2024-09-27 07:35:51 发布
网友
我用sklearn中的GridSearch来优化分类器的参数。有很多数据,所以整个优化过程需要一段时间:一天多。我想在执行期间观察已经尝试过的参数组合的性能。有可能吗?
sklearn
GridSearch
将GridSearchCV中的verbose参数设置为正数(数字越大,获得的详细信息越多)。例如:
GridSearchCV
verbose
GridSearchCV(clf, param_grid, cv=cv, scoring='accuracy', verbose=10)
签出the GridSearchCVProgressBar
刚刚找到它,我正在使用它。非常喜欢:
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
将
GridSearchCV
中的verbose
参数设置为正数(数字越大,获得的详细信息越多)。例如:签出the GridSearchCVProgressBar
刚刚找到它,我正在使用它。非常喜欢:
相关问题 更多 >
编程相关推荐