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。

SST 总平方和(Sum of Squared for Total)

SSE 误差平方和(Sum of Squares for Error)

SSR 回归平方和(Sum of Squares for Regression)

R^2 判定系数(Coefficient of Determination)

R 多重相关系数(Multiple Correlation Coeffient)

MSE 均方误差(Mean Squared Error)

RMSE 均方根误差(Root Mean Squared Error)

SAE/SAD 绝对误差(Sum of Absolute Error/Difference)

MAE/MAD 平均绝对误差(Mean Absolute Error/Difference)

MAPE 平均绝对百分误差(Mean Absolute Percentage Error)

count 行数

explained variance 解释方差

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 | | —- | —- | —- | —- | —- | —- | —- |

| 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