Imputing missing values using sklearn IterativeImputer class for MICE

问题

I'm trying to learn how to implement MICE in imputing missing values for my datasets. I've heard about fancyimpute's MICE, but I also read that sklearn's IterativeImputer class can accomplish similar results. From sklearn's docs:

Our implementation of IterativeImputer was inspired by the R MICE package (Multivariate Imputation by Chained Equations) [1], but differs from it by returning a single imputation instead of multiple imputations. However, IterativeImputer can also be used for multiple imputations by applying it repeatedly to the same dataset with different random seeds when sample_posterior=True

I've seen "seeds" being used in different pipelines, but I never understood them well enough to implement them in my own code. I was wondering if anyone could explain and provide an example on how to implement seeds for a MICE imputation using sklearn's IterativeImputer? Thanks!

回答1:

IterativeImputer behavior can change depending on a random state. The random state which can be set is also called a "seed".

As stated by the documentation, we can get multiple imputations when setting sample_posterior to True and changing the random seeds, i.e. the parameter random_state.

Here is an example of how to use it:

import numpy as np
from sklearn.experimental import enable_iterative_imputer
from sklearn.impute import IterativeImputer

X_train = [[1, 2],
           [3, 6],
           [4, 8],
           [np.nan, 3],
           [7, np.nan]]
X_test = [[np.nan, 2],
          [np.nan, np.nan],
          [np.nan, 6]]

for i in range(3):
    imp = IterativeImputer(max_iter=10, random_state=i, sample_posterior=True)
    imp.fit(X_train)
    print(f"imputation {i}:")
    print(np.round(imp.transform(X_test)))

It outputs:

imputation 0:
[[ 1.  2.]
 [ 5. 10.]
 [ 3.  6.]]
imputation 1:
[[1. 2.]
 [0. 1.]
 [3. 6.]]
imputation 2:
[[1. 2.]
 [1. 2.]
 [3. 6.]]

We can observe the three different imputations.

来源：https://stackoverflow.com/questions/58613108/imputing-missing-values-using-sklearn-iterativeimputer-class-for-mice

标签

python

dataframe

missing-data

sklearn-pandas