[Project] Some more fixes

diff --git a/src/plugins/lua/neural.lua b/src/plugins/lua/neural.lua
index fcf9ac5c2d78c94e80a91d1b5801c355da5ac547..e3518d3bd78dcbdb72b8b6a25b70cfb6d61f7f39 100644 (file)
--- a/src/plugins/lua/neural.lua
+++ b/src/plugins/lua/neural.lua
@@ -490,9 +490,7 @@ local function ann_push_task_result(rule, task, verdict, score, set)
       if not err and type(data) == 'table' then
         local nspam,nham = data[1],data[2]
 
-        if nspam > 0 and nham > 0 and
-            can_push_train_vector(rule, task, learn_type, nspam, nham) then
-
+        if can_push_train_vector(rule, task, learn_type, nspam, nham) then
           local vec = result_to_vector(task, set)
 
           local str = rspamd_util.zstd_compress(table.concat(vec, ';'))
@@ -518,6 +516,11 @@ local function ann_push_task_result(rule, task, verdict, score, set)
               'LPUSH', -- command
               { target_key, str } -- arguments
           )
+        else
+          lua_util.debugm(N, task,
+              "do not add %s train data for ANN rule " ..
+                  "%s:%s",
+              learn_type, rule.prefix, set.name)
         end
       else
         if err then
@@ -1100,6 +1103,7 @@ local function maybe_train_existing_ann(worker, ev_base, rule, set, profiles)
             -- at least (10 * (1 - 0.25)) = 8 trains
 
             local max_len = math.max(lua_util.unpack(lua_util.values(lens)))
+            local min_len = math.min(lua_util.unpack(lua_util.values(lens)))
 
             if rule.train.learn_type == 'balanced' then
               local len_bias_check_pred = function(_, l)
@@ -1117,7 +1121,7 @@ local function maybe_train_existing_ann(worker, ev_base, rule, set, profiles)
               end
             else
               -- Probabilistic mode, just ensure that at least one vector is okay
-              if max_len >= rule.train.max_trains then
+              if min_len > 0 and max_len >= rule.train.max_trains then
                 rspamd_logger.debugm(N, rspamd_config,
                     'can start ANN %s learn as it has %s learn vectors; %s required, after checking %s vectors',
                     ann_key, lens, rule.train.max_trains, what)