• ALINK(三十七):模型评估(二)回归评估 (EvalRegressionBatchOp)


    Java 类名:com.alibaba.alink.operator.batch.evaluation.EvalRegressionBatchOp

    Python 类名:EvalRegressionBatchOp

    功能介绍

    回归评估是对回归算法的预测结果进行效果评估,支持下列评估指标。

     

     

     

     

     

    参数说明

    名称

    中文名称

    描述

    类型

    是否必须?

    默认值

    labelCol

    标签列名

    输入表中的标签列名

    String

     

    predictionCol

    预测结果列名

    预测结果列名

    String

     

    代码示例

    Python 代码

    from pyalink.alink import *
    import pandas as pd
    useLocalEnv(1)
    df = pd.DataFrame([
        [0, 0],
        [8, 8],
        [1, 2],
        [9, 10],
        [3, 1],
        [10, 7]
    ])
    inOp = BatchOperator.fromDataframe(df, schemaStr='pred int, label int')
    metrics = EvalRegressionBatchOp().setPredictionCol("pred").setLabelCol("label").linkFrom(inOp).collectMetrics()
    print("Total Samples Number:", metrics.getCount())
    print("SSE:", metrics.getSse())
    print("SAE:", metrics.getSae())
    print("RMSE:", metrics.getRmse())
    print("R2:", metrics.getR2())

    Java 代码

    import org.apache.flink.types.Row;
    import com.alibaba.alink.operator.batch.BatchOperator;
    import com.alibaba.alink.operator.batch.evaluation.EvalRegressionBatchOp;
    import com.alibaba.alink.operator.batch.source.MemSourceBatchOp;
    import com.alibaba.alink.operator.common.evaluation.RegressionMetrics;
    import org.junit.Test;
    import java.util.Arrays;
    import java.util.List;
    public class EvalRegressionBatchOpTest {
      @Test
      public void testEvalRegressionBatchOp() throws Exception {
        List <Row> df = Arrays.asList(
          Row.of(0, 0),
          Row.of(8, 8),
          Row.of(1, 2),
          Row.of(9, 10),
          Row.of(3, 1),
          Row.of(10, 7)
        );
        BatchOperator <?> inOp = new MemSourceBatchOp(df, "pred int, label int");
        RegressionMetrics metrics = new EvalRegressionBatchOp().setPredictionCol("pred").setLabelCol("label").linkFrom(
          inOp).collectMetrics();
        System.out.println("Total Samples Number:" + metrics.getCount());
        System.out.println("SSE:" + metrics.getSse());
        System.out.println("SAE:" + metrics.getSae());
        System.out.println("RMSE:" + metrics.getRmse());
        System.out.println("R2:" + metrics.getR2());
      }
    }

    运行结果

    Total Samples Number: 6.0
    SSE: 15.0
    SAE: 7.0
    RMSE: 1.5811388300841898
    R2: 0.8282442748091603
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  • 原文地址:https://www.cnblogs.com/qiu-hua/p/14902328.html
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