mail::spamassassin::persistentaddrlist(3pm) [debian man page]

Mail::SpamAssassin::PersistentAddrList(3pm)		User Contributed Perl Documentation	       Mail::SpamAssassin::PersistentAddrList(3pm)

NAME

       Mail::SpamAssassin::PersistentAddrList - persistent address list base class

SYNOPSIS

	 my $factory = PersistentAddrListSubclass->new();
	 $spamtest->set_persistent_addr_list_factory ($factory);
	 ... call into SpamAssassin classes...

       SpamAssassin will call:

	 my $addrlist = $factory->new_checker($spamtest);
	 $entry = $addrlist->get_addr_entry ($addr);
	 ...

DESCRIPTION

       All persistent address list implementations, used by the auto-whitelist code to track known-good email addresses, use this as a base class.

       See "Mail::SpamAssassin::DBBasedAddrList" for an example.

METHODS

       $factory = PersistentAddrListSubclass->new();
	   This creates a factory object, which SpamAssassin will call to create a new checker object for the persistent address list.

       my $addrlist = $factory->new_checker();
	   Create a new address-list checker object from the factory. Called by the SpamAssassin classes.

       $entry = $addrlist->get_addr_entry ($addr);
	   Given an email address $addr, return an entry object with the details of that address.

	   The entry object is a reference to a hash, which must contain at least two keys: "count", which is the count of times that address has
	   been encountered before; and "totscore", which is the total of all scores for messages associated with that address.  From these two
	   fields, an average score will be calculated, and the score for the current message will be regressed towards that mean message score.

	   The hash can contain whatever other data your back-end needs to store, under other keys.

	   The method should never return "undef", or a hash that does not contain a "count" key and a "totscore" key.

       $entry = $addrlist->add_score($entry, $score);
	   This method should add the given score to the whitelist database for the given entry, and then return the new entry.

       $entry = $addrlist->remove_entry ($entry);
	   This method should remove the given entry from the whitelist database.

       $entry = $addrlist->finish ();
	   Clean up, if necessary.  Called by SpamAssassin when it has finished checking, or adding to, the auto-whitelist database.

perl v5.14.2							    2011-06-06			       Mail::SpamAssassin::PersistentAddrList(3pm)

Mail::SpamAssassin::Plugin::AutoLearnThreshold(3pm)	User Contributed Perl Documentation    Mail::SpamAssassin::Plugin::AutoLearnThreshold(3pm)

NAME

       Mail::SpamAssassin::Plugin::AutoLearnThreshold - threshold-based discriminator for Bayes auto-learning

SYNOPSIS

	 loadplugin	Mail::SpamAssassin::Plugin::AutoLearnThreshold

DESCRIPTION

       This plugin implements the threshold-based auto-learning discriminator for SpamAssassin's Bayes subsystem.  Auto-learning is a mechanism
       whereby high-scoring mails (or low-scoring mails, for non-spam) are fed into its learning systems without user intervention, during
       scanning.

       Note that certain tests are ignored when determining whether a message should be trained upon:

       o   rules with tflags set to 'learn' (the Bayesian rules)

       o   rules with tflags set to 'userconf' (user configuration)

       o   rules with tflags set to 'noautolearn'

       Also note that auto-learning occurs using scores from either scoreset 0 or 1, depending on what scoreset is used during message check.  It
       is likely that the message check and auto-learn scores will be different.

USER OPTIONS

       The following configuration settings are used to control auto-learning:

       bayes_auto_learn_threshold_nonspam n.nn	 (default: 0.1)
	   The score threshold below which a mail has to score, to be fed into SpamAssassin's learning systems automatically as a non-spam
	   message.

       bayes_auto_learn_threshold_spam n.nn	 (default: 12.0)
	   The score threshold above which a mail has to score, to be fed into SpamAssassin's learning systems automatically as a spam message.

	   Note: SpamAssassin requires at least 3 points from the header, and 3 points from the body to auto-learn as spam.  Therefore, the
	   minimum working value for this option is 6.

       bayes_auto_learn_on_error (0 | 1)	(default: 0)
	   With "bayes_auto_learn_on_error" off, autolearning will be performed even if bayes classifier already agrees with the new
	   classification (i.e.  yielded BAYES_00 for what we are now trying to teach it as ham, or yielded BAYES_99 for spam). This is a
	   traditional setting, the default was chosen to retain backwards compatibility.

	   With "bayes_auto_learn_on_error" turned on, autolearning will be performed only when a bayes classifier had a different opinion from
	   what the autolearner is now trying to teach it (i.e. it made an error in judgement). This strategy may or may not produce better future
	   classifications, but usually works very well, while also preventing unnecessary overlearning and slows down database growth.

perl v5.14.2							    2011-06-06		       Mail::SpamAssassin::Plugin::AutoLearnThreshold(3pm)