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<Row> 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<Row> 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);
        }
    }
}

# Simple program that trains a LinearSVC model and uses it for classification.
#
# 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
#
# https://nightlies.apache.org/flink/flink-ml-docs-master/docs/try-flink-ml/quick-start/

from pyflink.common import Types
from pyflink.datastream import StreamExecutionEnvironment
from pyflink.ml.core.linalg import Vectors, DenseVectorTypeInfo
from pyflink.ml.lib.classification.linearsvc import LinearSVC
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
input_table = t_env.from_data_stream(
    env.from_collection([
        (Vectors.dense([1, 2, 3, 4]), 0., 1.),
        (Vectors.dense([2, 2, 3, 4]), 0., 2.),
        (Vectors.dense([3, 2, 3, 4]), 0., 3.),
        (Vectors.dense([4, 2, 3, 4]), 0., 4.),
        (Vectors.dense([5, 2, 3, 4]), 0., 5.),
        (Vectors.dense([11, 2, 3, 4]), 1., 1.),
        (Vectors.dense([12, 2, 3, 4]), 1., 2.),
        (Vectors.dense([13, 2, 3, 4]), 1., 3.),
        (Vectors.dense([14, 2, 3, 4]), 1., 4.),
        (Vectors.dense([15, 2, 3, 4]), 1., 5.),
    ],
        type_info=Types.ROW_NAMED(
            ['features', 'label', 'weight'],
            [DenseVectorTypeInfo(), Types.DOUBLE(), Types.DOUBLE()])
    ))

# create a linear svc object and initialize its parameters
linear_svc = LinearSVC().set_weight_col('weight')

# train the linear svc model
model = linear_svc.fit(input_table)

# use the linear svc model for predictions
output = model.transform(input_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():
    features = result[field_names.index(linear_svc.get_features_col())]
    expected_result = result[field_names.index(linear_svc.get_label_col())]
    prediction_result = result[field_names.index(linear_svc.get_prediction_col())]
    raw_prediction_result = result[field_names.index(linear_svc.get_raw_prediction_col())]
    print('Features: ' + str(features) + ' \tExpected Result: ' + str(expected_result)
          + ' \tPrediction Result: ' + str(prediction_result)
          + ' \tRaw Prediction Result: ' + str(raw_prediction_result))