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

IndexToString #

IndexToStringModel transforms input index column(s) to string column(s) using the model data computed by StringIndexer. It is a reverse operation of StringIndexerModel.

Input Columns #

Param name Type Default Description
inputCols Integer null Indices to be transformed to string.

Output Columns #

Param name Type Default Description
outputCols String null Transformed strings.

Parameters #

Below are the parameters required by StringIndexerModel.

Key Default Type Required Description
inputCols null String yes Input column names.
outputCols null String yes Output column names.

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 creates an IndexToStringModelExample instance and uses it for feature
 * engineering.
public class IndexToStringModelExample {
    public static void main(String[] args) {
        StreamExecutionEnvironment env = StreamExecutionEnvironment.getExecutionEnvironment();
        StreamTableEnvironment tEnv = StreamTableEnvironment.create(env);

        // Creates model data for IndexToStringModel.
        StringIndexerModelData modelData =
                new StringIndexerModelData(
                        new String[][] {{"a", "b", "c", "d"}, {"-1.0", "0.0", "1.0", "2.0"}});
        Table modelTable = tEnv.fromDataStream(env.fromElements(modelData)).as("stringArrays");

        // Generates input data.
        DataStream<Row> predictStream = env.fromElements(Row.of(0, 3), Row.of(1, 2));
        Table predictTable = tEnv.fromDataStream(predictStream).as("inputCol1", "inputCol2");

        // Creates an indexToStringModel object and initializes its parameters.
        IndexToStringModel indexToStringModel =
                new IndexToStringModel()
                        .setInputCols("inputCol1", "inputCol2")
                        .setOutputCols("outputCol1", "outputCol2")

        // Uses the indexToStringModel object for feature transformations.
        Table outputTable = indexToStringModel.transform(predictTable)[0];

        // Extracts and displays the results.
        for (CloseableIterator<Row> it = outputTable.execute().collect(); it.hasNext(); ) {
            Row row =;

            int[] inputValues = new int[indexToStringModel.getInputCols().length];
            String[] outputValues = new String[indexToStringModel.getInputCols().length];
            for (int i = 0; i < inputValues.length; i++) {
                inputValues[i] = (int) row.getField(indexToStringModel.getInputCols()[i]);
                outputValues[i] = (String) row.getField(indexToStringModel.getOutputCols()[i]);

                    "Input Values: %s \tOutput Values: %s\n",
                    Arrays.toString(inputValues), Arrays.toString(outputValues));

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

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

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

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

# generate input data
predict_table = t_env.from_data_stream(
        (0, 3),
        (1, 2),
            ['input_col1', 'input_col2'],
            [Types.INT(), Types.INT()])

# create an index-to-string model and initialize its parameters and model data
model_data_table = t_env.from_data_stream(
        ([['a', 'b', 'c', 'd'], [-1., 0., 1., 2.]],),

model = IndexToStringModel() \
    .set_input_cols('input_col1', 'input_col2') \
    .set_output_cols('output_col1', 'output_col2') \

# use the index-to-string model for feature engineering
output = model.transform(predict_table)[0]

# extract and display the results
field_names = output.get_schema().get_field_names()
input_values = [None for _ in model.get_input_cols()]
output_values = [None for _ in model.get_input_cols()]
for result in t_env.to_data_stream(output).execute_and_collect():
    for i in range(len(model.get_input_cols())):
        input_values[i] = result[field_names.index(model.get_input_cols()[i])]
        output_values[i] = result[field_names.index(model.get_output_cols()[i])]
    print('Input Values: ' + str(input_values) + '\tOutput Values: ' + str(output_values))