多分类评估 (EvalMultiClassBatchOp)

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

功能介绍

多分类评估是对多分类算法的预测结果进行效果评估。
支持Roc曲线，LiftChart曲线，K-S曲线，Recall-Precision曲线绘制。
流式的实验支持累计统计和窗口统计，除却上述四条曲线外，还给出Auc/Kappa/Accuracy/Logloss随时间的变化曲线。
给出整体的评估指标包括：AUC、K-S、PRC, 不同阈值下的Precision、Recall、F-Measure、Sensitivity、Accuracy、Specificity和Kappa。

混淆矩阵

#### Precision

Recall

F-Measure

Sensitivity

Accuracy

Specificity

Kappa

Logloss

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 | | | |

| predictionDetailCol | 预测详细信息列名 | 预测详细信息列名 | String | | 所选列类型为 [STRING] | |

代码示例

Python 代码

from pyalink.alink import *
import pandas as pd
useLocalEnv(1)
df = pd.DataFrame([
    ["prefix1", "{\"prefix1\": 0.9, \"prefix0\": 0.1}"],
    ["prefix1", "{\"prefix1\": 0.8, \"prefix0\": 0.2}"],
    ["prefix1", "{\"prefix1\": 0.7, \"prefix0\": 0.3}"],
    ["prefix0", "{\"prefix1\": 0.75, \"prefix0\": 0.25}"],
    ["prefix0", "{\"prefix1\": 0.6, \"prefix0\": 0.4}"]])
inOp = BatchOperator.fromDataframe(df, schemaStr='label string, detailInput string')
metrics = EvalMultiClassBatchOp().setLabelCol("label").setPredictionDetailCol("detailInput").linkFrom(inOp).collectMetrics()
print("Prefix0 accuracy:", metrics.getAccuracy("prefix0"))
print("Prefix1 recall:", metrics.getRecall("prefix1"))
print("Macro Precision:", metrics.getMacroPrecision())
print("Micro Recall:", metrics.getMicroRecall())
print("Weighted Sensitivity:", metrics.getWeightedSensitivity())

Java 代码

import org.apache.flink.types.Row;
import com.alibaba.alink.operator.batch.BatchOperator;
import com.alibaba.alink.operator.batch.evaluation.EvalMultiClassBatchOp;
import com.alibaba.alink.operator.batch.source.MemSourceBatchOp;
import com.alibaba.alink.operator.common.evaluation.MultiClassMetrics;
import org.junit.Test;
import java.util.Arrays;
import java.util.List;
public class EvalMultiClassBatchOpTest {
    @Test
    public void testEvalMultiClassBatchOp() throws Exception {
        List <Row> df = Arrays.asList(
            Row.of("prefix1", "{\"prefix1\": 0.9, \"prefix0\": 0.1}"),
            Row.of("prefix1", "{\"prefix1\": 0.8, \"prefix0\": 0.2}"),
            Row.of("prefix1", "{\"prefix1\": 0.7, \"prefix0\": 0.3}"),
            Row.of("prefix0", "{\"prefix1\": 0.75, \"prefix0\": 0.25}")
        );
        BatchOperator <?> inOp = new MemSourceBatchOp(df, "label string, detailInput string");
        MultiClassMetrics metrics = new EvalMultiClassBatchOp().setLabelCol("label").setPredictionDetailCol(
            "detailInput").linkFrom(inOp).collectMetrics();
        System.out.println("Prefix0 accuracy:" + metrics.getAccuracy("prefix0"));
        System.out.println("Prefix1 recall:" + metrics.getRecall("prefix1"));
        System.out.println("Macro Precision:" + metrics.getMacroPrecision());
        System.out.println("Micro Recall:" + metrics.getMicroRecall());
        System.out.println("Weighted Sensitivity:" + metrics.getWeightedSensitivity());
    }
}

运行结果

Prefix0 accuracy: 0.6
Prefix1 recall: 1.0
Macro Precision: 0.8
Micro Recall: 0.6
Weighted Sensitivity: 0.6