Imputing missing values using sklearn IterativeImputer class for MICE

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.

A way to go about stacking the data might be to change @Stanislas' code around a bit like so:

mvi = {} # just my preference for dict, you can use a list too # mvi collects each dataframe into a dict of dataframes using index: 0 thru 2 for i in range(3): imp = IterativeImputer(max_iter=10, random_state=i, sample_posterior=True) mvi[i] = np.round(imp.fit_transform(X_train)) 

combine the imputations into a single dataset using

# a. pandas concat, or pd.concat(list(dfImp.values()), axis=0) #b. np stack dfs = np.stack(list(dfImp.values()), axis=0) 

pd.concat creates a 2D data, on the other hand,np.stack creates a 3D array that you can reshape into 2D. The breakdown of the numpy 3D is as follows:

• axis 0: num of iterated dataframes
• axis 1: len of original df (num of rows)
• axis 2: num of columns in original dataframe

create a 2D from 3D

You can use numpy reshape like so:

np.reshape(dfs, newshape=(dfs.shape[0]*dfs.shape[1], -1)) 

which means you essentially multiply axis 0 by axis 1 to stack the dataframes into one big dataframe. The -1 at the end just means that whatever axes is left off, use that, in this case it is the columns.