This documentation is for an unreleased version of Apache Flink Machine Learning Library. We recommend you use the latest stable version.

Imputer #

The imputer for completing missing values of the input columns.

Missing values can be imputed using the statistics(mean, median or most frequent) of each column in which the missing values are located. The input columns should be of numeric type.

Note The mean/median/most frequent value is computed after filtering out missing values and null values, null values are always treated as missing, and so are also imputed.

Note The parameter relativeError is only effective when the strategy is median.

Input Columns #

Param name Type Default Description
inputCols Number null Features to be imputed.

Output Columns #

Param name Type Default Description
outputCols Double null Imputed features.

Parameters #

Below are the parameters required by ImputerModel.

Key Default Type Required Description
inputCols null String[] yes Input column names.
outputCols null String[] yes Output column names.
missingValue Double.NaN Double no The placeholder for the missing values. All occurrences of missing values will be imputed.

Imputer needs parameters above and also below.

Key Default Type Required Description
strategy "mean" String no The imputation strategy. Supported values: ‘mean’, ‘median’, ‘most_frequent’.
relativeError 0.001 Double no The relative target precision for the approximate quantile algorithm.

Examples #

import org.apache.flink.streaming.api.datastream.DataStream;
import org.apache.flink.streaming.api.environment.StreamExecutionEnvironment;
import org.apache.flink.table.api.Table;
import org.apache.flink.types.Row;
import org.apache.flink.util.CloseableIterator;

import java.util.Arrays;

/** Simple program that trains a {@link Imputer} model and uses it for feature engineering. */
public class ImputerExample {

    public static void main(String[] args) {
        StreamExecutionEnvironment env = StreamExecutionEnvironment.getExecutionEnvironment();
        StreamTableEnvironment tEnv = StreamTableEnvironment.create(env);

        // Generates input training and prediction data.
        DataStream<Row> trainStream =
                        Row.of(Double.NaN, 9.0),
                        Row.of(1.0, 9.0),
                        Row.of(1.5, 9.0),
                        Row.of(2.5, Double.NaN),
                        Row.of(5.0, 5.0),
                        Row.of(5.0, 4.0));
        Table trainTable = tEnv.fromDataStream(trainStream).as("input1", "input2");

        // Creates an Imputer object and initialize its parameters
        Imputer imputer =
                new Imputer()
                        .setInputCols("input1", "input2")
                        .setOutputCols("output1", "output2")

        // Trains the Imputer model.
        ImputerModel model =;

        // Uses the Imputer model for predictions.
        Table outputTable = model.transform(trainTable)[0];

        // Extracts and displays the results.
        for (CloseableIterator<Row> it = outputTable.execute().collect(); it.hasNext(); ) {
            Row row =;
            double[] inputValues = new double[imputer.getInputCols().length];
            double[] outputValues = new double[imputer.getInputCols().length];
            for (int i = 0; i < inputValues.length; i++) {
                inputValues[i] = (double) row.getField(imputer.getInputCols()[i]);
                outputValues[i] = (double) row.getField(imputer.getOutputCols()[i]);
                    "Input Values: %s\tOutput Values: %s\n",
                    Arrays.toString(inputValues), Arrays.toString(outputValues));

# Simple program that creates an Imputer instance and uses it for feature
# engineering.

from pyflink.common import Types
from pyflink.datastream import StreamExecutionEnvironment
from import Imputer
from pyflink.table import StreamTableEnvironment

env = StreamExecutionEnvironment.get_execution_environment()

# create a StreamTableEnvironment
t_env = StreamTableEnvironment.create(env)

# generate input training and prediction data
train_data = t_env.from_data_stream(
        (float('NaN'), 9.0,),
        (1.0, 9.0,),
        (1.5, 7.0,),
        (1.5, float('NaN'),),
        (4.0, 5.0,),
        (None, 4.0,),
            ['input1', 'input2'],
            [Types.DOUBLE(), Types.DOUBLE()])

# Creates an Imputer object and initializes its parameters.
imputer = Imputer()\
    .set_input_cols('input1', 'input2')\
    .set_output_cols('output1', 'output2')\

# Trains the Imputer Model.
model =

# Uses the Imputer Model for predictions.
output = model.transform(train_data)[0]

# Extracts and displays the results.
field_names = output.get_schema().get_field_names()
for result in t_env.to_data_stream(output).execute_and_collect():
    input_values = []
    output_values = []
    for i in range(len(imputer.get_input_cols())):
    print('Input Values: ' + str(input_values) + '\tOutput Values: ' + str(output_values))