import org.apache.flink.ml.feature.kbinsdiscretizer.KBinsDiscretizer;
import org.apache.flink.ml.feature.kbinsdiscretizer.KBinsDiscretizerModel;
import org.apache.flink.ml.feature.kbinsdiscretizer.KBinsDiscretizerParams;
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 KBinsDiscretizer model and uses it for feature engineering. */
public class KBinsDiscretizerExample {
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(Vectors.dense(1, 10, 0)),
Row.of(Vectors.dense(1, 10, 0)),
Row.of(Vectors.dense(1, 10, 0)),
Row.of(Vectors.dense(4, 10, 0)),
Row.of(Vectors.dense(5, 10, 0)),
Row.of(Vectors.dense(6, 10, 0)),
Row.of(Vectors.dense(7, 10, 0)),
Row.of(Vectors.dense(10, 10, 0)),
Row.of(Vectors.dense(13, 10, 3)));
Table inputTable = tEnv.fromDataStream(inputStream).as("input");
// Creates a KBinsDiscretizer object and initializes its parameters.
KBinsDiscretizer kBinsDiscretizer =
new KBinsDiscretizer().setNumBins(3).setStrategy(KBinsDiscretizerParams.UNIFORM);
// Trains the KBinsDiscretizer Model.
KBinsDiscretizerModel model = kBinsDiscretizer.fit(inputTable);
// Uses the KBinsDiscretizer Model for predictions.
Table outputTable = model.transform(inputTable)[0];
// Extracts and displays the results.
for (CloseableIterator<Row> it = outputTable.execute().collect(); it.hasNext(); ) {
Row row = it.next();
DenseVector inputValue = (DenseVector) row.getField(kBinsDiscretizer.getInputCol());
DenseVector outputValue = (DenseVector) row.getField(kBinsDiscretizer.getOutputCol());
System.out.printf("Input Value: %s\tOutput Value: %s\n", inputValue, outputValue);
}
}
}
# Simple program that trains a KBinsDiscretizer model and uses it for feature
# engineering.
from pyflink.common import Types
from pyflink.ml.linalg import Vectors, DenseVectorTypeInfo
from pyflink.datastream import StreamExecutionEnvironment
from pyflink.ml.feature.kbinsdiscretizer import KBinsDiscretizer
from pyflink.table import StreamTableEnvironment
# Creates a new StreamExecutionEnvironment.
env = StreamExecutionEnvironment.get_execution_environment()
# Creates a StreamTableEnvironment.
t_env = StreamTableEnvironment.create(env)
# Generates input for training and prediction.
input_table = t_env.from_data_stream(
env.from_collection([
(Vectors.dense(1, 10, 0),),
(Vectors.dense(1, 10, 0),),
(Vectors.dense(1, 10, 0),),
(Vectors.dense(4, 10, 0),),
(Vectors.dense(5, 10, 0),),
(Vectors.dense(6, 10, 0),),
(Vectors.dense(7, 10, 0),),
(Vectors.dense(10, 10, 0),),
(Vectors.dense(13, 10, 0),),
],
type_info=Types.ROW_NAMED(
['input', ],
[DenseVectorTypeInfo(), ])))
# Creates a KBinsDiscretizer object and initializes its parameters.
k_bins_discretizer = KBinsDiscretizer() \
.set_input_col('input') \
.set_output_col('output') \
.set_num_bins(3) \
.set_strategy('uniform')
# Trains the KBinsDiscretizer Model.
model = k_bins_discretizer.fit(input_table)
# Uses the KBinsDiscretizer Model for predictions.
output = model.transform(input_table)[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():
print('Input Value: ' + str(result[field_names.index(k_bins_discretizer.get_input_col())])
+ '\tOutput Value: ' +
str(result[field_names.index(k_bins_discretizer.get_output_col())]))