GBDT回归预测 (GbdtRegPredictBatchOp)

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

功能介绍

• gbdt(Gradient Boosting Decision Trees)回归，是经典的基于boosting的有监督学习模型，可以用来解决回归问题

• 支持连续特征和离散特征

• 支持数据采样和特征采样

参数说明

| 名称 | 中文名称 | 描述 | 类型 | 是否必须？ | 取值范围 | 默认值 | | —- | —- | —- | —- | —- | —- | —- |

| predictionCol | 预测结果列名 | 预测结果列名 | String | ✓ | | |

| modelFilePath | 模型的文件路径 | 模型的文件路径 | String | | | null |

| reservedCols | 算法保留列名 | 算法保留列 | String[] | | | null |

| vectorCol | 向量列名 | 向量列对应的列名，默认值是null | String | | 所选列类型为 [DENSE_VECTOR, SPARSE_VECTOR, STRING, VECTOR] | null |

| numThreads | 组件多线程线程个数 | 组件多线程线程个数 | Integer | | | 1 |

代码示例

Python 代码

from pyalink.alink import *
import pandas as pd
useLocalEnv(1)
df = pd.DataFrame([
    [1.0, "A", 0, 0, 0],
    [2.0, "B", 1, 1, 0],
    [3.0, "C", 2, 2, 1],
    [4.0, "D", 3, 3, 1]
])
batchSource = BatchOperator.fromDataframe(
    df, schemaStr='f0 double, f1 string, f2 int, f3 int, label int')
streamSource = StreamOperator.fromDataframe(
    df, schemaStr='f0 double, f1 string, f2 int, f3 int, label int')
trainOp = GbdtRegTrainBatchOp()\
    .setLearningRate(1.0)\
    .setNumTrees(3)\
    .setMinSamplesPerLeaf(1)\
    .setLabelCol('label')\
    .setFeatureCols(['f0', 'f1', 'f2', 'f3'])\
    .linkFrom(batchSource)
predictBatchOp = GbdtRegPredictBatchOp()\
    .setPredictionCol('pred')
predictStreamOp = GbdtRegPredictStreamOp(trainOp)\
    .setPredictionCol('pred')
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.GbdtRegPredictBatchOp;
import com.alibaba.alink.operator.batch.regression.GbdtRegTrainBatchOp;
import com.alibaba.alink.operator.batch.source.MemSourceBatchOp;
import com.alibaba.alink.operator.stream.StreamOperator;
import com.alibaba.alink.operator.stream.regression.GbdtRegPredictStreamOp;
import com.alibaba.alink.operator.stream.source.MemSourceStreamOp;
import org.junit.Test;
import java.util.Arrays;
import java.util.List;
public class GbdtRegPredictBatchOpTest {
    @Test
    public void testGbdtRegPredictBatchOp() throws Exception {
        List <Row> df = Arrays.asList(
            Row.of(1.0, "A", 0, 0, 0),
            Row.of(2.0, "B", 1, 1, 0),
            Row.of(3.0, "C", 2, 2, 1),
            Row.of(4.0, "D", 3, 3, 1)
        );
        BatchOperator <?> batchSource = new MemSourceBatchOp(
            df, "f0 double, f1 string, f2 int, f3 int, label int");
        StreamOperator <?> streamSource = new MemSourceStreamOp(
            df, "f0 double, f1 string, f2 int, f3 int, label int");
        BatchOperator <?> trainOp = new GbdtRegTrainBatchOp()
            .setLearningRate(1.0)
            .setNumTrees(3)
            .setMinSamplesPerLeaf(1)
            .setLabelCol("label")
            .setFeatureCols("f0", "f1", "f2", "f3")
            .linkFrom(batchSource);
        BatchOperator <?> predictBatchOp = new GbdtRegPredictBatchOp()
            .setPredictionCol("pred");
        StreamOperator <?> predictStreamOp = new GbdtRegPredictStreamOp(trainOp)
            .setPredictionCol("pred");
        predictBatchOp.linkFrom(trainOp, batchSource).print();
        predictStreamOp.linkFrom(streamSource).print();
        StreamOperator.execute();
    }
}

运行结果

批预测结果

| f0 | f1 | f2 | f3 | label | pred | | —- | —- | —- | —- | —- | —- |

| 1.0000 | A | 0 | 0 | 0 | 0.0000 |

| 2.0000 | B | 1 | 1 | 0 | 0.0000 |

| 3.0000 | C | 2 | 2 | 1 | 1.0000 |

| 4.0000 | D | 3 | 3 | 1 | 1.0000 |

流预测结果

| f0 | f1 | f2 | f3 | label | pred | | —- | —- | —- | —- | —- | —- |

| 1.0000 | A | 0 | 0 | 0 | 0.0000 |

| 4.0000 | D | 3 | 3 | 1 | 1.0000 |

| 3.0000 | C | 2 | 2 | 1 | 1.0000 |

| 2.0000 | B | 1 | 1 | 0 | 0.0000 |