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Diffstat (limited to 'lualib/plugins/neural.lua')
| -rw-r--r-- | lualib/plugins/neural.lua | 779 |
1 files changed, 779 insertions, 0 deletions
diff --git a/lualib/plugins/neural.lua b/lualib/plugins/neural.lua new file mode 100644 index 000000000..4d4c44b5d --- /dev/null +++ b/lualib/plugins/neural.lua @@ -0,0 +1,779 @@ +--[[ +Copyright (c) 2020, 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. +]]-- + +local fun = require "fun" +local lua_redis = require "lua_redis" +local lua_settings = require "lua_settings" +local lua_util = require "lua_util" +local meta_functions = require "lua_meta" +local rspamd_kann = require "rspamd_kann" +local rspamd_logger = require "rspamd_logger" +local rspamd_tensor = require "rspamd_tensor" +local rspamd_util = require "rspamd_util" + +local N = 'neural' + +-- Used in prefix to avoid wrong ANN to be loaded +local plugin_ver = '2' + +-- Module vars +local default_options = { + train = { + max_trains = 1000, + max_epoch = 1000, + max_usages = 10, + max_iterations = 25, -- Torch style + mse = 0.001, + autotrain = true, + train_prob = 1.0, + learn_threads = 1, + learn_mode = 'balanced', -- Possible values: balanced, proportional + learning_rate = 0.01, + classes_bias = 0.0, -- balanced mode: what difference is allowed between classes (1:1 proportion means 0 bias) + spam_skip_prob = 0.0, -- proportional mode: spam skip probability (0-1) + ham_skip_prob = 0.0, -- proportional mode: ham skip probability + store_pool_only = false, -- store tokens in cache only (disables autotrain); + -- neural_vec_mpack stores vector of training data in messagepack neural_profile_digest stores profile digest + }, + watch_interval = 60.0, + lock_expire = 600, + learning_spawned = false, + ann_expire = 60 * 60 * 24 * 2, -- 2 days + hidden_layer_mult = 1.5, -- number of neurons in the hidden layer + symbol_spam = 'NEURAL_SPAM', + symbol_ham = 'NEURAL_HAM', + max_inputs = nil, -- when PCA is used + blacklisted_symbols = {}, -- list of symbols skipped in neural processing +} + +-- Rule structure: +-- * static config fields (see `default_options`) +-- * prefix - name or defined prefix +-- * settings - table of settings indexed by settings id, -1 is used when no settings defined + +-- Rule settings element defines elements for specific settings id: +-- * symbols - static symbols profile (defined by config or extracted from symcache) +-- * name - name of settings id +-- * digest - digest of all symbols +-- * ann - dynamic ANN configuration loaded from Redis +-- * train - train data for ANN (e.g. the currently trained ANN) + +-- Settings ANN table is loaded from Redis and represents dynamic profile for ANN +-- Some elements are directly stored in Redis, ANN is, in turn loaded dynamically +-- * version - version of ANN loaded from redis +-- * redis_key - name of ANN key in Redis +-- * symbols - symbols in THIS PARTICULAR ANN (might be different from set.symbols) +-- * distance - distance between set.symbols and set.ann.symbols +-- * ann - kann object + +local settings = { + rules = {}, + prefix = 'rn', -- Neural network default prefix + max_profiles = 3, -- Maximum number of NN profiles stored +} + +-- Get module & Redis configuration +local module_config = rspamd_config:get_all_opt(N) +settings = lua_util.override_defaults(settings, module_config) +local redis_params = lua_redis.parse_redis_server('neural') + +-- Lua script that checks if we can store a new training vector +-- Uses the following keys: +-- key1 - ann key +-- returns nspam,nham (or nil if locked) +local redis_lua_script_vectors_len = [[ + local prefix = KEYS[1] + local locked = redis.call('HGET', prefix, 'lock') + if locked then + local host = redis.call('HGET', prefix, 'hostname') or 'unknown' + return string.format('%s:%s', host, locked) + end + local nspam = 0 + local nham = 0 + + local ret = redis.call('LLEN', prefix .. '_spam') + if ret then nspam = tonumber(ret) end + ret = redis.call('LLEN', prefix .. '_ham') + if ret then nham = tonumber(ret) end + + return {nspam,nham} +]] + +-- Lua script to invalidate ANNs by rank +-- Uses the following keys +-- key1 - prefix for keys +-- key2 - number of elements to leave +local redis_lua_script_maybe_invalidate = [[ + local card = redis.call('ZCARD', KEYS[1]) + local lim = tonumber(KEYS[2]) + if card > lim then + local to_delete = redis.call('ZRANGE', KEYS[1], 0, card - lim - 1) + for _,k in ipairs(to_delete) do + local tb = cjson.decode(k) + redis.call('DEL', tb.redis_key) + -- Also train vectors + redis.call('DEL', tb.redis_key .. '_spam') + redis.call('DEL', tb.redis_key .. '_ham') + end + redis.call('ZREMRANGEBYRANK', KEYS[1], 0, card - lim - 1) + return to_delete + else + return {} + end +]] + +-- Lua script to invalidate ANN from redis +-- Uses the following keys +-- key1 - prefix for keys +-- key2 - current time +-- key3 - key expire +-- key4 - hostname +local redis_lua_script_maybe_lock = [[ + local locked = redis.call('HGET', KEYS[1], 'lock') + local now = tonumber(KEYS[2]) + if locked then + locked = tonumber(locked) + local expire = tonumber(KEYS[3]) + if now > locked and (now - locked) < expire then + return {tostring(locked), redis.call('HGET', KEYS[1], 'hostname') or 'unknown'} + end + end + redis.call('HSET', KEYS[1], 'lock', tostring(now)) + redis.call('HSET', KEYS[1], 'hostname', KEYS[4]) + return 1 +]] + +-- Lua script to save and unlock ANN in redis +-- Uses the following keys +-- key1 - prefix for ANN +-- key2 - prefix for profile +-- key3 - compressed ANN +-- key4 - profile as JSON +-- key5 - expire in seconds +-- key6 - current time +-- key7 - old key +-- key8 - optional PCA +local redis_lua_script_save_unlock = [[ + local now = tonumber(KEYS[6]) + redis.call('ZADD', KEYS[2], now, KEYS[4]) + redis.call('HSET', KEYS[1], 'ann', KEYS[3]) + redis.call('DEL', KEYS[1] .. '_spam') + redis.call('DEL', KEYS[1] .. '_ham') + redis.call('HDEL', KEYS[1], 'lock') + redis.call('HDEL', KEYS[7], 'lock') + redis.call('EXPIRE', KEYS[1], tonumber(KEYS[5])) + if KEYS[8] then + redis.call('HSET', KEYS[1], 'pca', KEYS[8]) + end + return 1 +]] + +local redis_script_id = {} + +local function load_scripts() + redis_script_id.vectors_len = lua_redis.add_redis_script(redis_lua_script_vectors_len, + redis_params) + redis_script_id.maybe_invalidate = lua_redis.add_redis_script(redis_lua_script_maybe_invalidate, + redis_params) + redis_script_id.maybe_lock = lua_redis.add_redis_script(redis_lua_script_maybe_lock, + redis_params) + redis_script_id.save_unlock = lua_redis.add_redis_script(redis_lua_script_save_unlock, + redis_params) +end + +local function create_ann(n, nlayers, rule) + -- We ignore number of layers so far when using kann + local nhidden = math.floor(n * (rule.hidden_layer_mult or 1.0) + 1.0) + local t = rspamd_kann.layer.input(n) + t = rspamd_kann.transform.relu(t) + t = rspamd_kann.layer.dense(t, nhidden); + t = rspamd_kann.layer.cost(t, 1, rspamd_kann.cost.ceb_neg) + return rspamd_kann.new.kann(t) +end + +-- Fills ANN data for a specific settings element +local function fill_set_ann(set, ann_key) + if not set.ann then + set.ann = { + symbols = set.symbols, + distance = 0, + digest = set.digest, + redis_key = ann_key, + version = 0, + } + end +end + +-- This function takes all inputs, applies PCA transformation and returns the final +-- PCA matrix as rspamd_tensor +local function learn_pca(inputs, max_inputs) + local scatter_matrix = rspamd_tensor.scatter_matrix(rspamd_tensor.fromtable(inputs)) + local eigenvals = scatter_matrix:eigen() + -- scatter matrix is not filled with eigenvectors + lua_util.debugm(N, 'eigenvalues: %s', eigenvals) + local w = rspamd_tensor.new(2, max_inputs, #scatter_matrix[1]) + for i=1,max_inputs do + w[i] = scatter_matrix[#scatter_matrix - i + 1] + end + + lua_util.debugm(N, 'pca matrix: %s', w) + + return w +end + +-- This function is intended to extend lock for ANN during training +-- It registers periodic that increases locked key each 30 seconds unless +-- `set.learning_spawned` is set to `true` +local function register_lock_extender(rule, set, ev_base, ann_key) + rspamd_config:add_periodic(ev_base, 30.0, + function() + local function redis_lock_extend_cb(_err, _) + if _err then + rspamd_logger.errx(rspamd_config, 'cannot lock ANN %s from redis: %s', + ann_key, _err) + else + rspamd_logger.infox(rspamd_config, 'extend lock for ANN %s for 30 seconds', + ann_key) + end + end + + if set.learning_spawned then + lua_redis.redis_make_request_taskless(ev_base, + rspamd_config, + rule.redis, + nil, + true, -- is write + redis_lock_extend_cb, --callback + 'HINCRBY', -- command + {ann_key, 'lock', '30'} + ) + else + lua_util.debugm(N, rspamd_config, "stop lock extension as learning_spawned is false") + return false -- do not plan any more updates + end + + return true + end + ) +end + +local function can_push_train_vector(rule, task, learn_type, nspam, nham) + local train_opts = rule.train + local coin = math.random() + + if train_opts.train_prob and coin < 1.0 - train_opts.train_prob then + rspamd_logger.infox(task, 'probabilistically skip sample: %s', coin) + return false + end + + if train_opts.learn_mode == 'balanced' then + -- Keep balanced training set based on number of spam and ham samples + if learn_type == 'spam' then + if nspam <= train_opts.max_trains then + if nspam > nham then + -- Apply sampling + local skip_rate = 1.0 - nham / (nspam + 1) + if coin < skip_rate - train_opts.classes_bias then + rspamd_logger.infox(task, + 'skip %s sample to keep spam/ham balance; probability %s; %s spam and %s ham vectors stored', + learn_type, + skip_rate - train_opts.classes_bias, + nspam, nham) + return false + end + end + return true + else -- Enough learns + rspamd_logger.infox(task, 'skip %s sample to keep spam/ham balance; too many spam samples: %s', + learn_type, + nspam) + end + else + if nham <= train_opts.max_trains then + if nham > nspam then + -- Apply sampling + local skip_rate = 1.0 - nspam / (nham + 1) + if coin < skip_rate - train_opts.classes_bias then + rspamd_logger.infox(task, + 'skip %s sample to keep spam/ham balance; probability %s; %s spam and %s ham vectors stored', + learn_type, + skip_rate - train_opts.classes_bias, + nspam, nham) + return false + end + end + return true + else + rspamd_logger.infox(task, 'skip %s sample to keep spam/ham balance; too many ham samples: %s', learn_type, + nham) + end + end + else + -- Probabilistic learn mode, we just skip learn if we already have enough samples or + -- if our coin drop is less than desired probability + if learn_type == 'spam' then + if nspam <= train_opts.max_trains then + if train_opts.spam_skip_prob then + if coin <= train_opts.spam_skip_prob then + rspamd_logger.infox(task, 'skip %s sample probabilisticaly; probability %s (%s skip chance)', learn_type, + coin, train_opts.spam_skip_prob) + return false + end + + return true + end + else + rspamd_logger.infox(task, 'skip %s sample; too many spam samples: %s (%s limit)', learn_type, + nspam, train_opts.max_trains) + end + else + if nham <= train_opts.max_trains then + if train_opts.ham_skip_prob then + if coin <= train_opts.ham_skip_prob then + rspamd_logger.infox(task, 'skip %s sample probabilisticaly; probability %s (%s skip chance)', learn_type, + coin, train_opts.ham_skip_prob) + return false + end + + return true + end + else + rspamd_logger.infox(task, 'skip %s sample; too many ham samples: %s (%s limit)', learn_type, + nham, train_opts.max_trains) + end + end + end + + return false +end + +-- Closure generator for unlock function +local function gen_unlock_cb(rule, set, ann_key) + return function (err) + if err then + rspamd_logger.errx(rspamd_config, 'cannot unlock ANN %s:%s at %s from redis: %s', + rule.prefix, set.name, ann_key, err) + else + lua_util.debugm(N, rspamd_config, 'unlocked ANN %s:%s at %s', + rule.prefix, set.name, ann_key) + end + end +end + +-- Used to generate new ANN key for specific profile +local function new_ann_key(rule, set, version) + local ann_key = string.format('%s_%s_%s_%s_%s', settings.prefix, + rule.prefix, set.name, set.digest:sub(1, 8), tostring(version)) + + return ann_key +end + +local function redis_ann_prefix(rule, settings_name) + -- We also need to count metatokens: + local n = meta_functions.version + return string.format('%s%d_%s_%d_%s', + settings.prefix, plugin_ver, rule.prefix, n, settings_name) +end + +-- This function receives training vectors, checks them, spawn learning and saves ANN in Redis +local function spawn_train(params) + -- Check training data sanity + -- Now we need to join inputs and create the appropriate test vectors + local n = #params.set.symbols + + meta_functions.rspamd_count_metatokens() + + -- Now we can train ann + local train_ann = create_ann(params.rule.max_inputs or n, 3, params.rule) + + if #params.ham_vec + #params.spam_vec < params.rule.train.max_trains / 2 then + -- Invalidate ANN as it is definitely invalid + -- TODO: add invalidation + assert(false) + else + local inputs, outputs = {}, {} + + -- Used to show sparsed vectors in a convenient format (for debugging only) + local function debug_vec(t) + local ret = {} + for i,v in ipairs(t) do + if v ~= 0 then + ret[#ret + 1] = string.format('%d=%.2f', i, v) + end + end + + return ret + end + + -- Make training set by joining vectors + -- KANN automatically shuffles those samples + -- 1.0 is used for spam and -1.0 is used for ham + -- It implies that output layer can express that (e.g. tanh output) + for _,e in ipairs(params.spam_vec) do + inputs[#inputs + 1] = e + outputs[#outputs + 1] = {1.0} + --rspamd_logger.debugm(N, rspamd_config, 'spam vector: %s', debug_vec(e)) + end + for _,e in ipairs(params.ham_vec) do + inputs[#inputs + 1] = e + outputs[#outputs + 1] = {-1.0} + --rspamd_logger.debugm(N, rspamd_config, 'ham vector: %s', debug_vec(e)) + end + + -- Called in child process + local function train() + local log_thresh = params.rule.train.max_iterations / 10 + local seen_nan = false + + local function train_cb(iter, train_cost, value_cost) + if (iter * (params.rule.train.max_iterations / log_thresh)) % (params.rule.train.max_iterations) == 0 then + if train_cost ~= train_cost and not seen_nan then + -- We have nan :( try to log lot's of stuff to dig into a problem + seen_nan = true + rspamd_logger.errx(rspamd_config, 'ANN %s:%s: train error: observed nan in error cost!; value cost = %s', + params.rule.prefix, params.set.name, + value_cost) + for i,e in ipairs(inputs) do + lua_util.debugm(N, rspamd_config, 'train vector %s -> %s', + debug_vec(e), outputs[i][1]) + end + end + + rspamd_logger.infox(rspamd_config, + "ANN %s:%s: learned from %s redis key in %s iterations, error: %s, value cost: %s", + params.rule.prefix, params.set.name, + params.ann_key, + iter, + train_cost, + value_cost) + end + end + + lua_util.debugm(N, rspamd_config, "subprocess to learn ANN %s:%s has been started", + params.rule.prefix, params.set.name) + + local ret,err = pcall(train_ann.train1, train_ann, + inputs, outputs, { + lr = params.rule.train.learning_rate, + max_epoch = params.rule.train.max_iterations, + cb = train_cb, + pca = (params.set.ann or {}).pca + }) + + if not ret then + rspamd_logger.errx(rspamd_config, "cannot train ann %s:%s: %s", + params.rule.prefix, params.set.name, err) + + return nil + end + + if not seen_nan then + local out = train_ann:save() + return out + else + return nil + end + end + + params.set.learning_spawned = true + + local function redis_save_cb(err) + if err then + rspamd_logger.errx(rspamd_config, 'cannot save ANN %s:%s to redis key %s: %s', + params.rule.prefix, params.set.name, params.ann_key, err) + lua_redis.redis_make_request_taskless(params.ev_base, + rspamd_config, + params.rule.redis, + nil, + false, -- is write + gen_unlock_cb(params.rule, params.set, params.ann_key), --callback + 'HDEL', -- command + {params.ann_key, 'lock'} + ) + else + rspamd_logger.infox(rspamd_config, 'saved ANN %s:%s to redis: %s', + params.rule.prefix, params.set.name, params.set.ann.redis_key) + end + end + + local function ann_trained(err, data) + params.set.learning_spawned = false + if err then + rspamd_logger.errx(rspamd_config, 'cannot train ANN %s:%s : %s', + params.rule.prefix, params.set.name, err) + lua_redis.redis_make_request_taskless(params.ev_base, + rspamd_config, + params.rule.redis, + nil, + true, -- is write + gen_unlock_cb(params.rule, params.set, params.ann_key), --callback + 'HDEL', -- command + {params.ann_key, 'lock'} + ) + else + local ann_data = rspamd_util.zstd_compress(data) + local pca_data + + fill_set_ann(params.set, params.ann_key) + if params.set.ann.pca then + pca_data = rspamd_util.zstd_compress(params.set.ann.pca:save()) + end + -- Deserialise ANN from the child process + ann_trained = rspamd_kann.load(data) + local version = (params.set.ann.version or 0) + 1 + params.set.ann.version = version + params.set.ann.ann = ann_trained + params.set.ann.symbols = params.set.symbols + params.set.ann.redis_key = new_ann_key(params.rule, params.set, version) + + local profile = { + symbols = params.set.symbols, + digest = params.set.digest, + redis_key = params.set.ann.redis_key, + version = version + } + + local ucl = require "ucl" + local profile_serialized = ucl.to_format(profile, 'json-compact', true) + + rspamd_logger.infox(rspamd_config, + 'trained ANN %s:%s, %s bytes (%s compressed); %s rows in pca (%sb compressed); redis key: %s (old key %s)', + params.rule.prefix, params.set.name, + #data, #ann_data, + #(params.set.ann.pca or {}), #(pca_data or {}), + params.set.ann.redis_key, params.ann_key) + + lua_redis.exec_redis_script(redis_script_id.save_unlock, + {ev_base = params.ev_base, is_write = true}, + redis_save_cb, + {profile.redis_key, + redis_ann_prefix(params.rule, params.set.name), + ann_data, + profile_serialized, + tostring(params.rule.ann_expire), + tostring(os.time()), + params.ann_key, -- old key to unlock... + pca_data + }) + end + end + + if params.rule.max_inputs then + fill_set_ann(params.set, params.ann_key) + -- Train PCA in the main process, presumably it is not that long + params.set.ann.pca = learn_pca(inputs, params.rule.max_inputs) + end + + params.worker:spawn_process{ + func = train, + on_complete = ann_trained, + proctitle = string.format("ANN train for %s/%s", params.rule.prefix, params.set.name), + } + -- Spawn learn and register lock extension + params.set.learning_spawned = true + register_lock_extender(params.rule, params.set, params.ev_base, params.ann_key) + return + + end +end + +-- This function is used to adjust profiles and allowed setting ids for each rule +-- It must be called when all settings are already registered (e.g. at post-init for config) +local function process_rules_settings() + local function process_settings_elt(rule, selt) + local profile = rule.profile[selt.name] + if profile then + -- Use static user defined profile + -- Ensure that we have an array... + lua_util.debugm(N, rspamd_config, "use static profile for %s (%s): %s", + rule.prefix, selt.name, profile) + if not profile[1] then profile = lua_util.keys(profile) end + selt.symbols = profile + else + lua_util.debugm(N, rspamd_config, "use dynamic cfg based profile for %s (%s)", + rule.prefix, selt.name) + end + + local function filter_symbols_predicate(sname) + if settings.blacklisted_symbols and settings.blacklisted_symbols[sname] then + return false + end + local fl = rspamd_config:get_symbol_flags(sname) + if fl then + fl = lua_util.list_to_hash(fl) + + return not (fl.nostat or fl.idempotent or fl.skip or fl.composite) + end + + return false + end + + -- Generic stuff + table.sort(fun.totable(fun.filter(filter_symbols_predicate, selt.symbols))) + + selt.digest = lua_util.table_digest(selt.symbols) + selt.prefix = redis_ann_prefix(rule, selt.name) + + lua_redis.register_prefix(selt.prefix, N, + string.format('NN prefix for rule "%s"; settings id "%s"', + rule.prefix, selt.name), { + persistent = true, + type = 'zlist', + }) + -- Versions + lua_redis.register_prefix(selt.prefix .. '_\\d+', N, + string.format('NN storage for rule "%s"; settings id "%s"', + rule.prefix, selt.name), { + persistent = true, + type = 'hash', + }) + lua_redis.register_prefix(selt.prefix .. '_\\d+_spam', N, + string.format('NN learning set (spam) for rule "%s"; settings id "%s"', + rule.prefix, selt.name), { + persistent = true, + type = 'list', + }) + lua_redis.register_prefix(selt.prefix .. '_\\d+_ham', N, + string.format('NN learning set (spam) for rule "%s"; settings id "%s"', + rule.prefix, selt.name), { + persistent = true, + type = 'list', + }) + end + + for k,rule in pairs(settings.rules) do + if not rule.allowed_settings then + rule.allowed_settings = {} + elseif rule.allowed_settings == 'all' then + -- Extract all settings ids + rule.allowed_settings = lua_util.keys(lua_settings.all_settings()) + end + + -- Convert to a map <setting_id> -> true + rule.allowed_settings = lua_util.list_to_hash(rule.allowed_settings) + + -- Check if we can work without settings + if k == 'default' or type(rule.default) ~= 'boolean' then + rule.default = true + end + + rule.settings = {} + + if rule.default then + local default_settings = { + symbols = lua_settings.default_symbols(), + name = 'default' + } + + process_settings_elt(rule, default_settings) + rule.settings[-1] = default_settings -- Magic constant, but OK as settings are positive int32 + end + + -- Now, for each allowed settings, we store sorted symbols + digest + -- We set table rule.settings[id] -> { name = name, symbols = symbols, digest = digest } + for s,_ in pairs(rule.allowed_settings) do + -- Here, we have a name, set of symbols and + local settings_id = s + if type(settings_id) ~= 'number' then + settings_id = lua_settings.numeric_settings_id(s) + end + local selt = lua_settings.settings_by_id(settings_id) + + local nelt = { + symbols = selt.symbols, -- Already sorted + name = selt.name + } + + process_settings_elt(rule, nelt) + for id,ex in pairs(rule.settings) do + if type(ex) == 'table' then + if nelt and lua_util.distance_sorted(ex.symbols, nelt.symbols) == 0 then + -- Equal symbols, add reference + lua_util.debugm(N, rspamd_config, + 'added reference from settings id %s to %s; same symbols', + nelt.name, ex.name) + rule.settings[settings_id] = id + nelt = nil + end + end + end + + if nelt then + rule.settings[settings_id] = nelt + lua_util.debugm(N, rspamd_config, 'added new settings id %s(%s) to %s', + nelt.name, settings_id, rule.prefix) + end + end + end +end + +-- Extract settings element for a specific settings id +local function get_rule_settings(task, rule) + local sid = task:get_settings_id() or -1 + local set = rule.settings[sid] + + if not set then return nil end + + while type(set) == 'number' do + -- Reference to another settings! + set = rule.settings[set] + end + + return set +end + +local function result_to_vector(task, profile) + if not profile.zeros then + -- Fill zeros vector + local zeros = {} + for i=1,meta_functions.count_metatokens() do + zeros[i] = 0.0 + end + for _,_ in ipairs(profile.symbols) do + zeros[#zeros + 1] = 0.0 + end + profile.zeros = zeros + end + + local vec = lua_util.shallowcopy(profile.zeros) + local mt = meta_functions.rspamd_gen_metatokens(task) + + for i,v in ipairs(mt) do + vec[i] = v + end + + task:process_ann_tokens(profile.symbols, vec, #mt, 0.1) + + return vec +end + +return { + can_push_train_vector = can_push_train_vector, + create_ann = create_ann, + default_options = default_options, + gen_unlock_cb = gen_unlock_cb, + get_rule_settings = get_rule_settings, + load_scripts = load_scripts, + module_config = module_config, + new_ann_key = new_ann_key, + plugin_ver = plugin_ver, + process_rules_settings = process_rules_settings, + redis_ann_prefix = redis_ann_prefix, + redis_params = redis_params, + redis_script_id = redis_script_id, + result_to_vector = result_to_vector, + settings = settings, + spawn_train = spawn_train, +} |