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SA-LEARN(1) User Contributed Perl Documentation SA-LEARN(1)
NAME
sa-learn - train SpamAssassin's Bayesian classifier
SYNOPSIS
sa-learn [options] [file]...
sa-learn [options] --dump [ all | data | magic ]
Options:
--ham Learn messages as ham (non-spam)
--spam Learn messages as spam
--forget Forget a message
--use-ignores Use bayes_ignore_from and bayes_ignore_to
--sync Syncronize the database and the journal if needed
--force-expire Force a database sync and expiry run
--dbpath Allows commandline override (in bayes_path form)
for where to read the Bayes DB from
--dump [all|data|magic] Display the contents of the Bayes database
Takes optional argument for what to display
--regexp 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
--mbx Input sources are in mbx format
--showdots Show progress using dots
--no-sync Skip syncronizing the database and journal
after learning
-L, --local Operate locally, no network accesses
--import Migrate data from older version/non DB_File
based databases
--clear Wipe out existing database
--backup Backup, to STDOUT, existing database
--restore Restore a database from filename
-u username, --username=username Override username taken from the runtime environment
-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
DESCRIPTION
Given a typical selection of your incoming mail classified as spam or
ham (non-spam), this tool will feed each mail to SpamAssassin, allow-
ing 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 globbing in the mail folder names is supported; in other
words, listing a folder name as "*" will scan every folder that
matches.
SpamAssassin remembers which mail messages it has learnt already, and
will not re-learn those messages again, unless you use the --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 'for-
get' the previous indications.
OPTIONS
--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 mes-
sages have already been filtered through SpamAssassin, the learner
will ignore any modifications SpamAssassin may have made.
--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.
--folders=filename, -f filename
sa-learn will read in the list of folders from the specified file,
one folder per line in the file. If the folder is prefixed with
"ham:" or "spam:", sa-learn will learn that folder appropriately,
otherwise the folders will be assumed to be of the type specified
by --ham or --spam.
--use-ignore
Don't learn the message if a from address matches configuration
file item "bayes_ignore_from" or a to address matches
"bayes_ignore_to". The option might be used when learning from a
large file of messages from which the hammy spam messages or
spammy ham messages have not been removed.
--sync
Syncronize the journal and databases. Upon successfully syncing
the database with the entries in the journal, the journal file is
removed.
--force-expire
Forces an expiry attempt, regardless of whether it may be neces-
sary or not. Note: This doesn't mean any tokens will actually
expire. Please see the EXPIRATION section below.
Note: "--force-expire" also causes the journal data to be syn-
cronized into the Bayes databases.
--forget
Forget a given message previously learnt.
--dbpath
Allows a commandline override of the bayes_path configuration
option.
--dump option
Display the contents of the Bayes database. Without an option or
with the all option, all magic tokens and data tokens will be dis-
played. magic will only display magic tokens, and data will only
display the data tokens.
Can also use the --regexp RE option to specify which tokens to
display based on a regular expression.
--clear
Clear an existing Bayes database by removing all traces of the
database.
WARNING: This is destructive and should be used with care.
--backup
Performs a dump of the Bayes database in machine/human readable
format.
The dump will include token and seen data. It is suitable for
input back into the --restore command.
--restore=filename
Performs a restore of the Bayes database defined by filename.
WARNING: This is a destructive operation, previous Bayes data will
be wiped out.
-h, --help
Print help message and exit.
-u username, --username=username
If specified this username will override the username taken from
the runtime environment. You can use this option to specify users
in a virtual user configuration.
-C path, --configpath=path, --config-file=path
Use the specified path for locating the distributed configuration
files. Ignore the default directories (usually "/usr/share/spa-
massassin" or similar).
--siteconfigpath=path
Use the specified path for locating site-specific configuration
files. Ignore the default directories (usually "/etc/mail/spamas-
sassin" or similar).
-p prefs, --prefspath=prefs, --prefs-file=prefs
Read user score preferences from prefs (usually "$HOME/.spamassas-
sin/user_prefs").
=item B<-D>, B<--debug-level>
Produce diagnostic output.
--no-sync
Skip the slow syncronization step which normally takes place after
changing database entries. If you plan to learn from many folders
in a batch, or to learn many individual messages one-by-one, it is
faster to use this switch and run "sa-learn --sync" once all the
folders have been scanned.
Clarification: The state of --no-sync overrides the
bayes_learn_to_journal configuration option. If not specified,
sa-learn will learn to the database directly. If specified, sa-
learn will learn to the journal file.
Note: --sync and --no-sync can be specified on the same command-
line, which is slightly confusing. In this case, the --no-sync
option is ignored since there is no learn operation.
-L, --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 Spa-
mAssassin will not perform network access while learning; but
future versions may.
--import
If you previously used SpamAssassin's Bayesian learner without the
"DB_File" module installed, it will have created files in other
formats, such as "GDBM_File", "NDBM_File", or "SDBM_File". This
switch allows you to migrate that old data into the "DB_File" for-
mat. It will overwrite any data currently in the "DB_File".
Can also be used with the --dbpath path option to specify the
location of the Bayes files to use.
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 probab-
listic analysis. Furthermore, once given a basis for the analysis, the
engine can continue to learn iteratively by applying both the non-
Bayesian and Bayesian rulesets together to create evolving "intelli-
gence".
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:
Can greatly reduce false positives and false negatives.
It learns from your mail, so it is tailored to your unique e-mail
flow.
Once it starts learning, it can continue to learn from SpamAssassin
and improve over time.
And the cons:
A decent number of messages are required before results are useful for
ham/spam determination.
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 is fairly easy to understand why.
With Bayesian analysis, it's all probabilities - "because the past
says it is 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 whitelist-
ing".)
It will take disk space and memory.
The databases it maintains take quite a lot of resources to store
and use.
GETTING STARTED
Still interested? Ok, here's the guidelines for getting this working.
First a high-level overview:
Build a significant sample of both ham and spam.
I suggest several thousand of each, placed in SPAM and HAM direc-
tories 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 is to be statistically useful. Oth-
erwise, the results may be pretty skewed.
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.
If you need SpamAssassin to forget about specific messages, use the
--forget option.
This can be applied to either ham or spam that has run through the
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).
Learning from single messages uses a command like this:
sa-learn --ham --no-sync mailmessage
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 "--sync" option.
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.
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 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
"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 is best to keep training the filter to make
sure it has fresh data to work from. There are various ways to do
this:
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 fold-
ers, 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 is easy
enough to keep up to date.)
2. Unsupervised learning from Bayesian classification
Another way to train is to chain the results of the Bayesian clas-
sifier 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).
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 addi-
tion, if possible.
This is the default, but can be turned off by setting the SpamAs-
sassin configuration parameter "bayes_auto_learn" to 0.
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.
FILES
sa-learn and the other parts of SpamAssassin's Bayesian learner, use a
set of persistent database files to store the learnt tokens, as fol-
lows.
bayes_toks
The database of tokens, containing the tokens learnt, their count
of occurrences in ham and spam, and the timestamp when the token
was last seen in a message.
This database also contains some 'magic' tokens, as follows: the
version number of the database, the number of ham and spam mes-
sages learnt, the number of tokens in the database, and timestamps
of: the last journal sync, the last expiry run, the last expiry
token reduction count, the last expiry timestamp delta, the oldest
token timestamp in the database, and the newest token timestamp in
the database.
This is a database file, using "DB_File". The database 'version
number' is 0 for databases from 2.5x, 1 for databases from certain
2.6x development releases, and 2 for all more recent databases.
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 has
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 "DB_File".
bayes_journal
While SpamAssassin is scanning mails, it needs to track which
tokens it uses in its calculations. To avoid the contention of
having each SpamAssassin process attempting to gain write access
to the Bayes DB, the token timestamps are written to a 'journal'
file which will later (either automatically or via "sa-learn
--sync") be used to syncronize the Bayes DB.
Also, through the use of "bayes_learn_to_journal", or when using
the "--no-sync" option with sa-learn, the actual learning data
will take be placed into the journal for later syncronization.
This is typically useful for high-traffic sites to avoid the same
contention as stated above.
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 expira-
tion periodically when certain criteria (listed below) are met.
SpamAssassin can sync the journal and expire the DB tokens either man-
ually or opportunistically. A journal sync is due if --sync is passed
to sa-learn (manual), or if the following is true (opportunistic):
- bayes_journal_max_size does not equal 0 (means don't sync)
- the journal file exists
and either:
- the journal file has a size greater than bayes_journal_max_size
or
- a journal sync has previously occured, and at least 1 day has passed
since that sync
Expiry is due if --force-expire is passed to sa-learn (manual), or if
all of the following are true (opportunistic):
- the last expire was attempted at least 12hrs ago
- bayes_auto_expire does not equal 0
- the number of tokens in the DB is > 100,000
- the number of tokens in the DB is > bayes_expiry_max_db_size
- there is at least a 12 hr difference between the oldest and newest
token atimes
EXPIRE LOGIC
If either the manual or opportunistic method causes an expire run to
start, here is the logic that is used:
- 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.
- if the reduction number is < 1000 tokens, abort (not worth the
effort).
- 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)
- if no expire has been done before, or the last expire looks "wierd",
do an estimation pass. The definition of "wierd" is:
- last expire over 30 days ago
- last atime delta was < 12 hrs
- last reduction count was < 1000 tokens
- estimated new atime delta is < 12 hrs
- the difference between the last reduction count and the goal
reduction count is > 50%
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 expira-
tion count. Use this to choose the atime delta to use, unless one of
the following occurs:
- 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.
- 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.
- the number of tokens that would be removed is < 1000. the benefit
isn't worth the effort. more tokens need to be learned.
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.
EXPIRY RELATED CONFIGURATION SETTINGS
"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).
"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.
"bayes_journal_max_size" specifies how large the Bayes journal will
grow before it is opportunistically synced. The default value is
102400.
INSTALLATION
The sa-learn command is part of the Mail::SpamAssassin Perl module.
Install this as a normal Perl module, using "perl -MCPAN -e shell", or
by hand.
SEE ALSO
spamassassin(1) spamc(1) Mail::SpamAssassin(3)
Gary Robinson's f(x) and combining algorithms, as used in
SpamAssassin
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