Mark Martinec wrote:
> Jef,
> 
>> At log level2, I see some autolearn entries. I started with an empty
>> logfile and used fetchmail to retrieve a pop3 mailbox with 278 messages
>> (almost all spam). They are all correctly marked a spam.
>> In the log, I see only 133 autolearn headers:
>>   - autolearn=no: 103
>>   - autolearn=spam: 1
>>   - autolearn=ham: 0
>>   - autolearn=unavailable: 29
>> So there is not much learning happening, even with very high spam scores
>> (20+ and even 30+).
> 
> Depends on your settings for bayes_auto_learn_threshold_nonspam and
> bayes_auto_learn_threshold_spam, and on your other rules, plugins
> and network tests, i.e. if spam usually collects high enough scores.
> It may be normal. SA debugging can be turned on selectively, e.g:
> 
>  # amavisd -d bayes,learn debug-sa

I have the default settings from ubuntu for spamassassin.

bayes_auto_learn_threshold_nonspam      0.1
bayes_auto_learn_threshold_spam         12.0
bayes_auto_learn                        1

I only added the SARE rules to increase scores. For amavis, I only 
changed these settings from the ubuntu defaults:

$myhostname = 'manta.localdomain.local';
$mydomain = 'localdomain.local';
@local_domains_acl = ( ".$mydomain" );
$sa_spam_subject_tag = undef;
$sa_tag_level_deflt  = -999;
$sa_tag2_level_deflt = 5.00;
$sa_kill_level_deflt = 5.00;
$sa_dsn_cutoff_level = 10;
$final_virus_destiny      = D_PASS;
$final_banned_destiny     = D_PASS;
$final_spam_destiny       = D_PASS;
$final_bad_header_destiny = D_PASS;


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