[Rework] Use a dedicated library for autolearn

conf/statistic.conf

@@ -41,34 +41,7 @@ classifier "bayes" {
     symbol = "BAYES_SPAM";
     spam = true;
   }
-  learn_condition =<<EOD
-return function(task, is_spam, is_unlearn)
-  local learn_type = task:get_request_header('Learn-Type')
-
-  if not (learn_type and tostring(learn_type) == 'bulk') then
-    local prob = task:get_mempool():get_variable('bayes_prob', 'double')
-
-    if prob then
-      local in_class = false
-      local cl
-      if is_spam then
-        cl = 'spam'
-        in_class = prob >= 0.95
-      else
-        cl = 'ham'
-        in_class = prob <= 0.05
-      end
-
-      if in_class then
-        return false,string.format('already in class %s; probability %.2f%%',
-          cl, math.abs((prob - 0.5) * 200.0))
-      end
-    end
-  end
-
-  return true
-end
-EOD
+  learn_condition = "return require("lua_bayes_learn").autolearn"
 
   .include(try=true; priority=1) "$LOCAL_CONFDIR/local.d/classifier-bayes.conf"
   .include(try=true; priority=10) "$LOCAL_CONFDIR/override.d/classifier-bayes.conf"

lualib/lua_bayes_learn.lua

@@ -0,0 +1,49 @@
+--[[
+Copyright (c) 2019, Vsevolod Stakhov <vsevolod@highsecure.ru>
+
+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
+
+Unless required by applicable law or agreed to in writing, software
+distributed under the License is distributed on an "AS IS" BASIS,
+WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
+See the License for the specific language governing permissions and
+limitations under the License.
+]]--
+
+-- This file contains functions to simplify bayes classifier auto-learning
+
+local exports = {}
+
+exports.autolearn = function(task, is_spam, is_unlearn)
+  local learn_type = task:get_request_header('Learn-Type')
+
+  if not (learn_type and tostring(learn_type) == 'bulk') then
+    local prob = task:get_mempool():get_variable('bayes_prob', 'double')
+
+    if prob then
+      local in_class = false
+      local cl
+      if is_spam then
+        cl = 'spam'
+        in_class = prob >= 0.95
+      else
+        cl = 'ham'
+        in_class = prob <= 0.05
+      end
+
+      if in_class then
+        return false,string.format(
+            'already in class %s; probability %.2f%%',
+            cl, math.abs((prob - 0.5) * 200.0))
+      end
+    end
+  end
+
+  return true
+end
+
+return exports