sa-learn-nonspam.raw

#!/usr/bin/perl -w -T

use strict;

use File::Spec;

my $PREFIX = '@@PREFIX@@';  # substituted at 'make' time
my $DEF_RULES_DIR = '@@DEF_RULES_DIR@@';  # substituted at 'make' time
my $LOCAL_RULES_DIR = '@@LOCAL_RULES_DIR@@';  # substituted at 'make' time

use lib '@@INSTALLSITELIB@@';  # substituted at 'make' time

BEGIN {
  # Locate locally installed SA libraries *without* using FindBin, which generates
  # warnings and causes more trouble than its worth.  We don't need to be too
  # smart about this BTW.
  my @bin = File::Spec->splitpath($0);
  my $bin = ($bin[0] ? File::Spec->catpath(@bin[0..1]) : $bin[1])  # /home/jm/foo -> /home/jm
            || File::Spec->curdir;                                 # foo          -> .

  # check to make sure it wasn't just installed in the normal way.
  # note that ./lib/Mail/SpamAssassin.pm takes precedence, for
  # building SpamAssassin on a machine where an old version is installed.
  if (-e $bin.'/lib/Mail/SpamAssassin.pm'
        || !-e '@@INSTALLSITELIB@@/Mail/SpamAssassin.pm')
  {
    # These are common paths where the SA libs might be found.
    foreach (qw(lib ../lib/site_perl
                ../lib/spamassassin ../share/spamassassin/lib))
    {
      my $dir = File::Spec->catdir($bin, split('/', $_));
      if(-f File::Spec->catfile($dir, "Mail", "SpamAssassin.pm")) {
        unshift(@INC, $dir); last;
      }
    }
  }
}

require Mail::SpamAssassin::CmdLearn;
exit Mail::SpamAssassin::CmdLearn::cmdline_run ({ isspam => 0 });

# ---------------------------------------------------------------------------

=head1 NAME

sa-learn-nonspam - train spamassassin with nonspam data

=head1 SYNOPSIS

B<sa-learn-nonspam> [options] --file I<message>

B<sa-learn-nonspam> [options] --mbox I<mailbox>

B<sa-learn-nonspam> [options] --dir I<directory>

B<sa-learn-nonspam> [options] --single < I<message>

Options:

 -f file, --folders=file           Read list of files/directories from file
 --dir                             Learn a directory of RFC 822 files
 --file                            Learn a file in RFC 822 format
 --mbox                            Learn a file in mbox format
 --showdots                        Show progress using dots
 --no-rebuild                      Skip building databases after scan
 -C file, --config-file=file       Path to standard configuration dir
 -p prefs, --prefs-file=file       Set user preferences file
 -a, --auto-whitelist              Use auto-whitelists
 --whitelist-factory               Select whitelist factory
 -D, --debug-level                 Print debugging messages
 -V, --version                     Print version
 -h, --help                        Print usage message

=head1 DESCRIPTION

Given a typical selection of your incoming mail classified as spam or non-spam,
this tool will feed each mail to SpamAssassin, allowing it to 'learn' what
signs are likely to mean spam, and which are likely to mean non-spam.

Simply run this command once for each of your mail folders, and it will
''learn'' from the mail therein.  If you make a mistake and scan a mail as
non-spam when it is spam or vice versa, simply rescan it correctly and the
mistake will be corrected.

SpamAssassin remembers which mail messages it's learnt already, and will not
re-learn those messages again, unless you use the B<sa-forget> command.

(Note: this command is not present in a released version of SpamAssassin yet.)

=head1 INTRODUCTION TO BAYESIAN FILTERING

(Thanks to Michael Bell for this section!)

For a more lengthy description of how this works, go to
http:///www.paulgraham.com and see "A Plan for Spam". It's reasonably
readable, even if statistics make me break out in hives.

The short semi-inaccurate version: Given training, a spam heuristics engine
can take the most "spammy" and "nonspammy" words and apply probablistic
analysis. Furthermore, once given a basis for the analysis, the engine can
continue to learn iteratively by applying both it's non-Bayesian and Bayesian
ruleset together to create evolving "intelligence".

SpamAssassin 2.50 supports Bayesian spam analysis, in the form of the BAYES
rules. This is a new feature, quite powerful, and disabled by default.

The pros of Bayesian spam analysis:

=over 4

=item Can greatly reduce false positives and false negatives.

It learns from your mail, so it's tailored to your unique e-mail flow.

=item Once it starts learning, it can continue to learn from SpamAssassin
and improve over time.

A planned feature is to add 'auto-learning' to SpamAssassin, so that
it can reliably self-train.  It's not present yet, though.

=back

And the cons:

=over 4

=item It requires a fair amount of work to get running.

A lot of hand-sorting of mail is crucial.

=item It's hard to explain why a message is or isn't marked as spam.

By which I mean: a straightforward rule, that matches, say, "VIAGRA" is
easy to understand. If it generates a false positive or false negative,
it's fairly easy to understand why.

With Bayesian analysis, it's all probabilities - "because the past says
it's likely as this falls into a probablistic distribution common to past
spam in your systems". Tell that to your users!  Tell that to the client
when he asks "what can I do to change this". (By the way, the answer in
this case is "use whitelisting".)

=item It will take disk space and memory.

The databases it maintains take quite a lot of resources to store and use.

=back

=head1 GETTING STARTED

Still interested? Ok, here's the guidelines for getting this working.

First a high-level overview:

=over 4

=item Build a significant sample of both spam and non-spam.

I suggest several thousand of each, placed in SPAM and NONSPAM directories or
mailboxes.  Yes, you MUST hand-sort this - otherwise the results won't be much
better than SpamAssassin on its own. Verify the spamminess/nonspammiess of
EVERY message. I urge you to avoid using a publicly available corpus (sample) -
this must be taken from YOUR mail server, if it's to be statistically useful.
Otherwise, the results may be pretty skewed.

=item Use the SpamAssassin tools (more below) B<sa-learn-spam> and
B<sa-learn-nonspam> to teach SpamAssassin about these samples, like
so:

	sa-learn-spam /path/to/spam/folder
	sa-learn-nonspam /path/to/nonspam/folder
	...

Let SpamAssassin proceed, learning stuff. When it find spam and non-spam
it will add the "interesting tokens" to the database.

=item Once you've learnt everything you want in this session, run
B<sa-learn-rebuild> to put those changes into the production database.

	sa-learn-rebuild

This will probably take 30 seconds to several minutes to complete, depending on
how much mail you trained with, and how fast your machine is.

=item If you need SpamAssassin to forget about specific messages, use
B<sa-forget>.

This can be applied to either spam or non-spam that has run through the
B<sa-learn> processes. It's a bit of a hammer, really, lowering the
weighting of the specific tokens in that message (only if that message has
been processed before).

=item Learning from single messages uses a command like this:

	cat mailmessage | sa-learn-nonspam --single

This is handy for binding to a key in your mail user agent.  It's very fast, as
all the time-consuming stuff is deferred until you run the C<sa-learn-rebuild>
command.

=back

=head1 EFFECTIVE TRAINING

Learning filters require training to be effective.  If you don't train
them, they won't work.  In addition, you need to train them with new
messages regularly to keep them up-to-date, or their data will become
stale and impact accuracy.

You need to train with both spam I<and> non-spam mails.  One type of mail
alone will not have any effect.

Note that if your mail folders contain things like forwarded spam,
discussions of spam-catching rules, etc., this will cause trouble.  You
should avoid scanning those messages if possible.  (An easy way to do this
is to move them aside, into a folder which is not scanned.)

Another thing to be aware of, is that typically you should aim to train
with at least 1000 messages of spam, and 1000 non-spam messages, if
possible.  More is better, but anything over about 5000 messages does not
improve accuracy significantly in our tests.

It's also worth noting that training with a very very small quantity of
non-spam, will produce atrocious results.  You should aim to train with at
least the same amount (or more if possible!) of non-spam data than spam.

On an on-going basis, it's best to keep training the filter to make
sure it has fresh data to work from.  There are various ways to do
this:

=over 4

=item 1. Supervised learning

This means keeping a copy of all or most of your mail, separated into spam
and nonspam piles, and periodically re-training using those.  It produces
the best results, but requires more work from you, the user.

(An easy way to do this, by the way, is to create a new folder for
'deleted' messages, and instead of deleting them from other folders,
simply move them in there instead.  Then keep all spam in a separate
folder and never delete it.  As long as you remember to move misclassified
mails into the correct folder set, it's easy enough to keep up to date.)

=item 2. Unsupervised learning from Bayesian classification

Another way to train is to chain the results of the Bayesian classifier
back into the training, so it reinforces its own decisions.  This is only
safe if you then retrain it based on any errors you discover.

SpamAssassin does not support this method, due to experimental results
which strongly indicate that it does not work well, and since Bayes is
only one part of the resulting score presented to the user (while Bayes
may have made the wrong decision about a mail, it may have been overridden
by another system).

=item 3. Unsupervised learning from SpamAssassin rules

Also called 'auto-learning' in SpamAssassin.  Based on statistical
analysis of the SpamAssassin success rates, we can automatically train the
Bayesian database with a certain degree of confidence that our training
data is accurate.

It should be supplemented with some supervised training in addition, if
possible.

This will be turned on by setting the SpamAssassin configuration parameter
C<auto_learn> to 1; note, however, that it is not supported in the current
version of SpamAssassin.

=back

=head1 OPTIONS

=over 4

=item B<-a>, B<--auto-whitelist>

Use auto-whitelists.  While learning, add addresses to the auto-whitelist
as appropriate.

=item B<-h>, B<--help>

Print help message and exit.

=item B<-C> I<config>, B<--config-file>=I<config>

Read configuration from I<config>.

=item B<-p> I<prefs>, B<--prefs-file>=I<prefs>

Read user score preferences from I<prefs>.

=item B<-D>, B<--debug-level>

Produce diagnostic output.

=item B<-M> I<factory>, B<--whitelist-factory>=I<factory>

Select alternative whitelist factory.

=item B<--no-rebuild>

Skip the slow rebuilding step which normally takes place after changing
database entries.  If you plan to scan many folders in a batch, it is faster to
use this switch and run B<sa-learn-rebuild> once all the folders have been
scanned.

=back

=head1 INSTALLATION

The B<sa-learn-nonspam> command is part of the B<Mail::SpamAssassin> Perl
module.  Install this as a normal Perl module, using C<perl -MCPAN -e shell>,
or by hand.

=head1 ENVIRONMENT

No environment variables, aside from those used by perl, are required to
be set.

=head1 SEE ALSO

Mail::SpamAssassin(3)
spamassassin(1)
sa-learn-spam(1)
sa-learn-nonspam(1)
sa-learn-rebuild(1)
sa-forget(1)
http:///www.paulgraham.com, "A Plan For Spam"

http://www.bgl.nu/~glouis/bogofilter/test6000.html, discussion of various Bayes
training regimes, including 'train on error' and unsupervised training

=head1 AUTHOR

Justin Mason E<lt>jm /at/ jmason.orgE<gt>

=cut