2 weeks ago
colingray83
SAS Employee
Member since
07-08-2021
- 6 Posts
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Latest posts by colingray83
Subject Views Posted 2427 04-25-2024 05:09 AM 2684 04-18-2024 04:00 AM 662 04-17-2024 12:00 PM 2811 04-17-2024 10:10 AM 1757 12-07-2023 08:30 AM 6400 02-13-2023 11:30 AM -
Activity Feed for colingray83
- Posted Re: how to connect to AWS with SAS on SAS Enterprise Guide. 04-25-2024 05:09 AM
- Posted Re: how to connect to AWS with SAS on SAS Enterprise Guide. 04-18-2024 04:00 AM
- Posted Re: Detecting Fraud Using Semisupervised Learning in SAS Viya on SAS Communities Library. 04-17-2024 12:00 PM
- Posted Re: how to connect to AWS with SAS on SAS Enterprise Guide. 04-17-2024 10:10 AM
- Liked Can I use API to automate business rules development on SAS-ID? for Saurabh_Rana. 12-07-2023 08:32 AM
- Posted Re: How can we use Network Analytics for Application Fraud Detection on SAS Communities Library. 12-07-2023 08:30 AM
- Posted Re: SAS for Anomaly Detection & Outlier Segmentation on SAS Communities Library. 02-13-2023 11:30 AM
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Posts I Liked
Subject Likes Author Latest Post 1
04-25-2024
05:09 AM
Hi,
It might be that your organisation doesn't permit connections out to s3, as if its timing out, this sounds like it might be a connection issue. You could try using curl from a command line prompt to your s3 bucket, this would at least check if your getting a connection.
Have a look at this website and check it meets your requirements
https://www.googlinux.com/retrieving-s3-objects-using-curl/
regards,
Colin
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04-18-2024
04:00 AM
Hi,
Not sure there is a debug option, though the following will list files to the log,
proc s3 config="c:/temp/aws/tks3.conf"; list "XXXsasdata"; /* will list files to the log */ run;
I've also used with Viya a libname statement that points directly to the folder and you can then read directly in the usual way. I'm not sure how AWS transfer charges apply, so i always disconnect afterwards !
caslib mys3 datasource=(srctype="s3" accesskeyid="AAAAAAAAAAAA" secretaccesskey="BBBBBBBBBBBBBBBB" region="EU_Ireland" bucket="XXXXsasdata" /* objectpath="/asr/projB" */ usessl=true);
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04-17-2024
12:00 PM
I guess a use case might be in the insurance sector where fraud is often hidden, though there are probably cases where an investigation has taken place and a result has been found of fraud or not-fraud.
Anyone used this technique in other applications?
Colin
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04-17-2024
10:10 AM
Hi,
It sounds like you need PROC S3, this reads / lists / creates files in S3 buckets. It has a variety of statements so check in support.sas.com for more details, but you will need something like:
/* read data in and import excel file*/ filename _s3file temp; %let _locals3file = %sysfunc(pathname(_s3file)); /* connect to S3 */
proc s3 config="/home/sas/Documents/tks3.conf"; get "/XXXXsasdata/fakenames100.csv" "&_locals3file"; /* XXXXsasdata is your bucket name */ run;
/* this bit hopefully works - i was using xlsx spreadsheet so haven't tested with csv */ proc import datafile =_s3file replace dbms=csv out=work.fake1; getnames=yes;
run;
you will also need a file called tks3.conf (or you can put the details in code, but this contains something like the following)
ssl=yes keyID=XXXXXXXX secret=YYYYYYYYYYYYYYYYYYYYYYYY region=euireland
Let me know how you get on - good luck.
Colin
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12-07-2023
08:30 AM
Hi,
Great article Celia. I'd also add in another feature of networks and that of repeated events. Probably best to explain by an example. Let's say we have 5 linked motor insurance claims on a network and all 5 occur at night. From our past records only 25% of claims occur at night. If we assume independence of events then the probability of all 5 claims being at night = (0.25)^5 = .001 (or around 1 in 1000). You can use the binomial distribution for 0,1,2,3 &4 claims at night.
The independence assumption is quite strong and probably OK in this case [though you may be able to think of exceptions e.g. maybe all night time shift staff etc.], in other cases may not work as well.
Good luck with your analysis.
Colin
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02-13-2023
11:30 AM
Hi,
I also like to use t-sne (or t stochastic neighbour embedding), which can highlight anomalies (and other patterns). In the example below using credit card fraud data from Kaggle, I've reduced the dimensionality from 27 down to 2 and then overlaid the fraud flags (the fraud flags played no part in building the model - only for display). Many of the frauds seem quite anomalous from the rest of the group, so might help to identify new fraud typologies. If you get a chance try it out on some data and see what you get. Good luck. Colin
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