XGBoost 回归训练 (XGBoostRegTrainBatchOp)

Java 类名：com.alibaba.alink.operator.batch.regression.XGBoostRegTrainBatchOp
Python 类名：XGBoostRegTrainBatchOp

功能介绍

XGBoost 组件是在开源社区的基础上进行包装，使功能和 PAI 更兼容，更易用。
XGBoost 算法在 Boosting 算法的基础上进行了扩展和升级，具有较好的易用性和鲁棒性，被广泛用在各种机器学习生产系统和竞赛领域。
当前支持分类，回归和排序。

参数说明

代码示例

以下代码仅用于示意，可能需要修改部分代码或者配置环境后才能正常运行！

Python 代码

df = pd.DataFrame([
    [0, 1, 1.1, 1.0],
    [1, -2, 0.9, 2.0],
    [0, 100, -0.01, 3.0],
    [1, -99, 0.1, 4.0],
    [0, 1, 1.1, 5.0],
    [1, -2, 0.9, 6.0]
])
batchSource = BatchOperator.fromDataframe(
    df, schemaStr='y int, x1 double, x2 double, x3 double'
)
streamSource = StreamOperator.fromDataframe(
    df, schemaStr='y int, x1 double, x2 double, x3 double'
)
trainOp = XGBoostRegTrainBatchOp()\
    .setNumRound(1)\
    .setPluginVersion('1.5.1')\
    .setLabelCol('y')\
    .linkFrom(batchSource)
predictBatchOp = XGBoostRegPredictBatchOp()\
    .setPredictionCol('pred')\
    .setPluginVersion('1.5.1')
predictStreamOp = XGBoostRegPredictStreamOp(trainOp)\
    .setPredictionCol('pred')\
    .setPluginVersion('1.5.1')
predictBatchOp.linkFrom(trainOp, batchSource).print()
predictStreamOp.linkFrom(streamSource).print()
StreamOperator.execute()

Java 代码

import org.apache.flink.types.Row;
import com.alibaba.alink.operator.batch.BatchOperator;
import com.alibaba.alink.operator.batch.regression.XGBoostRegPredictBatchOp;
import com.alibaba.alink.operator.batch.regression.XGBoostRegTrainBatchOp;
import com.alibaba.alink.operator.batch.source.MemSourceBatchOp;
import com.alibaba.alink.operator.stream.StreamOperator;
import com.alibaba.alink.operator.stream.regression.XGBoostRegPredictStreamOp;
import com.alibaba.alink.operator.stream.source.MemSourceStreamOp;
import org.junit.Test;
import java.util.Arrays;
import java.util.List;
public class XGBoostRegTrainBatchOpTest {
    @Test
    public void testXGBoostTrainBatchOp() throws Exception {
        List <Row> data = Arrays.asList(
            Row.of(0, 1, 1.1, 1.0),
            Row.of(1, -2, 0.9, 2.0),
            Row.of(0, 100, -0.01, 3.0),
            Row.of(1, -99, 0.1, 4.0),
            Row.of(0, 1, 1.1, 5.0),
            Row.of(1, -2, 0.9, 6.0)
        );
        BatchOperator <?> batchSource = new MemSourceBatchOp(data, "y int, x1 int, x2 double, x3 double");
        StreamOperator <?> streamSource = new MemSourceStreamOp(data, "y int, x1 int, x2 double, x3 double");
        BatchOperator <?> trainOp = new XGBoostRegTrainBatchOp()
            .setNumRound(1)
            .setPluginVersion("1.5.1")
            .setLabelCol("y")
            .linkFrom(batchSource);
        BatchOperator <?> predictBatchOp = new XGBoostRegPredictBatchOp()
            .setPredictionCol("pred")
            .setPluginVersion("1.5.1");
        StreamOperator <?> predictStreamOp = new XGBoostRegPredictStreamOp(trainOp)
            .setPredictionCol("pred")
            .setPluginVersion("1.5.1");
        predictBatchOp.linkFrom(trainOp, batchSource).print();
        predictStreamOp.linkFrom(streamSource).print();
        StreamOperator.execute();
    }
}

运行结果