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package org.apache.flink.ml.examples.classification;

import org.apache.flink.ml.classification.linearsvc.LinearSVC;
import org.apache.flink.ml.classification.linearsvc.LinearSVCModel;
import org.apache.flink.ml.linalg.DenseVector;
import org.apache.flink.ml.linalg.Vectors;
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;

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

        // Generates input data.
        DataStream inputStream =
                env.fromElements(
                        Row.of(Vectors.dense(1, 2, 3, 4), 0., 1.),
                        Row.of(Vectors.dense(2, 2, 3, 4), 0., 2.),
                        Row.of(Vectors.dense(3, 2, 3, 4), 0., 3.),
                        Row.of(Vectors.dense(4, 2, 3, 4), 0., 4.),
                        Row.of(Vectors.dense(5, 2, 3, 4), 0., 5.),
                        Row.of(Vectors.dense(11, 2, 3, 4), 1., 1.),
                        Row.of(Vectors.dense(12, 2, 3, 4), 1., 2.),
                        Row.of(Vectors.dense(13, 2, 3, 4), 1., 3.),
                        Row.of(Vectors.dense(14, 2, 3, 4), 1., 4.),
                        Row.of(Vectors.dense(15, 2, 3, 4), 1., 5.));
        Table inputTable = tEnv.fromDataStream(inputStream).as("features", "label", "weight");

        // Creates a LinearSVC object and initializes its parameters.
        LinearSVC linearSVC = new LinearSVC().setWeightCol("weight");

        // Trains the LinearSVC Model.
        LinearSVCModel linearSVCModel = linearSVC.fit(inputTable);

        // Uses the LinearSVC Model for predictions.
        Table outputTable = linearSVCModel.transform(inputTable)[0];

        // Extracts and displays the results.
        for (CloseableIterator it = outputTable.execute().collect(); it.hasNext(); ) {
            Row row = it.next();
            DenseVector features = (DenseVector) row.getField(linearSVC.getFeaturesCol());
            double expectedResult = (Double) row.getField(linearSVC.getLabelCol());
            double predictionResult = (Double) row.getField(linearSVC.getPredictionCol());
            DenseVector rawPredictionResult =
                    (DenseVector) row.getField(linearSVC.getRawPredictionCol());
            System.out.printf(
                    "Features: %-25s \tExpected Result: %s \tPrediction Result: %s \tRaw Prediction Result: %s\n",
                    features, expectedResult, predictionResult, rawPredictionResult);
        }
    }
}