수.GridSearchCV숫자가 클수록 더 많은 세부 정보를 얻을 수 있습니다.예를 들어:

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

저는 데이비드 S의 답변을 보완하고 싶습니다.

아이디어를 드리자면, 아주 간단한 경우에는, 다음과 같이 보입니다.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

그리고 이것이 어떻게 보이는가.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

저 같은 경우에는.verbose=1요령을 터득합니다.

그리드 검색CV 진행률 표시줄 확인

지금 막 찾아서 쓰고 있어요.매우 흥미로운 점:

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

빠른 해결 방법: 크롬에서 nb를 사용하는 경우 그리드 검색 출력에서 원하는 단어를 검색하면 됩니다.GridSearch가 nb에 더 많은 출력을 반환할 때 Chrome이 자동으로 진행 상황을 업데이트합니다.

언급URL : https://stackoverflow.com/questions/24121018/sklearn-gridsearch-how-to-print-out-progress-during-the-execution