use strict;
use File::Spec;
use vars qw/ $PREFIX $DEF_RULES_DIR $LOCAL_RULES_DIR /;
$PREFIX = '@@PREFIX@@'; $DEF_RULES_DIR = '@@DEF_RULES_DIR@@'; $LOCAL_RULES_DIR = '@@LOCAL_RULES_DIR@@';
use lib '@@INSTALLSITELIB@@';
BEGIN {
my @bin = File::Spec->splitpath($0);
my $bin = ($bin[0] ? File::Spec->catpath(@bin[0..1]) : $bin[1]) || File::Spec->curdir;
if (-e $bin.'/lib/Mail/SpamAssassin.pm'
|| !-e '@@INSTALLSITELIB@@/Mail/SpamAssassin.pm')
{
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 ();
=head1 NAME
sa-learn - train SpamAssassin's Bayesian classifier
=head1 SYNOPSIS
B<sa-learn> [options] [file]...
B<sa-learn> [options] --dump [ all | data | magic ]
Options:
--ham Learn messages as ham (non-spam)
--spam Learn messages as spam
--forget Forget a message
--rebuild Rebuild the database if needed
--force-expire Force an expiry run, rebuild every time
--dump [all|data|magic] Display the contents of the Bayes database
Takes optional argument for what to display
--dbpath <path> For dump/import only, specifies (in bayes_path form)
where to read the Bayes DB from
--regexp <re> For dump only, specifies which tokens to
dump based on a regular expression.
-f file, --folders=file Read list of files/directories from file
--dir Ignored; historical compatability
--file Ignored; historical compatability
--mbox Input sources are in mbox format
--showdots Show progress using dots
--no-rebuild Skip building databases after scan
-L, --local Operate locally, no network accesses
--import Upgrade data from an earlier database version
-C path, --configpath=path, --config-file=path Path to standard configuration dir
-p prefs, --prefspath=file, --prefs-file=file Set user preferences file
--siteconfigpath=path Path for site configs (def: /etc/mail/spamassassin)
-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 ham
(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 ham.
Simply run this command once for each of your mail folders, and it will
''learn'' from the mail therein.
Note that I<globbing> in the mail folder names is supported (except when
using perl 5.005 in taint-mode); in other words, listing a folder name
as C<*> will scan every folder that matches.
SpamAssassin remembers which mail messages it's learnt already, and will not
re-learn those messages again, unless you use the B<--forget> option.
Messages learnt as spam will have SpamAssassin markup removed, on the fly.
If you make a mistake and scan a mail as ham when it is spam, or vice
versa, simply rerun this command with the correct classification, and the
mistake will be corrected. SpamAssassin will automatically 'forget' the
previous indications.
=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 "hammy" 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 and later supports Bayesian spam analysis, in
the form of the BAYES rules. This is a new feature, quite powerful,
and is disabled until enough messages have been learnt.
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.
=back
And the cons:
=over 4
=item A decent number of messages are required before results are useful
for ham/spam determination.
=item It's hard to explain why a message is or isn't marked as spam.
i.e.: 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 ham and spam.
I suggest several thousand of each, placed in SPAM and HAM 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/haminess of EVERY
message. You're urged 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 this tool to teach SpamAssassin about these samples, like so:
sa-learn --spam /path/to/spam/folder
sa-learn --ham /path/to/ham/folder
...
Let SpamAssassin proceed, learning stuff. When it finds ham and spam
it will add the "interesting tokens" to the database.
=item If you need SpamAssassin to forget about specific messages, use
the B<--forget> option.
This can be applied to either ham or 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 --ham --no-rebuild
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 with the C<--rebuild>
option.
=item Autolearning is enabled by default
If you don't have a corpus of mail saved to learn, you can let
SpamAssassin automatically learn the mail that you receive. If you are
autolearning from scratch, the amount of mail you receive will determine
how long until the BAYES_* rules are activated.
=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> ham 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.)
If the messages you are learning from have already been filtered through
SpamAssassin, the learner will compensate for this. In effect, it learns what
each message would look like if you had run C<spamassassin -d> over it in
advance.
Another thing to be aware of, is that typically you should aim to train
with at least 1000 messages of spam, and 1000 ham messages, if
possible. More is better, but anything over about 5000 messages does not
improve accuracy significantly in our tests.
Be careful that you train from the same source -- for example, if you train
on old spam, but new ham mail, then the classifier will think that
a mail with an old date stamp is likely to be spam.
It's also worth noting that training with a very small quantity of
ham, will produce atrocious results. You should aim to train with at
least the same amount (or more if possible!) of ham 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 ham 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 is the default, but can be turned off by setting the SpamAssassin
configuration parameter C<bayes_auto_learn> to 0.
=item 4. Mistake-based training
This means training on a small number of mails, then only training on
messages that SpamAssassin classifies incorrectly. This works, but it
takes longer to get it right than a full training session would.
=back
=head1 OPTIONS
=over 4
=item B<--ham>
Learn the input message(s) as ham. If you have previously learnt any of the
messages as spam, SpamAssassin will forget them first, then re-learn them as
ham. Alternatively, if you have previously learnt them as ham, it'll skip them
this time around. If the messages have already been filtered through
SpamAssassin, the learner will ignore any modifications SpamAssassin may have
made.
=item B<--spam>
Learn the input message(s) as spam. If you have previously learnt any of the
messages as ham, SpamAssassin will forget them first, then re-learn them as
spam. Alternatively, if you have previously learnt them as spam, it'll skip
them this time around. If the messages have already been filtered through
SpamAssassin, the learner will ignore any modifications SpamAssassin may have
made.
=item B<--rebuild>
Rebuild the databases, typically done after learning with B<--no-rebuild>,
or if you wish to periodically clean the Bayes databases once a day on
a busy server.
=item B<--force-expire>
Forces an expiry attempt, regardless of whether it may be necessary
or not. Note: This doesn't mean any tokens will actually expire.
Please see the EXPIRATION section below.
=item B<--forget>
Forget a given message previously learnt.
=item B<--dump> I<option>
Display the contents of the Bayes database. Without an option or with
the I<all> option, all magic tokens and data tokens will be displayed.
I<magic> will only display magic tokens, and I<data> will only display
the data tokens.
Can also use the B<--dbpath> I<path> option to specify the location of
the Bayes files to use.
Can also use the B<--regexp> I<RE> option to specify which tokens to
display based on a regular expression.
=item B<-h>, B<--help>
Print help message and exit.
=item B<-C> I<path>, B<--configpath>=I<path>, B<--config-file>=I<path>
Use the specified path for locating the distributed configuration files.
Ignore the default directories (usually C</usr/share/spamassassin> or similar).
=item B<--siteconfigpath>=I<path>
Use the specified path for locating site-specific configuration files. Ignore
the default directories (usually C</etc/mail/spamassassin> or similar).
=item B<-p> I<prefs>, B<--prefspath>=I<prefs>, B<--prefs-file>=I<prefs>
Read user score preferences from I<prefs> (usually C<$HOME/.spamassassin/user_prefs>).
=item B<-D>, B<--debug-level>
Produce diagnostic output.
=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, or to learn
individual messages one-by-one, it is faster to use this switch and run
C<sa-learn --rebuild> once all the folders have been scanned.
=item B<-L>, B<--local>
Do not perform any network accesses while learning details about the mail
messages. This will speed up the learning process, but may result in a
slightly lower accuracy.
Note that this is currently ignored, as current versions of SpamAssassin will
not perform network access while learning; but future versions may.
=item B<--import>
If you previously used SpamAssassin's Bayesian learner without the C<DB_File>
module installed, it will have created files in other formats, such as
C<GDBM_File>, C<NDBM_File>, or C<SDBM_File>. This switch allows you to migrate
that old data into the C<DB_File> format. It will overwrite any data currently
in the C<DB_File>.
Can also be used with the B<--dbpath> I<path> option to specify the location of
the Bayes files to use.
=back
=head1 FILES
B<sa-learn> and the other parts of SpamAssassin's Bayesian learner,
use a set of persistent database files to store the learnt tokens, as follows.
=over 4
=item bayes_toks
The database of tokens, containing the tokens learnt, their count of
occurrences in ham and spam, and the message count of the last message
they were seen in.
This database also contains some 'magic' tokens, as follows: the number of ham
and spam messages learnt, the number of tokens in the database, the
message-count of the last expiry run, the message-count of the oldest token in
the database, and the message-count of the current message (to the nearest
5000).
This is a database file, using the first one of the following database modules
that SpamAssassin can find in your perl installation: C<DB_File>, C<GDBM_File>,
C<NDBM_File>, or C<SDBM_File>.
=item bayes_seen
A map of message-ID to what that message was learnt as. This is used
so that SpamAssassin can avoid re-learning a message it's already seen,
and so it can reverse the training if you later decide that message
was previously learnt incorrectly.
This is a database file, using the first one of the following database modules
that SpamAssassin can find in your perl installation: C<DB_File>, C<GDBM_File>,
C<NDBM_File>, or C<SDBM_File>.
=item bayes_journal
While SpamAssassin is scanning mails, it needs to track which tokens it uses in
its calculations. So that many processes can read the databases
simultaneously, but only one can write at a time, this uses a 'journal' file.
When you run C<sa-learn --rebuild>, the journal is read, and the tokens that
were accessed during the journal's lifetime will have their last-access time
updated in the C<bayes_toks> database.
=back
=head1 EXPIRATION
Since SpamAssassin can auto-learn messages, the Bayes database files
could increase perpetually until they fill your disk. To control this,
SpamAssassin performs journal synchronization and bayes expiration
periodically when certain criteria (listed below) are met.
SpamAssassin can sync the journal and expire the DB tokens either
manually or opportunistically. A journal sync is due if I<--rebuild>
is passed to sa-learn (manual), or if the following is true
(opportunistic):
=over 4
=item - bayes_journal_max_size does not equal 0 (means don't sync)
=item - the journal file exists
=back
and either:
=over 4
=item - the journal file has a size greater than bayes_journal_max_size
=back
or
=over 4
=item - at least 1 day has passed since the last journal sync
=back
Expiry is due if I<--force-expire> is passed to sa-learn (manual),
or if all of the following are true (opportunistic):
=over 4
=item - the last expire was attempted at least 12hrs ago
=item - bayes_auto_expire does not equal 0
=item - the number of tokens in the DB is > 100,000
=item - the number of tokens in the DB is > bayes_expiry_max_db_size
=item - there is at least a 12 hr difference between the oldest and newest token atimes
=back
=head2 EXPIRE LOGIC
If either the manual or opportunistic method causes an expire run
to start, here is the logic that is used:
=over 4
=item - figure out how many tokens to keep. take the larger of
either bayes_expiry_max_db_size * 75% or 100,000 tokens. therefore, the goal
reduction is number of tokens - number of tokens to keep.
=item - if the reduction number is < 1000 tokens, abort (not worth the effort).
=item - if an expire has been done before, guesstimate the new
atime delta based on the old atime delta. (new_atime_delta =
old_atime_delta * old_reduction_count / goal)
=item - if no expire has been done before, or the last expire looks
"wierd", do an estimation pass. The definition of "wierd" is:
=over 8
=item - last expire over 30 days ago
=item - last atime delta was < 12 hrs
=item - last reduction count was < 1000 tokens
=item - estimated new atime delta is < 12 hrs
=item - the difference between the last reduction count and the goal reduction count is > 50%
=back
=back
=head2 ESTIMATION PASS LOGIC
Go through each of the DB's tokens. Starting at 12hrs, calculate
whether or not the token would be expired (based on the difference
between the token's atime and the db's newest token atime) and keep
the count. Work out from 12hrs exponentially by powers of 2. ie:
12hrs * 1, 12hrs * 2, 12hrs * 4, 12hrs * 8, and so on, up to 12hrs
* 512 (6144hrs, or 256 days).
The larger the delta, the smaller the number of tokens that will
be expired. Conversely, the number of tokens goes up as the delta
gets smaller. So starting at the largest atime delta, figure out
which delta will expire the most tokens without going above the
goal expiration count. Use this to choose the atime delta to use,
unless one of the following occurs:
=over 8
=item - the largest atime (smallest reduction count) would expire
too many tokens. this means the learned tokens are mostly old and
there needs to be new tokens learned before an expire can
occur.
=item - all of the atime choices result in 0 tokens being removed.
this means the tokens are all newer than 12 hours and there needs
to be new tokens learned before an expire can occur.
=item - the number of tokens that would be removed is < 1000. the
benefit isn't worth the effort. more tokens need to be learned.
=back
If the expire run gets past this point, it will continue to the end.
A new DB is created since the majority of DB libraries don't shrink the
DB file when tokens are removed. So we do the "create new, migrate old
to new, remove old, rename new" shuffle.
=head2 EXPIRY RELATED CONFIGURATION SETTINGS
=over 4
=item C<bayes_auto_expire> is used to specify whether or not SpamAssassin
ought to opportunistically attempt to expire the Bayes databaase.
The default is 1 (yes).
=item C<bayes_expiry_max_db_size> specifies both the auto-expire token
count point, as well as the resulting number of tokens after expiry
as described above. The default value is 150,000, which is roughly
equivalent to a 6Mb database file if you're using DB_File.
=item C<bayes_journal_max_size> specifies how large the Bayes
journal will grow before it is opportunistically synced. The
default value is 102400.
=back
=head1 INSTALLATION
The B<sa-learn> 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)
http://www.paulgraham.com/ , Paul Graham's "A Plan For Spam" paper
http://radio.weblogs.com/0101454/stories/2002/09/16/spamDetection.html , Gary
Robinson's f(x) and combining algorithms, as used in SpamAssassin
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