多標籤分類評估 (EvalMultiLabelBatchOp)

Java 類名：com.alibaba.alink.operator.batch.evaluation.EvalMultiLabelBatchOp

Python 類名：EvalMultiLabelBatchOp

功能介紹

多label分類評估是對多label分類演算法的預測結果進行效果評估，支援下列評估指標。

f1

引數說明

程式碼示例

Python 程式碼

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 程式碼

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