ALINK(四十)：模型评估（五）排序评估 (EvalRankingBatchOp)

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

Python 类名：EvalRankingBatchOp

功能介绍

排序评估是对推荐排序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	✓
labelRankingInfo	Object列列名	Object列列名	String		"object"
predictionRankingInfo	Object列列名	Object列列名	String		"object"

代码示例

Python 代码

from pyalink.alink import *
import pandas as pd
useLocalEnv(1)
df = pd.DataFrame([
    ["{"object":"[1, 6, 2, 7, 8, 3, 9, 10, 4, 5]"}", "{"object":"[1, 2, 3, 4, 5]"}"],
    ["{"object":"[4, 1, 5, 6, 2, 7, 3, 8, 9, 10]"}", "{"object":"[1, 2, 3]"}"],
    ["{"object":"[1, 2, 3, 4, 5]"}", "{"object":"[]"}"]
])
inOp = BatchOperator.fromDataframe(df, schemaStr='pred string, label string')
metrics = EvalRankingBatchOp().setPredictionCol('pred').setLabelCol('label').linkFrom(inOp).collectMetrics()
print(metrics)

Java 代码

import org.apache.flink.types.Row;
import com.alibaba.alink.operator.batch.BatchOperator;
import com.alibaba.alink.operator.batch.evaluation.EvalRankingBatchOp;
import com.alibaba.alink.operator.batch.source.MemSourceBatchOp;
import com.alibaba.alink.operator.common.evaluation.RankingMetrics;
import org.junit.Test;
import java.util.Arrays;
import java.util.List;
public class EvalRankingBatchOpTest {
  @Test
  public void testEvalRankingBatchOp() throws Exception {
    List <Row> df = Arrays.asList(
      Row.of("{"object":"[1, 6, 2, 7, 8, 3, 9, 10, 4, 5]"}", "{"object":"[1, 2, 3, 4, 5]"}"),
      Row.of("{"object":"[4, 1, 5, 6, 2, 7, 3, 8, 9, 10]"}", "{"object":"[1, 2, 3]"}"),
      Row.of("{"object":"[1, 2, 3, 4, 5]"}", "{"object":"[]"}")
    );
    BatchOperator <?> inOp = new MemSourceBatchOp(df, "pred string, label string");
    RankingMetrics metrics = new EvalRankingBatchOp().setPredictionCol("pred").setLabelCol("label").linkFrom(inOp)
      .collectMetrics();
    System.out.println(metrics.toString());
  }
}

运行结果

-------------------------------- Metrics: --------------------------------
microPrecision:0.32
averageReciprocalHitRank:0.5
precision:0.2667
accuracy:0.2667
f1:0.3761
hitRate:0.6667
microRecall:1
microF1:0.4848
subsetAccuracy:0
recall:0.6667
map:0.355
hammingLoss:0.5667