[Doc] Improve classifiers documentation

diff --git a/doc/markdown/configuration/statistic.md b/doc/markdown/configuration/statistic.md
index f314a31a65de10fda16b7c59f2ed3923d7c5aed1..18e870652c40d07c25acf5cd4e8ea6e42b9bb283 100644 (file)
--- a/doc/markdown/configuration/statistic.md
+++ b/doc/markdown/configuration/statistic.md
@@ -17,7 +17,7 @@ This schema is displayed in the following picture:
 
 ![OSB algorithm](https://rspamd.com/img/rspamd-schemes.004.png "Rspamd OSB scheme")
 
-The main disadvantage is the amount of tokens which is multiplied by size of window. In rspamd, we use a window of 5 tokens that means that 
+The main disadvantage is the amount of tokens which is multiplied by size of window. In rspamd, we use a window of 5 tokens that means that
 the number of tokens is about 5 times larger than the amount of words.
 
 Statistical tokens are stored in statfiles which, in turn, are mapped to specific backends. This architecture is displayed in the following image:
@@ -30,15 +30,24 @@ Starting from rspamd 1.0, we propose to use `sqlite3` as backed and `osb` as tok
 metainformation in statistics. The following configuration demonstrates the recommended statistics configuration:
 
 ~~~ucl
-classifier {
-    type = "bayes";
+# Classifier's algorith is BAYES
+classifier "bayes" {
     tokenizer {
         name = "osb";
     }
+
+    # Unique name used to learn the specific classifier
+    name = "common_bayes";
+
     cache {
         path = "${DBDIR}/learn_cache.sqlite";
     }
+
+    # Minimum number of words required for statistics processing
     min_tokens = 11;
+    # Minimum learn count for both spam and ham classes to perform classification
+    min_learns = 200;
+
     backend = "sqlite3";
     languages_enabled = true;
     statfile {
@@ -67,15 +76,19 @@ It is also possible to create custom lua scripts to use customized user or langu
 of such a script for extracting domain names from recipients organizing thus per-domain statistics:
 
 ~~~ucl
-    classifier {
-        tokenizer {
-            name = "osb";
-        }
-        name = "bayes2";
-        min_tokens = 11;
-        backend = "sqlite3";
-        per_language = true;
-        per_user = <<EOD
+classifier "bayes" {
+    tokenizer {
+        name = "osb";
+    }
+
+    name = "bayes2";
+
+    min_tokens = 11;
+    min_learns = 200;
+
+    backend = "sqlite3";
+    per_language = true;
+    per_user = <<EOD
 return function(task)
     local rcpt = task:get_recipients(1)
 
@@ -89,15 +102,15 @@ end
 return nil
 end
 EOD
-        statfile {
-            path = "/tmp/bayes2.spam.sqlite";
-            symbol = "BAYES_SPAM2";
-        }
-        statfile {
-            path = "/tmp/bayes2.ham.sqlite";
-            symbol = "BAYES_HAM2";
-        }
+    statfile {
+        path = "/tmp/bayes2.spam.sqlite";
+        symbol = "BAYES_SPAM2";
+    }
+    statfile {
+        path = "/tmp/bayes2.ham.sqlite";
+        symbol = "BAYES_HAM2";
     }
+}
 ~~~
 
 ## Applying per-user and per-language statistics
@@ -114,42 +127,48 @@ It is different from 1.0 version where the second approach was used for both cas
 Rspamd allows to learn and to check multiple classifiers for a single messages. This might be useful, for example, if you have common and per user statistics. It is even possible to use the same statfiles for these purposes. Classifiers **might** have the same symbols (thought it is not recommended) and they should have a **unique** `name` attribute that is used for learning. Here is an example of such a configuration:
 
 ~~~ucl
-    classifier {
-        tokenizer {
-            name = "osb";
-        }
-        name = "bayes_user";
-        min_tokens = 11;
-        backend = "sqlite3";
-        per_language = true;
-        per_user = true;
-        statfile {
-            path = "/tmp/bayes.spam.sqlite";
-            symbol = "BAYES_SPAM_USER";
-        }
-        statfile {
-            path = "/tmp/bayes.ham.sqlite";
-            symbol = "BAYES_HAM_USER";
-        }
+classifier "bayes" {
+    tokenizer {
+        name = "osb";
     }
 
-    classifier {
-        tokenizer {
-            name = "osb";
-        }
-        name = "bayes";
-        min_tokens = 11;
-        backend = "sqlite3";
-        per_language = true;
-        statfile {
-            path = "/tmp/bayes.spam.sqlite";
-            symbol = "BAYES_SPAM";
-        }
-        statfile {
-            path = "/tmp/bayes.ham.sqlite";
-            symbol = "BAYES_HAM";
-        }
+    name = "users";
+    min_tokens = 11;
+    min_learns = 200;
+    backend = "sqlite3";
+    per_language = true;
+    per_user = true;
+
+    statfile {
+        path = "/tmp/bayes.spam.sqlite";
+        symbol = "BAYES_SPAM_USER";
+    }
+    statfile {
+        path = "/tmp/bayes.ham.sqlite";
+        symbol = "BAYES_HAM_USER";
     }
+}
+
+classifier "bayes" {
+    tokenizer {
+        name = "osb";
+    }
+
+    name = "common";
+    min_tokens = 11;
+    min_learns = 200;
+    backend = "sqlite3";
+    per_language = true;
+
+    statfile {
+        path = "/tmp/bayes.spam.sqlite";
+        symbol = "BAYES_SPAM";
+    }
+    statfile {
+        path = "/tmp/bayes.ham.sqlite";
+        symbol = "BAYES_HAM";
+    }
+}
 ~~~
 
 To learn specific classifier, you can use `-c` option for `rspamc` (or `Classifier` HTTP header):
@@ -162,12 +181,14 @@ To learn specific classifier, you can use `-c` option for `rspamc` (or `Classifi
 From version 1.1, it is also possible to specify redis as a backend for statistics and cache of learned messages. Redis is recommended for clustered configurations as it allows simultaneous learn and checks and, besides, is very fast. To setup redis, you could use `redis` backend for a classifier (cache is set to the same servers accordingly).
 
 ~~~ucl
-    classifier {
+    classifier "bayes" {
         tokenizer {
             name = "osb";
         }
+
         name = "bayes";
         min_tokens = 11;
+        min_learns = 200;
         backend = "redis";
         servers = "localhost:6379";
         #write_servers = "localhost:6379"; # If needed another servers for learning