RandomSampleImputer() replaces missing data with a random sample extracted from the
variable. It works with both numerical and categorical variables. A list of variables
can be indicated, or the imputer will automatically select all variables in the train
The random samples used to replace missing values may vary from execution to execution. This may affect the results of your work. Thus, it is advisable to set a seed.
Setting the seed#
There are 2 ways in which the seed can be set in the
seed = 'general' then the random_state can be either
None or an integer.
random_state then provides the seed to use in the imputation. All observations will
be imputed in one go with a single seed. This is equivalent to
pandas.sample(n, random_state=seed) where
n is the number of observations with
missing data and
seed is the number you entered in the
seed = 'observation', then the random_state should be a variable name
or a list of variable names. The seed will be calculated observation per
observation, either by adding or multiplying the values of the variables
indicated in the
random_state. Then, a value will be extracted from the train set
using that seed and used to replace the NAN in that particular observation. This is the
pandas.sample(1, random_state=var1+var2) if the
pandas.sample(1, random_state=var1*var2) if the
is set to
For example, if the observation shows variables color: np.nan, height: 152, weight:52, and we set the imputer as:
RandomSampleImputer(random_state=['height', 'weight'], seed='observation', seeding_method='add'))
the np.nan in the variable colour will be replaced using pandas sample as follows:
For more details on why this functionality is important refer to the course Feature Engineering for Machine Learning.
You can also find more details about this imputation in the following notebook.
Note, if the variables indicated in the
random_state list are not numerical
the imputer will return an error. In addition, the variables indicated as seed
should not contain missing values themselves.
Important for GDPR#
This estimator stores a copy of the training set when the
fit() method is
called. Therefore, the object can become quite heavy. Also, it may not be GDPR
compliant if your training data set contains Personal Information. Please check
if this behaviour is allowed within your organisation.
Below a code example using the House Prices Dataset (more details about the dataset here).
First, let’s load the data and separate it into train and test:
import numpy as np import pandas as pd import matplotlib.pyplot as plt from sklearn.model_selection import train_test_split from feature_engine.imputation import RandomSampleImputer # Load dataset data = pd.read_csv('houseprice.csv') # Separate into train and test sets X_train, X_test, y_train, y_test = train_test_split( data.drop(['Id', 'SalePrice'], axis=1), data['SalePrice'], test_size=0.3, random_state=0 )
In this example, we sample values at random, observation per observation, using as seed the value of the variable ‘MSSubClass’ plus the value of the variable ‘YrSold’. Note that this value might be different for each observation.
RandomSampleImputer() will impute all variables in the data, as we left the
default value of the parameter
# set up the imputer imputer = RandomSampleImputer( random_state=['MSSubClass', 'YrSold'], seed='observation', seeding_method='add' ) # fit the imputer imputer.fit(X_train)
fit() the imputer stored a copy of the X_train. And with transform, it will extract
values at random from this X_train to replace NA in the datasets indicated in the
# transform the data train_t = imputer.transform(X_train) test_t = imputer.transform(X_test)
The beauty of the random sampler is that it preserves the original variable distribution:
fig = plt.figure() ax = fig.add_subplot(111) X_train['LotFrontage'].plot(kind='kde', ax=ax) train_t['LotFrontage'].plot(kind='kde', ax=ax, color='red') lines, labels = ax.get_legend_handles_labels() ax.legend(lines, labels, loc='best')
In the following Jupyter notebook you will find more details on the functionality of the
RandomSampleImputer(), including how to set the different types of seeds.
All Feature-engine notebooks can be found in a dedicated repository.
You will also find a lot of information on why the seed matters in this notebook:
And finally, there is also a lot of information about this and other imputation techniques in this online course: