diff options
32 files changed, 3574 insertions, 413 deletions
diff --git a/.overcommit.yml b/.overcommit.yml index d26d3de52..9212c33b3 100644 --- a/.overcommit.yml +++ b/.overcommit.yml @@ -29,7 +29,8 @@ PreCommit: command: ['luacheck', 'lualib', 'src/plugins/lua'] ClangFormat: enabled: true - command: ['git', 'clang-format', '--diff'] + command: ['sh', '-c', 'git clang-format --diff --quiet || (echo "Running clang-format to fix issues..." && git clang-format && git add -u && echo "Files formatted and staged.")'] + on_warn: fail #PostCheckout: # ALL: # Special hook name that customizes all hooks of this type # quiet: true # Change all post-checkout hooks to only display output on failure diff --git a/conf/statistic.conf b/conf/statistic.conf index 36d418935..3ba460ff3 100644 --- a/conf/statistic.conf +++ b/conf/statistic.conf @@ -35,11 +35,11 @@ classifier "bayes" { statfile { symbol = "BAYES_HAM"; - spam = false; + class = "ham"; } statfile { symbol = "BAYES_SPAM"; - spam = true; + class = "spam"; } learn_condition = 'return require("lua_bayes_learn").can_learn'; diff --git a/lualib/lua_bayes_redis.lua b/lualib/lua_bayes_redis.lua index 782e6fc47..a7af80bf1 100644 --- a/lualib/lua_bayes_redis.lua +++ b/lualib/lua_bayes_redis.lua @@ -25,27 +25,44 @@ local ucl = require "ucl" local N = "bayes" local function gen_classify_functor(redis_params, classify_script_id) - return function(task, expanded_key, id, is_spam, stat_tokens, callback) - + return function(task, expanded_key, id, class_labels, stat_tokens, callback) local function classify_redis_cb(err, data) lua_util.debugm(N, task, 'classify redis cb: %s, %s', err, data) if err then callback(task, false, err) else - callback(task, true, data[1], data[2], data[3], data[4]) + -- Pass the raw data table to the C++ callback for processing + -- The C++ callback will handle both binary and multi-class formats + callback(task, true, data) + end + end + + -- Determine class labels to send to Redis script + local script_class_labels + if type(class_labels) == "table" then + -- Use simple comma-separated string instead of messagepack + script_class_labels = "TABLE:" .. table.concat(class_labels, ",") + else + -- Single class label or boolean compatibility + if class_labels == true or class_labels == "true" then + script_class_labels = "S" -- spam + elseif class_labels == false or class_labels == "false" then + script_class_labels = "H" -- ham + else + script_class_labels = class_labels -- string class label end end lua_redis.exec_redis_script(classify_script_id, { task = task, is_write = false, key = expanded_key }, - classify_redis_cb, { expanded_key, stat_tokens }) + classify_redis_cb, { expanded_key, script_class_labels, stat_tokens }) end end local function gen_learn_functor(redis_params, learn_script_id) - return function(task, expanded_key, id, is_spam, symbol, is_unlearn, stat_tokens, callback, maybe_text_tokens) + return function(task, expanded_key, id, class_label, symbol, is_unlearn, stat_tokens, callback, maybe_text_tokens) local function learn_redis_cb(err, data) - lua_util.debugm(N, task, 'learn redis cb: %s, %s', err, data) + lua_util.debugm(N, task, 'learn redis cb: %s, %s for class %s', err, data, class_label) if err then callback(task, false, err) else @@ -53,17 +70,24 @@ local function gen_learn_functor(redis_params, learn_script_id) end end + -- Convert class_label for backward compatibility + local script_class_label = class_label + if class_label == true or class_label == "true" then + script_class_label = "S" -- spam + elseif class_label == false or class_label == "false" then + script_class_label = "H" -- ham + end + if maybe_text_tokens then lua_redis.exec_redis_script(learn_script_id, { task = task, is_write = true, key = expanded_key }, learn_redis_cb, - { expanded_key, tostring(is_spam), symbol, tostring(is_unlearn), stat_tokens, maybe_text_tokens }) + { expanded_key, script_class_label, symbol, tostring(is_unlearn), stat_tokens, maybe_text_tokens }) else lua_redis.exec_redis_script(learn_script_id, { task = task, is_write = true, key = expanded_key }, - learn_redis_cb, { expanded_key, tostring(is_spam), symbol, tostring(is_unlearn), stat_tokens }) + learn_redis_cb, { expanded_key, script_class_label, symbol, tostring(is_unlearn), stat_tokens }) end - end end @@ -112,8 +136,7 @@ end --- @param classifier_ucl ucl of the classifier config --- @param statfile_ucl ucl of the statfile config --- @return a pair of (classify_functor, learn_functor) or `nil` in case of error -exports.lua_bayes_init_statfile = function(classifier_ucl, statfile_ucl, symbol, is_spam, ev_base, stat_periodic_cb) - +exports.lua_bayes_init_statfile = function(classifier_ucl, statfile_ucl, symbol, class_label, ev_base, stat_periodic_cb) local redis_params = load_redis_params(classifier_ucl, statfile_ucl) if not redis_params then @@ -137,7 +160,6 @@ exports.lua_bayes_init_statfile = function(classifier_ucl, statfile_ucl, symbol, if ev_base then rspamd_config:add_periodic(ev_base, 0.0, function(cfg, _) - local function stat_redis_cb(err, data) lua_util.debugm(N, cfg, 'stat redis cb: %s, %s', err, data) @@ -162,11 +184,22 @@ exports.lua_bayes_init_statfile = function(classifier_ucl, statfile_ucl, symbol, end end + -- Convert class_label to learn key + local learn_key + if class_label == true or class_label == "true" or class_label == "S" then + learn_key = "learns_spam" + elseif class_label == false or class_label == "false" or class_label == "H" then + learn_key = "learns_ham" + else + -- For other class labels, use learns_<class_label> + learn_key = "learns_" .. string.lower(tostring(class_label)) + end + lua_redis.exec_redis_script(stat_script_id, { ev_base = ev_base, cfg = cfg, is_write = false }, stat_redis_cb, { tostring(cursor), symbol, - is_spam and "learns_spam" or "learns_ham", + learn_key, tostring(max_users) }) return statfile_ucl.monitor_timeout or classifier_ucl.monitor_timeout or 30.0 end) @@ -178,7 +211,6 @@ end local function gen_cache_check_functor(redis_params, check_script_id, conf) local packed_conf = ucl.to_format(conf, 'msgpack') return function(task, cache_id, callback) - local function classify_redis_cb(err, data) lua_util.debugm(N, task, 'check cache redis cb: %s, %s (%s)', err, data, type(data)) if err then @@ -201,17 +233,16 @@ end local function gen_cache_learn_functor(redis_params, learn_script_id, conf) local packed_conf = ucl.to_format(conf, 'msgpack') - return function(task, cache_id, is_spam) + return function(task, cache_id, class_name, class_id) local function learn_redis_cb(err, data) lua_util.debugm(N, task, 'learn_cache redis cb: %s, %s', err, data) end - lua_util.debugm(N, task, 'try to learn cache: %s', cache_id) + lua_util.debugm(N, task, 'try to learn cache: %s as %s (id=%s)', cache_id, class_name, class_id) lua_redis.exec_redis_script(learn_script_id, { task = task, is_write = true, key = cache_id }, learn_redis_cb, - { cache_id, is_spam and "1" or "0", packed_conf }) - + { cache_id, tostring(class_id), packed_conf }) end end diff --git a/lualib/redis_scripts/bayes_cache_learn.lua b/lualib/redis_scripts/bayes_cache_learn.lua index 7d44a73ef..a7c9ac443 100644 --- a/lualib/redis_scripts/bayes_cache_learn.lua +++ b/lualib/redis_scripts/bayes_cache_learn.lua @@ -1,12 +1,15 @@ --- Lua script to perform cache checking for bayes classification +-- Lua script to perform cache checking for bayes classification (multi-class) -- This script accepts the following parameters: -- key1 - cache id --- key2 - is spam (1 or 0) +-- key2 - class_id (numeric hash of class name, computed by C side) -- key3 - configuration table in message pack local cache_id = KEYS[1] -local is_spam = KEYS[2] +local class_id = KEYS[2] local conf = cmsgpack.unpack(KEYS[3]) + +-- Use class_id directly as cache value +local cache_value = tostring(class_id) cache_id = string.sub(cache_id, 1, conf.cache_elt_len) -- Try each prefix that is in Redis (as some other instance might have set it) @@ -15,8 +18,8 @@ for i = 0, conf.cache_max_keys do local have = redis.call('HGET', prefix, cache_id) if have then - -- Already in cache, but is_spam changes when relearning - redis.call('HSET', prefix, cache_id, is_spam) + -- Already in cache, but cache_value changes when relearning + redis.call('HSET', prefix, cache_id, cache_value) return false end end @@ -30,7 +33,7 @@ for i = 0, conf.cache_max_keys do if count < lim then -- We can add it to this prefix - redis.call('HSET', prefix, cache_id, is_spam) + redis.call('HSET', prefix, cache_id, cache_value) added = true end end @@ -46,7 +49,7 @@ if not added then if exists then if not expired then redis.call('DEL', prefix) - redis.call('HSET', prefix, cache_id, is_spam) + redis.call('HSET', prefix, cache_id, cache_value) -- Do not expire anything else expired = true diff --git a/lualib/redis_scripts/bayes_classify.lua b/lualib/redis_scripts/bayes_classify.lua index e94f645fd..d6132e631 100644 --- a/lualib/redis_scripts/bayes_classify.lua +++ b/lualib/redis_scripts/bayes_classify.lua @@ -1,37 +1,68 @@ --- Lua script to perform bayes classification +-- Lua script to perform bayes classification (multi-class) -- This script accepts the following parameters: -- key1 - prefix for bayes tokens (e.g. for per-user classification) --- key2 - set of tokens encoded in messagepack array of strings +-- key2 - class labels: table of all class labels as "TABLE:label1,label2,..." +-- key3 - set of tokens encoded in messagepack array of strings local prefix = KEYS[1] -local output_spam = {} -local output_ham = {} +local class_labels_arg = KEYS[2] +local input_tokens = cmsgpack.unpack(KEYS[3]) -local learned_ham = tonumber(redis.call('HGET', prefix, 'learns_ham')) or 0 -local learned_spam = tonumber(redis.call('HGET', prefix, 'learns_spam')) or 0 +-- Parse class labels (always expect TABLE: format) +local class_labels = {} +if string.match(class_labels_arg, "^TABLE:") then + local labels_str = string.sub(class_labels_arg, 7) -- Remove "TABLE:" prefix + for label in string.gmatch(labels_str, "([^,]+)") do + table.insert(class_labels, label) + end +else + -- Legacy single class - convert to array + class_labels = { class_labels_arg } +end --- Output is a set of pairs (token_index, token_count), tokens that are not --- found are not filled. --- This optimisation will save a lot of space for sparse tokens, and in Bayes that assumption is normally held +-- Get learned counts for all classes (ordered) +local learned_counts = {} +for _, label in ipairs(class_labels) do + local key = 'learns_' .. string.lower(label) + -- Handle legacy keys for backward compatibility + if label == 'H' then + key = 'learns_ham' + elseif label == 'S' then + key = 'learns_spam' + end + table.insert(learned_counts, tonumber(redis.call('HGET', prefix, key)) or 0) +end -if learned_ham > 0 and learned_spam > 0 then - local input_tokens = cmsgpack.unpack(KEYS[2]) - for i, token in ipairs(input_tokens) do - local token_data = redis.call('HMGET', token, 'H', 'S') +-- Get token data for all classes (ordered) +local token_results = {} +for i, _ in ipairs(class_labels) do + token_results[i] = {} +end - if token_data then - local ham_count = token_data[1] - local spam_count = token_data[2] +-- Check if we have any learning data +local has_learns = false +for _, count in ipairs(learned_counts) do + if count > 0 then + has_learns = true + break + end +end - if ham_count then - table.insert(output_ham, { i, tonumber(ham_count) }) - end +if has_learns then + -- Process each token + for i, token in ipairs(input_tokens) do + local token_data = redis.call('HMGET', token, unpack(class_labels)) - if spam_count then - table.insert(output_spam, { i, tonumber(spam_count) }) + if token_data then + for j, _ in ipairs(class_labels) do + local count = token_data[j] + if count and tonumber(count) > 0 then + table.insert(token_results[j], { i, tonumber(count) }) + end end end end end -return { learned_ham, learned_spam, output_ham, output_spam }
\ No newline at end of file +-- Always return ordered arrays: [learned_counts_array, token_results_array] +return { learned_counts, token_results } diff --git a/lualib/redis_scripts/bayes_learn.lua b/lualib/redis_scripts/bayes_learn.lua index 5456165b6..ebc798fe0 100644 --- a/lualib/redis_scripts/bayes_learn.lua +++ b/lualib/redis_scripts/bayes_learn.lua @@ -1,14 +1,14 @@ --- Lua script to perform bayes learning +-- Lua script to perform bayes learning (multi-class) -- This script accepts the following parameters: -- key1 - prefix for bayes tokens (e.g. for per-user classification) --- key2 - boolean is_spam +-- key2 - class label string (e.g. "S", "H", "T") -- key3 - string symbol -- key4 - boolean is_unlearn -- key5 - set of tokens encoded in messagepack array of strings -- key6 - set of text tokens (if any) encoded in messagepack array of strings (size must be twice of `KEYS[5]`) local prefix = KEYS[1] -local is_spam = KEYS[2] == 'true' and true or false +local class_label = KEYS[2] local symbol = KEYS[3] local is_unlearn = KEYS[4] == 'true' and true or false local input_tokens = cmsgpack.unpack(KEYS[5]) @@ -18,15 +18,47 @@ if KEYS[6] then text_tokens = cmsgpack.unpack(KEYS[6]) end -local hash_key = is_spam and 'S' or 'H' -local learned_key = is_spam and 'learns_spam' or 'learns_ham' +-- Handle backward compatibility for boolean values +if class_label == 'true' then + class_label = 'S' -- spam +elseif class_label == 'false' then + class_label = 'H' -- ham +end + +local hash_key = class_label +local learned_key = 'learns_' .. string.lower(class_label) + +-- Handle legacy keys for backward compatibility +if class_label == 'S' then + learned_key = 'learns_spam' +elseif class_label == 'H' then + learned_key = 'learns_ham' +end redis.call('SADD', symbol .. '_keys', prefix) redis.call('HSET', prefix, 'version', '2') -- new schema -redis.call('HINCRBY', prefix, learned_key, is_unlearn and -1 or 1) -- increase or decrease learned count + +-- Update learned count, but prevent it from going negative +if is_unlearn then + local current_count = tonumber(redis.call('HGET', prefix, learned_key)) or 0 + if current_count > 0 then + redis.call('HINCRBY', prefix, learned_key, -1) + end +else + redis.call('HINCRBY', prefix, learned_key, 1) +end for i, token in ipairs(input_tokens) do - redis.call('HINCRBY', token, hash_key, is_unlearn and -1 or 1) + -- Update token count, but prevent it from going negative + if is_unlearn then + local current_token_count = tonumber(redis.call('HGET', token, hash_key)) or 0 + if current_token_count > 0 then + redis.call('HINCRBY', token, hash_key, -1) + end + else + redis.call('HINCRBY', token, hash_key, 1) + end + if text_tokens then local tok1 = text_tokens[i * 2 - 1] local tok2 = text_tokens[i * 2] @@ -38,7 +70,14 @@ for i, token in ipairs(input_tokens) do redis.call('HSET', token, 'tokens', tok1) end - redis.call('ZINCRBY', prefix .. '_z', is_unlearn and -1 or 1, token) + if is_unlearn then + local current_z_score = tonumber(redis.call('ZSCORE', prefix .. '_z', token)) or 0 + if current_z_score > 0 then + redis.call('ZINCRBY', prefix .. '_z', -1, token) + end + else + redis.call('ZINCRBY', prefix .. '_z', 1, token) + end end end end diff --git a/src/client/rspamc.cxx b/src/client/rspamc.cxx index 404359877..1dc48faae 100644 --- a/src/client/rspamc.cxx +++ b/src/client/rspamc.cxx @@ -59,6 +59,7 @@ static const char *user = nullptr; static const char *helo = nullptr; static const char *hostname = nullptr; static const char *classifier = nullptr; +static const char *learn_class_name = nullptr; static const char *local_addr = nullptr; static const char *execute = nullptr; static const char *sort = nullptr; @@ -90,6 +91,9 @@ static gboolean skip_attachments = FALSE; static const char *pubkey = nullptr; static const char *user_agent = "rspamc"; static const char *files_list = nullptr; +static const char *queue_id = nullptr; +static const char *log_tag = nullptr; +static std::string settings; std::vector<GPid> children; static GPatternSpec **exclude_compiled = nullptr; @@ -102,6 +106,11 @@ static gboolean rspamc_password_callback(const char *option_name, gpointer data, GError **error); +static gboolean rspamc_settings_callback(const char *option_name, + const char *value, + gpointer data, + GError **error); + static GOptionEntry entries[] = { {"connect", 'h', 0, G_OPTION_ARG_STRING, &connect_str, @@ -182,6 +191,12 @@ static GOptionEntry entries[] = "Use specific User-Agent instead of \"rspamc\"", nullptr}, {"files-list", '\0', 0, G_OPTION_ARG_FILENAME, &files_list, "Read one or more newline separated filenames to scan from file", nullptr}, + {"queue-id", '\0', 0, G_OPTION_ARG_STRING, &queue_id, + "Set Queue-ID header for the request", nullptr}, + {"log-tag", '\0', 0, G_OPTION_ARG_STRING, &log_tag, + "Set Log-Tag header for the request", nullptr}, + {"settings", '\0', 0, G_OPTION_ARG_CALLBACK, (void *) &rspamc_settings_callback, + "Set Settings header as JSON/UCL for the request", nullptr}, {nullptr, 0, 0, G_OPTION_ARG_NONE, nullptr, nullptr, nullptr}}; static void rspamc_symbols_output(FILE *out, ucl_object_t *obj); @@ -198,6 +213,7 @@ enum rspamc_command_type { RSPAMC_COMMAND_SYMBOLS, RSPAMC_COMMAND_LEARN_SPAM, RSPAMC_COMMAND_LEARN_HAM, + RSPAMC_COMMAND_LEARN_CLASS, RSPAMC_COMMAND_FUZZY_ADD, RSPAMC_COMMAND_FUZZY_DEL, RSPAMC_COMMAND_FUZZY_DELHASH, @@ -250,6 +266,15 @@ static const constexpr auto rspamc_commands = rspamd::array_of( .need_input = TRUE, .command_output_func = nullptr}, rspamc_command{ + .cmd = RSPAMC_COMMAND_LEARN_CLASS, + .name = "learn_class", + .path = "learnclass", + .description = "learn message as class", + .is_controller = TRUE, + .is_privileged = TRUE, + .need_input = TRUE, + .command_output_func = nullptr}, + rspamc_command{ .cmd = RSPAMC_COMMAND_FUZZY_ADD, .name = "fuzzy_add", .path = "fuzzyadd", @@ -527,8 +552,7 @@ rspamc_password_callback(const char *option_name, auto *map = (char *) locked_mmap.value().get_map(); value_view = std::string_view{map, locked_mmap->get_size()}; auto right = value_view.end() - 1; - for (; right > value_view.cbegin() && g_ascii_isspace(*right); --right) - ; + for (; right > value_view.cbegin() && g_ascii_isspace(*right); --right); std::string_view str{value_view.begin(), static_cast<size_t>(right - value_view.begin()) + 1}; processed_passwd.assign(std::begin(str), std::end(str)); processed_passwd.push_back('\0'); /* Null-terminate for C part */ @@ -557,6 +581,46 @@ rspamc_password_callback(const char *option_name, return TRUE; } +static gboolean +rspamc_settings_callback(const char *option_name, + const char *value, + gpointer data, + GError **error) +{ + if (value == nullptr) { + g_set_error(error, G_OPTION_ERROR, G_OPTION_ERROR_BAD_VALUE, + "Settings parameter cannot be empty"); + return FALSE; + } + + // Parse the settings string using UCL to validate it + struct ucl_parser *parser = ucl_parser_new(UCL_PARSER_KEY_LOWERCASE); + if (!ucl_parser_add_string(parser, value, strlen(value))) { + auto *ucl_error = ucl_parser_get_error(parser); + g_set_error(error, G_OPTION_ERROR, G_OPTION_ERROR_BAD_VALUE, + "Invalid JSON/UCL in settings: %s", ucl_error); + ucl_parser_free(parser); + return FALSE; + } + + // Get the parsed object and validate it + auto *obj = ucl_parser_get_object(parser); + if (obj == nullptr) { + g_set_error(error, G_OPTION_ERROR, G_OPTION_ERROR_BAD_VALUE, + "Failed to parse settings as JSON/UCL"); + ucl_parser_free(parser); + return FALSE; + } + + // Store the validated settings string + settings = value; + + ucl_object_unref(obj); + ucl_parser_free(parser); + + return TRUE; +} + /* * Parse command line */ @@ -649,6 +713,7 @@ check_rspamc_command(const char *cmd) -> std::optional<rspamc_command> {"report", RSPAMC_COMMAND_SYMBOLS}, {"learn_spam", RSPAMC_COMMAND_LEARN_SPAM}, {"learn_ham", RSPAMC_COMMAND_LEARN_HAM}, + {"learn_class", RSPAMC_COMMAND_LEARN_CLASS}, {"fuzzy_add", RSPAMC_COMMAND_FUZZY_ADD}, {"fuzzy_del", RSPAMC_COMMAND_FUZZY_DEL}, {"fuzzy_delhash", RSPAMC_COMMAND_FUZZY_DELHASH}, @@ -659,10 +724,33 @@ check_rspamc_command(const char *cmd) -> std::optional<rspamc_command> }); std::string cmd_lc = rspamd_string_tolower(cmd); + + // Handle learn_class:classname syntax + if (cmd_lc.find("learn_class:") == 0) { + auto colon_pos = cmd_lc.find(':'); + if (colon_pos != std::string::npos && colon_pos + 1 < cmd_lc.length()) { + auto class_name = cmd_lc.substr(colon_pos + 1); + // Store class name globally for later use + learn_class_name = g_strdup(class_name.c_str()); + // Return the learn_class command + auto elt_it = std::find_if(rspamc_commands.begin(), rspamc_commands.end(), [&](const auto &item) { + return item.cmd == RSPAMC_COMMAND_LEARN_CLASS; + }); + if (elt_it != std::end(rspamc_commands)) { + return *elt_it; + } + } + return std::nullopt; + } + auto ct = rspamd::find_map(str_map, std::string_view{cmd_lc}); + if (!ct.has_value()) { + return std::nullopt; + } + auto elt_it = std::find_if(rspamc_commands.begin(), rspamc_commands.end(), [&](const auto &item) { - return item.cmd == ct; + return item.cmd == ct.value(); }); if (elt_it != std::end(rspamc_commands)) { @@ -799,6 +887,10 @@ add_options(GQueue *opts) add_client_header(opts, "Classifier", classifier); } + if (learn_class_name) { + add_client_header(opts, "Class", learn_class_name); + } + if (weight != 0) { auto nstr = fmt::format("{}", weight); add_client_header(opts, "Weight", nstr.c_str()); @@ -852,6 +944,18 @@ add_options(GQueue *opts) hdr++; } + if (queue_id != nullptr) { + add_client_header(opts, "Queue-Id", queue_id); + } + + if (log_tag != nullptr) { + add_client_header(opts, "Log-Tag", log_tag); + } + + if (!settings.empty()) { + add_client_header(opts, "Settings", settings.c_str()); + } + if (!flagbuf.empty()) { if (flagbuf.back() == ',') { flagbuf.pop_back(); @@ -1918,7 +2022,7 @@ rspamc_client_cb(struct rspamd_client_connection *conn, if (raw_body) { /* We can also output the resulting json */ - rspamc_print(out, "{}\n", std::string_view{raw_body, (std::size_t)(rawlen - bodylen)}); + rspamc_print(out, "{}\n", std::string_view{raw_body, (std::size_t) (rawlen - bodylen)}); } } } @@ -1950,7 +2054,7 @@ rspamc_process_input(struct ev_loop *ev_base, const struct rspamc_command &cmd, p = strrchr(connect_str, ']'); if (p != nullptr) { - hostbuf.assign(connect_str + 1, (std::size_t)(p - connect_str - 1)); + hostbuf.assign(connect_str + 1, (std::size_t) (p - connect_str - 1)); p++; } else { @@ -1965,7 +2069,7 @@ rspamc_process_input(struct ev_loop *ev_base, const struct rspamc_command &cmd, if (hostbuf.empty()) { if (p != nullptr) { - hostbuf.assign(connect_str, (std::size_t)(p - connect_str)); + hostbuf.assign(connect_str, (std::size_t) (p - connect_str)); } else { hostbuf.assign(connect_str); diff --git a/src/controller.c b/src/controller.c index 0550ba6b8..6e0e4cac1 100644 --- a/src/controller.c +++ b/src/controller.c @@ -53,6 +53,7 @@ #define PATH_HISTORY_RESET "/historyreset" #define PATH_LEARN_SPAM "/learnspam" #define PATH_LEARN_HAM "/learnham" +#define PATH_LEARN_CLASS "/learnclass" #define PATH_METRICS "/metrics" #define PATH_READY "/ready" #define PATH_SAVE_ACTIONS "/saveactions" @@ -2126,6 +2127,7 @@ rspamd_controller_handle_learn_common( struct rspamd_controller_worker_ctx *ctx; struct rspamd_task *task; const rspamd_ftok_t *cl_header; + const char *class_name; ctx = session->ctx; @@ -2167,7 +2169,9 @@ rspamd_controller_handle_learn_common( goto end; } - rspamd_learn_task_spam(task, is_spam, session->classifier, NULL); + /* Use unified class-based learning approach */ + class_name = is_spam ? "spam" : "ham"; + rspamd_task_set_autolearn_class(task, class_name); if (!rspamd_task_process(task, RSPAMD_TASK_PROCESS_LEARN)) { msg_warn_session("<%s> message cannot be processed", @@ -2212,6 +2216,96 @@ rspamd_controller_handle_learnham( } /* + * Learn class command handler: + * request: /learnclass + * headers: Password, Class + * input: plaintext data + * reply: json {"success":true} or {"error":"error message"} + */ +static int +rspamd_controller_handle_learnclass( + struct rspamd_http_connection_entry *conn_ent, + struct rspamd_http_message *msg) +{ + struct rspamd_controller_session *session = conn_ent->ud; + struct rspamd_controller_worker_ctx *ctx; + struct rspamd_task *task; + const rspamd_ftok_t *cl_header, *class_header; + char *class_name = NULL; + + ctx = session->ctx; + + if (!rspamd_controller_check_password(conn_ent, session, msg, TRUE)) { + return 0; + } + + if (rspamd_http_message_get_body(msg, NULL) == NULL) { + msg_err_session("got zero length body, cannot continue"); + rspamd_controller_send_error(conn_ent, + 400, + "Empty body is not permitted"); + return 0; + } + + class_header = rspamd_http_message_find_header(msg, "Class"); + if (!class_header) { + msg_err_session("missing Class header for multiclass learning"); + rspamd_controller_send_error(conn_ent, + 400, + "Class header is required for multiclass learning"); + return 0; + } + + task = rspamd_task_new(session->ctx->worker, session->cfg, session->pool, + session->ctx->lang_det, ctx->event_loop, FALSE); + + task->resolver = ctx->resolver; + task->s = rspamd_session_create(session->pool, + rspamd_controller_learn_fin_task, + NULL, + (event_finalizer_t) rspamd_task_free, + task); + task->fin_arg = conn_ent; + task->http_conn = rspamd_http_connection_ref(conn_ent->conn); + task->sock = -1; + session->task = task; + + cl_header = rspamd_http_message_find_header(msg, "classifier"); + if (cl_header) { + session->classifier = rspamd_mempool_ftokdup(session->pool, cl_header); + } + else { + session->classifier = NULL; + } + + if (!rspamd_task_load_message(task, msg, msg->body_buf.begin, msg->body_buf.len)) { + goto end; + } + + /* Set multiclass learning flag and store class name */ + class_name = rspamd_mempool_ftokdup(task->task_pool, class_header); + rspamd_task_set_autolearn_class(task, class_name); + + if (!rspamd_task_process(task, RSPAMD_TASK_PROCESS_LEARN)) { + msg_warn_session("<%s> message cannot be processed", + MESSAGE_FIELD_CHECK(task, message_id)); + goto end; + } + +end: + /* Set session spam flag for logging compatibility */ + if (class_name) { + session->is_spam = (strcmp(class_name, "spam") == 0); + } + else { + session->is_spam = FALSE; + } + rspamd_session_pending(task->s); + + return 0; +} + +/* * Scan command handler: * request: /scan * headers: Password @@ -3292,7 +3386,7 @@ rspamd_controller_handle_unknown(struct rspamd_http_connection_entry *conn_ent, rspamd_http_message_add_header(rep, "Access-Control-Allow-Methods", "POST, GET, OPTIONS"); rspamd_http_message_add_header(rep, "Access-Control-Allow-Headers", - "Classifier,Content-Type,Password,Map,Weight,Flag,Hash"); + "Classifier,Class,Content-Type,Password,Map,Weight,Flag,Hash"); rspamd_http_connection_reset(conn_ent->conn); rspamd_http_router_insert_headers(conn_ent->rt, rep); rspamd_http_connection_write_message(conn_ent->conn, @@ -3456,7 +3550,7 @@ rspamd_controller_handle_lua_plugin(struct rspamd_http_connection_entry *conn_en */ static int rspamd_controller_handle_bayes_classifiers(struct rspamd_http_connection_entry *conn_ent, - struct rspamd_http_message *msg) + struct rspamd_http_message *msg) { struct rspamd_controller_session *session = conn_ent->ud; struct rspamd_controller_worker_ctx *ctx = session->ctx; @@ -4049,6 +4143,9 @@ start_controller_worker(struct rspamd_worker *worker) PATH_LEARN_HAM, rspamd_controller_handle_learnham); rspamd_http_router_add_path(ctx->http, + PATH_LEARN_CLASS, + rspamd_controller_handle_learnclass); + rspamd_http_router_add_path(ctx->http, PATH_METRICS, rspamd_controller_handle_metrics); rspamd_http_router_add_path(ctx->http, diff --git a/src/libserver/cfg_file.h b/src/libserver/cfg_file.h index 36941da7a..355046cac 100644 --- a/src/libserver/cfg_file.h +++ b/src/libserver/cfg_file.h @@ -139,7 +139,10 @@ struct rspamd_statfile_config { char *symbol; /**< symbol of statfile */ char *label; /**< label of this statfile */ ucl_object_t *opts; /**< other options */ - gboolean is_spam; /**< spam flag */ + char *class_name; /**< class name for multi-class classification */ + unsigned int class_index; /**< class index for O(1) lookup during classification */ + gboolean is_spam; /**< DEPRECATED: spam flag - use class_name instead */ + gboolean is_spam_converted; /**< TRUE if class_name was converted from is_spam flag */ struct rspamd_classifier_config *clcf; /**< parent pointer of classifier configuration */ gpointer data; /**< opaque data */ }; @@ -182,6 +185,8 @@ struct rspamd_classifier_config { double min_prob_strength; /**< use only tokens with probability in [0.5 - MPS, 0.5 + MPS] */ unsigned int min_learns; /**< minimum number of learns for each statfile */ unsigned int flags; + GHashTable *class_labels; /**< class_name -> backend_symbol mapping for multi-class */ + GPtrArray *class_names; /**< ordered list of class names */ }; struct rspamd_worker_bind_conf { @@ -621,12 +626,25 @@ void rspamd_config_insert_classify_symbols(struct rspamd_config *cfg); */ gboolean rspamd_config_check_statfiles(struct rspamd_classifier_config *cf); -/* - * Find classifier config by name +/** + * Multi-class configuration helpers + */ +gboolean rspamd_config_parse_class_labels(const ucl_object_t *obj, + GHashTable **class_labels); + +gboolean rspamd_config_migrate_binary_config(struct rspamd_statfile_config *stcf); + +gboolean rspamd_config_validate_class_config(struct rspamd_classifier_config *ccf, + GError **err); + +const char *rspamd_config_get_class_label(struct rspamd_classifier_config *ccf, + const char *class_name); + +/** + * Find classifier by name */ struct rspamd_classifier_config *rspamd_config_find_classifier( - struct rspamd_config *cfg, - const char *name); + struct rspamd_config *cfg, const char *name); void rspamd_ucl_add_conf_macros(struct ucl_parser *parser, struct rspamd_config *cfg); diff --git a/src/libserver/cfg_rcl.cxx b/src/libserver/cfg_rcl.cxx index 0a48e8a4f..da5845917 100644 --- a/src/libserver/cfg_rcl.cxx +++ b/src/libserver/cfg_rcl.cxx @@ -1197,31 +1197,73 @@ rspamd_rcl_statfile_handler(rspamd_mempool_t *pool, const ucl_object_t *obj, st->opts = (ucl_object_t *) obj; st->clcf = ccf; - const auto *val = ucl_object_lookup(obj, "spam"); - if (val == nullptr) { + /* Handle migration from old 'spam' field to new 'class' field */ + const auto *class_val = ucl_object_lookup(obj, "class"); + const auto *spam_val = ucl_object_lookup(obj, "spam"); + + if (class_val != nullptr && spam_val != nullptr) { + msg_warn_config("statfile %s has both 'class' and 'spam' fields, using 'class' field", + st->symbol); + } + + if (class_val == nullptr && spam_val == nullptr) { + /* Neither field present, try to guess by symbol name */ msg_info_config( - "statfile %s has no explicit 'spam' setting, trying to guess by symbol", + "statfile %s has no explicit 'class' or 'spam' setting, trying to guess by symbol", st->symbol); if (rspamd_substring_search_caseless(st->symbol, strlen(st->symbol), "spam", 4) != -1) { st->is_spam = TRUE; + st->class_name = rspamd_mempool_strdup(pool, "spam"); + st->is_spam_converted = TRUE; } else if (rspamd_substring_search_caseless(st->symbol, strlen(st->symbol), "ham", 3) != -1) { st->is_spam = FALSE; + st->class_name = rspamd_mempool_strdup(pool, "ham"); + st->is_spam_converted = TRUE; } else { g_set_error(err, CFG_RCL_ERROR, EINVAL, - "cannot guess spam setting from %s", + "cannot guess class setting from %s, please specify 'class' field", st->symbol); return FALSE; } - msg_info_config("guessed that statfile with symbol %s is %s", - st->symbol, - st->is_spam ? "spam" : "ham"); + msg_info_config("guessed that statfile with symbol %s has class '%s'", + st->symbol, st->class_name); + } + else if (class_val == nullptr && spam_val != nullptr) { + /* Only spam field present - migrate to class */ + msg_warn_config("statfile %s uses deprecated 'spam' field, please use 'class' instead", + st->symbol); + if (st->is_spam) { + st->class_name = rspamd_mempool_strdup(pool, "spam"); + } + else { + st->class_name = rspamd_mempool_strdup(pool, "ham"); + } + st->is_spam_converted = TRUE; } + else if (class_val != nullptr && spam_val == nullptr) { + /* Only class field present - set is_spam for backward compatibility */ + if (st->class_name != nullptr) { + if (strcmp(st->class_name, "spam") == 0) { + st->is_spam = TRUE; + } + else if (strcmp(st->class_name, "ham") == 0) { + st->is_spam = FALSE; + } + else { + /* For non-binary classes, default to not spam */ + st->is_spam = FALSE; + } + msg_debug_config("statfile %s with class '%s' set is_spam=%s for compatibility", + st->symbol, st->class_name, st->is_spam ? "true" : "false"); + } + } + /* If both fields are present, class takes precedence and was already parsed by the default parser */ return TRUE; } @@ -1229,6 +1271,31 @@ rspamd_rcl_statfile_handler(rspamd_mempool_t *pool, const ucl_object_t *obj, } static gboolean +rspamd_rcl_class_labels_handler(rspamd_mempool_t *pool, + const ucl_object_t *obj, + const char *key, + gpointer ud, + struct rspamd_rcl_section *section, + GError **err) +{ + auto *ccf = static_cast<rspamd_classifier_config *>(ud); + + if (obj->type != UCL_OBJECT) { + g_set_error(err, CFG_RCL_ERROR, EINVAL, + "class_labels must be an object"); + return FALSE; + } + + if (!rspamd_config_parse_class_labels(obj, &ccf->class_labels)) { + g_set_error(err, CFG_RCL_ERROR, EINVAL, + "invalid class_labels configuration"); + return FALSE; + } + + return TRUE; +} + +static gboolean rspamd_rcl_classifier_handler(rspamd_mempool_t *pool, const ucl_object_t *obj, const char *key, @@ -1301,6 +1368,22 @@ rspamd_rcl_classifier_handler(rspamd_mempool_t *pool, } } } + else if (g_ascii_strcasecmp(st_key, "class_labels") == 0) { + /* Parse class_labels configuration directly */ + if (ucl_object_type(val) != UCL_OBJECT) { + g_set_error(err, CFG_RCL_ERROR, EINVAL, + "class_labels must be an object"); + ucl_object_iterate_free(it); + return FALSE; + } + + if (!rspamd_config_parse_class_labels(val, &ccf->class_labels)) { + g_set_error(err, CFG_RCL_ERROR, EINVAL, + "invalid class_labels configuration"); + ucl_object_iterate_free(it); + return FALSE; + } + } } } @@ -1375,8 +1458,80 @@ rspamd_rcl_classifier_handler(rspamd_mempool_t *pool, } ccf->opts = (ucl_object_t *) obj; + + /* Validate multi-class configuration */ + GError *validation_err = nullptr; + if (!rspamd_config_validate_class_config(ccf, &validation_err)) { + if (validation_err) { + g_propagate_error(err, validation_err); + } + else { + g_set_error(err, CFG_RCL_ERROR, EINVAL, + "multi-class configuration validation failed for classifier '%s'", + ccf->name ? ccf->name : "unknown"); + } + return FALSE; + } + cfg->classifiers = g_list_prepend(cfg->classifiers, ccf); + /* Populate class_names array from statfiles - only for explicit multiclass configs */ + if (ccf->statfiles) { + GList *cur = ccf->statfiles; + gboolean has_explicit_classes = FALSE; + + /* Check if any statfile uses explicit class declaration (not converted from is_spam) */ + cur = ccf->statfiles; + while (cur) { + struct rspamd_statfile_config *stcf = (struct rspamd_statfile_config *) cur->data; + msg_debug("checking statfile %s: class_name=%s, is_spam_converted=%s", + stcf->symbol, stcf->class_name ? stcf->class_name : "NULL", + stcf->is_spam_converted ? "true" : "false"); + if (stcf->class_name && !stcf->is_spam_converted) { + has_explicit_classes = TRUE; + break; + } + cur = g_list_next(cur); + } + + msg_debug("has_explicit_classes = %s", has_explicit_classes ? "true" : "false"); + + /* Only populate class_names for explicit multiclass configurations */ + if (has_explicit_classes) { + msg_debug("populating class_names for multiclass configuration"); + } + else { + msg_debug("skipping class_names population for binary configuration"); + } + + if (has_explicit_classes) { + ccf->class_names = g_ptr_array_new(); + + cur = ccf->statfiles; + while (cur) { + struct rspamd_statfile_config *stcf = (struct rspamd_statfile_config *) cur->data; + if (stcf->class_name) { + /* Check if class already exists */ + bool found = false; + for (unsigned int i = 0; i < ccf->class_names->len; i++) { + if (strcmp((char *) g_ptr_array_index(ccf->class_names, i), stcf->class_name) == 0) { + stcf->class_index = i; /* Store the index for O(1) lookup */ + found = true; + break; + } + } + + if (!found) { + /* Add new class */ + stcf->class_index = ccf->class_names->len; + g_ptr_array_add(ccf->class_names, g_strdup(stcf->class_name)); + } + } + cur = g_list_next(cur); + } + } + } + return TRUE; } @@ -2457,7 +2612,7 @@ rspamd_rcl_config_init(struct rspamd_config *cfg, GHashTable *skip_sections) FALSE, TRUE, cfg->doc_strings, - "CLassifier options"); + "Classifier options"); /* Default classifier is 'bayes' for now */ sub->default_key = "bayes"; @@ -2476,7 +2631,7 @@ rspamd_rcl_config_init(struct rspamd_config *cfg, GHashTable *skip_sections) rspamd_rcl_add_default_handler(sub, "min_prob_strength", rspamd_rcl_parse_struct_double, - G_STRUCT_OFFSET(struct rspamd_classifier_config, min_token_hits), + G_STRUCT_OFFSET(struct rspamd_classifier_config, min_prob_strength), 0, "Use only tokens with probability in [0.5 - MPS, 0.5 + MPS]"); rspamd_rcl_add_default_handler(sub, @@ -2505,6 +2660,18 @@ rspamd_rcl_config_init(struct rspamd_config *cfg, GHashTable *skip_sections) "Name of classifier"); /* + * Multi-class configuration + */ + rspamd_rcl_add_section_doc(&top, sub, + "class_labels", nullptr, + rspamd_rcl_class_labels_handler, + UCL_OBJECT, + FALSE, + TRUE, + sub->doc_ref, + "Class to backend label mapping for multi-class classification"); + + /* * Statfile defaults */ auto *ssub = rspamd_rcl_add_section_doc(&top, sub, @@ -2522,11 +2689,17 @@ rspamd_rcl_config_init(struct rspamd_config *cfg, GHashTable *skip_sections) 0, "Statfile unique label"); rspamd_rcl_add_default_handler(ssub, + "class", + rspamd_rcl_parse_struct_string, + G_STRUCT_OFFSET(struct rspamd_statfile_config, class_name), + 0, + "Class name for multi-class classification"); + rspamd_rcl_add_default_handler(ssub, "spam", rspamd_rcl_parse_struct_boolean, G_STRUCT_OFFSET(struct rspamd_statfile_config, is_spam), 0, - "Sets if this statfile contains spam samples"); + "DEPRECATED: Sets if this statfile contains spam samples (use 'class' instead)"); } if (!(skip_sections && g_hash_table_lookup(skip_sections, "composite"))) { diff --git a/src/libserver/cfg_utils.cxx b/src/libserver/cfg_utils.cxx index c7bb20210..c22a9b877 100644 --- a/src/libserver/cfg_utils.cxx +++ b/src/libserver/cfg_utils.cxx @@ -3042,3 +3042,189 @@ rspamd_ip_is_local_cfg(struct rspamd_config *cfg, return FALSE; } + +gboolean +rspamd_config_parse_class_labels(const ucl_object_t *obj, GHashTable **class_labels) +{ + const ucl_object_t *cur; + ucl_object_iter_t it = nullptr; + + if (!obj || ucl_object_type(obj) != UCL_OBJECT) { + return FALSE; + } + + if (*class_labels == nullptr) { + *class_labels = g_hash_table_new_full(g_str_hash, g_str_equal, + g_free, g_free); + } + + while ((cur = ucl_object_iterate(obj, &it, true)) != nullptr) { + const char *class_name = ucl_object_key(cur); + const char *label = ucl_object_tostring(cur); + + if (class_name && label) { + /* Validate class name: alphanumeric + underscore, max 32 chars */ + if (strlen(class_name) > 32) { + msg_err("class name '%s' is too long (max 32 characters)", class_name); + g_hash_table_destroy(*class_labels); + *class_labels = nullptr; + return FALSE; + } + + for (const char *p = class_name; *p; p++) { + if (!g_ascii_isalnum(*p) && *p != '_') { + msg_err("class name '%s' contains invalid character '%c'", class_name, *p); + g_hash_table_destroy(*class_labels); + *class_labels = nullptr; + return FALSE; + } + } + + /* Validate label uniqueness */ + if (g_hash_table_lookup(*class_labels, label)) { + msg_err("backend label '%s' is used by multiple classes", label); + g_hash_table_destroy(*class_labels); + *class_labels = nullptr; + return FALSE; + } + } + + g_hash_table_insert(*class_labels, g_strdup(class_name), g_strdup(label)); + } + + return g_hash_table_size(*class_labels) > 0; +} + +gboolean +rspamd_config_migrate_binary_config(struct rspamd_statfile_config *stcf) +{ + if (stcf->class_name != nullptr) { + /* Already migrated or using new format */ + return TRUE; + } + + if (stcf->is_spam) { + stcf->class_name = g_strdup("spam"); + msg_info("migrated statfile '%s' from is_spam=true to class='spam'", + stcf->symbol ? stcf->symbol : "unknown"); + } + else { + stcf->class_name = g_strdup("ham"); + msg_info("migrated statfile '%s' from is_spam=false to class='ham'", + stcf->symbol ? stcf->symbol : "unknown"); + } + + return TRUE; +} + +gboolean +rspamd_config_validate_class_config(struct rspamd_classifier_config *ccf, GError **err) +{ + GList *cur; + GHashTable *seen_classes = nullptr; + struct rspamd_statfile_config *stcf; + unsigned int class_count = 0; + + if (!ccf || !ccf->statfiles) { + g_set_error(err, g_quark_from_static_string("config"), 1, + "classifier has no statfiles defined"); + return FALSE; + } + + seen_classes = g_hash_table_new_full(g_str_hash, g_str_equal, g_free, nullptr); + + /* Iterate through statfiles and collect classes */ + cur = ccf->statfiles; + while (cur) { + stcf = (struct rspamd_statfile_config *) cur->data; + + /* Migrate binary config if needed */ + if (!rspamd_config_migrate_binary_config(stcf)) { + g_set_error(err, g_quark_from_static_string("config"), 1, + "failed to migrate binary config for statfile '%s'", + stcf->symbol ? stcf->symbol : "unknown"); + g_hash_table_destroy(seen_classes); + return FALSE; + } + + /* Check class name */ + if (!stcf->class_name || strlen(stcf->class_name) == 0) { + g_set_error(err, g_quark_from_static_string("config"), 1, + "statfile '%s' has no class defined", + stcf->symbol ? stcf->symbol : "unknown"); + g_hash_table_destroy(seen_classes); + return FALSE; + } + + /* Track unique classes */ + if (!g_hash_table_contains(seen_classes, stcf->class_name)) { + g_hash_table_insert(seen_classes, g_strdup(stcf->class_name), GINT_TO_POINTER(1)); + class_count++; + } + + cur = g_list_next(cur); + } + + /* Validate class count */ + if (class_count < 2) { + g_set_error(err, g_quark_from_static_string("config"), 1, + "classifier must have at least 2 classes, found %ud", class_count); + g_hash_table_destroy(seen_classes); + return FALSE; + } + + if (class_count > 20) { + msg_warn("classifier has %ud classes, performance may be degraded above 20 classes", + class_count); + } + + /* Initialize classifier class tracking - only for explicit multiclass configurations */ + gboolean has_explicit_classes = FALSE; + + /* Check if any statfile uses explicit class declaration (not converted from is_spam) */ + cur = ccf->statfiles; + while (cur) { + stcf = (struct rspamd_statfile_config *) cur->data; + if (stcf->class_name && !stcf->is_spam_converted) { + has_explicit_classes = TRUE; + break; + } + cur = g_list_next(cur); + } + + /* Only populate class_names for explicit multiclass configurations */ + if (has_explicit_classes) { + if (ccf->class_names) { + g_ptr_array_unref(ccf->class_names); + } + ccf->class_names = g_ptr_array_new_with_free_func(g_free); + + /* Populate class names array */ + GHashTableIter iter; + gpointer key, value; + g_hash_table_iter_init(&iter, seen_classes); + while (g_hash_table_iter_next(&iter, &key, &value)) { + g_ptr_array_add(ccf->class_names, g_strdup((const char *) key)); + } + } + else { + /* Binary configuration - ensure class_names is NULL */ + if (ccf->class_names) { + g_ptr_array_unref(ccf->class_names); + ccf->class_names = nullptr; + } + } + + g_hash_table_destroy(seen_classes); + return TRUE; +} + +const char * +rspamd_config_get_class_label(struct rspamd_classifier_config *ccf, const char *class_name) +{ + if (!ccf || !ccf->class_labels || !class_name) { + return nullptr; + } + + return (const char *) g_hash_table_lookup(ccf->class_labels, class_name); +} diff --git a/src/libserver/task.c b/src/libserver/task.c index 9f5b1f00a..f655ab11b 100644 --- a/src/libserver/task.c +++ b/src/libserver/task.c @@ -730,7 +730,7 @@ rspamd_task_process(struct rspamd_task *task, unsigned int stages) if (all_done && (task->flags & RSPAMD_TASK_FLAG_LEARN_AUTO) && !RSPAMD_TASK_IS_EMPTY(task) && - !(task->flags & (RSPAMD_TASK_FLAG_LEARN_SPAM | RSPAMD_TASK_FLAG_LEARN_HAM))) { + !(task->flags & (RSPAMD_TASK_FLAG_LEARN_SPAM | RSPAMD_TASK_FLAG_LEARN_HAM | RSPAMD_TASK_FLAG_LEARN_CLASS))) { rspamd_stat_check_autolearn(task); } break; @@ -738,12 +738,32 @@ rspamd_task_process(struct rspamd_task *task, unsigned int stages) case RSPAMD_TASK_STAGE_LEARN: case RSPAMD_TASK_STAGE_LEARN_PRE: case RSPAMD_TASK_STAGE_LEARN_POST: - if (task->flags & (RSPAMD_TASK_FLAG_LEARN_SPAM | RSPAMD_TASK_FLAG_LEARN_HAM)) { + if (task->flags & (RSPAMD_TASK_FLAG_LEARN_SPAM | RSPAMD_TASK_FLAG_LEARN_HAM | RSPAMD_TASK_FLAG_LEARN_CLASS)) { if (task->err == NULL) { - if (!rspamd_stat_learn(task, - task->flags & RSPAMD_TASK_FLAG_LEARN_SPAM, - task->cfg->lua_state, task->classifier, - st, &stat_error)) { + gboolean learn_result = FALSE; + + if (task->flags & RSPAMD_TASK_FLAG_LEARN_CLASS) { + /* Multi-class learning */ + const char *autolearn_class = rspamd_task_get_autolearn_class(task); + if (autolearn_class) { + learn_result = rspamd_stat_learn_class(task, autolearn_class, + task->cfg->lua_state, task->classifier, + st, &stat_error); + } + else { + g_set_error(&stat_error, g_quark_from_static_string("stat"), 500, + "No autolearn class specified for multi-class learning"); + } + } + else { + /* Legacy binary learning */ + learn_result = rspamd_stat_learn(task, + task->flags & RSPAMD_TASK_FLAG_LEARN_SPAM, + task->cfg->lua_state, task->classifier, + st, &stat_error); + } + + if (!learn_result) { if (stat_error == NULL) { g_set_error(&stat_error, @@ -922,15 +942,14 @@ rspamd_learn_task_spam(struct rspamd_task *task, const char *classifier, GError **err) { + /* Use unified class-based approach internally */ + const char *class_name = is_spam ? "spam" : "ham"; + /* Disable learn auto flag to avoid bad learn codes */ task->flags &= ~RSPAMD_TASK_FLAG_LEARN_AUTO; - if (is_spam) { - task->flags |= RSPAMD_TASK_FLAG_LEARN_SPAM; - } - else { - task->flags |= RSPAMD_TASK_FLAG_LEARN_HAM; - } + /* Use the unified class-based learning approach */ + rspamd_task_set_autolearn_class(task, class_name); task->classifier = classifier; diff --git a/src/libserver/task.h b/src/libserver/task.h index 1c1778fee..a1742e160 100644 --- a/src/libserver/task.h +++ b/src/libserver/task.h @@ -104,9 +104,9 @@ enum rspamd_task_stage { #define RSPAMD_TASK_FLAG_LEARN_SPAM (1u << 12u) #define RSPAMD_TASK_FLAG_LEARN_HAM (1u << 13u) #define RSPAMD_TASK_FLAG_LEARN_AUTO (1u << 14u) +#define RSPAMD_TASK_FLAG_LEARN_CLASS (1u << 25u) #define RSPAMD_TASK_FLAG_BROKEN_HEADERS (1u << 15u) -#define RSPAMD_TASK_FLAG_HAS_SPAM_TOKENS (1u << 16u) -#define RSPAMD_TASK_FLAG_HAS_HAM_TOKENS (1u << 17u) +/* Removed RSPAMD_TASK_FLAG_HAS_SPAM_TOKENS and RSPAMD_TASK_FLAG_HAS_HAM_TOKENS - not needed in multi-class */ #define RSPAMD_TASK_FLAG_EMPTY (1u << 18u) #define RSPAMD_TASK_FLAG_PROFILE (1u << 19u) #define RSPAMD_TASK_FLAG_GREYLISTED (1u << 20u) @@ -114,7 +114,7 @@ enum rspamd_task_stage { #define RSPAMD_TASK_FLAG_SSL (1u << 22u) #define RSPAMD_TASK_FLAG_BAD_UNICODE (1u << 23u) #define RSPAMD_TASK_FLAG_MESSAGE_REWRITE (1u << 24u) -#define RSPAMD_TASK_FLAG_MAX_SHIFT (24u) +#define RSPAMD_TASK_FLAG_MAX_SHIFT (25u) /* Request has been done by a local client */ #define RSPAMD_TASK_PROTOCOL_FLAG_LOCAL_CLIENT (1u << 1u) diff --git a/src/libstat/MULTICLASS_BAYES_ARCHITECTURE.md b/src/libstat/MULTICLASS_BAYES_ARCHITECTURE.md new file mode 100644 index 000000000..dc8352374 --- /dev/null +++ b/src/libstat/MULTICLASS_BAYES_ARCHITECTURE.md @@ -0,0 +1,451 @@ +# Rspamd Multiclass Bayes Architecture + +## Overview + +This document describes the complete data flow for the multiclass Bayes classification system in Rspamd, covering the interaction between C++ core, Lua scripts, Redis backend, and the classification pipeline. + +## High-Level Data Flow + +``` +[Task Processing] → [Tokenization] → [Redis Backend] → [Lua Scripts] → [Redis Scripts] → [Results] → [Classification] +``` + +## 1. Classification Pipeline Entry Point + +### 1.1 Task Processing Start + +```c +// src/libstat/stat_process.c +rspamd_stat_classify(struct rspamd_task *task, struct rspamd_config *cfg) +``` + +**Flow:** + +1. Task arrives for classification +2. Iterates through configured classifiers +3. For each classifier, calls `rspamd_stat_classifiers[i].classify_func()` +4. For Bayes: calls `bayes_classify_multiclass()` + +### 1.2 Bayes Classification Entry + +```c +// src/libstat/classifiers/bayes.c +gboolean bayes_classify_multiclass(struct rspamd_classifier *ctx, + GPtrArray *tokens, + struct rspamd_task *task) +``` + +**Key Steps:** + +1. Validates `ctx->cfg->class_names` array +2. Sets up `bayes_task_closure` with class information +3. **Calls Redis backend to fetch token data** +4. Processes returned token values +5. Calculates probabilities and inserts symbols + +## 2. Redis Backend Data Flow + +### 2.1 Backend Runtime Creation + +```cpp +// src/libstat/backends/redis_backend.cxx +gpointer rspamd_redis_runtime(struct rspamd_task *task, + struct rspamd_statfile_config *stcf, + gboolean learn, gpointer c, int _id) +``` + +**Runtime Structure:** + +```cpp +template<class T> +class redis_stat_runtime { + struct redis_stat_ctx *ctx; // Redis connection context + struct rspamd_task *task; // Current task + struct rspamd_statfile_config *stcf; // Statfile configuration + const char *redis_object_expanded; // Expanded key prefix + int id; // Statfile ID (critical!) + std::optional<std::map<int, T>> results; // Token index → value mapping +}; +``` + +**Critical Insight: Statfile ID Mapping** + +- Each statfile has a unique ID (`id`) +- Token values are stored in `tok->values[id]` array +- **The `id` must match exactly between runtime and statfile** + +### 2.2 Multiple Runtime Creation (Classification Mode) + +For multiclass classification, the system creates multiple runtimes: + +```cpp +// For each statfile in classifier +for (cur = stcf->clcf->statfiles; cur; cur = g_list_next(cur)) { + auto *other_stcf = (struct rspamd_statfile_config *) cur->data; + + // Find correct statfile ID + struct rspamd_stat_ctx *st_ctx = rspamd_stat_get_ctx(); + int other_id = -1; + for (i = 0; i < st_ctx->statfiles->len; i++) { + struct rspamd_statfile *st = g_ptr_array_index(st_ctx->statfiles, i); + if (st->stcf == other_stcf) { + other_id = st->id; // ← This is the critical mapping! + break; + } + } + + // Create runtime with correct ID + auto *other_rt = new redis_stat_runtime<float>(ctx, task, object_expanded); + other_rt->id = other_id; // ← Must be set correctly! +} +``` + +### 2.3 Token Processing Call + +```cpp +gboolean rspamd_redis_process_tokens(struct rspamd_task *task, + GPtrArray *tokens, + int id, gpointer p) +``` + +**Flow:** + +1. Serializes tokens to MessagePack format +2. Builds class labels string (e.g., "TABLE:H,S,N,T") +3. Calls Lua function to execute Redis script +4. Registers callback for async result processing + +## 3. Lua Script Layer + +### 3.1 Lua Function Entry Point + +```lua +-- lualib/lua_bayes_redis.lua +local function gen_classify_functor(redis_params, classify_script_id) + return function(task, expanded_key, id, stat_tokens, callback) + -- Executes Redis script via lua_redis + lua_redis.exec_redis_script(classify_script_id, + { task = task, is_write = false, key = expanded_key }, + classify_redis_cb, + { expanded_key, class_labels, stat_tokens }) + end +end +``` + +**Key Components:** + +- `expanded_key`: Redis key prefix (e.g., "BAYES{user@domain}") +- `class_labels`: "TABLE:H,S,N,T" format for multiclass +- `stat_tokens`: MessagePack-encoded token array +- `callback`: Function to handle Redis script results + +### 3.2 Class Labels Format + +**Critical Detail**: The class labels format determines Redis script behavior: + +```lua +-- Binary mode (legacy) +class_labels = "H" -- Single class + +-- Multiclass mode +class_labels = "TABLE:H,S,N,T" -- Multiple classes with TABLE: prefix +``` + +## 4. Redis Script Execution + +### 4.1 Script Structure + +```lua +-- lualib/redis_scripts/bayes_classify.lua +local prefix = KEYS[1] -- "BAYES{user@domain}" +local class_labels_arg = KEYS[2] -- "TABLE:H,S,N,T" +local input_tokens = cmsgpack.unpack(KEYS[3]) -- [tok1, tok2, ...] +``` + +### 4.2 Class Label Parsing + +```lua +local class_labels = {} +if string.match(class_labels_arg, "^TABLE:") then + -- Multiclass mode + local labels_str = string.sub(class_labels_arg, 7) -- Remove "TABLE:" + for label in string.gmatch(labels_str, "([^,]+)") do + table.insert(class_labels, label) -- ["H", "S", "N", "T"] + end +else + -- Binary mode (single label) + table.insert(class_labels, class_labels_arg) +end +``` + +### 4.3 Redis Key Structure + +**Learning Counts:** + +``` +BAYES{user@domain}_H_learns → { learns: 1500 } +BAYES{user@domain}_S_learns → { learns: 800 } +BAYES{user@domain}_N_learns → { learns: 200 } +BAYES{user@domain}_T_learns → { learns: 150 } +``` + +**Token Counts:** + +``` +BAYES{user@domain}_H_tokens → { token1: 45, token2: 12, ... } +BAYES{user@domain}_S_tokens → { token1: 23, token2: 67, ... } +BAYES{user@domain}_N_tokens → { token1: 5, token2: 8, ... } +BAYES{user@domain}_T_tokens → { token1: 2, token2: 3, ... } +``` + +### 4.4 Token Lookup Process + +```lua +-- Get learning counts for each class +local learned_counts = {} +for i, class_label in ipairs(class_labels) do + local learns_key = prefix .. "_" .. class_label .. "_learns" + learned_counts[i] = tonumber(redis.call('HGET', learns_key, 'learns') or '0') +end + +-- Batch token lookup for all classes +local pipe = redis.call('MULTI') +for i, token in ipairs(input_tokens) do + for j, class_label in ipairs(class_labels) do + local token_key = prefix .. "_" .. class_label .. "_tokens" + redis.call('HGET', token_key, token) + end +end +local token_results = redis.call('EXEC') + +-- Parse results into ordered arrays +local token_data = {} +for j, class_label in ipairs(class_labels) do + token_data[j] = {} -- token_data[class_index][token_index] = count +end + +local result_idx = 1 +for i, token in ipairs(input_tokens) do + for j, class_label in ipairs(class_labels) do + local count = tonumber(token_results[result_idx]) or 0 + if count > 0 then + table.insert(token_data[j], {i, count}) -- {token_index, count} + end + result_idx = result_idx + 1 + end +end + +-- Return: [learned_counts, token_data] +return {learned_counts, token_data} +``` + +### 4.5 Return Format + +**Redis Script Returns:** + +```lua +{ + [1] = {1500, 800, 200, 150}, -- learned_counts per class + [2] = { -- token_data per class + [1] = {{1,45}, {2,12}, ...}, -- Class H tokens: {token_idx, count} + [2] = {{1,23}, {2,67}, ...}, -- Class S tokens + [3] = {{1,5}, {2,8}, ...}, -- Class N tokens + [4] = {{1,2}, {2,3}, ...} -- Class T tokens + } +} +``` + +## 5. Result Processing in C++ + +### 5.1 Redis Callback Handler + +```cpp +// src/libstat/backends/redis_backend.cxx +static int rspamd_redis_classified(lua_State *L) +{ + auto *rt = REDIS_RUNTIME(rspamd_mempool_get_variable(task->task_pool, cookie)); + bool result = lua_toboolean(L, 2); + + if (result && lua_istable(L, 3)) { + // Process learned_counts (table index 1) + lua_rawgeti(L, 3, 1); + if (lua_istable(L, -1)) { + // Store learned counts (implementation detail) + } + lua_pop(L, 1); + + // Process token_results (table index 2) + lua_rawgeti(L, 3, 2); + if (lua_istable(L, -1)) { + process_multiclass_token_results(L, rt, task); + } + lua_pop(L, 1); + } +} +``` + +### 5.2 Token Results Processing + +```cpp +static void process_multiclass_token_results(lua_State *L, + redis_stat_runtime<float> *rt, + struct rspamd_task *task) +{ + // L stack: token_results table at top + // Format: {[1] = {{1,45}, {2,12}}, [2] = {{1,23}, {2,67}}, ...} + + if (rt->stcf->clcf && rt->stcf->clcf->statfiles) { + GList *cur = rt->stcf->clcf->statfiles; + int class_idx = 1; + + while (cur) { + auto *stcf = (struct rspamd_statfile_config *)cur->data; + + // Find correct statfile ID + int statfile_id = find_statfile_id_for_config(stcf); + + // Get or create runtime for this statfile + auto maybe_statfile_rt = get_runtime_for_statfile(task, stcf, statfile_id); + if (maybe_statfile_rt) { + auto *statfile_rt = maybe_statfile_rt.value(); + + // Get token data for this class (class_idx) + lua_rawgeti(L, -1, class_idx); + if (lua_istable(L, -1)) { + parse_class_token_data(L, statfile_rt); + } + lua_pop(L, 1); + } + + cur = g_list_next(cur); + class_idx++; + } + } +} +``` + +### 5.3 Token Value Assignment + +```cpp +bool redis_stat_runtime<T>::process_tokens(GPtrArray *tokens) const +{ + rspamd_token_t *tok; + + if (!results) { + return false; + } + + // results maps: token_index → token_count + for (auto [token_idx, token_count] : *results) { + tok = (rspamd_token_t *) g_ptr_array_index(tokens, token_idx - 1); + + // CRITICAL: Set tok->values[id] where id is the statfile ID + tok->values[id] = token_count; + } + + return true; +} +``` + +## 6. Classification Algorithm Execution + +### 6.1 Multiclass Processing + +```c +// src/libstat/classifiers/bayes.c +gboolean bayes_classify_multiclass(struct rspamd_classifier *ctx, + GPtrArray *tokens, + struct rspamd_task *task) +{ + struct bayes_task_closure cl; + + // Initialize with class information from config + cl.num_classes = ctx->cfg->class_names->len; + cl.class_names = (char**)ctx->cfg->class_names->pdata; + + // Process all tokens + for (i = 0; i < tokens->len; i++) { + rspamd_token_t *tok = g_ptr_array_index(tokens, i); + bayes_classify_token_multiclass(ctx, tok, &cl); + } +} +``` + +### 6.2 Token Classification + +```c +static void bayes_classify_token_multiclass(struct rspamd_classifier *ctx, + rspamd_token_t *tok, + struct bayes_task_closure *cl) +{ + // For each statfile, check if it has data for this token + for (i = 0; i < ctx->statfiles_ids->len; i++) { + int id = g_array_index(ctx->statfiles_ids, int, i); + struct rspamd_statfile *st = g_ptr_array_index(ctx->ctx->statfiles, id); + + // CRITICAL: tok->values[id] must be set by Redis backend + double val = tok->values[id]; + + if (val > 0) { + // Find which class this statfile belongs to + for (j = 0; j < cl->num_classes; j++) { + if (strcmp(st->stcf->class_name, cl->class_names[j]) == 0) { + // Accumulate token evidence for this class + process_token_for_class(cl, j, val, st); + break; + } + } + } + } +} +``` + +## 7. Critical Data Mapping + +### 7.1 Statfile ID Assignment + +**The Core Problem**: Ensuring correct mapping between: + +1. **Redis script class order**: `[H, S, N, T]` (array indices 1,2,3,4) +2. **Statfile IDs**: Global statfile IDs assigned by `rspamd_stat_get_ctx()` +3. **Runtime IDs**: Must match statfile IDs for `tok->values[id]` assignment + +### 7.2 Configuration to Runtime Mapping + +```c +// Configuration defines classes +statfile "BAYES_HAM" { class = "ham"; symbol = "BAYES_HAM"; } // Gets ID=0 +statfile "BAYES_SPAM" { class = "spam"; symbol = "BAYES_SPAM"; } // Gets ID=1 +statfile "BAYES_NEWS" { class = "news"; symbol = "BAYES_NEWS"; } // Gets ID=2 + +// Redis backend maps: class_name → backend_label +class_labels = { + "ham" = "H"; // Maps to Redis "H" + "spam" = "S"; // Maps to Redis "S" + "news" = "N"; // Maps to Redis "N" +} + +// Redis script processes in label order: ["H", "S", "N"] +// Returns data in same order: [ham_data, spam_data, news_data] + +// C++ must map: +// redis_result[0] → statfile_id=0 (ham) +// redis_result[1] → statfile_id=1 (spam) +// redis_result[2] → statfile_id=2 (news) +``` + +### 7.3 Token Array Structure + +```c +// For each token in message +struct rspamd_token { + uint64_t data; // Token hash + float values[MAX_STATFILES]; // Values per statfile ID + // ... +}; + +// After Redis processing: +// tok->values[0] = ham_count (from redis_result[0]) +// tok->values[1] = spam_count (from redis_result[1]) +// tok->values[2] = news_count (from redis_result[2]) +``` diff --git a/src/libstat/backends/cdb_backend.cxx b/src/libstat/backends/cdb_backend.cxx index 0f55a725c..f6ca9c12d 100644 --- a/src/libstat/backends/cdb_backend.cxx +++ b/src/libstat/backends/cdb_backend.cxx @@ -393,7 +393,6 @@ rspamd_cdb_process_tokens(struct rspamd_task *task, gpointer runtime) { auto *cdbp = CDB_FROM_RAW(runtime); - bool seen_values = false; for (auto i = 0u; i < tokens->len; i++) { rspamd_token_t *tok; @@ -403,21 +402,13 @@ rspamd_cdb_process_tokens(struct rspamd_task *task, if (res) { tok->values[id] = res.value(); - seen_values = true; } else { tok->values[id] = 0; } } - if (seen_values) { - if (cdbp->is_spam()) { - task->flags |= RSPAMD_TASK_FLAG_HAS_SPAM_TOKENS; - } - else { - task->flags |= RSPAMD_TASK_FLAG_HAS_HAM_TOKENS; - } - } + /* No longer need to set flags - multi-class handles missing data naturally */ return true; } @@ -488,4 +479,4 @@ void rspamd_cdb_close(gpointer ctx) { auto *cdbp = CDB_FROM_RAW(ctx); delete cdbp; -}
\ No newline at end of file +} diff --git a/src/libstat/backends/mmaped_file.c b/src/libstat/backends/mmaped_file.c index 4430bb9a4..a6423a1e6 100644 --- a/src/libstat/backends/mmaped_file.c +++ b/src/libstat/backends/mmaped_file.c @@ -85,8 +85,7 @@ typedef struct { #define RSPAMD_STATFILE_VERSION \ { \ - '1', '2' \ - } + '1', '2'} #define BACKUP_SUFFIX ".old" static void rspamd_mmaped_file_set_block_common(rspamd_mempool_t *pool, @@ -958,12 +957,7 @@ rspamd_mmaped_file_process_tokens(struct rspamd_task *task, GPtrArray *tokens, tok->values[id] = rspamd_mmaped_file_get_block(mf, h1, h2); } - if (mf->cf->is_spam) { - task->flags |= RSPAMD_TASK_FLAG_HAS_SPAM_TOKENS; - } - else { - task->flags |= RSPAMD_TASK_FLAG_HAS_HAM_TOKENS; - } + /* No longer need to set flags - multi-class handles missing data naturally */ return TRUE; } diff --git a/src/libstat/backends/redis_backend.cxx b/src/libstat/backends/redis_backend.cxx index 7137904e9..302778bcb 100644 --- a/src/libstat/backends/redis_backend.cxx +++ b/src/libstat/backends/redis_backend.cxx @@ -22,6 +22,7 @@ #include "contrib/fmt/include/fmt/base.h" #include "libutil/cxx/error.hxx" +#include <map> #include <string> #include <cstdint> @@ -121,9 +122,9 @@ public: } static auto maybe_recover_from_mempool(struct rspamd_task *task, const char *redis_object_expanded, - bool is_spam) -> std::optional<redis_stat_runtime<T> *> + const char *class_label) -> std::optional<redis_stat_runtime<T> *> { - auto var_name = fmt::format("{}_{}", redis_object_expanded, is_spam ? "S" : "H"); + auto var_name = fmt::format("{}_{}", redis_object_expanded, class_label); auto *res = rspamd_mempool_get_variable(task->task_pool, var_name.c_str()); if (res) { @@ -147,9 +148,15 @@ public: rspamd_token_t *tok; if (!results) { + msg_debug_bayes("process_tokens: no results available for statfile id=%d", id); return false; } + if (results->size() > 0) { + msg_debug_bayes("processing %uz tokens for statfile id=%d, class=%s", + results->size(), id, stcf->class_name ? stcf->class_name : "unknown"); + } + for (auto [idx, val]: *results) { tok = (rspamd_token_t *) g_ptr_array_index(tokens, idx - 1); tok->values[id] = val; @@ -158,12 +165,14 @@ public: return true; } - auto save_in_mempool(bool is_spam) const + auto save_in_mempool(const char *class_label) const { - auto var_name = fmt::format("{}_{}", redis_object_expanded, is_spam ? "S" : "H"); + auto var_name = + rspamd_mempool_strdup(task->task_pool, + fmt::format("{}_{}", redis_object_expanded, class_label).c_str()); /* We do not set destructor for the variable, as it should be already added on creation */ - rspamd_mempool_set_variable(task->task_pool, var_name.c_str(), (gpointer) this, nullptr); - msg_debug_bayes("saved runtime in mempool at %s", var_name.c_str()); + rspamd_mempool_set_variable(task->task_pool, var_name, (gpointer) this, nullptr); + msg_debug_bayes("saved runtime in mempool at %s", var_name); } }; @@ -178,6 +187,26 @@ rspamd_redis_stat_quark(void) } /* + * Get the class label for a statfile (for multi-class support) + */ +static const char * +get_class_label(struct rspamd_statfile_config *stcf) +{ + /* Try to get the label from the classifier config first */ + if (stcf->clcf && stcf->clcf->class_labels && stcf->class_name) { + const char *label = rspamd_config_get_class_label(stcf->clcf, stcf->class_name); + if (label) { + return label; + } + /* If no label mapping found, use class name directly */ + return stcf->class_name; + } + + /* Fallback to legacy binary classification */ + return stcf->is_spam ? "S" : "H"; +} + +/* * Non-static for lua unit testing */ gsize rspamd_redis_expand_object(const char *pattern, @@ -235,6 +264,11 @@ gsize rspamd_redis_expand_object(const char *pattern, if (rcpt) { rspamd_mempool_set_variable(task->task_pool, "stat_user", (gpointer) rcpt, nullptr); + msg_debug_bayes("redis expansion: found recipient '%s'", rcpt); + } + else { + msg_debug_bayes("redis expansion: no recipient found (deliver_to=%s)", + task->deliver_to ? task->deliver_to : "null"); } } @@ -448,6 +482,7 @@ rspamd_redis_parse_classifier_opts(struct redis_stat_ctx *backend, users_enabled = ucl_object_lookup_any(classifier_obj, "per_user", "users_enabled", nullptr); + msg_debug_bayes_cfg("per-user lookup: users_enabled=%p", users_enabled); if (users_enabled != nullptr) { if (ucl_object_type(users_enabled) == UCL_BOOLEAN) { backend->enable_users = ucl_object_toboolean(users_enabled); @@ -485,9 +520,16 @@ rspamd_redis_parse_classifier_opts(struct redis_stat_ctx *backend, /* Default non-users statistics */ if (backend->enable_users || backend->cbref_user != -1) { backend->redis_object = REDIS_DEFAULT_USERS_OBJECT; + msg_debug_bayes_cfg("using per-user Redis pattern: %s (enable_users=%s, cbref_user=%d)", + backend->redis_object, backend->enable_users ? "true" : "false", + backend->cbref_user); } else { backend->redis_object = REDIS_DEFAULT_OBJECT; + msg_debug_bayes_cfg("using default Redis pattern: %s (enable_users=%s, cbref_user=%d)", + backend->redis_object, + backend->enable_users ? "true" : "false", + backend->cbref_user); } } else { @@ -541,7 +583,7 @@ rspamd_redis_init(struct rspamd_stat_ctx *ctx, ucl_object_push_lua(L, st->classifier->cfg->opts, false); ucl_object_push_lua(L, st->stcf->opts, false); lua_pushstring(L, backend->stcf->symbol); - lua_pushboolean(L, backend->stcf->is_spam); + lua_pushstring(L, get_class_label(backend->stcf)); /* Pass class label instead of boolean */ /* Push event loop if there is one available (e.g. we are not in rspamadm mode) */ if (ctx->event_loop) { @@ -606,11 +648,20 @@ rspamd_redis_runtime(struct rspamd_task *task, stcf->symbol); return nullptr; } + else { + msg_debug_bayes("redis object expanded: pattern='%s' -> expanded='%s' (learn=%s, symbol=%s)", + ctx->redis_object ? ctx->redis_object : "default", + object_expanded, + learn ? "true" : "false", + stcf->symbol); + } + + const char *class_label = get_class_label(stcf); /* Look for the cached results */ if (!learn) { auto maybe_existing = redis_stat_runtime<float>::maybe_recover_from_mempool(task, - object_expanded, stcf->is_spam); + object_expanded, class_label); if (maybe_existing) { auto *rt = maybe_existing.value(); @@ -624,24 +675,62 @@ rspamd_redis_runtime(struct rspamd_task *task, /* No cached result (or learn), create new one */ auto *rt = new redis_stat_runtime<float>(ctx, task, object_expanded); - if (!learn) { - /* - * For check, we also need to create the opposite class runtime to avoid - * double call for Redis scripts. - * This runtime will be filled later. - */ - auto maybe_opposite_rt = redis_stat_runtime<float>::maybe_recover_from_mempool(task, - object_expanded, - !stcf->is_spam); - - if (!maybe_opposite_rt) { - auto *opposite_rt = new redis_stat_runtime<float>(ctx, task, object_expanded); - opposite_rt->save_in_mempool(!stcf->is_spam); - opposite_rt->need_redis_call = false; + /* Find the statfile ID for the main runtime */ + int main_id = _id; /* Use the passed _id parameter */ + rt->id = main_id; + rt->stcf = stcf; + + /* For classification, create runtimes for all other statfiles to avoid multiple Redis calls */ + if (!learn && stcf->clcf && stcf->clcf->statfiles) { + GList *cur = stcf->clcf->statfiles; + + while (cur) { + auto *other_stcf = (struct rspamd_statfile_config *) cur->data; + const char *other_label = get_class_label(other_stcf); + + /* Find the statfile ID by searching through all statfiles */ + struct rspamd_stat_ctx *st_ctx = rspamd_stat_get_ctx(); + int other_id = -1; + for (unsigned int i = 0; i < st_ctx->statfiles->len; i++) { + struct rspamd_statfile *st = (struct rspamd_statfile *) g_ptr_array_index(st_ctx->statfiles, i); + if (st->stcf == other_stcf) { + other_id = st->id; + msg_debug_bayes("found statfile mapping: %s (class=%s) → id=%d", + st->stcf->symbol, other_label, other_id); + break; + } + } + + if (other_id == -1) { + msg_debug_bayes("statfile not found for class %s, skipping", other_label); + /* Skip if statfile not found */ + cur = g_list_next(cur); + continue; + } + + if (other_stcf == stcf) { + /* This is the main statfile, use the main runtime */ + rt->save_in_mempool(other_label); + msg_debug_bayes("main runtime: statfile %s (class=%s) → id=%d", + stcf->symbol, other_label, rt->id); + } + else { + /* Create additional runtime for other statfile */ + auto *other_rt = new redis_stat_runtime<float>(ctx, task, object_expanded); + other_rt->id = other_id; + other_rt->stcf = other_stcf; + other_rt->need_redis_call = false; + other_rt->save_in_mempool(other_label); + msg_debug_bayes("additional runtime: statfile %s (class=%s) → id=%d", + other_stcf->symbol, other_label, other_id); + } + + cur = g_list_next(cur); } } - - rt->save_in_mempool(stcf->is_spam); + else { + rt->save_in_mempool(class_label); + } return rt; } @@ -816,77 +905,306 @@ rspamd_redis_classified(lua_State *L) if (rt == nullptr) { msg_err_task("internal error: cannot find runtime for cookie %s", cookie); - return 0; } bool result = lua_toboolean(L, 2); if (result) { - /* Indexes: - * 3 - learned_ham (int) - * 4 - learned_spam (int) - * 5 - ham_tokens (pair<int, int>) - * 6 - spam_tokens (pair<int, int>) - */ - - /* - * We need to fill our runtime AND the opposite runtime - */ - auto filler_func = [](redis_stat_runtime<float> *rt, lua_State *L, unsigned learned, int tokens_pos) { - rt->learned = learned; - redis_stat_runtime<float>::result_type *res; - - res = new redis_stat_runtime<float>::result_type(); - - for (lua_pushnil(L); lua_next(L, tokens_pos); lua_pop(L, 1)) { - lua_rawgeti(L, -1, 1); - auto idx = lua_tointeger(L, -1); - lua_pop(L, 1); - - lua_rawgeti(L, -1, 2); - auto value = lua_tonumber(L, -1); - lua_pop(L, 1); - - res->emplace_back(idx, value); - } - - rt->set_results(res); - }; - - auto opposite_rt_maybe = redis_stat_runtime<float>::maybe_recover_from_mempool(task, - rt->redis_object_expanded, - !rt->stcf->is_spam); + /* Check we have enough arguments and the result data is a table */ + if (lua_gettop(L) < 3 || !lua_istable(L, 3)) { + msg_err_task("internal error: expected table result from Redis script, got %s", + lua_typename(L, lua_type(L, 3))); + rt->err = rspamd::util::error("invalid Redis script result format", 500); + return 0; + } - if (!opposite_rt_maybe) { - msg_err_task("internal error: cannot find opposite runtime for cookie %s", cookie); + /* Redis returns [learned_counts_array, token_results_array] + * Both ordered the same way as statfiles in classifier */ + size_t result_len = rspamd_lua_table_size(L, 3); + msg_debug_bayes("Redis result array length: %uz", result_len); + if (result_len != 2) { + msg_err_task("internal error: expected 2-element result from Redis script, got %uz", result_len); + rt->err = rspamd::util::error("invalid Redis script result format", 500); return 0; } - if (rt->stcf->is_spam) { - filler_func(rt, L, lua_tointeger(L, 4), 6); - filler_func(opposite_rt_maybe.value(), L, lua_tointeger(L, 3), 5); + /* Get learned_counts_array and token_results_array */ + lua_rawgeti(L, 3, 1); /* learned_counts -> position 4 */ + lua_rawgeti(L, 3, 2); /* token_results -> position 5 */ + + /* First, process learned_counts */ + if (lua_istable(L, 4) && rt->stcf->clcf) { + if (rt->stcf->clcf->class_names && rt->stcf->clcf->class_names->len > 0) { + /* Multi-class: use class_names order */ + for (unsigned int class_idx = 0; class_idx < rt->stcf->clcf->class_names->len; class_idx++) { + const char *class_name = (const char *) g_ptr_array_index(rt->stcf->clcf->class_names, class_idx); + + /* Find statfile with this class name */ + GList *cur = rt->stcf->clcf->statfiles; + while (cur) { + auto *stcf = (struct rspamd_statfile_config *) cur->data; + if (stcf->class_name && strcmp(stcf->class_name, class_name) == 0) { + const char *class_label = get_class_label(stcf); + + /* Get the runtime for this statfile */ + auto maybe_rt = redis_stat_runtime<float>::maybe_recover_from_mempool(rt->task, + rt->redis_object_expanded, + class_label); + if (maybe_rt) { + auto *statfile_rt = maybe_rt.value(); + + /* Extract learned count using class index (1-based for Lua) */ + lua_rawgeti(L, 4, class_idx + 1); + if (lua_isnumber(L, -1)) { + statfile_rt->learned = lua_tointeger(L, -1); + msg_debug_bayes("set learned count for class %s (label %s): %L", + class_name, class_label, statfile_rt->learned); + } + lua_pop(L, 1); /* Pop learned_counts[class_idx + 1] */ + } + break; /* Found the statfile for this class */ + } + cur = g_list_next(cur); + } + } + } + else { + /* Binary classification: process statfiles in order */ + GList *cur = rt->stcf->clcf->statfiles; + unsigned int statfile_idx = 0; + while (cur) { + auto *stcf = (struct rspamd_statfile_config *) cur->data; + const char *class_label = get_class_label(stcf); + + /* Get the runtime for this statfile */ + auto maybe_rt = redis_stat_runtime<float>::maybe_recover_from_mempool(rt->task, + rt->redis_object_expanded, + class_label); + if (maybe_rt) { + auto *statfile_rt = maybe_rt.value(); + + /* Extract learned count using statfile index (1-based for Lua) */ + lua_rawgeti(L, 4, statfile_idx + 1); + if (lua_isnumber(L, -1)) { + statfile_rt->learned = lua_tointeger(L, -1); + msg_debug_bayes("set learned count for statfile %s (label %s): %L", + stcf->symbol, class_label, statfile_rt->learned); + } + lua_pop(L, 1); /* Pop learned_counts[statfile_idx + 1] */ + } + cur = g_list_next(cur); + statfile_idx++; + } + } } - else { - filler_func(rt, L, lua_tointeger(L, 3), 5); - filler_func(opposite_rt_maybe.value(), L, lua_tointeger(L, 4), 6); + + /* Process token results */ + if (lua_istable(L, 5) && rt->stcf->clcf) { + if (rt->stcf->clcf->class_names && rt->stcf->clcf->class_names->len > 0) { + /* Multi-class: use class_names order */ + for (unsigned int class_idx = 0; class_idx < rt->stcf->clcf->class_names->len; class_idx++) { + const char *class_name = (const char *) g_ptr_array_index(rt->stcf->clcf->class_names, class_idx); + + /* Find statfile with this class name */ + GList *cur = rt->stcf->clcf->statfiles; + while (cur) { + auto *stcf = (struct rspamd_statfile_config *) cur->data; + if (stcf->class_name && strcmp(stcf->class_name, class_name) == 0) { + const char *class_label = get_class_label(stcf); + + /* Find the statfile ID */ + struct rspamd_stat_ctx *st_ctx = rspamd_stat_get_ctx(); + struct rspamd_statfile *st = nullptr; + for (unsigned int i = 0; i < st_ctx->statfiles->len; i++) { + struct rspamd_statfile *candidate = (struct rspamd_statfile *) g_ptr_array_index(st_ctx->statfiles, i); + if (candidate->stcf == stcf) { + st = candidate; + break; + } + } + + if (!st) { + msg_debug_bayes("statfile not found for class %s, skipping", class_name); + break; + } + + /* Get or create runtime for this statfile */ + auto *statfile_rt = rt; /* Use current runtime if it matches */ + if (stcf != rt->stcf) { + auto maybe_rt = redis_stat_runtime<float>::maybe_recover_from_mempool(task, + rt->redis_object_expanded, + class_label); + if (maybe_rt) { + statfile_rt = maybe_rt.value(); + } + else { + msg_debug_bayes("runtime not found for class %s, skipping", class_label); + break; + } + } + + /* Ensure correct statfile ID assignment */ + statfile_rt->id = st->id; + + /* Process token results using class index (1-based for Lua) */ + lua_rawgeti(L, 5, class_idx + 1); /* Get token_results[class_idx + 1] */ + if (lua_istable(L, -1)) { + /* Parse token results into statfile runtime */ + auto *res = new std::vector<std::pair<int, float>>(); + + lua_pushnil(L); /* First key for iteration */ + while (lua_next(L, -2) != 0) { + if (lua_istable(L, -1) && lua_objlen(L, -1) == 2) { + lua_rawgeti(L, -1, 1); /* token_index */ + lua_rawgeti(L, -2, 2); /* token_count */ + + if (lua_isnumber(L, -2) && lua_isnumber(L, -1)) { + int token_idx = lua_tointeger(L, -2); + float token_count = lua_tonumber(L, -1); + res->emplace_back(token_idx, token_count); + } + + lua_pop(L, 2); /* Pop token_index and token_count */ + } + lua_pop(L, 1); /* Pop value, keep key for next iteration */ + } + + statfile_rt->set_results(res); + } + lua_pop(L, 1); /* Pop token_results[class_idx + 1] */ + break; /* Found the statfile for this class */ + } + cur = g_list_next(cur); + } + } + } + else { + /* Binary classification: process statfiles in order */ + GList *cur = rt->stcf->clcf->statfiles; + unsigned int statfile_idx = 0; + while (cur) { + auto *stcf = (struct rspamd_statfile_config *) cur->data; + const char *class_label = get_class_label(stcf); + + /* Find the statfile ID */ + struct rspamd_stat_ctx *st_ctx = rspamd_stat_get_ctx(); + struct rspamd_statfile *st = nullptr; + for (unsigned int i = 0; i < st_ctx->statfiles->len; i++) { + struct rspamd_statfile *candidate = (struct rspamd_statfile *) g_ptr_array_index(st_ctx->statfiles, i); + if (candidate->stcf == stcf) { + st = candidate; + break; + } + } + + if (!st) { + msg_debug_bayes("statfile not found for %s, skipping", stcf->symbol); + cur = g_list_next(cur); + statfile_idx++; + continue; + } + + /* Get or create runtime for this statfile */ + auto *statfile_rt = rt; /* Use current runtime if it matches */ + if (stcf != rt->stcf) { + auto maybe_rt = redis_stat_runtime<float>::maybe_recover_from_mempool(task, + rt->redis_object_expanded, + class_label); + if (maybe_rt) { + statfile_rt = maybe_rt.value(); + } + else { + msg_debug_bayes("runtime not found for %s, skipping", class_label); + cur = g_list_next(cur); + statfile_idx++; + continue; + } + } + + /* Ensure correct statfile ID assignment */ + statfile_rt->id = st->id; + + /* Process token results using statfile index (1-based for Lua) */ + lua_rawgeti(L, 5, statfile_idx + 1); /* Get token_results[statfile_idx + 1] */ + if (lua_istable(L, -1)) { + /* Parse token results into statfile runtime */ + auto *res = new std::vector<std::pair<int, float>>(); + + lua_pushnil(L); /* First key for iteration */ + while (lua_next(L, -2) != 0) { + if (lua_istable(L, -1) && lua_objlen(L, -1) == 2) { + lua_rawgeti(L, -1, 1); /* token_index */ + lua_rawgeti(L, -2, 2); /* token_count */ + + if (lua_isnumber(L, -2) && lua_isnumber(L, -1)) { + int token_idx = lua_tointeger(L, -2); + float token_count = lua_tonumber(L, -1); + res->emplace_back(token_idx, token_count); + } + + lua_pop(L, 2); /* Pop token_index and token_count */ + } + lua_pop(L, 1); /* Pop value, keep key for next iteration */ + } + + statfile_rt->set_results(res); + msg_debug_bayes("set %uz token results for statfile %s (label %s, id=%d)", + res->size(), stcf->symbol, class_label, st->id); + } + lua_pop(L, 1); /* Pop token_results[statfile_idx + 1] */ + + cur = g_list_next(cur); + statfile_idx++; + } + } } - /* Mark task as being processed */ - task->flags |= RSPAMD_TASK_FLAG_HAS_SPAM_TOKENS | RSPAMD_TASK_FLAG_HAS_HAM_TOKENS; + /* Clean up stack */ + lua_pop(L, 2); /* Pop learned_counts and token_results */ - /* Process all tokens */ + /* Process tokens for all runtimes */ g_assert(rt->tokens != nullptr); - rt->process_tokens(rt->tokens); - opposite_rt_maybe.value()->process_tokens(rt->tokens); + + /* Process tokens for all statfiles */ + if (rt->stcf->clcf && rt->stcf->clcf->statfiles) { + GList *cur = rt->stcf->clcf->statfiles; + + while (cur) { + auto *stcf = (struct rspamd_statfile_config *) cur->data; + const char *class_label = get_class_label(stcf); + + auto maybe_rt = redis_stat_runtime<float>::maybe_recover_from_mempool(task, + rt->redis_object_expanded, + class_label); + if (maybe_rt) { + auto *statfile_rt = maybe_rt.value(); + statfile_rt->process_tokens(rt->tokens); + } + + cur = g_list_next(cur); + } + } + else { + /* Fallback: just process the main runtime */ + rt->process_tokens(rt->tokens); + } } else { /* Error message is on index 3 */ - const auto *err_msg = lua_tostring(L, 3); - rt->err = rspamd::util::error(err_msg, 500); - msg_err_task("cannot classify task: %s", - err_msg); + const char *err_msg = nullptr; + if (lua_gettop(L) >= 3 && lua_isstring(L, 3)) { + err_msg = lua_tostring(L, 3); + } + if (err_msg) { + rt->err = rspamd::util::error(err_msg, 500); + msg_err_task("cannot classify task: %s", err_msg); + } + else { + rt->err = rspamd::util::error("unknown Redis script error", 500); + msg_err_task("cannot classify task: unknown Redis script error"); + } } return 0; @@ -929,7 +1247,42 @@ rspamd_redis_process_tokens(struct rspamd_task *task, rspamd_lua_task_push(L, task); lua_pushstring(L, rt->redis_object_expanded); lua_pushinteger(L, id); - lua_pushboolean(L, rt->stcf->is_spam); + + /* Send all class labels for multi-class support */ + if (rt->stcf->clcf && rt->stcf->clcf->class_names && + rt->stcf->clcf->class_names->len > 0) { + /* Multi-class: send array of class labels in deterministic order */ + lua_createtable(L, rt->stcf->clcf->class_names->len, 0); + for (unsigned int i = 0; i < rt->stcf->clcf->class_names->len; i++) { + const char *class_name = (const char *) g_ptr_array_index(rt->stcf->clcf->class_names, i); + const char *class_label = nullptr; + + /* Find the class label for this class name from any statfile with this class */ + GList *cur = rt->stcf->clcf->statfiles; + while (cur) { + auto *stcf = (struct rspamd_statfile_config *) cur->data; + if (stcf->class_name && strcmp(stcf->class_name, class_name) == 0) { + class_label = get_class_label(stcf); + break; + } + cur = g_list_next(cur); + } + + if (class_label) { + lua_pushstring(L, class_label); + lua_rawseti(L, -2, i + 1); /* Lua arrays are 1-indexed */ + } + } + } + else { + /* Binary classification: send both spam and ham labels for optimization */ + lua_createtable(L, 2, 0); + lua_pushstring(L, "H"); /* ham */ + lua_rawseti(L, -2, 1); + lua_pushstring(L, "S"); /* spam */ + lua_rawseti(L, -2, 2); + } + lua_new_text(L, tokens_buf, tokens_len, false); /* Store rt in random cookie */ @@ -979,13 +1332,31 @@ rspamd_redis_learned(lua_State *L) bool result = lua_toboolean(L, 2); if (result) { - /* TODO: write it */ + /* Learning successful - no complex data to process like in classification */ + msg_debug_bayes("learned tokens successfully in Redis for symbol %s, class %s", + rt->stcf->symbol, get_class_label(rt->stcf)); + + /* Clear any previous error state */ + rt->err = std::nullopt; + + /* Learning operations don't return data structures to process, + * they just update Redis state. Success means the Redis script + * completed without errors. */ } else { /* Error message is on index 3 */ - const auto *err_msg = lua_tostring(L, 3); - rt->err = rspamd::util::error(err_msg, 500); - msg_err_task("cannot learn task: %s", err_msg); + const char *err_msg = nullptr; + if (lua_gettop(L) >= 3 && lua_isstring(L, 3)) { + err_msg = lua_tostring(L, 3); + } + if (err_msg) { + rt->err = rspamd::util::error(err_msg, 500); + msg_err_task("cannot learn task: %s", err_msg); + } + else { + rt->err = rspamd::util::error("unknown Redis script error", 500); + msg_err_task("cannot learn task: unknown Redis script error"); + } } return 0; @@ -1028,7 +1399,7 @@ rspamd_redis_learn_tokens(struct rspamd_task *task, rspamd_lua_task_push(L, task); lua_pushstring(L, rt->redis_object_expanded); lua_pushinteger(L, id); - lua_pushboolean(L, rt->stcf->is_spam); + lua_pushstring(L, get_class_label(rt->stcf)); /* Pass class label instead of boolean */ lua_pushstring(L, rt->stcf->symbol); /* Detect unlearn */ @@ -1056,6 +1427,8 @@ rspamd_redis_learn_tokens(struct rspamd_task *task, lua_new_text(L, text_tokens_buf, text_tokens_len, false); } + msg_debug_bayes("called lua learn script for %s (cookie=%s)", rt->stcf->symbol, cookie); + if (lua_pcall(L, nargs, 0, err_idx) != 0) { msg_err_task("call to script failed: %s", lua_tostring(L, -1)); lua_settop(L, err_idx - 1); diff --git a/src/libstat/backends/sqlite3_backend.c b/src/libstat/backends/sqlite3_backend.c index 973dc30a7..8f29a3b4e 100644 --- a/src/libstat/backends/sqlite3_backend.c +++ b/src/libstat/backends/sqlite3_backend.c @@ -589,12 +589,7 @@ rspamd_sqlite3_process_tokens(struct rspamd_task *task, } } - if (rt->cf->is_spam) { - task->flags |= RSPAMD_TASK_FLAG_HAS_SPAM_TOKENS; - } - else { - task->flags |= RSPAMD_TASK_FLAG_HAS_HAM_TOKENS; - } + /* No longer need to set flags - multi-class handles missing data naturally */ } diff --git a/src/libstat/classifiers/bayes.c b/src/libstat/classifiers/bayes.c index 93b5149da..dbae98cc2 100644 --- a/src/libstat/classifiers/bayes.c +++ b/src/libstat/classifiers/bayes.c @@ -1,11 +1,11 @@ -/*- - * Copyright 2016 Vsevolod Stakhov +/* + * Copyright 2025 Vsevolod Stakhov * * 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 + * 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, @@ -53,10 +53,26 @@ static double inv_chi_square(struct rspamd_task *task, double value, int freedom_deg) { double prob, sum, m; + double log_prob, log_m; int i; errno = 0; m = -value; + + /* Handle extreme negative values that would cause exp() underflow */ + if (value < -700) { + /* Very strong confidence, return 0 */ + msg_debug_bayes("extreme negative value: %f, returning 0", value); + return 0.0; + } + + /* Handle extreme positive values that would cause overflow */ + if (value > 700) { + /* No confidence, return 1 */ + msg_debug_bayes("extreme positive value: %f, returning 1", value); + return 1.0; + } + prob = exp(value); if (errno == ERANGE) { @@ -75,6 +91,8 @@ inv_chi_square(struct rspamd_task *task, double value, int freedom_deg) } sum = prob; + log_prob = value; /* log of current prob term */ + log_m = log(fabs(m)); /* log of |m| for numerical stability */ msg_debug_bayes("m: %f, probability: %g", m, prob); @@ -83,24 +101,60 @@ inv_chi_square(struct rspamd_task *task, double value, int freedom_deg) * prob is e ^ x (small value since x is normally less than zero * So we integrate over degrees of freedom and produce the total result * from 1.0 (no confidence) to 0.0 (full confidence) + * Use logarithmic arithmetic to prevent overflow */ for (i = 1; i < freedom_deg; i++) { - prob *= m / (double) i; + /* Calculate next term using logarithms to prevent overflow */ + log_prob += log_m - log((double) i); + + /* Check if the log probability is too negative (term becomes negligible) */ + if (log_prob < -700) { + msg_debug_bayes("term %d became negligible, stopping series", i); + break; + } + + /* Check if the log probability is too positive (would cause overflow) */ + if (log_prob > 700) { + msg_debug_bayes("series diverging at term %d, returning 1.0", i); + return 1.0; + } + + prob = exp(log_prob); sum += prob; - msg_debug_bayes("i=%d, probability: %g, sum: %g", i, prob, sum); + msg_debug_bayes("i=%d, log_prob: %g, probability: %g, sum: %g", i, log_prob, prob, sum); + + /* Early termination if sum is getting too large */ + if (sum > 1e10) { + msg_debug_bayes("sum too large (%g), returning 1.0", sum); + return 1.0; + } } return MIN(1.0, sum); } struct bayes_task_closure { - double ham_prob; - double spam_prob; + double ham_prob; /* Kept for binary compatibility */ + double spam_prob; /* Kept for binary compatibility */ + double meta_skip_prob; + uint64_t processed_tokens; + uint64_t total_hits; + uint64_t text_tokens; + struct rspamd_task *task; +}; + +/* Multi-class classification closure */ +struct bayes_multiclass_closure { + double *class_log_probs; /* Array of log probabilities for each class */ + uint64_t *class_learns; /* Learning counts for each class */ + char **class_names; /* Array of class names */ + unsigned int num_classes; /* Number of classes */ double meta_skip_prob; uint64_t processed_tokens; uint64_t total_hits; uint64_t text_tokens; struct rspamd_task *task; + struct rspamd_classifier_config *cfg; }; /* @@ -122,7 +176,6 @@ bayes_classify_token(struct rspamd_classifier *ctx, unsigned int spam_count = 0, ham_count = 0, total_count = 0; struct rspamd_statfile *st; struct rspamd_task *task; - const char *token_type = "txt"; double spam_prob, spam_freq, ham_freq, bayes_spam_prob, bayes_ham_prob, ham_prob, fw, w, val; @@ -211,41 +264,379 @@ bayes_classify_token(struct rspamd_classifier *ctx, if (!(tok->flags & RSPAMD_STAT_TOKEN_FLAG_META)) { cl->text_tokens++; } + } +} + +/* + * Multinomial token classification for multi-class Bayes + */ +static void +bayes_classify_token_multiclass(struct rspamd_classifier *ctx, + rspamd_token_t *tok, + struct bayes_multiclass_closure *cl) +{ + unsigned int i, j; + int id; + struct rspamd_statfile *st; + struct rspamd_task *task; + double val, fw, w; + guint64 *class_counts; + guint64 total_count = 0; + + task = cl->task; + + /* Skip meta tokens probabilistically if configured */ + if (tok->flags & RSPAMD_STAT_TOKEN_FLAG_META && cl->meta_skip_prob > 0) { + val = rspamd_random_double_fast(); + if (val <= cl->meta_skip_prob) { + return; + } + } + + /* Allocate array for class counts */ + class_counts = g_alloca(cl->num_classes * sizeof(guint64)); + memset(class_counts, 0, cl->num_classes * sizeof(guint64)); + + /* Collect counts for each class */ + for (i = 0; i < ctx->statfiles_ids->len; i++) { + id = g_array_index(ctx->statfiles_ids, int, i); + st = g_ptr_array_index(ctx->ctx->statfiles, id); + g_assert(st != NULL); + val = tok->values[id]; + + if (val > 0) { + /* Direct O(1) class index lookup instead of O(N) string comparison */ + if (st->stcf->class_name && st->stcf->class_index < cl->num_classes) { + unsigned int class_idx = st->stcf->class_index; + class_counts[class_idx] += val; + total_count += val; + cl->total_hits += val; + } + else { + msg_debug_bayes("invalid class_index %ud >= %ud for statfile %s", + st->stcf->class_index, cl->num_classes, st->stcf->symbol); + } + } + } + + /* Calculate multinomial probability for this token */ + if (total_count >= ctx->cfg->min_token_hits) { + /* Feature weight calculation */ + if (tok->flags & RSPAMD_STAT_TOKEN_FLAG_UNIGRAM) { + fw = 1.0; + } else { - token_type = "meta"; + fw = feature_weight[tok->window_idx % G_N_ELEMENTS(feature_weight)]; } - if (tok->t1 && tok->t2) { - msg_debug_bayes("token(%s) %uL <%*s:%*s>: weight: %f, cf: %f, " - "total_count: %ud, " - "spam_count: %ud, ham_count: %ud," - "spam_prob: %.3f, ham_prob: %.3f, " - "bayes_spam_prob: %.3f, bayes_ham_prob: %.3f, " - "current spam probability: %.3f, current ham probability: %.3f", - token_type, - tok->data, - (int) tok->t1->stemmed.len, tok->t1->stemmed.begin, - (int) tok->t2->stemmed.len, tok->t2->stemmed.begin, - fw, w, total_count, spam_count, ham_count, - spam_prob, ham_prob, - bayes_spam_prob, bayes_ham_prob, - cl->spam_prob, cl->ham_prob); + w = (fw * total_count) / (1.0 + fw * total_count); + + /* Apply multinomial model for each class */ + for (j = 0; j < cl->num_classes; j++) { + /* Skip classes with insufficient learns */ + if (ctx->cfg->min_learns > 0 && cl->class_learns[j] < ctx->cfg->min_learns) { + continue; + } + + double class_freq = (double) class_counts[j] / MAX(1.0, (double) cl->class_learns[j]); + double class_prob = PROB_COMBINE(class_freq, total_count, w, 1.0 / cl->num_classes); + + /* Ensure probability is properly bounded [0, 1] */ + class_prob = MAX(0.0, MIN(1.0, class_prob)); + + /* Skip probabilities too close to uniform (1/num_classes) */ + double uniform_prior = 1.0 / cl->num_classes; + if (fabs(class_prob - uniform_prior) < ctx->cfg->min_prob_strength) { + continue; + } + + cl->class_log_probs[j] += log(class_prob); + } + + cl->processed_tokens++; + if (!(tok->flags & RSPAMD_STAT_TOKEN_FLAG_META)) { + cl->text_tokens++; + } + + /* Per-token debug logging removed to reduce verbosity */ + } +} + +/* + * Multinomial Bayes classification with Fisher confidence + */ +static gboolean +bayes_classify_multiclass(struct rspamd_classifier *ctx, + GPtrArray *tokens, + struct rspamd_task *task) +{ + struct bayes_multiclass_closure cl; + rspamd_token_t *tok; + unsigned int i, j, text_tokens = 0; + int id; + struct rspamd_statfile *st; + rspamd_multiclass_result_t *result; + double *normalized_probs; + double max_log_prob = -INFINITY; + unsigned int winning_class_idx = 0; + double confidence; + + g_assert(ctx != NULL); + g_assert(tokens != NULL); + + /* Initialize multi-class closure */ + memset(&cl, 0, sizeof(cl)); + cl.task = task; + cl.cfg = ctx->cfg; + + /* Get class information from classifier config */ + if (!ctx->cfg->class_names) { + msg_debug_bayes("no class_names array in classifier config"); + return TRUE; /* Fall back to binary mode */ + } + if (ctx->cfg->class_names->len < 2) { + msg_debug_bayes("insufficient classes: %ud < 2", (unsigned int) ctx->cfg->class_names->len); + return TRUE; /* Fall back to binary mode */ + } + if (!ctx->cfg->class_names->pdata) { + msg_debug_bayes("class_names->pdata is NULL"); + return TRUE; /* Fall back to binary mode */ + } + + cl.num_classes = ctx->cfg->class_names->len; + cl.class_names = (char **) ctx->cfg->class_names->pdata; + + /* Debug: verify class names are accessible */ + msg_debug_bayes("multiclass setup: ctx->cfg->class_names=%p, len=%ud, pdata=%p", + ctx->cfg->class_names, (unsigned int) ctx->cfg->class_names->len, ctx->cfg->class_names->pdata); + msg_debug_bayes("multiclass setup: cl.num_classes=%ud, cl.class_names=%p", + cl.num_classes, cl.class_names); + cl.class_log_probs = g_alloca(cl.num_classes * sizeof(double)); + cl.class_learns = g_alloca(cl.num_classes * sizeof(uint64_t)); + + /* Initialize probabilities and get learning counts */ + for (i = 0; i < cl.num_classes; i++) { + cl.class_log_probs[i] = 0.0; + cl.class_learns[i] = 0; + } + + /* Collect learning counts for each class */ + for (i = 0; i < ctx->statfiles_ids->len; i++) { + id = g_array_index(ctx->statfiles_ids, int, i); + st = g_ptr_array_index(ctx->ctx->statfiles, id); + g_assert(st != NULL); + + for (j = 0; j < cl.num_classes; j++) { + if (st->stcf->class_name && + strcmp(st->stcf->class_name, cl.class_names[j]) == 0) { + cl.class_learns[j] += st->backend->total_learns(task, + g_ptr_array_index(task->stat_runtimes, id), ctx->ctx); + break; + } + } + } + + /* Check minimum learns requirement - count viable classes */ + unsigned int viable_classes = 0; + if (ctx->cfg->min_learns > 0) { + for (i = 0; i < cl.num_classes; i++) { + if (cl.class_learns[i] >= ctx->cfg->min_learns) { + viable_classes++; + } + else { + msg_info_task("class %s excluded from classification: %uL learns < %ud minimum", + cl.class_names[i], cl.class_learns[i], ctx->cfg->min_learns); + } + } + + if (viable_classes == 0) { + msg_info_task("no classes have sufficient training samples for classification"); + return TRUE; + } + + msg_info_bayes("multiclass classification: %ud/%ud classes have sufficient learns", + viable_classes, cl.num_classes); + } + + /* Count text tokens */ + for (i = 0; i < tokens->len; i++) { + tok = g_ptr_array_index(tokens, i); + if (!(tok->flags & RSPAMD_STAT_TOKEN_FLAG_META)) { + text_tokens++; + } + } + + if (text_tokens == 0) { + msg_info_task("skipped classification as there are no text tokens. " + "Total tokens: %ud", + tokens->len); + return TRUE; + } + + /* Set meta token skip probability */ + if (text_tokens > tokens->len - text_tokens) { + cl.meta_skip_prob = 0.0; + } + else { + cl.meta_skip_prob = 1.0 - (double) text_tokens / tokens->len; + } + + /* Process all tokens */ + for (i = 0; i < tokens->len; i++) { + tok = g_ptr_array_index(tokens, i); + bayes_classify_token_multiclass(ctx, tok, &cl); + } + + if (cl.processed_tokens == 0) { + /* Debug: check why no tokens were processed */ + msg_debug_bayes("examining token values for debugging:"); + for (i = 0; i < MIN(tokens->len, 10); i++) { /* Check first 10 tokens */ + tok = g_ptr_array_index(tokens, i); + for (j = 0; j < ctx->statfiles_ids->len; j++) { + id = g_array_index(ctx->statfiles_ids, int, j); + if (tok->values[id] > 0) { + struct rspamd_statfile *st = g_ptr_array_index(ctx->ctx->statfiles, id); + msg_debug_bayes("token %ud: values[%d] = %.2f (class=%s, symbol=%s)", + i, id, tok->values[id], + st->stcf->class_name ? st->stcf->class_name : "unknown", + st->stcf->symbol); + } + } + } + + msg_info_bayes("no tokens found in bayes database " + "(%ud total tokens, %ud text tokens), ignore stats", + tokens->len, text_tokens); + return TRUE; + } + + if (ctx->cfg->min_tokens > 0 && + cl.text_tokens < (int) (ctx->cfg->min_tokens * 0.1)) { + msg_info_bayes("ignore bayes probability since we have " + "found too few text tokens: %uL (of %ud checked), " + "at least %d required", + cl.text_tokens, text_tokens, + (int) (ctx->cfg->min_tokens * 0.1)); + return TRUE; + } + + /* Normalize probabilities using softmax */ + normalized_probs = g_alloca(cl.num_classes * sizeof(double)); + + /* Find maximum for numerical stability - only consider classes with sufficient training */ + for (i = 0; i < cl.num_classes; i++) { + msg_debug_bayes("class %s, log_prob: %.2f", cl.class_names[i], cl.class_log_probs[i]); + /* Only consider classes that have sufficient training data */ + if (ctx->cfg->min_learns > 0 && cl.class_learns[i] < ctx->cfg->min_learns) { + msg_debug_bayes("skipping class %s in winner selection: %uL learns < %ud minimum", + cl.class_names[i], cl.class_learns[i], ctx->cfg->min_learns); + continue; + } + if (cl.class_log_probs[i] > max_log_prob) { + max_log_prob = cl.class_log_probs[i]; + winning_class_idx = i; + } + } + + /* Apply softmax normalization */ + double sum_exp = 0.0; + for (i = 0; i < cl.num_classes; i++) { + normalized_probs[i] = exp(cl.class_log_probs[i] - max_log_prob); + sum_exp += normalized_probs[i]; + } + + if (sum_exp > 0) { + for (i = 0; i < cl.num_classes; i++) { + normalized_probs[i] /= sum_exp; + } + } + else { + /* Fallback to uniform distribution */ + for (i = 0; i < cl.num_classes; i++) { + normalized_probs[i] = 1.0 / cl.num_classes; + } + } + + /* Calculate confidence using Fisher method for the winning class */ + if (max_log_prob > -300) { + if (max_log_prob > 0) { + /* Positive log prob means very strong evidence - high confidence */ + confidence = 0.95; /* High confidence for positive log probabilities */ + msg_debug_bayes("positive log_prob (%g), setting high confidence", max_log_prob); } else { - msg_debug_bayes("token(%s) %uL <?:?>: weight: %f, cf: %f, " - "total_count: %ud, " - "spam_count: %ud, ham_count: %ud," - "spam_prob: %.3f, ham_prob: %.3f, " - "bayes_spam_prob: %.3f, bayes_ham_prob: %.3f, " - "current spam probability: %.3f, current ham probability: %.3f", - token_type, - tok->data, - fw, w, total_count, spam_count, ham_count, - spam_prob, ham_prob, - bayes_spam_prob, bayes_ham_prob, - cl->spam_prob, cl->ham_prob); + /* Negative log prob - use Fisher method as intended */ + double fisher_result = inv_chi_square(task, max_log_prob, cl.processed_tokens); + confidence = 1.0 - fisher_result; + + msg_debug_bayes("fisher_result: %g, max_log_prob: %g, condition check: fisher_result > 0.999 = %s, max_log_prob > -50 = %s", + fisher_result, max_log_prob, + fisher_result > 0.999 ? "true" : "false", + max_log_prob > -50 ? "true" : "false"); + + /* Handle case where Fisher method indicates extreme confidence */ + if (fisher_result > 0.999 && max_log_prob > -100) { + /* Large magnitude negative log prob means strong evidence */ + confidence = 0.90; + msg_debug_bayes("extreme negative log_prob (%g), setting high confidence", max_log_prob); + } } } + else { + confidence = normalized_probs[winning_class_idx]; + } + + /* Create and store multiclass result */ + result = g_new0(rspamd_multiclass_result_t, 1); + result->class_names = g_new(char *, cl.num_classes); + result->probabilities = g_new(double, cl.num_classes); + result->num_classes = cl.num_classes; + result->winning_class = cl.class_names[winning_class_idx]; /* Reference, not copy */ + result->confidence = confidence; + + for (i = 0; i < cl.num_classes; i++) { + result->class_names[i] = g_strdup(cl.class_names[i]); + result->probabilities[i] = normalized_probs[i]; + } + + rspamd_task_set_multiclass_result(task, result); + + msg_info_bayes("MULTICLASS_RESULT: winning_class='%s', confidence=%.3f, normalized_prob=%.3f, tokens=%uL", + cl.class_names[winning_class_idx], confidence, + normalized_probs[winning_class_idx], cl.processed_tokens); + + /* Insert symbol for winning class if confidence is significant */ + if (confidence > 0.05) { + char sumbuf[32]; + double final_prob = rspamd_normalize_probability(confidence, 0.5); + + rspamd_snprintf(sumbuf, sizeof(sumbuf), "%.2f%%", confidence * 100.0); + + /* Find the statfile for the winning class to get the symbol */ + for (i = 0; i < ctx->statfiles_ids->len; i++) { + id = g_array_index(ctx->statfiles_ids, int, i); + st = g_ptr_array_index(ctx->ctx->statfiles, id); + + if (st->stcf->class_name && + strcmp(st->stcf->class_name, cl.class_names[winning_class_idx]) == 0) { + msg_info_bayes("SYMBOL_INSERT: symbol='%s', final_prob=%.3f, confidence_display='%s'", + st->stcf->symbol, final_prob, sumbuf); + rspamd_task_insert_result(task, st->stcf->symbol, final_prob, sumbuf); + break; + } + } + + msg_debug_bayes("multiclass classification: winning class '%s' with " + "probability %.3f, confidence %.3f, %uL tokens processed", + cl.class_names[winning_class_idx], + normalized_probs[winning_class_idx], + confidence, cl.processed_tokens); + } + else { + msg_info_bayes("SYMBOL_SKIPPED: confidence=%.3f <= 0.05, no symbol inserted", confidence); + } + + return TRUE; } @@ -279,6 +670,37 @@ bayes_classify(struct rspamd_classifier *ctx, g_assert(ctx != NULL); g_assert(tokens != NULL); + /* Check if this is a multi-class classifier */ + msg_debug_bayes("classification check: class_names=%p, len=%uz", + ctx->cfg->class_names, + ctx->cfg->class_names ? ctx->cfg->class_names->len : 0); + + if (ctx->cfg->class_names && ctx->cfg->class_names->len >= 2) { + /* Verify that at least one statfile has class_name set (indicating new multi-class config) */ + gboolean has_class_names = FALSE; + for (i = 0; i < ctx->statfiles_ids->len; i++) { + int id = g_array_index(ctx->statfiles_ids, int, i); + struct rspamd_statfile *st = g_ptr_array_index(ctx->ctx->statfiles, id); + msg_debug_bayes("checking statfile %s: class_name=%s, is_spam_converted=%s", + st->stcf->symbol, + st->stcf->class_name ? st->stcf->class_name : "NULL", + st->stcf->is_spam_converted ? "true" : "false"); + if (st->stcf->class_name) { + has_class_names = TRUE; + } + } + + msg_debug_bayes("has_class_names=%s", has_class_names ? "true" : "false"); + + if (has_class_names) { + msg_debug_bayes("using multiclass classification with %ud classes", + (unsigned int) ctx->cfg->class_names->len); + return bayes_classify_multiclass(ctx, tokens, task); + } + } + + /* Fall back to binary classification */ + msg_debug_bayes("using binary classification"); memset(&cl, 0, sizeof(cl)); cl.task = task; @@ -286,14 +708,14 @@ bayes_classify(struct rspamd_classifier *ctx, if (ctx->cfg->min_learns > 0) { if (ctx->ham_learns < ctx->cfg->min_learns) { msg_info_task("not classified as ham. The ham class needs more " - "training samples. Currently: %ul; minimum %ud required", + "training samples. Currently: %uL; minimum %ud required", ctx->ham_learns, ctx->cfg->min_learns); return TRUE; } if (ctx->spam_learns < ctx->cfg->min_learns) { msg_info_task("not classified as spam. The spam class needs more " - "training samples. Currently: %ul; minimum %ud required", + "training samples. Currently: %uL; minimum %ud required", ctx->spam_learns, ctx->cfg->min_learns); return TRUE; @@ -374,7 +796,7 @@ bayes_classify(struct rspamd_classifier *ctx, final_prob = (s + 1.0 - h) / 2.; msg_debug_bayes( "got ham probability %.2f -> %.2f and spam probability %.2f -> %.2f," - " %L tokens processed of %ud total tokens;" + " %uL tokens processed of %ud total tokens;" " %uL text tokens found of %ud text tokens)", cl.ham_prob, h, @@ -549,3 +971,155 @@ bayes_learn_spam(struct rspamd_classifier *ctx, return TRUE; } + +gboolean +bayes_learn_class(struct rspamd_classifier *ctx, + GPtrArray *tokens, + struct rspamd_task *task, + const char *class_name, + gboolean unlearn, + GError **err) +{ + unsigned int i, j, total_cnt; + int id; + struct rspamd_statfile *st; + rspamd_token_t *tok; + gboolean incrementing; + unsigned int *class_counts = NULL; + struct rspamd_statfile **class_statfiles = NULL; + unsigned int num_classes = 0; + + g_assert(ctx != NULL); + g_assert(tokens != NULL); + g_assert(class_name != NULL); + + msg_info_bayes("LEARN_CLASS: class='%s', unlearn=%s, tokens=%ud", + class_name, unlearn ? "true" : "false", tokens->len); + + incrementing = ctx->cfg->flags & RSPAMD_FLAG_CLASSIFIER_INCREMENTING_BACKEND; + + /* Count classes and prepare arrays for multi-class learning */ + if (ctx->cfg->class_names && ctx->cfg->class_names->len > 0) { + num_classes = ctx->cfg->class_names->len; + class_counts = g_alloca(num_classes * sizeof(unsigned int)); + class_statfiles = g_alloca(num_classes * sizeof(struct rspamd_statfile *)); + memset(class_counts, 0, num_classes * sizeof(unsigned int)); + memset(class_statfiles, 0, num_classes * sizeof(struct rspamd_statfile *)); + } + + for (i = 0; i < tokens->len; i++) { + total_cnt = 0; + tok = g_ptr_array_index(tokens, i); + + /* Reset class counts for this token */ + if (num_classes > 0) { + memset(class_counts, 0, num_classes * sizeof(unsigned int)); + } + + for (j = 0; j < ctx->statfiles_ids->len; j++) { + id = g_array_index(ctx->statfiles_ids, int, j); + st = g_ptr_array_index(ctx->ctx->statfiles, id); + g_assert(st != NULL); + + /* Determine if this statfile matches our target class */ + gboolean is_target_class = FALSE; + if (st->stcf->class_name) { + /* Multi-class: exact class name match */ + is_target_class = (strcmp(st->stcf->class_name, class_name) == 0); + } + else { + /* Legacy binary: map class_name to spam/ham */ + if (strcmp(class_name, "spam") == 0 || strcmp(class_name, "S") == 0) { + is_target_class = st->stcf->is_spam; + } + else if (strcmp(class_name, "ham") == 0 || strcmp(class_name, "H") == 0) { + is_target_class = !st->stcf->is_spam; + } + } + + if (is_target_class) { + /* Learning: increment the target class */ + if (incrementing) { + tok->values[id] = 1; + } + else { + tok->values[id]++; + } + total_cnt += tok->values[id]; + + /* Track class counts for debugging */ + if (num_classes > 0) { + for (unsigned int k = 0; k < num_classes; k++) { + const char *check_class = (const char *) g_ptr_array_index(ctx->cfg->class_names, k); + if (st->stcf->class_name && strcmp(st->stcf->class_name, check_class) == 0) { + class_counts[k] += tok->values[id]; + class_statfiles[k] = st; + break; + } + } + } + } + else { + /* Unlearning: decrement other classes if unlearn flag is set */ + if (tok->values[id] > 0 && unlearn) { + if (incrementing) { + tok->values[id] = -1; + } + else { + tok->values[id]--; + } + total_cnt += tok->values[id]; + + /* Track class counts for debugging */ + if (num_classes > 0) { + for (unsigned int k = 0; k < num_classes; k++) { + const char *check_class = (const char *) g_ptr_array_index(ctx->cfg->class_names, k); + if (st->stcf->class_name && strcmp(st->stcf->class_name, check_class) == 0) { + class_counts[k] += tok->values[id]; + class_statfiles[k] = st; + break; + } + } + } + } + else if (incrementing) { + tok->values[id] = 0; + } + } + } + + /* Debug logging */ + if (tok->t1 && tok->t2) { + if (num_classes > 0) { + GString *debug_str = g_string_new(""); + for (unsigned int k = 0; k < num_classes; k++) { + const char *check_class = (const char *) g_ptr_array_index(ctx->cfg->class_names, k); + g_string_append_printf(debug_str, "%s:%d ", check_class, class_counts[k]); + } + msg_debug_bayes("token %uL <%*s:%*s>: window: %d, total_count: %d, " + "class_counts: %s", + tok->data, + (int) tok->t1->stemmed.len, tok->t1->stemmed.begin, + (int) tok->t2->stemmed.len, tok->t2->stemmed.begin, + tok->window_idx, total_cnt, debug_str->str); + g_string_free(debug_str, TRUE); + } + else { + msg_debug_bayes("token %uL <%*s:%*s>: window: %d, total_count: %d, " + "class: %s", + tok->data, + (int) tok->t1->stemmed.len, tok->t1->stemmed.begin, + (int) tok->t2->stemmed.len, tok->t2->stemmed.begin, + tok->window_idx, total_cnt, class_name); + } + } + else { + msg_debug_bayes("token %uL <?:?>: window: %d, total_count: %d, " + "class: %s", + tok->data, + tok->window_idx, total_cnt, class_name); + } + } + + return TRUE; +} diff --git a/src/libstat/classifiers/classifiers.h b/src/libstat/classifiers/classifiers.h index 22978e673..cab658146 100644 --- a/src/libstat/classifiers/classifiers.h +++ b/src/libstat/classifiers/classifiers.h @@ -54,6 +54,13 @@ struct rspamd_stat_classifier { gboolean unlearn, GError **err); + gboolean (*learn_class_func)(struct rspamd_classifier *ctx, + GPtrArray *input, + struct rspamd_task *task, + const char *class_name, + gboolean unlearn, + GError **err); + void (*fin_func)(struct rspamd_classifier *cl); }; @@ -73,6 +80,13 @@ gboolean bayes_learn_spam(struct rspamd_classifier *ctx, gboolean unlearn, GError **err); +gboolean bayes_learn_class(struct rspamd_classifier *ctx, + GPtrArray *tokens, + struct rspamd_task *task, + const char *class_name, + gboolean unlearn, + GError **err); + void bayes_fin(struct rspamd_classifier *); /* Generic lua classifier */ diff --git a/src/libstat/learn_cache/redis_cache.cxx b/src/libstat/learn_cache/redis_cache.cxx index 0de5cd094..afefeadcd 100644 --- a/src/libstat/learn_cache/redis_cache.cxx +++ b/src/libstat/learn_cache/redis_cache.cxx @@ -152,6 +152,33 @@ rspamd_stat_cache_redis_runtime(struct rspamd_task *task, return (void *) ctx; } +/* Get class ID using rspamd_cryptobox_fast_hash */ +static uint64_t +rspamd_stat_cache_get_class_id(const char *class_name) +{ + if (!class_name) { + return 0; + } + + if (strcmp(class_name, "spam") == 0 || strcmp(class_name, "S") == 0) { + return 1; + } + else if (strcmp(class_name, "ham") == 0 || strcmp(class_name, "H") == 0) { + return 0; + } + else { + /* For other classes, use rspamd_cryptobox_fast_hash */ + uint64_t hash = rspamd_cryptobox_fast_hash(class_name, strlen(class_name), 0); + + /* Ensure we don't get 0 or 1 (reserved for ham/spam) */ + if (hash == 0 || hash == 1) { + hash += 2; + } + + return hash; + } +} + static int rspamd_stat_cache_checked(lua_State *L) { @@ -161,23 +188,39 @@ rspamd_stat_cache_checked(lua_State *L) if (res) { auto val = lua_tointeger(L, 3); - if ((val > 0 && (task->flags & RSPAMD_TASK_FLAG_LEARN_SPAM)) || - (val <= 0 && (task->flags & RSPAMD_TASK_FLAG_LEARN_HAM))) { - /* Already learned */ - msg_info_task("<%s> has been already " - "learned as %s, ignore it", - MESSAGE_FIELD(task, message_id), - (task->flags & RSPAMD_TASK_FLAG_LEARN_SPAM) ? "spam" : "ham"); - task->flags |= RSPAMD_TASK_FLAG_ALREADY_LEARNED; + /* Get the class being learned */ + const char *autolearn_class = rspamd_task_get_autolearn_class(task); + if (!autolearn_class) { + /* Fallback to binary flags for backward compatibility */ + if (task->flags & RSPAMD_TASK_FLAG_LEARN_SPAM) { + autolearn_class = "spam"; + } + else if (task->flags & RSPAMD_TASK_FLAG_LEARN_HAM) { + autolearn_class = "ham"; + } } - else { - /* Unlearn flag */ - task->flags |= RSPAMD_TASK_FLAG_UNLEARN; + + if (autolearn_class) { + uint64_t expected_id = rspamd_stat_cache_get_class_id(autolearn_class); + + if ((uint64_t) val == expected_id) { + /* Already learned */ + msg_info_task("<%s> has been already " + "learned as %s, ignore it", + MESSAGE_FIELD(task, message_id), + autolearn_class); + task->flags |= RSPAMD_TASK_FLAG_ALREADY_LEARNED; + } + else { + /* Different class learned, unlearn flag */ + msg_debug_task("<%s> cached value %ld != expected %lu for class %s, will unlearn", + MESSAGE_FIELD(task, message_id), + val, expected_id, autolearn_class); + task->flags |= RSPAMD_TASK_FLAG_UNLEARN; + } } } - /* Ignore errors for now, as we can do nothing about them at the moment */ - return 0; } @@ -235,9 +278,20 @@ int rspamd_stat_cache_redis_learn(struct rspamd_task *task, lua_rawgeti(L, LUA_REGISTRYINDEX, ctx->learn_ref); rspamd_lua_task_push(L, task); lua_pushstring(L, h); - lua_pushboolean(L, is_spam); - if (lua_pcall(L, 3, 0, err_idx) != 0) { + /* Get the class being learned - prefer multiclass over binary */ + const char *autolearn_class = rspamd_task_get_autolearn_class(task); + if (!autolearn_class) { + /* Fallback to binary flag for backward compatibility */ + autolearn_class = is_spam ? "spam" : "ham"; + } + + /* Push class name and class ID */ + lua_pushstring(L, autolearn_class); + uint64_t class_id = rspamd_stat_cache_get_class_id(autolearn_class); + lua_pushinteger(L, class_id); + + if (lua_pcall(L, 4, 0, err_idx) != 0) { msg_err_task("call to redis failed: %s", lua_tostring(L, -1)); lua_settop(L, err_idx - 1); return RSPAMD_LEARN_IGNORE; diff --git a/src/libstat/stat_api.h b/src/libstat/stat_api.h index 811566ad3..aa6111a8b 100644 --- a/src/libstat/stat_api.h +++ b/src/libstat/stat_api.h @@ -108,6 +108,23 @@ rspamd_stat_result_t rspamd_stat_learn(struct rspamd_task *task, GError **err); /** + * Learn task as a specific class, task must be processed prior to this call + * @param task task to learn + * @param class_name name of the class to learn (e.g., "spam", "ham", "transactional") + * @param L lua state + * @param classifier NULL to learn all classifiers, name to learn a specific one + * @param stage learning stage + * @param err error returned + * @return TRUE if task has been learned + */ +rspamd_stat_result_t rspamd_stat_learn_class(struct rspamd_task *task, + const char *class_name, + lua_State *L, + const char *classifier, + unsigned int stage, + GError **err); + +/** * Get the overall statistics for all statfile backends * @param cfg configuration * @param total_learns the total number of learns is stored here @@ -120,6 +137,43 @@ rspamd_stat_result_t rspamd_stat_statistics(struct rspamd_task *task, void rspamd_stat_unload(void); +/** + * Multi-class classification result structure + */ +typedef struct { + char **class_names; /**< Array of class names */ + double *probabilities; /**< Array of probabilities for each class */ + unsigned int num_classes; /**< Number of classes */ + const char *winning_class; /**< Name of the winning class (reference, not owned) */ + double confidence; /**< Confidence of the winning class */ +} rspamd_multiclass_result_t; + +/** + * Set multi-class classification result for a task + */ +void rspamd_task_set_multiclass_result(struct rspamd_task *task, + rspamd_multiclass_result_t *result); + +/** + * Get multi-class classification result from a task + */ +rspamd_multiclass_result_t *rspamd_task_get_multiclass_result(struct rspamd_task *task); + +/** + * Free multi-class result structure + */ +void rspamd_multiclass_result_free(rspamd_multiclass_result_t *result); + +/** + * Set autolearn class for a task + */ +void rspamd_task_set_autolearn_class(struct rspamd_task *task, const char *class_name); + +/** + * Get autolearn class from a task + */ +const char *rspamd_task_get_autolearn_class(struct rspamd_task *task); + #ifdef __cplusplus } #endif diff --git a/src/libstat/stat_config.c b/src/libstat/stat_config.c index 8a5313df2..5ada7d468 100644 --- a/src/libstat/stat_config.c +++ b/src/libstat/stat_config.c @@ -28,6 +28,7 @@ static struct rspamd_stat_classifier lua_classifier = { .init_func = lua_classifier_init, .classify_func = lua_classifier_classify, .learn_spam_func = lua_classifier_learn_spam, + .learn_class_func = NULL, /* TODO: implement lua multi-class learning */ .fin_func = NULL, }; @@ -37,6 +38,7 @@ static struct rspamd_stat_classifier stat_classifiers[] = { .init_func = bayes_init, .classify_func = bayes_classify, .learn_spam_func = bayes_learn_spam, + .learn_class_func = bayes_learn_class, .fin_func = bayes_fin, }}; @@ -68,8 +70,7 @@ static struct rspamd_stat_tokenizer stat_tokenizers[] = { .dec_learns = rspamd_##eltn##_dec_learns, \ .get_stat = rspamd_##eltn##_get_stat, \ .load_tokenizer_config = rspamd_##eltn##_load_tokenizer_config, \ - .close = rspamd_##eltn##_close \ - } + .close = rspamd_##eltn##_close} #define RSPAMD_STAT_BACKEND_ELT_READONLY(nam, eltn) \ { \ .name = #nam, \ @@ -85,8 +86,7 @@ static struct rspamd_stat_tokenizer stat_tokenizers[] = { .dec_learns = NULL, \ .get_stat = rspamd_##eltn##_get_stat, \ .load_tokenizer_config = rspamd_##eltn##_load_tokenizer_config, \ - .close = rspamd_##eltn##_close \ - } + .close = rspamd_##eltn##_close} static struct rspamd_stat_backend stat_backends[] = { RSPAMD_STAT_BACKEND_ELT(mmap, mmaped_file), @@ -101,8 +101,7 @@ static struct rspamd_stat_backend stat_backends[] = { .runtime = rspamd_stat_cache_##eltn##_runtime, \ .check = rspamd_stat_cache_##eltn##_check, \ .learn = rspamd_stat_cache_##eltn##_learn, \ - .close = rspamd_stat_cache_##eltn##_close \ - } + .close = rspamd_stat_cache_##eltn##_close} static struct rspamd_stat_cache stat_caches[] = { RSPAMD_STAT_CACHE_ELT(sqlite3, sqlite3), diff --git a/src/libstat/stat_process.c b/src/libstat/stat_process.c index 176064087..11b31decc 100644 --- a/src/libstat/stat_process.c +++ b/src/libstat/stat_process.c @@ -32,6 +32,78 @@ static const double similarity_threshold = 80.0; +void rspamd_task_set_multiclass_result(struct rspamd_task *task, rspamd_multiclass_result_t *result) +{ + g_assert(task != NULL); + g_assert(result != NULL); + + rspamd_mempool_set_variable(task->task_pool, "multiclass_bayes_result", result, + (rspamd_mempool_destruct_t) rspamd_multiclass_result_free); +} + +rspamd_multiclass_result_t * +rspamd_task_get_multiclass_result(struct rspamd_task *task) +{ + g_assert(task != NULL); + + return (rspamd_multiclass_result_t *) rspamd_mempool_get_variable(task->task_pool, + "multiclass_bayes_result"); +} + +void rspamd_multiclass_result_free(rspamd_multiclass_result_t *result) +{ + if (result == NULL) { + return; + } + + g_free(result->class_names); + g_free(result->probabilities); + /* winning_class is a reference, not owned - don't free */ + g_free(result); +} + +void rspamd_task_set_autolearn_class(struct rspamd_task *task, const char *class_name) +{ + g_assert(task != NULL); + g_assert(class_name != NULL); + + /* Store the class name in the mempool */ + const char *class_name_copy = rspamd_mempool_strdup(task->task_pool, class_name); + rspamd_mempool_set_variable(task->task_pool, "autolearn_class", + (gpointer) class_name_copy, NULL); + + /* Set the appropriate flags */ + task->flags |= RSPAMD_TASK_FLAG_LEARN_CLASS; + + /* For backward compatibility, also set binary flags */ + if (strcmp(class_name, "spam") == 0 || strcmp(class_name, "S") == 0) { + task->flags |= RSPAMD_TASK_FLAG_LEARN_SPAM; + } + else if (strcmp(class_name, "ham") == 0 || strcmp(class_name, "H") == 0) { + task->flags |= RSPAMD_TASK_FLAG_LEARN_HAM; + } +} + +const char * +rspamd_task_get_autolearn_class(struct rspamd_task *task) +{ + g_assert(task != NULL); + + if (task->flags & RSPAMD_TASK_FLAG_LEARN_CLASS) { + return (const char *) rspamd_mempool_get_variable(task->task_pool, "autolearn_class"); + } + + /* Fallback to binary flags for backward compatibility */ + if (task->flags & RSPAMD_TASK_FLAG_LEARN_SPAM) { + return "spam"; + } + else if (task->flags & RSPAMD_TASK_FLAG_LEARN_HAM) { + return "ham"; + } + + return NULL; +} + static void rspamd_stat_tokenize_parts_metadata(struct rspamd_stat_ctx *st_ctx, struct rspamd_task *task) @@ -394,18 +466,9 @@ rspamd_stat_classifiers_process(struct rspamd_stat_ctx *st_ctx, } /* - * Do not classify a message if some class is missing + * Multi-class approach: don't check for missing classes + * Missing tokens naturally result in 0 probability */ - if (!(task->flags & RSPAMD_TASK_FLAG_HAS_SPAM_TOKENS)) { - msg_info_task("skip statistics as SPAM class is missing"); - - return; - } - if (!(task->flags & RSPAMD_TASK_FLAG_HAS_HAM_TOKENS)) { - msg_info_task("skip statistics as HAM class is missing"); - - return; - } for (i = 0; i < st_ctx->classifiers->len; i++) { cl = g_ptr_array_index(st_ctx->classifiers, i); @@ -565,7 +628,24 @@ rspamd_stat_cache_check(struct rspamd_stat_ctx *st_ctx, if (sel->cache && sel->cachecf) { rt = cl->cache->runtime(task, sel->cachecf, FALSE); - learn_res = cl->cache->check(task, spam, rt); + + /* For multi-class learning, determine spam boolean from class name if available */ + gboolean cache_spam = spam; /* Default to original spam parameter */ + const char *autolearn_class = rspamd_task_get_autolearn_class(task); + if (autolearn_class) { + if (strcmp(autolearn_class, "spam") == 0 || strcmp(autolearn_class, "S") == 0) { + cache_spam = TRUE; + } + else if (strcmp(autolearn_class, "ham") == 0 || strcmp(autolearn_class, "H") == 0) { + cache_spam = FALSE; + } + else { + /* For other classes, use a heuristic or default to spam for cache purposes */ + cache_spam = TRUE; /* Non-ham classes are treated as spam for cache */ + } + } + + learn_res = cl->cache->check(task, cache_spam, rt); } if (learn_res == RSPAMD_LEARN_IGNORE) { @@ -658,9 +738,63 @@ rspamd_stat_classifiers_learn(struct rspamd_stat_ctx *st_ctx, continue; } - if (cl->subrs->learn_spam_func(cl, task->tokens, task, spam, - task->flags & RSPAMD_TASK_FLAG_UNLEARN, err)) { - learned = TRUE; + /* Check if classifier supports multi-class learning and if we should use it */ + if (cl->subrs->learn_class_func && cl->cfg->class_names && cl->cfg->class_names->len > 2) { + /* Multi-class learning: determine class name from task flags or autolearn result */ + const char *class_name = NULL; + + if (task->flags & RSPAMD_TASK_FLAG_LEARN_SPAM) { + /* Find spam class name */ + for (unsigned int k = 0; k < cl->cfg->class_names->len; k++) { + const char *check_class = (const char *) g_ptr_array_index(cl->cfg->class_names, k); + /* Look for statfile with this class that is spam */ + GList *cur = cl->cfg->statfiles; + while (cur) { + struct rspamd_statfile_config *stcf = (struct rspamd_statfile_config *) cur->data; + if (stcf->class_name && strcmp(stcf->class_name, check_class) == 0 && stcf->is_spam) { + class_name = check_class; + break; + } + cur = g_list_next(cur); + } + if (class_name) break; + } + if (!class_name) class_name = "spam"; /* fallback */ + } + else if (task->flags & RSPAMD_TASK_FLAG_LEARN_HAM) { + /* Find ham class name */ + for (unsigned int k = 0; k < cl->cfg->class_names->len; k++) { + const char *check_class = (const char *) g_ptr_array_index(cl->cfg->class_names, k); + /* Look for statfile with this class that is ham */ + GList *cur = cl->cfg->statfiles; + while (cur) { + struct rspamd_statfile_config *stcf = (struct rspamd_statfile_config *) cur->data; + if (stcf->class_name && strcmp(stcf->class_name, check_class) == 0 && !stcf->is_spam) { + class_name = check_class; + break; + } + cur = g_list_next(cur); + } + if (class_name) break; + } + if (!class_name) class_name = "ham"; /* fallback */ + } + else { + /* Fallback to spam/ham based on the spam parameter */ + class_name = spam ? "spam" : "ham"; + } + + if (cl->subrs->learn_class_func(cl, task->tokens, task, class_name, + task->flags & RSPAMD_TASK_FLAG_UNLEARN, err)) { + learned = TRUE; + } + } + else { + /* Binary learning: use existing function */ + if (cl->subrs->learn_spam_func(cl, task->tokens, task, spam, + task->flags & RSPAMD_TASK_FLAG_UNLEARN, err)) { + learned = TRUE; + } } } @@ -759,9 +893,26 @@ rspamd_stat_backends_learn(struct rspamd_stat_ctx *st_ctx, backend_found = TRUE; if (!(task->flags & RSPAMD_TASK_FLAG_UNLEARN)) { - if (!!spam != !!st->stcf->is_spam) { - /* If we are not unlearning, then do not touch another class */ - continue; + /* For multiclass learning, check if this statfile has any tokens to learn */ + if (task->flags & RSPAMD_TASK_FLAG_LEARN_CLASS) { + /* Multiclass learning: only process statfiles that have tokens set up by the classifier */ + gboolean has_tokens = FALSE; + for (unsigned int k = 0; k < task->tokens->len && !has_tokens; k++) { + rspamd_token_t *tok = (rspamd_token_t *) g_ptr_array_index(task->tokens, k); + if (tok->values[id] != 0) { + has_tokens = TRUE; + } + } + if (!has_tokens) { + continue; + } + } + else { + /* Binary learning: use traditional spam/ham check */ + if (!!spam != !!st->stcf->is_spam) { + /* If we are not unlearning, then do not touch another class */ + continue; + } } } @@ -870,7 +1021,24 @@ rspamd_stat_backends_post_learn(struct rspamd_stat_ctx *st_ctx, if (cl->cache) { cache_run = cl->cache->runtime(task, cl->cachecf, TRUE); - cl->cache->learn(task, spam, cache_run); + + /* For multi-class learning, determine spam boolean from class name if available */ + gboolean cache_spam = spam; /* Default to original spam parameter */ + const char *autolearn_class = rspamd_task_get_autolearn_class(task); + if (autolearn_class) { + if (strcmp(autolearn_class, "spam") == 0 || strcmp(autolearn_class, "S") == 0) { + cache_spam = TRUE; + } + else if (strcmp(autolearn_class, "ham") == 0 || strcmp(autolearn_class, "H") == 0) { + cache_spam = FALSE; + } + else { + /* For other classes, use a heuristic or default to spam for cache purposes */ + cache_spam = TRUE; /* Non-ham classes are treated as spam for cache */ + } + } + + cl->cache->learn(task, cache_spam, cache_run); } } @@ -879,6 +1047,218 @@ rspamd_stat_backends_post_learn(struct rspamd_stat_ctx *st_ctx, return res; } +static gboolean +rspamd_stat_classifiers_learn_class(struct rspamd_stat_ctx *st_ctx, + struct rspamd_task *task, + const char *classifier, + const char *class_name, + GError **err) +{ + struct rspamd_classifier *cl, *sel = NULL; + unsigned int i; + gboolean learned = FALSE, too_small = FALSE, too_large = FALSE; + + if ((task->flags & RSPAMD_TASK_FLAG_ALREADY_LEARNED) && err != NULL && + *err == NULL) { + /* Do not learn twice */ + g_set_error(err, rspamd_stat_quark(), 208, "<%s> has been already " + "learned as %s, ignore it", + MESSAGE_FIELD(task, message_id), + class_name); + + return FALSE; + } + + /* Check whether we have learned that file */ + for (i = 0; i < st_ctx->classifiers->len; i++) { + cl = g_ptr_array_index(st_ctx->classifiers, i); + + /* Skip other classifiers if they are not needed */ + if (classifier != NULL && (cl->cfg->name == NULL || + g_ascii_strcasecmp(classifier, cl->cfg->name) != 0)) { + continue; + } + + sel = cl; + + /* Now check max and min tokens */ + if (cl->cfg->min_tokens > 0 && task->tokens->len < cl->cfg->min_tokens) { + msg_info_task( + "<%s> contains less tokens than required for %s classifier: " + "%ud < %ud", + MESSAGE_FIELD(task, message_id), + cl->cfg->name, + task->tokens->len, + cl->cfg->min_tokens); + too_small = TRUE; + continue; + } + else if (cl->cfg->max_tokens > 0 && task->tokens->len > cl->cfg->max_tokens) { + msg_info_task( + "<%s> contains more tokens than allowed for %s classifier: " + "%ud > %ud", + MESSAGE_FIELD(task, message_id), + cl->cfg->name, + task->tokens->len, + cl->cfg->max_tokens); + too_large = TRUE; + continue; + } + + /* Use the new multi-class learning function if available */ + if (cl->subrs->learn_class_func) { + if (cl->subrs->learn_class_func(cl, task->tokens, task, class_name, + task->flags & RSPAMD_TASK_FLAG_UNLEARN, err)) { + learned = TRUE; + } + } + else { + /* Fallback to binary learning with class name mapping */ + gboolean is_spam; + if (strcmp(class_name, "spam") == 0 || strcmp(class_name, "S") == 0) { + is_spam = TRUE; + } + else if (strcmp(class_name, "ham") == 0 || strcmp(class_name, "H") == 0) { + is_spam = FALSE; + } + else { + /* For unknown classes with binary classifier, skip */ + msg_info_task("skipping class '%s' for binary classifier %s", + class_name, cl->cfg->name); + continue; + } + + if (cl->subrs->learn_spam_func(cl, task->tokens, task, is_spam, + task->flags & RSPAMD_TASK_FLAG_UNLEARN, err)) { + learned = TRUE; + } + } + } + + if (sel == NULL) { + if (classifier) { + g_set_error(err, rspamd_stat_quark(), 404, "cannot find classifier " + "with name %s", + classifier); + } + else { + g_set_error(err, rspamd_stat_quark(), 404, "no classifiers defined"); + } + + return FALSE; + } + + if (!learned && err && *err == NULL) { + if (too_large) { + g_set_error(err, rspamd_stat_quark(), 204, + "<%s> contains more tokens than allowed for %s classifier: " + "%d > %d", + MESSAGE_FIELD(task, message_id), + sel->cfg->name, + task->tokens->len, + sel->cfg->max_tokens); + } + else if (too_small) { + g_set_error(err, rspamd_stat_quark(), 204, + "<%s> contains less tokens than required for %s classifier: " + "%d < %d", + MESSAGE_FIELD(task, message_id), + sel->cfg->name, + task->tokens->len, + sel->cfg->min_tokens); + } + } + + return learned; +} + +rspamd_stat_result_t +rspamd_stat_learn_class(struct rspamd_task *task, + const char *class_name, + lua_State *L, + const char *classifier, + unsigned int stage, + GError **err) +{ + struct rspamd_stat_ctx *st_ctx; + rspamd_stat_result_t ret = RSPAMD_STAT_PROCESS_OK; + + /* + * We assume now that a task has been already classified before + * coming to learn + */ + g_assert(RSPAMD_TASK_IS_CLASSIFIED(task)); + + st_ctx = rspamd_stat_get_ctx(); + g_assert(st_ctx != NULL); + + msg_debug_bayes("learn class stage %d has been called for class '%s'", stage, class_name); + + if (st_ctx->classifiers->len == 0) { + msg_debug_bayes("no classifiers defined"); + task->processed_stages |= stage; + return ret; + } + + if (task->message == NULL) { + ret = RSPAMD_STAT_PROCESS_ERROR; + if (err && *err == NULL) { + g_set_error(err, rspamd_stat_quark(), 500, + "Trying to learn an empty message"); + } + + task->processed_stages |= stage; + return ret; + } + + if (stage == RSPAMD_TASK_STAGE_LEARN_PRE) { + /* Process classifiers - determine spam boolean for compatibility */ + gboolean spam = (strcmp(class_name, "spam") == 0 || strcmp(class_name, "S") == 0); + rspamd_stat_preprocess(st_ctx, task, TRUE, spam); + + if (!rspamd_stat_cache_check(st_ctx, task, classifier, spam, err)) { + msg_debug_bayes("cache check failed, skip learning"); + return RSPAMD_STAT_PROCESS_ERROR; + } + } + else if (stage == RSPAMD_TASK_STAGE_LEARN) { + /* Process classifiers */ + if (!rspamd_stat_classifiers_learn_class(st_ctx, task, classifier, + class_name, err)) { + if (err && *err == NULL) { + g_set_error(err, rspamd_stat_quark(), 500, + "Unknown statistics error, found when learning classifiers;" + " classifier: %s", + task->classifier); + } + return RSPAMD_STAT_PROCESS_ERROR; + } + + /* Process backends - determine spam boolean for compatibility */ + gboolean spam = (strcmp(class_name, "spam") == 0 || strcmp(class_name, "S") == 0); + if (!rspamd_stat_backends_learn(st_ctx, task, classifier, spam, err)) { + if (err && *err == NULL) { + g_set_error(err, rspamd_stat_quark(), 500, + "Unknown statistics error, found when storing data on backend;" + " classifier: %s", + task->classifier); + } + return RSPAMD_STAT_PROCESS_ERROR; + } + } + else if (stage == RSPAMD_TASK_STAGE_LEARN_POST) { + /* Process backends - determine spam boolean for compatibility */ + gboolean spam = (strcmp(class_name, "spam") == 0 || strcmp(class_name, "S") == 0); + if (!rspamd_stat_backends_post_learn(st_ctx, task, classifier, spam, err)) { + return RSPAMD_STAT_PROCESS_ERROR; + } + } + + task->processed_stages |= stage; + + return ret; +} + rspamd_stat_result_t rspamd_stat_learn(struct rspamd_task *task, gboolean spam, lua_State *L, const char *classifier, unsigned int stage, @@ -1039,12 +1419,11 @@ rspamd_stat_check_autolearn(struct rspamd_task *task) if (mres) { if (mres->score > rspamd_task_get_required_score(task, mres)) { - task->flags |= RSPAMD_TASK_FLAG_LEARN_SPAM; - + rspamd_task_set_autolearn_class(task, "spam"); ret = TRUE; } else if (mres->score < 0) { - task->flags |= RSPAMD_TASK_FLAG_LEARN_HAM; + rspamd_task_set_autolearn_class(task, "ham"); ret = TRUE; } } @@ -1076,12 +1455,11 @@ rspamd_stat_check_autolearn(struct rspamd_task *task) if (mres) { if (mres->score >= spam_score) { - task->flags |= RSPAMD_TASK_FLAG_LEARN_SPAM; - + rspamd_task_set_autolearn_class(task, "spam"); ret = TRUE; } else if (mres->score <= ham_score) { - task->flags |= RSPAMD_TASK_FLAG_LEARN_HAM; + rspamd_task_set_autolearn_class(task, "ham"); ret = TRUE; } } @@ -1117,11 +1495,16 @@ rspamd_stat_check_autolearn(struct rspamd_task *task) /* We can have immediate results */ if (lua_ret) { if (strcmp(lua_ret, "ham") == 0) { - task->flags |= RSPAMD_TASK_FLAG_LEARN_HAM; + rspamd_task_set_autolearn_class(task, "ham"); ret = TRUE; } else if (strcmp(lua_ret, "spam") == 0) { - task->flags |= RSPAMD_TASK_FLAG_LEARN_SPAM; + rspamd_task_set_autolearn_class(task, "spam"); + ret = TRUE; + } + else { + /* Multi-class: any other class name */ + rspamd_task_set_autolearn_class(task, lua_ret); ret = TRUE; } } @@ -1139,79 +1522,138 @@ rspamd_stat_check_autolearn(struct rspamd_task *task) } } else if (ucl_object_type(obj) == UCL_OBJECT) { - /* Try to find autolearn callback */ - if (cl->autolearn_cbref == 0) { - /* We don't have preprocessed cb id, so try to get it */ - if (!rspamd_lua_require_function(L, "lua_bayes_learn", - "autolearn")) { - msg_err_task("cannot get autolearn library from " - "`lua_bayes_learn`"); - } - else { - cl->autolearn_cbref = luaL_ref(L, LUA_REGISTRYINDEX); + /* Check if this is a multi-class autolearn configuration */ + const ucl_object_t *multiclass_obj = ucl_object_lookup(obj, "multiclass"); + + if (multiclass_obj && ucl_object_type(multiclass_obj) == UCL_OBJECT) { + /* Multi-class threshold-based autolearn */ + const ucl_object_t *thresholds_obj = ucl_object_lookup(multiclass_obj, "thresholds"); + + if (thresholds_obj && ucl_object_type(thresholds_obj) == UCL_OBJECT) { + /* Iterate through class thresholds */ + ucl_object_iter_t it = NULL; + const ucl_object_t *class_obj; + const char *class_name; + + while ((class_obj = ucl_object_iterate(thresholds_obj, &it, true))) { + class_name = ucl_object_key(class_obj); + + if (class_name && ucl_object_type(class_obj) == UCL_ARRAY && class_obj->len == 2) { + /* [min_score, max_score] for this class */ + const ucl_object_t *min_elt = ucl_array_find_index(class_obj, 0); + const ucl_object_t *max_elt = ucl_array_find_index(class_obj, 1); + + if ((ucl_object_type(min_elt) == UCL_FLOAT || ucl_object_type(min_elt) == UCL_INT) && + (ucl_object_type(max_elt) == UCL_FLOAT || ucl_object_type(max_elt) == UCL_INT)) { + + double min_score = ucl_object_todouble(min_elt); + double max_score = ucl_object_todouble(max_elt); + + if (mres && mres->score >= min_score && mres->score <= max_score) { + rspamd_task_set_autolearn_class(task, class_name); + ret = TRUE; + msg_debug_bayes("multiclass autolearn: score %.2f matches class '%s' [%.2f, %.2f]", + mres->score, class_name, min_score, max_score); + break; /* Stop at first matching class */ + } + } + } + } } } - - if (cl->autolearn_cbref != -1) { - lua_pushcfunction(L, &rspamd_lua_traceback); - err_idx = lua_gettop(L); - lua_rawgeti(L, LUA_REGISTRYINDEX, cl->autolearn_cbref); - - ptask = lua_newuserdata(L, sizeof(struct rspamd_task *)); - *ptask = task; - rspamd_lua_setclass(L, rspamd_task_classname, -1); - /* Push the whole object as well */ - ucl_object_push_lua(L, obj, true); - - if (lua_pcall(L, 2, 1, err_idx) != 0) { - msg_err_task("call to autolearn script failed: " - "%s", - lua_tostring(L, -1)); + else { + /* Try to find autolearn callback */ + if (cl->autolearn_cbref == 0) { + /* We don't have preprocessed cb id, so try to get it */ + if (!rspamd_lua_require_function(L, "lua_bayes_learn", + "autolearn")) { + msg_err_task("cannot get autolearn library from " + "`lua_bayes_learn`"); + } + else { + cl->autolearn_cbref = luaL_ref(L, LUA_REGISTRYINDEX); + } } - else { - lua_ret = lua_tostring(L, -1); - if (lua_ret) { - if (strcmp(lua_ret, "ham") == 0) { - task->flags |= RSPAMD_TASK_FLAG_LEARN_HAM; - ret = TRUE; - } - else if (strcmp(lua_ret, "spam") == 0) { - task->flags |= RSPAMD_TASK_FLAG_LEARN_SPAM; - ret = TRUE; + if (cl->autolearn_cbref != -1) { + lua_pushcfunction(L, &rspamd_lua_traceback); + err_idx = lua_gettop(L); + lua_rawgeti(L, LUA_REGISTRYINDEX, cl->autolearn_cbref); + + ptask = lua_newuserdata(L, sizeof(struct rspamd_task *)); + *ptask = task; + rspamd_lua_setclass(L, rspamd_task_classname, -1); + /* Push the whole object as well */ + ucl_object_push_lua(L, obj, true); + + if (lua_pcall(L, 2, 1, err_idx) != 0) { + msg_err_task("call to autolearn script failed: " + "%s", + lua_tostring(L, -1)); + } + else { + lua_ret = lua_tostring(L, -1); + + if (lua_ret) { + if (strcmp(lua_ret, "ham") == 0) { + rspamd_task_set_autolearn_class(task, "ham"); + ret = TRUE; + } + else if (strcmp(lua_ret, "spam") == 0) { + rspamd_task_set_autolearn_class(task, "spam"); + ret = TRUE; + } + else { + /* Multi-class: any other class name */ + rspamd_task_set_autolearn_class(task, lua_ret); + ret = TRUE; + } } } - } - lua_settop(L, err_idx - 1); + lua_settop(L, err_idx - 1); + } } - } - if (ret) { - /* Do not autolearn if we have this symbol already */ - if (rspamd_stat_has_classifier_symbols(task, mres, cl)) { - ret = FALSE; - task->flags &= ~(RSPAMD_TASK_FLAG_LEARN_HAM | - RSPAMD_TASK_FLAG_LEARN_SPAM); - } - else if (mres != NULL) { - if (task->flags & RSPAMD_TASK_FLAG_LEARN_HAM) { - msg_info_task("<%s>: autolearn ham for classifier " - "'%s' as message's " - "score is negative: %.2f", - MESSAGE_FIELD(task, message_id), cl->cfg->name, - mres->score); - } - else { - msg_info_task("<%s>: autolearn spam for classifier " - "'%s' as message's " - "action is reject, score: %.2f", - MESSAGE_FIELD(task, message_id), cl->cfg->name, - mres->score); + if (ret) { + /* Do not autolearn if we have this symbol already */ + if (rspamd_stat_has_classifier_symbols(task, mres, cl)) { + ret = FALSE; + task->flags &= ~(RSPAMD_TASK_FLAG_LEARN_HAM | + RSPAMD_TASK_FLAG_LEARN_SPAM | + RSPAMD_TASK_FLAG_LEARN_CLASS); + /* Clear the autolearn class from mempool */ + rspamd_mempool_set_variable(task->task_pool, "autolearn_class", NULL, NULL); } + else if (mres != NULL) { + const char *autolearn_class = rspamd_task_get_autolearn_class(task); + + if (autolearn_class) { + if (strcmp(autolearn_class, "ham") == 0) { + msg_info_task("<%s>: autolearn ham for classifier " + "'%s' as message's " + "score is negative: %.2f", + MESSAGE_FIELD(task, message_id), cl->cfg->name, + mres->score); + } + else if (strcmp(autolearn_class, "spam") == 0) { + msg_info_task("<%s>: autolearn spam for classifier " + "'%s' as message's " + "action is reject, score: %.2f", + MESSAGE_FIELD(task, message_id), cl->cfg->name, + mres->score); + } + else { + msg_info_task("<%s>: autolearn class '%s' for classifier " + "'%s', score: %.2f", + MESSAGE_FIELD(task, message_id), autolearn_class, + cl->cfg->name, mres->score); + } + } - task->classifier = cl->cfg->name; - break; + task->classifier = cl->cfg->name; + break; + } } } } diff --git a/src/plugins/lua/bayes_expiry.lua b/src/plugins/lua/bayes_expiry.lua index 44ff9dafa..0d78f2272 100644 --- a/src/plugins/lua/bayes_expiry.lua +++ b/src/plugins/lua/bayes_expiry.lua @@ -41,32 +41,38 @@ local template = {} local function check_redis_classifier(cls, cfg) -- Skip old classifiers if cls.new_schema then - local symbol_spam, symbol_ham + local class_symbols = {} + local class_labels = {} local expiry = (cls.expiry or cls.expire) if type(expiry) == 'table' then expiry = expiry[1] end - -- Load symbols from statfiles + -- Extract class_labels mapping from classifier config + if cls.class_labels then + class_labels = cls.class_labels + end + -- Load symbols from statfiles for multi-class support local function check_statfile_table(tbl, def_sym) local symbol = tbl.symbol or def_sym - - local spam - if tbl.spam then - spam = tbl.spam - else - if string.match(symbol:upper(), 'SPAM') then - spam = true + local class_name = tbl.class + + -- Handle legacy spam/ham detection for backward compatibility + if not class_name then + if tbl.spam ~= nil then + class_name = tbl.spam and 'spam' or 'ham' + elseif string.match(tostring(symbol):upper(), 'SPAM') then + class_name = 'spam' + elseif string.match(tostring(symbol):upper(), 'HAM') then + class_name = 'ham' else - spam = false + class_name = def_sym end end - if spam then - symbol_spam = symbol - else - symbol_ham = symbol + if class_name then + class_symbols[class_name] = symbol end end @@ -87,10 +93,9 @@ local function check_redis_classifier(cls, cfg) end end - if not symbol_spam or not symbol_ham or type(expiry) ~= 'number' then + if next(class_symbols) == nil or type(expiry) ~= 'number' then logger.debugm(N, rspamd_config, - 'disable expiry for classifier %s: no expiry %s', - symbol_spam, cls) + 'disable expiry for classifier: no class symbols or expiry configured') return end -- Now try to load redis_params if needed @@ -108,17 +113,16 @@ local function check_redis_classifier(cls, cfg) end if redis_params['read_only'] then - logger.infox(rspamd_config, 'disable expiry for classifier %s: read only redis configuration', - symbol_spam) + logger.infox(rspamd_config, 'disable expiry for classifier: read only redis configuration') return end - logger.debugm(N, rspamd_config, "enabled expiry for %s/%s -> %s expiry", - symbol_spam, symbol_ham, expiry) + logger.debugm(N, rspamd_config, "enabled expiry for classes %s -> %s expiry", + table.concat(lutil.keys(class_symbols), ', '), expiry) table.insert(settings.classifiers, { - symbol_spam = symbol_spam, - symbol_ham = symbol_ham, + class_symbols = class_symbols, + class_labels = class_labels, redis_params = redis_params, expiry = expiry }) @@ -249,12 +253,11 @@ local expiry_script = [[ local keys = ret[2] local tokens = {} - -- Tokens occurrences distribution counters + -- Dynamic occurrence tracking for all classes local occur = { - ham = {}, - spam = {}, total = {} } + local classes_found = {} -- Expiry step statistics counters local nelts, extended, discriminated, sum, sum_squares, common, significant, @@ -264,24 +267,44 @@ local expiry_script = [[ for _,key in ipairs(keys) do local t = redis.call('TYPE', key)["ok"] if t == 'hash' then - local values = redis.call('HMGET', key, 'H', 'S') - local ham = tonumber(values[1]) or 0 - local spam = tonumber(values[2]) or 0 + -- Get all hash fields to support multi-class + local hash_data = redis.call('HGETALL', key) + local class_counts = {} + local total = 0 local ttl = redis.call('TTL', key) + + -- Parse hash data into class counts + for i = 1, #hash_data, 2 do + local class_label = hash_data[i] + local count = tonumber(hash_data[i + 1]) or 0 + class_counts[class_label] = count + total = total + count + + -- Track classes we've seen + if not classes_found[class_label] then + classes_found[class_label] = true + occur[class_label] = {} + end + end + tokens[key] = { - ham, - spam, - ttl + class_counts = class_counts, + total = total, + ttl = ttl } - local total = spam + ham + sum = sum + total sum_squares = sum_squares + total * total nelts = nelts + 1 - for k,v in pairs({['ham']=ham, ['spam']=spam, ['total']=total}) do - if tonumber(v) > 19 then v = 20 end - occur[k][v] = occur[k][v] and occur[k][v] + 1 or 1 + -- Update occurrence counters for all classes and total + for class_label, count in pairs(class_counts) do + local bucket = count > 19 and 20 or count + occur[class_label][bucket] = (occur[class_label][bucket] or 0) + 1 end + + local total_bucket = total > 19 and 20 or total + occur.total[total_bucket] = (occur.total[total_bucket] or 0) + 1 end end @@ -293,9 +316,10 @@ local expiry_script = [[ end for key,token in pairs(tokens) do - local ham, spam, ttl = token[1], token[2], tonumber(token[3]) + local class_counts = token.class_counts + local total = token.total + local ttl = tonumber(token.ttl) local threshold = mean - local total = spam + ham local function set_ttl() if expire < 0 then @@ -310,14 +334,39 @@ local expiry_script = [[ return 0 end - if total == 0 or math.abs(ham - spam) <= total * ${epsilon_common} then + -- Check if token is common (balanced across classes) + local is_common = false + if total == 0 then + is_common = true + else + -- For multi-class, check if any class dominates significantly + local max_count = 0 + for _, count in pairs(class_counts) do + if count > max_count then + max_count = count + end + end + -- Token is common if no class has more than (1 - epsilon) of total + is_common = (max_count / total) <= (1 - ${epsilon_common}) + end + + if is_common then common = common + 1 if ttl > ${common_ttl} then discriminated = discriminated + 1 redis.call('EXPIRE', key, ${common_ttl}) end elseif total >= threshold and total > 0 then - if ham / total > ${significant_factor} or spam / total > ${significant_factor} then + -- Check if any class is significant + local is_significant = false + for _, count in pairs(class_counts) do + if count / total > ${significant_factor} then + is_significant = true + break + end + end + + if is_significant then significant = significant + 1 if ttl ~= -1 then redis.call('PERSIST', key) @@ -361,33 +410,50 @@ local expiry_script = [[ redis.call('DEL', lock_key) local occ_distr = {} - for _,cl in pairs({'ham', 'spam', 'total'}) do + + -- Process all classes found plus total + local all_classes = {'total'} + for class_label in pairs(classes_found) do + table.insert(all_classes, class_label) + end + + for _, cl in ipairs(all_classes) do local occur_key = pattern_sha1 .. '_occurrence_' .. cl if cursor ~= 0 then - local n - for i,v in ipairs(redis.call('HGETALL', occur_key)) do - if i % 2 == 1 then - n = tonumber(v) - else - occur[cl][n] = occur[cl][n] and occur[cl][n] + v or v + local existing_data = redis.call('HGETALL', occur_key) + if #existing_data > 0 then + for i = 1, #existing_data, 2 do + local bucket = tonumber(existing_data[i]) + local count = tonumber(existing_data[i + 1]) + if occur[cl] and occur[cl][bucket] then + occur[cl][bucket] = occur[cl][bucket] + count + elseif occur[cl] then + occur[cl][bucket] = count + end end end - local str = '' - if occur[cl][0] ~= nil then - str = '0:' .. occur[cl][0] .. ',' - end - for k,v in ipairs(occur[cl]) do - if k == 20 then k = '>19' end - str = str .. k .. ':' .. v .. ',' + if occur[cl] and next(occur[cl]) then + local str = '' + if occur[cl][0] then + str = '0:' .. occur[cl][0] .. ',' + end + for k = 1, 20 do + if occur[cl][k] then + local label = k == 20 and '>19' or tostring(k) + str = str .. label .. ':' .. occur[cl][k] .. ',' + end + end + table.insert(occ_distr, cl .. '=' .. str) + else + table.insert(occ_distr, cl .. '=no_data') end - table.insert(occ_distr, str) else redis.call('DEL', occur_key) end - if next(occur[cl]) ~= nil then + if occur[cl] and next(occur[cl]) then redis.call('HMSET', occur_key, unpack_function(hash2list(occur[cl]))) end end @@ -446,8 +512,8 @@ local function expire_step(cls, ev_base, worker) '%s infrequent (%s %s), %s mean, %s std', lutil.unpack(d)) if cycle then - for i, cl in ipairs({ 'in ham', 'in spam', 'total' }) do - logger.infox(rspamd_config, 'tokens occurrences, %s: {%s}', cl, occ_distr[i]) + for _, distr_info in ipairs(occ_distr) do + logger.infox(rspamd_config, 'tokens occurrences: {%s}', distr_info) end end end diff --git a/test/functional/cases/110_statistics/300-multiclass-redis.robot b/test/functional/cases/110_statistics/300-multiclass-redis.robot new file mode 100644 index 000000000..278f7e0a0 --- /dev/null +++ b/test/functional/cases/110_statistics/300-multiclass-redis.robot @@ -0,0 +1,42 @@ +*** Settings *** +Documentation Multiclass Bayes Classification Tests with Redis Backend +Suite Setup Rspamd Redis Setup +Suite Teardown Rspamd Redis Teardown +Test Setup Set Test Hash Documentation +Resource multiclass_lib.robot + +*** Variables *** +${RSPAMD_REDIS_SERVER} ${RSPAMD_REDIS_ADDR}:${RSPAMD_REDIS_PORT} +${RSPAMD_STATS_HASH} siphash +${CONFIG} ${RSPAMD_TESTDIR}/configs/multiclass_bayes.conf + +*** Test Cases *** +Multiclass Basic Learning and Classification + [Documentation] Test basic multiclass learning and classification + [Tags] multiclass basic learning + Multiclass Basic Learn Test + +Multiclass Legacy Compatibility + [Documentation] Test that old learn_spam/learn_ham commands still work + [Tags] multiclass compatibility legacy + Multiclass Legacy Compatibility Test + +Multiclass Relearn + [Documentation] Test reclassifying messages to different classes + [Tags] multiclass relearn + Multiclass Relearn Test + +Multiclass Cross-Class Learning + [Documentation] Test learning message as different class than expected + [Tags] multiclass cross-learn + Multiclass Cross-Learn Test + +Multiclass Unlearn + [Documentation] Test unlearning (learning message as different class) + [Tags] multiclass unlearn + Multiclass Unlearn Test + +Multiclass Statistics + [Documentation] Test that statistics show all class information + [Tags] multiclass statistics + Multiclass Stats Test
\ No newline at end of file diff --git a/test/functional/cases/110_statistics/320-multiclass-peruser.robot b/test/functional/cases/110_statistics/320-multiclass-peruser.robot new file mode 100644 index 000000000..e8ca34616 --- /dev/null +++ b/test/functional/cases/110_statistics/320-multiclass-peruser.robot @@ -0,0 +1,31 @@ +*** Settings *** +Suite Setup Rspamd Redis Setup +Suite Teardown Rspamd Redis Teardown +Test Setup Set Test Hash Documentation +Resource multiclass_lib.robot + +*** Variables *** +${CONFIG} ${RSPAMD_TESTDIR}/configs/multiclass_bayes.conf +${REDIS_SCOPE} Suite +${RSPAMD_REDIS_SERVER} ${RSPAMD_REDIS_ADDR}:${RSPAMD_REDIS_PORT} +${RSPAMD_SCOPE} Suite +${RSPAMD_STATS_BACKEND} redis +${RSPAMD_STATS_HASH} null +${RSPAMD_STATS_KEY} null +${RSPAMD_STATS_PER_USER} true + +*** Test Cases *** +Multiclass Per-User Basic Learn Test + Multiclass Basic Learn Test test@example.com + +Multiclass Per-User Legacy Compatibility Test + Multiclass Legacy Compatibility Test test@example.com + +Multiclass Per-User Relearn Test + Multiclass Relearn Test test@example.com + +Multiclass Per-User Cross-Learn Test + Multiclass Cross-Learn Test test@example.com + +Multiclass Per-User Unlearn Test + Multiclass Unlearn Test test@example.com
\ No newline at end of file diff --git a/test/functional/cases/110_statistics/multiclass_lib.robot b/test/functional/cases/110_statistics/multiclass_lib.robot new file mode 100644 index 000000000..9f70e05fb --- /dev/null +++ b/test/functional/cases/110_statistics/multiclass_lib.robot @@ -0,0 +1,169 @@ +*** Settings *** +Library OperatingSystem +Resource lib.robot + +*** Variables *** +${CONFIG} ${RSPAMD_TESTDIR}/configs/multiclass_bayes.conf +${MESSAGE_HAM} ${RSPAMD_TESTDIR}/messages/ham.eml +${MESSAGE_SPAM} ${RSPAMD_TESTDIR}/messages/spam_message.eml +${MESSAGE_NEWSLETTER} ${RSPAMD_TESTDIR}/messages/newsletter.eml +${REDIS_SCOPE} Suite +${RSPAMD_REDIS_SERVER} null +${RSPAMD_SCOPE} Suite +${RSPAMD_STATS_BACKEND} redis +${RSPAMD_STATS_HASH} null +${RSPAMD_STATS_KEY} null +${RSPAMD_STATS_PER_USER} ${EMPTY} + +*** Keywords *** +Learn Multiclass + [Arguments] ${user} ${class} ${message} + # Extract filename from message path for queue-id + ${path} ${filename} = Split Path ${message} + IF "${user}" + ${result} = Run Rspamc -d ${user} -h ${RSPAMD_LOCAL_ADDR}:${RSPAMD_PORT_CONTROLLER} learn_class:${class} ${message} + ELSE + ${result} = Run Rspamc -h ${RSPAMD_LOCAL_ADDR}:${RSPAMD_PORT_CONTROLLER} learn_class:${class} ${message} + END + Check Rspamc ${result} + +Learn Multiclass Legacy + [Arguments] ${user} ${class} ${message} + # Test backward compatibility with old learn_spam/learn_ham commands + # Extract filename from message path for queue-id + ${path} ${filename} = Split Path ${message} + IF "${user}" + ${result} = Run Rspamc -d ${user} -h ${RSPAMD_LOCAL_ADDR}:${RSPAMD_PORT_CONTROLLER} learn_${class} ${message} + ELSE + ${result} = Run Rspamc -h ${RSPAMD_LOCAL_ADDR}:${RSPAMD_PORT_CONTROLLER} learn_${class} ${message} + END + Check Rspamc ${result} + +Multiclass Basic Learn Test + [Arguments] ${user}=${EMPTY} + Set Suite Variable ${RSPAMD_STATS_LEARNTEST} 0 + Set Test Variable ${kwargs} &{EMPTY} + IF "${user}" + Set To Dictionary ${kwargs} Deliver-To=${user} + END + + # Learn all classes + Learn Multiclass ${user} spam ${MESSAGE_SPAM} + Learn Multiclass ${user} ham ${MESSAGE_HAM} + Learn Multiclass ${user} newsletter ${MESSAGE_NEWSLETTER} + + # Test classification + Scan File ${MESSAGE_SPAM} &{kwargs} + Expect Symbol BAYES_SPAM + + Scan File ${MESSAGE_HAM} &{kwargs} + Expect Symbol BAYES_HAM + + Scan File ${MESSAGE_NEWSLETTER} &{kwargs} + Expect Symbol BAYES_NEWSLETTER + + Set Suite Variable ${RSPAMD_STATS_LEARNTEST} 1 + +Multiclass Legacy Compatibility Test + [Arguments] ${user}=${EMPTY} + Set Test Variable ${kwargs} &{EMPTY} + IF "${user}" + Set To Dictionary ${kwargs} Deliver-To=${user} + END + + # Test legacy learn_spam and learn_ham commands still work + Learn Multiclass Legacy ${user} spam ${MESSAGE_SPAM} + Learn Multiclass Legacy ${user} ham ${MESSAGE_HAM} + + # Should still classify correctly + Scan File ${MESSAGE_SPAM} &{kwargs} + Expect Symbol BAYES_SPAM + + Scan File ${MESSAGE_HAM} &{kwargs} + Expect Symbol BAYES_HAM + +Multiclass Relearn Test + [Arguments] ${user}=${EMPTY} + IF ${RSPAMD_STATS_LEARNTEST} == 0 + Fail "Learn test was not run" + END + + Set Test Variable ${kwargs} &{EMPTY} + IF "${user}" + Set To Dictionary ${kwargs} Deliver-To=${user} + END + + # Relearn spam message as ham + Learn Multiclass ${user} ham ${MESSAGE_SPAM} + + # Should now classify as ham or at least not spam + Scan File ${MESSAGE_SPAM} &{kwargs} + ${pass} = Run Keyword And Return Status Expect Symbol BAYES_HAM + IF ${pass} + Pass Execution Successfully reclassified spam as ham + END + Do Not Expect Symbol BAYES_SPAM + +Multiclass Cross-Learn Test + [Arguments] ${user}=${EMPTY} + Set Test Variable ${kwargs} &{EMPTY} + IF "${user}" + Set To Dictionary ${kwargs} Deliver-To=${user} + END + + # Learn newsletter message as ham to test cross-class learning + Learn Multiclass ${user} ham ${MESSAGE_NEWSLETTER} + + # Should classify as ham, not newsletter (since we trained it as ham) + Scan File ${MESSAGE_NEWSLETTER} &{kwargs} + Expect Symbol BAYES_HAM + Do Not Expect Symbol BAYES_NEWSLETTER + +Multiclass Unlearn Test + [Arguments] ${user}=${EMPTY} + Set Test Variable ${kwargs} &{EMPTY} + IF "${user}" + Set To Dictionary ${kwargs} Deliver-To=${user} + END + + # First learn spam + Learn Multiclass ${user} spam ${MESSAGE_SPAM} + Scan File ${MESSAGE_SPAM} &{kwargs} + Expect Symbol BAYES_SPAM + + # Then unlearn spam (learn as ham) + Learn Multiclass ${user} ham ${MESSAGE_SPAM} + + # Should no longer classify as spam + Scan File ${MESSAGE_SPAM} &{kwargs} + Do Not Expect Symbol BAYES_SPAM + +Check Multiclass Results + [Arguments] ${result} ${expected_class} + # Check that scan result contains expected class information + Should Contain ${result.stdout} BAYES_${expected_class.upper()} + # Check for multiclass result format [class_name] + Should Match Regexp ${result.stdout} BAYES_${expected_class.upper()}.*\\[${expected_class}\\] + +Multiclass Stats Test + # Check that rspamc stat shows learning counts for all classes + ${result} = Run Rspamc -h ${RSPAMD_LOCAL_ADDR}:${RSPAMD_PORT_CONTROLLER} stat + # Don't use Check Rspamc for stat command as it expects JSON success format + Should Be Equal As Integers ${result.rc} 0 + + # Should show statistics for all classes + Should Contain ${result.stdout} BAYES_SPAM + Should Contain ${result.stdout} BAYES_HAM + Should Contain ${result.stdout} BAYES_NEWSLETTER + +Multiclass Configuration Migration Test + # Test that old binary config can be automatically migrated + Set Test Variable ${binary_config} ${RSPAMD_TESTDIR}/configs/stats.conf + + # Start with binary config + ${result} = Run Rspamc --config ${binary_config} stat + Check Rspamc ${result} + + # Should show deprecation warning but work + Should Contain ${result.stderr} deprecated ignore_case=True + diff --git a/test/functional/configs/multiclass_bayes.conf b/test/functional/configs/multiclass_bayes.conf new file mode 100644 index 000000000..278aeeee9 --- /dev/null +++ b/test/functional/configs/multiclass_bayes.conf @@ -0,0 +1,129 @@ +options = { + filters = ["spf", "dkim", "regexp"] + url_tld = "{= env.TESTDIR =}/../lua/unit/test_tld.dat" + pidfile = "{= env.TMPDIR =}/rspamd.pid" + dns { + retransmits = 10; + timeout = 2s; + fake_records = [{ + name = "example.net"; + type = txt; + replies = ["v=spf1 -all"]; + }] + } +} + +logging = { + type = "file", + level = "debug" + filename = "{= env.TMPDIR =}/rspamd.log" +} + +metric = { + name = "default", + actions = { + reject = 100500, + } + unknown_weight = 1 +} + +worker { + type = normal + bind_socket = "{= env.LOCAL_ADDR =}:{= env.PORT_NORMAL =}" + count = 1 + keypair { + pubkey = "{= env.KEY_PUB1 =}"; + privkey = "{= env.KEY_PVT1 =}"; + } + task_timeout = 60s; +} + +worker { + type = controller + bind_socket = "{= env.LOCAL_ADDR =}:{= env.PORT_CONTROLLER =}" + count = 1 + keypair { + pubkey = "{= env.KEY_PUB1 =}"; + privkey = "{= env.KEY_PVT1 =}"; + } + secure_ip = ["127.0.0.1", "::1"]; + stats_path = "{= env.TMPDIR =}/stats.ucl"; +} + +# Multi-class Bayes classifier configuration +classifier { + languages_enabled = true; + tokenizer { + name = "osb"; + hash = {= env.STATS_HASH =}; + key = {= env.STATS_KEY =}; + } + backend = "{= env.STATS_BACKEND =}"; + + # Multi-class statfiles + statfile { + class = "spam"; + symbol = BAYES_SPAM; + server = {= env.REDIS_SERVER =} + } + statfile { + class = "ham"; + symbol = BAYES_HAM; + server = {= env.REDIS_SERVER =} + } + statfile { + class = "newsletter"; + symbol = BAYES_NEWSLETTER; + server = {= env.REDIS_SERVER =} + } + + # Backend class labels for Redis + class_labels = { + "spam" = "S"; + "ham" = "H"; + "newsletter" = "N"; + } + + cache { + server = {= env.REDIS_SERVER =} + } + + # Multi-class autolearn configuration + autolearn = { + classes = { + spam = { + threshold = 15.0; + verdict_mapping = { spam = true }; + }; + ham = { + threshold = -5.0; + verdict_mapping = { ham = true }; + }; + newsletter = { + symbols = ["NEWSLETTER_HEADER", "BULK_MAIL", "UNSUBSCRIBE_LINK"]; + threshold = 8.0; + }; + }; + + check_balance = true; + max_class_ratio = 0.6; + skip_threshold = 0.95; + } + + min_learns = 1; + min_tokens = 1; + min_token_hits = 1; + min_prob_strength = 0.05; + + {% if env.STATS_PER_USER ~= '' %} + per_user = <<EOD +return function(task) + return task:get_principal_recipient() +end +EOD; + {% endif %} +} + +lua = "{= env.TESTDIR =}/lua/test_coverage.lua"; + +settings {} diff --git a/test/functional/lib/rspamd.robot b/test/functional/lib/rspamd.robot index 5d23e3ceb..f61998f46 100644 --- a/test/functional/lib/rspamd.robot +++ b/test/functional/lib/rspamd.robot @@ -419,10 +419,23 @@ Run Nginx ${nginx_log} = Get File ${RSPAMD_TMPDIR}/nginx.log Log ${nginx_log} +Set Test Hash Documentation + ${log_tag} = Evaluate __import__('hashlib').md5('${TEST NAME}'.encode()).hexdigest()[:8] + Log TEST CONTEXT: [${log_tag}] ${TEST NAME} console=True + Run Rspamc [Arguments] @{args} - ${result} = Run Process ${RSPAMC} -t 60 --header Queue-ID\=${TEST NAME} - ... @{args} env:LD_LIBRARY_PATH=${RSPAMD_TESTDIR}/../../contrib/aho-corasick + ${log_tag} = Evaluate __import__('hashlib').md5('${TEST NAME}'.encode()).hexdigest()[:8] + # Check if --queue-id is already provided in the arguments + ${args_str} = Evaluate ' '.join(@{args}) + ${has_queue_id} = Evaluate '--queue-id' in '${args_str}' + IF ${has_queue_id} + ${result} = Run Process ${RSPAMC} -t 60 --log-tag ${log_tag} + ... @{args} env:LD_LIBRARY_PATH=${RSPAMD_TESTDIR}/../../contrib/aho-corasick + ELSE + ${result} = Run Process ${RSPAMC} -t 60 --queue-id ${TEST NAME} --log-tag ${log_tag} + ... @{args} env:LD_LIBRARY_PATH=${RSPAMD_TESTDIR}/../../contrib/aho-corasick + END Log ${result.stdout} [Return] ${result} diff --git a/test/functional/messages/newsletter.eml b/test/functional/messages/newsletter.eml new file mode 100644 index 000000000..93c996956 --- /dev/null +++ b/test/functional/messages/newsletter.eml @@ -0,0 +1,50 @@ +From: "Marketing Team" <newsletter@example.com> +To: user@example.org +Subject: 🎉 Monthly Newsletter - Exclusive Deals & Product Updates! +Date: Thu, 21 Jul 2023 10:00:00 +0000 +Message-ID: <newsletter-123@example.com> +MIME-Version: 1.0 +Content-Type: text/html; charset=utf-8 +List-Unsubscribe: <https://example.com/unsubscribe?id=123> +Precedence: bulk +X-Mailer: MailChimp/Pro 12.345 + +<!DOCTYPE html> +<html> +<head> + <meta charset="utf-8"> + <title>Monthly Newsletter</title> +</head> +<body> + <h1>🎉 Exclusive Monthly Offers!</h1> + + <p>Dear Valued Subscriber,</p> + + <p>This month we're excited to bring you our <strong>BIGGEST SALE</strong> of the year!</p> + + <h2>🔥 Hot Deals This Month:</h2> + <ul> + <li>50% OFF all premium products</li> + <li>FREE shipping on orders over $50</li> + <li>Buy 2 Get 1 FREE on selected items</li> + </ul> + + <p><a href="https://example.com/shop?utm_source=newsletter&utm_campaign=monthly">SHOP NOW</a></p> + + <h2>📱 New Product Launch</h2> + <p>Check out our revolutionary new gadget that everyone is talking about!</p> + + <h2>🎁 Refer a Friend</h2> + <p>Share this newsletter and both you and your friend get $10 credit!</p> + + <hr> + + <p><small> + You're receiving this because you subscribed to our newsletter.<br> + <a href="https://example.com/unsubscribe?id=123">Unsubscribe here</a> | + <a href="https://example.com/preferences">Update preferences</a><br> + Marketing Team, Example Corp<br> + 123 Business St, City, State 12345 + </small></p> +</body> +</html>
\ No newline at end of file diff --git a/test/functional/messages/transactional.eml b/test/functional/messages/transactional.eml new file mode 100644 index 000000000..e227aaa77 --- /dev/null +++ b/test/functional/messages/transactional.eml @@ -0,0 +1,18 @@ +From: noreply@example.com +To: user@example.org +Subject: Password Reset Request +Date: Thu, 21 Jul 2023 11:00:00 +0000 +Message-ID: <pwd-reset-456@example.com> +MIME-Version: 1.0 +Content-Type: text/plain + +Hello, + +You have requested a password reset for your account. + +Click here to reset your password: https://example.com/reset?token=abc123 + +This link expires in 24 hours. + +Best regards, +Security Team
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