From: Vsevolod Stakhov Date: Mon, 10 Jun 2024 15:10:26 +0000 (+0100) Subject: [Project] Add a tool to perform statistical analysis of classifiers X-Git-Tag: 3.9.0~18^2~7 X-Git-Url: https://source.dussan.org/?a=commitdiff_plain;h=8d3a787bef493d28297ee2cea13cec389c3bb690;p=rspamd.git [Project] Add a tool to perform statistical analysis of classifiers --- diff --git a/lualib/rspamadm/classifier_test.lua b/lualib/rspamadm/classifier_test.lua new file mode 100644 index 000000000..44d2fc9e6 --- /dev/null +++ b/lualib/rspamadm/classifier_test.lua @@ -0,0 +1,186 @@ +local rspamd_util = require "rspamd_util" +local lua_util = require "lua_util" +local argparse = require "argparse" +local fun = require "fun" + +local parser = argparse() + :name "rspamadm classifier_test" + :description "Learn bayes classifier and evaluate its performance" + :help_description_margin(32) + +parser:option "-H --ham" + :description("Ham directory") + :argname("") +parser:option "-S --spam" + :description("Spam directory") + :argname("") +parser:flag "-n --no-learning" + :description("Do not learn classifier") +parser:option "--nconns" + :description("Number of parallel connections") + :argname("") + :convert(tonumber) + :default(10) +parser:option "-t --timeout" + :description("Timeout for client connections") + :argname("") + :convert(tonumber) + :default(60) +parser:option "-c --connect" + :description("Connect to specific host") + :argname("") + :default('localhost:11334') +parser:option "-r --rspamc" + :description("Use specific rspamc path") + :argname("") + :default('rspamc') +parser:option "-c --cv-fraction" + :description("Use specific fraction for cross-validation") + :argname("") + :convert(tonumber) + :default('0.7') + +local opts + +-- Utility function to split a table into two parts randomly +local function split_table(t, fraction) + local shuffled = {} + for _, v in ipairs(t) do + local pos = math.random(1, #shuffled + 1) + table.insert(shuffled, pos, v) + end + local split_point = math.floor(#shuffled * tonumber(fraction)) + local part1 = { lua_util.unpack(shuffled, 1, split_point) } + local part2 = { lua_util.unpack(shuffled, split_point + 1) } + return part1, part2 +end + +local function shell_quote(argument) + if argument:match('^[%w%+%-%.,:/=@_]+$') then + return argument + end + argument = argument:gsub('[$`"\\]', '\\%0') + return '"' .. argument .. '"' +end + +-- Utility function to get all files in a directory +local function get_files(dir) + return fun.totable(fun.map(shell_quote, rspamd_util.glob(dir .. '/*'))) +end + +-- Function to train the classifier with given files +local function train_classifier(files, command, connections) + local rspamc_command = string.format("%s --connect %s -j --compact -n %s -t %.3f %s %s", + opts.rspamc, opts.connect, opts.nconns, opts.timeout, command, table.concat(files, " ")) + local result = assert(io.popen(rspamc_command)) + result = result:read("*all") +end + +-- Function to classify files and return results +local function classify_files(files) + local settings_header = '--header Settings=\"{symbols_enabled=[BAYES_SPAM, BAYES_HAM]}\"' + local rspamc_command = string.format("%s %s --connect %s --compact -n %s -t %.3f %s", + opts.rspamc, + settings_header, + opts.connect, + opts.nconns, + opts.timeout, table.concat(files, " ")) + local result = assert(io.popen(rspamc_command)) + local results = {} + for line in result:lines() do + if string.match(line, "BAYES_SPAM") then + table.insert(results, { result = "spam", output = line }) + elseif string.match(line, "BAYES_HAM") then + table.insert(results, { result = "ham", output = line }) + end + end + + return results +end + +-- Function to evaluate classifier performance +local function evaluate_results(results, true_label) + local true_positives, false_positives, true_negatives, false_negatives = 0, 0, 0, 0 + for _, res in ipairs(results) do + if res.result == true_label then + if string.match(res.file, true_label) then + true_positives = true_positives + 1 + else + false_positives = false_positives + 1 + end + else + if string.match(res.file, true_label) then + false_negatives = false_negatives + 1 + else + true_negatives = true_negatives + 1 + end + end + end + + local total = #results + local accuracy = (true_positives + true_negatives) / total + local precision = true_positives / (true_positives + false_positives) + local recall = true_positives / (true_positives + false_negatives) + local f1_score = 2 * (precision * recall) / (precision + recall) + + print("True Positives:", true_positives) + print("False Positives:", false_positives) + print("True Negatives:", true_negatives) + print("False Negatives:", false_negatives) + print("Accuracy:", accuracy) + print("Precision:", precision) + print("Recall:", recall) + print("F1 Score:", f1_score) +end + +local function handler(args) + opts = parser:parse(args) + local ham_directory = opts['ham'] + local spam_directory = opts['spam'] + -- Get all files + local spam_files = get_files(spam_directory) + local ham_files = get_files(ham_directory) + + -- Split files into training and cross-validation sets + + local train_spam, cv_spam = split_table(spam_files, opts.cv_fraction) + local train_ham, cv_ham = split_table(ham_files, opts.cv_fraction) + + print(string.format("Spam: %d train files, %d cv files; ham: %d train files, %d cv files", + #train_spam, #cv_spam, #train_ham, #cv_ham)) + if not opts.no_learning then + -- Train classifier + print(string.format("Start learn spam, %d messages, %d connections", #train_spam, opts.nconns)) + train_classifier(train_spam, "learn_spam") + print(string.format("Start learn ham, %d messages, %d connections", #train_ham, opts.nconns)) + train_classifier(train_ham, "learn_ham") + print("Learning done") + end + + -- Classify cross-validation files + local cv_files = {} + for _, file in ipairs(cv_spam) do + table.insert(cv_files, file) + end + for _, file in ipairs(cv_ham) do + table.insert(cv_files, file) + end + + -- Shuffle cross-validation files + cv_files = split_table(cv_files, 1) + + print(string.format("Start cross validation, %d messages, %d connections", #cv_files, opts.nconns)) + -- Get classification results + local results = classify_files(cv_files) + + -- Evaluate results + evaluate_results(results, "spam") + +end + +return { + name = 'classifiertest', + aliases = { 'classifier_test' }, + handler = handler, + description = parser._description +} \ No newline at end of file