Spamassassin autolearn=no

[BMWfan]

Hello,

i’am using postfix, amavis, spamassassin and clamav.

Amavis triggers spamassassin and clamav.

Every mail which arrives in kopano includes this entry in the mail meader:
autolearn=no autolearn_force=no

Perhaps this is a normal behaviour, but i thought when i use bayes then it should autolearn, or iam false?

To explain spamassassin which mails are spam iam using inotify-spamlearn and kopano-spamd.

This is my /etc/spamassassin/local.cf:

# This is the right place to customize your installation of SpamAssassin.
#
# See 'perldoc Mail::SpamAssassin::Conf' for details of what can be
# tweaked.
#
# Only a small subset of options are listed below
#
###########################################################################

#   Add *****SPAM***** to the Subject header of spam e-mails
#
# rewrite_header Subject *****SPAM*****


#   Save spam messages as a message/rfc822 MIME attachment instead of
#   modifying the original message (0: off, 2: use text/plain instead)
#
# report_safe 1


#   Set which networks or hosts are considered 'trusted' by your mail
#   server (i.e. not spammers)
#
# trusted_networks 212.17.35.


#   Set file-locking method (flock is not safe over NFS, but is faster)
#
# lock_method flock


#   Set the threshold at which a message is considered spam (default: 5.0)
#
# required_score 5.0


#   Use Bayesian classifier (default: 1)
#
use_bayes 1


#   Bayesian classifier auto-learning (default: 1)
#
bayes_auto_learn 1


#   Set headers which may provide inappropriate cues to the Bayesian
#   classifier
#
bayes_ignore_header X-Bogosity
bayes_ignore_header X-Spam-Flag
bayes_ignore_header X-Spam-Status


#   Whether to decode non- UTF-8 and non-ASCII textual parts and recode
#   them to UTF-8 before the text is given over to rules processing.
#
# normalize_charset 1

#   Some shortcircuiting, if the plugin is enabled
# 
ifplugin Mail::SpamAssassin::Plugin::Shortcircuit
#
#   default: strongly-whitelisted mails are *really* whitelisted now, if the
#   shortcircuiting plugin is active, causing early exit to save CPU load.
#   Uncomment to turn this on
#
# shortcircuit USER_IN_WHITELIST       on
# shortcircuit USER_IN_DEF_WHITELIST   on
# shortcircuit USER_IN_ALL_SPAM_TO     on
# shortcircuit SUBJECT_IN_WHITELIST    on

#   the opposite; blacklisted mails can also save CPU
#
# shortcircuit USER_IN_BLACKLIST       on
# shortcircuit USER_IN_BLACKLIST_TO    on
# shortcircuit SUBJECT_IN_BLACKLIST    on

#   if you have taken the time to correctly specify your "trusted_networks",
#   this is another good way to save CPU
#
# shortcircuit ALL_TRUSTED             on

#   and a well-trained bayes DB can save running rules, too
#
# shortcircuit BAYES_99                spam
# shortcircuit BAYES_00                ham

endif # Mail::SpamAssassin::Plugin::Shortcircuit

ifplugin Mail::SpamAssassin::Plugin::RelayCountry
add_header all Relay-Country _RELAYCOUNTRY_
header RELAYCOUNTRY_BAD X-Relay-Countries =~ /(CN|RU|UA|RO|VN)/
describe RELAYCOUNTRY_BAD Relayed through spammy country at some point
score RELAYCOUNTRY_BAD 2.0
 
header RELAYCOUNTRY_GOOD X-Relay-Countries =~ /^(DE|AT|CH)/
describe RELAYCOUNTRY_GOOD First untrusted GW is DE, AT or CH
score RELAYCOUNTRY_GOOD -0.5
endif # Mail::SpamAssassin::Plugin::RelayCountry

score RCVD_IN_BL_SPAMCOP_NET 0 5.246 0 5.347
score RCVD_IN_BRBL_LASTEXT 0 5.246 0 5.347
score URIBL_BLACK 0 5.7 0 5.7
score URIBL_WS_SURBL 0 2.659 0 2.608
score URIBL_MW_SURBL 0 2.263 0 2.263
score URIBL_CR_SURBL 0 2.263 0 2.263
score URIBL_GREY 0 2.084 0 1.424
score URIBL_DBL_SPAM    0 4.5 0 4.5
score URIBL_DBL_PHISH   0 4.5 0 4.5
score URIBL_DBL_MALWARE 0 4.5 0 4.5
score URIBL_DBL_BOTNETCC 0 4.5 0 4.5
score URIBL_DBL_ABUSE_SPAM 0 4.0 0 4.0
score URIBL_DBL_ABUSE_PHISH 0 4.5 0 4.5
score URIBL_DBL_ABUSE_MALW  0 4.5 0 4.5
score URIBL_DBL_ABUSE_BOTCC 0 4.5 0 4.5

I gave already more then 500 spam mails to learn to spamassassin.

I think some counts should be higher as zerosa-learn --dump magic

0.000          0          3          0  non-token data: bayes db version
0.000          0          0          0  non-token data: nspam
0.000          0          0          0  non-token data: nham
0.000          0          0          0  non-token data: ntokens
0.000          0          0          0  non-token data: oldest atime
0.000          0          0          0  non-token data: newest atime
0.000          0          0          0  non-token data: last journal sync atime
0.000          0          0          0  non-token data: last expiry atime
0.000          0          0          0  non-token data: last expire atime delta
0.000          0          0          0  non-token data: last expire reduction count

But the logs from inotify-spamlearn says it has Learned tokens:

INFO Processing [Inotify] /var/lib/kopano/spamd/spam/FC102D47DEDD4AEEABE91BBD90F08D0D.eml: Learned tokens from 1 message(s) (1 message(s) examined)
INFO Removing file: /var/lib/kopano/spamd/spam/FC102D47DEDD4AEEABE91BBD90F08D0D.eml
INFO Processing [Inotify] /var/lib/kopano/spamd/spam/F507443E09D24102911540197DC2C66B.eml: Learned tokens from 1 message(s) (1 message(s) examined)
INFO Removing file: /var/lib/kopano/spamd/spam/F507443E09D24102911540197DC2C66B.eml
INFO Processing [Inotify] /var/lib/kopano/spamd/spam/66113D3562C548219755ADCF12DD61EC.eml: Learned tokens from 1 message(s) (1 message(s) examined)
INFO Removing file: /var/lib/kopano/spamd/spam/66113D3562C548219755ADCF12DD61EC.eml
INFO Processing [Inotify] /var/lib/kopano/spamd/spam/D6E90D3DE8724866A21AE80146022E07.eml: Learned tokens from 1 message(s) (1 message(s) examined)
INFO Removing file: /var/lib/kopano/spamd/spam/D6E90D3DE8724866A21AE80146022E07.eml
INFO Processing [Inotify] /var/lib/kopano/spamd/spam/1A600DC9E18A40FA8A925837673A65F8.eml: Learned tokens from 1 message(s) (1 message(s) examined)
INFO Removing file: /var/lib/kopano/spamd/spam/1A600DC9E18A40FA8A925837673A65F8.eml

[BMWfan]

This problem is solved.

i forgot to set the bayes_auto_learn_threshold values.