Binarizer
This documentation is for an unreleased version of Apache Flink Machine Learning Library. We recommend you use the latest stable version.

Binarizer #

Binarizer binarizes the columns of continuous features by the given thresholds. The continuous features may be DenseVector, SparseVector, or Numerical Value.

Input Columns #

Param name Type Default Description
inputCols Number/Vector null Number/Vectors to be binarized.

Output Columns #

Param name Type Default Description
outputCols Number/Vector null Binarized Number/Vectors.

Parameters #

Key Default Type Required Description
inputCols null String[] yes Input column names.
outputCols null String[] yes Output column name.
thresholds null Double[] yes The thresholds used to binarize continuous features.

Examples #

import org.apache.flink.ml.feature.binarizer.Binarizer;
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;

import java.util.Arrays;

/** Simple program that creates a Binarizer instance and uses it for feature engineering. */
public class BinarizerExample {
    public static void main(String[] args) {
        StreamExecutionEnvironment env = StreamExecutionEnvironment.getExecutionEnvironment();
        StreamTableEnvironment tEnv = StreamTableEnvironment.create(env);

        // Generates input data.
        DataStream<Row> inputStream =
                env.fromElements(
                        Row.of(
                                1,
                                Vectors.dense(1, 2),
                                Vectors.sparse(
                                        17, new int[] {0, 3, 9}, new double[] {1.0, 2.0, 7.0})),
                        Row.of(
                                2,
                                Vectors.dense(2, 1),
                                Vectors.sparse(
                                        17, new int[] {0, 2, 14}, new double[] {5.0, 4.0, 1.0})),
                        Row.of(
                                3,
                                Vectors.dense(5, 18),
                                Vectors.sparse(
                                        17, new int[] {0, 11, 12}, new double[] {2.0, 4.0, 4.0})));

        Table inputTable = tEnv.fromDataStream(inputStream).as("f0", "f1", "f2");

        // Creates a Binarizer object and initializes its parameters.
        Binarizer binarizer =
                new Binarizer()
                        .setInputCols("f0", "f1", "f2")
                        .setOutputCols("of0", "of1", "of2")
                        .setThresholds(0.0, 0.0, 0.0);

        // Transforms input data.
        Table outputTable = binarizer.transform(inputTable)[0];

        // Extracts and displays the results.
        for (CloseableIterator<Row> it = outputTable.execute().collect(); it.hasNext(); ) {
            Row row = it.next();

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

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

# Simple program that creates a Binarizer instance and uses it for feature
# engineering.

from pyflink.common import Types
from pyflink.datastream import StreamExecutionEnvironment
from pyflink.ml.core.linalg import Vectors, DenseVectorTypeInfo
from pyflink.ml.lib.feature.binarizer import Binarizer
from pyflink.table import StreamTableEnvironment

# create a new StreamExecutionEnvironment
env = StreamExecutionEnvironment.get_execution_environment()

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

# generate input data
input_data_table = t_env.from_data_stream(
    env.from_collection([
        (1,
         Vectors.dense(3, 4)),
        (2,
         Vectors.dense(6, 2))
    ],
        type_info=Types.ROW_NAMED(
            ['f0', 'f1'],
            [Types.INT(), DenseVectorTypeInfo()])))

# create an binarizer object and initialize its parameters
binarizer = Binarizer() \
    .set_input_cols('f0', 'f1') \
    .set_output_cols('of0', 'of1') \
    .set_thresholds(1.5, 3.5)

# use the binarizer for feature engineering
output = binarizer.transform(input_data_table)[0]

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