import org.apache.flink.ml.feature.vectorassembler.VectorAssembler;
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 a VectorAssembler instance and uses it for feature engineering. */
public class VectorAssemblerExample {
    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(2.1, 3.1),
                                1.0,
                                Vectors.sparse(5, new int[] {3}, new double[] {1.0})),
                        Row.of(
                                Vectors.dense(2.1, 3.1),
                                1.0,
                                Vectors.sparse(
                                        5,
                                        new int[] {4, 2, 3, 1},
                                        new double[] {4.0, 2.0, 3.0, 1.0})));
        Table inputTable = tEnv.fromDataStream(inputStream).as("vec", "num", "sparseVec");

        // Creates a VectorAssembler object and initializes its parameters.
        VectorAssembler vectorAssembler =
                new VectorAssembler()
                        .setInputCols("vec", "num", "sparseVec")
                        .setOutputCol("assembledVec");

        // Uses the VectorAssembler object for feature transformations.
        Table outputTable = vectorAssembler.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[vectorAssembler.getInputCols().length];
            for (int i = 0; i < inputValues.length; i++) {
                inputValues[i] = row.getField(vectorAssembler.getInputCols()[i]);
            }

            Vector outputValue = (Vector) row.getField(vectorAssembler.getOutputCol());

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

# Simple program that creates a VectorAssembler instance and uses it for feature
# engineering.
#
# 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, SparseVectorTypeInfo
from pyflink.ml.lib.feature.vectorassembler import VectorAssembler
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([
        (Vectors.dense(2.1, 3.1),
         1.0,
         Vectors.sparse(5, [3], [1.0])),
        (Vectors.dense(2.1, 3.1),
         1.0,
         Vectors.sparse(5, [1, 2, 3, 4],
                        [1.0, 2.0, 3.0, 4.0])),
    ],
        type_info=Types.ROW_NAMED(
            ['vec', 'num', 'sparse_vec'],
            [DenseVectorTypeInfo(), Types.DOUBLE(), SparseVectorTypeInfo()])))

# create a vector assembler object and initialize its parameters
vector_assembler = VectorAssembler() \
    .set_input_cols('vec', 'num', 'sparse_vec') \
    .set_output_col('assembled_vec') \
    .set_handle_invalid('keep')

# use the vector assembler model for feature engineering
output = vector_assembler.transform(input_data_table)[0]

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