决策树回归预测 (DecisionTreeRegPredictStreamOp)

Java 类名：com.alibaba.alink.operator.stream.regression.DecisionTreeRegPredictStreamOp
Python 类名：DecisionTreeRegPredictStreamOp

功能介绍

• 决策树回归组件支持稠密数据格式

• 支持带样本权重的训练

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 |

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

| modelStreamFilePath | 模型流的文件路径 | 模型流的文件路径 | String | | | null |

| modelStreamScanInterval | 扫描模型路径的时间间隔 | 描模型路径的时间间隔，单位秒 | Integer | | | 10 |

| modelStreamStartTime | 模型流的起始时间 | 模型流的起始时间。默认从当前时刻开始读。使用yyyy-mm-dd hh:mm:ss.fffffffff格式，详见Timestamp.valueOf(String s) | String | | | null |

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 = DecisionTreeRegTrainBatchOp()\
    .setLabelCol('label')\
    .setFeatureCols(['f0', 'f1', 'f2', 'f3'])\
    .linkFrom(batchSource)
predictBatchOp = DecisionTreeRegPredictBatchOp()\
    .setPredictionCol('pred')
predictStreamOp = DecisionTreeRegPredictStreamOp(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.DecisionTreeRegPredictBatchOp;
import com.alibaba.alink.operator.batch.regression.DecisionTreeRegTrainBatchOp;
import com.alibaba.alink.operator.batch.source.MemSourceBatchOp;
import com.alibaba.alink.operator.stream.StreamOperator;
import com.alibaba.alink.operator.stream.regression.DecisionTreeRegPredictStreamOp;
import com.alibaba.alink.operator.stream.source.MemSourceStreamOp;
import org.junit.Test;
import java.util.Arrays;
import java.util.List;
public class DecisionTreeRegPredictStreamOpTest {
    @Test
    public void testDecisionTreeRegPredictStreamOp() throws Exception {
        List <Row> df = Arrays.asList(
            Row.of(1.0, "A", 0L, 0L, 0L),
            Row.of(2.0, "B", 1L, 1L, 0L),
            Row.of(3.0, "C", 2L, 2L, 1L),
            Row.of(4.0, "D", 3L, 3L, 1L)
        );
        BatchOperator <?> batchSource = new MemSourceBatchOp(
            df, "f0 double, f1 string, f2 long, f3 long, label long");
        StreamOperator <?> streamSource = new MemSourceStreamOp(
            df, "f0 double, f1 string, f2 long, f3 long, label long");
        BatchOperator <?> trainOp = new DecisionTreeRegTrainBatchOp()
            .setLabelCol("label")
            .setFeatureCols("f0", "f1", "f2", "f3")
            .linkFrom(batchSource);
        BatchOperator <?> predictBatchOp = new DecisionTreeRegPredictBatchOp()
            .setPredictionCol("pred");
        StreamOperator <?> predictStreamOp = new DecisionTreeRegPredictStreamOp(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 |

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

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

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