mirror of
https://github.com/rspamd/rspamd.git
synced 2024-08-27 17:55:15 +02:00
144 lines
4.4 KiB
Lua
144 lines
4.4 KiB
Lua
--[[
|
|
Copyright (c) 2019, Vsevolod Stakhov <vsevolod@highsecure.ru>
|
|
|
|
Licensed under the Apache License, Version 2.0 (the "License");
|
|
you may not use this file except in compliance with the License.
|
|
You may obtain a copy of the License at
|
|
|
|
http://www.apache.org/licenses/LICENSE-2.0
|
|
|
|
Unless required by applicable law or agreed to in writing, software
|
|
distributed under the License is distributed on an "AS IS" BASIS,
|
|
WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
|
|
See the License for the specific language governing permissions and
|
|
limitations under the License.
|
|
]]--
|
|
|
|
-- This file contains functions to simplify bayes classifier auto-learning
|
|
|
|
local lua_util = require "lua_util"
|
|
local lua_verdict = require "lua_verdict"
|
|
local N = "lua_bayes"
|
|
|
|
local exports = {}
|
|
|
|
exports.can_learn = function(task, is_spam, is_unlearn)
|
|
local learn_type = task:get_request_header('Learn-Type')
|
|
|
|
if not (learn_type and tostring(learn_type) == 'bulk') then
|
|
local prob = task:get_mempool():get_variable('bayes_prob', 'double')
|
|
|
|
if prob then
|
|
local in_class = false
|
|
local cl
|
|
if is_spam then
|
|
cl = 'spam'
|
|
in_class = prob >= 0.95
|
|
else
|
|
cl = 'ham'
|
|
in_class = prob <= 0.05
|
|
end
|
|
|
|
if in_class then
|
|
return false,string.format(
|
|
'already in class %s; probability %.2f%%',
|
|
cl, math.abs((prob - 0.5) * 200.0))
|
|
end
|
|
end
|
|
end
|
|
|
|
return true
|
|
end
|
|
|
|
exports.autolearn = function(task, conf)
|
|
local function log_can_autolearn(verdict, score, threshold)
|
|
local from = task:get_from('smtp')
|
|
local mime_rcpts = 'undef'
|
|
local mr = task:get_recipients('mime')
|
|
if mr then
|
|
for _,r in ipairs(mr) do
|
|
if mime_rcpts == 'undef' then
|
|
mime_rcpts = r.addr
|
|
else
|
|
mime_rcpts = mime_rcpts .. ',' .. r.addr
|
|
end
|
|
end
|
|
end
|
|
|
|
lua_util.debugm(N, task, 'id: %s, from: <%s>: can autolearn %s: score %s %s %s, mime_rcpts: <%s>',
|
|
task:get_header('Message-Id') or '<undef>',
|
|
from and from[1].addr or 'undef',
|
|
verdict,
|
|
string.format("%.2f", score),
|
|
verdict == 'ham' and '<=' or verdict == 'spam' and '>=' or '/',
|
|
threshold,
|
|
mime_rcpts)
|
|
end
|
|
|
|
-- We have autolearn config so let's figure out what is requested
|
|
local verdict,score = lua_verdict.get_specific_verdict("bayes", task)
|
|
local learn_spam,learn_ham = false, false
|
|
|
|
if verdict == 'passthrough' then
|
|
-- No need to autolearn
|
|
lua_util.debugm(N, task, 'no need to autolearn - verdict: %s',
|
|
verdict)
|
|
return
|
|
end
|
|
|
|
if conf.spam_threshold and conf.ham_threshold then
|
|
if verdict == 'spam' then
|
|
if conf.spam_threshold and score >= conf.spam_threshold then
|
|
log_can_autolearn(verdict, score, conf.spam_threshold)
|
|
learn_spam = true
|
|
end
|
|
elseif verdict == 'ham' then
|
|
if conf.ham_threshold and score <= conf.ham_threshold then
|
|
log_can_autolearn(verdict, score, conf.ham_threshold)
|
|
learn_ham = true
|
|
end
|
|
end
|
|
elseif conf.learn_verdict then
|
|
if verdict == 'spam' or verdict == 'junk' then
|
|
learn_spam = true
|
|
elseif verdict == 'ham' then
|
|
learn_ham = true
|
|
end
|
|
end
|
|
|
|
if conf.check_balance then
|
|
-- Check balance of learns
|
|
local spam_learns = task:get_mempool():get_variable('spam_learns', 'int64') or 0
|
|
local ham_learns = task:get_mempool():get_variable('ham_learns', 'int64') or 0
|
|
|
|
local min_balance = 0.9
|
|
if conf.min_balance then min_balance = conf.min_balance end
|
|
|
|
if spam_learns > 0 or ham_learns > 0 then
|
|
local max_ratio = 1.0 / min_balance
|
|
local spam_learns_ratio = spam_learns / (ham_learns + 1)
|
|
if spam_learns_ratio > max_ratio and learn_spam then
|
|
lua_util.debugm(N, task,
|
|
'skip learning spam, balance is not satisfied: %s < %s; %s spam learns; %s ham learns',
|
|
spam_learns_ratio, min_balance, spam_learns, ham_learns)
|
|
learn_spam = false
|
|
end
|
|
|
|
local ham_learns_ratio = ham_learns / (spam_learns + 1)
|
|
if ham_learns_ratio > max_ratio and learn_ham then
|
|
lua_util.debugm(N, task,
|
|
'skip learning ham, balance is not satisfied: %s < %s; %s spam learns; %s ham learns',
|
|
ham_learns_ratio, min_balance, spam_learns, ham_learns)
|
|
learn_ham = false
|
|
end
|
|
end
|
|
end
|
|
|
|
if learn_spam then
|
|
return 'spam'
|
|
elseif learn_ham then
|
|
return 'ham'
|
|
end
|
|
end
|
|
|
|
return exports |