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))