mirror of
https://github.com/NicolasBohn/NexQuant.git
synced 2026-07-28 16:07:46 +00:00
Xu Yang
d5a6a08210
* ignore result csv file * fix app scripts * rename taskgenerator to developer and generate to develop * fix a config bug in coder * fix a small bug in factor coder evaluators * remove a single logger in factor coder evaluators * fix a small bug in model coder main.py * rename Implementation to Workspace * move the prepare the inject_code into FBWorkspace to align all the behavior * fix a small bug in model feedback * remove debug lines for multi processing and simplify evaluators multi proc * add a copy function to workspace to freeze the workspace && add config prefix to speed up debugging * make hypothesisgen a abc class * use Qlib***Experiment * fix a small bug * rename Imp to Ws * rename sub_implementations to sub_workspace_list * fix a bug in feedback not presented as content in prompts * move proposal pys to proposal folder * reformat the folder * align factor and model qlib workspace and use template to handle the workspace * add a filter to evoagent to filter out false evo * align multi_proc_n into RDAGENT seeting * handle when runner gets empty experiment * fix logger merge remaining problems * fix black and isort automatically
Preparation
Install Pytorch
CPU CUDA will be enough for verify the implementation
Please install pytorch based on your system. Here is an example on my system
pip3 install torch torchvision torchaudio --index-url https://download.pytorch.org/whl/cpu
pip3 install torch_geometric
Tasks
Task Extraction
From paper to task.
# python rdagent/app/model_implementation/task_extraction.py
# It may based on rdagent/document_reader/document_reader.py
python rdagent/components/task_implementation/model_implementation/task_extraction.py ./PaperImpBench/raw_paper/
Complete workflow
From paper to implementation
# Similar to
# rdagent/app/factor_extraction_and_implementation/factor_extract_and_implement.py
Paper benchmark
# TODO: it does not work well now.
python rdagent/app/model_implementation/eval.py
TODO:
- Create reasonable benchmark
- with uniform input
- manually create task
- Create reasonable evaluation metrics