ALINK(三十九)：模型评估（四）多标签分类评估 (EvalMultiLabelBatchOp)

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。

 

f1

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":"[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());
  }
}

-------------------------------- 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