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.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))