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

NGram #

NGram converts the input string array into an array of n-grams, where each n-gram is represented by a space-separated string of words. If the length of the input array is less than n, no n-grams are returned.

Input Columns #

Param name Type Default Description
inputCol String[] "input" Input string array.

Output Columns #

Param name Type Default Description
outputCol String[] "output" N-grams.

Parameters #

Key Default Type Required Description
n 2 Integer no Number of elements per n-gram (>=1).
inputCol "input" String no Input column name.
outputCol "output" String no Output column name.

Examples #

import org.apache.flink.ml.feature.ngram.NGram;
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 NGram instance and uses it for feature engineering. */
public class NGramExample {
	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((Object) new String[0]),
				Row.of((Object) new String[] {"a", "b", "c"}),
				Row.of((Object) new String[] {"a", "b", "c", "d"}));
		Table inputTable = tEnv.fromDataStream(inputStream).as("input");

		// Creates an NGram object and initializes its parameters.
		NGram nGram = new NGram().setN(2).setInputCol("input").setOutputCol("output");

		// Uses the NGram object for feature transformations.
		Table outputTable = nGram.transform(inputTable)[0];

		// Extracts and displays the results.
		for (CloseableIterator<Row> it = outputTable.execute().collect(); it.hasNext(); ) {
			Row row = it.next();

			String[] inputValue = (String[]) row.getField(nGram.getInputCol());
			String[] outputValue = (String[]) row.getField(nGram.getOutputCol());

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

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

from pyflink.common import Types
from pyflink.datastream import StreamExecutionEnvironment
from pyflink.ml.lib.feature.ngram import NGram
from pyflink.table import StreamTableEnvironment

env = StreamExecutionEnvironment.get_execution_environment()

t_env = StreamTableEnvironment.create(env)

# Generates input data.
input_data_table = t_env.from_data_stream(
    env.from_collection([
        ([],),
        (['a', 'b', 'c'],),
        (['a', 'b', 'c', 'd'],),
    ],
        type_info=Types.ROW_NAMED(
            ["input", ],
            [Types.OBJECT_ARRAY(Types.STRING())])))

# Creates an NGram object and initializes its parameters.
n_gram = NGram() \
    .set_input_col('input') \
    .set_n(2) \
    .set_output_col('output')

# Uses the NGram object for feature transformations.
output = n_gram.transform(input_data_table)[0]

# Extracts and displays 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(n_gram.get_input_col())]
    output_value = result[field_names.index(n_gram.get_output_col())]
    print('Input Value: ' + ' '.join(input_value) + '\tOutput Value: ' + str(output_value))