import org.apache.flink.ml.feature.stringindexer.StringIndexer;
import org.apache.flink.ml.feature.stringindexer.StringIndexerModel;
import org.apache.flink.ml.feature.stringindexer.StringIndexerParams;
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 trains a StringIndexer model and uses it for feature engineering. */
public class StringIndexerExample {
    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("a", 1.0),
                        Row.of("b", 1.0),
                        Row.of("b", 2.0),
                        Row.of("c", 0.0),
                        Row.of("d", 2.0),
                        Row.of("a", 2.0),
                        Row.of("b", 2.0),
                        Row.of("b", -1.0),
                        Row.of("a", -1.0),
                        Row.of("c", -1.0));
        Table trainTable = tEnv.fromDataStream(trainStream).as("inputCol1", "inputCol2");

        DataStream<Row> predictStream =
                env.fromElements(Row.of("a", 2.0), Row.of("b", 1.0), Row.of("c", 2.0));
        Table predictTable = tEnv.fromDataStream(predictStream).as("inputCol1", "inputCol2");

        // Creates a StringIndexer object and initializes its parameters.
        StringIndexer stringIndexer =
                new StringIndexer()
                        .setStringOrderType(StringIndexerParams.ALPHABET_ASC_ORDER)
                        .setInputCols("inputCol1", "inputCol2")
                        .setOutputCols("outputCol1", "outputCol2");

        // Trains the StringIndexer Model.
        StringIndexerModel model = stringIndexer.fit(trainTable);

        // Uses the StringIndexer 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();

            Object[] inputValues = new Object[stringIndexer.getInputCols().length];
            double[] outputValues = new double[stringIndexer.getInputCols().length];
            for (int i = 0; i < inputValues.length; i++) {
                inputValues[i] = row.getField(stringIndexer.getInputCols()[i]);
                outputValues[i] = (double) row.getField(stringIndexer.getOutputCols()[i]);
            }

            System.out.printf(
                    "Input Values: %s \tOutput Values: %s\n",
                    Arrays.toString(inputValues), Arrays.toString(outputValues));
        }
    }
}

# Simple program that trains a StringIndexer model and uses it for feature
# engineering.

from pyflink.common import Types
from pyflink.datastream import StreamExecutionEnvironment
from pyflink.ml.feature.stringindexer import StringIndexer
from pyflink.table import StreamTableEnvironment

# 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_data_stream(
    env.from_collection([
        ('a', 1.),
        ('b', 1.),
        ('b', 2.),
        ('c', 0.),
        ('d', 2.),
        ('a', 2.),
        ('b', 2.),
        ('b', -1.),
        ('a', -1.),
        ('c', -1.),
    ],
        type_info=Types.ROW_NAMED(
            ['input_col1', 'input_col2'],
            [Types.STRING(), Types.DOUBLE()])
    ))

predict_table = t_env.from_data_stream(
    env.from_collection([
        ('a', 2.),
        ('b', 1.),
        ('c', 2.),
    ],
        type_info=Types.ROW_NAMED(
            ['input_col1', 'input_col2'],
            [Types.STRING(), Types.DOUBLE()])
    ))

# create a string-indexer object and initialize its parameters
string_indexer = StringIndexer() \
    .set_string_order_type('alphabetAsc') \
    .set_input_cols('input_col1', 'input_col2') \
    .set_output_cols('output_col1', 'output_col2')

# train the string-indexer model
model = string_indexer.fit(train_table)

# use the string-indexer model for feature engineering
output = model.transform(predict_table)[0]

# extract and display the results
field_names = output.get_schema().get_field_names()
input_values = [None for _ in string_indexer.get_input_cols()]
output_values = [None for _ in string_indexer.get_input_cols()]
for result in t_env.to_data_stream(output).execute_and_collect():
    for i in range(len(string_indexer.get_input_cols())):
        input_values[i] = result[field_names.index(string_indexer.get_input_cols()[i])]
        output_values[i] = result[field_names.index(string_indexer.get_output_cols()[i])]
    print('Input Values: ' + str(input_values) + '\tOutput Values: ' + str(output_values))