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Bayesian Tail
flok writes "We all know anti-spam-software using Bayesian filtering. The results with these are amazingly good. So that made me thinking: why not create a tool which monitors logfiles and determines using a Bayesian filter what events to display and what not? That's why I created btail. Btail is just that: it monitors a logfile and filters it with a Bayesian filter. The results are above my own expectations!"
This is a cool idea but I wouldn't want to use it on to filter logs on important systems... every line may be crucial.
Anyhow credits on a decent idea
A psychopath can't tell the difference between right and wrong. A sociopath knows the difference - he just doesn't care.
Do you have any examples of what type of stuff it learns to filter and what it learns to show? The btail site is kind of lacking of what it outputs versus what it filters
Still very preliminary at this point, but shows promise. Now, to build and try it out!
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Better be careful to train the filter about those warnings that don't happen very often, but when they do, you really want to know about them.
One line blog. I hear that they're called Twitters now.
I currently use CRM114 and on the mailing list, some one (Evan Prodromou) has created a program that does just this using the CRM114 language. It is called "Monkeyplexer" based on the idea that you could train a monkey to sort your mail box into folders.
r -0.7.tar.gz
If you pop over to the CRM114 site and search the general list archives for monkeyplexer to find the discussions about it.
Here is the last version announcement that I could find in my mailbox:
monkeyplexer is a tool for automatically sorting incoming email messages into appropriate folders. A new version of monkeyplexer, 0.7, is now available. http://bad.dynu.ca/~evan/monkeyplexer/monkeyplexe
This version includes the following changes:
You can specify which mailboxes to use, instead of which mailboxes to exclude. This can save some typing and some time at runtime, at the expense of dynamically updating the list. You can tell the monkeytrainer to only train messages that were received in the last few weeks, days, hours, minutes -- whatever. The monkeyplexer remembers which messages have been trained for which folders. If you train a message for a different folder, the monkeyplexer will automatically forget the first folder before training for the new one. Thanks to everyone who has installed monkeyplexer already. I hope this new version helps some people out. I find it easier and more accurate.
~ESP
Bayesian filtering could be used for lots of things outside of spam. One example could possibly be Wikis, determining spam from ham modifications (well, yes, it is spam here). I've had some other ideas that involve Bayesian, but they've escaped me for the moment.
Before you walk a mile in someone's shoes, you should insult them so you know how they are and what they're doing.
POPFile is exactly what you're looking for.
Give him a break--it is the first release, and I doubt he's had much feedback yet.
All due respect, you're being a bit hard on the guy. He's not doing badly here.
:|
The [brackets] used in the usage message are standard in the Unix world for specifying an optional or default argument. Just look at any man page. So that, actually, is pretty straightforward. The name of the default config file would likely also be spelled out in the man page, which I would expect, so that's not confusing.
As for changing the if construct into a switch, well, I'm trusting the accuracy of your excerpt, but I didn't find his code to be very difficult to read, to be honest, and certainly not a candidate for DailyWTF, which typically contains laughably horrible code.
As far as other code may go, the guy states that this is in a nascent stage, so jumping on his source files seems like a bit of an easy shot
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assert(expired(knowledge));
I thought this app was learning everything was in the log, and then only showed the new out-of-the-ordinary log entries that didn't quite fit in with the rest. This would allow to filter out freak events from the log and show them to the user. How different would such an app be from the proposed btail? And how confident would you be about such an unsupervised log analyzer?
--- Sigmentation Fault - Comments Dumped
Why would you run this on an MS system? The critical errors are so common that btail would discard them with the rest of the log file.
Now even geeks can get a little tail!
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Step 1: Allow the option to automatically discover and load canned training packages, eg: a directory under /etc. Make it automatically pick the right training file to use when called with a logfile (so eg: btail httpd.conf knows to look for the training for httpd.conf files).
/etc directory.
Step 2: Include btail with major distros
Step 3: Any package for an app that generates logs can come with a ready-made canned training package, which gets dropped into the
That way, you could apt-get a package, start btail-ing its logfiles immediately without the need to tediously train the filter first. Training would still be possible, to personalise the filter.
Bayesian tail might be neat. I like the idea of broadening the use, but I'd much rather see bayesian filters used on my in-box for more than just spam. I envision a filter that would sort out e-mails based on subject matter. This would have the net effect of improving the filter technology because it's trying to sort e-mails you actually want to look at.
We all know that if the filter makes a mistake and hides a message in the Spam box, and chances are you'll might miss many of them, another the chance to train the filter has been lost. But if an e-mail that was intended to land in the Irate Customer box, instead lands in the Clueless Customer box, the likelihood of noticing it is much greater.
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I love Bayes stuff - and there's a very nice Python module written by divmod.
I was playing around with AIML to cobble together a basic chat bot when I realised that I could use a Bayesian parser to radically cut down the amount of AIML that I needed to write. AIML is an XML style of chat bot repsonses, it's clever in that it's highly recursive but the downside is that you need to create a rule for every eventuality.
By adding in a bit of Bayesian guessing before the AIML parser got it hands on the conversation, I'm able to keep the AIML files very focused and give the chat bot a bit more sparkle - you don't have to train him about everything. After a while he realised that 'yo', 'hi' and 'hello' are all the same thing, so he just guesses that you're saying hello and pulls out the correct response from the AIML file (rather than creating an AIML rule to deal with all the variations on 'hello').
If you're interested I'd strongly recommend installing GrokitBot. You can get the source and a bit more explanation at my site, Suttree.com
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