import org.apache.flink.ml.feature.tokenizer.Tokenizer;
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 Tokenizer instance and uses it for feature engineering. */
public class TokenizerExample {
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("Test for tokenization."), Row.of("Te,st. punct"));
Table inputTable = tEnv.fromDataStream(inputStream).as("input");
// Creates a Tokenizer object and initializes its parameters.
Tokenizer tokenizer = new Tokenizer().setInputCol("input").setOutputCol("output");
// Uses the Tokenizer object for feature transformations.
Table outputTable = tokenizer.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(tokenizer.getInputCol());
String[] outputValues = (String[]) row.getField(tokenizer.getOutputCol());
System.out.printf(
"Input Value: %s \tOutput Values: %s\n",
inputValue, Arrays.toString(outputValues));
}
}
}
# Simple program that creates a Tokenizer instance and uses it for feature
# engineering.
from pyflink.common import Types
from pyflink.datastream import StreamExecutionEnvironment
from pyflink.ml.feature.tokenizer import Tokenizer
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([
('Test for tokenization.',),
('Te,st. punct',),
],
type_info=Types.ROW_NAMED(
['input'],
[Types.STRING()])))
# Creates a Tokenizer object and initializes its parameters.
tokenizer = Tokenizer() \
.set_input_col("input") \
.set_output_col("output")
# Uses the Tokenizer object for feature transformations.
output = tokenizer.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(tokenizer.get_input_col())]
output_value = result[field_names.index(tokenizer.get_output_col())]
print('Input Value: ' + str(input_value) + '\tOutput Values: ' + str(output_value))