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
data 中的 nan使用 sklearn.impute.IterativeImputer .一个 friend 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')
imputed_data是:
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 documentation ，默认估计量是 BayesianRidge() .但是如果我使用其他估算器，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。编码:
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:这是一种正确的方法还是我应该做些什么来修复警告消息？
谢谢你。
最佳答案
他们在这里遇到了同样的问题:
https://github.com/scikit-learn/scikit-learn/issues/14338
关于python - sklearn.impute.IterativeImputer 的实现，我们在Stack Overflow上找到一个类似的问题： https://stackoverflow.com/questions/57154209/