sklearn.impute.IterativeImputer的实现

本文介绍了sklearn.impute.IterativeImputer的实现的处理方法，对大家解决问题具有一定的参考价值，需要的朋友们下面随着小编来一起学习吧！

问题描述

考虑data，其中包含一些下面的nan:

Consider data which contains some nan below:

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

我想使用sklearn.impute.IterativeImputer替换data中的nan.一位朋友用下面的代码帮助了我:

I want to replace nan in the data using sklearn.impute.IterativeImputer. A friend helped me with the code below:

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

在IterativeImputer中文档，默认估算值为BayesianRidge().但是，如果我在下面的代码中使用诸如estimator=ExtraTreesRegressor(n_estimators=10, random_state=0)之类的其他估算器，它将返回警告消息.代码:

From the IterativeImputer documentation, the default estimator is BayesianRidge(). But if I use other estimators such as estimator=ExtraTreesRegressor(n_estimators=10, random_state=0) like in the code below, it returns a warning message.The code:

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一些措施来修正警告消息?
谢谢.

My question: is this a correct approach or should I do something to fix the warning message?
Thank you.

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sklearn.impute.IterativeImputer的实现

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