One Hot Encoder

One Hot Encoder #

One-hot encoding maps a categorical feature, represented as a label index, to a binary vector with at most a single one-value indicating the presence of a specific feature value from among the set of all feature values. This encoding allows algorithms that expect continuous features, such as Logistic Regression, to use categorical features.

OneHotEncoder can transform multiple columns, returning a one-hot-encoded output vector column for each input column.

Input Columns #

Param name Type Default Description
inputCols Integer null Label index

Output Columns #

Param name Type Default Description
outputCols Vector null Encoded binary vector

Parameters #

Key Default Type Required Description
inputCols null String yes Input column names.
outputCols null String yes Output column names.
handleInvalid HasHandleInvalid.ERROR_INVALID String No Strategy to handle invalid entries. Supported values: HasHandleInvalid.ERROR_INVALID, HasHandleInvalid.SKIP_INVALID
dropLast true Boolean no Whether to drop the last category.

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 OneHotEncoder model and uses it for feature engineering. */
public class OneHotEncoderExample {
    public static void main(String[] args) {
        StreamExecutionEnvironment env = StreamExecutionEnvironment.getExecutionEnvironment();
        StreamTableEnvironment tEnv = StreamTableEnvironment.create(env);

        // Generates input training and prediction data.
        DataStream<Row> trainStream =
                env.fromElements(Row.of(0.0), Row.of(1.0), Row.of(2.0), Row.of(0.0));
        Table trainTable = tEnv.fromDataStream(trainStream).as("input");

        DataStream<Row> predictStream = env.fromElements(Row.of(0.0), Row.of(1.0), Row.of(2.0));
        Table predictTable = tEnv.fromDataStream(predictStream).as("input");

        // Creates a OneHotEncoder object and initializes its parameters.
        OneHotEncoder oneHotEncoder =
                new OneHotEncoder().setInputCols("input").setOutputCols("output");

        // Trains the OneHotEncoder Model.
        OneHotEncoderModel model =;

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

        // Extracts and displays the results.
        for (CloseableIterator<Row> it = outputTable.execute().collect(); it.hasNext(); ) {
            Row row =;
            Double inputValue = (Double) row.getField(oneHotEncoder.getInputCols()[0]);
            SparseVector outputValue =
                    (SparseVector) row.getField(oneHotEncoder.getOutputCols()[0]);
            System.out.printf("Input Value: %s\tOutput Value: %s\n", inputValue, outputValue);

# Simple program that trains a OneHotEncoder model and uses it for feature
# engineering.
# Before executing this program, please make sure you have followed Flink ML's
# quick start guideline to set up Flink ML and Flink environment. The guideline
# can be found at

from pyflink.common import Row
from pyflink.datastream import StreamExecutionEnvironment
from import OneHotEncoder
from pyflink.table import StreamTableEnvironment, DataTypes

# create a new StreamExecutionEnvironment
env = StreamExecutionEnvironment.get_execution_environment()

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

# generate input training and prediction data
train_table = t_env.from_elements(
    [Row(0.0), Row(1.0), Row(2.0), Row(0.0)],
        DataTypes.FIELD('input', DataTypes.DOUBLE())

predict_table = t_env.from_elements(
    [Row(0.0), Row(1.0), Row(2.0)],
        DataTypes.FIELD('input', DataTypes.DOUBLE())

# create a one-hot-encoder object and initialize its parameters
one_hot_encoder = OneHotEncoder().set_input_cols('input').set_output_cols('output')

# train the one-hot-encoder model
model =

# use the one-hot-encoder model for predictions
output = model.transform(predict_table)[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(one_hot_encoder.get_input_cols()[0])]
    output_value = result[field_names.index(one_hot_encoder.get_output_cols()[0])]
    print('Input Value: ' + str(input_value) + ' \tOutput Value: ' + str(output_value))