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