[Project] Fix unlearn stuff

diff --git a/src/libstat/classifiers/bayes.c b/src/libstat/classifiers/bayes.c
index ffe96237ce715b47ddae2f31e3bcab28e5dfb9ac..f851fbb369714b883173007a501a3375d81ce98e 100644 (file)
--- a/src/libstat/classifiers/bayes.c
+++ b/src/libstat/classifiers/bayes.c
@@ -333,6 +333,11 @@ bayes_classify_token_multiclass(struct rspamd_classifier *ctx,
 
                /* 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);
 
@@ -431,16 +436,26 @@ bayes_classify_multiclass(struct rspamd_classifier *ctx,
                }
        }
 
-       /* Check minimum learns requirement */
+       /* 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) {
-                               msg_info_task("not classified as %s. The class needs more "
-                                                         "training samples. Currently: %uL; minimum %ud required",
+                       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);
-                               return TRUE;
                        }
                }
+
+               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 */
@@ -580,6 +595,10 @@ bayes_classify_multiclass(struct rspamd_classifier *ctx,
 
        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];
@@ -594,6 +613,8 @@ bayes_classify_multiclass(struct rspamd_classifier *ctx,
 
                        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;
                        }
@@ -605,6 +626,9 @@ bayes_classify_multiclass(struct rspamd_classifier *ctx,
                                                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;
 }
@@ -963,6 +987,9 @@ bayes_learn_class(struct rspamd_classifier *ctx,
        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 */