import org.apache.flink.ml.feature.variancethresholdselector.VarianceThresholdSelector;
import org.apache.flink.ml.feature.variancethresholdselector.VarianceThresholdSelectorModel;
import org.apache.flink.ml.linalg.DenseVector;
import org.apache.flink.ml.linalg.Vectors;
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 {@link VarianceThresholdSelector} model and uses it for feature
 * selection.
 */
public class VarianceThresholdSelectorExample {

    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(1, Vectors.dense(5.0, 7.0, 0.0, 7.0, 6.0, 0.0)),
                        Row.of(2, Vectors.dense(0.0, 9.0, 6.0, 0.0, 5.0, 9.0)),
                        Row.of(3, Vectors.dense(0.0, 9.0, 3.0, 0.0, 5.0, 5.0)),
                        Row.of(4, Vectors.dense(1.0, 9.0, 8.0, 5.0, 7.0, 4.0)),
                        Row.of(5, Vectors.dense(9.0, 8.0, 6.0, 5.0, 4.0, 4.0)),
                        Row.of(6, Vectors.dense(6.0, 9.0, 7.0, 0.0, 2.0, 0.0)));
        Table trainTable = tEnv.fromDataStream(trainStream).as("id", "input");

        // Create a VarianceThresholdSelector object and initialize its parameters
        double threshold = 8.0;
        VarianceThresholdSelector varianceThresholdSelector =
                new VarianceThresholdSelector()
                        .setVarianceThreshold(threshold)
                        .setInputCol("input");

        // Train the VarianceThresholdSelector model.
        VarianceThresholdSelectorModel model = varianceThresholdSelector.fit(trainTable);

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

        // Extracts and displays the results.
        System.out.printf("Variance Threshold: %s\n", threshold);
        for (CloseableIterator<Row> it = outputTable.execute().collect(); it.hasNext(); ) {
            Row row = it.next();
            DenseVector inputValue =
                    (DenseVector) row.getField(varianceThresholdSelector.getInputCol());
            DenseVector outputValue =
                    (DenseVector) row.getField(varianceThresholdSelector.getOutputCol());
            System.out.printf("Input Values: %-15s\tOutput Values: %s\n", inputValue, outputValue);
        }
    }
}

# Simple program that trains a VarianceThresholdSelector model and uses it for feature
# selection.

from pyflink.common import Types
from pyflink.datastream import StreamExecutionEnvironment
from pyflink.ml.linalg import Vectors, DenseVectorTypeInfo
from pyflink.ml.feature.variancethresholdselector import VarianceThresholdSelector
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_data = t_env.from_data_stream(
    env.from_collection([
        (1, Vectors.dense(5.0, 7.0, 0.0, 7.0, 6.0, 0.0),),
        (2, Vectors.dense(0.0, 9.0, 6.0, 0.0, 5.0, 9.0),),
        (3, Vectors.dense(0.0, 9.0, 3.0, 0.0, 5.0, 5.0),),
        (4, Vectors.dense(1.0, 9.0, 8.0, 5.0, 7.0, 4.0),),
        (5, Vectors.dense(9.0, 8.0, 6.0, 5.0, 4.0, 4.0),),
        (6, Vectors.dense(6.0, 9.0, 7.0, 0.0, 2.0, 0.0),),
    ],
        type_info=Types.ROW_NAMED(
            ['id', 'input'],
            [Types.INT(), DenseVectorTypeInfo()])
    ))

# create a VarianceThresholdSelector object and initialize its parameters
threshold = 8.0
variance_thread_selector = VarianceThresholdSelector()\
    .set_input_col("input")\
    .set_variance_threshold(threshold)

# train the VarianceThresholdSelector model
model = variance_thread_selector.fit(train_data)

# use the VarianceThresholdSelector model for predictions
output = model.transform(train_data)[0]

# extract and display the results
print("Variance Threshold: " + str(threshold))
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(variance_thread_selector.get_input_col())]
    output_value = result[field_names.index(variance_thread_selector.get_output_col())]
    print('Input Values: ' + str(input_value) + ' \tOutput Values: ' + str(output_value))