本文整理汇总了Python中sklearn.model_selection._search.BaseSearchCV方法的典型用法代码示例。如果您正苦于以下问题：Python _search.BaseSearchCV方法的具体用法？Python _search.BaseSearchCV怎么用？Python _search.BaseSearchCV使用的例子？那么恭喜您, 这里精选的方法代码示例或许可以为您提供帮助。您也可以进一步了解该方法所在类sklearn.model_selection._search的用法示例。
在下文中一共展示了_search.BaseSearchCV方法的3个代码示例，这些例子默认根据受欢迎程度排序。您可以为喜欢或者感觉有用的代码点赞，您的评价将有助于系统推荐出更棒的Python代码示例。
示例1: test__custom_fit_no_run_search
# 需要导入模块: from sklearn.model_selection import _search [as 别名]
# 或者: from sklearn.model_selection._search import BaseSearchCV [as 别名]
def test__custom_fit_no_run_search():
class NoRunSearchSearchCV(BaseSearchCV):
def __init__(self, estimator, **kwargs):
super().__init__(estimator, **kwargs)
def fit(self, X, y=None, groups=None, **fit_params):
return self
# this should not raise any exceptions
NoRunSearchSearchCV(SVC(), cv=5).fit(X, y)
class BadSearchCV(BaseSearchCV):
def __init__(self, estimator, **kwargs):
super().__init__(estimator, **kwargs)
with pytest.raises(NotImplementedError,
match="_run_search not implemented."):
# this should raise a NotImplementedError
BadSearchCV(SVC(), cv=5).fit(X, y) 示例2: print_cv_result
# 需要导入模块: from sklearn.model_selection import _search [as 别名]
# 或者: from sklearn.model_selection._search import BaseSearchCV [as 别名]
def print_cv_result(result, n):
if isinstance(result, BaseSearchCV):
result = result.cv_results_
scores = result['mean_test_score']
params = result['params']
if n < 0:
n = len(scores)
print("Cross Validation result in descending order: (totalling {} trials)".format(n))
for rank, candidate, in enumerate(heapq.nlargest(n, zip(scores, params), key=lambda tup: tup[0])):
print("rank {}, score = {}\n hyperparams = {}".format(rank + 1, *candidate)) 示例3: test_custom_run_search
# 需要导入模块: from sklearn.model_selection import _search [as 别名]
# 或者: from sklearn.model_selection._search import BaseSearchCV [as 别名]
def test_custom_run_search():
def check_results(results, gscv):
exp_results = gscv.cv_results_
assert sorted(results.keys()) == sorted(exp_results)
for k in results:
if not k.endswith('_time'):
# XXX: results['params'] is a list :|
results[k] = np.asanyarray(results[k])
if results[k].dtype.kind == 'O':
assert_array_equal(exp_results[k], results[k],
err_msg='Checking ' + k)
else:
assert_allclose(exp_results[k], results[k],
err_msg='Checking ' + k)
def fit_grid(param_grid):
return GridSearchCV(clf, param_grid, cv=5,
return_train_score=True).fit(X, y)
class CustomSearchCV(BaseSearchCV):
def __init__(self, estimator, **kwargs):
super().__init__(estimator, **kwargs)
def _run_search(self, evaluate):
results = evaluate([{'max_depth': 1}, {'max_depth': 2}])
check_results(results, fit_grid({'max_depth': [1, 2]}))
results = evaluate([{'min_samples_split': 5},
{'min_samples_split': 10}])
check_results(results, fit_grid([{'max_depth': [1, 2]},
{'min_samples_split': [5, 10]}]))
# Using regressor to make sure each score differs
clf = DecisionTreeRegressor(random_state=0)
X, y = make_classification(n_samples=100, n_informative=4,
random_state=0)
mycv = CustomSearchCV(clf, cv=5, return_train_score=True).fit(X, y)
gscv = fit_grid([{'max_depth': [1, 2]},
{'min_samples_split': [5, 10]}])
results = mycv.cv_results_
check_results(results, gscv)
for attr in dir(gscv):
if attr[0].islower() and attr[-1:] == '_' and \
attr not in {'cv_results_', 'best_estimator_',
'refit_time_'}:
assert getattr(gscv, attr) == getattr(mycv, attr), \
"Attribute %s not equal" % attr