BoxPlot异常检测 (BoxPlotOutlierStreamOp)

Java 类名：com.alibaba.alink.operator.stream.outlier.BoxPlotOutlierStreamOp
Python 类名：BoxPlotOutlierStreamOp

功能介绍

• BoxPlot算法又叫做箱线图算法, 是一种常用的异常检测算法.
• 它由五个数值点组成：最小值(min)，下四分位数(Q1)，中位数(median)，上四分位数(Q3)，最大值(max)，大于Q3 + K IQR和小于Q1 - K IQR的点定义为异常值(Outlier)。
• k通常取值为1.5或者3.

  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 | | —- | —- | —- | —- | —- | —- | —- | | precedingTime | 时间窗口大小 | 时间窗口大小 | String | ✓ | | | | predictionCol | 预测结果列名 | 预测结果列名 | String | ✓ | | | | direction | Not available! | Not available! | String | | “POSITIVE”, “NEGATIVE”, “BOTH” | “BOTH” | | featureCol | 特征列名 | 特征列名，默认选最左边的列 | String | | 所选列类型为 [BIGDECIMAL, BIGINTEGER, BYTE, DOUBLE, FLOAT, INTEGER, LONG, SHORT] | null | | groupCols | 分组列名数组 | 分组列名，多列，可选，默认不选 | String[] | | | null | | outlierThreshold | 异常评分阈值 | 只有评分大于该阈值才会被认为是异常点 | Double | | | | | precedingRows | 数据窗口大小 | 数据窗口大小 | Integer | | | null | | predictionDetailCol | 预测详细信息列名 | 预测详细信息列名 | String | | | | | timeCol | 时间戳列(TimeStamp) | 时间戳列(TimeStamp) | String | | | null | | numThreads | 组件多线程线程个数 | 组件多线程线程个数 | Integer | | | 1 |

代码示例

Python 代码

from pyalink.alink import *
import pandas as pd
useLocalEnv(1)
import time, datetime
import numpy as np
import pandas as pd
data = pd.DataFrame([
            [1, datetime.datetime.fromtimestamp(1), 10.0, 0],
            [1, datetime.datetime.fromtimestamp(2), 11.0, 0],
            [1, datetime.datetime.fromtimestamp(3), 12.0, 0],
            [1, datetime.datetime.fromtimestamp(4), 13.0, 0],
            [1, datetime.datetime.fromtimestamp(5), 14.0, 0],
            [1, datetime.datetime.fromtimestamp(6), 15.0, 0],
            [1, datetime.datetime.fromtimestamp(7), 16.0, 0],
            [1, datetime.datetime.fromtimestamp(8), 17.0, 0],
            [1, datetime.datetime.fromtimestamp(9), 18.0, 0],
            [1, datetime.datetime.fromtimestamp(10), 19.0, 0]
])
dataOp = dataframeToOperator(data, schemaStr='id int, ts timestamp, val double, label int', op_type='stream')
outlierOp = BoxPlotOutlierStreamOp()\
            .setGroupCols(["id"])\
            .setTimeCol("ts")\
            .setPrecedingRows(3)\
            .setFeatureCol("val")\
            .setPredictionCol("pred")\
            .setPredictionDetailCol("pred_detail")
dataOp.link(outlierOp).print()
StreamOperator.execute()

Java 代码

package com.alibaba.alink.operator.stream.outlier;
import org.apache.flink.types.Row;
import com.alibaba.alink.operator.stream.StreamOperator;
import com.alibaba.alink.operator.stream.sink.CollectSinkStreamOp;
import com.alibaba.alink.operator.stream.source.MemSourceStreamOp;
import junit.framework.TestCase;
import org.junit.Assert;
import org.junit.Test;
import java.sql.Timestamp;
import java.util.Arrays;
import java.util.List;
public class BoxPlotOutlierStreamOpTest extends TestCase {
    @Test
    public void test() throws Exception {
        List <Row> mTableData = Arrays.asList(
            Row.of(1, new Timestamp(1), 10.0, 0),
            Row.of(1, new Timestamp(2), 11.0, 0),
            Row.of(1, new Timestamp(3), 12.0, 0),
            Row.of(1, new Timestamp(4), 13.0, 0),
            Row.of(1, new Timestamp(5), 14.0, 0),
            Row.of(1, new Timestamp(6), 15.0, 0),
            Row.of(1, new Timestamp(7), 16.0, 0),
            Row.of(1, new Timestamp(8), 17.0, 0),
            Row.of(1, new Timestamp(9), 18.0, 0),
            Row.of(1, new Timestamp(10), 19.0, 0)
        );
        MemSourceStreamOp dataOp = new MemSourceStreamOp(mTableData, new String[] {"id", "ts", "val", "label"});
        BoxPlotOutlierStreamOp outlierOp = new BoxPlotOutlierStreamOp()
            .setGroupCols("id")
            .setTimeCol("ts")
            .setPrecedingRows(3)
            .setFeatureCol("val")
            .setPredictionCol("pred")
            .setPredictionDetailCol("pred_detail");
        dataOp.link(outlierOp).print();
        StreamOperator.execute();
    }
}

运行结果