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