Welcome to ML_Botting_Core!

Solving Complex UI Challenges w/ ML
pip install ml-botting-core

Public Eve Online Models

This package will auto download these models from Here at runtime and maintain a copy on your device. (and auto update)
Sample config Here with all models.

Training

Train your own models Here
Sort your training images into folders (images shape must be the same shape) and target the root for training. The classifer will train those image samples to the name of the folder they are in.

• Image names do not matter.
• PNGs only.
• Number of samples per folder do not matter, however you want enough, 80% for training, 20% for validation.

├── training_data
│ ├── char_select
│ │ ├── image_1.png
│ │ ├── image_2.png
│ │ ├── image_3.png
│ ├── connection_lost
│ │ ├── image_1.png
│ │ ├── image_2.png
│ │ ├── image_3.png
│ ├── in_flight
│ │ ├── image_1.png
│ │ ├── image_2.png
│ │ ├── image_3.png
│ ├── in_hanger
│ │ ├── image_1.png
│ │ ├── image_2.png
│ │ ├── image_3.png

Usage:

Check out the samples Here.

ml_botting_core_config.json

{
   "public_models":[
      {
         "game":"eve_online",
         "model_name":"game_state",
         "download_latest":1,
         "download_latest_from":"https://storage.googleapis.com/eve_online_models/",
         "model_root_directory":"O:\\eve_live_models\\game_state",
         "model_log_directory":"O:\\eve_live_logs\\game_state",
         "save_images":0
      }
   ]
}

Implementation

from ml_botting_core import universal_predictor

up_config = json.load(open(r'ml_botting_core_config.json'))
up = universal_predictor(config=up_config)

img = Image.open('some_image.png')
state_result = up.predict(img, 'game_state')

state_result

{
   "epoc_time":"1682138565007.508",
   "argmax_index":2,
   "value_at_argmax":"1.0",
   "class":"jump_though_first",
   "classes":[
      "dock_now",
      "invalid",
      "jump_though_first",
      "jump_through_second",
      "no_action",
      "warp_to_dock_3",
      "warp_to_dock_4"
   ],
   "scores":[
      1.2750345662162804e-15,
      1.4581948495906438e-11,
      1.0,
      5.21881417175057e-17,
      1.4712418422554443e-18,
      1.2777047215389858e-12,
      6.730089694497203e-17
   ],
   "id":"98ad373b-e0a6-11ed-9b27-2cf05d9fe8eb",
   "image_saved":0,
   "model_name":"nav_options"
}