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

StopWordsRemover #

A feature transformer that filters out stop words from input.

Note: null values from input array are preserved unless adding null to stopWords explicitly.

See Also: Stop words (Wikipedia)

Input Columns #

Param name Type Default Description
inputCols String[] null Arrays of strings containing stop words to remove.

Output Columns #

Param name Type Default Description
outputCols String[] null Arrays of strings with stop words removed.

Parameters #

Key Default Type Required Description
inputCols null String[] yes Input column names.
outputCols null String[] yes Output column name.
stopWords StopWordsRemover.loadDefaultStopWords("english") String[] no The words to be filtered out.
caseSensitive false Boolean no Whether to do a case-sensitive comparison over the stop words.
locale StopWordsRemover.getDefaultOrUS().toString() String no Locale of the input for case insensitive matching. Ignored when caseSensitive is true.

Examples #

import org.apache.flink.ml.feature.stopwordsremover.StopWordsRemover;
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 StopWordsRemover instance and uses it for feature engineering. */
public class StopWordsRemoverExample {
    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[] {"test", "test"}),
                        Row.of((Object) new String[] {"a", "b", "c", "d"}),
                        Row.of((Object) new String[] {"a", "the", "an"}),
                        Row.of((Object) new String[] {"A", "The", "AN"}),
                        Row.of((Object) new String[] {null}),
                        Row.of((Object) new String[] {}));
        Table inputTable = tEnv.fromDataStream(inputStream).as("input");

        // Creates a StopWordsRemover object and initializes its parameters.
        StopWordsRemover remover =
                new StopWordsRemover().setInputCols("input").setOutputCols("output");

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

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

            String[] inputValues = row.getFieldAs("input");
            String[] outputValues = row.getFieldAs("output");

            System.out.printf(
                    "Input Values: %s\tOutput Values: %s\n",
                    Arrays.toString(inputValues), Arrays.toString(outputValues));
        }
    }
}
# Simple program that creates a StopWordsRemover instance and uses it for feature
# engineering.

from pyflink.common import Types
from pyflink.datastream import StreamExecutionEnvironment
from pyflink.ml.lib.feature.stopwordsremover import StopWordsRemover
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_table = t_env.from_data_stream(
    env.from_collection([
        (["test", "test"],),
        (["a", "b", "c", "d"],),
        (["a", "the", "an"],),
        (["A", "The", "AN"],),
        ([None],),
        ([],),
    ],
        type_info=Types.ROW_NAMED(
            ['input'],
            [Types.OBJECT_ARRAY(Types.STRING())])))

# create a StopWordsRemover object and initialize its parameters
remover = StopWordsRemover().set_input_cols('input').set_output_cols('output')

# use the StopWordsRemover for feature engineering
output_table = remover.transform(input_table)[0]

# extract and display the results
field_names = output_table.get_schema().get_field_names()
for result in t_env.to_data_stream(output_table).execute_and_collect():
    input_value = result[field_names.index('input')]
    output_value = result[field_names.index('output')]
    print('Input Value: ' + str(input_value) + '\tOutput Value: ' + str(output_value))