Elementwise Product
This documentation is for an unreleased version of Apache Flink Machine Learning Library. We recommend you use the latest stable version.

Elementwise Product #

Elementwise Product multiplies each input vector with a given scaling vector using Hadamard product. If the size of the input vector does not equal the size of the scaling vector, the transformer will throw an IllegalArgumentException.

Input Columns #

Param name Type Default Description
inputCol Vector "input" Features to be scaled.

Output Columns #

Param name Type Default Description
outputCol Vector "output" Scaled features.

Parameters #

Key Default Type Required Description
inputCol "input" String no Input column name.
outputCol "output" String no Output column name.
scalingVec null String yes The scaling vector.

Examples #

import org.apache.flink.ml.feature.elementwiseproduct.ElementwiseProduct;
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;

/**
 * Simple program that creates an ElementwiseProduct instance and uses it for feature engineering.
 */
public class ElementwiseProductExample {
    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)), Row.of(1, Vectors.dense(2.1, 3.1)));

        Table inputTable = tEnv.fromDataStream(inputStream).as("id", "vec");

        // Creates an ElementwiseProduct object and initializes its parameters.
        ElementwiseProduct elementwiseProduct =
                new ElementwiseProduct()
                        .setInputCol("vec")
                        .setOutputCol("outputVec")
                        .setScalingVec(Vectors.dense(1.1, 1.1));

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

        // Extracts and displays the results.
        for (CloseableIterator<Row> it = outputTable.execute().collect(); it.hasNext(); ) {
            Row row = it.next();
            Vector inputValue = (Vector) row.getField(elementwiseProduct.getInputCol());
            Vector outputValue = (Vector) row.getField(elementwiseProduct.getOutputCol());
            System.out.printf("Input Value: %s \tOutput Value: %s\n", inputValue, outputValue);
        }
    }
}

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

from pyflink.common import Types
from pyflink.datastream import StreamExecutionEnvironment
from pyflink.ml.core.linalg import Vectors, DenseVectorTypeInfo
from pyflink.ml.lib.feature.elementwiseproduct import ElementwiseProduct
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(2.1, 3.1)),
        (2, Vectors.dense(1.1, 3.3))
    ],
        type_info=Types.ROW_NAMED(
            ['id', 'vec'],
            [Types.INT(), DenseVectorTypeInfo()])))

# create an elementwise product object and initialize its parameters
elementwise_product = ElementwiseProduct() \
    .set_input_col('vec') \
    .set_output_col('output_vec') \
    .set_scaling_vec(Vectors.dense(1.1, 1.1))

# use the elementwise product object for feature engineering
output = elementwise_product.transform(input_data_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():
    input_value = result[field_names.index(elementwise_product.get_input_col())]
    output_value = result[field_names.index(elementwise_product.get_output_col())]
    print('Input Value: ' + str(input_value) + '\tOutput Value: ' + str(output_value))