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

RandomSplitter #

An AlgoOperator which splits a table into N tables according to the given weights.

Parameters #

Key Default Type Required Description
weights [1.0, 1.0] Double[] no The weights of data splitting.

Examples #

import org.apache.flink.ml.feature.randomsplitter.RandomSplitter;
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 a RandomSplitter instance and uses it for data splitting. */
public class RandomSplitterExample {
	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(1, 10, 0),
				Row.of(1, 10, 0),
				Row.of(1, 10, 0),
				Row.of(4, 10, 0),
				Row.of(5, 10, 0),
				Row.of(6, 10, 0),
				Row.of(7, 10, 0),
				Row.of(10, 10, 0),
				Row.of(13, 10, 3));
		Table inputTable = tEnv.fromDataStream(inputStream).as("input");

		// Creates a RandomSplitter object and initializes its parameters.
		RandomSplitter splitter = new RandomSplitter().setWeights(4.0, 6.0);

		// Uses the RandomSplitter to split inputData.
		Table[] outputTables = splitter.transform(inputTable);

		// Extracts and displays the results.
		System.out.println("Split Result 1 (40%)");
		for (CloseableIterator<Row> it = outputTables[0].execute().collect(); it.hasNext(); ) {
			System.out.printf("%s\n", it.next());
		}
		System.out.println("Split Result 2 (60%)");
		for (CloseableIterator<Row> it = outputTables[1].execute().collect(); it.hasNext(); ) {
			System.out.printf("%s\n", it.next());
		}
	}
}

# Simple program that creates a RandomSplitter instance and uses it for data splitting.

from pyflink.common import Types
from pyflink.datastream import StreamExecutionEnvironment
from pyflink.ml.lib.feature.randomsplitter import RandomSplitter
from pyflink.table import StreamTableEnvironment

# Creates a new StreamExecutionEnvironment.
env = StreamExecutionEnvironment.get_execution_environment()

# Creates a StreamTableEnvironment.
t_env = StreamTableEnvironment.create(env)

# Generates input table.
input_table = t_env.from_data_stream(
    env.from_collection([
        (1, 10, 0),
        (1, 10, 0),
        (1, 10, 0),
        (4, 10, 0),
        (5, 10, 0),
        (6, 10, 0),
        (7, 10, 0),
        (10, 10, 0),
        (13, 10, 0)
    ],
        type_info=Types.ROW_NAMED(
            ['f0', 'f1', "f2"],
            [Types.INT(), Types.INT(), Types.INT()])))

# Creates a RandomSplitter object and initializes its parameters.
splitter = RandomSplitter().set_weights(4.0, 6.0)

# Uses the RandomSplitter to split the dataset.
output = splitter.transform(input_table)

# Extracts and displays the results.
print("Split Result 1 (40%)")
for result in t_env.to_data_stream(output[0]).execute_and_collect():
    print(str(result))

print("Split Result 2 (60%)")
for result in t_env.to_data_stream(output[1]).execute_and_collect():
    print(str(result))