org.apache.flink.ml.examples.feature.StringIndexerExample Maven / Gradle / Ivy

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 * to you under the Apache License, Version 2.0 (the
 * "License"); you may not use this file except in compliance
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 *     http://www.apache.org/licenses/LICENSE-2.0
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package org.apache.flink.ml.examples.feature;

import org.apache.flink.ml.feature.stringindexer.StringIndexer;
import org.apache.flink.ml.feature.stringindexer.StringIndexerModel;
import org.apache.flink.ml.feature.stringindexer.StringIndexerParams;
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.table.api.bridge.java.StreamTableEnvironment;
import org.apache.flink.types.Row;
import org.apache.flink.util.CloseableIterator;

import java.util.Arrays;

/** Simple program that trains a StringIndexer model and uses it for feature engineering. */
public class StringIndexerExample {
    public static void main(String[] args) {
        StreamExecutionEnvironment env = StreamExecutionEnvironment.getExecutionEnvironment();
        StreamTableEnvironment tEnv = StreamTableEnvironment.create(env);

        // Generates input training and prediction data.
        DataStream trainStream =
                env.fromElements(
                        Row.of("a", 1.0),
                        Row.of("b", 1.0),
                        Row.of("b", 2.0),
                        Row.of("c", 0.0),
                        Row.of("d", 2.0),
                        Row.of("a", 2.0),
                        Row.of("b", 2.0),
                        Row.of("b", -1.0),
                        Row.of("a", -1.0),
                        Row.of("c", -1.0));
        Table trainTable = tEnv.fromDataStream(trainStream).as("inputCol1", "inputCol2");

        DataStream predictStream =
                env.fromElements(Row.of("a", 2.0), Row.of("b", 1.0), Row.of("c", 2.0));
        Table predictTable = tEnv.fromDataStream(predictStream).as("inputCol1", "inputCol2");

        // Creates a StringIndexer object and initializes its parameters.
        StringIndexer stringIndexer =
                new StringIndexer()
                        .setStringOrderType(StringIndexerParams.ALPHABET_ASC_ORDER)
                        .setInputCols("inputCol1", "inputCol2")
                        .setOutputCols("outputCol1", "outputCol2");

        // Trains the StringIndexer Model.
        StringIndexerModel model = stringIndexer.fit(trainTable);

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

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

            Object[] inputValues = new Object[stringIndexer.getInputCols().length];
            double[] outputValues = new double[stringIndexer.getInputCols().length];
            for (int i = 0; i < inputValues.length; i++) {
                inputValues[i] = row.getField(stringIndexer.getInputCols()[i]);
                outputValues[i] = (double) row.getField(stringIndexer.getOutputCols()[i]);
            }

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