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ESD异常检测 (EsdOutlierStreamOp)

Java 类名:com.alibaba.alink.operator.stream.outlier.EsdOutlierStreamOp

Python 类名:EsdOutlierStreamOp

功能介绍

  • Esd算法又叫做箱线图算法, 是一种常用的异常检测算法.

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
predictionCol 预测结果列名 预测结果列名 String
alpha 置信度 置信度 Double 0.05
direction 方向 检测异常的方向 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
maxIter 最大迭代步数 最大迭代步数 Integer
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
precedingTime 时间窗口大小 时间窗口大小 String 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 = EsdOutlierStreamOp()\
			.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 EsdOutlierStreamOpTest 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"});

		EsdOutlierStreamOp outlierOp = new EsdOutlierStreamOp()
			.setGroupCols("id")
			.setTimeCol("ts")
			.setPrecedingRows(3)
			.setFeatureCol("val")
			.setPredictionCol("pred")
			.setPredictionDetailCol("pred_detail");

		dataOp.link(outlierOp).print();

		StreamOperator.execute();
	}
}

运行结果