import org.apache.flink.ml.feature.onehotencoder.OneHotEncoder;
import org.apache.flink.ml.feature.onehotencoder.OneHotEncoderModel;
import org.apache.flink.ml.linalg.SparseVector;
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 trains a OneHotEncoder model and uses it for feature engineering. */
public class OneHotEncoderExample {
public static void main(String[] args) {
StreamExecutionEnvironment env = StreamExecutionEnvironment.getExecutionEnvironment();
StreamTableEnvironment tEnv = StreamTableEnvironment.create(env);
// Generates input training and prediction data.
DataStream<Row> trainStream =
env.fromElements(Row.of(0.0), Row.of(1.0), Row.of(2.0), Row.of(0.0));
Table trainTable = tEnv.fromDataStream(trainStream).as("input");
DataStream<Row> predictStream = env.fromElements(Row.of(0.0), Row.of(1.0), Row.of(2.0));
Table predictTable = tEnv.fromDataStream(predictStream).as("input");
// Creates a OneHotEncoder object and initializes its parameters.
OneHotEncoder oneHotEncoder =
new OneHotEncoder().setInputCols("input").setOutputCols("output");
// Trains the OneHotEncoder Model.
OneHotEncoderModel model = oneHotEncoder.fit(trainTable);
// Uses the OneHotEncoder Model for predictions.
Table outputTable = model.transform(predictTable)[0];
// Extracts and displays the results.
for (CloseableIterator<Row> it = outputTable.execute().collect(); it.hasNext(); ) {
Row row = it.next();
Double inputValue = (Double) row.getField(oneHotEncoder.getInputCols()[0]);
SparseVector outputValue =
(SparseVector) row.getField(oneHotEncoder.getOutputCols()[0]);
System.out.printf("Input Value: %s\tOutput Value: %s\n", inputValue, outputValue);
}
}
}
# Simple program that trains a OneHotEncoder model and uses it for feature
# engineering.
from pyflink.common import Row
from pyflink.datastream import StreamExecutionEnvironment
from pyflink.ml.feature.onehotencoder import OneHotEncoder
from pyflink.table import StreamTableEnvironment, DataTypes
# create a new StreamExecutionEnvironment
env = StreamExecutionEnvironment.get_execution_environment()
# create a StreamTableEnvironment
t_env = StreamTableEnvironment.create(env)
# generate input training and prediction data
train_table = t_env.from_elements(
[Row(0.0), Row(1.0), Row(2.0), Row(0.0)],
DataTypes.ROW([
DataTypes.FIELD('input', DataTypes.DOUBLE())
]))
predict_table = t_env.from_elements(
[Row(0.0), Row(1.0), Row(2.0)],
DataTypes.ROW([
DataTypes.FIELD('input', DataTypes.DOUBLE())
]))
# create a one-hot-encoder object and initialize its parameters
one_hot_encoder = OneHotEncoder().set_input_cols('input').set_output_cols('output')
# train the one-hot-encoder model
model = one_hot_encoder.fit(train_table)
# use the one-hot-encoder model for predictions
output = model.transform(predict_table)[0]
# extract and display 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(one_hot_encoder.get_input_cols()[0])]
output_value = result[field_names.index(one_hot_encoder.get_output_cols()[0])]
print('Input Value: ' + str(input_value) + ' \tOutput Value: ' + str(output_value))