本文整理汇总了Python中sklearn.impute.IterativeImputer.n_iter_方法的典型用法代码示例。如果您正苦于以下问题：Python IterativeImputer.n_iter_方法的具体用法？Python IterativeImputer.n_iter_怎么用？Python IterativeImputer.n_iter_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.impute.IterativeImputer的用法示例。
在下文中一共展示了IterativeImputer.n_iter_方法的1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_iterative_imputer_zero_iters
# 需要导入模块: from sklearn.impute import IterativeImputer [as 别名]
# 或者: from sklearn.impute.IterativeImputer import n_iter_ [as 别名]
def test_iterative_imputer_zero_iters():
rng = np.random.RandomState(0)
n = 100
d = 10
X = sparse_random_matrix(n, d, density=0.10, random_state=rng).toarray()
missing_flag = X == 0
X[missing_flag] = np.nan
imputer = IterativeImputer(max_iter=0)
X_imputed = imputer.fit_transform(X)
# with max_iter=0, only initial imputation is performed
assert_allclose(X_imputed, imputer.initial_imputer_.transform(X))
# repeat but force n_iter_ to 0
imputer = IterativeImputer(max_iter=5).fit(X)
# transformed should not be equal to initial imputation
assert not np.all(imputer.transform(X) ==
imputer.initial_imputer_.transform(X))
imputer.n_iter_ = 0
# now they should be equal as only initial imputation is done
assert_allclose(imputer.transform(X),
imputer.initial_imputer_.transform(X))