import org.apache.flink.ml.feature.imputer.Imputer;
import org.apache.flink.ml.feature.imputer.ImputerModel;
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 {@link Imputer} model and uses it for feature engineering. */
public class ImputerExample {

    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(Double.NaN, 9.0),
                        Row.of(1.0, 9.0),
                        Row.of(1.5, 9.0),
                        Row.of(2.5, Double.NaN),
                        Row.of(5.0, 5.0),
                        Row.of(5.0, 4.0));
        Table trainTable = tEnv.fromDataStream(trainStream).as("input1", "input2");

        // Creates an Imputer object and initialize its parameters
        Imputer imputer =
                new Imputer()
                        .setInputCols("input1", "input2")
                        .setOutputCols("output1", "output2")
                        .setStrategy("mean")
                        .setMissingValue(Double.NaN);

        // Trains the Imputer model.
        ImputerModel model = imputer.fit(trainTable);

        // Uses the Imputer model for predictions.
        Table outputTable = model.transform(trainTable)[0];

        // Extracts and displays the results.
        for (CloseableIterator<Row> it = outputTable.execute().collect(); it.hasNext(); ) {
            Row row = it.next();
            double[] inputValues = new double[imputer.getInputCols().length];
            double[] outputValues = new double[imputer.getInputCols().length];
            for (int i = 0; i < inputValues.length; i++) {
                inputValues[i] = (double) row.getField(imputer.getInputCols()[i]);
                outputValues[i] = (double) row.getField(imputer.getOutputCols()[i]);
            }
            System.out.printf(
                    "Input Values: %s\tOutput Values: %s\n",
                    Arrays.toString(inputValues), Arrays.toString(outputValues));
        }
    }
}

# Simple program that creates an Imputer instance and uses it for feature
# engineering.

from pyflink.common import Types
from pyflink.datastream import StreamExecutionEnvironment
from pyflink.ml.feature.imputer import Imputer
from pyflink.table import StreamTableEnvironment

env = StreamExecutionEnvironment.get_execution_environment()

# create a StreamTableEnvironment
t_env = StreamTableEnvironment.create(env)

# generate input training and prediction data
train_data = t_env.from_data_stream(
    env.from_collection([
        (float('NaN'), 9.0,),
        (1.0, 9.0,),
        (1.5, 7.0,),
        (1.5, float('NaN'),),
        (4.0, 5.0,),
        (None, 4.0,),
    ],
        type_info=Types.ROW_NAMED(
            ['input1', 'input2'],
            [Types.DOUBLE(), Types.DOUBLE()])
    ))

# Creates an Imputer object and initializes its parameters.
imputer = Imputer()\
    .set_input_cols('input1', 'input2')\
    .set_output_cols('output1', 'output2')\
    .set_strategy('mean')\
    .set_missing_value(float('NaN'))

# Trains the Imputer Model.
model = imputer.fit(train_data)

# Uses the Imputer Model for predictions.
output = model.transform(train_data)[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_values = []
    output_values = []
    for i in range(len(imputer.get_input_cols())):
        input_values.append(result[field_names.index(imputer.get_input_cols()[i])])
        output_values.append(result[field_names.index(imputer.get_output_cols()[i])])
    print('Input Values: ' + str(input_values) + '\tOutput Values: ' + str(output_values))