ruff check --fix

api/app.py

@@ -1,10 +1,8 @@
 import asyncio
 import logging
-import time
 from typing import List
 
 import pandas as pd
-import requests
 from fastapi import HTTPException
 from pydantic import BaseModel
 from sklearn.model_selection import train_test_split
@@ -13,7 +11,6 @@
 from api.cogs import predict
 from api.cogs import requests as req
 from api.MachineLearning import classifier, data
-import aiohttp
 from datetime import date
 
 app = config.app
@@ -90,7 +87,7 @@ async def manual_startup(secret: str):
         await req.post_prediction(output)
 
         if len(hiscores) < config.BATCH_AMOUNT:
-            sleep=60
+            sleep = 60
             logger.info(f"{len(hiscores)=} < {config.BATCH_AMOUNT=}, sleeping: {sleep}")
             await asyncio.sleep(sleep)
     return {"detail": "ok"}
@@ -105,7 +102,7 @@ async def load(secret: str):
         This endpoint is used by the detector api to load the latest model.
     """
     if secret != config.secret_token:
-        raise HTTPException(status_code=404, detail=f"insufficient permissions")
+        raise HTTPException(status_code=404, detail="insufficient permissions")
 
     binary_classifier = binary_classifier.load()
     multi_classifier = multi_classifier.load()
@@ -120,7 +117,7 @@ async def predict_player(secret: str, hiscores, name: name) -> List[dict]:
     """
     logger.debug(f"predicting player {name}")
     if secret != config.secret_token:
-        raise HTTPException(status_code=404, detail=f"insufficient permissions")
+        raise HTTPException(status_code=404, detail="insufficient permissions")
     name = pd.DataFrame(name.dict())
     output = predict.predict(hiscores, name, binary_classifier, multi_classifier)
     return output
@@ -134,14 +131,14 @@ async def train(secret: str):
     """
     logger.debug("training model")
     if secret != config.secret_token:
-        raise HTTPException(status_code=404, detail=f"insufficient permissions")
+        raise HTTPException(status_code=404, detail="insufficient permissions")
 
     labels = await req.get_labels()
     players = []
     hiscores = []
 
     for label in labels:
-        if not label["label"] in config.LABELS:
+        if label["label"] not in config.LABELS:
             continue
 
         player_data = await req.get_player_data(label_id=label["id"])

api/cogs/predict.py

@@ -24,40 +24,40 @@ def predict(
     If the binary classifier predicts that the player is a bot, then the multi classifier is used to predict the type of bot.
     The output is a list of dictionaries with the predictions.
     """
-    logger.debug(f"Predicting hiscores for players")
+    logger.debug("Predicting hiscores for players")
     hiscores = data.hiscoreData(hiscores)
     low_level = hiscores.df_low.index
     hiscores = hiscores.features()
 
-    logger.debug(f"Predicting binary for players")
+    logger.debug("Predicting binary for players")
     # binary prediction
     binary_pred = binary_classifier.predict_proba(hiscores)
     binary_pred = pd.DataFrame(
         binary_pred, index=hiscores.index, columns=["Real_Player", "Unknown_bot"]
     )
 
     # multi prediction
-    logger.debug(f"Predicting multi for players")
+    logger.debug("Predicting multi for players")
     multi_pred = multi_classifier.predict_proba(hiscores)
     multi_pred = pd.DataFrame(
         multi_pred, index=hiscores.index, columns=np.unique(config.LABELS)
     )
 
     # remove real players from multi
-    logger.debug(f"Removing real players from multi for players")
+    logger.debug("Removing real players from multi for players")
     real_players = binary_pred.query("Real_Player > 0.5").index
     mask = ~(multi_pred.index.isin(real_players))
     multi_pred = multi_pred[mask]
 
 
     # remove bots from real players
-    logger.debug(f"Removing bots from binary for players")
+    logger.debug("Removing bots from binary for players")
     bots = multi_pred.index
     mask = ~(binary_pred.index.isin(bots))
     binary_pred = binary_pred[mask]
 
     # combine binary & player_pred
-    logger.debug(f"Combining binary and multi for players")
+    logger.debug("Combining binary and multi for players")
     output = pd.DataFrame(names).set_index("id")
     output = output.merge(binary_pred, left_index=True, right_index=True, how="left")
 
@@ -71,7 +71,7 @@ def predict(
 
 
     # cleanup predictions
-    logger.debug(f"Cleaning up predictions for players")
+    logger.debug("Cleaning up predictions for players")
     mask = output["Real_Player"].isna()  # all multi class predictions
 
 
@@ -83,15 +83,15 @@ def predict(
     output.fillna(0, inplace=True)
 
     # add Predictions, Predicted_confidence, created
-    logger.debug(f"Adding Predictions, Predicted_confidence, created for players")
+    logger.debug("Adding Predictions, Predicted_confidence, created for players")
     columns = [c for c in output.columns if c != "name"]
     output["Predicted_confidence"] = round(output[columns].max(axis=1) * 100, 2)
     output["Prediction"] = output[columns].idxmax(axis=1)
     output["created"] = time.strftime("%Y-%m-%d %H:%M:%S", time.gmtime())
     output.reset_index(inplace=True)
 
     # low level player predictions are not accurate
-    logger.debug(f"Removing low level players for players")
+    logger.debug("Removing low level players for players")
 
     mask = output['id'].isin(low_level)
     output.loc[mask, "Prediction"] = "Stats_Too_Low"

notes.md

@@ -1,38 +1,46 @@
 
 # api documentation
-```
+```sh
 http://127.0.0.1:8000/docs
 http://127.0.0.1:8000/redoc
 ```
 # extra info
-```
+```sh
 POST: to create data.
 GET: to read data.
 PUT: to update data.
 DELETE: to delete data.
 ```
 # keeping fork up to date
-```
+```sh
 git checkout develop
 git pull --rebase upstream develop
 git push
 ```
 # setup
+## windows
 creating a python venv to work in and install the project requirements
-```
+```sh
 python -m venv .venv
 .venv\Scripts\activate
 python -m pip install --upgrade pip
 pip install -r requirements.txt
 ```
+## linux
+```sh
+python3 -m venv .venv
+source .venv/bin/activate
+python -m pip install --upgrade pip
+pip install -r requirements.txt
+```
 # for admin purposes saving & upgrading
 when you added some dependancies update the requirements
-```
+```sh
 venv\Scripts\activate
 call pip freeze > requirements.txt
 ```
 when you want to upgrade the dependancies
-```
+```sh
 venv\Scripts\activate
 powershell "(Get-Content requirements.txt) | ForEach-Object { $_ -replace '==', '>=' } | Set-Content requirements.txt"
 call pip install -r requirements.txt --upgrade
@@ -41,6 +49,6 @@ powershell "(Get-Content requirements.txt) | ForEach-Object { $_ -replace '>=',
 ```
 # branch cleanup
 if your branch gets out of sync and for some reason you have many pushes and pulls, to become insync without pushing some random changes do this
-```
+```sh
 git fetch https://github.com/Bot-detector/bot-detector-ML.git
 ```