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

Max Abs Scaler #

Max Abs Scaler is an algorithm rescales feature values to the range [-1, 1] by dividing through the largest maximum absolute value in each feature. It does not shift/center the data and thus does not destroy any sparsity.

Input Columns #

Param name Type Default Description
inputCol Vector "input" Features to be scaled.

Output Columns #

Param name Type Default Description
outputCol Vector "output" Scaled features.

Parameters #

Key Default Type Required Description
inputCol "input" String no Input column name.
outputCol "output" String no Output column name.

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;

/** Simple program that trains a MaxAbsScaler model and uses it for feature engineering. */
public class MaxAbsScalerExample {
    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(Vectors.dense(0.0, 3.0)),
                        Row.of(Vectors.dense(2.1, 0.0)),
                        Row.of(Vectors.dense(4.1, 5.1)),
                        Row.of(Vectors.dense(6.1, 8.1)),
                        Row.of(Vectors.dense(200, 400)));
        Table trainTable = tEnv.fromDataStream(trainStream).as("input");

        DataStream<Row> predictStream =
                        Row.of(Vectors.dense(150.0, 90.0)),
                        Row.of(Vectors.dense(50.0, 40.0)),
                        Row.of(Vectors.dense(100.0, 50.0)));
        Table predictTable = tEnv.fromDataStream(predictStream).as("input");

        // Creates a MaxAbsScaler object and initializes its parameters.
        MaxAbsScaler maxAbsScaler = new MaxAbsScaler();

        // Trains the MaxAbsScaler Model.
        MaxAbsScalerModel maxAbsScalerModel =;

        // Uses the MaxAbsScaler Model for predictions.
        Table outputTable = maxAbsScalerModel.transform(predictTable)[0];

        // Extracts and displays the results.
        for (CloseableIterator<Row> it = outputTable.execute().collect(); it.hasNext(); ) {
            Row row =;
            DenseVector inputValue = (DenseVector) row.getField(maxAbsScaler.getInputCol());
            DenseVector outputValue = (DenseVector) row.getField(maxAbsScaler.getOutputCol());
            System.out.printf("Input Value: %-15s\tOutput Value: %s\n", inputValue, outputValue);

# Simple program that trains a MaxAbsScaler model and uses it for feature
# engineering.

from pyflink.common import Types
from pyflink.datastream import StreamExecutionEnvironment
from import Vectors, DenseVectorTypeInfo
from import MaxAbsScaler
from pyflink.table import StreamTableEnvironment

# create a new StreamExecutionEnvironment
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(
        (Vectors.dense(0.0, 3.0),),
        (Vectors.dense(2.1, 0.0),),
        (Vectors.dense(4.1, 5.1),),
        (Vectors.dense(6.1, 8.1),),
        (Vectors.dense(200, 400),),

predict_data = t_env.from_data_stream(
        (Vectors.dense(150.0, 90.0),),
        (Vectors.dense(50.0, 40.0),),
        (Vectors.dense(100.0, 50.0),),

# create a maxabs scaler object and initialize its parameters
max_abs_scaler = MaxAbsScaler()

# train the maxabs scaler model
model =

# use the maxabs scaler model for predictions
output = model.transform(predict_data)[0]

# extract and display the results
field_names = output.get_schema().get_field_names()
for result in t_env.to_data_stream(output).execute_and_collect():
    input_value = result[field_names.index(max_abs_scaler.get_input_col())]
    output_value = result[field_names.index(max_abs_scaler.get_output_col())]
    print('Input Value: ' + str(input_value) + ' \tOutput Value: ' + str(output_value))