KSigma异常检测 (KSigmaOutlierStreamOp)

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

Python 类名：KSigmaOutlierStreamOp

功能介绍

KSigma算法是一种常用的异常检测算法,如果整体数据服从正态分布，则如果一个点偏离均值K倍标准差，则该点被视为异常点.

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	✓
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
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 = KSigmaOutlierStreamOp()\
			.setGroupCols(["id"])\
			.setTimeCol("ts")\
			.setPrecedingRows(3)\
			.setFeatureCol("val")\
			.setPredictionCol("pred")\
			.setPredictionDetailCol("pred_detail")

dataOp.link(outlierOp).print()

StreamOperator.execute()

java示例

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 com.alibaba.alink.testutil.AlinkTestBase;
import org.junit.Assert;
import org.junit.Test;

import java.sql.Timestamp;
import java.util.Arrays;
import java.util.List;

public class KSigmaOutlierStreamOpTest extends AlinkTestBase {

	@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"});

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

		dataOp.link(outlierOp).print();

		StreamOperator.execute();

	}

}

运行结果

无