import org.apache.flink.ml.feature.minmaxscaler.MinMaxScaler;
import org.apache.flink.ml.feature.minmaxscaler.MinMaxScalerModel;
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 MinMaxScaler model and uses it for feature engineering. */
public class MinMaxScalerExample {
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(Vectors.dense(0.0, 3.0)),
Row.of(Vectors.dense(2.1, 0.0)),
Row.of(Vectors.dense(4.1, 5.1)),
Row.of(Vectors.dense(6.1, 8.1)),
Row.of(Vectors.dense(200, 400)));
Table trainTable = tEnv.fromDataStream(trainStream).as("input");
DataStream<Row> predictStream =
env.fromElements(
Row.of(Vectors.dense(150.0, 90.0)),
Row.of(Vectors.dense(50.0, 40.0)),
Row.of(Vectors.dense(100.0, 50.0)));
Table predictTable = tEnv.fromDataStream(predictStream).as("input");
// Creates a MinMaxScaler object and initializes its parameters.
MinMaxScaler minMaxScaler = new MinMaxScaler();
// Trains the MinMaxScaler Model.
MinMaxScalerModel minMaxScalerModel = minMaxScaler.fit(trainTable);
// Uses the MinMaxScaler Model for predictions.
Table outputTable = minMaxScalerModel.transform(predictTable)[0];
// Extracts and displays the results.
for (CloseableIterator<Row> it = outputTable.execute().collect(); it.hasNext(); ) {
Row row = it.next();
DenseVector inputValue = (DenseVector) row.getField(minMaxScaler.getInputCol());
DenseVector outputValue = (DenseVector) row.getField(minMaxScaler.getOutputCol());
System.out.printf("Input Value: %-15s\tOutput Value: %s\n", inputValue, outputValue);
}
}
}
# Simple program that trains a MinMaxScaler model 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.minmaxscaler import MinMaxScaler
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([
(Vectors.dense(0.0, 3.0),),
(Vectors.dense(2.1, 0.0),),
(Vectors.dense(4.1, 5.1),),
(Vectors.dense(6.1, 8.1),),
(Vectors.dense(200, 400),),
],
type_info=Types.ROW_NAMED(
['input'],
[DenseVectorTypeInfo()])
))
predict_data = t_env.from_data_stream(
env.from_collection([
(Vectors.dense(150.0, 90.0),),
(Vectors.dense(50.0, 40.0),),
(Vectors.dense(100.0, 50.0),),
],
type_info=Types.ROW_NAMED(
['input'],
[DenseVectorTypeInfo()])
))
# create a min-max-scaler object and initialize its parameters
min_max_scaler = MinMaxScaler()
# train the min-max-scaler model
model = min_max_scaler.fit(train_data)
# use the min-max-scaler model for predictions
output = model.transform(predict_data)[0]
# extract and display the results
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(min_max_scaler.get_input_col())]
output_value = result[field_names.index(min_max_scaler.get_output_col())]
print('Input Value: ' + str(input_value) + ' \tOutput Value: ' + str(output_value))