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