Dave> I want to set up spambayes to work with procmail on my mail Dave> server. Does anyone have experience with that? Dave, I use spambayes with procmail. The major issue is generally not one of getting messages classified, but of getting them trained. Here are the relevant bits out of my procmailrc file: PYCKSUM=$HOME/local/bin/pycksum HAMMIE=$HOME/local/bin/hammiefilter.py BAYESCUSTOMIZE=$HOME/hammie.opt :0 fw:hamlock | $HAMMIE -d $HOME/hammie.db :0 * ^X-Spambayes-Classification: spam { ### this recipe gobbles items with matching body checksums (taken ### loosely to try and avoid obvious tricks) :0 W: cksum.lock | $PYCKSUM -v $HOME/tmp/cksum.cache ### spam scores come in two flavors - equal to 1.00 and less than ### 1.00 scores are much more likely to be real spam, so require ### less sifting - therefore keep them separate :0: * ^X-Spambayes-Classification: spam; 1.00 $SPAM1 :0: $SPAM } :0 * ^X-Spambayes-Classification: unsure unsure ... You can dispense with the PYCKSUM stuff, though I find it does delete a fair number of duplicate spams. I get email for a large number of aliases at the same address however. YMMV. I've attached the version of the script which I use. It's similar to the loosecksum.py script in the Spambayes utilities directory, but incorporates the ideas Justin Mason detailed about the SpamAssassin checksummer. Skip

You can dispense with the PYCKSUM stuff, though I find it does delete a fair number of duplicate spams. I get email for a large number of aliases at the same address however. YMMV. I've attached the version of the script which I use. It's similar to the loosecksum.py script in the Spambayes utilities directory, but incorporates the ideas Justin Mason detailed about the SpamAssassin checksummer.

Maybe you can update integration.txt with these pertinent bits? Also, perhaps check in your checksummer? c'est moi - TimS http://www.fourstonesExpressions.com http://wecanstopspam.org There are 10 kinds of people in the world: those who understand binary, and those who don't.

Skip Montanaro <skip@pobox.com> writes:

>> I use spambayes with procmail. The major issue is generally not one >> of getting messages classified, but of getting them trained.

Dave> I figured it would be; I think that's what I meant by Dave> "classified". I do have a folder full of accumulated spam. What Dave> has been your strategy for training?

Here's what I do. It's sensitive to my particular mail setup, so you can probably only use this as a rough guide.

My mail reader is VM inside XEmacs.

I'm using GNUs, FWIW.

VM has a "l"abel command prefix. I added two new keys to its keymap, "h" and "s" (which were fortuitously unused) to copy messages to spam and ham folders:

Here's what I've never understood about this system: shouldn't it be enough to label spam? GNUs gives me a key to label a message as spam. If I collect all of those, shouldn't I be able to tell spambayes that everything in my INBOX that's been read and isn't in my SpamBox is ham?

(defun copy-to-spam () (interactive) (vm-save-message (expand-file-name "~/tmp/newspam")) (vm-undelete-message 1))

(defun copy-to-nonspam () (interactive) (vm-save-message (expand-file-name "~/tmp/newham")) (vm-undelete-message 1))

(define-key vm-mode-map "ls" 'copy-to-spam) (define-key vm-summary-mode-map "ls" 'copy-to-spam) (define-key vm-mode-map "lh" 'copy-to-nonspam) (define-key vm-summary-mode-map "lh" 'copy-to-nonspam)

~/tmp/new{ham,spam} are then processed using a fairly simple shell script:

#!/bin/bash

export BAYESCUSTOMIZE=$HOME/hammie.opt cd ~/tmp

base=new db=hammie.db

# touch the messages up a bit to avoid spurious "clues" if [ -f ${base}ham -a -f ${base}spam ] ; then unheader.py -p 'X-VM|X-Hammie|X-Spam' ${base}ham > ${base}ham.clean unheader.py -p 'X-VM|X-Hammie|X-Spam' ${base}spam > ${base}spam.clean

# do the deed hammie.py -d -p $db -g ${base}ham.clean -s ${base}spam.clean

# save the files for later retraining cat ${base}ham.clean >> ${base}ham.clean.save echo "" >> ${base}ham.clean.save rm ${base}ham ${base}ham.clean

cat ${base}spam.clean >> ${base}spam.clean.save echo "" >> ${base}spam.clean.save rm ${base}spam ${base}spam.clean else echo Missing ${base}ham and/or ${base}spam files fi

I run the train script periodically to train on new ham and spam, then copy the resulting hammie.db file to where it's really used:

% train Training ham (newham.clean): 12 Training spam (newspam.clean): 29 % cp -p hammie.db ~

This setup works fine for me, though probably won't be as attractive for people who aren't as addicted to the shell prompt as I am.

Well, I'm not sure I understand it yet, but I think I'll get there. Thanks! -- Dave Abrahams Boost Consulting www.boost-consulting.com

i think this speaks well to the point that training is a individual and manual proceess! :o) b Skip Montanaro wrote:

Here's what I do. It's sensitive to my particular mail setup, so you can probably only use this as a rough guide.

My mail reader is VM inside XEmacs. VM has a "l"abel command prefix. I added two new keys to its keymap, "h" and "s" (which were fortuitously unused) to copy messages to spam and ham folders:

(defun copy-to-spam () (interactive) (vm-save-message (expand-file-name "~/tmp/newspam")) (vm-undelete-message 1))

(defun copy-to-nonspam () (interactive) (vm-save-message (expand-file-name "~/tmp/newham")) (vm-undelete-message 1))

(define-key vm-mode-map "ls" 'copy-to-spam) (define-key vm-summary-mode-map "ls" 'copy-to-spam) (define-key vm-mode-map "lh" 'copy-to-nonspam) (define-key vm-summary-mode-map "lh" 'copy-to-nonspam)

~/tmp/new{ham,spam} are then processed using a fairly simple shell script:

#!/bin/bash

export BAYESCUSTOMIZE=$HOME/hammie.opt cd ~/tmp

base=new db=hammie.db

# touch the messages up a bit to avoid spurious "clues" if [ -f ${base}ham -a -f ${base}spam ] ; then unheader.py -p 'X-VM|X-Hammie|X-Spam' ${base}ham > ${base}ham.clean unheader.py -p 'X-VM|X-Hammie|X-Spam' ${base}spam > ${base}spam.clean

# do the deed hammie.py -d -p $db -g ${base}ham.clean -s ${base}spam.clean

# save the files for later retraining cat ${base}ham.clean >> ${base}ham.clean.save echo "" >> ${base}ham.clean.save rm ${base}ham ${base}ham.clean

cat ${base}spam.clean >> ${base}spam.clean.save echo "" >> ${base}spam.clean.save rm ${base}spam ${base}spam.clean else echo Missing ${base}ham and/or ${base}spam files fi

I run the train script periodically to train on new ham and spam, then copy the resulting hammie.db file to where it's really used:

% train Training ham (newham.clean): 12 Training spam (newspam.clean): 29 % cp -p hammie.db ~

This setup works fine for me, though probably won't be as attractive for people who aren't as addicted to the shell prompt as I am.

Skip

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