import org.apache.flink.ml.feature.interaction.Interaction;
import org.apache.flink.ml.linalg.Vector;
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;

import java.util.Arrays;

/** Simple program that creates an Interaction instance and uses it for feature engineering. */
public class InteractionExample {
    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(0, Vectors.dense(1.1, 3.2), Vectors.dense(2, 3)),
                        Row.of(1, Vectors.dense(2.1, 3.1), Vectors.dense(1, 3)));

        Table inputTable = tEnv.fromDataStream(inputStream).as("f0", "f1", "f2");

        // Creates an Interaction object and initializes its parameters.
        Interaction interaction =
                new Interaction().setInputCols("f0", "f1", "f2").setOutputCol("outputVec");

        // Transforms input data.
        Table outputTable = interaction.transform(inputTable)[0];

        // Extracts and displays the results.
        for (CloseableIterator<Row> it = outputTable.execute().collect(); it.hasNext(); ) {
            Row row = it.next();
            Object[] inputValues = new Object[interaction.getInputCols().length];
            for (int i = 0; i < inputValues.length; i++) {
                inputValues[i] = row.getField(interaction.getInputCols()[i]);
            }
            Vector outputValue = (Vector) row.getField(interaction.getOutputCol());
            System.out.printf(
                    "Input Values: %s \tOutput Value: %s\n",
                    Arrays.toString(inputValues), outputValue);
        }
    }
}

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

from pyflink.common import Types
from pyflink.datastream import StreamExecutionEnvironment
from pyflink.ml.linalg import Vectors, DenseVectorTypeInfo
from pyflink.ml.feature.interaction import Interaction
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_data_table = t_env.from_data_stream(
    env.from_collection([
        (1,
         Vectors.dense(1, 2),
         Vectors.dense(3, 4)),
        (2,
         Vectors.dense(2, 8),
         Vectors.dense(3, 4))
    ],
        type_info=Types.ROW_NAMED(
            ['f0', 'f1', 'f2'],
            [Types.INT(), DenseVectorTypeInfo(), DenseVectorTypeInfo()])))

# create an interaction object and initialize its parameters
interaction = Interaction() \
    .set_input_cols('f0', 'f1', 'f2') \
    .set_output_col('interaction_vec')

# use the interaction for feature engineering
output = interaction.transform(input_data_table)[0]

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