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https://github.com/NicolasBohn/NexQuant.git
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999bbac08dc393cc178fa12322392af85a3eec79
18 Commits
| Author | SHA1 | Message | Date | |
|---|---|---|---|---|
 XianBW
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6e19c9e632 |
feat: add LLM-finetune scenario (#1314)
* refine prompt * small update * fix a small bug * remove debug config after execution * fix: only remove <think> at start * feat: support creating dataset & multi-eval frame (#1302) * feat: add iterative evolve and evaluation support with partial chain stop * feat: add FTDataEvaluator and support multiple implement functions in finetune * feat: data implement for pre-proposal and proposal and add datasets (#1303) * feat:(1) support for multi layer dataset extraction (2) add category.json for dataset in datasets/ * fix: fix bug for generate category.json * feat: add get_dataset_folder_desc * init data proposal and merge qzli/ft * update data proposal prompts and add max_position_embeddings and resolve confilcts * remove sample counts in data proposal * turn data and train to unified hypo_gen * refine prompts * remove category.json and add it to dataset_info * fix jinja problem and proposal done * lint * add ai-generated description and raw readme into dataset_info.json * update prompt for description * add datasets * initial fix for proposal of data * final version for data proposal * lint * feat: add stats in dataset_info, and enable data coder (#1306) * refactor(dataset): add stats into dataset_info.json, and remove dataset from gitignore_folder * feat: enable data coder and run data process * feat: Merge data coder (#1307) * feat: implement finetune data coding, evaluation, and config improvements * fix: deepspeed config path * fix: dataset info columns --------- Co-authored-by: Young <afe.young@gmail.com> * replace str length with token_limit * add readme to dataset_info and remove useless blank lines in scenario description * feat: dataset prepare * fix: extract prams script name * feat: add loss&predictions samples to feedback * remove duplicate envs and and add llm_api_preferences and enhance reasoning token limits * feat: network for ft_env * fix: remove gpt-4o, which has low quota * feat: a simple ui * feat: merge data and train task type (#1309) * feat: filter redundant prams of lf * fix: ui bug caused by removing task_type * fix: force agent to use high concurrency, and remove redundant prompt * feat: extract info from llama factory log, and check data exists before download * fix: add compatibility rules * feat: llm evaluator for data coder * feat: openai package in ft docker, and refine prompt * feat: refine ft ui, add more info * feat: add raw logs * refine data coder prompt(for feedback debug) * feat: select dataset in scen init * fix: ui for docker log seperately * feat: sync log through blob * improve ui, and add llm feedback in Runner&Exp2FB (#1312) * fix: ui bug to visualize docker log, and lint * feat: unified docker log for ft env, and some refactor * fix bugs and improve ui * feat: save log of evaluator(single feedback) * feat: add evaluator, set cleanup docker log * feat: call llm in RunnerEvaluator and Feedback * fix: extract structured error message in RunnerEvaluator * feat: feedback improve, and fix some bugs * feat: feedback improve when runner fails * small update * feat(UI): add running info and benchmark metric in loop expander * feat(UI): add render markdown toggle * feat: refine prompts and add error type in exp2fb * feat: add filterd params reason, set default benchmark timeout to infinite, and refine train loss express * recover dataset deepscaler * feat: set timeout in .env * refactor: unifiied ft_env timeout * feat: debug mode for data coder * feat: deliver data_stats after generate debug_data * feat: use gpt-5.1 as judge model, set judge_retry, and refine debug mode prompt * refine prompt * refactor: llama factory manager logic, and refine data processing prompt * feat(DockerEnv): support GPU selection via CUDA_VISIBLE_DEVICES * feat: set api concurrency via .env * fix: ft env timeout bug * feat: enable CondaEnv run * fix: can't update bin path in first run, and path bug in lf manager * feat(ui): set log path through .env * refactor(ui): wrap_lines, remove css * feat(coder): retry when parse code-block fail * fix: refine single-fb in ui, and fix path bug(not allow proposal to decide path) * fix: opencompass CondaEnv torch compatible with vllm * fix: refine error text in coding * feat: deepspeed config for CondaEnv * feat: memory estimator * fix: deepspeed package for condaenv * fix: use `client.chat.completions.create()` only * feat: flash attention for condaenv * feat: strong and weak models interface * fix: condaenv package dependency * use multi round conversation in llm finetune proposal * refine prompt for data processing * enable evolving in data coder * maximize output token size * fix: refine ui * fix: optional packages for llama factory * fix: torch denpendency for b200 * fix: opencompass dependency * update cot prompts * skip the sub implement * skip conda preparation if env exists * update chemcot datasets * fix: unify docker to use litellm * update readme and instructions * fix: set CUDA_VISIBLE_DEVICES for CondaEnv * feat: add panorama dataset, refactor dataset interface * feat: calculate token using tiktoken, and ndarray bug * fix: download subtasks of chemcotdataset seperately * feat: customized prepare func for datasets * feat: update new benchmarks * add datasets package * docs: readme for llm finetune * feat: download raw data directly, with post-process function * feat: analyze raw dataset * suppress litellm debug info * feat(ui): summary page * feat: run multi-jobs * feat: improve ui * feat: add path and checkout options to LLM finetune loop entrypoint * feat: add FinanceIQ_ppl benchmark with auto-download and dataset desc rendering * refactor: remove unused imports and dead code, fix session folder logging * feat: enable tablebench and tableInstruct dataset * refine dataset readme, and coder prompt * refine proposal and coder prompt * fix: ui path (default log path) * feat: add automatic LoRA model merging for benchmarking with vLLM * refactor: reorganize finetune benchmark and merge modules under benchmark dir * refactor: modularize benchmark config and error extraction for finetune scenario * fix: update benchmark import paths and disable env cache for device info * refactor docke&conda env and fix import bugs * modify init python file * feat: add FinanceIQ dataset split utility and integrate with pipeline * feat: set weak and strong model by env, distribute workload across models * feat: sample dataset and rm params for tensorboard, wandb * update script to run jobs * refine proposal prompt, remove specific dataset name * fix(ui): auto switch log folder * fix: estimate the processed full data after sample * feat: filter raw data more aggressively, and lower data_eval standard * feat: sync workspace to blob * feat: rdkit for chemcotbench * update qwen2.5&llama3.1 context * fix: force failure on validation error and remove try/except in validator * feat: unified error sample extraction (with test scripts) * feat: set conda cache with .env * feat: skip data eval if data pass in last evo * fix: rm redundant param * fix ui bug * refactor: centralize assign_code_list_to_evo in MultiProcessEvolvingStrategy * feat: add test_params.yaml generation and workspace cleanup improvements for finetune * refactor: replace get_clear_ws_cmd with clear_workspace and update prompts for hard check criteria * add bioprobench dataset * fix: handle commas in training config extraction and refactor prompt includes * bioprobench description * add bioprobench readme * feat: merge lora adapter for blackwell gpu * feat: support for multi benchmarks in one job * change dfficult aware content for training * update difficulty-aware and logging principles * fix: resolve variable name conflict in FTRunnerEvaluator * set job id accuracy to minute * feat(ui): display one selected metric per benchmark * feat: store sota exp, and fix ws_ckp bug * fix: truncate data.json in feedback * fix: opencompass data for conda env * fix: save only the last model * feat: set log path and ws path * fix: set overwrite_cache to avoid lock contention(through injecting params) * feat: redirect stdout to file in localenv * add pickle cache to dataset desc * fix CI * fix: remove redundant wrapper * feat: set python_unbuffered * move redirect stdout to env run * fix a small bug * move model folder * feat(ui): display benchmark baseline * fix: enrich scenario and benchmark description * fix: rewrite runner eval to accept easier * feat: compare with baseline when no SOTA * update tablebench readme * fix: switch back to single benchmark (for baseline) * feat(ui): add ws path in ui * refactor: update SOTA tracking to use DAG traversal and parent selection * fix: prioritize local_selection in trace and refactor sibling retrieval logic * refactor: unify error handling in feedback generation and update workspace injection * feat: add skip_loop_error_stepname to control error skip step in LoopBase * fix: set local_selection to NEW_ROOT for experiments without parent * feat: set different ports for jobs * feat: set different ports for jobs * feat: add upper data size limit for LLM fine-tuning and update related prompts * fix: replace get_truncated_stdout() with stdout for consistent output handling * refactor: remove data.json from cache and workspace logic, focus on script-based reuse * fix: rm target_scenario * feat: add selective cache extraction and custom cache key for data processing * fix(ui): bug when displaying tablebench * fix: filter config in dataset_info.json * feat: add test set, set valid set * feat(ui): update test score, and set color for final decision * feat: add test score for baseline and update ui * fix: use [-100:] as test range * feat: update data_stats in runner * feat: wait for opencompass init when run multi jobs * fix: adjust test&valid split * feat: force to generate COT(with <think> token), and add answer format in scenarios.json * feat: improve ui * fix: unify benchmark volume mounts and set extra_volumes for conda env * fix(ui): number color * fix: update GPU memory handling to use total memory in GB and streamline code * fix: set use_cot_postprocessor * feat: add env_dict to config classes and merge env vars in Env run * fix: let coder obey proposal * fix(ui): direction bug and update chemcot core metirc * fix: set consistent benchmark mount points and env vars for docker and conda * fix: addintional target for LoRA * feat: workspace dir log for benchmark running * fix: tableInstruct path bug and update benchmark description * feat: timeout for whole job * fix: align FinanceIQ import to opencompass * feat: use llm_judge for FinanceIQ * feat: switch to turn on <think> or not * feat: using scripts to redirect stdout, and run in different windows * feat: sync litellm log * fix: gpu memory format * fix: escape special characters in benchmark desc * fix: set data processing timeout to 1h * feat: set valid_loss and save_best_model * fix: inject timeout and stage * fix: loss history extract logic * feat: inject output dir * feat: inject eval batch size * feat: inject save_total_limit * feat: update data prompt * fix: escape shell special characters * fix: tablebench visualization UI * fix: move implementation validation to coder, and ignore injected params * feat: README for FinanceIQ dataset * fix: bioprobench desc error * fix: remove task alignment when coder eval * fix: FinanceIQ now extracts last capital as answer * fix: stdout contains binary data * feat: recover estimate full output and set eval setting automatically * fix(ui): precision for summary table * fix(ui): import error * feat: try to use lora * fix(api): fix litellm bug for code block * fix: refine prompts to give agent more decision space * chore(ci): fix mypy typing issues * chore(ci): format code with black * chore(ci): fix ruff lint violations * chore(ci): sort imports with isort * chore(ci): format code with black * test: temporarily skip extract_parameters imports due to numpy pin * fix: compatibility issues for qlib scenarios on finetune branch * fix(fin_factor): skip to fb for coder error * fix(loop): default skip to feedback step on skip_loop_error When skip_loop_error exception happens and skip_loop_error_stepname is not explicitly set, default to jumping to 'feedback' step if it exists, otherwise fall back to the last step (record). This prevents KeyError when record step tries to access feedback data that doesn't exist because we skipped the feedback phase. Also removed redundant skip_loop_error_stepname from finetune loop since it's now the default behavior. * add 'skip to record' to DS scenario like other scenarios * fix 2 scenarios bug about rd_loop class * fix: lint(mypy, ruff, black) error * fix: mypy lint error * fix data science scenario bug --------- Co-authored-by: Xu Yang <peteryang@vip.qq.com> Co-authored-by: Qizheng Li <jenssenlee@163.com> Co-authored-by: you-n-g <you-n-g@users.noreply.github.com> Co-authored-by: amstrongzyf <201840057@smail.nju.edu.cn> Co-authored-by: Young <afe.young@gmail.com> Co-authored-by: amstrongzyf <amstrongzyf@126.com> Co-authored-by: chelsea97 <zhuowbrown@gmail.com> Co-authored-by: SunsetWolf <Lv.Linlang@hotmail.com> |
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you-n-g
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7fc09169bc |
feat: fallback to acceptable results (#1129)
* refactor: add is_acceptable, fallback logic and generify evolving agent * refine lint * small * lint * lint * lint * feat: add is_acceptable to CoSTEERMultiFeedback * feat: add in-memory workspace checkpoint and recovery * feat: preserve symbolic links in workspace checkpoints and recovery * lint * lint * feat: limit workspace checkpoint to files under 100KB * feat: add workspace checkpoint size limit setting * prompt * lint |
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Xu Yang
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305eff1c5e |
feat: enhance timeout management and knowledge base handling in CoSTEER components (#1130)
* feat: enhance timeout management and knowledge base handling in CoSTEER components * fix a little bug * fix small bug * fix a small bug * Update rdagent/scenarios/data_science/loop.py Co-authored-by: you-n-g <you-n-g@users.noreply.github.com> * add scale check * fix a small bug * fix CI * use dynamic chat_token_limit & remove repeated lines * fix CI * remove useless comment * fix small bug * update draft appendix * fix prompt * add code correctness as top priority --------- Co-authored-by: you-n-g <you-n-g@users.noreply.github.com> |
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you-n-g
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5baed909e7 |
refactor: refactor core framework to better propogate feedbacks (#599)
* refactor: Update type annotations and remove unused class in evolving modules * refactor: Simplify evolving agent and feedback handling in CoSTEER module * lint & CI * mypy * ruff for core * mypy * refactor: remove unnecessary comments and update feedback handling logic * refactor: Add prev_task_feedback parameter to evolving strategies * feat: Clear folder before extracting zip file in DockerEnv * fix: Correct retrieval of last experiment from history |
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you-n-g
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f78175b37a |
feat: refactor for general data science (#498)
* refine ds modal for more cases: eval and es * update model template * prompts for model and ensemble * fix a bug * fix a bug * init: ds workflow evovingstrategy * Adding ensemble (#505) * Initial Draft * Updating logic for init * Revising * Successful Testing * Updating to use the latest & right class * bug: bug-fixing for testing * data science loop changes * data science loop base * ds loop feedback * fix * remove measure_time because it's duplicated (in LoopBase) * add the knowledge query for data_loader & feature * edit ds workflow evaluator * data_loader bug fix * stop evolving when all tasks completed * llm app change * fix break all complete strategy * Adding queried knowledge (#508) Co-authored-by: XianBW <36835909+XianBW@users.noreply.github.com> * fix loop bug * ds workflow evaluator; test; refine prompts * workflow spec * fix ci * feature task changes * ds loop change * fix a bug in feat * add query knowledge for model and workflow * llm_debug info(for show) using pickle instead of json * remove NextLoopException * loop change * coder raise CoderError when all sub_tasks failed * rename code_dict to file_dict in FBWorkspace * add CoSTEER unittest * now show self.version in Task.get_task_information(), simplify CoSTEER sub tasks definition * remove some properties in ModelTask, add model_type in it. * fix llm app bug * llm web app bug fix * ds loop bug fix * fix: give component code to feature&ens eval * loop catch error bug * rename load_from_raw_data to load_data * feat: Add debug data creation functionality for data science scenarios * support local folder (#511) * support local folder * remove unnecessary random * KaggleScen Subclass * small fix * use template for style description * update default scen to kaggle * update sample data script * make sure frac < 1 * fix a bug * feature spec changes * fix * changeimport order * clear unnecessary std outputs * fix a typo * create sample folder after unzip kaggle data * feature/model test script update * Align the data types across modules. * fix a bug in model eval * show line number * move sample entry point to app * spec & model prompt changes * Refine the competition specification to address the data type problem and the coherence issue. * fix some bugs * add file filter in FBworkspace.code property * support non-binary prediction * avoid too much warnings * fix a bug in ensemble module * filtered the knowledge query in all modules * delete RAG in idea proposal * refine the code in ensemble * show exp workspace in llm_st * exp_gen bug fix * feedback bug fix * use `feature` instead of `feat01` * Trace & method of judging if exp is completed change * fix a bug in package calling and execute ci * fix code * bug fix * bug fix * fix a bug * fix some bugs * fix a bug * refactor: Enhance error handling and feedback in data science loop * support different use_azure on chat and embedding models * multi-model proposal logic * fix a small syntax error * loopBase and some changes * ensemble scores change * fbworkspace.code -> .all_codes * use all model codes in workflow coder * check scores.csv's keys(model_names) * model name changes * add a todo in ensemble test * sota_exp changes * give model info in exp gen * add runner time limit * config using debug data or not in evals * exp to feedback base * add feature code when writing model task * small problem * copying during sampling * update * refactor: Simplify code handling and improve workspace management * model part output fix * print model's execution time * bug fix * ensemble test fix * ens small change * ens_test bug fix * Refine partial expansion logic to display only a few subfolders when their structure is uniform, improving readability in nested directories. * several update on prompts * sample subfolders * Filter the stdout after code execution to remove irrelevant information e.g. progress bars, whitespace characters, excessive line breaks. * Add some more prompts and comments * several update on the first init rounds * model timeout as error * fix pattern of getting model codes in workspace * small bux fix on model prompts * remove get_code_with_key since we have regex pattern * fix: Correct tqdm progress bar update logic in LoopBase class * feat: Add diff generation and enhance feedback mechanism in data science loop * update some fix to model and workflow prompts * refine the logic of progress bar filter * add last_successful_exp in exp_gen * fix a one line bug * add a hint in prompt * fix data sample for bms * fix data sample for bms * hypothesis small fix * crawler readme update * fix component gen * fix bug * annotation change * load description.md if it exists * refactor: Simplify SOTA description handling in feedback and prompts * refactor: Use shared templates for feedback and experiment descriptions * change webapp for model codes changes * update proposal * add timeout message for docker run output * fix * refine the code in docker time processing * use .shape instead of len() when do shape eval * won't change size during iteration * support bson sample * sample support jsonl and bson * add former_code to coder prompts * a little speed us in debug data creating * filter progress bar when eval ens and main * avoid costeer makes no change to former code * fix several log error * add timeout judge threshold * fix some bugs in the evaluation of component output shapes * File structure for supporting litellm (#517) Co-authored-by: Young <afe.young@gmail.com> * ignore submission and show processing * ignore submission and show processing * add efficiency notice * refactor: Enhance error message with detailed feedback summary * refactor: Simplify component handling in DSExpGen class * refactor: Update code structure and add docstring for clarity * reserve one sample to each label in data sampling * add Evaluation info * refine costeer code to avoid giving same code twice * use raw_description as plain text * add a prompt hint to avoid same dict key * model task name bug in first model exp gen * fix a typo * add some debug info in costeer tests * task init change * enhance data sampling * refine the code in data_loader * more reasonable loop * fix a bug in data folder description * add error msg & traceback to execution feedback * fix llm error msg detection * add task information to costeer eval & add cache to docker run(use zipfile to store the whole workspace) * fix CI first round * fix CI second round * use txt to store test script to avoid pytest * remove zipfile in requirements * add azure.identity to requirements * ignore debug web page * component test changes * remove redundent task_desc in model coder * feat: Add APE module and prompts for automated prompt engineering * fix: Update .gitignore and improve text formatting in eval.py * refactor: Update print output and improve code comments and imports * style: Fix string formatting and import order in ape.py and fmt.py * exclude ape * add a data folder notice * reduce unnecessary output to stdout * refine the code of describe_data_folder * fix ci * style: streamlit style update (#522) * streamlit style update * fix import * fix format * fix llm_st loop progress bar * debugapp small change * fix model str * refine some prompts * fix model str * fix CI * refine the logic associated with the data_folder * fix ci * small change * set filter_progress_bar as default in execute * model proposal with workflow * add submission check in workflow eval * fix bug * small change * fix CI * fix CI * refactor: Move generate_diff to utils and update DSExpGen logic * more reasonable prompt describing metric direction * fix a minor jinja2 bug * quick fix exp_gen bugs * fix the following bug * fix * fix some bugs * remove workflow from model * add pending_tasks_list in data science to enable coding model and workflow * refine the code for handling JSON-formatted data descriptions * assert with information * ensure correct csv file name * add logging to help record the output * log competition * add log tag for debug llm app * test: Test ds refactor ll (#523) * fix bugs to former scenario * fix a bug because coding in rdloop changed * fix the bug when feedback gets no hypothesis * fix trace structure * change all trace hist when merging hypothesis to experiments * ignore some error in ruff * fix kaggle scenario bugs * refine one line * another bug * another small bug * fix ui bugs * chage kaggle train.py path --------- Co-authored-by: Xu Yang <peteryang@vip.qq.com> * fix CI * Update rdagent/app/data_science/loop.py Co-authored-by: Copilot <175728472+Copilot@users.noreply.github.com> * add samplecsv into spec prompts * fix CI --------- Co-authored-by: TPLin22 <tplin2@163.com> Co-authored-by: yuanteli <1957922024@qq.com> Co-authored-by: Xisen Wang <118058822+xisen-w@users.noreply.github.com> Co-authored-by: Bowen Xian <xianbowen@outlook.com> Co-authored-by: Xu Yang <peteryang@vip.qq.com> Co-authored-by: XianBW <36835909+XianBW@users.noreply.github.com> Co-authored-by: Tim <illking@foxmail.com> Co-authored-by: 炼金术师华华 <37462254+YeewahChan@users.noreply.github.com> Co-authored-by: Linlang <30293408+SunsetWolf@users.noreply.github.com> Co-authored-by: Copilot <175728472+Copilot@users.noreply.github.com> |
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Xu Yang
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fce241b9f9 |
feat: a unified CoSTEER to fit more scenarios (#491)
* Use ExtendedBaseSettings to replace BaseSettings * update a more general way to pass the default setting * update all code * fix CI * fix CI * fix qlib scenario * fix CI * fix CI * fix CI & add data science interfaces * remove redundant code * abandon costeer knowledge base v1 --------- Co-authored-by: Xu Yang <xuyang1@microsoft.com> Co-authored-by: XianBW <36835909+XianBW@users.noreply.github.com> |
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Linlang
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7672eaeaaa |
chore: remove package (#449)
* remove package * reformat with black * fix pytest error * fix pytest error * fix pytest error * add packages * change code |
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Xu Yang
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4ae7e6db19 |
feat: Initial version if Graph RAG in KAGGLE scenario (#301)
* Initial version if Graph RAG in KAGGLE scenario * fix CI * fix a small bug * fix CI * fix CI * fix CI |
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Linlang
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276b462f40 |
Fix ruff error1 (#81)
* fix_ruff_error1 * fix_ruff_error * fix ruff error * fix ruff error * pass model.py * rename exception class * rename exception class * rename func name generate_feedback * remove prepare args * optimize code * optimize code * fix code error |
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Xu Yang
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e0a24fb46f |
Several update on the repo (see desc) (#76)
* 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 |
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Xu Yang
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1d9b4cd2ec |
Align factor coder into new framework (#47)
* use CoSTEER as component name * rename factorimplementation to avoid confusion * rename modelimplementation * align benchmark and evolving evaluators * add scenario to evaluator init function * rename all factorimplementationknowledge in CoSTEER * remove all scenario related information in component * remove useless code --------- Co-authored-by: xuyang1 <xuyang1@microsoft.com> |
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Xu Yang
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1b31bcd2c2 |
refine class design and inheritance first version code (#41)
* refine class design and inheritance first version code * fix all typos --------- Co-authored-by: xuyang1 <xuyang1@microsoft.com> |
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Xu Yang
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6b626eb56d |
New Framework for idea proposal and implementation on RD-Agent (#34)
* Commit init framework * Co-authored-by: Yuante Li (FESCO Adecco Human Resources) <v-yuanteli@microsoft.com> Co-authored-by: XianBW <XianBW@users.noreply.github.com> * add an import * refine the whole framework * benchmark related framework * fix black and isort errors * move requirements to folder * fix black again --------- Co-authored-by: Young <afe.young@gmail.com> Co-authored-by: xuyang1 <xuyang1@microsoft.com> |
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Xu Yang
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a126c84c92 |
Refine all the implementation code to higher quality for release (#29)
* refine CI script * refine all the code to higher quality * refine the script to factor extraction and implementation * add task loader interface * add a task loader interface && move pdf analysis to pdf task loader * change the name to global variables --------- Co-authored-by: xuyang1 <xuyang1@microsoft.com> |
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USTCKevinF
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ebb659a018 |
reporeformat V2 (#23)
* reformat factor implement process * move some code to more reasonable place * fix the bug * add test function in factor_extract_and_implement.py * change select factor number to ratio , add some factor implement setting and fix some bug while using knowledgebase * change evoagent * add abstract class EvoAgent * add benchmark workflow * fix some bug in llm_utils * run wenjun's code * fix the knowledgebase instance check --------- Co-authored-by: xuyang1 <xuyang1@microsoft.com> |
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Xu Yang
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1e77557293 |
factor extraction pipeline ready (#16)
* run the code * update code * remove some redundant code --------- Co-authored-by: xuyang1 <xuyang1@microsoft.com> |
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Xu Yang
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c6833b0858 |
update code (#9)
Co-authored-by: xuyang1 <xuyang1@microsoft.com> |
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Xu Yang
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02173ffdcc |
add code security check CI and dependbot (#3)
* update code * init structure * add requirements and fix CI --------- Co-authored-by: xuyang1 <xuyang1@microsoft.com> |