考虑data其中包含以下一些 nan：
Column-1 Column-2 Column-3 Column-4 Column-5
0 NaN 15.0 63.0 8.0 40.0
1 60.0 51.0 NaN 54.0 31.0
2 15.0 17.0 55.0 80.0 NaN
3 54.0 43.0 70.0 16.0 73.0
4 94.0 31.0 94.0 29.0 53.0
5 99.0 52.0 77.0 91.0 58.0
6 84.0 19.0 36.0 NaN 97.0
7 41.0 91.0 62.0 67.0 68.0
8 44.0 38.0 27.0 53.0 37.0
9 58.0 NaN 63.0 57.0 28.0
10 66.0 68.0 89.0 36.0 47.0
11 7.0 81.0 5.0 99.0 16.0
12 43.0 55.0 64.0 88.0 NaN
13 8.0 90.0 91.0 44.0 4.0
14 29.0 52.0 94.0 71.0 47.0
15 22.0 21.0 68.0 61.0 38.0
16 76.0 36.0 70.0 99.0 50.0
17 38.0 31.0 66.0 79.0 99.0
18 94.0 22.0 92.0 39.0 58.0
我想替换 nan 中的data using sklearn.impute.IterativeImputer。一位朋友帮我写了下面的代码：
imp = IterativeImputer(missing_values=np.nan, sample_posterior=False,
max_iter=10, tol=0.001,
n_nearest_features=4, initial_strategy='median')
imp.fit(data)
imputed_data = pd.DataFrame(data=imp.transform(data),
columns=['Column-1', 'Column-2', 'Column-3', 'Column-4', 'Column-5'],
dtype='int')
The imputed_data is:
Column-1 Column-2 Column-3 Column-4 Column-5
0 59 15 63 8 40
1 60 51 66 54 31
2 15 17 55 80 48
3 54 43 70 16 73
4 94 31 94 29 53
5 99 52 77 91 58
6 84 19 36 59 97
7 41 91 62 67 68
8 44 38 27 53 37
9 58 46 63 57 28
10 66 68 89 36 47
11 7 81 5 99 16
12 43 55 64 88 47
13 8 90 91 44 4
14 29 52 94 71 47
15 22 21 68 61 38
16 76 36 70 99 50
17 38 31 66 79 99
18 94 22 92 39 58
来自IterativeImputer 文档 https://scikit-learn.org/stable/modules/generated/sklearn.impute.IterativeImputer.html#sklearn.impute.IterativeImputer，默认估计量是BayesianRidge()。但如果我使用其他估计器，例如estimator=ExtraTreesRegressor(n_estimators=10, random_state=0)就像下面的代码一样，它返回一条警告消息。
代码：
imp = IterativeImputer(estimator=ExtraTreesRegressor(n_estimators=10, random_state=0), missing_values=np.nan, sample_posterior=False,
max_iter=10, tol=0.001,
n_nearest_features=4, initial_strategy='median')
imp.fit(data)
消息：
C:\Users\...\sklearn\impute\_iterative.py:599: ConvergenceWarning: [IterativeImputer] Early stopping criterion not reached. " reached.", ConvergenceWarning).
我的问题：这是正确的方法还是我应该采取措施来修复警告消息？
谢谢。
