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.linalg import Vectors, DenseVectorTypeInfo
from pyflink.ml.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))