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.linalg import Vectors, DenseVectorTypeInfo
from pyflink.ml.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))