多标签分类评估 (EvalMultiLabelBatchOp)

Java 类名：com.alibaba.alink.operator.batch.evaluation.EvalMultiLabelBatchOp
Python 类名：EvalMultiLabelBatchOp

功能介绍

多label分类评估是对多label分类算法的预测结果进行效果评估，支持下列评估指标。

subsetAccuracy

#### hammingLoss #### accuracy #### microPrecision #### microRecall #### microF1 #### precision #### recall #### f1

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 | ✓ | | |

| 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\":\"[0.0, 1.0]\"}", "{\"object\":\"[0.0, 2.0]\"}"],
    ["{\"object\":\"[0.0, 2.0]\"}", "{\"object\":\"[0.0, 1.0]\"}"],
    ["{\"object\":\"[]\"}", "{\"object\":\"[0.0]\"}"],
    ["{\"object\":\"[2.0]\"}", "{\"object\":\"[2.0]\"}"],
    ["{\"object\":\"[2.0, 0.0]\"}", "{\"object\":\"[2.0, 0.0]\"}"],
    ["{\"object\":\"[0.0, 1.0, 2.0]\"}", "{\"object\":\"[0.0, 1.0]\"}"],
    ["{\"object\":\"[1.0]\"}", "{\"object\":\"[1.0, 2.0]\"}"]
])
source = BatchOperator.fromDataframe(df, "pred string, label string")
evalMultiLabelBatchOp: EvalMultiLabelBatchOp = EvalMultiLabelBatchOp().setLabelCol("label").setPredictionCol("pred").linkFrom(source)
metrics = evalMultiLabelBatchOp.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.EvalMultiLabelBatchOp;
import com.alibaba.alink.operator.batch.source.MemSourceBatchOp;
import com.alibaba.alink.operator.common.evaluation.MultiLabelMetrics;
import org.junit.Test;
import java.util.Arrays;
import java.util.List;
public class EvalMultiLabelBatchOpTest {
    @Test
    public void testEvalMultiLabelBatchOp() throws Exception {
        List <Row> df = Arrays.asList(
            Row.of("{\"object\":\"[0.0, 1.0]\"}", "{\"object\":\"[0.0, 2.0]\"}"),
            Row.of("{\"object\":\"[0.0, 2.0]\"}", "{\"object\":\"[0.0, 1.0]\"}"),
            Row.of("{\"object\":\"[]\"}", "{\"object\":\"[0.0]\"}"),
            Row.of("{\"object\":\"[2.0]\"}", "{\"object\":\"[2.0]\"}"),
            Row.of("{\"object\":\"[2.0, 0.0]\"}", "{\"object\":\"[2.0, 0.0]\"}"),
            Row.of("{\"object\":\"[0.0, 1.0, 2.0]\"}", "{\"object\":\"[0.0, 1.0]\"}"),
            Row.of("{\"object\":\"[1.0]\"}", "{\"object\":\"[1.0, 2.0]\"}")
        );
        BatchOperator <?> source = new MemSourceBatchOp(df, "pred string, label string");
        EvalMultiLabelBatchOp evalMultiLabelBatchOp =
            new EvalMultiLabelBatchOp().setLabelCol("label").setPredictionCol(
            "pred").linkFrom(source);
        MultiLabelMetrics metrics = evalMultiLabelBatchOp.collectMetrics();
        System.out.println(metrics.toString());
    }
}

运行结果

-------------------------------- Metrics: --------------------------------
microPrecision:0.7273
microF1:0.6957
subsetAccuracy:0.2857
precision:0.6667
recall:0.6429
accuracy:0.5476
f1:0.6381
microRecall:0.6667
hammingLoss:0.3333