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/*
* Copyright (c) 2009-2012, Vsevolod Stakhov
* All rights reserved.
*
* Redistribution and use in source and binary forms, with or without
* modification, are permitted provided that the following conditions are met:
* * Redistributions of source code must retain the above copyright
* notice, this list of conditions and the following disclaimer.
* * Redistributions in binary form must reproduce the above copyright
* notice, this list of conditions and the following disclaimer in the
* documentation and/or other materials provided with the distribution.
*
* THIS SOFTWARE IS PROVIDED BY AUTHOR ''AS IS'' AND ANY
* EXPRESS OR IMPLIED WARRANTIES, INCLUDING, BUT NOT LIMITED TO, THE IMPLIED
* WARRANTIES OF MERCHANTABILITY AND FITNESS FOR A PARTICULAR PURPOSE ARE
* DISCLAIMED. IN NO EVENT SHALL AUTHOR BE LIABLE FOR ANY
* DIRECT, INDIRECT, INCIDENTAL, SPECIAL, EXEMPLARY, OR CONSEQUENTIAL DAMAGES
* (INCLUDING, BUT NOT LIMITED TO, PROCUREMENT OF SUBSTITUTE GOODS OR SERVICES;
* LOSS OF USE, DATA, OR PROFITS; OR BUSINESS INTERRUPTION) HOWEVER CAUSED AND
* ON ANY THEORY OF LIABILITY, WHETHER IN CONTRACT, STRICT LIABILITY, OR TORT
* (INCLUDING NEGLIGENCE OR OTHERWISE) ARISING IN ANY WAY OUT OF THE USE OF THIS
* SOFTWARE, EVEN IF ADVISED OF THE POSSIBILITY OF SUCH DAMAGE.
*/
/*
* Bayesian classifier
*/
#include "classifiers.h"
#include "tokenizers/tokenizers.h"
#include "main.h"
#include "filter.h"
#include "cfg_file.h"
#include "binlog.h"
#include "lua/lua_common.h"
#define LOCAL_PROB_DENOM 16.0
static inline GQuark
bayes_error_quark (void)
{
return g_quark_from_static_string ("bayes-error");
}
struct bayes_statfile_data {
guint64 hits;
guint64 total_hits;
double value;
struct statfile *st;
stat_file_t *file;
};
struct bayes_callback_data {
statfile_pool_t *pool;
struct classifier_ctx *ctx;
gboolean in_class;
time_t now;
stat_file_t *file;
struct bayes_statfile_data *statfiles;
guint32 statfiles_num;
guint64 total_spam;
guint64 total_ham;
guint64 processed_tokens;
gsize max_tokens;
double spam_probability;
double ham_probability;
};
static gboolean
bayes_learn_callback (gpointer key, gpointer value, gpointer data)
{
token_node_t *node = key;
struct bayes_callback_data *cd = data;
gint c;
guint64 v;
c = (cd->in_class) ? 1 : -1;
/* Consider that not found blocks have value 1 */
v = statfile_pool_get_block (cd->pool, cd->file, node->h1, node->h2, cd->now);
if (v == 0 && c > 0) {
statfile_pool_set_block (cd->pool, cd->file, node->h1, node->h2, cd->now, c);
cd->processed_tokens ++;
}
else if (v != 0) {
if (G_LIKELY (c > 0)) {
v ++;
}
else if (c < 0){
if (v != 0) {
v --;
}
}
statfile_pool_set_block (cd->pool, cd->file, node->h1, node->h2, cd->now, v);
cd->processed_tokens ++;
}
if (cd->max_tokens != 0 && cd->processed_tokens > cd->max_tokens) {
/* Stop learning on max tokens */
return TRUE;
}
return FALSE;
}
/**
* Returns probability of chisquare > value with specified number of freedom
* degrees
* @param value value to test
* @param freedom_deg number of degrees of freedom
* @return
*/
static gdouble
inv_chi_square (gdouble value, gint freedom_deg)
{
long double prob, sum;
gint i;
if ((freedom_deg & 1) != 0) {
msg_err ("non-odd freedom degrees count: %d", freedom_deg);
return 0;
}
value /= 2.;
errno = 0;
#ifdef HAVE_EXPL
prob = expl (-value);
#elif defined(HAVE_EXP2L)
prob = exp2l (-value * log2 (M_E));
#else
prob = exp (-value);
#endif
if (errno == ERANGE) {
msg_err ("exp overflow");
return 0;
}
sum = prob;
for (i = 1; i < freedom_deg / 2; i ++) {
prob *= value / (gdouble)i;
sum += prob;
}
return MIN (1.0, sum);
}
/*
* In this callback we calculate local probabilities for tokens
*/
static gboolean
bayes_classify_callback (gpointer key, gpointer value, gpointer data)
{
token_node_t *node = key;
struct bayes_callback_data *cd = data;
guint i;
struct bayes_statfile_data *cur;
guint64 spam_count = 0, ham_count = 0, total_count = 0;
double spam_prob, spam_freq, ham_freq, bayes_spam_prob;
for (i = 0; i < cd->statfiles_num; i ++) {
cur = &cd->statfiles[i];
cur->value = statfile_pool_get_block (cd->pool, cur->file, node->h1, node->h2, cd->now);
if (cur->value > 0) {
cur->total_hits += cur->value;
if (cur->st->is_spam) {
spam_count += cur->value;
}
else {
ham_count += cur->value;
}
total_count += cur->value;
}
}
/* Probability for this token */
if (total_count > 0) {
spam_freq = ((double)spam_count / MAX (1., (double)cd->total_spam));
ham_freq = ((double)ham_count / MAX (1., (double)cd->total_ham));
spam_prob = spam_freq / (spam_freq + ham_freq);
bayes_spam_prob = (0.5 + spam_prob * total_count) / (1. + total_count);
cd->spam_probability += log (bayes_spam_prob);
cd->ham_probability += log (1. - bayes_spam_prob);
cd->processed_tokens ++;
}
if (cd->max_tokens != 0 && cd->processed_tokens > cd->max_tokens) {
/* Stop classifying on max tokens */
return TRUE;
}
return FALSE;
}
struct classifier_ctx*
bayes_init (rspamd_mempool_t *pool, struct classifier_config *cfg)
{
struct classifier_ctx *ctx = rspamd_mempool_alloc (pool, sizeof (struct classifier_ctx));
ctx->pool = pool;
ctx->cfg = cfg;
ctx->debug = FALSE;
return ctx;
}
gboolean
bayes_classify (struct classifier_ctx* ctx, statfile_pool_t *pool, GTree *input, struct worker_task *task, lua_State *L)
{
struct bayes_callback_data data;
gchar *value;
gint nodes, i = 0, selected_st = -1, cnt;
gint minnodes;
guint64 maxhits = 0, rev;
double final_prob, h, s;
struct statfile *st;
stat_file_t *file;
GList *cur;
char *sumbuf;
g_assert (pool != NULL);
g_assert (ctx != NULL);
if (ctx->cfg->opts && (value = g_hash_table_lookup (ctx->cfg->opts, "min_tokens")) != NULL) {
minnodes = strtol (value, NULL, 10);
nodes = g_tree_nnodes (input);
if (nodes > FEATURE_WINDOW_SIZE) {
nodes = nodes / FEATURE_WINDOW_SIZE + FEATURE_WINDOW_SIZE;
}
if (nodes < minnodes) {
return FALSE;
}
}
cur = call_classifier_pre_callbacks (ctx->cfg, task, FALSE, FALSE, L);
if (cur) {
rspamd_mempool_add_destructor (task->task_pool, (rspamd_mempool_destruct_t)g_list_free, cur);
}
else {
cur = ctx->cfg->statfiles;
}
data.statfiles_num = g_list_length (cur);
data.statfiles = g_new0 (struct bayes_statfile_data, data.statfiles_num);
data.pool = pool;
data.now = time (NULL);
data.ctx = ctx;
data.processed_tokens = 0;
data.spam_probability = 0;
data.ham_probability = 0;
data.total_ham = 0;
data.total_spam = 0;
if (ctx->cfg->opts && (value = g_hash_table_lookup (ctx->cfg->opts, "max_tokens")) != NULL) {
minnodes = parse_limit (value, -1);
data.max_tokens = minnodes;
}
else {
data.max_tokens = 0;
}
while (cur) {
/* Select statfile to classify */
st = cur->data;
if ((file = statfile_pool_is_open (pool, st->path)) == NULL) {
if ((file = statfile_pool_open (pool, st->path, st->size, FALSE)) == NULL) {
msg_warn ("cannot open %s", st->path);
cur = g_list_next (cur);
data.statfiles_num --;
continue;
}
}
data.statfiles[i].file = file;
data.statfiles[i].st = st;
statfile_get_revision (file, &rev, NULL);
if (st->is_spam) {
data.total_spam += rev;
}
else {
data.total_ham += rev;
}
cur = g_list_next (cur);
i ++;
}
cnt = i;
g_tree_foreach (input, bayes_classify_callback, &data);
if (data.processed_tokens == 0 || data.spam_probability == 0) {
final_prob = 0;
}
else {
h = 1 - inv_chi_square (-2. * data.spam_probability, 2 * data.processed_tokens);
s = 1 - inv_chi_square (-2. * data.ham_probability, 2 * data.processed_tokens);
final_prob = (s + 1 - h) / 2.;
}
if (data.processed_tokens > 0 && fabs (final_prob - 0.5) > 0.05) {
sumbuf = rspamd_mempool_alloc (task->task_pool, 32);
for (i = 0; i < cnt; i ++) {
if ((final_prob > 0.5 && !data.statfiles[i].st->is_spam) ||
(final_prob < 0.5 && data.statfiles[i].st->is_spam)) {
continue;
}
if (data.statfiles[i].total_hits > maxhits) {
maxhits = data.statfiles[i].total_hits;
selected_st = i;
}
}
if (selected_st == -1) {
msg_err ("unexpected classifier error: cannot select desired statfile");
}
else {
/* Calculate ham probability correctly */
if (final_prob < 0.5) {
final_prob = 1. - final_prob;
}
rspamd_snprintf (sumbuf, 32, "%.2f%%", final_prob * 100.);
cur = g_list_prepend (NULL, sumbuf);
insert_result (task, data.statfiles[selected_st].st->symbol, final_prob, cur);
}
}
g_free (data.statfiles);
return TRUE;
}
gboolean
bayes_learn (struct classifier_ctx* ctx, statfile_pool_t *pool, const char *symbol, GTree *input,
gboolean in_class, double *sum, double multiplier, GError **err)
{
struct bayes_callback_data data;
gchar *value;
gint nodes;
gint minnodes;
struct statfile *st, *sel_st = NULL;
stat_file_t *to_learn;
GList *cur;
g_assert (pool != NULL);
g_assert (ctx != NULL);
if (ctx->cfg->opts && (value = g_hash_table_lookup (ctx->cfg->opts, "min_tokens")) != NULL) {
minnodes = strtol (value, NULL, 10);
nodes = g_tree_nnodes (input);
if (nodes > FEATURE_WINDOW_SIZE) {
nodes = nodes / FEATURE_WINDOW_SIZE + FEATURE_WINDOW_SIZE;
}
if (nodes < minnodes) {
msg_info ("do not learn message as it has too few tokens: %d, while %d min", nodes, minnodes);
*sum = 0;
g_set_error (err,
bayes_error_quark(), /* error domain */
1, /* error code */
"message contains too few tokens: %d, while min is %d",
nodes, (int)minnodes);
return FALSE;
}
}
data.pool = pool;
data.in_class = in_class;
data.now = time (NULL);
data.ctx = ctx;
data.processed_tokens = 0;
data.processed_tokens = 0;
if (ctx->cfg->opts && (value = g_hash_table_lookup (ctx->cfg->opts, "max_tokens")) != NULL) {
minnodes = parse_limit (value, -1);
data.max_tokens = minnodes;
}
else {
data.max_tokens = 0;
}
cur = ctx->cfg->statfiles;
while (cur) {
/* Select statfile to learn */
st = cur->data;
if (strcmp (st->symbol, symbol) == 0) {
sel_st = st;
break;
}
cur = g_list_next (cur);
}
if (sel_st == NULL) {
g_set_error (err,
bayes_error_quark(), /* error domain */
1, /* error code */
"cannot find statfile for symbol: %s",
symbol);
return FALSE;
}
if ((to_learn = statfile_pool_is_open (pool, sel_st->path)) == NULL) {
if ((to_learn = statfile_pool_open (pool, sel_st->path, sel_st->size, FALSE)) == NULL) {
msg_warn ("cannot open %s", sel_st->path);
if (statfile_pool_create (pool, sel_st->path, sel_st->size) == -1) {
msg_err ("cannot create statfile %s", sel_st->path);
g_set_error (err,
bayes_error_quark(), /* error domain */
1, /* error code */
"cannot create statfile: %s",
sel_st->path);
return FALSE;
}
if ((to_learn = statfile_pool_open (pool, sel_st->path, sel_st->size, FALSE)) == NULL) {
g_set_error (err,
bayes_error_quark(), /* error domain */
1, /* error code */
"cannot open statfile %s after creation",
sel_st->path);
msg_err ("cannot open statfile %s after creation", sel_st->path);
return FALSE;
}
}
}
data.file = to_learn;
statfile_pool_lock_file (pool, data.file);
g_tree_foreach (input, bayes_learn_callback, &data);
statfile_inc_revision (to_learn);
statfile_pool_unlock_file (pool, data.file);
if (sum != NULL) {
*sum = data.processed_tokens;
}
return TRUE;
}
gboolean
bayes_learn_spam (struct classifier_ctx* ctx, statfile_pool_t *pool,
GTree *input, struct worker_task *task, gboolean is_spam, lua_State *L, GError **err)
{
struct bayes_callback_data data;
gchar *value;
gint nodes;
gint minnodes;
struct statfile *st;
stat_file_t *file;
GList *cur;
gboolean skip_labels;
g_assert (pool != NULL);
g_assert (ctx != NULL);
if (ctx->cfg->opts && (value = g_hash_table_lookup (ctx->cfg->opts, "min_tokens")) != NULL) {
minnodes = strtol (value, NULL, 10);
nodes = g_tree_nnodes (input);
if (nodes > FEATURE_WINDOW_SIZE) {
nodes = nodes / FEATURE_WINDOW_SIZE + FEATURE_WINDOW_SIZE;
}
if (nodes < minnodes) {
g_set_error (err,
bayes_error_quark(), /* error domain */
1, /* error code */
"message contains too few tokens: %d, while min is %d",
nodes, (int)minnodes);
return FALSE;
}
}
cur = call_classifier_pre_callbacks (ctx->cfg, task, TRUE, is_spam, L);
if (cur) {
skip_labels = FALSE;
rspamd_mempool_add_destructor (task->task_pool, (rspamd_mempool_destruct_t)g_list_free, cur);
}
else {
/* Do not try to learn specific statfiles if pre callback returned nil */
skip_labels = TRUE;
cur = ctx->cfg->statfiles;
}
data.pool = pool;
data.now = time (NULL);
data.ctx = ctx;
data.in_class = TRUE;
data.processed_tokens = 0;
if (ctx->cfg->opts && (value = g_hash_table_lookup (ctx->cfg->opts, "max_tokens")) != NULL) {
minnodes = parse_limit (value, -1);
data.max_tokens = minnodes;
}
else {
data.max_tokens = 0;
}
while (cur) {
/* Select statfiles to learn */
st = cur->data;
if (st->is_spam != is_spam || (skip_labels && st->label)) {
cur = g_list_next (cur);
continue;
}
if ((file = statfile_pool_is_open (pool, st->path)) == NULL) {
if ((file = statfile_pool_open (pool, st->path, st->size, FALSE)) == NULL) {
msg_warn ("cannot open %s", st->path);
if (statfile_pool_create (pool, st->path, st->size) == -1) {
msg_err ("cannot create statfile %s", st->path);
g_set_error (err,
bayes_error_quark(), /* error domain */
1, /* error code */
"cannot create statfile: %s",
st->path);
return FALSE;
}
if ((file = statfile_pool_open (pool, st->path, st->size, FALSE)) == NULL) {
g_set_error (err,
bayes_error_quark(), /* error domain */
1, /* error code */
"cannot open statfile %s after creation",
st->path);
msg_err ("cannot open statfile %s after creation", st->path);
return FALSE;
}
}
}
data.file = file;
statfile_pool_lock_file (pool, data.file);
g_tree_foreach (input, bayes_learn_callback, &data);
statfile_inc_revision (file);
statfile_pool_unlock_file (pool, data.file);
maybe_write_binlog (ctx->cfg, st, file, input);
msg_info ("increase revision for %s", st->path);
cur = g_list_next (cur);
}
return TRUE;
}
GList *
bayes_weights (struct classifier_ctx* ctx, statfile_pool_t *pool, GTree *input, struct worker_task *task)
{
/* This function is unimplemented with new normalizer */
return NULL;
}
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