Provided by: spamassassin_3.4.4-1ubuntu1.2_all 
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 Synchronize the database and the journal if needed
--force-expire Force a database sync and expiry run
--dbpath <path> 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 <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 compatibility
--file Ignored; historical compatibility
--mbox Input sources are in mbox format
--mbx Input sources are in mbx format
--max-size <b> Skip messages larger than b bytes;
defaults to 256 KB, 0 implies no limit
--showdots Show progress using dots
--progress Show progress using progress bar
--no-sync Skip synchronizing 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 <filename> Restore a database from filename
-u username, --username=username
Override username taken from the runtime
environment, used with SQL
-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
(default: /usr/etc/spamassassin)
--cf='config line' Additional line of configuration
-D, --debug [area=n,...] 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, 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 csh-style globbing in the mail folder names is supported; in other words, listing a folder name
as "*" will scan every folder that matches. See "Mail::SpamAssassin::ArchiveIterator" for more details.
If you are using mail boxes in format other than maildir you should use the --mbox or --mbx parameters.
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
'forget' the previous indications.
Users of "spamd" who wish to perform training remotely, over a network, should investigate the "spamc -L"
switch.
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 messages 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:type:" or "spam:type:", sa-learn will learn that folder
appropriately, otherwise the folders will be assumed to be of the type specified by --ham or --spam.
"type" above is optional, but is the same as the standard for ArchiveIterator: mbox, mbx, dir, file,
or detect (the default if not specified).
--mbox
sa-learn will read in the file(s) containing the emails to be learned, and will process them in mbox
format (one or more emails per file).
--mbx
sa-learn will read in the file(s) containing the emails to be learned, and will process them in mbx
format (one or more emails per file).
--use-ignores
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
Synchronize 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 necessary 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 synchronized 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 displayed. 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 when using SQL as the Bayes backend.
NOTE: This option will not change to the given username, it will only attempt to act on behalf of
that user. Because of this you will need to have proper permissions to be able to change files owned
by username. In the case of SQL this generally is not a problem.
-C path, --configpath=path, --config-file=path
Use the specified path for locating the distributed configuration files. Ignore the default
directories (usually "/usr/share/spamassassin" or similar).
--siteconfigpath=path
Use the specified path for locating site-specific configuration files. Ignore the default
directories (usually "/etc/spamassassin" or similar).
--cf='config line'
Add additional lines of configuration directly from the command-line, parsed after the configuration
files are read. Multiple --cf arguments can be used, and each will be considered a separate line of
configuration.
-p prefs, --prefspath=prefs, --prefs-file=prefs
Read user score preferences from prefs (usually "$HOME/.spamassassin/user_prefs").
--progress
Prints a progress bar (to STDERR) showing the current progress. In the case where no valid terminal
is found this option will behave very much like the --showdots option.
-D [area,...], --debug [area,...]
Produce debugging output. If no areas are listed, all debugging information is printed. Diagnostic
output can also be enabled for each area individually; area is the area of the code to instrument.
For example, to produce diagnostic output on bayes, learn, and dns, use:
spamassassin -D bayes,learn,dns
For more information about which areas (also known as channels) are available, please see the
documentation at:
C<http://wiki.apache.org/spamassassin/DebugChannels>
Higher priority informational messages that are suitable for logging in normal circumstances are
available with an area of "info".
--no-sync
Skip the slow synchronization 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 commandline, 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 SpamAssassin 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" format. 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.
MIGRATION
There are now multiple backend storage modules available for storing user's bayesian data. As such you
might want to migrate from one backend to another. Here is a simple procedure for migrating from one
backend to another.
Note that if you have individual user databases you will have to perform a similar procedure for each one
of them.
sa-learn --sync
This will sync any outstanding journal entries
sa-learn --backup > backup.txt
This will save all your Bayes data to a plain text file.
sa-learn --clear
This is optional, but good to do to clear out the old database.
Repeat!
At this point, if you have multiple databases, you should perform the procedure above for each of
them. (i.e. each user's database needs to be backed up before continuing.)
Switch backends
Once you have backed up all databases you can update your configuration for the new database backend.
This will involve at least the bayes_store_module config option and may involve some additional
config options depending on what is required by the module. (For example, you may need to configure
an SQL database.)
sa-learn --restore backup.txt
Again, you need to do this for every database.
If you are migrating to SQL you can make use of the -u <username> option in sa-learn to populate each
user's database. Otherwise, you must run sa-learn as the user who database you are restoring.
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 probabilistic 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 "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:
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 probabilistic 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".)
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 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 is to be statistically useful. Otherwise,
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 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 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 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).
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
"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 follows.
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 messages 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, 2 for 2.6x, and 3 for 3.0 and later
releases.
bayes_seen
A map of Message-Id and some data from headers and body 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 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 synchronize 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 synchronization. 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 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 --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 occurred, 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 "weird", do an estimation pass. The
definition of "weird" 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 expiration 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 database. 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) Mail::SpamAssassin::ArchiveIterator(3)
<http://www.paulgraham.com/> Paul Graham's "A Plan For Spam" paper
<http://www.linuxjournal.com/article/6467> Gary Robinson's f(x) and combining algorithms, as used in
SpamAssassin
<http://www.bgl.nu/~glouis/bogofilter/> 'Training on error' page. A discussion of various Bayes training
regimes, including 'train on error' and unsupervised training.
PREREQUISITES
"Mail::SpamAssassin"
AUTHORS
The SpamAssassin(tm) Project <https://spamassassin.apache.org/>