File Manager
--[[
Copyright (c) 2016, 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.
]]--
if confighelp then
return
end
local fun = require "fun"
local lua_redis = require "lua_redis"
local lua_util = require "lua_util"
local lua_verdict = require "lua_verdict"
local neural_common = require "plugins/neural"
local rspamd_kann = require "rspamd_kann"
local rspamd_logger = require "rspamd_logger"
local rspamd_tensor = require "rspamd_tensor"
local rspamd_text = require "rspamd_text"
local rspamd_util = require "rspamd_util"
local ts = require("tableshape").types
local N = "neural"
local settings = neural_common.settings
local redis_profile_schema = ts.shape{
digest = ts.string,
symbols = ts.array_of(ts.string),
version = ts.number,
redis_key = ts.string,
distance = ts.number:is_optional(),
}
local has_blas = rspamd_tensor.has_blas()
local text_cookie = rspamd_text.cookie
-- Creates and stores ANN profile in Redis
local function new_ann_profile(task, rule, set, version)
local ann_key = neural_common.new_ann_key(rule, set, version, settings)
local profile = {
symbols = set.symbols,
redis_key = ann_key,
version = version,
digest = set.digest,
distance = 0 -- Since we are using our own profile
}
local ucl = require "ucl"
local profile_serialized = ucl.to_format(profile, 'json-compact', true)
local function add_cb(err, _)
if err then
rspamd_logger.errx(task, 'cannot store ANN profile for %s:%s at %s : %s',
rule.prefix, set.name, profile.redis_key, err)
else
rspamd_logger.infox(task, 'created new ANN profile for %s:%s, data stored at prefix %s',
rule.prefix, set.name, profile.redis_key)
end
end
lua_redis.redis_make_request(task,
rule.redis,
nil,
true, -- is write
add_cb, --callback
'ZADD', -- command
{set.prefix, tostring(rspamd_util.get_time()), profile_serialized}
)
return profile
end
-- ANN filter function, used to insert scores based on the existing symbols
local function ann_scores_filter(task)
for _,rule in pairs(settings.rules) do
local sid = task:get_settings_id() or -1
local ann
local profile
local set = neural_common.get_rule_settings(task, rule)
if set then
if set.ann then
ann = set.ann.ann
profile = set.ann
else
lua_util.debugm(N, task, 'no ann loaded for %s:%s',
rule.prefix, set.name)
end
else
lua_util.debugm(N, task, 'no ann defined in %s for settings id %s',
rule.prefix, sid)
end
if ann then
local vec = neural_common.result_to_vector(task, profile)
local score
local out = ann:apply1(vec, set.ann.pca)
score = out[1]
local symscore = string.format('%.3f', score)
lua_util.debugm(N, task, '%s:%s:%s ann score: %s',
rule.prefix, set.name, set.ann.version, symscore)
if score > 0 then
local result = score
task:insert_result(rule.symbol_spam, result, symscore)
else
local result = -(score)
task:insert_result(rule.symbol_ham, result, symscore)
end
end
end
end
local function ann_push_task_result(rule, task, verdict, score, set)
local train_opts = rule.train
local learn_spam, learn_ham
local skip_reason = 'unknown'
if not train_opts.store_pool_only and train_opts.autotrain then
if train_opts.spam_score then
learn_spam = score >= train_opts.spam_score
if not learn_spam then
skip_reason = string.format('score < spam_score: %f < %f',
score, train_opts.spam_score)
end
else
learn_spam = verdict == 'spam' or verdict == 'junk'
if not learn_spam then
skip_reason = string.format('verdict: %s',
verdict)
end
end
if train_opts.ham_score then
learn_ham = score <= train_opts.ham_score
if not learn_ham then
skip_reason = string.format('score > ham_score: %f > %f',
score, train_opts.ham_score)
end
else
learn_ham = verdict == 'ham'
if not learn_ham then
skip_reason = string.format('verdict: %s',
verdict)
end
end
else
-- Train by request header
local hdr = task:get_request_header('ANN-Train')
if hdr then
if hdr:lower() == 'spam' then
learn_spam = true
elseif hdr:lower() == 'ham' then
learn_ham = true
else
skip_reason = 'no explicit header'
end
elseif train_opts.store_pool_only then
local ucl = require "ucl"
learn_ham = false
learn_spam = false
-- Explicitly store tokens in cache
local vec = neural_common.result_to_vector(task, set)
task:cache_set(rule.prefix .. '_neural_vec_mpack', ucl.to_format(vec, 'msgpack'))
task:cache_set(rule.prefix .. '_neural_profile_digest', set.digest)
skip_reason = 'store_pool_only has been set'
end
end
if learn_spam or learn_ham then
local learn_type
if learn_spam then learn_type = 'spam' else learn_type = 'ham' end
local function vectors_len_cb(err, data)
if not err and type(data) == 'table' then
local nspam,nham = data[1],data[2]
if neural_common.can_push_train_vector(rule, task, learn_type, nspam, nham) then
local vec = neural_common.result_to_vector(task, set)
local str = rspamd_util.zstd_compress(table.concat(vec, ';'))
local target_key = set.ann.redis_key .. '_' .. learn_type
local function learn_vec_cb(_err)
if _err then
rspamd_logger.errx(task, 'cannot store train vector for %s:%s: %s',
rule.prefix, set.name, _err)
else
lua_util.debugm(N, task,
"add train data for ANN rule " ..
"%s:%s, save %s vector of %s elts in %s key; %s bytes compressed",
rule.prefix, set.name, learn_type, #vec, target_key, #str)
end
end
lua_redis.redis_make_request(task,
rule.redis,
nil,
true, -- is write
learn_vec_cb, --callback
'LPUSH', -- command
{ target_key, str } -- arguments
)
else
lua_util.debugm(N, task,
"do not add %s train data for ANN rule " ..
"%s:%s",
learn_type, rule.prefix, set.name)
end
else
if err then
rspamd_logger.errx(task, 'cannot check if we can train %s:%s : %s',
rule.prefix, set.name, err)
elseif type(data) == 'string' then
-- nil return value
rspamd_logger.infox(task, "cannot learn %s ANN %s:%s; redis_key: %s: locked for learning: %s",
learn_type, rule.prefix, set.name, set.ann.redis_key, data)
else
rspamd_logger.errx(task, 'cannot check if we can train %s:%s : type of Redis key %s is %s, expected table' ..
'please remove this key from Redis manually if you perform upgrade from the previous version',
rule.prefix, set.name, set.ann.redis_key, type(data))
end
end
end
-- Check if we can learn
if set.can_store_vectors then
if not set.ann then
-- Need to create or load a profile corresponding to the current configuration
set.ann = new_ann_profile(task, rule, set, 0)
lua_util.debugm(N, task,
'requested new profile for %s, set.ann is missing',
set.name)
end
lua_redis.exec_redis_script(neural_common.redis_script_id.vectors_len,
{task = task, is_write = false},
vectors_len_cb,
{
set.ann.redis_key,
})
else
lua_util.debugm(N, task,
'do not push data: train condition not satisfied; reason: not checked existing ANNs')
end
else
lua_util.debugm(N, task,
'do not push data to key %s: train condition not satisfied; reason: %s',
(set.ann or {}).redis_key,
skip_reason)
end
end
--- Offline training logic
-- Utility to extract and split saved training vectors to a table of tables
local function process_training_vectors(data)
return fun.totable(fun.map(function(tok)
local _,str = rspamd_util.zstd_decompress(tok)
return fun.totable(fun.map(tonumber, lua_util.str_split(tostring(str), ';')))
end, data))
end
-- This function does the following:
-- * Tries to lock ANN
-- * Loads spam and ham vectors
-- * Spawn learning process
local function do_train_ann(worker, ev_base, rule, set, ann_key)
local spam_elts = {}
local ham_elts = {}
local function redis_ham_cb(err, data)
if err or type(data) ~= 'table' then
rspamd_logger.errx(rspamd_config, 'cannot get ham tokens for ANN %s from redis: %s',
ann_key, err)
-- Unlock on error
lua_redis.redis_make_request_taskless(ev_base,
rspamd_config,
rule.redis,
nil,
true, -- is write
neural_common.gen_unlock_cb(rule, set, ann_key), --callback
'HDEL', -- command
{ann_key, 'lock'}
)
else
-- Decompress and convert to numbers each training vector
ham_elts = process_training_vectors(data)
neural_common.spawn_train({worker = worker, ev_base = ev_base,
rule = rule, set = set, ann_key = ann_key, ham_vec = ham_elts,
spam_vec = spam_elts})
end
end
-- Spam vectors received
local function redis_spam_cb(err, data)
if err or type(data) ~= 'table' then
rspamd_logger.errx(rspamd_config, 'cannot get spam tokens for ANN %s from redis: %s',
ann_key, err)
-- Unlock ANN on error
lua_redis.redis_make_request_taskless(ev_base,
rspamd_config,
rule.redis,
nil,
true, -- is write
neural_common.gen_unlock_cb(rule, set, ann_key), --callback
'HDEL', -- command
{ann_key, 'lock'}
)
else
-- Decompress and convert to numbers each training vector
spam_elts = process_training_vectors(data)
-- Now get ham vectors...
lua_redis.redis_make_request_taskless(ev_base,
rspamd_config,
rule.redis,
nil,
false, -- is write
redis_ham_cb, --callback
'LRANGE', -- command
{ann_key .. '_ham', '0', '-1'}
)
end
end
local function redis_lock_cb(err, data)
if err then
rspamd_logger.errx(rspamd_config, 'cannot call lock script for ANN %s from redis: %s',
ann_key, err)
elseif type(data) == 'number' and data == 1 then
-- ANN is locked, so we can extract SPAM and HAM vectors and spawn learning
lua_redis.redis_make_request_taskless(ev_base,
rspamd_config,
rule.redis,
nil,
false, -- is write
redis_spam_cb, --callback
'LRANGE', -- command
{ann_key .. '_spam', '0', '-1'}
)
rspamd_logger.infox(rspamd_config, 'lock ANN %s:%s (key name %s) for learning',
rule.prefix, set.name, ann_key)
else
local lock_tm = tonumber(data[1])
rspamd_logger.infox(rspamd_config, 'do not learn ANN %s:%s (key name %s), ' ..
'locked by another host %s at %s', rule.prefix, set.name, ann_key,
data[2], os.date('%c', lock_tm))
end
end
-- Check if we are already learning this network
if set.learning_spawned then
rspamd_logger.infox(rspamd_config, 'do not learn ANN %s, already learning another ANN',
ann_key)
return
end
-- Call Redis script that tries to acquire a lock
-- This script returns either a boolean or a pair {'lock_time', 'hostname'} when
-- ANN is locked by another host (or a process, meh)
lua_redis.exec_redis_script(neural_common.redis_script_id.maybe_lock,
{ev_base = ev_base, is_write = true},
redis_lock_cb,
{
ann_key,
tostring(os.time()),
tostring(math.max(10.0, rule.watch_interval * 2)),
rspamd_util.get_hostname()
})
end
-- This function loads new ann from Redis
-- This is based on `profile` attribute.
-- ANN is loaded from `profile.redis_key`
-- Rank of `profile` key is also increased, unfortunately, it means that we need to
-- serialize profile one more time and set its rank to the current time
-- set.ann fields are set according to Redis data received
local function load_new_ann(rule, ev_base, set, profile, min_diff)
local ann_key = profile.redis_key
local function data_cb(err, data)
if err then
rspamd_logger.errx(rspamd_config, 'cannot get ANN data from key: %s; %s',
ann_key, err)
else
if type(data) == 'table' then
if type(data[1]) == 'userdata' and data[1].cookie == text_cookie then
local _err,ann_data = rspamd_util.zstd_decompress(data[1])
local ann
if _err or not ann_data then
rspamd_logger.errx(rspamd_config, 'cannot decompress ANN for %s from Redis key %s: %s',
rule.prefix .. ':' .. set.name, ann_key, _err)
return
else
ann = rspamd_kann.load(ann_data)
if ann then
set.ann = {
digest = profile.digest,
version = profile.version,
symbols = profile.symbols,
distance = min_diff,
redis_key = profile.redis_key
}
local ucl = require "ucl"
local profile_serialized = ucl.to_format(profile, 'json-compact', true)
set.ann.ann = ann -- To avoid serialization
local function rank_cb(_, _)
-- TODO: maybe add some logging
end
-- Also update rank for the loaded ANN to avoid removal
lua_redis.redis_make_request_taskless(ev_base,
rspamd_config,
rule.redis,
nil,
true, -- is write
rank_cb, --callback
'ZADD', -- command
{set.prefix, tostring(rspamd_util.get_time()), profile_serialized}
)
rspamd_logger.infox(rspamd_config,
'loaded ANN for %s:%s from %s; %s bytes compressed; version=%s',
rule.prefix, set.name, ann_key, #data[1], profile.version)
else
rspamd_logger.errx(rspamd_config,
'cannot unpack/deserialise ANN for %s:%s from Redis key %s',
rule.prefix, set.name, ann_key)
end
end
else
lua_util.debugm(N, rspamd_config, 'missing ANN for %s:%s in Redis key %s',
rule.prefix, set.name, ann_key)
end
if set.ann and set.ann.ann and type(data[2]) == 'userdata' and data[2].cookie == text_cookie then
-- PCA table
local _err,pca_data = rspamd_util.zstd_decompress(data[2])
if pca_data then
if rule.max_inputs then
-- We can use PCA
set.ann.pca = rspamd_tensor.load(pca_data)
rspamd_logger.infox(rspamd_config,
'loaded PCA for ANN for %s:%s from %s; %s bytes compressed; version=%s',
rule.prefix, set.name, ann_key, #data[2], profile.version)
else
-- no need in pca, why is it there?
rspamd_logger.warnx(rspamd_config,
'extra PCA for ANN for %s:%s from Redis key %s: no max inputs defined',
rule.prefix, set.name, ann_key)
end
else
-- pca can be missing merely if we have no max_inputs
if rule.max_inputs then
rspamd_logger.errx(rspamd_config, 'cannot unpack/deserialise ANN for %s:%s from Redis key %s: no PCA: %s',
rule.prefix, set.name, ann_key, _err)
set.ann.ann = nil
else
-- It is okay
set.ann.pca = nil
end
end
end
else
lua_util.debugm(N, rspamd_config, 'no ANN key for %s:%s in Redis key %s',
rule.prefix, set.name, ann_key)
end
end
end
lua_redis.redis_make_request_taskless(ev_base,
rspamd_config,
rule.redis,
nil,
false, -- is write
data_cb, --callback
'HMGET', -- command
{ann_key, 'ann', 'pca'}, -- arguments
{opaque_data = true}
)
end
-- Used to check an element in Redis serialized as JSON
-- for some specific rule + some specific setting
-- This function tries to load more fresh or more specific ANNs in lieu of
-- the existing ones.
-- Use this function to load ANNs as `callback` parameter for `check_anns` function
local function process_existing_ann(_, ev_base, rule, set, profiles)
local my_symbols = set.symbols
local min_diff = math.huge
local sel_elt
for _,elt in fun.iter(profiles) do
if elt and elt.symbols then
local dist = lua_util.distance_sorted(elt.symbols, my_symbols)
-- Check distance
if dist < #my_symbols * .3 then
if dist < min_diff then
min_diff = dist
sel_elt = elt
end
end
end
end
if sel_elt then
-- We can load element from ANN
if set.ann then
-- We have an existing ANN, probably the same...
if set.ann.digest == sel_elt.digest then
-- Same ANN, check version
if set.ann.version < sel_elt.version then
-- Load new ann
rspamd_logger.infox(rspamd_config, 'ann %s is changed, ' ..
'our version = %s, remote version = %s',
rule.prefix .. ':' .. set.name,
set.ann.version,
sel_elt.version)
load_new_ann(rule, ev_base, set, sel_elt, min_diff)
else
lua_util.debugm(N, rspamd_config, 'ann %s is not changed, ' ..
'our version = %s, remote version = %s',
rule.prefix .. ':' .. set.name,
set.ann.version,
sel_elt.version)
end
else
-- We have some different ANN, so we need to compare distance
if set.ann.distance > min_diff then
-- Load more specific ANN
rspamd_logger.infox(rspamd_config, 'more specific ann is available for %s, ' ..
'our distance = %s, remote distance = %s',
rule.prefix .. ':' .. set.name,
set.ann.distance,
min_diff)
load_new_ann(rule, ev_base, set, sel_elt, min_diff)
else
lua_util.debugm(N, rspamd_config, 'ann %s is not changed or less specific, ' ..
'our distance = %s, remote distance = %s',
rule.prefix .. ':' .. set.name,
set.ann.distance,
min_diff)
end
end
else
-- We have no ANN, load new one
load_new_ann(rule, ev_base, set, sel_elt, min_diff)
end
end
end
-- This function checks all profiles and selects if we can train our
-- ANN. By our we mean that it has exactly the same symbols in profile.
-- Use this function to train ANN as `callback` parameter for `check_anns` function
local function maybe_train_existing_ann(worker, ev_base, rule, set, profiles)
local my_symbols = set.symbols
local sel_elt
local lens = {
spam = 0,
ham = 0,
}
for _,elt in fun.iter(profiles) do
if elt and elt.symbols then
local dist = lua_util.distance_sorted(elt.symbols, my_symbols)
-- Check distance
if dist == 0 then
sel_elt = elt
break
end
end
end
if sel_elt then
-- We have our ANN and that's train vectors, check if we can learn
local ann_key = sel_elt.redis_key
lua_util.debugm(N, rspamd_config, "check if ANN %s needs to be trained",
ann_key)
-- Create continuation closure
local redis_len_cb_gen = function(cont_cb, what, is_final)
return function(err, data)
if err then
rspamd_logger.errx(rspamd_config,
'cannot get ANN %s trains %s from redis: %s', what, ann_key, err)
elseif data and type(data) == 'number' or type(data) == 'string' then
local ntrains = tonumber(data) or 0
lens[what] = ntrains
if is_final then
-- Ensure that we have the following:
-- one class has reached max_trains
-- other class(es) are at least as full as classes_bias
-- e.g. if classes_bias = 0.25 and we have 10 max_trains then
-- one class must have 10 or more trains whilst another should have
-- at least (10 * (1 - 0.25)) = 8 trains
local max_len = math.max(lua_util.unpack(lua_util.values(lens)))
local min_len = math.min(lua_util.unpack(lua_util.values(lens)))
if rule.train.learn_type == 'balanced' then
local len_bias_check_pred = function(_, l)
return l >= rule.train.max_trains * (1.0 - rule.train.classes_bias)
end
if max_len >= rule.train.max_trains and fun.all(len_bias_check_pred, lens) then
rspamd_logger.debugm(N, rspamd_config,
'can start ANN %s learn as it has %s learn vectors; %s required, after checking %s vectors',
ann_key, lens, rule.train.max_trains, what)
cont_cb()
else
rspamd_logger.debugm(N, rspamd_config,
'cannot learn ANN %s now: there are not enough %s learn vectors (has %s vectors; %s required)',
ann_key, what, lens, rule.train.max_trains)
end
else
-- Probabilistic mode, just ensure that at least one vector is okay
if min_len > 0 and max_len >= rule.train.max_trains then
rspamd_logger.debugm(N, rspamd_config,
'can start ANN %s learn as it has %s learn vectors; %s required, after checking %s vectors',
ann_key, lens, rule.train.max_trains, what)
cont_cb()
else
rspamd_logger.debugm(N, rspamd_config,
'cannot learn ANN %s now: there are not enough %s learn vectors (has %s vectors; %s required)',
ann_key, what, lens, rule.train.max_trains)
end
end
else
rspamd_logger.debugm(N, rspamd_config,
'checked %s vectors in ANN %s: %s vectors; %s required, need to check other class vectors',
what, ann_key, ntrains, rule.train.max_trains)
cont_cb()
end
end
end
end
local function initiate_train()
rspamd_logger.infox(rspamd_config,
'need to learn ANN %s after %s required learn vectors',
ann_key, lens)
do_train_ann(worker, ev_base, rule, set, ann_key)
end
-- Spam vector is OK, check ham vector length
local function check_ham_len()
lua_redis.redis_make_request_taskless(ev_base,
rspamd_config,
rule.redis,
nil,
false, -- is write
redis_len_cb_gen(initiate_train, 'ham', true), --callback
'LLEN', -- command
{ann_key .. '_ham'}
)
end
lua_redis.redis_make_request_taskless(ev_base,
rspamd_config,
rule.redis,
nil,
false, -- is write
redis_len_cb_gen(check_ham_len, 'spam', false), --callback
'LLEN', -- command
{ann_key .. '_spam'}
)
end
end
-- Used to deserialise ANN element from a list
local function load_ann_profile(element)
local ucl = require "ucl"
local parser = ucl.parser()
local res,ucl_err = parser:parse_string(element)
if not res then
rspamd_logger.warnx(rspamd_config, 'cannot parse ANN from redis: %s',
ucl_err)
return nil
else
local profile = parser:get_object()
local checked,schema_err = redis_profile_schema:transform(profile)
if not checked then
rspamd_logger.errx(rspamd_config, "cannot parse profile schema: %s", schema_err)
return nil
end
return checked
end
end
-- Function to check or load ANNs from Redis
local function check_anns(worker, cfg, ev_base, rule, process_callback, what)
for _,set in pairs(rule.settings) do
local function members_cb(err, data)
if err then
rspamd_logger.errx(cfg, 'cannot get ANNs list from redis: %s',
err)
set.can_store_vectors = true
elseif type(data) == 'table' then
lua_util.debugm(N, cfg, '%s: process element %s:%s',
what, rule.prefix, set.name)
process_callback(worker, ev_base, rule, set, fun.map(load_ann_profile, data))
set.can_store_vectors = true
end
end
if type(set) == 'table' then
-- Extract all profiles for some specific settings id
-- Get the last `max_profiles` recently used
-- Select the most appropriate to our profile but it should not differ by more
-- than 30% of symbols
lua_redis.redis_make_request_taskless(ev_base,
cfg,
rule.redis,
nil,
false, -- is write
members_cb, --callback
'ZREVRANGE', -- command
{set.prefix, '0', tostring(settings.max_profiles)} -- arguments
)
end
end -- Cycle over all settings
return rule.watch_interval
end
-- Function to clean up old ANNs
local function cleanup_anns(rule, cfg, ev_base)
for _,set in pairs(rule.settings) do
local function invalidate_cb(err, data)
if err then
rspamd_logger.errx(cfg, 'cannot exec invalidate script in redis: %s',
err)
elseif type(data) == 'table' then
for _,expired in ipairs(data) do
local profile = load_ann_profile(expired)
rspamd_logger.infox(cfg, 'invalidated ANN for %s; redis key: %s; version=%s',
rule.prefix .. ':' .. set.name,
profile.redis_key,
profile.version)
end
end
end
if type(set) == 'table' then
lua_redis.exec_redis_script(neural_common.redis_script_id.maybe_invalidate,
{ev_base = ev_base, is_write = true},
invalidate_cb,
{set.prefix, tostring(settings.max_profiles)})
end
end
end
local function ann_push_vector(task)
if task:has_flag('skip') then
lua_util.debugm(N, task, 'do not push data for skipped task')
return
end
if not settings.allow_local and lua_util.is_rspamc_or_controller(task) then
lua_util.debugm(N, task, 'do not push data for manual scan')
return
end
local verdict,score = lua_verdict.get_specific_verdict(N, task)
if verdict == 'passthrough' then
lua_util.debugm(N, task, 'ignore task as its verdict is %s(%s)',
verdict, score)
return
end
if score ~= score then
lua_util.debugm(N, task, 'ignore task as its score is nan (%s verdict)',
verdict)
return
end
for _,rule in pairs(settings.rules) do
local set = neural_common.get_rule_settings(task, rule)
if set then
ann_push_task_result(rule, task, verdict, score, set)
else
lua_util.debugm(N, task, 'settings not found in rule %s', rule.prefix)
end
end
end
-- Initialization part
if not (neural_common.module_config and type(neural_common.module_config) == 'table')
or not neural_common.redis_params then
rspamd_logger.infox(rspamd_config, 'Module is unconfigured')
lua_util.disable_module(N, "redis")
return
end
local rules = neural_common.module_config['rules']
if not rules then
-- Use legacy configuration
rules = {}
rules['default'] = neural_common.module_config
end
local id = rspamd_config:register_symbol({
name = 'NEURAL_CHECK',
type = 'postfilter,callback',
flags = 'nostat',
priority = 6,
callback = ann_scores_filter
})
neural_common.settings.rules = {} -- Reset unless validated further in the cycle
if settings.blacklisted_symbols and settings.blacklisted_symbols[1] then
-- Transform to hash for simplicity
settings.blacklisted_symbols = lua_util.list_to_hash(settings.blacklisted_symbols)
end
-- Check all rules
for k,r in pairs(rules) do
local rule_elt = lua_util.override_defaults(neural_common.default_options, r)
rule_elt['redis'] = neural_common.redis_params
rule_elt['anns'] = {} -- Store ANNs here
if not rule_elt.prefix then
rule_elt.prefix = k
end
if not rule_elt.name then
rule_elt.name = k
end
if rule_elt.train.max_train and not rule_elt.train.max_trains then
rule_elt.train.max_trains = rule_elt.train.max_train
end
if not rule_elt.profile then rule_elt.profile = {} end
if rule_elt.max_inputs and not has_blas then
rspamd_logger.errx('cannot set max inputs to %s as BLAS is not compiled in',
rule_elt.name, rule_elt.max_inputs)
rule_elt.max_inputs = nil
end
rspamd_logger.infox(rspamd_config, "register ann rule %s", k)
settings.rules[k] = rule_elt
rspamd_config:set_metric_symbol({
name = rule_elt.symbol_spam,
score = 0.0,
description = 'Neural network SPAM',
group = 'neural'
})
rspamd_config:register_symbol({
name = rule_elt.symbol_spam,
type = 'virtual',
flags = 'nostat',
parent = id
})
rspamd_config:set_metric_symbol({
name = rule_elt.symbol_ham,
score = -0.0,
description = 'Neural network HAM',
group = 'neural'
})
rspamd_config:register_symbol({
name = rule_elt.symbol_ham,
type = 'virtual',
flags = 'nostat',
parent = id
})
end
rspamd_config:register_symbol({
name = 'NEURAL_LEARN',
type = 'idempotent,callback',
flags = 'nostat,explicit_disable',
priority = 5,
callback = ann_push_vector
})
-- We also need to deal with settings
rspamd_config:add_post_init(neural_common.process_rules_settings)
-- Add training scripts
for _,rule in pairs(settings.rules) do
neural_common.load_scripts(rule.redis)
-- This function will check ANNs in Redis when a worker is loaded
rspamd_config:add_on_load(function(cfg, ev_base, worker)
if worker:is_scanner() then
rspamd_config:add_periodic(ev_base, 0.0,
function(_, _)
return check_anns(worker, cfg, ev_base, rule, process_existing_ann,
'try_load_ann')
end)
end
if worker:is_primary_controller() then
-- We also want to train neural nets when they have enough data
rspamd_config:add_periodic(ev_base, 0.0,
function(_, _)
-- Clean old ANNs
cleanup_anns(rule, cfg, ev_base)
return check_anns(worker, cfg, ev_base, rule, maybe_train_existing_ann,
'try_train_ann')
end)
end
end)
end
File Manager Version 1.0, Coded By Lucas
Email: hehe@yahoo.com