How to use SimpleImputer class to impute missing values in different columns with different constant values?

Question

I was using sklearn.impute.SimpleImputer(strategy='constant',fill_value= 0) to impute all columns with missing values with a constant value(0 being that constant value here).

But, it sometimes makes sense to impute different constant values in different columns. For example, i might like to replace all NaN values of a certain column with the maximum value from that column, or some other column's NaN values with minimum or let's suppose median/mean of that particular column values.

How can i achieve this?

Also, i'm actually new to this field, so i'm not really sure if doing this might improve my model's results. Your opinions are welcome.

Answer 1

If you want to impute different features with different arbitrary values, or the median, you need to set up several SimpleImputer steps within a pipeline and then join them with the ColumnTransformer:

import pandas as pd
from sklearn.compose import ColumnTransformer
from sklearn.pipeline import Pipeline
from sklearn.impute import SimpleImputer

# first we need to make lists, indicating which features
# will be imputed with each method

features_numeric = ['LotFrontage', 'MasVnrArea', 'GarageYrBlt']
features_categoric = ['BsmtQual', 'FireplaceQu']

# then we instantiate the imputers, within a pipeline
# we create one imputer for numerical and one imputer
# for categorical

# this imputer imputes with the mean
imputer_numeric = Pipeline(steps=[
    ('imputer', SimpleImputer(strategy='mean')),
])

# this imputer imputes with an arbitrary value
imputer_categoric = Pipeline(
    steps=[('imputer',
            SimpleImputer(strategy='constant', fill_value='Missing'))])

# then we put the features list and the transformers together
# using the column transformer

preprocessor = ColumnTransformer(transformers=[('imputer_numeric',
                                                imputer_numeric,
                                                features_numeric),
                                               ('imputer_categoric',
                                                imputer_categoric,
                                                features_categoric)])

# now we fit the preprocessor
preprocessor.fit(X_train)

# and now we can impute the data
# remember it returs a numpy array

X_train = preprocessor.transform(X_train)
X_test = preprocessor.transform(X_test)

Alternatively, you can use the package Feature-Engine which transformers allow you to specify the features:

from feature_engine import imputation as msi
from sklearn.pipeline import Pipeline as pipe

pipe = pipe([
    # add a binary variable to indicate missing information for the 2 variables below
    ('continuous_var_imputer', msi.AddMissingIndicator(variables = ['LotFrontage', 'GarageYrBlt'])),
     
    # replace NA by the median in the 3 variables below, they are numerical
    ('continuous_var_median_imputer', msi.MeanMedianImputer(imputation_method='median', variables = ['LotFrontage', 'GarageYrBlt', 'MasVnrArea'])),
     
    # replace NA by adding the label "Missing" in categorical variables (transformer will skip those variables where there is no NA)
    ('categorical_imputer', msi.CategoricalImputer(variables = ['var1', 'var2'])),
     
    # median imputer
    # to handle those, I will add an additional step here
    ('additional_median_imputer', msi.MeanMedianImputer(imputation_method='median', variables = ['var4', 'var5'])),
     ])

pipe.fit(X_train)
X_train_t = pipe.transform(X_train)

Feature-engine returns dataframes. More info in this link.

To install Feature-Engine do:

pip install feature-engine

Hope that helps