added knowledge to training about emoji amount

.env.example

@@ -34,3 +34,4 @@ ML_STOP_WORDS_IN_CHATS={"-123":["word1","word2"]}
 ML_MAX_NUMBER_OF_ITERATIONS=100
 UPDATE_CHAT_ADMINS_INTERVAL_SEC=86400
 UPDATE_CHAT_ADMINS=true
+ML_CUSTOM_EMOJI_THRESHOLD=20

src/VahterBanBot.Tests/TgMessageUtils.fs

@@ -33,8 +33,11 @@ type Tg() =
                 ChatInstance = Guid.NewGuid().ToString()
             )
         )
+
+    static member emoji(?offset: int) = MessageEntity(Type = MessageEntityType.CustomEmoji, Offset = defaultArg offset 0 , Length = 1)
+    static member emojies(n: int) = Array.init n (fun i -> Tg.emoji i)
 
-    static member quickMsg (?text: string, ?chat: Chat, ?from: User, ?date: DateTime, ?callback: CallbackQuery, ?caption: string, ?editedText: string) =
+    static member quickMsg (?text: string, ?chat: Chat, ?from: User, ?date: DateTime, ?callback: CallbackQuery, ?caption: string, ?editedText: string, ?entities: MessageEntity[]) =
         let updateId = next()
         let msgId = next()
         Update(
@@ -47,7 +50,8 @@ type Tg() =
                     From = (from |> Option.defaultValue (Tg.user())),
                     Date = (date |> Option.defaultValue DateTime.UtcNow),
                     Caption = (caption |> Option.defaultValue null),
-                    ReplyToMessage = null
+                    ReplyToMessage = null,
+                    Entities = (entities |> Option.defaultValue null)
                 ),
             EditedMessage =
                 if editedText |> Option.isSome then

src/VahterBanBot/Bot.fs

@@ -513,7 +513,7 @@ let justMessage
         if not shouldBeSkipped then
             let! usrMsgCount = DB.countUniqueUserMsg message.From.Id
 
-            match ml.Predict(message.TextOrCaption, usrMsgCount)  with
+            match ml.Predict(message.TextOrCaption, usrMsgCount, message.Entities)  with
             | Some prediction ->
                 %mlActivity.SetTag("spamScoreMl", prediction.Score)
 

src/VahterBanBot/DB.fs

@@ -162,10 +162,12 @@ let getUserById (userId: int64): Task<DbUser option> =
         return users |> Seq.tryHead
     }
 
+[<CLIMutable>]
 type SpamOrHamDb =
     { text: string
       spam: bool
       less_than_n_messages: bool
+      custom_emoji_count: int
       created_at: DateTime }
 
 let mlData (criticalMsgCount: int) (criticalDate: DateTime) : Task<SpamOrHamDb array> =
@@ -175,7 +177,11 @@ let mlData (criticalMsgCount: int) (criticalDate: DateTime) : Task<SpamOrHamDb a
         //language=postgresql
         let sql =
             """
-WITH less_than_n_messages AS (SELECT u.id, COUNT(DISTINCT m.text) < @criticalMsgCount AS less_than_n_messages
+WITH custom_emojis AS (SELECT message.id, COUNT(*) FILTER (WHERE entities ->> 'type' = 'custom_emoji') AS custom_emoji_count
+                       FROM message,
+                            LATERAL JSONB_ARRAY_ELEMENTS(raw_message -> 'entities') AS entities
+                       GROUP BY message.id),
+     less_than_n_messages AS (SELECT u.id, COUNT(DISTINCT m.text) < @criticalMsgCount AS less_than_n_messages
                               FROM "user" u
                                        LEFT JOIN message m ON u.id = m.user_id
                               GROUP BY u.id),
@@ -191,6 +197,7 @@ WITH less_than_n_messages AS (SELECT u.id, COUNT(DISTINCT m.text) < @criticalMsg
      spam_or_ham AS (SELECT x.text,
                             x.spam,
                             x.less_than_n_messages,
+                            x.custom_emoji_count,
                             MAX(x.created_at) AS created_at
                      FROM (SELECT DISTINCT COALESCE(m.text, re_id.message_text)                       AS text,
                                            CASE
@@ -210,6 +217,7 @@ WITH less_than_n_messages AS (SELECT u.id, COUNT(DISTINCT m.text) < @criticalMsg
                                                ELSE TRUE
                                                END                                                    AS spam,
                                            COALESCE(l.less_than_n_messages, TRUE)                     AS less_than_n_messages,
+                                           COALESCE(ce.custom_emoji_count, 0)                         AS custom_emoji_count,
                                            COALESCE(re_id.banned_at, re_text.banned_at, m.created_at) AS created_at
                            FROM (SELECT *
                                  FROM message
@@ -218,8 +226,9 @@ WITH less_than_n_messages AS (SELECT u.id, COUNT(DISTINCT m.text) < @criticalMsg
                                     FULL OUTER JOIN really_banned re_id
                                                     ON m.message_id = re_id.message_id AND m.chat_id = re_id.banned_in_chat_id
                                     LEFT JOIN really_banned re_text ON m.text = re_text.message_text
+                                    LEFT JOIN custom_emojis ce ON m.id = ce.id
                                     LEFT JOIN less_than_n_messages l ON m.user_id = l.id) x
-                     GROUP BY text, spam, less_than_n_messages)
+                     GROUP BY text, spam, less_than_n_messages, custom_emoji_count)
 SELECT *
 FROM spam_or_ham
 ORDER BY created_at;

src/VahterBanBot/ML.fs

@@ -21,6 +21,7 @@ type SpamOrHam =
     { text: string
       spam: bool
       lessThanNMessagesF: single
+      moreThanNEmojisF: single
       createdAt: DateTime }
 
 [<CLIMutable>]
@@ -75,6 +76,7 @@ type MachineLearning(
                     { text = x.text
                       spam = x.spam
                       createdAt = x.created_at
+                      moreThanNEmojisF = if x.custom_emoji_count > botConf.MlCustomEmojiThreshold then 1.0f else 0.0f
                       lessThanNMessagesF = if x.less_than_n_messages then 1.0f else 0.0f }
                 )
                 |> fun x ->
@@ -90,7 +92,7 @@ type MachineLearning(
             let dataProcessPipeline =
                 mlContext.Transforms.Text
                     .FeaturizeText(outputColumnName = "TextFeaturized", inputColumnName = "text")
-                    .Append(mlContext.Transforms.Concatenate(outputColumnName = "Features", inputColumnNames = [|"TextFeaturized"; "lessThanNMessagesF";|]))
+                    .Append(mlContext.Transforms.Concatenate(outputColumnName = "Features", inputColumnNames = [|"TextFeaturized"; "lessThanNMessagesF"; "moreThanNEmojisF"|]))
                     .Append(mlContext.BinaryClassification.Trainers.SdcaLogisticRegression(
                         labelColumnName = "spam",
                         featureColumnName = "Features",
@@ -121,14 +123,22 @@ type MachineLearning(
             logger.LogError(ex, "Error training model")
     }
 
-    member _.Predict(text: string, userMsgCount: int) =
+    member _.Predict(text: string, userMsgCount: int, entities: MessageEntity array) =
         try
             match predictionEngine with
             | Some predictionEngine ->
+                let emojiCount =
+                    entities
+                    |> Option.ofObj
+                    |> Option.defaultValue [||]
+                    |> Seq.filter (fun x -> x.Type = MessageEntityType.CustomEmoji)
+                    |> Seq.length
+
                 predictionEngine.Predict
                     { text = text
                       spam = false
                       lessThanNMessagesF = if userMsgCount < botConf.MlTrainCriticalMsgCount then 1.0f else 0.0f
+                      moreThanNEmojisF = if emojiCount > botConf.MlCustomEmojiThreshold then 1.0f else 0.0f
                       createdAt = DateTime.UtcNow }
                 |> Some
             | None ->

src/VahterBanBot/Program.fs

@@ -75,7 +75,8 @@ let botConf =
       MlTrainingSetFraction = getEnvOr "ML_TRAINING_SET_FRACTION" "0.2" |> float
       MlSpamThreshold = getEnvOr "ML_SPAM_THRESHOLD" "0.5" |> single
       MlWarningThreshold = getEnvOr "ML_WARNING_THRESHOLD" "0.0" |> single
-      MlMaxNumberOfIterations = getEnvOr "ML_MAX_NUMBER_OF_ITERATIONS" "50" |> int 
+      MlMaxNumberOfIterations = getEnvOr "ML_MAX_NUMBER_OF_ITERATIONS" "50" |> int
+      MlCustomEmojiThreshold = getEnvOr "ML_CUSTOM_EMOJI_THRESHOLD" "20" |> int
       MlStopWordsInChats = getEnvOr "ML_STOP_WORDS_IN_CHATS" "{}" |> fromJson }
 
 let validateApiKey (ctx : HttpContext) =

src/VahterBanBot/Types.fs

@@ -42,6 +42,7 @@ type BotConfiguration =
       MlSpamThreshold: single
       MlWarningThreshold: single
       MlMaxNumberOfIterations: int
+      MlCustomEmojiThreshold: int
       MlStopWordsInChats: Dictionary<int64, string list> }
 
 [<CLIMutable>]